# Draft Regulatory Impact Analysis

Proposed Rulemaking to Establish Light

Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards

### EPA-420-D-09-003 September 2009 

### Assessment and Standards Division Office of Transportation and Air Quality U.S. Environmental Protection Agency 

# Draft Regulatory Impact Analysis

Proposed Rulemaking to Establish Light

Duty Vehicle Greenhouse Gas Emission

Standards and Corporate Average Fuel Economy StandardsDraft Regulatory Impact Analysis

i Table of Contents

TABLE OF CONTEN TS............................................................................... i

LIST OF ACRONYMS................................................................................ vi

EXECUTIVE SUMMARY............................................................................ ix Greenhouse Emission Impacts of EPA’s Proposal................................................................ x Criteria Pollutant Impacts of EPA’s Proposal ..................................................................... xi Costs and Benefits of EPA’s Proposal ................................................................................. xiii

CHAPTER 1: TECHNOLOGY PACKAGES, COST AND EFFECTIVENESS 1-1

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

1.2 Technology Cost and Effectiveness ............

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

1.3 Package Cost and Effectiveness................................................................................ 1-10

1.3.1 Explanation of Technology Packages ............................................................ 1-10

1.3.2 Technology Package Costs & Effectiveness .................................................. 1-12

1.4 EPA’s Lumped Parameter Approach for Determining Effectiveness Synergies. 1-26

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......................................................... 1-31

1.4.5 Technologies Considered by EPA and Ricardo in the Vehicle Simulation 1-32

1.4.6 Choice of Technology Packages ..................................................................... 1-34

1.4.7 Simulation Results........................................................................................... 1-35

1.5 Comparison of Lumped-Parameter Results to Modeling Results......................... 1-36

1.6 Using the Lumped-Parameter Technique to Determine Synergies in a Technology Application Flowpath (Identifying “Technology Pairs” to account for synergies) ....... 1-38

CHAPTER 2: AIR CONDITIONING....

................................................ ............ 2-2

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

2.2 Air Conditioner Leakage............................................................................................. 2-4

2.2.1 Impacts of Refrigerant Leakage on Greenhouse Gas Emissions .................. 2-4

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

2.2.3 Technologies That Reduce Refrigerant Leakage and their Effectiveness.. 2-11

2.2.4 Technical Feasibility of Leakage-Reducing Technologies ........................... 2-14

2.2.5 Deterioration of Leakage Controls in A/C Systems ..................................... 2-14

2.2.6 Other Benefits of improving A/C Leakage Performance ............................ 2-18Draft Regulatory Impact Analysis

ii 2.3 CO 2 Emissions due to Air Conditioners................................................................... 2-19

2.3.1 Impact of Air Conditioning Use on Fuel Consumption and CO2 Emissions. 2- 19

2.3.2 Technologies That Improve Efficiency of Air Conditioning and Their Effectiveness ..................................................................................................................

.. 2-28

2.3.3 Technical Feasibility of Efficiency-Improving Technologies ...................... 2-32

2.3.4 A/C Efficiency Credits .................................................................................... 2-32

2.4 Costs of A/C reducing technologies.......................................................................... 2-37

2.5 Air Conditioning Credit Summary .......................................................................... 2-39

CHAPTER 3: TECHNICAL BASIS OF THE STANDARDS......................... 3-1

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

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

3.1.2 Overview of Equivalency Calculation. ............................................................ 3-2

3.2 Analysis of Footprint Approach for Establishing Individual Company Standards 3- 7

3.2.1 “Footprint” as a vehicle attribute .................................................................... 3-8

3.2.2 Alternative Attributes ..................................................................................... 3-14

3.2.3 EPA Selection of the Footprint Attribute...................................................... 3-20

3.3 Supplemental Analysis of Relative Car and Truck Standards.............................. 3-23

CHAPTER 4: RESULTS OF PROPOSED AND ALTERNATIVE STANDARDS 4-1

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

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

4.2.1 Representation of the CO2 Control T echnology 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 Per Vehicle Costs 2012-2016 ..................................................................................... 4-16

4.6 Technology Penetration...............................

.............................................................. 4-19

4.7 Manufacturer-Specific Standards ............................................................................... 24

4.8 Alternative Program Stringencies............................................................................... 25

4.9 Assessment of Manufacturer Differences ................................................................ 4-33

CHAPTER 5: EMISSIONS IMPACTS ............................................................. 5-1

5.1 Overview ....................................................................................................................... 5-1Draft Regulatory Impact Analysis

iii 5.2 Introduction.................................................................................................................. 5-3

5.2.1 Scope of Analysis ............................................................................................... 5-3

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

5.2.3 Upstream Contributions ................................................................................... 5-4

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

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

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

5.3.2 Description of Scenarios.................................................................................... 5-6

5.3.3 Calculation of Downstream Emissions .......................................................... 5-14

5.3.4 Calculation of Upstream Emissions............................................................... 5-24

5.4 Greenhouse Gas Emission Inventory....................................................................... 5-24

5.5 Non-Greenhouse Gas Emission Inventory............................................................... 5-26

5.5.1 Downstream Impacts of Program.................................................................. 5-28

5.5.2 Upstream Impacts of Program....................................................................... 5-29

5.5.3 Total Program Impact .................................................................................... 5-30

5.6 Model Year Lifetime Analyses.................................................................................. 5-31

5.6.1 Methodology..................................................................................................... 5-31

5.6.2 Results............................................................................................................... 5-33

5.7 Alternative 4% and 6% Scenarios ........................................................................... 5-34

5.7.1 4% Scenario ..................................................................................................... 5-35

5.7.2 6% Scenario ..................................................................................................... 5-37

5.A Appendix to Chapter 5: Details of the TLAAS Impacts Analysis........................ 5-40

5.A.1 Introduction and Summary............................................................................ 5-40

5.A.2 Factors Determining the Impact of the TLAAS ........................................... 5-40

5.A.3 Bounding Analysis of TLAAS Impact ........................................................... 5-41

5.A.4 Approach used for Estimating TLAAS Impact............................................ 5-42

CHAPTER 6: VEHICLE PROGRAM COSTS INCLUDING FUEL CONSUMPTION IMPACTS 6-1

6.1 Vehicle Program Costs ................................................................................................ 6-1

6.1.1 Vehicle Compliance Costs on a Per-Vehicle Basis.......................................... 6-1

6.1.2 Vehicle Compliance Costs on a Per-Year Basis.............................................. 6-7

6.2 Cost per Ton of Emissions Reduced......................................................................... 6-11

6.3 Fuel Consumption Impacts ....................................................................................... 6-12Draft Regulatory Impact Analysis

iv 6.4 Vehicle Program Cost Summary.............................................................................. 6-15

CHAPTER 7: ENVIRONMENTAL AND HEALTH IMPACTS . ..............7- 1

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- 13

7.2 Non-GHG Air Quality Impacts ................................................................................7- 26

7.2.1 Introduction .....................................................................................................7- 26

7.2.2 Current Levels of Pollutants...........................................................................7- 26

7.2.3 Impacts on Future Air Quality.......................................................................7- 28

7.3 Quantified and Monetized Co-Pollutant Health and Environmental Impacts ....7- 31

7.3.1 Economic Value of Reductions in Criteria Pollutants .................................7- 32

7.3.2 Human Health and Environmental Benefits for the Final Rule .................7- 37

7.4 Changes in Global Mean Temperature and Sea-Level Rise Associated with the Proposal’s GHG Emissions Reductions.............................................................................7- 43

7.4.1 Introduction .....................................................................................................7- 43

7.4.2 Estimated Projected Reductions in Global Mean Surface Temperature and Sea-Level Rise ................................................................................................................. 7- 43

7.5 SCC and GHG Benefits.............................................................................................7- 46

7.6 Weight Reduction and Vehicle Safety......................................................................7- 56

CHAPTER 8: OTHER ECONOMIC AND SOCIAL IMPACTS ................... 8-1

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

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

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

8.1.3 Consumer Payback Period and Lifetime Savings on New Vehicle Purchases 8-11

8.2 .......................................................................................................................................... 8-14

8.3 Energy Security Impacts ........................................................................................... 8-14

8.4 Other Externalities..................................................................................................... 8-16

8.4.1 Reduced Refueling Time................................................................................. 8-16

8.4.2 Value of Additional Driving ........................................................................... 8-17

8.4.3 Noise, Congestion, and Accidents................................................................... 8-17

8.4.4 Summary of Other Externalities.................................................................... 8-18

8.5 Summary of Costs and Benefits................................................................................ 8-20Draft Regulatory Impact Analysis

v CHAPTER 9: SMALL BUSINESS FLEXIBILITY ANALYSIS .................... 9- 1Draft Regulatory Impact 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 CO 2 : 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 YearDraft Regulatory Impact Analysis

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: Turbocharg er/Turbocharging V6: 6-cylinder engine in a “V” configuration V8: 8-cylinder engine in a “V” configuration WGII: Working group IIDraft Regulatory Impact Analysis

viii This Page Intentionally Left BlankDraft Regulatory Impact Analysis

ix 

## Executive Summary

The Environmental Protection Agency (EPA) and the National Highway Traffic Safety Administration (NHTSA) are issuing a jo int proposal to establish new standards for light-duty highway vehicles that will reduce greenhouse gas emissions and improve fuel economy. The joint proposed 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 proposing greenhouse gas emissions standards under the Clean Air Act, and NHTSA is proposing Corporate Average Fuel Economy standards u nder the Energy Policy and Conservation Act, as amended. These standards apply to passe nger cars, light-duty trucks, and medium-duty passenger vehicles, covering model years 2012 through 2016. They would require these vehicles to meet an estimated combined average emissions level of 250 grams of CO 2 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 a nd consistent national program (National Program). These standards are designed such that compliance can be achieved with a single national vehicle fleet whose emissions and fu el economy performance improves year over year. The proposed National Program would resu lt in approximately 950 million metric tons of CO 2 emission reductions and approximately 1.8 billion barrels of oil savings over the lifetime of vehicles sold in model years 2012 through 2016.

The vehicle categories covered by the rulemaking are responsible for almost 60 percent of all U.S. transportation-related gree nhouse gas emissions an d include cars, sport utility vehicles, minivans, and pickup trucks used for personal transportation. Transportation related emissions are responsibl e for approximately 30 percent of total U.S. greenhouse gas emissions. Under the National Program, automobile manufacturers would be able to build a single light-duty national fleet that satisfies all requirements under both programs while ensuring that consumers still have a full range of vehicle choices.

This draft regulatory impact analysis (DRIA) contains supporting documentation to the EPA proposal. NHTSA has prepared their ow n Proposed RIA (PRIA) in support of their proposal (this can be found in NHTSA’s docket for their proposal, NHTSA-2009-0059). While the two proposals are similar, there are al so differences in the analyses that require separate discussion. This is largely becaus e EPA and NHTSA act under different statutes. EPA’s authority comes under the Clean Air Ac t, and NHTSA’s authority comes under EPCA, and each statute has somewhat different requir ements 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 co ntained in the draft joint technical support document (draft joint TSD, which can be fo und in EPA’s docket EP A-HQ-OAR-2009-0472). Therefore, this DRIA should be viewed as a companion document to the draft joint TSD and the two documents together provide the details of EPA’s technical analysis in support of its proposal.Draft Regulatory Impact Analysis

x Specifically, this document contains, in Ch apter 1, a description of EPA’s use of technology packages in the OMEGA model. This discussion builds on the discussion contained in Chapter 3 of the draft joint TSD wh ich provides details of technology costs and effectiveness but only an overview of how technol ogies are put together into packages for the OMEGA model. Chapter 1 also contains a discussion of the lumped parameter model which is a major part of our determination of the effectiveness of these packages.

In Chapter 2, we present a detailed discussion of our AC credit program and the technology costs and effectiveness associated with new AC systems. This discussion is unique to this DRIA as the AC-related proposal is unique to EPA.

In Chapter 3, we present the technical basis of EPA’s proposed standards and an analysis of the “footprint” approach EPA is proposing for establishing standards. In Chapter 4, we present an overview of the OMEGA mo del and the modeling results (actual OMEGA model inputs and outputs) in support of the proposed program and the alternative standards that were considered. Chapter 5 presents the em ission reductions expected from the proposal. Chapter 6 presents the program costs and fuel savings associated with EPA’s proposal. Chapter 7 presents the environmental and health impacts, including EPA’s discussion of the social cost of carbon, and Chapter 8 presents other economic and social impacts—e.g., less time spent refueling due to higher fuel effici ency—of the proposal. Chapter 9 presents our analysis of the small business impacts due to EPA’s proposal. All of these discussions— Chapters 3 through 9—are unique to this DRIA since, even though many of the metrics are common between EPA and DOT, we have different results due to our use of different models (EPA’s OMEGA model versus DOT’s CAFE Compliance and Effects Modeling System (often referred to as "the CAFE model" or "the Volpe model")) and the differences in our programs (e.g., AC credits versus no AC cred its, plus many other program flexibilities).


### Greenhouse Emission Impacts of EPA’s Proposal

Table 1 shows reductions estimated from EPA’s proposed GHG standards assuming a pre-control case of 2011 MY CAFE standards continuing indefinitely beyond 2011, and a post-control case in which 2016 MY standards continue indefinitely beyond 2016. These reductions are broken down by upstream and downstream components, including air conditioning improvements, and also account for the offset from a 10 percent “rebound” effect in vehicle miles travelled as discussed in Chapter 4 of the joint draft TSD. A Including the reductions from upstream emissions, tota l reductions are estimated to reach 325 MMTCO 2 eq (million metric tons of CO 2 equivalent emissions) annually by 2030 (a 21 percent reduction in U.S. car and light truc k emissions), and grow to over 500 MMTCO 2 eq in 2050 as cleaner vehicles continue to come into the fleet (a 23 percent reduction in U.S. car and light truck emissions).

A “Rebound VMT” is the term used to describe the increase in driving that might occur as vehicle fuel consumption decreases (i.e., the fuel economy improves) since the cost per mile of operating the vehicle decreases. As a result of this rebound effect, the benefits of the proposed rule are offset slightly since owners of compliant vehicles drive more miles re sulting in slightly more GHG emissions . Importantly, the adverse effects, or disbenefits, of rebound VMT are far outweighed by the overall benefits of the proposal.Draft Regulatory Impact Analysis

xi Table 1. Projected Net GHG Reductions (MMT CO 2 Equivalent per year)

CALENDAR YEAR 2020 2030 2040 2050

Net reduction to tailpipe standards* 165.2 324.6 417.5 518.5

Tailpipe standards 107.7 211.4 274.1 344.0 A/C – indirect CO2 11.0 21.1 27.3 34.2 A/C – direct HFCs 13.5 27.2 32.1 34.9 Upstream 33.1 64.9 84.1 105.5

Percent reduction relative to U.S. reference (cars + light trucks) 12.4% 2.14% 22.8% 22.9%

Percent reduction relative to U.S. reference (all sectors) 2.2% 4.2% 5.2% 6.2%

Percent reduction relative to worldwide reference 0.3% 0.6% 0.7% 0.9%

* includes impacts of 10% VMT rebound rate presented in Table III.F.1-3



### Criteria Pollutant Impacts of EPA’s Proposal

As shown in Table 2, EPA estimates that the proposed program would result in

re ductions of oxides of nitrogen (NOx), volati le organic compounds (VOC), particulate matter (PM) and oxides of sulfur (SOx), but would increase carbon monoxide (CO) emissions. The CO increase is because gasoline fueled passeng er cars and light trucks contribute over 50 percent of the total CO emissions in the US, wh ereas for other pollutants the contribution is less than 40 percent. Thus, for CO the increase from VMT rebound outweighs the upstream CO reductions. For all pollutants the overall im pact of the program would be relatively small compared to total U.S. inventories across all sectors. In the year 2030, EPA estimates the proposed program would reduce these total NOx, PM and SOx inventories by 0.2 to 0.3 percent and reduce the VOC inventory by 1.2 pe rcent, while increasing the total national CO inventory by 0.4 percent.

As shown in Table 3, EPA estimates that the proposed program would result in small cha n ges for toxic emissions compared to total U.S. inventories across all sectors. In 2030, EPA estimates the program would reduce total be nzene and formaldehyde by 0.04 percent. Total acrolein, acetaldehyde, and 1,3-butadien e would increase by 0.03 to 0.2 percent.Draft Regulatory Impact Analysis

xii Other factors which may impact criteria, or non-GHG, emissions but are not estimated in this analysis include:

 Vehicle technologies used to reduce tailpipe CO 2 emissions; because the regulatory standards for non-GHG emissions are the primary driver for theseemissions, EPA expects the impact of today's program to be negligible on nonGHG emission rates per mile.

 The potential for increased market penetration of diesel vehicles; because these vehicles would be held to the same certification and in-use standards for criteria pollutants as their gasoline counterparts, EPA expects their impact to be negligible on criteria pollutants and other non-GHG emissions.

 Early introduction of electric vehicles and plug-in hybrid electric vehicles, which would reduce criteria emissions in cases where they are able to certify to lower certification standards. It would also likely reduce gaseous air toxics.

 Reduced refueling emissions due to less frequent refueling events and reduced annual refueling volumes resulting from the GHG standards.

 Increased hot soak evaporative emissions due to the likely increase in number of trips associated with VMT rebound modeled in this proposal.

 Increased market share of E10 relative to E0, due to the decreased overall gasoline consumption of today's proposal combined with an unchanged fuel ethanol volume.

Table 2. Annual Criteria Emission Impacts of Program (short tons)

TOTAL IMPACTS UPSTREAM IMPACATS DOWNSTREAM IMPACTS

2020 2030 2020 2030 2020 2030
VOC -73,739 -142,347 -75,437 -147,841 1,698 5,494 % of total inventory -0.60% -1.20% -0.61% -1.20% 0.01% 0.05% CO 70,614 227,832 -7,209 -14,107 77,823 241,939 % of total inventory 0.13% 0.38% -0.01% -0.02% 0.14% 0.40% NO X -17,206 -27,726 -22,560 -43,286 5,354 15,560 % of total inventory -0.14% -0.20% -0.18% -0.36% 0.04% 0.13% PM2.5 -2,856 -5,431 -3,075 -6,003 218 572 % of total inventory -0.08% -0.16% -0.09% -0.18% 0.01% 0.02% SOx -16,307 -31,965 -13,804 -27,060 -2,503 -4,906 % of total inventory -0.18% -0.34% -0.16% -0.29% -0.03% -0.05%Draft Regulatory Impact Analysis

xiii Table 3. Annual Air Toxic Emission Impacts of Program (short tons)

TOTAL IMPACTS UPSTREAM IMPACATS DOWNSTREAM IMPACTS

2020 2030 2020 2030 2020 2030
1,3-Butadiene 11 37 -1.8 -3.4 13.2 40.2 % of total inventory 0.07% 0.22% -0.01% -0.02% 0.08% 0.24% Acetaldehyde 17 61 -8 -15 24.8 75.5 % of total inventory 0.04% 0.13% -0.02% -0.03% 0.05% 0.17% Acrolein 0 2 -1.1 -2 1.3 3.9 % of total inventory 0.00% 0.03% 0.00% 0.00% 0.02% 0.06% Benzene -84 -77 -163 -320 79.6 242.2 % of total inventory -0.04% -0.04% -0.08% -0.15% 0.04% 0.11% Formaldehyde -28 -16 -60 -112 31.8 96.3 % of total inventory -0.03% -0.02% -0.07% -0.10% 0.04% 0.11%



### Costs and Benefits of EPA’s Proposal

Table 4 presents estimated annual net benefits for the indicated calendar years. The table also shows the net present values of thos e 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 five interim SCC values considered by EPA (please refer to Ch apter 7 of this DRIA for a discussion of the five interim SCC values). As noted in Chapter 7, there is a very high probability (very likely according to the IPCC) that the benefit estimates from GHG reductions are underestimates because, in part, models used to calculate SCC values do not include information about impacts that have not been quantified. Note that the quantified annual costs shown in Table 4 are negati ve because fuel savings are included. Fuel savings are considered as negative costs (i.e., positive benefits) of the proposed vehicle program. The fuel savings outweigh the costs associated with the addition of new technology and, therefore, the vehicle program costs are negative.Draft Regulatory Impact Analysis

xiv Table 4 Quantified Net Benefits Associated wi th the Proposed Light-Duty Vehicle GHG Program a, b
(Millions of 2007 dollars)

2020 2030 2040 2050 NPV, 3% NPV, 7% Quantified Annual Costs -$25,100 -$72,500 -$105,700 -$146,100 -$1,287,600 -$529,500
Quantified Annual Benefits at each assumed SCC value
SCC 5% $9,900 $21,100 $30,200 $42,100 $400,900 $177,200
SCC 5% Newell-Pizer $11,200 $24,400 $35,500 $51,600 $470,100 $205,700
SCC from 3% and 5% $13,400 $29,800 $46,500 $68,600 $594,700 $257,100
SCC 3% $16,900 $39,800 $62,500 $95,600 $788,600 $337,100
SCC 3% Newell-Pizer $22,700 $53,800 $87,500 $132,600 $1,093,100 $462,800 Quantified Net Benefits at each assumed SCC value
SCC 5% $35,000 $93,600 $135,900 $188,200 $1,688,500 $706,700
SCC 5% Newell-Pizer $36,300 $96,900 $141,200 $197,700 $1,757,700 $735,200
SCC from 3% and 5% $38,500 $102,300 $152,200 $214,700 $1,882,300 $786,600
SCC 3% $42,000 $112,300 $168,200 $241,700 $2,076,200 $866,600
SCC 3% Newell-Pizer $47,800 $126,300 $193,200 $278,700 $2,380,700 $992,300

a Note that the co-pollutant impacts associated with the standards pres ented here do not include the full complement of endpoints that, if quantified and monetized , would change the total monetized estimate of rulerelated impacts. Instead, the co-po llutant benefits are based on benefit-per-ton values that reflect only humanhealth impacts associated with reductions in PM2.5 exposure. Ideally, human health and environmental cobenefits would be based on changes in ambient PM2.5 and ozone as determined by full-scale air quality modeling. However, we were unable to conduct a full-scale air quality modeling analysis in time for the proposal. We intend to more fully capture the co-polluta nt benefits for the analysis of the final standards.
b Quantified annual costs are shown as negative here be cause fuel savings are included. Fuel savings are considered as negative costs (i.e., positive benefits) of the proposed vehicle program. The fuel savings outweigh the costs associated with the addition of new technology and, therefore, the vehicle program costs are negative. The fuel impacts included here were calculated using pre-tax fuel prices.Technology Packages, Cost and Effectiveness

1-1 

## CHAPTER 1: Technology Packages, Cost and Effectiveness

1.1 Overview of Technology

The proposed 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 ac hieved by the standards, and the impacts of the standards in terms of costs, quantified and unq uantified benefits, safety, and other impacts. The availability of technology to achieve redu ctions 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 cont rol 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 CO 2 . (CO 2 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 pr ocess 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 part iculate 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 CO 2 in the process. In the case of NOx, aftertreatment devices have focused on the chem ical 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) upgrad ing 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 CO 2 emitted, and on (2) aftertreatment devices that either continue this oxidation process and increase emissions of CO 2 , or otherwise change the compounds emitted by the engine. Since CO 2 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 availableDraft Regulatory Impact Analysis

1-2 to address CO 2 . Instead, the focus of the CO 2 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 va rious accessories during the operation of the vehicle such as the air conditioner. Technology can reduce CO 2 emissions by either making more efficient use of the energy that is produced through combustion of the fuel or 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 aff ects 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 wh eels, 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 consumptio n. 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 effi ciency 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 ot her 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 proposed 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 manu facturing 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 typically plan on a major redesign for the models approximately every 5 years. At the redesign stage, the manufacturer will upgrade or add all of the te chnology and make all of the other changes needed so the vehicle model will meet the manufacturer’s plans for the next several years. This includes meeting all of the emissions and other requirements that would apply during the years before the next major redesign of the vehicle.

This redesign often involves a package of changes, designed to work together to meet the various requirements and plans for the mode l for several model years after the redesign. This often involves significant engineering, development, manufacturing, and marketingTechnology Packages, Cost and Effectiveness

1-3 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. 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 mu ltiple systems of the vehicle thus occur at the vehicle redesign stage and not in the time period between redesigns.

As discussed below, there are a wide variety of emissions control technologies involving several different systems in the vehicl e 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 CO 2 , and as discussed below, to reduce emissions of HFCs from the air conditioner.

In determining the requisite technology and cost of these first ever GHG emissions standards for light-duty vehicles, EPA proposes to use 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 rede sign, 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 te chnology required by the GHG proposal is commercially available and already being employ ed to a limited extent across the fleet. The vast majority of the emission reductions which would result from the proposed rule would result from the increased the use of these techno logies. EPA also believes the proposed rule would encourage the development and limited us e of more advanced technologies, such as PHEVs and EVs.

In section 1.2 below, a summary of technology cost s and effect iveness is presented. In section 1.3, the process of combining technologies in to packages is described along with package costs and effectiveness. Sections 1.4 through 1.6 discuss the lumped parameter
ap pro ach which provides background and support for determining technology and package effectiveness.Draft Regulatory Impact Analysis

1-4 

### 1.2 Technology Cost and Effectiveness

EPA collected information on the cost and effectiveness of CO 2 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 consid ered to be most credible. Those sources included: the 2002 NAS report on the effectiven ess 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 pl ans 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 th e 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. EPA also has a contracted study ongoing with FEV (an engineering services firm) that consists of complete system tear-downs to evaluate technologies down to the nuts and bolts to a rrive at very detailed estimates of the costs associated with manufacturing them. Lastly , cost and effectiveness estimates have been adjusted slightly 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 CO 2 emission control, such as papers published by the Society of Automotive Engineers and the American Society of Mechanical Engineers. The results of all of the research and discussions are summarized in Chapter 3 of the draft Joint Technical Support Document.

EPA reviewed all this information in order to develop the best estimates of the cost and effectiveness of CO 2 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), transmi ssion-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

assoc ia ted 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

m u lti-point, port fuel injected gasoline engine ope rating 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. Th is 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 andTechnology Packages, Cost and Effectiveness

1-5 effectiveness of CO 2 reducing technologies can be found in Chapter 3 of the draft joint TSD, along with a more detailed description of the comprehensive technical evaluation underlying the estimates.

Table 1-1 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 $259OHC 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 $291OHV 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 Base engine $222 $287 $287 $287 $312 GDI – lean burn GDI - stoich $623 $623 $623 $623 $623 Turbocharge (single) Base engine $366 $366 $366 $366 $366 Turbocharge (twin) Base engine $663 $663 $663 $663 $663 Downsize to I4 DOHC V6 DOHC -$337 -$337 -$337 -$337 -$337 Downsize to I4 DOHC V6 SOHC -$53 -$53 -$53 -$53 -$53 Downsize to I4 DOHC V6 OHV $265 $265 $265 $265 $265 Downsize to I4 DOHC I4 DOHC (larger) -$47 -$47 -$47 -$47 -$47 Downsize to I3 DOHC I4 DOHC -$80 n/a n/a n/a n/a Downsize to V6 DOHC V8 DOHC n/a -$160 -$160 -$160 -$160 Downsize to V6 DOHC V8 SOHC 2V n/a $199 $199 $199 $199 Downsize to V6 DOHC V8 SOHC 3V n/a $111 $111 $111 $111Turbo w/o downsizeDownsize 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 $214 $214 $214 $214 $214

Downsize to I4 DOHC & add turbo V6 SOHC w/o turbo $453 $453 $453 $453 $453

Downsize to I4 DOHC & add turbo V6 OHV w/o turbo $797 $797 $797 $797 $797

Downsize to I4 DOHC & add turbo I4 DOHC (larger) w/o turbo $372 $372 $372 $372 $372

Downsize to I3 DOHC & add turbo I4 DOHC w/o turbo $344 n/a n/a n/a n/a

Downsize to V6 DOHC & add twin turbo V8 DOHC w/o turbo n/a $613 $613 $613 $613

Downsize to V6 DOHC & add twin turbo V8 SOHC 2V w/o turbo n/a $971 $971 $971 $971

Downsize to V6 DOHC & add twin turbo V8 SOHC 3V w/o turbo n/a $872 $872 $872 $872Turbo with downsize

Downsize to V6 DOHC V8 OHV w/o turbo n/a $1,096 $1,096 $1,096 $1,096Draft Regulatory Impact Analysis

1-6 & add twin turbo Convert to V6 DOHC V6 SOHC n/a $354 $354 $354 $354 Convert to V6 DOHC V6 OHV n/a $464 $464 $464 $464 Convert to V8 DOHC V8 SOHC 2V n/a $398 $398 $398 $398 Convert to V8 DOHC V8 SOHC 3V n/a $310 $310 $310 $310 Convert to V8 DOHC V8 OHV n/a $509 $509 $509 $509Gasoline HCCI dualmode GDI - stoich $253 $375 $375 $375 $659
Diesel – Lean NOx trap Base gasoline engine Diesel – urea SCR Base gasoline engine $2,655 $2,164 $2,164 $2,961

Table 1-2 EPA’s Incremental Piece Costs for Transmi ssion 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 $150 $150 $150 $150 $150 6-speed DCT – dry clutch 6-speed auto trans $65 $65 $65 $65 $65 6-speed DCT – wet clutch 6-speed auto trans $139 $139 $139 $139 $139 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

Full-Series hydraulic hybrid Base engine &
trans $750 $825 $825 $900 $1200
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/aTechnology Packages, Cost and Effectiveness

1-7 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 CO 2 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 draft joint TS D and each of these estimates is discussed in more detail there.Draft Regulatory Impact Analysis

1-8 Table 1-6 Engine Technology Effectiveness

Absolute CO 2 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 rulemakingTechnology Packages, Cost and Effectiveness

1-9 Table 1-7 Transmission T echnology Effectiveness

Absolute CO 2 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 CVT (from 4-speed auto) 6 6 6 n.a. n.a.

Table 1-8 Hybrid Technology Effectiveness

Absolute CO 2 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 CO 2 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 CO 2 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 1.5Draft Regulatory Impact Analysis

1-10 

### 1.3 Package Cost and Effectiveness

1.3.1 Explanation of Technology Packages

Individual technologies can be used by manufactures to achieve incremental CO 2 reductions. 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 CO 2 reductions 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, manufacturer s can more efficiently make use of their redesign resources and more ef fectively plan for changes necessary to meet future standards.

Therefore, the approach taken here is to group technologies into packages of increasing cost and effectiveness. EPA determin ed 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 an d 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 adequat e resolution for the cost basis as sociated 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 ef fectiveness 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 th at these 19 vehicle types span the range of vehicle footprints— smaller footprints for smaller vehicles and larg er footprints for larger vehicles—which serve as the basis for the proposed 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 p ackage. 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 lo w drag brakes. Subsequent packages include advanced gasoline engine and transmission tech nologies such as turbo/downsizing, GDI, mass reduction and dual-clutch transmission. The mo st 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 order to modify its fleet such that compliance could be achieved.Technology Packages, Cost and Effectiveness

1-11 The final step in creating the vehicle packag es 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 de monstrated 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 w ith advanced valvetrain technology was just as effective as a turbo charge I4, t hus excluding the additional cost of turbo charging;

(2) The OMEGA model was allowed to apply pa ckages 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 p ackage that was cost-effective. Most of the diesel packages and some of the hybrid packag es 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 fu el generates approx imately 15% more CO 2 than gasoline. As this rule is based on the reduction of CO 2 emissions 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.

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 MappingDraft Regulatory Impact Analysis

1-12 

### 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 al so 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 provide more attractive cost effectiveness). Table 1-12 shows the package cost s for t h e packages that were actually used as inputs to the OMEGA model (note that all packages are listed but only those for which costs are shown were actually used in 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 lo ng-term indirect cost markups on the package costs. For details of the learni ng effects and indirect cost markups used in this analysis refer to Chapter 3 of the draft 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
yea r 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 are, on

av er age, 117% 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 108% of those for 2016 as learning has occurred between 2012 and 2014. The costs for 2022 are 92% 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 proposed program. Table 1-12 also shows the effectiveness of e ach package used in the OMEGA model (note that the effectiveness of packages does not change with model year).Technology Packages, Cost and Effectiveness1-13Table 1-11 Package Descriptions and 2016MY Costs for 19 VehicleTypes (T1-T19), All Packages Considered, Costs in 2007 dollarsVehicleTechnologyPackageEngineTransmission System VoltageCamshaft changes(excluding those fordownsized engines) Lubes Friction Rdxn Downsize Aftertreatment Aggressive shift Early torque lock Alternator & electrificati o Power steering Aero Low RR tires Low drag brakes Axle disconnect Weight rdxn 2016 MY Costbaseline 1.5L - 4V DOHC I4AT 4 spd12 V11.5L - 4V I4AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$18921.5L - 4V I4 CCPDCT 6 spd 12 VLUB EFRIACC 12V EPS AERO 1 LRR3% $71631.2L I3 DVVL + CCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I3IACC 42V EPS AERO 1 LRR5% $1,42240.7L I3 (small) Turbo DCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I3IACC 42V EPS AERO 1 LRR 10% $1,9465150kW/lithium ion (range of FTP 150 miles) N/AHEVAERO 1 LRR$27,675baseline 2.4L-4V DOHC I4AT 4 spd12 V12.4L 4V I4AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$18922.0L I4 CCP + GDIAT 6 spd12 VLUB EFR I4 to I4ASL TORQ IACC 12V EPS AERO 1 LRR3% $81432.4L -4V I4 CCP + GDIDCT 6 spd 12 VLUB EFR I4 to I4IACC 12V EPS AERO 1 LRR3% $1,02542.0L I4 DVVL + CCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I4IACC 42V EPS AERO 1 LRR 10% $1,67551.5L I4 Turbo DCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I4IACC 42V EPS AERO 1 LRR 10% $2,00361.2L I4 HEV (IMA) + GDIdry DCT 6 spd HEVLUB EFR I4 to I4AERO 1 LRR$3,34571.2L I4 HEV Plug-in IMA + GDIdry DCT 6 spd HEVLUB EFR I4 to I4AERO 1 LRR$10,267baseline 2.4L-4V DOHC I4AT 4 spd12 V12.4L - 4V I4AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$18922.2L I4 CCP + GDIAT 6 spd12 VLUB EFR I4 to I4ASL TORQ IACC 12V EPS AERO 1 LRR3% $83332.2L I4 DVVL + CCP + GDIDCT 6 spd 12 VLUB EFR I4 to I4IACC 12V EPS AERO 1 LRR5% $1,01442.2L I4 DVVL + CCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I4IACC 42V EPS AERO 1 LRR 10% $1,73951.6L I4 Turbo DCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I4IACC 42V EPS AERO 1 LRR 10% $2,06661.6L Turbo DCP + GDI lean burndry DCT 6 spd 42 S-SLUB EFR I4 to I4GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,07571.4L I4 Turbo HEV (IMA) + GDIdry DCT 6 spd HEVLUB EFR I4 to I4AERO 1 LRR$3,76481.8L I4 HEV (Power Split) + GDIN/AHEVLUB EFR I4 to I4AERO 1 LRR$4,24391.8L I4 HEV Plug-in Power Split + GDI N/AHEVLUB EFR I4 to I4AERO 1 LRR$9,666baseline 3.0L-4V DOHC V6AT 4 spd12 V13.0L - 4V V6AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21422.0L I4 Turbo DCP + GDIAT 6 spd12 VLUB EFR V6 DOHC to I4ASL TORQ IACC 12V EPS AERO 1 LRR3% $1,30632.0L I4 Turbo DCP + GDIDCT 6 spd 12 VLUB EFR V6 DOHC to I4IACC 12V EPS AERO 1 LRR3% $1,39242.0L I4 Turbo DCP + GDIDCT 6 spd 42 S-SLUB EFR V6 DOHC to I4IACC 42V EPS AERO 1 LRR5% $1,97452.0L Turbo DCP + GDI lean burnDCT 6 spd 42 S-SLUB EFR V6 DOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,27362.4L I4 Turbo DieselDCT 6 spdLUB EFRDiesel-SCREPS AERO 1 LRR5% $2,92071.5L I4 Turbo HEV (IMA) + GDIDCT 6 spd HEVLUB EFR V6 DOHC to I4AERO 1 LRR$4,53082.8L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFRAERO 1 LRR$6,095

Subcomp I4 (T1) Compact Car I4 (T2)

Midsize Car/Small MPV (unibody) I4 (T3)

Compact Car/Small MPV (unibody) V6 (T4)Draft Regulatory Impact Analysis1-14Table 1-11 Continuedbaseline 3.3L-4V DOHC V6AT 4 spd12 V13.3L - 4V V6AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21423.0L V6 GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,02233.0L V6 w/ Deac GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,27543.0L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR 10% $2,10352.2L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 DOHC to I4IACC 42V EPS AERO 1 LRR 10% $2,24563.0L V6 HCCI GDIDCT 6spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR 30% $3,75472.2L Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V6 DOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,37382.5L I4 HEV (Power Split) + GDIN/AHEVLUB EFR V6 DOHC to I4AERO 1 LRR$6,005baseline 4.5L-4V DOHC V8AT 4 spd12 V14.5L - 4V V8AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21424.0L V6 GDI + CCPAT 6 spd12 VLUB EFR V8 DOHC to V6 DOHCASL TORQ IACC 12V EPS AERO 1 LRR3% $88334.0L V6 w/ Deac GDI + CCPAT 6 spd42 S-SLUB EFR V8 DOHC to V6 DOHCASL TORQ IACC 42V EPS AERO 1 LRR5% $1,63344.0L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR5% $1,71953.0L V6 Turbo DCP + GDIAT 6 spd42 S-SLUB EFR V8 DOHC to V6 DOHCASL TORQ IACC 42V EPS AERO 1 LRR5% $2,33363.0L V6 Turbo DCP + GDIDCT 6 spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR5% $2,41974.0L V6 HCCI GDIDCT 6 spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 30% $3,85383.0L V6 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR5% $3,45693.0L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFR V8 DOHC to V6 DOHCAERO 1 LRR$5,953baseline 2.6L-4V DOHC I4 (I5)AT 4 spd12 V12.6 L - 4V I4AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21422.4L I4 CCP + GDIAT 6 spd12 VLUB EFR I4 to I4ASL TORQ IACC 12V EPS AERO 1 LRR3% $98732.4L I4 DVVL + CCP + GDIDCT 6 spd 12 VLUB EFR I4 to I4IACC 12V EPS AERO 1 LRR3% $1,25542.4L I4 DVVL + CCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I4IACC 42V EPS AERO 1 LRR 10% $2,05452.0L I4 Turbo DCP + GDIdry DCT 6 spd 42 S-SLUB EFR I4 to I4IACC 42V EPS AERO 1 LRR 10% $2,36961.8L Turbo DCP + GDI lean burnDCT 6 spd 42 S-SLUB EFR I4 to I4GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,69171.8L I4 Turbo HEV (IMA) + GDIdry DCT 6 spd HEVLUB EFR I4 to I4AERO 1 LRR$4,62881.8L I4 Turbo HEV (Power Split) + GDI N/AHEVLUB EFR I4 to I4AERO 1 LRR$6,16491.8L I4 Turbo HEV Plug-in IMA + GDI dry DCT 6 spd HEVLUB EFR I4 to I4AERO 1 LRR$14,226baseline 3.7L-2V SOHC V6AT 4 spd12V13.7L 2V SOHC V6AT 4 spd12VLUB EFRASL TORQ IACC 12VLRR$21423.2L 2V SOHC V6 GDI + CCPAT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,04433.2L 2V SOHC V6 w/Deac GDI + CCP AT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,19442.8L 4V V6 GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,39852.8L 4V V6 GDI + CCP + DVVLAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,69662.8L 4V V6 w/ Deac GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,66572.8L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S V6 SOHC to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR 10% $2,52882.4L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 SOHC to I4IACC 42V EPS AERO 1 LRR 10% $2,55592.8L 4V V6 HCCI GDIDCT 6spd 42 S-S V6 SOHC to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR 30% $4,38710 2.4L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V6 SOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,89011 2.8L I4 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR5% $2,96812 3.0L 4V V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEV V6 SOHC to V6 DOHC LUB EFRAERO 1 LRR$4,71013 3.0L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V6 SOHC to V6 DOHC LUB EFRAERO 1 LRR$5,940

Midsize/Large Car V6 (T5) Midsize Car/Large Car V8 (T6)

Mid-sized MPV (unibody)/Small Truck I4 (T7) Midsize MPV (unibody)/Small Truck V6/V8 (T8)Technology Packages, Cost and Effectiveness1-15Table 1-11 Continuedbaseline 4.0L-2V SOHC V6AT 4 spd12V14.0L 2V SOHC V6AT 4 spd12VLUB EFRASL TORQ IACC 12VLRR$21423.6L 2V SOHC V6 GDI + CCPAT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,06433.6L 2V SOHC V6 w/Deac GDI + CCP AT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,21443.2L 4V V6 GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,41853.2L 4V V6 w/ Deac GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,69963.2L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S V6 SOHC to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR 10% $2,59672.4L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 SOHC to I4IACC 42V EPS AERO 1 LRR 10% $2,62383.2L 4V V6 HCCI GDIDCT 6spd 42 S-S V6 SOHC to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR 30% $4,65492.4L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V6 SOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR 30% $5,15810 2.0L I4 Turbo HEV (IMA) + GDIDCT 6 spd HEVLUB EFR V6 SOHC to I4AERO 1 LRR$4,81411 3.2L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V6 SOHC to V6 DOHC LUB EFRAERO 1 LRR$6,449baseline 4.7L-2V SOHC V8AT 4 spd12V14.7L 2V SOHC V8AT 4 spd12VLUB EFRASL TORQ IACC 12VLRR$21424.4L 2V SOHC V8 GDI + CCPAT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,08534.4L 2V SOHC V8 w/Deac GDI + CCP AT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,23544.2L 4V V6 GDI + CCPAT 6 spd12VLUB EFR V8 SOHC to V6 DOHCASL TORQ IACC 12V EPS AERO 1 LRR3% $1,28454.2L 4V V6 w/ Deac GDI + CCPAT 6 spd12VLUB EFR V8 SOHC to V6 DOHCASL TORQ IACC 12V EPS AERO 1 LRR5% $1,57964.2L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFR V8 SOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 10% $2,51272.8L 4V V6 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V8 SOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 10% $3,21184.2L 4V V6 HCCI GDIDCT 6spd 42 S-SLUB EFR V8 SOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 30% $4,77492.8L 4V V6 Turbo DCP + GDI lean burn DCT 6spd 42 S-SLUB EFR V8 SOHC to V6 DOHC GDI-LBIACC 42V EPS AERO 1 LRR 30% $5,95110 3.0L V6 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR5% $3,03711 4.2L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFR V8 SOHC to V6 DOHCAERO 1 LRR$6,294baseline 4.2L-2V SOHC V6AT 4 spd12V14.2L 2V SOHC V6AT 4 spd12VLUB EFRASL TORQ IACC 12VLRR$23923.9L 2V SOHC V6 GDI + CCPAT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,10433.9L 2V SOHC V6 w/Deac GDI + CCP AT 6 spd12VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,27343.6L 4V V6 GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,45853.6L 4V V6 GDI + CCP + DVVLAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,84263.6L 4V V6 w/ Deac GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC LUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,75173.6L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S V6 SOHC to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR LDB 10% $2,73582.5L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 SOHC to I4IACC 42V EPS AERO 1 LRR LDB 10% $2,74394.0L 4V V6 HCCI GDIDCT 6spd 42 S-S V6 SOHC to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR LDB 30% $4,96710 2.5L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V6 SOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR LDB 30% $5,18611 2.8L I4 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR LDB 5% $3,87112 3.6L 4V V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEV V6 SOHC to V6 DOHC LUB EFRAERO 1 LRR LDB$5,84913 3.6L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V6 SOHC to V6 DOHC LUB EFRAERO 1 LRR LDB$8,364baseline 3.8L 2V OHV V6AT 4 sp12 V13.8L-2V OHV V6AT 4 spd12VLUB EFRASL TORQ IACC 12VLRR$23923.2L 4V DOHC V6 GDI + CCPAT 6 spd12V V6 OHV to V6 DOHC LUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,32433.2L 4V DOHC V6 w/Deac GDI + CCP AT 6 spd12V V6 OHV to V6 DOHC LUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,49343.2L 4V DOHC V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S V6 OHV to V6 DOHC LUB EFRIACC 42V EPS AERO 1 LRR LDB 10% $2,84552.5L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 OHV to I4 DOHCIACC 42V EPS AERO 1 LRR LDB 10% $3,087

Large MPV (unibody) V6 (T9) Large MPV (unibody) V8 (T10) Large Truck (+ Van) V6 (T11)
Large Truck + Large MPV V6 (T12)Draft Regulatory Impact Analysis1-16Table 1-11 Continuedbaseline 5.7L 2V OHV V8AT 4 spd12V15.7L 2V OHV V8AT 4 spd12VLUB EFRASL TORQ IACC 12VLRR$23925.2L 2V OHV V8 GDI + CCPAT 6 spd12VLUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,05135.2L 2V OHV V8 w/Deac GDI + CCP AT 6 spd12VLUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,21944.6L 4V V8 GDI + CCPAT 6 spd12V V8 OHV to V8 DOHC LUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,55954.6L 4V V8 w/ Deac GDI + CCPAT 6 spd12V V8 OHV to V8 DOHC LUB EFRASL TORQ IACC 12V AERO 1 LRR5% $1,87964.6L 4V V8 w/ Deac GDI + CCPDCT 6 spd 42 S-S V8 SOHC to V8 DOHC LUB EFRIACC 42V EPS AERO 1 LRR LDB 10% $2,91373.5L 4V V6 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V8 OHV to V6 DOHCIACC 42V EPS AERO 1 LRR LDB 10% $3,52084.6L 4V V8 HCCI GDIDCT 6spd 42 S-S V8 OHV to V8 DOHC LUB EFRIACC 42V EPS AERO 1 LRR LDB 30% $5,64793.5L V6 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V8 OHV to V6 DOHC GDI-LBIACC 42V EPS AERO 1 LRR LDB 30% $6,35510 3.5L V6 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCRAERO 1 LRR LDB 5% $3,84411 4.6L 4V V8 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V8 OHV to V8 DOHC LUB EFRAERO 1 LRR LDB$8,519baseline 5.4L 3V SOHC V8AT 4 sp12 V15.4L 3V SOHC - V8AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$23924.6L 4V DOHC V8 GDI + CCPAT 6 spd12VV8 SOHC 3V to V8 DOH LUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,21034.6L 4V DOHC V8 w/ Deac GDI + CCP AT 6 spd12VV8 SOHC 3V to V8 DOH LUB EFRASL TORQ IACC 12V AERO 1 LRR5% $1,53044.6L 4V DOHC V8 w/ Deac GDI + CCP DCT 6 spd 42 S-SV8 SOHC 3V to V8 DOH LUB EFRIACC 42V EPS AERO 1 LRR LDB 10% $2,82553.5L V6 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V8 SOHC 3V to V6 DOHCIACC 42V EPS AERO 1 LRR LDB 10% $3,295baseline 3.2L-4V DOHC V6AT 4 spd12V13.2 L - 4V V6AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21422.8L V6 GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,04432.8L V6 GDI + CCP + DVVLAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,34242.8L V6 w/ Deac GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,31152.8L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR5% $1,88162.4L I4 Turbo DCP + GDIDCT 6 spd 42 S-SLUB EFR V6 DOHC to I4IACC 42V EPS AERO 1 LRR5% $2,02372.8L V6 HCCI GDIDCT 6spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR 30% $4,03382.4L I4 Turbo DCP + GDI lean burnDCT 6 spd 42 S-SLUB EFR V6 DOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,49492.8L I4 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR5% $2,96810 3.0L V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEVLUB EFRAERO 1 LRR$4,35611 3.0L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFRAERO 1 LRR$5,586baseline 3.5L-4V DOHC V6AT 4 spd12V13.5L - 4V V6AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21423.2L V6 GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,06433.2L V6 w/ Deac GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,34543.2L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR 10% $2,24352.4L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 DOHC to I4IACC 42V EPS AERO 1 LRR5% $2,05763.2L V6 HCCI GDIDCT 6spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR 30% $4,30072.4L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V6 DOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR 30% $4,91982.0L I4 Turbo HEV (IMA) + GDIDCT 6 spd HEVLUB EFR V6 DOHC to I4AERO 1 LRR$4,53093.2L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFRAERO 1 LRR$6,095

Large Truck (+ Van) V8 (T13) Large Truck (+Van) V8 (T14) Midsize MPV (unibody)/Small Truck V6/V8 (T15) Large MPV (unibody) V6 (T16)Technology Packages, Cost and Effectiveness1-17Table 1-11 Continuedbaseline 4.6L-4V DOHC V8AT 4 spd12V14.6L - 4V V8AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$21424.2L V6 GDI + CCPAT 8 spd12 VLUB EFR V8 DOHC to V6 DOHCASL TORQ IACC 12V EPS AERO 1 LRR3% $92634.2L V6 w/ Deac GDI + CCPAT 8 spd12 VLUB EFR V8 DOHC to V6 DOHCASL TORQ IACC 12V EPS AERO 1 LRR5% $1,22144.2L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 10% $2,15352.8L V6 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 10% $2,85364.2L V6 HCCI GDIDCT 6spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR 30% $4,41572.8L V6 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V8 DOHC to V6 DOHC GDI-LBIACC 42V EPS AERO 1 LRR 30% $5,59283.0L V6 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR5% $3,03794.2L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFR V8 DOHC to V6 DOHCAERO 1 LRR$5,935baseline 4.0L-4V DOHC V6AT 4 spd12V14.0L - 4V V6AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$23923.6L V6 GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR3% $1,10433.6L V6 GDI + CCP + DVVLAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,48843.6L V6 w/ Deac GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V EPS AERO 1 LRR5% $1,39853.6L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR LDB 10% $2,38162.5L I4 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V6 DOHC to I4IACC 42V EPS AERO 1 LRR LDB 10% $2,50474.0L V6 HCCI GDIDCT 6spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR LDB 30% $4,61382.5L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V6 DOHC to I4 GDI-LBIACC 42V EPS AERO 1 LRR LDB 30% $4,94792.8L I4 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCREPS AERO 1 LRR LDB 5% $3,87110 3.6L V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEVLUB EFRAERO 1 LRR LDB$5,49511 3.6L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFRAERO 1 LRR LDB$8,010baseline 5.6L 4V DOHC V8AT 4 spd12V15.6L 4V V8AT 4 spd12 VLUB EFRASL TORQ IACC 12VLRR$23924.6L V8 GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V AERO 1 LRR3% $1,05134.6L V8 w/ Deac GDI + CCPAT 6 spd12 VLUB EFRASL TORQ IACC 12V AERO 1 LRR5% $1,37144.6L V8 w/ Deac GDI + CCPDCT 6 spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR LDB 10% $2,51553.5L V6 Turbo DCP + GDIDCT 6spd 42 S-SLUB EFR V8 DOHC to V6 DOHCIACC 42V EPS AERO 1 LRR LDB 10% $3,03664.6L V8 HCCI GDIDCT 6spd 42 S-SLUB EFRIACC 42V EPS AERO 1 LRR LDB 30% $5,13973.5L V6 Turbo DCP + GDI lean burnDCT 6spd 42 S-SLUB EFR V8 DOHC to V6 DOHC GDI-LBIACC 42V EPS AERO 1 LRR LDB 30% $5,87283.5L V6 Turbo DieselDCT 6 spd12LUB EFRDiesel-SCRAERO 1 LRR LDB 5% $3,84494.6L V8 w/ Deac GDI + CCP HEV (2-mode) N/AHEVLUB EFRAERO 1 LRR LDB$8,010

Large MPV (unibody) V8 (T17) Large Truck (+ Van) V6 (T18) Large Truck (+ Van) V8 (T19)Notes to Table 1-11:DOHC=dual overhead cam; SOHC=single overhead cam; OHV=overhead valve; AT=automatic transmission; DCT=dual clutch transmission;LUB=low friction lubes;EFR=engine frictionreduction; ASL=aggressive shift logic; TORQ=early torque converter lockup; IACC=improved accessories; EPS=electric power steering; AERO 1=improved aerodynamics; LRR=low rollingresistance tires.Draft Regulatory Impact Analysis1-18Table 1-12 Package Costs & Effectiveness for 2012-2022+MY for 19 Vehicle Types (T1-T19), Packages Used as Inputs to the OMEGA Model, Costs in 2007 dollarsVehicleTechnologyPackageEngineTransmissionSystemVoltageCamshaft changes(excluding those fordownsized engines)2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 CO2 % Reductionbaseline 1.5L - 4V DOHC I4AT 4 spd12 V11.5L - 4V I4AT 4 spd12 V$206 $202 $197 $193 $189 $189 $189 $189 $189 $189 $182 7.6%21.5L - 4V I4 CCPDCT 6 spd 12 V$801 $779 $757 $736 $716 $716 $716 $716 $716 $716 $685 18.9%31.2L I3 DVVL + CCP + GDIdry DCT 6 spd 42 S-S$1,751 $1,717 $1,574 $1,454 $1,422 $1,422 $1,422 $1,422 $1,422 $1,422 $1,340 33.2%40.7L I3 (small) Turbo DCP + GDIdry DCT 6 spd 42 S-S$2,343 $2,291 $2,130 $1,994 $1,946 $1,946 $1,946 $1,946 $1,946 $1,946 $1,820 36.4%5150kW/lithium ion (range of FTP 150 miles) N/AHEVbaseline 2.4L-4V DOHC I4AT 4 spd12 V12.4L 4V I4AT 4 spd12 V$206 $202 $197 $193 $189 $189 $189 $189 $189 $189 $182 7.6%22.0L I4 CCP + GDIAT 6 spd12 V$911 $886 $861 $837 $814 $814 $814 $814 $814 $814 $784 17.5%32.4L -4V I4 CCP + GDIDCT 6 spd 12 V42.0L I4 DVVL + CCP + GDIdry DCT 6 spd 42 S-S$2,037 $1,994 $1,843 $1,715 $1,675 $1,675 $1,675 $1,675 $1,675 $1,675 $1,593 35.4%51.5L I4 Turbo DCP + GDIdry DCT 6 spd 42 S-S61.2L I4 HEV (IMA) + GDIdry DCT 6 spd HEV71.2L I4 HEV Plug-in IMA + GDIdry DCT 6 spd HEVbaseline 2.4L-4V DOHC I4AT 4 spd12 V12.4L - 4V I4AT 4 spd12 V$206 $202 $197 $193 $189 $189 $189 $189 $189 $189 $182 7.6%22.2L I4 CCP + GDIAT 6 spd12 V32.2L I4 DVVL + CCP + GDIDCT 6 spd 12 V$1,137 $1,105 $1,074 $1,043 $1,014 $1,014 $1,014 $1,014 $1,014 $1,014 $972 23.2%42.2L I4 DVVL + CCP + GDIdry DCT 6 spd 42 S-S$2,108 $2,063 $1,910 $1,780 $1,739 $1,739 $1,739 $1,739 $1,739 $1,739 $1,656 35.4%51.6L I4 Turbo DCP + GDIdry DCT 6 spd 42 S-S61.6L Turbo DCP + GDI lean burndry DCT 6 spd 42 S-S71.4L I4 Turbo HEV (IMA) + GDIdry DCT 6 spd HEV81.8L I4 HEV (Power Split) + GDIN/AHEV$4,785 $4,644 $4,506 $4,373 $4,243 $4,243 $4,243 $4,243 $4,243 $4,243 $3,710 35.9%91.8L I4 HEV Plug-in Power Split + GDI N/AHEVbaseline 3.0L-4V DOHC V6AT 4 spd12 V13.0L - 4V V6AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%22.0L I4 Turbo DCP + GDIAT 6 spd12 V32.0L I4 Turbo DCP + GDIDCT 6 spd 12 V$1,562 $1,518 $1,475 $1,433 $1,392 $1,392 $1,392 $1,392 $1,392 $1,392 $1,190 23.4%42.0L I4 Turbo DCP + GDIDCT 6 spd 42 S-S$2,392 $2,341 $2,168 $2,021 $1,974 $1,974 $1,974 $1,974 $1,974 $1,974 $1,725 31.6%52.0L Turbo DCP + GDI lean burnDCT 6 spd 42 S-S62.4L I4 Turbo DieselDCT 6 spd71.5L I4 Turbo HEV (IMA) + GDIDCT 6 spd HEV82.8L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV$9,234 $9,215 $7,493 $6,112 $6,095 $6,095 $6,095 $6,095 $6,095 $6,095 $5,310 36.5%

Subcomp I4 (T1) Compact Car I4 (T2)

Midsize Car/Small MPV (unibody) I4 (T3)

Compact Car/Small MPV (unibody) V6 (T4)Technology Packages, Cost and Effectiveness1-19Table 1-12 Continuedbaseline 3.3L-4V DOHC V6AT 4 spd12 V13.3L - 4V V6AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%23.0L V6 GDI + CCPAT 6 spd12 V$1,144 $1,112 $1,081 $1,051 $1,022 $1,022 $1,022 $1,022 $1,022 $1,022 $927 17.9%33.0L V6 w/ Deac GDI + CCPAT 6 spd12 V43.0L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S$2,537 $2,482 $2,305 $2,153 $2,103 $2,103 $2,103 $2,103 $2,103 $2,103 $1,942 34.2%52.2L I4 Turbo DCP + GDIDCT 6spd 42 S-S63.0L V6 HCCI GDIDCT 6spd 42 S-S72.2L Turbo DCP + GDI lean burnDCT 6spd 42 S-S82.5L I4 HEV (Power Split) + GDIN/AHEV$6,772 $6,572 $6,377 $6,188 $6,005 $6,005 $6,005 $6,005 $6,005 $6,005 $5,129 37.5%baseline 4.5L-4V DOHC V8AT 4 spd12 V14.5L - 4V V8AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%24.0L V6 GDI + CCPAT 6 spd12 V$986 $959 $933 $907 $883 $883 $883 $883 $883 $883 $780 17.9%34.0L V6 w/ Deac GDI + CCPAT 6 spd 42 S-S44.0L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S$2,103 $2,061 $1,896 $1,757 $1,719 $1,719 $1,719 $1,719 $1,719 $1,719 $1,552 31.9%53.0L V6 Turbo DCP + GDIAT 6 spd 42 S-S63.0L V6 Turbo DCP + GDIDCT 6 spd 42 S-S74.0L V6 HCCI GDIDCT 6 spd 42 S-S83.0L V6 Turbo DieselDCT 6 spd 1293.0L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV$9,082 $9,068 $7,346 $5,966 $5,953 $5,953 $5,953 $5,953 $5,953 $5,953 $5,143 44.4%baseline 2.6L-4V DOHC I4 (I5)AT 4 spd12 V12.6 L - 4V I4AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%22.4L I4 CCP + GDIAT 6 spd12 V$1,104 $1,074 $1,044 $1,015 $987 $987 $987 $987 $987 $987 $890 17.4%32.4L I4 DVVL + CCP + GDIDCT 6 spd 12 V$1,406 $1,367 $1,328 $1,291 $1,255 $1,255 $1,255 $1,255 $1,255 $1,255 $1,139 21.4%42.4L I4 DVVL + CCP + GDIdry DCT 6 spd 42 S-S$2,481 $2,428 $2,252 $2,102 $2,054 $2,054 $2,054 $2,054 $2,054 $2,054 $1,896 34.7%52.0L I4 Turbo DCP + GDIdry DCT 6 spd 42 S-S$2,837 $2,773 $2,587 $2,427 $2,369 $2,369 $2,369 $2,369 $2,369 $2,369 $2,172 36.3%61.8L Turbo DCP + GDI lean burnDCT 6 spd 42 S-S71.8L I4 Turbo HEV (IMA) + GDIdry DCT 6 spd HEV81.8L I4 Turbo HEV (Power Split) + GDI N/AHEV$6,951 $6,745 $6,545 $6,352 $6,164 $6,164 $6,164 $6,164 $6,164 $6,164 $5,335 39.6%91.8L I4 Turbo HEV Plug-in IMA + GDIdry DCT 6 spd HEVbaseline 3.7L-2V SOHC V6AT 4 spd12V13.7L 2V SOHC V6AT 4 spd12V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%23.2L 2V SOHC V6 GDI + CCPAT 6 spd12V$1,168 $1,136 $1,104 $1,073 $1,044 $1,044 $1,044 $1,044 $1,044 $1,044 $948 17.8%33.2L 2V SOHC V6 w/Deac GDI + CCP AT 6 spd12V$1,337 $1,300 $1,263 $1,228 $1,194 $1,194 $1,194 $1,194 $1,194 $1,194 $1,093 19.6%42.8L 4V V6 GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC52.8L 4V V6 GDI + CCP + DVVLAT 6 spd12V V6 SOHC to V6 DOHC62.8L 4V V6 w/ Deac GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC72.8L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S V6 SOHC to V6 DOHC $3,017 $2,948 $2,756 $2,591 $2,528 $2,528 $2,528 $2,528 $2,528 $2,528 $2,354 32.4%82.4L I4 Turbo DCP + GDIDCT 6spd 42 S-S92.8L 4V V6 HCCI GDIDCT 6spd 42 S-S V6 SOHC to V6 DOHC10 2.4L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-S11 2.8L I4 Turbo DieselDCT 6 spd 1212 3.0L 4V V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEV V6 SOHC to V6 DOHC13 3.0L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V6 SOHC to V6 DOHC $8,838 $8,807 $7,233 $5,968 $5,940 $5,940 $5,940 $5,940 $5,940 $5,940$5,193 36.3%

Midsize/Large Car V6 (T5) Midsize Car/Large Car V8 (T6)

Mid-sized MPV (unibody)/Small Truck I4 (T7) Midsize MPV (unibody)/Small Truck V6/V8 (T8)Draft Regulatory Impact Analysis1-20Table 1-12 Continuedbaseline 4.0L-2V SOHC V6AT 4 spd12V14.0L 2V SOHC V6AT 4 spd12V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%23.6L 2V SOHC V6 GDI + CCPAT 6 spd12V$1,191 $1,158 $1,126 $1,095 $1,064 $1,064 $1,064 $1,064 $1,064 $1,064 $969 17.4%33.6L 2V SOHC V6 w/Deac GDI + CCP AT 6 spd12V$1,360 $1,322 $1,285 $1,249 $1,214 $1,214 $1,214 $1,214 $1,214 $1,214 $1,113 19.4%43.2L 4V V6 GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC53.2L 4V V6 w/ Deac GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC63.2L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S V6 SOHC to V6 DOHC72.4L I4 Turbo DCP + GDIDCT 6spd 42 S-S$3,125 $3,052 $2,857 $2,690 $2,623 $2,623 $2,623 $2,623 $2,623 $2,623 $2,409 32.3%83.2L 4V V6 HCCI GDIDCT 6spd 42 S-S V6 SOHC to V6 DOHC92.4L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-S10 2.0L I4 Turbo HEV (IMA) + GDIDCT 6 spd HEV11 3.2L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V6 SOHC to V6 DOHC $9,633 $9,602 $7,869 $6,477 $6,449 $6,449 $6,449 $6,449 $6,449 $6,449$5,651 36.5%baseline 4.7L-2V SOHC V8AT 4 spd12V14.7L 2V SOHC V8AT 4 spd12V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%24.4L 2V SOHC V8 GDI + CCPAT 6 spd12V$1,215 $1,181 $1,148 $1,116 $1,085 $1,085 $1,085 $1,085 $1,085 $1,085 $990 17.4%34.4L 2V SOHC V8 w/Deac GDI + CCP AT 6 spd12V$1,384 $1,345 $1,307 $1,271 $1,235 $1,235 $1,235 $1,235 $1,235 $1,235 $1,134 19.4%44.2L 4V V6 GDI + CCPAT 6 spd12V54.2L 4V V6 w/ Deac GDI + CCPAT 6 spd12V64.2L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S$2,999 $2,930 $2,739 $2,575 $2,512 $2,512 $2,512 $2,512 $2,512 $2,512 $2,342 34.3%72.8L 4V V6 Turbo DCP + GDIDCT 6spd 42 S-S84.2L 4V V6 HCCI GDIDCT 6spd 42 S-S92.8L 4V V6 Turbo DCP + GDI lean burn DCT 6spd 42 S-S10 3.0L V6 Turbo DieselDCT 6 spd 1211 4.2L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV$9,458 $9,433 $7,704 $6,317 $6,294 $6,294 $6,294 $6,294 $6,294 $6,294 $5,486 36.5%baseline 4.2L-2V SOHC V6AT 4 spd12V14.2L 2V SOHC V6AT 4 spd12V$256 $252 $247 $243 $239 $239 $239 $239 $239 $239 $231 7.6%23.9L 2V SOHC V6 GDI + CCPAT 6 spd12V$1,233 $1,199 $1,167 $1,135 $1,104 $1,104 $1,104 $1,104 $1,104 $1,104 $984 18.3%33.9L 2V SOHC V6 w/Deac GDI + CCP AT 6 spd12V$1,424 $1,384 $1,346 $1,309 $1,273 $1,273 $1,273 $1,273 $1,273 $1,273 $1,146 19.9%43.6L 4V V6 GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC53.6L 4V V6 GDI + CCP + DVVLAT 6 spd12V V6 SOHC to V6 DOHC63.6L 4V V6 w/ Deac GDI + CCPAT 6 spd12V V6 SOHC to V6 DOHC73.6L 4V V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S V6 SOHC to V6 DOHC $3,256 $3,184 $2,977 $2,800 $2,735 $2,735 $2,735 $2,735 $2,735 $2,735 $2,529 34.9%82.5L I4 Turbo DCP + GDIDCT 6spd 42 S-S94.0L 4V V6 HCCI GDIDCT 6spd 42 S-S V6 SOHC to V6 DOHC10 2.5L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-S11 2.8L I4 Turbo DieselDCT 6 spd 1212 3.6L 4V V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEV V6 SOHC to V6 DOHC13 3.6L 4V V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V6 SOHC to V6 DOHCbaseline 3.8L 2V OHV V6AT 4 sp12 V13.8L-2V OHV V6AT 4 spd12V$256 $252 $247 $243 $239 $239 $239 $239 $239 $239 $231 7.6%23.2L 4V DOHC V6 GDI + CCPAT 6 spd12V V6 OHV to V6 DOHC $1,482 $1,441 $1,401 $1,362 $1,324 $1,324 $1,324 $1,324 $1,324 $1,324 $1,196 18.9%33.2L 4V DOHC V6 w/Deac GDI + CCP AT 6 spd12V V6 OHV to V6 DOHC $1,672 $1,625 $1,580 $1,536 $1,493 $1,493 $1,493 $1,493 $1,493 $1,493 $1,358 20.1%43.2L 4V DOHC V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S V6 OHV to V6 DOHC $3,381 $3,305 $3,095 $2,914 $2,845 $2,845 $2,845 $2,845 $2,845 $2,845 $2,635 34.9%52.5L I4 Turbo DCP + GDIDCT 6spd 42 S-S$3,653 $3,569 $3,351 $3,163 $3,087 $3,087 $3,087 $3,087 $3,087 $3,087 $2,820 35.1%

Large MPV (unibody) V6 (T9) Large MPV (unibody) V8 (T10) Large Truck (+ Van) V6 (T11)
Large Truck + Large MPV V6 (T12)Technology Packages, Cost and Effectiveness1-21Table 1-12 Continuedbaseline 5.7L 2V OHV V8AT 4 spd12V15.7L 2V OHV V8AT 4 spd12V$256 $252 $247 $243 $239 $239 $239 $239 $239 $239 $231 7.6%25.2L 2V OHV V8 GDI + CCPAT 6 spd12V$1,172 $1,141 $1,110 $1,080 $1,051 $1,051 $1,051 $1,051 $1,051 $1,051 $933 18.3%35.2L 2V OHV V8 w/Deac GDI + CCPAT 6 spd12V$1,363 $1,325 $1,289 $1,253 $1,219 $1,219 $1,219 $1,219 $1,219 $1,219 $1,096 19.9%44.6L 4V V8 GDI + CCPAT 6 spd12V V8 OHV to V8 DOHC54.6L 4V V8 w/ Deac GDI + CCPAT 6 spd12V V8 OHV to V8 DOHC64.6L 4V V8 w/ Deac GDI + CCPDCT 6 spd 42 S-S V8 SOHC to V8 DOHC $3,457 $3,379 $3,167 $2,984 $2,913 $2,913 $2,913 $2,913 $2,913 $2,913 $2,705 34.9%73.5L 4V V6 Turbo DCP + GDIDCT 6spd 42 S-S$4,142 $4,044 $3,812 $3,610 $3,520 $3,520 $3,520 $3,520 $3,520 $3,520 $3,223 35.1%84.6L 4V V8 HCCI GDIDCT 6spd 42 S-S V8 OHV to V8 DOHC93.5L V6 Turbo DCP + GDI lean burnDCT 6spd 42 S-S10 3.5L V6 Turbo DieselDCT 6 spd 1211 4.6L 4V V8 w/ Deac GDI + CCP HEV (2-mode) N/AHEV V8 OHV to V8 DOHCbaseline 5.4L 3V SOHC V8AT 4 sp12 V15.4L 3V SOHC - V8AT 4 spd12 V$256 $252 $247 $243 $239 $239 $239 $239 $239 $239 $239 7.6%24.6L 4V DOHC V8 GDI + CCPAT 6 spd12V V8 SOHC 3V to V8 DOHC $1,352 $1,315 $1,279 $1,244 $1,210 $1,210 $1,210 $1,210 $1,210 $1,210 $1,161 18.9%34.6L 4V DOHC V8 w/ Deac GDI + CCP AT 6 spd12V V8 SOHC 3V to V8 DOHC $1,714 $1,666 $1,619 $1,574 $1,530 $1,530 $1,530 $1,530 $1,530 $1,530 $1,456 21.2%44.6L 4V DOHC V8 w/ Deac GDI + CCP DCT 6 spd 42 S-S V8 SOHC 3V to V8 DOHC $3,357 $3,282 $3,073 $2,893 $2,825 $2,825 $2,825 $2,825 $2,825 $2,825 $2,687 34.9%53.5L V6 Turbo DCP + GDIDCT 6spd 42 S-S$3,889 $3,798 $3,573 $3,379 $3,295 $3,295 $3,295 $3,295 $3,295 $3,295 $3,085 35.1%baseline 3.2L-4V DOHC V6AT 4 spd12V13.2 L - 4V V6AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%22.8L V6 GDI + CCPAT 6 spd12 V$1,168 $1,136 $1,104 $1,073 $1,044 $1,044 $1,044 $1,044 $1,044 $1,044 $948 17.8%32.8L V6 GDI + CCP + DVVLAT 6 spd12 V42.8L V6 w/ Deac GDI + CCPAT 6 spd12 V52.8L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S$2,286 $2,239 $2,068 $1,924 $1,881 $1,881 $1,881 $1,881 $1,881 $1,881 $1,722 31.8%62.4L I4 Turbo DCP + GDIDCT 6 spd 42 S-S72.8L V6 HCCI GDIDCT 6spd 42 S-S82.4L I4 Turbo DCP + GDI lean burnDCT 6 spd 42 S-S92.8L I4 Turbo DieselDCT 6 spd 12$3,342 $3,244 $3,149 $3,057 $2,968 $2,968 $2,968 $2,968 $2,968 $2,968 $2,732 32.6%10 3.0L V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEV11 3.0L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV$8,439 $8,420 $6,857 $5,603 $5,586 $5,586 $5,586 $5,586 $5,586 $5,586 $4,852 35.8%baseline 3.5L-4V DOHC V6AT 4 spd12V13.5L - 4V V6AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%23.2L V6 GDI + CCPAT 6 spd12 V$1,191 $1,158 $1,126 $1,095 $1,064 $1,064 $1,064 $1,064 $1,064 $1,064 $969 17.4%33.2L V6 w/ Deac GDI + CCPAT 6 spd12 V43.2L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S52.4L I4 Turbo DCP + GDIDCT 6spd 42 S-S$2,485 $2,431 $2,255 $2,105 $2,057 $2,057 $2,057 $2,057 $2,057 $2,057 $1,806 31.6%63.2L V6 HCCI GDIDCT 6spd 42 S-S72.4L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-S82.0L I4 Turbo HEV (IMA) + GDIDCT 6 spd HEV93.2L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV$9,234 $9,215 $7,493 $6,112 $6,095 $6,095 $6,095 $6,095 $6,095 $6,095 $5,310 36.5%

Large Truck (+ Van) V8 (T13) Large Truck (+Van) V8 (T14) Midsize MPV (unibody)/Small Truck V6/V8 (T15) Large MPV (unibody) V6 (T16)Draft Regulatory Impact Analysis1-22Table 1-12 Continuedbaseline 4.6L-4V DOHC V8AT 4 spd12V14.6L - 4V V8AT 4 spd12 V$231 $227 $222 $218 $214 $214 $214 $214 $214 $214 $207 7.6%24.2L V6 GDI + CCPAT 8 spd12 V$1,035 $1,006 $979 $952 $926 $926 $926 $926 $926 $926 $823 17.4%34.2L V6 w/ Deac GDI + CCPAT 8 spd12 V$1,368 $1,329 $1,292 $1,256 $1,221 $1,221 $1,221 $1,221 $1,221 $1,221 $1,111 20.5%44.2L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S$2,593 $2,537 $2,358 $2,205 $2,153 $2,153 $2,153 $2,153 $2,153 $2,153 $1,984 33.7%52.8L V6 Turbo DCP + GDIDCT 6spd 42 S-S64.2L V6 HCCI GDIDCT 6spd 42 S-S72.8L V6 Turbo DCP + GDI lean burnDCT 6spd 42 S-S83.0L V6 Turbo DieselDCT 6 spd 1294.2L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEV$9,053 $9,040 $7,323 $5,948 $5,935 $5,935 $5,935 $5,935 $5,935 $5,935 $5,127 36.5%baseline 4.0L-4V DOHC V6AT 4 spd12V14.0L - 4V V6AT 4 spd12 V$256 $252 $247 $243 $239 $239 $239 $239 $239 $239 $231 7.6%23.6L V6 GDI + CCPAT 6 spd12 V$1,233 $1,199 $1,167 $1,135 $1,104 $1,104 $1,104 $1,104 $1,104 $1,104 $984 18.3%33.6L V6 GDI + CCP + DVVLAT 6 spd12 V43.6L V6 w/ Deac GDI + CCPAT 6 spd12 V$1,565 $1,521 $1,479 $1,437 $1,398 $1,398 $1,398 $1,398 $1,398 $1,398 $1,270 21.0%53.6L V6 w/ Deac GDI + CCPDCT 6 spd 42 S-S$2,856 $2,796 $2,601 $2,436 $2,381 $2,381 $2,381 $2,381 $2,381 $2,381 $2,187 34.4%62.5L I4 Turbo DCP + GDIDCT 6spd 42 S-S$2,995 $2,931 $2,732 $2,562 $2,504 $2,504 $2,504 $2,504 $2,504 $2,504 $2,220 34.5%74.0L V6 HCCI GDIDCT 6spd 42 S-S82.5L I4 Turbo DCP + GDI lean burnDCT 6spd 42 S-S92.8L I4 Turbo DieselDCT 6 spd 1210 3.6L V6 w/ Deac GDI + CCP HEV (IMA) DCT 6 spd HEV11 3.6L V6 w/ Deac GDI + CCP HEV (2-mode) N/AHEVbaseline 5.6L 4V DOHC V8AT 4 spd12V15.6L 4V V8AT 4 spd12 V$256 $252 $247 $243 $239 $239 $239 $239 $239 $239 $231 7.6%24.6L V8 GDI + CCPAT 6 spd12 V$1,172 $1,141 $1,110 $1,080 $1,051 $1,051 $1,051 $1,051 $1,051 $1,051 $933 18.3%34.6L V8 w/ Deac GDI + CCPAT 6 spd12 V$1,363 $1,325 $1,289 $1,253 $1,219 $1,219 $1,219 $1,219 $1,219 $1,219 $1,096 21.0%44.6L V8 w/ Deac GDI + CCPDCT 6 spd 42 S-S$1,556 $1,515 $1,475 $1,435 $1,398 $1,398 $1,398 $1,398 $1,398 $1,398 $1,272 34.4%53.5L V6 Turbo DCP + GDIDCT 6spd 42 S-S$2,145 $2,086 $2,029 $1,973 $1,919 $1,919 $1,919 $1,919 $1,919 $1,919 $1,703 34.5%64.6L V8 HCCI GDIDCT 6spd 42 S-S73.5L V6 Turbo DCP + GDI lean burnDCT 6spd 42 S-S83.5L V6 Turbo DieselDCT 6 spd 1294.6L V8 w/ Deac GDI + CCP HEV (2-mode) N/AHEV

Large MPV (unibody) V8 (T17) Large Truck (+ Van) V6 (T18) Large Truck (+ Van) V8 (T19)Technology Packages, Cost and Effectiveness

1-23 Table 1-13 Package Costs Measured Relative to the Package Costs for the 2016MY

YEAR PACKAGE COSTS RELATIVE TO 2016 2012 117% 2013 115% 2014 108% 2015 102% 2016 100% 2017 100% 2018 100% 2019 100% 2020 100% 2021 100% 2022+ 92%

We do not show cost and effectiveness estimates for a number of the technology listed in Table 1-12, as we determined that these packages were not cost effective relative to other
pac k ages available for a specific vehicle type. The process used to make these determinations is discussed below.

As discussed in detail in Chapter 4 of th is DRIA, 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 packag es 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 pack age. 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 t echn o logy 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 only exception is that the package including an HCCI engine has been removed as this technology is not expected to be commercially available in time for widespread introduction by 2016. The information listed in the first six columns is taken from Table 1-12. The values in the seventh column, which are explai ned below, are used to remove pack ages which would not likely be applied by a manufacturer and therefore, should not be included in the OMEGA modeling.Draft Regulatory Impact Analysis

1-24 Table 1-14 Evaluation of Technology Packages for Vehicle Type #6

Engine Transmission System Voltage Weight Reduction Total CO 2 Reduction Total 2016 Cost $/delta CO 2 % 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $2,816 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $883 $6,497 4.0L V6 w/ Deac GDI + CCP AT 6 spd 42 S-S 5% 28.2% $1633 $7,274 3.0L V6 Turbo DCP + GDI AT 6 spd 42 S-S 5% 28.5% $2333 $284,727 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1719 $(17,617) 3.0L V6 Turbo DCP + GDI DCT 6spd 42 S-S 5% 32.1% $2419 $2,363 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 35.0% $3456 $35,737 3.0L V6 w/ Deac GDI+CCP HEV 2-mode HEV 0% 44.4% $5953 $26,586 Remove Turbo with AT 6 spd 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $2,816 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $883 $6,497 4.0L V6 w/ Deac GDI + CCP AT 6 spd 42 S-S 5% 28.2% $1633 $7,274 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1719 $2,305 3.0L V6 Turbo DCP + GDI DCT 6spd 42 S-S 5% 32.1% $2419 $452,082 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 35.0% $3456 $35,737 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5953 $26,586 Remove Turbo with DCT 6 spd 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $2,816 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $883 $6,497 4.0L V6 w/ Deac GDI + CCP AT 6 spd 42 S-S 5% 28.2% $1633 $7,274 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1719 $2,305 3.0L V6 Turbo Diesel DCT 6 spd 12V 5% 35.0% $3456 $56,828 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5953 $26,586 Remove Diesel 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $2,816 4.0L V6 GDI + CCP AT 6 spd 12V 3% 17.9% $883 $6,497 4.0L V6 w/ Deac GDI + CCP AT 6 spd 42 S-S 5% 28.2% $1633 $7,274 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1719 $2,305 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5953 $34,012 Remove two AT 6 spd steps 4.5L DOHC 4-Valve V8 AT 4 spd 12V 0% 7.6% $214 $2,816 4.0L V6 w/ Deac GDI + CCP DCT 6 spd 42 S-S 5% 31.9% $1719 $6,184 3.0L V6 w/ Deac GDI + CCP HEV 2-mode HEV 0% 44.4% $5953 $34,012

The seventh, or last column of Table 1-14 is a measure of the incremental cost effe ctiveness of each package relative to the previous package. Specifically, it is the ratio of the incremental cost of the current package ov er the previous package to the incremental effectiveness of the current package over the pr evious 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 CO 2 emissions. Here, however, the arithmetic difference in the effec tiveness of two technology packages provides the best comparison across packages, since the base CO 2 emissions 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% effectiv eness, respectively, represents the same CO 2 emission reduction as a 10% difference between two packages with 27% and 37%Technology Packages, Cost and Effectiveness

1-25 effectiveness, respectively. Generally, a low ratio of incremental cost to incremental effectiveness is better than a high ratio. Ideal ly, 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 except f o r the HCCI engine package. The order of the packages has been rearranged slightly from that in Table 1-12 in order to place the packages in order of increasing total effectiveness. As can be see n , 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 is very high, while that for the engine with cylind er deactivation with a dual clutch transmission is negative. The cause of this is that the cost of the latter package is lower than the former. If the latter package can achieve a 31.9% reduction in CO 2 emissions at a cost of $1,719, then there is no point in considering a package which only achieves a 28.5% reduction in CO 2 emissions for a cost of $2,333. Therefore, we removed the package for the turbocharged engine with a 6 speed automatic transmission and repeated the calculations. (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 technol ogy packages is shown in the second section of Table 1-14.

The seco nd set of packages now shows one obvious anomaly. The ratio of incremental cost to incremental effectiveness fo r the turbocharged engine with a dual clutch transmission is more than a factor of 10 higher than any of the others. This occurs because this package only reduces CO 2 emissions by 0.2% over the previous package for an incremental cost of $700. A manufacturer woul d be better off applying the next package (the diesel) to a portion of its vehicles than to ap ply the turbocharged engine with a dual clutch transmission package to a higher percentage of its vehicles. Therefore, we removed the package for the turbocharged engine with the du al clutch transmission and again repeated the calculations. The revised set of technology packages is shown in the third section of Table 1-1 4

. The greatest anomaly in the third set of ratios is that for the diesel package. It is more than twice the value of the ratio for the 2-mode hybrid. If we believed that manufacturers would prefer to implement diesel technology over strong hybridization for some reason, we could have left both packages in the modeling. However, absent such a reason, we removed the diesel engine package from vehicle type #6. The revised set of technology packages is shown in the fourth section of Table 1-14.

The great est anomaly in the fourth section of the table is that the ratio of incremental cost to incremental effectiveness for the engine with cylinder deactivation and a dual clutch transmission is much lower than those for th e two prior packages. In order to assess the benefit of removing the two prior packages, we do so in the fifth and last section of Table 1-14. As ca n be seen, the ratio of incremental cost to incremental effectiveness for the engine with cylinder deactivation and a dual clutch tran smission increases to over $6,184. This is only marginally lower than th e ratios for the two packages which have been excluded. Retaining the two packages provides a more gradual application of technology. This provides the model the choice of applying technology whic h is currently widespread in the fleet (6- speed automatic transmissions and 12 volt electri cal systems) to a greater percentage of salesDraft Regulatory Impact Analysis

1-26 before applying more extensive technology (dual clutch transmissions and start stop technology). Therefore, we did not exclude th e two technologies based on a strict use of the ratio of incremental cost to incremental effectiveness.



### 1.4 EPA’s Lumped Parameter Approach for Determ ining Effectiveness Synergies

EPA engineers reviewed existing tools that could be used to develop estimates of the technology synergies, including the NEMS model. 1 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 lumped-parameter 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 CO 2 emissions than the lumped-parameter approa ch, particularly with regard to applying synergy effects differentiated across different vehicle classes, and therefore augmented this approach with the detailed vehicle simula tion 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 in herent in the combustion of fuel),

 Heat lost from the combustion pro cess 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;

with the remaining energy available to propel th e 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 tec hnology that reduces the losses in an already improved category has less of a potential impact than it would if applied on its own. TableTechnology Packages, Cost and Effectiveness

1-27 1-15 below is an example spreadsheet used by EPA to estimate the synergistic impacts of a technology package for a standard-size car.

Table 1-15 Sample Lumped Parameter Spreadsheet

EPA Staff Deliberative Materials--Do Not Quote or Cite

Vehicle type: Standard Car Descr iption: 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

ReductionDraft Regulatory Impact Analysis

1-28 Table 1-16 below lists the technologies considered in this example, their
corresp on ding individual technology effectiveness values, and a comparison of the gross combined package CO 2 reduction (i.e. disregarding synergies) to the lumped parameter results. The difference is the implied synergisti c effects of these technologies combined on a package.

Table 1-16 Comparison of Lumped Parameter Analysis with Standard Car Package

TECHNOLOGY INDIVIDUAL CO 2 REDUCTION CUMULATIVE CO 2 REDUCTION 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 ve hicle simulation work. EPA will continue to use the lumped parameter approach as an analy tical tool, and (using the output data from the vehicle simulation as a basis) may adjust the synergies as necessary in the future.



### 1.4.1 Ricardo’s Vehicle Simulation

Vehicle simulation modeling was performed by Ricardo, Inc. The simulation work addressed gaps in existing synergy modeling tool s, and served to both supplement and update the earlier vehicle simulation work published by NESCCAF. Using a physics-based, secondby-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.Technology Packages, Cost and Effectiveness

1-29 In total, Ricardo modeled five baseline ve hicles 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 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 performan ce 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 Time to accelerate from 0-30 MPH Launch Acceleration Vehicle speed and distance after a 3-second acceleration from rest Time to accelerate from 30 to 50 MPH Passing Performance 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 (wid e 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 CO 2 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, method ology 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 whic h 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 CO 2 emissions over the EPA combined cycle federal test procedure (FTP) for five baseline vehicles, for validation with EPA certification data.

 Simulation of the vehicle technology combinations (applied to the baseline vehicles)Draft Regulatory Impact Analysis

1-30  Incremental technology effectiveness estima tes, 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 se lection process for each of the baseline vehicles and the technology packages, and summarizes the results of the vehicle simulation.



### 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 t echnologies could be evaluated. The main distinguishing attributes of the five vehicle cl asses considered by EPA and Ricardo are given below in Table 1-18.
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.Technology Packages, Cost and Effectiveness

1-31  It should be among the sale s 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 proposed 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, Ch rysler/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.



### 1.4.4 Description of Baseline Vehicle Models

The baseline vehicles selected to repres ent their respective vehicle classes are described below in Table 1-19, listed with the critical attributes that EPA used as selection crite r ia. 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.Draft Regulatory Impact Analysis

1-32 Table 1-19 Description of Baseline Vehicles

*-Estimated CO 2 equivalent, taken from EPA adjusted combined fuel economy ratings.



### 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 technolog ies. 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 potenti al feasibility in the next one to ten years. 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

CO 2 Emissions* (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 67Vehicle 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 2.16PerformanceCharacteristics

Power / Weight Ratio

(HP/ton) 99.1 134.4 88.4 95.8 119.9Technology Packages, Cost and Effectiveness

1-33 The list of technologies considered therefore includes those that are available today (e.g., variable valve timing, six-speed automatic tran smissions) 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 bel o w lists the technologies that Ricardo included in the vehicle simulation models.

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 engi ne 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 Variab le 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%)Draft Regulatory Impact Analysis

1-34 

### 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 th ese technology combinations, EPA considered available cost and effectiveness numbers from the literature, and applied engineering judgment to match technologies that were comp atible 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
pac k age simulated by Ricardo.
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, HEAStandardCar

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, HEASmall 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, HEAFull 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, HEALarge 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, EACCLargeTruck

10 3.6L V6, GDI, DCP DCT6 EPS, EACC, HEATechnology Packages, Cost and Effectiveness

1-35 Turbo
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 X2 5.4L V8, GDI, HCCI DCP, CVVL DCT6 EPS, EACC, HEA

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 a ssumed 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 CO 2 emissions results from the vehicle simulation are summarized below in Table 1-22 (for cars) and Table 1-23 (for light-duty trucks). The CO 2 estimates are given for the combined city and highway test cycles, according to the EPA Federal Test Procedure (FTP), with the technology package results compar ed 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 th e proposed 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 CO 2 Emissions Estimates Obtained from Vehicle Simulation (Cars)

CO 2 CITY CO 2 HWY CO 2 COMB CO 2 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%Full SizeCar

Y1 GDI, CVA, DCT 278 199 242 32.0%Draft Regulatory Impact Analysis

1-36 Y2 GDI, HCCI, DCT 290 197 248 30.4%

6a GDI, DCP, CVVL, DCT 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

Table 1-23 CO 2 Emissions Estimates Obta ined from Vehicle Simulation (Light-Duty Trucks)

CO 2 CITY CO 2 HWY CO 2 COMB CO 2 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:Technology Packages, Cost and Effectiveness

1-37 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. 2) EPA is confi d ent 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.

0% 5% 10% 15% 20% 25% 30% 35% 40%Std CarStd CarStd CarSm MPVSm MPVSm MPVSm MPVSm MPVSm MPVSm MPVLarge CarLarge CarLarge CarLarge CarLarge CarLarge CarLarge MPVLarge MPVLarge MPVTruckTruckTruckTruckTruckTruckTruck

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
PackageFC 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 pack ages that garner additional investigation). EPA will continue to analyze this data, focu sing on those packages where the differences between the two approaches are large.

The simulation results may present opport unities 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 some technologies by comparing and isolating Ricardo package results across different vehicle platforms.Draft Regulatory Impact Analysis

1-38 

### 1.6 Using the Lumped-Parameter Techni que to Determine Synergies in a Technology Application Flowpath (Ide ntifying “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 us ing an approach in which technologies are added sequentially in a pre-determined applicat ion path to each individual vehicle model, as used in NHTSA’s 2006 fuel economy rule for light trucks 3 , these interactions may be accounted for by considering a series of interact ing 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. Wh en 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 applie d, regardless of whether any of them have also been superseded. Figure 1-3 below provides an example of how technology pairs are ide n tified for a specific technology application pa th similar to one used by NHTSA. In this example, an interaction is identified betwee n 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.

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 pathTechnology Packages, Cost and Effectiveness

1-39 References

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472.

1 National Energy Modeling System, Energy Informa tion 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 EPA-HQ-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.Draft Regulatory Impact Analysis

2-2 

## 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 air conditioner (MAC) systems. In the 1970’s an d 1980’s, air conditioner systems were an optional (luxury) feature, but it now comes standard on almost all new vehicle models. The Mobile Air Conditioner (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. Firs t, 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 wh ile the vehicle is moving down the road or being tested on a dynamometer for the fuel econo my 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 offcycle loads that can be switched on by the oc cupant that affect the engine; these include lights, wipers, stereo systems, electrical defroste r/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 directly into the air - even when the vehicle is not in operation. No other vehicle system does this. Because of these factors, a distinct approach to control of MAC systems is justified, and a separa te 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 firs t is through direct leakage of the refrigerant into the air. The hydrofluorocarbon refrigeran t 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 warm ing potential of 1,430 grams of CO 2 (which has a GWP

A The global warming potentials (GWP) used in the NPRM analysis are cons istent 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 agre ed 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 fr amework. When inventories are recalculated for the final rule, changes in GWP used may lead to adjustments.Air Conditioning

2-3 of 1). 1 In order for the A/C system to take ad vantage 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 pre ssures can range from 50-80 psi depending on the temperature, and during operation, these pressure s 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 sc rappage (especially when refrigerant capture and recycling programs are less efficient). Small amounts of leakage can also occur continuously even in extremely “leak-tight” sy stems by permeating through hose membranes. This last mechanism is not dissimilar to fu el permeation through porous fuel lines. Manufacturers may be able to reduce these leak age emissions through the implementation of technologies 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 al so 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 syst em hardware year-round. The additional fuel required to run the system is converted into CO 2 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 CO 2 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, thes e (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 CO 2 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 greenh ouse 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 discusses some similar features and others that emphasize additional emission reduction mechanisms.Draft Regulatory Impact Analysis

2-4 

### 2.2 Air Conditioner Leakage

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 averag e 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 end-of-life was 8.5%.

2.2.1.2 Emission Inventory

The EP A 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 inventory source. The HFC refrigerant emissions from light duty vehi cle A/C systems was estimated to be 61.8 Tg CO 2 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 CO 2 , N 2 O and CH 4 emissions are from MOVES, the refrigerant emissions are from the Vintaging model, and the A/C CO 2 emissions are from EPA and the National Renewable Energy Laboratory (NREL) as described below.

B EPA reported the MVAC emissions at 56.6 Tg CO 2 EQ, using a GWP of 1300. This number has been adjusted using a GWP of 1430.Air Conditioning

2-5 Table 2-1. CO 2 equivalent emissions from light duty vehicles broken up by source or process.

Emissions source or process Tg CO 2 (equivalent) Percentage of total Tailpipe CO 2 (w/o A/C) 1,076 88.6% CO 2 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 “regul ar” emissions), 49% for service and maintenance (or “irregular” emissions), and 9% occurs at disp osal 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 ch arge 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 can be converted to CO 2 equivalent by multiplying by 1430 GWP. 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



### 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 – HF C-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 lo w levels in a test chamber (or shed). Since the J2727 method has been correlated to “min i-shed” results (where select components are tested in a small chamber, simulating real-wor ld 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 Rule?

As wit h 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 orDraft Regulatory Impact Analysis

2-6 standard. Whether a design-bas ed approach is used for emis sions 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 market-driven incentive of assuring customer satisfaction will drive manufac turers to design A/C systems that perform as promised, and never need to be recharged. 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 me asured in bench- and field-test studies of vehicles and components.

In the case of refrigerant leakage, it woul d be very costly and burdensome to design, develop, and implement a test procedure and facility for measuring refrigerant leakage on each and every vehicle type a manufacturer produces. With leakage levels on many new vehicles expected to be as low as 9 g/yr (0.001 g/hr), it would be difficult to accurately measure the actual leakage rate. Even if it were possible to build a suitable facility capable of accurately measuring very low levels of refrige rant leakage, such a facility would still not exercise the A/C system across it s normal range of operation, both in terms of engine and vehicle speeds as well as ambient conditions (e.g. under high compressor load, leakage past the compressor shaft seal on a running system ca n be 20 times higher than the static leakage level). 7 In addition, it is very likely that any performance-based test would become obsolete in the timeframe of this rulemaking, as low-GW P refrigerants are likely to be adopted by manufacturers.

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 is proposing to rely upon the best available design metrics for quantifying system performance. 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 credi t available to manufacturers will be calculated based on how much a particular vehicle’s annual leakage value is re duced 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 co mponents enumerated in the proposed rule (expressed in the proposed rule 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:Air Conditioning

2-7 Equation 1 – Credit Equation

A/C Credit = (MaxCredit) * [ 1 - (§86.166-12 Score/AvgImpact C ) * (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 whethe r HFC-134a or an alternative refrigerant are used. The values are shown in Table 2-3, and are documented in the following sections.

 “MaxCredit” i s a term for the maximum amou nt of credit entered into the equation before constraints are applied to terms. Th e maximum credits that could 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 potent ial 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 Alternative Refrigerant Cars Trucks Cars Trucks MaxCredit equation input (grams /mile CO 2 EQ) 12.6 15.6 13.8 17.2 A/C credit maximum (grams /mile CO 2 EQ) 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

2.2.2.2.1 Max Credit Term

In orde r 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

C Proposed section 86.166-12 sets out the individual component leakage values based on the SAE value.Draft Regulatory Impact Analysis

2-8 (VMT). D 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 presente d in DRIA chapter 5, the HFC emission inventories were estimated from a number of existing data sources. The per-vehicle per-year HFC emission of the current (reference) vehicle fleet was determined using averaged 2005 and 2006 registration data from the Transport ation Energy Databook (TEDB) and 2005 and 2006 mobile HFC leakage estimates from the EPA Emissions and Sinks report described above. 8,9 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), by increasing the car contribution proportionally by the percentage of fo rmer 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 2005/200 6 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 fl eet 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 a n d are expected, regardless of prevention efforts. The accuracy of the J2727 approach (as expressed in proposed §86.112), as a design based test, decrea ses as the amount of expected leakage diminishes. 50% of the base rate is therefore proposed to be set as the maximum potential leakage credit for improvemen ts 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 ra te 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

D In short, leakage emissions occur even while the car is parked, so the connection to a gram/mile credit is not straightforward. However, HFC emissi ons must be converted to a gram/mile basis in order to create a relevant credit.Air Conditioning

2-9 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 additi on, 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.

A final adjustment was made to each credit to account for the difference between realworld HFC emissions and test-cycle CO 2 emissions. It has been sh own that the tests currently used for CAFE certification represents an approximately 20% gap from real world fuel consumption and the resulting CO 2 emissions. 10 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 CO 2 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 CO 2 EQ) VMT (Billions of Miles) Total HFC EmissionsPer Mile (CO 2 EQ Gram/mile) HFC Leakage and Service EmissionsPer Mile (CO 2 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 Proposed section 86.166-12, impl ementi ng 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 studi es discussed in section 2.2.1.1 which discussed the HFC

em issi on rate of the in-use fleet (which includes vehicles at all stages of life), the SAE J2727 estimates leakage from new vehicles. In the deve lopment of J2727, two relevant studies wereDraft Regulatory Impact Analysis

2-10 assessed to quantify new vehicle emission rates. In the first study, measurements from relatively new (properly functioning and ma nufactured) Japanese-market 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. 11 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. 12 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 w ith a standard deviation of 4.4 g/yr. European vehicles, because 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 fa ctor 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 pe r 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 Minumum Score = Car or truck av erage 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 to tal fleet average of 18 grams per year, with a maximum reduction fraction of 50%

The GWP Refrigerant term in Equation 1 al lows 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 Mi nimum 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 highAir Conditioning

2-11 penetrations within the 2012-2016 timeframe, as this technology is likely to accompany hybrid vehicle and stop-start technologies, and not conventional vehicles.

2.2.2.2.3 AvgImpact Term

AvgImpa ct is the average annual impact of A/C leakage, which is 16.6 and 20.7 g/yr for cars and trucks respectively. This wa s derived using Equation 2, but by setting the minimum fraction to one.

2.2.2.2.4 GWPRefrigerant Term

T h is term is relates to the global warm ing 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. 13 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 refri gerant 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 proposed 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. 14 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 Minnesot a’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 ma ss-produced vehicles in the United States. In these mass-produced vehicles, the need to main tain A/C system integrity (and the need to avoid the customer inconvenience of having their A/C system serv iced due to loss of refrigerant) is often balanced against the co st 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 mayDraft Regulatory Impact Analysis

2-12 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. 15 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. Howe ver, 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 leakag e 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. 16 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 fl exi ble hoses on an automotive A/C syst em are needed to isolate the system from engine vibration and to allow for the engine to roll within its mounts as the vehicle acceleratesand decelerates. Since the compressor is typically mounted to the engine, the lines going toand-from the compressor (i.e. the suction and pre ssure 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 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-Air Conditioning

2-13 low permeation’, and can be used in a ven eer 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 cond itions, 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 ven eer hoses have 25% the permeation rate of rubber hose, and ultra low permeable barrier ve neer 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

Wit h in 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 emi ssion chart, these interfaces are described as fittings and connections, and each type of fitti ng 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 multipl e o-rings, seal washers, and seal washers with o-rings, are available to manufacturers for the purp ose 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 exa m ple, the relative leakage factor of 125 for the baseline single O-ring is 125 times more “leaky” than the best technology - the metal gasket.

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 1Draft Regulatory Impact Analysis

2-14 2.2.3.4 Compressor Shaft Seal

A m ajor 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 sy stem 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 th e 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 lowe r, 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. 7 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 le akage, 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.



### 2.2.4 Technical Feasibility of Le akag e-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 demons trated 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 Deterioration of 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 deterioration that are modeled: the normal deterioration that results in increasing leakage and the “avoidable” deterioration of the condenser & compressor components. This model is developed to help us estimate the costs of the A/CAir Conditioning

2-15 reductions. It is especially needed to determ ine the period over which the discounted cost savings should be applied. E

Normal deterioration occurs throughout all components of the A/C system. Hoses, fittings, compressors, etc all wear with age an d exposure to heat (temperature changes), vibration, and the elements. It is assumed that the system deterioration rates decrease (proportionally) as the base leakage rates are decreased with the use of improved parts and components. The base deterioration rate is modeled as a linear function, such that the (new vehicle) leakage rate is 18 g/yr at age zero an d 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
gra m 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
gram s) per y ear and a servicing loss rate of 8.8% (68 grams) per year averaged over the vehicle’s life. The model assumes an average re frigerant charge of 7 70 grams for vehicles sold in 2002 or later and does not currently assu me 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 59-6 8 g/yr is higher with Schwarz’s European 2 study and lower than CARB’s study, 3 and thus is within the range of results in the literature.

E Air conditioning leakage controls are the only t echnology in this propos ed rule that has 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 powe rtrain 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. Simi larly, air conditioning leakage technologies can and do deteriorate, contributing to si gnificantly 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 fe w technologies where the emissions are no longer relevant, i.e. an A/C system that no longer functions, no longer emits indirect emissions.Draft Regulatory Impact Analysis

2-16 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 th is 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 us ed later to estimate the cost of maintenance savings due to low leak technologies (from refills) as well as the benefits of leakage controls.

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 800charge 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 proposed 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 inc l udes a line indicating when a recharge is re quired (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.Air Conditioning

2-17 

### 0 20 40 60 80 100 120

0 2 4 6 8 10 12 14 Age of VehicleLeakage (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.



### 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 lineDraft Regulatory Impact Analysis

2-18 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 th e 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. 17 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. 7 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 ca uses of compressor failure is a shortage of lubricating oil, which originates from a shorta ge of refrigerant flowing through the system (and it is a refrigerant-oil mixture which carries lubricating oil to the compressor). 18

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 to day 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 lo ss, and that further operation of the system in this state can result in additional componen t 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.Air Conditioning

2-19 

### 2.3 CO 2 

### Emissions due to Air Conditioners

2.3.1 Impact of Air Conditioning Use on Fuel Consumption and CO 2 

### Emissions

Three studies have been performed in recen t 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 At mospheric Programs (OAP) within EPA have performed a series of A/C related fuel use studies. 19,20 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 pe rcentage 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 pa rt of their GHG emission rulemaking. 21 The primary technical analysis utilized by ARB is summari zed in a report published by NESCCAF for ARB. The bulk of the technical work was pe rformed 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. Th e 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.

The NESCAFF results were taken from Table 3-1 of their report and are summarized in Table 2-7. 22

Table 2-7: CO 2 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 totalDraft Regulatory Impact Analysis

2-20 CO 2 emissions were determined using a 55%/45% weighting of CO 2 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 sy stem fuel use, the percentage of CO 2 emissions and fuel use are equivalent, since the type of fuel being used is always gasoline. F

In order to compare the NESCCAF and ARB estimates to that of NREL-OAP, weighting factors for the five vehicle classe s were developed. NESCCAF presented sales percentages for the five vehicle clas ses in Table 2-1 of their report. 22 These are shown below in Table 2-8. Since these sales percentages do not sum to 100% (possibly due to round-off or bec ause som e 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, norm alized to that of cars. G 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.

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. 23 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/m 2 . In addition, prior to the test, the vehicle has been pre-heated for

F 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.

G 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.Air Conditioning

2-21 10 minutes under these conditions , so the interior cabin starts the test at an elevated temperature. Testing of 500 late model vehicl es 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 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. 24 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 conditi ons 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. 25 Resulting in 14.3 g/mi per vehicle CO 2 -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). H

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 defrost er usage would increase the percentage of time that the compressor was engaged at a load equivale nt 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

H 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 automa tic A/C (e.g. middle luxury car, specialty, middle luxury SUV, large luxury SUV, et. al.)Draft Regulatory Impact Analysis

2-22 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 NRELOAP 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 co ntain 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. Ho wever, EPA was able to identify a couple of aspects of the studies which could be improved fo r the purpose of this analysis. Doing so, the overall difference between the studies was redu ced 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 ar e 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 th e NESCCAF and EPA methodologies and results below. With respect to EPA’s estimates fro m the 2006 rulemaking, the estimate including defroster use will be used, since NESCCAF consid ered 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. NESCCA F 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 compre ssor 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 theAir Conditioning

2-23 frequency that the compressor is engaged when th e 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%, ag ain ignoring defroster use. Since EPA utilized NREL-OAP’s estimate of defroster use in its anal ysis, this estimate does not contribute to the difference in the two estimates. Also, fuel us e is very low 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 itsel f (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 explai ns 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 ou tside and wearing one of two different sets of clothes. A number of assumptions had to be ma de 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 reason able, 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 inde x, which combines temperature and humidity into a single metric. Heat index is conceptua lly 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 accura cy 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 a ssumptions or extrapola tions 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.Draft Regulatory Impact Analysis

2-24 With respect to the operation of the A/C compressor at various ambient and driving conditions, EPA bases its estimate on the Phoenix ve hicle 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. Th e 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 appears to have been tested. It is not clear how well these curves would apply to other manufacturers’ syst ems, 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 part icular 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 unde r 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 cy cling 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 SC 03, 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 SC 03 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. 26 The vehicle pre-heating, short test duration and the requirement that the driver window be rolled down, make it extremely likely that the vehicle compartmentAir Conditioning

2-25 never reaches a comfortable temperature by the en d of the test. However, it is possible that the compressor still cycled to some degree duri ng 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 fu el 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 possible that this assumption that the A/C compressor is engaged 100% during SC03 is causing EPA’s estima te of A/C fuel use to be under-estimated to some degree.

There are additional uncertainties involved in EPA’s assumption th at 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 reas onable, 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 st atistical analysis of these data would be.

An A/C load adjustment factor is also appl ied 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). 27

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 estim ate the load curves applicable to the ambient conditions existing during driving times for a la rge number of cities across the U.S. The resulting curves are averaged using the VMT es timated 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 consumpt ion over the FTP and HFET cycles. The CRUISE model simulates vehicle operation and fuel co nsumption over specified driving conditions. The load of the A/C compressor (based on benc h 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 im pact than applying the average load of theDraft Regulatory Impact Analysis

2-26 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.

A detailed comparison of this aspect of the two analyses would require reconstructing both models to produce A/C fuel use estimate s 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 whic h 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 th e 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 fu el, 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 percen tage of fuel due to A/C use.

NESCCAF estimated the impact of the A/C co mpressor 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 en gine speed and load between city and highway driving. This allows a detail ed 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 engi ne 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 high way 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 othe r 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 CO 2 emissions over the FTP/HFET shown above in Table 2-7 are multiplied by a factor of 1.25 to represent

on road CO 2 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 us e divided by the estimated onroad fuel use.Air Conditioning

2-27 These figures are shown in Table 2-9 below. The VMT weighted average of these
pe rcentages is 4.4%, 0.9% lower than the estimate presented above.

Table 2-9: Adjusted NESCCAF CO 2 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 Us e 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 incr em ental CO 2 emissions from the A/C compressor load from th e CRUISE simulations of the FTP and HFET cycles. The top half of the table shows th e incremental fuel use in terms of grams CO 2 per mile. These figures were taken from Tables B-20 through B-23 of the NESCCAF report. 28 For the large car, two base vehicles were simulated. EPA selected the vehicle with the conventional gasoline engine with variable valv e 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 CO 2 per minute. These figures were calculated by multip lying the A/C fuel impacts in grams per mile by the average speeds of the FTP and HFET cycl es: 19.6 and 48.2 mph and converting hours to minutes. The final line of the table shows th e ratio of the incremental fuel use in terms of grams CO 2 per minute for the HFET cycle to that over the FTP.

Table 2-10: Impact of A/C on Fuel Use: System

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 CO 2 emissions over the HFET to that over the FTP is greater than 1.0 for each of the five vehicles. VMT weighting the CO 2 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 esti mating 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 impactDraft Regulatory Impact Analysis

2-28 of A/C on onroad fuel consumption, the 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 th e 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 es timated the percentage of vehicles on the road with inoperative A/C systems as a functi on 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 CO 2 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 CO 2 emissions of approximately 14 g/mi per vehicle. The A/C usage is inherently higher in hotter months an d states; however, vehicle owners may use the A/C systems throughout the year in all parts of th e 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. Significan t 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 proposing a program that would result in improved efficiency of the A/C system (without sacrificing passenger comfort) while improving th e fuel efficiency of the vehicle, which has a direct impact on CO 2 emissions.

The cooperative IMAC program described above has demonstrated that average A/C efficiency can be improved by 36.4% (compa red 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 evap orators and condensers; and systems which circulate compressor oil throughout the A/C system. These baseline systems are also extraordinarily wasteful in their energy consum ption because they add heat to the cooled airAir Conditioning

2-29 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 alr eady-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 proposing would encourage the reduction of A/C CO 2 emissions from cars and trucks by 40% from current baseline levels through a credit system. EPA believes that the component efficiency impr ovements demonstrated in the IMAC program, combined with improvements in the control of the supporting mechanical and electrical devices (i.e. engine speeds and electrical h eat exchanger fans), can go beyond the IMAC levels and achieve a total efficiency improvem ent 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 C o mpressor
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 di splacement of the compressor based on conditions within the A/C system, such has head pressu re, 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 SA E IMAC team determined that an annual efficiency improvement of 24.1% wa s possible using this technology. 29 EPA estimates that additional improvements with this technol ogy, 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 effi ciency improvement of 40%, compared to the baseline system.

2.3.2.2 Reduced Reheat Using a Externally-Controlled, Fixed-Displacement or Pneumatic V a riable-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 temperatur e. This disengaging, or cycling, of the compressor clutch must be externally-controlled in a manner similar to that described inDraft Regulatory Impact Analysis

2-30 2.3.2.1. EPA believes that a reduced reheat strategy for fixed-displacement and pneumatic variable-displacement compressors can result in an efficiency improvement of 20%. This lower efficiency improvement estimate (compa red to an externally-controlled variable displacement compressor) is due to the therma l and kinetic energy losses resulting from cycling a compressor clutch off-and-on repeatedly.

2.3.2.3 Defaulting to Recirculated Cabin Air

In a mbient 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 it’s supply air from the cooler air inside the vehicle (also known as ‘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 30% improvement is possible. A mechanically-controlled door on the A/C system’s air intake typically controls whether outs ide 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 recirculat ed air position), EPA is proposing that, as cabin comfort and de-fogging conditions allow, an efficiency credit be granted if a manufacturer defaults to recirculated air whenever the outsid e 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 equip their A/ C systems with humidity sensors, which will allow them to adjust the blend of fresh-to-recirculated air and optimize the controls for maximum efficiency.

2.3.2.4 Improved Blower and Fan Motor Controls

In cont r olling the speed of the direct cu rrent (DC) electric motors in an air conditioning system, manufacturers often utiliz e resistive elements to reduce the voltage supplied to the motor, which in turn reduces it s 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 cont rolling DC voltage is to use a pulsewidth modulated (PWM) controllers for each 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 both the blower and fan motors, an overall improvement in A/C system efficiency of 15% is possible. 30

2.3.2.5 Electronic Expansion Valve

Th e e xpansion 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 controlledAir Conditioning

2-31 by an internal temperature reference to assure a constant temperature level for 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 w ill be necessary to reheat it by directing some of the airflow through the heater core. It is th is reheating of the air which results in reducedsystem efficiency, as additional compressor energy is consumed in the process of overcooling the air. However, if the expansion of th e 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 nece ssary to meet the current cooling needs of the passenger compartment. This electronic expa nsion 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 estimates the when fully developed, calibrated, an d optimized to the requirements of particular system design, use of EXV technology can result in a 20% efficiency improvement over the baseline TXV system.

2.3.2.6 Improved-Efficiency Evaporators and Condensers

The e v aporators and condensers in an A/C syst em 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 effectiv eness of these components is to determine the Coefficient of Performance (COP) for the sy stem using the industry-consensus method described in the SAE surface vehicle standard J2765 – Procedure for Measuring System COP of a Mobile Air Conditioning System on a Test Bench. 31 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.

2.3.2.7 Oil Separator

The oil pr esent in a typical A/C system circulates throughout the system for the purpose of lubricating the compressor. Because th is oil is in contact with inner surfaces of evaporator and condenser, and a coating of oil re duces the heat transfer effectiveness of these devices, the overall system efficiency is reduced. 32 It also adds inefficiency to the system to be “pushing around and cooling” an extraneo us 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 compresso r – 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 IM AC team estimated that overall system COP could be improved by 8% if an oil separator was used. 14 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.Draft Regulatory Impact Analysis

2-32 

### 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 CO 2 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 ne eded to qualify for a certain level of CO 2 credit. For model years 2014 and later, the design-ba sed approach will be supplemented with a vehicle performance test, which has been modified slightly from that proposed in the GHG Mandatory Reporting Rule. In particular, EP A is proposing 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 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 is proposing a modification to 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 au tomatic 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 contro ls). In recognition of this disparity in the proposed test procedure, EPA is revising the te st to allow vehicles with manual A/C controls to average the result obtained on the high fan sp eed 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 discusse s how a modified SC03 test could also be used to measure the efficiency of A/C systems.

To establish an average A/C CO 2 rate for the A/C systems in todays vehicles, the EPA conducted laboratory tests to measure the amount of additional CO 2 a vehicle generated due to A/C use on the proposed Idle Test. 33 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. TheAir Conditioning

2-33 average A/C CO 2 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 CO 2 (g/min) 21.3 Standard Deviation of Test Results (+ g/min) 5.8Draft Regulatory Impact Analysis

2-34 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 2-12). The individual test results from this testing are shown in Figure 2-4. EPA attempted to find a correlation between the A/C CO 2 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-to-test 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 ve hicle’s interior volume, footprint, or engine displacement, but no clear correlation could be fo und. What is clear, however, is that load placed on the engine by the A/C system is not co nsistent, and in certain cases, larger vehicles perform better than smaller one s, in terms of their A/C CO 2 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 NumberA/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 proposed 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 ty pes (e.g. a compressor may be ‘over-sized’ 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 of their vehicles which produced a below-average A/C CO 2 result, is also known for having A/C performance at idle which does not meet customer expectations, but off-idle, performs very well. Therefore, itAir Conditioning

2-35 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 vehi cle’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 “roadspeed” 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 perform ance and efficiency elsewhere in the drive cycle. However, since idle is significant part of real-world 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.

The average A/C CO 2 result for the vehicles tested was 21.3 g/min. Starting in the year 2014, in order to qualify for A/C Effi ciency 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 credit, it will be necessary for each model to achieve an A/C CO 2 result less than or equal to 14.9 g/min (which is 30% less than the average value obse rved in the EPA testing). EPA chose the 30% improvement over the “average” value to driv e the fleet of vehicles toward A/C systems which approach or exceed the efficiency of current best-in-class vehicles. 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 occu r. Once manufacturers begin using the technologies described in Table 2-12 – and develop these tech no lo gies 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 CO 2 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 u tilized by manufacturers and are assigning a CO 2 ‘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 qu alify 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 2-12. If the more than one technology is utilized by a manufacturer f o r 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 CO 2 -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 re flect the increased prevalence of “automatic” A/C controls in modern vehicles (the Phoenix st udy used in the EPA’s 2006 estimate was fromDraft Regulatory Impact Analysis

2-36 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 co mpressor 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. 34 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.

Table 2-12: Efficiency-Improving A/C Technologies and Credits
Technology Description Estimated Reduction in A/C CO 2 Emissions A/C Credit (g/mi CO 2 )

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 whenever ambient temperature is greater than 75 °F 30% 1.7

Blower motor and cooling fan controls which limit waste energy (e.g. pulsewidth modulated power controller) 15% 0.9

Electronic expansion valve 20% 1.1

Improved evaporators and condensers (with system analysis on each component indicating a COP improvement greater than 10%, when compared to previous design) 20% 1.1

Oil Separator 10% 0.6

The estimates for the percent reduction in A/C CO 2 for each technology is based in part on the results of SAE IMAC Team 2 (Im proved 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 CO 2 emissions forAir Conditioning

2-37 each was adjusted upward to re flect 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 CO 2 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 re sult from improved technolo gies. 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 th an 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 leak age 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 th e ones included in the J2727 leakage charts. I 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 compli ance 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 2-13 shows how these costs may be distributed on a year by year basi s as the proposed 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 estimate d 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. 35 With the new AC systems, such $100 or $147 maintenance charge s could be moved delayed until later in the vehicle life and, possibly, one of more events c ould be eliminated completely. This provides potential savings to consumers as a result of the new technology. Note that these potential maintenance savings are not included in the cost and benefit analysis presented in Chapters 6 and 8 of this DRIA. However, EPA intends to include an estimate of maintenance savings in the final rule analysis and believe that this high er estimate for the cost of recharging an A/C system would serve as the basis for those maintena nce savings in the cost analysis of the final rule.

I Author unknown, Alternative Refr igerant Assessment Workshop, SAE Au tomotive Alterna tive Refrigerant Symposium, Arizona, 2003.Draft Regulatory Impact Analysis

2-38 For indirect CO 2 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. J The IMAC Efficiency Improvement team of the Society of Automotive Engineers realized an efficiency improvement of 36.4% based on existing technologies and processes. 29 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 Jo int 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 th e 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 poten tially significant cost savings for the vehicle owner compared to the cost of the efficien cy 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 DRIA, the indirect AC fuel savings has been included in the total fuel savings resulting from the proposal.

Table 2-13 presents the compliance costs associated with new AC technology with esti m ates 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.

Table 2-13: Estimated Costs in each Model Year for New AC Technology, 2007 dollars

2012 2013 2014 2015 2016 Penetration 25% 40% 55% 75% 85% AC Leakage (Direct) $4 $7 $9 $13 $14 AC Indirect $13 $21 $29 $40 $45 Total $18 $28 $39 $53 $60

J 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

2-39 

### 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 we re drawn from an adjusted version of AEO 2009, as documented in DRIA Chapter 5. As documented above, no use of alternative refrigerant is projected in this in this analys is, although this assumption may be revisited in the final rule ( Table 2-14).

Table 2-14 : Credit Summary with Estimated Penetration Rates

Model Year 2012 2013 2014 2015 2016 Estimated Penetration 25% 40% 60% 80% 85% Car Sales Fraction 63% 64% 64% 66% 66% Truck Sales Fraction 37% 36% 36% 34% 34%

Car Direct Credit 2.0 3.1 4.7 6.2 6.6 Car Indirect Credit 1.4 2.3 3.4 4.6 4.8 Total Car Credit 3.0 4.8 7.2 9.6 10.2

Truck Direct Credit 1.6 2.5 3.8 5.0 5.4 Truck Indirect Credit 1.4 2.3 3.4 4.6 4.8 Total Truck credit 3.4 5.4 8.1 10.8 11.5

Fleet average credits 3.1 5.0 7.5 10.0 10.6Draft Regulatory Impact Analysis

2-40 References

A ll references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472

.

1 IPCC. Chapter 2. Changes in Atmospheric Constituents and in Radi ative Forcing. September 2007.

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.

3 Vincent, R., Cleary, K., Ayala, A., Corey, R. 2004. “Emissions of HFC-134a from Light-Duty Vehicles in California.” SAE 2004-01-2256.

4 EPA, 2009, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007

. http://www.epa.gov/climatechange/emissions/usinventoryreport.html

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.

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.

7 Society of Automotive Engineers, “IMAC Team 4 – Reducing Refrigerant Emissions at Service and Vehicle End of Life,” June, 2007.

8 Office of Energy Efficiency and Renewable Energy, U. S. Department of Energy. Transportation Energy Data Book: Edition 27. 2008.

9 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2005. http://epa.gov/climat echange/emissions/ downloads06/07CR.pdf

10 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

11 Ikegama, T., Kikuchi, K.. 2006. “Field Test Results and Correlation with SAEJ2727.” Proceedings of the SAE 7 th Alternative Refrigeran t Systems Symposium.

12 Atkinson, W., Baker, J., Ikegami, T., Nickels, P. 2006. “Revised SAEJ2727: SAE Interior Climate Control Standards Committee Presentation to the European Commission.”

13 EPA. 2009 U.S. Greenhouse Gas Inventory Report. INVENTORY OF U.S. GREENHOUSE GAS EMISSIONS AND SINKS: 1990-2007 (April 2009)

14 Society of Automotive Engineers, “IMAC Team 1 – Refrigerant Leakage Re duction, Final Report to Sponsors,” 2006.

15 Society of Automotive Engineers Surface Vehicle St andard J2727, issued August 2008, http://www.sae.org

16 Minnesota Pollution Control Agency, “Model Year 2009 Leakage Rate List,” http://www.pca.state.mn.us/clim atechange/mobileair.html.Air Conditioning

2-41 17 Schwarz, W., “Emission of Refrigerant R-134a from Mobile Air-Conditioning Systems,” Study conducted for the German Federal Environmental Office, September, 2001.

18 “A/C Triage – Ensuring Replacem ent Compressor Survival, “ AC Delco Tech Connect, Volume 12, Number 5, January/February 2005, http://www.airsept.com/Articles/Compressor Guard/ACDelcoTechConnectJanFeb05.pdf.

19 Johnson, Valerie H., “Fuel Used for Vehicle Air Conditioning: A State-by-State Thermal Comfort-Based Approach,” SAE 2002-01-1957, 2002.

20 Rugh, John P, Valerie Hovland, and Stephen O. Ande rsen, “Significant Fuel Sa vings and Emission Reductions by Improving Vehicle Air C onditioning,” Mobile Air Conditioning Su mmit, Washington DC., April 14-15, 2004.

21 California Environmental Protection Agency Air Reso urces 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.

22 “Reducing Greenhouse Gas Emissions from Light-Duty Mo tor Vehicles,” Northeast States Center for a Clean Air Future, September 2004.

23 Fuel Economy Labeling of Motor Vehicles: Revision to Improve Calculation of Fuel Economy Estimates; Final Rule, 71 FR 77872, December 27, 2006.

24 Koupal, J., “Air Conditioning Activity Effects in MOBILE6” EPA Report Number M6.ACE.001, 1998.

25 EPA Technical Support Document for Fuel Economy Label Regulations, November, 2006.

26 Nam, E.K., “Understanding and Modeling NOx Emissions from Automobiles During Hot Operation,” PhD Thesis, University of Michigan, 1999.

27 Nam, E. K., “Understanding and Modeling NOx Em issions from Air Conditioned Automobiles,” SAE Technical Paper Series 2000-01-0858, 2000.

28 “Reducing Greenhouse Gas Emissions from Light-Duty Mo tor Vehicles,” Northeast States Center for a Clean Air Future, September 2004.

29 Society of Automotive Engineers, “IMAC Team 2 – Improved Efficiency, Final Report,” April 2006.

30 Memo to docket, “Meeting with Delphi and Presentation to EPA,” March 2009.

31 Society of Automotive Engineers Surface Vehicle Sta ndard J2765, issued October 2008, http://www.sae.org.

32 Barbat, Tiberiu, et al, “CFD Study of Phase Separators in A/C Automotive Systems,” SAE 2003-01-0736, 2003.

33 EPA, “AC Idle Test Results_Summary for docket,” August 2009.

34 “2009 Ward’s Automotive Yearbook,” Ward’s Automotive Group, 2009. ISBN Number 978-0-9105-89-26-0.

35 California Air Resources Board, “S taff Analysis of Proposed Early Action for Climate Change Mitigation in California,” 2007, http://www.arb.ca. gov/cc/hfc-mac/documents/hfcdiy.pdf.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 proposed rule, in developing the proposed standard, EPA built on the technica l 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 sa me degree of technology penetration that would be required for California vehicles unde r the California program. In essence, EPA evaluated the stringency of the California Pa vley 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 CO 2 performance standards for model year 2016 which would result in the new vehicle fleet in the State of California having CO 2 performance equal to the performance from the California Pavley 1 standards. This techni cal analysis is documented in this sub-chapter of the DRIA.

Table 3-1 presents the calculated emission levels at which the national GHG standard

wo ul d ensure that vehicle sales in California of federally compliant vehicles would have fleet average GHG emissions that are equal to the flee t average that would be achieved under the California program described in Sections 1900, 1 960 and 1961.1 of Title 13, California Code of Regulations (“Pavley I”) by model year 2016:

Table 3-1: Fleet Average National CO 2 Emission Levels for Model Years 2012-2016

MODEL YEAR 2012 2013 2014 2015 2016 Fleet Average Tailpipe Emission Level (CO 2 gram / mile) 288 281 275 263 250

Manufacturer’s use of credits and other pr ogram flexibilities may alter the program stringency beyond that which is shown here.Draft Regulatory Impact Analysis

3-2 

### 3.1.2 Overview of Equivalency Calculation.

The calculation of the fleet-wide national MY 2015 and MY 2016 CO 2 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 CO 2 emissions achieved in CA from the 2015 and 2016 model year fleets.

2. The estimate of fleetwide average CO 2 emissions was disaggregated into achieved car and truck CO 2 emission levels at the national level using the new car and truck definitions proposed for this rule.

3. Based on the anticipated national fleet mix, the achieved car and truck levels were weighted together to determine the nation al targets which would achieve reductions equivalent to Pavley I in California.

This calculation accounts for the compositi onal difference between the CA vehicle fleet and the National fleet (i.e., CA has a higher pr oportion of cars than the average state), and for various parameters in the CA program.

3.1.2.1 Calculating CO 2 Equivalent Emissions under the California Program

To calculate the CO 2 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 un der the California standards from the California fleet.

The Passenger Car and Light Truck Standa rds 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 CO 2 equivalent emissions due to methane (CH 4 ) and nitrous oxide (N 2 O), are shown in Table 3-2. C H 4 and N 2 O were excluded because the proposed EPA program separately addresses these emissions (Preamble section III).Technical Basis of the Standards

3-3 Table 3-2: California Regulatory Standards excluding CH 4 and N 2 O (grams CO 2 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 cl assification scheme are found in the California regulations.

In order to estimate the emission contribu tion 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 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 vehicl e sales in the intermediate and small volume manufacturer categories. These manufacturers, wh ich 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, ma nufacturers 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.Draft Regulatory Impact Analysis

3-4 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 CO 2 emissions for the new California fleet. As an example in 2016:

Achieved Fleetwide CO 2 Equivalent Emissions =

(PC/LDT1 standard x PC/LDT1 Percentage) + (LT2 st andard x LT2 Percentage) + (Intermediate Volume PC/LDT1 standard x Interm ediate 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 a chieved fleetwide CO 2 equivalent tailpipe emission level expected in California in 2016 is 248 grams / mile.

This analysis was repeated for model year 2015. In order to achieve equivalency, the national program must produce a fleetwide aver age emission level in California that is no higher than 261 grams CO 2 / mile in 2015 and 248 grams CO 2 / 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 flee t, the California fleet-wide average CO 2 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 ve hicle weight as well as classic cars. The remaining light duty fleet is defined as Non-Passenger Automobiles (NPA) ( Table 3-4).Technical Basis of the Standards

3-5 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 es timates 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 CO 2 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 CO 2 emission Targets under the EPA Proposal

To determine the MY 2015 and MY 2016 fleetwide targets under the EPA proposal, the achieved emission levels from PA and NPA ( Table 3-5) were reweighted into a national f l eet-wide average based upon the anticipated national fleet of 60% passenger car, 40% light duty truck. Based on NHTSA analysis presente d in the MY 2011 CAFE final rule, this fleet 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 CO 2 / mile, while the 2016 target is 250 grams CO 2 /mile.Draft Regulatory Impact Analysis

3-6 .
3.1.2.4 Calculation of 2012-2014 “California Equivalent” Targets

Th e 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 sa les 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 subs tituted in these earlier years. Using the methodology described in section Error! Reference source not found. , 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%

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 th e 20 15/16 analysis, small manufacturers we re 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 CH 4 and N 2 O (grams CO 2 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 Comment [TSh1]: Where is this meant to point?Technical Basis of the Standards

3-7 The resulting achieved emission levels in California are 286 grams CO 2 / mile in MY 2012, 279 grams CO 2 / mile in MY 2013 and 273 grams CO 2 / mile in MY 2014. In order to derive PA and NPA achieved emission levels, th ese achieved emission levels were multiplied by MY-specific ratios derived from Nation al 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 nation al 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 CO 2 emission standard is the structure of the standard; i.e., th e basis for the determination of the standard for each vehicle manufacturer.

Vehicle CO 2 emissions are closely related to fuel economy. Over 99 percent of the carbon atoms in motor fuel are typically converted to tailpipe CO 2 , and therefore, for any given fuel with a fixed hydrogen-to-carbon ratio, the amount of CO 2 emitted (grams) is directly correlated to the volume of fuel that is consumed (gallons), and therefore CO 2 g/mile is essentially inversely proportional to vehicle fuel economy, expressed as miles per gallon. As part of the CAFE program, EPA measures vehicle CO 2 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 proposing footprint-based CO 2 standards for cars and light trucks. EPA believes that this program design has the potential to promote CO 2 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 CO 2 emission targets, across all manufacturers.

In 2007, EPA evaluated several vehicle attributes on which to base proposed CO 2 standards for both cars and light trucks: footpr int, curb weight, engine displacement, interiorDraft Regulatory Impact Analysis

3-8 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 CO 2 emissions. Since emissions of CO 2 are controlled, there must be a reasonable degree of correlation from a technical perspective between a proposed attribute and vehicle CO 2 emissions performance. 2) The relationship between the attribute and potential CO 2 reducing technologies. In order to promote emissions reductions, choice in technology for the manufacturers, and cost-effective solutions, it is important that an attrib ute not discourage the use of important CO 2 control strategies. 3) How much the attribute would encourage compliance strategies that tend to circumvent the goal of CO 2 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 or proposed 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 proposing to base the individual manufacturers fleetwide CO 2 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 20 11 and beyond. NHTSA used projected sales, footprint, and mpg data from automakers’ produc t 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

econom y targets for every model’s footprint va lue. Chapter V of NHTSA’s RIA for the MY 2011 CAFE program contains more detailed information how the MY 2011 car and truck curves were generated.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 (ft 2 )MPG

Figure 3-1 NHTSA Reformed CAFE Curve for MY 2011 CarsDraft Regulatory Impact Analysis

3-10 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 (ft 2 )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 th at manufacturer. In the rulemaking process, NHTSA also considered weight, towing cap acity, 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 CO 2 standards based on the four criteria outlined above.

3.2.1.1 Correlation to tailpipe CO 2 emissions

Figure 3-3 and Figure 3-4 describe the relationship of tailpipe CO 2 emissions and vehicle footprint. These figures were genera ted using the manufacturer’s 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 are not expected to have changed.Technical Basis of the Standards

3-11 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 CO 2 emissions were correlated, a linear least-squares regression was performed for cars and trucks separately. It should be noted that NHTSA used non-sales- weighted minimum abso lute difference (MAD) regressions to develop the slopes of the proposed fuel economy and CO 2 emission standards. The reader is referred to the preamble to the pr oposed rule for a discussion of 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 (ft 2 )CO2(g/mi)

Figure 3-3 Model Year 2007 Cars; Sales-weighted Linear Regression of CO 2 Tailpipe Emissions and FootprintDraft Regulatory Impact Analysis

3-12 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 (ft 2 )CO2(g/mi)

Figure 3-4 Model Year 2007 Trucks; Sales-weighted Linear Regression of CO 2 Tailpipe Emissions and Footprint

As illustrated in the above figures, the R 2 values for model year 2007 cars and trucks are 0.283 and 0.331 respectively (both statistica lly significant to a confidence level greater than 99%), indicating that there is a non-random correlation to CO 2 emissions. As vehicle size increases, its CO 2 emissions tend to increase.

3.2.1.2 Relationship with CO 2 -reducing strategies

The footprint attribute would encourage all CO 2 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 CO 2 emissions. However, smaller vehicles would have smaller footprints and would be subject to lower, more stringent, CO 2 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.Technical Basis of the Standards

3-13 A footprint attribute also would not discourage the use of lightweight materials, as a lighter vehicle with no change in footpr int would more easily comply with its CO 2 target. Therefore, in choosing the footprint attribute, the use of lightweight material would remain a viable compliance option, an important factor as lightweight materials can simultaneously reduce mobile CO 2 emissions and improve vehicle safety. NHTSA came to the same conclusion in its Reformed CAFE rulemaking. 7 Though there can be a trend between weight and size, EPA is not equating the two. More over, EPA is assuming that manufacturers can and will lightweight their vehicles at a given footprint level as a potential compliance strategy.

3.2.1.3 Sensitivity of CO 2 control to compliance-related vehicle adjustments

Depending on the attribute, manufacturers ma y 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 ha ve the opportunity to increase its average fleet footprint over time in order to comply with a less stringent standard, which would circumvent the CO 2 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. NHTSA made this same fi nding in the Reformed CAFE rulemaking. 8 While definitive historical footprint data is not ava ilable, EPA believes that footprint has grown more modestly in the past than many other attributes.

3.2.1.4 Consistency with other existing or pr oposed 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 it s CAFE program and is proposing to continue using this metric, 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 Calif ornia 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 prog ram 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. 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 Chapter 2 of the RIA, which offers an additional layer of consistency.Draft Regulatory Impact Analysis

3-14 

### 3.2.2 Alternative Attributes

Some manufacturers have suggested using a vehicle’s curb weight for an attributebased standard. 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 plo t s of tailpipe CO 2 emissions versus curb weight for 2007 car and truck models respectively, where circle size indicates the sales volume of each model.

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 CO 2 Tailpipe Emissions and Curb WeightTechnical Basis of the Standards

3-15 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 CO 2 Tailpipe Emissions and Curb Weight

For both cars and trucks, curb weight has a relatively high correlation with tailpipe CO 2 emissions. A sales-weighted linear least squares regression determined R 2 values of 0.582 for cars and 0.521 for trucks, indicating a substantial relationship of the current fleet’s curb weight and CO 2 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 st rong, lightweight materials, occupant safety is not necessarily compromised by a reduction in vehicle weight. 9 In fact, these studies have shown that a vehicle’s size is a more important factor than weight in its effect on occupant safety. In a weight-based attribute system, a lower weight would correspond to a more stringent CO 2 standard. While this would discourage downsizing as a compliance strategy, it’s important to recognize that weight as an attribute for determining tailpipe CO 2 standards would discourage the use of lightweight materials, even though advanced lightweight materials could simultaneously reduce CO 2 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 co uld add weight to their vehicles in order to be subject to and comply with a less stringent standard. This potential is reinforced by the relatively high rate of growth of vehicle wei ght; it has grown 1.0 – 1.5% per year since theDraft Regulatory Impact Analysis

3-16 late 1980s. 10 This development would have negative environmental consequences by increasing overall CO 2 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 CO 2 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

con t ain sales-weighted linear regression plot of tailpipe CO 2 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

01234567 Engine Displacement (L)CO2(g/mi)

Figure 3-7 Model Year 2007 Cars; Sales-weighted Linear Regression of CO 2 Tailpipe Emissions and Engine Displacement.Technical Basis of the Standards

3-17 y = 45.094x + 217.45
R 2 = .619

150 250 350 450 550 650 750

1234567 Engine Displacement (L)CO2(g/mi)

Figure 3-8 Model Year 2007 Trucks; Sales-weighted Linear Regression of CO 2 Tailpipe Emissions and Engine Displacement

Engine displacement correlates well to tailpipe emissions, with R 2 values of 0.667 for cars and 0.619 for trucks. This is because in creasing engine displacement typically increases the amount of fuel burned per cycle.

EPA believes that a standard based on en gine displacement does not guarantee any environmental benefit because of the disincentive to add certain CO 2 -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 displ acements: 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 CO 2 emissions (See RIA Section on Tech Feasibility ) and are increasingly prevalent in the European market. Thus EPA believes that the use of engine displacement for establishing CO 2 tailpipe standards will undermine readily achievable and feasible reductions of CO 2 emissions.Draft Regulatory Impact Analysis

3-18 EPA also examined interior volume and o ccupant 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 co nsider when purchasing a new vehicle. Figure 3-9 be low contains a plot of interior volume and tailpipe CO 2 for model year 2007 cars.

MY 2007 Cars: Tailpipe CO 2 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 (ft 3 )Tailpipe CO2(g/mi)

Figure 3-9 Model Year 2007 Cars; Linear Trend of CO 2 Tailpipe Emissions and Engine Displacement

EPA confirmed that interior volume is not at all correlated to vehicle CO 2 emissions with a R 2 value of 0.0036 for cars. The correlation of interior volume and tailpipe CO 2 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 CO 2 standard for light trucks that is based on interior volume, since pick-up trucks would be re quired 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 proposing interior volume for the standard.

Alternatively, occupant capacity does not share the same safety implications as interior volume. Furthermore, since it is diff icult to game and does not discourage the use of any CO 2 -reducing technologies, there is significant potential for CO 2 improvement. Figure 3- 10 and Figure 3-11 below illustrate the breakdown of the model year 2007 fleet in terms of

o c cupant capacity.Technical Basis of the Standards

3-19 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%

2456
Number of SeatsPercentage

Figure 3-10 Model Year 2007 Cars; Percentage of Sales by Occupant CapacityDraft Regulatory Impact Analysis

3-20 MY 2007 Cars: Range of Tailpipe CO 2 Emissions by Occupant Capacity

0 100 200 300 400 500 600 700

1234567 Number of OccupantsTailpipe CO2(g/mi)

Figure 3-11 Model Year 2007 Cars; Range of Tailpipe CO 2 Emissions by Occupant Capacity

However, occupant capacity and CO 2 emissions do not relate well. Since 84% of the 2007 car fleet has 5 seats, an occupant-based st andard 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 CO 2 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 vehi cle attributes that could be used to set CO 2 standards. To summarize key results from the 2007 analysis, interior volume and passenger carrying capacity have extremely po or correlation with fuel economy, and EPA is not proposing them for that reason. The thr ee remaining attribute options—footprint, curb weight, and engine displacement—are all reasonable choices in terms of correlation with CO 2 emissions levels, with weight having the best correlation to CO 2 emissions levels. However, it should be noted that correlation is not the pr imary 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 (engi ne 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 economy 11,12 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.Technical Basis of the Standards

3-21 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 CO 2 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. La rger footprints amount to more space inside the vehicle to carry passengers or cargo, whic h are important considerations for consumers. Thus, it is an additional benefit that the fo otprint-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 CO 2 standards then the form of the standards would be compatible with NHTSA’s use of footprint in their Reformed CAFE program. EPA requestscomment on the proposed selection of the footprint attribute for establishing manufacturerspecific CO 2 standards.

For these reasons, EPA therefore believes that the footprint attribute is the best choice of the attributes discussed, from both an engineering as well as from a public policy standpoint. EPA therefore proposes to use footprint in the CO2 standard-setting process for this rule.

EPA is proposing to implement the footprint attribute in the proposed CO2 control program via a piecewise linear function. As me ntioned above, this is the equivalent to the shape selected by NHTSA for its proposed CAFE standards for light trucks for model years 2012-2016. The shape of this function with re spect to CO2 is reflected in Figures I.D.3-3 and I.D.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

.Draft Regulatory Impact Analysis

3-22 200 250 300 350 400

30 35 40 45 50 55 60 65 70 Footprint (sq feet)CO2 (grams per mile)

2012

2016 `

Figure 3-12 CO 2 (g/mi) Car standard curves

200 250 300 350 400

30 35 40 45 50 55 60 65 70 Footprint (sq feet)CO2 (grams per mile)

2012

2016

Figure 3-13 CO2 (g/mi) Truck standard curvesTechnical Basis of the Standards

3-23 Implementing the proposed CO2 emission standard s in this manner provides consistency with NHTSA’s proposed CAFE standards.

Other forms of a footprint-based CO2 standard are possible. Examples are a logistic curve, simple straight line, a line which levels out at a certain point to discourage vehicle upsizing, etc. Section II of the preamble as well as Chapter 2 of the draft joint TSD contains more information on how EPA defined the piecewise linear CO2 target function.

3.3 Supplemental Analysis of Relative Car and Truck Standards

Th e methodology used to set the standards, and thus, the relative stringency of the car and truck standards is described in Chapter 2 of the draft Joint TSD. The car and truck standards were set based on achieved levels of car and truck fuel economy from NHTSA’s Volpe Model under conditions where estimated net social benefits were maximized, while the application of strong hybrid and diesel technolo gy was excluded. These achieved levels were then adjusted upwards arithmetically until the combined car-truck fleet met the CO2 levels described in Section 3.1 above. EPA and NHTSA worked jointly in th at analysis and EPA believes it is a reasonable basis for determining the relative car-truck stringency for the proposed GHG standards.

In this section, a few alternative methods to setting this relative stringency are investigated. These methods use the OMEGA mod el, though it is expected that the results would be similar if the Volpe Model was used. EPA’s OMEGA model and its use in support of this proposed rule is described in Chapter 4 of this draft RIA.

In performing these runs of the OMEGA model, the technology packages were removed which added either hybrid or diesel technology. This was done because the CO2 reduction required to meet the overall level of CO2 emissions being proposed for 2016, 250 g/mi, is generally not requiring significant levels of either hybrid or diesel technology when the potential for A/C credits is considered. This is the same approach that was used in the Volpe modeling used to develop the car and truck standards as described in Chapter 2 of the draft Joint TSD. The technology penetratio n of the other technologies was limited as described in Chapter 1 for MY 2016; 100% for the technologies included in package 1 for each vehicle type and 85% for the more signif icant technologies (any technology package number 2 or higher). In order to be comparable to the Volpe modeling, A/C related technologies were not included. Thus, the CO2 emission target was 261 g/mi in 2016. Fuel prices and the value of externalities were the same as those used to project the benefits from the proposal.

As described in greater detail in Chapte r 4, the OMEGA model applies technology incrementally. It determines which vehicle receives the next step of control using the manufacturer-based net cost effectiveness metr ic. This metric includes the cost of the technology, the value of the fuel savings which accrues from the CO2 technology over a specified time period (here, 5 years) and the de gree of CO2 emission reduction. This rankingDraft Regulatory Impact Analysis

3-24 process includes the decision of whether to apply th e next level of control to a car or a truck. This allows the evaluation of car an d truck CO2 control simultaneously.

In the first set of OMEGA runs used to evaluate relative car-truck stringency, all manufacturers’ vehicles were combined into a single fleet. Determining manufacturerspecific CO2 standards or levels of control re quires some initial assump tion of relative car and truck stringency. Modeling the industry as one, single fleet avoids this. It also makes the analysis more technical in nature and focuses on the technical capability of the industry to reduce car and truck CO2 emissions efficiently, as manufacturer distinctions are eliminated. The disadvantage is that it does not reflect the fact that manufacturers vehicles differ in their fundamental level of CO2 emissions and therefore, manufacturers differ in the projected level of technology needed to meet the same footprint-based standard. With this simplification, a single run of the OMEGA model can provide an indication of the relative potential to control car and truck CO2 emissions.

We ran the OMEGA model with a CO2 emis sion standard which was beyond the capability of the technology available (i.e., maximum technology). The combined level of car plus truck CO2 emissions was determined, as we ll as for cars and trucks separately. The overall level of control was relaxed until net so cietal benefits were maximized. The benefits quantified included reduced externalities relate d to crude oil imports, upstream emissions, refueling time and CO2 emission reductions. They also included costs and benefits associated with increased VMT due to the fuel economy reb ound effect (e.g., vehicle emissions, noise, congestion, value of additional driving, etc.) CO2 was valued at $20 per ton in 2007 and a 3% per annum discount rate was used. Finally, the overall CO2 emissions was increased in several steps from the maximum net benefit level to evaluate how relative car and truck emissions changed with the overall level of stringency. The results of this analysis are summarized in Table 3-9.

Table 3-9 Relative Car and Truck CO2 Emissions At Various Levels of CO2 Control – Single Industrywide 2016 Fleet (Excludes Impact of A/C Technology)

Car CO2

(g/mi) Truck CO2 (g/mi) Combined CO2 (g/mi) Ratio of Truck/Car CO2 Maximum Application of Technology 209.4 286.6 235.3 1.37

Maximum Net Societal Benefits 210.1 286.6 235.8 1.36 +5 g/mi CO2 214.8 291.7 240.6 1.36 +10 g/mi CO2 218.6 298.3 245.3 1.36 +15 g/mi CO2 222.0 305.5 250.0 1.38 +20 g/mi CO2 222.2 318.5 254.5 1.43 +25 g/mi CO2 226.0 325.2 259.2 1.44

As can be seen, applying all the non-hybr id and non-diesel technology achieved an overall level of CO2 control of 235 g/mi. This is equivalent to a fuel economy of 37.8 mpg, since the use of diesel engines in only that in the reference fleet. (If A/C-related CO2 emission reduction of 11 g/mi were subtracted from this figure, it would decrease to 224 g/mi, equivalent to 39.6 mpg.) The ratio of car to truck CO2 emissions at this overall level of CO2 emissions is 1.37. Relaxing this level until ne t societal benefits reach their maximum only increased CO2 emissions by 0.5 g/mi (equivalent to a decrease in fuel economy of 0.1 mpg).Technical Basis of the Standards

3-25 This indicates that net incremental cost of ne arly all the non-hybrid, non-diesel technology packages are less than the societal benefits provi ded. The ratio of car to truck CO2 emissions at this overall level of CO2 emissions decreases slightly to 1.36. The ratio for the proposed truck and car standards in 2016 is 1.35 (302/224), or nearly identical.

As the overall level of CO2 control is rela xed, the ratio of truck to car emissions increases. This indicates that, on the increment, the least cost effective steps of control were applied primarily to trucks and not cars. EPA’s and NHTSA’s methodology for setting the relative stringency for the car and truck standards in 2012 through 2016 began with the car and truck CO2 levels where estimated social be nefits were maximized and increased the CO2 levels of both cars and trucks by the same increment until the combined CO2 level was 261 g/mi. Because the cars emit less than trucks, th ese shifts upward increase car emissions by a greater percentage than truck emissions. Increasing the overall level of CO2 emissions based on the relative cost effectiveness of technolo gy would increase truck emissions by a greater percentage than car emissions.Draft Regulatory Impact Analysis

3-26 References

All references can be f ound in the 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.

3 NHTSA Model Year 2011 Rule. RIN 2127-AK29. Average Fuel Economy Standards. Passenger Cars and Light Trucks. Model Year 2011.

4 NHTSA Model Year 2011 Rule. RIN 2127-AK29. Average Fuel Economy Standards. Passenger Cars and Light Trucks. Model Year 2011.

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. 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.

8 71 FR at 17613.

9 71 FR at 17596; 2002 NAS Report at 24.

10 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

11 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

12 Guidelines for Analytically Derived Fuel Economy. March 11, 2004 http://www.epa.gov/otaq/cert/dearmfr/ccd0406.pdf

### Results of Proposed and Alternative Standards

4-1 

## CHAPTER 4: Results of Proposed an d Altern ative Standards

4.1 Introduction

There are many ways for a manufacturer to reduce CO 2 

### emissions from any given vehicle during a redesign. A manufacturer can choose from a myriad of CO 2 

### reducing technologies and can apply one or more of these technologies to some or all of its vehicles. Thus, for a variety of levels of CO 2 

### emissions control, there are an almost infinite number of technology combinations which produce the desired CO 2 

### reduction. EPA has created a new vehicle model, th e 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 CO 2 

### emissions level.



### 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 requi red for each vehicle are its manufacturer, CO 2 

### 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 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 Draft Regulatory Impact Analysis (DRIA) 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 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 Chapter 1 of this DRIA and Chapter 3 of the Draft Joint TSD. In all cases, the order of the technologies or technology packages for a partic ular vehicle type is designat ed by the model user in the input files prior to running the model. Se veral criteria can be used to develop a reasonable ordering of technologies or packages. These are described in Chapter 1 of the Draft 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 describe d in chapter 4 of the Draft Joint TSD.

### Draft Regulatory Impact Analysis

4-2 The fourth type of data describes the CO 2 

### emission standards being modeled. These include the CO 2 

### emission equivalents of the 2011 MY CAFE standards and the proposed CO 2 

### standards for 2016. As described in more detail in Chapter 2 of this DRIA and briefly in section 4.2.1 below, the appl ication of A/C technology is evaluated in a separate analysis from those technologies which impact CO 2 

### 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 Contro l Te chnology 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 vehicles may 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.

The first step is to develop a list of individual technologies which are either contained in each technology package, or would supplant the relevant portion of each technology package (e.g., the engine, the tran smission, etc.). For example, variable intake valve timing would be associated with a downsized, turbocharged, direct injection engine. Thus, the effectiveness and cost of variable intake valve timing would be considered to be already present for any technology package which included either variable intake valve timing or included an engine technology which provided greater effectiveness. The reverse case would be an example of a technology which would supplant another technology. If a vehicle already had a downsized, turbocharged, direct injection engine, the effectiveness and cost of this technology would be considered to be already fully present when evaluating the application of a technology package which included variable intake valve timing or any other engine technology up to and including a downsized, turbocharged, direct injection engi ne. In either case, the effectiveness and cost present on the 2008 MY vehicle would be li mited to the effectiveness and cost of the engine technology This would allow the a pplication of non-engine related technologies also included in that package (e.g., an improved transmission) to still be applied.

A specific example would be a 2008 MY ve hicle falling into EPA vehicle type 1 equipped with a dry DCT. A dry DCT is ad ded as part of EPA’s technology package number 3 for vehicle type 1. Thus, the effectiveness and cost of a dry DCT would be considered to be already present when applying the effectiveness and cost of package 3 to this vehicle. If the dry DCT contributed to 50% of the total effectiveness and 40% of the cost of package 3 over package 2 and 40% of the cost of package 3 over package 2 for this vehicle, then these percentages would be included in the market data file for this vehicle. If the level of CO2 control led to th e application of technology package 3 to this

### Results of Proposed and Alternative Standards

4-3 vehicle in a run of the OMEGA model, the model would only apply 50% of the effectiveness of package 3 to this vehicle and 60% of the cost of this package.

If two consecutive technology packages both contain a dry DCT, packages 3 and 4, for example, the model user does not need to declare it as technology already present in the baseline. Instead, the benefit of pa ckage 4 is due to engine-related technology is considered incremental to package 3 and is co ntained in the technology input file. When applying package 4 to this vehicle, the OM EGA model will apply its full incremental effectiveness and cost. The same would be true for packages which replace a dry DCT with a hybrid technology 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 beyo nd this technology in terms of engine related CO2 control efficiency.

Since vehicle models are grouped into vehi cle types for technology addition, and there are relatively few vehicle types comp ared to the number of vehicle models analyzed, there may be special cases when the applicable technology packages are less effective than baseline technologies; EPA theref ore limits the applicability of the relevant technology packages to the affected vehi cle models by adjusting the cost and effectiveness reflected in the baseline by the same method described above. An example of a single technology which supplants several technologies would be a 2008 MY vehicle which was equipped with a diesel engine. The effectiveness of this technology would be considered to be present for technology packages which included small improvements to a gasoline engine, since the resultant gaso line engine would othe rwise accrue technology packages which are not as effective at reducing CO2 than the baseline 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 vehicl e would be considered. The transmission related portion of the package’s cost and effec tiveness would be allowed to be applied in order to comply with future CO2 emission standards.

Six technologies were considered to be contained in all technology packages available for all 19 vehicle types: low viscosit y lubrication, improved engine friction, low rolling resistance tires, high efficiency acce ssories, electric power steering and improved automatic transmission controls and lock-up. The total effectiveness and cost estimates for every technology package described in Ch apter 1 includes the effectiveness and cost of these five technologies. Thus, their presence on a 2008 MY vehicle must be deducted from the effectiveness of any package added in the modeling.

All the packages described in Chapter 1 which are numbered 2 or higher (i.e., all packages other than the initial package available to each vehicle type) contain at least dual cam phasing and a 6-speed automatic tran smission. Thus, the followed technologies were considered to be contained in all tec hnology packages numbered 2 and above for all 19 vehicle types: intake valve timing, c oupled cam phasing, dual cam phasing, 6-speed (or higher) automatic or manual transmission, or a continuously variable transmission. In addition, the following technologies supplant either dual cam phasing or a 6-speed

### Draft Regulatory Impact Analysis

4-4 automatic transmission: variable valve lift and timing, cylinder deactivation, diesel combustion, dual clutch transmission, and IMA, power-split and 2-mode hybridization. Thus, the effectiveness and cost of these technologies were also included when determining the percentages of the effectiven ess and cost of each package number 2 or higher which was already utilized on each 2008 MY vehicle.

All the packages described in Chapter 1 which are numbered 2 or also include conversion to direct injection gasoline combus tion, except for package 2 for vehicle type 1. Thus, the effectiveness and cost of direct injection gasoline combustion was also included when determining the percentages of the effectiveness and cost of each package number 2 or higher which was already utiliz ed on each 2008 MY vehicle, with the exception of package number 2 for vehicle type 1.

Table 4-1 depicts the packages which first turbocharge an engine.

Table 4-1 Technology Packages Which Initially Include Turbocharging


### Vehicle

Type Technology Package Vehicle Type Technology Package
1 4 12 5 3 4 13 5 4 2 14 5 5 4 15 4 6 4 16 3 7 5 17 5 8 5 18 5 9 4 19 5 10 5

Thus, the effectiveness and cost of turbocharging was included when determining the percentages of the effectiveness and cost of each package shown in Table 4-1 or higher w i thin each vehicle type which was already utilized on each 2008 MY vehicle.

Table 4-2 depicts the packages which firs t add star t-stop tech nology to a vehicle.

### Results of Proposed and Alternative Standards

4-5 Table 4-2 Technology Packages Which Initially Add Start-Stop


### Vehicle

Type Technology Package Vehicle Type Technology Package
1 3 11 4 2 3 12 4 3 3 13 4 4 3 14 4 5 3 15 3 6 3 16 3
7 4 17 4 8 4 18 4 9 4 19 4 10 4

Thus, the effectiveness and cost of start-stop technology was included when determining the percentages of the effectiveness and cost of each package shown in Table 4- 2 or higher w ithin each vehicle type which was already utilized on each 2008 MY vehicle.

The second step in this process is to determine the total cost and CO2 effectiveness of the technologies already present and relevant to each available package. Determining the total cost usually simply involves adding up the costs of the individual technologies present. In order to determine the total effectiveness of the technologies already present on each vehicle, the lumped parameter model described above is used. Because the specific technologies present on each 2008 vehi cle are known, the applicable synergies and dis-synergies can be fully accounted for.

The third step in this process is to divide the total cost and CO2 effectiveness values determined in step 2 by the total cost and CO2 effectiveness of the relevant technology packages. These fractions are capped at a value of 1.0 or less, since 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 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 co st of each package is the same for each vehicle in a vehicle type. For effectiveness, the individual percentages are combined by weighting them by both sales and base CO2 em ission 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

### Draft Regulatory Impact Analysis

4-6 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 proposed CO2 standards.

Table 4-3 and Table 4-4 show the degree to which the baseline fleet, adjusted for sales in 201 6, includes the effectiveness and co st of the various technology packages by vehicle type.

Table 4-3 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 6

1 6.50% 19.70% 2.30% 0.00% N/A* N/A 2 13.20% 28.10% 12.60% N/A N/A N/A 3 11.80% 21.70% 0.50% 24.30% N/A N/A 4 2.80% 35.30% 0.00% 0.00% N/A N/A 5 11.40% 38.30% 3.70% 1.00% N/A N/A 6 2.20% 47.70% 1.60% 4.10% N/A N/A 7 8.30% 22.10% 19.40% 0.10% 0.00% 0.00% 8 0.00% 0.00% 0.00% 0.00% 0.00% N/A 9 0.50% 0.00% 0.00% 0.00% 0.00% N/A 10 0.50% 4.70% 0.60% 0.00% 0.00% N/A 11 1.40% 0.00% 0.00% 0.00% N/A N/A 12 7.50% 13.00% 2.40% 0.00% 0.00% N/A 13 22.90% 6.80% 57.40% 0.00% 0.00% N/A 14 0.00% 33.70% 0.00% 0.00% 0.00% N/A 15 0.00% 0.00% 0.00% 0.00% 0.00% N/A 16 5.60% 31.30% 3.30% 0.00% N/A N/A 17 4.00% 57.70% 9.40% 0.00% 0.00% N/A 18 19.40% 15.80% 0.00% 0.00% 0.00% N/A 19 17.80% 47.40% 0.00% 0.00% 0.00% N/A

* N/A: No such package for that vehicle type

### Results of Proposed and Alternative Standards

4-7 Table 4-4 Presence of Technology on 2008 MY Vehicles In Terms of Cost (Weighted Average Across Car and Truck Sales in 2016) 

### Technology Package Number Vehicle

Type 1 2 3 4 5 6

1 1.40% 16.50% 1.90% 0.00% N/A N/A 2 7.60% 23.20% 16.40% N/A N/A N/A 3 8.10% 15.40% 3.40% 5.70% N/A N/A 4 2.00% 31.90% 0.00% 0.00% N/A N/A 5 10.90% 22.40% 3.40% 0.20% N/A N/A 6 0.70% 33.80% 2.40% 3.00% N/A N/A 7 6.20% 19.90% 27.90% 0.20% 0.20% 0.00% 8 0.00% 0.00% 0.00% 0.00% 0.00% N/A 9 0.10% 0.00% 0.00% 0.00% 0.00% N/A 10 0.10% 2.60% 0.60% 0.00% 0.00% N/A 11 0.20% 0.00% 0.00% 0.00% N/A N/A 12 1.30% 3.10% 2.40% 0.00% 0.00% N/A 13 3.30% 2.80% 58.20% 0.10% 0.10% N/A 14 0.00% 8.70% 0.00% 0.00% 0.00% N/A 15 0.00% 0.00% 0.00% 0.00% 0.00% N/A 16 1.80% 22.90% 6.90% 0.00% N/A N/A 17 1.40% 46.90% 12.00% 9.50% 0.00% N/A 18 12.60% 11.10% 0.00% 0.00% 0.00% N/A 19 2.70% 26.60% 0.00% 0.00% 0.00% N/A

As mentioned above for the market data input file utilized by OMEGA, which characterizes the vehicle fleet, the modeling mu st and does account for the fact that many 2008 MY vehicles are already equipped with one or more of the technologies discussed in the Draft TSD Chapter 3. Because EPA chose to apply technologies in packages, 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 effectiven ess 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.

### Draft Regulatory Impact Analysis

4-8 An example of a technology which supplants several technologies would be a 2008 MY vehicle which was equipped with a diesel engi ne. The effectiveness of this technology 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 li kely 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 m odel’s calculations and enables synergistic effects among technology packages to be in cluded 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% for reasons described in the preamble. 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 implem ented 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 re ceive the technology package, splitting sales between the old and new vehicles. If subseque nt 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 effectiv eness of adding the subsequent technology may depend on which vehicles are receiving it.

### Results of Proposed and Alternative Standards

4-9 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 be cases when a vehicle model in the base line may already contain some CO2-reducing technology; OMEGA considers this when de termining 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 rece ive 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 desc ribed 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.

OMEGA adds technology effectiv eness 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.



##  

TEB A I E AIECAP CO 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.



### ) (* CEBCAP TechCost lCost Incrementa

 

MFR MFR 

### Sales TotalFleet ModelSales TechCost Cost AvgVehicle      

 *

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 im plemented in the Volpe model because each step in OMEGA has incorporated several technologies. However, for simplification in

### Draft Regulatory Impact Analysis

4-10 the core model calculations, the user must enter into the technology input file the technology costs which are incremental to th e 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.



### 4.3 Modeling Process

In order to determine the technology c osts associated with this proposed rule, EPA performed two separate modeling exercises. The first was to determine the costs associated with meeting any existing regulati on of CO2 or MPG. The latest regulation that has been promulgated is NHTSA’s CAFE program for MY 2011, directed under the Energy Independence and Security Act (EIS A). EPA considers the MY 2011 CAFE regulations to constitute the “reference case” for calculating the costs and benefits of this GHG proposal. In other words, absent any fu rther 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 proposed rule alone, EPA seeks to subtract out any costs associated with meeting any existing standards related to GHG emissions. EPA ran OMEGA a second time to calculate the cost of meeting the EPA’s proposed standards in 2016, and then subtracted the results of the reference case model run to determine the costs of this proposed 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 proposed 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 des cribing 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 in the proposed regulations whic h would govern 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 their footprint values. When evaluating the 2011 MY CAFE standards, the car-truck trading was limited to 1.2 mpg. When evaluating the proposed CO2 st andards, 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 proposed CO2 standards, 2012. Estimates of the technolog y 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 DRIA to this proposed rule. When evaluating the 2016 standards using OMEGA, the proposed 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 11 g/mi of A/C credit in 2016. Thus, the 2016 CO2 target for the fleet evaluated using OMEGA was 261 g/mi instead of 250 g/mi.

### Results of Proposed and Alternative Standards

4-11 The cost of the improved A/C systems required to generate the 11 g/mi credit was estimated separately. This is consistent with the proposed 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 stan dard. 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 CO 2 

### the technology will reduce. One component of the cost of adding a technology is its production cost, as discussed above. However, it is expected that new vehicle purchaser’s value improved fuel economy since it reduces the cost of operating the vehicle. Typical vehicle purchasers are assumed to value the fuel savings accrue d 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 CO 2 

### emission reduction is the change in CO 2 

### 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-effectiv eness value (relatively low technology cost or high fuel savings leads to lower values). Because th e 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:

### Draft Regulatory Impact Analysis

4-12 

##  
      

#  

 



###    

 35 1



### 1 1 2 )1( 1

i

i i PP

i i i



### Gap VMT dCO Gap VMTdFS TechCost ff ManufCostE

Where

ManufCostEff = Manufacturer-Based Cost Ef fectiveness (in dollars per kilogram CO 2 

### ),

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,

dFS i 

### = Difference in fuel consumption due to the addition of technology times fuel price in year i,

dCO 2 

### = Difference in CO 2 

### 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 th e manufacturer-based net cost-effectiveness equation is not a measure of the social cost of this proposal, 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 CO 2 

### 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 proposal, this factor was not included in the determination of manufacturer-based net cost-effectiveness in the analyses performed in

### Results of Proposed and Alternative Standards

4-13 support of this proposed rule. Comments are requested on the benefit of including an increase in the vehicle’s residual value after five years in the calculation of effective cost.

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 CO 2 

### 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 CO 2 

### reduction of a percentage an incremental reduction in fuel consumption depends on the CO 2 

### level of the vehicle prior to adding the technology. EPA requests comment on the use of manufacturer-based net costeffectiveness to rank CO 2 

### emission reduction technologies in the context of evaluating alternative fleet average standards for this rule. EPA believes this manufacturer-based net cost-effectiveness metric is appropriate for ranking technology in this proposed program because it considers effectivene ss values that may vary widely among technology packages when determining the order of technology addition. Comments are requested on this option and on any others thought to be appropriate.



### 4.4 Modeling of CAA Compli ance Flexibilities

EPA’s proposed rule incorporates several compliance flexibilities. 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 proposed CO2 standards. OMEGA was designed to be able to address the first two types of flexibilities directly through the appropriate specification of model inputs a nd scenario definition. However, for several reasons, the expected impact of A/C cred its 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 spe cifying 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 above, EP A 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.

### Draft Regulatory Impact Analysis

4-14 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 incl uded 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 emi ssions 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 pro duce 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 direct A/C emissions are included in the base level of vehicles’ emissions. Their remaining levels after the application of technology are considere d when determining whether a manufacturer is in compliance with the specified standards. However, this is not consistent with the design of the prop osed A/C credit system. Neither direct nor indirect A/C emissions are included in the compliance determination towards the proposed 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 essent ially 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 applicab le to each manufacturer in 2016 and adjust their proposed CO2 emission standards accordingly. Thus, the effective 2016 proposed 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 proposed standards after accounting for A/C credits. After modeling, EPA then added a uniform AC cost of $60 per vehicle to each manufacture r’s per vehicle technology cost.

With respect to car-truck trading, OMEG A was originally designed to allow the trading of car-truck credits on a vehicle-g/mi CO 2 basis. For example, if a manufacturer over-complies with its applicable CO2 standard for cars by 3 g/mi and it sells 1,000,000 cars, it generates 3,000,000 vehicle-g/mi CO2 worth of credits. If these credits are used to compensate for under-compliance towards the truck CO2 standard and truck sales are 500,000, the manufacturer’s truck CO2 emission level could be as much as 6 g/mi CO2 above the standard. This is the credit trading approach used in EPA’s Tier 2 light-duty vehicle emission control program.

As described in section III.B.4 of the Preamble to this proposed rule, EPA is proposing to trade car and truck credits on a li fetime CO2 emission basis. In the above

### Results of Proposed and Alternative Standards

4-15 example, cars are assumed to have a lifetime VMT of 152,000 miles. Therefore, the value of the 3 g/mi over-compliance is multiplied by 1,000,000 vehicles and 190,971 miles and converted to metric tons, or 573,000 metric tons of CO2. If these credits are used to compensate for under-compliance towards the truck CO2 standard and truck sales are again 500,000, the manufacturer’s truck CO2 em ission level could only be as much as 5.2 g/mi CO2 above the standard, as the lifetime VMT of trucks is 221,199 miles.

In order to simulate trading on a lifetime emission basis, we ran the OMEGA model with a single vehicle fleet (i.e., as if cars and trucks were being regulated identically). We adjusted the base CO2 emissions of trucks (in g/mi) to account for their higher lifetime VMT by multiplying them by 1.158 (the ratio of 221,199 to 190,971). We also adjusted the proposed CO2 emission sta ndard applicable to each manufacturer in 2016 to account for these higher truck emissions. Because each manufacturer has a different car-truck sales split, the degree to which their standards were increased varied. Thus, each manufacturer’s standard was specified as a universal or flat standard in the Scenario file (each manufacturer’s compliance was evaluated separately). This universal standard was essentially determined by applying the proposed 2016 car standard to each manufacturer’s cars and 1.158 times the proposed 2016 truck standard to each manufacturer’s trucks. The OMEGA input and ou tput files using the latest version of the model, including the adjusted truck base CO2 levels and adjusted standards can be found under “EPA OMEGA Model” in the docket to this rule.

Under the proposal, FFV credits are only available through model year 2015. Since we use the OMEGA model directly to ev aluate 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 manufactu rers’ 2008 baseline fleets (adjusted for projected sales in 2011) do not meet the 2011 CAFE stand ards which comprise the reference case for this analysis. FFV credits are available under this 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. U nder the current CAFE program, FFV credits are limited to 1.2 mpg in 2011. Car-truck trading is also allowed under this program, up to 1.0 mpg in 2011. However, our reference case is a 2016 vehicle fleet complying with the2011 CAFE standards. In 2016, the limit on FFV credits is reduced to 0.8 mpg and cartruck trading is increased to 1.5 mpg. We use these latter levels here.

Because fuel economy is the inverse of fuel consumption, a specified change in fuel economy (e.g., either the limit on FFV cre dits 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 mpg if the manufacturer generated sufficient credits from its car fleet. These two fuel economy levels represent CO2 emission levels of 395 and 423 g/mi, respectively, assuming all the vehicles are fueled with gasoline, a difference of 28 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 363 and 386 g/mi, respectively, a difference of 23 g/mi CO2. In both cases, the difference in terms of mpg is 1.5.

### Draft Regulatory Impact Analysis

4-16 However, the difference in terms of CO2 emissions decreases as the base fuel economy increases.

As mentioned above, the OMEGA model is not designed to incorporate a credit which is specified in terms of mpg. Since the effect in terms of CO2 emissions changes with the level of fuel economy standard and the footprint-based 2011 CAFE standards impose a different effective fuel economy st andard on each manufacturer, these credits cannot be simulated in an OMEGA model run which represents the CO2 standard as a footprint-based standard. Thus, as was the case with the proposed 2016 standards, we determined the effective flat standard applicable to each manufacturer’s sales in 2011 after accounting for expected use of FFV credits. The 2011 CAFE standards for cars and trucks were converted to CO2 emissions assumin g that all vehicles were fueled with gasoline (i.e., 8887/mpg). Truck emissions were again increased by a factor of 1.158 and the car and truck standards for each manufacturer were sales weighted after again increasing the truck standard by this same factor. An exception was made for those manufacturers which traditionally pay CAFEE fines in lieu of compliance. For these manufacturers, we substituted the achieved fuel economy levels from NHTSA’s Volpe Model evaluations of the 2011 CAFEE standards for these manufacturers’ CAFEE standards. These manufacturers were BMW, Daimler, Porsche, Tata and Volkswagen. Also, several manufacturers were not run through the model, as their 2008 vehicles already complied with both 2011 car and truc k CAFE standards: Honda, Kia, Mazda, and Toyota.

This approach allows unlimited trading of car-truck credits to occur. This is consistent with the proposal for 2016, but not 2011. In order to determine whether the 1.5 mpg car-truck trading limit had been exce eded, EPA converted the final car and truck CO2 emissions levels for each manufacturer to their fuel economy equivalents and compared these values to the applicable 2011 CAFE standards. If the fuel economy of the vehicle class which under-complied with it s standard exceeded 1.5 mpg, the shortfall was reduced to 1.5 mpg and the fuel economy of the over-complying class decreased. The OMEGA model was then run for cars and trucks separately to determine the overall cost of these trading limited manufacturers. Only two manufacturers were found to exceed the trading limit in the unlimited trading runs, Daimler and Hyundai. The impact of limiting trading on projected technology costs in 2011 was very small, $8 and $3 per vehicle, respectively. Hyundai’s 2008 vehicles complied with the 2011 CAFEE standards with unlimited trading (i.e., zero compliance cost). However, limited trading required Hyundai to apply technology to their trucks. The OMEGA input and output files using the latest version of the model, including the adjusted truck base CO2 levels and adjusted standards can be found under “EPA OMEGA Model” in the docket to this rule.



### 4.5 Per Vehicle Costs 2012-2016

As described above, the per-vehicle technol ogy costs for this program alone must account for any cost that incurred by compli ance with existing vehicle programs. EPA

### Results of Proposed and Alternative Standards

4-17 first used OMEGA to calculate costs reflected in the existing CAFE program. OMEGA estimates that, on average, manufacturers will ne ed to spend $78 per vehicle to meet the current MY 2011 CAFE standards. A 

### Reference case costs are provided in Table 4-5 below.

Table 4-5 Incremental Technology Cost of the Reference Case



### Cars Trucks Combined BMW $ 319 $ 479 $ 361 Chrysler $ 7 $ 125 $ 59 Daimler $ 431 $ 632 $ 495
Ford $ 28 $ 211 $ 109 General Motors $ 28 $ 136 $ 73 Honda $ 0 0 $ 0 Hyundai $ 0 $ 76 $ 14
Kia $ 0 $ 48 $ 8 Mazda $ 0 $ 0 $ 0 Mitsubishi $ 96 $ 322 $ 123 Nissan $ 0 $ 19 $ 6 Porsche $ 535 $ 1,074 $ 706 Subaru $ 64 $ 100 $ 77 Suzuki $ 99 $ 231 $ 133
Tata $ 691 $ 1,574 $ 1,161 Toyota $ 0 $ 0 $ 0 Volkswagen $ 269 $ 758 $ 354 Overall $ 47 $ 141 $ 78

EPA then used OMEGA to calculate the costs of meeting the proposed 2016 standards, which are displayed in Table 4-6 below, and two alternative scenarios for sensitivit y. In Table 4-13 and Table 4-14, EPA presents the per-vehicle cost for these scenarios, r e spectively. 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 ca se 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.

### Draft Regulatory Impact Analysis

4-18 Table 4-6 Incremental Technology Cost of the Proposed 2016 CO2 Standards


### Cars Trucks All
BMW $1,701 $1,665 $1,691 Chrysler $1,331 $1,505 $1,408
Daimler $1,631 $1,357 $1,543
Ford $1,435 $1,485 $1,457
General Motors $969 $1,782 $1,311
Honda $606 $695 $633 Hyundai $739 $1,680 $907
Kia $741 $1,177 $812
Mazda $946 $1,030 $958
Mitsubishi $1,067 $1,263 $1,090
Nissan $1,013 $1,194 $1,064
Porsche $1,549 $666 $1,268
Subaru $903 $1,329 $1,057
Suzuki $1,093 $1,263 $1,137
Tata $1,270 $674 $952 Toyota $600 $436 $546 Volkswagen $1,626 $949 $1,509
Overall $968 $1,214 $1,051

EPA estimates that the additional technology required for manufacturers to meet the GHG standards in this proposed rule will cost on average $1051/vehicle.

The majority of manufacturers representing the vast majority of sales in 2016 are projected to comply with the proposed 2016 standards with the addition of technology under the penetration limits described a bove. However, several smaller volume manufacturers (at least with respect to U.S. sales) are projected to fall short of compliance. The CO2 standards applicable to each manufacturer based on its distribution of sales and vehicle footprints are shown in Table 4-7 along with the projected achieved level of C O2 emissions from the OMEGA model.

### Results of Proposed and Alternative Standards

4-19 Table 4-7 CO2 Standards and Projected Achieved Levels in 2016



### Car Truck Manufacturer Standard Achieved CO2 Standard Achieved CO2 BMW * 237.1 245.8 295.2 339.7 Chrysler 241.7 220.1 312.2 334.1 Daimler * 244.1 246 305.6 363 Ford 238.5 225.1 326 353.6 General Motors 238.5 221.2 333.9 349.4 Honda 231.6 186.3 292.7 302.8 Hyundai 231.8 196.8 290.4 308 Kia 233.7 191.6 300.7 310.5 Mazda 230.5 205.6 283.4 285.4 Mitsubishi 227.5 212.4 281.7 319.7 Nissan 235.3 212.5 315.7 351.5 Porsche * 214.8 247.8 298.7 398.5 Subaru 224.7 208.3 279.1 270.7 Suzuki 216.8 197.4 283.7 312.1
Tata * 258.6 270.6 287.1 397.4 Toyota 230 188.1 303.2 307.3 Volkswagen * 227.2 240.6 304.8 394.9 Overall 234 208.8 314 334.6



### 4.6 Technology Penetration

The major technologies chosen by OMEGA are described in the Table 4-8 through Table 4-11 below for the reference case and control case cars and trucks. 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 Analysis4-20Table 4-8 2016 Technology Penetration in the Reference Case-CarsCars VVT VVTL GDIGDIDeacGDITurbo Diesel6 SpdAutoWetDCTDryDCT42 SS IMAPowerSplit2-Mode EPSBMW 85.9% 11.9% 8.9% 9.0% 0.0% 0.0% 88.5% 9.0% 0.0% 9.0% 0.0% 0.2% 0.0% 0.0%Chrysler 48.0% 0.0% 0.0% 0.0% 0.0% 0.0% 23.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.9%Daimler 47.0% 0.0% 5.2% 15.2% 0.0% 1.6% 45.1% 31.9% 0.0% 15.2% 0.0% 0.0% 0.0% 0.0%Ford 12.7% 0.0% 0.0% 0.0% 0.0% 0.0%36.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3.6%General Motors 54.9% 0.0% 5.6% 0.0% 0.0%0.0% 13.2% 0.0% 0.0% 0.2% 0.0% 0.0% 0.1% 0.0%Honda 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%5.9% 0.0% 0.0% 0.0%3.0% 0.0% 0.0% 0.0%Hyundai 29.9% 0.0% 0.0% 0.0% 0.0% 0.0%0.1% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0%Kia 24.2% 0.0%0.0% 0.0% 0.0% 0.0% 0.8% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 2.5%Mazda 96.0% 3.3% 13.4% 0.0% 0.0% 0.0% 36.0% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.6%Mitsubishi 77.8% 0.0% 0.0% 0.0% 0.0% 0.0% 77.4% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 4.9%Nissan 99.8% 0.0% 5.1% 0.0% 0.0% 0.0%26.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%Porsche 31.1% 0.0% 0.0% 14.8% 16.3% 0.0% 0.0% 31.1% 0.0% 14.8% 0.0% 0.0% 0.0% 0.0%Subaru 48.7% 3.3% 0.0% 0.0% 0.0% 0.0%26.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Suzuki 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Tata 64.4% 0.0%31.1% 33.4% 0.0% 0.0%31.1% 33.4% 0.0% 33.4% 0.0% 0.0% 0.0% 0.0%Toyota 99.7% 0.0% 7.1% 0.0% 0.0% 0.0%35.7% 0.0% 0.0% 0.0% 0.0% 15.8% 0.0% 63.8%Volkswagen 16.8% 0.7% 41.0% 4.7% 0.0% 0.0% 73.6% 17.9% 0.0% 4.7% 0.0% 0.0% 0.0% 0.0%Overall 53.0% 15.0% 5.6% 1.0% 0.1% 0.0%27.8% 2.0% 0.0% 1.0% 0.4% 3.1% 0.0% 13.3%Results of Proposed and Alternative Standards4-21Table 4-9 2016 Technology Penetration in the Reference Case-TrucksTrucks VVT VVTL GDIGDIDeacGDITurbo Diesel6 SpdAutoWetDCTDryDCT42 SS IMAPowerSplit2-Mode EPSBMW 97.0% 0.0% 3.0% 16.9% 0.0% 0.0% 80.1% 16.9% 0.0% 16.9% 0.0% 0.0% 0.0% 0.0%Chrysler 4.8% 0.0% 0.1% 0.0% 0.0% 0.1% 34.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.9%Daimler 92.9% 0.0% 40.3% 0.0% 44.7% 16.1%47.3% 44.7% 0.0% 44.7% 0.0% 0.0% 0.0% 0.0%Ford 37.7% 0.0% 1.3% 0.0% 0.0% 0.0%17.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3.6%GeneralMotors 24.6% 0.0% 0.0% 0.0% 0.0%0.0% 14.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0%Honda 0.0% 100.0% 4.2% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Hyundai 28.6% 0.0% 0.0% 0.0% 0.0% 0.0%27.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Kia 98.8% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.5%Mazda 49.9% 0.0% 2.5% 0.0% 0.0% 0.0%43.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6%Mitsubishi 100.0% 0.0% 0.0% 18.4% 0.0% 0.0% 64.9% 18.4% 0.0% 18.4% 0.0% 0.0% 0.0% 4.9%Nissan 100.0% 49.5% 49.5% 0.0% 0.0% 0.0%50.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%Porsche 91.6% 0.0% 0.0% 47.0% 38.0% 0.0% 0.0% 85.0% 0.0% 85.0% 0.0% 0.0% 0.0% 0.0%Subaru 32.6% 0.0% 0.0% 0.0% 0.0% 0.0%32.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Suzuki 100.0% 0.0% 17.8% 0.0% 0.0% 0.0%100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%Tata 85.0% 0.0% 0.0% 85.0% 0.0% 0.0% 0.0% 85.0% 0.0% 85.0% 0.0% 0.0% 0.0% 0.0%Toyota 85.5% 0.0% 8.1% 0.0% 0.0% 0.0%20.0% 0.0% 0.0% 0.0% 0.0% 6.6% 0.0% 63.8%Volkswagen 99.3% 0.0% 100.0% 17.3% 67.7% 0.7% 0.0% 85.0% 0.0% 85.0% 0.0% 0.0% 0.0% 0.0%Overall 48.6% 16.0% 9.0% 1.6% 2.2% 0.3%20.5% 3.8% 0.0% 3.8% 0.0% 1.3% 0.0% 13.3%Regulatory Impact Analysis4-22Table 4-10 2016 Technology Penetration in the Control Case-CarsCarsVVT VVTL GDIGDIDeacGDITurboDiesel6 SpdAutoWetDCTDryDCT42 S-SIMAPowerSplit2-ModeEPSRBMW 97.8% 2.0% 4.9% 37.2% 44.1% 0.0% 14.8% 65.5% 5.7% 71.0% 0.0% 3.9% 10.3% 100.0%Chrysler 46.2% 44.3% 59.0%19.1% 0.8% 0.0% 24.2% 18.0% 44.3% 62.2% 0.0% 0.0% 0.0% 100.0%Daimler 89.2% 0.0% 3.4% 42.0% 39.9% 1.6% 9.4% 60.8% 14.2% 72.5% 0.0% 3.2% 9.3% 100.0%Ford 51.9%27.0%27.0%37.7% 12.2% 0.0% 11.1% 46.0% 27.0% 73.0% 0.0% 0.0% 0.0% 100.0%General Motors 43.5% 37.2% 40.6% 11.0% 2.9%0.0% 5.0% 13.4% 37.2% 50.6% 0.0% 0.0% 0.1% 100.0%Honda 0.0%100.0%24.0%0.0% 0.0% 0.0% 5.8% 0.0% 24.0%24.0% 3.0% 0.0% 0.0% 100.0%Hyundai 16.9% 31.3% 34.1%1.8% 0.8% 0.0% 2.8% 2.6% 31.3%33.9% 0.0% 0.0% 0.0% 100.0%Kia 4.5%35.6%35.6%0.0% 0.0% 0.0% 0.0% 0.0% 35.6% 35.6% 0.0% 0.0% 0.0% 100.0%Mazda 64.3% 28.4% 61.3% 1.5% 11.6% 0.0% 34.5% 13.1% 28.2% 41.3% 0.0% 0.0% 0.0% 100.0%Mitsubishi 21.8% 65.5% 74.1%0.0% 0.0% 0.0% 22.6% 0.0% 65.5% 65.5% 0.0% 0.0% 0.0% 100.0%Nissan 63.8% 36.2% 36.2% 22.6% 3.2% 0.0% 33.3% 25.8% 36.2%60.5% 0.0% 1.0% 0.0% 100.0%Porsche 100.0% 0.0% 2.6% 27.2% 57.8% 0.0% 7.8% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Subaru 14.6% 42.5% 42.4%3.5% 6.6% 0.0% 0.4% 10.1% 42.4%52.6% 0.0% 0.0% 0.0% 100.0%Suzuki 0.0% 76.2% 76.2%0.0% 0.0% 0.0% 0.0% 0.0% 76.2%76.2% 0.0% 0.0% 0.0% 100.0%Tata 100.0% 0.0% 3.6%81.4% 0.0% 0.0% 13.7% 70.0% 0.0%70.0% 0.0% 3.6% 11.4% 100.0%Toyota 64.9% 34.8% 41.6% 2.3% 0.0% 0.0% 31.2% 26.5% 21.1% 23.4% 0.0% 15.8% 0.0% 100.0%Volkswagen 87.4% 0.1% 7.7% 25.2% 58.4% 0.0% 11.9% 72.0% 0.0% 70.0% 0.0% 1.4% 13.5% 100.0%Overall 47.0% 40.3% 33.2%13.8% 8.0% 0.0% 15.6% 24.3% 27.1% 46.4% 0.4% 3.4% 1.2% 100.0%Results of Proposed and Alternative Standards4-23Table 4-11 2016 Technology Penetration in the Control Case-TrucksTrucksVVT VVTL GDIGDIDeacGDITurboDiesel6 SpdAutoWetDCTDryDCT42 S-SIMAPowerSplit2-ModeEPSWReBMW 99.5% 0.0% 0.5% 29.0%56.0% 0.0% 15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Chrysler 81.6% 4.1% 41.1% 39.3% 4.6% 0.1%52.5% 33.5% 4.1% 37.6% 0.0% 0.0% 0.0% 100.0%Daimler 92.9% 0.0% 2.4%47.8% 37.2% 16.1% 15.0% 70.1% 0.0%70.1% 0.0% 0.0% 14.9% 100.0%Ford 84.5% 6.1%32.3%39.6% 13.2% 0.0% 28.6% 53.2% 5.7% 58.6% 0.0% 0.0% 0.0% 100.0%General Motors 83.1% 3.5% 24.3% 45.6% 13.0%0.0% 23.0% 58.6% 3.5% 62.1%0.0% 0.0% 0.1% 100.0%Honda 30.7% 69.3% 24.4% 3.5% 7.9% 0.0% 19.3% 11.5% 4.5% 16.0% 0.0% 0.0% 0.0% 100.0%Hyundai 89.3% 0.0% 0.0% 8.5% 76.5% 0.0%4.0% 85.0% 0.0% 85.0% 0.0% 0.0% 0.0% 100.0%Kia 77.3%0.0%42.7%0.0% 33.0% 0.0% 42.7% 33.0% 0.0% 33.0% 0.0% 0.0% 0.0% 100.0%Mazda 92.9% 7.0% 9.9%5.8% 40.8% 0.0% 7.2% 46.6% 7.0%53.6% 0.0% 0.0% 0.0% 100.0%Mitsubishi 90.3% 0.0% 0.0% 18.4% 55.2% 0.0% 16.7% 73.6% 0.0% 73.6%0.0% 0.0% 0.0% 100.0%Nissan 98.6% 1.4% 11.8% 36.0% 8.4% 0.0% 34.1% 44.4% 1.4% 45.9% 0.0% 0.0% 0.0% 100.0%Porsche 85.0% 15.0% 15.0%53.7% 31.3% 0.0% 15.0% 70.0% 0.0%70.0% 0.0% 0.0% 15.0% 100.0%Subaru 34.4% 57.0% 51.5%5.8% 27.7% 0.0% 0.0% 69.3% 15.7%49.2% 0.0% 0.0% 0.0% 100.0%Suzuki 85.0% 0.0% 35.7% 17.8% 31.6% 0.0%35.7% 49.3% 0.0% 49.3% 0.0% 0.0% 0.0% 100.0%Tata 100.0% 0.0% 4.1%80.9% 0.0% 0.0% 15.0% 70.0% 0.0%70.0% 0.0% 4.1% 10.9% 100.0%Toyota 93.6% 0.0% 27.4% 2.1% 0.0% 0.0%28.2% 2.1% 0.0% 2.1% 0.0% 6.6% 0.0% 100.0%Volkswagen 87.9% 11.9% 15.0%29.2% 55.8% 0.7% 15.0% 70.0% 0.0%70.0% 0.0% 0.0% 15.0% 100.0%Overall 80.7% 11.8% 24.5% 27.3% 14.0% 0.3%25.0% 40.6% 3.1% 43.1% 0.0% 1.3% 1.1% 100.0%Regulatory Impact Analysis

4-24 As can be seen, the overall reduction in vehi cle weight is projected to be 4%. This reduction varies across the two vehicle classes and vehicle base weight. For cars below 2950 pounds curb weight, the reduction is 2.3% (62 p ounds), while it was 4.4% (154 pounds) for cars above 2950 curb weight. For trucks below 3850 pounds curb weight, the 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 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 4-12.

Table 4-12 Breakdown of Weight Reduction in Modeling Results

Weight Category % Weight Reduction Average Weight Reduction < 2950 lbs 2.3% 62 lbs Cars > 2950 lbs 4.4% 154 lbs < 3850 lbs 3.5% 119 lbs Trucks with 3850

lb break point > 3850 lbs 4.5% 215 lbs < 5000 lbs 3.3% 140 lbs Trucks with 5000

lb break point > 5000 lbs 6.7% 352 lbs



### 4.7 Manufacturer-Specific Standards

As described in Section 3.2, in any attrib ute-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 averages calculated for the proposed 2016 model year are presented in Table 4-13.Results of Proposed and Alternative Standards

4-25 Table 4-13 2016 Projected Standards by Manufacturer

Standards Cars Trucks Combined BMW 226.9 283.7 241.8 Chrysler 231.5 300.7 262.1 Daimler 233.9 294.1 253.2 Ford 228.3 314.5 266.5 General Motors 228.3 322.4 267.9 Honda 221.4 281.2 239.2 Hyundai 221.6 278.9 231.8 Kia 223.5 289.2 234.2 Mazda 220.3 271.9 227.7 Mitsubishi 217.3 270.2 223.5 Nissan 225.1 304.2 247.5 Porsche 204.6 287.2 230.8 Subaru 214.5 267.6 233.7 Suzuki 206.6 272.2 223.4 Tata 248.4 275.6 262.8 Toyota 219.8 291.7 243.5 Volkswagen 217.0 293.3 230.3 Overall 223.8 302.5 250.2

These car and truck standards average out to an overall industry CO2 stringency of 250 g/mi, consistent with President Obama’s announcement on May 19, 2009.



### 4.8 Alternative Program Stringencies

EPA analyzed the technology cost of two alternative stringency scenarios: 4%/year and 6%/year. With the reference case the same as th at described above in Section 4.1, the costs of the two alternative control cases along with th e technology penetrations and the manufacturerspecific standards are presented in Table 4-14 through Table 4-19, below. The manufacturers’s C O 2 targets for these alternativ e standards are presented in Table 4-20 and Table 4-21.Regulatory Impact Analysis

4-26 Table 4-14 2016 Technology Cost in the 4% sensitivity case

Cars Trucks All
BMW $ 1,701 $ 1,665 $ 1,691 Chrysler $ 1,167 $ 1,505 $ 1,316 Daimler $ 1,631 $ 1,357 $ 1,543
Ford $ 1,339 $ 1,417 $ 1,373 General Motors $ 850 $ 1,769 $ 1,237 Honda $ 606 $ 460 $ 563 Hyundai $ 685 $ 1,505 $ 832
Kia $ 741 $ 738 $ 741 Mazda $ 819 $ 1,030 $ 849 Mitsubishi $ 1,004 $ 1,263 $ 1,034 Nissan $ 910 $ 1,194 $ 991 Porsche $ 1,549 $ 666 $ 1,268 Subaru $ 903 $ 1,131 $ 985 Suzuki $ 1,093 $ 1,026 $ 1,076
Tata $ 1,270 $ 674 $ 952 Toyota $ 479 $ 436 $ 465 Volkswagen $ 1,626 $ 949 $ 1,509 Overall $ 893 $ 1,154 $ 980Results of Proposed and Alternative Standards

4-27 Table 4-15 2016 Technology Cost in the 6% sensitivity case

Cars Trucks All
BMW $ 1,701 $ 1,665 $ 1,691 Chrysler $ 1,642 $ 2,211 $ 1,893 Daimler $ 1,631 $ 1,357 $ 1,543
Ford $ 2,175 $ 2,396 $ 2,273 General Motors $ 1,718 $ 2,158 $ 1,903 Honda $ 777 $ 1,580 $ 1,016 Hyundai $ 1,275 $ 1,680 $ 1,347
Kia $ 1,104 $ 1,772 $ 1,213 Mazda $ 1,321 $ 1,293 $ 1,317 Mitsubishi $ 1,495 $ 2,065 $ 1,563 Nissan $ 1,654 $ 2,274 $ 1,830 Porsche $ 1,549 $ 666 $ 1,268 Subaru $ 1,440 $ 1,615 $ 1,503 Suzuki $ 1,718 $ 2,219 $ 1,846
Tata $ 1,270 $ 674 $ 952 Toyota $ 755 $ 1,182 $ 895 Volkswagen $ 1,626 $ 949 $ 1,509 Overall $ 1,381 $ 1,866 $ 1,544Regulatory Impact Analysis4-28Table 4-16 2016 Technology Penetration in the 4% sensitivity case- CarsCarsVVT VVTL GDIGDIDeacGDITurboDiesel6 SpdAutoWetDCTDryDCT42 S-SIMAPowerSplit2-ModeEPSRBMW 97.8% 2.0% 4.9% 37.2% 44.1% 0.0% 14.8% 65.5% 5.7% 71.0%0.0% 3.9% 10.3% 100.0%Chrysler 33.3% 44.3% 51.7% 13.6% 0.8% 0.0%16.9% 12.4% 44.3% 56.7%0.0% 0.0% 0.0% 100.0%Daimler 89.2% 0.0% 3.4% 42.0% 39.9% 1.6%9.4% 60.8% 14.2% 72.5%0.0% 3.2% 9.3% 100.0%Ford 50.0% 27.0%36.1%26.8% 12.2% 0.0% 16.2% 39.0% 27.0% 66.1%0.0% 0.0% 0.0% 100.0%General Motors 56.1% 23.8% 40.3% 10.6% 2.9%0.0% 17.6% 13.4% 23.8% 37.3%0.0% 0.0% 0.1% 100.0%Honda 0.0% 100.0% 24.0% 0.0% 0.0% 0.0%5.8% 0.0% 24.0% 24.0% 3.0% 0.0% 0.0% 100.0%Hyundai 17.4% 31.3% 31.3% 0.0% 0.8% 0.0%0.0% 0.8% 31.3% 32.1% 0.0% 0.0% 0.0% 100.0%Kia 4.5%35.6%35.6%0.0% 0.0% 0.0% 0.0% 0.0% 35.6% 35.6% 0.0% 0.0% 0.0% 100.0%Mazda 40.7% 28.4% 37.7% 1.5% 11.6% 0.0% 10.9% 13.1% 28.2% 41.3%0.0% 0.0% 0.0% 100.0%Mitsubishi 29.2% 58.2% 74.1% 0.0% 0.0% 0.0% 29.9% 0.0% 58.2% 58.2% 0.0% 0.0% 0.0% 100.0%Nissan 63.8% 36.2% 42.3% 16.6% 0.0% 0.0% 41.9% 16.6% 36.2% 52.8%0.0% 1.0% 0.0% 100.0%Porsche 100.0% 0.0% 2.6% 27.2% 57.8% 0.0% 7.8% 70.0% 0.0% 70.0%0.0% 0.0% 15.0% 100.0%Subaru 14.6% 42.5% 42.4% 3.5% 6.6% 0.0%0.4% 10.1% 42.4% 52.6%0.0% 0.0% 0.0% 100.0%Suzuki 0.0% 76.2% 76.2% 0.0% 0.0% 0.0%0.0% 0.0% 76.2% 76.2% 0.0% 0.0% 0.0% 100.0%Tata 100.0% 0.0% 3.6% 81.4% 0.0% 0.0%13.7% 70.0% 0.0% 70.0%0.0% 3.6% 11.4% 100.0%Toyota 82.0% 17.7% 24.5% 2.3% 0.0% 0.0% 31.2% 43.6% 4.0% 6.3% 0.0% 15.8% 0.0% 100.0%Volkswagen 87.4% 0.1% 7.7% 25.2% 58.4% 0.0% 11.9% 72.0% 0.0% 70.0%0.0% 1.4% 13.5% 100.0%Overall 51.3% 34.8% 30.7% 11.4% 7.7% 0.0%18.3% 25.6% 21.6% 39.0%0.4% 3.4% 1.2% 100.0%Results of Proposed and Alternative Standards4-29Table 4-17 2016 Technology Penetration in the 4% sensitivity case- TrucksTrucksVVT VVTL GDIGDIDeacGDITurboDiesel6 SpdAutoWetDCTDryDCT42 S-SIMAPowerSplit2-Mode EPSBMW 99.5% 0.0% 0.5% 29.0% 56.0% 0.0% 15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Chrysler 81.6% 4.1% 41.1% 39.3% 4.6% 0.1% 52.5% 33.5% 4.1% 37.6% 0.0% 0.0% 0.0% 100.0%Daimler 92.9% 0.0% 2.4% 47.8% 37.2% 16.1%15.0% 70.1% 0.0% 70.1% 0.0% 0.0% 14.9% 100.0%Ford84.5% 2.8% 29.9% 38.7% 13.2% 0.0%31.1% 51.9% 2.8% 54.7% 0.0% 0.0% 0.0% 100.0%General Motors 82.0% 3.5% 23.1% 45.6% 13.0%0.0% 21.8% 58.6% 3.5% 62.1% 0.0% 0.0% 0.1% 100.0%Honda 10.3% 69.3% 11.9% 3.5% 0.0% 0.0%6.8% 3.5% 4.5% 8.1% 0.0% 0.0% 0.0% 100.0%Hyundai 84.0% 0.0% 7.8% 8.5% 63.5% 0.0%11.8% 71.9% 0.0% 71.9% 0.0% 0.0% 0.0% 100.0%Kia34.6% 0.0% 7.7% 0.0% 25.3% 0.0%7.7% 25.3% 0.0% 25.3% 0.0% 0.0% 0.0% 100.0%Mazda 92.9% 7.0% 9.9% 5.8% 40.8% 0.0%7.2% 46.6% 7.0% 53.6% 0.0% 0.0% 0.0% 100.0%Mitsubishi 90.3% 0.0% 0.0% 18.4% 55.2% 0.0% 16.7% 73.6% 0.0% 73.6% 0.0% 0.0% 0.0% 100.0%Nissan 98.6% 1.4% 11.8% 36.0% 8.4% 0.0%34.1% 44.4% 1.4% 45.9% 0.0% 0.0% 0.0% 100.0%Porsche 85.0% 15.0% 15.0% 53.7% 31.3% 0.0%15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Subaru 61.5% 29.9% 51.5% 5.8% 27.7% 0.0%27.1% 57.9% 0.0% 33.5% 0.0% 0.0% 0.0% 100.0%Suzuki 85.0% 0.0% 59.5% 17.8% 7.7% 0.0%59.5% 25.5% 0.0% 25.5% 0.0% 0.0% 0.0% 100.0%Tata 100.0% 0.0% 4.1%80.9% 0.0% 0.0%15.0% 70.0% 0.0% 70.0% 0.0% 4.1% 10.9% 100.0%Toyota 93.6% 0.0% 27.4% 2.1% 0.0% 0.0% 28.2% 2.1% 0.0% 2.1% 0.0% 6.6% 0.0% 100.0%Volkswagen 87.9% 11.9% 15.0% 29.2% 55.8% 0.7% 15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Overall 77.6% 10.7% 22.1% 27.1% 12.6% 0.3%24.0% 38.7% 2.2% 40.6% 0.0% 1.3% 1.1% 100.0%Regulatory Impact Analysis4-30Table 4-18 2016 Technology Penetration in the 6% Sensitivity Case-CarsCarsVVT VVTL GDIGDIDeacGDITurboDiesel6 SpdAutoWetDCTDryDCT42 S-SIMAPowerSplit2-ModeEPSWeReduBMW 97.8% 2.0% 4.9%37.2% 44.1% 0.0% 14.8% 65.5% 5.7% 71.0% 0.0% 3.9% 10.3% 100.0%Chrysler 43.7% 48.5% 48.5% 33.6% 2.9% 0.0% 3.7% 36.2% 48.5% 84.7% 0.0% 0.0% 0.0% 100.0%Daimler 89.2% 0.0% 3.4% 42.0% 39.9% 1.6%9.4% 60.8% 14.2% 72.5% 0.0% 3.2% 9.3% 100.0%Ford79.3% 7.6% 13.3% 35.8% 35.8% 0.0%5.5% 41.1% 32.0% 73.1% 1.6% 5.5% 4.8% 100.0%General Motors 53.8% 39.4% 40.2% 42.7% 2.9%0.0% 8.1% 39.5% 39.4% 78.9% 0.0% 0.0% 0.1% 100.0%Honda 5.7%86.5%41.1% 0.0% 0.0% 0.0% 11.0% 0.0% 35.4% 35.4%3.0% 0.0% 0.0% 100.0%Hyundai 39.8% 35.2% 43.4% 9.3% 17.8% 0.0%8.2% 27.1% 35.2% 62.3% 0.0% 0.0% 0.0% 100.0%Kia 35.2%35.6%56.8%0.0% 13.2% 0.0% 21.3% 13.2% 35.6% 48.7% 0.0% 0.0% 0.0% 100.0%Mazda 32.5% 67.3% 68.5% 1.5% 11.6% 0.0%2.7% 13.1% 67.2% 80.3% 0.0% 0.0% 0.0% 100.0%Mitsubishi 28.1% 64.2% 66.8% 4.3% 13.9% 0.0% 6.9% 7.1% 74.1% 81.2% 1.9% 1.2% 0.8% 100.0%Nissan 61.8% 38.2% 46.5% 19.3% 19.2% 0.0% 10.8% 21.7% 53.6% 75.3% 4.3% 5.7% 0.7% 100.0%Porsche 100.0% 0.0% 2.6% 27.2% 57.8% 0.0%7.8% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Subaru 14.6% 75.0% 74.9% 3.5% 6.6% 0.0%0.4% 17.6% 67.4% 77.6% 0.0% 0.0% 0.0% 100.0%Suzuki 85.0% 0.0% 1.5% 7.2% 76.2% 0.0%0.0% 7.2% 76.2% 83.5% 0.0% 1.5% 0.0% 100.0%Tata 100.0% 0.0% 3.6%81.4% 0.0% 0.0% 13.7% 70.0% 0.0% 70.0% 0.0% 3.6% 11.4% 100.0%Toyota 54.4% 45.3% 52.2% 2.3% 0.0% 0.0% 31.2% 2.3% 45.3% 47.6% 0.0% 15.8% 0.0% 100.0%Volkswagen 87.4% 0.1% 7.7% 25.2% 58.4% 0.0%11.9% 72.0% 0.0% 70.0% 0.0% 1.4% 13.5% 100.0%Overall 52.8% 39.7% 37.9% 19.1% 14.8% 0.0%13.4% 24.9% 38.1% 62.8% 1.1% 4.7% 2.0% 100.0%Results of Proposed and Alternative Standards4-31Table 4-19 2016 Technology Penetration in the 6% Sensitivity Case-TrucksTrucksVVT VVTL GDIGDIDeacGDITurboDiesel6 SpdAutoWetDCTDryDCT42 S-SIMAPowerSplit2-ModeEPSWeReduBMW 99.5% 0.0% 0.5%29.0% 56.0% 0.0% 15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Chrysler 81.6% 4.1% 4.1% 70.1% 10.8% 0.1% 5.1% 80.9% 4.1% 85.0% 0.0% 0.0% 0.0% 100.0%Daimler 92.9% 0.0% 2.4% 47.8% 37.2% 16.1% 15.0% 70.1% 0.0% 70.1% 0.0% 0.0% 14.9% 100.0%Ford 90.7%0.0%2.2%38.0% 45.0% 0.0%2.6% 71.0% 5.0% 76.1% 0.0% 3.1% 5.8% 100.0%General Motors 87.0% 1.7% 1.7% 68.5% 14.8%0.0% 2.1% 81.5% 3.5% 85.0% 0.0% 0.0% 0.1% 100.0%Honda 54.0% 46.0% 31.6% 3.5% 50.4% 0.0%0.0% 54.0% 31.0% 85.0% 0.0% 0.0% 0.0% 100.0%Hyundai 89.3% 0.0% 0.0% 8.5% 76.5% 0.0%4.0% 85.0% 0.0% 85.0% 0.0% 0.0% 0.0% 100.0%Kia 85.4%1.1%1.1%0.0% 83.9% 0.0% 0.0% 83.9% 1.1% 85.0% 0.0% 0.0% 0.0% 100.0%Mazda 63.4% 36.5% 41.3% 5.8% 40.8% 0.0%9.0% 76.2% 7.0% 53.6% 0.0% 0.0% 0.0% 100.0%Mitsubishi 90.3% 0.0% 3.2% 24.9% 56.9% 0.0% 5.3% 60.6% 9.4% 70.0% 0.0% 5.3% 9.7% 100.0%Nissan 100.0% 0.0% 0.8% 9.8% 74.4% 0.0%3.7% 59.2% 17.7% 76.9% 0.0% 4.6% 3.6% 100.0%Porsche 85.0% 15.0% 15.0% 53.7% 31.3% 0.0% 15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Subaru 34.4% 57.0% 51.5% 5.8% 27.7% 0.0%0.0% 33.5% 51.5% 85.0% 0.0% 0.0% 0.0% 100.0%Suzuki 85.0% 0.0% 3.1% 26.5% 55.4% 0.0%0.0% 70.0% 0.0% 70.0% 0.0% 3.1% 11.9% 100.0%Tata 100.0% 0.0% 4.1%80.9% 0.0% 0.0% 15.0% 70.0% 0.0% 70.0% 0.0% 4.1% 10.9% 100.0%Toyota 82.0% 3.2% 15.0% 17.6% 34.2% 0.0% 13.6% 51.8% 3.2% 55.0% 0.0% 6.6% 0.0% 100.0%Volkswagen 87.9% 11.9% 15.0% 29.2% 55.8% 0.7%15.0% 70.0% 0.0% 70.0% 0.0% 0.0% 15.0% 100.0%Overall 83.4% 8.1% 9.2% 33.8% 39.0% 0.3%5.3% 67.0% 8.5% 75.2% 0.0% 2.3% 2.7% 100.0%Regulatory Impact Analysis

4-32 Table 4-20 2016 Standards by Manufacturer in the 4% Sensitivity Case

Standards Cars Trucks Combined BMW 230.3 288.3 245.5 Chrysler 235.0 305.3 266.0 Daimler 237.4 298.7 257.0 Ford 231.7 319.0 270.4 General Motors 231.8 327.0 271.8 Honda 224.8 285.8 242.9 Hyundai 225.0 283.5 235.4 Kia 226.9 293.8 237.8 Mazda 223.7 276.5 231.3 Mitsubishi 220.7 274.7 227.1 Nissan 228.5 308.7 251.3 Porsche 208.0 291.8 234.6 Subaru 217.9 272.2 237.5 Suzuki 210.0 276.8 227.1 Tata 251.8 280.1 266.9 Toyota 223.2 296.3 247.3 Volkswagen 220.5 297.8 233.9 Overall 227.2 307.1 254.0Results of Proposed and Alternative Standards

4-33 Table 4-21 2016 Standards by Manufacturer in the 6% Sensitivity Case

Standards Cars Trucks Combined BMW 208.7 259.4 222.0 Chrysler 213.4 276.4 241.2 Daimler 215.8 269.8 233.1 Ford 210.2 290.1 245.6 General Motors 210.2 298.1 247.2 Honda 203.2 256.9 219.2 Hyundai 203.4 254.6 212.6 Kia 205.4 264.9 215.1 Mazda 202.1 247.6 208.7 Mitsubishi 199.1 245.8 204.7 Nissan 207.0 279.8 227.6 Porsche 186.4 262.9 210.7 Subaru 196.3 243.3 213.3 Suzuki 188.5 247.9 203.6 Tata 230.2 251.2 241.4 Toyota 201.7 267.4 223.3 Volkswagen 198.9 268.9 211.1 Overall 205.7 278.2 230.0



### 4.9 Assessment of Manufacturer Differences

The levels of requisite technologies sh own 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 ma nufacturers. Yet, large differences remain in the level of technology projected to be requ ired for various manufacturers to meet the proposed standards. Therefore, several analys es 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 CO 2 emission levels.

Manufacturers’ average CO 2 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 vary ing degrees, as indicated in Table 4-22 below.Regulatory Impact Analysis

4-34 Table 4-22 Penetration of Technology in 2008 Vehicles with 2016 Sales: Cars and Trucks

GDI GDI+ Deac GDI+ Turbo Diesel 6 Speed or CV Trans Dual Clutch Trans StartStop Hybrid BMW 6.70% 0.00% 0.00% 0.00% 98.80% 0.80% 0.00% 0.10% Chrysler 0.00% 0.00% 0.00% 0.00% 27.90% 0.00% 0.00% 0.00% Daimler 6.20% 0.00% 0.00% 6.20% 74.70% 11.40% 0.00% 0.00% Ford 0.60% 0.00% 0.00% 0.00% 28.10% 0.00% 0.00% 0.00% General Motors 3.30% 0.00% 0.00% 0.00% 13.70% 0.00% 0.10% 0.10% Honda 1.20% 0.00% 0.00% 0.00% 4.20% 0.00% 0.00% 2.10% Hyundai 0.00% 0.00% 0.00% 0.00% 4.90% 0.00% 0.00% 0.00% Kia 0.00% 0.00% 0.00% 0.00% 0.90% 0.00% 0.00% 0.00% Mazda 11.80% 0.00% 0.00% 0.00% 37.10% 0.00% 0.00% 0.00% Mitsubishi 0.00% 0.00% 0.00% 0.00% 76.10% 0.00% 0.00% 0.10% Nissan 17.70% 0.00% 0.00% 0.00% 33.30% 0.00% 0.00% 0.00% Porsche 0.00% 0.00% 0.00% 0.00% 3.90% 0.00% 0.00% 0.00% Subaru 0.00% 0.00% 0.00% 0.00% 29.00% 0.00% 0.00% 0.00% Suzuki 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00%
Tata 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Toyota 7.50% 0.00% 0.00% 0.00% 30.60% 0.00% 0.00% 12.80% Volkswagen 52.20% 0.00% 0.00% 0.10% 82.80% 10.90% 0.00% 0.00% Overall 6.40% 0.00% 0.00% 0.10% 27.10% 0.60% 0.00% 2.80%

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 techno logy in 2008 essentially catch up to those which did. The exception is the use of hybr id technology in 2008, since hybrids are not projected to be needed by most manufacturers to meet the proposed 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 various manufacturers would produce the same level of CO2 emissions if their CO2 reducing technology wa s 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, difference s in CO2 emissions due to differences thedistribution of sales by vehicle size should be largely eliminated by the use of a footprintbased 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 us e of CO2 reducing technology, so that the otherResults of Proposed and Alternative Standards

4-35 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 eff ect of this technology was then removed from each vehicle to produce CO 2 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” CO 2 levels and their required CO 2 emission level under the proposed 2016 standards. This difference is the total reduction in CO2 emissions required for each manufacturer relative to a “no technolo gy” 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 emissi on reduction requirements. The results are shown in Figure 4-1.

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Figure 4-1 CO2 Emissions Relative to Flee t Adjusted for Technology and Footprint
As can be seen, the manufacturers projected in Table 4-22 to require the greatest levels of tech nology 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 100Regulatory Impact Analysis

4-36 g/mi greater CO 2 emissions than the aver age 2008 MY truck after accounting for differences in the use of fuel saving technology and footpr int. The lowest adjustment is for Subaru’s trucks, which have 50 g/mi CO 2 lower emissions than the average truck.

While this comparison confirms the differences in the technology penetrations shown in Table 4-22, it does not yet explain why these d i fferences exist. Two well known factors affecting vehicle fuel efficiency are vehicle weight and performance. The footprint-based form of the proposed CO 2 standard accounts for most of th e 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 CO 2 emissions over the two-cycle test procedure. So manufacturers wi th higher average performance levels will tend to have higher average CO 2 emissions for any given footprint. Table 4-23 shows each

ma nufacturer’s average ratios of weight to footprint and horsepower to weight.

Table 4-23 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” CO 2 emissions was estimated. First, the “no technology” CO 2 emissions levels were statisticallyResults of Proposed and Alternative Standards

4-37 analyzed to determine the average impact of wei ght and the ratio of horsepower to weight on CO 2 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 4-24.

Table 4-24 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’ CO 2 emissions for cars and over 70 percent for truc ks. These relationships were then used to adjust each vehicle’s “no technology” CO 2 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” CO 2 levels and their required CO 2 emission level under the proposed 2016 standards and subtracted the difference for the industry as a whole. The results are shown in Figure 4-2.Regulatory Impact Analysis

4-38 -40 -30 -20 -10

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Car
Truc k

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 f ootpri nt) and performance dramatically reduce s the differences in various manufacturers’ CO 2 emissions. Most of the manufacturers with high offsets in Figure 4-1 now show low or

ne gative offsets. For example, BMW’s and VW’s trucks show very low CO 2 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. Th is analysis indicates that the primary reasons for the differences in technology penetrations shown for the various manufacturers in Table 4-24 a r e 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 thei r sales towards these consumers. However, the cost in terms of CO 2 emissions is clear. Producing relatively heavy or high performance vehicles increases CO 2 emissions and will require greater levels of technology in order to meet the proposed CO 2 standards.Emissions Impacts

5-1 

## CHAPTER 5: Emissions Impacts

5.1 Overview

The domestic transportation sector emits appr oximately 28% of total U.S. greenhouse gas (GHG) emissions in 2010 based on the standard accounting methodology used by EPA in compiling the inventory of U.S. GHG emissions pursuant to the United Nations Framework Convention on Climate Change. This number is potentially even higher, as the standard methodology excludes upstream transportation fuel emissions associated with extraction, shipping, refining, and distribution from the emi ssions of the transportation sector. Within the transportation sector, emissions from light duty vehicles such as passenger cars, passenger trucks, and light duty commercial trucks account for 18% of total US GHG emissions, or approximately 1,300 million metric tons (MMT) of CO 2 equivalent (CO 2 EQ) greenhouse gas emissions in calendar year 2010.

Today’s proposal quantifies anticipated impacts from the EPA vehicle CO 2 emission standards. The emissions from the GHGs carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O) and hydrofluorocarbons (HFCs) were qu antified. In addition to reducing the emissions of greenhouse gases, today’s proposal would also influence the emissions of “criteria” air pollutants, including carbon mo noxide (CO), fine particulate matter (PM 2.5 ) and sulfur dioxide (SOx) and the ozone precursors hydrocarbons (VOC) and oxides of nitrogen (NOx); and air toxics (hazardous air poll utants, including benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein).

Downstream (tailpipe) emission impacts we re developed using a spreadsheet analysis based on data from two EPA models. Computation algorithms and achieved CO 2 levels were derived from EPA’s Optimization Model for redu cing Emissions of Greenhouse gases from Automobiles (OMEGA) and were coupled with non-CO 2 emission rates from EPA’s Motor Vehicle Emission Simulator (MOVES).

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. 1 Based on these analyses, the control programs proposed in this chapter would account for 325 MMT CO 2 EQ of annual GHG reduction by the year 2030 and 519 MMT per year by 2050. Fuel savings resulting from the GHG standards are projected at 42.6 bi llion gallons of fuel savings in Calendar Year 2050 ( Table 5-1).Draft Regulatory Impact Analysis

5-2 Table 5-1 - Impacts of Proposed Progr am on GHG Emissions and Fuel Savings

CALENDAR YEAR ANNUAL GHG REDUCTION (CO 2 EQ MMT) FUEL SAVINGS (MILLION BARRELS PER DAY OF

GASOLINE EQUIVALENT) ANNUAL FUEL SAVINGS (BILLION GALLONS OF GASOLINE EQUIVALENT) 2020 165.2 0.9 13.4 2030 324.6 1.7 26.2 2040 417.5 2.2 33.9 2050 518.5 2.8 42.6

The emissions of non-GHG air pollutants due to light duty vehicles are also expected to be affected by today’s proposal. These effects are largely due to changes in fuel production, but are also driven by changes in driver behavior. A The delta values shown here include both upstream and downstream contributions.

Table 5-2 - Impacts of Proposed 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 Δ 1,3-Butadiene 11.5 0.07% 36.8 0.22% Δ Acetaldehyde 16.8 0.037% 60.6 0.134% Δ Acrolein 0.2 0.00% 1.8 0.03% Δ Benzene -83.6 -0.04% -77.5 -0.04% Δ Carbon Monoxide 70,614 0.13% 227,832 0.38% Δ Formaldehyde -28.3 -0.03% -15.7 -0.02% Δ Oxides of Nitrogen -17,206 -0.14% -27,726 -0.23% Δ Particulate Matter (below 2.5 micrometers) -2,856 -0.08% -5,431 -0.16%

Δ Oxides of Sulfur -16,307 -0.18% -31,965 -0.34% Δ Volatile Organic Compounds -73,739 -0.60% -142,347 -1.17%

We also analyzed the emission reductions ov er the full model year lifetime of the 2012- 2016 model year cars and trucks affected by today’s proposal. These results, including both upstream and downstream GHG contributions, are presented below ( Table 5-3).

A A rebound of 10% is used in this analysis. See secti on 5.3.3.1.1 for a brief definition of rebound, and the joint Technical Support Document for a more complete discussion.Emissions Impacts

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 81 6.6 157 2013 125 10.0 239 2014 174 13.9 331 2015 243 19.5 463 2016 323 26.3 626 Total Program Benefit 947 76.2 1,815



### 5.2 Introduction

5.2.1 Scope of Analysis

Today’s program proposes new standards for the greenhouse gas (GHG) emissions of light duty vehicles from model year 2012 through model year 2016. The proposed program affects light duty gasoline and diesel fueled vehi cles. 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 proposal will significantly decrease the magnitude of these emissions. Because of antic ipated changes to driving behavior and fuel production, a number of co-pollutants would also be affected by today’s proposal.

This chapter describes the development of inventories for emissi ons of the gaseous pollutants impacted by the proposed rule. These pollutants are divided into greenhouse gases, or gases that in an atmosphere absorb and emit radiation within the thermal infrared range, and non-greenhouse gases. Such impacts may o ccur "upstream" in the agricultural sector and 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

ana l ysis presents the projected impacts of to day’s proposal on greenhouse gases in calendar years 2020, 2030, 2040 and 2050. Non-greenhouse gases are shown in 2020 and 2030. The program was quantified as the difference in mass emissions between the proposed standards and a reference case as described in Section 5.3.2.2.Draft Regulatory Impact Analysis

5-4 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

Delta inventories for the four gree nhouse gases carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O) and hydrofluorocarbons (HFC) ar e presented herein. The sole HFC discussed in this inventory is R-134a, which is the refrigerant in most current vehicle air conditioning systems. Delta inventories for the non-ghg pollutants 1,3-butadiene, acetaldehyde, acrolein, benzene, carbon monoxid e (CO), formaldehyde, oxides of nitrogen (NOx), particulate matter below 2.5 micrometers, oxides of sulfur (SO x ), and volatile organic compounds (VOC) are also presented.



### 5.2.2 Downstream Contributions

The largest source of GHG reductions from today’s proposal is new standards for tailpipe emissions produced during vehicle opera tion (termed “downstream” emissions). Absolute reductions from tailpipe GHG standards are projected to grow over time as the fleet turns over to vehicles affected by the standa rds, meaning the benefit of the program will continue to grow as long as the older vehicl es in the fleet are replaced by newer, lower CO 2 emitting vehicles.

As described herein, the downstream reductions in greenhouse gases due to the proposed 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, improving fuel econom y is anticipated to increase total vehicle miles traveled, which has impacts on both G HG and non-GHG emissions. These impacts are detailed in Section 5.3.3.1.1



### 5.2.3 Upstream Contributions

In addition to downstream emission reductions, reductions are expected in the emissions associated with the pr ocesses involved in getting petroleum to the pump, including the extraction and transportation of crude oil, a nd the production and distribution of finished gasoline (termed “upstream” emissions). Change s are anticipated in upstream emissions due to the expected reduction in the volume of fu el consumed. Less gasoline consumed means less gasoline transported, less gasoline refined, and less crude oil extracted and transported toEmissions Impacts

5-5 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 signific ant source of upstream emissions and is not considered in this analysis. 2



### 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 CO 2 equivalent (CO 2 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 pu blications by the Intergovernmental Panel on Climate Change (IPCC). 3, B

Table 5-5 - Global Warming Potentials for the Inventory GHGs

Gas Global Warming potential (CO 2 Equivalent)
CO 2 1
CH 4 25
N 2 O 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). Broadly

B The global warming potentials (GWP) used in the NPRM inventory analysis are consistent with Intergovernmental Panel on Climate Cha nge (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 ch ange framework. When inventories are recalculated for the final rule, changes in GW P used may lead to adjustments.Draft Regulatory Impact Analysis

5-6 speaking, OMEGA is used to determine the most likely paths by which manufacturers would meet tailpipe CO 2 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 (t he benefits post-processor). The OMEGA benefits post-processor produces a national scale analysis of the benefits (emission reductions, monetized co-benefits) of the analyzed program

Inputs to the OMEGA post-processor were updated with emission rates from EPA’s Motor Vehicle Emission Simulator (MOVES). 45 CO 2 emission and fuel consumption rates are drawn from OMEGA results, with all co-po llutant emission rates derived from the Draft MOVES emission rate database. C,6 Air conditioning inventories (including HFC and CO 2 contributions) were separately calculated in sp readsheet analyses, and are based on previous EPA research. 7 Both MOVES and OMEGA are established models and continue to be actively developed. 8,9

Upstream emissions were calculated using the same tools as were used for the Renewable Fuel Standard 2 (R FS2) proposed rule analysis, 10 but for the current analysis it was assumed that all impacts are related to changes in volume of gasoline produced and consumed, with no changes in volumes of other petroleum-based fuels, ethanol, or other renewable fuels. The estimate of emissions asso ciated with production of gasoline from crude oil is based on emission factors in the GREET model developed by DOE's Argonne National Lab. 11, 12 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 base d 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 proposal 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.

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 proposal, and each is described below. 13 The two scenarios shown are differentiated by their regulatory CO 2 emission standards. The reference scenario CO 2 emissions are based upon the National Highway Traffic Safety Administration (NHTSA) Model Year 2011 Corporate Average Fuel Economy (CAFE) standards, 14 while the control scenario CO 2 emissions are based upon the program proposed 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.

C Two tables were updated in the Draft MOVES database. These tables are available in the docket.Emissions Impacts

5-7 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. However, due to the decreased gasoline volume associated with today's proposal, a greater market share of E10 is expected relative to E0, which would be expected to have some effect on fleetwide average non-GHG emission rates. The increased market share of ethanol blended gasoline, which is likely small relative to the other effects considered here, has not been accounted for in the downstream emission modeling conducted for today's proposal, but is planned to be addressed in the final rule air quality analysis, for which localized impacts could be more significant. 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 ap proximately 1-2%. A more comprehensive analysis of the impacts of different ethanol and gasoline volume scenarios is being prepared as part of EPA's RFS2 rulemaking package. D

5.3.2.1 Sales and Fleet Composition

Fle e t composition has a significant effect upon the impacts of the proposed rule. Consequently, it is significant that the cars and tr ucks 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 defi ni tions used by the National Highway Saf ety Transit Association in the MY 2011 CAFE standards. 15 While the formal definitions are length y, 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

Based on EPA analysis of the projected MY 2012-2016 fleet, 16 approximately 22% of the classic truck fleet is anticipated to be reclassified as Passenger Automobiles under the new standards. Projected sales of classic cars and trucks for calendar years 2012-2030 were drawn from the Energy Information Administration Annual Energy Outlook (AEO) April 2009 projection. 17 The AEO 2009 sales projections, based on the classic fleet, were then

D XX [Insert RFS2 NPRM reference, since FRM will not be available in time for GHG NPRM.]Draft Regulatory Impact Analysis

5-8 reclassified using PA and NPA definitions. For calendar years 2030-2050, which are beyond the scope of AEO’s projections, 0.76% annual growth in the sales of cars and trucks was assumed.

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 14,850,955 15,653,713 16,216,393 16,575,580 16,581,055 Classic Car Fraction 51.8% 52.9% 54.3% 55.8% 57.1%

PA Fraction 62.6% 63.6% 64.3% 65.6% 66.5% PAs Sold 8,235,204 9,255,624 9,977,341 10,479,350 10,890,967

5.3.2.2 Proposed Standards

In dividual P A and NPA tailpipe CO 2 fleet average emission standards are shown for reference and control scenarios along with an anticipated fleet average combined standard. Rather than an absolute standard, the values re ferred to here as standards are the production weighted average standard predicted by the co efficients of the relevant equation. As documented in Preamble section II, under both reference and control scenarios, each manufacturer has a unique fleet average standard based on their vehicle footprints and production.

Fleet average standards are calculated here by weighting the individual PA and NPA standards by the respective proportions of anticipated production (Section 5.3.2.1). These CO 2 emission values are unadjusted values (i.e . in CAFE space), so they are lower than the anticipated on-road emissions. In all scenarios, vehicles are assumed to maintain model year 2016 emissions for post-2016 vehicles. Because the fleet composition continues to change post-MY 2016, the fleet average emission level continues to vary

5.3.2.2.1 Reference Case

5.3.2.2.1.1 CO 2 Emission Standards
The reference scenario standards 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 draft joint TSD and chapter 4 of this DRIA). 18 Average car and truck fuel economy standards were calculated from the coefficients in the MY 2011 rule and EPA analysis of the projected MY 2012-2016 fleet. 19,20 Average fuel economy levels were calculated for each manufacturer’s fleet, and then combined based on projected sales.Emissions Impacts

5-9 A ratio of 8887 grams of CO 2 emitted per gallon of gasoline was used to convert to the calculated fuel economy standards to CO 2 (gram/mile) emission factors. The basic derivation of the 8887 factor can be seen in previous EPA publications. 21

CO 2 emission standards were calculated by applying the CAFE coefficients to the footprint of each model, and calculating fleet averages based on projected model sales. Minor changes in the emission standard are expe cted due to projected changes in the average new vehicle footprint between 2012 and 2016 ( Table 5-8).

Table 5-8 – Reference Case Average Emission Standards (grams/mile CO 2 )

MODEL

YEAR PA EMISSION

LEVEL NPA EMISSION LEVEL MY EMISSION LEVEL

2012 291 366 319 2013 291 366 319 2014 291 368 319 2015 291 368 318 2016 291 368 317

5.3.2.2.1.2 Achieved CO 2 Emission Levels

The emission standards shown in Table 5-8 do not reflect the impact of several program fl exibilities in CAFE, 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 mor e than the standard seemingly requires. Additionally, some manufacturers overcomply with the standards, and th is overcompliance is not reflected in the CAFE standards.

While the CAFE program is complex, the mo st significant portio ns of the program flexibilities are accounted for. In this analys is, manufacturer overcompliance, credit trading, FFV credits, and fine paying manufacturers were accounted for. Banked credits from the calculation of achieved standards were excluded.

In general, achieved emission levels were estimated by beginning with the more stringent of either (A) manufacturer’s CAFE score (in CO 2 space) or (B) estimated achieved MY 2008 CO 2 level 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 is anticipated that they will take advantage. Consistent wi th the use of the MY 2011 standards, the credits and trading levels available for MY 2011 are assumed available in all years of the reference case. Manufacturers were always assumed to pe rform at least as well as they did in 2008.

Overcompliance and Credit TradingDraft Regulatory Impact Analysis

5-10 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 score (in CO 2 space) was calculated by manufacturer for PA and NPA separately. To estimate the effects of overcompliance, each manufacturer’s achieve d 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 “traded” within a manufacturer in order to make up a shortfall in the remaining ve hicle class. Credits are generated on a sales and VMT weighted basis, and traded between ve hicle classes. The MY 2011 CAFE cap on credit trading of 1.0 mpg was used. This trad ing 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 th ey are able to trade 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 th e 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 adva ntage 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 projecte d 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 impacts of these program flexibilities on the standards, achieved levels based on program flexibilities and manufacturer overcompliance are shown in Table 5-10.

Table 5-9 - Impacts of credits (grams/mile CO2 EQ)

MODEL

YEAR OVERCOMPLIANCE, CREDITS AND TRADING FFV FINES NET

2012 -6.1 6.7 0.7 1.2 2013 -6.4 6.7 0.2 0.5 2014 -6.8 6.7 0.1 0.0 2015 -7.1 6.5 0.1 -0.5 2016 -7.3 6.4 0.0 -0.9Emissions Impacts

5-11 Table 5-10 – Reference Case Achieved Emissions (grams/mile CO 2 )

MODEL

YEAR ANTICIPATED PA EMISSION LEVEL ANTICIPATED NPA EMISSION LEVEL ANTICIPATED MY EMISSION LEVEL 2012 284 382 320 2013 283 382 319 2014 283 383 319 2015 282 384 317 2016 282 384 316

5.3.2.2.2 Control Case

5.3.2.2.2.1 CO 2 Emission Standards
Similar to the reformed CAFE program, EPA is proposing to establish a footprint attribute based function in order to determine the CO 2 (gram/mile) emission standard for a given vehicle. The piecewise linear function us ed by EPA is documented in Section II of the preamble. 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 standards for the MY2012-2016 vehicles. Average PA and NPA fuel economy standards were calculated by applying this function to the EPA fleet file ( Table 5-11). 22

Table 5-11 - Control Case Average Emission Standards (grams/mile CO2)

MODEL

YEAR PA EMISSION

LEVEL NPA EMISSION LEVEL PROJECTED MY EMISSION

STANDARD LEVEL 2012 261 351 295 2013 253 341 286 2014 246 332 276 2015 235 317 263 2016 223 302 250Draft Regulatory Impact Analysis

5-12 5.3.2.2.2.2 Achieved CO 2 Emission Levels

Just as with the reference scenario, the emission standards ( Table 5-11) do not include the effec t of several program flexibilities built into the EPA program.

The same basic methodology was used to calculate achieved fleet standards for the control case. In general, achieved standards were estimated by beginning with the more stringent of either (A) manufacturer’s calcula ted footprint-based emission standard or (B) estimated achieved CO 2 level based on the EPA fleet data file. Using that starting point, each manufacturer’s emissions were increased by the impact of the credits of which were anticipated that they will take advantage. Manu facturers were always assumed to perform at least as well as they did in 2008.

Overcompliance and Trading

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 CAFE.

The achieved overcompliance on either PA or NPA could be “traded” 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, an d traded between vehicle classes. Under the EPA proposed program, there are no limits on such trading. This trading of the overcompliance credit negates nearly all of the ov ercompliance anticipated in the early years.

Under the unlimited within-fleet trad ing allowed under the EPA program, manufacturers can potentially invest in their fleet differently than the precise achieved levels described here. Because the credit trading is VMT weighted, the resulting changes will be essentially environmentally neutral on both GHG and criteria pollutants.

Flex Fueled Vehicles

The flex fueled vehicle credit, per the discussion in the preamble (Section III), is set at 1.2 MPG for MY 2012-2014, 1.0 MPG for MY 2015, and 0 MPG for MY 2016+. As in the reference case, it was assumed that the manufacturers General Motors, Chrysler and Ford would take advantage of 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 CO 2 per mile for the fleet, while direct A/C credits were set at 6.9 grams CO 2 per mile for PA and 8.6 grams CO 2 per mile for NPA). EPA assumed market penetration of the technology according to Table 5-16. A moreEmissions Impacts

5-13 complete discussion of the A/C credit program and inventories is provided in section 5.3.3.2, as well as DRIA chapter 2.

Temporary Lead Time Allowance Alternative Standards (TLAAS)

We assumed that every potentially eligible manufacturer took advantage of the temporary lead time allowance. Each qualif ying manufacturer was assumed to use the full vehicle allocation according to the default producti on schedule shown in Preamble Section III. The allocation was split evenly between cars and trucks for each manufacturer. This vehicle allocation was assumed to emit as much CO 2 per mile as the highest emitting car or truck in each manufacturer’s fleet. Th ese vehicles were then proportionally averaged into the manufacturer’s GHG score. For more on the TLAAS program, please see Appendix A to this RIA chapter.

The aggregate impacts of these pr ogram flexibilities are listed in Table 5-12.

Table 5-12 – Estimated Impacts of Proposed Program Flexibilities (grams/mile CO2 EQ)

MODEL

YEAR OVERCOMPLIANCE, CREDITS AND TRADING FFV DIRECT A/C INDIRECT A/C TLAAS NET

2012 0.1 6.0 1.9 1.4 0.3 9.6 2013 -0.3 5.7 3.0 2.3 0.2 10.9 2014 0.0 5.4 4.1 3.1 0.2 12.7 2015 0.0 4.1 5.6 4.3 0.1 14.0 2016 0.0 0.0 6.3 4.8 0.0 11.1

Based on these impacts, the achieved emission level by PA, NPA and fleet are displayed in Table 5-13. Please note that the achieved emission levels include the increase in
test procedure e missions due to the use of the A/C credit. The impacts of A/C improvements are discussed in section 5.3.3.2.

Table 5-13 – Federal GHG Program Anticipated Emission Levels (grams/mile CO2)

MODEL

YEAR ANTICIPATED PA EMISSION LEVEL ANTICIPATED NPA EMISSION LEVEL ANTICIPATED MY EMISSION LEVEL 2012 266 368 304 2013 261 359 297 2014 256 349 289 2015 246 335 277 2016 234 314 261

Table 5-13 differs slightly from the OMEGA cost-side model results in 2016. OM EGA 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 shownDraft Regulatory Impact Analysis

5-14 here. However, because the trading is VMT we ighted, it is environmentally neutral and has no GHG emissions impacts.

OMEGA also predicts slight undercompliance in 2016 for several manufacturers, while the results presented here assume full compliance. Based on preliminary analysis, the OMEGA cost-side results are estimated to pr oduce approximately 0.5% less GHG benefit.



### 5.3.3 Calculation of Downstream Emissions

The fleet inputs (achieved CO 2 emission levels by model year and vehicle sales) described above were incorporated in to a spreadsheet along with emission ratesderived from Draft MOVES 2009 and benefits calculations from the OMEGA postprocessor. The resulting spreadsheet project s 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.

A model year lifetime analysis, considering only the five model years regulated underneath the program, is shown in Section 5.6. In contrast to the calendar
year anal ysis, 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 Tailpipe GHG Emissions

Two basi c 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 3 - Emissions
This equation is adjusted in calculations fo r 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.

5.3.3.1.1 Base VMT

Th e d ownstream analysis is based upon a “bottom-up” estimate of total VMT and vehicle population. The VMT inputs are documented more fully in draft joint TSD chapter 4, but a description of their use in the emissions calculations are provided below.

The analysis spreadsheet contains MY-s pecific estimates of per vehicle VMT by vehicle age, as well as the fractions of new vehicles still on the road as a functionEmissions Impacts

5-15 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 for that model year, age, and vehicle class.

Future vehicle sales were drawn from AEO 2009 (as discussed in Section 5.3.2.1), while historic vehicle sales are dr aw n from the Transportation Energy Data Book, 23 Post MY 2011 vehicles were reclassified in order to correspond to the PA/NPA definitions.

As described in the draft technical support document, mileage accumulation by age was calculated using inputs from the NHTSA “Vehicle Survivability and Travel Mileage Schedules” and additional inputs unique to this analysis. 24,25 In brief, a 1.15% per vehicle annual VMT growth rate was assumed, but additional factors such as achieved fuel efficiency and the price of gasoline also contributed to the precise schedule for each MY.

The 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 5-14). 26Draft Regulatory Impact Analysis

5-16 Table 5-14 - 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 0.0334Emissions Impacts

5-17 A complete discussion of the derivation of the MY specific VMT schedules is provided in draft joint TSD chapter 4.

5.3.3.1.2 Rebound

T h e tailpipe CO 2 standards are expected to result in greater fuel efficiency. Per the discussion of the rebound effect in the draft joint TSD chapter 4, improved fuel efficiency is expected to lead to a propor tional 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 new old reb 

### FleetFC FleetFC FleetFC REB VMT 
  *

%

Equation 4 - 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 vehicl e, which is consistent with the assumption that fuel economy is constant throughout vehicle life.

This analysis assumes a 10% rebound effect; the analysis behind 10% is explored in greater depth in Chapter 4 of the draft joint TSD.

5.3.3.1.3 Emission Factors

The derivati on of the emission factors used in this analysis is documented in chapter 4 of the technical support document. Briefly, CO 2 emission rates are derived from the achieved vehicle emission levels in Table 5-10 & Table 5-13, SO 2 emission rates are derived from fuel sulfur levels, and the emission rates for the remaining pollutants are derived from the draft MOVES 2009 database. For a more complete di scussion of these emission rates, please see the draft joint TSD chapter 4. 27

EPA is not projecting any reductions in tailpipe CH 4 or N 2 O emissions as a result of these proposed emission caps, which are meant to prevent emission backsliding and to bring diesel vehicles equipped with advanced technolo gy aftertreatment into alignment with current gasoline vehicle emissions. Similar to other pollutants, there are emission impacts due to reduced fuel production and increased driving (rebound).Draft Regulatory Impact Analysis

5-18 5.3.3.1.4 Tailpipe CO2 Emissions from Vehicles

CO 2 emission rates were derived from the achieved levels in Table 5-10 & Table 5-13. Previous EPA analysis has shown that an approximately 20% gap exists bet w een CAFE space fuel econo my and on-road fuel economy. 28 The on-road gap is more fully documented in the draft joint TSD chapter 4.

The 20% gap, while approximate, includes average effects of fuel efficiency contributors such as road roughness, wind, and high acceleration ev ents. 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 fuel consumption impacts of running a mobile vehicle air conditioning system. In this analysis, CO 2 emissions are assumed to remain constant throughout the vehicle’s lifetime.

By dividing a CAFE-space CO 2 emission rate by (1-on-road gap), one can approximate the actual on-road CO 2 emissions experienced by drivers.

On road tailpipe CO 2 emissions = Achieved CO 2 Emission Level /(1-on-road gap) x VMT including rebound Equation 5- Tailpipe CO2 Emissions Excluding A/C

Based on Equation 5, the baseline CO 2 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 5-15 - Tailpipe CO 2 Emissions including Baseline A/C Usage (MMT)

2020 2030 2040 2050 Tailpipe CO 2 Emissions (Reference) 1,209 1,373 1,662 2,069

Δ CO 2 Emissions (Control) including 10% rebound -108 -212 -274 -344

Δ CO 2 Emissions due to 10% rebound 11 21 27 33

5.3.3.2 Air Conditioning Emissions

Out s ide of the tailpipe CO 2 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 CO 2 . With regard to air conditioning-related emissions, significant opportunity exists to reduce HFC emissions fro m refrigerant leakage (direct emissions) and CO 2 from A/C induced engine loads (indirect emissions).Emissions Impacts

5-19 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 system s contribute to the emissions of GHGs. The first is through direct leakage of refrigerant (c urrently 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 w a rming impact than a similar amount of emissions from other mobile source GHGs. Leakage can occur slowly through seals, gasket s, 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 DRIA Chapter 2.

EPA’s analysis indicates that together, these A/C- related emissions account for approximately 8% of the GHG emissions from cars and light trucks. EPA is proposing credit provisions which we expect all manufacturers to utilize which are expected to reduce direct leakage emissions by 50% and to reduce the incremental increase of A/C related CO 2 emissions by 40% in model year 2016 vehicles, wi th 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 differe nces 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 cont ainment of a fluid is the key control feature, while efficiency improvements are more similar to the vehicle-based control of CO 2 in that they would be achieved through specific hardware and controls.

The anticipated phase-in of air co nditioning controls is shown in Table 5-16. The

m a rket penetration is based upon analysis from the OMEGA model. OMEGA projections show improved A/C technology market penetration at 85% of the market in 2016. This 85% cap is then roughly linearized across the five year period ( Table 5-16). Because HFC leakage is som e what independent of vehicle miles traveled, HFC reduction % is based on the proportion of new vehicles that have HFC leakage containment technology. By contrast, indirect A/C reduction % is dependent upon the travel fraction, and is proportional to the VMT attributable to vehicles with the control technology.

Table 5-16 – AC Control by Model Year (Reduction from Base Emissions)

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016+ Market Penetration of technology 25% 40% 55% 75% 85% HFC Reduction % -13% -21% -28% -38% -43% Indirect Reduction % -10% -16% -22% -30% -34%

The penetrations of A/C control technology and HFC reductions shown in this section differ slightly from those shown in DRIA chapter 2, and in Preamble section III. The HFC credits discussed in this section are slightly larger (9%) than the proposed program credits, while the penetration schedule of the credits is slig htly lower. The net effect is a less than 1%Draft Regulatory Impact Analysis

5-20 overstatement of total program GHG related benefits. EPA will address this issue in the final rulemaking.

5.3.3.2.1 Direct A/C (HFC) Emissions The pro j ected HFC baseline inventories are derived from previous EPA analyses. 29

HFC emissions are a leakage type emission, similar to other evaporative emissions from a vehicle. 30 Consequently, HFC emissions are tied more closely to vehicle stock than to VMT.

To calculate HFC emissions, the per-vehic le per-year emission contribution of the current vehicle fleet was determined using averaged 2005 and 2006 registration data from the Transportation Energy Databook (TEDB) 31 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 fu ture year using data from the emission modeling analysis. This analysis assumes that the leakage rates of the current fleet remain constant into the future. Preliminary EPA estimates suggest that air conditioner charge size is decreasing, which implies that the current analysis may s omewhat 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 proposed EPA program is expected to impact only two of these phases of the HFC inventory by reducing leakage and re ducing need for servicing.

The vehicle population model from the emissi on 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 6).

Emissions Reductions = Reduction % by Calendar Year x Total CY inventory Reduction % by CY = ∑ Calendar Year (Reduction % by MY x Vehicle Popula tion by MY)/Total Vehicle Population Equation 6 - HFC Inventory Calculation
Table 5-17 shows the calculated penetration of technology into the vehicle fleet and consequent reduction from baseline HFC Inventory.

Table 5-17 - HFC (Direct A/C) Emissions

Calendar

Year Baseline HFC (MMT CO2EQ) Penetration of Technology (Population Based) Reduction From Baseline

(%) Reduction from Baseline (MMT CO2EQ)

2010 55.0 0% 0% 0.0 2020 61.5 43% -21% -13.5 2030 70.5 76% -38% -27.2 2040 76.4 84% -42% -32.1 2050 82.1 85% -42% -34.9Emissions Impacts

5-21 5.3.3.2.2 Indirect A/C (CO2) Emissions

By a d ding an additional load to the powertr ain, A/C indirectly causes an increase in tailpipe CO 2 emissions. Thus, where HFC inventory is prop ortional to penetration of the technology into the 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 us age were taken from the EPA analysis documented in DRIA chapter 2. There, indir ect A/C usage is calculated to add 14.25 grams of CO 2 emissions to the certification emissions of either cars or trucks. The indirect A/C controls proposed in the rule are estimated to re move up to 40% of the emission impact of air conditioning systems, or 5.7 grams per mile.

The OMEGA post processor was used to calculate the contribution the indirect A/C program to the overall inventory. Reference and Control scenario emissions due to anticipated improvements to indirect A/C systems are shown in Table 5-18

.

Table 5-18 –Indirect A/C Emissions

Calendar

Year Baseline Indirect A/C (MMT CO2EQ) Reduction From Baseline

(%) Reduction from Baseline (MMT CO2EQ) 2010 52.9 -0% 0 2020 55.2 -20% -11.0 2030 65.9 -32% -21.1 2040 80.6 -34% -27.2 2050 100.5 -34% -34.1

It should be noted that the baseline indire ct 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 Co-pollutant Emissions

Du e to th e rebound effect, the downstream emissions of several co-pollutants are anticipated to increase. These inventories are calculated in a similar manner to the CO 2 emissions. Rebound VMT, which is the additional driving, is broken into distribution by vehicle age. VMT by each age was then multipli ed by the appropriate emission factor. These emissions by age were then summed by calendar year ( Equation 7, Table 5-19).

The EP A reference fleet assumes a small number of diesel vehicles are sold in each year (~20 thousand out of ~13-16 million). Fo r the criteria emission analysis, it was assumed that 0.5% of new light duty vehicles sold were diesels. Because diesel fueled vehicles areDraft Regulatory Impact Analysis

5-22 subject to the same Tier 2 emission standards as gasoline fueled vehicles, the emission rates of criteria pollutants are similar. E

Emissions Calendar Year = ∑ Calendar Year (Rebound VMT by Age * Emission Factor by Age ) Equation 7 - Emissions by Calendar Year

Table 5-19 - Delta GHG Emissions Due to Rebound (Metric Tons)

CALENDAR YEAR 2020 2030 2040 2050 Gasoline Fueled Vehicles Δ CH 4 307 644 857 1079 Δ N 2 O 134 284 377 3475

Diesel Fueled Vehicles Δ CH 4 0.13 0.25 0.32 0.40 Δ N 2 O 0.20 0.40 0.52 0.66

E 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 engi nes emit differently during st art, as well as during the various modes of operation.Emissions Impacts

5-23 Table 5-20 - Delta Downstream non-GHG Emissions (Short Tons)

CALENDAR YEAR 2020 2030 Gasoline Fueled Vehicles Δ 1,3-Butadiene 13.08 39.75 Δ Acetaldehyde 24.58 74.86 Δ Acrolein 1.22 3.72 Δ Benzene 79.27 241.26 Δ CO 77,648.80 241,314.67 Δ Formaldehyde 31.08 94.43 Δ NO x 5,128.09 15,110.31 Δ PM 2.5 217.31 569.82 Δ SO 2 -2,502.6 -4,905.6 Δ VOC 1,678.90 5,442.80

Diesel Fueled Vehicles Δ 1,3-Butadiene 0.17 0.44 Δ Acetaldehyde 0.23 0.60 Δ Acrolein 0.07 0.17 Δ Benzene 0.37 0.98 Δ CO 174.13 624.39 Δ Formaldehyde 0.72 1.89 Δ NO x 225.72 449.95 Δ PM 2.5 0.88 1.87 Δ SO 2 Attributed to Gasoline Δ VOC 19.33 51.03

In general, downstream emissions are predicted to increase a small amount due to rebound driving. The one exception is sulfur emissions (SO 2 ), which are predicted to decrease as a result of the decrease in fu el consumption. As shown in section 5.3.4, the inc r eases in non-ghg pollutants are generally le ss than the projected decreases on the upstream side. The exceptions are in those pollutant s, such as carbon monoxide (CO), where a relatively small of US emissions comes from upstream sources.

5.3.3.4 Fuel Savings

The proposed 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 calcul ate d from total tailpipe CO 2 avoided (including CO 2 due to driving and indirectDraft Regulatory Impact Analysis

5-24 A/C use) using a conversion factor of 8887 grams of CO 2 per gallon of gasoline. All fuel saved is considered 100% gasoline without any oxygenate. F,32

Fuel savings were calculated from total tailpipe CO 2 avoided (including CO 2 due to driving and indirect A/C) using a conversion factor of 8887 grams of CO 2 per gallon of gasoline. 33

Table 5-21 - Downstream Fuel Consumption Changes by Calendar Year (Billions of Gallons of Gasoline Equivalent)

2020 2030 2040 2050 Fuel Consumption (Reference) 142.2 161.9 196.2 244.1 Δ Total Fuel Consumption due to EPA Program -13.4 -26.2 -33.9 -42.6

Δ Fuel Consumption due to 10% rebound 1.2 2.3 3.0 3.8

Δ Fuel Consumption due to A/C controls -1.2 -2.4 -3.1 -3.8



### 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 finish ed fuel transport, storage, and distribution. As shown above in Table 5-4 this includes all the stages prior to the final filling of vehicle f u el tanks at retail service stations. The details of the assumptions, data sources, and calculations that were used to estimate the em ission impacts presented here can be found in the Technical Support Document and the docket memo, "Calculation of Upstream Emissions for the GHG Vehicle Rule." 34



### 5.4 Greenhouse Gas Emission Inventory

This section presents total program calendar year impacts by sector ( Table 5-22, Table 5-23

, Table 5-24). Upstream, downstream, and total program impact are presented.

F Based on the documentation of the on-road gap, it would be justifiable to assume an ethanol percentage of 0.8%. This volume of ethanol would result in a total en ergy difference of less than 0.5%. See the fuel labeling rule technical support document, EPA420-R-06-017, for further details.Emissions Impacts

5-25 Table 5-22 – Downstream GHG and Fuel Consumption Changes vs. Reference Case
2020 2030 2040 2050 Δ CO2 (Metric Tons) -118,682,739 - 232,643,716 -301,498,777 -378,287,357 Δ CH4 (Metric tons) 308 645 857 1,080 Δ N2O (Metric tons) 134 284 378 476 Δ HFC (Metric tons) -9,429 -18,987 -22,420 -24,407 Δ GHG (MMT CO2 EQ) -132.1 -259.7 -333.4 -413.0 Δ Fuel Consumption (billion gallons per year) -13.4 -26.2 -33.9 -42.6

Table 5-23 – Upstream GHG Change vs. Reference Case
2020 2030 2040 2050 Δ CO2 (Metric Tons) -28,857,236 - 56,566,395 -73,308,230 -91,979,068 Δ CH4 (Metric tons) -163,638 -320,765 -415,701 -521,576 Δ N2O (Metric tons) -464 -909 -1,178 -1,478 Δ GHG (MMT CO2 EQ) -33.1 -64.9 -84.1 -105.5

Table 5-24 – Total GHG and Fuel Consumption Changes vs. Reference Case
2020 2030 2040 2050 Δ CO2 (Metric Tons) -147,539,975 -289,210,111 -374,807,007 -470,266,424 Δ CH4 (Metric tons) -163,330 -320,120 -414,844 -520,496 Δ N2O (Metric tons) -329 -625 -800 -1,002 Δ HFC (Metric tons) -9,429 -18,987 -22,420 -24,407 Δ GHG (MMT CO2 EQ) -165.2 -324.6 -417.5 -518.5 Δ Fuel Consumption (billion gallons per year) -13.4 -26.2 -33.9 -42.6

5.4.1 Impact on US and Global GHG Inventory

A s stated in the introduction , 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 obs erved global warming over the last 50 years. All mobile sources emitted 31.5 percent of all US GHG in 2006, and have been the fastest - growing source of US GHG since 1990. Light-duty vehicles are responsible for nearly 60 percent of all mobile source GHGs. For ligh t-duty vehicles, CO2 emissions represent about 95 percent of all greenhouse emissions, and the CO2 emissions measur ed over the EPA testsDraft Regulatory Impact Analysis

5-26 used for fuel economy complian ce represent over 90 percent of total light-duty vehicle greenhouse gas emissions.

This action is an important step towards curbing steady growth of GHG emissions from cars and light trucks. In the absence of control, GHG emissions worldwide and in the U.S. are projected to continue steady growth ; U.S. GHGs are estimated to make up roughly 15 percent of total worldwide emissions, and the contribution of direct emissions from cars and light-trucks to this U.S. share is growing over time, reaching an estimated 20 percent of U.S. emissions by 2030 in the absence of control.

As discussed elsewhere in this proposal, this steady rise in GHG emissions is associated with numerous adverse impacts on hu man health, food and agriculture, air quality, and water and forestry resources.



### 5.5 Non-Greenhouse Gas Emission Inventory

The reference case emission inventories used for this proposed 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 andFuture Year National Emission Summaries," from Madeleine Strum to Docket EPA-HQOAR-2007-0491. That memo summarizes th e methodologies and additional reference documents for criteria air pollutants (CAP) an d mobile source air toxics (MSATs). The effects of the Clean Air Interstate rule are not included here.

For onroad mobile sources, the MOVES Draf t 2009 model was used that estimates emissions from light-duty and heavy-duty gasoline and diesel vehicles, except for motorcycles. For motorcycles, the MOBILE6.2 model was used as run using the NMIM platform that applies county specific fuel properties and temperatures. For the MOVES model runs the Vehicle Miles Traveled (VMT) of light-duty gasoline vehicles was adjusted to account for factors related to this proposal, such as the ten percent rebound effect as described above in Sections 5.3.3.1.1 and 5.3.3.1.2.

Most nonroad equipm ent was modeled w ith NONROAD2005d using NMIM, 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). 35, 36

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. 35, 37 Locomotives and C1/C2 commercial marine vessel inventories come from the spring 2008Emissions Impacts

5-27 final rule, and the C3 commercial marine emission inventory is from the base case inventories in the June 2009 proposed rule.

Table 5-25 and Table 5-26 show the total 2020 and 2030 mobile and non-mobile

so ur ce inventory projections that were used as the reference case against which impacts of the rule were applied. The impacts, expressed as percentages, are presen ted below in Sections 5.5.1 through 5.5.3.

Table 5-25. 2020 Reference Case Emissions by Sector (annual short tons)

VOC CO NOX PM10 PM2.5 SO2

Onroad Gasoline 1,973,180 29,211,716 1,93 4,488 96,380 58,990 30,922

Onroad Diesel 129,321 260,238 1,353, 773 32,733 40,071 4,218

Nonroad SI a 1,289,918 14,286,250 242, 828 53,092 49,019 15,413
Other Nonroad b 234,870 1,424,643 3,389,7 61 230,553 210,509 943,226 Stationary/Area 8,740,057 11,049,239 5,773,927 3,194,610 3,047, 714 7,864,681

Total 12,367,346 56,232,087 12,694,778 3, 607,368 3,406,303 8,858,459

TABLE 5-25 CONTINU E D BENZENE 1,3- BUTADIENE ACETALDEHYDE FORMALDEHYDE ACROLEIN

Onroad Gasoline 60,742 7,518 14,604 18,716 903

Onroad Diesel 1,571 830 3,743 10,010 475

Nonroad SI a 36,862 5,895 4,768 10,240 584
Other Nonroad b 3,760 929 9,542 22,324 1,013 Stationary/Area 111,337 1,847 13,118 23,846 3,412
Total 214,273 17,019 45,777 85,136 6,387

a Nonroad gasoline, LPG, and CNG engines plus portable fuel containers
b Nonroad diesel engines and all locomotive, aircraft, and commercial marineDraft Regulatory Impact Analysis

5-28 Table 5-26. 2030 Reference Case Emissions by Sector (annual short tons)

VOC CO NOX PM10 PM2.5 SO2

Onroad Gasoline 1,800,856 32,038,635 1,504, 390 110,796 67,416 36,011

Onroad Diesel 140,959 219,594 1,120, 656 34,746 26,498 5,478

Nonroad SI a 1,198,679 15,815,805 243, 515 55,011 50,816 17,270 Other Nonroad b 238,652 1,411,393 3,427,832 253,572 229,183 1,426,994 Stationary/Area 8,740,057 11,049,239 5,773,927 3,194,610 3,047, 714 7,864,681
Total 12,119,203 60,534,666 12,070,321 3, 648,735 3,421,628 9,350,433

Table 5-26

c o ntinued Benzene 1,3- Butadiene Acetaldehyde Formaldehyde Acrolein

Onroad Gasoline 55,692 6,840 13,354 17,071 812

Onroad Diesel 1,706 915 4,050 10,903 517

Nonroad SI a 39,871 6,279 5,118 11,229 629
Other Nonroad b 3,764 979 9,579 22,487 1,055 Stationary/Area 111,337 1,847 13,118 23,846 3,412

Total 212,371 16,859 45,220 85,536 6,425

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

As described in the introduction, downs tream inventories were generated using algorithms from EPA’s Optimization Model fo r reducing Emissions of Greenhouse gases from Automobiles (OMEGA). The OMEGA bene fits post-processor produces a national scale analysis of the benefits (emission reduct ions, monetized co-benefits) of the analyzed program. The non-GHG emission results shown here ( Table 5-27) were calculated in a spreadsheet analysis using algorithms from the Draft MOVES 2009 emission database and algorithms from the OMEGA post-processor.Emissions Impacts

5-29 Table 5-27. Downstream Emission Changes of Proposed Program

CALENDAR YEAR 2020 CALENDAR YEAR 2030

POLLUTANT Short Tons Percent Change in US Total Short Tons Percent Change in US Total
Δ 1,3-Butadiene 13.2 0.078% 40.2 0.238% Δ Acetaldehyde 25 0.054% 75 0.167% Δ Acrolein 1.29 0.020% 3.89 0.061% Δ Benzene 80 0.037% 242 0.114% Δ Carbon Monoxide 77,823 0.138% 241,939 0.400% Δ Formaldehyde 32 0.037% 96 0.113% Δ Oxides of Nitrogen 5,354 0.042% 15,560 0.129% Δ Particulate Matter (below 2.5 micrometers) 218 0.006% 572 0.017%

Δ Oxides of Sulfur -2,503 -0.028% -4,906 -0.052% Δ Volatile Organic Compounds 1,698 0.014% 5,494 0.045%



### 5.5.2 Upstream Impacts of Program

Fuel production and distribution emission impacts of the proposed 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) proposed rule. It relies pa rtially 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 stre ngthen and expand on the GREET analysis.

Updates and enhancements to the GREET model assumptions include updated crude oil and gasoline transport emissi on factors that account for recent EPA emission standards and modeling, such as the Tier 4 diesel truck sta ndards published in 2001 and the locomotive and commercial marine standards finalized in 2008. In addition, GREET does not include air toxics. Thus emission factor s 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 Emissi ons Inventory (NEI), a risk and technology review for petroleum refineries, speciated emission profiles in EPA's SPECIATE database, orDraft Regulatory Impact Analysis

5-30 the Mobile Source Air Toxics rule (MSAT) invent ory for benzene; these pollutant tons were divided by refinery energy use or gasoline distribution quantities published by the DOE Energy Information Administration (EIA) to get emission factors in terms of grams per million BTU of finished gasoline. The resulti ng emission factors are presented in Chapter 4 of the draft joint TSD for today's proposed rule.

Results of these emission inventory impact calculations relative to the reference case for 2020 and 2030 are shown in Table 5-28 for the criteria pollutants and individual air toxic pollutant s.
The proposed 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 5-28. Upstream Emission Changes of Proposed Program

CALENDAR YEAR 2020 CALENDAR YEAR 2030

POLLUTANT Short Tons Percent Change in US Total Short Tons Percent Change in US Total
Δ 1,3-Butadiene -1.8 -0.010% -3.4 -0.020% Δ Acetaldehyde -8 -0.017% -15 -0.033% Δ Acrolein -1 -0.017% -2 -0.032% Δ Benzene -163 -0.076% -320 -0.151% Δ Carbon Monoxide -7,209 -0.013% -14,107 -0.023% Δ Formaldehyde -60 -0.071% -112 -0.131% Δ Oxides of Nitrogen -22,560 -0.178% -43,286 -0.359% Δ Particulate Matter (below 2.5 micrometers) -3,075 -0.090% -6,003 -0.175%

Δ Oxides of Sulfur -13,804 -0.156% -27,060 -0.289% Δ Volatile Organic Compounds -7 5,437 -0.610% -147,841 -1.220%



### 5.5.3 Total Program Impact

Table 5-29 shows the combined impacts of downstream and upstream aspects of the proposed program. The fuel production and di stribution impacts of the proposed program on VOC, NOx, PM2.5, and SOx 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 are driven by the rebound effect on VMT, which are only partially offset by upstream reductions.

Air toxic emission impacts depend on the relative reductions from upstream emissionsEmissions Impacts

5-31 versus increases due to rebound on the downst ream emissions. Relative to 2030 US total reference case emissions, formaldehyde and benzene emissions are projected to decrease by 0.02 to 0.04 percent, but 1,3-butadiene, acetal dehyde, and acrolein emissions would increase by 0.03 to 0.22 percent.

Table 5-29. Total Non-GHG Emissi on Changes of Proposed Program

CALENDAR YEAR 2020 CALENDAR YEAR 2030

POLLUTANT Short Tons Percent Change in US Total Short Tons Percent Change in US Total
Δ 1,3-Butadiene 11.5 0.07% 36.8 0.22% Δ Acetaldehyde 17 0.04% 61 0.13% Δ Acrolein 0.2 0.00% 1.8 0.03% Δ Benzene -84 -0.04% -77 -0.04% Δ Carbon Monoxide 70,614 0.13% 227,832 0.38% Δ Formaldehyde -28 -0.03% -16 -0.02% Δ Oxides of Nitrogen -17,206 -0.14% -27,726 -0.23% Δ Particulate Matter (below 2.5 micrometers) -2,856 -0.08% -5,431 -0.16%

Δ Oxides of Sulfur -16,307 -0.18% -31,965 -0.34% Δ Volatile Organic Compounds -73,739 -0.60% -142,347 -1.17%



### 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 life time 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 draft joint TSD. In brief, it was derived using the same methodology as the MY-specific VM T schedules and is the average of the VMT schedules from 2012-2030 ( Table 5-30).

All ot her inputs, including sales, emission factors and achieved emission levels are the same between the two analyses.Draft Regulatory Impact Analysis

5-32 Table 5-30 - Updated Survival Fraction and Mileage Accumulation by Age

AGE PA SURVIVAL FRACTION PA MILEAGE NPA SURVIVAL FRACTION NPA MILEAGE

0 0.9950 16,932 0.9950 18,847 1 0.9900 16,603 0.9741 18,408 2 0.9831 16,257 0.9603 18,050 3 0.9731 15,814 0.9420 17,575 4 0.9593 15,414 0.9190 17,142 5 0.9413 14,993 0.8913 16,593 6 0.9188 14,545 0.8590 16,095 7 0.8918 14,105 0.8226 15,493 8 0.8604 13,624 0.7827 14,891 9 0.8252 13,192 0.7401 14,336 10 0.7866 12,668 0.6956 13,689 11 0.7170 12,222 0.6501 13,160 12 0.6125 11,705 0.6042 12,554 13 0.5094 11,191 0.5517 11,945 14 0.4142 10,727 0.5009 11,342 15 0.3308 10,283 0.4522 10,822 16 0.2604 9,878 0.4062 10,383 17 0.2028 9,482 0.3633 9,900 18 0.1565 9,090 0.3236 9,433 19 0.1200 8,691 0.2873 9,033 20 0.0916 8,366 0.2542 8,692 21 0.0696 8,126 0.2244 8,499 22 0.0527 8,003 0.1975 8,246 23 0.0399 7,774 0.1735 8,261 24 0.0301 7,587 0.1522 8,066 25 0.0227 7,424 0.1332 8,066 26 0.0000 7,334 0.1165 8,101 27 0.0000 7,200 0.1017 8,098 28 0.0000 7,103 0.0887 8,096 29 0.0000 7,044 0.0773 8,095 30 0.0000 7,042 0.0673 8,093 31 0.0000 7,039 0.0586 8,092 32 0.0000 7,033 0.0509 8,086 33 0.0000 7,021 0.0443 8,080 34 0.0000 7,007 0.0385 8,064 35 0.0000 6,988 0.0334 8,050Emissions Impacts

5-33 

### 5.6.2 Results

The GHG emission reductions are shown for each model year, as are the co-pollutant impacts ( Table 5-31, Table 5-32).

T a ble 5-31 – Lifetime GHG Emissions vs. Reference Case (MMT CO2 EQ)

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Program Total Δ Downstream Tailpipe Emission -53.19 -79.99 -110.75 -154.99 -213.28 -612.19 Δ Downstream Indirect A/C -5.33 -9.04 -12.88 -17.87 -20.28 -65.39 Δ Downstream Direct A/C -6.92 -11.73 -16.72 -23.20 -26.32 -84.89 Δ Downstream CH4 0.00 0.01 0.01 0.01 0.02 0.05 Δ Downstream N2O 0.02 0.03 0.05 0.06 0.10 0.26 Total Δ Downstream -65.41 -100.71 -140.29 -195.99 -259.76 -762.17

Δ Upstream CO 2 -14.23 -21.65 -30.06 -42.03 -56.79 -164.75 Δ Upstream CH4 -1.69 -2.58 -3.58 -5.01 -6.76 -19.62 Δ Upstream N2O -0.07 -0.11 -0.15 -0.21 -0.28 -0.82 Total Δ Upstream -15.99 -24.33 -33.79 -47.25 -63.83 -185.19

Total Program Δ GHG Emissions -81.41 -125.05 -174.08 -243.23 -323.59 -947.36

Total Program Fuel Savings (Billion Barrels) 0.16 0.24 0.33 0.46 0.63 1.82Draft Regulatory Impact Analysis

5-34 Table 5-32 – Lifetime non-GHG Emissions vs. Reference Case (Short Tons)
MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Program Total
Downstream Δ VOC 1,495 2,373 3,336 4,751 6,763 18,717 Δ NO X 3,991 6,602 9,469 13,485 20,082 53,629 Δ PM 2.5 152 241 338 482 686.074 1,899 Δ CO 63,783 102,779 145,598 207,338 295,494 814,992 Δ SO 2 -1,234 -1,877 -2,607 -3,645 -4,925 -14,288 Δ Benzene 65.1 103.4 145.4 207.1 294.2 815 Δ 1,3 Butdiene 10.8 17.1 24.1 34.3 48.8 135 Δ Formaldehyde 25.9 41.1 57.9 82.4 117.1 324 Δ Acetaldehyde 20.3 32.2 45.3 64.5 91.6 254 Δ Acrolein 1.0 1.7 2.3 3.3 4.7 13

Upstream Δ VOC -37,188 -56,572 -78,562 -109,850 -148,418 -430,591 Δ NO X -10,888 -16,564 -23,002 -32,163 -43,455 -126,072 Δ PM 2.5 -1,510 -2,297 -3,190 -4,460 -6,026 -17,484 Δ CO -3,549 -5,398 -7,496 -10,482 -14,162 -41,087 Δ SO 2 -6,807 -10,354 -14,379 -20,106 -27,165 -78,812 Δ Benzene -80.4 -122.3 -169.9 -237.6 -321.0 -931 Δ 1,3 Butdiene -0.9 -1.3 -1.8 -2.6 -3.5 -10 Δ Formaldehyde -28.2 -42.9 -59.5 -83.2 -112.5 -326 Δ Acetaldehyde -3.7 -5.7 -7.9 -11.0 -14.9 -43 Δ Acrolein -0.5 -0.8 -1.1 -1.5 -2.1 -6

Total Δ VOC -35,694 -54,199 -75,226 -105,099 -141,655 -411,874
Δ NO X -6,897 -9,962 -13,533 -18,678 -23,373 -72,443
Δ PM 2.5 -1,358 -2,056 -2,852 -3,978 -5,340 -15,585
Δ CO 60,234 97,381 138,101 196,856 281,332 773,905
Δ SO 2 -8,041 -12,232 -16,986 -2 3,751 -32,090 -93,099
Δ Benzene -15 -19 -24 -30 -27 -116
Δ 1,3 Butdiene 10 16 22 32 45 125
Δ Formaldehyde -2 -2 -2 -1 5 -2
Δ Acetaldehyde 17 27 37 53 77 211
Δ Acrolein 1 1 1 2 3 7



### 5.7 Alternative 4% and 6% Scenarios

For this proposal, two alternative control scenarios were evaluated characterized by 4% and 6% annual growth in the GHG standards from the MY 2011 standard. Other than theEmissions Impacts

5-35 standards, these scenarios share all inputs with the proposed 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 p rogram standards are shown in Table 5-33 and the achieved levels are shown in Table 5-34

.

Table 5-33: 4% Scenario Standards

MODEL

YEAR PA EMISSION

LEVEL NPA EMISSION LEVEL ANTICIPATED MY EMISSION LEVEL 2012 276 366 310 2013 265 355 298 2014 255 342 286 2015 246 331 275 2016 237 318 264

Table 5-34: 4% Scenario Achieved Levels

MODEL

YEAR ANTICIPATED PA EMISSION LEVEL ANTICIPATED NPA EMISSION LEVEL ANTICIPATED MY EMISSION LEVEL 2012 275 378 314 2013 266 368 304 2014 258 352 292 2015 249 338 279 2016 237 319 264

5.7.1.2 Results

Re sults a re shown relative to the same reference scenario as the proposed EPA program. Both calendar year and model year lifetime results are shown.Draft Regulatory Impact Analysis

5-36 Table 5-35 – Downstream CY GHG Reductions and Fuel Savings vs. Reference Case
CY 2020 CY 2030 CY 2040 CY 2050 Downstream Δ CO 2 excluding indirect A/C controls (MMT CO 2 EQ) -97.7 -196.6 -255.5 -320.7 Δ Indirect A/C CO 2 (MMT CO 2 EQ) -11.0 -21.1 -27.2 -34.1 Δ Direct A/C HFC (MMT CO 2 EQ) -13.5 -27.2 -32.1 -34.9 Δ CH 4 (MMT CO 2 EQ) 0 0 0 0 Δ N 2 O (MMT CO 2 EQ) 0 0 0.1 0.1 Δ Total GHG (MMT CO 2 EQ) -122.2 -244.8 -314.7 -389.6

Upstream Δ CO 2 (MMT CO 2 EQ) -26.4 -52.9 -68.7 -86.2 Δ CH 4 (MMT CO 2 EQ) -4.1 -8.0 -10.4 -13.0 Δ N 2 O (MMT CO 2 EQ) -0.1 -0.3 -0.3 -0.4 Δ Total GHG -30.5 -61.2 -79.4 -99.6

Total Δ Total GHG -152.7 -306.0 -394.4 -489.2 Δ Fuel Consumption (Annual, Billio n gallons) -12.2 -24.5 -31.8 -39.9Emissions Impacts

5-37 Table 5-36 – Total Model Year Lifetime GHG Reductions vs. Baseline

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Program Total Downstream Δ CO 2 excluding indirect A/C controls (MMT CO 2 EQ) -22.1 -55.2 -100.3 -145.4 -198.8 -521.8 Δ Indirect A/C CO 2 (MMT CO 2 EQ) -5.3 -9.0 -12.9 -17.9 -20.3 -65.4 Δ Direct A/C HFC (MMT CO 2 EQ) -6.9 -11.7 -16.7 -23.2 -26.3 -84.9 Δ CH 4 (MMT CO 2 EQ) 0.0 0.0 0.0 0.0 0.0 0.0 Δ N 2 O (MMT CO 2 EQ) 0.0 0.0 0.0 0.1 0.1 0.2 Δ Total GHG (MMT CO 2 EQ) -34.3 -75.9 -129.9 -186.4 -245.3 -671.8

Upstream Δ CO 2 (MMT CO 2 EQ) -6.7 -15.6 -27.5 -39.7 -53.3 -142.8 Δ CH 4 (MMT CO 2 EQ) -0.8 -1.9 -3.3 -4.7 -6.3 -17.0 Δ N 2 O (MMT CO 2 EQ) 0.0 -0.1 -0.1 -0.2 -0.3 -0.7 Δ Total GHG -7.5 -17.6 -30.9 -44.6 -59.9 -160.5

Total Δ Total GHG -41.8 -93.5 -160.8 -231.0 -305.2 -832.3 Δ Fuel Consumption (Annual, Billion gallons) -3.09 -7.3 -12.7 -18.4 -24.7 -66.1



### 5.7.2 6% Scenario

5.7.2.1 Standards and Achieved Levels

The program st andards are shown in Table 5-33 and the achieved levels are shown in Table 5-34.

Table 5-37: 6% Scenario Standards

MODEL

YEAR ANTICIPATED PA EMISSION LEVEL ANTICIPATED NPA EMISSION LEVEL ANTICIPATED MY EMISSION LEVEL 2012 269 362 304 2013 255 342 287 2014 241 323 270 2015 228 306 255 2016 216 290 241Draft Regulatory Impact Analysis

5-38 Table 5-38: 6% Scenario Achieved Levels

MODEL

YEAR ANTICIPATED PA EMISSION LEVEL ANTICIPATED NPA EMISSION LEVEL ANTICIPATED MY EMISSION LEVEL 2012 270 375 309 2013 258 352 292 2014 244 332 275 2015 230 313 259 2016 216 290 241

5.7.2.2 Results

Re sults a re shown relative to the same re ference scenario as the proposed EPA program. Both calendar year and model year lifetime results are shown.

Table 5-39 –CY GHG Emissions and Fuel Consumption vs. Reference Case
CY 2020 CY 2030 CY 2040 CY 2050 Downstream Δ CO 2 excluding indirect A/C controls (MMT CO 2 EQ) -146.8 -290.8 -377.2 -473.3 Δ Indirect A/C CO 2 (MMT CO 2 EQ) -11.0 -21.3 -27.5 -34.4 Δ Direct A/C HFC (MMT CO 2 EQ) -13.5 -27.2 -32.1 -34.9 Δ CH 4 (MMT CO 2 EQ) 0.0 0.0 0.0 0.0 Δ N 2 O (MMT CO 2 EQ) 0.0 0.0 0.1 0.1 Δ Total GHG (MMT CO 2 EQ) -171.3 -339.2 -436.6 -542.4

Upstream Δ CO 2 (MMT CO 2 EQ) -38.4 -75.9 -98.4 -123.4 Δ CH 4 (MMT CO 2 EQ) -5.5 -10.7 -14.0 -17.4 Δ N 2 O (MMT CO 2 EQ) -0.1 -0.4 -0.4 -0.5 Δ Total GHG -44.0 -87.0 -112.7 -141.4

Total Δ Total GHG -215.2 -426.2 -549.3 -683.9 Δ Fuel Consumption (Annual, Billion gallons) -17.8 -35.1 -45.5 -57.1Emissions Impacts

5-39 Table 5-40 –MY Lifetime GHG Emissions and Fuel Consumption vs. Reference Case

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Program Total Downstream Δ CO 2 excluding indirect A/C controls (MMT CO 2 EQ) -36.5 -96.8 -162.5 -225.7 -292.9 -814.4 Δ Indirect A/C CO 2 (MMT CO 2 EQ) -5.3 -9.0 -12.9 -17.9 -20.3 -65.4 Δ Direct A/C HFC (MMT CO 2 EQ) -6.9 -11.7 -16.7 -23.2 -26.3 -84.9 Δ CH 4 (MMT CO 2 EQ) 0.0 0.0 0.0 0.0 0.0 0.1 Δ N 2 O (MMT CO 2 EQ) 0.0 0.0 0.1 0.1 0.1 0.4 Δ Total GHG (MMT CO 2 EQ) -48.7 -117.5 -192.0 -266.7 -339.3 -964.2

Upstream Δ CO 2 (MMT CO 2 EQ) -10.2 -25.7 -42.6 -59.2 -76.1 -213.9 Δ CH 4 (MMT CO 2 EQ) -1.2 -3.1 -5.1 -7.1 -9.1 -25.5 Δ N 2 O (MMT CO 2 EQ) -0.1 -0.1 -0.2 -0.3 -0.4 -1.1 Δ Total GHG -11.4 -28.9 -47.9 -66.6 -85.6 -240.4

Total Δ Total GHG -60.2 -146.4 - 239.9 -333.3 -424.9 -1204.7 Δ Fuel Consumption (Annual, Billion gallons) -4.7 -11.9 -19.7 -27.4 -35.2 -99.0Draft Regulatory Impact Analysis

5-40 

### 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 vehi cles from model years 2012-2015 into a separate fleet. This separate fl eet is subject to a 25% less stringent standard than the manufacturer’s primary fleet (subject to various further constraints described in section III of the preamble and in the proposed rule itself).

Several manufacturer decisions and market place events determine the impacts of the TLAAS program. This appendix presents a sensitiv ity 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 presente d here range from 0 to 25 MMT of CO 2 emissions, in all cases the TLAAS program has a proportionally small impact (< 3%) on the total program benefits over the model years 2012-2016.

Under the estimation procedure used in the emission inventory analysis (as opposed to the bracketing analysis mentioned immediately ab ove), the TLAAS is projected to result in an approximately 3.4 MMT decrease in greenhouse gas benefits from this rule over the lifetime of vehicles manufactured in model years 2012-201 5 (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 th e very short lead times available).



### 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 po tentially eligible for TLAAS based on preliminary EPA analysis of projected domestic sales for model year 2009. These manufacturers are Porsche, Tata, Mazda, Mi tsubishi, Suzuki, Daimler, Subaru, BMW, Volkswagen, Hyundai, and Kia.

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.Emissions Impacts

5-41 The impacts of the program therefore partia lly depend on manufacturer eligibility.

Manufacturer Usage

By reducing the compliance burden, the TLAAS provides needed lead time flexibility to manufacturers in order to comply with the Light Duty Vehicle Greenhouse Gas Program in the short term, and provides needed lead time for these manufacturers to bring their entire fleet into compliance with the stringent 2016 MY standards. 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.

Vehicles in the TLAAS fleet may consume more fuel than comparably sized vehicles in the primary fleet. Assuming consumers place some weight on fuel economy when purchasing a vehicle, manufacturers with TLAAS fleets may thus place their vehicles at a competitive disadvantage in the marketplace.

Further, at the cessation of the TLAAS progr am, manufacturers will need their fleet to 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 wh en 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, manufacturer s may likewise choose to not fully utilize the TLAAS vehicle production volumes.

Size and Classification of the Vehicles Placed in the TLAAS Fleet

As the TLAAS program allows 25% addition al emissions over the footprint-based main fleet standards, the size of the vehicles placed in the TLAAS fleet is significant. 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 who utilized the TLAAS fleet for small vehicles would necessarily have a proportionally lower net impact. Similarl y, 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 Light Duty Vehicle Greenhouse Gas Program.Draft Regulatory Impact Analysis

5-42 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 manuf acturer could use their full allocation on their largest vehicle, could potentially increase sales of those vehicles to 100,000 over the four year period, 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 950 MMT GHG reduced over lifetime of model years 2012-2016 (i.e. impact of the TLAAS is zero), while the upper bound impact was 925 MMT GHG reduced over the same period. Thus, the maximum potential impact of the program, even under this most extreme scenario is approximately 25 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 proposed 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 DRIA chapter 5. In this analysis , all eleven manufacturers were assumed to use the default vehicle allocation schedule from the TLAAS. 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 5-41:TLAAS Default Vehicle Production Volumes

MODEL

YEAR 2012 2013 2014 2015

Sales Volume 40,000 30,000 20,000 10,000Emissions Impacts

5-43 The allocation was split evenly between cars and trucks for each manufacturer. The TLAAS fleet was assumed to emit as much CO 2 per mile as expected from the largest footprint car or truck in each manufacturer’s flee t. 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 score. This resulted in an emission impact of approximately 3.4 MMT CO 2 over the lifetime of the 2012-2015 MY vehicles.

The gram per mile impacts are listed here for each of these scenarios.

TLAAS impact (Grams CO 2 Emissions Per Mile) Model Year Lower Bound Scenario Upper Bound Scenario Estimate Used In Emission Analysis 2012 0 2.0 0.3 2013 0 1.5 0.2 2014 0 1.0 0.2 2015 0 0.5 0.1 2016 0 0 0.0Draft Regulatory Impact Analysis

5-44 References

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472.

1 Argonne National Laboratory. The Greenhouse Ga ses, Regulated Emissions, and Energy Use in Transportation (GREET) Model versions 1.7 and 1.8. http://www.transportation.anl.gov/modeling_simulation/GREET/

2 Mcculloch A.; Lindley A. A. From mine to refrigerati on: a life cycle inventory analysis of the production of HFC-134a .. ; International journal of refrigeration 2003, vol. 26, no8, pp. 865-872

3 Intergovernmental Panel on Climate Change. Chapter 2. Changes in Atmospheric Constituents and in Radiative Forcing. September 2007. http://www.ipcc. ch/pdf/assessment-report/ar4 /wg1/ar4-wg1-chapter2.pdf

4 EPA. Draft MOVES 2009. http://www.epa.gov/otaq/models/moves/index.htm

5 U.S. EPA 2009. Updated OMEGA Post-Processor Spreadsheet. August 15, 2009.

6 Updated Tables to MOVES in Docket. Samplevehiclepopulation and emissionbyage.

7 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

8 MOVES documentation and technical documents can be seen at http://www.epa.gov/otaq/models/moves/index.htm

.

9 Reference OMEGA Peer Review

10 U.S. EPA. Draft Regulatory Impact Analysis: Change s to Renewable Fuel Standard Program. Chapters 2 and 3.May 26, 2009.

11 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/

12 U.S. EPA. 2008. RFS2 Modified version of GREET1.7 Upstream Emissions Spreadsheet, October 31, 2008.

13 U.S. EPA, Achieved CO 2 standards worksheet. 2009.

14 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_downl oader.jsp?file=/staticfile s/DOT/NHTSA/Rulemaking/Ru les/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf

15 NHTSA. 2009. Average Fuel Economy Standards, Pass enger Cars and Light Trucks, Model Year 2011. http://www.nhtsa.dot.gov/portal/nhtsa_static_file_downl oader.jsp?file=/staticfile s/DOT/NHTSA/Rulemaking/Ru les/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf

16 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. August 2009..

17 Energy Information Administration. Annual Energy Outlook 2009. Supplemental Transportation Tables. April 2009. http://www.eia.doe.gov /oiaf/aeo/supplem ent/sup_tran.xlsEmissions Impacts

5-45 18 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_downl oader.jsp?file=/staticfile s/DOT/NHTSA/Rulemaking/Ru les/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf

19 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_downl oader.jsp?file=/staticfile s/DOT/NHTSA/Rulemaking/Ru les/Associated Files/CAFE_Updated_Final_Rule_MY2011.pdf

20 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. August 2009..

21 EPA. Emission Facts: Average Carbon Dioxide Em issions Resulting from Gasoline and Diesel Fuel. EPA420-F-05-001 February 2005

22 U.S. EPA. Baseline and Reference Fleet File, as documented in TSD chapter 1. August 2009..

23 Office of Energy Efficiency and Renewable Ener gy, U.S. Department of Energy. Transportation Energy Data Book: Edition 27. Chapter 4. 2008.

24 NHTSA. Vehicle Survivability and Travel Mileage Schedules. 2006.

25 Draft TSD Chapter 4

26 NHTSA. Vehicle Survivability and Travel Mileage Schedules. 2006.

27 Draft TSD Chapter 4

28 EPA. Final Technical Support Document.Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates

29 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

30 Draft RIA chapter 2.

31 Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy. Transportation Energy Data Book: Edition 27. 2008.

32 EPA. Final Technical Support Document. Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates

33 Average Carbon Dioxide Emissions Resulting from Gasoline and Diesel Fuel. EPA420-F-05-001 February 2005. http://www.epa.gov/otaq/climate/420f05001.htm

34 Craig Harvey, EPA, “Calculation of Upstream Emissions for the GHG Vehicle Rule.” 2009.

35 Control of Emissions of Air Pollution From Loco motive 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).

36 Control of Emissions From Nonroad Spark-Ignition Engi nes and Equipment, Final Rule (Federal Register Vol 73, No. 196, page 59034, October 8, 2008).Draft Regulatory Impact Analysis

5-46 37 Draft Regulatory Impact Analysis: Control of Emi ssions of Air Pollution from Category 3 Marine Diesel Engines, Chapter 3. This is available in Docket OAR-2007-0121at h ttp://www.regulations.gov/Vehicle Program Costs Including Fuel Consumption Impacts

6-1 

## CHAPTER 6: Vehicle Program Costs Including Fuel Consum ption Impacts

This chapter presents the costs of the proposed GHG vehicle program including the costs associated with addition of new technolo gy and savings associated with improved fuel consumption. In section 6.1, vehicle comp liance costs are presented on a per-car and pertruck basis for each manufacturer and the industr y as a whole. Vehicle compliance costs are also presented on an annual basis for each manu facturer 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 proposal. 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 proposed program. Such impacts are pres ented in Chapter 8 of this draft RIA.



### 6.1 Vehicle Program Costs

Chapter 4 of this draft RIA presents the outputs of the OMEGA model for the model year 2016. Here, we build on those results and calculate estimated costs for each model year beginning with 2012 and going through 2050. We do this both on a per-vehicle basis and an annual basis. Costs here include costs associat ed with the proposed A/C credit program. For details on the individual technology costs please refer to Chapter 3 of the draft joint TSD. For details on the OMEGA model inputs (i.e., how the individual technology costs are combined into package costs) please refer to Chapter 1 of this draft RIA. For details on the A/C costs, please refer to Chapter 2 of this draft RIA.



### 6.1.1 Vehicle Compliance Costs on a Per-Vehicle Basis

As stated above, Chapter 4 of this draft RIA presents the cost per vehicle for each manufacturer in the 2016 model year. Those 2016 MY costs are reproduced in Table 6-1. To esti m ate the cost per vehicle for model years 2012 through 2015, we first looked at the projected CO2 levels for each manufacturer’s fl eet for each year 2011 though 2016. Those CO2 levels are presented in Table 6-2 for cars and Table 6-3 for trucks. A The achieved CO2 levels for 2012-2015 were derived using the same process described in chapter 5 of the DRIA. Starting with the calculated manufacturer, vehicle class, and model year specific achieved standards, we estimated the cost effective envi ronmentally neutral credit trading based on the

A Note that the 2012-2015 CO2 levels are estimates based upon assumptions of manufacturer fleetwide CO2 averages in 2011, which are extrapolated from a 2008 base fleet. Consequently, the average CO2 emission levels for some manufacturers are potentially too hi gh for the 2011MY which make s 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 Suzuki as shown in Table 6-5 is approximately 60% of the 2016MY cost. In re ality, the transition be tween MY 2011 and MY 2016 may be significantly sm oother, and is likely to be smoother due to multiyear planning.Regulatory Impact Analysis

6-2 2016 achieved levels predicted by OMEGA. Based on this process, we projected the most likely achieved levels for 2012-2015 for each manufacturer.

There are some differences between cost e ffective achieved levels and the achieved levels shown in DRIA chapter 5. As shown he re, the cost effective achieved levels for the intermediate years were derived in the foll owing manner. MY 20 11 baseline CO2 was determined from the reference fleet file. 1 MY 2016 achieved CO 2 was determined from the OMEGA output described in DRIA chapter 4. To determine the intermediate years, an interpolation was performed between these two points. Two different forms of interpolation were used. For manufacturers that fully comply with the 2016 standard s, the change between 2011 and 2016 was weighted by the percent chan ge in their achieved target for each year (as determined in DRIA chapter 5). For the manufacturers that do not fully comply in 2016, these manufacturers improved by 20% of their total change each year.

Two manufacturers, Subaru 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. 2

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. This methodology is based, in part, on the credit carry-forward and carry-back provisions contained in the proposal. However, we must also remember that the technology costs and, subsequently, the package costs in the 2016 MY have undergone some learning effects as described in Chapter 3 of the draft joint TSD. We compared the 2016 MY package costs to each of the intervening years and the results, on a pe rcentage basis, are shown in Table 6-4. We have also done this f o r the years following 2016 to reflect the effe cts of the near term and long term ICMs as described in Chapter 3 of the draft 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 $969 in the 2016 MY while achieving a CO2 average of 240 g/mi; for the 2011 and 2012 MYs, GM cars are projec ted to achieve a CO2 average of 306 and 276, respectively. We can apply the ratio (306-276)/(306-240)=0.45 to GM’s 2016 cost of $969, and then apply the 2012 relative to 2016 cost factor of 117%, to arrive at an estimated 2012 cost of $507. C We then carry out this process 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 for cars and
truc ks c ombined. Table 6-8 shows the industry average cost p er car, cost per truck, and cost per vehicle (car/truck combined) fo r the 2012 and later model years. D

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.

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 no t include maintenance costs a ssociated with low friction lubes and low rolling resistenance tires. We include high er new vehicle costs for thes e latter items but do notVehicle Program Costs Including Fuel Consumption Impacts

6-3 Table 6-1 Cost per Car and Truck, including A/C, for the 2016 MY (2007 dollars)

MANUFACTURER $/CAR $/TRUCK BMW $1,700 $1,664 Chrysler $1,331 $1,505 Daimler $1,630 $1,356 Ford $1,434 $1,485 GeneralMotors $969 $1,781 Honda $606 $695 Hyundai $739 $1,679 Kia $741 $1,177 Mazda $946 $1,029 Mitsubishi $1,067 $1,263 Nissan $1,012 $1,193 Porsche $1,548 $666 Subaru $902 $1,328 Suzuki $1,093 $1,263 Tata $1,269 $673 Toyota $599 $435 Volkswagen $1,626 $949 Overall $968 $1,213

Table 6-2 Projected CO2 Levels for MYs 2011-2016, Cars Only (g/mi CO2)

MANUFACTURER 2011MY 2012MY 2013MY 2014MY 2015MY 2016MY

BMW 308.0 292.5 278. 2 263.3 248.4 235.3 Chrysler 313.1 279.0 270.2 261.3 250.0 219.6 Daimler 308.6 293.1 278.8 263.9 249.0 236.0 Ford 311.0 276.5 26 8.3 259.5 246.2 226.4 GeneralMotors 305.8 276. 4 267.7 259.1 246.0 239.8 Honda 263.2 251.2 24 0.3 228.9 217.4 207.7 Hyundai 282.2 260.0 25 1.8 243.5 232.3 226.8 Kia 280.5 263.0 254. 6 245.8 234.4 223.1 Mazda 286.8 260.1 25 1.8 243.0 231.4 221.0 Mitsubishi 287.1 258.1 246.5 231.0 215.6 211.7 Nissan 278.4 262.7 25 5.1 246.9 235.8 217.4 Porsche 339.1 317.9 29 7.9 277.3 256.7 237.9 Subaru 304.0 272.9 25 8.6 249.9 241.3 231.3 Suzuki 284.5 247.0 23 8.4 229.7 217.9 203.3 Tata 347.3 328.9 31 1.8 294.1 276.4 260.4 Toyota 250.9 240.5 23 1.3 221.5 211.7 203.7 Volkswagen 291.5 278.3 266.4 253.8 241.2 230.4 Overall 285.1 264.0 25 4.5 244.4 232.4 220.9

account for higher replacement costs during vehicle lifetim es even though oil is changed many times and tires are changed once or twice. We inte nd to include these maintenance costs a nd savings in our final rule analysis.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 357.3 341.2 326. 5 311.2 295.8 282.4 Chrysler 386.7 365.7 354.6 344.0 326.5 314.1 Daimler 423.0 397.6 373.7 349.0 324.3 301.7 Ford 413.4 382.1 37 0.3 360.2 342.5 316.6 GeneralMotors 410.5 392. 7 380.8 369.3 351.0 308.4 Honda 346.7 338.3 33 1.2 323.5 315.8 310.0 Hyundai 357.6 330.6 32 0.3 308.4 293.1 257.6 Kia 353.9 339.2 329. 1 317.8 302.8 291.4 Mazda 332.7 326.0 31 4.9 302.7 287.4 267.9 Mitsubishi 337.2 327.2 322.4 317.9 311.5 307.0 Nissan 387.3 370.3 35 8.9 348.7 331.1 321.5 Porsche 369.6 361.0 35 3.7 345.8 337.9 332.0 Subaru 334.9 294.4 27 5.9 264.9 254.0 240.9 Suzuki 356.7 314.7 30 6.1 296.7 283.8 280.7 Tata 362.5 355.2 34 9.3 342.7 336.1 331.5 Toyota 353.4 345.8 33 9.6 332.8 325.9 321.0 Volkswagen 417.6 398.9 381.5 363.4 345.3 329.2 Overall 383.6 365.3 35 5.5 345.8 331.7 310.9

Table 6-4 Package Costs Measured Relative to the Package Costs for the 2016MY

YEAR PACKAGE COSTS RELATIVE TO 2016 2012 117% 2013 115% 2014 108% 2015 102% 2016 100% 2017 100% 2018 100% 2019 100% 2020 100% 2021 100% 2022+ 92%Vehicle Program Costs Including Fuel Consumption Impacts

6-5 Table 6-5 Cost per Car, including A/C, by Manufacturer (2007 dollars)

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY
BMW $426 $801 $1,130 $1,425 $1,700 Chrysler $571 $703 $797 $918 $1,331 Daimler $408 $768 $1,084 $1,367 $1,630 Ford $686 $831 $945 $1,123 $1,434 GeneralMotors $507 $643 $740 $897 $969 Honda $155 $288 $406 $512 $606 Hyundai $348 $466 $558 $680 $739 Kia $265 $385 $484 $608 $741 Mazda $451 $578 $681 $814 $946 Mitsubishi $483 $661 $858 $1,035 $1,067 Nissan $306 $446 $566 $724 $1,012 Porsche $381 $725 $1,022 $1,289 $1,548 Subaru $454 $648 $726 $796 $902 Suzuki $593 $713 $798 $916 $1,093 Tata $315 $596 $841 $1,060 $1,269 Toyota $155 $286 $404 $509 $599 Volkswagen $412 $770 $1,086 $1,369 $1,626 Overall $374 $531 $663 $813 $968

Table 6-6 Cost per Truck, including A/C, by Manufacturer (2007 dollars)

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY
BMW $420 $786 $1,109 $1,398 $1,664 Chrysler $510 $763 $956 $1,276 $1,505 Daimler $333 $634 $895 $1,128 $1,356 Ford $564 $760 $882 $1,112 $1,485 GeneralMotors $364 $596 $777 $1,061 $1,781 Honda $188 $337 $475 $599 $695 Hyundai $532 $720 $893 $1,107 $1,679 Kia $325 $536 $735 $984 $1,177 Mazda $125 $326 $515 $736 $1,029 Mitsubishi $488 $709 $874 $1,101 $1,263 Nissan $363 $592 $758 $1,042 $1,193 Porsche $179 $323 $455 $574 $666 Subaru $673 $959 $1,069 $1,169 $1,328 Suzuki $820 $967 $1,079 $1,238 $1,263 Tata $186 $330 $465 $586 $673 Toyota $120 $213 $300 $378 $435 Volkswagen $237 $446 $629 $794 $949 Overall $358 $539 $682 $886 $1,213Regulatory Impact Analysis

6-6 Table 6-7 Cost per Vehicle (car/truck combined), including A/C, by Manufacturer (2007 dollars)

MANUFACTURER 2012MY 2013MY 2014MY 2015MY 2016MY
BMW $424 $797 $1,124 $1,418 $1,691 Chrysler $534 $738 $887 $1,080 $1,408 Daimler $376 $710 $998 $1,259 $1,509 Ford $641 $806 $922 $1,119 $1,452 GeneralMotors $443 $622 $756 $968 $1,311 Honda $167 $305 $428 $538 $632 Hyundai $384 $516 $623 $759 $907 Kia $304 $473 $635 $813 $973 Mazda $384 $529 $651 $801 $959 Mitsubishi $484 $670 $861 $1,045 $1,095 Nissan $328 $501 $639 $829 $1,070 Porsche $354 $661 $944 $1,201 $1,444 Subaru $527 $747 $831 $905 $1,023 Suzuki $683 $806 $892 $1,019 $1,147 Tata $281 $528 $738 $935 $1,117 Toyota $142 $260 $368 $465 $545 Volkswagen $376 $712 $1,003 $1,265 $1,508 Overall $368 $534 $670 $838 $1,050

Table 6-8 Industry Average Cost per Car, Truck, and Combined by Year (2007 dollars)

YEAR $/CAR $/TRUCK $/VEHICLE
2012 $374 $358 $368 2013 $531 $539 $534 2014 $663 $682 $670 2015 $813 $886 $838 2016 $968 $1,213 $1,050 2017 $968 $1,213 $1,047 2018 $968 $1,213 $1,044 2019 $968 $1,213 $1,042 2020 $968 $1,213 $1,040 2021 $968 $1,213 $1,039 2022 $890 $1,116 $955 2023 $890 $1,116 $955 2024 $890 $1,116 $955 2025 $890 $1,116 $955 2026 $890 $1,116 $954 2027 $890 $1,116 $954 2028 $890 $1,116 $954 2029 $890 $1,116 $953 2030 $890 $1,116 $953 2031 $890 $1,116 $953 2032 $890 $1,116 $953 2033 $890 $1,116 $953 2034 $890 $1,116 $953 2035 $890 $1,116 $953Vehicle Program Costs Including Fuel Consumption Impacts

6-7 2036 $890 $1,116 $953 2037 $890 $1,116 $953 2038 $890 $1,116 $953 2039 $890 $1,116 $953 2040 $890 $1,116 $953 2041 $890 $1,116 $953 2042 $890 $1,116 $953 2043 $890 $1,116 $953 2044 $890 $1,116 $953 2045 $890 $1,116 $953 2046 $890 $1,116 $953 2047 $890 $1,116 $953 2048 $890 $1,116 $953 2049 $890 $1,116 $953 2050 $890 $1,116 $953



### 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,

re spectively, we can calculate annual costs by multiplying by estimated sales. Table 6-9 sh ow s projected car sales by manufacturer for model years 2012-2016. Table 6-10 shows
p r ojected truck sales by manufacturer for model years 2012-2016. Table 6-11 shows combi ned 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 b y the entire industry for cars, trucks, and to tal for the years 2012 through 2050 with net present values using both a 3 percent and a 7 percent discount rate. E

E Note that the vehicle compliance costs presented here do not include costs associated with upgrading testing facilities to accommodate N2O testing. While including t hose costs would likely have very little impact on the costs presented here for new vehicle technology, the costs should be included and we intend to do so in the final rule analysis.Regulatory Impact Analysis

6-8 Table 6-9 Estimated Annual Car Sales by Manufacturer (# of Units)

MANUFACTURER 2012MY 2013M Y 2014MY 2015MY 2016MY

BMW 283,471 323,191 352,248 371,668 380,804 Chrysler 178,635 175,072 169,046 136,583 138,602 Daimler 1,348,260 1,424,3 45 1,446,097 1,503,175 1,511,354 Ford 149,192 135,946 135,141 130,588 131,022 General Motors 1,484,580 1,62 0,301 1,708,507 1,789,813 1,820,234 Honda 1,155,008 1,352,607 1,493,242 1,562,496 1,593,092 Hyundai 580,538 558,975 562,862 590,579 596,891 Kia 22,878 32,822 35,534 40,586 41,584 Mazda 304,524 309,667 331,198 347,533 351,081 Mitsubishi 313,489 318,669 338,487 340,069 345,489 Nissan 172,172 190,133 204,335 229,562 235,205 Porsche 64,843 61,169 56,478 52,368 53,459 Subaru 958,696 1,031,569 1,073,307 1,104,272 1,123,486 Suzuki 31,605 35,813 38,470 36,175 37,064 Tata 86,537 86,220 81,480 75,965 77,427 Toyota 1,697,762 1,862,201 1,985,033 2,114,273 2,154,115 Volkswagen 423,433 458,641 467,885 465,263 476,699 Industry 9,255,624 9,977,3 41 10,479,350 10,890,967 11,067,608

Table 6-10 Estimated Annual Truck Sales by Manufacturer (# of Units)

MANUFACTURER 2012MY 2013M Y 2014MY 2015MY 2016MY

BMW 142,861 141,949 144,022 139,034 135,569 Chrysler 282,472 244,441 222,545 112,381 109,674 Daimler 1,025,575 1,107,5 35 1,202,141 1,230,889 1,202,442 Ford 88,877 77,593 76,913 73,425 74,135 General Motors 1,209,642 1,32 0,438 1,342,856 1,355,820 1,322,512 Honda 669,948 724,500 687,208 691,641 675,173 Hyundai 141,028 135,751 134,274 132,978 129,763 Kia 42,168 46,256 53,335 48,583 47,372 Mazda 78,789 74,596 72,192 69,894 68,310 Mitsubishi 71,930 71,136 66,919 59,361 57,998 Nissan 109,351 115,684 125,189 113,366 110,541 Porsche 10,098 11,440 9,124 7,349 7,171 Subaru 477,897 478,571 474,558 454,488 444,471 Suzuki 20,767 20,639 19,379 16,822 17,273 Tata 30,391 29,750 30,545 27,204 26,526 Toyota 1,019,375 1,019,048 1,045,671 1,081,323 1,057,837 Volkswagen 109,415 100,952 104,023 102,743 100,186 Industry 5,530,583 5,720,2 80 5,810,895 5,717,300 5,586,953Vehicle Program Costs Including Fuel Consumption Impacts

6-9 Table 6-11 Estimated Annual Costs by Manufacturer, including A/C, for Cars ($Millions of 2007 dollars)

MANUFACTURER 2012MY 2013M Y 2014MY 2015MY 2016MY

BMW $120 $260 $400 $530 $650 Chrysler $100 $120 $130 $130 $180 Daimler $70 $150 $220 $310 $380 Ford $930 $1,180 $1,370 $1,690 $2,170 General Motors $750 $1,040 $1,260 $1,600 $1,760 Honda $180 $390 $610 $800 $960 Hyundai $200 $260 $310 $400 $440 Kia $80 $120 $160 $210 $260 Mazda $140 $180 $230 $280 $330 Mitsubishi $30 $40 $50 $50 $60 Nissan $290 $460 $610 $800 $1,140 Porsche $10 $30 $40 $50 $60 Subaru $70 $90 $100 $100 $120 Suzuki $50 $60 $60 $70 $80 Tata $10 $20 $30 $40 $50 Toyota $260 $530 $800 $1,080 $1,290 Volkswagen $170 $350 $510 $640 $780 Industry $3,460 $5,300 $6,950 $8,850 $10,710

Table 6-12 Estimated Annual Costs by Manufacturer , including A/C, for Trucks ($Millions of 2007 dollars)

MANUFACTURER 2012MY 2013M Y 2014MY 2015MY 2016MY

BMW $60 $110 $160 $190 $230 Chrysler $140 $190 $210 $140 $170 Daimler $40 $70 $110 $130 $150 Ford $580 $840 $1,060 $1,370 $1,790 General Motors $440 $790 $1,040 $1,440 $2,360 Honda $130 $240 $330 $410 $470 Hyundai $80 $100 $120 $150 $220 Kia $30 $40 $50 $70 $80 Mazda $10 $20 $30 $40 $60 Mitsubishi $0 $10 $10 $10 $10 Nissan $170 $280 $360 $470 $530 Porsche $0 $10 $10 $10 $10 Subaru $60 $70 $80 $90 $100 Suzuki $20 $30 $30 $30 $30 Tata $10 $20 $20 $30 $30 Toyota $120 $220 $310 $410 $460 Volkswagen $30 $50 $70 $80 $100 Industry $1,980 $3,090 $3,960 $5,060 $6,780Regulatory Impact Analysis

6-10 Table 6-13 Estimated Annual Costs by Manufacturer, including A/C, for Cars and Trucks Combined ($Millions of 2007 dollars)

MANUFACTURER 2012MY 2013M Y 2014MY 2015MY 2016MY

BMW $180 $370 $560 $720 $880 Chrysler $240 $310 $340 $270 $350 Daimler $110 $220 $330 $440 $530 Ford $1,510 $2,020 $2,430 $3,060 $3,960 General Motors $1,190 $1,830 $2,300 $3,040 $4,120 Honda $310 $630 $940 $1,210 $1,430 Hyundai $280 $360 $430 $550 $660 Kia $110 $160 $210 $280 $340 Mazda $150 $200 $260 $320 $390 Mitsubishi $30 $50 $60 $60 $70 Nissan $460 $740 $970 $1,270 $1,670 Porsche $10 $40 $50 $60 $70 Subaru $130 $160 $180 $190 $220 Suzuki $70 $90 $90 $100 $110 Tata $20 $40 $50 $70 $80 Toyota $380 $750 $1,110 $1,490 $1,750 Volkswagen $200 $400 $580 $720 $880 Industry $5,440 $8,390 $10,910 $13,910 $17,490

Table 6-14 Annual Sales & Costs for Cars & Trucks (Monetary Values in 2007 dollars)

YEAR CAR SALES TRUCK SALES CAR COSTS ($MILLIONS) TRUCK COSTS ($MILLIONS) TOTAL COSTS ($MILLIONS) 2012 9,255,624 5,530,583 $3,460 $1,980 $5,440 2013 9,977,341 5,720,280 $5,300 $3,090 $8,390 2014 10,479,350 5,810,895 $6,950 $3,960 $10,910 2015 10,890,967 5,717,300 $8,850 $5,060 $13,910 2016 11,067,608 5,586,953 $10,710 $6,780 $17,490 2017 11,398,169 5,403,989 $11,030 $6,560 $17,590 2018 11,684,257 5,282,864 $11,310 $6,410 $17,720 2019 11,948,850 5,191,459 $11,570 $6,300 $17,870 2020 12,190,082 5,088,666 $11,800 $6,170 $17,970 2021 12,184,615 5,018,346 $11,790 $6,090 $17,880 2022 12,224,907 4,966,015 $10,880 $5,540 $16,420 2023 12,393,064 4,990,624 $11,030 $5,570 $16,600 2024 12,615,769 5,057,793 $11,230 $5,640 $16,870 2025 12,867,956 5,154,435 $11,450 $5,750 $17,200 2026 13,056,941 5,196,282 $11,620 $5,800 $17,420 2027 13,146,812 5,220,321 $11,700 $5,830 $17,530 2028 13,245,293 5,211,789 $11,790 $5,820 $17,610 2029 13,390,625 5,172,196 $11,920 $5,770 $17,690 2030 13,550,044 5,250,009 $12,060 $5,860 $17,920 2031 13,653,024 5,289,909 $12,150 $5,900 $18,050 2032 13,756,787 5,330,112 $12,250 $5,950 $18,200 2033 13,861,339 5,370,621 $12,340 $5,990 $18,330 2034 13,966,685 5,411,438 $12,430 $6,040 $18,470Vehicle Program Costs Including Fuel Consumption Impacts

6-11 2035 14,072,832 5,452,565 $12,530 $6,080 $18,610 2036 14,179,785 5,494,004 $12,620 $6,130 $18,750 2037 14,287,552 5,535,759 $12,720 $6,180 $18,900 2038 14,396,137 5,577,830 $12,820 $6,220 $19,040 2039 14,505,548 5,620,222 $12,910 $6,270 $19,180 2040 14,615,790 5,662,936 $13,010 $6,320 $19,330 2041 14,726,870 5,705,974 $13,110 $6,370 $19,480 2042 14,838,794 5,749,339 $13,210 $6,420 $19,630 2043 14,951,569 5,793,034 $13,310 $6,460 $19,770 2044 15,065,201 5,837,061 $13,410 $6,510 $19,920 2045 15,179,696 5,881,423 $13,510 $6,560 $20,070 2046 15,295,062 5,926,122 $13,620 $6,610 $20,230 2047 15,411,305 5,971,160 $13,720 $6,660 $20,380 2048 15,528,430 6,016,541 $13,820 $6,710 $20,530 2049 15,646,447 6,062,267 $13,930 $6,770 $20,700 2050 15,765,360 6,108,340 $14,030 $6,820 $20,850 NPV, 3% $257,680 $132,320 $390,000 NPV, 7% $141,860 $74,700 $216,550



### 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 proposal using the costs shown in Table 6-14 and the emissions

r e ductions described in Chapter 5. We have calculated the cost per metric ton of GHG emissions reductions in the years 2020, 2030, 2040, and 2050 using the annual vehiclecompliance costs and emission reductions for each of those years. The value in 2050 represents the long-term cost per ton of the emissions reduced. Note that we have not included the savings associated with reduced fuel consumption, nor any of the other benefits of this proposal in the cost per ton calculations. If we were to include fuel savings in the cost estimates, the cost per ton would be less than $0, since the fuel savi ngs outweigh the costs (see Section 6.3 below). With regard to the proposed CH4 and N2O standards, since these standards would be emissions caps designed to ensure manufacturers do not backslide from current levels, we have not estimated costs asso ciated with the standards (since the standards would not require any change from current practices nor do we estimate they would result in emissions reductions).

The results for CO2e costs per ton under the proposed vehicle program are shown in Table 6-15.Regulatory Impact Analysis

6-12 Table 6-15 Annual Cost Per Metric Ton of CO2e Reduced, in $2007 dollars

YEAR COST ($MILLIONS) * CO2-EQUIVLANET REDUCTION (MILLION METRIC TONS) COST PER TON

2020 $18,000 170 $110 2030 $17,900 320 $60 2040 $19,300 420 $50 2050 $20,900 520 $40

* Costs here include vehicle compliance costs and do not in clude any fuel savings (discussed in section 6.3) or other benefits of this proposal (discussed in Chapter 8).



### 6.3 Fuel Consumption Impacts

In this section, we present the impact of the proposed program 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 ga llons of fuel expected to be consumed as a result of the proposal.

The proposed CO2 standards would result in significant improvements in the fuel efficiency of affected vehicles. Drivers of those vehicles would see corresponding savings associated with reduced fuel expenditures. We have estimated the impacts on fuel consumption for both the proposed tailpipe CO2 standards and the proposed A/C credit program. To do this, fuel consumption is calculated using both current CO2 emission levels and the proposed CO2 standards. The difference between these estimates represents the net savings from the proposed CO2 standards.

The expected impacts on fuel consumption are shown in Table 6-16. The gallons sho w n in the tables reflect impacts from the pr oposed CO2 standards, including the proposed A/C credit program, and include increased consumption resulting from the rebound effect. Using these fuel consumption estimates, we can calculate the monetized fuel savings associated with the proposed CO2 standards. To do this, we multiply reduced fuel consumption in each year by the corresponding estimated average fuel price in that year, using the reference case taken from the AEO 2009. AEO is the government consensus estimate used by NHTSA and many other government agencies to estimate the projected price of fuel. We have included all fuel taxes in th ese estimates since these are the prices paid by consumers. As such, the savings shown reflec t savings to the consumer. These results are also shown in Table 6-16. Note that we present the mon eti zed fuel sa vings using pre-tax fuel prices in Chapter 8 of this draft RIA. The fuel savings based on pre-tax fuel prices reflect the societal savings in contrast to the consumer savings presented in Table 6-16. Also in Chapter 8, w e present the benefit-cost of the proposal an d, for that reason, present the fuel impacts as negative costs of the program while here we present them as positive savings.Vehicle Program Costs Including Fuel Consumption Impacts

6-13 Table 6-16 Annual Fuel Consumption Impacts of the Proposed Vehicle Standards and A/C Credit Programs (Monetary values in 2007 dollars)

YEAR GALLONS (MILLIONS) FUEL PRICE INCLUDING

TAXES ($/GALLON) SAVINGS ($MILLIONS)

2012 500 $2.70 $1,400 2013 1,300 $2.85 $3,800 2014 2,400 $3.00 $7,200 2015 3,900 $3.16 $12,400 2016 5,900 $3.27 $19,400 2017 7,900 $3.39 $26,700 2018 9,800 $3.48 $34,100 2019 11,600 $3.56 $41,300 2020 13,400 $3.62 $48,400 2021 15,000 $3.64 $54,600 2022 16,600 $3.67 $60,800 2023 18,100 $3.69 $66,700 2024 19,500 $3.69 $72,000 2025 20,900 $3.68 $76,800 2026 22,100 $3.72 $82,200 2027 23,300 $3.72 $86,500 2028 24,300 $3.76 $91,600 2029 25,300 $3.87 $97,800 2030 26,200 $3.82 $100,000 2031 27,000 $3.84 $103,800 2032 27,800 $3.86 $107,500 2033 28,600 $3.88 $111,100 2034 29,400 $3.90 $114,700 2035 30,100 $3.92 $118,300 2036 30,900 $3.95 $121,900 2037 31,600 $3.97 $125,500 2038 32,400 $3.99 $129,200 2039 33,200 $4.01 $133,000 2040 33,900 $4.03 $136,800 2041 34,700 $4.05 $140,700 2042 35,500 $4.07 $144,700 2043 36,300 $4.10 $148,800 2044 37,200 $4.12 $153,100 2045 38,000 $4.14 $157,400 2046 38,900 $4.16 $161,900 2047 39,800 $4.19 $166,500 2048 40,700 $4.21 $171,200 2049 41,600 $4.23 $176,100 2050 42,600 $4.25 $181,000 NPV, 3% $1,850,200 NPV, 7% $826,900Regulatory Impact Analysis

6-14 As shown in Table 6-16, we are projecting that co n sumers would realize very large fuel savings as a result of the standards contained in today’s proposal. There are several ways to view this value. Some, as demonstrated be low in Chapter 8 of this draft RIA, view these fuel savings as a reduction in the cost of ow ning a vehicle, whose full benefits consumers realize. This approach assumes that, regardle ss how consumers in fact make their decisions on how much fuel economy to purchase, they will gain these fuel savings. Another view says that consumers do not necessarily value fuel saving s as equal to the results of this calculation. 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 draft RIA.

If we limit the analysis to the five model years 2012-2016—in other words, the fuel consumption savings during the lifetimes of thos e five model years, the results would be as shown in Table 6-17.
Table 6-17 Annual Fuel Savings for 2012-2016 MY Ve hicles Using Pre-tax Fuel Prices ($Millions of 2007 dollars)

YEAR 2012MY 2013MY 2014MY 2015MY 2016MY SUM 2012 $1,300 $1,300 2013 $1,300 $2,100 $3,500 2014 $1,400 $2,200 $3,100 $6,700 2015 $1,400 $2,200 $3,200 $4,600 $11,500 2016 $1,400 $2,200 $3,200 $4,700 $6,500 $18,100 2017 $1,400 $2,200 $3,200 $4,700 $6,600 $18,100 2018 $1,400 $2,200 $3,200 $4,700 $6,600 $18,000 2019 $1,300 $2,100 $3,100 $4,600 $6,500 $17,600 2020 $1,200 $2,000 $3,000 $4,400 $6,300 $17,000 2021 $1,100 $1,900 $2,800 $4,200 $6,000 $16,100 2022 $1,100 $1,700 $2,600 $4,000 $5,700 $15,100 2023 $900 $1,600 $2,400 $3,700 $5,400 $14,100 2024 $800 $1,400 $2,200 $3,400 $5,000 $12,800 2025 $600 $1,200 $2,000 $3,100 $4,600 $11,500 2026 $500 $1,000 $1,700 $2,800 $4,200 $10,200 2027 $400 $800 $1,400 $2,300 $3,700 $8,700 2028 $300 $700 $1,100 $2,000 $3,200 $7,300 2029 $300 $600 $1,000 $1,700 $2,700 $6,200 2030 $200 $400 $800 $1,300 $2,200 $5,000 2031 $200 $300 $600 $1,100 $1,800 $4,000 2032 $100 $300 $500 $900 $1,500 $3,300 2033 $100 $200 $400 $700 $1,200 $2,600 2034 $100 $200 $300 $600 $1,000 $2,100 2035 $100 $200 $300 $500 $800 $1,700 2036 $100 $100 $200 $400 $600 $1,400 2037 $100 $100 $200 $300 $500 $1,200 2038 $0 $100 $200 $300 $400 $1,000 2039 $0 $100 $100 $200 $400 $800 2040 $0 $100 $100 $200 $300 $700 2041 $0 $0 $100 $100 $300 $600 2042 $0 $0 $100 $100 $200 $500 2043 $0 $0 $100 $100 $200 $400Vehicle Program Costs Including Fuel Consumption Impacts

6-15 2044 $0 $0 $100 $100 $100 $300 2045 $0 $0 $100 $100 $100 $300 2046 $0 $0 $0 $100 $100 $300 2047 $0 $0 $0 $100 $100 $200 2048 $0 $0 $0 $100 $100 $200 2049 $0 $0 $0 $0 $100 $200 2050 $0 $0 $0 $0 $100 $100 NPV, 3% $15,600 $24,400 $34, 800 $49,800 $68,500 $193,100 NPV, 7% $12,100 $19,000 $27, 200 $38,900 $53,700 $150,900



### 6.4 Vehicle Program Cost Summary

The vehicle program costs consist of the vehi cle compliance costs and the fuel savings (fuel savings are expressed here as negative fuel costs) that would result from the reduction in fuel consumption. These costs are summarized in Table 6-18.

Table 6-18 Annual Vehicle Pr ogram Costs Including Fuel Costs Using Post-Tax Fuel Prices ($Millions of 2007 dollars)

YEAR VEHICLE COMPLIANCE COSTS FUEL COSTS (NEGATIVE COSTS

ARE SAVINGS) TOTAL COSTS (NEGATIVE COSTS

ARE SAVINGS) 2012 $5,400 -$1,400 $4,000 2013 $8,400 -$3,800 $4,600 2014 $10,900 -$7,200 $3,700 2015 $13,900 -$12,400 $1,500 2016 $17,500 -$19,400 -$1,900 2017 $17,600 -$26,700 -$9,100 2018 $17,700 -$34,100 -$16,400 2019 $17,900 -$41,300 -$23,400 2020 $18,000 -$48,400 -$30,400 2021 $17,900 -$54,600 -$36,700 2022 $16,400 -$60,800 -$44,400 2023 $16,600 -$66,700 -$50,100 2024 $16,900 -$72,000 -$55,100 2025 $17,200 -$76,800 -$59,600 2026 $17,400 -$82,200 -$64,800 2027 $17,500 -$86,500 -$69,000 2028 $17,600 -$91,600 -$74,000 2029 $17,700 -$97,800 -$80,100 2030 $17,900 -$100,000 -$82,100 2031 $18,100 -$103,800 -$85,700 2032 $18,200 -$107,500 -$89,300 2033 $18,300 -$111,100 -$92,800 2034 $18,500 -$114,700 -$96,200 2035 $18,600 -$118,300 -$99,700 2036 $18,800 -$121,900 -$103,100 2037 $18,900 -$125,500 -$106,600 2038 $19,000 -$129,200 -$110,200 2039 $19,200 -$133,000 -$113,800Regulatory Impact Analysis

6-16 2040 $19,300 -$136,800 -$117,500 2041 $19,500 -$140,700 -$121,200 2042 $19,600 -$144,700 -$125,100 2043 $19,800 -$148,800 -$129,000 2044 $19,900 -$153,100 -$133,200 2045 $20,100 -$157,400 -$137,300 2046 $20,200 -$161,900 -$141,700 2047 $20,400 -$166,500 -$146,100 2048 $20,500 -$171,200 -$150,700 2049 $20,700 -$176,100 -$155,400 2050 $20,900 -$181,000 -$160,100 NPV, 3% $390,000 -$1,850,200 -$1,460,200 NPV, 7% $216,600 -$826,900 -$610,300Vehicle Program Costs Including Fuel Consumption Impacts

6-17 References

All references can be found in th e EPA DOCKET: EPA-HQ-OAR-2009-0472.

1 U.S. EPA. Baseline and Refere nce Fleet File, as documented in TSD chapter 1. August 2009.

2 US EPA 2009. 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 (NO X ), sulfur oxides (SO X ), carbon monoxide and air toxics. These pollutants would not be directly regulated by the proposed standards, but the proposed standards would affect emissions of these pollutants and precursors. Reductions in these pollutants would be co-benefits of this proposal (that is, benefits in addition to the benefits of reduced GHGs).

7.1.1.1 Particulate Matter

7.1.1.1.1 Background

Part iculate m atter (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 tracheobronc hial and alveolar regions) of the respiratory tract (referred to as thoracic particles). Current national ambient air quality standards (NAAQS) use PM 2.5 as the indicator for fine particles (with PM 2.5 referring to particles with a nominal mean aerodynamic diameter less than or equal to 2.5 μm), and use PM 10 as the indicator for purposes of regu lating the coarse fraction of PM 10 (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 PM 10-2.5 ). Ultrafine particles are a subset of fine particles, generally less than 100 nanometers (0.1 μ m) in aerodynamic diameter.

Particles span many sizes and shapes and consist of hundreds of different chemicals. Particles originate from sources and are also formed through atmospheri c chemical reactions; the former are often referred to as “primary” part icles, and the latter as “secondary” particles. In addition, there are also physical, non-chem ical 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 partic le’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., SO X , NO X and VOCs) in the atmosphere. The chemical and physical properties of PM 2.5 may vary greatly with time, region, meteorology and source category. Thus, PM 2.5 may include a complex mixture of different pollutants including sulfates, nitrates, organic compounds, elem ental 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. 1Draft Regulatory Impact Analysis

7-2 7.1.1.1.2 Health Effects of PM

This sec tion provides a summary of the h ealth effects associated with exposure to ambient concentrations of PM. A The information in this section is based on the data and conclusions in the PM Air Quality Criteria Document (PM AQCD) and PM Staff Paper prepared by the U.S. Environmental Protection Agency (EPA). B,2,3 We also present additional recent studies published after the cut-off date for the PM AQCD. 4, C Taken together this information supports the conclusion that exposure to ambient concentrations of PM are associated with adverse health effects.

7.1.1.1.2.1 Short-term Exposure Morta lity and M orbidity Studies

As discussed in the PM AQCD, short-term exposure to PM 2.5 is associated with premature mortality from cardiopulmonary diseases, 5 hospitalization and emergency department visits for cardiopulmonary diseases, 6 increased respiratory symptoms, 7 decreased lung function 8 and physiological changes or biomarkers for cardiac changes. 9,10 In addition, the PM AQCD described a limited body of ne w evidence from epidemiologic studies for potential relationships between short term exposu re to PM and health endpoints such as low birth weight, preterm birth, and neonatal and infant mortality. 11

Among the studies of effects associat ed with short-term exposure to PM 2.5 , several specifically address the contribution of mobile sources to short-term PM 2.5 -related effects on premature mortality. The results from these studies generally indicated that several combustion-related fine particle source-types are likely associated with mortality, including

A Personal exposure includes contributions from many differ ent 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 PM NAAQS is currently under review and the EPA is considering all available science on PM health effects, including information which has been publishe d since 2004, in the development of the upcoming PM Integrated Science Assessment Document (ISA). A second draft of the PM ISA was co mpleted in Ju ly 2009 and was submitted for review by the Clean Air Scientific Advisory Committee (C ASAC) of EPA’s Science Advisory Board. Comments from the general public have also been requested. For more information, see http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=210586.
C These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. The provisional assessmen t did not and could not (given a very short timeframe) undergo the extensive critical review by CASAC and the public, as did the PM AQCD. The provisional assessment found that the “new” studies expand the scientific information and provide important insights on the relationship between PM exposure and health effects of PM. The provisional assessment also found that “new” studies generally strengthen the evidence that acute and chronic exposure to fine particles and acute exposure to thoracic coarse particles are associated with health effects. Further, the provisional science assessment found that the results reported in the studies did not dramatically diverge from previous findings, and taken in context with the findings of the AQCD, the new information and findings did not materially change any of the broad scientific conclusions regarding the health effects of PM exposure made in the AQCD. However, it is important to note that this assessment was limited to screening, su rveying, and preparing a provisional assessment of these studies. For reasons outlined in Section I.C of the pr eamble for the final PM NAAQ S rulemaking in 2006 (see 71 FR 61148-49, October 17, 2006), EPA based its N AAQS decision on the science presented in the 2004 AQCD.Environmental and Health Impacts

7-3 motor vehicle emissions as well as other sources. 12 The analyses incorporate source apportionment tools into short-term exposure studies and are briefly mentioned here. Analyses incorporating source apportionment by fa ctor analysis with daily time-series studies of daily death rates indicated a relationship between mobile source PM 2.5 and mortality. 13,14,15,16 Another recent study in 14 U.S. cities examined the effect of PM 10 exposures on daily hospital admissions for cardiovascular disease. This study found that the effect of PM 10 was significantly greater in areas with a larger proportion of PM 10 coming from motor vehicles, indicating that PM 10 from these sources may have a greater effect on the toxicity of ambient PM 10 when compared with other sources. 17 These studies provide evidence that PM-related emissions, specific ally from mobile sources, are associated with adverse health effects.

7.1.1.1.2.2 Long-term Exposure Mortality and Morbidity Studies
Long-term exposure to ambient PM 2.5 is associated with premature mortality from cardiopulmonary diseases and lung cancer, 18 and effects on the resp iratory system such as decreased lung function or the development of chronic respiratory disease. 19 Of specific importance, the PM AQCD also noted that the PM components of gasoline and diesel engine exhaust represent one class of hypothesized li kely important contributors to the observed ambient PM-related increases in lung cancer incidence and mortality. 20

The PM AQCD and PM Staff Paper empha sized the results of two long-term epidemiologic studies, the Six Cities and American Cancer Society (ACS) prospective cohort studies, based on several factors – the large air quality data set for PM in the Six Cities Study, the fact that the study populations were similar to the general population, and the fact that these studies have undergone extensive reanalysis. 21,22,23,2425,26 These studies indicate that there are positive associations for all-cause, ca rdiopulmonary, and lung cancer mortality with long-term exposure to PM 2.5 . One analysis of a subset of the ACS cohort data, which was published after the PM AQCD was finalized but in time for the 2006 Provisional Assessment, found a larger association than had previo usly been reported between long-term PM 2.5 exposure and mortality in the Los Angeles area us ing a new exposure estimation method that accounted for variations in concentration within the city. 27

As discussed in the PM AQCD, the morbidity studies that combine the features of cross-sectional and cohort studies provide the best evidence for chronic exposure effects. Long-term studies evaluating the effect of ambient PM on children’s development have shown some evidence indicating effects of PM 2.5 and/or PM 10 on reduced lung function growth. 28 In another recent publication included in the 2006 Provisional Assessment, investigators in southern California reported th e results of a cross-sectional study of outdoor PM 2.5 and a measure of atherosclerosis development in the Los Angeles basin. 29 The study found significant associations between ambient residential PM 2.5 and carotid intima-media thickness (CIMT), an indicator of subclinical atherosclerosis, an underlying factor in cardiovascular disease.

7.1.1.2 Ozone

7

. 1.1.2.1 BackgroundDraft Regulatory Impact Analysis

7-4 Ground-level ozone pollution is typically formed by the reaction of VOCs and NO X 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.

The science of ozone formatio n, transport, and accumulati on is complex. Groundlevel ozone is produced and destroyed in a cycl ical 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 typica lly 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 NO X emissions.

The highest levels of ozone are produced when both VOC and NO X emissions are present in significant quantities on clear su mmer days. Relatively small amounts of NO X enable ozone to form rapidly when VOC levels are relatively high, but ozone production is quickly limited by removal of the NO X . Under these conditions NO X reductions are highly effective in reducing ozone while VOC reductions have little effect. Such conditions are called “NO X -limited.” Because the contribution of VOC emissions from biogenic (natural) sources to local ambient ozone concentrations ca n be significant, even some areas where manmade VOC emissions are relatively low can be NO X -limited.

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide (NO) with ozone, forming nitrogen dioxide (NO 2 ); as the air moves downwind and the cycle continues, the NO 2 forms additional ozone. The importan ce of this reaction depends, in part, on the relative concentrations of NO X , VOC, and ozone, all of which change with time and location. When NO X levels are relatively high and VOC levels relatively low, NO X formsinorganic nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOClimited”. Under these conditions, VOC reductions are effective in reducing ozone, but NO Xreductions can actually increase local ozone under certain circumstances. Even in VOClimited urban areas, NO X reductions are not expected to increase ozone levels if the NO X reductions are sufficiently large.

Rural areas are usually NO X -limited, due to the relatively large amounts of biogenic VOC emissions in such areas. Urban areas can be either VOC- or NO X -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. 30,31 We are relying on the data

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 ambien t concentrations but also by the individuals breathing route and rate.Environmental and Health Impacts

7-5 and conclusions in the ozone AQCD and staff pa per, regarding the health effects associated with ozone exposure.

Ozone-related health effects include lung function decrements, respiratory symptoms, aggravation of asthma, increased hospital an d emergency room visi ts, increased asthma medication usage, and a variety of other respirator y 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 cardiov ascular-related morbidity and highly suggestive evidence that short-term ozone exposure direct ly or indirectly contributes to non-accidental and cardiopulmonary-related mortality, but additional research is needed to clarify theunderlying 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 oz one-related mortality should be included in estimates of the health benefits of reducing ozone exposure. 32 People who are more susceptible to effects associated with exposure to ozone can 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 stud ies, EPA has identified several key health effects associated with exposure to levels of oz one found today in many areas of the country. Short-term (1 to 3 hours) and prolonged ex posures (6 to 8 hours) to ambient ozone concentrations have been linked to lung func tion decrements, respiratory symptoms, increased hospital admissions and emergency room visits for respiratory problems. 33, 34, 35, 36, 37, 38 Repeated exposure to ozone can increase sus ceptibility to respiratory infection and lung inflammation and can aggravate preexisting respiratory diseases, such as asthma. 39, 40, 41, 42, 43 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 premat ure aging of the lungs and/or the development of chronic respiratory illnesses, such as emphysema and chronic bronchitis. 44, 45, 46, 47

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. 48 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. 49 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. 50, 51, 52, 53, 54, 55, 56, 57 Further, children are more at risk of experiencing health effects from ozone exposure than adults because their respiratory systems are still developing. These individuals (a s well as people with respiratory illnesses, such as asthma, especially asthmatic children) can experience reduced lung function and increased respiratory symptoms, such as chest pa in and cough, when exposed to relatively low ozone levels during prolonged periods of moderate exertion. 58, 59, 60, 61Draft Regulatory Impact Analysis

7-6 7.1.1.3 Nitrogen Oxides and Sulfur Oxides

7

. 1.1.3.1 Background

Sulfur dioxide (SO 2 ), a member of the sulfur oxide (SO X ) family of gases, is formed from burning fuels containing sulfur (e.g., coal or oil derived), extracting gasoline from oil, or extracting metals from ore. Nitrogen dioxide (NO 2 ) is a member of the nitrogen oxide (NO X ) family of gases. Most NO 2 is formed in the air through the oxidation of nitric oxide (NO) emitted when fuel is burned at a high temperature.

SO 2 and NO 2 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 heal th effects of ambient PM are discussed in Section 7.1.1.1.2. NO X 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 Sulfur Oxides

Information on the health effects of SO 2 can be found in the U.S. Environmental Protection Agency Integrated Science Assessment for Sulfur Oxides. 62 SO 2 has long been known to cause adverse respiratory health effects, particularly among individuals with asthma. Other potentially sensitive groups include children and the elderly. During periods of elevated ventilation, asthmatics may experien ce symptomatic bronchoconstriction within minutes of exposure. Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the EPA has concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO 2 . Separately, based on an evaluation of the epidemiologic evidence of associations between short-term exposure to SO 2 and mortality, the EPA has concluded that the overall evidence is suggestive of a causal relationship between short-term exposure to SO 2 and mortality.

7.1.1.3.3 Health Effects of Nitrogen Oxides

Information on the health effects of NO 2 can be found in the U.S. Environmental Protection Agency Integrated Science Assessment (ISA) for Nitrogen Oxides. 63 The U.S. EPA has concluded that the findings of epid emiologic, controlled human exposure, and animal toxicological studies provide evidence that is sufficient to infer a likely causal relationship between respiratory effects and short-term NO 2 exposure. The ISA concludes that the strongest evidence for such a rela tionship 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 NO 2 exposure. First, the ISA concludes that NO 2 exposure may enhance the sensitivity to allergen-induced decrements in lung function and increase the allergen-induced airway inflammatory response at exposures as low as 0.26 ppm NO 2 for 30 minutes. Second, exposure to NO 2 has been found to enhance the inhere nt responsiveness of the airway to subsequent nonspecific challenges in controlled human exposure studies of asthmaticEnvironmental and Health Impacts

7-7 subjects. Enhanced airway responsiveness co uld have important clinical implications for asthmatics since transient increases in airway responsiveness following NO 2 exposure have the potential to increase symptoms and worsen asthma control. Toge ther, the epidemiologic and experimental data sets form a plausible, consistent, and coherent description of a relationship between NO 2 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, NO 2 has also been linked to other 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

We are relying on the data and conclusion s in the EPA Air Quality Criteria Document for CO (CO Criteria Document), which was published in 2000, regarding the health effects associated with CO exposure. E,64 Carbon monoxide enters the bloodstream through the lungs and forms carboxyhemoglobin (COHb), a compound that inhibits the blood’s capacity to carry oxygen to organs and tissues. 65,66 Carbon monoxide has long been known to have substantial adverse effects on human health, incl uding toxic effects on blood and tissues, and effects on organ functions. Although there ar e effective compensatory increases in blood flow to the brain, at some concentrations of COHb somewhere above 20 percent, these compensations fail to maintain sufficient oxygen delivery, and metabolism declines. 67 The subsequent hypoxia in brain tissue then produces behavioral effects, including decrements in continuous performance and reaction time. 68

Carbon monoxide has been linked to increased risk for people with heart disease, reduced visual perception, cognitive functions and aerobic capacity, and possible fetal effects. 69 Persons with heart disease are especially sensitive to CO poisoning and may experience chest pain if they breathe the gas while exercising. 70 Infants, elderly persons, and individuals with respiratory diseases are also particularly sensitive. Carbon monoxide can affect healthy individuals, impairing exercise capacity, visual perception, manual dexterity, learning functions, and ability to perform complex tasks. 71

Several epidemiological studies have shown a link between CO and premature morbidity (including angina, congestive heart fa ilure, and other cardiovascular diseases). Several studies in the United States and Canada have also reported an association between ambient CO exposures and frequency of cardiovascular hospital admissions, especially for congestive heart failure (CHF). An association between ambient CO exposure and mortality has also been reported in epidem iological studies, though not as consistently or specifically as with CHF admissions. EPA reviewed these studies as part of the CO Criteria Document

E The CO NAAQS is currently under review and the EPA is considering all available science on CO health effects, including information which has been publishe d since 2000, in the development of the upcoming CO Integrated Science Assessment Document (ISA). A first draft of the CO ISA was co mpleted in March 2009 and was submitted for review by the Clean Air Scientific Advisory Committee (C ASAC) of EPA’s Science Advisory Board. For more information, see http://cfpub. epa.gov/ncea/cfm/recordispl ay.cfm?deid=203935.Draft Regulatory Impact Analysis

7-8 review process and noted the possibility that the average ambient CO levels used as exposure indices in the epidemiology studies may be surrogates for ambient air mixes impacted by combustion sources and/or other constituent toxic components of such mixes. More research will be needed to better clarify CO’s role. 72

7.1.1.5 Air Toxics

Mo tor vehicle emissions contribute to ambien t levels of air toxics known or suspected as human or animal carcinogens, or that ha ve noncancer health effects. The population experiences an elevated risk of cancer and othe r noncancer health effects from exposure to air toxics. 73 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 Assessme nt (NATA) and have significant inventory contributions from mobile sources.

Table 7-1 Mobile Source Inventory Contribution to 2002 Emissions of NATA Risk Drivers a

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 C ounty section of the 2002 NATA webpage (http://www.epa.gov/ttn/atw/nata2002/ta bles.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: be nzo[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 mo bile 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 em issions of mobile source air toxics by 330,000 tons in 2030, including 61,000 tons of benzene. 74Environmental and Health Impacts

7-9 Noncancer health effects can result from chronic, F subchronic, G or acute H 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 noncan cer respiratory health effects. This will continue to be the case in 2030, even though to xics 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 confiden ce in NATA estimates of population noncancer hazard from ambient exposure to this pollutant is low. 75,76

The NATA modeling framework has a number of limitations which prevent its use as the sole basis for setting regulatory standa rds. These limitations and uncertainties are discussed on the 2002 NATA website. 77 Even so, this modeling framework is very useful in identifying air toxic pollutants and sources of gr eatest 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 conclu des 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. 78,79,80 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 Internat ional 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. 81,82

A number of adverse noncancer health effects including blood disorders, such as preleukemia and aplastic anemia, have also be en associated with long-term exposure to benzene. 83,84 The most sensitive noncancer effect obse rved in humans, based on current data, is the depression of the absolute lymphocyte count in blood. 85,86 In addition, recent work, including studies sponsored by the Health Effect s Institute (HEI), provides evidence that biochemical responses are occurring at lower levels of benzene exposure than previously known. 87,88,89,90 EPA’s IRIS program has not yet evaluated these new data.

7.1.1.5.2 1,3-Butadiene

F 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).
G Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an organism.
H Defined in the IRIS database as exposure by the oral , dermal, or inhalation route for 24 hours or less.Draft Regulatory Impact Analysis

7-10 EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation. 91,92 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. 93,94 There are numerous studies consistently demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by experimental animals and humans. The spec ific mechanisms of 1,3-butadiene-induced carcinogenesis are unknown; however, the scie ntific evidence strongly suggests that the carcinogenic effects are mediated by genotoxic metabolites. Animal data suggest that females may be more sensitive than males for cancer effects associated with 1,3-butadiene exposure; there are insufficient data in humans from which to draw conclusions about sensitive subpopulations. 1,3-butadiene also causes a variety of reproductive and developmental effects in mice; no human data on these effects are available. The most sensitive effect was ovarian atrophy observed in a lifetime bioassay of female mice. 95

7.1.1.5.3 Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in ra ts, mice, hamsters, and monkeys. 96 EPA is currently reviewing recently published epidemiological data . For instance, research conducted by the National Cancer Institute (NCI) found an incr eased risk of nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to formaldehyde. 97,98 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. 99 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. 100 Extended follow-up of a cohort of British chemical workers did not fi nd evidence of an increase in nasopharyngeal or lymphohematopoietic cancers, but a continuing st atistically significant excess in lung cancers was reported. 101

In the past 15 years there has been substant ial research on the inhalation dosimetry for formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the Chemical Industry Institute of To xicology), with a focus on use of rodent data for refinement of the quantitative cancer dose-response assessment. 102,103,104 CIIT’s risk assessment of formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. However, it should be noted that recent rese arch published by EPA indicates that when twostage modeling assumptions are varied, resulting dose-response estimates can vary by several orders of magnitude. 105,106,107,108 These findings are not supportive of interpreting the CIIT model results as providing a conservative (hea lth protective) estimate of human risk. EPA research also examined the co ntribution 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 pub lished 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.

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 classificationEnvironmental and Health Impacts

7-11 than previous IARC evaluations. After reviewing the currently available epidemiological evidence, the IARC (2006) characterize d the human evidence for formaldehyde carcinogenicity as “sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was characterized as “strong.” 109 EPA is reviewing the recent work cited above from th e NCI and NIOSH, as well as th e analysis by the CIIT Centers for Health Research and other studies, as pa rt 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 inflammati on – including eosinophil infiltration into the airways. There are several studies that suggest that formaldehyde may increase the risk of asthma – particularly in the young. 110,111

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. 112 Acetaldehyde is reasonably anticipate d to be a human carcinogen by the U.S. DHHS in the 11 th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC. 113,114 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. 115 In short-term (4 week) rat studies, degeneration of olfactory epithelium was observed at various concentration levels of acetaldehyde
exposure. 116,117 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 volu me (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation. 118 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 ca rcinogenic 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. 119 The IARC determined in 1995 that acrolein was not classifiable as to its carcinogenicity in humans. 120

Acrolein is extremely acrid and irritati ng to humans when inhaled, with acute exposure resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. Levels considerably lower than 1 ppm (2.3 mg/m 3 ) elicit subjective complaints of eye and nasal irritation and a decrease in the respiratory rate. 121,122 Lesions to the lungs and upper respiratory tract of rats, rabbits, and hamsters have been observed after subchronic exposureDraft Regulatory Impact Analysis

7-12 to acrolein. Based on animal data, individu als with compromised respiratory function (e.g., emphysema, asthma) are expected to be at incr eased risk of developing adverse responses to strong respiratory irritants such as acrolein. This was demonstrated in mice with allergic airway-disease by comparison to non-diseased mice in a study of the acute respiratory irritant effects of acrolein. 123

EPA is currently in the process of conducting an assessment of acute exposure effects for acrolein. The intense irritancy of this car bonyl has been demonstrated during controlled tests in human subjects, who suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure. 124

7.1.1.5.6 Polycyclic Organic Matter (POM)

POM is generally defined as a large cla ss 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 h ydrocarbons (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 we re associated with several adverse birth outcomes, including low birth weight and reduced length at birth, as well as impaired cognitive development at age three. 125,126 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 qua ntities 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 numb er of recent animal carcinogenicity studies. 127 The draft reassessment completed external peer review. 128 Based on external peer review comments received, additional analyses are being undertaken. This external review draft does not represent official agency opinion and was rel eased solely for the purposes of external peer review and public comment. Once EPA evaluates public and peer reviewer comments, the document will be revised. 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 an d some evidence of carcinogenicity in mice. 129 California EPA has released a new risk a ssessment for naphthalene, and the IARC has reevaluated naphthalene and re-classified it as Group 2B: possibly carcinogenic to humans. 130 Naphthalene also causes a number of chronic non-cancer effects in animals, including abnormal cell changes and growth in respiratory and nasal tissues. 131

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 today’s proposed action.Environmental and Health Impacts

7-13 Mobile source air toxic compounds that would po tentially be impacted include ethylbenzene, polycyclic organic matter, propionaldehyde, toluene, and xylene. Information regarding the health effects of these compounds can be found in EPA’s IRIS database. 132



### 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, NO X , SO X , carbon monoxide and air toxics.

7.1.2.1 Visibility Degradation

Em ission s from LD vehicles contribute to poor visibility in the U.S. through their primary PM 2.5 and NO X emissions (which contribute to the formation of secondary PM 2.5 ). These airborne particles degrade visibility by scattering and absorbing light. Good visibility increases the quality of life where individual s live and work, and where they engage in recreational activities.

The U.S. government places special emphasis on protecting visibility in national parks and wilderness areas. Section 169 of the Clean Air Act requires the U.S. government to address existing visibility impairment and future visibility impairment in the 156 national parks (see Figure 7-1) exceeding 6,000 acres, and wilderness ar e as exceeding 5,000 acres, which are categorized as mandatory class I fede ral areas (62 FR 38680, July 18, 1997).

7.1.2.1.1 Visibility Monitoring

In conjunction with the U.S. National Par k Service, the U.S. Forest Service, other Federal land managers, and State organization s 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 aeros ol measurements at all sites, and optical and scene measurements at some of the sites. Aerosol measurements are taken for PM 10 and PM 2.5 mass, and for key constituents of PM 2.5 , such as sulfate, nitrate, organic and elemental carbon, soil dust, and several other elements. Measurements for specific aerosol constituents are used to calculate "reconstructed" aerosol light extinction by multiplying the mass for each constituent by its empirically-derived scattering and/or absorption efficiency, with adjustment for the relative humidity. Knowledge of the main constituents of a site's light extinction "budget" is critical for source apportionment an d control strategy development. Optical measurements are used to directly measure light extinction or its components. Such measurements are taken principally with either a transmissometer, which measures total light extinction, or a nephelometer, which measures pa rticle scattering (the largest human-caused component of total extinction). Scene characteristics are typically recorded three times daily with 35 millimeter photography and are used to determine the quality of visibility conditionsDraft Regulatory Impact Analysis

7-14 (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 curre nt 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 (refl ecting light extinction due to both anthropogenic and non-anthropogenic sources) vary regionally across the U.S. The rural East generally has higher levels of impairment than remote sites in the West, with the exception of urban-influenced sites such as San Gorgonio Wilderness (CA) and Point Reyes National Seashore (CA), which have annual average levels comparable to certain sites in the Northeast. Regional differences are illustrated by Figure s 4-39a and 4-39b in the Air Quality Criteria Document for Particulate Matter, which show that, for Class I areas, visibility levels on the 20% haziest days in the West are about equal to levels on the 20% best days in the East. 133

Higher visibility impairment levels in the East are due to generally higher concentrations of anthropogenic fine particle s, particularly sulfates, and higher average relative humidity levels. In fact, sulfates account for 60-86% of the haziness in eastern sites. 134 Aerosol light extinction due to sulfate on the 20% haziest days is significantly larger in eastern Class I areas as compared to western areas (Figures 4-40a and 4-40b in the Air Quality Criteria Document for Particulate Matter). 135 With the exception of remote sites in the northwestern U.S., visibility is typically wors e in the summer months. This is particularly true in the Appalachian region, where averag e light extinction in the summer exceeds the annual average by 40%. 136

7.1.2.1.2 Addressing Visibility in the U.S.

The U.S. EPA is pursuing a two-part strate gy to address visibility. First, to address the welfare effects of PM on visibility, EPA set secondary PM 2.5 standards which act in conjunction with the establishment of a regional haze program. In setting this secondary standard, EPA has concluded that PM 2.5 causes adverse effects on visibility in various locations, depending on PM concentrations an d factors such as chemical composition and average relative humidity. Second, section 169 of the Clean Air Act provides additional authority to address existing visibility impairme nt and prevent future visibility impairment in the 156 national parks, forests and wilderness areas categorized as mandatory Class I federal areas (62 FR 38680-81, July 18, 1997). I Figure 7-1 below identifies where each of these parks are locat ed in the U.S. In July 1999, th e regional haze rule (64 FR 35714) was put in place to protect the visibility in mandatory Class I federal areas. Visibility can be said to be impaired in both PM 2.5 nonattainment areas and mandatory Class I federal areas. J

I These areas are defined in section 162 of the Act 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.
J As mentioned above, the EPA recently amended the PM NAAQS, making the secondary NAAQS equal, in all respects, to the primary standards for both PM 2.5 and PM 10 , (71 FR 61144, Oct. 17, 2006). In February 2009, the D.C. Circuit Court remanded the secondary standards for fi ne particles, based on EP A’s failure to adequatelyEnvironmental and Health Impacts

7-15 Figure 7-1 Mandatory Class I Areas in the U.S.

7.1.2.2 Plant and Ecosystem Effects of Ozone

The re are a number of environmental or public welfare effects associated with the presence of ozone in the ambient air. 137 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 throughou t the United States, impairing crops, native vegetation, and ecosystems more than any other air pollutant”. 138 Like carbon dioxide (CO 2 ) and other gaseous substances, ozone enters plant tissues primarily through apertures (stomata) in leaves in a process called “uptake”. 139 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, incl uding enzyme activities, lipids, and cellular membranes, disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns. 140,141 If enough tissue becomes damaged from these effects, a plant’s capacity to fix carbon to form carbohydrates, which are the pr imary form of energy used by plants is

explain why setting the secondary PM2.5 NAAQS equivale nt to the primary standards provided the required protection for public welf are including protection from visibility impairment.Draft Regulatory Impact Analysis

7-16 reduced, 142 while plant respiration increases. With fewer resources available, the plant reallocates existing resources away from root growth and storage, above ground growth or yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies have shown that plants stressed in these ways may exhibit a general loss of vigor, which can lead to secondary impacts that modify plants' responses to other environmental factors. Speci fically, 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. Fu rthermore, there is evidence that ozone can interfere with the formation of mycorrhiza, esse ntial 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. 143,144

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 l eaf 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 inju ry 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) 145,146,147 Other resistance mechanisms may i nvolve the intercellular production of detoxifying substances. Several biochemical su bstances 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. 148

Because of the differing sensitivities among pl ants to ozone, ozone pollution can also exert a selective pressure that leads to change s 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, ecosyst ems, agronomic crops and urban ornamentals.

Ozone also has been conclusively shown to cause discernible injury to forest
trees. 149,150 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. 151,152

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 ofEnvironmental and Health Impacts

7-17 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 an d temporal variation of tropospheric ozone, species composition, soil properties and climatic factors. 153 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-le vel forest decline in sensitive ecosystems. 154,155,156 It is not yet possible to predict ecosystem respon ses to ozone with much certainty; however, considerable knowledge of potential ecosyste m 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 importan t crop species are sensitive to ozone levels typical of those found in the United States.” 157 In addition, economic studies have shown reduced economic benefits as a result of predicte d reductions in crop yields associated with observed ozone levels. 158,159,160

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/te nants and by governmental units responsible for public areas. 161 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. 162 In the U.S., ozone in the lower atmosphere is one of the pollutants of prim ary 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 in dicates impaired physiological processes in the leaves. 163

In the U.S. this indicator is based on da ta from the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3program, 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 woodlot s and urban trees. Sites are selected using a systematic sampling grid, based on a global sampling design. 164,165 At each site that has at least 30 individual plants of at least thr ee ozone-sensitive species and enough open space to ensure that sensitive plants are not protected from ozone exposure by the forest canopy, FIA looks for damage on the foliage of ozone-sensitive forest plant species. Monitoring of ozone injury to plants by the USDA Forest Servi ce 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.Draft Regulatory Impact Analysis

7-18 7.1.2.2.1 Recent Ozone Data for the U.S.

There is considerable regional variatio n 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 fore st 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. 166, 167 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 indictor in Figure 7-2 are based on averages of all observa tions coll ected in 2002, the latest year fo r 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-cat egory biosite index based on expert opinion, but designed to be equivalent from site to site. Ra nges 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. 168, 169

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, Delawa re, 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, an d 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, Ha waii and Arizona). The percentage of sites showing some ozone damage was about 45% in each of these EPA Regions.Environmental and Health Impacts

7-19 Figure 7-2 Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002 ab

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 poll ution. Other forest plant species, or evenDraft Regulatory Impact Analysis

7-20 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 regiona lly, 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. 170 Ozone may have other adverse impacts on plants (e.g., reduced productivity) that do not show signs of visible foliar injury. 171

Though FIA has extensive spatial coverage based on a robust sample design, not all

forested areas in the U.S. are monitored for ozon e injury. Even though the biosite data have been collected over multiple years, most biosit es 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 co mmercial and ecologically important forest tree species throughout the United States. 172

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 tr ee species from ambient ozone concentrations and accounting for the prevalence of those species within the forest. As a way to quantify therisks to particular plants from ground-level ozone, scientists have developed ozoneexposure/tree-response functions by exposing tree seedlings to different ozone levels and measuring reductions in growth as “biomass loss.” Typically, seedlings are used because they are easy to manipulate and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone within the leaves of seedlings and mature trees are identical, though the magnitude of the effect may be higher or lower depending on the tree species. 173Environmental and Health Impacts

7-21 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 ha ve 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 lo cations, 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, re gardless of their source or predominant form (particle, gas or liquid). Reflecting this fact , the PM AQCD concludes that regardless of size fractions, particles containing nitrates and sulfates have the greatest potential for widespread environmental significance, while effects are also related to other chemical constituents found in ambient PM, such as trace metals and orga nics. The following characterizations of the nature of these welfare effects are based on the information contained in the PM AQCD and PM Staff Paper. 174,175

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 soil and wa ter acidification, nutrient enrichment, and eutrophication. 176

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 te rrestrial and aquatic biota. The lower pH concentrations and higher aluminum levels re sulting from acidification make it difficult for some fish and other aquatic organisms to surviv e, 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 cy cling 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, magn esium and potassium are essential nutrients for plant growth and physiology. Changes in the relative proportions of these nutrients, especially in comparison with aluminum concen trations, have been asso ciated 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, theDraft Regulatory Impact Analysis

7-22 responses of forest trees to ac id precipitation (rain, snow) incl ude accelerated weathering of leaf cuticular surfaces, increased permeability of leaf surfaces to toxic materials, water, and disease agents; increased leaching of nutr ients from foliage; and altered reproductive processes—all which serve to weaken trees so th at 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 curre ntly found in some locations in the eastern U.S. 177 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. 178 Lichens are sensitive indicators of nitr ogen deposition effects to terrestrial ecosystems and the lichen studies in the Columb ia 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 syndrome known as nitrogen saturation. These effects 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 increa ses significantly above background and critical thresholds are exceeded; (2) leaching of excess ni trate and associated base cations from soils into streams, lakes, and rivers, and mobiliza tion 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. 179

In the U.S. numerous forests now show se vere 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, Nort h Carolina; mixed hardwood watersheds at Fernow Experimental Forest in West Virginia; American beech forests in Great Smoky Mountains National Park, Tennessee; mixed conifer forests and chaparral watersheds in southern California and the southwestern Sierra Nevada in Central California; the alpine tundra/subalpine conifer forests of the Colorado Front Range; and red alder forests in the Cascade Mountains in Washington.

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; habita t 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 n itrogen entering estuaries that is ultimately attributable to atmospheric deposition is not well-defined. On an annual basis, atmosphericEnvironmental and Health Impacts

7-23 nitrogen deposition may contribute significantl y to the total nitrogen load, depending on the size and location of the watershed. In additi on, 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, an d 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 dec lines 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 wh en 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. 180

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 influe ncing forest growth (PM AQCD, p. 4-87). 181 Investigation of trace metals near roadways and industrial facilities indicate that a substantial load of heavy metals can accumulate on vegeta tive surfaces. Copper, zinc, and nickel have been documented to cause direct toxicity to vegetation under field conditions (PM AQCD, p. 4-75). Little research has been conducted on the effects associated with mixtures of contaminants found in ambient PM. While meta ls typically exhibit low solubility, limiting their bioavailability and direct toxicity, chem ical transformations of metal compounds occur in the environment, particularly in the presen ce 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, 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 (PM AQCD, p. 4-76). This hypothesized relationship/correlation was furthe r explored in high elevation forests in the northeastern U.S. These studies measured leve ls of a group of intracellular compounds found in plants that bind with metals and are prod uced 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 (PM AQCD 4-76,77). 182 Contamination of plant leaves by heavy metals can lead to elevated soil levels. Trace me tals absorbed into the plant frequently bind to the leaf tissue, and then are lost when the l eaf drops (PM AQCD, p. 4-75). As the fallenDraft Regulatory Impact Analysis

7-24 leaves decompose, the heavy metals are transferred into the soil. 183,184

The environmental sources and cycling of me rcury 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 partic le 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. 185,186 Over fifty percent of the mercury in the Chesapea ke Bay has been attributed to atmospheric deposition. 187 Overall, the National Science and T echnology Council identifies atmospheric deposition as the primary source of mercury to aquatic systems. 188 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 b een identified in streambed sediments, and these elevated levels have been correlated with population density and motor vehicle

use. 189,190 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 elev ated levels along roadsides. 191 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. 192 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 par ticulate 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. 193

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. 194,195 Analyses of PAH deposition in Chesapeake and Galveston Bay indicate that dry deposition and gas exchange from the atmosphere to the surface water predominate. 196,197 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. 198 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. 199 Thus, dry deposition is likely the mainEnvironmental and Health Impacts

7-25 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 consistenc y in monitoring methods, and the significant influence of local sources on deposition levels. 200 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. 201

Cousins et al. estimate that more than ninety percent of semi-volatile organic compound (SVOC) emissions in the United Kingdom deposit on soil. 202 An analysis of PAH concentrations near a Czechoslovakian roadway indicated that concentrations were thirty times greater than background. 203

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 physic ally 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 particle s or hygroscopic coarse particles contribute to these physical effects. In addition, the depo sition of ambient PM can reduce the aesthetic appeal of buildings and culturally important ar ticles through soiling. Particles consisting primarily of carbonaceous compounds cause soili ng 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. 204 In laboratory experiments, a wide range of tolerance to VOCs has been observed. 205 Decreases in harvested seed pod weight have been reported for the more sensitive plants, and some studies have reported effects on seed germinat ion, 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. 206

Research suggests an adverse impact of ve hicle exhaust on plants, which has in some cases been attributed to aromatic compounds and in other cases to nitrogen oxides. 207,208,209 The impacts of VOCs on plant reproduction may ha ve long-term implications for biodiversity and survival of native species near major road ways. 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 th e potential for metabolites of these compounds to affect herbivores or insects.Draft Regulatory Impact Analysis

7-26 

### 7.2 Non-GHG Air Quality Impacts

7.2.1 Introduction

Chapter 5 of this DRIA presents the proj ected emissions changes due to the proposed rule. Once the emissions changes are projected the next step is to look at how the ambient air quality would be impacted by those emissions changes. Although the purpose of this proposal is to address greenhouse gas emissions, this pr oposed rule would also impact emissions of criteria and hazardous air pollutants. Section 7. 2.2 describes current ambient levels of PM, ozone, CO and some air toxics without the standards being proposed in this rule. No air quality modeling was done for this DRIA to project the impacts of the proposed rule. Air quality modeling will be done for the final rule, however, and those plans are discussed in Section 7.2.3.



### 7.2.2 Current Levels of Pollutants

7.2.2.1 Particulate Matter

As described in Section 7.1.1.1, PM causes adverse health effects, and the U.S. government has set national standards to provide requisite protection against those health effects. There are two U.S. national am bient air quality standards (NAAQS) for PM 2.5 : an annual standard (15 μ g/m 3 ) and a 24-hour standard (35 μ g/m 3 ). The most recent revisions to these standards were in 1997 and 2006. In 2005 the U.S. EPA designated nonattainment areas for the 1997 PM 2.5 NAAQS (70 FR 19844, April 14, 2005). K As of June 5, 2009 there are 39 1997 PM 2.5 nonattainment areas comprised of 208 full or partial counties with a total population exceeding 88 million. Area designations for the 2006 24-hour PM 2.5 NAAQS are expected to be promulgated in 2009 and become effective 90 days after publication in the Federal Register.

States with PM 2.5 nonattainment areas will be required to take action to bring those areas into compliance in the future. Most 1997 PM 2.5 nonattainment areas are required to attain the 1997 PM 2.5 NAAQS in the 2010 to 2015 time frame and then be required to maintain the 1997 PM 2.5 NAAQS thereafter. 210 The 2006 24-hour PM 2.5 nonattainment areas will be required to attain the 2006 24-hour PM 2.5 NAAQS in the 2014 to 2019 time frame and then be required to maintain the 2006 24-hour PM 2.5 NAAQS thereafter. 211

7.2.2.2 Ozone

As described in Section 7.1.1.2, ozone causes adverse health effects, and the U.S. government has set national standards to protect against those health effects. The NAAQS for ozone is an 8-hour standard set at 0.075 ppm. The most recent revision to this standard was in

K 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.Environmental and Health Impacts

7-27 2008; the previous 8-hour ozone standard, set in 1997, had been 0.08 ppm. In 2004 the U.S. EPA designated nonattainment areas for the 19 97 8-hour ozone NAAQS (69 FR 23858, April 30, 2004). L As of June 5, 2009 there are 55 1997 8-hour ozone nonattainment areas comprised of 290 full or partial counties wi th a total population of approximately 132 million. 212 Nonattainment designations for the 2008 8-hour ozone standard are currently under development.

States with ozone nonattainment areas are re quired 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 2 013 time frame and then to maintain it thereafter. M The attainment dates associated with the potential nonattainment areas based on the 2008 8-hour ozone NAAQS will likely be in the 2013 to 2021 tim eframe, depending on the severity of the problem in each area. Table 7-2 provides an estimate, based on 2004-06 air
q u ality data, of the counties with design values greater than the 2008 ozone NAAQS.

Table 7-2 Counties with Design Values Greater Than the 2008 Ozone NAAQS Based on 2005-2007 Air Quality Data

NUMBER OF COUNTIES POPULATION A

1997 Ozone Standard: counties within the 57 areas currently designated as nonattainment 293 131,977,890

2008 Ozone Standard: additional counties that would not meet the 2008 NAAQS b 227 41,285,262

Total 520 173,263,152

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 populati ons that will eventually be included in areas with multiple counties designated nonattainment.

7.2.2.3 Carbon Monoxide

As described in Section 7.1.1.4, CO causes adverse health effects, and the U.S. government has set national standards to protect against those health effects. There are two CO NAAQS. The 8-hour average CO NAAQS is 9 ppm, not to be exceeded more than once per year, and the 1-hour average CO NAAQS is 35 ppm, not to be exceeded more than once

L 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.
M The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area is designated as severe and will have to attain before June 15, 2021. The South Coast Air Basin has requested to be reclassified as an extreme nonattainment area which will make its attainment date June 15, 2024. Th e San Joaquin Valley Air Basin 8-hour ozone nonattainment area is de signated as serious and will ha ve to attain before June 15, 2013. The San Joaquin Valley Air Basin has requested to be reclassified as an extreme nonattainment area which will make its attainment date June 15, 2024.Draft Regulatory Impact Analysis

7-28 per year. The two standards are the identical standards that were promulgated in 1971. Reviews of the CO NAAQS in both 1985 and 1994 kept the standards at the same levels. As of June 5, 2009 there are approximately 479,0 00 people living in a portion of Clark Co., NV which is currently the only area in the country th at is designated as nonattainment for CO, see Table 7-3. 213 The CO NAAQS is currently under review and a final rule is expected to be final in May 2011.

Table 7-3 Classified Carbon Monoxide Nonattainment Areas as of June 2009

AREA CLASSIFICATION POPULATION

Clark County (p), NV Nonattainment 478,766 Total 478,766

7.2.2.4 Air Toxics

According to the National Air Toxics Assessment (NATA) for 2002, mobile sources were responsible for 47 percent of outdoor toxi c emissions and over 50 percent of the cancer risk. 214 Nearly the entire U.S. population was ex posed to an average concentration of air toxics that has the potential for adverse noncance r respiratory health effects. EPA recently finalized vehicle and fuel controls to reduce mobile source air toxics. 215 In addition, over the years, EPA has implemented a number of mobile source and fuel controls resulting in VOC reductions, which also reduce air toxic emissi ons. Modeling from the recent Mobile Source Air Toxics (MSAT) rule suggests that the mobile source contribution to ambient benzene concentrations is projected to decrease over 40% by 2015, with a decrease in ambient benzene concentration from all sources of about 25%. Al though benzene is used as an example, the downward trend is projected for other air toxics as well. See the RIA for the final MSAT rule for more information on ambient air toxics projections. 216



### 7.2.3 Impacts on Future Air Quality

Air quality models use mathematical and numerical techniques to simulate the physical and chemical processes that affect air pollutants as they disperse and react in the atmosphere. Based on inputs of meteorological data and source information, these models are designed to characterize primary pollutants that are emitted directly into the atmosphere and secondary pollutants that are formed as a resu lt of complex chemical reactions within the atmosphere. Photochemical air quality models have become widely recognized and routinely utilized tools for regulatory analysis by asse ssing the effectiveness of control strategies. These models are applied at multiple spatial scales from local, regional, national, and global. Section 7.2.3.1 provides more detail on the photochemical model, the Community Multi-scale Air Quality (CMAQ) model, which will be utilized for the final rule analysis.Environmental and Health Impacts

7-29 7.2.3.1 Community Multi-scale Air Quality (CMAQ) Modeling Plans

Full-scale photochemical air quality modeling is necessary to accurately project levels of PM 2.5 , ozone, CO and air toxics. For the final rule, a national-scale air quality modeling analysis will be performed to analyze the impacts of the vehicle standards on PM 2.5 , ozone, and selected air toxics (i.e., benzene, formaldehyde, acetaldehyde, acrolein and 1,3- butadiene). The length of time needed to pr epare the necessary emissions inventories, in addition to the processing time associated with the modeling itself, has precluded us from performing air quality modeling for this proposal.

Section II.G.1 of the preamble presents projections of the changes in criteria pollutant and air toxics emissions due to the proposed vehicle standards; the basis for those estimates is set out in Chapter 5 of the DRIA. The atmospheric chemistry related to ambient concentrations of PM 2.5 , ozone and air toxics is very complex, and making predictions based solely on emissions changes is extremely difficu lt. However, based on the magnitude of the emissions changes predic ted to result from the proposed vehicle standards, we expect that there will be an improvement in ambient air quality, pending a more comprehensive analysis for the final rule.

For the final rule, EPA intends to use a 2005-based Community Multi-scale Air Quality (CMAQ) modeling platform as the tool for the air quality modeling. The CMAQ modeling system is a comprehensive three-dime nsional grid-based Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, primary and secondary PM concentrations and deposition, and air toxics, over regional and urban spatial scales (e.g., over the contiguous U.S.). 217,218,219 The CMAQ model is a well-known and well-established tool and is commonly used by EPA for regulatory an alyses, for instance the recent ozone NAAQS proposal, and by States in developing attainment demonstrations for their State Implementation Plans. 220 The CMAQ model (version 4.6) was peer-reviewed in February of 2007 for EPA as reported in “Third Peer Review of CMAQ Model,” and the EPA Office of Research and Development (ORD) peer review re port which includes version 4.7 is currently being finalized. 221

CMAQ includes many science modules that simulate the emission, production, decay, deposition and transport of organic and inorgani c gas-phase and particle-phase pollutants in the atmosphere. We intend to use the most recent CMAQ version (version 4.7), which was officially released by EPA’s Office of Research and Development (ORD) in December 2008 and reflects updates to earlier versions in a number of areas to improve the underlying science. These include (1) enhanced secondar y organic aerosol (SOA) mechanism to include chemistry of isoprene, sesquiterpene, and aged in-cloud biogenic SOA in addition to terpene; (2) improved vertical convective mixing; (3 ) improved heterogeneous reaction involving nitrate formation; and (4) an updated gas- phase chemistry mechanism, Carbon Bond 05 (CB05), with extensions to model explicit concentrations of air toxic species as well as chlorine and mercury. This mechanism, CB05 -toxics, also computes concentrations of species that are involved in aqueous chemistry and that are precursors to aerosols.

The CMAQ modeling domain will encompass all of the lower 48 States and portions of Canada and Mexico. The modeling domain will include a large continental U.S. 36 kmDraft Regulatory Impact Analysis

7-30 grid and two 12 km grids (an Eastern US and a Western US domain), as shown in Figure 7-3. The m odeling domain will contain 14 vertical la yers with the top of the modeling domain at about 16,200 meters, or 100 millibars (mb).

Figure 7-3 CMAQ 12-km Eastern and Western US modeling domains

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic sources, meteorological data, an d initial and boundary conditions. The CMAQ meteorological input files will be derived from simulations of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model 222 for the entire year of 2005. 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. 223 The meteorology for the national 36 km grid and the 12 km Eastern and Western U.S. grids will be developed by EPA and described in more detail within the final RIA and the technical support document for the final rule air quality modeling.

The lateral boundary and initial species concentrations will be provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model. 224 The global GEOS-CHEM model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing SystemEnvironmental and Health Impacts

7-31 (GEOS). This model will be run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitude-longitude) and 20 vertical layers. Th e predictions will be 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 will be used as the initial/boundary state for all subsequent 12 km finer grid modeling.



### 7.3 Quantified and Monetized Co-Pollu tant Health and Environmental Impacts

This section presents EPA’s analysis of the co-pollutant health and environmental impacts that can be expected to occur as a result of the proposed light-duty vehicle GHG rule. GHG emissions are predominantly the byproduct of fossil fuel combustion processes that also produce criteria and hazardous air pollutants. Th e vehicles that are subject to the proposed standards are also significant so urces of mobile source air polluti on such as direct PM, NOx, VOCs and air toxics. The proposed standards would affect exhaust emissions of these pollutants from vehicles. They would also affect emissions from upstream sources related to changes in fuel consumption. Changes in ambient ozone, PM 2.5 , and air toxics that would result from the proposed standards are expected to affect human health in the form of premature deaths and other serious human health effects, as well as other important public health and welfare effects.

It is important to quantify the health and environmental impacts associated with the proposed standard because a failure to adequa tely consider these ancillary co-pollutant impacts could lead to an incorrect assessment oftheir net costs and benefits. Moreover, copollutant 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.

EPA typically quantifies and monetizes the health and environmental impacts related to both PM and ozone in its regulatory impact analyses (RIAs), when possible. However, we were unable to do so in time for this propos al. EPA attempts to make emissions and air quality modeling decisions early in the analy tical process so that we can complete the photochemical air quality modeling and use that data to inform the health and environmental impacts analysis. Resource and time constraints precluded the Agency from completing this work in time for the proposal. Instead, EPA is using PM-related benefits-per-ton values as an interim approach to estimating the PM-related be nefits of the proposal. We also provide a complete characterization of the health and environmental impacts that will be quantified and monetized for the final rulemaking.

This section is split into two sub-sections: the first presents the PM-related benefitsper-ton values used to monetize the PM-related co-benefits associated with the proposal; the second explains what PM- and ozone-related health and environmental impacts EPA will quantify and monetize in the analysis for the final rule. EPA bases its analyses on peerreviewed studies of air quality and health and welfare effects and peer-reviewed studies of the monetary values of public health and welfare im provements, and is generally consistent withDraft Regulatory Impact Analysis

7-32 benefits analyses performed for the analysis of the final Ozone National Ambient Air Quality Standard (NAAQS) and the final PM NAAQS analysis, as well as the recent Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA (U.S. EPA, 2009a), and NO 2 NAAQS (U.S. EPA, 2009b). 225,226, 227,228

Though EPA is characterizing the change s in emissions asso ciated with toxic pollutants, we will not be able to quantify or monetize the human health effects associated with air toxic pollutants for either the proposal or the final rule analyses. Please refer to Chapter 5.5 for more information about the air toxics emissions impacts associated with the proposed standards.



### 7.3.1 Economic Value of Reductions in Criteria Pollutants

As described in Chapter 5.5, the propo sed standards would reduce emissions of several criteria and toxic pollutants and precursors. In this analysis, EPA estimates the economic value of the human health be nefits associated with reducing PM 2.5 exposure. Due to analytical limitations, this analysis does not estimate benefits related to other criteria pollutants (such as ozone, NO 2 or SO 2 ) or toxic pollutants, nor does it monetize all of the potential health and welfare effects associated with PM 2.5
. This analysis uses a “benefit-per-ton” me thod to estimate a selected suite of PM 2.5 - related health benefits described below. These PM 2.5 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 PM 2.5 , or its precursors (such as NOx, SOx, and VOCs), from a specified source. Ideally, the human health benefits would be estimated based on changes in ambient PM 2.5 as determined by full-scale air quality modeling. However, this modeling was not possible in the timeframe for this proposal.

The dollar-per-ton estimates used in this analysis are provided in Table 7-4. In the

summa ry of costs and benefits, Chapter 8.3 of this RIA, we present the monetized value of PM-related improvements associated with the proposal.

Table 7-4: Benefits-per-ton Values (2007$) Deri ved Using the ACS Cohort Study for PM-related Premature Mortality (Pope et al., 2002) a and a 3% Discount Rate b

All Sources d Stationary (Non-EGU) Sources Mobile Sources Year c SOx VOC NOx Direct PM2.5 NOx Direct PM2.5 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

a The benefit-per-ton estimates presente d in this table are based on an estimate of premature mortality derived from the ACS study (Pope et al., 2002). If the bene fit-per-ton estimates were based on the Six Cities study (Laden et al., 2006), the values would be approx imately 145% (nearly two-an d-a-half times) larger. b The benefit-per-ton estimates presented in this table a ssume a 3% discount rate in the valuation of premature mortality to account for a twenty-year segmented cessation la g. If a 7% discount rate had been used, the values would be approximately 9% lower.Environmental and Health Impacts

7-33 c Benefit-per-ton values were estimated for the year s 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 (U.S. EPA, 2008a), 229 proposed Portland Cement National Emissi ons Standards for Hazardous Air Pollutants (NESHAP) RIA (U.S. EPA, 2009a), 230 and proposed NO 2 primary NAAQS (U.S. EPA, 2009b). 231 Table 7-5 shows the quantified and unquantified PM 2.5 -related co-benefits captured in those benefit-per-ton estimates.

Table 7-5: Human Health and Welfare Effects of PM 2.5 Pollutant /

Effect Quantified and Monetized in Primary Estimates Unquantified Effects Changes in: PM 2.5 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 exacerbatio ns (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Visibility Household soiling

Consistent with the proposed NO 2 NAAQS, N 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) 232 accompanying the recent final ozone NAAQS RIA (U.S. EPA, 2008a). Readers can also refer to Fann et al. (2009) 233 for a detailed description of the benefit-per-ton methodology. O A more detailed description of the benefit-per-ton estimates is al so provided in the TSD that accompanies this rulemaking.

As described in the documentation for th e benefit per-ton estimates cited above, national per-ton estimates were developed for selected pollutant/source category

N Although we summarize th e main issues in this chapter, we encourag e interested readers to see benefits chapter of the proposed primary NO2 NAAQS RIA for a more detaile d description of recent ch anges to the PM benefits presentation and preference for the no-threshold model.
O 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 $6.3 million (year 200 0$), up from an estimate of $5.5 million (year 2 000$) 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.htmlDraft Regulatory Impact Analysis

7-34 combinations. The per-ton values calculated therefore apply only to tons reduced from those specific pollutant/source combinations (e.g., NO 2 emitted from mobile sources; direct PM emitted from stationary sources). Our estimate of PM 2.5 benefits is therefore based on the total direct PM 2.5 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 incl ude incremental benefits down to the lowest modeled PM 2.5 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 invest igated methods to characterize uncertainty in the relationship between PM 2.5 exposure and premature mortality. EPA’s final PM 2.5 NAAQS analysis provides a more complete picture about the overall uncertainty in PM 2.5 benefits estimates. For more information, please consult the PM 2.5 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 PM 2.5 and premature mortality that EPA usually conducts in associ ation with its benefits analysis was not conducted for this proposal.

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) 234 and the Harvard Six Cities cohort (Laden et al., 2006). 235 The concentration-response (C -R) function developed from the extended analysis of American Cancer Societ y (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 co hort, as reported by Laden et al (2006), was published after the completion of the Staff Paper for the 2006 PM 2.5 NAAQS and has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 co-benefits estimates in analyses completed since the PM 2.5 NAAQS. These are logical choices for anchor points when presenting PM-related benefits because, al though 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 PM 2.5 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 epidemiolo gy-based estimates (Roman et al., 2008; IEc, 2006). 236,237 However, due to the analytical limitations associat ed with this analysis, we have chosen to use the benefit-per-ton value derived from the AC S study and note that benefits would beEnvironmental and Health Impacts

7-35 approximately 145% (or nearly two-and-a-half ti mes) larger if the Harvard Six Cities values were used.

As a note to those who might be comparing th e 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 studie s. 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) 238 meta-analysis of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) 239 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) 240 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 th e SAB-EEAC advice it has received. Therefore, EPA has decided to apply the VSL that was vett ed and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 241 while they continue efforts to update their guidance on this issue. P 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.

 They do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an overestimate or underestimate of the actual benefits of controlling fine particulates. EPA will conduct full-sc ale air quality modeling for the final rulemaking in an effort to capture this variability. Please refer to Section VII.E for a description of EPA’s modeling plans and to Section VIII.G.2 for the description of the quantification and monetization of health impacts for the FRM.

 This analysis assumes that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an

P In the (draft) update of the Economic Guidelines (U.S. EPA, 2008c), EPA retained the VSL endorsed by the SAB with the understanding that furt her updates to the morta lity 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>.Draft Regulatory Impact Analysis

7-36 important assumption, because PM 2.5 produced via transported precursors emitted from stationary sources may differ significantly from direct PM 2.5 released from diesel engines and other in dustrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.

 This analysis assumes that the health impact function for fine particles is linear within the range of ambient concentrat ions under consideration. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard down to the lowest modeled concentrations.

 There are several health benefits categories that we were unable to quantify due to limitations associated with using benefits-per-ton estimates, several of which could be substantial. Because the NO X and VOC emission reductions associated with this proposal are also precursors to ozone, reductions in NO X and VOC would also reduce ozone formation and the health effects associated with ozone exposure. Unfortunately, benefits-per-ton estimates do not exist due to issues associated with the complexity of the atmospheric air chemistry and nonlinearities associated with ozone formation. The PM-related benefitsper-ton estimates also do not include any human welfare or ecological benefits. Please refer to VIII.G.2 for a description of the quantification and monetization of health impact for the FRM and a desc ription of the unquantified co-pollutant benefits associated with this rulemaking.

 There are many uncertainties associated with the health impact functions used in this modeling effort. These include: within-study variability (the precision with which a given study estimates the relationship between air quality changes and health effects); across-study variation (different published studies of the same pollutant/health effect relatio nship typically do not report identical findings and in some instances the diff erences are substantial); the application of C-R functions nationwide (does not account for any relationship between region and health effect, to the extent that such a relationship exists); extrapolation of impact functions across population (we assumed that certain health impact functions applied to age ranges broader than that considered in the original epidemiological study); and various uncertainties in the C-Rfunction, including causality and thresholds. These uncertainties may underor over-estimate benefits.

 EPA has investigated methods to characterize uncertainty in the relationship between PM 2.5 exposure and premature mortality. EPA’s final PM 2.5 NAAQS analysis provides a more complete picture about the overall uncertainty in PM 2.5 benefits estimates. For more information, please consult the PM 2.5 NAAQS RIA (Table 5.5).

 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.Environmental and Health Impacts

7-37  As described above, using the benefit-per-ton value derived from the ACS study (Pope et al., 2002) alone provides an incomplete characterization of PM 2.5 benefits. When placed in the contex t of the Expert Elicitation results, this estimate falls toward the lower end of the distribution. By contrast, the estimated PM 2.5 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.

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a good indication of local or regional air quality and health impacts, as there may be localized impacts associated with the proposed rulema king. Additionally, the atmospheric chemistry related to ambient concentrations of PM 2.5 , 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 welf are impacts. As discussed above, timing and resource constraints precluded EPA from conduc ting a full-scale photochemical air quality modeling analysis in time for the NPRM. For the final rule, however, a national-scale air quality modeling analysis will be performed to analyze the impacts of the standards on PM 2.5 , ozone, and selected air toxics. The benefits anal ysis plan for the final rulemaking is discussed in the next section.



### 7.3.2 Human Health and Environmenta l Benefits for the Final Rule

7.3.2.1 Human Health and Environmental Impacts

To model the ozone and PM air quality benefits of the final rule, EPA will use the Community Multiscale Air Quality (CMAQ) model (see Section 7.2.3.1 for a description of the CMAQ model). The modeled ambient air quality data will serve as an input to the Environmental Benefits Mapping and Analysis Program (BenMAP). 242 BenMAP is a computer program developed by EPA that integrates a number of the modeling elements used in previous RIAs (e.g., interpolation functi ons, population projections, health impact functions, valuation functions, analysis an d pooling methods) to translate modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates.

Table 7-6 lists the co-pollutant health effect exposure-response functions we will use
to quant ify the co-pollutant incidence impacts associated with the final light-duty vehicles standard.Draft Regulatory Impact Analysis

7-38 Table 7-6: Health Impact Functions Used in BenMAP to Estimate Impacts of PM 2.5 and Ozone Reductions

ENDPOINT POLLUTANT STUDY STUDY POPULATION Premature Mortality

Premature mortality – daily time series O 3 Multi-city Bell et al (2004) (NMMAPS study) 243 – Nonaccidental Huang et al (2005) 244 - Cardiopulmonary Schwartz (2005) 245 – Non-accidental Meta-analyses: Bell et al (2005) 246 – All cause Ito et al (2005) 247 – Non-accidental Levy et al (2005) 248 – All cause All ages

Premature mortality—cohort study, allcause PM 2.5 Pope et al. (2002) 249 Laden et al. (2006) 250 >29 years >25 years

Premature mortality, total exposures PM 2.5 Expert Elicitation (IEc, 2006) 251 >24 years

Premature mortality — all-cause PM 2.5 Woodruff et al. (1997) 252 Infant (<1 year)

Chronic Illness

Chronic bronchitis PM 2.5 Abbey et al. (1995) 253 >26 years
Nonfatal heart attacks PM 2.5 Peters et al. (2001) 254 Adults (>18 years) Hospital Admissions
Pooled estimate: Schwartz (1995) - ICD 460-519 (all resp) 255 Schwartz (1994a; 1994b) - ICD 480-486 (pneumonia) 256,257 Moolgavkar et al. (1997) - ICD 480-487 (pneumonia) 258 Schwartz (1994b) - ICD 491-492, 494-496 (COPD) Moolgavkar et al. (1997) – ICD 490-496 (COPD) >64 years O 3

Burnett et al. (2001) 259 <2 years
PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 490-496 (COPD) 260 Ito (2003)—ICD 490-496 (COPD) 261 >64 years

PM 2.5 Moolgavkar (2000)—ICD 490-496 (COPD) 262 20–64 years

PM 2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years RespirFSatory

PM 2.5 Sheppard (2003)—ICD 493 (asthma) 263 <65 years

PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 390-429 (all cardiovascular) Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia, heart failure) >64 years Cardiovascular

PM 2.5 Moolgavkar (2000)—ICD 390-429 (all cardiovascular) 20–64 yearsEnvironmental and Health Impacts

7-39 Asthma-related ER visits O 3 Pooled estimate: Jaffe et al (2003) 264 Peel et al (2005) 265 Wilson et al (2005) 266 5–34 years All ages All ages
Asthma-related ER visits (con’t) PM 2.5 Norris et al. (1999) 267 0–18 years

Other Health Endpoints

Acute bronchitis PM 2.5 Dockery et al. (1996) 268 8–12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) 269 Asthmatics, 9–11
years Lower respiratory symptoms PM 2.5 Schwartz and Neas (2000) 270 7–14 years

Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) 271 (cough, wheeze and shortness of breath) Vedal et al. (1998) 272 (cough) 6–18 years a

Work loss days PM 2.5 Ostro (1987) 273 18–65 years School absence days O 3 Pooled estimate: Gilliland et al. (2001) 274 Chen et al. (2000) 275 5–17 years b

O 3 Ostro and Rothschild (1989) 276 18–65 years Minor Restricted Activity Days (MRADs) PM 2.5 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 An alysis –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 Pr oposed 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.

7.3.2.2 Monetized Estimates of Impacts of Reductions in Co-Pollutants

Table 7-7 presents the monetary values we will apply to changes in the incidence of health and welfare effects associated with reductions in non-GHG pollutants that will occur when these GHG control strategies are finalized.Draft Regulatory Impact Analysis

7-40 Table 7-7: Valuation Metrics Used in BenM AP to Estimate Monetary Co-Benefits

Endpoint Valuation Method Valuation (2000$) Premature mortality Assumed Mean VSL $6,300,000 Chronic Illness Chronic Bronchitis WTP: Av erage Severity $340,482 Myocardial Infarctions, Nonfatal Medical Costs Over 5 Years. Varies by age and discount rate. Russell (1998) 277 ---

Medical Costs Over 5 Years. Varies by age and discount rate. Wittels (1990) 278 ---

Hospital Admissions Respiratory, Age 65+ COI: Medical Costs + Wage Lost $18,353 Respiratory, Ages 0-2 COI: Medical Costs $7,741 Chronic Lung Disease (less Asthma) COI: Medical Costs + Wage Lost $12,378

Pneumonia COI: Medical Costs + Wage Lost $14,693 Asthma COI: Medical Costs + Wage Lost $6,634 Cardiovascular COI: Medical Costs + Wage Lost (20- 64) $22,778

COI: Medical Costs + Wage Lost (65- 99) $21,191

ER Visits, Asthma COI: Smith et al. (1997) 279 $312 COI: Standford et al. (1999) 280 $261 Other Health Endpoints Acute Bronchitis WTP: 6 Da y Illness, CV Studies $356 Upper Respiratory Symptoms WTP: 1 Day, CV Studies $25 Lower Respiratory Symptoms WTP: 1 Day, CV Studies $16 Asthma Exacerbation WTP: Bad Asthma Day, Rowe and Chestnut (1986) 281 $43

Work Loss Days Median Daily Wage, County-Specific --- Minor Restricted Activity Days WTP: 1 Day, CV Studies $51 School Absence Days Median Daily Wage, Women 25+ $75 Worker Productivity Me dian Daily Wage, Outdoor Workers, County-Specific ---
Environmental Endpoints Recreational Visibility WTP: 86 Class I Areas --- Source: Dollar amounts for each valuation method were extracted from BenMAP version 3.0.

7.3.2.3 Other Unquantified Health and Environmental Impacts

I n addition to the co-pollutant health and environmental impacts we will quantify for the analysis of the Light-Duty Vehicle GHG stan dard, there are a number of other health and human welfare endpoints that we will not be able to quantify because of current limitations in the methods or available data. These impacts are associated with emissions of air toxics (including benzene, 1,3-butadiene, formalde hyde, acetaldehyde, acrolein, and ethanol), ambient ozone, and ambient PM 2.5 exposures. For example, we have not quantified a number of known or suspected health effects linked w ith ozone and PM for which appropriate health impact functions are not available or which do not provide easily interpretable outcomes (i.e.,Environmental and Health Impacts

7-41 changes in heart rate variability). In additio n, we are currently unable to quantify a number of known welfare effects, including reduced acid and particulate deposition damage to cultural monuments and other materials, and environmental benefits due to reductions of impacts of eutrophication in coastal areas. For air toxics, the available tools and methods to assess risk from mobile sources at the national scale are not adequate for extrapolation to benefits assessment. In addition to inherent limitations in the tools for national-scale modeling of air toxics and exposure, there is a l ack of epidemiology data for air toxics in the general population. Table 7-8 lists these unquantified health and environmental impacts.

Table 7-8: Unquantified and Non-Monetized Potential Effects

POLLUTANT/EFFECTS EFFECTS NOT INCLUDED IN ANALYSIS - CHANGES IN:
Ozone Health a Chronic respiratory damage Premature aging of the lungs Non-asthma respiratory emergency room visits Exposure to UVb (+/-) d

Ozone Welfare Yields for -commercial forests -some fruits and vegetables -non-commercial crops Damage to urban ornamental plants Impacts on recreational demand from damaged forest aesthetics Ecosystem functions Exposure to UVb (+/-)
PM Health b Premature mortality - short term exposures c Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Exposure to UVb (+/-) PM Welfare Residential and recreational visibility in non-Class I areas Soiling and materials damage Damage to ecosystem functions Exposure to UVb (+/-) Nitrogen and Sulfate Deposition Welfare Commercial forests due to acidic sulfate and nitrate deposition Commercial freshwater fishing due to acidic deposition Recreation in terrestrial ecosystems due to acidic deposition Existence values for currently healthy ecosystems Commercial fishing, agriculture, and forests due to nitrogen deposition Recreation in estuarine ecosystems due to nitrogen deposition Ecosystem functions Passive fertilization CO Health Behavioral effects Hydrocarbon (HC)/Toxics Health e Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde, ethanol) Anemia (benzene) Disruption of production of blood components (benzene) Reduction in the number of blood platelets (benzene) Excessive bone marrow formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmental effects (1,3-butadiene, ethanol) Irritation of eyes and mucus membranes (formaldehyde) Respiratory irritation (formaldehyde) Asthma attacks in asthmatics (formaldehyde)Draft Regulatory Impact Analysis

7-42 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 f Direct toxic effects to animals Bioaccumulation in the food chain Damage to ecosystem function Odor

a In addition to primary economic endpoints, there are a number of biological responses that have been associated with ozone health effects including increased airway responsiveness to stim uli, inflammation in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection. The public health impact of these biological responses may be partly represented by our quantified endpoints. b In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints.

c While some of the effects of short-term exposures ar e likely to be captured in the estimates, there may be premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis. However, the PM mortality results de rived from the expert e licitation do take into acc ount premature mortality effects of short term exposures. d May result in benefits or disbenefits.

e Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act. Please refer to Chapter 8.4 for additional information on the health effects of air toxics.
f Please refer to Chapter 8.4 fo r additional information on the welfare effects of air toxics.

In addition to the co-pollutant health and environmental impacts we will quantify for the analysis of the final standard, there are a number of other health and human welfare endpoints that we will not be able to quantify or monetize because of current limitations in the methods or available data. These impacts are associated with emissions of air toxics (including benzene, 1,3-butadiene, formalde hyde, acetaldehyde, acrolein, and ethanol), ambient ozone, and ambient PM 2.5 exposures. Chapter 7.3 of the RIA lists these unquantified health and environmental impacts.

While there will be impacts associated with air toxic pollutant emission changes that result from the final standard, we will not at tempt to monetize those impacts. This is primarily because currently available tools and methods to assess air to xics 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 curren tly available for assessment at the national scale are those used in the National-Scale Air Toxics Assessment (NATA). The EPA Science Advisory Board specifically commen ted 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. 282 While EPA has since improved th e tools, there remain critical limitations for estimating incidence and assessing benefits of reducing mobi le source air toxics. EPA continues to work to address these limitations; howev er, we do not anticipate having methods and tools availableEnvironmental and Health Impacts

7-43 for national-scale application in time for the analysis of the final rules. Q



### 7.4 Changes in Global Mean Temperature and Sea-Level Rise Associated with the Proposal’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 Proposal will affect climate change projections. Because GHGs mix well in the atmosphere and have long atmospheric lifetimes, changes in GHG emissions will affect atmospheric concentrations of gree nhouse gases and future climate for decades to centuries. Two common indicators of climate change are global mean surface temperature andsea-level rise. This section estimates the response in global mean surface temperature and sealevel rise projections to the estimated net gl obal GHG emissions reductions associated with the Proposal (see Chapter 5 for the estimated net reductions in global emissions over time by GHG).



### 7.4.2 Estimated Projected Reductions in Gl obal Mean Surface Temperature and Sea-Level Rise

We estimated changes in projected atmospheric CO 2 concentrations, global mean surface temperature and sea-level rise to 2100 using the MiniCAM (Mini Climate Assessment Model) integrated assessment model 283 coupled with the MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change) simple climate model. 284 MiniCAM 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 CO 2 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 proposal was developed by applying the proposal’s estimated emissions reductions to the MiniCA M reference (no climate policy or baseline) scenario (used as the basis for the Re presentative Concentration Pathway RCP4.5 285 ). Specifically, the CO 2 , N 2 O, CH 4 , and HFC-134a emissions redu ctions from Chapter 5 were

Q In April, 2009, EPA hosted a workshop on estim ating the benefits or reducing hazardous air pollutants. This workshop built upon the work accomplis hed in the June 2000 Science Advisory Board/EPA Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on approaches to estimating human health be nefits 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/toxica ir/2009workshop.html for more information about the workshop and its associated materials.Draft Regulatory Impact Analysis

7-44 applied as net reductions to the MiniCAM 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 suppli ed (in Chapter 5) for 2020. The emissions reductions past 2050 were scaled with total U. S. road transportation fuel consumption from the MiniCAM reference scenario. Using MAGICC, the atmospheric CO 2 concentration, the global mean temperature, and sea-level change were projected at five-year time steps to 2100 for both the reference (no climate policy) scenar io and the emissions scenario specific to the Proposal. To capture some of the uncertainty in the climate system, the changes in projected temperatures and sea level were estimated across the most current Intergovernmental Panel on Climate Change (IPCC) range of clim ate sensitivities, 1.5°C to 6.0°C. 286

To compute the reductions in atmospheric CO 2 concentration, temperature, and sealevel rise specifically attributable to the Pr oposal, the output from the Proposal’s emissions scenario was subtracted from the reference (no po licy or baseline) emi ssions case scenario. As a result of the Proposal’s emissions reductions, the atmospheric CO 2 concentration is projected to be reduced by approximately 2.9 to 3.2 parts per million (ppm), the global mean temperature is projected to be reduced by approximately 0.007-0.016°C by 2100 and global mean sea-level rise is projected to be reduced by approximately 0.06-0.15 cm by 2100.

Figure 7-4 provides the estimated reduct io ns in projected global mean surface temperatures associated with the Proposal. Figure 7-5 provides the estimated reductions in global m ean sea-level rise associated with the Proposal.



### Global Mean Temperature Change

-0.0200 -0.0150 -0.0100 -0.0050 0.0000 2000 2050 2100

YearDifference (°C) ClimSens=1.5
ClimSens=2.0
ClimSens=2.5
ClimSens=3.0
ClimSens=4.5
ClimSens=6.0

Figure 7-4 Estimated Projected Reductions in Global Mean Surface Temperatures from Baseline for the Proposed Vehicles Rulemaking (for climate sensitivities ranging from 1.5-6°C)Environmental and Health Impacts

7-45 

### Global Mean Sea Level Rise Change

-0.20 -0.15 -0.10 -0.05 0.00 2000 2050 2100

YearDifference (cm) ClimSens=1.5
ClimSens=2.0
ClimSens=2.5
ClimSens=3.0
ClimSens=4.5
ClimSens=6.0

Figure 7-5 Estimated Projected Reductions in Gl obal Mean Sea-Level Rise from Baseline for the Proposed Vehicles Rulemaking (for climate sensitivities ranging from 1.5-6°C)

The results in both Figure 7-4 and Figure 7-5 show a relatively small reduction in the
pr ojected global mean surface temperature an d 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. 287 These projected reductions are proportionally representative of changes to U.S. GHG emissions in the transportation sector. While not formally estima ted for the proposed rulemaking, a reduction in projected global mean temperature and sea- level rise implies a reduction in the risks associated with of 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 bo th qualitatively and quantitatively, some of which can be monetized (seeChapter 7.5). There are substantial uncertainties in modeling the global risks of climate change, which complicates quantification and benefit-cost assessments. Changes in climate variables are a meaningful pr oxy 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 av ailability), as well as those that have not yet been quantified or are extremely difficult to qua ntify (e.g., respectively forest disturbance and catastrophic events such as collapse of large ice sheets and subsequent sea-level rise).Draft Regulatory Impact Analysis

7-46 

### 7.5 SCC and GHG Benefits

We assigned a monetary value to reductions in CO 2 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 GHGs, such as property damage from sea level rise, forced migration due to dr y 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 gr owth, climate sensitivity to GHG emissions, procedures used to model the economic impacts of climate change, and the choice of discount rates. Furthermore, some of the likely and potential damages from climate change—for example, the loss of endangered species—are generally not included in current SCC estimates. These omissions may turn out to be significant, in the sense that they may mean that the best current estimates are too low. As noted by the IPCC Fourth Assessment Report, “It is very likely that globally aggregated fi gures underestimate the damage costs because they cannot include many non-quantifiable impacts." 288

Today’s joint proposals present a set of interim SCC values reflecting a federal interagency group’s interpretation of the releva nt climate economics literature. The interim SCC values, which reflect an interim interpretation of the current literature, are derived using several discount rates. The interim SCC values include:

 $5 (based on a 5% discount rate);

 $10(5% using Newell-Pizer adjustment);,

 $20(average SCC value from the average SCC estimates based on 5% and 3%);

 $34 (3%);

 $56 (3% using Newell-Pizer adjustment).

These interim SCC values are in 2007 dollars, and are based on a CO 2 emissions change of 1 metric ton in 2007. Section III.H.6 of the Preamble provides a complete discussion about SCC and the interim set of values.

The tables below summarize the total GHG benefits for the lifetime of the rule, which are calculated by using the five interim SCC valu es. Specifically, total monetized benefits in each year are calculated by multiplying the marginal benefits estimates per metric ton of CO 2 (the SCC) by the reductions in CO 2 for that year. We have also approximated the total monetized benefits for non-CO 2 GHGs by multiplying the SCC value by the reductions in non-CO 2 GHGs for that year. Marginal benefit estimates per metric ton of non-CO 2 GHGs are currently unavailable, but work is on-going to monetize benefits related to the mitigation of other non-CO 2 GHGs.Environmental and Health Impacts

7-47 Table 7-9: Upstream and Downstream CO2 Benefits for th e Given SCC Value, Calendar Year Analysis (Millions of 2007 dollars)

YEAR 5% 5%NEWELLPIZER FROM 3% AND 5% 3% 3%NEWELLPIZER 2012 $35 $70 $132 $230 $383 2013 $89 $179 $340 $590 $984 2014 $168 $336 $639 $1,109 $1,849 2015 $281 $562 $1,068 $1,855 $3,092 2016 $439 $878 $1,668 $2,898 $4,829 2017 $601 $1,203 $2,285 $3,968 $6,614 2018 $768 $1,536 $2,919 $5,069 $8,449 2019 $939 $1,878 $3,568 $6,197 $10,328 2020 $1,112 $2,225 $4,227 $7,342 $12,237 2021 $1,288 $2,577 $4,896 $8,503 $14,172 2022 $1,465 $2,929 $5,566 $9,666 $16,111 2023 $1,645 $3,290 $6,250 $10,855 $18,092 2024 $1,827 $3,654 $6,942 $12,057 $20,095 2025 $2,014 $4,027 $7,652 $13,290 $22,151 2026 $2,200 $4,400 $8,359 $14,519 $24,198 2027 $2,386 $4,772 $9,067 $15,748 $26,247 2028 $2,568 $5,136 $9,759 $16,949 $28,249 2029 $2,749 $5,497 $10,445 $18,141 $30,235 2030 $2,931 $5,861 $11,137 $19,342 $32,237 2031 $3,117 $6,234 $11,844 $20,571 $34,285 2032 $3,306 $6,611 $12,562 $21,818 $36,363 2033 $3,501 $7,002 $13,305 $23,108 $38,513 2034 $3,702 $7,404 $14,068 $24,434 $40,723 2035 $3,912 $7,824 $14,865 $25,819 $43,031 2036 $4,129 $8,258 $15,691 $27,253 $45,421 2037 $4,357 $8,715 $16,558 $28,758 $47,930 2038 $4,594 $9,188 $17,458 $30,322 $50,536 2039 $4,844 $9,687 $18,406 $31,969 $53,281 2040 $5,104 $10,209 $19,396 $33,688 $56,147 2041 $5,379 $10,758 $20,441 $35,502 $59,170 2042 $5,668 $11,336 $21,539 $37,409 $62,349 2043 $5,973 $11,945 $22,696 $39,420 $65,700 2044 $6,294 $12,587 $23,916 $41,538 $69,229 2045 $6,632 $13,263 $25,200 $43,768 $72,947 2046 $6,987 $13,975 $26,552 $46,117 $76,861 2047 $7,362 $14,724 $27,976 $48,590 $80,983 2048 $7,756 $15,512 $29,473 $51,191 $85,318 2049 $8,171 $16,342 $31,049 $53,927 $89,878 2050 $8,607 $17,214 $32,706 $56,805 $94,675 NPV, 3% $62,100 $124,200 $236,000 $409,800 $683,100 NPV, 7% $25,600 $51,200 $97,200 $168,800 $281,300Draft Regulatory Impact Analysis

7-48 Table 7-10: Upstream and Downstream non-CO2 GHG Benefits for the Given SCC Value, Calendar Year Analysis (Millions of 2007 dollars)

YEAR 5% 5%NEWELLPIZER FROM 3% AND 5% 3% 3%NEWELLPIZER 2012 $5 $11 $21 $36 $60 2013 $13 $27 $51 $88 $147 2014 $24 $48 $91 $158 $263 2015 $38 $76 $145 $252 $419 2016 $55 $109 $208 $361 $601 2017 $72 $144 $274 $476 $793 2018 $91 $181 $344 $597 $996 2019 $110 $220 $417 $725 $1,208 2020 $133 $266 $506 $879 $1,465 2021 $154 $308 $585 $1,016 $1,694 2022 $175 $351 $667 $1,158 $1,929 2023 $198 $395 $751 $1,304 $2,173 2024 $220 $440 $837 $1,453 $2,422 2025 $243 $487 $925 $1,606 $2,677 2026 $267 $533 $1,013 $1,760 $2,934 2027 $290 $580 $1,102 $1,913 $3,189 2028 $313 $626 $1,189 $2,064 $3,441 2029 $334 $668 $1,269 $2,204 $3,674 2030 $358 $716 $1,361 $2,364 $3,940 2031 $379 $758 $1,441 $2,503 $4,172 2032 $400 $801 $1,521 $2,642 $4,403 2033 $422 $843 $1,602 $2,782 $4,637 2034 $443 $886 $1,683 $2,924 $4,873 2035 $465 $930 $1,766 $3,067 $5,112 2036 $487 $974 $1,851 $3,214 $5,357 2037 $510 $1,019 $1,937 $3,364 $5,607 2038 $533 $1,066 $2,025 $3,517 $5,862 2039 $557 $1,114 $2,116 $3,675 $6,124 2040 $581 $1,163 $2,209 $3,836 $6,394 2041 $607 $1,214 $2,306 $4,005 $6,676 2042 $633 $1,267 $2,407 $4,180 $6,967 2043 $661 $1,321 $2,511 $4,361 $7,268 2044 $689 $1,378 $2,619 $4,548 $7,580 2045 $719 $1,437 $2,730 $4,742 $7,904 2046 $749 $1,498 $2,846 $4,944 $8,240 2047 $781 $1,561 $2,967 $5,153 $8,588 2048 $814 $1,627 $3,091 $5,369 $8,949 2049 $847 $1,695 $3,220 $5,593 $9,322 2050 $883 $1,765 $3,354 $5,826 $9,710 NPV, 3% $7,100 $14,200 $27,100 $47,000 $78,400 NPV, 7% $3,000 $6,000 $11,400 $19,700 $32,900Environmental and Health Impacts

7-49 Table 7-11: Upstream and Downstream CO2-Equivalent Benefits for the Given SCC Value, Calendar Year Analysis (Millions of 2007 dollars)

YEAR 5% 5%NEWELLPIZER FROM 3% AND 5% 3% 3%NEWELLPIZER 2012 $40 $81 $153 $266 $443 2013 $103 $206 $391 $678 $1,131 2014 $192 $384 $729 $1,267 $2,112 2015 $319 $638 $1,213 $2,107 $3,511 2016 $494 $987 $1,876 $3,258 $5,430 2017 $673 $1,347 $2,559 $4,444 $7,407 2018 $859 $1,717 $3,263 $5,667 $9,444 2019 $1,049 $2,098 $3,985 $6,922 $11,537 2020 $1,246 $2,491 $4,734 $8,222 $13,703 2021 $1,442 $2,885 $5,481 $9,520 $15,866 2022 $1,640 $3,280 $6,232 $10,824 $18,040 2023 $1,842 $3,685 $7,001 $12,159 $20,265 2024 $2,047 $4,094 $7,779 $13,510 $22,517 2025 $2,257 $4,514 $8,577 $14,897 $24,828 2026 $2,466 $4,933 $9,373 $16,279 $27,131 2027 $2,676 $5,352 $10,169 $17,661 $29,435 2028 $2,881 $5,762 $10,947 $19,014 $31,690 2029 $3,083 $6,165 $11,714 $20,345 $33,909 2030 $3,289 $6,578 $12,498 $21,707 $36,178 2031 $3,496 $6,992 $13,285 $23,074 $38,457 2032 $3,706 $7,412 $14,083 $24,460 $40,766 2033 $3,923 $7,845 $14,906 $25,890 $43,150 2034 $4,145 $8,290 $15,751 $27,357 $45,595 2035 $4,377 $8,753 $16,631 $28,886 $48,143 2036 $4,616 $9,232 $17,541 $30,467 $50,778 2037 $4,867 $9,734 $18,494 $32,122 $53,537 2038 $5,127 $10,254 $19,483 $33,839 $56,398 2039 $5,400 $10,801 $20,522 $35,643 $59,405 2040 $5,686 $11,371 $21,605 $37,525 $62,541 2041 $5,986 $11,972 $22,747 $39,507 $65,846 2042 $6,301 $12,603 $23,945 $41,589 $69,315 2043 $6,633 $13,267 $25,207 $43,781 $72,968 2044 $6,983 $13,965 $26,534 $46,086 $76,809 2045 $7,350 $14,700 $27,930 $48,510 $80,851 2046 $7,736 $15,473 $29,398 $51,060 $85,101 2047 $8,143 $16,286 $30,943 $53,743 $89,571 2048 $8,570 $17,139 $32,565 $56,560 $94,267 2049 $9,018 $18,036 $34,269 $59,520 $99,201 2050 $9,490 $18,979 $36,060 $62,631 $104,385 NPV, 3% $69,200 $138,400 $263,000 $456,900 $761,400 NPV, 7% $28,600 $57,100 $108,500 $188,500 $314,200

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 yearDraft Regulatory Impact Analysis

7-50 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 DRIA chapter 5. The GHG benefits of the full life of each of the five model years from 2012 through 2016 are shown in Table 7-12 through Table 7-16 for each of the five differe nt social cost of carbon values. Th e GHG benefits are shown for each year in the model year life and in net present value using both a 3 percent and a 7 percent discount rate.Environmental and Health Impacts

7-51 Table 7-12: Upstream and Downstream CO2-Equivalent Benefits for the 5% SCC Value, Model Year Analysis (Millions of 2007 dollars)

YEAR 2012 2013 2014 2015 2016 SUM
2012 $42 $0 $0 $0 $0 $42 2013 $42 $67 $0 $0 $0 $109 2014 $42 $66 $95 $0 $0 $204 2015 $42 $66 $95 $137 $0 $340 2016 $41 $66 $95 $137 $188 $526 2017 $40 $65 $94 $136 $187 $522 2018 $39 $63 $92 $135 $187 $516 2019 $37 $61 $90 $133 $185 $507 2020 $36 $59 $88 $130 $182 $494 2021 $34 $56 $84 $126 $178 $478 2022 $32 $53 $80 $121 $172 $459 2023 $29 $50 $76 $115 $166 $436 2024 $25 $46 $71 $109 $158 $409 2025 $21 $40 $65 $102 $150 $378 2026 $18 $34 $57 $94 $140 $341 2027 $15 $28 $48 $81 $128 $300 2028 $12 $23 $40 $69 $112 $257 2029 $10 $19 $34 $58 $95 $216 2030 $8 $16 $28 $49 $80 $181 2031 $7 $13 $23 $40 $67 $150 2032 $5 $11 $19 $33 $56 $124 2033 $5 $9 $16 $28 $46 $103 2034 $4 $8 $13 $23 $38 $85 2035 $3 $6 $11 $19 $31 $71 2036 $3 $5 $9 $16 $26 $60 2037 $2 $5 $8 $14 $22 $51 2038 $2 $4 $7 $12 $19 $43 2039 $1 $3 $6 $10 $16 $37 2040 $1 $3 $4 $9 $14 $31 2041 $1 $2 $4 $6 $12 $26 2042 $1 $2 $4 $6 $9 $21 2043 $1 $2 $3 $5 $8 $19 2044 $1 $2 $3 $5 $7 $17 2045 $1 $2 $3 $4 $6 $15 2046 $1 $1 $2 $4 $6 $14 2047 $1 $1 $2 $3 $5 $12 2048 $0 $1 $2 $3 $5 $11 2049 $0 $0 $2 $3 $4 $8 2050 $0 $0 $0 $2 $4 $6 NPV,

3% $477 $733 $1,021 $1,426 $1,897 $5,555

NPV, 7% $368 $543 $727 $978 $1,251 $3,866Draft Regulatory Impact Analysis

7-52 Table 7-13: Upstream and Downstream CO2-Equivalent Benefits for the 5% Newell-Pizer SCC Value, Model Year Analysis (Millions of 2007 dollars)

YEAR 2012 2013 2014 2015 2016 SUM
2012 $84 $0 $0 $0 $0 $84 2013 $84 $133 $0 $0 $0 $217 2014 $84 $133 $191 $0 $0 $407 2015 $83 $133 $190 $274 $0 $680 2016 $82 $131 $190 $273 $376 $1,052 2017 $80 $129 $187 $273 $374 $1,044 2018 $78 $126 $185 $270 $374 $1,033 2019 $75 $122 $180 $266 $370 $1,013 2020 $71 $118 $175 $260 $364 $988 2021 $68 $112 $168 $252 $356 $956 2022 $64 $107 $160 $242 $345 $918 2023 $58 $100 $152 $231 $332 $872 2024 $50 $91 $142 $219 $316 $818 2025 $42 $79 $130 $205 $300 $756 2026 $35 $67 $113 $187 $280 $683 2027 $29 $56 $96 $163 $256 $601 2028 $24 $47 $81 $139 $223 $513 2029 $20 $39 $68 $116 $190 $432 2030 $16 $32 $56 $97 $160 $361 2031 $13 $26 $47 $81 $133 $300 2032 $11 $22 $38 $67 $111 $249 2033 $9 $18 $32 $55 $92 $206 2034 $8 $15 $26 $46 $76 $171 2035 $7 $13 $22 $38 $63 $142 2036 $5 $11 $19 $32 $52 $120 2037 $5 $9 $16 $27 $44 $102 2038 $3 $8 $14 $23 $37 $86 2039 $3 $6 $12 $20 $32 $73 2040 $3 $5 $9 $17 $28 $62 2041 $2 $5 $8 $13 $24 $52 2042 $2 $4 $7 $12 $18 $43 2043 $2 $4 $6 $10 $16 $38 2044 $2 $3 $6 $9 $14 $35 2045 $2 $3 $5 $8 $13 $31 2046 $1 $3 $5 $7 $12 $28 2047 $1 $2 $4 $7 $10 $25 2048 $0 $2 $4 $6 $9 $21 2049 $0 $0 $3 $5 $8 $17 2050 $0 $0 $0 $5 $7 $12 NPV,

3% $955 $1,467 $2,042 $2,853 $3,794 $11,109

NPV, 7% $736 $1,086 $1,453 $1,955 $2,503 $7,733Environmental and Health Impacts

7-53 Table 7-14: Upstream and Downstream CO2-Equivalent Bene fits for the from 3% and 5% SCC Value, Model Year Analysis (Millions of 2007 dollars)

YEAR 2012 2013 2014 2015 2016 SUM
2012 $160 $0 $0 $0 $0 $160 2013 $160 $253 $0 $0 $0 $413 2014 $160 $252 $362 $0 $0 $774 2015 $158 $252 $361 $521 $0 $1,292 2016 $156 $249 $360 $519 $714 $1,998 2017 $152 $245 $356 $519 $711 $1,984 2018 $148 $240 $351 $513 $711 $1,962 2019 $142 $233 $343 $505 $702 $1,925 2020 $136 $224 $333 $493 $692 $1,878 2021 $129 $213 $320 $479 $676 $1,816 2022 $121 $203 $304 $460 $655 $1,743 2023 $110 $190 $289 $438 $630 $1,657 2024 $95 $173 $270 $416 $600 $1,555 2025 $81 $150 $247 $389 $569 $1,436 2026 $67 $128 $215 $355 $532 $1,298 2027 $56 $107 $183 $309 $487 $1,141 2028 $46 $89 $154 $263 $424 $976 2029 $38 $74 $128 $221 $361 $821 2030 $31 $61 $107 $185 $303 $686 2031 $25 $50 $88 $154 $253 $570 2032 $21 $41 $73 $127 $211 $473 2033 $17 $34 $60 $105 $175 $391 2034 $14 $29 $50 $87 $144 $324 2035 $12 $24 $42 $72 $119 $270 2036 $10 $21 $36 $61 $99 $227 2037 $9 $18 $31 $52 $84 $193 2038 $6 $15 $26 $45 $71 $164 2039 $6 $11 $23 $38 $62 $139 2040 $5 $10 $17 $33 $53 $118 2041 $4 $9 $15 $24 $46 $99 2042 $4 $8 $14 $22 $34 $81 2043 $4 $7 $12 $20 $30 $73 2044 $3 $6 $11 $18 $27 $66 2045 $3 $6 $10 $16 $25 $59 2046 $3 $5 $9 $14 $22 $53 2047 $2 $5 $8 $13 $20 $47 2048 $0 $4 $7 $11 $18 $40 2049 $0 $0 $6 $10 $16 $32 2050 $0 $0 $0 $9 $14 $23 NPV,

3% $1,814 $2,786 $3,879 $5,420 $7,208 $21,108

NPV, 7% $1,398 $2,063 $2,762 $3,715 $4,756 $14,693Draft Regulatory Impact Analysis

7-54 Table 7-15: Upstream and Downstream CO2-Equivalent Benefits for the 3% SCC Value, Model Year Analysis (Millions of 2007 dollars)

YEAR 2012 2013 2014 2015 2016 SUM
2012 $278 $0 $0 $0 $0 $278 2013 $277 $439 $0 $0 $0 $717 2014 $277 $438 $629 $0 $0 $1,344 2015 $274 $437 $627 $905 $0 $2,243 2016 $270 $432 $626 $902 $1,240 $3,471 2017 $264 $426 $619 $901 $1,235 $3,445 2018 $257 $416 $610 $890 $1,234 $3,407 2019 $247 $404 $595 $878 $1,220 $3,344 2020 $236 $389 $578 $857 $1,202 $3,261 2021 $224 $371 $555 $831 $1,174 $3,154 2022 $210 $352 $529 $799 $1,138 $3,028 2023 $191 $329 $502 $761 $1,094 $2,878 2024 $166 $301 $469 $722 $1,042 $2,700 2025 $140 $261 $429 $676 $989 $2,494 2026 $117 $222 $373 $617 $925 $2,254 2027 $97 $186 $318 $537 $845 $1,982 2028 $80 $154 $267 $457 $736 $1,694 2029 $65 $128 $223 $384 $627 $1,427 2030 $53 $105 $186 $321 $527 $1,192 2031 $43 $87 $153 $267 $440 $990 2032 $36 $71 $127 $221 $367 $821 2033 $30 $59 $105 $182 $303 $679 2034 $25 $50 $87 $151 $250 $563 2035 $21 $42 $74 $125 $207 $469 2036 $18 $36 $62 $106 $173 $395 2037 $16 $31 $54 $90 $146 $336 2038 $11 $27 $46 $78 $124 $284 2039 $10 $19 $40 $66 $107 $242 2040 $9 $17 $29 $58 $91 $204 2041 $8 $15 $26 $42 $80 $172 2042 $7 $14 $24 $38 $59 $141 2043 $6 $12 $21 $34 $53 $127 2044 $6 $11 $19 $31 $47 $114 2045 $5 $10 $17 $27 $43 $102 2046 $4 $9 $15 $25 $38 $91 2047 $4 $8 $14 $22 $34 $82 2048 $0 $7 $12 $20 $31 $70 2049 $0 $0 $11 $18 $27 $56 2050 $0 $0 $0 $16 $24 $40 NPV,

3% $3,151 $4,840 $6,738 $9,414 $12,519 $36,661

NPV, 7% $2,428 $3,584 $4,796 $6,452 $8,260 $25,519Environmental and Health Impacts

7-55 Table 7-16: Upstream and Downstream CO2-Equivalent Benefits for the 3% Newell-Pizer SCC Value, Model Year Analysis (Millions of 2007 dollars)

YEAR 2012 2013 2014 2015 2016 SUM
2012 $463 $0 $0 $0 $0 $463 2013 $462 $732 $0 $0 $0 $1,194 2014 $462 $730 $1,048 $0 $0 $2,240 2015 $457 $729 $1,044 $1,508 $0 $3,739 2016 $451 $721 $1,043 $1,503 $2,067 $5,784 2017 $441 $711 $1,031 $1,501 $2,059 $5,742 2018 $428 $694 $1,016 $1,484 $2,057 $5,679 2019 $412 $674 $992 $1,463 $2,033 $5,573 2020 $393 $648 $963 $1,428 $2,003 $5,435 2021 $373 $618 $925 $1,385 $1,956 $5,257 2022 $350 $586 $881 $1,332 $1,897 $5,046 2023 $319 $549 $836 $1,269 $1,824 $4,796 2024 $276 $501 $782 $1,204 $1,737 $4,500 2025 $233 $435 $715 $1,126 $1,648 $4,157 2026 $195 $369 $622 $1,029 $1,541 $3,756 2027 $161 $309 $530 $896 $1,408 $3,304 2028 $133 $257 $445 $762 $1,227 $2,824 2029 $109 $213 $371 $640 $1,044 $2,378 2030 $89 $176 $309 $534 $878 $1,986 2031 $72 $144 $256 $445 $733 $1,651 2032 $60 $119 $211 $368 $611 $1,368 2033 $50 $98 $174 $303 $506 $1,132 2034 $42 $83 $145 $251 $417 $938 2035 $36 $70 $123 $209 $345 $782 2036 $30 $60 $104 $177 $288 $658 2037 $26 $51 $90 $149 $243 $560 2038 $18 $44 $77 $129 $206 $474 2039 $16 $32 $67 $110 $178 $403 2040 $14 $29 $49 $96 $152 $340 2041 $13 $26 $44 $70 $133 $286 2042 $12 $23 $40 $63 $98 $236 2043 $10 $21 $36 $57 $88 $212 2044 $9 $18 $32 $51 $79 $190 2045 $8 $17 $29 $46 $71 $170 2046 $7 $15 $26 $41 $64 $152 2047 $7 $13 $23 $37 $57 $137 2048 $0 $12 $20 $33 $51 $116 2049 $0 $0 $18 $29 $46 $93 2050 $0 $0 $0 $26 $41 $67 NPV,

3% $5,251 $8,066 $11,229 $15,690 $20,865 $61,102

NPV, 7% $4,046 $5,973 $7,994 $10,753 $13,766 $42,531Draft Regulatory Impact Analysis

7-56 

### 7.6 Weight Reduction and Vehicle Safety

Over the past 20 years there has been a ge nerally increasing trend in the weight of vehicles (see figure III.X-1 below from EPA’s Fuel Economy Trends Report). 289 There have been a number of factors contributing to this in cluding: 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, et c). 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. Unfortunately, as the figure also shows, none of this efficiency improvement has been realized in fuel economy gains or GHG emissions reductions.

Figure 7-6: Weight, 0-to-60 MPH acceleration time and adjusted fuel economy for light-duty vehicles
During this same period, 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 drivers are able to avoid accidents with anti-lock brakes (ABS), electronic stability control (ESC), and improved tires and suspension. NHTSA anticipates a 12.6 percent reduction in fatality levels between 2007 and 2020 with safety improvements due to pending NHTSA FMVSS and other factors. Assuming that safety improvements wi ll 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 CO 2 emissions, the reduction of vehicle weight is a controversial and complicated topic. In a joint technical analysis, EPA and NHTSA agree that automakers could reduce weight as one part of the industries’ strategy forEnvironmental and Health Impacts

7-57 meeting the proposed standards. As shown in table III.D.6-3 of the Preamble, EPA is expecting that vehicle manufacturers will reduce the weight of their vehicles by approximately 4% on average between 2011 and 2016 although individual vehicles may have greater or smaller weight reduction (NHTSA’s results are similar using the Volpe model.) The penetration and magnitude of these changes are consistent with the public announcements made by many of the manufacturers since early 2008 and are consistent with the 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 May 2009, EPA met with 11 major auto companies: GM, Chrysler, Ford, Nissan, Honda, Toyota, M itsubishi, 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 the 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 platforms

 Toyota: 30% weight reduction on 2015 Corolla and a 10% weight reduction on midsize vehicles by 2015

 Nissan: 15% average weight reduction by 2015

 Mazda: 100 kg (220 pound) weight redu ction by 2011 and an additional 100 kg weight reduction by 2016

 Mercedes: 5% average weig ht reduction by 2015

Reducing vehicle mass without reducing the si ze, 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 DRIA. 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 fu nction of the component. This includes substitution of high-strength steels, alumin um, 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, th ere 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.Draft Regulatory Impact Analysis

7-58  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. Sm art 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 fram e” vehicles are redesigned with a lighter “unibody” construction with little comp romise 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 redu ction is due to thes e reduced powertrain output requirements from reduced engine power output and/or displacement, lighter weight transmission and final drive gear ratios. 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, an d thus allow further reductions in the weight of these subsystems. The reduction s in weight for unsp rung masses such as brakes, control arms, wheels and tires can further reduce stresses in the suspension mounting points which can allow still furt her reductions in weight. For example, lightweighting 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. 290

Weight reduction is broadly applicable acr oss 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 believes 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 task s. We request 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 projects that automakers will not reduce footprint in order to meet the proposed CO 2 standards in our modeling analysis. NHTSA and EPA have taken two measures to help ensure that the proposed rules provide no 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 de sign feature of the proposed rule is that the CO 2 or fuel economy targets are based on the attr ibute of footprint (which is a surrogate forEnvironmental and Health Impacts

7-59 vehicle size). R The second design feature is that the shape of the footprint curve (or function) has been carefully chosen such that it neither encourages manufacturers to increase, nor decrease the footprint of their fleet. Thus , the standard curves are designed to be approximately “footprint neutral” within the sloped portion of the function. S For further discussion on this, refer to Section II.C of the preamble, or Chapter 2 of the joint TSD. Thus the agencies are assuming in their modeling an alysis that the manufacturers could reduce vehicle mass without reducing vehicle footprint as one way to respond to the proposed rule. T

In Section IV of the preamble, NHTSA presen ts a safety analysis of the proposed CAFE standards based on the 2003 Kahane 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. 291,292 The results from 1991-1999 vehicle data indicate that a heavier vehicle is safer th an a lighter one based on the assumption that historical vehicle mass reductions are accompanied with vehicle size and footprint reductions.

As discussed in Section IV of the Preamble, NHTSA has developed a worse case estimate of the impact of weight reductions on fatalities. The underlying data used for that analysis does not allow NHTSA to analyze the spec ific impact of weight reduction at constant footprint because historically there have not be en a large number of vehicles produced that relied substantially on material substitution. Rather, the data set includes vehicles that were either smaller and lighter or larger and heavier. The numbers in the NHTSA analysis predict the safety-related fatality consequences that wo uld occur in the unlikely event that weight reduction for model years 2012-2016 is accomplished by reducing mass and reducing footprint. EPA concurs with NHTSA that the safety analysis conducted by NHTSA and presented in Section IV is a worst case analysis for fatalities and we expect the actual impacts on vehicle safety could be much less. EPA and NHTSA are not able to quantify the lowerbound or the best-case potential impacts at this time.

The 2005 Dynamic Research, Incorporated (DRI) studies 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 studies, 293,294 one of which was published as a Society of Automotive Engineers Technical Paper and received peer review through that body, DRI presented result s that indicate that vehicle weight reduction tends to decrease fatalities, but vehicle whee lbase and track reduction tends to increase fatalities. The DRI work focused on four major points, with #1 and #4 being discussed with additional detail below:

R As the footprint attribute is defined as wheelbase tim es 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.

S This neutrality with respect to foot print does not extend to the smallest and largest vehicles, because the function is limited, or flattened, in these footprint ranges

T See Chapter 1 of the joint TSD for a description of potential footprint changes in the 2016 reference fleetDraft Regulatory Impact Analysis

7-60 1. 2-Door vehicles represented a signif icant 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.

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 lower centers of gravity and lower loads on the roof structures.

The data used for the DRI analysis was similar to NHTSA’s 2003 Kahane study, using Fatality Analysis Reporting System (FARS) data for vehicle model year s 1985 thr ough 1998 for cars, and 1985 through 1997 trucks. This data overlaps Kahane’s FARS data on model year 1991 to 1999 vehicles. DRI also used a logistical 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. These vehicles have relatively high weight relative to their wheelbase, and are also disproportionately involved in crashes. Thus, Kahane concluded that including these vehicl es 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. The majority of 2-door vehicles excluded in the 2003 Kahane study and included in DRI's analysis were high-sales volume light-duty vehicles and vehicles shared common vehicle platforms and architectures with 4-door vehicles that were included in the 2003 Kahane study. 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. 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.

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 sta tistically significant when 2-door cars were removed. EPA and NHTSA recognize 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 for the Kahane study abov e. Thus, the agencies seek comment on how to ensure that any analysis supporting the final rule accounts as fully as possible for the range of safety impacts due to weight reduction on the variety of vehicles regulated under these proposed standards.

The DRI and Kahane studies also differ 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 analysisEnvironmental and Health Impacts

7-61 indicates 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 d ecrease rollover fatalities, and wheelbase and track reduction would increase rollover fataliti es. DRI offers two potential root causes for higher curb weight resulting in higher rollover fata lities. The first is that a taller vehicle tends to be heavier than a shorter vehicle; therefore he avier 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 ha d relatively less rollover crashworthiness.

NHTSA has rejected the DRI analysis, and has not relied on it for its evaluation of safety impact changes in CAFE standards. See Section IV.G.6 of this Notice, as well as NHTSAs March 2009 Final Rulemaking for MY2011 CAFE standards (see 74 FR at 14402- 05).

The DRI and Kahane analysis of the FARS data appear to be quite similar in one respect because the results are reproducible be tween the two studies when using aggregated vehicle attributes for 4-door cars. 293,294,295 The two analyses differ when individual vehicle attributes of mass, wheelbase and track width are separately analyzed. NHTSA has raised this as a concern with the DRI study. When 2-do or vehicles are removed from the data set EPA is concerned that the results may no longer be statistically significant with respect to independent vehicle attributes due to the small size of the remaining data set, as DRI stated in the 2005 study.

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 notes 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. EPA invites comment on all aspects of the issue of the impact of this kind of weight reduction on safety, including the usefulne ss of the DRI study in ev aluating this issue.

The agencies are committed to continuing to analyze vehicle safety issues so a more informed evaluation can be made. We request comment on this issue. These comments should include not only further discussion and analysis of the relevant studies but data and analysis which can allow the agencies to more accurately quantify any potential safety issues with the proposed standards.Draft Regulatory Impact Analysis

7-62 References

References can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472 or are publically available.

1 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-05-005a. Retrieved March 19, 2009 from http://www.epa. gov/ttn/naaqs/standards/pm/d ata/pmstaffpaper_2005 1221.pdf. Section 2.2.

2 U.S. EPA (2004). Air Quality Criteria for Particulate Matter . Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/.

3 U.S. EPA. (2005). Review of the National Ambient Air Qua lity Standard for Particulate Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. EPA-452/R-05-005a. Retrieved March 19, 2009 from http://www.epa .gov/ttn/naaqs/standards/pm/data/pmstaffpap er_20051221.pdf.

4 U.S. EPA. (2006). Provisional Assessment of Recent Studies on Hea lth Effects of Particulate Matter Exposure. EPA/600/R-06/063. Retrieved on March 19, 2009 from http://www.epa.gov/air/particlepollution/pdfs/ord_report_20060720.pdf.

5 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 8-305.

6 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 9-93.

7 U.S. EPA (2004). Air Quality Criteria for Particulate Matter . Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Section 8.3.3.1.

8 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Table 8-34.

9 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. Section 8.3.1.3.4.

10 U.S. EPA. (2006). National Ambient Air Quality Standards for Particulate Matter ; Proposed Rule. 71 FR 2620, January 17, 2006.

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12 U.S. EPA (2004). Air Quality Criteria for Particulate Matter . Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 8-85.

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18 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 8-307.

19 U.S. EPA (2004). Air Quality Criteria for Particulate Matter . Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 8-313, 8-314.

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66 Coburn, R.F. (1979) Mechanisms of carbon monoxide toxicity. Prev. Med. 8:310-322.

67 Helfaer, M.A., and Traystman, R.J. (1996) Cerebrovascular effects of carbon monoxide. In: Carbon Monoxide (Penney, D.G., ed). Boca Raton, CRC Press, 69-86.

68 Benignus, V.A. (1994) Behavioral effects of carbon mo noxide: meta analyses and extrapolations. J. Appl. Physiol. 76:1310-1316.

69 U.S. EPA (2000). Air Quality Cr iteria for Carbon Monoxide. EPA600 -P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http ://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is availabl e in Docket EPA-HQ-OAR-2005-0161.

70 U.S. EPA (2000). Air Quality Cr iteria for Carbon Monoxide. EPA600 -P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http ://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is availabl e in Docket EPA-HQ-OAR-2005-0161.

71 U.S. EPA (2000). Air Quality Cr iteria for Carbon Monoxide. EPA600 -P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http ://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is availabl e in Docket EPA-HQ-OAR-2005-0161.

72 U.S. EPA (2000). Air Quality Cr iteria for Carbon Monoxide. EPA600 -P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http ://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is availabl e in Docket EPA-HQ-OAR-2005-0161.

73 U. S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/n ata2002/risksum.html

74 U.S. EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources. 72 FR 8428; February 26, 2007.

75 U.S. EPA (2003) Integrated Risk Information System File of Acro lein. National Center for Environmental Assessment, Office of Research and Development, Wa shington, D.C. 2003. This material is available electronically at http://www .epa.gov/iris/subst/0364.htm.

76 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. This mate rial is available electronically at http://www.epa.gov/ttn/atw/n ata2002/risksum.html.

77 U.S. EPA (2009) National-Scale Air Toxics Assessm ent for 2002. http://www.e pa.gov/ttn/atw/nata2002.Environmental and Health Impacts

7-67 78 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.

79 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.

80 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henr y, V.A. (1992) Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of granuloc yte/macrophage co lony-stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691-3695.

81 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.

82 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.n iehs.nih.gov /go/16183.

83 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193-197.

84 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541-554.

85 Rothman, N., G.L. Li, M. Do semeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q. Xi, W. Lu, M.T. Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among Chinese workers heavily exposed to benzene. Am. J. Ind. Med. 29: 236-246.

86 U.S. EPA 2002 Toxicological Review of Benzene (Nonc ancer Effects). Environmental Protection Agency, Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental Assessment, Washington DC. This mate rial is available electronically at http://www.epa.gov/iris/subst/0276.htm.

87 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Me likian, 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, Valida tion & Evaluation of Biomarkers in Workers Exposed to Benzene in China.

88 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohe n, et al. (2002). Hematological changes among Chinese workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 275-285.

89 Lan, Qing, Zhang, L., Li, G., Vermeu len, R., et al. (2004). Hematot oxically in Workers Exposed to Low Levels of Benzene. Science 306: 1774-1776.

90 Turtletaub, K.W. and Mani, C. (2003). Benzene metabol ism in rodents at doses relevant to human exposure from Urban Air. Research Reports Health Effect Inst. Report No.113.

91 U.S. EPA. 2002. Health Assessment of 1,3-Butadiene. Office of Research and Development, National Center for Environmental Assessment, Washington Office, Wa shington, DC. Report No. EPA600-P-98-001F. This document is available electronically at h ttp://www.epa.gov/iris/supdocs/buta-sup.pdf.

92 U.S. EPA. 2002 “Full IRIS Summary for 1,3-buta diene (CASRN 106-99-0)” Environmental Protection Agency, Integrated Risk Informa tion System (IRIS), Research and Development, National Center for Environmental Assessment, Washington, DC http://www.epa.gov/ir is/subst/0139.htm.

93 International Agency for Research on Cancer (IARC) (1999) Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 71, Re-evaluati on of some organic chemicals, hydrazine and hydrogen peroxide and Volume 97 (in preparation), World Health Organization, Lyon, France.

94 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.n iehs.nih.gov /go/16183.

95 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) S ubchronic toxicity of 4-vinylcy clohexene in rats and mice by inhalation. Fundam. Appl. Toxicol. 32:1-10.

96 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents fromDraft Regulatory Impact Analysis

7-68 Exposure to Formaldehyde, Office of Pesticides and Toxic Substances, April 1987.

97 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Ha yes, R. B.; Blair, A. 2003. Mortality from lymphohematopoetic malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615-1623.

98 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.

99 Beane Freeman, L. E.; Blair, A.; Lubi n, 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.

100 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

101 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.

102 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically motivated computational modeling of formaldehyde carci nogenicity in the F344 rat. Tox Sci 75: 432-447.

103 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human respiratory tract cancer risks of inhaled formaldeh yde: Dose-response predictions derived from biologicallymotivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279-296.

104 Chemical Industry Institute of To xicology (CIIT).1999. Formaldehyde: Hazard characterization and doseresponse assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC.

105 U.S. EPA. Analysis of the Sensitivity and Uncertai nty in 2-Stage Clonal Growth Models for Formaldehyde with Relevance to Other Biological ly-Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-08/103, 2008

106 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.

107 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertaintie s in the CIIT 2-stage model for formaldehyde-induced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237

108 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sens itivity analysis of biologi cally motivated model for formaldehyde-induced respiratory cancer in humans. Ann Occup Hyg 52:481-495.

109 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tertButoxypropan-2-ol. Monographs Volume 88. World Health Organization, Lyon, France.

110 Agency for Toxic Substances and Disease Registry (A TSDR). 1999. Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of Health a nd Human Services, Pub lic Health Service. http://www.atsdr.cdc.gov/to xprofiles/tp111.html

111 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint sponsorship of the United Nations Environment Prog ramme, the Intern ational Labour Organization, and the World Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound Management of Chemicals. Geneva.

112 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.Environmental and Health Impacts

7-69 113 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.n iehs.nih.gov /go/16183.

114 International Agency for Research on Cancer (IARC). 1999. Re-evalua tion of some organic chemicals, hydrazine, and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.

115 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Th is material is available electronically at http://www .epa.gov/iris/subst/0290.htm.

116 U.S. EPA. 2003. Integrated Risk Information System F ile of Acrolein. Research and Development, National Center for Environmental Assessment, Washington, DC . This material is ava ilable electronically at http://www.epa.gov/iris/subst/0364.htm.

117 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.

118 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acetaldehyde induces histamine-mediated bronchoconstriction in asthmatic s. Am. Rev. Respir.Dis .148(4 Pt 1): 940-943.

119 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

120 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.

121 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

122 Sim, VM; Pattle, RE. (1957) Effect of possible smog irritants on human subjects. J Am Med Assoc 165(15):1908-1913.

123 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. I mmediate sensory nerve-mediated respiratory responses to irritants in healthy and allergic airway-d iseased mice. J Appl Physiol 94(4):1563-1571.

124 Sim VM, Pattle RE. Effect of possible smog irrita nts on human subjects JAMA165: 1980-2010, 1957.

125 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.

126 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Ta ng, 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.

127 U. S. EPA. 2004. Toxicological Review of Naph thalene (Reassessment of the Inhalation Cancer Risk), Environmental Protection Agency, Integrated Risk Info rmation 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.

128 Oak Ridge Institute for Science and Ed ucation. (2004). External Peer Review for the IRIS Reassessment of the Inhalation Carcinogenicity of Naphthalene. August 2004. http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=84403

129 National Toxicology Program (NTP). (2004). 11th Report on Carcinogens. Public Health Service, U.S. Department of Health and Human Services, Research Triangle Park, NC. Av ailable from: http://ntpserver.niehs.nih.gov.

130 International Agency for Research on Cancer (IARC). (2002). Monographs on the Evaluation of theDraft Regulatory Impact Analysis

7-70 Carcinogenic Risk of Ch emicals for Humans. Vo l. 82. Lyon, France.

131 U. S. EPA. 1998. Toxicological Review of Naphthalene , Environmental Protection Agency, Integrated Risk Information System, Research and Development, Nationa l Center for Environmental Assessment, Washington, DC. This material is available electronica lly at http://www.epa.g ov/iris/subst/0436.htm

132 U.S. EPA Integrated Risk Information System (I RIS) database is available at: www.epa.gov/iris

133 U.S. EPA (2004). Air Quality Criteria for Particulate Matter . Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 4-179.

134 U.S. EPA (2004). Air Quality Criteria for Particulate Matter . Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/ . p. 4-236.

135 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. p. 4-182.

136 Sisler, J.F. (1996) Spatial and seasonal patterns and long term variability of the comp osition of the haze in the United States: an analysis of data from the IMPROVE network . CIRA Report, ISSN 0737-5352-32, Colorado State University.

137 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 Radi ation, Office of Policy Analysis and Review, Washington, DC, November; EPA report no. EPA410-R-99-001. This document is contained in Docket Identification EPA-HQ-OAR-2004- 0008-0485.

138 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

139 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.

140 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 EPAHQ-OAR-2004-0008-0455 to 0457
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141 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

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142 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket Identification EPAHQ-OAR-2004-0008-0455 to 0457
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143 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This docu ment is contained in Dock et Identification EPA-HQOAR-2004-0008-0455 to 0457
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144 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 EPAHQ-OAR-2004-0008-0455 to 0457.

145 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 docu ment is contained in Dock et Identification EPA-HQOAR-2004-0008-0455 to 0457
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146 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.Environmental and Health Impacts

7-71 147 Winner, W.E. (1994). Mechanistic analysis of plant responses to air pollution. Ecological Applications, 4(4), 651-661.

148 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket Identification EPAHQ-OAR-2004-0008-0455 to 0457.

149 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket Identification EPAHQ-OAR-2004-0008-0455 to 0457.

150 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.

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152 Pye, J.M. (1988). Impact of ozone on the growth and yield of trees: A review. Journal of Environmental Quality, 17 , 347-360.

153 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

154 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

155 McBride, J.R., Miller, P.R., Laven, R. D. (1985). Effects of oxidant air pol lutants 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.

156 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 Protecti on Agency, Environmental Research Laboratory (EPA600-D-82-276).

157 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.

158 Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. ( 1985). Implications of environmental policy for U.S. agriculture: the ca se of ambient ozone standards. Journal of Environmental Management, 20 , 321-331.

159 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.

160 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

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161 Abt Associates, Inc. 1995. Urban ornamental plants: se nsitivity to ozone and pote ntial 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.

162 U.S. EPA. Air Quality Criteria for Ozone an d Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.This do cument is available in Docket EPA-HQ-OAR-2005- 0036

163 Grulke, N.E. (2003). The physiological basis of ozone in jury 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.

164 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.Draft Regulatory Impact Analysis

7-72 165 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.

166 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.

167 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.

168 Coulston, J.W., Riitters, K.H., Smith, G.C. (2004). A preliminary assessment of th e Montréal process indicators of air pollution for the United States. Environmental Monitoring and Assessment, 95, 57-74.

169 U.S. EPA (United States Envir onmental Protection Agency). 2006. Air quality criteria for ozone and related photochemical oxidants. EPA/600/R-05/004aF-cF. Research Triangle Park, NC. <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923>

170 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.

171 U.S. EPA (United States Envir onmental Protection Agency). 2006. Air quality criteria for ozone and related photochemical oxidants. EPA/600/R-05/004aF-cF. Research Triangle Park, NC. <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923>

172 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.

173 Chappelka, A.H., Samuelson, L.J. (1998). Ambient ozon e effects on forest trees of the eastern United States: a review. New Phytologist, 139, 91-108.

174 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I Document No. EPA600/P99/002aF and Volume II Document No. EPA600/P-99/002bF. This document is available in Docket EPA-HQOAR-2005-0161.

175 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate Matter: Policy Assessment of Scientific and Technical Information, O AQPS Staff Paper. EPA-452/R-05-005. This document is available in Docket EPA-HQ-OAR-2005-0161.

176 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulfur- Ecological Criteria (Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F.

177 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 Agen cy. Research Triangle Park, NC. EPA 620/R–03/001.

178 Fenn, M.E. and Blubaugh, T.J. (2005) Winter Depos ition of Nitrogen and Sulfur in the Eastern Columbia River Gorge National Scenic Ar ea, USDA Forest Service.

179 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of change. Ambio 31: 64– 71.

180 Bricker, Suzanne B., et al., National Estuarine Eutrophi cation Assessment, Effects of Nutrient Enrichment in the Nation’s Estuaries, National Ocea n Service, National Oceanic and Atmo spheric Administra tion, September, 1999.Environmental and Health Impacts

7-73 181 Smith, W.H. 1991. “Air pollution a nd Forest Damage.” Chemical E ngineering News, 69(45): 30-43.

182 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.

183 Cotrufo, M.F.; DeSanto, A.V.; Alfa ni, A.; et al. 1995. “Effects of urban heavy metal pollution on organic matter decomposition in Quercus ilix L. wood s.” Environmental Po llution, 89: 81-87.

184 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.

185 Mason, R.P. and Sullivan, K.A. 1997. “Mercury in Lake Michigan.” E nvironmental Scienc e & Technology, 31: 942-947. (from Delta Repo rt “Atmospheric deposition of toxics to the Great Lakes”).

186 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.

187 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 Sta ndards, Research Triangle Park, North Carolina. Error! Main Document Only. This document is available in Docket EPA-HQ-OAR-2004-0008.

188 NSTC 1999

189 Callender, E. and Rice, K.C. 2000. “The Urban Enviro nmental Gradient: Anthropogenic Influences on the Spatial and Temporal Distributions of Lead and Zinc in Sediments.” Environmental Science & Technology, 34: 232-238.

190 Rice, K.C. 1999. “Trace Element Concentrations in Streambed Sediment Across the Conterminous United States.” Environmental Science & Technology, 33: 2499-2504.

191 Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. “Implica tions of Platinum-Group Element Accumulation along U.S. Roads from Catalytic-Converter Attritio n.” Environ. Sci. Technol. 35: 3816-3822.

192 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Es timating Air Emissions from Sources of Polycyclic Organic Matter,” Office of Air Quality Planning and Standards, Research Triangle Park , North Carolina. This document is available in Docket EPA-HQ-OAR-2004-0008.

193 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Es timating Air Emissions from Sources of Polycyclic Organic Matter,” Office of Air Quality Planning and Standards, Research Triangle Pa rk, North Carolina. Error! Main Document Only. This document is available in Docket EPA-HQ-OAR-2004-0008.

194 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
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195 Simcik, M.F.; Eisenreich, S.J.; and Lioy, P.J. 1999. “Source apportionmen t and source/sink relationship of PAHs in the coastal atmosphere of Chicago and Lake Michigan.” Atmospheric Environment, 33: 5071-5079.

196 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.

197 Park, J.S.; Wade, T.L.; and Sweet, S. 2001. “Atmospheric distribution of polycyclic aromatic hydrocarbons and deposition to Galveston Bay, Texas, US A.” Atmospheric Environment, 35: 3241-3249.Draft Regulatory Impact Analysis

7-74 198 Poor, N.; Tremblay, R.; Kay, H.; et al. 2002. “Atm ospheric concentrations and dry deposition rates of polycyclic aromatic hydrocarbons (PAHs) for Tampa Bay, Florida, USA.” Atmosphe ric Environment 38: 6005- 6015.

199 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.

200 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 Standa rds, Research Triangle Park, North Carolina. This document is available in Docket EPA-HQ-OAR-2004-0008.

201 Van Metre, P.C.; Mahler, B.J.; a nd Furlong, E.T. 2000. “Urban Sprawl Leaves its PAH Signature.” Environmental Science & Technology, 34: 4064-4070.

202 Cousins, I.T.; Beck, A.J.; and Jones, K.C. 1999. “A revi ew of the processes involved in the exchange of semivolatile organic compounds across the air-soil interface.” The Science of the Total Environment, 228: 5-24.

203 Tuhackova, J. et al. (2001) Hydrocarbon deposition and soil microflora as affected by highway traffic. Environmental Pollution, 113: 255-262.

204 US EPA. 1991. Effects of organic chemicals in the atmosphere on terrestrial plants. EPA/600/3-91/001.

205 C ape 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-to p chamber experiment. Envi ron. Pollut. 124:341-343.

206 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-to p chamber experiment. Envi ron. Pollut. 124:341-343.

207 Viskari E-L. 2000. Epicuticular wax of Norway spruce needles as indicator of traffic pollutant deposition. Water, Air, and Soil Pollut. 121:327-337.

208 Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake and transformation of benzene and toluene by plant leaves. Ecotox. Environ. Safety 37:24-29.

209 Kammerbauer H, H Selinger, R Rommelt, A Ziegler- Jons, D Knoppik, B Hock. 1987. Toxic components of motor vehicle emissions for the spruce Pc iea abies. Environ. Pollut. 48:235-243.

210 U.S. EPA. (2007). PM2.5 National Ambient Air Quality Standard Implemen tation Rule (Final). Washington, DC: U.S. EPA. Retrieved on May 14, 200 9 from Docket EPA-HQ-OAR-2003-0062 at http://www.regulations.gov/. 72 FR 20586. U.S. EPA. (2006 ). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrie ved on March 19, 2009 from Docket EPA-HQ-OAR-2003- 0190 at http://www.regulations.gov/

.

211 PM Standards Revision – 2006: Timeline. Retrieved on March 19, 2009 from http://www.epa.gov/oar/particlepollution/naaqsrev2006.html#timeline
212 US EPA: 8-hour Ozone Nonattainment Areas. Retrieved on March 19, 2009 http://www.epa.gov/ttn/atw /nata2002/risksum.html

213 Carbon Monoxide Nonattainment Area Summary: http://www.epa .gov/air/oaqps/gr eenbk/cnsum.html

214 U.S. EPA. (2009) 2002 Nationa l-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/n ata1999/risksum.html

215 Control of Hazardous Air Pollutants From Mobile Sources (72 FR 8428; February 26, 2007)Environmental and Health Impacts

7-75 216 US EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources Regulatory Impact Analysis. EPA document number 420-R-07-002, February 2007.

217 U.S. Environmental Protection Agency, Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeling system, EPA/600/R-99/030, Office of Research and Development).
218 Byun, D.W., and Schere, K.L., 2006. Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Mode ling System, J. Applied Mechanics Reviews, 59 (2), 51-77.
219 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 qua lity modeling: EPA’s Models-3, Atmo spheric Environmen t, 30, 1925-1938.
220 US EPA (2007). Regulatory Impact Analysis of the Pr oposed Revisions to the Na tional Ambient Air Quality Standards for Ground-Level Ozone. EPA document number 442/R-07-008, July 2007.
221 Aiyyer, A, Cohan, D., Russell, A., St ockwell, W., Tanrikulu, S., Vizuete, W., Wilczak, J., 2007. Final Report: Third Peer Review of the CMAQ Model. p. 23.

222 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

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223 Gr ell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generat ion Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR. , 138 pp, National Center for Atmospheric Research, Boulder CO.

224 Yantosca, B. (2004). GEOS-CHEMv7-01-02 User’s Guide , Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.

225 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. March.

226 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.

227 U.S. Environmental Protection Agency (U.S. EPA). 2009a. 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/RI As/portlandcementria_4-20-09.pdf>.

228 U.S. Environmental Protection Agency (U.S. EPA). 2009b. Proposed NO2 NAAQS Regulatory Impact Analysis (RIA). Office of Air Quality Planning and Stan dards, Research Triangle Pa rk, NC. April. Available on the Internet at http://www.epa.gov/ttn /ecas/regdata/RIAs/proposedno2ria.pdf.

229 U.S. Environmental Protection Agency (U.S. EPA) . 2008a. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Available at <http://www.epa .gov/ttn/ecas/regdata/RIAs/6- ozoneriachapter6.pdf>.

230 U.S. Environmental Protection Agency (U.S. EPA). 2009a. 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/RI As/portlandcementria_4-20-09.pdf>.

231 U.S. Environmental Protection Agency (U.S. EPA). 2009b. Proposed NO2 NAAQS Regulatory Impact Analysis (RIA). Office of Air Quality Planning and Stan dards, Research Triangle Pa rk, NC. April. Available on the Internet at http://www.epa.gov/ttn /ecas/regdata/RIAs/proposedno2ria.pdf.Draft Regulatory Impact Analysis

7-76 232 U.S. Environmental Protection Agency (U.S. EPA) . 2008b. 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>.

233 Fann, N. et al. (2009). The influence of location, s ource, 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.

234 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.

235 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dock ery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respiratory a nd Critical Care Medicine 173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regu lations. Washington, DC: The National Academies Press.

236 Roman, Henry A., Katherine D. Wa lker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. Expert Judgmen t Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. Environ. Sci. Technol., 42(7):2268-2274.

237 Industrial Economics, Inc. 2006. Expanded Expe rt Judgment Assessment of the Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Pr epared for the U.S. EPA, Office of Air Quality Planning and Standards, September. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdat a/Uncertainty/pm_ee_report.pdf>

238 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270.

239 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Re view of Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.

240 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analys is. Environmental and Resource Economics. 34: 385- 406.

241 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/Guidelin es.html/$file/cover.pdf>.

242 Information on BenMAP, including down loads of the software, can be fo und at http://www. epa.gov/ttn/ecas/ benmodels.html.

243 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urba n communities, 1987-2000. JAMA

, 2004. 292(19): p. 2372-8.

244 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-re spiratory mortality. Environmetrics . 16: 547-562.

245 Schwartz, J. (2005) How sensitive is the associa tion between ozone and daily deaths to control for temperature? Am. J. Respir. Crit. Care Med. 171: 627-631.

246 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 mo rbidity, mortality, and air pollution study. Epidemiology . 16(4): p. 436-45.Environmental and Health Impacts

7-77 247 Ito, K., S.F. De Leon, and M. Lippmann (2005). Asso ciations between ozone and daily mortality: analysis and meta-analysis. Epidemiology . 16(4): p. 446-57.

248 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005 ). Ozone exposure and mo rtality: an empiric bayes metaregression analysis. Epidemiology . 16(4): p. 458-68.

249 Pope, C.A., III, R.T. Burnett, M.J. T hun, 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.

250 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.

251 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.

252 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. ( 1997). The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particul ate Air Pollution in the United States. Environmental Health Perspectives. 105(6):608-612.

253 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancur en, 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.

254 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittle man. (2001). Increased Particulate Air Pollution and the Triggering of Myocardial Infarction. Circulation. 103:2810-2815.

255 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax. 50(5):531-538.

256 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, Minnesota. Arch Environ Health. 49(5):366-374.

257 Schwartz J. (1994b). Air Po llution and Hospital Admissions For th e Elderly in Detroit, Michigan. Am J Respir Crit Care Med. 150(3):648-655.

258 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology . 8(4):364-370.

259 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.

260 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.

261 Ito, K. (2003). “Associations of Particulate Matt er 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.

262 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.

263 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994. In Revised Analyses of Time-Series St udies of Air Pollution and Health . Special Report. Boston, MA: Health Effects Institute.

264 Jaffe DH, Singer ME, Rimm AA. (2003). Air polluti on and emergency department visits for asthma among Ohio Medicaid recipients, 1991-1996. Environ Res 91(1):21-28.Draft Regulatory Impact Analysis

7-78 265 Peel, J. L., P. E. Tolbert, M. Kl ein, et al. (2005). Ambient air pollutio n and respiratory emergency department visits. Epidemiology . Vol. 16 (2): 164-74.

266 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution, weather, and respiratory emergency room visits in two northern New England ci ties: an ecological time-series study. Environ Res . Vol. 97 (3): 312-21.

267 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 He alth Perspectives 107(6):489-493.

268 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 Hea lth Perspectives 104(5):500-505.

269 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory Health and PM 10 Pollution: A Daily Time Series Analysis. American Review of Respiratory Diseases 144:668-674.

270 Schwartz, J., and L.M. Neas. (2000). Fine Particles ar e More Strongly Associated than Coarse Pa rticles with Acute Respiratory Health Effects in Schoolchildren. Epidemiology 11:6-10.

271 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. (2001). Air Pollution and Exacerbation of Asthma in African-American Children in Los Angeles. Epidemiology 12(2):200-208.

272 Vedal, S., J. Petkau, R. White, and J. Blair. (1998 ). Acute Effects of Ambi ent Inhalable Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and Critical Care Medicine 157(4):1034-1043.

273 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of Environmental Economics Management 14:87-98.

274 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.

275 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air pollution. Inhal Toxicol 12(11):997-1016.

276 Ostro, B.D. and S. Rothschild. (1989). Air Pollu tion and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants. Environmental Research 50:238-247.

277 Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, an d S.C. Hartz. (1998). Direct Medical Costs of Coronary Artery Disease in the United States. American Journal of Cardiology 81(9):1110-1115.

278 Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. (1990) . Medical Costs of Corona ry Artery Disease in the United States. American Journal of Cardiol ogy 65(7):432-440.

279 Smith, D.H., D.C. Malone, K.A. La wson, L.J. Okamoto, C. Battista, and W.B. Saunders. (1997). A National Estimate of the Econom ic Costs of Asthma. American Journal of Respiratory and Critical Care Medicine 156(3 Pt 1):787-793.

280 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.

281 Rowe, R.D., and L.G. Chestnut. (1986). Oxidants and Asthmatics in Los Angeles: A Benefits Analysis— Executive Summary. Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental Protection Agency, Office of Policy Analysis. EPA-230-09-86-018. Washington, DC.

282 Science Advisory Board. 2001. NATA – Evaluatin g the National-Scale Air Toxics Assessment for 1996 – an SAB Advisory. http://www.ep a.gov/ttn/atw/sab/sabrev.html.

283 MiniCAM is a long-term, global integrated assessment model of energy, economy, agriculture and land use, that considers the sources of emissions of a suit e of greenhouse gases (GHG's), emitted in 14 globally disaggregated regions, the fate of emissions to the atmos phere, and the consequences of changing concentrationsEnvironmental and Health Impacts

7-79 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 ener gy, agriculture, land-use, and economic developments that in turn shape global emissions.

Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2 003: Model Documentation for the MiniCAM. PNNL-14337, Pacific Northwest National Labo ratory, Richland, Washington.

284 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 determin e changes in greenhouse-gas concentrations, global-mean surface air temperature and sea-level resulting from an thropogenic 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-mea n temperature responses of more sophisticated coupled Atmosphere/Ocean General Circulation Mo dels (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 St rategies 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 la rger warming projections in the IPCC Third Assessment Report J. Climate 15, 2945-2952.

285 This 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. 200 8). The MiniCAM RCP4.5 is based on the scenarios presented in Clarke et al. (2007) with non-CO2 and pollu tant 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 availabl e 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 Biologica l & Environmental Research, Washington, DC., USA, 154 pp.).
Moss, Richard, Mustafa Babiker, Sander Brinkman, Eduard o 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).

286 In IPCC reports, equilibrium climate sensitivity refers to the equilibriu m change in the annual mean global surface temperature following a doubling of the atmospheri c equivalent carbon dioxide concentration. The IPCC states that climate sensitivity is “likel y” 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/.

287 IPCC WGI, 2007. The baseline temperature increase s by 2100 from our MiniCAM-MAGICC runs are 1.8°C to 4.5°C.Draft Regulatory Impact Analysis

7-80 288 IPCC WGII. 2007. Climate Change 2007 - Impact s, Adaptation and Vulnerability Contribution of Working Group II to the Fourth Asse ssment Report of the IPCC

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289 “ Light-Duty Automotive Technology and Fuel Econom y Trends: 1975 Through 2008”, EPA420-R-08-015, U.S. Environmental Protection Agency Office of Transportation and Air Quality, September 2008

290 “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.

291 “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

292 “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

293 “Supplemental Results on the Independent Effects of Cu rb Weight, Wheelbase and Track on Fatality Risk”, Dynamic Research, Inc., DRI-TR-05-01, May 2005

294 “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.

295 FR Vol. 74, No. 59, beginning on pg. 14402Other 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.4 of the preamble to the proposal and presented below in Table 8-1 and Table 8-2 were derived using the following

m e thodology. For additional disc ussion of the assumptions used in the vehicles sales impacts, see Section III.H of the preamble. The calcula tion 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 assessmen t of models that examine these questions.

The analysis starts with the increase in co sts estimated by OMEGA. 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 passthrough 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 on to 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 financing costs, as well as increases in the resale value of the vehicle. These factors weigh against each other: the higher sales tax and in surance 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 insuran ce costs that depend on vehicle value. The Insurance Information Institute 2 provides the average value of collision plus comprehensive insurance in 2006 as $4 48. The average value of a new vehi cle 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 increa se 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 population-Draft Regulatory Impact Analysis

8-2 weighted sales tax. That estimate of the state sa les 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 August 24, 2009, the national average in terest rate for a 5 year new car loan was 7.41 percent. 6 Converting the up-front payment to an annual value paid over five years results in a consumer paying 24.7% of the up-front amount every year. The present value of these five payments results in an increase of 12.9% of the cost, using a 3% discount rate; with a 7% discount rate, the increase is 1.1%. NHTSA’ s PRIA notes that 70% of auto purchases use financing; applying that fraction to this cost increase results in an addition of 9.0% in financing costs with a 3% discount rate, and 0.8% 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 re cover that amount after 5 years, it reduces the effect of the increased cost of the vehicle. Di scounted 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 + 0.090 – 0.302) = 0.932 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 + 0.008 – 0.250) = 0.892 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 5.6 of this DRIA 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 fi ve 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 th us 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 th e increase in costs associated with the rule to get the net effect of the rule on consumer ex penditure. The higher cost leads consumers to purchase fewer new vehicles, but the fuel savings can counteract this effect. This calculation uses an elasticity of dema nd for new vehicles of -1 8 : 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 thatOther Economic and Social Impacts

8-3 the demand elasticity for new vehicles under this rule is the same as the elasticity for older vehicles. 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 66,600 0.7 27,300 0.5 2013 93,300 0.9 161,300 2.8 2014 134,400 1.3 254,400 4.4 2015 236,300 2.2 368,400 6.5 2016 375,400 3.4 519,000 9.4

Table 8-1shows vehicle sales increasing. Becau se the fuel savings associated with this rule are expected to exceed the technology co sts, 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. Th is 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 ex pected to lead to an increase in sales for both cars and trucks. Both the absolute and the perc ent increases for truck sales are larger than those for cars (except in 2012).

Table 8-2 Vehicle Sales Impacts Using a 7% Discount Rate

CHANGE IN CAR SALES % CHANGE CHANGE IN TRUCK SALES % CHANGE

2012 61,900 0.7 25,300 0.5 2013 86,600 0.9 60,000 1 2014 125,200 1.2 122,900 2.1 2015 221,400 2 198,100 3.5 2016 353,100 3.2 291,500 5.3

Table 8-2 shows the same calculations using a 7% discount rate. Qualitatively, the results are identical to those using a 3% disco unt 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, th e percent increases are larger for trucks.

This calculation focuses on changes in co nsumer expenditures as the explanatory variable for changes in aggregate new vehicle sale s. This is a simplification, since consumers typically consider a number of factors in addition to expenditures when they decide on purchasing a vehicle. In addition, it does not co nsider changes in the mix of vehicles sold thatDraft Regulatory Impact Analysis

8-4 may result from this rule. 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 vehicl e 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 ab ility 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 proposed 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 continue its efforts to review the literatu re, but, given the known difficulties, neither NHTSA nor EPA has conducted an analysis using these models for this proposal.

This rule will lead automakers to change characteristics – in particular, the fuel economy -- of the vehicles they produce. These changes will affect the cost of manufacturing the vehicle; as a result, the prices of the vehicles will also change.

In response to these changes, the number and types of vehicles sold is likely to change. When consumers buy vehicles, they consider both their personal characteristics (such as age, family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, consumers will reconsider their purchases. Increases in fuel economy are likely to be attractive to consumers, but increases in price, as well as 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 ab sence of the rule. The changes in prices and vehicle characteristics are likely to influence co nsumers on multiple mark et scales: the total number of new vehicles sold; the mix of new vehi cles 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 characte ristics and prices. In principle, it should produce more accurate estimates of compliance co sts 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 surplu s, 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 incl uded in the models, the research questions theyOther Economic and Social Impacts

8-5 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

Co n sumer 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 numeri cal 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 typi cally modeled as discrete choices, where the choice is whether to purchase a specified vehicl e. 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 is, essentially, the probability that consumer s will purchase a specific vehicle, these approaches are similar in process; they differ mo stly in the kinds of data that they use.

The primary methods used to model vehicle ch oices 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 Kleit 15 and Austin and Dinan 16 ) 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 a nd Dinan used the elasticities from an internal GM vehicle choice model.

8.1.2.2 Data Sources

The predicti on s of vehicle purchases fro m 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%.Draft Regulatory Impact Analysis

8-6 The parameters in CCMs can be develope d 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 de velop 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 Administration 19 and the New Vehicle Market Model developed by NERA Economic Consulting 20 are examples of calibrated models.

8.1.2.3 Factors Included in the Models

Consu m er choice models vary in their comple xity 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

Co n sumer 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 sect or 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 taxes 31 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

Consu m er vehicle choice models typically consider the effect of fuel economy on vehicle purchase decisions. It can appear in various forms.Other Economic and Social Impacts

8-7 Some models 33 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 perh aps 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 es timate 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 Kurani 34 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 eco nomy 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 d ecisions. Other models use fuel consumption 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 wh en 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 Turre ntine and Kurani examined, it suggests a calculating consumer. On the other hand, it is al so a way to recognize the role of fuel prices in consumers’ purchase of fuel economy: Busse et al. 39 present 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.

More recent studies do not suggest agreemen t on the value of increased fuel economy to consumers. For instance, some papers 41 find that the role of fuel cost (price per gallon divided by miles per gallon, or the cost of driv ing one mile) decreases for larger vehicles; in contrast, Gramlich 42 finds that owners of fuel-inefficient vehicles have the greatest willingness to pay for improved fuel economy. 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; asDraft Regulatory Impact Analysis

8-8 a result, “fuel economy” may in fact represent several characteristics at the same time. Indeed, Gramlich 43 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.

Espey and Nair 44 find, using data from model year 2001, that consumers might be willing to pay roughly $500 for a 1-mpg increase in city driving, approximately $250 for a 1- mpg increase in highway driving, or approximat ely $600 for an increase in combined fuel economy; they argue that these values approxim ately correspond to the fuel savings that consumers might expect over the lifetime of the vehicle. McManus 45 finds, in 2005, that consumers were willing to pay $578 for a 1-mpg increase in fuel economy. Gramlich 46 finds willingness to pay for an increase from 25 mpg to 30 mpg to range between $4100 (for luxury cars, when gasoline costs $2/gallon) to $20,600 (for SUVs when gasoline costs $3.50/gallon).

Some studies 47 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 studies 48 have found that increasing fuel economy standards imposes welfare losses on co nsumers 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 studies 49 notes that its baseline model implies that consumers are willing to buy more fuel economy than producers have provided; they have to adjust their model to eliminate these “neg ative-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 Pr od uc ers 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 su pplying them, when they appear to “pay for themselves” even in the absence of regulation?

On the consumer side, this disconnect betw een net present value estimates of energyconserving cost savings and what consumers act ually spend on energy conservation is often referred to as the Energy Paradox, 50 since consumers appear to routinely undervalue a wide range of investments in energy conservation. Some possible explanations for the paradox include:

 Consumers put little weight on benefits from fuel savings in the future;

 Consumers consider other attributes more important than fuel economy at the time of vehicle purchase;

 Consumers may not be able to find the vehicles they want with improved fuel
economy;Other Economic and Social Impacts

8-9  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; in contrast, at the time of purchase the increased costs of fuel-saving technol ogies are certain and immediate;

 There is likely to be variation among co nsumers in the benefits they get from improved fuel economy, due to different miles driven and driving styles.

The producer side of this paradox is much less studied. Hypotheses for underprovision of fuel economy by producers include:

 Producers put more effort into attributes that consumers have regularly sought in the past, such as size and power, rather than fuel 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 may not make that list;

 While consumer preferences for fuel economy may change rapidly as fuel prices fluctuate, producers cannot change their de sign or production decisions as rapidly; as a result, vehicle designs may end up not satisfying consumer desires at a particular time;

 Producers may have misestimated 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 fuel sa vings. 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

Consum e r vehicle choice modeling in principle can provide a great deal of useful information for regulatory analysis. All models estimate changes in fleet mix of new vehicles; some also provide estimates of tota l 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.Draft Regulatory Impact Analysis

8-10 First, consumer vehicle choice modeling has the potential to describe more accurately the impact of a policy, by identifying market sh ifts. More accurate description of the market resulting from a policy can improve other estimate s of policy impacts, such as the change in vehicle emissions or vehicle miles traveled. Th e 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 ha ve not previously been offered. Welch, 51 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. 52 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 co mpliance costs of a rule. Most current modeling is based on a fleet mix determined ou tside 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 whic h 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 mod el 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 co llected by taxing fuel-inefficient vehicles), Greene et al. found that 95% of the increas e in fuel economy was due to addition of technology rather than changes in vehicles sold. 53 Consideration of consumer behavior in welfare estimates will improve regulatory analys is, but only to the extent that the predicted changes in consumer purchase patterns reflect actual changes.

An additional feature of consumer choice models, as noted above, is that they can be used to calculate consumer surplus impacts. 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 gain s 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.

At this point, it is unclear whether two models given the same scenario would produce similar results in either prediction of changes in the vehicles 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 additio n, the predictive ability of consumer vehicleOther Economic and Social Impacts

8-11 choice models may be limited as consumers face new vehicle choices that they previously did not have. If the results across models are not cons istent or are highly sensitive to parameters or other features, then careful thought needs to be given to model selection and development.

Nonetheless, because there are potential ad vantages to using consumer vehicle choice models if these difficulties can be addressed, EPA is continuing to explore options for including consumer and producer choice in mod eling the impacts of fuel economy-related regulations. This effort includes further review of existing consumer vehicle choice models and the estimates of consumers’ willingness to pay for increased fuel economy. 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 sh ould help to shed light on whether it is more costly to rely solely on the application of te chnologies to vehicles to meet a given fuel standard than when consumer and producer beha vior is taken into account. EPA plans to evaluate this work within the context of the over all literature on consumer vehicle choices, to determine its usefulness in informing the analys is for the final rule. We seek comment on the usefulness of consumer choice modeling results and the consistency and reliability of results from these models.



### 8.1.3 Consumer Payback Period and Lifetime Saving s on New Vehicle Purchases

Another factor of interest is the payback period on the purchase of a new vehicle that complies with the proposed standards. In other words, how long would it take for the expected fuel savings to outweigh the increased cost of a new vehicle? For example, a new 2016 MY vehicle is estimated to cost $1,050 more (on average, and relative to the reference case vehicle) due to the addition of new GHG re ducing 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 5 for details on fuel savings). But how many months or years would pass before the fuel savings exceed the upfront cost of $1,050?

Table 8-3 provides the answer to this question for a vehicle purchaser who pays for the

n e w vehicle upfront in cash (we discuss later in this section the payback period for consumers who finance the new vehicle purchase with a loan). The table uses annual miles driven (vehicle miles traveled, or VMT) and survival ra tes consistent with the emission and benefits analyses presented in Chapter 4 of the draft joint TSD. We have included rebound VMT in the control case but not in the reference case, co nsistent 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 DRIA. Further, this analysis does not include other societal impacts such as the value of increase d 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 describedDraft Regulatory Impact Analysis

8-12 in Chapter 1 of the draft joint TSD. As can be se en in the table, it will take under 3 years (2 years and 8 months at a 3% discount rate, 2 years and 10 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 48,000 miles, depending on the discount rate. For the driver that drives more than the aver age, the payback would come sooner. For the driver that drives less than the aver age, the payback would come later.

Table 8-3 Payback Period on a 2016MY New Vehicle Purchase via Cash (2007 dollars)

Increased Vehicle

Cost a Fuel Price b Reference VMT c Control VMT c Reference Fuel Costs d Control Fuel Costs d Annual Fuel Savings Cumulative Discounted Fuel Savings at 3% Cumulat i Discoun t Fuel Savings 7% Year of Ownership ($) ($/gal) (miles) (miles) ($) ($) ($) ($) ($) 1 -$1,128 $3.27 17,481 17,813 $2,544 $2,101 $443 $436 $428 2 $3.39 16,934 17,256 $2,549 $2,106 $444 $860 $829 3 $3.48 16,432 16,744 $2, 545 $2,102 $443 $1,272 $1,20 3 4 $3.56 15,777 16,077 $2, 495 $2,061 $434 $1,663 $1,54 6

a Increased cost of the proposed rule is $1,050; the value here includes nationwide average sales tax of 5.3% and increased insu rance premiums of 1.98%; both of these percentages are discussed in section 8.1.1.
b AEO 2009 reference case fuel price including taxes.

c VMT is calculated as the weighted car/tr uck VMT with cars estimated to account for 67% of the fleet and trucks 33%; VMT shown here includes survival fraction and, for the control case, rebound VMT.
d Fuel costs calculated using the reference and control case achiev ed CO2 levels as presented in Chapter 5 with 8887 grams of CO 2 p 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 no t the expected A/C-rela ted maintenance savings.
Most people purchase a new vehicle using cred it rather than paying cash up front. The typical car loan today is a five year, 60 month loan. As of August 24, 2009, the national average interest rate for a 5 year new car loan was 7.41 percent. If the increased vehicle cost is spread out over 5 years at 7.41 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 $162 in discounted net savings (3% discount rate) saved in the first year and similar savings for th e 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 averag e owner is making a payment for the financing of the average new vehicle their monthly fuel savings would be greater than the increase in the loan payments. This amounts to a savings on the order of $9 to $14 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 th e case for the remaining years of ownership).Other Economic and Social Impacts

8-13 Table 8-4 Payback Period on a 2016 MY New Vehicle Purchase via Credit (2007 dollars)

Increased Vehicle

Cost a Fuel Price b Reference VMT c Control VMT c Reference Fuel Costs d Control Fuel Costs d Annual Fuel Savings Annual Discounted Net Savings at 3% Annua Discoun t Net Savings 7% Year of Ownership ($) ($/gal) (miles) (miles) ($) ($) ($) ($) ($) 1 $278 $3.27 17,481 17,813 $2,544 $2,101 $443 $162 $159 2 $278 $3.39 16,934 17,256 $2,549 $2,106 $444 $158 $150 3 $278 $3.48 16,432 16,744 $2,545 $2,102 $443 $153 $139 4 $278 $3.56 15,777 16,077 $2,495 $2,061 $434 $141 $123 5 $278 $3.62 15,109 15,396 $2,432 $2,009 $423 $127 $107 6 $3.64 14,338 14,611 $2,318 $1,914 $403 $343 $278

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 7.41 percent.
b AEO 2009 reference case fuel price including taxes.

c VMT is calculated as the weighted car/tr uck VMT with cars estimated to account for 67% of the fleet and trucks 33%; VMT shown here includes survival fraction and, for the control case, rebound VMT.
d Fuel costs calculated using the reference and control case achiev ed CO2 levels as presented in Chapter 5 with 8887 grams of CO 2 p 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 no t the expected A/C-rela ted maintenance savings.

We can also calculate the lifetime fuel savings an d 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

sa vin gs is greater than $3,200 at a 3% discount rate, or $2,400 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 Savings b,c Lifetime Discounted Net Savings Purchase Option ($) ($) ($) 3% discount rate Cash $1,128 $4,558 $3,446 Credit a $1,293 $4,558 $3,265 7% discount rate Cash $1,128 $3,586 $2,495 Credit a $1,180 $3,586 $2,406

a Assumes a 5 year loan at 7.41 percent.
b VMT is calculated as the weighted car/tr uck VMT with cars estimated to account for 67% of the fleet and trucks 33%; VMT shown here includes survival fraction and, for the control case, rebound VMT.

c Fuel savings here were calculated using AEO 2009 reference case fuel price including taxes.Draft Regulatory Impact Analysis

8-14 

### 8.2

8.3 Energy Security Impacts

This chapter will only describe the energy security analysis that was conducted beyond that described in Chapter 4.B.10 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.B.10 of the TSD.

After the EPA-sponsored peer review of the Oak Ridge National Laboratory’s (ORNL) Energy Security Analysis was complete d 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. S i nce the AEO 2009 forecasts ends in 2030, EP A 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 no t change. The final set of values that was used by the OMEGA model is shown in Table 8-7.Other Economic and Social Impacts

8-15 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 secu rity premium price. These values are shown in Table 8-8. 55 The reduced oil estimates were derive d from the OMEGA model, as explained in Chapter 5 of EPA’s DRIA. EPA used the same assumption that NHTSA used in its Corporate Average Fuel Economy and CAFE Reform for MY 2008-2011 Light Trucks proposal, which assumed each gallon of fuel sa ved reduces total U.S. imports of crude oil or refined products by 0.95 gallons 56 . Section 5.3 of this RIA contains a discussion regarding caveats for the fuel savings estimated due to impl ementation of this rule. Section III.H.8.b 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.Draft Regulatory Impact Analysis

8-16 Table 8-8 Total Annual Energy Secu rity Benefits in 2015, 2020, 2030, and 2040 (Billions of 2007 dollars)

YEAR BENEFITS 2015 $0.59 2020 $2.30 2030 $4.81 2040 $6.23



### 8.4 Other Externalities

There are other impacts associated with the proposed GHG emissions standards and associated reduced fuel consumption. Lower fuel consumption would, presumably, result in fewer trips to the filling station to refuel and, thus, time saved. The rebound effect, discussed in detail in Chapter 4 of the draft joint TSD, pr oduces additional benefits to vehicle owners in the form of consumer surplus fr om the increase in vehicle-miles driven, but may also increase the societal costs associated with traffic cong estion, 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 proposed standards, but they are neverthele ss important to include. We summarize the value of these other impacts in section 8.4.4 of this DRIA. Please refer to the draft joint TSD that accompanies this proposal for more info rmation about these impacts and how EPA and NHTSA use them in their analyses.



### 8.4.1 Reduced Refueling Time

Improving the fuel economy of passenger car s and light-duty trucks may also increase their driving range before they require refuel ing. By reducing the frequency with which drivers typically refuel their vehicles and ex tending the upper limit of the range they can travel before requiring refueling, improving fu el economy provides some additional benefits to their owners. Alternatively, if manufact urers 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 so, this presumably reflects their judgment th at the value to economic benefits to vehicle buyers from lower purchase prices exceeds th at 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 (abo ut 2/3) and rural (about 1/3) driving and average vehicle occupancy for all driving trip s (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. 58,59Other Economic and Social Impacts

8-17 

### 8.4.2 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 el ect 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 i mproved 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 meas ures 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 approximatio n, 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 draft joint TSD. Again, the negative effect that rebound driving has on the fuel consumption savings associated with the proposed GHG standards is included in the fuel economy savings presented in section 8.5 of this DRIA. Note th at 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.



### 8.4.3 Noise, Congestion, and Accidents

Although it provides some benefits to driv ers, increased vehicle use associated with the rebound effect also contributes to increased traffic congestion, motor vehicle accidents, and highway noise. Depending on how the addition al travel is distributed over the day and on where it takes place, additional vehicle use can co ntribute to traffic congestion and delays by increasing traffic volumes on facilities that are al ready heavily traveled during peak periods. These added delays impose higher costs on drivers and other vehicle occupants in the form of increased travel time and operating expenses. Because drivers do not take these added costs into account in deciding when and where to trav el, 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 eff ect 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 co sts, 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 h eavier traffic (although their severity may be reduced by the slower speeds at which heavier traffic typically moves).Draft Regulatory Impact Analysis

8-18 Finally, added vehicle use from the rebound effect may also increase traffic noise. Noise generated by vehicles causes inconvenien ce, irritation, and potentially even discomfort to occupants of other vehicles, to pedestrians and other bystanders, and to residents or occupants of surrounding property. Because th ese 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 consid erable uncertainty in measuring their value, any increase in the economic costs of traffic noise resulting from added vehicle use must be included together with ot her 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 an alysis 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 cost s 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 “Middl e” 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 pe r 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 estima ted 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.

Table 8-9 $/mile Inputs used for External Costs

EXTERNAL COSTS $/VMT Congestion $ 0.052 Accidents $ 0.023 Noise $ 0.001



### 8.4.4 Summary of Other Externalities

Table 8-10 summarizes the other economic impacts discussed in sections 8.4.1 throu gh 8.4.3.Other Economic and Social Impacts

8-19 Table 8-10. Estimated Economic Externalities Associ ated with the Proposed Light-Duty Vehicle GHG Program (Millions of 2007 dollars)

YEAR REDUCED REFUELING VALUE OF INCREASED

DRIVING ACCIDENTS, NOISE, CONGESTION ANNUAL QUANTIFIED

BENEFITS 2012 $100 $100 -$100 $200 2013 $200 $400 -$200 $400 2014 $500 $700 -$400 $800 2015 $700 $1,200 -$700 $1,300 2016 $1,100 $2,000 -$1,100 $2,000 2017 $1,500 $2,700 -$1,400 $2,800 2018 $1,800 $3,400 -$1,800 $3,500 2019 $2,200 $4,200 -$2,100 $4,200 2020 $2,500 $4,900 -$2,400 $5,000 2021 $2,800 $5,500 -$2,700 $5,600 2022 $3,100 $6,100 -$3,000 $6,200 2023 $3,400 $6,700 -$3,300 $6,800 2024 $3,700 $7,200 -$3,600 $7,300 2025 $3,900 $7,700 -$3,900 $7,800 2026 $4,200 $8,200 -$4,100 $8,300 2027 $4,400 $8,600 -$4,300 $8,700 2028 $4,600 $9,100 -$4,500 $9,200 2029 $4,800 $9,800 -$4,700 $9,800 2030 $4,900 $10,000 -$4,900 $10,000 2031 $5,100 $10,400 -$5,000 $10,400 2032 $5,200 $10,700 -$5,200 $10,800 2033 $5,400 $11,100 -$5,300 $11,100 2034 $5,500 $11,400 -$5,500 $11,500 2035 $5,700 $11,800 -$5,600 $11,800 2036 $5,800 $12,100 -$5,800 $12,200 2037 $6,000 $12,500 -$5,900 $12,600 2038 $6,100 $12,900 -$6,000 $12,900 2039 $6,200 $13,200 -$6,200 $13,300 2040 $6,400 $13,600 -$6,300 $13,700 2041 $6,500 $14,000 -$6,500 $14,100 2042 $6,700 $14,400 -$6,600 $14,500 2043 $6,800 $14,800 -$6,800 $14,900 2044 $7,000 $15,200 -$6,900 $15,300 2045 $7,200 $15,700 -$7,100 $15,700 2046 $7,300 $16,100 -$7,200 $16,200 2047 $7,500 $16,600 -$7,400 $16,700 2048 $7,700 $17,000 -$7,600 $17,100 2049 $7,800 $17,500 -$7,800 $17,600 2050 $8,000 $18,000 -$7,900 $18,100 NPV, 3% $89,600 $184,700 -$88,200 $186,100 NPV, 7% $41,000 $82,700 -$40,200 $83,500Draft Regulatory Impact Analysis

8-20 

### 8.5 Summary of Costs and Benefits

In this section we present a summary of costs, benefits, and net benefits of the proposal. We present fuel consumption impact s as negative costs of the vehicle program.

Table 8-11 shows the estimated annual societal costs of the vehicle program for the i n dicated calendar years. The table also shows the net present values of those costs for the calendar years 2012-2050 using both a 3 percent and a seven percent discount rate. In this table, fuel savings are calculated using pre-tax fuel prices and are presented as negative costs associated with the vehicle program (rather than positive savings).

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 $18,000 $17,900 $19,300 $20,900 $390,000 $216,600 Fuel Savings a -$43,100 -$90,400 -$125,000 -$167,000 -$1,677,600 -$746,100 Quantified Annual Costs -$25,100 -$72,500 -$105,700 -$146,100 -$1,287,600 -$529,500

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 qua ntified benefits (i.e., total benefits)—for each of five interim SCC values considered by EPA.

The interim SCC values are derived using several discount rates and include:

 $5 (based on a 5% discount rate);

 $10 (5% using Newell-Pizer adjustment);,

 $20 (average SCC value from the average SCC estimates based on 5% and 3%);

 $34 (3%);

 $56 (3% using Newell-Pizer adjustment).

These interim SCC values are in 2007 dollars, and are based on a CO 2 emissions change of 1 metric ton in 2007. Section III.H.2.a of the Preamble provides a complete discussion about SCC and the interim set of values.

Section III.H.2.a of the preamble to this rule also notes that there is a very high probability (very likely acco rding to the IPCC) that the benefit estimates from GHG reductions are underestimates. One of the primar y reasons is that models used to calculate SCC values do not include information about impacts that have not been quantified.Other Economic and Social Impacts

8-21 In addition, the total GHG reduction benefits presented below likely underestimate the value of GHG reductions because they were calculated using the marginal values for CO 2 emissions. The impacts of non-CO 2 emissions vary from those of CO 2 emissions because of differences in atmospheric lifetimes and radiative forcing. As a result, the marginal benefit values of non-CO 2 GHG reductions and their growth rates over time will not be the same as the marginal benefits measured on a CO 2 –equivalent scale. Marginal benefit estimates per metric ton of non- CO 2 GHGs are currently unavailable, bu t work is on-going to monetize benefits related to the mitigation of other non-CO 2 GHGs.

Table 8-12 Use Estimated Societal Benefits Associ ated with the Proposed Light-Duty Vehicle GHG Program (Millions of 2007 dollars)

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

Reduced GHG Emissions at each assumed SCC value

SCC 5% $1,200 $3,300 $5,700 $9, 500 $69,200 $28,600
SCC 5% Newell-Pizer $2,500 $6,600 $11,000 $19,000 $138,400 $57,100
SCC from 3% and 5% $4,700 $12,000 $22,000 $36,000 $263,000 $108,500
SCC 3% $8,200 $22,000 $38,000 $63,000 $456,900 $188,500
SCC 3% Newell-Pizer $14,000 $36,000 $63,000 $100,000 $761,400 $314,200 PM 2.5 Related Benefits a,b,c $1,400 $3,000 $4,600 $6, 700 $59,800 $26,300 Energy Security Impacts (price shock) $2,300 $4,800 $6,200 $7, 800 $85,800 $38,800
Reduced Refueling $2,500 $4,900 $6,400 $8,000 $89,600 $41,000 Value of Increased Driving d $4,900 $10,000 $13,600 $18,000 $184,700 $82,700 Accidents, Noise, Congestion -$2,400 -$4,900 -$6,300 -$7,900 -$88,200 -$40,200
Quantified Annual Benefits at each assumed SCC value

SCC 5% $9,900 $21,100 $30,200 $42,100 $400,900 $177,200
SCC 5% Newell-Pizer $11,200 $24,400 $35,500 $51,600 $470,100 $205,700
SCC from 3% and 5% $13,400 $29,800 $46,500 $68,600 $594,700 $257,100
SCC 3% $16,900 $39,800 $62,500 $95,600 $788,600 $337,100
SCC 3% Newell-Pizer $22,700 $53,800 $87,500 $132,600 $1,093,100 $462,800

a Note that the co-po llutant impacts associated with the standa rds presented here do not include the full complement of endpoints that, if quantified and monetiz ed, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant benefits are based on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 expos ure. 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, we were unable to conduct a full-scale air qu ality modeling analysis in time for the proposal. We intend to more fully capture the co-pollutant benefits for the analysis of the final standards.
b The PM2.5-related benefits (derived from benefit-per- ton values) presented in th is 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 twoand-a-half times) larger

c 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 segm ented cessation lag. If a 7% discount rate had been used, the values would be approximately 9% lower.
d 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-Draft Regulatory Impact Analysis

8-22 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 five interim SCC values considered by EPA. As noted above, there is a very high probability (very likely according to the IPCC) that the benefit estimates from GHG reductions are underestimates because, in part, models used to calculate SCC values do not include information about impacts that have not been quantified.

Table 8-13. Quantified Net Benefits Associated with the Proposed Ligh t-Duty Vehicle GHG Program a, b
(Millions of 2007 dollars)

2020 2030 2040 2050 NPV, 3% NPV, 7% Quantified Annual Costs -$25,100 -$72,500 -$105,700 -$146,100 -$1,287,600 -$529,500
Quantified Annual Benefits at each assumed SCC value
SCC 5% $9,900 $21,100 $30,200 $42,100 $400,900 $177,200
SCC 5% Newell-Pizer $11,200 $24,400 $35,500 $51,600 $470,100 $205,700
SCC from 3% and 5% $13,400 $29,800 $46,500 $68,600 $594,700 $257,100
SCC 3% $16,900 $39,800 $62,500 $95,600 $788,600 $337,100
SCC 3% Newell-Pizer $22,700 $53,800 $87,500 $132,600 $1,093,100 $462,800 Quantified Net Benefits at each assumed SCC value

SCC 5% $35,000 $93,600 $135,900 $188,200 $1,688,500 $706,700
SCC 5% Newell-Pizer $36,300 $96,900 $141,200 $197,700 $1,757,700 $735,200
SCC from 3% and 5% $38,500 $102,300 $152,200 $214,700 $1,882,300 $786,600
SCC 3% $42,000 $112,300 $168,200 $241,700 $2,076,200 $866,600
SCC 3% Newell-Pizer $47,800 $126,300 $193,200 $278,700 $2,380,700 $992,300

a Note that the co-po llutant impacts associated with the standa rds presented here do not include the full complement of endpoints that, if quantified and monetiz ed, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant benefits are based on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 expos ure. Ideally, human health and environmental benefits would be based on changes in ambient PM2.5 an d ozone as determined by full- scale air quality modeling. However, we were unable to conduct a full-scale air qu ality modeling analysis in time for the proposal. We intend to more fully capture the co-pollutant benefits for the analysis of the final standards.
b Fuel impacts were calculated using pre-tax fuel prices.

EPA also conducted a separate analysis of the total benefits over the model year lifetimes of the 2012 through 2016 model year ve hicles. 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 DRIA chapter 5. The societal benef its 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 di scount 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 8-15include fuel savings as a positive benefit. A s such, the quantified annual costs as shown in Table 8-16 and Table 8-17 do not include f u el savings since those are includ ed as benefits. Also note that Table 8-14 through Table 8

- 17 include the benefits of reduced CO 2 emissions—and consequently the total benefits—for each of five interim SCC values considered by EPA. As noted above, there is a very highOther Economic and Social Impacts

8-23 probability ( very likely according to the IPCC) that the benefit estimates from GHG reductions are underestimates because, in part , models used to calculate SCC values do not include information about impacts that have not been quantified.

Table 8-14 Estimated Societal Benefits Associated with the Proposed Ligh t-Duty Vehicle GHG Program, Model Year Analysis (Millions of 2007 dollars; 3% Discount Rate)

MONETIZED VALUES 2012MY 2013MY 2014MY 2015MY 2016MY SUM
Cost of Noise, Accident, Congestion ($) -$900 -$1,400 -$1,900 -$2,800 -$3,900 -$11,000
Pretax Fuel Savings ($) $15,600 $24,400 $34,800 $49,800 $68,500 $193,300 Energy Security ($) (price shock) $400 $600 $900 $1,200 $1,600 $4,700
Change in no. of Refueling (#) 500 700 1,000 1,300 1,800 5,300
Change in Refueling Time (hours) 0 100 100 100 200 400
Value of Reduced Refueling time ($) $900 $1,400 $1,900 $2,700 $3,700 $10,500
Value of Additional Driving ($) $2,000 $3,000 $4,100 $5,700 $7,900 $22,700

Value of PM 2.5 related Health Impacts ($) a,b,c $600 $900 $1,200 $1,700 $2,200 $6,600
Social Cost of Carbon (SCC) at each assumed SCC value

SCC 5% $500 $700 $1,000 $1,400 $1,900 $5,600
SCC 5% Newell-Pizer $1,000 $1,500 $2,000 $2,900 $3,800 $11,000
SCC from 3% and 5% $1,800 $2,800 $3,900 $5,400 $7,200 $21,000
SCC 3% $3,200 $4,800 $6,700 $9,400 $13,000 $37,000
SCC 3% Newell-Pizer $5,300 $8,100 $11,000 $16,000 $21,000 $61,000
Total Benefits at each assumed SCC value

SCC 5% $19,100 $29,600 $42,000 $59,700 $81,900 $232,400
SCC 5% Newell-Pizer $19,600 $30,400 $43,000 $61,200 $83,800 $237,800
SCC from 3% and 5% $20,400 $31,700 $44,900 $63,700 $87,200 $247,800
SCC 3% $21,800 $33,700 $47,700 $67,700 $93,000 $263,800
SCC 3% Newell-Pizer $23,900 $37,000 $52,000 $74,300 $101,000 $287,800

a 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, we were unable to conduct a full-scale air quality modeling analysis in time for the proposal. We intend to more fully capture the co-pollutant benefits for the analysis of the final standards.
b 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.

c 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.Draft Regulatory Impact Analysis

8-24 Table 8-15. Estimated Societal Benefits Associated with the Proposed Light-Duty Vehicle GHG Program, Model Year Analysis (Millions of 2007 dollars; 7% Discount Rate)

MONETIZED VALUES 2012MY 2013MY 2014MY 2015MY 2016MY SUM
Cost of Noise, Accident, Congestion ($) -$700 -$1,100 -$1,500 -$ 2,200 -$3,100 -$8,700
Pretax Fuel Savings ($) $12,100 $19,000 $27,200 $39,000 $53,700 $150,900 Energy Security ($) (price shock) $300 $500 $700 $900 $1,300 $3,700
Change in no. of Refueling (#) 400 500 800 1,100 1,500 4,200 Change in Refueling Time (hours) 0 0 100 100 100 300
Value of Reduced Refueling time ($) $700 $1,100 $1,500 $2,100 $2,900 $8,300
Value of Additional Driving ($) $1,500 $2,400 $3,200 $4,500 $6,300 $18,000

Value of PM 2.5 related Health Impacts ($) a,b,c $500 $700 $1,000 $1,300 $1,800 $5,300
Social Cost of Carbon (SCC) at each assumed SCC value

SCC 5% $400 $500 $700 $1,000 $1,300 $3,900
SCC 5% Newell-Pizer $700 $1,100 $1,500 $2,000 $2,500 $7,700
SCC from 3% and 5% $1,400 $2,100 $2,800 $3,700 $4,800 $15,000
SCC 3% $2,400 $3,600 $4,800 $6,500 $8,300 $26,000
SCC 3% Newell-Pizer $4,000 $6,000 $8,000 $11,000 $14,000 $43,000
Total Benefits at each assumed SCC value

SCC 5% $14,800 $23,100 $32,800 $46,600 $64,200 $181,400
SCC 5% Newell-Pizer $15,100 $23,700 $33,600 $47,600 $65,400 $185,200
SCC from 3% and 5% $15,800 $24,700 $34,900 $49,300 $67,700 $192,500
SCC 3% $16,800 $26,200 $36,900 $52,100 $71,200 $203,500
SCC 3% Newell-Pizer $18,400 $28,600 $40,100 $56,600 $76,900 $220,500

a 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, we were unable to conduct a full-scale air quality modeling analysis in time for the proposal. We intend to more fully capture the co-pollutant benefits for the analysis of the final standards.
b 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.

c 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.Other Economic and Social Impacts

8-25 Table 8-16. Quantified Net Benefits Associated with the Proposed Light-Duty Vehicle GHG Program, Model Year Analysis a (Millions of 2007 dollars; 3% Discount Rate)

2012MY 2013MY 2014MY 2015MY 2016MY SUM Quantified Annual Costs (excluding fuel savings) $5,400 $8,400 $10,900 $13,900 $17,500 $56,100
Quantified Annual Benefits at each assumed SCC value

SCC 5% $19,100 $29,600 $42,000 $59,700 $81,900 $232,400
SCC 5% Newell-Pizer $19,600 $30,400 $43,000 $61,200 $83,800 $237,800
SCC from 3% and 5% $20,400 $31,700 $44,900 $63,700 $87,200 $247,800
SCC 3% $21,800 $33,700 $47,700 $67,700 $93,000 $263,800
SCC 3% Newell-Pizer $23,900 $37,000 $52,000 $74,300 $101,000 $287,800 Quantified Net Benefits at each assumed SCC value

SCC 5% $13,700 $21,200 $31,100 $45,800 $64,400 $176,300
SCC 5% Newell-Pizer $14,200 $22,000 $32,100 $47,300 $66,300 $181,700
SCC from 3% and 5% $15,000 $23,300 $34,000 $49,800 $69,700 $191,700
SCC 3% $16,400 $25,300 $36,800 $53,800 $75,500 $207,700
SCC 3% Newell-Pizer $18,500 $28,600 $41,100 $60,400 $83,500 $231,700

a Note that the co-po llutant impacts associated with the standa rds presented here do not include the full complement of endpoints that, if quantified and monetiz ed, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant benefits are based on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 expos ure. 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, we were unable to conduct a full-scale air qu ality modeling analysis in time for the proposal. We intend to more fully capture the co-pollutant benefits for the analysis of the final standards.

Table 8-17. Quantified Net Benefits Associated with the Proposed Light-Duty Vehicle GHG Program, Model Year Analysis a (Millions of 2007 dollars; 7% Discount Rate)

2012MY 2013MY 2014MY 2015MY 2016MY SUM Quantified Annual Costs (excluding fuel savings) $5,400 $8,400 $10,900 $1 3,900 $17,500 $56,100
Quantified Annual Benefits at each assumed SCC value

SCC 5% $14,800 $23,100 $32,800 $46,600 $64,200 $181,400
SCC 5% Newell-Pizer $15,100 $23,700 $33,600 $47,600 $65,400 $185,200
SCC from 3% and 5% $15,800 $24,700 $34,900 $49,300 $67,700 $192,500
SCC 3% $16,800 $26,200 $36,900 $52,100 $71,200 $203,500
SCC 3% Newell-Pizer $18,400 $28,600 $40,100 $56,600 $76,900 $220,500 Quantified Net Benefits at each assumed SCC value

SCC 5% $9,400 $14,700 $21,900 $32,700 $46,700 $125,300
SCC 5% Newell-Pizer $9,700 $15,300 $22,700 $33,700 $47,900 $129,100
SCC from 3% and 5% $10,400 $16,300 $24,000 $35,400 $50,200 $136,400
SCC 3% $11,400 $17,800 $26,000 $38,200 $53,700 $147,400
SCC 3% Newell-Pizer $13,000 $20,200 $29,200 $42,700 $59,400 $164,400

a Note that the co-po llutant impacts associated with the standa rds presented here do not include the full complement of endpoints that, if quantified and monetiz ed, would change the total monetized estimate of rulerelated impacts. Instead, the co-pollutant benefits are based on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 expos ure. 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, we were unable to conduct a full-scale air qu ality modeling analysis in time for the proposal. We intend to more fully capture the co-pollutant benefits for the analysis of the final standards.Draft Regulatory Impact Analysis

8-26 References

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472.

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.

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.

3 U.S. Department of Energy, 2008, “Ave rage Price of a New Car, 1970-2006,” http://www1.eere.energy.gov/vehiclesandfuels/facts /2008_fotw520.html, acce ssed April 23, 2009.

4 Solheim, Mark, 2006“State Car Tax Rankings,” http://www.kiplinger.com/features/archives/2006 /04/cartax.html, acce ssed April 23, 2009.

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://w ww.census.gov/popest/states/st ates.html, accessed April 23, 2009.

6 http://auto-loan.interest.com , 8/24/09 at 9:20AM.

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-thatcar-really-costs-to-own-ov.htm , accessed April 23, 2009.

8 Kleit A.N., 1990, “The Effect of Annual Changes in Automobile Fuel Economy Standards,” Journal of Regulatory Economics 2: 151-172; McCarthy, Patrick S. , 1996. “Market Price and Income Elasticities of New Vehicle Demands.” Review of Economics and Statistics 78: 543-547; Goldberg, Pinelopi K., 1998. “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1): 1-33; Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375- 384

.

9 Goldberg, Pinelopi Koujianou, “Product Differentiation a nd Oligopoly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951; 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.

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; 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.

11 McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Auto makers Mitigate Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14, 2006.

12 Berry, Steven, James Levinsohn, an d Ariel Pakes, “Automobile Prices in Market Equilibri um,” Econometrica 63(4) (July 1995): 841-940; Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a Combination of Micr o and Macro Data: The New Car Mark et,” Journal of Political Economy 112(1) (2004): 68-105.

### Other Economic and Social Impacts

8-27 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.

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.

15 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294.

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.

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; 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.

18 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940.

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 un der contract No. DE -AC0500OR22725, 2005.

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_Analysis_Of_20112015 _CAFE_Standards.html.

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.

22 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940.

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.

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; 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.

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.

26 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940.

### Draft Regulatory Impact Analysis

8-28 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.

28 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129; Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338; 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; 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.

29 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729; 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.

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.

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; 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.

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; 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; Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research Part D (2009) (in press).

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.

34 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223.

35 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323.

36 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594.

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.

38 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940.

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).

### Other Economic and Social Impacts

8-29 40 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218.

41 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; Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940.

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.

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.

44 Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323.

45 McManus, Walter, “Can Proactive Fuel Economy Strategies Help Automakers Mitigate Fuel-Price Risks?” Ann Arbor, MI: University of Michigan Transportation Research Institute; Munich Personal RePEc Archive Paper No. 3460, September 14, 2006; http://mpra.ub.uni-muenchen.de/3460/1/MPRA_paper_3460.pdf, accessed 6/16/09.

46 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.

47 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; 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.

48 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294; 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; Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Federal Reserve Bank of Chicago WP 2008-13, at http://www.chicagofed.org/publications/workingpapers/wp2008_13.pdf (accessed 5/6/09); 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).

49 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.

50 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency

. ” Paris, France: International Energy Agency; 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; 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; 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.

### Draft Regulatory Impact Analysis

8-30 51 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=magazine+channel_what's +next , accessed 7/7/09.

52 Berry, Steven, James Levinsohn, and Ariel Pakes (July 1995). “Automobile Prices in Market Equilibrium,” Econometrica 63(4): 841-940.

53 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.

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 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 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 reduc total U.S. imports of crude petroleum or refined fuel by 0.95 gallons..

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). Available at http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed July 27, 2009).

### Small Business Flexibility Analysis

9-1 

## CHAPTER 9: Small Business Flex ibility 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, unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. As a part of this analysis, an agency is directed to convene a Small Business Advocacy Review Panel (SBAR Pa nel or ‘the Panel’). During the Panel process, we would gather information and recommendations from Small Entity Representatives (SERs) on how to reduce th e impact of the rule on small entities.

The following 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 proposed 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 th at 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 ente rprise 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 proposed regulation.



### Table 9-1: Primary Vehicle SBA Small Business Categories NAICS a 

### 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 proposed 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 a preliminary assessment, EPA has identified a total of about 47 vehicle entities, 33 of which are vehicle manufacturers. Of a total of 33 manufacturers, 2

### Draft Regulatory Impact Analysis

9-2 manufacturers fit the SBA 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 converter s must seek a certificate for all of their vehicle models. Model year 1993 and newer vehi cles 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 businesse s 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 would 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 deferring standards for them will have a negligible impact on the GHG emissions reductions from the proposed standards.

EPA has not conducted a Regulatory Flexibility Analysis or a SBREFA SBAR Panel for the proposed rule beca use we are proposing to certif y that the rule would not have a significant economic impact on a substantial number of small entities. EPA is proposing to defer standards for manufactu rers meeting SBA’s definition of small business as described in 13 CFR 121.201. EPA would 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 busi ness. 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 proposed deferment will have a negligible impact on the GHG emissions reductions from the proposed standards.

To ensure that EPA is aware of which companies would be deferred, EPA is proposing 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. 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. Because such entities are not automatically exempted from other EPA regulations for light-duty vehicles and light-duty trucks, absent such a declaration, EPA would assume that the entity was subject to the greenhouse gas control requirements in this GHG proposal. The declaration would need to be submitted at time of vehicle emissions certification under the EPA Tier 2 program. EPA expects that the additional paperwork burden associated with completing and submitting a small

### Small Business Flexibility Analysis

9-3 entity declaration to gain deferral from the pr oposed GHG standards w ould be negligible and easily done in the context of other routine submittals to EPA. However, EPA has accounted for this cost with a nominal estimate included in the Information Collection Request completed under the Paperwork Reduc tion Act. Additional information can be found in the Paperwork Reduction Act discussion in section III.I.2. Based on this, EPA is proposing to certify that the rule would not have a significant economic impact on a substantial number of small entities.