Final Rulemaking to Establish Light-Duty 

Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel 

Economy Standards

Regulatory Impact Analysis
EPA-420-R-10-009

April 2010

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Final Rulemaking to Establish Light-Duty 

Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel 

Economy Standards

Regulatory Impact Analysis
i 

This Page Intentionally Left Blank 
ii 

EPA FINAL RIA TABLE OF CONTENTS 

 

Executive Summary.............................................................................................................ES-1 

CHAPTER 1: TECHNOLOGY PACKAGES, COST AND EFFECTIVENESS 

1.1 Overview of Technology.................................................................................................1-1 

1.2 Technology Cost and Effectiveness ................................................................................1-4 

1.3 Package Cost and Effectiveness ....................................................................................1-11 

 1.3.1 Explanation of Technology Packages ..................................................................1-11 

 1.3.2 Technology Package Costs & Effectiveness ........................................................1-13 

1.4 EPA’s Lumped Parameter Approach for Determining Effectiveness Synergies ..........1-25 

 1.4.1 Ricardo’s Vehicle Simulation...............................................................................1-28 

 1.4.2 Description of Ricardo’s Report...........................................................................1-29 

 1.4.3 Determination of representative vehicle classes...................................................1-30 

 1.4.4 Description of Baseline Vehicle Models..............................................................1-32 

 1.4.5 Technologies Considered by EPA and Ricardo in the Vehicle Simulation .........1-33 

 1.4.6 Choice of Technology Packages ..........................................................................1-35 

 1.4.7 Simulation Results................................................................................................1-37 

1.5 Comparison of Lumped-Parameter Results to Modeling Results .................................1-38 

1.6 Using the Lumped-Parameter Technique to Determine Synergies in a Technology 

Application Flowpath ...........................................................................................................1-40 

CHAPTER 2: AIR CONDITIONING 

2.1 Overview of Air Conditioning Impacts and Technologies..............................................2-1 

2.2 Air Conditioner Leakage .................................................................................................2-3 

 2.2.1 Impacts of Refrigerant Leakage on Greenhouse Gas Emissions............................2-3 

 2.2.2 A/C Leakage Credit................................................................................................2-5 

 2.2.3 Technologies That Reduce Refrigerant Leakage and their Effectiveness............2-12 

 2.2.4 Technical Feasibility of Leakage-Reducing Technologies ..................................2-16 

 2.2.5 Leakage Controls in A/C Systems........................................................................2-16 

 2.2.6 Other Benefits of improving A/C Leakage Performance .....................................2-19 

2.3 CO2 Emissions due to Air Conditioners .......................................................................2-20 

 2.3.1 Impact of Air Conditioning Use on Fuel Consumption and CO2 Emissions.......2-20 

 2.3.2 Technologies That Improve Efficiency of Air Conditioning and Their 

Effectiveness.........................................................................................................................2-30 

 2.3.3 Technical Feasibility of Efficiency-Improving Technologies..............................2-35 

 2.3.4 A/C Efficiency Credits .........................................................................................2-35 

2.4 Costs of A/C reducing technologies ..............................................................................2-43 

2.5 Air Conditioning Credit Summary ................................................................................2-45 

CHAPTER 3: TECHNICAL BASIS OF THE STANDARDS.................................................. 

3.1 Technical Basis of the Standards....................................................................................3-1 

 3.1.1 Summary.................................................................................................................3-1 

 3.1.2 Overview of Equivalency Calculation....................................................................3-1 
iii 

3.2 Analysis of Footprint Approach for Establishing Individual Company Standards.........3-6 

 3.2.1 “Footprint” as a Vehicle Attribute..........................................................................3-7 

 3.2.2 Alternative Attributes ...........................................................................................3-12 

 3.2.3 EPA Selection of the Footprint Attribute .............................................................3-19 

CHAPTER 4: RESULTS OF FINAL AND ALTERNATIVE STANDARDS 

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

4.2 Model Inputs....................................................................................................................4-1 

 4.2.1 Representation of the CO2 Control Technology Already Applied to 2008 MY 

Vehicles ..................................................................................................................................4-2 

 4.2.2 Technology Package Approach..............................................................................4-8 

4.3 Modeling Process ..........................................................................................................4-10 

4.4 Modeling of CAA Compliance Flexibilities .................................................................4-13 

4.5 Manufacturer-Specific Standards and Achieved CO2 Levels.......................................4-16 

4.6 Per Vehicle Costs 2012-2016 ........................................................................................4-17 

4.7 Technology Penetration.................................................................................................4-19 

4.8 Alternative Program Stringencies..................................................................................4-26 

4.9 Assessment of Manufacturer Differences .....................................................................4-36 

CHAPTER 5: EMISSIONS IMPACTS 

5.1 Overview .........................................................................................................................5-1 

5.2 Introduction .....................................................................................................................5-4 

 5.2.1 Scope of Analysis...................................................................................................5-4 

 5.2.2 Downstream Contributions.....................................................................................5-4 

 5.2.3 Upstream Contributions..........................................................................................5-5 

 5.2.4 Global Warming Potentials ....................................................................................5-5 

5.3 Program Analysis and Modeling Methods ......................................................................5-6 

 5.3.1 Models Used...........................................................................................................5-6 

 5.3.2 Description of Scenarios.........................................................................................5-7 

 5.3.3 Calculation of Downstream Emissions ................................................................5-16 

 5.3.4 Calculation of Upstream Emissions .....................................................................5-30 

5.4 Greenhouse Gas Emission Inventory ............................................................................5-30 

5.5 Non-Greenhouse Gas Emission Inventory ....................................................................5-31 

 5.5.1 Downstream Impacts of Program on Non-GHG Emissions.................................5-33 

 5.5.2 Upstream Impacts of Program on Non-GHG Emissions .....................................5-34 

 5.5.3 Total non-GHG Program Impact..........................................................................5-35 

5.6 Model Year Lifetime Analyses .....................................................................................5-36 

 5.6.1 Methodology.........................................................................................................5-36 

 5.6.2 Results ..................................................................................................................5-38 

5.7 Alternative 4% and 6% Scenarios .................................................................................5-40 

 5.7.1 4% Scenario..........................................................................................................5-40 

 5.7.2 6% Scenario..........................................................................................................4-42 

5.8 Inventories Used for Air Quality Analyses ...................................................................5-44 

 5.8.1 Upstream Emissions .............................................................................................5-44 

5.A Appendix to Chapter 5: Details of the TLAAS Impacts 

Analysis ................................................................................................................................5-49 
iv 

 5.A.1 Introduction and Summary ..................................................................................5-49 

 5.A.2 Factors Determining the Impact of the TLAAS ..................................................5-50 

 5.A.3 Bounding Analysis of TLAAS Impact ................................................................5-51 

 5.A.4 Approach used for Estimating TLAAS Impact ...................................................5-51 

5.B .......... Appendix to Chapter 5: Impacts of Advanced Technology Vehicle Incentives for 

Electric Vehicles...................................................................................................................5-54 

 5.B.1 Introduction and Summary ..................................................................................5-54 

 5.B.2 Assumptions behind the Analysis........................................................................5-54 

 5.B.3 Inputs ...................................................................................................................5-55 

 5.B.4 Computation.........................................................................................................5-56 

CHAPTER 6: VEHICLE PROGRAM COSTS INCLUDING FUEL CONSUMPTION 

IMPACTS.................................................................................................................................... 

6.1 Vehicle Program Costs....................................................................................................6-2 

 6.1.1 Vehicle Compliance Costs on a Per-Vehicle Basis................................................6-2 

 6.1.2 Vehicle Compliance Costs on a Per-Year Basis...................................................6-10 

6.2 Cost per Ton of Emissions Reduced .............................................................................6-14 

6.3 Fuel Consumption Impacts............................................................................................6-14 

6.4 Vehicle Program Cost Summary...................................................................................6-18 

CHAPTER 7: ENVIRONMENTAL AND HEALTH IMPACTS ............................................. 

7.1 Health and Environmental Effects of Non-GHG Pollutants ...........................................7-1 

 7.1.1 Health Effects Associated with Exposure to Pollutants .........................................7-1 

 7.1.2 Environmental Effects Associated with Exposure to Pollutants ..........................7-17 

7.2 Non-GHG Air Quality Impacts .....................................................................................7-30 

 7.2.1 Air Quality Modeling Methodology.....................................................................7-31 

 7.2.2 Air Quality Modeling Results ..............................................................................7-44 

7.3 Quantified and Monetized Non-GHG Health and Environmental Impacts ..................7-86 

 7.3.1 Quantified and Monetized Non-GHG Human Health Benefits of the 2030 Calendar 

Year (CY) Analysis ..............................................................................................................7-87 

 7.3.2 PM-related Monetized Benefits of the Model Year (MY) Analysis ..................7-117 

7.4 Changes in Global Mean Temperature and Sea Level Rise Associated with the Rule’s 

GHG Emissions Reductions...............................................................................................7-122 

 7.4.1 Introduction ........................................................................................................7-122 

 moran7.4.2 Estimated Projected Reductions in Atmospheric CO2 Concentrations, Global 

Mean Surface Temperatures and Sea Level Rise ...............................................................7-122 

 7.4.3 Summary.............................................................................................................7-126 

7.5 SCC and GHG Benefits...............................................................................................7-127 

7.6 Weight Reduction and Vehicle Safety ........................................................................7-135 

 7.6.1 What did EPA say in the NPRM and in the Draft RIA with regard to Potential Safety 

Effects? ...............................................................................................................................7-136 

 7.6.2 What Public Comments did EPA Receive in Regard to its Safety Discussion and 

what is EPA’s Response? ...................................................................................................7-142 

 7.6.3 NHTSA’s 2010 Study of Accident Fatalities by Vehicle Size and Weight .......7-146 

 7.6.4 Suggested Next Steps To Increase Our Understanding of the Effects of Vehicle 

Size and Weight on Fatalities.............................................................................................7-148 
v 

CHAPTER 8: OTHER ECONOMIC AND SOCIAL IMPACTS 

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

 8.1.1 How Vehicle Sales Impacts were Estimated for this Rule .....................................8-1 

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

 8.1.3 Consumer Payback Period and Lifetime Savings on New Vehicle Purchases ....8-13 

8.2 Energy Security Impacts................................................................................................8-16 

8.3 Other Impacts ................................................................................................................8-18 

 8.3.1 Reduced Refueling Time......................................................................................8-18 

 8.3.2 Value of Additional Driving.................................................................................8-19 

 8.3.3 Noise, Congestion, and Accidents........................................................................8-19 

 8.3.4 Summary of Other Impacts ..................................................................................8-21 

8.4 Summary of Costs and Benefits ....................................................................................8-23 

CHAPTER 9: SMALL BUSINESS FLEXIBILITY ANALYSIS 
vi 

List of Acronyms 

2-mode: 2-mode hybrid electric vehicle 

2V: 2-valves per cylinder 

4V: 4-valves per cylinder 

12V: 12 Volts 

42V: 42 Volts 

A/C: Air conditioner/conditioning 

AERO: Improved aerodynamics 

ASL: Aggressive Shift Logic 

AT: Automatic transmission 

CAFE: Corporate Average Fuel Economy 

CCP: Couple Cam Phasing 

CO2: carbon dioxide 

CVA: Camless Valve Actuation (full) 

CVT: Continuously Variable Transmission 

CVVL: Continuous Variable Valve Lift 

Deac: Cylinder Deactivation 

DICE: Dynamic Integrated Model of Climate and the Economy 

DCP: Dual (independent) Cam Phasing 

DCT: 6-speed Dual Clutch Transmission 

DOHC: Dual Overhead Camshafts 

DOT: Department of Transportation 

DVVL: Discrete (two-step) Variable Valve Lift 

EFR: Engine Friction Reduction 

EIS: Environmental Impact Statement 

EPS: Electric Power Steering 

FUND:Climate Framework for Uncertainty, Negotiation, and Distribution 

GDI: Gasoline Direct Injection 

GHG: Greenhouse gas 

HCCI: Homogenous Charge Compression Ignition (gasoline) 

HEV: Hybrid Electric Vehicle 

I3: In-line 3-cylinder engine 

I4: In-line 4-cylinder engine 

IACC: Improved Accessories 

IAM: Integrated Assessment Model 

IMA: Integrated Motor Assist 

IPCC: Intergovernmental Panel on Climate Change 

L4: Lock-up 4-speed automatic transmission 

L5: Lock-up 5-speed automatic transmission 

L6: Lock-up 6-speed automatic transmission 

LDB: Low drag brakes 

LRR: Low Rolling Resistance 

LUB: Low-friction engine lubricants 

MPV: Multi-Purpose Vehicle 

MY: Model Year 
vii 

NESCCAF: Northeast States Center for a Clean Air Future 

NHTSA: National Highway Transportation Safety Administration 

OECD:Organization for Economic Cooperation and Development 

OHV: Overhead Valve (pushrod) 

OMB: Office of Management and Budget 

ORNL: Oak Ridge National Laboratory

PAGE: Policy Analysis for the Greenhouse Effect 

PHEV: Plug-in Hybrid Electric Vehicle 

PRTP: Pure Rate of Time Preference 

S&P: Standard and Poor’s 

SCC: Social Cost of Carbon 

SCR: Selective Catalytic Reduction 

SOHC: Single Overhead Camshaft 

SRES: Special Report on Emissions Scenarios 

S-S: Stop-start hybrid system 

THC: Thermohaline circulation 

TORQ: Early torque converter lockup 

Turbo: Turbocharger/Turbocharging 

V6: 6-cylinder engine in a “V” configuration 

V8: 8-cylinder engine in a “V” configuration 

WGII: Working group II 
ES-1 

Executive Summary 

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

Safety Administration (NHTSA) are issuing a joint final rulemaking to establish new 

standards for light-duty highway vehicles that will reduce greenhouse gas emissions (GHG) 

and improve fuel economy. The joint rulemaking is consistent with the National Fuel 

Efficiency Policy announced by President Obama on May 19, 2009, responding to the 

country’s critical need to address global climate change and to reduce oil consumption. EPA 

is finalizing greenhouse gas emissions standards under the Clean Air Act, and NHTSA is 

finalizing Corporate Average Fuel Economy standards under the Energy Policy and 

Conservation Act (EPCA), as amended. These standards apply to passenger cars, light-duty 

trucks, and medium-duty passenger vehicles, covering model years (MY) 2012 through 2016. 

The standards will require these vehicles to meet an estimated combined average emissions 

level of 250 grams of CO2 per mile in MY 2016 under EPA’s GHG program, and 34.1 mpg 

in MY 2016 under NHTSA’s CAFE program and represent a harmonized and consistent 

national program (National Program). These standards are designed such that compliance can 

be achieved with a single national vehicle fleet whose emissions and fuel economy 

performance improves year over year. The National Program will result in approximately 960 

million metric tons of CO2 emission reductions and approximately 1.8 billion barrels of oil 

savings over the lifetime of vehicles sold in model years 2012 through 2016. 

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.1

 The health- and 

environmentally-related effects associated with these emissions are a classic example of an 

externality-related market failure. An externality occurs when one party's actions impose 

uncompensated costs on another party. EPA’s final rule will deliver additional environmental 

and energy benefits, as well as cost savings, on a nationwide basis that would likely not be 

available if the rule were not in place. 

Table 1 shows EPA’s estimated lifetime discounted cost, benefits and net benefits for 

all vehicles projected to be sold in model years 2012-2016. 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. 

 

1

 “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-2009-0472-11292, 

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"). 
ES-2 

Table 1 EPA’s Estimated 2012-2016 Model Year Lifetime Discounted Costs, 

Benefits, and Net Benefits assuming the $21/ton SCC Valuea,b,c,d

(Millions of 2007 dollars) 

3% Discount Rate 

Costs $51,500 

Benefits $240,200 

Net Benefits $188,700 

7% Discount Rate 

Costs $51,500 

Benefits $191,700 

Net Benefits $140,200 

a

 As noted in Section III.H, SCC increases over time. The $21/ton value applies to 

2010 CO2 emissions and grows larger over time.

b Although EPA estimated the benefits associated with four different values of a one 

ton GHG reduction ($5, $21, $35, $65), for the purposes of this overview presentation 

of estimated costs and benefits EPA is showing the benefits associated with the 

marginal value deemed to be central by the interagency working group on this topic: 

$21 per ton of CO2e, in 2007 dollars and 2010 emissions. The $21/ton value applies 

to 2010 CO2 emissions and grows over time.

c

 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. 

d

 Monetized GHG benefits exclude the value of reductions in non-CO2 GHG 

emissions (HFC, CH4 and N2O) expected under this final rule. Although EPA has not 

monetized the benefits of reductions in these non-CO2 emissions, the value of these 

reductions should not be interpreted as zero. Rather, the reductions in non-CO2 GHGs 

will contribute to this rule’s climate benefits, as explained in Section III.F.2. The SCC 

TSD notes the difference between the social cost of non-CO2 emissions and CO2 

emissions, and specifies a goal to develop methods to value non-CO2 emissions in 

future analyses. 

 This Regulatory Impact Analysis (RIA) contains supporting documentation to the 

EPA rulemaking. NHTSA has prepared their own RIA in support of their rulemaking (this 

can be found in NHTSA’s docket for the rulemaking, NHTSA-2009-0059). 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 

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 joint Technical Support Document (joint 

TSD can be found in EPA’s docket EPA-HQ-OAR-2009-0472). Therefore, this RIA should 

be viewed as a companion document to the Joint TSD and the two documents together 

provide the details of EPA’s technical analysis in support of its rulemaking. 

While NHTSA and EPA each modeled their respective regulatory programs under the 

National Program, the analyses were generally consistent and featured similar parameters. 

EPA did not conduct an overall uncertainty analysis of the impacts associated with its 

regulatory program, though it did conduct uncertainty and sensitivity analyses of individual 

components of the analysis (e.g., uncertainty ranges associated with quantified and monetized 
ES-3 

non-GHG health impacts). NHTSA, however, conducted a Monte Carlo simulation of the 

uncertainty associated with its regulatory program. The focus of the simulation model was 

variation around the chosen uncertainty parameters and their resulting impact on the key 

output parameters, fuel savings, and net benefits. Among other parameters, NHTSA varied 

technology costs, technology effectiveness, fuel prices, the values of oil consumption 

externalities and the rebound effect. Because of the similarities between the two analyses, 

EPA references NHTSA RIA Chapter XII as indicative of the relative magnitude, uncertainty 

and sensitivities of parameters of the cost/benefit analysis. 

This document contains the following; 

Chapter 1: Technology Packages, Cost and Effectiveness. The details of the vehicle 

technology 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 the 

basis of the technology costs and effectiveness analyses discussed in Chapter 3 of the Joint 

TSD. This chapter also contains details on the lumped parameter model, which is a major 

part of EPA’s determination of the effectiveness of these packages. 

Chapter 2: Air Conditioning. EPA’s unique air conditioning (A/C) program is 

discussed. Details for this chapter include the A/C credit program and the related technology 

costs and effectiveness associated with new A/C systems. The A/C credit program allows 

manufacturers to earn credit for both direct and indirect CO2eq emissions. Direct emission 

credits are earned through reducing refrigerant leakage (as the current refrigerant, R-134a, has 

a very high global warming potential) from the A/C system. The amount of direct emission, or 

leakage, credit that a manufacturer can earn is determined by using a design-based method to 

calculate the yearly refrigerant leakage from a vehicle’s A/C system. This leakage value is 

then used to calculate a “grams-per-mile” credit, with allowances made for the global 

warming potential of the refrigerant. Indirect emission credits are earned through improving 

the efficiency of the A/C system, which reduces the amount of power required to operate the 

A/C system as well as the amount of CO2 emitted by the vehicle. The amount of indirect 

emission credit is determined by using a menu-based approach, where the inclusion of 

specific efficiency-improving components or design elements into a vehicle’s A/C system 

results in an assigned credit value. 

Chapter 3: Technical Basis of the Standards. This chapter contains two subchapters. 

In the first, EPA evaluates the stringency of the California Pavley 1 program but for a national 

standard. However, as further explained in the preamble, before being able to do so, technical 

analysis was necessary in order to be able to assess what would be an equivalent national new 

vehicle fleet-wide CO2 performance standards for model year 2016 which would result in the 

new vehicle fleet in the State of California having CO2 performance equal to the performance 

from the California Pavley 1 standards. This technical analysis is documented in this subchapter of the RIA. In the second subchapter, EPA discusses an analysis of the “footprint” 

approach EPA is using for establishing standards. 

Chapter 4: Results of Final and Alternative Standards. A conceptual overview of 

EPA’s OMEGA model and technology cost results for the program and alternative standards 
ES-4 

considered. For each manufacturer, EPA estimates the following achieved fleetwide CO2 

levels and technology costs over the reference case, see Table 2. 

Table 2 Fleetwide Costs in 2016 

 Fleetwide Cost 

BMW $ 1,453 

Chrysler $ 1,329 

Ford $ 1,231 

Subaru $ 899 

General Motors $ 1,219 

Honda $ 575 

Hyundai $ 745 

Tata $ 984 

Kia $ 594 

Mazda $ 808 

Daimler $ 1,343 

Mitsubishi $ 978 

Nissan $ 810 

Porsche $ 1,257 

Suzuki $ 937 

Toyota $ 455 

Volkswagen $ 1,694 

Total $ 948 

 Key results of the alternative stringencies analyzed are presented in Table 3: 

Table 3 Key Results Per Reduction Scenarios of 4% and 6% Per Year 

Reduction Scenarios 

Industry 

achieved CO2 

level (g/mi) 

Average 

industry cost 

per vehicle 

4% per year 256.9 $883 

6% per year 236.1 $1,343 
ES-5 

Chapter 5: Emissions Impacts. This chapter contains the greenhouse gas and nongreenhouse gas emission impacts of this rule and includes the impact of credits. 

Greenhouse Emission Impacts of EPA’s Rulemaking

Table 4 shows reductions estimated from EPA’s GHG standards. The analyses 

assume a pre-control case of MY 2011 CAFE standards continuing indefinitely beyond 2011, 

and a post-control case in which MY 2016 standards continue indefinitely beyond 2016. 

Including the reductions from upstream emissions (fuel production and transport), 

total reductions are estimated to reach 307 MMTCO2eq (million metric tons of CO2

equivalent emissions) annually by 2030 (equivalent to a 21 percent reduction in U.S. car and 

light truck emissions as compared to the reference scenario), and grow to over 500 

MMTCO2eq in 2050 as cleaner vehicles continue to come into the fleet (equivalent to a 23 

percent reduction in U.S. car and light truck emissions relative to the control case that year). 

Table 4. Projected Net GHG Reductions (MMT CO2 Equivalent per year) 

Calendar Year: 2020 2030 2040 2050 

Net Reduction* 156.4 307.0 401.5 505.9 

Net CO2 139.1 273.3 360.4 458.7 

Net other GHG 17.3 33.7 41.1 47.2 

Downstream Reduction 125.2 245.7 320.7 403.0

CO2 (excluding A/C) 101.2 199.5 263.2 335.1

A/C – indirect CO2 10.6 20.2 26.5 33.8

 A/C – direct HFCs 13.3 26.0 30.9 34.2

 CH4 (rebound effect) 0.0 0.0 0.0 0.0 

 N2O (rebound effect) 0.0 -0.1 -0.1 -0.1 

Upstream Reduction 31.2 61.3 80.8 102.9

CO2 27.2 53.5 70.6 89.9 

CH4 3.9 7.6 10.0 12.7 

N2O 0.1 0.3 0.3 0.4 

 * includes impacts of 10% VMT rebound rate 

Impacts of EPA’s Rulemaking on Emissions of Criteria and Toxic Pollutants

 The results of EPA’s analyses on the impacts of the program on annual criteria 

emissions are listed in Table 5. For all criteria pollutants the overall impact of the program 

will be relatively small compared to total U.S. inventories across all sectors. In 2030, EPA 

estimates the program will reduce total NOX, PM and SOX inventories by 0.1 to 0.8 percent 

and reduce the VOC inventory by 1.0 percent, while increasing the total national CO 

inventory by 0.6 percent. 
ES-6 

EPA estimates that the GHG program will result in small changes for toxic emissions 

compared to total U.S. inventories across all sectors, as listed in Table 6. In 2030, EPA 

estimates the program will reduce total benzene and 1,3 butadiene emissions by 0.1 to 0.3 

percent. Total acrolein and formaldehyde emissions will increase by 0.1 percent. 

Acetaldehyde emissions will increase by 2.2 percent. 

Table 5 Annual Criteria Emission Impacts of Program (short tons) 

Total Impacts Upstream Impacts Downstream Impacts 

2020 2030 2020 2030 2020 2030 

VOC -60,187 -115,542 -64,506 -126,749 4,318 11,207 

 % of total 

inventory -0.51% -1.01% -0.55% -1.11% 0.04% 0.01% 

CO 3,992 170,675 -6,165 -12,113 10,156 182,788 

 % of total 

inventory 0.01% 0.56% -0.02% -0.04% 0.01% 0.6% 

NOX -5,881 -21,763 -19,291 -37,905 13,410 16,143 

 % of total 

inventory -0.02 -0.07% -0.06% -0.12% 0.04% 0.05% 

PM2.5 -2,398 -4,564 -2,629 -5,165 231.0 602.3 

 % of total 

inventory -0.03% -0.05% -0.03% -0.06% 0.00% 0.01% 

SOX -13,832 -27,443 -11,804 -23,194 -2,027 -4,249 

 % of total 

inventory -0.41% -0.82% -0.35% -0.69% -0.06% -0.13% 

Table 6. Annual Air Toxic Emission Impacts of Program (short tons) 

Total Impacts Upstream Impacts Downstream Impacts 

 2020 2030 2020 2030 2020 2030 

1,3-Butadiene -95 -21 -1.5 -3.0 -93.6 -18.1 

 % of total 

inventory -0.38% -0.10% -0.01% -0.01% -0.37% -0.09% 

Acetaldehyde 760 668 -6.8 -13.4 766.9 681.5 

 % of total 

inventory 2.26% 2.18% -0.02% -0.04% 2.28% 2.22% 

Acrolein 1 5 -0.9 -1.8 1.7 6.5 

 % of total 

inventory 0.01% 0.07% -0.01% -0.03% 0.03% 0.10% 

Benzene -890 -523 -139.6 -274.3 -750.0 -248.3 

% of total 

inventory -0.48% -0.29% -0.08% -0.15% -0.40% -0.14% 

Formaldehyde -49 15 -51.4 -101.0 2.1 116.3 

 % of total 

inventory -0.06% 0.02% -0.06% -0.12% 0.00% 0.14% 
ES-7 

Chapter 6: Vehicle Program Costs Including Fuel Consumption Impacts. The 

program costs and fuel savings associated with EPA’s rulemaking. In Chapter 6, we present 

briefly some of the outputs of the OMEGA model (costs per vehicle) and how we use those 

outputs to estimate the annual costs (and fuel savings) of the program through 2050. We also 

present our cost per ton analysis showing the cost incurred for each ton of GHG reduced by 

the program. 

Chapter 7: Environmental and Health Impacts. This Chapter provides details on the 

non-GHG health and environmental impacts associated with criteria pollutants and air toxics. 

We also present the results of our non-GHG air quality modeling analysis and the quantified 

and monetized estimates of PM2.5- and ozone-related health impacts. Our air quality modeling 

indicates that the final standards have relatively little impact on ambient concentrations of 

modeled PM2.5, ozone, and air toxics (See Chapter 7.2.). The criteria pollutant-related 

benefits of the rule are associated with small reductions in PM2.5. 

As described in Chapter 7.5, EPA used four new 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 used 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 2012 through 2016 model year vehicles. 

Chapter 7.6 also includes additional information about EPA’s mass reduction and 

safety analysis. 

Chapter 8: Other Economic and Social Impacts. This chapter provides a description 

of other economic and social impacts associated with the rule, including vehicle sales impacts, 

consumer vehicle choice, energy security, and other economic impacts associated with 

reduced refueling, the value of increased driving, and the cost associated with additional 

noise, congestion and accidents. 

Vehicle Sales Impacts: Our analysis predicts vehicle sales increasing as a result of the 

rule. Because the fuel savings associated with this rule are expected to exceed the technology 

costs, the effective prices of vehicles – the adjusted increase in technology cost less the fuel 

savings over five years -- to consumers will fall, and consumers will buy more new vehicles. 

This effect is expected to increase over time. As a result, if consumers consider at least five 

years of fuel savings at the time that they make their vehicle purchases, the lower net cost of 

the vehicles is expected to lead to an increase in sales for both cars and trucks. Both the 

absolute and the percent increases for truck sales are larger than those for cars (except in 

2012). 

Consumer Choice Impacts: Consumer vehicle choice models could in principle be 

used to examine the effects of this rule on the mix of vehicles sold. In practice, however, 
ES-8 

EPA finds that the state of the art of these models is not yet settled. The models show great 

variation, and there has been very little comparative assessment of them. Like NHTSA, EPA 

will continue its efforts to review the literature, but, given the known difficulties, neither 

NHTSA nor EPA has conducted an analysis using these models for this rule. 

Payback Period on New Vehicle Purchases: We also conducted 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 within three 

years for people purchasing new vehicles with cash. For those purchasing new vehicles with 

a typical five-year car note, the fuel savings will outweigh increased costs in the first month of 

ownership. 

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. Based on these estimates of fuel savings, over the lifetimes of model years 2012-

2016, we estimate the discounted energy security impacts at $10.1 billion dollars, in 2007 

dollars, assuming a 3 percent discount rate, and $8.0 billion dollars, assuming a 7 percent 

discount rate. 

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

expected under the final rule. Table 7 presents these economics impacts. 

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

impacts due to EPA’s rulemaking. 
ES-9 

Table 7 Economic Impacts of the Light-Duty GHG Rule 

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

Vehicle Costs $15,600 $15,800 $17,400 $19,000 $345,900 $191,900

Fuel Savingsb -$35,700 -$79,800 -$119,300 -$171,200 -$1,545,600 -$672,600

Reduced Refueling $2,400 $4,800 $6,300 $8,000 $87,900 $40,100

Value of Increased Drivingb $4,200 $8,800 $13,000 $18,400 $171,500 $75,500

Benefits from Reduced CO2 Emissions at each assumed SCC valuec,d,e

Avg SCC at 5% $900 $2,700 $4,600 $7,200 $34,500 $34,500

Avg SCC at 3% $3,700 $8,900 $14,000 $21,000 $176,700 $176,700

Avg SCC at 2.5% $5,800 $14,000 $21,000 $30,000 $299,600 $299,600

95th percentile SCC at 3% $11,000 $27,000 $43,000 $62,000 $538,500 $538,500

Other Impacts 

Criteria Pollutant 

Benefitsf,g,h,i 

 B 

$1,200-

$1,300 

$1,200-

$1,300 

$1,200-

$1,300 $21,000 $14,000

Energy Security Impacts 

(price shock) $2,200 $4,500 $6,000 $7,600 $81,900 $36,900

Accidents, Noise, 

Congestion -$2,300 -$4,600 -$6,100 -$7,800 -$84,800 -$38,600

Quantified Net Benefits at each assumed SCC valuec,d,e

Avg SCC at 5% $27,500 $81,500 $127,000 $186,900 $1,511,700 $643,100

Avg SCC at 3% $30,300 $87,700 $136,400 $200,700 $1,653,900 $785,300

Avg SCC at 2.5% $32,400 $92,800 $143,400 $209,700 $1,776,800 $908,200

95th percentile SCC at 3% $37,600 $105,800 $165,400 $241,700 $2,015,700 $1,147,100

a

 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 Chapter 7 for more detail. 

b

 Calculated using pre-tax fuel prices. 

c

 Monetized GHG benefits exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4 and N2O) 

expected under this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 

emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO2 

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F.2 of the preamble. The SCC 

Technical Support Document (TSD) notes the difference between the social cost of non-CO2 emissions and CO2 

emissions, and specifies a goal to develop methods to value non-CO2 emissions in future analyses. 

d

 Section III.H.6 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates 

range as follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 

2.5%: $35-$65; and for 95th percentile SCC at 3%: $65-$136. Section III.H.6 also presents these SCC 

estimates. 

e

 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. 

f

 Note that “B” indicates unquantified criteria pollutant benefits in the year 2020. For the final rule, we only 

modeled the rule’s PM2.5- and ozone-related impacts in the calendar year 2030. For the purposes of estimating 

a stream of future-year criteria pollutant benefits, we assume that the benefits out to 2050 are equal to, and no 

less than, those modeled in 2030 as reflected by the stream of estimated future emission reductions. The NPV of 

criteria pollutant-related benefits should therefore be considered a conservative estimate of the potential benefits 

associated with the final rule. 

g

 The benefits presented in this table include an estimate of PM-related premature mortality derived from Laden 

et al., 2006, and the ozone-related premature mortality estimate derived from Bell et al., 2004. If the benefit 

estimates were based on the ACS study of PM-related premature mortality (Pope et al., 2002) and the Levy et al., 

2005 study of ozone-related premature mortality, the values would be as much as 70% smaller. 

h

 The calendar year benefits presented in this table assume either a 3% discount rate in the valuation of PMrelated premature mortality ($1,300 million) or a 7% discount rate ($1,200 million) to account for a twenty-year 

segmented cessation lag. Note that the benefits estimated using a 3% discount rate were used to calculate the 
ES-10 

NPV using a 3% discount rate and the benefits estimated using a 7% discount rate were used to calculate the 

NPV using a 7% discount rate. For benefits totals presented at each calendar year, we used the mid-point of the 

criteria pollutant benefits range ($1,250). 

i

 Note that the co-pollutant impacts presented here do not include the full complement of endpoints that, if 

quantified and monetized, would change the total monetized estimate of impacts. The full complement of 

human health and welfare effects associated with PM and ozone remain unquantified because of current 

limitations in methods or available data. We have not quantified a number of known or suspected health effects 

linked with ozone and PM for which appropriate health impact functions are not available or which do not 

provide easily interpretable outcomes (e.g., changes in heart rate variability). Additionally, we are unable to 

quantify a number of known welfare effects, including reduced acid and particulate deposition damage to 

cultural monuments and other materials, and environmental benefits due to reductions of impacts of 

eutrophication in coastal areas. 
Technology Packages, Cost and Effectiveness 

1-1 

CHAPTER 1:Technology Packages, Cost and Effectiveness 

1.1 Overview of Technology 

The final GHG program is based on the need to obtain significant GHG emissions 

reductions from the transportation sector, and the recognition that there are cost effective 

technologies to achieve such reductions in the 2012-2016 time frame. 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. 

At the same time, the technological problems and solutions involved in this 

rulemaking differ in many ways from prior mobile source rulemakings. In the past the 

assessment of exhaust emissions control technology has focused on how to reduce the amount 

of various unwanted chemical compounds that are generated when fuel is combusted. The 

emissions are often the result of incomplete combustion, such as emissions of HC, CO, and 

PM. In some cases the combustion products are the result of the specific conditions under 

which combustion occurs, such as the relationship between emissions of NOx and the 

temperature of combustion. Technology to control exhaust emissions has focused, in part, on 

changing the fuel delivery and engine systems so there is more complete combustion of the 

fuel which generates less HC, CO, and PM in the engine exhaust but, by design, generates 

more CO2. (CO2 is one of ultimate combustion products of any carbon containing fuel, such 

as gasoline and diesel fuel.). Other changes to the fuel delivery and engine systems have 

been designed to change the combustion process to reduce the amount of NOx and PM 

generated by the engine. Very large reductions have been achieved by installing and 

optimizing aftertreatment (post-combustion, post- engine generated pollution) devices, such 

as catalytic converters and catalyzed diesel particulate filters (DPF), that reduce the amount of 

emissions of HC, CO, and PM by oxidizing or combusting these compounds in the 

aftertreatment device, again generating CO2 in the process. In the case of NOx, 

aftertreatment devices have focused on the chemical process of reduction, or removal of 

oxygen from the compound. Therefore the exhaust emissions control technologies of the past 

have focused almost exclusively on (1) upgrading the fuel delivery and engine systems to 

control the combustion process to reduce the amount of unwanted emissions from the engine 

and in the process increase the amount of CO2 emitted, and on (2) aftertreatment devices that 

either continue this oxidation process and increase emissions of CO2, or otherwise change the 

compounds emitted by the engine. Since CO2 is a stable compound produced by the complete 

combustion of the fuel – indeed serving as a marker of how efficiently fuel has been 

combusted, these two methods employed to address HC, CO, PM, and NOx are not available 
Regulatory Impact Analysis 

1-2 

to address CO2. Instead, the focus of the CO2 emissions control technology must be entirely 

different—reducing the amount of fuel that is combusted. 

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. 

The braking system the aerodynamics 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. Finally, there are other advanced technologies under development, 

such as lean burn gasoline engines, which offer the potential of improved energy generation 

through improvements in the basic combustion process. 

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 final 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. 
Technology Packages, Cost and Effectiveness 

1-3 

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 

thus occur at the vehicle redesign stage and not in the time period between redesigns. 

Given that the regulatory timeframe of the GHG program is five years (2012 through 

2016), 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 once during that timeframe, a five year redesign cycle has been 

used in our final rule. This same redesign cycle was used in the proposal. This means that the 

analysis assumes that each vehicle platform in the US fleet can undergo at least one full 

redesign during our regulatory timeframe. While some may undergo more than one, the 

analysis assumes that the extra redesign comes at the expense of another vehicle that would, 

in effect, undergo no redesign during the regulatory timeframe. 

Commenters were generally supportive of the use of a five year redesign cycle. 

However, at least one commenter argued that shorter redesign cycles are possible and that the 

final GHG standards are too low in light of the ability of manufacturers to conduct redesigns 

at a faster pace. EPA’s response on both sides of this issue can be found in the Response to 

Comments document (see issue 3.1). 

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 five model years at issue in this rulemaking, 2012-2016, EPA projects that 

almost the entire fleet of light-duty vehicles (i.e., 85 percent) will have gone through a 

redesign cycle. If the technology to control greenhouse gas emissions is efficiently folded 

into this redesign process, then by 2016 almost 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 HFCs from the air conditioner. 
Regulatory Impact Analysis 

1-4 

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 required by the GHG rule is 

commercially available and already being employed to a limited extent across the fleet, 

although far greater penetration of these technologies into the fleet is projected as a result of 

the final rule. The vast majority of the emission reductions which will result from the rule 

would result from the increased use of these technologies. EPA also believes the rule would 

encourage the development and limited use of more advanced technologies, such as PHEVs 

and EVs, and is structuring the rule to encourage these technologies’ use. 

In section 1.2 below, a summary of technology costs and effectiveness is presented. In 

section 1.3, the process of combining technologies into packages is described along with 

package costs and effectiveness. Sections 1.4 through 1.6 discuss the lumped parameter 

approach which provides background and support for determining technology and package 

effectiveness. 

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 

NHTSA’s 2011 CAFE FRM and EPA’s 2008 Staff Technical Report. In those analyses, 

piece costs and effectiveness were estimated based on a number of sources. The objective 

was to use those sources of information considered to be most credible. Those sources 

included: the 2002 NAS report on the effectiveness and impact of CAFE standards; the 2004 

study done by the Northeast States Center for a Clean Air Future (NESCCAF); the 

California Air Resources Board (CARB) Initial Statement of Reasons in support of their 

carbon rulemaking; a 2006 study done by Energy and Environmental Analysis (EEA) for the 

Department of Energy; a study done by the Martec Group for the Alliance of Automobile 

Manufacturers, and an update by the Martec Group to that study; and vehicle fuel economy 

certification data. In addition, confidential data submitted by vehicle manufacturers in 

response to NHTSA’s request for product plans were considered, as was confidential 

information shared by automotive industry component suppliers in meetings with EPA and 

NHTSA staff held during the second half of the 2007 calendar year. 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. 

Since publication of the 2011 CAFE FRM and EPA’s 2008 Staff Technical Report, 

EPA began a contracted study with FEV (an engineering services firm) that consists of 

complete system tear-downs to evaluate technologies down to the nuts and bolts to arrive at 
Technology Packages, Cost and Effectiveness 

1-5 

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 staff in the first half of 2009 where both piece costs and fuel consumption 

efficiencies were discussed in detail. 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. The results of 

these efforts and early results of the FEV contracted study were used in the proposal for this 

rule. 

Since the proposal, EPA has carefully examined all information on technology cost 

and effectiveness received during the comment period. Importantly, the FEV contracted 

study has progressed and provides many more new cost estimates that have been incorporated 

in the final analysis. As a result, while some FEV teardown costs were used in the proposal, 

we have expanded our use of FEV costs for the final rule using new information available to 

us shortly after the proposal. For more detail on our technology cost estimates and how they 

have changed since the proposal refer to Chapter 3 of the joint TSD and, specifically, section 

3.3.2.2 of the joint TSD for how costs have changed. 

EPA reviewed all this information in order to develop the best estimates of the cost 

and effectiveness of CO2 reducing technologies. These estimates were developed for five 

vehicle classes: small car, large car, minivan, small truck and large truck. All vehicle types 

were mapped into one of these five classes in EPA’s analysis (see Chapter 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., electronic power steering), or vehicle-related (e.g., low 

rolling resistance tires). Table 1-1 through 

Table 1-5 show estimates of the near term cost associated with various technologies for 

the five vehicle classes used in this analysis. These estimates shown in Table 1-1 through 

Table 1-5 are relative to a baseline vehicle having a multi-point, port fuel injected 

gasoline engine operating at a stoichiometric air-fuel ratio with fixed valve timing and lift and 

without any turbo or super charging and equipped with a 4-speed automatic transmission. 

This configuration was chosen as the baseline vehicle because it is the predominant 

technology package sold in the United States. Costs are presented in terms of their hardware 

incremental compliance cost. This means that they include all potential costs associated with 

their application on vehicles, not just the cost of their physical parts. A more detailed 

description of these and the following estimates of cost and effectiveness of CO2 reducing 

technologies can be found in Chapter 3 of the joint TSD, along with a more detailed 

description of the comprehensive technical evaluation underlying the estimates. 
Regulatory Impact Analysis 

1-6 

Table 1-1 EPA’s Incremental Piece Costs for Engine Technologies Marked up to include both Direct and 

Indirect Costs in 2016 (2007 Dollars per Vehicle) 

Vehicle Class 

Technology Incremental to 

Small Car Large Car Minivan Small Truck Large Truck 

Low friction lubricants Base engine $3 $3 $3 $3 $3 

 Engine friction reduction Base engine $50 $75 $75 $75 $100 

VVT – intake cam 

phasing Base engine $40 $80 $80 $80 $80 

VVT – coupled cam 

phasing Base engine $40 $80 $80 $80 $80 

VVT – dual cam phasing Base engine $73 $157 $157 $157 $157 

Cylinder deactivation Base engine n/a $150 $150 $150 $169 

Discrete VVLT Base engine $125 $181 $181 $181 $259 

OHC Engines 

Continuous VVLT Base engine $245 $449 $449 $449 $489 

Cylinder deactivation Base engine n/a $150 $150 $150 $169 

VVT – coupled cam 

phasing Base engine $40 $40 $40 $40 $40 

Discrete VVLT Base engine $141 $204 $204 $204 $291 

OHV Engines 

Continuous VVLT 

(includes conversion to 

Overhead Cam) 

Base engine w/ VVTcoupled $497 $1,048 $1,048 $1,048 $1,146 

 Camless valvetrain 

(electromagnetic) Base engine $501 $501 $501 $501 $501 

 GDI – stoichiometric I4 Base I4 $209 209 $209 209 209 

 GDI – stoichiometric V6 Base V6 n/a $301 $301 $301 $301 

 GDI – stoichiometric V8 Base V8 n/a $346 $346 n/a $346 

 GDI – lean burn GDI - stoich $623 $623 $623 $623 $623 

Turbocharge (single) Base I4 $397 n/a $397 n/a n/a 

Turbocharge (single) Base V6 n/a $420 $420 $420 $420 

Turbocharge (twin) Base engine $666 $666 $666 $666 $666 

Downsize to I4 DOHC V6 DOHC n/a -$384 -$384 -$384 -$384 

Downsize to I4 DOHC V6 SOHC n/a -$177 -$177 -$177 -$177 

Downsize to I4 DOHC V6 OHV n/a $265 $265 $265 $265 

Downsize to I4 DOHC I4 DOHC (larger) -$67 -$67 -$67 -$67 -$67 

Downsize to I3 DOHC I4 DOHC -$116 n/a n/a n/a n/a 

Downsize to V6 DOHC V8 DOHC n/a -$188 -$188 -$188 -$188 

Downsize to V6 DOHC V8 SOHC 2V n/a $59 $59 $59 $59 

Downsize to V6 DOHC V8 SOHC 3V n/a -$17 -$17 -$17 -$17 

Turbo w/o downsize 

Downsize w/o turbo 

Downsize to V6 DOHC V8 OHV n/a $310 $310 $310 $310 

Downsize to I4 DOHC 

& add turbo V6 DOHC w/o turbo n/a $149 $149 $149 $149 

Downsize to I4 DOHC 

& add turbo V6 SOHC w/o turbo n/a $323 $323 $323 $323 

Downsize to I4 DOHC 

& add turbo V6 OHV w/o turbo $771 $771 $771 $771 $771 

Downsize to I4 DOHC 

& add turbo 

I4 DOHC (larger) 

w/o turbo $391 n/a $391 n/a n/a 

Downsize to I3 DOHC 

& add turbo I4 DOHC w/o turbo $349 n/a n/a n/a n/a 

Downsize to V6 DOHC 

& add twin turbo V8 DOHC w/o turbo n/a $592 $592 $592 $592 

Turbo with downsize 

Downsize to V6 DOHC 

& add twin turbo 

V8 SOHC 2V w/o 

turbo n/a $816 $816 $816 $816 
Technology Packages, Cost and Effectiveness 

1-7 

Downsize to V6 DOHC 

& add twin turbo 

V8 SOHC 3V w/o 

turbo n/a $736 $736 $736 $736 

Downsize to V6 DOHC 

& add twin turbo V8 OHV w/o turbo n/a $1,099 $1,099 $1,099 $1,099 

 Convert to V6 DOHC V6 SOHC n/a $258 $258 $258 $258 

 Convert to V6 DOHC V6 OHV n/a $464 $464 $464 $464 

 Convert to V8 DOHC V8 SOHC 2V n/a $292 $292 $292 $292 

 Convert to V8 DOHC V8 SOHC 3V n/a $213 $213 $213 $213 

 Convert to V8 DOHC V8 OHV n/a $509 $509 $509 $509 

 Gasoline HCCI dualmode GDI - stoich $253 $375 $375 $375 $659 

 Diesel – Lean NOx trap Base gasoline engine $1,877 

 Diesel – urea SCR Base gasoline engine $2,655 $2,164 $2,164 $2,961 

Table 1-2 EPA’s Incremental Piece Costs for Transmission Technologies Marked up to include both 

Direct and Indirect Costs in 2016 (2007 Dollars per Vehicle) 

Vehicle Class 

Technology Incremental to Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

Aggressive shift logic Base trans $28 $28 $28 $28 $28 

Early torque converter lockup Base trans $25 $25 $25 $25 $25 

5-speed automatic 4-speed auto trans $90 $90 $90 $90 $90 

6-speed automatic 4-speed auto trans $99 $99 $99 $99 $99 

6-speed DCT – dry clutch 6-speed auto trans -$52 -$52 -$52 -$52 -$52 

6-speed DCT – wet clutch 6-speed auto trans -$7 -$7 -$7 -$7 -$7 

6-speed manual 5-speed manual trans $79 $79 $79 $79 $79 

CVT 4-speed auto trans $192 $224 $224 n/a n/a 

Table 1-3 EPA’s Incremental Piece Costs for Hybrid Technologies Marked up to include both Direct and 

Indirect Costs in 2016 (2007 Dollars per Vehicle) 

Vehicle Class Technology Incremental to 

Small Car Large Car Minivan Small Truck Large Truck 

Stop-Start Base engine & 

trans $351 $398 $398 $398 $437 

IMA/ISA/BSG 

(includes engine 

downsize) 

Base engine & 

trans $2,854 $3,612 $3,627 $3,423 $4,431 

2-Mode hybrid 

electric vehicle 

Base engine & 

trans $4,232 $5,469 $5,451 $4,943 $7,236 

Power-split 

hybrid electric 

vehicle 

Base engine & 

trans 

$3,967 $5,377 $5,378 $4,856 $7,210 

Plug-in hybrid 

electric vehicle 

IMA/ISA/BSG 

hybrid $6,922 $9,519 $9,598 $9,083 $12,467 

Plug-in hybrid 

electric vehicle 

Power-split 

hybrid $5,423 $7,431 $7,351 $7,128 $9,643 

Full electric 

vehicle 

Base engine & 

trans $27,628 n/a n/a n/a n/a 
Regulatory Impact Analysis 

1-8 

Table 1-4 EPA’s Incremental Piece Costs for Accessory Technologies Marked up to include both Direct 

and Indirect Costs in 2016 (2007 Dollars per Vehicle) 

Vehicle Class Technology Incremental to 

Small Car Large Car Minivan Small Truck Large Truck

Improved high efficiency 

alternator & 

electrification of 

accessories 

Base accessories $76 $76 $76 $76 $76 

Upgrade to 42 volt 

electrical system 

12 volt electrical 

system 

$86 $86 $86 $86 $86 

Electric power steering 

(12 or 42 volt) 

Base power 

steering $94 $94 $94 $94 $94 

Table 1-5 EPA’s Incremental Piece Costs for Vehicle Technologies Marked up to include both Direct and 

Indirect Costs in 2016 (2007 Dollars per Vehicle) 

Vehicle Class Technology Incremental to 

Small Car Large Car Minivan Small Truck Large Truck 

Aero drag reduction (20% 

on cars, 10% on trucks) Base vehicle $42 $42 $42 $42 $42 

Low rolling resistance 

tires Base tires $6 $6 $6 $6 $6 

Low drag brakes (ladder 

frame only) Base brakes n/a n/a n/a $63 $63 

Secondary axle disconnect 

(unibody only) Base vehicle $514 $514 $514 $514 n/a 

Front axle disconnect 

(ladder frame only) Base vehicle n/a n/a n/a $84 $84 

Table 1-6 through Table 1-10 summarize the CO2 reduction estimates of various 

technologies which can be applied to cars and light-duty trucks. A similar summary of costs 

is provided in Chapter 3 of the joint TSD and each of these estimates is discussed in more 

detail there. 
Technology Packages, Cost and Effectiveness 

1-9 

Table 1-6 Engine Technology Effectiveness 

Absolute CO2 Reduction (% from baseline vehicle) 

Technology 

Small Car Large 

Car Minivan Small Truck 

Large 

Truck 

Low friction lubricants – incremental to base engine 0.5 0.5 0.5 0.5 0.5 

Engine friction reduction – incremental to base engine 1-3 1-3 1-3 1-3 1-3 

Overhead Cam Branch 

VVT – intake cam phasing 2 1 1 1 2 

VVT – coupled cam phasing 3 4 2 3 4 

VVT – dual cam phasing 3 4 2 2 4 

Cylinder deactivation (includes imp. oil pump, 

if applicable) n.a. 6 6 6 6 

Discrete VVLT 4 3 3 4 4 

Continuous VVLT 5 6 4 5 5 

Overhead Valve Branch 

Cylinder deactivation (includes imp. oil 

pump, if applicable) n.a. 6 6 6 6 

VVT – coupled cam phasing 3 4 2 3 4 

Discrete VVLT 4 4 3 4 4 

Continuous VVLT (includes conversion to 

Overhead Cam) 5 6 4 5 5 

Camless valvetrain (electromagnetic) ** 5-15 5-15 5-15 5-15 5-15 

Gasoline Direct Injection–stoichiometric (GDI-S) 1-2 1-2 1-2 1-2 1-2 

Gasoline Direct Injection–lean burn (incremental to 

GDI-S) ** 8-10 9-12 9-12 9-12 10-14 

Gasoline HCCI dual-mode (incremental to GDI-S) ** 10-12 10-12 10-12 10-12 10-12 

Turbo+downsize (incremental to GDI-S) 5-7 5-7 5-7 5-7 5-7 

Diesel – Lean NOx trap[ ]* 15-26 

[25-35] 

21-32 

[30-40] 

21-32 

[30-40] 

21-32 

[30-40] 

21-32 

[30-40] 

Diesel – urea SCR [ ]* 15-26 

[25-35] 

21-32 

[30-40] 

21-32 

[30-40] 

21-32 

[30-40] 

21-32 

[30-40] 

* Note: estimates for % reduction in fuel consumption are presented in brackets. 

** Note: for reference only, not used in this rulemaking 
Regulatory Impact Analysis 

1-10 

Table 1-7 Transmission Technology Effectiveness 

Absolute CO2 Reduction (% from baseline vehicle) 

Technology Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

 5-speed automatic (from 4-speed auto) 2.5 2.5 2.5 2.5 2.5 

 Aggressive shift logic 1-2 1-2 1-2 1-2 1-2 

 Early torque converter lockup 0.5 0.5 0.5 0.5 0.5 

 6-speed automatic (from 4-speed auto) 4.5-6.5 4.5-6.5 4.5-6.5 4.5-6.5 4.5-6.5 

 6-speed AMT (from 4-speed auto) 9.5-14.5 9.5-14.5 9.5-14.5 9.5-14.5 9.5-14.5 

 6-speed manual (from 5-speed manual) 0.5 0.5 0.5 0.5 0.5 

Table 1-8 Hybrid Technology Effectiveness 

Absolute CO2 Reduction (% from baseline vehicle) 

Technology Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

 Stop-Start with 42 volt system 7.5 7.5 7.5 7.5 7.5 

 IMA/ISA/BSG (includes engine downsize) 30 25 20 20 20 

 2-Mode hybrid electric vehicle n.a. 40 40 40 25 

 Power-split hybrid electric vehicle 35 35 35 35 n.a. 

 Full-Series hydraulic hybrid 40 40 40 40 30 

 Plug-in hybrid electric vehicle 58 58 58 58 47 

 Full electric vehicle (EV) 100 100 n.a. n.a. n.a. 

Table 1-9 Accessory Technology Effectiveness 

Absolute CO2 Reduction (% from baseline vehicle) 

Technology Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

Improved high efficiency alternator & electrification of 

accessories (12 volt) 1-2 1-2 1-2 1-2 1-2 

Electric power steering (12 or 42 volt) 1.5 1.5-2 2 2 2 

Improved high efficiency alternator & electrification of 

accessories (42 volt) 2-4 2-4 2-4 2-4 2-4 

Table 1-10 Other Vehicle Technology Effectiveness 

Absolute CO2 Reduction (% from baseline vehicle) 

Technology Small 

Car 

Large 

Car Minivan Small 

Truck 

Large 

Truck 

 Aero drag reduction (20% on cars, 10% on trucks) 3 3 3 2 2 

 Low rolling resistance tires (10%) 1-2 1-2 1-2 1-2 n.a. 

 Low drag brakes (ladder frame only) n.a. n.a. n.a. 1 1 

 Secondary axle disconnect (unibody only) 1 1 1 1 n.a. 

 Front axle disconnect (ladder frame only) n.a. n.a. n.a. 1.5 1.5 
Technology Packages, Cost and Effectiveness 

1-11 

1.3 Package Cost and Effectiveness 

1.3.1 Explanation of Technology Packages 

Individual technologies can be used by manufactures to achieve incremental 

CO2reductions. However, as mentioned in Section 1.1, EPA believes that manufacturers are 

more likely to bundle technologies into “packages” to capture synergistic aspects and reflect 

progressively larger CO2reductions with additions or changes to any given package. In 

addition, manufacturers typically apply new technologies in packages during model 

redesigns—which occur once roughly 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. No commenter took issue with this concept. 

Therefore, the approach taken here is to group technologies into packages of 

increasing cost and effectiveness. EPA determined that 19 different vehicle types provided 

adequate resolution required to accurately model the entire fleet. This was the result of 

analyzing the existing light duty fleet with respect to vehicle size and powertrain 

configurations. All vehicles, including cars and trucks, were first distributed based on their 

relative size, starting from compact cars and working upward to large trucks. Next, each 

vehicle was evaluated for powertrain, specifically the engine size, I4, V6, and V8, and finally 

by the number of valves per cylinder. Note that each of these 19 vehicle types was mapped 

into one of the five classes of vehicles mentioned in Figure 1-1. While the five classes 

provide adequate resolution for the cost basis associated with technology application, they do 

not adequately account for all vehicle attributes such as base vehicle powertrain configuration 

and mass reduction. For example, costs and effectiveness estimates for the small car class 

were used to represent costs for three vehicle types: subcompact cars, compact cars, and 

small multi-purpose vehicles (MPV) equipped with a 4-cylinder engine, however the mass 

reduction associated for each of these vehicle types was based on the vehicle type sales 

weighted average. Note also that these 19 vehicle types span the range of vehicle footprints—

smaller footprints for smaller vehicles and larger footprints for larger vehicles—which serve 

as the basis for the GHG standards. 

Within each of the 19 vehicle types multiple technology packages were created in 

increasing technology content and, hence, increasing effectiveness. Important to note is that 

the effort in creating the packages attempted to maintain a constant utility 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. The initial packages represent what a 

manufacturer will most likely implement on all vehicles, including low rolling resistance tires, 

low friction lubricants, engine friction reduction, aggressive shift logic, early torque converter 

lock-up, improved electrical accessories, and low drag brakes. Subsequent packages include 

advanced gasoline engine and transmission technologies such as turbo/downsizing, GDI, mass 

reduction and dual-clutch transmission. The most technologically advanced packages within 

a segment included HEV, PHEV and EV designs. The end result being a list of several 

packages for each of 19 different vehicle types from which a manufacturer could choose in 
Regulatory Impact Analysis 

1-12 

order to modify its fleet such that compliance could be achieved. No commenter took issue 

with this concept or the list of packages that were developed. 

The final step in creating the vehicle packages was to evaluate each package within 

the 19 vehicle types for cost-effectiveness. This was accomplished by dividing the 

incremental cost of the technology package by its incremental effectiveness and assessing the 

overall step in cost-effectiveness. Technology packages that demonstrated little to no increase 

in effectiveness and a significant increase in cost were eliminated as a choice for the model. 

This process provided several positive aspects in the package creation: 

(1) Vehicle packages were not limited by any preconceived assumptions of which 

technologies should be more prominent. An example of this is turbo-downsizing a 

V6 engine. In some cases the GDI V6 with advanced valvetrain technology was 

just as effective as a turbo charge I4, thus excluding the additional cost of turbo 

charging; 

(2) The OMEGA model was allowed to apply packages in an increasing order of both 

effectiveness and cost. 

 Some of the intermediate packages were not cost-effective. As a result, the model 

might be blocked from choosing a subsequent package that was cost-effective. Most of the 

diesel packages and some of the hybrid packages exhibited this condition. Due to the high 

cost of these packages, and effectiveness on par with advanced gas, the model would not 

move through these packages and choose a more cost effective package, thus blocking the 

model’s logical progression. This is the reason for the absence of diesel and hybrid packages 

in some of the 19 vehicle types available for the OMEGA model. The specific criteria used to 

remove certain packages from use the model inputs is discussed further below. It is important 

to note that the burning of diesel fuel generates approximately 15% more CO2than gasoline. 

As this rule is based on the reduction of CO2emissions and not on fuel economy, this creates 

an additional effectiveness disadvantage for the diesel packages as compared to the advanced 

gas and gas hybrid packages. 
Technology Packages, Cost and Effectiveness 

1-13 

Small car

Subcompact; 16V I4 DOHC (T1)

Large car

Minivan

Small 

truck

Large 

truck

Compact; 16V I4 DOHC (T2)

Midsize car/small MPV; 16V I4 DOHC (T3)

Midsize/large car; 24V V6 DOHC (T5)

Midsize/large car; 32V V8 DOHC (T6)

Compact car/small MPV; 24V V6 DOHC (T4)

Midsize MPV/small truck; 16V I4 DOHC (T7)

Large MPV; 12V V6 SOHC (T9)

Large MPV; 16V V8 SOHC (T10)

Large MPV; 24V V6 DOHC (T16)

Large MPV; 32V V8 DOHC (T17)

Midsize MPV/small truck; 12V V6 SOHC (T8)

Midsize MPV/small truck; 24V V6 DOHC (T15)

Large truck; 12V V6 SOHC (T11)

Large truck/MPV; 12V V6 OHV (T12)

Large truck; 16V V8 OHV (T13)

Large truck/van; 16V V8 SOHC (T14)

Large truck/van; 24V V6 DOHC (T18)

Large truck/van; 32V V8 DOHC (T19)

Figure 1-1 Scaling classes to Vehicle Type Mapping 

1.3.2 Technology Package Costs & Effectiveness 

As described above, technology packages were created for each of 19 different vehicle 

types. These packages are described in Table 1-11 and the 2016 MY costs for each package 

are also presented. Note that Table 1-11 includes all the packages created and considered by 

EPA. Only a subset of these packages was actually used as inputs to the OMEGA model 

because some of the packages were not desirable from a cost effectiveness standpoint (in 

other words, some packages would be skipped over if the next package provides superior cost 

effectiveness). Table 1-12 shows the package costs for the packages that were actually used 

as inputs to the OMEGA model. This table shows the package costs for each model year 

2012 through 2022 and later. This shows the impact of both learning effects and short-term 

versus long-term indirect cost markups on the package costs. For details of the learning 

effects and indirect cost markups used in this analysis refer to Chapter 3 of the Joint TSD. By 

taking a simple average of the technology package costs for each year shown in Table 1-12 

and then normalizing the averages to the 2016 model year average, the package costs for each 

year can be expressed as a percentage relative to 2016. These results are shown in Table 

1-13. This table shows that package costs in 2012 are, on average, 118% of the costs for 

2016. This higher cost is due to backing out the learning effects that are built into the 2016 

model year estimates. For 2014, the costs are 109% of those for 2016 as learning has 

occurred between 2012 and 2014. The costs for 2022 are 94% of those for 2016. This is the 

result of the long-term ICM kicking in as some indirect costs are no longer attributable to the 

GHG program. Table 1-12 also shows the effectiveness of each package used in the OMEGA 

model (note that the effectiveness of packages does not change with model year). No 

commenter took issue with this concept. 
Regulatory Impact Analysis 

1-14 

Table 1-11 Package Descriptions and 2016MY Costs for 19 Vehicle Types (T1-T19), All Packages Considered, Costs 

Vehicle Technology Package # Engine Transmission System Voltage Camshaft changes (not used for downsized engines)

Lubes

Friction Rdxn

Fuel system

Downsize

Aftertreatment

Aggressive shift

Early torque lock

100 1.5L 4V DOHC I4 AT 4 spd 12V 

101 1.5L 4V I4 AT 4 spd 12V LUB EFR ASL TORQ IA102 1.5L 4V I4 + CCP DCT 6 spd 12V LUB EFR IA103 1.2L 4V I3 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4>I3 I4 to I3 IA104 1.0L 4V I3 (small) Turbo + DCP + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4>I3 I4 to I3 IA105 29KWH (FTP 150 miles @160 WH/mi) N/A EV 

200 2.4L 4V DOHC I4 AT 4 spd 12V 

201 2.4L 4V I4 AT 4 spd 12V LUB EFR ASL TORQ IA202 2.0L 4V I4 + CCP + GDI AT 6 spd 12V LUB EFR GDI-I4 I4 to I4 ASL TORQ IA203 2.0L 4V I4 + CCP + GDI DCT 6 spd 12V LUB EFR GDI-I4 I4 to I4 IA204 2.0L 4V I4 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4 I4 to I4 IA205 1.5L 4V I4 Turbo + DCP + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4 I4 to I4 IA206 1.2L 4V I4 HEV (IMA) + GDI dry DCT 6 spd HEV LUB EFR GDI-I4 I4 to I4 207 1.2L 4V I4 Plug-in HEV (IMA) + GDI (50% UF) dry DCT 6 spd HEV LUB EFR GDI-I4 I4 to I4 

300 2.4L 4V DOHC I4 AT 4 spd 12V 

301 2.4L 4V I4 AT 4 spd 12V LUB EFR ASL TORQ IA302 2.2L 4V I4 + CCP + GDI AT 6 spd 12V LUB EFR GDI-I4 I4 to I4 ASL TORQ IA303 2.2L 4V I4 + CCP + DVVL + GDI DCT 6 spd 12V LUB EFR GDI-I4 I4 to I4 IA304 2.2L 4V I4 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4 I4 to I4 IA305 1.6L 4V I4 Turbo + DCP + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4 I4 to I4 IA307 1.4L 4V I4 Turbo HEV (IMA) + GDI dry DCT 6 spd HEV LUB EFR GDI-I4 I4 to I4 308 1.8L 4V I4 HEV (Power Split) + GDI N/A HEV LUB EFR GDI-I4 I4 to I4 309 1.8L 4V I4 Plug-in HEV (Power Split) + GDI (50% UF) N/A HEV LUB EFR GDI-I4 I4 to I4 

400 3.0L 4V DOHC V6 AT 4 spd 12V 

401 3.0L 4V V6 AT 4 spd 12V LUB EFR ASL TORQ IA402 2.0L 4V I4 Turbo + DCP + GDI AT 6 spd 12V LUB EFR GDI-V6>I4 V6 DOHC to I4 ASL TORQ IA403 2.0L 4V I4 Turbo + DCP + GDI DCT 6 spd 12V LUB EFR GDI-V6>I4 V6 DOHC to I4 IA404 2.0L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 DOHC to I4 IA406 2.4L I4 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 407 1.5L 4V I4 Turbo HEV (IMA) + GDI DCT 6 spd HEV LUB EFR GDI-V6>I4 V6 DOHC to I4 408 2.8L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V6 

Subcompact 

Car I4 

(Type 1)

Compact Car I4 

(Type 2)

Midsize Car/Small 

MPV (unibody) I4

(Type 3)

Compact 

Car/Small MPV 

(unibody) V6

(Type 4)
Technology P

1-15 

Table 1-11 Continued 

Vehicle Technology Package # Engine Transmission System Voltage Camshaft changes (not used for downsized engines)

Lubes

Friction Rdxn

Fuel system

Downsize

Aftertreatment

Aggressive shift

Early torque lock

500 3.3L 4V DOHC V6 AT 4 spd 12V 

501 3.3L 4V V6 AT 4 spd 12V LUB EFR ASL TORQ IA502 3.0L 4V V6 + CCP +GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA503 3.0L 4V V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA504 3.0L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6 IA505 2.2L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 DOHC to I4 IA508 2.5L 4V I4 HEV (Power Split) + GDI N/A HEV LUB EFR GDI-V6>I4 V6 DOHC to I4 

600 4.5L 4V DOHC V8 AT 4 spd 12V 

601 4.5L 4V V8 AT 4 spd 12V LUB EFR ASL TORQ IA602 4.0L 4V V6 + CCP+ GDI AT 6 spd 12V LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC ASL TORQ IA603 4.0L 4V V6 + CCP + Deac + GDI AT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC ASL TORQ IA604 4.0L 4V V6 + CCP+ Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC IA605 3.0L 4V V6 Turbo + DCP + GDI AT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC ASL TORQ IA606 3.0L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC IA608 3.0L 4V V6 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 609 3.0L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC 

700 2.6L 4V DOHC I4 (I5) AT 4 spd 12V 

701 2.6L 4V I4 AT 4 spd 12V LUB EFR ASL TORQ IA702 2.4L 4V I4 + CCP + GDI AT 6 spd 12V LUB EFR GDI-I4 I4 to I4 ASL TORQ IA703 2.4L 4V I4 + CCP + DVVL + GDI DCT 6 spd 12V LUB EFR GDI-I4 I4 to I4 IA704 2.4L 4V I4 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4 I4 to I4 IA705 2.0L 4V I4 Turbo + DCP + GDI dry DCT 6 spd 42V S-S LUB EFR GDI-I4 I4 to I4 IA707 1.8L 4V I4 Turbo HEV (IMA) + GDI dry DCT 6 spd HEV LUB EFR GDI-I4 I4 to I4 708 1.8L 4V I4 Turbo HEV (Power Split) + GDI N/A HEV LUB EFR GDI-I4 I4 to I4 709 1.8L 4V I4 Turbo Plug-in HEV (IMA) + GDI (50% UF) dry DCT 6 spd HEV LUB EFR GDI-I4 I4 to I4 

800 3.7L 2V SOHC V6 AT 4 spd 12V 

801 3.7L 2V SOHC V6 AT 4 spd 12V LUB EFR ASL TORQ IA802 3.2L 2V SOHC V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA803 3.2L 2V SOHC V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA804 2.8L 4V V6 + CCP + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA805 2.8L 4V V6 + CCP + DVVL + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA806 2.8L 4V V6 + CCP + Deac + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA807 2.8L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S V6 SOHC to V6 DOHC LUB EFR GDI-V6 IA808 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 SOHC to I4 IA811 2.8L I4 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 812 3.0L 4V V6 HEV (IMA) + CCP + Deac + GDI DCT 6 spd HEV V6 SOHC to V6 DOHC LUB EFR GDI-V6 813 3.0L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV V6 SOHC to V6 DOHC LUB EFR GDI-V6 

Midsize/Large 

Car V6

(Type 5)

Midsize Car/Large 

Car V8

(Type 6)

Mid-sized MPV 

(unibody)/Small 

Truck I4

(Type 7)

Midsize MPV 

(unibody)/Small Truck 

V6/V8

(Type 8)
Regulatory Impact Analysis 

1-16 

Table 1-11 Continued

Vehicle Technology Package # Engine Transmission System Voltage Camshaft changes (not used for downsized engines)

Lubes

Friction Rdxn

Fuel system

Downsize

Aftertreatment

Aggressive shift

Early torque lock

900 4.0L 2V SOHC V6 AT 4 spd 12V 

901 4.0L 2V SOHC V6 AT 4 spd 12V LUB EFR ASL TORQ IA902 3.6L 2V SOHC V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA903 3.6L 2V SOHC V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA904 3.2L 4V V6 + CCP + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA905 3.2L 4V V6 + CCP + Deac + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA906 3.2L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S V6 SOHC to V6 DOHC LUB EFR GDI-V6 IA907 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 SOHC to I4 IA910 2.0L 4V I4 Turbo HEV (IMA) + GDI DCT 6 spd HEV LUB EFR GDI-V6>I4 V6 SOHC to I4 911 3.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV V6 SOHC to V6 DOHC LUB EFR GDI-V6 

1000 4.7L 2V SOHC V8 AT 4 spd 12V 

1001 4.7L 2V SOHC V8 AT 4 spd 12V LUB EFR ASL TORQ IA1002 4.4L 2V SOHC V8 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V8 ASL TORQ IA1003 4.4L 2V SOHC V8 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V8 ASL TORQ IA1004 4.2L 4V V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V8>V6 V8 SOHC to V6 DOHC ASL TORQ IA1005 4.2L 4V V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V8>V6 V8 SOHC to V6 DOHC ASL TORQ IA1006 4.2L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 SOHC to V6 DOHC IA1007 2.8L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 SOHC to V6 DOHC IA1010 3.0L V6 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 1011 4.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V8>V6 V8 SOHC to V6 DOHC 

1100 4.2L 2V SOHC V6 AT 4 spd 12V 

1101 4.2L 2V SOHC V6 AT 4 spd 12V LUB EFR ASL TORQ IA1102 3.9L 2V SOHC V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1103 3.9L 2V SOHC V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1104 3.6L 4V V6 + CCP + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA1105 3.6L 4V V6 + CCP + DVVL + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA1106 3.6L 4V V6 + CCP + Deac + GDI AT 6 spd 12V V6 SOHC to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA1107 3.6L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S V6 SOHC to V6 DOHC LUB EFR GDI-V6 IA1108 2.5L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 SOHC to I4 IA1111 2.8L I4 Turbo Diesel DCT 6 spd 12v LUB EFR Diesel Diesel-SCR 1112 3.6L 4V V6 HEV (IMA) + CCP + Deac + GDI DCT 6 spd HEV V6 SOHC to V6 DOHC LUB EFR GDI-V6 1113 3.6L 4V V6 HEV (2-mode)+ CCP + Deac + GDI N/A HEV V6 SOHC to V6 DOHC LUB EFR GDI-V6 

1200 3.8L 2V OHV V6 AT 4 spd 12V 

1201 3.8L 2V OHV V6 AT 4 spd 12V LUB EFR ASL TORQ IA1202 3.2L 4V DOHC V6 + CCP + GDI AT 6 spd 12V V6 OHV to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA1203 3.2L 4V DOHC V6 + CCP + Deac + GDI AT 6 spd 12V V6 OHV to V6 DOHC LUB EFR GDI-V6 ASL TORQ IA1204 3.2L 4V DOHC V6 + CCP + Deac + GDI DCT 6 spd 42V S-S V6 OHV to V6 DOHC LUB EFR GDI-V6 IA1205 2.5L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 OHV to I4 DOHC IA

Large MPV (unibody) 

V6

(Type 9)

Large MPV (unibody) 

V8

(Type 10)

Large Truck (+ Van) V6

(Type 11)

Large Truck + 

Large MPV V6

(T12)
Technology P

1-17 

Table 1-11 Continued

Vehicle Technology Package # Engine Transmission System Voltage Camshaft changes (not used for downsized engines)

Lubes

Friction Rdxn

Fuel system

Downsize

Aftertreatment

Aggressive shift

Early torque lock

1300 5.7L 2V OHV V8 AT 4 spd 12V 

1301 5.7L 2V OHV V8 AT 4 spd 12V LUB EFR ASL TORQ IA1302 5.2L 2V OHV V8 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V8 ASL TORQ IA1303 5.2L 2V OHV V8 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V8 ASL TORQ IA1304 4.6L 4V V8 + CCP + GDI AT 6 spd 12V V8 OHV to V8 DOHC LUB EFR GDI-V8 ASL TORQ IA1305 4.6L 4V V8 + CCP + Deac + GDI AT 6 spd 12V V8 OHV to V8 DOHC LUB EFR GDI-V8 ASL TORQ IA1306 4.6L 4V V8 + CCP + Deac + GDI DCT 6 spd 42V S-S V8 OHV to V8 DOHC LUB EFR GDI-V8 IA1307 3.5L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 OHV to V6 DOHC IA1310 3.5L V6 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 1311 4.6L 4V V8 HEV (2-mode) + CCP + Deac + GDI N/A HEV V8 OHV to V8 DOHC LUB EFR GDI-V8 

1400 5.4L 3V SOHC V8 AT 4 spd 12V 

1401 5.4L 3V SOHC V8 AT 4 spd 12V LUB EFR ASL TORQ IA1402 4.6L 4V DOHC V8 + CCP + GDI AT 6 spd 12V V8 SOHC 3V to V8 DOHC LUB EFR GDI-V8 ASL TORQ IA1403 4.6L 4V DOHC V8 + CCP + Deac + GDI AT 6 spd 12V V8 SOHC 3V to V8 DOHC LUB EFR GDI-V8 ASL TORQ IA1404 4.6L 4V DOHC V8 + CCP + Deac + GDI DCT 6 spd 42V S-S V8 SOHC 3V to V8 DOHC LUB EFR GDI-V8 IA1405 3.5L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 SOHC 3V to V6 DOHC IA

1500 3.2L 4V DOHC V6 AT 4 spd 12V 

1501 3.2L 4V V6 AT 4 spd 12V LUB EFR ASL TORQ IA1502 2.8L 4V V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1503 2.8L 4V V6 + CCP + DVVL + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1504 2.8L 4V V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1505 2.8L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6 IA1506 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 DOHC to I4 IA1509 2.8L I4 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 1510 3.0L 4V V6 HEV (IMA) + CCP + Deac + GDI DCT 6 spd HEV LUB EFR GDI-V6 1511 3.0L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V6 

1600 3.5L 4V DOHC V6 AT 4 spd 12V 

1601 3.5L 4V V6 AT 4 spd 12V LUB EFR ASL TORQ IA1602 3.2L 4V V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1603 3.2L 4V V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1604 3.2L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6 IA1605 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 DOHC to I4 IA1608 2.0L 4V I4 Turbo HEV (IMA) + GDI DCT 6 spd HEV LUB EFR GDI-V6>I4 V6 DOHC to I4 1609 3.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V6 

Large Truck (+ Van) V8

(Type 13)

Large Truck 

(+Van) V8

(Type 14)

Midsize MPV 

(unibody)/Small Truck 

V6/V8

(Type 15)

Large MPV 

(unibody) V6

(Type 16)
Regulatory Impact Analysis 

1-18 

Table 1-11 Continued

Vehicle Technology Package # Engine Transmission System Voltage Camshaft changes (not used for downsized engines)

Lubes

Friction Rdxn

Fuel system

Downsize

Aftertreatment

Aggressive shift

Early torque lock

1700 4.6L 4V DOHC V8 AT 4 spd 12V 

1701 4.6L 4V V8 AT 4 spd 12V LUB EFR ASL TORQ IA1702 4.2L 4V V6 + CCP + GDI AT 8 spd 12V LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC ASL TORQ IA1703 4.2L 4V V6 + CCP + Deac + GDI AT 8 spd 12V LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC ASL TORQ IA1704 4.2L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC IA1705 2.8L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC IA1708 3.0L V6 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 1709 4.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC 

1800 4.0L 4V DOHC V6 AT 4 spd 12V 

1801 4.0L 4V V6 AT 4 spd 12V LUB EFR ASL TORQ IA1802 3.6L 4V V6 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1803 3.6L 4V V6 + CCP + DVVL + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1804 3.6L 4V V6 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V6 ASL TORQ IA1805 3.6L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6 IA1806 2.5L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V6>I4 V6 DOHC to I4 IA1809 2.8L I4 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 1810 3.6L 4V V6 HEV (IMA) + CCP + Deac + GDI DCT 6 spd HEV LUB EFR GDI-V6 1811 3.6L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V6 

1900 5.6L 4V DOHC V8 AT 4 spd 12V 

1901 5.6L 4V V8 AT 4 spd 12V LUB EFR ASL TORQ IA1902 4.6L 4V V8 + CCP + GDI AT 6 spd 12V LUB EFR GDI-V8 ASL TORQ IA1903 4.6L 4V V8 + CCP + Deac + GDI AT 6 spd 12V LUB EFR GDI-V8 ASL TORQ IA1904 4.6L 4V V8 + CCP + Deac + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8 IA1905 3.5L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S LUB EFR GDI-V8>V6 V8 DOHC to V6 DOHC IA1908 3.5L V6 Turbo Diesel DCT 6 spd 12V LUB EFR Diesel Diesel-SCR 1909 4.6L 4V V8 HEV (2-mode) + CCP + Deac + GDI N/A HEV LUB EFR GDI-V8 

Large Truck (+ 

Van) V8

(Type 19)

Large MPV 

(unibody) V8

(Type 17)

Large Truck (+ Van) V6

(Type 18)

Notes to Table 1-11: DOHC=dual overhead cam; SOHC=single overhead cam; OHV=overhead valve; AT=automatic transmission; DCT=dual clutch transmission; LUB=

reduction; ASL=aggressive shift logic; TORQ=early torque converter lockup; IACC=improved accessories; EPS=electric power steering; AERO 1=imresistance tires. 
Technology P

1-19 

Table 1-12 Package Costs & Effectiveness for 2012-2022+MY for 19 Vehicle Types (T1-T19), Packages Used as Inputs to the OMEG

Technology

Package Engine Transmission System Voltage 2012 2013 2014 2015 2016 2017 2018 

100 1.5L 4V DOHC I4 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

101 1.5L 4V I4 AT 4 spd 12V $206 $202 $197 $193 $189 $189 $189 102 1.5L 4V I4 + CCP DCT 6 spd 12V $578 $562 $547 $532 $518 $518 $518 103 1.2L 4V I3 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S $1,506 $1,479 $1,343 $1,230 $1,205 $1,205 $1,205 $104 1.0L 4V I3 (small) Turbo + DCP + GDI dry DCT 6 spd 42V S-S $2,144 $2,098 $1,943 $1,812 $1,770 $1,770 $1,770 $

200 2.4L 4V DOHC I4 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

201 2.4L 4V I4 AT 4 spd 12V $206 $202 $197 $193 $189 $189 $189 203 2.0L 4V I4 + CCP + GDI DCT 6 spd 12V $748 $728 $708 $688 $669 $669 $669 204 2.0L 4V I4 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S $1,810 $1,774 $1,629 $1,508 $1,475 $1,475 $1,475 $207 1.2L 4V I4 Plug-in HEV (IMA) + GDI (50% UF) dry DCT 6 spd HEV $15,595 $15,587 $12,525 $10,074 $10,066 $10,066 $10,066 $1

300 2.4L 4V DOHC I4 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

301 2.4L 4V I4 AT 4 spd 12V $206 $202 $197 $193 $189 $189 $189 303 2.2L 4V I4 + CCP + DVVL + GDI DCT 6 spd 12V $1,018 $989 $961 $934 $908 $908 $908 304 2.2L 4V I4 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S $1,882 $1,843 $1,697 $1,573 $1,538 $1,538 $1,538 $309 1.8L 4V I4 Plug-in HEV (Power Split) + GDI (50% UF) N/A HEV $13,222 $13,081 $11,250 $9,762 $9,634 $9,634 $9,634 $

400 3.0L 4V DOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

401 3.0L 4V V6 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 403 2.0L 4V I4 Turbo + DCP + GDI DCT 6 spd 12V $1,176 $1,143 $1,111 $1,081 $1,051 $1,051 $1,051 $404 2.0L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,006 $1,967 $1,804 $1,668 $1,633 $1,633 $1,633 $406 2.4L I4 Turbo Diesel DCT 6 spd 12V $3,063 $2,974 $2,887 $2,803 $2,722 $2,722 $2,722 $408 2.8L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $9,249 $9,230 $7,508 $6,127 $6,109 $6,109 $6,109 $

500 3.3L 4V DOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

501 3.3L 4V V6 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 502 3.0L 4V V6 + CCP +GDI AT 6 spd 12V $1,102 $1,071 $1,041 $1,013 $985 $985 $985 503 3.0L 4V V6 + CCP + Deac + GDI AT 6 spd 12V $1,387 $1,348 $1,310 $1,274 $1,238 $1,238 $1,238 $505 2.2L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,311 $2,263 $2,092 $1,947 $1,903 $1,903 $1,903 $508 2.5L 4V I4 HEV (Power Split) + GDI N/A HEV $6,008 $5,831 $5,658 $5,491 $5,329 $5,329 $5,329 $

600 4.5L 4V DOHC V8 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

601 4.5L 4V V8 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 602 4.0L 4V V6 + CCP+ GDI AT 6 spd 12V $912 $887 $863 $839 $817 $817 $817 604 4.0L 4V V6 + CCP+ Deac + GDI DCT 6 spd 42V S-S $1,863 $1,828 $1,670 $1,538 $1,506 $1,506 $1,506 $609 3.0L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $9,065 $9,052 $7,330 $5,951 $5,939 $5,939 $5,939 $
Regulatory Impact Analysis 

1-20 

Table 1-12 Continued

Technology

Package Engine Transmission System Voltage 2012 2013 2014 2015 2016 2017 2018 

700 2.6L 4V DOHC I4 (I5) AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

701 2.6L 4V I4 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 703 2.4L 4V I4 + CCP + DVVL + GDI DCT 6 spd 12V $1,072 $1,042 $1,014 $986 $959 $959 $959 704 2.4L 4V I4 + CCP + DVVL + GDI dry DCT 6 spd 42V S-S $2,181 $2,136 $1,969 $1,828 $1,788 $1,788 $1,788 $708 1.8L 4V I4 Turbo HEV (Power Split) + GDI N/A HEV $6,884 $6,680 $6,482 $6,290 $6,104 $6,104 $6,104 $709 1.8L 4V I4 Turbo Plug-in HEV (IMA) + GDI (50% UF) dry DCT 6 spd HEV $21,528 $21,504 $17,349 $14,020 $13,999 $13,999 $13,999 $1

800 3.7L 2V SOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

801 3.7L 2V SOHC V6 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 802 3.2L 2V SOHC V6 + CCP + GDI AT 6 spd 12V $1,126 $1,094 $1,064 $1,035 $1,006 $1,006 $1,006 $803 3.2L 2V SOHC V6 + CCP + Deac + GDI AT 6 spd 12V $1,295 $1,259 $1,223 $1,189 $1,156 $1,156 $1,156 $808 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,588 $2,532 $2,353 $2,200 $2,149 $2,149 $2,149 $813 3.0L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $8,746 $8,718 $7,146 $5,884 $5,858 $5,858 $5,858 $

900 4.0L 2V SOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

901 4.0L 2V SOHC V6 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 902 3.6L 2V SOHC V6 + CCP + GDI AT 6 spd 12V $1,149 $1,117 $1,086 $1,056 $1,027 $1,027 $1,027 $903 3.6L 2V SOHC V6 + CCP + Deac + GDI AT 6 spd 12V $1,318 $1,281 $1,245 $1,210 $1,176 $1,176 $1,176 $907 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,665 $2,607 $2,425 $2,270 $2,217 $2,217 $2,217 $911 3.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $9,541 $9,513 $7,782 $6,392 $6,367 $6,367 $6,367 $

1000 4.7L 2V SOHC V8 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1001 4.7L 2V SOHC V8 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 1004 4.2L 4V V6 + CCP + GDI AT 6 spd 12V $1,239 $1,204 $1,170 $1,138 $1,106 $1,106 $1,106 $1006 4.2L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S $2,632 $2,574 $2,394 $2,240 $2,187 $2,187 $2,187 $1011 4.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $9,316 $9,294 $7,570 $6,187 $6,167 $6,167 $6,167 $

1100 4.2L 2V SOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1101 4.2L 2V SOHC V6 AT 4 spd 12V $256 $252 $247 $243 $239 $239 $239 1102 3.9L 2V SOHC V6 + CCP + GDI AT 6 spd 12V $1,057 $1,029 $1,001 $974 $948 $948 $948 1103 3.9L 2V SOHC V6 + CCP + Deac + GDI AT 6 spd 12V $1,248 $1,213 $1,180 $1,148 $1,117 $1,117 $1,117 $1108 2.5L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,778 $2,721 $2,528 $2,365 $2,312 $2,312 $2,312 $

1200 3.8L 2V OHV V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1201 3.8L 2V OHV V6 AT 4 spd 12V $256 $252 $247 $243 $239 $239 $239 1202 3.2L 4V DOHC V6 + CCP + GDI AT 6 spd 12V $1,582 $1,537 $1,495 $1,453 $1,413 $1,413 $1,413 $1204 3.2L 4V DOHC V6 + CCP + Deac + GDI DCT 6 spd 42V S-S $3,145 $3,076 $2,873 $2,700 $2,637 $2,637 $2,637 $1205 2.5L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $3,285 $3,212 $3,005 $2,827 $2,760 $2,760 $2,760 $
Technology P

1-21 

Table 1-12 Continued 

Technology

Package Engine Transmission System Voltage 2012 2013 2014 2015 2016 2017 2018 

1300 5.7L 2V OHV V8 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1301 5.7L 2V OHV V8 AT 4 spd 12V $256 $252 $247 $243 $239 $239 $239 1302 5.2L 2V OHV V8 + CCP + GDI AT 6 spd 12V $1,107 $1,077 $1,048 $1,020 $993 $993 $993 1303 5.2L 2V OHV V8 + CCP + Deac + GDI AT 6 spd 12V $1,298 $1,262 $1,228 $1,194 $1,162 $1,162 $1,162 $1306 4.6L 4V V8 + CCP + Deac + GDI DCT 6 spd 42V S-S $3,397 $3,320 $3,110 $2,929 $2,859 $2,859 $2,859 $1307 3.5L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S $3,910 $3,819 $3,593 $3,398 $3,314 $3,314 $3,314 $

1400 5.4L 3V SOHC V8 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1401 5.4L 3V SOHC V8 AT 4 spd 12V $256 $252 $247 $243 $239 $239 $239 1402 4.6L 4V DOHC V8 + CCP + GDI AT 6 spd 12V $1,393 $1,355 $1,317 $1,281 $1,246 $1,246 $1,246 $1404 4.6L 4V DOHC V8 + CCP + Deac + GDI DCT 6 spd 42V S-S $3,063 $2,996 $2,796 $2,624 $2,564 $2,564 $2,564 $1405 3.5L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S $3,501 $3,421 $3,208 $3,024 $2,952 $2,952 $2,952 $

1500 3.2L 4V DOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1501 3.2L 4V V6 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 1502 2.8L 4V V6 + CCP + GDI AT 6 spd 12V $1,126 $1,094 $1,064 $1,035 $1,006 $1,006 $1,006 $1505 2.8L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S $2,078 $2,037 $1,873 $1,734 $1,697 $1,697 $1,697 $1511 3.0L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $8,454 $8,435 $6,872 $5,618 $5,600 $5,600 $5,600 $

1600 3.5L 4V DOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1601 3.5L 4V V6 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 1602 3.2L 4V V6 + CCP + GDI AT 6 spd 12V $1,149 $1,117 $1,086 $1,056 $1,027 $1,027 $1,027 $1605 2.4L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,099 $2,057 $1,892 $1,753 $1,715 $1,715 $1,715 $1609 3.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $9,249 $9,230 $7,508 $6,127 $6,109 $6,109 $6,109 $

1700 4.6L 4V DOHC V8 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1701 4.6L 4V V8 AT 4 spd 12V $231 $227 $222 $218 $214 $214 $214 1702 4.2L 4V V6 + CCP + GDI AT 8 spd 12V $960 $934 $908 $884 $860 $860 $860 1704 4.2L 4V V6 + CCP + Deac + GDI DCT 6 spd 42V S-S $2,354 $2,304 $2,132 $1,986 $1,941 $1,941 $1,941 $1709 4.2L 4V V6 HEV (2-mode) + CCP + Deac + GDI N/A HEV $9,037 $9,024 $7,308 $5,933 $5,921 $5,921 $5,921 $

1800 4.0L 4V DOHC V6 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1801 4.0L 4V V6 AT 4 spd 12V $256 $252 $247 $243 $239 $239 $239 1802 3.6L 4V V6 + CCP + GDI AT 6 spd 12V $1,057 $1,029 $1,001 $974 $948 $948 $948 1806 2.5L 4V I4 Turbo + DCP + GDI DCT 6 spd 42V S-S $2,582 $2,530 $2,343 $2,185 $2,138 $2,138 $2,138 $

1900 5.6L 4V DOHC V8 AT 4 spd 12V $0 $0 $0 $0 $0 $0 $0 

1901 5.6L 4V V8 AT 4 spd 12V $256 $252 $247 $243 $239 $239 $239 1902 4.6L 4V V8 + CCP + GDI AT 6 spd 12V $1,152 $1,121 $1,091 $1,061 $1,033 $1,033 $1,033 $1904 4.6L 4V V8 + CCP + Deac + GDI DCT 6 spd 42V S-S $2,822 $2,763 $2,569 $2,405 $2,351 $2,351 $2,351 $1905 3.5L 4V V6 Turbo + DCP + GDI DCT 6 spd 42V S-S $3,338 $3,263 $3,055 $2,875 $2,807 $2,807 $2,807 $
Regulatory Impact Analysis 

1-22 

Table 1-13 Package Costs Measured Relative to the Package Costs for the 2016MY 

YEAR PACKAGE COSTS 

RELATIVE TO 2016

2012 119% 

2013 117% 

2014 109% 

2015 102% 

2016 100% 

2017 100% 

2018 100% 

2019 100% 

2020 100% 

2021 100% 

2022+ 94% 

A number of the packages shown in Table 1-11 are not shown in Table 1-12 

because it was determined that those packages were not cost effective relative to other 

packages available for a specific vehicle type. The process used to make these 

determinations is discussed below. 

As discussed in detail in Chapter 4 of this RIA, the order of technology which will 

be applied to any specific vehicle by the OMEGA model is set in the Technology input 

file. Since the goal of adding technology is to move the manufacturer closer to 

compliance with the GHG standard, the available technology packages should be placed 

in order of their total GHG effectiveness. Otherwise, the model is adding technology 

which moves the manufacturer further from compliance. At the same time, the cost of 

each successive package should be greater than that of the prior package. In this case, a 

greater degree of GHG reduction is available at a lower cost. The package with the 

greater cost and lower overall effectiveness should therefore be removed from the list. 

Table 1-14 presents the complete list of technology packages which were 

described for vehicle type #6, which includes midsize and large cars equipped with a V8 

engine with either SOHC or DOHC and 4 valves per head. The information listed in the 

first seven columns is taken from Table 1-11 and/or Table 1-12. The values in the eighth 

column, which are explained below, are used to remove packages which would not likely 

be applied by a manufacturer and, therefore, should not be included in the OMEGA 

modeling. 
Technology Packages, Cost and Effectiveness 

1-23 

Table 1-14 Evaluation of Technology Packages for Vehicle Type #6 

Technology 

Package Engine Transmission System Voltage 

Weight 

Reduction 

Total CO2 

Reduction 

Total 

2016 

Cost 

$/delta 

CO2% 

601 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $28 

602 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $817 $59 

603 4.0L V6 w/ Deac GDI + CCP AT 6 spd 42 S-S 5% 28.2% $1567 $73 

605 3.0L V6 Turbo DCP + GDI AT 6 spd 42 S-S 5% 28.5% $2274 $288 

604 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1506 $(220) 

606 3.0L V6 Turbo DCP + GDI DCT 6spd 42 S-S 5% 32.1% $2214 $4,568 

608 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 32.3% $3258 $4,128 

609 3.0L V6 w/ Deac GDI+CCP HEV 2-mode HEV 0% 44.4% $5939 $223 

Remove Package 605 

601 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $28 

602 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $817 $59 

603 4.0L V6 w/ Deac GDI + CCP AT 6 spd 42 S-S 5% 28.2% $1567 $73 

604 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1506 ($16) 

606 3.0L V6 Turbo DCP + GDI DCT 6spd 42 S-S 5% 32.1% $2214 $4,568 

608 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 32.3% $3258 $4,128 

609 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5939 $223 

Remove Package 603 

601 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $28 

602 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $817 $59 

604 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1506 $49 

606 3.0L V6 Turbo DCP + GDI DCT 6spd 42 S-S 5% 32.1% $2214 $4,568 

608 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 32.3% $3258 $4,128 

609 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5939 $223 

Remove Package 606 

601 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $28 

602 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $817 $59 

604 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1506 $49 

608 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 32.3% $3258 $4,295 

609 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5939 $223 

Remove Package 608 

601 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $28 

602 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $817 $59 

604 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1506 $49 

609 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5939 $356 
Regulatory Impact Analysis 

1-24 

The eighth, or last column of Table 1-14 is a measure of the incremental cost 

effectiveness of each package relative to the previous package. Specifically, it is the ratio 

of the incremental cost of the current package over the previous package to the 

incremental effectiveness of the current package over the previous package. In both 

cases (cost and effectiveness), the increment is the arithmetic difference. As discussed 

above, OMEGA uses a different measure of incremental effectiveness in its calculation of 

CO2emissions. Here, however, the arithmetic difference in the effectiveness of two 

technology packages provides the best comparison across packages, since the base 

CO2emissions inherent in the total effectiveness estimates is the same; that of the base 

vehicle. Therefore, a 10% difference between two packages with 7% and 17% 

effectiveness, respectively, represents the same CO2emission reduction as a 10% 

difference between two packages with 27% and 37% effectiveness, respectively. 

Generally, a low ratio of incremental cost to incremental effectiveness is better than a 

high ratio. Ideally, the technology packages included in the model would progress from 

lower ratios to higher ratios. 

The topmost section of Table 1-14 shows all of the packages described earlier. 

The order of the packages has been rearranged slightly from that in Table 1-11 in order to 

place the packages in order of increasing total effectiveness. As can be seen, there are 

two very large anomalies in the ratios of incremental cost to incremental effectiveness. 

The ratio for the turbocharged engine with a 6 speed automatic transmission (package 

605) is very high, while that for the engine with cylinder deactivation with a dual clutch 

transmission (package 604) is negative. The cause of this is that the cost of package 604 

is lower than that for package 605. If package 604 can achieve a 31.9% reduction in 

CO2emissions at a cost of $1,506, then there is no point in considering a package which 

only achieves a 28.5% reduction in CO2emissions for a cost of $2,274. Therefore, 

package 605 was removed and the calculations were repeated. (In general, the package 

just prior to one with a negative ratio of incremental cost to incremental effectiveness 

should be removed.) The revised set of technology packages is shown in the second 

section of Table 1-14 after removing package 605. 

The second set of packages now shows one obvious anomaly. Again, the ratio of 

incremental cost to incremental effectiveness for package 604 is negative. This occurs 

because package 604 achieves a higher CO2 reduction for less cost than package 603. As 

done above, package 603 was eliminated and the calculations were repeated using the 

revised set of packages shown in the third section of Table 1-14. 

The third set of packages shows another anomaly. Package 606 is roughly a 

factor of 10 higher than any of the prior packages. This occurs because package 606 

reduces CO2 emissions by only 0.2% over package 604 for an incremental cost of around 

$700. A manufacturer would be better off skipping package 606 and moving straight to 

package 608 (the diesel) since package 608 has a more attractive (although not much) 

ratio than does package 606. Therefore, package 606 was eliminated and the calculations 

were repeated using the revised set of packages shown in the fourth section of Table 

1-14. 
Technology Packages, Cost and Effectiveness 

1-25 

The greatest anomaly in the fourth set of ratios is that for the diesel package 608. 

It is considerably less attractive than package 609 (the 2-mode hybrid). Therefore, 

package 608 is removed and results in the list of packages shown in the fifth and last 

section of Table 1-14. If EPA believed that manufacturers would prefer to implement 

diesel technology over strong hybridization for some reason, both packages could have 

been left in the modeling. However, absent such a reason, the diesel engine package was 

removed from vehicle type #6. The revised set of technology packages is shown in the 

fourth section of Table 1-14. 

1.4 EPA’s Lumped Parameter Approach for Determining 

Effectiveness Synergies 

EPA engineers reviewed existing tools that could be used to develop estimates of 

the technology synergies, including the NEMS model1. However, the synergies in the 

NEMS model depend heavily upon an assumed technology application flow path; those 

technologies that the model would apply first would be expected to have fewer synergies 

than those applied later on. For this reason, and because this report includes many new 

technologies not available in NEMS, it was necessary for EPA to develop its own set of 

estimates. EPA used a well-documented engineering approach known as a lumpedparameter technique to determine values for synergies. At the same time, however, EPA 

recognized the availability of more robust methods for determining the synergistic 

impacts of multiple technologies on vehicle CO2 emissions than the lumped-parameter 

approach, particularly with regard to applying synergy effects differentiated across 

different vehicle classes, and therefore augmented this approach with the detailed vehicle 

simulation modeling described in Section 1.4.7. 

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, 

• Accessory losses, related directly to the parasitics associated with the engine 

accessories and indirectly to the fuel efficiency losses related to engine 

warmup, 

• Vehicle road load (tire and aerodynamic) losses; 
Regulatory Impact Analysis 

1-26 

with the remaining energy 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 categorized into the major types of engine losses it reduces, so 

that interactions between multiple technologies applied to the vehicle may be determined. 

When a technology is applied, its effects are estimated 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. Table 1-15 below is an example spreadsheet used by EPA to estimate 

the synergistic impacts of a technology package for a standard-size car. 
Technology Packages, Cost and Effectiveness 

1-27 

Table 1-15 Sample Lumped Parameter Spreadsheet 

EPA Staff Deliberative Materials--Do Not Quote or Cite

Vehicle type: Standard Car Description: Technology picklist

Family Package: Z

Heat 

Lost To

Vehicle Parasitics Gearbox, Exhaust &

Mass Drag Tires T.C. Coolant

Inertia Aero Rolling Access Trans Friction Pumping Ind Eff Second

Load Load Load Losses Losses Losses Losses Losses Law Check

13.0% 4.0% 4.0% 1.8% 4.2% 6.6% 4.4% 32.0% 30.0% 100.0% OK

0% 16% 8% 64% 33% 16% 75%

13.0% 3.4% 3.7% 0.8% 3.3% 5.6% 1.1% 31.8% 30%

Indicated Mech Brake Drivetrain Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Loads

Baseline 38.0% 71.1% 27.0% 77.8% 21.0% 100.0%

New 38.2% 82.5% 31.5% 87.2% 27.5% 95.4%

PMEP Brake

72.9% Fuel Consumption Original friction/brake ratio Losses Efficiency

27.1% FC Reduction Based on PMEP/IMEP >>>> 11% 27%

37.2% FE Improvement (GM study) =71.1% mech efficiency

N/A Diesel FC Reduction

Independent User Picklist

Technology FC Estimate Loss Category Implementation into estimator Include? (0/1) Gross FC Red

Aero Drag Reduction 3.0% Aero 16% aero (cars), 10.5% aero (trucks) 1 3.0%

Rolling Resistance Reduction 1.5% Rolling 8% rolling 1 1.5%

Low Fric Lubes 0.5% Friction 2% friction 1 0.5%

EF Reduction 2.0% Friction 8.5% friction 1 2.0%

ICP 2.0% Pumping 12% pumping, 38.2% IE, -2% fric 0 0.0%

DCP 3.0% total VVT Pumping 18.5% pumping, 38.2% IE, -2% fric 0 0.0%

CCP 3.0% total VVT Pumping 18.5% pumping, 38.2% IE, -2% fric 1 3.0%

Deac 6.0% Pumping, friction 39% pumping 0 0.0%

DVVL 4.0% Pumping 30% pumping, -3% friction 1 4.0%

CVVL 5.0% Pumping 37% pumping, -3% friction 0 0.0%

Camless 10.0% Pumping 76% pumping, -5% friction 0 0.0%

GDI 1.5% Ind Eff 38.6% Ind Eff 0 0.0%

Turbo/Dnsize 6.0% Pumping 39% pumping 0 0.0%

5-spd 2.5% Trans, pumping 22% pumping, -5% trans 0 0.0%

CVT 6.0% Trans, pumping 46% pumping, -5% trans 0 0.0%

ASL 1.5% Pumping 9.5% pumping 1 1.5%

Agg TC Lockup 0.5% Trans 2.5% trans 1 0.5%

6-spd auto 5.5% Trans, pumping 42% pumping, -5% trans 1 5.5% Or #44/45

AMT 6.5% Trans 35% trans (increment) 1 6.5%

42V S-S 7.5% F, P, A 13% friction, 19% pumping, 38% access 1 7.5%

12V acc + Imp alt 1.5% Access 18% access 0 0.0% Or #53

EPS 1.5% Access 18% access 1 1.5%

42V acc + imp alt 3.0% Access 36% access 1 3.0% Or #51

HCCI dual-mode 11.0% Ind. Eff, pumping 41% IE, 25% pumping 0 0.0%

GDI (lean) 10.5% Ind. Eff, pumping 40% IE, 38% pumping 0 0.0%

Diesel - LNT 30.0% over gas Ind Eff, pumping 48% IE, 85% pumping, -13% friction 0 0.0%

Diesel - SCR 30.0% over gas Ind Eff, pumping 46% IE, 80% pumping, -13% friction 0 0.0%

Opt. E25 8.5% Ind. Eff, pumping 39% IE, 40% pumping 0 0.0%

33.6%

% of original fuel

Pick one or 

6-spd

Pick one

Vehicle Energy Effects Estimator

Pick one

Pick one

Current Results

Indicated Energy

Brake Energy Engine Friction

Road Loads

Baseline % of fuel

Reduction

Table 1-16 below lists the technologies considered in this example, their 

corresponding individual technology effectiveness values, and a comparison of the gross 

combined package CO2 reduction (i.e. disregarding synergies) to the lumped parameter 

results. The difference is the implied synergistic effects of these technologies combined 

on a package. 
Regulatory Impact Analysis 

1-28 

Table 1-16 Comparison of Lumped Parameter Analysis with Standard Car Package 

TECHNOLOGY INDIVIDUAL CO2

REDUCTION 

CUMULATIVE 

CO2REDUCTION 

Aero Drag 3% 3% 

Rolling Resistance Reduction 1.5% 4.5% 

Low Friction Lubricants 0.5% 4.9% 

 Engine Friction Reduction 2.0% 6.8% 

VVT – Coupled Cam Phasing 3.0% 9.6% 

VVT – Discrete Variable Lift 4.0% 13.2% 

Aggressive Shift Logic 1.5% 14.5% 

Early Torque Convertor Lock-up 0.5% 15.0% 

6-speed Automatic Transmission 5.5% 19.6% 

6-speed Dual Clutch Transmission 6.5% 24.9% 

Stop-start with 42 volt system 7.5% 30.5% 

Electric Power Steering 1.5% 31.5% 

42V acc + improved alternator 3.0% 33.6% 

Gross combined effectiveness 33.6% 

Lumped Parameter Estimate 27.1% 

Estimated synergistic effects -6.5% 

The synergy estimates obtained using the lumped parameter technique were 

subsequently compared to the results from the vehicle simulation work. EPA will 

continue to use the lumped parameter approach as an analytical tool, and (using the 

output data from the vehicle simulation as a basis) may adjust the synergies as necessary 

in the future. No commenter took issue with this concept. 

1.4.1 Ricardo’s Vehicle Simulation 

Vehicle simulation modeling was performed by Ricardo, Inc. The simulation 

work addressed gaps in existing synergy modeling tools, and served to both supplement 

and update the earlier vehicle simulation work published by NESCCAF. Using a 

physics-based, second-by-second model of each individual technology applied to various 

baseline vehicles, the Ricardo model was able to estimate the effectiveness of the 

technologies acting either individually or in combination. This information could then be 

used to estimate the synergies of these technology combinations, and also to differentiate 

the synergies across different vehicle classes. 

In total, Ricardo modeled five baseline vehicles and twenty-six distinct 

technology combinations, covering the full range of gasoline and diesel powertrain 

technologies used in the Volpe model, with the exception of the powersplit, plug-in and 

two-mode hybrid vehicle technologies. The five generalized vehicle classes modeled 
Technology Packages, Cost and Effectiveness 

1-29 

were a standard car, a full-size car, a small multi-purpose vehicle (MPV), a large MPV 

and a large truck. The complete list of vehicles and technology packages is given below 

in this section, along with a detailed explanation of the selection criteria. 

Each technology package was modeled under a constraint of “equivalent 

performance” to the baseline vehicle. To quantify the performance, a reasonably 

comprehensive, objective set of vehicle performance criteria were used as a basis to 

compare with the baseline vehicle, characterizing the launch acceleration, passing 

performance and grade capability that a vehicle buyer might expect when considering a 

technology package. The main metrics used to compare vehicle performance are listed 

below in Table 1-17. 

Table 1-17 Performance Metrics Used as Basis for “Equivalent Performance” 

CHARACTERISTIC PERFORMANCE METRIC 

Overall Performance Time to accelerate from 0-60 MPH 

Launch Acceleration Time to accelerate from 0-30 MPH 

Vehicle speed and distance after a 3-second acceleration 

from rest 

Passing Performance Time to accelerate from 30 to 50 MPH 

Time to accelerate from 50 to 70 MPH 

Maximum % grade at 70 MPH 

(standard car, large car, small MPV and large MPV) 

Grade Capability 

Maximum % grade at 60 MPH at GCVWR (large truck) 

Notes: All accelerations are assumed at WOT (wide open throttle) condition. GCVWR = Gross Combined 

Vehicle Weight Rating 

A summary of the vehicle simulation results is given below in Section 1.4.7, 

including the CO2 emissions reduction effectiveness for each technology package. The 

full Ricardo vehicle simulation results, including the acceleration performance data, may 

be found in Ricardo’s final report posted publicly at EPA’s website.2

1.4.2 Description of Ricardo’s Report 

In this section, the structure, methodology and results from the Ricardo vehicle 

simulation report are summarized. EPA worked closely with Ricardo to develop baseline 

models of five generalized vehicle classes that could be validated against EPA 

certification data, and then used as a platform upon which to add various technology 

packages. The vehicle simulation modeling results generated by Ricardo consist of the 

following: 

• Baseline vehicle characterization, to determine the baseline fuel consumption and 

CO2 emissions over the EPA combined cycle federal test procedure (FTP) for five 

baseline vehicles, for validation with EPA certification data. 
Regulatory Impact Analysis 

1-30 

• Simulation of the vehicle technology combinations (applied to the baseline 

vehicles) 

• Incremental technology effectiveness estimates, to examine the effect of adding 

technologies one-by-one. These could then be used more directly to validate 

synergies estimated using the lumped parameter method. 

This section describes the selection process for each of the baseline vehicles and 

the technology packages, and summarizes the results of the vehicle simulation. No 

commenter took issue that the Ricardo work was a legitimate way to validate the lumped 

parameter methodology, and that it did in fact confirm that methodology’s reasonable use 

in this rule 

1.4.3 Determination of representative vehicle classes 

In an effort to establish a reasonable scope for the vehicle simulation work and to 

update the earlier simulation done by NESCCAF, EPA chose five representative vehicle 

classes as the basis for evaluating technology benefits and synergies, representing the 

vehicle attributes of the projected highest-volume light-duty car and truck sales segments. 

These five classes covered a broad range of powertrain and vehicle characteristics, over 

which the effectiveness and synergies of each of the technologies could be evaluated. 

The main distinguishing attributes of the five vehicle classes considered by EPA and 

Ricardo are given below in Table 1-18. 
Technology Packages, Cost and Effectiveness 

1-31 

Table 1-18 Attributes of the Five Generalized Vehicle Classes Considered by Ricardo 

VEHICLE 

CLASS 

STANDARD 

CAR 

LARGE 

CAR 

SMALL 

MPV 

LARGE 

MPV 

LARGE 

TRUCKS 

EPA Vehicle 

Types Included 

Compact, 

Midsize 

Large CAR Small SUV, 

Small 

Pickup 

Minivans, 

Mid-SUV’s 

Large SUV’s, 

Large Pickups

Curb Weight 

Range 

2800-3600 lbs >3600 lbs 3600-4200 

lbs 

4200-4800 lbs >4800 lbs 

Engine Type I4 V6 I4 V6 V8 

Drivetrain FWD RWD/AWD FWD FWD/AWD 4WD 

Body Type Unibody Unibody Unibody Unibody Ladder Frame

Towing 

Capability 

None None Partial Partial Full 

Example vehicles Toyota Camry, 

Chevy Malibu, 

Honda Accord 

Chrysler 300, 

Ford 500 / 

Taurus 

Saturn Vue, 

Ford 

Escape, 

Honda CRV 

Dodge Grand 

Caravan, 

GMC Acadia, 

Ford Flex 

Ford F-150, 

Chevy 

Silverado 

1500, Dodge 

Ram 

EPA then selected representative vehicle models for each of these classes, based on three 

main criteria: 

• The vehicle should possess major attributes and technology characteristics that are 

near the average of its class, including engine type and displacement, transmission 

type, body type, weight rating, footprint size and fuel economy rating. 

• It should be among the sales volume leaders in its class, or where there is not a 

clearly-established volume leader, the model should share attributes consistent 

with major sellers. 

• The vehicle should have undergone a recent update or redesign, such that the 

technology in the baseline model could be considered representative of vehicles 

sold at the beginning of the regulatory timeframe. 

Consideration was also given to include the sales-leading vehicle manufacturers 

among the baseline models. Hence, the U. S. domestic manufacturers account for four of 

the five models (Chrysler 300, GM/Saturn Vue, Chrysler/Dodge Caravan, and the Ford 

F-150), while import manufacturers are represented in their strongest sales segment, the 

standard car class, by the Toyota Camry. 
Regulatory Impact Analysis 

1-32 

1.4.4 Description of Baseline Vehicle Models 

The baseline vehicles selected to represent their respective vehicle classes are 

described below in Table 1-19, listed with the critical attributes that EPA used as 

selection criteria. While each attribute for these baseline vehicles does not match the 

precise average for its class, each of these baselines is an actual vehicle platform that 

allows validation of the simulation data with “real world” certification data. 

Table 1-19 Description of Baseline Vehicles 

*Estimated CO2 equivalent, taken from EPA adjusted combined fuel economy ratings. 

VEHICLE CLASS STANDARD 

CAR 

LARGE 

CAR 

SMALL 

MPV 

LARGE 

MPV 

LARGE 

TRUCKS 

Baseline Vehicle Toyota 

Camry Chrysler 300 Saturn VUE 

Dodge Grand 

Caravan Ford F-150 

CO2Emissions* 

(g/mi) 327 409 415 435 575 

Base Engine DOHC I4 SOHC V8 DOHC I4 OHV V6 SOHC V8 

Displacement 

(L) 2.4 3.5 2.4 3.8 5.4 

Rate Power 

(HP) 154 250 169 205 300 

Torque (ft-lbs) 160 250 161 240 365 

Valvetrain Type VVT (DCP) Fixed VVT (DCP) Fixed VVT (CCP) 

Valves/Cylinder 4 4 4 2 3 

Drivetrain FWD RWD FWD FWD 4WD 

Transmission Auto Auto Auto Auto Auto 

# of Forward 

Speeds 5 5 4 4 4 

Curb Weight 

(lbs) 3108 3721 3825 4279 5004 

ETW (lbs) 3500 4000 4000 4500 6000 

GVWR (lbs) -- -- 4300 5700 6800 

GCWR (lbs) -- -- -- -- 14000 

Front Track 

Width (in.) 62 63 61.4 63 67 

Vehicle Attributes 

Wheelbase (in.) 109.3 120 106.6 119.3 144.5 

Displacement / 

Weight Ratio 

(L/ton) 

1.54 1.88 1.25 1.78 2.16 

Performance 

Characteristics 

Power / 

Weight Ratio 

(HP/ton) 

99.1 134.4 88.4 95.8 119.9 
Technology Packages, Cost and Effectiveness 

1-33 

1.4.5 Technologies Considered by EPA and Ricardo in the Vehicle 

Simulation 

A number of advanced gasoline and diesel technologies were considered in the 

Ricardo study, comprising the majority of the technologies used in the Volpe model, with 

the exception of the hybrid electric vehicle technologies. In developing a comprehensive 

list of technologies to be modeled, EPA surveyed numerous powertrain and vehicle 

technologies and technology trends, in order to assess their potential feasibility in the 

next one to ten years. The list of technologies considered therefore includes those that 

are available today (e.g., variable valve timing, six-speed automatic transmissions) as 

well as some that may not be ready for five to ten years (e.g., camless valve actuation and 

HCCI engines). Table 1-20 below lists the technologies that Ricardo included in the 

vehicle simulation models. 
Regulatory Impact Analysis 

1-34 

Table 1-20 Technologies Included in the Ricardo Vehicle Simulation 

ENGINE TECHNOLOGIES 

Abbreviation Description 

DOHC Dual Overhead Camshafts 

SOHC Single Overhead Camshaft 

OHV Overhead Valve (pushrod) 

CCP Couple Cam Phasing 

DCP Dual (independent) Cam Phasing 

DVVL Discrete (two-step) Variable Valve Lift 

CVVL Continuous Variable Valve Lift 

Deac Cylinder Deactivation 

CVA Camless Valve Actuation (full) 

Turbo Turbocharging and engine downsizing 

GDI Gasoline Direct Injection 

Diesel Diesel with advanced aftertreatment 

HCCI Homogenous Charge Compression Ignition (gasoline) 

LUB Low-friction engine lubricants 

EFR Engine Friction Reduction 

TRANSMISSION TECHNOLOGIES 

Abbreviation Description 

L4 Lock-up 4-speed automatic transmission 

L5 Lock-up 5-speed automatic transmission 

L6 Lock-up 6-speed automatic transmission 

DCT6 6-speed Dual Clutch Transmission 

CVT Continuously Variable Transmission 

ASL Aggressive Shift Logic 

TORQ Early Torque Converter Lock-up 

ACCESSORY TECHNOLOGIES 

Abbreviation Description 

ISG (42V) 42V Integrated Starter-Generator 

EPS Electric Power Steering 

EACC Electric Accessories (water pump, oil pump, fans) 

HEA High-Efficiency Alternator 

VEHICLE TECHNOLOGIES 

Abbreviation Description 

AERO Aerodynamic drag reduction (10~20%) 

ROLL Tire Rolling Resistance reduction (10%) 
Technology Packages, Cost and Effectiveness 

1-35 

1.4.6 Choice of Technology Packages 

EPA chose a number of technology packages representing a range of options that 

manufacturers might pursue. In determining these technology combinations, EPA 

considered available cost and effectiveness numbers from the literature, and applied 

engineering judgment to match technologies that were compatible with each other and 

with each vehicle platform. Also, where appropriate, the same technologies were applied 

to multiple vehicle classes, to determine where specific vehicle attributes might affect 

their benefits and synergies. Table 1-21 below describes in detail the technology content 

in each technology package simulated by Ricardo. 
Regulatory Impact Analysis 

1-36 

Table 1-21 Description of the Vehicle Technology Packages Modeled by Ricardo 

VEHICLE 

CLASS 

TECHNOLOGY 

PACKAGE ENGINE VALVETRAIN TRANSMISSION ACCESSORIES 

Baseline 2.4 Liter I4 DOHC, DCP L5 -- 

Z 2.4L I4, PFI CCP, DVVL DCT6 ISG (42V), EPA, 

EACC 

1 2.4L I4, GDI DCP, DVVL CVT EPS, EACC, HEA 

Standard 

Car 

2 2.4L I4, GDI DCP L6 ISG (42V), EPS, 

EACC 

Baseline 2.4 Liter I4 DOHC, DCP L6 EPS 

Z 2.4L I4, PFI CCP, DVVL DCT6 ISG (42V), EPA, 

EACC 

1 2.4L I4, GDI DCP, DVVL CVT EPS, EACC, HEA 

2 2.4L I4, GDI DCP L6 ISG (42V), EPA, 

EACC 

15 1.5L I4, GDI, 

Turbo DCP DCT6 EPS, EACC, HEA 

15a 2.4L I4, GDI CVA DCT6 EPS, EACC, HEA 

15b 2.4L I4, GDI, 

HCCI DCP, CVVL DCT6 EPS, EACC, HEA 

 Small MPV 

5 1.9L I4, Diesel DOHC DCT6 EPS, EACC, HEA 

Baseline 3.5 Liter V6 SOHC L5 -- 

4 2.2L I4, GDI, 

Turbo DCP L6 EPS, EACC, HEA 

5 2.8L I4, Diesel DOHC DCT6 EPS, EACC, HEA 

Y1 3.5L V6, GDI CVA DCT6 EPS, EACC, HEA 

Y2 3.5L V6, GDI, 

HCCI DCP, CVVL DCT6 EPS, EACC, HEA 

6a 3.0L V6, GDI DCP, CVVL DCT6 EPS, EACC, HEA 

 Full Size Car 

16 3.5L V6, GDI CCP, Deac L6 ISG (42V), EPA, 

EACC 

Baseline 3.8 Liter V6 OHV L4 -- 

4 2.1L I4, GDI, 

Turbo DCP L6 EPS, EACC, HEA 

6b 3.0L V6, GDI CCP, Deac DCT6 EPS, EACC, HEA 

Large MPV 

16 3.8L V6, GDI CCP, Deac L6 ISG (42V), EPA, 

EACC 

Baseline 5.4 Liter, V8 SOHC, CCP L4 -- 

9 5.4L V8, GDI CCP, Deac DCT6 ISG (42V), EPA, 

EACC 

10 3.6L V6, GDI, 

Turbo DCP DCT6 EPS, EACC, HEA 

11 4.8L V8, Diesel DOHC DCT6 EPS, EACC, HEA 

12 5.4L V8, GDI CCP, Deac L6 ISG (42V), EPA, 

EACC 

17 5.4L V8, GDI DCP, DVVL L6 EPS, EACC, HEA 

X1 5.4L V8, GDI CVA DCT6 EPS, EACC, HEA 

Large Truck 

X2 5.4L V8, GDI, 

HCCI DCP, CVVL DCT6 EPS, EACC, HEA 
Technology Packages, Cost and Effectiveness 

1-37 

Other: 20% Aerodynamic drag reduction, 10% tire rolling resistance reduction assumed for all 

vehicles, except Large Trucks. 10% Aerodynamic drag reduction assumed for Large Truck. Low-Friction 

lubricants and moderate engine friction reductions are assumed for all vehicles. Aggressive shift logic and 

early torque converter lockup strategies are assumed for all vehicles, where applicable. 

1.4.7 Simulation Results 

The CO2 emissions results from the vehicle simulation are summarized below in 

Table 1-22 (for cars) and Table 1-23 (for light-duty trucks). The CO2 estimates are given 

for the combined city and highway test cycles, according to the EPA Federal Test 

Procedure (FTP), with the technology package results compared with the baseline vehicle 

as shown. 

It is important to reiterate that each of the technology package results were 

obtained with performance determined to be equivalent to the baseline vehicle. No 

attempt was made to project trends in performance during the regulatory period, nor was 

the performance downgraded to give improved fuel efficiency. A full comparison of 

vehicle acceleration performance is given in the Ricardo final report. 

Table 1-22 CO2 Emissions Estimates Obtained from Vehicle Simulation (Cars) 

CO2 

CITY 

CO2 

HWY 

CO2 

COMB 

CO2 

REDUCTION 

VEHICLE TECHNONOLGY 

PACKAGE 

MAJOR 

FEATURES* 

g/mi g/mi g/mi % 

Baseline 2.4L I4, DCP, L5 338 217 284 -- 

Z CCP, DVVL, 

DCT, ISG 250 170 214 24.7% 

1 GDI, DCP, 

DVVL, CVT 294 198 251 11.5% 

Standard Car

2 GDI, DCP, L6, 

ISG 277 180 233 17.8% 

Baseline 3.5L V6, L5 420 279 356 -- 

4 2.2L I4, GDI, 

Turbo, DCP, L6 346 236 296 16.9% 

5 2.8L I4 Diesel, 

DCT 315 221 273 23.5% 

Y1 GDI, CVA, DCT 278 199 242 32.0% 

Y2 GDI, HCCI, DCT 290 197 248 30.4% 

6a GDI, DCP, 

CVVL, DCT 

Full Size Car

331 235 288 19.2% 

16 GDI, CCP, Deac, 

L6, ISG 301 205 257 27.7% 

*-Please refer to Table 1-20 for a full description of the vehicle technologies 
Regulatory Impact Analysis 

1-38 

Table 1-23 CO2 Emissions Estimates Obtained from Vehicle Simulation (Light-Duty Trucks) 

CO2 

CITY 

CO2 

HWY 

CO2 

COMB 

CO2 

REDUCTION

VEHICLE TECHNONOLGY 

PACKAGE 

MAJOR 

FEATURES* 

g/mi g/mi g/mi % 

Baseline 2.4L I4, DCP, EPS 367 253 316 -- 

Z CCP, DVVL, DCT, 

ISG 272 208 243 23.0% 

1 GDI, DCP, DVVL, 

CVT 310 227 272 13.7% 

2 GDI, DCP, L6, ISG 291 211 255 19.3% 

15 1.5L I4 GDI, Turbo, 

DCP, DCT 272 212 245 22.5% 

15a GDI, CVA, DCT 262 193 231 26.8% 

15b GDI, HCCI, DCT 270 197 237 24.8% 

Small MPV 

5 1.9L I4 Diesel, DCT 282 205 247 21.8% 

Baseline 3.8L V6 458 313 393 -- 

4 2.1L I4, GDI, Turbo, 

DCP, L6 357 256 312 20.6% 

6b GDI, CCP, Deac, DCT 333 248 295 24.9% 

Large MPV 

16 GDI, CCP, Deac, L6, 

ISG 325 225 280 28.7% 

Baseline 5.4L V8, CCP 612 402 517 -- 

9 GDI, CCP, Deac, DCT, 

ISG 432 315 379 26.7% 

10 3.6L V6, GDI, Turbo, 

DCP, DCT 404 319 366 29.3% 

11 4.8L V8 Diesel, DCT 444 326 391 24.4% 

12 GDI, CCP, Deac, L6, 

ISG 459 328 400 22.6% 

17 GDI, DCP, DVVL, L6 492 333 420 18.8% 

X1 GDI, CVA, DCT 422 314 374 27.8% 

Large Truck 

X2 GDI, HCCI, DCT 425 311 374 27.7% 

*-Please refer to Table 1-20 for a full description of the vehicle technologies 

1.5 Comparison of Lumped-Parameter Results to Modeling Results 

Considering the following: 

1) EPA’s lumped-parameter package estimates are comparable with those obtained 

from the detailed Ricardo simulations. This is illustrated in Figure 1-2 below. 
Technology Packages, Cost and Effectiveness 

1-39 

2) EPA is confident in the plausibility of the individual technology effectiveness 

estimates in, based on the sources from which that information was assimilated, as 

detailed in Section 2 of this report. 

3) Additionally, EPA expresses confidence in the overall Ricardo package results 

due to the robust methodology used in building the models and generating the 

results. No commenter took issue with this concept. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

Std CarStd CarStd Car

Sm MPVSm MPVSm MPVSm MPVSm MPVSm MPVSm MPV

Large CarLarge CarLarge CarLarge CarLarge CarLarge Car

Large MPVLarge MPVLarge MPV

TruckTruckTruckTruckTruckTruckTruck

Z 1 2 Z 1 2 15 15a 15b 5 4 5 6a 16 Y1 Y2 4 6b 16 9 10 11 12 17 X1 X2

Package

FC Reduction

Ricardo

L-P

Figure 1-2 Comparison of Ricardo package results to equivalent lumped parameter package results 

Based on this, EPA concludes that the synergies derived from the lumped 

parameter approach are generally plausible (with a few packages that garner additional 

investigation). EPA will continue to analyze this data, focusing on those packages where 

the differences between the two approaches are large. 

The simulation results may present opportunities to improve the fidelity of the 

lumped-parameter approach by identifying differences between different platforms or 

important vehicle traits (such as displacement-to-weight ratio, e.g.). There might also be 

opportunity to infer (through detailed analysis) the individual effectiveness values for 
Regulatory Impact Analysis 

1-40 

some technologies by comparing and isolating Ricardo package results across different 

vehicle platforms. 

1.6 Using the Lumped-Parameter Technique to Determine Synergies 

in a Technology Application Flowpath (Identifying “Technology 

Pairs” to account for synergies) 

In order to account for the real world synergies of combining of two or more 

technologies, the product of their individual effectiveness values must be adjusted based 

on known interactions, as noted above. When using an approach in which technologies 

are added sequentially in a pre-determined application path to each individual vehicle 

model, as used in NHTSA’s 2006 fuel economy rule for light trucks3, these interactions 

may be accounted for by considering a series of interacting technology pairs. EPA 

believes that a lumped parameter approach can be used as a means to estimate and 

account for synergies for such a technology application method. When using a sequential 

technology application approach which applies more than one technology, it is necessary 

to separately account for the interaction of each unique technology pair. Moreover, if the 

sequential technology application approach applies a technology that supersedes another, 

for example, where a VVLT system is substituted in place of a cylinder deactivation 

system, its incremental effectiveness must be reduced by the sum of the synergies of that 

technology with each individual technology that was previously applied, regardless of 

whether any of them have also been superseded. Figure 1-3 below provides an example 

of how technology pairs are identified for a specific technology application path similar 

to one used by NHTSA. In this example, an interaction is identified between each of the 

engine technologies (except GDI) with each of the transmission technologies. So, in this 

example, were the model to couple a turbocharged and downsized GDI engine with a 6-

speed transmission, it would apply a series of many synergy pairs to the combined 

individual effectiveness values to arrive at the overall effectiveness. 
Technology Packages, Cost and Effectiveness 

1-41 

Engine Technology Trans Technology

VVT (ICP)

VVT (CCP) L5

DISP L6

VVLT (DVVL)

GDI

TURB

(Lines indicate potential synergies)

Figure 1-3 Illustration of technology pairings for a specific technology application path 
Regulatory Impact Analysis 

1-42 

References 

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472.

1

 National Energy Modeling System, Energy Information Administration, U. S. Dept of 

Energy. 

2

 “A Study of Potential Effectiveness of Carbon Dioxide Reducing Vehicle 

Technologies,” EPA Report No. EPA420-R-08-004, available in the EPA docket EPAHQ-OAR-2009-0472 and on the Internet at 

http://www.epa.gov/otaq/technology/420r08004a.pdf. 

3

 NHTSA 2008-2011 CAFE FRM at 71 FR 17566. Docket Number: EPA-HQ-OAR2009-0472-0162
Air Conditioning 

2-1 

CHAPTER 2:Air Conditioning 

2.1 Overview of Air Conditioning Impacts and Technologies 

Over 95% of the new cars and light trucks in the United States are equipped with 

mobile air conditioning (A/C) systems. In the 1970’s and 1980’s, A/C systems were an 

optional (luxury) feature, but these systems are now standard on almost all new vehicle 

models. The A/C system is a unique and distinct technology on the automobile. It is 

different from the other technologies described in Chapter 3 of the joint Technical 

Support Document (TSD) in several ways. First, most of the technologies described in 

the joint TSD directly affect the efficiency of the engine, transmission, and vehicle 

systems. As such, these systems are almost always active while the vehicle is moving 

down the road or being tested on a dynamometer for the fuel economy and emissions test 

drive cycles. A/C on the other hand, is a parasitic load on the engine that only burdens 

the engine when the vehicle occupants demand it. Since it is not tested as a normal part 

of the fuel economy and emissions test drive cycles, it is referred to as an “off-cycle” 

effect. There are many other off-cycle loads that can be switched on by the occupant that 

affect the engine; these include lights, wipers, stereo systems, electrical 

defroster/defogger, heated seats, power windows, etc. However, these electrical loads 

individually amount to a very small effect on the engine (although together they can be 

significant). The A/C system (by itself) adds a significantly higher load on the engine as 

described later in this chapter. Secondly, present A/C systems leak a powerful 

greenhouse gas (GHG) directly into the air - even when the vehicle is not in operation. 

No other vehicle system has associated GHG leakage. Because of these factors, a distinct 

approach to control of MAC systems is justified, and a separate technical discussion is 

also warranted. 

As just mentioned above, there are two mechanisms by which A/C systems 

contribute to the emissions of greenhouse gases. The first is through direct leakage of the 

refrigerant into the air. The hydrofluorocarbon (HFC) refrigerant compound currently 

used in all recent model year vehicles is R134a (also known as 1,1,1,2-Tetrafluoroethane, 

or HFC-134a). Based on the higher global warming potential of HFCs, a small leakage 

of the refrigerant has a greater global warming impact than a similar amount of emissions 

of some other mobile source GHGs. R134a has a global warming potential (GWP) of 

1430.A This means that 1 gram of R134a has the equivalent global warming potential of 

 

A

 The global warming potentials (GWP) used in the NPRM analysis are consistent with Intergovernmental 

Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). At this time, the IPCC Second 

Assessment Report (SAR) global warming potential values have been agreed upon as the official U.S. 

framework for addressing climate change. The IPCC SAR GWP values are used in the official U.S. 

greenhouse gas inventory submission to the United Nations climate change framework. When inventories 

are recalculated for the final rule, changes in GWP used may lead to adjustments. 
Regulatory Impact Analysis 

2-2 

1,430 grams of CO2 (which has a GWP of 1).1

 In order for the A/C system to take 

advantage of the refrigerant’s thermodynamic properties and to exchange heat properly, 

the system must be kept at high pressures even when not in operation. Typical static 

pressures can range from 50-80 psi depending on the temperature, and during operation, 

these pressures can get to several hundred psi. At these pressures leakage can occur 

through a variety of mechanisms. The refrigerant can leak slowly through seals, gaskets, 

and even small failures in the containment of the refrigerant. The rate of leakage may 

also increase over the course of normal wear and tear on the system. Leakage may also 

increase more quickly through rapid component deterioration such as during vehicle 

accidents, maintenance or end-of-life vehicle scrappage (especially when refrigerant 

capture and recycling programs are less efficient). Small amounts of leakage can also 

occur continuously even in extremely “leak-tight” systems by permeating through hose 

membranes. This last mechanism is not dissimilar to fuel permeation through porous fuel 

lines. Manufacturers may be able to reduce these leakage emissions through the 

implementation of technologies/designs such as leak-tight, non-porous, durable 

components. The global warming impact of leakage emissions also can be addressed by 

using alternative refrigerants with lower global warming potential. Refrigerant emissions 

can also occur during maintenance and at the end of the vehicle’s life (as well as 

emissions during the initial charging of the system with refrigerant), and these emissions 

are already addressed by the CAA Title VI stratospheric ozone program, as described 

below. 

The second mechanism by which vehicle A/C systems contribute to GHG 

emissions is through the consumption of additional fuel required to provide power to the 

A/C system and from carrying around the weight of the A/C system hardware year-round. 

The additional fuel required to run the system is converted into CO2 by the engine during 

combustion. These increased emissions due to A/C operation can be reduced by 

increasing the overall efficiency of the vehicle’s A/C system, as described below. EPA 

will not be addressing modifications to the excess weight of the A/C system, since the 

incremental increase in CO2 emissions and fuel consumption due to carrying the A/C 

system is directly measured during the normal federal test procedure, and is thus already 

subject to the normal control program. 

EPA’s analysis indicates that together, these (A/C related) emissions account for 

about 9% of the greenhouse gas emissions from cars and light trucks. In this document, 

EPA will separate the discussion of these two categories of A/C-related emissions 

because of the fundamental differences in the emission mechanisms and the methods of 

emission control. Refrigerant leakage control is akin in many respects to past EPA fuel 

evaporation control programs (in that containment of a fluid is the key feature), while 

efficiency improvements are more similar to the vehicle-based control of CO2 set out in 

the joint TSD (in that they would be achieved through specific hardware and controls). 

EPA recognizes that California and the European Union also believe that A/C 

related emissions account for a significant part of greenhouse gas emissions. Both 

California and the European Union have either proposed or discussed programs to limit 

GHGs from A/C systems. EPA has evaluated these programs and this document 
Air Conditioning 

2-3 

discusses some similar features and others that emphasize additional emission reduction 

mechanisms. 

2.2 Air Conditioner Leakage 

No substantive public comments were received during the public comment period on the 

size of the HFC credit or the HFC inventories presented here. Consequently, the NPRM 

analysis is presented here unchanged. The NPRM inventories differ slightly from the 

updated emission inventory analysis provided in the FRM (RIA Chapter 5) due to slight 

changes in sales and VMT. The global warming potentials used in this analysis are 

discussed in RIA chapter 5. 

2.2.1 Impacts of Refrigerant Leakage on Greenhouse Gas Emissions 

There have been several studies in the literature which have attempted to quantify the 

emissions (and impact) of air conditioner HFC emissions from light duty vehicles. In this 

section, several of these studies are discussed. 

2.2.1.1 In-Use Leakage Rates 

Based on measurements from 300 European vehicles (collected in 2002 and 

2003), Schwarz and Harnisch estimate that the average HFC direct leakage rate from 

modern A/C systems was estimated to be 53 g/yr.2 This corresponds to a leakage rate of 

6.9% per year. This was estimated by extracting the refrigerant from recruited vehicles 

and comparing the amount extracted to the amount originally filled (as per the vehicle 

specifications). The fleet and size of vehicles differs from Europe and the United States, 

therefore it is conceivable that vehicles in the United States could have a different 

leakage rate. The authors measured the average charge of refrigerant at initial fill to be 

about 747 grams (it is somewhat higher in the U.S. at 770g), and that the smaller cars 

(684 gram charge) emitted less than the higher charge vehicles (883 gram charge). 

Moreover, due to the climate differences, the A/C usage patterns also vary between the 

two continents, which may influence leakage rates. 

Vincent et al., from the California Air Resources Board estimated the in-use 

refrigerant leakage rate to be 80 g/yr.3

 This is based on consumption of refrigerant in 

commercial fleets, surveys of vehicle owners and technicians. The study assumed an 

average A/C charge size of 950 grams and a recharge rate of 1 in 16 years (lifetime). The 

recharges occurred when the system was 52% empty and the fraction recovered at endof-life was 8.5%. 

2.2.1.2 Emission Inventory 

The EPA publishes an inventory of greenhouse gases and sinks on an annual 

basis. The refrigerant emissions numbers that are used in the present analysis are from 

the Vintaging model, which is used to generate the emissions included in this EPA 
Regulatory Impact Analysis 

2-4 

inventory source. The HFC refrigerant emissions from light duty vehicle A/C systems 

was estimated to be 61.8 Tg CO2 equivalent in 2005 by the Vintaging model.4,B

 

In 2005, refrigerant leakage accounted for about 5.1% of total greenhouse gases 

from light duty sources. The following table shows the breakdown of greenhouse gases 

as broken down by the different emissions processes in 2005. The baseline tailpipe CO2, 

N2O and CH4 emissions are from MOVES, the refrigerant emissions are from the 

Vintaging model, and the A/C CO2 emissions are from EPA and the National Renewable 

Energy Laboratory (NREL) as described below. 

Table 2-1 CO2 Equivalent Emissions from Light Duty Vehicles Broken Up by Source or Process 

Emissions source or process Tg CO2 (equivalent) Percentage of total 

Tailpipe CO2 (w/o A/C) 1,076 88.6% 

CO2 from A/C 47.2 3.9% 

HFC-134a (Leakage) 61.8 5.1% 

N2O 28.2 2.3% 

CH4 1.9 0.2% 

Total 1,215 

From a vehicle standpoint, the Vintaging model assumes that 42% of the 

refrigerant emissions are due to direct leakage (or “regular” emissions), 49% for service 

and maintenance (or “irregular” emissions), and 9% occurs at disposal or end-of-life as 

shown in the following table. These are based on assumptions of the average amount of 

chemical leaked by a vehicle every year, how much is lost during service of a vehicle 

(from professional service center and do-it-yourself practices), and the amount lost at 

disposal. These numbers vary somewhat over time based on the characteristics (e.g. 

average charge size and leakage rate) of each “vintage” of A/C system, assumptions of 

how new A/C systems enter the market, and the number of vehicles disposed of in any 

given year. 

Table 2-2 Light Duty Vehicle HFC-134a Emissions in 2005 from Vintaging Model - HFC Emissions 

Multiplied by 1430 GWP to Convert to CO2 Equivalent 

Emission Process HFC emissions (metric 

tons) 

Fraction of total 

Leakage 18,151 0.42 

Maintenance/servicing 21,176 0.49 

Disposal/end-of-life 3,890 0.09 

Total 43,217 1.0 

 

B

 EPA reported the MVAC emissions at 56.6 Tg CO2 EQ, using a GWP of 1300. This number has been 

adjusted using a GWP of 1430. 
Air Conditioning 

2-5 

2.2.2 A/C Leakage Credit 

The level to which each technology can reduce leakage can be calculated using 

the SAE Surface Vehicle Standard J2727 – HFC-134a Mobile Air Conditioning System 

Refrigerant Emission Chart. This industry standard was developed by SAE and the 

cooperative industry and government IMAC (Improved Mobile Air Conditioning) 

program using industry experience, laboratory testing of components and systems, and 

field data to establish a method for calculating leakage. With refrigerant leakage rates as 

low as 10 g/yr, it would be exceedingly difficult to measure such low levels in a test 

chamber (or shed). Since the J2727 method has been correlated to “mini-shed” results 

(where select components are tested in a small chamber, simulating real-world driving 

cycles), the EPA considers this method to be an appropriate surrogate for vehicle testing 

of leakage. It is also referenced by the California Air Resources Board in their 

Environmental Performance Label regulation and the State of Minnesota in their GHG 

reporting regulation.5,6

 

2.2.2.1 Why Is EPA Relying on a Design-Based Approach to Quantify Leakage? 

As with any design-based rule, it is possible to achieve compliance by simply 

selecting the minimum number of design attributes needed to meet a particular threshold 

or standard. Whether a design-based approach is used for emissions compliance or 

earning voluntary GHG credits, manufacturers will rightly choose the combination of 

design attributes which yield the maximum benefit at the lowest cost. However, there is 

a risk that some manufacturers may select poor quality, cheap parts, or implement the 

changes poorly, resulting in vehicles which ostensibly meet the rule’s provisions, but in 

practice, fail to achieve their stated benefits. However, EPA believes that the marketdriven incentive of assuring customer satisfaction will drive manufacturers to design A/C 

systems that perform as promised, and never need to be recharged. In addition, at time of 

certification, manufacturers are required to attest that the components used in these 

systems are durable. Also, it should be noted that the relative leakage rates assigned to 

various components, materials, and technologies in SAE J2727 are based on (and 

correlated to) actual leakage rates, as measured in bench- and field-test studies of vehicles 

and components. 

As discussed in the preamble and Response to Comments document, in the 

absence of a vehicle-level performance test to measure the how a particular A/C system 

design functions (and the difficulty in creating such a test), EPA will rely on the best 

available design metrics to quantify system performance. A few commenters suggested 

that we allow manufacturers, as an option, to use an industry-developed “mini-shed” test 

procedure (SAE J2763 – Test Procedure for Determining Refrigerant Emissions from 

Mobile Air Conditioning Systems) to measure and report annual refrigerant leakage.C

 

However, while EPA generally prefers performance testing, for an individual vehicle A/C 

 

C

 Honeywell and Volvo supported this view; most other commenters did not. 
Regulatory Impact Analysis 

2-6 

system or component, there is not a strong inherent correlation between a performance 

test using SAE J2763 and the design-based approach we are adopting (based on SAE 

J2727, as discussed below).D

 Establishing such a correlation would require testing of a 

fairly broad range of current-technology systems in order to establish the effects of such 

factors as production variability and assembly practices (which are included in J2727 

scores, but not in J2763 measurements). To EPA's knowledge, such a correlation study 

has not been done. At the same time, as discussed below, there are indications that much 

of the industry will eventually be moving toward alternative refrigerants with very low 

GWPs. EPA believes such a transition would diminish the value of any correlation 

studies that might be done to confirm the appropriateness of the SAE J2763 procedure as 

an option in this rule. For these reasons, EPA is therefore not adopting such an optional 

direct measurement approach to addressing refrigerant leakage at this time. EPA believes 

that the SAE J2727 method as an appropriate method for quantifying the expected yearly 

refrigerant leakage rate from A/C systems. 

2.2.2.2 How Are Credits Calculated? 

The A/C credit available to manufacturers will be calculated based on how much 

a particular vehicle’s annual leakage value is reduced against the average new vehicle, 

and will be calculated using a method drawn directly from the SAE J2727 approach. By 

scoring the minimum leakage rate possible on the J2727 components enumerated in the 

rule (expressed as a measure of annual leakage), one earns the maximum A/C credit (on a 

gram per mile basis). 

The A/C credit available to manufacturers will be calculated based on the 

reduction to a vehicle’s yearly leakage rate, using the following equation: 

Equation 1 – Credit Equation 

A/C Credit = (MaxCredit) * [ 1 - (§86.166-12 Score/AvgImpactE

) * 

(GWPRefrigerant/1430)] 

There are four significant terms to the credit equation. Each is briefly summarized 

below, and is then explained more thoroughly in the following sections. Please note that 

the values of many of these terms change depending on whether HFC-134a or an 

alternative refrigerant are used. The values are shown in Table 2-3, and are documented 

in the following sections. 

• “MaxCredit” is a term for the maximum amount of credit entered into the 

equation before constraints are applied to terms. The maximum credits that could 

 

D

 However, there is a correlation in the fleet between J2763 measurements and J2727 scores. 

E

 Section 86.166-12 sets out the individual component leakage values based on the SAE value. 
Air Conditioning 

2-7 

be earned by a manufacturer is limited by the choice of refrigerant and by 

assumptions regarding maximum achievable leakage reductions. 

• “Score/AvgImpact” is the leakage score of the A/C system as measured according 

to the §86.166-12 calculation in units of g/yr, where the minimum score which is 

deemed feasible is fixed. 

• “AvgImpact” is the annual average impact of A/C leakage. 

• “GWPRefrigerant” is the global warming potential for direct radiative forcing of 

the refrigerant as defined by EPA (or IPCC). 

Table 2-3 Components of the A/C Credit Calculation 

 HFC-134a 

Lowest-GWP 

Refrigerant 

(GWP=1) 

 Cars Trucks Cars Trucks 

MaxCredit equation input (grams /mile CO2 EQ) 12.6 15.6 13.8 17.2 

A/C credit maximum (grams /mile CO2 EQ)a

6.3 7.8 13.8 17.2 

§86.166-12 Score AvgImpact (grams / HFC year) 8.3 10.4 8.3 10.4 

Avg Impact (grams / HFC year) 16.6 20.7 16.6 20.7 

 

a

IWith electric compressor, value increases to 9.5 and 11.7 for cars and trucks, respectively. 

2.2.2.2.1 Max Credit Term 

In order to determine the maximum possible credit on a gram per mile basis, it 

was necessary to determine the projected real world HFC emissions per mile in 2016. 

Because HFC is a leakage type emission, it is largely disconnected from vehicle miles 

traveled (VMT).F

 Consequently, the total HFC inventory in 2016 was calculated, and 

then calculated the relevant VMT. The quotient of these two terms is the HFC 

contribution per mile. 

Consistent with the methodology presented in RIA chapter 5, the HFC emission 

inventories were estimated from a number of existing data sources. The per-vehicle peryear HFC emission of the current (reference) vehicle fleet was determined using averaged 

2005 and 2006 registration data from the Transportation Energy Databook (TEDB) and 

2005 and 2006 mobile HFC leakage estimates from the EPA Emissions and Sinks report 

described above.4,7 The per-vehicle per-year emission rates were then adjusted to 

account for the new definitions of car and truck classes (described in preamble section I), 

 

F

 In short, leakage emissions occur even while the car is parked, so the connection to a gram/mile credit is 

not straightforward. However, HFC emissions must be converted to a gram/mile basis in order to create a 

relevant credit. 
Regulatory Impact Analysis 

2-8 

by increasing the car contribution proportionally by the percentage of former trucks that 

are reclassified as cars. This inventory calculation assumes that the leakage rates and 

charge sizes of future fleets are equivalent to the fleet present in the 2005/2006 reference 

years. Preliminary EPA analysis indicates that this may increasingly overstate the future 

HFC inventory, as charge sizes are decreasing. 

The per-vehicle per-year average emission rate was then scaled by the projected 

vehicle fleet in each future year (using the fleet predicted in the emissions analysis) to 

estimate the HFC emission inventory if no controls were enacted on the fleet. After 

dividing the 2016 inventory by total predicted VMT in 2016, an average per mile HFC 

emission rate (“base rate”) was obtained. 

The base rate is an average in-use number, which includes both old vehicles with 

significant leakage, as well as newer vehicles with very little leakage. The new vehicle 

leakage rate is discussed in section 2.2.2.2.2, while deterioration is discussed in section 

2.2.5. 

• Max Credit with Conventional Refrigerant (HFC-134a)

Two adjustments were made to the base rate in order to calculate the Maximum 

HFC credit with conventional refrigerant. First, EPA has determined that 50% 

leakage prevention is the maximum potentially feasible prevention rate in the 

2012-2016 timeframe (section 2.2.3). Some leaks will occur and are expected, 

regardless of prevention efforts. The accuracy of the J2727 approach (as 

expressed in §86.112), as a design based test, decreases as the amount of expected 

leakage diminishes. 50% of the base rate is therefore set as the maximum 

potential leakage credit for improvements to HFC leakage using conventional 

refrigerant. 

Second, EPA expects that improvements to conventional refrigerant systems will 

affect both leakage and service emissions, but will not affect end of life 

emissions. EPA expects that reductions in the leakage rate from A/C systems will 

result in fewer visits for maintenance and recharges. This will have the side 

benefit of reducing the emissions leftover from can heels (leftover in the recharge 

cans) and the other releases that occur during maintenance. However, as 

disposal/end of life emissions will be unaffected by the leakage improvements 

(and also are subject to control under the rules implementing Title VI of the 

CAA), the base rate was decreased by a further 9% (Table 2-2). 

• Max Credit with Alternative Refrigerant 

Emission reductions greater than 50% are possible with alternative refrigerants. 

As an example, if a refrigerant with a GWP of 0 were used, it would be possible 

to eliminate all refrigerant GHG emissions. In addition, for alternative 

refrigerants, the EPA believes that vehicles with reduced GWP refrigerants should 

get credit for end of life emission reductions. Thus, the maximum credit with 

alternative refrigerant is about 9% higher than twice the maximum leakage 

reduction. 
Air Conditioning 

2-9 

AIAM commented that EPA should not set a lower limit on the leakage score, 

even for non-electric compressors. EPA has determined not to do so. First, although 

there do exist vehicles in the Minnesota data with lower scores than our proposed (and 

now final) minimum scores, there are very few car models that have scores less than 8.3, 

and these range from 7.0 to about 8.0 and the difference are small compared to our 

minimum score.G

 More important, lowering the leakage limit would necessarily increase 

credit opportunities for equipment design changes, and EPA believes that these changes 

could discourage the environmentally optimal result of using low GWP refrigerants. 

Introduction of low GWP refrigerants could be discouraged because it may be less costly 

to reduce leakage than to replace many of the A/C system components. Moreover, due to 

the likelihood of in-use factors, even a leak-less (according to J2727) R134a system will 

have some emissions due to manufacturing variability, accidents, deterioration, 

maintenance, and end of life emissions, a further reason to cap the amount of credits 

available through equipment design. The only way to guarantee a near zero emission 

system in-use is to use a low GWP refrigerant. The EPA has therefore decided for the 

purposes of this final rule to not change the minimum score for belt driven compressors 

due to the reason cited above and to the otherwise overwhelming support for the program 

as proposed from commenters. 

In addition, as discussed above, EPA recognizes that substituting a refrigerant 

with a significantly lower GWP will be a very effective way to reduce the impact of all 

forms of refrigerant emissions, including maintenance, accidents, and vehicle scrappage. 

To address future GHG regulations in Europe and California, systems using alternative 

refrigerants -- including HFO1234yf, with a GWP of 4 and CO2 with a GWP of 1 -- are 

under serious development and have been demonstrated in prototypes by A/C component 

suppliers. The European Union has enacted regulations phasing in alternative refrigerants 

with GWP less than 150 starting this year, and the State of California proposed providing 

credits for alternative refrigerant use in its GHG rule. Within the timeframe of MYs 

2012-2016, EPA is not expecting widespread use of low-GWP refrigerants. However, 

EPA believes that these developments are promising, and, as proposed, has included in 

the A/C Leakage Credit formula above a factor to account for the effective GHG 

reductions that could be expected from refrigerant substitution. The A/C Leakage Credits 

that will be available will be a function of the GWP of the alternative refrigerant, with the 

largest credits being available for refrigerants with GWPs at or approaching a value of 1. 

For a hypothetical alternative refrigerant with a GWP of 1 (e.g., CO2 as a refrigerant), 

effectively eliminating leakage as a GHG concern, our credit calculation method could 

result in maximum credits equal to total average emissions, or credits of 13.8 and 17.2 

g/mi CO2eq for cars and trucks, respectively, as incorporated into the A/C Leakage Credit 

formula above as the "MaxCredit" term. 

 

G

 The Minnesota refrigerant leakage data can be found at 

http://www.pca.state.mn.us/climatechange/mobileair.html#leakdata 
Regulatory Impact Analysis 

2-10 

A final adjustment was made to each credit to account for the difference between 

real-world HFC emissions and test-cycle CO2 emissions. It has been shown that the tests 

currently used for CAFE certification represents an approximately 20% gap from real 

world fuel consumption and the resulting CO2 emissions.8 Because the credits from 

direct a/c improvements are taken from a real world source, and are being traded for an 

increase in fuel consumption due to increased CO2 emissions, the credit was multiplied 

by 0.8 to maintain environmental neutrality (Table 2-4). 

Table 2-4 HFC Credit Calculation for Cars and Trucks Based on a GWP of 1430 

 HFC 

Inventory 

(MMT 

CO2 EQ) 

VMT 

(Billions 

of Miles) 

Total HFC 

EmissionsPer 

Mile 

(CO2 EQ 

Gram/mile) 

HFC 

Leakage and 

Service 

EmissionsPer 

Mile 

(CO2 EQ 

Gram/mile) 

Maximum 

Credit w/ 

alternative 

refrigerant 

 (Adjusted 

for Onroad gap & 

including 

end of life)

Maximum 

Credit w/o 

alternative 

refrigerant 

(50% of 

Adjusted 

HFC & 

excluding 

end of life) 

Car 27.4 1,580 17.2 15.5 13.8 6.3 

Truck 30.4 1,392 21.5 19.6 17.2 7.8 

Total 57.8 2,972 18.6 16.9 14.9 6.8 

2.2.2.2.2 Section 86.166-12, implementing the J2727 Score Term 

The J2727 score is the SAE J2727 yearly leakage estimate of the A/C system as 

calculated according to the J2727 procedure. The minimum score for cars and trucks is a 

fixed value, and the section below describes the derivation of the minimum leakage 

scores that can be achieved using the J2727 procedure. 

In contrast to the studies discussed in section 2.2.1.1 which discussed the HFC 

emission rate of the in-use fleet (which includes vehicles at all stages of life), the SAE 

J2727 estimates leakage from new vehicles. In the development of J2727, two relevant 

studies were assessed to quantify new vehicle emission rates. In the first study, 

measurements from relatively new (properly functioning and manufactured) Japanesemarket vehicles were collected. This study was based on 78 in-use vehicles (56 single 

evap, 22 dual evap) from 7 Japanese auto makers driven in Tokyo and Nagoya from 

April, 2004 to December, 2005. The study also measured a higher emissions level of 16 

g/yr for 26 vehicles in a hotter climate (Okinawa). This study indicated the leakage rate 

to be close to 8.6 g/yr for single evaporator systems and 13.3 g/yr for dual evaporator 

systems.9

 A weighted (test) average gives 9.9 g/yr. In the second study, emissions were 

measured on European-market vehicles up to seven years age driven from November, 

2002 to January, 2003.10 The European vehicle emission rates were slightly higher than 

the Japanese fleet, but overall, they were consistent. The average emission rate from this 

analysis is 17.0 g/yr with a standard deviation of 4.4 g/yr. European vehicles, because 
Air Conditioning 

2-11 

they have smaller charge sizes, likely understate the leakage rate relative to the United 

States. To these emission rates, the J2727 authors added a factor to account for 

occasional defective parts and/or improper assembly and to calibrate the result of the 

SAE J2727 calculation with the leakage measured in the vehicle and component leakage 

studies. 

We adjust this rate up slightly by a factor proportional to the average European 

refrigerant charge to the average United States charge (i.e. 770/747 from the Vintaging 

model and Schwarz studies respectively). The newer vehicle emission rate is thus 18 g/yr 

for the average newer vehicle emissions. This number is a combined car and truck 

number, and although based on the limited data, it was not possible to separate them. 

To derive the minimum score, the 18 gram per year rate was used as a ratio to 

convert the gram per mile emission impact into a new vehicle gram per year for the test. 

The car or truck direct a/c emission factor (gram per mile) was divided by the average 

emission factor (gram per mile) and then multiplied by the new vehicle average leakage 

rate (gram per year) 

Equation 2 – J2727 Minimum Score 

J2727 Minimum Score = Car or truck average pre control emissions (gram 

per mile)/ Fleet average pre-control emissions (grams per mile) x New 

vehicle annual leakage rate (grams per year) x Minimum Fraction 

By applying this equation, the minimum J2727 score is fixed at 8.3 g/yr for cars 

and 10.4 g/yr for trucks. This corresponds to a total fleet average of 18 grams per year, 

with a maximum reduction fraction of 50%. 

The GWP Refrigerant term in Equation 1 allows for the accounting of refrigerants 

with lower GWP (so that this term can be as low as zero in the equation), which is why 

the same minimum score is kept regardless of refrigerant used. 

It is technically feasible for the J2727 Minimum score to be less than the values 

presented in the table. But this will usually require the use of an electric compressor (see 

below for technology description), which the EPA does not expect to see with high 

penetrations within the 2012-2016 timeframe, as this technology is likely to accompany 

hybrid vehicle and stop-start technologies, and not conventional vehicles. However, 

several commenters noted that electric A/C compressors are an enabler to lower leakage 

rates – beyond the minimum levels we specified - and when this technology is used in 

conjunction with other leakage-reducing technologies, the resulting system leakage can 

be lower than the minimum levels we proposed (8.3 g/yr for cars and 10.4 g/yr for 

trucks). We agree with the commenters that it is feasible for A/C systems with electric 

compressors to achieve lower leak rates than belt-driven compressors. Since compressor 

leakage can be responsible for more than 50% of the refrigerant leakage from a system, 

we are lowering the minimum leakage score for cars and trucks with electric compressors 

by 50%, to 4.1 and 5.2 g/yr respectively. The effect of this change will be that vehicles 
Regulatory Impact Analysis 

2-12 

with electric compressors will be able to qualify for credit, on a grams-per-mile basis, 

than would have been possible with the limitations of the original minimum leakage 

score. For vehicles which do use an electric compressor, the 8.3 and 10.4 g/yr minimum 

leakage scores for cars and trucks are retained. 

2.2.2.2.3 AvgImpact Term 

AvgImpact is the average annual impact of A/C leakage, which is 16.6 and 20.7 

g/yr for cars and trucks respectively. This was derived using Equation 2, but by setting 

the minimum fraction to one. 

2.2.2.2.4 GWPRefrigerant Term 

This term is relates to the global warming potential (GWP) of the refrigerant as 

documented by EPA. A full discussion of GWP and its derivation is too lengthy for this 

space, but can be found in many EPA documents.4c This term is used to correct for 

refrigerants with global warming potentials that differ from HFC-134a. As just 

explained, this term accounts for the GWP of any refrigerant used, and can be as low as 

zero. 

2.2.3 Technologies That Reduce Refrigerant Leakage and their Effectiveness 

In this section, the baseline technologies which were used in the EPA’s analysis 

of refrigerant leakage are described as well as the effectiveness of the leakage-reducing 

technologies that are believed will be available to manufacturers in the 2012-to-2016 

timeframe of this rulemaking. An EPA analysis to determine a baseline leakage emission 

rate was conducted in the 2006-to-2007 timeframe, and at that time, it was estimated that 

the A/C system in new vehicles would leak refrigerant at an average rate of 18 g/yr, 

which represents the types of A/C components and technologies currently in use. EPA 

believes, through utilization of the leakage-reducing technologies described below, that it 

will be possible for manufacturers to reduce refrigerant leakage 50%, relative to the 18 

g/yr baseline level.11 EPA also believes that all of these leakage-reducing technologies 

are currently available, and that many manufacturers have already begun using them to 

improve system reliability and in anticipation of the State of California’s Environmental 

Performance Label regulations and the State of Minnesota’s reporting requirements for 

High Global Warming Potential Gases. 

In describing the technologies below, only the relative effectiveness figures are 

presented, as the individual piece costs are not known. The EPA only has costs of 

complete systems based on the literature, and the individual technologies are described 

below. 

2.2.3.1 Baseline Technologies 

The baseline technologies assumed for A/C systems which have an average 

annual leak rate of 18 g/yr are common to many mass-produced vehicles in the United 

States. In these mass-produced vehicles, the need to maintain A/C system integrity (and 
Air Conditioning 

2-13 

the need to avoid the customer inconvenience of having their A/C system serviced due to 

loss of refrigerant) is often balanced against the cost of the individual A/C components. 

For manufacturers seeking improved system reliabilty, components and technologies 

which reduce leakage (and possibly increase cost) are selected, whereas other 

manufacturers may choose to emphasize lower system cost over reliabilty, and choose 

components or technologies prone to increased leakage. In the absence of standards or 

credits concerning refrigerant leakage, it is the market forces of cost and reliability which 

determine the technology a manufacturer chooses. In EPA’s baseline scenario, the 

following assumptions were made concerning the definition of a baseline A/C system: 

− all flexible hose material is rubber, without leakage-reducing 

barriers or veneers, of approximately 650 mm in length for both the 

high and low pressure lines 

− all system fittings and connections are sealed with a single o-rings 

− the compressor shaft seal is a single-lip design 

− one access port each on the high and low pressure lines 

− two of the following components: pressure switch, pressure relief 

valves, or pressure transducer 

− one thermostatic expansion valve (TXV) 

The design assumptions of EPA baseline scenario are also similar to the sample 

worksheet included in SAE’s surface vehicle standard J2727 – HFC-134a Mobile Air 

Conditioning System Refrigerant Emission Chart.12 In the J2727 emission chart, it is the 

baseline technologies which are assigned the highest leakage rates, and the inclusion of 

improved components and technologies in an A/C system will reduce this annual leakage 

rate, as a function of their effectiveness relative to the baseline. EPA considers these 

‘baseline’ technologies to be representative of recent model year vehicles, which, on 

average, can experience a refrigerant loss of 18 g/yr. However, depending on the design 

of a particular vehicle’s A/C system (e.g. materials, length of flexible hoses, number of 

fittings and adaptor plates, etc.), it is possible to achieve a leakage score much higher (i.e. 

worse) than 18 g/yr. According to manufacturer data submitted to the State of 

Minnesota, 19% of 2009 model year vehicles have a J2727 refrigerant score greater than 

18 g/yr, with the highest-scoring vehicle reporting a leakage rate of 30.1 g/yr.13 The 

average leakage was found to be 15.1 g/yr, though this value is not sales weighted. 

2.2.3.2 Flexible Hoses 

The flexible hoses on an automotive A/C system are needed to isolate the system 

from engine vibration and to allow for the engine to roll within its mounts as the vehicle 

accelerates and decelerates. Since the compressor is typically mounted to the engine, the 

lines going to-and-from the compressor (i.e. the suction and pressure lines) must be 

flexible, or unwanted vibration would be transferred to the body of the vehicle (or other 

components), and excessive strain on the lines would result. It has been industry practice 

for many years to manufacture these hoses from rubber, which is relatively inexpensive 

and durable. However, rubber hoses are not impermeable, and refrigerant gases will 

eventually migrate into the atmosphere. To reduce permeation, two alternative hose 
Regulatory Impact Analysis 

2-14 

material can be specified. The first material, is known as a standard ‘veneer’ (or 

‘barrier’) hose, where a polyamide (polymer) layer - which has lower permeability than 

rubber - is encased by a rubber hose. The barrier hose is similar to a veneer hose, except 

that an additional layer of rubber is added inside the polyamide layer, creating three-layer 

hose (rubber-polyamide-rubber). The second material is known as ‘ultra-low 

permeation’, and can be used in a veneer or barrier hose design. This ultra-low 

permeation hose is the most effective at reducing permeation, followed by the standard 

veneer or barrier hose. Permeation is most prevalent during high pressure conditions, 

thus it is even more important that these low permeable hoses are employed on the high 

pressure side, more so than on the low pressure side. EPA expects that many 

manufacturers will begin using these technologies (and many have already begun doing 

so) to reduce refrigerant leakage. 

According to J2727, standard barrier veneer hoses have 25% the permeation rate 

of rubber hose, and ultra low permeable barrier veneer hoses have 10% the permeation 

rate (as compared to a standard baseline rubber hose of the same length and diameter). 

2.2.3.3 System Fittings and Connections 

Within an automotive A/C system and the various components it contains (e.g. 

expansion valves, hoses, rigid lines, compressors, accumulators, heat exchangers, etc.), it 

is necessary that there be an interface, or connection, between these components. These 

interfaces may exists for design, manufacturing, assembly, or serviceability reasons, but 

all A/C systems have them to some degree, and each interface is a potential path for 

refrigerant leakage to the atmosphere. In SAE J2727 emission chart, these interfaces are 

described as fittings and connections, and each type of fitting or connection type is 

assigned an emission value based on its leakage potential; with a single o-ring (the 

baseline technology) having the highest leak potential; and a metal gasket having the 

lowest. In between these two extremes, a variety of sealing technologies, such as 

multiple o-rings, seal washers, and seal washers with o-rings, are available to 

manufacturers for the purpose of reducing leakage. It is expected that manufacturers will 

choose from among these sealing technology options to create an A/C system which 

offers the best cost-vs-leakage rate trade-off for their products. 

The relative effectiveness of the fitting and connector technology is presented in 

Table 2-5. For example, the relative leakage factor of 125 for the baseline single O-ring 

is 125 times more “leaky” than the best technology - the metal gasket. 
Air Conditioning 

2-15 

Table 2-5 Effectiveness of Fitting and Connector Technology 

Fitting or Connector Relative 

Leakage 

Single O-ring 125

Single Captured O-ring 75

Multiple O-ring 50

Seal Washer 10

Seal Washer with O-ring 5

Metal Gasket 1

2.2.3.4 Compressor Shaft Seal 

A major source of refrigerant leakage in automotive A/C systems is the 

compressor shaft seal. This seal is needed to prevent pressurized refrigerant gasses from 

escaping the compressor housing. As the load on the A/C system increases, so does the 

pressure, and the leakage past the seal increases as well. In addition, with a belt-driven 

A/C compressor, a side load is placed on the compressor shaft by the belt, which can 

cause the shaft to deflect slightly. The compressor shaft seal must have adequate 

flexibility to compensate for this deflection, or movement, of the compressor shaft to 

ensure that the high-pressure refrigerant does not leak past the seal lip and into the 

atmosphere. When a compressor is static (not running), not only are the system pressures 

lower, the only side load on the compressor shaft is that from tension on the belt, and 

leakage past the compressor shaft is at a minimum. However, when the compressor is 

running, the system pressure is higher and the side load on the compressor shaft is higher 

(i.e. the side load is proportional to the power required to turn the compressor shaft) - 

both of which can increase refrigerant leakage past the compressor shaft seal. It is 

estimated that the rate of refrigerant leakage when a compressor is running can be 20 

times that of a static condition.14 Due to the higher leakage rate under running 

conditions, SAE J2727 assigns a higher level of impact to the compressor shaft seal. In 

the example shown in the August 2008 version of the J2727 document, the compressor is 

responsible for 58% of the system refrigerant leakage, and of that 58%, over half of that 

leakage is due to the shaft seal alone (the remainder comes from compressor housing and 

adaptor plate seals). To address refrigerant leakage past the compressor shaft, 

manufacturers can use multiple-lip seals in place of the single-lip seals. 
Regulatory Impact Analysis 

2-16 

2.2.4 Technical Feasibility of Leakage-Reducing Technologies 

EPA believes that the leakage-reducing technologies discussed in the previous 

sections are available to manufacturers today, are relatively low in cost, and that their 

feasibility and effectiveness have been demonstrated by the SAE IMAC teams. EPA also 

believes – as has been demonstrated in the J2727 calculations submitted by 

manufacturers to the State of Minnesota – that reductions in leakage from 18 g/yr to 9 

g/yr are possible (e.g. the 2009 Saturn Vue has a reported leakage score of 8.5 g/yr). In 

addition to earning credit for reduced refrigerant leakage, some manufacturers may, 

within the timeframe of this rulemaking, choose to introduce alternative refrigerant 

systems, such as HFO-1234yf. 

2.2.5 Leakage Controls in A/C Systems 

In order to determine the cost savings from the improvements to the leakage 

system, it is necessary to project the point at which the vehicle will require servicing and 

an additional refrigerant charge. 

There are two mechanisms of leakage that are modeled: the “normal” leakage that 

results in annual refrigerant loss, and the “avoidable” leakage which results in total 

refrigerant loss due to failure of the A/C components (e.g. evaporator, condenser, or 

compressor). This model is developed to help us estimate the costs of the A/C leakage 

reductions. It is especially needed to determine the period over which the discounted cost 

savings should be applied.H

 

Normal refrigerant leakage occurs throughout all components of the A/C system. 

Hoses, fittings, compressors, etc all wear with age and exposure to heat (temperature 

changes), vibration, and the elements. It is assumed that the system leakage rates 

decrease (proportionally) as the base leakage rates are decreased with the use of 

improved parts and components. The base leakage rate is modeled as a linear function, 

 

H Air conditioning leakage controls are the only technology in this rule that have an assumed 

deterioration that affects the effectiveness of the technology. This is partly because sufficient 

data is not available for many of the technologies in chapter 3 of the TSD. Moreover, it is not 

expected that deterioration of powertrain technologies will lead to emissions increases on the 

scale of those seen when criteria pollutant technologies deteriorate. The deterioration from the 

latter can increase emissions by factors of 10 or even 100 or more. Similarly, air conditioning 

leakage technologies can and do deteriorate, contributing to significantly higher emissions over 

time. For this reason, a deterioration model is proposed below. This model only applies for 

leakage, and not for indirect CO2 (tailpipe) emissions due to A/C. For the latter, a partly 

functioning system may lead to somewhat higher emissions, but when it finally fails, it is one of 

the few technologies where the emissions are no longer relevant, i.e. an A/C system that no 

longer functions, no longer emits indirect emissions. 
Air Conditioning 

2-17 

such that the (new vehicle) leakage rate is 18 g/yr at age zero and 59 g/yr at the “average” 

age of 5 years old. The 18 gram leakage rate for new vehicles has been documented in 

section 2.2.2, while the 59 gram mid-life leakage rate is drawn from the Vintaging model 

and is documented below. 

The Vintaging model assumes a constant leakage + servicing emission rate of 

18% per year for modern vehicles running with HFC-134a refrigerant. As the emission 

rates do not change by age in vintaging, the emission rate is the average rate of loss over 

the vehicle’s life. 

 Applying the percentages in Table 2-2, this corresponds to a leakage rate of 7.6% 

(59 grams) per year and a servicing loss rate of 8.8% (68 grams) per year averaged over 

the vehicle’s life. The model assumes an average refrigerant charge of 770 grams for 

vehicles sold in 2002 or later and does not currently assume that these charge sizes will 

change in the future; however, the model may be updated as new information becomes 

available. The resulting vehicle emission rates are presented in Table 2-6. 

Table 2-6 Annual In-Use Vehicle HFC-134a Emission Rate from Vintaging Model

Emission Process Leak rate (%/year) Leak rate (g/year) 

Leakage 7.6% 59 

Servicing/maintenance 8.8% 68 

The average leakage emissions rate of 59-68 g/yr is higher with Schwarz’s 

European2

 study and lower than CARB’s study,3 and thus is within the range of results in 

the literature. 

This model is presented in Figure 2-1 with the assumption that the average 

vehicle (A/C system) last about 10 years. Technically, the assumption is that the A/C 

system lasts 10 years and not the vehicle per se. Inherent in this assumption is that the 

vehicle owner will not repair the A/C system on an older vehicle due to the expensive 

nature of most A/C repairs late in life relative to the value of the vehicle. It is also 

assumed that the refrigerant requires a recharge when the state of charge reaches 50% for 

the analysis in this section. This deterioration/leakage model approach will be used later 

to estimate the cost of maintenance savings due to low leak technologies (from refills) as 

well as the benefits of leakage controls. 
Regulatory Impact Analysis 

2-18 

 

0

20

40

60

80

100

120

0 2 4 6 8 10 12

Age (years)

Leakage Emissions (g/yr)

0

100

200

300

400

500

600

700

800

charge remaining (grams)

leakage

remaining charge

recharge 

required

Figure 2-1 Deterioration Rate of Refrigerant Leakage 

Figure 2-2 shows how the leakage rates vary with age as the initial leakage rates 

are decreased to meet new standards (with improved components and parts). The 

deterioration lines of the lower leakage rates were determined by applying the appropriate 

ratio to the 17 g/yr base deterioration rate. Figure 2-3 shows the refrigerant remaining, 

which includes a line indicating when a recharge is required (50% charge remaining out 

of an initial charge of 770g). So a typical vehicle meeting a leakage score of 8.5 g/yr 

(new) will not require a recharge until it is about 12 years old. 

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14

Age of Vehicle

Leakage (g/yr)

18 g/yr

13.5 g/yr

9 g/yr

4.5 g/yr

 

Figure 2-2 A/C Refrigerant Leakage Rate for Different Technologies as Vehicles Age 
Air Conditioning 

2-19 

 

0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14

Age (years)

Refrigerant Remaining (grams)

18 g/yr

13.5 g/yr

9 g/yr

4.5 g/yr

refill line

Figure 2-3 A/C Refrigerant Remaining in a Typical System as Vehicles Age and Deteriorate 

2.2.6 Other Benefits of improving A/C Leakage Performance 

The EPA is assuming that a reduction in leakage emissions from new vehicles 

will also improve the leakage over the lifetime of the vehicle. There is ample evidence to 

show that A/C systems that leak more also have other problems that occur (especially 

with the compressor) due to the lack of oil circulating in the system. Thus, it is expected 

that an A/C system which utilizes leak-reducing components and technologies should, on 

average, last longer than one which does not. 

An European study conducted in 2001 (by Schwarz) found that the condenser is 

the component most likely to fail and result in a total leak.15 The study also found that 

compressor component was most likely the culprit when other malfunctions were present 

(other than total loss). A more recent (and larger) study found that condensers required 

replacement at half the rate of a compressor (10% vs 19% of the entire part replacement 

rate), and that evaporators and accumulators failed more often.16 The same study also 

found that many of the repairs occurred when the vehicles were aged 5-10 years. Both 

these studies indicate that the condenser and compressor are among the major causes of 

failure in an A/C system. Leakage reductions in the system are expected to greatly 

reduce the incidence of compressor repair, since one of the main root causes of 

compressor failure is a shortage of lubricating oil, which originates from a shortage of 

refrigerant flowing through the system (and it is a refrigerant-oil mixture which carries 

lubricating oil to the compressor).16
Regulatory Impact Analysis 

2-20 

Monitoring of refrigerant volume throughout the life of the A/C system may 

provide an opportunity to circumvent some previously described failures specifically 

related to refrigerant loss. Similar to approaches used today by the engine on-board 

diagnostic systems (OBD) to monitor engine emissions, a monitoring system that 

informed the vehicle operator of a low refrigerant level could potentially result in 

significant reductions in A/C refrigerant emissions due to component failure(s) by 

creating an opportunity for early repair actions. While most A/C systems contain sensors 

capable of detecting the low refrigerant pressures which result from significant 

refrigerant loss, these systems are generally not designed to inform the vehicle operator 

of the refrigerant loss, and that further operation of the system in this state can result in 

additional component damage (e.g. compressor failure). Electronic monitoring of the 

refrigerant may be achieved by using a combination of existing A/C system sensors and 

new software designed to detect refrigerant loss before it progresses to a level where 

component failure is likely to occur. 

2.3 CO2 Emissions due to Air Conditioners 

2.3.1 Impact of Air Conditioning Use on Fuel Consumption and CO2

Emissions 

Three studies have been performed in recent years which estimate the impact of 

A/C use on the fuel consumption of motor vehicles. In the first study, the National 

Renewable Energy Laboratory (NREL) and the Office of Atmospheric Programs (OAP) 

within EPA have performed a series of A/C related fuel use studies.17,18 The energy 

needed to operate the A/C compressor under a range of load and ambient conditions was 

based on testing performed by Delphi, an A/C system supplier. They used a vehicle 

simulation model, ADVISOR, to convert these loads to fuel use over the EPA’s FTP test 

cycle. They developed a personal “thermal comfort”-based model to predict the 

percentage of drivers which will turn on their A/C systems under various ambient 

conditions. Overall, NREL estimated A/C use to represent 5.5% of car and light truck 

fuel consumption in the U.S. 

In the second study, the California Air Resources Board (ARB) estimated the 

impact of A/C use on fuel consumption as part of their GHG emission rulemaking.19 The 

primary technical analysis utilized by ARB is summarized in a report published by 

NESCCAF for ARB. The bulk of the technical work was performed by two contractors: 

AVL Powertrain Engineering and Meszler Engineering Services. This work is founded 

on that performed by NREL-OAP. Meszler used the same Delphi testing to estimate the 

load of the A/C compressor at typical ambient conditions. The impact of this load on 

onroad fuel consumption was estimated using a vehicle simulation model developed by 

AVL - the CRUISE model - which is more sophisticated than ADVISOR. These 

estimates were made for both the EPA FTP and HFET test cycles. (This is the 

combination of test cycle results used to determine compliance with NHTSA’s current 

CAFE standards.) NREL’s thermal comfort model was used to predict A/C system use in 

various states and seasons. 
Air Conditioning 

2-21 

The NESCAFF results were taken from Table 3-1 of their report and are 

summarized in Table 2-7.20

Table 2-7 CO2 Emissions Over 55/45 FTP/HFET Tests and From A/C Use (g/mi) 

Small Car Large Car Minivan Small Truck Large Truck 

55/45 FTP/HFET 278 329 376 426 493 

Indirect A/C 

Fuel Use 16.8 19.1 23.5 23.5 23.5 

Total 294.8 348.1 399.5 449.5 516.5 

Indirect A/C 

Fuel Use 5.7% 5.5% 5.9% 5.2% 4.6% 

NESCAFF estimated that nationwide, the average impact of A/C use on vehicle 

fuel consumption ranged from 4.6% for a large truck or SUV, to 5.9% for a minivan. 

The total CO2 emissions were determined using a 55%/45% weighting of CO2 emissions 

from EPA FTP and HFET tests plus A/C fuel use (hereafter referred to simply as 

FTP/HFET). .For the purposes of this analysis of A/C system fuel use, the percentage of 

CO2 emissions and fuel use are equivalent, since the type of fuel being used is always 

gasoline.I 

In order to compare the NESCCAF and ARB estimates to that of NREL-OAP, 

weighting factors for the five vehicle classes were developed. NESCCAF presented sales 

percentages for the five vehicle classes in Table 2-1 of their report.20 These are shown 

below in Table 2-8. Since these sales percentages do not sum to 100% (possibly due to 

round-off or because some vehicles do not fit into any of the five categories) the 

percentages were normalized so that they summed to 100%. The car and truck categories 

were then weighted by their lifetime VMT, normalized to that of cars. J This meant a 

relative weighting factor for the three truck categories of 1.11 relative to a factor of 1.0 

for cars. The percentage of lifetime VMT represented by each vehicle class were then 

determined. These estimates are shown on the last line of Table 2-8. 

 

I

 Because NESCCAF estimated A/C fuel use nationwide, while ARB focused on that in California, the 

NESCCAF and EPA methodologies and results are coempared below. 

J

 Based on annual mileage per vehicle from the Volpe Model discounted at 7% per year. Discounted 

lifetime mileages are 102,838 for cars and 114,350 for trucks. 
Regulatory Impact Analysis 

2-22 

Table 2-8 Sales and VMT by Vehicle Class 

Small Car Large Car Minivan Small Truck Large Truck 

NESCCAF sales 22% 25% 7% 23% 21% 

Normalized 

NESCCAF sales 22.4% 25.5% 7.1% 23.5% 21.4% 

Lifetime VMT 

weighting factor 1.00 1.00 1.11 1.11 1.11 

VMT 21.2% 24.1% 7.5% 24.6% 22.5% 

 Using the percentages of VMT represented by each vehicle class, the A/C fuel 

use impacts of NESCCAF and ARB were weighted and determined that they represent 

5.3% and 4.2% of fuel use over the FTP/HFET, respectively, including the A/C fuel use. 

In the final study, EPA evaluated the impact of A/C use on fuel consumption as 

part of its recent rulemaking which revised the onroad fuel economy labeling procedures 

for new motor vehicles.21 EPA estimated the impact of the A/C compressor on fuel 

consumption from vehicle emission measurements taken over its SC03 emissions test. 

SC03 is a 10 minute test where the vehicle is operated at city speeds, at 95 degrees F, 

40% relative humidity and a solar load of 850 Watts/m2. In addition, prior to the test, the 

vehicle has been pre-heated for 10 minutes under these conditions, so the interior cabin 

starts the test at an elevated temperature. Testing of 500 late model vehicles over both 

the FTP and SC03 test cycles indicated that fuel consumption was 27% higher on the 

SC03 test than over a combination of Bag 2 and Bag 3 fuel consumption designed to 

match the vehicle load of the SC03 test. EPA assumed that the A/C compressor was 

engaged 100% of the time over SC03 due to the high ambient temperature, short duration 

and vehicle pre-heating test conditions. 

EPA does not measure A/C emissions at highway speeds. Thus, this impact had 

to be estimated based on the city-like SC03 test. EPA tested six vehicles (four 

conventional and two hybrid) over the FTP, SC03, and HFET emission tests in a standard 

test cell at 60 F, 75 F, and 95 F with and without the A/C system operating in order to 

assess the relative impact of A/C use at city and highway speeds. The data indicated that 

it was more accurate to assume that the impact of the A/C compressor on fuel 

consumption was the same at city and highway speeds when compared in terms of fuel 

burned per unit time than when compared in terms of fuel use per mile. Thus, EPA 

estimated the impact of A/C in terms of fuel use per mile at highway speeds by 

multiplying the A/C related fuel use at city speeds by the ratio of the speed of the city test 

to that of the highway test. For average driving in the U.S., this ratio was estimated to be 

0.348. The result was that the impact of engaging the A/C compressor 100% of the time 

at highway speeds increased fuel use by 9.7%, versus 27% at city speeds. These 
Air Conditioning 

2-23 

percentages are based on the assumptions that fuel is only consumed during warmed up 

driving, hence ignoring cold start fuel use. 

EPA’s estimate in the Fuel Economy Labeling rule of in-use A/C compressor 

engagement was based on a test program covering 1004 trips made by 19 vehicles being 

operated by their owners in Phoenix, Arizona.8

 The results of this testing were correlated 

against heat index, a function of temperature and humidity, and time of day, to represent 

solar load. Nationwide, EPA estimated that the A/C compressor was engaged 15.2% of 

the time. However, much of this time, the ambient conditions are less severe than those 

of the SC03 test. Therefore, EPA reduced this percentage to 13.3% to normalize usage to 

the load experienced during SC03 conditions. On a nationwide basis, EPA estimated that 

the A/C system was turned on an average of 23.9% of the time.22 Resulting in 14.3 g/mi 

per vehicle CO2 -equivalent impact due to A/C use (where 30% of the vehicle fleet is 

equipped with automatic A/C controls, and 70% of the fleet is equipped with manual 

controls).K

This estimate does not include defroster usage, while the NREL-OAP and ARBNESCCAF estimates do include this. EPA considered adding the impact of defroster 

usage based in large part on NREL-OAP estimates. NREL-OAP estimates that the 

defroster is in–use 5.4% of the time. However, the load of the compressor under 

defrosting conditions is very low. EPA estimated that including defroster usage would 

increase the percentage of time that the compressor was engaged at a load equivalent to 

that over SC03 from 13.3% to 13.7%. While this defroster impact was quantified, EPA 

decided not to include it in its final 5-cycle fuel economy formulae. Based on the A/C 

usage factor of 13.3% and EPA’s 5-cycle formulae, A/C system use increases onroad fuel 

consumption by 2.4%. Including defroster use modestly increased this value to 2.5%. 

Comparing the results of the three studies, the EPA estimate gives the smallest 

A/C system impact, while the NREL-OAP estimate is the highest. The NESCCAF and 

NREL-OAP studies give very similar results. The overall difference between the 

estimates is more than a factor of two. 

It is difficult to directly compare the three estimates. The NREL-OAP and ARBNESCCAF methodologies are very similar. However, the EPA methodology is quite 

different, as will be discussed further below. This complicates the comparison, making it 

difficult to compare smaller segments of each study directly. In addition, as will be seen, 

each study utilizes assumptions or estimates which contain uncertainties. These 

uncertainties are not well characterized. EPA concluded that it is not possible to 

determine a single best estimate of A/C fuel use from these studies. However, EPA was 

able to identify a couple of aspects of the studies which could be improved for the 

 

K

 Fraction of fleet equipped with automatic A/C control is based on is based on industry estimates and an 

EPA analysis of the percentage of 2008 U.S. car sales – as published in the 2009 Ward’s Automotive 

Yearbook - for vehicle categories likely to be equipped with automatic A/C (e.g. middle luxury car, 

specialty, middle luxury SUV, large luxury SUV, et. al.) 
Regulatory Impact Analysis 

2-24 

purpose of this analysis. Doing so, the overall difference between the studies was 

reduced by roughly one half. This process is described below. 

The first step in this comparison will reduce the number of studies from three to 

two. The NREL-OAP and ARB-NESCCAF methodologies are very similar, since both 

utilize the NREL-OAP comfort model to estimate A/C usage onroad. They also both use 

essentially the same estimate of A/C compressor load from Delphi to estimate the load 

which the compressor puts on the engine. ARB-NESCCAF utilized the vehicle 

simulation tool, AVL’s CRUISE model, to estimate the impact of A/C load on fuel 

economy, while NREL employed the ADVISOR model (both models assumed a rather 

simple A/C system load). In addition, ARB-NESCCAF modeled both city and highway 

driving (i.e., the 55/45 FTP/HFET), while NREL-OAP only modeled the FTP. Thus, 

EPA will focus on the NESCCAF estimate over that of NREL-OAP, though as 

mentioned above, their overall estimates are very similar. Also, because NESCCAF 

estimated A/C fuel use nationwide, while ARB focused on that in California, EPA will 

focus on comparing the NESCCAF and EPA methodologies and results below. With 

respect to EPA’s estimates from the 2006 rulemaking, the estimate including defroster 

use will be used, since NESCCAF considered defroster use, as well. As way of reminder, 

on a nationwide average basis, the NESCAFF estimates indicate that A/C use represents 

5.3% of total fuel consumption, while EPA estimates this at 2.5%. 

NESCCAF and EPA break down the factors which determine the impact of A/C 

use on onroad fuel consumption differently. NESCCAF breaks down the process into 

three parts. The first is the frequency that drivers turn on their A/C system. The second 

is the average load of the A/C compressor at various ambient conditions, including 

compressor cycling. The third is the impact of this average A/C compressor load on fuel 

economy over various driving conditions. 

In contrast, in the fuel labeling rulemaking, EPA breaks down the process into 

two parts. The first is the frequency that the A/C compressor is engaged at various 

ambient conditions. This includes both the frequency that the driver turns on the A/C 

unit and the frequency that the compressor is engaged when the system is turned on. The 

second is the impact of the A/C compressor on fuel economy over various driving 

conditions when the compressor is engaged. 

The most direct comparison that can be made between the two studies is the 

estimate of A/C system use. Because EPA measured both A/C system on/off condition 

as well as compressor engaged/disengaged condition in the Phoenix test program, it is 

possible to compare the percentage of A/C system use as measured in the Phoenix study 

and extrapolated to the U.S. to that of the NREL-OAP comfort model. 

In its rulemaking analysis, based on its Phoenix study and extrapolation 

procedure, EPA estimated that on average, the A/C unit was turned on 23.9% of the time. 

This does not include defroster use. There, EPA also determined that the NREL-OAP 

thermal comfort model predicts a higher percentage of 29%, again ignoring defroster use. 

Since EPA utilized NREL-OAP’s estimate of defroster use in its analysis, this estimate 

does not contribute to the difference in the two estimates. Also, fuel use is very low 
Air Conditioning 

2-25 

during defroster use compared to air conditioning at high ambient temperatures, so the 

difference between the 23.9% and 29% estimates is the most relevant factor. By itself 

(ignoring fuel use during defrosting), this difference would cause the NESCCAF A/C 

fuel use estimate to be 27% higher than that of EPA. The overall difference between the 

5.3% and 2.5% estimates is 112%. Thus, the difference in estimated A/C system use 

explains about one-fourth of the overall difference between the two studies. 

NREL’s thermal comfort model for vehicle A/C use is based on a model designed 

to the represent the comfort of a person walking outside and wearing one of two different 

sets of clothes. A number of assumptions had to be made in order to extrapolate this 

outdoor model to a person sitting in a vehicle. The predictions of NREL-OAP’s thermal 

comfort model have not been confirmed with any vehicle/occupant testing and their air 

conditioner settings. Therefore, its predictions, while reasonable, are of an unknown 

accuracy. 

EPA’s Phoenix study was performed over a relatively short period of time, 

roughly seven weeks. It was conducted in only one city, Phoenix. Thus, the variation in 

climate evaluated was limited. The number of vehicles tested was also fairly small, 

nineteen. However, over 1000 trips were monitored by these 19 vehicles. EPA 

extrapolated the measured A/C compressor engagement under these limited ambient 

conditions to other conditions using a metric called the heat index, which combines 

temperature and humidity into a single metric. Heat index is conceptually similar to 

NREL-OAP’s comfort model. This allowed the results found in the generally dry climate 

of Phoenix to be extrapolated to both cooler and more humid conditions typical of the 

rest of the U.S. No testing has yet been performed to confirm the accuracy of this 

extrapolation. 

Given the two very different approaches to estimating vehicle A/C system use, it 

is notable that the difference in the two estimates is only a relative 27%. As both the 

EPA and NREL-OAP models of A/C system use involve assumptions or extrapolations 

which have not been verified, it is not possible to determine which one is more accurate. 

Thus, the differences in the EPA and ARB estimates of the impact of A/C use on onroad 

fuel consumption due to these two different sources of A/C usage cannot be resolved at 

this time. 

With respect to the operation of the A/C compressor at various ambient and 

driving conditions, EPA bases its estimate on the Phoenix vehicle test study. This is 

subject to the same uncertainties described above, due mainly to the limited scope of the 

data. NREL-OAP relies on test results published by W.O. Forrest of Delphi. Forrest 

describes the factors which affect the load of the A/C system on the engine: the 

percentage of time the compressor is engaged, compressor displacement, compressor 

speed, air flow across the evaporator, engine operating condition and ambient conditions. 

The load curves presented by Forrest apply to a 210 cc compressor and show load as a 

function of compressor speed for six sets of ambient conditions. The loads include the 

effect of compressor cycling. However, no mention is made of airflow rates across the 

evaporator, which would vary with engine speed. It is not clear whether these curves 

were based on bench testing or onroad vehicle testing. Also, only one A/C system 
Regulatory Impact Analysis 

2-26 

appears to have been tested. It is not clear how well these curves would apply to other 

manufacturers’ systems, nor even to others produced by Delphi. Forrest states that the 

loads for other compressor displacements can be approximated by assuming that the load 

is proportional to compressor displacement. However, this is clearly an approximation 

and does not address differences inherent in particular A/C system applications. The fact 

that the NESCCAF analysis is based on the testing of only a single A/C system and does 

not address the effect of varying airflow rates under different driving conditions appears 

to be the largest sources of uncertainty in their estimate. 

It is not possible to directly compare these two estimates of compressor operation. 

EPA’s Phoenix study provides an estimate of the percentage of time that the compressor 

is engaged when the A/C system is on. On the other hand, compressor cycling is 

implicitly included in the Delphi load curves. Since the load curves of a continuous 

operating compressor were not presented, the degree of cycling cannot be determined. 

Thus, the effect of any differences in the NESCCAF and EPA estimates of compressor 

engagement cannot be quantified. 

With respect to the impact of the A/C compressor load on fuel economy, EPA 

relies on a comparison of measured fuel economy over the two warmed up bags of its 

FTP test (when the A/C system is inoperative) and its SC03, A/C emissions test. The 

vehicles on both tests are run at city speeds. EPA based its estimates on the testing of 

over 600 recent model year vehicles. Thus, for the conditions addressed by the SC03 

test, EPA’s estimate of the impact of A/C system load on fuel economy is well supported. 

However, in order to combine this measurement with the Phoenix study, EPA needed an 

estimate of the percentage of time that the compressor was engaged during the SC03 test. 

The SC03 test does not include a measurement of this factor, so EPA had to estimate the 

percentage of time that the compressor was engaged during the test. As noted above, 

EPA assumed that the A/C compressor was engaged 100% of the time during the SC03 

test given its short duration and the pre-heating of the vehicle. Thus, for a given ambient 

condition, if the compressor was estimated to be engaged 25% of the time, then the 

incremental amount of fuel used due to A/C system was 25% of the difference between 

the fuel use over the SC03 test and a 39%/61% weighting of the fuel use over Bags 2 and 

3 of the FTP, respectively. 

EPA has evidence to show that most vehicles’ A/C compressors are engaged 

100% of the time over SC03.23 The vehicle pre-heating, short test duration and the 

requirement that the driver window be rolled down, make it extremely likely that the 

vehicle compartment never reaches a comfortable temperature by the end of the test. 

However, it is possible that the compressor still cycled to some degree during the test. 

All compressors shut down when the heat exchanger nears 32 F in order to avoid icing. 

The cold heat exchanger continues to cool the refrigerant while the compressor is shut 

down, but the compressor is not putting an additional load on the engine and increasing 

fuel consumption. As it is impossible for the compressor to operate more than 100% of 

the time, any error in EPA’s assumption can only lower the actual compressor use below 

100%. If compressor engagement was lower than 100%, this would mean that fuel use at 

100% compressor engagement would be higher than currently estimated. Thus, it is 
Air Conditioning 

2-27 

possible that this assumption that the A/C compressor is engaged 100% during SC03 is 

causing EPA’s estimate of A/C fuel use to be under-estimated to some degree. 

There are additional uncertainties involved in EPA’s assumption that a vehicle’s 

A/C fuel use is constant in terms of gallons per hour, and thus inversely proportional to 

vehicle speed when presented in terms of gallons per mile. EPA testing of six vehicles as 

part of the Fuel Economy Labeling rulemaking (used to estimate A/C compressor usage 

in highway driving conditions, as noted above) confirmed that A/C fuel use was roughly 

constant in terms of gallons per hour. However, this testing was performed in a standard 

emission test cell. Air flow through the engine compartment was the same at city and 

highway speeds. The city test was only 20 minutes long and the highway test was only 

10 minutes long. There was also significant variability in the individual vehicle test 

results. Thus, while the testing showed that EPA’s assumption was reasonable, there is 

an unknown degree of uncertainty associated with extrapolating the measured A/C fuel 

use at city speeds to highway speeds. One could attempt to quantify the uncertainty using 

the test results of the six vehicles. However, these vehicles were not randomly selected 

and two of the six vehicles were Prius hybrids. Thus, it is not clear how representative 

the results of a statistical analysis of these data would be. 

An A/C load adjustment factor is also applied to account for the change in 

compressor load which occurs when the compressor is engaged at different temperatures. 

The study which developed this data data is based on an A/C model developed by Nam 

(2000).8

NESCCAF starts with A/C compressor load curves which describe the A/C 

compressor load as a function of compressor speed for six ambient conditions. These 

curves, along with A/C - on percentages from the thermal comfort model, were used to 

interpolate between the six compressor load curves to estimate the load curves applicable 

to the ambient conditions existing during driving times for a large number of cities across 

the U.S. The resulting curves are averaged using the VMT estimated to occur in each 

city to produce a single load curve representing the entire U.S. 

NESCCAF then input this national average load curve into AVL’s CRUISE 

model to estimate the effect of A/C on fuel consumption over the FTP and HFET cycles. 

The CRUISE model simulates vehicle operation and fuel consumption over specified 

driving conditions. The load of the A/C compressor (based on bench testing) was added 

to the other loads being placed on the vehicle, such as inertia, friction, aerodynamic drag, 

etc. The A/C loads included the cycling of the compressor as a function of ambient 

condition. In actuality, the engine will experience the full load of the compressor at some 

times and no load at other times. This could produce a slightly different fuel use impact 

than applying the average load of the compressor all of the time. However, this error is 

likely very small. The A/C load curves vary as a function of engine speed, but not 

vehicle speed. However, as air flow by the heat exchanger will vary as a function of 

vehicle speed, compressor cycling and evaporator cooling efficiency is likely to vary, as 

well. However, the degree of error associated with any of these simplifications is 

unknown. 
Regulatory Impact Analysis 

2-28 

A detailed comparison of this aspect of the two analyses would require 

reconstructing both models to produce A/C fuel use estimates for specific ambient 

conditions. This is beyond the scope of the study. Also, once the differences were 

known, it would still be difficult to decide which estimate was superior. 

There is one aspect of each analysis which appears to be an improvement over the 

other. In addition to A/C, EPA evaluated a number of other reasons why onroad fuel 

economy differs from that measured over the FTP and HFET cycles. Among these were 

higher speed and more aggressive driving, ambient temperatures below 75 F, short trips, 

wind, under-inflated tires, ethanol containing fuel, etc. This does not affect the absolute 

volume of fuel used by the A/C system, but it does raise the total amount of fuel 

consumed onroad, effectively lowering the percentage of fuel due to A/C use. 

NESCCAF estimated the impact of the A/C compressor load on fuel use during 

city and highway driving using the CRUISE model. While it is not clear that this is 

superior to EPA’s SC03 data, the CRUISE model is likely more accurate for highway 

driving than an extrapolation of the SC03 data (i.e. EPA’s six vehicle study described 

above). While CRUISE was not able to represent all aspects of vehicle operation, such as 

airflow across the evaporator, it does simulate the difference in engine speed and load 

between city and highway driving. This allows a detailed simulation of the A/C 

compressor speed during this driving, which is a primary factor in estimating A/C 

compressor load. EPA’s extrapolation of the impact over SC03 essentially assumes that 

engine speed and airflow over the evaporator are the same during both city and highway 

driving, or that any differences cancel each other. This is unlikely. Therefore, 

NESCCAF’s highway estimates are likely more accurate than EPA’s. 

Since the two analyses were performed so differently, the CRUISE results for 

highway driving cannot be simply substituted for EPA’s estimates. However, one way to 

utilize the CRUISE highway results is to determine the ratio of the impact of the A/C 

load on fuel use over the HFET to that over the FTP. This ratio can then be substituted 

for EPA’s assumption that the impact of A/C load is constant with time (inversely 

proportional to vehicle speed in terms of gallons per mile. 

Adjusting the NESCCAF estimates for the other factors reducing onroad fuel 

economy relative to the FTP/HFET is straightforward. EPA found that all such factors, 

including A/C, reduced onroad fuel economy to 80% of the FTP/HFET. In other words, 

onroad fuel consumption is 25% higher (1/0.8) than over the FTP/HFET. Thus, the CO2

emissions over the FTP/HFET shown above in Table 2-7 are multiplied by a factor of 

1.25 to represent onroad CO2 emissions. A/C fuel use is unaffected. A/C fuel use as a 

percentage of onroad fuel use is simply the ratio of the A/C fuel use divided by the 

estimated onroad fuel use. These figures are shown in Table 2-9 below. The VMT 

weighted average of these percentages is 4.4%, 0.9% lower than the estimate presented 

above. 
Air Conditioning 

2-29 

Table 2-9 Adjusted NESCCAF CO2 Emissions Over 55/45 FTP/HFET Tests and From A/C Use 

(g/mi) 

Small Car Large Car Minivan Small Truck Large Truck 

55/45 FTP/HFET 349 413 472 535 619 

Indirect A/C Fuel Use 16.8 19.1 23.5 23.5 23.5 

Indirect A/C Fuel Use 4.8% 4.6% 5.0% 4.4% 3.8% 

Incorporating the relative impact of A/C load on fuel consumed over the HFET 

versus FTP cycles from CRUISE requires a few steps. Table 2-10 shows the incremental 

CO2 emissions from the A/C compressor load from the CRUISE simulations of the FTP 

and HFET cycles. The top half of the table shows the incremental fuel use in terms of 

grams CO2 per mile. These figures were taken from Tables B-20 through B-23 of the 

NESCCAF report.24 For the large car, two base vehicles were simulated. EPA selected 

the vehicle with the conventional gasoline engine with variable valve timing and lift. The 

large truck was not modeled using CRUISE. Further in the study, Meszler assumed that 

the A/C fuel impact was proportional to compressor displacement. The large truck is 

assumed to have the same compressor displacement as the minivan and small truck. 

Thus, the A/C fuel impact was estimated for the large truck as the average of the impacts 

for the minivan and small truck. The bottom half of the table shows the incremental fuel 

use in terms of grams CO2 per minute. These figures were calculated by multiplying the 

A/C fuel impacts in grams per mile by the average speeds of the FTP and HFET cycles: 

19.6 and 48.2 mph and converting hours to minutes. The final line of the table shows the 

ratio of the incremental fuel use in terms of grams CO2 per minute for the HFET cycle to 

that over the FTP. 

Table 2-10 Impact of A/C System on Fuel Use 

 Small Car Large Car Minivan Small Truck Large Truck 

A/C impact: 100% A/C System On Time (g/mi) 

FTP 67.4 56.6 81.8 89.7 85.8 

HFET 32.3 31.9 45.0 47.4 46.2 

A/C impact: 100% A/C System On Time (g/minute (g/min)) 

FTP 22.02 18.49 26.7 29.3 28.0 

HFET 25.95 25.63 36.2 38.1 37.1 

HFET/FTP (g/min)/(g/min) 1.18 1.39 1.35 1.30 1.32 

As can be seen in the last line of Table 2-10, the ratio of A/C CO2 emissions over 

the HFET to that over the FTP is greater than 1.0 for each of the five vehicles. VMT 

weighting the CO2 emissions for each of the five vehicle groups produces an average 

ratio of 1.30. EPA assumed that this ratio was 1.0. Thus, EPA likely underestimated the 

impact of A/C fuel use during highway driving by 30%. For the purposes of EPA’s 

onroad fuel economy labeling rule, this under-estimation is small, because the impact of 

A/C on highway fuel economy is small. However, when estimating the impact of A/C 

fuel use, the difference is more significant. EPA’s five cycle formulae for estimating 

onroad fuel economy was adjusted to reflect this 1.32 factor. The impact of A/C fuel use 

on onroad fuel economy including defrosting increased from 2.5% to 2.8%. Thus, 

instead of a range of 2.5-5.3% for the impact of A/C on onroad fuel consumption, the 
Regulatory Impact Analysis 

2-30 

range is now 2.8-4.4%. The difference between the two estimates has been cut almost in 

half. 

There is one more adjustment that should be made to both estimates. Both EPA 

and NESCCAF assume that all A/C systems are in working condition. However, A/C 

systems do leak refrigerant, sometimes to the point where the system no longer works. 

Since the cost of repairing a leak can be significant, some vehicle owners do not always 

choose to repair the system. For its MOBILE6 emission model, EPA estimated the 

percentage of vehicles on the road with inoperative A/C systems as a function of vehicle 

age. Coupling these estimates with the amount of VMT typically driven by vehicles as a 

function of age, EPA estimates that 8% of all the VMT in the U.S. is by vehicles with 

inoperative A/C systems. These systems do not impact fuel consumption. Thus, both the 

NESCCAF and EPA estimates should be multiplied by 0.92. Doing this, the impact of 

A/C on onroad fuel consumption is estimated to be 2.6-to-4.1%. 

2.3.2 Technologies That Improve Efficiency of Air Conditioning and Their 

Effectiveness 

EPA estimates that the CO2 emissions from A/C related load on the engine 

accounts for about 3.9% of total greenhouse gas emissions from passenger vehicles in the 

United States. This is equivalent to CO2 emissions of approximately 14 g/mi per vehicle. 

The A/C usage is inherently higher in hotter months and states; however, vehicle owners 

may use the A/C systems throughout the year in all parts of the nation. That is, people 

use A/C systems to cool and dry the cabin air for passenger comfort on hot humid days, 

as well as to de-humidify the air used for defogging/de-icing the front windshield to 

improve visibility. 

Most of the excess load on the engine comes from the compressor, which pumps 

the refrigerant around the system loop. Significant additional load on the engine may 

also come from electrical or hydraulic fan units used for heat exchange across the 

condenser and radiator. The controls that EPA believes manufacturers would use to earn 

credits for improved A/C efficiency would focus primarily, but not exclusively, on the 

compressor, electric motor controls, and system controls which reduce load on the A/C 

system (e.g. reduced ‘reheat’ of the cooled air and increased of use recirculated cabin 

air). EPA is finalizing a program that will result in improved efficiency of the A/C 

system (without sacrificing passenger comfort) while improving the fuel efficiency of the 

vehicle, which has a direct impact on CO2 emissions. 

The cooperative IMAC program described above has demonstrated that average 

A/C efficiency can be improved by 36.4% (compared to a baseline A/C system), when 

utilizing “best-of-best” technologies. EPA considers a baseline A/C system contains the 

following components and technologies; internally-controlled fixed displacement 

compressor (in which the compressor clutch is controlled based on ‘internal’ system 

parameters, such as head pressure, suction pressure, and/or evaporator outlet 

temperature); blower and fan motor controls which create waste heat (energy) when 

running at lower speeds; thermostatic expansion valves; standard efficiency evaporators 

and condensers; and systems which circulate compressor oil throughout the A/C system. 
Air Conditioning 

2-31 

These baseline systems are also extraordinarily wasteful in their energy consumption 

because they add heat to the cooled air out of the evaporator in order to control the 

temperature inside the passenger compartment. Moreover, many systems default to a 

fresh air setting, which brings hot outside air into the cabin, rather than recirculating the 

already-cooled air within the cabin. 

The IMAC program indicates that improvements can be accomplished by a 

number of methods related only to the A/C system components and their controls 

including: improved component efficiency, improved refrigerant cycle controls, and 

reduced reheat of the cooled air. The program EPA is finalizing will encourage the 

reduction of A/C CO2 emissions from cars and trucks by up to 40% from current baseline 

levels through a credit system. EPA believes that the component efficiency 

improvements demonstrated in the IMAC program, combined with improvements in the 

control of the supporting mechanical and electrical devices (i.e. engine speeds and 

electrical heat exchanger fans), can go beyond the IMAC levels and achieve a total 

efficiency improvement of 40%. The following sections describe the technologies EPA 

believes manufacturers can use to attain these efficiency improvements. 

2.3.2.1 Reduced Reheat Using a Externally-Controlled, Variable-Displacement 

Compressor 

The term ‘external control’ of a variable-displacement compressor is defined as a 

mechanism or control strategy where the displacement of the compressor adjusted 

electronically, based on the temperature setpoint and/or cooling demand of the A/C 

system control settings inside the passenger compartment. External controls differ from 

‘internal controls’ that internal controls adjust the displacement of the compressor based 

on conditions within the A/C system, such has head pressure, suction pressure, or 

evaporator outlet temperature. By controlling the displacement of the compressor by 

external means, the compressor load can be matched to the cooling demand of the cabin. 

With internal controls, the amount of cooling delivered by the system may be greater than 

desired, at which point the cooled cabin air is then ‘reheated’ to achieve the desired cabin 

comfort. It is this reheating of the air which results reduces the efficiency of the A/C 

system – compressor power is consumed to cool air to a temperature less than what is 

desired. 

Reducing reheat through external control of the compressor is a very effective 

strategy for improving A/C system efficiency. The SAE IMAC team determined that an 

annual efficiency improvement of 24.1% was possible using this technology.25 EPA 

estimates that additional improvements with this technology, when fully developed, 

calibrated, and optimized to particular vehicle’s cooling needs - and combined with 

increased use of recirculated cabin air - can result in an efficiency improvement of 40%, 

compared to the baseline system. 
Regulatory Impact Analysis 

2-32 

2.3.2.2 Reduced Reheat Using a Externally-Controlled, Fixed-Displacement or 

Pneumatic Variable-Displacement Compressor 

When using a fixed-displacement or pneumatic variable-displacement compressor 

(which controls the stroke, or displacement, of the compressor based on system suction 

pressure), reduced reheat can be realized by disengaging the compressor clutch 

momentarily to achieve the desired evaporator air temperature. This disengaging, or 

cycling, of the compressor clutch must be externally-controlled in a manner similar to 

that described in 2.3.2.1. EPA believes that a reduced reheat strategy for fixeddisplacement and pneumatic variable-displacement compressors can result in an 

efficiency improvement of 20%. This lower efficiency improvement estimate (compared 

to an externally-controlled variable displacement compressor) is due to the thermal and 

kinetic energy losses resulting from cycling a compressor clutch off-and-on repeatedly. 

2.3.2.3 Defaulting to Recirculated Cabin Air 

In ambient conditions where air temperature outside the vehicle is much higher 

that the air inside the passenger compartment, most A/C systems draw air from outside 

the vehicle and cool it to the desired comfort level inside the vehicle. This approach 

wastes energy because the system is continuously cooling the hotter outside air instead of 

having the A/C system draw its supply air from the cooler air inside the vehicle (also 

known as recirculated air, or ‘recirc’). By only cooling this inside air (i.e. air that has 

been previously cooled by the A/C system), less energy is required, and A/C Idle Tests 

conducted by EPA indicate that an efficiency improvement of 35-to-40% improvement is 

possible under the conditions of this test. A mechanically-controlled door on the A/C 

system’s air intake typically controls whether outside air, inside air, or a mixture of both, 

is drawn into the system. Since the typical ‘default’ position of this air intake door is 

outside air (except in cases where maximum cooling capacity is required, in which case, 

many systems automatically switch this door to the recirculated air position), EPA is 

specifying that, as cabin comfort and de-fogging conditions allow, an efficiency credit be 

granted if a manufacturer defaults to recirculated air whenever the outside ambient 

temperature is greater than 75°F. To maintain the desired quality inside the cabin (in 

terms of freshness and humidity), EPA believes some manufacturers will control the air 

supply in a ‘closed-loop’ manner, equipping their A/C systems with humidity sensors or 

fog sensors (which detect condensation on the inside glass), allowing them to adjust the 

blend of fresh-to-recirculated air and optimize the controls for maximum efficiency. In 

response to comments concerning the allowance of additional credit for humidity sensors 

(i.e. closed-loop control), we are redefining the credit available for recirculated cabin air 

based on how the air supply is controlled. Vehicles with closed-loop control of the air 

supply (i.e. sensor feedback is used to control the interior air quality) will qualify for a 

1.7 g/mi CO2 credit and vehicles with open-loop control (sensor feeback is not used to 

control interior air quality) will qualify for a 1.1 g/mi CO2 credit. We believe that the 

closed-loop control system will be inherently more efficient than the open-loop control 

system because the former can maximize the amount to recirculation to achieve a desired 

air quality, whereas the latter will use a fixed ‘default’ amount of recirculated air which 

provides the desired air quality under worst case conditions (e.g. maximum number of 

passengers in the vehicle). 
Air Conditioning 

2-33 

2.3.2.4 Improved Blower and Fan Motor Controls 

In controlling the speed of the direct current (DC) electric motors in an air 

conditioning system, manufacturers often utilize resistive elements to reduce the voltage 

supplied to the motor, which in turn reduces its speed. In reducing the voltage however, 

these resistive elements produce heat, which is typically dissipated into the air ducts of 

the A/C system. Not only does this waste heat consume electrical energy, it contributes 

to the heat load on the A/C system. One method for controlling DC voltage is to use a 

pulsewidth modulated (PWM) controller on the motor. A PWM controller can reduce the 

amount of energy wasted, and based on Delphi estimates of power consumption for these 

devices, EPA believes that when more efficient speed controls are applied to either the 

blower or fan motors, an overall improvement in A/C system efficiency of 15% is 

possible.26 We changed the definition for this credit from requiring waste heat reducing 

control on both the blower and fan motors to requiring it ‘only’ on the blower motor - 

whether or not similar control is used on the fan motor. This change was made because 

commenters noted the majority of the efficiency gain due to waste-heat-reducing control 

technology is realized on the blower motor, and not the fan motor. Since the blower 

motor is consuming energy almost 100% of the time (whether for heating, cooling, or 

ventilation, the motor is usually running at some speed whenever the vehicle is being 

driven), the efficiency to be gained from improved control technology is greatest on this 

motor, and thus credit for waste heat reducing technology will apply only when it is used 

on the blower motor. 

2.3.2.5 Internal Heat Exchanger 

An internal heat exchanger (IHX), which is alternatively described as a suction line heat 

exchanger, transfers heat from the high pressure liquid entering the evaporator to the gas 

exiting the evaporator, which reduces compressor power consumption and improves the 

efficiency of the A/C system. Previously, we considered that IHX technology would be 

required with the changeover to an alternative refrigerant such as HFO-1234yf, as the 

different expansion characteristics of that refrigerant (compared to R-134a) would 

necessitate an IHX. However, several commenters noted that an IHX can be used on R134a systems as well, and that a significant efficiency improvement can be realized in 

doing so. It is estimated that use of an IHX can improve the coefficient of performance 

(COP) for the system can be improved by 7%, resulting in a fuel consumption reduction 

of 1-to-2%.27 EPA believes that a 20% improvement in efficiency relative to the baseline 

configuration can be realized if the system includes an IHX, and a 1.1 g/mi credit for an 

IHX will be added to the list of efficiency improving technologies. 

2.3.2.6 Electronic Expansion Valve 

The expansion valve in an A/C system is used to “throttle” the flow high pressure 

liquid refrigerant upstream of the evaporator. By throttling the refrigerant flow, it is 

possible to control the amount of expansion (superheat) that the refrigerant will undergo, 

and by extension, the amount of heat removed from air passing through the evaporator. 

With a conventional, or thermostatic, expansion valve (TXV), the amount of expansion is 

controlled by an internal temperature reference to assure a constant temperature level for 
Regulatory Impact Analysis 

2-34 

the expanded refrigerant gas, which is typically a few degrees Celsius above the freezing 

point of water (which may be too cool for the desired cabin comfort level). In the case 

where the air exiting the evaporator is too cool (or over-cooled), it will be necessary to 

reheat it by directing some of the airflow through the heater core. It is this reheating of 

the air which results in reduced system efficiency, as additional compressor energy is 

consumed in the process of over-cooling the air. However, if the expansion of the 

refrigerant is controlled externally – such as by an electronic signal from the A/C control 

unit – it is possible to adjust the level of expansion, or superheat, to only to the level 

necessary to meet the current cooling needs of the passenger compartment. This 

electronic expansion valve (EXV) approach is similar to the reduced reheat strategy, 

except that instead of controlling the mass of refrigerant flowing through the system by 

controlling the compressor output, the mass flow is controlled by the EXV. By reducing 

the amount of refrigerant expanding, or controlling the level of superheat in the gas-phase 

refrigerant, the temperature of the evaporator can be increased and controlled to the point 

where reheating of the air is not necessary, the SAE IMAC team determined that an 

annual efficiency improvement of 16.5% is possible. EPA estimated that when fully 

developed, calibrated, and optimized to the requirements of particular system design, use 

of EXV technology could result in a 20% efficiency improvement over the baseline TXV 

system. However, many commenters stated that the EPA estimate for EXV efficiency 

was over-stated, that no manufacturers were developing this technology within the 

timeframe of this rulemaking, and that it should not be included on the list of efficiencyimproving technologies. These commenters noted that the SAE IMAC report (from 

which we referenced the expected efficiency improvement) utilized an EXV in 

conjunction with a more efficient compressor – and not as a standalone technology. 

Given the uncertainty in the effectiveness of EXV technology, and the statements that no 

manufacturers plan on utilizing it, we are removing this technology from the list of 

efficiency improving technologies and credits. 

2.3.2.7 Improved-Efficiency Evaporators and Condensers 

The evaporators and condensers in an A/C system are designed to transfer heat to 

and from the refrigerant – the evaporator absorbs heat from the cabin air and transfers it 

to the refrigerant, and the condenser transfer heat from the refrigerant to the outside 

ambient air. The efficiency, or effectiveness, of this heat transfer process directly effects 

the efficiency of the overall system, as more work, or energy, is required if the process is 

inefficient. A method for measuring the heat transfer effectiveness of these components 

is to determine the Coefficient of Performance (COP) for the system using the industryconsensus method described in the SAE surface vehicle standard J2765 – Procedure for 

Measuring System COP of a Mobile Air Conditioning System on a Test Bench.28 We 

solicited comments as to how we should define the “baseline” evaporator and condenser 

designs which are compared to the “improved” design. The bench test based engineering 

analysis that a manufacturer will submit at time of certification. We will consider the 

baseline component to be the version which a manufacturer most recently had in 

production on the same vehicle or a vehicle in a similar EPA vehicle classification. The 

design characteristics of the baseline component (e.g. tube 

configuration/thickness/spacing and fin density) are to be documented in an engineering 

analysis and compared to the improved components, along with data demonstrating the 
Air Conditioning 

2-35 

COP improvement. This same engineering analysis can be applied to evaporators and 

condensers on other vehicles and models (even if the overall size of the heat exchanger is 

different), as long as the design characteristics of the baseline and improved components 

are the same. If these components can demonstrate a 10% improvement in COP versus 

the baseline components, EPA estimates that a 20% improvement in overall system 

efficiency is possible. 

2.3.2.8 Oil Separator 

The oil present in a typical A/C system circulates throughout the system for the 

purpose of lubricating the compressor. Because this oil is in contact with inner surfaces 

of evaporator and condenser, and a coating of oil reduces the heat transfer effectiveness 

of these devices, the overall system efficiency is reduced.29 It also adds inefficiency to 

the system to be “pushing around and cooling” an extraneous fluid that results in a 

dilution of the thermodynamic properties of the refrigerant. If the oil can be contained 

only to that part of the system where it is needed – the compressor – the heat transfer 

effectiveness of the evaporator and condenser will improve. The overall COP will also 

improve due to a reduction in the flow of diluent. The SAE IMAC team estimated that 

overall system COP could be improved by 8% if an oil separator was used.11 EPA 

believes that if oil is prevented from prevented from circulating throughout the A/C 

system, an overall system efficiency improvement of 10% can be realized. Whether the 

oil separator is a standalone component or is integral to the compressor design, 

manufacturers can submit an engineering analysis to demonstrate the effectiveness of the 

oil separation technology. 

2.3.3 Technical Feasibility of Efficiency-Improving Technologies 

EPA believes that the efficiency-improving technologies discussed in the previous 

sections are available to manufacturers today, are relatively low in cost, and that their 

feasibility and effectiveness has been demonstrated by the SAE IMAC teams and various 

industry sources. EPA also believes that when these individual components and 

technologies are fully designed, developed, and integrated into A/C system designs, 

manufacturers will be able to achieve the estimated reductions in CO2 emissions and earn 

appropriate A/C Efficiency Credits, which are discussed in the following section. 

2.3.4 A/C Efficiency Credits 

In model years 2012 through and 2016, manufacturers would be required to 

demonstrate that vehicles receiving credit for A/C efficiency improvements are equipped 

with the type of components and/or controls needed to qualify for a certain level of CO2

credit. For model years 2014 and later, the design-based approach will be supplemented 

with a vehicle performance test. In particular, EPA is specifying that the range of 

allowable ambient temperature for a valid A/C Idle Test be limited to 75 + 2 °F (as 

opposed to 68-to-86 °F for a valid FTP test) and that the humidity in the test cell be 

limited to 50 + 5 grains of water per pound of dry air (where there are no such humidity 

constraints on an FTP test, only a humidity correction for NOx). This narrowing of the 

allowable range of ambient conditions was done to improve the accuracy and 
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2-36 

repeatability of the test results. Since the performance of an A/C system (and the amount 

of fuel consumed by the A/C system) are directly influenced by the heat energy, or 

enthalpy, of the air within the test cell – where criteria pollutants are not - it was 

necessary to control the enthalpy, and limit its effect on the test results. In addition, EPA 

has modified the interior fan settings for vehicles with manual A/C controls. In the 

proposed reporting rule, vehicle with manual A/C controls were to be run on the ‘high’ 

fan setting for the duration of the A/C on portion of the test. However, EPA believes that 

this fan speed setting would unduly penalize vehicles with manual controls when 

compared to those with automatic control - as automatic controls adjust the fan speed to 

lower setting as the target interior temperature is reached (which is similar to what a 

driver does on a vehicle with manual controls). In recognition of this disparity in the 

proposed test procedure, EPA has revised the test to allow vehicles with manual A/C 

controls to average the result obtained on the high fan speed setting with the result 

obtained on the low fan speed setting. The additional 10-minute idle sequence on the low 

fan speed setting is to be run immediately following the high fan sequence (no additional 

prep cycle is required). This revised performance test will assure that the A/C 

components and/or system control strategies a manufacturer chooses to implement are 

indeed delivering the efficiency gains projected for each. The performance test discussed 

in section II of the preamble is the A/C Idle Test, but in that section, EPA also discusses 

how a modified SC03 test could also be used to measure the efficiency of A/C systems. 

To establish an average A/C CO2 rate for the A/C systems in todays vehicles, the 

EPA conducted laboratory tests to measure the amount of additional CO2 a vehicle 

generated due to A/C use on the Idle Test. 30 The results of this test program are 

summarized in Table 2-11, and represent a wide cross-section of vehicle types in the U.S. 

market. The average A/C CO2 result from this group of vehicles is the value against 

which results from vehicle testing (beginning in 2014) will be compared. The EPA 

conducted laboratory tests to tested over 60 vehicles representing a wide range of vehicle 

types (e.g. compact cars, midsize cars, large cars, sport utility vehicles, small station 

wagons, and standard pickup trucks). 

Table 2-11 Summary of A/C Idle Test Study Conducted by EPA at the National Vehicle Fuel and 

Emissions Laboratory 

Vehicle Makes Tested 19 

Vehicle Models Tested 29 

Model Years Represented (number of vehicles in each model 

year) 

1999 (2), 2006 (21), 2007 (39) 

EPA Size Classes Represented Minicompact, Compact, Midsize, and 

Large Cars 

Sport Utility Vehicles 

Small Station Wagons 

Standard Pickup Trucks 

Total Number of A/C Idle Tests 62 

Average A/C CO2 (g/min) 21.3 

Standard Deviation of Test Results (+ g/min) 5.8 
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2-37 

The majority of vehicles tested were from the 2006 and 2007 model years and 

their A/C systems are representative of the ‘baseline’ technologies, in terms of efficiency 

(i.e. to EPA’s knowledge, these vehicles do not utilize any of the efficiency-improving 

technologies described in Table 2-13). The individual test results from this testing are 

shown in Figure 2-4. EPA attempted to find a correlation between the A/C CO2 results 

and a vehicle’s interior volume, footprint, and engine displacement, but was unable to do 

so, as there is significant “scatter” in the test results. This scatter is generally not test-totest variation, but scatter amongst the various vehicle models and types – there is no clear 

correlation between which vehicles perform well on this test, and those which do not. 

EPA did attempt to find a correlation between the idle test results and a vehicle’s interior 

volume, footprint, or engine displacement, but no clear correlation could be found. What 

is clear, however, is that load placed on the engine by the A/C system is not consistent, 

and in certain cases, larger vehicles perform better than smaller ones, in terms of their 

A/C CO2 result. 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0 10 20 30 40 50 60 70

Test Number

A/C CO2 (g/min)

Average A/C CO2 rate = 21.3 g/min

Figure 2-4 EPA A/C Idle Test Results from Various Vehicle Model Types 

Part of this variation in the A/C Idle Test results may be due to the components a 

manufacture chooses to use in a particular vehicle. Where components such as 

compressors are shared across vehicle model types (e.g. a compressor may be ‘oversized’ for one application, but the use of a common part amongst multiple model types 

results in a cost savings to the manufacturer). Some of the variation may also be due to 

the amount of cooling capacity a vehicle has at idle. One manufacturer indicated that one 
Regulatory Impact Analysis 

2-38 

of their vehicles which produced a below-average A/C CO2 result, is also known for 

having A/C performance at idle which does not meet customer expectations, but off-idle, 

performs very well. Therefore, it will be necessary for manufacturers to balance the 

cooling capacity of the A/C system under idle conditions against the overall A/C system 

efficiency. 

Some of this variation between various models may also be due to the efficiency 

of the fan(s) which draw air across the condenser – since an external fan is not placed in 

front of the vehicle during the A/C Idle Test, it is the vehicle’s fan which is responsible 

for rejecting heat from the condenser (and some models may do this more efficiently than 

others). In this case, EPA believes that an SC03-type test – run in a full environmental 

chamber with a “road-speed” fan on the front of the vehicle – would be a better measure 

of how a vehicle’s A/C system performs under transient conditions, and any limitations 

the system may have at idle could be counter-balanced by improved performance and 

efficiency elsewhere in the drive cycle. However, since idle is significant part of realworld and FTP drive cycles (idle represents 18% of the FTP), EPA believes that the focus 

in this rulemaking on A/C system efficiency under idle conditions is justified. Many 

commenters questioned the ability of the A/C Idle Test to measure the effect of certain 

A/C technologies (e.g. technologies which improve performance under higher cooling 

load conditions), and stated that the test was not representative of real-world driving 

conditions. While we acknowledge that there are limitations to the Idle Test, we have 

determined that it is still a valid tool evaluating the efficiency of a vehicle’s A/C system 

under conditions encountered in daily driving. Moreover, we believe that a performance 

test is necessary to assure that efficiency-improving technologies are implemented 

properly and that the vehicle’s A/C system operates in an efficient manner under idle 

conditions. In the future, EPA will continue to work with industry groups, 

manufacturers, component suppliers, and other government organizations to develop a 

procedure for determining A/C system efficiency which incorporates the appropriate testbench, modeling, and drive cycle tools. The goal of this exercise is the development of a 

reliable, accurate, and verifiable assessment and testing method which minimizes a 

manufacturers testing burden. This effort could include component-level assessment of 

A/C technologies, modeling of system control strategies, and development of a vehiclebased test procedure for validating the findings of component-level and system modeling 

analyses. 

 The average A/C CO2 result for the vehicles tested was 21.3 g/min. Starting in 

the year 2014, in order to qualify for A/C Efficiency Credits, it will be necessary for 

manufacturers to demonstrate the efficiency of their systems by running an A/C Idle Test 

on each vehicle model for which they are seeking credit. To qualify for the full credit, it 

will be necessary for each model to achieve an A/C CO2 result less than or equal to 14.9 

g/min (which is 30% less than the average value observed in the EPA testing). EPA chose 

the 30% improvement over the “average” value to drive the fleet of vehicles toward A/C 

systems which approach or exceed the efficiency of current best-in-class vehicles. 

Several commenters disagreed with the EPA’s threshold for full credit, arguing that the 

30% improvement was too aggressive. However, EPA test results on three vehicle size 

classes (large car, SUV, and pickup truck) indicate that significant reductions in fuel 

consumption can be achieved by simply switching A/C control from outside air (OSA) to 
Air Conditioning 

2-39 

recirculated cabin air. As shown in , the percentage reduction in the CO2 due to A/C use 

was greater than 30% in all three cases. 

Table 2-12 Effect of Outside Air and Recirculated Cabin Air on A/C Idle Test Results (EPA Testing) 

A/C CO2 Result (g/min) 

Vehicle Type 

w/Outside Air w/Recirc Cabin Air 

Change in A/C CO2 

w/Recirc (%) 

Large Car 25.9 14.0 -45.9 

SUV 17.4 11.4 -34.5 

Pickup Truck 14.1 9.0 -36.2 

EPA believes this approach will cause manufacturers to tailor the size A/C 

components and systems to the cooling needs of a particular vehicle model and focus on 

the overall efficiency of their A/C systems. EPA believes this approach strikes a 

reasonable balance between avoiding granting credits for improvements which would 

occur in any case, and encouraging A/C efficiency improvements which would not 

otherwise occur. However, to avoid having an all-or-nothing threshold of 14.9 g/min on 

the Idle Test to qualify for credits, EPA will allow amount of credit to be scaled to Idle 

Test result, with vehicles achieving 14.9 g/min or better receiving full credit, and vehicles 

achieving 21.3 g/min or higher receiving no credit, as shown in Figure 2-5. 
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2-40 

Figure 2-5 A/C Credit Adjustment Factor. 

 Once manufacturers begin using the technologies described in Table 2-13 – and 

develop these technologies for the requirements of each vehicle, with a focus on 

achieving optimum efficiency – EPA believes it will be possible to demonstrate that a 

vehicle is indeed achieving the reductions in A/C CO2 emissions that are estimated for 

this rulemaking. 

We believe that it is possible to identify the A/C efficiency-improving 

components and control strategies most-likely to be utilized by manufacturers and are 

assigning a CO2 ‘credit’ to each. In addition, EPA recognizes that to achieve the 

maximum efficiency benefit, some components can be used in conjunction with other 

components or control strategies. Therefore, the system efficiency synergies resulting 

from the grouping of three or more individual components are additive, and will qualify 

for a credit commensurate with their overall effect on A/C efficiency. A list of these 

technologies – and the credit associated with each – is shown in Table 2-13. If the more 

than one technology is utilized by a manufacturer for a given vehicle model, the A/C 

credits can be added, but the maximum credit possible is limited to 5.7 g/mi. This 

maximum credit represents a 40% improvement over a 14.3 g/mi per vehicle CO2 -

equivalent impact due to A/C use. This 14.3 g/mi impact is derived from the EPA’s 2006 

estimate of fuel consumption due to A/C use of 12.11 g/mi. However, the 2006 estimate 

needed to be adjusted upward to reflect the increased prevalence of “automatic” A/C 

controls in modern vehicles (the Phoenix study used in the EPA’s 2006 estimate was 
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from 1990s-vintage vehicles, which do not include a significant number of vehicles with 

automatic climate control systems). To derive the newer estimate, a scenario was first 

modeled in which 100% of vehicles used in the Phoenix study were equipped with 

automatic A/C systems (which increases the amount of time the compressor is engaged in 

moderate ambient conditions), which resulted in the 12.11 g/mi estimate increasing to 

17.85 g/mi. Industry and supplier estimates were then used for the number of vehicles 

equipped with automatic A/C systems - as well as vehicle sales data from the 2009 

Ward’s Automotive Yearbook – and projected that 38% of new vehicles are equipped 

with automatic A/C systems.31 Finally, the percentages of vehicles with and without 

automatic A/C systems were multiplied by their respective impact on fuel consumption 

(0.62 x 12.11 + 0.38 x 17.85) to produce our estimate of 14.3 g/mi. This credit is the 

same for cars and trucks because the A/C components, cooling requirements, and system 

functions are similar for both vehicle classes. Therefore, EPA believes the level of 

efficiency improvement and the maximum credit possible should be similar for cars and 

trucks as well. 
Regulatory Impact Analysis 

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Table 2-13 Efficiency-Improving A/C Technologies and Credits 

Technology Description Estimated Reduction 

in A/C CO2

Emissions 

A/C Credit (g/mi 

CO2) 

Reduced reheat, with externally-controlled, 

variable-displacement compressor 

30% 1.7 

Reduced reheat, with externally-controlled, 

fixed-displacement or pneumatic variable 

displacement compressor 

20% 1.1 

Default to recirculated air with closed-loop 

control of the air supply (sensor feedback to 

control interior air quality) whenever the 

outside ambient temperature is 75 °F or 

higher (although deviations from this 

temperature are allowed if accompanied by 

an engineering analysis) 

30% 1.7 

Default to recirculated air with open-loop 

control of the air supply (no sensor 

feedback) whenever the outside ambient 

temperature is 75 °F or higher (although 

deviations from this temperature are 

allowed if accompanied by an engineering 

analysis) 

20% 1.1 

Blower motor control which limit wasted 

electrical energy (e.g. pulsewidth modulated 

power controller) 

15% 0.9 

Internal heat exchanger (or suction line heat 

exchanger) 

20% 1.1 

 

Improved evaporators and condensers (with 

engineering analysis on each component 

indicating a COP improvement greater than 

10%, when compared to previous design) 

20% 1.1 

Oil Separator (internal or external to 

compressor) 

10% 0.6 
Air Conditioning 

2-43 

The estimates for the percent reduction in A/C CO2 for each technology are based 

in part on the results of SAE IMAC Team 2 (Improved Efficiency) final report, which 

both provides a baseline for calculating creditable improvements, and also provides a 

level of improvement for each technology. The estimated percent reduction in A/C CO2

emissions for each was adjusted upward to reflect continuous improvement in the design, 

calibration, and implementation of these technologies. These technologies, which, when 

combined, can allow manufacturers to achieve the 40% reduction in CO2 emissions. 

2.4 Costs of A/C Reducing Technologies 

This section describes the cost estimates for reductions in air conditioner related 

GHG emissions as well as the cost savings that result from improved technologies. 

These estimates are largely determined from literature reviews of publications and public 

presentations made by parties involved in the development and manufacture of A/C 

systems as well as from EPA analyses. The cost savings are estimated from the literature 

as well as the supplemental deterioration models based analysis described above. 

For leakage, or direct, emissions, EPA assumes that reductions can be achieved 

without a change in refrigerant, though it is possible that by 2020 a new technology and 

refrigerant will be a much more viable option than it is today. For example, an 

alternative refrigerant with a GWP less than 150 and can be used directly in current A/C 

systems will be able to meet the leakage credit requirements without significant 

engineering changes or cost increases. However, in order to reduce the leakage in 

conventional R134a systems by 50%, it has been estimated that the manufacturer cost 

would increase by $15 per vehicle in 2002 dollars, employing existing off-the-shelf 

technologies such as the ones included in the J2727 leakage charts.L

 Converting this to 

2007 dollars using the GDP price deflator (see Appendix 3.A of the Draft Joint TSD) 

results in a cost of $17. With the indirect cost markup factor of 1.11 for a low complexity 

technology the compliance cost becomes $19. Using this as the 2012MY cost and 

applying time based learning results in a 2016MY cost of $17 for leakage reduction 

technology. Table 2-14 shows how these costs may be distributed on a year by year basis 

as the program phases in over 5 years. 

We expect that a reduction in leakage will lead to fewer servicing events for 

refrigerant recharge. In 2006, the EPA estimated the average cost to the vehicle owner 

for a recharge maintenance visit was $100. However, recent information indicates that 

the industry average cost of recharging an automotive air conditioner is $147.32 With the 

new AC systems, such $100 or $147 maintenance charges could be moved delayed until 

later in the vehicle life and, possibly, one of more events could be eliminated completely. 

This provides potential savings to consumers as a result of the new technology. Note that 

 

L

 Author unknown, Alternative Refrigerant Assessment Workshop, SAE Automotive Alternative 

Refrigerant Symposium, Arizona, 2003. 
Regulatory Impact Analysis 

2-44 

these potential maintenance savings are not included in the cost and benefit analysis 

presented in Chapters 6 and 8 of this RIA. However, EPA intends to include an estimate 

of maintenance savings in the final rule analysis and believe that this higher estimate for 

the cost of recharging an A/C system would serve as the basis for those maintenance 

savings in the cost analysis of the final rule. 

For indirect CO2 emissions due to A/C, it has been estimated that a 25-30% 

reduction can be achieved at a manufacturer cost of 44€, or $51 in 2005 dollars.M

 The 

IMAC Efficiency Improvement team of the Society of Automotive Engineers realized an 

efficiency improvement of 36.4% based on existing technologies and processes.25 For 

the idle test, EPA estimates that further reductions with software controls can achieve a 

total reduction of 40%. Converting the $51 value to 2007 dollars results in $54 (using the 

GDP price deflator as explained in Appendix 3.A of the Draft Joint TSD) and applying a 

1.11 indirect cost multiplier for a low complexity technology (as described in Chapter 3 

of the Draft Joint TSD) gives a total compliance cost of $60. Using this as the 2012MY 

cost and applying time based learning (as described in Chapter 3 of the Draft Joint TSD) 

results in a 2016MY cost of $53. 

In the 2008 Advance Notice of Proposed Rule, EPA presented a quick analysis of 

the potential fuel savings associated with the control of indirect emissions via new AC 

technology. There EPA assumes a reference 2010 fuel economy of 30 mpg for cars and 

24 for trucks. With a 20% real-world shortfall, this becomes 24 and 19 mpg respectively. 

As described in appendix A of the GHG advanced notice (and above), A/C impacts 

overall fuel consumption by 2.6-to-4.1%, and that an ultimate efficiency improvement of 

40% is achievable. EPA used the AEO 2008 fuel price, discount values, vehicle 

scrappage and VMT figures employed elsewhere in the advanced proposal to calculate a 

$96 cost savings for cars and $130 for trucks for the life of the vehicle. Assuming the 

same 0.23 factor to account for rebound and emissions, these savings increase to $118 for 

cars and $159 for trucks. This was noted in the GHG advance notice as being a 

potentially significant cost savings for the vehicle owner compared to the cost of the 

efficiency improvements. EPA has not updated this analysis for this rule. For the 

analysis in support of this rule, as presented in Chapter 6 of this RIA, the indirect AC fuel 

savings has been included in the total fuel savings resulting from this rulemaking. 

Table 2-14 presents the compliance costs associated with new AC technology 

with estimates for how those costs might change as vehicles with the technology are 

introduced into the fleet. Costs shown are averages per vehicle since not all vehicles 

would include the new technology but would, instead, include the technology according 

to the penetration estimates shown in the table. 

 

M

 The 0.87 Euro-US dollar conversion is dated today but was valid in 2005. 2005 Euros are converted to 

2005 US dollars then 2005 US dollars are converted to 2007 US dollars. 
Air Conditioning 

2-45 

Table 2-14 Estimated Costs in Each Model Year for New AC Technology, 2007 Dollars 

2012 2013 2014 2015 2016 

Penetration 28% 40% 60% 80% 85% 

AC Leakage (Direct) $5 $7 $11 $14 $15 

AC Indirect $15 $21 $32 $42 $45 

Total $20 $28 $42 $56 $60 

 

2.5 Air Conditioning Credit Summary 

A summary table is shown with the estimated usage of the A/C credits. EPA 

projected the penetration rates as a reasonable ramp to the 85% penetration cap in 2016. 

The 85% penetration cap was set to maintain consistency with the technology penetration 

caps used in OMEGA. The car and truck sales fractions were drawn from an adjusted 

version of AEO 2009, as documented in RIA Chapter 5. As documented above, no use 

of alternative refrigerant is projected in this in this analysis, although this assumption 

may be revisited in the final rule (Table 2-15). 

Table 2-15 Credit Summary with Estimated Penetration Rates

Model Year 

2012 2013 2014 2015 2016 

Estimated Penetration 28% 40% 60% 80% 85% 

Car Sales Fraction 61.1% 61.9% 63.2% 64.6% 65.6% 

Truck Sales Fraction 38.9% 38.1% 36.8% 35.4% 34.4% 

Car Direct Credit 1.8 2.5 3.8 5.0 5.4 

Car Indirect Credit 1.6 2.3 3.4 4.6 4.8 

Total Car Credit 3.4 4.8 7.2 9.6 10.2 

Truck Direct Credit 2.2 3.1 4.7 6.2 6.6 

Truck Indirect Credit 1.4 2.3 3.4 4.6 4.8 

Total Truck credit 3.8 5.4 8.1 10.8 11.5 

Fleet average credits 3.5 5.0 7.5 10.0 10.6 
Regulatory Impact Analysis 

2-46 

References ` 

1

 IPCC, Chapter 2, “Changes in Atmospheric Constituents and in Radiative Forcing,” 

September, 2007. This document is available in Docket EPA-HQ-OAR-2009-0472-0117. 

2

 Schwarz, W., Harnisch, J. 2003, “Establishing Leakage Rates of Mobile Air 

Conditioners,” Prepared for the European Commission (DG Environment), Doc B4-

3040/2002/337136/MAR/C1. This document is available in Docket EPA-HQ-OAR-2009-

0472-0157. 

3

 Vincent, R., Cleary, K., Ayala, A., Corey, R., “Emissions of HFC-134a from LightDuty Vehicles in California,” SAE 2004-01-2256, 2004. This document is available in 

Docket EPA-HQ-OAR-2009-0472-0186. 

4

 EPA, 2009, “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007,” 

http://www.epa.gov/climatechange/emissions/usinventoryreport.html. This document is 

available in Docket EPA-HQ-OAR-2009-0472. 

5

 State of California, Manufacturers Advisory Correspondence MAC #2009-01, 

“Implementation of the New Environmental Performance Label,” 

http://www.arb.ca.gov/msprog/macs/mac0901/mac0901.pdf. This document is available 

in Docket EPA-HQ-OAR-2009-0175. 

6

 State of Minnesota, “Reporting Leakage Rates of HFC-134a from Mobile Air 

Conditioners,” http://www.pca.state.mn.us/climatechange/mac-letter-082908.pdf. This 

document is available in Docket EPA-HQ-OAR-2009-0472-0178. 

7

 Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy, 

“Transportation Energy Data Book: Edition 27,” 2008. This document is available in 

Docket EPA-HQ-OAR-2009-0472 

8

 EPA, “Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of 

Fuel Economy Estimates - Final Technical Support Document,” 179 pp, 2.1MB, 

EPA420-R-06-017, December, 2006. This document is available in Docket EPA-HQOAR-2009-0472. 

9

 Ikegama, T., Kikuchi, K., “Field Test Results and Correlation with SAEJ2727,” 

Proceedings of the SAE 7th Alternative Refrigerant Systems Symposium, 2006. This 

document is available in Docket EPA-HQ-OAR-2009-0472. 

10 Atkinson, W., Baker, J., Ikegami, T., Nickels, P., “Revised SAEJ2727: SAE Interior 

Climate Control Standards Committee Presentation to the European Commission,” 2006. 

This document is available in Docket EPA-HQ-OAR-2009-0472. 
Air Conditioning 

2-47 

11 Society of Automotive Engineers, “IMAC Team 1 – Refrigerant Leakage Reduction, 

Final Report to Sponsors,” 2006. This document is available in Docket EPA-HQ-OAR2009-0472-0188. 

12 Society of Automotive Engineers Surface Vehicle Standard J2727, issued August, 

2008, http://www.sae.org. This document is available in Docket EPA-HQ-OAR-2009-

0472-0160. 

13 Minnesota Pollution Control Agency, “Model Year 2009 Leakage Rate List,” 

http://www.pca.state.mn.us/climatechange/mobileair.html. This document is available in 

Docket EPA-HQ-OAR-2009-0472-0161. 

14 Society of Automotive Engineers, “IMAC Team 4 – Reducing Refrigerant Emissions 

at Service and Vehicle End of Life,” June, 2007. This document is available in Docket 

EPA-HQ-OAR-2009-0472. 

15 Schwarz, W., “Emission of Refrigerant R-134a from Mobile Air-Conditioning 

Systems,” Study conducted for the German Federal Environmental Office, September, 

2001. This document is available in Docket EPA-HQ-OAR-2009-0472. 

16 “A/C Triage – Ensuring Replacement Compressor Survival, “ AC Delco Tech Connect, 

Volume 12, Number 5, January/February, 2005, 

http://www.airsept.com/Articles/CompressorGuard/ACDelcoTechConnectJanFeb05.pdf. 

This document is available in Docket EPA-HQ-OAR-2009-0472-0167. This document is 

available in Docket EPA-HQ-OAR-2009-0472-0167. 

17 Johnson, V., “Fuel Used for Vehicle Air Conditioning: A State-by-State Thermal 

Comfort-Based Approach,” SAE 2002-01-1957, 2002. This document is available in 

Docket EPA-HQ-OAR-2009-0472-0179. 

18 Rugh, J., Johnson, V., Andersen, S., “Significant Fuel Savings and Emission 

Reductions by Improving Vehicle Air Conditioning,” Mobile Air Conditioning Summit, 

Washington DC., April 14-15, 2004. This document is available in Docket EPA-HQOAR-2009-0472-0179. 

19 California Environmental Protection Agency Air Resources Board, “STAFF REPORT: 

Initial statement of reasons for proposed rulemaking, public hearing to consider adoption 

of regulations to control greenhouse gas emissions from motor vehicles,” 2004. This 

document is available in Docket EPA-HQ-OAR-2009-0472. 

20 “Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” Northeast 

States Center for a Clean Air Future, September, 2004. This document is available in 

Docket EPA-HQ-OAR-2009-0472. 

21 Fuel Economy Labeling of Motor Vehicles: Revision to Improve Calculation of Fuel 

Economy Estimates; Final Rule, 71 FR 77872, December 27, 2006. This document is 

available in Docket EPA-HQ-OAR-2009-0472. 
Regulatory Impact Analysis 

2-48 

22 EPA Technical Support Document for Fuel Economy Label Regulations, November, 

2006. This document is available in Docket EPA-HQ-OAR-2009-0472. 

23 Nam, E.K., “Understanding and Modeling NOx Emissions from Automobiles During 

Hot Operation,” PhD Thesis, University of Michigan, 1999. This document is available in 

Docket EPA-HQ-OAR-2009-0472. 

24 “Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” Northeast 

States Center for a Clean Air Future, September, 2004. This document is available in 

Docket EPA-HQ-OAR-2009-0472-0182. 

25 Society of Automotive Engineers, “IMAC Team 2 – Improved Efficiency, Final 

Report,” April, 2006. This document is available in Docket EPA-HQ-OAR-2009-0472-

0156. 

26 Memo to docket, “Meeting with Delphi and Presentation to EPA,” March, 2009. This 

document is available in Docket EPA-HQ-OAR-2009-0472-0196. 

27 Mathur, G., “Experimental Investigation with Cross Fluted Double-Pipe Suction Line 

Heat Exchanger to Enhance AC System Performance,” SAE 2009-01-0970, 2009. This 

document is available in Docket EPA-HQ-OAR-2009-0472. 

28 Society of Automotive Engineers Surface Vehicle Standard J2765, issued October, 

2008, http://www.sae.org. This document is available in Docket EPA-HQ-OAR-2009-

0472-0171. 

29 Barbat, T. et. Al., “CFD Study of Phase Separators in A/C Automotive Systems,” SAE 

2003-01-0736, 2003. This document is available in Docket EPA-HQ-OAR-2009-0472-

0187. 

30 EPA, “AC Idle Test Results_Summary for docket,” August, 2009. This document is 

available in Docket EPA-HQ-OAR-2009-0472-0183. 

31 “2009 Ward’s Automotive Yearbook,” Ward’s Automotive Group, 2009. ISBN 

Number 978-0-9105-89-26-0. This document is available in Docket EPA-HQ-OAR2009-0472-0248. 

32 California Air Resources Board, “Staff Analysis of Proposed Early Action for Climate 

Change Mitigation in California,” 2007, http://www.arb.ca.gov/cc/hfcmac/documents/hfcdiy.pdf. This document is available in Docket EPA-HQ-OAR-2009-

0472.
Technical Basis of the Standards 

3-1 

CHAPTER 3: Technical Basis of the Standards 

3.1 Technical Basis of the Standards 

3.1.1 Summary 

As explained in section III.D of the preamble to the rule, in developing the standard, 

EPA built on the technical work performed by the State of California during its development 

of its statewide GHG program. This led EPA to evaluate a Clean Air Act national standard 

which would require the same degree of technology penetration that would be required for 

California vehicles under the California program. In essence, EPA evaluated the stringency 

of the California Pavley 1 program but for a national standard. However, as further explained 

in the preamble, before being able to do so, technical analysis was necessary in order to be 

able to assess what would be an equivalent national new vehicle fleet-wide CO2 performance 

standards for model year 2016 which would result in the new vehicle fleet in the State of 

California having CO2 performance equal to the performance from the California Pavley 1 

standards. This technical analysis is documented in this sub-chapter of the RIA. 

Table 3-1 presents the calculated emission levels at which the national GHG standard 

would ensure that vehicle sales in California of federally compliant vehicles would have fleet 

average GHG emissions that are equal to the fleet average that would be achieved under the 

California program described in Sections 1900, 1960 and 1961.1 of Title 13, California Code 

of Regulations (“Pavley I”) by model year 2016: 

Table 3-1 Fleet Average National CO2 Emission Levels for Model Years 2012-2016 

MODEL YEAR 

2012 2013 2014 2015 2016 

Fleet Average Tailpipe Emission Level 

(CO2 gram / mile) 288 281 275 263 250 

Manufacturer’s use of credits and other program flexibilities may alter the program 

stringency beyond that which is shown here. 

3.1.2 Overview of Equivalency Calculation. 

The calculation of the fleet-wide national MY 2015 and MY 2016 CO2 emission levels 

which would be equivalent to California’s Pavley I program is briefly outlined here. 

1. Based on the California new vehicle fleet mix (predicted sales) and the CA program 

provisions, EPA calculated the fleetwide average CO2 emissions achieved in CA from 

the 2015 and 2016 model year fleets. 

2. The estimate of fleetwide average CO2 emissions was disaggregated into achieved car 

and truck CO2 emission levels at the national level using the new car and truck 

definitions for this rule. 
Regulatory Impact Analysis 

3-2 

3. Based on the anticipated national fleet mix, the achieved car and truck levels were 

weighted together to determine the national targets which would achieve reductions 

equivalent to Pavley I in California. 

This calculation accounts for the compositional difference between the CA vehicle fleet 

and the National fleet (i.e., CA has a higher proportion of cars than the average state), and for 

various parameters in the CA program. 

3.1.2.1 Calculating CO2 Equivalent Emissions under the California Program 

To calculate the CO2 equivalent emissions in California under Pavley I, the California 

Passenger Car and Light Truck standards were combined with the California fleet mix in 

order to calculate the anticipated emissions under the California standards from the California 

fleet. 

The Passenger Car and Light Truck Standards were drawn from Sections 1900, 1960 

and 1961.1 of Title 13, California Code of Regulations. Intermediate and small volume 

manufacturer standards were calculated based on guidance within the regulation, as well as 

EPA analysis of current manufacturer product mix. These standards, less 2 grams per mile of 

CO2 equivalent emissions due to methane (CH4) and nitrous oxide (N2O), are shown in Table 

3-2. CH4 and N2O were excluded because the EPA program separately addresses these 

emissions (Preamble section III). 

Table 3-2 California Regulatory Standards excluding CH4 and N2O (grams CO2 equivalent per mile) 

MY 2015 

Standard 

MY 2016 

Standard 

California Car (PC/LDT1) Standard 211 203 

Intermediate/Small Volume 

Manufacturer California Car Standard 314 229 

CA LDT2/MDPV Standard 339 330 

Intermediate/Small Volume 

Manufacturer LDT2/MDPV Standard 360 357 

The projected fleet mix, as defined under Pavley I, was then determined in California. 

Significantly, the California program deviates from historic definitions of “classic” cars and 

trucks. In brief, Pavley I defines “PC/LDT1” as passenger cars and light duty trucks below 

3,750 pounds, while “LDT2” include all trucks intended to convey passengers that weigh less 

than 10,000 pounds. The details of this classification scheme are found in the California 

regulations. 

In order to estimate the emission contribution of PC/LDT1 and LDT2 in California, 

EPA estimated the respective fleet fractions. EPA estimated the national sales mix in 2015 

and 2016 at 60% passenger cars and 40% light duty trucks. This estimate is supported by the 

Energy Information Administrations’ Annual Energy Outlook 2009, which estimated 

passenger cars at 59.4% of 2016 new vehicle sales in its published reference case.1

 Due to the 
Technical Basis of the Standards 

3-3 

American Recovery and Reinvestment Act of 2009, the Annual Energy Outlook reference 

case has since been updated to project 2016 sales at 57.1% passenger cars. 

The projected 60% passenger cars, 40% light duty trucks sales fraction was then 

applied to the California vehicle fleet mix. In such a scenario, the California Air Resource 

Board (ARB) estimated that PC/LDT1s comprise approximately 66% of the new light duty 

vehicle fleet in California and that LDT2s comprise the remainder (34%). 

Once the PC/LDT1 and LDT2 fractions of California new vehicle sales were 

determined, EPA estimated the fraction of vehicle sales in the intermediate and small volume 

manufacturer categories. These manufacturers, which sell less than 60,000 vehicles per year 

in California, are subject to less stringent emission standards under Pavley I. While estimates 

of future sales by manufacturer fluctuate, manufacturers such as Subaru, Porsche, Hyundai 

and Volkswagen were considered beneath this threshold for the purpose of this analysis. 

Based on EPA market analysis, small/intermediate volume manufacturers were estimated at 

9% of total California PC/LDT1 sales and 5% of total California LDT2 Sales. The final 

product mix assumed in California in 2015 and 2016 under a 60/40 national sales scenario is 

shown in Table 3-3. 

Table 3-3 California Sales Mix under a 60% Classic Car 40% Classic Truck National Sales Scenario 

Sales % 

PC/LDT1 Sales 60% 

Intermediate Volume PC/LDT1 

sales 

6% 

California LDT2 Sales 32% 

Intermediate Volume LT2 sales 2% 

The product mix was multiplied by the relevant standard and summed in order to 

calculate the achieved average CO2 emissions for the new California fleet. As an example in 

2016: 

Achieved Fleetwide CO2 Equivalent Emissions = 

(PC/LDT1 standard x PC/LDT1 Percentage) + (LT2 standard x LT2 Percentage) + (Intermediate Volume 

PC/LDT1 standard x Intermediate Volume PC/LDT1 Percentage) + Intermediate Volume LT2 standard x 

Intermediate Volume LT2 Percentage) 

= 

(0.6 x 203) + (0.06 x 229) + (0.32 x 330) + (0.02 x 357) = 248 grams. 

(eq.1)

Based on the projected 60% passenger car, 40% light duty truck national sales mix (Table 

3-3); the achieved fleetwide CO2 equivalent tailpipe emission level expected in California in 

2016 is 248 grams / mile. 
Regulatory Impact Analysis 

3-4 

This analysis was repeated for model year 2015. In order to achieve equivalency, the 

national program must produce a fleetwide average emission level in California that is no 

higher than 261 grams CO2 / mile in 2015 and 248 grams CO2 / mile in 2016.

3.1.2.2 Translating the CA Fleetwide Average Emissions into Cars (Passenger 

Automobiles) and Trucks (Non-Passenger Automobiles) 

In order to describe the national fleet, the California fleet-wide average CO2 emission 

level was translated into car and truck achieved emissions levels. However, the regulatory 

definitions in EPA’s Title II programs differ. Passenger Automobiles (PA) are defined as 

two wheel drive SUVs below 6,000 lbs. gross vehicle weight as well as classic cars. The 

remaining light duty fleet is defined as Non-Passenger Automobiles (NPA) (Table 3-4). 

Table 3-4 Summary of Fleet Description Methods 

REGULATOR CAR DEFINITION TRUCK DEFINITION 

National Highway Transit 

Safety Association (CAFE 

Through MY 2010) 

Car – Passenger Car Truck – LDT1-4 and MDPV 

California ARB Car – PC + LDT1 Light Truck – LDT2-4 and MDPV 

EPA Passenger Automobile – PC 

+ 2 wheel drive SUVs below 

6,000 GVW 

Non-Passenger Automobile – Remaining 

light duty fleet 

To disaggregate the combined California fleet emission level into PA and NPA 

vehicles, the 2015 and 2016 California achieved levels were multiplied by ratios derived from 

National Highway Transit Association (NHTSA) analysis of the emissions from PA and NPA 

vehicles.2

 Based on the NHTSA analysis, EPA estimates that PAs have an emission 

contribution equivalent to 91% of the California MY 2016 fleet average, while NPA have an 

emission contribution equivalent to 119% of the California achieved CO2 fleet average 

emissions. These ratios, and the PA/NPA achieved emission levels, are shown in Table 3-5. 

Table 3-5 PA and NPA Emission Levels under Pavley I 

Regulatory Class Ratio MY 2015 

Achieved 

Emission 

Level 

MY 2016 

Achieved 

Emission 

Level 

PA 0.91 238 227 

NPA 1.19 312 297 

3.1.2.3 Calculating the 2015 and 2016 Fleetwide CO2 Emission Targets under the EPA 

Final Rule 

To determine the MY 2015 and MY 2016 fleetwide targets under the EPA final rule, 

the achieved emission levels from PA and NPA (Table 3-5) were reweighted into a national 

fleet-wide average based upon the anticipated national fleet of 60% passenger car, 40% light 

duty truck. Based on NHTSA analysis presented in the MY 2011 CAFE final rule, this fleet 
Technical Basis of the Standards 

3-5 

is expected to be comprised of approximately 66.4% PA and 33.6% NPA.3

 The PA and NPA 

achieved emission levels were weighted into a national fleetwide average based upon these 

percentages. The resulting 2015 fleetwide target is 263 grams CO2 / mile, while the 2016 

target is 250 grams CO2 /mile. 

3.1.2.4 Calculation of 2012-2014 “California Equivalent” Targets 

The methodology used to calculate the 2015 and 2016 California Equivalent levels 

was repeated for the 2012-2014 model years. The most significant departure from the 

previously described methodology is that sales projections differ in MY 2012-2014 as 

compared to MY 2015-2016. 

EPA assessment of projected vehicle sales during MY 2012-2014 supported a lower 

proportion of car sales than the 60% fraction projected during MY 2015-2016. March 2009 

AEO vehicle sales estimates were therefore substituted in these earlier years. Using the 

methodology described in section 3.1.2.1, the AEO estimates were used to project PC/LDT1 

fractions in CA, and PA and NPA sales fractions nationally (Table 3-6). 

Table 3-6 National PA and NPA Sales Fractions estimated in March 2009 AEO Projections 

Regulatory Class MY 2012 MY 2013 MY 2014 

AEO Car fraction 55.0% 56.1% 57.4% 

AEO Truck fraction 45.0% 43.9% 42.6% 

PC/LDT1 in CA 61.0% 62.1% 63.4% 

LT2 in CA 39.0% 37.9% 36.6% 

PA fraction Nationally 62.1% 63.0% 64.1% 

NPA fraction Nationally 37.9% 37.0% 35.9% 

One commenter, Yuli Chew, stated that he thought that the 6% per year alternative 

was more representative of Pavley I levels. As this analysis shows, the national GHG 

standard would provide that vehicle sales in California of federally compliant vehicles would 

have fleet average GHG emissions that are equal to the fleet average that would be achieved 

under the California program In their comments on the proposal, the California Air 

Resources Board agreed that the standards presented in this rulemaking align with 

California’s Pavley greenhouse gas emissions standards, and ultimately arrive at the same 

stringency as California’s standards in MY 2016. 
Regulatory Impact Analysis 

3-6 

Per the previously described methodology, the calculated CA sales fractions were then 

multiplied by the Pavley I standards for MY 2012 – MY 2014 (Table 3-7). Consistent with 

the 2015/16 analysis, small manufacturers were assumed to remain a constant 9% of 

California PC/LDT1 sales and 5% of California LDT2 Sales. 

Table 3-7 2012-2014 California Regulatory Standards 

excluding CH4 and N2O (grams CO2 equivalent per mile) 

MY 2012 MY 2013 MY 2014 

California Car (PC/LDT1) Standard 231 225 220 

Intermediate/Small Volume 

Manufacturer California Car Standard 314 314 314 

CA LDT2/MDPV Standard 359 353 348 

Intermediate/Small Volume 

Manufacturer LDT2/MDPV Standard 360 360 360 

The resulting achieved emission levels in California are 286 grams CO2 / mile in MY 

2012, 279 grams CO2 / mile in MY 2013 and 273 grams CO2 / mile in MY 2014. In order to 

derive PA and NPA achieved emission levels, these achieved emission levels were multiplied 

by MY-specific ratios derived from National Highway Transit Association (NHTSA) 

analysis. 4

The projected PA and NPA emission levels were then recombined into a national fleet 

achieved emission level based on the national PA and NPA sales fractions shown in Table 3-6 

(Table 3-8). 

Table 3-8: PA and NPA Emission Levels under Pavley I 

Regulatory Class MY 2012 

Achieved 

Emission 

Level 

MY 2013 

Achieved 

Emission 

Level 

MY 2014 

Achieved 

Emission 

Level 

PA 260 253 248 

NPA 334 328 323 

Fleet Average 288 281 275 

3.2 Analysis of Footprint Approach for Establishing Individual Company 

Standards 

One of the fundamental issues associated with the vehicle fleet average CO2 emission 

standard is the structure of the standard; i.e., the basis for the determination of the standard for 

each vehicle manufacturer. 

Vehicle CO2 emissions are closely related to fuel economy. Over 99 percent of the 

carbon atoms in motor fuel are typically converted to tailpipe CO2, and therefore, for any 

given fuel with a fixed hydrogen-to-carbon ratio, the amount of CO2 emitted (grams) is 
Technical Basis of the Standards 

3-7 

directly correlated to the volume of fuel that is consumed (gallons), and therefore CO2 g/mile 

is essentially inversely proportional to vehicle fuel economy, expressed as miles per gallon. 

As part of the CAFE program, EPA measures vehicle CO2 emissions and converts them to 

mpg and generates and maintains the federal fuel economy database. Additionally, EPA 

calculates the individual manufacturers’ CAFE values each year, and submits these values to 

NHTSA. 

EPA is finalizing footprint-based CO2 standards for cars and light trucks. EPA 

believes that this program design has the potential to promote CO2 reductions across a broad 

range of vehicle manufacturers, while simultaneously accounting for other important societal 

objectives cognizable under section 202 (a) such as consumer choice and vehicle safety. EPA 

believes a footprint-based system will also provide a more level playing field among 

manufacturers, as all models with similar size will have the same CO2 emission targets, 

across all manufacturers. 

In 2007, EPA evaluated several vehicle attributes on which to base CO2 standards for 

both cars and light trucks: footprint, curb weight, engine displacement, interior volume, and 

passenger carrying capacity. All of these attributes have varied advantages and 

disadvantages. EPA’s evaluation centered on three primary criteria (all of which reflect 

factors relevant under section 202 (a)). 1) Correlation with tailpipe CO2 emissions. Since 

emissions of CO2 are controlled, there must be a reasonable degree of correlation from a 

technical perspective between an attribute and vehicle CO2 emissions performance. 2) The 

relationship between the attribute and potential CO2 reducing technologies. In order to 

promote emissions reductions, choice in technology for the manufacturers, and cost-effective 

solutions, it is important that an attribute not discourage the use of important CO2 control 

strategies. 3) How much the attribute would encourage compliance strategies that tend to 

circumvent the goal of CO2 reduction. EPA believes that it is important to choose an attribute 

that minimizes the risk that manufacturers would change the magnitude of the attribute as a 

method of compliance. 4) The consistency of the attribute with existing regulations. EPA 

does not want to create a program that competes with others that accomplish similar goals. 

The 2007 analysis examines potential attributes against these criteria and is outlined below. 

3.2.1 “Footprint” as a Vehicle Attribute 

EPA is basing the individual manufacturers fleetwide CO2 standards on the vehicle 

footprint attribute. Footprint is defined as a vehicle’s wheelbase multiplied by average track 

width. In other words, footprint is the area enclosed by the points at which the wheels meet 

the ground. 

In 2006, NHTSA adopted footprint as the basis for fuel economy standards in its 

Reformed CAFE program for light trucks, and in 2008, the agency extended this program 

structure to regulate passenger cars for MY 2011 and beyond. NHTSA used projected sales, 

footprint, and mpg data from automakers’ product plans, along with information on the cost 

and effectiveness of fuel economy technologies, to create a footprint versus fuel economy 

curve shown below in Figure 3-1 for cars and Figure 3-2 for trucks that establishes fuel 

economy targets for every model’s footprint value. Chapter V of NHTSA’s RIA for the MY 
Regulatory Impact Analysis 

3-8 

2011 CAFE program contains more detailed information how the MY 2011 car and truck 

curves were generated. 

NHTSA Final MY 2011 Standards for Cars and Trucks

20

22

24

26

28

30

32

34

36

38

40

35 40 45 50 55 60 65 70 75

Footprint (ft2

)

MPG

Figure 3-1 NHTSA Reformed CAFE Curve for MY 2011 Cars 
Technical Basis of the Standards 

3-9 

NHTSA Final MY 2011 Standards for Cars and Trucks

20

22

24

26

28

30

32

34

36

38

40

35 40 45 50 55 60 65 70 75

Footprint (ft2

)

MPG

Figure 3-2 NHTSA Reformed CAFE Curve for MY 2011 Trucks 

The overall fleet-wide fuel economy compliance value for an individual manufacturer 

is then calculated at the end of the model year by a sales-weighted, harmonic average of the 

fuel economy targets for all models sold by that manufacturer. In the rulemaking process, 

NHTSA also considered weight, towing capacity, and four wheel drive capability as 

alternative attributes, but rejected them in favor of footprint.5

 

EPA evaluated footprint as the attribute for setting vehicle CO2 standards based on the 

four criteria outlined above. 

3.2.1.1 Correlation to Tailpipe CO2 Emissions 

Figure 3-3 and Figure 3-4 describe the relationship of tailpipe CO2 emissions and 

vehicle footprint. These figures were generated using the manufacturers’ 2007 confidential 

product plans, the most current projections at the time of the analysis. EPA has since received 

new product plans and developed a new baseline dataset from publicly available information. 

However, EPA has not redone the analysis below with this new data as the general trends do 

not appear to have changed. 

The first plot describes the model year 2007 car fleet and the second plot describes the 

model year 2007 truck fleet. The circles represent the sales volume of a particular model, 

where a larger circle corresponds to higher sales projection and a smaller circle corresponds to 

a lower sales projection. In order to determine how closely footprint and CO2 emissions were 

correlated, a linear least-squares regression was performed for cars and trucks separately. It 
Regulatory Impact Analysis 

3-10 

should be noted that NHTSA used non-sales-weighted minimum absolute difference (MAD) 

regressions to develop the slopes of the fuel economy and CO2 emission standards. The 

preamble of this final rule discusses the reasons for use of non-sales-weighted MAD 

regressions for this purpose. 

y = 8.46x - 89.866

R

2

 = .283

100

150

200

250

300

350

400

450

500

36 38 40 42 44 46 48 50 52 54 56

Footprint (ft2

)

CO2 (g/mi)

Figure 3-3 Model Year 2007 Cars; Sales-weighted Linear Regression of CO2 Tailpipe Emissions and 

Footprint 
Technical Basis of the Standards 

3-11 

y = 4.172x + 170.17

R

2

 = .331

150

250

350

450

550

650

750

35 40 45 50 55 60 65 70 75 80 85

Footprint (ft2

)

CO2 (g/mi)

Figure 3-4 Model Year 2007 Trucks; Sales-weighted Linear Regression of CO2 Tailpipe Emissions and 

Footprint 

As illustrated in the above figures, the R2 values for model year 2007 cars and trucks 

are 0.283 and 0.331 respectively (both statistically significant to a confidence level greater 

than 99%), indicating that there is a non-random correlation to CO2 emissions. As vehicle 

size increases, its CO2 emissions tend to increase. 

3.2.1.2 Relationship with CO2-Reducing Strategies 

The footprint attribute would encourage all CO2 control strategies with the exception 

of vehicle downsizing. All other things being equal, vehicle downsizing tends to correspond 

to lower vehicle weight, which results in lower CO2 emissions. However, smaller vehicles 

would have smaller footprints and would be subject to lower, more stringent, CO2 emissions 

targets, discouraging downsizing as a compliance strategy. Also, absent other design 

changes, decreasing vehicle size could reduce vehicle safety for that vehicle's driver, 

especially for those vehicles less than 4000 pounds.6

 Thus, the fact that footprint discourages 

vehicle downsizing is viewed by many safety advocates as a positive aspect. This continues 

to be an important factor in NHTSA’s adoption of footprint in its Reformed CAFE program. 

A footprint attribute also would not discourage the use of lightweight materials, as a 

lighter vehicle with no change in footprint would more easily comply with its CO2 target. 

Therefore, in choosing the footprint attribute, the use of lightweight material would remain a 
Regulatory Impact Analysis 

3-12 

viable compliance option, an important factor as lightweight materials can simultaneously 

reduce mobile CO2 emissions and improve vehicle safety. NHTSA came to the same 

conclusion in its Reformed CAFE rulemaking.7

 EPA is assuming that manufacturers can and 

will lightweight their vehicles at a given footprint level as a potential compliance strategy. 

EPA discusses the relationship of vehicle weight and safety in section 7.6 of this RIA . 

3.2.1.3 Sensitivity of CO2 Control to Compliance-Related Vehicle Adjustments 

Depending on the attribute, manufacturers may find it more economically attractive to 

comply in a way that tends to compromise the expected emission reduction benefits of the 

program. Specifically, a manufacturer would have the opportunity to increase its average 

fleet footprint over time in order to comply with a less stringent standard, which would 

circumvent the CO2 reduction goals of the program. However, major changes in a vehicle’s 

footprint typically require a substantial redesign of the vehicle, which typically occurs every 

5-7 years. While definitive historical footprint data is not available, EPA believes that 

footprint has grown more modestly in the past than many other attributes. 

3.2.1.4 Consistency with Other Existing Regulatory Programs 

EPA and NHTSA have coordinated closely in developing parallel GHG and MPG 

standards in order to avoid creating a “patchwork” of regulations. Since NHTSA has in 

recent history used footprint as the basis for its CAFE program and is finalizing this metric in 

today’s final rule, footprint remains the simplest, most natural option with respect to the goal 

of avoiding excessive regulatory burden on the manufacturers. 

Under the Clean Air Act, the State of California may petition EPA for the authority to 

create more stringent mobile source emissions regulations at the state level. EPA has granted 

California this privilege and the California program outlined does not utilize the footprint (or 

any) attribute; instead the regulatory structure is based on a universal (or unreformed) 

standard. Despite differences in the structure of the standards, the EPA federal program is 

expected to have an equivalent stringency when compared to the California program, thus 

making it a 50-state program. See Section 3.1. In order to account for early AC credits 

offered by the California program, EPA has also chosen to adopt a very similar credit system 

outlined in section III.C.1 of the preamble and Chapter 2 of the RIA, which offer an additional 

layer of consistency. 

3.2.2 Alternative Attributes 

Curb weight is defined in EPA regulations (CFR 86.1803-01) as the actual or 

estimated weight of the vehicle with all standard equipment, plus the fuel weight at nominal 

tank capacity, plus the weight of optional equipment. Figure 3-5 and Figure 3-6 below show 

plots of tailpipe CO2 emissions versus curb weight for 2007 car and truck models 

respectively, where circle size indicates the sales volume of each model. 
Technical Basis of the Standards 

3-13 

y = 0.0885x + 6.764

R2 = .582

100

150

200

250

300

350

400

450

500

2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 4400

Curb Weight (lbs)

CO2 (g/mi)

Figure 3-5 Model Year 2007 Cars; Sales-weighted Linear Regression of CO2 Tailpipe Emissions and Curb 

Weight 
Regulatory Impact Analysis 

3-14 

y = 0.0593x + 126.85

R

2

 = .521

150

250

350

450

550

650

750

3000 3500 4000 4500 5000 5500 6000 6500 7000 7500

Curb Weight (lbs)

CO2 (g/mi)

Figure 3-6 Model Year 2007 Trucks; Sales-weighted Linear Regression of CO2 Tailpipe Emissions and 

Curb Weight 

For both cars and trucks, curb weight has a relatively high correlation with tailpipe 

CO2 emissions. A sales-weighted linear least squares regression determined R2

 values of 

0.582 for cars and 0.521 for trucks, indicating a substantial relationship of the current fleet’s 

curb weight and CO2 emissions. 

Historically, some vehicle safety advocates have preferred weight for an attributebased standard since a standard with a steep relationship with weight discourages downweighting. However, with recent advances in strong, lightweight materials, occupant safety is 

not necessarily compromised by a reduction in vehicle weight.8

 In fact, these studies have 

shown that a vehicle’s size is a more important factor than weight in its effect on occupant 

safety. Section 7.6 of this RIA discusses in greater detail EPA’s perspective on vehicle weight 

and safety. In a weight-based attribute system, a lower weight would correspond to a more 

stringent CO2 standard. While this would discourage downsizing as a compliance strategy, 

it’s important to recognize that weight as an attribute for determining tailpipe CO2 standards 

would discourage the use of lightweight materials, even though advanced lightweight 

materials could simultaneously reduce CO2 emissions and improve vehicle safety. 

Furthermore, since a vehicle’s weight is much easier to change than most other 

attributes, it is more likely that manufacturers could add weight to their vehicles in order to be 

subject to and comply with a less stringent standard. This potential is reinforced by the 
Technical Basis of the Standards 

3-15 

relatively high rate of growth of vehicle weight; it has grown 1.0 – 1.5% per year since the 

late 1980s.9

 This development would have negative environmental consequences by 

increasing overall CO2 emissions, contrary to the chief goal of section 202 (a) of the Act. 

EPA also examined engine displacement as a potential attribute for determining 

manufacturer CO2 standards. Engine displacement is defined as the volume swept as the 

piston moves from top dead center to bottom dead center. Figure 3-7 and Figure 3-8 below 

contain sales-weighted linear regression plot of tailpipe CO2 emissions and engine 

displacement for 2007 cars and trucks, respectively. 

y = 45.333x + 171.53

R2 = .667

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7

Engine Displacement (L)

CO2 (g/mi)

Figure 3-7 Model Year 2007 Cars; Sales-weighted Linear Regression of CO2 Tailpipe Emissions and 

Engine Displacement. 
Regulatory Impact Analysis 

3-16 

y = 45.094x + 217.45

R

2

 = .619

150

250

350

450

550

650

750

1 2 3 4 5 6 7

Engine Displacement (L)

CO2 (g/mi)

Figure 3-8 Model Year 2007 Trucks; Sales-weighted Linear Regression of CO2 Tailpipe Emissions and 

Engine Displacement 

Engine displacement correlates well to tailpipe emissions, with R2 values of 0.667 for 

cars and 0.619 for trucks. This is because increasing engine displacement typically increases 

the amount of fuel burned per cycle. 

EPA believes that a standard based on engine displacement does not guarantee any 

environmental benefit because of the disincentive to add certain CO2-reducing technologies 

and the potential for manufacturers to adjust the sales of higher-displacement models 

regardless of whether or not it reflects market demand. Hypothetically, a model could have 

three trim lines with three different displacements: A 4-cylinder 2.0L Turbo, a 4-cylinder 

2.5L, and a 6-cylinder 3.0L. Since these models would have three standards ranging from 

most to least stringent, correspondingly, this type of standard would be a disincentive to sell 

models with smaller engines or turbochargers. These strategies can dramatically reduce CO2 

emissions (See Chapter 1 of the RIA) and are increasingly prevalent in the European market. 

Thus EPA believes that the use of engine displacement for establishing CO2 tailpipe 

standards will undermine readily achievable and feasible reductions of CO2 emissions. 
Technical Basis of the Standards 

3-17 

EPA also examined interior volume and occupant capacity as potential attributes 

because they characterize vehicle utility well. Increasing interior volume creates more space 

for people and cargo, and increasing occupant capacity creates the potential to carry more 

people, both important factors consumers consider when purchasing a new vehicle. Figure 

3-9 below contains a plot of interior volume and tailpipe CO2 for model year 2007 cars. 

MY 2007 Cars: Tailpipe CO2 Emissions by Interior Volume

R

2

 = 0.0036

100

200

300

400

500

600

700

50 70 90 110 130 150 170 190

Interior Volume (ft3

)

Tailpipe CO2 (g/mi)

Figure 3-9 Model Year 2007 Cars; Linear Trend of CO2 Tailpipe Emissions and Engine Displacement 

EPA confirmed that interior volume is not at all correlated to vehicle CO2 emissions 

with a R2 value of 0.0036 for cars. The correlation of interior volume and tailpipe CO2 is 

worse for light trucks by definition, since cargo space for pickup trucks is a separate exterior 

bed. Thus, it does not make sense to have a CO2 standard for light trucks that is based on 

interior volume, since pick-up trucks would be required to meet a stricter CO2 standard than 

SUVs and minivans, which are typically regulated in the general “truck” category. For these 

reasons, EPA is not finalizing interior volume for the standard. 

Alternatively, occupant capacity does not share the same safety implications as 

interior volume. Furthermore, since it is difficult to game and does not discourage the use of 

any CO2-reducing technologies, there is significant potential for CO2 improvement. Figure 

3-10 and Figure 3-11 below illustrate the breakdown of the model year 2007 fleet in terms of 

occupant capacity. 
Regulatory Impact Analysis 

3-18 

Model Year 2007 Cars: Percentage of Sales by Occupant Capacity

2.2%

84.0%

5.7%

8.1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2 4 5 6

Number of Seats

Percentage

Figure 3-10 Model Year 2007 Cars; Percentage of Sales by Occupant Capacity 
Technical Basis of the Standards 

3-19 

MY 2007 Cars: Range of Tailpipe CO2 Emissions by Occupant Capacity

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7

Number of Occupants

Tailpipe CO2 (g/mi)

Figure 3-11 Model Year 2007 Cars; Range of Tailpipe CO2 Emissions by Occupant Capacity 

However, occupant capacity and CO2 emissions do not relate well. Since 84% of the 

2007 car fleet has 5 seats, an occupant-based standard would essentially result in a universal 

standard for a majority of vehicles. Since the car models falling into the 5-seat category have 

a tailpipe CO2 range of 133 to 472 g/mi, an occupancy-based standard would negate the 

benefits from relative equity of the attribute-based system to full line manufacturers. 

3.2.3 EPA Selection of the Footprint Attribute 

EPA has considered a range of potential vehicle attributes that could be used to set 

CO2 standards. To summarize key results from the 2007 analysis, interior volume and 

passenger carrying capacity have extremely poor correlation with fuel economy, and EPA is 

not finalizing them for that reason. The three remaining attribute options—footprint, curb 

weight, and engine displacement—are all reasonable choices in terms of correlation with CO2 

emissions levels, with weight having the best correlation to CO2 emissions levels. However, 

it should be noted that correlation is not the primary deciding factor for the selection of an 

attribute. One could easily get an excellent correlation by choosing a function that combines 

the effects of weight, displacement, N/v ratio (engine speed to vehicle speed ratio at top gear), 

and frontal area (as a product with the aerodynamic coefficient). There are many other, but 

these are the four variables that most define a vehicle’s fuel economy10,11 The choice of an 

attribute is not only an engineering decision, it also a policy decision. It is linked with the 

outcomes that are desired in a future fleet. 
Regulatory Impact Analysis 

3-20 

 With respect to the remaining criteria, EPA believes footprint is clearly superior to 

both weight and engine displacement. Footprint does not inherently discourage any key CO2 

control strategies (except for vehicle downsizing), while weight would discourage the use of 

lightweight materials. Engine displacement would discourage engine downsizing with 

turbocharging, a strategy increasingly popular in the United States and Europe. Footprint is 

somewhat less susceptible to modifications for compliance, since major changes would 

generally require a significant platform redesign; in contrast, it is easier for manufacturers to 

change weight and engine displacement. 

EPA notes that the footprint attribute also correlates well with the "utility" or 

"usefulness" of the vehicle to the consumer. Larger footprints amount to more space inside 

the vehicle to carry passengers or cargo, which are important considerations for consumers. 

Thus, it is an additional benefit that the footprint-based approach would not discourage 

changes to vehicle designs that can provide more utility to consumers. EPA also recognizes 

that if footprint is used for the vehicle CO2 standards then the form of the standards would be 

compatible with NHTSA’s use of footprint in their Reformed CAFE program. 

For these reasons, EPA therefore believes that the footprint attribute is the best choice 

of the attributes discussed, from both an engineering and public policy standpoint and is using 

footprint in the CO2 standard-setting process for this rule. 

EPA is implementing the footprint attribute in this CO2 control program via a 

piecewise linear function. As mentioned above, this is the equivalent to the shape finalized by 

NHTSA for its CAFE standards for model years 2012-2016. The shape of this function with 

respect to CO2 is reflected in Figures I.B.3-3 and I.B.3-4 of the preamble. The difference is 

that it moves from low CO2 values on the left to high CO2 values on the right (see Figure 

3-12 and Figure 3-13 below for example) due to its inverse relation to MPG. 
Technical Basis of the Standards 

3-21 

Figure 3-12 CO2 (g/mi) Car standard curves 

Figure 3-13 CO2 (g/mi) Truck standard curves 
Regulatory Impact Analysis 

3-22 

Implementing the CO2 emission standards in this manner provides consistency with 

NHTSA’s CAFE standards. Section II of the preamble and Chapter 2 of the joint TSD 

contain more information on how EPA and NHTSA defined the piecewise linear CO2 target 

function. 

References 

References can be found in EPA docket EPA-HQ-OAR-2009-0472.

1

 Energy Information Administration. Annual Energy Outlook 2009. 

http://www.eia.doe.gov/oiaf/aeo/index.html

2

 NHTSA Model Year 2011 Rule. RIN 2127-AK29. Average Fuel Economy Standards. 

Passenger Cars and Light Trucks. Model Year 2011. [Docket No. NHTSA-2009-0062] 

3

 NHTSA Model Year 2011 Rule. RIN 2127-AK29. Average Fuel Economy Standards. 

Passenger Cars and Light Trucks. Model Year 2011. [Docket No. NHTSA-2009-0062] 

4

 NHTSA Model Year 2011 Rule. RIN 2127-AK29. Average Fuel Economy Standards. 

Passenger Cars and Light Trucks. Model Year 2011. [Docket No. NHTSA-2009-0062] 

5

 See generally 71 FR at 17595-96. 

6

 National Academy of Sciences, “Effectiveness and Impact of Corporate Average Fuel 

Economy (CAFE) Standards,” National Academy Press, Washington, DC, 2002. ISBN 0-

309-07601-3. Available for online viewing or hard copy purchase from the National 

Academy Press at http://books.nap.edu/openbook.php?isbn=0309076013. 

7

 71 FR at 17620-21; see also 2002 NAS Report at 24 (ISBN 0-309-07601-3). 

8

 71 FR at 17596; 2002 NAS Report at 24 (ISBN 0-309-07601-3). 

9

 Light-Duty Automotive Technology and Fuel Economy Trends: 1975 through 2007," U.S. 

Environmental Protection Agency, EPA420-S-07-001, September 2007, 

"http://www.epa.gov/otaq/fetrends.htm 

10 Nam, E.K., Giannelli, R, Fuel Consumption Modeling of Conventional and Advanced 

Technology Vehicles in the Physical Emission Rate Estimator (PERE), EPA document 

number EPA420-P-05-001, 2004

11Guidelines for Analytically Derived Fuel Economy. March 11, 2004 

http://www.epa.gov/otaq/cert/dearmfr/ccd0406.pdf
Results of Final and Alternative Standards 

4-1 

CHAPTER 4: Results of Final and Alternative Standards 

4.1 Introduction 

There are many ways for a manufacturer to reduce CO2 emissions from any given 

vehicle. A manufacturer can choose from a myriad of CO2 reducing technologies and can 

apply one or more of these technologies to some or all of its vehicles (within the 

constraints of sufficient lead time). Thus, for a variety of levels of CO2 emissions 

control, there are an almost infinite number of technology combinations which produce 

the desired CO2 reduction. As part of the process of developing the proposed rule, EPA 

created a new vehicle model, the Optimization Model for Emissions of Greenhouse gases 

from Automobiles (OMEGA) in order to make a reasonable estimate of how 

manufacturers will add technologies to vehicles in order to meet a fleet-wide CO2

emissions level. EPA created OMEGA in 2008 and has continued to update its 

algorithms through the present. OMEGA underwent a formal peer review process in the 

Spring of 2009, and version 1.0 became publicly available in the NPRM docket and on 

EPA’s web site shortly after publication of the NPRM. The model and a summary of the 

peer review process can be found on EPA’s web site at: 

http://www.epa.gov/otaq/climate/models.htm. EPA continues to use the OMEGA model 

here to estimate the technology and cost associated with the final CO2 emission 

standards. 

4.2 Model Inputs 

OMEGA utilizes four basic sets of input data. The first is a description of the 

vehicle fleet. The key pieces of data required for each vehicle are its manufacturer, CO2

emission level, fuel type, projected sales and footprint. The model also requires that each 

vehicle be assigned to one of the 19 vehicle types, which tells the model which set of 

technologies can be applied to that vehicle. Chapter 1 of the Joint TSD contains a 

description of how the 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 also 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 4.2.1 of this 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 used by the model is a description of the 

technologies available to manufacturers, primarily their cost and effectiveness. Note that 

the five vehicle classes which determine the individual technology cost and effectiveness 

values (see chapter 1 of this RIA) are not explicitly used by the model; instead, the costs 

and effectiveness used by the model are associated with each vehicle package, and are 

based on their associated vehicle types (of 19). This information was described in 
 Regulatory Impact Analysis 

4-2 

Chapter 1 of this RIA and Chapter 3 of the Joint TSD. In all cases, the order of the 

technologies or technology packages for a particular vehicle type is designated by the 

model user in the input files prior to running the model. Several criteria can be used to 

develop a reasonable ordering of technologies or packages. These are described in 

Chapter 1 of the RIA. 

The third type of input data describes vehicle operational data, such as annual 

scrap rates and mileage accumulation rates, and economic data, such as fuel prices and 

discount rates. These estimates are described in chapter 4 of the Joint TSD. 

The fourth type of data describes the CO2 emission standards being modeled. 

These include the CO2 emission equivalents of the 2011 MY CAFE standards and the 

final CO2 standards for 2016. As described in more detail in Chapter 2 of this RIA and 

briefly in section 4.2.1 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 discuss in Section 4.2.1, below. 

4.2.1 Representation of the CO2 Control Technology Already Applied to 

2008 MY Vehicles 

The market data input file utilized by OMEGA, which characterizes the vehicle 

fleet, is designed to account for the fact that the 2008 model year vehicles which 

comprise our baseline fleet may already be equipped with one or more of the 

technologies available in general to reduce CO2 emissions. As described in Chapter 1 of 

this RIA, EPA decided to apply technologies in packages, as opposed to one at a time. 

However, 2008 vehicles were equipped with a wide range of technology combinations, 

many of which cut across the packages. Thus, EPA developed a method to account for 

the presence of the combinations of applied technologies in terms of their proportion of 

the EPA packages described in Chapter 1. This analysis can be broken down into four 

steps. While we received no adverse comment on how this process was conducted for the 

NPRM, we have improved this process and hopefully made it easier for interested parties 

to perform their own analyses in the future. 

The first step in the updated process is to breakdown 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. Table 4-1 

presents the technologies and the rankings which we developed for the analyses 
Results of Final and Alternative Standards 

4-3 

supporting the final rule. We do not show any rankings in Table 4-1 for the “other” 

technologies, such as improved rolling resistance, electric power steering, etc. These 

technologies are assumed to be added to baseline vehicles which do not already have 

these technologies. 

Table 4-1 Rankings of Individual Technologies 

Ranking Engine Transmission Hybrid Weight 

1 Intake Cam Phasing 

Cylinder Deactivation - 

OHV 

CVT 

6 Speed 

Auto 

 3% 

2 Dual or Coupled Cam 

Phasing 

6 Speed 

Manual 

Dual 

Clutch 

42 Volt 

Start-Stop 

5% 

3 Cylinder Deactivation - 

OHC 

 Integrated Motor 

Assist 

10% 

4 Variable valve lift 

5 Diesel 

6 Power-Split 

2-Mode 

Power-Split

2-Mode 

Power-Split 

2-Mode 

7 Plug-In Electric Plug-In 

Electric 

Plug-In Electric 

8 Battery Electric Battery 

Electric 

Battery Electric 

Each baseline vehicle was assigned a ranking in each of four categories based on 

the maximum ranking of any of its applicable technologies in each technology category. 

For example, a vehicle with an OHC engine with both coupled cam phasing and cylinder 

deactivation was assigned an engine technology ranking of “3”, the ranking applicable to 

cylinder deactivation, since its ranking is higher than that for coupled cam phasing. The 

same was done for the technology packages. The engine technology for this example 

baseline vehicle was left alone whenever a technology package had an engine ranking of 

3 or less. 

It should be noted that the strong hybrid packages were assigned engine and 

transmission rankings, as well as hybrid rankings. The application of strong hybrid 

technology affects the type of engine used in the vehicles. For example, it is not 

reasonable to add cylinder deactivation or variable valve lift to vehicles which already 

have power-split or 2-model hybrid systems. 

Two engine-related technologies are not shown in Table 4-1: gasoline direct 

injection and turbocharging. Whenever a technology package included gasoline direct 

injection, the baseline engine was converted to gasoline direct injection. If the baseline 

engine was already of gasoline direct injection design, this aspect of the engine was left 

unchanged. 
 Regulatory Impact Analysis 

4-4 

The possibility that a baseline engine was already turbocharged was handled 

slightly differently in order to maintain the manufacturer’s established tendency to 

turbocharge its engines. If the engine of a baseline vehicle was turbocharged, this 

turbocharging was assumed to continue with the addition of any technology package 

which did not include strong hybridization (i.e., power-split, 2-mode, plug-in or battery 

electric). In addition, if a package included either cylinder deactivation or variable valve 

lift, neither of these technologies was added with the addition of that package. The 

turbocharger was assumed to supplant this technology. 

In the second step of the process, we used these rankings to estimate the complete 

list of technologies which would be present on each baseline vehicle after the application 

of each technology package. 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. This process was repeated 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 determine the degree of each technology 

package’s incremental effectiveness and incremental cost is affected by the technology 

already present on the baseline vehicle. The degree to which a technology package’s 

incremental effectiveness is reduced by technology already present on the baseline 

vehicle is termed the technology effectiveness basis, or TEB, in the OMEGA model The 

value of each vehicle’s TEB for each applicable technology package is determined as 

follows: 

















−

−

−

















−

−

×

















−

−

=

−

−

−

1i,p

i,p

1i,p

i,p

i,v

1i,v

i

1 tTotalEffec

1 tTotalEffec

1

1 tTotalEffec

1 tTotalEffec

1 tTotalEffec

tTotalEffec

1

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 
Results of Final and Alternative Standards 

4-5 

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 

The values of CEB and TEB are capped at 1.0 or less, since a vehicle cannot have 

more than the entire package already present on it. In other words, the addition of a 

technology package cannot increase emissions nor reduce costs. (A value of 1.0 causes 

the OMEGA model to not change either the cost or CO2 emissions of a vehicle when that 

technology package is added.) The value of a specific TEB or CEB can be negative, 

however. This implies that the incremental effectiveness or the incremental cost of 

adding a package can be greater than that when adding the packages in sequence to a 

vehicle with no baseline technology. 

An example of this is a baseline vehicle with a 6 speed manual transmission. All 

of our technology package effectiveness and cost estimates are estimated for specified 

baseline vehicles, all of which have 4 speed automatic transmissions. Our technology 

packages improve this transmission, sometimes to a 6 speed automatic transmission and 

then a dual clutch transmission and sometimes directly to a dual clutch transmission. 

Subsequent packages may then strongly hybridize the vehicle. If a baseline vehicle has a 

6 speed manual transmission, this transmission is unaffected by the technology packages 

which include either a 6 speed automatic transmission or a dual clutch transmission, since 

the manual transmission is both cheaper and/or more efficient than these other 

transmissions. However, when the vehicle is hybridized, this manual transmission is 

replaced. The incremental cost of changing this vehicle to a power-split hybrid design, 

for example, is greater than that for a vehicle with a dual clutch transmission, since the 

credit for removing the manual transmission is less than that for the dual clutch 

transmission. The negative CEB causes the OMEGA model to apply a cost for this 

power-split package which is slightly higher than that for the typical baseline vehicle. 

The fourth step is to combine the fractions of the cost and effectiveness of each 

technology package already present on the individual 2008 vehicles models for each 

vehicle type. 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 vehicle type. For effectiveness, the individual percentages are combined by 
 Regulatory Impact Analysis 

4-6 

weighting them by both sales and base CO2 emission level. This appropriately weights 

vehicle models with either higher sales or CO2 emissions within a vehicle type. 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 2011 MY CAFE standards and 

the final CO2 standards. 

Table 4-2 and Table 4-3 show the degree to which the baseline fleet, adjusted for 

sales in 2016, includes the effectiveness and cost of the various technology packages by 

vehicle type. 

Table 4-2 Presence of Technology on 2008 MY Vehicles In Terms of CO2 Effectiveness (Weighted 

Average Across Car and Truck Sales in 2016) 

Technology Package Number 

Vehicle 

Type 1 2 3 4 5 

1 12.7% 16.9% 1.2% -2.3% 0.0% 

2 21.2% 24.5% 12.5% -0.7% 0.0% 

3 18.5% 19.6% 2.9% 0.0% 0.0% 

4 17.6% 33.6% 0.0% -3.6% -0.5% 

5 21.3% 33.5% 5.9% -0.1% -0.6% 

6 18.2% 41.4% 6.6% -0.7% 0.0% 

7 14.2% 15.6% 0.2% 2.5% -4.5% 

8 0.2% 0.2% -0.9% 0.2% -0.1% 

9 1.0% 0.1% -0.5% 0.1% -0.1% 

10 4.1% 5.2% 0.6% 0.0% 0.0% 

11 5.3% 0.8% -4.1% 0.9% 0.0% 

12 11.2% 13.4% 0.1% 0.2% 0.0% 

13 34.0% 32.0% 6.5% -0.1% 0.3% 

14 8.5% 32.1% 0.0% 0.5% 0.0% 

15 0% 0% 0% 0% 0% 

16 15.0% 27.4% 2.8% 2.0% 0.0% 

17 19.1% 40.8% 0.3% 3.5% 0.0% 

18 21.7% 13.0% 1.0% 0.0% 0.0% 

19 26.2% 45.0% 0.0% 0.0% 0.0% 

* N/A: No such package for that vehicle type 
Results of Final and Alternative Standards 

4-7 

Table 4-3 Presence of Technology on 2008 MY Vehicles In Terms of Cost (Weighted Average Across 

Car and Truck Sales in 2016) 

Vehicle 

Type 1 2 3 4 5 

1 1.8% 30.4% 1.5% -0.6% 0.0% 

2 3.7% 33.7% 19.1% 0.0% 0.0% 

3 7.9% 24.5% 4.0% 0.0% 0.0% 

4 4.3% 36.4% 0.0% 4.6% -3.3% 

5 10.2% 25.5% 8.4% -0.2% 0.1% 

6 3.1% 32.4% 6.4% 3.7% 0.0% 

7 6.4% 27.9% 0.2% 1.6% -0.1% 

8 0.2% 0.1% 0.0% 0.0% 0.0% 

9 0.3% 0.1% 0.0% 0.0% 0.0% 

10 0.6% 4.2% 0.4% 0.5% 0.0% 

11 1.4% 1.0% 0.0% 0.0% 0.0% 

12 1.5% 4.6% 0.0% 0.0% 0.0% 

13 3.9% 14.2% 7.7% -0.5% 0.0% 

14 0.0% 14.1% 0.0% 0.4% 0.0% 

15 0.0% 0.0% 0.0% 0.0% 0.0% 

16 2.6% 52.5% 3.7% 2.6% 0.0% 

17 2.3% 48.3% 1.5% 4.3% 0.0% 

18 11.9% 27.8% 0.0% 0.0% 0.0% 

19 2.1% 48.3% 0.0% 0.0% 0.0% 

As mentioned above, for the market data input file utilized by OMEGA 

characterizing the vehicle fleet, the modeling must and does account for the fact that 

many 2008 MY vehicles are already equipped with one or more of the technologies 

discussed in the TSD Chapter 3. Because EPA chose to apply technologies in packages, 

(a methodology endorsed by many commenters and not challenged by any) and 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. The first step in 

this analysis is to develop a list of individual technologies which are either contained in 

each technology package, or would supplant the addition of the relevant portion of each 

technology package. An example would be a 2008 MY vehicle equipped with variable 

valve timing and a 6-speed automatic transmission. The cost and effectiveness of 

variable valve timing would be considered to be already present for any technology 

packages which included the addition of variable valve timing or technologies which 

went beyond this technology in terms of engine related CO2 control efficiency. An 

example of a technology which supplants several technologies would be a 2008 MY 

vehicle which was equipped with a diesel engine. The effectiveness of this technology 
 Regulatory Impact Analysis 

4-8 

would be considered to be present for technology packages which included 

improvements to a gasoline engine, since the resultant gasoline engines have a lower 

CO2 control efficiency than the diesel engine. However, if these packages which 

included improvements also included improvements unrelated to the engine, like 

transmission improvements, only the engine related portion of the package already 

present on the vehicle would be considered. The transmission related portion of the 

package’s cost and effectiveness would be allowed to be applied in order to comply with 

future CO2 emission standards.. 

4.2.2 Technology Package Approach 

 Consistent with its streamlined redesign cycle approach, EPA designed OMEGA 

to allow the user to add GHG-reducing technologies in packages that would reasonably 

and likely be added by manufacturers within a redesign cycle. In addition, the user can 

combine similar vehicle models into “vehicle type” groups which are likely to receive the 

same list of technology packages. For each vehicle type, the user must rank the 

technology packages in order of how OMEGA should add them to that specific vehicle 

type. This approach puts some onus on the user to develop a reasonable sequence of 

technologies. However, the model also produces information which helps the user 

determine when a particular technology or bundle of technologies might be “out of 

order”. The approach also simplifies the model’s calculations and enables synergistic 

effects among technology packages to be included to the fullest degree possible. 

When technology is sufficiently new, or the lead time available prior to the end of 

the redesign cycle is such that it is not reasonable to project that the technology could be 

applied to all vehicle models that are of the same specific vehicle type, the user can limit 

the technology application through the use of a market penetration cap (“market cap”) of 

less than 100%. This cap can vary by redesign cycle. When a technology package is 

applied to fewer than 100% of the sales of a vehicle model due to the market cap, the 

effectiveness of the technology group is simply reduced proportionately to reflect the 

total net effectiveness of applying that technology package to that vehicle’s sales. Most 

of the technologies for the analysis conducted in this rule had a market cap of 85%, 

though hybrids were restricted to 15%. A small number of technologies had a 100% 

phase in cap. These include: low friction lubricants, electric power steering, improved 

accessories, and low rolling resistance tires. These simple to apply technologies may be 

implemented outside of a vehicle’s normal redesign schedule. 

 OMEGA does not create a new vehicle with the technology package and 

retain the previous vehicle which did not receive the technology package, splitting sales 

between the old and new vehicles. If subsequent technology packages can be applied to 

the vehicle, the user must consider whether in reality the new technology would likely be 

applied to those vehicles which received the previous technology or those which did not, 

or a combination of the two. The effectiveness of adding the subsequent technology may 

depend on which vehicles are receiving it. 

 In OMEGA, the costs and effectiveness of technologies are assumed to be 

the same for all vehicle models that belong to the sale vehicle type category. There may 
Results of Final and Alternative Standards 

4-9 

be cases when a vehicle model in the baseline may already contain some CO2-reducing 

technology; OMEGA considers this when determining whether a technology can or 

cannot be applied to it. In the inputs to the model, the user can limit the volume of a 

specific vehicle model’s sales which can receive a technology package by indicating the 

fraction of its baseline that already contains some effectiveness and cost of each specific 

technology package. In addition, as described above, the volume of a given vehicle 

type’s sales which can receive a specific technology package can also be limited in an 

input file with a market penetration “cap”, if desired. The effectiveness and application 

limits of each technology package can vary over time, if desired. The development of 

these factors is described in detail in the previous sub-section. 

 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 the effectiveness of the current technology 

package and the denominator serves to “back out” any effectiveness that is present in the 

baseline. CAP refers to the market penetration cap, 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. 

( )

AIE TEB

CO AIECAP

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 the baseline. 

IncrementalCost = TechCost (* − CEBCAP )

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. To simplify the model’s algorithm, EPA 

has chosen to input the package costs and effectiveness values on a step-wise basis. This 

is not the same “incremental” approach implemented in the Volpe model because each 

step in OMEGA has incorporated several technologies. However, for simplification in 

the core model calculations, the user must enter into the technology input file the 
 Regulatory Impact Analysis 

4-10 

technology costs which are incremental to the technology package immediately preceding 

it. In the case of the first technology package, this is simply the full technology package 

cost, since it is going on a baseline vehicle and since any technology in the baseline is 

considered in the equations, as described in the equations above. 

EPA received no adverse comment on this approach and no changes in this 

methodology have been made since the NPRM. 

4.3 Modeling Process 

 In order to determine the technology costs associated with this final rule, 

EPA performed two separate modeling exercises. The first was to determine the costs 

associated with meeting any existing regulation of CO2 or MPG. The latest regulation 

that has been promulgated is NHTSA’s CAFE program for MY 2011, pursuant to the 

Energy Independence and Security Act (EISA). EPA considers the MY 2011 CAFE 

regulations to constitute the “reference case” for calculating the costs and benefits of this 

GHG rule. In other words, absent any further rulemaking, this is the vehicle fleet EPA 

would expect to see through 2016 -- the “status quo”. In order to calculate the costs and 

benefits of this final rule alone, EPA seeks to subtract out any costs associated with 

meeting any existing standards related to GHG emissions. EPA consequently ran 

OMEGA a second time to calculate the cost of meeting the EPA’s final standards in 

2016, and then subtracted the results of the reference case model run to determine the 

costs of this final GHG program. 

Conceptually, OMEGA begins by determining the specific CO2 emission 

standard applicable for each manufacturer and its vehicle class (i.e., car or truck). Since 

the final rule allows 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 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 (for example) in the final regulations which governs credit trading between 

these two vehicle classes. For both the 2011 CAFE and 2016 CO2 standards, these 

standards are a function of each manufacturer’s sales of cars and truck and these vehicles’ 

footprint values. When evaluating the 2011 MY CAFE standards, the car-truck trading 

was limited to 1.2 mpg. When evaluating the final CO2 standards, the OMEGA model 

was run only for MY 2016. OMEGA is designed to evaluate technology addition over a 

complete redesign cycle and 2016 represents the final year of a redesign cycle starting 

with the first year of the final CO2 standards, 2012. Estimates of the technology and cost 

for the interim model years are developed from the model projections made for 2016. 

This process is discussed in Chapter 6 of EPA’s RIA to this final rule. When evaluating 

the 2016 standards using OMEGA, the final CO2 standard which manufacturers would 

otherwise have to meet to account for the anticipated level of A/C credits generated was 

adjusted. On an industry wide basis, the projection shows that manufacturers would 

generate 10.2 g/mi of A/C credit in 2016 for each car sold and 11.5 g/mi of A/C credit for 

each truck sold. Thus, the sales-weighted 2016 CO2 target for the fleet evaluated using 

OMEGA was 261 g/mi instead of 250 g/mi. 
Results of Final and Alternative Standards 

4-11 

The cost of the improved A/C systems required to generate this credit was 

estimated separately. This is consistent with the final A/C credit procedures, which 

would grant manufacturers A/C credits based on their total use of improved A/C systems, 

and not on the increased use of such systems relative to some base model year fleet. 

Some manufacturers may already be using improved A/C technology. However, this 

represents a small fraction of current vehicle sales. To the degree that such systems are 

already being used, EPA is over-estimating both the cost and benefit of the addition of 

improved A/C technology relative to the true reference fleet to a small degree. 

The model then works with one manufacturer at a time to add technologies until 

that manufacturer meets its applicable standard. The OMEGA model can utilize several 

approaches to determining the order in which vehicles receive technologies. For this 

analysis, EPA used a “manufacturer-based net cost-effectiveness factor” to rank the 

technology packages in the order in which a manufacturer would likely 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, and is estimated to be roughly five years. It is also assumed that 

consumers discount these savings at the same rate as that used in the rest of the analysis 

(3 or 7 percent). Any residual value of the additional technology which might remain 

when the vehicle is sold is not considered. 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, again discounted to the year of vehicle 

purchase. 

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: 

[ ]

[ ][ ]

( ) ∑

∑

+

=

−

××

−

××−

= 35

1

1

1

2

)1(

1

i

i

i

PP

i

i i

Gap

dCO VMT

Gap

TechCost VMTdFS

ManufCostEff

Where 
 Regulatory Impact Analysis 

4-12 

ManufCostEff = Manufacturer-Based Cost Effectiveness (in dollars per kilogram 

CO2), 

TechCost = Marked up cost of the technology (dollars), 

PP = 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, 

dFSi

 = Difference in fuel consumption due to the addition of technology times 

fuel price in year i, 

dCO2 = Difference in CO2 emissions due to the addition of technology 

VMTi = product of annual VMT for a vehicle of age i and the percentage of 

vehicles of age i still on the road, 

1- Gap = Ratio of onroad fuel economy to two-cycle (FTP/HFET) fuel economy 

When calculating the fuel savings, 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 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. 

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. It is reasonable to estimate that the added technology to improve CO2

level and fuel economy would retain this same percentage of value when the vehicle is 

five years old. However, it is less clear whether first purchasers, and thus, manufacturers 

would consider this residual value when ranking technologies and making vehicle 

purchases, respectively. For this rule, this factor was not included in the determination of 

manufacturer-based net cost-effectiveness in the analyses performed in support of this 

final rule. 

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. EPA believes this manufacturer-based net cost-effectiveness metric is 

appropriate for ranking technology in this final program because it considers 
Results of Final and Alternative Standards 

4-13 

effectiveness values that may vary widely among technology packages when determining 

the order of technology addition.. 

4.4 Modeling of CAA Compliance Flexibilities 

EPA’s final rule incorporates several compliance flexibilities. See generally 

section III.C of the preamble to the final rule. Three of these flexibilities, the credit for 

air conditioning system improvements, car-truck credit trading, and FFV credits, are 

expected to be used extensively by manufacturers and have been factored into our 

estimates of the cost of the final CO2 standards. OMEGA was designed to be able to 

address the first two types of flexibilities directly through the appropriate specification of 

model inputs and scenario definition. However, for several reasons, the expected impact 

of A/C credits was handled outside of OMEGA. The impact of car-truck credit trading 

was accomplished in a slightly more complex fashion than will be the case with future 

versions of the model. OMEGA was not originally designed to include FFV credits in 

terms of miles per gallon. The methods used to account for these three flexibilities are 

described below. 

OMEGA is capable of including both the impact of air conditioning use on CO2 

emissions from the tailpipe (indirect A/C emissions) and refrigerant emissions (direct 

A/C emissions). The current approach to specifying refrigerant emissions in the Market 

file and the effectiveness of refrigerant emission control in the Technology file allows for 

the straightforward accounting of EPA’s current approach to estimating both of these 

factors. As described in Chapter 2 of this RIA, EPA currently estimates the same base 

level of direct A/C emissions from cars and a distinct level of emissions from trucks. 

These levels can be input directly into Column AD of the Market file. The reduction in 

direct A/C emissions associated with improved A/C systems can be input into Column U 

of the Technology file. 

Accounting for indirect A/C emissions, consistent with our approach to estimating 

these emissions in Chapter 2, however, is more difficult. In Chapter 2, we estimate a 

single level of 14 g/mi CO2 from A/C usage and a potential reduction of 40% for a high 

efficiency A/C design (maximum A/C credit of 5.7 g/mi CO2). OMEGA currently 

combines all sources of CO2 tailpipe emissions (i.e., those measured over the 2-cycle 

compliance test and those from A/C usage). Adding 14 g/mi CO2 from A/C usage to the 

base emission level of all vehicles could be easily accomplished. However, specifying a 

consistent 40% reduction of this incremental emission level would not be. The CO2 

effectiveness of technologies included in the Technology file applies to all sources of 

CO2 emissions. Since the base 2-cycle CO2 emission level of vehicles varies, the 

additional 14 g/mi of indirect A/C emissions would represent a different percentage of 

total CO2 emissions of each vehicle. A single effectiveness value for the benefit of high 

efficiency A/C systems would therefore produce a slightly different CO2 emission 

reduction for each vehicle. 

In addition, OMEGA is currently designed to include both indirect and direct A/C 

emissions in the accounting of emissions towards compliance with the specified 

standards. This means that the 14 g/mi of indirect A/C emissions and 17-21 g/mi of 
 Regulatory Impact Analysis 

4-14 

direct A/C emissions are included in the base level of vehicles’ emissions. Their 

remaining levels after the application of technology are considered when determining 

whether a manufacturer is in compliance with the specified standards. However, this is 

not consistent with the design of the final A/C credit system. Neither direct nor indirect 

A/C emissions are included in the compliance determination towards the final CO2 

emission standards. Compliance is determined based on CO2 emissions measured over 

the 2-cycle test procedure which does not include these A/C emissions. Then, reductions 

in A/C emissions are essentially subtracted from the measured 2-cycle CO2 emissions. 

With the current OMEGA model design, it was more straightforward to determine 

the total A/C credit applicable to each manufacturer in 2016 and adjust their final CO2 

emission standards accordingly. Thus, the effective 2016 final car and truck standards 

were increased by 10.2 g/mi and 11.5 g/mi, respectively. OMEGA was then run to 

determine the level of non-A/C technology needed to meet the final standards after 

accounting for A/C credits. After modeling, EPA then added a uniform AC cost of $60 

per vehicle to each manufacturer’s per vehicle technology cost. 

With respect to car-truck trading, the OMEGA model published with the NPRM 

has been upgraded to directly facilitate the trading of car-truck credits on a total lifetime 

CO2 emission basis, consistent with the provisions of the proposed and final CO2 rule. 

For example, if a manufacturer over-complies with its applicable CO2 standard for cars 

by 3 g/mi,sells 1,000,000 cars, and cars have a lifetime VMT of 195,264 miles, it 

generates 585,792 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 5.2 g/mi CO2 above the standard. 

Under the final rule, FFV credits are only available through model year 2015. 

Since we use the OMEGA model directly to evaluate technical feasibility and costs only 

for the 2016 model year, FFV credits are not a factor. (FFV credits use in earlier years is 

accounted for in projecting the cost of technology for 2012-2015 below.) However, as 

discussed above, some manufacturers’ 2008 baseline fleets (adjusted for projected sales 

in 2011) do not meet the 2011 CAFE standards which comprise the reference case for 

this analysis. FFV credits are available under the CAFE program and expected be used at 

the maximum allowable level by Chrysler, Ford and General Motors for both their cars 

and trucks and by Nissan for their trucks. Under the current CAFE program, FFV credits 

are limited to 1.2 mpg in 2011. This credit decreases to 0.8 mpg in 2016. Car-truck 

trading is also allowed under the CAFE program, up to 1.0 mpg in 2011. This car-truck 

credit trading limitation increases to 1.5 mpg in 2016. Our reference case is a 2016 

vehicle fleet complying with the 2011 CAFE standards. Thus, there is some basis for 

utilizing the FFV and car-truck credit limits applicable in 2016. However, as the changes 

to the FFV and car-truck credit limits over time are part of EISA itself, and the fuel 

economy side of these joint NHTSA-EPA rules implements a provision of EISA, these 

changes to the FFV credit and car-truck credit trading can be considered to be part of the 

fuel economy regulation being promulgated and not part of the baseline or reference case 

existing prior to this rule. We believe that this latter classification is the most appropriate 
Results of Final and Alternative Standards 

4-15 

for this rule analysis. Thus, in our reference case, we limit FFV credits to 1.2 mpg and 

car-truck trading is limited to 1.0 mpg. 

Because fuel economy is the inverse of fuel consumption, a specified change in 

fuel economy (e.g., either the limit on FFV credits or car-truck trading) represents a 

varying change in fuel consumption (and CO2 emissions) depending on the initial level 

of fuel economy. For example, for a manufacturer whose truck standard is 22.5 mpg, its 

trucks could be as low as 21.5 mpg if the manufacturer generated sufficient credits from 

its car fleet. These two fuel economy levels represent CO2 emission levels of 395 and 

413 g/mi, respectively, assuming all the vehicles are fueled with gasoline, a difference of 

18 g/mi CO2. If the manufacturer’s truck standard is 24 mpg, its trucks could be as low 

as 23 mpg if the manufacturer generated sufficient credits from its car fleet. These two 

fuel economy levels represent CO2 emission levels of 370 and 386 g/mi, respectively, a 

difference of 16 g/mi CO2. In both cases, the difference in terms of mpg is 1.0. 

However, the difference in terms of CO2 emissions decreases as the base fuel economy 

increases. 

The fact that the same limit in terms of fuel economy translates into differing 

limits in terms of CO2 emissions complicates the modeling of CO2 emission compliance 

using the OMEGA model. The model currently only accepts a single limit on car-truck 

trading in terms of g/mi CO2 emissions. However, since the limit of 1.0 mpg on cartruck trading results in a different limit for each manufacturer, this necessitates a separate 

model run for each manufacturer when the trading of credits might approach the 1.0 mpg 

limit. Also, the OMEGA model is not yet set up to accept FFV credits in terms of mpg. 

Thus, the CO2 standards applicable to those manufacturers expected to utilize FFV 

credits must be adjusted outside of the model. (Work is underway to facilitate these 

credits within the model, but was not completed in time for this final rule analysis.) 

Thus, we adjusted the footprint-based standard for each manufacturer expected to 

use FFV credits by the level of CO2 emissions equivalent to the maximum 1.2 mpg FFV 

credit. The 2011 CAFE standards for cars and trucks were converted to CO2 emissions 

assuming that all vehicles were fueled with gasoline (i.e., 8887/mpg). 

In addition, for manufacturers expected to pay CAFE fines in lieu of compliance, 

we substituted the achieved fuel economy levels from NHTSA’s Volpe Model 

evaluations of the 2011 CAFE standards for these manufacturers’ CAFE standards. The 

only manufacturer found to prefer paying fines over compliance was Porsche, and then 

only for its cars. 

We initially ran the OMEGA model with unlimited trading of car-truck credits to 

determine the degree of trading which was likely to occur. We then determined the cartruck trading limit in terms of g/mi CO2 for each manufacturer equivalent to 1.0 mpg and 

determined if this limit had been exceeded. Only three manufacturers were found to 

exceed the trading limit in the unlimited trading runs, Mitsubishi, Suzuki and Tata. The 

OMEGA input and output files using the latest version of the model can be found under 

“EPA OMEGA Model” in the docket to this rule. 
 Regulatory Impact Analysis 

4-16 

4.5 Manufacturer-Specific Standards and Achieved CO2 Levels 

 As described in RIA Section 3.2, in any attribute-based regulatory structure, 

manufacturers are bound to have different overall GHG targets, since they are based on 

the size and sales mix of each manufacturer. The fleet-wide targets calculated for the 

final 2016 model year are presented in Error! Reference source not found.Table 4-4. 

Table 4-4 2016 Projected Standards by Manufacturer 

Car Truck 

Production

Weighted 

Average◊

VMT Weighted 

Average* 

BMW 228.4 282.5 243.9 245.6 

Chrysler 232.2 295.0 265.8 268.1 

Daimler 238.3 294.3 256.1 257.9 

Ford 229.2 304.7 257.1 259.7 

General Motors 230.5 315.7 270.5 273.6 

Honda 222.1 280.6 243.7 245.7 

Hyundai 222.2 278.3 230.6 231.7 

Kia 224.3 289.3 235.5 237.0 

Mazda 221.2 270.8 228.4 229.4 

Mitsubishi 219.4 269.1 239.3 241.1 

Nissan 225.7 294.4 245.4 247.5 

Porsche 206.1 286.9 233.0 235.7 

Subaru 215.5 267.1 234.2 235.9 

Suzuki 207.5 271.9 218.0 219.3 

Tata 249.9 272.5 258.8 259.6 

Toyota 221.1 294.4 245.0 247.4 

Volkswagen 218.6 292.7 231.6 233.2 

Overall 225.1 297.7 250.1 252.5 

◊

 Production weighted CO2 levels include reductions from A/C improvements and are weighted by 

production only.

*VMT weighted CO2 levels include reductions from A/C improvements and are weighted by both 

production and VMT for consistency with CO2 standard levels. 

The VMT weighted car and truck standards average out to an overall industry 

CO2 stringency of 252.5 g/mi. This number is based on sales and lifetime VMT 

weightings of the applicable car and truck standards. The 2016 industry combined CO2 

level of 250 g/mi presented by President Obama in his announcement on May 19, 2009 

was calculated by weighting car and truck CO2 by sales only and did not consider trading 

on a lifetime VMT basis. As shown above, when the combined car and truck standards 

above are calculated using a sales weighting alone, the industry combined average results 

in 250.1g/mi. 

The majority of manufacturers representing the vast majority of sales in 2016 are 

projected to comply with the final 2016 standards with the addition of technology under 
Results of Final and Alternative Standards 

4-17 

the penetration limits described in Section 4.7 below. However, several smaller volume 

manufacturers (at least with respect to U.S. sales) are projected to fall short of 

compliance. For a more complete discussion of the feasibility of the standards, please see 

Section III.D in the preamble. Table 4-5 below contains the projected achieved levels of 

CO2 emissions for each manufacturer from the OMEGA model. Overall, these levels are 

very similar to those projected in the NPRM. 

Table 4-5 Projected Achieved CO2 Levels in 2016 

◊

 Production weighted CO2 levels include reductions from A/C 

improvements and are weighted by production only. 

*VMT weighted CO2 levels include reductions from A/C 

improvements and are weighted by both production and VMT for 

consistency with CO2 standard levels. 

4.6 Per Vehicle Costs 2012-2016 

As described above, the per-vehicle technology costs for this program alone must 

account for any cost that incurred by compliance with existing vehicle programs. EPA 

first used OMEGA to calculate costs reflected in the existing CAFE program, which is 

Car Truck Production 

Weighted 

Average◊

VMT 

Weighted 

Average* 

BMW 236.3 278.7 248.5 249.8

Chrysler 227.9 298.2 265.6 268.1

Ford 233.4 298.3 257.3 259.6

Subaru 218.2 263.0 234.4 235.9

General Motors 241.3 305.1 271.3 273.6

Honda 207.6 302.0 242.5 245.7

Hyundai 214.5 315.6 229.8 231.7

Tata 258.6 323.6 284.2 286.5

Kia 213.1 335.2 234.3 237.0

Mazda 218.2 285.6 228.1 229.4

Daimler 246.3 297.8 262.6 264.3

Mitsubishi 223.3 264.0 239.6 241.1

Nissan 223.2 299.8 245.2 247.5

Porsche 244.1 332.0 273.4 276.3

Suzuki 197.3 317.7 216.8 219.3

Toyota 212.8 308.6 244.0 247.1

Volkswagen 223.5 326.6 241.6 243.9

Overall 223.8 302.5 250.8 253.5
 Regulatory Impact Analysis 

4-18 

the reference case for this analysis. OMEGA estimates that, on average, manufacturers 

will need to spend $78 per vehicle to meet the current MY 2011 CAFE standards. A

 

Reference case costs are provided in Table 4-6 below. 

Table 4-6 Incremental Technology Cost of the Reference Case 

 Car Truck Combined 

BMW $ 346 $ 423 $ 368 

Chrysler $ 33 $ 116 $ 77 

Ford $ 73 $ 161 $ 106 

Subaru $ 68 $ 62 $ 66 

General Motors $ 31 $ 181 $ 102 

Honda $ - $ - $ - 

Hyundai $ - $ 69 $ 10 

Tata $ 611 $ 1,205 $ 845 

Kia $ - $ 42 $ 7 

Mazda $ - $ - $ - 

Daimler $ 468 $ 683 $ 536 

Mitsubishi $ 328 $ 246 $ 295 

Nissan $ - $ 61 $ 18 

Porsche $ 473 $ 706 $ 550 

Suzuki $ 49 $ 232 $ 79 

Toyota $ - $ - $ - 

Volkswagen $ 228 $ 482 $ 272 

Total $ 63 $ 138 $ 89 

EPA then used OMEGA to calculate the costs of meeting the final 2016 

standards, which are displayed in Table 4-7 below, and two alternative scenarios for 

sensitivity. In Table 4-7 and Table 4-17, EPA presents the per-vehicle cost for these 

scenarios, respectively. EPA has accounted for the cost to meet the standards in the 

reference case. In other words, the following tables contain results of the OMEGA 

control case runs after the reference case values have been subtracted. 

 

 

A

 It should be noted that the latest version of OMEGA projects slightly different costs than those shown 

here. This is usually due to an error when the model eliminates over-compliance which occurs with the last 

step of technology addition. The costs presented here reflect the correction of this error. The latest version 

of the model also reflects several improvements to the model’s algorithms when selecting between car and 

truck control. These revisions generally only change the projected cost by a dollar or two per vehicle and 

do not affect the overall conclusions of this analysis. 
Results of Final and Alternative Standards 

4-19 

Table 4-7 Incremental Technology Cost of the Final 2016 CO2 Standards 

 Car Truck Combined 

BMW $ 1,558 $ 1,195 $ 1,453 

Chrysler $ 1,129 $ 1,501 $ 1,329 

Ford $ 1,108 $ 1,442 $ 1,231 

Subaru $ 962 $ 790 $ 899 

General Motors $ 899 $ 1,581 $ 1,219 

Honda $ 635 $ 473 $ 575 

Hyundai $ 802 $ 425 $ 745 

Tata $ 1,181 $ 680 $ 984 

Kia $ 667 $ 247 $ 594 

Mazda $ 855 $ 537 $ 808 

Daimler $ 1,536 $ 931 $ 1,343 

Mitsubishi $ 817 $ 1,218 $ 978 

Nissan $ 686 $ 1,119 $ 810 

Porsche $ 1,506 $ 759 $ 1,257 

Suzuki $ 1,015 $ 537 $ 937 

Toyota $ 381 $ 609 $ 455 

Volkswagen $ 1,848 $ 972 $ 1,694 

Total $ 870 $ 1,099 $ 948 

EPA estimates that the additional technology required for manufacturers to meet 

the GHG standards for this final rule will cost on average $948/vehicle. This cost is 

roughly $100 lower than that projected in the NPRM. This difference is due primarily to 

a reduction in the estimated cost for the various technologies being added to the vehicles. 

4.7 Technology Penetration 

 The major technologies chosen by OMEGA are described in the Table 4-8 

through Table 4-12 for the reference case and in Tables 4-11 through 4-13 for the control 

case for cars, trucks, and combined fleets. The values in the table containing the control 

case technology are for that alone – EPA has subtracted out the impact of the reference 

case. 
Regulatory Impact Analysis 

4-20 

Table 4-8 2016 Technology Penetration in the Reference Case-Cars 

Manufacturer SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 2-Mo

BMW 53% 10% 43% 0% 40% 45% 12% 0% 0% Chrysler 0% 0% 1% 0% 5% 0% 0% 0% 0% Daimler 23% 20% 5% 2% 47% 32% 21% 0% 0% Ford 0% 0% 4% 0% 32% 0% 0% 0% 0% General Motors 6% 0% 3% 0% 14% 0% 0% 0% 0% Honda 0% 9% 0% 0% 0% 0% 0% 3% 0% Hyundai 0% 0% 0% 0% 0% 0% 0% 0% 0% Kia 0% 0% 0% 0% 0% 0% 0% 0% 0% Mazda 11% 0% 11% 0% 15% 0% 0% 0% 0% Mitsubishi 45% 0% 3% 0% 25% 50% 0% 0% 0% Nissan 0% 0% 0% 0% 0% 0% 0% 0% 1% Porsche 88% 0% 88% 0% 0% 41% 15% 0% 0% Subaru 0% 0% 12% 0% 0% 0% 0% 0% 0% Suzuki 80% 0% 0% 0% 0% 80% 80% 0% 0% Tata 85% 64% 0% 0% 34% 64% 64% 0% 0% Toyota 7% 0% 0% 0% 21% 0% 0% 0% 15% Volkswagen 87% 3% 84% 0% 13% 77% 4% 0% 0% Fleet 12% 2% 8% 0% 15% 8% 2% 0% 3% 
Results of Final and A

4-21 

Table 4-9 2016 Technology Penetration in the Reference Case-Trucks 

Manufacturer SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 2-Mo

BMW 20% 16% 0% 0% 84% 16% 16% 0% 0% Chrysler 0% 0% 0% 0% 28% 0% 0% 0% 0% Daimler 24% 24% 16% 16% 62% 38% 38% 0% 0% Ford 1% 0% 0% 0% 19% 0% 0% 0% 0% General Motors 

0% 0% 0% 0% 17% 0% 0% 0% 0% 

Honda 4% 0% 4% 0% 0% 0% 0% 0% 0% Hyundai 0% 0% 0% 0% 23% 0% 0% 0% 0% Kia 0% 0% 0% 0% 0% 0% 0% 0% 0% Mazda 26% 0% 26% 0% 48% 0% 0% 0% 0% Mitsubishi 13% 0% 0% 0% 25% 13% 0% 0% 0% Nissan 0% 0% 0% 0% 0% 0% 0% 0% 0% Porsche 100% 0% 50% 0% 15% 84% 85% 0% 0% Subaru 0% 0% 3% 0% 0% 0% 0% 0% 0% Suzuki 18% 0% 0% 0% 18% 0% 0% 0% 0% Tata 85% 37% 51% 0% 15% 85% 85% 0% 0% Toyota 7% 0% 0% 0% 16% 0% 0% 0% 6% Volkswagen 99% 17% 69% 1% 15% 85% 85% 0% 0% Fleet 7% 2% 3% 0% 18% 4% 4% 0% 1% 
Regulatory Impact Analysis 

4-22 

Table 4-10 2016 Technology Penetration in the Reference Case - Combined Cars and Trucks 

Manufacturer SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 2-

BMW 44% 12% 30% 0% 53% 37% 13% 0% 0% Chrysler 0% 0% 0% 0% 18% 0% 0% 0% 0% Daimler 23% 22% 8% 6% 52% 34% 26% 0% 0% Ford 0% 0% 3% 0% 27% 0% 0% 0% 0% General Motors 3% 0% 1% 0% 15% 0% 0% 0% 0% Honda 2% 6% 2% 0% 0% 0% 0% 2% 0% Hyundai 0% 0% 0% 0% 3% 0% 0% 0% 0% Kia 0% 0% 0% 0% 0% 0% 0% 0% 0% Mazda 13% 0% 13% 0% 20% 0% 0% 0% 0% Mitsubishi 32% 0% 2% 0% 25% 35% 0% 0% 0% Nissan 0% 0% 0% 0% 0% 0% 0% 0% 1% Porsche 92% 0% 75% 0% 5% 55% 38% 0% 0% Subaru 0% 0% 9% 0% 0% 0% 0% 0% 0% Suzuki 70% 0% 0% 0% 3% 67% 67% 0% 0% Tata 85% 54% 20% 0% 27% 73% 73% 0% 0% Toyota 7% 0% 0% 0% 19% 0% 0% 0% 12% Volkswagen 89% 5% 81% 0% 14% 78% 18% 0% 0% Fleet 10% 2% 7% 0.2% 16% 7% 3% 0.2% 2.5% 
Results of Final and A

4-23 

Table 4-11 2016 Technology Penetration in the Control Case-Cars 

Manufacturer SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 

BMW 78% 19% 62% 8% 13% 61% 63% 0% 4% Chrysler 85% 1% 7% 0% 30% 55% 56% 0% 0% Daimler 78% 28% 54% 6% 10% 72% 65% 0% 3% Ford 85% 12% 15% 0% 32% 56% 57% 0% 0% General Motors 56% 3% 13% 0% 4% 53% 53% 0% 0% Honda 59% 9% 0% 0% 0% 70% 21% 3% 0% Hyundai 61% 0% 1% 0% 0% 61% 38% 0% 0% Kia 39% 0% 1% 0% 0% 63% 4% 0% 0% Mazda 59% 0% 12% 1% 11% 48% 47% 0% 0% Mitsubishi 67% 0% 7% 1% 21% 67% 67% 0% 0% Nissan 62% 0% 3% 0% 1% 57% 52% 0% 1% Porsche 75% 15% 73% 12% 0% 34% 58% 0% 0% Subaru 82% 0% 12% 0% 0% 79% 58% 0% 0% Suzuki 80% 0% 0% 0% 0% 80% 80% 0% 0% Tata 85% 64% 17% 0% 14% 70% 70% 0% 4% Toyota 22% 2% 4% 0% 15% 49% 2% 0% 15% Volkswagen 79% 16% 74% 13% 10% 67% 57% 0% 2% Fleet 58% 6% 14% 1% 11% 58% 39% 0% 4% 
Regulatory Impact Analysis 

4-24 

Table 4-12 2016 Technology Penetration in the Control Case-Trucks 

SGDI DEAC-OHC Turbo Diesel 6 SPD 

Auto 

DCT 42 V S-S IMA Power 

Split 

BMW 86% 28% 57% 0% 15% 69% 70% 0% 0% Chrysler 74% 25% 25% 0% 32% 50% 53% 0% 0% Daimler 72% 35% 52% 2% 15% 70% 70% 0% 0% Ford 83% 35% 25% 0% 18% 68% 68% 0% 0% General Motors 80% 50% 16% 0% 12% 70% 70% 0% 0% Honda 16% 0% 4% 0% 0% 12% 12% 0% 0% Hyundai 44% 0% 0% 0% 56% 0% 0% 0% 0% Kia 1% 0% 0% 0% 0% 1% 1% 0% 0% Mazda 65% 0% 27% 0% 48% 39% 7% 0% 0% Mitsubishi 85% 0% 72% 0% 4% 85% 85% 0% 0% Nissan 76% 23% 29% 0% 3% 72% 73% 0% 0% Porsche 100% 15% 42% 0% 15% 69% 70% 0% 0% Subaru 21% 0% 3% 0% 0% 21% 21% 0% 0% Suzuki 61% 0% 0% 0% 61% 0% 0% 0% 0% Tata 85% 40% 42% 0% 15% 70% 70% 0% 6% Toyota 34% 17% 0% 0% 9% 20% 17% 0% 6% Volkswagen 99% 29% 56% 0% 15% 70% 70% 0% 0% Fleet 62% 27% 18% 0% 13% 49% 48% 0% 1% 
Results of Final and A

4-25 

Table 4-13 2016 Technology Penetration in the Control Case – Combined Cars and Trucks 

Manufacturer SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 2-Mod

BMW 80% 21% 61% 6% 13% 63% 65% 0% 3% 12Chrysler 79% 13% 17% 0% 31% 52% 54% 0% 0% 0Daimler 76% 30% 53% 5% 12% 72% 67% 0% 2% 12Ford 84% 21% 19% 0% 27% 60% 61% 0% 0% 0General Motors 67% 25% 14% 0% 8% 61% 61% 0% 0% 0Honda 43% 6% 2% 0% 0% 49% 18% 2% 0% 0Hyundai 59% 0% 1% 0% 8% 52% 32% 0% 0% 0Kia 33% 0% 1% 0% 0% 52% 4% 0% 0% 0Mazda 60% 0% 14% 1% 17% 47% 41% 0% 0% 0Mitsubishi 74% 0% 33% 0% 14% 74% 74% 0% 0% 0Nissan 66% 7% 11% 0% 2% 62% 58% 0% 1% 0Porsche 83% 15% 62% 8% 5% 45% 62% 0% 0% 15Subaru 60% 0% 9% 0% 0% 58% 44% 0% 0% 0Suzuki 77% 0% 0% 0% 10% 67% 67% 0% 0% 0Tata 85% 55% 27% 0% 14% 70% 70% 0% 4% 11Toyota 26% 7% 3% 0% 13% 40% 7% 0% 12% 0Volkswagen 82% 18% 71% 11% 10% 68% 60% 0% 1% 14Fleet 60% 13% 15% 0.9% 12% 55% 42% 0% 3% 1
Regulatory Impact Analysis 

4-26 

As can be seen, the overall reduction in vehicle weight is projected to be 4.3%. As there 

has been a concern in the past that weight reductions are associated with increased safety risk, a 

more specific breakdown of the projected weight reduction by vehicle class and weight range is 

provided below. For cars below 2950 pounds curb weight, the estimated reduction is 2.3% (62 

pounds), while it was estimated to be 4.4% (154 pounds) for cars above 2950 curb weight. For 

trucks below 3850 pounds curb weight, the projected reduction is 3.5% (119 pounds), while it 

was 4.5% (215 pounds) for trucks above 3850 curb weight. Splitting trucks at a higher weight, 

for trucks below 5000 pounds curb weight, the estimated reduction is 3.3% (140 pounds), while 

it was 6.7% (352 pounds) for trucks above 5000 curb weight. These results are tabulated below 

in Table 4-14. 

Table 4-14 Breakdown of Weight Reduction in Modeling Results 

 

Weight 

Category 

Average 

Weight 

Reduction 

% Weight 

Reduction 

< 2950 lbs 75 lbs 2.8% 

Cars 

> 2950 lbs 153 lbs 4.3% 

< 3850 lbs 163 lbs 4.7% 

Trucks with 3850 

lb break point 

> 3850 lbs 240 lbs 5.1% 

< 5000 lbs 186 lbs 4.4% 

Trucks with 5000 

lb break point 

> 5000 lbs 376 lbs 7.0% 

4.8 Alternative Program Stringencies 

 EPA also analyzed the technology cost of two alternative stringency scenarios: 4%/year 

and 6%/year. The manufacturers’s CO2 targets and achieved levels for standards with these 

alternative stringincies are presented in Table 4-15 and Table 4-16 below. 
Results of Final and Alternative Standards 

4-27 

Table 4-15 2016 Standards by Manufacturer in the 4% Sensitivity Case 

 Achieved CO2 Levels CO2 Standards 

 Car Truck Combined Car Truck Combined 

BMW 236.3 278.7 249.8 232.1 287.4 249.7 

Chrysler 228.7 305.2 272.5 235.9 299.9 272.5 

Ford 237.3 302.7 263.7 242.0 299.3 265.1 

Subaru 218.2 271.7 239.4 233.0 309.6 263.3 

General 

Motors 245.6 308.1 277.3 234.2 320.6 278.0 

Honda 211.1 305.3 249.1 225.8 285.5 249.9 

Hyundai 216.2 329.3 235.5 225.9 283.3 235.7 

Tata 258.6 323.6 286.5 228.0 294.3 256.4 

Kia 217.7 335.7 240.7 224.9 275.7 234.8 

Mazda 222.9 285.6 233.3 223.1 274.0 231.5 

Daimler 246.3 297.8 264.3 229.4 299.3 253.9 

Mitsubishi 227.5 268.4 245.3 209.8 291.8 245.6 

Nissan 229.2 299.8 251.6 219.2 272.1 236.0 

Porsche 244.1 332.0 276.3 211.3 276.9 235.3 

Suzuki 197.3 334.3 222.4 253.6 277.4 258.0 

Toyota 217.5 311.6 251.2 224.8 299.3 251.5 

Volkswagen 223.5 326.6 243.9 222.3 297.7 237.2 

Overall 227.3 305.9 256.9 229.2 299.7 255.8 

Table 4-16 2016 Standards by Manufacturer in the 6% Sensitivity Case 

 Achieved CO2 Levels CO2 Standards 

 Car Truck Combined Car Truck Combined 

BMW 236.3 278.7 249.8 210.4 258.8 225.8 

Chrysler 210.7 273.9 246.9 214.2 271.3 246.9 

Ford 214.2 285.0 242.7 220.3 270.7 240.6 

Subaru 207.6 227.8 215.6 211.3 281.0 238.9 

General Motors 213.6 290.9 252.7 212.5 292.1 252.8 

Honda 194.7 270.9 225.5 204.1 256.9 225.5 

Hyundai 202.9 260.9 212.8 204.2 254.7 212.8 

Tata 258.6 323.6 286.5 206.3 265.7 231.8 

Kia 189.4 335.2 217.9 203.2 247.1 211.8 

Mazda 203.9 243.8 210.5 201.4 245.4 208.7 

Daimler 246.3 297.8 264.3 207.8 270.7 229.8 

Mitsubishi 212.2 260.6 233.4 188.1 263.2 220.9 

Nissan 200.2 286.8 227.7 197.5 243.5 212.1 

Porsche 244.1 332.0 276.3 189.6 248.3 211.1 

Suzuki 186.8 260.6 200.3 231.9 248.8 235.0 
Regulatory Impact Analysis 

4-28 

Toyota 192.9 288.3 227.1 203.1 270.7 227.4 

Volkswagen 223.5 326.6 243.9 200.6 269.1 214.2 

Overall 206.6 284.9 236.1 207.5 271.2 231.5 

With the reference case the same as that described above in Section 4.1, the costs of the 

two alternative control cases are presented in Tables 4-17 and 4-18, respectively, and the 

technology penetrations are presented in Table 4-17 through Table 4-24, below. 

Table 4-17 2016 Technology Cost in the 4% sensitivity case 

Car Truck Combined 

BMW $ 1,558 $ 1,195 $ 1,453 

Chrysler $ 1,111 $ 1,236 $ 1,178 

Ford $ 1,013 $ 1,358 $ 1,140 

Subaru $ 962 $ 616 $ 836 

General Motors $ 834 $ 1,501 $ 1,148 

Honda $ 598 $ 411 $ 529 

Hyundai $ 769 $ 202 $ 684 

Tata $ 1,181 $ 680 $ 984 

Kia $ 588 $ 238 $ 527 

Mazda $ 766 $ 537 $ 733 

Daimler $ 1,536 $ 931 $ 1,343 

Mitsubishi $ 733 $ 1,164 $ 906 

Nissan $ 572 $ 1,119 $ 729 

Porsche $ 1,506 $ 759 $ 1,257 

Suzuki $ 1,015 $ 179 $ 879 

Toyota $ 323 $ 560 $ 400 

Volkswagen $ 1,848 $ 972 $ 1,694 

Total $ 811 $ 1,020 $ 883 
Results of Final and Alternative Standards 

4-29 

Table 4-18 2016 Technology Cost in the 6% sensitivity case 

 Car Truck Combined 

BMW $ 1,558 $ 1,195 $ 1,453 

Chrysler $ 1,447 $ 2,156 $ 1,827 

Ford $ 1,839 $ 2,090 $ 1,932 

Subaru $ 1,173 $ 1,316 $ 1,225 

General Motors $ 1,728 $ 2,030 $ 1,870 

Honda $ 894 $ 891 $ 893 

Hyundai $ 1,052 $ 1,251 $ 1,082 

Tata $ 1,181 $ 680 $ 984 

Kia $ 1,132 $ 247 $ 979 

Mazda $ 1,093 $ 1,083 $ 1,092 

Daimler $ 1,536 $ 931 $ 1,343 

Mitsubishi $ 1,224 $ 1,840 $ 1,471 

Nissan $ 1,151 $ 1,693 $ 1,306 

Porsche $ 1,506 $ 759 $ 1,257 

Suzuki $ 1,426 $ 1,352 $ 1,414 

Toyota $ 747 $ 906 $ 799 

Volkswagen $ 1,848 $ 972 $ 1,694 

Total $ 1,296 $ 1,538 $ 1,379 
Regulatory Impact Analysis 

4-30 

Table 4-19 2016 Technology Penetration in the 4% sensitivity case- Cars 

Manufacturer SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 2-

BMW 78% 19% 62% 8% 13% 61% 63% 0% 4% Chrysler 85% 0% 7% 0% 30% 55% 55% 0% 0% Daimler 78% 28% 54% 6% 10% 72% 65% 0% 3% Ford 73% 8% 15% 0% 26% 56% 57% 0% 0% General Motors 57% 3% 6% 0% 11% 46% 46% 0% 0% Honda 48% 9% 0% 0% 0% 59% 21% 3% 0% Hyundai 61% 0% 1% 0% 0% 61% 33% 0% 0% Kia 45% 0% 1% 0% 0% 68% 0% 0% 0% Mazda 82% 0% 12% 1% 11% 71% 69% 0% 0% Mitsubishi 85% 0% 3% 0% 25% 63% 64% 0% 0% Nissan 65% 0% 3% 0% 1% 60% 56% 0% 1% Porsche 75% 15% 73% 12% 0% 34% 58% 0% 0% Subaru 82% 0% 12% 0% 0% 79% 58% 0% 0% Suzuki 80% 0% 0% 0% 0% 80% 80% 0% 0% Tata 85% 64% 17% 0% 14% 70% 70% 0% 4% Toyota 9% 2% 0% 0% 18% 35% 2% 0% 15% Volkswagen 79% 16% 74% 13% 10% 67% 57% 0% 2% Fleet 55% 5% 12% 1% 12% 54% 38% 0% 4% 
Results of Final and A

4-31 

Table 4-20 2016 Technology Penetration in the 4% sensitivity case- Trucks 

SGDI DEACOHC Turbo Diesel 6 SPD Auto DCT 42 V S-S IMA Power Split 2-Mod

BMW 86% 28% 57% 0% 15% 69% 70% 0% 0% 15Chrysler 52% 24% 25% 0% 24% 50% 52% 0% 0% 0Daimler 72% 35% 52% 2% 15% 70% 70% 0% 0% 15Ford 83% 35% 19% 0% 24% 62% 62% 0% 0% 0General Motors 69% 49% 16% 0% 4% 69% 69% 0% 0% 0Honda 37% 0% 4% 0% 0% 32% 4% 0% 0% 0Hyundai 0% 0% 0% 0% 23% 0% 0% 0% 0% 0Kia 1% 0% 0% 0% 0% 1% 0% 0% 0% 0Mazda 65% 0% 27% 0% 48% 39% 7% 0% 0% 0Mitsubishi 85% 0% 72% 0% 4% 85% 85% 0% 0% 0Nissan 76% 23% 29% 0% 3% 72% 73% 0% 0% 0Porsche 100% 15% 42% 0% 15% 69% 70% 0% 0% 15Subaru 54% 0% 3% 0% 0% 54% 0% 0% 0% 0Suzuki 18% 0% 0% 0% 18% 0% 0% 0% 0% 0Tata 85% 40% 42% 0% 15% 70% 70% 0% 6% 9Toyota 27% 17% 0% 0% 2% 20% 17% 0% 6% 0Volkswagen 99% 29% 56% 0% 15% 70% 70% 0% 0% 15Fleet 58% 27% 17% 0% 10% 50% 46% 0% 1% 1
Regulatory Impact Analysis 

4-32 

Table 4-21 2016 Technology Penetration in the 4% sensitivity case – Cars and Trucks Combined 

SGDI 

DEACOHC Turbo Diesel

6 

SPD Auto DCT 42 V S-S IMA 

Power 

Split 

2-

Mode PHEV/EV MS1 

BMW 80% 21% 61% 6% 13% 63% 65% 0% 3% 12% 0.1% 0% Chrysler 67% 13% 17% 0% 26% 52% 54% 0% 0% 0% 0.0% 14% Daimler 76% 30% 53% 5% 12% 72% 67% 0% 2% 12% 0.0% 0% Ford 77% 18% 16% 0% 25% 58% 59% 0% 0% 0% 0.0% 18% General Motors 62% 24% 11% 0% 7% 57% 57% 0% 0% 0% 0.0% 5% Honda 44% 6% 2% 0% 0% 49% 15% 2% 0% 0% 0.0% 34% Hyundai 52% 0% 1% 0% 3% 52% 28% 0% 0% 0% 0.0% 24% Kia 37% 0% 1% 0% 0% 57% 0% 0% 0% 0% 0.0% 43% Mazda 79% 0% 14% 1% 17% 66% 60% 0% 0% 0% 0.0% 9% Mitsubishi 85% 0% 31% 0% 16% 72% 72% 0% 0% 0% 0.0% 13% Nissan 69% 7% 11% 0% 2% 64% 61% 0% 1% 0% 0.0% 7% Porsche 83% 15% 62% 8% 5% 45% 62% 0% 0% 15% 0.0% 0% Subaru 72% 0% 9% 0% 0% 70% 37% 0% 0% 0% 0.0% 35% Suzuki 70% 0% 0% 0% 3% 67% 67% 0% 0% 0% 0.0% 3% Tata 85% 55% 27% 0% 14% 70% 70% 0% 4% 11% 0.0% 0% Toyota 15% 7% 0% 0% 13% 30% 7% 0% 12% 0% 0.0% 23% Volkswagen 82% 18% 71% 11% 10% 68% 60% 0% 1% 14% 0.0% 0% Fleet 56% 13% 14% 0.8% 11% 53% 41% 0% 3% 1% 0.0% 16% 
Results of Final and A

4-33 

Table 4-22 2016 Technology Penetration in the 6% Sensitivity Case - Cars 

SGDI 

DEAC-OHC 

Turb

o 

Diese

l 

6 

SPD Auto DCT 

42 V 

S-S IMA 

Powe

r Split

2-

Mode 

PHEV/E

V MS1 M

BMW 78% 19% 62% 8% 13% 61% 63% 0% 4% 10% 0.2% 0% 4Chrysler 85% 4% 37% 0% 1% 84% 85% 0% 0% 0% 0.0% 0% Daimler 78% 28% 54% 6% 10% 72% 65% 0% 3% 10% 0.0% 0% 4Ford 85% 11% 55% 0% 5% 74% 75% 0% 5% 5% 0.0% 0% 1General Motors 85% 6% 50% 0% 2% 83% 84% 0% 0% 1% 0.0% 0% Honda 72% 9% 0% 0% 0% 70% 70% 3% 0% 0% 0.0% 0% 1Hyundai 70% 0% 1% 0% 9% 61% 61% 0% 0% 0% 0.0% 9% 2Kia 75% 0% 1% 0% 0% 75% 74% 0% 0% 0% 0.0% 1% 2Mazda 85% 0% 14% 1% 3% 80% 83% 0% 0% 0% 0.0% 0% 2Mitsubishi 84% 1% 28% 1% 4% 78% 78% 0% 6% 1% 0.0% 0% Nissan 84% 0% 37% 1% 0% 80% 83% 0% 1% 0% 0.0% 0% Porsche 75% 15% 73% 12% 0% 34% 58% 0% 0% 15% 0.0% 0% 7Subaru 83% 0% 12% 1% 2% 79% 80% 0% 0% 0% 0.0% 2% 4Suzuki 85% 0% 85% 0% 0% 85% 85% 0% 0% 0% 0.0% 0% Tata 85% 64% 17% 0% 14% 70% 70% 0% 4% 11% 0.0% 0% 5Toyota 71% 2% 4% 0% 15% 56% 53% 0% 15% 0% 0.0% 3% 3Volkswagen 79% 16% 74% 13% 10% 67% 57% 0% 2% 13% 0.0% 0% 6Fleet 79% 7% 30% 1% 6% 71% 70% 0% 4% 2% 0.0% 1% 2
Regulatory Impact Analysis 

4-34 

Table 4-23 2016 Technology Penetration in the 6% Sensitivity Case-Trucks 

SGDI 

DEAC-OHC Turbo 

Diese

l 

6 

SP

D 

Aut

o DCT 

42 

V SS IMA Powe r Split 2- Mode PHEV/E V MS1 M

2 

BMW 86% 28% 57% 0% 15% 69% 70% 0% 0% 15% 0.0% 0% 57Chrysler 85% 21% 61% 0% 4% 79% 82% 0% 0% 3% 0.0% 0% 5Daimler 72% 35% 52% 2% 15% 70% 70% 0% 0% 15% 0.0% 0% 38Ford 85% 16% 60% 0% 3% 76% 76% 0% 4% 5% 0.0% 0% 11General Motors 85% 46% 34% 0% 2% 82% 82% 0% 0% 3% 0.0% 0% 12Honda 61% 0% 28% 0% 4% 56% 56% 0% 0% 0% 0.0% 4% 24Hyundai 85% 9% 76% 0% 12% 76% 76% 0% 0% 0% 0.0% 0% 85Kia 1% 0% 0% 0% 0% 1% 1% 0% 0% 0% 0.0% 0% 0Mazda 89% 0% 45% 0% 13% 80% 80% 0% 0% 0% 0.0% 5% 41Mitsubishi 85% 9% 62% 0% 4% 70% 70% 0% 6% 9% 0.0% 0% 42Nissan 85% 29% 39% 0% 0% 74% 74% 0% 4% 6% 0.0% 0% 24Porsche 100% 15% 42% 0% 15% 69% 70% 0% 0% 15% 0.0% 0% 31Subaru 85% 0% 28% 0% 6% 79% 79% 0% 0% 0% 0.0% 6% 25Suzuki 85% 0% 85% 0% 0% 85% 85% 0% 0% 0% 0.0% 0% 67Tata 85% 40% 42% 0% 15% 70% 70% 0% 6% 9% 0.0% 0% 0Toyota 70% 17% 7% 0% 32% 34% 34% 0% 6% 0% 0.0% 29% 0Volkswagen 99% 29% 56% 0% 15% 70% 70% 0% 0% 15% 0.0% 0% 56Fleet 78% 23% 37% 0% 10% 66% 66% 0% 2% 4% 0.0% 6% 16
Results of Final and A

4-35 

Table 4-24 2016 Technology Penetration in the 6% Sensitivity Case – Cars and Trucks combined 

SGDI 

DEACOHC Turbo Diesel

6 

SPD Auto DCT 

42 

V SS IMA Power Split 2- Mode PHEV/EV MS1 

BMW 80% 21% 61% 6% 13% 63% 65% 0% 3% 12% 0.1% 0% Chrysler 85% 13% 50% 0.05% 3% 82% 83% 0% 0% 2% 0.0% 0% Daimler 76% 30% 53% 5% 12% 72% 67% 0% 2% 12% 0.0% 0% Ford 85% 13% 57% 0% 4% 74% 75% 0% 5% 5% 0.0% 0% General Motors 85% 25% 43% 0% 2% 83% 83% 0% 0% 2% 0.0% 0% Honda 68% 6% 10% 0% 1% 65% 65% 2% 0% 0% 0.0% 1% Hyundai 73% 1% 12% 0% 9% 64% 64% 0% 0% 0% 0.0% 8% Kia 62% 0% 1% 0% 0% 62% 61% 0% 0% 0% 0.0% 1% Mazda 85% 0% 19% 1% 4% 80% 82% 0% 0% 0% 0.0% 1% Mitsubishi 85% 4% 42% 0% 4% 75% 75% 0% 6% 4% 0.0% 0% Nissan 85% 8% 38% 0% 0% 78% 81% 0% 2% 2% 0.0% 0% Porsche 83% 15% 62% 8% 5% 45% 62% 0% 0% 15% 0.0% 0% Subaru 84% 0% 18% 1% 3% 79% 80% 0% 0% 0% 0.0% 4% Suzuki 85% 0% 85% 0% 0% 85% 85% 0% 0% 0% 0.0% 0% Tata 85% 55% 27% 0% 14% 70% 70% 0% 4% 11% 0.0% 0% Toyota 71% 7% 5% 0% 20% 49% 47% 0% 12% 0% 0.0% 12% Volkswagen 82% 18% 71% 11% 10% 68% 60% 0% 1% 14% 0.0% 0% Fleet 79% 12% 33% 0.9% 7% 69% 69% 0% 4% 3% 0.0% 3% 
Regulatory Impact Analysis 

4-36 

4.9 Assessment of Manufacturer Differences 

The levels of requisite technologies shown above differ significantly across the various 

manufacturers. This is to be expected for universal, or flat fuel economy or CO2 standards, since 

manufacturers’ sales mixes differ dramatically in average size. However, use of footprint-based 

standards should eliminate the effect of vehicle size, and thus, market mix, on the relative 

stringency of a standard across manufacturers. Yet, large differences remain in the level of 

technology projected to be required for various manufacturers to meet the final standards. 

Therefore, several analyses were performed to ascertain the cause of these differences. Because 

the baseline case fleet consists of 2008 MY vehicle designs, these analyses were focused on 

these vehicles, their technology and their CO2 emission levels. 

Manufacturers’ average CO2 emissions vary for a wide range of reasons. In addition to 

widely varying vehicle styles, designs, and sizes, manufacturers have implemented fuel efficient 

technologies to varying degrees, as indicated in Table 4-25 below. 

Table 4-25 Penetration of Technology in 2008 Vehicles with 2008 Sales: Cars and Trucks 

 SGDI 

DEACOHC Turbo Diesel 

6 

Speed 

Auto 

Trans DCT 

42 V 

S-S Hybrid PHEV/EV 

BMW 7.50% 0.00% 6.10% 0.00% 86.00% 0.90% 0.00% 0.00% 0.10%

Chrysler 0.00% 0.00% 0.50% 0.10% 14.00% 0.00% 0.00% 0.00% 0.00%

Daimler 0.00% 0.00% 6.50% 5.60% 76.00% 7.50% 0.00% 0.00% 0.00%

Ford 0.40% 0.00% 2.20% 0.00% 29.00% 0.00% 0.00% 0.00% 0.00%

General Motors 3.10% 0.00% 1.40% 0.00% 15.00% 0.00% 0.00% 0.30% 0.00%

Honda 1.40% 7.10% 1.40% 0.00% 0.00% 0.00% 0.00% 2.10% 0.00%

Hyundai 0.00% 0.00% 0.00% 0.00% 3.00% 0.00% 0.00% 0.00% 0.00%

Kia 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Mazda 13.60% 0.00% 13.60% 0.00% 26.00% 0.00% 0.00% 0.00% 0.00%

Mitsubishi 0.00% 0.00% 0.00% 0.00% 10.00% 0.00% 0.00% 0.00% 0.00%

Nissan 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.80% 0.00%

Porsche 58.60% 0.00% 14.90% 0.00% 49.00% 0.00% 0.00% 0.00% 0.00%

Subaru 0.00% 0.00% 9.80% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Suzuki 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Tata 0.00% 0.00% 17.30% 0.00% 99.00% 0.00% 0.00% 0.00% 0.00%

Toyota 6.80% 0.00% 0.00% 0.00% 21.00% 0.00% 0.00% 11.60% 0.00%

Volkswagen 50.60% 0.00% 39.50% 0.00% 69.00% 13.10% 0.00% 0.00% 0.00%

Overall 3.80% 0.80% 2.60% 0.10% 19.10% 0.50% 0.00% 2.20% 0.00%

Once significant levels of technology are added to these vehicles in order to comply with 

future standards, the impact of existing technology diminishes dramatically. Manufacturers 

which did not utilize much technology in 2008 essentially catch up to those which did. The 

exception is the use of hybrid technology in 2008, since hybrids are not projected to be needed 

by most manufacturers to meet the final standards. This primarily affects Toyota, and to a lesser 

extent, Honda. Their use of hybrid technology in their 2008 fleet will continue to provide 

relatively greater CO2 reductions even in the 2016 projections. As long as the vehicle designs of 
Results of Final and Alternative Standards 

4-37 

various manufacturers would produce the same level of CO2 emissions if their CO2 reducing 

technology was removed, for the most part, difference in the application of technology in 2008 

will not affect the level of technology needed in 2016. 

In addition, as mentioned above, differences in CO2 emissions due to differences the 

distribution of sales by vehicle size should be largely eliminated by the use of a footprint-based 

standard. Thus, just because a manufacturer produces larger vehicles than another manufacturer 

does not explain the differences in required technology seen above. 

In order to focus this analysis on the 2008 MY fleet, it would be helpful to remove the 

effect of differences in vehicle size and the use of CO2 reducing technology, so that the other 

causes of differences can be highlighted. EPA used the EPA lumped parameter model described 

in Chapter 1 to estimate the degree to which technology present on each 2008 MY vehicle was 

improving fuel efficiency. The effect of this technology was then removed from each vehicle to 

produce CO2 emissions which did not reflect any differences due to the use of CO2 reducing 

technology. This set of adjusted CO2 emission levels is referred to as “no technology” 

emissions. 

The differences in the relative sizes of vehicles sold by each manufacturer were 

accounted for by determining the difference between the sales-weighted average of each 

manufacturer’s “no technology” CO2 levels and their required CO2 emission level under the final 

2016 standards. This difference is the total reduction in CO2 emissions required for each 

manufacturer relative to a “no technology” baseline. The same difference for the industry as a 

whole is 71 g/mi CO2 for cars and 1.7 g/mi CO2 for trucks. This industry-wide difference was 

subtracted from each manufacturer’s difference to highlight which manufacturers had lower and 

higher CO2 emission reduction requirements. The results are shown in Figure 4-1. 
Regulatory Impact Analysis 

4-38 

-60

-40

-20

0

20

40

60

80

100

120

BMW

Chrysler

Daimler

Ford

GeneralMotors

Honda

Hyundai

Kia

Mazda

Mitsubishi

Nissan

Porsche

Subaru

Suzuki

Tata

Toyota

Volkswagen

CO2 Emissions (g/mi)

Car

Truck

Figure 4-1 CO2 Emissions Relative to Fleet Adjusted for Technology and Footprint 

The manufacturers projected in Table 4-25 to require the greatest levels of technology 

also show the highest offsets relative to the industry. The greatest offset shown in Figure 4-1 is 

for Tata’s trucks (Land Rover). These vehicles are estimated to have 100 g/mi greater CO2

emissions than the average 2008 MY truck after accounting for differences in the use of fuel 

saving technology and footprint. The lowest adjustment is for Subaru’s trucks, which have 50 

g/mi CO2 lower emissions than the average truck. 

While this comparison confirms the differences in the technology penetrations shown in 

Table 4-25, it does not yet explain why these differences exist. Two well known factors 

affecting vehicle fuel efficiency are vehicle weight and performance. The footprint-based form 

of the final CO2 standard accounts for most of the difference in vehicle weight seen in the 2008 

MY fleet. However, even at the same footprint, vehicles can have varying weights. Also, higher 

performing vehicles also tend to have higher CO2 emissions over the two-cycle test procedure. 

So manufacturers with higher average performance levels will tend to have higher average CO2

emissions for any given footprint. Table 4-26 shows each manufacturer’s average ratios of 

weight to footprint and horsepower to weight. 

 
Results of Final and Alternative Standards 

4-39 

Table 4-26 Vehicle Weight to Footprint and Performance 

 Car Truck 

Manufacturer 

Weight / 

Footprint 

(lb/sq ft) 

Horsepower/ 

Weight 

(hp/lb) 

Weight / 

Footprint 

(lb/sq ft) 

Horsepower/ 

Weight (hp/lb) 

BMW 78 0.073 94 0.059 

Chrysler 74 0.054 85 0.053 

Daimler 73 0.068 97 0.057 

Ford 77 0.057 84 0.052 

General Motors 76 0.057 83 0.059 

Honda 67 0.051 83 0.055 

Hyundai 70 0.052 84 0.056 

Kia 67 0.05 79 0.057 

Mazda 73 0.05 80 0.055 

Mitsubishi 74 0.052 83 0.056 

Nissan 72 0.059 80 0.058 

Porsche 82 0.106 96 0.073 

Subaru 73 0.057 79 0.054 

Suzuki 70 0.049 81 0.062 

Tata 78 0.077 110 0.057 

Toyota 71 0.054 80 0.062 

Volkswagen 80 0.059 108 0.052 

Overall 73 0.056 83 0.058 

The impact of these two factors on each manufacturer’s “no technology” CO2 emissions 

was estimated. First, the “no technology” CO2 emissions levels were statistically analyzed to 

determine the average impact of weight and the ratio of horsepower to weight on CO2 emissions. 

Both factors were found to be statistically significant at the 95 percent confidence level. The 

results of the statistical analysis are summarized in Table 4-27. 
Regulatory Impact Analysis 

4-40 

Table 4-27 Effect of Weight and Performance on “No Technology” Vehicle CO2 

 Intercept 

(g/mi CO2) 

Effect of 

weight 

(g/mi 

CO2/lb) 

Effect of 

Horsepower / 

Weight (g/mi 

CO2*lb/hp) 

R-Square 

Car -45.8 0.0819 1590 0.82 

Truck -21 0.0782 1838 0.71 

Together, these two factors explain over 80 percent of the variability in vehicles’ CO2

emissions for cars and over 70 percent for trucks. These relationships were then used to adjust 

each vehicle’s “no technology” CO2 emissions to the average weight for its footprint value and 

to the average horsepower to weight ratio of either the car or truck fleet, as follows: 

For Cars: 

CO2 Emissions adjusted for weight and performance = “No Technology” CO2 - 

 (Vehicle Weight - Vehicle Footprint * 73) * 0.0819 – 

 (Vehicle hp/wt – 0.056 ) * 1590 

For Truck: 

CO2 Emissions adjusted for weight and performance = “No Technology” CO2 - 

 (Vehicle Weight - Vehicle Footprint * 83) * 0.0782 – 

 (Vehicle hp/wt – 0.058 ) * 1838 

We then recomputed the difference between the sales-weighted average of each 

manufacturer’s adjusted “no technology” CO2 levels and their required CO2 emission level under 

the final 2016 standards and subtracted the difference for the industry as a whole. The results are 

shown in Figure 4-2. 
Results of Final and Alternative Standards 

4-41 

-40

-30

-20

-10

0

10

20

30

40

BMW

Chrysler

Daimler

Ford

GeneralMotors

Honda

Hyundai

Kia

Mazda

Mitsubishi

Nissan

Porsche

Subaru

Suzuki

Tata

Toyota

Volkswagen

CO2 Emissions (g/mi)

Car

Truck

Figure 4-2 CO2 Emissions Relative to Fleet Adjusted for Technology, Footprint, Weight at Footprint, and 

Performance 

First, note that the scale in Figure 4-2 is much smaller by a factor of 3 than that in Figure 

4-1. In other words, accounting for differences in vehicle weight (at constant footprint) and 

performance dramatically reduces the differences in various manufacturers’ CO2 emissions. 

Most of the manufacturers with high offsets in Figure 4-1 now show low or negative offsets. For 

example, BMW’s and VW’s trucks show very low CO2 emissions. Tata’s emissions are very 

close to the industry average. Daimler’s vehicles are no more than 10 g/mi above the average for 

the industry. This analysis indicates that the primary reasons for the differences in technology 

penetrations shown for the various manufacturers in Table 4-27 are weight and performance. 

EPA has not determined why some manufacturer’s vehicle weight is relatively high for its 

footprint value, nor whether this weight provides additional utility for the consumer. 

Performance is more straightforward. Some consumers desire high performance and some 

manufacturers orient their sales towards these consumers. However, the cost in terms of CO2

emissions is clear. Producing relatively heavy or high performance vehicles increases CO2

emissions and will require greater levels of technology in order to meet the final CO2 standards. 
Emissions Impacts 

5-1 

CHAPTER 5: Emissions Impacts 

5.1 Overview 

Climate change is widely viewed as the most significant long-term threat to the global 

environment. According to the Intergovernmental Panel on Climate Change, anthropogenic 

emissions of greenhouse gases are very likely (90 to 99 percent probability) the cause of most 

of the observed global warming over the last 50 years. The primary GHGs of concern are 

carbon dioxide (CO2), methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and 

sulfur hexafluoride.1 Mobile sources emitted 31 percent of all U.S. GHG in 2007 

(transportation sources, which do not include certain off-highway sources, account for 28 

percent) and have been the fastest-growing source of U.S. GHG since 1990.2

 Mobile sources 

addressed in the recent endangerment finding under CAA section 202(a)--light-duty vehicles, 

heavy-duty trucks, buses, and motorcycles--accounted for 23 percent of all U.S. GHG in 

2007.3

 Light-duty vehicles emit four GHGs--CO2 , methane, nitrous oxide, and 

hydrofluorocarbons--and are responsible for nearly 60 percent of all mobile source GHGs and 

over 70 percent of Section 202(a) mobile source GHG. For light-duty vehicles in 2007, CO2

emissions represent about 94 percent of all greenhouse emissions (including HFCs), and the 

CO2 emissions measured over the EPA tests used for fuel economy compliance represent 

about 90 percent of total light-duty vehicle greenhouse gas emissions.4,5 

Today’s rule quantifies anticipated impacts from the EPA vehicle CO2 emission 

standards. The emissions from the GHGs carbon dioxide (CO2), methane (CH4), nitrous 

oxide (N2O) and hydrofluorocarbons (HFCs) were quantified. In addition to reducing the 

emissions of greenhouse gases, today’s rule would also influence the emissions of “criteria” 

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

dioxide (SOx) and the ozone precursors hydrocarbons (VOC) and oxides of nitrogen (NOx); 

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

acrolein). 

Most analyses in this chapter of the RIA were updated between proposal and final 

rulemaking. Most significantly, as a result of public comments and updated economic data, 

the attribute based CO2 curves have been revised, as discussed in detail in Section II.B of this 

preamble and Chapter 2 of the Joint TSD. This update in turn affects costs, benefits, and 

other impacts of the final standards. Thus EPA's overall projection of the impacts of the final 

rule standards have been updated and the results are different than for the NPRM, though in 

general not by a large degree. 

 Beyond updated CO2 curves, other new inputs includes revised sales projections of the 

MY 2012-2016 fleet, and updated economic input data. All changes to inputs are documented 

in the TSD, and are further described in this document. 

Downstream (tailpipe) emission impacts were developed using two EPA models. 

Computation algorithms and achieved CO2 levels were derived from EPA’s Optimization 

Model for reducing Emissions of Greenhouse gases from Automobiles (OMEGA). Non-CO2
Regulatory Impact Analysis 

5-2 

emissions were calculated using data from EPA’s Motor Vehicle Emission Simulator 

(MOVES2010). 

Upstream (fuel production and distribution) emission changes resulting from the 

decreased fuel consumption predicted by the downstream models were calculated using a 

spreadsheet model based on emission factors from GREET.6

 Based on these analyses, the 

control programs set forth in this chapter would account for 307 MMT CO2EQ of annual 

GHG reduction in the year 2030 and 506 MMT per year in 2050. Fuel savings resulting from 

the GHG standards are projected at 41.5 billion gallons of fuel savings in Calendar Year 2050 

(Table 5-1). 

Table 5-1 Impacts of Program on GHG Emissions and Fuel Savings 

CALENDAR 

YEAR 

ANNUAL GHG 

REDUCTION (CO2

EQ MMT) 

FUEL SAVINGS 

(MILLION BARRELS 

PER DAY OF 

GASOLINE 

EQUIVALENT) 

ANNUAL FUEL 

SAVINGS 

(BILLION GALLONS 

OF GASOLINE 

EQUIVALENT) 

2020 156.3 0.8 12.6 

2030 307.4 1.6 24.7 

2040 401.5 2.1 32.6 

2050 505.9 2.7 41.5 

The emissions of non-GHG air pollutants due to light duty vehicles are also expected 

to be affected by today’s final rule. These effects are due to changes in driver behavior (the 

“rebound effect”)A

 and also reflect ethanol volume assumptions that are not due to the new 

GHG vehicle standards. The delta values shown here include both upstream and downstream 

contributions. 

 

A

 A rebound effect of 10% is used in this analysis. See section 5.3.3.1.1 for a brief definition of rebound, and 

chapter IV of the joint Technical Support Document for a more complete discussion. 
Emissions Impacts 

5-3 

Table 5-2 Impacts of Program on Non-GHG Emissions (Short Tons per year) 

POLLUTANT 

CALENDAR 

YEAR 

2020 

% 

CHANGE 

VS. 2020 

REFEREN

CE 

CALEND

AR YEAR

2030 

% 

CHANGE 

VS. 2030 

REFEREN

CE

∆ 1,3-Butadiene -95.1 -0.38% -21.1 -0.10%

∆ Acetaldehyde 760.0 2.26% 668.1 2.18%

∆ Acrolein 0.8 0.01% 4.7 0.07%

∆ Benzene -889.9 -0.48% -523.1 -0.29%

∆ Carbon Monoxide 3,980.3 0.01% 170,648.6 0.56%

∆ Formaldehyde -49.4 -0.06% 15.1 0.02%

∆ Oxides of Nitrogen -5,916.1 -0.02% -21,845.0 -0.07%

∆ Particulate Matter 

(below 2.5 micrometers) -2,402.9 -0.03% -4,574.8 -0.05%

∆ Oxides of Sulfur -13,853.4 -0.42% -27,492.8 -0.82%

∆ Volatile Organic Compounds -60,305.4 -0.51% -115,816.5 -1.02%

We also analyzed the emission reductions over the full model year lifetime of the 2012-

2016 model year cars and trucks affected by today’s final rule. These results, including both 

upstream and downstream GHG contributions, are presented below (Table 5-3). 

Table 5-3 Model Year Lifetime Fuel Savings and GHG Reductions 

Model Year Lifetime GHG 

Reduction 

(MMT CO2 EQ) 

Lifetime Fuel Savings

(Billion Gallons Of 

Gasoline Equivalent) 

Lifetime Fuel Savings 

(Million Barrels of 

Gasoline Equivalent) 

2012 88.8 7.3 173.1 

2013 130.2 10.5 250.35 

2014 174.2 13.9 330.5 

2015 244.2 19.5 464.7 

2016 324.7 26.5 630.7 

Total 

Program 

Benefit 962.0 77.6 1,849.3 
Regulatory Impact Analysis 

5-4 

5.2 Introduction 

5.2.1 Scope of Analysis 

Today’s program finalizes new standards for the greenhouse gas (GHG) emissions of 

light duty vehicles from model year 2012 through model year 2016. The program affects 

light duty gasoline and diesel fueled vehicles. Most passenger vehicles such as cars, sport 

utility vehicles, vans, and pickup trucks are light duty vehicles. Such vehicles are used for 

both commercial and personal uses and are significant contributors to the total United States 

(U.S.) GHG emission inventory. Today’s final rule will significantly decrease the magnitude 

of these emissions. Because of anticipated changes to driving behavior and fuel production, a 

number of co-pollutants would also be affected by today’s final rule. 

This chapter describes the development of inventories for emissions of the gaseous 

pollutants impacted by the rule. These pollutants are divided into greenhouse gases, or gases 

that in an atmosphere absorb and emit radiation within the thermal infrared range, and nongreenhouse gases. Such impacts may occur "upstream" in the fuel production and distribution 

processes, or "downstream" in direct emissions from the transportation sector. Table 5-4 

presents the processes considered in each domain. This analysis presents the projected 

impacts of today’s final rule on greenhouse gases in calendar years 2020, 2030, 2040 and 

2050. Non-greenhouse gas inventories are shown in 2020 and 2030. The program was 

quantified as the difference in mass emissions between the standards and a reference case as 

described in Section 5.3.2.2. 

Table 5-4 Processes Considered 

PROCESS UPSTREAM / DOWNSTREAM 

Crude Oil Extraction Upstream 

Crude Oil Transport Upstream 

Oil Refining Upstream 

Fuel Transport and Distribution Upstream 

Fuel Tailpipe Emissions Downstream 

Air Conditioning System Leakage Downstream 

Inventories for the four greenhouse gases carbon dioxide (CO2), methane (CH4), nitrous 

oxide (N2O) and hydrofluorocarbons (HFC) are presented herein. The sole HFC discussed in 

this inventory is R-134a, which is the refrigerant in most current vehicle air conditioning 

systems. Inventories for the non-GHG pollutants 1,3-butadiene, acetaldehyde, acrolein, 

benzene, carbon monoxide (CO), formaldehyde, oxides of nitrogen (NOx), particulate matter 

below 2.5 micrometers, oxides of sulfur (SOx), and volatile organic compounds (VOC) are 

also presented. 

5.2.2 Downstream Contributions 

The largest source of GHG reductions from today’s final rule is new standards for 

tailpipe emissions produced during vehicle operation. Absolute reductions from tailpipe 

GHG standards are projected to grow over time as the fleet turns over to vehicles affected by 
Emissions Impacts 

5-5 

the standards, meaning the benefit of the program will continue to grow as long as the older 

vehicles in the fleet are replaced by newer, lower CO2 emitting vehicles. 

As described herein, the downstream reductions in greenhouse gases due to the program 

are anticipated to be achieved through improvements to both fuel economy and air 

conditioning system operation. Improvements to air conditioning systems can be further 

separated into reducing leakage of HFCs (direct improvement) and reducing fuel consumption 

by increasing the efficiency of the air conditioning system (indirect). 

Due to the rebound effect,B

 improving fuel economy is anticipated to increase total 

vehicle miles traveled, which has impacts on both GHG and non-GHG emissions. These 

impacts are detailed in Section 5.3.3.1.1. The implications for non-GHG emissions of 

changes in fuel supply were analyzed for the final rulemaking and are discussed in Section 

5.3.3.5. 

5.2.3 Upstream Contributions 

In addition to downstream emission reductions, reductions are expected in the 

emissions associated with the processes involved in getting petroleum to the pump, including 

the extraction and transportation of crude oil, and the production and distribution of finished 

gasoline. Changes are anticipated in upstream emissions due to the expected reduction in the 

volume of fuel consumed. Less gasoline consumed means less gasoline transported, less 

gasoline refined, and less crude oil extracted and transported to refineries. Thus, there should 

be reductions in the emissions associated with each of these steps in the gasoline production 

and distribution process. 

HFC manufacture is not considered a significant source of upstream emissions and is not 

considered in this analysis.7

5.2.4 Global Warming Potentials 

Throughout this document, in order to refer to the four inventoried greenhouse gases on 

an equivalent basis, Global Warming Potentials (GWPs) are used. In simple terms, GWPs 

provide a common basis with which to combine several gases with different heat trapping 

abilities into a single inventory (Table 5-5). When expressed in CO2 equivalent (CO2 EQ) 

terms, each gas is weighted by its heat trapping ability relative to that of carbon dioxide. The 

GWPs used in this chapter are drawn from publications by the Intergovernmental Panel on 

Climate Change (IPCC).8 

The global warming potentials (GWP) used in this analysis are consistent with the 2007 

Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). At 

this time, the 1996 IPCC Second Assessment Report (SAR) global warming potential values 

 

B

 Described in Joint TSD Chapter 4 
Regulatory Impact Analysis 

5-6 

have been agreed upon as the official U.S. framework for addressing climate change and are 

used in the official U.S. greenhouse gas inventory submission to the United Nations climate 

change framework. This is consistent with the use of the SAR global warming potential 

values in current international agreements. 

Table 5-5 Global Warming Potentials for the Inventory GHGs 

Gas Global Warming potential 

(CO2 Equivalent) 

CO2 1 

CH4 25 

N2O 298 

HFC (R134a) 1430 

5.3 Program Analysis and Modeling Methods 

5.3.1 Models Used 

The inventories presented in this document were developed from established EPA 

models. 

Downstream inventories were generated using algorithms from EPA’s Optimization 

Model for reducing Emissions of Greenhouse gases from Automobiles (OMEGA) in 

conjunction with EPA’s Motor Vehicle Emission Simulator (MOVES2010). Broadly 

speaking, OMEGA 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 benefits analyses 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) of the analyzed program. 

The OMEGA post-processor was updated with emission rates MOVES2010.9,10 CO2

emission and fuel consumption rates are drawn from OMEGA results, with all co-pollutant 

emission rates derived from the MOVES2010 emission rate database. Air conditioning 

inventories (including HFC and CO2 contributions) were separately calculated in spreadsheet 

analyses, and are based on previous EPA research.11 Both MOVES and OMEGA are 

published, publicly available models and continue to be actively developed. 12,13 No public 

comments were received on the selection of either MOVES or the OMEGA post-processor for 

calculating the impacts of this rule. 

Upstream emissions were calculated using the same tools as were used for the 

Renewable Fuel Standard 2 (RFS2) rule analysis,14 but for the current analysis it was assumed 

that all impacts are related to changes in volume of gasoline produced and consumed, with no 
Emissions Impacts 

5-7 

changes in volumes of other petroleum-based fuels, ethanol, or other renewable fuels. The 

estimate of emissions associated with production of gasoline from crude oil is based on 

emission factors in the GREET model developed by DOE's Argonne National Lab.15, 16 The 

actual calculation of the emission inventory impacts of the decreased gasoline production is 

done in EPA's spreadsheet model for upstream emission impacts. This model uses the 

decreased volumes of the crude based fuels and the various crude production and transport 

emission factors from GREET to estimate the net emissions impact. As just noted, the 

analysis for today's rulemaking assumes that all changes in volumes of fuel used affect only 

gasoline, with no effects on use of other petroleum-based fuels, ethanol, or other renewable 

fuels. No public comments were received on EPA’s use of the modified version of GREET in 

this analysis. 

The following sections provide an in-depth description of the inputs and methodology 

used in each analysis. 

5.3.2 Description of Scenarios 

One reference and one control scenario are modeled in this analysis, and each is 

described below.17 The two scenarios shown are differentiated by their regulatory CO2

emission standards. The reference scenario CO2 emissions are based upon the National 

Highway Traffic Safety Administration (NHTSA) Model Year 2011 Corporate Average Fuel 

Economy (CAFE) standards,18 while the control scenario CO2 emissions are based upon the 

program set forth herein. Otherwise, the scenarios share fleet composition, sales, base vehicle 

miles traveled (VMT), and all other relevant aspects. Vehicles are modeled as compliant with 

Tier 2 criteria emission standards. 

As in the proposal, for this analysis we attribute decreased fuel consumption from this 

program to gasoline only, while assuming no effect on volumes of ethanol and other 

renewable fuels because they are mandated under the Renewable Fuel Standard (RFS2). 

However, because this rule does not assume RFS2 volumes of ethanol in the baseline, the 

result is a greater projected market share of E10 in the control case.19 In fact, the GHG 

standards will not be affecting the market share of E10, because EPA’s analysis for the RFS2 

rule predicts 100% E10 penetration by 2014.20

In the proposal, EPA stated these same fuel assumptions and qualitatively noted that 

there were likely unquantified impacts on non-GHG emissions between the two cases. In RIA 

Chapter 5, EPA indicated its plans to quantify these impacts in the air quality modeling and in 

the final rule inventories. Upstream emission impacts depend only on fuel volumes, so the 

impacts presented here reflect only the reduced gasoline consumption. 

The inventories presented in this rulemaking include an analysis of these fuel effects 

which was conducted using EPA’s Motor Vehicle Emission Simulator (MOVES2010). The 

most notable impact, although still relatively slight, is a 2.2 percent increase in 2030 in 

national acetaldehyde emissions over the baseline scenario. It should be noted that these 

emission impacts are not due to the new GHG vehicle standards. These impacts are instead a 

consequence of the assumed ethanol volumes. This program does not mandate an increase in 
Regulatory Impact Analysis 

5-8 

E10, nor any particular fuel blend. The emission impact of this shift was also modeled in the 

RFS2 rule. 

 Ethanol use was modeled at the volumes projected in AEO2007 for the reference 

and control case; thus no changes are projected in upstream emissions related to ethanol 

production and distribution. Due to the lower energy content of ethanol blended gasoline, the 

increase in ethanol market share is also projected to decrease the fuel savings predicted by this 

analysis by less than 1% (Section 5.3.3.5). 

The relationship between fuel composition and emission impacts used in 

MOVES2010 and applied in this analysis match those developed for the recent Renewable 

Fuels Standard (RFS2) requirement, and are extensively documented in the RFS2 RIA and 

supporting documents.21

5.3.2.1 Sales and Fleet Composition 

Fleet composition has a significant effect upon the impacts of the program. 

Consequently, it is significant that the cars and trucks in this analysis are defined differently 

than their historic EPA classifications. Passenger Automobiles (PA), as used herein, are 

defined as classic cars and two-wheel drive SUVs below 6,000 lbs. gross vehicle weight. The 

remaining light duty fleet is defined as Non-Passenger Automobiles (NPA). The NPA 

classification includes most classic light duty trucks such as four-wheel drive SUVs, pickup 

trucks, and similar vehicles. 

As shown in Table 5-6, the vehicle classifications used herein are consistent with the 

definitions used by the National Highway Safety Transit Association in the MY 2011 CAFE 

standards.22 While the formal definitions are lengthy, brief summaries of the classifications 

are shown here. 

Table 5-6 Definitions of Vehicle Classes 

REGULATOR CAR DEFINITION TRUCK DEFINITION 

National Highway Traffic 

Safety Administration CAFE 

Program (pre-MY 2011) 

Classic Car – Passenger Car Classic Truck – Light Duty Trucks 1-4 and 

Medium Duty Passenger Vehicles. 

EPA Program 

(MY 2012+) 

Passenger Automobile – PC 

+ 2 wheel drive SUVs below 

6,000 GVW 

Non-Passenger Automobile – Remaining 

light duty fleet 

As explained in section II.B of the preamble to the final rule and chapter 1 of the Joint 

TSD, EPA updated its fleet projection for the final rulemaking analysis.23 As a result of this 

change, all calculations which depend upon fleet composition (ie, emission inventories, 

impacts of flexibilities, and fuel savings) were updated from those in chapter 5 of the RIA. 

Total volumes of projected sales of classic cars and trucks for calendar years 2012-

2035 were drawn from the Energy Information Administration Annual Energy Outlook 

(AEO) 2010 Early Release projection (December 2010).24 The AEO 2010 Early Release is an 
Emissions Impacts 

5-9 

update of the April 2009 AEO projections used in the proposal analysis.25 Based on EPA 

analysis of the projected MY 2012-2016 fleet,26 approximately 20% of the classic truck fleet 

is anticipated to be reclassified as Passenger Automobiles under the new standards. The 

AEO 2010 sales projections, which are based on the classic fleet, were then reclassified using 

PA and NPA definitions by shifting 19.91% of AEO’s truck sales projection to car sales. For 

calendar years 2035-2050, which are beyond the scope of AEO’s projections, 0.88% annual 

growth in the sales of cars and trucks was assumed. The annual growth rate of 0.88% is the 

average year-on-year sales growth projected by AEO from years 2017-2035. 

A more complete discussion of the process for developing the MY 2012-2016 fleet is 

available in TSD chapter 1. 

Table 5-7 Projected Total Vehicle Sales and Car Fractions 

Model Year 

2012 

Model Year 

2013 

Model Year 

2014 

Model Year 

2015 

Model Year 

2016 

Total Light 

Duty Sales 

 

14,921,031 15,835,190 16,178,725 16,452,676 16,501,102 

Classic Car 

Fraction 

51.8% 52.9% 54.3% 55.8% 57.1%

PA Fraction 59.2% 61.1% 61.9% 63.2% 64.6%

PAs Sold 

 7,922,992 9,123,197 9,797,738 

 

10,231,974 

 

10,627,055 

5.3.2.2 Fleet Average CO2 Targets 

In this section, the term “target” is used to refer to the output of the footprint equations 

described in Preamble Section II. The term “achieved emission level” is similar, but includes 

the impacts of program flexibilities. 

As documented in Preamble Section II, under both reference and control scenarios, 

each manufacturer has a unique fleet average target based on their vehicle footprints and 

production. 

Fleet average targets are calculated by weighting the individual PA and NPA targets 

by the respective proportions of anticipated production (Section 5.3.2.1). These CO2 emission 

values are unadjusted values (i.e. in CAFE space), so they are lower than the anticipated onroad emissions. In all scenarios, post- 2016 vehicles are assumed to maintain model year 

2016 emissions. Because the fleet composition continues to change post-MY 2016, the fleet 

average emission level continues to vary. No public comments were received on this 

methodology. 

Below, PA and NPA tailpipe CO2 fleet average emission targets and achieved 

emission levels during MY 2012-2016 are shown for reference and control scenarios. 
Regulatory Impact Analysis 

5-10 

5.3.2.2.1 Reference Case 

5.3.2.2.1.1 CO2 Emission Targets 

The reference scenario targets were derived from the NHTSA model year 2011 

Corporate Average Fuel Economy (CAFE) standards applied to the MY 2012-2016 reference 

fleet (see chapter 1 of the joint TSD and chapter 4 of the RIA).27 Average car and truck fuel 

economy targets were calculated from the coefficients in the MY 2011 rule and the projected 

MY 2012-2016 fleet.28,29 Average fuel economy targets were calculated for each 

manufacturer’s fleet, and then combined based on projected sales. 

A ratio of 8,887 grams of CO2 emitted per gallon of gasoline was used to convert to 

the calculated fuel economy standards to CO2 (gram/mile) emission factors. The basic 

derivation of the 8,887 factor can be seen in previous EPA publications.30

Minor changes in the emission targets are due to projected changes in the average new 

vehicle footprint between 2012 and 2016 (Table 5-8). 

Table 5-8 Reference Case Average Emission Targets (grams/mile CO2) 

MODEL 

YEAR 

PA 

EMISSION 

LEVEL 

NPA EMISSION 

LEVEL 

MY EMISSION 

LEVEL 

2012 292 365 320 

2013 291 365 319 

2014 291 364 318 

2015 292 364 317 

2016 292 364 316 

5.3.2.2.1.2 Achieved CO2 Emission Levels 

The emission targets shown in Table 5-8 do not reflect the impact of several program 

flexibilities in CAFE program, nor do they account for manufacturer overcompliance. 

Projected achieved emission levels include the effects of manufacturers who pay fines rather 

than comply with the emission standards, as well as a number of credit programs under 

EPCA/EISA that allow manufacturers to emit more than the standard otherwise allows. 

Additionally, some manufacturers overcomply with the standards, and this overcompliance is 

not reflected in the CAFE targets. 

While the CAFE program is complex, the most significant portions of the program 

flexibilities were accounted for. In this analysis, manufacturer overcompliance, credit trading, 

FFV credits, and fine paying manufacturers were included. Credit banking was excluded. 

In general, achieved emission levels were estimated by beginning with the more 

stringent of either (A) a manufacturer’s CAFE target (in CO2 space) or (B) estimated actual 
Emissions Impacts 

5-11 

MY 2008 CO2 emission levels based on the EPA fleet data file. Using that starting point, 

each manufacturer’s emissions was increased by the impact of the credits of which it is 

anticipated that they will take advantage. Consistent with the use of the MY 2011 standards, 

the credits and trading limits available for MY 2011 were assumed available in all years of the 

reference case. Manufacturers were always assumed to perform at least as well as they did in 

2008. 

Overcompliance and Credit Transfers 

Using the EPA fleet file, the fleet mix was estimated by manufacturer for model year 

2012 through model year 2016. For each model year, the CAFE target (in CO2 space) was 

calculated by manufacturer for PA and NPA separately. To estimate the effects of 

overcompliance, each manufacturer’s achieved 2008 PA/NPA emissions were compared 

against the PA/NPA emissions required by CAFE in 2011. 

The overcompliance on either PA or NPA could be “transfered” within a manufacturer 

in order to make up a shortfall in the remaining vehicle class. Credits are generated on a sales 

and VMT weighted basis, and transfered between vehicle classes. The MY 2011 CAFE cap 

on credit trading of 1.0 mpg was used. This trading of the overcompliance credit negates 

some, but not all of the overcompliance anticipated. Certain manufacturers, such as Toyota 

and Honda, overcomply by a great deal more than they are able to transfer between vehicle 

classes. 

Flex Fueled Vehicle Credits

The 2007 Energy Independence and Security Act allows for CAFE credits due to 

production of “flex-fueled” vehicles. Under the model year 2011 standards, such credits can 

be used to meet up to 1.2 MPG of the CAFE standard. The manufacturers General Motors, 

Chrysler and Ford were assumed to take advantage of this credit for both cars and trucks, 

while Nissan was assumed to utilize this credit solely for trucks. 

Fines 

In this analysis, EPA used estimates of fine paying manufacturers from NHTSA’s 

Volpe model. That model supplied projected maximum stringencies that a manufacturer 

would meet before it was more cost effective to pay a non-compliance fine. The 

manufacturers who are projected to pay fines are Tata, Daimler, BMW, Porsche, and 

Volkswagen. 

The projected achieved levels based on program flexibilities and manufacturer 

overcompliance are shown in 

Table 5-10. 
Regulatory Impact Analysis 

5-12 

Table 5-9 Impacts of credits (grams/mile CO2 EQ) 

MODEL 

YEAR 

OVERCOMPLIANCE, 

CREDITS AND 

TRANSFERS 

FFV FINES NET 

2012 -5.1 7.5 1.0 3.5

2013 -6.1 7.1 0.6 1.5

2014 -6.4 6.6 0.2 0.3

2015 -6.7 6.4 0.1 -0.2

2016 -7.0 6.3 0.1 -0.6

Table 5-10 Reference Case Achieved Emissions (grams/mile CO2) 

MODEL 

YEAR 

ANTICIPATED 

PA EMISSION 

LEVEL 

ANTICIPATED 

NPA EMISSION 

LEVEL 

ANTICIPATED 

MY EMISSION 

LEVEL 

2012 286 383 324 

2013 284 381 321 

2014 283 379 318 

2015 283 378 317 

2016 283 378 316 

5.3.2.2.2 Control Case 

5.3.2.2.2.1 CO2 Emission Standards 

Similar to the reformed CAFE program, EPA is establishing a footprint attribute based 

function in order to determine the CO2 (gram/mile) emission standard for a given vehicle. 

The piecewise linear function used by EPA is documented in Section II.B of the preamble to 

the final rule. Based on this function, and the same vehicle fleet as was used in the reference 

scenario, EPA calculated projected PA and NPA fleet average emission targets for the 

MY2012-2016 vehicles (Table 5-11).31

 

 Table 5-11 Control Case Average Emission Targets (grams/mile CO2) 

MODEL 

YEAR 

PA 

EMISSION 

LEVEL 

NPA EMISSION 

LEVEL 

PROJECTED 

MY EMISSION 

TARGET 

2012 263 346 295 

2013 256 337 286 

2014 247 327 276 
Emissions Impacts 

5-13 

2015 236 312 263 

2016 225 298 250 

 

5.3.2.2.2.2 Achieved CO2 Emission Levels 

Just as with the reference scenario, the control case emission targets (Table 5-11) do 

not include the effect of several flexibilities built into the EPA program. 

The same basic methodology was used to calculate achieved fleet emission levels for 

the control case as in the reference case. In general, achieved emission levels were estimated 

by beginning with the more stringent of either (A) a manufacturer’s calculated footprint-based 

emission target or (B) the estimated achieved CO2 level based on the EPA fleet data file. 

Using that starting point, each manufacturer’s emissions were increased by the impact of the 

credits which we anticipate manufacturers will utilize. Manufacturers were always assumed to 

perform at least as well as they did in 2008. 

Overcompliance and Credit Transfers

Using the EPA fleet file, the fleet mix was estimated by manufacturer for model year 

2012 through model year 2016. For each model year, the GHG standard was calculated by 

manufacturer for PA and NPA separately. To estimate the effects of overcompliance, each 

manufacturer’s achieved PA/NPA emissions was compared against the PA/NPA emissions 

required by their target. 

The achieved overcompliance on either PA or NPA could be “transfered” within a 

manufacturer in order to make up a shortfall in the remaining vehicle class. Credits are 

generated on a sales and VMT weighted basis, and traded between vehicle classes. Under the 

EPA program, there are no limits within the light duty fleet on such trading.C

 This 

transferance of the overcompliance credit negates nearly all of the overcompliance anticipated 

in the early years. 

Under the unlimited within-fleet trading allowed under the EPA program, 

manufacturers can potentially invest in their fleet differently than the precise achieved levels 

described here. Because the credit transfers are VMT weighted, the resulting changes will be 

essentially environmentally neutral on both GHG and criteria pollutants. 

Flex Fueled Vehicles

The flex fueled vehicle credit,consistent with the final rule is set at 1.2 MPG for MY 

2012-2014, 1.0 MPG for MY 2015, and 0 MPG for MY 2016+. See also preamble section 

 

C

 Preamble section III.B and III.C discusses credit transfers in more detail, including limits on credit life and 

various other restrictions. 
Regulatory Impact Analysis 

5-14 

III.C.2 As in the reference case, it was assumed that the manufacturers General Motors, 

Chrysler and Ford would utilize this credit for both cars and trucks, while Nissan would 

utilize this credit solely for trucks. 

A/C

 Indirect A/C credits were set at 5.7 grams CO2 per mile for the fleet, while direct A/C 

credits were set at 6.3 grams CO2 per mile for PA and 7.8 grams CO2 per mile for NPA). In 

the proposal, we noted the inconsistent values between the direct A/C credit presented here, 

and the direct A/C credit discussed in RIA Chapter 2. We corrected this minor inconsistency 

for this FRM analysis. EPA assumed market penetration of the technology according to Table 

5-17. A more complete discussion of the A/C credit program and inventories is provided in 

section 5.3.3.2, as well as RIA chapter 2. 

Temporary Lead Time Allowance Alternative Standards (TLAAS)

 In response to public comment, the TLAAS program includes certain additional 

features which expand its range. See preamble section III.B.5 and Appendix A to this RIA 

chapter. Specifically, the TLAAS program has been expanded into two distinct tiers, which 

are manufacturers with fewer than 400,000 sales and manufacturers with fewer than 50,000 

sales. Manufacturers with less than 5,000 vehicles in sales were also temporarily exempted 

from this rulemaking. A brief summaryof the inputs used in this analysis appear below. For 

more on the TLAAS program, please see Appendix A to this RIA chapter. 

For the larger manufacturers, we assumed that every potentially eligible manufacturer 

utilized the TLAAS program. Each qualifying manufacturer was assumed to use the full 

vehicle allocation according to the default production schedule shown in Section III.B.5 of the 

proposal preamble and reproduced in Table 5-12. 

Table 5-12 – TLAAS default production schedule 

MODEL 

YEAR 

2012 2013 2014 2015 

Sales Volume 40,000 30,000 20,000 10,000 
Emissions Impacts 

5-15 

 The allocation was split evenly between cars and trucks for each manufacturer. For 

these companies, this vehicle allocation was assumed to emit as much CO2 per mile as the 

highest emitting car or truck in each manufacturer’s fleet. 

For the smaller manufacturers, the program was expanded to allow 50,000 vehicles in 

2016, and 200,000 vehicles though 2015. These fleets were assumed to gradually phase into 

compliance. These TLAAS fleets are assumed to emit 1.25x more emissions than the 

manufacturer’s sales weighted target in 2012, and by 2016, they were assumed to emit 1.05x 

more emissions. 

 In each case, the TLAAS vehicles were then proportionally averaged into the 

manufacturer’s achieved emission level. 
Regulatory Impact Analysis 

5-16 

The aggregate impacts of these program flexibilities are listed in Table 5-13. 

Table 5-13 Estimated Impacts of Program Flexibilities (grams/mile CO2 EQ) 

MODEL 

YEAR 

OVERCOMPLIANCE, 

CREDITS AND 

TRANSFERS 

FFV DIRECT 

A/C 

INDIRECT 

A/C 

TLAAS NET 

2012 -0.1 6.5 1.7 1.4 1.2 10.7 

2013 0.0 5.8 2.7 2.3 0.9 11.7 

2014 0.0 5.0 4.1 3.4 0.6 13.1 

2015 0.0 3.7 5.5 4.6 0.3 14.0 

2016 0.0 0.0 5.8 4.8 0.1 10.7 

Based on these impacts, the achieved emission level by PA, NPA and fleet are 

displayed in Table 5-14. Please note that the achieved emission levels include the increase in 

test procedure emissions due to the use of the A/C credit. The impacts of A/C improvements 

are discussed in section 5.3.3.2. 

Table 5-14 Federal GHG Program Anticipated Emission Levels (grams/mile CO2) 

MODEL 

YEAR 

ANTICIPATED 

PA EMISSION 

LEVEL 

ANTICIPATED 

NPA EMISSION 

LEVEL 

ANTICIPATED 

MY EMISSION 

LEVEL 

2012 270 365 307 

2013 264 354 298 

2014 258 344 290 

2015 248 330 277 

2016 236 309 261 

 Table 5-14 differs slightly from the OMEGA cost-side model results in 2016. 

OMEGA assumes environmentally neutral trading between PA and NPA within a 

manufacturer’s fleet in order to minimize technology costs. Consequently, the distribution of 

fleet emission reductions differs slightly between cars and trucks from that which is shown 

here. However, because the trading is VMT weighted, it is environmentally neutral and has 

no GHG emissions impacts. 

As in the proposal, the OMEGA also predicts slight undercompliance in 2016 for 

several manufacturers, while the results presented here assume full compliance. The net 

undercompliance is approximately 0.8 grams in 2016. A more complete discussion of the 

OMEGA cost modeling is available in RIA chapter 4. 

5.3.3 Calculation of Downstream Emissions 

As stated in Section 5.1, the downstream analysis conducted in the proposal has been 

updated in the analysis shown here. To reiterate, the 2012-2016 CO2 standards (i.e., the 
Emissions Impacts 

5-17 

attribute-based curves for cars and light trucks) were revised slightly,D

 and several tools were 

updated; Draft MOVES2009 was updated to MOVES2010 and minor changes were made to 

the OMEGA post-processor. Beyond these changes, the analysis of GHG emissions was 

similarly conducted between NPRM and FRM. While public comments were received on 

several of the economic inputs used in the modeling (see TSD chapter 4), no substantive 

comments were received concerning the methodology or resulting inventories. 

As mentioned in Section 5.2.2, the analysis of non-GHG emissions was updated to 

include fuel effects. The FRM upstream analysis was updated with the new fuel savings 

volumes, but is otherwise unchanged. 

A model year lifetime analysis, considering only the five model years 

specifically regulated by the program, is shown in Section 5.6. In contrast to the 

calendar year analysis, the model year lifetime analysis shows the lifetime impacts of 

the program on each MY fleet over the course of that fleet’s existence. 

5.3.3.1 Calculation of Tailpipe CO2 Emissions 

The fleet inputs (achieved CO2 emission levels by model year and vehicle 

sales) were incorporated into a spreadsheet along with emission rates derived from 

MOVES2010 and benefits calculations from the OMEGA post-processor. The 

resulting spreadsheet projects emission impacts in each calendar year. The effects of 

the program grow over time as the fleet turns over to vehicles subject to the more 

stringent new standards. 

 Two basic elements feed into OMEGA’s calculation of vehicle tailpipe 

emissions. These elements are VMT and emission rates. 

Total Emissions = VMT miles * Emission rate grams/mile

 Equation 1 - 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 are applied to 

each model year within the calendar year. Emissions are then summed across all model years. 

The following sections describe the VMT and emission factor components of this 

analysis. 

 

D

 See Preamble Section II.B 
Regulatory Impact Analysis 

5-18 

5.3.3.1.1 Base VMT 

The downstream analysis is based upon a “bottom-up” estimate of total VMT 

and vehicle population. The VMT inputs are documented more fully in joint TSD 

chapter 4, but a description of their use in the emissions calculations are provided 

below. 

The analysis spreadsheet contains MY-specific estimates of per-vehicle VMT 

by vehicle age, as well as the fractions of new vehicles still on the road as a function 

of age. The total VMT for vehicles in a specific model year during a specific calendar 

year is determined by multiplying 1) new vehicle sales for that model year, 2) the 

fraction of new vehicles remaining on the road according to the age of those vehicles 

in that calendar year and 3) the annual VMT accumulation schedule for that vehicle 

class, model year, and age. 

Future vehicle sales were drawn from AEO 2010 Early Release (as discussed 

in Section 5.3.2.1), while historic vehicle sales are drawn from the Transportation 

Energy Data Book,32 Post MY 2011 vehicles were reclassified in order to correspond 

to the PA/NPA definitions. 

As described in the TSD, mileage accumulation by age was calculated using 

inputs from the NHTSA “Vehicle Survivability and Travel Mileage Schedules” and 

additional inputs unique to this analysis.33,34 In brief, a 1.15% per vehicle annual 

VMT growth rate was assumed, but additional factors such as achieved fuel 

consumption and the price of gasoline also contributed to the precise schedule for each 

MY. 

The vehicle survival schedule was taken without emendation from “Vehicle 

Survivability and Travel Mileage Schedules.” While adjustments may be necessary to 

this schedule to accommodate the change between classic cars/trucks and PA/NPA, 

EPA is unaware of any extant data supporting specific adjustments. Because of the 

lack of data, the survival rates from “Vehicle Survivability and Travel Mileage 

Schedules” were used without further adjustment (Table 5-15).35
Emissions Impacts 

5-19 

Table 5-15 Survival Fraction by Age 

AGE PA SURVIVAL 

FRACTION 

NPA 

SURVIVAL 

FRACTION 

0 0.9950 0.9950 

1 0.9900 0.9741 

2 0.9831 0.9603 

3 0.9731 0.9420 

4 0.9593 0.9190 

5 0.9413 0.8913 

6 0.9188 0.8590 

7 0.8918 0.8226 

8 0.8604 0.7827 

9 0.8252 0.7401 

10 0.7866 0.6956 

11 0.7170 0.6501 

12 0.6125 0.6042 

13 0.5094 0.5517 

14 0.4142 0.5009 

15 0.3308 0.4522 

16 0.2604 0.4062 

17 0.2028 0.3633 

18 0.1565 0.3236 

19 0.1200 0.2873 

20 0.0916 0.2542 

21 0.0696 0.2244 

22 0.0527 0.1975 

23 0.0399 0.1735 

24 0.0301 0.1522 

25 0.0227 0.1332 

26 0.0000 0.1165 

27 0.0000 0.1017 

28 0.0000 0.0887 

29 0.0000 0.0773 

30 0.0000 0.0673 

31 0.0000 0.0586 

32 0.0000 0.0509 

33 0.0000 0.0443 

34 0.0000 0.0385 

35 0.0000 0.0334 
Regulatory Impact Analysis 

5-20 

A complete discussion of the derivation of the MY specific VMT schedules is 

provided in joint TSD chapter 4. 

5.3.3.1.2 Rebound 

The tailpipe CO2 standards are expected to result in greater fuel efficiency. Per 

the discussion of the rebound effect in the joint TSD chapter 4, improved fuel 

efficiency is expected to lead to a proportional increase in VMT. Consequently, the 

VMT differs between the reference and control cases. 

The rebound effect is formally defined as the ratio of the percentage change in 

VMT to the percentage change in incremental driving cost, which is typically assumed 

to be the incremental cost of fuel consumed per mile. Since VMT increases with a 

reduction in fuel consumption, the sign of the rebound effect is negative. The 

percentage increase in VMT for a given change in fuel consumption per mile is 

calculated as follows: 

( )

old

old new

reb FleetFC

FleetFC FleetFC VMT REB −

∆% −= *

Equation 2 - VMT Rebound 

As fuel consumption changes by model year, each model year’s vehicles 

reflect a different change in VMT. In OMEGA, this change in VMT is assumed to 

continue throughout the life of the vehicle, which is consistent with the assumption 

that fuel economy is constant throughout vehicle life. 

This analysis assumes a 10% rebound effect; the analysis supporting that figure 

is explored in greater depth in chapter 4 of the joint TSD. 

5.3.3.1.3 Emission Factors 

The derivation of the emission factors used in this analysis is documented in chapter 4 

of the technical support document. Briefly, CO2 emission rates are derived from the achieved 

vehicle emission levels in 

Table 5-10 & Table 5-14, SO2 emission rates are derived from fuel sulfur levels, and 

the emission rates for the remaining pollutants are derived from the MOVES2010 database. 

For a more complete discussion of these emission rates, please refer to joint TSD chapter 4.36 

EPA is not projecting any reductions in tailpipe CH4 or N2O emissions as a result of 

these emission caps.Similar to other pollutants, there are downstream emission impacts due to 

changes in fuel supply and increased driving (rebound), as well as upstream impacts due to 

decreases in fuel production, transport, and distribution. 
Emissions Impacts 

5-21 

5.3.3.1.4 Tailpipe CO2 Emissions from Vehicles 

CO2 emission rates were derived from the achieved CO2 emission levels in 

Table 5-10 & Table 5-14. Previous EPA analysis has shown that an 

approximately 20% gap exists between CAFE space fuel economy and on-road fuel 

economy.37 The on-road gap is more fully documented in the joint TSD chapter 4. 

The 20% gap, while approximate, includes average effects of energy 

consumption contributors such as road roughness, wind, and high acceleration events. 

The gap also reflects the different energy content between certification fuel and real 

world fuel (which frequently contains some oxygenate or ethanol.), as well as the CO2

emission impacts of running a mobile vehicle air conditioning system. In this 

analysis, CO2 emissions are assumed to remain unchanged throughout the vehicle’s 

lifetime. 

By dividing a CAFE-space CO2 emission rate by one minus the on-road gap, 

one can approximate the actual on-road CO2 emission rate experienced by drivers, and 

this analysis used this means of reflecting the on-road gap. By including VMT, we 

estimate the on-road tailpipe CO2 emissions. 

On road tailpipe CO2 emissions = 

Achieved CO2 Emission Level / (1-on-road gap) x VMT including rebound 

Equation 3 - Tailpipe CO2 Emissions Excluding A/C

Based on Equation 3, the baseline CO2 emissions and change in tailpipe 

emissions due to the new control program were calculated. Emissions due to rebound 

were also calculated. The contributions of the A/C control program are excluded from 

this table. 

Table 5-16 Tailpipe CO2 Emissions including Baseline A/C Usage (MMT) 

2020 2030 2040 2050 

Tailpipe CO2 Emissions 

(Reference) 1,173 1,313 1,609 2,030 

∆ Tailpipe CO2 Emissions 

(Control) including 10% 

rebound -101.2 -199.6 -263.4 -335.3 

∆ Tailpipe CO2 Emissions 

due to 10% rebound 10.3 19.9 26.1 33.2 

5.3.3.2 Air Conditioning Emissions 

Outside of the tailpipe CO2 emissions directly attributable to driving, EPA has 

analyzed how new control measures might be developed for air conditioning (“A/C”) related 

emissions of HFCs and CO2. With regard to air conditioning-related emissions, significant 
Regulatory Impact Analysis 

5-22 

opportunity exists to reduce HFC emissions from refrigerant leakage (direct emissions) and 

CO2 from A/C induced engine loads (indirect emissions). 

Over 95% of the new cars and light trucks in the U.S. are equipped with A/C systems. 

There are two mechanisms by which A/C systems contribute to the emissions of GHGs. The 

first is through direct leakage of refrigerant (currently the HFC compound R134a) into the air. 

Based on the high GWP of HFCs (Table 5-5), a small leakage of the refrigerant has a greater 

global warming impact than a similar amount of emissions from other mobile source GHGs. 

Leakage can occur slowly through seals, gaskets, hose permeation and even small failures in 

the containment of the refrigerant, or more quickly through rapid component deterioration, 

vehicle accidents or during maintenance and end of-life vehicle scrappage (especially when 

refrigerant capture and recycling programs are less efficient). The leakage emissions can be 

reduced through the choice of leak-tight, durable components, or the global warming impact 

of leakage emissions can be addressed by using an alternative refrigerant with lower GWP. 

These options are described more fully in RIA Chapter 2. 

EPA’s analysis, shown in RIA chapter 2, indicates that A/C- related emissions 

accounted for approximately 8% of the GHG emissions from in-use cars and light trucks in 

2005. EPA is finalizing credit provisions which we expect all manufacturers to utilize which 

are expected to reduce direct leakage emissions by 50% and to reduce indirect A/C emissions 

(A/C related CO2 tailpipe emissions) by 40% in model year 2016 vehicles, with a gradual 

phase-in starting in model year 2012. It is appropriate to separate the discussion of these two 

categories of A/C-related emissions because of the fundamental differences in the emission 

mechanisms and the methods of emission control. Refrigerant leakage control is akin in many 

respects to past EPA fuel evaporation control programs in that containment of a fluid is the 

key control feature, while efficiency improvements are more similar to the vehicle-based 

control of CO2 in that they would be achieved through specific hardware and controls. 

The anticipated phase-in of air conditioning controls is shown in Table 5-17. The 85% 

cap is roughly linearized across the five year period (Table 5-17). Because HFC leakage is 

somewhat independent of vehicle miles traveled, the HFC fraction is based upon the 

proportion of new vehicles that have HFC leakage containment technology. By contrast, the 

indirect A/C reduction fraction is dependent upon the travel fraction, and is proportional to the 

VMT traveled by vehicles with the control technology. 
Emissions Impacts 

5-23 

Table 5-17 – AC Control by Model Year (Reduction from Base Emissions) 

MY 

2012 

MY 

2013 

MY 

2014 

MY 

2015 

MY 

2016+ 

Market Penetration of technology 25%E40% 60% 80% 85%

HFC Reduction % -13% -21% -30% -40% -43%

Indirect Reduction % -10% -16% -24% -32% -34%

5.3.3.2.1 Direct A/C (HFC) Emissions 

The projected HFC baseline inventories are derived from previous EPA analyses.38 

The methodology used in the proposal was updated with the new estimates of vehicle sales 

and miles traveled. 

As noted, HFC emissions are a leakage type emission, similar to other evaporative 

emissions from a vehicle.39 Consequently, HFC emissions are tied more closely to vehicle 

stock than to VMT. 

To calculate HFC emissions, the per-vehicle per-year emission contribution of the 

current vehicle fleet was determined using averaged 2005 and 2006 registration data from the 

Transportation Energy Databook (TEDB)40 and 2005 and 2006 mobile HFC leakage estimates 

from the EPA Emissions and Sinks report. This per-vehicle per-year contribution was then 

scaled to the projected vehicle fleet in each future year using data from the emission modeling 

analysis. This analysis assumes that the leakage rates of the current fleet remain constant into 

the future. As noted in the proposal and reiterated here, preliminary EPA analysis indicates 

that air conditioner charge size is decreasing, which implies that the analysis presented here 

may overstate the HFC emission inventory. 

The resulting HFC inventory is a combination top-down/bottom up inventory and 

includes leakage, maintenance/servicing, and disposal/end of life phases of HFC. The EPA 

program is expected to impact only two of these phases of the HFC inventory by reducing 

leakage and reducing need for servicing. 

The vehicle population model from the emission analysis was used to calculate the 

penetration of the technology into the market by calendar year. The equation used for 

calculating the reductions in HFC is shown below ( Equation 4). 

 

E

 In Preamble Section III, the expected penetration of A/C control technology is shown to be 28% in MY 2012. 

The slightly lower penetration number used in the emission modeling indicates a slight underestimation of the 

emission reductions from MY 2012, and consequently the benefits from this rule. 
Regulatory Impact Analysis 

5-24 

Emissions Reductions = Reduction % by Calendar Year x Total CY inventory 

Reduction % by CY = ∑Calendar Year (Reduction % by MY x Vehicle Population by MY)/Total Vehicle Population

 Equation 4 - HFC Inventory Calculation

Table 5-18 shows baseline HFC inventory and control scenario reductions. 

Table 5-18 HFC (Direct A/C) Emissions 

Calendar

Year 

Baseline HFC 

(MMT CO2EQ) 

Reduction From 

Baseline 

(%) 

Reduction from 

Baseline 

(MMT CO2EQ) 

2010 56.9 0% 0.0 

2020 61.3 -22% -13.3 

2030 67.8 -38% -26.0 

2040 73.7 -42% -30.9 

2050 80.4 -42% -34.2 

5.3.3.2.2 Indirect A/C (CO2) Emissions 

By adding an additional load to the powertrain, A/C indirectly causes an increase in 

tailpipe CO2 emissions. Thus, where HFC inventory is proportional to vehicle population, the 

indirect A/C emission inventory is proportional to VMT of those vehicles. Because newer 

vehicles are assumed to be driven more, indirect A/C control technology benefits the fleet 

more quickly than HFC control technology. 

The emission rates for indirect A/C usage were taken from the EPA analysis 

documented in RIA chapter 2. There, indirect A/C usage is calculated to add 14.25 grams of 

CO2 emissions to the certification emissions of either cars or trucks. The indirect A/C 

controls put forth in the rule are estimated to remove up to 40% of the emission impact of air 

conditioning systems, or 5.7 grams per mile. 

 The methodology used in the proposal was updated with the new estimates of vehicle 

sales and mileage traveled. The OMEGA post processor was used to calculate the 

contribution of the indirect A/C program to the overall inventory. Reference and control 

scenario emissions attributable to indirect A/C systems are shown in Table 5-19. 

Table 5-19 –Indirect A/C Emissions 

Calendar

Year 

Baseline Indirect A/C 

(MMT CO2EQ) 

Reduction From 

Baseline 

(%) 

Reduction from 

Baseline 

(MMT CO2EQ) 

2010 53.1 -0% 0 

2020 53.6 -20% -10.6 

2030 63.1 -32% -20.2 

2040 78.5 -34% -26.5 
Emissions Impacts 

5-25 

2050 99.3 -34% -33.8 

It should be noted that the baseline indirect A/C emissions are included within the onroad adjustment factor. The baseline inventory is not double counted when aggregating the 

components of this program. 

5.3.3.3 Tailpipe Methane (CH4) and Nitrous Oxide (N2O) Emissions from Vehicles 

MOVES2010 does not include fuel effects for either nitrous oxide or methane emissions. 

Therefore, the only modeled difference in N2O and CH4 between control and reference cases 

are emissions which occur during rebound driving. These emissions, like all rebound 

emissions, were calculated in the modified OMEGA post-processor. 

 The reference inventories shown in Table 5-20 were calculate using MOVES2010 as 

described in Section 5.3.3.5.1. 

Table 5-20 Downstream CH4 and N2O Emissions (Metric Tons) 

Reference Emissions Control Emissions 

(including rebound) 

Delta Emissions 

2020 2030 2020 2030 2020 2030 

Gasoline Vehicles

CH4 41,828 44,464 42,130 45,096 302 632

N2O 29,898 21,620 29,898 21,904 135 284

 

Diesel Vehicles 

CH4 661.6 810.3 661.7 810.6 0.1 0.3

N2O 829.8 999.3 830.0 999.7 0.2 0.4

5.3.3.4 Fuel Savings 

The EPA program is anticipated to create significant fuel savings as compared to the 

reference case. Projected fuel savings are shown in Table 5-21. Fuel savings can be 

calculated from total tailpipe CO2 avoided (including CO2 due to driving and indirect A/C 

use) using a conversion factor of 8887 grams of CO2 per gallon of gasoline. All fuel saved is 

considered 100% gasoline without any oxygenate.F,41 

Fuel savings were calculated from total tailpipe CO2 avoided (including CO2 due to 

driving and indirect A/C) using a conversion factor of 8887 grams of CO2 per gallon of 

gasoline. 42

 

F

 Based on the documentation of the on-road gap, it would be justifiable to assume an ethanol percentage of 

approximately 2.3%. This volume of ethanol would result in a total energy difference of less than 1%. See the 

fuel labeling rule technical support document, EPA420-R-06-017, for further details. 
Regulatory Impact Analysis 

5-26 

Table 5-21 - Fuel Consumption Changes by Calendar Year 

(Billions of Gallons of Gasoline Equivalent) 

2020 2030 2040 2050 

Fuel Consumption (Reference) 137.9 154.8 190 239.6 

∆ Total Fuel Consumption due 

to EPA Program -12.6 -24.7 -32.6 -41.6 

∆ Fuel Consumption due to 10% 

rebound 1.2 2.2 2.9 3.7 

∆ Fuel Consumption due to A/C 

controls -1.2 -2.3 -3.0 -3.8 

5.3.3.5 Downstream Criteria and Air Toxic Emissions 

This rule affects tailpipe co-pollutant emissions in two significant ways. The first, 

modeled in the proposal, is an increase in emissions due to the rebound effect. The second 

effect, newly analyzed here, is an increase in the market share of ethanol blended gasoline. 

As modeled, this rule will reduce the consumption and production of gasoline (E0), while the 

production of ethanol is held constant due to the Renewable Fuel Standards. Consequently, 

the fraction of fuel which is blended to 10% ethanol (E10) is assumed to increase. However, 

the increased E10 market share is projected to occur regardless of this rule; in the RFS2 

analysis we project 100% E10 by 2014. These fuel effects were not quantified in the proposal; 

as a result the proposal emission inventories differ from those shown here. 

For today’s analysis, MOVES2010 was used to generate base inventories for both 

reference and control fuel supplies using a single base VMT. Using the control scenario fuel 

supply, the OMEGA post-processor provided rebound emission quantities. 

5.3.3.5.1 Base Criteria and Air Toxic Emission Inventories 

MOVES2010 was run in the following manner in order to provide base inventories for 

both reference and control cases. 

The fuel supplies in each case were calculated by sequentially: 

A) estimating the light duty energy demand in 2020 and 2030 based on the 

 energy consumption projections from this rule. 

B) determining the total energy demand from light duty, heavy duty, motorcycle 

and non-road sources from AEO and other reference sources 

C) determining the total ethanol volume from AEO 2007, which does not include 

increased renewable fuel volumes due to EISA.G

 

G

 Due to the long lead times required for this analysis, it was completed before the second Renewable Fuel 

Standard (RFS2) was signed. The increased renewable fuel volume attributable to this regulation is therefore not 

assumed in this analysis. 
Emissions Impacts 

5-27 

D) calculating E10 market share 

The resulting 2020 and 2030 E10 market shares are shown in Table 5-22. 

Table 5-22 E10 market shares 

Calendar

Year 

Reference Case 

E10 fraction 

Control Case 

E10 Fraction 

Delta 

2020 85% 92% 7% 

2030 82% 97% 15%H

For simplicity, we used the same MOVES fuel supply table as used in the Renewable 

Fuel Standard 2 analysis as our reference case fuel supply. This table is approximately 90% 

E10. For the control case, we created a table where 100% of the fuel supply was E10. These 

tables were used in both of the modeled years (2020 and 2030). This slightly overstates the 

delta in E10 usage in 2020 and slightly understates the delta in 2030. The simplified analysis 

still captures the approximate deltas shown in Table 5-22. 

To maintain consistency with the MOVES runs conducted for the Air Quality analysis, 

temperatures from 2005 were also input to MOVES, and used in both years of analysis. 

Four separate MOVES runs were conducted. All valid sourcetype/fuel type 

combinations in the MOVES2010 database were included in the MOVES runs. For each of 

the control case and reference case, one run was used to calculate evaporative emissions, and 

one run was set for all other processes (running exhaust, start exhaust, brakewear, tirewear, 

crankcase, refueling, and extended idle). Diesel toxic emissions were not produced by the 

MOVES model, but were post-processed from VOC emissions using published ratios 

 

H As the production of petroleum based fuels decreases, the market share of E10 is projected to 

gradually increase. E10 has slightly less energy than E0, a consequence of which may be a slight 

reduction in the quantity of retail gasoline gallons saved by this rule. The total energy savings would 

remain as predicted by this rule. 

Assuming that a gallon of ethanol contains approximately 77,000 BTU of energy and that a gallon of 

gasoline contains approximately 115,000, a gallon of E10 contains 3.3% less energy than a gallon of 

gasoline. A 15% increase in E10 market share in 2030, as described in table 5-22, would indicate that 

the average gallon of gasoline sold in the control case in 2030 would contain 0.5% less energy than in 

the reference case. This difference in energy content would be less in the near term, before the 

program is fully phased in. 

All else being equal, the difference in energy content would result in additional gallons of fuel being 

purchased to meet the energy demands of the control case. Assuming that gasoline prices would not 

be affected by ethanol or energy content, this would result in a very slight overestimate of the 

monetized fuel savings predicted by this rule and discussed in RIA Chapter 6. 
Regulatory Impact Analysis 

5-28 

identical to those in MOVES.43 All emission results except for evaporative emissions were 

post-processed and scaled based on the ratios of VMT between the MOVES output and the 

VMT used for air quality modeling. Evaporative emissions, which are largely dependent on 

vehicle population, were not scaled. As the air quality modeling was only conducted in 2030, 

2020 VMT was created by scaling the air quality modeling VMT by the ratio of 2020 to 2030 

VMT in MOVES. The resulting factor (82%) was universally applied to the 2030 VMT to 

produce 2020 VMT for each sourcetype. 

The VMT developed for air quality modeling is more completely documented in 

Section 5.8. 

5.3.3.5.2 Criteria and Air Toxic Emissions due to the Rebound Effect 

As a result of the additional rebound VMT, the downstream emissions of several copollutants increase in the control case. The emissions due to rebound were calculated in the 

OMEGA post-processor in a similar manner to the CO2 emissions. Rebound VMT was 

broken into distribution by vehicle age and was then multiplied by the appropriate emission 

factor. These emissions by age were then summed by calendar year (Equation 5). 

EmissionsCalendar Year = ∑Calendar Year (Rebound VMT by Age * Emission Factor by Age ) 

Equation 5 - Emissions by Calendar Year 

The EPA reference fleet assumes a small number of diesel vehicles are sold in each 

year (approximately 20 thousand vehicles out of approximately13-16 million). For the 

analysis of criteria emissions due to the rebound effect, it was assumed that 0.5% of new light 

duty vehicles sold were diesels. Because diesel fueled vehicles are subject to the same Tier 2 

emission standards as gasoline fueled vehicles, the emission rates of criteria pollutants are 

similar.I

5.3.3.5.3 Tabulation of Downstream Criteria and Air Toxic Impacts 

This section contains a table of the downstream criteria and air toxic emissions. 

 

I

 Emissions rates between tier 2 gasoline and diesel vehicles are similar but not identical due to the particulars of 

operations of the engine types. Diesel and gasoline engines emit differently during start, as well as during the 

various modes of operation. 
Emissions Impacts 

5-29 

Table 5-23 - Downstream non-GHG Emissions (Short Tons) 

Reference Emissions Control Emissions 

(including rebound) 

Delta Emissions 

CY 2020 CY 2030 CY 2020 CY 2030 CY 2020 CY 2030 

Gasoline Vehicles

1,3-Butadiene 4,777 3,448 4,683 3,429 -93.7 -18.5

Acetaldehyde 10,805 7,909 11,571 8,590 766.6 681.0

Acrolein 569 419 571 426 1.7 6.4

Benzene 30,633 22,048 29,882 21,799 -750.4 -249.2

CO 20,764,531 20,615,741 20,774,455 20,797,866 9,924.0 182,125.6

Formaldehyde 11,268 8,196 11,270 8,311 1.4 114.6

NOx 1,493,306 1,059,567 1,506,643 1,075,547 13,336.7 15,979.4

PM 2.5 40,685 42,855 40,915 43,455 229.8 599.7

SO2 22,130 25,700 20,103 21,451 -2,027.7 -4,248.5

VOC 1,290,008 1,006,387 1,294,309 1,017,550 4,301.6 11,163.3

Diesel Vehicles 

1,3-Butadiene 1,397 1,434 1,397 1,434 0.2 0.4

Acetaldehyde 5,562 5,709 5,562 5,709 0.2 0.5

Acrolein 741 761 741 761 0.1 0.2

Benzene 2,620 2,689 2,620 2,690 0.3 0.9

CO 612,037 567,933 612,270 568,595 232.2 662.2

Formaldehyde 15,279 15,684 15,280 15,686 0.7 1.7

NOx 1,502,844 1,192,334 1,502,917 1,192,498 73.7 163.2

PM 2.5 41,483 15,678 41,484 15,680 1.2 2.6

SO2 4,565 5,538 4,565 5,538 Attributed to gasoline 

VOC 203,977 209,384 203,994 209,428 17.1 44.1

In summary, the downstream emissions of the criteria pollutants CO, NOx, PM2.5, and 

VOC increase due to the additional rebound VMT. SO2 emissions decrease because the CO2

standards lead to a decreased volume of fuel consumption and less resulting emissions of 

sulfur compounds. 

 Air toxic emissions, which are sensitive to fuel effects, vary more between cases. 

Acetaldehyde emissions increase roughly proportionally to the increase in ethanol penetration. 

Similarly, benzene and 1,3 butadiene decrease proportionally to the decrease in gasoline 

emissions. These changes are the result of our ethanol volume assumptions and are not due to 

the new GHG vehicle standards. For a more complete discussions of ethanol effects on air 

toxic emissions, please refer to the EPA RFS2 analysis.44

 

As will be shown in section 5.3.4, the increases in non-GHG pollutants are generally 

less than the projected decreases on the upstream side. The exceptions are those pollutants 
Regulatory Impact Analysis 

5-30 

such as carbon monoxide (CO), acetaldehyde, and formaldehyde, where a relatively small of 

US emissions comes from upstream sources. 

5.3.4 Calculation of Upstream Emissions 

The term "upstream emissions" refers to air pollutant emissions generated from all 

crude oil extraction, transport, refining, and finished fuel transport, storage, and distribution. 

As shown above in Table 5-4 this includes all the stages prior to the final filling of vehicle 

fuel tanks at retail service stations. The details of the assumptions, data sources, and 

calculations that were used to estimate the emission impacts presented here can be found in 

the Technical Support Document and the docket memo, "Calculation of Upstream Emissions 

for the GHG Vehicle Rule."45 The results of this analysis are shown in Table 5-30. No public 

comments were received on the methodologies used in the calculation of upstream 

inventories. 

5.4 Greenhouse Gas Emission Inventory 

This section presents total program calendar year impacts by sector (Table 5-24, Table 

5-25,Table 5-26). Upstream, downstream, and total program impact are presented. 

Table 5-24 Downstream GHG and Fuel Consumption Changes vs. Reference Case 

2020 2030 2040 2050 

∆ CO2 (Metric Tons) -111,867,639 -219,811,320 -289,887,109 -368,990,880

∆ CH4 (Metric tons) 302.0 631.8 853.1 1,087.4

∆ N2O (Metric tons) 134.9 284.1 383.9 489.6

∆ HFC (Metric tons) -9,324 -18,189 -21,642 -23,899

∆ GHG (MMT CO2 EQ) -125.2 -245.7 -320.7 -403.0

∆ Fuel Consumption (billion 

gallons per year) 

-12.6 -24.7 -32.6 -41.5

Table 5-25 Upstream GHG Change vs. Reference Case 

2020 2030 2040 2050 

∆ CO2 (Metric Tons) 

-27,200,175.2 -53,446,255.6 -70,484,907.4 -89,718,677.3

∆ CH4 (Metric tons) -154,246.0 -303,081.5 -399,703.8 -508,774.1

∆ N2O (Metric tons) -437.2 -859.1 -1,133.0 -1,442.2

∆ GHG (MMT CO2 EQ) -31.2 -61.3 -80.8 -102.9
Emissions Impacts 

5-31 

Table 5-26 Total GHG and Fuel Consumption Changes vs. Reference Case 

2020 2030 2040 2050 

∆ CO2 (Metric Tons) -139,067,814.2 -273,257,576.1 -360,372,016.8 -458,709,557.6

∆ CH4 (Metric tons) -153,944.0 -302,449.7 -398,850.7 -507,686.7

∆ N2O (Metric tons) -302.3 -575.0 -749.1 -952.6

∆ HFC (Metric tons) -9,324.1 -18,189.3 -21,641.7 -23,899.2

∆ GHG (MMT CO2 EQ) -156.3 -307.0 -401.5 -505.9

∆ Fuel Consumption (billion 

gallons per year) 

-12.6 -24.7 -32.6 -41.5

5.5 Non-Greenhouse Gas Emission Inventory 

The reference case emission inventories used for this rule are obtained from different 

sources depending on sector. 

 For stationary/area sources and aircraft, 2020 projections were used from the 2002 

National Emissions Inventory (NEI), Version 3. The development of these inventories is 

documented in the November 27, 2007, memo titled, "Approach for Developing 2002 and 

Future Year National Emission Summaries," from Madeleine Strum to Docket EPA-HQOAR-2007-0491. That memo summarizes the methodologies and additional reference 

documents for criteria air pollutants (CAP) and mobile source air toxics (MSATs). The 

effects of the Clean Air Interstate rule are not included here. 

The onroad mobile source numbers have been updated from the NPRM with the 

MOVES data produced for this final rule analysis. For onroad mobile sources, the MOVES 

2010 model was used as described in Section 5.3.3.5. This model estimates emissions from 

light-duty and heavy-duty gasoline and diesel vehicles. These inventories have previously 

been shown in Section 5.3.3.5.1. In some cases, particularly VOC, CO and NOx, the change 

from the MOBILE model to a MOVES based inventory has led to large changes in reference 

inventories from the proposal. These changes are due to model updates rather than program 

changes. 

Most nonroad equipment was modeled with NONROAD2005d, which is a version of 

the NONROAD that includes the benefits of the two nonroad regulations published in 2008 

(the locomotive and marine diesel rule and the small spark-ignition and recreational marine 

engine rule).46, 47 This version of NONROAD does not include the county specific detail that 

is provided when NONROAD is run using NMIM. Some precision is lost using this method. 

Inventories for locomotives and commercial marine vessels are not covered by the 

NONROAD model, and they have been updated since the 2002 NEI and its future year 

projections were completed. Thus the more recent inventory projections published in the 

regulatory impact analyses of their respective recent rulemakings were used.46, 48

 

Locomotives and C1/C2 commercial marine vessel inventories come from the spring 2008 
Regulatory Impact Analysis 

5-32 

final rule, and the C3 commercial marine emission inventory is from the base case inventories 

in the June 2009 proposed rule. 

Table 5-27 and Table 5-28 show the total 2020 and 2030 mobile and non-mobile source 

inventory projections that were used as the reference case against which impacts of the rule 

were applied. The impacts, expressed as percentages, are presented below in Sections 5.5.1 

through 5.5.3. 

Table 5-27 2020 Reference Case Emissions by Sector (annual short tons) 

VOC CO NOX SO2 PM2.5 

Onroad 

Gasoline 1,290,008 20,764,531 1,493,306 22,130 40,685

Onroad Diesel 203,977 612,037 1,502,844 4,565 41,483

Nonroad SIa

1,289,918 14,286,250 242,828 49,019 15,413

Other 

Nonroadb 234,870 1,424,643 3,389,761 210,509 943,226

Stationary/Area 8,740,057 11,049,239 5,773,927 3,047,714 7,864,681

Total 11,758,830 29,756,282 30,783,084 3,333,937 8,905,488

a

 Nonroad gasoline, LPG, and CNG engines plus portable fuel containers 

b

 Nonroad diesel engines and all locomotive, aircraft, and commercial marine 

TABLE 5-27 

CONTINUED 

BENZENE 1,3-

BUTADIENE

ACETALDEHYDE FORMALDEHYDE ACROLEIN

Onroad 

Gasoline 30,633 4,777 10,805 11,268 569

Onroad Diesel 2,620 1,397 5,562 15,279 741

Nonroad SIa 36,862 5,895 4,768 10,240 584

Other 

Nonroadb

3,760 929 9,542 22,324 1,013

Stationary/Area 111,337 1,847 13,118 23,846 3,412

Total 

185,212 25,038 33,602 82,957 6,319

a

 Nonroad gasoline, LPG, and CNG engines plus portable fuel containers 

b

 Nonroad diesel engines and all locomotive, aircraft, and commercial marine 
Emissions Impacts 

5-33 

Table 5-28 2030 Reference Case Emissions by Sector (annual short tons) 

VOC CO NOX SO2 PM2.5 

Onroad 

Gasoline 1,006,387 20,615,741 1,059,567 25,700 42,855

Onroad Diesel 209,384 567,933 1,192,334 5,538 15,680

Nonroad SIa

1,198,679 15,815,805 243,515 50,816 17,270

Other 

Nonroadb 238,652 1,411,393 3,427,832 229,183 1,426,994

Stationary/Area 8,740,057 11,049,239 5,773,927 3,047,714 7,864,681

Total 

11,393,159 30,528,338 30,628,948 3,358,951 9,367,480

Table 5-28 

continued Benzene 1,3-

Butadiene

Acetaldehyde Formaldehyde Acrolein

Onroad Gasoline 22,048 3,448 7,909 8,196 419

Onroad Diesel 2,689 1,434 5,709 15,684 761

Nonroad SIa

39,871 6,279 5,118 11,229 629

Other Nonroadb

3,764 979 9,579 22,487 1,055

Stationary/Area 111,337 1,847 13,118 23,846 3,412

Total 179,709 20,523 30,683 81,442 6,276

a

 Nonroad gasoline, LPG, and CNG engines plus portable fuel containers 

b

 Nonroad diesel engines and all locomotive, aircraft, and commercial marine 

5.5.1 Downstream Impacts of Program on Non-GHG Emissions 

The non-GHG emission results shown here (Table 5-29) are a summary of the 

previous analysis, and are combination of output from MOVES2010 and the OMEGA postprocessor. 
Regulatory Impact Analysis 

5-34 

Table 5-29 Downstream Emission Changes of Program 

CALENDAR YEAR 

2020 

CALENDAR YEAR 

2030 

POLLUTANT 

Short Tons

Percent 

Change in 

US Total 

Short Tons 

Percent 

Change in 

US Total 

∆ 1,3-Butadiene -93.6 -0.37% -18.1 -0.09%

∆ Acetaldehyde 766.9 2.28% 681.5 2.22%

∆ Acrolein 1.7 0.03% 6.5 0.10%

∆ Benzene -750.0 -0.40% -248.3 -0.14%

∆ Carbon Monoxide 10,156.3 0.03% 182,787.8 0.60%

∆ Formaldehyde 2.1 0.00% 116.3 0.14%

∆ Oxides of Nitrogen 13,410.3 0.04% 16,142.6 0.05%

∆ Particulate Matter 

(below 2.5 micrometers) 231.0 0.00% 602.3 0.01%

∆ Oxides of Sulfur -2,027.7 -0.06% -4,248.5 -0.13%

∆ Volatile Organic Compounds 4,318.7 0.04% 11,207.4 0.10%

5.5.2 Upstream Impacts of Program on Non-GHG Emissions 

Non-GHG fuel production and distribution emission impacts of the program were 

estimated in conjunction with the development of life cycle GHG emission impacts, and the 

GHG emission inventories discussed above. The basic calculation is a function of fuel 

volumes in the analysis year and the emission factors associated with each process or 

subprocess. 

In general this life cycle analysis uses the same methodology as the Renewable Fuel 

Standard (RFS2) rule. It relies partially on the “Greenhouse Gases, Regulated Emissions, and 

Energy Use in Transportation” (GREET) model, developed by the Department of Energy’s 

Argonne National Laboratory (ANL), but takes advantage of additional information and 

models to significantly strengthen and expand on the GREET analysis. 

Updates and enhancements to the GREET model assumptions include updated crude 

oil and gasoline transport emission factors that account for recent EPA emission standards and 

modeling, such as the Tier 4 diesel truck standards published in 2001 and the locomotive and 

commercial marine standards finalized in 2008. In addition, GREET does not include air 

toxics. Thus emission factors for the following air toxics were added: benzene, 1,3-

butadiene, formaldehyde, acetaldehyde, and acrolein. These upstream toxics emission factors 

were calculated from the 2002 National Emissions Inventory (NEI), a risk and technology 

review for petroleum refineries, speciated emission profiles in EPA's SPECIATE database, or 

the Mobile Source Air Toxics rule (MSAT) inventory for benzene; these pollutant tons were 

divided by refinery energy use or gasoline distribution quantities published by the DOE 
Emissions Impacts 

5-35 

Energy Information Administration (EIA) to get emission factors in terms of grams per 

million BTU of finished gasoline. The resulting emission factors are presented in Chapter 4 

of the joint TSD for today's rule. 

Results of these emission inventory impact calculations relative to the reference case 

for 2020 and 2030 are shown in Table 5-30 for the criteria pollutants and individual air toxic 

pollutants. 

The program is projected to provide reductions in all pollutants associated with 

gasoline production and distribution as the projected fuel savings reduce the quantity of 

gasoline needed. 

Table 5-30 Upstream Emission Changes of Program 

CALENDAR YEAR 

2020 

CALENDAR YEAR 

2030 

POLLUTANT 

Short Tons

Percent 

Change in 

US Total 

Short 

Tons 

Percent 

Change in 

US Total 

∆ 1,3-Butadiene -1.5 -0.01% -3.0 -0.01%

∆ Acetaldehyde -6.8 -0.02% -13.4 -0.04%

∆ Acrolein -0.9 -0.01% -1.8 -0.03%

∆ Benzene -139.6 -0.08% -274.3 -0.15%

∆ Carbon Monoxide -6,164.6 -0.02% -12,113.0 -0.04%

∆ Formaldehyde -51.4 -0.06% -101.0 -0.12%

∆ Oxides of Nitrogen -19,291.0 -0.06% -37,905.4 -0.12%

∆ Particulate Matter 

(below 2.5 micrometers) -2,629.1 -0.03% -5,165.9 -0.06%

∆ Oxides of Sulfur -11,804.1 -0.35% -23,194.1 -0.69%

∆ Volatile Organic Compounds -64,505.9 -0.55% -126,749.1 -1.11%

5.5.3 Total non-GHG Program Impact 

Table 5-31 shows the combined impacts of downstream and upstream aspects of the 

program. The net impacts of the program on VOC, NOx, and PM2.5, are mainly due to 

reductions in emissions associated with gasoline production and distribution as the projected 

fuel savings of the program reduce the quantity of gasoline needed. Increases in CO 

emissions are driven by the rebound effect on VMT, which are only partially offset by 

upstream reductions. 

Net emissions depend on the relative impacts of the reductions from upstream 

emissions versus increases due to the rebound effect and ethanol volume assumptions (that are 

not due to the GHG vehicle standards) on the downstream emissions. All changes in non-
Regulatory Impact Analysis 

5-36 

GHG emissions are less than 2.5% of the national inventory, with the net impact on most nonGHG emissions at less than a single percent. 

Table 5-31 Total Non-GHG Emission Changes of Program 

CALENDAR YEAR 

2020 

CALENDAR YEAR 

2030 

POLLUTANT 

Short Tons

Percent 

Change in 

US Total 

Short 

Tons 

Percent 

Change in 

US Total 

∆ 1,3-Butadiene -95.1 -0.38% -21.1 -0.10%

∆ Acetaldehyde 760.0 2.26% 668.1 2.18%

∆ Acrolein 0.8 0.01% 4.7 0.07%

∆ Benzene -889.6 -0.48% -522.5 -0.29%

∆ Carbon Monoxide 3,991.6 0.01% 170,674.8 0.56%

∆ Formaldehyde -49.3 -0.06% 15.3 0.02%

∆ Oxides of Nitrogen -5,880.7 -0.02% -21,762.8 -0.07%

∆ Particulate Matter 

(below 2.5 micrometers) -2,398.1 -0.03% -4,563.6 -0.05%

∆ Oxides of Sulfur -13,831.8 -0.42% -27,442.5 -0.82%

∆ Volatile Organic Compounds -60,187.1 -0.51% -115,541.7 -1.01%

5.6 Model Year Lifetime Analyses 

5.6.1 Methodology 

EPA also conducted a separate analysis of the total benefits over the model year 

lifetime of 2012 through 2016 model year vehicles. In contrast to the calendar year analysis, 

the model year lifetime analysis shows the lifetime impacts of the program on each MY fleet 

over the course of its existence. 

In this analysis, a simplified VMT schedule is used. Rather than using a MY specific 

VMT schedule for each MY, a single VMT schedule is used for all five model years. This 

VMT schedule is more fully described in the joint TSD chapter 4. In brief, it was derived 

using the same methodology as the MY-specific VMT schedules and is the average of the 

VMT schedules from 2012-2030 (Table 5-32). 

The ethanol volumes used in this analysis are from AEO 2007. As there are 

proportionally few vehicles subject to the new GHG standards in the first few years of these 

vehicle’s lifetimes, which is when the majority of driving occurs, little change is anticipated 

in the fuel supply which these vehicles use. Therefore, no fuel effects are calculated in the 

MY lifetime analysis. 
Emissions Impacts 

5-37 

All other inputs, including sales and achieved emission levels are the same between 

the two analyses. 

Table 5-32 Updated Survival Fraction and Mileage Accumulation by Age 

AGE PA 

SURVIVAL 

FRACTION 

PA 

MILEAGE 

NPA 

SURVIVAL 

FRACTION 

NPA 

MILEAGE 

0 0.9950 17,270 0.9950 19,219 

1 0.9900 16,943 0.9741 18,782 

2 0.9831 16,599 0.9603 18,419 

3 0.9731 16,163 0.9420 17,946 

4 0.9593 15,761 0.9190 17,502 

5 0.9413 15,337 0.8913 16,952 

6 0.9188 14,881 0.8590 16,439 

7 0.8918 14,429 0.8226 15,829 

8 0.8604 13,940 0.7827 15,218 

9 0.8252 13,495 0.7401 14,648 

10 0.7866 12,964 0.6956 13,992 

11 0.7170 12,510 0.6501 13,450 

12 0.6125 11,990 0.6042 12,832 

13 0.5094 11,470 0.5517 12,212 

14 0.4142 10,997 0.5009 11,600 

15 0.3308 10,543 0.4522 11,069 

16 0.2604 10,125 0.4062 10,617 

17 0.2028 9,714 0.3633 10,125 

18 0.1565 9,307 0.3236 9,650 

19 0.1200 8,891 0.2873 9,238 

20 0.0916 8,546 0.2542 8,882 

21 0.0696 8,285 0.2244 8,667 

22 0.0527 8,136 0.1975 8,400 

23 0.0399 7,896 0.1735 8,395 

24 0.0301 7,699 0.1522 8,197 

25 0.0227 7,530 0.1332 8,188 

26 0.0000 7,432 0.1165 8,218 

27 0.0000 7,297 0.1017 8,216 

28 0.0000 7,198 0.0887 8,213 

29 0.0000 7,138 0.0773 8,211 

30 0.0000 7,136 0.0673 8,210 

31 0.0000 7,133 0.0586 8,208 

32 0.0000 7,128 0.0509 8,203 

33 0.0000 7,117 0.0443 8,196 

34 0.0000 7,103 0.0385 8,182 

35 0.0000 7,086 0.0334 8,167 
Regulatory Impact Analysis 

5-38 

5.6.2 Results 

The GHG emission reductions are shown for each model year, as are the co-pollutant 

impacts (Table 5-33, Table 5-34). 

Table 5-33 Lifetime GHG Emissions vs. Reference Case (MMT CO2 EQ) 

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Program Total 

∆ Downstream Tailpipe 

Emission -59.1 -84.0 -108.9 -153.9 -214.5 -620.4

∆ Downstream Indirect A/C -5.5 -9.4 -14.4 -19.6 -20.9 -69.9

∆ Downstream 

Direct A/C -6.6 -11.2 -17.2 -23.4 -25.0 -83.4

∆ Downstream CH4 0.0 0.0 0.0 0.0 0.0 0.1

∆ Downstream N2O 0.0 0.0 0.1 0.1 0.1 0.3

Total ∆ Downstream -71.2 -104.6 -140.5 -196.8 -260.3 -773.4

 

∆ Upstream CO2 -15.7 -22.7 -30.0 -42.2 -57.2 -167.8

∆ Upstream CH4 -1.9 -2.7 -3.6 -5.0 -6.8 -20.0

∆Upstream N2O -0.1 -0.1 -0.1 -0.2 -0.3 -0.8

Total ∆ Upstream -17.7 -25.5 -33.7 -47.4 -64.3 -188.7

 

Total Program ∆ GHG 

Emissions -88.8 -130.2 -174.2 -244.2 -324.6 -962.0

 

∆ Fuel Consumption 

(Billion Barrels) -0.17 -0.25 -0.33 -0.46 -0.63 -1.85
Emissions Impacts 

5-39 

Table 5-34 Lifetime non-GHG Emissions vs. Reference Case (Short Tons) 

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Total 

Downstream 

∆ VOC 2,246.9 3,281.5 4,336.4 6,055.7 8,181.5 24,102.0

∆ NOX 4,334.4 6,414.4 8,465.9 11,745.4 15,872.5 46,832.5

∆ PM 2.5 202.8 296.7 392.0 547.0 739.1 2,177.6

∆ CO 86,963.0 127,079.2 167,922.1 234,434.9 316,325.0 932,724.2

∆ SO2 -1,362.3 -1,970.4 -2,601.0 -3,657.6 -4,964.0 -14,555.2

∆ Benzene 76.1 111.2 146.9 205.1 276.7 816.0

∆ 1,3 Butdiene 12.8 18.7 24.7 34.5 46.5 137.2

∆ Formaldehyde 30.7 44.8 59.2 82.7 111.5 328.9

∆ Acetaldehyde 29.2 42.6 56.3 78.7 106.2 313.1

∆ Acrolein 1.4 2.1 2.7 3.8 5.2 15.3

 

Upstream 

∆ VOC -41,066.1 -59,394.3 -78,404.6 -110,253.7 -149,633.5 -438,752.1

∆ NOX -12,281.2 -17,762.4 -23,447.5 -32,972.3 -44,749.1 -131,212.5

∆ PM 2.5 -1,673.7 -2,420.7 -3,195.5 -4,493.6 -6,098.6 -17,882.2

∆ CO -3,924.6 -5,676.1 -7,492.9 -10,536.6 -14,300.0 -41,930.1

∆ SO2 -7,514.8 -10,868.7 -14,347.4 -20,175.5 -27,381.7 -80,288.1

∆ Benzene -88.9 -128.5 -169.7 -238.6 -323.8 -949.4

∆ 1,3 Butdiene -1.0 -1.4 -1.8 -2.6 -3.5 -10.3

∆ Formaldehyde -32.7 -47.3 -62.5 -87.9 -119.3 -349.8

∆ Acetaldehyde -4.4 -6.3 -8.3 -11.7 -15.9 -46.5

∆ Acrolein -0.6 -0.9 -1.1 -1.6 -2.2 -6.4

 

Total 

∆ VOC -38,819.2 -56,112.8 -74,068.1 -104,197.9 -141,452.0 -414,650.1

∆ NOX -7,946.8 -11,348.0 -14,981.6 -21,226.9 -28,876.7 -84,379.9

∆ PM 2.5 -1,470.9 -2,124.1 -2,803.5 -3,946.6 -5,359.6 -15,704.6

∆ CO 83,038.4 121,403.1 160,429.2 223,898.3 302,025.0 890,794.0

∆ SO2 -8,877.1 -12,839.0 -16,948.4 -23,833.1 -32,345.7 -94,843.3

∆ Benzene -12.7 -17.4 -22.8 -33.4 -47.1 -133.4

∆ 1,3 Butdiene 11.8 17.3 22.9 31.9 43.0 126.9

∆ Formaldehyde -2.1 -2.6 -3.3 -5.2 -7.8 -20.9

∆ Acetaldehyde 24.9 36.3 48.0 67.0 90.3 266.6

∆ Acrolein 0.8 1.2 1.6 2.2 3.0 8.9
Regulatory Impact Analysis 

5-40 

5.7 Alternative 4% and 6% Scenarios 

For this final rule, two alternative control scenarios were evaluated characterized by 

4% and 6% annual growth in the GHG standards from the MY 2011 standard. Like the 

previous analyses, this analysis has been updated from the proposal using the new economic 

inputs. Other than the standards, these scenarios share all inputs with the EPA program. Only 

GHG reductions and fuel savings are shown for these programs. 

5.7.1 4% Scenario 

5.7.1.1 Standards and Achieved Levels 

The program standards are shown in Table 5-35 and the achieved levels are shown in Table 

5-36. 

Table 5-35 4% Scenario Standards 

MODEL 

YEAR 

PA 

EMISSION 

LEVEL 

NPA EMISSION 

LEVEL 

ANTICIPATED 

MY EMISSION 

LEVEL 

2012 277 365 311 

2013 267 352 299 

2014 257 329 287 

2015 248 327 276 

2016 239 315 265 

Table 5-36 4% Scenario Achieved Levels 

MODEL 

YEAR 

ANTICIPATED 

PA EMISSION 

LEVEL 

ANTICIPATED 

NPA EMISSION 

LEVEL 

ANTICIPATED 

MY EMISSION 

LEVEL 

2012 277 380 317 

2013 269 367 306 

2014 261 349 293 

2015 251 335 281 

2016 239 315 265 

5.7.1.2 Results 

Results are shown relative to the same reference scenario as the EPA program. Both 

calendar year and model year lifetime results are shown. 
Emissions Impacts 

5-41 

Table 5-37 Downstream CY GHG Reductions and Fuel Savings vs. Reference Case 

CY 

2020 

CY 

2030 

CY 

2040 

CY 

2050 

Downstream 

∆ CO2 excluding indirect A/C controls (MMT CO2 EQ) -89.8 -183.4 -243.0 -309.5 

∆Indirect A/C CO2(MMT CO2 EQ) -10.6 -20.1 -26.5 -33.7 

∆ Direct A/C HFC (MMT CO2 EQ) -13.3 -26.0 -30.9 -34.2 

∆ CH4 (MMT CO2 EQ) 0.0 0.0 0.0 0.0 

∆ N2O (MMT CO2 EQ) 0.0 0.0 0.1 0.1 

∆ Total GHG (MMT CO2 EQ) -113.9 -229.5 -300.3 -377.2 

 

Upstream 

∆ CO2 (MMT CO2 EQ) -24.5 -49.5 -65.5 -83.5 

∆ CH4 (MMT CO2 EQ) -3.5 -7.0 -9.3 -11.8 

∆ N2O (MMT CO2 EQ) -0.1 -0.2 -0.3 -0.3 

∆ Total GHG -28.0 -56.7 -75.1 -95.7 

 

Total 

∆ Total GHG -141.9 -286.2 -375.4 -472.9 

∆ Fuel Consumption (Annual, Billion gallons) -11.3 -22.9 -30.3 -38.6 
Regulatory Impact Analysis 

5-42 

Table 5-38 Total Model Year Lifetime GHG Reductions vs. Baseline 

MY 

2012 

MY 

2013 

MY 

2014 

MY 

2015 

MY 

2016 

Program 

Total 

Downstream 

∆ CO2 excluding indirect A/C controls 

(MMT CO2 EQ) -20.7 -53.3 -94.2 -140.0 -198.0 -506.2

∆Indirect A/C CO2(MMT CO2 EQ) -5.5 -9.4 -14.4 -19.6 -20.9 -69.8

∆ Direct A/C HFC (MMT CO2 EQ) -6.6 -11.2 -17.2 -23.4 -25.0 -83.3

∆ CH4 (MMT CO2 EQ) 0.0 0.0 0.0 0.0 0.0 0.0

∆ N2O (MMT CO2 EQ) 0.0 0.0 0.0 0.1 0.1 0.2

∆ Total GHG (MMT CO2 EQ) -32.8 -73.9 -125.7 -182.9 -243.8 -659.0

Upstream 

∆ CO2 (MMT CO2 EQ) -6.4 -15.2 -26.4 -38.8 -53.2 -140.1

∆ CH4 (MMT CO2 EQ) -0.8 -1.8 -3.1 -4.6 -6.3 -16.7

∆ N2O (MMT CO2 EQ) 0.0 -0.1 -0.1 -0.2 -0.3 -0.7

∆ Total GHG -7.2 -17.1 -29.7 -43.6 -59.8 -157.4

Total 

∆ Total GHG -39.9 -100.0 -155.4 -226.5 -303.6 -816.4

∆ Fuel Consumption (Billion gallons) -2.9 -7.1 -12.2 -18.0 -24.6 -64.8

5.7.2 6% Scenario 

5.7.2.1 Standards and Achieved Levels 

The program standards are shown in Table 5-35 and the achieved levels are shown in 

Table 5-36. 

Table 5-39 6% Scenario Standards 

MODEL 

YEAR 

ANTICIPATED 

PA EMISSION 

LEVEL 

ANTICIPATED 

NPA EMISSION 

LEVEL 

ANTICIPATED 

MY EMISSION 

LEVEL 

2012 272 358 305 

2013 257 339 288 

2014 243 320 272 

2015 230 303 256 

2016 217 286 241 
Emissions Impacts 

5-43 

Table 5-40 6% Scenario Achieved Levels 

MODEL 

YEAR 

ANTICIPATED 

PA EMISSION 

LEVEL 

ANTICIPATED 

NPA EMISSION 

LEVEL 

ANTICIPATED 

MY EMISSION 

LEVEL 

2012 274 374 313 

2013 261 350 295 

2014 247 329 277 

2015 232 310 260 

2016 217 286 241 

5.7.2.2 Results 

Results are shown relative to the same reference scenario as the EPA program. Both 

calendar year and model year lifetime results are shown. 

Table 5-41 CY GHG Emissions and Fuel Consumption vs. Reference Case 

CY 

2020 

CY 

2030 

CY 

2040 

CY 

2050 

Downstream 

∆ CO2 excluding indirect A/C controls (MMT CO2 EQ) -137.4 -274.9 -363.2 -462.4 

∆Indirect A/C CO2(MMT CO2 EQ) -10.7 -20.3 -26.7 -34.0 

∆ Direct A/C HFC (MMT CO2 EQ) -13.3 -26.0 -30.9 -34.2 

∆ CH4 (MMT CO2 EQ) 0.0 0.0 0.0 0.0 

∆ N2O (MMT CO2 EQ) 0.0 0.1 0.1 0.2 

∆ Total GHG (MMT CO2 EQ) -161.4 -321.1 -420.7 -530.4 

 

Upstream 

∆ CO2 (MMT CO2 EQ) -36.0 -71.8 -94.8 -120.7 

∆ CH4 (MMT CO2 EQ) -5.1 -10.2 -13.4 -17.1

∆ N2O (MMT CO2 EQ) -0.2 -0.3 -0.5 -0.6

∆ Total GHG -41.3 -82.3 -108.7 -138.4 

 

Total 

∆ Total GHG -202.7 -403.4 -529.4 -668.8 

∆ Fuel Consumption (Annual, Billion gallons) -16.7 -33.2 -43.9 -55.9 
Regulatory Impact Analysis 

5-44 

Table 5-42 MY Lifetime GHG Emissions and Fuel Consumption vs. Reference Case 

MY 

2012 

MY 

2013 

MY 

2014 

MY 

2015 

MY 

2016 

Program 

Total 

Downstream 

∆ CO2 excluding indirect A/C controls 

(MMT CO2 EQ) -37.7 -96.9 -158.1 -222.8 -295.1 -810.6

∆Indirect A/C CO2(MMT CO2 EQ) -5.5 -9.4 -14.5 -19.7 -21.1 -70.2

∆ Direct A/C HFC (MMT CO2 EQ) -6.6 -11.2 -17.3 -23.5 -25.1 -83.8

∆ CH4 (MMT CO2 EQ) 0.0 0.0 0.0 0.0 0.0 0.1

∆ N2O (MMT CO2 EQ) 0.0 0.0 0.1 0.1 0.1 0.4

∆ Total GHG (MMT CO2 EQ) -49.8 -117.5 -189.8 -265.9 -341.2 -964.2

Upstream 

∆ CO2 (MMT CO2 EQ) -10.5 -25.8 -42.0 -59.0 -76.9 -214.2

∆ CH4 (MMT CO2 EQ) -1.3 -3.1 -5.0 -7.0 -9.2 -25.5

∆ N2O (MMT CO2 EQ) -0.1 -0.1 -0.2 -0.3 -0.4 -1.1

∆ Total GHG -11.8 -29.0 -47.2 -66.3 -86.4 -240.7

Total 

∆ Total GHG -61.7 -146.5 -237.0 -332.2 -427.6 -1,204.9

∆ Fuel Consumption (Billion gallons) 

-4.9 

 

-12.0 

 

-19.4 

 

-27.3 

 

-35.6 

 

-99.1 

5.8 Inventories Used for Air Quality Analyses 

This section describes the processes used in calculating the inventories for the air quality 

(AQ) modeling analysis. Air quality modeling requires significant lead time, and 

consequently the air quality inventories were completed significantly before the inventories 

presented in this final rule. 

5.8.1 Upstream Emissions 

5.8.1.1 Petroleum Production and Refining Emissions 

Petroleum production includes crude oil extraction and transport to refineries. As in 

the nationwide analysis presented in the proposed rule as well as this final rule, we assumed 

that (a) 50% of the change in gasoline supply was projected to come from domestic refineries, 

and (b) 10% of the change in crude being used by domestic refineries would be domestic 

crude. Thus, using our assumption that 1.0 gallon less of gasoline equates to approximately 

1.0 gallon less crude throughput, the reduction in crude extraction and transport would equal 

about 5% of the change in gasoline volume. To generate the emission inventory adjustment 

factors for air quality modeling these reductions were applied to the projected crude supply to 

US refineries, per AEO 2009 (stimulus version).49 The resulting estimates are shown in Table 

5-43. Only the 2030 values were used in the air quality modeling. The percent reductions 

were applied to the NEI projected inventories for 2030. The 0.61% reduction was applied to 
Emissions Impacts 

5-45 

all SCCs associated with petroleum extraction, and the 6.09% reduction was applied to all 

SCCs associated with gasoline refining.50

 

Table 5-43 Crude Oil and Gasoline Volume Reductions Associated with LD GHG Rule

PARAMETER 2020 2030 

Crude Supply to US Refineries (bgal/yr)2

211.96 214.94 

Reduction in Gasoline Consumption (bgal/yr) 13.35 26.18

Reduction in Domestic-Refined Gasoline (bgal/yr) 6.68 13.09

Reduction in Domestic Refining of Crude (US & Imported 

Crude) (bgal/yr)

6.68 13.09

Reduction in Domestic-Refined Gasoline from Domestic Crude 

(bgal/yr)

0.67 1.31

Reduction in Domestic Crude Production & Transport to 

refineries (bgal/yr)

0.67 1.31

Percent Reduction in Domestic Refining 3.17% 6.09%

Percent Reduction in Domestic Crude Production & Transport 0.32% 0.61%

Note that this method used for AQ county allocation is not directly comparable with 

the method used for nationwide impacts, for which we used the GREET-based upstream 

impacts spreadsheet model to calculate the absolute change in tons for each stage of the 

upstream inventory. 

5.8.1.2 Gasoline Transport, Storage and Distribution emissions (vapor) 

With the reduced gasoline consumption associated with this rule there would be 

changes in the quantity of vapor losses during the transport and distribution of gasoline. The 

analysis of these impacts was separated into two segments: refinery to bulk terminal (RBT) 

and bulk terminal to pump (BTP). The reference case analyzed would include some amount 

of E0 (zero percent ethanol) in the BTP segment, but the reduced gasoline production 

projected with this rule, combined with unchanged ethanol volumes, means that essentially all 

gasoline would be blended with at least 10 percent ethanol. Thus the transport of E0 gasoline 

would only occur between refineries and blending terminals in the control case, i.e., the RBT 

segment. The BTP segment would include both E10 and E85. No changes in volumes of E10 

or E85 were assumed for this analysis. 

E0 – Refinery to Bulk Terminal (RBT) 

E0 – Bulk Terminal to Pump (BTP, used for reference cases only) 

E10 – Bulk Terminal to Pump (BTP) 

E85 – Bulk Terminal to Pump (BTP) 

For each of the above fuel type and transport stage combinations, nationwide VOC 

impacts (ton deltas) (and benzene and ethanol vapor) were calculated using EPA’s upstream 

impacts spreadsheet model for the control scenario versus the reference case. For air quality 
Regulatory Impact Analysis 

5-46 

modeling the three BTP values were combined into a total BTP impact for each scenario. 

These impact values were renormalized to be ton deltas relative to the reference case. Then 

all the SCCs in the NEI related to gasoline transport, storage, and distribution (TS&D) were 

categorized as either RBT or BTP, and the NEI VOC emissions were summed for each 

category. The nationwide VOC percent change for the control case relative to the reference 

case for RBT was calculated as the control case RBT delta tons (versus AEO) divided by the 

NEI RBT tons. Similarly, the nationwide VOC percent change for the control case for BTP 

was calculated as the control case BTP delta tons (versus AEO) divided by the NEI BTP tons. 

The projected reference case VOC and calculated adjustments are shown in Table 5-44. 

Table 5-44 Gasoline Distribution Emission Reductions Associated with LD GHG Rule 

GASOLINE 

DISTRIBUTION 

SEGMENT 

FUEL NEI VOC 

(TON/YR) 

VOC 

CHANGE 

(TON/YR) 

PERCENT 

CHANGE VS 

NEI 

Refinery to Bulk 

Terminal 

E0 273,513 -50,042 -18.30% 

E0 -92,034 

E10 0 

E85 0 

Bulk Terminal to 

Pump 

Total 490,236 -92,034 -18.77% 

The county level AQ inventories for the control case were then calculated by applying 

these percent changes in VOC to the corresponding sets of SCCs (point and non-point 

sources) for every county. The same adjustment factors were applied to benzene evaporative 

emissions.51

 

5.8.1.3 Downstream Emissions from Onroad vehicles and Nonroad Equipment 

except Aircraft, Locomotives, and Commercial Marine Vessels 

5.8.1.4 Introduction 

Downstream emissions are those resulting from mobile-source operation, including 

onroad vehicles and nonroad vehicles and equipment. This section describes the development 

of emissions from all onroad vehicles and from nonroad equipment modeled by the 

NONROAD Model. Emissions from aircraft, locomotives, and commercial marine vessels 

are discussed in Section 0. 

The emissions discussed in this section were developed using three EPA models: 

MOVES, MOBILE6, and NONROAD. MOBILE6 and NONROAD were run using the 

National Mobile Inventory Model (NMIM), which is software that runs MOBILE6 and 

NONROAD at the county-month level by accessing a county database, preparing input files, 

and aggregating the output. 

Similar to the emission inventories for the final rule, both VMT rebound and the 

effects of increasing ethanol proportions in the fuel supply (which are not due to the GHG 

vehicle standards) were accounted for in the air quality modeling inventories. Additional 
Emissions Impacts 

5-47 

details on the downstream inventories used for the air quality modeling are available in a 

docket memo.52

5.8.1.5 Onroad 

For onroad mobile sources except motorcycles, EPA executed an internal draft version 

of MOVES dated 8/25/2009 (MOVES20090825), which is similar to Draft MOVES200953

with a number of improvements to the fuel effects data and code. This version of MOVES 

used default database MOVESDB20090902. A slightly later version, MOVES20091113, 

was run for evaporative runs to correct a bug in the code that processed evaporative 

emissions. This version of MOVES used default database MOVESDB20091109. Usersupplied fuel and temperature tables were used for these runs. Historical temperature and 

humidity data for 2005 was used for all years. For motorcycles, we relied on the MOBILE6.2 

model as run using the NMIM platform with county-specific fuel properties and temperatures. 

MOVES supplied all pollutants except SO2 and NH3, which came from NMIM runs. Onroad 

inventories were generated by multiplying MOVES emission factors by VMT developed for 

the Office of Air Quality Planning and Standards's 2002 Version 3 Modeling Platform 

(PF02v3)54 and used in the recently published Locomotive-Marine Rule.55 This VMT, which 

was based on AEO2006,56 was adjusted to match the annual VMT from AEO2009,57 but with 

county allocations preserved. AEO2009 growth factors from 2005 to 2030 were applied to 

2005 NEI VMT using the three AEO categories: light duty, commercial light trucks and 

heavy duty. Assignments to MOBILE6 categories are straightforward except for Commercial 

Light Trucks, which are only gasoline in the AEO classification. The MOBILE6 Model was 

M6203ChcOxFixNMIM, a special version that includes cold-start VOC and the cold-start 

controls of the Mobile Source Air Toxics Rule that go into effect in 2011 and used in the 

recently published Locomotive-Marine rule. Onroad emissions generated at the state-month 

level from MOVES were distributed to the county-month level using the results from 

MOBILE6 as run by NMIM. 

5.8.1.6 Nonroad 

Nonroad equipment except for aircraft, locomotives, and commercial marine vessels 

was modeled with the latest publically released NONROAD version NONROAD2008a.58

This version of the NONROAD includes the benefits of the two nonroad regulations 

published in 2008 (the Locomotive and Marine Rule and the Small Spark-Ignition And 

Recreational Marine Engine Rule59) plus all previous nonroad regulations. 

5.8.1.7 Summaries 

The two tables below are national-annual emissions in U.S. tons by mobile-source 

sector for the reference and control cases in 2030. There are differences between these 

inventories and those produced for the Final Rule because the latter used the final version of 

MOVES2010 and an earlier version was used for the air quality modeling inventories. This 

difference was unavoidable due to the long lead time required for air quality modeling. 

However, the proportional difference in downstream inventories between the Control and 

Reference cases is nearly the same in the two sets of inventories, so the air quality modeling 

adequately reflects the effects of the rule. 
Regulatory Impact Analysis 

5-48 

Table 5-45 2030 Reference Case. National annual emissions in U.S. tons 

pollutant Sector 

Light duty 

Gasoline 

Light duty 

Diesel 

Heavy Duty 

 Gasoline 

Heavy Duty 

Diesel 

Non-road 

Diesel 

Non-Road 

Gasoline 

VOC 635,169 1,122 22,921 167,574 67,377 1,170,391

NOx 1,245,018 7,776 63,982 979,298 481,698 207,374

CO 17,901,362 8,441 783,117 477,617 165,956 15,148,540

SO2 33,384 884 2,185 3,633 1,314 1,104

PM2.5 24,224 112 829 16,258 18,817 48,012

Benz 15,723 23 606 1,843 1,371 23,952

Acet 5,823 14 231 5,054 3,576 3,284

Buta 2,520 10 98 1,070 125 3,924

Formal 5,996 44 235 13,723 7,961 6,188

Acro 275 4 22 614 204 358

Table 5-46 2030 Control Case. National annual emissions in U.S. tons. 

pollutant Sector 

 Light duty 

Gasoline 

Light duty 

Diesel 

Heavy Duty 

Gasoline 

Heavy Duty 

Diesel 

Non-road 

Diesel 

Non-Road 

Gasoline 

VOC 651,538 1,139 23,160 167,574 67,377 1,170,521

NOx 1,268,205 7,893 64,179 979,298 481,698 216,640

CO 18,126,698 8,578 780,432 477,617 165,956 14,706,415

SO2 27,975 740 2,185 3,633 1,314 1,106

PM2.5 24,575 113 829 16,258 18,817 48,012

Benz 15,652 23 593 1,843 1,371 23,009

Acet 6,341 14 248 5,054 3,576 3,470

Buta 2,519 10 97 1,070 125 3,974

Formal 6,108 44 235 13,723 7,961 6,258

Acro 280 4 22 614 204 352
Emissions Impacts 

5-49 

5.A Appendix to Chapter 5: Details of the TLAAS Impacts Analysis 

5.A.1 Introduction and Summary 

The TLAAS program allows manufacturers with total domestic sales of less than 

400,000 vehicles during model year 2009 to place up to 100,000 vehicles from model years 

2012-2015 into a separate fleet. As a change from the proposed rule, the final rule allows that 

manufacturers with total domestic sales of less than 50,000 in MY 2009 to place up to 

250,000 vehicles from model years 2012-2016 into a separate fleetJ,K. This separate fleet is 

subject to a 25% less stringent standard than the manufacturer’s primary fleet (subject to 

various further constraints described in section III.B.5 of the preamble). One commenter, the 

American Council for an Energy Efficient Economy, voiced concerns that EPA (A) 

underestimated the impact of the TLAAS program, and (B) did not provide documentation of 

the relevant calculations. As in the proposal, EPA has provided documentation of the 

calculation in this appendix, with the relevant spreadsheets available in the docket.60 EPA has 

also revised its estimates of the TLAAS provision for this final rule. Please see the Response 

to Comments document for additional details. Several manufacturer decisions and 

marketplace events will ultimately determine the impacts of the TLAAS program. This 

appendix presents a sensitivity analysis that brackets the impact of the program, and provides 

additional details on the assumptions made in the EPA emission analysis. 

Although the bracketing analyses presented here range from 0 to 37 MMT of CO2 

emissions, in all cases the TLAAS program has a proportionally small impact (< 4%) on the 

total program benefits over the model years 2012-2016. The maximum impact presented here 

has increased approximately proportional to the increase in program size from the proposal; 

i.e., the maximum potential impact described in the NPRM was 25 MMT for 1.1 million 

vehicles, while the maximum impact described here is 37 MMT for 1.55 million vehicles. 

Under the estimation procedure used in the emission inventory analysis (as opposed to 

the bracketing analysis mentioned immediately above), the TLAAS program is projected to 

result in an approximately 14 MMT decrease in greenhouse gas benefits from this rule over 

the lifetime of vehicles manufactured in model years 2012-2016 (assuming that it is 

technically feasible for all TLAAS-eligible producers to meet the otherwise-applicable GHG 

standards for those years, a dubious assumption given the very short lead times available). 

While 14 MMT is a small fraction of the overall program benefits (approximately one 

percent of the estimated GHG reductions), 14 MMT is an increase over the 3.4 MMT impact 

 

JThese manufacturers could place up to 200,000 vehicles into the TLAAS fleet between MYs 2012-2015, and up 

to an additional 50,000 vehicles into that fleet in MY 2016. 

KThe final rule also deters regulation of manufacturers of vehicles with 2008 or 2009 domestic sales of less than 

5,000 vehicles whose three-year rolling average of domestic sales remain less than 5,000 vehicles, and which 

demonstrate inability to purchase credits. The deferral is respect to the CO2 standards only. These vehicles are 

not considered in the analysis above. 
Regulatory Impact Analysis 

5-50 

estimated in the proposal. This is largely due to several changes in the TLAAS program made 

in response to public comment, as discussed below. There was also a calculation error in the 

analysis for the proposal, which would have increased the estimate of the TLAAS, as 

proposed to 4.9 MMT. 

5.A.2 Factors Determining the Impact of the TLAAS 

The greatest challenge to accurately estimating the impacts of the TLAAS are 

uncertainties about manufacturer eligibility and manufacturer usage of the program. There is 

a third, albeit smaller uncertainty, concerning the size of the vehicles placed in the program. 

Eligibility

Up to eleven major manufacturers are potentially eligible for TLAAS based on 

preliminary EPA analysis of projected domestic sales for model year 2009. These 

manufacturers are Porsche, Jaguar, Mazda, Mitsubishi, Suzuki, Daimler, Subaru, BMW, 

Volkswagen, Hyundai, and Kia. Three of the above manufacturers are expected to be eligible 

for the expanded TLAAS program. These manufacturers are Suzuki, Porsche and Jaguar. 

Manufacturers such as Hyundai, Kia, Mazda, and Volkswagen are preliminarily 

estimated at 2009 domestic sales bordering 400,000. If none of these four manufacturers are 

eligible for the TLAAS program, the program covers up to 700,000 vehicles. If all four are 

included, the program increases in size by approximately 50% to 1.1 million vehicles. 

The impacts of the program therefore partially depend on manufacturer eligibility. 

Manufacturer Usage 

As explained in section III.B of the preamble to the final rule, the TLAAS program is 

predicated on the need for additional lead time for certain manufacturers, and is a reasonable 

exercise of EPA’s section 202 (a) to consider lead time in crafting standards. The TLAAS 

provides needed flexibility to manufacturers in order to comply with the CO2 standards for the 

earlier model years, , thereby providing needed lead time for these manufacturers to bring 

their entire fleet into compliance with the stringent 2016 MY standards or, for manufacturers 

eligible for TLAAS in MY 2016, to be fully compliant with the standards by the 2017 model 

year. However, it is unclear whether manufacturers will participate in the TLAAS program to 

the fullest extent allowed, as there are two disincentives to fully utilizing the TLAAS. 

Further, when the TLAAS program ends, manufacturers’ entire fleets must meet the 

more stringent main program standards. If a manufacturer takes full advantage of the 

program by using the maximum 25% additional emission allotment, they may place 

themselves at a technological disadvantage when the program ends. Both in terms of 

engineering and manufacturing, a manufacturer is unlikely to want to fall behind its 

competitors. To avoid this scenario, a manufacturer may make gradual gains over the 

TLAAS program, and gradually use less of the 25% additional emission allotment. 

Because of these disincentives, manufacturers may likewise choose to not fully utilize 

the TLAAS vehicle production volumes. 
Emissions Impacts 

5-51 

Size and Classification of the Vehicles Placed in the TLAAS Fleet

As the TLAAS program allows 25% additional emissions over the footprint-based 

main fleet standards, the size of the vehicles placed in the TLAAS fleet is significant in 

estimating its impacts. If a manufacturer places small but high emitting vehicles in the 

TLAAS fleet (ie, Porsche Carrera), the impact of the program is less than if large and high 

emitting vehicles are placed in the TLAAS fleet. 

A manufacturer which utilized the TLAAS fleet for small vehicles would necessarily 

have a proportionally lower net impact. Similarly, due to the two distinct footprint curves, the 

choice whether to place cars or trucks in the TLAAS fleet will also determine impact. 

5.A.3 Bounding Analysis of TLAAS Impact 

This section provides upper and lower bounds for the potential impacts from the 

TLAAS, and then describes the inputs used in the emission analysis. 

TLAAS is an optional program which can be used for a limited number of eligible 

vehicles to achieve compliance with the CO2 emission standard. Consequently, no 

manufacturer is obligated to use the program, and the lower bound of the program impact 

could theoretically be zero. This is considered a highly unlikely scenario, as several 

manufacturers are anticipated to use the TLAAS to meet their compliance targets given the 

lack of lead time for these manufacturers to make the major conversions necessary to meet the 

standards. 

Conversely, as an upper bound, every manufacturer could use their full allocation on 

their largest vehicle, could potentially increase sales of those vehicles to 100,000 over the four 

year period, plus 250,000 vehicles over a five-year period for eligible smaller manufacturers 

and could use the full 25% “cushion” for each of these vehicles. This is also an unlikely 

scenario, as it would require companies such as Porsche and BMW to sell specific vehicle 

models (such as the Porsche Boxster, or the Rolls Royce Phantom) in unprecedented numbers. 

As a boundary analysis, EPA analyzed these upper and lower bound scenarios. The 

GHG savings from the lower bound program was estimated at 976 MMT GHG reduced over 

lifetime of model years 2012-2016 (i.e. impact of the TLAAS is zero), while the upper bound 

impact was 938 MMT GHG reduced over the same period. Thus, the maximum potential 

impact of the program, even under this most extreme scenario is approximately 37 MMT. 

As noted, neither of these scenarios is remotely likely. However, the point of the 

bounding analysis is to show that the greatest possible impact of the TLAAS is still relatively 

minimal. 

5.A.4 Approach used for Estimating TLAAS Impact 

Having bounded the analysis, a third approach was used for the emission modeling 

described in RIA chapter 5. In this analysis, all eight TLAAS manufacturers were assumed to 

use the example vehicle allocation schedule from Section III of the proposal Preamble, 
Regulatory Impact Analysis 

5-52 

replicated in Table 5-47. This is a conservative estimate, as several of the manufacturers are 

unlikely to utilize their allocation due to either lack of need, or the disincentives discussed 

above. 

Table 5-47 TLAAS Default Vehicle Production Volumes 

MODEL 

YEAR 

2012 2013 2014 2015 

Sales Volume 40,000 30,000 20,000 10,000 

The allocation was split evenly between cars and trucks for each manufacturer. For 

these eight manufacturers, the TLAAS fleet allotment was assumed to emit as much CO2 per 

mile as expected from the largest complying footprint car or truck in each manufacturer’s 

fleet. For emission estimation purposes, upsizing the fleet effectively lowers the stringency of 

the target. This estimate combines the impact of the 25% additional emission allotment and 

the vehicle size factors discussed above. These vehicles were then proportionally averaged 

into the manufacturer’s GHG emission level. 

For the three manufacturers eligible for the expanded TLAAS, the phase-in schedule 

shown in the table below was used. As this allocation encompasses almost all of the 

manufacturers’ sales in the early years, a different allocation was used for each manufacturer. 

Almost all of Porsche’s fleet, both cars and trucks, is covered. Tata is assumed to split the 

allocation between car and trucks. Suzuki is assumed to use the entire allocation for its cars. 

As the compliance gap for the smaller manufacturers is on average significantly larger 

than the average compliance gap for the other 8 manufacturers, these manufacturers were 

assumed to make a more intense use of the TLAAS program. For these manufacturers, their 

TLAAS fleets are assumed to emit 1.25x more emissions than the manufacturer’s sales 

weighted target in 2012, and by 2016, they were assumed to emit 1.05x more emissions. This 

schedule assumes a gradual decrease in CO2 emissions, which would project that the 

manufacturers to reach complicance with the main program CO2 standards by 2017. The 

volumes assumes slight growth from the 2009 base year and are based on the EPA fleet data 

file. There is uncertainty in these estimates, as discussed above. 

Table 5-48 Volumes and Usage Ratios in Expanded TLAAS Analysis 

MODEL 

YEAR 

2012 2013 2014 2015 2016 

Sales 

Volume 

60,000 60,000 50,000 40,000 40,000 

Usage Ratio 1.25 1.20 1.15 1.10 1.05 
Emissions Impacts 

5-53 

The expanded TLAAS program grows the program by approximately 40% (1.55 

million vehicles now / 1.1 million vehicles in the proposal). The increased program size, 

combined with the assumption that the users of the expanded TLAAS program will use it 

more heavily accounts for the increased estimate of impacts. As in the proposal, small 

volume manufacturers are not included in this final rule analysis. 

In this analysis, the total TLAAS program results in an emission impact of 

approximately 14 MMT CO2 over the lifetime of the 2012-2016 MY vehicles. 

The gram per mile impacts are listed here for each of these scenarios. 

Table 5-49 Gram per Mile per Year 

 TLAAS impact (Grams CO2 

Emissions Per Mile) 

Model Year Lower 

Bound 

Scenario 

Upper 

Bound 

Scenario 

Estimate 

Used In 

Emission 

Analysis 

2012 0.0 2.6 1.2

2013 0.0 2.1 0.9

2014 0.0 1.6 0.6

2015 0.0 1.0 0.3

2016 0.0 0.5 0.1
Regulatory Impact Analysis 

5-54 

5.B Appendix to Chapter 5: Impacts of Advanced Technology Vehicle 

Incentives for Electric Vehicles 

5.B.1 Introduction and Summary 

As described in Preamble Section III, EPA is finalizing provisions that provide a 

temporary regulatory incentive for the commercialization of certain advanced vehicle power 

trains—electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell 

vehicles (FCVs)—for model year 2012-2016 light-duty and medium-duty passenger vehicles. 

EPA is finalizing two changes to the proposed incentive program—deleting the vehicle 

multiplier and adding an individual automaker cap on the cumulative vehicle production 

eligible for the zero gm/mi compliance value —that will limit the loss in GHG savings due to 

these incentives. These incentives apply for the model years 2012-2016 covered by this final 

rule, and EPA will revisit this issue in rulemakings for future model years. 

 This section provides an analysis of the emission impacts of 500,000 electric vehicles 

produced under the zero gram per mile incentive during the 2012-2016 timeframe. As stated 

in Preamble Section III, it is impossible to predict the number of EVs that will be produced 

between 2012 and 2016. EPA believes that sales of 500,000 “un-capped” EVs is a reasonable 

scenario. Fewer EVs, or a combination of 500,000 EVs and PHEVs, would lessen the loss in 

GHG benefits. Conversely, additional sales of “un-capped” EVs would increase the loss in 

GHG benefits. 

Based on the analysis presented here, sales of 500,000 uncapped electric vehicles 

would produce a net reduction in GHG benefits of 25 MMT over the program without this 

provision. 

5.B.2 Assumptions behind the Analysis 

This analysis is intended as a preliminary exercise in an emerging field. The net 

impacts of the EV provision are dependent on several assumptions, and the assessment 

published here is intended to be demonstrative. Several assumptions are conservative; most 

significantly, the assumptions regarding manufacturer usage of the EV provision are meant to 

be a boundary analysis. 

We assume that manufacturers utilize the full benefit of the EV provision to 

manufacture internal combustion engine vehicles that emit more than they would otherwise. 

As an example, the fleet target of a typical manufacturer would be 250 grams CO2 per mile in 

2016. If the manufacturer sold EVs under this program, the manufacturer could place less 

technology on their conventional vehicles, so long as their net achieved level met their target. 

In essence, the downstream (tailpipe) benefit of the EV would be canceled out. Because the 

same amount of gasoline would be consumed regardless of the EV provision, sales of EVs 

would therefore not impact total fuel savings or total emissions from gasoline vehicles. 

Upstream emissions for electric vehicles sold beyond the individual automaker cap would be 

accounted for by this rule, and would therefore have no negative environmental impact. 
Emissions Impacts 

5-55 

We assume 500,000 electric vehicles are sold in the 2012-2016 timeframe, which is a 

significant increase over current electric vehicle sales, which are near zero in EPA’s 2008 

baseline market data file. Similarly, EPA’s OMEGA modeling does not predict the need for 

significant electrification of the fleet over the time frame presented in this rulemaking. 

We assume that EVs are only sold as cars, and that they are driven for the same 

lifetime VMT as a conventional vehicle. 

5.B.3 Inputs 

As stated previously, we assume that there is no downstream benefit of electric 

vehicles, and therefore the net impact of electric vehicles is equivalent to the net impact of the 

electricity generated to fuel these vehicles. To calculate the net impact over the vehicle 

lifetime of all the EVs sold, the formula is as follows: 

EV emission impact = (Lifetime VMT) x (kWh/mile) x (Emissions/kWh) x (sales) 

Equation 6- EV Impacts 

The inputs used for this calculation are shown below (Table 5-51). Given that 

Equation 6 is linear, a change to any of these variables would produce a proportional and 

linear change in the results of the analysis. 

Table 5-50 Inputs for EV Analysis 

LIFETIME 

VMT 

195,264 

kWh/mile 0.329 

GHG Emissions 

g CO2e/kWh 

768 

The lifetime VMT is described in TSD Chapter 4 and in Table 5-32, and is assumed to 

be the same between conventional vehicles and electric vehicles. 

The kWh per mile value is intended as a rough estimate of the potential electricity 

usage of typical mid-size EV in the 2012-2016 timeframe. Based on preliminary EPA 

analysis, an EV of approximately 3200 pounds is projected to consume 230 Wh/mile over the 

combined FTP and HWFE tests. It is assumed that on-road energy consumption will be about 
Regulatory Impact Analysis 

5-56 

43% higher than the tested energy consumptionL

. Based on these assumptions, we project a 

mid-size EV would have a real world electrical energy consumption rate of 329 Wh/mile. 

Table 5-51 EV energy required 

FTP/HWFE 

Fuel Energy 

required 

230 

FTP/HWFE 

Real World 

Energy Required

329 

Accounting for the CO2e emissions from EVs requires accounting for emissions 

during the feedstock gathering, power generation, power distribution, and vehicle charging 

stages. In other words, accounting for electricity for EVs requires accounting for the 

efficiencies of the various stages of the combustion of fossil fuels at the power plant, as well 

as the inefficiencies in transmitting the electricity from plant to the vehicle. For this analysis, 

the electricity is generated at the 2005 national average emission level (633 g CO2/ kWh). 61M 

This value must be adjusted for emissions due to charging inefficiencies (“wall to 

vehicle” losses) of 10%, and transmission and distribution losses (“plant to wall”) of 7%, 

resulting in an actual upstream emission impact of 768 g CO2e for each kWh used at the 

vehicle. 

 The emission factors used in this analysis could be higher or lower depending on 

when users charge their vehicles, and whether this causes additional natural gas or coal power 

plants to be shunted on-line. If the CO2e emissions from powerplants were 10% higher or 

lower, the resulting impacts from the EV provision would be proportionally higher or lower. 

5.B.4 Computation 

The inputs from Table 5-51 were inserted in Equation 6, with the resulting impact 

shown below. 

 

L

 Based on preliminary data, we assume that on-road EV shortfall is greater for electric vehicles than typical ICE 

vehicles. This accounts for performance in different climates, as well as other issues. We assume this shortfall 

to be about 30%, if measured in terms of fuel economy. If converted to an increase in energy consumption, that 

factor becomes 1/0.7 or a 43% increase. 

M

 The value 633 g CO2/kWh was derived by beginning with the EPA eGrid 2007 v 1.1 emissions, combining 

N2O, CH4 and CO2 using GWP values stated in Table 5-5, and adding 6% for feedstock gathering based on 

Greet 1.8. 
Emissions Impacts 

5-57 

195,265 miles x 329 kWh/mile x 0.768 g CO2/kWh x 500,000 sales / (1012 conversion of grams to 

MMT) 

= 24.8MMT 

References

1

 Intergovernmental Panel on Climate Change Working Group I. 2007. Climate Change 2007 

- The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. 

2

 U.S. Environmental Protection Agency. 2009. Inventory of U.S. Greenhouse Gas Emissions 

and Sinks: 1990-2007. EPA 430-R-09-004. Available at

http://epa.gov/climatechange/emissions/downloads09/GHG2007entire_report-508.pdf 

3

 U.S. EPA. 2009 Technical Support Document for Endangerment and Cause or Contribute 

Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act. Washington, DC. 

pp. 180-194. Available at

http://epa.gov/climatechange/endangerment/downloads/Endangerment%20TSD.pdf 

4

 U.S. Environmental Protection Agency. 2009. Inventory of U.S. Greenhouse Gas Emissions 

and Sinks: 1990-2007. EPA 430-R-09-004. Available at

http://epa.gov/climatechange/emissions/downloads09/GHG2007entire_report-508.pdf 

5

 RIA Chapter 2 

6

 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-HQOAR-2009-0472-0215 

7

 McCulloch A.; Lindley A. A. From mine to refrigeration: a life cycle inventory analysis of 

the production of HFC-134a .. ; International journal of refrigeration 

 2003, vol. 26, no8, pp. 865-872 

8

 Intergovernmental Panel on Climate Change. Chapter 2. Changes in Atmospheric 

Constituents and in Radiative Forcing. September 2007. http://www.ipcc.ch/pdf/assessmentreport/ar4/wg1/ar4-wg1-chapter2.pdf . Docket ID: EPA-HQ-OAR-2009-0472-0117 

9

 EPA. MOVES 2010. http://www.epa.gov/otaq/models/moves/index.htm 

10 U.S. EPA 2009. Updated OMEGA Post-Processor Spreadsheet. February 2010. 

11 John Koupal, Richard Rykowski, Todd Sherwood, Ed Nam. “Documentation of Updated 

Light-duty Vehicle GHG Scenarios.” Memo to Docket ID No. EPA-HQ-OAR-2008-0318 

Docket ID: EPA-HQ-OAR-2009-0472-0116 
Regulatory Impact Analysis 

5-58 

12 MOVES documentation and technical documents can be seen at 

http://www.epa.gov/otaq/models/moves/index.htm. 

13 EPA’s OMEGA model, documentation, and technical documents can be found 

http://www.epa.gov/oms/climate/models.htm. The model is also docketed EPA-HQ-OAR2009-0472-0192. 

14 U.S. EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. 

EPA-420-R-10-006. February 2010. Docket EPA-HQ-OAR-2009-0472-11332. Chapters 2 

and 3. 

15 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-HQOAR-2009-0472-0215 

16 U.S. EPA. 2008. RFS2 Modified version of GREET1.7 Upstream Emissions Spreadsheet, 

October 31, 2008. Docket ID: EPA-HQ-OAR-2009-0472-0191 

17 U.S. EPA, FRM Achieved CO2 standards worksheet. 2010. 

18 NHTSA. 2009. Average Fuel Economy Standards, Passenger Cars and Light Trucks, 

Model Year 2011. Docket ID: NHTSA-2009-0062-0001. Docket ID : EPA-HQ-OAR2009-0472-0060 

http://www.nhtsa.dot.gov/portal/nhtsa_static_file_downloader.jsp?file=/staticfiles/DOT/NHT

SA/Rulemaking/Rules/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf 

19 When this rule’s analysis was initiated, the RFS2 rule was not yet final. Therefore, it 

assumes the ethanol volumes in Annual Energy Outlook 2007 (U.S. Energy Information 

Administration, Annual Energy Outlook 2007, Transportation Demand Sector Supplemental 

Table. http://www.eia.doe.gov/oiaf/archive/aeo07/supplement/index.html) 

20 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA420-R-10-006. February 2010. Docket EPA-HQ-OAR-2009-0472-11332. see also 75 FR 

14670, March 26, 2010 

21 EPA. 2009. Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard 

Program. EPA–HQ–OAR–2005–0161. http://www.epa.gov/otaq/renewablefuels/rfs2_1-

5.pdf. Docket ID: EPA-HQ-OAR-2009-0472-0119 

22 NHTSA. 2009. Average Fuel Economy Standards, Passenger Cars and Light Trucks, 

Model Year 2011. 

http://www.nhtsa.dot.gov/portal/nhtsa_static_file_downloader.jsp?file=/staticfiles/DOT/NHT

SA/Rulemaking/Rules/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf. Docket 

ID : EPA-HQ-OAR-2009-0472-0060 

23 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. August 

2009. EPA-HQ-OAR-2009-0472-0085. 
Emissions Impacts 

5-59 

24 Energy Information Administration. Annual Energy Outlook 2010 Early Release. 

Supplemental Transportation Tables. December 2009. 

http://www.eia.doe.gov/oiaf/aeo/supplement/sup_tran.xls. 

25 Energy Information Administration. Annual Energy Outlook 2009. Supplemental 

Transportation Tables. April 2009. 

http://www.eia.doe.gov/oiaf/aeo/supplement/sup_tran.xls. EPA-HQ-OAR-2009-0472-0121 

26U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. February 

2009.. 

27 NHTSA. 2009. Average Fuel Economy Standards, Passenger Cars and Light Trucks, 

Model Year 2011. Docket ID: NHTSA-2009-0062-0001. 

http://www.nhtsa.dot.gov/portal/nhtsa_static_file_downloader.jsp?file=/staticfiles/DOT/NHT

SA/Rulemaking/Rules/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf. Docket 

ID : EPA-HQ-OAR-2009-0472-0060 

28 NHTSA. 2009. Average Fuel Economy Standards, Passenger Cars and Light Trucks, 

Model Year 2011. Docket ID: NHTSA-2009-0062-0001. 

http://www.nhtsa.dot.gov/portal/nhtsa_static_file_downloader.jsp?file=/staticfiles/DOT/NHT

SA/Rulemaking/Rules/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf. Docket 

ID : EPA-HQ-OAR-2009-0472-0060 

29 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. February 

2009.. 

30 EPA. Emission Facts: Average Carbon Dioxide Emissions Resulting from Gasoline and 

Diesel Fuel. EPA420-F-05-001 February 2005. Docket ID: EPA-HQ-OAR-2009-0472-0122 

31 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. February 

2009. 

32 Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy. 

Transportation Energy Data Book: Edition 27. Chapter 4. 2008. Docket ID: EPA-HQOAR-2009-0472-0280. 

33 NHTSA. Vehicle Survivability and Travel Mileage Schedules. 2006. Docket ID: EPAHQ-OAR-2009-0472-0126 

34 Joint TSD Chapter 4 

35 NHTSA. Vehicle Survivability and Travel Mileage Schedules. 2006. Docket ID: EPAHQ-OAR-2009-0472-0126 

36 Joint TSD Chapter 4 
Regulatory Impact Analysis 

5-60 

37 EPA. Final Technical Support Document.Fuel Economy Labeling of Motor Vehicle 

Revisions to Improve Calculation of Fuel Economy Estimates. EPA-HQ-OAR-2009-0472-

0281 

38 John Koupal, Richard Rykowski, Todd Sherwood, Ed Nam. “Documentation of Updated 

Light-duty Vehicle GHG Scenarios.” Memo to Docket ID No. EPA-HQ-OAR-2008-0318. 

Docket ID: EPA-HQ-OAR-2009-0472-0116 

39 RIA chapter 2. 

40 Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy. 

Transportation Energy Data Book: Edition 27. 2008. Docket ID: EPA-HQ-OAR-2009-0472-

0280. 

41 EPA. Final Technical Support Document. Fuel Economy Labeling of Motor Vehicle 

Revisions to Improve Calculation of Fuel Economy Estimates. EPA-HQ-OAR-2009-0472-

0281 

42 EPA. Emission Facts: Average Carbon Dioxide Emissions Resulting from Gasoline and 

Diesel Fuel. EPA420-F-05-001 February 2005. Docket ID: EPA-HQ-OAR-2009-0472-0122 

43 EPA. Technical Description of the Toxics Module for MOBILE6.2 and Guidance on Its 

use for Emission Inventory Preparation. November 2002. 

44 U.S. EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. 

EPA-420-R-10-006. February 2010. Docket EPA-HQ-OAR-2009-0472-11332. Chapters 2 

and 3. 

45 Craig Harvey, EPA, “Calculation of Upstream Emissions for the GHG Vehicle Rule.” 

2009. Docket ID: EPA-HQ-OAR-2009-0472-0216 

46 Control of Emissions of Air Pollution From Locomotive Engines and Marine 

Compression-Ignition Engines Less Than 30 Liters per Cylinder, Republication, Final Rule 

(Federal Register Vol 73, No. 126, page 37096, June 30, 2008). Docket ID: EPA-HQ-OAR2009-0472-0139 

47 Control of Emissions From Nonroad Spark-Ignition Engines and Equipment, Final Rule 

(Federal Register Vol 73, No. 196, page 59034, October 8, 2008). Docket ID: EPA-HQOAR-2009-0472-0282 

48 Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 

Marine Diesel Engines, Chapter 3. This is available in Docket OAR-2007-0121at 

http://www.regulations.gov/ . Docket ID: EPA-HQ-OAR-2009-0472-0283 

49 Energy Information Administration. Annual Energy Outlook 2009. Supplemental 

Transportation Tables. April 2009. 

http://www.eia.doe.gov/oiaf/aeo/supplement/sup_tran.xls. EPA-HQ-OAR-2009-0472-0121 
Emissions Impacts 

5-61 

50 U.S. EPA. 2009. “For LD GHG AQ Modeling, 2030 Control Case: Adjustments to Oil 

Refining, and Crude Production/Transport SCCs,” spreadsheet file: 

“Oil_Prod_Transp_Refine_2030_adjust.xls,” 11/6/2009. 

51 U.S. EPA. 2009. “Gasoline Distribution SCCs and Adjustments to apply for the LD GHG 

AQ Modeling 2030 Control Case,” spreadsheet file: “LDGHG_SCC_GasDistrib_Adjust2.xls” 

11/9/2009. 

52 Harvey Michaels, US EPA. “NMIM and MOVES Runs for LD GHG Air Quality 

Modeling: Memorandum” 3/18/2010. Accompanying physical DVD which contains relevant 

software and processing scripts. Title: “NMIM and MOVES Runs for LGR Air Quality 

Modeling: DVD”. 

53 For information on Draft MOVES2009, see 

http://www.epa.gov/otaq/models/moves/movesback.htm. 

54 See http://www.epa.gov/ttn/chief/emch/index.html#2002. 

55 Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine 

Compression-Ignition Engines Less Than 30 Liters per Cylinder (published May 6, 2008 and 

republished June 30, 2008). For details, see 

http://www.epa.gov/otaq/locomotives.htm#2008final. 

56 Energy Information Administration. Annual Energy Outlook 2009. See 

http://www.eia.doe.gov/oiaf/archive/aeo06/index.htmle 

57 Energy Information Administration. Annual Energy Outlook 2009. See 

http://www.eia.doe.gov/oiaf/archive/aeo09/index.html 

58 NONROAD Model 2008a. http://www.epa.gov/otaq/nonrdmdl.htm 

59 Final Rule: Control of Emissions from Nonroad Spark-Ignition Engines and Equipment 

(published October 8, 2008). For details, see http://www.epa.gov/otaq/equip-ld.htm. Docket 

ID: EPA-HQ-OAR-2009-0472-0282 

60 U.S. EPA, FRM Achieved CO2 standards worksheet. 2010. 

61 U.S. EPA. 2009. eGrid2007 dataset. http://www.epa.gov/cleanenergy/energyresources/egrid/index.html. Accessed February 3, 2010.
Vehicle Program Costs Including Fuel Consumption Impacts 

6-1 

CHAPTER 6: Vehicle Program Costs Including Fuel 

Consumption Impacts 

This chapter presents the costs of the GHG vehicle program including the costs 

associated with addition of new technology and savings associated with improved fuel 

consumption. In section 6.1, vehicle compliance costs are presented on a per-car and pertruck basis for each manufacturer and the industry as a whole. Vehicle compliance costs are 

also presented on an annual basis for each manufacturer and the industry as a whole. Where 

appropriate, net present values are presented at both a 3 percent and a 7 percent discount rate 

for annual costs in the years 2012 through 2050. In section 6.2, the cost per ton of GHG 

reduced is presented as a result of the rule. In section 6.3, fuel consumption impacts are 

presented on a per-year basis for cars and trucks in terms of gallons saved and in terms of 

dollars saved. In section 6.4, the vehicle program costs and fuel consumption impacts are 

summarized. This chapter does not present costs associated with noise, congestion, accidents 

and other economic impacts associated with increased driving that could result from the rule. 

Such impacts are presented in Chapter 8 of this RIA. 

The costs presented here differ slightly from those presented in the proposal. The 

different costs for the vehicle program are the result of revised costs for a limited set of 

technologies expected to be used for compliance. Those revised costs stem from the 

continuing teardown cost estimation work being done by FEV for EPA. See 74 FR at 48502. 

At proposal, we used the tear down data to estimate the cost of stoichiometric gasoline direct 

injection and turbocharging with engine downsizing. We have expanded our use of FEV tear 

down costs for the final rule to estimate costs for the following technologies using new 

information available to us shortly after the proposal: stoichiometric gasoline direct injection 

and turbo charging with engine downsizing for a single overhead cam (SOHC) 3 

valve/cylinder V8 engine downsized to a SOHC V6 engine; stoichiometric gasoline direct 

injection and turbo chargin with engine downsizing for a dual overhead cam (DOHC) V6 

engine to a DOHC 4 cylinder engine; a 6 speed automatic transmission replacing a 5 speed 

automatic transmission; and a 6 speed wet dual clutch transmission replacing a 6 speed 

automatic transmission . 

This costing methodology has been published and gone through a peer review.1

 In 

addition, FEV and EPA extrapolated the engine downsizing costs for the following scenarios 

that were outside of the noted study cases:2 

1. Downsizing a SOHC 2 valve/cylinder V8 engine to a DOHC V6 engine. 

2. Downsizing a DOHC V8 engine to a DOHC V6 engine. 

3. Downsizing a SOHC V6 engine to a DOHC 4 cylinder engine. 

4. Downsizing a DOHC 4 cylinder engine to a DOHC 3 cylinder engine. 

For more detail on those revised technology costs refer to Chapter 3 (section 3.3.2.2) 

of the joint TSD. EPA fuel cost estimates have also been updated using the recent AEO 2010 
Regulatory Impact Analysis 

6-2 

Early Release. For more information about the updated fuel prices refer to Chapter 4 of the 

joint TSD. EPA has not changed technology cost or fuel cost estimates in response to 

comment and, in general, commenters agreed with or otherwise did not question EPA’s cost 

estimates and methodology. This is true not only for our estimates of 2016 MY costs but also 

our estimate of costs for the intermediate years 2012-2015. 

6.1 Vehicle Program Costs 

Chapter 4 of this RIA presents the outputs of the OMEGA model for the model year 

2016. Here, EPA builds on those results and calculates estimated costs for each model year 

beginning with 2012 and going through 2050. This is done on a per-vehicle basis and an 

annual basis. Costs here include costs associated with the A/C credit program. For details on 

the individual technology costs refer to Chapter 3 of the joint TSD. For details on the 

OMEGA model inputs (i.e., how the individual technology costs are combined into package 

costs) refer to Chapter 1 of this RIA. For details on the A/C costs, refer to Chapter 2 of this 

RIA. 

6.1.1 Vehicle Compliance Costs on a Per-Vehicle Basis 

As stated above, Chapter 4 of this RIA presents the estimated cost per 2016 MY 

vehicle for each manufacturer. Those 2016 MY costs are reproduced in Table 6-1. To 

estimate the cost per vehicle for model years 2012 through 2015, EPA projected CO2

levels for each manufacturer’s fleet for each model year 2011 though 2016. Those 

CO2 levels are presented in 

Table 6-2 for cars and Table 6-3 for trucks.A

 

 

A

 Note that the 2012-2015 CO2 levels are estimates based upon assumptions of manufacturer fleetwide CO2

averages in 2011, which are extrapolated from the 2008 baseline fleet. Consequently, the average CO2 emission 

levels for some manufacturers are potentially too high for the 2011MY which makes the transition to the 

2012MY appear as a more significant change. As a result, 2012MY costs represent a large percentage of the 

total costs. As an example, the 2012MY cost for Subaru as shown in Table 6-5 is approximately 45% of the 

2016MY cost. In reality, the transition between MY 2011 and MY 2016 may be significantly smoother, and is 

likely to be smoother due to multiyear planning. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-3 

Table 6-1 Cost per Car and Truck, including A/C, for the 2016 MY Relative to the Cost of Complying 

with the 2011 CAFE Standards (2007 dollars) 

MANUFACTURER $/CAR $/TRUCK

BMW $1,557 $1,195 

Chrysler $1,128 $1,501 

Daimler $1,535 $930 

Ford $1,108 $1,441 

General Motors $898 $1,581 

Honda $634 $473 

Hyundai $802 $425 

Kia $667 $247 

Mazda $854 $537 

Mitsubishi $817 $1,217 

Nissan $686 $1,118 

Porsche $1,506 $758 

Subaru $961 $789 

Suzuki $1,014 $536 

Tata $1,180 $679 

Toyota $380 $609 

Volkswagen $1,847 $971 

Overall $869 $1,098 

Table 6-2 Projected CO2 Levels for MYs 2011-2016, Cars Only (g/mi CO2) 

MANUFACTURER 2011MY 2012MY 2013MY 2014MY 2015MY 2016MY

BMW 313.8 297.0 282.1 266.3 250.4 236.3 

Chrysler 314.7 288.4 271.6 253.9 240.0 227.9 

Daimler 323.2 306.5 291.7 276.0 260.2 246.3 

Ford 311.3 276.9 268.8 259.9 247.1 233.4 

General Motors 305.4 278.2 270.4 261.5 248.3 241.3 

Honda 265.0 247.4 236.6 224.7 215.2 207.6 

Hyundai 273.8 255.7 244.5 232.3 222.5 214.5 

Kia 280.6 257.2 244.3 230.4 219.4 213.1 

Mazda 290.2 261.6 252.9 243.9 232.2 218.2 

Mitsubishi 286.8 262.1 252.7 239.6 226.6 223.3 

Nissan 280.0 263.1 255.8 247.5 236.3 223.2 

Porsche 339.0 318.7 300.3 281.0 261.6 244.1 

Subaru 286.4 259.8 246.8 238.6 230.4 218.2 

Suzuki 285.2 266.3 249.3 231.4 213.4 197.3 

Tata 348.2 328.9 311.6 293.4 275.1 258.6 

Toyota 253.1 243.7 236.3 227.8 219.4 212.8 

Volkswagen 291.5 276.6 263.6 249.6 235.6 223.5 

Overall 288.0 266.8 256.0 245.2 233.7 223.8 
Regulatory Impact Analysis 

6-4 

Table 6-3 Projected CO2 Levels for MYs 2011-2016, Trucks Only (g/mi CO2) 

MANUFACTURER 2011MY 2012MY 2013MY 2014MY 2015MY 2016MY

BMW 369.8 350.1 332.6 313.9 295.3 278.7 

Chrysler 380.9 355.7 339.9 322.9 309.5 298.2 

Daimler 423.5 396.9 372.4 346.9 321.3 297.8 

Ford 394.5 373.3 362.5 350.8 332.1 298.3 

General Motors 402.5 384.1 372.5 362.2 344.0 305.1 

Honda 354.9 338.7 328.8 317.8 308.9 302.0 

Hyundai 356.8 344.1 336.6 327.9 320.8 315.6 

Kia 366.9 356.1 350.4 343.7 337.9 335.2 

Mazda 341.2 324.7 312.5 302.3 286.5 285.6 

Mitsubishi 336.9 311.6 300.8 292.0 277.0 264.0 

Nissan 399.3 361.0 350.8 339.5 321.0 299.8 

Porsche 364.2 356.3 350.5 343.7 336.8 332.0 

Subaru 335.8 307.5 293.6 284.8 275.9 263.0 

Suzuki 338.3 332.7 329.2 324.7 320.2 317.7 

Tata 377.5 365.2 355.1 343.9 332.8 323.6 

Toyota 375.4 360.6 347.9 334.1 320.3 308.6 

Volkswagen 420.3 400.1 382.0 362.9 343.7 326.6 

Overall 384.4 364.7 352.8 339.7 324.5 302.5 

The achieved CO2 levels for 2012-2015 were derived using a similar process to that 

described in Chapter 5 of this RIA. As in Chapter 5, EPA estimated model year specific 

emission targets based on the GHG standard curves and each manufacturer’s projected fleet 

mix. From these targets, EPA adjusted CO2 emissions by the impact of anticipated FFV 

credit, A/C credit, and TLAAS usage. For the emission analysis presented in RIA Chapter 5, 

EPA also estimated the impact of credit transfers which increased net emissions; i.e., several 

manufacturers 2008 baseline vehicles overcomplied with the 2011 MY CAFE standard for 

cars in the early program years. The credits associated with this overcompliance with the car 

standards could be traded to these manufacturers’ trucks fleets with the result of a net 

emission increase relative to a scenario that restricted transfers (i.e., required each 

manufacturer’s trucks to meet the 2011 CAFE standard while retaining over-compliance for 

cars). Credit transfers beyond baseline overcompliance is environmentally neutral and were 

not required in the emission analysis presented in Chapter 5. 

For the cost analysis presented here, EPA additionally considered environmentally 

neutral credit transfers. In order to reduce overall compliance costs, manufacturers may 

choose to overcomply with the GHG standard in either car or truck categories and “trade” (on 

a VMT weighted basis), their overcompliance to the other category. As detailed in RIA 

Chapter 4, OMEGA incorporates this flexibility and projects technology application based on 

the most cost effective path to compliance. Thus, the fleetwide CO2 achieved emission levels 

from RIA chapter 5 had to be adjusted to incorporate environmentally neutral credit transders. 

In these adjustments, the VMT adjusted fleet average CO2 by manufacturer remained the 

same, but the average emission levels for cars and trucks may be above or below the 

applicable CO2 standards and was based on the results of the OMEGA modeling. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-5 

The cost effective achieved levels for the intermediate years were derived in the 

following manner. Car and truck CO2 emissions in MY 2011 were taken directly from the 

reference fleet file.3 This fleet consists of 2008 MY vehicles, with sales projected to 2011 and 

with emissions reduced to the extent necessary for each manufacturer to comply with the 

2011 MY CAFEE standards, with one exception. This exception was that those 

manufacturers which traditionally have paid CAFE fines in lieu of compliance were allowed 

to do so when NHTSA’s VOLPE model estimated that it was less expensive for these 

manufacturers to pay fines instead of adding additional technology to meet the 2011 MY 

standards. MY 2016 achieved CO2 was determined from the OMEGA output described in 

RIA Chapter 4. To determine the CO2 emissions by manufacturer for the intermediate years, 

an interpolation was performed between these two points. Two different forms of 

interpolation were used, as appropriate. Generally, for manufacturers projected by the 

OMEGA modeling to achieve the 2016 MY standards, the change between each 

manufacturer’s 2011 and 2016 emission levels was weighted by the percent change between 

their fleet average (i.e., car plus truck) standard for each year (as determined in RIA Chapter 

5) relative to their 2011 MY emission level. For manufacturers that are projected by the 

OMEGA modeling to not achieve their MY 2016 standard, we assumed a linear improvement 

between their 2011 MY and 2016 MY emission levels (i.e., 20% of the total change between 

2011-2016 emission levels was applied each year). 

Several manufacturers, including Subaru, Kia, Mazda, and Mitsubishi, had their 

improvement front loaded in order to produce early year compliance. These companies are 

anticipated to comply with the intermediate year standards, but the 2008 base fleet may 

understate their expected performance.B The analysis behind the cost effective achieved 

levels is contained in the EPA docket.4

We then used these CO2 values to generate ratios that could be applied to the 2016 

MY costs to arrive at cost estimates for each of the intervening years. However, it is 

important to remember that the technology costs and, subsequently, the package costs in the 

2016 MY have undergone some adjustments to account for learning effects as described in 

Chapter 3 of the joint TSD. EPA compared the 2016 MY package costs to each of the 

intervening years and the results, on a percentage basis, are shown in Table 6-4. This was 

also done for the years following 2016 to reflect the effects of the near term and long term 

indirect cost multipliers (ICMs) as described in Chapter 3 of the joint TSD. The process for 

estimating costs in the intervening years is best understood by way of an example: General 

Motors cars are estimated to incur a cost of $898 in the 2016 MY while achieving a CO2

average of 241 g/mi; for the 2011 and 2012 MYs, GM cars are projected to achieve a CO2

average of 305 and 278, respectively. The ratio (305-278)/(305-241) can be applied to GM’s 

2016 cost of $898, and then apply the 2012 relative to 2016 cost factor of 119%, to arrive at 

an estimated 2012 cost of $453.C

 This process is carried out for each manufacturer for each 

year to arrive at the results presented in Table 6-5 for cars, Table 6-6 for trucks, and Table 6-7 

 

B

Ibid.

C

 Numbers in the text are rounded for clarity so results using numbers shown in the text may not match those in 

tables. 
Regulatory Impact Analysis 

6-6 

for cars and trucks combined. Table 6-8 shows the estimated industry average cost per car, 

cost per truck, and cost per vehicle (car/truck combined) for the 2012 and later model years.D

 

Table 6-4 Package Costs Measured Relative to the Package Costs for the 2016MY 

YEAR PACKAGE COSTS 

RELATIVE TO 2016

2012 119% 

2013 117% 

2014 109% 

2015 102% 

2016 100% 

2017 100% 

2018 100% 

2019 100% 

2020 100% 

2021 100% 

2022+ 94% 

 

D

 Note that the costs per car, truck and vehicle presented here do not include possible maintenance savings 

associated with the new A/C systems. They also do not include maintenance costs associated with low friction 

lubes and low rolling resistenance tires. Higher new vehicle costs are included for these latter items but do not 

account for higher replacement costs during vehicle lifetimes even though oil is changed many times and tires 

are changed once or twice. Using the incremental increase in maintenance costs (and savings) and discounting 

them back to present value would have little impact on the present value of the costs presented here. Note also 

that the expected penetration of A/C control technology is 28% in 2012 but was 25% in our emission modeling 

work (similarly small discrepancies exist for 2013-2015). The slightly lower penetration number used in the 

emission modeling indicates a slight underestimation of the emission reductions from MY 2012, and 

consequently a slight underestimation of the costs in 2012-2015. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-7 

Table 6-5 Cost per Car, including A/C, by Manufacturer Relative to the Cost of Complying with the 2011 

CAFE Standards (2007 dollars) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY

BMW $402 $746 $1,042 $1,300 $1,557

Chrysler $408 $656 $862 $991 $1,128

Daimler $397 $735 $1,027 $1,282 $1,535

Ford $583 $708 $797 $932 $1,108

General Motors $453 $575 $670 $816 $898

Honda $230 $367 $485 $560 $634

Hyundai $291 $464 $612 $708 $802

Kia $275 $420 $540 $616 $667

Mazda $404 $518 $599 $702 $854

Mitsubishi $378 $514 $662 $790 $817

Nissan $243 $342 $428 $538 $686

Porsche $383 $718 $1,004 $1,252 $1,506

Subaru $445 $653 $734 $805 $961

Suzuki $259 $484 $677 $844 $1,014

Tata $302 $563 $787 $982 $1,180

Toyota $105 $186 $260 $324 $380

Volkswagen $482 $887 $1,240 $1,547 $1,847

Overall $342 $507 $631 $749 $869

Table 6-6 Cost per Truck, including A/C, by Manufacturer Relative to the Cost of Complying with the 

2011 CAFE Standards (2007 dollars) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY

BMW $307 $571 $799 $996 $1,195

Chrysler $543 $871 $1,146 $1,321 $1,501

Daimler $235 $442 $618 $771 $930

Ford $377 $561 $714 $953 $1,441

General Motors $355 $569 $713 $968 $1,581

Honda $172 $273 $361 $419 $473

Hyundai $156 $244 $325 $379 $425

Kia $101 $150 $197 $230 $247

Mazda $190 $324 $410 $538 $537

Mitsubishi $502 $706 $818 $1,021 $1,217

Nissan $512 $638 $732 $898 $1,118

Porsche $222 $377 $527 $657 $758

Subaru $365 $535 $603 $662 $789

Suzuki $174 $276 $385 $481 $536

Tata $184 $330 $461 $575 $679

Toyota $161 $294 $410 $512 $609

Volkswagen $249 $464 $649 $810 $971

Overall $314 $496 $652 $820 $1,098
Regulatory Impact Analysis 

6-8 

Table 6-7 Cost per Vehicle (car/truck combined), including A/C, by Manufacturer Relative to the Cost of 

Complying with the 2011 CAFE Standards (2007 dollars) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY

BMW $363 $679 $959 $1,209 $1,453

Chrysler $493 $781 $1,020 $1,171 $1,328

Daimler $337 $622 $871 $1,089 $1,312

Ford $511 $658 $768 $940 $1,228

General Motors $406 $572 $691 $889 $1,219

Honda $206 $330 $439 $507 $574

Hyundai $273 $419 $553 $656 $745

Kia $197 $312 $386 $459 $501

Mazda $356 $471 $559 $672 $799

Mitsubishi $400 $545 $685 $826 $876

Nissan $334 $449 $537 $657 $823

Porsche $334 $614 $825 $1,009 $1,206

Subaru $419 $615 $692 $762 $912

Suzuki $239 $432 $583 $719 $855

Tata $295 $524 $728 $913 $1,099

Toyota $125 $222 $312 $387 $455

Volkswagen $437 $794 $1,106 $1,412 $1,693

Overall $331 $503 $639 $774 $948
Vehicle Program Costs Including Fuel Consumption Impacts 

6-9 

Table 6-8 Industry Average Cost per Car, Truck, and Combined by Year Relative to the Cost of 

Complying with the 2011 CAFE Standards (2007 dollars) 

YEAR $/CAR $/TRUCK $/VEHICLE

2012 $342 $314 $331 

2013 $507 $496 $503 

2014 $631 $652 $639 

2015 $749 $820 $774 

2016 $869 $1,098 $948 

2017 $869 $1,098 $947 

2018 $869 $1,098 $945 

2019 $869 $1,098 $943 

2020 $869 $1,098 $940 

2021 $869 $1,098 $939 

2022 $817 $1,032 $882 

2023 $817 $1,032 $881 

2024 $817 $1,032 $881 

2025 $817 $1,032 $880 

2026 $817 $1,032 $880 

2027 $817 $1,032 $879 

2028 $817 $1,032 $879 

2029 $817 $1,032 $878 

2030 $817 $1,032 $878 

2031 $817 $1,032 $877 

2032 $817 $1,032 $877 

2033 $817 $1,032 $876 

2034 $817 $1,032 $876 

2035 $817 $1,032 $875 

2036 $817 $1,032 $875 

2037 $817 $1,032 $875 

2038 $817 $1,032 $875 

2039 $817 $1,032 $875 

2040 $817 $1,032 $875 

2041 $817 $1,032 $875 

2042 $817 $1,032 $875 

2043 $817 $1,032 $875 

2044 $817 $1,032 $875 

2045 $817 $1,032 $875 

2046 $817 $1,032 $875 

2047 $817 $1,032 $875 

2048 $817 $1,032 $875 

2049 $817 $1,032 $875 

2050 $817 $1,032 $875 
Regulatory Impact Analysis 

6-10 

6.1.2 Vehicle Compliance Costs on a Per-Year Basis 

Given the cost per car and cost per truck estimates shown in Table 6-5 and Table 6-6, 

respectively, annual costs can be calculated by multiplying by estimated sales. Table 6-9 

shows projected car sales by manufacturer for model years 2012-2016. Table 6-10 shows 

projected truck sales by manufacturer for model years 2012-2016. Table 6-11 shows 

combined sales by manufacturer for 2012-2016. Table 6-11 shows annual costs attributable 

to cars by manufacturer for MYs 2012-2016, Table 6-12 shows the same for trucks, and Table 

6-13 shows the same for cars and trucks combined. Table 6-14 then shows the annual costs 

by the entire industry for cars, trucks, and total for the years 2012 through 2050 with net 

present values using both a 3 percent and a 7 percent discount rate.E

 

Table 6-9 Estimated Annual Car Sales by Manufacturer (# of Units) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY 

BMW 289,631 293,905 369,979 411,653 422,874

Chrysler 409,462 426,454 411,319 392,483 399,762

Daimler 211,652 202,559 244,554 263,751 270,940

Ford 1,468,182 1,485,801 1,567,762 1,542,470 1,559,310

General Motors 1,586,094 1,544,975 1,452,559 1,487,318 1,514,479

Honda 906,096 1,064,848 1,087,076 912,434 930,350

Hyundai 376,284 395,573 395,515 511,236 518,445

Kia 299,611 326,652 427,191 538,717 548,055

Mazda 283,128 329,911 378,291 413,328 420,516

Mitsubishi 110,284 104,555 88,150 82,310 82,688

Nissan 824,030 831,607 854,131 925,478 946,518

Porsche 41,117 43,299 34,024 32,426 33,309

Subaru 183,486 175,170 184,521 204,746 206,903

Suzuki 72,297 81,781 90,597 100,600 103,003

Tata 36,377 50,527 49,316 63,751 65,489

Toyota 1,591,054 1,941,480 2,079,011 2,176,644 2,226,522

Volkswagen 434,412 498,641 517,978 567,711 583,185

Industry 9,123,197 9,797,738 10,231,974 10,627,055 10,832,348

 

E

 Note that the vehicle compliance costs presented here do not include costs associated with upgrading testing 

facilities to accommodate N2O testing. Including those costs would have very little impact on the costs 

presented here for new vehicle technology. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-11 

Table 6-10 Estimated Annual Truck Sales by Manufacturer (# of Units) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY 

BMW 204,197 183,550 191,010 175,612 170,749

Chrysler 692,115 594,092 514,802 475,312 462,150

Daimler 108,053 114,531 136,455 129,878 126,281

Ford 851,877 940,080 965,589 936,781 910,840

General Motors 1,510,917 1,536,070 1,336,797 1,379,813 1,341,604

Honda 634,705 676,729 634,606 560,745 545,217

Hyundai 58,164 101,529 103,857 94,606 91,986

Kia 87,643 102,773 114,423 118,391 115,113

Mazda 60,783 64,784 67,780 74,213 72,158

Mitsubishi 48,290 46,179 52,835 56,896 55,320

Nissan 405,017 391,572 406,045 391,733 380,886

Porsche 13,190 14,608 16,033 17,145 16,670

Subaru 97,935 89,944 99,293 116,055 117,295

Suzuki 4,593 16,557 20,060 20,547 19,978

Tata 29,647 33,749 40,294 43,703 42,493

Toyota 886,621 990,315 1,095,949 1,107,261 1,076,598

Volkswagen 104,842 141,421 151,992 127,888 124,346

Industry 5,798,588 6,038,484 5,947,819 5,826,579 5,669,683

 

Table 6-11 Estimated Annual Costs by Manufacturer, including A/C, for Cars Relative to the Cost of 

Complying with the 2011 CAFE Standards ($Millions of 2007 dollars) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY 

BMW $120 $220 $390 $540 $660

Chrysler $170 $280 $350 $390 $450

Daimler $80 $150 $250 $340 $420

Ford $860 $1,050 $1,250 $1,440 $1,730

General Motors $720 $890 $970 $1,210 $1,360

Honda $210 $390 $530 $510 $590

Hyundai $110 $180 $240 $360 $420

Kia $80 $140 $230 $330 $370

Mazda $110 $170 $230 $290 $360

Mitsubishi $40 $50 $60 $70 $70

Nissan $200 $280 $370 $500 $650

Porsche $20 $30 $30 $40 $50

Subaru $80 $110 $140 $160 $200

Suzuki $20 $40 $60 $80 $100

Tata $10 $30 $40 $60 $80

Toyota $170 $360 $540 $710 $850

Volkswagen $210 $440 $640 $880 $1,080

Industry $3,120 $4,970 $6,460 $7,960 $9,410
Regulatory Impact Analysis 

6-12 

Table 6-12 Estimated Annual Costs by Manufacturer, including A/C, for Trucks Relative to the Cost of 

Complying with the 2011 CAFE Standards ($Millions of 2007 dollars) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY 

BMW $60 $100 $150 $170 $200

Chrysler $380 $520 $590 $630 $690

Daimler $30 $50 $80 $100 $120

Ford $320 $530 $690 $890 $1,310

General Motors $540 $870 $950 $1,340 $2,120

Honda $110 $180 $230 $240 $260

Hyundai $10 $20 $30 $40 $40

Kia $10 $20 $20 $30 $30

Mazda $10 $20 $30 $40 $40

Mitsubishi $20 $30 $40 $60 $70

Nissan $210 $250 $300 $350 $430

Porsche $0 $10 $10 $10 $10

Subaru $40 $50 $60 $80 $90

Suzuki $0 $0 $10 $10 $10

Tata $10 $10 $20 $30 $30

Toyota $140 $290 $450 $570 $660

Volkswagen $30 $70 $100 $100 $120

Industry $1,820 $2,990 $3,880 $4,780 $6,230

Table 6-13 Estimated Annual Costs by Manufacturer, including A/C, for Cars and Trucks Combined 

Relative to the Cost of Complying with the 2011 CAFE Standards ($Millions of 2007 dollars) 

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY 

BMW $180 $320 $540 $710 $860

Chrysler $550 $800 $940 $1,020 $1,140

Daimler $110 $200 $330 $440 $540

Ford $1,180 $1,580 $1,940 $2,330 $3,040

General Motors $1,260 $1,760 $1,920 $2,550 $3,480

Honda $320 $570 $760 $750 $850

Hyundai $120 $200 $270 $400 $460

Kia $90 $160 $250 $360 $400

Mazda $120 $190 $260 $330 $400

Mitsubishi $60 $80 $100 $130 $140

Nissan $410 $530 $670 $850 $1,080

Porsche $20 $40 $40 $50 $60

Subaru $120 $160 $200 $240 $290

Suzuki $20 $40 $70 $90 $110

Tata $20 $40 $60 $90 $110

Toyota $310 $650 $990 $1,280 $1,510

Volkswagen $240 $510 $740 $980 $1,200

Industry $4,940 $7,960 $10,340 $12,740 $15,640
Vehicle Program Costs Including Fuel Consumption Impacts 

6-13 

Table 6-14 Annual Sales & Costs for Cars & Trucks Relative to the Cost of Complying with the 2011 

CAFE Standards (Monetary Values in 2007 dollars) 

YEAR CAR SALES TRUCK SALES CAR COSTS 

($MILLIONS) 

TRUCK COSTS 

($MILLIONS) 

TOTAL COSTS

($MILLIONS) 

2012 9,123,197 5,798,588 $3,120 $1,820 $4,940

2013 9,797,738 6,038,484 $4,970 $2,990 $7,960

2014 10,231,974 5,947,819 $6,460 $3,880 $10,340

2015 10,627,055 5,826,579 $7,960 $4,780 $12,740

2016 10,832,348 5,669,683 $9,410 $6,230 $15,640

2017 10,694,686 5,490,258 $9,290 $6,030 $15,320

2018 10,688,658 5,281,918 $9,290 $5,800 $15,090

2019 10,930,973 5,191,411 $9,500 $5,700 $15,200

2020 11,387,037 5,154,530 $9,900 $5,660 $15,560

2021 11,411,597 5,048,217 $9,920 $5,540 $15,460

2022 11,406,245 4,938,711 $9,320 $5,100 $14,420

2023 11,512,039 4,900,413 $9,400 $5,060 $14,460

2024 11,744,448 4,938,251 $9,590 $5,100 $14,690

2025 11,997,261 4,968,893 $9,800 $5,130 $14,930

2026 12,196,567 4,995,378 $9,960 $5,160 $15,120

2027 12,379,457 5,018,975 $10,110 $5,180 $15,290

2028 12,554,527 5,025,873 $10,260 $5,190 $15,450

2029 12,711,837 5,027,891 $10,380 $5,190 $15,570

2030 12,888,819 5,068,246 $10,530 $5,230 $15,760

2031 13,022,913 5,068,435 $10,640 $5,230 $15,870

2032 13,193,076 5,078,420 $10,780 $5,240 $16,020

2033 13,386,235 5,109,052 $10,940 $5,270 $16,210

2034 13,601,889 5,135,430 $11,110 $5,300 $16,410

2035 13,814,706 5,151,342 $11,290 $5,320 $16,610

2036 13,937,501 5,197,131 $11,390 $5,370 $16,760

2037 14,061,386 5,243,327 $11,490 $5,410 $16,900

2038 14,186,373 5,289,933 $11,590 $5,460 $17,050

2039 14,312,471 5,336,953 $11,690 $5,510 $17,200

2040 14,439,690 5,384,392 $11,800 $5,560 $17,360

2041 14,568,040 5,432,252 $11,900 $5,610 $17,510

2042 14,697,530 5,480,537 $12,010 $5,660 $17,670

2043 14,828,171 5,529,252 $12,110 $5,710 $17,820

2044 14,959,974 5,578,399 $12,220 $5,760 $17,980

2045 15,092,948 5,627,984 $12,330 $5,810 $18,140

2046 15,227,104 5,678,009 $12,440 $5,860 $18,300

2047 15,362,453 5,728,479 $12,550 $5,910 $18,460

2048 15,499,005 5,779,398 $12,660 $5,970 $18,630

2049 15,636,770 5,830,769 $12,770 $6,020 $18,790

2050 15,775,760 5,882,596 $12,890 $6,070 $18,960

NPV, 3% $226,730 $119,200 $345,940

NPV, 7% $124,010 $67,850 $191,860
Regulatory Impact Analysis 

6-14 

6.2 Cost per Ton of Emissions Reduced 

We have calculated the cost per ton of GHG (CO2 equivalent, or CO2e) reductions 

associated with this GHG rule using the costs shown in Table 6-14 and the emissions 

reductions described in Chapter 5. The cost per metric ton of GHG emissions reductions in 

the years 2020, 2030, 2040, and 2050 is calculated 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 

(see section 6.3 below). This latter calculation does not include the other benefits associated 

with this rule such as those associated with criteria pollutant reductions or energy security 

benefits as discussed in Chapter 8 of this RIA. By including the fuel savings in the cost 

estimates, the cost per ton is less than $0, since the estimated value of fuel savings outweighs 

the vehicle program costs. With regard to the CH4 and N2O standards, since these standards 

would be emissions caps designed to ensure that manufacturers do not backslide from current 

levels, the costs associated with the standards were not estimated (since the standards would 

not require any change from current practices nor is it estimated they would result in 

emissions reductionsF

). 

The results for CO2e costs per ton under the final rule are shown in Table 6-15. 

Table 6-15 Annual Cost Per Metric Ton of CO2e Reduced, in $2007 dollars 

Year 

Vehicle 

Compliance 

Cost a

($Millions) 

Fuel Savings b

($Millions) 

CO2-equivalent 

Reduction 

(Million metric tons) 

Cost per Ton – 

Vehicle 

Program only 

Cost per Ton – 

Vehicle Program 

with Fuel Savings 

2020 $15,600 -$35,700 160 $100 -$130 

2030 $15,800 -$79,800 310 $50 -$210 

2040 $17,400 -$119,300 400 $40 -$250 

2050 $19,000 -$171,200 510 $40 -$300 

a Costs here include vehicle compliance costs and do not include any fuel savings 

b Fuel savings calculated using pre-tax fuel prices.

6.3 Fuel Consumption Impacts 

In this section, EPA presents the impact of the final rule on fuel consumption and the 

consumer savings realized due to the lower fuel consumption. Chapter 5 provides more detail 

on the estimated reduction in the gallons of fuel expected to be consumed as a result of the 

rule. 

The new CO2 standards will result in significant improvements in the fuel efficiency of 

affected vehicles. Drivers of those vehicles will see corresponding savings associated with 

 

F

 Including those costs would have very little impact on the costs presented here for new vehicle technology. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-15 

reduced expenditures for fuel. EPA has estimated the impacts on fuel consumption for both 

the tailpipe CO2 standards and the A/C credit program. To do this, fuel consumption is 

calculated using both current CO2 emission levels and the new CO2 standards. The difference 

between these estimates represents the net savings from the new CO2 standards. 

The expected impacts on fuel consumption are shown in Table 6-16. The gallons 

shown in the tables reflect impacts from the new CO2 standards, including the A/C credit 

program, and include increased consumption resulting from the rebound effect. Using these 

fuel consumption estimates, the monetized fuel savings associated with the new CO2

standards can be calculated. To do this, the reduced fuel consumption in each year is 

multiplied by the corresponding estimated average fuel price in that year, using the reference 

case taken from the AEO 2010 Early Release. AEO is the government consensus estimate 

used by NHTSA and many other government agencies to estimate the projected price of fuel. 

The calculation has been done using both the pre-tax and the post-tax fuel prices. The latter 

of these is what consumers actually pay for the fuel and, therefore, the post-tax fuel savings 

are those savings that consumers will realize. The pre-tax fuel savings represent the savings 

to society. 

Table 6-16 Annual Fuel Consumption Impacts of the Vehicle Standards and A/C Credit Programs 

 (Monetary values in 2007 dollars) 

YEAR GALLONS 

(MILLIONS) 

FUEL PRICE 

EXCLUDING 

TAXES 

($/GALLON) 

FUEL PRICE 

INCLUDING 

TAXES 

($/GALLON) 

PRE-TAX 

FUEL 

SAVINGS 

($MILLIONS) 

POST-TAX 

FUEL SAVINGS 

($MILLIONS) 

2012 500 $2.08 $2.61 $1,100 $1,400

2013 1,300 $2.21 $2.84 $2,900 $3,800

2014 2,300 $2.45 $2.95 $5,700 $6,900

2015 3,800 $2.56 $3.00 $9,600 $11,300

2016 5,700 $2.61 $3.07 $14,800 $17,400

2017 7,500 $2.68 $3.13 $20,100 $23,500

2018 9,200 $2.75 $3.19 $25,400 $29,400

2019 10,900 $2.80 $3.22 $30,600 $35,200

2020 12,600 $2.84 $3.27 $35,700 $41,100

2021 14,200 $2.89 $3.29 $40,900 $46,700

2022 15,600 $2.92 $3.34 $45,600 $52,200

2023 17,100 $2.96 $3.37 $50,500 $57,400

2024 18,400 $2.99 $3.38 $55,100 $62,200

2025 19,700 $3.01 $3.42 $59,200 $67,300

2026 20,800 $3.05 $3.46 $63,600 $72,100

2027 22,000 $3.09 $3.49 $67,900 $76,600

2028 22,900 $3.13 $3.54 $71,800 $81,200

2029 23,900 $3.18 $3.59 $76,000 $85,600

2030 24,700 $3.23 $3.60 $79,800 $89,100

2031 25,600 $3.25 $3.64 $83,100 $93,200

2032 26,400 $3.29 $3.69 $86,800 $97,300
Regulatory Impact Analysis 

6-16 

2033 27,200 $3.34 $3.72 $90,700 $101,100

2034 27,900 $3.37 $3.77 $94,200 $105,200

2035 28,700 $3.42 $3.83 $98,200 $109,900

2036 29,500 $3.48 $3.87 $102,700 $114,100

2037 30,300 $3.53 $3.91 $106,700 $118,400

2038 31,000 $3.57 $3.95 $110,800 $122,700

2039 31,800 $3.61 $3.99 $115,000 $127,100

2040 32,600 $3.66 $4.04 $119,300 $131,700

2041 33,400 $3.70 $4.08 $123,800 $136,400

2042 34,300 $3.75 $4.12 $128,400 $141,200

2043 35,100 $3.79 $4.17 $133,200 $146,300

2044 36,000 $3.84 $4.21 $138,100 $151,400

2045 36,800 $3.89 $4.26 $143,200 $156,800

2046 37,700 $3.93 $4.30 $148,400 $162,300

2047 38,700 $3.98 $4.35 $153,900 $168,100

2048 39,600 $4.03 $4.39 $159,500 $173,900

2049 40,500 $4.08 $4.44 $165,300 $180,000

2050 41,500 $4.12 $4.49 $171,200 $186,300

NPV, 

3% $1,545,600 $1,723,900

NPV, 

7% $672,600 $755,700

As shown in Table 6-16, we are projecting that consumers will realize very large fuel 

savings as a result of the new CO2 standards. There are several ways to view this value. 

Some, as demonstrated below in Chapter 8 of this RIA, view these fuel savings as a reduction 

in the cost of owning a vehicle, whose full benefits consumers realize. This approach 

assumes that, regardless of how consumers in fact make their decisions on how much fuel 

economy to purchase, they will necessarily gain these fuel savings. Another view says that 

consumers do not necessarily value fuel savings as equal to the results of this calculation, 

notwithstanding actual dollars accruing to them. Instead, consumers may either undervalue or 

overvalue fuel economy relative to these savings, based on their personal preferences. This 

issue is discussed further in Section 8.1.2 of this RIA. 

If the analysis is limited to the five model years 2012-2016—in other words, to the 

fuel consumption savings during the vehicle lifetimes of those five model years, the results 

would be as shown in Table 6-17. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-17 

Table 6-17 Annual Fuel Savings for 2012-2016 MY Vehicles Using Pre-tax Fuel Prices ($Millions of 2007 

dollars) 

YEAR 2012MY 2013MY 2014MY 2015MY 2016MY SUM 

2012 $1,300 $1,300

2013 $1,300 $2,000 $3,400

2014 $1,400 $2,200 $2,900 $6,500

2015 $1,500 $2,200 $3,000 $4,300 $10,900

2016 $1,400 $2,100 $2,900 $4,300 $6,000 $16,700

2017 $1,400 $2,100 $2,900 $4,200 $5,900 $16,600

2018 $1,300 $2,000 $2,800 $4,200 $5,900 $16,300

2019 $1,300 $2,000 $2,700 $4,100 $5,800 $15,800

2020 $1,200 $1,800 $2,600 $3,900 $5,600 $15,200

2021 $1,100 $1,700 $2,500 $3,800 $5,400 $14,500

2022 $1,000 $1,600 $2,300 $3,500 $5,100 $13,600

2023 $900 $1,500 $2,200 $3,300 $4,900 $12,800

2024 $800 $1,300 $2,000 $3,100 $4,600 $11,800

2025 $700 $1,100 $1,800 $2,800 $4,200 $10,600

2026 $500 $1,000 $1,500 $2,500 $3,900 $9,400

2027 $400 $800 $1,300 $2,200 $3,500 $8,200

2028 $400 $700 $1,100 $1,800 $3,000 $6,900

2029 $300 $500 $900 $1,500 $2,500 $5,800

2030 $200 $400 $700 $1,200 $2,100 $4,700

2031 $200 $400 $600 $1,000 $1,700 $3,900

2032 $200 $300 $500 $800 $1,400 $3,200

2033 $100 $200 $400 $700 $1,100 $2,600

2034 $100 $200 $300 $600 $900 $2,100

2035 $100 $200 $300 $500 $800 $1,700

2036 $100 $100 $200 $400 $600 $1,500

2037 $100 $100 $200 $300 $500 $1,200

2038 $0 $100 $200 $300 $400 $1,000

2039 $0 $100 $100 $200 $400 $900

2040 $0 $100 $100 $200 $300 $700

2041 $0 $100 $100 $100 $300 $600

2042 $0 $100 $100 $100 $200 $500

2043 $0 $0 $100 $100 $200 $400

2044 $0 $0 $100 $100 $200 $400

2045 $0 $0 $100 $100 $100 $300

2046 $0 $0 $0 $100 $100 $300

2047 $0 $0 $0 $100 $100 $300

2048 $0 $0 $0 $100 $100 $200

2049 $0 $0 $0 $100 $100 $200

2050 $0 $0 $0 $0 $100 $100

NPV, 3% $15,600 $23,300 $31,600 $45,300 $62,500 $178,300

NPV, 7% $12,100 $18,100 $24,600 $35,400 $48,800 $139,000
Regulatory Impact Analysis 

6-18 

6.4 Vehicle Program Cost Summary 

The vehicle program costs consist of the vehicle compliance costs relative to the cost 

of complying with the 2011 CAFE standards, and the fuel savings that would result from the 

reduction in fuel consumption. These costs are summarized in Table 6-18. 
Vehicle Program Costs Including Fuel Consumption Impacts 

6-19 

Table 6-18 Annual Vehicle Program Costs Including Fuel Savings Using Post-Tax Fuel Prices ($Millions 

of 2007 dollars) 

YEAR 

VEHICLE 

COMPLIANCE 

COSTS 

FUEL SAVINGS) TOTAL 

2012 $4,900 -$1,400 $3,500

2013 $8,000 -$3,800 $4,200

2014 $10,300 -$6,900 $3,400

2015 $12,700 -$11,300 $1,400

2016 $15,600 -$17,400 -$1,800

2017 $15,300 -$23,500 -$8,200

2018 $15,100 -$29,400 -$14,300

2019 $15,200 -$35,200 -$20,000

2020 $15,600 -$41,100 -$25,500

2021 $15,500 -$46,700 -$31,200

2022 $14,400 -$52,200 -$37,800

2023 $14,500 -$57,400 -$42,900

2024 $14,700 -$62,200 -$47,500

2025 $14,900 -$67,300 -$52,400

2026 $15,100 -$72,100 -$57,000

2027 $15,300 -$76,600 -$61,300

2028 $15,500 -$81,200 -$65,700

2029 $15,600 -$85,600 -$70,000

2030 $15,800 -$89,100 -$73,300

2031 $15,900 -$93,200 -$77,300

2032 $16,000 -$97,300 -$81,300

2033 $16,200 -$101,100 -$84,900

2034 $16,400 -$105,200 -$88,800

2035 $16,600 -$109,900 -$93,300

2036 $16,800 -$114,100 -$97,300

2037 $16,900 -$118,400 -$101,500

2038 $17,100 -$122,700 -$105,600

2039 $17,200 -$127,100 -$109,900

2040 $17,400 -$131,700 -$114,300

2041 $17,500 -$136,400 -$118,900

2042 $17,700 -$141,200 -$123,500

2043 $17,800 -$146,300 -$128,500

2044 $18,000 -$151,400 -$133,400

2045 $18,100 -$156,800 -$138,700

2046 $18,300 -$162,300 -$144,000

2047 $18,500 -$168,100 -$149,600

2048 $18,600 -$173,900 -$155,300

2049 $18,800 -$180,000 -$161,200

2050 $19,000 -$186,300 -$167,300

NPV, 3% $345,900 -$1,723,900 -$1,378,000

NPV, 7% $191,900 -$755,700 -$563,800
Regulatory Impact Analysis 

6-20 

References 

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472. 

1

 EPA-420-R-09-020, EPA docket number EPA-HQ-OAR-2009-0472-11282; peer review 

report dated November 6, 2009, is at EPA-HQ-OAR-2009-0472-11285. 

2

 “Binning of FEV Costs to GDI, Turbo-charging, and Engine Downsizing,” memorandum to 

Docket EPA-HQ-OAR-2009-0472, from Michael Olechiw, U.S. EPA, dated March 25, 2010. 

3

 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. August 

2009. 

4

 US EPA 2010. Cost effective achieved levels spreadsheet.
 Environmental and Health Impacts 

7-1 

CHAPTER 7:Environmental and Health Impacts 

7.1 Health and Environmental Effects of Non-GHG Pollutants 

7.1.1 Health Effects Associated with Exposure to 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 would not be directly regulated by the standards, 

but the standards would affect emissions of these pollutants and precursors. 

7.1.1.1 Particulate Matter 

7.1.1.1.1 Background 

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). Current national ambient air quality standards 

(NAAQS) use PM2.5 as the indicator for fine particles (with PM2.5 referring to particles with a 

nominal mean aerodynamic diameter less than or equal to 2.5 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 aerodynamic diameter. 

Particles span many sizes and shapes and consist of numerous different chemicals. 

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 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 chemicals including 
Regulatory Impact Analysis 

7-2 

sulfates, nitrates, organic compounds, elemental carbon and metal compounds. These 

particles can remain in the atmosphere for days to weeks and travel through the atmosphere 

hundreds to thousands of kilometers.1

 

7.1.1.1.2 Particulate Matter Health Effects 

This section provides a summary of the health effects associated with exposure to 

ambient concentrations of PM.A

 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).B

 

The ISA concludes that ambient concentrations of PM are associated with a number of 

adverse health effects.C 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 both short-term and long-term exposure periods. 

7.1.1.1.2.1 Effects Associated with PM2.5

Short-term Exposure 

The ISA concludes that cardiovascular effects and all-cause cardiovascular- and 

respiratory-related mortality are causally associated with short-term exposure to PM2.5.

2

 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. 

Long-term Exposure 

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 cardiopulmonary causes.3

 It also 

 

A

 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 both components may contribute to adverse health effects. 

B

 The ISA is available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 

C

 The ISA evaluates the health evidence associated with different health effects, assigning one of five “weight of 

evidence” determination: 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. The following text summarizes only those 

health effects with at least a “suggestive” weight of evidence determination. 
 Environmental and Health Impacts 

7-3 

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. 

7.1.1.1.2.2 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.

4

 The ISA concludes that available 

evidence is suggestive of a causal relationship between short-term exposures to PM10-2.5 and 

cardiovascular effects, such as hospitalizations for ischemic heart disease. 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 

and pulmonary inflammation. 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.

5

7.1.1.1.2.3 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).6 

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.7

7.1.1.2 Ozone 

7.1.1.2.1 Background 

 Ground-level ozone pollution is typically formed by the reaction of VOCs and NOX

in the lower atmosphere in the presence of heat and 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. 
Regulatory Impact Analysis 

7-4 

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) 

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. 

7.1.1.2.2 Health Effects of Ozone 

Exposure to ambient ozone contributes to a wide range of adverse health effects.D

 

These health effects are well documented and are critically assessed in the EPA ozone air 

quality criteria document (ozone AQCD) and EPA staff paper.8,9 We are relying on the data 

and conclusions in the ozone AQCD and staff paper, regarding the health effects associated 

 

D

 Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people 

move between locations which have notable different ozone concentrations. Also, the amount of ozone 

delivered to the lung is not only influenced by the ambient concentrations but also by the individuals breathing 

route and rate. 
 Environmental and Health Impacts 

7-5 

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 recent report on the estimation of ozonerelated 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.10 People who appear to be more 

susceptible to effects associated with exposure to ozone include children, asthmatics and the 

elderly. Those with greater exposures to ozone, for instance due to time spent outdoors (e.g., 

children and outdoor workers), are also of concern. 

Based on a large number of scientific studies, EPA has identified several key health 

effects associated with exposure to levels of ozone found today in many areas of the country. 

Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to ambient ozone 

concentrations have been linked to lung function decrements, respiratory symptoms, increased 

hospital admissions and emergency room visits for respiratory problems.11, 12, 13, 14, 15, 16 

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung 

inflammation and can aggravate preexisting respiratory diseases, such as asthma.17, 18, 19, 20, 21

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.22, 23, 24, 25 

Children and adults who are outdoors and active during the summer months, such as 

construction workers, are among those most at risk of elevated ozone exposures.26 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.27 For example, summer camp studies in the Eastern United 

States and Southeastern Canada have reported statistically significant reductions in lung 

function in children who are active outdoors.28, 29, 30, 31, 32, 33, 34, 35 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.36, 37, 38, 39
Regulatory Impact Analysis 

7-6 

7.1.1.3 Nitrogen Oxides and Sulfur Oxides 

7.1.1.3.1 Background 

 Sulfur dioxide (SO2), a member of the sulfur oxide (SOX) family of gases, is formed 

from burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or 

extracting metals from ore. 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 vapor 

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 7.1.1.1.2. NOX along with non-methane hydrocarbons 

(NMHC) are the two major precursors of ozone. The health effects of ozone are covered in 

Section 7.1.1.2.2. 

7.1.1.3.2 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.40 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 exposure to SO2. The 

immediate effect of SO2 on the respiratory system in humans is bronchoconstriction. 

Asthmatics are more sensitive to the effects of SO2 likely resulting from preexisting 

inflammation associated with this disease. In laboratory studies involving controlled human 

exposures to SO2, respiratory effects have consistently been observed following 5-10 min 

exposures at SO2 concentrations ≥ 0.4 ppm in asthmatics engaged in moderate to heavy levels 

of exercise, with more limited evidence of respiratory effects among exercising asthmatics 

exposed to concentrations as low as 0.2-0.3 ppm. A clear concentration-response relationship 

has been demonstrated in these studies following exposures to SO2 at concentrations between 

0.2 and 1.0 ppm, both in terms of increasing severity of respiratory symptoms and decrements 

in lung function, as well as the percentage of asthmatics adversely affected. 

In epidemiologic studies, respiratory effects have been observed in areas where the 

mean 24-hour SO2 levels range from 1 to 30 ppb, with maximum 1 to 24-hour average SO2

values 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 
 Environmental and Health Impacts 

7-7 

on the SO2 effect estimates, the effect of SO2 on respiratory health outcomes appears to be 

generally robust and independent of the effects of gaseous and particulate copollutants, 

suggesting that the observed effects of SO2 on respiratory endpoints occur independent of the 

effects of other ambient air pollutants. 

Consistent associations between short-term exposure to SO2 and mortality have been 

observed in epidemiologic studies, with larger effect estimates reported for respiratory 

mortality than for cardiovascular mortality. While this finding is consistent with the 

demonstrated effects of SO2 on respiratory morbidity, uncertainty remains with respect to the 

interpretation of these 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. 

7.1.1.3.3 Health Effects of NO2

Information on the health effects of NO2 can be found in the EPA Integrated Science 

Assessment (ISA) for Nitrogen Oxides.41 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 

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. In addition, small but significant increases in nonspecific airway hyperresponsiveness were reported following 1-hour exposures of asthmatics 

to 0.1 ppm NO2. 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. 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 
Regulatory Impact Analysis 

7-8 

health endpoints. These include all-cause (nonaccidental) mortality, hospital admissions or 

emergency department visits for cardiovascular disease, and decrements in lung function 

growth associated with chronic exposure. 

7.1.1.4 Carbon Monoxide 

Information on the health effects of carbon monoxide (CO) can be found in the EPA 

Integrated Science Assessment (ISA) for Carbon Monoxide.42 The ISA concludes that 

ambient concentrations of CO are associated with a number of adverse health effects.E

 This 

section provides a summary of the health effects associated with exposure to ambient 

concentrations of CO.F 

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 

between short-term exposures to CO and cardiovascular morbidity. It also concludes that 

available data are inadequate to conclude that a causal relationship exists between long-term 

exposures to CO and cardiovascular morbidity. 

Animal studies show various neurological effects with in-utero CO exposure. 

Controlled human exposure studies report inconsistent neural and behavioral effects following 

low-level CO exposures. The ISA concludes the evidence is suggestive of a causal 

relationship with both short- and long-term exposure to CO and central nervous system 

effects. 

A number of epidemiologic and animal toxicological studies cited in the ISA have 

evaluated associations between preterm birth and cardiac birth defects and CO exposure. The 

epidemiologic studies provide limited evidence of a CO-induced effect on pre-term births and 

 

E

 The ISA evaluates the health evidence associated with different health effects, assigning one of five “weight of 

evidence” determination: 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. 

F

 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. 
 Environmental and Health Impacts 

7-9 

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 

copollutant 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. 

7.1.1.5 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.43 These compounds include, but are not limited to, benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene. 

These compounds, except acetaldehyde, were identified as national or regional risk drivers in 

the 2002 National-scale Air Toxics Assessment (NATA) and have significant inventory 

contributions from mobile sources. 
Regulatory Impact Analysis 

7-10 

Table 7-1 Mobile Source Inventory Contribution to 2002 Emissions of NATA Risk Driversa 

 2002 NATA Risk Driver Percent of National 

Emissions Attributable to 

All Mobile Sources 

Percent of National 

Emissions Attributable to 

Light-Duty Vehicles 

Benzene 59% 41% 

1,3-Butadiene 58% 37% 

Formaldehyde 43% 19% 

Acrolein 18% 9% 

Polycyclic organic matter (POM)b 6% 3% 

Naphthalene 35% 22% 

Diesel PM and Diesel exhaust 

organic gases 

100% 1% 

a

 This table is generated from data contained in the pollutant specific Microsoft Access database files found 

in the State-Specific Emission by County section of the 2002 NATA webpage 

(http://www.epa.gov/ttn/atw/nata2002/tables.html) and data from the 2002 National Emissions Inventory 

(NEI; http://www.epa.gov/ttn/chief/net/2002inventory.html), which is the underlying basis for the emissions 

used in the 2002 NATA (http://www.epa.gov/ttn/atw/nata2002/methods.html). 

b This POM inventory includes the 15 POM compounds: benzo[b]fluoranthene, benz[a]anthracene, indeno(1,2,3-

c,d)pyrene, benzo[k]fluoranthene, chrysene, benzo[a]pyrene, dibenz(a,h)anthracene, anthracene, pyrene, 

benzo(g,h,i)perylene, fluoranthene, acenaphthylene, phenanthrene, fluorine, and acenaphthene. 

According to NATA for 2002, mobile sources were responsible for 47 percent of 

outdoor toxic emissions, over 50 percent of the cancer risk, and over 80 percent of the 

noncancer hazard. Benzene is the largest contributor to cancer risk of all 124 pollutants 

quantitatively assessed in the 2002 NATA and mobile sources were responsible for 59 percent 

of benzene emissions in 2002. In 2007, EPA finalized vehicle and fuel controls that address 

this public health risk; it will reduce total emissions of mobile source air toxics by 330,000 

tons in 2030, including 61,000 tons of benzene. 44

Noncancer health effects can result from chronic,G subchronic,H or acuteI 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 2002 

NATA, nearly the entire U.S. population was exposed to an average concentration of air 

toxics that has the potential for adverse noncancer respiratory health effects. This will 

 

G

 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). 

H

 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 

I

 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 
 Environmental and Health Impacts 

7-11 

continue to be the case in 2030, even though toxics concentrations will be lower. Mobile 

sources were responsible for over 80 percent of the noncancer (respiratory) risk from outdoor 

air toxics in 2002. The majority of this risk was from exposure to acrolein. The confidence in 

the RfC for acrolein is medium and confidence in NATA estimates of population noncancer 

hazard from ambient exposure to this pollutant is low.45,46

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 2002 NATA website.47 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. 

7.1.1.5.1 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.48,49,50 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.51,52

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.53,54 The most sensitive noncancer effect observed in humans, based on current data, 

is the depression of the absolute lymphocyte count in blood.55,56 In addition, recent work, 

including studies sponsored by the Health Effects Institute (HEI), provides evidence that 

biochemical responses are occurring at lower levels of benzene exposure than previously 

known.57,58,59,60 EPA’s IRIS program has not yet evaluated these new data. 

7.1.1.5.2 1,3-Butadiene 

 EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.61,62

 

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.63,64 There are numerous studies 

consistently demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by 

experimental animals and humans. The specific mechanisms of 1,3-butadiene-induced 

carcinogenesis are unknown; however, the scientific evidence strongly suggests that the 

carcinogenic effects are mediated by genotoxic metabolites. Animal data suggest that females 

may be more sensitive than males for cancer effects associated with 1,3-butadiene exposure; 
Regulatory Impact Analysis 

7-12 

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.65

7.1.1.5.3 Formaldehyde 

 Since 1987, EPA has classified formaldehyde as a probable human carcinogen based 

on evidence in humans and in rats, mice, hamsters, and monkeys.66 EPA is currently 

reviewing recently published epidemiological data. For instance, research conducted by the 

National Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and 

lymphohematopoietic malignancies such as leukemia among workers exposed to 

formaldehyde.67,68 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 exposures.69 A recent National Institute of 

Occupational Safety and Health (NIOSH) study of garment workers also found increased risk 

of death due to leukemia among workers exposed to formaldehyde.70 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.71

In the past 15 years there has been substantial research on the inhalation dosimetry for 

formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the 

Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement 

of the quantitative cancer dose-response assessment.72,73,74 CIIT’s risk assessment of 

formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. 

However, it should be noted that recent research published by EPA indicates that when twostage modeling assumptions are varied, resulting dose-response estimates can vary by several 

orders of magnitude.75,76,77,78 These findings are not supportive of interpreting the CIIT model 

results as providing a conservative (health protective) estimate of human risk.79 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.80

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.”81 EPA is reviewing the 
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7-13 

recent work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers 

for Health Research and other studies, as part of a reassessment of the human hazard and 

dose-response associated with formaldehyde. 

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.82,83

7.1.1.5.4 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.84 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.85,86 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.87 In short-term (4 week) rat studies, degeneration of 

olfactory epithelium was observed at various concentration levels of acetaldehyde 

exposure.

88,89 Data from these studies were used by EPA to develop an inhalation reference 

concentration. Some asthmatics have been shown to be a sensitive subpopulation to 

decrements in functional expiratory volume (FEV1 test) and bronchoconstriction upon 

acetaldehyde inhalation.90 The agency is currently conducting a reassessment of the health 

hazards from inhalation exposure to acetaldehyde. 

7.1.1.5.5 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.91 The IARC determined in 1995 that acrolein was not 

classifiable as to its carcinogenicity in humans.92

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.

93 These data and additional studies regarding acute effects of human exposure to 
Regulatory Impact Analysis 

7-14 

acrolein are summarized in EPA’s 2003 IRIS Human Health Assessment for acrolein.94

 

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.95 Lesions to 

the lungs and upper respiratory tract of rats, rabbits, and hamsters have been observed after 

subchronic exposure to acrolein.96 Acute exposure effects in animal studies report bronchial 

hyper-responsiveness.97 In a recent 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.98 Based on these 

animal data and demonstration of similar effects in humans (i.e., 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. 

7.1.1.5.6 Polycyclic Organic Matter (POM) 

 POM is generally defined as a large class of organic compounds which have multiple 

benzene rings and a boiling point greater than 100 degrees Celsius. Many of the compounds 

included in the class of compounds known as POM are classified by EPA as probable human 

carcinogens based on animal data. One of these compounds, naphthalene, is discussed 

separately below. Polycyclic aromatic hydrocarbons (PAHs) are a subset of POM that 

contain only hydrogen and carbon atoms. A number of PAHs are known or suspected 

carcinogens. Recent studies have found that maternal exposures to PAHs (a subclass of 

POM) 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 at age three.99,100 EPA has not yet evaluated these recent studies. 

7.1.1.5.7 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.101

 

The draft reassessment completed external peer review.102 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.103 

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.104

 
 Environmental and Health Impacts 

7-15 

Naphthalene also causes a number of chronic non-cancer effects in animals, including 

abnormal cell changes and growth in respiratory and nasal tissues.105

7.1.1.5.8 Other Air Toxics 

 In addition to the compounds described above, other compounds in gaseous 

hydrocarbon and PM emissions from vehicles would be affected by this final rule. 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.J

7.1.1.6 Exposure and Health Effects Associated with Traffic 

Populations who live, work, or attend school near major roads experience elevated 

exposure concentrations to a wide range of air pollutants, as well as higher risks for a number 

of adverse health effects. While the previous sections of this RIA have focused on the health 

effects associated with individual criteria pollutants or air toxics, this section discusses the 

mixture of different exposures near major roadways, rather than the effects of any single 

pollutant. As such, this section emphasizes traffic-related air pollution, in general, as the 

relevant indicator of exposure rather than any particular pollutant. 

Concentrations of many traffic-generated air pollutants are elevated for up to 300-500 

meters downwind of roads with high traffic volumes.106 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.107 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.108 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” 

 

J

 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 
Regulatory Impact Analysis 

7-16 

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.109

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.110

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.111

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.112 Therefore, at current population of 

approximately 309 million, assuming that population and housing similarly distributed, there 

are over 48 million people in the U.S. living near such sources. The HEI report also notes that 

in two North American cities, Los Angeles and Toronto, over 40% of each city’s population 

live within 500 meters of a highway or 100 meters of a major road. It also notes that about 

33% of each city’s population resides within 50 meters of major roads. Together, the 

evidence suggests that a large U.S. population lives in areas with elevated traffic-related air 

pollution. 

People living near roads are often socioeconomically disadvantaged. According to the 

2007 American Housing Survey, a renter-occupied property is over twice as likely as an 

owner-occupied property to be located near a highway with 4+ lanes, railroad or airport. In 

the same survey, the median household income of rental housing occupants was less than half 

that of owner-occupants ($28,921/$59,886). Numerous studies in individual urban areas 

report higher levels of traffic-related air pollutants in areas with high minority or poor 

populations.113,114,115

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 
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7-17 

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.116 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. 117,118,119 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.116 

7.1.2 Environmental Effects Associated with Exposure to Pollutants 

 In this section we will discuss the environmental effects associated with non-GHG copollutants, specifically: particulate matter, ozone, NOX, SOX, carbon monoxide and air toxics. 

7.1.2.1 Visibility Degradation 

 Emissions from LD vehicles contribute to poor visibility in the U.S. through their 

emissions of primary PM2.5 and secondary PM2.5 precursors such as NOX. 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. First, EPA has concluded 

that PM2.5 causes adverse effects on visibility in various locations, depending on PM 

concentrations and factors such as chemical composition and average relative humidity, and 

has set secondary PM2.5 standards.K

 The secondary PM2.5 standards act in conjunction with 

the regional haze program. The regional haze rule (64 FR 35714) 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 38680-81, 

July 18, 1997).L Visibility can be said to be impaired in both PM2.5 nonattainment areas and 

mandatory class I federal areas. Figure 7-1 shows the location of the 156 mandatory class I 

federal areas. 

 

K

 The existing annual primary and secondary PM2.5 standards have been remanded and are being addressed in 

the currently ongoing PM NAAQS review. 

L 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. 
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Figure 7-1 Mandatory Class I Areas in the U.S. 

7.1.2.1.1 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 

7-1). 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 
 Environmental and Health Impacts 

7-19 

constituent by its empirically-derived scattering and/or absorption efficiency, with adjustment 

for the relative humidity. Knowledge of the main constituents of a site's light extinction 

"budget" is critical for source apportionment and control strategy development. In addition to 

this indirect method of assessing light extinction, there are optical measurements which 

directly measure light extinction or its components. Such measurements are taken principally 

with either a transmissometer, which measures total light extinction, or by combining the PM 

light scattering measured by integrating nephelometers with the PM light absorption measured 

by an aethalometer. Scene characteristics are typically recorded three times daily with 35 

millimeter 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 proposed 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.120

7.1.2.2 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.121 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”.122 Like carbon dioxide (CO2) 

and other gaseous substances, ozone enters plant tissues primarily through apertures (stomata) 

in leaves in a process called “uptake”.123 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.124,125 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,126 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 
Regulatory Impact Analysis 

7-20 

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.127,128 

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)129,130,131 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.132

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. 

Ozone also has been conclusively shown to cause discernible injury to forest 

trees.133,134 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.135,136 

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 
 Environmental and Health Impacts 

7-21 

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.137 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.138,139,140 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. 

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.”141 In addition, economic studies have shown 

reduced economic benefits as a result of predicted reductions in crop yields associated with 

observed ozone levels.142,143,144 

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.145 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. 

Air pollution can have noteworthy cumulative impacts on forested ecosystems by 

affecting regeneration, productivity, and species composition.146 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.147

 

In the U.S. this indicator is based on data from the U.S. Department of Agriculture 

(USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 

program, formerly known as Forest Health Monitoring, FIA examines ozone injury to ozonesensitive 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.148,149 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 
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looks for damage on the foliage of ozone-sensitive forest plant species. Monitoring of ozone 

injury to plants by the USDA Forest Service has expanded over the last 10 years from 

monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 2002. 

7.1.2.2.1 Recent Ozone Data for the U.S. 

 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 U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and 

Analysis (FIA) program which examines ozone injury to ozone-sensitive plant species at 

ground monitoring sites in forest land across the country (This indicator does not include 

woodlots and urban trees). Sites are selected using a systematic sampling grid, based on a 

global sampling design.150, 151 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. The data underlying the indicator in Figure 7-2 are 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 are 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.152, 153

 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. 
 Environmental and Health Impacts 

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Figure 7-2 Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002ab 
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7-24 

7.1.2.2.1.1 Indicator Limitations 

 Field and laboratory studies were reviewed to identify the forest plant species in each 

region that are highly sensitive to ozone air pollution. Other forest plant species, or even 

genetic variants of the same species, may not be harmed 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 is considerably reduced under conditions of low soil 

moisture, but most of the variability in the index (70%) was explained by ozone 

concentration.154 Ozone may have other adverse impacts on plants (e.g., reduced 

productivity) that do not show signs of visible foliar injury.155

 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. 

7.1.2.3 Ozone Impacts on Forest Health 

 Air pollution can impact the environment and affect ecological systems, leading to 

changes in the biological community (both in the diversity of species and the health and vigor 

of individual species). As an example, many studies have shown that ground-level ozone 

reduces the health of plants including many commercial and ecologically important forest tree 

species throughout the United States.156

 

When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Since photosynthesis occurs in cells within leaves, the ability of 

the plant to produce energy by photosynthesis can be compromised if enough damage occurs 

to these cells. If enough tissue becomes damaged it can reduce carbon fixation and increase 

plant respiration, leading to reduced growth and/or reproduction in young and mature trees. 

Ozone stress also increases the susceptibility of plants to disease, insects, fungus, and other 

environmental stressors (e.g., harsh weather). Because ozone damage can consist of visible 

injury to leaves, it also reduces the aesthetic value of ornamental vegetation and trees in urban 

landscapes, and negatively affects scenic vistas in protected natural areas. 

Assessing the impact of ground-level ozone on forests in the eastern 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 ozone-
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exposure/tree-response functions by exposing tree seedlings to different ozone levels and 

measuring reductions in growth as “biomass loss.” Typically, seedlings are used because they 

are easy to manipulate and measure their growth loss from ozone pollution. The mechanisms 

of susceptibility to ozone within the leaves of seedlings and mature trees are identical, though 

the magnitude of the effect may be higher or lower depending on the tree species. 157

 

Some of the common tree species in the United States that are sensitive to ozone are 

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), 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. 

7.1.2.4 Particulate Matter Deposition 

 Particulate matter contributes to adverse effects on vegetation and ecosystems, and to 

soiling and materials damage. These welfare effects result 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.158,159 

7.1.2.4.1 Deposition of Nitrogen and Sulfur 

Nitrogen and sulfur interactions in the environment are highly complex. Both 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.160

 

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 

acid neutralization, and because calcium, magnesium and potassium are essential nutrients for 
Regulatory Impact Analysis 

7-26 

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.161 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.162 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.163

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 
 Environmental and Health Impacts 

7-27 

tundra/subalpine conifer forests of the Colorado Front Range; and red alder forests in the 

Cascade Mountains in Washington. 

Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or 

oceans) either from direct atmospheric deposition, surface runoff, or leaching from nitrogen 

saturated soils into ground or surface waters can contribute to conditions of severe water 

oxygen depletion; eutrophication and algae blooms; altered fish distributions, catches, and 

physiological states; loss of biodiversity; habitat degradation; and increases in the incidence 

of disease. 

Atmospheric deposition of nitrogen is a significant source of total nitrogen to many 

estuaries in the United States. The amount of nitrogen entering estuaries that is ultimately 

attributable to atmospheric deposition is not well-defined. On an annual basis, atmospheric 

nitrogen deposition may contribute significantly to the total nitrogen load, depending on the 

size and location of the watershed. In addition, episodic nitrogen inputs, which may be 

ecologically important, may play a more important role than indicated by the annual average 

concentrations. Estuaries in the U.S. that suffer from nitrogen enrichment often experience a 

condition known as eutrophication. Symptoms of eutrophication include changes in the 

dominant species of phytoplankton, low levels of oxygen in the water column, fish and 

shellfish kills, outbreaks of toxic alga, 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.164

7.1.2.4.2 Deposition of Heavy Metals 

Heavy metals, including cadmium, copper, lead, chromium, mercury, nickel and zinc, 

have the greatest potential for impacting forest growth.165 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 
Regulatory Impact Analysis 

7-28 

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 

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.166 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.167,168 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.169

 

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.170,171 Over 

fifty percent of the mercury in the Chesapeake Bay has been attributed to atmospheric 

deposition.172 Overall, the National Science and Technology Council identifies atmospheric 

deposition as the primary source of mercury to aquatic systems.173 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 
 Environmental and Health Impacts 

7-29 

use.174,175 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.176 Plant uptake of platinum has been 

observed at these locations. 

7.1.2.4.3 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.177 Polycyclic aromatic hydrocarbons (PAHs) are a 

class of POM that contains compounds which are known or suspected carcinogens. 

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.178

 

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.179,180 Analyses of PAH deposition in 

Chesapeake and Galveston Bay indicate that dry deposition and gas exchange from the 

atmosphere to the surface water predominate.181,182 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.183 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.184 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.185 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.186 

Cousins et al. estimate that more than ninety percent of semi-volatile organic 

compound (SVOC) emissions in the United Kingdom deposit on soil.187 An analysis of PAH 

concentrations near a Czechoslovakian roadway indicated that concentrations were thirty 

times greater than background.188
Regulatory Impact Analysis 

7-30 

7.1.2.4.4 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. 

7.1.2.5 Environmental Effects of Air Toxics 

 Fuel combustion emissions contribute to ambient levels of pollutants that contribute 

to adverse effects on vegetation. Volatile organic compounds (VOCs), some of which are 

considered air toxics, have long been suspected to play a role in vegetation damage.189 In 

laboratory experiments, a wide range of tolerance to VOCs has been observed.190 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.191

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.192,193,194 

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. 

7.2 Non-GHG Air Quality Impacts 

This section presents the methodology and results of EPA’s air quality modeling to 

determine the projected impact of the vehicle standards finalized in this rule on ambient 

concentrations of criteria and air toxic pollutants. Section 7.1 above describes the health and 

environmental effects associated with the criteria and air toxic pollutants that are impacted by 

this rule, and Section 7.3 describes the methodology for calculating monetized benefits due to 

reductions in adverse health effects associated with PM2.5 and ozone. 
 Environmental and Health Impacts 

7-31 

7.2.1 Air Quality Modeling Methodology 

Air quality models use mathematical and numerical techniques to simulate the 

physical and chemical processes that affect air pollutants as they disperse and react in the 

atmosphere. Based on inputs of meteorological data and source information, these models are 

designed to characterize primary pollutants that are emitted directly into the atmosphere and 

secondary pollutants that are formed as a result of complex chemical reactions within the 

atmosphere. Photochemical air quality models have become widely recognized and routinely 

utilized tools for regulatory analysis by assessing the effectiveness of control strategies. 

These models are applied at multiple spatial scales - local, regional, national, and global. This 

section provides detailed information on the photochemical model used for our air quality 

analysis (the Community Multi-scale Air Quality (CMAQ) model), atmospheric reactions and 

the role of chemical mechanisms in modeling, and model uncertainties and limitations. 

Further discussion of the modeling methodology is included in the Air Quality Modeling 

Technical Support Document (AQM TSD) found in the docket for this rule. Results of the air 

quality modeling are presented in Section 7.2.2. 

7.2.1.1 Modeling Methodology 

A national-scale air quality modeling analysis was performed to estimate future year 

annual PM2.5 concentrations, 24-hour PM2.5 concentrations, 8-hour ozone concentrations, air 

toxics concentrations, and nitrogen and sulfur deposition levels for future years. The 2005-

based CMAQ modeling platform was used as the basis for the air quality modeling of the 

future reference case and the future control scenario for this final rule. This platform 

represents a structured system of connected modeling-related tools and data that provide a 

consistent and transparent basis for assessing the air quality response to projected changes in 

emissions. The base year of data used to construct this platform includes emissions and 

meteorology for 2005. The platform was developed by the U.S. EPA’s Office of Air Quality 

Planning and Standards in collaboration with the Office of Research and Development and is 

intended to support a variety of regulatory and research model applications and analyses. 

The CMAQ modeling system is a non-proprietary, publicly available, peer-reviewed, 

state-of-the-science, three-dimensional, grid-based Eulerian air quality grid model designed to 

estimate the formation and fate of oxidant precursors, primary and secondary PM 

concentrations, acid deposition, and air toxics, over regional and urban spatial scales for given 

input sets of meteorological conditions and emissions.195,196,197 The CMAQ model version 4.7 

was most recently peer-reviewed in February of 2009 for the U.S. EPA.M

 The CMAQ model 

 

M Report on the peer-review is still being finalized. Draft available upon request from Director S.T.Rao, 

Atmospheric Modeling and Analysis Division; rao.st@epa.gov; 919-541-4541. Allen, D., Burns, D., Chock, D., 

Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the Peer Review of the Atmospheric 

Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC. 
Regulatory Impact Analysis 

7-32 

is a well-known and well-respected tool and has been used in numerous national and 

international applications.198,199,200 This 2005 multi-pollutant modeling platform used CMAQ 

version 4.7.1N with a minor internal change made by the U.S. EPA CMAQ model developers 

intended to speed model runtimes when only a small subset of toxics species are of interest. 

CMAQ includes many science modules that simulate the emission, production, decay, 

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in 

the atmosphere. We used CMAQ v4.7.1 which reflects updates to version 4.7 to improve the 

underlying science. These include aqueous chemistry mass conservation improvements, 

improved vertical convective mixing and lowered CB05 mechanism unit yields for acrolein 

from 1,3-butadiene tracer reactions which were updated to be consistent with laboratory 

measurements. Section 7.2.1.2.2 of this RIA discusses the chemical mechanism and SOA 

formation. 

7.2.1.1.1 Model Domain and Configuration 

The CMAQ modeling domain encompasses all of the lower 48 States and portions of 

Canada and Mexico. The modeling domain is made up of a large continental U.S. 36 

kilometer (km) grid and two 12 km grids (an Eastern US and a Western US domain), as 

shown in Figure 7-3. The modeling domain contains 14 vertical layers with the top of the 

modeling domain at about 16,200 meters, or 100 millibars (mb). 

 

N

 CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and 

Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
 Environmental and Health Impacts 

7-33 

Figure 7-3 Map of the CMAQ Modeling Domain 

7.2.1.1.2 Model Inputs 

The key inputs to the CMAQ model include emissions from anthropogenic and 

biogenic sources, meteorological data, and initial and boundary conditions. The CMAQ 

meteorological input files were derived from simulations of the Pennsylvania State 

University/National Center for Atmospheric Research Mesoscale Model201 for the entire year 

of 2005 over model domains that are slightly larger than those shown in Figure 7-3. 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.202 The meteorology for the national 36 km grid and the two 12 km 

grids were developed by EPA and are described in more detail within the AQM TSD. The 

meteorological outputs from MM5 were processed to create model-ready inputs for CMAQ 

using the Meteorology-Chemistry Interface Processor (MCIP) version 3.4, for example: 

horizontal wind components (i.e., speed and direction), temperature, moisture, vertical 

diffusion rates, and rainfall rates for each grid cell in each vertical layer.203

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.204 The global 

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 
Regulatory Impact Analysis 

7-34 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS). This model was run for 2005 with a grid resolution of 2 degree x 2.5 degree 

(latitude-longitude) and 20 vertical layers. The predictions were used to provide one-way 

dynamic boundary conditions at three-hour intervals and an initial concentration field for the 

36 km CMAQ simulations. The future base conditions from the 36 km coarse grid modeling 

were used as the initial/boundary state for all subsequent 12 km finer grid modeling. 

The emissions inputs used for the 2005 base year and each of the future year base 

cases and control scenarios analyzed for this rule are summarized in Chapter 5 of this RIA. 

7.2.1.1.3 CMAQ Evaluation 

An operational model performance evaluation for ozone, PM2.5 and its related 

speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.), nitrate 

and sulfate deposition, and specific air toxics (formaldehyde, acetaldehyde, benzene, 1,3-

butadiene, and acrolein) was conducted using 2005 state/local monitoring data in order to 

estimate the ability of the CMAQ modeling system to replicate base year concentrations. 

Model performance statistics were calculated for observed/predicted pairs of 

daily/monthly/seasonal/annual concentrations. Statistics were generated for the following 

geographic groupings: domain wide, Eastern vs. Western (divided along the 100th meridian), 

and each Regional Planning Organization (RPO) region.O

 The “acceptability” of model 

performance was judged by comparing our results to those found in recent regional PM2.5

model applications for other, non-EPA studies.P Overall, the performance for the 2005 

modeling platform is within the range or close to that of these other applications. The 

performance of the CMAQ modeling was evaluated over a 2005 base case. The model was 

able to reproduce historical concentrations of ozone and PM2.5 over land with low bias and 

error results. Model predictions of annual formaldehyde, acetaldehyde and benzene showed 

relatively small bias and error results when compared to observations. The model yielded 

larger bias and error results for 1,3 butadiene and acrolein based on limited monitoring sites. 

A more detailed summary of the 2005 CMAQ model performance evaluation is available 

within the AQM TSD found in the docket of this rule. 

 

O

 Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANE-VU), 

Midwest Regional Planning Organization – Lake Michigan Air Directors Consortium (MWRPO-LADCO), 

Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air 

Partnership (CENRAP), and Western Regional Air Partnership (WRAP).

P These other modeling studies represent a wide range of modeling analyses which cover various models, model 

configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules.
 Environmental and Health Impacts 

7-35 

7.2.1.1.4 Model Simulation Scenarios 

As part of our analysis for this rulemaking, the CMAQ modeling system was used to 

calculate daily and annual PM2.5 concentrations, 8-hour ozone concentrations, annual and 

seasonal (summer and winter) air toxics concentrations, and annual nitrogen and sulfur 

deposition total levels for each of the following emissions scenarios: 

- 2005 base year 

- 2030 reference case projection 

- 2030 control case projection 

The emission inventories used in the air quality and benefits modeling are different 

from the final rule inventories due to the considerable length of time required to conduct the 

modeling. However, the air quality modeling inventories are generally consistent with the 

final emission inventories, so the air quality modeling adequately reflects the effects of the 

rule. The emission inventories used for air quality modeling are discussed in Section 5.8 of 

this RIA. The emissions modeling TSD, found in the docket for this rule (EPA-HQ-OAR2009-0472), contains a detailed discussion of the emissions inputs used in our air quality 

modeling. 

We use the predictions from the model in a relative sense by combining the 2005 baseyear predictions with predictions from each future-year scenario and applying these modeled 

ratios to ambient air quality observations to estimate daily and annual PM2.5 concentrations, 

and 8-hour ozone concentrations for each of the 2030 scenarios. The ambient air quality 

observations are average conditions, on a site-by-site basis, for a period centered around the 

model base year (i.e., 2003-2007). 

The projected daily and annual PM2.5 design values were calculated using the 

Speciated Modeled Attainment Test (SMAT) approach. The SMAT uses a Federal Reference 

Method (FRM) mass construction methodology that results in reduced nitrates (relative to the 

amount measured by routine speciation networks), higher mass associated with sulfates 

(reflecting water included in FRM measurements), and a measure of organic carbonaceous 

mass that is derived from the difference between measured PM2.5 and its non-carbon 

components. This characterization of PM2.5 mass also reflects crustal material and other 

minor constituents. The resulting characterization provides a complete mass balance. It does 

not have any unknown mass that is sometimes presented as the difference between measured 

PM2.5 mass and the characterized chemical components derived from routine speciation 

measurements. However, the assumption that all mass difference is organic carbon has not 

been validated in many areas of the U.S. The SMAT methodology uses the following PM2.5

species components: sulfates, nitrates, ammonium, organic carbon mass, elemental carbon, 

crustal, water, and blank mass (a fixed value of 0.5 μg/m3

). More complete details of the 
Regulatory Impact Analysis 

7-36 

SMAT procedures can be found in the report "Procedures for Estimating Future PM2.5 Values 

for the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test 

(SMAT)".205 For this latest analysis, several datasets and techniques were updated. These 

changes are fully described within the technical support document for the Small SI Engine 

Rule modeling AQM TSD.206 The projected 8-hour ozone design values were calculated 

using the approach identified in EPA's guidance on air quality modeling attainment 

demonstrations.207

 

Additionally, we conducted an analysis to compare the absolute and percent 

differences between the 2030 control case and the 2030 reference cases for annual and 

seasonal ethanol and five air toxics of interest (formaldehyde, acetaldehyde, benzene, 1,3-

butadiene, and acrolein), as well as annual nitrate and sulfate deposition. These data were not 

compared in a relative sense due to the limited observational data available. 

7.2.1.2 Chemical Mechanisms in Modeling 

This rule presents inventories for NOX, VOC, CO, PM2.5, SO2, NH3, and five air 

toxics: benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. The five air toxics 

are explicit model species in the CMAQv4.7 model with carbon bond 5 (CB05) 

mechanisms.208 In addition to direct emissions, photochemical processes mechanisms are 

responsible for formation of some of these compounds in the atmosphere from precursor 

emissions. For some pollutants such as PM, formaldehyde, and acetaldehyde, many 

photochemical processes are involved. CMAQ therefore also requires inventories for a large 

number of other air toxics and precursor pollutants. Methods used to develop the air quality 

inventories can be found in Chapter 5 of the RIA. 

 In the CB05 mechanism, the chemistry of thousands of different VOCs in the 

atmosphere are represented by a much smaller number of model species which characterize 

the general behavior of a subset of chemical bond types; this condensation is necessary to 

allow the use of complex photochemistry in a fully 3-D air quality model.209

Complete combustion of ethanol in fuel produces carbon dioxide (CO2) and water 

(H2O). Incomplete combustion results in the production of other air pollutants, such as 

acetaldehyde and other aldehydes, and the release of unburned ethanol. Ethanol is also 

present in evaporative emissions. In the atmosphere, ethanol from unburned fuel and 

evaporative emissions can undergo photodegradation to form aldehydes (acetaldehyde and 

formaldehyde) and peroxyacetyl nitrate (PAN), and also plays a role in ground-level ozone 

formation. Mechanisms for these reactions are included in CMAQ. Additionally, other 

aromatic hydrocarbons (AHC) and hydrocarbons are considered because any increase in 

acetyl peroxy radicals due to ethanol increases might be counterbalanced by a decrease in 

radicals resulting from decreases in AHC and other hydrocarbons. 
 Environmental and Health Impacts 

7-37 

CMAQ includes 63 inorganic reactions to account for the cycling of all relevant 

oxidized nitrogen species and cycling of radicals, including the termination of NO2 and 

formation of nitric acid (HNO3) without PAN formation.Q

NO2 + ·OH + M → HNO3 + M k = 1.19 x 10-11 cm3molecule-1s

-1

 

210

The CB05 mechanism also includes more than 90 organic reactions that include 

alternate pathways for the formation of acetyl peroxy radical, such as by reaction of 

methylglyoxal, which is also formed from reactions of AHC. Alternate reactions of acetyl 

peroxy radical, such as oxidation of NO to form NO2, which again leads to ozone formation, 

are also included. 

Atmospheric reactions and chemical mechanisms involving several key formation 

pathways are discussed in more detail in the following sections. 

7.2.1.2.1 Acetaldehyde 

Acetaldehyde is the main photodegradation product of ethanol, as well as other 

precursor hydrocarbons. Acetaldehyde is also a product of fuel combustion. In the 

atmosphere, acetaldehyde can react with the OH radical and O2 to form the acetyl peroxy 

radical [CH3C(O)OO·].R

 This radical species can then further react with nitric oxide (NO), to 

produce formaldehyde (HCHO), or with nitrogen dioxide (NO2), to produce PAN 

[CH3C(O)OONO2]. An overview of these reactions and the corresponding reaction rates are 

provided below. S

CH3CHO + ·OH → CH3C·O + H2O k = 1.5 x 10-11 cm

3molecule-1

s

-1 211

CH3C·O + O2 + M → CH3C(O)OO· + M 

CH3C(O)OO· + NO → CH3C(O)O· + NO2 k = 2.0 x 10-11 cm

3molecule-1

s

-1

 

212

CH3C(O)O· → ·CH3 + CO2 

·CH3 + O2 + M → CH3OO· + M 

CH3OO· + NO → CH3O· + NO2

 

Q

 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 

R

 Acetaldehyde is not the only source of acetyl peroxy radicals in the atmosphere. For example, dicarbonyl 

compounds (methylglyoxal, biacetyl, and others) also form acetyl radicals, which can further react to form 

peroxyacetyl nitrate (PAN). 

S

 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 
Regulatory Impact Analysis 

7-38 

CH3O· + O2 → HCHO + HO2

CH3C(O)OO· + NO2 + M → CH3C(O)OONO2 + M k = 1.0 x 10-11 

cm3molecule-1s

-1

 

213

Acetaldehyde can also photolyze (hν), which predominantly produces ·CH3 and HCO: 

CH3CHO + hν → ·CH3 + HCO λ = 240-380 nm 214

As mentioned above, ·CH3 is oxidized in the atmosphere to produce formaldehyde 

(HCHO). Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere, 

formaldehyde undergoes photolysis and reaction with the OH radical, NO3 radical, and ozone, 

and the resulting lifetimes are ~4 hours, 1.2 days, 83 days, and >4.5 years, respectively.T 

Formaldehyde is removed mainly by photolysis whereas the higher aldehydes, those with two 

or more carbons such as acetaldehyde, react predominantly with OH radicals. The photolysis 

of formaldehyde is a source of additional radicals, and as shown above, these radicals can 

react with NO2 to form PAN in the atmosphere. 

HCHO + hν → H + HCO λ = 240-360 nm 215

CB05 mechanisms for acetaldehyde formation warrant a detailed discussion given the 

increase in vehicle and engine exhaust emissions for this pollutant and ethanol, which can 

form acetaldehyde in the air. Acetaldehyde is represented explicitly in the CB05 chemical 

mechanism216,217 by the ALD2 model species, which can be both formed from other VOCs 

and can decay via reactions with oxidants and radicals. The reaction rates for acetaldehyde, 

as well as for the inorganic reactions that produce and cycle radicals, and the representative 

reactions of other VOCs have all been updated to be consistent with recommendations in the 

literature.218

The decay reactions of acetaldehyde are fewer in number and can be characterized 

well because they are explicit representations. Acetaldehyde can photolyze in the presence of 

sunlight or react with molecular oxygen (O3(P)), hydroxyl radical (OH), or nitrate radicals. 

Of these reactions, both photolysis and reaction with OH are the most important reactions 

determining loss of acetaldehyde. The reaction rates are based on expert recommendations,219

and the photolysis rate is from IUPAC recommendations. 

In CMAQ v4.7, the acetaldehyde that is formed from photochemical reactions is 

tracked separately from that which is due to direct emission and transport of direct emissions. 

In CB05, there are 25 different reactions that form acetaldehyde in molar yields ranging from 

 

T

 Lifetime calculated using the following: for photolysis, with overhead sun (at noontime during the summer); 

for OH radical reactions, a 12-hour daytime average of 2.0 x 106

 molecule cm-3; for NO3 radical reactions, a 12-

hour nighttime average of 5 x 108 molecule cm-3; and for ozone, a 24-hour average of 7 x 1011 molecule cm-3. 
 Environmental and Health Impacts 

7-39 

0.02 (ozone reacting with lumped products from isoprene oxidation) to 2.0 (cross reaction of 

acylperoxy radicals, CXO3). The specific parent VOCs that contribute the most to 

acetaldehyde concentrations vary spatially and temporally depending on characteristics of the 

ambient air, but alkenes in particular are found to play a large role. The IOLE model species, 

which represents internal carbon-carbon double bonds, has high emissions and relatively high 

yields of acetaldehyde. The OLE model species, representing terminal carbon double bonds, 

also plays a role because it has high emissions although lower acetaldehyde yields. 

Production from peroxyproprional nitrate and other peroxyacylnitrates (PANX) and aldehydes 

with 3 or more carbon atoms also play an important role. Thus, the amount of acetaldehyde 

(and formaldehyde as well) formed in the ambient air as well as emitted in the exhaust (the 

latter being accounted for in emission inventories) is affected by changes in these precursor 

compounds due to the addition of ethanol to fuels (e.g., decreases in alkenes would cause 

some decrease of acetaldehyde, and to a larger extent, formaldehyde). 

The reaction of ethanol (CH3CH2OH) with OH is slower than some other important 

reactions but can be an important source of acetaldehyde if the emissions are large. Based on 

kinetic data for molecular reactions, the only important chemical loss process for ethanol (and 

other alcohols) is reaction with the hydroxyl radical (·OH).220 This reaction produces 

acetaldehyde (CH3CHO) with a 90% yield.221 The lifetime of ethanol in the atmosphere can 

be calculated from the rate coefficient, k, and due to reaction with the OH radical, occurs on 

the order of a day in polluted urban areas or several days in unpolluted areas. U

 

CH3CH2OH + ·OH → CH3C·HOH + H2O k = 3.2 x 10-12

 cm

3molecule-1

s

-1

 

222

CH3C·HOH + O2 → CH3CHO + HO2

In CB05, reaction of one molecule of ethanol yields 0.90 molecules of acetaldehyde. 

It assumes the majority of the reaction occurs through H-atom abstraction of the more 

weakly-bonded methylene group, which reacts with oxygen to form acetaldehyde and 

hydroperoxy radical (HO2), and the remainder of the reaction occurs at the –CH3 and -OH 

groups, creating formaldehyde (HCHO), oxidizing NO to NO2 (represented by model species 

XO2) and creating glycoaldehyde, which is represented as ALDX: 

CH3CHOH + OH → HO2 + 0.90 CH3CHO + 0.05 ALDX + 0.10 HCHO + 0.10 XO2 

7.2.1.2.2 Secondary Organic Aerosols 

Secondary organic aerosol (SOA) chemistry research described below has led to 

implementation of new pathways for secondary organic aerosol (SOA) in CMAQ 4.7, based 

on recommendations of Edney et al. and the recent work of Carlton et al.223, 224 In previous 

 

U

 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 
Regulatory Impact Analysis 

7-40 

versions of the CMAQ model, all SOA was treated as semi-volatile, whereas in CMAQ v4.7, 

non-volatile SOA are simulated as well, including SOA originating from aromatic oxidation 

under low-NOX conditions. 

7.2.1.2.2.1 SOA Research 

SOA results when products of atmospheric transformation or photooxidation of a 

volatile organic compound (VOC) form or partition to the particle phase. Current research 

suggests SOA contributes significantly to ambient organic aerosol (OA) concentrations, and 

in Southeast and Midwest States may make up more than 50% (although the contribution 

varies from area to area) of the organic fraction of PM2.5 during the summer (but less in the 

winter).225,226 A wide range of laboratory studies conducted over the past twenty years show 

that anthropogenic aromatic hydrocarbons and long-chained alkanes, along with biogenic 

isoprene, monoterpenes, and sesquiterpenes, contribute to SOA formation.227,228,229,230,231

 

Anthropogenic SOA is a small portion of all SOA; most is biogenic and varies with season. 

Based on these laboratory results, SOA chemical mechanisms have been developed and 

integrated into air quality models such as the CMAQ model and have been used to predict OA 

concentrations.232 

Over the past 10 years, ambient OA concentrations have been routinely measured in 

the U.S. and some of these data have been used to determine, by employing source/receptor 

methods, the contributions of the major OA sources, including biomass burning and vehicular 

gasoline and diesel exhaust. Since mobile sources are a significant source of VOC emissions, 

currently accounting for approximately 50% of anthropogenic VOC,233 mobile sources are 

also an important source of SOA. 

Toluene is an important contributor to anthropogenic SOA. Other aromatic 

compounds contribute as well, but the extent of their contribution has not yet been quantified. 

Mobile sources are the most significant contributor to ambient toluene concentrations as 

shown by analyses done for the 2002 National Air Toxics Assessment (NATA)234 and the 

Mobile Source Air Toxics (MSAT) Rule.235 2002 NATA indicates that onroad and nonroad 

mobile sources accounted for 70% (2.24 μg/m3

) of the total average nationwide ambient 

concentration of toluene (3.24 μg/m3

), when the contribution of the estimated “background” is 

apportioned among source sectors. 

The amount of toluene in gasoline influences the amount of toluene emitted in vehicle 

exhaust and evaporative emissions, although, like benzene, some toluene is formed in the 

combustion process. In turn, levels of toluene and other aromatics in gasoline are potentially 

influenced by the amount of ethanol blended into the fuel. Due to the high octane quality of 

ethanol, it greatly reduces the need for and levels of other high-octane components such as 

aromatics including toluene (which is the major aromatic compound in gasoline). Since 

toluene contributes to SOA and the toluene level of gasoline is decreasing, it is important to 

assess the effect of these reductions on ambient PM. 
 Environmental and Health Impacts 

7-41 

It is unlikely that ethanol would directly form SOA or affect SOA formation indirectly 

through changes in the radical populations from increasing ethanol exhausts. Nevertheless, 

scientists at the U.S. EPA’s Office of Research and Development’s National Exposure 

Research Laboratory recently directed experiments to investigate ethanol’s SOA forming 

potential.236 The experiments were conducted under conditions where peroxy radical 

reactions would predominate (irradiations performed in the absence of NOX and OH produced 

from the photolysis of hydrogen peroxide). This was the most likely scenario under which 

SOA formation could occur, since a highly oxygenated C4 organic would be potentially 

made. As expected, no SOA was produced. From these experiments, the upper limit for the 

aerosol yield would have been less than 0.01% based on scanning mobility particle sizer 

(SMPS) data. Given the expected negative result based on these initial smog chamber 

experiments, these data were not published. 

In general, a review of the literature shows limited data on SOA concentrations, 

largely due to the lack of analytical methods for identifying and determining the 

concentrations of the highly polar organic compounds that make up SOA. The most widely 

applied method of estimating total ambient SOA concentrations is the EC tracer method using 

ambient data which estimates of the OC/EC ratio in primary source emissions.237,238 SOA 

concentrations have also been estimated using OM (organic mass) to OC (organic carbon) 

ratios, which can indicate that SOA formation has occurred, or by subtracting the 

source/receptor-based total primary organic aerosol (POA) from the measured OC 

concentration.239 Such methods, however, may not be quantitatively accurate and provide no 

information on the contribution of individual biogenic and anthropogenic SOA sources, which 

is critical information needed to assess the impact of specific sources and the associated 

health risk. These methods assume that OM containing additional mass from oxidation of OC 

comes about largely (or solely) from SOA formation. In particular, the contributions of 

anthropogenic SOA sources, including those of aromatic precursors, are required to determine 

exposures and risks associated with replacing fossil fuels with biofuels. 

Upon release into the atmosphere, numerous VOC compounds can react with free 

radicals in the atmosphere to form SOA. While this has been investigated in the laboratory, 

there is relatively little information available on the specific chemical composition of SOA 

compounds themselves from specific VOC precursors. This absence of compositional data 

from the precursors has largely prevented the identification of aromatically-derived SOA in 

ambient samples which, in turn, has prevented observation-based measurements of the 

aromatic and other SOA contributions to ambient PM levels. 

As a first step in determining the ambient SOA concentrations, EPA has developed a 

tracer-based method to estimate such concentrations.240,241 The method is based on using 

mass fractions of SOA tracer compounds, measured in smog chamber-generated SOA 

samples, to convert ambient concentrations of SOA tracer compounds to ambient SOA 

concentrations. This method consists of irradiating the SOA precursor of interest in a smog 

chamber in the presence of NOx, collecting the SOA produced on filters, and then analyzing 
Regulatory Impact Analysis 

7-42 

the samples for highly polar compounds using advanced analytical chemistry methods. 

Employing this method, candidate tracers have been identified for several VOC compounds 

which are emitted in significant quantities and known to produce SOA in the atmosphere. 

Some of these SOA-forming compounds include toluene, a variety of monoterpenes, isoprene, 

and β-caryophyllene, the latter three of which are emitted by vegetation and are more 

significant sources of SOA than toluene. Smog chamber work can also be used to investigate 

SOA chemical formation mechanisms.242,243,244,245

Although these concentrations are only estimates, due to the assumption that the mass 

fractions of the smog chamber SOA samples using these tracers are equal to those in the 

ambient atmosphere, there are presently no other means available for estimating the SOA 

concentrations originating from individual SOA precursors. Among the tracer compounds 

observed in ambient PM2.5 samples are two tracer compounds that have been identified in 

smog chamber aromatic SOA samples.246 To date, these aromatic tracer compounds have 

been identified, in the laboratory, for toluene and m-xylene SOA. Additional work is 

underway by the EPA to determine whether these tracers are also formed by benzene and 

other alkylbenzenes (including o-xylene, p-xylene, 1,2,4-trimethylbenzene, and 

ethylbenzene). 

One caveat regarding this work is that a large number of VOCs emitted into the 

atmosphere, which have the potential to form SOA, have not yet been studied in this way. It 

is possible that these unstudied compounds produce SOA species which are being used as 

tracers for other VOCs. This means that the present work could overestimate the amount of 

SOA formed in the atmosphere by the VOCs studied to date. This approach may also 

estimate entire hydrocarbon classes (e.g., all methylsubstituted-monoaromatics or all 

monoterpenes) and not individual precursor hydrocarbons. Thus the tracers could be broadly 

representative and not indicative of individual precursors. This is still unknown. Also, 

anthropogenic precursors play a role in formation of atmospheric radicals and aerosol acidity, 

and these factors influence SOA formation from biogenic hydrocarbons. This anthropogenic 

and biogenic interaction, important to EPA and others, needs further study. The issue of SOA 

formation from aromatic precursors is an important one to which EPA and others are paying 

significant attention. For benzene, smog chamber studies show that benzene forms SOA 

possibly through reactions with NOx. Early smog chamber work suggests benzene might be 

relatively inert in forming SOA, although this study may not be conclusive.247 However, 

more recent work shows that benzene does form SOA in smog chambers.248,249 This new 

smog chamber work shows that benzene can be oxidized in the presence of NOX to form SOA 

with maximum mass of SOA being 8-25% of the mass of benzene. As mentioned above, 

work is needed to determine if a tracer compound can be found for benzene SOA which might 

indicate how much of ambient SOA comes from benzene. 

The aromatic tracer compounds and their mass fractions have also been used to 

estimate monthly ambient aromatic SOA concentrations from March 2004 to February 2005 

in five U.S. Midwestern cities.250 The annual tracer-based SOA concentration estimates were 
 Environmental and Health Impacts 

7-43 

0.15, 0.18, 0.13, 0.15, and 0.19 μg carbon/m3

 for Bondville, IL, East St. Louis, IL, 

Northbrook, IL, Cincinnati, OH and Detroit, MI, respectively, with the highest concentrations 

occurring in the summer. On average, the aromatic SOA concentrations made up 17 % of the 

total SOA concentration. Thus, this work suggests that we are finding ambient PM levels on 

an annual basis of about 0.15 μg/m3 associated with present toluene levels in the ambient air 

in these Midwest cities. Based on preliminary analysis of recent laboratory experiments, it 

appears the toluene tracer could also be formed during photooxidation of some of the 

xylenes.251

Over the past decade a variety of modeling studies have been conducted to predict 

ambient SOA levels, with most studies focusing on the contributions of biogenic 

monoterpenes and anthropogenic aromatic hydrocarbons. More recently, modelers have 

begun to include the contribution of the isoprene SOA to ambient OC concentrations.252 In 

general, the studies have been limited to comparing the sum of the POA and SOA 

concentrations with ambient OC concentrations. The general consensus in the atmospheric 

chemistry community appears to be that monoterpene contributions, which are clearly 

significant, and the somewhat smaller aromatic contributions, are insufficient to account for 

observed ambient SOA levels. 253 Part of this gap has been filled recently by SOA predictions 

for isoprene. Furthermore, the identification in ambient SOA of a tracer compound for the 

sesquiterpene β-caryophyllene,254 coupled with the high sesquiterpene SOA yields measured 

in the laboratory,255 suggests this class of hydrocarbons should be included in SOA chemical 

mechanisms. In addition, recent data on SOA formation from aromatic hydrocarbons suggest 

their contributions, while much smaller than biogenic hydrocarbons, could be larger than 

previously thought.256,257

7.2.1.2.3 Ozone 

As mentioned above, the addition of ethanol to fuels has been shown to contribute to 

PAN formation and this is one way for it to contribute therefore to ground-level ozone 

formation. PAN is a reservoir and carrier of NOX and is the product of acetyl radicals reacting 

with NO2 in the atmosphere. One source of PAN is the photooxidation of acetaldehyde 

(Section 7.2.1.2.1), but any hydrocarbon having a methyl group has the potential for forming 

acetyl radicals and therefore PAN.V

 PAN can undergo thermal decomposition with a lifetime 

of approximately 1 hour at 298K or 148 days at 250K. W

CH3C(O)OONO2 + M → CH3C(O)OO· + NO2 + M k = 3.3 x 10-4 

s

-1 258

 

V

 Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, pxylene, and 1,3,5-, 1,2,4-trimethylbenzene), form methylglyoxal and biacetyl, which are also strong generators 

of acetyl radicals (Smith, D.F., T.E. Kleindienst, C.D. McIver (1999) Primary product distribution from the 

reaction of OH with m-, p-xylene and 1,2,4- and 1,3,5-Trimethylbenzene. J. Atmos. Chem., 34: 339- 364.). 

W All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 
Regulatory Impact Analysis 

7-44 

The reaction above shows how NO2 is released in the thermal decomposition of PAN. 

NO2 can also be formed in photodegradation reactions where NO is converted to NO2 (see 

OH radical reaction of acetaldehyde in Section 3.4.1.2.1). In both cases, NO2 further 

photolyzes to produce ozone (O3). 

NO2 + hν → NO + O(3

P) λ = 300-800 nm 259

O(3P) + O2 + M → O3 + M 

The temperature sensitivity of PAN allows it to be stable enough at low temperatures 

to be transported long distances before decomposing to release NO2. NO2 can then participate 

in ozone formation in regions remote from the original NOX source.260 A discussion of CB05 

mechanisms for ozone formation can be found in Yarwood et al. (2005).261

7.2.1.3 Modeling Uncertainties and Limitations 

All the results presented below must be interpreted with the understanding that there 

are uncertainties in inventories, atmospheric processes in CMAQ, and other aspects of the 

modeling process. While it is beyond the scope of this Regulatory Impact Analysis to include 

a comprehensive discussion of all limitations and uncertainties associated with air quality 

modeling, several sources of uncertainty that impact analyses for this rule are addressed. 

A key source of uncertainty is the photochemical mechanisms in CMAQ 4.7. 

Pollutants such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and 1,3-butadiene can be 

formed secondarily through atmospheric chemical processes. Since secondarily formed 

pollutants can result from many different reaction pathways, there are uncertainties associated 

with each pathway. Simplifications of chemistry must be made in order to handle reactions of 

thousands of chemicals in the atmosphere. Mechanisms for formation of ozone, PM, 

acetaldehyde and peroxyacetyl nitrate (PAN) are discussed in Section 7.2.1.2. 

For PM, there are a number of uncertainties associated with SOA formation that 

should be addressed explicitly. As mentioned in Section 7.2.1.2.2, a large number of VOCs 

emitted into the atmosphere, which have the potential to form SOA, have not yet been studied 

in detail. In addition, the amount of ambient SOA that comes from benzene is uncertain. 

Simplifications to the SOA treatment in CMAQ have also been made in order to preserve 

computational efficiency. These simplifications are described in release notes for CMAQ 4.7 

on the Community Modeling and Analysis System (CMAS) website.262

 

7.2.2 Air Quality Modeling Results 

As described above, we performed a series of air quality modeling simulations for the 

continental U.S in order to assess the impacts of the vehicle rule. We looked at impacts on 

future ambient PM2.5, ozone, ethanol and air toxics levels, as well as nitrogen and sulfur 
 Environmental and Health Impacts 

7-45 

deposition levels and visibility impairment. In this section, we present information on current 

levels of pollution as well as model projected levels of pollution for 2030. 

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the inventories used in the air quality modeling and the benefits modeling, 

which are presented in Section 5.8, are slightly different than the final vehicle standard 

inventories presented in Section 5.5. However, the air quality inventories and the final rule 

inventories are generally consistent, so the air quality modeling adequately reflects the effects 

of the rule. 

7.2.2.1 Particulate Matter (PM2.5 and PM10) 

As described in Section 7.1, PM causes adverse health effects, and the EPA has set 

national ambient air quality standards (NAAQS) to protect against those health effects. In this 

section we present information on current and model-projected future PM levels. 

7.2.2.1.1 Current Levels of PM 

Figure 7-4 and Figure 7-5 show a snapshot of annual and 24-hour PM2.5

concentrations in 2008. There are two National Ambient Air Quality Standards (NAAQS) for 

PM2.5: an annual standard (15 μg/m3) and a 24-hour standard (35 μg/m3). In 2008, the highest 

annual average PM2.5 concentrations were in California, Arizona, and Hawaii and the highest 

24-hour PM2.5 concentrations were in California and Virginia. 
Regulatory Impact Analysis 

7-46 

Figure 7-4 Annual Average PM2.5 Concentrations in μg/m3 for 2008X 

 

X

 From U.S. EPA, 2010. Our Nation’s Air: Status and Trends through 2008. EPA-454/R-09-002. February 

2010. Available at: http://www.epa.gov/airtrends/2010/index.html. 
 Environmental and Health Impacts 

7-47 

Figure 7-5 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in μg/m3 for 2008Y 

The most recent revisions to the PM 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).Z As of January 6, 2010, approximately 88 million people live in the 39 areas that 

are designated as nonattainment for the 1997 PM2.5 National Ambient Air Quality Standard 

(NAAQS). These PM2.5 nonattainment areas are comprised of 208 full or partial counties. 

Nonattainment areas for the 1997 PM2.5 NAAQS are pictured in Figure 7-6. On October 8, 

2009, the EPA issued final nonattainment area designations for the 2006 24-hour PM2.5

NAAQS (74 FR 58688, November 13, 2009). These designations include 31 areas composed 

 

Y

 From U.S. EPA, 2010. Our Nation’s Air: Status and Trends through 2008. EPA-454/R-09-002. February 

2010. Available at: http://www.epa.gov/airtrends/2010/index.html. 

Z

 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. 
Regulatory Impact Analysis 

7-48 

of 120 full or partial counties with a population of over 70 million. Nonattainment areas for 

the 2006 PM2.5 NAAQS are pictured in Figure 7-7. In total, there are 54 PM2.5 nonattainment 

areas composed of 243 counties with a population of almost 102 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 will be 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.263 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.264 The vehicle 

standards finalized here first apply to model year 2012 vehicles. 

 

Figure 7-6 1997 PM2.5 Nonattainment Areas 
 Environmental and Health Impacts 

7-49 

Figure 7-7 2006 PM2.5 Nonattainment Areas 

As of January 6, 2010, approximately 26 million people live in the 47 areas that are 

designated as nonattainment for the PM10 NAAQS. There are 40 full or partial counties that 

make up the PM10 nonattainment areas. Nonattainment areas for the PM10 NAAQS are 

pictured in Figure 7-8. 
Regulatory Impact Analysis 

7-50 

Figure 7-8 PM10 Nonattainment Areas 

7.2.2.1.2 Projected Levels of PM2.5

Generally, our modeling indicates that the vehicle standards will reduce PM2.5

concentrations in some localized areas of the country. In the following sections we describe 

projected PM2.5 levels in the future, with and without the vehicle standards. Information on 

the air quality modeling methodology is contained in Section 7.2.1.1. Additional detail can be 

found in the air quality modeling technical support document (AQM TSD). 
 Environmental and Health Impacts 

7-51 

7.2.2.1.2.1 Projected Levels of PM2.5 without this Rule 

EPA has already adopted many mobile source emission control programs that are 

expected to reduce ambient PM levels. These control programs include the New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder rule,AA the Marine SparkIgnition and Small Spark-Ignition Engine rule (73 FR 59034, October 8, 2008), the 

Locomotive and Marine Compression-Ignition Engine Rule (73 FR 25098, May 6, 2008), the 

Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), the Heavy Duty Engine and 

Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 

18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control 

Requirements (65 FR 6698, Feb. 10, 2000). As a result of these and other federal, state and 

local programs, the number of areas that fail to meet the PM2.5 NAAQS in the future is 

expected to decrease. However, even with the implementation of all current state and federal 

regulations, there are projected to be counties violating the PM2.5 NAAQS well into the 

future. 

The air quality modeling conducted projects that in 2030, with all current controls in 

effect but excluding the emissions changes expected to occur as a result of the vehicle 

standards being finalized here, at least 9 counties, with a projected population of nearly 28 

million people, may not attain the annual standard of 15 μg/m3 and at least 26 counties, with a 

projected population of over 41 million people, may not attain the 2006 24-hour standard of 

35 μg/m3. Since the emission changes from this rule go into effect during the period when 

some areas are still working to attain the PM2.5 NAAQS, the projected emission changes will 

impact state and local agencies in their effort to attain and maintain the PM2.5 standard. In the 

following section we discuss projected nonattainment areas and how they compare to the 

areas which are projected to experience PM2.5 reductions or increases from the vehicle 

standards. 

7.2.2.1.2.2 Projected Annual Average PM2.5 Design Values with this Rule 

This section summarizes the results of our modeling of annual average PM2.5 air 

quality impacts in the future due to the vehicle standards. Specifically, we compare a 2030 

reference scenario, a scenario without the vehicle standards, to a 2030 control scenario which 

includes the vehicle standards. Our modeling indicates that the majority of the modeled 

counties will see decreases of less than 0.05 μg/m3

 in their annual PM2.5 design values due to 

 

AA This rule was signed on December 18, 2009 but has not yet been published in the Federal Register. The 

signed version of the rule is available at http://epa.gov/otaq/oceanvessels.htm). 
Regulatory Impact Analysis 

7-52 

the vehicle standards. Figure 7-9 presents the changes in annual PM2.5 design values in 

2030.BB

 

Figure 7-9 Projected Change in 2030 Annual PM2.5 Design Values Between the Reference Case and 

Control Case 

As shown in Figure 7-9, six counties will see decreases of more than 0.05 μg/m3. 

These counties are in southern California, central North Dakota, eastern Missouri, southwest 

Louisiana and the Houston area in Texas. The maximum projected decrease in an annual 

PM2.5 design value is 0.07 μg/m3 in Harris County, Texas. The decreases in annual PM2.5

design values that we see in some counties are likely due to emission reductions related to 

lower gasoline production at existing oil refineries; reductions in direct PM2.5 emissions and 

PM2.5 precursor emissions (NOX and SOX) contribute to reductions in ambient concentrations 

 

BB An annual PM2.5 design value is the concentration that determines whether a monitoring site meets the annual 

NAAQS for PM2.5. The full details involved in calculating an annual PM2.5 design value are given in appendix N 

of 40 CFR part 50. 
 Environmental and Health Impacts 

7-53 

of both direct PM2.5 and secondarily-formed PM2.5. Additional information on the upstream 

emissions reductions that are projected with this final rule is available in Section 5.5. 

There are also a few counties that will see small, no more than 0.01 μg/m3

, design 

value increases. These small increases in annual PM2.5 design values are likely related to 

downstream emission increases. Additional information on the downstream emissions 

increases that are projected with this final rule is also available in Section 5.3.3.5. 

There are 9 counties, all in California, that are projected to have annual PM2.5 design 

values above the NAAQS in 2030 with the vehicle standards in place. Table 7-2 below 

presents the changes in design values for these counties. 

Table 7-2 Change in Annual PM2.5 Design Values (μg/m3) for Counties Projected to be Above the Annual 

PM2.5 NAAQS in 2030 

County Name Change in 

Annual 

PM2.5

Design 

Value 

(μg/m3) 

Population 

in 2030a

Riverside Co., California -0.02 2,614,198 

San Bernardino Co., California -0.03 2,784,489 

Los Angeles Co., California -0.06 10,742,722 

Kern Co., California -0.01 981,806 

Tulare Co., California -0.01 528,662 

Orange Co., California -0.03 4,431,070 

Kings Co., California 0.00 195,067 

Fresno Co., California 0.00 1,196,949 

San Diego Co., California 0.00 4,399,983 

Note: 

a

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. 

Population by Single Year of Age CD. 

Table 7-3 shows the average change in 2030 annual PM2.5 design values for: (1) all 

counties with 2005 baseline design values, (2) counties with 2005 baseline design values that 

exceeded the annual PM2.5 standard, (3) counties with 2005 baseline design values that did not 

exceed the standard, but were within 10% of it, (4) counties with 2030 design values that 

exceeded the annual PM2.5 standard, and (5) counties with 2030 design values that did not 

exceed the standard, but were within 10% of it. Counties within 10% of the standard are 

intended to reflect counties that although not violating the standards, will also be impacted by 

changes in PM2.5 as they work to ensure long-term maintenance of the annual PM2.5 NAAQS. 

These statistics show either no change or a decrease in annual PM2.5 design values in 2030. 
Regulatory Impact Analysis 

7-54 

On a population-weighted basis, the average modeled future-year annual PM2.5 design values 

are projected to decrease by 0.01 μg/m3

 due to the vehicle standards. On a populationweighted basis annual PM2.5 design values in those counties that are projected to be above the 

annual PM2.5 standard in 2030 will see a slightly larger decrease of 0.03 μg/m3 due to the 

vehicle standards. 

Table 7-3 Average Change in Projected Annual PM2.5 Design Values 

Averagea Number 

of US 

Counties 

2030 

Populationb

Change in 

2030 design 

value 

(μg/m3) 

All 0.00 

All, population-weighted 576 247,415,381 -0.01 

Counties whose 2005 base year is violating the 

1997 annual PM2.5 standard 0.00 

Counties whose 2005 base year is violating 

the1997 annual PM2.5 standard, populationweighted 70 65,106,709 -0.02 

Counties whose 2005 base year is within 10 

percent of the1997 annual PM2.5 standard 0.00 

Counties whose 2005 base year is within 10 

percent of the 1997 annual PM2.5 standard, 

population-weighted 102 33,008,932 0.00 

Counties whose 2030 control case is violating 

the 1997 annual PM2.5 standard -0.02 

Counties whose 2030 control case is violating 

the 1997 annual PM2.5 standard, populationweighted 9 27,874,946 -0.03 

Counties whose 2030 control case is within 10% 

of the 1997 annual PM2.5 standard 0.00 

Counties whose 2030 control case is within 10% 

of the 1997 annual PM2.5 standard, populationweighted 5 5,864,401 0.00 

Note: 

a

 Averages are over counties with 2005 modeled design values 

b Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by 

Single Year of Age CD. 

7.2.2.1.2.3 Projected 24-hour Average PM2.5 Design Values with this Rule 

This section summarizes the results of our modeling of 24-hour PM2.5 air quality 

impacts in the future due to the vehicle standards. Specifically, we compare a 2030 reference 
 Environmental and Health Impacts 

7-55 

scenario, a scenario without the vehicle standards, to a 2030 control scenario which includes 

the vehicle standards. Our modeling indicates that the majority of the modeled counties will 

see changes of between -0.05 μg/m3

 and +0.05 μg/m3 in their 24-hour PM2.5 design values. 

Figure 7-10 presents the changes in 24-hour PM2.5 design values in 2030.CC 

Figure 7-10 Projected Change in 2030 24-hour PM2.5 Design Values Between the Reference Case and the 

Control Case 

As shown in Figure 7-10, 17 counties will see decreases of more than 0.05 μg/m3. 

These counties are in southern California, northern Utah, central North Dakota, eastern 

Missouri, southern Arkansas, northern Oklahoma, southwest Louisiana and the Houston area 

in Texas. The maximum projected decrease in a 24-hour PM2.5 design value is 0.21 μg/m3 in 

Harris County, Texas. The decreases in 24-hour PM2.5 design values that we see in some 

counties are likely due to emission reductions related to lower gasoline production at existing 

 

CC A 24-hour PM2.5 design value is the concentration that determines whether a monitoring site meets the 24-

hour NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5 design value are given in 

appendix N of 40 CFR part 50. 
Regulatory Impact Analysis 

7-56 

oil refineries; reductions in direct PM2.5 emissions and PM2.5 precursor emissions (NOX and 

SOX) contribute to reductions in ambient concentrations of both direct PM2.5 and secondarilyformed PM2.5. Additional information on the upstream emissions reductions that are projected 

with this final rule is available in Section 5.5. 

There are also some counties that will see small, less than 0.05 μg/m3

, design value 

increases. These small increases in 24-hour PM2.5 design values are likely related to 

downstream emissions increases. 

There are 26 counties, mainly in California, that are projected to have 24-hour PM2.5

design values above the NAAQS in 2030 with the vehicle standards in place. Table 7-4 

below presents the changes in design values for these counties. 
 Environmental and Health Impacts 

7-57 

 

Table 7-4 Change in 24-hour PM2.5 Design Values (μg/m3) for Counties Projected to be Above the 24-hour 

PM2.5 NAAQS in 2030 

County Name Change in 24-

hour PM2.5

Design Value 

(μg/m3) 

Population in 

2030a

Riverside Co., California -0.05 2,614,198 

Kern Co., California -0.02 981,806 

Allegheny Co., Pennsylvania 0.04 1,234,931 

Fresno Co., California -0.01 1,196,950 

San Bernardino Co., California -0.02 2,784,490 

Los Angeles Co., California -0.10 10,742,722 

Kings Co., California -0.01 195,067 

Tulare Co., California 0.00 528,663 

Lane Co., Oregon 0.00 460,993 

Sacramento Co., California -0.01 1,856,971 

Cache Co., Utah 0.01 141,446 

Salt Lake Co., Utah -0.02 1,431,946 

Orange Co., California -0.09 4,431,071 

Butte Co., California 0.00 287,236 

Stanislaus Co., California -0.01 688,246 

Klamath Co., Oregon 0.00 77,200 

Utah Co., Utah 0.04 661,456 

Lincoln Co., Montana 0.00 20,454 

Pierce Co., Washington 0.03 1,082,579 

Santa Clara Co., California -0.01 2,320,199 

Merced Co., California 0.00 313,334 

Imperial Co., California 0.00 174,175 

Wayne Co., Michigan -0.01 1,838,270 

San Diego Co., California -0.02 4,399,983 

Milwaukee Co., Wisconsin 0.00 927,986 

Brooke Co., West Virginia 0.00 24,095 

Note: 

a

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. 

Population by Single Year of Age CD. 

Table 7-5 shows the average change in 2030 24-hour PM2.5 design values for: (1) all 

counties with 2005 baseline design values, (2) counties with 2005 baseline design values that 

exceeded the 24-hour PM2.5 standard, (3) counties with 2005 baseline design values that did 

not exceed the standard, but were within 10% of it, (4) counties with 2030 design values that 
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exceeded the 24-hour PM2.5 standard, and (5) counties with 2030 design values that did not 

exceed the standard, but were within 10% of it. Counties within 10% of the standard are 

intended to reflect counties that although not violating the standards, will also be impacted by 

changes in PM2.5 as they work to ensure long-term maintenance of the 24-hour PM2.5

NAAQS. On a population-weighted basis, the average modeled future-year 24-hour PM2.5

design values are projected to decrease by 0.01 μg/m3

 due to the vehicle standards. On a 

population-weighted basis 24-hour PM2.5 design values in those counties that are projected to 

be above the 24-hour PM2.5 standard in 2030 will see a slightly larger decrease of 0.05 μg/m3. 

Table 7-5 Average Change in Projected 24-hour PM2.5 Design Values 

Averagea Number 

of US 

Counties 

2030 

Populationb

Change in 

2030 design 

value 

(μg/m3) 

All 0.00 

All, population-weighted 579 247,228,608 -0.01 

Counties whose 2005 base year is violating the 

2006 24-hour PM2.5 standard 0.00 

Counties whose 2005 base year is violating the 

2006 24-hour PM2.5 standard, populationweighted 105 86,013,770 -0.02 

Counties whose 2005 base year is within 10 

percent of the 2006 24-hour PM2.5 standard 0.00 

Counties whose 2005 base year is within 10 

percent of the 2006 24-hour PM2.5 standard, 

population-weighted 139 53,848,276 0.00 

Counties whose 2030 control case is violating 

the 2006 24-hour PM2.5 standard -0.01 

Counties whose 2030 control case is violating 

the 2006 24-hour PM2.5 standard, populationweighted 26 41,416,465 -0.05 

Counties whose 2030 control case is within 10% 

of the 2006 24-hour PM2.5 standard 0.00 

Counties whose 2030 control case is within 10% 

of the 2006 24-hour PM2.5 standard, 

population-weighted 24 18,526,165 0.01 

Note: 

a

 Averages are over counties with 2005 modeled design values 

b

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by 

Single Year of Age CD. 
 Environmental and Health Impacts 

7-59 

7.2.2.2 Ozone 

As described in Section 7.1, ozone causes adverse health effects, and the EPA has set 

national ambient air quality standards (NAAQS) to protect against those health effects. In this 

section, we present information on current and model-projected future ozone levels. 

7.2.2.2.1 Current Levels of Ozone 

Figure 7-11 shows a snapshot of ozone concentrations in 2007. The highest ozone 

concentrations were located in California. Thirty-two percent of the sites were above 0.075 

ppm, the level of the 2008 standard. 

Figure 7-11 Ozone Concentrations (fourth highest daily maximum 8-hour concentration) in ppm for 

2008DD 

 

DD From U.S. EPA, 2010. Our Nation’s Air: Status and Trends through 2008. EPA-454/R-09-002. February 

2010. Available at: http://www.epa.gov/airtrends/2010/index.html. 
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7-60 

 

Figure 7-12 1997 Ozone Nonattainment Areas 

The primary and secondary national ambient air quality standards (NAAQS) for ozone 

are 8-hour standards set at 0.075 ppm. The most recent revision to the ozone standards was in 

2008; the previous 8-hour ozone standards, set in 1997, had been set at 0.08 ppm. In 2004, 

the U.S. EPA designated nonattainment areas for the 1997 8-hour ozone NAAQS (69 FR 

23858, April 30, 2004). As of January 6, 2010, there are 51 8-hour ozone nonattainment areas 

for the 1997 ozone NAAQS composed of 266 full or partial counties with a total population 

of over 122 million. Figure 7-12 presents the 1997 NAAQS ozone nonattainment areas. On 

January 6, 2010, EPA proposed to reconsider the 2008 ozone NAAQS to ensure that they are 

requisite to protect public health with an ample margin of safety, and requisite to protect 

public welfare. EPA intends to complete the reconsideration by August 31, 2010. If, as a 

result of the reconsideration, EPA promulgates different ozone standards, the new 2010 ozone 
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7-61 

standards would replace the 2008 ozone standards and the requirement to designate areas for 

the replaced 2008 standards would no longer apply. Because of the significant uncertainty the 

reconsideration proposal creates regarding the continued applicability of the 2008 ozone 

NAAQS, EPA has extended the deadline for designating areas for the 2008 NAAQS by one 

year. This will allow EPA to complete its reconsideration of the 2008 ozone NAAQS before 

determining whether designations for those standards are necessary. 

If EPA promulgates new ozone standards in 2010, EPA intends to accelerate the 

designations process for the primary standard so that the designations would be effective in 

August 2011. EPA is considering two alternative schedules for designating areas for a new 

seasonal secondary standard, an accelerated schedule or a 2-year schedule. 

Table 7-6 includes an estimate, based on 2006-08 air quality data, of the counties with 

design values greater than the 2008 ozone NAAQS. 

Table 7-6 Counties with Design Values Greater Than the Ozone NAAQS 

NUMBER OF 

COUNTIES 

POPULATIONA

1997 Ozone Standard: counties within the 54 

areas currently designated as nonattainment (as 

of 1/6/10) 

266 122,343, 799 

2008 Ozone Standard: additional counties that 

would not meet the 2008 NAAQS (based on 

2006-2008 air quality data)b

156 36,678,478 

Total 422 159,022,277 

Notes: 

a

 Population numbers are from 2000 census data. 

b

 Attainment designations for the 2008 ozone NAAQS have not yet been made. Nonattainment for the 2008 

Ozone NAAQS will be based on three years of air quality data from later years. Also, the county numbers in the 

table include only the counties with monitors violating the 2008 Ozone NAAQS. The numbers in this table may 

be an underestimate of the number of counties and populations that will eventually be included in areas with 

multiple counties designated nonattainment.

 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 be required to maintain 

it thereafter.EE In addition, there will be attainment dates associated with the designation of 

 

EE 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 their attainment date June 15, 2024. The San Joaquin Valley Air Basin 8-
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7-62 

nonattainment areas as a result of the reconsideration of the 2008 ozone NAAQS. If the 

ozone NAAQS reconsideration action is completed on the proposed schedule, the primary 

NAAQS attainment dates would be in the 2014-2031 time frame. The vehicle standards first 

apply to model year 2012 vehicles. 

7.2.2.2.2 Projected Levels of Ozone 

In the following sections, we describe projected ozone levels in the future with and 

without the vehicle standards. We do not expect this rule to have a meaningful impact on 

ozone concentrations, given the small magnitude of the ozone impacts and the fact that much 

of the impact is due to ethanol assumptions that are independent of this rule. Our modeling 

indicates that there will be increases in ozone design value concentrations in many areas of 

the country and decreases in ozone design value concentrations in a few areas. However, the 

increases in ozone design values are not due to the standards finalized in this rule, but are 

related to our assumptions about the volume of ethanol that will be blended into gasoline. 

The ethanol volumes will be occurring as a result of the recent Renewable Fuel Standards 

(RFS2) rule.265 Information on the air quality modeling methodology is contained in Section 

7.2.1.1. Additional detail can be found in the air quality modeling technical support 

document (AQM TSD). 

7.2.2.2.2.1 Projected Levels of Ozonewithout this Rule 

EPA has already adopted many emission control programs that are expected to reduce 

ambient ozone levels. These control programs include the New Marine Compression-Ignition 

Engines at or Above 30 Liters per Cylinder rule,FF the Marine Spark-Ignition and Small 

Spark-Ignition Engine rule (73 FR 59034, October 8, 2008), the Locomotive and Marine Rule 

(73 FR 25098, May 6, 2008), the Clean Air Interstate Rule (70 FR 25162, May 12, 2005), the 

Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), and the Heavy Duty Engine and 

Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 

18, 2001). As a result of these and other federal, state and local programs, 8-hour ozone 

levels are expected to improve in the future. However, even with the implementation of all 

current state and federal regulations, there are projected to be counties violating the ozone 

NAAQS well into the future. 

The air quality modeling projects that in 2030, with all current controls in effect but 

excluding the emissions changes expected to occur as a result of this final rule, at least 16 

 

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 

their attainment date June 15, 2024. 

FF This rule was signed on December 18, 2009 but has not yet been published in the Federal Register. The 

signed version of the rule is available at http://epa.gov/otaq/oceanvessels.htm). 
 Environmental and Health Impacts 

7-63 

counties, with a projected population of almost 35 million people, may not attain the 2008 8-

hour ozone standard of 75 ppb. Since the emission changes from this rule go into effect 

during the period when some areas are still working to attain the ozone NAAQS, the projected 

emission changes will impact state and local agencies in their effort to attain and maintain the 

ozone standard. In the following section we discuss projected nonattainment areas and how 

they compare to the areas which are projected to experience ozone reductions from the 

vehicle standards. 

7.2.2.2.2.2 Projected Levels of Ozonewith this Rule 

This section summarizes the results of our modeling of ozone air quality impacts in 

the future with the vehicle standards. Specifically, we compare a 2030 reference scenario, a 

scenario without the vehicle standards, to a 2030 control scenario which includes the vehicle 

standards. We do not expect this rule to have a meaningful impact on ozone concentrations, 

given the small magnitude of the ozone impacts and the fact that much of the impact is due to 

ethanol assumptions that are independent of this rule. 

Our modeling indicates ozone design value concentrations will increase in many areas 

of the country and decrease in a few areas. The increases in ozone design values are not due 

to the standards finalized in this rule, but are related to our assumptions about the volume of 

ethanol that will be blended into gasoline. The ethanol volumes will be occurring as a result 

of the recent RFS2 rule. As discussed in Sections 5.3.2 and 5.3.3.5 of this RIA, we attribute 

decreased fuel consumption and production from this program to gasoline only, while 

assuming constant ethanol volumes in our reference and control cases. Holding ethanol 

volumes constant while decreasing gasoline volumes increases the market share of 10% 

ethanol (E10) in the control case. However, the increased E10 market share is projected to 

occur regardless of this rule, and the air quality impacts of this effect are included in our 

analyses for the recent RFS2 rule. As the RFS2 analyses indicate, increasing usage of E10 

fuels (when compared with E0 fuels) can increase NOX emissions and thereby increase ozone 

concentrations, especially in NOX-limited areas where relatively small amounts of NOX

enable ozone to form rapidly.266 Figure 7-13 presents the changes in 8-hour ozone design 

value concentration in 2030 between the reference case and the control case.GG 

 

GG An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour 

ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in appendix I of 

40 CFR part 50. 
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7-64 

Figure 7-13 Projected Change in 2030 8-hour Ozone Design Values Between the Reference Case and 

Control Case 

As can be seen in Figure 7-13, the majority of the design value increases are less than 

0.1 ppb. However, there are some counties that will see 8-hour ozone design value increases 

above 0.1 ppb; these counties are along the mid-Atlantic coast and in southern Arizona. The 

maximum projected increase in an 8-hour ozone design value is 0.25 ppb in Richland County, 

South Carolina. There are also some counties that are projected to see 8-hour ozone design 

value decreases. The decreases in ambient ozone concentration are likely due to projected 

upstream emissions decreases in NOX and VOCs from reduced gasoline production. The 

counties with ozone design value decreases greater than 0.1 ppb are in California, Texas, 

Louisiana, Mississippi, Kentucky, Ohio and West Virginia. The maximum decrease projected 

in an 8-hour ozone design value is 0.22 ppb in Riverside, CA. 

There are 16 counties, half of them in California, that are projected to have 8-hour 

ozone design values above the 2008 NAAQS in 2030 with the vehicle standards in place. 

Table 7-7 below presents the changes in design values for these counties. Increases in design 

values in Maryland and Connecticut are a reflection of our ethanol volume assumptions as 
 Environmental and Health Impacts 

7-65 

discussed above (also Sections 5.3.2 and 5.3.3.5) and are not due to the standards finalized in 

this rule. 

Table 7-7 Change in Ozone Design Values (ppb) for Counties Projected to be Above the 2008 Ozone 

NAAQS in 2030 

County Name Change in 

8-hour 

Ozone

Design 

Value 

(ppb) 

Population 

in 2030a

San Bernardino Co., 

California -0.18 60,710,005

Riverside Co., California -0.22 60,658,001

Los Angeles Co., California -0.06 60,376,012

Kern Co., California -0.07 60,295,001

Harris Co., Texas -0.17

482,010,05

5

Tulare Co., California -0.02 61,070,009

Suffolk Co., New York -0.02

361,030,00

2

Fresno Co., California -0.04 60,190,008

Brazoria Co., Texas -0.15

480,391,00

4

Orange Co., California -0.13 60,595,001

Harford Co., Maryland 0.06

240,251,00

1

Fairfield Co., Connecticut 0.00 90,013,007

East Baton Rouge Co., 

Louisiana -0.10

220,330,00

3

Calaveras Co., California -0.10 60,090,001

Ventura Co., California -0.03 61,112,002

New Haven Co., Connecticut 0.04 90,093,002

San Bernardino Co., 

California -0.18 60,710,005

Note: 

a

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. 

Population by Single Year of Age CD. 

Table 7-8 shows the average change in 2030 8-hour ozone design values for: (1) all 

counties with 2005 baseline design values, (2) counties with 2005 baseline design values that 

exceeded the 2008 ozone standard, (3) counties with 2005 baseline design values that did not 
Regulatory Impact Analysis 

7-66 

exceed the 2008 standard, but were within 10% of it, (4) counties with 2030 design values 

that exceeded the 2008 ozone standard, and (5) counties with 2030 design values that did not 

exceed the standard, but were within 10% of it. Counties within 10% of the standard are 

intended to reflect counties that although not violating the standards, will also be impacted by 

changes in ozone as they work to ensure long-term maintenance of the ozone NAAQS. Many 

of these statistics, except for counties with 2030 design values that exceed the 2008 ozone 

standard, show an increase in 2030. Again, increases in ozone design value concentrations 

are a reflection of our ethanol volume assumptions, as discussed above (also Sections 5.3.2 

and 5.3.3.5), and are not due to the standards finalized in this rule. On a population-weighted 

basis, the average modeled future-year 8-hour ozone design values are projected to increase 

by 0.28 ppb in 2030. On a population-weighted basis those counties that are projected to be 

above the 2008 ozone standard in 2030 will see a decrease of 0.10 ppb due to the vehicle 

standards. 

Table 7-8 Average Change in Projected 8-hour Ozone Design Value 

Averagea Number 

of US 

Counties 

2030 

Populationb

Change in 

2030 design 

value (ppb) 

All 0.03 

All, population-weighted 678 262,264,195 0.01 

Counties whose 2005 base year is violating the 

2008 8-hour ozone standard 0.03 

Counties whose 2005 base year is violating the 

2008 8-hour ozone standard, populationweighted 389 192,026,888 0.01 

Counties whose 2005 base year is within 10 

percent of the 2008 8-hour ozone standard 0.03 

Counties whose 2005 base year is within 10 

percent of the 2008 8-hour ozone standard, 

population-weighted 208 47,276,756 0.02 

Counties whose 2030 control case is violating 

the 2008 8-hour ozone standard -0.07 

Counties whose 2030 control case is violating 

the 2008 8-hour ozone standard, populationweighted 16 34,751,421 -0.10 

Counties whose 2030 control case is within 10% 

of the 2008 8-hour ozone standard 0.00 

Counties whose 2030 control case is within 10% 

of the 2008 8-hour ozone standard, populationweighted 80 61,467,398 0.01 

Note: 

a

 Averages are over counties with 2005 modeled design values 
 Environmental and Health Impacts 

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b

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by 

Single Year of Age CD.

Ground-level ozone pollution is formed by the reaction of VOCs and NOX in the 

atmosphere in the presence of heat and sunlight. The science of ozone formation, transport, 

and accumulation is complex.267 The projected ozone decreases which are seen in the air 

quality modeling for this final rule are likely a result of the emissions changes due to the 

vehicle standards combined with the photochemistry involved, the different background 

concentrations of VOCs and NOX in different areas of the country, and the different 

meteorological conditions in different areas of the country. 

When VOC levels are relatively high, relatively small amounts of NOX enable ozone 

to form rapidly. Under these conditions, VOC reductions have little effect on ozone and 

while NOX reductions are highly effective in reducing ozone, NOX increases lead to increases 

in ozone. Such conditions are called “NOX -limited.” Because the contribution of VOC 

emissions from biogenic (natural) sources to local ambient ozone concentrations can be 

significant, even some areas where man-made VOC emissions are relatively low can be NOX

-limited. Rural areas are usually NOX -limited, due to the relatively large amounts of biogenic 

VOC emissions in such areas. 

When NOX levels are relatively high and VOC levels relatively low, NOX forms 

inorganic nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOClimited.” Under these conditions, VOC reductions are effective in reducing ozone, but NOX

reductions can actually increase local ozone under certain circumstances. 

7.2.2.3 Air Toxics 

7.2.2.3.1 Current Levels of Air Toxics 

The majority of Americans continue to be exposed to ambient concentrations of air 

toxics at levels which have the potential to cause adverse health effects.268 The levels of air 

toxics to which people are exposed vary depending on where people live and work and the 

kinds of activities in which they engage, as discussed in detail in U.S. EPA’s most recent 

Mobile Source Air Toxics Rule.269 In order to identify and prioritize air toxics, emission 

source types and locations which are of greatest potential concern, U. S. EPA conducts the 

National-Scale Air Toxics Assessment (NATA). The most recent NATA was conducted for 

calendar year 2002, and was released in June 2009.270 NATA for 2002 includes four steps: 

1) Compiling a national emissions inventory of air toxics emissions from outdoor 

sources 

2) Estimating ambient concentrations of air toxics across the United States 

3) Estimating population exposures across the United States 
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4) Characterizing potential public health risk due to inhalation of air toxics including 

both cancer and noncancer effects 

Figure 7-14 and Figure 7-15 depict estimated county-level carcinogenic risk and 

noncancer respiratory hazard from the assessment. The respiratory hazard is dominated by a 

single pollutant, acrolein. 

According to NATA for 2002, mobile sources were responsible for 47 percent of 

outdoor toxic emissions, over 50 percent of the cancer risk, and over 80 percent of the 

noncancer hazard. 271,HH Benzene is the largest contributor to cancer risk of all 124 pollutants 

quantitatively assessed in the 2002 NATA, and mobile sources were responsible for 59 

percent of benzene emissions in 2002. Over the years, EPA has implemented a number of 

mobile source and fuel controls which have resulted in VOC reductions, which also reduced 

benzene and other air toxic emissions. 

Figure 7-14 County Level Average Carcinogenic Risk, 2002 NATA 

 

HH 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 rule 

were modeled with CMAQ 4.7, which has only recently been updated to include air toxics. 
 Environmental and Health Impacts 

7-69 

Figure 7-15 County Level Average Noncancer Hazard Index, 2002 NATA 

7.2.2.3.2 Projected Levels of Air Toxics 

In the following sections, we describe results of our modeling of air toxics levels in 

the future with the standards finalized in this action. Although there are a large number of 

compounds which are considered air toxics, we focused on those which were identified as 

national and regional-scale cancer and noncancer risk drivers in past NATA assessments and 

were also likely to be significantly impacted by the standards. These compounds include 

benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Ethanol impacts were also 

included in our analyses. Information on the air quality modeling methodology is contained 

in Section 7.2.1. Additional detail such as the seasonal concentration maps for the modeled 

air toxics can be found in the air quality modeling technical support document (AQM TSD) in 

the docket for this rule. 

It should be noted that EPA has adopted many mobile source emission control 

programs that are expected to reduce ambient air toxics levels. These control programs 

include the Heavy-duty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI 

and Marine SI Engine Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial 

Marine Rule (73 FR 25098, May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, 

February 26, 2007), Clean Air Nonroad Diesel Rule (69 FR 38957, June 29, 2004), Heavy 
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Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements 

(66 FR 5002, Jan. 18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline 

Sulfur Control Requirements (65 FR 6698, Feb. 10, 2000). As a result of these programs, the 

ambient concentration of air toxics in the future is expected to decrease. The reference case 

and control case scenarios include these controls. 

Our modeling indicates that the GHG standards have relatively little impact on 

national average ambient concentrations of the modeled air toxics. Because overall impacts 

are small, we concluded that assessing exposure to ambient concentrations and conducting a 

quantitative risk assessment of air toxic impacts was not warranted. However, we did develop 

population metrics, including the population living in areas with increases or decreases in 

concentrations of various magnitudes. 

Acetaldehyde 

Our air quality modeling does not show substantial overall nationwide impacts on 

ambient concentrations of acetaldehyde as a result of the standards finalized in this rule. 

Annual percent changes in ambient concentrations of acetaldehyde are less than 1% across the 

country (Figure 7-16). Decreases in ambient concentrations of acetaldehyde seen in the much 

of the eastern half of the U.S. and parts of the West are generally less than 0.01 μg/m3 (Figure 

7-16). 

Figure 7-16 Changes in Acetaldehyde Ambient Concentrations Between the Reference Case and the 

Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 
 Environmental and Health Impacts 

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Formaldehyde 

Our modeling projects that the standards finalized in this rule will not have a 

significant impact on ambient formaldehyde concentrations. As shown in Figure 7-17, annual 

percent changes in ambient concentrations of formaldehyde are less than 1% across the 

country, with the exception of a 1 to 5% decrease in a small area of southern Kansas and 

northern Oklahoma. Figure 7-17 also shows that absolute changes in ambient concentrations 

of formaldehyde are generally less than 0.1 μg/m3. Increases in ambient formaldehyde 

concentrations, which range from 0.001 to 0.1 μg/m3, are a reflection of our ethanol volume 

assumptions as discussed above in Section 7.2.2.2.2.2 (also Sections 5.3.2 and 5.3.3.5) and are 

not due to the standards finalized in this rule. 

Figure 7-17 Changes in Formaldehyde Ambient Concentrations Between the Reference Case and the 

Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 

Ethanol 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of ethanol from the vehicle GHG standards. While Figure 7-18 shows 

increases in ambient ethanol concentrations ranging between 1 and 50% in some areas of the 

country, these increases are a reflection of our ethanol volume assumptions as discussed 
Regulatory Impact Analysis 

7-72 

above in Section 7.2.2.2.2.2 (also Sections 5.3.2 and 5.3.3.5) and are not due to the standards 

finalized in this rule. 

Figure 7-18 Changes in Ethanol Ambient Concentrations Between the Reference Case and the Control 

Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 

Benzene 

Our modeling projects that the standards finalized in this rule will not have a 

significant impact on ambient benzene concentrations. Figure 7-19 shows decreases in 

ambient benzene concentrations ranging between 1 and 10% and between 0.001 and 0.1 

μg/m3. Because this rule will reduce consumption and production of gasoline, some of these 

decreases in benzene concentrations are likely due to the vehicle GHG standards. However, 

decreases in benzene concentrations may also be a reflection of our ethanol volume 

assumptions as discussed above for ozone, ethanol and formaldehyde, and are not due to the 

standards finalized in this rule. For example, the percent change map in Figure 7-19 below 

shows benzene decreases occurring in the same areas of the country as ozone, ethanol, and 

formaldehyde increases. 
 Environmental and Health Impacts 

7-73 

Figure 7-19 Changes in Benzene Ambient Concentrations Between the Reference Case and the Control 

Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 

1,3-Butadiene 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of 1,3-butadiene from the GHG standards. Small decreases ranging from 1 to 

10% occur in some southern areas of the country and increases ranging from 1 to over 100% 

occur in some northern areas and areas with high altitudes (Figure 7-20). Changes in absolute 

concentrations of ambient 1,3-butadiene are less than 0.001 μg/m3 except in some areas of the 

Northeast and Utah (Figure 7-20). Annual increases in ambient concentrations of 1,3-

butadiene are driven by wintertime rather than summertime changes (seasonal maps can be 

found in the AQM TSD). These increases appear in rural areas with cold winters and low 

ambient levels but high contributions of emissions from snowmobiles, and a major reason for 

this modeled increase may be deficiencies in available emissions test data used to estimate 

snowmobile 1,3-butadiene emission inventories. These data were based on tests using only 

three engines, which showed significantly higher 1,3-butadiene emissions with 10% ethanol. 

However, they may not have been representative of real-world response of snowmobile 

engines to ethanol. Regardless, these increases are a reflection of our ethanol volume 

assumptions and are not due to the standards finalized in this rule. 
Regulatory Impact Analysis 

7-74 

Figure 7-20 Changes in 1,3-Butadiene Ambient Concentrations Between the Reference Case and the 

Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 

Acrolein 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of acrolein from the standards finalized in this rule. Small decreases ranging 

from 1 to 2.5% occur in a few areas of the country and increases ranging from 1 to 100% 

occur in some northern areas and areas with high altitudes (Figure 7-21). Changes in absolute 

concentrations of acrolein are less than 0.001 μg/m3 across the country (Figure 7-21). 

Ambient acrolein increases are driven by wintertime changes (see the AQM TSD for seasonal 

maps), and occur in the same areas of the country that have wintertime rather than 

summertime increases in ambient 1,3-butadiene. 1,3-butadiene is a precursor to acrolein, and 

these increases are likely associated with the same emission inventory uncertainties in areas of 

high snowmobile usage seen for 1,3-butadiene. As described above, these increases are a 

reflection of our ethanol volume assumptions and are not due to the standards finalized in this 

rule. 
 Environmental and Health Impacts 

7-75 

Figure 7-21 Changes in Acrolein Ambient Concentrations Between the Reference Case and the Control 

Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 

Population Metrics 

To assess the impact of projected changes in air quality with the GHG standards, we 

developed population metrics that show population experiencing increases and decreases in 

annual ambient concentrations across the modeled air toxics. Table 7-9 illustrates the 

percentage of the population impacted by changes of various magnitudes in annual ambient 

concentrations between the reference case and the control case. As discussed above, increases 

in ambient ethanol, 1,3-butadiene, and acrolein concentration are due to our ethanol volume 

assumptions, and are not the result of the GHG vehicle standards. 
Regulatory Impact Analysis 

7-76 

Table 7-9 Percent of Total Population Impacted by Changes in Annual Ambient Concentrations of Toxic 

Pollutants Between the Reference and Control Cases in 2030 

Percent Change 

in Annual 

Ambient 

Concentration Acetaldehyde Acrolein Benzene 1,3-Butadiene Ethanol Formaldehyde 

≤-100 

>-100 to ≤-50 

>-50 to ≤-10 

>-10 to ≤-5 0.97% 0.60% 0.64% 0.44% 0.54% 

>-5 to ≤-2.5 0.61% 0.18% 4.65% 3.26% 1.84% 0.41% 

>-2.5 to ≤-1 5.13% 1.92% 16.22% 8.43% 15.11% 0.27% 

> -1 to <1 94.86% 94.82% 78.63% 75.36% 67.59% 99.75% 

≥1 to <2.5 2.19% 8.45% 4.92% 

≥2.5 to <5 0.93% 2.25% 2.77% 

≥5 to <10 0.14% 0.82% 3.96% 

≥10 to <50 0.63% 0.78% 3.82% 

≥50 to <100 0.27% 

≥100 0.13% 

7.2.2.4 Deposition of Nitrogen and Sulfur 

7.2.2.4.1 Current Levels of Nitrogen and Sulfur Deposition 

Over the past two decades, the EPA has undertaken numerous efforts to reduce 

nitrogen and sulfur deposition across the U.S. Analyses of long-term monitoring data for the 

U.S. show that deposition of both nitrogen and sulfur compounds has decreased over the last 

17 years although many areas continue to be negatively impacted by deposition. Deposition 

of inorganic nitrogen and sulfur species routinely measured in the U.S. between 2004 and 

2006 were as high as 9.6 kilograms of nitrogen per hectare per year (kg N/ha/yr) and 21.3 

kilograms of sulfur per hectare per year (kg S/ha/yr). Figure 7-22 and Figure 7-23 show that 

annual total deposition (the sum of wet and dry deposition) decreased between 1989-1999 and 

2004-2006 due to sulfur and NOX controls on power plants, motor vehicles and fuels in the 

U.S. The data show that reductions were more substantial for sulfur compounds than for 

nitrogen compounds. These numbers are generated by the U.S. national monitoring network 

and they likely underestimate nitrogen deposition because neither ammonia nor organic 

nitrogen is measured. In the eastern U.S., where data are most abundant, total sulfur 

deposition decreased by about 44% between 1990 and 2007, while total nitrogen deposition 

decreased by 25% over the same time frame.272

 
 Environmental and Health Impacts 

7-77 

Figure 7-22 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2004 -2006 
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7-78 

Figure 7-23 Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2004-2006 
 Environmental and Health Impacts 

7-79 

7.2.2.4.2 Projected Levels of Nitrogen and Sulfur Deposition 

Our air quality modeling does not show substantial overall nationwide impacts on the 

annual total sulfur and nitrogen deposition occurring across the U.S. as a result of the vehicle 

standards required by this rule. Figure 7-24 shows that for sulfur deposition the vehicle 

standards will result in annual percent decreases of 0.5% to more than 2% in locations with 

refineries as a result of the lower output from refineries due to less gasoline usage. These 

locations include the Texas and Louisiana portions of the Gulf Coast; the Washington D.C. 

area; Chicago, IL; portions of Oklahoma and northern Texas; Bismarck, North Dakota; 

Billings, Montana; Casper, Wyoming; Salt Lake City, Utah; Seattle, Washington; and San 

Francisco, Los Angeles, and San Luis Obispo, California. The remainder of the country will 

see only minimal changes in sulfur deposition, ranging from decreases of less than 0.5% to 

increases of less than 0.5%. The impacts of the vehicle standards on nitrogen deposition are 

minimal, ranging from decreases of up to 0.5% to increases of up to 0.5%. 

Figure 7-24 Percent Change in Annual Total Sulfur over the U.S. Modeling Domain as a Result of the 

Required Vehicle Standards 
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7-80 

7.2.2.5 Visibility Degradation 

7.2.2.5.1 Current Visibility Levels 

Recently designated PM2.5 nonattainment areas indicate that, as of January 6, 2010, 

approximately 101 million people live in nonattainment areas for the PM2.5 NAAQS. Thus, at 

least these populations would likely be experiencing visibility impairment, as well as many 

thousands of individuals who travel to these areas. In addition, while visibility trends have 

improved in mandatory class I federal areas, the most recent data show that these areas 

continue to suffer from visibility impairment. In eastern areas, average visual range has 

decreased from 90 miles to 15-25 miles. In western areas, visual range has decreased from 

140 miles to 35-90 miles.273 In summary, visibility impairment is experienced throughout the 

U.S., in multi-state regions, urban areas, and remote mandatory class I federal areas. 

7.2.2.5.2 Projected Visibility Levels 

Air quality modeling conducted for this final rule was used to project visibility 

conditions in 138 mandatory class I federal areas across the U.S. in 2030. The results show 

that all the modeled areas will continue to have annual average deciview levels above 

background in 2030.II The results also indicate that the majority of the modeled mandatory 

class I federal areas will see no change in their visibility, but some mandatory class I federal 

areas will see improvements in visibility due to the vehicle standards and a few mandatory 

class I federal areas will see visibility decreases. The average visibility at all modeled 

mandatory class I federal areas on the 20% worst days is projected to improve by 0.002 

deciviews, or 0.01%, in 2030. The greatest improvement in visibilities will be seen in Bosque 

de Apache (New Mexico) and the San Gorgonio Wilderness (near Los Angeles, California). 

Bosque de Apache will see a 0.15% improvement (0.02 DV) and the San Gorgonio 

Wilderness will see a 0.10% improvement (0.02 DV) in 2030 due to the vehicle standards. 

The following six areas will see a degradation of 0.01 DV in 2030 as a result of the vehicle 

standards: Hells Canyon Wilderness (Oregon), 0.06% degradation; Kalmiopsis Wilderness 

(Oregon), 0.06% degradation; Strawberry Mountain Wilderness (Oregon), 0.06% 

degradation; Petrified Forest National Park (Arizona), 0.08% degradation; Rocky Mountain 

National Park (Colorado), 0.08% degradation; and Three Sisters Wilderness (Oregon), 0.06% 

degradation. Table 7-10 contains the full visibility results from 2030 for the 138 analyzed 

areas. 

 

II The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility 

index, called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a scale for 

perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, 

the average person can generally perceive a change of one deciview. The higher the deciview value, the worse 

the visibility. Thus, an improvement in visibility is a decrease in deciview value. 
 Environmental and Health Impacts 

7-81 

Table 7-10 Visibility Levels in Deciviews for Individual U.S. Class I Areas on the 20% Worst Days for 

Several Scenarios 

CLASS 1 

AREA 

(20% WORST 

DAYS) 

STATE 

2005 

BASELINE 

VISIBILITY 

2030 

BASE 

2030 

CONTROL 

NATURAL 

BACKGROUND 

Sipsey 

Wilderness 

AL 29.62 23.41 23.41 11.39 

Caney Creek 

Wilderness 

AR 26.78 22.52 22.51 11.33 

Upper Buffalo 

Wilderness 

AR 27.09 23.06 23.05 11.28 

Chiricahua NM AZ 13.33 13.28 13.28 6.92 

Chiricahua 

Wilderness 

AZ 13.33 13.27 13.27 6.91 

Galiuro 

Wilderness 

AZ 13.33 13.20 13.20 6.88 

Grand Canyon 

NP 

AZ 11.85 11.58 11.58 6.95 

Mazatzal 

Wilderness 

AZ 13.80 13.10 13.10 6.91 

Mount Baldy 

Wilderness 

AZ 11.27 11.10 11.10 6.95 

Petrified Forest 

NP 

AZ 13.73 13.31 13.32 6.97 

Pine Mountain 

Wilderness 

AZ 13.80 13.12 13.12 6.92 

Saguaro NM AZ 14.53 14.04 14.04 6.84 

Sierra Ancha 

Wilderness 

AZ 14.37 13.82 13.82 6.92 

Superstition 

Wilderness 

AZ 14.01 13.46 13.46 6.88 

Sycamore 

Canyon 

Wilderness 

AZ 15.34 15.04 15.04 6.96 

Agua Tibia 

Wilderness 

CA 23.09 24.56 24.55 7.17 

Ansel Adams 

Wilderness 

(Minarets) 

CA 14.90 14.78 14.77 7.12 

Caribou 

Wilderness 

CA 14.19 13.98 13.97 7.29 

Cucamonga 

Wilderness 

CA 19.35 18.23 18.22 7.17 

Desolation 

Wilderness 

CA 12.52 12.54 12.54 7.13 

Emigrant 

Wilderness 

CA 17.37 17.21 17.20 7.14 

Hoover 

Wilderness 

CA 11.92 11.85 11.85 7.12 
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7-82 

John Muir 

Wilderness 

CA 14.90 14.81 14.80 7.14 

Joshua Tree 

NM 

CA 19.40 18.68 18.68 7.08 

Kaiser 

Wilderness 

CA 14.90 14.71 14.71 7.13 

Kings Canyon 

NP 

CA 23.41 22.81 22.80 7.13 

Lassen 

Volcanic NP 

CA 14.19 14.00 14.00 7.31 

Lava Beds NM CA 14.77 14.31 14.31 7.49 

Mokelumne 

Wilderness 

CA 12.52 12.50 12.49 7.14 

Pinnacles NM CA 18.22 18.10 18.09 7.34 

Point Reyes NS CA 22.89 22.98 22.98 7.39 

Redwood NP CA 18.66 19.22 19.22 7.81 

San Gabriel 

Wilderness 

CA 19.35 18.06 18.05 7.17 

San Gorgonio 

Wilderness 

CA 21.80 20.23 20.21 7.10 

San Jacinto 

Wilderness 

CA 21.80 20.12 20.11 7.12 

San Rafael 

Wilderness 

CA 19.04 18.94 18.93 7.28 

Sequoia NP CA 23.41 22.64 22.64 7.13 

South Warner 

Wilderness 

CA 14.77 14.58 14.58 7.32 

Thousand 

Lakes 

Wilderness 

CA 14.19 14.00 14.00 7.32 

Ventana 

Wilderness 

CA 18.22 18.58 18.58 7.32 

Yosemite NP CA 17.37 17.24 17.24 7.14 

Black Canyon 

of the Gunnison 

NM 

CO 10.18 9.82 9.82 7.06 

Eagles Nest 

Wilderness 

CO 9.38 9.19 9.19 7.08 

Flat Tops 

Wilderness 

CO 9.38 9.27 9.27 7.07 

Great Sand 

Dunes NM 

CO 12.49 12.29 12.28 7.10 

La Garita 

Wilderness 

CO 10.18 10.00 10.00 7.06 

Maroon BellsSnowmass 

Wilderness 

CO 9.38 9.23 9.23 7.07 
 Environmental and Health Impacts 

7-83 

Mesa Verde NP CO 12.78 12.44 12.44 7.09 

Mount Zirkel 

Wilderness 

CO 10.19 10.08 10.08 7.08 

Rawah 

Wilderness 

CO 10.19 9.99 9.99 7.08 

Rocky Mountain 

NP 

CO 13.54 13.33 13.34 7.05 

Weminuche 

Wilderness 

CO 10.18 9.99 9.99 7.06 

West Elk 

Wilderness 

CO 9.38 9.20 9.20 7.07 

Everglades NP FL 22.48 21.34 21.34 11.15 

Okefenokee GA 27.24 23.44 23.44 11.45 

Wolf Island GA 27.24 23.44 23.44 11.42 

Craters of the 

Moon NM 

ID 14.19 13.56 13.56 7.13 

Sawtooth 

Wilderness 

ID 14.33 14.24 14.24 7.15 

Mammoth Cave 

NP 

KY 31.76 25.48 25.48 11.53 

Acadia NP ME 23.19 22.20 22.20 11.45 

Moosehorn ME 21.94 21.03 21.03 11.36 

Roosevelt 

Campobello 

International 

Park 

ME 21.94 21.03 21.03 11.36 

Isle Royale NP MI 21.33 19.42 19.42 11.22 

Seney MI 24.71 22.45 22.45 11.37 

Voyageurs NP MN 19.82 17.79 17.79 11.09 

HerculesGlades 

Wilderness 

MO 27.15 23.60 23.60 11.27 

AnacondaPintler 

Wilderness 

MT 13.91 13.72 13.72 7.28 

Bob Marshall 

Wilderness 

MT 14.54 14.32 14.32 7.36 

Cabinet 

Mountains 

Wilderness 

MT 14.15 13.81 13.81 7.43 

Gates of the 

Mountains 

Wilderness 

MT 11.67 11.47 11.47 7.22 

Glacier NP MT 19.13 18.55 18.55 7.56 
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7-84 

Medicine Lake MT 17.78 16.81 16.81 7.30 

Mission 

Mountains 

Wilderness 

MT 14.54 14.25 14.25 7.39 

Scapegoat 

Wilderness 

MT 14.54 14.30 14.29 7.29 

SelwayBitterroot 

Wilderness 

MT 13.91 13.79 13.79 7.32 

UL Bend MT 14.92 14.63 14.63 7.18 

Linville Gorge 

Wilderness 

NC 29.40 23.36 23.36 11.43 

Shining Rock 

Wilderness 

NC 28.72 23.04 23.04 11.45 

Lostwood ND 19.50 17.95 17.95 7.33 

Theodore 

Roosevelt NP 

ND 17.69 16.29 16.29 7.31 

Great Gulf 

Wilderness 

NH 22.13 20.19 20.18 11.31 

Presidential 

Range-Dry 

River 

Wilderness 

NH 22.13 20.19 20.18 11.33 

Brigantine NJ 29.28 25.88 25.87 11.28 

Bandelier NM NM 11.87 11.29 11.28 7.02 

Bosque del 

Apache 

NM 13.89 13.18 13.16 6.97 

Gila Wilderness NM 13.32 13.03 13.03 6.95 

Pecos 

Wilderness 

NM 10.10 9.82 9.82 7.04 

Salt Creek NM 18.20 17.21 17.20 6.99 

San Pedro 

Parks 

Wilderness 

NM 10.39 10.06 10.06 7.03 

Wheeler Peak 

Wilderness 

NM 10.10 9.70 9.70 7.07 

White Mountain 

Wilderness 

NM 13.52 12.94 12.94 6.98 

Jarbidge 

Wilderness 

NV 12.13 12.09 12.09 7.10 

Wichita 

Mountains 

OK 23.79 20.50 20.49 11.07 

Crater Lake NP OR 14.04 13.76 13.76 7.71 

Diamond Peak 

Wilderness 

OR 14.04 13.71 13.71 7.77 
 Environmental and Health Impacts 

7-85 

Eagle Cap 

Wilderness 

OR 18.25 17.64 17.63 7.34 

Gearhart 

Mountain 

Wilderness 

OR 14.04 13.88 13.88 7.46 

Hells Canyon 

Wilderness 

OR 18.73 17.90 17.91 7.32 

Kalmiopsis 

Wilderness 

OR 16.31 16.38 16.39 7.71 

Mount Hood 

Wilderness 

OR 14.79 14.49 14.49 7.77 

Mount Jefferson 

Wilderness 

OR 15.93 15.75 15.75 7.81 

Mount 

Washington 

Wilderness 

OR 15.93 15.72 15.72 7.89 

Mountain Lakes 

Wilderness 

OR 14.04 13.75 13.74 7.57 

Strawberry 

Mountain 

Wilderness 

OR 18.25 17.65 17.66 7.49 

Three Sisters 

Wilderness 

OR 15.93 15.72 15.73 7.87 

Cape Romain SC 27.14 24.09 24.09 11.36 

Badlands NP SD 16.73 15.52 15.51 7.30 

Wind Cave NP SD 15.96 14.93 14.93 7.24 

Great Smoky 

Mountains NP 

TN 30.43 24.30 24.30 11.44 

Joyce-KilmerSlickrock 

Wilderness 

TN 30.43 24.30 24.30 11.45 

Big Bend NP TX 17.39 16.43 16.42 6.93 

Carlsbad 

Caverns NP 

TX 16.98 15.89 15.88 7.02 

Guadalupe 

Mountains NP 

TX 16.98 15.89 15.88 7.03 

Arches NP UT 11.04 10.82 10.81 6.99 

Bryce Canyon 

NP 

UT 11.73 11.52 11.52 6.99 

Canyonlands 

NP 

UT 11.04 10.88 10.88 7.01 

Capitol Reef NP UT 10.63 10.74 10.74 7.03 

James River 

Face 

Wilderness 

VA 29.32 23.18 23.17 11.24 

Shenandoah 

NP 

VA 29.66 23.73 23.72 11.25 

Lye Brook 

Wilderness 

VT 24.17 20.72 20.72 11.25 
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7-86 

Alpine Lake 

Wilderness 

WA 17.35 17.29 17.28 7.86 

Glacier Peak 

Wilderness 

WA 13.78 14.06 14.05 7.80 

Goat Rocks 

Wilderness 

WA 12.88 12.32 12.32 7.82 

Mount Adams 

Wilderness 

WA 12.88 12.33 12.33 7.78 

Mount Rainier 

NP 

WA 17.56 17.23 17.22 7.90 

North Cascades 

NP 

WA 13.78 14.20 14.19 7.78 

Olympic NP WA 16.14 16.35 16.35 7.88 

Pasayten 

Wilderness 

WA 15.39 14.99 14.99 7.77 

Dolly Sods 

Wilderness 

WV 29.73 23.14 23.14 11.32 

Otter Creek 

Wilderness 

WV 29.73 23.14 23.14 11.33 

Bridger 

Wilderness 

WY 10.93 10.80 10.80 7.08 

Fitzpatrick 

Wilderness 

WY 10.93 10.80 10.80 7.09 

Grand Teton 

NP 

WY 10.94 10.61 10.61 7.09 

North Absaroka 

Wilderness 

WY 11.12 10.98 10.98 7.09 

Red Rock 

Lakes 

WY 10.94 10.68 10.68 7.14 

Teton 

Wilderness 

WY 10.94 10.70 10.70 7.09 

Washakie 

Wilderness 

WY 11.12 10.98 10.98 7.09 

Yellowstone NP WY 10.94 10.66 10.66 7.12 

7.3 Quantified and Monetized Non-GHG Health and Environmental 

Impacts 

This section presents EPA’s analysis of the non-GHG health and environmental 

impacts that can be expected to occur as a result of the 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 standards 

are also significant sources of mobile source air pollution such as direct PM, NOX, VOCs and 

air toxics. The standards will affect exhaust emissions of these pollutants from vehicles. 

They will also affect emissions from upstream sources related to changes in fuel consumption. 
 Environmental and Health Impacts 

7-87 

Changes in ambient ozone, PM2.5, and air toxics that will result from the standards are 

expected to affect human health in the form of premature deaths and other serious human 

health effects, as well as other important public health and welfare effects. 

It is important to quantify the health and environmental impacts associated with the 

final rule because a failure to adequately consider these ancillary co-pollutant impacts could 

lead to an incorrect assessment of their net costs and benefits. Moreover, co-pollutant impacts 

tend to accrue in the near term, while any effects from reduced climate change mostly accrue 

over a time frame of several decades or longer. 

This section is split into two sub-sections: the first presents the PM- and ozone-related 

health and environmental impacts associated with final rule in calendar year (CY) 2030; the 

second presents the PM-related benefits-per-ton values used to monetize the PM-related cobenefits associated with the model year (MY) analysis of the final rule.JJ

 

7.3.1 Quantified and Monetized Non-GHG Human Health Benefits of the 2030 

Calendar Year (CY) Analysis 

This analysis reflects the impact of the final light-duty GHG rule in 2030 compared to 

a future-year reference scenario without the rule in place. Overall, we estimate that the final 

rule will lead to a net decrease in PM2.5-related health impacts (see Chapter 7.2.2.2 for more 

information about the air quality modeling results). While the PM-related air quality impacts 

are relatively small, the decrease in population-weighted national average PM2.5 exposure 

results in a net decrease in adverse PM-related human health impacts (the decrease in national 

population weighted annual average PM2.5 is 0.0036 μg/m3). 

The air quality modeling also projects very small increases in ozone concentrations in 

many areas (see Chapter 7.2.2.1), but these are driven by the ethanol production volumes 

mandated by the recently finalized RFS2 rule and are not due to the standards finalized in this 

rule (see Chapters 5.3.2 and 5.3.3.5 for more information). While the ozone-related impacts 

are very small, the overall increase in population-weighted national average ozone exposure 

results in a small increase in ozone-related health impacts (population weighted maximum 8-

hour average ozone increases by 0.0104 ppb). 

 

JJ EPA typically analyzes rule impacts (emissions, air quality, costs and benefits) in the year in which they occur; 

for this analysis, we selected 2030 as a representative future year. We refer to this analysis as the “Calendar 

Year” (CY) analysis. EPA also conducted a separate analysis of the impacts over the model year lifetimes of the 

2012 through 2016 model year vehicles. We refer to this analysis as the “Model Year” (MY) analysis. In 

contrast to the CY analysis, the MY lifetime analysis shows the lifetime impacts of the program on each of these 

MY fleets over the course of its lifetime. 
Regulatory Impact Analysis 

7-88 

We base our analysis of the final rule’s impact on human health in 2030 on peerreviewed studies of air quality and human health effects.274,275 Our benefits methods are also 

consistent with recent rulemaking analyses such as the proposed Portland Cement National 

Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA,276 the final NO2 NAAQS,

277 and the final Category 3 Marine Engine rule.278 To model the ozone and PM air quality 

impacts of the final rule, we used the Community Multiscale Air Quality (CMAQ) model (see 

Section 7.2.1). The modeled ambient air quality data serves as an input to the Environmental 

Benefits Mapping and Analysis Program (BenMAP).KK BenMAP is a computer program 

developed by the U.S. EPA that integrates a number of the modeling elements used in 

previous analyses (e.g., interpolation functions, population projections, health impact 

functions, valuation functions, analysis and pooling methods) to translate modeled air 

concentration estimates into health effects incidence estimates and monetized benefits 

estimates. 

The range of total monetized ozone- and PM-related health impacts is presented in 

Table 7-11. We present total benefits based on the PM- and ozone-related premature 

mortality function used. The benefits ranges therefore reflect the addition of each estimate of 

ozone-related premature mortality (each with its own row in Table 7-11) to estimates of PMrelated premature mortality. These estimates represent EPA’s preferred approach to 

characterizing a best estimate of benefits. As is the nature of Regulatory Impact Analyses 

(RIAs), the assumptions and methods used to estimate air quality benefits evolve to reflect the 

Agency’s most current interpretation of the scientific and economic literature. 

Table 7-11 Estimated 2030 Monetized PM-and Ozone-Related Health Benefitsa 

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysis and 

Six-Cities Analysisa

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Millions, 2007$, 3% 

Discount Rate)b,c,d 

Total Benefits 

(Millions, 2007$, 7% 

Discount Rate) b,c,d

Bell et al., 2004 Total: $510 - $1,300 

PM: $550 - $1,300 

Ozone: -$40 

Total: $460 - $1,200 

PM: $500 - $1,200 

Ozone: -$40 

Huang et al., 2005 Total: $490 - $1,300 

PM: $550 - $1,300 

Ozone: -$64 

Total: $440 - $1,200 

PM: $500 - $1,200 

Ozone: -$64 

Multi-city analyses 

Schwartz, 2005 Total: $490 - $1,300 

PM: $550 - $1,300 

Ozone: -$60 

Total: $440 - $1,200 

PM: $500 - $1,200 

Ozone: -$60 

Meta-analyses Bell et al., 2005 Total: $430 - $1,200 

PM: $550 - $1,300 

Ozone: -$120 

Total: $380 - $1,100 

PM: $500 - $1,200 

Ozone: -$120 

 

KK Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. 
 Environmental and Health Impacts 

7-89 

Ito et al., 2005 Total: $380 - $1,200 

PM: $550 - $1,300 

Ozone: -$170 

Total: $330 - $1,000 

PM: $500 - $1,200 

Ozone: -$170 

Levy et al., 2005 Total: $380 - $1,200 

PM: $550 - $1,300 

Ozone: -$170 

Total: $330 - $1,000 

PM: $500 - $1,200 

Ozone: -$170 

Notes: 

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was 

developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-

related premature mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities 

study (Laden et al., 2006).

b

 Note that total benefits presented here do not include a number of unquantified benefits categories. A 

detailed listing of unquantified health and welfare effects is provided in Table 7-12. 

c

 Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines 

for Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits 

for ease of presentation and computation. 

d

 Negatives indicate a disbenefit, or an increase in health effect incidence. 

The benefits in Table 7-11 include all of the human health impacts we are able to 

quantify and monetize at this time. However, the full complement of human health and 

welfare effects associated with PM and ozone remain unquantified because of current 

limitations in methods or available data. We have not quantified a number of known or 

suspected health effects linked with ozone and PM for which appropriate health impact 

functions are not available or which do not provide easily interpretable outcomes (e.g., 

changes in heart rate variability). Additionally, we are unable to quantify a number of known 

welfare effects, including reduced acid and particulate deposition damage to cultural 

monuments and other materials, and environmental benefits due to reductions of impacts of 

eutrophication in coastal areas. These are listed in Table 7-12. As a result, the health benefits 

quantified in this section are likely underestimates of the total benefits attributable to the final 

rule. 

Table 7-12 Unquantified and Non-Monetized Potential Effects 

Pollutant/Effects Effects Not Included in Analysis - Changes in: 

Ozone Healtha Chronic respiratory damageb 

Premature aging of the lungsb 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e

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 (+/-)e 

PM Healthc Premature mortality - short term exposuresd

Low birth weight 
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7-90 

Pulmonary function 

Chronic respiratory diseases other than chronic bronchitis 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e 

PM Welfare Residential and recreational visibility in non-Class I areas 

Soiling and materials damage 

Damage to ecosystem functions 

Exposure to UVb (+/-)e 

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 

HC/Toxics Healthf Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde) 

Anemia (benzene) 

Disruption of production of blood components (benzene) 

Reduction in the number of blood platelets (benzene) 

Excessive bone marrow formation (benzene) 

Depression of lymphocyte counts (benzene) 

Reproductive and developmental effects (1,3-butadiene) 

Irritation of eyes and mucus membranes (formaldehyde) 

Respiratory irritation (formaldehyde) 

Asthma attacks in asthmatics (formaldehyde) 

Asthma-like symptoms in non-asthmatics (formaldehyde) 

Irritation of the eyes, skin, and respiratory tract (acetaldehyde) 

Upper respiratory tract irritation and congestion (acrolein) 

HC/Toxics Welfare Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor 

Notes: 

a

 The public health impact of biological responses such as increased airway responsiveness to stimuli, 

inflammation in the lung, acute inflammation and respiratory cell damage, and increased susceptibility 

to respiratory infection are likely partially represented by our quantified endpoints. 

b

 The public health impact of effects such as chronic respiratory damage and premature aging of the 

lungs may be partially represented by quantified endpoints such as hospital admissions or premature 

mortality, but a number of other related health impacts, such as doctor visits and decreased athletic 

performance, remain unquantified. 

c

 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. 

d

 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 
 Environmental and Health Impacts 

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analysis. However, the PM mortality results derived from the expert elicitation do take into account 

premature mortality effects of short term exposures. 

e

 May result in benefits or disbenefits. 

f

 Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the CAA. 

While there will be impacts associated with air toxic pollutant emission changes that 

result from the final rule, 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.279

 

While EPA has since improved the tools, there remain critical limitations for estimating 

incidence and assessing benefits of reducing mobile source air toxics. EPA continues to work 

to address these limitations; however, we did not have the methods and tools available for 

national-scale application in time for the analysis of the final rule.LL 

EPA is also unaware of specific information identifying any effects on listed 

endangered species from the small fluctuations in pollutant concentrations associated with this 

rule (see Section 7.2). Furthermore, our current modeling tools are not designed to trace 

fluctuations in ambient concentration levels to potential impacts on particular endangered 

species. 

7.3.1.1 Human Health and Environmental Impacts 

Table 7-13 and Table 7-14 present the annual PM2.5 and ozone health impacts in the 48 

contiguous U.S. states associated with the final rule for 2030. For each endpoint presented in 

Table 7-13 and Table 7-14, we provide both the point estimate and the 90% confidence 

interval. 

Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and 

Six-Cities studies and no threshold assumption in the model of mortality, we estimate that the 

 

LL 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, the workshop 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. 
Regulatory Impact Analysis 

7-92 

final rule will result in between 60 and 150 cases of avoided PM2.5-related premature deaths 

annually in 2030. As a sensitivity analysis, when the range of expert opinion is used, we 

estimate between 22 and 200 fewer premature mortalities in 2030. 

The range of ozone impacts is based on changes in risk estimated using several 

sources of ozone-related mortality effect estimates. This analysis presents six alternative 

estimates for the association based upon different functions reported in the scientific 

literature, derived from both the National Morbidity, Mortality, and Air Pollution Study 

(NMMAPS) (Bell et al., 2004; Huang et al., 2005; Schwartz, 2005) and from a series of recent 

meta-analyses (Bell et al., 2005, Ito et al., 2005, and Levy et al., 2005). This approach is not 

inconsistent with recommendations provided by the NRC in their recent report (NRC, 2008) 

on the estimation of ozone-related mortality risk reductions, “The committee recommends 

that the greatest emphasis be placed on estimates from new systematic multicity analyses that 

use national databases of air pollution and mortality, such as in the NMMAPS, without 

excluding consideration of meta-analyses of previously published studies.” For ozone-related 

premature mortality in 2030, we estimate a range of between 4 to 18 additional premature 

mortalities related to the ethanol production volumes mandated by the recently finalized RFS2 

ruleMM (and reflected in the air quality modeling for this rule), but are not due to the final 

standards themselves. 

Following these tables, we also provide a more comprehensive presentation of the 

distributions of incidence generated using the available information from empirical studies 

and expert elicitation. Table 7-15 presents the distributions of the reduction in PM2.5-related 

premature mortality based on the C-R distributions provided by each expert, as well as that 

from the data-derived health impact functions, based on the statistical error associated with 

the ACS study (Pope et al., 2002) and the Six-Cities study (Laden et al., 2006). The 90% 

confidence interval for each separate estimate of PM-related mortality is also provided. 

In 2030, the effect estimates of nine of the twelve experts included in the elicitation 

panel fall within the empirically-derived range provided by the ACS and Six-Cities studies. 

Only one expert falls below this range, while two of the experts are above this range. 

Although the overall range across experts is summarized in these tables, the full uncertainty in 

the estimates is reflected by the results for the full set of 12 experts. The twelve experts’ 

judgments as to the likely mean effect estimate are not evenly distributed across the range 

illustrated by arraying the highest and lowest expert means. 

 

MM EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA-420-R-10-006. 

February 2010. Docket EPA-HQ-OAR-2009-0472-11332. see also 75 FR 14670, March 26, 2010. 
 Environmental and Health Impacts 

7-93 

Table 7-13 Estimated PM2.5-Related Health Impactsa

Health Effect 

2030 Annual Reduction in 

Incidence 

(5th% - 95th%ile) 

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002) 

 Adult, age 25+, Six-Cities Study (Laden et al., 2006) 

 Infant, age <1 year (Woodruff et al., 1997) 

60 

(23 – 96) 

150 

(83 – 220) 

0 

(0-1) 

Chronic bronchitis (adult, age 26 and over) 42 

(8 – 77) 

Non-fatal myocardial infarction (adult, age 18 and over) 100 

(38 – 170) 

Hospital admissions - respiratory (all ages)c13 

(7 – 20) 

Hospital admissions - cardiovascular (adults, age >18)d 32 

(23 – 38) 

Emergency room visits for asthma (age 18 years and younger) 42 

(25 – 59) 

Acute bronchitis, (children, age 8-12) 95 

(0 – 190) 

Lower respiratory symptoms (children, age 7-14) 1,100 

(540 – 1,700) 

Upper respiratory symptoms (asthmatic children, age 9-18) 850 

(270 – 1,400) 

Asthma exacerbation (asthmatic children, age 6-18) 1,000 

(120 – 2,900) 

Work loss days 7,600 

(6,600 – 8,500) 

Minor restricted activity days (adults age 18-65) 45,000 

(38,000 – 52,000) 

Notes: 

a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous 

United States. 

b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 

2002) and the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of 

adult mortality and should not be summed. PM-related infant mortality based upon a study by 

Woodruff, Grillo, and Schoendorf, (1997).280

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease 

(COPD), pneumonia and asthma. 

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic 

heart disease, dysrhythmias, and heart failure. 
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Table 7-14 Estimated Ozone-Related Health Impactsa 

Health Effect 

2030 Annual Reduction in 

Incidence 

(5th% - 95th%ile) 

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental 

 Huang et al. (2005) – Cardiopulmonary 

 Schwartz (2005) – Non-accidental 

Meta-analyses: 

 Bell et al. (2005) – All cause 

 Ito et al. (2005) – Non-accidental 

 Levy et al. (2005) – All cause 

-4 

(-8 – 0) 

-7 

(-14 – 1) 

-6 

(-13 – 1) 

-13 

(-24 - -2) 

-18 

(-30 - -6) 

-18 

(-28 - -9) 

Hospital admissions- respiratory causes (adult, 65 and 

older)c 

-38 

(-86 - -6) 

Hospital admissions -respiratory causes (children, under 

2) 

-6 

(-13 – 1) 

Emergency room visit for asthma (all ages) -16 

(-51 – 8) 

Minor restricted activity days (adults, age 18-65) -18,000 

(-40,000 – 3,700) 

School absence days -7,700 

(-16,000 – 1,200) 

Notes: 

a

 Negatives indicate a disbenefit, or an increase in health effect incidence. Incidence is rounded to two 

significant digits. Estimates represent incidence within the 48 contiguous U.S. 

b

 Estimates of ozone-related premature mortality are based upon incidence estimates derived from several 

alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. 

(2005); Levy et al. (2005). The estimates of ozone-related premature mortality should therefore not be 

summed. 

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and 

subcategories for COPD and pneumonia. 
 Environmental and Health Impacts 

7-95 

Table 7-15 Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 

2030 Associated with the Final Rule 

Source of Mortality 2030 Primary Option 

Estimate 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 23 60 96 

Laden et al. (2006) 83 150 220 

Expert A 30 160 300 

Expert B 17 120 270 

Expert C 22 120 270 

Expert D 18 86 140 

Expert E 100 200 310 

Expert F 78 110 160 

Expert G 0 72 130 

Expert H 0 91 210 

Expert I 19 120 220 

Expert J 29 98 220 

Expert K 0 22 100 

Expert L 14 87 170 

7.3.1.2 Monetized Estimates of Impacts of Changes in Non-GHG Pollutants 

Table 7-16 presents the estimated monetary value of changes in the incidence of ozone 

and PM2.5-related health effects. Total aggregate monetized benefits are presented in Table 

7-17. All monetized estimates are presented in 2007$. Where appropriate, estimates account 

for growth in real gross domestic product (GDP) per capita between 2000 and 2030.NN The 

monetized value of PM2.5-related mortality also accounts for a twenty-year segmented 

 

NN Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for 

most goods (such as environmental protection) will increase if real incomes increase. Benefits are therefore 

adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account for income 

growth over time. For growth between 2000 and 2030, this factor is 1.23 for long-term mortality, 1.27 for 

chronic health impacts, and 1.08 for minor health impacts. For a complete discussion of how these adjustment 

factors were derived, we refer the reader to the PM NAAQS regulatory impact analysis.9 Note that similar 

adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit value regardless 

of the future year of analysis. 
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7-96 

cessation lag.OO To discount the value of premature mortality that occurs at different points in 

the future, we apply both a 3% and 7% discount rate. We also use both a 3% and 7% discount 

rate to value PM-related nonfatal heart attacks (myocardial infarctions).PP As the results 

indicate, total benefits are driven primarily by the reduction in PM2.5-related premature 

fatalities each year. 

In addition to omitted benefits categories such as air toxics and various welfare 

effects, not all known PM2.5- and ozone-related health and welfare effects could be quantified 

or monetized. The estimate of total monetized health benefits of the final rule is thus equal to 

the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify plus 

the sum of the nonmonetized health and welfare benefits. Our estimate of total monetized 

benefits in 2030 for the final rule, using the ACS and Six-Cities PM mortality studies and the 

range of ozone mortality assumptions, is between $380 and $1,300 million, assuming a 3 

percent discount rate, or between $330 and $1,200 million, assuming a 7 percent discount 

rate. As the results indicate, total benefits are driven primarily by the reduction in PM2.5-

related premature fatalities each year. 

The next largest benefit is for reductions in chronic illness (chronic bronchitis and 

nonfatal heart attacks), although this value is more than an order of magnitude lower than for 

premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor 

restricted activity days, and work loss days account for the majority of the remaining benefits. 

The remaining categories each account for a small percentage of total benefit; however, they 

represent a large number of avoided incidences affecting many individuals. A comparison of 

the incidence table to the monetary benefits table reveals that there is not always a close 

correspondence between the number of incidences avoided for a given endpoint and the 

monetary value associated with that endpoint. For example, there are over 100 times more 

work loss days than PM-related premature mortalities (based on the ACS study), yet work 

loss days account for only a very small fraction of total monetized benefits. This reflects the 

fact that many of the less severe health effects, while more common, are valued at a lower 

level than the more severe health effects. Also, some effects, such as hospital admissions, are 

 

OO Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in 

pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, 

this term is often referred to as “cessation lag”. The existence of such a lag is important for the valuation of 

premature mortality incidence because economic theory suggests that benefits occurring in the future should be 

discounted. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is 

consistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – 

Science Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency 

Request on Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection 

Agency Administrator, December. 

PP Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost 

earnings and direct medical costs over a 5-year period following a nonfatal MI. 
 Environmental and Health Impacts 

7-97 

valued using a proxy measure of willingness-to-pay (e.g., cost-of-illness). As such, the true 

value of these effects may be higher than that reported here. 

Table 7-16 Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects (in millions 

of 2007$) a,b

2030 

PM2.5-Related Health Effect (5th and 95th %ile) 

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

 3% discount rate 

 7% discount rate 

$510 

($70 - $1,300) 

$460 

($63 - $1,200) 

Adult, age 25+ - Six-Cities study 

(Laden et al., 2006) 

 3% discount rate 

 7% discount rate 

$1,300 

($190 - $3,300) 

$1,200 

($180 - $3,000) 

Premature Mortality – 

Derived from Epidemiology 

Studiesc,d,

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

$1.8 

($0 - $7.0) 

Chronic bronchitis (adults, 26 and over) $22 

($1.9 - $77) 

Non-fatal acute myocardial infarctions 

 3% discount rate 

 7% discount rate 

$14 

($3.9 - $35) 

$14 

($3.6 - $35) 

Hospital admissions for respiratory causes $0.20 

($0.01 - $0.29) 

Hospital admissions for cardiovascular causes $0.91 

($0.58 - $1.3) 

Emergency room visits for asthma $0.016 

($0.009 - $0.024) 

Acute bronchitis (children, age 8–12) $0.007 

($0 – $0.018) 

Lower respiratory symptoms (children, 7–14) $0.022 

($0.009 - $0.043) 

Upper respiratory symptoms (asthma, 9–11) $0.027 

($0.008 - $0.061) 

Asthma exacerbations $0.058 

($0.006 - $0.17) 

Work loss days $1.2 

($1.0 - $1.3) 
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Minor restricted-activity days (MRADs) $2.9 

($1.7 - $4.2) 

Ozone-related Health Effect (5th and 95th %ile) 

Bell et al., 2004 -$38 

(-$110 - $4.2) 

Huang et al., 2005 -$62 

(-$180 - $4.7) 

Premature Mortality, All ages – 

Derived from Multi-city analyses

Schwartz, 2005 -$58 

(-$170 - $8.8) 

Bell et al., 2005 -$120 

(-$330 - -$7.9) 

Ito et al., 2005 -$170 

(-$430 - -$19) 

Premature Mortality, All ages – 

Derived from Meta-analyses 

Levy et al., 2005 -$170 

(-$410 - -$21) 

Hospital admissions- respiratory causes (adult, 65 and older) -$0.92 

(-$2.1 - $0.27) 

Hospital admissions- respiratory causes (children, under 2) -$.21 

(-$.45 - $0.031) 

Emergency room visit for asthma (all ages) -$0.006 

(-$0.018 - $0.003) 

Minor restricted activity days (adults, age 18-65) -$1.2 

(-$2.7 - $0.25) 

School absence days -$0.71 

(-$1.4 - $0.11) 

Notes: 

a

 Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary benefits are 

rounded to two significant digits for ease of presentation and computation. PM and ozone benefits are 

nationwide. 

b

 Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the 

analysis year (2030). 

c

 Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses. 
 Environmental and Health Impacts 

7-99 

Table 7-17 Total Monetized Ozone and PM-related Benefits Associated with the Final Rule in 2030 

Total Ozone and PM Benefits (billions, 2007$) – 

PM Mortality Derived from the ACS and Six-Cities Studies 

3% Discount Rate 7% Discount Rate 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Bell et al., 

2004 

$510 - $1,300 Bell et al., 

2004 

$460 - $1,200 

Huang et al., 

2005 

$490 - $1,300 Huang et al., 

2005 

$440 - $1,200 Multi-city 

Schwartz, 

2005 

$490 - $1,300 

Multi-city 

Schwartz, 

2005 

$440 - $1,200 

Bell et al., 

2005 

$430 - $1,200 Bell et al., 

2005 

$380 - $1,100 

Meta-analysis Ito et al., 2005 $380 - $1,200 Ito et al., 2005 $330 - $1,000 

Levy et al., 

2005 

$380 - $1,200 

Meta-analysis 

Levy et al., 

2005 

$330 - $1,000 

Total Ozone and PM Benefits (billions, 2007$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

3% Discount Rate 7% Discount Rate 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Bell et al., 

2004 

$190 - $1,700 Bell et al., 

2004 

$170 - $1,600 

Huang et al., 

2005 

$160 - $1,700 Huang et al., 

2005 

$140 - $1,500 Multi-city 

Schwartz, 

2005 

$170 - $1,700 

Multi-city 

Schwartz, 

2005 

$150 - $1,500 

Bell et al., 

2005 

$100 - $1,600 Bell et al., 

2005 

$86 - $1,500 

Meta-analysis Ito et al., 2005 $56 - $1,600 Ito et al., 2005 $37 - $1,400 

Levy et al., 

2005 

$55 – $1,600 

Meta-analysis 

Levy et al., 

2005 

$36 - $1,400 

7.3.1.3 Methodology 

7.3.1.3.1 Human Health Impact Functions 

Health impact functions measure the change in a health endpoint of interest, such as 

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact 

functions are derived from primary epidemiology studies, meta-analyses of multiple 

epidemiology studies, or expert elicitations. A standard health impact function has four 
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7-100 

components: (1) an effect estimate from a particular study; (2) a baseline incidence rate for 

the health effect (obtained from either the epidemiology study or a source of public health 

statistics such as the Centers for Disease Control); (3) the size of the potentially affected 

population; and (4) the estimated change in the relevant ozone or PM summary measures. 

A typical health impact function might look like: 

 ( 1) 0 −⋅=∆

∆⋅ x

eyy

β

, 

where y0 is the baseline incidence (the product of the baseline incidence rate times the 

potentially affected population), β is the effect estimate, and ∆x is the estimated change in the 

summary pollutant measure. There are other functional forms, but the basic elements remain 

the same. The following subsections describe the sources for each of the first three elements: 

size of the potentially affected populations; PM2.5 and ozone effect estimates; and baseline 

incidence rates. We also describe the treatment of potential thresholds in PM-related health 

impact functions. Section 7.2 describes the ozone and PM air quality inputs to the health 

impact functions. 

7.3.1.3.1.1 Potentially Affected Populations 

The starting point for estimating the size of potentially affected populations is the 

2000 U.S. Census block level dataset.281 Benefits Modeling and Analysis Program 

(BenMAP) incorporates 250 age/gender/race categories to match specific populations 

potentially affected by ozone and other air pollutants. The software constructs specific 

populations matching the populations in each epidemiological study by accessing the 

appropriate age-specific populations from the overall population database. BenMAP projects 

populations to 2030 using growth factors based on economic projections.282

7.3.1.3.1.2 Effect Estimate Sources 

The most significant quantifiable benefits of reducing ambient concentrations of ozone 

and PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria 

Documents283,284 and the World Health Organization’s 2003 and 2004285,286 reports outline 

numerous human health effects known or suspected to be linked to exposure to ambient ozone 

and PM. EPA recently evaluated the ozone and PM literature for use in the benefits analysis 

for the final 2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same 

literature in this analysis; for more information on the studies that underlie the health impacts 

quantified in this RIA, please refer to those documents. 

It is important to note that we are unable to separately quantify all of the possible PM 

and ozone health effects that have been reported in the literature for three reasons: (1) the 

possibility of double counting (such as hospital admissions for specific respiratory diseases 
 Environmental and Health Impacts 

7-101 

versus hospital admissions for all or a sub-set of respiratory diseases); (2) uncertainties in 

applying effect relationships that are based on clinical studies to the potentially affected 

population; or (3) the lack of an established concentration-response (CR) relationship. Table 

7-18 lists the health endpoints included in this analysis. 

Table 7-18 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)287 – Nonaccidental 

Huang et al (2005)288 - Cardiopulmonary 

Schwartz (2005)289 – Non-accidental 

Meta-analyses:

Bell et al (2005)290 – All cause 

Ito et al (2005)291 – Non-accidental 

Levy et al (2005)292 – All cause 

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 Pope et al. (2002)293

Laden et al. (2006)294

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)295 >24 years 

Premature mortality 

— all-cause 

PM2.5 Woodruff et al. (1997)296 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 Abbey et al. (1995)297 >26 years 

Nonfatal heart attacks PM2.5 Peters et al. (2001)298 Adults (>18 years) 

Hospital Admissions 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)299

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)300,301 

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)302

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

O3 

Burnett et al. (2001)303 <2 years 

Respiratory 

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)304

Ito (2003)—ICD 490-496 (COPD)305

>64 years 
Regulatory Impact Analysis 

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PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD)306 20–64 years 

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years 

PM2.5 Sheppard (2003)—ICD 493 (asthma)307 <65 years 

PM2.5 Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic 

heart disease, dysrhythmia, heart failure) 

Cardiovascular >64 years 

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER 

visits 

O3 Pooled estimate:

Jaffe et al (2003)308

Peel et al (2005)309

Wilson et al (2005)310

5–34 years 

All ages 

All ages 

Asthma-related ER 

visits (con’t) 

PM2.5 Norris et al. (1999)311 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)312 8–12 years 

Upper respiratory 

symptoms 

PM2.5 Pope et al. (1991)313 Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000)314 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

Ostro et al. (2001)315 (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998)316 (cough) 

6–18 yearsa

Work loss days PM2.5 Ostro (1987)317 18–65 years 

School absence days 

O3 

Pooled estimate:

Gilliland et al. (2001)318

Chen et al. (2000)319

5–17 yearsb

O3 Ostro and Rothschild (1989)320 Minor Restricted 18–65 years 

Activity Days 

(MRADs) 

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. 
 Environmental and Health Impacts 

7-103 

In selecting epidemiological studies as sources of effect estimates, we applied several 

criteria to develop a set of studies that is likely to provide the best estimates of impacts in the 

U.S. To account for the potential impacts of different health care systems or underlying 

health status of populations, we give preference to U.S. studies over non-U.S. studies. In 

addition, due to the potential for confounding by co-pollutants, we give preference to effect 

estimates from models including both ozone and PM over effect estimates from singlepollutant models.321,322

 

7.3.1.3.1.3 Baseline Incidence Rates 

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the 

relative risk of a health effect, rather than estimating the absolute number of avoided cases. 

For example, a typical result might be that a 100 ppb decrease in daily ozone levels might, in 

turn, decrease hospital admissions by 3 percent. The baseline incidence of the health effect is 

necessary to convert this relative change into a number of cases. A baseline incidence rate is 

the estimate of the number of cases of the health effect per year in the assessment location, as 

it corresponds to baseline pollutant levels in that location. To derive the total baseline 

incidence per year, this rate must be multiplied by the corresponding population number. For 

example, if the baseline incidence rate is the number of cases per year per 100,000 people, 

that number must be multiplied by the number of 100,000s in the population. 

Table 7-19 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentrationresponse functions to individual age groups and then summed over the relevant age range to 

provide an estimate of total population benefits. In most cases, we used a single national 

incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the 

national rates used are national averages, because these data are most applicable to a national 

assessment of benefits. For some studies, however, the only available incidence information 

comes from the studies themselves; in these cases, incidence in the study population is 

assumed to represent typical incidence at the national level. Regional incidence rates are 

available for hospital admissions, and county-level data are available for premature mortality. 

We have projected mortality rates such that future mortality rates are consistent with our 

projections of population growth.323
Regulatory Impact Analysis 

7-104 

Table 7-19 National Average Baseline Incidence Ratesa

Rate per 100 people per yeard Endpoint Source Notes by Age Group 

<18 18-24 25-34 35-44 45-54 55-64 65+

Mortality CDC Compressed Mortality File, 

accessed through CDC Wonder 

(1996-1998) 

nonaccidental 0.025 0.022 0.057 0.150 0.383 1.006 4.937 

Respiratory 

Hospital 

Admissions. 

1999 NHDS public use data filesb incidence 0.043 0.084 0.206 0.678 1.926 4.389 11.62 

Asthma ER visits 2000 NHAMCS public use data 

filesc; 1999 NHDS public use data 

filesb

incidence 1.011 1.087 0.751 0.438 0.352 0.425 0.232 

Minor Restricted 

Activity Days 

(MRADs) 

Ostro and Rothschild 

(1989, p. 243) 

incidence – 780 780 780 780 780 – 

School Loss 

Days 

National Center for Education 

Statistics (1996) and 1996 HIS 

(Adams et al., 1999, Table 47); 

estimate of 180 school days per 

year 

all-cause 990.0 – – – – – – 

Notes: 

a

 The following abbreviations are used to describe the national surveys conducted by the National Center for Health 

Statistics: HIS refers to the National Health Interview Survey; NHDS - National Hospital Discharge Survey; NHAMCS - 

National Hospital Ambulatory Medical Care Survey. 

b

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/

c

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/ 

d

 All of the rates reported here are population-weighted incidence rates per 100 people per year. Additional details on the 

incidence and prevalence rates, as well as the sources for these rates are available upon request. 
 Environmental and Health Impacts 

105 

Table 7-19 National Average Baseline Incidence Rates (continued) 

Endpoint Source Notes Rate per 100 people per 

year 

Ostro et al. (2001) Incidence (and 

prevalence) 

among 

asthmatic 

AfricanAmerican 

children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.076 (0.173) 

0.067 (0.145) 

0.037 (0.074) 

Asthma Exacerbations 

Vedal et al. (1998) Incidence (and 

prevalence) 

among 

asthmatic 

children 

Daily wheeze 

Daily cough 

Daily dyspnea 

0.038 

0.086 

0.045 

7.3.1.3.1.4 Treatment of Potential Thresholds in PM2.5-Related Health Impact Functions 

In past analyses, OTAQ has estimated PM2.5-related benefits assuming that a threshold 

exists in the PM-related concentration-response functions (at 10 μg/m3) below which there are no 

associations between exposure to PM2.5 and health impacts. Based on our review of the body of 

scientific literature, however, EPA’s preferred benefits estimation approach assumes a nothreshold model that calculates incremental benefits down to the lowest modeled PM2.5 air 

quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. EPA's Integrated Science Assessment,QQ which was recently reviewed by EPA’s 

Clean Air Scientific Advisory Committee,RR,SS concluded that the scientific literature 

consistently finds that a no-threshold log-linear model most adequately portrays the PMmortality concentration-response relationship while recognizing potential uncertainty about the 

 

QQ U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter . 

National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139F. December. 

Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. Accessed March 15, 

2010. 

RR U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). Review of EPA’s 

Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPACOUNCIL-09-008. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73ACCA834AB44A1085

2575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>. 

SS U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). Consultation on EPA’s 

Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure 

Assessment. EPA-COUNCIL-09-009. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723FE644C5D758DF852

575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>. 
Regulatory Impact Analysis 

106 

exact shape of the concentration-response function.TT Although this document does not 

necessarily represent final agency policy, it provides a basis for reconsidering the application of 

thresholds in PM2.5 concentration-response functions used in EPA’s RIAs. It is important to note 

that while CASAC provides advice regarding the science associated with setting the National 

Ambient Air Quality Standards, typically other scientific advisory bodies provide specific advice 

regarding benefits analysis.UU This approach reflects EPA’s most current interpretation of the 

scientific literature on PM2.5 and mortality. Please refer to the documentation associated with the 

proposed Portland Cement MACT RIA for a description of the history of the treatment of 

thresholds in our analyses.324

As can be seen in Table 7-7-20, we conducted a sensitivity analysis for premature 

mortality, with alternative thresholds at 3 μg/m3 (the “background,” or no-threshold, 

assumption), 7.5 μg/m3, 10 μg/m3, 12 μg/m3, and 14 μg/m3. By replacing the no-threshold 

assumption in the ACS premature mortality function with a 10 μg/m3 threshold model, the 

number of avoided incidences of premature mortality would decrease by approximately 22 

percent. 

 

TT It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually 

distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a 

discontinuity, which is a specific example of non-linearity. 

UU In the proposed Portland Cement RIA, EPA solicited comment on the use of the no-threshold model for benefits 

analysis within the preamble of that proposed rule. The comment period for the Portland Cement proposed 

NESHAP closed on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at 

http://www.regulations.gov). EPA is currently reviewing those comments. U.S. Environmental Protection Agency. 

(2009). Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland 

Cement Manufacturing Industry. Office of Air and Radiation. Retrieved on May 4, 2009, from 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf 
 Environmental and Health Impacts 

107 

Table 7-7-20 PM-Related Mortality Benefits Associated with the Final Rule: Threshold Sensitivity Analysis 

Using the ACS Study (Pope et al., 2002)a

PM Mortality Incidence Level of Assumed Threshold 

2030 

14 μg/m3 b 30 

12 μg/m3 35 

10 μg/m3 c 47 

7.5 μg/m3 d

56 

3 μg/m3 e 60 

Notes: 

a

 Note that this table only presents the effects of a threshold on 

PM-related mortality incidence based on the ACS study. 

b

 Alternative annual PM NAAQS. 

c Previous threshold assumption 

d

 SAB-HES (2004)86 

e

 NAS (2002)87

7.3.1.3.2 Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk. For example, suppose a 

pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 

to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 

the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk). 

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary 

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing 

the risk of a health effect, because they reflect the direct expenditures related to treatment, but 

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We 

provide unit values for health endpoints (along with information on the distribution of the unit 

value) in Table 7-7-21. All values are in constant year 2000 dollars, adjusted for growth in real 
Regulatory Impact Analysis 

108 

income out to 2020 and 2030 using projections provided by Standard and Poor’s. Economic 

theory argues that WTP for most goods (such as environmental protection) will increase if real 

income increases. Many of the valuation studies used in this analysis were conducted in the late 

1980s and early 1990s. Because real income has grown since the studies were conducted, 

people’s willingness to pay for reductions in the risk of premature death and disease likely has 

grown as well. We did not adjust cost of illness-based values because they are based on current 

costs. Similarly, we did not adjust the value of school absences, because that value is based on 

current wage rates. For details on valuation estimates for PM-related endpoints, see the 2006 

PM NAAQS RIA. For details on valuation estimates for ozone-related endpoints, see the 2008 

Ozone NAAQS RIA. 
 Environmental and Health Impacts 

109 

Table 7-7-21 Unit Values Used for Economic Valuation of Health Endpoints (2000$)a

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimate

Premature Mortality (Value of a Statistical Life): PM2.5- and 

Ozone-related 

$6,320,000 $7,590,000 $7,800,000 EPA currently recommends a default central V

based on a Weibull distribution fitted to twentestimates (5 contingent valuation and 21 labor underlying studies, the distribution parametersinformation are available in Appendix B of EPfor Preparing Economic Analyses. The guideat: http://yosemite.epa.gov/ee/epa/eermfile.ns01.pdf/$File/EE-0516-01.pdf 

Chronic Bronchitis (CB) $340,000 $420,000 $430,000 Point estimate is the mean of a generated distr

avoid a case of pollution-related CB. WTP to pollution-related CB is derived by adjusting WViscusi et al., 1991325) to avoid a severe case o

in severity and taking into account the elasticito severity of CB. 

Nonfatal Myocardial Infarction (heart attack)  3% discount rate  Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over  7% discount rate  Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

Age-specific cost-of-illness values reflect lost medical costs over a 5-year period following a earnings estimates are based on Cropper and K

Direct medical costs are based on simple averRussell et al. (1998)327 and Wittels et al. (199

Lost earnings: Cropper and Krupnick (1990). Present discouof lost earnings: age of onset: at 3% at 7% 25-44 $8,774 $7,855 45-54 $12,932 $11,578 55-65 $74,746 $66,920 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($102,658—no discou2. Russell et al. (1998), 5-year period ($22,33$21,113 at 7% discount rate) 
Regulatory Impact Analysis 

110 

Table 7-7-21 Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continu

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimat

Hospital Admissions Chronic Obstructive Pulmonary Disease (COPD) (ICD codes 490-492, 494-496) 

$12,378 $12,378 $12,378 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted category illnesses) reported in Agency for HQuality (2000)329 (www.ahrq.gov). 

Pneumonia (ICD codes 480-487) 

$14,693 $14,693 $14,693 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted pneumonia category illnesses) reported in AResearch and Quality (2000) (www.ahrq.gov

Asthma Admissions $6,634 $6,634 $6,634 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted category illnesses) reported in Agency for HQuality (2000) (www.ahrq.gov). 

All Cardiovascular (ICD codes 390-429) 

$18,387 $18,387 $18,387 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted cardiovascular category illnesses) reported in Research and Quality (2000) (www.ahrq.gov

Emergency Room Visits for Asthma 

$286 $286 $286 Simple average of two unit COI values: 

(1) $311.55, from Smith et al. (1997)330 and 

(2) $260.67, from Stanford et al. (1999).331
 Environmental and Health Impacts 

111 

Table 7-7-21 Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continu

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estimat

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) 

$25 $27 $27 Combinations of the three symptoms for whi

available that closely match those listed by Pdifferent “symptom clusters,” each describindollar value was derived for each type of URestimates of WTP (IEc, 1994)332 to avoid ea

cluster and assuming additivity of WTPs. Tis the average of the dollar values for the sevURS. 

Lower Respiratory Symptoms (LRS) 

$16 $17 $17 Combinations of the four symptoms for whic

available that closely match those listed by S11 different “symptom clusters,” each descridollar value was derived for each type of LRestimates of WTP IEc, 1994) to avoid each sand assuming additivity of WTPs. The dollaaverage of the dollar values for the 11 differe

Asthma Exacerbations $42 $45 $45 Asthma exacerbations are valued at $42 per 

mean of average WTP estimates for the four “bad asthma day,” described in Rowe and Ch

study surveyed asthmatics to estimate WTP asthma day,” as defined by the subjects. For an asthma attack is assumed to be equivalent asthma is moderate or worse as reported in th(1986) study. 

Acute Bronchitis $360 $380 $390 Assumes a 6-day episode, with daily value e

low and high values for related respiratory syin Neumann et al. (1994).334
Regulatory Impact Analysis 

112 

Table 7-7-21 Unit Values Used for Economic Valuation of Health Endpoints (2000$)a (continued) 

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

1990 Income 

Level 

2020 Income 

Levelb

2030 Income 

Levelb Derivation of Estim

Restricted Activity and Work/School Loss Days Work Loss Days (WLDs) Variable 

(national median = ) 

 County-specific median annual wages divid

of vacation) and then by 5—to get median dCensus, compiled by Geolytics, Inc. 

School Absence Days $75 $75 $75 Based on expected lost wages from parent s

Estimated daily lost wage (if a mother must child) is based on the median weekly wage older in 2000 (U.S. Census Bureau, StatisticStates: 2001, Section 12: Labor Force, EmTable No. 621). This median wage is $551estimated median daily wage of $103. The expected loss in wages due to a day of mother would have to stay home with her cprobability that the mother is in the workforwould lose if she missed a day = 72.85% of 

Worker Productivity $0.95 per 

worker per 10% change in ozone per day 

$0.95 per worker per 10% change in ozone per day 

$0.95 per worker per 10% change in ozone per day 

Based on $68 – median daily earnings of wand fishing – from Table 621, Statistical Ab(“Full-Time Wage and Salary Workers – N2000") (Source of data in table: U.S. BureaBulletin 2307 and Employment and Earning

Minor Restricted Activity Days (MRADs) 

$51 $54 $55 Median WTP estimate to avoid one MRAD 

a All monetized annual benefit estimates are presented in year 2000 dollars. We use the Consumer Price Indexes to adjust both WTP- a

estimates to 2007 dollars from 2000 dollars.336 For WTP-based estimates, we use an inflation factor of 1.20 based on the CPI-U for “a

estimates, we use an inflation factor of 1.35 based on the CPI-U for medical care. 

b Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as envi

increase if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment 

growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM NAAQS regu

that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit value regardless of the futu
 Environmental and Health Impacts 

7-113 

7.3.1.3.3 Manipulating Air Quality Modeling Data for Health Impacts Analysis 

In Section 7.2, we summarized the methods for and results of estimating air quality for 

the final rule. These air quality results are in turn associated with human populations to 

estimate changes in health effects. For the purposes of this analysis, we focus on the health 

effects that have been linked to ambient changes in ozone and PM2.5 related to emission 

reductions estimated to occur due to the implementation of the final rule. We estimate 

ambient PM2.5 and ozone concentrations using the Community Multiscale Air Quality model 

(CMAQ). This section describes how we converted the CMAQ modeling output into fullseason profiles suitable for the health impacts analysis. 

7.3.1.3.3.1 General Methodology 

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid 

cell from the standard CMAQ output files. For ozone, these model predictions are used in 

conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.VV,WW 

The predicted changes in ozone concentrations from the future-year base case to future-year 

control scenario serve as inputs to the health and welfare impact functions of the benefits 

analysis (i.e., BenMAP). 

To estimate ozone-related health effects for the contiguous United States, full-season 

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, 

we generated full-season ozone profiles for each location in two steps: (1) we combined 

monitored observations and modeled ozone predictions to interpolate hourly ozone 

concentrations to a grid of 12-km by 12-km population grid cells for the contiguous 48 states, 

and (2) we converted these full-season hourly ozone profiles to an ozone measure of interest, 

such as the daily 8-hour maximum.XX,YY 

For PM2.5, we also use the model predictions in conjunction with observed monitor 

data. CMAQ generates predictions of hourly PM species concentrations for every grid. The 

species include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size 

range), a primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and 

several secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the 

sum of the primary fine fraction and all of the secondarily formed particles. Future-year 

estimates of PM2.5 were calculated using relative reduction factors (RRFs) applied to 2002 

ambient PM2.5 and PM2.5 species concentrations. A gridded field of PM2.5 concentrations was 

created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient 

 

VV The ozone season for this analysis is defined as the 5-month period from May to September.

WW Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting 

at least nine hourly observations per day [8 am to 8 pm] during the ozone season).

XX The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

YY This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap.
Regulatory Impact Analysis 

7-114 

data. Gridded fields of PM2.5 species concentrations were created by interpolating EPA 

speciation network (ESPN) ambient data and IMPROVE data. The ambient data were 

interpolated to the CMAQ 12 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft guidance 

for modeling the PM2.5 standard (EPA, 1999). The guidance recommends that model 

predictions be used in a relative sense to estimate changes expected to occur in each major 

PM2.5 species. The procedure for calculating future-year PM2.5 design values is called the 

“Speciated Modeled Attainment Test (SMAT).” EPA used this procedure to estimate the 

ambient impacts of the final rule. 

Table 7-7-22 provides those ozone and PM2.5 metrics for grid cells in the modeled 

domain that enter the health impact functions for health benefits endpoints. The populationweighted average reflects the baseline levels and predicted changes for more populated areas 

of the nation. This measure better reflects the potential benefits through exposure changes to 

these populations. 

Table 7-7-22 Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air Quality Metrics for 

Health Benefits Endpoints Associated with the Final Rule 

2030 

Statistica Baseline Changeb

Ozone Metric: National Population-Weighted Average (ppb)c

Daily Maximum 8-Hour Average 

Concentration 

43.5620 -0.0104 

PM2.5 Metric: National Population-Weighted Average (ug/m3) 

Annua Average Concentration 9.7548 0.0036 

Notes: 

a

 Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects 

estimates. Ozone metrics are calculated over relevant time periods during the daylight hours 

of the “ozone season” (i.e., May through September). 

b

 The change is defined as the base-case value minus the control-case value. A negative value 

indicates an increase in population-weighted average air quality from the baseline to the 

control scenario. 

c

 Calculated by summing the product of the projected CMAQ grid-cell population and the 

estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total 

population.

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the emission control scenarios used in the air quality and benefits modeling 

are slightly different than the final emission inventories estimated for the final rule. Please 

refer to Chapter 5.5 for more information about the inventories used in the air quality 

modeling that supports the health impacts analysis. 

7.3.1.4 Methods for Describing Uncertainty 

The National Research Council (NRC)337 highlighted the need for EPA to conduct 

rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 
 Environmental and Health Impacts 

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uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is 

developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in 

key modeling elements on both health incidence and benefits estimates. Components of that 

process include emissions modeling, air quality modeling, health effects incidence estimation, 

and valuation. 

 

In benefit analyses of air pollution regulations conducted to date, the estimated impact 

of reductions in premature mortality has accounted for 85% to 95% of total benefits. 

Therefore, it is particularly important to characterize the uncertainties associated with 

reductions in premature mortality. The health impact functions used to estimate avoided 

premature deaths associated with reductions in ozone have associated standard errors that 

represent the statistical errors around the effect estimates in the underlying epidemiological 

studies.ZZ In our results, we report credible intervals based on these standard errors, reflecting 

the uncertainty in the estimated change in incidence of avoided premature deaths. We also 

provide multiple estimates, to reflect model uncertainty between alternative study designs. 

 

 For premature mortality associated with exposure to PM, we follow the same approach 

that has been used in several recent RIAs.338,339,340 First, we use Monte Carlo methods for 

estimating random sampling error associated with the concentration response functions from 

epidemiological studies and economic valuation functions. Monte Carlo simulation uses 

random sampling from distributions of parameters to characterize the effects of uncertainty on 

output variables, such as incidence of premature mortality. Specifically, we used Monte 

Carlo methods to generate confidence intervals around the estimated health impact and dollar 

benefits. Distributions for individual effect estimates are based on the reported standard 

errors in the epidemiological studies. Distributions for unit values are described in Table 7-

7-21. 

Second, as a sensitivity analysis, we use the results of our expert elicitation of the 

concentration response function describing the relationship between premature mortality and 

ambient PM2.5 concentration.AAA, 341 Incorporating only the uncertainty from random 

sampling error omits important sources of uncertainty (e.g., in the functional form of the 

model; whether or not a threshold may exist). This second approach attempts to incorporate 

these other sources of uncertainty. 

Use of the expert elicitation and incorporation of the standard errors approaches 

provide insights into the likelihood of different outcomes and about the state of knowledge 

regarding the benefits estimates. Both approaches have different strengths and weaknesses, 

which are fully described in Chapter 5 of the PM NAAQS RIA. 

These multiple characterizations, including confidence intervals, omit the contribution 

 

ZZ Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a 

given change in ambient ozone or PM concentration. 

AAA Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, 

usually of multiple experts, are obtained (Ayyb, 2002).
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to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. 

Furthermore, the approach presented here does not yet include methods for addressing 

correlation between input parameters and the identification of reasonable upper and lower 

bounds for input distributions characterizing uncertainty in additional model elements. As a 

result, the reported confidence intervals and range of estimates give an incomplete picture 

about the overall uncertainty in the estimates. This information should be interpreted within 

the context of the larger uncertainty surrounding the entire analysis. 

As mentioned above, total benefits are driven primarily by the reduction in PM2.5-

related premature mortalities each year. Some key assumptions underlying the premature 

mortality estimates include the following, which may also contribute to uncertainty: 

• Inhalation of fine particles is causally associated with premature death at 

concentrations near those experienced by most Americans on a daily basis. 

Although biological mechanisms for this effect have not yet been completely 

established, the weight of the available epidemiological, toxicological, and 

experimental evidence supports an assumption of causality. The impacts of 

including a probabilistic representation of causality were explored in the expert 

elicitation-based results of the PM NAAQS RIA. 

• All fine particles, regardless of their chemical composition, are equally potent 

in causing premature mortality. This is an important assumption, because PM 

produced via transported precursors emitted from engines may differ 

significantly from PM precursors released from electric generating units and 

other industrial sources. However, no clear scientific grounds exist for 

supporting differential effects estimates by particle type. 

• The C-R function for fine particles is approximately linear within the range of 

ambient concentrations under consideration. Thus, the estimates include health 

benefits from reducing fine particles in areas with varied concentrations of PM, 

including both regions that may be in attainment with PM2.5 standards and 

those that are at risk of not meeting the standards. 

• There is uncertainty in the magnitude of the association between ozone and 

premature mortality. The range of ozone impacts associated with the final rule 

is estimated based on the risk of several sources of ozone-related mortality 

effect estimates. In a recent report on the estimation of ozone-related 

premature mortality published by the National Research Council, 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.342

 

EPA has requested advice from the National Academy of Sciences on how best 

to quantify uncertainty in the relationship between ozone exposure and 

premature mortality in the context of quantifying benefits. 
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Acknowledging omissions and uncertainties, we present a best estimate of the total 

benefits based on our interpretation of the best available scientific literature and methods 

supported by EPA’s technical peer review panel, the Science Advisory Board’s Health Effects 

Subcommittee (SAB-HES). The National Academies of Science (NRC, 2002) has also 

reviewed EPA’s methodology for analyzing the health benefits of measures taken to reduce 

air pollution. EPA addressed many of these comments in the analysis of the final PM 

NAAQS.343,344 This analysis incorporates this most recent work to the extent possible. 

7.3.2 PM-related Monetized Benefits of the Model Year (MY) Analysis 

As described in Chapter 5.5, the final standards will reduce emissions of several 

criteria and toxic pollutants and precursors. In the MY analysis, EPA estimates the economic 

value of the human health benefits associated with reducing PM2.5 exposure. Due to 

analytical limitations, this analysis does not estimate benefits related to other criteria 

pollutants (such as ozone, NO2 or SO2) or toxics pollutants, nor does it monetize all of the 

potential health and welfare effects associated with PM2.5. 

The MY analysis uses a “benefit-per-ton” method to estimate a selected suite of PM2.5-

related health benefits described below. These PM2.5 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 associated with the 

MY analysis would be estimated based on changes in ambient PM2.5 as determined by fullscale air quality modeling. However, this modeling was not possible in the timeframe for the 

final rule due to the time and resource constraints associated with running full-scale 

photochemical air quality modeling. 

The dollar-per-ton estimates used in this analysis are provided in Table 7-23. In the 

summary of costs and benefits, Chapter 8.4 of this RIA, EPA presents the monetized value of 

PM-related improvements associated with the final rule. 
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Table 7-23 Benefits-per-ton Values (2007$) Derived Using the ACS Cohort Study for PM-related 

Premature Mortality (Pope et al., 2002)a

 

All Sourcesd Stationary (Non-EGU) 

Sources 

Year Mobile Sources c

SOX VOC NOX Direct PM2.5 NOX Direct PM2.5

Estimated Using a 3 Percent Discount Rateb 

2015 $28,000 $1,200 $4,700 $220,000 $4,900 $270,000 

2020 $31,000 $1,300 $5,100 $240,000 $5,300 $290,000 

2030 $36,000 $1,500 $6,100 $280,000 $6,400 $350,000 

2040 $43,000 $1,800 $7,200 $330,000 $7,600 $420,000 

Estimated Using a 7 Percent Discount Rateb 

2015 $26,000 $1,100 $4,200 $200,000 $4,400 $240,000 

2020 $28,000 $1,200 $4,600 $220,000 $4,800 $270,000 

2030 $33,000 $1,400 $5,500 $250,000 $5,800 $320,000 

2040 $39,000 $1,600 $6,600 $300,000 $6,900 $380,000 

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 145% (nearly two-and-a-half times) larger. 

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 2040, EPA 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. 

The benefit per-ton technique has been used in previous analyses, including EPA’s 

recent Ozone National Ambient Air Quality Standards (NAAQS) RIA,345 the proposed 

Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA 

(U.S. EPA, 2009a),346 and the final NO2 NAAQS (U.S. EPA, 2009b).347 Table 7-24 shows 

the quantified and unquantified PM2.5-related co-benefits captured in those benefit-per-ton 

estimates. 

Table 7-24 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 
 Environmental and Health Impacts 

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Consistent with the final NO2 NAAQS,BBB the benefits estimates utilize the 

concentration-response functions as reported in the epidemiology literature. Readers 

interested in reviewing the complete methodology for creating the benefit-per-ton estimates 

used in this analysis can consult the Technical Support Document (TSD)348 accompanying the 

recent final ozone NAAQS RIA. Readers can also refer to Fann et al. (2009)349 for a detailed 

description of the benefit-per-ton methodology.CCC A more detailed description of the 

benefit-per-ton estimates is also provided in the TSD that accompanies this rulemaking. 

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 PM-related precursor emissions controlled by sector and multiplied by 

each per-ton value. 

The benefit-per-ton coefficients in this analysis were derived using modified versions 

of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. 

Specifically, this analysis uses the benefit-per-ton estimates first applied in the Portland 

Cement NESHAP RIA (U.S. EPA, 2009a), which incorporated functions directly from the 

epidemiology studies without an adjustment for an assumed threshold. Removing the 

threshold assumption is a key difference between the method used in this analysis to estimate 

PM co-benefits and the methods used in analyses prior to EPA’s proposed Portland Cement 

NESHAP. The benefit-per-ton estimates now include incremental benefits down to the lowest 

modeled PM2.5 air quality levels. 

PM-related mortality provides the majority (85-95%) of the monetized value in each 

benefit-per-ton estimate. As such, EPA deems it important to characterize the uncertainty 

underlying the concentration-response (C-R) functions used in its benefits analyses of 

regulations affecting PM levels. 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). 

However, due to the limitations of the benefit-per-ton methodology employed here, the 

quantitative uncertainty analysis related to the C-R relationship between PM2.5 and premature 

 

BBB Although we summarize the main issues in this chapter, we encourage interested readers to see the benefits 

chapter of the final primary NO2 NAAQS RIA for a more detailed description of recent changes to the PM 

benefits presentation and preference for the no-threshold model. 

CCC 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 
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mortality that EPA usually conducts in association with its benefits analysis was not 

conducted for this analysis. 

Typically, the premature mortality-related effect coefficients that underlie the benefitsper-ton estimates are drawn from epidemiology studies that examine two large population 

cohorts: the American Cancer Society cohort (Pope et al., 2002)350 and the Harvard Six-Cities 

cohort (Laden et al., 2006).351 The concentration-response (C-R) function developed from the 

extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. 

(2002), has previously been used by EPA to generate its primary benefits estimate. The 

extended analysis of the Harvard Six-Cities cohort, as reported by Laden et al (2006), was 

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS and has been 

used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 co-benefits estimates in 

analyses completed since the PM2.5 NAAQS. These are logical choices for anchor points 

when presenting PM-related benefits because, although both studies are well designed and 

peer reviewed, there are strengths and weaknesses inherent in each, which argues for using 

both studies to generate benefits estimates. Using the alternate relationships between PM2.5 

and premature mortality supplied by experts as part of EPA’s 206 Expert Elicitation Study,

higher and lower benefits estimates are plausible, but most of the expert-based estimates fall 

between the two epidemiology-based estimates (Roman et al., 2008; IEc, 2006).352,353

 

However, due to the analytical limitations associated with this analysis, we have chosen to use 

the benefit-per-ton value derived from the ACS study and note that benefits would be 

approximately 145% (or nearly two-and-a-half times) larger if the Harvard Six-Cities values 

were used. 

As a note to those who might be comparing the benefits estimates in this rule to those 

in previous EPA analyses, it is the nature of benefits analyses for assumptions and methods to 

evolve over time to reflect the most current interpretation of the scientific and economic 

literature. For a period of time (2004-2008), EPA’s Office of Air and Radiation (OAR) 

valued mortality risk reductions using a value of statistical life (VSL) estimate derived from a 

limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 

million to $10 million (2000$) consistent with two meta-analyses of the wage-risk literature. 

The $1 million value represented the lower end of the interquartile range from the Mrozek and 

Taylor (2002)354 meta-analysis of 33 studies. The $10 million value represented the upper 

end of the interquartile range from the Viscusi and Aldy (2003)355 meta-analysis of 43 studies. 

The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 

million estimated in the Kochi et al. (2006)356 meta-analysis. However, the Agency neither 

changed its official guidance on the use of VSL in rule-makings nor subjected the interim 

estimate to a scientific peer-review process through the Science Advisory Board (SAB) or 

other peer-review group. 

Until updated guidance is available, EPA determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, 

EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 
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for Preparing Economic Analyses (U.S. EPA, 2000)357 while they continue efforts to update 

their guidance on this issue.DDD This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$). The dollar-per-ton 

estimates used in this analysis are based on this VSL. 

The benefit-per-ton estimates are subject to a number of assumptions and 

uncertainties. 

• Dollar-per-ton estimates do not reflect local variability in population density, 

meteorology, exposure, baseline health incidence rates, or other local factors 

that might lead to an overestimate or underestimate of the actual benefits of 

controlling fine particulates. In Chapter 7.2, we describe the full-scale air 

quality modeling conducted for the 2030 calendar year analysis in an effort to 

capture this variability. 

• There are several health benefits categories that EPA was unable to quantify in 

the MY analysis due to limitations associated with using benefits-per-ton 

estimates, several of which could be substantial. Because NOX and VOC 

emissions are also precursors to ozone, changes in NOX and VOC would also 

impact ozone formation and the health effects associated with ozone exposure. 

Benefits-per-ton estimates for ozone, however, 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-perton estimates also do not include any human welfare or ecological benefits. 

Please refer to the previous section of this RIA (Chapter 7.3.1)for a description 

of the quantification and monetization of health impacts for the CY analysis 

and a description of the unquantified co-pollutant benefits associated with this 

rulemaking. 

• 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. 

 

DDD In the (draft) update of the Economic Guidelines (U.S. EPA, 2008c), EPA retained the VSL endorsed by the 

SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming 

in the near future. Therefore, this report does not represent final agency policy. The draft update of the 

Economic Guidelines is available on the Internet at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE0516-01.pdf/$File/EE-0516-01.pdf>. 
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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 this 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. Timing and resource constraints 

precluded EPA from conducting full-scale photochemical air quality modeling for the MY 

analysis. We have, however, conducted national-scale air quality modeling for the CY 

analysis to analyze the impacts of the standards on PM2.5, ozone, and selected air toxics. 

7.4 Changes in Global Mean Temperature and Sea Level Rise Associated 

with the Rule’s GHG Emissions Reductions 

7.4.1 Introduction 

Based on modeling analysis performed by the EPA, reductions in CO2 and other 

GHGs associated with the rule will affect climate change projections. Since GHGs are wellmixed in the atmosphere and have long atmospheric lifetimes, changes in GHG emissions will 

affect atmospheric concentrations of greenhouse gases and future climate for decades to 

centuries. Two common indicators of climate change are global mean surface temperature and 

sea level rise. This section provides estimates for the response in global mean surface 

temperature and sea level rise projections to the estimated net global GHG emissions 

reductions associated with the this rule (see Chapter 5 for the estimated net reductions in 

global emissions over time by GHG). 

7.4.2 Estimated Projected Reductions in Atmospheric CO2 Concentrations, 

Global Mean Surface Temperatures and Sea Level Rise 

 To assess the impact of the emissions reductions from the 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 modelEEE coupled with the MAGICC (Model for the 

 

EEE MiniCAM 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. MiniCAM 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. 

Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2003: Model Documentation for the MiniCAM. PNNL-14337, 

Pacific Northwest National Laboratory, Richland, Washington.
 Environmental and Health Impacts 

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Assessment of Greenhouse-gas Induced Climate Change) simple climate model.FFF GCAM 

was used to create the globally and temporally consistent set of climate relevant variables 

required for running MAGICC. MAGICC was then used to estimate the change in the 

atmospheric CO2 concentrations, global mean surface temperature and sea level rise over 

time. Given the magnitude of the estimated emissions reductions associated with the rule, a 

simple climate model such as MAGICC is reasonable for estimating the atmospheric and 

climate response. 

An emissions scenario for the rule was developed by applying the rule’s estimated 

emissions reductions to the GCAM reference (no climate policy or baseline) scenario (used as 

the basis for the Representative Concentration Pathway RCP4.5GGG). Specifically, the CO2, 

N2O, CH4, and HFC-134a emissions reductions from Chapter 5 were applied as net reductions 

to the GCAM global baseline net emissions for each GHG. All emissions reductions were 

assumed to begin in 2012, with zero emissions change in 2011 and linearly increasing to 

equal the value supplied (in Chapter 5) for 2020. The emissions reductions past 2050 were 

 

FFF MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single 

framework. The framework allows the user to determine changes in greenhouse-gas concentrations, global-mean 

surface air temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane 

(CH4), nitrous oxide (N2O), reactive gases (CO, NOx, VOCs), the halocarbons (e.g. HCFCs, HFCs, PFCs) and 

sulfur dioxide (SO2). MAGICC emulates the global-mean temperature responses of more sophisticated coupled 

Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy. 

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. 

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. 

GGGThis scenario is used because it contains a comprehensive suite of greenhouse and pollutant gas emissions. 

The four RCP scenarios will be used as common inputs into a variety of Earth System Models for inter-model 

comparisons leading to the IPCC AR5 (Moss et al. 2008). The MiniCAM RCP4.5 is based on the scenarios 

presented in Clarke et al. (2007) with non-CO2 and pollutant gas emissions implemented as described in Smith 

and Wigley (2006). Base-year information has been updated to the latest available data for the RCP process. The 

final RCP4.5 scenario will be available at the IAMC scenario Web site (www.iiasa.ac.at/web-apps/tnt/RcpDb/). 

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.). 

Moss, Richard, Mustafa Babiker, Sander Brinkman, Eduardo Calvo, Tim Carter, Jae Edmonds, Ismail Elgizouli, 

Seita Emori, Lin Erda, Kathy Hibbard, Roger Jones, Mikiko Kainuma, Jessica Kelleher, Jean Francois 

Lamarque, Martin Manning, Ben Matthews, Jerry Meehl, Leo Meyer, John Mitchell, Nebojsa Nakicenovic, 

Brian O’Neill, Ramon Pichs, Keywan Riahi, Steven Rose, Paul Runci, Ron Stouffer, Detlef van Vuuren, John 

Weyant, Tom Wilbanks, Jean Pascal van Ypersele, and Monika Zurek (2008) Towards New Scenarios for 

Analysis of Emissions, Climate Change, Impacts, and Response Strategies (Intergovernmental Panel on Climate 

Change, Geneva) 132 pp. 

Smith, Steven J. and T.M.L. Wigley (2006) "Multi-Gas Forcing Stabilization with the MiniCAM" Energy 

Journal (Special Issue #3). 
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scaled with total U.S. road transportation fuel consumption from the GCAM reference 

scenario. Using MAGICC, 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 this 

action. 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.HHH

 

To compute the reductions in atmospheric CO2 concentration, temperature, and sea 

level rise specifically attributable to the rule, the output from the rule’s emissions scenario 

was subtracted from the reference (no policy or baseline) emissions case scenario. As a result 

of the rule’s specified emissions reductions, the atmospheric CO2 concentration is projected to 

be reduced by approximately 2.7 to 3.1 parts per million (ppm), the global mean temperature 

is projected to be reduced by approximately 0.006-0.015°C by 2100 and global mean sea level 

rise is projected to be reduced by approximately 0.06-0.14 cm by 2100. 

Figure 7-25 provides the results for the estimated reductions in atmospheric CO2

concentration associated with the rule. Figure 7-26 provides the estimated reductions in 

projected global mean temperatures associated with the rule. Figure 7-27 provides the 

estimated reductions in global mean sea level rise associated with the rule. 

 

HHH 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/. 
 Environmental and Health Impacts 

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CO2 Concentration Change

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

2000 2020 2040 2060 2080 2100

Year

Difference (ppmv)

ClimSens=1.5

ClimSens=2.0

ClimSens=2.5

ClimSens=3.0

ClimSens=4.5

ClimSens=6.0

Figure 7-25 Estimated Projected Reductions in Atmospheric CO2 Concentrations (parts per million by 

volume) from Baseline for the Final Vehicles Rulemaking (for climate sensitivities ranging from 1.5-6°C) 

Global Mean Temperature Change

-0.0180

-0.0160

-0.0140

-0.0120

-0.0100

-0.0080

-0.0060

-0.0040

-0.0020

0.0000

2000 2020 2040 2060 2080 2100

Year

Difference (°C)

ClimSens=1.5

ClimSens=2.0

ClimSens=2.5

ClimSens=3.0

ClimSens=4.5

ClimSens=6.0

 

Figure 7-26 Estimated Projected Reductions in Global Mean Surface Temperatures from Baseline for the 

Final Vehicles Rulemaking (for climate sensitivities ranging from 1.5-6°C) 
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Global Mean Sea level Rise

-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

2000 2020 2040 2060 2080 2100

Year

Difference (cm)

ClimSens=1.5

ClimSens=2.0

ClimSens=2.5

ClimSens=3.0

ClimSens=4.5

ClimSens=6.0

Figure 7-27 Estimated Projected Reductions in Global Mean Sea Level Rise from Baseline for the Final 

Vehicles Rulemaking (for climate sensitivities ranging from 1.5-6°C) 

The results in both Figure 7-26 and Figure 7-27 show a relatively small reduction in 

the projected global mean temperature and sea level respectively, across all climate 

sensitivities. The projected reductions are small relative to the IPCC’s 2100 “best estimates” 

for global mean temperature increases (1.8 – 4.0oC) and sea level rise (0.20-0.59m) for all 

global GHG emissions sources for a range of emissions scenarios.358

 

In today’s rule, EPA analyzes another climate-related variable and calculates the 

projected changes in tropical ocean pH. EPA estimated the change in ocean pH using the 

Program CO2SYS,III version 1.05, a program which performs calculations relating parameters 

of the carbon dioxide (CO2) system in seawater. EPA used the program to calculate ocean pH 

as a function of atmospheric CO2, among other specified input conditions. Based on the 

projected atmospheric CO2 concentration reductions (average of 2.9 ppm by 2100) that would 

result from this rule, the program calculates an increase in ocean pH of approximately 0.0014 

pH units in 2100. Thus, this analysis indicates the projected decrease in atmospheric CO2

concentrations from today’s rule would result in an increase in ocean pH. 

7.4.3 Summary 

EPA’s analysis of the rule’s effect on global climate conditions is intended to quantify 

these potential reductions using the best available science. While EPA’s modeling results of 

 

III 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. 
 Environmental and Health Impacts 

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the effect of this rule alone show small differences in climate effects (CO2 concentration, 

temperature, sea-level rise, ocean pH), when expressed in terms of global climate endpoints 

and global GHG emissions, they yield results that are repeatable and consistent within the 

modeling frameworks used. 

These projected reductions are proportionally representative of changes to U.S. GHG 

emissions in the transportation sector. While not formally estimated for this rulemaking, a 

reduction in projected global mean temperature and sea level rise implies a reduction in the 

risks associated with climate change. Both figures illustrate that the distribution for projected 

global mean temperature and sea level rise increases has shifted down. The benefits of GHG 

emissions reductions can be characterized both qualitatively and quantitatively, some of 

which can be monetized (see Chapter 7.5). There are substantial uncertainties in modeling the 

global risks of climate change, which complicates quantification and cost-benefits 

assessments. Changes in climate variables are a meaningful proxy for changes in the risk of 

all potential impacts--including those that can be monetized, and those that have not been 

monetized but can be quantified in physical terms (e.g., water availability), as well as those 

that have not yet been quantified or are extremely difficult to quantify (e.g., forest disturbance 

and catastrophic events such as collapse of large ice sheets and subsequent sea level rise). 

7.5 SCC and GHG Benefits 

We assigned a monetary value to reductions in CO2 emissions using the marginal 

dollar value (i.e., cost) of climate-related damages resulting from carbon emissions, also 

referred to as “social cost of carbon” (SCC). The SCC is intended to measure the monetary 

value society places on impacts resulting from increased CO2 emissions, such as property 

damage from sea level rise, forced migration due to dry land loss, and mortality changes 

associated with vector-borne diseases. Published estimates of the SCC vary widely, however, 

as a result of uncertainties about future economic growth, climate sensitivity to CO2

emissions, procedures used to model the economic impacts of climate change, and the choice 

of discount rates. Furthermore, as noted by the IPCC Fourth Assessment Report, “It is very 

likely that globally aggregated figures underestimate the damage costs because they cannot 

include many non-quantifiable impacts."359 

In today’s final rule, EPA assigned a dollar value to reductions in CO2 emissions using 

SCC estimates that were recently developed by an interagency process. The general approach 

to estimating SCC values was to run three integrated assessment models (FUND, DICE, and 

PAGE) using inputs agreed upon by the interagency group. The technical support document, 

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, (i.e., 

SCC TSD) presents a more detailed description of the methodology used to generate the new 

estimates, the underlying assumptions, and the limitations of the new SCC estimates.360

For 2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars), and are based on 

a CO2 emissions change of 1 metric ton in 2010. The first three estimates are based on the 

average SCC across models and socio-economic and emissions scenarios at the 5, 3, and 2.5 

percent discount rates, respectively. The fourth value is included to represent the higher-thanexpected impacts from temperature change further out in the tails of the SCC distribution. For 
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this purpose, EPA has used the SCC value for the 95th percentile at a 3 percent discount rate. 

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. These SCC estimates also 

grow over time. For instance, the central value increases to $24 per ton of CO2 in 2015 and 

$26 per ton of CO2 in 2020. The table below shows how the SCC estimates change between 

2010 and 2050. The complete model results are available in the docket for this final rule 

[EPA-HQ-OAR-2009-0472]. 

Table 7-25 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) 

Discount Rate 5% 3% 2.5% 3% 

Year Avg Avg Avg 95th 

2010 5 21 35 65 

2020 7 26 42 81 

2030 10 33 50 100 

2040 13 39 58 119 

2050 16 45 65 136 

The tables below summarize the total GHG benefits for the lifetime of the rule, which 

are calculated by using the four new SCC values. Specifically, total monetized benefits in 

each year are calculated by multiplying the marginal benefits estimates per metric ton of CO2

(the SCC) by the reductions in CO2 for that year. However, these monetized GHG benefits 

exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4, N2O) expected under 

this final rule. 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 reductions in nonCO2 GHGs will contribute to this rule’s climate benefits, as explained in Section III.F. The 

SCC TSD notes the difference between the social cost of non-CO2 emissions and SCC and 

specifies a goal to develop methods to value non-CO2 emissions in future analyses. 
 Environmental and Health Impacts 

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Table 7-26 Upstream and Downstream CO2 Benefits for the Given SCC Value, Calendar Year Analysisa (Millions of 

2007 dollars) 

YEAR 

5% 

(AVERAGE SCC) 

$5 IN 2010 

3% 

(AVERAGE SCC) 

$21 IN 2010 

2.5% 

(AVERAGE SCC) 

$35 IN 2010 

3% 

(95TH PERCENTILE) 

$65 IN 2010 

2012 $31 $135 $220 $412 

2013 $78 $334 $541 $1,016 

2014 $143 $602 $972 $1,834 

2015 $239 $989 $1,593 $3,020 

2016 $373 $1,525 $2,447 $4,660 

2017 $512 $2,060 $3,295 $6,301 

2018 $651 $2,586 $4,123 $7,918 

2019 $797 $3,120 $4,960 $9,563 

2020 $946 $3,657 $5,799 $11,223 

2021 $1,111 $4,221 $6,663 $12,944 

2022 $1,277 $4,768 $7,496 $14,615 

2023 $1,449 $5,322 $8,333 $16,303 

2024 $1,621 $5,868 $9,151 $17,966 

2025 $1,799 $6,418 $9,972 $19,639 

2026 $1,973 $6,947 $10,756 $21,250 

2027 $2,151 $7,475 $11,534 $22,855 

2028 $2,321 $7,973 $12,261 $24,365 

2029 $2,494 $8,468 $12,981 $25,867 

2030 $2,664 $8,949 $13,677 $27,326 

2031 $2,839 $9,438 $14,381 $28,807 

2032 $3,012 $9,915 $15,065 $30,252 

2033 $3,191 $10,408 $15,770 $31,744 

2034 $3,372 $10,900 $16,472 $33,234 

2035 $3,561 $11,412 $17,202 $34,785 

2036 $3,752 $11,926 $17,932 $36,339 

2037 $3,951 $12,460 $18,689 $37,954 

2038 $4,152 $12,998 $19,451 $39,582 

2039 $4,362 $13,558 $20,243 $41,274 

2040 $4,577 $14,127 $21,046 $42,992 

2041 $4,802 $14,690 $21,815 $44,691 

2042 $5,033 $15,267 $22,601 $46,428 

2043 $5,274 $15,866 $23,417 $48,234 

2044 $5,523 $16,481 $24,253 $50,087 

2045 $5,781 $17,118 $25,117 $52,005 

2046 $6,046 $17,773 $26,004 $53,976 

2047 $6,322 $18,449 $26,919 $56,013 

2048 $6,606 $19,143 $27,857 $58,103 

2049 $6,899 $19,859 $28,823 $60,258 

2050 $7,202 $20,596 $29,816 $62,476 

NPVb$34,500 $176,700 $299,600 $538,500

a Monetized GHG benefits exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4 and N2O) 

expected under this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 

emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO2 

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F.2. The SCC TSD notes the 

difference between the social cost of non-CO2 emissions and CO2 emissions, and specifies a goal to develop 

methods to value non-CO2 emissions in future analyses. 
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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. 

EPA also conducted a separate analysis of the GHG benefits over the model year 

lifetimes of the 2012 through 2016 model year vehicles. In contrast to the calendar year 

analysis, the model year lifetime analysis shows the lifetime impacts of the program on each 

of these MY fleets over the course of its lifetime. Full details of the inputs to this analysis can 

be found in RIA chapter 5. The GHG benefits of the full life of each of the five model years 

from 2012 through 2016 are shown in Table 7-27 through Table 7-30 for each of the four 

different social cost of carbon values. The GHG benefits are shown for each year in the 

model year life and in net present value. The same discount rate used to discount the value of 

damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present 

value of SCC for internal consistency. 
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7-131 

Table 7-27 Upstream and Downstream CO2 Benefits for the 5% (Average SCC) Value, Model Year Analysisa

(Millions of 2007 dollars) 

YEAR MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 SUM 

2012 $36 $0 $0 $0 $0 $36

2013 $36 $53 $0 $0 $0 $89

2014 $36 $54 $73 $0 $0 $163

2015 $36 $54 $74 $107 $0 $271

2016 $36 $54 $74 $108 $151 $422

2017 $35 $53 $74 $108 $151 $421

2018 $34 $52 $73 $107 $152 $418

2019 $33 $51 $71 $106 $150 $411

2020 $31 $49 $69 $104 $148 $402

2021 $30 $47 $67 $102 $147 $393

2022 $29 $45 $65 $99 $144 $382

2023 $26 $43 $62 $95 $140 $366

2024 $23 $39 $59 $91 $134 $346

2025 $20 $35 $54 $86 $128 $322

2026 $17 $30 $47 $79 $120 $293

2027 $14 $25 $41 $69 $111 $259

2028 $12 $21 $34 $59 $97 $223

2029 $10 $18 $29 $50 $83 $189

2030 $8 $15 $24 $42 $70 $158

2031 $7 $12 $20 $35 $58 $132

2032 $6 $10 $17 $29 $49 $110

2033 $5 $8 $14 $24 $40 $91

2034 $4 $7 $11 $20 $33 $76

2035 $3 $6 $10 $16 $27 $63

2036 $3 $5 $8 $14 $23 $53

2037 $3 $4 $7 $12 $19 $45

2038 $2 $4 $6 $10 $16 $38

2039 $2 $3 $5 $9 $14 $32

2040 $1 $3 $4 $7 $12 $27

2041 $1 $2 $4 $5 $10 $23

2042 $1 $2 $3 $5 $8 $19

2043 $1 $2 $3 $4 $7 $17

2044 $1 $2 $2 $4 $6 $15

2045 $1 $1 $2 $3 $5 $13

2046 $1 $1 $2 $3 $5 $12

2047 $1 $1 $2 $3 $4 $10

2048 $0 $1 $2 $2 $4 $9

2049 $0 $0 $1 $2 $3 $7

2050 $0 $0 $0 $2 $3 $5

NPV, 

5% $400 $500 $700 $1,000 $1,300 $3,800

a As noted above, these monetized GHG benefits exclude the value of reductions in non-CO2 GHG 

emissions expected under this final rule. Although EPA has not monetized the benefits of reductions in 

non-CO2 GHGs, the value of these reductions should not be interpreted as zero. 
Regulatory Impact Analysis 

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Table 7-28 Upstream and Downstream CO2 Benefits for the 3% (Average SCC) SCC Value, Model Year Analysisa

(Millions of 2007 dollars) 

YEAR MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 SUM 

2012 $155 $0 $0 $0 $0 $155

2013 $154 $230 $0 $0 $0 $383

2014 $152 $227 $310 $0 $0 $689

2015 $149 $225 $306 $445 $0 $1,124

2016 $146 $220 $303 $439 $616 $1,723

2017 $141 $215 $296 $435 $608 $1,695

2018 $136 $208 $289 $425 $601 $1,659

2019 $129 $199 $279 $415 $589 $1,611

2020 $122 $189 $268 $401 $574 $1,554

2021 $115 $179 $256 $387 $557 $1,494

2022 $107 $169 $242 $369 $537 $1,425

2023 $97 $157 $228 $350 $513 $1,345

2024 $84 $142 $212 $329 $485 $1,253

2025 $71 $124 $192 $306 $457 $1,149

2026 $59 $105 $167 $277 $424 $1,032

2027 $49 $88 $141 $240 $384 $902

2028 $40 $73 $118 $203 $332 $766

2029 $33 $60 $98 $169 $281 $641

2030 $27 $50 $81 $140 $234 $532

2031 $22 $41 $67 $116 $194 $439

2032 $18 $34 $55 $95 $160 $362

2033 $15 $28 $45 $78 $131 $297

2034 $13 $23 $37 $64 $107 $244

2035 $11 $19 $31 $52 $88 $202

2036 $9 $17 $26 $44 $73 $168

2037 $8 $14 $22 $37 $61 $142

2038 $6 $12 $19 $31 $51 $119

2039 $5 $9 $16 $26 $43 $100

2040 $5 $8 $12 $23 $37 $84

2041 $4 $7 $11 $17 $31 $70

2042 $4 $6 $9 $15 $23 $57

2043 $3 $6 $8 $13 $20 $51

2044 $3 $5 $7 $12 $18 $45

2045 $2 $4 $7 $10 $16 $39

2046 $2 $4 $6 $9 $14 $35

2047 $2 $3 $5 $8 $12 $31

2048 $0 $3 $4 $7 $11 $25

2049 $0 $0 $4 $6 $10 $20

2050 $0 $0 $0 $5 $8 $14

NPV, 

3% $1,700 $2,400 $3,100 $4,400 $5,900 $17,000

a As noted above, these monetized GHG benefits exclude the value of reductions in non-CO2 GHG 

emissions expected under this final rule. Although EPA has not monetized the benefits of reductions in 

non-CO2 GHGs, the value of these reductions should not be interpreted as zero. 
 Environmental and Health Impacts 

7-133 

Table 7-29 Upstream and Downstream CO2 Benefits for the from 2.5% (Average SCC) SCC Value, Model Year 

Analysisa

 (Millions of 2007 dollars)

YEAR MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 SUM 

2012 $253 $0 $0 $0 $0 $253

2013 $249 $372 $0 $0 $0 $621

2014 $246 $366 $500 $0 $0 $1,113

2015 $241 $362 $492 $716 $0 $1,810

2016 $234 $353 $486 $705 $988 $2,766

2017 $226 $344 $474 $695 $972 $2,711

2018 $216 $331 $461 $679 $959 $2,646

2019 $205 $317 $444 $660 $936 $2,562

2020 $193 $300 $425 $636 $910 $2,463

2021 $181 $283 $404 $611 $880 $2,359

2022 $168 $266 $381 $581 $845 $2,240

2023 $152 $246 $357 $547 $803 $2,106

2024 $131 $222 $331 $514 $757 $1,954

2025 $110 $192 $299 $475 $710 $1,786

2026 $92 $162 $258 $429 $656 $1,597

2027 $76 $135 $218 $370 $593 $1,392

2028 $62 $112 $182 $312 $511 $1,178

2029 $51 $93 $150 $259 $430 $983

2030 $41 $76 $124 $214 $358 $813

2031 $34 $62 $102 $176 $295 $670

2032 $28 $51 $83 $144 $243 $550

2033 $23 $42 $68 $118 $199 $450

2034 $19 $35 $56 $96 $162 $369

2035 $16 $29 $47 $79 $133 $304

2036 $14 $25 $39 $66 $109 $253

2037 $12 $21 $34 $55 $91 $213

2038 $9 $18 $28 $47 $76 $178

2039 $8 $14 $24 $39 $65 $150

2040 $7 $12 $18 $34 $54 $125

2041 $6 $11 $16 $25 $47 $104

2042 $5 $9 $14 $22 $34 $85

2043 $5 $8 $12 $19 $30 $75

2044 $4 $7 $11 $17 $27 $66

2045 $4 $6 $10 $15 $23 $58

2046 $3 $6 $8 $13 $21 $51

2047 $3 $5 $7 $12 $18 $45

2048 $0 $4 $6 $10 $16 $37

2049 $0 $0 $6 $9 $14 $29

2050 $0 $0 $0 $8 $12 $20

NPV, 

2.5% $2,700 $3,900 $5,200 $7,200 $9,700 $29,000

a As noted above, these monetized GHG benefits exclude the value of reductions in non-CO2 GHG 

emissions expected under this final rule. Although EPA has not monetized the benefits of reductions in 

non-CO2 GHGs, the value of these reductions should not be interpreted as zero. 
Regulatory Impact Analysis 

7-134 

Table 7-30 Upstream and Downstream CO2 Benefits for the 3% (95th Percentile) SCC Value, Model Year Analysisa

(Millions of 2007 dollars)

YEAR MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 SUM 

2012 $473 $0 $0 $0 $0 $473

2013 $468 $699 $0 $0 $0 $1,167

2014 $465 $691 $944 $0 $0 $2,100

2015 $456 $685 $933 $1,357 $0 $3,432

2016 $446 $672 $925 $1,342 $1,882 $5,267

2017 $432 $657 $907 $1,329 $1,859 $5,184

2018 $415 $636 $886 $1,303 $1,841 $5,081

2019 $395 $611 $856 $1,272 $1,804 $4,939

2020 $373 $581 $822 $1,230 $1,761 $4,767

2021 $352 $550 $785 $1,186 $1,709 $4,582

2022 $327 $518 $743 $1,132 $1,647 $4,367

2023 $297 $481 $699 $1,071 $1,572 $4,120

2024 $257 $436 $649 $1,008 $1,486 $3,836

2025 $217 $378 $589 $936 $1,398 $3,517

2026 $181 $320 $510 $848 $1,297 $3,156

2027 $150 $268 $432 $733 $1,175 $2,757

2028 $124 $222 $361 $619 $1,016 $2,342

2029 $101 $184 $299 $517 $858 $1,959

2030 $83 $152 $248 $428 $715 $1,625

2031 $68 $124 $204 $353 $592 $1,341

2032 $56 $102 $167 $290 $489 $1,104

2033 $47 $85 $137 $237 $401 $906

2034 $39 $71 $113 $194 $328 $745

2035 $33 $59 $95 $160 $268 $616

2036 $28 $51 $79 $134 $221 $513

2037 $24 $43 $68 $112 $185 $432

2038 $18 $37 $58 $96 $154 $362

2039 $16 $28 $50 $81 $132 $305

2040 $14 $25 $37 $69 $111 $256

2041 $12 $22 $33 $51 $95 $213

2042 $11 $19 $29 $45 $70 $174

2043 $10 $17 $25 $40 $62 $154

2044 $8 $15 $22 $35 $55 $136

2045 $7 $13 $20 $31 $48 $120

2046 $6 $12 $17 $27 $43 $106

2047 $6 $10 $15 $24 $38 $93

2048 $0 $9 $14 $21 $33 $77

2049 $0 $0 $12 $19 $29 $60

2050 $0 $0 $0 $16 $26 $42

NPV, 

3% $5,100 $7,300 $9,600 $13,000 $18,000 $53,000

a As noted above, these monetized GHG benefits exclude the value of reductions in non-CO2 GHG 

emissions expected under this final rule. Although EPA has not monetized the benefits of reductions in 

non-CO2 GHGs, the value of these reductions should not be interpreted as zero. 
 Environmental and Health Impacts 

7-135 

7.6 Weight Reduction and Vehicle Safety 

Over the past 20 years there has been a generally increasing trend in the weight of 

vehicles (see Figure 7-28 below from EPA’s Fuel Economy Trends Report). 361 There have 

been a number of factors contributing to this including: greater penetration of heavier trucks, 

introduction of SUVs, and an increasing amount of content in vehicles (including features for 

safety, noise reduction, added comfort, luxury, etc). This increased weight has been partially 

enabled by the increased efficiency of vehicles, especially in engines and transmissions. The 

impressive improvements in efficiency during this period have not only allowed for greater 

weight carrying capacity (and towing), but it has also allowed for greater acceleration 

performance in the fleet. As the figure also shows, little of this efficiency improvement has 

been realized in fuel economy gains or GHG emissions reductions. 

Figure 7-28 Weight, 0-to-60 MPH acceleration time and adjusted fuel economy for light-duty vehicles 

 

During this same period, due in part to increasing numbers of Federal Motor Vehicle 

Safety Standards and increasingly stringent NCAP standards from NHTSA, the safety of 

vehicles has also undergone tremendous improvement. Vehicles are designed to better 

withstand both frontal and side impacts, occupants are protected better with increased seat 

belt usage and air bags, and anti-lock brakes (ABS), electronic stability control (ESC), and 

improved tires and suspensions help drivers avoid accidents. NHTSA stated in its NPRM that 

it anticipated a 12.6 percent reduction in fatality levels between 2007 and 2020 with safety 

improvements due to pending NHTSA FMVSS and other factors, such as behavioral 

improvements (less drunk driving and increased seat belt usage, for example).JJJ,KKK

 

 

JJJNHTSA stated in section IX of their PRIA for this rule: “The agency examined the impacts of identifiable 

safety trends over the lifetime of the vehicles produced in each model year. An estimate of these impacts was 
Regulatory Impact Analysis 

7-136 

Assuming that safety improvements will be made evenly throughout that period, EPA 

estimates the reduction in fatalities between 2007 and 2016 to be 8.7%. 

The interplay between vehicle weight and potential impact on safety is complex. 

While certainly an effective option for reducing CO2 emissions, the reduction of vehicle 

weight is a controversial and complicated topic. The EPA believes that though much work 

has been done to advance the understanding of the effects of vehicle weight on safety, there is 

much yet to do. EPA also acknowledges that for the analysis of various topics contained in 

this RIA and in the preamble, there are a number of uncertainties which exist with these 

analysis and any results must be viewed in light of those uncertainties. In the context of 

safety, for example, due to limitations in modeling consumer and producer behavioral 

responses to the new standards, this analysis does not explore the net fleetwide safety 

implications of potential changes in the distribution of vehicles in the on road fleet (e.g., 

changes in the variance of size, weight, vehicle types on the road) if consumers respond to the 

rulemaking by purchasing a distribution of vehicles which are significantly different than 

what EPA has forecast. 

This section of the RIA describes and compares some of the key studies that have been 

conducted in the recent past – including NHTSA’s recent 2010 analysis, and presents some 

potential research options going forward. 

7.6.1 What did EPA say in the NPRM and in the Draft RIA with regard to 

Potential Safety Effects? 

In the NPRM, EPA discussed potential safety effects of the proposed standards. In the 

joint technology analysis, EPA and NHTSA predicted that automakers could reduce vehicle 

weight as one part of the industry’s strategy for meeting the proposed standards. EPA’s 

modeling projected (and still projects) that vehicle manufacturers will reduce the weight of 

their vehicles by 4% on average between 2011 and 2016, with the average per-vehicle mass 

reduction in absolute terms being greater for light-trucks than for passenger cars. For the 

NPRM, this mass reduction estimate was generally smaller on both an absolute and a 

percentage basis for smaller car than for larger vehicles. Specifically, we estimated an 

average reduction of 2.3% (62 lbs) for cars with a curb weight below 2,950 lbs, 4.4% (154 

lbs) for cars with a curb weight above 2,950, 3.5% (119 lbs) for trucks with a curb weight 

below 3,850 lbs, and 4.5% for trucks with a curb weight above 3,850 lbs (215 lbs). The 

 

contained in a previous agency report. (The next citation in this document, Blincoe, L. and Shankar, U, January 

2007.) The impacts were estimated on a year-by-year basis, but could be examined in a combined fashion. The 

agency assumed that the safety trends will result in a reduction in the target population of fatalities from which 

the weight impacts are derived. Using this method, we found a 12.6 percent reduction in fatality levels between 

2007 and 2020. The estimates derived from applying Kahane’s percentages to a baseline of 2007 fatalities were 

thus multiplied by 0.874 to account for changes that the agency believes will take place in passenger car and 

light truck safety between the 2007 baseline on-road fleet used for this particular analysis and year 2020.

KKK Blincoe, L. and Shankar, U, “The Impact of Safety Standards and Behavioral Trends on Motor Vehicle 

Fatality Rates,” DOT HS 810 777, January 2007. See Table 4 comparing 2020 to 2007 (37,906/43,363 = 12.6% 

reduction. 
 Environmental and Health Impacts 

7-137 

penetration and magnitude of these modeled changes are consistent with the public 

announcements made by many manufacturers since early 2008 and are consistent with 

meetings that EPA has had with senior engineers and technical leadership at many of the 

automotive companies during 2008 and 2009. 

Between September 2008 and March 2009, EPA met with 11 major auto companies: 

GM, Chrysler, Ford, Nissan, Honda, Toyota, Mitsubishi, Hyundai/Kia, BMW, Mercedes and 

Volkswagen. Each company announced plans to reduce vehicle weight broadly across the 

passenger car vehicle and light truck categories within the 2012 to 2016 timeframe. Their 

plans for vehicle weight reduction are not limited to a single weight class but instead are 

expected to be implemented widely across their products. The following statements 

summarize a number of automotive manufacturers’ future plans to reduce vehicle weight 

announced in the public domain within the past two year: 

• Ford: 250 to 750 pound weight reductions 2012 to 2020 across all vehicle platforms362

• Toyota: 30% weight reduction on 2015 Corolla and a 10% weight reduction on midsize vehicles by 2015363

• Nissan: 15% average weight reduction by 2015363

• Mazda: 100 kg (220 pound) weight reduction by 2011 and an additional 100 kg 

weight reduction by 2016363, 364

• Mercedes: 2-3 % weight reduction on recently introduced 2009 “BlueEFFICIENCY” 

models365 

The EPA believes that reducing vehicle mass of the magnitude we estimated for the proposal 

and the final rule (up to 10% for certain vehicles by model year 2016)without reducing the 

size, footprint or the structural integrity of the vehicle is technically feasible. Many of the 

technical options for doing so are outlined in Chapter 3 of the joint TSD and in this RIA. In 

the NPRM, EPA described how weight reduction can be accomplished by the proven methods 

described below. Every manufacturer can employ these methodologies to some degree, the 

magnitude to which each will be used will depend on opportunities within individual vehicle 

design. 

• Material Substitution: Substitution of lower density and/or higher strength materials 

in a manner that preserves or improves the function of the component. This includes 

substitution of high-strength steels, aluminum, magnesium or composite materials for 

components currently fabricated from mild steel, e.g., the magnesium-alloy front 

structure used on the 2009 Ford F150 pickups (we note that since these MY 2009 

F150s have only begun to enter the fleet, there is little real-world crash data available 

to evaluate the safety impacts of this new design). Light-weight materials with 

acceptable energy absorption properties can maintain structural integrity and 

absorption of crash energy relative to previous designs while providing a net decrease 

in component weight. In their comments to the proposed rule, the American Iron and 

Steel Institute (AISI) noted: “AISI has shown in its research with the Auto/Steel 

Partnership and in programs supported by the U.S. Department of Energy, the use of 

new Advanced High Strength Steel (AHSS) steel grades can enable the mass of 
Regulatory Impact Analysis 

7-138 

critical crash structures, such as front rails and bumper systems, to be reduced by 

25%.” 

• Smart Design: Computer aided engineering (CAE) tools can be used to better 

optimize load paths within structures by reducing stresses and bending moments 

without adversely affecting structural integrity. This allows better optimization of the 

sectional thicknesses of structural components to reduce mass while maintaining or 

improving the function of the component. Smart designs also integrate separate parts 

in a manner that reduces mass by combining functions or the reduced use of separate 

fasteners. In addition, some “body on frame” vehicles are redesigned with a lighter 

“unibody” construction with little compromise in vehicle functionality. 

• Reduced Powertrain Requirements: Reducing vehicle weight sufficiently allows for 

the use of a smaller, lighter and more efficient engine while maintaining or increasing 

performance. Approximately half of the mass reduction that can be realized in the 

powertrain is due to reduced powertrain output requirements. The subsequent reduced 

rotating mass (e.g. transmission, driveshafts/halfshafts, wheels and tires) via weight 

and/or size reduction of components are made possible by reduced torque output 

requirements. 

• Mass Decompounding: Following from the point above, the compounded weight 

reductions of the body, engine and drivetrain can reduce stresses on the suspension 

components, steering components, brakes, and thus allow further reductions in the 

weight of these subsystems. The reductions in weight for unsprung masses such as 

brakes, control arms, wheels and tires can further reduce stresses in the suspension 

mounting points which can allow still further reductions in weight. For example, mass 

reduction can allow for the reduction in the size of the vehicle brake system, while 

maintaining the same stopping distance. It is estimated that 1.25 kilograms of 

secondary weight savings can be achieved for every kilogram of weight saved on a 

vehicle when all subsystems are redesigned to take into account the initial primary 

weight savings.366

 

The EPA stated in the NPRM that it believes that weight reduction is broadly 

applicable across all vehicle subsystems including the engine, exhaust system, transmission, 

chassis, suspension, brakes, body, closure panels, glazing, seats and other interior 

components, engine cooling systems and HVAC systems. EPA mentioned that it is both 

technically feasible to reduce weight without reducing vehicle size, footprint or structural 

strength and manufacturers have indicated to the agencies that they will use these approaches 

to accomplish these goals. We requested written comment on this assessment and this 

projection, including up-to-date plans regarding the extent of use by each manufacturer of 

each of the methodologies described above. 

EPA also projected that automakers will not reduce vehicle footprint in response to the 

proposed CO2 standards in our modeling analysis. NHTSA and EPA have taken two 

measures to help ensure that this final rule does not provide an incentive for mass reduction to 

be accompanied by a corresponding decrease in the footprint of the vehicle (with its 

concomitant decrease in crush and crumple zones). The first design feature of the rule is that 
 Environmental and Health Impacts 

7-139 

the CO2 or fuel economy targets are based on the attribute of footprint (which is a surrogate 

for vehicle size).LLL The second design feature is that the shape of the footprint curve (or 

function) has been carefully designed with the intention that it neither encourages 

manufacturers to increase, nor decrease the footprint of their fleet. Changes in relative safety 

are related to shifts in the distribution of vehicles on the road. A policy that induces a 

widening in the size distribution of vehicles on the road, could result in negative impacts on 

safety. The primary mechanism in this rulemaking for mitigating the potential negative 

effects on safety is the application of footprint-based standards, which create a disincentive 

for manufacturers to produce smaller-footprint vehicles. This is because as footprint 

decreases, the corresponding fuel economy/GHG emission target becomes more 

stringent.MMM The shape of the footprint curves themselves have also been designed to be 

approximately “footprint neutral” within the sloped portion of the functions – that is, to 

neither encourage manufacturers to increase the footprint of their fleets, nor to decrease it. 

Upsizing also is discouraged through a “cut-off” at larger footprints. For both cars and light 

trucks there is a “cut-off” that affects vehicles smaller than 41 square feet. The agencies 

recognize that for manufacturers who make small vehicles in this size range, this cut off 

creates some incentive to downsize (i.e. further reduce the size and/or increase the production 

of models currently smaller than 41 square feet) to make it easier to meet the target. The cut 

off may also create some incentive for manufacturers who do not currently offer such models 

to do so in the future. However, at the same time, the agencies believe that there is a limit to 

the market for cars smaller than 41 square feet - most consumers likely have some minimum 

expectation about interior volume, among other things. In addition, vehicles in this market 

segment are the lowest price point for the light-duty automotive market, with a number of 

models in the $10,000 to $15,000 range. In order to justify selling more vehicles in this 

market in order to generate fuel economy or CO2 credits (that is, for this final rule to be the 

incentive for selling more vehicles in this small car segment), a manufacturer would need to 

add additional technology to the lowest price segment vehicles, which could be challenging. 

Therefore, due to these two reasons (a likely limit in the market place for the smallest sized 

cars and the potential consumer acceptance difficulty in adding the necessary technologies in 

order to generate fuel economy and CO2 credits), the agencies believe that the incentive for 

manufacturers to increase the sale of vehicles smaller than 41 square feet due to this 

rulemaking, if present, is small. For further discussion on these aspects of the standards, 

please see Section II.C above and Chapter 2 of the Joint TSD. However, EPA acknowledges 

 

LLL As the footprint attribute is defined as wheelbase times track width, the footprint target curves do not 

discourage manufacturers from reducing vehicle size by reducing front, rear, or side overhang, which can impact 

safety by resulting in less crush space. However, EPA does acknowledge that front and rear and side overhang 

are not included in footprint and that there may be changes in this part of the vehicle in the future. 

MMM We note, however, that vehicle footprint is not synonymous with vehicle size. Since the footprint is only 

that portion of the vehicle between the front and rear axles, footprint-based standards do not discourage 

downsizing the portions of a vehicle in front of the front axle and to the rear of the rear axle, or to other portions 

of the vehicle outside the wheels. The crush space provided by those portions of a vehicle can make important 

contributions to managing crash energy. At least one manufacturer has confidentially indicated plans to reduce 

overhang as a way of reducing mass on some vehicles during the rulemaking time frame. Additionally, simply 

because footprint-based standards create no incentive to downsize vehicles, does not mean that manufacturers 

may not choose to do so if doing so makes it easier to meet the overall standard (as, for example, if the smaller 

vehicles are so much lighter that they exceed their targets by much greater amounts). 
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some uncertainty regarding how consumer purchases will change in response to the vehicles 

designed to meet the model years 2012-2016 standards. This could potentially affect the mix 

of vehicles sole in the future, including the mass and footprint distribution, and thus result in a 

different impact on safety than is discussed and presented in this final rule. 

EPA also discussed safety in the context of NHTSA’s analysis presented in Section IV 

of the NPRM preamble, in which NHTSA presented an analysis of the proposed CAFE 

standards based on the 2003 Kahane safety analysis. NHTSA’s Dr. Charles Kahane 

performed a thorough review on historical data regarding the relationship between mass 

reduction, wheel base, track width and fatality risk.367,368 The results from 1991-1999 vehicle 

data indicate that a heavier vehicle is safer than a lighter one based on the assumption that 

historical vehicle mass reductions are accompanied with vehicle size and footprint reductions. 

Based on this, NHTSA developed and presented a worst case estimate of the effect of weight 

reductions on fatalities. The underlying data used for that analysis did not allow NHTSA to 

analyze the specific effect of weight reduction at constant footprint because historically there 

have not been a large number of vehicles produced that relied substantially on material 

substitution. Rather, the data set included vehicles that were either smaller and lighter or 

larger and heavier. The numbers in the NHTSA analysis predicted the safety-related fatality 

consequences that would occur in the unlikely event that weight reduction for model years 

2012-2016 is accomplished by reducing mass and reducing footprint (as well as structural 

integrity). EPA acknowledged that the safety analysis conducted by NHTSA and presented in 

Section IV of the NPRM Preamble could be a worst case analysis for fatalities, but that the 

actual effects on vehicle safety could (and would likely) be much less. However, EPA and 

NHTSA were not able to quantify the lower-bound potential effects at that time. 

Thus, the 2003 and earlier Kahane studies (as summarized in the NPRM) concerning 

weight reductions and safety indicate that there is not a clear cut answer to the issue before the 

agencies of potential safety impacts of the rule due to mass reduction which may occur in the 

fleet absent size reductions. These studies draw upon historical vehicle data where mass 

reduction were (for the most part) linked with size reductions, thus they occurred in ways 

different from the way the agencies project weight reduction will occur for MY 2012-2016 

vehicles. As such the pre-2010 Kahane studies may not be directly relevant to predicting or 

quantifying the safety impact of the projected use of weight reduction technologies in this 

rule. Several commenters to the proposed rule support this conclusion, including the 

International Council on Clean Transportation (ICCT): “The results of the Kahane study 

would be great for analyzing the safety impacts of a weight-based attribute system.... 

Kahane’s methodology was simply not designed to assess the safety impacts of lightweight 

materials.”, and the National Association of Clean Air Agencies (NACAA): “significant 

portions of NHTSA’s safety analysis are based on out-of-date data”. 

In contrast to the pre-2010 Kahane studies, Dynamic Research Incorporated (DRI) has 

assessed the independent effects of vehicle weight and size on safety in order to determine if 

there are tradeoffs between improving vehicle safety and fuel consumption In their 2005 
 Environmental and Health Impacts 

7-141 

studies, 369,370 ,NNN DRI presented results that indicated that vehicle weight reduction tended to 

decrease fatalities, but vehicle wheelbase and track reduction tended to increase fatalities. 

In the RIA for the proposed rule, EPA attempted to summarize DRI’s results in four 

major points. These points are re-summarized below to reflect comments from DRI as to 

present this information more accurately:OOO 

1. 2-Door vehicles represented a significant portion of the light duty fleet and should 

not be ignored. 

2. Directional control and therefore crash avoidance improves with a reduction in 

curb weight and/or increases in wheel base and track. 

3. The occupants of the impacted vehicle, or “collision partner” benefit from being 

impacted by a lighter vehicle. 

4. Rollover fatalities are reduced by a reduction in curb weight due to a potentially 

lower center of gravity and lower loads on the roof structures. 

The data used for the DRI analysis was similar to that used in NHTSA’s 2003 Kahane 

study, using Fatality Analysis Reporting System (FARS) data for vehicle model years 1985 

through 1998 for cars, and 1985 through 1997 trucks. This data overlaps Kahane’s FARS 

data on model year 1991 to 1998 vehicles. DRI also used a logistic regression method 

similar to the approach taken by the 2003 Kahane study. However, DRI included 2-door 

passenger cars, whereas the Kahane study excluded all 2-door vehicles. The 2003 Kahane 

study excluded 2-door passenger cars because it found that for MY 1991-1999 vehicles; 

sports and muscle cars constituted a significant proportion of those vehicles. NHTSA stated 

that these vehicles have relatively high weight relative to their wheelbase, and are also 

disproportionately involved in crashes. Thus, Kahane concluded that including these vehicles 

in the analysis excessively skewed the regression results. As of July 1, 1999, 2-door 

passenger cars represented 29% of the registered cars in the United States.PPP The majority of 

2-door vehicles excluded in the 2003 Kahane study and included in DRI's analysis were highsales volume light-duty vehicles and vehicles shared common vehicle platforms and 

architectures with 4-door vehicles that were included in the 2003 Kahane study. DRI’s 

position was that this is a significant portion of the light duty fleet, too large to be ignored, 

and conclusions regarding the effects of weight and safety should be based on data for all 

cars, not just 4-doors. 

 

NNN One of these studies was published as a Society of Automotive Engineers Technical Paper and received peer 

review through that body 

OOO DRI stated: “The DRI work focused on the effects of vehicle size and weight (i.e. curb weight, wheelbase, 

and track) on vehicle crash avoidance, crashworthiness, and compatibility, based on accident and fatality data. 

There were numerous conclusions in addition to those listed, including the benefits of both increased size and 

weight.” 

PPP Specific examples include the Chevrolet Cavalier and Monte Carlo, Oldsmobile Achieva and Supreme, Buick 

Riviera, Ford Escort and Probe, Mercury Tracer, Honda Civic, Hyundai Accent, and VW Golf which do not 

necessarily represent high-weight, short-wheelbase sports and high-performance vehicle types. 
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DRI did, however, state in their conclusions that the results are sensitive to removing 

data for 2-doors and wagons, and that the results for 4-door cars with respect to the effects of 

wheelbase and track width were no longer statistically significant when 2-door cars were 

removed. EPA and NHTSA (along with many commenters) recognized the technical 

challenges of properly accounting for 2-door cars in a regression analysis evaluating the 

impacts of vehicle weight on safety, due to the concerns discussed in the 2003 Kahane study 

above. 

The DRI and Kahane studies also differed with respect to the impact of vehicle weight 

on rollover fatalities. The Kahane study treated curb weight as a surrogate for size and weight 

and analyzed them as a single variable. Using this method, the 2003 Kahane analysis 

indicated that curb weight reductions would increase fatalities due to rollovers. The DRI 

study differed by analyzing curb weight, wheelbase, and track as multiple variables and 

concluded that curb weight reduction would decrease rollover fatalities, and wheelbase and 

track reduction would increase rollover fatalities. DRI offered two potential root causes for 

higher curb weight resulting in higher rollover fatalities. The first is that a taller vehicle tends 

to be heavier than a shorter vehicle; therefore heavier vehicles may be more likely to rollover 

because the vehicle height and weight are correlated with vehicle center of gravity height. 

The second is that FMVSS 216 for roof crush strength requirements for passenger cars of 

model years 1995 through 1999 were proportional to the unloaded vehicle weight if the 

weight is less than 3,333 lbs, however they were a constant if the weight is greater than 3,333 

lbs. Therefore heavier vehicles may have had relatively less rollover crashworthiness. 

In the NPRM, NHTSA rejected many elements of the DRI analysis, and did not rely 

on it for its evaluation of safety impact changes from the proposed CAFE standards. See 

Section IV.G.6 of the Notice of Proposed Rulemaking, as well as NHTSA March 2009 Final 

Rulemaking for MY2011 CAFE standards (74 FR at 14402-05). 

The DRI analysis concluded that there would be small additional reductions in 

fatalities for cars and trucks if the weight reduction occurs without accompanying vehicle 

footprint or size changes. EPA noted that if DRI’s results were to be applied using the curb 

weight reductions predicted by the OMEGA model, an overall reduction in fatalities would be 

predicted. There were many commenters who supported this notion. Further discussion of 

these comments is included below. 

7.6.2 What Public Comments did EPA Receive in Regard to its Safety Discussion 

and what is EPA’s Response? 

EPA requested and received public comments from several sources regarding the 

NPRM Preamble positions noted above. Many of the comments received were in support of 

EPA’s safety assessment, such as those from Dynamics Research Institute (DRI), 

International Council on Clean Transportation (ICCT), Public Citizen, Union of Concerned 

Scientists, California Air Resources Board (CA-ARB) and the National Association of Clean 

Air Agencies (NACAA). 

In a technical comment, DRI’s agreed with EPA that directional control and therefore 

crash avoidance improves with a reduction in curb weight. DRI went further and commented 
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7-143 

that control also improved with an increase in wheel base and track width., The latter point 

acknowledges that a corresponding potential decrease in wheel base or track width could 

result in an increase in fatalities. DRI offered results from the application of the “quasi-steady 

vehicle directional equations of motion”. These results show “that passenger cars with shorter 

wheelbases tend to have smaller characteristic speeds, resulting in higher [yaw rate] natural 

frequency and less damping, based on analysis of quasi-steady vehicle equations of motion”. 

EPA concurs with DRI’s comments regarding their analysis. This supports the 

argument made by EPA in the NPRM that, vehicles with reduced mass are better able to avoid 

accidents that cause fatalities due to increased vehicle maneuverability that better matches a 

driver’s intended steering inputs.QQQ EPA analyzed Consumer Reports Double Lane Change 

DataRRR to determine how curb weight affects emergency handling. Consumer Reports 

provided EPA with a summary of their double lane change tests for vehicles from MY 2003 

through MY 2010 along with the vehicles’ curb weight.SSS By plotting the vehicle double 

lane change speed with respect to curb weight, EPA is able to show how the speed at which a 

double lane change can be safely executed goes up as curb weight decreases, as shown in 

Figure 7-29 below. As the weight of all vehicles is decreased, their maneuverability and 

handling will increase, thus giving the driver greater control and increased capability to avoid 

accidents. This relationship held true for both vehicles equipped with and without Electronic 

Stability Control (ESC), although the same data show that capability is further enhanced with 

the addition of ESC. At any given curb weight, the addition of ESC resulted in a higher 

average double lane change speed than those vehicles without stability control. Under all 

stability control events, the ESC system will be working against the momentum of the 

vehicle. Reductions in mass will generally increase ESC effectiveness in terms of the ability 

of a vehicle to enter a double lane change maneuver at a higher speed. However, NHTSA’s 

review of historical FARS data for MY 1991-1999 cars has found that smaller cars are 

responsible for a higher number of crashes than other vehicle types. 

 

QQQ FMVSS126 - “Oversteering and understeering are typically cases of loss-of-control where vehicles move in 

a direction different from the driver’s intended direction”. 

RRR The Consumer Reports double lane change maneuver is referred to as “Emergency Handling” within 

Consumer Reports vehicle assessments. The maneuver consists of a set of traffic cones arranged in such a 

pattern as to force the vehicle into a left lane, and then a return to the right lane over a controlled distance. The 

double lane change speed is based on the driver’s entrance speed at the beginning of the course. The highest 

entrance speed with which the driver is able to negotiate the course without disturbing the cones is deemed the 

“Emergency Handling” metric. 

SSS The data used for Figure 7.6.2 is available in the memorandum “Vehicle Double Lane Change Data Provided 

by Consumer Reports”, which has been placed in the EPA docket for this final rule. 
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Vehicles equipped with ESC: 

y = -0.0026x + 62.114

R

2

 = 0.5282

Vehicles without ESC

y = -0.0025x + 59.921

R

2

 = 0.4085

40

45

50

55

60

65

1000 2000 3000 4000 5000 6000 7000

Vehicle Curb Weight (lbs)

Double Lane Change Speed (MPH)

Non-ESC

ESC Vehicles

Linear (ESC Vehicles)

Linear (Non-ESC)

Figure 7-29 Double Lane Change Speed vs. Vehicle Curb Weight 

The International Council on Clean Transportation (ICCT) endorsed the DRI 

comments (described in the previous section) in their comments to the proposal. ICCT went a 

step further and applied the DRI results to calculate a potential benefit from reducing curb 

weight while maintaining footprint. Accounting for the downward trend in annual vehicle 

fatalities, and the actual weight reduction anticipated by agencies’ modeling results, ICCT 

determined that there would be a reduction of 599 fatalities and 354 fatalities for a 100 lb 

weight reduction in cars and trucks respectively. 

In contrast, there were comments received from the American Iron and Steel Institute 

(AISI) and the Competitive Enterprise Institute (CEI) that contended EPA’s conclusions. 

Both AISI and CEI noted in their comments that historically heavier vehicles have 

demonstrated a safety advantage over lighter vehicles. AISI presented the results from 

Desapriya that “sedans are two times more likely to be injured than drivers or passengers in 

larger pickup trucks and SUV’s”, and CEI endorsed the results from the 2003 Kahane study in 

support of their position. 

AISI also noted that: “AISI has shown in its research with the Auto/Steel Partnership 

and in programs supported by the U.S. Department of Energy, the use of new (Advanced High 

Stength Steel) AHSS steel grades can enable mass of critical crash structures, such as front 

rails and bumper systems, to be reduced by 25 percent. Such vehicle structures with reduced 

mass can perform as well as their heavier counterparts in standard NHTSA frontal or IIHS 

offset instrumented crash tests.” This is exactly the type of material substitution that EPA 
 Environmental and Health Impacts 

7-145 

noted in the NPRM. In this case a vehicle can be made lighter without a concomitant 

decrease in crush and crumple zones. 

CEI noted the historical studies that analyzed the relationships between CAFE 

standards and vehicle fatalities, specifically, R.W. Crandall and J.D. Graham, authors of “The 

Effect of Fuel Economy Standards on Automobile Safety” and the 2003 Kahane report. 

According to CEI, the Crandall and Graham analysis reported that CAFE regulations had a 

downsizing effect of 500 pounds per car in 1989. The applicability of the 1989 analyses is 

questionable in the context of this rule and its associated effectiveness from MY 2012 through 

2016. Vehicle design capability, including computer aided engineering (CAE) has changed 

substantially in the 20 years since this report was completed. Increased stringency of FMVSS 

standards combined with the improved strength of materials available and used in automobile 

manufacturing has allowed auto manufacturers to improve impact performance without a 

commensurate increase in vehicle weight. In addition, the 500 pound reduction analyzed by 

Crandall and Graham is substantially greater than the estimated weight reduction associated 

with this final rule. Regarding the 2003 Kahane report, NHTSA has recognized some of the 

limitations of the 2003 analysis in the context of this rulemaking and presents a summary and 

review of the 2010 Kahane report in the preamble and within NHTSA’s FRIA. 

CEI further comments that “new technologies and attribute-based regulation will not 

eliminate the safety tradeoff”. CEI states that “some proponents of higher CAFE standards, 

and of CO2 emission limits, claim that new technologies can eliminate these lethal effects. 

This claim is simply false, even if such technologies do not themselves involve downsizing. 

Consider a hi-tech prototype car capable of meeting either a higher CAFE standard, or a 

stringent CO2 emissions standard. Imagine that you then increase this car’s size and weight 

by adding several cubic feet of trunk space and occupant space. The result would be an even 

safer car.” EPA recognizes that more recent vehicles have more safety features than 1990s 

vehicles, which are likely to make them safer overall. To account for this, NHTSA did adjust 

the results of both its NPRM and final rule analysis to include known safety improvements, 

like ESC and increases in seat belt use, that have occurred since MYs 1991-1999.TTT

 

However, simply because newer vehicles have more safety countermeasures, does not mean 

that the weight/safety relationship necessarily changes. More likely, it would change the 

target population (the number of fatalities) to which one would apply the weight/safety 

relationship. Thus, EPA acknowledges that while mass reduction can be done in a safety 

neutral manner, some mass reduction techniques for both passenger cars and light trucks can 

make them less safe in certain crashes as discussed in NHTSA’s FRIA.UUU

 

TTT See Chapter IX of the NHTSA FRIA for details on this adjustment. 

UUU If one has a vehicle (vehicle A), and both reduces the vehicle’s mass and adds new safety equipment to it, 

thus creating a variant (vehicle A1), the variant might conceivably have a level of overall safety for its occupants 

is equal to that of the original vehicle (vehicle A). However, vehicle A1 might not be as safe as second variant 

(vehicle A2) of vehicle A, one that is produced by adding to vehicle A the same new safety equipment added to 

the first variant, but this time without any mass reduction. 
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Additional comments received from the AISI challenge the agencies’ conclusions with 

regard to vehicle size. While they agree that “the historical relationship between vehicle size, 

weight, and collision severity may be influenced by design and structural improvements”, 

they assert that “the aggressive schedule for implementing the proposed rule assures that 

carmakers will be manufacturing smaller, lighter vehicles in order to comply.” Porsche and 

IIHS had similar comments implying that footprint standards will increase the risk that 

manufacturers will make vehicles smaller. In response, EPA does not feel that the schedule is 

overly aggressive. The standards and their phase-in period is feasible and economically 

practical as described in section III.D of the preamble. That same discussion, as well as the 

supporting analysis in the joint TSD, demonstrates a clear compliance path to meeting the 

standards based on wider penetration of existing technologies, at reasonable and affordable 

cost. In addition, the attribute-based approach, along with the shape of footprint curve has 

been developed with the objective of minimizing the incentive to downsize (since, among 

other things, downsizing simply creates a more stringent regulatory target corresponding to 

the downsized footprint) nor to upsize (as that could incur significant redesign costs and also 

would likely increase the mass of the vehicle which could off-set any benefit in upsizing 

from a stringency perspective as a higher mass vehicle will produce more CO2 emissions). 

EPA consequently does not accept the commenter’s assertion. However, as discussed in 

Sections III.H.1 and IV.G.6 of the preamble, the agencies acknowledge some uncertainty 

regarding how consumer purchases will change in response to the vehicles designed to meet 

the MYs 2012-2016 standards. This could potentially affect the mix of vehicles sold in the 

future, including the mass and footprint distribution. 

7.6.3 NHTSA’s 2010 Study of Accident Fatalities by Vehicle Size and Weight 

In response to comments received and in an effort to follow through on some of its 

NRPM pledges for consideration in the future, NHTSA has significantly revised its 2003 

study on the relationship between vehicle mass and fatalities in the context of this 2012-2016 

rulemaking. A copy of this new report, “Relationships Between Fatality Risk, Mass, and 

Footprint in Model Year 1991-1999 and Other Passenger Cars and LTVs”, Charles J. Kahane, 

NCSA, NHTSA, March 2010, has been placed in the docket for this rulemaking, hereafter 

referred to as the 2010 Kahane report.VVV In the new 2010 Kahane report, based on the 

original MY 1991-1999 vehicle data set, NHTSA addresses several criticisms, specifically 

including 2-door vehicles in its passenger car analysis, attempting to separate weight and 

footprint as independent variables and their associated contributions to vehicle fatalities, and 

comparing their results to those from DRI. We note that this analysis looks specifically at 

impacts on fatalities. EPA and NHTSA have not analyzed the impact of mass reduction 

predicted from this final rule on non-fatal accidents. We also note that the results of the 2010 

Kahane analysis, as applied to the mass reductions predicted from this rulemaking, must be 

viewed in the overall context of our projection of the 2012-2016 new vehicle fleet distribution 

(See Chapter 1 of the Joint TSD for a discussion of how EPA and NHTSA have developed the 

 

VVV NHTSA intends for this 2010 Kahane report to undergo a peer review in accordance with OMB guidance 

for peer review, and the results of this peer review and any subsequent revisions to the report will be made 

available to the public upon completion.
 Environmental and Health Impacts 

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projected future new vehicle fleet). EPA acknowledges some uncertainty regarding how 

consumer purchases may change in response to the vehicles designed to meet the MYs 2012-

2016 standards. This could potentially affect the mix of vehicles sold in this time frame, 

including the mass and footprint distribution – which would impact the projection of fatalities 

from the 2010 Kahane analysis as applied to this final rule. 

The 2010 Kahane report presents three sets of results each with an estimate of 

fatalities based on the weight reductions projected in the feasibility analysis for the final rule. 

Each of these results presents a significant departure from the NPRM. The first set of results 

is a straight regression of result for passenger cars and LTVs. The passenger car data now 

includes 2-door vehicles, although a decision was made to continue to exclude muscle cars. 

The second and third sets of results, termed “upper bound scenario” and “lower bound 

scenario” respectively, are the result of expert opinion and judgment by NHTSA as to how 

mass may be reduced and the potential effects, both primary for the driver and occupants of a 

vehicle, and secondary or societal effects. NHTSA was able to perform these analyses with 

both mass and footprint treated as independent variables in the 2010 study. The unmodified 

straight regression results now reflect an expected reduction of 301 fatalities for the life of 

MY 2012-2016 vehicles, down from the absolute worst-case 493 in the CAFE NPRM. The 

new “upper bound scenario” estimates that there will be 22 additional fatalities as the result of 

the CAFE rule, and the “lower bound scenario” an 80 fatality decreaseWWW. These results are 

consistent with EPA’s NPRM claim that fatalities as a result of the rule could be close to zero. 

However, NHTSA states in the new 2010 Kahane analysis that the potential fatality increases 

associated with mass reduction in the passenger cars would be to a large extent offset by the 

benefits of mass reduction in the heavier LTVs. As was stated in the NPRM, EPA continues 

to believe that weight can be reduced from passenger cars safely through smart design, and 

other methods which can be used in the model years 2012 to 2016 time frame. This is based 

on a number of studies in the literature including those from DRI, Wenzel, Ross and 

Robertson referenced above and elsewhere in this final rule. 

Furthermore, in an effort to address public comments that promoted the DRI results as 

a legitimate alternative to NHTSA’s analysis, the 2010 Kahane report presents several 

conclusions. NHTSA first focused on the issue of “near multicollinearity” of the data. This 

statistical characteristic can result in increased uncertainty of regression coefficients, which 

according to sources cited by NHTSA, can result in the “wrong sign or implausible 

magnitude”. There are statistical tests that can be run on data used in regression models to 

determine the level of multicollinearity between independent variables. NHTSA performed 

tests on their data set and determined that mass and footprint did exhibit “near 

multicollinearity”. Subsequently, NHTSA applied a 2-step regression, in accordance with 

DRI’s methodology, to its own data and the results showed a corresponding decrease in 

fatalities for a 100 pound mass reduction. NHTSA did not accept these results for many 

 

WWW The “upper-estimate scenario” and “lower-estimate scenario” are based on NHTSA’s judgment as a vehicle 

safety agency, and are not meant to convey a sense of confidence in the precision of the results, but more to 

convey a sense of bounding for potential safety effects. 
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reasons, as outlined in the 2010 Kahane report, however their underlying conclusion was that 

the 2-step regression applied by DRI exacerbated the effects of “near multicollinearity”. 

The EPA has found one additional peer-reviewed study of historical FARS data. 

Robertson 2006371 analyzed somewhat newer model year passenger cars and light-trucks 

(1999-2002) and used a logistic regression approach similar to the 2003 Kahane and 2005 

DRI studies, including consideration of driver gender and age. We note that this peer 

reviewed paper was published as a commentary in response to previous work published by the 

author, and this commentary does include new original work. The reference list contained in 

this 2006 Robertson paper includes the references to the previous work by the author The 

study found multicollinearity to be a problem for regressions, including vehicle curb-weight 

and wheelbase, but found turning radius could be substituted as an indicator of vehicle size 

without introducing significant multicollinearity into the regression analysis. Robertson’s 

analysis tested the hypothesis of reducing vehicle curb-weight to the minimum achievable by 

the 1999-2002 model year population vs. vehicle size. The regression results showed a 

societal benefit of a 28% reduction in fatalities for minimization of mass. Mass minimization 

vs a vehicle size metric is not the same as removing a fixed percent or fixed quantity of 

vehicle mass and thus the results are not directly comparable to either the 2010 Kahane or 

2005 DRI results, but they are directionally consistent with both studies. The analysis showed 

similar trends to the 2010 Kahane report with increased risk of fatality to drivers of lighter 

vehicles, which was more than offset by the reduction in risk of fatality to other drivers, 

similar to Kahane’s 2010 results with respect to light trucks and to DRI’s results for both 

light-trucks and passenger cars. 

The EPA believe that while NHTSA’s new 2010 analysis significantly adds to the 

literature and understanding of the effects of mass reduction on safety, that there still are 

many opportunities for further study. 

7.6.4 Suggested Next Steps To Increase Our Understanding of the Effects of 

Vehicle Size and Weight on Fatalities 

NHTSA and EPA believe that it is important for the agencies to conduct further study 

and research into the interaction of mass, size and safety to assist future rulemakings. The 

agencies intend to begin working collaboratively and to explore with DOE, CARB, and 

perhaps other stakeholders an interagency/ intergovernmental working group to evaluate all 

aspects of mass, size and safety. It would also be the goal of this team to coordinate 

government supported studies and independent research, to the extent possible, to help ensure 

the work is complementary to previous and ongoing research and to guide further research in 

this area. DOE’s EERE office has long funded extensive research into component advanced 

vehicle materials and vehicle mass reduction. Other agencies may have additional expertise 

that will be helpful in establishing a coordinated work plan. The agencies are interested in 

looking at the weight-safety relationship in a more holistic (complete vehicle) way, and 

thanks to this CAFE rulemaking NHTSA has begun to bring together parts of the agency— 

crashworthiness, and crash avoidance rulemaking offices and the agency’s Research & 

Development office—in an interdisciplinary way to better leverage the expertise of the 

agency. Extending this effort to other agencies will help to ensure that all aspects of the 

weight-safety relationship are considered completely and carefully with our future research. 
 Environmental and Health Impacts 

7-149 

The agencies also intend to carefully consider comments received in response to the NPRM in 

developing plans for future studies and research and to solicit input from stakeholders. 

The agencies also plan to watch for safety effects as the U.S. light-duty vehicle fleet 

evolves in response both to the CAFE/GHG standards and to consumer preferences over the 

next several years. Additionally, as new and advanced materials and component smart 

designs are developed and commercialized, and as manufacturers implement them in more 

vehicles, it will be useful for the agencies to learn more about them and to try to track these 

vehicles in the fleet to understand the relationship between vehicle design and injury/fatality 

data. Specifically, the agencies intend to follow up with study and research of the following: 

First, NHTSA is in the process of contracting with an independent institution to 

review the statistical methods that NHTSA and DRI have used to analyze historical data 

related to mass, size and safety, and to provide recommendation on whether the existing 

methods or other methods should be used for future statistical analysis of historical data. This 

study will include an consideration of potential multicollinearity in the historical data and how 

best to address it in a regression analysis. This study is being initiated because, in response to 

the NPRM, NHTSA received a number of comments related to the methodology NHTSA 

used for the NPRM to determine the relationship between mass and safety, as discussed in 

detail above. 

Second, NHTSA and EPA, in consultation with DOE, intend to begin updating the 

MYs 1991-1999 database on which the safety analyses in the NPRM and final rule are based 

with newer vehicle data in the next several months. This task will take at least a year to 

complete. This study is being initiated in response to the NPRM comments related to the use 

of data from MYs 1991-1999 in the NHTSA analysis, as discussed in the section II.G of the 

preamble. 

Third, in order to assess if the design of recent model year vehicles that incorporate 

various mass reduction methods affect the relationships among vehicle mass, size and safety, 

NHTSA and EPA intend to conduct collaborative statistical analysis, beginning in the next 

several months. The agencies intend to work with DOE to identify vehicles that are using 

material substitution and smart design. After these vehicles are identified, the agencies intend 

to assess if there are sufficient data for statistical analysis. If there are sufficient data, 

statistical analysis would be conducted to compare the relationship among mass, size and 

safety of these smart design vehicles to vehicles of similar size and mass with more traditional 

designs. This study is being initiated because, in response to the NPRM, NHTSA received 

comments related to the use of data from MYs 1991-1999 in the NHTSA analysis that did not 

include new designs that might change the relationship among mass, size and safety, as 

discussed in detail above. 

NHTSA may initiate a two-year study of the safety of the fleet through an analysis of 

the trends in structural stiffness and whether any trends identified impact occupant injury 

response in crashes. Vehicle manufacturers may employ stiffer light weight materials to limit 

occupant compartment intrusion while controlling for mass that may expose the occupants to 

higher accelerations resulting in a greater chance of injury in real-world crashes. This study 
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7-150 

would provide information that would increase the understanding of the effects on safety of 

newer vehicle designs. 

In addition, EPA and NHTSA, possibly in collaboration with DOE, may conduct a 

longer-term computer modeling-based design and analysis study to help determine the 

maximum potential for mass reduction in the MYs 2017-2021 timeframe, through direct 

material substitution and smart design while meeting safety regulations and guidelines, and 

maintaining vehicle size and functionality. This study may build upon prior research 

completed on vehicle mass reduction. This study would further explore the comprehensive 

vehicle effects, including dissimilar material joining technologies, manufacturer feasibility of 

both supplier and OEM, tooling costs, and crash simulation and perhaps eventual crash 

testing. 
 Environmental and Health Impacts 

7-151 

References 

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0101.

17 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

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from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Docket EPA-HQOAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-

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18 Devlin, R. B., McDonnell, W. F., Mann, R., Becker, S., House, D. E., Schreinemachers, D., 

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20 Koren, H. S., Devlin, R. B., Graham, D. E., Mann, R., McGee, M. P., Horstman, D. H., 

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21 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-HQOAR-2009-0472-0324. 

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EPA600-P-93-004aF. Washington. D.C.: U.S. EPA. Retrieved on March 19, 2009 from EPAHQ-OAR-2005-0161. p. 7-171. Docket EPA-HQ-OAR-2009-0472-0099, EPA-HQ-OAR2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101.
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23 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 

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24 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. 

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25 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-HQOAR-2009-0472-0361. 

26 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/R07-003. Washington, DC, U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQOAR-2003-0190 at http://www.regulations.gov/. Docket EPA-HQ-OAR-2009-0472-0106. 

27 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 

from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Docket EPA-HQOAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-

0101.

28 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 

from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Docket EPA-HQOAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-

0101.

29 Avol, E.L., Trim, S. C., Little, D.E., Spier, C.E., Smith, M. N., Peng, R.-C., Linn, W.S., 

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exposure and lung function in children attending a southern California summer camp. Paper 

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30 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., 

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31 Raizenne, M.E., Burnett, R.T., Stern, B., Franklin, C.A., Spengler, J.D. (1989) Acute lung 

function responses to ambient acid aerosol exposures in children. Environ. Health Perspect., 

79,179-185. Docket EPA-HQ-OAR-2009-0472-0363. 

32 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 

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33 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 
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Angeles, CA. Pittsburgh, PA: Air & Waste Management Association; pp. 83-89. (A&WMA 

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34 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-2009-0472-0380. 

35 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-2009-0472-

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36 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 

from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Docket EPA-HQOAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-

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37 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. 

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38 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 

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39 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-2009-0472-0384. 

40 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-HQOAR-2009-0472-0335. 

41 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-HQOAR-2009-0472-0350. 

42 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. 

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11322. 
 Environmental and Health Impacts 

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44 U.S. EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources. 72 FR 8428; 

February 26, 2007. Docket EPA-HQ-OAR-2009-0472-0271. 

45 U.S. EPA (2003) Integrated Risk Information System File of Acrolein. National Center for 

Environmental Assessment, Office of Research and Development, Washington, D.C. 2003. 

This material is available electronically at http://www.epa.gov/iris/subst/0364.htm. Docket 

EPA-HQ-OAR-2009-0472-0391. 

46 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. This material is 

available electronically at http://www.epa.gov/ttn/atw/nata2002/risksum.html. Docket EPAHQ-OAR-2009-0472-11322. 

47 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. 

http://www.epa.gov/ttn/atw/nata2002. Docket EPA-HQ-OAR-2009-0472-11321. 

48 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-OAR2009-0472-1659. 

49 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, p. 345-389, 1982. Docket EPA-HQ-OAR-2009-0472-0366. 

50 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-2009-0472-0370. 

51 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. EPA-HQ-OAR-2009-

0472-0367. Docket EPA-HQ-OAR-2009-0472-0367. 

52 U.S. Department of Health and Human Services National Toxicology Program 11th Report 

on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 

53 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health 

Perspect. 82: 193-197. EPA-HQ-OAR-2009-0472-0368 

54 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art 

Reviews. 3: 541-554. Docket EPA-HQ-OAR-2009-0472-0325. 

55 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, 

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Hayes (1996) Hematotoxicity among Chinese workers heavily exposed to benzene. Am. J. 

Ind. Med. 29: 236-246. Docket EPA-HQ-OAR-2009-0472-0326. 

56 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-2009-0472-

0327. 
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57 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-2009-0472-0328.

58 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes 

among Chinese workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 

275-285. Docket EPA-HQ-OAR-2009-0472-0329. 

59 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-2009-

0472-0330. 

60 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-2009-0472-0385. 

61 U.S. EPA. 2002. Health Assessment of 1,3-Butadiene. Office of Research and 

Development, National Center for Environmental Assessment, Washington Office, 

Washington, DC. Report No. EPA600-P-98-001F. This document is available electronically 

at http://www.epa.gov/iris/supdocs/buta-sup.pdf. Docket EPA-HQ-OAR-2009-0472-0386. 

62 U.S. EPA. 2002 “Full IRIS Summary for 1,3-butadiene (CASRN 106-99-0)” Environmental 

Protection Agency, Integrated Risk Information System (IRIS), Research and Development, 

National Center for Environmental Assessment, Washington, DC 

http://www.epa.gov/iris/subst/0139.htm. Docket EPA-HQ-OAR-2009-0472-1660. 

63 International Agency for Research on Cancer (IARC) (1999) Monographs on the evaluation 

of carcinogenic risk of chemicals to humans, Volume 71, Re-evaluation of some organic 

chemicals, hydrazine and hydrogen peroxide and Volume 97 (in preparation), World Health 

Organization, Lyon, France. Docket EPA-HQ-OAR-2009-0472-0387. 

64 U.S. Department of Health and Human Services National Toxicology Program 11th Report 

on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 

65 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-2009-0472-0388. 

66 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-OAR-2009-0472-0389. 

67 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-2009-0472-0336. 

68 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from 

solid cancers among workers in formaldehyde industries. American Journal of Epidemiology 

159: 1117-1130. Docket EPA-HQ-OAR-2009-0472-0337. 
 Environmental and Health Impacts 

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69 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-2009-0472-0338. 

70 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-2009-

0472-0339. 

71 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-2009-0472-0340. 

72 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-2009-0472-0341. 

73 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 biologically-motivated computational modeling of a combined 

rodent and human dataset. Tox Sci 82: 279-296. Docket EPA-HQ-OAR-2009-0472-0342. 

74 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard 

characterization and dose-response assessment for carcinogenicity by the route of inhalation. 

CIIT, September 28, 1999. Research Triangle Park, NC. Docket EPA-HQ-OAR-2009-0472-

0429. 

75 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-2009-0472-0369. 

76 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-HQ-OAR-2009-0472-0371. 

77 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage 

model for formaldehyde-induced nasal cancer in the F344 rat: a limited sensitivity analysis-I. 

Risk Anal 27:1237. Docket EPA-HQ-OAR-2009-0472-0444. 

78 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-2009-0472-0447. 

79 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-2009-0472-0447. 

80 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage 

model for formaldehyde-induced nasal cancer in the F344 rat: a limited sensitivity analysis-I. 

Risk Anal 27:1237. Docket EPA-HQ-OAR-2009-0472-0444. 
Regulatory Impact Analysis 

7-158 

81 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 

1-tert-Butoxypropan-2-ol. Monographs Volume 88. World Health Organization, Lyon, 

France. Docket EPA-HQ-OAR-2009-0472-1164. 

82 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-OAR2009-0472-1191. 

83 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-2009-0472-1199. 

84 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 EPAHQ-OAR-2009-0472-0390. 

85 U.S. Department of Health and Human Services National Toxicology Program 11th Report 

on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 

86 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. 

87 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-OAR2009-0472-0390. 

88 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 EPAHQ-OAR-2009-0472-0391. 

89 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 EPAHQ-OAR-2009-0472-0392. 

90 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized 

acetaldehyde induces histamine-mediated bronchoconstriction in asthmatics. Am. Rev. 

Respir.Dis.148(4 Pt 1): 940-943. Docket EPA-HQ-OAR-2009-0472-0408. 

91 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-2009-0472-0391. 

92 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-2009-0472-0393. 
 Environmental and Health Impacts 

7-159 

93 Sim VM, Pattle RE. Effect of possible smog irritants on human subjects JAMA165: 1980-

2010, 1957. Docket EPA-HQ-OAR-2009-0472-0395. 

94 U.S. EPA (U.S. Environmental Protection Agency). (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. 

95 Weber-Tschopp, A; Fischer, T; Gierer, R; et al. (1977) Experimentelle reizwirkungen von 

Acrolein auf den Menschen. Int Arch Occup Environ Hlth 40(2):117-130. In German. Docket 

EPA-HQ-OAR-2009-0472-0394. 

96 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-2009-0472-0391. 

97 U.S. EPA (U.S. Environmental Protection Agency). (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. 

98 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-2009-0472-0396. 

99 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. Docket EPA-HQ-OAR-2009-0472-0372. 

100 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. Docket EPA-HQ-OAR-2009-

0472-0373. 

101 U. S. EPA. 2004. 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-2009-0472-0272. 

102 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-2009-

0472-0273. 

103 National Toxicology Program (NTP). (2004). 11th Report on Carcinogens. Public Health 

Service, U.S. Department of Health and Human Services, Research Triangle Park, NC. 

Available from: http://ntp-server.niehs.nih.gov. 
Regulatory Impact Analysis 

7-160 

104 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-2009-0472-0274. 

105 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 

106 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-2009-0472-11375. 

107 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-OAR-2009-0472-11382. 

108 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-2009-0472-11381. 

109 Holguin, F. (2008) Traffic, outdoor air pollution, and asthma. Immunol Allergy Clinics 

North Am 28: 577-588. 

110 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-2009-0472-11376. 

111 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-2009-

0472-11380. 

112 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] 

113 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-2009-0472-11379. 

114 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-2009-0472-11373. 

115 Forkenbrock, D.J. and L.A. Schweitzer, Environmental Justice and Transportation 

Investment Policy. Iowa City: Univerity of Iowa, 1997. 

116 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-2009-0472-11378. 
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117 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 EPAHQ-OAR-2009-0472-11377. 

118 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-HQOAR-2009-0472-11383. 

119 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-2009-

0472-11374. 

120 U.S. EPA (2009). final PM ISA, pg 9-19 through 9-23

121 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-HQOAR-2009-0472-0343. 

122 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101.

123 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-HQOAR-2009-0472-0344. 

124 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101.

125 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-2009-0472-0345. 

126 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

127 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

128 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

129 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 
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130 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-OAR-2009-0472-0346. 

131 Winner, W.E. (1994). Mechanistic analysis of plant responses to air pollution. Ecological 

Applications, 4(4), 651-661. Docket EPA-HQ-OAR-2009-0472-0347. 

132 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

133 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

134 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. 

135 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-2009-0472-0348. 

136 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-2009-0472-0349. 

137 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

138 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

139 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-HQOAR-2009-0472-0374. 

140 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-2009-

0472-0375. 

141 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

142 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-2009-0472-0376. 

143 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-2009-0472-0397. 
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144 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-HQ-OAR-2009-0472-0398.

145 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-2009-0472-0399. 

146 U.S. EPA. 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-2009-

0472-0099, EPA-HQ-OAR-2009-0472-0100, and EPA-HQ-OAR-2009-0472-0101.

147 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-2009-0472-0400.

148 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-2009-0472-1200. 

149 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-OAR2009-0472-1201. 

150 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-2009-0472-1200. 

151 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-OAR2009-0472-1201. 

152 Coulston, J.W., Riitters, K.H., Smith, G.C. (2004). A preliminary assessment of the 

Montréal process indicators of air pollution for the United States. Environmental Monitoring 

and Assessment, 95, 57-74. Docket EPA-HQ-OAR-2009-0472-1202. 

153 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants.

EPA/600/R-05/004aF-cF. Docket EPA-HQ-OAR-2009-0472-0099, EPA-HQ-OAR-2009-

0472-0100, and EPA-HQ-OAR-2009-0472-0101. 

154 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-OAR2009-0472-1201. 

155 U.S. EPA (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants. 

EPA/600/R-05/004aF-cF. Docket EPA-HQ-OAR-2009-0472-0099, EPA-HQ-OAR-2009-

0472-0100, and EPA-HQ-OAR-2009-0472-0101. 
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7-164 

156 US EPA. (2007) Review of the National Ambient Air Quality Standards for Ozone: Policy 

assessment of scientific and technical information. Office of Air Quality Planning and 

Standards staff paper. EPA-452/R-07-003. Docket EPA-HQ-OAR-2009-0472-0106. 

157 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-OAR2009-0472-1203 

158 U.S. EPA (2009) Final PM ISA

159 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate 

Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. 

EPA-452/R-05-005. Docket EPA-HQ-OAR-2009-0472-0098.

160 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological Criteria (Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. Docket EPAHQ-OAR-2009-0472-0014.

161 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-2009-0472-0095.

162 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 EPAHQ-OAR-2009-0472-0112.

163 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of 

change. Ambio 31: 64–71. Docket EPA-HQ-OAR-2009-0472-0377.

164 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-2009-0472-0378 and 

EPA-HQ-OAR-2009-0472-0378.1.

165 Smith, W.H. 1991. “Air pollution and Forest Damage.” Chemical Engineering News, 

69(45): 30-43. Docket EPA-HQ-OAR-2009-0472-0275.

166 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-2009-0472-0276.

167 Cotrufo, M.F.; DeSanto, A.V.; Alfani, A.; et al. 1995. “Effects of urban heavy metal 

pollution on organic matter decomposition in Quercus ilix L. woods.” Environmental 

Pollution, 89: 81-87. Docket EPA-HQ-OAR-2009-0472-1205.

168 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-2009-0472-1206.

169 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-2009-0472-11295
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170 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-HQ-OAR-2009-0472-1207.

171 Landis, M.S. and Keeler, G.J. 2002. “Atmospheric mercury deposition to Lake Michigan 

during the Lake Michigan Mass Balance Study.” Environmental Science & Technology, 21: 

4518-24. Docket EPA-HQ-OAR-2009-0472-1208.

172 U.S. EPA. 2000. EPA453/R-00-005, “Deposition of Air Pollutants to the Great Waters: 

Third Report to Congress,” Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. Docket EPA-HQ-OAR-2009-0472-0091.

173 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-2009-

0472-1209.

174 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-2009-0472-

1895.

175 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-2009-0472-1896.

176 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-OAR-2009-0472-1897.

177 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-2009-0472-1908.

178 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-2009-0472-1908.

179 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-2009-0472-

1909.

180 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-2009-0472-1910.

181 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-2009-0472-0256.

182 Park, J.S.; Wade, T.L.; and Sweet, S. 2001. “Atmospheric distribution of polycyclic 

aromatic hydrocarbons and deposition to Galveston Bay, Texas, USA.” Atmospheric 

Environment, 35: 3241-3249. Docket EPA-HQ-OAR-2009-0472-0352.
Regulatory Impact Analysis 

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183 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-2009-0472-0354.

184 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-2009-0472-0256.

185 U.S. EPA. 2000. EPA453/R-00-005, “Deposition of Air Pollutants to the Great Waters: 

Third Report to Congress,” Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. Docket EPA-HQ-OAR-2009-0472-0091.

186 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-OAR2009-0472-0355.

187 Cousins, I.T.; Beck, A.J.; and Jones, K.C. 1999. “A review of the processes involved in the 

exchange of semi-volatile organic compounds across the air-soil interface.” The Science of 

the Total Environment, 228: 5-24. Docket EPA-HQ-OAR-2009-0472-0359.

188 Tuhackova, J. et al. (2001) Hydrocarbon deposition and soil microflora as affected by 

highway traffic. Environmental Pollution, 113: 255-262. Docket EPA-HQ-OAR-2009-0472-

0356.

189 U.S. EPA. 1991. Effects of organic chemicals in the atmosphere on terrestrial plants. 

EPA/600/3-91/001. Docket EPA-HQ-OAR-2009-0472-0401.

190 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-2009-0472-0357.

191 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-2009-0472-0357.

192 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-2009-

0472-1128.

193 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-2009-

0472-1142.

194 Kammerbauer H, H Selinger, R Rommelt, A Ziegler-Jons, D Knoppik, B Hock. 1987. 

Toxic components of motor vehicle emissions for the spruce Pciea abies. Environ. Pollut. 

48:235-243. Docket EPA-HQ-OAR-2009-0472-0358.

195 Byun, D.W., Ching, J. K.S. (1999). Science algorithms of the EPA models-3 community 

multiscale air quality (CMAQ) modeling system. Washington, DC: U.S. Environmental 

Protection Agency, Office of Research and Development. Docket EPA-HQ-OAR-2009-0472-

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196 Byun, D.W., Schere, K.L. (2006). Review of the Governing Equations, Computational 

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System. Journal of Applied Mechanics Reviews, 59(2), 51-77. Docket 

EPA-HQ-OAR-2009-0472-1916 

197 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A. 

(1996). The next generation of integrated air quality modeling: EPA’s Models-3, Atmospheric 

Environment, 30, 1925-1938. Docket EPA-HQ-OAR-2009-0472-1917 

198 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004). 

Simulating regional-scale ozone climatology over the eastern United States: model evaluation 

results. Atmospheric Environment, 38(17), 2627-2638. Docket EPA-HQ-OAR-2009-0472-

11350 

199 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Longrange 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 

200 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 

201 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for 

Atmospheric Research, Boulder CO. Docket EPA-HQ-OAR-2009-0472-2104 

202 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for 

Atmospheric Research, Boulder CO. Docket EPA-HQ-OAR-2009-0472-2104 

203 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 

204 Le Sager, P. Yantosca, B., Carouge, C. (2008). GEOS-CHEM v8-01-02 User’s Guide, 

Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, December 

18, 2008. Docket EPA-HQ-OAR-2009-0472-11344 

205 U.S. EPA, (2004), Procedures for Estimating Future PM2.5 Values for the CAIR Final 

Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)- Updated 

11/8/04. Docket EPA-HQ-OAR-2009-0472-11327 

206 U.S. EPA, (2008), Control of Emissions from Nonroad Spark-Ignition Engines and 

Equipment, Technical Support Document. EPA 454/R-08-005. Docket EPA-HQ-OAR-2009-

0472-11330 

207 U.S. EPA (2007) Guidance on the Use of Models and Other Analyses For Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze; EPA-454/B-07-002; 

Research Triangle Park, NC; April 2007. Docket EPA-HQ-OAR-2009-0472-11328 
Regulatory Impact Analysis 

7-168 

208 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical 

Mechanism: CB05. Final Report to the US EPA, RT-0400675, December 8, 2005. 

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf. Docket EPA-HQ-OAR2009-0472-0111

209 Dodge, M.C., 2000. Chemical oxidant mechanisms for air quality modeling: critical 

review. Atmospheric Environment 34, 2103-2130. Docket EPA-HQ-OAR-2009-0472-11338

210 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

211 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

212 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

213 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

214 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, 

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical 

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. 

NASA Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html. Docket EPAHQ-OAR-2009-0472-11320

215 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, 

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical 

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. 

NASA Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html. Docket EPA-HQOAR-2009-0472-11320

216 Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon Bond 

Mechanism: CB05. Final Report to the US EPA, RT-0400675. Yocke and Company, Novato, 

CA. Docket EPA-HQ-OAR-2009-0472-0111
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217 Luecken, D.J., Phillips, S., Sarwar, G., Jang, C., 2008b. Effects of using the CB05 vs. 

SAPRC99 vs. CB4 chemical mechanism on model predictions: Ozone and gas-phase 

photochemical precursor concentrations. Atmospheric Environment 42, 5805-5820. Docket 

EPA-HQ-OAR-2009-0472-11343

218 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, 

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical 

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. 

NASA Jet Propulsion Laboratory. Docket EPA-HQ-OAR-2009-0472-11320

219 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, 

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical 

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. 

NASA Jet Propulsion Laboratory. Docket EPA-HQ-OAR-2009-0472-11320

220 Atkinson R, Arey J (2003) Atmospheric Degradation of Volatile Organic Compounds. 

Chem Rev 103: 4605-4638. Docket EPA-HQ-OAR-2009-0472-11334

221 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

222 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

223 Edney, E. O., T. E. Kleindienst, M. Lewandowski, and J. H. Offenberg, 2007. Updated 

SOA chemical mechanism for the Community Multi-Scale Air Quality model, EPA 600/X07/025, U.S. EPA, Research Triangle Park, NC. Docket EPA-HQ-OAR-2009-0472-11345

224 Carlton, A.G., B. J. Turpin, K. Altieri, S. Seitzinger, R. Mathur, S. Roselle, R. J. Weber, 

(2008), CMAQ model performance enhanced when in-cloud SOA is included: comparisons 

of OC predictions with measurements, Environ. Sci. Technol. 42 (23), 8798–8802. Docket 

EPA-HQ-OAR-2009-0472-11336

225 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ 

Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern 

United States, Environ Sci Technol 42(9):3303-3309. http://pubs.acs.org/cgibin/article.cgi/esthag/2008/42/i09/html/es0720412.html. Docket EPA-HQ-OAR-2009-0472-

11340

226 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of 

the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at 
Regulatory Impact Analysis 

7-170 

a southeastern U.S. location, Atmos Environ 41(37):8288-8300. Docket EPA-HQ-OAR2009-0472-11356

227 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) 

Contributions of Toluene and -pinene to SOA Formed in an Irradiated Toluene/-

pinene,NOx/Air Mixture: Comparison of Results Using 14C Content and SOA Organic Tracer 

Methods, Environ Sci Technol 41: 3972-3976. Docket EPA-HQ-OAR-2009-0472-11318 

228 Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H. (1992) Secondary organic aerosol 

formation and transport. Atmos Environ 26, 2269–2282. Docket EPA-HQ-OAR-2009-0472-

11319

229 Takekawa, H. Minoura, H. Yamazaki, S. (2003) Temperature dependence of secondary 

organic aerosol formation by photo-oxidation of hydrocarbons. Atmos Environ 37: 3413–

3424. Docket EPA-HQ-OAR-2009-0472-11358

230 Kleeman, M.J., Ying, Q., Lu, J., Mysliwiec, M.J., Griffin, R.J., Chen, J., Clegg, S. (2007) 

Source apportionment of secondary organic aerosol during a severe photochemical smog 

episode. Atmos Environ 41: 576–591. Docket EPA-HQ-OAR-2009-0472-11313

231 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; 

Grieshop, A. P.; Lane, T. E.; Pierce, J. R.; Pandis, S. N. (2007) Rethinking organic aerosol: 

Semivolatile emissions and photochemical aging. Science 315: 1259-1262. Docket EPA-HQOAR-2009-0472-11357

232 Byun DW, Schere, KL (2006) Review of the Governing Equations, Computational 

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System, J Applied Mechanics Reviews 59: 51-76. Docket EPA-HQOAR-2009-0472-1916 

233 U. S. EPA (2002) National Air Quality and Emissions Trends Report, 2001. EPA 454/K02-001, September 2002. http://www.epa.gov/air/airtrends/aqtrnd01/summary.pdf. Docket 

EPA-HQ-OAR-2009-0472-0092 

234 U. S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. 

http://www.epa.gov/ttn/atw/nata2002/risksum.html. Docket EPA-HQ-OAR-2009-0472-

11321

235 U. S. EPA (2007) Regulatory Impact Analysis for the Control of Hazardous Air Pollutants 

from Mobile Sources Rule, Chapter 3, Air Quality and Resulting Health and Welfare Effects 

of Air Pollution from Mobile Sources. 72 FR 8428, February 26, 2007. 

http://www.epa.gov/otaq/regs/toxics/420r07002.pdf. Docket EPA-HQ-OAR-2009-0472-0107

236 Kleindienst, T.E. (2008) Hypothetical SOA Production from Ethanol Photooxidation. 

Memo to the Docket EPA-HQ-OAR-2005-0161. Docket EPA-HQ-OAR-2009-0472-11314
 Environmental and Health Impacts 

7-171 

237 Turpin, B.J., Huntzicker, J.J., Larson, S.M., Cass, G.R. (1991) Los Angeles Summer 

Midday Particulate Carbon: Primary and Secondary Aerosol. Environ Sci Technol 25: 1788-

1793. Docket EPA-HQ-OAR-2009-0472-11359

238 Turpin, B.J., Huntzicker, J.J. (1995) Identification of Secondary Organic Aerosol Episodes 

and Quantitation of Primary and Secondary Organic Aerosol Concentrations During SCAQS. 

Atmos Environ 29(23): 3527-3544. Docket EPA-HQ-OAR-2009-0472-11360

239 Bae M-S, Schauer JJ, Turner JR (2006) Estimation of the Monthly Average Ratios of 

Organic Mass to Organic Carbon for Fine Particulate Matter at an Urban Site, Aerosol Sci 

Technol 40(12): 1123-1139. http://dx.doi.org/10.1080/02786820601004085. Docket EPAHQ-OAR-2009-0472-11335

240 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of 

the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at 

a southeastern U.S. location. Atmos Environ 41(37):8288-8300. Docket EPA-HQ-OAR2009-0472-11356

241 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) 

Contributions of Toluene and -pinene to SOA Formed in an Irradiated Toluene/-

pinene,NOx/Air Mixture: Comparison of Results Using 14C Content and SOA Organic Tracer 

Methods, Environ Sci Technol 41: 3972-3976. Docket EPA-HQ-OAR-2009-0472-11318 

242 Claeys M, R Szmigielski, I Kourtchev, P Van der Veken, R Vermeylen, W Maenhaut, M 

Jaoui, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2007) 

Hydroxydicarboxylic acids: Markers for secondary organic aerosol from the photooxidation 

of -pinene. Environ Sci Technol 41(5): 1628-1634. Docket EPA-HQ-OAR-2009-0472-

11337

243 Edney EO, TE Kleindienst, M Jaoui, M Lewandowski, JH Offenberg, W Wang, M Claeys 

(2005) Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol 

from laboratory irradiated isoprene/NOX/SO2/air mixtures and their detection in ambient 

PM2.5 samples collected in the Eastern United States. Atmos Environ 39: 5281-5289. Docket 

EPA-HQ-OAR-2009-0472-11339

244 Jaoui M, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2005) Identification 

and quantification of aerosol polar oxygenated compounds bearing carboxylic or hydroxyl 

groups. 2. Organic tracer compounds from monoterpenes. Environ Sci Technol 39: 5661-

5673. Docket EPA-HQ-OAR-2009-0472-11352

245 Kleindienst TE, TS Conver, CD McIver, EO Edney (2004) Determination of secondary 

organic aerosol products from the photooxidation of toluene and their implications in ambient 

PM2.5. J Atmos Chem 47: 70-100. Docket EPA-HQ-OAR-2009-0472-11315

246 Kleindienst TE, TS Conver, CD McIver, EO Edney (2004) Determination of secondary 

organic aerosol products from the photooxidation of toluene and their implication in ambient 

PM2.5, J Atmos Chem 47: 70-100. Docket EPA-HQ-OAR-2009-0472-11315
Regulatory Impact Analysis 

7-172 

247 Izumi L, T Fukuyama (1990) Photochemical aerosol formation from aromatic 

hydrocarbons in the presence of NOx, Atmos Environ 24A: 1433. Docket EPA-HQ-OAR2009-0472-11351

248 Martin-Reviego M, K Wirtz (2005) Is benzene a precursor for secondary organic aerosol? 

Environ Sci Technol 39: 1045-1054. Docket EPA-HQ-OAR-2009-0472-11343

249 Ng, N. L., Kroll, J. H., Chan, A. W. H., Chabra, P. S., Flagan, R. C., Seinfield, J. H., 

Secondary organic aerosol formation from m-xylene, toluene, and benzene, Atmospheric 

Chemistry and Physics Discussion, 7, 3909-3922, 2007. Docket EPA-HQ-OAR-2009-0472-

11316 

250 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ 

Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern 

United States, Environ Sci Technol 42(9):3303-3309. http://pubs.acs.org/cgibin/article.cgi/esthag/2008/42/i09/html/es0720412.html. Docket EPA-HQ-OAR-2009-0472-

11340

251 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of 

the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at 

a southeastern U.S. location. Atmos Environ 41(37):8288-8300. Docket EPA-HQ-OAR2009-0472-11356

252 Henze DK, JH Seinfeld (2006) Global secondary organic aerosol from isoprene oxidation. 

Geophys Res Lett 33: L09812. doi:10.1029/2006GL025976. Docket EPA-HQ-OAR-2009-

0472-11347

253 Henze DK.;J Seinfeld (2006) Global secondary organic aerosol from isoprene oxidation. 

Geophys. Res. Lett 33 L09812. doi:10.1029/2006GL025976. Docket EPA-HQ-OAR-2009-

0472-11347

254 Jaoui M, M Lewandowski, TE Kleindienst, JH Offenberg, EO Edney (2007) β -

Caryophyllinic acid: An atmospheric tracer for β-caryophyllene secondary organic aerosol. 

Geophys Res Lett 34: L05816. doi:10.1029/2006GL028827. Docket EPA-HQ-OAR-2009-

0472-1917 

255 Griffin RJ, DR Cocker III, RC Flagan, JH Seinfeld (1999) Organic aerosol formation from 

oxidation of biogenic hydrocarbons, J Geophys Res 104: 3555-3567. Docket EPA-HQ-OAR2009-0472-11346

256 Ng NL, JH Kroll, AWH Chan, PS Chabra, RC Flagan, JH Seinfeld (2007) Secondary 

organic aerosol formation from m-xylene, toluene, and benzene. Atmos Chem Phys 7: 3909-

3922. Docket EPA-HQ-OAR-2009-0472-11316 

257 Hildebrandt1, L., Donahue1, N. M, Pandis1, S. N. (2009) High formation of secondary 

organic aerosol from the photo-oxidation of toluene. Atmos Chem Phys 9: 2973-2986. 

Docket EPA-HQ-OAR-2009-0472-11349
 Environmental and Health Impacts 

7-173 

258 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., 

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical 

Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for 

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html. Docket EPA-HQ-OAR-2009-0472-11333

259 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, 

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical 

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. 

NASA Jet Propulsion Laboratory. http://jpldataeval.jpl.nasa.gov/index.html. Docket EPAHQ-OAR-2009-0472-11320

260 Finlayson-Pitts BJ, Pitts JN Jr. (1986) Atmospheric Chemistry: Fundamentals and 

Experimental Techniques, Wiley, New York.

261 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical 

Mechanism: CB05. Final Report to the US EPA, RT-0400675, December 8, 2005. 

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf. Docket EPA-HQ-OAR2009-0472-0111

262 http://www.cmascenter.org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt

263 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-HQOAR-2003-0062 at http://www.regulations.gov/.72 FR 20586.

264 PM Standards Revision – 2006: Timeline. Retrieved on March 19, 2009 from 

http://www.epa.gov/oar/particlepollution/naaqsrev2006.html#timeline

265 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA420-R-10-006. February 2010. Docket EPA-HQ-OAR-2009-0472-11332. see also 75 FR 

14670, March 26, 2010. 

266 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA420-R-10-006. February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-OAR-2009-

0472-11332

267 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in 

Docket Identification EPA-HQ-OAR-2004-0008-0455 to 0457. 

268 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from 

Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007. Docket EPA-HQ-OAR-2009-

0472-0271, 0271.1 and 0271.2
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7-174 

269 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from 

Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007. Docket EPA-HQ-OAR-2009-

0472-0271, 0271.1 and 0271.2

270 U. S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. 

http://www.epa.gov/ttn/atw/nata2002/. Docket EPA-HQ-OAR-2009-0472-11321

271 U. S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. 

http://www.epa.gov/ttn/atw/nata2002/risksum.html. Docket EPA-HQ-OAR-2009-0472-

11322

272 U.S. EPA. U.S. EPA's 2008 Report on the Environment (Final Report). U.S. 

Environmental Protection Agency, Washington, D.C., EPA/600/R-07/045F (NTIS PB2008-

112484). Docket EPA-HQ-OAR-2009-0472-11298. Updated data available online at: 

http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewInd&ch=46&subtop=341&lv=list.

listByChapter&r=201744 

273 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I 

Document No. EPA600/P-99/002aF and Volume II Document No. EPA600/P-99/002bF. 

Docket EPA-HQ-OAR-2009-0472-0096

274 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) 

for the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared 

by: Office of Air and Radiation. Retrieved March, 26, 2009 at 

http://www.epa.gov/ttn/ecas/ria.html. EPA-HQ-OAR-2009-0472-0240 

275 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact 

Analysis. Prepared by: Office of Air and Radiation, Office of Air Quality Planning and 

Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html. EPA-HQOAR-2009-0472-0238

276 U.S. Environmental Protection Agency (U.S. EPA). 2009. 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. April. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>. EPA-HQ-OAR2009-0472-0241

277 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. EPA-HQ-OAR-2009-0472-0237

278 U.S. Environmental Protection Agency. 2009. Regulatory Impact Analysis: Control of 

Emissions of Air Pollution from Category 3 Marine Diesel Engines. EPA-420-R-09-019, 

December 2009. Prepared by Office of Air and Radiation. 
 Environmental and Health Impacts 

7-175 

http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09019.pdf. Accessed February 9, 2010. 

EPA-HQ-OAR-2009-0472-0283

279 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. EPAHQ-OAR-2009-0472-0244

280 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between 

Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United 

States.” Environmental Health Perspectives 105(6):608-612. EPA-HQ-OAR-2009-0472-

0382

281 GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD-ROM 

Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ 

[accessed 29 September 2004]

282 Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM. 

Woods & Poole Economics, Inc. Washington, D.C. EPA-HQ-OAR-2009-0472-0011

283 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related 

photochemical oxidants (second external review draft). Research Triangle Park, NC: 

National Center for Environmental Assessment; report no. EPA/600R-05/004aB-cB, 3v. 

Available: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 2006] EPAHQ-OAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, EPA-HQ-OAR-2009-0472-

0101

284 U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Matter 

Volume II of II. National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

EPA/600/P-99/002bF. EPA-HQ-OAR-2009-0472-0097

285 World Health Organization (WHO). (2003). Health Aspects of Air Pollution with 

Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. World 

Health Organization. Bonn, Germany. EUR/03/5042688.

286 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Metaanalysis of time-series studies and panel studies of Particulate Matter (PM) and Ozone (O3): 

Report of a WHO task group. Copenhagen, Denmark: World Health Organization. 

287 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. EPA-HQ-OAR-2009-0472-1662

288 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.

EPA-HQ-OAR-2009-0472-0233

289 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. EPA-HQ-OAR-2009-

0472-1678
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290 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. EPA-HQ-OAR-2009-0472-0222

291 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. EPA-HQ-OAR-2009-

0472-0231

292 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an 

empiric bayes metaregression analysis. Epidemiology. 16(4): p. 458-68. EPA-HQ-OAR-2009-

0472-0236

293 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. 

Thurston. (2002). “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to 

Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. 

EPA-HQ-OAR-2009-0472-0263

294 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine 

Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care 

Medicine. 173: 667-672. EPA-HQ-OAR-2009-0472-1661

295 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. EPA-HQ-OAR2009-0472-0242

296 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Between 

Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United 

States. Environmental Health Perspectives. 105(6):608-612. EPA-HQ-OAR-2009-0472-0382 

297 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1995). 

Estimated Long-Term Ambient Concentrations of PM(10) and Development of Respiratory 

Symptoms in a Nonsmoking Population. Archives of Environmental Health. 50(2): 139-152. 

EPA-HQ-OAR-2009-0472-0432 

298 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. EPAHQ-OAR-2009-0472-0239 

299 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the 

elderly for respiratory disease. Thorax. 50(5):531-538.

300 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in 

Minneapolis St Paul, Minnesota. Arch Environ Health. 49(5):366-374. EPA-HQ-OAR-2009-

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301 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, 

Michigan. Am J Respir Crit Care Med. 150(3):648-655. EPA-HQ-OAR-2009-0472-1674

302 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital 

admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology. 

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303 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). 

Association between ozone and hospitalization for acute respiratory diseases in children less 

than 2 years of age. Am J Epidemiol. 153(5):444-452. EPA-HQ-OAR-2009-0472-0223

304 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. 

305 Ito, K. (2003). “Associations of Particulate Matter Components with Daily Mortality and 

Morbidity in Detroit, Michigan.” In Revised Analyses of Time-Series Studies of Air Pollution 

and Health. Special Report. Health Effects Institute, Boston, MA. EPA-HQ-OAR-2009-0472-

1674 

306 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the 

Circulatory System in Three U.S. Metropolitan Areas. Journal of the Air and Waste 

Management Association 50:1199-1206. EPA-HQ-OAR-2009-0472-1664

307 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions 

in Seattle, Washington, 1987-1994. In Revised Analyses of Time-Series Studies of Air 

Pollution and Health. Special Report. Boston, MA: Health Effects Institute. EPA-HQOAR-2009-0472-0318

308 Jaffe DH, Singer ME, Rimm AA. (2003). Air pollution and emergency department visits 

for asthma among Ohio Medicaid recipients, 1991-1996. Environ Res 91(1):21-28. EPA-HQOAR-2009-0472-0234

309 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory 

emergency department visits. Epidemiology. Vol. 16 (2): 164-74. EPA-HQ-OAR-2009-0472-

1663

310 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution, weather, and respiratory 

emergency room visits in two northern New England cities: an ecological time-series study. 

Environ Res. Vol. 97 (3): 312-21. EPA-HQ-OAR-2009-0472-0246

311 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. 

(1999). An Association between Fine Particles and Asthma Emergency Department Visits for 

Children in Seattle. Environmental Health Perspectives 107(6):489-493. EPA-HQ-OAR2009-0472-0318 

312 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, 

J.H. Ware, M. Raizenne, and F.E. Speizer. (1996). Health Effects of Acid Aerosols On North 

American Children-Respiratory Symptoms. Environmental Health Perspectives 104(5):500-

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313 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory 

Health and PM10 Pollution: A Daily Time Series Analysis. American Review of Respiratory 

Diseases 144:668-674. EPA-HQ-OAR-2009-0472-1672

314 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than 

Coarse Particles with Acute Respiratory Health Effects in Schoolchildren. Epidemiology

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315 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

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316 Vedal, S., J. Petkau, R. White, and J. Blair. (1998). Acute Effects of Ambient Inhalable 

Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and 

Critical Care Medicine 157(4):1034-1043. EPA-HQ-OAR-2009-0472-1671 

317 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal 

of Environmental Economics Management 14:87-98. EPA-HQ-OAR-2009-0472-1670 

318 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. 

(2001). The effects of ambient air pollution on school absenteeism due to respiratory 

illnesses. Epidemiology 12(1):43-54. EPA-HQ-OAR-2009-0472-1675

319 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and 

air pollution. Inhal Toxicol 12(11):997-1016. EPA-HQ-OAR-2009-0472-0224

320 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: 

An Observational Study of Multiple Pollutants. Environmental Research 50:238-247. EPAHQ-OAR-2009-0472-0364 

321 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. EPA-HQ-OAR-2009-0472-4664

322 National Research Council (NRC). (2002). Estimating the Public Health Benefits of 

Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 

323 Abt Associates, Inc. October 2005. Methodology for County-level Mortality Rate 

Projections. Memorandum to Bryan Hubbell and Zachary Pekar, U.S. EPA. 

324 U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National 

Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing 

Industry. Office of Air and Radiation. Retrieved on May 4, 2009, from 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf EPA-HQ-OAR2009-0472-0241

325 Viscusi, W.K., W.A. Magat, and J. Huber. (1991). Pricing Environmental Health Risks: 

Survey Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis. 

Journal of Environmental Economics and Management 21:32-51. 

326 Cropper, M.L., and A.J. Krupnick. (1990). The Social Costs of Chronic Heart and Lung 

Disease. Resources for the Future. Washington, DC. Discussion Paper QE 89-16-REV. 

327 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. EPA-HQ-OAR-2009-0472-1666 

328 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. EPA-HQOAR-2009-0472-1669 
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329 Agency for Healthcare Research and Quality (AHRQ). (2000). HCUPnet, Healthcare 

Cost and Utilization Project. Rockville, MD. Accessed April 10, 2009, from 

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330 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. EPA-HQ-OAR-2009-0472-

1667 

331 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. EPA-HQ-OAR-2009-0472-1668 

332 Industrial Economics, Incorporated (IEc). (1994). Memorandum to Jim DeMocker, 

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333 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. 

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09-86-018. Washington, DC. EPA-HQ-OAR-2009-0472-0243

334 Neumann, J.E., M.T. Dickie, and R.E. Unsworth. (1994). Linkage Between Health Effects 

Estimation and Morbidity Valuation in the Section 812 Analysis—Draft Valuation Document. 

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Review. March 31. 

335 Tolley, G.S. et al. January (1986). Valuation of Reductions in Human Health Symptoms 

and Risks. University of Chicago. Final Report for the U.S. Environmental Protection 

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336 Council of Economic Advisors. (2005). The Annual Report of the Council of Economic 

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Office: Washington, DC. 

337 National Research Council (NRC). (2002). Estimating the Public Health Benefits of 

Proposed Air Pollution Regulations. The National Academies Press: Washington, D.C. 

338 U.S. Environmental Protection Agency, (2004a). Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and 

Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf. EPA-HQ-OAR-2009-0472-0140

339 U.S. Environmental Protection Agency, (2005). Regulatory Impact Analysis for the Clean 

Air Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Retrieved on 

April 10, 2009, from http://www.epa.gov/interstateairquality/tsd0175.pdf. 

340 U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from 

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341 Industrial Economics, Inc. (2006). Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

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342 National Research Council (NRC), 2008. Estimating Mortality Risk Reduction and 

Economic Benefits from Controlling Ozone Air Pollution. The National Academies Press: 

Washington, D.C. EPA-HQ-OAR-2009-0472-0322

343 National Research Council (NRC). 2002. Estimating the Public Health Benefits of 

Proposed Air Pollution Regulations. The National Academies Press: Washington, D.C.

344 U.S. Environmental Protection Agency. October 2006. Final Regulatory Impact Analysis 

(RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter. 

Prepared by: Office of Air and Radiation. Available at http://www.epa.gov/ttn/ecas/ria.html. 

EPA-HQ-OAR-2009-0472-0240

345 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 

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Air Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. EPA-HQ-OAR-2009-

0472-0238

346 U.S. Environmental Protection Agency (U.S. EPA). 2009. 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. April. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>. EPA-HQ-OAR2009-0472-0241

347 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 

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15, 2010. EPA-HQ-OAR-2009-0472-0237

348 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>. 

349 Fann, N. et al. (2009). The influence of location, source, and emission type in estimates of 

the human health benefits of reducing a ton of air pollution. Air Qual Atmos Health. 

Published online: 09 June, 2009. EPA-HQ-OAR-2009-0472-0229
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7-181 

350 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. 

Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine 

Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. 

EPA-HQ-OAR-2009-0472-0263

351 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine 

Particulate Air Pollution and Mortality.” American Journal of Respiratory and Critical Care 

Medicine 173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. EPA-HQ-OAR-2009-0472-

1661

352 Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. 

Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. Expert Judgment Assessment of 

the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. Environ. Sci. 

Technol., 42(7):2268-2274. EPA-HQ-OAR-2009-0472-0242

353 Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

the U.S. EPA, Office of Air Quality Planning and Standards, September. Available on the 

Internet at http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf EPA-HQOAR-2009-0472-0242

354 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A MetaAnalysis.” Journal of Policy Analysis and Management 21(2):253-270. EPA-HQ-OAR-2009-

0472-1677

355 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of 

Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. EPAHQ-OAR-2009-0472-0245

356 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis. Environmental 

and Resource Economics. 34: 385-406. EPA-HQ-OAR-2009-0472-0235

357 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing 

Economic Analyses. EPA 240-R-00-003. National Center for Environmental Economics, 

Office of Policy Economics and Innovation. Washington, DC. September. Available on the 

Internet at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. EPAHQ-OAR-2009-0472-0232 

358 IPCC WGI, 2007. The baseline temperature increases by 2100 from our MiniCAMMAGICC runs are 1.8°C to 4.5°C.

359 IPCC. 2007. Climate Change 2007 - Synthesis Report Contribution of Working Groups I, 

II and III to the Fourth Assessment Report of the IPCC.
Regulatory Impact Analysis 

7-182 

360

 Interagency Working Group on Social Cost of Carbon, U.S. Government, 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, “Social Cost of Carbon for Regulatory 

Impact Analysis Under Executive Order 12866,” February 2010, available in docket EPAHQ-OAR-2009-0472.

361 “Light-Duty Automotive Technology and Fuel Economy Trends: 1975 Through 2008”, 

EPA420-R-08-015, U.S. Environmental Protection Agency Office of Transportation and Air 

Quality, September 2008 

362 “2008/9 Blueprint for Sustainability – Our Future Works” Ford Motor Company. Last 

accessed on the Internet on March 24, 2010 at the following URL: 

http://www.ford.com/go/sustainability and available in Docket EPA-HQ-OAR-2009-0472.

363 “Japan’s new diet plan: Slim down, ounce by ounce” Automotive News, July 7, 2008.

364 “Mazda: Don’t believe the hot air being emitted by hybrid hype” Automotive News, 

March 30, 2009.

365 “Improved aerodynamics, lightweight construction, tyres and drive technology - 

BlueEFFICIENCY in the C-Class: Fuel consumption reduced by 12 per cent” Daimler Press 

Release, February 28, 2008. Last accessed on the Internet on March 24, 2010 at the following 

URL: http://media.daimler.com/dcmedia/0-921-1050602-1-1050604-1-0-0-0-0-1-11701-

854934-0-1-0-0-0-0-0.html and available in Docket EPA-HQ-OAR-2009-0472.

366 “Future Generation Passenger Compartment-Validation” in “Lightweighting Materials – 

FY 2008 Progress Report”, U.S. Department of Energy, Office of Energy Efficiency and 

Renewable Energy, Vehicle Technologies Program, May 2009.

367 “Relationship between Vehicle Size and Fatality Risk in Model Year 1985-93 Passenger 

cars and Light Trucks”, DOT HS 808 570, NHTSA Technical Report, January 1997

368 “Vehicle Weight, Fatality Risk and Crash Compatibility of Model Year 1991-99 Passenger 

Cars and Light Trucks”, DOT HS 809 663, NHTSA Technical Report, October 2003

369 “Supplemental Results on the Independent Effects of Curb Weight, Wheelbase and Track 

on Fatality Risk”, Dynamic Research, Inc., DRI-TR-05-01, May 2005

370 “An Assessment of the Effects of Vehicle Weight and Size on Fatality Risk in 1985 to 

1998 Model Year Passenger Cars and 1985 to 1997 Model Year Light Trucks and Vans”, Van 

Auken, M., Zellner J.W., SAE Technical Paper Number 2005-01-1354, 2005.

371 “Blood and Oil: Vehicle Characteristics in Relation to Fatality Risk and Fuel Economy”, 

L.S. Robertson, American Journal of Public Health, Vol. 96, No. 11, November 2006.
Other Economic and Social Impacts 

8-1 

CHAPTER 8:Other Economic and Social Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 How Vehicle Sales Impacts were Estimated for this Rule 

The vehicle sales impacts discussed in Section III.H.5 of the preamble to the rule and 

presented below in Table 8-1 and Table 8-2 were derived using the following methodology. 

For additional discussion of the assumptions used in the vehicles sales impacts, see Section 

III.H of the preamble. The calculation is performed for an average car and an average truck, 

rather than for individual vehicles. The analysis conducted for this rule does not have the 

precision to examine effects on individual manufacturers or different vehicle classes. Chapter 

8.1.2 provides our assessment of models that examine these questions. 

The analysis compares two effects. On the one hand, the vehicles will become more 

expensive, which would, by itself, discourage sales. On the other hand, the vehicles will have 

improved fuel economy and thus lower operating costs, which makes them more attractive to 

consumers. If consumers do not accurately compare the value of fuel savings with the 

increased cost of fuel economy technology in their vehicle purchase decisions, as discussed in 

Preamble III.H.1, they will continue to behave in this way after this rule. If auto makers have 

accurately gauged how consumers consider fuel economy when purchasing vehicles and have 

provided the amount that consumers want in vehicles, then consumers should not be expected 

to want the more fuel-efficient vehicles. After all, auto makers would have provided as much 

fuel economy as consumers want. If, on the other hand, auto makers underestimated 

consumer demand for fuel economy, as suggested by some commenters and discussed in 

Preamble Section III.H.1 and RIA Section 8.1.2, then this rule may lead to production of more 

desirable vehicles, and vehicle sales may increase. This assumption implies that auto makers 

have missed some profit-making opportunities. The results presented in this analysis depend 

on the assumption that more fuel efficient vehicles that yield net consumer benefits over five 

years would not otherwise be offered on the vehicle market due to market failures on the part 

of vehicle manufacturers. If vehicles that achieve the fuel economy standards prescribed by 

today’s rulemaking would already be available, but consumers chose not to purchase them, 

then this rulemaking would not result in an increase in vehicle sales, because it does not alter 

how consumers make decisions about which vehicles to purchase 

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 likely that the cost increase is split between consumers and automakers, 

and the price is not likely to increase as much as costs.1 Thus, the assumption of full cost 

pass-through is probably an overestimate, and price is not likely to increase as much as 

estimated here. 

The next step in the analysis is to adjust this cost increase for other effects on the 

consumer. We assume that the consumer holds onto this vehicle for 5 years and then sells it. 

The higher vehicle price is likely to lead to an increase in sales tax, insurance, and vehicle 
Regulatory Impact Analysis 

8-2 

financing costs, as well as increases in the resale value of the vehicle. These factors weigh 

against each other: the higher sales tax, insurance, and financing costs increase costs to 

consumers; the higher resale value allows consumers to recover a portion of these 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 Institute2

 provides the average value of collision plus comprehensive 

insurance in 2006 as $448. The average value of a new vehicle in 2006, according to the U.S. 

Department of Energy, was $22,651.3 (This value is for a 2006 vehicle in 2006 and is used 

only for the insurance adjustment; it does not correspond to the new vehicle prices, described 

below, used in the vehicle sales impact calculation.) Dividing the insurance cost by the 

average price of a new vehicle gives the proportion of comprehensive plus collision insurance 

as 1.98% 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 1.98% of the increase in vehicle cost. For the 

five-year period, the present value of this increase in insurance cost would be worth 9.0% of 

the vehicle cost increase, using a 3% discount rate (8.1% at a 7% discount rate). 

Calculating the average increase in sales tax starts with the vehicle sales tax for each 

state in 2006.4 The sales tax per state was then multiplied by the 2006 population of the 

state;5 those values were summed and divided by total U.S. population, to give a populationweighted sales tax. That estimate of the state sales taxes for vehicles in the U.S. is 5.3% in 

2006. This value is assumed to be a one-time cost incurred when the vehicle is purchased. 

As of February 9, 2010, the national average interest rate for a 5 year new car loan was 

6.54 percent.6

 Converting the up-front payment to an annual value paid over five years results 

in a consumer paying 24.1% of the up-front amount every year. The present value of these 

five payments results in an increase of 10.3% of the cost, using a 3% discount rate; with a 7% 

discount rate, the increase is -1.2%. NHTSA’s RIA notes that 70% of auto purchases use 

financing; applying that fraction to this cost increase results in an addition of 7.2% in 

financing costs with a 3% discount rate, and -0.9% for a 7% discount rate. 

The average resale price of a vehicle after 5 years is about 35%7

 of the original 

purchase price. Because the consumer can recover that amount after 5 years, it reduces the 

effect of the increased cost of the vehicle. Discounted to a present value at a 3% interest rate, 

the increase in price should be worth about 30.2% to the vehicle purchaser (25.0% at a 7% 

discount rate). This approach is premised on the idea that the resale value of a vehicle is 

directly proportional to the initial value, and that proportion does not change. 

Thus, the effect on a consumer’s expenditure of the cost of the new technology (with 

some rounding) should be (1 + 0.090 + 0.053 + 0.072 – 0.302) = 0.914 times the cost of the 

technology at a 3% discount rate. At a 7% discount rate, the effect on a consumer’s 

expenditure of the cost of the new technology should be (1 + 0.081 + 0.053 - 0.009 – 0.250) = 

0.876 times the cost of the technology. 

The fuel cost savings are based on the five years of consumer ownership of the 

vehicle. The analysis is done for each model-year for an average vehicle. Section 6.3 of this 
Other Economic and Social Impacts 

8-3 

RIA discusses the source of aggregate fuel savings, in gallons, for cars and trucks for each 

model year by year. These values are divided by the total number of the vehicles produced to 

get per-vehicle savings per year for the first five years of the vehicle’s life. This method 

ignores the few vehicles of the new model year that are scrapped. Because incorporating 

scrappage would reduce the denominator, and thus increase per-vehicle fuel savings, it 

underestimates per-vehicle fuel savings by a small amount. The per-vehicle fuel savings in 

gallons are multiplied by the price of fuel to get the per-vehicle fuel savings in dollars. For 

each model year, then, the first five years of fuel savings are discounted and summed to 

produce the present value of fuel savings for that vintage vehicle. For instance, the 2016 fuel 

savings per vehicle are the present value in year 2016 of fuel savings estimated for 2016 

through 2020. 

The prices for new vehicles are assumed to be constant at the 2008 value (in 2007$) of 

$26,201 for a car, and $29,678 for a truck. These are the values used in NHTSA’s 2011 rule 

on CAFE standards. 

The fuel cost savings are subtracted from the increase in costs associated with the rule 

to get the net effect of the rule on consumer expenditure. The higher cost leads consumers to 

purchase fewer new vehicles, but the fuel savings can counteract this effect. This calculation 

uses an elasticity of demand for new vehicles of -18

: that is, an increase of 1% in the price of 

a new vehicle will lead to a 1% reduction in new vehicle sales. Using this value assumes that 

the demand elasticity for new vehicles under this rule is the same as the elasticity for new 

vehicles in the past. This change in consumer expenditure as a percent of the average price of 

a new vehicle, with the elasticity of demand of -1, is the negative of the percent change in 

vehicle purchases. The net effect of this calculation on vehicle purchases is in Table 8-1 and 

Table 8-2. 

Table 8-1 Vehicle Sales Impacts Using a 3% Discount Rate

CHANGE IN 

CAR SALES 

% CHANGE CHANGE IN 

TRUCK SALES 

% CHANGE 

2012 67,500 0.7 62,100 1.1 

2013 76,000 0.8 190,200 3.2 

2014 114,000 1.1 254,900 4.3 

2015 222,200 2.1 352,800 6.1 

2016 360,500 3.3 488,000 8.6 

Table 8-1 shows vehicle sales increasing. Because the fuel savings associated with 

this rule are expected to exceed the technology costs, the effective prices of vehicles – the 

adjusted increase in technology cost less the fuel savings over five years -- to consumers will 

fall, and consumers will buy more new vehicles. This effect is expected to increase over time. 

As a result, if consumers consider fuel savings at the time that they make their vehicle 

purchases, the lower net cost of the vehicles is expected to lead to an increase in sales for both 

cars and trucks. Both the absolute and the percent increases for truck sales are larger than 
Regulatory Impact Analysis 

8-4 

those for cars (except in 2012). This approach may not accurately reflect the role of fuel 

savings in consumers’ purchase decisions, as the discussion in Preamble Section III.H.1 

suggests. If consumers consider fuel savings in a different fashion than modeled here, then 

this approach will not accurately reflect the impact of this rule on vehicle sales. 

Table 8-2 Vehicle Sales Impacts Using a 7% Discount Rate

CHANGE IN 

CAR SALES 

% CHANGE CHANGE IN 

TRUCK SALES 

% CHANGE 

2012 62,800 0.7 58,300 1 

2013 70,500 0.7 92,300 1.5 

2014 106,100 1 127,700 2.1 

2015 208,400 2 194,200 3.3 

2016 339,400 3.1 280,000 4.9 

Table 8-2 shows the same calculations using a 7% discount rate. Qualitatively, the 

results are identical to those using a 3% discount rate: the fuel savings outweigh the increase 

in technology costs for all years. As a result, vehicle sales are expected to be higher under 

this rule than in the absence of the rule. In addition, while the increased numbers of car sales 

are larger than the numbers for trucks, the percent increases are larger for trucks. 

This calculation focuses on changes in consumer expenditures as the explanatory 

variable for changes in aggregate new vehicle sales. This is a simplification, since consumers 

typically consider a number of factors in addition to expenditures when they decide on 

purchasing a vehicle. Some of the factors that might affect consumer vehicle purchases 

include changing market conditions, changes in vehicle characteristics that might accompany 

improvements in fuel economy, or consumers considering a different “payback period” for 

their fuel economy purchases. These complications add considerable uncertainty to our 

vehicle sales impact analysis. 

The next section discusses more complex modeling of the vehicle purchase decision. 

8.1.2 Consumer Vehicle Choice Modeling 

In this section we describe some of the consumer vehicle choice models EPA has 

reviewed in the literature, and we describe the models’ results and limitations that we have 

identified. The evidence from consumer vehicle choice models indicates a huge range of 

estimates for consumers’ willingness to pay for additional fuel economy. Because consumer 

surplus estimates from consumer vehicle choice models depend critically on this value, we 

would consider any consumer surplus estimates of the effect of our rule from such models to 

be unreliable. In addition, the predictive ability of consumer vehicle choice models 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. Like NHTSA, EPA will 
Other Economic and Social Impacts 

8-5 

continue its efforts to review the literature, but, given the known difficulties, neither NHTSA 

nor EPA has conducted an analysis using these models for this rule. 

This rule will lead automakers to change characteristics – in particular, the fuel 

economy -- of the vehicles they produce. These changes will affect the cost of manufacturing 

the vehicle; as a result, the prices of the vehicles will also change. 

In response to these changes, the number and types of vehicles sold is likely to change. 

When consumers buy vehicles, they consider both their personal characteristics (such as age, 

family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., 

vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, 

consumers will reconsider their purchases. Increases in fuel economy are likely to be 

attractive to consumers, but increases in price, as well as some 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 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 for each vehicle. Because the market share 
Regulatory Impact Analysis 

8-6 

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. 

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,9

 Greene et al.,10 and McManus.11

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,12 Bento et al.,13 and Train and Winston.14 

While discrete choice modeling appears to be the primary method for consumer choice 

modeling, others (such as Kleit15 and Austin and Dinan16) 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.17

 

Because they draw on the behavior of individuals, they provide what is sometimes called 

micro-level data. Other studies, that estimate market shares instead of discrete purchase 

decisions, use aggregated data that can cover long time periods.18 

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 Administration19 and the New Vehicle Market Model 

developed by NERA Economic Consulting20 are examples of calibrated models. 
Other Economic and Social Impacts 

8-7 

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;21 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);22 others explicitly consider the relationship 

between the new and used vehicle markets.23 Some models include consideration of vehicle 

miles traveled,24 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;25 others include varying numbers and kinds of vehicle and consumer attributes. 

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,26 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 a 

stakeholder in many public policy discussions, research questions cover a wide gamut. These 

topics have included the effects of voluntary export restraints on Japanese vehicles compared 

to tariffs and quotas,27 the market acceptability of alternative-fuel vehicles,28 the effects of 

introduction and exit of vehicles from markets,29 causes of the decline in market shares of 

U.S. automakers,30 and the effects of gasoline taxes31 and “feebates”32 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

Consumer vehicle choice models typically consider the effect of fuel economy on 

vehicle purchase decisions. It can appear in various forms. 

Some models33 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 Kurani34 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, the miles per gallon of vehicles is one of the vehicle 

characteristics included to explain purchase decisions. Other models use fuel consumption 
Regulatory Impact Analysis 

8-8 

per mile, the inverse of miles per gallon, as a measure:35 since consumers pay for gallons of 

fuel, then this measure can assess fuel savings relatively directly.36 Yet other models multiply 

fuel consumption per mile by the cost of fuel to get the price of driving a mile,37 or they 

divide fuel economy by fuel cost to get miles per dollar.38 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. 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, it is also a way to recognize the role of fuel prices 

in consumers’ purchase of fuel economy: Recent research39 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,40 in a paper published in 1988, reviewed 10 papers using consumer 

vehicle choice models and estimated for each one how much consumers would be willing to 

pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that 

people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating 

costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that 

people are not willing to pay $1 once to get $1 per year reduced costs of operating their 

vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the 

present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% 

discount rate produces a present value of $8.78. While this study is quite old, it suggests that, 

at least as of that time, consumer vehicle choice models produced widely varying estimates of 

the value of reduced vehicle operating costs. 

A new review from David Greene41 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, Gramlich42 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 one value that represents the role of fuel economy in consumer 

purchase decisions. 

Some studies43 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 studies44 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 studies45 notes that its baseline model implies that 
Other Economic and Social Impacts 

8-9 

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 Consumers May Not Buy, and Producers May Not Provide, Fuel 

Economy that Pays for Itself 

If consumers are willing to pay for fuel-saving technologies, why does the market not 

already take advantage of these low-cost technologies? Why aren’t consumers demanding 

these vehicle improvements, and manufacturers supplying them, when they appear to “pay for 

themselves” even in the absence of regulation? While existing research does not offer full 

answers, it is important to attempt to explore these questions, because under certain 

assumptions, the purely private benefits of fuel economy (fuel savings, time savings, increases 

in driving time) are likely to be accompanied by private losses. If there is no such offset, or if 

it is small or insignificant, the reason lies in some kind of market failure. 

A detailed literature attempts to identify possible market failures that would justify the 

assumption that the degree of consumer welfare loss is relatively small. On the consumer 

side, 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,46 since consumers appear to undervalue a wide range of investments in energy 

conservation. Some possible explanations for the paradox47 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; 

• 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; 
Regulatory Impact Analysis 

8-10 

• 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. 

Both theoretical and empirical research suggests that, in the context of fuel economy 

and elsewhere, many consumers do not make energy-efficient investments even when those 

investments would pay off in the relatively short-term.48 This conclusion is in line with 

related findings that consumers may underweight benefits and costs that are less salient or that 

will be realized only in the future.49 At the same time, it is worth noting that many of these 

behaviors can be accounted for in standard economic models. For instance, accounting for 

uncertainty in future fuel savings is a common practice. For some consumers, high observed 

discount rates may reflect the illiquidity of investments in fuel economy or high opportunity 

costs of such investments where consumers are carrying high-interest-rate debt. There is 

disagreement in the literature about the degree to which these explanations contribute to 

understanding the Energy Paradox, and additional empirical investigation is still needed. 

The producer side of this paradox is much less studied. Hypotheses for 

underprovision of fuel economy by producers, related to those involving consumers, include: 

• Producers put more effort into attributes that consumers have regularly sought in 

the past, such as size and power, than into fuel and time savings with uncertain 

future returns; 

• In selecting a limited number of vehicle attributes among which consumers can 

choose, producers may aim to provide choices related to characteristics (such as 

numbers of doors or transmission types) that strongly influence what vehicle a 

consumer will buy, and fuel economy and time savings may not make that list; 

• While consumer preferences for fuel economy may change rapidly as fuel prices 

fluctuate, producers cannot change their design or production decisions as rapidly; 

as a result, vehicle designs may end up not satisfying consumer desires at a 

particular time; 

• Producers may have underestimated the value that consumers place on fuel 

economy. 

How consumers buy, and producers provide, fuel economy involves complex 

decisions on both sides of the market. Both sides of the market rely heavily in their 

calculations on the uncertain benefits of savings from fuel economy improvements. In 

addition, consumers trade off fuel economy with many other vehicle attributes, and producers 

do not provide the full range of attributes possible for consumers. From this perspective, it 

may not be a surprise that, at a given point in time, consumer preferences for fuel economy 

may not match up with producer provision of it. 

8.1.2.7 Assessment of the Literature 

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 
Other Economic and Social Impacts 

8-11 

relevant effects on consumer welfare. All models estimate changes in fleet mix of new 

vehicles; some also provide estimates of total new vehicle sales; and a few incorporate the 

used vehicle market, potentially to the decision on when a vehicle is scrapped. Being able to 

model these changes has several advantages. 

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 

vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is not 

proven. It is likely that, in coming years, new vehicles will be developed, and existing 

vehicles will be redesigned, perhaps to have improvements in both fuel economy and safety 

factors in combinations that consumers have not previously been offered. Welch,50 for 

instance, argues that auto producers are likely to increase the sizes of vehicles in response to 

the footprint-based fuel economy standard. Models based on the existing vehicle fleet may, 

however, not do well in predicting consumers’ choices among the new vehicles offered. One 

attempt to analyze the effect of the oil shock of 1973 on consumer vehicle choice found that, 

after two years, the particular model did not predict well due to changes in the vehicle fleet.51

 

Thus, consumer vehicle choice models, even if they did produce robust results in analyzing 

the short-term effects of policy changes, may miss changes associated with new and 

redesigned vehicles. 

The modeling may improve estimates of the compliance costs of a rule. Most current 

modeling is based on a fleet mix determined outside the model; neither vehicle manufacturers 

nor consumers respond directly to cost increases and other vehicle changes by a change in the 

fleet mix. With the use of consumer vehicle choice modeling, both consumers and producers 

have greater choices in response to these changes: they can either accept the new costs and 

vehicle characteristics, or they can change which vehicles are sold. The fact that consumers 

and producers have additional options suggests that compliance costs are likely to be lower 

through incorporation of a consumer choice model than through use of a technology-cost 

model alone. On the other hand, the effect may not be large: in the context of “feebates” 

(subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient vehicles), 

Greene et al. found that 95% of the increase in fuel economy was due to addition of 

technology rather than changes in vehicles sold.52 Consideration of consumer behavior in 

welfare estimates will improve regulatory analysis, but only to the extent that the predicted 

changes in consumer purchase patterns reflect actual changes. 

An additional complication associated with consumer choice modeling is accurate 

prediction of producers’ responses to the rule. Auto makers not only predict consumers’ 

preferences for vehicles; they also may seek to influence those preferences through marketing 

and advertising.53 In addition, 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. Including these market features into consumer vehicle 

choice models is a complex undertaking. Not all consumer vehicle choice models include a 

producer model, and those that do may not include much detail, due to computational limits. 

Technology costs still represent an accurate measure of the opportunity cost of resources to 

society, but they may overestimate or underestimate the effect on the prices that consumers 
Regulatory Impact Analysis 

8-12 

face. Firms with market power usually pass along less than full cost increases, in order not to 

reduce sales very much. As a result, for most vehicles the increased technology costs may not 

equal the price increases that consumers will see. 

An additional feature of consumer choice models, as noted above, is that they can be 

used to calculate consumer surplus impacts on vehicle purchase decisions. Consumer surplus 

is a standard measurement of consumer impacts in benefit-cost analysis. Consumer surplus 

calculations from these models estimate how much consumers appreciate the gains in fuel 

economy relative to the increased vehicle costs that they face, based on the assumption that 

consumers, at the time of vehicle purchase, have made the best decisions for themselves on 

the amount of fuel economy in the vehicles they purchase. These values, though, are based on 

the relationship between consumer willingness to pay for fuel economy and the costs of 

improved fuel economy. Because the estimates of consumer willingness to pay for fuel 

economy appear to be highly inconsistent, consumer surplus measures from any one model 

are unlikely to be reliable. 

Principles of welfare analysis can be useful for understanding the role of consumer 

vehicle choice models in benefit-cost analysis. Consumer welfare is commonly measured as 

the change in income that would leave the consumer as well off in the presence of the change 

(in this case, the increase in fuel economy and vehicle price) as in the absence of the change; 

this amount is known as compensating variation, since the consumer is compensated for the 

change.A

 If the vehicle has not changed other than those two characteristics, then a consumer 

has the choices of (i) paying the higher price for the vehicle, (ii) choosing to buy a different 

vehicle, or (iii) not buying a new vehicle. The only reason the consumer would decide to pay 

the higher price, (i), is if this option is preferable to the other two options. If the consumer 

cares nothing about the increased fuel economy but is given an amount of money equal to the 

price increase, she is at least as well off as before the price increase: she can still buy the 

original vehicle (which has improved fuel economy but is otherwise identical), or she can still 

choose options (ii) or (iii). Thus, the price increase due to the rule is an upper bound on the 

consumer’s welfare loss, if no other vehicle characteristics of interest to the consumer has 

changed. If the consumer actually appreciates the improved fuel economy, the welfare loss is 

even smaller. However, if the vehicle has changed due to the fuel economy increase in ways 

other than price and fuel economy, or if there are additional costs associated with these 

vehicles not included in the analysis, then there may be additional welfare impacts that are not 

included in the technology cost estimates. 

At this point, it is unclear whether two consumer vehicle choice models given the 

same scenario would produce similar results in either prediction of changes in the vehicles 

 

A

 A closely related concept, equivalent variation, measures the change in income that would be a perfect 

substitute for the increased fuel economy and vehicle cost. These measures differ based on whether the basis for 

evaluation is the consumer’s utility before the change (compensating variation) or the consumer’s utility after the 

change (equivalent variation). In practice, the difference between these two measures is typically very small for 

marketed goods. 
Other Economic and Social Impacts 

8-13 

purchased or in estimates of consumer surplus effects. The estimates of consumer surplus 

from consumer vehicle choice models depend heavily on the value to consumers of improved 

fuel economy, a value for which estimates are highly varied. In addition, the predictive 

ability of consumer vehicle choice models may be limited as consumers face new vehicle 

choices that they previously did not have. If the results across models are not consistent or 

are highly sensitive to parameters or other features, then careful thought needs to be given to 

model selection and development. 

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 

rulemaking EPA compares the fuel and other savings that consumers will receive with the 

technology costs of the vehicles. The regulations have been carefully designed so that the full 

range of vehicle choices in the marketplace could be maintained; rigorous technological 

feasibility, cost, and lead-time analysis has shown that the standards could be met while 

maintaining current levels of other vehicle attributes. For these reasons, EPA believes that 

consumers will enjoy significant savings that substantially outweigh any likely consumer 

welfare losses. Nonetheless, EPA continues to consider these questions, and is continuing 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 efects on consumer welfare. 

In addition, EPA is developing capacity to examine the factors that may affect the 

results of consumer vehicle choice models, and to explore their impact on analysis of 

regulatory scenarios. Under contract with EPA, Resources for the Future (RFF) is developing 

a model of the vehicle market that can be used to evaluate different policy designs and 

compare regulatory scenarios on the basis of changes in cost, changes in the prices paid by 

consumers, changes in consumer welfare, and changes in industry profits. It should help to 

shed light on whether it is more costly to rely solely on the application of technologies to 

vehicles to meet a given fuel standard than when consumer and producer behavior is taken 

into account. EPA plans to evaluate this work within the context of the overall literature on 

consumer vehicle choices, to determine its usefulness in informing the analysis for future 

rules. 

8.1.3 Consumer Payback Period and Lifetime Savings on New Vehicle Purchases 

Another factor of interest is the payback period on the purchase of a new vehicle that 

complies with these 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 2016 MY 

vehicle is estimated to cost $948 more (on average, and relative to the reference case vehicle) 

due to the addition of new GHG reducing technology (see Chapter 4 for details on this cost 

estimate). This new technology will result in lower fuel consumption and, therefore, savings 

in fuel expenditures (see Chapter 6 for details on fuel savings). But how many months or 

years would pass before the fuel savings exceed the upfront cost of $948? 

Table 8-3 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 
Regulatory Impact Analysis 

8-14 

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 joint TSD. We have included rebound VMT in the 

control case but not in the reference case, consistent with other parts of our analysis. We have 

also included fuel savings associated with A/C controls (in the control case only), but have 

not included expected A/C-related maintenance savings. We discuss the likely maintenance 

savings in Chapter 2 of this RIA. Further, this analysis does not include other societal 

impacts such as the value of increased driving, or noise, congestion and accidents since we 

really want to focus on those factors consumers consider most while in the showroom 

considering a new car purchase. Car/truck fleet weighting is handled as described in Chapter 

1 of the joint TSD. As can be seen in the table, it will take under 3 years (2 years and 7 

months at a 3% discount rate, 2 years and 9 months at a 7% discount rate) for the cumulative 

fuel savings to exceed the upfront increase in vehicle cost. For the average driver, this 

payback would occur at around 46,000 to 49,000 miles, depending on the discount rate. For 

the driver that drives more than the average, the payback would come sooner. For the driver 

that drives less than the average, the payback would come later. 

Table 8-3 Payback Period on a 2016MY New Vehicle Purchase via Cash (2007 dollars) 

Increased 

Vehicle 

Costa

Fuel Priceb

Reference 

VMTc

Control 

VMTc

Reference 

Fuel 

Costsd

Control 

Fuel 

Costsd

Annual 

Fuel 

Savings 

Cumulative 

Discounted 

Fuel 

Savings at 

3% 

Cumulative 

Discounted 

Fuel 

Savings at 

Year of 7% 

Ownership ($) ($/gal) (miles) (miles) ($) ($) ($) ($) ($) 

1 $1,018 $3.07 17,850 18,186 $2,437 $2,013 $424 $418 $410 

2 $3.13 17,297 17,623 $2,410 $1,990 $420 $820 $790 

3 $3.19 16,789 17,105 $2,377 $1,963 $414 $1,204 $1,139 

4 $3.22 16,133 16,437 $2,310 $1,908 $402 $1,567 $1,457 

a

 Increased cost of the rule is $948; the value here includes nationwide average sales tax of 5.3% and increased 

insurance premiums of 1.98%; both of these percentages are discussed in section 8.1.1. 

b

 AEO 2010 Early Release reference case fuel price including taxes. 

c

 VMT is calculated as the weighted car/truck VMT with cars estimated to account for 66% of the fleet and 

trucks 34%; VMT shown here includes survival fraction and, for the control case, rebound VMT. 

d

 Fuel costs calculated using the reference and control case achieved CO2 levels as presented in Chapter 5 with 

8887 grams of CO2 per gallon of gasoline and include the 20 percent road fuel economy gap, as discussed in 

Chapter 5; the control case also includes the effects of A/C controls on CO2 emissions but not the expected A/Crelated maintenance savings. 

Most people purchase a new vehicle using credit rather than paying cash up front. The 

typical car loan today is a five year, 60 month loan. As of February 9, 2010, the national 

average interest rate for a 5 year new car loan was 6.54 percent. If the increased vehicle cost 

is spread out over 5 years at 6.54 percent, the analysis would look like that shown in Table 

8-4. As can be seen in this table, the fuel savings immediately outweigh the increased 

payments on the car loan, amounting to $177 in discounted net savings (3% discount rate) 

saved in the first year and similar savings for the next two years before reduced VMT starts to 

cause the fuel savings to fall. 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 
Other Economic and Social Impacts 

8-15 

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 $9 to $15 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 (as would be the case for the remaining years of ownership). 

Table 8-4 Payback Period on a 2016 MY New Vehicle Purchase via Credit (2007 dollars) 

Increased 

Vehicle 

Costa

Fuel 

Priceb

Reference 

VMTc

Control 

VMTc

Reference 

Fuel 

Costsd

Control 

Fuel 

Costsd

Annual 

Fuel 

Savings 

Annual 

Discounted 

Net 

Savings at 

3% 

Annual 

Discounted 

Net 

Savings at 

Year of 7% 

Ownership ($) ($/gal) (miles) (miles) ($) ($) ($) ($) ($) 

1 $245 $3.07 17,850 18,186 $2,437 $2,013 $424 $177 $173 

2 $245 $3.13 17,297 17,623 $2,410 $1,990 $420 $167 $158 

3 $245 $3.19 16,789 17,105 $2,377 $1,963 $414 $157 $142 

4 $245 $3.22 16,133 16,437 $2,310 $1,908 $402 $142 $124 

5 $245 $3.27 15,451 15,742 $2,244 $1,853 $391 $127 $107 

6 $0 $3.29 14,668 14,944 $2,148 $1,774 $374 $318 $258 

a

 This uses the same increased cost as Table 8-3 but spreads it out over 5 years assuming a 5 year car loan at 6.54 

percent. 

b

 AEO 2010 Early Release reference case fuel price including taxes. 

c

 VMT is calculated as the weighted car/truck VMT with cars estimated to account for 66% of the fleet and 

trucks 34%; VMT shown here includes survival fraction and, for the control case, rebound VMT. 

d

 Fuel costs calculated using the reference and control case achieved CO2 levels as presented in Chapter 5 with 

8887 grams of CO2 per gallon of gasoline and include the 20 percent road fuel economy gap, as discussed in 

Chapter 5; the control case also includes the effects of A/C controls on CO2 emissions but not the expected A/Crelated maintenance savings. 

We can also calculate the lifetime fuel savings and net savings 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 

8-5. In either case, the present value of the lifetime net savings is greater than $3,100 at a 3% 

discount rate, or $2,300 at a 7% discount rate. 

Table 8-5 Lifetime Discounted Net Savings on a 2016 MY New Vehicle Purchase (2007 dollars) 

Increased 

Discounted Vehicle 

Cost 

Lifetime 

Discounted Fuel 

Savingsb,c 

Lifetime 

Discounted Net 

Savings 

Purchase Option ($) ($) ($) 

3% discount rate 

Cash $1,018 $4,306 $3,303 

Credita$1,140 $4,306 $3,166 

7% discount rate 

Cash $1,018 $3,381 $2,396 

Credita$1,040 $3,381 $2,340 

a

 Assumes a 5 year loan at 6.54 percent. 
Regulatory Impact Analysis 

8-16 

b

 VMT is calculated as the weighted car/truck VMT with cars estimated to account for 

66% of the fleet and trucks 34%; VMT shown here includes survival fraction and, for 

the control case, rebound VMT. 

c Fuel savings here were calculated using AEO 2010 Early Release reference case fuel 

price including taxes. 

8.2 Energy Security Impacts 

This chapter will only describe the energy security analysis that was conducted 

beyond that described in Chapter 4 of the TSD. Additional analysis was conducted to provide 

inputs to EPA’s OMEGA model. For a detailed discussion of the development of the energy 

security estimates, please refer to Chapter 4 of the joint TSD. 

After the EPA-sponsored peer review of the Oak Ridge National Laboratory’s 

(ORNL) Energy Security Analysis was completed in 2008, ORNL, at EPA’s request, updated 

the analysis using values from the AEO 2009 rather than the 2007 values. The methodology 

used to update this analysis was the same one that was peer-reviewed.54 The results are 

shown in Table 8-6. ORNL estimated the energy security premium for 2015, 2020, and 2030. 

Since the AEO 2009 forecasts ends in 2030, EPA assumed that the post-2030 energy security 

premium did not change through 2040. 

Table 8-6 Energy Security Premium in 2015, 2020, 2030, and 2040 (2007$/Barrel) 

YEAR MONOPSONY 

(RANGE) 

MACROECONOMIC 

DISRUPTION/ADJUSTMENT 

COSTS (RANGE) 

TOTAL MID-POINT 

(RANGE) 

2015 $11.79 

($4.26 - $21.37) 

$6.70 

($3.11 - $10.67) 

$18.49 

($9.80 - $28.08) 

2020 $12.31 

($4.46 - $22.53) 

$7.62 

($3.77 - $12.46) 

$19.94 

($10.58 - $30.47) 

2030 $10.57 

($3.84 - $18.94) 

$8.12 

($3.90 - $13.04) 

$18.69 

($10.52 - $27.89) 

2040 $10.57 

($3.84 - $18.94) 

$8.12 

($3.90 - $13.04) 

$18.69 

($10.52 - $27.89) 

EPA linearly interpolated the values for the years 2016 through 2019, using the 2015 

and 2020 values as endpoints. EPA followed the same procedure to estimate the 2021 

through 2029 estimates, using the 2020 and 2030 values as endpoints. Post-2030, EPA 

assumed that the energy security estimate did not change. The final set of values that was 

used by the OMEGA model is shown in Table 8-7. 
Other Economic and Social Impacts 

8-17 

Table 8-7 Energy Security Premium Estimates for Years 2015-2040 (2007$/Barrel) 

YEAR MONOPSONY MACRO/DISRUPT TOTAL 

2015 $11.79 $6.70 $18.49 

2016 $11.89 $6.88 $18.78 

2017 $12.00 $7.07 $19.07 

2018 $12.10 $7.25 $19.36 

2019 $12.21 $7.44 $19.65 

2020 $12.31 $7.62 $19.94 

2021 $12.14 $7.67 $19.82 

2022 $11.96 $7.72 $19.69 

2023 $11.79 $7.77 $19.57 

2024 $11.61 $7.82 $19.44 

2025 $11.44 $7.87 $19.32 

2026 $11.27 $7.92 $19.19 

2027 $11.09 $7.97 $19.07 

2028 $10.92 $8.02 $18.94 

2029 $10.74 $8.07 $18.82 

2030 $10.57 $8.12 $18.69 

2031 $10.57 $8.12 $18.69 

2032 $10.57 $8.12 $18.69 

2033 $10.57 $8.12 $18.69 

2034 $10.57 $8.12 $18.69 

2035 $10.57 $8.12 $18.69 

2036 $10.57 $8.12 $18.69 

2037 $10.57 $8.12 $18.69 

2038 $10.57 $8.12 $18.69 

2039 $10.57 $8.12 $18.69 

2040 $10.57 $8.12 $18.69 

The total energy security benefits are derived from the estimated reductions in imports 

of finished petroleum products and crude oil using only the macroeconomic 

disruption/adjustment portion of the energy security premium price. These values are shown 

in Table 8-8.55 The reduced oil estimates were derived from the OMEGA model, as explained 

in Chapter 5 of EPA’s RIA. EPA used the same assumption that NHTSA used in its 

Corporate Average Fuel Economy and CAFE Reform for MY 2008-2011 Light Trucks rule, 

which assumed each gallon of fuel saved reduces total U.S. imports of crude oil or refined 

products by 0.95 gallonsB56 . Section 5.3 of this RIA contains a discussion regarding caveats 

 

B

 Preliminary Regulatory Impacts Analysis, April 2008. Based on a detailed analysis of differences in fuel 

consumption, petroleum imports, and imports of refined petroleum products among the Reference Case, High 
Regulatory Impact Analysis 

8-18 

for the fuel savings estimated due to implementation of this rule. Section III.H. of the 

preamble contains a detailed discussion of how the monopsony and macroeconomic 

disruption/adjustment components were treated for this analysis. Note that if the monopsony 

effects were included in this analysis, they could be significant. 

Table 8-8 Total Annual Energy Security Benefits in 2015, 2020, 2030, and 2040 (Billions of 2007 dollars) 

YEAR BENEFITS 

2015 $0.57 

2020 $2.17 

2030 $4.55 

2040 $6.00 

8.3 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 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 these 

standards, but they are nevertheless important to include. We summarize the value of these 

other impacts in section 8.4.4 of this RIA. Please refer to the joint TSD for more information 

about these impacts and how EPA and NHTSA use them in their analyses. 

8.3.1 Reduced Refueling Time 

Improving the fuel economy of passenger cars and light-duty trucks may also increase 

their driving range before they require refueling. By reducing the frequency with which 

drivers typically refuel their vehicles and extending the upper limit of the range they can 

travel before requiring refueling, improving fuel economy provides some additional benefits 

to their owners. Alternatively, if manufacturers respond to improved fuel economy by 

reducing the size of fuel tanks to maintain a constant driving range, the resulting cost saving 

will presumably be reflected in lower vehicle sales prices. If manufacturers respond by doing 

 

Economic Growth, and Low Economic Growth Scenarios presented in the Energy Information Administration’s 

Annual Energy Outlook 2007, NHTSA estimated that approximately 50 percent of the reduction in fuel 

consumption is likely to be reflected in reduced U.S. imports of refined fuel, while the remaining 50 percent 

would be expected to be reflected in reduced domestic fuel refining. Of this latter figure, 90 percent is 

anticipated to reduce U.S. imports of crude petroleum for use as a refinery feedstock, while the remaining 10 

percent is expected to reduce U.S. domestic production of crude petroleum. Thus on balance, each gallon of fuel 

saved is anticipated to reduce total U.S. imports of crude petroleum or refined fuel by 0.95 gallons. 
Other Economic and Social Impacts 

8-19 

so, this presumably reflects their judgment that the value to economic benefits to vehicle 

buyers from lower purchase prices exceeds that from extended refueling range. 

No direct estimates of the value of extended vehicle range are readily available, so this 

analysis calculates the reduction in the annual number of required refueling cycles that results 

from improved fuel economy, and applies DOT-recommended values of travel time savings to 

convert the resulting time savings to their economic value.57

 

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and 

average vehicle occupancy for all driving trips (1.6 persons), the DOT-recommended value of 

travel time per vehicle-hour is $24.00 (in 2006 dollars). We assume that the average tank 

refill is 55%, that the average fuel tank is 19.3 gallons, and that the average time to find and 

use a gas station is five minutes.58,59

8.3.2 Value of Additional Driving 

The increase in travel associated with the rebound effect produces additional benefits 

to vehicle owners, which reflect the value to drivers and other vehicle occupants of the added 

(or more desirable) social and economic opportunities that become accessible with additional 

travel. As evidenced by the fact that they elect to make more frequent or longer trips when 

the cost of driving declines, the benefits from this added travel exceed drivers’ added outlays 

for the fuel it consumes (measured at the improved level of fuel economy resulting from 

stricter GHG standards ).60 The amount by which the benefits from this increased driving 

travel exceed its increased fuel costs measures the net benefits they receive from the 

additional travel, usually referred to as increased consumer surplus. 

EPA estimates the economic value of the increased consumer surplus provided by 

added driving using the conventional approximation, which is one half of the product of the 

decline in vehicle operating costs per vehicle-mile and the resulting increase in the annual 

number of miles driven. Because it depends on the extent of improvement in fuel economy, 

the value of benefits from increased vehicle use changes by model year 

We discuss the rebound effect in more detail in Chapter 4 of the joint TSD. Again, the 

negative effect that rebound driving has on the fuel consumption savings associated with the 

GHG standards is included in the fuel economy savings presented in section 8.5 of this RIA. 

Note that in section 8.4.4 below, where we present the benefit associated with rebound 

driving, we have used pre-tax fuel prices since those prices reflect the societal value of the 

driving. 

8.3.3 Noise, Congestion, and Accidents 

Although it provides some benefits to drivers, increased vehicle use associated with 

the rebound effect also contributes to increased traffic congestion, motor vehicle accidents, 

and highway noise. Depending on how the additional travel is distributed over the day and on 

where it takes place, additional vehicle use can contribute to traffic congestion and delays by 

increasing traffic volumes on facilities that are already heavily traveled during peak periods. 

These added delays impose higher costs on drivers and other vehicle occupants in the form of 
Regulatory Impact Analysis 

8-20 

increased travel time and operating expenses. Because drivers do not take these added costs 

into account in deciding when and where to travel, they must be accounted for separately as a 

cost of the added driving associated with the rebound effect. 

Increased vehicle use due to the rebound effect may also increase the costs associated 

with traffic accidents. Drivers may take account of the potential costs they (and their 

passengers) face from the possibility of being involved in an accident when they decide to 

make additional trips. However, they probably do not consider all of the potential costs they 

impose on occupants of other vehicles and on pedestrians when accidents occur, so any 

increase in these “external” accident costs must be considered as another cost of additional 

rebound-effect driving. Like increased delay costs, any increase in these external accident 

costs caused by added driving is likely to depend on the traffic conditions under which it takes 

place, since accidents are more frequent in heavier traffic (although their severity may be 

reduced by the slower speeds at which heavier traffic typically moves). 

Finally, added vehicle use from the rebound effect may also increase traffic noise. 

Noise generated by vehicles causes inconvenience, irritation, and potentially even discomfort 

to occupants of other vehicles, to pedestrians and other bystanders, and to residents or 

occupants of surrounding property. Because these effects are unlikely to be taken into 

account by the drivers whose vehicles contribute to traffic noise, they represent additional 

externalities associated with motor vehicle use. Although there is considerable uncertainty in 

measuring their value, any increase in the economic costs of traffic noise resulting from added 

vehicle use must be included together with other increased external costs from the rebound 

effect. 

EPA relies on estimates of congestion, accident, and noise costs caused by 

automobiles and light trucks developed by the Federal Highway Administration to estimate 

the increased external costs caused by added driving due to the rebound effect.61 NHTSA 

employed these estimates previously in its analysis accompanying the MY 2011 final rule, 

and continues to find them appropriate for this analysis after reviewing the procedures used 

by FHWA to develop them and considering other available estimates of these values. They 

are intended to measure the increases in costs from added congestion, property damages and 

injuries in traffic accidents, and noise levels caused by automobiles and light trucks that are 

borne by persons other than their drivers (or “marginal” external costs). 

Updated to 2007 dollars, FHWA’s “Middle” estimates for marginal congestion, 

accident, and noise costs caused by automobile use amount to 5.2 cents, 2.3 cents, and 0.1 

cents per vehicle-mile (for a total of 7.6 cents per mile), while those for pickup trucks and 

vans are 4.7 cents, 2.5 cents, and 0.1 cents per vehicle-mile (for a total of 7.3 cents per 

mile).62, 63 These costs are multiplied by the annual increases in automobile and light truck 

use from the rebound effect to yield the estimated increases in congestion, accident, and noise 

externality costs during each future year. 

EPA uses a single value for both cars and trucks, as shown in Table 8-9. 
Other Economic and Social Impacts 

8-21 

Table 8-9 $/mile Inputs used for External Costs 

EXTERNAL COSTS $/VMT 

Congestion $ 0.052 

Accidents $ 0.023 

Noise $ 0.001 

8.3.4 Summary of Other Impacts 

Table 8-10 summarizes the other economic impacts discussed in sections 8.3.1 

through 8.3.3. 
Regulatory Impact Analysis 

8-22 

Table 8-10 Other Impacts Associated with the Light-Duty Vehicle GHG Program (Millions of 2007 

dollars) 

YEAR 

VALUE OF 

REDUCED 

REFUELING 

VALUE OF 

INCREASED 

DRIVING 

ACCIDENTS, 

NOISE, 

CONGESTION 

2012 $100 $200 -$100

2013 $300 $400 -$200

2014 $500 $700 -$400

2015 $700 $1,200 -$700

2016 $1,100 $1,800 -$1,000

2017 $1,500 $2,400 -$1,400

2018 $1,800 $3,000 -$1,700

2019 $2,100 $3,600 -$2,000

2020 $2,400 $4,200 -$2,300

2021 $2,700 $4,700 -$2,600

2022 $3,000 $5,300 -$2,900

2023 $3,300 $5,800 -$3,200

2024 $3,600 $6,200 -$3,400

2025 $3,800 $6,700 -$3,700

2026 $4,000 $7,200 -$3,900

2027 $4,200 $7,600 -$4,100

2028 $4,400 $8,100 -$4,300

2029 $4,600 $8,500 -$4,500

2030 $4,800 $8,800 -$4,600

2031 $4,900 $9,200 -$4,800

2032 $5,100 $9,600 -$4,900

2033 $5,300 $10,000 -$5,100

2034 $5,400 $10,400 -$5,200

2035 $5,600 $10,900 -$5,400

2036 $5,700 $11,300 -$5,500

2037 $5,900 $11,700 -$5,700

2038 $6,000 $12,100 -$5,800

2039 $6,200 $12,500 -$5,900

2040 $6,300 $13,000 -$6,100

2041 $6,500 $13,500 -$6,300

2042 $6,600 $13,900 -$6,400

2043 $6,800 $14,400 -$6,600

2044 $7,000 $14,900 -$6,700

2045 $7,100 $15,500 -$6,900

2046 $7,300 $16,000 -$7,100

2047 $7,500 $16,600 -$7,200

2048 $7,700 $17,200 -$7,400

2049 $7,800 $17,800 -$7,600

2050 $8,000 $18,400 -$7,800

NPV, 3% $87,900 $171,500 -$84,800

NPV, 7% $40,100 $75,500 -$38,600
Other Economic and Social Impacts 

8-23 

8.4 Summary of Costs and Benefits 

In this section we present a summary of costs, benefits, and net benefits of the rule. 

Table 8-11 shows the estimated annual societal costs of the vehicle program for the indicated 

calendar years. The table also shows the net present values of those costs for the calendar 

years 2012-2050 using both 3 and 7 percent discount rates. In this table, fuel savings are 

calculated using pre-tax fuel prices. 

Table 8-11 Estimated Societal Costs of the Light-Duty Vehicle GHG Program (Millions of 2007 dollars) 

COSTS 2020 2030 2040 2050 NPV, 3% NPV, 7% 

Vehicle Compliance Costs $15,600 $15,800 $17,400 $19,000 $345,900 $191,900 

Fuel Savings a -$35,700 -$79,800 -$119,300 -$171,200 -$1,545,600 -$672,600

Quantified Annual Costs -$20,100 -$64,000 -$101,900 -$152,200 -$1,199,700 -$480,700

a

 Calculated using pre-tax fuel prices. 

Table 8-12 presents estimated annual societal 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 a 3 percent and a 7 percent discount rate. The table shows the benefits of reduced 

GHG emissions—and consequently the annual quantified benefits (i.e., total benefits)—for 

each of four SCC values considered by EPA. As discussed in [the SCC TSD for this final 

rule], the models used to estimate SCC may not capture the economic effects of all possible 

adverse consequences of climate change and may therefore lead to underestimates of the 

SCC. 

In addition the monetized GHG benefits presented below exclude the value of 

reductions in non-CO2 GHG emissions (HFC, CH4, N2O) expected under this final rule. 

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 reductions in non-CO2 GHGs 

will contribute to this rule’s climate benefits, as explained in Section III.F.2. The technical 

support document, Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866, (i.e., SCC TSD) notes the difference between the social cost of non-CO2

emissions and CO2 emissions, and specifies a goal to develop methods to value non-CO2

emissions in future analyses.64
Regulatory Impact Analysis 

8-24 

Table 8-12 Use Estimated Societal Benefits Associated with the Light-Duty Vehicle GHG Program 

(Millions of 2007 dollars) 

BENEFITS 2020 2030 2040 2050 NPV, 3%A NPV, 7%A 

Reduced CO2 Emissions at each assumed SCC valueb,c

Avg SCC at 5% $900 $2,700 $4,600 $7,200 $34,500 $34,500

Avg SCC at 3% $3,700 $8,900 $14,000 $21,000 $176,700 $176,700

Avg SCC at 2.5% $5,800 $14,000 $21,000 $30,000 $299,600 $299,600

95th percentile SCC at 3% $11,000 $27,000 $43,000 $62,000 $538,500 $538,500

Criteria Pollutant 

Benefitsd,e,f,g B $1,200-

$1,300 

$1,200-

$1,300 

$1,200-

$1,300 $21,000 $14,000

Energy Security Impacts 

(price shock) $2,200 $4,500 $6,000 $7,600 $81,900 $36,900

Reduced Refueling $2,400 $4,800 $6,300 $8,000 $87,900 $40,100

Value of Increased Drivingh $4,200 $8,800 $13,000 $18,400 $171,500 $75,500

Accidents, Noise, 

Congestion -$2,300 -$4,600 -$6,100 -$7,800 -$84,800 -$38,600

Quantified Annual Benefits at each assumed SCC valueb,c

Avg SCC at 5% $7,400 $17,500 $25,100 $34,700 $312,000 $162,400

Avg SCC at 3% $10,200 $23,700 $34,500 $48,500 $454,200 $304,600

Avg SCC at 2.5% $12,300 $28,800 $41,500 $57,500 $577,100 $427,500

95th percentile SCC at 3% $17,500 $41,800 $63,500 $89,500 $816,000 $666,400

a Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to 

calculate net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. 

b

 Monetized GHG benefits exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4 and N2O) 

expected under this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 

emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO2 

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F.2. The SCC TSD notes the 

difference between the social cost of non-CO2 emissions and CO2 emissions, and specifies a goal to develop 

methods to value non-CO2 emissions in future analyses. 

c Section 7.5 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates 

range as follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 

2.5%: $35-$65; and for 95th percentile SCC at 3%: $65-$136. Section 7.5 also presents these SCC estimates. 

d Note that “B” indicates unquantified criteria pollutant benefits in the year 2020. For the final rule, we only 

modeled the rule's PM2.5- and ozone-related impacts in the calendar year 2030. For the purposes of estimating a 

stream of future-year criteria pollutant benefits, we assume that the benefits out to 2050 are equal to, and no less 

than, those modeled in 2030 as reflected by the stream of estimated future emission reductions. The NPV of 

criteria pollutant-related benefits should therefore be considered a conservative estimate of the potential benefits 

associated with the final rule.

e The benefits presented in this table include an estimate of PM-related premature mortality derived from Laden 

et al., 2006, and the ozone-related premature mortality estimate derived from Bell et al., 2004. If the benefit 

estimates were based on the ACS study of PM-related premature mortality (Pope et al., 2002) and the Levy et al., 

2005 study of ozone-related premature mortality, the values would be as much as 70% smaller.

f

 The calendar year benefits presented in this table assume either a 3% discount rate in the valuation of PMrelated premature mortality ($1,300 million) or a 7% discount rate ($1,200 million) to account for a twenty-year 

segmented cessation lag. Note that the benefits estimated using a 3% discount rate were used to calculate the 

NPV using a 3% discount rate and the benefits estimated using a 7% discount rate were used to calculate the 

NPV using a 7% discount rate. For benefits totals presented at each calendar year, we used the mid-point of the 

criteria pollutant benefits range ($1,250). 

g

 Note that the co-pollutant impacts presented here do not include the full complement of endpoints that, if 

quantified and monetized, would change the total monetized estimate of impacts. The full complement of 
Other Economic and Social Impacts 

8-25 

human health and welfare effects associated with PM and ozone remain unquantified because of current 

limitations in methods or available data. We have not quantified a number of known or suspected health effects 

linked with ozone and PM for which appropriate health impact functions are not available or which do not 

provide easily interpretable outcomes (e.g., changes in heart rate variability). Additionally, we are unable to 

quantify a number of known welfare effects, including reduced acid and particulate deposition damage to 

cultural monuments and other materials, and environmental benefits due to reductions of impacts of 

eutrophication in coastal areas. 

h

 Calculated using pre-tax fuel prices. 

Table 8-13 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 a 3 percent and a 7 percent discount rate. The table includes the benefits of 

reduced GHG emissions—and consequently the annual net benefits—for each of four SCC 

values considered by EPA. 

Table 8-13 Quantified Net Benefits Associated with the Light-Duty Vehicle GHG Programa

(Millions of 2007 dollars) 

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

Quantified Annual 

Costs -$20,100 -$64,000 -$101,900 -$152,200 -$1,199,700 -$480,700

Quantified Annual Benefits at each assumed SCC valueb,c

Avg SCC at 5% $7,400 $17,500 $25,100 $34,700 $312,000 $162,400

Avg SCC at 3% $10,200 $23,700 $34,500 $48,500 $454,200 $304,600

Avg SCC at 2.5% $12,300 $28,800 $41,500 $57,500 $577,100 $427,500

95th percentile SCC 

at 3% $17,500 $41,800 $63,500 $89,500 $816,000 $666,400

Quantified Net Benefits at each assumed SCC valueb,c

Avg SCC at 5% $27,500 $81,500 $127,000 $186,900 $1,511,700 $643,100

Avg SCC at 3% $30,300 $87,700 $136,400 $200,700 $1,653,900 $785,300

Avg SCC at 2.5% $32,400 $92,800 $143,400 $209,700 $1,776,800 $908,200

95th percentile SCC 

at 3% $37,600 $105,800 $165,400 $241,700 $2,015,700 $1,147,100

a Fuel impacts were calculated using pre-tax fuel prices. 

b

 Monetized GHG benefits exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4 and N2O) 

expected under this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 

emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO2 

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F.2. The SCC TSD notes the 

difference between the social cost of non-CO2 emissions and CO2 emissions, and specifies a goal to develop 

methods to value non-CO2 emissions in future analyses. 

c Section 7.5 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates 

range as follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 

2.5%: $35-$65; and for 95th percentile SCC at 3%: $65-$136. Section 7.5 also presents these SCC estimates. 

Note also that net present value of reduced CO2 emissions is calculated differently than other benefits. The 

same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is 

used to calculate net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. 

EPA also conducted a separate analysis of the total benefits over the model year 

lifetimes of the 2012 through 2016 model year vehicles. In contrast to the calendar year 

analysis presented in Table 8-11 through Table 8-13, the model year lifetime analysis shows 

the lifetime impacts of the program 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 5 of this RIA. The societal 
Regulatory Impact Analysis 

8-26 

benefits of the full life of each of the five model years from 2012 through 2016 are shown in 

Table 8-14 and Table 8-15 at both a 3 percent and a 7 percent discount rate, respectively. The 

net benefits are shown in Table 8-16 and Table 8-17 for both a 3 percent and a 7 percent 

discount rate, respectively. Note that the quantified annual benefits shown in Table 8-14 and 

Table 8-15 include fuel savings as a positive benefit. As such, the quantified annual costs as 

shown in Table 8-16 and Table 8-17 do not include fuel savings since those are included as 

benefits. Also note that Table 8-14 through Table 8-17 include the benefits of reduced CO2

emissions—and consequently the total benefits—for each of four SCC values considered by 

EPA. 

Table 8-14 Estimated Societal Benefits Associated with the Lifetimes of 2012-2016 Model year Vehicles 

(Millions of 2007 dollars; 3% Discount Rate) 

MONETIZED VALUES 2012MY 2013MY 2014MY 2015MY 2016MY SUM 

Cost of Noise, Accident, Congestion ($) -$1,100 -$1,600 -$2,100 -$2,900 -$3,900 -$11,600

Pretax Fuel Savings ($) $16,100 $23,900 $32,200 $46,000 $63,500 $181,800

Energy Security ($) (price shock) a $900 $1,400 $1,800 $2,500 $3,500 $10,100

Value of Reduced Refueling time ($) $1,100 $1,600 $2,100 $3,000 $4,000 $11,900

Value of Additional Driving ($) $2,400 $3,400 $4,400 $6,000 $7,900 $24,000

Value of PM2.5 related Health Impacts ($)b,c,d $700 $900 $1,300 $1,800 $2,400 $7,000

Reduced CO2 Emissions at each assumed SCC value e,f

Avg SCC at 5% $400 $500 $700 $1,000 $1,300 $3,800

Avg SCC at 3% $1,700 $2,400 $3,100 $4,400 $5,900 $17,000

Avg SCC at 2.5% $2,700 $3,900 $5,200 $7,200 $9,700 $29,000

95th percentile SCC at 3% $5,100 $7,300 $9,600 $13,000 $18,000 $53,000

Total Benefits at each assumed SCC value e,f

Avg SCC at 5% $20,500 $30,100 $40,400 $57,400 $78,700 $227,000

Avg SCC at 3% $21,800 $32,000 $42,800 $60,800 $83,300 $240,200

Avg SCC at 2.5% $22,800 $33,500 $44,900 $63,600 $87,100 $252,200

95th percentile SCC at 3% $25,200 $36,900 $49,300 $69,400 $95,400 $276,200

a Note that, due to a calculation error in the rule, the energy security impacts for the model year analysis were roughly half 

what they should have been. 

 b Note that the co-pollutant impacts associated with the standards presented here do not include the full complement of 

endpoints that, if quantified and monetized, would change the total monetized estimate of rule-related 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 associated with the vehicle model year lifetimes for the final rule. 

c 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). If the benefit-per-ton estimates were based on the Six 

Cities study (Laden et al., 2006), the values would be approximately 145% (nearly two-and-a-half times) larger. 

d

 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. 

e Monetized GHG benefits exclude the value of reductions in non-CO2

 GHG emissions (HFC, CH4 and N2O) expected under 

this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 emissions, the value of these 

reductions should not be interpreted as zero. Rather, the reductions in non-CO2 GHGs will contribute to this rule’s climate 

benefits, as explained in Section III.F.2. The SCC TSD notes the difference between the social cost of non-CO2 emissions 

and CO2 emissions, and specifies a goal to develop methods to value non-CO2 emissions in future analyses. 

f Section 7.5 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates range as 

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 2.5%: $35-$65; and for 

95th percentile SCC at 3%: $65-$136. Section 7.5 also presents these SCC estimates. Note that net present value of reduced 

CO2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages 

from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer 

to the SCC TSD for more detail. 
Other Economic and Social Impacts 

8-27 

Table 8-15 Estimated Societal Benefits Associated with the Lifetimes of 2012-2016 Model year Vehicles 

(Millions of 2007 dollars; 7% Discount Rate) 

MONETIZED VALUES 2012MY 2013MY 2014MY 2015MY 2016MY SUM 

Cost of Noise, Accident, Congestion ($) -$900 -$1,200 -$1,600 -$2,300 -$3,100 -$9,200

Pretax Fuel Savings ($) $12,500 $18,600 $25,100 $36,000 $49,600 $141,900

Energy Security ($) (price shock) a $800 $1,100 $1,400 $2,000 $2,700 $8,000

Value of Reduced Refueling time ($) $900 $1,300 $1,700 $2,400 $3,200 $9,400

Value of Additional Driving ($) $1,900 $2,700 $3,500 $4,700 $6,200 $19,000

Value of PM2.5 related Health Impacts ($)b,c,d $500 $800 $1,000 $1,400 $1,900 $5,600

Reduced CO2 Emissions at each assumed SCC value e,f

Avg SCC at 5% $400 $500 $700 $1,000 $1,300 $3,800

Avg SCC at 3% $1,700 $2,400 $3,100 $4,400 $5,900 $17,000

Avg SCC at 2.5% $2,700 $3,900 $5,200 $7,200 $9,700 $29,000

95th percentile SCC at 3% $5,100 $7,300 $9,600 $13,000 $18,000 $53,000

Total Benefits at each assumed SCC value e,f

Avg SCC at 5% $16,100 $23,800 $31,800 $45,200 $61,800 $178,500

Avg SCC at 3% $17,400 $25,700 $34,200 $48,600 $66,400 $191,700

Avg SCC at 2.5% $18,400 $27,200 $36,300 $51,400 $70,200 $203,700

95th percentile SCC at 3% $20,800 $30,600 $40,700 $57,200 $78,500 $227,700

a Note that, due to a calculation error in the rule, the energy security impacts for the model year analysis were roughly half 

what they should have been. 

 b Note that the co-pollutant impacts associated with the standards presented here do not include the full complement of 

endpoints that, if quantified and monetized, would change the total monetized estimate of rule-related 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 associated with the vehicle model year lifetimes for the final rule. 

c 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). If the benefit-per-ton estimates were based on the Six 

Cities study (Laden et al., 2006), the values would be approximately 145% (nearly two-and-a-half times) larger. 

d

 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. 

e Monetized GHG benefits exclude the value of reductions in non-CO2

 GHG emissions (HFC, CH4 and N2O) expected under 

this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 emissions, the value of these 

reductions should not be interpreted as zero. Rather, the reductions in non-CO2 GHGs will contribute to this rule’s climate 

benefits, as explained in Section III.F.2. The SCC TSD notes the difference between the social cost of non-CO2 emissions 

and CO2

 emissions, and specifies a goal to develop methods to value non-CO2 emissions in future analyses.

f Section 7.5 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates range as 

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 2.5%: $35-$65; and for 

95th percentile SCC at 3%: $65-$136. Section 7.5 also presents these SCC estimates. Note that net present value of reduced 

CO2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages 

from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer 

to the SCC TSD for more detail. 
Regulatory Impact Analysis 

8-28 

Table 8-16. Quantified Net Benefits Associated with the Lifetimes of 2012-2016 Model Year Vehicles 

(Millions of 2007 dollars; 3% Discount Rate) 

2012MY 2013MY 2014MY 2015MY 2016MY SUM 

Quantified Annual Costs 

(excluding fuel savings)a $4,900 $8,000 $10,300 $12,700 $15,600 $51,500

Quantified Annual Benefits at each assumed SCC valueb,c 

Avg SCC at 5% $20,500 $30,100 $40,400 $57,400 $78,700 $227,000

Avg SCC at 3% $21,800 $32,000 $42,800 $60,800 $83,300 $240,200

Avg SCC at 2.5% $22,800 $33,500 $44,900 $63,600 $87,100 $252,200

95th percentile SCC at 3% $25,200 $36,900 $49,300 $69,400 $95,400 $276,200

Quantified Net Benefits at each assumed SCC valueb,c 

Avg SCC at 5% $15,600 $22,100 $30,100 $44,700 $63,100 $175,500

Avg SCC at 3% $16,900 $24,000 $32,500 $48,100 $67,700 $188,700

Avg SCC at 2.5% $17,900 $25,500 $34,600 $50,900 $71,500 $200,700

95th percentile SCC at 3% $20,300 $28,900 $39,000 $56,700 $79,800 $224,700

a Quantified annual costs as shown here are the increased costs for new vehicles in each given model year. Since 

those costs are assumed to occur in the given model year (i.e., not over a several year time span), the discount 

rate does not affect the costs.

b Monetized GHG benefits exclude the value of reductions in non-CO2

 GHG emissions (HFC, CH4 and N2O) expected under 

this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 emissions, the value of these 

reductions should not be interpreted as zero. Rather, the reductions in non-CO2 GHGs will contribute to this rule’s climate 

benefits, as explained in Section III.F.2. The SCC TSD notes the difference between the social cost of non-CO2 emissions 

and CO2 emissions, and specifies a goal to develop methods to value non-CO2 emissions in future analyses.

c Section 7.5 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates range as 

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 2.5%: $35-$65; and for 

95th percentile SCC at 3%: $65-$136. Section 7.5 also presents these SCC estimates. Note that net present value of reduced 

CO2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages 

from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer 

to the SCC TSD for more detail. 
Other Economic and Social Impacts 

8-29 

Table 8-17 Quantified Net Benefits Associated with the Lifetimes of 2012-2016 Model Year Vehicles 

(Millions of 2007 dollars; 7% Discount Rate) 

2012MY 2013MY 2014MY 2015MY 2016MY SUM 

Quantified Annual Costs 

(excluding fuel savings)a $4,900 $8,000 $10,300 $12,700 $15,600 $51,500

Quantified Annual Benefits at each assumed SCC valueb,c 

Avg SCC at 5% $16,100 $23,800 $31,800 $45,200 $61,800 $178,500

Avg SCC at 3% $17,400 $25,700 $34,200 $48,600 $66,400 $191,700

Avg SCC at 2.5% $18,400 $27,200 $36,300 $51,400 $70,200 $203,700

95th percentile SCC at 3% $20,800 $30,600 $40,700 $57,200 $78,500 $227,700

Quantified Net Benefits at each assumed SCC valueb,c 

Avg SCC at 5% $11,200 $15,800 $21,500 $32,500 $46,200 $127,000

Avg SCC at 3% $12,500 $17,700 $23,900 $35,900 $50,800 $140,200

Avg SCC at 2.5% $13,500 $19,200 $26,000 $38,700 $54,600 $152,200

95th percentile SCC at 3% $15,900 $22,600 $30,400 $44,500 $62,900 $176,200

a Quantified annual costs as shown here are the increased costs for new vehicles in each given model year. Since 

those costs are assumed to occur in the given model year (i.e., not over a several year time span), the discount 

rate does not affect the costs.

b Monetized GHG benefits exclude the value of reductions in non-CO2

 GHG emissions (HFC, CH4 and N2O) expected under 

this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 emissions, the value of these 

reductions should not be interpreted as zero. Rather, the reductions in non-CO2 GHGs will contribute to this rule’s climate 

benefits, as explained in Section III.F.2. The SCC TSD notes the difference between the social cost of non-CO2 emissions 

and CO2

 emissions, and specifies a goal to develop methods to value non-CO2 emissions in future analyses.

c Section 7.5 notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates range as 

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $21-$45; for Average SCC at 2.5%: $35-$65; and for 

95th percentile SCC at 3%: $65-$136. Section 7.5 also presents these SCC estimates. Note that net present value of reduced 

CO2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages 

from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer 

to the SCC TSD for more detail. 
Regulatory Impact Analysis 

8-30 

References 

1

 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-HQOAR-2009-0472-0007). 

2

 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 

2005-2006,” http://www.iii.org/media/facts/statsbyissue/auto/, accessed April 23, 2009 

(Docket EPA-HQ-OAR-2009-0472-0008). 

3

 U.S. Department of Energy, 2008, “Average Price of a New Car, 1970-2006,” 

http://www1.eere.energy.gov/vehiclesandfuels/facts/2008_fotw520.html, accessed April 23, 

2009 (Docket EPA-HQ-OAR-2009-0472-0009). 

4

 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). 

5

 U.S. Census Bureau, “Population, Population change and estimated components of 

population change: April 1, 2000 to July 1, 2008” (NST-EST2008-alldata), 

http://www.census.gov/popest/states/states.html, accessed April 23, 2009 (Docket EPA-HQOAR-2009-0472-0011). 

6

“Auto Loan Interest Rate”, EPA-HQ-OAR-2009-0472-11575 http://auto-loan.interest.com, 

1/22/10. 

7

 Consumer Reports, August 2008,“What That Car Really Costs to Own,” 

http://www.consumerreports.org/cro/cars/pricing/what-that-car-really-costs-to-own-4-

08/overview/what-that-car-really-costs-to-own-ov.htm , accessed April 23, 2009 (Docket 

EPA-HQ-OAR-2009-0472-0013). 

8

 Kleit A.N., 1990, “The Effect of Annual Changes in Automobile Fuel Economy Standards,” 

Journal of Regulatory Economics 2: 151-172 (Docket EPA-HQ-OAR-2009-0472-0015); 

McCarthy, Patrick S., 1996. “Market Price and Income Elasticities of New Vehicle 

Demands.” Review of Economics and Statistics 78: 543-547 (Docket EPA-HQ-OAR-2009-

0472-0016); Goldberg, Pinelopi K., 1998. “The Effects of the Corporate Average Fuel 

Efficiency Standards in the US,” Journal of Industrial Economics 46(1): 1-33 (Docket EPAHQ-OAR-2009-0472-0017); Greene, David L., “Feebates, Footprints and Highway Safety,” 

Transportation Research Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR-2009-0472-

0019). 

9

 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-2009-0472-0021); 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-2009-0472-0017). 
Other Economic and Social Impacts 

8-31 

10 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2009-0472-0022); 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-2009-0472-0023). 

11 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-2009-0472-0024). 

12 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2009-0472-

0025); 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-2009-0472-0026). 

13 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 

(Docket EPA-HQ-OAR-2009-0472-0029). 

14 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-2009-0472-0030). 

15 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-2009-

0472-0031). 

16 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-2009-0472-0032). 

17 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: An 

Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 

282-287 (Docket EPA-HQ-OAR-2009-0472-0029); 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-OAR2009-0472-0030). 

18 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-2009-0472-

0025). 
Regulatory Impact Analysis 

8-32 

19 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2009-0472-0022). 

20 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_Anal

ysis_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2009-0472-0033). 

21 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-2009-0472-0030). 

22 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-2009-0472-

0025). 

23 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: An 

Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 

282-287 (Docket EPA-HQ-OAR-2009-0472-0029). 

24 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 

(Docket EPA-HQ-OAR-2009-0472-0029); 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-2009-0472-0034). 

25 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the 

California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” comments 

submitted to the U.S. Environmental Protection Agency at Regulations.gov, document 

number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2009-0472-0035). 

26 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2009-0472-

0025). 

27 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-2009-0472-0021). 

28 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with 

Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPAHQ-OAR-2009-0472-0036); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint 
Other Economic and Social Impacts 

8-33 

Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” 

Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2009-0472-

00357); 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-2009-0472-0038); Greene, David L., K. G. 

Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in the U.S. 

Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for the U.S. 

Department of Energy, August 2004 (Docket EPA-HQ-OAR-2009-0472-0039). 

29 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-2009-0472-

0040); 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-2009-0472-0026). 

30 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-2009-0472-0030). 

31 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 

(Docket EPA-HQ-OAR-2009-0472-0029); 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-2009-0472-0034). 

32 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-2009-0472-0023); 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-2009-0472-0034); Greene, David L., “Feebates, 

Footprints and Highway Safety,” Transportation Research Part D (2009) (in press) (Docket 

EPA-HQ-OAR-2009-0472-0019). 

33 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-2009-0472-0032). 

34 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2009-0472-0041). 

35 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-2009-0472-

0042). 
Regulatory Impact Analysis 

8-34 

36 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 

1593-1594 (Docket EPA-HQ-OAR-2009-0472-0043). 

37 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-2009-0472-0021). 

38 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-2009-0472-

0025). 

39 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://www.econ.ucdavis.edu/faculty/knittel/papers/gaspaper_latest.pdf (accessed 

6/16/09) (Docket EPA-HQ-OAR-2009-0472-0044); 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). Effects of Gasoline Prices on Driving Behavior and Vehicle Markets. The Congress 

of the United States, Pub. No. 2883; 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. 

40 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-2009-0472-0045). 

41 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

EPA Report EPA-420-R-10-008. (Docket EPA-HQ-OAR-2009-0472-11575). 

42 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://www.econ.yale.edu/seminars/apmicro/am08/gramlich-081216.pdf , accessed 

5/11/09 (Docket EPA-HQ-OAR-2009-0472-0046). 

43 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://www.econ.yale.edu/seminars/apmicro/am08/gramlich-081216.pdf , accessed 

5/11/09 (Docket EPA-HQ-OAR-2009-0472-0046); 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-2009-0472-0054). 

44 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-OAR2009-0472-0031); 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-OAR2009-0472-0032); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and 

the Cost of the Corporate Average Fuel Economy Standard,” Federal Reserve Bank of 
Other Economic and Social Impacts 

8-35 

Chicago WP 2008-13, at 

http://www.chicagofed.org/publications/workingpapers/wp2008_13.pdf (accessed 5/6/09) 

(Docket EPA-HQ-OAR-2009-0472-0047); Jacobsen, Mark, 2008. “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 5/6/09) (Docket EPA-HQOAR-2009-0472-0048). 

45 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-2009-0472-0032). 

46 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2009-0472-0049); Jaffe, 

Adam B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies 

and Climate Change Policies: Issues and Evidence." In Climate Change Economics and 

Policy, Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 

(Docket EPA-HQ-OAR-2009-0472-0050); 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 EPAHQ-OAR-2009-0472-0051); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: 

Pipe Dream or Pipeline to the Future?” Review of Environmental Economics and Policy. 

Vol.3, 2: 304-320 (Docket EPA-HQ-OAR-2009-0472-0052). 

47 Helfand, Gloria, and Ann Wolverton (2009). “Evaluating the Consumer Response to Fuel 

Economy: A Review of the Literature.” U.S. Environmental Protection Agency, National 

Center for Environmental Economics Working Paper 2009-4, available at 

http://yosemite.epa.gov/EE/epa/eed.nsf/WPNumber/2009-04?OpenDocument, accessed 

1/25/10 (Docket EPA-HQ-OAR-2009-0472-11520). 

48 Jaffe, A.B., and R.N Stavins (1994). “The Energy Paradox and the Diffusion of 

Conservation Technology.” Resource and Energy Economics 16(2): 91-122 (Docket EPAHQ-OAR-2009-0472-11415); Allcott, Hunt, and Nathan Wozny, “Gasoline Prices, Fuel 

Economy, and the Energy Paradox,” working paper, available at 

http://web.mit.edu/allcott/www/Allcott%20and%20Wozny%202010%20-

%20Gasoline%20Prices,%20Fuel%20Economy,%20and%20the%20Energy%20Paradox.pdf 

(Docket EPA-HQ-OAR-2009-0472-11554). 

49 Barber, Brad, Terrance Odean, and Lu Zheng. “Out of Sight, Out of Mind: The Effects of 

Expenses on Mutual Fund Flows.” Journal of Business 78(6) (2005): 2095-2119 (Docket 

EPA-HQ-OAR-2009-0472-11555).

50 Welch, David. “CAFE: A Prize for Making Gas-Guzzlers?” Business Week April 16, 

2009, 

http://www.businessweek.com/magazine/content/09_17/b4128048030307.htm?chan=magazin

e+channel_what's+next , accessed 7/7/09 (Docket EPA-HQ-OAR-2009-0472-0053). 
Regulatory Impact Analysis 

8-36 

51 Berry, Steven, James Levinsohn, and Ariel Pakes (July 1995). “Automobile Prices in 

Market Equilibrium,” Econometrica 63(4): 841-940 (Docket EPA-HQ-OAR-2009-0472-

0025). 

52 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-2009-0472-0023). 

53 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing 

can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576. 

54 Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil 

Imports, ICF, Inc., September 2007. 

55 Estimated reductions in imports of finished petroleum products and crude oil are 95% of 

108 MMB in 2015, 353MMB in 2020, 637MMB in 2030,and 740MMB in 2040. 

56 Preliminary RIA in support of the "Corporate Average Fuel Economy Standards For 

MY 2011 - 2015 Passenger Cars and Light Trucks", April 2008 

57 See http://ostpxweb.dot.gov/policy/Data/VOT97guid.pdf and 

http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf

58 California Environmental Protection Agency, Air Resources Board. Draft Assessment of 

the Real-World Impacts of Commingling California Phase 3 Reformulated Gasoline. August 

2003 

59 The 19.3 gallon average tank size is from EPA calculations conducted on the Volpe Model 

Market Data file used in NHTSA’s Model Year 2011 CAFE Standards Final Rule. 

60 These benefits are included in the value of fuel savings reported in Tables VIII-5 through 

VIII-9. 

61 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost 

Allocation Study; see http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed 

July 29, 2009). 

62

 See Federal Highway Administration, 1997 Federal Highway Cost Allocation Study, 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm, Tables V-22, V-23, and V-24 (last 

accessed July 27, 2009). 

63 The Federal Highway Administration’s estimates of these costs agree closely with some 

other recent estimates. For example, recent published research conducted by Resources for 

the Future (RFF) estimates marginal congestion and external accident costs for increased 

light-duty vehicle use in the U.S. to be 3.5 and 3.0 cents per vehicle-mile in year-2002 dollars. 

See Ian W.H. Parry and Kenneth A. Small, “Does Britain or the U.S. Have the Right Gasoline 

Tax?” Discussion Paper 02-12, Resources for the Future, 19 and Table 1 (March 2002). 
Other Economic and Social Impacts 

8-37 

Available at http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed July 27, 

2009). 

64 Interagency Working Group on Social Cost of Carbon, U.S. Government, 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, “Social Cost of Carbon for Regulatory Impact Analysis Under 

Executive Order 12866,” February 2010, available in docket EPA-HQ-OAR-2009-0472. 
Small Business Flexibility Analysis 

9-1 

CHAPTER 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’). During the Panel process, we would gather information 

and recommendations from Small Entity Representatives (SERs) on how to reduce the 

impact of the rule on small entities. This requirement does not apply if the agency 

certifies that the rule will not have a significant economic impact on a substantial number 

of small entities. 

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); (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 provides an overview of the primary 

SBA small business categories potentially affected by this regulation. 

Table 9-1 Primary Vehicle SBA Small Business Categories 

NAICSa Codes Defined by SBA As a 

small business if less than 

or equal to :b

Light-duty vehicle manufacturers 336111 1,000 employees. 

Vehicle importers 81111, 811112 $7 million annual sales. 

Alternative fuel vehicle converters 811198 $7 million annual sales. 

a. North American Industry Classification System 

b. According to SBA’s regulations (13 CFR 121), businesses with no more than the listed number of 

employees or dollars in annual receipts are considered “small entities” for RFA purposes.

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 2008 model year certification databases. These companies are already certifying 

their vehicles for compliance with applicable EPA emissions standards (e.g., Tier 2). We 

then identified companies that appear to meet the definition of small business provided in 

the table 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 47 vehicle entities, 33 of 

which are vehicle manufacturers. Of a total of 33 manufacturers, two fit the SBA 
Regulatory Impact Analysis 

9-2 

definition of a small entity. These businesses produce vehicles for small niche markets, 

and all of these entities manufacture limited production, high performance cars. 

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). There are currently eight 

ICIs, all of which are small entities. 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 small 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. We 

identified six alternative fuel converters in the light-duty vehicle market, and three of 

these qualify as small entities under SBA’s definition. 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. 

EPA has not conducted a Regulatory Flexibility Analysis or a SBREFA SBAR 

Panel for the rule because we are certifying that the rule would not have a significant 

economic impact on a substantial number of small entities. EPA is exempting 

manufacturers, domestic and foreign, meeting SBA’s size definitions of small business as 

described in 13 CFR 121.201. EPA will instead consider appropriate GHG standards for 

these entities as part of a future regulatory action. This includes small entities in three 

distinct categories of businesses for light-duty vehicles: small volume manufacturers, 

independent commercial importers (ICIs), and alternative fuel vehicle converters. EPA 

has identified about 13 entities that fit the Small Business Administration (SBA) criterion 

of a small business. EPA estimates that these small entities comprise less than 0.1 

percent of the total light-duty vehicle sales in the U.S., and therefore the exemption will 

have a negligible impact on the GHG emissions reductions from the final standards.A

 

 

To ensure that EPA is aware of which companies would be exempt, EPA 

proposed to require that such entities submit a declaration to EPA containing a detailed 

written description of how that manufacturer qualifies as a small entity under the 

provisions of 13 CFR 121.201. EPA has reconsidered the need for this additional 

submission under the regulations and is deleting it as not necessary. We already have 

 

A

 It should be noted that EPA is deferring CO2 standards for small volume manufacturers with annual sales 

less than 5,000 vehicles. See preamble section III.B.6. This deferral is not dependent on whether the entity 

in question meets the SBA definition of small entity. 
Small Business Flexibility Analysis 

9-3 

information on the limited number of small entities that we expect would receive the 

benefits of the exemption, and do not need the proposed regulatory requirement to be 

able to effectively implement this exemption for those parties who in fact meet its terms. 

Small entities are currently covered by a number of EPA motor vehicle emission 

regulations, and they routinely submit information and data on an annual basis as part of 

their compliance responsibilities. The net effect is that these entities are not regulated by 

the light duty vehicle greenhouse gas rule.

Responses to comments that the rule has an adverse impact on small entity 

stationary sources which are not regulated by the rule can be found at Section 5.14 of the 

Response to Comments.