Record of ROCIS Upload of Joint Preamble and TSD 2-19-10

From: Mariana Cubeddu/DC/USEPA/US To: Alexander Cristofaro/DC/USEPA/US@ EPA, Eileen Pritchard/DC/USEPA/US@EPA, Lesley Schaaff/DC/USEPA/US@EPA, Nicole Owens/DC/USEPA/US@EPA, Robin Kime/DC/USEPA/US@EPA, Br uce Schillo/DC/USEPA/US@EPA, Paul Balserak/DC/USEPA/US@EPA, Philip Schwartz/DC/USEPA/US@EPA Cc: Robin Moran/AA/USEPA/ US@EPA, Tad Wysor/AA/USEPA/US@EPA Date: 02/19/2010 03:20 PM Subject: RETURNED WITH APPROVAL TO GO TO OMB: SAN 5344: EPA / NHTSA Joint Rulemaking to Establish Light-Duty GHG Emission Standards and Corporate Average Fuel Economy Standards

OPEI approved sections of the tier 1 final rule SAN 5344: Joint Rulemaking to Establish LightDuty GHG Emission Standards to go to OMB. This action was uploaded into ROCIS today.ENVIRONMENTAL PRO TECTION AGENCY 40 CFR Parts 9, 86, and 600

DEPARTMENT OF TRANSPORTATION National Highway Traffic Safety Administration 49 CFR Parts 531, 533, 537 and 538

[EPA–HQ–OAR–2009–0472; FRL_8959-4; NHTSA-2009-0059] RIN 2060-AP58; RIN 2127-AK90

Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final Rule

AGENCIES: Environmental Protection Agency (EPA) and National Highway Traffic Safety Administration (NHTSA).

ACTION: Final Rule.

_______________________________________________________________________ SUMMARY: EPA and NHTSA are issuing this joint Final Rule to establish a National Program consisting of new standards for light -duty vehicles that will reduce greenhouse gas emissions and improve fuel economy. This joint Final Rule is consistent with the National Fuel Efficiency Policy ann ounced by President Obama on May 19, 2009, responding to the country ’s critical need to address global climate change and to reduce oil consumption. EPA is finalizing greenhouse gas emi ssions standards under the Clean Air Act, and NHTSA is finalizing Corporat e Average Fuel Economy standards under the Energy Policy and Conservation Act, as ame nded. These standards apply to passenger cars, light-duty trucks, and medium-duty pa ssenger vehicles, covering model years 2012 through 2016, and represent a harmonized and consistent National Program. Under the National Program, automobile manufacturers wi ll be able to build a single light-duty national fleet that satisfie s all requirements under both pr ograms while ensuring that consumers still have a full range of vehicle choices.

DATES: This final rule is effective on [Insert date 60 days a fter publication in the Federal Register], sixty days after date of publication in the Federal Register

.

ADDRESSES: EPA and NHTSA have established dockets for this action under Docket ID No. EPA-HQ-OAR-2009-0472 and NHTSA-2009-0059, respectively. All documents in the docket are listed on the www.regulat ions.gov web site. Although listed in the index, some information is not publicly avai lable, e.g., CBI or other information whose disclosure is restricted by stat ute. Certain other material, su ch as copyrighted material, is not placed on the Internet and will be publicly available only in hard copy form. Publicly available docket materials are avai lable either electronically through www.regulations.gov or in hard copy at the following locations: EPA: EPA Docket Center, EPA/DC, EPA West, Room 3334, 1301 Constitution Ave., NW., Washington,

1DC. The Public Reading Room is open from 8:30 a.m. to 4:30 p.m., Monday through Friday, excluding legal holidays. The telephone number for the Public Reading Room is (202) 566–1744 . NHTSA: Docket Management Facilit y, M-30, U.S. Department of Transportation, West Building, Ground Floor , Rm. W12-140, 1200 New Jersey Avenue, SE, Washington, DC 20590. The Docket Management Facility is open between 9 a.m. and 5 p.m. Eastern Time, Monday thr ough Friday, except Federal holidays.

FOR FURTHER INFORMATION CONTACT: EPA: Tad Wysor, Office of Transportation and Air Quality, Assessmen t and Standards Division, Environmental Protection Agency, 2000 Traverwood Drive, Ann Arbor MI 48105; telephone number: 734-214-4332; fax number: 734-214-4816; email address: wysor.tad@epa.gov , or Assessment and Standards Division Hotline; telephone number (734) 214-4636; e-mail address asdinfo@epa.gov . NHTSA: Rebecca Yoon, Office of Chief Counsel, National Highway Traffic Safety Administration, 1200 New Jersey Avenue, SE, Washington, DC 20590. Telephone: (202) 366-2992.

SUPPLEMENTARY INFORMATION:

Does this Action Apply to Me?

This action affects companies that manu facture or sell new light-duty vehicles, light-duty trucks, and medium-duty passenge r vehicles, as defined under EPA’s CAA regulations, 1 and passenger automobiles (passenger cars) and non-passenger automobiles (light trucks) as defined unde r NHTSA’s CAFE regulations. 2 Regulated categories and entities include:

Category NAICS Codes A Examples of Potentially Regulated Entities

Industry 336111 336112 Motor vehicle manufacturers.

Industry 811112 811198 541514 Commercial Importers of Vehicles and Vehicle Components.

A North American Industry Clas sification System (NAICS)

This list is not intended to be exhausti ve, but rather provide s a guide regarding entities likely to be regulated by this action. To determine whether particular activities may be regulated by this action, you should car efully examine the regulations. You may direct questions regarding the applicability of this action to the person listed in FOR FURTHER INFORMATION CONTACT

.

1 “Light-duty vehicle,” “light-duty truck,” and “medium-duty passenger vehicle” are defined in 40 CFR 86.1803-01. Generally, the term “light-duty vehicle” means a passenger car, the term “light-duty truck” means a pick-up truck, sport-utility vehicle, or miniva n of up to 8,500 lbs gross vehicle weight rating, and “medium-duty passenger vehicle” means a sport-utility vehicle or passenger van from 8,500 to 10,000 lbs gross vehicle weight rating. Medium-duty passenger vehicles do not include pick-up trucks.
2 “Passenger car” and “light truck” are defined in 49 CFR Part 523.

2XX [Insert Outline once it's confirmed]

I. Overview of Joint EPA/NHTSA National Program

A. Introduction

The National Highway Traffic Safety Administration (NHTSA) and the Environmental Protection Agency (EPA) are each announcing final rules whose benefits will address the urgent and closely intertwined challenges of energy independence and security and global warming. These rules will implement a strong and coordinated federal greenhouse gas and fuel economy progr am for passenger cars, light-duty-trucks, and medium-duty passenger vehicl es (hereafter light -duty vehicles), referred to as the National Program. The rules will achieve substantial reductions of greenhouse gas (GHG) emissions and improvements in fuel eco nomy from the light- duty vehicle part of the transportation sector, based on technology th at is already being commercially applied in most cases and that can be inco rporated at a reasonable cost.

This joint rulemaking is consistent w ith the President’s announcement on May 19, 2009 of a National Fuel Efficiency Policy of establishing consistent, harmonized, and streamlined requirements that would reduce greenhouse gas emissions and improve fuel economy for all new cars and light-duty trucks sold in the United States. 3 The National Program will deliver additional environmental and energy benefits, cost savings, and administrative efficiencies on a nationwide basi s that would likely not be available under a less coordinated approach. The Nationa l Program also represents regulatory convergence by making it possible for the standa rds of two different federal agencies and the standards of California and other states to act in a unifi ed fashion in providing these benefits. The National Program will allow automakers to produce and sell a single fleet nationally, mitigating the additional costs th at manufacturers would otherwise face in having to comply with multiple sets of federal and state standards. This joint notice is also consistent with the No tice of Upcoming Joint Rulemaking issued by DOT and EPA on May 19, 2009 4 and responds to the Presiden t’s January 26, 2009 memorandum on CAFE standards for model years 2011 and beyond, 5 the details of which can be found in Section IV of this joint notice.

Climate change is widely viewed as th e most significant long-term threat to the global environment. According to the In tergovernmental Panel on Climate Change, anthropogenic emissions of greenhouse gase s are very likely (90 to 99 percent probability) the cause of most of the observed global warming over the last 50 years. The primary GHGs of concern are carbon dioxide (CO 2 ), methane, nitrous oxide,

3 President Obama Announces National Fuel Effici ency Policy, The White House, May 19, 2009. Available at: http://www.whitehouse.gov/the_press_ office/President-Obama-Announces-National-FuelEfficiency-Policy/. Remarks by the President on Na tional Fuel Efficiency Standards, The White House, May 19, 2009. Available at: http://www.whitehouse.gov /the_press_office/Remarks-by-the-President-onnational-fuel-efficiency-standards/.
4 74 FR 24007 (May 22, 2009).
5 Available at: http://www.whitehouse.gov/the_press_office/Presidential_Memorandum_Fuel_Economy/.

3hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. 6 Mobile sources emitted 31 percent of all U.S. GHG in 2007 (transpor tation sources, which do not include certain off-highway sources, account for 28 percent) an d have been the fastest-growing source of U.S. GHG since 1990. 7 Mobile sources addressed in the recent endangerment and contribution findings under CAA section 202(a) --light-duty vehicles, heavy-duty trucks, buses, and motorcycles--accounted for 23 percent of all U.S. GHG in 2007. 8 Light-duty vehicles emit four GHGs--CO 2 , methane, nitrous oxide, a nd hydrofluorocarbons--and are responsible for nearly 60 percent of all mobile source GHGs and over 70 percent of Section 202(a) mobile source GHGs. For light-duty vehicles in 2007, CO 2 emissions represent about 94 percent of all greenhous e emissions (including HFCs), and the CO 2 emissions measured over the EPA tests used for fuel economy compliance represent about 90 percent of total light-dut y vehicle greenhouse gas emissions. 9
, 10

Improving energy security by reducing our dependence on foreign oil has been a national objective since the first oil price shocks in th e 1970s. Net petroleum imports now account for approximately 60 percent of U.S. petroleum consumption. World crude oil production is highly concentrated, exacerbating the risks of supply disruptions and price shocks. Tight global oil markets led to prices over $100 per barrel in 2008, with gasoline reaching as high as $4 per gallon in many parts of the U.S., causing financial hardship for many families. The export of U.S. assets for oil imports continues to be an important component of the historically unprecedented U.S. trade deficits. Transportation accounts for about two-thirds of U.S. petroleum consumption. Light-duty vehicles account for about 60 pe rcent of transportation oil us e, which means that they alone account for about 40 percen t of all U.S. oil consumption.

1. Building Blocks of the National Program

The National Program is both needed and possible because the relationship between improving fuel economy and reducing CO 2 tailpipe emissions is a very direct and close one. The amount of those CO 2 emissions is essentially constant per gallon combusted of a given type of fuel. Thus, the more fuel efficient a vehicle is, the less fuel it burns to travel a given distance. The less fuel it burns, the less CO 2 it emits in traveling

6 Intergovernmental Panel on Climate Change Working Group I. 2007. Climate Change 2007-The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
7 U.S. Environmental Protection Agency. 2009. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007. EPA 430-R-09-004. Available at http://epa.gov/climatechange/emissions/downloads09/GHG2007entire_report-508.pdf
8 U.S. EPA. 2009 Technical Support Document for Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act. Washington, DC. pp. 180-194. Available at http://epa.gov/climatechange/endangerment/downloads/Endangerment%20TSD.pdf
9 U.S. Environmental Protection Agency. 2009. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007. EPA 430-R-09-004. Available at http://epa.gov/climatechange/emi ssions/downloads09/GHG2007entire_report-508.pdf
10 U.S. Environmental Protection Agency. RIA, Chapter 2

4that distance. 11 While there are emission control te chnologies that reduce the pollutants (e.g

. , carbon monoxide) produced by imperfect combustion of fuel by capturing or converting them to other compounds, there is no such technology for CO 2 . Further, while some of those pollutants can also be redu ced by achieving a more complete combustion of fuel, doing so only increases the tailpipe emissions of CO 2 . Thus, there is a single pool of technologies for addressing these twin problems, i.e., those that reduce fuel consumption and thereby reduce CO 2 emissions as well.

a. DOT’s CAFE Program

In 1975, Congress enacted the Energy Policy and Conservation Act (EPCA), mandating that NHTSA establish and implemen t a regulatory program for motor vehicle fuel economy to meet the various facets of the need to conserve energy, including ones having energy independence and security, enviro nmental and foreign policy implications. Fuel economy gains since 1975, due both to th e standards and market factors, have resulted in saving billions of barrels of oil and avoiding billions of metric tons of CO 2 emissions. In December 2007, Congress enacte d the Energy Independence and Securities Act (EISA), amending EPCA to require substant ial, continuing increa ses in fuel economy standards.

The CAFE standards address most, but not all, of the real world CO 2 emissions because a provision in EPCA as originally enacted in 1975 require s the use of the 1975 passenger car test procedures under which vehicle air condi tioners are not turned on during fuel economy testing. 12 Fuel economy is determined by measuring the amount of CO 2 and other carbon compounds emitted from th e tailpipe, not by attempting to measure directly the amount of fuel consumed during a vehicle test, a difficult task to accomplish with precision. The carbon content of the test fuel 13 is then used to calculate the amount of fuel that had to be consumed per m ile in order to produce that amount of CO 2 . Finally, that fuel consumption figure is converted in to a miles-per-gallon figure. CAFE standards also do not address the 5-8 percen t of GHG emissions that are not CO 2 , i.e., nitrous oxide (N 2 O), and methane (CH 4 ) as well as emissions of CO 2 and hydrofluorocarbons (HFCs) related to operation of th e air conditioning system.

b. EPA’s Greenhouse Gas Standa rds for Light-duty Vehicles

Under the Clean Air Act EPA is responsib le for addressing ai r pollutants from motor vehicles. On April 2, 2007, the U.S. Supreme Court issued its opinion in Massachusetts v. EPA
, 14 a case involving a 2003 order of the EPA denying a petition for rulemaking to regulate greenhouse gas emissi ons from motor vehicles under section

11 Panel on Policy Implications of Greenhouse Warming, National Academy of Sciences, National Academy of Engineering, Institute of Medicine, “Po licy Implications of Greenhouse Warming: Mitigation, Adaptation, and the Science Base,” Natio nal Academies Press, 1992. p. 287.
12 Although EPCA does not require the use of 1975 test procedures for light trucks, those procedures are used for light truck CAFE standard testing purposes.
13 This is the method that EPA uses to dete rmine compliance with NH TSA’s CAFE standards.
14 549 U.S. 497 (2007).

5202(a) of the Clean Air Act (CAA). 15 The Court held that greenhouse gases were air pollutants for purposes of the Clean Air Act a nd further held that the Administrator must determine whether or not emissions from new motor vehicles cause or contribute to air pollution which may reasonably be anticipated to endanger public health or welfare, or whether the science is too uncertain to make a reasoned decision. The Court further ruled that, in making these decisions, the EPA Admini strator is required to follow the language of section 202(a) of the CAA. The Court rejected the argu ment that EPA cannot regulate CO 2 from motor vehicles because to do so would de facto tighten fuel economy standards, authority over which has been assi gned by Congress to DOT. The Court stated that “[b]ut that DOT sets mileage standards in no way licenses EPA to shirk its environmental responsibilities. EPA has been charged with protecting the public‘s ‘health’ and ‘welfare’, a stat utory obligation wholly independent of DOT’s mandate to promote energy efficiency.” The Court c oncluded that “[t]he two obligations may overlap, but there is no reason to think the two agencies cannot both administer their obligations and yet avoid inconsistency.” 16 The Court remanded th e case back to the Agency for reconsideration in light of its findings. 17

On December 15, 2009, EPA published two findings (74 FR 66496): that emissions of GHGs from new motor vehicles an d motor vehicle engine s contribute to air pollution, and that the air pollution may reason ably be anticipated to endanger public health and welfare.

c. California Air Resources Board Greenhouse Gas Program

In 2004, the California Air Resources Bo ard approved standards for new lightduty vehicles, which regulate the emission of not only CO 2 , but also other GHGs. Since then, thirteen states and the District of Columbia, comprisi ng approximately 40 percent of the light-duty vehicle market, have adopted California’s standards. These standards apply to model years 2009 through 2016 and require CO 2 emissions for passenger cars and the smallest light trucks of 323 g/mi in 2009 and 205 g/mi in 2016, and for the remaining light trucks of 439 g/mi in 2009 and 332 g/mi in 2016. On June 30, 2009, EPA granted California’s request for a waiver of preemption under the CAA. 18 The granting of the waiver permits California and the other states to proceed with implementing the California emission standards.

In addition, to promote the Nationa l Program, California announced its commitment to take several actions in support of the National program, including revising its program for MYs 2012-2016 such that compliance with the federal GHG standards will be deemed to be compliance wi th California's GHG standards. This will

15 68 FR 52922 (Sept. 8, 2003).
16 549 U.S. at 531-32.
17 For further information on Massachusetts v. EPA see the July 30, 2008 Advance Notice of Proposed Rulemaking, “Regulating Greenhouse Gas Emissions under the Clean Air Act”, 73 FR 44354 at 44397. There is a comprehensive discussion of the litigation’s history, the Supreme Court’s findings, and subsequent actions undertaken by the Bush Administration and the EPA from 2007-2008 in response to the Supreme Court remand.
18 74 FR 32744 (July 8, 2009).

6allow the single national fleet produced by automakers to meet the two Federal requirements and to meet California requirement s as well. California is proceeding with a rulemaking intended to revise its 2004 re gulations to meet its commitments.

2. Public Participation

The agencies proposed their respective rules on September 28, 2009 (74 FR 49454), and received a large number of commen ts representing many perspectives on the proposed rule. The agencies r eceived oral testimony at three public hearings in different parts of the country, and received written comments from more than 130 organizations, including auto manufacturer s and suppliers, states, e nvironmental and other nongovernmental organizations (NGOs), and over 129,000 comments from private citizens.

The vast majority of commenters supporte d the central tenets of the proposed CAFE and GHG programs. That is, there was broad support from most organizations for a National Program that achieves a le vel of 250 gram/mile fleet average CO 2 , which would be 35.5 miles per gallon if the automakers were to meet this CO 2 level solely through fuel economy improvements. The standa rds will be phased in over model years 2012 through 2016 which will allow manufacturer s to build a common fleet of vehicles for the domestic market. In general, comment ers from the automobile industry supported the proposed standards as well as the credit opportunities an d other compliance provisions providing flexibility, while also ma king some recommendations for changes. Environmental and public interest non-govern mental organizations (NGOs), as well as most states that commented, were also generally supportive of the National Program standards. Many of these organizations also expressed concern about the possible impact on program benefits, depending on how the cr edit provisions and flexibilities are designed. The agencies also received specific comments on many aspects of the proposal.

Throughout this notice, the agencies discu ss many of the key issues arising from the public comments and the agencies' responses. In addition, the agencies have addressed all of the public comments in the Joint Response to Comments document associated with this final rule.

B. Summary of the Joint Final Rule and Differences from the Proposal

In this joint rulemaking, EPA is estab lishing GHG emissions standards under the Clean Air Act (CAA), and NHTSA is estab lishing Corporate Average Fuel Economy (CAFE) standards under the Energy Policy a nd Conservation Action of 1975 (EPCA), as amended by the Energy Independence and Securi ty Act of 2007 (EISA). The intention of this joint rulemaking is to set forth a caref ully coordinated and ha rmonized approach to implementing these two statutes , in accordance with all substantive and procedural requirements imposed by law.

NHTSA and EPA have coordinated closely and worked jointly in developing their respective final rules. This is reflected in many aspects of this joint rule. For example,

7the agencies have developed a comprehensive Joint Technical Support Document (TSD) that provides a solid technical underpinning for each agency’s modeling and analysis used to support their standards. Also, to the extent allowed by law, the agencies have harmonized many elements of program design, such as the form of the standard (the footprint-based attribute curves), and the defi nitions used for cars and trucks. They have developed the same or similar compliance flexibilities, to the extent allowed and appropriate under their respect ive statutes, such as aver aging, banking, an d trading of credits, and have harmonized the compliance te sting and test protocols used for purposes of the fleet average standards each agency is finalizing. Finally, under their respective statutes, each agency is called upon to exerci se its judgment and determine standards that are an appropriate balance of various rele vant statutory factors. Given the common technical issues before each agency, the si milarity of the factors each agency is to consider and balance, and the authority of each agency to take into consideration the standards of the other agency, both EPA and NHTSA are establishing standards that result in a harmonized National Program.

This joint final rule covers passenge r cars, light-duty-truc ks, and medium-duty passenger vehicles built in model years 2012 through 2016. These vehicle categories are responsible for almost 60 percent of all U. S. transportatio n-related GHG emissions. EPA and NHTSA expect that automobile manufact urers will meet these standards by utilizing technologies that will redu ce vehicle GHG emissions and improve fuel economy. Although many of these technologies are ava ilable today, the emissions reductions and fuel economy improvements finalized in this notice will involve more widespread use of these technologies across the light-duty vehicle fleet. Th ese include improvements to engines, transmissions, and tires, increased use of start-stop technology, improvements in air conditioning systems, increased use of hybrid and ot her advanced te chnologies, and the initial commercialization of electric ve hicles and plug-in hybrids. NHTSA’s and EPA’s assessments of likely vehicle technologie s that manufacturers will employ to meet the standards are discussed in deta il below and in the Joint TSD.

The National Program will result in approxi mately 960 million metric tons of total carbon dioxide equivalent emissions reductions and approximately 1.8 billion barrels of oil savings over the lifetime of vehicles sold in model years (MYs) 2012 through 2016. In total, the combined EPA and NHTSA 2012-2016 standards will reduce GHG emissions from the U.S. light-duty fleet by approximately 21 percent by 2030 over the level that would occur in the absence of the National Program. These actions also will provide important energy secu rity benefits, as light-duty vehicles are about 95 percent dependent on oil-based fuels. The agencies project that the benefits of the National Program will total more than $240 billion at a 3% discount rate, or more than $190 billion at a 7% discount rate. In the discussion that follows in Sections III and IV, each agency explains the related benefits for their individual standards.

Together, EPA and NHTSA estimate that the average cost increase for a model year 2016 vehicle due to the National Program will be less than $1,000. U.S. consumers who purchase their vehicle outright will save enough in lower fuel costs over the first three years to offset these higher vehicle cost s. However, most U.S. consumers purchase

8a new vehicle using credit rath er than paying cash and the typi cal car loan today is a five year, 60 month loan. These consumers will s ee immediate savings due to their vehicle’s lower fuel consumption in the form of a net reduction in annual costs of $130-$180 throughout the duration of the loan (that is, th e fuel savings will outweigh the increase in loan payments by $130-$180 per year). Whet her a consumer takes out a loan or purchases a new vehicle outright, over the lifetime of a model year 2016 vehicle, the consumer's net savings should be more than $3,000. The average 2016 MY vehicle will emit 16 fewer metric tons of CO 2 -equivalent emissions (that is, CO 2 emissions plus HFC air conditioning leakage emissions ) emissions during its lifetime.

This joint rule also results in important regulatory c onvergence and certainty to automobile companies. Absent this rule, th ere would be three separate federal and state regimes independently regulating light-duty vehicles to reduce fuel consumption and GHG emissions: NHTSA’s CAFE standards, EPA’s GHG standards, and the GHG standards applicable in Calif ornia and other states adopti ng the California standards. This joint rule will allow automakers to meet both the NHTSA and EPA requirements with a single national fleet, greatly simp lifying the industry's technology, investment and compliance strategies. In addition, to promote the National Program, California announced its commitment to take several actions, including revising its program for MYs 2012-2016 such that compliance with the federal GHG standards will be deemed to be compliance with California's GHG standards. This will allow the single national fleet used by automakers to meet the two Federal requiremen ts and to meet California requirements as well. California is proceedi ng with a rulemaking intended to revise its 2004 regulations to meet its commitments. EPA and NHTSA are confident that these GHG and CAFE standards will successfully harmonize both the federal and state programs for MYs 2012-2016 and will allow our country to achieve the increased benefits of a single, nationwide program to reduce light-duty vehicle GHG emissions and reduce the country’s dependence on fossil fu els by improving these vehicles’ fuel
economy.

A successful and sustainable automo tive industry depends upon, among other things, continuous technology innovation in general, and low greenhouse gas emissions and high fuel economy vehicles in particular. In this respect, this action will help spark the investment in technology innovation nece ssary for automakers to successfully compete in both domestic and export markets, and thereby continue to support a strong economy.

While this action covers MYs 2012-2016, many stakeholders encouraged EPA and NHTSA to also begin working toward standards for MY 2017 and beyond that would maintain a single nationwide program. The ag encies recognize the importance of and are committed to a strong, coordinated national pr ogram for light-duty vehicles for model years beyond 2016.

Key elements of the National Program fina lized today are the level and form of the GHG and CAFE standards, the availa ble compliance mechanisms, and general implementation elements. These elements ar e summarized in the following section, with

9more detailed discussions about EPA’s GHG program following in Se ction III, and about NHTSA’s CAFE program in Section IV. This joint final rule responds to the wide array of comments that the agencies received on the proposed rule. This section summarizes many of the major comments on the primary elements of the proposal and describes whether and how the final rule has cha nged, based on the comments and additional analyses. Major comments and the agencies’ responses to them ar e also discussed in more detail in later sections of this pr eamble. For a full summary of public comments and EPA’s and NHTSA’s responses to them, please see the Joint Response to Comments document associated with this final rule.

1. Joint Analytical Approach

NHTSA and EPA have worked closely toge ther on nearly every aspect of this joint final rule. The extent a nd results of this collaboration are reflected in the elements of the respective NHTSA and EPA rules, as well as the analytical work contained in the Joint Technical Support Document (Joint TSD). The Joint TSD, in particular, describes important details of the analytical work that are shared, as well as any differences in approach. These include the build up of the baseline and reference fleets, the derivation of the shape of the curves that define the st andards, a detailed descri ption of the costs and effectiveness of the technology choices that are available to vehicle manufacturers, a summary of the computer models used to es timate how technologies might be added to vehicles, and finally the economic inputs used to calculate the impacts and benefits of the rules, where practicable.

EPA and NHTSA have jointly developed at tribute curve shapes that each agency is using for its final standards. Further deta ils of these functions can be found in Sections III and IV of this preamble as well as Chapter 2 of the Joint TSD. A critical technical underpinning of each agency’s analysis is the cost and effectiveness of the various control technologies. These are used to anal yze the feasibility a nd cost of potential GHG and CAFE standards. A detailed descri ption of all of the technology information considered can be found in Chapter 3 of th e Joint TSD (and for A/C, Chapter 2 of the EPA RIA). This detailed technology data forms the inputs to computer models that each agency uses to project how vehicle manufactur ers may add those techno logies in order to comply with the new standards. These are the OMEGA and Volpe models for EPA and NHTSA, respectively. The models and their i nputs can also be found in the docket. Further description of the model and outputs ca n be found in Sections III and IV of this preamble, and Chapter 3 of the Joint TSD. Th is comprehensive joint analytical approach has provided a sound and consistent technical basis for each agency in developing its final standards, which are summarized in the sections below.

The vast majority of public comments expressed strong support for the joint analytical work performed for the proposal . Commenters generally agreed with the analytical work and its results, and supported the transparency of the analysis and its underlying data. Where commenter s raised specific points, th e agencies have considered them and made changes where appropriate. The agencies' further evaluation of various technical issues also led to a limited number of changes. A detailed discussion of these

10issues can be found in Section II of this preamble, and the Joint TSD.

2. Level of the Standards

In this notice, EPA and NHTSA are estab lishing two separate se ts of standards, each under its respective statutory author ities. EPA is setting national CO 2 emissions standards for light-duty vehicles under sec tion 202 (a) of the Clean Air Act. These standards will require these vehicles to meet an estimated combined average emissions level of 250 grams/mile of CO 2 in model year 2016. NHTSA is setting CAFE standards for passenger cars and light trucks under 49 U.S.C. 32902. These standards will require manufacturers of those vehicles to meet an estimated combined average fuel economy level of 34.1 mpg in model ye ar 2016. The standards for bot h agencies begin with the 2012 model year, with standards increasing in stringency through model year 2016. They represent a harmonized approach that will allow industry to build a single national fleet that will satisfy both the GHG require ments under the CAA and CAFE requirements under EPCA/EISA.

Given differences in their re spective statutory authoritie s, however, the agencies’ standards include some important differences. Under the CO 2 fleet average standards adopted under CAA section 202(a), EPA expects manufacturers to take advantage of the option to generate CO 2 -equivalent credits by reducing em issions of hydrofluorocarbons (HFCs) and CO 2 through improvements in their air conditioner systems. EPA accounted for these reductions in developing its final CO 2 standards. NHTSA did not do so because EPCA does not allow vehicle manufacturers to use air conditioning credits in complying with CAFE standard s for passenger cars. 19 CO 2 emissions due to air conditioning operation are not measured by the test pr ocedure mandated by statute for use in establishing and enforcing CAFE standards fo r passenger cars. As a result, improvement in the efficiency of passenger car air conditione rs is not considered as a possible control technology for purposes of CAFE.

These differences regarding the treatment of air conditioning improvements (related to CO 2 and HFC reductions ) affect the relati ve stringency of the EPA standard and NHTSA standard for MY 2016. The 250 grams per mile of CO 2 equivalent emissions limit is equivalent to 35.5 mpg 20 if the automotive industry were to meet this CO 2 level all through fuel economy improvements. As a consequence of the prohibition against NHTSA’s allowing credits for air conditioning improvements for purposes of passenger car CAFE compliance, NHTSA is se tting fuel economy standards that are estimated to require a combined (passenger car and light truck) average fuel economy level of 34.1 mpg by MY 2016.

The vast majority of public comments expressed strong support for the National

19 There is no such statutory limitation with respect to light trucks.
20 The agencies are using a common co nversion factor between fuel economy in units of miles per gallon and CO 2 emissions in units of grams per mile. This conversion factor is 8,887 grams CO 2 per gallon gasoline fuel. Diesel fuel has a c onversion factor of 10,180 grams CO 2 per gallon diesel fuel though for the purposes of this calculation, we are assuming 100% gasoline fuel.

11Program standards, including the stringency of the agencies’ respective standards and the phase-in from model year 2012 through 2016. There were a number of comments supporting standards more stringent than proposed, and a few others supporting less stringent standards, in particular for the 2012-2015 model years. The agencies' consideration of comments and their updated te chnical analyses led to only very limited changes in the footprint curves and did not change the agencies’ projections that the nationwide fleet will achieve a level of 250 grams/mile by 2016 (equivalent to 35.5 mpg). The responses to these comments are discusse d in more detail in Sections III and IV, respectively, and in the Joint Response to Comments document.

As proposed, NHTSA and EPA’s final st andards, like the standards NHTSA promulgated in March 2009 for MY 2011, are expressed as mathematical functions depending on vehicle footprint. Footprint is one measure of vehicle size, and is determined by multiplying the vehicle’s wheelbase by the vehicle’s average track width. 21 The standards that must be met by each manufacturer's fleet will be determined by computing the sales-weighted average (h armonic average for CAFE) of the targets applicable to each of the manufacturer’s pa ssenger cars and light trucks. Under these footprint-based standards, the levels require d of individual manufacturers will depend, as noted above, on the mix of vehicles sold. NHTSA's and EPA’s resp ective sta ndards are shown in the tables below. It is important to note that the standards are the attributebased curves established by each agency. Th e values in the tables below reflect the agencies’ projection of the corr esponding fleet levels that will result from these attributebased curves.

As a result of public comments and updated economic and future fleet projections, EPA and NHTSA have updated the attribute based curves for this final rule, as discussed in detail in Section II.B of this preamble and Chapter 2 of the Joint TSD. This update in turn affects costs, benefits, a nd other impacts of the final standards. Thus, the agencies have updated their overall projec tions of the impacts of the final rule standards, and these results are only slight ly different from those presented in the proposed rule.

As shown in Table I.B.2-1, NHTSA’s fleet-wide CAFE-required levels for passenger cars under the final standards are projected to increase from 33.3 to 37.8 mpg between MY 2012 and MY 2016. Similarly, fleet-w ide CAFE levels for light trucks are projected to increase from 25.4 to 28.8 mpg. NHTSA has also estimated the average fleet-wide required levels for the combined car and truck fleets. As shown, the overall fleet average CAFE level is expected to be 34.1 mpg in MY 2016. These numbers do not include the effects of other fl exibilities and credits in the program. These standards represent a 4.3 percent aver age annual rate of incr ease relative to the MY 2011 standards. 22

21 See 49 CFR 523.2 for the exact definition of “footprint.”
22 Because required CAFE levels depend on the mix of vehicles sold by manufacturers in a model year, NHTSA’s estimate of future required CAFE levels depe nds on its estimate of the mix of vehicles that will be sold in that model year. NHTSA currently esti mates that the MY 2011 standards will require average

12Table I.B.2-1 Average Required Fuel Economy (mpg) under Final CAFE Standards 2011-base 2012 2013 2014 2015 2016 Passenger Cars 30.4 33.3 34.2 34.9 36.2 37.8 Light Trucks 24.4 25.4 26.0 26.6 27.5 28.8 Combined Cars & Trucks 27.6 29.7 30.5 31.3 32.6 34.1

Accounting for the expectation that some manufacturers could continue to pay civil penalties rather than achieving required CAFE levels, and the ability to use FFV credits, 23 NHTSA estimates that the CAFE standa rds will lead to the following average achieved fuel economy levels, based on the proj ections of what each manufacturer’s fleet will comprise in each year of the program: 24

Table I.B.2-2 Projected Fleet-Wide Ac hieved CAFE Levels under the Final Footprint-Based CAFE Standards (mpg) 2012 2013 2014 2015 2016 Passenger Cars 32.3 33.5 34.2 35.0 36.2 Light Trucks 24.5 25.1 25.9 26.7 27.5 Combined Cars & Trucks 28.7 29.7 30.6 31.5 32.7

NHTSA is also required by EISA to se t a minimum fuel economy standard for domestically manufactured passe nger cars in addition to the attribute-based passenger car standard. The minimum standard “shall be the greater of (A) 27.5 miles per gallon; or (B) 92 percent of the average fuel economy projected by the Secretary for the combined domestic and non-domestic passenger automobi le fleets manufactured for sale in the United States by all manufacturers in the model year....” 25

Based on NHTSA’s current market forecas t, the agency’s estimates of these minimum standards under the MY 2012-2016 CAFE standards (and, for comparison, the final MY 2011 standard) are summarized below in Table I.B.2-3. 26 For eventual compliance calculations, the final calculated minimum standards will be updated to

fuel economy levels of 30.4 mpg for passenger cars, 24.4 mpg for light trucks, and 27.6 mpg for the combined fleet.
23 The penalties are similar in function to e ssentially unlimited, fixed-price allowances.
24 NHTSA’s estimates account for availab ility of CAFE credits for the sale of flexible-fuel vehicles (FFVs), and for the potential that some manufacturers will pay civil penalties rather than comply with the CAFE standards. This yields NHTSA’s estimates of the r eal-world fuel economy that will likely be achieved under the final CAFE standards. NHTSA has not incl uded any potential impact of car-truck credit transfer in its estimate of the achieved CAFE levels.
25 49 U.S.C. 32902(b)(4).
26 In the March 2009 final rule establishing MY 2011 standards for passenger cars and light trucks, NHTSA estimated that the minimum required CAFE standard for domestically manufactured passenger cars would be 27.8 mpg under the MY 2011 passe nger car standard. Based on the agency’s current forecast of the MY 2011 passenger car market, NHTSA now estimates that the minimum required CAFE standard will be 28.0 mpg in MY 2011.

13reflect the average fuel economy level required under the final standards.

Table I.B.2-3 Estimated Minimum Standard for Domestically Manufactured Passenger Cars under MY 2011 and MY 2012-2016 CAFE Standards for Passenger Cars (mpg) 2011 2012 2013 2014 2015 2016 28.0 30.7 31.4 32.1 33.3 34.7

EPA is establishing GHG emissions standa rds, and Table I.B.2-4 provides EPA’s estimates of their proj ected overall fleet-wide CO 2 equivalent emission levels. 27 The g/mi values are CO 2 equivalent values because they include the projected use of air conditioning (A/C) credits by manufactur ers, which include both HFC and CO 2 reductions.

Table I.B.2-4 Projected Fleet -Wide Emissions Compliance Levels under the FootprintBased CO 2 Standards (g/mi) 2012 2013 2014 2015 2016 Passenger Cars 263 256 247 236 225 Light Trucks 346 337 326 312 298 Combined Cars & Trucks 295 286 276 263 250

As shown in Table I.B.2-4, fleet-wide CO 2 emission level requirements for cars are projected to increase in stringency fr om 263 to 225 g/mi between MY 2012 and MY 2016. Similarly, fleet-wide CO 2 equivalent emission level requirements for trucks are projected to increase in st ringency from 346 to 298 g/mi. As shown, the overall fleet average CO 2 level requirements are projected to in crease in stringency from 295 g/mi in MY 2012 to 250 g/mi in MY 2016.

EPA anticipates that manufacturers will ta ke advantage of program flexibilities such as flexible fueled vehicle credits a nd car/truck credit tradi ng. Due to the credit trading between cars and trucks, the estimated improvements in CO 2 emissions are distributed differently than shown in Tabl e I.B.2-4, where full manufacturer compliance without credit trading is assumed. Table I. B.2-5 shows EPA’s proj ection of the achieved emission levels of the fleet for MY 2012 through 2016, which does consider the impact of car/truck credit transfer and the incr ease in emissions due to certain program flexibilities including flex fueled vehicle cr edits and the temporary lead time allowance alternative standards. The use of optional ai r conditioning credits is considered both in this analysis of achieved levels and of the compliance levels described above. As can be seen in Table I.B.2-5, the projected achieved levels are slightly higher for model years 2012-2015 due to EPA's assumptions about ma nufacturers' use of the regulatory flexibilities, but by model year 2016 the achieved level is projected to be 250 g/mi for the fleet.

27 These levels do not include the effect of flexible fu el credits, transfer of cred its between cars and trucks, temporary lead time allowance, or any other cr edits with the exception of air conditioning.

14Table I.B.2-5 Projected Fleet-Wide Ac hieved Emission Levels under the Footprint-Based CO 2 Standards (g/mi) 2012 2013 2014 2015 2016 Passenger Cars 267 256 245 234 223 Light Trucks 365 353 340 324 303 Combined Cars & Trucks 305 293 280 266 250

Several auto manufacturers stated that th e increasingly stringent requirements for fuel economy and GHG emissions in the early years of the program should follow a more linear phase-in. The agencies’ consideration of comments and of their updated technical analyses did not lead to cha nges to the phase-in of the sta ndards discussed above. This issue is discussed in more detail in Sections II.D, and in Sections III and IV.

NHTSA’s and EPA’s technology assessment i ndicates there is a wide range of technologies available for manu facturers to consider in upgrading vehicles to reduce GHG emissions and improve fuel economy. Commenters were in general agreement with this assessment. 28 As noted, these include improvements to the engines such as use of gasoline direct injection and downsized engines that use turbochargers to provide performance similar to that of larger e ngines, the use of advanced transmissions, increased use of start-stop technology, improve ments in tire rolling resistance, reductions in vehicle weight, increased use of hybrid and other advanced technologies, and the initial commercialization of elect ric vehicles and plug-in hybri ds. EPA is also projecting improvements in vehicle air conditioners incl uding more efficient as well as low leak systems. All of these technologies are al ready available today, and EPA’s and NHTSA’s assessments are that manufacturers will be able to meet the standards through more widespread use of these t echnologies across the fleet.

With respect to the practicability of th e standards in terms of lead time, during MYs 2012-2016 manufacturers are expected to go through the normal automotive business cycle of redesigning and upgrading their light-duty vehicle products, and in some cases introducing entirely new vehicles not on the market today. This rule allows manufacturers the time needed to incorpor ate technology to achieve GHG reductions and improve fuel economy during the vehicle redesign process. This is an important aspect of the rule, as it avoids the much higher co sts that would occur if manufacturers needed to add or change technology at times other than their scheduled redesigns. This time period also provides manufactur ers the opportunity to plan for compliance using a multiyear time frame, again consistent with normal business practice. Over these five model years, there will be an opportunity for manufact urers to evaluate almost every one of their vehicle model platforms and add technology in a cost effective way to control GHG emissions and improve fuel economy. This includes redesign of the air conditioner systems in ways that will further reduce GHG emissions. Various commenters stated that the proposed phase-in of the standards shoul d be introduced more aggressively, less

28 The close relationship between emissions of CO 2 – the most prevalent greenhouse gas emitted by motor vehicles--and fuel consumption, means that the technologies to control CO 2 emissions and to improve fuel economy overlap to a great degree.

15aggressively, or in a more linear manner. Ho wever, our consideration of these comments about the phase-in, as well as our revised anal yses, lead us to conc lude that the general rate of introduction of the standards as proposed remains appropriate. This conclusion is also not affected by the slight difference from the proposal in the final footprint-based curves. These issues are addressed further in Sections III and IV.

Both agencies considered other standards as part of the rulemaking analyses, both more and less stringent than those pr oposed. EPA’s and NHTSA’s analyses of alternative standards are contained in Sections III and IV of this preamble, respectively, as well as the agencies' respective RIAs.

The CAFE and GHG standards describe d above are based on determining emissions and fuel economy using the city and highway test procedures that are currently used in the CAFE program. Some envir onmental and other organizations commented that the test procedures shoul d be improved to reflect more real-world driving conditions; auto manufacturers in general do not support such changes to th e test procedures at this time. Both agencies recognize that these test procedures are not fu lly representative of real world driving conditions. For example, EPA has adopted more representative test procedures that are used in determini ng compliance with emissions standards for pollutants other than GHGs. These test proce dures are also used in EPA’s fuel economy labeling program. However, as discussed in Section III, the current information on effectiveness of the individual emissions c ontrol technologies is based on performance over the CAFE test procedures. For that reason, EPA is using the current CAFE test procedures for the CO 2 standards and is not changing those test procures in this rulemaking. NHTSA, as discussed above, is li mited by statute in what test procedures can be used for purposes of passenger car te sting, although there is no such statutory limitation with respect to test procedures for trucks. However, the same reasons for not changing the truck test procedures apply for CAFE as well.

Both EPA and NHTSA are interested in developing programs that employ test procedures that are more repr esentative of real world driv ing conditions, to the extent authorized under their respectiv e statutes. This is an impor tant issue, and the agencies intend to continue to evaluate it in the context of a future rulemaking to address standards for model year 2017 and thereafter. This coul d include consideration of a range of test procedure changes to better represent real-w orld driving conditions in terms of speed, acceleration, deceleration, ambient temperatures , use of air conditioners, and the like. With respect to air conditioner operation, EP A discusses the public comments on these issues and the final procedures for determ ining emissions credit s for controls on air conditioners in Section III.

Finally, based on the information EPA deve loped in its recent rulemaking that updated its fuel economy labeling program to better reflect average real-world fuel economy, the calculation of fuel savings and CO 2 emissions reductions that will be achieved by the CAFE and GHG standards in cludes adjustments to account for the difference between the fuel economy level meas ured in the CAFE test procedure and the fuel economy actually achieved on average unde r real world drivi ng conditions. These

16adjustments are industry averages for the vehi cles’ performance as a whole, however, and are not a substitute for the information on effectiveness of individual control technologies that will be explored for purposes of a futu re GHG and CAFE rulemaking.

3. Form of the Standards

NHTSA and EPA proposed attribute-based standards for passenger cars and light trucks. NHTSA adopted an attribute approach based on vehicle footprint in its Reformed CAFE program for light truc ks for model years 2008-2011, 29 and recently extended this approach to passenger cars in the CAFE rule for MY 2011 as required by EISA. 30 The agencies also proposed using vehicle footpr int as the attribute for the GHG and CAFE standards. Footprint is defined as a vehicle’ s wheelbase multiplied by its track width -- in other words, the area enclosed by the points at which the wheels meet the ground. Most commenters that expressed a view on this topic supported basing the standards on an attribute, and almost all of these supported the proposed choi ce of vehicle footprint as an appropriate attribute. The agencies conti nue to believe that th e standards are best expressed in terms of an attri bute, and that the f ootprint attribute is the most appropriate attribute on which to base the standards. Thes e issues are further discussed later in this notice and in Chapter 2 of the Joint TSD.

Under the footprint-based standards, each manufacturer will have a GHG and CAFE target unique to its fleet, depending on the footprints of the vehicle models produced by that manufacturer. A manufact urer will have separate footprint-based standards for cars and for trucks. Generally, la rger vehicles (i.e., ve hicles with larger footprints) will be subject to less stringent standards (i.e., higher CO 2 grams/mile standards and lower CAFE standa rds) than smaller vehicles. This is because, generally speaking, smaller vehicles are more capab le of achieving lower levels of CO 2 and higher levels of fuel economy than larger vehicl es. While a manufacturer’s fleet average standard could be estimated throughout th e model year based on projected production volume of its vehicle fleet, the standard to which the manufacturer must comply will be based on its final model year production figur es. A manufacturer’s calculation of fleet average emissions at the end of the mode l year will thus be based on the productionweighted average emissions of each model in its fleet.

The final footprint-based st andards are very similar in shape to those proposed. NHTSA and EPA include more discussion of the development of the final curves in Section II below, with a full discussion in the Joint TSD. In additi on, a full discussion of the equations and coefficients that define th e curves is included in Section III for the CO 2 curves and Section IV for the mpg curves. Th e following figures illust rate the standards. First, Figure I.B.3-1 shows the fuel economy (mpg) car standard curve.

Under an attribute-based st andard, every vehicle model has a performance target (fuel economy for the CAFE standards, and CO 2 g/mile for the GHG emissions standards), the level of which depends on the vehi cle’s attribute (for th is rule, footprint).

29 71 FR 17566 (Apr. 6, 2006).
30 74 FR 14196 (Mar. 30, 2009).

17The manufacturers’ fleet average performance is determined by the productionweighted 31 average (for CAFE, harmonic average) of those targets. NHTSA and EPA are setting CAFE and CO 2 emissions standards defined by constrained linear functions and, equivalently, piecewise linear functions. 32 As a possible option for future rulemakings, the constrained linear form was introduced by NHTSA in the 2007 NPRM proposing CAFE standards for MY 20 11-2015.

NHTSA is establishing the attribute curv es below for assigning a fuel economy level to an individual vehicle’s footpr int value, for model years 2012 through 2016. These mpg values will be production weighted to determine each manufacturer’s fleet average standard for cars and trucks. Alt hough the general model of the equation is the same for each vehicle category and each year, the parameters of the equation differ for cars and trucks. Each paramete r also changes on an annual basi s, resulting in the yearly increases in stringency. Figur e I.B.3-1 below illustrates th e passenger car CAFE standard curves for model years 2012 through 2016 while Figure I.B.3-2 below illustrates the light truck standard curves for model years 2012-2016. The MY 2011 final standards for cars and trucks, which are specified by a constr ained logistic function rather than a constrained linear function, are shown for comparison.

31 Based on vehicles produced for sale in the United States.
32 The equations are equivalent but are specified differe ntly due to differences in the agencies’ respective models.

182025303540453540455055606570Target(mpg)Footprint (sf)Figure I.B.3-1 MY 2011 and MY 2012-2016 Passenger Car Fuel Economy Targets201120162015201420132012

19Figure I.B.3-2. MY 2011 and MY 2012-2016 Light Truck Fuel Economy Targets2025303540453540455055606570Target(mpg)Footprint (sf)201120162015201420132012

20EPA is establishing the attribut e curves below for assigning a CO 2 level to an individual vehicle’s footprint value, for model years 2012 through 2016. These CO 2 values will be production weighted to de termine each manufacturer’s fleet average standard for cars and trucks. As with the CAFE curves above, the general form of the equation is the same for each vehicle categor y and each year, but the parameters of the equation differ for cars and trucks. Again, each parameter also changes on an annual basis, resulting in the yearly increases in stringency. Figure I.B.3-3 below illustrates the CO 2 car standard curves for model year s 2012 through 2016 while Figure I.B.3-4 shows the CO 2 truck standard curves for model years 2012-2016.

21Figure I.B.3-3 CO2(g/mi) Car Standard Curves

22Figure I.B.3-4 CO2(g/mi) Truck Standard Curves

23NHTSA and EPA received a number of comments about the shape of the car and truck curves. We address these comments fu rther in Section II.C below as well as in Sections III and IV.

As proposed, NHTSA and EPA will use the same vehicle category definitions for determining which vehicles are subject to th e car curve standards ve rsus the truck curve standards. In other words, a vehicle clas sified as a car under the NHTSA CAFE program will also be classified as a car under the EPA GHG program, and likewise for trucks. Auto industry commenters generally agreed wi th this approach and believe it is an important aspect of harmoni zation across the two agencies ’ programs. Some other commenters expressed concern a bout potential conseque nces, especially in how cars and trucks are distinguished. However, EPA and NHTSA are employing the same car and truck definitions for the MY 2012-2016 CAFE and GHG standards as those used in the CAFE program for the 2011 model year standards. 33 This issue is further discussed for the EPA standards in Section III, and for th e NHTSA standards in Section IV. This approach of using CAFE definitions allows EPA’s CO 2 standards and the CAFE standards to be harmonized across all vehicles for this program. However, EPA is not changing the car/truck definition for the purpos es of any other previous rules.

Generally speaking, a smaller footprint vehicle will have higher fuel economy and lower CO 2 emissions relative to a larger foot print vehicle. A footprint-based CO 2 or CAFE standard can be relatively neutral with respect to vehicle size and consumer choice. All vehicles, whether smaller or larger, must make improvements to reduce CO 2 emissions or improve fuel economy, and theref ore all vehicles will be relatively more expensive. With the footprint-based sta ndard approach, EPA and NHTSA believe there should be no significant effect on the relative distribution of di fferent vehicle sizes in the fleet, which means that consumers will still be able to purchase the size of vehicle that meets their needs. Table I.B.3-1 illustrates the fact that different ve hicle sizes will have varying CO 2 emissions and fuel economy ta rgets under the final standards.

Table I.B.3-1 Model Year 2016 CO 2 and Fuel Economy Targets for Various MY 2008 Vehicle Types

Vehicle

Type Example

Models Show Desktop.scf Example Model Footprint (sq. ft.) CO 2 Emissions Target (g/mi) Fuel Economy Target (mpg) Example Passenger Cars

Compact

car Honda Fit 40 206 41.1 Midsize

car Ford Fusion 46 230 37.1 Fullsize

car Chrysler 300 53 263 32.6

33 49 CFR Part 523.

24Example Light-duty Trucks Small SUV 4WD Ford Escape 44 259 32.9 Midsize

crossover Nissan Murano 49 279 28.2 Minivan Toyota Sienna 55 303 24.7

Large pickup

truck Chevy Silverado 67 348 30.6

4. Program Flexibilities

EPA’s and NHTSA’s programs as establis hed in this rule provide compliance flexibility to manufacturers, especially in th e early years of the National Program. This flexibility is expected to provide sufficient lead time for manufactur ers to make necessary technological improvements and reduce the overall cost of the program, without compromising overall environmental and fuel economy objectives. The broad goal of harmonizing the two agencies’ standards include s preserving manufactur ers’ flexibilities in meeting the standards, to the extent a ppropriate and required by law. The following section provides an overview of this fina l rule’s flexibility provisions. Many auto manufacturers commented in support of thes e provisions as critical to meeting the standards in the lead time provided. Envi ronmental groups, some states, and others raised concerns about the possi bility for windfall credits a nd loss of program benefits. The provisions in the final rule are in most cases the same as those proposed. However consideration of the issues raised by commenters has led to modifications in certain provisions. These comments and the agencies ’ response are discussed in Sections III and IV below and in the Joint Response to Comments document.

a. CO 2 /CAFE Credits Generated Based on Fleet Average Performance

Under this NHTSA and EPA final rule, th e fleet average standards that apply to a manufacturer’s car and truck fl eets are based on the applicable footprint-based curves. At the end of each model year, when produc tion of the model year is complete, a production-weighted fleet average will be cal culated for each averaging set (cars and trucks). Under this approach, a manufacturer’s car and/or tr uck fleet that achieves a fleet average CO 2 /CAFE level better than the standard can generate credits. Conversely, if the fleet average CO 2 /CAFE level does not meet the standa rd, the fleet would incur debits (also referred to as a shortfall).

Under the final program, a manufacturer whose fleet generates credits in a given model year would have several options for us ing those credits, in cluding credit carryback, credit carry-forwar d, credit transfers, and credit tr ading. These provisions exist in the MY 2011 CAFE program under EPCA and EI SA, and similar provisions are part of EPA’s Tier 2 program for light -duty vehicle criteria polluta nt emissions, as well as many other mobile source standards issued by EPA un der the CAA. The manufacturer will be

25able to carry back credits to offset a defi cit that had accrued in a prior model year and was subsequently carried over to the current model year. EPCA also provides for this. EPCA restricts the carry-back of CAFE credit s to three years, a nd as proposed EPA is establishing the same limitati on, in keeping with the goal of harmonizing both sets of standards.

After satisfying any need to offset pre- existing deficits, remaining credits can be saved (banked) for use in future years. Under the CAFE program, EISA allows manufacturers to apply credits earned in a m odel year to compliance in any of the five subsequent model years. 34 As proposed, under the GHG program, EPA is also allowing manufacturers to use these banked credits in th e five years after the year in which they were generated (i.e., five years carry-forward).

EISA required NHTSA to establish by regulation a CAFE credits transferring program, which NHTSA established in a March 2009 final rule codified at 49 CFR Part 536, to allow a manufacturer to transfer credits between it s vehicle fleets to achieve compliance with the standards. For example, credits earned by over-compliance with a manufacturer’s car flee t average standard could be used to offset debits incurred due to that manufacturer’s not meeting the truck flee t average standard in a given year. EPA’s Tier 2 program also provides for this type of credit transfer. As proposed for purposes of this rule, EPA allows unlimited credit transfer s across a manufacturer’s car-truck fleet to meet the GHG standard. This is based on the expectation that this flexibility will facilitate manufacturers' ability to co mply with the GHG standards in the lead time provided, and will allow the required GHG emissions reductions to be achieved in the most cost effective way. Under the CAA, unlike under EISA, there is no statutory limitation on car-truck credit transfers. Therefore, EPA is not constraining car-truck credit transfers, as doing so would reduce the flexibility for lead time, and would increase costs with no corresponding environmental benefit. For the CAFE program, however, EISA limits the amount of credits that may be transferred, wh ich has the effects of limiting the extent to which a manufacturer can rely upon cr edits in lieu of making fuel economy improvements to a particular portion of its vehi cle fleet, but also of potentially increasing the costs of improving the manufacturer’s overa ll fleet. EISA also prohibits the use of transferred credits to meet the statutory minimum level for the domestic car fleet standard. 35 These and other statutory limits will continue to apply to the determination of compliance with the CAFE standards.

EISA also allowed NHTSA to establis h by regulation a CAFE credit trading program, which NHTSA established in the Ma rch 2009 final rule at 40 CFR Part 536, to allow credits to be traded (sold) to othe r vehicle manufacturers. As proposed, EPA allows credit trading in the GHG program. These sorts of exchanges are typically allowed under EPA’s current mobile source emission credit programs, although manufacturers have seldom made such ex changes. Under the NHTSA CAFE program, EPCA also allows these types of credit tr ades, although, as with transferred credits, traded credits may not be used to meet the minimum domestic car standards specified by

34 49 U.S.C. 32903(a)(2).
35 49 U.S.C. 32903(g)(4).

26statute. 36 Comments discussing these provisi ons supported the proposed approach. These final provisions are the same as proposed.

As further discussed in sec tion IV of this preamble, NHTSA sought to find a way to provide credits for improving the efficiency of light truck air conditioners (A/Cs) and solicited public comments to that end. The ag ency did so because the power necessary to operate an A/C compressor places a significant additional load on the engine, thus reducing fuel economy and increasing CO 2 tailpipe emissions. See section III.C.1 below. The agency would have made a similar e ffort regarding cars , but a 1975 statutory provision made it unfruitful even to explore th e possibility of admi nistratively proving such credits for cars. The agency did not identify a workable way of providing such credits for light trucks in th e context of this rulemaking.

b. Air Conditioning Credits Under the EPA Final Rule

Air conditioning (A/C) systems contri bute to GHG emissions in two ways. Hydrofluorocarbon (HFC) refrigerants, whic h are powerful GHG pollutants, can leak from the A/C system (direct A/C emissions). As just noted, operation of the A/C system also places an additional load on the engine, which results in additional CO 2 tailpipe emissions (indirect A/C related emissions). EPA is allowing manuf acturers to generate credits by reducing either or both types of GHG emissions related to A/C systems. Specifically, EPA is establis hing a method to calculate CO 2 equivalent reductions for the vehicle's full useful life on a grams/mile basis that can be used as credits in meeting the fleet average CO 2 standards. EPA’s analysis indicat es that this approach provides manufacturers with a highly cost-effective way to achieve a por tion of GHG emissions reductions under the EPA program. EPA is estimating that manufacturers will on average generate 11 g/mi GHG credit towa rd meeting the 250 g/mi by 2016 (though some companies may generate more). EPA will also allow manufacturers to earn early A/C credits starting in MY 2009 through 2011, as discussed further in a later section. There were many comments on th e proposed A/C provisions. Nearly every one of these was supportive of EPA including A/C control as part of this rule, though there was some disagreement on some of the details of th e program. The HFC crediting scheme was widely supported. The comments mainly we re concentrated on i ndirect A/C related credits. The auto manufacturers and supp liers had some technical comments on A/C technologies, and there were many concerns wi th the proposed idle test. EPA has made some minor adjustments in bot h of these areas that we be lieve are responsive to these concerns. EPA addresses A/C issues in greater detail in Section III of this preamble and in Chapter 2 of EPA’s RIA.

c. Flexible-fuel and Alternative Fuel Vehicle Credits

EPCA authorizes a complia nce flexibility incentive under the CAFE program for production of dual-fueled or flexible-fuel vehicles (FFV) and dedicated alternative fuel vehicles. FFVs are vehicles that can run both on an alte rnative fuel and conventional fuel. Most FFVs are E-85 capable vehicles, wh ich can run on either gasoline or a mixture

36 49 U.S.C. 32903(f)(2).

27of up to 85 percent ethanol and 15 percent gasolin e. Dedicated alternative fuel vehicles are vehicles that run exclusively on an alte rnative fuel. EPCA was amended by EISA to extend the period of availability of the FFV incentive, but to begin phasing it out by annually reducing the amount of FFV incentive that can be us ed toward compliance with the CAFE standards. 37 Although NHTSA expressed c oncern about the non-use of alternative fuel by FFVs in a 2002 report to Congress (Effects of th e Alternative Motor Fuels Act CAFE Incentives Policy), EISA does not premise the availability of the FFV credits on actual use of alternative fuel by an FFV vehicle. Under NHTSA’s CAFE program, pursuant to EISA, no FFV credits will be availa ble for CAFE compliance after MY 2019. 38 For dedicated alternative fuel vehicl es, there are no limits or phase-out of the credits. As required by the statute, NHT SA will continue to allow the use of FFV credits for purposes of compliance with th e CAFE standards until the end of the EISA phase-out period.

For the GHG program, as proposed, EPA w ill allow FFV credits in line with EISA limits, but only during the period fr om MYs 2012 to 2015. After MY 2015, EPA will only allow FFV credits based on a manufact urer’s demonstration that the alternative fuel is actually being used in the vehicles and based on the vehicle’s actual performance. EPA discusses this in more detail in Secti on III.C of the preamble, including a summary of key comments. These provisions are be ing finalized as proposed, with further discussion in Section III.C of how manufacturers can demonstrat e that the alternative fuel is being used.

d. Temporary Lead-time Allowance Al ternative Standards Under the EPA Final Rule

Manufacturers with limited product lines ma y be especially challenged in the early years of the National Program, and need additional lead time. Manufacturers with narrow product offerings may not be able to take full advantage of averaging or other program flexibilities due to the limited scope of the types of vehicles they sell. For example, some smaller volume manufacturer flee ts consist entirely of vehicles with very high baseline CO 2 emissions. Their vehicles are above the CO 2 emissions target for that vehicle footprint, but do not have other types of vehicles in their production mix with which to average. Often, these manufactur ers pay fines under the CAFE program rather than meet the applicable CAFE standar d. EPA believes that these technological circumstances call for more lead time in th e form of a more gr adual phase-in of standards.

EPA is finalizing a temporary lead-time allowance for manufacturers that sell vehicles in the U.S. in MY 2009 and for which vehicle sales in that model year are below 400,000 vehicles. This allowance will be available only during the MY 2012-2015 phase-in years of the program. A manufacturer that satisfies th e threshold criteria will be

37 EPCA provides a statutory incentive for production of FFVs by specifying that their fuel economy is determined using a special calculation procedure that results in those vehicles being assigned a higher fuel economy level than would otherwise occur. This is typically referred to as an FFV credit.
38 Id.

28able to treat a limited number of vehicles as a separate averaging fleet, which will be subject to a less stringent GHG standard. 39 Specifically, a standard of 25 percent above the vehicle’s otherwise appli cable foot-print target le vel will apply to up to 100,000 vehicles total, spread ov er the four year period of MY 2012 through 2015. Thus, the number of vehicles to which the flexibility could apply is limited. EPA also is setting appropriate restrictions on cred it use for these vehicles, as discussed further in Section III. By MY 2016, these allowance vehicles must be averaged into the manufacturer’s full fleet (i.e., they will no longer be eligible for a different standard). EPA discusses this in more detail in Section III.B of the preamble.

EPA received comments from several smaller manufacturers that the TLAAS program was insufficient to allow manufactur ers with very limited product lines to comply. These manufacturers commented that th ey need additional lead time to meet the standards, because their CO 2 baselines are significantly hi gher and their vehicle product lines are even more limited, reducing their abi lity to average across their fleets compared even to other TLAAS manufacturers. EPA fully summarizes the public comments on the TLAAS program, including comments not suppor ting the program, in Section III.B. In summary, in response to the lead time issues raised by manufacturers, EPA is modifying the TLAAS program that applies to manufacturers with between 5,000 and 50,000 vehicle sales in MY 2009. EPA believes th ese provisions are necessary given that, compared with other TLAAS manufacturers, these manufacturers have even more limited product offerings across which to average and higher baseline CO2 emissions, and thus need additional leadtime to meet th e standards. These manufactures would have an increased allotment of vehicles, a tota l of 250,000, compared to 100,000 vehicles (for other TLAAS-eligible manufacturers). In addition, the TLAAS program for these manufacturers would be exte nded by one year, through MY2016 for these vehicles. The other provisions of the TLAAS program would continue to appl y, such as the restrictions on credit trading and the level of the standard. Additional restrictions would also apply to these vehicles, as discussed in Section III. In addition, for the smallest volume manufacturers, those with below 5,000 vehicle sa les, EPA is not setting standards at this time but is instead deferring standards until a future rulemaking. This is the same approach we are using for small businesses. The unique issues involved with these manufacturers will be addressed in that future rulemaking. Further discussion of the public comment on these issues and details on these changes from the proposed program are included in Section III.

e. Additional Credit O pportunities under the Clean Air Act (CAA)

EPA is establishing additional opportun ities for early credits in MYs 2009-2011 through over-compliance with a ba seline standard. The baseline standard is set to be equivalent, on a national level, to the California standards. Credits can be generated by over-compliance with this baseline in one of two ways – over-compliance by the fleet of vehicles sold in California and the CAA sec tion 177 states (i.e., thos e states adopting the

39 EPCA does not permit such an allowance. Consequently, manufacturers who may be able to take advantage of a lead-time allowanc e under the GHG standards would be required to comply with the applicable CAFE standard or be su bject to penalties for non-compliance.

29California program), or over-compliance with the fleet of vehicles sold in the 50 states. EPA is also providing for early credits ba sed on over-compliance with CAFE, but only for vehicles sold in states outside of California and the CAA section 177 states. Under the early credit provisions, no early FFV cred its would be allowed, except those achieved by over-compliance with the California program based on California’s provisions that manufacturers demonstrate actual use of th e alternative fuel. EPA’s early credits provisions are designed to ensure that there would be no double counting of early credits. NHTSA notes, however, that credits for ove rcompliance with CAFE standards during MYs 2009-2011 will still be available for manufacturers to use toward compliance in future model years, just as before.

EPA received comments from some envi ronmental organizations and states expressing concern that these early credits were inappropriate windfall credits because they provided credits for actions that were not surplus, that is above what would otherwise be required for compliance with either state or federal motor vehicle standards. This focused on the credits for over-complianc e with the California standards generated during model years 2009 and perhaps 2010, where according to commenters the CAFE requirements were in effect more stringent than the California standards. EPA believes that early credits provide a valuable incenti ve for manufacturers that have implemented fuel efficient technologies in excess of their CAFE compliance obligations prior to MY2012. With appropriate restrictions, thes e credits, reflecting ove r-compliance over a three model year time frame (MY2009-2011) a nd not just over one or two model years, will be surplus reductions and not otherwise required by law. Therefore, EPA is finalizing these provisions largely as propose d, but in response to comments, with an additional restriction on the tr ading of MY 2009 credits. Th e overall structure of this early credit program addresses concerns about the potential for windfall credits in the first one or two model years. This issu e is fully discussed in Section III.C.

EPA is providing an additional temporary incentive to encourage the commercialization of advanced GHG/fuel economy control technologies--including electric vehicles (EVs), plug- in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs)--for model years 2012-2016. EPA’s pr oposal included an emissions compliance value of zero grams/mile for EVs and FCVs , and the electric porti on of PHEVs, and a multiplier in the range of 1.2 to 2.0, so th at each advanced technology vehicle would count as greater than one vehicle in a manu facturer’s fleetwide compliance calculation. EPA received many comments on the proposed incentives. Many state and environmental organization commenters believed that the combination of these incentives could undermine the GHG benefits of the ru le, and believed the emissions compliance values should take into account the ne t upstream GHG emissions associated with electrified vehicles compared to vehicles powered by petroleum based fuel. Auto manufacturers generally supporte d the incentives, some believi ng the incentives to be a critical part of the National Program. Mo st auto makers supported both the zero grams/mile emissions compliance value and the higher multipliers.

Upon considering the public comments on this issue, EPA is finalizing an advanced technology vehicle in centive program that includes a zero gram/mile emissions

30compliance value for EVs and FCVs, and the electric portion of PH EVs, for up to the first 200,000 EV/PHEV/FCV vehicles produ ced by a given manufacturer during MY2012-2016. For any production greater than this amount, the compliance value for the vehicle will be greater than zero gram/mile, set at a level that reflects the vehicle’s net increase in upstream GHG emissions in compar ison to the gasoline vehicle it replaces. In addition, EPA is not finalizing a multiplier. EPA will also allow this early advanced technology incentive program beginning in MYs 2009-2011. The purpose of these provisions is to provide a temporary incentiv e to promote technologies which have the potential to produce very large GHG reductio ns in the future. The tailpipe GHG emissions from EVs, FCVs, and PHEVs ope rated on grid electricity are zero, and traditionally the emissions of the vehicle itself are all that EPA takes into account for purposes of compliance with standards set unde r section 202(a). This has not raised any issues for criteria pollutants, as upstream emissions associated with production and distribution of the fuel are addressed by co mprehensive regulatory programs focused on the upstream sources of those emissions. At this time, however, there is no such comprehensive program addressing upstream emissions of GHGs, and the upstream GHG emissions associated with production and dist ribution of electricity are higher than the corresponding upstream GHG emissions of gasoline or other petroleum based fuels. In the future, if there were a program to c ontrol upstream emissions of GHGs, then the tailpipe level of zero gram/mile from these vehicles has the potential to produce very large reductions in GHG emissions, and to transform the transportation sector’s contribution to the country’s GHG emissions. Recognizing the potential of these vehicle technologies for very large future reductions, EPA will continue to use the tailpipe value of zero gram/mile as the compliance value as an incentive for the initial production of these vehicles. EPA will reassess the issue of how to address EVs, PHEVs, and FCVs in rulemakings for model years 2017 and beyond, ba sed on the status of advanced vehicle technology commercialization, the status of upstream GHG control programs, and other relevant factors. Further discussion of the temporary advanced technology vehicle incentives, including more detail on the pub lic comments and EPA’s response, is found in Section III.C.

EPA is also providing an option for ma nufacturers to generate credits for employing new and innovative technologies th at achieve GHG reductions that are not reflected on current test procedures, as proposed. Examples of such “off-cycle” technologies might include solar panels on hybr ids, adaptive cruise control, and active aerodynamics, among other technologies. These three credit provision s are discussed in more detail in Section III.

5. Coordinated Compliance

Previous NHTSA and EPA regulations and statutory provisions establish ample examples on which to develop an effective compliance program that achieves the energy and environmental benefits from CAFE and motor vehicle GHG standards. NHTSA and EPA have developed a program that recognizes, and replicates as closely as possible, the compliance protocols associated with the existing CAA Tier 2 vehicle emission standards, and with CAFE standards. The cer tification, testing, repor ting, and associated

31compliance activities closely track current practices and are thus familiar to manufacturers. EPA already oversees test ing, collects and processes test data, and performs calculations to determine complia nce with both CAFE and CAA standards. Under this coordinated approach, the comp liance mechanisms for both programs are consistent and non-duplicative. EPA will also apply the CAA authoritie s applicable to its separate in-use require ments in this program.

The compliance approach allows manuf acturers to satisfy the new program requirements in the same general way they comply with existing applicable CAA and CAFE requirements. Manufacturers would de monstrate compliance on a fleet-average basis at the end of each model year, allowing model-level testing to continue throughout the year as is the current practice for CA FE determinations. The compliance program design establishes a single se t of manufacturer reporting re quirements and relies on a single set of underlying data. This approach still allo ws each agency to assess compliance with its respec tive program under its respec tive statutory authority.

NHTSA and EPA do not anticipate any significant noncompliance under the National Program. However, failure to meet the fleet average standards (after credit opportunities are exhausted) would ultimately result in the potentia l for penalties under both EPCA and the CAA. The CAA allows EP A considerable discretion in assessment of penalties. Penalties under the CAA are typically determined on a vehicle-specific basis by determining the number of a manuf acturer’s highest emitting vehicles that caused the fleet average standa rd violation. This is the same mechanism used for EPA’s National Low Emission Vehicle a nd Tier 2 corporate average standards, and to date there have been no instances of noncompliance. CAFE penalties are specified by EPCA and would be assessed for the enti re noncomplying fleet at a rate of $5.50 times the number of vehicles in the fleet, times the number of tenths of mpg by which the fleet average falls below the standard. In the event of a complia nce action arising out of the same facts and circumstances, EPA could consider CAFE penalties when determining appropriate remedies for the EPA case.

Several stakeholders commented on the proposed coordinated compliance approach. The comments indicated broad support for the overall approach EPA proposed. In particular, both regulated i ndustry and the public interest community appreciated the attempt to streamline co mpliance by adopting current practice where possible and by coordinating EPA and NHTSA co mpliance requirements. Thus the final compliance program design is largely unchanged from the proposal. Some commenters requested additional detail or clarification in certain areas and others suggested some relatively narrow technical changes, and EP A has responded to these suggestions. EPA and NHTSA summarize these comments and the ag encies’ responses in Sections III and IV, respectively, below. The Joint Respons e to Comments document associated with this document includes all of the comment s and responses of the two agencies.

C. Summary of Costs and Benefits of the National Program

This section summarizes the projected costs and benefits of the CAFE and GHG

32emissions standards. These projections he lped inform the agencies’ choices among the alternatives considered and provide further confirmation that the final standards are an appropriate choice within the spectrum of choices allowable under their respective statutory criteria. The costs and benefits projected by NHTSA to result from these CAFE standards are presented first, followed by those from EPA’s analysis of the GHG emissions standards.

For several reasons, the estimates for co sts and benefits presented by NHTSA and EPA, while consistent, are not directly compar able, and thus should not be expected to be identical. Most important, NHTSA and EPA’s standards would require slightly different fuel efficiency improvements. EPA’s GHG standa rd is more stringent in part due to its assumptions about manufacturers’ use of air conditioning credits, which result from reductions in air conditioning-re lated emissions of HFCs and CO 2 . NHTSA was unable to make assumptions about manufacturers’ im proving the efficiency of air conditioners due to statutory limitations. In addition, the CAFE and GHG standards offer different program flexibilities, and the agencies’ an alyses differ in their accounting for these flexibilities (for example, FFVs), primarily because NHTSA is statut orily prohibited from considering some flexibilities when establ ishing CAFE standards, while EPA is not. These differences contribute to differences in the agencies’ respective estimates of costs and benefits resulting from the new standards.

NHTSA performed two analyses: a primar y analysis that shows the estimates of costs, fuel savings, and related benefits th at the agency considered for purposes of establishing new CAFE standards, and a supplem ental analysis that re flects the agency’s best estimate of the potential real-world effects of the CAFE standards, including manufacturers’ potential use of FFV credits in accordance with the provisions of EISA concerning their availability. Because EPCA prohibits NHTSA from considering the ability of manufacturers to use of FFV cred its to increase their fleet average fuel economy when establishing CAFE standards, the agency ’s primary analysis does not include them. However, EPCA does not prohi bit NHTSA from considering the fact that manufacturers may pay civil penalties rather than complying with CAFE standards, and NHTSA’s primary analysis accounts for some manufacturers’ tendency to do so. In addition, NHTSA’s supplemental analysis of th e effect of FFV credits on benefits and costs from its CAFE standards, demonstrat es the real-world impacts of FFVs, and the summary estimates presented in Section IV in clude these effects. Including the use of FFV credits reduces estimated per-vehicle comp liance costs of the pr ogram. However, as shown below, including FFV credits does not significantly change the projected fuel savings and CO 2 reductions, because FFV credits re duce the fuel economy levels that manufacturers achieve not only under the sta ndards, but also under the baseline MY 2011 CAFE standards.

Also, EPCA, as amended by EISA, allows manufacturers to transfer credits between their passenger car a nd light truck fleets. Howe ver, EPCA also prohibits NHTSA from considering manufact urers’ ability to increase their average fuel economy through the use of CAFE credits when determining the stringency of the CAFE standards. Because of this prohibition, NHTSA’s primary an alysis does not account for the extent to

33which credit transfers might actually occur. For purposes of its supplemental analysis, NHTSA considered accounting for the possibility that some manufacturers might utilize the opportunity under EPCA to transfer some CAFE credits between the passenger car and light truck fleets, but determined th at in NHTSA’s year-by-year analysis, manufacturers’ credit transfers cannot be reasonably estimated at this time. 40

EPA made explicit assumptions about ma nufacturers’ use of FFV credits under both the baseline and control alte rnatives, and its estimates of costs and benefits from the GHG standards reflect these assumptions. However, under the GHG standards, FFV credits would be available through MY 2015; starting in MY 2016, EPA will only allow FFV credits based on a manufactur er’s demonstration that the alternative fuel is actually being used in the vehicles and the actual GHG performance for the vehicle run on that alternative fuel.

EPA’s analysis also assumes that manuf acturers would transf er credits between their car and truck fleets in the MY 2011 baseline subject to the maximum value allowed by EPCA, and that unlimited car-truck cr edit transfers would occur under the GHG standards. Including these assumptions in EPA’s analysis increases the resulting estimates of fuel savings and reductions in GHG emissions, while reducing EPA’s estimates of program compliance costs.

Finally, under the EPA GHG program, there is no ability for a manufacturer to intentionally pay fines in lie u of meeting the standard. Under EPCA, however, vehicle manufacturers are allowed to pay fines as an alternative to compliance with applicable CAFE standards. NHTSA’s analysis explicit ly estimates the level of voluntary fine payment by individual manufacturers, whic h reduces NHTSA’s estimates of both the costs and benefits of its CAFE standards. In contrast, the CAA does not allow for fine payment (civil penalties) in lieu of comp liance with emission standards, and EPA’s analysis of benefits from its standard t hus assumes full compliance. This assumption results in higher estimates of fuel savings , of reductions in GHG emissions, and of manufacturers’ compliance costs to sell fleets that comply with both NHTSA’s CAFE program and EPA’s GHG program.

In summary, the projected costs and benefits presented by NHTSA and EPA are not directly comparable, because the GHG em ission levels establ ished by EPA include air conditioning-related improvements in equi valent fuel efficiency and HFC reductions, because of the assumptions incorporated in EPA’s analysis regarding car-truck credit transfers, and because of EPA's projec tion of complete compliance with the GHG standards. It should also be expected th at overall, EPA’s estimates of GHG reductions and fuel savings achieved by the GHG standard s will be slightly higher than those projected by NHTSA only for the CAFE sta ndards because of the reasons described

40 NHTSA’s analysis estimates multi-year planning e ffects within a context in which each model year is represented explicitly, and technologies applied in one model year carry forward to future model years. NHTSA does not currently have a reasonable basis to estimate how a manufactur er might, for example, weigh the transfer of credits from the passenger car to the light truck fleet in MY 2013 against the potential to carry light truck technologies forward from MY 2013 through MY 2016.

34above. For the same reasons, EPA’s estimates of manufacturers’ costs for complying with the passenger car and light trucks GHG standards are slightly higher than NHTSA’s estimates for complying with the CAFE standards.

A number of stakeholders commen ted on NHTSA’s and EPA’s analytical assumptions in estimating costs and benefits of the program. These comments and any changes from the proposed values are summarized in Section II.F, and further in Sections III (for EPA) and IV (for NHTSA); the Re sponse to Comments document presents the detailed responses to each of the comments.

1. Summary of Costs and Benefits of NHTSA's CAFE Standards

Without accounting for the compliance fl exibilities that NHTSA is prohibited from considering when determining the level of new CAFE standards, since manufacturers’ decisions to use those flexibilities are voluntary, NHTSA estimates that these fuel economy increases would lead to fuel savings totaling 61 billion gallons throughout the useful lives of vehicles so ld in MYs 2012-2016. At a 3% discount rate, the present value of the economic benefits resulting from those fuel savings is $143 billion.

The agency further estimates that th ese new CAFE standards will lead to corresponding reductions in CO 2 emissions totaling 655 million metric tons (mmt) during the useful lives of vehicles sold in MY s 2012-2016. The present value of the economic benefits from avoiding those emissions is $14.5 billion, based on a gl obal social cost of carbon value of approximately $21 per metric ton (in 2010, and growing thereafter). NHTSA also estimated the benefits associated with three more estimates of a one ton GHG reduction in 2010 ($5, $35, and $65), which w ill likewise grow thereafter. See Section II for a more detailed discussion of the social cost of carbon. It is important to note that NHTSA’s CAFE standards and EPA’ s GHG standards will both be in effect, and each will lead to increases in average fuel economy and CO 2 emissions reductions. The two agencies’ standards together compri se the National Program, and this discussion of costs and benefits of NHTSA ’s CAFE standards does not ch ange the fact that both the CAFE and GHG standards, jointly, are the source of the benefits and costs of the National Program.

Table I.C.1-1 NHTSA Fuel Sa ved (billion gallons) and CO 2 Emissions Avoided (mmt) under CAFE Standards (without FFV Credits) 2012 2013 2014 2015 2016 Total Fuel (b. gal.) 4.2 8.9 12.5 16.0 19.5 61.0 CO 2 (mmt) 44 94 134 172 210 655

Considering manufacturers’ ability to earn credit toward compliance by selling FFVs, NHTSA estimates very little change in incremental fuel savings and avoided CO 2 emissions, assuming FFV credits would be us ed toward both the baseline and final

35standards:

Table I.C.1-2 NHTSA Fuel Sa ved (billion gallons) and CO 2 Emissions Avoided (million metric tons, mmt) under CAFE Standards (with FFV Credits) 2012 2013 2014 2015 2016 Total Fuel (b. gal.) 4.9 8.2 11.3 15.0 19.1 58.6 CO 2 (mmt) 53 89 123 163 208 636

NHTSA estimates that these fuel economy increases would pr oduce other benefits both to drivers (e.g., reduced time spent refueli ng) and to the U.S. (e.g., reductions in the costs of petroleum imports beyond the direct savings from re duced oil purchases, as well as some disbenefits (e.g., increase traffic congestion) caused by drivers’ tendency to travel more when the cost of driving declin es (as it does when fuel economy increases). NHTSA has estimated the total monetary va lue to society of these benefits and disbenefits, and estimates th at the standards will produce significant net benefits to society. Using a 3% discount rate, NHTSA es timates that the present value of these benefits would total more than $180 billion ove r the useful lives of vehicles sold during MYs 2012-2016. More discussion regarding mone tized benefits can be found in Section IV of this notice and in NHTSA’s Regulatory Impact Analysis. Note that the benefit calculation in Tables I.C.1-3 through 1- 6 includes the benefits of reducing CO 2 emissions, 41 but not the benefits of reducing other GHG emissions.

Table I.C.1-3 NHTSA Discounted Benefits ($billion) under the CAFE Standards (before FFV Cr edits, using 3 percent Discount Rate) 2012 2013 2014 2015 2016 Total Passenger Cars 6.8 15.2 21.6 28.7 35.2 107.5 Light Trucks 5.1 10.7 15.5 19.4 24.3 75.0 Combined 11.9 25.8 37.1 48 59.5 182.5

Using a 7% discount rate, NHTSA estimat es that the presen t value of these benefits would total more than $145 billion over the same time period.

Table I.C.1-4 NHTSA Discounted Benefits ($billion) Under the CAFE Standards (before FFV Cr edits, using 7 percent Discount Rate) 2012 2013 2014 2015 2016 Total Passenger Cars 5.5 12.3 17.5 23.2 28.6 87.0 Light Trucks 4.0 8.4 12.2 15.3 19.2 59.2 Combined 9.5 20.7 29.7 38.5 47.8 146.2

41 CO 2 benefits for purposes of these tables are calculated using the $21/ton SCC values. Note that net present value of reduced GHG emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net present value of SCC for internal consistency.

36NHTSA estimates that FFV credits coul d reduce achieved bene fits by about 3.8%:

Table I.C.1-5a NHTSA Discount ed Benefits ($billion) under the CAFE Standards (with FFV Credits, using a 3 percent Discount Rate) 2012 2013 2014 2015 2016 Total Passenger Cars 7.6 13.7 19.1 25.6 34.0 100.0 Light Trucks 6.4 10.4 14.6 19.8 24.4 75.6 Combined 14.0 24.1 33.7 45.4 58.4 175.6

Table I.C.1-5b NHTSA Discount ed Benefits ($billion) under the CAFE Standards (with FFV Credits, using a 7 percent Discount Rate) 2012 2013 2014 2015 2016 Total Passenger Cars 6.1 11.1 15.5 20.7 27.6 80.9 Light Trucks 5.0 8.2 11.5 15.6 19.3 59.7 Combined 11.2 19.3 27.0 36.4 46.9 140.7

NHTSA attributes most of these benefits—about $143 billion (at a 3% discount rate and excluding consideration of FFV credits ), as noted above—to reductions in fuel consumption, valuing fuel (for societal purposes ) at the future pre-ta x prices projected in the Energy Information Administration’s (A EO’s) reference case forecast from the Annual Energy Outlook (AEO) 2010 Early Release. NHTSA’s Final Regulatory Impact Analysis (FRIA) accompanying this rule presen ts a detailed analysis of specific benefits of the rule.

Table I.C.1-6 Summary of Be nefits Fuel Savings and CO 2 Emissions Reduction Due to the Rule (before FFV Credits) Monetized Value (Discounted) Amount 3% Discount Rate 7% Discount Rate Fuel savings 61.0 billion gallons $143.0 billion $112.0 billion CO 2 emissions reductions 655 mmt $14.5 billion $14.5 billion

NHTSA estimates that the increases in technology application necessary to achieve the projected improvements in fuel economy will entail considerable monetary outlays. The agency estimates that incrementa l costs for achieving its standards – that is, outlays by vehicle manufacturers over and above those required to comply with the MY 2011 CAFE standards – will total about $52 billion (i.e., during MYs 2012-2016).

Table I.C.1-7 NHTSA Incrementa l Technology Outlays ($billion) under the CAFE Standards (before FFV Credits) 2012 2013 2014 2015 2016 Total Passenger Cars 4.1 5.4 6.9 8.2 9.5 34.2 Light Trucks 1.8 2.5 3.7 4.3 5.4 17.6

37Combined 5.9 7.9 10.5 12.5 14.9 51.7

NHTSA estimates that use of FFV cr edits could significantly reduce these outlays:

Table I.C.1-8 NHTSA Incrementa l Technology Outlays ($billion) under CAFE Standards (with FFV Credits) 2012 2013 2014 2015 2016 Total Passenger Cars 2.6 3.6 4.8 6.1 7.5 24.6 Light Trucks 1.1 1.5 2.5 3.4 4.4 12.9 Combined 3.7 5.1 7.3 9.5 11.9 37.5

The agency projects that manufacturers will recover most or all of these additional costs through higher selling pri ces for new cars and light truc ks. To allow manufacturers to recover these increased outlays (and, to a much lesser extent, the civil penalties that some companies are expected to pay for nonc ompliance), the agency estimates that the standards would lead to increases in av erage new vehicle prices ranging from $457 per vehicle in MY 2012 to $985 per vehicle in MY 2016:

Table I.C.1-9 NHTSA Incremental Increases in Average New Vehicle Costs ($) under CAFE Standards (before FFV Credits) 2012 2013 2014 2015 2016 Passenger Cars 505 573 690 799 907 Light Trucks 322 416 621 752 961 Combined 434 513 665 782 926

NHTSA estimates that use of FFV credits could significantly reduce these costs, especially in earlier model years:

Table I.C.1-10 NHTSA Incremental Increa ses in Average New Vehicle Costs ($) under CAFE Standards (with FFV Credits) 2012 2013 2014 2015 2016 Passenger Cars 303 378 481 593 713 Light Trucks 194 260 419 581 784 Combined 261 333 458 589 737

NHTSA estimates, therefore, that the to tal benefits of these CAFE standards will be more than three times the magnitude of th e corresponding costs. As a consequence, its standards would produce net benefits of $130.7 b illion at a 3 percent discount rate (with FFV credits, $135.0 billion) or $94.5 billion at a 7 percent discount ra te over the useful lives of vehicles sold during MYs 2012-2016.

382. Summary of Costs and Benefits of EPA's GHG Standards

EPA has analyzed in detail the costs a nd benefits of the final GHG standards. Table I.C.2-1 shows EPA’s estimated lifetime fuel savings and CO 2 equivalent emission reductions for all vehicles sold in the mode l years 2012-2016. The values in Table I.C.2- 1 are projected lifetime totals for each model year and are not discounted. As documented in EPA’s Final RIA, the potential credit transfer betw een cars and trucks may change the distribution of the fuel sa vings and GHG emission impacts between cars and trucks. As discussed above with re spect to NHTSA’s CAFE standards, it is important to note that NHTSA’s CAFE sta ndards and EPA’s GHG standards will both be in effect, and each will lead to increases in average fuel economy and reductions in CO 2 emissions. The two agencies’ standards together comprise the National Program, and this discussion of costs and benefits of EPA’s GHG standards does not change the fact that both the CAFE and GHG standards, jointly, are the source of the benefits and costs of the National Program.

Table I.C.2-1 EPA’s Estimated 2012-2016 M odel Year Lifetime Fuel Saved and GHG Emissions Avoided 2012 2013 2014 2015 2016 Total Fuel (billion gallons) 4.0 5.5 7.3 10.5 14.3 41.6 Fuel (billion barrels) 0.10 0.13 0.17 0.25 0.34 0.99 Cars

CO 2 EQ (mmt) 49.3 68.5 92.7 134 177 521

Fuel (billion gallons) 3.3 5.0 6.6 9.0 12.2 36.1 Fuel (billion barrels) 0.08 0.12 0.16 0.21 0.29 0.86 Light Trucks

CO 2 EQ (mmt) 39.6 61.7 81.6 111 147 441

Fuel (billion gallons) 7.3 10.5 13.9 19.5 26.5 77.7 Fuel (billion barrels) 0.17 0.25 0.33 0.46 0.63 1.85 Combined

CO 2 EQ (mmt) 88.8 130 174 244 325 962

Table I.C.2-2 shows EPA’s estimated lifetim e discounted benefits for all vehicles sold in model years 2012-2016. Although EPA es timated the benefits associated with four different values of a one ton GHG reduction ($5, $21, $35, $65), for the purposes of this overview presentation of estimated benefi ts EPA is showing the benefits associated with one of these marginal values, $21 per ton of CO 2 , in 2007 dollars and 2010 emissions. Table I.C.2-2 presents benefits ba sed on the $21 value. Section III.H presents the four marginal values used to estimate monetized benefits of GHG reductions and Section III.H presents the program benefits us ing each of the four marginal values, which represent only a partial accounting of total benefits due to omitted climate change impacts and other factors that are not readil y monetized. The values in the table are discounted values for each model year of ve hicles throughout their projected lifetimes. The benefits include all benefits cons idered by EPA such as fuel savings, GHG reductions, PM benefits, energy se curity and other externalitie s such as reduced refueling and accidents, congestion and noise. The life time discounted benefits are shown for one

39of four different social cost of carbon (SCC) values considered by EPA. The values in Table I.C.2-2 do not include costs associated with new technology required to meet the GHG standard.

Table I.C.2-2 EPA’s Estimated 2012-2016 Mode l Year Lifetime Discounted Benefits Assuming the $21/ton SCC Value a,b (billions of 2007 dollars) Model Year Discount Rate 2012 2013 2014 2015 2016 Total 3% $21.9 $32.4 $43.3 $61.3 $84.0 $243 7% $17.5 $26.0 $34.7 $49.1 $67.1 $194

a The benefits include all benefits considered by EPA such as the economic value of reduced fuel co nsumption and accompanying savings in refueling time, climate-related economic benefits from reducing emissions of CO 2 (but not other GHGs), economic benefits from reducing emissions of PM and other air pollutants that contribute to its formation, and reductions in energy security externalities caused by U.S. petroleum consumption and imports. The analysis also includes disbenefits stemming from additional vehicle use, such as as the economic damages caused by accidents, congestion and noise.
b Note that net present value of reduced GHG emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net present valu e of SCC for internal consistency. Refer to Section III.F for more detail.

Table I.C.2-3 shows EPA’s estimated lifetime fuel savings, lifetime CO 2 emission reductions, and the monetized net present values of those fuel savings and CO 2 emission reductions. The gall ons of fuel and CO 2 emission reductions are projected lifetime values for all vehicles sold in the model years 2012-201 6. The estimated fuel savings in billions of barrels and the GHG reductions in million metric tons of CO 2 shown in Table I.C.2-3 are totals for the five model years thr oughout their projected lifetime and are not discounted. The monetized values shown in Ta ble I.C.2-3 are the summed values of the discounted monetized-fuel savings and monetized-CO 2 reductions for the five model years 2012-2016 throughout their lifetimes. Th e monetized values in Table I.C.2-3 reflect both a 3 percent and a 7 percent discount rate as noted.

Table I.C.2-3 EPA’s Estimated 2012-2016 M odel Year Lifetime Fuel savings, CO 2

Emission Reductions, and Discounted Monetized Benefits at a 3% Discount Rate (monetized values in 2007 dollars) Amount $ value (billions) Fuel savings 1.8 billion barrels $182, 3% discount rate $142, 7% discount rate CO 2 emission reductions (valued assuming $21/ton CO 2 in 2010) 962 MMT CO 2 e $20 a

a Note that net present value of reduced GHG emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer to Section III.F for more detail.

40Table I.C.2-4 shows EPA’s estimated incr emental and total technology outlays for cars and trucks for each of the model y ears 2012-2016. The technology outlays shown in Table I.C.2-4 are for the industry as a whole and do not account for fuel savings associated with the program.

Table I.C.2-4 EPA’s Estimated Incremental Technology Outlays (billions of 2007 dollars) 2012 2013 2014 2015 2016 Total Cars $3.1 $5.0 $6.5 $8.0 $9.4 $31.9 Trucks $1.8 $3.0 $3.9 $4.8 $6.2 $19.7 Combined $4.9 $8.0 $10.3 $12.7 $15.6 $51.6

Table I.C.2-5 shows EPA’s estimated incremental cost increase of the average new vehicle for each model year 2012-2016. The values shown are incremental to a baseline vehicle and are not cumulative. In other words, the estimated increase for 2012 model year cars is $342 relativ e to a 2012 model year car ab sent the National Program. The estimated increase for a 2013 model year car is $507 relative to a 2013 model year car absent the National Program (not $342 plus $507).

Table I.C.2-5 EPA’s Estimated Incremental Increase in Average New Vehicle Cost (2007 dollars per unit) 2012 2013 2014 2015 2016 Cars $342 $507 $631 $749 $869 Trucks $314 $496 $652 $820 $1,098 Combined $331 $503 $639 $774 $948

D. Background and Comparison of NHTSA and EPA Statutory Authority

Section I.C of the proposal containe d a detailed overview discussion of the NHTSA and EPA statutory authorities. In a ddition to the discussion in the proposal, each agency discusses comments pertaining to its statutory authority and the agency’s responses in Sections III and IV of this notice, respectively.

II. Joint Technical Work Completed for This Final Rule

A. Introduction

In this section NHTSA and EPA discuss several aspects of the joint technical analyses on which the two agencies collaborated. These analyses are common to the development of each agency’s final standards. Specifically we discuss: the development of the vehicle market forecast used by each agency for assessing costs, benefits, and effects, the development of the attribute-ba sed standard curve shapes, the determination

41of the relative stringency betw een the car and truck fleet st andards, the technologies the agencies evaluated and their costs and eff ectiveness, and the economic assumptions the agencies included in their analyses. Th e Joint Technical Support Document (TSD) discusses the agencies’ joint technical work in more detail.

B. Developing the Future Fleet for As sessing Costs, Benefits, and Effects

1. Why Did the Agencies Establish a Baseline and Reference Vehicle Fleet?

In order to calculate the impacts of the EPA and NHTSA regulations, it is necessary to estimate the composition of the futu re vehicle fleet absent these regulations, to provide a reference point relative to wh ich costs, benefits, and effects of the regulations are assessed. As in the pr oposal, EPA and NHTSA have developed this comparison fleet in two parts. The first st ep was to develop a ba seline fleet based on model year 2008 data. The second step was to project that fleet into model years 2011- 2016. This is called the reference fleet. The third step was to modify that MY 2011- 2016 reference fleet such that it had suffi cient technology to meet the MY 2011 CAFE standards. This final version of the reference fleet is the light-duty fleet estimated to exist in MY 2012-2016 in the absence of today’s standards, based on the assumption that manufacturers would continue to meet the MY 2011 CAFE standards (or pay civil penalties allowed under EPCA 42 ) in the absence of further in creases in the stringency of CAFE standards. Each agency used this appr oach to develop a final reference fleet to use in its modeling. All of the agencies’ estimates of emission reductions, fuel economy improvements, costs, and societal impacts are developed in relation to the respective reference fleets.

EPA and NHTSA proposed a transparent a pproach to developi ng the baseline and reference fleets, largely working from publicly available data. This proposed approach differed from previous CAFE rules, which relied on confidential manufacturers’ product plan information to develop the baseline. Most of the public comments to the NPRM addressing this issue supported this methodol ogy for developing the in puts to the rule's analysis. Because the input sh eets can be made public, stak eholders can verify and check EPA’s and NHTSA’s modeling, and perform their own analyses with these datasets. In this final rulemaking, EPA and NHTSA are us ing an approach very similar to that proposed, continuing to rely on publicly availabl e data as the basis for the baseline and reference fleets.

2. How did the Agencies Develop the Baseline Vehicle Fleet?

At proposal, EPA and NHTSA developed a baseline fleet comprised of model year 2008 data gathered from EPA’s emission certification and fuel economy database. MY 2008 was used as the basis for the baseli ne vehicle fleet because it was the most

42 That is, the manufacturers who have traditionally paid fines under EPCA instead of complying with the CAFE standards were “allowed,” for purposes of the re ference fleet, to reach only the CAFE level at which paying fines became more cost-effective than adding technology, even if that fell short of the MY 2011 standards.

42recent model year for which a complete set of da ta is publicly available. This remains the case. Manufacturers are not required to submit final sale s and mpg figures for MY 2009 until April 2010, 43 after the CAFE standard’s mandated promulgation date. Consequently, in this final rule, EPA and NHTSA made no changes to the method or the results of the MY 2008 baseline fleet used at proposal, except for some specific corrections to engineering inputs for some vehi cle models reflected in the market forecast input to NHTSA’s CAFE model. More detail s about how the agencies constructed this baseline fleet can be found in Chapter 1.2 of the Joint TSD. Corrections to engineering inputs for some vehicle models in the mark et forecast input to NHTSA’s CAFE model are discussed in Chapter 2 of the Joint TSD.

3. How Did the Agencies Develop the Projected MY 2011-2016 Vehicle Fleet?

EPA and NHTSA have based the projection of total car and total light truck sales for MYs 2011-2016 on projections made by the Department of Energy’s Energy Information Administration (EIA). EIA pub lishes a mid-term projection of national energy use called the Annual Energy Outlook (AEO ). This projection utilizes a number of technical and econometric models which are designed to refl ect both economic and regulatory conditions expected to exist in the future. In support of its projection of fuel use by light-duty vehicles, EIA projects sale s of new cars and light trucks. In the proposal, the agencies used the three reports published by EIA as part of the AEO 2009. We also stated that updated versions of th ese reports could be used in the final rules should AEO timely issue a new version. EIA published an early version of its AEO 2010 in December 2009, and the agencies are making use of it in this final rulemaking. The differences in projected sales in the 2009 report (used in the NP RM) and the early 2010 report are very small, so NHTSA and EPA have decided to simply scale the NPRM volumes for cars and trucks (in the aggr egate) to match those in the 2010 report. 44 We thus employ the sales projections from the scaled updated 2009 Annual Energy Outlook, which is equivalent to AEO 2010 Early Release, for the final rule. The scaling factors for each model year are presented in Chapter 1 of the Joint TSD for this final rule.

In the AEO 2010 Early Release, EIA project s that total light-duty vehicle sales will gradually recover from their currently depressed levels by around 2013. In 2016, car sales are projected to be 9.4 million (57 percen t) and truck sales are projected to be 7.1 million (43 percent). Although the total level of sales of 16.5 million units is similar to pre-2008 levels, the fraction of car sales is project ed to be higher than that existing in the 2000-2007 timeframe. This projection reflects the impact of higher fuel prices, as well as EISA’s requirement that the new vehicle fl eet average at least 35 mpg by MY 2020. The agencies note that AEO does not represent the fleet at a level of detail sufficient to explicitly account for the reclassification—prom ulgated as part of NHTSA’s final rule for

43 40 CFR 600.512-08, Model Year Report
44 EPA and NHTSA have evaluated the differences between the AEO 2010 (early draft) and AEO 2009 and found little difference in the fleet projections (or fuel prices). This analysis can be found in the memo to the docket: (Docket EPA-HQ-OAR-2009-0472). The ag encies therefore conclude that EIA has made no significant changes to its projections.

43MY 2011 CAFE standards—of a number of 2-wheel drive sport utility vehicles from the truck fleet to the car fleet for MYs 2011 and after. Sales projections of cars and trucks for future model years can be found in th e Joint TSD for these final rules.

In addition to a shift towards more car sale s, sales of segments within both the car and truck markets have been changing and are expected to continue to change. Manufacturers are introducing more crossover m odels which offer much of the utility of SUVs but use more car-like designs. The AEO 2010 report does not, however, distinguish such changes within the car and truck classes. In order to reflect these changes in fleet makeup, EPA and NHTSA consid ered several other available forecasts. EPA purchased and shared with NHTSA forecasts from two well-known industry analysts, CSM Worldwide (CSM), and J.D. Po wers. NHTSA and EPA decided to use the forecast from CSM, modified as described be low, for several reasons presented in the NPRM preamble 45 and draft Joint TSD. The cha nges between company market share and industry market segments were mo st significant from 2011-2014, while for 2014- 2015 the changes were relatively small. Noti ng this, and lacking a credible foreca company and segment shares after 2015, th e agencies assumed 2016 market share and market segments to be the same as for 2015. st of

CSM Worldwide provides quarterly sales forecasts for the automotive industry. In the NPRM, the agencies identified a concern with the 2 nd quarter CSM forecast that was used as a basis for the projection. CSM projections at that time were based on an industry that was going through a significant financial transition, a nd as a result the market share forecasts for some companies were impacted in surprising ways. As the industry’s situation has settled somewhat over the past year, the 4 th quarter projection appears to address this issue – for example, it shows nearly a two-fold increase in sales for Chrysler compared to significant loss of market share shown for Chrysler in the 2 nd quarter projection. Additionally, some comme nters, such as GM, recognized that the fleet appeared to include an unusuall y high number of large pickup trucks. 46 In fact, the agencies discovered (independently of th e comments) that CSM’s standard forecast included all vehicles below 14,000 GVWR, incl uding class 2b and 3 heavy duty vehicles, which are not regulated by this final rule. 47 The commenters were thus correct that light duty reference fleet projections at proposal had more full size trucks and vans due to the mistaken inclusion of the heavy duty versions of those vehicles. The agencies requested a separate data forecast from CSM that filtered their 4 th quarter projec tion to exclude these heavy duty vehicles. The agen cies then used this filtered 4 th quarter forecast for the final rule. A detailed comparison of the ma rket by manufacturer can be found in the final TSD. For the public’s reference, copies of the 2 nd , 3 rd , and 4 th quarter CSM forecasts

45 See , e.g., 74 FR 49484.
46 GM argued that the unusually large volume of large pickups led to higher overall requirements for those vehicles. As discussed below, the agencies’ analysis fo r the final rule corrects the number of large pickups. With this correction and other updates to the agenci es’ market forecast and other analytical inputs, the target functions defining the final standards (and achieving the average required performance levels defining the national program) are very similar to those from the NPRM, especially for light trucks, as illustrated below in Figur es II.C-7 and II.C-8.
47 These include the Ford F-250 & F-350, Econoline E-250, & E-350; Chevy Express, Silverado 2500, & 3500; GMC Savana, Dodge 2500, & 3500; among others.

44have been placed in the docket for this rulemaking. 48

We then projected the CSM forecasts fo r relative sales of cars and trucks by manufacturer and by market segment onto the total sales estimates of AEO 2010. Tables II.B.3-1 and II.B.3-2 show the resulting proj ections for the refere nce 2016 model year and compare these to actual sa les that occurred in baseline 2008 model year. Both tables show sales using the traditional definition of cars and light trucks.

Table II.B.3-1 Annual Sales of Light-Duty Vehicles by Manufacturer in 2008 and Estimated for 2016
Cars Light Trucks Total
2008 MY 2016 MY 2008 MY 2016 MY 2008 MY 2016 MY

BMW 291,796 424,923 61,324 171,560 353,120 596,482

Chrysler 537,808 340,908 1,119,397 525,128 1,657,205 866,037

Daimler 208,052 272,252 79,135 126,880 287,187 399,133

Ford 709,583 1,118,727 1,158,805 1,363,256 1,868,388 2,481,983

General Motors 1,370,280 230,705 1,749,227 95,054 3,119,507 325,760

Honda 899,498 1,283,937 612,281 1,585,828 1,511,779 2,869,766

Hyundai 270,293 811,214 120,734 671,437 391,027 1,482,651

Kia 145,863 401,372 135,589 211,996 281,452 613,368

Mazda 191,326 455,643 111,220 210,717 302,546 666,360

Mitsubishi 76,701 350,055 24,028 144,992 100,729 495,047

Porsche 18,909 49,914 18,797 88,754 37,706 138,668

Nissan 653,121 33,471 370,294 16,749 1,023,415 50,220

Subaru 149,370 876,677 49,211 457,114 198,581 1,333,790

Suzuki 68,720 97,466 45,938 26,108 114,658 123,574

Tata 9,596 65,806 55,584 42,695 65,180 108,501

48 The CSM Sales Forecast Excel file (“CSM North America Sales Forecasts 2Q 09 3Q09 4Q09 for the Docket”) is available in the docket (Docket EPA-HQ-OAR-2009-0472).

45Toyota 1,143,696 2,069,283 1,067,804 1,249,719 2,211,500 3,319,002

Volkswagen 290,385 586,011 26,999 124,703 317,384 710,011

Total 7,034,997 9,468,365 6,806,367 7,112,689 13,841,364 16,580,353

Table II.B.3-2 Annual Sales of Light-Duty Vehicles by Market Segment in 2008 and Estimated for 2016

Cars Light Trucks

2008 MY 2016 MY 2008 MY 2016 MY

Full-Size Car 829,896 530,945 Full-Size Pickup 1,331,989 1,379,036 Luxury Car 1,048,341 1,548,242 Mid-Size Pickup 452,013 332,082

Mid-Size Car 2,166,849 2,550,561 Full-Size Van 33,384 65,650

Mini Car 617,902 1,565,373 Mid-Size Van 719,529 839,194

Small Car 1,912,736 2,503,566 Mid-Size MAV* 110,353 116,077 Specialty Car 459,273 769,679 Small MAV 231,265 62,514

Full-Size SUV* 559,160 232,619

Mid-Size SUV 436,080 162,502

Small SUV 196,424 108,858

Full-Size CUV* 264,717 260,662

Mid-Size CUV 923,165 1,372,200

Small CUV 1,548,288 2,181,296

Total Sales** 7,034,997 9,468,365 6,806,367 7,079,323 * MAV – Multi-Activity Vehicle, SUV – Sport U tility Vehicle, CUV – Crossover Utility Vehicle **Total Sales are based on the classic Car/Truck definition.

Determining which traditionally-defined trucks will be defined as cars for purposes of this final rule using the revi sed definition establis hed by NHTSA for MYs 2011 and beyond requires more detailed informa tion about each vehicle model. This is described in greater detail in Chapter 1 of the final TSD.

The forecasts obtained from CSM provided estimates of car and truck sales by segment and by manufacturer, but not by manuf acturer for each market segment. Therefore, NHTSA and EPA needed other in formation on which to base these more detailed projected market splits. For this ta sk, the agencies used as a starting point each manufacturer’s sales by market segment fr om model year 2008, which is the baseline fleet. Because of the larger number of segmen ts in the truck market, the agencies used

46slightly different methodologi es for cars and trucks.

The first step for both cars and trucks was to break down each manufacturer’s 2008 sales according to the market segment defi nitions used by CSM. For example, the agencies found that Ford’s 49 cars sales in 2008 were broken down as shown in Table II.B.3-3:

Table II.B.3-3: Breakdown of Ford’s 2008 Car Sales Full-size cars 160,857 units Mid-size Cars 170,399 units Small/Compact Cars 180,249 units Subcompact/Mini Cars None Luxury cars 87,272 units Specialty cars 110,805 units

EPA and NHTSA then adjusted each manuf acturer’s sales of each of its car segments (and truck segments, separately) so that the manufacturer’s total sales of cars (and trucks) matched the total estimated for each future model year based on AEO and CSM forecasts. For example, as indicated in Table II.B.3-1, Ford’s total car sales in 2008 were 709,583 units, while the agencies projec t that they will in crease to 1,113,333 units by 2016. This represents an in crease of 56.9 percent. Thus, the agencies increased the 2008 sales of each Ford car segment by 56.9 percent. This produced estimates of future sales which matched total car and truck sale s per AEO and the manufacturer breakdowns per CSM. However, the sales splits by market segment would not necessarily match those of CSM (shown for 2016 in Table II.B.3-2).

In order to adjust the market segment mix for cars, the agencies first adjusted sales of luxury, specialty a nd other cars. Since the to tal sales of cars for each manufacturer were already set, any changes in the sales of one car segment had to be compensated by the opposite change in anothe r segment. For the luxury, specialty and other car segments, it is not clear how cha nges in sales would be compensated. For example, if luxury car sales decreased, would sales of full-size cars increase, mid-size cars, and so on? The agencies have assumed that any changes in the sales of cars within these three segments were compensated for by proportional changes in the sales of the other four car segments. For example, for 2016, the figures in Table II .B.3-2 indicate that luxury car sales in 2016 are 1,548,242 units. Luxury car sales are 1,048,341 units in 2008. However, after adjusting 2008 car sales by the change in total car sales for 2016 projected by EIA and a change in manufactur er market share per CSM, luxury car sales decreased to 1,523,171 units. Thus, overall fo r 2016, luxury car sales had to increase by 25,071 units or 6 percent. The agencies acco rdingly increased the luxury car sales by each manufacturer by this percentage. The absolute decrease in luxury car sales was spread across sales of full-size, mid-size, compact and subcompact cars in proportion to each manufacturer’s sales in th ese segments in 2008. The same adjustment process was

49 Note: In the NPRM, Ford’s 2008 sales per segment, and the total number of cars was different than shown here. The change in values is due to a correction of vehicle segments for some of Ford’s vehicles.

47used for specialty cars and the “other cars” segment defined by CSM.

The agencies used a slightly different appr oach to adjust for changing sales of the remaining four car segments. Starting with fu ll-size cars, the agencies again determined the overall percentage change that needed to o ccur in future year full-size car sales after 1) adjusting for total sales per AEO 2010, 2) adjusting for manufacturer sales mix per CSM and 3) adjusting the luxury, specialty and other car segments, in order to meet the segment sales mix per CSM. Sales of each ma nufacturer’s large cars were adjusted by this percentage. However, instead of spr eading this change over the remaining three segments, the agencies assigned the entire chan ge to mid-size vehicl es. The agencies did so because the CSM data followed the trend of increasing volumes of smaller cars while reducing volumes of larger cars . If a consumer had previous ly purchased a full-size car, we thought it unlikely that their next purchase would d ecrease by two size categories, down to a subcompact. It seemed more reas onable to project that they would drop one vehicle size category smaller. Thus, the change in each manu facturer’s sales of full-size cars was matched by an opposite change (in abso lute units sold) in mid-size cars.

The same process was then applied to mid- size cars, with the change in mid-size car sales being matched by an opposite change in compact car sales. This process was repeated one more time for compact car sales, with changes in sales in this segment being matched by the opposite change in the sales of subcompacts. The overall result was a projection of car sales for model years 2012-2016--the reference fleet--which matched the total sales projections of the AEO forecas t and the manufacturer a nd segment splits of the CSM forecast. These sales splits can be found in Chapter 1 of the Joint TSD for this final rule.

As mentioned above, the agencies applied a slightly different process to truck sales, because the agencies could not confid ently project how the change in sales from one segment preferentially went to or came fr om another particular segment. Some trend from larger vehicles to smaller vehicles w ould have been possible. However, the CSM forecasts indicated large changes in total sp ort utility vehicle, multi-activity vehicle and cross-over sales which could not be connected. Thus, the agen cies applied an iterative, but straightforward process for adjusting 2008 truck sales to match the AEO and CSM forecasts.

The first three steps were exactly the sa me as for cars. EPA and NHTSA broke down each manufacturer’s truck sales into th e truck segments as defined by CSM. The agencies then adjusted all manufacturers’ truc k segment sales by the same factor so that total truck sales in each mode l year matched AEO projections for truck sales by model year. The agencies then adjusted each manufacturer’s truck sales by segment proportionally so that each manu facturer’s percentage of to tal truck sales matched that forecast by CSM. This again left the need to adjust truck sales by segment to match the CSM forecast for each model year.

In the fourth step, the agencies adjust ed the sales of each truck segment by a common factor so that total sales for that segment matched the combination of the AEO

48and CSM forecasts. For example, project ed sales of large pickups across all manufacturers were 1,286,184 units in 2016 afte r adjusting total sales to match AEO’s forecast and adjusting each manufacturer’s tr uck sales to match CSM’s forecast for the breakdown of sales by manufacturer. Appl ying CSM’s forecast of the large pickup segment of truck sales to AEO’s total sales fo recast indicated total large pickup sales of 1,379,036 units. Thus, we increased each manuf acturer’s sales of large pickups by 7 percent. 50 The agencies applied the same type of adjustment to all the other truck segments at the same time. The result wa s a set of sales projections which matched AEO’s total truck sales projection and CSM’s market segment forecast. However, after this step, sales by manufacturer no longer met CSM’s forecast. Thus, we repeated step three and adjusted each manufacturer’s truck sa les so that they met CSM’s forecast. The sales of each truck segment (by manufacturer) were adjusted by the same factor. The resulting sales projection matched AEO’ s total truck sales projection and CSM’s manufacturer forecast, but sales by market segment no longer met CSM’s forecast. However, the difference between the sales projec tions after this fifth step was closer to CSM’s market segment forecast than it was afte r step three. In ot her words, the sales projection was converging to the desired result. The agencies repeated these adjustments, matching manufacturer sales mix in one step and then market segment in the next a total of 19 times. At this point, we were able to match the market segment splits exactly and the manufacturer splits were within 0.1 percen t of our goal, which is well within the needs of this analysis.

The next step in developing the referen ce fleets was to characterize the vehicles within each manufacturer-seg ment combination. In large part, this was based on the characterization of the specific vehicle m odels sold in 2008 -- i.e., the vehicles comprising the baseline fleet. EPA and NHTSA chose to base our estimates of detailed vehicle characteristics on 2008 sales for several reasons. One, these vehicle characteristics are not confidential and can t hus be published here for careful review by interested parties. Two, because it is constr ucted beginning with actual sales data, this vehicle fleet is limited to vehicle models know n to satisfy consumer demands in light of price, utility, performance, safety, and other vehicle attributes.

As noted above, the agencies gathered most of the information about the 2008 baseline vehicle fleet from EPA’s emission certification a nd fuel economy database. The data obtained from this source included ve hicle production volume, fuel economy, engine size, number of engine cylinders, transmission type, fuel type, etc. EPA’s certification database does not include a detailed description of th e types of fuel economyimproving/CO 2 -reducing technologies considered in th is final rule. Thus, the agencies augmented this description with publicly available data which includes more complete technology descriptions from Ward’s Automotive Group. 51 In a few instances when required vehicle information (such as vehicle footprint) was not available from these two sources, the agencies obtained this informa tion from publicly accessible internet sites

50 Note: In the NPRM this example showed 29 percen t instead of 7 percent. The significant decrease was due to using the filtered 4th quarter CSM forecast. Commenters, such as GM, had commented that we had too many full-size trucks and vans, and this change addresses their comment.
51 Note that WardsAuto.com is a fee-based service, but all information is public to subscribers.

49such as Motortrend.com and Edmunds.com. 52

The projections of future car and truc k sales described above apply to each manufacturer’s sales by market segment. Th e EPA emissions certifi cation sales data are available at a much finer level of detail, esse ntially vehicle configur ation. As mentioned above, the agencies placed each vehicle in th e EPA certification database into one of the CSM market segments. The agencies then totaled the sales by each manufacturer for each market segment. If the combinati on of AEO and CSM forecasts indicated an increase in a given manufacturer ’s sales of a particular market segment, then the sales of all the individual vehicle confi gurations were adjusted by the same factor. For example, if the Prius represented 30 percent of Toyota’s sales of compact cars in 2008 and Toyota’s sales of compact cars in 2016 was projected to double by 2016, then the sales of the Prius were doubled, and the Prius sales in 2016 remained 30 percent of Toyota’s compact car sales.

The projection of average f ootprint for both cars and trucks remained virtually constant over the years covered by the final rulemaking. This occurrence is strictly a result of the CSM projections. There are a number of trends that occur in the CSM projections that caused the average footprint to remain constant. First, as the number of subcompacts increases, so do the number of 2-wheel drive crossover vehicles (that are regulated as cars). Seco nd, truck volumes have many segment changes during the rulemaking time frame. There is no specific f ootprint related trend in any segment that can be linked to the unchanging footprint, bu t there is a trend that non-pickups’ volumes will move from truck segments that are ladder frame to those that are unibody-type vehicles. A table of the footprint projections is available in the TSD as well as further discussion on this topic.

4. How Was the Development of the Base line and Reference Fleets for this Final Rule Different from NH TSA’s Historical Approach?

NHTSA has historically based its anal ysis of potential new CAFE standards on detailed product plans the agency has reque sted from manufacturers planning to produce light vehicles for sale in the United States . Although the agency has not attempted to compel manufacturers to submit such info rmation, most major manufacturers and some smaller manufacturers have voluntar ily provided it when requested.

The proposal discusses many of the advant ages and disadvantages of the market forecast approach used by the agencies, including the agencies’ interest in examining product plans as a check on the reference fl eet developed by the agencies for this rulemaking. One of the primary reasons for the request for data in 2009 was to obtain permission from the manufactur ers to make public their pr oduct plan information for model years 2010 and 2011. There are a numbe r of reasons that this could be advantageous in the development of a refere nce fleet. First, some known changes to the fleet may not be captured by the approach of solely using publicly available information. For example, the agencies’ current market forecast includes some vehicles for which

52 Motortrend.com and Edmunds.com are free, no-fee internet sites.

50manufacturers have announced plans for elim ination or drastic production cuts such as the Chevrolet Trailblazer, the Chrysler PT Cruiser, the Chrysler Pacifica, the Dodge Magnum, the Ford Crown Victoria, the Merc ury Sable, the Pontiac Grand Prix, the Pontiac G5 and the Saturn Vue. These vehicle models appear explicit ly in market inputs to NHTSA’s analysis, and are among those ve hicle models included in the aggregated vehicle types appearing in market inputs to EPA’s analysis. However, although the agencies recognize that these specific vehi cles will be discontinued, we continue to include them in the market forecast because they are useful as a surrogate for successor vehicles that may appear in the rulema king time frame to replace the discontinued vehicles in that market segment. 53

Second, the agencies’ market forecast does not include some forthcoming vehicle models, such as the Chevrolet Volt, the Ford Fiesta and several publicly announced electric vehicles, including the announcemen ts from Nissan regarding the Leaf. Nor does it include several MY 2009 or 2010 vehicles, such as the Honda Insight, the Hyundai Genesis and the Toyota Venza, as our starting point for defining specific vehicle models in the reference fleet was Model Year 2008. Additionally, the market forecast does not account for publicly announced technology introductions, such as Ford’s EcoBoost system, whose product plans specify which ve hicles and how many are planned to havethis technology. Chrysler Group LLC has announced plans to offer small- and mediumsized cars using Fiat powertrains. Were th e agencies to rely on manufacturers’ product plans (that were submitted), the market forecas t would account for not only these specific examples, but also for similar examples that have not yet be en announced publicly.

Some commenters, such as CBD and NESCAUM, suggest ed that the agencies’ omission of known future vehicles and t echnologies in the reference fleet causes inaccuracies, which CBD further suggested could lead the agencies to set lower standards. On the other hand, CARB commente d that “the likely impact of this omission is minor.” Because the agencies’ analysis examines the costs and benefits of progressively adding technology to manufacturers’ fleets, the omission of future vehicles and technologies primarily affects how much additional technology (and, therefore, how much incremental cost and benefit) is ava ilable relative to the point at which the agencies’ examination of potential new standa rds begins. Thus, in fact, the omission only reflects the reference fleet, rather than the agencies’ conclusions re garding how stringent the standards should be. This is discussed further below. The agencies believe the above-mentioned comments by CBD, NESCAUM, and others are based on a misunderstanding of the agencies’ approach to analyzing potential incr eases in regulatory stringency. The agencies also note that manufacturers do not always use technology solely to increase fuel economy, and that use of technology to increase vehicles’ acceleration performance or utility would probably make that technology unavailable toward more stringent standards. Consider ing the incremental nature of the agencies’ analysis, and the counterbalancing aspects of potentially omitted technology in the reference fleet, the agencies believe thei r determination of the stringency of new

53 An example of this is in the GM Pontiac line, which is in the process of being phased out during the course of this rulemaking. GM has similar vehicles within their other brands (like Chevy) that will presumable pick up the loss in Pontiac share. We m odel this simply by leaving the Pontiac brand in.

51standards has not been impact ed by any such omissions.

Moreover, EPA and NHTSA believe that not including such ve hicles after MY 2008 does not significantly impact our estimates of the technology required to comply with the standards. If included, these ve hicles could increase the extent to which manufacturers are—in the reference case—e xpected to over-comply with the MY 2011 CAFE standards, and could thereby make the ne w standards appear to cost less and yield less benefit relative to the reference case . However, in the agencies’ judgment, production of the most advanced technology ve hicles, such as the Chevy Volt or the Nissan Leaf (for example), will most lik ely be too limited during MY 2011 through MY 2016 to significantly impact manufacture rs’ compliance positions. While we are projecting the characteri stics of the future fl eet by extrapolating fr om the MY 2008 fleet, the primary difference between the future fl eet and the 2008 fleet in the same vehicle segment is the use of additional CO 2 -reducing and fuel-saving technologies. Both the NHTSA and EPA models add such technologies to evaluate means of complying with the standards, and the costs of doing so. Thus, our future projections of the vehicle fleet generally shift vehicle designs towards those mo re likely to be typical of newer vehicles. Compared to using product plans that show continued fuel economy increases planned based on expectations that CAFE standards will continue to increase, this approach helps to clarify the costs and benefits of the new stan dards, as the costs and benefits of all fuel economy improvements beyond those required by the MY 2011 CAFE standards are being assigned to the final rules. In some cases, the “actual” (vs. projected or “modeled”) new vehicles being introduced into th e market by manufacturers are done so in anticipation of this rulemaking. On the othe r hand, manufacturers ma y plan to continue using technologies to improve vehicle performance and/or ut ility, not just fuel economy. Our approach prevents some of these act ual technological improvements and their associated cost and fuel economy improveme nts from being assumed in the reference fleet. Thus, the added technology will not be considered to be free (or having no benefits) for the purposes of this rule.

In this regard, the agencies further note that manufacturer announcements regarding forward models (or future vehicl e models) need not be accepted automatically. Manufacturers tend to limit accurate production in tent information in these releases for reasons such as: (a) competitors will closely examine their information for data in their product planning decisions; (b) the press c overage of forward model announcements is not uniform, meaning highly anticipated models have more coverage and materials than models that may be less exciting to the pub lic and consistency and uniformity cannot be ensured with the usage of press information; and (c) these market projections are subject to change (sometimes significant), and manufacturers may not want to give the appearance of being indecisive , or under/over-confident to their shareholders and the public with premature rel ease of information.

NHTSA has evaluated the use of public manufacturer forward model press information to update the vehicle fleet inputs to the baseline and reference fleet. The challenges in this approach are evidenced by the continuous stream of manufacturer press releases throughout a defined rulemaking peri od. Manufacturers’ press releases suffer

52from the same types of inaccuracies that many commenters believe can affect product plans. Manufacturers can often be overly optimistic in their press releases, both on projected date of rele ase of new models and on sales volumes.

More generally and more critically, as discussed in the proposal and as endorsed by many of the public comments, there are seve ral advantages to the approach used by the agencies in this final rule. Most impor tantly, today’s market forecast is much more transparent. The information sources used to develop today’s market forecast are all either in the public domain or available comm ercially. Another sign ificant advantage of today’s market forecast is the agencies’ abilit y to assess more fully the incremental costs and benefits of the proposed standards. In addition, by de veloping baseline and reference fleets from common sources, the agencies have been able to avoid some errors—perhaps related to interpretation of requests—that have been observed in past responses to NHTSA’s requests. An additional advantage of the approach used for this proposal is a consistent projection of the ch ange in fuel economy and CO 2 emissions across the various vehicles from the application of new technology. With the approach used for this final rule, the baseline market data comes from actua l vehicles (on the road today) which have actual fuel economy test data (in contrast to manufacturer estimates of future product fuel economy) – so there is no question what is the basis for the fuel economy or CO 2 performance of the baseline market data as it is.

5. How does manufacturer produc t plan data factor into the baseline used in this final rule?

In the spring and fall of 2009, many manufacturers submitted product plans in response to NHTSA’s recent requests that they do so. NHTSA and EPA both have access to these plans, and both agencies have re viewed them in detail. A small amount of product plan data was used in the development of the baseline. The specific pieces of data are:

• Wheelbase

• Track Width Front

• Track Width Rear

• EPS (Electric Power Steering)

• ROLL (Reduced Rolling Resistance)

• LUB (Advance Lubrication i.e. low weight oil)

• IACC (Improved Electrical Accessories)

• Curb Weight

• GVWR (Gross Vehicle Weight Rating)

The track widths, wheelbase, curb weight , and GVWR for vehicles could have been looked up on the internet (159 were), bu t were taken from the product plans when available for convenience. To ensure accur acy, a sample from each product plan was used as a check against the numbers available from Motortrend.com. These numbers will be published in the baseline file since they can be easily looked up on the internet. On the other hand, EPS, ROLL, LUB, and IACC are difficult to determine without using manufacturer’s product plans. These items will not be published in the baseline file, but

53the data has been aggregated into the agenci es’ baseline in the technology effectiveness and cost effectiveness for each vehicle in a way that allows the baseline for the model to be published without revealing the manufacturer’s data.

Also, some technical information that manufacturers have provided in product plans regarding specific vehicl e models is, at least insofar as NHTSA and EPA have been able to determine, not available from public or commercial sources. While such gaps do not bear significantly on the agencies’ anal ysis, the diversity of pickup configurations necessitated utilizing a sales-weighted average footprint value 54 for many manufacturers’ pickups. Since our modeling only utilizes footprint in order to estimate each manufacturer’s CO 2 or fuel economy standard and all th e other vehicle characteristics are available for each pickup configuration, this approximation has no practical impact on the projected technology or cost associated with compliance with the various standards evaluated. The only impact which could arise w ould be if the relative sales of the various pickup configurations changed, or if the ag encies were to explore standards with a different shape. This would necessitate recalculating the average foot print value in order to maintain accuracy.

Additionally, as discussed in the NPRM, in an effort to update the 2008 baseline to account for the expected changes in the fleet in the near-ter m model years 2009-2011 described above, NHTSA requested permission from the manufactur ers to make this limited product plan information public. Unfortunately, virtually no manufacturers agreed to allow the use of their data after 2009 model year. A few ma nufacturers, such as GM and Ford, stated we could use their 2009 product plan data after the end of production (December 31), but this would not have afforded us sufficient time to do the analysis for the final rule. Since the agenci es were unable to obtai n consistent updates, the baseline and reference fleets were not updated beyond 2008 model year for the final rule. The 2008 baseline fleet and projections were instead updated using the latest AEO and CSM data as discussed earlier.

NHTSA and EPA recognize that the appro ach applied for the current rule gives transparency and openness of the vehicl e market forecast high priority, and accommodates minor inaccuracies that may be introduced by not accounting for future product mix changes anticipated in manufacturer s’ confidential product plans. For any future fleet analysis that the agencies are required to perform, NHTSA and EPA plan to

54 A full-size pickup might be offered with various combinations of cab style (e.g., regular, extended, crew) and box length (e.g., 51⁄2’, 61⁄2’, 8’) and, therefore, multiple footprint sizes. CAFE compliance data for MY 2008 data does not contain footprint information, and does not contain information that can be used to reliably identify which pickup entries correspond to fo otprint values estimable from public or commercial sources. Therefore, the agencies have used the known production levels of averag e values to represent all variants of a given pickup line (e.g., all variants of the F-150 and the Sierra/Silverado) in order to calculate the sales-weighted average footprint value for each pickup family. Again, this has no impact on the results of our modeling effort, although it would require re-estim ation if we were to examine light truck standards of a different shape. In the extreme, one single footprint value could be used for every vehicle sold by a single manufacturer as long as the fuel economy standa rd associated with this footprint value represented the sales-weighted, harmonic average of the fuel economy standards associat ed with each vehicle’s footprint values.

54request that manufacturers submit product plans and allow some public release of information. In performing this analysis, the agencies plan to reexamine potential tradeoffs between transparency and technica l reasonableness, and to explain resultant choices.

C. Development of Attribute-Based Curve Shapes

In the NPRM, NHTSA and EPA proposed to set attribute-based CAFE and CO 2 standards that are defined by a mathematical function for MYs 2012-2016 passenger cars and light trucks. EPCA, as amended by EISA , expressly requires that CAFE standards for passenger cars and light trucks be based on one or more vehicle at tributes related to fuel economy, and be expressed in th e form of a mathematical function. 55 The CAA has no such requirement, though in past rules, EPA has relied on both uni versal and attributebased standards (e.g., for nonroad engines, EPA uses the attribute of horsepower). However, given the advantages of using attr ibute-based standards and given the goal of coordinating and harmonizing CO 2 standards promulgated under the CAA and CAFE standards promulgated under EPCA, EPA also proposed to issue standards that are attribute-based and defined by mathematical functions. There was consensus in the public comments that EPA should develop attribute-based CO 2 standards.

Comments received in response to the ag encies’ decision to base standards on vehicle footprint were largely supportiv e. Several commenters (BMW, NADA, NESCAUM) expressed support for attribute-ba sed (as opposed to flat or universal) standards generally, and agreed with EPA’s decision to harmonize with NHTSA in this respect. Many commenters (Aluminum Association, BMW, ICCT, NESCAUM, NY DEC, Schade, Toyota) also supported the agen cies’ decision to con tinue setting CAFE standards, and begin setting GHG standards, on the basis of vehicle footprint, although one commenter (NJ DEP) opposed the use of foot print due to concern that it encourages manufacturers to upsize vehi cles and undercut the gains of the standard. Of the commenters supporting the use of footprint, several fo cused on the benefits of harmonization—both between EPA and NHTSA, and between the U.S. and the rest of the world. BMW commented, for example, that many other countries use weight-based standards rather than foot print-based. While BMW did not object to NHTSA’s and EPA’s use of footprint-based standards, it emphasized the impact of this nonharmonization on manufacturers who sell vehicl es globally, and asked the agencies to consider these effects. NAD A supported the use of footpr int, but cautioned that the agencies must be careful in setting the foot print curve for light trucks to ensure that manufacturers can continue to provide functionality like 4WD and towing/hauling capacity.

Some commenters requested that the agencies consider othe r or more attributes in addition to footprint, largely reitera ting comments submitted to the MYs 2011-2015 CAFE NPRM. Cummins supported the agencies using a secondary at tribute to account for towing and hauling capacity in large tr ucks, for example, while Ferrari asked the agencies to consider a multi-attribute appr oach incorporating curb weight, maximum

55 49 U.S.C. 32902(a)(3)(A).

55engine power or torque, and/or engine displ acement, as it had requested in the previous round of CAFE rulemaking. An individual, Mr. Kenneth Johnson, commented that weight-based standards would be preferable to footprint- based ones, because weight correlates better with fuel economy than footpr int, because the use of footprint does not necessarily guarantee safety th e way the agencies say it does, and because weight-based standards would be fairer to manufacturers.

In response, EPA and NHTSA continue to believe that the benefits of footprintattribute-based standards out weigh any potential drawback s raised by commenters, and that harmonization between the two agencies shou ld be the overriding goal on this issue. As discussed by NHTSA in the MY 2011 CAFE final rule, 56 the agencies believe that the possibility of gaming is lowest with footpr int-based standards, as opposed to weightbased or multi-attribute-based standards. Sp ecifically, standards that incorporate weight, torque, power, towing capability, and/or off-ro ad capability in addition to footprint would not only be significantly more complex, but by providing degrees of freedom with respect to more easily-adjusted attributes, they woul d make it less certain that the future fleet would actually achieve the average fuel economy and CO 2 levels projected by the agencies. The agencies are theref ore finalizing MYs 2012-2016 CAFE and GHG standards based on footprint.

The agencies also recognize that th ere could be benefits for a number of manufacturers if there was greater interna tional harmonization of fuel economy and GHG standards, but this is largely a question of how stringent standards are and how they are enforced. It is entirely possible that footprint-based and weight-based systems can coexist internationally and not present an undue burden for manufact urers if they are carefully crafted. Different c ountries or regions may find diff erent attributes appropriate for basing standards, depending on the particular challenges they face – from fuel prices, to family size and land use, to safety c oncerns, to fleet composition and consumer preference, to other environmental challenges besides climate change. The agencies anticipate working more closely with other coun tries and regions in the future to consider how to mitigate these issues in a way that least burdens manufacturers while respecting each country’s need to meet its own particular challenges.

Under an attribute-based st andard, every vehicle model has a performance target (fuel economy and CO 2 emissions for CAFE and CO 2 emissions standards, respectively), the level of which depends on the vehicle’s a ttribute (for th e proposal, footprint). The manufacturers’ fleet average performan ce is determined by the production-weighted 57 average (for CAFE, harmonic average) of those targets. NHTSA and EPA are promulgating CAFE and CO 2 emissions standards defined by constrained linear functions and, equivalently, piecewise linear functions. 58 As a possible option for future rulemakings, the constrained linear form was introduced by NHTSA in the 2007 NPRM proposing CAFE standards for MY 2011-2015. Described mathematically, the proposed

56 See 74 FR 14359 (Mar. 30, 2009).
57 Production for sale in the United States.
58 The equations are equivalent but are specified differe ntly due to differences in the agencies’ respective models.

56constrained linear function was define d according to the following formula: 59

1
1 1

, , TARGET
MIN MAX c FOOTPRINT d a b = ⎡ ⎤ ⎛ ⎞ × + ⎜ ⎟ ⎢ ⎥ ⎝ ⎠ ⎣ ⎦

where

TARGET = the fuel economy target (in mpg) applicable to vehicles of a given footprint ( FOOTPRINT , in square feet),

a = the function’s upper limit (in mpg), b = the function’s lower limit (in mpg),

c = the slope (in gpm per square foot) of the sloped portion of the function, d

= the intercept (in gpm) of the sloped portion of the function (that is, the value the sloped portion would take if extended to a footprint of 0 square feet, and the MIN and MAX functions take the minimum and maximum, respectively, of the included values; for example, MIN (1,2) = 1, MAX (1,2) = 2, and MIN [ MAX (1,2),3)]=2.

Because the format is linear on a gallons-p er-mile basis, not on a miles-per-gallon basis, it is plotted as fuel consumption be low. Graphically, the constrained linear form appears as shown in Figure II.C-1.

59 This function is linear in fuel consumption but not in fuel economy.

570.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0503540455055606570Footprint (sf)

Fuel Consumption (gpm)1/LIMITUPPER1/LIMITLOWERINTERCEPTSLOPEFigure II.C-1 The Shape of the Constrained Linear Form

58The specific form and stringency for each fleet (passenger car and light trucks) and model year are defined through specific valu es for the four coefficients shown above.

EPA proposed the equivalent equation below for assigning CO 2 targets to an individual vehicle’s footprin t value. Although the general model of the e quation is the same for each vehicle category and each year, the parameters of the equation differ for cars and trucks and for each model year. Described mathematically, EPA’s proposed piecewise linear function was as follows:

Target = a, if x ≤ l Target = cx + d, if l < x ≤ h Target = b, if x > h

In the constrained linear form similar in fo rm to the fuel economy equation above, this equation takes the simplified form:

Target = MIN [ MAX ( c * x + d , a) , b]

where

Target = the CO 2 target value for a given footprint (in g/mi) a = the minimum target value (in g/mi CO 2 ) 60 b = the maximum target value (in g/mi CO 2 ) c = the slope of the linear function (in g/mi per sq ft CO 2 ) d = is the intercept or zero-offset for the line (in g/mi CO 2 ) x = footprint of the vehicle model (in s quare feet, rounded to the nearest tenth) l & h are the lower and higher footprint lim its or constraints or (“kinks”) or the boundary between the flat regions and th e intermediate sloped line (in sq ft)

Graphically, piecewise linear form, like th e constrained linear form, appears as shown in Figure II.C-2.

60 These a, b, d coefficients differ from the a, b, d coefficients in the constrained linear fuel economy equation primarily by a factor of 8887 (plus an additive factor for air conditioning).

59303540455055606570Footprint (sq feet)

Fuel Consumption (gallons/mile)lower limit = lIntercept = d

CO2 (gram/mile) orupperlimit = hslope = cmin = amax = bFigure II.C-2 The Shape of the Piecewise Linear Form

60As for the constrained linear form, the specific form and stringency of the piecewise linear function for each fleet (passe nger car and light trucks) and model year are defined through specific values fo r the four coefficients shown above.

For purposes of the proposed rules, NHT SA and EPA developed the basic curve shapes using methods similar to those applie d by NHTSA in fitting the curves defining the MY 2011 standards. The first step involved defining the relevant vehicle characteristics in the form used by NHTSA’ s CAFE model (e.g., fuel economy, footprint, vehicle class, technology) descri bed in Section II.B of this pr eamble and in Chapter 1 of the Joint TSD. However, because the baseline fleet utilizes a wide ra nge of available fuel saving technologies, NHTSA used the CAFE mode l to develop a fleet to which all of the technologies discussed in Chapter 3 of the Joint TSD 61 were applied, ex cept dieselization and strong hybridization. This was accomplished by taki ng the following steps: (1) treating all manufacturers as unwilling to pay civil pena lties rather than applying technology, (2) applying any technology at a ny time, irrespective of scheduled vehicle redesigns or freshening, and (3) ignoring “phase-in caps” that constrain the overall amount of technology that can be applied by the model to a gi ven manufacturer’s fleet. These steps helped to increase technologi cal parity among vehicle models, thereby providing a better basis (than the baseline or reference fleets) for estimating the statistical relationship between vehicl e size and fuel economy.

In fitting the curves, NHTSA and EPA also continued to fit the sloped portion of the function to vehicle models between the footprint values at which the agencies continued to apply constraints to limit the function’s value for both the smallest and largest vehicles. Without a limit at the sma llest footprints, the f unction—whether logistic or linear—can reach values that would be unfairly burdensome for a manufacturer that elects to focus on the market for small vehicles; depending on the underlying data, an unconstrained form, could result in stringency levels that are tec hnologically infeasible and/or economically impracticab le for those manufacturers that may elect to focus on the smallest vehicles. On the other side of the function, without a limit at the largest footprints, the function may pr ovide no floor on required fuel economy. Also, the safety considerations that suppor t the provision of a disincentive for downsizing as a compliance strategy apply weakly—if at all—to the very largest vehicles. Limiting the function’s value for the largest vehicles lead s to a function with an inherent absolute minimum level of performance, while remaini ng consistent with safety considerations.

Before fitting the sloped portion of the constrained linear form, NHTSA and EPA selected footprints above and below whic h to apply constraints (i.e., minimum and maximum values) on the function. The agencies believe that the linear form performs well in describing the observed relationship between footprint and fuel consumption or CO 2 emissions for vehicle models within th e footprint ranges covering most vehicle

61 The agencies excluded diesel engi nes and strong hybrid vehicle technologies from this exercise (and only this exercise) because the agencies expect that ma nufacturers would not need to rely heavily on these technologies in order to comply with the proposed standards. NHTSA and EPA did include diesel engines and strong hybrid vehicle t echnologies in all other portions of their analyses.

61models, but that the single (a s opposed to piecewise) linear form does not perform well in describing this relationship for the smallest and largest vehicle models. For passenger cars, the agency noted that several manufact urers offer small, sporty coupes below 41 square feet, such as the BMW Z4 and Mi ni, Honda S2000, Mazda MX-5 Miata, Porsche Carrera and 911, and Volkswagen New Beetle. Because such vehicles represent a small portion (less than 10 percent) of the passenge r car market, yet often have performance, utility, and/or structural ch aracteristics that could make it technologically infeasible and/or economically impract icable for manufacturers focusing on such vehicles to achieve the very challenging average requirements that coul d apply in the absence of a constraint, EPA and NHTSA proposed to “cut off” the linear portion of the passenger car function at 41 square feet. For consistenc y, the agency proposed to do the same for the light truck function, although no light trucks ar e currently offered below 41 square feet. The agencies further noted that above 56 square feet, the only passenger car model present in the MY 2008 fleet were four l uxury vehicles with extremely low sales volumes—the Bentley Arnage and three versio ns of the Rolls Royce Phantom. NHTSA and EPA therefore also proposed to “cut off” the linear portion of the passenger car function at 56 square feet. Finally, the agen cies noted that although public information is limited regarding the sales volumes of the many different configuratio ns (cab designs and bed sizes) of pickup trucks, most of the largest pickups (e.g., the Ford F-150, GM Sierra/Silverado, Nissan Titan, and Toyota Tundr a) appear to fall just above 66 square feet in footprint. EPA and NHTSA therefor e proposed to “cut off” the linear portion of the light truck function at 66 square feet.

Having developed a set of vehicle emissi ons and footprint data which represent the benefit of all non-diesel, non-hybrid technolog ies, we determined the initial values for parameters c and d were determined for cars and trucks separately. c and d were initially set at the values for which the average (equivalently, sum) of the absolute values of the differences was minimized between the “max imum technology” fleet fuel consumption (within the footprints between the upper and lower limits) and the straight line of the function defined above at the same corre sponding vehicle foot prints. That is, c and d were determined by minimizing the average absolute residual, commonly known as the MAD (Mean Absolute Deviation) approach, of the corresponding straight line.

Finally, NHTSA calculated the values of the upper and lower parameters ( a and b ) based on the corresponding f ootprints discussed above (41 and 56 square feet for passenger cars, and 41 and 66 squa re feet for light trucks).

The result of this methodology is shown below in Figures II.C-3 and II.C-4 for passenger cars and light trucks, respectivel y. The fitted curves are shown with the underlying “maximum technology” passenger car and light truck fleets. For passenger cars, the mean absolute deviat ion of the sloped portion of the function was 14 percent. For trucks, the corresponding MAD was 10 percent.

620.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.05535 40 45 50 55 60 65 70 75 80Footprint (sf)

Fuel Consumption (gpm)Figure II.C-3 “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function0.060

630.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.0550.06035 40 45 50 55 60 65 70 75 80Footprint (sf)

Fuel Consumption (gpm)Figure II.C--4 “Maximum Technology” Light Truck with Fitted Constrained Linear Function

64The agencies used these functional fo rms as a starting point to develop mathematical functions defining the actual proposed standards as discussed above. The agencies then transposed these func tions vertically (i.e., on a gpm or CO 2 basis, uniformly downward) to produce the sa me fleetwide fuel economy (and CO 2 emission levels) for cars and light tr ucks described in the NPRM.

A number of public comments generally supported the agencies’ choice of attribute-based mathematical functions, as well as the methods applied to fit the function. Ferrari indicated support for the use of a constr ained linear form rather than a constrained logistic form, support for the application of limits on the functions’ values, support for a generally less steep passenger car curve compared to MY 2011, and support for the inclusion of all manufacturers in the analysis used to fit the curves. ICCT also supported the use of a constrained linear form. Toyot a expressed general support for the methods and outcome, including a less-s teep passenger car curve, and the application of limits on fuel economy targets applicable to the smallest vehicles. The UAW commented that the shapes and levels of the curves are reasonable.

Other commenters suggested that changes to the agencies’ methods and results would yield better outcomes. GM suggested that steeper cu rves would provide a greater incentive for limited-line manufacturers to apply technology to smaller vehicles. GM argued that steeper and, in their view, fairercurves could be obtained by using salesweighted least-squares regression rather than minimization of the unweighted mean absolute deviation. Conversely, students fr om UC Santa Barbara commented that the passenger car and light truck curves should be flatter and should converge over time in order to encourage the market to turn, as the agencies’ analysis assumes it will, away from light trucks and toward passenger cars.

NADA commented that there should be no “cut-off” points (i.e., lower limits or floors), because these de facto “backstops” might limit consumer choice, especially for light trucks—a possibility also suggested by the Alliance. The Alliance and several individual manufacturers also co mmented that the cut-off point for light trucks should be shifted to 72 square feet (from the pr oposed 66 square feet), arguing that the preponderance of high-volume light truck models with footprints greater than 66 square feet is such that a 72 square foot cu t-off point makes it unduly challenging for manufacturers serving the large pickup market and thereby constitutes a de facto backstop. Also, with respect to the smallest light truck models, Honda commented that the cut-off point should be set at the point defining the smallest 10 percent of the fleet, both for consistency with the passenger ca r cut-off point, and to provide a greater incentive for manufacturers to downsize the sm allest light truck m odels (which provide greater functionality than passenger cars).

Other commenters focused on whether the ag encies should have separate curves for different fleets or whether they should have a single curve that applied to both passenger cars and light trucks. This issue is related, to so me extent, to commenters who discussed whether car and truck definitions should change. CARB, Ford, and Toyota

65supported separate curves for cars and trucks, ge nerally stating that different fleets have different functional characteris tics and these characteristics are appropriately addressed by separate curves. Likewise, AIAM, Chry sler, and NADA supported leaving the current definitions of car and truck the same. CBD, ICCT, and NESCAUM supported a single curve, based on concerns about manufactur ers gaming the system and reclassifying passenger cars as light trucks in order to obtain the often-less stringent light truck standard, which could lead to lower bene fits than anticipated by the agencies.

In addition, the students from UC Sa nta Barbara reported being unable to reproduce the agencies’ an alysis to fit curves to the pa ssenger car and light truck fleets, even when using the model, inputs, and exte rnal analysis files posted to NHTSA’s web site when the NPRM was issued.

Having considered public comments, NHTSA and EPA have re-examined the development of curves underlying the st andards proposed in the NPRM, and are promulgating standards based on the same unde rlying curves. The agencies have made this decision considering that, while EISA ma ndates that CAFE sta ndards be defined by a mathematical function in terms of one or more attributes related to fuel economy, neither EISA nor the CAA require that the mathema tical function be limited to the observed or theoretical dependence of fuel economy on the selected attribute or attributes. As a means by which CAFE and GHG standards are specified, the mathematical function can and does properly play a normative role. Therefore, NHTSA and EPA have concluded that, as supported by comments, the mathemati cal function can reasonably be based on a blend of analytical and policy considerati ons, as discussed below and in the Joint Technical Support Document.

With respect to GM’s recommendation th at NHTSA and EPA use weighted leastsquares analysis, the agencies find that the market forecast used for analysis supporting both the NPRM and the final rule exhibits the two key characteristics that previously led NHTSA to use minimization of the unweighted Mean Absolute Deviation (MAD) rather than weighted least-squares analysis. First, projected model-specific sales volumes in the agencies’ market forecast cover an extremel y wide range, such that, as discussed in NHTSA’s rulemaking for MY 2011, while unw eighted regression gives low-selling vehicle models and high-selling vehicle models equal emphasis, sales-weighted regression would give some vehicle models considerably more emphasis than other vehicle models. 62 The agencies’ intention is to f it a curve that describes a technical relationship between fuel economy and footprin t, given comparable levels of technology, and this supports weighting discrete vehicl e models equally. On the other hand, sales

62 For example, the agencies’ market forecast shows MY 2016 sales of 187,000 units for Toyota’s 2WD Sienna, and shows 27 model configurations with MY 2 016 sales of fewer than 100 units. Similarly, the agencies’ market forecast shows MY 2016 sales of 268,000 for the Toyota Prius, and shows 29 model configurations with MY 2016 sales of fewer than 100 units. Sales-weighted analysis would give the Toyota Sienna and Prius more than a thousand times the consideration of many vehicle model configurations. Sales-weighted analysis would, therefore, cause a large number of vehicle model configurations to be virtually ignored. See discussion in NHTSA’s final rule for MY 2011 passenger car and light truck CAFE standards, 74 FR 14368 (Mar. 30, 2009), and in NHTSA’s NPRM for that rulemaking, 73 FR 24423-24429 (May 2, 2008).

66weighted regression would allow the differen ce between other vehicle attributes to be reflected in the analysis, and also would reflect consumer demand.

Second, even after NHTSA’s “maximum technology” analysis to increase technological parity of vehicle models before fitting curves, the agencies’ market forecast contains many significant out liers. As discussed in NHTSA’s rulemaking for MY 2011, MAD is a statistical procedur e that has been demonstrated to produce more efficient parameter estimates than least-squares analys is in the presence of significant outliers. 63 In addition, the agencies remain concerned that the steeper curves resulting from weighted least-squares analysis would in crease the risk that energy savings and environmental benefits would be lower than projected, because the steeper curves would provide a greater incentive to increase sales of larger vehicles with lower fuel economy levels. Based on these techni cal considerations and these concerns regarding potential outcomes, the agencies have decided not to re-fit curves using weighted least-squares analysis, but note that they may reconsider using least-squares regression in future analysis.

NHTSA and EPA have considered GM’s comment that steeper curves would provide a greater incentive for limited-line ma nufacturers to apply technology to smaller vehicles. While the agencies agree that a steeper curve would, absent any changes in fleet mix, tend to shift average compliance burdens away from GM and toward companies that make smaller vehicles, the ag encies are concerned, as stated above, that steeper curves would increase the risk that induced increases in vehicle size could erode projected energy and en vironmental benefits.

NHTSA and EPA have also considered the comments by the students from UC Santa Barbara indicating that the passenger ca r and light truck curves should be flatter and should converge over time. The agencies conclude that flatter curves would reduce the incentives intended in shifting from “flat” CAFE standards to attribute-based CAFE and GHG standards—those being the incentive to res pond to attribute-ba sed standards in ways that minimize compromises in vehicle safety, and the incentive for more manufacturers (than primarily t hose selling a wider range of vehicles) across the range of the attribute to have to increas e the application of fuel-savi ng technologies. With regard to whether the agencies should set separate cu rves or a single one, NHTSA also notes that EPCA requires NHTSA to establish standard s separately for passenger cars and light

63 Id. In the case of a dataset not drawn from a sample with a Gaussian, or normal, distribution, there is often a need to employ robust estimation methods rather than rely on least-squares approach to curve fitting. The least-squares approach has as an underlyi ng assumption that the data are drawn from a normal distribution, and hence fits a curve using a sum-of-squ ares method to minimize errors. This approach will, in a sample drawn from a non-normal distribution, give excessive weight to outliers by making their presence felt in proportion to the square of their dist ance from the fitted curve, and, hence, distort the resulting fit. With outliers in the sample, the typical solution is to use a robust method such as a minimum absolute deviation, rather than a squared term, to estimate the fit ( see , e.g., ‘‘AI Access: Your Access to Data Modeling,’’ at http://www.aiaccess.net/Englis h/Glossaries/GlosMod/e_gm _O_Pa.htm#Outlier). The effect on the estimation is to let the presence of each observation be felt more uniformly, resulting in a curve more representative of the data ( see , e.g., Peter Kennedy, A Guide to Econometrics , 3 rd edition, 1992, MIT Press, Cambridge, MA).

67trucks, and thus concludes that the sta ndards for each fleet should be based on the characteristics of vehicles in each fleet. In other words, the passenger car curve should be based on the characteristics of passenge r cars, and the light truck curve should be based on the characteristics of light trucks—thus to the extent that those characteristics are different, an artificially-forced convergence would not accurately reflect those differences. However, such convergence coul d be appropriate depending on future trends in the light vehicle market, specifically further reduction in the differences between passenger car and light truck characteristics. While that trend was more apparent when car-like 2WD SUVs were classified as light trucks, it seems likely to diminish for the model year vehicles subject to these rules as the truck fleet will be more purely “trucklike” than has been the case in recent years.

NHTSA and EPA have also considered comments on the maxima and minima that the agencies have applied to “cut o ff” the linear function underlying the proposed curves for passenger cars and light trucks. Contrary to NADA’s suggestion that there should be no such cut-off points, the agenci es conclude that curves lacking maximum fuel economy targets (i.e., minimum CO 2 targets) would result in average fuel economy and GHG requirements that would not be te chnologically feasible or economically practicable for manufacturer s concentrating on those mark et segments. In addition, minimum fuel economy targets (i.e., maximum CO 2 targets) are important to mitigate the risk to energy and environmental benefits of potential market shifts toward large vehicles. The agencies also disagree with comments by the Alliance and several individual manufacturers that the cut-off point for light trucks should be shifted to 72 square feet (from the proposed 66 square feet) to ease compliance burdens facing manufacturers serving the large pickup market. Such a sh ift would increase the risk that energy and environmental benefits of the standards w ould be compromised by induced increases in the sales of large pickups, in situations where the increased compliance burden is feasible and appropriate. Also, the agencies’ market fo recast suggests that most of the light trucks models with footprints larger than 66 square feet have curb weights near or above 5,000 pounds. This suggests, in turn, that in terms of highway safety, there is little or no need to discourage downsizing of light trucks with footprints larger than 66 square feet. Based on these energy, environmental, technol ogical feasibility, ec onomic practicability, and safety considerations, the agencies conclu de that the light truc k curve should be cut off at 66 square feet, as proposed, rather than at 72 square feet. The agencies also disagree with Honda’s suggesti on that the cut-off point for th e smallest trucks be shifted to a larger footprint value, because doing so could potentially increase the incentive to reclassify vehicles in that size range as light trucks, and coul d thereby increase the possibility that energy and environmental be nefits of the rule would be less than projected.

Finally, considering comments by the UC Santa Barbara students regarding difficulties reproducing NHTSA’s analysis, NHTSA reexamined its analysis, and discovered some erroneous en tries in model inputs underlyi ng the analysis used to develop the curves proposed in the NPRM. These errors ar e discussed in NHTSA’s final Regulatory Impact Analysis (F RIA) and have since been corrected. They include the following: incorrect valvetrain phasing and lift inputs for many BMW engines, incorrect

68indexing for some Daimler models, incorrectly enabled valvetrain technologies for rotary engines and Atkinson cycle engines, omitted baseline applications of cylinder deactivation in some Honda and GM engines, incorrect valve phasing codes for some 4- cylinder Chrysler engines, omitted baseline applications of advanced transmissions in some VW models, incorrectly enabled advanced electrificat ion technologies for several hybrid vehicle models, and incorrect DCT effectiveness estimates for subcompact passenger cars. These errors, while not signi ficant enough to impact the overall analysis of stringency, did affect the fitted slope for the passenger car curve and would have prevented precise replication of NHTSA’s NPRM analysis by outside parties.

After correcting these errors and rep eating the curve development analysis presented in the NPRM, NHTSA obtained the cu rves shown below in Figures II.C-5 and II.C-6 for passenger cars and light trucks, resp ectively. The fitted curves are shown with the underlying “maximum technology” passe nger car and light truck fleets. For passenger cars, the mean absolute deviation of the sloped portion of the function was 14 percent. For trucks, the co rresponding MAD was 10 percent.

690.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060

35 40 45 50 55 60 65 70 75 80 F u e l C o n s u m p t i o n ( g p m )

Footprint (sf) Figure II.C-5 Revised “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function

700.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060

35 40 45 50 55 60 65 70 75 80 F u e l C o n s u m p t i o n ( g p m )

Footprint (sf)

Figure II.C-6 Revised “Maximum Technology” Light Truck with Fitted Constrained Linear Function

71This refitted passenger car curve is simila r to that presented in the NPRM, and the refitted light truck curve is nearly identical the corres ponding curve in the NPRM. However, the slope of the refitted passenger car curve is about 27 percent steeper (on a gpm per sf basis) than the curve presented in the NPRM. For passenger cars and light trucks, respectively, Figures II.C-7 and II.C- 8 show the results of adjustment—discussed in the next section—of the above curves to yield the average required fuel economy levels corresponding to the final standards.

7220 25 30 35 40 45

35 40 45 50 55 60 65 70 75 80 T a r g e t ( m p g )

Footprint (sf) NPRM

Final

Re-Fitted

Figure II.C-7 MY 2016 Passenger Car Targets: NPRM, Final Rule, and if Using ReFitted Curve

7320 25 30 35 40 45

35 40 45 50 55 60 65 70 75 80 T a r g e t ( m p g )

Footprint (sf) NPRM

Final

Re-Fitted

Figure II.C-8 MY 2016 Light Truc k Targets: NPRM, Final Ru le, and if Using Re-Fitted Curve

74While the resultant light truck curves ar e visually indistinguishable from one another, the refitted curve fo r passenger cars would increase stringency for the smallest cars, decrease stringency for th e largest cars, and provide a greater incentive to increase vehicle size throughout the ra nge of footprints within which NHTSA and EPA project most passenger car models will be sold through MY 2016. The agencies are concerned that these changes would make it unduly di fficult for manufacturers to introduce new small passenger cars in the United States , and unduly risk losses in energy and environmental benefits by increasing incenti ves for the passenger car market to shift toward larger vehicles.

Also, the agencies note that the re fitted passenger car curve produces only a slightly closer fit to the corrected fleet than would the curve estimated in the NPRM; with respect to the corrected fleet (between th e “cut off” footprint values, and after the “maximum technology” analysis discussed above ), the mean absolute deviation for the refitted curve is 13.887 percent, a nd that of a refitted curve held to the original slope is 13.933 percent. In other words, the data support the original slope very nearly as well as they support the refitted slope.

Considering NHTSA’s and EPA’s concerns regarding the change in incentives that would result from a refitt ed curve for passenger cars, and considering that the data support the original curves about as well as they woul d support refitted curves, the agencies are finalizing CAFE and GHG standa rds based on the curves presented in the NPRM.

Finally, regarding some commenters’ in ability to reproduce the agencies’ NPRM analysis, NHTSA believes that its correction of the errors discussed above and its release (on NHTSA’s web site) of the updated Vo lpe model and all accompanying inputs and external analysis files should enable outside parties to independently reproduce the agencies’ analysis. If outside parties conti nue to experience difficulty in doing so, we encourage them to contact NHTSA, and the agen cy will do its best to provide assistance.

Thus, in summary, the agencies’ appro ach to developing the attribute-based mathematical functions for MY 2012-2016 CAFE and CO 2 standards represents the agencies’ best technical judgment and consider ation of potential outcomes at this time, and we are confident that the conclusions have resulted in appropriate and reasonable standards. The agencies recognize, however, that aspects of thes e decisions may merit updating or revision in future an alysis to support CAFE and CO 2 standards or for other purposes. Consistent with best rulemaking pr actices, the agencies will take a fresh look at all assumptions and appr oaches to curve fitting, a ppropriate attributes, and mathematical functions in the c ontext of future rulemakings.

The agencies also recognized in the NPRM the possibility that lower fuel prices could lead to lower fleetwide fuel economy (and higher CO 2 emissions) than projected in this rule. One way of addressing that concern is through the use of a universal standard—that is, an average standard set at a (single) absolute le vel. This is often

75described as a “backstop standard.” Th e agencies explained that under the CAFE program, EISA requires such a minimum aver age fuel economy standard for domestic passenger cars, but is silent with regard to similar backstops for imported passenger cars and light trucks, while under the CAA, a back stop could be adopted under section 202(a) assuming it could be justified under the relevant statutory criteria. NHTSA and EPA also noted that the flattened portions of the curv es at the largest footprints directionally address the issue of a backstop (i.e., the m pg “floor” or gpm “ceiling” applied to the curves provides a universal and absolute value for that range of footpr ints). The agencies sought comment on whether backstop standards, or any other method within the agencies’ statutory authority, should and can be implemented in order to guarantee a level of CO 2 emissions reductions and fuel savings under the attribute-based standards.

The agencies received a number of co mments regarding the need for a backstop beyond NHTSA’s alternative minimum standard. Comments were divided fairly evenly between support for and opposition to additi onal backstop standards. The following organizations supported the need for EP A and NHTSA to have explicit backstop standards: American Council for an Ener gy Efficient Economy (ACEEE), American Lung Association, California Air Resources Board (CARB), Environment America, Environment Defense Fund, Massachusetts De partment of Environmental Protection, Natural Resources Defense Council (NRDC), No rtheast States for Coordinated Air Use Management (NESCAUM), Public Citizen a nd Safe Climate Campaign, Sierra Club, State of Washington Department of Ecol ogy, Union of Concerned Scientists, and a number of private citizens. Commenters in favor of additional backstop standards for all fleets for both NHTSA and EPA 64 generally stated that the em issions reductions and fuel savings expected to be achieved by MY 2016 depended on assumptions about fleet mix that might not come to pass, and that vari ous kinds of backstop standards or “ratchet mechanisms” 65 were necessary to ensure that those reductions were achieved in fact. In addition, some commenters 66 stated that manufacturers might build larger vehicles or more trucks during MYs 2012-2016 than the agen cies project, for example, because 1) any amount of slope in target curves encour ages manufacturers to upsize, and 2) lower targets for light trucks than for passenger cars encourage manufacturers to find ways to reclassify vehicles as light trucks, such as by dropping 2WD versions of SUVs and offering only 4WD versions, perhaps spu rred by NHTSA’s reclas sification of 2WD SUVs as passenger cars. Both of these mechanisms will be addressed further below. Some commenters also disc ussed EPA authority under the CAA to set backstops, 67 agreeing with EPA’s analysis that section 202 (a) allows su ch standards since EPA has wide discretion under that secti on to craft standards.

The following organizations opposed a backstop: Alliance of Automobile Manufacturers (AAM), Association of Intern ational Automobile Manufacturers (AIAM),

64 ACEEE, American Lung Associati on, CARB, Christopher Lish, Environment America, EDF, MA DEP, NRDC, NESCAUM, Public Citizen, Sierra Club et al. , SCAQMD, UCS, WA DE
65 Commenters generally defined a “ratchet mechanism” as an automatic re-calcu lation of stringency to ensure cumulative goals are reached by 2016, even if emissions reductions and fuel savings fall short in the earlier years covered by the rulemaking.
66 CBD, MA DEP, NJ DEP, Public Citizen, Sierra Club et al. , UCS
67 CARB, Public Citizen, Sierra Club et al.

76Ford Motor Company, National Automobile Dealers Association (NADA), Toyota Motor Company, and the United Auto Workers Uni on. Commenters stating that additional backstops would not be necessary disagreed that upsizing was likely, 68 and emphasized the anti-backsliding characteristics of the ta rget curves. Others argued that universal absolute standards as backstops could restrict consumer choice of vehicles. Commenters making legal arguments under EPCA/EISA 69 stated that Congre ss’ silence regarding backstops for imported passenger cars and light trucks should be construed as a lack of authority for NHTSA to create further back stops. Commenters making legal arguments under the CAA 70 focused on the lack of clear authority under the CAA to create multiple GHG emissions standards for the same fleets of vehicles based on the same statutory criteria, and opposed EPA taking steps that would reduce harmoniza tion with NHTSA in standard setting. Furthermor e, AIAM indicated that EI SA’s requirement that the combined (car and truck) fuel economy level reach at least 35 mpg by 2020 itself constitutes a backstop. 71 One individual 72 commented that while additional backstop standards might be necessary given optimism of fleet mix assumptions, both agencies’ authorities would probably need to be revi sed by Congress to clarify that backstop standards (whether for indivi dual fleets or for the nationa l fleet as a whole) were permissible.

In response, EPA and NHTSA remain c onfident that their projections of the future fleet mix are reliable, and that future changes in the fleet mix of footprints and sales are not likely to lead to more than mode st changes in projected emissions reductions or fuel savings. 73 Both agencies thus remain confid ent in these fleet projections and the

68 For example, the Alliance and Toyota said that up sizing would not be likely because (1) it would not necessarily make compliance with applicable standards eas ier, since larger vehicles tend to be heavier and heavier vehicles tend to achieve worse fuel economy/emissions levels; (2) it may require expensive platform changes; (3) target curves become increasi ngly more stringent from year to year, which reduces the benefits of upsizing; and (4) the mpg floor and gpm ceiling for the largest vehicles (the point at which the curve is “cut off”) discourages manufacturers from continuing to upsize beyond a point because doing so makes it increasingly difficult to meet the flat standard at that part of the curve.
69 AIAM, Alliance, Ford, NADA, Toyota
70 Alliance, Ford, NADA, UAW
71 NHTSA and EPA agree with AIAM that the EISA 35 mpg requirement in MY 2020 has a backstop-like function, in that it requires a certain level of achieved fleetwide fuel economy by a certain date, although it is not literally a backstop standard. Considering that NHTSA’s MY 2011 CA FE standards increased projected average fuel economy requirements (relative to the MY 2010 standards) at a significantly faster rate than would be required to achieve the 35-in-2020 requirement, and considering that the standards being finalized today would increase projected average combin ed fuel economy requiremen ts to 34.1 mpg in MY 2016, four years before MY 2020, the agencies believe that the U.S. vehicle market would have to shift in highly unexpected ways in order to put the 35-in-2020 requirement at risk, even despite the fact that due to the attribute-based standards, average fuel econom y requirements will vary depending on the mix of vehicles produced for sale in the U.S. in each m odel year. The agencies further emphasize that both NHTSA and EPA plan to conduct and document retros pective analyses to evaluate how the market’s evolution during the rulemaking timeframe compares with the agencies’ forecasts employed for this rulemaking. Additionally, we emphasize that both agenci es have the authority, given sufficient lead time, to revise their standards upwards if necessary to avoid missing the 35-in-2020 requirement.
72 Schade
73 For reference, NHTSA’s March 2009 final rule establishing MY 2011 CAFE standards was based on a forecast that passenger cars would represent 57.6 percent of the MY 2011 fleet, and that MY 2011 passenger cars and light trucks would average 45.6 square feet (sf) and 55.1 sf, respectively, such that

77resulting emissions reductions and fuel savings from the st andards. As explained in Section II.B above, the agencies’ projections of the future fleet are based on the most transparent information currently available to the agencies. In addi tion, there are only a relatively few model years at issue. Moreover, market trends today are consistent with the agencies’ estimates, showing shifts from light trucks to passenger cars and increased emphasis on fuel economy from all vehicles.

Finally, the shapes of the curves, including the “flattening” at the largest footprint values, tend to avoid or minimize regulatory in centives for manufacturers to upsize their fleet to change their compliance burden. Give n the way the curves are fit to the data points (which represent vehicl e models’ fuel economy mapped against their footprint), the agencies believe that there is little re al benefit to be gained by a manufacturer upsizing their vehicles. As discussed above, the agencies’ analysis indica tes that, for passenger car models with footprints falling between the two flatte ned portions of the corresponding curve, the actual slope of fuel economy with resp ect to footprint, if fit to that data by itself, is about 27 percent steeper than the curve the agencies are promulgating today. This difference sugge sts that manufacturers would, if anything, have more to gain by reducing vehicle footprin t than by increasing vehicle footprint. For light trucks, the agencies’ analysis indicates that, for models with footprints falling between the two flatted portions of the co rresponding curve, the slope of fuel economy with respect to footprint is nearly identical to the curve the agencies are promulgating today. This suggests that, within this range , manufacturers would typically have little incentive to either incrementally increase or reduce vehicle footprint.

At the same time, adding another back stop standard would have virtually no effect if the standard was weak, but a more stringent backstop could compromise the objectives served by attribute-based standard s – that they distribute compliance burdens more equally among manufacturers, and at th e same time encourage manufacturers to apply fuel-saving technologies rather than si mply downsizing their vehicles, as they did in past decades under flat st andards. This is why Congr ess mandated attribute-based CAFE standards in EISA. This compromise in objectives could occur for any manufacturer whose fleet average was above the backstop, irrespective of why they were above the backstop and irrespective of whet her the industry as a whole was achieving the emissions and fuel economy benefits projecte d for the final standards, the problem the backstop is supposed to address. For exampl e, the projected industry wide level of 250 gm/mile for MY 2016 is based on a mix of manufacturer levels, ranging from approximately 205 to 315 gram/mile 74 but resulting in an indust ry wide basis in a fleet average of 250 gm/mile. Unless the backstop wa s at a very weak le vel, above the high end of this range, then some percentage of manufacturers would be above the backstop even if the performance of the entire industr y remains fully consistent with the emissions

average required CAFE levels would be 30.2 mpg, 24.1 mpg, and 27.3 mpg, respectively, for passenger cars, light trucks, and the overall light -duty fleet. Based on the agencies’ current market fo recast, even as soon as MY 2011, passenger cars will comprise a larger share (59.2 percent) of the light vehicle market; passenger cars and light trucks will, on average, be sm aller by 0.5 sf and 1.3 sf, respectively; and average required CAFE levels will be higher by 0.2 mpg, 0. 3 mpg, and 0.3 mpg, respectively, for passenger cars, light trucks, and the overall light-duty fleet.
74 Based on estimated standards presented in tables III.B.1-1 and III.B.1-2.

78and fuel economy levels projected for the fi nal standards. For th ese manufacturers and any other manufacturers who were above the backstop, the objectives of an attribute based standard would be compromised and unnecessary costs would be imposed. This could directionally impose in creased costs for some manufac turers. It would be difficult if not impossible to establish th e level of a backstop standard such that costs are likely to be imposed on manufacturers only when ther e is a failure to achieve the projected reductions across the industry as a whole. An example of this kind of industry wide situation could be when there is a significant sh ift to larger vehicles across the industry as a whole, or if there is a general market shift from cars to trucks. The problem the agencies are concerned about in those circum stances is not with respect to any single manufacturer, but rather is ba sed on concerns over shifts ac ross the fleet as a whole, as compared to shifts in one manufacturer's fleet that may be more than offset by shifts the other way in another manufacturer's fleet. However, in this respect, a traditional backstop acts as a manufacturer specific standard.

The concept of a ratchet mechanism r ecognizes this problem, and would impose the new more stringent standard only when the problem arises acr oss the industry as a whole. While the new more stringent stan dards would enter into force automatically, any such standards would still need to provide adequate lead time fo r the manufacturers. Given the limited number of model years cove red by this rulemaking and the short leadtime already before the 2012 model year, a ra tchet mechanism in this rulemaking that would automatically tighten the standards at some point after model year 2012 is finished and apply the new more stringent standards fo r model years 2016 or ear lier, would fail to provide adequate lead time for any new, more stringent standards

Additionally, we do not believe that th e risk of vehicle upsizing or changing vehicle offerings to “game” the passenger car an d light truck definitions is as great as commenters imply for the model years in question. 75 The changes that commenters suggest manufacturers might make are neither so simple nor so likely to be accepted by consumers. For example, 4WD versions of vehicles tend to be more expensive and, other things being equal, have i nherently lower fuel economy than their 2WD equivalent models. Therefore, although th ere is a market for 4WD vehicles, and some consumers might shift from 2WD vehicles to 4WD vehicles if 4WD becomes available at little or no extra cost, many consumers still may not desire to purchase 4WD vehicles because of concerns about cost premium and additio nal maintenance requirements; conversely, many manufacturers often require the 2WD option to satisfy demand for base vehicle models. Additionally, increasing the footprint of vehicles requires platform changes, which usually requires a product redesign phase (the agencies estimate that this occurs on average once every 5 years for most models ). Alternatively, turning many 2WD SUVs into 2WD light trucks would re quire manufacturers to squeeze a third row of seats in or significantly increase their GVWR, which al so requires a significant change in the

75 We note that NHTSA’s recent clarification of th e light truck definitions has significantly reduced the potential for gaming, and resulted in the reclassification of over a million vehicles from the light truck to the passenger car fleet.

79vehicle. 76 The agencies are confident that the anticipated increases in average fuel economy and reductions in average CO 2 emission rates can be achieved without backstops under EISA or the CAA. As noted above, the agencies plan to conduct retrospective analysis to monitor progress. Both agencies have the authority to revise standards if warranted, as long as sufficient lead time is provided.

The agencies acknowledge that the MY 2016 fleet emissions and fuel economy goals of 250 g/mi and 34.1 mpg for EPA and NHTSA respectively are estimates and not standards (the MY 2012-2016 curves are the standards). Changes in fuel prices, consumer preferences, and/or vehicle surv ival and mileage accumulation rates could result in either smaller or larger oil and GHG savings. As explained above and elsewhere in the rule, the agencies believe that the possibility of not meeting (or, alternatively, exceeding) fuel economy and emissions goals exists, but is not likely Given this, and given the potential complexi ties in designing an appropria te backstop, the agencies believe the balance here points to not adopti ng additional backstops at this time for the MYs 2012-2016 standards other than NHTSA’s finalizing of the ones required by EPCA/EISA for domestic passenger cars. If , during the timeframe of this rule, the agencies observe a significant shift in th e manufacturer’s product mix resulting in a relaxation of their estimated targets, NHTSA and EPA will reconsider options, both for MYs 2012-2016 and future rulemakings. This issue will be discussed further in Sections III and IV.

D. Relative Car-Truck Stringency

The agencies proposed fleetwide standards with the projected levels of stringency of 34.1 mpg or 250 g/mi in MY 2016 (as well as the corresponding intermediate year fleetwide standards) for NHTSA and EPA respectively. To determine the relative stringency of passenger car and light truck standards for thos e model years, the agencies were concerned that increasing the difference between the car and truck standards (either by raising the car standards or lowering the truck standards) could encourage manufacturers to build fewer cars and more trucks, likely to the detriment of fuel economy and CO 2 reductions. 77 In order to maintain consis tent car/truck standards, the agencies applied a constant ratio between the estimated average required performance under the passenger car and light truck standards, in order to mainta in a stable set of incentives regarding ve hicle classification.

To calculate relative car-truck stringency for the proposal, the agencies explored a number of possible alternatives, and for the reasons described in the proposal used the Volpe model in order to estimate stringencies at which net benefits would be maximized

.

76 Increasing the GVWR of a light truck (assuming this was the only goal) can be accomplished in a number of ways, and must include consideration of: (1) redesign of wheel axles; (2) improving the vehicle suspension; (3) changes in tire specification (which w ill likely affect ride quality); (4) vehicle dynamics development (especially with vehicles equipped with electronic stability control); and (5) brake redesign. Depending on the vehicle, some of these changes may be easier or more difficult than others.
77 For example, since many 2WD S UVs are classified as passenger cars, manufacturers have already warned that high car standards rela tive to truck standards could create an incentive for them to drop the 2WD version and sell only the 4WD version.

80The agencies have followed the same approach in calculating the relative car-truck stringency for the final standards promulgate d today. Further details of the development of this approach can be found in Section IV of this preamble as well as in NHTSA’s RIA and EIS. NHTSA examined passenger car and light truck standard s that would produce the proposed combined average fuel economy levels from Table I.B.2-2 above. NHTSA did so by shifting downward the curves that maximize net benefits, holding the relative stringency of passenger car and light truck standards constant at the level determined by maximizing net benefits, such that the aver age fuel economy required of passenger cars remained 31 percent higher than the average fuel economy required of light trucks. This methodology resulted in the average fuel ec onomy levels for passenger cars and light trucks during MYs 2012-2016 as shown in Tabl e I.B.1-1. The followi ng chart illustrates this methodology of shifting the standards from the levels maximizing net benefits to the levels consistent with the combined fuel economy standards in this final rule.

81Figure II.D-1 Shifting the Standards from the Maximizing Net Benefit Levels to the Levels Consistent with the CombinedFuel Economy Standards in this Rule202530354045201120122013201420152016Ave.RequiredFE(mpg)Model YearCombined (National Policy)Cars (Maximizing Net Benefits)Trucks (Maximizing Net Benefits)Combined (Maximizing Net Benefits)Cars (Pending)Trucks (Pending)Combined (Pending)Light TrucksPassenger CarsCombined Ave.

82The final car and truck standards fo r EPA (Table I.B.1-4 above) were subsequently determined by first converting th e average required fuel economy levels to average required CO 2 emission rates, and then applyi ng the expected air conditioning credits for 2012-2016. These A/C credits are shown in the following table. Further details of the derivation of thes e factors can be found in Section III of this preamble or in the EPA RIA.

Table II.D-1 Expected Fleet A/C Credits (in CO 2 equivalent g/mi) from 2012-2016

Average Technology Penetration Average credit for cars Average credit for

trucks Average credit for combined fleet 2012 28% 78 3.4 3.8 3.5 2013 40% 4.8 5.4 5.0 2014 60% 7.2 8.1 7.5 2015 80% 9.6 10.8 10.0 2016 85% 10.2 11.5 10.6

The agencies sought comment on the use of this methodology for apportioning the fleet stringencies to relative car and tr uck standards for 2012-2016. General Motors commented that, compared to the passenger ca r standard, the light tr uck standard is too stringent because “the most fuel efficient cars and sma ll trucks already meet the 2016 MY requirements” but “the most fuel effici ent large trucks must increase fuel economy by 20 percent to meet the 2016 MY requirement s.” GM recommended that the agencies relax stringency specifically for large pickups, such as the Silverado.

The agencies disagree with the premise of the comment that the standard is too stringent under the applicable statutory provi sions because some existing large trucks are not already meeting a later model year standa rd. Our analysis shows that the standards are not too stringent for manu facturers selling these vehicl es. The agencies’ analyses demonstrate a means by which manufacturers could apply cost-effec tive technologies in order to achieve the standards, and we ha ve provided adequate lead time for the technology to be applied. More important, th e agencies’ analysis demonstrate that the fleetwide emission standards for MY 2016 ar e technically feasible, for example by implementing technologies such as engine downsizing, turbochargi ng, direct injection, improving accessories and tire rolling resistance, etc.

GM did not comment on the use of the methodology applied by the agencies to develop the gap between th e passenger car and light truck standards—only on the outcome of the methodology. For the reasons discussed below, the agencies maintain that the methodology applied above provides an appropriate basis to determine the gap between the passenger car and light truck sta ndards, and disagree with GM’s arguments

78 We assume slightly higher A/C penetration in 2012 than was assumed in the proposal only to correct for rounding that occurred in the curve setting process.

83that the outcome is unfair.

First, GM’s argument incorrectly suggest s that every individual vehicle model must achieve its fuel economy and emissions targets. CAFE standards and new GHG emissions standards apply to fleetwide average performance, not model-specific performance, even though aver age required levels are base d on average model-specific targets, and the agencies’ analysis demons trates that GM and other manufacturers of large trucks can cost-effectively comply with the new standards.

Second, GM implies that every manufacturer must be challenged equally with respect to fuel economy and emissions. Although NHTSA and EPA maintain that attribute-based CAFE and GHG emissions standards can more evenly balance compliance challenges, attribute-based sta ndards are not intended to and cannot make these challenges equal, and while the agencies are mindful of the potential impacts of the standards on the relative competitiveness of different vehicle manufacturers, there is nothing in EPCA or the CAA 79 requiring that these challenges be equal.

We have also already addressed and reject ed GM’s suggestion of shifting the “cut off” point for light trucks from 66 square f eet to 72 square feet, thereby “dropping the floor” of the target function for light trucks. As discussed in the preceding section, this is so as not to forego the rules’ energy and e nvironmental benefits, and because there is little or no safety basis to discourage dow nsizing of the largest light trucks.

Finally, NHTSA and EPA disagree with GM ’s claim that the outcome of the agencies’ approach is unfairly burdensome for light trucks as compared to passenger cars. Based on the agencies’ market forecast, NHTSA ’s analysis indicates that incremental technology outlays could, on average, be compar able for passenger cars and light trucks under the final CAFE standards, and further indicates that the ratio of total benefits to total costs could be greater under the final light truck standards than under the final passenger car standards.

79 As NHTSA explained in the NPRM, the Conference Report for EPCA, as enact ed in 1975, makes clear, and the case law affirms, “a determination of maximum feasible average fuel economy should not be keyed to the single manufacturer which might have the most difficulty achieving a given level of average fuel economy.” CEI-I , 793 F.2d 1322, 1352 (D.C. Cir. 1986). Instead, NHTSA is compelled “to weigh the benefits to the nation of a higher fuel economy standa rd against the difficulties of individual automobile manufacturers.” Id . The law permits CAFE standards exceedin g the projected capability of any particular manufacturer as long as the standard is economically practicable for the industry as a whole. Similarly, EPA is afforded great discretion under section 202 (a) of the CAA to balance issues of technical feasibility, cost, adequacy of lead time, and safe ty, and certainly is not required to do so in a manner that imposes regulatory obligatio ns uniformly on each manufacturer. See NRDC v. EPA , 655 F. 2d 318, 322, 328 (D.C. Cir. 1981) (wide discretion afforded by the statutory factors, and EPA predictions of technical feasibility afforded considerable discretion subject to constraints of reasonableness EPA predictions of technical feasibility afforded considerable discretion subject to constraints of r easonableness); and cf. International Harvester Co. v. Ruckelshaus , 479 F. 2d 615, 640 (D.C. Cir. 1973) ( “as long as feasible technology permits the demand for new passenger automobiles to be generally met, the basic requirements of the Act would be satisfied, even though this might occasion fewer models and a more limited choice of engine types”).

84E. Joint Vehicle Technology Assumptions

Vehicle technology assumptions, i.e., a ssumptions about technologies' cost, effectiveness, and the rate at which they can be incorporated into new vehicles, are often controversial as they have a significant impact on the levels of the standards. The agencies must, therefore, take great care in developing and justifying these estimates. In developing technology inputs for the anal ysis of the MY 2012-2016 standards, the agencies reviewed the technology assumptions that NHTSA used in setting the MY 2011 standards, the comments that NHTSA received in respons e to its May 2008 Notice of Proposed Rulemaking (NPRM), and the comments received in response to the NPRM for this rule. This review is consistent with the request by President Obama in his January 26 memorandum to DOT. In addition, the agencies reviewed the techno logy input estimates identified in EPA’s July 2008 Advance Noti ce of Proposed Rulemaking. The review of these documents was supplemented with updated information from more current literature, new product plans from manufactur ers, and from EPA cer tification testing.

As a general matter, EPA and NHTSA be lieve that the best way to derive technology cost estimates is to conduct real-world tear down studies. Most of the commenters on this issue agreed. The adva ntages not only lie in the rigor of the approach, but also in its transparency. Th ese studies break down each technology into its respective components, evaluate the costs of each component, and build up the costs of the entire technology based on the contribu tion of each component and the processes required to integrate them. As such, tear down studies re quire a significant amount of time and are very costly. EPA has been c onducting tear down studies to assess the costs of vehicle technologies under a contract with FEV. Further details for this methodology is described below and in the TSD.

Due to the complexity and time in curred in a tear down study, only a few technologies evaluated in this rulemaking have been costed in this manner thus far. The agencies prioritized the technologies to be costed first based on how prevalent the agencies believed they might be likely to be during the rulemaking time frame, and based on their anticipated cost-effectiveness. Th e agencies believe that the focus on these important technologies (listed below) is sufficien t for the analysis in this rule, but EPA is continuing to analyze more technologies beyond th is rule as part of studies both already underway and in the future. For most of the other technologies, because tear down studies were not yet available, the agencies d ecided to pursue, to the extent possible, the Bill of Materials (BOM) approach as ou tlined in NHTSA’s MY 2011 final rule. A similar approach was used by EPA in the EPA 2008 Staff Technical Report. This approach was recommended to NHTSA by Ricardo, an international engineering consulting firm retained by NHTSA to aid in the analysis of public comments on its proposed standards for MYs 2011-2015 because of its expertise in the area of fuel economy technologies. A BOM approach is on e element of the process used in tear down studies. The difference is that under a BOM approach, the build up of cost estimates is conducted based on a review of cost and effectiveness estimates for each component from available literature, while under a tear down study, the cost estimates which go into the BOM come from the tear down study itself. To the extent that the

85agencies departed from the MY 2011 CAFE fina l rule estimates, the agencies explained the reasons and provided supporting analyses in the Technical Support Document.

Similarly, the agencies followed a BOM approach for developing the technology effectiveness estimates, insofar as the BOM de veloped for the cost estimates helped to inform the appropriate effectiveness values derived from the literature review. The agencies supplemented the information with results from available simulation work and real world EPA certification testing.

The agencies would also like to note that per the Energy Independence and Security Act (EISA), the National Academies of Sciences has been conducting a study for NHTSA to update Chapter 3 of their 2002 NAS Report, wh ich presents technology effectiveness estimates for light-duty vehicles . The update takes a fresh look at that list of technologies and their associated cost and effectiveness values. The updated NAS report was expected to be available on September 30, 2009, but has not been completed and released to the public. The results from this study thus are unavailable for this rulemaking. The agencies look forward to cons idering the results from this study as part of the next round of rulemaking for CAFE/GHG standards.

1. What Technologies Did the Agencies Consider?

The agencies considered over 35 vehicle technologies that manufacturers could use to improve the fuel economy and reduce CO 2 emissions of their vehicles during MYs 2012-2016. The majority of the technologies described in this section are readily available, well known, and could be incorporat ed into vehicles once production decisions are made. Other technologies considered may not currently be in production, but are beyond the research phase and under developmen t, and are expected to be in production in the next few years. These are technologies which can, for the most part, be applied both to cars and trucks, and which are capab le of achieving significant improvements in fuel economy and reductions in CO 2 emissions, at reasonable co sts. The agencies did not consider technologies in the res earch stage because th e lead time available for this rule is not sufficient to move most of these technologies from research to production.

The technologies considered in the agencies ’ analysis are briefly described below. They fall into five broad cat egories: engine technologi es, transmission technologies, vehicle technologies, electrifi cation/accessory technologies, and hybrid technologies. For a more detailed description of each technology and their costs and effectiveness, we refer the reader to Chapter 3 of the Joint TSD, Chapter III of NHTSA’s FRIA, and Chapter 1 of EPA’s final RIA. Technologies to reduce CO 2 and HFC emissions from air conditioning systems are discussed in Secti on III of this preamble and in EPA’s final RIA.

Types of engine technologies that improve fuel economy and reduce CO 2 emissions include the following:

86• Low-friction lubricants – low viscosity and advanced low friction lubricants oils are now available with improved pe rformance and better lubrication. If manufacturers choose to make use of thes e lubricants, they would need to make engine changes and possibly conduct durab ility testing to accommodate the lowfriction lubricants.

• Reduction of engine friction losses – can be achieved th rough low-tension piston rings, roller cam followers, improved mate rial coatings, more optimal thermal management, piston surface treatments, a nd other improvements in the design of engine components and subsystems that improve engine operation.

• Conversion to dual overhead cam with dual cam phasing – as applied to overhead valves designed to increase the air flow with more than two valves per cylinder and reduce pumping losses.

• Cylinder deactivation – deactivates the intake and exhaust valves and prevents fuel injection into some cylinders duri ng light-load operation. The engine runs temporarily as though it were a smalle r engine which substantially reduces pumping losses.

• Variable valve timing – alters the timing of the inta ke valve, exhaust valve, or both, primarily to reduce pumping losses, increase specific power, and control residual gases.

• Discrete variable valve lift – increases efficiency by optimizing air flow over a broader range of engine operation which reduces pumping losses. Accomplished by controlled switching between two or more cam profile lobe heights.

• Continuous variable valve lift – is an electromechanica lly controlled system in which valve timing is changed as lift hei ght is controlled. This yields a wide range of performance optimization and vol umetric efficiency, including enabling the engine to be valve throttled.

• Stoichiometric gasoline di rect-injection technology – injects fuel at high pressure directly into the combustion chamber to improve cooling of the air/fuel charge within the cylinder, which allows fo r higher compression ratios and increased thermodynamic efficiency.

• Combustion restart – can be used in conjunction with gasoline direct-injection systems to enable idle-off or start-stop functionality. Similar to other start-stop technologies, additional enablers, such as electric power steering, accessory drive components, and auxiliary oil pump, might be required.

• Turbocharging and downsizing – increases the available airflow and specific power level, allowing a reduced engine si ze while maintaining performance. This reduces pumping losses at lighter loads in comparison to a larger engine.

87• Exhaust-gas recirculation boost – increases the exhaust-ga s recirculation used in the combustion process to increase thermal efficiency and reduce pumping losses.

• Diesel engines – have several characteristics that give superior fuel efficiency, including reduced pumping losses due to la ck of (or greatly reduced) throttling, and a combustion cycle that operates at a higher compression ratio, with a very lean air/fuel mixture, relative to an eq uivalent-performance gasoline engine. This technology requires additiona l enablers, such as NO x trap catalyst after-treatment or selective catalytic reduction NO x after-treatment. The cost and effectiveness estimates for the diesel engine and aftertreatment system utilized in this final rule have been revised from the NHTSA MY 2011 CAFE final rule. Additionally, the diesel technology option has been made available to small cars in the Volpe and OMEGA models. Though this is not expect ed to make a significant difference in the modeling results, the agencies agreed with the commenters that supported such a revision.

Types of transmission technol ogies considered include:

• Improved automatic tr ansmission controls – optimizes shift schedule to maximize fuel efficiency under wide ranging cond itions, and minimizes losses associated with torque converter slip through lock-up or modulation.

• Six-, seven-, and eight-speed automatic transmissions – the gear ratio spacing and transmission ratio are optimized to enable the engine to operate in a more efficient operating range over a broader range of vehicle operating conditions.

• Dual clutch or automated shift manual transmissions – are similar to manual transmissions, but the vehicle controls shifting and launch functions. A dualclutch automated shift manual transmissi on uses separate clutches for evennumbered and odd-numbered gears, so the next expected gear is pre-selected, which allows for faster and smoother shifting.

• Continuously variable transmission – commonly uses V-shaped pulleys connected by a metal belt rather than gears to provide ratios for operation. Unlike manual and automatic transmissions with fixed transmission ratios, continuously variable transmissions can provide fully variable and an infinite number of transmission ratios that enable the engine to operate in a more efficient operating range over a broader range of vehicle operating conditions.

• Manual 6-speed transmission –offers an additional gear ratio, often with a higher overdrive gear ratio, than a 5-speed manual transmission.

Types of vehicle technologi es considered include:

• Low-rolling-resistance tires – have characteristics that reduce frictional losses associated with the energy dissipated in the deformation of the tires under load, thereby improving fuel economy and reducing CO 2 emissions.

88• Low-drag brakes – reduce the sliding friction of disc brake pads on rotors when the brakes are not engaged because the brake pads are pulled away from the
rotors.

• Front or secondary axle disconnect for four-wheel drive systems – provides a torque distribution disconnect between fr ont and rear axles when torque is not required for the non-driving axle. This results in the reduction of associated parasitic energy losses.

• Aerodynamic drag reduction – is achieved by changing vehicle shape or reducing frontal area, including sk irts, air dams, underbody c overs, and more aerodynamic side view mirrors.

• Mass reduction and material substitution – Mass reduction encompasses a variety of techniques ranging from improved desi gn and better component integration to application of lighter and higher-strength materials. Mass reduction is further compounded by reductions in engine power and ancillary systems (transmission, steering, brakes, suspension, etc.). The agencies recognize th ere is a range of diversity and complexity for mass reduction and material substitution technologies and there are many techni ques that automotive suppliers and manufacturers are using to achieve the leve ls of this technology that the agencies have modeled in our analysis for the final standards.

Types of electrification/ accessory and hybrid technol ogies considered include:

• Electric power steering (EPS) – is an electrically-assist ed steering system that has advantages over traditio nal hydraulic power steeri ng because it replaces a continuously operated hydraulic pump, ther eby reducing parasitic losses from the accessory drive.

•

• Improved accessories (IACC) – may include high efficiency alternators, electrically driven (i.e., on-demand) wate r pumps and cooling fans. This excludes other electrical accessories such as electric oil pumps and electrically driven air conditioner compressors. The latter is c overed explicitly within the A/C credit
program.

• Air Conditioner Systems – These technologies include improved hoses, connectors and seals for leakage cont rol. They also include improved compressors, expansion valves, heat ex changers and the control of these components for the purposes of improving tailpipe CO 2 emissions as a result of A/C use. These technologies are discu ssed later in this pr eamble and covered separately in the EPA RIA.

• 12-volt micro-hybrid (MHEV) – also known as idle-stop or start-stop and commonly implemented as a 12-volt belt-dri ven integrated starter-generator, this is the most basic hybrid system that faci litates idle-stop capa bility. Along with

89other enablers, this system replaces a common alternator with a belt-driven enhanced power starter-alternator, an d a revised accessory drive system.

• Higher Voltage Stop-Start/Belt Integr ated Starter Generator (BISG) – provides idle-stop capability and uses a higher vol tage battery with increased energy capacity over typical automotive batteries. The higher system voltage allows the use of a smaller, more powerful electric mo tor. This system replaces a standard alternator with an enhanced power, hi gher voltage, higher efficiency starteralternator, that is belt driven and that can recover braking energy while the vehicle slows down (regenerative braking).

• Integrated Motor Assist (IMA)/Crank integrated starter generator (CISG) – provides idle-stop capability and uses a high voltage battery with increased energy capacity over typical automotive ba tteries. The higher system voltage allows the use of a smaller, more powerfu l electric motor and reduces the weight of the wiring harness. This system replaces a standard alternator with an enhanced power, higher voltage, higher e fficiency starter-alternator that is crankshaft mounted and can recover brak ing energy while the vehicle slows down (regenerative braking).

• 2-mode hybrid (2MHEV) – is a hybrid electric drive system that uses an adaptation of a conventional stepped-ra tio automatic transmission by replacing some of the transmission clutches with two electric motors that control the ratio of engine speed to vehicle speed, while clut ches allow the motors to be bypassed. This improves both the transmission torque capacity for heavy-duty applications and reduces fuel consumption and CO 2 emissions at highway speeds relative to other types of hybrid electric drive systems.

• Power-split hybrid (PSHEV) – a hybrid electric drive system that replaces the traditional transmission with a single planetary gearset and a motor/generator. This motor/generator uses the engine to either charge the battery or supply additional power to the drive motor. A second, more powerful motor/generator is permanently connected to the vehicle’s final drive and always turns with the wheels. The planetary gear splits engine power between the first motor/generator and the drive motor to eith er charge the battery or supply power to the wheels.

• Plug-in hybrid electric vehicles (PHEV) – are hybrid electric vehicles with the means to charge their battery packs from an outside source of electricity (usually the electric grid). These vehicles have larger battery packs with more energy storage and a greater capabil ity to be discharged than other hybrids. They also use a control system that allows the batte ry pack to be substantially depleted under electric-only or blended m echanical/elect ric operation.

• Electric vehicles (EV) – are vehicles with all-elec tric drive and with vehicle systems powered by energy-optimized batt eries charged primarily from grid electricity.

90The cost estimates for the various hybrid systems have been revised from the estimates used in the MY 2011 CAFE final rule , in particular with respect to estimated battery costs.

2. How did the Agencies Determine the Costs and Effectiveness of Each of These Technologies?

As mentioned above, EPA and NHTSA belie ve that the best way to derive technology cost estimates is to conduct real-world tear down studies. To date, the costs of the following five technologies have been ev aluated with respect to their baseline (or replaced) technologies. For these technologies noted below, the agencies relied on the tear down data available and scaling me thodologies used in EPA’s ongoing study with FEV. Only the cost estimate for the first technology on the list below was used in the NPRM. The others were completed subse quent to the publication of the NPRM.

1. Stoichiometric gasoline direct inje ction and turbo charging with engine downsizing (T-DS) for a large DOHC 4 cylinder engine to a small DOHC (dual overhead cam) 4 cylinder engine. 2. Stoichiometric gasoline direct inje ction and turbo charging with engine downsizing for a SOHC si ngle overhead cam) 3 valve/cylinder V8 engine to a SOHC V6 engine. 3. Stoichiometric gasoline direct inje ction and turbo charging with engine downsizing for a DOHC V6 engine to a DOHC 4 cylinder engine. 4. 6-speed automatic transmission replacing a 5-speed automatic transmission. 5. 6-speed wet dual clutch transmission (DCT) replacing a 6-speed automatic transmission.

This costing methodology has been pub lished and gone through a peer review. 80 Using this tear down costing methodology, FE V has developed costs for each of the above technologies. In addi tion, FEV and EPA extrapolated the engine downsizing costs for the following scenarios that were outside of the noted study cases: 81

1. Downsizing a SOHC 2 valve/cylinder V8 engine to a DOHC V6. 2. Downsizing a DOHC V8 to a DOHC V6. 3. Downsizing a SOHC V6 engine to a DOHC 4 cylinder engine. 4. Downsizing a DOHC 4 cylinder engine to a DOHC 3 cylinder engine.

The agencies relied on the findings of FEV in part for estimating the cost of these technologies in this rulema king. However, for some of the technologies, NHTSA and EPA modified FEV’s estimated costs. FEV made the assumption that these technologies would be mature when produced in large vo lumes (450,000 units or more). The agencies believe that there is some uncertainty rega rding each manufacturer’s near-term ability to employ the technology at the volumes assumed in the FEV analysis. There is also the

80 EPA-F-09-020; EPA docket number [insert docket ID #].
81 the methodology for which can be found in a memo to the docket titled " "

91potential for near term (earli er than 2016) supplier-level Engineering, Design and Testing (ED&T) costs to be in excess of those c onsidered in the FEV analysis as existing equipment and facilities are converted to pr oduction of new technologies. The agencies have therefore decided to average the FEV resu lts with the NPRM values in an effort to account for these near-term factors. This methodology was done for the following technologies:

1. Converting a port-fuel injected (PFI) DOHC I4 to a turbocharged-downsizedstoichiometric GDI DOHC I3. 2. Converting a PFI DOHC V6 engine to a T-DS-stoichiometric GDI DOHC I4. 3. Converting a PFI SOHC V6 engine to a T-DS-stoichiometric GDI DOHC I4. 4. Converting a PFI DOHC V8 engine to a T-DS-stoichiometric GDI DOHC V6. 5. Converting a PFI SOHC 3V V8 engine to a T-DS-stoichiometric GDI DOHC V6. 6. Converting a PFI SOHC 2V V8 engine to a T-DS-stoichiometric GDI DOHC V6. 7. Replacing a 4-speed automatic transmission with a 6-speed automatic transmission. 8. Replacing a 5-speed automatic transmission with a 6-speed automatic transmission. 9. Replacing a 6-speed automatic transmi ssion with a 6-speed wet dual clutch transmission.

For the I4 to Turbo GDI I4 study applie d in the NPRM, the agencies requested from FEV an adjusted cost estimate which accounted for these uncertainties as an adjustment to the base technology burden rate. 82 These new costs are used in the final rules. These details are also furthe r described in the memo to the docket. 83 The confidential information provided by manuf acturers as part of their product plan submissions to the agencies or discussed in meetings between the agencies and the manufacturers and suppliers served largel y as a check on publicly-available data.

For the other technologies, considering all sources of information (including public comments) and using the BOM approach, the agencies worked together intensively to determine component costs fo r each of the technologies and build up the costs accordingly. Where estimates diffe r between sources, we have used our engineering judgment to arrive at what we believe to be the best available cost estimate, and explained the basis for that exercise of judgment in the TSD. Building on NHTSA’s estimates developed for the MY 2011 CAFE final rule and EPA’s Advance Notice of

82 Burden costs include the following fixed and variable costs: rented and leased equipment; manufacturing equipment depreciation; plant office equipment depreciation; utilities expense; insurance (fire and general); municipal taxes; plant floor space (e quipment and plant offices); mainte nance of manufacturing equipment - non-labor; maintenance of manufacturing building - general, internal and external, parts, and labor; operating supplies; perishable and supplier-owned tooling; all other plant wages (excluding direct, indirect and MRO labor); returnable dunnage maintenance; an d intra-company shipping costs (see EPA-HQ-OAR2009-0472-0149).
83 Reference memo to docket.

92Proposed Rulemaking, which relied on the EPA 2008 Staff Technical Report, 84 the agencies took a fresh look at technology cost and effectiveness values for purposes of the joint rulemaking under the National Program. Fo r costs, the agencies reconsidered both the direct or “piece” costs and indirect cost s of individual component s of technologies. For the direct costs, the agencies followed a bill of materials (BOM) approach employed in NHTSA’s MY 2011 final rule based on recommendation from Ricardo, Inc., as described above. EPA used a similar appro ach in the EPA 2008 Staff Technical Report. A bill of materials, in a general sense, is a list of components or sub-systems that make up a system—in this case, an item of fuel economy-improving technology. In order to determine what a system costs, one of the fi rst steps is to determine its components and what they cost.

NHTSA and EPA estimated these component s and their costs based on a number of sources for cost-related information. The objective was to use those sources of information considered to be most credible for projecting the costs of individual vehicle technologies. For example, while NHTSA a nd Ricardo engineers had relied considerably in the MY 2011 final rule on the 2008 Mart ec Report for costing contents of some technologies, upon further joint review and for purposes of the MY 2012-2016 standards, the agencies decided that some of the cos ting information in that report was no longer accurate due to downward trends in commodity pr ices since the publicati on of that report. The agencies reviewed, then revalidated or updated cost estimates for individual components based on new information. Thus, while NHTSA and EPA found that much of the cost information used in NHTSA’s MY 2011 final rule and EPA’s staff report was consistent to a great extent, the agencies , in reconsidering information from many
sources, 85
, 86
, 87
, 88
, 89
, 90
, 91 revised several component costs of several major technologies: turbocharging with engine downsizing (as described above), mild and strong hybrids, diesels, stoichiometric gasoline direct injection fuel systems, and valve train lift technologies. These are discu ssed at length in the Joint TSD and in NHTSA’s final RIA.

Once costs were determined, they were ad justed to ensure that they were all

84 EPA Staff Technical Report: Cost and Effectiven ess Estimates of Technologies Used to Reduce LightDuty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, March 2008. (docket number)
85 National Research Council, “Effectiveness and Imp act of Corporate Averag e Fuel Economy (CAFE) Standards,” National Academy Press, Washington, DC (2002) (the “2002 NAS Report”), available at http://www.nap.edu/openbook.p hp?isbn=0309076013 (last accessed August 7, 2009-update) (docket #?)
86 Northeast States Center for a Clean Air Future (NESCCAF), “Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” 2004 (the “2004 NESCCAF Report”), available at http://www.nesccaf .org/documents/rpt04 0923ghglightduty.pdf (last accesse d August 7, 2009-update) ) (docket #?)
87 “Staff Report: Initial Statement of Reasons fo r Proposed Rulemaking, Public Hearing to Consider Adoption of Regulations to Control Greenhouse Gas Emissions from Motor Vehicles,” California Environmental Protection Agency, Air Resources Board, August 6, 2004.(docket number?)
88 Energy and Environmental Analysis, Inc., “Technology to Improve the Fuel Economy of Light Duty Trucks to 2015,” 2006 (the “2006 EEA Report”), Docket EPA-HQ-OAR-2009-0472.(document #)
89 Martec, “Variable Costs of Fuel Economy Technologies,” June 1, 2008, (the “2008 Martec Report”) available at Docket No. NHTSA-2008-0089-0169.1
90 Vehicle fuel economy certification da ta (way to access? Or in docket?)
91 Confidential data submitted by manufacturers in response to the March 2009 and other requests for product plans.

93expressed in 2007 dollars using a ratio of GD P values for the associated calendar years, 92
and indirect costs were accounted for usi ng the ICM (indirect cost multiplier) approach explained in Chapter 3 of the Joint TSD, rather than using the traditional Retail Price Equivalent (RPE) multiplier approach. A report explaining how EPA developed the ICM approach can be found in the docket for this rule. The comments addressing the ICM approach were generally positive and encourag ing. However, one commenter suggested that we had mischaracterized the complexity of a few of our technologies, which would result in higher or lower markups than pr esented in the NPRM. That commenter also suggested that we had used the ICMs as a means of placing a higher level of manufacturer learning on the cost estimates. The latter comment is not true and the methodology behind the ICM approach is explai ned in detail in the reports that are available in the docket for this rule. 93 The former is open to debate given the subjective nature of the engineering analysis behi nd it, but upon further thought both agencies believe that the complexities used in the NPRM were appropriate and have, therefore, carried those forward into the final rule. We discuss this in greater detail in the Response to Comments document.

Regarding estimates for technology e ffectiveness, NHTSA and EPA also reexamined the estimates from NHTSA’s MY 2011 final rule and EPA’s ANPRM and 2008 Staff Technical Report, which were la rgely consistent with NHTSA’s 2008 NPRM estimates. The agencies also reconsidered other sources such as the 2002 NAS Report, the 2004 NESCCAF report, recent CAFE compliance data (by comparing similar vehicles with different technol ogies against each other in fu el economy testing, such as a Honda Civic Hybrid versus a directly comp arable Honda Civic conventional drive), and confidential manufacturer estimates of t echnology effectiveness. NHTSA and EPA engineers reviewed effectiveness information from the multiple sources for each technology and ensured that such effectiv eness estimates were based on technology hardware consistent with the BOM components used to estimate costs. The agencies also carefully examined the pertinent public comm ents. Together, they compared the multiple estimates and assessed their validity, taking ca re to ensure that common BOM definitions and other vehicle attributes such as performa nce, refinement, and drivability were taken into account. However, because the agen cies’ respective models employ different numbers of vehicle subclasses and use differe nt modeling techniques to arrive at the standards, direct comparison of BOMs was so mewhat more complicated. To address this and to confirm that the outputs from the di fferent modeling techniques produced the same result, NHTSA and EPA developed mapping t echniques, devising technology packages and mapping them to corresponding incremen tal technology estimates. This approach helped compare the outputs from the increm ental modeling technique to those produced by the technology packaging approach to ensure results that are cons istent and could be translated into the respectiv e models of the agencies.

92 NHTSA examined the use of the CPI multiplier instead of GDP for adjusting these dollar values, but found the difference to be exceedingly small – only $0.14 over $100.
93 Rogozhin, Alex, Michael Gallaher, and Walter McManus, “Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers,” EPA 420-R-09-003 , Docket EPA Docket EP A-HQ-OAR-2009-0472-0142, February 2009, http://epa.gov/otaq/ld-h wy/420r09003.pdf; need to insert mention of more recent reports
too.

94In general, most effectiveness estimates used in both the MY 2011 final rule and the 2008 EPA staff report were determined to be accurate and were carried forward without significant chan ge first into the NPRM, and now into these final rules. When NHTSA and EPA’s estimates for effectiveness diverged slightly due to differences in how the agencies apply technologies to vehicl es in their respective models, we report the ranges for the effectiveness values used in each model. There were only a few comments on the technology effectiveness estimates used in the NPRM. Most of the technologies that were mentioned in the comments were the more advanced technologies that are not assumed to have large penetrations in the market within the timeframe of this rule, notably hybrid technologies. Even if the e ffectiveness figures for hybrid vehicles were adjusted, it would have made little difference in the NHTSA and EPA analysis of the impacts and costs of the rule. The respons e to comments document has more specific responses to these comments.

The agencies note that the effectivene ss values estimated for the technologies considered in the modeling analyses may repr esent average values, and do not reflect the enormous spectrum of possible values that could result from adding the technology to different vehicles. For exampl e, while the agencies have estimated an effectiveness of 0.5 percent for low friction lubricants, each vehicle could have a unique effectiveness estimate depending on the baseline vehicle’s o il viscosity rating. Similarly, the reduction in rolling resistance (and thus the improveme nt in fuel economy and the reduction in CO 2 emissions) due to the application of low ro lling resistance tires depends not only on the unique characteristics of the tires origin ally on the vehicle, but on the unique characteristics of the tires being applied, char acteristics which must be balanced between fuel efficiency, safety, and performance. Aerodynamic drag reduction is much the same—it can improve fuel economy and reduce CO 2 emissions, but it is also highly dependent on vehicle-specific functional objecti ves. For purposes of the final standards, NHTSA and EPA believe that employing av erage values for t echnology effectiveness estimates, as adjusted depending on vehicl e subclass, is an appropriate way of recognizing the potential va riation in the specific benefits that individual manufacturers (and individual vehicles) might obtain from adding a fuel-saving technology.

Chapter 3 of the Joint Technical S upport Document contains a detailed description of our assessment of vehicle technology cost and effectiveness estimates. The agencies note that the technology costs included in this final rule take into account only those associated with th e initial build of the vehicl e. Although comments were received to the NPRM that suggested there c ould be additional maintenance required with some new technologies (e.g., turbocharging, hybrids, etc.), and that additional maintenance costs could occur as a result, th e agencies do not believe that the amount of additional cost will be significant in the timeframe of this rulemaking, based on the relatively low application rate s for these technologies. Th e agencies will undertake a more detailed review of these potential costs in preparation for the next round of CAFE/GHG standards.

95F. Joint Economic Assumptions

The agencies’ final analysis of alte rnative CAFE and GHG standards for the model years covered by this final rulemaking rely on a range of forecast information, economic estimates, and input parameters. Th is section briefly describes the agencies’ choices of specific parameter values. These economic values play a significant role in determining the benefits of bot h CAFE and GHG standards.

In reviewing these variables and the ag ency’s estimates of their values for purposes of this final rule, NHTSA and EP A reconsidered previous comments that NHTSA had received, reviewed newly availa ble literature, and reviewed comments received in response to the proposed rule. For this final rule, we made three major changes to the economic assumptions. First, we revised the tec hnology costs to reflect more recently available data. Second, we updated fuel price and transportation demand assumptions to reflect the Annual Energy Outlo ok (AEO) 2010 Early Release. Third, we have updated our estimates of the social cost of carbon (SCC). The key economic assumptions are summarized below, and are discussed in greater detail in Section III (EPA) and Section IV (NHTSA), as well as in Chapter 4 of the Joint TSD, Chapter VIII of NHTSA’s RIA and Chapter 8 of EPA’s RIA.

• Costs of fuel economy-improving technologies – These estimates are presented in summary form above and in more detail in the agencies’ resp ective sections of this preamble, in Chapter 3 of the Join t TSD, and in the agencies’ respective RIAs. The technology cost estimates used in this analysis are intended to represent manufacturers’ direct costs fo r high-volume production of vehicles with these technologies and sufficient experience with their applicati on so that all cost reductions due to “learning curve” effects have been fully realized. Costs are then modified by applying near-term indirect cost multipliers ranging from 1.11 to 1.64 to the estimates of vehicle manufact urers’ direct cost s for producing or acquiring each technology to impr ove fuel economy, depending on the complexity of the technology and the tim e frame over which costs are estimated. This accounts for both the direct and indi rect costs associated with implementing new technologies in response to this final rule. The t echnology cost estimates for a select group of technologies have cha nged since the NPRM. These changes, as summarized in Section II.E and in Chapte r 3 of the Joint TSD, were made in response to updated cost estimates ava ilable to the agencies shortly after publication of the NPRM, not in response to comments. In general, commenters were supportive of the cost estimates used in the NPRM and the transparency of the methodology used to generate them.

• Potential opportunity costs of improved fuel economy – This estimate addresses the possibility that achieving the fuel economy improvements required by alternative CAFE or GHG st andards would require manuf acturers to compromise the performance, carrying capacity, safety, or comfort of their vehicle models. If it did so, the resulting sacrifice in the valu e of these attributes to consumers would represent an additional cost of achieving the required improvements, and thus of

96manufacturers’ compliance with stricter st andards. Currently the agencies assume that these vehicle attributes do not cha nge, and include the co st of maintaining these attributes as part of the cost es timates for technologies. However, it is possible that the technology co st estimates do not include adequate allowance for the necessary efforts by manufacturers to maintain vehicle performance, carrying capacity, and utility while improving fuel economy and reducing GHG emissions. While, in principle, consumer vehicle de mand models can measure these effects, these models do not appear to be robust across specifications, si nce authors derive a wide range of willingness-to-pay values for fuel economy from these models, and there is not clear guidance from the literature on whether one specification is clearly preferred over another. This i ssue is discussed in EPA’s RIA, Section 8.1.2 and NHTSA’s RIA Section XXX. The agencies requested comment on how to estimate explicitly the changes in ve hicle buyers’ welfare from the combination of higher prices for new vehicle models, increases in their fuel economy, and any accompanying changes in vehicle attributes such as performance, passenger- and cargo-carrying capacity, or other dimensi ons of utility. Commenters did not provide recommendations for how to evaluate the quality of different models or identify a model appropriate for the agencies’ purposes. Some commenters expressed various concerns about the us e of existing consumer vehicle choice models. While EPA and NHTSA are not using a consumer vehicle choice model to analyze the effects of this rule, we continue to investig ate these models.

• The on-road fuel economy “gap” – Actual fuel economy levels achieved by lightduty vehicles in on-road dr iving fall somewhat short of their levels measured under the laboratory-like test conditions used by NHTSA and EPA to establish compliance with the final CAFE and GHG standards. The agencies use an onroad fuel economy gap for light-duty vehi cles of 20 percent lower than published fuel economy levels. For example, if the measured CAFE fuel economy value of a light truck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of that vehicle is expected to be 16 mpg (20*.80). 94 NHTSA previously used this estimate in its MY 2011 final rule, and the agencies confirmed it based on independent analysis for use in this FRM. No substantive comments were received on this input.

• Fuel prices and the value of saving fuel – Projected future fuel prices are a critical input into the preliminary economic analys is of alternative standards, because they determine the value of fuel savi ngs both to new vehicle buyers and to society. For the proposed rule, the agenci es had relied on the then most recent fuel price projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) 2009 (Revised Up dated). However, for this final rule, the agencies have updated the analyses based on AEO 2010 (December 2009 Early Release) Reference Case forecasts of inflation-adjusted (constant-dollar) retail gasoline and diesel fuel prices, which represent the EIA’s most up-to-date

94 U.S. Environmental Protection Agency, Final Technical Support Document, Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates, EPA420-R-06-017, December 2006.

97estimate of the most likely course of future prices for petroleum products. 95 AEO 2010 includes slightly lower petroleu m prices compared to AEO 2009.

The forecasts of fuel prices reported in EIA’s AEO 2010 Early Release Reference Case extends through 2035, compared to the AEO 2009 which only went through 2030. As in the proposal, fuel prices beyond the time frame of AEO’s forecast were estimated using an average growth rate.

While EIA revised AEO 2010, the vehicle MPG standards are similar to those that were published in AEO 2009. No s ubstantive comments were received on the use of AEO as a source of fuel prices. 96

• Consumer valuation of fuel economy and payback period – In estimating the value of fuel economy improvements that would result from alternative CAFE and GHG standards to potential vehicle buyers, the agencies assume that buyers value the resulting fuel savings over only part of the expected lifetime of the vehicles they purchase. Specifically, we assume that buyers value fuel savings over the first five years of a new vehicle’ s lifetime, and that buyers discount the value of these future fuel savings using rates of 3% and 7%. The five-year figure represents the current average term of c onsumer loans to finance the purchase of new vehicles. One commenter argued that higher-fuel-economy vehicles should have higher resale prices than vehicles with lower fuel economy, but did not provide supporting data. This revision, if made, would increase the net benefits of the rule. Another commenter supported th e use of a five-year payback period for this analysis. In the ab sence of data to support changes, EPA and NHTSA have kept the same assumptions.

• Vehicle sales assumptions – The first step in estimating lifetime fuel consumption by vehicles produced during a model year is to calculate the nu mber of vehicles expected to be produced and sold. 97 The agencies relied on the AEO 2010 Early Release for forecasts of total vehicle sa les, while the baseline market forecast developed by the agencies (see Section II.B) divided total projected sales into sales of cars and light trucks.

95 Energy Information Administration, Annual Energy Outlook 2010, Early Release Reference Case (December 2009), Table 12. Availa ble at http://www.eia.doe.gov/oiaf/ aeo/aeoref_tab.html (last accessed February 02, 2010).
96 Cite memo to docket concerning this issue.
97 Vehicles are defined to be of age 1 during the cal endar year corresponding to the model year in which they are produced; thus for example, model year 2000 vehicles are c onsidered to be of age 1 during calendar year 2000, age 2 during calen dar year 2001, and to reach their maximum age of 26 years during calendar year 2025. NHTSA considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles orig inally produced during a model year remain in service. Applying these conventions to vehicle registration data indicates that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 years

. See Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at h ttp://www-nrd.nhtsa.dot.g ov/Pubs/809952.pdf (last accessed Feb. 15, 2010)

.

98• Vehicle survival assumptions -- We then applied updated values of age-specific survival rates for cars and light trucks to these adjusted for ecasts of passenger car and light truck sales to determine the numb er of these vehicles remaining in use during each year of their expected lif etimes. No substantive comments were received on vehicle su rvival assumptions.

• Total vehicle use – We then calculated the total number of miles that cars and light trucks produced in each model year wi ll be driven during each year of their lifetimes using estimates of annual vehicle use by age tabulated from the Federal Highway Administration’s 2001 National Household Transportation Survey (NHTS), 98 adjusted to account for the effect on vehicle use of subsequent increases in fuel prices. Due to the lo wer fuel prices projected in AEO 2010, the average vehicle is estimated to be used s lightly more (~3 perc ent) over its lifetime than assumed in the proposal. In orde r to insure that the resulting mileage schedules imply reasonable estim ates of future growth in total car and light truck use, we calculated the rate of growth in annual car and light truck mileage at each age that is necessary for total car and li ght truck travel to increase at the rates forecast in the AEO 2010 Early Release Re ference Case. The growth rate in average annual car and light truck use produced by this calculation is approximately 1.1 percent per year. 99 This rate was applie d to the mileage figures derived from the 2001 NHTS to estimate annual mileage during each year of the expected lifetimes of MY 2012-2016 cars and light trucks. 100 While commenters requested further detail on the assumptions regarding total vehicle use, no specific issues were raised.

• Accounting for the rebound effe ct of higher fuel economy – The rebound effect refers to the fraction of fuel savings expected to result from an increase in vehicle fuel economy – particularly an increase re quired by the adoption of more stringent CAFE and GHG standards – that is offset by additional vehicle use. The increase in vehicle use occurs because higher fu el economy reduces the fuel cost of driving, typically the largest single component of the monetary cost of operating a vehicle, and vehicle owners respond to this reduction in operating costs by driving slightly more. We received comments supporting our proposed value of 10 percent, although we also received comments recommending higher and lower values. However, we did not receive a ny new data or comments that justify revising the 10 percent value for th e rebound effect at this time.

• Benefits from increased vehicle use – The increase in vehicle use from the rebound effect provides additional benefits to their owners, who may make more frequent trips or travel farther to r each more desirable destinations. This

98 For a description of the Survey, see http://nhts.ornl.gov/qui ckStart.shtml (last accessed July 27, 2009).
99 It was not possible to estimate separate growth ra tes in average annual use for cars and light trucks, because of the significant reclassi fication of light truck models as passenger cars discussed previously.
100 While the adjustment for future fuel prices reduces average mileage at each age from the values derived from the 2001 NHTS, the adjustment for expected future growth in average vehicle use increases it. The net effect of these two adjustments is to increase expected lifetime mileage by about 18 percent for passenger cars and about 16 percent for light trucks.

99additional travel provides benefits to drivers and their passengers by improving their access to social and economic oppor tunities away from home. These benefits are measured by the net “consum er surplus” resulting from increased vehicle use, over and above the fuel expe nses associated with this additional travel. We estimate the economic value of the consumer surplus provided by added driving using the c onventional approximation, whic h 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 and varies among alternative standards.

• The value of increased driving range – By reducing the frequency with which drivers typically refuel their vehicles, and by extending the upper limit of the range they can travel before requir ing refueling, improving fuel economy and reducing GHG emissions thus provides some additional benefits to their owners. No direct estimates of the value of extende d vehicle range are readily available, so the agencies’ analysis calculates the re duction in the annual number of required refueling cycles that results from improved fuel economy, and applies DOTrecommended values of travel time savi ngs to convert the resulting time savings to their economic value. 101 Please see the Chapter 4 of the Joint TSD for details.

• Added costs from congestion, crashes and noise – Although it provides some benefits to drivers, increased vehicle us e associated with the rebound effect also contributes to increased tr affic congestion, motor vehicle accidents, and highway noise. Depending on how the additional trav el is distributed over the day and on where it takes place, additional vehicle us e can contribute to traffic congestion and delays by increasing tr affic volumes on facilities that are already heavily traveled during peak periods. These a dded delays impose higher costs on drivers and other vehicle occupants in the form of increased travel time and operating expenses, increased costs associated with traffic accidents, and increased traffic noise. The agencies rely on estimates of congestion, accident, and noise costs caused by automobiles and light trucks developed by the Federal Highway Administration to estimate the increase d external costs caused by added driving due to the rebound effect. 102

• Petroleum consumption and import externalities – U.S. consumption and imports of petroleum products also impose costs on the domestic economy that are not reflected in the market price for crude petroleum, or in the prices paid by consumers of petroleum products such as gasoline. In economics literature on this subject, these costs include (1) highe r prices for petroleum products resulting

101 Department of Transportation, Guidance Memorandum, “The Value of Saving Travel Time: Departmental Guidance for Conducting Economic Evaluations,” Apr. 9, 1997. http://ostpxweb.dot.gov/policy/Da ta/VOT97guid.pd f (last accessed Feb. 15, 2010); update available at http://ostpxweb.dot.gov/policy/Da ta/VOTrevision1_2-11- 03.pdf (last accessed Feb. 15, 2010).
102 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study ; http://www.fhwa.dot.gov/policy/ hcas/final/index.htm (last accessed Feb. 15, 2010).

100from the effect of U.S. oil import demand on the world oil price (“monopsony costs”); (2) the expected costs from the risk of disruptions to the U.S. economy caused by sudden reductions in the supply of imported oil to the U.S.; and (3) expenses for maintaining a U.S. military presence to secure imported oil supplies from unstable regions, and fo r maintaining the strategic petroleum reserve (SPR) to cushion against resulting price increases. 103 Reducing U.S. imports of crude petroleum or refined fuels can reduce the ma gnitude of these external costs. Any reduction in their total value that resu lts from lower fuel consumption and petroleum imports represents an econom ic benefit of setting more stringent standards over and above the dollar valu e of fuel savings itself. Since the agencies are taking a global perspective with respect to the estimate of the social cost of carbon for this rulemaking, the ag encies do not include the value of any reduction in monopsony payments as a be nefit from lower fuel consumption, because those payments from a global perspe ctive represent a tr ansfer of income from consumers of petroleum products to oil suppliers rather than a savings in real economic resources. Similarly, th e agencies do not include any savings in budgetary outlays to support U.S. militar y activities among the benefits of higher fuel economy and the resulting fuel savi ngs. Based on a recently-updated ORNL study, we estimate that each gallon of fuel saved that results in a reduction in U.S. petroleum imports (either crude petroleum or refined fuel) will reduce the expected costs of oil supply disruptio ns to the U.S. economy by $0.169 (2007$). Each gallon of fuel saved as a consequen ce of higher standards is anticipated to reduce total U.S. imports of crude pe troleum or refined fuel by 0.95 gallons. 104

EPA and NHTSA received comments about the treatment of the monopsony effect, macroeconomic disruption effect, a nd the military costs associated with the energy security benefits of this rule. The agencies did not receive any comments that justify changing the en ergy security analysis. As a result, the agencies continue to only use the macroeconomic disruption component of the energy security analysis under a global context wh en estimating the total energy security benefits associated with this rule. Fu rther, the Agencies did not receive any information that they could use to quan tity that component of military costs directly related to energy security, and thus did not modify that part of its analysis. A more complete discussion of the energy security analysis can be found in Chapter 4 of the Joint TSD, and Sections III and IV of this preamble.

103 See, e.g., Bohi, Douglas R. and W. David Montgomery (1982). Oil Prices, Energy Security, and Import Policy Washington, DC: Resources for the Future, Johns Hopkins University Press; Bohi, D. R., and M. A. Toman (1993). "Energy and Security: Externalities and Policies," Energy Policy 21:1093-1109; and Toman, M. A. (1993). "The Economics of Energy Se curity: Theory, Evidence, Policy," in A. V. Kneese and J. L. Sweeney, eds. (1993). Handbook of Natural Resource and Energy Economics, Vol. III. Amsterdam: North-Holland, pp. 1167-1218.
104 Each gallon of fuel saved is assumed to reduce imports of refined fuel by 0.5 gallons, and the volume of fuel refined domestically by 0.5 gallons. Domestic fu el refining is assumed to utilize 90 percent imported crude petroleum and 10 percent dome stically-produced crude petroleum as feedstocks. Together, these assumptions imply that each gallon of fuel saved will reduce imports of refined fuel and crude petroleum by 0.50 gallons + 0.50 gallons*90 percent = 0.50 gallons + 0.45 gallons = 0.95 gallons.

101• Air pollutant emissions

o Impacts on criteria air pollutant emissions – While reductions in domestic fuel refining and distribution that re sult from lower fuel consumption will reduce U.S. emissions of criteria pollutants, additional vehicle use associated with the rebound effect will increase emissions of these pollutants. Thus the net effect of stricter standards on emissions of each criteria pollutant depends on the relative magnitudes of reduced emissions from fuel refining and distribution, a nd increases in emissions resulting from added vehicle use. Criteria air pollutants emitted by vehicles and during fuel production include carbon monoxide (CO), hydrocarbon compounds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides (NO X ), fine particulate matter (PM 2.5 ), and sulfur oxides (SO X ). It is assumed that the emission rates (per mile) stay constant for future year vehicles.

o Economic value of reductions in criteria air pollutants – For the purpose of the joint technical analysis, EPA and NHTSA estimate the economic value of the human health benefits associated with reducing exposure to PM 2.5 using a “benefit -per-ton” method. These PM 2.5 -related benefit-perton estimates provide the total moneti zed benefits to human health (the sum of reductions in premature mort ality and premature morbidity) that result from eliminating one ton of directly emitted PM 2.5 , or one ton of a pollutant that contributes to secondarily-formed PM 2.5 (such as NO X , SO X , and VOCs), from a specified source. Chapter 4.2.9 of the Technical Support Document that accompanies th is rule includes a description of these values. Separately, EPA also conducted air quality modeling to estimate the change in ambient concen trations of criteria pollutants and used this as a basis for estimating the human health benefits and their economic value. Section III.H.7 presents these benefits estimates.

o Reductions in GHG emissions – Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the process of producing and distributing transportation fuels, as we ll as from fuel combustion itself. By reducing the volume of fuel consumed by passenger cars and light trucks, higher standards will thus reduce GHG emissions generated by fuel use, as well as throughout the fuel s upply cycle. The agencies estimated the increases of GHGs other than CO 2 , including methane and nitrous oxide, from additional vehicle use by multiplying the increase in total miles driven by cars and light truc ks of each model year and age by emission rates per vehicle-mile for these GHGs. These emission rates, which differ between cars and light truc ks as well as between gasoline and diesel vehicles, were estimated by EPA using its recently-developed Motor Vehicle Emission Simulator (Draft MOVES 2010). 105 Increases in

105 The MOVES model assumes that the per-mile rates at which cars and light trucks emit these GHGs are determined by the efficiency of fu el combustion during engine operatio n and chemical reactions that occur

102emissions of non-CO 2 GHGs are converted to equivalent increases in CO 2 emissions using estimates of the Global Warming Potential (GWP) of methane and nitrous oxide.

o Economic value of reductions in CO 2 emissions - EPA and NHTSA assigned a dollar 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 “socia l 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 dry land loss, and mortality changes associated with vector-borne diseas es. Published estimates of the SCC vary widely as a result of uncertain ties about future economic growth, climate sensitivity to GHG emissions, procedures used to model the economic impacts of climate change, a nd the choice of discount rates.

EPA and NHTSA received extensive comments about how to improve the characterization of the SCC and ha ve since developed new estimates through an interagency modeling ex ercise. The comments addressed various issues, such as discount rate selection, tr eatment of uncertainty, and emissions and socioeconomic trajec tories, and justified the revision of SCC for the final rule. The modeling exercise involved running three integrated assessment models using inputs agreed upon by the interagency group for climate sensitivity, socioec onomic and emissions trajectories, and discount rates. A more complete discussion of SCC can be found in [Section xxx]; revised SCC estimates corresponding to assumed values of the discount rate are sh own in Table II.F-1.

Table II.F-1 Social Cost of CO 2 , 2010 (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%
Source of Estimate Mean of Estimates Values 95 th percentile estimate 2010 Estimate $5 $21 $35 $65

• Discounting future benefits and costs —Discounting future fuel savings and other benefits is intended to account for the reduction in their value to society when they are deferred until some future date, rather than received immediately. The discount rate expresses the percent dec line in the value of these benefits—as viewed from today’s perspective—for each ye ar they are deferred into the future. In evaluating the non-climate related benefits of the final standa rds, the agencies have employed discount rates of both 3 per cent and 7 percent. We received some

during catalytic after-treatment of engine exhaust, and are thus independent of vehicles’ fuel consumption rates. Thus MOVES’ emission factors for these GHGs , which are expressed per mile of vehicle travel, are assumed to be unaffected by changes in fuel economy.

103comments on the discount rates used in the proposal, most of which were directed at the discount rates used to value future fuel savings and the rates used to value of the social cost of carbon. In genera l, commenters were supporting one of the discount rates over the other, although so me suggested that our rates were too high or too low. We have revised the discounting used when calculating the net present value of social cost of carbon as explained [in Sectio n XXX] but have not revised our discounting procedures for other costs or benefits.

For the reader’s reference, Table II.F-2 below summarizes the values used to calculate the impacts of each final standard. The values presented in this table are summaries of the inputs used for the models ; specific values us ed in the agencies’ respective analyses may be aggregated, expande d, or have other relevant adjustments. See the respective RIAs for details.

In addition, the agencies have conducted a range of sensitivities and present them in their respective RIAs. For example, NHTSA has conducted a sens itivity analysis on several assumptions including (1) forecasts of future fuel prices, (2) the discount rate applied to future benefits and costs, (3) th e magnitude of the rebound effect, (4) the value to the U.S. economy of reducing carbon di oxide emissions, (5) inclusion of the monopsony effect, and (6) the reduction in exte rnal economic costs resulting from lower U.S. oil imports. This informati on is provided in NHTSA’s RIA.

104105 Table II.F-2 Economic Values fo r Benefits Computations (2007$) Fuel Economy Rebound Effect 10% "Gap" between test and on-road MPG 20% Value of refueling time per ($ per vehicle-hour) $ 24.64 Annual growth in average vehicle use 1.15% Fuel Prices (2012-50 average, $/gallon) Retail gasoline price $3.66 Pre-tax gasoline price $3.29 Economic Benefits from Re ducing Oil Imports ($/gallon) "Monopsony" Component $ 0.00 Price Shock Component $ 0.17 Military Security Component $ 0.00 Total Economic Costs ($/gallon) $ 0.17 Emission Damage Costs (2020, $/ton or $/metric ton) Carbon monoxide $ 0 Volatile organic compounds (VOC) $ 1,300 Nitrogen oxides (NO X ) – vehicle use $ 5,100 Nitrogen oxides (NO X ) – fuel production and distribution $ 5,300 Particulate matter (PM 2.5 ) – vehicle use $ 240,000 Particulate matter (PM 2.5 ) – fuel production and distribution $ 290,000, Sulfur dioxide (SO 2 ) $ 31,000

Carbon dioxide (CO 2 ) emissions in 2010 $ 5 $ 21 $ 35 $ 65

Annual Increase in CO 2 Damage Cost variable, depending on estimate External Costs from Additional Automobile Use ($/vehicle-mile)
Congestion $ 0.054 Accidents $ 0.023 Noise $ 0.001 Total External Costs $ 0.078 External Costs from Additional Light Truck Use ($/vehicle-mile) Congestion $0.048 Accidents $0.026 Noise $0.001 Total External Costs $0.075 Discount Rates Applied to Future Benefits 3%, 7%The Baseline and Reference Vehicle Fleet



## CHAPTER 1: The Baseline and Reference Vehicle Fleet

The passenger cars and light trucks sold currently in the United States, and those which are anticipated to be sold in th e MY 2012-2016 timeframe, are highly varied and satisfy a wide range of consumer needs. Fr om two-seater miniature cars to 11-seater passenger vans to large extended cab pickup trucks, American consumers have a great number of vehicle options to accommodate thei r utility needs and preferences. Recent volatility in oil prices and th e state of the economy have demonstrated that consumer demand and choice of vehicles within this wide range can be sensitiv e to these factors. Although it is impossible for anyone or any orga nization to precisely predict the future, a characterization and quantification of the future fleet are requir ed to assess the impacts of rules which would affect that future fleet. In order to do this , the various leading publicly-available sources are examined, and a series of mode ls are relied upon that help us to project the composition of a reference fl eet. This chapter de scribes the process for accomplishing this.

Most of the public comments to the NPRM supported this methodology for developing the inputs to the rule's analysis. Because the input sheets can be made public, stakeholders can verify and check EPA’s and NHTSA’s modeling, and perform their own analyses with these datasets. Many commenters stated that creating a transparent fleet from public sources was a significant impr ovement over previous rulemakings, although other commenters raised accuracy issues with regard to the continuation in the agencies’ analysis of MY 2008 vehicles into the futu re model years covered by the rulemaking. There were no comments on me thodology, but GM did comment that they believe the agencies had projected more full size trucks and full size vans than they believe would be produced. EPA had already noticed, after the NPRM had been published, that the standard CSM forecast included heavy duty clas s 2b and class 3 vehicles. EPA requested that CSM make a custom forecast with thes e vehicles removed for the final rulemaking.



### 1.1 Why do the agencies establish a baseline and reference vehicle fleet?

In order to calculate the impacts of the EPA and NHTSA final rule, it is necessary to estimate the composition of the future vehicl e fleet absent the final CAFE/GHG standards in order to conduct comparisons. EPA in consultation with NHTSA has developed a comparison fleet in two parts. The first st ep was to develop a ba seline fleet based on model year 2008 data. EPA and NHTSA create a baseline fleet in order to track the volumes and types of fuel economy-improving and CO 2 -reducing technologies which are already present in today’s fleet . Creating a baseline fleet help s to keep, to some extent, the agencies’ models from adding technologies to vehicles that already have these technologies, which would result in “double counting” of technologies’ costs and benefits. The second step was to project the baseline fleet sales into MYs 2011-2016. This is called the reference fleet, and it repr esents the fleet that would exist in MYs 2011- 2016 absent any change from cu rrent regulations. The third step was to add technologies

1-1Joint Technical Support Document

to that fleet such that each manu facturer’s average car and truck CO 2 levels are in compliance with their MY 2011 CAFE standards. This final “reference fleet” is the light duty fleet estimated to exist in MYs 2012-2016 without the final CAFE/GHG standards. All of the agencies’ estimates of emission reductions/fuel economy improvements, costs, and societal impacts are developed in relati on to the respective re ference fleets. The chapter describes the first two steps of the development of the baseline and reference fleets. The third step of technology addition is developed separately by each agency as the outputs of the OMEGA and Volpe models. The process is describe d in section II of the preamble and in each agency’s respective RIAs.



### 1.2 The 2008 baseline vehicle fleet

1.2.1 Why did the agencies choose 20 08 as the baseline model year?

The baseline vehicle fleet developed by EPA in consultation with NHTSA and is comprised of model year 2008 data. MY 2008 was used as the basis for the baseline vehicle fleet, because it is the most recent model year for which a complete set of data is publicly available. Vehicle manufacturers have 90 days after their last vehicle is produced to submit their CAFE data to EPA. 1 Most manufacturers interpret this to mean 90 days after the end of the calendar year. For example, in calendar year 2007, model year 2008 vehicles were tested and certifie d by the EPA. These MY 2008 vehicles were then sold in the latter part (often fall) of 2007 until the following fall of 2008. In early 2009 (calendar year), the ma nufacturers then submit th eir total sales of MY 2008 vehicles. After these sales figures were submitted, EPA and NHTSA combined the sales with the previously measured and reported fu el economies to calculate the sales-weighted average fleet fuel economy. Even though the fuel economies (and some other specifications) of the MY 2009 vehicles were kn own, since they were tested earlier, the sales were not yet known for each company ex actly. Full MY 2009 sales data is not available until April 2010, due to the fact that manufacturers have 90 days after the end of the model year to submit their data. a Therefore, the agencies chose to use MY 2008 as the baseline since it was the latest comple te transparent data set available.



### 1.2.2 On what data is the base line vehicle fleet based?

As part of the CAFE progra m, EPA measures vehicle CO 2 emissions and converts them to mpg and generates and maintains the federal fuel economy database. Most of the information about the 2008 vehicle fleet was gathered from EPA’s emission certification and fuel economy database , most of which is available to the public

. The data obtained from this source included vehicle production volume, fuel economy, carbon dioxide emissions, fuel type, number of engine cy linders, displacement, valves per cylinder, engine cycle, transmission type, drive, hybr id type, and aspiration. However, EPA’s certification database does not include a detailed descrip tion of the types of fuel economy-improving/CO 2 -reducing technologies considered in this final rule, because this level of information is not necessary for emission certification or fuel economy testing.

a 40 CFR § 600.512-08, Model Year Report

1-2The Baseline and Reference Vehicle Fleet

Thus, the agency augmented this description with publicly-available data which includes more complete technology descriptions from Ward’s Automotive Group. 2

, b In a few instances when required vehicle information was not available from these two sources (such as vehicle footprint), this inform ation was obtained from publicly-accessible internet sites such as Motortrend.com, Edm unds.com and other source s to a lesser extent (such as articles about specific vehicles reveal ed from internet search engine research. 3

, c

The baseline vehicle fleet for the analysis in this rule is comprised of publiclyavailable data to the largest extent possible. However, a few relatively low-impact technologies were added based on confid ential information provided from some manufacturers (within their pr oduct plan submissions to NHTSA and EPA). This was done because the data were not available fr om any other source. These technologies include low friction lubricants, electric power steering, improved accessories, and low rolling resistance tires. This confidential in formation has been excised from the baseline data submitted to the docket, though the su mmary results are still used, so that any specific information cannot be traced back to any specific manufacturer. This discrepancy between the public baseline and th e one used by the agencies is relatively minor and results in only result small differences in the outputs of the Volpe and OMEGA models for certain manufacturers.

Creating the 2008 baseline fleet Excel file was an extremely labor intensive process. EPA in consultation with NHT SA first considered using EPA’s CAFE certification data, which contains most of the required information. However, since the deadline for manufacturers to report this data did not allow enough time for early modeling review, it was necessary to start this process using an alternative data source.

The agencies next considered using EP A’s vehicle emissions certification data, which contains much of the required in formation, however it lacked the production volumes that are necessary for the OMEGA and Volpe models. The data set also contains some vehicle models manufacturers have certified, but not produced. A second data source which would supply production vo lumes and eliminate extraneous vehicles was needed. Data from a paid subscription to Ward’s Automotive Group was used as the second source for data, which contains produc tion volumes and vehicle specifications.

The vehicle emissions certification dataset came in two parts, an engine file and a vehicle file. Since there was a common index in the two files, th e engine and vehicle data were easily combined into one spread sheet. The agencies had hoped to supplement this dataset with production volume data fr om Ward’s Automotive Group but the Ward’s data does not have production volumes for i ndividual vehicles down to the resolution of the specific engine and transmission level. Although production volumes from Ward’s Automotive Group could not be used, the subs cription did provide specific details on individual vehicles and engines. The Ward’s data used came in two parts (engine file and vehicle file), and also required mapping. In this case, mapping was more difficult since there was no common index between the two f iles. A new index was implanted in the

b Note that WardsAuto.com is a fee-based service, but all information is public to subscribers.

c Motortrend.com and Edmunds.com are free, no-fee internet sites.

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engine file and a search equa tion in the vehicle file, which identified most of the vehicle and engine combinations. Each vehicle and engine combination was reviewed and corrections were made manually when the search routine failed to give the correct engine and vehicle combination. The combined Ward ’s data was then mapped to the vehicle emissions certification data by creating a ne w index in the combined Ward’s data and using the same process that was used to combine the Ward’s engine and vehicle files. In the next step, CAFE certificati on data had to be merged in order to fill out the needed production volumes.

NHTSA and EPA reviewed the CAFE certi fication data for model year 2008 as it became available. The CAFE certification data set could have been used with the Ward’s data without the vehicle emission certificati on dataset, but was instead appended to the combined Ward’s and vehicle cer tification dataset. The two former datasets were then mapped into the CAFE dataset using the same Excel mapping technique described above. Finally, EPA and NHTSA obtained the remaini ng attribute and techno logy data, such as footprint, curb weight, and othe rs (for a complete list of da ta with sources see Table 1-1 below) from other sources (such as the intern et and the confidential product plan data), thus completing the baseline dataset.

This was the first time a baseline fleet was created using this method. Given the long delay before the CAFE certification data became available, EPA explored creating the alternative dataset. It is possible to cr eate the same baseline with CAFE certification data, the Ward’s engine data, a limited amount of product plan data, and some internet searches.

Table 1-1 below shows the columns of the complete fleet file, which includes the 2008 MY baseline data that was compiled. Each column has its name, definition (description) and source. The EV and Aero co lumns were added to the fleet file to more accurately describe vehicles for the final rule. The data that is marked “not available” is data NHTSA would normally get from product pl ans. As mentioned above, some of the desired model inputs, such as the presence of low rolling resistance tires, reduced engine friction, improved accessories, etc., are not available from public sources and the agencies had to rely on manufacturers’ confidential product plans. The Technology Effectiveness Basis and the Cost Effectiven ess Basis values reflect the percent of a technology package’s effectiveness and cost pr esent in the baseline fleet, and they are described in further detail in chapters 3.1 and 3.5, respectively. Those technologies that are not accounted for in the baseline—that is , the ones marked “not available”—run the risk of getting double counted by the agencies’ models, but those effects are expected to be small.

1-4The Baseline and Reference Vehicle Fleet

Table 1-1 Data, Definitions, and Sources

Data Item Definition Where The Data is From

Manufacturer Common name of company that manufactured vehicle. May include more name plates than Cert Manufacturer Name. Certification data

CERT Manufacturer Name Certification name of company that manufactured vehicle Certification data Name Plate Name of Division Certification data

Model Name of Vehicle Certification data Reg Class EPA Fuel Economy Class Name Certification data

Class If a car’s Footprint<43 then "SubCmpctAuto" If a car’s 43<=Footprint<46 then "CompactAuto" If a car’s 46<=Footprint<53 then "MidSizeAuto” If a car’s Footprint >=53 then “LargeAuto” If a S.U.V.’s Footprint < 43 then “SmallSuv” If a S.U.V.’s 43<=Footprint<46 then "MidSizeSuv” If a S.U.V’s Footprint >=46 then “LargeSuv” If a Truck’s Footprint < 50 then “SmallPickup” If a Truck’s Footprint>=50 then “LargPickup” If a Van’s Structure is Ladder then “CargoVan” If a Van’s Structure is Unibody then “Minivan” Derived From Certification data and Footprint

CSM Class CSM Worldwides class for the vehicle. Used to weight vehicles based on CSM data. CSM Worldwide

Traditional Car/Truck C= Car, T=Truck. As defined in the certification database. Not used in calculations. Certification data Total Production Volume Total number of vehicles produced fo r that model. Certification data Fuel Econ.

(mpg) EPA Unadjusted Fuel Economy Certification data

Curb Weight (lb) Vehicles Curb Weight Some from Edmonds.com or Motortrend.com, Others from

product plans with a subset verified with Edmonds.com or Motortrend.com for accuracy.
Area (sf) Average Track x Wheelbase Calculated from track width and wheel base Fuel Gas or Diesel Wards Disp (lit.) Engine Cylinder Displacement Size in Liters Wards/Certification data
Effective Cyl Number of Cylinder + 2 if the engine has a turbo or super charger. Derived From Certification data. Actual Cylinders Actual Number of Engine Cylinders Certification data Valves Per Cylinder Number of Valves Per Actual Cylinder Certification data
Valve Type Type of valve actuation. Wards (Note:Type E is from Cert Data) Engine Cycle As Defined by EPA Ce rt. Definition Wards Horsepower Max. Horsepower of the Engine Wards

Torque Max. Torque of the Engine Wards
Trans Type A=Auto AMT=Automate d Manual M=Manual CVT= Continuously Variable Transmission Certification data Trans Type Code with number of Gears Certification data Num of Gears Number of Gears Certification data

Structure Ladder or Unibody General Internet Searches using Google.com Drive Fwd, Rwd, Awd, 4wd Certification data

Wheelbase Length of Wheelbase Some from Edmonds.com or Motortrend.com, Others from

product plans with a subset

1-5Joint Technical Support Document

verified with Edmonds.com or Motortrend.com for accuracy.

Track Width (front) Length of Track Width in inches Some from Edmonds.com or Motortrend.com, Others from

product plans with a subset verified with Edmonds.com or Motortrend.com for accuracy.

Track Width (rear) Length of Track Width in inches Some from Edmonds.com or Motortrend.com, Others from

product plans with a subset verified with Edmonds.com or Motortrend.com for accuracy.
Footprint Average Track x Wheelbase Calculated from track width and wheel base

Curb Weight Curb Weight of the Vehicle Some from Edmonds.com or Motortrend.com, Others from

product plans with a subset verified with Edmonds.com or Motortrend.com for accuracy.

GVWR

Gross Vehicle Weight Rating of the Vehicle Some from Edmonds.com or Motortrend.com, Others from

product plans with a subset verified with Edmonds.com or Motortrend.com for accuracy. HYBRID Is the Vehicle a Hybrid? Certification data HYBRID TYPE Type of Hybrid Certification data Turbo Turbo Charged Engine Certification data Super Charged Super Charged Certification data Turbo and/or Super Charged Either or both Turbo charged / Super Charged Certification data SOHC Single Overhead Cam Engine Wards DOHC Dual Overhead Cam Engine Wards OHV Overhead Valve Engine Wards

1 LUB Low friction lubricants—incremental to base engine Obtained from product plans from manufacturers that report this information. 2

2 EFR Engine friction reduction—i ncremental to base engine Obtained from product plans from manufacturers that report this information. 2
3 CCPS VVT - Coupled Cam Phasing (CCP) on SOHC Wards 1

4 DVVLS Discrete Variable Valve Lift (DVVL) on SOHC Wards 1
5 DEACS Cylinder Deactivation on SOHC Wards 1
6 ICP VVT - Intake Cam Phasing (ICP) Wards 1

7 DCP VVT - Dual Cam Phasing (DCP) (independent) Wards 1

8 DVVLD Discrete Variable Valve Lift (DVVL) on DOHC Wards 1
9 CVVL Continuously Variable Valve Lift (CVVL) Wards 1

10 DEACD Cylinder Deactiva tion on DOHC Wards 1
11 DEACO Cylinder Deactiva tion on OHV Wards 1
12 CCPO VVT - Coupled Cam Phasing (CCP) on OHV Wards 1

13 DVVLO Discrete Variable Valv e Lift (DVVL) on OHV Wards 1

1-6The Baseline and Reference Vehicle Fleet

14 CDOHC Conversion to DOHC with DCP Not available 15 SGDI Stoichiometric Gasoline Direct Injection (GDI) Wards 1

16 CBRST Combustion Restart Not available 18 EGRB Exhaust Gas Recirculation (EGR) Boost Not available

19 DSLT Conversion to Diesel (from TRBDS) Not available

20 DSLC Conversion to Diesel (fro m CBRST) Not available

21 6MAN 6-Speed Manual/Improved Inte rnals Certification data

22 IATC Improved Auto. Trans. Controls/Externals Not available

23 CVT Continuously Variable Transmission Certification data

24 NAUTO 6/7/8-Speed Auto. Trans with Impr oved Internals Certification data 25 DCTAM Dual Clutch or Automated Manual Transmission Certification data

26 EPS Electric Power Steering Obtained from product plans from manufacturers that report this information. 2

27 IACC Improved Accessories Obtained from product plans from manufacturers that report this information. 2
28 MHEV 12V Micro-Hybrid Not available

29 HVIA Higher Voltage/Improved Alternator Not available

30 BISG Belt Mounted Integrated Starter Generator Certification data

31 CISG Crank Mounted Integrated Starter Generator Certification data

32 PSHEV Power Split Hybrid Certification data 33 2MHEV 2-Mode Hybrid Certification data 34 PHEV Plug-in Hybrid Not available

35 MS1 Material Substitution (1%) Not available

36 MS2 Material Substitution (2%) Not available

37 MS5 Material Substitution (5%) Not available

38 ROLL Low Rolling Resistance Tires Obtained from product plans from manufacturers that report this information. 2
39 LDB Low Drag Brakes Not available

EV Electric Vehicle Not available

Aero Aerodynamics Optimised Not available

1-7Joint Technical Support Document

Import Car Cars Imported Certification data Index Index Used to link EPA and NHTSA baselines Created

Volume 2011 Projected Production Volume for 2011 Calculated based on 2008 volume and AEO and CSM adjustment factors.

Volume 2012 Projected Production Volume for 2012 Calculated based on 2008 volume and AEO and CSM adjustment factors.

Volume 2013 Projected Production Volume for 2013 Calculated based on 2008 volume and AEO and CSM adjustment factors.

Volume 2014 Projected Production Volume for 2014 Calculated based on 2008 volume and AEO and CSM adjustment factors.

Volume 2015 Projected Production Volume for 2015 Calculated based on 2008 volume and AEO and CSM adjustment factors.

Volume 2016 Projected Production Volume for 2016 Calculated based on 2008 volume and AEO and CSM adjustment factors.

Vehicle Type Number Vehicle Type Number assigned to a vehicle based on its number of cylinders, valves per cylinder, and valve actuation technology. Mapping done by Agencies

Footprint: PU Average Car Foot Print is normal. Truck footprint is the production weighted average for each vehicle. Derived from data from Edmunds.com or Motortrend.com. Production volumes or specific footprints from product plans.

Footprint: PU Cutoff Same as above, but footprint valve for trucks limited to 66 before weighting. Meaning valves greater than 66 equal 66. Derived from data from Edmunds.com or Motortrend.com. Production volumes or specific footprints from product plans. NHTSA Defined New NHTSA Car/Truck New NHTSA Car Truck valu e as determined by NHTSA. Used in calculations. NHTSA

CO 2 CO 2 calculated from MPG. CO 2 weighted 1.15 times higher for diesel vehi cles. Certification data

Thresholded FootPrint Footprint valve that will be set to 41 for values less than 41, Will be set to 56 for car values > 56, and will be set to 66 for truck values >66 Used in Summary Calculations Derived from data from Edmunds.com or Motortrend.com. Production volumes or specific footprints from product plans.

TEB Tech 1 Percentage of Tech Package 1 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

TEB Tech 2 Percentage of Tech Package 2 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

TEB Tech 3 Percentage of Tech Package 3 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

TEB Tech 4 Percentage of Tech Package 4 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

TEB Tech 5 Percentage of Tech Package 5 Calculated using a macro in another spread sheet.

1-8The Baseline and Reference Vehicle Fleet

Calculated based on the technology in the "Data" sheet.

TEB Tech 6 Percentage of Tech Package 6 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

CEB Tech 1 Percentage of Cost of Tech Package 1 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

CEB Tech 2 Percentage of Cost of Tech Package 2 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

CEB Tech 3 Percentage of Cost of Tech Package 3 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

CEB Tech 4 Percentage of Cost of Tech Package 4 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

CEB Tech 5 Percentage of Cost of Tech Package 5 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

CEB Tech 6 Percentage of Cost of Tech Package 6 Calculated using a macro in another spread sheet. Calculated based on the technology in the "Data" sheet.

Notes: 1. For engines not available in the Ward sAuto.com engine file, an internet sear ch was done to find this information. 2. These data were obtained from manufact urer’s product plans. They were used to block (where possible) the model from adding technology that was already on a vehicle. 3. Ward’s Automotive Group data obtained from "2008 Light Vehicle Engines."

The sales volumes for the MY 2008 baseline fleet are included in the section below on reference fleet under the MY 2008 columns. Table 1-2 displays the technologies present in the baseline fleet.

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Table 1-2 2008 Technology PercentagesTurboSuper ChargedSOHCDOHCOHVLUBCCPSDVVLSDEACSICPDCPDVVLD

All Trucks 1.0% 0.2% 23.7% 47.6% 28.7% 61.4% 4.1% 5.8% 0.0% 5.3% 36.6% 1.5%
All Cars 3.4% 0.2% 17.4% 73.3% 9.2% 48.5% 1.4% 7.4% 1.3% 3.2% 47.9% 0.2%
BMW Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 94.7% 0.0%
BMW Cars 6.3% 1.1% 13.9% 86.0% 0.0% 0.0% 11.9% 11.9% 0.0% 0.0% 85.8% 1.5% Chrysler Trucks 0.2% 0.0% 38.9% 4.1% 56.9% 76.9% 0.0% 0.0% 0.0% 0.0% 3.9% 0.0% Chrysler Cars 0.9% 0.0% 20.8% 71.5% 7.7% 28.5% 0.0% 0.0% 0.0% 0.0% 41.7% 0.0%
Ford Trucks 0.0% 0.1% 62.9% 34.3% 2.8% 3.9% 26.5% 0.0% 0.0% 0.0% 9.3% 0.0%
Ford Cars 3.1% 0.9% 14.2% 85.8% 0.0% 6.4% 4.0% 0.0% 0.0% 0.0% 5.2% 0.0%
Subaru Trucks 3.2% 0.0% 70.5% 29.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 29.5% 27.4%
Subaru Cars 14.5% 0.0% 69.2% 30.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 30.8% 0.9%
GM Trucks 0.0% 0.0% 0.0% 30.8% 69.2% 92.7% 0.0% 0.0% 0.0% 14.7% 30.8% 0.0%
GM Cars 2.6% 0.0% 0.0% 56.2% 43.8% 92.0% 0.0% 0.0% 0.0% 11.5% 28.8% 0.0%
Honda Trucks 4.2% 0.0% 64.3% 35.7% 0.0% 0.0% 0.0% 64.3% 0.0% 0.0% 31.5% 4.2%
Honda Cars 0.0% 0.0% 57.2% 42.8% 0.0% 0.0% 0.0% 57.2% 10.7% 0.0% 42.6% 0.3% Hyundai Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 29.6% 0.0% Hyundai Cars 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 32.2% 0.0%
Tata Trucks 0.0% 20.3% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%
Tata Cars 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%
Kia Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 17.2% 0.0%
Kia Cars 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 10.2% 0.0%
Mazda Trucks 23.5% 0.0% 0.6% 99.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 99.3% 0.0%
Mazda Cars 11.4% 0.0% 0.0% 100.0% 0.0% 0.0% 0.9% 0.9% 0.9% 0.9% 99.9% 0.9%
Daimler Trucks 15.9% 0.7% 36.2% 63.8% 0.0% 0.0% 0.0% 0.0% 0.0% 46.7% 0.0% 0.0%
Daimler Cars 2.4% 0.3% 54.6% 45.4% 0.0% 0.0% 0.0% 0.0% 0.0% 30.3% 0.0% 0.0%
Mitsubishi Trucks 0.0% 0.0% 100.0% 0.0% 0.0% 100.0% 37.7% 0.0% 0.0% 0.0% 0.0% 0.0%
Mitsubishi Cars 0.0% 0.0% 100.0% 0.0% 0.0% 100.0% 42.9% 0.0% 0.0% 0.0% 0.0% 0.0%
Nissan Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%
Nissan Cars 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%
Porsche Trucks 12.4% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 100.0% 100.0%
Porsche Cars 17.4% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 28.7% 82.6% 0.0%
Suzuki Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Suzuki Cars 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Toyota Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 89.1% 0.0% 0.0% 0.0% 0.0% 78.3% 0.0% Toyota Cars 0.0% 0.0% 0.0% 100.0% 0.0% 69.9% 0.0% 0.0% 0.0% 0.2% 99.4% 0.0%
VW Trucks 0.6% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 78.9% 99.4% 78.9%
VW Cars 43.1% 0.0% 85.0% 15.0% 0.1% 0.0% 0.0% 0.0% 0.0% 1.9% 14.1% 0.8%

1-10The Baseline and Reference Vehicle Fleet

Table 1-2 2008 Technology PercentagesCVVLDEACDDEACOCCPODVVLOSGDI6MANCVTNAUTODCTAMEPSBISGCISG

All Trucks 2.8% 0.0% 10.5% 1.4% 0.0% 2.9% 1.0% 2.6% 19.9% 0.0% 0.9% 0.0% 0.0%
All Cars 5.2% 0.0% 1.2% 5.7% 0.0% 4.7% 2.3% 11.5% 18.5% 0.8% 12.6% 0.0% 0.4%
BMW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 6.3% 0.7% 0.0% 99.3% 0.0% 0.0% 0.0% 0.0%
BMW Cars 0.0% 0.0% 0.0% 0.0% 0.0% 7.7% 14.6% 0.5% 83.3% 1.1% 0.0% 0.0% 0.0% Chrysler Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 4.3% 3.6% 21.4% 0.0% 0.0% 0.0% 0.0% Chrysler Cars 0.0% 0.0% 5.2% 0.0% 0.0% 0.0% 0.9% 18.5% 3.9% 0.0% 0.0% 0.0% 0.0%
Ford Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 1.0% 0.0% 1.8% 18.8% 0.0% 0.0% 0.0% 0.0%
Ford Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% 1.9% 36.8% 0.0% 0.0% 0.0% 0.0%
Subaru Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Subaru Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 4.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
GM Trucks 0.0% 0.0% 39.1% 5.0% 0.0% 0.0% 0.0% 0.0% 18.6% 0.0% 0.0% 0.0% 0.0%
GM Cars 0.0% 0.0% 4.0% 28.8% 0.0% 6.0% 1.1% 0.0% 12.3% 0.0% 0.0% 0.1% 0.0%
Honda Trucks 31.5% 0.0% 0.0% 0.0% 0.0% 4.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Honda Cars 42.6% 0.0% 0.0% 0.0% 0.0% 0.0% 3.1% 3.2% 0.0% 0.0% 0.0% 0.0% 3.2% Hyundai Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% Hyundai Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Tata Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%
Tata Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 91.6% 0.0% 0.0% 0.0% 0.0%
Kia Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Kia Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Mazda Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 23.5% 0.0% 0.1% 57.2% 0.0% 0.0% 0.0% 0.0%
Mazda Cars 0.9% 0.9% 0.9% 0.9% 0.9% 11.4% 3.8% 0.1% 19.2% 0.0% 0.0% 0.0% 0.0%
Daimler Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 15.9% 0.0% 0.0% 99.3% 0.0% 0.0% 0.0% 0.0%
Daimler Cars 0.0% 0.0% 0.0% 0.0% 0.0% 1.7% 0.3% 0.0% 67.4% 10.4% 0.0% 0.0% 0.0%
Mitsubishi Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 14.4% 23.3% 0.0% 0.0% 0.0% 0.0%
Mitsubishi Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.4% 44.2% 7.6% 0.0% 0.0% 0.0% 0.0%
Nissan Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% 16.5% 0.0% 0.0% 16.5% 0.0% 0.0%
Nissan Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 4.5% 69.4% 0.0% 0.0% 32.3% 0.0% 0.0%
Porsche Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 1.4% 0.0% 98.6% 0.0% 0.0% 0.0% 0.0%
Porsche Cars 0.0% 0.0% 0.0% 0.0% 0.0% 17.4% 51.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Suzuki Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Suzuki Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Toyota Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 5.7% 1.4% 4.7% 19.2% 0.0% 0.0% 0.0% 0.0% Toyota Cars 0.0% 0.0% 0.0% 0.0% 0.0% 7.6% 0.1% 16.7% 22.5% 0.0% 64.0% 0.0% 0.0%
VW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0%
VW Cars 0.0% 0.0% 0.0% 0.0% 0.0% 46.0% 6.4% 5.4% 65.7% 14.3% 0.0% 0.0% 0.0%

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Table 1-2 2008 Technology PercentagesPSHEV2MHEVPHEVAuto 4 SpeedAuto 5 SpeedAuto 6 SpeedAuto 7 SpeedAuto 8 SpeedManual 5 SpeedManual 6 SpeedCVTAutomated Manual5 SpeedAutomated Manual6 SpeedAutomated Manual7 Speed

All Trucks 0.8% 0.1% 0.0% 37.9% 37.3% 18.6% 1.4% 0.0% 1.1% 1.0% 2.6% 0.0% 0.0% 0.0%
All Cars 2.7% 0.0% 0.0% 39.1% 22.9% 16.4% 1.7% 0.4% 4.9% 2.3% 11.5% 0.3% 0.5% 0.0%
BMW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 99.3% 0.0% 0.0% 0.0% 0.7% 0.0% 0.0% 0.0% 0.0%
BMW Cars 0.2% 0.0% 0.2% 0.0% 0.0% 82.6% 0.8% 0.0% 0.3% 14.6% 0.5% 0.0% 0.0% 1.1% Chrysler Trucks 0.0% 0.0% 0.0% 34.9% 35.5% 21.4% 0.0% 0.0% 0.3% 4.3% 3.6% 0.0% 0.0% 0.0% Chrysler Cars 0.0% 0.0% 0.0% 67.0% 8.2% 3.9% 0.0% 0.0% 1.4% 0.9% 18.5% 0.0% 0.0% 0.0%
Ford Trucks 0.0% 0.0% 0.0% 55.9% 20.9% 18.8% 0.0% 0.0% 2.7% 0.0% 1.8% 0.0% 0.0% 0.0%
Ford Cars 0.0% 0.0% 0.0% 36.3% 18.2% 36.8% 0.0% 0.0% 5.6% 1.2% 1.9% 0.0% 0.0% 0.0%
Subaru Trucks 0.0% 0.0% 0.0% 62.1% 29.5% 0.0% 0.0% 0.0% 8.4% 0.0% 0.0% 0.0% 0.0% 0.0%
Subaru Cars 0.0% 0.0% 0.0% 69.6% 2.4% 0.0% 0.0% 0.0% 23.9% 4.1% 0.0% 0.0% 0.0% 0.0%
GM Trucks 0.0% 0.5% 0.0% 75.5% 5.0% 19.0% 0.0% 0.0% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0%
GM Cars 0.0% 0.1% 0.0% 77.9% 5.4% 12.3% 0.0% 0.0% 3.2% 1.1% 0.0% 0.0% 0.0% 0.0%
Honda Trucks 0.0% 0.0% 0.0% 0.0% 99.7% 0.0% 0.0% 0.0% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0%
Honda Cars 0.0% 0.0% 0.0% 0.0% 87.3% 0.0% 0.0% 0.0% 6.4% 3.1% 3.2% 0.0% 0.0% 0.0% Hyundai Trucks 0.0% 0.0% 0.0% 29.8% 45.0% 25.0% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% Hyundai Cars 0.0% 0.0% 0.0% 76.4% 16.7% 0.0% 0.0% 0.0% 6.7% 0.2% 0.0% 0.0% 0.0% 0.0%
Tata Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Tata Cars 0.0% 0.0% 0.0% 0.0% 8.4% 91.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Kia Trucks 0.0% 0.0% 0.0% 23.1% 75.7% 0.0% 0.0% 0.0% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0%
Kia Cars 0.0% 0.0% 0.0% 51.5% 41.0% 0.0% 0.0% 0.0% 7.5% 0.0% 0.0% 0.0% 0.0% 0.0%
Mazda Trucks 0.0% 0.0% 0.0% 10.1% 29.6% 57.2% 0.0% 0.0% 3.0% 0.0% 0.1% 0.0% 0.0% 0.0%
Mazda Cars 0.0% 0.0% 0.0% 33.3% 29.7% 19.2% 0.0% 0.0% 13.9% 3.8% 0.1% 0.0% 0.0% 0.0%
Daimler Trucks 0.0% 0.0% 0.0% 0.0% 0.7% 0.0% 99.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Daimler Cars 0.0% 0.0% 0.0% 0.0% 22.0% 0.0% 67.4% 0.0% 0.0% 0.3% 0.0% 10.4% 0.0% 0.0%
Mitsubishi Trucks 0.0% 0.0% 0.0% 62.3% 0.0% 23.3% 0.0% 0.0% 0.0% 0.0% 14.4% 0.0% 0.0% 0.0%
Mitsubishi Cars 0.0% 0.0% 0.0% 30.8% 4.5% 7.6% 0.0% 0.0% 11.6% 1.4% 44.2% 0.0% 0.0% 0.0%
Nissan Trucks 0.0% 0.0% 0.0% 0.0% 81.6% 0.0% 0.0% 0.0% 0.7% 1.2% 16.5% 0.0% 0.0% 0.0%
Nissan Cars 1.1% 0.0% 0.0% 7.0% 19.1% 0.0% 0.0% 0.0% 0.0% 4.5% 69.4% 0.0% 0.0% 0.0%
Porsche Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 98.6% 0.0% 0.0% 0.0% 1.4% 0.0% 0.0% 0.0% 0.0%
Porsche Cars 0.0% 0.0% 0.0% 0.0% 33.7% 0.0% 0.0% 0.0% 14.4% 51.9% 0.0% 0.0% 0.0% 0.0%
Suzuki Trucks 0.0% 0.0% 0.0% 0.0% 99.5% 0.0% 0.0% 0.0% 0.5% 0.0% 0.0% 0.0% 0.0% 0.0%
Suzuki Cars 0.0% 0.0% 0.0% 76.2% 13.1% 0.0% 0.0% 0.0% 10.7% 0.0% 0.0% 0.0% 0.0% 0.0% Toyota Trucks 4.7% 0.0% 0.0% 9.0% 63.9% 19.2% 0.0% 0.0% 1.9% 1.4% 4.7% 0.0% 0.0% 0.0% Toyota Cars 16.9% 0.0% 0.0% 35.7% 19.5% 19.8% 0.0% 2.6% 5.5% 0.1% 16.7% 0.0% 0.0% 0.0%
VW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
VW Cars 0.0% 0.0% 0.0% 0.0% 0.0% 65.7% 0.0% 0.0% 8.2% 6.4% 5.4% 0.0% 14.3% 0.0%

As noted at proposal, these technology tables indicate that some manufacturers are already implementing some of these tec hnologies in their 2008 fl eet. VW stands out as having a significant number of turbocharg ed direct injection engines, though it is

1-12The Baseline and Reference Vehicle Fleet

uncertain whether their engines are also downsized. Some of the valve and cam technologies are quite common today: for exam ple, nearly half the fleet has dual cam phasing, while Honda and GM are have consid erable levels of engines with cylinder deactivation (DEAC). Honda also has alrea dy implemented continuously variable valve lift (CVVL) on a majority of their engines. Moreover, Toyota has the highest penetration of hybrid technologies. Finally, regarding th e technologies obtained from the product plans, many of the manufacturers who reporte d the lubricating oil information indicated that low friction (or low viscosity) oil will be used more frequently in the future. Therefore, it is likely that th is technology is underreported for the fleet as a whole since it was assumed that the usage of low viscosity oils was zero for the companies who did not provide product plans. The l ack of reporting of these types of technologies (if they exist in the fleet) will probably lead to a sl ightly higher cost of compliance for these companies. However, these technologies alone do not necessarily guarantee a better fuel economy compared to other vehicles in thei r footprint class. There are many other considerations at play, most notably, weight and power.

The section below provides further de tail on the conversion of the MY 2008 baseline into the MY 2011-2016 reference fleet. It also describes more of the data contained in the baseline spreadsheet.



### 1.3 The MY 2011-2016 Reference Fleet

The reference fleet aims to reflect the cu rrent market conditions and expectations about conditions of the vehicl e fleet during the model years to which the agencies’ rules apply. Fundamentally, constructing this fl eet involved projecting the MY 2008 baseline fleet into the MY 2011-2016 model years. It also included the assumption that none of the models had changes during this period. Pr ojecting what the fleet will look like in the future is a process that is inherently uncertain. NHTSA and EPA therefore relied on many sources of reputable information to make these projections.



### 1.3.1 On what data is the reference vehicle fleet based?

As explained at proposal, EPA and NHTSA have based the proj ection of total car and light truck sales on recent projections made by the Energy Information Administration (EIA). EIA publishes a mi d-term projection of national energy use annually called the Annual Energy Outlook (AEO). 4 No commenter challenged the appropriateness of using the AEO. At propos al, the agencies used the various AEO 2009 reports for the sales projections used in cons tructing the reference fleet used as the basis for the proposed rule. However, EIA also recently published an early version of its 2010 Annual Energy Outlook. This early 2010 report was published in December, in time to be used in this rule making. The differences in projected sales in the updated 2009 report and the early 2010 report are very small, so th e agencies just scaled the data from the 2009 report data to the absolute number of vehi cles (cars and trucks ) in the 2010 report. Table II.B.2-1 below shows the scaling factors that were used for each model year. We use the sales projections from the scaled updated 2009 Annual Energy Outlook.

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Table II.B.2-1 Annual Energy Outlook 2010 Scaling Factor d

2011 2012 2013 2014 2015 2016 Multiplier 0.958339 1.004719 1.011593 0.997677 0.992585 0.995178

In the AEO 2010 Early Release, EIA project s that total light-duty vehicle sales gradually recover from their currently depressed levels by roughly 2013. In 2016, car and light truck sales are projected to be 9.4 and 7.1 million units, respectively. While the total level of sales of 16.5 m illion units is similar to pre-2008 levels, the fraction of car sales is projected to be higher than that existing in the 2000-2007 timeframe. This projection reflects the im pact of higher fuel prices, as well as the effects of the EISA (which called for a minimum fleet average of 35 mpg by 2020). Note that EIA’s definition of cars and trucks follows that used by NHTSA prior to the MY 2011 CAFE final rule published earlier th is year. The MY 2011 CAFE final rule reclassified a number of 2-wheel drive sport utility vehicles from th e truck fleet to the car fleet. EIA’s sales projections of cars and trucks for the 2011-2016 model years under both the new and old NHTSA truck definition are shown in Table 1-3 and Table 1-4 below, respectively. Actual vehicle sales for MY 2008 are also shown under the new NHTSA truck definition. Slight differences exist between the tota l car and truck sales projections. These differences are less than 0.1 pe rcent and are due to the itera tive process described further below to shift sales between car and truck market segments.

Table 1-3 AEO 2010 Volumes New NHTSA Car Truck Definition After Projections

2008 Baseline

Sales 2011 Projected Volume 2012 Projected Volume 2013 Projected Volume 2014 Projected Volume 2015 Projected Volume 2016 Projected Volume

All Trucks 5,620,847 5,457,758 5,798,588 6,038,484 5,947,819 5,826,579 5,669,683

All Cars 8,220,517 7,922,992 9,123,197 9,797,738 10,231,974 10,627,055 10,832,348

Total 13,841,364 13,380,750 14,921,784 15,836,222 16,179,793 16,453,634 16,502,032

Table 1-4 AEO 2010 Volumes Old NHTSA Car Truck Definition Before Projections

2011 Projected Volume 2012 Projected Volume 2013 Projected Volume 2014 Projected Volume 2015 Projected Volume 2016 Projected Volume

All Trucks 6,665,386 7,160,069 7,478,667 7,423,880 7,280,946 7,079,323

All Cars 6,715,364 7,761,715 8,357,555 8,755,912 9,172,688 9,422,709

Total 13,380,750 14,921,784 15,836,222 16,179,793 16,453,634 16,502,032

d The scaling factors were determined by dividing the absolute number of vehicles in AEO 2010 for each model year shown by the absolute number of vehi cles in AEO 2009 for each of those model years.

1-14The Baseline and Reference Vehicle Fleet

In addition to a shift towards more car sale s, sales of segments within both the car and truck markets have also b een changing and are expected to continue to change in the future. Manufacturers are continuing to introduce more crossover models which offer much of the utility of SUVs but use more car-like designs and unibody structures. In order to reflect these changes in fleet makeup, EPA and NHTSA considered several available forecasts. After reviewing info rmation from a number of forecasting organizations, the agencies decided to purchase forecasts from two well-known industry analysts, CSM Worldwide (CSM) and J.D. Powers. 5 NHTSA and EPA decided to use the forecast from CSM, for se veral reasons. One, CSM agreed to allow us to publish their high level data, on which the forecast is based, in the public domain. Two, it covered nearly all the timefr ame of greatest relevance to this rule (2012-2015 model years). Three, it provided projections of vehicle sales both by manufacturer and by market segment. Four, it utilized market segments similar to those used in the EPA emission certification program and fuel econom y guide. EPA did need to have CSM’s standard forecast (that covers all vehicles below 14,000 lbs GVW) customized to cover only light duty vehicles and medium duty pa ssenger vehicles. As discussed further below, the CSM forecast is combined with other data obtained by NHTSA and EPA. The breakdown of car and truck sa les by manufacturer and by market segment for the 2016 model year and beyond were assumed to be the same as CSM’s forecast for the 2015 calendar year, because CSM forecasts fo r 2016 will not be available until the 1 st quarter of 2010.



### 1.3.2 How do the agencies develop the reference vehicle fleet?

The process of producing the 2011-2016 refe rence fleet involved combining the baseline fleet with the projection data de scribed above. This was a complex multistep procedure, which is descri bed in this section.

1.3.2.1 How was the 2008 baseline data merged with the CSM data?

As explained at proposal, merging the 2008 baseline data with the 2011-2016 CSM data required a thorough mapping of certif ication vehicles to CSM vehicles by individual make and model. One challenge the agencies faced when determining a reference case fleet was that the sales data projected by CSM had different market segmentation than the data contained in EPA’s internal database. In order to create a common segmentation between the two databa ses, side-by-side comparison of the specific vehicle models in both datasets was performed, and an additional “CSM segment” modifier in the spreadsheet was cr eated, thus mapping the two datasets. The reference fleet sales based on the “CSM segmentation” was then projected.

The bas

bs. Truck eline data and reference fleet volumes are available to the public. The baseline Excel spreadsheet in the docket is the result of the merged files. 6 It provides specific details on the sources and definitions for the data. The Excel file contains several ta They are: “Data”, “Data Tech Definitions”, “SUM”, “SUM Tech Definitions”, “ Vehicle Type Map”, and “Car Vehicle Type Ma p”. “Data” is the tab with the raw data.

1-15Joint Technical Support Document

“Data Tech Definitions” is the tab where ea ch column is defined and its data source named. “SUM” is the tab where the raw data is processed to be us ed in the OMEGA and Volpe models. The “SUM” tab minus columns A-F and minus the Generic vehicles is the input file for the models. The “Generic” manufacturer (shown in the “SUM” tab) is the sum of all manufacturers and is calculated as a reference, and for data verification purposes. It is used to validate the manufacturer s’ totals. It also gi ves an overview of th fleet. e

1-16The Baseline and Reference Vehicle Fleet

Table 1-5 shows odels is determined by the number of unique segm binations. These combinations of segm vehicle type (the vehicle type number is the same as the technology package n packages discussed in.the EPA DRIA (chapter where the columns of the “SUM” tab are defin d “Car Vehicle Type Map” map the number of c e “tech package” vehicle type number. the sum of the models chosen. The number of m
ent and vehicle type com ent and umber) are determined by the technology 1). “SUM Tech Defin itions” is the tab ed. The “Truck Vehicle Type Map” an ylinder and valve actuati on technology to th

1-17Joint Technical Support Document

Table 1-5 Models from the SUM Tab Model

Model Car Like LargeSuv >=V8 Vehicle Type: 13 Car Like LargeSuv V6 Vehicle Type: 16 Car Like LargeSuv V6 Vehicle Type: 12 Car Like LargeSuv V6 Vehicle Type: 9 Car Like LargeSuv I4 and I5 Vehicle Type: 7 Car Like MidSizeSuv V6 Vehicle Type: 8 Car Like MidSizeSuv V6 Vehicle Type: 5 Car Like MidSizeSuv I4 Vehicle Type: 7 Car Like SmallSuv V6 Vehicle Type: 12 Car Like SmallSuv V6 Vehicle Type: 4 Car Like SmallSuv I4 Vehicle Type: 3 La rgeAuto >=V8 Vehicle Type: 13 La rgeAuto >=V8 Vehicle Type: 10 Larg eAuto >=V8 Vehicle Type: 6 La rgeAuto V6 Vehicle Type: 12 LargeAuto V6 Vehicle Type: 5 MidSizeAuto >=V8 Vehicle Type: 13 MidSizeAuto >=V8 Vehicle Type: 10 MidSizeAuto >=V8 (7 or >) Vehicle Type: 6 MidSizeAuto V6 Vehicle Type: 12 MidSizeAuto V6 Vehicle Type: 8 MidSizeAuto V6 Vehicle Type: 5 MidSizeAuto I4 Vehicle Type: 3

In the combin mode ed EPA certification and CSM database, all of the 2008 vehicle

propo in

e 2009 e mapped to the existing vehicles by and overall segment distribution. The map in

eet

r and by market segment was normalized (set equal) to the total sales estim

data ell ls were assumed to continue out to 2016, though their volumes changed in rtio ith n to CSM projections. Also, any new models expected to be introduced w -2016 timeframe are not included in th e data. These volumes are reassigned to th the existing models. All MY 2011-2016 vehicles ar a process of mapping to manufacturer market share
p gs are discussed in the next section. Further discussion of this limitation is discussed below in section 1.3.4. The statistics of this fleet will be presented below since further modifications were required to the volumes as the next section describes.

1.3.2.2 How were the CSM forecasts norma lized to the AEO forecasts?

As also explained at proposal, the next st ep in the production of the reference fl is one of the more complicated steps to e xplain. Here, the projected CSM forecasts for relative sales of cars and trucks by manufact ure ates of the preliminary 2010 Annual Energy Outlook (AEO). NHTSA and EPA used projected ca r and truck volumes for this period from AEO 2009. However, the AEO projects sales only at the car and truck level, not at the manufacturer and model-specific level, which are needed for the analysis. The CSM provided year-by-year percentages of cars and trucks sold by each manufacturer as w

1-18The Baseline and Reference Vehicle Fleet

as the percentages of each vehicle segment. Using these percentages normalized to the AEO-projected volumes then provided th e manufacturer-specific market share and model-specific sales for mode l years 2011-2016 (it is worth cl arifying that the agen are not using the model-specific sales volumes from CSM, only the volumes by manufacturer and segment). This pro cies

cess is described in greater de tail in the following paragraphs.

e-

. Adjust total car and truck sa les to match AEO projections.

. Adjust Car sales to match CSM market share projections for each manufacturer

ta l car and truck sales in 2008 to match AEO

the volumes in 2008 to the AEO
pro car or t

Examp In order to determine future producti on volumes, multipliers were developed by manufacturer and vehicle segment that c ould be applied to MY 2008 volumes. The process for developing the multipliers is complicated, but is easiest to explain as a thre step process, though the first step is combin ed with both the second and third step, so only one multiplier per manufacturer and vehicle segment is developed.

The three steps are: 1 2

an d car segment. 3. Adjust Truck sales to match CSM market share projections for each manufacturer and truck segment.

The first step is the adjustment of to
pr ojections of total car and tr uck sales in 2011-2016. The volumes for all of the trucks in 2008 were added up (TruckSum2008), and so were the volumes of all the cars (CarSum2008). A multiplier was developed to scale jections. The example equation below show s the general form of how to calculate a ruck multiplier. The AEO projections are shown above in Table 1-3

. le Equation : uckMultiplier(Year X) = AEOProjectionforTrucks(Year X) / TruckSum2008

CarMultiplier(Year X) = AEOProj ectionforCars(Year X) / CarSum2008

Year X is the model year of the multiplier.

The AEO projection is different for each model year. Therefo Tr

Where:

re, the multipliers

as

ent, ive the best multipliers.

M
t ar e different for each model year. The multipliers can be applied to each 2008 vehicle a first adjustment, but multipliers based solely on AEO have limited value since it can only give an adjustment that will give the co rrect total numbers of cars and trucks without the correct market share or vehicle mix. A correction fa ctor based on the CSM data, which does contain market share and vehicl e segment mix, is therefore necessary, so combining the AEO multiplier with CSM multipliers (one per manufacturer, segm and model year) will g

Th ere were several steps in developing an adjustment for Cars based on the CS data. CSM provided data on the market share and vehicle segment di stribution. The firs step in determining the adjustment for Cars was to total the number of Cars in each vehicle segment by manufacturer in MY 2008. A total for all manufacturers in each

1-19Joint Technical Support Document

segment was also calculated. The next step was to multiply the volume of each segmen for each manufacturer by the CSM m t arket share. The AEO multiplier was also applied at this tim
correct Market ble 1-6

. The next step egment distribution. The process for adjusting ent is more complicated than a simple one step multiplication. In order to keep manufactur ers’ volumes constant and still have the correct vehicle segm bution, vehicles need to move from segment to segment while m ng co f S an um e d plis i ar) eople ving to vehicle rul tim le detai ple o edur de rea tion

Vehicles from CSM’s “Luxury Car,” “Speci alty Car,” and “Other Car” segments if reduced will b lly distri o the re g fou ories -Size Car,” “Mid-Size Car, ese sales increased, they were taken from the remaining four categor ies so that the re les in these four tegories rem ined cons Vehicles from CSM’s “Luxury Car,” “Speci alty Car,” and “Other Car” segments, if increased will take equally fr remaining categories (“Full-Size Car,” “Mid-Size Car,” “Small Car,” “ ar”). All m nufacturers ave the sam p lier for a iven segm nt shift ba d on moving all vehicles in that segm achieve th CSM dis ution. Table 1-6 shows how the 2011 v ers that were created for each adjustment. This does not mean that new vehicle segments will be added (except for Generic Mini Car desc in the next step) to ma ufacturers at do not produce them. Vehicles within each manufact rer will b fted as c se to the distribution as possible given the other rules. Table 1-7 has the percentages of Cars per CSM segment. These perc ntages are multiplied by the total number of vehicles in a given year to get the total sales in the segment. Table 1-6 shows the totals for 2011 in the “2011 AEO-CSM Sales Goal” column. e processed, if vehicles need to mo t of the next smaller segm ove to en from Small Cars. Note: In or acco this, neric egment was added to ma turer did n e a M pe) C ction in 2008 eeded ift do hicle the Car
ent. ata must be processed in the following order: “Luxury Ca cial r,” “Other Car,” ize M id ar,” oes not need roc par y u s or ork hicles will a move rd th t di n. There are t peci r,” a e. This gave projected volumes with AEO total volumes and market share ion for Cars. This is shown in the “Adjusted for 2011 AEO and Manufacturer Share” column of Ta

is to adjust the sales vo lumes for CSM vehicle s for vehicle segm

ent distri
nstant manu h the shift. T aintaini
to ac acturers’ to tals. he assumpti on (b ix rules ased on the d one ass shift in veh ption wer cle sales in appli e last ye com is that p the

are mo smalle r s in the emaking e frame. A higher vel (less led) exam f this proc e is provi d in the p mble sec II.

1.

, e equa buted t mainin r categ (“Full ” “Small Car,” “Mini Ca r”). If th lative sa

ca a tant. 2. om th e Mini C 3. a h e multi g e se

en t to e trib ehicles moved and th e multipli

ribed n th

u e shi lo

e

4. When “Full-Size Car,” “Mid-Size Car,” “S mall Car” ar ve in or out of the segment, they will move into or ou ent. So, if Mid-Size Cars are being processed they can only m or be tak der to mplish a “Ge Mini Car” s nufac s who ot hav ini (ty ar in produ , but n to sh wn ve s from Small
segm 5. The d r,” “Spe ty C a Ca “Full-S Car,” “ -Size C “Small Car.” The “M ini r” d that ve to be p essed se a tely. B sing thi der, it w s out lways towa e c corre s io tribut

,” “S w o nd exceptions, BMW and Porsche only have “Luxury Car alty Ca

1-20The Baseline and Reference Vehicle Fleet

“Other Car” vehicles, so their volumes were not changed or shifted since these rules did not apply to them. 6. When an individual manufacturer multiplier is applied for a segment, the vehicles move to or from the appropriate segments as specified in the previous rules and a shown in Table 1-6

.

Table 1-6 Example: 2011 Model Year Volume Shift*

t 2008 MY les Adjusted for 2011 AEO and Manufacturer Market Share 201 s

CSM Segmen Luxury, Specialty, Other Adjustment Full Size Adjustment Midsize Adjustment Small Car Adjustment 1 AEOCSM Sales Goal Sa
All Full-Size Car 411,025 730,355 501, 245 520,885 411,025 411,025 411,025
All Luxur 970,666 y Car 1,057,875 1,076, 470 970,680 970,680 970,680 970,680
All Mid-S 1,838,095 ize Car 1,970,494 1,946,981 2,033,087 2,142,948 1,838, 095 1,838,095
All Mini C 991,349 ar 599,643 686,738 733,339 733,339 733,339 991,309
All Small Car 1,850,522 2,007,527 2, 099,343 2,099,343 2,404, 196 2,146,226 2,146,226 All Specia 637,759 lty Car 754,547 783, 982 637,785 637,785 637,785 637,785
All Others 12,178 3,259 4,355 12,178 12,178 12,178 12,178
Number Vehicles that shift and Where
All Full-S ize Car 19,640 (109,861) - - All Luxur (105,790) - - - y Car
All Mid-Size Car 86,107 109,861 (304,853) -
All Mini Car 46,601 - - 257,970
All Small Car 91,816 - 304,853 (257,970) All Specia lty Car (146,198) - - - All Others 7,823 - - - Individual Manufacturer Multiplier

All Full-S ize Car 0.789

All Luxur y Car 0.902
All Mid-Size Car 0.858
All Mini C 1.352 ar All Sm al l Car All Specialty C ar 0.814
All Others 2 .796 *Note: The data in this ta ble is based on the NPRM data.

CS T able 1-7 Example: CSM – Percent of Cars per Segment*
M Segment 2011 2012 2013 2014 2015 2016 Com c pa t Car 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Full-Siz 4.9% e Car 5.9% 4.3% 4.7% 4.9% 4.9% Luxury C 15.1% ar 13.9% 13.7% 14. 6% 15.0% 15.1% Mid-Size Car 26.2% 29.0% 27.9% 27.5% 27.9% 27.9% Mini Car 14.1% 15.5% 15. 5% 15.4% 15.4% 15.4% Small Car 30.6% 27.5% 26. 5% 26.1% 25.8% 25.8% Specialty Car 9.1% 9.8% 10.7% 10.9% 10.6% 10.6% Others 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% *Note: The data in this ta ble is based on the NPRM data.

1-21Joint Technical Support Document

Mathematically, an individual manufacturer multiplier is calculated by making the segment the goal and dividing by the previ ous total for the segment (shown in Table 1-6 ). If the number is g if the number is less than 1, the vehicles are leaving the segmen t. So, for example, if Luxury Cars have

a 1.5, the sp ma nufacturer who has Luxury Cars, a multiplier

o to its lu ar v , an tota r of vehicles that shifted into the Luxury segment is subtracted from aining segm m that

c ket share he and rge ave jus f 0.7,
t fic manu r w La rs, iplier of 0.7 is applied to its L the tota er le g m into that

m id-Siz .

e vehicl es te th rules, a total for each

m nd vehic en t h or anufacturer

s specific m ear 01 ral s L Car ded by
t total for th ufa seg ( e.g. Ge Mo xury Car volume for each vehicle

odel. This is done by taking the multiplier (which is for a specific manufacturer and
segmen eneral

t is
nt

s

ar sed on the AEO and CSM data. This is the goal for market share. The total number of trucks in each truck segment in a sp ecific future model year based on the AEO and CSM data. This is the goal for vehicle segment distribution. Table 1-8 has the percentages of Trucks per CSM segment reater than 1, the vehicles are entering the segment, and

n adjustment of n for a ecific f 1.5 is applied xury c olume d the l numbe the rem ents to aintain ompany’s mar . On t other h

, if La Cars h an ad tment o hen for a speci facture ho has rg e Ca a mult

arge Cars, and l numb of vehic s l eavin that seg ent is trans ferred anufacturer’s M e Cars

After th

a e volum are shif d using e above anufacturer le segm t is main 2 ained. T

e e total f

r each m egment for a odel y ( e.g. , 1 Gen Moto uxury s) divi he MY 2008 at man cturer ment , 2008 neral tors Lu s) is the new multiplier used to determin e the future vehicle

m t) times the MY 2008 volume for the specific vehicle model ( e.g. , 2008 G Motors Luxury Car Cadillac CTS). This process is repeated for each model year (2011- 2016).

The method used to adjust CSM Trucks to the AEO market share was different than the method used for Cars. The process for Cars is different than Trucks because i not possible to predict how vehicles would shift between segments based on curre market trends. This is because of the added utility of some trucks that makes their sale more insensitive to factors like fuel pri ce. Again, CSM provided data on the market share and vehicle segment di stribution. The process for having the fleet match CSM’s market share and vehicle segmen t distribution was iterative.

The following totals were determined:

• The total number of trucks for each manufacturer in 2008 model year.

• The total number of trucks in each truck segment in 2008 model year.

• The total number of truck in each segm ent for each manufacturer in 2008 model
year.

• The total number of trucks for each manuf acturer in a specific future model ye ba

•

1-22The Baseline and Reference Vehicle Fleet

Table 1-8 Example: CSM – Percent of Trucks per Segment*
CSM Segment 2011 2012 2013 2014 2015 2016 F ull-Siz % % e CUV 4.1 3.3% 4.0% 4.4% 4.5 4.5% Full-Si ze Pickup 20.0% 17.9% 18.1% 20.1% % 2 20.8 0.8% Fu 1.3% 1.3% ll-Size SUV 3.3% 2.8% 1.6% 1.2% Full-Size Van 3.5% 3.3% 4.0% 3.9% 4.0% 4.0% Mid-Size C UV 14.5% 15.8% 17.5% 18.1% 18.4% 18.4% Mid-Size MAV 3.8% 3.3% 2.6% 2.2% 2.2% 2.2% Mid-Size Pickup 3.6% 6.4% 6.6% 5.8% 6.4% 6.6% Mid-Size SU V 3 .2% 1.8% % 1.6% 2.3% 1.6% 1.6 Mid-Siz e Van 9 0. 8.7% 11. % 11.3% 1 7% 10.0% 8.7% Small C UV 7. 6.2% 27.3 % 28.4% 2 0% 26.1% 2 26.2% Small M AV .9% 4.1 4.1% 1 3.4% % 4.1% 4.1% Small S UV .8% 2.2 1.8% 2 2.4% % 2.0% 1.8% *No te: T s th

To start, two es re ad each m w total 8 he data in thi ta ble is based on e NPRM data.

different typ of tables were c ated. One table h anufacturer ith its sales for 200 (similar to

1-23Joint Technical Support Document

anufacturer. The second table starts out with a “Generic ” manufacturer ( Table 1-10 ) whi le

lated and ad ded to the table. This is not shown in the bles below. A process of first adjusting th e numbers in the tables to the goal for market sha

or

ng by the current total (sta rting with 2008 model year olumes) for a manufacturer. A segment dist ribution-based multiplier is calculated by taki Table 1-9 ). This table will have the goal for each manufacturer, and a column added for each iteration with the current total. Th e second table has a truck segment total by

m ch is the table where the goal resides. Each manufacturer (BMW is shown in Tab 1-11 ) is then listed below the “Generic” manufactur er. With each iteration, a new total is added for each segment that is calcu
ta re distribution was done. This was fo llowed by adjusting to the goal for vehicle segment distribution. Each time an adjustme nt was done a new column was added. An adjustment was done by creating a multiplier (either segment distribution-based manufacturer distribution-based) and applyi ng it to each vehicle segment total in the current iteration. A manufacturer-based multip lier is calculated by taking the goal total for a manufacturer and dividi

v ng the goal distribution volumes in the Generic manufacturer se t and dividing them by the current volume.

1-24The Baseline and Reference Vehicle Fleet

, Table 1-10 nd Table 1-11 W. Table 1-9 , a below illu strate two ite rations using BM

1-25Joint Technical Support Document

Table 1- mple:M cturer k Tota

ear Sales tu on e i i 9 Exa anufa Truc ls*

2008 Model Y Manufac rer Distribu ti 2011Volum Goal Mult plier for Iterat on 1
BMW 61,324 1399 1 24= 48 39948/613 2.28 *Note: The data in this ta sed o P .

Table 1-10 Exampl S c A t

CSM Segment l Y Segment Distrib 2 e Go ble is ba n the N RM data

e: Segm ent pecific Tru k Totals for ll Manufac urers*

Manufacturer 2008 Mod Sales e ear ut ion al 011Volum Multipliers

Generic 1 Full-Size Pickup ,07 3 1,195 3 1,390,34 1.30
Generic Mid-Size Picku p ,1 598 97 251,4 3 3 0.38
Generic Full-Size Va n ,3 9 33 84 244,3 3 8.30
Generic M id-Size Van 33 71 9 ,529 826,7 1.37
Generic Mid-Size MAV 19 1 65 ,448 263,7 1.26
Generic Sma ll MAV 5 0 23 ,524 133,3 9 0.57
Generic F ull-Size SUV 0 7 53 ,7 48 232,3 5 0.46
Generic Mid-Size SUV ,0 7 347 26 221,60 0.70
Generic Small SUV ,26 6 377 2 194,49 0.75
Generic Full-Size CUV ,55 3 406 4 287,31 0.69
Generic Mid-Size CUV ,3 3 79 8 35 1,007,5 8 0.99
Generic Small CUV 1,5 9 1,44 89 1,901,22 1.16 Note: 1. Generic means all manuf ta in this table is bas NP acturers. * The da ed on the RM data.

Table 1-11 Exa c T ls

Segment Y Sa er t Ma e io r ent Dist m ple: Segme nt Sp ecifi ruck Tota f or BMW*

Manufacturer CSM 2008 Model ear It les ation 1 Adjus for Iterat rket Shar n 2 Adjust fo Segm ribution BMW F ull-Size Pickup BMW Mid-Size Pickup BMW Full-Size Van BMW Mi d-Size Van BMW Mid-Size MAV 3 8,5 =11 ,882 2. 28*3,882= 51 1.26*8,851 ,137 BMW Small MAV BMW Full-Size SUV BMW Mid-Size S UV Small SUV BMW BMW Full-Size CUV B MW Mid-Size CUV 36,409 2.28*36,409=83,013 0.99*83,013=82,6 03 BMW Small CUV 21,033 2.28*21,033=47,955 1.16*47,955=55,592 Total BM W Vehicles 61,324 139,819 149,332 *Note: The data in this ta ble is based on the NPRM data.

1-26The Baseline and Reference Vehicle Fleet

Using this process, the numbers will get cl oser to the goal of matching CSM’s market share for each manufacturer egment after each of the iterations. The iterative pro cess is carried out until the totals nearly match the goals.

rations, all numb 1% ’s d ions. teratio ha top er w ue rv bers w ve

er the market share and di w lete, the totals need to be sed to create multipliers that could b iginal ndividual 2008 model year umes (e e ur vo he ea er segment divided by the 2008 ear r eac fact es a mu ha pp ch as ma er

. bove process is done f mod need 11-2016). The m ers pplied t ic m , es f he technology eing applied to 2012-2016 fleet to meet the 2011 cafe standard) is discussed below in section 4.1, with the results b



### 1.3.3 How has th ference change m the M to t nal Ru

The agenci ted the ted re (201 fleet the fi

SM W utlook. ce nal a fi ion

’s fo 2 i n

nnua s D 2

Wo e s icles h to have u s e d di f PA u e i E CSM duty pa c t a rate

.

1-1 h e rule e e d full s l n trucks d a

ince C e r c r in cars e 2 m olum A g and distribution for each vehicle s

After 19 ite ers were with in 0.0 of CSM ist ribut T he calculation i ns could ve been s ped soon , but they ere contin d to obse e how the num ould con rge.

Aft segment stributi on ere comp

u e applied to the or i vehicle vol ach uniqu manufact e models lume). T total for ch manufactur model y total fo h manu urer segment g iv
ent ltiplier t t can be a l ied to ea vehicle b ed on its nufactur and segm

The a or each el year ed (20 ultipli are then a o each veh le in 2008 odel year which giv a volume or each vehicle in 2011 through 2016 model year. The “reference case” (which is t b

e ing presented in section 4.2.



### e re
es upd 

### fle

ro 

### et

jec 

### d

re n 

### fr o

ce 

### N

20 

### PR

16) 

### he

le 

### Fi

for 

### le

l 

### ?

a p fe 1- fi na rule, though the baseline fleet (MY 2008) re mains unchanged. The NPRM reference , 2009 projections and the April 2009 Annual fleet used C Energy O orldwide’s second quarter The referen for the fi rule uses lter ed vers of CSM Worldwide urth quarter, 009 project ons, and (as oted above) an early vers ion of the 2010 A l Energy Out look that wa released in ecember of 009.

CSM rldwide’s sta ndard foreca st incl udes h avy duty cla s 2b and hea vy duty class 3 veh . Including th ese extra ve icl es in the s econd quarte r forecast cau sed the baseline an increased number of f ll size truck and full siz vans. EPA id not discover this screpancy bet ween CSM’s defi nition o heavy truck and the one u sed by NHTSA/E ntil after the p roposal had been publish d. For the f nal rule, the PA requested to filter their 4th quarter p rojections to only include light duty an d medium ssenger vehi les. The fil ered CSM d ta allowed th e agencies to produce

a more accu projection of future sales

Table 2 and 1-13 b elow show t e difference in volumes p r each CSM segment in the final baseline and the NPRM r ference. Th significant ecrease in th e number of ize vans can be attributed to the new fi tered versio of the CSM data. Full size show some d ecrease, but id not have s obvious an impact from the data filtering s SM increase their predict d volume fo full size tru ks. The ove all decrease and trucks fo r some of th year in the 011-2016 ti e frame is d ue to the decreased v es projected in the 2010 nnual Ener y Outlook.

1-27Joint Technical Support Document

Table 1-12 D ifference Truc k Segment Volu P

2015 mes (Final-N RM)

Model 2011 2012 2013 2014 2016
Full-Size Pic kup 3 0 9 (36,6 9) 209,466 224,624 (74,421) (11 ,407) (103,4 5)
Mid-Size Pi cku ) 5 9 p 56,1 53 (91,457 (61,634) (90,397) (14 ,580) (180,0 9)
Full-Size Van 4 ) ( 5 7 (138,3 6) (118,196 (214,794) 216,209) (22 ,771) (218,7 6)
Mid-Size Va n 8 ) 2 (119,4 0) (39,675 136,637 159,127 2 4,405 219,7 98
Mid-Size M AV 1 ) 5 1 (969 1) (92,668 (68,927) (58,747) (4 ,077) (43,4 3)
Small MA V 4 ) ( 4 6 (745 5) (172,269 (231,829) 230,774) (23 ,930) (227,6 7)
Full-Size SU V 4 75,9 09 54,140 90,095 128,390 1 4,629 140,8 61
Mid-Size SUV 5 85,2 65 114,268 99,228 64,284 2,735 51,5 63
Small SU V 0 ) 6 6 (42,8 7) (31,396 (41,079) (31,821) (1 ,856) (16,0 4)
Full-Size CUV (48,893) 11,197 (32,095) (35,457) (62,356) (59,796)
Mid-Size CUV 85,731 80,897 (67,541) 15,205 57,194 59,019
Small CUV (35,350) 109,182 253,249 353,827 307,846 304,197
All Trucks (289,913) 33,489 85,933 (16,993) (54,166) (73,873)

Table 1-13 Difference Car Seg ment Volumes (Final-NPRM)

Model 2011 2012 2013 2014 2015 2016
Full-Size Car 245,467 219,990 117,266 60,568 52,287 54,945
Mid-Size Car 179,734 (88,360) (109,151) (102,313) (119,548) (116,040)
Small Car (3 ,821 26,360 33,401 64,311) (47,099) 91,824 39 Subcompact/Mini Car 422 202,059 167,808 138371 146,009 10,613 107,
Lux 1,97 98,741 32 25 ury Car 1 ,604 55,014 78055 83,9 Specialty Car (17 ,256) (267,196) 6,546) (255347 59,743) 3,163) (176 (29 ) (2
Others (12, 2,350) (14,739) (17,811) (20646) (21,153) 178) (1
All Ca (111,86 102,089 52,668 ,460) (10,0467 (78,656) rs 8) (93 )

1-14 below shows ifferences in projected manufacturer volu between the final rule projected volumes and the NPRM projected volumes. The 2010 Annual Energy Outlook reduced the volumes for 2011, and 2014-2016. This would have reduced all of the manufacturer projected pr oduction volu if we had not us e 4th quarter

re optimistic, and the baseline for the final rule has 399,762 for Chrysler’s cars and 462,150 for Chrysler’s trucks in 2016. Table the d mes

mes ed th CSM projections. All manufactures that have an increase shown in those years are projected by CSM to have increased mark et share. The most notable change in projected production volume is from Chrysl er. Chrysler was projected to have 138,602 cars, and 109,674 trucks for 2016 in the NPRM based on using 2nd quarter CSM (and 2009 Annual Energy Outlook). 4th quarter CSM pr ojections for Chrysler are much mo

1-28The Baseline and Reference Vehicle Fleet

Table 1-14 Difference Car and Truck Vo lumes by Manufactu rer (Final-NPRM)

Manufacturer 2011 2012 2013 2014 2015 2016

Total Vehicles All Manufacturers (401,780) 135,578 138,601 (110,453) (154,633) (152,529)

All Manufactur 279 82,731 ers Trucks (89,568) 268,005 318,204 136,923 109,

All Manufacturers Cars (312,2 ) (235,260) 12) (132,427) (179,603) (247,376) (263,912

BMW T 6 9 rucks 2,770 61,336 41,601 46,988 36,57 35,180

BMW Cars ( 4 1,5 6 (2 5 70 87) ,160 9,286) 17,73 1 39,9 8 42, 0 Chrysler Trucks 33 4, 409 34 2 76 846 ,643 9,651 92,257 362,932 352,4 Chrysler Cars 13 2, 230 25 2 60 623 ,827 1,382 42,273 255,900 261,1

Ford Trucks (15 1,0 73 (16 (2 ( ) 2) 56) (1 ,698) 7,456) 36,552) 294,109 (291,60

Ford Cars 114,699 119,923 61,456 121,665 39,295 47,956

Subaru Trucks (13,064) 9,058 12,351 22,381 42,630 43,160

Subaru Cars ,158 75,882 16,044 34,293 39,224 49,380 74

GENERAL MOTORS Trucks 33 93 19,092 ,594 301,275 215,632 (6,059) 23,9

GENERAL MOTORS Cars 92 5) (305,754) ,543 10 (7 ) (2 8) 1,513 5,325 55,94 (302,49

HONDA Trucks 14 (35 (4 ) ) 6)

, 156

, 243) 7 ,772) (5 2,602 (1 30,896 ( 129,95

HONDA Cars (1 45 (248 (28 (4 ) ) 1) ,521) ,912) 7,759) 06,166 (650,062 (662,74

HYUNDAI Trucks ( 80 (82 (3 ) 7) ,886) ,864) 4,222) (30,417 (38,372) (37,77

DAI Cars (188,1 81) (204,2 (163,4 (167,3 ) (79,342) (78,446) 55) 01) 47 HYUN
Tat a Trucks (618) (12,521) (12,506) (13,041) (4,879) (4,879)

T ata Cars 4,615 13,499 17,706 13,782 23,165 23, 905

Kia Trucks (9,888) 8,854 28,177 42,230 48,498 46, 803

Kia Cars 3,362 (4,913) 16,985 95,993 191,184 196,973

Mazda Trucks (1, 909) (11,147) (6,352) 861 14,853 14,160

Mazda C ars (25,356) (30,361) 11,242 39,804 73,259 75,028

Daimler Trucks (1,296) (1,299) (1,152) 11,265 16,512 15,7 40

Daimler Cars 36,309 39,481 12,426 40,218 34,189 35 ,735

Mitsubishi Trucks 36,069 38,193 34,739 43,711 49,546 48,149

Mitsubishi Cars 36,473 45,440 43,385 31,673 29,942 29,228

Nissan Trucks (89,599) (72,880) (87,000) (68,513) (62,754) (63, 585)

Nissan Cars (182,244) (134,666) (199,962) (219,176) (178,793) (176,968)

HE Trucks (8,270) (7,577) (6,031) (3,346) 323 (603) PORSC
PORSC (3,755) HE Cars (5,853) 9,511 7,486 (4,446) (3,750)

Suzuki Trucks (18,299) (25,798) (13,193) (10,485) (6, 657) (6,548)

Suzuki Cars (41,871) (14,240) (4,439) 9,117 24,635 25 ,575

TOYOTA Trucks (171,045) (132,755) (28,733) 50,277 25,938 18,761

TOYOTA Cars (118,360) (106,708) 79,278 93,978 62,371 72,407

VOLKSWAGEN Trucks (25,074) (4,573) 40,469 47,969 25,144 24 ,160

VOLKSWAGEN Cars 92 10,979 40,000 50,093 102,448 106,486



### .3.4 What are the sales volumes an 1 d charac teristics of the reference fleet?

Table 1-15 and Table 1-16 below contain the sales vol umes and percentages that result from the process above for MY 2008 and 2011-2016.

1-29Joint Technical Support Document

Table 1-15 Vehicle Segment Volumes

Actual and Projected Sales Volume Refe rence Class Segm 015 2016 ent a 2008 2011 2012 2013 2014 2 Large Auto 557,693 479,329 438,780 422,094 438,346 461,663 474,260 Mid-Size Auto 3,097,859 2,747,665 3, 028,262 3,174,985 3,361,926 3,524,000 3,619,798 Compact Auto 1,976,424 1,671,674 2, 024,030 2,232,209 2,340,408 2,454,466 2,520,777 Sub-Compact Auto 1,364,434 1,763,860 2,222,516 2,483,365 2,564,754 2,663,349 2,735,809
All Cars 6,996,410 6,662,527 7,713, 589 8,312,653 8,705,434 9,103,479 9, 350,644

Large Pickup 1,581,880 1,515,406 1,649,043 1,774,535 1,629,457 1,594,363 1,550,212 Small Pickup 177,497 120,816 128,754 168,070 150,225 129,421 125,837 Large SUV 2,783,949 2,692,979 2,808, 069 2,796,947 2,904,358 2,932,045 2,851,169 Mid-Size SUV 1,263,360 1,462,069 1,630, 310 1,677,100 1,722,783 1,687,445 1, 645,170
Small SUV 285,355 113,652 111, 539 97,954 90,729 80,754 78,518
Mini Van 642,055 631,390 665, 307 799,280 784,215 737,003 716,594 Cargo Van 110,858 181,911 215, 174 209,684 192,590 189,124 183,887
A ll Trucks 6,844,954 6,718,223 7,208,196 7,523,569 7,474,358 7,350,155 7,151,387
a This is n o longer the CSM segment, but the segmen t used for the subsequent analysis in this rule.

Table 1-16 Manufacturer Volumes2008BaselineSales2011ProjectedVolume2012ProjectedVolume2013ProjectedVolume2014ProjectedVolume2015ProjectedVolume2016ProjectedVolume

Total Vehicles 13,841,364 13,380,750 14,921, 784 15,836,222 16,179,793 16,453,6 34 16,502 ,032
All Trucks 6,844,954 6,718,223 7,208,196 7,523,569 7,474,358 7,350,155 7,151,387
All Cars 6,996,410 6,662,527 7,713, 589 8,312,653 8,705,434 9,103 ,479 9,350,644
BMW Trucks 61,324 202,729 204,197 183,550 191,010 175,612 170,749
BMW Cars ,653 422,874 291,796 230,986 289,631 293,905 369,979 411 Chrysler 462,150 Trucks 956,792 737,504 692,115 594,092 514,802 475,312 Chrysler Cars 700,413 326,327 409,462 426,454 411,319 392,483 399,762 Ford Trucks 846,596 792,527 851,877 940,080 965,589 936,781 910,840 Ford Cars 1,021,792 1,344,461 1,468,182 1,485,801 1,567,762 1,5 42,470 1,55 9,310 Subaru Trucks 82,546 104,832 97,935 89,944 99,293 116 ,05 5 117,295 Subaru Cars 116,035 169,688 183 ,486 175,170 184,5 21 204,746 206,903 GM Trucks 1,512,047 1,347,521 1,510,917 1,536,070 1,336,797 1,379,8 13 1,34 1,604 GM Cars 1,607,460 1,248,909 1,586,094 1,544,975 1,452,559 1,487,318 1,514, 479 Honda Trucks 505,140 585,334 634,705 676,729 634,606 560 ,745 545,217 Honda Cars 1,006,639 850,875 906,096 1,064,848 1,087,076 912,434 930,350 Hyundai Trucks 53,158 46,314 58,164 101,529 103,857 94,6 06 91,986 Hyundai Cars 337,869 382,250 376 ,284 395,573 395,515 511,236 518,445 Tata Trucks 55,584 30,726 29,647 33,749 40,294 43,70 3 42,493 Tata Cars 9,596 23,187 36,377 50,527 49,316 63,7 51 65,489 Kia Trucks 59,472 87,819 87,643 102,773 114,423 118 ,391 115,113 Kia Cars 221,980 305,709 299,611 326,652 427,191 53 8,717 548,055 Mazda Trucks 55,885 57,873 60,783 64,784 67,780 74 ,2 13 72,158 Mazda Cars 246,661 292,592 283,128 329,911 378,291 413,328 420,516

1-30The Baseline and Reference Vehicle Fleet

Daimler Trucks 79,135 96,630 108,053 114,531 136,455 129,87 8 126,281 Daimler Cars 208,052 213,801 211,652 202,559 244,554 263,751 270,940 Mitsubishi Trucks 15,371 45,337 48,290 46,179 52,835 56,89 6 55,320 Mitsubishi Cars 85,358 104,252 110,284 104,555 88,150 82,3 10 82,688 Nissan Trucks 305,546 331,067 405,017 391,572 406,045 3 91,733 380,886 Nissan Cars 717,869 611 ,629 824, 030 831,607 854,131 925,478 946,518 Porsche Trucks 18,797 13,131 13,190 14,608 16,033 17 ,145 16,670
Porsche C 26 33,309 ars 18,909 25,056 41,117 43,299 34,024 32,4 Suzuki Trucks 35,319 6,895 4,593 16,557 20,060 2 0,547 19,978 Suzuki Cars 79,339 49,484 72,297 81,781 90,597 100,6 00 103,003 Toyota Trucks 951,136 888,116 886,621 990,315 1,095,949 1,107,261 1,076,598 Toyota Cars 1,260,364 1,355,639 1,591,054 1,941,480 2,079,011 2,176,644 2, 226,522 VW Trucks 26,999 83,403 104,842 141,421 151,992 127,888 124,346 VW Cars 290,385 388,146 434,412 498,641 517,978 567,711 583,185

Table 1-17 also shows how the change in fleet make-up may affect the footprint distributions over time. The resulting data indicate that footprint will not change significantly between 2008 and 2016. There will be an increase in the number of cars sold, which will cause the average footprints fo r cars and t rucks combined to be slightly

ections were made w ithout accounting for the effe

Foot Print Mean for Car & Truck

Combined Foot Print Mean for Car Foot Print Mean for Truck small er (about 2%). This is the result of AEO projecting an increased number of cars, and CSM predicting that most of that increase will be in the subcompact segment. It is important to note that the AEO and CSM proj cts of this rulemaking, and thus are not influenced by the new regulations. e

Table 1-17 Production Foot Print Mean

Model Year

2 008 49.0 45.4 54.1
2 011 48.9 45.2 54.2
2 012 48.6 45.0 54.3
2 013 48.5 44.9 54.3 2014 48.2 44.9 54.0
2 015 4 8.2 44.9 54.0 2016 48.1 44.9 54.0

Table 1-18 and Table 1-19 below show the changes in engine cylinders over the model years. The current assumptions show that engines will be downsized over the model years to which these rules apply. The biggest projected shift occurs between MY 2008 and 2013. This shift is a projected conseq uence of the expected changes in class

e Refer to the memo to the docket (Docket EPA-HQ-OAR-2009-0472) for a more detailed description of the updated AEO2010 fl eet projection.

1-31Joint Technical Support Document

and segment mix as predicted by AEO a nd CSM, and does not represent engine downsizing attributable to the rules.

Table 1-18 Truck Percentages of 4, 6, 8 Cylinder Engines by Model Year
Percent of Total Sales of 4,6,8 Cylinders Trucks Model 2008 2011 2012 2013 2014 2015 2016 All Trucks 8 Cylinders 33.7% 31.4% 30.9% 29.7% 28.3% 28.8% 28.8% All Trucks 6 Cylinders 55.8% 57.1% 57.1% 58.2% 59.4% 59.0% 59.0% All Trucks 4 Cylinders 10.5% 11.5% 12.0% 12.0% 12.4% 12.2% 12.3%

Table 1-19 Car Percentages of 4, 6, 8 Cylinder Engines by Model Year
Percent of Total Sales of 4,6,8 Cylinders Cars Model 2008 2011 2012 2013 2014 2015 2016 All Cars 8 Cylinders 5.2% 5.2% 5.0% 4.8% 4.8% 5.0% 5.1% All Cars 6 Cylinders 36.7% 34.5% 33.0% 31.9% 32.2% 32.3% 32.2% All Cars 4 Cylinders 58.1% 60.3% 62.0% 63.3% 63.0% 62.6% 62.7%



### 1.3.5 How is the development of the baselin e and reference fleet for this final rule different from NHTSA’s histor ical approach and why is this approach preferable?

As described in detail at the opening of this chapter, and as explained at proposal, NHTSA has historically based its analysis of potential new CAFE standards on detailed product plans the agency has requested from manufacturers planning to produce light vehicles for sale in the United States. Although the agency has never compelled manufacturers to submit such information, mo st major manufacturers and some smaller manufacturers have voluntarily provided it when requested.

As in other prior rulemakings, NHTSA has requested extensive and detailed information regarding the models that ma nufacturers plan to offer, as well as manufacturers’ estimates of th e volume of each model they e xpect to produce for sale in the U.S. NHTSA’s recent requests have sought information regarding a range of engineering and planning characte ristics for each vehicle model ( e.g. , fuel economy, engine, transmission, physical dimensions, wei ghts and capacities, redesign schedules), each engine ( e.g. , fuel type, fuel delivery, aspira tion, valvetrain configuration, valve timing, valve lift, power and torque ratings), and each transmission ( e.g. , type, number of gears, logic).

The information that manufacturers have pr ovided in response to this request has varied in completeness and detail. Some ma nufacturers have submitted nearly all of the information NHTSA has requested, for most or all of the model years covered by NHTSA’s requests, and have closely fo llowed NHTSA’s guidance regarding the structure of the information. Other manuf acturers have submitted partial information,

1-32The Baseline and Reference Vehicle Fleet

1-33 information for only a few model years mation in a structure less amenable

analysis. Still other manufacturers have not responded to NHTSA’s requests or have esponded on occasion, usually with partial information.

One of the primary reasons for the request for data in 2009 was to obtain ission from the manufactur ers to make public their pr oduct plan information for model years 2010 and 2011. There are a numbe r of reasons that this could be

may not be captured by this approach of sole ly using publicly available information. For s some vehicles for which anufacturers have announced plans for elim ination or drastic production cuts such as odge

ly in market inputs

e amaro, the Ford Fiesta and several publicly announced electric vehicles, incl uding the announcements from Nissan. Nor does it include several MY 2009 and 2010 vehi cles, such as the Honda Insight, the Hyundai Genesis and the Toyota Venza, as the starting point for vehicle definitions was MY 2008. Additionally, the market forecast does not account for publicly-announced technology introductions, such as Ford’s Ec oBoost system, whose product plans specify which vehicles and how many are planned to ha ve this technology. Were the agencies to rely on manufacturers’ product plans, this market forecast would account for not only these specific examples, but also for simila r examples that have not yet been announced publicly.

Finally, some technical information manu facturers that have provided in product plans regarding specific vehicl e models is, at least as far as NHTSA and EPA have been able to determine, not available from public or commercial sources. While such gaps do not bear significantly on the agencies’ anal ysis, the diversity of pickup configurations necessitated utilizing a sales-weighted average footprint value f for many manufacturers’ pickups. Since the modeling only utilizes footprint in order to estimate each manufacturer’s CO 2 or fuel economy standard and all th e other vehicle characteristics are , and/or infor
to

r

perm

advantageous in the development of a refere nce fleet. First some changes to the fleet

example, the agencies’ current market forecast include

m the Chevrolet Trailblazer, the Chrysler PT Cruiser, the Chrysler Pacifica, the D Magnum, the Ford Crown Victoria, the Humm er H2, the Mercury Sable, the Pontiac and Prix, and the Pontiac G5. These vehicle models appear explicit Gr to NHTSA’s analysis, and are among those ve hicle models included in the aggregated vehicle types appearing in market inputs to EPA’s analysis.

Second, the agencies’ market forecast does not include some forthcoming vehicl models, such as the Chevrolet Volt, the Chevrolet C

f A full-size pickup might be offered with various combinations of cab style ( e.g. , regular, extended, crew) and box length ( e.g. , 51⁄2’, 61⁄2’, 8’) and, therefore, multiple footprint sizes. CAFE compliance data for MY2008 data does not contain footprint information, and does not contain information that can be used to reliably identify which pickup entries correspond to fo otprint values estimable from public or commercial sources. Therefore, the agencies have used the known production levels of averag e values to represent all variants of a given pickup line ( e.g. , all variants of the F-150 and the Sierra/Silverado) in order to calculate the sales-weighted average footprint value for each pickup family. Again, this has little impact on the results of the modeling effort. In the extreme, one single footprint value could be used for every vehicle sold by a single manufacturer as long as the fuel ec onomy standard associated with this footprint value represented the sales-weighted, ha rmonic average of the fuel economy standards associated with each vehicle’s footprint values.Joint Technical Support Document

1-34 available for each pickup configuration, this approximation has no practical impact on the projected technology or cost associated with compliance with the various standards evaluated. The only impact which could arise w ould be if the relative sales of the various pickup configurations changed, or if the ag encies were to explore standards with a different shape or attribute. This would necessitate recalculating the average footprint value in order to maintain accuracy.

In an effort to update the 2008 baseline to account for the expected changes in the fleet in the near-term model years 20 09-2011 described above, NHTSA requested permission from the manufactur ers to make this limited product plan information public. Unfortunately, virtually no manuf acturers agreed to allow the use of their da ta after the 2009 model year. A few manufacturers stated we could use their 2009 product plan data after the end of production (December 31). Unfortunately, this does not afford us sufficient time to do the analysis for the fina l rule. Also, the 2009 product plan data that was permitted to be used was not consistent with the data already in the baseline, and in some cases was determined to be inaccurate. Since the agencies were unable to obtain consistent updates, the base line and reference fleets we re not updated beyond 2008 model year for this final rule. The 2008 baseline fleet and projections were instead updated using the latest AEO and CSM data, as explained above.



### 1.3.6

1 B 

### How does manufacturer product plan da ta factor into the baseline used in this final rule?

In the spring and fall of 2009, many manufacturers submitted product plans in response to NHTSA’s recent requests that they do so. NHTSA and EPA both have access to these plans, and both agencies have re viewed them in detail. A small amount of product plan data was used in the development of the baseline. The specific pieces of data are:

• Wheelbase

• Track Width Front

• Track Width Rear

• EPS (Electric Power Steering)

• ROLL (Reduced Rolling Resistance)

• LUB (Advance Lubrication i.e. low weight oil)

• IACC (Improved Electrical Accessories)

• Curb Weight

• GVWR (Gross Vehicle Weight Rating)

The track widths, wheelbase, curb weight , and GVWR for vehicles could have been looked up on the internet (159 were), bu t were taken from the product plans when available for convenience. To ensure accur acy, a sample from each product plan was used as a check against the numbers available from Motortrend.com. These numbers will be published in the baseline file since they can be easily looked up on the internet. On the other hand, EPS, ROLL, LUB, and IACC are difficult to determine without using manufacturer’s product plans. These items w ill not be published in the baseline file, but the data has been aggregated into the agenci es’ baseline in the technology effectivenessThe Baseline and Reference Vehicle Fleet

1-35 and cost effectiveness for each vehicle in a way that allows the baseline for the model to be published without reveali ng the manufacturers data.Joint Technical Support Document

1-36 References

1 CFEIS (Certification Fuel Emissions Information Syst em) Database: CFEIS is an EPA database system.

2 WardsAuto.com: Used as a source for engine specifications.

3 Motortrend.com and Edmunds.com: Used as a source for foot print and vehicle weight data.

4 Energy Information Administration’s 2009 Annual Energy Outlook.

5 CSM World Wide, CSM World Wide is a paid service prov ider; J.D. Power Automotive Forecasting, J.D. Power Automotive Forecasting is a paid service provider.

6 The baseline Excel file (“2008-2016 Production Su mmary Data _Definitions Docket 08_27_2009”) is available in the docket (Docket EPA-HQ-OAR-2009-0472).What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?



## CHAPTER 2: What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed?

2.1 Standards are attribute-based and are defined by a mathematical function

As discussed in Section II.B of the final rule preamble, NHTSA and EPA are setting attribute-based CAFE and CO 2 standards for MYs 2012-2016 passenger cars and light trucks, which are expressed by a mathematical func tion. EPCA, as amended by EISA, expressly requires that CAFE standards for passenger cars and light trucks be based on one or more vehicle attributes related to fuel economy, and be expressed in the form of a mathematical function. 1 The CAA has no such requirement, although such an approach is permissible under section 202 (a) and EPA has used the attr ibute-based approach in issuing standards under analogous provisions of the CAA ( e.g. , standards for non-road diesel engines using engine size as the attribute). Given the advantages of using a ttribute-based standards for light duty vehicles, and given the goal of coordinating and harmonizing CO 2 standards promulgated under the CAA and CAFE standards promulga ted under CAA and EPCA, as expressed by President Obama in his announcement of the ne w National Program and by the agencies in the joint NOI and NPRM, EPA is also finaliz ing standards that are attribute-based and expressed as mathematical functions.

Under an attribute-based st andard approach, each vehi cle model has a fuel economy (mpg) or CO 2 target, the stringency of which depends on the value of the attribute for the vehicle model. Thus, fuel economy and CO 2 targets are set for individual vehicles, and vary directly with the attribute, i.e. , becoming more stringent as th e attribute decreases and vice versa. For example, size-based ( i.e. , size-indexed) standards assign higher fuel economy targets (lower CO 2 targets) to smaller vehicles and lower fuel economy targets (higher CO 2 targets) to larger vehicles. However, we emphasize that the target s themselves are not standards. The CAFE or fleet-wide average CO 2 level required of a particular manufacturer is actually determined by calculati ng the production-weighted averag e of the applicable modelspecific fuel economy or CO 2 targets.

The form of the fuel economy or CO 2 standard can have a dramatic effect on the level of improvement required from various types of vehicles and on the relative stringency of the standard for various manufacturers. These disparate impacts derive from two facts concerning the current automotive market: 1) co nsumers currently demand a wide variety of vehicle types and sizes, and 2) individual ma nufacturers focus thei r product offerings on different portions of this demand. Therefore, a comparison of the advantages and disadvantages of attribute-based and flat (universal) st andards needs to cons ider both of these factors.

Methods to reduce the CO 2 emissions from and increase the fuel economy of individual vehicles can be ge nerally grouped into f our main categories: 1) reducing vehicle size, 2) reducing vehicle perfor mance and/or utility, 3) increasing the efficiency of the powertrain and accessories while maintaining constant performance and utility, and 4)

1Joint Technical Support Document

reducing weight and aerodynamic drag while maintaining vehicl e size and utility. a Stringent flat standards encourage all f our of these methods. However, the level of encouragement differs between manufacturers. Manufacturers of generally larger vehicles face a more difficult task in meeting a flat standard than those of generally smaller vehicles. Thus, the stringency of flat standards will tend to be li mited by the capabilities of the manufacturers of larger vehicles. Conversely, manufacturers of smaller vehicles will tend to face lower compliance costs and may not even need to change their vehicles at all. b Thus, the overall level of achieved stringency of a flat standard ma y be limited. A flat standard will encourage smaller, less powerful vehicles, as these vehicl es face the least compliance costs. Sales will tend to be shifted to manufactur ers of generally smaller vehicl es as their overall compliance costs will be lower than those of manufacturers of average or la rger vehicles. Finally, because smaller vehicles face lower costs than larger vehicles, consumers will be encouraged to purchase smaller vehicles. Or, to avoid these out comes, the standard may not be of sufficient stringency to result in significant fuel economy improvements or emission reductions.

Attribute-based fuel economy and CO 2 standards are preferable to universal industrywide average standards for several reasons. Fi rst, if the shape is chosen properly, every manufacturer is more likely to be required to continue adding more fuel efficient technology each year, because the level of the compliance ob ligation of each manufac turer is based on its own particular product mix.

Second, attribute-based standards minimize th e incentive for manufacturers to respond to CAFE and CO 2 standards in ways that may be harmful to safety. c Because each vehicle model has its own target based on the attrib ute chosen, attribute-based standards can, depending on the corresponding mathematical function ( i.e. , the shape of the attribute based standards), reduce or eliminate the incentive to bu ild smaller vehicles simply to meet a fleetwide average. Since smaller vehicles are subj ect to more stringent fuel economy targets, a manufacturer’s increasing its proportion of smaller vehicles would not necessarily ease its compliance obligation. For the purposes of this rule, the agencies believe that setting attribute-based standards with car eful attention to th e slope of the target curves will not encourage wholesale changes in fleet mix to either smaller or larger vehicles.

a Another method of increasing fleet fuel economy is to increase the number of high-fuel-economy vehicles and reduce production of low-fuel-economy vehicles, for inst ance, by changing their relative prices. EPA and NHTSA believe that existing technologies allow automake rs to meet the requiremen ts of the rules without changing the mix of vehicles produ ced (although the rules do not prohibit such changes in mix).

b Trading of credits could encourag e fuel economy improvements or CO 2 emission reductions from manufacturers of smaller vehicles. This would probabl y create a shift of wealth between the two sets of manufacturers. However, most manufacturers have indicated that they would be unlikely to trade credits with their direct competitors. In addition, EPCA does not permit NHTSA, in determining maximum feasible CAFE standards, to consider manufacturers’ ability to earn, transfer, trade, and use CAFE credits.

c The 2002 NAS Report described at length and quantified the potential safety problem with average fuel economy standards that specify a single numerical requirement for the entire industry. See NAS Report at 5, finding 12.

2What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

Third, attribute-based standards provide a more equitable regulatory framework for different vehicle manufacturers. 2 A flat industry-wide average standard imposes disproportionate compliance obligat ions on (full-line) manufacturers that need to significantly change their product plans and little obligati on on those manufacturers that have minimal need to change their plans because, for example, they may build smaller vehicles. Attributebased standards that have the proper shape spread the regulatory cost burden for fuel economy more broadly across all of the vehicl e manufacturers within the industry.

And fourth, attribute-based standards re spect economic conditi ons and consumer choice. Manufacturers are require d to invest in technologies th at improve the fuel economy of their fleets, regardless of ve hicle mix. Thus the consumer is likely to enjoy the same wide range of vehicles to choose from in 2016 as they do now. The differe nce will be, of course, that the vehicles will be more fuel efficient.

The agencies recognize that, because ma nufacturers’ compliance obligations under attribute-based standards are base d in part on the mix of vehicles that they produce, the fuel savings and emissions reductions produced under attribute-based standards can vary depending on market conditions. For example, fuel prices lower than those anticipated at the time of rulemaking could tend to shift consum er demand toward larger vehicles. If manufacturers sell a greater number of larger vehicles than the agencies anticipate, fuel savings and CO 2 reductions would be lower th an anticipated. In contrast, if fuel prices rise significantly, more fuel savings and CO 2 reductions than anticipate d should be likely.

One potential way to mitigate the variability of results under attribute-based standards due to market conditions is through the use of a universal standard—that is, an average standard set at a (single) absolute level below which manufacturers’ fuel economy/CO 2 standards may not drop/exceed. This is ofte n described as a “back stop standard.” For purposes of the CAFE program, EISA expre ssly requires a backstop for domesticallymanufactured passenger cars—a universal minimu m, non-attribute-based standard of either “27.5 mpg or 92 percent of the average fuel economy projected by the Secretary of Transportation for the combined domestic a nd non-domestic passenger automobile fleets manufactured for sale in the United States by all manufacturers in the model year...” 3

, whichever is greater. Backstops are generally permissible unde r section 202(a) of the CAA (as are universal st andards ge nerally).

For purposes of the CAFE and CO 2 standards proposed in the NPRM, NHTSA and EPA recognized that the risk, even if small, does exist that low fuel prices in MYs 2012-2016 might lead indirectly to less than currently an ticipated fuel savings and emissions reductions. The NPRM sought comment on whether backstop st andards, or any other method within the agencies’ statutory authority, should and can be implemented for the import and light truck fleets in order to achieve the fuel savings that attribute-based standard s might not absolutely guarantee.

As discussed in Section II.C of the final rule preamble, the agencies have reviewed the comments received on this issue and have made the policy choice not to adopt backstop standards for MYs 2012-2016 other than those for domestic passenger cars, as required by EISA. EPA and NHTSA remain confident that th eir projections of the future fleet mix are

3Joint Technical Support Document

reliable, and that future changes in the fleet mix of footprints and sales ar e not likely to lead to more than modest changes in projected emissions reductions or fuel savings. Both agencies remain confident in these fleet projections a nd the resulting emissions reductions and fuel savings from the standards. As explained in section II.B of the preamble and chapter 1 of the TSD, the agencies’ projections of the future fleet are based on the best, transparent data obtained to date. In addition, th ere are only a relatively few mode l years at issue. Moreover, market trends today are consistent with the agencies’ estimates showing shifts from light trucks to passenger cars and increased emphasi s on fuel economy. Finally, the shapes of the curves, including the “flattening” at the larges t footprint values, tend to avoid or minimize regulatory incentives for a manufacturer to upsi ze their fleet to change their compliance burden.

At the same time, adding another backst op standard would directionally impose increased costs for some manufacturers, with greater costs commensurate with more stringent backstops. This would occur unless the backstop standard was relatively weak, in which case it would have a minimal effect. It would be difficult to establish the level of a backstop standard such that costs are li kely to be imposed on manufacturer s only when there is a failure to achieve the projected reductions across the indu stry as a whole. An example of this could be when there is a significant sh ift to larger vehicles across the industry as a whole, or if there is a general market shift from cars to trucks. The problem the agencies are concerned about is not with respect to any single manufacturer, but rather is base d on concerns over shifts across the fleet as a whole, as compared to shifts in one manufacturer's fleet that may be more than offset by shifts the other way in another manuf acturer's fleet. However a backstop does not directly deal with fleetwide tr ends because it acts as a manuf acturer specific standard. The concept of a ratchet mechanism recognizes this issue, and aims to impose the new more stringent standard only when the problem arises across the industry as a whole. However, this approach fails to provide the lead time necessary for making vehicle changes to meet a new, more stringent standard.

In addition, we do not believe that the risk of vehicle upsizing or changing vehicle offerings to “game” the passenger car and light tr uck definitions is as great as commenters imply for the model years in question. The ch anges that commenters suggest manufacturers might make are neither so simple nor so lik ely to be accepted by consumers. For example, 4WD versions of vehicles tend to be more e xpensive and, other things being equal, have inherently lower fuel economy than their 2WD equivalent models. Therefore, many consumers may not desire to purchase 4WD vehicles because of the cost premium; conversely, many manufacturers often require th e 2WD option to satisfy demand for base vehicle models. Additionally, increasing the foot print of vehicles requires platform changes, which usually requires a product redesign phase (the agencies estimate that this occurs on average once every 5 years for a model). Alternatively, turning many 2WD SUVs into (2WD) light trucks would require manufacturer s to squeeze a third row of seats in or significantly increase their GVWR , which also requires a signifi cant change in the vehicle.

For these reasons, and in the interest of harmonization and given that neither EISA nor the CAA provide specific directiv es on this issue plus the agenci es’ belief that backstops are not the only mechanisms to ensure fuel savi ngs and emissions reductions, the agencies have reached the conclusion not to incorporate backst ops in these final rules but will consider the

4What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

issue further if the evidence warrants. The agenci es discuss this issue further in the final rule preamble.



### 2.2 What attribute do the agencies use, and why?

Consistent with the MY 2011 CAFE standa rds, EPA and NHTSA are using footprint as the attribute for the MY 2012-2016 CAFE sta ndards and CO2 emissions standards. There are several policy reasons why the agencies beli eve that footprint is the most appropriate attribute on which to base the st andards, as discussed below.

It is important that the CAFE and CO 2 standards be set in a way that does not encourage manufacturers to respond by selling vehicles that are in any way less safe. Although NHTSA’s research also indicates that certain types of reductions in vehicle mass tend to compromise vehicle safety, footprint-ba sed standards provide an incentive to use new and advanced lightweight materi als and structures that coul d be otherwise discouraged by weight-based standards, because manufacturers can use them to improve a vehicle’s fuel economy without their use necessarily resulting in a change in the vehicle’s target level of fuel economy or CO 2 emissions and without a substantial impact on th e safety (in terms of crashworthiness) of that vehicle.

Further, although the agencies recognize th e point raised by some commenters that weight is better correlated with fuel economy and CO 2 emissions than is footprint, the agencies continue to believe that there is less risk of “gaming” (artif icial manipulation of the attribute(s) to achieve a more favorable target ) by increasing footprin t under footprint-based standards than by increasing ve hicle mass under weight-based st andards—it is relatively easy for a manufacturer to add enough weight to a vehi cle to decrease its appl icable fuel economy target by a significant amount, as compared to in creasing vehicle footprint. For a manufacturer to change a vehi cle’s footprint, a significant re design of the chassis and many components would be required. (See the relate d discussion of this issue above in the discussion of whether to adopt universal backstop standards.) In addition, despite requests from some manufacturers that the agencies cons ider multi-attribute standards, or adjust the target curves to “accommodate” other attributes like towing/hauling capa bility, the agencies continue to agree with concerns raised in 2008 by some commenters to NHTSA’s MY 2011 CAFE rulemaking that there would be greate r potential for gaming under multi-attribute standards, such as standards under which targ ets would also depend on attributes such as weight, torque, power, towing capability, and/ or off-road capability. Standards that incorporate such attributes in conjunction with footprint would not only be significantly more complex, but by providing degrees of freedom with respect to more easily -adjusted attributes, they would make it less certain that the future fleet would actually achieve the average fuel economy improvement and CO 2 reduction levels projec ted by the agencies.

5Joint Technical Support Document



### 2.3 What mathematical function do the agencies use, and why?

The MY 2011 CAFE standards are defined by a continuous, cons trained logistic function, which takes the form of an S-curve, and is defined according to the following formula:

( )
( ) 1
1 11
1 FOOTPRINT c d

FOOTPRINT c d TARGET

e a ba e −

− = ⎛ ⎞ + −

⎜ ⎟
+ ⎝ ⎠

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint ( FOOTPRINT , in square feet), b and a are the function’s lower and upper asymptotes (also in mpg), e is approximately equal to 2.718, d c is the footprint (in square feet) at which the inverse of the fuel economy targ et falls halfway between the inverses of the lower and upper asymptotes, and d is a parameter (in square feet) that determines how gradually the fuel economy target transitions from the upper toward the lower asymptote as the footprint increases. Figure 2-1 shows an example of a logi stic target function, where b = 20 mpg, a = 30 mpg, c = 40 square feet, and d = 5 square feet.

0 5 10 15 20 25 30 35

0 10 20 30 40 50 60 70 80 90 10 Footprint (Square Feet)Target (Miles per Gallon)

0

L

IMIT UPPER

L

IMIT L OWER

Figure 2-1 Sample Logistic Curve

d e is the irrational number for which the slope of the function y = number x is equal to 1 when x is equal to zero. The first 8 digits of e are 2.7182818.

6What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

After fitting this mathematical form (separately) to the passenger car and light truck fleets and determining the maximum feas ible stringency of the standards ( i.e. , the vertical positions of the curves), NHTSA arrived at the curves shown in Figure 2-2 to define the MY 2011 standards.

20 25 30 35 40 45

35 40 45 50 55 60 65 70 Footprint (sf)Target (mpg) Passenger Cars
Light Trucks

Figure 2-2 MY 2011 CAFE Standards fo r Passenger Cars and Light Trucks

In finalizing the MY 2011 sta ndards, NHTSA noted that the agency is not required to use a constrained logistic functi on and indicated that the agency may consider defining future CAFE standards in terms of a different math ematical function. NHTSA and EPA have done so jointly for the MYs 2012-2016 CAFE standards and CO 2 emissions standards.

In revisiting this question jointly, EP A and NHTSA found that the final MY 2011 CAFE standard for passenger cars, though le ss steep than the MY 2011 standard NHTSA proposed in 2008, continues to concentrate the sl oped portion of the curve (from a compliance perspective, the area in which upsizing results in a slightly lower applicable target) within a relatively narrow footprint range (a pproximately 47-55 square feet). In practical terms, this results in a portion of the fleet that has a larg e change in emissions and fuel consumption with just a minor change in footprint. This potentially increases the incentive for gaming by upsizing slightly to meet less stringent standard s. Further, most passenger car models have

7Joint Technical Support Document

footprints smaller than the curve’s 51.4 squa re foot inflection point , and many passenger car models have footprints at whic h the curve is relatively flat (see figure above). The resulting fit to the data was forced by choice of asymptot es defining the constrai ned logistic function. The agencies discuss below the alternative functi on that seeks to prevent this from occurring.

For both passenger cars and light trucks, a mathematical function that has some slope at most footprints where vehicles are produced is advantageous in term s of fairly balancing regulatory burdens among manufacturers, and in terms of providing a disincentive to respond to new standards by downsizing vehicles in wa ys that compromise vehicle safety. For example, a flat standard has dr awbacks discussed above: it may be very difficult for a full-line manufacturer to meet, while re quiring very little of a manuf acturer concentrating on small vehicles, and a flat standard may provide an incentive to manufacturer s to downsize certain vehicles, in order to “balance out” other vehicles subject to the same standard.

As a potential alternative to the constrained logis tic function, NHTSA had, in proposing MY 2011 standards, presented informa tion regarding a constrained linear function (or piecewise linear function). As shown in the 2008 NPRM, a constrained linear function has the potential to avoid creating a localized re gion (in terms of vehicl e footprint) over which the slope of the function is relatively steep. Although NHTSA did not receive public comments on this option, the agency indicated that it still believed a linear function constrained by upper (on a gpm basis) and possibl y lower limits could merit reconsideration in future CAFE rulemakings.

Having re-examined a piecewise linear func tion or constrained linear function for purposes of the proposed standards, NHTSA a nd EPA tentatively concluded that for both passenger cars and light trucks, it remained important to maintain a function with a meaningfully sloped section covering a wide footprint range, thereby providing a welldistributed disincentive to downsi ze vehicles in ways that coul d compromise highway safety. Also, the function proposed was not so steeply sloped that it would pr ovide a strong incentive to increase vehicle size in order to obta in a lower CAFE requirement and higher CO 2 limit, thereby compromising energy and environmenta l benefits. Therefore, the CAFE and CO 2 emissions standards proposed in the NPRM we re defined by constrained linear or piecewise linear functions.

The NHTSA constrained linear function was defined according to the following formula:

1
1 1

, , TARGET
MIN MAX c FOOTPRINT d a b = ⎡ ⎤ ⎛ ⎞ × + ⎜ ⎟ ⎢ ⎥ ⎝ ⎠ ⎣ ⎦

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint ( FOOTPRINT , in square feet), b and a are the function’s lower and upper asymptotes (also in mpg), respectively, c is the slope (in gpm per square foot) of the sloped portion of the function, and d is the intercept (in gpm) of the sloped portion of the function (that is, the value the sloped por tion would take if extended to a footprint of 0 square feet. The MIN and MAX functions take the minimum and ma ximum, respectively of the included

8What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

values; for example, MIN (1,2) = 1, MAX (1,2) = 2, and MIN [ MAX (1,2),3)]=2. Figure 2-3 shows an example of a linear target function, where a = 0.0241 gpm (41.6 mpg), b = 0.032 gpm (31.2 mpg), c = 0.000531 gpm per square foot, and d = 0.002292 gpm (436 mpg). Because the function is linear on a gpm basis, not an mpg basis, it is plotted on this basis.

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050

35 40 45 50 55 60 65 70

Footprint (sf)Fuel Consumption (gpm)

1/LIMIT UPPER 1/LIMIT LOWER

I NTERCEPT SLOPE

Figure 2-3 Sample Linear Function

The EPA piecewise linear function has th e same shape as the NHTSA equation but has a slightly different mathematical specification. It is defined according to the following formula:

Target = a, if x ≤ l

Target = cx + d, if l < x ≤ h

Target = b, if x > h

This equation also takes the simplified form (consistent with NHTSA’s functional form):

Target = MIN [ MAX ( c * x + d , a) , b]

Where,

9Joint Technical Support Document

Target = the CO2 target value for a given footprint (in g/mi) a = the minimum target value (in g/mi CO 2 ) b = the maximum target value (in g/mi CO 2 ) c = the slope of the linear function (in g/mi per sq ft CO 2 ) d = is the intercept or zero-offset for the line (in g/mi CO 2 ) x = footprint of the vehicle model (in s quare feet, rounded to the nearest tenth) l & h are the lower and higher footprint limits or constraints or the boundary (“kinks”) between the flat regions and the in termediate sloped line (in sq ft)

Figure 2-4 shows the basic curve.

30 35 40 45 50 55 60 65 70 Footprint (sq feet)CO2 (gram/mile) orFuel Consumption (gallons/mile)

lower limit = l Intercept = d upper limit = h

slope = c

min = a max = b

Figure 2-4 The Shape of the Piecew ise Linear Attribute Based Curve
For purposes of the NPRM, NHTSA, working with EPA, developed the basic curve shapes for both agencies’ respective standards, using statistical fitt ing methods similar to those applied by NHTSA in fitting the curves de fining the MY 2011 standards. The first step began with the market inputs discussed above, but because the baseline fleet was technologically heterogeneous, NHTSA used th e CAFE model to deve lop a fleet to which nearly all the technologies listed in Ch. 3 of the TSD e were applied, by taking the following steps: (1) applying all non-diesel and non-hybrid technologies to each vehicles according to

e The agencies excluded diesel engine s and strong hybrid vehicle technologies from this exercise (and only this exercise) because the agencies expect that manufacturers would not need to rely heavily on these technologies in order to comply with the proposed standards. NHTSA and EPA did include diesel engines and strong hybrid vehicle technologies in all other portions of their analyses.

10What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

their applicability, (2) treating all manufacturers as unwilling to pay civil penalties rather than applying technology, (3) applying any technology at any time, irrespective of scheduled vehicle redesigns or freshening, and (4) ignoring “phase-in caps ” that constrain the overall amount of technology that can be applied by the model to a given manufacturer’s fleet. These steps helped to increase technol ogical parity among vehicle mode ls, thereby providing a better basis (than the baseline fleet) for estimating th e statistical relationshi p between vehicle size and fuel economy. In other words, this proce ss normalized the fleet such that differences in technology had a minimal contribution to the variation (scatter) in the data. This process also served to eliminate the influence of differing degrees of pre-existing fuel-saving technology at various footprint values. This slightly fla ttened the slope of the line, indicating that a regression without adjusting fo r different 2008 vehicle techno logies would not only have overestimated the dependence of fuel economy on footprint, but would also have provided an inappropriate incentive to upsize vehicles.

In fitting the curves, EPA and NHTSA also continued to apply constraints to limit the function’s value for both the smallest and larges t vehicles. Without a limit at the smallest footprints, the function—whether logistic or linear—can reach values that would be unfairly burdensome for a manufacturer that elects to focus on the market for small vehicles; depending on the underlying data, an unc onstrained form could apply targets to the smallest vehicles that are simply unachievable. Limiti ng the function’s value for the smallest vehicles thus assures that the function remains t echnologically achievable and economically practicable for manufacturers focusing on small vehicles. Obviously, passenger compartments can only become so small; this means, among other things, that a vehicle’s frontal area—a key determinant of aerodynamic drag—can only become so small. Also, as vehicle size decreases, complyi ng with federal motor vehicle safety standards can necessitate the addition of structural elements that add mass and thereby reduce fuel economy and increase CO2 emissions. On the other side of the function, without a limit at the largest footprints, the function may pr ovide no floor on required fuel economy. Also, the safety considerations that support th e provision of a disincentive for downsizing as a compliance strategy apply weakly—if at all— to the very largest vehicles. Limiting the function’s value for the largest vehicles leads to a function with an inherent absolute minimum level of fuel economy (or maximum emissions), while remaining consistent with safety considerations.

Before fitting the sloped portion of the constrained linear form, NHTSA and EPA selected footprints above and be low which to apply constraints ( i.e. , minimum and maximum values) on the function. For pa ssenger cars, the agencies no ted that several manufacturers offer small and, in some cases, sporty coupes below 41 square feet, examples including the BMW Z4 and Mini, Saturn Sky, Honda F it and S2000, Hyundai Tiburon, Mazda MX-5 Miata, Suzuki SX4, Toyota Yaris, and Volksw agen New Beetle. Because such vehicles represent a small portion (less than 10 percent) of the passenger car market, yet often have characteristics that could make it infeasible to achieve the very challenging targets that could apply in the absence of a cons traint, the agencies proposed to “cut off” the linear portion of the passenger car function at 41 square feet. For consistency, the agencies proposed to do the same for the light truck function, although no lig ht trucks are currently offered below 41 square feet. If the agencies se t the limit for small trucks at a level higher than that for cars, there could be an undue incentive for manufacturers to change th eir car designs to qualify as trucks between the two footprint limits. The ag encies further noted that above 56 square feet,

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the only passenger car model present in the MY 2008 fleet were four luxury vehicles with extremely low sales volumes—the Bentley Arna ge and three versions of the Rolls Royce Phantom. The agencies therefore proposed to “c ut off” the linear por tion of the passenger car function at 56 square feet. Finally, the agen cies noted that although public information is limited regarding the sales volumes of the many di fferent configurations (cab designs and bed sizes) of pickup trucks, the largest pickups ( e.g. , the Ford F-150, GM Sierra/Silverado, Nissan Titan, and Toyota Tundra), appear to fall above 66 square feet in foot print. The agencies therefore proposed to “cut off” the linear portion of the light truck function at 66 square feet. These “cut-off” values of footprint serve as th e constraints to the functions shown above. The lower and upper levels are determined from wh ere the sloped line meets these cut-off points.

Having developed a set of baseline data to which to fit the mathematical fuel consumption function, the initial values for parameters c and d were determined for cars and trucks separately. Values for c and d were initially set such th at the average (equivalently, sum) of the absolute values of the differences was minimized between the “maximum technology” fleet fuel consumption (within the f ootprints between the upper and lower limits) and the straight line function defined above at the same co rresponding vehicle footprints. That is, c and d were determined by minimizing the average absolute residual, commonly known as the MAD (Mean Absolute Deviation) a pproach, of the correspo nding straight line. The curve was fit in fuel consumption ( i.e. , CO 2 ) space rather than fuel economy space because the manufacturer targets are in te rms of the harmonic average fuel economy (or average CO 2 ), and so it is more important that the curve fit the fuel consumption ( i.e. , CO 2 ) data well than that it fit the fuel economy data well. NHTSA also explained in the MY 2011 final rule that it chose to use MAD in this s tep instead of minimizing the sum of the square errors (“least squares,” a more common approach in curve fitting) in order to lessen the influence of outliers.

NHTSA and EPA currently believe that it is more appropriate to use unweighted data in fitting the curve rather than weighting the da ta by sales because of la rge variations in model sales. The agencies find that the market for ecast used for analysis supporting both the NPRM and the final rule exhibits the two key charac teristics that previously led NHTSA to use minimization of the unweighted Mean Absolute Deviation (MAD) rather than weighted leastsquares analysis. First, proj ected model-specific sales volumes in the agencies’ market forecast cover an extremely wide range, such that, as discussed in NHTSA’s rulemaking for MY 2011, although unweighted regre ssion gives low-selling vehicl e models and high-selling vehicle models equal emphasis, sales-weighted regression would give some vehicle models considerably more emphasis than other vehicle models. f The agencies’ intention is to fit a

f For example, the agencies’ market fo recast shows MY 2016 sales of 187, 000 units for Toyota’s 2WD Sienna, and shows 27 model configurations with MY 2016 sales of fewer than 100 units. Similarly, the agencies’ market forecast shows MY 2016 sales of 268, 000 for the Toyota Prius, and shows 29 model configurations with MY 2016 sales of fewer than 100 units. Sales-weighted analysis would give the Toyota Sienna and Prius more than a thousand times the consideration of many vehicle mode l configurations. Sales-weighted analysis would, therefore, cause a large number of vehicle model configurations to be virtually ignored. See discussion in NHTSA’s final rule for MY 2011 passenger car and light truck CAFE standards, 74 Fed. Reg. at 14368 (Mar. 30, 2009), and in NHTSA’s NPRM for that rulemaking, 73 Fed. Reg. at 24423-24429 (May 2, 2008).

12What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

curve that describes a technical relationship between fuel economy and footprint, given comparable levels of technology, and this support s weighting discrete ve hicle models equally. On the other hand, sales weighted regressi on would allow the difference between other vehicle attributes to be reflected in the an alysis, and also would reflect consumer demand.

Second, even after NHTSA’s “maximum technology” analysis to increase technological parity of vehicle models before fitting curves, the agencies’ market forecast contains many significant out liers. As discussed in NHTSA’s rulemaking for MY 2011, MAD is a statistical procedure that has b een demonstrated to produce more efficient parameter estimates than least-squares analys is in the presence of significant outliers. g In addition, the agencies remain concerned that the steeper curves resulti ng from weighted leastsquares analysis would increase the risk that energy savings and environmental benefits would be lower than projected, because the steep er curves would provide a greater incentive to increase sales of larger vehicles with lowe r fuel economy levels. Based on these technical considerations and these concerns regarding po tential outcomes, the ag encies have decided not to re-fit curves using weighted least-square s analysis, but note that they may reconsider using least-squares regre ssion in future analyses.

Finally, the agencies calculated the values of the upper and lower limits based on the corresponding footprint constraint s discussed above (41 and 56 square feet for passenger cars, and 41 and 66 square feet for light trucks).

The result of this methodology is shown below in Figure 2-5 and Figure 2-6 for passenger cars and light trucks, respectively. Th e fitted curves are shown with the underlying “maximum technology” passenger car and light tr uck fleets. For passenger cars, the mean absolute deviation of the sloped portion of th e function was 14 percent. For trucks, the corresponding mean absolute deviation was 10 percent.

g Id. In the case of a dataset not drawn from a sample with a Gaussian, or normal, distribution, there is often a need to employ robust estimation methods rather than rely on least-squares approach to curve fitting. The leastsquares approach has as an underlying assumption that the data are drawn fr om a normal distribution, and hence fits a curve using a sum-of-squares method to minimize er rors. This approach will, in a sample drawn from a non-normal distribution, give excessive weight to outliers by making their presence felt in proportion to the square of their distance from the fitted curve, and, hence, distort the resulting fit. With outliers in the sample, the typical solution is to use a robust method such as a minimum absolute deviation, rather than a squared term, to estimate the fit ( see , e.g. , ‘‘AI Access: Your Access to Data Modeling,’’ at http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_O_P a.htm#Outlier). The effect on the estimation is to let the presence of each observation be felt more uniformly, resulting in a curve more representative of the data ( see , e.g. , Peter Kennedy, A Guide to Econometrics , 3 rd edition, 1992, MIT Press, Cambridge, MA).

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0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060

35 40 45 50 55 60 65 70 75 80 Footprint (sf)Fuel Consumption (gpm)

Figure 2-5 “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function

14What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060

35 40 45 50 55 60 65 70 75 80 Footprint (sf)Fuel Consumption (gpm)

Figure 2-6 “Maximum Technology” Light Truc k with Fitted Constrai ned Linear Function

The agencies used these functional forms as a starting point to develop mathematical functions defining actual proposed standards. As discussed in Sections II.C of the preamble, the agencies transposed these functions vertically ( i.e. , on a gpm basis, uniformly downward) to produce the maximum feasible passenger car and light truck CAFE standards, and corresponding CO 2 emissions standards.

Having considered public comments as discus sed in greater detail in the final rule preamble, NHTSA and EPA have re-examined the development of curves underlying the standards proposed in the NPRM, and are promulgating standards based on the same underlying curves. The agencies have made this decision considering that, while EISA mandates that CAFE standards be defined by a ma thematical function in terms of one or more attributes related to fuel economy, neither EI SA nor the CAA expres sly require that the mathematical function be purely descriptive of an observed or theoretic al dependence of fuel economy on the selected attri bute or attributes. As a means by which CAFE and GHG standards are specified, the mathematical function plays a normative role. h Therefore,

h That is, tending to encourages some types of responses more than others – in this case, generally preferring increased use of fuel-saving technologies over simple reductions in vehicle size.

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NHTSA and EPA have concluded that the mathem atical function can r easonably be selected based on a blend of analytical and policy considerations.

Considering comments by UC Santa Barbara students regarding difficulties reproducing NHTSA’s analysis, NHTSA re-exa mined its analysis, and discovered some erroneous entries in model inputs underlying the analysis used to develop the curves proposed in the NPRM. These errors, which are also discussed in NHTSA’s fi nal Regulatory Impact Analysis (RIA) and have since been corrected, include the fo llowing: incorrect valvetrain phasing and lift inputs for many BMW engines, incorrect indexing for some Daimler models, incorrectly enabled valvetrain technologies for rotary engine s and Atkinson cycle engines, omitted baseline applications of cylinder deac tivation in some Honda and GM engines, incorrect valve phasing codes for some 4-cylinder Chrysler engines, omitted baseline applications of advanced transmissions in so me VW models, incorrectly enabled advanced electrification tec hnologies for several hybrid vehicle models, and incorrect DCT effectiveness estimates for subcompact passenger cars.

After correcting these errors and repeating the curve development analysis presented in the NPRM, NHTSA obtained curves shown be low in Figures 2-7 and 2-8 for passenger cars and light trucks, respectively. The fitted curves are shown with the underlying “maximum technology” passenger car and light tr uck fleets. For passenger cars, the mean absolute deviation of the sloped portion of th e function was 14 percent. For trucks, the corresponding mean absolute deviation was 10 percent.

Figure 2-7 Revised “Maximum Technology” Passenger Fl eet with Fitted Constrained Linear Function

16What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

Figure 2-8 Revised “Maximum Technology” Light Tr uck with Fitted Constrained Linear Function
This refitted passenger car curve is simila r to that presented in the NPRM, and the refitted light truck curve is n early identical the co rresponding curve in the NPRM. However, the slope of the passenger car cu rve is about 27 percent steeper (on a gpm per sf basis) than the curve presented in the NP RM. For passenger cars and light trucks, respectively, figures II.A.2-7 and II.A.2-8 show the results of adju stment—discussed below—of the above curves to yield the average required fuel economy levels corresponding to the announced national policy.

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20 25 30 35 40

35 40 45 50 55 60 65 70 75 80 T a r g e t ( m p g )

Footprint (sf)

45

NPRM

Final

Re-Fitted

Figure 2-9 MY2016 Passenger Car Targets: NPRM, Final Rule, and if Using Re-Fitted Curve

20 25 30 35 40

35 40 45 50 55 60 65 70 75 80 T a r g e t ( m p g )

Footprint (sf)

45

NPRM

Final

Re-Fitted

18What are the Attribute-Based Curves the Agen cies are Using, and How Were They Developed?

Figure 2-10 MY2016 Light Truck Targets: NPRM, Final Rule, and if Using Re-Fitted Curve

As the figures show, the resultant light truck curves are visually i ndistinguishable from one another. For passenger cars, in contrast, the refitted curve would increase stringency for the smallest cars, decrease stringency for the la rgest cars, and provide a greater incentive to increase car size throughout th e range of footprints within which NHTSA and EPA project most passenger car models will be sold th rough MY 2016. The agencies are concerned that these changes would make it unduly difficult for manufacturers to introduce new small passenger cars in the United States, and undul y risk forgoing achievable and cost-effective energy and environmental benefits by increasi ng incentives for the passenger car market to shift toward larger vehicles.

Also, the agencies note that the refitted passenger car curve produces only a slightly closer fit to the corrected fleet than would th e curve estimated in the NPRM; with respect to the corrected fleet (between the “cut off” footprint values, and after the “maximum technology” analysis discussed above), the mean absolute deviation for the refitted curve is 13.887 percent, and that of a ref itted curve held to the original slope is 13.933 percent. In other words, the data support the original slope very nearly as well as they support the refitted slope.

Although these refitted curves reflect the agenci es’ best technical judgment at this time regarding the use of statistical analysis for this purpose, based on NHTSA’s and EPA’s concerns regarding the undesirable changes in incentives that would result from a refitted curve for passenger cars, and given that the data support the original curves essentially as well as they would support refitted curves, the ag encies are finalizing CAFE and GHG standards based on the curves presented in the NPRM.

Finally, regarding some commenters’ inab ility to reproduce the agencies’ NPRM analysis, NHTSA believes that its correction of the errors discussed above and its release (on NHTSA’s web site) of the updated Volpe model and all accompanying inputs and external analysis files should enable outside parties to independently reproduce th e agencies’ analysis. If outside parties continue to experience difficu lty in doing so, we encourage them to contact NHTSA, and the agency will do its best to provide assistance.

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20 References

1 49 U.S.C. 32902(a)(3)(A).

2 Id . at 4-5, finding 10.

3 49 U.S.C. 32902(b)(4).Technologies Considered in the Agencies’ Analysis



## CHAPTER 3: Technologies Considered in the Agencies’ Analysis

This Chapter of the joint TSD desc ribes the technologies NHTSA and EPA evaluated as potential inputs in their respective models a nd provides estimates of the technologies’ costs, effectivene ss and availability. This Chapter also describes, in general terms, how the agencies use these inputs in their respective models . For greater detail on this subject, please see Section V of NHT SA’s FRIA and Chapter 4 of EPA’s RIA.



### 3.1 How do the agencies decide which technologies to include in the analysis?

Technology assumptions, i.e. , assumptions about their availability, cost, effectiveness, and the rate at which they can be incorporated into new vehicles, are often very controversial as they have a significan t impact on the levels of the standards. Agencies must, therefore, take great care in developing a nd justifying these assumptions. In developing technology i nputs for MY 2012-2016 standards the agencies reviewed the technology assumptions th at NHTSA used in setting the MY 2011 standards, the agencies revi ewed the technology input assump tions identified in EPA’s 2008 Staff Technical Report, 1 the comments that NHTSA received in response to its May 2008 Notice of Proposed Rulemaking, and the co mments that the agencies received in response to its September 2009 Notice of Proposed Rulemaking. In addition, the agencies supplemented their review with in formation from the sources described in the following section.



### 3.1.1 Reports and papers in the literature

NHTSA and EPA have done extensive resear ch in identifying the most credible sources of information. These sources incl uded: the 2002 NAS report on the effectiveness and impact of CAFE standards; 2 the 2004 study done by NESCCAF; 3 the California Air Resources Board (CARB) Initial Statemen t of Reasons in su pport of their carbon rulemaking; 4 a 2006 study done by Energy and Environmental Analysis (EEA) for the Department of Energy; 5 a study done by the Martec Group for the Alliance of Automobile Manufacturers, and an upda te by the Martec Group to that study; 6 and vehicle fuel economy certification data. Bo th agencies also reviewed the published technical literature which addressed the issue of CO 2 emission control and fuel economy, such as papers published by the Society of Automotive Engineers and the American Society of Mechanical Engineers, as well as papers submitted by commenters (for example, the Aluminum Asso ciation) to the agencies’ joint NPRM. In addition, confidential data submitted by vehicle manufacturers in resp onse to NHTSA’s request for product plans, 7 and confidential information shared by automotive industry component suppliers in meetings with EPA and NHTSA st aff held during the second half of the 2007 calendar year were used as a cross check of the public data menti oned above and not as a significant basis for this rulemaking.

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EPA also has a contracted study ongoing with FEV that consists of complete system tear-downs to evaluate technologies dow n to the nuts and bolts to arrive at very detailed estimates of the costs associated with manufacturing them. For the NPRM, the agencies used a completed analysis to estim ate costs of turbocha rging with downsizing for I4 engines only. The NPRM stated that tear-down cost estimates from FEV for additional engine and transmission technologi es became available shortly before the release of the NPRM, but not in time to be incorporated into the agencies’ cost analysis of the proposed standards. These preliminary results were made available for review and the agencies stated they would consider this information for use in the final rule analysis. 8 The NPRM also stated that a detailed report would be submitted to the docket on these additional technologies during the public comment period for this proposal. That deadline was not met but all additional technologies for which cost study tear downs have been completed from this study with FEV (studies have now been completed on turbocharging and downsizing for V6 and V8 engines, stoichiometr ic gasoline direct injection, 6/7/8-speed automatic transmission and dual clutch transmission technologies) have been considered for the final rule and de tails are contained in two reports placed in the docket. 9
, 10 EPA and NHTSA reviewed all of the above information in order to develop the best estimates ofavailability, cost and effectiveness of these fuelsaving/CO 2 -reducing technologies.

The agencies would also like to note that per the Energy Independence and Security Act (EISA), the National Academies of Sciences has been conducting a study for NHTSA to update chapter 3 of their 2002 NAS Report, wh ich presents technology effectiveness estimates for light-duty vehicles . The update takes a fresh look at that list of technologies and their associated cost and effectiveness values. The updated NAS report was expected to be available on September 30, 2009, but has not been completed and released to the public. The resu lts from this study thus are unavailable for this rulemaking. The agencies look forward to cons idering the results from this study as part of the next round of rulemaking for CAFE/GHG standards.



### 3.1.2 Fuel economy certification data

Where available, data from recent model years of EPA’s Fuel Economy Certification Data were used for helping to determine the effectiveness of specific technologies. CO 2 and fuel consumption reduction es timates were estimated from EPA’s fuel economy database on the two-cycle (FTP city & highway) fuel economy test results. During the standard fuel economy test cycles, direct measurements of CO 2 emissions are made. This data, along with other measurements , are then used to ca lculate the estimated fuel economy performance in gallons of fuel consumed per mile. Vehicle certification data are an obviously reliable source for determination of the CO 2 and fuel consumption reduction potential when a dire ctly comparable vehicle was offered both with and without the specific CO 2 and fuel consumption reduci ng technology, because a comparison between the emissions data betw een the two vehicles directly reflect the application of the technologies on the vehicle test cycl es. Where possible, technology-specific effectiveness numbers were extracted for vehicles wher e only the specific technology would be changed from a reference vehicle, in order to eliminate any confounding of

3-2Technologies Considered in the Agencies’ Analysis

values across several technologies. In some hybrid vehicle cases, the exact same vehicle may not be offered, and a similar ve hicle was selected for comparison.



### 3.2 Which technologies will be applic able during the model years to which the rules apply?

One of the key factors that NHTSA a nd EPA considered in developing the standards for each model year is the availa bility and feasibility of fuel-saving/CO 2 - reducing technologies. A variet y of technologies can be used to reduce fuel consumption and CO 2 emissions, whether they be engine-related ( e.g. , turbocharging), transmissionrelated ( e.g. , six forward gears in place of four), accessory-related ( e.g. , electric power steering), or vehicle-related ( e.g. , low rolling resistance tires). In taking a fresh look at the availability and feasibili ty of technologies for purpos es of setting the MYs 2012-2016 standards, the agencies reviewed NHTSA ’s MY 2011 CAFE final rule and EPA’s 2008 Staff Report, as well as th e public comments to the propos ed rule and updated the assumptions based on the most current information. As a check on this new analysis, NHTSA and EPA also reviewed the produc t plans submitted by manufacturers to the agencies in response to NHTSA’s Nove mber 2009 request for product plans.

Many technologies considered by the agencies, such as variable valve timing (VVT), low rolling resistance tires, etc., ar e well known and readily available during the rulemaking time period, and could be inco rporated into vehicles once production decisions are made. The agencies did not c onsider technologies in the research stage, such as homogeneous charge compression igni tion (HCCI), lean-bur n direct injection technology, etc., because their effectiveness and/ or cost estimates co ntain a high level of uncertainty.

The technologies considered by the NHTSA and EPA models are briefly described below. They fall under the five broad categories of engine, transmission, vehicle, electrification/accesso ry, and hybrid technologies. A more detailed description of each technology, and the technology’s costs a nd effectiveness, is described in greater detail in section 3.5 of this TSD. Types of engine technologies that improve fuel economy and reduce CO 2 emissions include the following:

• Low-friction lubricants – low viscosity and advan ced low friction lubricants oils are now available with improved performance and better lubrication.

• Reduction of engine friction losses – can be achieved through low-tension piston rings, roller cam followers, impr oved material coatings, more optimal thermal management, piston surface treatments, and other improvements in the design of engine components and subsystems that improve engine operation.

• Conversion to dual overhead cam with dual cam phasing – as applied to overhead valves designed to increase the air flow with more than two valves per cylinder and reduce pumping losses.

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• Cylinder deactivation – deactivates the intake and exhaust valves and prevents fuel injection into some cylinders du ring light-load operation. The engine runs temporarily as though it were a smaller engine which substantially reduces pumping losses

• Variable valve timing – alters the timing or phase of the intake valve, exhaust valve, or both, primarily to reduce pu mping losses, increase specific power, and control residual gases.

• Discrete variable valve lift – increases efficiency by optimizing air flow over a broader range of engine operation which reduces pumping losses. Accomplished by controlled switching betw een two or more cam profile lobe heights.

• Continuous variable valve lift – is an electromechanica lly controlled system in which cam period and phasing is changed as lift height is controlled. This yields a wide range of performance optimization and volumetric efficiency, including enabling the engine to be valve throttled.

• Stoichiometric gasoline di rect-injection technology – injects fuel at high pressure directly into the combusti on chamber to improve cooling of the air/fuel charge within the cylinder, wh ich allows for higher compression ratios and increased thermodynamic efficiency.

• Combustion restart – can be used in conjunction with gasoline direct-injection systems to enable idle-off or start-st op functionality. Similar to other startstop technologies, additiona l enablers, such as electric power steering, accessory drive components, and auxili ary oil pump, might be required.

• Turbocharging and downsizing – increases the available airflow and specific power level, allowing a reduced engine size while maintaining performance. This reduces pumping losses at lighter lo ads in comparison to a larger engine.

• Exhaust-gas recirculation boost – increases the exhaust- gas recirculation used in the combustion process to increase thermal efficiency and reduce pumping losses.

• Diesel engines – have several characteristics that give superior fuel efficiency, including reduced pumping losses due to lack of (or greatly reduced) throttling, and a combustion cycle that operates at a higher compression ratio, with a very lean air/fuel mixture, than an equivalent-performance gasoline engine. This technology requires additional enablers, such as NO x trap catalyst after-treatment or se lective catalytic reduction NO x after-treatment.

Types of transmission technol ogies considered include:

3-4Technologies Considered in the Agencies’ Analysis

• Improved automatic tr ansmission controls – optimizes shift schedule to maximize fuel efficiency under wide ranging conditions, and minimizes losses associated with torque converter slip through lock-up or modulation.

• Six-, seven-, and eight-speed automatic transmissions – the gear ratio spacing and transmission ratio are optimized to en able the engine to operate in a more efficient operating range over a broader range of vehicle operating conditions.

• Dual clutch or automated shift manual transmissions – are similar to manual transmissions, but the vehicle controls shifting and launch functions. A dualclutch automated shift manual transmissi on uses separate clutches for evennumbered and odd-numbered gears, so the next expected gear is pre-selected, which allows for faster and smoother shifting.

• Continuously variable transmission – commonly uses V-shaped pulleys connected by a metal belt rather than gears to provide ratios for operation. Unlike manual and automatic transmissions with fixed transmission ratios, continuously variable transmissions can pr ovide fully variable and an infinite number of transmission ratios that enab le the engine to operate in a more efficient operating range over a broader range of vehicle operating conditions.

• Manual 6-speed transmission – offers an additional gear ratio, often with a higher overdrive gear ratio, than a 5-speed manual transmission.

Types of vehicle technologi es considered include:

• Low-rolling-resistance tires – have characteristics that reduce frictional losses associated with the energy dissipated in the deformation of the tires under load, therefore reducing the energy needed to move the vehicle.

• Low-drag brakes – reduce the sliding friction of disc brake pads on rotors when the brakes are not engaged becaus e the brake pads are pulled away from the rotors.

• Front or secondary axle disconnect for four-wheel drive systems – provides a torque distribution disconnect between fr ont and rear axles when torque is not required for the non-driving axle. This results in the reduction of associated parasitic energy losses.

• Aerodynamic drag reduction – is achieved by changing vehicle shape or reducing frontal area, including skirts , air dams, underbody covers, and more aerodynamic side view mirrors.

• Mass reduction and material substitution – Mass reduction encompasses a variety of techniques ranging from improved design and better component integration to application of lighter and higher-strength materials. Mass

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reduction is further compounded by reducti ons in engine power and ancillary systems (transmission, steering, brakes, suspension, etc.).

Types of electrification/ accessory and hybrid technol ogies considered include:

• Electric power steering (EPS) – is an electrically-assi sted steering system that has advantages over traditional hydraulic power steering because it replaces a continuously operated hydraulic pump, th ereby reducing parasitic losses from the accessory drive.

• Improved accessories (IACC) – may include high efficiency alternators, electrically driven (i.e., on-demand) water pumps and cooling fans. This excludes other electrical accessories such as electric oil pumps and electrically driven air conditioner compressors.

• Air Conditioner Systems
– These technologies include improved hoses, connectors and seals for leakage cont rol. They also include improved compressors, expansion valves, heat ex changers and the control of these components for the purposes of improving tailpipe CO 2 emissions as a result of A/C use. These technologies ar e covered separately in the EPA RIA.

• 12-volt micro-hybrid (MHEV) – also known as idle-stop or start stop and commonly implemented as a 12-volt belt-d riven integrated starter-generator, this is the most basic hybrid system th at facilitates idle-s top capability. Along with other enablers, this system re places a common alternator with an enhanced power starter-alternator, both belt driven, and a revised accessory drive system.

• Higher Voltage Stop-Start/Belt Integr ated Starter Generator (BISG) – provides idle-stop capability and uses a high voltage battery with increased energy capacity over typical automotive ba tteries. The higher system voltage allows the use of a smaller, more pow erful electric motor and reduces the weight of the motor, inverter, and ba ttery wiring harnesses. This system replaces a standard alternator with an enhanced power, higher voltage, higher efficiency starter-alternator, that is be lt driven and that can recover braking energy while the vehicle slows down (regenerative braking).

• Integrated Motor Assist (IMA)/Crank integrated starter generator (CISG) – provides idle-stop capability and uses a high voltage battery with increased energy capacity over typical automotive ba tteries. The higher system voltage allows the use of a smaller, more pow erful electric motor and reduces the weight of the motor, inverter, and ba ttery wiring harnesses. This system replaces a standard alternator with an enhanced power, higher voltage, higher efficiency starter-alternator that is crankshaft mounted and can recover braking energy while the vehicle slows down (regenerative braking).

3-6Technologies Considered in the Agencies’ Analysis

• 2-mode hybrid (2MHEV) – is a hybrid electric drive system that uses an adaptation of a conventional steppe d-ratio automatic transmission by replacing some of the transmission clut ches with two electric motors that control the ratio of engine speed to vehicle speed, while clutches allow the motors to be bypassed. This improves both the transmission torque capacity for heavy-duty applications and reduces fuel consumption and CO 2 emissions at highway speeds relative to other types of hybrid electric drive systems.

• Power-split hybrid (PSHEV) – a hybrid electric drive system that replaces the traditional transmission with a single planetary gearset and a motor/generator. This motor/generator uses the engine to either charge the battery or supply additional power to the drive motor. A second, more powerful motor/generator is permanently connect ed to the vehicle’s final drive and always turns with the wheels. The planetary gear splits engine power between the first motor/generator and the drive mo tor to either charge the battery or supply power to the wheels.

• Plug-in hybrid electric vehicles (PHEV) – are hybrid electric vehicles with the means to charge their battery packs fr om an outside source of electricity (usually the electric grid). These vehicl es have larger battery packs with more energy storage and a greater capability to be discharged. They also use a control system that allows the battery p ack to be substantia lly depleted under electric-only or blended m echanical/electric operation.

• Electric vehicles (EV)
– are vehicles with all-electr ic drive and with vehicle systems powered by energy-optimized batt eries charged primarily from grid electricity.



### 3.3 What technology assumptions have the agencies used for the final rule?

3.3.1 How are the technologies applied in the agencies’ respective models?

Although both NHTSA and EPA are basi ng their fuel economy and emission modeling on the same baseline vehicle fleet and cost and effectiveness estimates for control technologies, differen ces in the Volpe and OMEGA models require that this common information be processed in different ways prior to their use in the respective models.. With respect to th e vehicle fleet, the Volpe Mode l evaluates the addition of technology to individual vehi cle configurations or mode ls, while the OMEGA model does so for vehicle platforms broken down fu rther by engine size. Thus, NHTSA used the vehicle sales estimates described above directly, while EPA combined the sales of certain vehicle models. The Volpe Model evaluates technologies individually. This,

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coupled with the modeling of individual vehicl e models, means that on ly the presence or absence of any particular technology needs to be indicated, as de scribed above. OMEGA applies technology in combinati ons or packages. This, pl us the grouping of individual vehicle models, requires that the total effectiveness of th e technology already applied in the baseline fleet must be calculated and must be reflected as a percentage of the various technology packages available to be added to those vehicles.

With respect to the cost and effectiv eness of technologies, as mentioned above, the Volpe Model applies technologies indivi dually. It does this following certain specified pathways for several categories of technologies ( e.g. , engine, transmission, accessories, etc.). The Volpe Model a pplies technology incrementally, so the effectiveness of each subsequent technology ne eds to be determined relative to the previous one. The same is true for cost. In addition, because of interaction in the effectiveness of certain technologies, herein referred to as the s ynergy/dis-synergy, any such interaction between the next technol ogy on a specified pathway with those which have already been potentially applied in other pathways must be determined. For example, the incremental effectiveness of switching from a six-speed automatic transmission to a dual clutch transmission wi ll depend on the level of engine technology already applied ( e.g. , intake cam phasing on a port-fuel injected engine or a down-sized, turbocharged, direct in jection engine).

EPA’s OMEGA model applies technologies in packages and according to a fixed sequence for any particular group of vehicles. This requires that the overall cost and effectiveness of each package be determined first, considering any and all dis-synergies which may exist. Then, the incremental co st and effectiveness of each subsequent package is determined relative to the prior one.

Thus, while the same baseline vehicle fleet and cost and effectiveness estimates for technologies are being used in both th e Volpe and OMEGA models, the form of the actual inputs to the model will appear to be different. For more information on EPA’s and NHTSA’s unique approaches to modeling, please refer to each agency’s respective final RIA.

In order to estimate both technol ogy costs and fuel consumption/CO 2 reduction estimates, it is necessary for each agency to describe the baseline vehicle characteristics from which the estimates can be compared. This “baseline” is different from the usage in Chapter 1 of this joint TSD. In Chapter 1, th e baseline fleet is the projected fleet in MY 2016 before accounting for technologies needed to meet the MY 2011 CAFE standards and before accounting for changes in fleet co mposition attributable to that rule (those later steps accounted for independently by ea ch agency in developing their separate reference fleets). In the present context, it indicates the vehicl e types and technologies that will be used for comparison from a strict cost and effectiveness point of view. These baselines may be slightly different for the two agencies. For EPA, unless noted elsewhere, the baseline vehicle is defined as a vehicle with a port-fuel injected, naturally aspirated gasoline engine with two intake a nd two exhaust valves and fixed valve timing and lift. The baseline transmission is a 4- speed automatic, and the vehicle has no hybrid

3-8Technologies Considered in the Agencies’ Analysis

systems. For NHTSA, unless noted elsewhere, the baseline vehicle is the actual vehicle as it exists in the baseline fleet, because NHT SA models each unique vehicle separately. For details on how the technology cost and e ffectiveness estimates presented below are used in each agency’s model, refer to each agency’s respective final RIA.



### 3.3.2 How did the agencies develop technology cost and effectiveness estimates for the final rule?

3.3.2.1 Considerations that affect technology effectiveness

As mentioned above, regarding estimates for technology effectiveness, NHTSA and EPA’s primary approach was to reex amine the estimates from NHTSA’s MY 2011 CAFE final rule and EPA’s ANPRM and Sta ff Technical Report, which largely mirrored NHTSA’s NPRM estimates in the 2008 proposal to establish CAFE standards for MYs 2011-2015. The agencies also reconsidered ot her sources such as the 2002 NAS Report, the 2004 NESCCAF report, recent CAFE complia nce data (comparing similar vehicles with different technologies against each ot her in fuel economy test ing, such as a Honda Civic Hybrid versus a directly comparab le Honda Civic conventional drive), and confidential manufacturer estimates of t echnology effectiveness. Using the BOM framework utilized in the MY 2011 CAFE final rule, NHTSA and EPA engineers reviewed effectiveness information from the multiple sources for each technology. Together, they compared the multiple estimate s and assessed their validity, taking care to ensure that common BOM defini tions and other vehicle attri butes such as performance, refinement, and drivability were taken into account. However, because the agencies’ respective models employ different numbers of vehicle subclasses and use different technology decision trees to ar rive at the standards, di rect comparison of BOMs was somewhat more complicated. To address this and to assure an apples-to-apple comparison, NHTSA and EPA developed mapping techniques, devising technology packages and corresponding incr emental technology estimates. This approach helped compare incremental and packaged estimates an d derive results that are consistent and could be translated into the respective mode ls of the agencies. In general, most effectiveness estimates used in the 2008 EP A Staff Report, the MY 2011 CAFE final rule and the MY 2012-2016 joint NPRM were determin ed to be accurate and were carried forward without significant change into the analysis for this final rule. When NHTSA and EPA’s estimates for effectiveness diverged slightly due to di fferences in how the agencies apply technologies to vehicles in their respective models, the agencies report the ranges for the effectiveness values used in each model, as well as the reasons why the range is reasonable. There were only a fe w comments on technology effectiveness. For the most part, the non-confidential comment s were consistent with the range of effectiveness reported in this rule. Wher e there were differences, EPA and NHTSA did

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not see sufficient evidence to warrant a change in the technology effectiveness. Further details can be found in the response to comments document. a

3.3.2.1.1 Technology synergies

The benefits of these technologies are gene rally but not always additive. That is, adding cylinder deactivation to dual cam phasing does not necessarily result in a 10 percent improvement. This is true because some of the technologies address the same shortcomings of the internal combustion engi ne, although in different ways. Where that is the case, negative (or dis-) synergies are said to exist between two (or more) technologies. It is also tr ue that combining some technol ogies can provide more benefit than a simple additive effect. In those cases, positive synergies are said to exist. The analysis accounts for these posit ive and negative synergies wher ever appropriate so as to ensure no double or insufficient counting of eff ects. Synergies are discussed in more detail in each agency’s respective final RIA.

3.3.2.2 Technology Cost Considerations

As a general matter, NHTSA and EPA be lieve the best way to derive technology cost estimates is to conduct real-world tear down studies. This position is supported by commenters and we received no comments to the contrary. b These studies are based to a large degree on tear downs of vehicles or vehicle systems that employ the new technologies, and of similar vehicles or sy stems without the new technologies. Analysts with expertise in automotive design, material s, and manufacturing th en compare the tear down components and evaluate the differences . Using databases fo r materials, labor, manufacturing overhead, and mark-up costs, th e overall costs to manufacture individual parts are calculated and summed into final results. However, as such, tear down studies require a significant amount of time and are very costly. EPA has begun conducting tear down studies to assess the costs of 4-5 techno logies under a contract with FEV Inc., an independent engine and power train systems research, desi gn and development company. To date, four technologies (downsizing and turbocharging, stoichiometric gasoline directinjection, dual clutch transmission and 6- speed automatic transmission) have been evaluated. The tear down study has been pe er-reviewed and the report for these tear down studies and the peer-review report ha ve been made public in the rulemaking docket. 11 The agencies have considered thes e studies and the comments received on them, as practicable and appropriate, in deve loping technology cost assumptions for this final rule.

These recently completed tear-downs include the following technologies:

a Insert info re where to go in both or the RtC docs.
b See comments from ICCT (EPA-HQ-OAR-2009-0472-7156), CARB (EPA-HQ-OAR-2009-0472-7189), NESCAUM (EPA-HQ-OAR-2009-0472-7235)

3-10Technologies Considered in the Agencies’ Analysis

1. 3.0L V6 port fuel-injected (PFI) downsi zed and turbocharged to a 2.0L I4 gasoline direct injection (GDI) 2. 5.4L V8 PFI downsized and turbocharg ed to a 3.0L V6 twin-turbo GDI 3. 5-speed automatic transmission to 6-speed automatic transmission 4. 6-speed automatic transmission to 6-speed wet dual-clutch transmission

A comparison between costs reported in the NPRM and the final rule can be found in Table 3-1 below.

Table 3-1 A comparison of NPRM costs and the final rule for five updated technologies ($2007 in 2012).

Technology Incremental To NPRM Direct Manufacturing Cost Final Rule Direct Manufacturing Cost Change

2.0L I4 Turbo GDI 3.0L V6 MPFI $248 $152 -$96 3.0L V6 twin-turbo GDI 5.4L V8 MPFI $1,081 $964 -$117 5-speed auto trans 4-speed auto trans $91 $91 $0 6-speed auto trans 4-speed auto trans $153 $101 -$52 6-speed auto trans 5-speed auto trans $62 * $9 -$53 6-speed dual-clutch trans (wet) 6-speed auto trans $126 -$11 -$137 * Calculated as the difference between the 4 to 6 speed trans ($153) and the 4 to 5 speed trans ($91).

FEV tear down cost analysis studies were conducted based on the assumption that the analyzed technologies and the manufactur ing for those technologies were both fully “mature,” in that designs a nd manufacturing processes have been reasonably optimized. The studies also assumed that manufacturing facilities have annual production levels of 450,000 units. EPA and NHTSA recognize that in early implementation years, designs and manufacturing processes may not be optimi zed to that extent, and investment cost may exceed those of fully mature technologies. To account for higher cost in the earlier implementation years of the rulemaking period, NHTSA and EPA estimated MY 2012 costs as the average of the FEV tear down st udy cost and the NPRM cost for technologies for which there were completed FEV tear dow n studies. Time-based learning is used to reflect cost in later years. This approach is applied to downsizi ng and turbocharging for V6 and V8, stoichiometric gasoline dire ct-injection for V6 and V8, dual clutch transmission and 6-speed automatic tran smission. In the NPRM, the costs for turbocharging, downsizing and SGDI for I4 e ngines were based on an FEV teardown cost study that was completed prior to release of the NPRM. For the final rule, these costs were carefully reviewed and updated to bett er account for early year implementation
costs.

Regarding the other technologies, becau se tear down studies were not yet available, the agencies decided to continue the BOM approach as outlined in NHTSA’s MY 2011 final rule and EPA’s 2008 Staff Report. This approach was recommended to NHTSA by Ricardo, an international engineerin g consulting firm retained by NHTSA to aid in the analysis of public comments on its proposed standards for MYs 2011-2015 because of its expertise in th e area of fuel economy technologies. A BOM approach is

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similar in concept to the approach used in te ar down studies. The di fference is that under a BOM approach, the build up of cost estima tes is conducted based on a review of cost and effectiveness estimates for each component from available literature. To the extent that the agencies departed from the MY 2011 CAFE final rule and the 2008 Staff Report estimates, the agencies have explained the re asons and provided supporting analyses. For example, while NHTSA and Ri cardo engineers had relied c onsiderably in the MY 2011 final rule on the 2008 Martec Report for co sting contents of some technologies, upon further joint review and for purposes of the MY 2012-2016 standards, the agencies decided that some of the costing information in that report was no longer accurate due to downward trends in commodity prices since the publication of that report. The agencies reviewed, revalidated or updated cost estim ates for individual components based on new information.

The subsequent sections describe the costs associated with the new vehicle technologies. The costs represent the piece co sts for an individual piece of hardware or system, e.g. , an intake cam phaser to provide vari able valve timing. Costs are presented in terms of their hardware incremental complia nce cost. This means that they include all potential costs associated with their applica tion on vehicles, not just the cost of their physical parts. As discussed, the basis for the piece costs presented here is NHTSA’s MY 2011 CAFE final rule and EPA’s 2008 Staff Report. 12
, 13 In those analyses, piece costs were estimated based on a number of s ources for cost related information. The objective was to use those sources of informa tion considered to be most credible for projecting the costs of individual vehicle tec hnologies. As a result of almost daily meetings between EPA and NHTSA staff dur ing 2009, where both piece costs and fuel consumption efficiencies were discussed in detail, cost estimates have been adjusted slightly. Where estimates differ between sources, engineering judgment was used to arrive at what is believed to be the best cost estimate available today, and explained the basis for that exercise of judgment.

Note that EPA’s 2008 Staff Report presented costs in terms of 2006 dollars. For this analysis, costs are expre ssed in terms of 2007 dollars. Any costs presented here that are based on costs expressed in 2006 dollars ha ve been adjusted to reflect 2007 dollars using the Gross Domestic Product price deflator (see Appendix 3.A). 14
, c The following discussion summarizes the piece cost estimates and how these data sources were used to arrive at the best estimate of piece costs for individual technologi es, as well as their respective effectiveness estimates. For purposes of this joint TSD, these estimates were developed jointly by NHTSA and EPA. Du e to differences in the modeling and technology application methodologies employed by the two agencies, the estimates used by each agency may not match exactly but will be consistent given the different modeling tools used. Additional details are also provide d in each agency’s respective final RIAs.

c The adjustment used to convert from 2006 dollars to 2007 dollars was 1.03.

3-12Technologies Considered in the Agencies’ Analysis

3.3.2.2.1 Direct manufacturing costs

Building on NHTSA’s estimates developed for the MY 2011 CAFE final rule and EPA’s Advanced Notice of Proposed Rulemaking, which relied on the 2008 Staff Technical Report, 15 the agencies took a fresh look at technology cost and effectiveness values for purposes of the joint final rule under the National Program. For costs, the agencies reconsidered both the direct or “piece” costs and indirect costs of individual components of technologies. For the direct co sts, the agencies generally followed a bill of materials (BOM) approach as noted above. A bill of materials, in a general sense, is a list of components that make up a system—i n this case, an item of fuel economyimproving technology. In order to determine what a system costs, one of the first steps is to determine its components and what they cost.

NHTSA and EPA estimated these component s and their costs based on a number of sources for cost-related information. The objective was to use those sources of information considered to be most credible for projecting the costs of individual vehicle technologies. As discussed above, the agencies reviewed, revalidated or updated cost estimates for individual components based on new information. Thus although NHTSA and EPA found that much of the cost information used in NHTSA’s MY 2011 CAFE final rule and EPA’s staff report was cons istent, the agencies, in reconsidering information from many sources (such as the new FEV cost studies), ultimately revised several component costs of several major technologies (turbochar ging downsizing, mild and strong hybrids, diesels, SGDI, Valve Train Lift Technologies, DCT and 6-speed automatic transmissions). 16 These are discussed at lengt h below. For turbocharging downsizing, SGDI, DCT and 6-speed automatic transmission, the agencies relied, to the extent possible, on the tear down data available and scali ng methodologies used in EPA’s ongoing study with FEV. As discussed above in detail in Section 3.3.2.2, this study consists of complete system tear down to evaluate technologies down to the nuts and bolts to arrive at very detailed estimates of the costs associated with manufacturing them. 17 The confidential information provided by manufacturers under their product plan submissions to the agencies or discussed in meetings between the agencies and the manufacturers and suppliers served largel y as a check on publicly-available data.

Once costs were determined, they were ad justed to ensure that they were all expressed in 2007 dollars using the GDP pri ce deflator as described in Appendix 3.A. Indirect costs were accounted for using a new approach developed by EPA for this rulemaking and explained below rather than using the Retail Price Equivalent (RPE) multiplier of 1.5 as was done in NHTSA’s MY 2011 final rule and EPA’s 2008 Staff Report. NHTSA and EPA also considered how costs should be adjusted to reflect manufacturer learning as discu ssed below. Lastly, costs were adjusted by modifying or scaling content assumptions to account for di fferences across the range of vehicle sizes and functional requirements, and adjusted the associated material cost impacts to account for the revised content, although these adjustme nts were different for each agency due to the different vehicle subclasses us ed in their respective models.

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3.3.2.2.2 Cost markups to account for indirect costs

Indirect costs include pr oduction-related costs (research, development, and other engineering), business-related costs (corpor ate salaries, pensions, and manufacturer profits), and retail-sales-related costs (dealer support, marketing, and de aler profits). For this analysis, direct cost estimates were firs t developed for each technology or system at the auto manufacturer level, i.e. , the price paid by the manufacturer to a Tier 1 component supplier. To these costs, an indir ect cost markup factor was then applied that varied by the best estimate of the particular technology’s complexity. This section describes the approach to determining the in direct cost multipliers (ICM) used in this analysis and the specific multipli ers used for each piece of technology.

3.3.2.2.3 Concept behind and development of indirect cost multipliers

If all desirable data were available, when a new technology is implemented, the costs of that technology would include the dire ct and indirect costs particular to that technology. For instance, some changes may involve new tooling, while others may not; some may affect the way the car is marketed, while others are of limited interest to consumers. In a world of full information, the indirect costs of a new technology would be calculated specifically for that technology. In practice, though, it is often difficult, if not impossible, to identify the indirect cost s specific to a particular new technology.

The automotive industry, EPA, and NHTS A have commonly used retail price equivalent (RPE) multipliers to approximate the indirect costs associated with a new technology. The RPE is a ratio of total revenues to direct manufacturing costs. Because, by definition, total revenues = direct costs + indirect costs + profit, the RPE is the factor that, when multiplied by direct manufacturing costs, recovers total revenue. d This multiplication is accurate only in the aggregate; it does not in reality apply to any specific technology. The RPE is a way to estimate indi rect costs on the assu mption that indirect costs are constant across all technologies and processes in a company. In the MY 2011 CAFE final rule NHTSA u tilized a 1.5 RPE multiplier.

In fact, however, the indirect costs of new technologies vary, both with the complexity of the technology and with the tim e frame. For instance, a hybrid-electric engine is likely to involve greater research and developm ent and marketing costs per dollar of direct costs than low-rolling-resist ance tires; the research and development costs of any technology are likely to decrease over time. In rec ognition of this concern, EPA contracted with RTI International to provide a current estimate of the RPE multiplier and to examine whether the indir ect costs of new technologies are likely to vary across technologies. The report “Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers,” by Alex Rogozhin, Michael Gallaher, and Walter McManus, 18 calculates the RPE multiplier as 1.46 in 2007. The re port then develops indirect cost (IC)

d Note that unlike the RPE, the ICM does not include profits.

3-14Technologies Considered in the Agencies’ Analysis

multipliers that vary with the complexity of technology and the time frame. While any multiplier is only an approximation of the true indirect costs of a new technology, the IC multipliers in this report move away from th e assumption that the proportion of indirect costs is constant across all technologies and take into account some of the variation in these costs. The multipliers developed in this report are presented in Table 3-2

. The indirect cost multipliers used adjustme nt factors, developed by a team of EPA engineers with expertise in the auto indus try, which accounted for the differences in complexity of the specific technologies under study. To examine the sensitivity of the results to different technologies of the same complexity, and to provide more detailed documentation of the development of the adjustment factors, EPA convened a second panel, 19 with NHTSA’s input, to develop adjustment factors for three different technologies. This latter pro cess allowed for estimates of the variation in adjustment factors, and thus in the variation of indire ct cost multipliers. These results are also presented in Table 3-2

.

Table 3-2 Indirect Cost Multipliers

TECHNOLOGY COMPLEXITY Short Run Long Run STUDY
Low Medium High Low Medium High
RTI Report 1.05 1.20 1.45 1.02 1.05 1.26

EPA Memo: Average 1.16 1.29 1.64 1.12 1.20 1.39

Standard Deviation 0. 14 0.15 0.21 0.14 0.13 0.15

Median 1.12 1.26 1.66 1.06 1.20 1.40

Max 1.43 1.53 2.15 1.42 1.45 1.69

Min 1.00 1.02 1.37 1.00 1.01 1.12
Multipliers Used in this Analysis 1.11 1.25 1.45 1.64 1.07 1.13 1.26 1.39

Table 3-2 shows minor differences in the multipliers for low- and mediumcomplexity technologies (roughly 0.1), but la rger differences in the high-complexity technologies. The EPA and NHT SA engineers who reviewed th e results believed that the differences reflected actual differences in th e technologies under study. In particular, for low complexity, low-rolling-resistance tires (the application in the RTI Report) would involve lower indirect costs than aerodynamic improvements (the application in the EPA memo); and, for medium complexity, dual-clut ch transmissions (the application in the RTI Report) should have a smaller multiplier than engine downsizing done in conjunction with turbocharging (the application in th e EPA Memo). For these two cases, EPA and NHTSA considered these techno logies to span the range of technologies assigned to those classes; the costs in this study, then, use the averages of the values of the two reports, as shown in the last line of Table 3-2 . For high complexity technologies, the agencies felt the technologies assigned to these categories—hybrid-electric vehicles in the RTI Report; plug-in hybrid electric vehi cles in the EPA Memo—were sufficiently

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different that each deserved a different categor y. This is discussed in more detail in the next section which highlights the multip liers used for each specific technology.

3.3.2.2.3.1 Application of specific indirect cost multipliers to each technology

As noted in the previous section, and in the NPRM, a different ICM was applied to each technology’s direct cost to arrive at its compliance cost. These different ICMs were chosen based on the complexity of inte grating the technology into the vehicle in the opinions of staff engineers at EPA and NHTSA , most of whom have several years of experience in the auto industry. As shown in Table 3-2 , ICMs were developed via two separate processes: that pr esented in the RTI report; and that presented in the EPA Memo. While all of the ICMs generated vi a these two processes were in general agreement, some differences did exist. In de termining how to deal with these differences, EPA and NHTSA agreed that, for the low and medium complexity technologies, a simple average of the two values would be used. However, for the high complexity technologies, it was decided that two separate high-multipliers should be used. The lower multiplier, deemed high, would be applied to those technologies of high complexity but with some level of use in the marketplace today. Such technologies would be power-split and 2-mode hybrid elect ric vehicles. The higher multiplier, deemed high+, would be applied to those technologi es of high complexity but with no, or essentially no, use in the current fleet. Su ch technologies would be plug-in hybrids and full electric vehicles. Table 3-3 shows the complexity level for each technology considered in this analysis.
Table 3-3 Complexity Levels of Technologies

LOW COMPLEXITY MEDIUM COMPLEXITY HIGH COMPLEXTIY HIGH+ COMPLEXITY Low friction lubes VVLT-conti nuous (OHV) VVLT-continuous (OHC) Plug-in hybrid

Engine friction reduction VVLT-di screte (OHV) Camle ss valve actuation Full electric vehicle VVT-intake (OHC) GDI-lean burn Homogeneous charge CI VVT-coupled (OHC & OHV) Turbocharge with downsize Weight reduction – 30% VVT-dual (OHC) Conversion to dies el Integrated motor assist hybrid Cylinder deactivation (OHC & OHV) Dual clutch transmission 2-mode hybrid

VVLT-discrete (OHC) Continually variable trans Power-split hybrid GDI-stoich 42 Volt conversion Turbocharge (with no downsize) Weight reduction – 20% Downsize (with no turbocharge) Integrated starter generator (stop-start) Cam configuration changes (with no downsize) Combustion restart

Aggressive shift logic Cooled EGR/EGR boost Early torque converter lockup 5-speed auto transmission 6-speed auto transmission 6-speed manual transmission Improved accessories Electric power steering

3-16Technologies Considered in the Agencies’ Analysis

Low rolling resistance tires Low drag brakes Secondary axle disconnect Improved aerodynamics Weight reduction – 3% Weight reduction – 5% Weight reduction – 10% A/C Leakage Reduction A/C Efficiency Improvement

The estimates of vehicle compliance costs cover the years of implementation of the program – MYs 2012 through 2016. In EPA’ s analysis, compliance costs have also been estimated for the years following impl ementation to shed li ght on the long term – 2022 and later – cost impacts of the rule. The year 2022 is used by EPA because the short-term and long-term markup factors de scribed above are applied in five year increments with the 2012 through 2016 impl ementation span and the 2017 through 2021 span both representing the short-term.

Commenters in general agreed with th is methodology for determining indirect cost multipliers, though there was some disagreement as to the magnitude of the multipliers.

3.3.2.2.4 Cost reduction through manufacturer learning

For some of the technologies considered in this analysis, manufacturer learning effects would be expected to play a role in the actual end costs. The “learning curve” or “experience curve” describes the reduction in unit production costs as a function of accumulated production volume. In theory, th e cost behavior it describes applies to cumulative production volume measured at th e level of an individual manufacturer, although it is often assumed—as both agencies ha ve done in past regu latory analyses—to apply at the industry-wide le vel, particularly in industr ies that utilize many common technologies and component supply sources. 20 Both agencies believe there are indeed many factors that cause costs to decrease over time. Research in the costs of manufacturing has consistently shown that , as manufacturers gain experience in production, they are able to apply innovati ons to simplify machining and assembly operations, use lower cost materials, and re duce the number or complexity of component parts. All of these factors allow manufactur ers to lower the per- unit cost of production ( i.e. , the manufacturing learning curve). 21

NHTSA has a detailed description of the learning effect in the MY 2011 CAFE final rule, and EPA has a detailed descrip tion of the learning e ffect in the 2008 Staff Report and in several past rules. 22 The description here is focused on how the learning effect has been used in the analys es support each agency’s rule.

Most studies of the effect of experience or learning on production costs appear to assume that cost reductions begin only af ter some initial volume threshold has been

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reached, but not all of these studies specify this threshold volume. The rate at which costs decline beyond the initial threshold is usually expresse d as the percent reduction in average unit cost that results from each successive doubling of cumulative production volume, sometimes referred to as the learning rate. Many estimates of experience curves do not specify a cumulative production volum e beyond which cost reductions would no longer occur, instead depending on the asympto tic behavior of the effect for learning rates below 100 percent to esta blish a floor on costs. In past rulemaking analyses, as noted above, both agencies have used a learning curve factor of 20 percent for each doubli ng of production volume. NHTSA has used this approach in analyses supporting recen t CAFE rules. In its analysis, EPA has simplified the approach by using an “every two years” based lear ning progression rather than a pure production volume progression ( i.e. , after two years of production it was assumed that production volumes would have doubled and, therefore, costs would be reduced by 20 percent).

In the MY 2011 CAFE final rule, NHTSA c onsidered not only this volume-based learning as described above, but also “ time-based” learning. Time-based learning, estimated by NHTSA at three percent per year, occurs in years following the volumebased learning steps and represents the sm aller scale learning that occurs as manufacturers continue to i nnovate. The time-based learning is, in effect, represented by the flattened out, asymptotic por tion of the learning curve.

For this analysis, each agency has employed both volume-based and time-based learning effects. In NHTSA’s analysis, volume- based learning is esti mated to result in 20 percent lower costs as production volumes of the given technology reach 300,000 units and again when they reach 600,000 units with in the entire industry. The count of production units begins in the 2012 model year and/or with the first units produced, whichever is earlier. In EPA’s analysis, as noted a bove, volume-based learning is estimated to result in 20 percent lower co sts after two full years of implementation ( i.e.
, the 2014 MY costs are 20 percent lower than the 2012 and 2013 model year costs). In each agency’s analysis, time-based learning is estimated to result in 3 percent lower costs in each year following first introduction of a given technology. Once two volume-based learning steps have occurred, or once the fi ve year time-based learning period is completed, learning is assumed to be complete, at least for purposes of the analyses done to support each agency’s rule.

Both agencies considered learning impacts on most but not all of the technologies expected to be used because some of the e xpected technologies are already used rather widely in the industry and, presumably, le arning impacts have already occurred. Volume-based learning was considered for only a handful of technologies that are considered to be new or emerging technologies such as the hybrids and electric vehicles. Most technologies have been c onsidered to be more establis hed given their current use in

3-18Technologies Considered in the Agencies’ Analysis

the fleet and, hence, the lower time-based lear ning has been applied. The learning effects applied to each technology are summarized in Table 3-4

.

Table 3-4 Learning Effects Applied to Each Technology

TECHNOLOGY LEARNING EFFECT Low friction lubricants None Engine friction reduction None VVT – intake cam phasing Time VVT – coupled cam phasing Time VVT – dual cam phasing Time Cylinder deactivation Time Discrete VVLT Time Continuous VVLT Time GDI – stoichiometric Time GDI – lean burn Time Turbocharge (with no downsize) Time Downsize (with no turbocharge) Time Turbocharge with downsize Time Cam configuration changes (with no downsize) Time Homogeneous charge CI Time Conversion to diesel Time Aggressive shift logic Time Early torque converter lockup Time 5-speed automatic Time 6-speed automatic Time 6-speed DCT – dry clutch Time 6-speed DCT – wet clutch Time 6-speed manual Time CVT Time Stop-Start Volume IMA/ISA/BSG (includes engine downsize) Volume 2-Mode hybrid electric vehicle Volume Power-split hybrid electric vehicle Volume e Plug-in hybrid electric vehicle Volume Full electric vehicle Volume Improved high efficiency al ternator & electrification of accessories Time Upgrade to 42 volt electrical system Time Electric power steering (12 or 42 volt) Time Aero drag reduction (20% on cars, 10% on trucks) Time Low rolling resistance tires None Low drag brakes (ladder frame only) None Secondary axle disconnect (unibody only) None Front axle disconnect (ladder frame only) Time A/C Leakage Reduction Time A/C Efficiency Improvements Time

Table 3-25 through Table 3-30 show Volpe model costs and effectiveness values for each particular technology described thr oughout this chapter. The costs shown are applicable for the 2012 model year. The Volp e Model handles learni ng effects within the model itself so that individua l technology costs in the 2016 model year would be lower than those shown in the tables.

e Due to an error, the NPRM showed this as time-based learning. .

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Table 3-31 through Table 3-40 show OMEGA model costs and effectiveness values for each particular technology describe d throughout this chapter. The costs shown are applicable for the 2016 model year and, therefore, repr esent fully learned costs in the context of EPA’s analysis. For technologies added in years prior to MY 2016, EPA has backed out the learning effects relative to th e costs shown in the tables. For example, Table 3-33 shows the small car stop-start vehi cle cost as $351 in 2016. In the 2012 model year, this cost would be higher since the volume-based learning reflected in the 2016 cost would not have occurred yet. Backing out two volume- based learning steps ( i.e. , dividing $351 by 80% twice) would re sult in a 2012 cost estimate of $548.

The different handling of learning eff ects across the two analyses (that done by EPA and that by NHTSA) would have little impact on the overall result s of the analyses. NHTSA handles learning effects in the CAFE model. EPA handles learning effects outside of the OMEGA model since OMEGA is run for only one model year - 2016. Because the CAFE model adds technology on a year-by-year basis, it applies volumebased learning effects once the applicable production volume thresholds have been exceeded. In contrast, EPA would not a pply volume-based learning until year 3 (assumed to be the point at which initia l production volumes have doubled). Most technologies in both analyses are subject to time-based learning which is applied on an annual basis in the same way by both agenci es (with the exception that NHTSA applies time-based learning in the CAFE model while EPA applies it outside the OMEGA model). Importantly, by the 2016 model year , both agencies will have applied two volume-based learning steps to the appropriate technologies (hybrid and electric vehicles only) and four time-based learning steps to the appropriate tec hnologies (most other technologies) to arrive at the same technology costs.



### 3.4 Specific technologies considered and estimates of costs and effectiveness

It is important to note that the cost a nd effectiveness values presented by NHTSA and EPA in this Final TSD are used when cr eating input files for th e agencies’ respective models, but are not litera lly the inputs themselves. The Dock et to this final rule contains the specific input files EPA and NHTSA used when modeling the technology cost.



### 3.4.1 What data sources did the agencies evaluate?

Refer to section 3.1 for a discussion of the specific data sources and reference material used for the analysis of this rulemaking.



### 3.4.2 Individual technology descriptions and cost/effectiveness estimates

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The sections that follow describe th e technologies applied by both EPA and NHTSA. It is important to note that there are distinct differences between the agencies in how cost and effectiveness estimations are reported and applied.

EPA reports the total cost and effect iveness associated with a respective technology. The technologies are then applied to a vehicle type as a package. To calculate a cost for a vehicle package, th e individual technologies are summed. To estimate the overall effectiveness, the lumped parameter method is applied.

NHTSA applies cost and effectiveness th rough a series of decision trees. The progressive nature of this methodology results in conditions for which a portion of a technology has already been added. In this case, NHTSA would add an incremental value which is the difference between the total cost of a technology and the portion already existing. An example of this is cyli nder deactivation. At the point in the decision tree that it is applied, valvetrain technologi es which could enable this feature have already been added. NHTSA will add the delta in cost, which in this example accounts for the NVH attributes.

3.4.2.1 Engine Technologies

NHTSA and EPA have reviewed the e ngine technology estimates used in NHTSA’s MY 2011 CAFE final rule and EPA’s 2008 Staff Report and available comments to the NPRM. In doing so NHTSA and EPA reconsidered all available sources and updated the estimates as appropria te. The section below describes each of the engine technologies cons idered for this rulemaking.

3.4.2.1.1 Low Friction Lubricants (LUB)

One of the most basic methods of reduci ng fuel consumption in gasoline engines is the use of lower viscosity engine lubricants. More advanced multi-viscosity engine oils are available today with improved perfor mance in a wider temperature band and with better lubricating properties. This can be accomplished by changes to the oil base stock ( e.g. , switching engine lubricants from a Group I base oils to lower-friction, lower viscosity Group III synthetic) and through chan ges to lubricant additive packages ( e.g. , friction modifiers and viscosity improvers). The use of 5W-30 motor oil is now widespread and auto manufacturers are introduc ing the use of even lower viscosity oils, such as 5W-20 and 0W-20, to improve cold-flo w properties and reduce cold start friction. However, in some cases, changes to the cranks haft, rod and main bear ings and changes to the mechanical tolerances of engine component s may be required. In all cases, durability testing would be required to ensure that durab ility is not compromised. The shift to lower viscosity and lower friction lubricants will also improve the effectiveness of valvetrain technologies such as cylinder deactivation, which rely on a minimum oil temperature (viscosity) for operation.

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Several manufacturers have previously commented confidentially, that low friction lubricants could have an effectiv eness value between 0 to 1 percent. For purposes of this final rule, NHTSA and EPA ar e using effectiveness estimates within this range. Therefore 0.5 percent was used in both models.

The 2002 NAS study estimated the low friction lubricant RPE at $8 to $11 using a 1.4 markup factor. The NESCCAF study s howed an RPE of $5 to $15 with a 1.4 markup. The EEA report to DOE showed manufacturer costs of $10 to $20 with no markup. Confidential Business Information (CBI) data estimates an average incremental cost of $3 for the use of low friction lubricants. EPA’s 2008 Staff Report also confirms this $3 cost (2006$). Both NHTSA and EPA be lieve that manufacturer’s estimates are the most accurate, and thus continue to believe that the $3 cost estimate is appropriate and independent of vehicle class since the en gineering work required should apply to any engine size. Applying an indirect cost multiplier (ICM) of 1.11, for a low complexity technology, results in a complia nce cost of $3 per vehicle (2007$) for a MY 2012 through MY 2016 vehicle (learning effects are not applied to low friction lubes). f

3.4.2.1.2 Engine Friction Reduction (EFR)

In addition to low friction lubricants, manufacturers can also reduce friction and improve fuel consumption by improving the design of engine components and subsystems. Approximately 10 percent of the energy consumed by a vehicle is lost to friction, and just over half is due to frictional losses w ithin the engine. 23 Examples include improvements in low-tension pi ston rings, piston skir t design, roller cam followers, improved crankshaft design and bearings, material coatings, material substitution, more optimal thermal ma nagement, and piston and cylinder surface treatments. Additionally, as computer-aided modeling software continues to improve, more opportunities for evolutionary fric tion reductions may become available.

All reciprocating and rotating components in the engine are potential candidates for friction reduction, and minute improvement s in several components can add up to a measurable fuel economy improvement. Th e 2002 NAS, NESCCAF and EEA reports as well as confidential manufacturer data sugge sted a range of effectiveness for engine friction reduction to be between 1 to 3 per cent. NHTSA and EPA continue to believe that this range is accurate. Because of the incremental nature of the CAFE model, NHTSA needed to continue to use the narro wer range of 1-2 percent, which was also used in the MY 2011 CAFE final rule.

In the MY 2011 CAFE final rule, NH TSA estimated a range from $13 to $49 using a 1.5 RPE on a per cylinder basis, or $9 to $33 without RPE (2007$). In the 2008 NPRM engine friction reduction was estimated to cost up to $14 without RPE on a per cylinder basis (2006$). EPA’ s 2008 Staff Report estimated this at $11 using a 1.5 RPE

f Note that the costs developed for low friction lubes fo r this analysis reflect the costs associated with any engine changes that would be required as well as any durability testing that may be required.

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on a per cylinder basis, or $7 without RP E (2006$). After review, NHTSA and EPA believe that the cost estimate is closer to the lower end of the MY 2011 CAFE final rule range and thus for this rulemaking is $13 pe r cylinder compliance cost (2007$), including the low complexity ICM markup value of 1.11, for a MY 2012 through MY 2016 vehicle (learning effects are not applied to engine fr iction reduction). This cost is multiplied by the number of engine cylinders.

3.4.2.1.3 Variable Valve Timing (VVT)

Variable valve timing (VVT) classifies a family of valve-train designs that alter the timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, increase specific power, and control the leve l of residual gases in the cylinder. VVT reduces pumping losses when the engine is lightly loaded by controlling valve timing closer to an optimum needed to sustain hor sepower and torque. VVT can also improve volumetric efficiency at higher engine speeds and loads. Additionally, VVT can be used to alter (and optimize) the effective compre ssion ratio where it is advantageous for certain engine operating modes ( e.g. , in the Atkinson Cycle).

VVT has now become a widely adopted te chnology: in MY 2007, over half of all new cars and light trucks had engines with some method of va riable valve timing. 24 Manufacturers are currently using many differe nt types of variable valve timing, which have a variety of different names and methods . The three major types of VVT are listed below.

Each of the three implementations of VVT uses a cam phaser to adjust the camshaft angular position rela tive to the crankshaft positi on, referred to as “camshaft phasing.” The phase adjustment results in changes to the pumping work required by the engine to accomplish the gas exchange pro cess. The majority of current cam phaser applications use hydraulically-actuated un its, powered by engine oil pressure and managed by a solenoid that controls th e oil pressure supplied to the phaser.

3.4.2.1.3.1 Intake Cam Phasing (ICP) Valvetrains with ICP, which is the simp lest of the cam phasing technologies, can modify the timing of the inlet valves by pha sing the intake camshaft while the exhaust valve timing remains fixed. This requires the addition of a cam phaser on each bank of intake valves on the engine. An in-line 4-cy linder engine has one bank of intake valves, while V-configured engines have two banks of intake valves.

NHTSA’s MY 2011 CAFE final rule and EPA 2008 Staff Report estimated an effectiveness of 1 to 2 percent for ICP, which was supported by the NESCCAF report and a majority of confidential manufacturer comments. The agencies have found no additional sources to suggest strongly that this estimate is inaccurate, and so have employed it for this rulemaking.

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As for costs, NHTSA’s MY 2011 CAFE final rule estimated a $61 RPE ($41 nonRPE) cost per cam phaser, based on the 2008 Martec Report and confidential manufacturer data (2007$). In the 2008 staff report, EPA estimated this at $59 using a 1.5 RPE or $40 without RPE (2006$). NHTSA and EPA believe that this estimate remains accurate. Using the new indirect cost multiplier of 1.11, for a low complexity technology, the compliance cost per cam phase r would be $45 per bank , yielding a $45 cost for and in-line engine configurations and $90 for V-engine configurations for a MY 2012 vehicle (2007$). Time-based learning is applied to ICP so the MY 2016 cost would be $40 for in in-line engine and $80 for the V-engine (2007$).

3.4.2.1.3.2 Coupled Cam Phasing (CCPS and CCPO) Valvetrains with coupled (or coordinate d) cam phasing can modify the timing of both the inlet valves and the exhaust valves an equal amount by phasing the camshaft of a single overhead cam (SOHC) engine or an overhead valve (OHV) engine. g For overhead cam engines, this requires the addition of a cam phaser on each bank of the engine. Thus, an in-line 4-cylinder engine has one cam pha ser, while SOHC V-engines have two cam phasers. For overhead valve (OHV) engines, which have only one camshaft to actuate both inlet and exhaust valves, CCP is the only VVT implementation option available and requires only one cam phaser. h

Based on NHTSA’s MY 2011 CAFE final ru le, previously-received confidential manufacturer data, and the NESCCAF report, NHTSA and EPA estimated the effectiveness of CCP to be between 1 to 4 percent. NHTSA and EPA reviewed this estimate for purposes of the final rule , and continue to find it accurate.

The same cam phaser has been assumed for ICP and CCP applications, thus CCPs cost per cam phaser is identical to ICPs. This results in a cost of $45 for in-line SOHC and OHV engines and $90 for SOHC V-engine configurations for a MY 2012 vehicle (2007$). With time-based learning applied, these costs for a 2016 MY vehicle would be $40 and $80, respectively (2007$).

3.4.2.1.3.3 Dual Cam Phasing (DCP) The most flexible VVT design is dual (independent) cam phasing, where the intake and exhaust valve opening and closing events are controlled independently. This option allows the option of cont rolling valve overlap, which can be used as an internal EGR strategy. At low engine loads, DCP cr eates a reduction in pum ping losses, resulting in improved fuel consumption/reduced CO 2 emissions. Increased internal EGR also results in lower engine-out NO X emissions. The amount by which fuel consumption is

g Although CCP appears only in the SOHC and OHV branch es of the decision tree, it is noted that a single phaser with a secondary chain drive would allow CCP to be applied to DOHC engines. Since this would potentially be adopted on a limited number of DOHC engines NHTSA did not include it in that branch of the decision tree.
h It is also noted that coaxial camshaft developments would allow other VVT options to be applied to OHV engines. However, since they would potentially be adopted on a limited number of OHV engines NHTSA did not include them in the decision tree.

3-24Technologies Considered in the Agencies’ Analysis

improved and CO 2 emissions are reduced depends on the residual tolerance of the combustion system. Additional improvements are observed at idle, where low valve overlap could result in impr oved combustion stability, poten tially reducing idle fuel consumption.

NHTSA MY 2011 CAFE final rule estimated the effectiveness of DCP to be between 3 to 5 percent relative to a base engine or 2 to 3 relative to an engine with ICP. NHTSA and EPA believe that this estimate remains applicable for the final rule.

As above, the same cam phaser has been assumed for ICP and DCP applications. Thus, DCP’s cost per cam phaser is identical to ICP’s. DCP requires two cam phasers per cylinder bank, one to control the intake valves and one to control the exha ust valves. This results in a cost of $90, relative to an engine without ICP, or $45 relative to an engine with ICP, minus $6 for the removal of the EGR valve, ultimately yielding costs of $84 and $39 respectively for in-line DOHC c onfigurations (all in 2007$). For Vconfiguration engines, the cost is $180 relative to an engine without ICP, or $90 relative to an engine with ICP, minus $6 for the re moval of the EGR valve, ultimately yielding costs of $174 and $84, respectively (all in 2007$). These costs are appropriate for a MY 2012 vehicle. With time-based learning a pplied, these costs for a 2016 MY vehicle would be $157 and $73, respectively (2007$).

3.4.2.1.4 Variable Valve Lift (VVL) Controlling the lift of the valves prov ides a potential for further efficiency improvements. By optimizing the valve-lift prof ile for specific engine operating regions, the pumping losses can be reduced by reduc ing the amount of th rottling required to produce the desired engine power output. By moving the throttling losses further downstream of the throttle valve, the heat tr ansfer losses that occur from the throttling process are directed into the fresh charge-air mixture just prior to compression, delaying the onset of knock-limited combustion processes. Variable valve lift control can also be used to induce in-cylinder mixture motion, wh ich improves fuel-air mixing and can result in improved thermodynamic efficiency. Variab le valve lift control can also potentially reduce overall valvetrain friction. At the same time, such systems may also incur increased parasitic losses associated with their actuation mechanisms. A number of manufacturers have already implemented VVL into their fleets (Toyota, Honda, and BMW), but overall this technology is still ava ilable for most of the fleet. There are two major classifications of variable valve lift, described below:

1. Discrete Variable Valve Lift (DVVLS, DVVLD, DVVLO) DVVL systems allow the selection between two or three discrete cam profiles by means of a hydraulically-actuated mechanical system. By optimizing the cam profile for specific engine operating regions, the pump ing losses can be reduced by reducing the amount of throttling required to produce the desired engine power output. This increases the efficiency of the engine. These cam prof iles consist of a low and a high-lift lobe, and may include an inert or blank lobe to incor porate cylinder deactivati on (in the case of a 3-

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step DVVL system). DVVL is normally applied together with VVT control. DVVL is also known as Cam Profile Switching (CPS). DVVL is a mature technology with low technical risk.

NHTSA’s MY 2011 CAFE final rule, previously-received confidential manufacturer data, and the NESCCAF report, all estimate the effectiveness of DVVL to be between 1 to 4 percent above that re alized by VVT system s. NHTSA and EPA believe this estimate continues to be applicable for the final rule.

In the MY 2011 CAFE final rule, NHTSA estimated an RPE (1.5 markup factor) of $201 for an inline 4-cylinder engine, $306 for a V6 engine and $396 for a V8 engine or without the RPE markup $134, $204, $264, resp ectively (all in 2007$) . In the 2008 Staff Report, EPA estimated this at $169, $246, and $322, respectively, using a 1.5 RPE factor (2006$) or $113, $164 and $215, respectiv ely without markup (all in 2006$). For this analysis, the agencies have estima ted the compliance cost s, including $25 for controls and using a 1.11 ICM for a low co mplexity technology, at $141 for an inline 4- cylinder engine, $205 for a V6 engine and $293 for a V8 for a 2012 MY vehicle (2007$). With time-based learning applied, these costs would be $125, $181, and $259, respectively for a 2016 MY vehicle (2007$).

2. Continuously Variable Valve Lift (CVVL) In CVVL systems, valve lift is varied by means of a mechanical linkage, driven by an actuator controlled by the engine control unit. The valve opening and phasing vary as the lift is changed and the relation depends on the geometry of the mechanical system. BMW has considerable production experience with CVVL systems and has sold portinjected “Valvetronic” engines since 2001. CVVL allows the airflow into the engine to be regulated by means of intake valv e opening reduction, which improves engine efficiency by reducing pumping losses from th rottling the intake syst em further upstream as with a conventiona lly throttled engine.

Variable valve lift gives a further reduc tion in pumping losses compared to that which can be obtained with cam phase c ontrol only, with CVVL providing greater effectiveness than DVVL, since it can be fully optimized for all engine speeds and loads, and is not limited to a two or three step compromise. There may also be a small reduction in valvetrain friction when operati ng at low valve lift, resulting in improved low load fuel consumption for cam phase contro l with variable valve lift as compared to cam phase control only. Most of the fuel economy effectiveness is achieved with variable valve lift on the intake valves only. CVVL is only applicable to double overhead cam (DOHC) engines.

NHTSA’s MY 2011 CAFE final rule estimat ed the effectiveness for CVVL at 1.5 to 3.5 percent over an engine with DCP, but also recognize that it could go up as high as 5 percent above and beyond DCP to account for the implementation of more complex CVVL systems such as BMW’s “Valvetronic” e ngines. This coincides with EPA Staff

3-26Technologies Considered in the Agencies’ Analysis

Report estimates of the contribution of CVVL, which were ba sed on the NESCCAF report, in which CVVL could improve effec tiveness by 4 percent (minivans) and up to 6 percent (large cars) over dual cam phasing. T hus, the effectiveness range for CVVL in this joint TSD ranges from 1.5 to 6 percent depending on the complexity level of the application. However, due to the complexity and cost of this t echnology, the GHG model did not consider it and the CAFE model projected no app lication of the less complex version of this technology. The most recen t submission of manufacturers’ product plans confirmed that this technology will not be applied by most manufacturers.

In the MY 2011 CAFE final rule, NHTS A estimated and RPE (1.5 factor) of continuously variable valve lift to be $306 for an inline 4-cylinder engine, $432 for a V6 engine and $582 for a V8 engine or without RPE $204, $287, $388, respectively. In the 2008 Staff Report, EPA estimated this at $254, $466, and $508, resp ectively, using a 1.5 RPE factor (2006$) or $169, $311 and $339, resp ectively without markup (all in 2006$) For this analysis, the agencies have estimat ed the compliance costs, using a 1.45 ICM for a high complexity technology, at $277 for an inline 4-cylinder engine, $509 for a V6 engine and $554 for a V8 engine for a MY 2012 vehicle (2007$). With time-based learning applied, these costs would become $245, $449, and $489, respectively for a MY 2016 vehicle (2007$).

3.4.2.1.5 Cylinder Deactivation (DEACS, DEACD, DEACO) In conventional spark-ignited engines thrott ling the airflow controls engine torque output. At partial loads, efficiency can be improved by using cylinder deactivation instead of throttling. Cylinde r deactivation (DEAC) can im prove engine efficiency by disabling or deactivating (usually ) half of the cylinders when the load is less than half of the engine’s total torque capabi lity – the valves are kept closed, and no fuel is injected – as a result, the trapped air within the deact ivated cylinders is simply compressed and expanded as an air spring, with reduced friction and heat lo sses. The active cylinders combust at almost double the load required if all of the cylinders were operating. Pumping losses are significantly reduced as long as the engine is operated in this “partcylinder” mode.

Cylinder deactivation cont rol strategy relies on setting maximum manifold absolute pressures or predicted torque within which it can deactivate the cylinders. Noise and vibration issues reduce th e operating range to which cyli nder deactivati on is allowed, although manufacturers are exploring vehicle ch anges that enable increasing the amount of time that cylinder deactivation might be suitable. Some manufacturers may choose to adopt active engine mounts and/or active noise cancellations systems to address NVH concerns and to allow a greater operating range of activation. Manufacturers have stated that use of DEAC on 4 cylinder engines would cause unacceptable NVH; therefore cylinder deactivation has not been applied to 4-cylinder engines.

Cylinder deactivation has seen a recent resurgence thanks to better valvetrain designs and engine controls. General Moto rs and Chrysler Group have incorporated

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cylinder deactivation across a substantial porti on of their V8-power ed lineups. Honda (Odyssey, Pilot) offers V6 models with cylinder deactivation.

Effectiveness improvements scale roughly with engine displa cement-to-vehicle weight ratio: the higher displacement-to-weight vehicles, operating at lower relative loads for normal driving, have the potential to operate in part-cylinder mode more frequently.

NHTSA and EPA reviewed the MY 2011 CAFE final rule estimate and confirmed a range of 0.5 to 6 percent depending on the valvetrain configuration and other existing technologies. The OMEGA model, which is based on packages, applies 6 percent reduction in CO 2 emissions in all applications. The CAFE model, due to its incremental nature, uses a range depending on the engine va lvetrain configurati on. For example, for DOHC engines which are already equipped with DCP and DVVLD, there is little benefit that can be achieved from adding cylinde r deactivation since the pumping work has already been minimized and internal E xhaust Gas Recirculation (EGR) rates are maximized, so the effectiveness is only up to 0.5 percent for DEACD. For SOHC engines which have CCP and DVVLS applied, effectiveness ranged from 2.5 to 3 percent for DEACS. For OHV engines, without VVT or VVL technologies, the effectiveness for DEACO ranged from 3.9 to 5.5 percent.

NHTSA and EPA considered a range of $170 to $190 (or $28 to $190 depending on whether an engine already has lost moti on devices, oil control valves and camshaft position sensors). This is a departure from NHTSA’s MY 2011 CAFE final rule, which used a range of $306 to $400. That range was primarily based on the 2008 Martec Report and applied a higher RPE value. In reviewing these assumptions, NHTSA and EPA amended the MY 2011 CAFE final rule estimates and adjusted the estimates to include the new ICM low complexity mar kup of 1.11. The EPA Staff Report estimated this ranging from $203 to $229 (RPE using a 1.5 factor) or $135 to $153 without RPE (2006$). NHTSA’s MYs 2011-2015 NPRM showed estimates of a $170 for a 6-cylinder engine and $190 for an 8-cylinder engine—when adjusted for 2007 dollars and using the new ICM multipliers— for engines that do not have lost motion devices. These numbers were within the ranges described by the 2002 NAS and NESCCAF reports. The $170 and $190 costs are appropriate of a MY 2012 ve hicle (2007$). With time-based learning applied, these would become $150 and $169, respectively, for a MY 2016 vehicle (2007$).

If lost motion devices are on the engine, the cost of DEAC as applied to SOHC and DOHC engines could be as low as $28. i This $28 j accounts for the potential additional application of active engine mounts on SOHC and DOHC engines and can

i The $28 is an adjustment from th e $75 estimate used in th e MY 2011 final rule to account for the new ICM markup factor and the fact that it could only be applied on up to 50 percent of the vehicles.

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only be applied on 50 percent of the vehicles . Further, this SOHC and DOHC engine estimate is relevant to the CAFE model only because the OMEGA model does not apply technologies in the same incremen tal fashion as the CAFE model.

3.4.2.1.6 Conversion to Double Overhead Camshaft Engine with Dual Cam Phasing (CDOHC)

Double overhead camshaft engines achiev e increased airflow at high engine speeds, improve volumetric efficiency and re ductions of the valvet rain’s moving mass. Such engines typically develop higher power at high engine speed s. Manufacturers may choose to replace OHV engines with DOHC engine designs with dual cam phasing (DCP). EPA and NHTSA reviewed the analys is of effectiveness for CDOHC in the MY 2011 CAFE final rule and found no information to indicate that these estimates were inaccurate. EPA and NHTSA estimate the effec tiveness to be between 1 to 2.5 percent.

As for costs, NHTSA’s MY 2011 CAFE final rule assumed that CDOHC would have an RPE of $746 ($497 non-RPE) for a V8 engine, $590 ($393 non-RPE) for a V6 engine and $373 ($249 non-RPE) for inline 4- cylinder engine (2007$) . In the 2008 Staff Report, EPA did not estimate a cost for this specific technology. For purposes of this rulemaking, NHTSA revised the costs only by identifying this tec hnology as a low complexity technology and applying an indi rect cost multiplier of 1.11 resulting in a compliance cost of $552 for V8 engine, $436 for a V6 and $276 for an inline 4-cylinder engine. The above costs are for a MY 2012 vehicle and with the application of timebased learning will be slightly lower for MY 2016 vehicle

3.4.2.1.7 Stoichiometric Gasoline Direct Injection (SGDI)

Stoichiometric gasoline direct injection (S GDI), or Spark Ignition Direct injection (SIDI), engines inject fuel at high pressure directly into the combustion chamber (rather than the intake port in port fu el injection). SGDI requires changes to the injector design, an additional high pressure fuel pump, new fu el rails to handle the higher fuel pressures and changes to the cylinder head and piston cr own design. Direct in jection of the fuel into the cylinder improves cooling of the air/fu el charge within the cylinder, which allows for higher compression ratios and increased thermodynamic efficiency without the onset of combustion knock. Recent injector desi gn advances, improved electronic engine management systems and the introduction of mu ltiple injection events per cylinder firing cycle promote better mixing of the air and fuel, enhance combustion rates, increase residual exhaust gas tolerance and improve co ld start emissions. SGDI engines achieve higher power density and match well with other technologies, such as boosting and variable valvetrain designs.

Several manufacturers have recently intr oduced vehicles with SGDI engines, including VW/Audi, BMW, Toyota (Lexus IS 350) and General Motors (Chevrolet

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Impala and Cadillac CTS 3.6L). BMW, GM, Ford and VW/Audi have announced their plans to increase dramatically the number of SGDI engines in their portfolios.

NHTSA’s MY 2011 CAFE final rule estimated the effectiveness of SGDI to be between 2 and 3 percent. NHTSA and EPA reviewed estimates from the Alliance of Automobile Manufactures, which projects 3 pe rcent gains in fuel efficiency and a 7 percent improvement in torque. The tor que increase provides the opportunity to downsize the engine allowing an increase in efficiency of up to a 5.8 percent. NHTSA and EPA also reviewed other published literature, reporting 3 percent effectiveness for SGDI. 25 Another source reports a 5 percent improvement on the NEDC drive cycle. 26 Confidential manufacturer data reported an efficiency effectiveness range of 1 to 2 percent. NHTSA and EPA determined that th e range of 2 to 3 percent continues to be appropriate. Combined w ith other technologies ( i.e. , boosting, downsizing, and in some cases, cooled EGR), SGDI can achieve grea ter reductions in fuel consumption and CO 2 emissions compared to engines of similar power output.

The NHTSA and EPA cost estimates for SGDI take into account the changes required to the engine hardware, engine electr onic controls, ancillar y and Noise Vibration and Harshness (NVH) mitigation systems. Through contacts with industry NVH suppliers, and manufacturer pre ss releases, the agencies be lieve that the NVH treatments will be limited to the mitigation of fuel system noise, specifically from the injectors and the fuel lines. In the final rule, the agen cies have revised the SGDI costs based on the FEV work that was not yet available for th e NPRM. Focusing on direct manufacturing costs, the NPRM estimates, the FEV values, and the final values are shown in Table 3-5

. FEV did not directly estimate the SGDI cost s shown here. Instead, FEV estimated costs associated with downsizing and turbocharging a V8 and V6 engine to a V6 and I4 engine, respectively, and simultaneously converting the PFI fuel system to a SGDI fuel system. The agencies, working closely with FEV, then “b inned” the costs into three distinct bins: downsize, turbocharge, and SGDI. As such, the FEV results shown in Table 3-5 cannot be found in the FEV reports, but instead are de tailed in a memo to the docket which also provides details of this binning process. 27 Because the methodology used by FEV presumes high volume production, instead of usin g the FEV results directly the agencies have averaged those results with the NPRM re sults to estimate the final values (this is noted in section 3.3.2.2, 3.4.2.1.9, and 3.4.2.2.5). Note that the costs for the I3 and I4 engines have changed since the NPRM but have not been averaged with the NPRM values. The costs changed based on a more rigorous binning process than that conducted for the NPRM, but were not averaged because they are based on the I4 to I4 teardown conducted by FEV which was, in fact, used in the NPRM. While the final value of $213 is lower than the NPRM value of $226, the $13 difference has simply been shifted from SGDI to the downsizing bin.
Table 3-5 Direct Manufacturing Co sts for SGDI (2007$ in 2012)
Technology NPRM FEV Results Final Rule I3 $226 $213 $213 I4 $226 $213 $213 V6 $293 $321 $307 V8 $317 $386 $352

3-30Technologies Considered in the Agencies’ Analysis

For the final rule marked up costs, the agencies estimate SGDI costs at $236 for an inline 4-cylinder and $341 for V6 and $390 for V8 including the low complexity ICM markup value of 1.11 (2007$ in 2012). With time-based learning applied, these costs would be $209, $301, and $346 for a MY 2016 vehicl e (2007$). As noted above, all of these costs differ slightly from those us ed in the NPRM analysis as shown in Table 3-6

. These costs were not changed in response to public comments, but instead were changed due to updated information from the FEV teardown studies. 28

Table 3-6 Marked up Costs for Stoi chiometric GDI (2007$ in 2012)
Technology NPRM Final Rule I3 $251 $236 I4 $251 $236 V6 $326 $341 V8 $353 $390

3.4.2.1.8 Combustion Restart (CBRST)

Combustion restart allows “start-stop” functionality of DI engines through the implementation of an upgraded starter withbi-directional rotation to allow precise crankshaft positioning prior to subsequent fuel injection and spark ignition, allowing engine restart. This method of implementing engine st op/start functionality allows not only the fuel savings from not idling the engine , but also reduces fuel consumption as the engine speeds up to its operational speed. A Di rect Injection (DI) fuel system is required for implementation of this technology.

NHTSA reviewed the MY 2011 CAFE final ru le assumptions and determined that due to technical risks implem entation of combustion restart would likely not be feasible prior to MY 2014. Some of the risks are asso ciated with unresolved issues regarding the impact of very high or very low ambient air te mperatures on the ability to start the engine in the described manner. Although the starter motor can provide fail-safe starting capability in these temperature limited areas, st rategies must be developed to manage the transitions. Other risks rela te to production readiness.

Additional hardware is required to imple ment combustion restart, beyond SGDI. This includes a battery sensor, incremen tal wiring and high current switching, an incremental crank position sensor, and, in the case of an automatic transmission applications, a transmission oil pump to allo w for torque converter continuity.

BMW has published a 3.5 percent fuel c onsumption effectiveness over the NEDC drive cycle for combustion restart, 29 and AVL a 4.8 percent effectiveness. 30 However, these reported effectiveness levels could poten tially be reduced significantly on the EPA

3-31Draft Joint Technical Support Document

combined drive cycle, as combustion restar t does not save fuel on the highway drive cycle. Therefore, NHTSA estimates the fu el consumption effectiveness for CBRST to range from 2 to 2.5 percent.

Regarding the cost estimate, NHTSA determ ined that the estimate of $118 from the 2008 Martec Report cost estimates for indivi dual pieces was the best available. The total RPE cost (excluding transmission pump) is $141 at high volumes, which includes $70 for upgrading the starter, $10 for a batt ery sensor and wiring, $10 for high current switch and $4 for crank sensor a totaling $94 ( non-RPE) cost. Applyi ng an indirect cost multiplier of 1.25, for a medium complexity technology, results in a compliance cost of $118 for a MY 2012 vehicle and will be reduced in future years with the application of time-based learning.

3.4.2.1.9 Turbocharging and Downsizing (TRBDS)

The specific power of a naturally aspirated engine is primarily limited by the rate at which the engine is able to draw air in to the combustion chambe rs. Turbocharging and supercharging (grouped together here as boosti ng) are two methods to increase the intake manifold pressure and cylinder charge-air mass above naturally aspirated levels. Boosting increases the airflow into the engine, thus increasing the specific power level, and with it the ability to reduce engine di splacement while maintaining performance. This effectively reduces the pumping losses at lighter loads in comparison to a larger, naturally aspirated engine. Almost every major manufacturer currently markets a vehicle with some form of boosting. While boosting has been a common practice for increasing performance for several decades, turbocharging has consider able potential to imp rove fuel economy and reduce CO 2 emissions when the engine displaceme nt is also reduced. Specific power levels for a boosted engine often exceed 100 hp/L, compared to average naturally aspirated engine power dens ities of roughly 70 hp/L. As a result, engines can conservatively be downsized roughly 30 percent to achieve similar peak output levels. In the last decade, improvements to turbocha rger turbine and compressor design have improved their reliability and performance acr oss the entire engine operating range. New variable geometry turbines and ball-bearing center cartridges allow faster turbocharger spool-up (virtually elimina ting the once-common “turbo la g”) while maintaining high flow rates for increased boost at high engine speeds. However, even with turbocharger improvements, maximum engine torque at very low engine speed conditions, for example launch from standstill, is increased less than at mid and high engine speed conditions. The potential to downsize engines may be less on vehicles with low displacement to vehicle mass ratios in order to provide ade quate acceleration from st andstill, particularly up grades or at high altitudes. Use of GDI systems with tu rbocharged engines and air-to-air charge air cooling also reduces the fuel octane requirements for knock limited combustion and allows the use of higher compression ratios. Ford’s “Ecoboost” downsized, turbocharged GDI engines introduced on MY 2010 vehicles allow the replacement of V8 engines with V6

3-32Technologies Considered in the Agencies’ Analysis

engines with improved in 0-60 mph accelerat ion and with fuel economy improvements of up to 12 percent. 31

NHTSA and EPA reviewed NHTSA’s MY 2011 CAFE final rule estimates that a turbocharged and downsized engine at equi valent performance levels would offer an effectiveness improvement of 2 to 5 percent over a naturally -aspirated SGDI engine of comparable performance. This would equa te to a 12 to 14 effectiveness improvement over baseline fixed-valve engine, similar to the estimate for Ford’s Ecoboost. NHTSA and EPA kept these estimates for this final rule. Recently published data with advanced sp ray-guided injection systems and more aggressive engine downsizing targeted towards reduced fuel consumption and CO 2 emissions reductions indicate that the potential for reducing CO 2 emissions for turbocharged, downsized GDI engines may be as much as 15 to 30 percent relative to port-fuel-injected engines. 14,15,16,17,18 Confidential manufactur er data suggests an incremental range of fuel consumption and CO 2 emission reduction of 4.8 to 7.5 percent for turbocharging and downsizing. Other pub licly-available sources suggest a fuel consumption and CO 2 emission reduction of 8 to 13 percent compared to currentproduction naturally-aspirated engines without friction reduction or other fuel economy technologies: a joint techni cal paper by Bosch and Ricardo suggesting fuel economy gain of 8 to 10 percent for downsizing from a 5.7 lite r port injection V8 to a 3.6 liter V6 with direct injection using a wall- guided direct injection system; 32 a Renault report suggesting a 11.9 percent NEDC fuel consumption gain for downsizing from a 1.4 liter port injection in-line 4-cylinder engine to a 1.0 liter in-line 4- cylinder engine, also with wall-guided direct injection; 33 and a Robert Bosch paper suggesting a 13 percent NEDC gain for downsizing to a turbocharged DI engi ne, again with wall -guided injection. 34 These reported fuel economy benefits show a wi de range depending on the SGDI technology employed. Public comment was received on this issue and a summary of those comments with the agencies’ response can be found in section X of the Response to Comments document. As noted above, the agencies relied on engine teardown analyses conducted by EPA, FEV and Munro to develop co sts for turbocharged GDI engines. 35 Teardown studies are one of the most effective ways to estimate technology costs. For the proposal, only the 2.4L I4 DOHC to 1.6L I4 DOHC teardown study had been completed in time for inclusion in the NPRM, and we used that study’s results as the cost in the 2012 model year. For other turbo-downsize costs, the NP RM primarily used values developed for the 2008 staff report. Since issuing the NPRM, two more teardown studies have been completed and those results are being used in the final analysis with some adjustment. The agencies have adjusted the I4 to I4 turbo-downsize costs slightly to reflect an updated burden rate k employed by FEV and an updated binning approach employed by

k Burden costs include the following fixed and variable costs: rented and leased equipment; manufacturing equipment depreciation; plant office equipment depreciation; utilities expense; insurance (fire and general); municipal taxes; plant floor space (e quipment and plant offices); mainte nance of manufacturing equipment - non-labor; maintenance of manufacturing building - general, internal and external, parts, and labor; operating supplies; perishable and supplier-owned tooling; all other plant wages (excluding direct, indirect and MRO labor); returnable dunnage maintenance; an d intra-company shipping costs (see EPA-HQ-OAR2009-0472-0149).

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the agencies to distinguish turbo-rela ted costs from downsize-related costs. l NHTSA and EPA modified the other FEV estimated costs as well. FEV made the assumption that these technologies would be mature when produced in large volumes (450,000 units or more). The agencies believe that there is potential for near term supplier-level engineering, design, and testing (ED&T) costs to be in excess of those considered in the FEV analysis (as existing equipment and fac ilities must be converted to production of new technologies). The agencies have theref ore decided to average the FEV results with the NPRM values. We have also used the FEV results, where possible, to estimate costs for turbo-downsize scenarios th at were not done via actual teardown study (e.g., FEV did not conduct a teardown of a V8 DOHC to turboc harged V6 DOHC). We have also used these values to estimate costs for other camshaft configuration changes that do not involve engine downsizing. Table 3-7 shows the NPRM, FEV and final rule direct manufacturing costs used for th e turbo-downsize technologies.

Table 3-7 Direct Manufacturing Costs for Tu rbocharging, Downsizing, and Other Camshaft Configuration Changes applicable in the 2012 Model Year (2007 dollars)

Technology NPRM FEV Result Final Rule Comment
Turbo (I4 to I4 only) $372 $404 $404 Use FEV result directly, no averaging with NPRM cost Turbo (V6 to I4) $450 $404 $427 Average of NPRM & actual FEV teardown Turbo (V8 to V6) $675 $681 $678 Average of NPRM & actual FEV teardown I4 DOHC to I3 DOHC -$101 -$193 -$147 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost I4 DOHC to smaller I4 DOHC -$60 -$85 -$85 Use FEV result directly, no averaging with NPRM cost

I4 SOHC to smaller I4 DOHC -$80 -$48 -$48 FEV result calculated using binni ng of other teardown results then used directly, no averaging with NPRM cost I4 OHV to smaller I4 DOHC $65 n/a $65 Not possible to calculate us ing binning of other teardown results; NPRM cost used as final V6 DOHC to I4 DOHC -$428 -$547 -$488 Average of NPRM & actual FEV teardown

V6 SOHC to I4 DOHC -$68 -$382 -$225 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost V6 OHV to I4 DOHC $270 n/a $270 Not possible to calculate us ing binning of other teardown results; NPRM cost used as final V8 DOHC to V6 DOHC -$203 -$274 -$238 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost V8 SOHC 3V to V6 DOHC $113 -$155 -$21 Average of NRPM & actual FEV teardown

V8 SOHC 2V to V6 DOHC $203 -$84 $60 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost V8 OHV to V6 DOHC $315 n/a $315 Not possible to calculate us ing binning of other teardown results; NPRM cost used as final V6 SOHC to V6 DOHC $360 $165 $262 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost V6 OHV to V6 DOHC $473 n/a $473 Not possible to calculate us ing binning of other teardown results; NPRM cost used as final V8 SOHC 3V to V8 DOHC $315 $119 $217 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost V8 SOHC 2V to V8 DOHC $405 $190 $298 FEV result calculated using binni ng of other teardown results then averaged with NPRM cost

l This “binning” approach simply bins the turbo-downsize-GDI total cost generated by FEV into separate cost bins for the turbo portion, the downsize portion, and the GDI portion.

3-34Technologies Considered in the Agencies’ Analysis

3-35 V8 OHV to V8 DOHC $518 n/a $518 Not possible to calculated us ing binning of other teardown results; NPRM cost used as final

For the OHV applications, the agencies maintained consistency with the EPA 2008 Staff Report and estimated direct manufact uring costs associated with downsizing to be $50 per cylinder, $10 per valve, a nd $100 per cam shaft for the 2015 model year (2006$). Therefore, these costs have not changed relative to the NPRM. Table 3-8 shows all of these turbo and downsize costs, the applicable ICMs, and the final costs for the 2012 MY (2007$). The agencies have applied time based learning to all of these costs. Estimated costs for the 2016 MY can be found in section 3.5.2 of this TSD.Draft Joint Technical Support DocumentTable 3-8 Direct Manufacturingand Marked up Costs for Turbocharging and Downsizing and Other Camshaft Configuration Changes in 2012 (2007dollars)Direct Manufacturing CostTechnologyIncrementaltoTurboDownsizeIC MarkupFinal Cost *Turbo (I4 to I4 only)Base engine$404n/a1.11$448Turbo (V6 to I4)Base engine$427n/a1.11$474Turbo (V8 to V6)Base engine$678n/a1.11$753Downsize to I4 DOHCV6 DOHCn/a-$4881.11-$434Downsize to I4 DOHCV6 SOHCn/a-$2251.11-$200Downsize to I4 DOHCV6 OHVn/a$2701.11$300Downsize to I4 DOHCI4 DOHC (larger)n/a-$851.11-$76Downsize to I3 DOHCI4 DOHCn/a-$1471.11-$131Downsize to V6 DOHCV8 DOHCn/a-$2381.11-$212Downsize to V6 DOHCV8 SOHC 2Vn/a$601.11$66Downsize to V6 DOHCV8 SOHC 3Vn/a-$211.11-$19

Turbo w/o Downsize Downsize w/o TurboDownsize to V6 DOHCV8 OHVn/a$3151.11$350Downsize to I4 DOHC & add turboV6 DOHC w/o turbo$427-$4881.25$168Downsize to I4 DOHC & add turboV6 SOHC w/o turbo$427-$2251.25$365Downsize to I4 DOHC & add turboV6 OHV w/o turbo$427$2701.25$872Downsize to I4 DOHC & add turboI4 DOHC (larger) w/o turbo$404-$851.25$441Downsize to I3 DOHC & add turboI4 DOHC w/o turbo$404-$1471.25$395Downsize to V6 DOHC & add twin turboV8 DOHC w/o turbo$675-$2381.25$669Downsize to V6 DOHC & add twin turboV8 SOHC 2V w/o turbo$675$601.25$923Downsize to V6 DOHC & add twin turboV8 SOHC 3V w/o turbo$675-$211.25$832

Turbo with DownsizeDownsize to V6 DOHC & add twin turboV8 OHV w/o turbo$675$3151.25$1,242Convert to V6 DOHCV6 SOHCn/a$2621.11$291Convert to V6 DOHCV6 OHVn/a$4731.11$525Convert to V8 DOHCV8 SOHC 2Vn/a$2981.11$330Convert to V8 DOHCV8 SOHC 3Vn/a$2171.11$241

Camshaft ChangesConvert to V8 DOHCV8 OHVn/a$5181.11$575* Note that, where downsizing results in cost savings, the compliance cost is calculated as the IC markup less 1 which is thenmultiplied by the absolute value of the directmanufacturing cost. The absolute value of the direct manufacturingcost is then subtracted from that to arrive at the end result. For example, for the V6 DOHC downsized to theI4 DOHC at a direct manufacturing cost of -$488, the compliance cost would be (1.11-1) x│-$488│-│-$488│= -$434.3-363.4.2.1.10 Cooled Exhaust Gas Recirc ulation/EGR Boost (EGRB)

Cooled exhaust gas recirculation (coole d EGR) or EGR Boost is a combustion concept that involves utilizi ng EGR as a charge dilutant for controlling combustion temperatures and cooling the EGR prior to its introduction to the combustion system. Higher exhaust gas residual levels at part load conditions reduce pumping losses for increased fuel economy. Cooled EGR reduces knock sensitivity which enables the use of more optimal spark advance or enables comp ression ratio to be increased for improved thermal efficiency, and increased fuel econom y. Currently available turbo, charge air cooler, and EGR cooler technolog ies are sufficient to demonstrate the feasibility of this concept.

However, this remains a technology with a num ber of issues that still need to be addressed and for which there is no produc tion experience. EGR system fouling characteristics could be poten tially worse than diesel EGR system fouling, due to the higher HC levels found in gasoline exhaust. Turbocharger compressor contamination may also be an issue for low pressure EGR sy stems. Additionally, transient controls of boost pressure, EGR rate, cam phasers and in take charge temperature to exploit the cooled EGR combustion concept will requir e development beyond what has already been accomplished by the automotive industry. These are all “implementation readiness” issues that must be resolv ed prior to putting EGR Boos t into high volume production.

NHTSA has concluded that these implem entation issues could be resolved and this technology could be brought to production by MY 2013. Supporting this conclusion, MEMA has previously suggested a 5 to 7 percent effectiveness for cooled EGR systems, although without boosting. 36

Two public sources indicate a 10 to 20 percent fuel consumption effectiveness for a downsized DI engine w ith cooled EGR compared to a naturally aspirated baseline engine 37 and a 4 percent fuel cons umption effectiveness for cooled EGR compared to a conventional downsized DI turbocharged engine. 38 Based on the data from these reports, NHTSA estimates the incremental reduction in fuel consumption for EGR Boost to be 4 percent over a turbocharged and downsized DI engine. Thus, if TRBDS pr ecedes EGRB, adding the 12 percent gain from TRBDS to the 4 percent gain from EGRB results in total fuel consumption reduction of 16 percent. This is in agreement with the range suggested in the Lotus report.

Regarding costs, the addition of EGR c ooler and EGR valve were estimated in NHTSA’s MY 2011 rule to have an incremen tal RPE cost impact of approximately $173 based on confidential individua l component cost data fr om 2008 Martec describing EGR cooler costs of $75, EGR valve costs of $20 and associated piping costs of $20, totaling $115 (non-RPE). For purposes of this rulemaking, NHTSA found no information to indicate that these estimates were inaccurate . To that end, NHTSA applied an indirect

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cost multiplier of 1.25, for a medium comple xity technology, resulting in a compliance cost of $144 for MY 2012 vehicles with time-based learning applied.

3.4.2.1.11 Diesel Engine Technologies

Diesel engines have several characteristics that give them superior fuel efficiency compared to conventional gasoline, spark-i gnited engines. Pumping losses are much lower due to lack of (or great ly reduced) throttling. The di esel combustion cycle operates at a higher compression ratio, w ith a very lean air/fuel mixture, and turbocharged lightduty diesels typically achieve much higher torq ue levels at lower engine speeds than equivalent-displacement natura lly-aspirated gasoline engines. Additionally, diesel fuel has a higher energy content per gallon. m However, diesel fuel also has a higher carbon to hydrogen ratio, which in creases the amount of CO 2 emitted per gallon of fuel used by approximately 15 percent over a gallon of gasoline.

Diesel engines have emissions characteri stics that present challenges to meeting federal Tier 2 NOx emissions standards. It is a significant systems- engineering challenge to maintain the fuel consumption advantage of the diesel engine while meeting U.S. emissions regulations. Fuel consumpti on can be negatively impacted by emissions reduction strategies depending on the combin ation of strategies employed. Emission compliance strategies for diesel vehicles so ld in the U.S. are expected to include a combination of combustion improvements and aftertreatment. These emission control strategies are being intr oduced on Tier 2 light-dut y diesel vehicles today

To achieve U.S. Tier 2 emissions limits, roughly 45 to 65 percent more NO x reduction is required compared to the Euro VI standards. Additionally, as discussed below, there may be a fuel consumption pena lty associated with diesel aftertreatment since extra fuel is needed for the aftertreatme nt, and this extra fuel is not used in the combustion process of the engine that provides torque to propel the vehicle.

Light-duty diesel emissions control systems capable of meeting Tier 2 Bin 5 emission standards are already in production. Several key advances in diesel technology have made it possible to reduce emissions coming from the engine prior to aftertreatment. These technologies include improved fuel systems (higher injection pressure and multiple-injection capability), advanced contro ls and sensors to optimize combustion and emissions performance, higher EGR levels and EGR cooling to reduce NO x , and advanced turbocharging systems.

On the aftertreatment side, the traditi onal 3-way catalyst aftertreatment found on gasoline-powered vehicles is in effective due to the lean-burn combustion of a diesel. All diesels will require a diesel particulate filter (DPF) or catalyzed diesel particulate filter (CDPF), a diesel oxidation catalyst (DOC), and a NO x reduction strategy to comply with

m

Burning one gallon of diesel fuel produces about 15 percent more carbon dioxide than gasoline due to the higher density and carbon to hydrogen ratio.

3-38Tier 2 emissions standards. The most common NO x reduction strategi es include the use of lean NO x traps (LNT) or selective catalytic reduction (SCR), which are outlined below.

1. Diesel Engine with Lean NO x Trap (LNT) Catalyst AfterTreatment

A lean NO x trap operates, in principle, by oxidizing NO to NO2 in the exhaust and storing NO2 on alkali sorbent material. When the control system determines (via mathematical model or a NO x sensor) that the trap is saturated with NO x , it switches the engine into a rich operating mode or may in some cases inject fuel directly into the exhaust stream to produce excess hydrocarbons th at act as a reducing agent to convert the stored NO x to N 2 and water, thereby “regeneratin g” the LNT and opening up more locations for NO x to be stored. LNTs preferentia lly store sulfate compounds from the fuel, which can reduce catalytic performan ce. The system must undergo periodic desulfurization by operating at a net-fuel-rich condition at high temperatures in order to retain NO x trapping efficiency.

NHTSA and EPA have concluded that dies el engines on small vehicles would be LNT-based. In the proposed rule, we did not include a diesel option for small vehicles because it did not appear to be a cost eff ective solution. Based on comments received we are including a diesel option for small vehicles in the final rule, and we are allowing the respective models to choose wh ether to apply the technology.

2. Diesel Engine with Selective Catalytic Reduction (SCR) AfterTreatment

An SCR aftertreatment system uses a re ductant (typically, ammonia derived from urea) that is injected into the exhaust st ream ahead of the SCR catalyst. Ammonia combines with NO x in the SCR catalyst to form N 2 and water. The hardware configuration for an SCR system is more comp licated than that of an LNT, due to the onboard urea storage and delivery system (w hich requires a urea pump and injector to inject urea into the exhaust stream). While a rich engine-operating mode is not required for NO x reduction, the urea is typically injected at a rate of approxima tely 3 percent of the fuel consumed. Manufacturers designing SCR systems intend to align urea tank refills with standard maintenance practices such as oil changes. As is the case with LNTbased diesels, EPA and NHTSA project that SCR-based diesel engines will be available within the next couple of years. Mercedes-Benz recently introduced two 2009 model year vehicles R320 and GL320, both of whic h are certified to Tier 2, Bin 5 emission standards. Based on public announcements from several other companies, an increased number of product offerings from multiple companies are expected over the next few
years.

In order to maintain equivalent perf ormance to comparable gasoline-engine vehicles, an in-line 4-cylinder diesel engi ne, with displacement varying around 2.0 liters was assumed to replace an I4 gasoline base engine for Subcompact, Compact, and

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Midsize Passenger Car, Performance Subcomp act Car and Small Light Truck. An in-line 4-cylinder diesel engine, with displaceme nt varying around 2.8 liters was assumed to replace a V6 gasoline base engine for Performance Compact, Performance Midsize, Large Passenger Car, Minivan, and Midsize Truc k for the CAFE model. A V6 diesel engine, with displacement varying around 4.0 liters to m eet vehicle performance requirements, was assumed to replace a V8 gasoline base engine for Large Truck and Performance Large Car vehicle classes for the CAFE model. It was also assumed that diesel engines for these classes would utilize SCR aftertreatment systems.

Confidential manufacturer and non-conf idential comment data submitted in response to NHTSA’s past rulemaking for di esel engines showed a fuel consumption reduction in the range of 16.7 to 26.7 percent or a 15 to 23 percent CO 2 emission reduction over a baseline gasoline engine. NHTSA’s MY 2011 CAFE final rule, which was supported by confidential manufacturer data, estimated the fuel consumption reduction of a SCR-based diesel engine to be between 19 to 25 percent over a baseline gasoline engine. NHTSA and EPA have revi sited these values and found them to be reasonable. EPA estimates a 15 to 26 percent CO 2 reduction for light-duty diesels equipped with SCR.

Diesel engines are more costly than port -injected spark-ignitio n gasoline engines. These higher costs result from:

• Fuel systems (higher pressures a nd more responsive injectors);

• Controls and sensors to optimize co mbustion and emissions performance;

• Engine design (higher cylinder pressure s require a more robust engine, but higher torque output means diesel engi nes can have reduced displacement);

• Turbocharger(s);

• Aftertreatment systems, which tend to be more costly for diesels;

Due to a significant decrease in platin um group metal prices since NHTSA’s MY 2011 CAFE final rule analysis, the agencies c hose to re-analy ze diesel costs. In EPA’s 2008 Staff Report, costs were considered for tw o types of diesel systems: one using a lean-NO X trap (LNT) along with a diesel par ticulate filter (DPF); and one using a selective catalytic re duction (SCR) system along with a DPF. In that report, EPA estimated direct manufacturing costs to ra nge from $1,860 for the small car (LNT plus DPF) to $2,710 for the large truck (SCR plus DPF). For comparison, the NESCCAF study showed direct manufacturing costs of $1,500 to $1,950. More recently, NHTSA’s MY 2011 CAFE final rule showed direct manufacturing costs of $2,670 for a 4-cylinder engine using a LNT plus DPF system, $3,735 fo r a 6-cylinder engine using a SCR plus DPF system, and $4,668 for an 8-cylinder e ngine using a SCR plus DPF system. NHTSA noted that estimates in the MY 2011 CA FE final rule were higher than those

3-40shown in this rule due largely to the spik e in platinum group metal prices that had occurred in the months just prior to issuing the 2011 CAFE final rule.

The following diesel engine costs were developed by first taking a look back at EPA’s 2008 Staff Report, which reveals a couple of factors that re sulted in somewhat misleading costs. First, the engine costs estimated there did not take into account the downsizing that would occur when moving from a gasoline engine to a diesel engine (provided equivalent performance was maintained ). Second, the engine costs used in that analysis were actually stated in terms of 2002 dollars rather than 2006 dollars in which the report was meant to be stated. The agen cies decided that an update to the enginerelated costs would provide a much better cost estimate for converting to diesel. This was done by starting with the source for engi ne costs in the 2008 staff report which was an October 2005 EPA Interim Report 39 which, in turn, sourced estimates from a 2003 study done by FEV for EPA contained within a 2004 EPA Interim Technical Report. 40 These direct manufacturing costs are reproduced in Table 3-9

.

Table 3-9 Diesel Engine Direct Manufacturing Source Costs, Incremental to a Baseline Gasoline Engine (2002 dollars)
Component(s) Large SUV Midsize Gasoline engine (baseline) 5L V8 2.4L I4 Diesel engine 4L V8 2.2L I4 Add high-pressure, common rail diesel fuel injection system $980 $630 Delete gasoline fuel injection system -$245 -$165 Add variable geometry turbocharger $175 $126 Delete gasoline ignition system -$120 -$75 Delete fuel pump and other changes to fuel system -$94 -$75 Enhance powertrain mounting system $87 $107 Other engine changes $80 $70 Add air intercooler, ducts, and sensor $80 $55 Larger battery and starter, add glow plugs $72 $50 Delete exhaust gas oxygen sensor* -$60 -$30 Add supplemental heater $50 $15 Modify transmission $25 $25 Enhance sound insulation package $25 $10 Smaller radiator -$13 -$4 Total $1,042 $739 Note: Table reproduced from EPA420-R-05-012, October 2005

Building on the direct manufacturing costs shown in Table 3-9 , the agencies used appropriate scaling to estimate the costs fo r replacing a baseline ga soline engine with a diesel engine for the following five situa tions: a small car converted from a 2.4L I4 gasoline to a 2L I4 Diesel; a large car converted from a 4.5L V8 gasoline to a 3L V6 diesel; a medium/large MPV converted from a 3.2L V6 to a 2.8L I4 diesel; a small truck converted from a 3.2L V6 gasoline to a 2.8L I4 diesel; and a large truck converted from a 5.6L V8 gasoline to a 4L V6 diesel. The resu lts for the five base gasoline to diesel conversions are shown in Table 3-10 . Values from Table 3-9 have been updated to 2007 dollars using the GDP price deflator factor of 1.15 (see Appendix 3.A). Since the source

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costs were developed in 2003, th is analysis conservatively considers the costs shown in Table 3-10 as being applicable to the 2012 model year.
Table 3-10 Diesel Engine Direct Manufacturing Scaled-Costs in 2012, Incremental to Baseline Gasoline Engine (2007 dollars)

Component(s) Small

car Large

car Med/large MPV Small truck Large truck Notes (see text below)
Gasoline engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8
Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 Add high-pressure, common rail diesel fuel injection system $517 $1,026 $724 $724 $1,026 1
Delete gasoline fuel injection system -$52 -$89 -$73 -$73 -$89 2
Add variable geometry turbocharger $145 $173 $145 $145 $201 3
Delete gasoline ignition system -$69 -$138 -$112 -$112 -$138 4 Delete fuel pump and other changes to fuel system -$62 -$108 -$86 -$86 -$108 5
Enhance powertrain mounting system $123 $100 $123 $123 $100 6
Other engine changes $57 $86 $80 $80 $86 7 Add air intercooler, ducts, and

sensor $45 $78 $63 $63 $92 8
Larger battery and starter, add glow plugs $57 $70 $57 $57 $70 9
Delete exhaust gas oxygen sensor* $0 $0 $0 $0 $0 10
Add supplemental heater $17 $37 $17 $17 $57 11 Modify transmission $29 $29 $29 $29 $29 12 Enhance sound insulation package $11 $20 $11 $11 $29 13 Smaller radiator -$7 -$15 -$10 -$10 -$15 14 Engine downsize credit $0 -$185 -$390 -$390 -$185 15 Total $813 $1,085 $580 $580 $1,156 * Note: Oxygen sensor removals ar e included in aftertreatment costs.

The costs shown in Table 3-10 were scaled in the following ways:

1. Large car and large truck calculated as 75% the cost of Table 3-9 ’s large SUV and 25% of midsize car; medium/large MPV a nd small truck calculated as equal to Table 3-9 ’s midsize car; small car calculated using Table 3-10 ’s Med/large MPV value of $724 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

2. The estimates generated by FEV for eliminating the gasoline fuel injection systems were considerably larger than EPA & NHT SA believed was appropriate. Therefore, for a more accurate estimate, these costs we re estimated, in 2007 dollars as follows: large car and large truck were calculated using incremental costs of $8/injector, $20/fuel rail, and $5 for a pressure damper or $8x8+$20+$5=$89; medium/large MPV and small truck were calculated using incremental costs of $8/injector, $20/fuel

3-42rail, and $5 for a pressure damper or $8x6+$20+$5=$73; small car calculated using Table 3-10 ’s Med/large MPV value of -$73 and a pplying the ratio of diesel engine sizes (2.0/2.8).

3. Large car calculated as the average of Table 3-9 ’s large SUV and midsize car; medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car, and large truck calcu lated as equal to Table 3-9 ’s large SUV; small car calculated as equal to medium/large MPV. Values conve rted to 2007 dollars using GDP factor of 1.15.

4. Medium/large MPV and small truck calculated as the average of Table 3-9 ’s large SUV and midsize car; Large car and la rge truck calculated as equal to Table 3-9 ’s large SUV; small car calcula ted as equal to half of Table 3-10 ’s large truck value of - $138. Values converted to 2007 dollars using GDP factor of 1.15.

5. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car; Large car and large truck calculated as equal to Table 3-9 ’s large SUV; small car calculated using Table 3-10 ’s Med/large MPV value of -$86 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

6. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s large SUV; Large car and large truck calculated as equal to Table 3-9 ’s midsize car; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

7. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car; Large car and large truck calculated as the average of Table 3-9 ’s large SUV and midsize car; small car calculated using Table 3-10 ’s Med/large MPV value of $80 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

8. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car; Large car calculated as the average of Table 3-9 ’s large SUV and midsize car; Large truck calculated as equal to Table 3-9 ’s large SUV; small car calculated using Table 3-10 ’s Med/large MPV value of $63 and applyi ng the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

9. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car; Large car and large truck calculated as the average of Table 3-9 ’s large SUV and midsize car; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

10. Oxygen sensor costs are included in the aftertreatment costs discussed below.

11. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car; Large car calculated as the average of Table 3-9 ’s large SUV and midsize car; Large

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truck calculated as equal to Table 3-9 ’s large SUV; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

12. Values from Table 3-9 converted to 2007 dollars using GDP factor of 1.15.

13. Medium/large MPV and small truck calculated as equal to Table 3-9 ’s midsize car; Large car calculated as the average of Table 3-9 ’s large SUV and midsize car; Large truck calculated as equal to Table 3-9 ’s large SUV; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

14. Medium/large MPV and small truck calculated as the average of Table 3-9 ’s large SUV and midsize car; Large car and la rge truck calculated as equal to Table 3-9 ’s large SUV; small car calculated using Table 3-10 ’s Med/large MPV value of -$10 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

15. Based on the approach presented in the tu rbocharging/downsizing section (see section 3.5.2.1.11), the savings associated with dow nsizing the gasoline engine were calculated by estimating the cost in 2007 do llars of each cylinder at $51, each valve at $10, and each cam at $103. n Therefore, the large car a nd large truck, which each lose two cylinders (-$102), eight valves (-$82) and no cams realize a $185 savings. The medium/large MPV and small truck would each lose two cylinders (-$102) and eight valves (-$82) and two cams (-$205) for a savings of $390. The small car downsizing credit was left at $0 given the small disp lacement change and lack of cylinder or valve removals.

For the diesel aftertreatment systems, th e approach taken is consistent with the approach taken in EPA’s 2007/2010 Highway Di esel rule and EPA’s recent locomotive and marine rule. 41 For platinum group metal (PGM) cost s, monthly average prices as of March 2009 as reported by Johnson-Matthey were used. 42
, o Those values were $1,085/troy ounce for platinum and $1,169/troy ounce for rhodium. Aftertreatment devices were sized according to the diesel e ngine displacement with a 1:1 ratio for both the SCR catalyst and the DPF, and a 0.5:1 ratio for the DOC ( i.e. , the DOC is half the

n These are the correct costs for th e 2015MY in 2007$. But, they are used here erroneously as 2012MY values. Technically, 3 years of time-based learning s hould have been backed out to get 2012MY values. So, the 2012MY costs are slightly underestimated by roughly $10. This was true in the proposed analysis and, because it has no meaningful im pact on the analysis (1-2% of the es timated diesel costs), continues to be true in the final analysis.

o These are the PGM prices used in the NPRM. As pr ecious metals, PGM prices swing widely year-to-year and even month-to-month. Given economic conditions during the past year and the tendency of investors during tough economic times to invest in valuable metals such as gold and PGMs, PGM prices have increased considerably since the NPRM. Were the January 2010 PGM prices used, the diesel costs presented here would increase on the order of 10-20 percent. That would serve to make diesels less cost effective options for improving fuel economy or re ducing GHGs. Since diesels comprise such a small percentage of each agency’s analysis from a technology penetration standpoint, even using the lower March 2009 PGM prices, the choice of what PGM prices to use is of little consequence to the analytical results for purposes of this rulemaking.

3-44displacement of the engine). The end result for aftertreatment devices, including a urea dosing unit, urea tank and necessary br ackets and heaters, are shown in Table 3-11 . Also shown in Table 3-11 are the savings associated with re moval of the gasoline catalyst. Note that the gasoline catalyst was sized acco rding to the gasoline engine that served as the baseline engine.

Table 3-11 Diesel Aftertre atment Direct Manufac turing Costs in 2012 (2007 dollars)

Component(s) Small car Large car Med/large MPV Small truck Large truck
Gasoline engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8 Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 DOC $216 $277 $257 $257 $339 DPF (includes a $20 pressure sensor for OBD & sensing) $401 $534 $503 $503 $668
SCR system (includes a $50 NOx sensor for OBD & sensing) n/a $904 $904 $914 $996
LNT System includes $50 NOx sensor for OBD and sensing $442 n/a n/a n/a n/a
Removal of gasoline catalysts & sensors -$175 -$401 -$288 -$298 -$483 Total $883 $1,314 $1,376 $1,376 $1,520

The incremental costs to convert fr om a gasoline to a diesel engine— Table 3-10 and Table 3-11 combined—are shown in Table 3-12

.

Table 3-12 Direct Manufacturing Co sts to Convert from a Gasoline to Diesel System in 2012 (2007 dollars)

Component(s) Small car Large car Med/large MPV Small truck Large truck
Gasoline engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8
Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 Engine-related costs $813 $1,085 $580 $580 $1,156 Aftertreatment $883 $1,314 $1,376 $1,376 $1,520 Total $1,697 $2,399 $1,956 $1,956 $2,676

This analysis applies time-based learni ng to diesel systems and a 1.25 ICM for a medium complexity rating. Therefore, the MY 2012 compliance costs are as shown in Table 3-13

.

Table 3-13 Compliance Costs to Convert from a Ga soline to Diesel System in 2012 (2007 dollars)

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Component(s) Small car Large car Med/large MPV Small truck Large truck
Gasoline engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8
Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 Total $2,121 $2,999 $2,445 $2,445 $3,345

The diesel costs for the 2016 MY, which in clude 4 years of time-based learning, are shown in section 3.5.2. In the NHTSA analysis, and consistent with the above analysis, diesel costs for a 2.0L in-line 4- cylinder from an I4 gasoline engine were estimated to be $2,121 in MY 2012. A 2.8L in-l ine 4-cylinder from a V6 gasoline engine were estimated to be $2,445 in MY 2012. A MY 2012 cost of $3,345 was estimated for a conversion to 4.0L V6 diesel from a V8 ga soline engine. These MY 2012 estimates were obtained using the in-line 4-cylinder 2.0L co st of $1,697, the in-line 4-cylinder 2.8L cost of $1956 and V6 cost of $2,676, from Table 3-12 , and multiplying them by the medium complexity ICM markup of 1.25 a nd are eligible for time-based learning cost reductions.

3. 4.2.2 Transmission Technologies

NHTSA and EPA have also reviewed th e transmission technology estimates used in the MY 2011 CAFE fina l rule. In doing so, NHTSA and EPA considered or reconsidered all available sources and updated the estimates as appropriate. The section below describes each of the transmission tec hnologies considered for this rulemaking.

3. 4.2.2.1 Improved Automatic Transmission Control (IATC) (Aggressive Shift Logic and Early Torque Converter Lockup)

Calibrating the transmission shift schedule to upshift earlier and quicker, and to lock-up or partially lock-up the torque converter under a broader range of operating conditions can reduce fuel consumption and CO 2 emissions. However, this operation can result in a perceptible degradation in noise, vibration, and hars hness (NVH). The degree to which NVH can be degraded before it beco mes noticeable to the driver is strongly influenced by characteristics of the vehicle, and although it is somewhat subjective, it always places a limit on how much fuel c onsumption can be improved by transmission control changes. Given that the Aggressi ve Shift Logic and Early Torque Converter Lockup are best optimized simultaneously due to the fact that adding both of them primarily requires only minor modifications to the transmis sion or calibration software, these two technologies are combined in the modeling.

3. 4.2.2.2 Aggressive Shift Logic

3-46During operation, an automatic transmission’s controller manages the operation of the transmission by scheduling the upshift or downshift, and locking or allowing the torque converter to slip based on a prepr ogrammed shift schedule. The shift schedule contains a number of lookup table functions, which define the shift points and torque converter lockup based on vehicle speed and th rottle position, and other parameters such as temperature. Aggressive shift logic (A SL) can be employed in such a way as to maximize fuel efficiency by modifying the sh ift schedule to upshift earlier and inhibit downshifts under some conditions, which reduc es engine pumping losses and engine friction. The application of this technology does require a ma nufacturer to confirm that drivability, durability, and NVH ar e not significantly degraded.

The agencies have estimated the effectiven ess of aggressive shift logic at 1 to 2 percent, as explained below. The agencies have estimated the cost at $32 for a MY 2012 vehicle, regardless of size (2007$). p With time-based learning applied, this cost would be $28 for a MY 2016 vehicle (2007$) based on the 2002 NAS report.

3. 4.2.2.3 Early Torque Converter Lockup

A torque converter is a fluid coup ling located between the engine and transmission in vehicles with automatic transmissions and continuously-variable transmissions (CVT). This fluid coupling allo ws for slip so the engine can run while the vehicle is idling in gear (as at a stop li ght), provides for smoothness of the powertrain, and also provides for torque multiplication during acceleration, and especially launch. During light acceleration and cruising, the inhe rent slip in a tor que converter causes increased fuel consumption, so modern automa tic transmissions utilize a clutch in the torque converter to lock it and prevent this slippage. Fuel consumption can be further reduced by locking up the torque converter at lower vehicle speeds, provided there is sufficient power to propel the vehicle, a nd noise and vibrati on are not excessive. q If the torque converter cannot be fully locked up for maximum efficiency, a partial lockup strategy can be employed to reduce slippage. Early torque converter lockup is applicable to all vehicle types with au tomatic transmissions. Some torque converters will require upgraded clutch materials to withstand addi tional loading and the slipping conditions during partial lock-up. As w ith aggressive shift logic, confirmation of acceptable drivability, performance, durab ility and NVH characteristics is required to successfully implement this technology.

Regarding the effectiveness of Improved Automatic Transmission Control, the MY 2011 CAFE final rule, which was supported by the 2002 NAS and NESCCAF reports as well as confidential manufactur er data, estimated an effectiveness improvement of 1 to 2 percent for aggressive shift logic and 0.5 per cent for early torque

p This reflects a revisiting of component costs for NHTSA since the MY 2011 CAFE final rule.

qAlthough only modifications to the transmission calibration software are considered as part of this technology, very aggressive early torque converter lock up may require an adjustment to damper stiffness and hysteresis inside the torque converter.

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converter lockup. These estimates are in ag reement with the values stated in the NESCCAF report and confidential manufacturer data. For the purpose of this final rule, NHTSA and EPA concluded that the combin ed estimated effectiveness is 1.5 to 2.5 percent reduction in CO 2 emissions and fuel consumption.

Regarding cost estimates, the agencies es timate the cost at $29, regardless of vehicle size and including a low complexity ICM of 1.11 fo r a MY 2012 vehicle (2007$). With time-based learning, this cost w ould be $25 for a MY 2016 vehicle (2007$). For IATC (or the combination of aggressive shif t logic and early torque converter lockup), the cost, including the 1.11 ICM, would be $60 for a MY 2012 vehicle and $53 for a MY 2016 vehicle after time-based learning (2007$).

3. 4.2.2.4 Automatic 6-, 7- and 8-Speed Transmissions (NAUTO)

Manufacturers can also choose to replace 4- and 5-speed transmission with 6-, 7-, or 8-speed automatic transmissions. Additional ratios allow for further optimization of engine operation over a wider range of conditio ns, but this is subject to diminishing returns as the number of speeds increases. As additional planetary gear sets are added (which may be necessary in some cases to achieve the higher number of ratios), additional weight and friction are introduced. Also, the additional shifting of such a transmission can be perceived as bothersome to some consumers, so manufacturers need to develop strategies for smooth shifts. So me manufacturers are repl acing 4- and 5-speed automatics with 6-speed automatics, and 7- and 8-speed automatics have also entered production, albeit in lower-volum e applications in luxury and performance oriented cars.

As discussed in the MY 2011 CAFE fina l rule, confidential manufacturer data projected that 6-speed transmissions could in crementally reduce fuel consumption by 0 to 5 percent from a baseline 4-speed automatic transmission, while an 8-speed transmission could incrementally reduce fuel consumption by up to 6 percent from a baseline 4-speed automatic transmission. GM has publicly claimed a fuel economy improvement of up to 4 percent for its new 6-speed automatic transmissions. 43 The 2008 EPA Staff Technical Report found a 4.5 to 6.5 percent fuel consum ption improvement for a 6-speed over a 4- speed automatic transmission. 44 Based on this information, NHTSA estimated in the MY 2011 rule, that the conversion to a 6-,7- and 8-speed transm ission (NAUTO) from a 4 or 5-speed automatic transmission with IATC wo uld have an incremental fuel consumption benefit of 1.4 percent to 3.4 per cent, for all vehicle classes. From a baseline 4 or 5 speed transmission without IATC, the incremental fuel consumption benefit would be approximately 3 to 6 percent, which is cons istent with the EPA Staff Report estimate.

NHTSA and EPA reviewed these effectiveness estimates and concluded that they remain accurate. While the CAFE model follows the incremental approach discussed above, the GHG model estimates the packag ed effectiveness of 4.5 to 6.5 percent.

3-48In the NPRM, NHTSA and EPA reviewed the cost estimates from the MY 2011 CAFE final rule which used cost estim ates from 2008 Martec report and the 2008 EPA Staff Report (which assumed use of a Lepelleti er gear set) and conc luded that some but not all 6-speed automatic transmissions would be equipped with Lepelle tier gear set. As such, the estimates were revised to establish the cost for the 6 speed transmission to be equally divided between application using Le pelletier, and applica tion of a standard planetary gear set 6-speed automatic transmi ssion as estimated in the 2008 Martec Report (and the MY 2011 CAFE final rule). The 2008 Martec report estimated a cost of $323 with RPE adjustment and $215 without RPE ad justment for converting a 4-speed to a 6- speed transmission and a cost of $638 with RPE adjustment or $425 without RPE adjustment for converting a 4-speed to an 8- speed transmission. As a result, the final incremental cost estimate in the NPRM was $170, independent of vehi cle type and size and including a low complexity 1.11 ICM (2007$ ). In the CAFE model, due to the structure of the vehicle classes used, an additional $102 (2007$) was included to account for performance vehicle classes and for medium and large trucks. This is because for performance classes, additional gear ratios, such as 7 and 8 speed transmissions may be utilized, and for medium and large trucks h eavier duty transmissions are required. These estimates represented MY 2012 vehicle costs

For the final analysis, as noted earlier, the agencies have considered the teardown work done by FEV. In its teardown work, FEV determined the 6 speed automatic transmission to be $106 less costly than a 5 speed automatic transmission (direct manufacturing cost without markups). This is counterintuitive but can be attributed to the type of gear set employed in the 6 speed au tomatic transmission studied by FEV. That 6 speed auto transmission was equipped with a Lepelletier-like gear se t and therefore does not require a 1-way clutch that is essential to other designs. The cost analysis performed in this rule requires that the cost differe nce between both a 4 speed and 5 speed auto relative to 6 speed auto be established becau se there are a substantial number of both 4 speed and 5 speed automatic transmissions us ed in the baseline fleet. This was done using the NPRM analysis for a 4 speed to 5 speed and adding the FEV results for the transition from a 5 speed to a 6 speed. Our anal ysis resulted in a cost for a 5 speed auto transmission relative to a 4 speed auto of $91 direct manufacturing cost (2007$ in 2012). As done in the NPRM, the agencies averaged the non-Lepelletier gear set cost with the Lepelletier-like gear set cost (FEV tear-down value of -$106). In the 2011 FRM, NHTSA estimated the cost of a 6 speed auto transmission, without a Lepelletier gear set, relative to a 4 speed auto transmission at $215 (2007$). Therefore, we have used the $215 value (4 speed to 6 speed) from NHTSA ’s 2011 FRM and subtracted $91 from that (4 speed to 5 speed) to arrive at a cost of $124 as the non- Lepelletier cost for a 6 speed auto transmission relative to a 5 speed auto transmission. We have then averaged the $124 value with the FEV value of -$106 (Lepelleti er-like cost) to get an end result of $9 (2007$ in 2012). This $9 cost represents the cost for a 6 speed auto transmission relative to a 5 speed auto transmission in the final an alysis. This $9 can then be added to the $91 to get a cost of $101 for a 6 speed auto transmission relative to a 4 speed auto transmission. Table 3-14 shows the direct manufacturing co sts used in the NPRM and the final analyses. With the low comple xity markup of 1.11, the compliance cost for a 6 speed automatic transmission relative to a 4 speed automatic transmission in 2012 is $112

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(2007$). With time based learning, the compliance costs in 2016 to move from a 4 speed auto to a 5 speed auto and then from a 5 speed auto to a 6 speed auto transmission would be $90 and $9, respectively (2007$).

Table 3-14 Direct Manuf acturing Costs for Automatic Transmissions (2007$ in 2012)

Technology Non-Lepelletier Cost Used in 2011

CAFE Analysis NPRM FEV tear down (Lepelletier-like) Final Rule Comments

5s auto relative to 4s
auto $91 n/a $91 Final rule uses NPRM value 6s auto relative to 5s
auto -$106 $9 $215-$91=$124 [$124+(-$106)]/2=$9 6s auto relative to 4s
auto $215 $153 n/a $101 $91+$9=$101 (values are rounded) Notes: Blank cells represent values not considered in this an alysis; n/a means that FEV did no t conduct a tear down of the technology; refer to text for more detail on the comments.

3. 4.2.2.5 Dual Clutch Transmissions / Automated Manual Transmissions (DCTAM)

An Automated Manual Transmission (AMT) is mechanically similar to a conventional manual transmission, but shifti ng and launch functions are automatically controlled by the electronics. There are two basic types of AMTs, si ngle-clutch and dualclutch (DCT). A single-clutch AMT is esse ntially a manual transmission with automated clutch and shifting. Because of shift quality issues with single-clutch designs, DCTs will likely be far more common in the U.S. and are the basis of the estimates that follow. A DCT uses separate clutches (and separate g ear shafts) for the even-numbered gears and odd-numbered gears. In this way, the next e xpected gear is pre-se lected, which allows for faster and smoother shifting. For example, if the vehicle is accelerating in third gear, the shaft with gears one, three and five has gear three engaged and is transmitting power. The shaft with gears two, four, a nd six is idle, but has gear fo ur engaged. When a shift is required, the controller disenga ges the odd-gear clutch whil e simultaneously engaging the even-gear clutch, thus making a smooth shift. If, on the other hand, the driver slows down instead of continuing to accelerate, the transmission will have to change to second gear on the idling shaft to antic ipate a downshift. This shif t can be made quickly on the idling shaft since there is no to rque being transferred on it.

In addition to single-clutch and dual-clut ch AMTs, there are also wet clutch and dry clutch designs which are used for different types of vehicle app lications. Wet clutch AMTs offer a higher torque capacity that come s from the use of a hydraulic system that cools the clutches. Wet clutch systems are le ss efficient than the dry clutch systems due to the losses associated with hydraulic pumpi ng. Additionally, wet AMTs have a higher cost due to the additional hydraulic hardware required.

3-50Overall, DCTs likely offer the greatest potential for effectiveness improvements among the various transmission options presente d in this report because they offer the inherently lower losses of a manual transmi ssion with the efficiency and shift quality advantages of electronic contro ls. The lower losses stem from the elimination of the conventional lock-up torque converter, and a greatly reduced need for high pressure hydraulic circuits to hold clut ches or bands to maintain gear ratios (in automatic transmissions) or hold pulleys in position to maintain gear ratio (in Continuously Variable Transmissions). However, the lack of a torque converter will affect how the vehicle launches from rest, so a DCT will most likely be paired with an engine that offers sufficient torque at low engine speeds to allow for adequate launch performance.

For the MY 2011 CAFE final rule, NHT SA estimated a 5.5 to 9.5 percent improvement in fuel consumption over a base line 4/5-speed automatic transmission for a wet clutch DCT, which was assumed for all but the smallest of vehicle subclasses, Subcompact and Compact cars and small LT. Th is results in an incremental effectiveness estimate of 2.7 to 4.1 percent over a 6-speed automatic transmission with IATC. For Subcompact and Compact Cars and small LT, which were assumed to use a dry clutch DCT, NHTSA estimated an 8 to 13 percent fuel consumption improvement over a baseline 4/5-speed automatic transmissi on, which equates to a 5.5 to 7.5 percent incremental improvement over the 6-speed tran smission. This is cons istent with the EPA Staff Report, which estimated that these tran smissions offer an effectiveness of 9.5 to 14.5 percent over a 4-speed automatic transmission. EPA and NHTSA therefore conclude that 8 to 13 percent effectiv eness is appropriate for this rule. In the NPRM analysis, the agencies estimated costs for 6 speed dual-clutch transmissions relative to 6 speed auto tran smissions. For the dry clutch DCT, the agencies estimated a direct manufacturing cost of $59 (2007$ in 2012). For the wet clutch DCT, the estimate was $126 (2007$ in 2012). For the final rule, we have considered the teardown work done by FEV. In that teardown work, FEV found a wet clutch DCT to be $147 less costly than a 6 speed auto transmission. Th is cost savings is not unexpected when one considers that th e DCT is less complex than an auto transmission. However, such a cost savings presumes that capacity exists to produce dual-clutch transmissions at volumes of 450,000 units, an assumption made by FEV. Since such capacity may not currently exist in the US, we have not used the FEV value directly but have instead av eraged the FEV value (-$147) with the value used in the NPRM analysis ($126). Therefore, in the fi nal rule, the wet clutch DCT is estimated to save $11 relative to a 6 speed auto transm ission (2007$ in 2012). To generate a dry clutch DCT cost for the final analysis, we ha ve applied the same cost difference from the NPRM for wet clutch versus dry clutch. In other words, the NPRM analysis had a cost difference of $67 which was then subtracted fr om the final wet-clut ch DCT cost of -$11 to arrive at a result of -$78 for a dry-clutch DCT relative to a 6 sp eed auto transmission (2007$ in 2012). Table 3-15 shows the direct manufacturing costs used in the NPRM and final rule for dual clutch transmissions. Applying the medium complexity ICM of 1.25 results in costs of -$59 for the wet-clutch DCT and -$8 for the dry clutch DCT (2007$ in 2012), both relative to a 6 speed auto transmission. With tim e based learning, these costs become -$52 and -$7, respectively, in 2016 (2007$).

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Table 3-15 Direct Manufacturing Costs for Dual-Clutch Transmissions (2007$ in 2012)

Technology NPRM FEV tear down Final Rule Comments

6s wet DCT relative to 6s auto $126 -$147 -$11 [$126+(-$147)]/2=-$11 6s dry DCT relative to 6s auto $59 n/a -$78 -$11-($126-$59) = -$78 Notes: n/a means that FEV did not conduct a tear down of the technology; refer to text for more detail on the comments.

3. 4.2.2.6 Continuously Variable Transmission (CVT)

A Continuously Variable Transmission (CVT ) is unique in that it does not use gears to provide ratios for operation. Inst ead, the most common CV T design uses two Vshaped pulleys connected by a metal belt. E ach pulley is split in half and a hydraulic actuator moves the pulley halves together or apart. This causes the belt to ride on either a larger or smaller diameter section of the pul ley which changes the effective ratio of the input to the output shafts. A dvantages of the CVT are that the engine can operate at its most efficient speed-load point more of th e time, since there are no fixed ratios. However, CVTs are limited by engine power and cannot be applied to high torque applications. Also, CVTs often have a wide r range of ratios comp ared to conventional automatic transmissions which can provide for more options in engine optimization. While CVTs by definition are fully continuous, some automakers choose to emulate conventional stepped automatic operation becau se some drivers are not used to the sensation of the engine speed opera ting independently of vehicle speed.

Considering the confidential data togeth er with independent review, NHTSA has estimated the fuel consumption effectiveness for CVTs at 2.2 to 4.5 percent over a 4/5- speed automatic transmission, which translates into a 0.7 to 2.0 incr emental effectiveness improvement over an automatic transmi ssion with the IATC technology. EPA and NHTSA reviewed these estimates and found them to be accurate.

The agencies adjusted the original estimates used in the MY 2011 CAFE final rule to account for medium complexity ICM of 1.25. This resulted in estimated incremental compliance cost of $250 for the MY 2012 vehicles. In the Volpe model, this technology was only applied to vehicles manufactured with unibody construction methods. CVTs are an established and readily available technology, so time based learning is applied.

3. 4.2.2.7 6-Speed Manual Transmissions (6MAN)

3-52Manual transmissions are enti rely dependent upon driver input to shift gears: the driver selects when to perfor m the shift and which gear to select. This is the most efficient transfer of energy of all transmissi on layouts, because it has the lowest internal gear losses, with a minimal hydraulic syst em, and the driver provides the energy to actuate the clutch. From a systems vi ewpoint, however, vehicles with manual transmissions have the drawback that the driv er may not always select the optimum gear ratio for fuel economy. Nonetheless, increa sing the number of available ratios in a manual transmission can improve fuel economy by allowing the driver to select a ratio that optimizes engine operation more often. Typically, this is achieved through adding overdrive ratios to reduce engine speed at cr uising velocities (which saves fuel through reduced engine pumping losses) and pushing th e torque required of the engine towards the optimum level. However, if the gear ra tio steps are not properly designed, this may require the driver to change gears more of ten in city driving, resulting in customer dissatisfaction. Additionally, if gear ratios are selected to achieve improved launch performance instead of to improve fuel ec onomy, then no fuel saving effectiveness is realized.

NHTSA’s MY 2011 CAFE final rule estimat ed an effectiveness increase of 0.5 percent for replacing a 5-speed manual with a 6-speed manual transmission, which was derived from confidential manufacturer da ta. NHTSA and EPA have found no evidence to dispute this estimate and chosen to use 0.5 percent reduction in CO 2 emissions and fuel consumption for replacing a 5-speed manual wi th a 6-speed manual transmission for this rule. NHTSA updated costs to reflect the IC M low complexity markup of 1.11 which resulted in a incremental compliance cost of $250 as compared to $338 for MY 2012, with lower costs in later MYs due to the appl ication of time-based learning factors. In the 2008 Staff Report, EPA estimated this RP E (1.5 RPE factor) at $107 (2006$) relative to a 5-speed manual transmission and for a MY 2015 vehicle. EPA updated this value to 2007$ and used a 1.11 ICM to get costs of $89 for a MY 2012 vehicle and $69 for a MY 2016 vehicle, both relative to a 5-speed manual transmission (2007$).

3. 4.2.3 Hybrid and Electrification/Accessory Technologies

A Hybrid is a vehicle that combines two or more sources of propulsion energy, where one uses a consumable fuel (like ga soline), and one is rechargeable (during operation, or by another energy source). Hybr id technology is established in the U.S. market and more manufacturers are adding hybrid models to thei r lineups. Hybrids reduce fuel consumption through three major mechanisms:

• Th e internal combustion engine can be optimized (through downsizing, modifying the operating cycle, or other control techniques) to operate at or near its most efficient point more of the time. Power loss from engine downsizing can be mitigated by employing power assist from the secondary
power source.

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• So me of the energy normally lost as h eat while braking can be captured and stored in the energy storage system for later use.

• Th e engine is turned off when it is not needed, such as when the vehicle is coasting or when stopped.

Hybrid vehicles utilize some combina tion of the three above mechanisms to reduce fuel consumption and CO 2 emissions. A fourth mechanism to reduce petroleum fuel consumption, available only to plug-in hybrids, is by substituting the petroleum fuel energy with energy from another source, such as the electric grid. The effectiveness of fuel consumption and CO 2 reduction depends on the utilizat ion of the above mechanisms and how aggressively they are pursued. One area where this variation is particularly prevalent is in the choice of engine size and its effect on balancing fuel economy and performance. Some manuf acturers choose not to downsi ze the engine when applying hybrid technologies. In these cases, perf ormance is vastly improved, while fuel efficiency improves significantly less than if the engine was downsized to maintain the same performance as the conventional version. While this approach has been used in cars such as the Honda Accord Hybrid (now disc ontinued), it is more likely to be used for vehicles like trucks where towi ng and/or hauling is an integr al part of their performance requirements. In these cases, if the engine is downsized, the battery can be quickly drained during a long hill climb with a heavy load, leaving only a downsized engine to carry the entire load. Because towing capab ility is currently a heavily-marketed truck attribute, manufacturers are he sitant to offer a truck with downsized engine which can lead to a significantly diminished towing pe rformance with a low battery, and therefore engines are traditionally not downsized for these vehicles.

Although hybrid vehicles using other energy storage concepts (flywheel, hydraulic) have been developed, the systems currently in production in the U.S. for passenger cars and light trucks use battery st orage and electric drive systems. Hybrid electric vehicles (HEV) are pa rt of a continuum of vehicles using systems with differing levels of electric drive and electric energy storage. This range of vehicles includes relatively basic engine start/stop systems, HE V systems with varying degrees of electric storage and electric drive syst em capability, plug-in hybrid el ectric vehicles (PHEV) with differing degrees of all electric range and battery electric vehicl es (EV) that rely entirely on electric drive and battery electric energy storage.

Different HEV, PHEV and EV concepts u tilize these mechanisms differently, so they are treated separately for the purposes of this analysis. Below is a discussion of battery energy storage and the major hybrid concepts that were determined to be available in the near term. In many applic ations, particularly w ith PHEV and EV, the battery represents the most costly and system-limiting sub-component of the hybrid system. Currently, there are many battery ch emistries being developed and refined for hybrid applications that are expected to enhance the performance of future hybrid vehicles.

3-543.4 .2.3.1 Batteries for MHEV, HEV, PHEV and EV Applications

The design of battery secondary cells can vary considerably between MHEV, HEV, PHEV and EV applications.

MHEV batteries : Due to their lower voltage (12-42 VDC) and reduced power and energy requirements, MHEV systems will lik ely continue to use lead-acid batteries. MHEV battery designs differ from those of current starved-electrolyte (typical maintenance free batteries) or flooded-electr olyte (the older styl e lead-acid batteries requiring water “top-off”) batteries used fo r starting, lighting and ignition (SLI) in automotive applications. Standard SLI batt eries are primarily de signed to provide highcurrent for engine start-up and then recharge immediately after startup via the vehicle’s charging system. Deeply discharging a standard SLI battery will great ly shorten its life. MHEV applications are expected to use:

• Ex tended-cycle-life flooded (ELF) lead-acid batteries

• Ab sorptive glass matt, valve-regulated lead-acid (AGM/V RLA) batteries –

or –

• Asymme tric lead-acid ba ttery/capacitor hybrids ( e.g. , flooded ultrabatteries)

MHEV systems using electrolytic double-laye r capacitors are also under development.

HEV batteries : HEV applications operate in a narrow, short-cycling, chargesustaining state of charge (SOC). Energy capacity in HEV applications is somewhat limited by the ability of the battery and power electronics to accept charge and by space and weight constraints within the vehicle design. HEV battery designs tend to be optimized for high power density rather than high energy density, w ith thinner cathode and anode layers and more numerous current collectors and separators ( Figure 3-1 ).

EV batteries : EV batteries tend to be optimi zed for high energy density and are considerably larger than HEV batteries. PH EV battery designs are intermediate between power-optimized HEV and energy-optim ized EV battery cell designs.

PHEV batteries : PHEV batteries must provide both charge depleting operation similar to an EV and charge sustaining operation similar to an HEV. Unlike HEV applications, charge-sustaining operation with PHEVs occurs at a relatively low battery state of charge (SOC) which can pose a signif icant challenge with respect to attaining acceptable battery cycle life. In the case of the GM Volt, this limits charge depleting operation to a minimum SOC of approximately 30 percent. 45 An alternative approach for PHEV applications that has potential to allo w extension of charge depletion to a lower battery SOC is using energy-optimized lith ium-ion batteries for charge depleting operation in combination with the use of supercapacitors for charge sustaining operation. 46

Figure 3-1 : Schematic representation of power and energy optimized

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prismatic-layered battery cells

Collector (-)
Cathode (-)
Separator
Anode (+)
Collector (+) HEV Power-optimized Battery Cell

EV Energy-optimized Battery Cell

Power-split hybrid vehicles from Toyota, Ford and Nissan, integrated motor assist hybrid vehicles from Honda a nd the GM 2-mode hybrid vehicles currently use nickelmetal hydride (NiMH) batteries. Lithium-ion (Li-ion) batteries offer the potential to approximately double both the energy and power density relative to current NiMH batteries, enabling much more electrical-ene rgy-intensive automotive applications such as PHEVs and EVs. Li-ion batteries for high-volume automotive applications differ substantially from those used in consumer electronics applications with respect to cathode chemistry, construction and cell size. Li-ion battery designs currently under development by CPI (LG-Chem) for the GM Volt PHEV and by AESC, GS-Yuasa and A123 Systems (respectively) for the upcomi ng Nissan, Mitsubishi and Chrysler EVs use large-format, layered-prismatic cells assemb led into battery modules. The modules are then combined into battery packs.

Cathodes for large-format, automotive Li -ion batteries are becoming increasingly focused on two chemistries – LiMn2O4-spin el (CPI, GS-Yuasa, AESC) and LiFePO4 (A123 Systems).

In addition to the purely hybrid technol ogies, which decrease the proportion of propulsion energy coming from the fuel by increasing the proportion of that energy coming from electricity, there are other steps th at can be taken to improve the efficiency of auxiliary functions ( e.g. , power-assisted steering or air-conditioning) which also reduce CO 2 emissions and fuel consumption. Th ese steps, together with the hybrid technologies, are collectively referred to as “vehicle electrification” because they generally use electricity inst ead of engine power. In or der to achieve consistency

3-56between the two modeling techniques, and to improve the number and range of technology offerings, the CAFE model was revised to includ e one additional mild hybrid technology. The high voltage or improved e fficiency alternator (HVIA) technology, which was used in the MY 2011 CAFE final ru le, is no longer repres ented as a separate technology and has instead been incorporated into this new mild hybrid technology, as discussed further below.

3.4.2.3.2 Hybrid System Sizing and Cost Estimating Methodology

NHTSA and EPA reviewed estimates of cost and effectiveness for hybrid and related electrical technologies and adjusted them as appropria te. Both agencies found the hybrid technology cost estimating methodol ogy that Ricardo a nd NHTSA developed during the MY 2011 CAFE final rule to be reasonable and used it to estimate hybrid systems costs and account for variation in component sizing across both the hybrid types and vehicle subclasses. That method utilizes four pieces of data: (1) key component sizes for a midsize car by hybrid system type; (2) normalized costs for each key component; (3) component scaling factors that are applied to each vehicle class/subclass by hybrid system type; and (4) vehicle characte ristics for the subclasse s which are used as the basis for the scaling factors. Duri ng development of the methodology, NHTSA and Ricardo made several assumptions:

1) Hybrid c ontrols hardware varies with the level of functionality offered by the hybrid technology. Assumed hybrid cont rols complexity for a 12V micro hybrid (MHEV) and belt integrated star ter generator (BISG) was 25 percent of a strong hybrid controls system and th e complexity for a Crank Integrated Starter Generator (CISG) was 50 percent. These ratios were estimates based on the directional need for increased functionality as system complexity increases. 2) Li-ion b atteries for hybrid electric ve hicles are currently entering production, including a 2010 MY Mercedes and Hyunda i. One estimate from Anderman indicates that Li-ion market pene tration will achieve 35 percent by 2015. 47 However, as was discussed above, significant development effort is underway by a number of battery producers which c ould impact cost and overcome other technical concerns. Therefore it was assumed that mild hybrids (MHEV, BISG, CISG) and strong hybrids (PSHEV , 2MHEV, PHEV) will use either Li-ion or NiMH batteries, depending on cost considerations. However, plugin hybrids will use Li-ion batteries only. Battery usage is discussed further below. 3) The plug -in hybrid battery pack was si zed for a mid-sized car by assuming: the vehicle has a 20 mile all electric range and consumes an average of 300 W-hr per mile; the battery pack can be discharged down to 30 percent depth of

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discharge; r and the capacity of a new battery p ack is 20 percent greater than at end of life ( i.e. , range on a new battery pack is 24 miles). 4) All hybr id systems included a DC/DC converter which was sized to accommodate vehicle electrical loads appropriate for increased vehicle electrification in the time frame considered. 5) High vol tage wiring scaled with hybrid vehicle functionality and could be represented as a fraction of strong hybr id wiring. These ratios were estimates based on the directional need for increased functionality as system complexity increases. 6) All hybr id systems included a supplementa l heater to provide vehicle heating when the engine is stopped; however, in th is analysis, it is assumed that only half of the vehicles will adapt this technology, as discussed further below. Only the strong hybrids included electric air conditioning to enable engine stop/start when vehicle air conditio ning was requested by the operator.

Furthermore, NHTSA and Ricardo recogni zed that some strong hybrid systems replaced a conventional transm ission with a hybrid-specific transmission, resulting in a cost offset ( i.e. , a cost credit) for the re moval of a portion of the clutches and gear sets within the transmission. In the MY 2011 CA FE final rule, the transmission cost in Table 3-16 below expressed hybrid transmission costs as a percentage of traditional automatic transmission cost, as described in the 2008 Martec Report, at $850 direct manufacturing costs (non-RPE/ICM). The method assumed th at the mechanical aspect of a power-split transmission with a reduced number of gear sets and clutches resulted in a cost savings of 50 percent ($425) over a conventional transmissi on with torque converter. For a 2-mode hybrid, the mechanical aspect s of the transmission are si milar in complexity to a conventional transmission, so no cost savi ngs was appropriate. The plug-in hybrid assumed a highly simplified transmission for elec tric motor drive, thus 25 percent of the base vehicle transmission cost was applied (resulting in a $638 credit).

The NHTSA MY 2011 CAFE final rule disc usses in detail how the hybrid cost estimating methodology uses the information pr ovided in the tables below to calculate costs for each of the strong hybrid systems used in this analysis. Italso includes a stepby-step example for the midsize vehicle mild hybrid systems used in the MY 2011 CAFE final rule. 48 As in that analysis, it is important to understand that the CISG technology replaces existing mild hybrid systems. s

NHTSA and EPA in reviewing the above made the following revisions. First, NHTSA and EPA revalidated the component size s that were estimated for a midsize car for each type of hybrid system as shown in Table 3-16 . However, NHTSA and EPA added an additional component, front e ngine accessory drive (FEAD), because hybridization often involves revi sion to the FEAD design such th at certain devices (belts, pulleys, idlers, etc.) as well as other engine components (alt ernator, A/C compressor, and

r The GM Volt operates between 30% DOD and 85% DOD. So there is 55% useable DOD, but charge sustaining operation starts at 30% and cycles between 30 and 35% DOD.

s For the incremental CAFE model, be fore CISG is applied, the costs fo r MHEV and BISG are subtracted if they were previously applied.

3-58starter) may no longer be needed and can thus be eliminated, or may be de-specified to lower cost alternatives. This is applicable to CISG and the strong hybrid technologies, and is intended to account for cost savings associated with items that changed or are no longer required as a result of these technology applications.

Table 3-16

. Component Sizes by Hybrid Type for a Midsize Car

Component MHEV BISG CISG PSHEV 2MHEV PHEV
Primary Motor power, continuous (kW) 3 11 45 45 45 Secondary Motor power, continuous (kW) na na 30 45 30 Primary Inverter power, continuous (kW) 3 11 45 45 45 Secondary Inverter power, continuous (kW) na na 30 45 30 Controls complexity (relative to strong hybrid) 25% 50% 100% 100% 100% NiMH Batter y Pack ca p acit y ( kW-hr ) 1 na 1 2 2 na Li-Ion Batter y

Pack ca p acit y ( kW-hr ) 1 na 1 2 2 15 DC/DC Converter power (kW) 0.7 2 2 2 2 High Voltage Wiring (relative to strong hybrid) na 50% 100% 100% 100%
Su pp lemental heatin g 2 50% 50% 50% 50% 50% Mechanical Transmission (relative to baseline vehicle) 100% 100% 50% 100% 25% Electric AC No No Yes Yes Yes Blended Brakes No Yes Yes Yes Yes FEAD Credit No Yes Yes Yes Yes Charger power, continuous (kW) na na na na 3

2 - Implemented through a reduction in component cost (50%) 1 - Assumes the use of either NiMH or Li-Ion, and not both. Hybrid Type

Second, the costs estimates of the key components were revised. The MY 2011 CAFE final rule was developed at a time when economic conditions were significantly different than those that currently exist, a time when many of the commodity materials used in the hybrid systems were more expe nsive than today. These changes in economic conditions were one of the factors leading to some of the cost revisions EPA and NHTSA jointly discussed and made. Differences in estimates provided by confidential sources to either EPA or NHTSA also played a part in the revisions. In addition, the agencies applied the new ICM mark-up factors instead of the RPE that was used previously. An appropriate ICM factor (1.45 for most mild and strong hybrid technol ogies) replaces the previous RPE factor (1.5), as discussed in section 3.3.2.2.2 . Specifically, the primary and secondary inverter cost per kilowatt were revised downward from $10 to $7, the controls cost was revised upward from $100 to $115, th e DC/DC converter costs were revised from $100 to $88, the blended brake system that was revised from $400 to $310, and finally the fully learned, high volume produc tion, cost per kilowatt hour (kW-hr) for NiMH batteries was revised from $350 to $320 to match the updated costs for Li-ion batteries discussed below. t

t In the MY 2011 final rule NHTSA had misstated this $350 per kW-hr cost as $50 per kW-hr due to a typographical error.

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The cost for Li-ion batteries was also revised. As pr eviously stated, Li-ion batteries are being implemented in seri es production in model year 2010. Battery technology is changing rapidly in the marketplace today, as discussed above, and is expected to continue along this path throughout the rulemaking period. OEMs are now forming relationships with batt ery manufacturers in an effort to research and develop not only new and improved battery technology, but also more efficient manufacturing processes capable of supporting high volume production. Accordingly, as shown in Table 3-17 , the $600 per kW-hr used in the MY 2011 CAFE final rule was revised downward to $320 per kW-hr. The revision downward from $600/kW-hr in the MY 2011 CAFE final rule to $320/kW-hr in this an alysis was done based a study by Deutsche Bank that estimated Li-ion batte ry costs at 300-400 €/kW-hr. 49 This was converted to $500/kW-hr then learned twice using volume-based learning to arrive at the $320/kW-hr applicable in the 2015 timeframe.

Li-ion batteries were originally restricted to plug-in hybrids only. Recent vehicle introductions confirm either battery technol ogy can be used in any mild or strong HEV application. However, manufact urers are likely to consider co st highly in their selection of battery technology. If Li-ion battery prices remain high, NiMH would be the default battery technology for all hybrid electric vehicles. For plug-in hybrids Li-ion would continue to be required because plug-in hybrids demand higher energy density than NiMH can provide. Neither the CAFE nor OM EGA model predicts a high penetration of plug-in technology in achieving the standards.

Finally, the agencies assessed the cost sa vings associated with the FEAD credit discussed above. This cost wa s not previously represented in the hybrid cost model. As shown in Table 3-17 below, a $100 credit is shown which offsets directly the costs of the other components specified. This is the best approximation of the value of these items, based on NHTSA and EPA engineering assessment.

Estimates of each key component are shown in Table 3-17 below along with the sources of those estimates. The cost basi s estimates assume fully learned, high-volume (greater than 1.2 million units per annum) production, and the costs shown are direct manufacturing costs that are not RPE or ICM ad justed. This table does not show a cost applicable to the belt integrated starter gene rator system (BISG) since it is a fixed cost that, like the automatic transmission pump co st, is not scaled by subclass as described later.

Table 3-17. Component Cost Basis at High Volumes and Data Sources stated in 2015 (2007 dollars)

COMPONENT COST BASIS DATA SOURCE Primary Motor ($/kW) $15 Secondary Motor ($/kW) $15 Martec 2008
Primary Inverter ($/kW) $7 Secondary Inverter ($/kW) $7 Controls $115 Confidential Business Information

NiMH Battery Pack ($/k W-hr.) $320 2011 CAFE FRM

3-60(with revision) Li-ion Battery Pack ($/kW-hr.) $320 Deutsche Bank 2008 DC/DC Converter (Size: 2kW) $88 Confidential Business Information High Voltage Wiring $200 Supplemental Heating $42 Martec 2008

Mechanical Transmission $850 Ma rtec 2008 (to 4-spd auto) Electric Air Conditioning $450 Blended Brakes $310 Charger $100 Confidential Business Information

Automatic Transmission Pump $75 Martec 2008 FEAD Credit $(100) Confidential Business Information

Third, NHTSA and EPA also revised co mponent size/scaling assumptions for some vehicles ( i.e. , large trucks). NHTSA and EPA recognized that some manufacturers may choose not to use supplemental cabin he ating, opting instead to continue engine operation in the event heat demand occurs; ther efore supplemental heat ing is specified for only half of the vehicles. Table 3-17 above indicates the 50 pe rcent application rate implemented in the hybrid cost estimati ng methodology reducing the component cost from $84 to $42.

EPA and NHTSA also reviewed the choi ce of a 3 kW DC/DC converter as a component size input for a midsize vehicle, which represented a 250 amp current capability. In retrospect this is a high speci fication for a midsize vehicle, so the agencies revised the estimate to a 2 kW DC/DC converter, as shown in Table 3-17 above, which would represent a more reasonable 150 amp current capacity.

The scaling factor used for the primary and secondary motors and invertors on the large truck and SUV vehicles was revised. As in the MY 2011 CAFE final rule, a linear extrapolation was used from the midsize ve hicle and extended it out to the largest of vehicles, the large truck class. This resulted in projected component sizes that are larger than those used on a commercially realized tr uck in this vehicle class, the Chevrolet Tahoe two-mode HEV. Accordi ngly, the scaling factors have been revised for this class (and the agencies have verified scaling factor s for the other classes) . This more closely approximates the motor and inverter sizes speci fied in the Tahoe application. For future analysis, the agencies are considering whether it may be more accurate to use one set of scaling for passenger cars and anothe r different set for light trucks.

Another revision involves the addition of a stand-alone higher voltage Start-Stop/ BISG mild hybrid system. NHTSA and EPA dete rmined that by applying a cost increase to the MHEV technology to allow for a voltage increase (lead acid batteries) and efficiency improvements to the alternator, th e system would then approximate the higher voltage Start-Stop/BISG applied by EPA. Ba sed on confidential sources, the estimates provided were first converte d to 2007 dollars and then reverse-learned through two cycles, since volume learning is applicable, to arrive at a non-RPE/ICM incremental

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compliance cost of $229. This cost is applicab le to all classes that use higher voltage Start-Stop/BISG, and is not scal ed by any vehicle attribute.

Component scaling factors for each type of hybrid system are as shown in Table 3-18 below.

Table 3-18. Component Scaling Factors applie d to Vehicle Class for each Hybrid System

Component MHEV CISG PSHEV 2MHEV PHEV
Primary Motor Engine displacement Curb weight Engine power

Secondary Motor na na Curb weight 2

Primary Inverter Secondary Inverter na na Controls NiMH Battery Pack na na Li-Ion Battery Pack na Curb weight DC/DC Converter High Voltage Wiring na Supplemental heating Mechanical Transmission Electric AC na na Blended Brakes na

Charger na na na na Same for all vehicle classes

(1) For all vehicle classes except for performance classes which use Engine Torque
(2) Curb weight used as surrogate for vehicle road load
(3) Curb weight used as surrogate for vehicle electrical load Same for all vehicle classes Hybrid Type

Engine displacement Curb weight 1

Curb weight 3 Vehicle footprint

Same for all vehicle classes Vehicle footprint

Vehicle footprint Primary motor power Secondary motor power Complexity
Curb weight

Regarding the market data file from the MY 2011 CAFE final rule, NHTSA and EPA did not make any revisions to the aver age vehicle characteristics for each vehicle subclass as shown in Table 3-19 , which defines the average vehicle characteristics for each vehicle subclass. These characteristics were used as the basis of the scaling factors in the CAFE and OMEGA models. EPA used these vehicle specifications solely for the purpose of scaling hybrid vehicle attributes in order to remain consistent with the NHTSA methodology.

3-62Table 3-19

. Key Vehicle Characteristics For Ea ch Vehicle Subclass for CAFE Model



### Vehicle Subclass Curb Weight

(lbs) Footprint (ft2) Engine Disp. (L) Engine Power

(hp) Torque (ft-lb)

Subcompact Car 2795 41 1.9 134 133 Compact Car 3359 44 2.2 166 167 Midsize Car 3725 47 2.9 205 206 Large Car 4110 50 3.4 258 248 Performance Subcompact Car 3054 40 2.7 260 260 Performance Compact Car 3516 44 3.0 269 260 Performance Midsize Car 3822 47 3.9 337 318 Performance Large Car 4189 51 4.8 394 388 Minivan 4090 50 3.3 247 242 Small Truck 3413 45 2.6 178 185 Medium Truck 4260 50 3.6 250 256 Large Truck 5366 63 5.0 323 352

3.4.2.3 .3 Electrical Power Steering (EPS)

Electric power steering (EPS) pr ovides a potential reduction in CO 2 emissions and fuel consumption over hydraulic power steering because of reduced overall accessory loads. This eliminates the parasitic losses associated with belt-driven power steering pumps which consistently draw load from the engine to pump hydraulic fluid through the steering actuation systems even when the wheels are not being turned. EPS is an enabler for all vehicle hybridization technologies si nce it provides power steering when the engine is off. EPS may be implemented on mo st vehicles with a standard 12V system. Some heavier vehicles may require a highe r voltage system which may add cost and complexity.

In the MY 2011 CAFE final rule, NHTSA es timated a 1 to 2 percent effectiveness based on the 2002 NAS report, a Sierra Resear ch report, and confidential manufacturer data. NHTSA and EPA reviewed these eff ectiveness estimates and found them to be accurate, thus they have been retained for this final rule.

Regarding costs, in the MY 2011 CAFE final rule, NHTSA estimated EPS at $105-120 at 1.5 RPE mark-up factor. NHTSA a nd EPA adjusted the EPS cost for the current rulemaking based on a review of the specification of the system. Adjustments were made to include potentially higher volta ge or heavier duty system operation, such as would be required on some hybrid trucks. A ccordingly, higher costs were estimated for systems with higher capability. After accountin g for the differences in system capability and applying the ICM markup of low comple xity technology of 1.11, the estimated costs

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for this rulemaking are $106 for a MY 2012 ve hicle (2007$). With time-based learning, this cost would be $94 for a MY 2016 vehicle (2007$). As EPS systems are in widespread usage today, time-base d learning is deemed applicable.

3 .4.2.3.4 Improved Accessories (IACC)

The accessories on an engine, including the alternator, coolant and oil pumps are traditionally mechanically-driven. A reduction in CO 2 emissions and fuel consumption can be realized by driving them electricall y, and only when needed (“on-demand”).

Electric water pumps and electric fans can provide better control of engine cooling. For example, coolant flow from an electric water pump can be reduced and the radiator fan can be shut off during engine warm-up or cold ambient temperature conditions which will reduce warm-up time, re duce warm-up fuel enrichment, and reduce parasitic losses.

Indirect benefit may be obtained by re ducing the flow from the water pump electrically during the engine warm-up period, allowing the engine to heat more rapidly and thereby reducing the fuel enrichment n eeded during cold star ting of the engine. Further benefit may be obtained when elec trification is combined with an improved, higher efficiency engine alternator. Intel ligent cooling can more easily be applied to vehicles that do not typically carry heavy payloads, so larger vehicles with towing capacity present a challenge, as these vehicles have high cooling fan loads. u

The agencies considered whether to in clude electric oil pump technology for the rulemaking. Because it is necessary to operate the oil pump any time the engine is running, electric oil pump technology has insign ificant effect on efficiency. Therefore, the agencies decided to not include electr ic oil pump technology for this rulemaking.

NHTSA and EPA jointly reviewed the estimates of 1 to 2 percent effectiveness estimates used in the MY 2011 CAFE final rule and found them to be accurate for Improved Electrical Accessories. For its anal ysis, NHTSA simply adjusted the MY 2011 CAFE final rule costs to re flect the new ICM markup of low complexity value of 1.11, resulting in a cost estimat e for this rulemaking of $128 at MY 2012 (2007$). Improved accessory systems are in production currently and thus time-based learning is applied. For its analysis, EPA considered both an accessory upgrade to a 12 Volt system and a combined accessory upgrade with conversi on to a 42 Volt system. The accessory upgrade itself is estimated to cost $86 in MY 2012 and $76 in MY 2016 (with time-based learning; both in 2007$). The conversion to a 42 Volt electrical system is estimated to

u In the CAFE model, IACC refers solely to improved engine cooling. However, EPA has included a high efficiency alternator in this category, as well as improvements to the cooling system.

3-64cost $97 for a MY 2012 vehicle and $86 for a MY 2016 vehicle (again with time-based learning; both in 2007$).

3 .4.2.3.5 12V Micro Hybrid (MHEV)

12V Micro-Hybrid (MHEV) systems are the most basic of hybrid systems and offer only the ability to turn the engine o ff when the vehicle is stopped or potentially during deceleration ( i.e. , idle stop). Their low cost and adaptability to existing powertrains and platforms can make them attractive for some applications. The conventional belt-driven alternator is repl aced with a belt-driven, enhanced power starter-alternator and a redesigned front-e nd accessory drive system. A conventional 12V gear-reduction starter is retained to en sure reliable cold-weather starting. Also, during idle-stop, some functions such as power steering and automatic transmission hydraulic pressure are lost, so electric power steering a nd an auxiliary transmission pump are needed.

Although EPA does not use the 12 V vers ion of this stop-start technology, NHTSA and EPA jointly reviewed the assumptions. The effectiveness estimates for this technology ranged from 2.0 to 4.0 percent, depe nding on whether the vehicle is equipped with a 4-, 6- or 8-cylinder engine, with the 4-cylinder engine having the lowest range and the 8-cylinder having the highest. The estima tes reflect the limited capability of 12 volt systems; sources citing higher estimates typi cally involve higher voltage systems that have increased capability.

For this final rule, the system specifica tions assumed in the MY 2011 CAFE final rule were applied ( i.e. , use of a 3 kW motor and a DC/DC converter) and the hybrid technology cost method produced costs like those in MY 2011 final rule; however, the use of new ICM markups resulted in costs ra nging from $288 for Subcompact subclass to $410 for the Large Performance subclass. Th is technology is not applied to the Large Truck and SUV subclass due to the heavy duty utility requirements of these vehicles. Time-based learning is considered applicable , and a schematic of the MHEV system is shown in Figure 3-2

. Figure 3-2 Schematic of MHEV type System [Husted, 2003]

En g ine Motor/ Generato r

Trans. Controller Inverte r

Final Drive Wheel +

-

Batter y

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3 .4.2.3.6 Higher Voltage Stop-Start/Belt Integr ated Starter Generator (BISG)

Higher Voltage Stop-Start and Belt M ounted Integrated Starter Generator (BISG) systems are similar to a micro-hybrid system, offering idle-stop functionality, except that they utilize larg er electric machine and a hi gher capacity battery, typically 42 volts or above, thus enabling a limited level of regenerative braking which is generally not applicable to 12 volt systems. The larger electric machine and battery also enables a limited degree of power assist, which MHEV cannot provide. However, because of the limited torque capacity of th e belt-driven design, these systems have a smaller electric machine, and thus less cap ability than crank-in tegrated or stronger hybrid systems. These systems replace the conventional alternator with a belt-driven starter/alternator and may add electric power steering and an auxiliary automatic transmission pump. The limited electrical requirements of these systems allow the use of lead-acid batteries or super capacitors for energy storage.

The effectiveness estimates used for th ese technologies range from 3.0 to 7.5 percent depending on vehicle subclass. The CAFE model, which applies this effectiveness incrementally to the prior 12 Volt technology, uses estimates of 4 to 6 percent. The estimates are based on t hose found in the 2002 NAS and 2004 NESCCAF reports, and confidential manufacturer data.

EPA estimates an incremental complian ce cost range of $549 (small car) to $682 (large truck) for a MY 2012 vehicle and in cluding a medium co mplexity ICM of 1.25 (2007$). With volume-based learning app lied, these become $351 (small car) and $437 (large truck) for a MY 2016 vehicle (2007$). The cost estimate in the CAFE model is incremental to the 12 Volt micro hybrid systems as noted above, and therefore is adjusted upwards to $286 to reflect the additional ba ttery capacity, wiring upgr ades, and a larger optimized electric machine only. The $286 re flects volume-based learning factors and the ICM medium-complexity markup of 1.25.

3.4.2.3.7 Integrated Motor Assist (IMA)/Crank Integrated Starter Generator (CISG)

IMA is a system developed and marketed by Honda 50 and is similar to CISG. They both utilize a thin axial electric motor bolted to the engine’s crankshaft and connected to the transmission through a torque converter or clutch. The axial motor is motor/generator that typically operates above 100 volts (b ut lower than the stronger hybrid systems discussed below, which ty pically operate at around 300 volts) and can provide sufficient torque for launch as well as generate sufficient current to provide significant levels of brake ener gy recovery. The motor/generato r also acts as the starter for the engine and can replace a typical acce ssory-driven alternator. Current IMA/CISG systems typically do not launch the vehicl e on electric power alone, although some commercially available systems can crui se on electric power and dual-clutch IMA/CISG systems capable of all-electri c drive are under development. IMA and CISG could be applied to all classes of vehicles. A schematic of the Honda IMA system is shown in Figure 3-3

.

3-66Figure 3-3 Schematic of Honda IMA System [Husted, 2003]

Final Drive Engine Motor/ Generator Trans. + -

Battery Controller

Inverte r Wheel

NHTSA did not have an equivalent t echnology to CISG in the MY 2011 CAFE final rule (the ISG technology used in the MY 2011 final rule was envisioned to be less capable than the CISG technology defined here). For the CISG technology, NHTSA estimated a net effectiveness range of 16 to 20 percent, relative to the baseline vehicle and across all vehicle subclasses in this anal ysis. The CAFE model therefore applies an incremental effectiveness of approximately 8.6 to 8.9 percent relative to the BISG technology and dependent on vehi cle subclass, except for large truck and SUV in which case BISG does not apply. Note that the net e ffectiveness assumptions used in this rule do not include engine downsizing, or any other effectiveness gains from engine, transmission, or vehicle technologies adde d to the vehicle by the modeling process.

EPA relied on a combination of certificati on data (comparing vehicles available with and without a hybrid system and backi ng out other components where appropriate) and manufacturer-supplied information to de termine that the effectiveness of these systems in terms of CO 2 reduction is 30 percent for small cars, 25 percent for large cars, and 20 percent for minivans and small trucks similar to the range estimated by NHTSA for the respective vehicle classes. The effectiveness for small cars assumes engine downsizing to maintain approximately equiva lent performance. The large car, minivan, and small truck effectiveness values assume less engine downsizing in order to improve vehicle performance and/or maintain towing and hauling performance.

EPA has estimated these costs, relativ e to a base engine/transmission, as $4,459 (small car) to $6,924 (large truck) for a MY 2012 vehicle an d including a high complexity ICM of 1.45 (2007$). With vol ume-based learning, these costs become $2,854 and $4,431, respectively, for a MY 2016 vehi cle (2007$). NHTSA has estimated the MY 2012 compliance cost, relative to a BISG baseline, to range from $2,791 for Subcompact vehicles to $3,611 for Midsize Li ght Truck and Minivan vehicles. These include a high complexity ICM markup factor of 1.45 for this technology. As this

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technology is still in limited production use, volume-based learning is applied, resulting in lower costs in later years.

3 .4.2.3.8 Power Split Hybrid

The Power Split hybrid (PSHEV) has the ability to move the vehicle on electric power only. It replaces the ve hicle’s transmission with a single planetary gear and a motor/generator. A second, more powerful moto r/generator is direc tly connected to the vehicle’s final drive. The planetary gear splits engine power between the first motor/generator and the final drive. The first motor/generator uses power from the engine to either charge the ba ttery or supply power to the wh eels. The speed of the first motor/generator determines the relative speed of the engine to the wheels. In this way, the planetary gear allows the engine to opera te independently of ve hicle speed, much like a CVT. The Toyota Prius and the Ford Hybrid Escape are two examples of power split hybrid vehicles.

In addition to providing the functions of idle engine stop, subsequent restart and regenerative braking, this hybrid system a llows for pure EV operation. The power split system provides very good fuel consumption in city driving. During highway cycles, the hybrid functions of regenerative braking, e ngine start/stop and op timal engine operation cannot be applied as often as in city driving, and so the effectiveness in fuel consumption is less. Additionally, it is less efficient at highway speeds due to the fact that the first motor/generator must be spinning at a relativ ely high speed and therefore incurs losses. Newer designs incorporate a gear-reducti on motor to provide improved high speed efficiency and improved matching of motor torque to engine torque.

The Power Split hybrid also reduces the cost of the transmission, replacing a conventional multi-speed unit with a single plan etary gear. The electric components are bigger than those in mild hybrid and IMA/ CISG configurations so the costs are correspondingly higher.

EPA and NHTSA used a combination of manufacturer-supplied information and a comparison of vehicles available with and without a hybrid system from EPA’s fuel economy test data to determine that the effec tiveness is 19 to 36 percent for the classes to which it is applied. The estimate would depe nd on whether engine downsizing is also assumed. As an example, See Table 3-20 and Table 3-21 , which assume some level of engine downsizing. In the CAFE incremental model, the range of effectiveness used was 23 to 33 percent as engine downsizing is not assumed (and accounted for elsewhere).

Table 3-20 Large Car Pow er Split Certification Data

Tailpipe CO 2 City Hwy 55/45 comb. N issan Altima 3.5L CVT 444 306 386 HEV 2.5L PS 317 254 286 Net % difference -26%

3-68Toyota Camry 3.0L 5-auto 404 286 355 HEV 2.4L PS 222 234 228 Net % difference -36%

Lexus GS 4.3L 6-auto 493 355 423 HEV 3.5L PS 355 317 341 Net % difference -19%

Table 3-21 Small Truck Power Split Certification Data

Tailpipe CO 2 City Hwy 55/45 comb. Ford Escape 4X4 3.0L 4-auto 467 386 423 HEV 2.3L PS 277 306 286 Net % difference -32%

Ford Escape 4X2 3.0L 4-auto 444 370 404 HEV 2.3L PS 247 286 261 Net % difference -35%

Toyota Highlander 4X4 3.3L 5-auto 493 370 423 HEV 3.3L PS 286 329 306 Net % difference -28%

Using the hybrid cost estimating me thodology with updated component costs, EPA established a compliance cost range of $4,481 (small cars) to $8,145 (large trucks) relative to the base engine and transmission for a MY 2012 ve hicle (all in 2007$). With time-based learning, these costs become $3,967 and $7,210, respectively, for a MY 2016 vehicle (2007$). Using the same met hodology, NHTSA established overall PSHEV system costs with electric power steer ing (EPS) and improved accessories (IACC) ranging from $5,509 to $11,534 for MY 2012, which leads to incremental costs in the CAFE model ranging from $1,600 to $6,723 de pending on the subclass (all in 2007$). The estimates were determined with a 1.45 ICM for a high complexity technology. Based on Toyota Prius and Ford Escape/Mari ner market sales, PSHEV technology with NiMH battery is considered mature from the perspective of cost learning, and time-based learning is applicable. However, the Li -ion battery technology is undergoing rapid development and it is expected that so me manufacturers may choose to use Li-ion batteries on PSHEV applications during this rulemaking period. Because this combination of technology is new, volume-ba sed learning would be appropriate for such applications. For cost analysis in the NP RM and final rule, volume-based learning is used for PSHEV which assumes use of Li-ion batteries. This volume-based learning for Li-ion is supported by th e 2008 Deutsche Bank study.

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3 .4.2.3.9 2-Mode Hybrid

The 2-Mode Hybrid (2MHEV) uses an ad aptation of a conventional stepped-ratio automatic transmission which replaces some of the transmission clutches with two electric motor/generators, allowing the transmission to act like a CVT. The motor/generators control the ratio of engine speed to vehicle speed. The clutches allow the motors to be bypassed improving the transmission’s torque capacity and the efficiency for improved fuel economy at highway speeds. This type of system is used in the Chevrolet Tahoe Hybrid.

In addition to providing the hybrid functions of engine stop and subsequent restart and regenerative braking, the 2MHEV allows for pure EV operation. The two motor/generators allow the engine to be r un in efficient operating zones. The 2-mode system provides significantly reduced CO 2 emissions and fuel consumption. The primary motor/generator is comparable in size to that in the PSHEV system, but the secondary motor/generator is larger. The 2-mode system cost is greater than that for the power split system due to the additional transmission complexity and secondary motor sizing.

The battery pack for 2MHEV is assumed to be 300V NiMH as is used in current 2MHEV systems today, however GM recently announced an agreement with Hitachi to provide Li-ion battery packs for 2012 and later 2MHEVs.

For this rule the CAFE model consider ed a range of 23 to 33 percent with a midpoint of 28 percent, assuming no engine dow nsizing to preserve th e utility nature of medium and large trucks ( e.g. , maintaining full towing capability even in situations with low battery charge) and EPA estimates CO 2 emissions reduction effectiveness to be 25 percent for large trucks (LDT3 and LDT4 categor ies) based on vehicle certification data. EPA estimates an effectiveness of 40 percent for smaller vehicles.

EPA and NHTSA estimated MY 2012 costs using the updated component costs and scaling factors in the hybrid cost estimating methodology discussed above. EPA estimated the compliance cost to range fr om $5,469 for large car to $7,236 for large truck/SUV relative to the ba se engine/transmission (all in 2007$). NHTSA determined incremental cost estimates ranging from $3,521 to $5,779 for light-duty truck applications in the CAFE model. Thes e include the 1.45 ICM markup value for high complexity technology. Given that the CAFE and OMEGA models classify vehicles differently, these numbers varied slightly in the modeling to reflect the different classification methods of the OMEGA and CAFE models, as described in the individual NHTSA and EPA FRIAs. Volume-based lear ning is applicable to this technology.

3 .4.2.3.10 Plug-In Hybrid

3-70Plug-In Hybrid Electric Ve hicles (PHEVs) are very si milar to Hybrid Electric Vehicles, but with three signifi cant functional differences. Th e first is the addition of a means to charge the battery pack from an outside source of electricity ( e.g. , the electric grid). Second, a PHEV would ha ve a larger battery pack with more energy storage, and a greater capability to be discha rged. Finally, a PHEV would have a control system that allows the battery pack to be signifi cantly depleted during normal operation.

Table 3-22 below, illustrates how PHEVs co mpare functionally to both hybrid electric vehicles (HEV) and el ectric vehicles (EV). Thes e characteristics can change significantly within each class/subclass, so th is is simply meant as an illustration of the general characteristics. In real ity, the design options are so va ried that all these vehicles exist on a continuum with HEVs on one end and EVs on the other.

Table 3-22 Conventional, HEVs, PHEVs, and EVs Compared

Increasing Electrification Attribute Conventional HEV PHEV EV

Drive Power Engine Blended Engine/Electric Blended Engine/Electric Electric

Engine Size Full Size Full Size or Smaller Smaller or Much Smaller No Engine
Electric Range None None to Very Short Short to Medium Medium to Long Battery Charging None On-Board Grid/On-Board Grid Only

Deriving some of their propul sion energy from the electric grid provides several advantages for PHEVs. PHEVs offer a signif icant opportunity to replace petroleum used for transportation energy with domestically-produced electricity. The reduction in petroleum usage does, of course, depend on th e amount of electric drive the vehicle is capable of under its duty cycle. PHEVs also provide electric utilitie s the possibility to increase electric generation during “off-peak” periods overn ight when there is excess generation capacity and electricity prices are lo wer. Utilities like to increase this “base load” because it increases overall system effici ency and lowers average costs. Utilities are also investigating the us e of PHEV and EV batteries as a source of grid storage capacity to provide ancillary services for grid stabilization purposes. PHEVs can lower localized emissions of criteria pollutants and air toxics especially in urban areas by operating on electric power. The emissions from the power generation occur outside the urban area at the power generation plant which provides health benefits for residents of the more densely populated urban areas by m oving emissions of ozone precursors out of the urban air shed. Unlike most other altern ative fuel technologies, PHEVs can initially use an existing infrastructure for refueling (c harging and liquid refueling) so investments in infrastructure may be reduced.

In analyzing the impacts of grid-connect ed vehicles like PHEVs and EVs, the emissions from the electrical generation can be accounted for if a full upstream and downstream analysis is desired. While this issue is being studied on an on-going basis,

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upstream CO 2 emissions are not unique to grid-c onnected technologies and so are not included in this analysis.

PHEVs will be considerably more costly than conventional vehicles and some other advanced technologies. To take a dvantage of their capab ility, consumers would have to be willing to charge the vehicles nightly, and would need access to electric power where they park their vehicles. For many ur ban dwellers who may pa rk on the street, or in private or public lots or garages, char ging may not be practical. Charging may be possible at an owner’s place of work, but th at would increase grid loading during peak hours which would eliminate some of the benefits to utilities of off-peak charging versus on-peak. Oil savings will still be the same in this case assuming the vehicle can be charged fully. The effectiveness potential of PHEVs depends on many factors, the most important being the energy stor age capacity designed into th e battery pack. To estimate the fuel consumption and tailpipe CO 2 reduction potential of PHEVs, EPA has developed an in-house vehicle energy model (PEREGRIN) to estimate the fuel consumption/CO 2 emissions reductions of PHEVs. This mode l is based on the PERE (Physical Emission Rate Estimator) physics-based model used as a fuel consumption input for EPA’s MOVES mobile source emissions model.

The PHEV small car, large car, minivan and small trucks were modeled using parameters from a midsize car similar to to day’s hybrids and scaled to each vehicle’s weight. The large truck PHEV was modeled separately as suming no engine downsizing. PHEVs can have a wide variati on in the All Electric Range (A ER) that they offer. Some PHEVs are of the “blended” type where the engine is on during most of the vehicle operation, but the proportion of electric energy that is used to pr opel the vehicle is significantly higher than that used in a PSHEV or 2MHEV. Each PHEV was modeled with enough battery capacity for a 20-mile-e quivalent AER and a power requirement to provide similar performance to a hybrid vehicle. 20 miles was selected because it offers a good compromise for vehicle performance, we ight, battery packaging and cost. Given expected near-term battery capability, a 20 mile range represents the likely capability that will be seen in PHEVs in the near-to-mid term.

To calculate the total energy use of a PHEV, the PHEV can be thought of as operating in two distinct mode s, electric (EV) mode, and hybrid (HEV) mode. At the tailpipe, the CO 2 emissions during EV operation are zero. The EV mode fuel economy can then be combined with the HEV mode fuel economy using the Utility Factor calculation in SAE J1711 to determine a to tal MPG value for the vehicle. (See Table 3-23 )
Table 3-23 Sample Calculation of PHEV Gasoline-Equivalent CO 2 Reduction

Midsize Car Large Truck EV energy comb (0.55 city / 0. 45 hwy) 0.252 kwh/mi 0.429 kwh/mi EV range (from PEREGRIN) 20 miles 20 miles

3-72SAE J1711 utility factor 0.30 0.30 HEV mode comb FE (0.55 city / 0.45 hwy) 49.1 mpg 25.6 mpg Total UF-adjusted FE (UF*FCEV + (1-UF)*FCHEV) 70.1 mpg 36.6 mpg Baseline FE 29.3 mpg 19.2 mpg Percent FE gain 139% 90% Percent CO 2 reduction -58% -47%

Calculating a total fuel c onsumption and tailpipe CO 2 reduction based on model outputs and the Utility Factor calculations resu lts in a 58 percent reduction for small cars, large cars, minivans, and small trucks. For large trucks, the resu lt is a 47 percent reduction. The lower improvement is due to less engine downsizing in the large truck class. The CAFE model does not apply this technology to me dium and large trucks and SUVs.

Using the hybrid cost estimating mode l and updated component costs, EPA and NHTSA determined MY 2012 incremental cost estimates for the CAFE model ranging from $11,527 for a subcompact car to $19,265 for a midsize performance car. This includes the 1.64 ICM markup va lue for very high complexity technology. In the OMEGA model, and relative to a IMA/IS A/BSG, these values range from $6,922 for small car to $12,467 for a large truck, and relative to power-split hybrid, these values range from $5,423 for small car to $9,643 for large truck (all values for MY 2016 in 2007$). Volume-based learning is applied to plug-in hybrid costs since this is a newly emerging technology.

3.4.2.4 

### Ve hicle Technologies

3. 4.2.4.1 Mass Reduction

Reducing a vehicle’s mass, or down-wei ghting the vehicle, decreases fuel consumption by reducing the energy demand needed to overcome forces resisting motion, and rolling resistance. Manufacturers empl oy a systematic approach to mass reduction, where the net mass reduction is the addition of a direct component or system mass reduction plus the additional mass reduction ta ken from indirect ancillary systems and components, as a result of full vehicle optimization, effectively compounding or obtaining a secondary mass reduction from a primary mass reduction. For example, use of a smaller, lighter engine with lower torq ue-output subsequently allows the use of a smaller, lighter-weight transmission and drive line components. Likewise, the compounded weight reductions of the body, engi ne and drivetrain reduce stresses on the suspension components, steering components, wheels, tires and brakes, allowing further reductions in the mass of these subsystems. The reductions in unsprung masses such as brakes, control arms, wheels and tires furthe r reduce stresses in the suspension mounting points. This produces a compoundi ng effect of mass reductions.

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Estimates of the synergistic effects of mass reduction and the compounding effect that occurs along with it can vary significantl y from one report to another. For example, in discussing its estimate, an Auto-Steel Pa rtnership report states that “These secondary mass changes can be considerable—estimated at an additional 0.7 to 1.8 times the initial mass change.” 51 This means for each one pound reducti on in a primary component, up to 1.8 pounds can be reduced from othe r structures in the vehicle ( i.e. , a 180 percent factor). The report also discusses that a primary va riable in the realized secondary weight reduction is whether or not the powertrain components can be included in the mass reduction effort, with the lower end estimates being applicable when powertrain elements are unavailable for mass reduction. Howe ver, another report by the Aluminum Association, which primarily focuses on the us e of aluminum as an alternative material for steel, estimated a factor of 64 percent for secondary mass reduction even though some powertrain elements were considered in the analysis. 52 That report also notes that typical values for this factor vary from 50 to 100 pe rcent. Although there is a wide variation in stated estimates, synergistic mass reductions do exist, and the effects result in tangible mass reductions. Mass reductions in a single vehicle component, for example a door side impact/intrusion system, may actually result in a significantly higher weight savings in the total vehicle, depending on how well the manufacturer integrates the modification into the overall vehicle design. Accordingly, care must be taken wh en reviewing reports on weight reduction methods and practices to ascertain if compounding effects have been considered or not. Mass reduction is broadly applicable acr oss all vehicle subs ystems including the engine, exhaust system, transmission, chassi s, suspension, brakes, body, closure panels, glazing, seats and other interior components, engine cooling systems and HVAC systems. It is estimated that up to 1.25 kilograms of s econdary 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. 53
, 54
Mass reduction can be accomplished by proven methods such as:

• Smart Design: Computer aided engineer ing (CAE) tools can be used to better

optimize load paths within structures by reducing stresses and bending moments applied to structures ( Figure 3-4 through Figure 3-6 ). This allows better optimization of the sectional thicknesses of structural components to reduce mass while maintaining or improving the func tion of the component. Smart designs also integrate separate parts in a manner that reduces mass by combining functions or the reduced use of separate fasteners. In addition, some “body on frame” vehicles are redesigned with a lighter “unibody” construction.

• Material Substitution: Substitution of lower density and/or higher strength

materials into a design in a manner that preserves or improves the function of the component. This includes substituti on of high-strength steels, aluminum, magnesium or composite materials for com ponents currently fabricated from mild steel.

• Reduced Powertrain Requirements: Reduci ng vehicle weight sufficiently allows

for the use of a smaller, lighter and more efficient engine while maintaining or

3-74increasing performance. Approximately ha lf of the reduction is due to these reduced powertrain output requirements fr om reduced engine power output and/or displacement, changes to transmission and final drive gear ratios. The subsequent reduced rotating mass ( e.g. , transmission, driveshafts/halfshafts, wheels and tires) via weight and/or size re duction of components are made possible by reduced torque output requirements.

Using smart design, material substitution a nd space efficient designs have already been integrated into vehicles as new platfo rms cycle into producti on although net vehicle weight and performance has continued to increase ( Figure 3-4 ).

Figure 3-4: Example of CAE optimization of the design and position of the roof rails and front seat

cross-member for the “Future Gene ration Passeng er Compartment.” 55 The thin lines represent relative positions for ro of bows and front cross-members cons idered during iterations of the CAE analysis of the vehicle structure. The different co lors given to the structural components represent differences in sectional material thickness with in this particular CAE modeling run.

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Figure 3-5: Example of CAE optimization of a roof-rail design for the “Future Generation Passenger Compartment” that reduces component mass by 41%. 55

Figure 3-6: Example of CAE optimization of a door-beam design for the “Future Generation Passenger Compartment” that reduces componen t mass by 57% and reduces part count and assemby. 55

3-76Figure 3-7: Lightweight, tubular front door intrusion beams (2007 Civic)

• A utomotive companies have largely used weight savings in some vehicle subsystems to offset or mitigate weight gains in other subsystems from increased feature content (sound insulation, entert ainment systems, improved climate control, panoramic roof, etc.).

• L ightweight designs have also been used to improve vehicle performance parameters by increased acceleration perf ormance or superior vehicle handling and braking.

Many manufacturers have already announ ced proposed future products plans reducing the weight of a vehicle body thr ough the use of high st rength steel body-inwhite, composite body panels, magnesium a lloy front and rear energy absorbing structures reducing vehicle we ight sufficiently to allow a smaller, lighter and more efficient engine. Nissan will be reduci ng average vehicle curb weight by 15% by 2015. 56 Ford has identified weight reductions of 250 to 750 lbs per vehicle as part of its implementation of known technology within its sustainability st rategy between 2011 and 2020. 57 Mazda plans to reduce vehicle weight by 220 pounds per vehicle or more as models are redesigned. 58
, 59 Ducker International estimates that the average curb weight of light duty vehicle fleet will decreas e approximately 2.8% from 2009 to 2015 and approximately 6.5% from 2009 to 2020 via changes in automotive materials and increased change-over from previously used body-on-frame automobile and light-truck designs to newer unibody designs. 56

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NHTSA in the CAFE model has used a tiere d approach in material substitution to reduce mass without considering other mean s of vehicle mass reduction. MS1, a 1.5 percent mass reduction, is appli cable in any year at refres h or redesign. MS2, a 3.5 to 8.5 percent mass reduction incremental to MS1, is only available in MY 2014 and beyond in an attempt to mimic manufacturers’ planne d applications of ma ss reduction/material substitution in later years. For smaller vehicles, MS2 was limited to a 3.5 percent mass reduction of mass reduction/material substitution, but for larger cars and trucks up to 8.5 percent mass reduction was allowed. In the CAFE model, MS2 is cumulative to MS1, as it is only applied after MS1; therefore the maximum weight reduction that can occur for smaller subclass vehicles is 5 percent, while large cars, truck, and SUVs could experience up to a 10 percent weight reduction. Making the MS2 technology una vailable until MY 2014 is intended to recognize the lead time required to implement pl atform redesigns that would be necessary for these levels of weight reduction. NHTSA and EPA estimate that a 10 pe rcent mass reduction with engine downsizing results in a 6.5 percent reduction in fuel c onsumption while maintaining equivalent vehicle performance ( i.e. , 0-60 mph time, towing capacity, etc.) which is consistent with estimates in the 2002 NAS report. For small am ounts of mass reduction, such as the 1.5 percent used at vehicle refresh in NHTSA’s modeling, no engine downsizing will be used and a 10 percent ma ss reduction without engine downsizing will result in a 3.5 percent re duction in fuel consumption. 60
, 61
, 62

NHTSA and EPA have reviewed three studies of mass reduction/material substitution and the associated cost. The 2002 NAS,Report estimated that vehicle weight could be reduced for approximately $1.50 pe r pound, along with 3 to 4 percent reductions in fuel consumption, without engine downsiz ing, from a 5 percent reduction in vehicle weight, at a cost of $210- $350. This translates into $1.50 per pound, assuming a 3,800 pound base vehicle and using the midpoint cost. Additionally, Sierra Research estimated that a 10 percent reduction, with compounding, could be accomplished for a cost of $1.01 per pound. 63 Finally, an MIT study 64 estimated that the weightof a vehicle could be reduced by 14 percent, with no compounding, fo r a cost of $1.36 per pound. The final cost estimate is $1.32 per pound and is based on the average of th e three referenced studies. Applying an ICM factor of 1.11 for a low complexity tec hnology results in a compliance cost of $1.48 per pound. For the vehicle mass reduction technologies, neither volume-based nor time-based cost reductions are applied, since many of the materials under consideration are commodity based.

3. 4.2.4.2 Low Drag Brakes (LDB)

Low drag brakes reduce the sliding friction of disc brake pads on rotors when the brakes are not engaged because the brake pa ds are pulled away from the rotating disc either by mechanical or electric methods. While most passenger cars have already adopted this technology with th e standardization of electroni c brake control, there are indications that this technology is still available for body-on-frame trucks and for some large passenger cars.

3-78NHTSA’s MY 2011 CAFE final rule estimated the effectiveness of LDB to be as much as 1 percent, based on confidential manufacturer data. NHTSA and EPA have reviewed this estimate and believe it to be applicable for this final rule.

NHTSA and EPA have reviewed the co st estimates from the MY 2011 CAFE final rule and have not found any evidence to sugge st that this cost is not a valid cost and have chosen to maintain this estimate. Howe ver, the agencies adjusted the costs to apply the ICM of 1.11, for a low-complexity tec hnology. The compliance cost for LDB is estimated to be $63 for MY 2012 through MY 2016 vehicles as no learning effects are applied to this technology.

3. 4.2.4.3 Low Rolling Resistance Tires (ROLL)

Tire rolling resistance is the frictiona l loss associated mainly with the energy dissipated in the deformation of the tires under load and thus influences fuel economy and CO 2 emissions. Other tire design characteristics ( e.g. , materials, construction, and tread design) influence durability, traction ( both wet and dry grip), vehicle handling, and ride comfort in addition to rolling resistan ce. A typical low rolling resistance tire’s attributes would include: increased tire infl ation pressure, material changes, and tire construction with less hysteresis, geometry changes ( e.g. , reduced aspect ratios), and reduction in sidewall and tread deflection. These changes would generally be accompanied with additional changes to su spension tuning and/or suspension design.

EPA and NHTSA estimated a 1 to 2 percen t increase in effectiveness with a 10 percent reduction in rolling resistance, wh ich was based on the 2002 NAS report findings and consistent with NHTSA’s MY 2011 CAFE final rule estima te. NHTSA and EPA still believe that this NAS effectiveness estimate is valid for this final rule. Lower rolling resistance tires are widely available today.

Based on NHTSA’ MY 2011 CAFE final ru le and the 2006 NAS/NRC report, the agencies have estimated the cost for low ro lling resistance tires to be $6 per vehicle. 65 This is based on a cost of $1 per tire as estimated by NAS/NRC 2006 report, which is $5 per vehicle, including the sp are tire. When applying th e ICM low complexity markup factor, of 1.11, results in compliance cost of $6 per vehicle for MY 2012 through MY 2016 vehicles (2007$). v Due to the commodity-based nature of this technology, cost learning is not applied.

3. 4.2.4.4 Front or Secondary Axle Disconnect for Four-Wheel Drive Systems (SAX)

v Note that the costs developed for low rolling resistance tires for this analysis do not include the increase in lifetime costs that would be expected at each tire replacement. Instead, the analysis includes only the upfront increase in costs.

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Energy is required to conti nually drive the front, or s econdary, axle in a fourwheel drive system even when the syst em is not required during most operating conditions. This energy loss directly resu lts in increased fuel consumption and CO 2 emissions. Many part-time four-wheel driv e systems use some type of front axle disconnect to provide shift-on-the-fly capabilities. The front axle disconnect is normally part of the front differential assembly. As part of a shift-on-the-fly four-wheel drive system, the front axle disconnect serves two basic purposes. First, in two-wheel drive mode, it disengages the front ax le from the front driveline so the front wheels do not turn the front driveline at road speed, saving wearand tear. Second, when shifting from twoto four-wheel drive “on the fly” (while moving), the front axle disconnect couples the front axle to the front differential side gear only when the transf er case’s synchronizing mechanism has spun the front driveshaft up to the same speed as the rear driveshaft. Four-wheel drive systems that have a front axle disconnect typically do not have either manual- or automatic-locking hubs. To isolate th e front wheels from the rest of the front driveline, front axle disconnect s use a sliding sleeve to conne ct or disconnect an axle shaft from the front differential side g ear. NHTSA and EPA are not aware of any manufacturer offering this technology in the U.S. today on unibody frame vehicles; however, it is possible this te chnology could be introduced by manufacturers within the rulemaking time period.

Based on confidential manufacturer data , NHTSA’s MY 2011 CAFE final rule estimated an effectiveness improvement of 1.0 to 1.5 percent for axle disconnect. NHTSA and EPA believe this to still be an accurate estimate for this rulemaking.

Regarding costs, the agencies believe th at the incremental compliance cost from NHTSA’s MY 2011 final rule is an accurate estimate. However, for this final rule, this estimate was revised by applying the new IC M factor markup, for a low complexity technology, of 1.11. Thus, the compliance cost es timate for this final rule, is $87 for a MY 2012 vehicle (2007$). With time-based l earning this cost would be $84 for a MY 2016 vehicle (2007$).

3.4.2.4.5 Aerodynamic Drag Reduction (AERO)

Many factors affect a vehicle’s aero dynamic drag and the resulting power required to move it through the air. While th ese factors change with air density and the square and cube of vehicle speed, respectively, the overall dr ag effect is determined by the product of its frontal area and drag coeffi cient. Reductions in these quantities can therefore reduce fuel consumption and CO 2 emissions. Although frontal areas tend to be relatively similar within a vehicle class (mostly due to market-competitive size requirements), significant variations in dr ag coefficient can be observed. Significant changes to a vehicle’s aerodynamic performan ce may need to be implemented during a redesign ( e.g. , changes in vehicle shape). However, shorter-term aerodynamic reductions, with a somewhat lower effectiven ess, may be achieved through the use of

3-80revised exterior components (typically at a model refresh in mid-cycle) and add-on devices that currently being a pplied. The latter list would in clude revised front and rear fascias, modified front air dams and rear valances, addition of rear deck lips and underbody panels, and lower aerodynamic drag exterior mirrors.

The MY 2011 CAFE final rule estimated that a fleet av erage of 10 to 20 percent total aerodynamic drag reduction is attain able (with a caveat for “high-performance” vehicles described below) which equates to incremental reductions in fuel consumption and CO 2 emissions of 2 to 3 percent for both cars and trucks. These numbers are generally supported by confidentia l manufacturer data and pub lic technical l iterature and therefore NHTSA and EPA are retaining these estimates, as confirmed by joint review, for the purposes of this final rule.

In the MY 2011 CAFE final rule, NH TSA estimated a range from $60 to $116 (1.5 RPE); $40 to $75 (without RPE). NHTSA and EPA reviewed these estimates and updated these estimates to reflect the new ICM markup factor of 1.11 for a low complexity technology. In addition, given its low complexity and the facility of its implementation during design and refresh cycl es, NHTSA and EPA believe that a cost estimate at the lower end of the range is more appropriate. Thus, the cost estimate used in this rulemaking is $48, including the 1.11 ICM markup value, for a low complexity technology (2007$). This compliance cost is for a MY 2012 vehicle. With time-based learning, this cost would be $42 for a MY 2016 vehicle (2007$)

3.4.2.5 

### Te chnologies considered but not included in the final rule analysis

NHTSA and EPA have identified six technolog ies that will not be available in the time frame considered under this rulemaking. These technologies while considered were not made available in the CAFE and OMEGA models. They are: electric vehicles, camless valve actuation (CVA), lean burn gaso line direct injecti on (LBDI), homogeneous charge compression ignition (H CCI), and electric assist tu rbocharging and full series hydraulic hybrids (HHV). While electric vehicles are likely become available in small numbers in the near future, the costs for th is technology (as estimated by the agencies) are non-competitive with the more conventi onal technologies listed above. Therefore, the OMEGA and Volpe model analysis would preclude their selection, when estimating the costs of the rule. Lean Burn direct injection engines are cu rrently available in Europe, however, these vehicles cannot be designed at this time to be both efficient and meet the NOx standards in the United States with the current sulfur levels in the fuels. The other technologies listed ar e still in the research phase of development. NHTSA and EPA will continue to monitor the industry and system suppliers for progress on these technologies, and should they become more av ailable, consider them for use in future rulemaking activity. More details are described below.

3.4.2.5.1 Electric Vehicles

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The recent intense interest in Hybrid vehicles and th e development of Hybrid vehicle battery and motor technology has he lped make Electric Vehicle technology more viable than it has ever been. Electric Vehicles (EVs) require much larger batteries than either HEVs or PHEVs, but the batteries must be of a high-energy and lower-power design to deliver an appropriate amount of power over the usef ul charge of the battery. These high-energy batteries are generally le ss expensive per kilowatt-hour than highpower batteries required for hybrids, but the size of the battery pack still incurs a considerable cost.

Electric motor and power electronics desi gns are very similar to HEV and PHEV designs, but they must be larger, more powerful, and more robust since they provide the only motive power for the vehicle. On the other hand, the internal combustion engine, fuel system, and possibly the transmission can all be removed for significant weight, complexity and cost savings.

As for PHEVs, two electric vehicles we re modeled, a small car and a large car, using the same model (PEREGRIN) and sim ilar assumptions. EVs are only considered for these two classes because the larger, heav ier vehicles would requ ire too much battery capacity to be practical in the short-to-mid term and the agencies do not see any serious development activities for these vehicle types in the market.

We chose to model the EVs with a range of 150 miles on the urban driving cycle because this range offers a good compromise in capability and battery cost, weight and size with expected technology in the near- to mid-term. Using the same methodology as used for PHEVs to calculate gasoline-equiva lent fuel consumption, the results obtained are shown in Table 3-24 , below.

Table 3-24 Electric Vehicle Gasoline-Equivalent CO 2 Reduction (excluding A/C)

Small Car Large Car EV energy comb (0.55 city / 0.45 hwy) 0.202 kwh/mi 0.244 kwh/mi City cycle EV range 150 miles 150 miles Highway cycle EV range 166 miles 162 miles Baseline FE 35.5 25.3 mpg Tailpipe CO 2 reduction 100% 100%

3.4.2.5.2 Camless Valve Actuation

Camless valve actuation relies on elec tromechanical actuators instead of camshafts to open and close the cylinder valv es. When electromechanical actuators are used to replace cams and coupled with sens ors and microprocessor controls, valve timing

3-82and lift can be optimized over all conditions . An engine valvetrain that operates independently of any mechanical means provide s the increased flexibility for intake and exhaust timing and lift optimization. With it comes increased ability to vary valve overlap, the rapid response required to change between combustion operating modes (such as HCCI and GDI), intake valve thro ttling, cylinder deactiva tion, and elimination of the camshafts (reduced fric tion and rotating mass). This level of control can enable even further incremental reducti ons in fuel consumption and CO 2 emissions.

This technology has been under research for many decades and although progress is being made, NHTSA and EPA have not f ound evidence to support that the technology can be successfully implemented within the 2012 through 2016 timeframe of these regulations. Thus NHTSA and EPA have not estimated cost or effectiveness for this technology at this time.

3.4.2.5.3 Lean-Burn Gasoline Direct Injection Technology

Direct injection, especially with diesel -like “spray-guided” injection systems, enables operation with excess air in a stratified or partially-stratified fuel-air mixture, as a way of reducing the amount of in take throttling. Also, with higher-pressure fuel injection systems, the fuel may be added late enough during the compression stroke so as to delay the onset of autoignition, even with highe r engine compression ratios or with boosted intake pressure. Taken together, an optim ized “lean-burn” dire ct injection gasoline engine may achieve high engine thermal effi ciency which approaches that of a diesel engine. European gasoline direct-injection engines have implemented stratified-charge lean-burn GDI, although at higher NOx emissions levels than are allowed at under U.S. Federal Tier 2 emissions standards. Fuel system improvements, changes in combustion chamber design and repositioning of the injectors have allowed for better air/fuel mixing and combustion efficiency. There is currently a shift from wall-guided injection to spray guided injection, which improves injection pr ecision and targeti ng towards the spark plug, increasing lean combustion stabilit y. Combined with advances in NO x aftertreatment, lean-burn GDI engines may eventually be a possibility in North America.

EPA and NHTSA’s current assessment is that the availability of ultra-low sulfur (ULS less than 15 ppm sulfur) gasoline is a key technical requirement for lean-burn GDI engines to meet EPA’s Tier 2 NO x emissions standards. Since we do not believe that ULS gasoline will be available during the mode l years applicable to these rules, the technology was not applied in EPA or NHTSA analyses.

3.4.2.5.4 Homogeneous Charge Compression Ignition

Gasoline homogeneous charge compression ig nition (HCCI), also referred to as controlled autoignition (CAI), is an alternate engine operating mode that does not rely on a spark event to initiate co mbustion. The principles are more closely aligned with a

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diesel combustion cycle, in which the compressed charge exceeds a temperature and pressure necessary for spontaneous autoignition although it differs from diesel by having a homogenous fuel/air charge rather than be ing a diffusion-controlled combustion event. The subsequent combustion event is much shorter in duration with higher thermal efficiency.

An HCCI engine has inherent advantages in its overall efficiency for two main

reasons:

• The engin e is operated with a higher compression ratio, and with a shorter combustion duration, resulting in a higher thermodynamic efficiency, and

• The engine can be operated virtually unthrottled, even at light loads,

Combined, these effects have shown an increase in engine brake efficiency (typically 25 to 28 percent) to greater th an 35 percent at the high end of the HCCI operating range. 66

Criteria pollutant emissions are very favorable duri ng HCCI operation. Lower peak in-cylinder temperatures (due to high dilution) keep en gine-out NOx emissions to a minimum – realistically below Ti er 2 levels without aftertreat ment – and particulates are low due to the homogeneous nature of the premixed charge.

Due to the inherent difficulty in ma intaining combustion stability withoutencountering engine knock, HCCI is difficult to control, requiring feedback from incylinder pressure sensors and rapid engine control logic to optimize combustion timing, especially considering the transi ent nature of operat ing conditions seen in a vehicle. Due to the highly dilute conditions under which gasoline-HCCI combustion is stable, the range of engine loads achievable in a natura lly-aspirated engine is somewhat limited. Because of this, it is likely that any commer cial application would operate in a “dualmode” strategy between HCCI and spark ig nition combustion modes, in which HCCI would be utilized for best e fficiency at light engine load s and spark ignition would be used at higher loads and at idle. This type of dual-m ode strategy has already been employed in diesel HCCI engi nes in Europe and Asia (not ably the Toyota Avensis D-Cat and the Nissan light-duty “MK” combustion diesels).

Until recently, gasoline-HCCI technology wa s considered to still be in the research phase. However, most manufacturer s have made public statements about the viability of incorporating HCCI into light-duty passenger vehicles, and have significant vehicle demonstration programs aimed at produ cing a viable product within the next 5-10
years.

3-84There is widespread optimism as to the CO 2 reduction potential for HCCI in the literature. Based on confidential manufact urer information, EPA and NHTSA believe that a gasoline HCCI / GDI dual-mode engi ne might achieve 10 to 12 percent reduction in CO 2 , compared to a comparable SI engine. Despite its promise, application of HCCI in light duty vehicles is not ye t ready for the market. It is not anticipated to be seen in volume for at least the next 5-10 years, wh ich is concurrent with many manufacturers’ public estimates. NHTSA also noted in its MY 2011 CAFE final rule that the technology will not be available within the time frame c onsidered based on a review of confidential product plan information.

3.4.2.5 .5 Electric Assist Turbocharging

The Alliance commented in NHTSA’s previous rulemaking that global development of electric assist turbocharging has not demonstrated the fuel efficiency effectiveness of a 12V EAT up to 2kW pow er levels since the 2004 NESCCAF study, and stated that it saw remote probability of its application over the next decade. While hybrid vehicles lower the incremental hard ware requirements for higher-voltage, higherpower EAT systems, NHTSA and EPA agree that significant developmental work is required to demonstrate effective systems a nd that implementation in significant volumes will not occur in the 2012 to 2016 time frame considered in this rulemaking. Thus, this technology was not included in the final rule.

3.4.2.5.6 Full Series Hydraulic Hybrid

A Full Series Hydraulic Hybrid Vehicle ( HHV) is somewhat similar in concept to a full series electric hybrid vehicle, except that the energy is stor ed in the form of compressed nitrogen gas and the power is transmitted in the form of hydraulic fluid. Series HHV technology currently under develo pment by EPA is capable of a 40 percent decrease in tailpipe CO 2 emissions in the small car, larg e car, minivan, and small truck classes. In the large truck class, a 30 percent CO 2 reduction is possible. The large truck benefit is somewhat lower than the other clas ses because it is assumed that a large truck requires a larger engine to maintain towing and hauling performance after the energy in the high pressure hydraulic accumulator is exhausted. This te chnology is still under development and not yet commercialized, however there are technology demonstration vehicles in service with UPS in daily package delivery service.



### 3 .5 Cost and effectiveness tables

The tables representing the Volpe mode l input files for incremental technology costs by vehicle subclass are presented below. The tables have been divided into passenger cars, performance passeng er cars, and light trucks to make them easier to read. It should be noted that the Volpe model i nput files define costs for MY 2012 and learn down costs for subsequent years.

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### 3 .5.1 NHTSA cost and effectiveness tables

Table 3-25 Technology Incremental Cost Estimates for Volpe model, Passenger Cars in 2012

VEHICLE TECHNOLOGY ICM COSTS PE R VEHICLE (2007$) BY VEHICLE SUBCLASS - PASSENGER CARS

Subcompact Compact Midsize Large
Car Car Car Car Nominal Baseline Engine (For Cost Basis) Inline 4 Inline 4 Inline 4 V6 Low Friction Lubricants 3 3 3 3 Engine Friction Reduction EFR 50 50 50 75 VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 45 45 45 90 Discrete Variable Valve Li ft (DVVL) on SOHC DVVLS 142 142 142 205 Cylinder Deactivation on SOHC DEACS n.a. n.a. n.a. 0 - 56 VVT - Intake Cam Phasing (ICP) ICP 45 45 45 90 VVT - Dual Cam Phasing (DCP) DCP 38 38 38 83 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 142 142 142 205 Continuously Variable Va lve Lift (CVVL) CVVL 277 277 277 509 Cylinder Deactivation on DOHC DEACD n.a. n.a. n.a. 0 - 56 Cylinder Deactivation on OHV DEACO n.a. n.a. n.a. 170 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 45 45 45 45 Discrete Variable Valve Lift (DVVL) on OHV DVVLO 142 142 142 0 - 56 Conversion to DOHC with DCP CDOHC 276 276 276 436 Stoichiometric Gasoline Direct Injection (GDI) SGDI 236 236 236 341 Combustion Restart CBRST 118 118 118 118 Turbocharging and Downsizing TRBDS 445 445 445 325 Exhaust Gas Recirculation (EGR) Boost EGRB 144 144 144 144
Conversion to Diesel following CBRST DSLC 1,527. 1,527 1,527 1,556 - 1,612

Conversion to Diesel following TRBDS DSLT 938. 938 938 1,088- 1,143 6-Speed Manual/Improved Internals 6MAN 250 250 250 250 Improved Auto. Trans. Controls/Externals IATC 60 60 60 60 Continuously Variable Transmission CVT 250 250 250 250 6/7/8-Speed Auto. Trans with Improved Internals NAUTO 112 112 112 112

Dual Clutch or Automated Manual Transmission DCTAM (59) (59) (8) (8) Electric Power Steering EPS 106 106 106 106 Improved Accessories IACC 128 128 128 128 12V Micro-Hybrid MHEV 288 311 342 367 Belt mounted Integrated Starter Generator BISG 286 286 286 286 Crank mounted Integrated Starter Generator CISG 2,791 3,107 3,319 3,547

3-86Power Split Hybrid PSHEV 1,600 2,133 2,742 3,261 2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. Plug-in Hybrid PHEV 11,520 14,135 16,215 n.a. Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 Low Rolling Resistance Tires ROLL 6 6 6 6 Low Drag Brakes LDB n.a. n.a. n.a. 63 Secondary Axle Disconnect SAX 87 87 87 87 Aero Drag Reduction AERO 48 48 48 48

Table 3-26

. Technology Incremental Cost Estimates for Volpe model, Performance Passenger Cars in 2012

VEHICLE TECHNOLOGY ICM COSTS PE R VEHICLE (2007$) BY VEHICLE SUBCLASS - PERFORMANCE PASSENGER CARS

Perform. Perform. Perform. Perform. Subcomp. Compact Midsize Large
Car Car Car Car Nominal Baseline Engine (For Cost Basis) Inline 4 V6 V6 V8 Low Friction Lubricants LUB 3 3 3 3 Engine Friction Reduction EFR 50 75 75 101 VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 45 90 90 90

Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 142 205 205 293
Cylinder Deactivation on SOHC DEACS n.a. 0 - 56 0 - 56 0 – 56 VVT - Intake Cam Phasing (ICP) ICP 45 90 90 90
VVT - Dual Cam Phasing (DCP) DCP 38 83 83 82

Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 142 205 205 293
Continuously Variable Valve Lift (CVVL) CVVL 277 509 509 555
Cylinder Deactivation on DOHC DEACD n.a. 0 - 56 0 - 56 0 – 56
Cylinder Deactivation on OH V DEACO n.a. 170 170 190 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 45 45 45 45

Discrete Variable Valve Lift (DVVL) on OHV DVVLO 142 0 - 56 0 - 56 0 – 56

Conversion to DOHC with DCP CDOHC 276 436 436 552 Stoichiometric Gasoline Direct Injection (GDI) SGDI 236 341 341 552

Combustion Restart CBRST 118 118 118 118 Turbocharging and Downsizing TRBDS 445 325 325 919

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Exhaust Gas Recirculation (EGR) Boost EGRB 144 144 144 144
Conversion to Diesel following CBRST DSLC 1,527 1,556 – 1,612 1,556 – 1,612 2,294 – 2,349

Conversion to Diesel following TRBDS DSLT 938 1,088 – 1,143 1,088 – 1,143 1,231 – 1,287
6-Speed Manual/Improved Internals 6MAN 250 250 250 250
Improved Auto. Trans. Controls/Externals IATC 60 60 60 60
Continuously Variable Transmission CVT 250 250 250 250
6/7/8-Speed Auto. Trans with Improved Internals NAUTO 112 – 214 112 – 214 112 – 214 112 - 214

Dual Clutch or Automated Manual Transmission DCTAM (8) (8) (8) (8)
Electric Power Steering EPS 106 106 106 106 Improved Accessories IACC 128 128 128 128 12V Micro-Hybrid MHEV 314 337 372 410 Belt mounted Integrated Starter Generator BISG 286 286 286 286
Crank mounted Integrated Starter Generator CISG 2,839 3,149 3,335 3,571

Power Split Hybrid PSHEV 3,661 4,018 5,287 6,723
2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. Plug-in Hybrid PHEV 14,987 16,792 19,265 n.a.
Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 Low Rolling Resistance Tires ROLL n.a. n.a. n.a. n.a. Low Drag Brakes LDB n.a. n.a. n.a. 63 Secondary Axle Disconnect SAX 87 87 87 87 Aero Drag Reduction AERO n.a. n.a. n.a. n.a.

Table 3-27 . Technology Incremental Cost Estimates for Volpe model, Light Trucks in 2012

VEHICLE TECHNOLOGY ICM COSTS PE R VEHICLE (2007$) BY VEHICLE SUBCLASS - LIGHT TRUCKS

Minivan Small Midsize Large
LT LT LT LT Nominal Baseline Engine (For Cost Basis) V6 Inline 4 V6 V8 Low Friction Lubricants LUB 3 3 3 3 Engine Friction Reduction EFR 75 50 75 101 VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 90 45 90 90

3-88Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 205 142 205 293
Cylinder Deactivation on SOHC DEACS 0 - 56 n.a. 0 - 56 0 – 56 VVT - Intake Cam Phasing (ICP) ICP 90 45 90 90 VVT - Dual Cam Phasing (DCP) DCP 83 38 83 83 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 205 142 205 293
Continuously Variable Valve Lift (CVVL) CVVL 509 277 509 555
Cylinder Deactivation on DOHC DEACD 0 - 56 n.a. 0 - 56 0 – 56 Cylinder Deactivation on OH V DEACO 170 n.a. 170 190 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 45 45 45 45
Discrete Variable Valve Lift (DVVL) on OHV DVVLO 0 - 56 142 0 - 56 0 – 56

Conversion to DOHC with DCP CDOHC 436 276 436 552 Stoichiometric Gasoline Direct Injection (GDI) SGDI 341 236 341 391

Combustion Restart CBRST 118 118 118 118 Turbocharging and Downsizing TRBDS 325 445 325 919 Exhaust Gas Recirculation (EGR) Boost EGRB 144 144 144 144
Conversion to Diesel following CBRST DSLC 1,556 - 1,612 1,527 1,556 -1,612 2,294 – 2,349

Conversion to Diesel following TRBDS DSLT 1,088-1,143 938 1,088 – 1,143 1,231 – 1,287

6-Speed Manual/Improved Internals 6MAN 250 250 250 250 Improved Auto. Trans. Controls/Externals IATC 60 60 60 60
Continuously Variable Transmission CVT 250 250 250 n.a. 6/7/8-Speed Auto. Trans with Improved Internals NAUTO 112 112 112 - 214 112 - 214

Dual Clutch or Automated Manual Transmission DCTAM (8) (8) (8) (8)
Electric Power Steering EPS 106 106 106 106 Improved Accessories IACC 128 128 128 n.a. 12V Micro-Hybrid MHEV 367 325 376 n.a. Belt mounted Integrated Starter Generator BISG 286 286 286 n.a. Crank mounted Integrated Starter Generator CISG 3,547 3,141 3,611 5,124

Power Split Hybrid PSHEV 3,261 2,377 3,462 n.a.

2-Mode Hybrid 2MHEV n.a. 3,661 4,887 - 4,989 5,902 – 6,004
Plug-in Hybrid PHEV n.a. 14,721 n.a. n.a. Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 Low Rolling Resistance Tires ROLL 6 6 6 6 Low Drag Brakes LDB 63 n.a. 63 63

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Secondary Axle Disconnect SAX 87 87 87 87 Aero Drag Reduction AERO 48 48 48 48

Table 3-28. Technology Incrementa l Effectiveness Estimates for Vo lpe model, Passenger Cars in 2012
VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION (-%) BY VEHICLE SUBCLASS - PASSENGER CAR
Subcompact Compact Midsize Large
Car Car Car Car Nominal Baseline Engine (For Cost Basis) Inline 4 Inline 4 Inline 4 V6
Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 Engine Friction Reduction EFR 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Cylinder Deactivation on SOHC DE ACS n.a. n.a. n.a. 2.5 - 3.0 VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Continuously Variable Valve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5
Cylinder Deactivation on DOHC DE ACD n.a. n.a. n.a. 0 - 0.5 Cylinder Deactivation on OHV DEAC O n.a. n.a. n.a. 3.9 - 5.5 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5
Discrete Variable Valve Lift (DVVL) on OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5

Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 Stoichiometric Gasoline Direct Injection (GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 Turbocharging and Downsizing TRBDS 4.2 - 4.8 4.2 - 4.8 4.2 - 4.8 1.8 - 1.9 Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 Conversion to Diesel following CBRST DSLC 13.5 – 13.9 13.5 – 13.9 13.5 – 13.9 10.8 - 11.7 Conversion to Diesel following TRBDS DSLT 5.3 – 6.9 5.3 – 6.9 5.3 – 6.9 5.3 - 6.9
6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 Improved Auto. Trans. Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5
Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0

3-906/7/8-Speed Auto. Trans with Improved Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4

Dual Clutch or Automated Manual Transmission DCTAM 5.5 – 7.5 5.5 – 7.5 2.7 - 4.1 2.7 - 4.1
Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 12V Micro-Hybrid MHEV 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.5 - 3.5 Belt mounted Integrated Starter Generator BISG 4.0 - 6.0 4.0 - 6.0 4.0 - 6.0 3.5 - 5.5
Crank mounted Integrated Starter Generator CISG 8.6 - 8.9 8.6 - 8.9 8.6 - 8.9 8.7 - 8.9

Power Split Hybrid PSHEV 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. Plug-in Hybrid PHEV 45.2 - 47.7 45.2 - 47.7 45.2 - 47.7 n.a. Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 Mass Reduction (3.5 to 8.5%) MS2 2.7 2.7 4.4 6.0 Low Rolling Resistance Tires ROLL 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Low Drag Brakes LDB n.a. n.a. n.a. 0.5 - 1.0 Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Aero Drag Reduction AERO 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Table 3-29. Component Technology Incrementa l Effectiveness Estimates for Volpe model, Performance Cars in 2012

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION (-%) BY VEHICLE SUBCLASS - PERFORMANCE PASSENGER CAR

Perform. Perform. Perform. Perform. Subcomp. Compact Midsize Large Car Car Car Car Nominal Baseline Engine (For Cost Basis) Inline 4 V6 V6 V8 Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 Engine Friction Reduction EFR 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Cylinder Deactivation on SOHC DEACS n.a. 2.5 - 3.0 2.5 - 3.0 2.5 - 3.0 VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Continuously Variable Valve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 Cylinder Deactivation on DOHC DEACD n.a. 0.0 - 0.5 0.0 - 0.5 0.0 - 0.5 Cylinder Deactivation on OHV DEACO n. a. 3.9 - 5.5 3.9 - 5.5 3.9 - 5.5 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Discrete Variable Valve Lift (DVVL) on OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5

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Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 Stoichiometric Gasoline Direct Injection (GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 Turbocharging and Downsizing TRBDS 4.2 - 4.8 1.8 - 1.9 1.8 - 1.9 1.8 - 1.9 Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0
Conversion to Diesel following CBRST DSLC 13.5 – 13.9 10.8 - 11.7 10.8 - 11.7 10.8 - 11.7 Conversion to Diesel following TRBDS DSLT 5.3 – 6.9 5.3 - 6.9 5.3 - 6.9 5.3 - 6.9 6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 Improved Auto. Trans. Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 n.a. 6/7/8-Speed Auto. Trans with Improved Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4

Dual Clutch or Automated Manual Transmission DCTAM 2.7 - 4.1 2.7 - 4.1 2.7 - 4.1 2.7 - 4.1
Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 12V Micro-Hybrid MHEV 2.0 - 3.0 2.5 - 3.5 2.5 - 3.5 3.0 - 4.0 Belt mounted Integrated Starter Generator BISG 4.0 - 6.0 3.5 - 5.5 3.5 - 5.5 3.0 - 5.0 Crank mounted Integrated Starter Generator CISG 8.6 - 8.9 8.7 - 8.9 8.7 - 8.9 8.7 - 8.9
Power Split Hybrid PSHEV 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. Plug-in Hybrid PHEV 45.2 - 47.7 45.2 - 47.7 45.2 - 47.7 n.a. Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 Mass Reduction (3.5 to 8.5%) MS2 2.7 2.7 4.4 6.0 Low Rolling Resistance Tires ROLL n.a. n.a. n.a. n.a. Low Drag Brakes LDB n.a. n.a. n.a. 0.5 - 1.0 Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Aero Drag Reduction AERO n.a. n.a. n.a. n.a.

Table 3-30. Component Technology Incremental Ef fectiveness Estimates for Volpe model, Light Trucks in 2012

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION

REDUCTION (-%) BY VEHICLE SUBCLASS - LIGHT TRUCKS

Minivan Small Midsize Large
LT LT LT LT Nominal Baseline Engine (For Cost Basis) V6 Inline 4 V6 V8 Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 Engine Friction Reduction EFR 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0

3-92Cylinder Deactivation on SOHC DEACS 2.5 - 3.0 n.a. 2.5 - 3.0 2.5 - 3.0 VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 Continuously Variable Va lve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 Cylinder Deactivation on DOHC DEACD 0 - 0.5 n.a. 0.0 - 0.5 0.0 - 0.5 Cylinder Deactivation on OHV DEACO 3.9 - 5.5 n.a. 3.9 - 5.5 3.9 - 5.5 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Discrete Variable Valve Lift (DVVL) on OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5

Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 Stoichiometric Gasoline Direct Injection (GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 Turbocharging and Downsizing TRBDS 1.8 - 1.9 4.2 - 4.8 1.8 - 1.9 1.8 - 1.9 Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0
Conversion to Diesel following CBRST DSLC 10.8 - 11.7 13.5 – 13.9. 10.8 - 11.7 10.8 - 11.7 Conversion to Diesel following TRBDS DSLT 5.3 - 6.9 n.a. 5.3 - 6.9 5.3 - 6.9 6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 Improved Auto. Trans. Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 n.a. 6/7/8-Speed Auto. Trans with Improved Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4

Dual Clutch or Automated Manual Transmission DCTAM 2.7 - 4.1 2.7 -4.1 2.7 - 4.1 2.7 - 4.1
Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 n.a. 12V Micro-Hybrid MHEV 2.5 - 3.5 2.0 - 3.0 2.5 - 3.5 n.a. Belt mounted Integrated Starter Generator BISG 3.5 - 5.5 4.0 - 6.0 3.5 - 5.5 n.a. Crank mounted Integrated Starter Generator CISG 8.7 - 8.9 8.6 - 8.9 8.7 - 8.9 14.1 - 16.3
Power Split Hybrid PSHEV 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 n.a. 2-Mode Hybrid 2MHEV n.a. 3.0 - 7.3 3.0 - 7.3 4.1 - 9.5
Plug-in Hybrid PHEV n.a. 45.2 - 47.7 n.a. n.a. Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 Mass Reduction (3.5 to 8.5%) MS2 6.0 4.4 4.4 6.0 Low Rolling Resistance Tires ROLL 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Low Drag Brakes LDB 0.5 - 1.0 n.a. 0.5 - 1.0 0.5 - 1.0 Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Aero Drag Reduction AERO 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0



### 3 .5.2 EPA cost and effectiveness tables

The tables below show costs and effectiveness values for each particular technology described throughout this chapter (t hose considered by EPA in its model). The costs are shown as incremental to a partic ular baseline as noted in the tables. In

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general, the costs are meant to reflect the cost of a given technology relative to a baseline vehicle or engine with fixed timing 4 valves per cylinder, port fuel injection and a 4 speed automatic transmission. Where this is not the case is made clea r in the tables. The same is true of the effectiveness tables below. As such, the costs and effectiveness values shown in the tables below will differ from the values contained in the NHTSA tables due to the different ways that the agency’s m odels handles them. However, the underlying cost and effectiveness values used by each agency in their respective analyses are consistent.

Table 3-31 EPA’s Incremental Piece Costs for Engine Tec hnologies Marked up to include both Direct and Indirect Costs in 2016 (20 07 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL TO Small Car Large Car Miniva n 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 Ba se 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 I4 Base I4 $209 209 $209 209 209 GDI – stoichiometric V6 Base V6 n/a $301 $301 $301 $301 GDI – stoichiometric V8 Base V8 n/a $346 $346 n/a $346 GDI – lean burn GDI - stoich $623 $623 $623 $623 $623 Turbocharge (single) Base I4 $397 n/a $397 n/a n/a Turbocharge (single) Base V6 n/a $420 $420 $420 $420 Turbocharge (twin) Base engine $666 $666 $666 $666 $666 Downsize to I4 DOHC V6 DOHC n/a -$384 -$384 -$384 -$384 Downsize to I4 DOHC V6 SOHC n/a -$177 -$177 -$177 -$177 Downsize to I4 DOHC V6 OHV n/a $265 $265 $265 $265 Downsize to I4 DOHC I4 DOHC (larger) -$67 -$67 -$67 -$67 -$67 Downsize to I3 DOHC I4 DOHC -$116 n/a n/a n/a n/a Downsize to V6 DOHC V8 DOHC n/a -$188 -$188 -$188 -$188 Downsize to V6 DOHC V8 SOHC 2V n/a $59 $59 $59 $59 Downsize to V6 DOHC V8 SOHC 3V n/a -$17 -$17 -$17 -$17Turbo 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 n/a $149 $149 $149 $149withdownsiz

Downsize to I4 DOHC & add turbo V6 SOHC w/o turbo n/a $323 $323 $323 $323

3-94Downsize to I4 DOHC & add turbo V6 OHV w/o turbo $771 $771 $771 $771 $771

Downsize to I4 DOHC & add turbo I4 DOHC (larger) w/o turbo $391 n/a $391 n/a n/a

Downsize to I3 DOHC & add turbo I4 DOHC w/o turbo $349 n/a n/a n/a n/a

Downsize to V6 DOHC & add twin turbo V8 DOHC w/o turbo n/a $592 $592 $592 $592

Downsize to V6 DOHC & add twin turbo V8 SOHC 2V w/o turbo n/a $816 $816 $816 $816

Downsize to V6 DOHC & add twin turbo V8 SOHC 3V w/o turbo n/a $736 $736 $736 $736

Downsize to V6 DOHC & add twin turbo V8 OHV w/o turbo n/a $1,099 $1,099 $1,099 $1,099

Convert to V6 DOHC V6 SOHC n/a $258 $258 $258 $258 Convert to V6 DOHC V6 OHV n/a $464 $464 $464 $464 Convert to V8 DOHC V8 SOHC 2V n/a $292 $292 $292 $292 Convert to V8 DOHC V8 SOHC 3V n/a $213 $213 $213 $213 Convert to V8 DOHC V8 OHV n/a $509 $509 $509 $509Gasoline HCCI dualmode GDI - stoich $253 $375 $375 $375 $659
Diesel – Lean NOx trap Base gasoline engine $1,877 Diesel – urea SCR Base gaso line engine $2,655 $2,164 $2,164 $2,961

Table 3-32 EPA’s Incremental Piece Costs for Transmission Technologies Mark ed up to include both Direct and Indirect Costs in 2016 (20 07 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL TO Small Car Large Car Minivan Small Truck Large Truck Aggressive shift logic Base trans $28 $28 $28 $28 $28 Early torque converter lockup Base trans $25 $25 $25 $25 $25 5-speed automatic 4-speed auto trans $90 $90 $90 $90 $90 6-speed automatic 4-speed auto trans $99 $99 $99 $99 $99 6-speed DCT – dry clutch 6-speed auto trans -$52 -$52 -$52 -$52 -$52 6-speed DCT – wet clutch 6-speed auto trans -$7 -$7 -$7 -$7 -$7 6-speed manual 5-speed manual trans $79 $79 $79 $79 $79 CVT 4-speed auto trans $192 $224 $224 n/a n/a

Table 3-33 EPA’s Incremental Piece Costs for Hybrid Tec hnologies Marked up to include both Direct and Indirect Costs in 2016 (20 07 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL TO Small Car Large Car Miniva n 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

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Plug-in hybrid electric vehicle IMA/ISA/BSG hybrid $6,922 $9,519 $9,598 $9,083 $12,467
Plug-in hybrid electric vehicle Power-split hybrid $5,423 $7,431 $7,351 $7,128 $9,643

Full electric vehicle Base engine &
trans $27,628 n/a n/a n/a n/a

Table 3-34 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 Miniva n 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 3-35 EPA’s Incremental Piece Cost s for Vehicle Technologies Marked up to include both Direct and Indirect Costs in 2016 (20 07 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL TO Small Car Large Car Miniva n 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 3-36 through Table 3-40 summarize the estimates for the CO 2 reduction estimates of various technologies which can be applied to cars and light-duty trucks.

3-96Table 3-36 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 (electroma gnetic) 5-15 5-15 5-15 5-15 5-15 Gasoline Direct Injection–stoichiom etric (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 15-26 15-26 15-26 15-26 Diesel – urea SCR []* 15-26 15-26 15-26 15-26 15-26

Table 3-37 Transmission Technology 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.

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Table 3-38 Hybrid Tech nology 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 3-39 Accessory T echnology Effectiveness

Absolute CO 2 Reduction (% from baseline vehicle) Technology Small Car Large Car Minivan Small Truck Large Truck Improved high efficiency altern ator & 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 alte rnator & electrification of accessories (42 volt) 2-4 2-4 2-4 2-4 2-4

Table 3-40 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 fr ame only) n.a. n.a. n.a. 1.5 1.5

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### Appendix 3.A

Gross Domestic Product Adjustments Used to Adjust 2002-2006 Dollars to 2007 Dollars

Throughout this analysis, the incremental cost estimates have been presented in terms of 2007 dollars. However, most of the data sources relied upon as the basis for the estimates have presented costs in terms of 2006 dollars. To conve rt the 2006 dollars to 2007 dollars, the “chain-type” Gross Domestic Product (GDP) deflator was used. Table 3.A-1 shows the GDP deflator data used for this analysis.

Table 3.A-1 Price Indexes for Gross Domestic Product

LINE 2002 2003 2004 2005 2006 2007 2008

1 Gross domestic product 104.193 106.409 109.462 113.039 116.676 119.819 122.5

2 Personal consumption expenditures 103.542 105.597 108.392 111.581 114.675 117.659 121.596

3 Durable goods 95.766 92.366 90.696 89.984 88.772 87.154 86.071 4 Nondurable goods 102.089 104.145 107.626 111.606 114.984 118.407 124.666 5 Services 106.018 109.379 112.929 116.7 120.752 124.712 128.752

6 Gross private domestic investment 101.64 103.191 106.686 111.381 116.102 117.735 118.509

7 Fixed investment 101.66 103.313 106.845 111.638 116.38 117.995 118.727 8 Nonresidential 99.513 99.591 100.896 103.829 107.277 108.739 110.513 9 Structures 110.03 113.872 120.912 135.177 151.822 157.662 163.298 10 Equipment and software 95.956 94.912 94.6 94.534 94.594 94.87 95.485 11 Residential 107.24 112.372 120.587 129.268 136.897 138.884 135.587 12 Change in private inventories

13 Net exports of goods and services 14 Exports 99.273 101.429 104.997 108.814 112.618 116.586 122.788 15 Goods 98.661 100.64 104.323 107.536 111.131 115.062 121.147 16 Services 100.762 103.333 106.632 111.855 116.156 120.211 126.684 17 Imports 96.341 99.685 104.526 111.154 115.932 120.168 132.698 18 Goods 95.288 98.063 102.915 109.568 114.171 118.326 131.408 19 Services 101.878 108.291 113.049 119.496 125.257 129.928 139.217

20 Government consumption expenditures and gross investment 105.507 109.849 114.754 121.47 127.239 132.941 139.234

21 Federal 105.631 110.094 115.322 120.834 125.806 130.076 134.289 22 National defense 105.792 110.751 115.932 121.944 127.381 131.874 136.574 23 Nondefense 105.345 108.898 114.218 118.744 122.803 126.636 129.869 24 State and local 105.435 109.712 114.431 121.862 128.109 134.671 142.219 Source: Bureau of Economic Analysis , U.S. Department of Commerce, http://www.bea.gov/national/nipaweb/ TableView.asp?SelectedTable=4

Using Line 1 in the table, 2006 dollars can be adjusted to 2007 dollars by multiplying the ratio 119.8/116.7, or 1.027, by any value expressed in 2006 dollars. For example, $1 in 2006 dollars would be $1 x 1.027, or $1.03 in 2007 dollars.

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3-100 References

1 “EPA Staff Technical Report: Cost and Effectiven ess Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions,” EPA420-R-08-008, March 2008, Docket EPA-HQ-OAR-2009- 0472-0132.

2 “Effectiveness and Impact of Co rporate Average Fuel Economy (CAF E) Standards,” National Research Council, National Academy of Sciences, 2002, Docket EPA-HQ-OAR-2009-0472-0147.

3 “Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” Northeast States Center for a Clean Air Future, September 2004, Docket EPA-HQ-OAR-2009-0472-0148.

4 “Staff Report: Initial Statement of Reasons fo r Proposed Rulemaking,” California Environmental Protection Agency, Air Resources Board, Regulations to Control Greenhouse Gas Emissions from Motor Vehicles, August 6, 2004, Docket EPA-HQ-OAR-2009-0472-0134.

5 “Technology to Improve the Fuel Economy of Light Duty Trucks to 2015,” Energy and Environmental Analysis, Inc., May 2006, Docket EPA-HQ-OAR-2009-0472-0136.

6 “Variable Costs of Fuel Economy Technologie s,” prepared for The A lliance of Automobile Manufacturers, June 1, 2008, Docket EPA-HQ-OAR-2009-0472-0144; and, “Variable Costs of Fuel Economy Technologies,” prepared for The Alliance of Automobile Manufacturers, June 1, 2008, Amended December 10, 2008, Docket EPA-HQ-OAR-2009-0472-0145.

7 National Highway Traffic Safety Administration, Re quest for Product Plan Information, 74 FR 48192, September 22, 2009.

8 Memorandum from Don Kopinski, U.S. EPA, Docket No. EPA-HQ-OAR-2009-0472-0217, dated September 11, 2009.

9 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis PilotStudy,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149.

10 Insert docket info for the FEV final report on additional case studies beyond the I4 to I4 (should be available in mid-Feb).

11 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis PilotStudy,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149; Insert docket info for the FEV final report on additional case studies beyond the I4 to I4 (should be available in mid-Feb).

12 NHTSA’s 2011 CAFE final rule at 74 FR 14196.

13 “EPA Staff Technical Report: Cost and Effectiven ess Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions,” EPA420-R-08-008, March 2008, Docket EPA-HQ-OAR-2009- 0472-0132.

14 National Income and Product Accounts Table 1.1.4: Price Indexes for Gross Domestic Product; downloaded on 6/25/2009 At 12:00:25 PM, Last Revised June 25, 2009; Bureau of Economic Analysis, U.S. Department of Commerce, www.bea.gov.3-101 15 “EPA Staff Technical Report: Cost and Effectiven ess Estimates of Technologies Used to Reduce LightDuty Vehicle Carbon Dioxide Emissions,” EPA420-R-08-008, March 2008, Docket EPA-HQ-OAR-2009- 0472-0132.

16 The 2002 NAS Report, Docket EPA-HQ-OAR-2009-0472-0147; the 2004 study done by NESCCAF, Docket EPA-HQ-OAR-2009-0472-0148; the California Air Resources Board (CARB) Initial Statement of Reasons in support of their carbon rulemaking, Docket EPA-HQ-OAR-2009-0472-0134; a 2006 study done by Energy and Environmental Analysis (EEA) for the Department of Energy, Docket EPA-HQ-OAR-2009- 0472-0136; a study done by Martec for the Alliance of Automobile Manufacturers, Docket EPA-HQ-OAR2009-0472-0144, and the 2008 Martec Report which updated that study, Docket EPA-HQ-OAR-2009- 0472-0145; and vehicle fuel economy certification da ta, and confidential data submitted by manufacturers in response to the March and November 2009 requests for product plans.

17 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis PilotStudy,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149.

18 Rogozhin, Alex, Michael Gallaher, and Walter McManus, “Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers,” EPA 420-R-09-003 , Docket EPA Docket EP A-HQ-OAR-2009-0472-0142, February 2009, http://epa.gov/otaq/ld-hwy/420r09003.pdf.

19 “Memorandum: Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies,” Gloria Helfand and Todd Sherwood, Office of Transportation and Air Quality, U.S. Environmental Protection Agency, August 2009, Docket EPA-HQ-OAR-2009-0472-0158.

20 “Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines,” EPA420-R-04-007, May 2004, Docket EPA-HQ-OAR-2009-0472-0140; “Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder,” EPA420-D-08-001a, May 2008, Docket EPA-HQ-OAR-2009-0472-0139; NHTSA 2011 CAFE final rule at 74 FR 14196.

21 “Learning Curves in Manufacturing,” Linda Argote and Dennis Epple, Science, February 23, 1990, Vol. 247, pp. 920-924, Docket EPA-HQ-OAR-2009-0472-0143.

22 NHTSA 2011 CAFE final rule at 74 FR 14196; “EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Light-D uty Vehicle Carbon Dioxide Emissions,” EPA420-R08-008, March 2008, Docket EPA-HQ-OAR-2009-0472-0132; “Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines,” EPA420-R-04-007, May 2004, Docket EPA-HQ-OAR-2009- 0472-0140; “Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder,” EPA420-D-08-001a, May 2008, Docket EPA-HQ-OAR-2009-0472-0139.

23 “Impact of Friction Reduction Technologies on Fuel Economy,” Fenske, G. Presented at the March 2009 Chicago Chapter Meeting of the ‘Society of Tribologists and Lubricated Engineers’ Meeting, March 18th, 2009. Available at: http://www.chicagostle.org/program/2008- 2009/Impact%20of%20Friction%20Reduction%20Technologies%20on%20Fuel%20Economy%20- %20with%20VGs%20removed.pdf (l ast accessed July 9, 2009).

24 “Light-Duty Automotive Technology and Fuel Economy Trends: 1975 Through 2007”, EPA420-S-07- 001, September 2007, Docket EPA-HQ-OAR-2009-0472-0137. Available at http://www.epa.gov/oms/cert/mpg/fetrends/fetrend s-archive.htm (last acce ssed July 9, 2009).

25 Paul Whitaker, Ricardo, Inc., “Gasoline Engine Performance And Emissions – Future Technologies and Optimization,” ERC Symposium, Low Emission Combustion Technologies for Future IC Engines,Draft Joint Technical Support Document

3-102 Madison, WI, June 8-9, 2005, Docket EPA-HQ-OAR-2009-0472-0155. Available at http://www.erc.wisc.edu/symposiums/2005_Sympos ium/June%208%20PM/Whitaker_Ricardo.pdf (last accessed Nov. 9, 2008).

26 Stefan Trampert, FEV Motorentechnik GmbH, “Engine and Transmission Development Trends - Rising Fuel Cost Pushes Technology,” Symposium on International Automotive Technology, Pune, India, January 2007.

27 Insert docket info for Mike’s memo to the docket providing details of the FEV binning of costs.

28 Insert docket info for Mike’s memo to the docket providing details of the FEV binning of costs.

29 Stefan Wolff, Dirk Abendroth, Werner Weigl, Claus-Peter Linner, Rupert Neudecker, Michael Schneider, Wolfgang Huber, and Andreas Rau, BM W, “Introducing The Auto matic Start-Stop (ASS) Function In Series Models,” 7th Stuttgart Automotive Vehicle and Engine Symposium, Organised by FKFS, Mar 2007, Vol. 1.

30 G.K. Fraidl, P.E. Kapus, and H. Friedl, AVL List GmbH, “Future Gasoline Engine Technologies for 130 g/Km CO2,” VKM-THD 11th Symposium on the Working Process of Combustion Engines, TU Graz, Sept. 2007.

31 “Development and Optimization of the Ford 3.5L V6 EcoBoost Combustion System,” Yi,J., Wooldridge, S., Coulson, G., Hilditch, J. Iyer, C.O., Moilane n, P., Papaioannou, G., Reiche, D. Shelby, M., VanDerWege, B., Weaver, C. Xu, Z., Davis, G., Hinds, B. Schamel, A. SAE Technical Paper No. 2009-01- 1494, 2009, Docket EPA-HQ-OAR-2009-0472-2860.

32 David Woldring and Tilo Landenfeld of Bosch, and Mark J. Christie of Ricardo, “DI Boost: Application of a High Performance Gasoline Direct Injection Concept,” SAE 2007-01-1410. Available at http://www.sae.org/technical/papers/2007-01 -1410 (last accessed Nov. 9, 2008)

33 Yves Boccadoro, Loïc Kermanac’h, Laurent Siauve , and Jean-Michel Vincent, Renault Powertrain Division, “The New Renault TCE 1.2L Turbocharged Gasoline Engine,” 28th Vienna Motor Symposium, April 2007.

34 Tobias Heiter, Matthias Philipp, Robert Bosch, “Gaso line Direct Injection: Is There a Simplified, CostOptimal System Approach for an Attractive Future of Gasoline Engines?” AVL Engine & Environment Conference, September 2005.

35 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis PilotStudy,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149.

36 Docket No. NHTSA-2008-0089-0193.1

37 Cairns et al. , Lotus, “Low Cost Solutions for Improved Fuel Economy in Gasoline Engines,” Global Powertrain Congress September 27-29, 2005, vol. 33. Available at http://www.gpcicpem.org/pages/publications.html (last accessed Nov. 9, 2008).

38 Tim Lake, John Stokes, Richard Murphy, and Richard Osborne of Ricardo and Andreas Schamel of Ford-Werke, “Turbocharging Concepts for Downsized DI Gasoline Engines,” VKA/ika Aachen Colloquium 2003. Available at http://cat.inist.fr/?aModele=afficheN&cpsidt=16973598 (last accessed Nov. 9, 2008).3-103 39 “Interim Report: New Powertrain Technologies and Their Projected Costs,” October 2005, EPA420-R05-012, Docket EPA-HQ-OAR-2009-0472-0138.

40 “Cost and Fuel Economy Comparison of Diesel an d Gasoline Powertrains in Passenger Cars and Light Trucks,” submitted by FEV Engine Technology, Inc., Ap ril 23, 2003, contained as Appendix I within EPA Interim Technical Report EPA420-R-04-002, Docket EPA-HQ-OAR-2009-0472-0141.

41 EPA’s 2007/2010 diesel heavy-duty highway final rule at66 FR 5002; EPA’s Locomotive and Marine final rule at 73 FR 37096.

42 http://www.platinum.matthey.com/.

43 General Motors, news release, “From Hybrids to Six-Speeds, Direct Injection And More, GM’s 2008 Global Powertrain Lineup Provides More Mile s with Less Fuel” (releas ed Mar. 6, 2007). Available at http://www.gm.com/experience/fuel_economy/news/2 007/adv_engines/2008-powertrain-lineup-082707.jsp (last accessed Sept. 18, 2008).

44 “EPA Staff Technical Report: Cost and Effectiven ess Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions” Environmental Protection Agency, EPA420-R-08-008, March 2008, at page 17, Docket EPA-HQ-OAR-2009-0472-0132.

45 “Latest Chevrolet Volt Battery Pack and Generator Details and Clarifications.” Lyle Dennis interview of Rob Peterson (GM) regarding the all-electric drive range of the GM Volt, August 29, 2007. Accessed onthe Internet on June 30, 2009 at: http://gm-volt.com/2007/08/29/latest-Chevrolet-volt-battery-pack-andgenerator-details-and-clarifications/

46 “Active Combination of Ultracapacitorsand Batteries for PHEV ESS.” Bohn, T. U.S. Department of Energy 2009 Vehicle Technologies Merit Review, May 20, 2009, Docket EPA-HQ-OAR-2009-0472-0163.

47 Anderman, Advanced Automotive Battery Conference, May 2008. Pro ceedings available for purchase at http://www.advancedautobat.com /Proceedings/index.html (last accessed October 17, 2008).

48 74 FR 14291 (Mar. 30, 2009)

49 “Electric Cars: Plugged In, Batteries must be included,” Deutsche Bank Global Markets Research Company, June 9, 2008, Docket EPA-HQ-OAR-2009-0472-0154.

50 http://automobiles.honda.com/insight-hybrid/features.aspx?Feature=ima (last accessed on September 10, 2009).

51 “Preliminary Vehicle Mass Estimation Using Empirical Subsystem Influence Coefficients,” Malen, D.E., Reddy, K. Auto-Steel Partnership Report, May 2 007, Docket EPA-HQ-OAR-2 009-0472-0169. Accessed on the Internet on May 30, 200 9 at: http://www.a-sp.org/data base/custom/Mass%20Compounding%20- %20Final%20Report.pdf

52 “Benefit Analysis: Use of Aluminum Structures in Conjunction with Alternative Powertrain Technologies in Automobiles,” Bull, M. Chavali, R., Mascarin, A., Aluminum Association Research Report, May 2008, Docket EPA-HQ-OAR-2009-0472-0168. Accessed on the Internet on April 30, 2009 at: http://www.autoaluminum.org/downloads/IBIS-Powertrain-Study.pdf

53 “Future Generation Passenger Compartment-Va lidation (ASP 241)” Villa no, P.J., Shaw, J.R., Polewarczyk, J., Morgans, S., Carpenter, J.A., Yo cum, A.D., in “Lightweighting Materials – FY 2008 Progress Report,” U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program, May 2009, Docket EPA-HQ-OAR-2009-0472-0190.Draft Joint Technical Support Document

3-104 54 “Preliminary Vehicle Mass Estimation Using Empirical Subsystem Influence Coefficients,” Malen, D.E., Reddy, K. Auto-Steel Partnership Report, May 2 007, Docket EPA-HQ-OAR-2 009-0472-0169. Accessed on the Internet on May 30, 200 9 at: http://www.a-sp.org/data base/custom/Mass%20Compounding%20- %20Final%20Report.pdf

55 “Future Generation Passenger Compartment – Final Report,” Auto-Steel Partnership Report, June 2007. Accessed on the Internet on May 30, 2009 at: http://www.a-sp.org/database/custom/FGPC%20Phase %201%20Final%20Report.pdf

56 “Lighten Up!,” Brooke, L., Evans, H. Automive Engineering International, Vol. 117, No. 3, March 2009.

57 “2008/9 Blueprint for Sustainability,” Ford Motor Company. Available at: http:// www.ford.com/go/sustainability (last accessed February 8, 2010).

58 “Mazda to cut vehicle fuel consumption 30 percent by 2015,” Mazda press release, June 23, 2009. Available at: http://www.mazda.com/publicity/rel ease/2008/200806 /080623.html (last accessed February 8, 2010).

59 “Mazda: Don’t believe hot air being emitted by hybrid hype,” Greimel, H. Automotive News, March 30, 2009.

60 “Factor of Two: Halving the Fuel Consumption of New U.S. Automobiles by 2035,” Cheah, L., Evans, C., Bandivadekar, A., Heywood, J., Massachusetts Ins titute of Technology, Publication No. LFEE 2007-04 RP, October 2007, Docket EPA-HQ-OAR-2009-0472-0174.

61 “FY 2007 Progress Report for Lightweighting Material s”, Davis, P., Sullivan, R., Carpenter, J., U.S. Department of Energy Office of Vehicle Techno logies, December 2008, Docket EPA-HQ-OAR-2009- 0472-0165. Accessed on the Internet on April 30, 2009 at: http://www1.eere.energy.gov/vehiclesa ndfuels/resources/vt_lm_fy07.html

62 “Benefit Analysis: Use of Aluminum Structures in Conjunction with Alternative Powertrain Technologies in Automobiles,” Bull, M. Chavali, R., Mascarin, A., Aluminum Association Research Report, May 2008, Docket EPA-HQ-OAR-2009-0472-0168. Accessed on the Internet on April 30, 2009
at: http://www.autoaluminum.org/downloads/IBIS-Powertrain-Study.pdf

63 “Basic Analysis of the Cost and Long-Term Impact of the Energy Independence and Security Act Fuel Economy Standards” Sierra Research Report No. SR2008-04-01, Docket EPA-HQ-OAR-2009-0472-0173.

64 “Factor of Two: Halving the Fuel Consumption of New U.S. Automobiles by 2035” L. Cheah, C. Evans, A. Bandivadekar and J. Heywood, Publication No . LFEE 2007-04 RP, October 2007, Docket EPA-HQOAR-2009-0472-0174.

65 “Tires and Passenger Vehicle Fuel Economy,” Tr ansportation Research Boar d Special Report 286, National Research Council of the National Academ ies, 2006, Docket EPA-HQ-OAR-2009-0472-0146.

66 “An HCCI Engine Power Plant for a Hybrid Vehicl e,” Sun, R., R. Thomas and C. Gray, Jr., SAE Technical Paper No. 2004-01-0933, 2004.Economic Assumptions Used in the Agencies’ Analyses



## CHAPTER 4: Economic Assumptions Used in the Agencies’ Analysis

4.1 How the agencies use the economic assumptions in their analyses

Improving new vehicles’ fuel efficiency a nd reducing greenhouse gas (GHG) emissions provides direct benefits to their buyers and us ers by reducing fuel cons umption and fuel costs throughout those vehicles’ lifetimes, stimulating increased vehicle use through the fuel economy rebound effect, and increasing vehicles’ driving range so that they require less frequent refueling. At the same time, the reduction in fuel use thatresults from requiring higher fuel economy and reducing GHGs also produces wider benefits to the U.S. economy by lowering the cost of economic externalities that re sult from U.S. petroleum consumption and imports, including reducing the price of petroleum, lowering the potential costs from disruption in the flow of oil imports, and possibly reducing federal outlays to secure imported oil supp lies and cushion the U.S. economy against their pote ntial interruption. Reducing fu el consumption and GHGs also lowers the economic costs of environmental extern alities resulting from fuel production and use, including reducing the impacts on human health fr om emissions of criteri a air pollutants, and reducing future economic damages from potentia l changes in the global climate caused by greenhouse gas emissions.

These benefits are partly offset by the increase in fuel use that results from added vehicle use due to the fuel economy rebound effect, as well as by added costs from the increased congestion, crashes, and noise cause d by increased vehicle use. They might also be offset by any loss in the utility that new vehicles provide to their buyers (and subsequent owners) if manufacturers include reductions in vehicles’ performance, carryi ng capacity, or comfort as part of their strategies to comply with higher fuel economy requirements and GHG standards. Nevertheless, the total economic benefits from requiring higher fuel economy and reducing GHGs are likely to be substantia l, and the EPA and NHTSA have attempted to develop detailed estimates of the economic benefits from adopting more stringent standards.

This section discusses the common economic values used by both NHTSA and EPA. These economic inputs incorporate a range of forecast information, economic estimates, and input parameters. This section describes the sources that EPA and NHTSA have relied upon for this information, the rationale underlying each assumption, and the agencies’ estimates of specific parameter values. These common economic values are then used as inputs into each Agencies’ respective modeling and other analyses of the economic benefits and costs of the EPA and NHTSA programs. While the underlying economic input values are common to both Agencies, it is the differences in the way each Agency assesses its program that result in differing benefits estimates. This issue is discus sed further in Section I. C of the preamble to the joint rulemaking.

1Draft Joint Technical Support Document



### 4.2 What economic assumptions do the agencies use?

4.2.1 The on-road fuel economy “gap”

Actual fuel economy levels achieved by vehicl es in on-road driving fall significantly short of their levels measured under the labora tory-like test conditions used under the CAFE program to establish its published fuel economy ratings for different models. In analyzing the fuel savings from passenger car and light truck fuel efficiency and GHG standards, the agency adjusts the actual fuel economy performance of each passenger car and light truck model downward from its rated value to re flect the expected size of this on-road fuel economy “gap.” In December 2006, EPA adopted changes to its regulations on fuel economy labeling, which were intended to bring vehicles ’ rated fuel economy levels clos er to their actual on-road fuel economy levels. 1

Comparisons of on-road and CAFE fuel econom y levels developed by EPA as part of its Final Rule implementing new fuel economy labeli ng requirements for new vehicles indicate that actual on-road fuel economy for light-duty vehicl es averages 20 percent lower than published fuel economy levels. 2 For example, if the overall EPA fuel economy rating of a light truck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of that vehicle is expected to be 16 mpg (20*.80). In its anal ysis supporting the Final Rule establishing CAFE standards for MY 2011, NHTSA employed EPA’s revised estimate of this on-road fuel economy gap in its analysis of the fuel savings resulting from alternative fuel efficiency standards.

An analysis conducted by NHTSA confirmed th at EPA’s estimate of a 20 percent gap between test and on-road fuel economy is well- founded. The agency used data on the number of passenger cars and light trucks of each model year that were in se rvice (registered for use) during each calendar year from 2000 through 2006, averag e fuel economy for passenger cars and light trucks produced during each model year, and estima tes of average miles driven per year by cars and light trucks of different ages during each calendar year over that period. These data were combined to develop estimates of the usage-weighted average fuel economy that the U.S. passenger car and light truck fleets would have achieved during each year from 2000 through 2006 under test conditions.

Table 4-1 compares NHTSA’s estimates of fleet -wide average fuel economy under test conditions for 2000 through 2006 to the Federal Highway Administration’s (FHWA) published estimates of actual on-road fuel economy achie ved by passenger cars a nd light trucks during each of those years. As it shows, FHWA’s estim ates of actual fuel economy for passenger cars ranged from 21 percent to 23 percent lower than NHTSA’s estimates of its fleet-wide average value under test conditions over this period. Similarly, FHWA’s estimates of actual fuel economy for light trucks ranged fr om 16 percent to 18 percent lowe r than NHTSA’s estimates of average light truck fuel economy und er test conditions. Thus, th ese results appear to confirm that the 20 percent on-road fuel economy discount or gap represents a reasonable estimate for use in evaluating the fuel savings likely to result from alternativ e fuel efficiency standards for MY 2012-2016 vehicles.

We are aware of two potential is sues involved in these estimates. One, the estimates of total annual car and truck VMT are developed by the states and submitted to FHWA. Each state

2Economic Assumptions Used in the Agencies’ Analyses

uses its own definition of a car and a truck. For example, some states classify minivans as cars and some as trucks. Thus, there are known inc onsistencies with these estimates when evaluated separately for cars and trucks. Also, total ga soline consumption can be reasonably estimated from excise tax receipts, but separate estimates fo r cars and trucks are not available. We are not aware of the precise methodology used to develo p the distinct on-road fuel economy estimates for cars and trucks developed by FHWA. We do not believe that they are based on direct measurements from substantial numbers of vehi cles, as no such test programs were found by EPA during its fuel economy labeling rule in 200 6. Also, the year-to-year consistency for both car and truck fuel economy implies some methodol ogy other than direct measurement. For this reason, NHTSA and EPA are not us ing distinct on-road fuel economy gaps for cars and trucks, but one common value of 20 percent for both vehicl e classes for purposes of estimating the fuel savings of the standards.

Table 4-1 Estimated Fleet-Wide Fuel Economy of Passenger Cars and Light Trucks Compared to Reported Fuel Economy

PASSENGER CARS LIGHT-DUTY TRUCKS

YEAR NHTSA Estimated Test MPG FHWA Reported Actual MPG Percent Difference NHTSA Estimated Test MPG FHWA Reported Actual MPG Percent Difference

2000 28.2 21.9 -22.2% 20.8 17.4 -16.3% 2001 28.2 22.1 -21.7% 20.8 17.6 -15.5% 2002 28.3 22.0 -22.3% 20.9 17.5 -16.2% 2003 28.4 22.2 -21.9% 21.0 17.2 -18.0% 2004 28.5 22.5 -21.1% 21.0 17.2 -18.3% 2005 28.6 22.1 -22.8% 21.1 17.7 -16.3%
2006 28.8 22.5 -21.8% 21.2 17.8 -16.2% Avg., 2000- 2006 28.4 22.2 -22.0% 21.0 17.5 -16.7%



### 4.2.2 Fuel prices and the value of saving fuel

Projected future fuel prices ar e a critical input into the pr eliminary economic analysis of alternative fuel efficiency and GHG standards, because they determ ine the value of fuel savings both to new vehicle buyers and to society. As in the proposal, EPA and NHTSA relied on the most recent fuel price projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) for this analysis. In the proposal, we used the AEO 2009 (April 2009 release). For the final rulemaking presente d here, we updated to the more recent AEO 2010 Early Release. 3 The Reference Case forecasts infl ation-adjusted (constant-dollar) retail gasoline and diesel fuel prices and represents the EIA’s most up-to-date estimate of the most likely course of future prices for petroleum products. 4

As compared to the gasoline prices used in the proposal, AEO 2010 Early Release fuel prices are generally lower (in absolute 2007 dollars). A compar ison table is presented below,

3Draft Joint Technical Support Document

Table 4-2 . In broad strokes, future projections of fuel price are generally lower in AEO 2010 than in AEO 2009.

Table 4-2 Gasoline Prices for Selected Years in AEO 2009 and 2010 (Presented in constant 2007$ and including all taxes) 2015 2020 2030 AEO 2010 $3.00 $3.27 $3.60 AEO 2009 $3.16 $3.62 $3.82

Federal government agencies generally use EI A’s projections in their assessments of future energy-related policies. The retail fuel price forecasts presented in AEO 2010 span the period from 2007 through 2035. Measured in constant 2007 dollars, the AEO 2010 Reference Case forecast of retail gasolin e prices during calendar year 2020 is $3.27 per gallon, rising gradually to $3.83 by the year 2035 (these values include federal, stat e and local taxes). However, valuing fuel savings over the maximum lifetimes of passenger cars and light trucks used in this analysis requires fuel price forecasts that extend through 2050, approximately the last year during which a signi ficant number of MY 2016 vehicles will remain in service. a To obtain fuel price forecasts for the years 2036 th rough 2050, the agency assumes that retail fuel prices will continue to increas e after 2035 at the average annual rates projected for 2020-2035 in the AEO 2010 Reference Case. b This assumption results in a projected retail price of gasoline that reaches $4.49 in 2050.

The value of fuel savings resulting from im proved fuel economy and GHG emissions to buyers of light-duty vehicles is determined by th e retail price of fuel, which includes federal, state, and any local taxes imposed on fuel sales. Total taxes on gasoline, in cluding federal, state, and local levies averaged $0.42 per gallon duri ng 2008, while those levied on diesel averaged $0.50. Because fuel taxes represent transfers of resources from fuel buyers to government agencies, however, rather than real resources that are consumed in the process of supplying or using fuel, their value must be deducted from reta il fuel prices to determine the value of fuel savings resulting from more stringe nt fuel efficiency and GHG standards to the U.S. economy as a whole.

In estimating the economy-wide value of fuel savings from improved fuel efficiency standards and GHG emission standards, EPA and NHTSA follow the assumptions used by EIA in AEO 2010 that state and local gasoline taxes w ill keep pace with inflation in nominal terms,

a The agency defines the maximum lifetim e of vehicles as the highest age at which more than 2 percent of those originally produced during a model year remain in service. In the case of light-duty trucks, for example, this age has typically been 36 years for recent model years.

b This projection uses the rate of incr ease in fuel prices for 2020-2030 rather than that over the complete forecast period (2009-2030) because th ere is extreme volatility in the forecasts for the years 2009 through approximately 2020. Using the average rate of change over the complete 2009-2030 forecast period w ould result in projections of declining fuel prices after 2030.

4Economic Assumptions Used in the Agencies’ Analyses

and thus remain constant when expressed in constant 2007 dollars. c In contrast, EIA assumes that federal gasoline taxes will remain unchanged in nominal terms, and thus decline throughout the forecast period when expressed in constant 2007 dollars. These differing assumptions about the likely future behavior of federal and state/loca l fuel taxes are consistent with recent historical experience, which reflects the fact that federal as well as most state motor fuel taxes are specified on a cents-per-gallon basis, and ty pically require legislation to ch ange. The projected value of total taxes is deducted from each future year’s forecast of retail gasoline and diesel prices reported in AEO 2010 to determine the economic valu e of each gallon of fuel saved during that year as a result of improved fuel economy. Subtr acting fuel taxes results in a projected value for saving gasoline of $2.89 per gallon during 2020, rising to $3.48 per gallon by the year 2035.

EIA is widely-recognized as an impartial and authoritative sour ce of analysis and forecasts of U.S. energy production, consumption, and prices. The agency has published annual forecasts of energy prices and consumption leve ls for the U.S. economy since 1982 in its AEOs. These forecasts have been widely relied upon by fe deral agencies for use in regulatory analysis and for other purposes. Since 1994, EIA’s annual forecasts have been based upon the agency’s National Energy Modeling System (NEMS), which includes detailed representation of supply pathways, sources of demand, and their interacti on to determine prices for different forms of
energy.
From 1982 through 1993, EIA’s forecasts of worl d oil prices – the primary determinant of prices for gasoline, diesel, and other tr ansportation fuels derived from petroleum – consistently overestimated actual prices during future years, often very significantly. Of the total of 119 forecasts of future world oil prices for the years 1985 through 2005 that EIA reported in its 1982-1993 editions of AEO, 109 overestimated the subsequent actual values for those years, on average exceeding their correspondi ng actual values by 75 percent.

Since that time, however, EIA’s forecasts of future world oil prices show a more mixed record for accuracy. The 1994-2005 editions of AE O reported 91 separate forecasts of world oil prices for the years 1995-2005, of which 33 ha ve subsequently proven too high while the remaining 58 have underestimated actual pr ices. The average absolute error ( i.e. , regardless of its direction) of these forecasts has been 21 per cent, but over- and underestimates have tended to offset one another, so that on average EIA’s mo re recent forecasts have underestimated actual world oil prices by 7 percent. Although both it s overestimates and underestimates of future world oil prices for recent years have often been large, the most recent editions of AEO have significantly underestimated petroleum prices during those year s for which actual prices are now available.

c To calculate economy-wide impacts, th e agencies value fuel savings at pre-ta x prices, since this measures the value of the resources that are saved when less fuel is produced and consumed. Th e economy-wide value of saving fuel includes this plus the reductions in economic and enviro nmental externalities that result from consuming less fuel, importing less petroleum, and so on.

5Draft Joint Technical Support Document

However, EPA and NHTSA does not regard EIA’s recent tendency to underestimate future prices for petroleum and refined products or the high level of current fuel prices as adequate justification to employ forecasts that differ from the Reference Case forecast presented in the AEO 2010 Reference Case. This is particularly the case because both the AEO 2009 and AEO 2010 Reference Cases forecast si gnificantly higher future prices than those projected in the initial release of AEO 2008, whic h in turn represented a major upward revision from EIA’s fuel price forecast reported previous ly in AEO 2007. Comparing recent forecasts of world oil prices also shows that EIA’s Reference Case forecast reported in AEO 2009 is among the highest of all six publicly-available forecasts of worl d oil prices over the 2010-30 time horizon. 5 Because world petroleum prices are the primary determ inant of retail prices for refined petroleum products such as transportation fuel s, this suggests that the Referen ce Case forecast of U.S. fuel prices reported in AEO 2010 is likely to be the highest of those projec ted by major forecasting services.



### 4.2.3 Vehicle survival and use assumptions

The agencies’ analyses of fuel savings and related benefits from adopting higher fuel efficiency standards and establishing GHG emi ssion standards for MY 2012-2016 passenger cars and light trucks begin by estimating the resulting changes in fuel use over the entire lifetimes of these vehicles. The change in total fuel cons umption by vehicles produced during each of these model years is calculated as the difference in their lifetime fuel use under the baseline assumption that the CAFE standard previously es tablished for model year 2011 remains in effect, and under the alternative assumption that CAFE and GHG emissions standards improve for each model year from 2012-2016.

The first step in estimating lifetime fuel consumption by vehicles produced during a model year is to calculate the number of those ve hicles that is expected to remain in service during each future calendar year after they are produced and sold. d This number is calculated by multiplying the number of vehicles originally produced during a model year by the proportion expected to remain in service at the age they will have reach ed during each subsequent calendar year, often referred to as a “survival rate.”

NHTSA previously updated its estimates of car and light truck survival rates using 2008 registration data (the most rece nt available) for passenger cars and light trucks produced during recent model years, in order to ensure that its forecasts of age-specific survival rates reflect recent increases in the durability and exp ected life spans of cars and light trucks. 6 Both agencies

d Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they are produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year 2000, age 1 during calendar year 2001, and to reach their maximum age of 26 years during calendar year 2025. NHTSA considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles originally produced during a model year remain in service. Applying these conventions to vehicle registration data indicates that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 years. See Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf (last accessed Feb. 15, 2010)

.

6Economic Assumptions Used in the Agencies’ Analyses

used these updated survival estimates in their an alyses of fuel savings and other benefits from adopting improved fuel efficien cy and GHG standards for MY 2012-2106 cars and light trucks. The proportions of passenger cars and light trucks expected to remain in service at each age up to their maximum lifetimes (26 and 36 y ears, respectively) are shown in Table 4-3 and Table 4-4

. e

The next step in estimating lifetime fuel use by the cars or light trucks produced during a future model year is to calculate the total numbe r of miles that they wi ll be driven during each year of their expected lifetimes. To estimate total miles driven, the number of cars and light trucks projected to remain in use during each future calendar year calculated in the previous step is multiplied by the average number of miles a surviving car or light truck is expected to be driven at the age it will have reached in that year.

Updated estimates of average annual miles dr iven by cars and light trucks at each age were developed by NHTSA from the Fede ral Highway Administ ration’s 2001 National Household Transportation Survey. These updated es timates, differ from those employed in past NHTSA analyses, were used previously by NHTSA to analyze the effects of the previouslyestablished CAFE standards fo r MY 2011 cars and light trucks. f

Table 4-3 and Table 4-4 report NHTSA’s updated estimates of average car and light truck use which were used in the analyses for th e MY 2011 final rule. As these tables show, the resulting survival-weighted mileages calculated for that rule over the 26-year maximum lifetime of passenger cars is 161,847 miles, while that over the 36-year maximum lif etime of light trucks is 190,066 miles. It should be noted, however, that survival-weighted mil eage is extremely low (less than 1,000 miles per year) after age 20 for cars and afte r age 25 for light trucks, and thus has little impact on lifetime fuel savings or ot her benefits from higher fuel economy, particularly after discounting thos e benefits to thei r present values.

The survival and annual mileage estimates re ported in the tables reflect the convention that vehicles are defined to be of age 1 during the calendar year that coincides with their model year Thus for example, model year 2012 vehicl es will be considered to be of age 1 during calendar year 2012. This convention is used in order to account for the fact that vehicles produced during a model year typical are first offe red for sale in June through September of the preceding calendar year (for example, sales of a model year typically begin in June through September of the previous calendar year, depending on manufacturer). Thus virtually all of the

e The maximum age of cars and light trucks was defined as the age when the number remaining in service has declined to approximately two percent of those originally produced. Based on an examination of recent registration data for previous model years, typical maximum ages appear to be 26 years for passenger cars and 36 years for light trucks.

f See also NHTSA, “Vehicle Survival and Travel Mileage Schedules,” Office of Regulatory Analysis and Evaluation, January 2006, pp. 15-17. The original source of information on annual use of passenger cars and light trucks by age used in this analysis is the 2001 National Household Travel Survey (NHTS), jointly sponsored by the Federal Highway Administration, Bureau of Transporta tion Statistics, and Nationa l Highway Traffic Safety Administration.

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vehicles produced during a model year will be in use for some or all of the calendar year coinciding with their model year, and they are considered to be of age 1 during that year. g

g As an illustration, virtually the entire production of model year 2012 cars and light trucks will have been sold by the end of calendar year 2012, so those vehicles are defined to be of age 1 during calendar year 2012. Model year 2012 vehicles are subsequently defined to be of age 2 during calendar year 2013, age 3 during calendar year 2014, and so on. One complication arises because registration data are typically collected for July 1 of each calendar year, so not all vehicles produced during a model year will appe ar in registration data until the calendar year when they have reached age 2 (and sometimes age 3) under this convention.

8Economic Assumptions Used in the Agencies’ Analyses

Table 4-3 Survival Rates and Unadjusted Annual Miles Traveled (VMT) by Age for Passenger Cars (calculated from 2001 NHTS) Used in MY 2011 Final Rule

Vehicle

Age Estimated Survival Fraction Estimated Annual VMT Survival-Weighted Annual VMT

1 0.9950 14,231 14,160 2 0.9900 13,961 13,821 3 0.9831 13,669 13,438 4 0.9731 13,357 12,998 5 0.9593 13,028 12,497 6 0.9413 12,683 11,938 7 0.9188 12,325 11,324 8 0.8918 11,956 10,662 9 0.8604 11,578 9,961 10 0.8252 11,193 9,237 11 0.7866 10,804 8,499 12 0.7170 10,413 7,466 13 0.6125 10,022 6,138 14 0.5094 9,633 4,907 15 0.4142 9,249 3,831 16 0.3308 8,871 2,934 17 0.2604 8,502 2,214 18 0.2028 8,144 1,652 19 0.1565 7,799 1,220 20 0.1200 7,469 896 21 0.0916 7,157 656 22 0.0696 6,866 478 23 0.0527 6,596 348 24 0.0399 6,350 253 25 0.0301 6,131 185 26 0.0227 5,940 135
Estimated Passenger Car Lifetime VMT 161,847

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Table 4-4 Survival Rates and Unadjusted Annual Vehicle-Miles Traveled (VMT) by Age for Light Trucks (calculated from 2001 NHTS) Used in MY 2011 Final Rule

VEHICLE AGE ESTIMATED SURVIVAL FRACTION ESTIMATED ANNUAL VMT SURVIVALWEIGHTED ANNUAL VMT 1 0.9950 16,085 16,004 2 0.9741 15,782 15,374 3 0.9603 15,442 14,829 4 0.9420 15,069 14,195 5 0.9190 14,667 13,479 6 0.8913 14,239 12,691 7 0.8590 13,790 11,845 8 0.8226 13,323 10,960 9 0.7827 12,844 10,053 10 0.7401 12,356 9,145 11 0.6956 11,863 8,252 12 0.6501 11,369 7,391 13 0.6042 10,879 6,573 14 0.5517 10,396 5,735 15 0.5009 9,924 4,971 16 0.4522 9,468 4,281 17 0.4062 9,032 3,669 18 0.3633 8,619 3,131 19 0.3236 8,234 2,665 20 0.2873 7,881 2,264 21 0.2542 7,565 1,923 22 0.2244 7,288 1,635 23 0.1975 7,055 1,393 24 0.1735 6,871 1,192 25 0.1522 6,739 1,026 26 0.1332 6,663 887 27 0.1165 6,648 774 28 0.1017 6,648 676 29 0.0887 6,648 590 30 0.0773 6,648 514 31 0.0673 6,648 447 32 0.0586 6,648 390 33 0.0509 6,648 338 34 0.0443 6,648 294 35 0.0385 6,648 256 36 0.0334 6,648 222 Estimated Lifetime Light Truck VMT 190,066

10Economic Assumptions Used in the Agencies’ Analyses

4.2.3.1 Adjusting vehicle use for future fuel prices

The estimates of average annual miles driven by passenger cars and light trucks reported in Table 4-3 and Table 4-4 reflect the historically low gasoline prices that prevailed at the time the 2001 NHTS was conducted. To account for the eff ect on vehicle use of subsequent increases in fuel prices in the proposal, the estimates of annual vehicle use derived from the NHTS were adjusted to reflect the forecasts of future gaso line prices reported in the Energy Information Administration’s Annual Energy Outlook 2009 Upda ted Reference Case. For this final rulemaking, the agencies updated the analysis wi th the forecasts of future gasoline prices reported in the Energy Information Admi nistration’s Annual Energy Outlook 2010 Early Release. This adjustment accounts for the differe nce between the average retail price per gallon of fuel forecast during each cal endar year over the expected li fetimes of model year 2012-2016 passenger cars and light trucks, and the aver age price that prevailed when the NHTS was conducted in 2001. The elasticity of annual vehicl e use with respect to fuel cost per mile corresponding to the 10 percen t fuel economy rebound effect us ed in this analysis ( i.e. , an elasticity of -0.10) was used in conjunction with the difference between each future year’s fuel prices and those prevailing in 2001 to adjust the estimates of vehicle use derived from the NHTS to reflect the effect of higher future fuel pri ces. This procedure was applied to the mileage figures reported previously in Table 4-3 and Table 4-4 to adjust annual mileage by age during each calendar year of the expected lifetim es of MY 2012-2016 cars and light trucks.

4.2.3.2 Ensuring consistency with growth in total vehicle use

The estimates of annual miles driven by passe nger cars and light trucks at each age were also adjusted to reflect projecte d future growth in average vehicle use. Increases in the average number of miles cars and trucks ar e driven each year have been an important source of historical growth in total car and light truck use, and are expected to represent an important source of future growth in total light-duty vehicle travel as well. As an illustration of the importance of growth in average vehicle use, the total number of miles driven by passenger cars increased 35 percent from 1985 through 2005, equivalent to a co mpound annual growth rate of 1.5 percent. 7 During that time, however, the total number of pa ssenger cars registered for in the U.S. grew by only about 0.3 percent annually. h Thus growth in the average number of miles automobiles are driven each year accounted fo r the remaining 1.2 percent (= 1. 5 percent - 0.3 percent) annual growth in total automobile use. i Further, the AEO 2010 Reference Case forecasts of total car and light truck use and of the number of cars and light trucks in use sugge st that their average annual use will continue to increase gradually from 2010 through 2035.

In order to develop reasonable estimates of future growth in aver age car and light truck use, the agencies calculated the rate of growth in the mileage schedules shown previously in

h A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for a small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to various ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the agency’s Center for Statistical Analysis.

i See supra note k above.

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Table 4-6 and Table 4-7 that would be necessary for total car and light truck travel to increase at the rate forecast in the AEO 2010 Reference Case. Th is calculated rate is also consistent with future changes in the overall size and age distri butions of the U.S. passenger car and light truck fleets that are implied by the agency’s forecasts of total car and light truck sales and the survival rates reported in Table 4-3 and Table 4-4 . The growth rate in averag e annual car and light truck use produced by this calculation is approximately 1.15 percent per year. j This rate was applied to the mileage figures reported previously in Table 4-3 and Table 4-4 to estimate annual mileage by age during each calendar year of the exp ected lifetimes of MY 2012-2016 cars and light trucks. k

j It was not possible to estimate separate growth rates in average annual use for cars and light trucks, because of the significant reclassification of light truck models as passenger cars discussed previously.

k The agency has also identified an unintentional discrepancy between the estimates of expected lifetime VMT used by EPA and NHTSA in their respective analyses of proposed GHG and CAFE standards reported in the NPRM. EPA’s estimates of expected lifetime VMT for model year 2012-26 vehicles reflect the shared assumption that the average number of miles driven annually by cars and light trucks of all ages would grow at a rate of 1.15 percent per year beginning in 2001. Although NHTSA’s description of the procedures used to calculate its estimates of expected lifetime VMT for passenger cars and light trucks indi cated that it had also adju sted the base-year estimates of vehicle use to account for annual growth (see Prelimin ary Regulatory Impact Assessment, Chapter VIII, p. 367), this adjustment was not in fact applied in NHTSA’s analys is of the effects of proposed increases in CAFE standards for model years 2012-16. This omission caused the estimates of expected lifetime VMT for passenger cars and light trucks that NHTSA actually used in its NPRM analysis of CAFE standards for model years 2012-16 to be lower than those used by EPA. This error has been corrected by NHTSA, and this correction is reflected in its analysis of the impacts of CAFE standards for model year 2012-16 passenger cars and light trucks reported in the Final Rule. This error did not occur in the analysis of environmental impacts of alternative incr eases in CAFE standards reported in the agency’s recent Draft Enviro nmental Impact Statement.

12Economic Assumptions Used in the Agencies’ Analyses

Table 4-6 and Table 4-7 report the results of a pplying the adjustments for both future fuel prices and annual growth in car and light truck use to the mileage schedules reported previously in Table 4-2 and Table 4-3. Separate adjustments for projected fuel prices and growth in car and light truck use were made for each calendar year from 2012 through 2030. Because the effects of both fuel prices and cumulative growth in av erage vehicle use vary by year, these adjustments result in differing VMT schedules for each futu re year. The adjusted annual VMT estimates reported in Table 4-4 and Table 4-5 a nd their lifetime totals represent the averages of the adjusted values of annual car and light truck use by age for calendar years 2012-2030. However, the estimates of fuel savings and other impacts of improved fuel efficiency for individual calendar years over the lifetimes of model ye ar 2012-16 cars and light trucks employ the adjusted values of car and light truck us e by age during those sp ecific calendar years.

While the adjustment for future fuel prices reduces average mileage at each age from the values shown previously, the adjustment for ex pected future growth in average vehicle use increases it. Comparing the mileage estimates in

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Table 4-6 and Table 4-7 to those shown previously in Table 4-2 and Table 4-3 shows that the net effect of these two adjustments is to increase expected lifetime mileag e significantly. As an illustration, expected lifetime mileage for passenge r cars, this figure rises to 195,264 miles from the 161,847 miles reported previously in Table 4-3 (or by 21 percent), wh ile expected lifetime mileage for light trucks increases from the 190,066 miles reported previously in Table 4-4 to 225,865 miles (19 percent). As previously noted, the estimates of surv ival-weighted mileage decline to less than 1,000 miles pe r year after age 20 for cars and after age 27 for light trucks. Thus they have relatively little impact on lifetime fuel savings or other be nefits from higher fuel economy, particularly after discounting the benefits that occur in those di stant future years to their present values.

The VMT schedules used in the proposal a nd in this final rulemaking are presented below. To summarize the earlier discussion, the slight (~2.5 percent) in crease in lifetime VMT schedule is attributable to the lower fuel prices projected in AEO 2010 Early Release as compared to the updated AEO 2009.

Table 4-5 Summary of Expected Lifetime VMT Schedules (Presented in constant 2007$ and including all taxes) MY 2012-2016 Proposal MY 2012-2016 Final Rulemaking Car 190,271 195,264 Trucks 221,199 225,865

14Economic Assumptions Used in the Agencies’ Analyses

Table 4-6 Survival Rates and Adjusted Annual Vehicle-Miles Traveled (VMT) by Age for Passenger Cars (average values for 2012-2030)

Vehicle Age Estimated Survival Fraction Adjusted Annual VMT Survival-Weighted Annual VMT 1 0.9950 17,270 17,183 2 0.9900 16,943 16,774 3 0.9831 16,599 16,319 4 0.9731 16,163 15,728 5 0.9593 15,761 15,119 6 0.9413 15,337 14,437 7 0.9188 14,881 13,672 8 0.8918 14,429 12,868 9 0.8604 13,940 11,994 10 0.8252 13,495 11,136 11 0.7866 12,964 10,198 12 0.7170 12,510 8,970 13 0.6125 11,990 7,344 14 0.5094 11,470 5,843 15 0.4142 10,997 4,555 16 0.3308 10,543 3,488 17 0.2604 10,125 2,637 18 0.2028 9,714 1,970 19 0.1565 9,307 1,456 20 0.1200 8,891 1,067 21 0.0916 8,546 783 22 0.0696 8,285 577 23 0.0527 8,136 429 24 0.0399 7,896 315 25 0.0301 7,699 232 26 0.0227 7,530 171 Adjusted Lifetime Passenger Car VMT 195,264

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Table 4-7 Survival Rates and Adjusted Annua l Vehicle-Miles Traveled (VMT) by Age for Light Trucks (average values for 2012-2030)

Vehicle Age Estimated Survival Fraction Adjusted Annual VMT Survival-Weighted Annual VMT 1 0.9950 19,219 19,123 2 0.9741 18,782 18,296 3 0.9603 18,419 17,688 4 0.9420 17,946 16,905 5 0.9190 17,502 16,085 6 0.8913 16,952 15,109 7 0.8590 16,439 14,121 8 0.8226 15,829 13,021 9 0.7827 15,218 11,911 10 0.7401 14,648 10,841 11 0.6956 13,992 9,733 12 0.6501 13,450 8,744 13 0.6042 12,832 7,753 14 0.5517 12,212 6,737 15 0.5009 11,600 5,811 16 0.4522 11,069 5,005 17 0.4062 10,617 4,313 18 0.3633 10,125 3,679 19 0.3236 9,650 3,123 20 0.2873 9,238 2,654 21 0.2542 8,882 2,258 22 0.2244 8,667 1,945 23 0.1975 8,400 1,659 24 0.1735 8,395 1,456 25 0.1522 8,197 1,248 26 0.1332 8,188 1,091 27 0.1165 8,218 957 28 0.1017 8,216 836 29 0.0887 8,213 728 30 0.0773 8,211 635 31 0.0673 8,210 553 32 0.0586 8,208 481 33 0.0509 8,203 418 34 0.0443 8,196 363 35 0.0385 8,182 315 36 0.0334 8,167 273 Adjusted Lifetime Light Truck VMT 225,865

16Economic Assumptions Used in the Agencies’ Analyses



### 4.2.4 Accounting for the fuel economy rebound effect

The rebound effect refers to the fraction of fuel savings e xpected to result from an increase in vehicle fuel economy that is offset by additional vehicle use. The increase in vehicle use that stems from improved fuel economy oc curs because vehicle owners respond to the resulting reduction in vehicle fu el consumption and operating cost s by driving slightly more.

The magnitude of the rebound effect is one of the determinants of the actual fuel savings that are likely to result from adopting stricter fuel economy or emissions standards, and thus an important parameter affecting EPA’s and NHTSA’s ev aluation of alternative standards for future model years. It can be measured directly by esti mating the elasticity of vehicle use with respect to fuel economy itself, or indirectly by the elastic ity of vehicle use with respect to fuel cost per mile driven. l When expressed as a positive percentage, either of these parameters gives the fraction of fuel savings that would otherwise result from adopting stricter st andards, but is offset by the increase in fuel consumption that results when vehicles with increased fuel economy are driven more.

The fuel economy rebound effect for light-duty vehicles has been th e subject of a large number of studies since the early 1980s. Although they have repor ted a wide range of estimates of its exact magnitude, these st udies generally concl ude that a significant rebound effect occurs when vehicle fuel efficiency improves. m The most common approach to estimating its magnitude has been to analyze household survey data on vehicle use, fuel consumption, fuel prices (often obtained from ex ternal sources), and other dete rminants of household travel demand to isolate the response of vehicle use to higher fuel economy. Other studies have relied on econometric analysis of annual U.S. data on ve hicle use, fuel economy, fuel prices, and other variables to identify the response of total or av erage vehicle use to changes in fleet-wide average fuel economy and its effect of fuel cost per m ile driven. Two recent studies analyzed yearly variation in vehicle ownership and use, fuel prices, and fuel economy among individual states over an extended time period in order to measure the response of vehicle use to changing fuel
economy. n

This section surveys thes e previous studies, summarizes recent work on the rebound effect, and explains the basis for the 10 percent rebound effect EP A and NHTSA are using in this rulemaking. 8

l Fuel cost per mile is equal to the price of fuel in dollars per gallon divided by fuel economy in miles per gallon, so this figure declines when a ve hicle’s fuel economy increases.

m

Some studies estimate that the long-run rebound effect is significantly larger than the immediate response to increased fuel efficiency. Al though their estimates of the adjustment period required for the rebound effect to reach its long-run magnitude vary, this long-run effect is most appropriate for evaluating the fuel savings and emissions reductions resulting from stricter standards that would apply to future model years.

n In effect, these studies treat U.S. states as a data “p anel” by applying appropriate estimation procedures to data consisting of each year’s average values of these variables for the separate states.

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4.2.4.1 Summary of historical literature on rebound effect

It is important to note that a majority of the studies pr eviously conducted on the rebound effect rely on data from the 1950-1990s. While these older studies provid e valuable information on the potential magnitude of the rebound effect, studies that include more recent information (e.g., data within the last decade) may provide more reliable estimates of how this rule will affect future driving behavior. Therefore, the more r ecent studies have been described in more detail below.

Estimates based on aggregate U.S. vehicle tr avel data published by the U.S. Department of Transportation, Federal Highway Administra tion, covering the period from roughly 1950 to 1990, have found long-run rebound effects on the order of 10-30 percent. Some of these studies are summarized in the following table.

Table 4-8 Estimates of the Rebound Effect Using U.S. Aggregate Time-Series Data on Vehicle Travel 1

AUTHOR

(YEAR) SHORTRUN LONG-RUN TIME PERIOD

Mayo & Mathis (1988) 22% 26% 1958-84

Gately (1992) 9% 9% 1966-88 Greene (1992) Linear 5- 19% Log-linear 13% Linear 5-19% Log-linear 13% 1957-89

Jones (1992) 13% 30% 1957-89 Schimek (1996) 5-7% 21-29% 1950-94

1 Source: Sorrell and Dimitropolous (2007) table 4.6.

Table 4-9 Estimates of the Rebound E ffect Using U.S. State Level Data 1

AUTHOR

(YEAR) SHORT-RUN LONG-RUN TIME PERIOD

Haughton & Sarkar (1996) 9-16% 22% 1973-1992

Small and Van Dender (2007) 4.5% 22% 1961-2001

1 Source: Sorrell and Dimitropolous (2007) table 4.7.

While national (Table 4-6) and state level ( Table 4-9 ) data have found relatively consistent long-run estimates of the rebound eff ect, household surveys display more variability ( Table 4-10 ). There are several possible explanations fo r this larger variabili ty. One explanation is that some of these studies do not include vehi cle age as an explanatory variable, thus leading to omitted variable bias. 9 Another explanation is that it is difficult to differentiate between the

18Economic Assumptions Used in the Agencies’ Analyses

impacts of residential density and fuel prices, since households with higher fuel prices are more likely to be in urban areas. 10

Table 4-10 Estimates of the Rebound Effect Using U.S. Survey Data

AUTHOR

(YEAR) SHORTRUN LONG-RUN TIME PERIOD

Goldberg (1966) 0% CES 1984-90

Greene (1999) 23% EIA RTECS 1979-1994 Pickrell & Schimek (1999) 4-34% NPTS 1995 Single year

Puller & Greening (1999) 49% CES 1980-90 Single year, cross-sectional West (2004) 87% CES 1997 Single year

An important distinction among studies of the rebound effect is whether they assume that the effect is constant, or varies over time in response to the absolute le vels of fuel costs, personal income, or household vehicle ownership. Most st udies using aggregate annual data for the U.S. assume a constant rebound effect, although some of these studies test whethe r the effect can vary as changes in retail fuel prices or average fuel economy alter fuel cost per mile driven. Many studies using household survey data estimate significantly different rebound effects for households owning varying numbers of vehicles, although they a rrive at differing conclusions about whether the rebound effect is larger among households that own more vehicles. Finally, one recent study using state-level data concludes that the rebound effect varies directly in response to changes in personal income and the de gree of urbanization of U.S. cities, as well as fuel costs.

In order to provide a more comprehensive ove rview of previous es timates of the rebound effect, EPA and NHTSA reviewed 22 studies of the rebound eff ect conducted from 1983 through 2005. The agencies then performed a detailed anal ysis of the 66 separate estimates of the longrun rebound effect reported in these st udies, which is summarized in

Table 4-11 below. o As the table indicates, these 66 estimates of the long-run rebound effect range from as low as 7 percent to as high as 75 percent, with a mean value of 23 percent. Limiting the sample to 50 estimates reported in the 17 published studies of the rebound effect yields the same range, but a slightly higher mean estimate (24 percent).

o In some cases, NHTSA derived estimates of the overall rebo und effect from more detailed results reported in the studies. For example, where studies estimated different rebound effects for households owning different numbers of vehicles but did not report an overall value, the agency computed a weighted average of the reported values using the distribution of households among vehicle ownership categories.

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The type of data used and authors’ assu mption about whether the rebound effect varies over time have important effects on its estimat ed magnitude. The 34 estimates derived from analysis of U.S. annual time-series data produce a mean estimate of 18 percent for the long-run rebound effect, while the mean of 23 estimates ba sed on household survey data is considerably larger (31 percent), and the mean of 9 estimates ba sed on state data (25 percent) is close to that for the entire sample. The 37 estimates assu ming a constant rebound effect produce a mean of 23 percent, identical to the mean of the 29 esti mates reported in studies that allowed the rebound effect to vary in response to fuel pric es, vehicle ownership, or household income.

Table 4-11 Summary Statistics for Estimates of the Rebound Effect

Range Distribution Category of Estimates Number of Studies Number of Estimates Low High Median Mean Std. Dev.

All Estimates 22 66 7% 75% 22% 23% 14%
Published Estimates 17 50 7% 75% 22% 24% 14%
U.S. Time-Series Data 7 34 7% 45% 14% 18% 9% Household Survey Data 13 23 9% 75% 31% 31% 16%
Pooled U.S. State Data 2 9 8% 58% 22% 25% 14% Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16% Variable Rebound Effect: (1) 10 29 10% 45% 23% 23% 10%

4.2.4.2 Summary of recent studies and analyses of the rebound effect

More recent studies since 2007 i ndicate that the impacts of fu el prices and fuel economy have been decreasing over time as incomes increas e and real gasoline prices fall. The theoretical argument for why the rebound effect should vary is that the responsiveness to the fuel cost of driving will be larger when it is a larger proportion of the total cost of driving. As incomes rise (or fuel prices fall), the responsiveness to the fuel cost per mile of driving will decrease if people view the time cost of driving – wh ich is likely to be related to their income levels – as a larger component of the total cost.

Small and Van Dender combined time series data for each of the 50 States and the District of Columbia to estimate the rebound e ffect, allowing the magnit ude of the rebound to vary over time. 11 For the time period from 1966-2001, their study found a long-run rebound effect of about 22 percent, whic h is consistent with previously published studies. But for the most recent five year period ( 1997-2001), the long-run rebound effect decreased to 11 percent. Furthermore, when the authors updated their estimates with data through 2004, the long-run rebound effect for the most recent five y ear period (2000-2004) dropped to 6 percent. 12

New research conducted by David Greene in 2008-2009 at EPA's request further appears to support the theory that th e magnitude of the rebound effect is declining over time. 13 Over the entire time period analyzed (1966-2007), Greene found that fuel prices had a statistically significant impact on VMT, while fuel economy did not . In spite of this result, Greene also tested Small and Van Dender’s formulation which a llows the elasticity of fuel cost per mile to decrease with increasing per capita income. The results of estimation using national time series

20Economic Assumptions Used in the Agencies’ Analyses

21 data confirmed the results obtained by Small and Van Dender using a time series of state level data.

While Small and Van Dender did not find a stat istically significant coefficient for fuel efficiency, they did find a statis tically significant coefficient for the price of fuel. They then tested the hypothesis that the elas ticity of vehicle trav el with respect to the price of fuel was equal to its elasticity with resp ect to the rate of fuel consump tion (gallons per mile), and found that the data could not reject that hypothesis. As a conse quence, they estimated the rebound effect as the elasticity of vehicle travel with respect to fuel cost per mile, which constrains these elasticities to be equal in magn itude. Greene also attempted to re-estimate the Small and Van Dender results using his national data set. Wh en using Greene’s preferred functional form, the projected rebound effect is approximately 12 percent in 2007, and drops to 10 percent in 2010, 9 percent in 2016 and 8 percent in 2030.

4.2.4.3 NHTSA analysis of the rebound effect

To provide additional insight into the rebound e ffect for the purposes of this rulemaking, NHTSA developed several new estimates of its magnitude. These estimates were developed by estimating and testing several ec onometric models of the relationship between vehicle milestraveled and factors that influence it, includi ng household income, fuel prices, vehicle fuel efficiency, road supply, the number of vehicles in use, vehicle prices, and other factors.

As the study by Small and Van Dender pointed out, it is important to account for the effect of fuel prices when attempting to es timate the rebound effect. Failing to control for changes in fuel prices is likely to bias estimates of the rebound effect. Therefore, changes in fuel prices are taken into account in Volpe’s analysis of the re bound effect. Several different approaches were used to estimate the fuel economy rebound effect for light duty vehicles, including single equation OLS re gressions, multiple equation systems estimated using two- and three-stage least squares regres sion, vector autoregression, and v ector error-correction models. With the except of single-equation regressions es timated using OLS, all of these procedures attempt to account for the endogenous relationshi p of fuel efficiency to fuel prices.

The results from each of these approaches are presented in Table 4-12 below. The table reports the value of the rebound effect calcul ated over the entire period from 1950 through 2006, as well as for the final year of that period. In addition, the table presents forecasts of the average rebound effect between 2010 and 2030, which utilize fo recasts of personal income, fuel prices, and fuel efficiency from EI A’s AEO 2009 Reference Case.

The results of NHTSA’s analysis are broadly consistent with the findings from previous research summarized above. The historical aver age long-run rebound effect is estimated to range from 16-30 percent, and comparing these estimates to its calculated values for 2006 (which range from 8-14 percent) gives some an indication that it is declining in magnitude. The forecast values of the rebound effect shown in the table also suggest that this decline is likely to continue through 2030, as they range from 4-16 percent.Economic Assumptions Usedin the Agencies’ AnalysesTable4-12Summary of NHTSA Estimates of the Rebound EffectRebound Effects:ModelVMTMeasureVariables Included in VMT EquationEstimationTechnique1950-200620062010-2030*Small-Van Dendersingle VMTequationannual VMTper adultfuel cost per mile, per Capita income, vehicle stock, roadmiles per adult, fraction of population that is adult,fraction of population living in urban areas, fraction ofpopulation living in urban areas with heavy rail, dummyvariables for fuel rationing, time trendOLS 33.0% 15.8% 8.0%Small-Van Denderthree-equationsystemannual VMTper adultfuel cost per mile, per Capita income, vehicle stock, roadmiles per adult, fraction of population that is adult,fraction of population living in urban areas, fraction ofpopulation living in urban areas with heavy rail, dummyvariables for fuel rationing, time trend3SLS 21.6% 5.8% 3.4%Single-equationVMT modelannual VMTper adultpersonal income, road miles per Capita, time trendOLS 18.4% 11.7% 9.2%Single-equationVMT modelannual VMTper vehiclefuel cost per mile, personal income, road miles perCapita, time trendOLS 17.6% 15.2% 15.7%Single-equationVMT modelannual VMTper adultfuel cost per mile, personal income, road miles perCapita, dummy variables for fuel rationing, time trendOLS 34.0% 20.8% 13.6%Single-equationVMT modelannual VMTper vehiclefuel cost per mile, personal income, vehicles per roadmile, % of fleet manufactured under CAFE standards,new vehicle pricesIV (for fuelcost per mile)16.3% 9.2% 7.0%Three-equationsystem for VMT,fuel efficiency, andvehicle stockannual VMTper vehiclefuel cost per mile, personal income, vehicles per capita,vehicles per road mile, fraction of adult populationlicensed to drive, new vehicle prices, % of fleetmanufactured under CAFE standards2SLS 29.5% 13.4% 15.9%Three-equationsystem for VMT,fuel efficiency, andvehicle stockannual VMTper vehiclefuel cost per mile, personal income, vehicles per capita,vehicles per road mile, fraction of adult populationlicensed to drive, new vehicle prices, % of fleetmanufactured under CAFE standards3SLS 29.8% 13.7% 16.2%Three-equationsystem for VMT,annual VMTper vehiclefuel cost per mile, personal income, vehicles per capita,vehicles per road mile, fraction of adult populationVector autoregression19.9% 10.8% --22Economic Assumptions Usedin the Agencies’ Analyses23Economic Assumptions Used in the Agencies’ Analyses

4.2.4 .4 Basis for rebound effect used by EPA and NHTSA in this rule

Based on a combination of hi storical estimates of the rebound effect and more recent analyses conducted by EPA and NHTSA, the agenci es used an estimate of 10 percent for the rebound effect in their analyses of proposed CAFE and GHG emi ssion standards. Although this value represents the lower bound of the historical range of values for the rebound effect, more recent analysis suggests that the rebound effect is declining over time. The agencies sought comment on other alternatives for estimating th e rebound effect. As one illustration, under the assumption that drivers respond similarly to chan ges in the cost of driving whether they are caused by variation in fuel prices or fuel econo my, the short-run price el asticity of demand for gasoline – which measures the sensitivity of gaso line consumption to changes in its price per gallon – may provide some indication about the magn itude of the rebound effect itself. Thus, the agencies sought comment on whether it would be appropriate to use the price elasticity of demand for gasoline, or other a lternative approaches, to guide their choice of a value for the rebound effect.

During the public comment period, EPA a nd NHTSA did not receive new data or analysis on the level of the rebound effect, but did receive so me supportive comments on the 10 percent level. Hence, EPA and NHTSA have elect ed to continue to use the 10 percent estimate of the rebound effect in the anal yses supporting this final rule.



### 4.2.5 Benefits from increased vehicle use

The increase in vehicle use from the rebound effect provides additiona l benefits to their owners, who may make more frequent trips or travel farther to reach more desirable destinations. This additional travel provides benefits to drivers and their passengers by improving their access to social and economic opportunitie s away from home. As eviden ced by their decisions to make more frequent or longer trip s when improved fuel economy redu ces their costs for driving, the benefits from this additional travel exceed the costs drivers and passengers incur in making more frequent or longer trips.

The analysis estimates the economic benefits from increased rebound-effect driving as the sum of fuel costs drivers incur plus the c onsumer surplus they receive from the additional accessibility it provides. p Because the increased in travel depend on the extent of improvement in fuel economy, the value of benefits it prov ides differs among model years and alternative CAFE standards. Under even those alternativ es that would impose th e highest standards, however, the magnitude of these benefits repr esents a small fraction of total benefits.

p The consumer surplus provided by added travel is estimated as one-half of th e product of the dec line in fuel cost per mile and the resulting increase in th e annual number of miles driven.

24Economic Assumptions Used in the Agencies’ Analyses



### 4.2.6 Added costs from increased vehicle use

Although it provides some benefits to drivers, increased vehi cle use associated with the rebound effect also contributes to increased traffic congestion, motor vehicle accidents, and highway noise. Depending on how the additional trav el is distributed over the day and on where it takes place, additional vehicle use can contribute to traffic c ongestion and delays by increasing traffic volumes on facilities that are already heavily traveled dur ing peak periods. These added delays impose higher costs on driv ers 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 travel, they must be accounted for separately as a cost of the added driving associated w ith the rebound effect.

Increased vehicle use due to the rebound effect may also in crease the costs associated with traffic accidents. Drivers may take accoun t of the potential costs they (and their passengers) face from the possibility of bei ng involved in an accident when they decide to make additional trips. However, they probably do not consid er 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 additiona l rebound-effect driving. Like increased delay costs, any increase in thes e external accident cost s caused by added driving is likely to depend on the traffic conditions under which it takes pl ace, since accidents are more frequent in heavier traffic (although their severity may be reduced by the slower speeds at which heavier traffic ty pically moves).

Finally, added vehicle use from the rebound eff ect may also increase traffic noise. Noise generated by vehicles causes inconvenience, irritation, and potentially even discomfort to occupants of other vehicles, to pedestrians and ot her bystanders, and to re sidents or occupants of surrounding property. Because these effects are unlikely to be ta ken into account by the drivers whose vehicles contribute to tra ffic noise, they represent additiona l externalities associated with motor vehicle use. Although ther e is considerable uncertainty in measuring their value, any increase in the economic costs of traffic noise re sulting from added vehicle use must be included together with other increased exte rnal costs from the rebound effect.

EPA and NHTSA rely on estimates of conge stion, accident, and noise costs caused by automobiles and light trucks developed by the Federal Highway Administ ration to estimate the increased external costs caused by a dded driving due to the rebound effect. 14 NHTSA employed these estimates previously in its analysis accompanying the MY 2011 final rule, and continues to find them appropriate for this NPRM after review ing the procedures used by FHWA to develop them and considering other available estimates of these values. They are intended to measure the increases in costs from added c ongestion, property damages and inju ries in traffic accidents, and noise levels caused by automobiles and light truc ks that are borne by persons other than their drivers (or “marginal” external costs).

Updated to 2007 dollars, FHWA’s “Middle” es timates for marginal congestion, accident, and noise costs caused by automobile use amount to 5.2 cents, 2.3 cents, and 0.1 cents per

25Draft Joint Technical Support Document

vehicle-mile (for a total of 7.6 cents per mile), while those fo r pickup trucks and vans are 4.7 cents, 2.5 cents, and 0.1 cents per vehicle-m ile (for a total of 7.3 cents per mile). 15

, q These costs are multiplied by the annual increases in automobile and light truck use from the rebound effect to yield the estimated increases in congestion, accident, and noise externality costs during each future year.



### 4.2.7 Pe troleum and energy security impacts

4.2.7.1 Impact on U.S. petroleum imports

In 2008, U.S. petroleum import expenditures represented 21 percent of total U.S. imports of all goods and services. 16 In 2008, the United States imported 66 percent of the petroleum it consumed, and the transportation sector acc ounts for 70 percent of total U.S. petroleum consumption. This compares roughly to 37 perc ent of petroleum from imports and 55 percent consumption of petroleum in th e transportation sector in 1975. 17 It is clear that petroleum imports have a significant impact on the U.S. economy. Requiring lower-GHG vehicle technology and improved fuel economy in the U.S. is expected to lower U.S. petroleum imports.

4.2.7.2 Background on U.S. energy security

U.S. energy security is broadly define d as protecting the U.S. economy against circumstances that threaten significant short- an d long-term increases in energy costs. Most discussion of U.S. energy security revolves ar ound the topic of the ec onomic costs of U.S. dependence on oil imports. The problem is that the U.S. relies on sources of imported oil from potentially unstable sources. In addition, oil export ers have the ability to raise the price of oil by exerting monopoly power through the formation of a cartel, the Organization of Petroleum Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. economy to episodic oil supply shocks and price spikes. In 2008, U. S. imports of crude oil were $326 billion (in 2007$, see Figure 4-1 ).

q The Federal Highway Administration’s es timates of these costs agree closely with some other recent estimates. For example, recent published research conducted by Resour ces 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 Feb. 15, 2010).

26Economic Assumptions Used in the Agencies’ Analyses

U.S. Expenditures on Crude Oil

0 100 200 300 400 500 600 700

1970 1975 1980 1985 1990 1995 2000 2005

Year(2007$ Billions) Domestic Imported

Figure 4-1 U.S. Expenditures on Crude Oil from 1970 through 2008 r

One effect of the EPA/NHTSA joint rule is that it promotes more efficient use of transportation fuels in the U.S. The result is that it reduces U.S. oil imports, which reduces both financial and strategic risks associ ated with a potential disruption in supply or a spike in the cost of a particular energy source. This reduction in risks is a measure of improved U.S. energy security. For this rule, an “oil premium” approa ch is utilized to identify those energy-security related impacts which are not reflected in the market price of oil, and which are expected to change in response to an incremental change in the level of U.S. oil imports.

4.2.7.2.1 Me thodology Used to Estimate U.S. Energy Security Benefits

In order to understand the ener gy security implications of reducing U.S. oil imports, EPA has worked with Oak Ridge Na tional Laboratory (ORNL), which has developed approaches for evaluating the social costs and energy security implications of oil use. The energy security estimates provide below are based upon a me thodology developed in a peer-reviewed study entitled, “ The Energy Security Benefits of Reduced Oil Use, 2006-2015 ,” completed in March 2008. This recent study is included as part of the docket for this rulemaking. 18 This ORNL study is an update version of the approach to estimating the energy security benefits of U.S. oil

r For historical data through 2006: EIA Annual Energy Review, various editions. For data 2006-2008: EIA Annual Energy Outlook (AEO) 2009 (Update Reference (Stimulus) Base Case). See file "aeostimtab_11.xls" available at http:// www.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.html

27Draft Joint Technical Support Document

import reductions developed in an ORNL 1997 Re port by Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, entitled “ Oil Imports: An Assessment of Benefits and Costs. ”

When conducting this recent analysis, ORNL considered the full cost of importing petroleum into the U.S. The full economic cost is defined to include tw o components in addition to the purchase price of petroleum itself. These are: (1) the higher costs for oil imports resulting from the effect of U.S. import demand on the world oil price and on OPEC market power ( i.e.

, the “demand” or “monopsony” costs); and (2) the risk of reductions in U.S. economic output and disruption of the U.S. economy caused by sudden disr uptions in the supply of imported oil to the U.S. ( i.e. , macroeconomic disruption/adjustment costs). Maintaining a U.S. military presence to help secure stable oil supply from potentially vulnerable regions of the world was not included in this analysis because its attribution to particul ar missions or activities is difficult (as discussed further below).

Section III.H of the preamble contains a de tailed discussion of how the monopsony and macroeconomic disruption/adjustment components were treated for this analysis. As that discussion indicates, the monopsony co st represents a transfer of resources from consumers of petroleum products to oil produc ers, and does not involve the consumption of real economic resources. Reducing the magnitude of the m onopsony cost does not produce a savings in economic resources, although it does reduce the va lue of resources transferred from U.S. consumers of petroleum products to foreign s uppliers of petroleum. In contrast, the macroeconomic disruption and adjustment costs that arise from U.S. petroleum imports do represent real economic costs, and reductions in their magnitu de do represent real economic benefits from reducing U.S. consumption and impor ts of petroleum. Theref ore, the remainder of this discussion focuses on estimating the magn itude of the macroeconomic disruption and adjustment costs.

As part of the process for using the ORNL energy security estimates, EPA sponsored an independent-expert peer review of this ORNL study. A report compiling the peer reviewers’ comments is provided in the docket. 19 In addition, EPA has worked with ORNL to address comments raised in the peer review and to deve lop estimates of the energy security benefits associated with a reduction in U.S. oil imports for this rule. In res ponse to peer reviewer comments, ORNL modified its model by chan ging several key parameters involving OPEC supply behavior, the responsiveness of oil demand and supply to a change in the world oil price, and the responsiveness of U.S. economic output to a change in the world oil price.

For this rule, ORNL further updated the en ergy security premium by incorporating the most recent oil price forecast and energy market tr ends in AEO 2009 into its model. In order for the energy security premium estimated to be us ed in EPA’s VECTOR m odel, ORNL developed energy security estimates for a number of different years; i.e. , 2015, 2020, 2030, and 2040. NHTSA used just the 2015 estimates to estimate the energy security benefits associated with this rule.

For 2015, ORNL has estimated that the total en ergy security premium associated with a reduction of imported oil is $18.49/barrel. On a do llar per gallon basis, en ergy security benefits for 2015 are $0.44/gallon. Based upon alternative sensitivities regarding OPEC supply behavior, and the responsiveness of oil demand and supply to a change in the world oil price, the energy

28Economic Assumptions Used in the Agencies’ Analyses
security premium ranges from $9.80 to $28.08/barrel. In terms of dollars per gallon, the energy security premium ranges from $0.23/gal lon to $0.67/gallon. Please refer to Table 4-13 for similar information for years 2015, 2020, 2030 and 2040, s as well as a breakdown of the components of the energy security premium for each year. The components of the energy security premium and their values are discussed below.

Table 4-13 Energy Security Premium in 2015, 2020, 2030 and 2040 t

(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)

4.2.7.2.1.1 Effect of oil use on long-run oil pr ice, U.S. import costs, and economic output

The first component of the full economic co sts of importing petroleum into the U.S. follows from the effect of U.S. import demand on the world oil price over the long-run. Because the U.S. is a sufficiently large pur chaser of foreign oil supplies, its purchases can affect the world oil price. This monopsony power means that in creases in U.S. petroleum demand can cause the world price of crude oil to ri se, and conversely, that reduced U.S. petroleum demand can reduce the world price of crude oil. Thus, one benef it of decreasing U.S. oil purchases, due to the increased availability and use of other transportation fuels, is the potential decrease in the crude oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illustrated with an example. If the U.S. imports 10 million barrels per day at a world oil pr ice of $50 per barrel, its total daily bill for oil imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the world oil price to drop to $49 per barrel, the da ily U.S. oil import bill drops to $441 million (9 million barrels times $49 per barrel). While the world oil price only declines $1, the resulting

s AEO 2009 forecasts energy market tre nds and values only to 2030. The en ergy security premia post-2030 were assumed to be the 2030 estimate.

t AEO 2009 world oil prices were used, as in the propos ed rule. The analysis was not updated using the recentlyreleased AEO 2010 forecast of world o il prices, because EPA and NHTSA believe that the changes in the fuel prices would be too small to cause a change in the Energy Security Premium estimates.

29Draft Joint Technical Support Document
decrease in oil purchase payments of $59 m illion per day ($500 million minus $441 million) is equivalent to an incremental benefit of $59 per barrel of oil imports re duced, or $10 more than the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost premium” represents the incremental external benefits to the U.S. for avoided import costs beyond the price paid oil purchases . This additional benefit aris es only to the extent that reduction in U.S. oil imports affects the world oil price. ORNL estimates this component of the energy security benefit in 2015 to be $11.79/ba rrel, with a range of $4.26 – $21.37/barrel of imported oil reduced.

4.2.7.2.1.2 Sh ort-run disruption premium from expected costs of sudden supply disruptions

The second component of the oil import premium, the “macroeconomic disruption/adjustment costs premium,” arises from the effect of oil imports on the expected cost of disruptions. A sudden increase in oil prices triggered by a disruption in world oil supplies has two main effects: (1) it increases the costs of imports in the short run, further expanding the transfer of U.S. wealth to foreign producers, and (2) it can lead to macroeconomic contraction, dislocation and Gross Domestic Product (GDP) losse s. ORNL estimates the composite estimate of these two factors that comprise the macroec onomic disruption/adjustment costs premium to be $6.70/barrel in 2015, with a range of $3.11 – $10.67/barrel of imported oil reduced.

4.2.7.2.1.2.1 Higher costs of oil imports and wealth transfer during shocks

During oil price shocks, the higher price of imported oil causes increased payments for imports and a transfer of wealth from the U.S to oil exporters. This increased claim on U.S. economic output is a loss to the U.S. that is separate from and additional to any reduction in economic output due to the shock. The increased wealth transfer during shocks is counted as a loss to the degree that the expect ed price increase is not anticip ated and internalized by oil

consumers.

4.2.7.2.1 .2.2 Macroeconomic costs: potential output lo ss and dislocation/adjustment costs

Macroeconomic losses during price shocks reflect both aggregate output losses and “allocative” losses. The former are a reduction in the level of output that the U.S. economy can produce fully using its available resources; and th e latter stem from temporary dislocation and underutilization of available reso urces due to the shock, such as labor unemployment and idle plant capacity. The aggregate output effect, a re duction in “potential” economic output, will last so long as the price is elevated. It depends on the extent and duration of any disruption in the world supply of oil, since these factors determ ine the magnitude of th e resulting increase in prices for petroleum products, as well as whether and how rapidly these prices return to their predisruption levels.

In addition to the aggregate contraction, th ere are “allocative” or “adjustment” costs associated with dislocated energy markets. Because supply disruptions and resulting price increases occur suddenly, empirical evidence shows they also impose additional costs on businesses and households which must adjust thei r use of petroleum and other productive factors more rapidly than if the same price increase had occurred gradually. Dislocational effects

30Economic Assumptions Used in the Agencies’ Analyses

include the unemployment of workers and othe r resources during the time needed for their intersectoral or interregional reallocation, and pauses in capital investment due to uncertainty. These adjustments temporarily reduce the level of economic output that can be achieved even below the “potential” output level that w ould ultimately be reached once the economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on businesses and households for making these adjustments reflect their limited ability to adjust prices, output levels, and their use of energy, labor and other inputs quickly and smoothl y in response to rapid changes in prices for petroleum products.

Since future disruptions in fo reign oil supplies are an uncer tain prospect, each of the disruption cost components must be weighted by the probability that the supply of petroleum to the U.S. will actually be disrupte d. Thus, the “expected value” of these costs – the product of the probability that a supply disruption will occur and the sum of costs from reduced economic output and the economy’s abrupt adjustment to shar ply higher petroleum pri ces – is the relevant measure of their magnitude. Further, when asse ssing the energy security value of a policy to reduce oil use, it is only the cha nge in the expected costs of di sruption that results from the policy that is relevant. The e xpected costs of disruption may change from lowering the normal ( i.e. , pre-disruption) level of domestic petroleum us e and imports, from any induced alteration in the likelihood or size of disrupti on, or from altering the short-run flexibility (e.g., elasticity) of petroleum use.

In summary, the steps needed to calculate the disruption or security premium are: 1) determine the likelihood of an oil supply disruption in the future; 2) assess the likely impacts of a potential oil supply disruption on th e world oil price; 3) assess th e impact of the oil price shock on the U.S. economy (in terms of import costs and macroeconomic losse s); and 4) determine how these costs change with oil imports. The value of price spike cost s avoided by reducing oil imports becomes the oil security portion of the premium.

4.2.7.2.2 Cost of existing U.S. energy security policies

The last often-identifie d component of the full economic co sts of U.S. oil imports is the costs to the U.S. taxpayers of existing U.S. ener gy security policies. Th e two primary examples are maintaining a military presence to help secure a stable oil supply from potentially vulnerable regions of the world and maintaining the Strate gic Petroleum Reserve (SPR). The SPR is the largest stockpile of government-owned emergency crude oil in the world. Established in the aftermath of the 1973-74 oil embargo, the SPR provides the U.S. a response option should a disruption in commercial oil supplie s threaten the U.S. economy. It also allows the U.S. to meet part of its Internati onal Energy Agency obligation to main tain emergency oil stocks, and it provides a national defense fuel reserve.

U.S. military costs are excluded from the analysis performed by ORNL because their attribution to particular missions or activities is difficult. Most military forces serve a broad range of security and foreign policy objectives. A ttempts to attribute some share of U.S. military costs to oil imports are further challenged by the need to estima te how those costs might vary with incremental variations in U.S. oil impor ts. Similarly, while the costs for building and maintaining the SPR are more clearly related to U. S. oil use and imports, hi storically these costs

31Draft Joint Technical Support Document

have not varied in response to changes in U.S. oil import levels. Thus, while SPR is factored into the ORNL analysis, the cost of maintaining the SPR is excluded.

4.2.7.2.3 Modifications to analysis based upon peer reviewer comments

The EPA commissioned ORNL to conduct a number of sensitivity analys es to address the comments of the peer reviewers. Based upon the peer reviewer comments, key parameters that influence the “oil import” premium were assess ed. Since not all the comments were in agreement with each other, several ranges of different parameters were developed for the analyses. These sensitivities used the most recent price forecasts and energy market trends available at the time the peer review wa s being conducted and completed, the AEO 2007 Reference Case. Thus, the results presented be low are suggestive of how the energy security premium is influenced by alternative assumptions of key parameters that influence world oil markets. A summary of the result s of the analyses are shown in Table 4-14

. Three key parameters were varied in orde r to assess their impacts on the oil import premium: (1) the response of OPEC supply, (2 ) the combined response of non-U.S., non-OPEC demand and supply and (3) the GDP response to a ch ange in the world oil as a result of reduced U.S. oil imports. The cases used updated s upply/demand elasticities for non-U.S./non-OPEC region after considering more r ecent estimates than those used in 1997 study. As a result, the total market responsiveness is gr eater than previous ORNL estimat es. Only small changes to the world oil price are anticipated from a substant ial reduction in U.S. de mand, on average, about $0.70/barrel for every million-barrel s-per-day reduction in demand.

In the ORNL framework, OPEC-behavior is trea ted parametrically, w ith a wide range of possible responses represented by a range of supply elasticities. Case One in Table 4-14 below refers to the AEO 2007 estimates of energy market trends and uses the elas ticity parameters from the original 1997 ORNL study. In Case Two, the OP EC supply elasticities range for 0.25 to 6 with a mean elasticity of 1.7. Ca se Three alters the distribution of the OPEC supply elasticities so that the mean elasticity is 2.2 instead of 1.7. With the more elastic OPEC oil supply in Case Three, the oil premium is lower. Alternativel y, a candidate rule for OPEC strategic response behavior, adapted from a lead article on what behavior maximi zes OPEC’s long run net revenue in a robust way, 20 would have OPEC respondi ng to preserve its worldwide oil market share. This is presented as Case Seven. Application of this rule instead of the range of OPEC supply responses used leads to an estimate of the o il import premium that is between Case Two and Case Three.

The second key parameter that was varied based upon peer reviewer comments was nonOPEC, non-U.S. demand and supply responsiveness to a change in the U.S. oil import demand and, hence, the world oil price. In Case F our, the mean non-U.S./non-OPEC demand and supply elasticities are taken to each be 0.3 in absolute value terms. When combined together, the net elasticity of import demand from this regi on — the non-U.S./non-OPEC region is — roughly 1.6. Case Five takes the Case Four assumptions of a more elastic OP EC supply behavior and combines those assumptions with the 1.6 net elasticity of import demand for the non-U.S./nonOPEC region. Case Six looks at the consequences of a yet hi gher net elasticity of import demand — 2.28 — for the non-U.S./non-OPEC region. The impact on the oil import premium is relatively modest.

32Economic Assumptions Used in the Agencies’ Analyses

33 Cases Eight and Nine consider a reduced GDP elasticity, the parameter which summarizes the sensitivity of GD P to oil price shocks. Several reviewers suggested a lower estimate for this parameter. In response to their comments, a couple of cases were examined where the GDP elasticity was lowered to 0.032 in comparison to the original ORNL estimate of 0.0495. As anticipated, this change lowered the oil import premium modestly. For example, compared with Case Four where OPEC supply is more elastic, lowering th e GDP elasticity with respect to the world oil price reduced the oil import premium by roughly $0.40/barrel. This is because the GDP-dislocation component is only about one-quarter of the total premium, and there are offsetting changes in other components. The last case examined, Case Nine, looks at the consequences for the oil import premium with a reduced elasticity of GDP if OPEC attempts to maintain its share of the world oil market.

Clearly there is an unavoidable degree of un certainty about the magnitude of marginal economic costs from the U.S. importation of petrol eum, and the size of the oil import premium. ORNL sought to reflect this with probabilistic ri sk analysis over key input factors, guided by the available literature and the best judgment of oil market experts. Cases shown in Table 4-14 explore some reasonable variati ons in the ranges of input assu mptions and the mean oil premium estimates vary in a fairly moderate range betw een roughly $11 and $15/barrel of imported oil. On balance, Case Eight suggested a reasonable and cautious assessment of the premium value to ORNL, and is ORNL’s recommended case. This is based on a review of important driving factors, the numerical evaluati ons and simulations over major uncertainties, and taking into consideration the many comments and suggestio n from the reviewers, the EPA and other Agencies. This recommended case, and the prem ium range resulting from 90 percent of the simulated outcomes, encompasses a wide array of perspectives and potential market outcomes in response to a reduction of U.S. imports.

As mentioned previously, this recommende d case relied on the most recent available projections of the U.S. and world oil market for the next ten years based upon the AEO 2007 Reference Case. OPEC-behavior was treated pa rametrically, with a wi de range of possible responses represented by a wide range of supply elasticities, from small to quite large. This recommended case recognized that the OPEC respons e is the most uncertain single element of this analysis. It could vary be tween inelastic defense of output le vels, or market share, or could be highly elastic in defense of price, probabl y at the expense of l onger run cartel power and discounted net profits. The balance between pos sible elastic and inelas tic OPEC response was essentially even over a fairly wide range of elastic ities. ORNL concluded that this is the best way to estimate OPEC behavior until greater pr ogress can be made in s ynthesizing what insights are available from the evolving strategic game-theoretic and empirical research on OPEC behavior, and advancing that rese arch. An alternative would have been to use OPEC strategic response behavior to maximize long-run net revenue, which may well correspond to marketshare preservation behavior ( e.g. , Case Seven), and a somewhat higher premium value.

Finally, ORNL’s recommended case used a GDP elasticity range, the parameter which summarizes the sensitivity of GDP to oil price shocks, which is reduced compared to earlier estimates, and compared to the full range of histor ically-based estimates. This helped address the concerns of those who either question the conc lusions of past empirical estimates or expect that the impacts of oil sh ocks may well be declining.Draft Joint Technical Support DocumentTable 4-14 Summary Results – Oil ImportPremium Under Various Cases ($2007/BBL)ComponentStatistic1) AEO2007Base Outlook,1997 StudyElasticities2) AEO2007Base Outlook,Wider Range ofOPEC SupplyElasticities3) Case 2 withRevised WiderRange of OPECSupplyElasticities4) Case 2 withUpdated NonOPECSupply/DemandElasticities5) Case 4 plusRevised WiderRange of OPECSupplyElasticities6) Case 4Variant withWider, HigherRange of NonU.S./Non-OPECSupply/DemandElasticities7) Case 6 withApplied StrategicOPEC BehavioralRule: MaintainMarket Share8) Case 4 withReduced GDPElasticity9) Case 7 withReduced GDPElasticityMean $5.57$10.26$8.16$7.77$6.52$6.44$9.36$7.86$9.42Range ($3.60 - $8.19) ($3.10 - $21.22) ($3.12 - $19.30) ($2.94 - $13.75) ($2.90 - $13.06) ($2.69 - $11.33) ($6.69 - $12.42) ($2.94 - $13.89) ($6.72 - $12.54)Mean $2.36$2.33$2.34$1.92$1.94$1.93$1.89$2.20$2.16Range ($0.51 - $4.66) ($0.58 - $4.57) ($0.66 - $4.61) ($0.39 - $3.75) ($0.38 - $3.84) ($0.42 - $3.70) ($0.44 - $3.66) ($0.63 - $4.07) ($0.62 - $3.96)Mean $3.83$3.70$3.76$3.42$3.45$3.47$3.41$2.67$2.64Range ($1.06 - $6.69) ($1.04 - $6.57) ($1.06 - $6.74) ($0.92 - $6.23) ($0.94 - $6.10) ($0.92 - $6.13) ($0.87 - $6.26) ($0.90 - $4.84) ($0.87 - $4.87)Mean $6.19$6.03$6.10$5.34$5.39$5.40$5.30$4.87$4.81Range ($2.94 - $10.01) ($2.94 - $9.75) ($2.91 - $9.85) ($2.53 - $8.69) ($2.60 - $8.62) ($2.58 - $8.92) ($2.54 - $8.74) ($2.23 - $7.85) ($2.25 - $7.77)Mean $11.75 $16.29 $14.27 $13.11 $11.90 $11.86 $14.65 $12.71$14.23Range ($8.04 - $15.96) ($8.10 - $27.42) ($7.90 - $25.49) ($7.26 - $19.59) ($6.96 - $18.62) ($6.79 - $17.23) ($11.03 - $18.61) ($7.07 -$19.02) ($10.76 - $18.25)Total Premium, in $/Gallon Mean $0.28$0.39$0.34$0.31$0.28$0.28$0.35$0.30$0.34Price Reduction ($/MMBD) Mean $0.52$1.04$0.81$0.75$0.61$0.60$0.90$0.75$0.90Cases7) Applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy). This rule implies that OPEC Supply elasticity matches that of all non-OPEC supply. As aresult non-U.S. elasticity of import demand ranges from8) Variant on version Case 4, considered reduced GDP elasticity for future disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032, reduced from mean value of -0.0495). OPECbehavior treated parametrically.3) Revise Case 2, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76)4) Updated Case 2 supply/demand elasticities for non-OPEC region with more recent estimates. Elasticity of non-U.S. demand -0.2 to -0.4, with mean and mode -0.3, non-U.S. Supply = 0.2 to 0.4, withmean and mode 0.3, implying (mode) net elasticity of impo5) Revise Case 4, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76). Net elasticity of importdemand is -1.6 for the non-U.S./non-OPEC r6) Alternative to Case 4 with expanded (and higher) range of non-U.S. supply/demand elasticities. Elasticity of non-U.S. demand = -0.3 to -0.7, triangular distribution with mode -0.4 mean -0.467, elasticity ofnon-U.S. supply = 0.2 to 0.6, mode 0.3 and mEconomic Disruption/AdjustmentCostsTotal Mid-Point1) Based on AEO2007. Updated oil market outlook from AEO1994 Base Case to AEO2007 Base Case. Among other things, this means average crude price rises from $20.33 to $48.34. All elasticitiesmatch 1997 values. Non-U.S. elasticity of import demand = -0.2) AEO2007 Base Outlook, with wider range of OPEC supply elasticities, 0.25 to 6.0 and a mean elasticity of 1.76.Monopsony ComponentDisruption Import CostsDisruption Dislocation Costs9) Revise Case 7 (which applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy)) with reduced GDP elasticity for future disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032, reduc34Economic Assumptions Used in the Agencies’ Analyses

4.2.7.2.4 Energy security premium values for this rule

After the peer review process was comple ted, EPA worked with ORNL to further update the analysis for this vehicle program . Using the same methodology as the peer reviewed model, but updating the analysis using AEO 2009 world oil price values, ORNL calculated energy security pr emium estimates for the years 2015, 2020, and 2030. Since the AEO 2009 ends its forecast in the year 2030, EPA and NHTSA assumed that the post-2030 energy security premium estimates were the same as the 2030 estimate. The results of this analysis are shown above in Table 4-13 in Chapter 4.2.8.

The monopsony cost represents a transfer of resources from consumers of petroleum products to oil producers, and does not entail the us e of real economic resources. Reducing the magnitude of the monopsony cost does not produce a savings in economic resources, although it does reduce the value of resources tr ansferred from U.S. consumers of petroleum products to foreign suppliers of petroleum. In contrast, the macroeconomic disruption and adjustment costs that arise from U.S. petroleu m imports do represent real economic costs, and reductions in their magnitude do represent r eal economic benefits from reducing U.S. consumption and imports of petroleum. Hence the agencies’ estimates of economic benefits from reducing fuel consumption via CAFE and GHG emission standa rds include ORNL’s estimates of the economic value of resulting reductions in macroeconomic and disruption costs, but exclude reductions in the monopsony cost component of the oil import premium.

4.2.7.2.5 The impact of fuel savings on U.S. petroleum imports

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 Scenar ios presented in AEO 2009, NHTSA and EPA estimate that approximately 50 percent of the re duction in fuel consumption resulting from adopting improved fuel efficiency standards a nd GHG standards 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. u 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. v Thus on balance, each gallon of fuel save d as a consequence of improved fuel efficiency standards and GHG standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel

u Differences between forecas t annual U.S. imports of crude petroleum and refine d products among these three scenarios range from 24-89 percent of differences in projected annual gasoline and diesel fuel consumption in the U.S. These differences average 49 percent over the forecast period spanned by AEO 2009. This analysis has not been updated using forecasts from AEO 2010, because to date only the Reference Case from AEO 2010 has been released by EIA.

v Differences between forecas t annual U.S. imports of crude petroleum among these three scenarios range from 67-97 percent of differences in total U.S. refining of crude petroleum, and average 85 percent over the forecast period spanned by AEO 2009.

35Draft Joint Technical Support Document

by 0.95 gallons. w Section 5.3 of the RIA contains a di scussion regarding caveats for the fuel savings estimated due to implementation of this rule.



### 4.2.8 Air pollutant emissions

Car and light truck use, fuel refining, and fu el distribution and reta iling also generate emissions of certain criteria air polluta nts, including carbon monoxide (CO), hydrocarbon compounds (usually referred to as “volatile organic compounds ,” or VOC), nitrogen oxides (NO x ), fine particulate matter (PM 2.5 ), and sulfur dioxide (SO 2 ). Emissions of most of these pollutants are associated with the number of vehicle miles driven, rather than with the quantity of fuel consumed. While reductions in fuel refining and distribu tion that result from lower fuel consumption will reduce emissions of criteria pollutants, additional vehicle use associated with the rebound effect from higher fuel economy will increase emissions of most of these pollutants. Sulfur dioxide is an exception, as described below.

Thus the net effect of stricter fuel effi ciency and GHG standards on total emissions of each criteria pollutant depends on the relative magnitudes of reduced emissions in fuel refining and distribution, and in creases in emissions from vehicle use. Because the relationship between emission rates (emissions per gallon refined of fuel or mile driven) in fuel refining and vehicle use is different for each criteria pollu tant, the net effect of fuel savings from increased fuel efficiency and GHG standards on total emissions of each pollutant is likely to differ.

4.2.8.1 Reductions in emissions of criteria air pollutants

For the analysis of criteria emissions ove r the lifetime of the model years covered by this rule, EPA and NHTSA estimate the increases in emissions of each criteria air pollutant from additional vehicle use by multiplying the increase in total miles driven by cars and light trucks of each model year and age by their esti mated emission rates per vehicle-mile of each pollutant. These emission rates differ between cars and light trucks as well as between gasoline and diesel vehicles, and both their values for new vehicles and the rates at which they increase with age and accumulated mileage can vary among model years. With the exception of SO 2 , the agencies calculated the increase in em issions of these criteria pollutants from added car and light truck use by multiplying the estimated increases in their vehicles’ use during each year over their exp ected lifetimes by per-mile emission rates appropriate to each vehicle type, fuel used, model year, a nd age as of that future year.

These emission rates were estimated by U.S. EPA using its newly updated Motor Vehicle Emission Simulator (MOVES2010). For the proposal, emission rates were estimated using Draft MOVES2009.

The MOVES model assumes that the per-m ile rates at which these pollutants are emitted are determined by EPA regulations and th e effectiveness of catalytic after-treatment

w This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95.

36Economic Assumptions Used in the Agencies’ Analyses

of engine exhaust emissions, and are thus unaff ected by changes in car and light truck fuel economy. As a consequence, the effects of requ ired increases in fuel economy on emissions of these pollutants from car and light truck use are determined entirely by the increases in driving that result from the fuel economy rebound effect. As discussed in the EPA RIA, anticipated changes in the fuel s upply composition will also affect crit eria pollutant emissions.

Emission factors in the MOVES database ar e expressed in the form of grams per vehicle-hour of operation. To convert these emission factors to grams per mile for use in EPA and NHTSA’s calculations, MOVES was run fo r the year 2050, and was programmed to report aggregate emissions from vehicle starti ng and running exhaust operation. EPA analysts selected the year 2050 in order to generate emission factors that we re representative of lifetime average emission rates for vehicles meeting the agency’s Tier 2 emission standard. x Separate estimates were developed for each vehi cle type and model year, as well as for each state and month, in order to reflect the eff ects of regional and temporal variation in temperature and other relevant variables on emissions.

The MOVES emissions estimates were then summed to the model year level and divided by average distance traveled in order to produce per-mile emission factors for each pollutant. The resulting emission rates repr esent average values across the nation, and incorporate typical temperatur e variations over an entire calendar year. These national average rates also reflect count y-specific differences in fuel composition, as well as in the presence and type of vehicle in spection and maintenance programs. y

Emission rates for the criteria pollutant SO 2 were calculated by EPA and NHTSA using average fuel sulfur content estimates supplied by EPA, together with the assumption that the entire sulfur content of fuel is emitted in the form of SO 2 . These calculations assumed that national average gasoline and diesel sulf ur levels would remain at current levels. z There, unlike many other criteria pollutants, sulfur dioxide emissions from vehicle use decrease proportionally to the decrease in fuel consumption.

Emissions of criteria air pollutants also o ccur during each phase of fuel production and distribution, including crude oil extraction and transportation, fuel refining, and fuel storage and transportation. The reduction in emissions during each of these phases depends on the extent to which fuel savings result in lower im ports of refined fuel, or in reduced domestic

x Because all light-duty emission rates in MOVES2010 are assumed to be invariant after MY 2010, a calendar-year 2050 run produced a full set of emission rates that reflect anticipated deterioration in the effectiveness of vehicles’ emission control syst ems with increasing age and accumulated mileage for post-MY 2010 vehicles.

y The national mix of fuel types includes county-level market shares of conventional and reformulated gasoline, as well as county-level variation in sulfur content, ethanol fractions, and other fuel properties. Inspection/maintenance programs at the county level account for detailed program design elements such as test type, inspection frequency, and program coverage by vehicle type and age.

z These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively, which produces emission rates of 0.17 grams of SO 2 per gallon of gasoline and 0.10 grams per gallon of diesel.

37Draft Joint Technical Support Document

fuel refining. To a lesser extent, they also depend on whether reductions in domestic gasoline refining are reflected in reduced imports of cr ude oil or in reduced domestic extraction of petroleum.

Both EPA’s and NHTSA’s analyses assume th at reductions in imports of refined fuel would reduce criteria pollutant emissions during fuel storage and distribution only. Reductions in domestic fuel re fining using imported crude oil as a feedstock are assumed to reduce emissions during fuel refining, storag e, and distribution, b ecause each of these activities would be reduced. Finally, reduced domestic fuel refining using domesticallyproduced crude oil is assumed to reduce emissi ons during all four phases of fuel production and distribution. aa

EPA estimated the reductions in criteria pollutant emissions from producing and distributing fuel that would o ccur with alternative fuel effi ciency and GHG standards using emission rates obtained from Argonne Nati onal Laboratories’ Greenhouse Gases and Regulated Emissions in Transportation (GREET) model. 21 The GREET model provides separate estimates of air polluta nt emissions that occur in fo ur phases of fuel production and distribution: crude oil extracti on, crude oil transportation and st orage, fuel refining, and fuel distribution and storage. bb EPA modified the GREET model to change certain assumptions about emissions during crude petr oleum extraction and transportati on, as well as to update its emission rates to reflect adopted and pe nding EPA emission standards. EPA also incorporated emission factors fo r the air toxics estimated in this analysis: benzene, 1,3- butadiene, acetaldehyde, acrolein, and formal dehyde. The resulting emission factors are shown in Table 4-15

.

aa In effect, this assumes that the di stances crude oil travels to U.S. refineries are approximately the same regardless of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline travels from refineries to retail stations are approximately the same as those from import terminals to gasoline stations.

bb Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of volatile organic compounds, or VOCs) are already accounted for in the “tailpipe” emission factors used to estimate the emissions generated by increased light truck use. GREET estimates emissions in each phase of gasoline production and distribution in mass per unit of gasoline energy content; these factors are then converted to mass per gallon of gasoline using the average energy content of gasoline.

38Economic Assumptions Used in the Agencies’ Analyses

Table 4-15 Emissions by Stage of Fuel Pro duction and Distribution (grams/million Btu)

Pollutant Fuel Type Petroleum Extraction & Transportation 1 Refinery Energy

Use Upstream Emissions Petroleum Refining On-Site Petroleum Refining 2 Fuel Transport,

Storage, Distribution 3

Conventional Gasoline 4.908 0.928 5.596 6.525 0.748 Reformulated Gasoline 4.908 0.908 5.662 6.571 0.768 CO Low Sulfur Diesel 4.908 0.800 5.103 5.903 0.780 Conventional Gasoline 3.035 0.602 2.560 3.162 42.91 Reformulated Gasoline 3.035 0.627 2.584 3.211 42.92 VOC Low Sulfur Diesel 3.035 0.552 2.511 3.063 1.261 Conventional Gasoline 14.91 3.328 14.442 17.771 3.691 Reformulated Gasoline 14.91 3.288 14.575 17.863 3.786 NOx Low Sulfur Diesel 14.91 2.895 12.972 15.866 3.570 Conventional Gasoline 3.926 4.398 9.678 14.076 0.886 Reformulated Gasoline 3.926 4.422 9.922 14.344 0.909 SOx Low Sulfur Diesel 3.926 3.893 9.187 13.080 0.840 Conventional Gasoline 0.645 1.442 1.789 3.231 0.155 Reformulated Gasoline 0.645 1.487 1.838 3.325 0.159 PM2.5 Low Sulfur Diesel 0.645 1.309 1.635 2.943 0.133 Air Toxics Conventional Gasoline 0.0017 0.0003 0.0014 0.0017 0.0001 Reformulated Gasoline 0.0017 0.0003 0.0014 0.0018 0.0001 1,3-Butadiene
Low Sulfur Diesel 0.0017 0.0003 0.0014 0.0017 0.0001 Conventional Gasoline 0.0002 0.0000 0.0002 0.0002 0.0046 Reformulated Gasoline 0.0002 0.0000 0.0002 0.0002 0.0047 Acetaldehyde Low Sulfur Diesel 0.0002 0.0000 0.0002 0.0002 0.0044 Conventional Gasoline 0.0001 0.0000 0.0001 0.0001 0.0006 Reformulated Gasoline 0.0001 0.0000 0.0001 0.0001 0.0006 Acrolein Low Sulfur Diesel 0.0001 0.0000 0.0001 0.0001 0.0006 Conventional Gasoline 0.0313 0.0062 0.0264 0.0326 0.0787 Reformulated Gasoline 0.0313 0.0064 0.0264 0.0328 0.0788 Benzene Low Sulfur Diesel 0.0313 0.0058 0.0264 0.0322 0.0015 Conventional Gasoline 0.0050 0.0010 0.0042 0.0052 0.0326 Reformulated Gasoline 0.0050 0.0010 0.0042 0.0052 0.0335 Formaldehyde
Low Sulfur Diesel 0.0050 0.0009 0.0042 0.0051 0.0316

1 The petroleum extraction and transport emission fact ors are only applied to domestic crude oil.
2 Refinery emissions factors are applied to domestic refineries, whether refining domestic or imported crude.
3 Fuel transport, storage, and distribution emission f actors represent domestic emissi ons and are applied to all finished fuel, whether refined domestically or internationally.
The agency converted these emission rates from the mass per fuel energy content basis on which GREET reports them to mass per gallon of fuel supplied using the estimates of fuel energy content reported by GREET. The resultin g emission rates were applied to both EPA’s and NHTSA’s estimates of fuel consumption unde r alternative fuel effi ciency standards to develop estimates of total emissions of each criteria polluta nt during fuel production and distribution. The assumptions about the effects of changes in fuel consumption on domestic

39Draft Joint Technical Support Document

and imported sources of fuel supply discussed above were then employed to calculate the effects of reductions in fuel use from alte rnative fuel efficiency and GHG standards on changes in domestic emissions of each criteria pollutant.

Finally, EPA and NHTSA calculated the net changes in domestic emissions of each criteria pollutant by summing the increases in its emissions projected to result from increased vehicle use, and the reductions in emissions anticipated to result from lower domestic fuel refining and distribution. cc As indicated previously, the effect of adopting improved fuel efficiency and GHG standards on total emissions of each criteria pollutant depends on the relative magnitudes of the resul ting reduction in emissions from fuel refining and distribution, and the increase in emissions from additional vehicle use. Although these net changes vary significantly among individual criteria pollutant s, we project that on balance, adopting improved fuel efficiency and GHG standards wo uld reduce emissions of all criteria air pollutants except carbon monoxide (CO). The an alysis of criteria emissions during specific calendar years is described independently in NHTSA’s Environmental Impact Statement and EPA’s RIA.

4.2.8.2 Estimated values of reducing PM related emissions in the model year analysis

In this analysis, EPA and NHTSA estimate the economic value of the human health benefits associated with reducing exposure to PM 2.5 using benefit-per-ton estimates for the analysis of PM-related benefits over the lifetime of specific mode l years. Due to analytical limitations, the estimated benefit-per-ton values do not include comparable benefits related to reductions in other criteria pollutants (such as ozone, NO 2 or SO 2 ) or toxic air pollutants, nor do they monetize all of the potential health and welfare effects associated with PM 2.5 or the other criteria pollutants. As a result these va lues underestimate the benefits associated with reductions of these pollutants. Note that EP A conducted full-scale photochemical air quality modeling for the calendar year analysis. Pl ease refer to Chapter 7.2 of the RIA for a description of EPA’s air quality modeling results and to Chapte r 7.3 for a description of the quantified and monetized PM- and ozone-r elated health impacts of the FRM.

The analysis of PM-related benefits over the lifetime of specific model years uses a “benefit-per-ton” method to estimate selected PM 2.5 -related health benefits. These PM 2.5 - related benefit-per-ton estimates provide the to tal monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of directly emitted PM 2.5 , or one ton of a pollutant that contributes to s econdarily-formed PM 2.5 (such as NOx, SOx, and VOCs) from a specified source. Id eally, the human health benefits would be estimated based on changes in ambient PM 2.5 concentrations and popul ation exposure, as determined by full-scale air quality and exposure modeling.

The dollar-per-ton estimate s for reductions in PM 2.5 emissions used in this analysis are provided in Table 4-16

.

cc All emissions from increased vehicle use are assumed to occur within the U.S., since fuel efficiency standards would apply only to vehicles produced for sale in the U.S.

40Economic Assumptions Used in the Agencies’ Analyses
Table 4-16 Benefits-per-ton Values (2007$) Derived Using the ACS Cohort Study for PM-related Premature Mo rtality (Pope et al., 2002) a and a 3% Discount Rate b

All Sources d Stationary (Non-EGU)
Sources e Mobile Sources Year c

SO x V OC NOx Direct PM2.5 NO x 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 presented in this table ar e based on an estimate of premature mortality derived from the ACS study (Pope et al., 2002). If the benef it-per-ton estimates were based on the Six Cities study (Laden et al., 2006), the values would be approxim ately 145% (nearly two-and- a-half times) larger.
b The benefit-per-ton estimates presented in this table assume a 3% discount rate in the valuation of premature mortality to account for a twenty-year se gmented cessation lag. If a 7% discount rate had been used, the values would be approximately 9% lower.

c Benefit-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA and NHTSA extrapolated exponentially based on the growth between 2020 and 2030.
d Note that the benefit-per-ton value for SOx is based on the value for Stationary (Non-EGU) sources; no SOx value was estimated for mobile sources. The benefit-pe r-ton value for VOCs was estimated across all sources.

e Non-EGU denotes stationary sources of emissions other than electric generating units.

As Table 4-16 indicates, EPA projects that the per-ton values for reducing emissions of criteria pollutants from both ve hicle use and stationary sources such as fuel refineries and storage facilities will increase over time. These projected increases reflect rising income levels, which are assumed to increase affected individuals’ willingne ss to pay for reduced exposure to health threats from air pollution, as well as population growth into the future, expanding the population exposed to air po llution in both urban and rural areas. 22

For certain PM 2.5 -related pollutants (such as direct PM 2.5 and NOx), EPA estimates different per-ton values for reducing mobile source emissions than for reductions in emissions of the same pollutant from stationary sources such as fuel refineries and storage facilities. These reflect differences in the ty pical geographic distribut ions of emissions of each pollutant, their contributi ons to ambient levels of PM 2.5 , and resulting changes in population exposure. EPA and NHTSA apply thes e separate values to its estimates of changes in emissions from vehi cle use and from fuel production and distribution to determine the net change in total economic damages from emissions of those pollutants.

The benefit per-ton technique has been used in previous analyses, including EPA’s recent Ozone National Ambient Air Quality Standards (NAAQS) RIA (U.S. EPA, 2008a), 23
Portland Cement National Emissions Standard s for Hazardous Air Pollutants (NESHAP) RIA (U.S. EPA, 2009a), 24 and NO 2 NAAQS (U.S. EPA, 2009b). 25 Table 4-17 shows the quantified and unquantified PM 2.5 -related co-benefits capture d in these be nefit-per-ton estimates.

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Table 4-17 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 a ttacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Visibility Household soiling

Consistent with the NO 2 NAAQS, dd the benefits estimates utilize concentrationresponse functions as reported in the epidemiol ogy literature. Readers in terested in reviewing the complete methodology for creating the benefit-per-ton estimates used in this analysis can consult the Technical Support Document (TSD) 26 accompanying the recent final ozone NAAQS RIA (U.S. EPA, 2008a). Reader s can also refer to Fann et al. (2009) 27 for a detailed description of the benefit-per-ton methodology. ee

As described above, national per-ton es timates were developed for selected pollutant/source category combinations. The pe r-ton values calculated th erefore 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 total PM 2.5 benefits is therefore ba sed on the total direct PM 2.5 and PM 2.5 –related precursor emissions (NOx, SOx, and VOCs) controlled from each s ource and multiplied by the respective per-ton values of reducing emissions from that source.

The benefit-per-ton coefficients in this anal ysis were derived using modified versions of the health impact functi ons used in the PM NAAQS Re gulatory Impact Analysis.

dd Although we summarize the main issues in this chapte r, we encourage interested readers to see benefits chapter of the NO2 NAAQS for a more detailed description of recent changes to the PM benefits presentation and preference for the no-threshold model.

ee The values included in this report are different from those presented in the article cited above. Benefits methods change to reflect new information and evaluation of the science. Since publication of the June 2009 article, EPA has made two significant changes to its benefits methods: (1) We no longer assume that a threshold exists in PM-related models of health impacts, which is consistent with the findings reported in published research; and (2) We have revised the Value of a Statistical Life to equal $6.3 million (year 2000$), up from an estimate of $5.5 million (year 2000$) used in the June 2009 report. Please refer to the following website for updates to the dollar-per-ton estimates: http://www.epa.gov/air/benmap/bpt.html

42Economic Assumptions Used in the Agencies’ Analyses

Specifically, this analysis uses the benefit-pe r-ton estimates first applied in the Portland Cement NESHAP RIA (U.S. EP A, 2009a), which incorporat ed concentration-response functions directly from the epidemiology studi es, without any adjustment for an assumed threshold. Removing the threshold assumption is a key difference between the method used in this analysis to estimate PM co-benefits a nd the methods used in analyses prior to EPA’s Portland Cement NESHAP. As a consequence, the benefit-per-ton estimates used in this analysis include incremental benefits of reductions in PM 2.5 concentrations down to their lowest modeled levels.

Reductions in PM-related mort ality provide the majority of the monetized value in each benefit-per-ton estimate. Typically, the pr emature mortality-relate d effect coefficients that underlie the benefits-per-t on estimates are drawn from epid emiology studies that examine two large population cohorts: the American Cancer Soci ety cohort (Pope et al., 2002) 28 and the Harvard Six Cities cohort (Laden et al., 2006). 29 The concentration-response (C-R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), has previously been used by EPA to generate its primary benefits estimate. The extended analysis of the Harvard Six Cities cohort, as reported by Laden et al (2006), was published after the completion of the Staff Paper for the 2006 PM 2.5 NAAQS and has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 cobenefits estimates in analyses completed since the PM 2.5 NAAQS.

These studies provide logical choices for anchor points when presenting PM-related benefits because, while both st udies are well designed and pee r-reviewed, there are strengths and weaknesses inherent in ea ch. Although this argues for us ing both studies to generate benefits estimates, due to the analytical lim itations associated with this analysis, EPA and NHTSA have chosen to use the benefit-per- ton value derived from the ACS study. The agencies note that benefits would be approxi mately 145 percent (or nearly two-and-a-half times) larger if the per-ton benefit values based on the Harvard Six Cities were used instead.

As is the nature of benefits analyses, assumptions and methods evolve over time to reflect the most current interpretation of the sc ientific 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 sta tistical life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrive d at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-a nalyses of the wage-risk literature.
The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) 30 meta-analysis of 33 studies. The $10 million value represented the upper end of the inte rquartile range from the Viscusi and Aldy (2003) 31 meta-analysis of 43 studies. The mean estimate of $5.5 million (2 000$) was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) 32 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 pe er-review group.

Until updated guidance is available, EPA de termined that a single, peer-reviewed estimate applied consistently best reflects th e SAB-EEAC advice it has received. Therefore,

43Draft Joint Technical Support Document

EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 33 while they continue efforts to update their guidance on this issue. ff This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation st udies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). The dollar-per-ton estimates used in this analysis are based on this revised VSL.

The benefit-per-ton estimates are subject to a number of assumptions and uncertainties.

a. They do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or othe r local factors that might lead to an overestimate or underestimate of the actual bene fits of controlling fine particulates in specific locations. EPA conducted full-scale air quality modeling fo r the calendar year analysis in an effort to capture this variab ility. Please refer to Chapter 7.2 of the RIA for a description of EPA’s air quality modeling results and to Chapter 7.3 for the description of the agency’s quantification and monetizati on of PM- and ozone-related health impacts for the FRM. b. This analysis assumes that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mort ality. This is an 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 industrial sources. At the present time, however, no clear scientific grounds exist for supporting differential effects estimates by particle type.

c. This analysis assumes that the health impact function for fine partic les is linear within the range of ambient concentrations under c onsideration. Thus, the estimates include health benefits from reducing fine particles in areas with varied initial 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. d. There are several health be nefits categories that EPA and NHTSA 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 rule 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, be nefits-per-ton estimates for ozone do not exist due to issues associated with the complexity of the atmospheric air chemistry and nonlinearities associated w ith ozone formation. The PM-related benefits-per-ton estimates also do not include any human welfar e or ecological benefits . Please refer to Chapter 7.3 of the RIA for a description of the quantification and monetization of both

ff

In the (draft) update of the Economic Guidelines (U .S. EPA, 2008c), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this report does not represent final agency policy. The draft update of the Economic Guidelines is available on the Internet at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE0516-01.pdf/$File/EE-0516-01.pdf>.

44Economic Assumptions Used in the Agencies’ Analyses

PM- and ozone-related health impacts fo r the FRM and a description of the unquantified co-pollutant benefits associated with this rulemaking.

As mentioned above, emissions changes and benefits-per-ton estimat es alone are not a good indication of local or region al air quality and health impact s, as the localized impacts associated with the rulemaking may vary si gnificantly. 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 accurate ly estimate the changes in ambient levels of these pollutants and their associated health and welfare impacts. For the final rule, EPA conducted a national-scale air quality modeling analysis in 203 0 to analyze the impacts of the standards on PM 2.5 , ozone, and selected air toxics.



### 4.2.9 Reductions in emission s of greenhouse gases

Emissions of carbon dioxide and other gr eenhouse gases (GHGs) occur throughout the process of producing and distributing transporta tion fuels, as well as from fuel combustion itself. By reducing the volume of fuel c onsumed by passenger cars and light trucks, higher fuel efficiency standards will thus reduce GHG emissions generated by fuel use, as well as throughout the fuel supply cycle. Lowering thes e emissions is likely to slow the projected pace and reduce the ultimate extent of future changes in the global climate, thus reducing future economic damages that changes in the glob al climate are otherwis e expected to cause. Further, by reducing the probability that clim ate changes with potentially catastrophic economic or environmental impacts will occur, lowering GHG emissions may also result in economic benefits that exceed the resulting redu ction in the expected future economic costs caused by gradual changes in the earth’s clim atic systems. Quantifying and monetizing benefits from reducing GHG emi ssions is thus an important step in estimating the total economic benefits likely to result from es tablishing improved fuel efficiency and GHG standards.

4.2.9.1 Estimating reductions in GHG emissions

The agencies estimated emissions of carbon dioxide (CO 2 ) from passenger car and light truck use by multiplying the number of gallons of each type of fuel (gasoline and diesel) they are projected to consume w ith each alternative CAFE standa rd in effect by the quantity or mass of CO 2 emissions released per gallon of fuel consumed. This calculation assumes that the entire carbon content of each fuel is ultimately converted to CO 2 emissions during the combustion process. The weighted average CO 2 content of gasoline is estimated to be approximately 8,900 grams per gallon, while that of diesel fuel is estimated to be approximately 10,200 grams per gallon. For de tails, please see EPA’s RIA and NHTSA’s RIA.

Although carbon dioxide emissions account for nearly 95 percent of total GHG emissions that result from fuel combustion during vehicle use, emissions of other GHGs are potentially significant as well because of their higher “poten cy” as GHGs than that of CO 2 itself. EPA and NHTSA estimated the in creases in emissions of methane (CH 4 ) and nitrous oxide (N 2 O) from additional vehicle use by multiplying the increase in total miles driven by

45Draft Joint Technical Support Document

cars and light trucks of each model year and ag e by emission rates per vehicle-mile for these GHGs. These emission rates, which differ between cars and light trucks as well as between gasoline and diesel vehicles, were estima ted by EPA using its recently-developed Motor Vehicle Emission Simulator (Dra ft MOVES 2009), as subsequen tly updated for use in this analysis.

The MOVES model assumes that the per-mile rates at which cars and light trucks emit these GHGs are determined by the efficiency of fuel combustion during engine operation and chemical reactions that occur during catalytic af ter-treatment of engine exhaust, and are thus independent of vehicles’ fuel consumption ra tes. Thus MOVES emission factors for these GHGs, which are expressed per mile of vehicl e travel, are assumed to be unaffected by changes in fuel economy.

Increases in emissions of non-CO 2 GHGs are converted to equivalent increases in CO 2 emissions using estimates of the Global Warm ing Potential (GWP) of methane and nitrous oxide. These GWPs account for the higher radiat ive forcing capacity of methane and nitrous oxide when they are released into the earth’s atmosphere, measured relative to that of CO 2 itself. The CO 2 equivalents of increases in emissions of these gases are then added to the increases in emissions of CO 2 itself to determine the effect of the total increase in CO 2 - equivalent GHG emissions from vehicle use.

4.2.9.2 Economic benefits from reducing GHG emissions

INSERT EXECUTIVE SUMMARY OR OTHER SUMMARY OF FINAL INTERAGENCY SCC GROUP FINAL GUIDANCE DOCUMENT?



### 4.2.10 The value of increa sed driving range

Improving the fuel economy of passenger cars and light-duty trucks may also increase their driving range before they require refu eling. By reducing the frequency with which drivers typically refuel their vehicles and extending the uppe r limit of the range they can travel before requiring refueling, improving fuel economy thus provides some additional benefits to their owners. Alternatively, if manufacturers respond to improved fuel economy by reducing the size of fuel tank s to maintain a constant driv ing range, the resulting cost saving will presumably be reflected in lower vehicle sales prices. If manufacturers respond by doing so, this presumably reflects their judgm ent that the value to economic benefits to vehicle buyers from lower purchase prices exceed s that from extended refueling range.

No direct estimates of the value of extended vehicle range are readily available, so the agencies’ analyses calculate 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. 34 As a coarse illustration of how the value of extended refueling range is estimated, a typical small light

46Economic Assumptions Used in the Agencies’ Analyses

truck model has an average fuel tank size of approximately 20 gallons. gg Based on a California Air Resources Board Study, the averag e fuel purchase is approximately 55% of tank volume. 35 Therefore, increasing this model’s act ual on-road fuel economy from 24 to 25 mpg would extend its driving range from 216 mile s (= 9 gallons x 24 mpg) to 225 miles (= 9 gallons x 25 mpg). Assuming that this vehicl e is driven 12,000 miles/year, this reduces the number of times it needs to be refueled each year from 55.5 (= 12,000 miles per year / 216 miles per refueling) to 53.3 (= 12,000 miles pe r year / 225 miles per refueling), or by 2.2 refuelings per year.

Weighted by the nationwide mix of urban (a bout 2/3) and rural (a bout 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). hh Assuming that locating a station and filling up requires five minutes, the annual va lue of time saved as a result of less frequent refueling amounts to $4.40 (calculated as 5/60 x 2.2 x $24.00). This calc ulation is repeated for each future calendar year that light trucks of each model year affected by the alternative fuel economy standards considered in this rule would remain in service. Like fuel savings and other benefits, however, the value of this benefit declines over a model year’s lifetime, because a smaller number of vehicles originally produced during that model year remain in service each year, and those remaining in service are driven fewer miles. The detailed values used in this calculation by each agency are available in EPA’s RIA and NHTSA’s FRIA respectively.

The agencies’ estimate of benefits from le ss frequent refueling is subject to several sources of uncertainty.

First, this analysis assumes that manuf acturers will not adjust fuel tank capacities downward (from the current average of 19.3 gallons ) when they improve the fuel economy of their vehicle models, so that the entire increase in fuel economy will be reflected in increased driving range. Should manufacturer s choose to downsize fuel tanks, and all other factors have been estimated with no error, th e current estimates of refueling benefits would be overstated. On the other hand, vehicle space, utility and value could increase and vehicle weight will decrease, improving fuel economy and CO 2 emissions (all other things being equal). In the context of the rule, this will decrease th e cost of compliance with the standards

. Second, the agencies’ analysis assumes that fuel purchases average 55 percent of fuel tank capacity. However, as shown in the Ca lifornia Air Resource Board (CARB) report,

gg Based on the Volpe Model Market Data file for Mode l Year 2011, average tank volumes for cars and trucks are 16.6 gallons and 23.0 gallons, respectively. This produces a production weighted average of 19.3 gallons.

hh The hourly wage rate during 2006 is estimated to be $24.00. Personal travel (94.4 percent of urban travel) is valued at 50 percent of the hourly wage rate. Business travel (5.6 percent of urban travel) is valued at 100 percent of the hourly wage rate. For intercity travel, pers onal travel (87 percent) is valued at 70 percent of the wage rate, while business travel (13 percent) is valued at 100 percent of the wage rate. The resulting values of travel time are $12.67 for urban trav el and $17.66 for intercity travel, and must be multiplied by vehicle occupancy (1.6) to obtain the estimate value of time per vehicle hour.

47Draft Joint Technical Support Document

refueling patterns vary. Mo reover, the 55 percent estimate implies that drivers, on average , are either refueling when nearly a half tank of gas remains in their vehicles, or that they are habitually not filling their tanks. Since many drivers only refuel when their tanks are very low, and since many drivers habitually refuel, this in turn implies that many drivers in the CARB study are refueling when their tanks are still well above 50 percent full. While based on field data, this estimate may thus overestimate the impact of refueling benefits.

For the primary analysis in both the propos al and this final rulemaking, the agencies assume that 100 percent of all refueling is demand-based; i.e. , that every gallon of fuel which is saved would reduce the need to return to the refueling station. Based on anecdotal evidence, this value is potentially an overestim ate. As an example, some people may refuel every Sunday morning at the same time as they buy their newspaper and chewing gum. Barring unusually long trips, these people would not benefit from an in creased driving range.

In order to understand how sensitive the overa ll estimate of the refueling benefit is to this specific input, we provide the following example. If 25 pe rcent of gallons are refueled on a habitual rather than demand-based schedule, and the rest of the assumptions remain constant, the value of increased driving range decreases by 25 percent. There would be no value of increased driving range derived from these gallons. Returning to the example light truck discussed above, which generated a value of increased driving ra nge worth $4.40 in the first year, if this owner refille d 1⁄4 of his annual fuel on a hab itual rather than demand based schedule, the value of increas ed driving range would decreas e to $3.30. Unfortunately, the agencies do not have a basis for this 25 percent value, thus this example is for illustrative purposes only. However, as noted below, DOT is undertaking a new survey which may provide a data-based basis for revising our 100 percent assumption for future analysis.

Third, the agencies’ estimate of refueling benefits assumes that refueling stops involve the same number of vehicle occupants as the overa ll average for all vehicle trips (1.6 persons). To the extent that drivers refuel while doi ng other errands or in advance of picking up passengers, this figure may overestimate the t ypical vehicle occupanc y during refueling, and thus the total savings in refueling time. Si milarly, the hourly value used to estimate the economic value of savings in refueling time reflects the typical mix of personal and business travel purposes, and drivers are likely to assign different values to their time when traveling for these different purposes. To the extent th at drivers seek to refuel when traveling for purposes that typically use less valuable time, th e hourly value used in the agencies’ analysis may overstate the benefits from saving refueling time.

Finally, the agencies assume that both finding and using a refueling station takes, on average, five minutes. There are few, if any, da ta sources on average refueling time, and this estimate is subject to significant uncertainty.

For these reasons, the agencies’ estimate of savings in refueling time is uncertain. To reduce the uncertainty, a new proj ect is being planned by DOT wh ich will include a detailed study of refueling events, using a random sample of refueling stations ac ross the U.S. It is projected to include ~7,000 observations (time to refuel, # gallons refueled, etc.) and ~5,000 surveys of refueling participants. The agencies anticipate that this will provide a robust data

48Economic Assumptions Used in the Agencies’ Analyses

set on which to revise many of the key inputs to the refueling benefit calc ulation. Some of the specific data categories which will be surveyed are listed below:

• Miles driven and time out of way to get to gas station

• Fuel gage level before and after refueling

• # gallons purchased

• # people in vehicle, above and below 16 years old

• Reasons for travel

• Reason for stopping at gas station ( e.g. , fuel level too low, or other)

While the study results are not available in time for this final rulemaking, it is anticipated that the data will improve future estimations of the value of increased driving
range.



### 4.2.11 Discounting future be nefits and costs

Discounting future fuel savings and other benefits is intended to account for the reduction in their value to society when they ar e deferred until some future date, rather than received immediately. The discount rate expres ses the percent decline in the value of these benefits – as viewed from current perspective – for each year they are de ferred into the future. In evaluating the benefits from alternative in creases in fuel economy and GHG standards for MY 2012-2016 passenger cars and light trucks , EPA and NHTSA have employed a discount rate of 3 percent per year.

The primary reason that the agencies have se lected 3 percent as the appropriate rate for discounting future benefits from increased fuel economy and GHG standards is that most or all of vehicle manufacturers’ costs for complying with improved fuel economy and GHG standards are likely to be reflected in higher sa les prices for their new vehicle models. By increasing sales prices for new cars and light trucks, CAFE regulation will thus primarily affect vehicle purchases and other private consumption decisions.

Both economic theory and OMB guidance on discounting indicate that the future benefits and costs of regulations that mainly affect private consumption should be discounted at the consumption rate of time preference. 36 OMB guidance indicates that savers appear to discount future consumption at an average real (that is, adjust ed to remove the effect of inflation) rate of about 3 percent when they f ace little risk about its likely level, which makes it a reasonable estimate of the cons umption rate of time preference. 37 Thus EPA and NHTSA have employed the 3 percent rate to discount pr ojected future benefits and costs resulting from improved fuel economy and GHG standa rds for MY 2012-2016 passenger cars and light trucks.

Because there is some uncertainty about th e extent to which vehicle manufacturers will be able to recover their costs for co mplying with improved fuel economy and GHG standards by increasing vehicle sales prices, however, the agen cies have also tested the sensitivity of these benefit and cost estimates to the use of a higher percent discount rate. OMB guidance indicates that the real economy- wide opportunity cost of capital is the

49Draft Joint Technical Support Document

appropriate discount rate to appl y to future benefits and costs when the primary effect of a regulation is “...to displa ce or alter the use of capital in the private sector,” and estimates that this rate currently av erages about 7 percent. 38 Thus the agencies have also tested the sensitivity of its benefit a nd cost estimates for alternative MY 2012-2016 fuel economy and GHG standards to the use of a 7 percent real discount rate.

One important exception to these values are the rates used to di scount benefits from reducing CO 2 emissions from the years in which reduced emissions occur, which span the lifetimes of model year 2012-16 car s and light trucks, to their pr esent values. In order to ensure consistency in the derivation and use of the interagency group’s estimates of the unit values of reducing CO 2 emissions, the total benefits fr om reducing those emissions during each future year are discounted using the same ra tes that were used to derive the alternative values of reducing each ton of CO 2 emissions. As described previously in Section 4.2.10.2, these rates are 2.5, 3.0, and 5.0 percent.

50Economic Assumptions Used in the Agencies’ Analyses

51 Reference

1 EPA, Fuel Economy Labeling of Motor Vehicles: Revisions To Improve Calculation of Fuel Economy Estimates; Final Rule, 40 CFR Part s 86 and 600, Federal Register, December 27, 2006, pp. 77872-77969, http://www.epa.gov/fedrgstr/EPA-AIR /2006/December/Day-27/a9749.pdf

.
2 EPA, Final Technical Support Document: Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates , Office of Transportation and Air Quality EPA420-R-06-017 December 2006, Chapter II, http://www.epa.gov/fueleconomy/420r06017.pdf

.

3 Energy Information Administration, Annual Energy Outlook 2010, Early Release Reference Case (December 2009), Table 12. Available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/excel/aeostimtab_12.xls

4 Energy Information Administration, Annual Energy Outlook 2009, Revised Updated Reference Case (April 2009), Table 12. Available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/excel/aeostimtab_12.xls (last accessed July 26, 2009). EIA’s Updated Reference Case reflects the effects of the American Reinvestment and Recovery Act of 2009, as well as the most recent revi sions to the U.S. and global economic outlook.

5 http://www.eia.doe.gov /oiaf/aeo/forecast.html (last accessed July 26, 2009), and Table 16, http://www.eia.doe.g ov/oiaf/aeo/pdf/table16.pdf (l ast accessed July 26, 2009).

6 Lu, S., NHTSA, Regu latory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/pdf/nrd30/NCSA/Rpts/2006/809952.pdf (last accessed Feb. 15, 2010). These updated survival rates suggest that the expected lifetimes of recent-model passenger cars and light trucks are 13.8 and 14.5 years.

7 FHWA, Highway Statistics, Summary to 1995, Table vm201at http://www.fhwa.dot.gov/ohim/summary95/vm201a.xlw

, and annual editions 1996-2005, Table VM-1 at http://www.fhwa.dot.gov/pol icy/ohpi/hss/hsspubs.htm (last accessed Feb. 15, 2010).

8 Sorrell, S. and J. Dimitropoulos, 2007. “UKERC Review of Evidence for the Rebound Effect, Technical Report 2: Econometric Studies”, UKERC/WP/TPA/2007/010, UK Energy Research Centre, London, October and Greening, L.A., D.L. Greene and C. Difiglio, 2000. “Ene rgy Efficiency and Consumption – The Rebound Effect – A Survey”, Energy Policy, vol. 28, pp. 389-401.

9 Greening et al. 2000.

10 Pickrell, D. and P. Schimek, 1999. “Growth in Motor Vehicle Ownership and Use: Evidence from the Nationwide Personal Transportation Survey”, Journal of Tr ansportation and Statistics, vol. 2, no. 1, pp. 1-17.

11 Small, K. and K. Van Dender, 2007a. “Fuel Efficien cy and Motor Vehicle Trav el: The Declining Rebound Effect”, The Energy Journal, vol. 28, no. 1, pp. 25-51.

12 Small, K. and K. Van Dender, 2007b. "Long Run Trends in Transport Demand, Fuel Price Elasticities and Implications of the Oil Outlook for Transport Policy," OECD/ITF Joint Transport Re search Centre Discussion Papers 2007/16, OECD, International Transport Forum.

13 Greene paper accepted by Energy Journal. [EPA will update with publication citation]

14 FHWA. 1997 Federal Highway Cost Allocation Study ; http://www.fhwa.dot.gov/po licy/hcas/final/index.htm (last accessed July 29, 2009).

15 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 Feb. 15, 2010).

16 U.S. Bureau of Economic Analysis, U.S. Internationa l Transactions Accounts Data, as shown on June 24, 2009.

17 U.S. Department of Energy, Annual Energy Review 2008, Report No. DOE/EIA-0384(2008), Tables 5.1 and 5.13c, June 26, 2009.Draft Joint Technical Support Document

52 18 Leiby, Paul N. "Estimating the Energy Security Bene fits of Reduced U.S. Oil Imports," Oak Ridge National Laboratory, ORNL/TM-2007/028, Final Report, 2008.

19 Peer Review Report Summary: Estimating the Energy Security Benefits of Re duced U.S. Oil Imports , ICF, Inc., September 2007.

20 Gately, Dermot 2004. "OPEC's Incentives for Faster Output Growth," The Energy Journal, 25(2):75-96, "What Oil Export Levels Should We Expect From OPEC?" The Energy Journal, 28(2):151-173, 2007

21 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Transportation (GREET) Model , Version 1.8, June 2007, available at http://www.transportation.anl.g ov/software/GREET/index.html (last accessed April 20, 2008).

22 The issue is discussed in more detail in the PM NAAQS RIA from 2006. See U.S. Environmental Protection Agency. 2006. Final Regulatory Impact Analysis (RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office of Air and Radiation. October 2006. Available at http://www.epa.gov/ttn /ecas/ria.html.

23 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. Av ailable at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6- ozoneriachapter6.pdf>.

24 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portla nd Cement Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the Internet at <http://www.epa.gov/ttn /ecas/regdata/RIAs/portland cementria_4-20-09.pdf>.

25 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 Park, NC. April. Available on the Internet at http://www.epa.gov/ttn /ecas/regdata/RIAs/proposedno2ria.pdf.

26 U.S. Environmental Protection Agency (U.S. EPA). 2008b. Technical Support Document: Calculating Benefit Per-Ton estimates, Ozone NAAQS Dock et #EPA-HQ-OAR-2007-0225-0284. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Av ailable on the Internet at <http://www.regulations.gov>.

27 Fann, N. et al. (2009). The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual Atmos Health. Published online: 09 June, 2009.

28 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. It o, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Asso ciation 287:1132-1141.

29 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine 173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regu lations. Washington, DC: The National Academies Press.

30 Mrozek, J.R., and L.O. Taylor. 2002. “What Determin es the Value of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270.

31 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.

32 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Anal ysis. Environmental and Resource Economics. 34: 385- 406.

33 U.S. Environmental Protection Agency (U.S. EPA). 200 0. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. National Center for Environmental Economics, Office of Policy Economics andEconomic Assumptions Used in the Agencies’ Analyses

53 Innovation. Washington, DC. September. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eed.nsf/web pages/Guidelines.html/$file/cover.pdf>.

34 See http://ostpxweb.dot.gov/policy/Data/VOT97guid.pdf and http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf

35 California Environmental Protection Agency, Air Resources Board. Draft Assessment of the Real-World Impacts of Commingling California Phase 3 Reformulated Gasoline. August 2003

36 Id.

37 Office of Management and Budget, Circular A-4, “Regulatory Analysis,” September 17, 2003, 33. Available

at

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf (last accessed July 24, 2009).

38 Id.