U.S. Department Of Transportation



### PRELIMINARY REGULATORY IMPACT ANALYSIS



# CORPORATE AVERAGE FUEL
ECONOMY FOR
MY 2011-2015
PASSENGER CARS and
LIGHT TRUCKS

Office of Regulatory Analysis and Evaluation

National Center for Statistics and Analysis April 2008TABLE OF CONTENTS

Executive Summary.........................................................................................i

I. Introduction........................................................................................ I-1

II. Need of the Nation to Conserve Energy ............................................II-1

III. Regulatory Alternatives ....................................................................III-1

IV. Impact of Other Motor Vehicle Standards on Fuel Economy ..........IV-1 Vehicle Size and Safety ....................................................................IV-7 The Impact of Emission Standards ..................................................IV-18

V. Fuel Economy Enhancing Technolog ies and the Volpe Model........ V-1 The Volpe Model ............................................................................. V-52 Penetration of Technologies by Alternative ................................... V-127

VI. Manufacturer Specific CAFE Capabilities .......................................VI-1

VII. Cost Impacts..................................................................................... VII-1 The Impact of Higher Prices on Sales ........................ .........VII-41 Potential Impact on Employment .......................................VII-53

VIII. Benefits .......................................................................................... VIII-1 Discount Rate.................................................................................. VIII-1 Sales Projections ............................................................................. VIII-3 The “Rebound Effect”..................................................................... VIII-5 On-Road Fuel Economy Adjustment....................................VIII-8 Benefits from Fuel Savings............................................................. VIII-9 Other Economic Benefits from Reducing Petroleum Use ............. VIII-21TABLE OF CONTENTS cont.

Emissions Reductions Resulting from Fuel Savings ..................... VIII-25 Summary of Benefits .................................................................... VIII-36

IX. Net Benefits and Sensitivity Analysis...............................................IX-1 Payback Period.................................................................................IX-14

X. Probabilistic Uncertainty Analysis ................................................... X-1

XI. Regulatory Flexibility Act and Unf unded Mandates Reform Act ...XI-1

Appendix A: Required MPG Levels............................................................ A-1i

EXECUTIVE SUMMARY

This assessment examines the costs and benef its of improving the fuel economy of passenger cars and light trucks for model years (MY) 2011- 2015. It includes a discussion of the technologies that can improve fuel economy, analys is of the potential im pact on retail prices, safety, lifetime fuel savings and their value to co nsumers, and other societal benefits such as improved energy security and reduced emi ssions of pollutants and greenhouse gases 1

.
In the previous rulemaking, the agency reform ed the corporate average fuel economy (CAFE) standards for light trucks with a si ze-based standard based on footprint 2 . This rulemaking continues this approach for both passenger cars and light trucks. A continuous mathematical function provides a separate fuel economy target for each footprint. Different parameters for the continuous mathematical function are derived fo r each model year. Individual manufacturers will be required to comply with a single fuel econo my level that is based on the distribution of its production among the footprints of its vehicles in each particular model year. Although the same reformed CAFE scheme is proposed for both passe nger cars and light trucks , they are established with different continuous mathematical functions specific to their desi gn capabilities.

The agency is proposing the “Optimized (7%)” altern ative. In this alternative the agency uses a 7 percent discount rate to value benefits and se ts the proposed mpg levels where marginal costs equal marginal benefits. It is one of six alternatives examined in the analysis. We also examined a second optimized scenario when discounting benef its at 3 percent “Optimized (3%). In general order of increasing severity (see Table 1), the seven scenarios examined are:

1: “25% Below Optimized”: This alternativ e mirrors the absolute difference in mpg derived from the 25% Above Optimized scenario in goi ng the same mpg amount below the Optimized 7% alternative 2: “Optimized (7%) An increase in the standa rd based upon availability of technologies and a marginal cost/benefit analysis, as was used in setting the MY 2008-2011 light truck standard. The mpg levels are set using a 7 perc ent discount rate for benefits. 3: “25% Above Optimized”: This alternative looks at the mpg levels of the Optimized (7%) and the Total Cost Equals Total Benefit alterna tive and picks mpg levels that are 25 percent of that difference.

1 This analysis does not contain NHTSA’s assessment of the potential environmental impacts of the proposed standards and reasonable alternatives for purposes of the National Environmental Policy Act (NEPA), 42 U.S.C. 4321-4347. On March 28, 2008, NHTSA published a notice of intent to prepare an environmental impact statement (EIS) and opened the NEPA scoping process (73 FR 1661 5). NHTSA will consider the potential environmental impacts of the proposed standards and reasonable alte rnatives through the NEPA process, and NHTSA’s NEPA analysis will inform any further action on the propo sed standards, consistent with NEPA and EPCA. 2 Vehicle Footprint is defined as the wheelbase (the distance from the center of the front axle to the center of the rear axle) times the average track widt h (the distance between the centerline of the tires) of the vehicle (in square feet).ii

4: “50% Above Optimized”: This alternative looks at the mpg levels of the Optimized (7%) and the Total Cost Equals Total Benefit alterna tive and picks mpg levels that are 50 percent of that difference. 5: “Optimized (3%) An increase in the standa rd based upon availability of technologies and a marginal cost/benefit analysis, as was used in setting the MY 2008-2011 light truck standard, except that the mpg levels are set using a 3 percent discount rate for benefits. 6: “Total Costs Equal Total Benefits”: An incr ease in the standard to a point where essentially total costs of the technologies adde d equals total benefits. In this analysis, for brevity, at times it is labeled “TC = TB”. 7: “Technology Exhaustion”: An increase in the standard based upon the maximum usage (based on NHTSA’s perspective) of available tec hnologies, disregarding the cost impacts.

Table 1a shows the agency’s projection of the actual harmonic average that would be achieved by the manufacturers, assuming those manufact urers whose plans are above the requirements would achieve those higher leve ls. Table 1b shows the estimated required levels. All of the tables in this analysis compare an adjusted baseline to the achieved harmonic average in Table 1a.

Costs : Costs were estimated based on the specific technologies that we re applied to improve each manufacturer’s fuel economy up to the leve l required under each alternative. Table 2 provides those cost estimates on an average per-vehicle basi s, and Table 3 provides those estimates on a fleet-wide basis in millions of dollars. Costs are not discounted.

Benefits : Benefits are determined mainly from fuel savings over the lifetim e of the vehicle, but also include externalities such as reductions in criteria pollutants. Table 4 provides those estimates on an industry-wide basis.

Net Benefits : Table 5 compares costs and benefits of each alternative. The values in Table 5 compare societal benefits to societ al costs of each alternative. T hus, it does not use the values of Table 2, which include fines paid by manufacturers and transferre d to consumers to pay.

Fuel Savings : Table 6 shows the lifetime fuel sa vings in millions of gallons.iii

Table 1a Alternative CAFE Levels Projected Harmonic Average for the Fleet 3 (in mpg)

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 25% Below Optimized 30.5 31.2 31.9 32.8 33.5 Optimized (7%) 31.0 32 .3 33.1 33.9 34.7 25% Above Optimized 31.5 33.3 34.2 35.3 36.1 50% Above Optimized 31.7 34.0 35.1 36.4 37.6 Optimized (3%) 32.2 34 .5 35.5 37.0 38.2

TC = TB 32.3 35.0 36.1 37.6 38.8 Technology Exhaustion 32.3 35.2 36.6 38.5 39.9

Light Trucks 25% Below Optimized 24.3 25.5 27.3 27.3 27.4 Optimized (7%) 24.4 25 .8 27.5 28.0 28.4 25% Above Optimized 24.4 26.1 27.8 28.5 29.5 50% Above Optimized 24.6 26.3 28.0 28.9 30.0 Optimized (3%) 24.4 25 .8 27.7 28.2 28.8

TC = TB 24.7 26.5 28.5 29.5 30.5 Technology Exhaustion 24.7 26.6 29.4 30.3 31.3

3 The values represent the higher of the manufacturer’s plans and the alternative level of the standard.iv

Table 1b Estimated Required Average for the Fleet (in mpg)

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 25% Below Optimized 29.6 30.3 31.7 32.7 33.9 Optimized (7%) 31.2 32.8 34.0 34.8 35.7 25% Above Optimized 32.8 35.3 36.1 36.8 37.5 50% Above Optimized 34.3 37.8 38.5 38.9 39.5 Optimized (3%) 37.1 39.1 39.3 40.7 40.9

TC = TB 37.5 42.7 43.0 43.1 43.3 Technology Exhaustion 38.6 45.4 48.9 50.1 52.6

Light Trucks 25% Below Optimized 24.9 26.0 27.5 27.5 27.5 Optimized (7%) 25.0 26.4 27.8 28.2 28.6 25% Above Optimized 25.1 26.9 28.0 28.8 29.8 50% Above Optimized 25.3 27.3 28.3 29.5 30.9 Optimized (3%) 25.0 26.4 28.0 28.5 29.0

TC = TB 25.6 28.1 28.8 30.8 33.1 Technology Exhaustion 25.9 28.6 32.2 33.1 34.7

See Appendix A for more information on the required levels.v

Table 2 Average Incremental Cost Per Vehicle – Consumer Perspective (2006 Dollars)

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 25% Below Optimized 126 126 187 294 428 Optimized (7%) 276 334 404 512 649 25% Above Optimized 494 778 871 1,078 1,185 50% Above Optimized 620 1,133 1,251 1,501 1,694 Optimized (3%) 896 1,284 1,376 1,706 1,915
TC = TB 966 1,685 1,829 2,159 2,367 Technology Exhaustion 1,038 2,032 2,406 2,889 3,264

Light Trucks 25% Below Optimized 185 526 738 705 708 Optimized (7%) 224 617 861 924 979 25% Above Optimized 279 873 1,141 1,352 1,655 50% Above Optimized 385 1,008 1,347 1,644 2,041 Optimized (3%) 227 616 955 1,028 1,145
TC = TB 501 1,325 1,770 2,171 2,509 Technology Exhaustion 536 1,364 2,255 2,507 2,785vi

Table 3 Incremental Total Cost – Consumer Perspective (Millions of 2006 Dollars)

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 5 year total 25% Below Optimized 835 818 1,253 2,153 3,209 8,268 Optimized (7%) 1,884 2,373 2,879 3,798 4,862 15,796 25% Above Optimized 3,387 5,653 6,445 8,240 9,084 32,808 50% Above Optimized 4,010 7,885 8,986 11,207 12,981 45,070 Optimized (3%) 5,467 8,791 9,821 12,447 14,484 51,011
TC = TB 5,913 10,796 12,303 15,403 17,398 61,812 Technology Exhaustion 6,079 12,595 14,701 18,759 21,110 73,245

Light Trucks 25% Below Optimized 1,349 4,296 6,329 6,212 6,326 24,512 Optimized (7%) 1,649 4,986 7,394 8,160 8,761 30,949 25% Above Optimized 2,072 7,034 9,815 11,903 14,781 45,606 50% Above Optimized 2,922 8,098 11,586 14,386 17,969 54,961 Optimized (3%) 1,662 4,974 8,190 9,058 10,253 34,136
TC = TB 3,788 10,525 15,196 18,762 21,364 69,635 Technology Exhaustion 3,933 10,670 18,275 21,051 23,479 77,408

Combined PC +LT 25% Below Optimized 2,184 5,114 7,582 8,365 9,534 32,780 Optimized (7%) 3,534 7,358 10,273 11,957 13,623 46,745 25% Above Optimized 5,459 12,687 16,261 20,143 23,865 78,414 50% Above Optimized 6,932 15,983 20,572 25,593 30,950 100,030 Optimized (3%) 7,128 13,765 18,011 21,505 24,737 85,147

TC = TB 9,702 21,321 27,499 34,164 38,761 131,447 Technology Exhaustion 10,013 23,266 32,976 39,810 44,589 150,653vii

Table 4a Present Value of Lifetime Soci etal Benefits by Alternative (Millions of 2006 Dollars) (Discounted 3% )

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5 years
25% Below Optimized 1,418 2,581 3,977 6,399 8,387 22,762
Optimized (7%) 3,181 6,054 7,561 9,716 11,616 38,128
25% Above Optimized 4,604 8,939 10,403 13,109 14,907 51,962
50% Above Optimized 5,241 10,842 12,571 15,503 17,893 62,050
Optimized (3%) 6,798 12,188 13,519 16,833 19,216 68,554

TC = TB 7,075 13,366 14,881 18,059 20,364 73,745
Technology Exhaustion 7,156 13,865 15,967 19,654 22,312 78,954

Light Trucks 25% Below Optimized 4,414 9,959 15,910 15,715 15,745 61,743
Optimized (7%) 4,919 11,055 17,120 18,866 20,506 72,466
25% Above Optimized 5,286 12,599 17,972 20,984 24,662 81,503
50% Above Optimized 5,848 13,249 18,955 22,375 26,475 86,902
Optimized (3%) 4,939 11,075 17,976 19,902 22,246 76,138

TC = TB 6,343 14,452 20,631 24,704 28,352 94,482
Technology Exhaustion 6,420 14,528 24,517 27,951 31,387 104,803

Passenger Cars and Light Trucks Combined 25% Below Optimized 5,832 12,540 19,887 22,114 24,132 84,505 Optimized (7%) 8,100 17,109 24,681 28,582 32,122 110,594 25% Above Optimized 9,890 21,538 28,375 34,093 39,569 133,465 50% Above Optimized 11,089 24,091 31,526 37,878 44,368 148,952 Optimized (3%) 11,737 23,263 31,495 36,735 41,462 144,692

TC = TB 13,418 27,818 35,512 42,763 48,716 168,227 Technology Exhaustion 13,576 28,393 40,484 47,605 53,699 183,757viii

Table 4b Present Value of Lifetime Soci etal Benefits by Alternative (Millions of 2006 Dollars) (Discounted 7 % )

Passenger Car MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5 years 25% Below Optimized 1,156 2,104 3,235 5,197 6,799 18,491 Optimized (7%) 2,596 4,933 6,148 7,889 9,420 30,986 25% Above Optimized 3,755 7,280 8,454 10,638 12,083 42,210 50% Above Optimized 4,274 8,825 10,213 12,576 14,495 50,383 Optimized (3%) 5,543 9,922 10,983 13,654 15,569 55,671

TC = TB 5,769 10,878 12,087 14,644 16,492 59,870 Technology Exhaustion 5,834 11,282 12,968 15,930 18,061 64,075

Light Trucks 25% Below Optimized 3,508 7,910 12,603 12,432 12,441 48,894 Optimized (7%) 3,909 8,779 13,560 14,915 16,192 57,355 25% Above Optimized 4,201 9,990 14,236 16,587 19,457 64,471 50% Above Optimized 4,642 10,507 15,011 17,687 20,892 68,739 Optimized (3%) 3,926 8,794 14,251 15,752 17,589 60,312

TC = TB 5,027 11,453 16,330 19,515 22,367 74,692 Technology Exhaustion 5,088 11,513 19,395 22,074 24,759 82,829

Passenger Cars and Light Trucks Combined 25% Below Optimized 4,664 10,014 15,838 17,629 19,240 67,385 Optimized (7%) 6,505 13,712 19,708 22,804 25,612 88,341 25% Above Optimized 7,956 17,270 22,690 27,225 31,540 106,681 50% Above Optimized 8,916 19,332 25,224 30,263 35,387 119,122 Optimized (3%) 9,469 18,716 25,234 29,406 33,158 115,983

TC = TB 10,796 22,331 28,417 34,159 38,859 134,562 Technology Exhaustion 10,922 22,795 32,363 38,004 42,820 146,904ix

Table 5a

Net Total Benefits Over the Vehicle’s Lifetime – Present Value Societal Perspective (Millions of 2006 Dollars) (Discounted 3% )

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5 years
25% Below Optimized 583 1,763 2,724 4,246 5,178 14,494
Optimized (7%) 1,297 3,681 4,682 5,918 6,754 22,332
25% Above Optimized 1,217 3,286 3,958 4,869 5,823 19,154
50% Above Optimized 1,231 2,957 3,585 4,296 4,912 16,980
Optimized (3%) 1,331 3,397 3,698 4,386 4,732 17,543

TC = TB 1,162 2,570 2,578 2,656 2,966 11,933
Technology Exhaustion 1,077 1,270 1,266 895 1,202 5,709
Light Trucks 25% Below Optimized 3,065 5,663 9,581 9,503 9,419 37,231
Optimized (7%) 3,270 6,069 9,726 10,706 11,745 41,517
25% Above Optimized 3,214 5,565 8,157 9,081 9,881 35,897
50% Above Optimized 2,926 5,151 7,369 7,989 8,506 31,941
Optimized (3%) 3,277 6,101 9,786 10,844 11,993 42,002

TC = TB 2,555 3,927 5,435 5,942 6,988 24,847
Technology Exhaustion 2,487 3,858 6,242 6,900 7,908 27,395 Passenger Cars and Light Trucks Combined 25% Below Optimized 3,648 7,426 12,305 13,749 14,598 51,725 Optimized (7%) 4,566 9,751 14,408 16,625 18,499 63,849 25% Above Optimized 4,431 8,851 12,114 13,950 15,704 55,051 50% Above Optimized 4,157 8,108 10,954 12,285 13,418 48,922 Optimized (3%) 4,609 9,498 13,484 15,230 16,725 59,545

TC = TB 3,716 6,497 8,013 8,599 9,955 36,780 Technology Exhaustion 3,563 5,127 7,508 7,795 9,110 33,104x

Table 5b Net Total Benefits Over the Vehicle’s Lifetime – Present Value Societal Perspective (Millions of 2006 Dollars) (Discounted 7 % )

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5 years 25% Below Optimized 321 1,286 1,982 3,044 3,590 10,223 Optimized (7%) 712 2,560 3,269 4,091 4,558 15,190 25% Above Optimized 368 1,627 2,009 2,398 2,999 9,402 50% Above Optimized 264 940 1,227 1,369 1,514 5,313 Optimized (3%) 76 1,131 1,162 1,207 1,085 4,660

TC = TB -144 82 -216 -759 -906 -1,942 Technology Exhaustion -245 -1,313 -1,733 -2,829 -3,049 -9,170 Light Trucks 25% Below Optimized 2,159 3,614 6,274 6,220 6,115 24,382 Optimized (7%) 2,260 3,793 6,166 6,755 7,431 26,406 25% Above Optimized 2,129 2,956 4,421 4,684 4,676 18,865 50% Above Optimized 1,720 2,409 3,425 3,301 2,923 13,778 Optimized (3%) 2,264 3,820 6,061 6,694 7,336 26,176

TC = TB 1,239 928 1,134 753 1,003 5,057 Technology Exhaustion 1,155 843 1,120 1,023 1,280 5,421 Passenger Cars and Light Trucks Combined 25% Below Optimized 2,480 4,900 8,256 9,264 9,706 34,605 Optimized (7%) 2,971 6,354 9,435 10,847 11,989 41,596 25% Above Optimized 2,497 4,583 6,429 7,082 7,675 28,267 50% Above Optimized 1,984 3,349 4,652 4,670 4,437 19,092 Optimized (3%) 2,341 4,951 7,223 7,901 8,421 30,836

TC = TB 1,094 1,010 918 -5 98 3,115 Technology Exhaustion 909 -471 -613 -1,806 -1,769 -3,749

Negative values mean that societal costs exceed societal benefits.xi

Table 6 Savings in Millions of Gallons of Fuel Undiscounted Over the Lifetime of the Model Year

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5 years 25% Below Optimized 708 1,261 1,946 3,135 4,151 11,201 Optimized (7%) 1,563 2,968 3,717 4,771 5,716 18,735 25% Above Optimized 2,313 4,480 5,221 6,601 7,476 26,091 50% Above Optimized 2,641 5,523 6,422 7,913 9,121 31,620 Optimized (3%) 3,463 6,197 6,905 8,587 9,784 34,936

TC = TB 3,599 6,860 7,676 9,320 10,461 37,916 Technology Exhaustion 3,677 7,143 8,261 10,233 11,562 40,876

Light Trucks 25% Below Optimized 2,157 4,933 7,902 7,799 7,808 30,599 Optimized (7%) 2,404 5,478 8,509 9,392 10,195 35,978 25% Above Optimized 2,585 6,339 9,070 10,592 12,534 41,120 50% Above Optimized 2,909 6,780 9,697 11,458 13,584 44,428 Optimized (3%) 2,414 5,488 8,978 9,959 11,127 37,966

TC = TB 3,228 7,471 10,640 12,778 14,602 48,719 Technology Exhaustion 3,263 7,506 12,659 14,448 16,147 54,023

Passenger Cars and Light Trucks Combined 25% Below Optimized 2,865 6,194 9,848 10,934 11,959 41,800 Optimized (7%) 3,967 8,446 12,226 14,163 15,911 54,713 25% Above Optimized 4,898 10,819 14,291 17,193 20,010 67,211 50% Above Optimized 5,550 12,303 16,119 19,371 22,705 76,048 Optimized (3%) 5,877 11,685 15,883 18,546 20,911 72,902

TC = TB 6,827 14,331 18,316 22,098 25,063 86,635 Technology Exhaustion 6,940 14,649 20,920 24,681 27,709 94,899I-1

I. INTRODUCTION

The purpose of this study is to analyze the eff ects of changes in the fuel economy standards for passenger cars and for light trucks from MY 201 1 to MY 2015. It includ es a discussion of the technologies that can improve fuel economy, the potential impacts on retail prices, safety, the discounted lifetime net benefits of fuel savings , and the potential gallons of fuel saved.

The agency issued a final rule on April 7, 2003 (68 FR 16868), setti ng the CAFE standard applicable to light trucks for MY 2005 at 21.0 mpg, for MY 2006 at 21.6 mpg, and for MY 2007 at 22.2 mpg. On April 6, 2006 (71 FR 17566), the agency issued a final rule for MYs 2008 to 2011 under a new “CAFE Reform” structure. Simila r to this report, a Final Regulatory Impact Analysis accompanied that final rule. 4 Much of the technical and cost information used in the 2006 analysis was taken from the findings in the National Academy of Sciences study 5 published in January 2002.

The new attribute-based Reformed CAFE system is based on the vehicle footprint (wheel base 6 x average wheel track width 7 ). The anticipated advantages of the ne w reformed CAFE system are:

First, the energy-saving potential of the CAFE program was hampered by the original regulatory structure. Manufacturers who o ffer predominately small vehicles had little or no regulatory incentive to enhance fuel economy, because their vehi cles tend to be more fuel efficient than the CAFE level. Moreover, the difference between the fuel economy standards for passenger cars and light trucks (27.5 mpg and 20.7 mpg, resp ectively, for MY 2004) encouraged vehicle manufacturers to offer vehicles classified as light trucks for purposes of CAFE, possibly inducing design changes that hurt overall fuel economy. A CAFE system that more closely links fuel economy standards to the various market se gments and their fuel economy performance may reduce the incentive to design vehicles which are functionally similar to passenger cars but are classified as li ght trucks.

Second, we were concerned that the original light truck CAFE standards could create safety risks. Vehicle manufacturers are encouraged to achieve greater fuel economy by downsizing and downweighting. Alternatively, manu facturers may offer small vehicl es to offset their offerings of large vehicles. The resulting in crease in the disparity between the smallest and largest vehicle sizes and weights in the on-road vehicle fleet is widely believed to have increased the number of fatalities in crashes involving passenger cars and light-duty trucks. The National Academy of Sciences (NAS) report and a NHTSA study 8 have suggested that if downweighting were concentrated on the heaviest vehicles in the fleet, there could be a small fl eetwide safety benefit, but downweighting of passenger cars and the lighter light trucks would increase fatalities.

4 “Final Regulatory Impact Analysis, Corporate Average Fuel Economy and CAFE Reform for MY 2008-2011 Light Trucks”, March 2006, Docket No. 24309-5.

5 “Effectiveness and Impact of Corp orate Average Fuel Economy (CAFE) St andards”, National Research Council,
2002. The link for the NAS report is http://www.nap.edu/books/0309076013/html/

6 “Wheel base” is essentially the dist ance between the centers of the axles.

7 “Track width” is the lateral distan ce between the centerline of the tires.

8 “Vehicle Weight, Fatality Risk and Crash Compatibilit y of Model Year 1991-99 Passenger Cars and Light Trucks”, Charles J. Kahane, Ph.D., NHTSA, October 2003, DOT HS 809-662.I-2

A third reason for considering CAFE reform rela tes to the adverse economic impacts that may result from such future increases in the stringe ncy of CAFE standards. Rapid increases in the level of the CAFE standard could have substa ntial economic consequences on manufacturers, especially those full-line manufacturers with prod uct mixes dominated by large heavier vehicles. For example, full-line manufacturers – especially those with substantial sales in the heavier end of the light truck market – may generate fewer CAFE credits and incur larger compliance costs than vehicle manufacturers who focus their sales in the smaller, lighter end of the light truck market. As CAFE standards become more stri ngent under the or iginal structure, the full-line manufacturers may experience adverse financial consequences, with resulting disruptions for employees in these firms and their suppliers.

EPCA also gives NHTSA authority to set pass enger car CAFE standards for each model year, but sets a default standard of 27.5 mpg. NHTSA ha s not raised the passenger car CAFE standard from 27.5 mpg since Congress lifted the ban on CAFE rulemakings in 2002 because it did not believe that it had authority to reform passenger car CAFE as it had for light trucks. Reforming the CAFE program achieves larger fuel savings while enhancing safety and preventing adverse economic consequences—objectives which apply equa lly to passenger cars as to light trucks. NHTSA was unwilling to raise the passenger car CA FE standard without also reforming it, because of the same fuel savings, safety, and econo mic concerns that led it to reform the light truck CAFE standards.

In December 2007, Congress passed the Energy Inde pendence and Security Act (EISA). EISA mandates the setting of separate standards for passenger cars and for light trucks at levels sufficient to ensure that the average fuel economy of the combined fleet of all passenger cars and light trucks sold by all manufact urers in the U.S. in model year 2020 equals or exceeds 35 miles per gallon. That is a 40 percent increase above the average of approximately 25 miles per gallon for the current combined fleet.

This notice proposes standards for model ye ars (MY) 2011-2015, the maximum number of model years under EISA for which NHTSA can es tablish standards in a single rulemaking. However, the Act gives NHTSA authority to refo rm passenger car CAFE, allowing the agency to set standards for those vehicles according to an attribute-based mathem atical function as it currently does for light trucks.

The dual fuel incentive program, through which manu facturers may improve their calculated fuel economies by producing vehicles capable of operating on alternative fuels, is not considered in this analysis. By law, the agency has always analyzed fuel economy without considering the dual fuel credits, since it is an incentive progr am designed to increase the availability of alternative fuel vehicles.

Throughout this analysis, unless ot herwise noted, the agency has not considered the ability of manufacturers to use credits or cr edit trading in achieving the a lternative fuel economy levels.

Throughout this document, confidential info rmation is presented in brackets [ ].II-1

II. NEED OF THE NATION TO CONSERVE ENERGY

The Energy Policy and Conservati on Act (EPCA) states that:

“When deciding maximum feasible aver age fuel economy ... the Secretary of Transportation shall consider technologica l feasibility, economic practicability, the effect of other motor vehicle standards of the Government on fuel economy, and the need of the United States to conserve energy.” 9

Thus, (EPCA) specifically directs the Departme nt to balance the technological and economic challenges related to fuel economy with the nati on's need to conserve energy. The concerns about energy security and the effects of en ergy prices and supply on national economic wellbeing that led to the enactmen t of EPCA persist today. The demand for petroleum is steadily growing in the U.S. and around the world.

Since 1970, there have been a seri es of events that suggest th at the behavior of petroleum markets is a matter for public concern.

• Crude oil prices are currently in the nei ghborhood of $100 per barrel. As recently as 1998, crude prices averaged about $ 13 per barrel ($15.85 in 2006 dollars). 10 Gasoline prices have more than d oubled during this period. 11

• U.S. domestic oil production peaked in 1970 at 11.3 million barrels per day. Between 1970 and 2006, U.S. domestic production has dec lined by nearly 40 percent, while U.S. petroleum consumption has increased by 40 pe rcent. Net petroleum imports now account for 60 percent of U.S. domestic petroleum consumption. 12

• Worldwide oil demand is very inelastic: decl ining prices do not induc e large increases in consumption, while higher prices do not rest rain consumption. Thus, relatively minor changes in quantity demanded can induce larg e changes in price. Within the United States, demand for gasoline, diesel, and jet fuel within the transportation sector is particularly inelastic.

• Demand for oil may increase significantly in As ia and worldwide in the future resulting in competition for oil supplies.

• Oil production facili ties, refineries, and supply chains have been disrupted from time to time, either by wars, political ac tion by oil producers, civil unrest , or natural disasters.

• High oil prices, sometimes induced by disruptio ns in oil markets, have often coincided with rising inflation and subse quent economic recessions.

9 49 USC 32902(f)

10 Energy Information Administration, Annual Energy Review 2006 , Table 5.21, p. 171. See: http://www.eia.doe.gov/emeu/ aer/pdf/pages/sec5_51.pdf

11 Energy Information Administration, Annual Energy Review 2006 , Table 5.24, p. 177. See: http://www.eia.doe.gov/emeu/ aer/pdf/pages/sec5_57.pdf

12 Energy Information Administration, Annual Energy Review 2006 , Table 5.1, p. 125. See: http://www.eia.doe.gov/eme u/aer/pdf/pages/sec5_5.pdfII-2

• Greenhouse gas emissions from the consumption of petroleum have become a subject of increasing public policy con cern, both in the United States and internationally. Greenhouse gases in general and carbon dioxide in particular have not thus far been subject to national regula tion. Studies by multiple sources suggest that rising atmospheric concentrations of greenhouse ga ses will damage human health and welfare. 13 There is a direct linkage between the consum ption of fossil energy and emissions of the greenhouse gas carbon dioxide, as essentially all of the carbon in hydrocarbon fuels is oxidized into carbon dioxide when the fuel is combusted. Reducing U.S. fossil energy consumption will generally induce a proportional reduction in carbon dioxide emissions.

Energy is an essential input to the U.S. economy, and having a strong economy is essential to maintaining and strengthening our national security. Secure, reliable, and affordable energy sources are fundamental to economic stability and development. Rising energy demand poses a challenge to energy security, given increased reliance on global energy markets. As noted above, U.S. energy consumption has increasingly been outstripping U.S. energy production.

Table II-1 presents trend data on the production and consumpti on of petroleum. Domestic petroleum production has been decreasing over ti me, while imports of petroleum have been increasing to meet the rising U.S. demand for petroleum.

Conserving energy, especially reducing the nation’s dependence on petroleum, benefits the U.S. in several ways. Improving energy e fficiency has benefits for economic growth and the environment, as well as other bene fits, such as reduci ng pollution and improving security of energy supply. More specificall y, reducing total petroleum use decreases our economy’s vulnerability to oil price shocks . Reducing dependence on oil imports from regions with uncertain conditi ons enhances our energy securi ty and can reduce the flow of oil profits to certain states now hostile to the U.S.

This reformed CAFE proposal would encourag e broader use of fuel saving technologies, resulting in more fuel-efficient vehicl es and greater overall fuel economy.

13 IPCC 2007: Climate Change 2007: Synthesis Report: Contributions of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, [Core writing team, Pachauri, R.K. and Reisinger, A. 9eds.)] (Published by the Intergovernmental Panel on Climate Change, 2008). A vailable at http://www.ipcc.ch/

, .II-3

Table II-1 Petroleum Production and Supply (millions of barrels per day)

Domestic Petroleum Production Net Petroleum

Imports U.S. Petroleum

Supply World Petroleum Consumption Import Share of U.S. Supply 1975 10.01 5.85 15.86 56.20 36.9% 1985 10.58 4.29 14.87 60.09 28.9% 1995 8.32 7.89 16.21 68.91 48.7% 2005 6.89 12.55 19.44 83.00 64.6% DOE Predictions 2015 5.91 12.52 18.43 67.9% 2025 5.58 14.87 20.45 72.7% 2030 5.39 16.37 21.76 75.2%

“Petroleum Production and Consumption and Some Important Percent Shares, 1950-2006”, Transportation Energy Data B ook: Edition 26 (2007), Table 1.12. http://cta.ornl.gov/data/Index.shtml “Comparison of petroleum projections, 2015, 2025, and 2030”, Department of Energy, Energy Information Administration, Annual Energy Outlook 2007, Table 23. http://www.eia.doe.gov/emeu/aer/contents.html

Table II-2 Transportation Consumption by Mode (millions of gallons per year)

Passenger Cars Light Trucks Total Light Vehicles Total Transportation Light Vehicles as % of Trans. 1975 74 19 93 139 67% 1985 72 27 99 157 63% 1995 68 46 114 188 61% 2005 74 65 139 219 63%

“Automobile Fuel Use and Fuel Type Shares for Calculation of Energy Use”, Transportation Energy Data Book: Edition 26 (2007) Table A.1, (Page A-7, appendix) http://cta.ornl.gov/data/Index.shtml “Light Truck Fuel Use and Fuel Type Shares for Calculation of Energy Use”, Transportation Energy Data Book: Edition 26 (2007) Table A.1, (Page A-7, appendix) http://cta.ornl.gov/data/Index.shtmlIII-1

III. REGULATORY ALTERNATIVES

In developing the proposed standards, the ag ency considered the four statutory factors underlying maximum feasibility as defined in EPCA (technol ogical feasibili ty, economic practicability, the effect of other standards of the Govern ment on fuel economy, and the need of the nation to conserve energy) as well as other rele vant considerations such as safety. NHTSA assessed what fuel saving technologies would be available, how effective they are, and how quickly they could be introduced. This assessment considered technological feas ibility, economic practicabil ity and associated energy conservation. We also considered othe r standards to the extent captured by EPCA 14 and environmental and safety concerns. This in formation was factored into the computer model used by NHTSA for applying technolog ies to particular vehicle models. The agency then balanced the factors relevant to standard setting. NHTSA’s NEPA analysis, discussed in the preamble of the accomp anying notice, also will inform NHTSA’s consideration of the proposed standards and re asonable alternatives in developing a final rule.

In balancing these factors, NHTSA generally observes that the incr easing application of technologies increases fuel economy and associat ed benefits, but it also increases costs. Initial applications of t echnologies provide far more fuel savings per dollar of expenditure than applications of remaining technologies, which provide less incremental fuel savings at greater cost a nd, with progressive additions of technologies, eventually far greater cost. At some stage, the increasing ap plication of technologies is not justified. A significant question is what methodology and de cisionmaking criteria are used in the balancing to determine when to cease adding technologies and thus arrive at regulatory fuel economy targets.

In developing its proposed standards, the ag ency used a net benefit-maximizing analysis that placed monetary values on relevant externalities (both energy security and environmental externalities, including the benefits of reductions in CO 2 emissions) and produced what is called the “optimized scenario .” The optimized standards reflect levels such that, considering the seve n largest manufacturers, net bene fits (that is, total benefits minus total costs) are higher than at every other examined level of stringency. The agency also reviewed the results of the mode l’s estimates of stringencies maximizing net benefits to assure that the results made se nse in terms of balanc ing EPCA’s statutory factors and in meeting EISA’s re quirements for improved fuel economy.

In addition to the optimized scenario, NHTSA considered and analyzed five additional regulatory alternatives that do not rely upon marginal benefit- cost analysis. In ascending order of stringency, the six alternatives are: •Standards that fall below the optimized scenario by the same absolute amount by which the +25 percent alternative exceeds th e optimized scenario (“25 percent below optimized” alternative),

14 71 Fed. Reg. 17566, 17669-70; April 6, 2006.III-2

•Standards based on applying technologies until net benefits are maximized (optimized scenario), and •Standards that exceed the optimized scenario by 25 percent of the interval between the optimized scenario and the TC =TB alternative (see below) (“25 percent above optimized” alternative), •Standards that exceed the optimized scenario by 50 percent of the interval between the optimized scenario and th e TC=TB alternative (“50 percent above optimized” alternative), •Standards based on applying technologies until total costs equal total benefits, i.e, until there are zero net benefits (TC=TB alternative), 15 and •Standards based on applying all feasible technologies without regard to cost (technology exhaustion alternative). 16

NHTSA chose these alternatives in order to consider and evaluate the impacts of balancing the EPCA factors di fferently in determining maximum feasibility than the agency has in prior rulemakings. In Center for Biological Diversity v. NHTSA , the Ninth Circuit Court recognized that “EPCA gives NHTSA discretion to decide how to balance the statutory factors—as long as NHTSA’s balancing does not undermine the fundamental purpose of EPCA: energy conser vation.” 508 F.3d 508, 527 (9th Cir. 2007). The Court also raised the possibility that NHTSA’s current balancing of the statutory factors might be different from the agency’s balancing in the past, given the greater importance today of the need of the nation to conserve energy and more advanced understanding of climate change. Id. at 530-31.

NHTSA recognizes that numerous alternative CAFE levels are theo retically conceivable and that the alternatives described above essentially represent only several on a continuum of alternatives. Along the continuum, each alternative represents a different way in which NHTSA conceivably could assign weight to each of the four EPCA factors and NEPA’s policies. For the alternatives that fall above th e optimized scenario (the +25, +50 and TC=TB alternatives), the agency woul d evaluate policies that put increasingly more emphasis on reducing energy consumption and CO 2 emissions, given their impact on global warming, and less on the other factor s, including the ec onomic impacts on the industry. Conversely, for the alternative that fa lls below the optimum scenario, the agency would evaluate policie s that place relatively more weight on the economic health of the industry and less on re ducing energy consumption and CO 2 emissions.

15 The agency considered the “TC=TB” alternative because one or more commenters in the rulemaking on standards for MY 2008-2011 light trucks urged NHTS A to consider setting the standards on this basis rather than on the basis of maximizing net benefits. In addition, while the Ninth Circuit Court of Appeals concluded that EPCA neither requires nor prohibits the setting of standards at the level at which net benefits are maximized, the Court ra ised concerns about tilting the balance more toward reducing energy consumption and CO 2 . 16 This was accomplished by determin ing the stringency at which a reform ed standard would require every manufacturer to apply every technology estimated to be potentially available. At such stringencies, all but one manufacturer would be expected to fail to comply with the standard, and many manufacturers would owe large civil penalties as a result. The agency consid ered this alternative becau se the agency wished to explore the stringency and consequences of standards based solely on the potential availability of technologies at the individual manufacturer level.III-3

For reasons such as those set forth in this section, NHTSA’s provisi onal analysis of the alternatives described above suggests that so me of them may not sa tisfy the four EPCA factors that NHTSA must a pply in setting “maximum f easible” CAFE standards. NHTSA seeks comments on the proposed standa rds and the regulatory alternatives to aid the agency’s determination of what st andards to adopt in the final rule.

The graphs below show, for passenger cars, light trucks, and the combined fleet, the average annual fuel economy levels for the four alternatives as compared to the proposed standards. Subsequent graphs and tables pr esent their estimated co sts, benefits, and net benefits (in billions of dolla rs). In addition, tables are provided summarizing the average extent to which manufacturers’ CAFE levels are projected to fall short of CAFE standards—i.e., the average shortfall—under each of these alternatives. Manufacturerspecific shortfall is shown for the proposed and TC=TB alternative.III-4

Figure III-1. Average Required CAFE Le vels (mpg) for Passenger Cars under Proposed and Alternative Standards202530354045502011 2012 2013 2014 2015Model Year

Average Required CAFE Level (mpg)25% Below ProposedProposed (Maximum Net Benefit)25% Above Proposed50% Above ProposedTotal Costs Equal to Total BenefitsMaximum TechnologyIII-5

Figure III-2. Average Required CAFE Le vels (mpg) for Light Trucks under Proposed and Alternative Standards202530354045502011 2012 2013 2014 2015Model Year

Average Required CAFE Level (mpg)25% Below ProposedProposed (Maximum Net Benefit)25% Above Proposed50% Above ProposedTotal Costs Equal to Total BenefitsMaximum TechnologyIII-6

Figure III-3. Average Required CAFE Le vels (mpg) for Overall Fleet under Proposed and Alternative Standards202530354045502011 2012 2013 2014 2015Model Year

Average Required CAFE Level (mpg)25% Below ProposedProposed (Maximum Net Benefit)25% Above Proposed50% Above ProposedTotal Costs Equal to Total BenefitsMaximum TechnologyIII-7

Figure III-4. Total Benefits under Proposed and Alternative Standards05,00010,00015,00020,00025,00030,00035,00040,00045,0002011 2012 2013 2014 2015Model Year

Total Benefits ($m)25% Below ProposedProposed (Maximum Net Benefit)25% Above Proposed50% Above ProposedTotal Costs Equal to Total BenefitsMaximum TechnologyIII-8

Figure III-5. Total Costs under Proposed and Alternative Standards05,00010,00015,00020,00025,00030,00035,00040,00045,0002011 2012 2013 2014 2015Model Year

Total Costs ($m)25% Below ProposedProposed (Maximum Net Benefit)25% Above Proposed50% Above ProposedTotal Costs Equal to Total BenefitsMaximum TechnologyIII-9

Figure III-6. Net Benefits under Pr oposed and Alternative Standards

For the proposal and each regulatory alternativ e, the Tables 22 and 23 show the total net benefits in millions of dollars at a 7 percent discount rate fo r the projected fleet of sales for each model year.05,00010,00015,00020,00025,00030,00035,00040,00045,0002011 2012 2013 2014 2015Model Year

Net Benefits ($m)25% Below ProposedProposed (Maximum Net Benefit)25% Above Proposed50% Above ProposedTotal Costs Equal to Total BenefitsMaximum TechnologyIII-10

Table III-1 Total Benefits Over the Vehicle’s Lifetime – Present Value (Millions of 2006 Dollars)

(Discounted 7 % )

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Passenger Cars

25% Below 1,156 2,104 3,235 5,197 6,799

Optimized 2,596 4,933 6,148 7,889 9,420

25% Above 3,755 7,280 8,454 10,638 12,083

50% Above 4,274 8,825 10,213 12,576 14,495

TC = TB 5,769 10,878 12,087 14,644 16,492
Technology Exhaustion 5,834 11,282 12,968 15,930 18,061

Light Trucks

25% Below 3,508 7,910 12,603 12,433 12,441

Optimized 3,909 8,779 13,560 14,915 16,192

25% Above 4,201 9,990 14,236 16,587 19,457

50% Above 4,642 10,507 15,011 17,687 20,892

TC = TB 5,027 11,453 16,330 19,515 22,367
Technology Exhaustion 5,088 11,457 19,418 22,093 24,779

Combined PC+LT

25% Below 4,664 10,014 15,838 17,630 19,240

Optimized 6,505 13,712 19,708 22,804 25,612

25% Above 7,956 17,270 22,690 27,225 31,540

50% Above 8,916 19,331 25,224 30,263 35,387

TC = TB 10,796 22,331 28,417 34,159 38,860
Technology Exhaustion 10,922 22,795 32,363 38,004 42,820III-11

Table III-2 Total Costs (Millions of 2006 Dollars)

(Discounted 7 % )

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Passenger Cars

25% Below 835 818 1,253 2,153 3,209

Optimized 1,884 2,373 2,879 3,798 4,862

25% Above 3,387 5,653 6,445 8,240 9,084

50% Above 4,010 7,885 8,986 11,207 12,981

TC = TB 5,913 10,796 12,303 15,403 17,398

Max Technology 6,079 12,595 14,701 18,759 21,110

Light Trucks

25% Below 1,349 4,296 6,329 6,212 6,326

Optimized 1,649 4,986 7,394 8,160 8,761

25% Above 2,072 7,034 9,815 11,903 14,781

50% Above 2,922 8,098 11,586 14,386 17,969

TC = TB 3,788 10,525 15,196 18,762 21,364

Max Technology 3,933 10,670 18,275 21,051 23,479

Combined PC+LT

25% Below 2,184 5,114 7,582 8,365 9,534

Optimized 3,534 7,358 10,273 11,957 13,623

25% Above 5,459 12,687 16,261 20,143 23,865

50% Above 6,932 15,983 20,572 25,593 30,950

TC = TB 9,702 21,321 27,499 34,164 38,761

Max Technology 10,013 23,266 32,976 39,810 44,589III-12

Table III-3 Net Total Benefits Over the Vehicle’s Lifetime – Present Value (Millions of 2006 Dollars)

(Discounted 7 % )

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Passenger Cars
25% Below 321 1,285 1,982 3,045 3,590
Optimized 711 2,560 3,269 4,092 4,558
25% Above 368 1,627 2,009 2,398 2,999
50% Above 264 940 1,226 1,370 1,514

TC = TB -144 82 -216 -759 -906
Max Technology -245 -1,313 -1,733 -2,829 -3,049

Light Trucks
25% Below 2,154 3,633 6,348 6,288 6,258
Optimized 2,260 3,793 6,167 6,755 7,432
25% Above 2,129 2,956 4,421 4,684 4,676
50% Above 1,720 2,408 3,426 3,301 2,924

TC = TB 1,239 928 1,134 753 1,004

Max Technology 1,155 843 1,120 1,023 1,280

Combined PC+LT

25% Below 2,476 4,919 8,330 9,333 9,848

Optimized 2,971 6,353 9,435 10,847 11,989

25% Above 2,497 4,583 6,430 7,082 7,675

50% Above 1,984 3,349 4,652 4,670 4,437

TC = TB 1,094 1,010 918 -5 98

Max Technology 909 -471 -613 -1,806 -1,769

Negative values mean that costs exceed benefits.

In tentatively deciding which al ternative to propose, the ag ency looked at a variety of factors. The agency notes that once string ency levels exceed the point at which net benefits are maximized, the societal costs of each incremental increase in stringency exceed the accompanying societal benefits. If we have valued benefits appropriately, it does not make economic sense to mandate the spending of more money than society receives in return. The resources used to meet overly stringent CAFE standards, insteadIII-13

of the optimized scenario sta ndards, would better be allocat ed to other uses such as technology research and development, or improvements in vehicle safety.

The agency considered the burden placed on specific manufacturers, consumers and employment. As CAFE standa rds increase, the incremental benefits are approximately constant while the incremental costs increase rapidly. Figure III-5 above shows that as stringency is increased, costs rise out of proportion compared to the benefits or the fuel savings. Increasingly higher costs have a ne gative impact on sales and employment. Each of the alternatives that is more stringent than the optimized alternative negatively impact sales and employment.

The agency also considered technological feasibility. The Volpe model assumes that major manufacturers will e xhaust all available technology before paying noncompliance civil penalties, even though the latter is often less costly. Historically, the large manufacturers have never paid civil penalties. NHTSA believes that there is a stigma to paying penalties that goes beyond economics. In the more stringent alternatives, the Volpe model predicts that increasing nu mbers of manufacturers will run out of technology to apply and, theoretic ally, resort to penalty paymen t. Setting standards this high is not technologically feasib le, nor does it serve the need of the nation to conserve fuel. Paying a CAFE penalty does not result in any fuel savings.

In analyzing the “-25 percent below optimized” alternative , the agency notes that these standards are more aggressive than the standa rds that the agency has proposed since the first years of the program and would impos e unprecedented costs on manufacturers. The agency also recognizes that ev en this pace of increase in the standards may burden some of the manufacturers, particul arly since the agency is now increasing car and light truck standards simultaneously. However, in light of the need of the nation to conserve energy and reduce global warming, the agency does not believe that this alternative would be maximum feasible under the statute. The agen cy is also concerned that the combined fleet might not reach the 35 mpg requirement by 2020 under EISA.

Underlying the differences in costs, benefits, and net benefits for the other alternatives are differences in the degree to which NHTSA has estimated that technologies might be applied in response to the standards corres ponding to each of these alternatives. The following tables show estimates of the aver age penetration rates of some selected technologies in the MY2015 pa ssenger car and light truck fleets under each of the alternatives discussed here:III-14

Table III-4 Estimated Average Technology Penetra tion (Largest Seven Manufacturers) MY2015 Passenger Cars

Technology Product
Plan Adjusted
Baseline 25%
Below Proposed Proposed Standard 25%
Above Proposed 50%
Above Proposed TC = TB Tech.
Exhaustion Automatically Shifted Manual Transmissions 10% 10% 23% 39% 47% 55% 63% 69% Spark Ignited Direct Injection Engines 22% 22% 22% 30% 37% 48% 68% 63% Turbocharging & Engine Downsizing 5% 5% 8% 17% 30% 40% 62% 57% Diesel Engines 0% 0% 3% 2% 7% 13% 18% 21% Hybrid Electric Vehicles 5% 5% 14% 15% 22% 28% 35% 38% Average Among Seven Largest Manufacturers

Table III-5 Estimated Average Technology Penetra tion (Largest Seven Manufacturers) MY2015 Light Trucks

Technology Product Plan Adjusted Baseline 25%
Below Proposed Proposed Standard 25%
Above Proposed 50%
Above Proposed TC = TB Tech.
Exhaustion Automatically Shifted Manual Transmissions 10% 14% 42% 55% 58% 60% 59% 70% Spark Ignited Direct Injection Engines 23% 24% 31% 40% 42% 55% 60% 69% Turbocharging & Engine Downsizing 9% 11% 21% 31% 38% 51% 54% 65% Diesel Engines 3% 6% 8% 10% 20% 23% 26% 28% Hybrid Electric Vehicles 2% 6% 15% 25% 29% 31% 30% 30% Average Among Seven Largest Manufacturers

As the first of the above tables indicate s, the Volpe model estimated that, under the standards proposed today, manufacturers might triple the planned utilization of turbochargers and hybrid electric powertrains in the passenger car fleet. This table also indicates that the use of tur bochargers in passenger cars mi ght increase by an additional factor of two under the “25% above proposed” alternative.

Similarly, the second table indicates that manufacturers might triple the planned utilization of diesel engines in the light truck fleet, and incr ease the utilization of hybrid electric powertrains by more than an orde r of magnitude. This table also shows a significant difference between the proposed and “25% above proposed” alternative, including an additional d oubling in the utilizati on of diesel engines.

NHTSA has examined the extent to which each alternative would (as estimated by the Volpe model and using the input informati on discussed in preceding sections) cause manufacturers to exhaust technologies projected to be available during MY2011- MY2015. The following chart summarizes the frequency with which this was estimated

to occur — i.e. , the number of instances in which an individual manufacturer exhausted technologies and thus fell below a standa rd in individual model years divided by 35 (seven manufacturers times five model years.III-15

Figure III-6 Calculated Prevalence of Technology Exhaustion

As this analysis indicates, the “25% belo w proposed” alternative caused technologies to be exhausted 3% of the time for passenger cars , and 17% of the time for light trucks. Under the proposed standards, the rate of technology exhaustion increased to 11% for passenger cars, but did not change for light trucks. However, under the “25% above proposed” alternative, the co rresponding rates incr eased to 26% and 37%, respectively. In other words, under this alternative, th e Volpe model estimated that, more than a quarter of the time, manufact urers would be unable to co mply with the passenger car standards solely using technologi es expected to be availabl e, and that they would be0%10%20%30%40%50%60%70%80%90%100%25% Below Proposed 25% Above 50% Above Total Cost = TotalBenefitTechnologyExhaustionAlternative

Calculated Prevalance of Technology ExhaustionCarsTrucksIII-16

unable to comply with the light truck standard s using available technologies more than a third of the time. These rates were estimated to be considerably higher for the remaining three alternatives.

These estimates of technology utilization a nd the exhaustion of available technologies indicate that all of the alternatives NHTSA ha s considered entail risk that one or more manufacturers would not be able comply w ith both the passenger car and light truck standards in every model year solely by a pplying technology. This risk is mitigated somewhat by the fact that our analysis ma y not encompass every technology that will potentially be available during MY2011-MY 2015. For example, some manufacturers have made public statements regarding hopes to offer “plug-in” HEVs before MY2015, but such vehicles are not re presented in our analysis. 17 Nonetheless, the agency has tentatively concluded that th e scope of technologies it ha s included is comprehensive enough that the analysis shown above indicate s that under some alternatives, there is considerable risk that some manufacturers would exhaust ava ilable technologies in some model years.

In tentatively concluding that the propos ed standards are the maximum feasible standards, NHTSA has balanced this risk agai nst the other considerations it must take into account, in particular the need of th e nation to conserve energy, which encompasses concerns regarding carbon di oxide emissions. The agency’s analysis includes economic measures of these needs—that is, economic me asures of the extern alities of petroleum consumption and the damages associated with carbon dioxide emissions. These measures are reflected in the agency’s estimates of the total and net benefits of each of the alternatives.

NHTSA is proposing standards that it estimate s will entail risk that some manufacturers will exhaust available technologies in some m odel years. However, relative to the less stringent “25% below proposed” al ternative, the agency has te ntatively concluded that the additional risk is outweighed by the significan t increase in estimated net benefits to society, ranging from an additional $0.5b in MY2011 to an additional $2.1b in MY2015. Conversely, the agency has tentatively conclude d that, relative to th e proposed standards, the more than doubling of risk posed by the “25% above proposed” alternative is not warranted, especially consideri ng that this alternative is estimated to significantly reduce net benefits, by $0.5b in MY2011 and, eventually, $4.3b in MY2015.

The agency invites comment regarding whether it has struck a proper balance and, if not, how it should do so. The alternatives identified by the agency are inte nded to aid public commenters in helping the agency to explore that issue.

Notwithstanding the tentativ e conclusions described a bove, NHTSA seeks comment on these regulatory alternatives to aid in determ ining what standards to adopt in the final rule. Specific sensitivity runs that vary fuel prices, the rebound effect, CO 2 and discount rate were conducted for the proposed optimized standards. These analyses have an

17 If included in the new product plans that the agency is requesting, these vehicles will be included in our analysis for the final rule.III-17

impact on the standards, costs and benefits. For example, in analyzing the “optimized alternative”, we estimated that following the same methods and criteria for setting the standards, but applying a 3% discount rate ra ther than a 7% discount rate, would suggest standards reaching about 33.6 mpg (average required fuel economy among both passenger cars and light trucks) in MY 2015, 2 mpg higher than the 31.6 mpg average resulting from the standards we are proposi ng based on a 7% discount rate. The more stringent standards durin g MY2011-MY2015 would reduce CO 2 emissions by 672 million metric tons (mmt), or 29% more than the 521 mmt achieved by the proposed standards. On the other hand, we estimate th at standards increasing at this pace would require about $85b in technology outlays during MY2011-MY2015, or 89% more than the $45b in technology outlays associated with the standards proposed today. The impact of the 3% rate is shown in the body of the PR IA along with the 6 form al alternatives. All other sensitivity analyses are shown in Chapter IX.IV-1

IV. IMPACT OF OTHER FEDERAL MO TOR VEHICLE STANDARDS ON FUEL ECONOMY

Introduction The Energy Policy and Conservation (EPCA or the Act) requires that fuel economy standards be set at the maximum feasible level after taking into account the following criteria: (1) technological f easibility, (2) economic practicab ility, (3) the impact of other Federal Motor Vehicle Standards on fuel ec onomy, and (4) the need of the Nation to conserve energy. This section discusses th e effects of other gove rnment regulations on model year (MY) 2011-2015 passenger car and light truck fuel economy.

The Impact of Safety Standards a nd Voluntary Safety Improvements The fuel economy impact of safety improvements will typically take the form of increased vehicle weight, which reduces the fuel economy of the vehicle. The manufacturer’s estimates of we ight and fuel economy impact have already been included in their baseline fuel economy projections. In some instances the manufacturers’ weight estimates are similar to NHTSA’s, in some instances they are less than NHTSA’s, but often they are more than NHTSA’s. The agen cy’s estimates are based on cost and weight tear-down studies of a few ve hicles and cannot possibly cove r all the variations in the manufacturers’ fleets. NHTSA requested a nd various manufacturers provided estimates of increases in weight resu lting from safety improvements, based on a MY 2007 baseline. However, for the passenger car and light tr uck proposal MY 2010 is the baseline. Thus, only safety equipment required to be installed or voluntarily installed after these dates are included in this analysis.

We have broken down our analysis of the impact of safety standards that might affect the MY 2011-2015 fleets into two parts, those fina l rules with known e ffective dates, and proposed rules without final effective dates or currently voluntary safety improvements.

Weight Impacts of Required Safety Standards (Final Rules) The National Highway Traffic Safety Administ ration (NHTSA) has issued two final rules on safety standards that become effective for passenger cars and light trucks between MY 2011 and MY 2015. These have been analyzed for their potential impact on passenger car and light truck weights, us ing MY 2010 as a baseline.

1. FMVSS 126, Electronic Stability Control 2. FMVSS 214, Side Impact Oblique Pole Test

FMVSS 126, Electronic Stability Control The phase-in schedule for vehicle manufacturers is: Model Year Production Beginning Date Requirement 2009 September 1, 2008 55% with carryover credit 2010 September 1, 2009 75% with carryover credit 2011 September 1, 2010 95% with carryover credit 2012 September 1, 2011 All light vehiclesIV-2

The final rule requires 75 percent of all light vehicles to meet the ESC requirement for MY 2010, 95 percent of all light vehicles to meet the ESC requirements by MY 2011, and all light vehicles must meet the requirements by MY 2012.

The agency’s analysis of weight impacts found that ABS adds 10.7 lbs. and ESC adds 1.8 lbs. per vehicle for a total of 12.5 lbs. Based on manufacturers’ plans for voluntary installation of ESC, 85 per cent of passenger cars in MY 2010 would have ABS and 52 percent would have ESC. Thus, the total incremental added weight over manufacturers’ plans in MY 2011 for passenger cars would be about 1.8 lbs. (0.10*10.7 + 0.43*1.8) and in MY 2012 an incremental 0.6 lbs. (0.05*10.7 + 0.05*1.8). Light trucks manufacturers’ plans show that 99 percent of all light truc ks would have ABS and that 74 percent would have ESC by MY 2010. Thus, for light trucks the incremental weight impacts of adding ESC would be 0.4 lbs. (0.21*1.8) in MY 2011 and an incremental 0.1 lbs. (0.01*10.7 + 0.05*1.8) in MY 2012.

FMVSS 214, Oblique Pole Side Impact Test The phase-in requirements for the side impact test are as shown below in Table IV-1 18 :

Table IV-1 FMVSS 214 Final Rule Phase-In Schedule Phase-in Date Percent of each manufacturer’s li ght vehicles that must comply during the production period September 1, 2009 to August 31, 2010 20 percent (excluding vehicles GVWR > 8,500 lbs.)

September 1, 2010 to August 31, 2011 50 percent vehicles (excluding vehicles GVWR > 8,500 lbs.)

September 1, 2011 to August 31, 2012 75 percent vehicles (excluding vehicles GVWR > 8,500 lbs.)

September 1, 2012 to August 31, 2013 All vehicles including limited line vehicles, except vehicles with GVWR > 8,500 lbs., alterers, and multi-stage manufacturers On or after September 1, 2013 All vehicles, including vehicles with GVWR > 8,500 lbs., alterers and multi-stage manufacturers

Based on manufacturers’ plans to voluntarily provide window cu rtains and torso bags, we estimate that 90 percent of passenger cars and light trucks would have window curtains for MY 2010 and 72 percent would have torso bags. A very similar percentage is estimated for MY 2011. A teardown study of 5 t horax air bags resulted in an average weight increase per ve hicle of 4.77 pounds (2.17 kg). 19 A second study 20 performed teardowns of 5 window curtain systems. One of the window curtain systems was very

18 The agency has received several petitions for re consideration to extend the lead time.

19 Khadilkar, et al. “Teardo wn Cost Estimates of Automotive Equipment Manufactured to Comply with Motor Vehicle Standard – FMVSS 214(D) – Side Impact Protection, Side Air Bag Features”, April 2003, DOT HS 809 809.

20 Ludtke & Associates, “Perform Cost and Weight Analysis, Head Protection Air Bag Systems, FMVSS 201”, page 4-3 to 4-5, DOT HS 809 842.IV-3

heavy (23.45 pounds). The other four window curtain systems had an average weight increase per vehicle of 6.78 pounds (3.08 kg), a fi gure which is assumed to be average for all vehicles in the future.

Assuming in the future that the typical system used to comply with the requirements of FMVSS No. 214 will be thorax bags with a wi ndow curtain, the average weight increase would be 2 pounds (0.10*6.78 + 0.28*4.77) in MY 2013. However, there is the potential that some light trucks might n eed to add structure to meet the test. The agency has no estimate of this potential weight impact for structure.

Weight Impacts of Proposed/Planned Safety Standards

Proposed FMVSS 216, Roof Crush On August 23, 2005, NHTSA proposed amending the roof crush standard to increase the roof crush standard from 1.5 times the vehi cle weight to 2.5 times the vehicle weight 21

. The NPRM proposed to extend the standard to vehicles with a GVWR of 10,000 pounds or less, thus including many light trucks that had not been re quired to meet the standard in the past. The proposed effective date wa s the first September 1 occurring three years after publication of the final rule. In the PRIA, the aver age passenger car weight was estimated to increase by 4.0 pounds and the aver age light truck weight was estimated to increase by 6.1 pounds for a 2.5 strength to we ight ratio. Based on comments to the NPRM, the agency believes that this weight estimate is likely to increase. However, the agency does not yet have an estimate for the final rule.

Planned NHTSA initiative on Ejection Mitigation The agency is planning on issuing a proposal on ejection mitigation. The likely result of the planned proposal is for window curtain side air bags to be larger and for a rollover sensor to be installed. The likely result will be an increase in weight of at least one pound, however, this analysis is not completed. In additi on, advanced glazing is one alternative that manufacturers might pursue for specific window applications (possibly for fixed windows for third row applications ) or more broadly. Advanced glazing is likely to have weight implications. Again th e agency has not made an estimate of the likelihood that advanced glazing might be used or its weight implications.

Possible NHTSA initiative on Pedestrian Protection The agency has started to analyze the costs and benefits of a potential Global Technical Regulation on pedestrian protec tion. Whether the agency will propose a rulemaking and the effective dates have not been decided, howev er, it is possible that a rule on pedestrian protection could start to be phased in by the end of the period of this proposed rulemaking. Potential weight increases for pe destrian head and leg protection have not yet been identified.

Summary – Overview of Anticipated Weight Increases

21 See 70 FR 53753, the PRIA is in Docket No. 22143 , entry #2 “Preliminary Regulatory Impact Analysis, FMVSS 216, Roof Crush Resistance,” August 2005.IV-4

The following table summarizes estimates ma de by NHTSA regardi ng the weight added in MY 2011 or later to institute the above discussed standards or likely rulemakings. In summary, NHTSA estimates that weight addi tions required by final rules and likely NHTSA regulations effective in MY 2011 and beyond, compared to the MY 2010 fleet and manufacturers’ plans, will increase passeng er car weight by at least 9.4 lbs. and light truck weight by at least 9.6 lbs.

Table IV-2 Weight Additions Due to Final Ru les or Likely NHTSA Regulations Compared to MY 2010 Baseline fleet

Standard No. Added Weight in

pounds Passenger Car Added Weight in kilograms Passenger Car Added Weight in

pounds

Light Trucks Added Weight in kilograms Light trucks

126 2.4 1.1 0.5 0.2 214 2.0 0.9 2.0 - ? 0.9 - ? 216 4.0 - ? 1.8 - ? 6.1 - ? 2.8 - ? Ejection Mitigation 1.0 - ? 0.4 - ? 1.0 - ? 0.4 - ?

Total 9.4 - ? 4.2 - ? 9.6 - ? 4.3 - ?

Based on NHTSA’s weight-versus-fuel-ec onomy algorithms, a 3-4 pound increase in weight equates to a loss of 0.01 mpg in fuel economy. Thus, the agen cy’s estimate of the safety/weight effects are 0.024 to 0.032 mpg or mo re for already issued or likely future safety standards.

CONFIDENTIAL SUBMISSIONS Weight Impacts of Potential Futu re Voluntary Safety Improvements At the time the agency requested informati on about fuel economy plans and capabilities for the future, the agency also requested information on weight increases that could occur due to safety improvements. Several manuf acturers provided confidential information about plans they had to meet final rules, proposed safety standard s, or to voluntarily increase safety. The baseline for these plan s was MY 2007. Several of these plans were to meet IIHS offset frontal of side impact testing. Most of these improvements will be installed on vehicles by MY 2011. [ .] Thus, many of them will have occurred by the time this rulemaking is effective. The areas covered above and the remaining areas desc ribed as voluntary safety initiatives that have weight implications and the confidentia l weight estimates are shown in Table IV-3 after MY 2007 and on Table IV-3a for those initia tives taking affect after MY 2011.IV-5

[ Table IV-3 Confidential Submissions on Safety Standa rds and Voluntary Safety Improvements Any model year after MY 2007

DCX Ford GM Pounds Kg. Pounds Kg. Pounds Kg. ESC Side Impact Roof Crush Vehicle Compatibility Ejection Mitigation Pedestrian Protection IIHS frontal pole

test Lane Departure Warning

Total all actions after MY 2007

]IV-6

[ Table IV-3a Confidential Submissions of We ight Impacts After MY 2011

DCX Ford GM Pounds Kg. Pounds Kg. Pounds Kg. ESC Side Impact Roof Crush Vehicle Compatibility Ejection Mitigation Pedestrian Protection Misc. structure for ratings IIHS frontal pole

test Lane Departure Warning

Total all actions after MY 2011IV-7

Vehicle Size and Safety NHTSA believes that an attribute based Refo rmed CAFE system removes the incentive to downsize that is inherent in the traditiona l fleet-wide CAFE flat standard requirement. The agency believes that the attribute based st andard is likely to ha ve beneficial impacts on safety compared to the flat standard. Ot her things being equa l, smaller vehicles provide less protection to their occupants in the event of a crash because there is less vehicle mass to absorb the crash energy and le ss interior space to buffer occupants from sheet metal intrusion. In additi on, smaller vehicles are generall y more likely to roll over. In single vehicle crashes, smaller vehicles ar e less safe than larger vehicles. In multivehicle crashes, both individual vehicle size and the relative size of the involved vehicles play a role in determining the injury outcome of occupants. Gene rally, larger vehicles will provide better protection, but often at the expense of occupants of smaller vehicles. If larger vehicles were to be reduced in si ze, it would likely decrease the chance of injury in crashes with smaller vehicles, but it would also likely increase the chance of injury for the occupants of the larger vehicles. The make up of any future mix-shifts in vehicle sales is purely speculative and the overall impact on injuries in multi-vehicle crashes of any future mix-shifts in vehicle size is unknown. However, downsizing is likely to increase the crash risk for vehicle occupants in singl e vehicle crashes, which make up 30% of all crashes and 57% of all fatalities. An attri bute based system will require improvements in fuel economy for all vehicle sizes, and w ill thus minimize incentives to downsize vehicles.

The NAS study The 2002 National Academy of Sciences (NAS) 22 report made explicit links between weight and vehicle safety. The NAS study conclusions we re divided, with 11 of 13 committee members representing the majority view and 2 of 13 the minority view. The findings of the majority presented on page 77 were: “In summary, the majority of the committ ee finds that the downsizing and weight reduction that occurred in the late 1970s and early 1980s mo st likely produced between 1,300 and 2,600 crash fatalities and between 13,000 and 26,000 serious in juries in 1993. The proportion of these casualties attributable to CAFE standard s is uncertain. It is not clear that significant weight reduction can be achieved in the future without some downsizing, and similar downsizing would be e xpected to produce sim ilar results. Even if weight reduction occurred without any downsizing, casualties would be expected to increase. Thus, any increase in CAFE as cu rrently structured could produce additional road casualties, unless it is specifically targeted at the largest, heavie st light trucks.” ... “Some might argue that this improving safety pi cture means that there is room to improve fuel economy without adverse safety conseque nces. However, such a measure would not achieve the goal of avoiding the adverse safety consequences of fuel economy increases. Rather, the safety penalty imposed by increa sed fuel economy (if we ight reduction is one of the measures) will be more difficult to identify in the light of the continuing improvement in traffic safety. Just because these anticipated sa fety innovations will

22 “ Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards”, National Research Council, 2002. The link for the NAS report is http://www.nap.edu/books/0309076013/html/IV-8

improve the safety of vehicles of all sizes does not mean that downsizing to achieve fuel economy improvements will have no safety costs.

If an increase in fuel economy is effe cted by a system that encourages either downweighting or the production and sale of more small cars, some additional traffic fatalities would be expected. Without a t houghtful restructuring of the program, that would be the trade-off that must be made if CAFE standards are increased by any significant amount.”

The minority view summarized on page 123 was: “The relationship between vehicle we ight and safety are complex and not measurable with any reasonable degree of certain ty at present. The relationship of fuel economy to safety is even more tenuous. But this does not mean that there is no reason for concern. Significant fuel economy impr ovements will require major changes in vehicle design. Safety is always an issu e whenever vehicles must be redesigned. In addition, the distribution of vehicle weights is an importa nt safety issue. Safety benefits should be possible if the weight dist ribution of light-duty ve hicles could be made more uniform, and economic gains might result from even partly correcting the negative externality that encourages i ndividuals to transfer safety risks to others by buying ever larger and heavier vehicles. Finally, it appears that in certain kinds of accidents, reducing we ight will increase safety risk, while in others it may reduce it. Reducing the weights of light-duty vehicles will neither benefit nor harm all highway users, there will be winners and losers....”

The Kahane Study The Kahane study 23 estimates the effect of 100-pound re ductions in heavy light trucks and vans (LTVs), light LTVs, heavy passe nger cars, and light passenger cars. It compares the fatality rates of LTVs and car s to quantify differences between vehicle types, given drivers of the same age/ge nder, etc. Some of its findings are: “Heavy vehicles had lower fatality rates per billion miles of travel than lighter vehicles of the same general type. When tw o vehicles collide, the laws of physics favor the occupants on the heavier vehicle (momen tum conservation). Furthermore, heavy vehicles were in most cases, longer, wider a nd less fragile than light vehicles. In part because of this, they usually had greater crashworthiness, structural integrity and directional stability. They we re less rollover-prone and easier for the average driver to control in a panic situation. In other wo rds, heavier vehicles tended to be more crashworthy and less crash-prone. Some of th e advantages for heavier vehicles are not preordained by the laws of physics, but were nevertheless character istic of the MY 9991- 99 fleet. Offsetting those advant ages, heavier vehicles tended to be more aggressive in crashes, increasing risk to occupants of the vehicles they collide with.”

Six different crash modes were analyzed (principal rollover, fixed object, pedestrian/bicycle/motorcyclist, and multi-vehicle crashes with heavy truck, light trucks,

23 “Vehicle Weight, Fatality Risk and Crash Compa tibility of Model Year 1991-99 Passenger Cars and Light Trucks”, Charles J. Kahane, Ph. D., NHTSA, October 2003, DOT HS 809-662.IV-9

and passenger cars). Summing all these cras h modes together, the net annual effects per 100-pound weight reduction were: For passenger cars weighing less than 2,950 pounds – fatalities increased by 597 For passenger cars weighing 2,950 pounds or more – fatalities increased by 216 For light trucks weighing less than 3,870 pounds – fatalities increased by 234 For light trucks weighing 3,870 pounds or more – fatalities increased by 71

In all cases, annual fatalities increased with a reduction in weight. However, further analysis of the Kahane study found that the net safety effect of removing 100 pounds from a light truck is zero for the group of all light trucks with a curb weight greater than 3,900 lbs. 24 Given the significant statistical uncerta inty around that figure, we assumed a confidence bound of approximately 1,000 lbs. an d used 5,000 lbs. as the threshold for considering weight reduc tion of specific models . In the MY 2008-2011 light truck final rule, NHTSA included weight reduction as a fuel improving technology for light trucks over 5,000 lbs. curb weight where we determin ed that weight reduction would not reduce overall safety and would be a cost-effec tive choice. We are applying the same methodology in this proposal, weight reduction is considered a technology that can be applied to light trucks over 5,000 lbs. curb weight.

The agency believes a number of conclu sions can be drawn from these studies:

• Heavier vehicles are more cr ashworthy and less crash prone. 25

• The net impacts on safety, considering the six different crash modes, of reducing weight are negative for all but the larger light trucks. Howeve r, this type of analysis can not examine extreme cases. For example, if there were a large mix shift from 50 percent passenger car and 50 pe rcent light truck sales, to 80 percent compact or smaller passenger cars and 20 pe rcent pickup truck sale s, this analysis cannot determine the net impacts on safety. Nothing in the ma nufacturer’s plans suggests a drastic change in the mix of ve hicles, nor is there any incentive, in our opinion, for such a change based on NHTSA ’s attribute based proposal on fuel
economy.

• Lighter vehicles fare worse in single vehicle collisions. In 2006, 57 percent of all passenger car and light truck fatalities we re in single vehicle crashes and 43 percent were in multi-vehicle crashes. Fatalities are almost split between rollovers (29 percent) and fixed or non-fixed objects (28 percent).

• Reducing weight increases the likelihood of rolling over. When you are sliding sideways and digging into mud or grass or hit a curb, all things being equal, the lighter vehicle is more prone to rolling ove r. Increasing track width (part of the footprint calculation) reduces the likelihood of rolling over. Track width is more important than weight for rollovers. Ro llover is the only area in which track

24 Kahane, Charles J., PhD, Vehicle Weight, Fatality Ri sk and Crash Compatibility of Model Year 1991-99 Passenger Cars and Light Trucks, October 2003. DOT HS 809 662. Page 161. Docket No. NHTSA-2003- 16318 (http://www.nhtsa.dot.gov/cars/rul es/regrev/evaluate/pdf/809662.pdf)

25 See Kahane study, page xiv Table 3 for prorated fatal crash involvements per billion miles.IV-10

width is the most important factor. Weight is more im portant than track width or wheelbase in the other five crash modes investigated. 26

• Reducing weight increases the likelihood of being killed in a fixed or non-fixed object crash. If you run into a tree, you are safer if you knock that tree down than if the tree stops your vehicle. A heavie r vehicle has a better chance of knocking the tree down.

The Kahane report also examined the total fa tality crash rates in all crash modes; including fatalities to occupant s of the case vehicle (i.e. in rollovers, single vehicle and multi-vehicle crashes), occupants of the othe r vehicle it collided with (to account for aggressive vehicles) and pede strians. Kahane used VM T data based on CDS odometer readings and controlled for age and gende r based on State data on nonculpable crash involvements (induced exposure). With these controls, the societal fatality rates per billion miles were:

TABLE IV-4

ADJUSTED FATAL-CRASH INVOLVEMENT RATES PER BILLION CASE VEHICLE MILES, BY VEHICLE TYPE

(Case vehicles are MY 1996-99 light trucks and 4-door cars with air bags in CY 1996-

2000, adjusted for age/gender, rural/urban, da y/night, speed limit, and other factors)

Average Fatal Curb Crash Involvements Vehicle Type and Size Weight Per Billion Miles

Very small 4-door cars 2,105 15.73 Small 4-door cars 2,469 11.37 Mid-size 4-door cars 3,061 9.46 Large 4-door cars 3,596 7.12 Compact pickup trucks 3,339 11.74 Large (100-series) pickup trucks 4,458 9.56 Small 4-door SUVs 3,147 10.47 Mid-size 4-door SUVs 4,022 13.68 Large 4-door SUVs 5,141 10.03 Minivans 3,942 7.97

In other words, mid-size cars had somewhat lo wer societal fatal crash rates than SUVs that weighed considerably more. Large ca rs and minivans had the lowest rates.

26 See Kahane (Docket No. 2003-16318-16)IV-11

Advocates for Highway and Auto Safety commented that Kahane’s (NHTSA’s) 2003 analysis may not apply if the effects of si ze and weight reducti ons are disaggregated, “weight reductions without corresponding re ductions in vehicle wheelbase length and track width could be expected to produce net benefits in re ducing occupant crash risks.”

NHTSA’s response is that Footpr int (especially track width) is an important variable in terms of a vehicle’s propensity for rollovers, a type of crash that accounts for 29 percent of all light vehicle occupant traffic fatal ities. Track width is one of the two vehicle properties that define Static Stability Factor (SSF). SSF was used as a single predominant factor to predict rollover rate in the agency’s original rollover NCAP, and it is still the most powerful element in the agen cy’s current rollover NCAP risk model that also factors in a road maneuver test. Wheelba se does not have a direct effect on rollover resistance. However, there are hypotheses th at an increase in wheelbase could reduce loss-of-control crashes by ma king the vehicle react slower in yaw and thereby reduce the number of single-vehicle pave ment departure crashes that produce most rollovers. Currently, the agency does not have a ny data to substantiate this theory. 27

We would like to clarify that our analysis does not manda te weight reduction, or any specific technology application fo r that matter. Our analysis relied exclusively on other fuel-saving technologies for passenger cars and only applied weight reduction to light trucks that have a curb weight greater than 5,000 lbs. to demonstrat e that manufacturers can comply with the proposed fuel economy levels without the need for potentially unsafe compliance measures.

Honda cited several reports, which it asserted demonstr ated that limited weight reductions would not reduce safe ty and could possibly decrease overall fatalities. Honda stated that the 2003 study by DRI found th at reducing weight without reducing size slightly decreased fatalities, and that this was confirmed in a 2004 study by DRI 28 that assessed new data and methodology changes in the 2003 Kahane Study. Honda asserted that the DRI results tend to confirm “that cu rb weight reduction w ould be expected to decrease the overall number of fatalities.”

DRI submitted an additional study, Supplemental Results on the Independent Effects of Curb Weight, Wheelbase, and Track Width on Fatality Risk in 1985-1998 Model Year Passenger Cars and 1985-1997 Model Year LTVs , Van Auken, R.M. and J. W. Zellner, May 20, 2005 (Docket No. 2003-16128-1456). This DRI study concluded that reductions in footprint are harmful to safety, whereas reductions in mass while holding footprint constant would benefit safety. The DRI st udy disagreed with NHTSA’s finding that mass had greater influence than track width or wheel base on the fatality risk of passenger cars in non-rollover crashes.

While NHTSA agrees that limited weight re duction to heavier vehicles will not reduce safety, we disagree with DRI’s overall conclusion, cited by H onda, that weight reductions

27 See Kahane (Docket No. 2003-16318-16)

28 See Docket Nos. 2003-16318-2, 2003-16318-3, and 2003-16318-7.IV-12

while holding footprint constant would signifi cantly benefit safety in lighter vehicles. NHTSA’s analyses of the relationships betw een fatality risk, mass, track width and wheelbase in 4-door 1991-1999 passenge r cars (Docket No. 2003-16318-16) found a strong relationship between track width and th e rollover fatality ra te, but only a modest (although significant) relationship between track width and fatality rate in non-rollover crashes. Even controllin g for track width and wheelba se – e.g., by holding footprint constant – weight reduction in the lighter cars is strongly, si gnificantly associated with higher non-rollover fatality rates in the NHTSA analysis. By contrast, the DRI study of May 20, 2005 analyzed 4-door cars and found a strong relationship between track width and fatality risk, and non-signi ficant associations of mass and wheelbase with fatality risk (Docket No. 2005-22223-78, p. 31). In other word s, when DRI analyzed the same group of vehicles as NHTSA, they did not get the sa me results. This difference indicates that DRI’s analytical method and/or databa se are not the same as NHTSA’s.

The agency continues to believe that weight reduction in lighter vehicles would reduce safety. However, we also believe that weight reductions in the heavier light trucks, while holding footprint constant, will not likely result in a net reduction in safety. In our opinion, it is impossible to reduce weight and maintain footprint unless you (a) substitute light for heavy materials in a big way or (b) remove features that customers want and are willing to pay for. In that sense, DRI’s contention that weight is unimportant could only be true for material substitution, because under present circumst ance weight reduction usually also means size reduction, and DRI agrees with NHTSA that a reduction in footprint is harmful to safety.

General Motors (Docket No. 2005-22223-1493) and the Alliance (Docket No. 2005- 22223-1642) were more explicit in their concerns over the safety impact associated with weight reduction. The Alliance stated that the fundamental laws of physics dictate that smaller and/or lighter vehicles are less sa fe than larger/heavier counterparts with equivalent safety designs and equipment. General Motors agreed that improvements in material strength, flexibility, and vehicle de sign have helped improve overall vehicle and highway safety. But, General Motors added, for a given vehicle, reducing mass generally reduces net safety. Further, General Motors stated that it does not intentionally reduce mass by replacing it with advanced materials, presuming that such action alone will result in improved protection for the occupants in a lighter vehicle; instead GM continues to believe that vehicles with larger mass will pr ovide better protection to occupants involved in a crash than a vehicle of the same design with less mass, given e quivalent crashes.

General Motors also questioned the agency ’s reliance on a 5,000 lbs. minimum vehicle weight for considering weight reduction, which was based on the finding of the 2003 Kahane report. General Motors stated that the agency’s conclusion is inconsistent with the sensitivity analysis performed by William E. Wecker Associates, Inc. 29 and submitted to the ANPRM docket. General Motors stat ed that the inflection point on the Wecker report’s graph for General Motors light tr ucks in both the periods of MYs 1991-1995 and MYs 1996-1999 is higher than 5,000 pounds.

29 Docket No. 2003-16128-1112IV-13

Additionally, General Motors stated that the NPRM did not acknowledge or respond to the main point of the Wecker report, which was that Dr. Kahane’s “analysis alone does not support the proposition that a crossover weight at or near 5,085 pounds is a robust, accurate description of the field performance of the fleet.”

We believe that General Motors was confus ing the 5,085 lbs. cross over weight (where the safety effect of mass reduction in a vehi cle weighing exactly 5,085 lbs., is zero) with the breakeven point described in the NPRM, which is the point where the total effect of reducing all vehicles heavier than the break even weight by an equal amount is zero. NHTSA estimated that the breakeven point as described in the NPRM is 3,900 lbs., if footprint is held constant.

If the 3,900 lbs. estimate were perfectly accu rate, we would be confident that weight reductions in vehicles down to 3,900 pounds woul d not result in net harm to safety. However, we agree with commenters that th ere is considerable uncertainty about the crossover weight and also the breakeven point . Therefore, in our analysis, we limited weight reduction to vehicles with a curb weight greater than 5,000 pounds. We believe that the 5,000 lbs. limit is sufficient so that we can be confident that such weight reductions will not have net harm on safety.

SUVOA encouraged NHTSA to emphasize th e importance of making sure that CAFE requirements do not encourage vehicle downsiz ing “or any other action that might have an adverse effect on safety.” SUVOA cited se veral reports in support of its assertion that downsizing harms safety 30 . As explained above, the agen cy has applied weight reduction only to those vehicles for which we are conf ident that such reduction will not negatively impact safety, however, our analysis does not mandate the use of sp ecific technologies or weight reductions.

Environmental Defense (Docket No. 2005- 22223-1805) commented that by limiting the use of weight reduction to heavier vehicles , the agency disregar ded the likelihood that manufacturers would rely on weight reduction in sma ller, lighter vehicles. Environmental Defense suggested that the im proved baselines should reflect this weight reduction strategy.

Environmental Defense asserted that weight reduction is among the most common and cost-effective options availa ble to manufacturers for improving vehicle fuel economy

30 SUVOA (see Docket No. 2003-16128-1067) provided the following cites in support of its assertion:

• 2001, the National Academy of Sciences affirmed that earlier downsizing of vehicles following the imposition of CAFE regulations resulted in an additional 1,300 to 2,600 deaths and an additional 20,000 serious injuries per year.

• A Harvard School of Public Health-Brookings Institution study in the 1990s found that vehicle downsizing due to federal fuel economy mandates increased occupant deaths by 14 to 27 percent.

• An in-depth analysis by USA Today in 1999, using NHTSA and auto insurance industry data, found that since 1975, 7,700 additional deaths occurred for every mile per gallon gained. By 1999, vehicle downsizing had killed more than 46,000 Americans. Factoring in the ensuing six years through 2005, the total conservatively eclipses 55,000 deaths.IV-14

across the light truck fleet. Environmental Defense referenced estimates presented in DeCicco (2005) that suggest that the cost per pound of weight reduced through use of high-strength steel and advanced engineeri ng techniques has been as low as, or lower than, 31 cents per pound reduced.

Moreover, Environmental Defense stated, th e exclusion of mass reduction in NHTSA’s analysis bears no relation to what will actual ly happen in the marketplace when standards are implemented. Environmental Defense argued that absent safety regulations prohibiting the use of mass reductions, ma nufacturers are likely to choose this compliance alternative in vehicl es of all weights as a cost-effective way to comply with CAFE. Environmental Defense stated that NHTSA should consider the potential for mass reduction among its compliance alternatives for all light trucks.

As stated above, the agency does not dictate which fuel-savings technologies must be applied to vehicles. Mass re duction is a compliance alternat ive for all light vehicles. However, one of the considerations in setti ng fuel economy standards is to set standards that will not lead to a reduction in safet y. The standards propos ed by the agency are those capable of being achieved by the manuf acturers without the need to reduce safety. If the agency were to consider weight reduction as a compliance option, we are concerned that the resulting increased stringency would force unsafe downweighting.

A group of experts at a workshop sponsored by the International Council on Clean Transportation (ICCT) examined many of the size/safety reports and wrote a June 2007 report “Sipping Fuel and Saving Lives: In creasing Fuel Economy Without Sacrificing Safety.” 31 NHTSA agrees with two of the thr ee ICCT report findings. We agree that fuel economy technologies exist that don’t affect safety. We agree that reducing weight (on vehicles over 5, 000 lbs) can make certain vehicles less aggre ssive and reduce their weight and probably improve sa fety. Many, but not all of the experts at the workshop, agreed with the last conclu sion: “Advanced technologies can decouple size from mass, creating important new possibilities for incr easing fuel economy and safety without compromising functionality”. We continue to believe, until someone demonstrates to the contrary with some kind of rigorous, scientif ic analysis, that reducing weight on smaller lighter vehicles will only make them more dangerous in single-vehicle crashes, because of fundamental physics.

A study examined similar safety issues - “The “Arms Race” on American Roads: The Effect of Sport Utility Vehicl es and Pickup Trucks of Traffi c Safety”, Michelle J. White, University of California San Diego, Journa l of Law and Economics, Volume XLVII, October 2004. The White paper finds that “W hen drivers shift from cars to light trucks or SUVS, each crash that involves fatalities fr om light truck or SUV occupants that is prevented comes at a cost of at least 4.3 addi tional crashes that i nvolve deaths of car occupants, pedestrians, bicyclis ts, or motorcyclists.”

The White study is an analysis of NHTSA ’s National Automotive Sampling System, General Estimates System and, as such, looks at the fatality risk given that a crash

31 See www.theicct.org/documents/ICCT_Sipping FuelFull_2007.pdfIV-15

occurred. However, it does not control for VMT (likelihood of a crash given a mile of driving). Furthermore, the study does not a ddress the safety of bi g cars vs. small cars or big LTVs versus small LTVs. Whether overall safety would be improved by shifting sales from SUV and pickups to passenger ca rs depends on what size of passenger cars you shifted to (see the table a bove), if you shifted to small or very small passenger cars, overall safety would decrease.

Another study examined the size/safety issues – “The Fatality Risks of Sports Utility Vehicles, Vans, and Pickups Relative to Cars ”, Ted Gayer, Georgetown University, The Journal of Risk and Uncertainty, 28-2, 103-133, 2004. This study finds that “Using a cross-sectional variation in snow depth as an instrument to determine VMT, the results suggest that light trucks are 2.63 to 4.00 times more likely to crash than cars.” “...once one adjusts for the greater frequency of crashe s by light trucks, the aggregate risk they pose substantially dominates the risk from car s. Indeed a world of light trucks would lead to three to ten times more fatal ities than a world of cars.”

This study does not address the safety of big cars vs. small cars or big LTVs versus small LTVs. This analysis using snow depth exa ggerates the difference in crash frequency and fatality rates between passenger cars and light trucks. Kahane’s study also adjusted the raw data for VMT, but we have used odometer readings by ag e of vehicle to control for VMT and found no such discrepancy in crash ra tes. The table above from the Kahane study does not show light trucks having substan tially higher fatality rates than passenger

cars.

A 2001 study by Dr. Leonard Evans, 32 modeled the risk of driver fatality in car 1 in a head-on collision with car 2. The equations in the report indi cate that reducing the curb weight of car 1 would increase the risk to the driver of car 1, while reducing the curb weight of car 2 would decrease the risk to the driver of car 1. However, the equations also indicate that reducing the wheelbase of either car increases the total risk to both drivers.

In a 2004 SAE paper, Dr. Evans claimed th at increasing the amount of lightweight materials in vehicle design can provide redu ced occupant risk bot h in two-vehicle and single-vehicle crashes, and also reduce risk for occupants in other vehicles 33 . However, he produced no analysis using real world data of vehicles with light weight material to substantiate that claim 34

.

32 Evans, L., “Causal Influence of Car Mass and Size on Driver Fatality Risk”, American Journal of Public Health, Vol. 91, No

. 7, July 2001, pp 1076-1081.

33 Evans, L., “How to make a car lighter and safer,” SAE 2004-01-1 172, Society of Automotive Engineers, 11 March 2004.

34 In NHTSA’s opinion, there are not enough vehicles made from lightweight material on the road to support an analysis using real world crash data.IV-16

In an amicus brief, the Insura nce Institute for Highway Safety 35 stated “Crash safety should be a consideration in how the balance is struck between programs to improve air quality and our efforts to protect people in cras hes. Physics dictates that vehicle weight and size will always matter in a crash. Research in the private, public, and nonprofit sectors have demonstrated the relationship between vehicle size and weight and crash injuries. Simply put, Vermont’s regulation encourages production of smaller, lighter vehicles which will lead to increased traffi c fatalities.” IIHS discusses research by NHTSA and IIHS that have led to the c onclusion that “Vehicle downsizing has compromised safety because in most cases , smaller and lighter vehicles are less protective of their occupants than larger, heav ier vehicles.” IIHS calculated the vehicle death rates by make model using driver de aths per million registered vehicle years, presented this data, and ranked them. “None of the 15 vehicles with the lowest driver death rates were mini or small models. ... El even of the 16 vehicles with the highest driver death rates were small cars and none we re large or very large. ... The pattern is unmistakable. There is an inverse relations hip between driver death rates and vehicle size...”

Footprint and safety The impact of CAFE standards on motor ve hicle and passenger safety has long been recognized as an integral part of the agency 's process of determining maximum feasible average fuel economy. The agency notes th at there are no compelling studies that quantify the precise and separa te effects of vehicle size a nd weight on safety, in part because there is a high degree of correlation between size and weight among vehicles now in widespread use. The agency has dete rmined that an attri bute system based on footprint with the continuous function would minimize incentives for design changes that would reduce motor vehicle safety. In a we ight-based system, a manufacturer can add weight to a vehicle in order to take adva ntage of a category with a lower fuel economy target. As discussed above, this up-wei ghting can have positive and negative safety implications, with possibly negative impacts for the fleet as a whole if weight is added to heavier light trucks. A manuf acturer could not as readily in crease footprint as it could vehicle weight.

In order to increase footprint, a manufacturer would have to either extend a vehicle’s track width, wheelbase, or both. Maintaini ng and increasing track width should play a positive role in limiting rollover vulnerability, whereas maintaining and increasing wheelbase should play a positive role in im proving handling – especially directional stability, which is crucial in preventing unintended off-road ex cursions that often lead to rollovers – and maximizing crush space (t hough total length is probably more closely correlated with crush space than is wheelbase).

This is mentioned in Dr. Kahane’s res ponse to safety studies submitted by Dynamic Research, Inc., by Marc Ross (University of Michigan) and Tom Wenzel (Lawrence Berkeley National Laboratory), and submitte d by William E. Wecker Associates.

35 Filed in the United States Court of Appeals for the Second Circuit, March 21, 2008, No. 07-4342-cv(L), March 21, 2008, Green Mountain Chrysler Plymouth Jeep..., by Michele Fields and Stephen L. Oesch of IIHS.IV-17

Dr. Kahane wrote: ”The objective of the NHTSA study was to calibrate the historical (MY 1991-99) relationships of vehicle mass and fatali ty risk, after controlling for driver age/gender, geographical lo cation, and vehicle equipment. In this type of analysis, “vehicle mass” incorporates not only the effects of ma ss per se but also the effects of many other size attributes that are historically and/or causally related to mass, such as wheelbase, track width and structur al integrity. (As vehicles get longer and wider, th ey almost always get heavier.)

The study does not claim that mass per se is the specific factor that increases or decreases fatality risk (except in its role in determining the relative Delta V of two vehicles that collide). On the contra ry, Chapter 5 of the NHTSA report shows that certain 4,000-pound SUVs have signifi cantly higher fatal- crash rates than 3,500-pound cars. The study only shows the historical relationship between mass – taking into account all the ot her size attributes that ha ve typically varied with mass – and fatality risk, for vehicles of th e same type. If historical relationships between mass and other size attributes c ontinue, in the abse nce of compelling reasons that would change those relationshi ps, future changes in mass are likely to be associated with similar changes in fata lity risk. (However, the increased use of advanced restraint systems and sophisticat ed crash avoidance safety devices in recent and future production vehicles could have a noticeable impact on the historical relationship between vehicle ma ss and fatality risk in future vehicle fleets.)

In that sense, it is irrelevant whether mass, wheelbase, track width or some other attribute is the principal cau sal factor on fatality risk. If you decrease mass, you will also tend to reduce wheelbase, track wi dth and other dimensions of size. If manufacturers respond to this proposal by building lighter vehicles of constant size, the historical relationship be tween mass and safety would gradually weaken.”

Changes in technology could influence the rela tionship between weight and size. There is emerging evidence that vehicle weight can be reduced without re ductions in size or safety through the use of high strength, lig htweight materials. Currently, we do not observe many vehicles built with lightweight mate rials in the historical data and therefore cannot separate the impact of size versus wei ght when lightweight materials are utilized. However, the impact of weight, whether it comes from reducing size or material substitution, will be the same for single vehicle impacts (rollovers and fixed and nonfixed object impacts).

Attribute-based standards eliminate the incen tive for manufacturers to respond to CAFE standards in ways harmful to safety. 36 Because each vehicle model has its own target

36 The 2002 NAS Report, on which NHTSA relied in reforming the CAFE program for light trucks, described at length and quantified the potential safety problem with average fuel economy standards that specify a single numerical require ment for the entire industry. See National Academy of Sciences, “Effectiveness and Impact of Corp orate Average Fuel Economy (CAF E) Standards,” (“NAS Report”)IV-18

(based on the attribute chosen), attribute- based standards provide no incentive to build smaller vehicles simply to meet a fleet-wide average, because the smaller vehicles will be subject to more stringent fuel economy and emissions targets.

The Impact of Emission Standards

EPA Labeling Rule: Fuel Economy Labe ling of Motor Vehicles: Revisions to Improve Calculation of Fuel Economy Estimates

The EPA Fuel Economy Labeling Rule 37 employs a new vehicle-specific, 5-cycle approach to calculating fuel economy labels which incorporates estimates of the fuel efficiency of each vehicle during high speed, aggressive driving, air conditioning operation and cold temperatures into each vehicl e’s fuel economy label. The rule became effective January 26, 2007, and will ta ke effect starting with MY 2008.

The new testing procedures will combine m easured fuel economy over the two current fuel economy tests, the FTP and HFET, as well as that over the US06, SC03 and cold FTP tests into estimates of city and highw ay fuel economy for labeling purposes. The test results from each cycle will be weighted to represent the contribution of each cycle’s attributes to on-road driving and fuel consumption. The la beling rule does not alter the FTP and HFET driving cycles, the measuremen t techniques, or the calculation methods used to determine CAFE.

The EPA Labeling Rule will not im pact CAFE standards or test procedures or other U.S. Government regulations. 38 Rather, the changes to existing test procedures will allow for the collection of appropriate fu el economy data to ensure th at existing test procedures better represent real -world conditions. 39 Further, the labeling ru le does not have a direct effect upon a vehicle’s weight, nor on the fuel economy level th at a vehicle can achieve. Instead, the labeling law serves to provide consumers with a more accurate estimate of fuel economy based on more comprehensive fact ors reflecting real-wor ld driving use. For this PRIA, the agency assumes that on-road mileage will be 80 percent of the measured CAFE, based on the FTP and HFET driving cycles, the measurement techniques, and the calc ulation methods used to determine CAFE.

National Academy Press, Washington, DC (2002), 5, finding 12. Available at http://www.nap.edu/openbook.php?record_id=10172 &page=R1 (last accessed Dec. 2, 2007).

37 See 71 FR 77872.

38 See 71 FR 77872, section I.F.

39 See 71 FR 77872, section II., IV.V-1

V. FUEL ECONOMY ENHANCING TECHNOLOGIES AND THE V OLPE M ODEL

A. Comparison of current with past approaches to analysis of technologies, their cost and effectiveness

In the Agency’s last two rulemakings cove ring light truck CAFE standards for MYs 2005 – 2007 and MYs 2008 – 2011, the agency relied on the 2002 National Academy of Sciences’ report, Effectiveness and Impact of Corporate Average Fuel Economy Standards (“the 2002 NAS Report”) 40 for estimating potential fuel economy benefits and associated retail costs of applying combinations of t echnologies in 10 classes of production vehicles. The NAS cost and effec tiveness numbers were the best available estimates at this time, determined by a panel of experts formed by the National Academy of Sciences, and the report had been peer re viewed by individuals chosen for their diverse perspectives and technical expertise in accordance with procedures approved by the Report Review Committee of the National Re search Council. However, since the publication of the 2002 NAS Report, there has been substantial advancement in fuelsaving technologies, including technologies not discussed in the NAS Report that are expected to appear on vehicles in the MY 2011-2015 timeframe. There also have been reports issued and studies conducted by severa l other organizations and companies that discuss fuel economy technologies and their be nefits and costs. NHTSA has contracted with the NAS to update the fuel economy s ection, Chapter 3, of the 2002 NAS Report. However, this update will not be available in time for this rulemaking. Due to the expedited nature of this rulemaking, NHTSA, in consultation with the Environmental Protection Agency (EPA), developed an update d technology cost and e ffectiveness list to be used in this notice.

This list presents NHTSA and EPA technical staff’s current assessment of the costs and effectiveness from a broad range of technologi es which can be appl ied to cars and lightduty trucks. EPA published the results of this collaboration in a report and submitted it to the NAS committee 41 . A copy of the report and other studies used in the technology update will be placed in NHTSA’s docket.

NHTSA believes that the estimates used for th is notice, which rely on the best available public and confidential information, are defens ible and reasonable predictions for the next five years. Nevertheless, NHTSA still believes that the ideal source for this information comes from a peer reviewed process such as the NAS. NHTSA will continue to work with NAS to update this lis t on a five year interv al as required by the Energy Independence and Security Act of 2007.

The majority of the technologies discussed in this section are in production and available on vehicles today, either in the United St ates, Japan, or Europe. A number of the

40 National Research Council, “Effectiveness and Imp act of Corporate Averag e Fuel Economy (CAFE) Standards,” National Academy Press, Washington, DC (2002). Available at http://www.nap.edu/openbook.p hp?isbn=0309076013 (last accessed Feb. 5, 2008).

41 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, March, 2008V-2

technologies are commonly available, while others have only recently been introduced into the market. In a few cases, we provide estimates on technologies which are not currently in production, but are expected to be so in the next few years. These technologies which can be applied to cars a nd trucks that are capable of achieving significant improvements in fuel economy a nd reductions in carbon dioxide emissions, and improve vehicle fuel economy, at reasonable costs.

NHTSA and EPA conducted the technology ex amination using concepts from the 2002 NAS report which constituted a starting point for the analysis. In the NAS Report, there were three exemplary technology paths or scenarios identified for each class of production vehicles, which lead to succe ssively greater improvements in fuel consumption and greater costs. Path I in cluded production-intent technologies that will be available within 10 years and could be implemented under current economic and regulatory conditions. Path II included more costly production-intent technologies that are technically feasible for introduction w ithin 10 years if economic and regulatory conditions justify their use. Path III in cluded emerging technologies that will be available within 10 to 15 years but that may require further development prior to commercial introduction. These three paths represented vehicle development steps that would offer increasing levels of fuel economy gains (as incremental gains) at incrementally increasing cost. As stated earlier, since the publi cation of the 2002 NAS Report, automotive technology has continued to advance and many of the technologies that were identified in the report as emergi ng have already entered the marketplace.

In this rulemaking, NHTSA working with EPA have examined a variety of technologies and looked beyond path I and path II to path III and to emerging technologies beyond path III. These technologies were in th eir infancy when the 2002 NAS Report was being formulated. In addition, unlike for past ru lemakings where NHTSA projected the use of different variants of a technology as a co mbined technology, in this rulemaking, NHTSA working with EPA examined advanced forms and subcategories of existing technologies and reflected the effectiveness and cost for each of the variants separately for all ten vehicle classes. The specific technologies affected are variable valve timing (VVT), variable valve lift and timi ng (VVLT) and cylinder deactivation. Manufacturers are currently using many different types of VVTs and VVLTs, which have a variety of different names and methods. This rulemaki ng employs specific co st and effectiveness estimates for variants of VVT, including Intake Camshaft Phasing (ICP), Coupled Camshaft Phasing (CCP), and Dual (Independe nt) Camshaft Phasing (DCP). It also employs specific cost and effectiveness es timates for variants of VVLT, including Discrete Variable Valve Lift (DVVL) and Continuous Variable Valve Lift (CVVL). We also now include the effectiveness and cost es timates for each of the variants of cylinder deactivation. The most common type of cyli nder deactivation is one in which an eightcylinder overhead valve engine di sables four of its cylinders under light loads. Cylinder deactivation could be incorporated on overhead cam engines, and can be applied to four and six cylinder engines as we ll (we have restricted appl ication to 6 and 8 cylinder engines). Thus, the variants of cylinder d eactivation that now have specific cost and effectiveness estimates include both overhead valve engine cylinder deactivation and overhead cam engine cylinder deactivation.V-3

The update also revisited technology lead time issues and took a fresh look at technology application rates, how to link certain technologies to certain redesign and refresh patterns, synergistic impacts resulting from adding t echnology packaging, and learning costs.

1. Data sources for technology assumptions

A large number of technical reports and pape rs are available which contain data and estimates of the fuel economy improvements of various vehicle technologies. In addition to specific peer-reviewed papers respecting individual technologies, we also utilized a number of recent reports which had been uti lized by various State and Federal Agencies and which were specifically undertaken for th e purpose of estimating future vehicle fuel economy reduction effectiveness or improveme nts in fuel economy. The reports we utilized most frequently were:

• 2002 National Academy of Science (NAS) re port titled "Effectiveness and Impact of Corporate Average Fuel Economy Standa rds”. At the time it was published, the NAS report was considered by many to be the most comprehensive summary of current and future fuel efficiencies imp rovements which could be obtained by the application of individual t echnologies. The focus of this report was fuel economy, which can be directly correlated wi th CO2 emissions. The 2002 NAS report contains effectiveness estimates for ten di fferent vehicle classifications (small car, mid-SUV, large truck, etc), but did not di fferentiate these effectiveness values across the classes. Where other sources or engineering principles indicated that a differentiation was warranted, we utilized the 2002 NAS effectiveness estimates as a starting point and furt her refined the estimate to one of the vehicle classes using engineering judgment or by cons ulting additional reliable sources.

• 2004 Northeast States Center for a Clean Air Future (NESCCAF) report "Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles". This report, which was utilized by the Califo rnia Air Resources Board for their 2004 regulatory action on vehicle CO2 emissi ons, includes a comprehensive vehicle simulation study undertaken by AVL, a worl d-recognized leader in automotive technology and engineering. In addition, the report include d cost estimates developed by the Martec Group, a market -based research and consulting firm which provides services to the au tomotive industry. The NESCCAF report considered a number of technologies not examined in the 2002 NAS report. In addition, through the use of vehicle simulation modeling, the 2004 NESCCAF report provides a scientifically rigorous es timation of the syne rgistic impacts of applying multiple fuel economy t echnologies to a given vehicle.

• 2006 Energy and Environmental Analysis Inc (EEA) report “Technology to Improve the Fuel Economy of Light Duty Trucks to 2015” Prepared for The U.S. Department of Energy and The U.S. Depart ment of Transportation. This update of technology characteristics is based on ne w data obtained by EEA from technology suppliers and auto-manufactur ers, and these data are compared to data fromV-4

studies conducted earlier by EEA, the Nati onal Academy of Sciences (NAS), the Northeast States Center for a Clean Ai r future (NESCCAF) and California Air Resources Board (CARB).

• Data from Vehicle Manufacturers, Com ponent Suppliers, and other reports. We also evaluated confidential data from a number of vehicle manufacturers as well as a number of technology component s uppliers. In February of 2007, the NHTSA published a detailed Request for Comment (RFC) in the Federal Register. This RFC included, among other items, a reque st for information from automotive manufacturers and the public on the fu el economy improvement potential of a large number of vehicle technologies. The manufacturer’s submissions to this RFC were supplemented by confidential briefing and data provided by vehicle component suppliers, who for many of the technologies considered are the actual manufacturers of the specific te chnology and often unde rtake their own development and testing efforts to inve stigate the fuel economy improvement potential of their products. Manufactur ers that provided NHTSA and EPA with fuel economy cost and effectiveness es timates include BMW, Chrysler, Ford, General Motors, Honda, Nissan, Toyota and Volkswagen. The major suppliers that provided NHTSA with fuel economy cost and effectiveness estimates include Borg-Warner, Bosch, Corning, Delphi, and Siemens.

• Finally, to verify that the fuel economy cost and effectiveness estimates for each of the technologies was reasonable and w ithin currently available estimates for these technologies, NHTSA examined t hose estimates provided by other reports or sources, such as the Martec (con tained in the 2004 NESCAFF report) and Sierra Research reports 42

.

2. Technologies and estimates of costs and effectiveness

This section describes each technology and associated cost and effectiveness numbers. The technologies can be classified into fi ve main groups similar to how they were classified in the NAS Report: engine technologies; transmi ssion technologies; accessory technologies; vehicle technologi es; and hybrid technologies.

While NHTSA and EPA followed the general approach taken by the NAS in estimating the cost and effectiveness numbers, we deci ded to update some of these estimates to reflect better the changed marketplace and regulatory environment, as well as the advancement in and greater penetration of some production-in tent and emerging technologies, which have led to lower cost s. The values contained in the 2002 NAS report were used to establish a baseline for the fuel economy cost and effectiveness estimates for each of the technologies. We th en examined all other estimates provided by manufacturers and major suppliers or other sour ces. In examining these values, we gave

42 “Alternative and Future T echnologies for Reducing Greenhouse Gas Emissions from Road Vehicles” Sierra Research Report for Environment Canada, 1999 (SR99-07-01). http://www.sierraresear ch.com/ReportListing.htmV-5

more weight to values or estimates pr ovided by manufacturers that have already implemented these technologies in their fleet, especially those that have introduced them in the largest quantities. Likewi se, for technologies that have not penetrated the fleet to date, but will by early in the next decade (according to confidentia l manufacturer plans), we gave more weight to valu es or estimates provided by manu facturers that have stated that they will be introducing th ese technologies in their fleet, es pecially those that plan to introduce them in the largest quantities. In addition, for the technologies that will appear on vehicles by early in the next decade, we carefully examined the values provided by those suppliers who have developed these tec hnologies and may have contracts in place to provide them to manufacturers.

Because not all technologies can be applie d on all types of vehicles, engines or transmissions, we separately evaluated 10 classe s of vehicles to estimate fuel economy cost and effectiveness for each of the tec hnologies. As discussed above, these ten classes, also used in NHTSA’s 2006 light tr uck CAFE rule, were derived from the 2002 NAS Report, which estimated the feasibility, potential incremental fuel consumption benefit and the incremental cost of three pr oduct development paths for the following ten vehicle classes: subcompact passenger cars, compact passenger cars, midsize passenger cars, large passenger cars, small sport utility ve hicles, midsize sport utility vehicles, large sport utility vehicles, small pic kups, large pickups, and minivans.

The application of technologies to a vehi cle class is limited not only by whether the manufacturer is capable of appl ying it within a particular de velopment cycle, but also by whether the technology may physically be ap plied to the vehicle. For example, continuously variable transmissions (CVTs) were only allowed to be projected on vehicles with unibody construction, which incl udes all passenger cars and minivans and some small and midsize SUVs. CVTs could not be projected for use on vehicles with ladder-frame construction, which includes all pickups and large SUVs and some small and midsize SUVs. Another example is cylinder deactivation being limited to vehicles with 6- or 8-cylinder engines. To simplify the analysis, NHTSA assumed that each class of vehicles would typically ha ve vehicle construction and e ngines with a specific number of cylinders that is most repres entative of that vehicle class.

Although we looked at ten vehicle classes separa tely, for some technologies the estimated incremental fuel consumption benefit and in cremental cost were the same across all vehicle classes (as for engine accessory im provement), while for other technologies the estimated incremental fuel consumption benefit and incremental cost differed across classes (as for hybrid drivetrains). The ma in difference was with which path(s) each technology was expected to be associated. .

The exact cost and benefit of a give n technology depends on specific vehicle characteristics (size, weight, base engi ne, etc.) and the existence of additional technologies that were already applied to the vehicle. In the section below, ranges of incremental cost and fuel consumption re duction values are listed where the values depend on vehicle characteristics and are inde pendent of the order in which they are applied to a vehicle. All costs, which are re flective of estimated retail price equivalentsV-6

(RPEs) were inflated by the producer price index (if needed ) and are presented in year 2006 dollars, because this is the last year for which final econo mic indexing is available. Some cost estimates are based on supplier cost s. In those instances, multipliers were included in those costs so that they would be treated in the same manner as cost estimates that are based on manufacturer costs. Thes e incremental values were calculated by subtracting out all same-pat h synergies associated with a given technology and any preceding items on the same path. Essentiall y, the incremental percent reduction in fuel consumption and cost impacts represent impr ovements beyond the ones realized due to technologies already applied to the vehicle. As an example, a 5-speed automatic transmission could incrementally reduce fuel consumption by 2 to 3 percent at an incremental cost of $75 to $165 per vehicle, relative to a 4-speed automatic transmission. In turn, a 6-speed automatic transmission c ould incrementally reduce fuel consumption by 4.5 to 6.5 percent at an incremental cost of $10 to $20 per vehicle, relative to a 5- speed transmission.

NHTSA acknowledges that this approach is different from the one it followed in establishing the reformed light truck st andards for MYs 2008-2011, where we relied nearly exclusively on the 2002 NAS report’s es timates. Our preference remains to rely upon peer-review and credible studies, such as the 2002 NAS report; however we believe that the estimates made by the joint EPA/ NHTSA team are accurate and defensible. The agency seeks comments on our assumptions and the cost, effectiveness and availability estimates provided. NHTSA also seeks comm ents on whether the order in which these technologies was applied by the Volpe model is proper and whether we have accurately accounted for technologies already included on vehicles and whether we have accurately accounted for technologies that are projected to be applied to vehicles. The agency also seeks comments on the “synergy” factors (discu ssed below) it has applied in order to adjust the estimated incremental effectiv eness of some pairs of technology and on whether similar adjustments to the estimated incremental cost of some technologies should be made. In preparation for a final rule, NHTSA intends to update its technologyrelated methodologies and estimates, and exp ects that these antic ipated updates will affect the form and stringency of the final standards.

a. Engine technologies

Low-Friction Lubricants

The use of lower viscosity engine and tr ansmission lubricants can reduce fuel consumption. More advanced multi-viscosity engine and transmission oils are now available with improved performance in a wi der temperature band, with better lubricating properties. However, even without any cha nges to fuel economy standards, most MY 2011-2015 vehicles are likely to use 5W-30 motor oil, and some will use even less viscous oils, such as 5W-20 or possibly even 0W-20 to reduce cold start friction. This may directionally benefit the fuel economy im provements of valvetrain technologies such as cylinder deactivation, which rely on a minimum oil temperature (viscosity) for operation. Most manufacturers therefore attr ibuted smaller potential fuel economy reductions and cost increases to lubricant improvements.V-7

The NAS Report projected that low-friction lu bricants could incrementally reduce fuel consumption by 1 percent at an incremental cost of $8 to $11. 43 The NESCCAF study projected that low-friction lubricants coul d incrementally reduce fuel consumption by 1 percent at an incremental cost of $5 to $15; while the EEA repor t projected that lowfriction lubricants could incrementally re duce fuel consumption by 1 percent at an incremental cost of $10 to $20. In contrast, ma nufacturer data projec ted an estimated fuel consumption potential of 0% to 1% at an incremental cost that ranged from $1 to $11, with many of them stating the costs as ranging from $1 to $5. NHTSA believes that these manufacturer estimates are more accurate and estimates that low-friction lubricants could reduce fuel consumption by 0.5 percent for all ve hicle types at an incremental cost of $3, which represents the mid-point of $2.50, rounded up to the next dollar.

Reduction of Engine Friction Losses

All reciprocating and rotating components in the engine are ca ndidates for friction reduction, and minute improvements in several components can add to a measurable fuel economy improvement. The amount of energy an engine loses to fric tion can be reduced in a variety of ways. Improvements in the design of engine components and subsystems will result in friction reduction, improved engine operation, greater fuel economy and reduced emissions. Examples include low-te nsion piston rings, roller cam followers, crankshaft design, improved material coatin gs, material substitution, more optimal thermal management, piston surface treatment s, and as lubricant friction reduction. Additionally, as computer-aided modeling software continues to improve, more opportunities for incremental frict ion reduction might become apparent. Even without any changes to fuel economy standards, most MY 2010-2015 vehicles are likely to employ one or more such techniques to reduc e engine friction and other mechanical and hydrodynamic losses.

The NAS Report predicted that such technol ogies could incrementally reduce fuel consumption by 1 to 5 percent at an in cremental cost of $36 to $146. NESCCAF predicted that such technol ogies could incrementally reduce fuel consumption by 0.5 percent at an incremental cost of $5 to $15; while the EEA report predicted that such technologies could reduce fuel consumpti on at an incremental cost of $10 to $55. Confidential manufacturer data indicate s that engine friction reduction could incrementally reduce fuel consumption by 1 to 3 percent at an incremental cost of $0 to $168. Based on available information from th ese reports and confidential manufacturer data, NHTSA estimates that friction reducti on could reduce fuel consumption for all vehicles by 1 to 3 percent at a cost of $21 pe r cylinder. Thus, the incremental cost of engine friction reduction for a 4-cylinder engi ne is $0 to $84 (applicable to subcompact and compact cars); for a 6-cylinder engine is $0 to $126 (applicable to midsize cars, large cars, small pickups, small SUVs, minivans and midsize SUVs); and for an 8-cylinder engine is $0 to $168 (applicable to large pickups and SUVs).

43 The price increases noted in this chapter are sligh tly higher than shown in the NAS study, since they have been converted into calendar year 2006 prices.V-8

Multi-Valve Overhead Camshaft Engine

It appears likely that many vehicles would still use overhead valve (OHV) engines with pushrods and one intake and one exhaust valv e per cylinder during the early part of the next decade. Engines with overhead cams (O HC) and more than two valves per cylinder achieve increased airflow at high engine speeds and reductions of the valve train’s moving mass and enable central positioning of spark plugs. Such engines, which are already used in some light trucks, typically develop higher power at high engine speeds. The NAS Report projected that multi-valve OHC engines could incrementally reduce fuel consumption by 2 percent to 5 percent at an incremental cost of $109 to $146, and NHTSA found no sources to update these projections.

For purposes of this rule, OHV engines and OHC engines were considered separately, and the model was generally not allowed to apply mu ltivalve OHC technology to OHV engines, except where continuous variable valve timing and lift (CVVL) is applied to OHV engines. In that case, the model assumes conversion to DOHC valvetrain, because DOHC valvetrains are prerequisites for the application of any adva nced engine technology over and above CVVL. Since applying CVVL to an OHV is the last im provement that could be made to such an engine, it’s logical to assume that manufacturers would redesi gn that engine as a DOHC and include CVVL as part of that redesign.

For 4-cylinder engines we estimated that th e cost to redesign an OHV engine as a DOHC that includes CVVL would be $599 ($169 fo r conversion to DVVL, $254 for conversion to CVVL, and $176 for conversion to DOHC, which comprises an additional camshaft and valves), with estimated fuel consumption reduction of 2 to 3 percent. For 6-cylinder engines we estimated that th e cost to redesign an OHV engi ne as a DOHC that includes CVVL would be $1262 ($246 for conversion to DVVL, $488 for conversion to CVVL, and $550 for conversion to DOHC, which compri ses an additional camshaft and valves), with estimated fuel consumption reduction of 1 to 4 percent. For 8-cylinder engines we estimated that the cost to redesign an OHV engine as a DOHC that includes CVVL would be $1380 ($322 for conversion to DVVL, $508 for conversion to CVVL, and $550 for conversion to DOHC, which comprises an additional camshaft and valves), with estimated fuel consumption reduction of 2 to 3 percent. Incremental cost estimates for DVVL and CVVL are discussed below.

NHTSA believes that the NESCCAF report and confidential manufactur er data are more accurate, and thereby estimates that a conversi on of an OHV engine to a DOHC engine with CVVL could incrementally reduce fuel consumption by 1 to 4 percent at an incremental cost of $599 to $1,380 compared to an OHV with VVT.

Cylinder Deactivation

For the vast majority of vehicles, each cyli nder is always active while the engine is running. Under partial load condi tions, the engine’s specific fuel consumption could be reduced if some cylinders coul d be disabled, such that th e active cylinders operate at higher load. In cylinder deactivation, some (usually half) of th e cylinders are “shutV-9

down” during light load operation – the valves are kept close d, 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 minimal fric tion and heat losses. 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.

The theoretical engine operating region for cylinder deactivation is limited to no more than roughly 50% of peak power at any gi ven engine speed. In practice, however, cylinder deactivation is employed primarily at lower engine cruising loads and speeds, where the transitions in and out of deactivation mode are less apparent to the operator and where the noise and vibration (NVH) associat ed with fewer firing cylinders may be less of an issue. Manufacturers are exploring the possibilities of increasing the amount of time that part-cylinder mode might be suitable to a vehicle with more refined powertrain and NVH treatment strategies.

General Motors and Chrysler Group have in corporated cylinder deactivation across a substantial portion of their V8-powered lineups. Honda (Odyssey, Pilot) and General Motors (Impala, Monte Carlo) offer V6 models with cyli nder deactivation.

There are two variants of cylinder deactivation. The most common type of cylinder deactivation is one in which an eight-cyli nder overhead valve engi ne disables four cylinders under light loads. Thus an eight -cylinder engine could disable four cylinders under light loads, such as when the vehicle is cruising at highway speed. This technology could be applied to four and six cylinder engines as well. General Motors and Chrysler Group have incorporated cylinde r deactivation across a substantial porti on of their V8- powered overhead valve lineups.

Cylinder deactivation could be incorporated on overhead cam engines and can be applied to four and six cylinder engines as well. Honda has already begun offering three V6 models with cylinder deactiva tion (Accord, Odyssey, and Pilo t) and GM will soon release cylinder deactivation on its 3.9L 6-cylinder engine. Fuel economy improvement potential scales roughly with engine displacement-to-vehic le weight ratio: the higher displacement-to-weight vehicl es, operating at lower rela tive loads for normal driving, have the potential to operate in pa rt-cylinder mode more frequently.

Honda’s technology includes the use of active engine mounts and noise damping amongst other items added to its V6 engines with cyli nder deactivation. This, of course increases the cost relative to a four or eight cylinder OHC engine.

Some manufacturers are getting results in ex cess of 6 percent and most are at the high end of the range. This higher number is suppor ted by official fuel economy test data on a V6 Honda Odyssey with cylinder deactivation compared to the same vehicle (and engine displacement) without cylinder deactiva tion and by confidential manufacturer information.V-10

The NAS Report projected that cylinder deac tivation could incrementally reduce fuel consumption by 3 percent to 6 percent at an incremental cost of $112 to $252. The NESCCAF study projected that cylinder deac tivation could incrementally reduce fuel consumption by 1.7 percent to 4.2 percent at an incremental cost of $161 to $210; while the EEA report projected that cylinder deactivation could incrementally reduce fuel consumption by 5.2 percent to 7.2 percent at an incremental cost of $105 to $135. Confidential manufacturer data a nd official fuel economy test data indicates that cylinder deactivation could incrementally reduce fuel consumption by at least 6 percent at an incremental cost of $203 to $229. NHTSA be lieves that these manufacturer estimates are more accurate and thus estimates that cylinder deactivation could reduce fuel consumption by 4.5 percent to 6 percent at an incremental cost of $203 to $229.

Variable Valve Timing

Variable valve timing is a classification of va lvetrain designs that alter the timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, increase specific power, and control residual gases. VVT re duces pumping losses when the engine is lightly loaded by positioning the valve at the optimum position needed to sustain horsepower and torque. VVT can also improve thermal efficiency at higher engine speeds and loads. Additionally, VVT can be used to alter (and optimize) the effective compression ratio where it is advantageous for certain engine operating modes.

Variable valve timing has been available in the market for quite a while. By the early 1990s, VVT had made a significant market pe netration with the arrival of Honda’s “VTEC” line of engines. VVT has now beco me a widely adopted technology: for the 2007 model year, over half of all new cars a nd light trucks have engines with some method of variable valve timi ng. Therefore, the degree of further improvement across the fleet is limited to vehicles that ha ve not already implemented this technology.

Manufacturers are currently using many differe nt types of variable valve timing, which have a variety of different names and methods. The major types of VVT are listed below:

Intake Camshaft Phasing (ICP)

Valvetrains with ICP – the simplest type of cam phasing - can modify the timing of the intake valve while the exhaust valve timing rema ins fixed. This requires the addition of a cam phaser for each bank of intake valves on the engine. An in-line 4-cylinder engine has one bank of intake valves, while V-configured engines would have two banks of intake valves. The NAS Report projected that ICP c ould incrementally reduce fuel consumption by 3 percent to 6 percent at an incremental cost of $35; while the EEA report projected that ICP could reduce fuel consumption at an incremental cost of $35. The NESCCAF study projected that ICP could incrementally reduce fuel consumption by 1 percent to 2 percent at an incremental cost of $49. C onsistent with the EEA report and NESCCAF study, we have used this $35 ma nufacturer cost to arrive at incremental cost of $59 per cam phaser or $59 for an in-line 4 cylinder and $119 for a V-type, thus NHTSA estimatesV-11

that ICP could incrementally reduce fuel cons umption by 1 to 2 percent at an incremental cost of $59 to $119 above fixed-cam valvetrains.

Coupled Camshaft Phasing (CCP)

Coupled (or coordinated) cam phasing is a desi gn in which both the intake and exhaust valve timing are varied with the same cam phase r. For an overhead cam engine, the same phaser added for ICP would be used for CCP co ntrol. As a result, its costs should be identical to those for ICP. For an overh ead valve engine, only one phaser would be required for both inline and V-configured engines since only one camshaft exists. Therefore, for overhead valve engines, the co st is estimated at $59 regardless of engine configuration, using the logic provided for ICP.

The NESCCAF study projected that CCP coul d incrementally reduce fuel consumption by 1 percent to 3 percent above that obtaine d by ICP. Confidentia l manufacturer data also projects that that CCP could incrementa lly reduce fuel consumption by 1 percent to 3 percent above that obtained by ICP. Acco rding to the NESCCAF report and confidential manufacturer data, NHTSA estimates that CCP could incrementally reduce fuel consumption by 1 to 3 percent at an incremental cost of $59 to $119.

Dual (Independent) Ca mshaft Phasing (DCP)

The most flexible VVT design is dual cam phasing, where the intake and exhaust valve opening and closing events are controlled independently. Th is design allows the option of controlling valve overlap, which can be used as an internal EGR strategy. Our estimated incremental compliance cost for this technology is built upon that for VVT-ICP where an additional cam phaser is added to control each bank of exhaust valves less the cost to the manufacturer of the removed EG R valve. The incremental compliance cost for a 4-cylinder engine is estimated to be $59 for each bank of valves, plus an estimated piece cost of $30 for the valves, for a tota l incremental compliance cost of $89. The incremental compliance cost for a V6 or a V8 engine is estimated to be $59 for each bank of intake valves (i.e., two banks times $59/bank = $119), $59 for each bank of exhaust valves (i.e., another $119) minus an estimat ed $29 incremental compliance cost for the removed EGR valve; the total increm ental compliance cost being $209.

According to the NESCCAF report and confid ential manufacturer data, it is estimated that DCP could incrementally reduce fuel consumption by 1 to 3 percent at an incremental cost of $89 to $209 compar ed to engines with ICP or CCP.

Because ICP and CCP have the same cost and similar effectiveness, it is assumed that manufacturers will choose the technology that be st fits the specific engine architecture and application.

Variable Valve Lift and TimingV-12

Some vehicles have engines for which both va lve timing and lift can be at least partially optimized based on engine operating conditions. Engines with variable valve timing and lift (VVLT) can achieve further reductions in pumping losses and further increases in thermal efficiency. Controlling the lift height of the valves provides additional flexibility and potential for further fuel consumption reduction. By reducing the valve lift, engines can decrease the volumetric flow at lower ope rating loads, improving fuel-air mixing and in-cylinder mixture motion whic h results in improved thermodynamic efficiency and also potentially reduced overall valvetrain fric tion. Also, by moving the throttling losses further downstream of the throttle valve, the heat transfer losses that occur from the throttling process are directed into the fresh charge-air mixt ure just prior to compression, delaying the onset of knock-limited combusti on processes. At the same time, such systems may also incur increased parasitic losses associated with their actuation mechanisms.

The NAS report projected that VVLT could in crementally reduce fuel consumption by 1 to 2 percent over VVT alone at an incremental cost of $73 to 218.

Manufacturers are currently using many different types of variable valve lift and timing, which have a variety of different names and methods. The major types of VVLT are listed below:

Discrete Variable Valve Lift

Discrete variable valve lift (DVVL) is a me thod in which the valvetrain switches between multiple cam profiles, usually 2 or 3, for each valve. These cam profiles consist of a low and a high-lift lobe, and may include an in ert or blank lobe to incorporate cylinder deactivation (in the case of a 3-step DVVL system). According to the NESCCAF report and confidential manufacturer data, it is es timated that DVVL could incrementally reduce fuel consumption by 0.5 to 3 percent at an incremental cost of $169 to $322 compared to an engine with VVT and cylinder deactiv ation depending on engine size and overhead cam versus overhead valve engines. Included in this cost estimate is $25 for controls and associated oil supply needs (these costs not reflected in the NESCCAF study). We also project that a single valve lifte r could control valve pairs, t hus engines with dual intake and/or dual exhaust valves woul d require only one lifter per pa ir of valves. Due to this, the estimated costs for applying DVVL to ove rhead cam and overhead valve engines are the same.

Continuous Variable Valve Lift

Continuous variable valve lift (CVVL) employs a mechanism that varies the pivot point in the rocker arm. This design is realis tically limited to overhead cam engines. Currently, BMW has implemented this type of system in its Valvetronic engines, which employs fully flexible valve timing to allow an extra set of rocker arms to vary the valve lift height. CVVL enables intake valve throttling in engines, which allows for the use of more complex systems of sensors and electroni c controls to enable further optimization of valve lift.V-13

The NESCCAF study projected incremen tal costs from $210 to $420, depending on vehicle class, while the EEA report pr ojected incremental costs of $180 to $350, depending on vehicle class. Confidential ma nufacturer data projects that CVVL could incrementally reduce fuel consumption by 1.5 by 4 percent at an incr emental cost of $200 to $515. NHTSA believes that these manufact urer estimates are more accurate than NESCCAF estimates, thus it gives more wei ght to them. According to the NESCCAF report and confidential manu facturer data, NHTSA estimates that CVVL could incrementally reduce fuel consumption by 1.5 by 4 percent at an incr emental cost of $254 to $508 compared to an engine with VVT a nd cylinder deactivation with cost estimates varying from $254, $466, and $508 for a 4-, 6- , and 8-cylinder engine, respectively.

Camless Valve Actuation

Camless valve actuation relies on electromechan ical actuators instead of camshafts to open and close the cylinder valves. When electromechanical actuators are used to replace cams and coupled with sensors and microproces sor controls, valve timing and lift can be optimized over all conditions. An engine valv etrain that operates independently of any mechanical means provides the ultimate in flexibility for intake and exhaust timing and lift optimization. With it comes infinite valve overlap variabil ity, the rapid response required to change between operating modes (such as HCCI and GDI), intake valve throttling, cylinder deactivation, and eliminati on of the camshafts (re duced friction). This level of control can enable even further incr emental reductions in fuel consumption.

Camless valvetrains have been under rese arch for many decades due to the design flexibility and the attractive fuel econom y improvement potential they might provide. Despite the promising features of camless valvetrains, significant challenges remain. High costs and design complexity have reduced manufacturers’ ent husiasm for camless engines in light of other competing valv etrain technologies. The advances in VVT, VVLT, and cylinder deactivation systems demonstrated in re cent years have reduced the potential efficiency advantage of camless valvetrains.

The NAS Report projected that camless valve actuation could incrementally reduce fuel consumption by 5 to 10 percent over VVLT at an incremental cost of $336 to $673. Confidential manufacturer information provides incremental fuel consumption losses that range from 2 to 10 percent at costs th at range from $300 to $1,100. The NESCCAF study projected that camless valve actuation c ould incrementally reduce fuel consumption by 11 to 13 percent at an incremental co st of $805 to $1,820; while the EEA report projected that camless valve actuation coul d incrementally reduce fuel consumption by 10 to 14 percent at an incremental cost of $210 to $600. These benefits and costs are believed to be incremental to engines with VVT.

In reviewing our sources for costs, we have determined that the adjusted costs presented in the 2002 NAS study, which ranged from $336 to $673 - depending on vehicle class – represent the best available estimates. Subt racting out the improvements associated withV-14

the application of VVLT provides an estim ated fuel consumption reduction of 2.5 percent.

Stoichiometric Gasoline Di rect Injection Technology

Gasoline direct injection (GDI, or SIDI) engines inject fuel at high pressure directly into the combustion chamber (rather than the intake port in port fuel injection). Direct injection improves cooling of the air/fuel ch arge within the cylinder, which allows for higher compression ratios and increased th ermodynamic efficiency. Injector design advances and increases in fuel pressure have promoted better mixing of the air and fuel, enhancing combustion rates, increasing exhaust gas tolerance and improving cold start emissions. GDI engines achieve higher pow er density and match well with other technologies, such as boosting a nd variable valvetrain designs.

Several manufacturers (Audi, BMW, and Vo lkswagen) have recently released GDI engines while General Motors and Toyota will be introducing GDI engines. In addition, BMW and GM have announced their plans to dramatically increase the number of GDI engines in thei r portfolios.

The NESCCAF report projected that the increm ental cost for GDI of $189 to $294; while the EEA report projected an incremental co st of $77 to $135. Confidential manufacturer data provides data with higher upper end costs than these estimates, with incremental fuel consumption estimates ranging from 1 to 2 per cent. For our analysis, we have estimated the costs of individual compone nts of a GDI system and used a “bottom up” approach looking at incremental costs for injectors, fuel pumps, etc., to arrive at system incremental compliance costs ranging from $122 to $420 for small cars and up to $228 to $525 for large trucks. The lower end of th e ranges represent our best estimate using a bottom up approach while the upper e nd of the ranges represent levels more consistent with the manufacturer CBI s ubmittals. As a result, we estimate that stoichiometric GDI could incrementally reduce fuel consumption by 1 to 2 percent at an incremental cost of $122 to $525 compared to engines of similar power output.

Gasoline Engine Turbocharging and Engine Downsizing The specific power of a naturally aspirated engi ne is limited, in part, by the rate at which the engine is able to draw air into th e combustion chambers. Turbocharging and supercharging are two methods to increas e the intake manifold pressure and cylinder charge-air mass above naturally aspi rated levels. By increasing the pressure differential between the atmosphere and the charging cylinders, superchargers and turbochargers increase this available airflow, and thus increase the specific power level, and with it the ability to reduce engine size while maintaining performance. This effectively reduces the pumping losses at light er loads in comparison to a larger, naturally aspirated engine, while at the same time reducing net friction losses.

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, it has consider able fuel economy potential wh en the engine displacementV-15

is reduced. Specific power levels for a boos ted engine often exceed 100 hp/L - compared to average naturally aspirate d engine power densities of roughly 70 hp/L. As a result, engines can conservatively be downsized r oughly 30 percent to achieve similar peak output levels.

In the last decade, improvements to turbin e design have improved their reliability and performance across the entire engine operating range. New variable geometry turbines spool up to speed faster (eliminating the once-common “turbo lag”) while maintaining high flow rates for increased boost at high speeds.

Turbocharging and downsizing i nvolves the addition of a boo st system, removal of two cylinders in most cases (from an 8-cylinder to a 6, or a 6 to a 4) and associated valves, and the addition of some form of cold start c ontrol system (e.g., air injection) to address possible cold start emission control. The NAS Report projected that turbocharging and downsizing could incrementally reduce fuel consumption by 5 to 7 percent at an incremental cost of $364 to $582. The EEA report projected turbocharging and downsizing could incrementally reduce fu el consumption by 5.2 to 7.8 percent.

In developing estimated costs for turbocha rging and downsizing an engine, NHTSA, in conjunction with EPA, relied upon piece cost estimates contained in the NESCCAF report. The cost estimates provided by th e NESCCAF report are as follows: $600 for the turbocharger and associated parts; $90 for an air injection pump and associated parts (each turbocharger requires an air injection pump); $75 pe r cylinder and associated components: $15 per each valve and associat ed components; and $150 per camshaft.

In developing the cost estimates for each of th e ten classes of vehicles, we determined the most logical type of downsiz ing that would occur for each class and starting with the turbocharger and air injector cost, eith er added or deleted cost, depending on the situation. These cost estimates are incremen tal to an engine with GDI. For subcompact and compact cars, we determined that the downsizing wouldn’t involve the removal of any cylinders, valves and camshafts, but inst ead would result in a manufacturer using a smaller displacement 4-cylinder engine and ad ding the turbocharger and the air injector to the smaller engine. Thus, for subcompact and compact cars, we estimated the cost of turbocharging and downsizing to be $690 ($600 for the turboc harger plus $90 for the air injector).

For large trucks and large SUVs we determin ed that the most logical engine downsizing would involve replacing an 8-cylinder overh ead valve engine with a turbocharged 6- cylinder dual overhead cam engine. This ch ange would result in the removal of 2 cylinders, and the addition of a turbocharger, an air inject or, 8 valves and 2 camshafts. Thus, for, we have estimated the cost of turbocharging and downsizing to be $810 ($600 for the turbocharger plus $90 for the air inje ctor, plus $120 for eight valves plus $150 for a camshaft and minus $150 for the removal of two cylinders).

For midsize cars, large cars, small trucks, small SUVs, midsize SUVs and minivans, we determined that the most logical engine dow nsizing would involve replacing a 6-cylinderV-16

dual overhead cam engine with a turbocharg ed 4-cylinder dual overhead cam engine. This change would result in the removal of 2 cylinders, 8 valves and 2 camshafts and the addition of a turbocharger and air injector. Thus, for, we have estimated the cost of turbocharging and downsizing to be $120 ($600 for the turboc harger plus $90 for the air injector, minus $150 for the removal of two cylinders, minus $120 for the removal of eight valves and minus $300 for the removal of two camshafts).

Thus, we have estimated the cost for a boosted/downsized engine system at $690 for small cars, $810 for large trucks, and $120 for other vehicle classes. Projections of the fuel consumption reduction potential of a tu rbocharged and downsized engine from the NAS Report are backed by EEA estimates a nd confidential manufacturer data. . According to the NAS Report, the EEA report, cost estimates developed in conjunction with EPA and confidential manufacturer data, NHTSA estimates that downsized turbocharged engines could incrementally re duce fuel consumption from 5 to 7.5 percent at an incremental cost of $120 to $810.

Diesel Engine

Diesel engines have several characteristics th at give them superior fuel efficiency to conventional gasoline, spark-ignited engines. Pumping losses are gr eatly reduced due to lack of (or greatly reduced) throttling. The diesel combustion cycle operates at a higher compression ratio, with a very lean air/fuel mi xture, and typically at much higher torque levels than an equivalent-displacement gasoli ne engine. Turbocharged light-duty diesels typically achieve much higher torque levels at lower engine sp eeds than equivalentdisplacement naturally-aspirated gasoline engi nes. Additionally, diesel fuel has higher energy content per gallon. However, diesel engines have emissions characteristics that present challenges to meeting Tier 2 emissions standards.

Compliance strategies are expected to include a combina tion of combustion improvements and after-treatment. Several key advances in diesel technology have made it possible to reduce emissions coming from the engine (prior to after-treatment). These technologies include improved fuel system s (higher pressures and more responsive injectors), advanced controls and sens ors to optimize combustion and emissions performance, higher EGR levels to reduce NO x , lower compression ratios and advanced turbocharging systems.

For after-treatment, the traditional 3-way catalyst found on gasoline-powered vehicles is ineffective due to the lean-burn combustion of a diesel. All diesels will require a particulate filter, an oxidation catalyst, and a NO x reduction strategy to comply with Tier 2 emissions standards. The NO x reduction strategies most common are outlined below:

Lean NO x Trap Catalyst After-Treatment

A lean NO x trap (LNT) operates, in principle, by storing NO x (NO and NO 2 ) when the engine is running in its normal (lean) state. When the control system determines (viaV-17

mathematical model or a NO x sensor) that the trap is saturated with NO x , it switches to a rich operating mode. This rich mode produ ces excess hydrocarbons that act as a reducing agent to convert the stored NO x to N 2 and water, thereby “regenerating” the LNT and opening up more locations for NO x to be stored. LNTs are sensitive to sulfur deposits which can reduce catalytic performance, but periodically undergo a desulfation engine operating mode to clean it of sulfur buildup.

According to confidential manufacturer data , NHTSA estimates that LNT-based diesels can incrementally reduce fuel consumption by 8 to 15 percent at an incremental cost of $1,500 to $1,600 compared to a direct injected turbocharged and downsized internal combustion engine. These costs are based on a “bottom up” cost analysis that was performed with EPA which then subtracted th e costs of all previous steps on the decision tree prior to diesel engines.

Selective Catalytic Reduction NO x After-Treatment

SCR uses a reductant (typically, ammonia deri ved from urea) continuously injected into the exhaust stream 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 complicated that that of an LNT, due to the onboard urea storage and delivery system (which requires a urea pump a nd injector into the exhaust stream). While there is no required rich engine operating mode prescribed for NO x reduction, the ur ea is typically injected at a rate of 3 to 4 percent of th at of fuel consumed. Manufacturers designing SCR systems are intending to align urea tank re fills with standard maintenance practices such as oil changes. Incremental fuel consumption reduction estimates for diesel engines with an SCR system range from 11 to 20 pe rcent at an incremen tal cost of $2,051 to $2,411 compared to a direct injected turboc harged and downsized internal combustion engine. These costs are based on a “bottom up” cost analysis that was performed with EPA, which then subtracted the costs of all pr evious steps on the de cision tree prior to diesel engines.

Based on public information and on recent di scussions that NHTSA and EPA have had with auto manufacturers and aftertreatment device manufacturers, NHTSA has received strong indications that LNT systems would pr obably be used on smaller vehicles while the SCR systems would be used on larger vehi cles and trucks. The primary reason given for this choice is the trade off between the rhodium needed for the LNT and the urea injection system needed for SCR. The break even point between these two cost factors appears to occur around 3.0 liters. Thus, it is believed that it is cheap er to manufacturer diesel engines smaller than 3.0 liters with an LNT system, and that conversely, it is cheaper to manufacturer diesel engines larger than 3.0 lite rs with a SCR system. Of course, there are other factors that influe nce a manufacturer’s decision on which system to use, but we have used this rule-of thumb for our analysis.

b. Transmission technologies

Five-, Six-, Seven- and Eight-Spe ed Automatic TransmissionsV-18

The number of available transm ission speeds influences the width of gear ratio spacing and overall coverage and, therefore, the degree of transmission ratio optimization available under different operating conditions. In general, transmissions can offer a greater available degree of engine optimiza tion and can therefore achieve higher fuel economy when the number of gears is increas ed. However, potential gains may be reduced by increases in transmission weight and rotating mass. Regardless of possible changes to fuel economy standards, manufact urers are increasingly introducing 5- and 6- speed automatic transmissions on their vehicl es. Additionally, some manufacturers are introducing 7- and 8-speed automatic tr ansmissions, with 7-speed automatic transmissions appearing w ith increasing frequency.

Automatic 5-speed Transmissions

As automatic transmissions have been deve loped over the years, more forward speeds have been added to improve fuel efficiency and performance. Increasing the number of available ratios provides the opportunity to optimize engine operation under a wider variety of vehicle speeds and load conditions. Also, additional gears allow for overdrive ratios (where the output shaft of the transmission is turni ng at a higher speed than the input shaft) which can lower the engine speed at a given road speed (provided the engine has sufficient power at the lower rpm point ) to reduce pumping losses. However, additional gears can add weight, rotating mass, and friction. Nevert heless, manufacturers are increasingly adding 5-speed automatic transmissions to replace 3- and 4-speed automatic transmissions.

The 2002 NAS study projected that 5-speed auto matic transmissions could incrementally reduce fuel consumption by 2 to 3 percent at an incremental cost of $76 to $167. The NESCCAF study projected that 5-speed auto matic transmissions could incrementally reduce fuel consumption by 1 percent at an incremental cost of $140; while the EEA report projected that 5-speed automatic tran smissions could incrementally reduce fuel consumption by 2 to 3 percent at an incremen tal cost of $130. Conf idential manufacturer data projected that 5-speed automatic tran smissions could incrementally reduce fuel consumption by 1 to 6 percent at an in cremental cost of from $60 to $281. NHTSA believes that the NAS study’s estimates are still valid and estimates that 5-speed automatic transmissions could incrementally reduce fuel consumption by 2.5 percent at an incremental cost of $76 to $167 (relativ e to a 4-speed automatic transmission).

Automatic 6-, 7- and 8-speed Transmissions

In addition to 5-speed automatic transmissions , manufacturers can also choose to utilize 6-, 7-, or 8-speed automatic transmissi ons. Additional ratios allow for further optimization of engine operation over a wider ra nge of conditions, but this is subject to diminishing returns as the number of speeds increases. As additional planetary gearsets are added (which may be necessary in some cas es to achieve the higher number of ratios), additional weight and friction are introduced. Also, the additional sh ifting of such a transmission can be perceived as bothersome to some consumers, so manufacturers needV-19

to develop strategies for smooth shifts. Some manufacturers are replacing 4-speed automatics with 6-speed automatics (there are also increasing numbers of 5-speed automatic transmissions that are being repla ced by 6-speed automatic transmissions), and 7-, and 8-speed automatics have entered product ion, albeit in lower-volume applications.

The NAS study projected that 6-, 7- or 8-sp eed transmissions could incrementally reduce fuel consumption by 1 to 2 percent at an incremental cost of $70 to $126. Confidential manufacturer data projected that 6-, 7- or 8-speed tran smissions could incrementally reduce fuel consumption by 1 to 3 percent at an incremental cost of $20 to $120. However, according to the EEA report, a Lepelletier gear set design provides for 6- speeds at the same cost as a 5-speed automatic . Based on that analysis, we have estimated the cost of a 6-speed automatic to be equivale nt to that for a 5-speed automatic. We have not developed any estimate costs for 7- or 8-speed transmissions because of the diminishing returns in efficiency versus the costs for transmissions beyond 6-speeds. NHTSA estimates that 6-, 7-, or 8-speed au tomatic transmissions could incrementally reduce fuel consumption by 0.5 to 2.5 percen t at an incremental cost of $0 to $20 (relative to a 5-speed automatic transmission) . We are estimating up to an additional $20 in costs because we have tried to account fo r the engineering effort in addition to the hardware which we believe the EEA did not a nd we wanted to capture some of the higher costs reported by manufacturers.

Aggressive Shift Logic

In operation, an automatic transmission’s cont roller decides when to upshift or downshift based on a variety of inputs such as ve hicle speed and throttl e position according to programmed logic. Aggressive shift logic (ASL) can be em ployed so that a transmission is engineered in such a way as to maximi ze fuel efficiency by upshifting earlier and inhibiting downshifts under some conditions. Through partial lock-up under some operating conditions and early lock-up under ot hers, automatic transmissions can achieve some reduction in overall fuel consumption. A ggressive shift logic is applicable to all vehicle types with automatic transmissions, a nd since in most cases it would require no significant hardware modifications, it can be adopted during vehicl e redesign or refresh or even in the middle of a vehicle’s produc t cycle. The application of this technology does, however, require a manuf acturer to confirm that driv eability, durabili ty, and noise, vibration, and harshness (NVH) ar e not significantly degraded.

The NAS study projected that aggressive sh ift logic could increm entally reduce fuel consumption by 1 to 2 percent at an incr emental cost of $0 to $70. Confidential manufacturer data projected th at aggressive shift logic c ould incrementally reduce fuel consumption by 0.5 to 3 percent at an incremental cost of $18 to $70. The NAS study estimates and confidential manufacturer data are within the same ranges, thus NHTSA believes that the NAS estimates are still accu rate. Thus, NHTSA estimates aggressive shift logic could incrementally reduce fuel consumption by 1 to 2 percent at an incremental cost of $38, which is approximate ly the average of the midpoint of the NAS cost range and the manu facturer cost range.V-20

Early Torque Converter Lockup

A torque converter is a fluid coupling locat ed between the engine and transmission in vehicles with automatic transmissions and continuously-variable transmissions (CVTs). This fluid coupling allows for slip so the e ngine can run while the vehicle is idling in gear, provides for smoothne ss of the powertrain, and also provides for torque multiplication during acceleration. During light acceleration and cruising, this slip 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 converte r early, and/or by us ing partial-lockup strategies to reduce slippage.

Some torque converters will require upgraded clutch materials to withstand additional loading and the slipping conditions during partial lock-up. As w ith aggressive shift logic, confirmation of acceptable driv eability, performance, durab ility and NVH characteristics is required to successfully implement this technology.

The 2002 NAS study did not include any esti mates for this technology. The NESCCAF study projected that early torque converte r lockup could incrementally reduce fuel consumption by 0.5 percent at an incrementa l cost of $0 to $10; while the EEA report projected that low-friction lubricants coul d incrementally reduce fuel consumption by 0.5 percent at an incremental cost of $5. NHTSA estimates the cost of this technology (i.e., the calibration effort) at $30 based in part on NESCCAF and the CBI submissions which provided costs with a midpoint of $30. We have used a higher value here than NESCCAF and EEA because we have tried to account for the engineering effort in addition to the hardware which we belie ve NESCCAF and EEA did not do and which were captured in the manuf acturers’ higher costs.

NHTSA estimates that early torque convert er lockup could incrementally reduce fuel consumption by approximately 0.5 percent at an incremental cost of approximately $30.

Automated Shift Manual Transmissions

An automated manual transmission (AMT) is mechanically similar to a conventional transmission, but shifting and launch functions are controlled by the vehicle. There are two basic types of AMTs, single-clutch a nd dual-clutch. A si ngle-clutch AMT is essentially a manual transmission with automa ted clutch and shifting. Because there are some shift quality issues with single-clu tch designs, dual-clutch AMTs are more common. A dual-clutch AMT uses separate clutches 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.

Overall, AMTs likely offer the greatest pot ential for fuel consumption reduction among the various transmission options presented in th is report because they offer the inherently lower losses of a manual transmission with th e efficiency and shift quality advantages of computer control. AMTs offer the lower losses of a manual transmission with theV-21

efficiency advantages of computer control. The lower losses stem from the elimination of the conventional lock-up tor que converter and a greatly re duced need for high pressure hydraulic circuits to hold clutches to maintain gear ratios (in automatic transmissions) or hold pulleys in position to maintain gear ra tio (in continuously variable transmissions, discussed below). However, the lack of a torque converter will affect how the vehicle launches from rest, so an AMT will most likely be paired with an engine that offers enough torque in the low-RPM range to al low for adequate launch performance.

An AMT is mechanically similar to a conve ntional manual transmission, but shifting and launch functions are controlled by the vehicl e rather than the driver. A switch from a conventional automatic transmission with torque converter to an AMT incurs some costs but also allows for some cost savings. Savings can be reali zed through elimination of the torque converter which is a very costly part of a traditional auto matic transmission, and through reduced need for high pressure hydrauli c circuits to hold clutches (to maintain gear ratios in automatic transmissions) or hold pulleys (to maintain gear ratios in Continuously Variable Transmissions). Cost in creases would be incurred in the form of calibration efforts since transmission calibra tions would have to be redone, and the addition of a clutch assembly for launce and gear changes.

The NESCCAF study projected that AMTs coul d incrementally reduce fuel consumption by 5 to 8 percent at an incremental cost of $0 to $280; while the EEA report projected that low-friction lubricants could increm entally reduce fuel consumption by 6 to 7 percent at an incremental cost of $195 to $225. Confidential manufact urer data projected that AMTs could incrementally reduce fuel consumption by 2 to 5 percent at an incremental cost of $70 to $400.

Taking all these estimates into consider ation, NHTSA estimates that AMTs could incrementally reduce fuel consumption by 4.5 to 7.5 percent at an in cremental cost of approximately $141. We believe that, overall, the hardware associated with an AMT, whether single clutch or dual clutch, is no more costly than that for a traditional automatic transmission given the savings associ ated with removal of the torque converter and high pressure hydraulic ci rcuits, which is estimated to amount to at least $30. Nonetheless, given the need for engin eering effort (e.g., cal ibration and vehicle integration work) when transitioning from a traditional automatic to an AMT, we have estimated the incremental compliance cost at $1 41, independent of vehicle class, which is the midpoint of the NESCCAF estimates a nd within the range provided confidential manufacturer data.

Continuously Variable Transmission

A Continuously Variable Transmission (CVT) is unique in that it does not use gears to provide ratios for operation. Unlike manual a nd automatic transmissions with fixed transmission ratios, CVTs provide, within their operating ranges, fully variable transmission ratios with an in finite number of gears. This enables even finer optimization of the transmission ratio under different op erating conditions and, therefore, someV-22

reduction of pumping and engine friction loss es. CVTs use either a belt or chain on a system of two pulleys.

The main advantage of a CVT is that the engi ne can operate at its most efficient point more often, since there are no fixed ratios. Al so, CVTs often have a wider range of ratios than conventional automa tic transmissions.

The most common CVT design us es two V-shaped pulleys connected by a metal belt. Each pulley is split in half and a hydraulic actuator moves th e pulley halves together or apart. This causes the belt to ride on either a larger or smaller diameter section of the pulley which changes the effective ratio of the input to th e output shafts. It is assumed that CVTs will only be used on cars, small SUVs, midsize crossover vehicles and minivans because they are currently used mainly in lower-torque applications. While a high-torque CVT could be developed for small pickup trucks and large pickup trucks and large SUVs, it would like ly have to be treated separately in terms of effectiveness. We do not see developm ent in the area of high-torque CVTs and therefore did not include th is type in our analysis.

The 2002 NAS study projected that CVTs coul d incrementally reduce fuel consumption by 4 to 8 percent at an incremental cost of $140 to $350. The NESCCAF study projected that CVTs could incrementally reduce fuel consumption by 4 percen t at an incremental cost of $210 to $245. Confiden tial manufacturer data pr ojected that CVTs could incrementally reduce fuel consumption by 3 to 9 percent at an incremental cost of $140 to $800. These values are incremental to a 4-speed transmission.

Based on an aggregation of ma nufacturers’ information, we estimate a CVT benefit of about 6% over a 4-speed automatic. This is above the NESCCAF value, but in the ra nge of NAS. In reviewing our sources for costs, we have determined that the adjusted costs pres ented in the 2002 NESCCAF study represent the best available estimates. Subtracting the es timated fuel consumption reduction and costs of replacing a 4-speed automatic transmissi on with a 5-speed automatic transmission results in NHTSA projecting that CVTs coul d incrementally reduce fuel consumption by 3.5 percent when compared to a conventiona l 5-speed automatic transmission at an incremental cost of $100 to $139.

Manual 6-, 7-, and 8-speed Transmissions

As with automatic transmissions, increasing the number of available ratios in a manual transmission can improve fuel economy by a llowing the driver to select a ratio that optimizes engine operation at a given speed. Typically, this is achieved through adding additional overdrive ratios to reduce engine speed (which saves fuel through reduced pumping losses). Six-speed manual transm issions have already achieved significant market penetration, so manufacturers have c onsiderable experience with them and the associated costs. For those vehicles with five-speed manual transmissions, an upgrade to a six-speed could incrementally reduce fuel consumption by 0.5 percent. Based on CBI submissions, which provided costs with a midpoint of $107, NHTSA estimates that 6-V-23

speed manual transmissions could incremen tally reduce fuel consumption by 0.5 percent when compared to 5-speed automatic tran smission at an incremental cost of $107.

c. Vehicle technologies

Rolling Resistance Reduction

Tire characteristics (e .g., materials, construction, and tr ead design) infl uence durability, traction control, vehicle handling, and comfort. They also influence rolling resistance – the 30 frictional losses associated mainly with the energy dissipated in the deformation of the tires under load – and ther efore, CO2 emissions. This t echnology is applicable to all vehicles, except for body-on-frame light trucks and performance vehicles (described in the next section). Based on a 2006 NAS/NRC re port, a 10% rolling resistance reduction would provide an increase in fuel economy of 1 to 2 percent. The same report estimates a $1 per tire cost for low rolling resistance tire s. For four tires, our incremental compliance cost estimate is $6 per vehicle, independent of vehicle class, alt hough not applicable to large trucks.

Low Drag Brakes

Low drag brakes reduce the sliding friction of disc brake pads on rotors when the brakes are not engaged because the brake shoes are pulled away from the rotating drum. While most passenger cars have already adopted th is technology, there are in dications that this technology is still available for body-on-fram e trucks. According to confidential manufacturer data, low drag brakes could incrementally reduce fuel consumption by 1 percent at an incremental cost of $85 to $90. NHTSA has adopted these values for its analysis.

Front or Secondary Axle Disconnect for Four-Wheel Drive Systems

To provide shift-on-the-fly capabilities, ma ny part-time four-wheel drive systems use some type of axle disconnect: front ax le disconnect in ladder-frame vehicles, and secondary ( i.e. , either front or rear) axle disconnect in unibo dy vehicles. Front and secondary axle disconnect serv e two basic purposes. Using front axle disconnect as an example, in two-wheel drive mode, the tec hnology disengages the front axle from the front driveline so the front wheels do not tu rn the front driveline at road speed, saving wear and tear. Then, when shifting from two- to four-wheel drive “on the fly” (while moving), the front axle disconnect couples the front axle to th e front differential side gear only when the transfer case's synchronizing mechanism has spun the front driveshaft up to the same speed as the rear driveshaft.

Four-wheel drive systems that have axle di sconnect typically do not have either manualor automatic-locking hubs. To isolate (for ex ample) the front wheels from the rest of the front driveline, front axle disconnects use a sliding sleeve to conn ect or disconnect an axle shaft from the front differential side gear.V-24

This technology has been used by ladder-fra me vehicles for some time, but has only started to appear on unibody vehicles recentl y. The incremental costs and benefits of applying front axle disconnect differ, dependi ng on the vehicle’s type of construction. According to confidential manufacturer da ta, front axle disconnects for ladder frame vehicles could achieve incremental fuel c onsumption reductions of 1.5 percent at an incremental cost of $114, while secondary axle disconnects for unibody vehicles could achieve incremental fuel consumption reductions of 1 percent at an incremental cost of $676. NHTSA has adopted these estimates for its analysis.

Aerodynamic Drag Reduction

A vehicle’s size and shape determine the am ount of power needed to push the vehicle through the air at different speeds. Changes in vehicle shape or frontal area can therefore reduce CO2 emissions. Areas for potential aerodynamic drag improvements include skirts, air dams, underbody covers, and mo re aerodynamic side view mirrors. NHTSA and EPA estimate a fleet average of 20% tota l aerodynamic drag redu ction is attainable for passenger cars, whereas a fleet average of 10% reduction is more realistic for trucks (with a caveat for “high-performance” vehicles, described below). These drag reductions equate to increases in fuel economy of 2% and 3% for trucks and cars, respectively. These numbers are in agreement with th e technical literature and supported by confidential manufacturer information. The CBI submittals generally showed the RPE associated with these changes at less than $100. NHTSA and EPA estimate that the incremental compliance cost to range from $0 to $75, independent of vehicle class. Aerodynamic drag reduction technologies are readily available t oday, although the phasein time required to distribute over a manufacturer ’s fleet is relatively long (6 years or so).

Weight Reduction

The term weight reduction encompasses a vari ety of techniques with a variety of costs and lead times. These include lighter-weight materials, higher strength materials, component redesign, and size matching of comp onents. Lighter-weigh t materials involve using lower density materials in vehicle com ponents, such as replacing steel parts with aluminum or plastic. The use of higher strength materials involves the substitution of one material for another that possesses higher st rength and less weight. An example would be using high strength alloy steel versus cold rolled steel. Component redesign is an ongoing process to reduce costs and/or we ight of components, while improving performance and reliability. An example would be a subsystem replacing multiple components and mounting hardware.

The cost of reducing weight is difficult to determine and is dependent upon the methods used. For example, a change in design that reduces weight on a new model may or may not save money. On the other hand, material substitution can result in an increase in price per application of th e technology if more expens ive materials are used.

For purposes of this proposed rule, NHTSA has considered onl y vehicles weighing greater than 5,000 pounds for wei ght reduction through material s substitution. ProvidedV-25

that those vehicles remain above 5,000 lbs we ight, vehicles may realize up to roughly 2 percent incremental fuel consumption thr ough materials substitution (corresponding to a 3 percent reduction in vehicle weight) at incremental costs of $0.75 to $1.25 per pound reduced.

d. Accessory technologies

Electric Power Steering

Electric power steering (EPS) is advantageous over hydraulic steeri ng in that it only draws power when the wheels are being turne d, which is only a small percentage of a vehicle’s operating time. EPS may be impl emented on many vehicles with a standard 12V system; however, for heavier vehicles, a 42V system may be re quired, which adds cost and complexity.

The NAS study projected that a 12V EPS system could incrementally reduce fuel consumption by 1.5 to 2.5 percent at an incremental cost of $105 to $150. The NESCCAF study projected that a 12V EPS coul d incrementally reduce fuel consumption by 1 percent at an incremental cost of $28 to $56; while the EEA re port projected that a 12V EPS could incrementally reduce fuel consumption by 1.5 to 1.9 percent at an incremental cost of $70 to $90. According to confidential manufacturer data, electric power steering could achieve incremental fuel consumption reductions of 1.5 to 2.0 percent at an incremental cost of $118 to $197.

NHTSA believes that these manufacturer estimates are more accurate and thus estimates that a 12V EPS system could incrementally reduce fuel consumption by 1.5 to 2 percent at an incremental cost of $118 to $197, independent of vehicle class.

Engine Accessory Improvement

The accessories on an engine, like the alternat or, coolant, and oil pumps, are traditionally driven by the accessory belt. Improving the effi ciency or outright electrification (12V) of these accessories (in the case of the mechanically driven pumps) would provide an opportunity to reduce the accessory loads on the engine. However, the potential for such replacement will be greater for vehicles with 42V electrical systems. Some large trucks also employ mechanical fans, some of whic h could also be improved or electrified. Additionally, there are now higher efficiency alternators which require less of an accessory load to achieve the same power flow to the battery.

According to the NAS Report engine accessory improvement could achieve incremental fuel consumption reductions of 1 to 2 percent at an incremental cost of $124 to $166. Confidential manufacturer information is al so within these ranges. The NESCCAF study estimated a cost of $56, but that estimate in cluded only a high efficiency generator and did not include electrification of other accessories. In reviewin g our sources for costs, we have determined that the adjusted costs presented in the 2002 NAS study, which ranged from $124 to $166 - depending on vehicle class – represent the best av ailable estimates.V-26

Based on the NAS study and confidential ma nufacturer information, NHTSA estimates that accessory improvement could incrementally reduce fuel consumption by 1 to 2 percent at an incremental cost of $124 to $166.

Forty-Two Volt (42V) Electrical System

Most vehicles today (aside from hybrids) op erate on 12V electrical systems. At higher voltages, which appear to be under considera tion to meet expected increases in on-board electrical demands, the power density of motors, solenoids, and other electrical components may increase to the point that ne w and more efficient systems, such as electric power steering, may be feasible. A 42V system can also accommodate an integrated starter generato r. According to the NAS Report, 42V engine accessory improvement could achieve incremental fuel co nsumption reductions of 1 to 2 percent at an incremental cost of $194 to $259. Accordi ng to confidential manuf acturer data, a 42V system could achieve incremental fuel consum ption reductions of 0 to 4 percent at an incremental cost of $62 to $280.

We believe that the state of 42V technology has evolved to where it is on par with the incremental costs and benefits of 12V engine accessory improvement. In reviewing our sources, we have determined that the numbers provided in the 2002 NAS study, which estimated that engine accessory improvement could achieve incremental fuel consumption reductions of 1 to 2 percent at an incremental cost of $124 to $166 - depending on vehicle class – represent the best available estimates for both 12V and 42V systems. Thus, we are estimating that a 42V electrical system could achieve incremental fuel consumption reductions of 1 to 2 percent at an incremental cost of $124 to $166. These estimates are independent of vehicle class and exclusive of improvements to the efficiencies or electrificati on of 12V accessories. These estimates are incremental to a 12V system, regardless of whet her the 12V system has improved efficiency or not.

e. Hybrid technologies

A hybrid describes a vehicle th at 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 ids reduce fuel consumption through three major mechanisms: by optimizing the operati on of the internal combustion engine (through downsizing, or other control techniques) to operate at or near its most efficient point more of the time; by recapturing lost br aking energy and storing it for later use; and by turning off the engine when it is not neede d, such as when the vehicle is coasting or when stopped.

Hybrid vehicles utilize some combination of the above three mechanisms to reduce fuel consumption. The effectiveness of a hybrid depends on the utilization of the above mechanisms and how aggressively they are pursued. Different hybr id concepts utilize these mechanisms differently, so they are treate d separately in this analysis. Below is a discussion of the major hybrid concepts judg ed to be available for use within the timeframe of this rulemaking.V-27

Integrated Starter-Generator with Idle-Off

Integrated Starter-Generator (ISG) systems ar e the most basic of hybrid systems and offer mainly idle-stop capability. They offer the l east power assist and regeneration capability of the hybrid approaches, but their low cost and easy adaptability to existing powertrains and platforms can make them attractive for so me applications. ISG systems operate at around 42V and so have smaller electric moto rs and less battery cap acity than other HEV designs because of their lower power demand.

ISG systems replace the conventional belt-dri ven alternator with a belt-driven, higher power starter-alternator. The starter-altern ator starts the engi ne during idle-stop operation, but often a conventional 12V gear-reduc tion starter is retain ed to ensure coldweather startability. Also, dur ing idle-stop, some functions such as power steering and automatic transmission hydraulic pressure are lost with conventiona l arrangements, so electric power steering and an auxiliary transmission pump are added. These components are similar to those that would be used in ot her hybrid designs. An ISG system could be capable of providing some launch assist, but it would be limited in comparison to other hybrid concepts. According to the NAS Report, an EEA report and confidential manufacturer data, ISG systems could achieve incremental fuel consumption reductions that range from 5 to 10 percent.

In addition, when idle-off is used (i.e., the pe troleum fuelled engine is shut off during idle operation), an electric power steering and auxiliary transmission pump are added to provide for functioning of these systems which, in a traditional vehi cle, were powered by the petroleum engine. The 2002 NAS study estimat ed the cost of these systems at $210 to $350 with a 12 volt electrical system and i ndependent of vehicle class, while the NESCCAF study estimated the cost for thes e systems at $280 with a 12 volt electrical system for a small car. The 2002 NAS study estimated the cost of these systems to be $210 to $350 with a 12 volt electrical system and independent of vehicle class, while the NESCCAF study estimated the cost for these systems of $280 with a 12 volt electrical system for a small car. Confid ential manufacturer informati on provides cost estimates for ISGs that range from $418 to $800. We believe that the NAS and the NESCCAF estimates are still accurate for ISGs with a 12V system. Thus, if you add these cost estimates to those we estimated for 42V system s plus associated equipment, which results an estimated incremental compliance cost of these systems, including the costs associated with upgrading to a 42 volt el ectrical system of $563 to $600, depending on vehicle class.

Therefore, NHTSA estimates that ISG sy stems could achieve incremental fuel consumption reductions of 5 to 10 per cent at incremental costs of $563 to $600, depending on vehicle class (this includes the costs associated with upgrading to a 42 volt electrical system). If a 42V system wa s already applied to a vehicle, the fuel consumption reductions and incremental cost fo r that technology would be subtracted in our analysis.

Integrated Motor Assist (IMA)/Integrated Starter-Alternator-Dam pener (ISAD) HybridV-28

Honda is the only manufacturer that uses Inte grated Motor Assist (IM A), which utilizes a thin axial electric motor bolted to the e ngine’s crankshaft and connected to the transmission through a torque converter or clutch. This electric motor acts as both a motor for helping to launch the vehicle and a generator for re covering energy while slowing down. It also acts as the starter for the engine and the electrical system’s main generator. Since it is rigidly fixed to the e ngine, if the motor turns, the engine must turn also, but combustion does not ne cessarily need to occur. Th e Civic Hybrid uses cylinder deactivation on all four cylinders for d ecelerations and some cruise conditions. The main advantage of the IMA system is it’s relatively low cost and ease of adaptability to conventiona l vehicles and powertrains while providing excellent efficiency gains. Packaging space is a c oncern for the physically longer engine-motortransmission assembly as well as the n ecessary battery pack, cabling and power electronics. According to EPA test da ta and confidential manufacturer product information, the IMA system could achieve in cremental fuel consumption reductions of 3.5 to 8.5 percent. 44 NHTSA has adopted these estimates for its analysis.

The 2002 NAS study did not consider this technology while the NESCCAF study estimated the cost for thes e systems at $2310 to $2940 for a small car and large car, respectively. We have used these estimates combined with confidential manufacturer data as the basis for our incremental complianc e costs of $1636 for the small car and $2274 for the large car, expressed in 2006 dollars. We have not estimated incremental compliance costs for the other vehicle classes because we do not believe those classes would use this technology and would, inst ead, use the hybrid technologies discussed below.

2-Mode Hybrids

GM, DaimlerChrysler, and BMW have formed a joint venture to develop a new HEV system based on HEV transmission technology originally developed by GM’s Allison Transmission Division for heavy-duty vehicles like city buses. Th is technology uses an adaptation of a conventional stepped-ratio automatic transmission by replacing some of the transmission clutches with two electric motors, which makes the transmission act like a CVT. Like Toyota’s Power Split design, these motors control the ratio of engine speed to vehicle speed. But unlike the Power Split system, clutches allow the motors to be bypassed, which improves both the transmissi on’s torque capacity for heavy-duty applications and fuel economy at highwa y speeds. According to confidential manufacturer data, 2-mode hybrids could achieve incremental fuel consumption reductions of 25 to 40 percent. NHTSA estimat es that 2-mode hybrids could achieve fuel reductions of 3.5 percent to 7 percent incremental to an Integrated Motor Assist (IMA)/Integrated Starter-Altern ator-Dampener (ISAD) Hybrid.

The 2002 NAS study did not consider this technology, while the NESCCAF study estimated the costs to range from $4340 to $5600, depending on vehicle class. These

44 Honda’s cost estimates are protected per Ho nda’s confidentiality ag reement with NHTSA.V-29

estimates are not incremental to an Integrated Motor Assist (IMA)/Integrated StarterAlternator-Dampener (ISAD) Hybrid. To accura tely project the cost of 2-mode hybrids when they were applied to midsize and large cars we subtracted the estimated costs of an Integrated Motor Assist (IMA)/Integrated St arter-Alternator-Dampene r (ISAD) Hybrid We have used the NESCCAF estimates as the basis for our incremental compliance costs of $1,501 to $5,127 in 2006 dollars, incremental to an Integrated Motor Assist (IMA)/Integrated Starter-Alternator-Dampe ner (ISAD) Hybrid or an ISG system depending on vehicle class. 45 We have not estimated incremental compliance costs for small cars because we believe that this ISG or IMA/ISAD technology is a better fit for small cars.

Power Split Hybrid

Toyota’s Hybrid Synergy Drive system as used in the Prius is a completely different approach than Honda’s IMA system and us es a “Power Split” device in place of a conventional transmission. The Power Split sy stem replaces the vehicle’s transmission with a single planetary gear and a moto r/generator. A second, more powerful motor/generator is permanently connected to the vehicle’s final drive and always turns with the wheels. The planetary gear split s the engine’s torque between the first motor/generator and the drive motor. The firs t motor/generator uses its engine torque to either charge the battery or supply additional power to the drive motor. The speed of the first motor/generator determines the relative sp eed of the engine to the wheels. In this way, the planetary gear allows the engine to operate completely independently of vehicle speed, much like a CVT.

The Power Split system allows for outstanding fuel economy in city driving. The vehicle also avoids the cost of a conventional tr ansmission, replacing it with a much simpler single planetary and motor/generator. However, it is less efficient at highway speeds due to the requirement that the first motor/ge nerator must be constantly spinning at a relatively high speed to maintain the correct ra tio. Also, load capacity is limited to the first motor/generator’s capacity to resist the reaction torque of the drive train.

A version of Toyota’s Power Split system is also used in the Lexus RX400h and Toyota Highlander sport utility vehicles. This vers ion has more powerful motor/generators to handle higher loads and also adds a third moto r/generator on the rear axle of four-wheeldrive models. This provides the vehicle with four wheel drive capability and four wheel regenerative braking capability. Ford’s CVT system used in the hybrid Escape is another version of the Power Split system, but f our-wheel-drive models use a conventional transfer case and drive shaft to power the rear wheels.

Other versions of this system are used in the Lexus GS450h and Lexus LS600h luxurysedans. These systems have modifications and additional hardware for sustained highspeed operation and/or all-wheel-drive capability. However, the Power Split system isn’t planned for usage on full-size trucks and SUVs due to its limited ability to provide the

45 GM’s cost estimates are protected per GM’s confidentiality agreement with NHTSA.V-30

torque needed by these vehicles . It’s anticipated that full-s ize trucks and SUVs would use the 2-mode hybrid system. The 2002 NAS st udy didn’t consider th is technology, while the NESCCAF study estimated the incremental costs at to be $3500 prior to any cost adjustment. Based on the NESCCAF study a nd fuel economy test data from EPA’s certification database which shows these systems being capable of reducing fuel consumption by 25 to 35 percent, NHTSA esti mates that Power Split hybrids can achieve incremental fuel consumption reductions of 25 to 35 percent over conventionally powered vehicles at an incremental co st of $3,700 to $3,850. Because NHTSA applies technologies incrementally to the technologies preceding them on our decision trees, the incremental fuel consumption reductions for Po wer Split hybrids are estimated to be 5 to 6.5 percent incremental to 2-Mode Hybrids (the technology that precedes Power Split hybrids on the decision tree), because the tec hnologies applied prior to and including 2- Mode hybrids are estimated to have incremen tal fuel consumption reductions of 20 to 28.5 percent over conventionally powered vehi cles. The technologies discussed below were not projected for use during the MY 2011 to 2015 timeframes because NHTSA isn’t aware that any manufacturer is including thes e technologies in any vehicle for which we have production plans for nor has any manufact urer publicly stated that any of these technologies will definitively be included on future products. If NHTSA receives such information regarding any technology(ies), it wi ll revisit this decision for the final rule. NHTSA is including its discussion of these t echnologies and their estimated costs and fuel consumption reductions as a reference for commenters and in anticipation of their possible inclusion in the final rule.

Variable Compression Ratio

A spark-ignited engine’s specific power is limited by the engine’s compression ratio, which is, in turn, currently limited by the e ngine’s susceptibility to knock, particularly under high load conditions. Engines with va riable compression ratio (VCR) improve fuel economy by the use of higher compression rati os at lower loads and lower compression ratios under higher loads. The NAS Report projected that VCR could incrementally reduce fuel consumption by 2 to 6 percent ove r 4-valve VVT at an incremental cost of $218 to $510. NHTSA has no information wh ich suggests that VCR will be included on any vehicles during the MY 2011-2015 timef rame, thus NHTSA does not use this technology in its analysis. Additionally, no updates to these estimates were sought.

Lean-Burn Gasoline Direct Injection Technology

One way to improve dramatically an engine ’s thermodynamic efficiency is by operating at a lean air-fuel mixture (excess air). Fu el 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 be a possibility in North America.V-31

However, a key technical requirement for l ean-burn GDI engines to meet EPA’s Tier 2 NO x emissions levels is the availability of lo w-sulfur gasoline, which is projected to be unavailable during MY 2011 – 2015.

According to the NESCCAF report and confid ential manufacturer data NHTSA estimates that lean-burn GDI engines could incremen tally reduce fuel consumption from 9 to 16 percent at an incremental cost of $500 to $750 compared to a port-fueled (stoichiometric) engine. NHTSA did not project the use of this technology during the time frame covered by this proposal, due to large uncertainties surrounding the availability of low-sulfur gasoline. Nonetheless, we have estimated the incremental compliance cost for these systems at $750, independent of vehicle class, and incremental to a stoichiometric GDI engine.

Homogeneous Charge Compression Ignition

Homogeneous charge compression ignition (HCCI), also referre d to as controlled auto ignition (CAI), is an alternat e engine operating mode that do es not rely on a spark event to initiate combustion. The principles are mo re closely aligned with a diesel combustion cycle, in which the compressed charge exceed s a temperature and pressure necessary for spontaneous ignition. The resulting burn is mu ch shorter in duration with higher thermal efficiency.

An HCCI engine has inherent a dvantages in its overall effici ency for several reasons. An extremely lean fuel/air charge increases th ermodynamic efficiency. Shorter combustion times and higher EGR tolerance permit very high compression ratios (which also increase thermodynamic efficiency). Additionall y, pumping losses are reduced because the engine can run unthrottled.

However, due to the nature of its combusti on process, HCCI is difficult to control, requiring in-cylinder pressure sensors and ve ry fast engine control logic to optimize combustion timing, especially considering th e variable nature of operating conditions seen in a vehicle. To be used in a comme rcially acceptable vehicle application, an HCCIequipped engine would most likely be “ dual-mode,” in which HCCI operation is complemented with a traditional SI combustion process at idle and at higher loads and speeds.

Until recently, HCCI technology was considered to still be in the research phase. However, several manufacturers have made public statements about the viability of incorporating HCCI into pr oduction vehicles over the next 10 years. The NESCCAF study estimated the cost to range from $560 to $840, depending on vehicle class, including the costs for a stoichiometric GDI system with DVVL. We have based our estimated incremental compliance cost on th e NESCCAF estimates a nd, after subtracting out the estimated incremental cost for a stoichiometric GDI system with DVVL, we estimate the incremental cost for HCCI to be from $263 to $685, depending on vehicle class. This estimated incremental compliance cost is incremental to a stoichiometric GDI engine.V-32

Advanced CVT

Advanced CVTs have the ability to deliver higher torques than existing CVTs and have the potential for broader market penetration. These new designs incorporate toroidal friction elements or cone-and-ring assemblies w ith varying diameters. According to the NAS Report, advanced CVT could incremen tally reduce fuel consumption by up to 2 percent at an incremental cost of $364 to $874. NHTSA has no information which suggests that VCR will be included on a ny vehicles during the MY 2011-2015 timeframe, thus NHTSA does not use this technology in its analysis. Additionally, no updates to these estimates were sought.

Plug-in Hybrids

Plug-In Hybrid Electric Vehicl es (PHEVs) are very similar to hybrid electric vehicles, but with three significant functional differences . The first is the addition of a means to charge the battery pack from an outside source of electricity (usually the electric grid). Second, a PHEV would have a larger batter y 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 significa ntly depleted during normal operation.

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.

The fuel consumption reduction potential of PHEVs depends on many factors, the most important being the electrical capacity desi gned into the battery pack. To estimate the fuel consumption reduction potential of PHEV s, EPA has developed an in-house vehicle energy model (PEREGRIN) which is base d on the PERE (Physical Emission Rate Estimator) physics-based model used as a fuel consumption input for EPA’s MOVES mobile source emissions modelB.

EPA modeled the PHEV small car , large car, minivan and small trucks using parameters from a midsize car similar to today’s hybrid s and scaled to each vehicle’s weight. The large truck PHEV was modeled separately a ssuming very little engine downsizing. Each PHEV was assumed to have enough battery cap acity for a 20-mile-equivalent all-electric range and a power requirement to provide si milar performance to a hybrid vehicle. A twenty mile range was selected because it offers a good compromise for vehicle performance, weight, battery packaging and cost. To calculate the total energy use of a PHEV, a vehicle can be thought of as operating in two distinct modes, electri c (EV) mode, and hybrid (HEV) mode. The energy consumed during EV operation can be accounted for and calculated in terms of gasoline-equivalent MPG by using 10CFR474, Electric and Hybrid Vehicle Research, Development, and Demonstration Program; Petroleum-Equivalent Fuel EconomyV-33

Calculation . 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 total MPG value for the vehicle. Calculating a total fuel consumption reduction based on model outputs, gasoline-equivalent calculations, and the Utility Factor calculations, results in a 28% fuel consumption reduction for small cars, large cars, minivans, and small trucks and a 31% fuel consumption reduction for large trucks.

The fuel consumption reduction potential of PHEVs will vary based on the electrical capacity designed into the batt ery pack. Assuming a 20-mile “all-electric range” design, a PHEV might incrementally reduce fu el consumption by 28 to 31 percent. 46 Based on discussions with EPA, we have estimated th e incremental cost of PHEVs to be from $4500 to $10200, depending on vehicle class.

NHTSA is aware that manufacturers have made public statements about their intent to produce PHEVs during the MY 2011 – 2015 timefra me. However, NHTSA has received no information from these manufacturers that provides all the specifications and production data that is necessary for NHTSA to include these vehicles in this analysis. NHTSA hopes to receive complete product pl an information from these manufacturers concerning any PHEVs that they pl an on producing during the MY 2011- 2015 timeframe.

NHTSA would like to note that if it receiv es new and/or updated information from manufacturers regarding th e likelihood of PHEV produc tion during the MY 2011 to 2015 timeframe, it will make every effort to include PHEVs as a technology in its final rule. To enable the possible inclus ion of PHEVs as a technology, NHTSA would also have to configure the Volpe model to account for th e estimated source(s) that would supply the electricity for electrical grid charging of the battery. Work has started on this effort, but has not yet been completed.

Additionally, NHTSA is unaware of the exis tence of any batteries that are deemed acceptable for the performance characteristics necessary for a plug-in hybrid. Therefore, although we discuss them here, the m odel does not currently apply them.

The tables below summarize for each of the 10 classes of vehicles the cost and effectiveness assumptions used in this rulema king as well as the year of availability of each technology. The agency seeks comments on our assumptions and the cost and effectiveness estimates provided.

46 This estimate is based on the EPA test cycle. We are unable to provide cost estimates for PHEV technology due to the great amount of uncertainty surrounding the chemistry, safety and performance requirements of the appropriate battery technology that would be used.V-34Table V-1 Technology Cost EstimatesTechnologies Vehicle Technology Incremental Retail Price Equivalent per Vehicle ($) by Vehicle ClassSubcompactCarCompactCarMidsizeCarLarge Car SmallPickupSmallSUVMinivanMidsizeSUVLargePickupLargeSUVLow friction lubricants –incremental to base engine3 3 3 3 3 3 3 3 3 3Engine friction reduction –incremental to base engine0-84 0-84 0-126 0-126 0-126 0-126 0-126 0-126 0-168 0-168Overhead Cam BranchVVT – intake camphasing59 59 119 119 119 119 119 119 119 119VVT – coupled camphasing59 59 119 119 119 119 119 119 119 119VVT – dual cam phasing 89 89 209 209 209 209 209 209 209 209Cylinder deactivation n.a. n.a. 203 203 203 203 203 203 229 229Discrete VVLT 169 169 246 246 246 246 246 246 322 322Continuous VVLT 254 254 466 466 466 466 466 466 508 508Overhead Valve BranchCylinder deactivation n.a. n.a. 203 203 203 203 203 203 229 229VVT – coupled camphasing59 5959 59 59 59 59 59 59 59Discrete VVLT 169 169 246 246 246 246 246 246 322 322Continuous VVLT(includes conversion toOverhead Cam)59959912621262126212621262126213801380Camless valvetrain(electromagnetic)336-673 336-673 336-673 336-673 336-673 336-673 336-673 336-673 336-673 336-673GDI – stoichiometric 122-420 122-420 204-525 204-525 204-525 204-525 204-525 204-525 228-525 228-525GDI – lean burn 750 750 750 750 750 750 750 750 750 750Gasoline HCCI dual-mode 263 263 390 390 390 390 390 390 685 685Turbocharge & downsize 690 690 120 120 120 120 120 120 810 810Diesel – Lean NOx trap 1586 1586Diesel – urea SCR 2051 2051 2411 2411 2126 2411 2261 2261Aggressive shift logic 38 38 38 38 38 38 38 38 38 38Early torque converterlockup30 30 30 30 30 30 30 30 30 305-speed automatic 76-167 76-167 76-167 76-167 76-167 76-167 76-167 76-167 76-167 76-1676-speed automatic 76-187 76-187 76-187 76-187 76-187 76-187 76-187 76-187 76-187 76-1876-speed AMT 141 141 141 141 141 141 141 141 141 141V-356-speed manual 107 107 107 107 107 107 107 107 107 107CVT 100 100 139 139 n.a. 139 139 139 n.a. n.a.V-36Stop-Start with 42 voltsystem563 563 600 600 600 600 600 600 600 600IMA/ISA/BSG (includesengine downsize)1636 1636 2274 2274 n.a n.a n.a n.a n.a n.a2-Mode hybrid electricvehiclen.a n.a 4655 4655 4655 4655 4655 4655 6006 6006Power-split hybrid electricvehicle (P-S HEV)3700-3850 3700-3850 3700-3850 3700-3850 3700-3850 3700-3850 3700-3850 3700-3850Plug-in hybrid electricvehicle (PHEV)4500 4500 6750 6750 6750 6750 6750 6750 10200 10200Improved high efficiencyalternator & electrification ofaccessories (12 volt)124-166 124-166 124-166 124-166 124-166 124-166 124-166 124-166 124-166 124-166Electric power steering (12or 42 volt)118-197 118-197 118-197 118-197 118-197 118-197 118-197 118-197 118-197 118-197Improved high efficiencyalternator & electrification ofaccessories (42 volt)124-166 124-166 124-166 124-166 124-166 124-166 124-166 124-166 124-166 124-166Aero drag reduction (20% oncars, 10% on trucks)0-75 0-75 0-75 0-75 0-75 0-75 0-75 0-75 0-75 0-75Low rolling resistance tires(10%)6 6 6 6 6 6 6 6Low drag brakes (ladderframe only)87 87 87 87 87Secondary axle disconnect(unibody only)676 676 676 676 676 676 676 676Front axle disconnect (ladderframe only)114 114 114 114 114Weight reduction (1%) –above 5,000 lbs only2/pound 2/poundWeight reduction (2%) –incremental to 1%2/pound 2/poundWeight reduction (3%) –incremental to 2%3/pound 3/poundSubcompactCarCompactCarMidsizeCarLarge Car SmallPickupSmallSUVMinivanMidsizeSUVLargePickupLargeSUVV-37Table V-2: Technology Percent Effectiveness EstimatesTechnologies Vehicle Technology Incremental FuelConsumption Reduction (%) by Vehicle ClassSubcompactCarCompactCarMidsizeCarLargeCarSmallPickupSmallSUVMinivanMidsizeSUVLargePickupLargeSUVLow friction lubricants –incremental to base engine0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5Engine friction reduction –incremental to base engine1-3 1-3 1-3 1-3 1-3 1-3 1-3 1-3 1-3 1-3Overhead Cam BranchVVT – intake cam phasing 2 2 1 1 1 1 1 1 2 2VVT – coupled cam phasing 1 1 3 3 2 2 1 1 2 2VVT – dual cam phasing 1 1 3 3 1 1 1 1 2 2Cylinder deactivation n/a n/a 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5Discrete VVLT 3 3 1.5 1.5 1.5 1.5 0.5 0.5 1.5 1.5Continuous VVLT 4 4 3.5 3.5 2.5 2.5 1.5 1.5 2.5 2.5Overhead Valve BranchCylinder deactivation n/a n/a 6 6 6 6 6 6 6 6VVT – coupled cam phasing 3 3 2.5 2.5 1.5 1.5 0.5 0.5 2.5 2.5Discrete VVLT 1.5 1.5 1.5 1.5 1.5 1.5 0.5 0.5 1.5 1.5Continuous VVLT (includesconversion to OverheadCam)2.5 2.5 3.5 3.5 2.5 2.5 1.5 1.5 2.5 2.5Camless valvetrain(electromagnetic)2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5GDI – stoichiometric 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2GDI – lean burn - - - - - - - - - -Gasoline HCCI dual-mode 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12 10-12Turbocharge & Downsize 5.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.55.0 – 7.5Diesel – Lean NOx trap 11.5 11.5 n/a n/a n/a n/a n/a n/a n/a n/aDiesel – urea SCR n/a n/a 15.5 15.5 15.5 15.5 15.5 15.5 15.5 15.5Aggressive shift logic 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2Early torque converterlockup0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.55-speed automatic 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.56-speed automatic 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.5 0.5-2.56-speed AMT 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.5 4.5-7.56-speed manual 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5CVT 3.5 3.5 3.5 3.5 n/a 3.5 3.5 3.5 n/a n/aV-38Stop-Start with 42 voltsystem7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5IMA/ISA/BSG (includesengine downsize)8.5 8.5 3.5 3.5 n/a n/a n/a n/a n/a n/a2-Mode hybrid electricvehiclen/a n/a 3.5 3.5 7 7 7 7 3.5 3.5Power-split hybrid electricvehicle (P-S HEV)5 5 6.5 6.5 6.5 6.5 6.5 6.5 n/a n/aPlug-in hybrid electricvehicle (PHEV)28 28 28 28 28 28 28 28 31 31Improved high efficiencyalternator & electrification ofaccessories (12 volt)1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2Electric power steering (12or 42 volt)1.5 1.5 1.5-2 1.5-2 2 2 2 2 2 2Improved high efficiencyalternator & electrification ofaccessories (42 volt)1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2Aero drag reduction (20% oncars, 10% on trucks)3 3 3 3 2 2 3 3 2 2Low rolling resistance tires(10%)1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 n/a n/aLow drag brakes (ladderframe only)n/a n/a n/a n/a 1 1 n/a n/a 1 1Secondary axle disconnect(unibody only)1 1 1 1 1 1 1 1 n/a n/aFront axle disconnect (ladderframe only)n/a n/a n/a n/a 1.5 1.5 n/a n/a 1.5 1.5Weight reduction (1%) –above 5,000 lbs onlyn/a n/a n/a n/a n/a n/a n/a n/a 0.7 0.7Weight reduction (2%) –incremental to 1%n/a n/a n/a n/a n/a n/a n/a n/a 0.7 0.7Weight reduction (3%) –incremental to 2%n/a n/a n/a n/a n/a n/a n/a n/a 0.7 0.7SubcompactCarCompactCarMidsizeCarLargeCarSmallPickupSmallSUVMinivanMidsizeSUVLargePickupLargeSUVV-39

Table V-3: Year of Availability

Technologies Year of Availability Low friction lubricants – incremental to base engine Present Engine friction reduction – incremental to base engine Present Overhead Cam Branch VVT – intake cam phasing Present VVT – coupled cam phasing Present VVT – dual cam phasing Present Cylinder deactivation Present Discrete VVLT Present Continuous VVLT Present Overhead Valve Branch Cylinder deactivation Present VVT – coupled cam phasing Present Discrete VVLT Present Continuous VVLT (includes conversion to Overhead Cam) Present Camless valvetrain (electromagnetic) 2020 GDI – stoichiometric Present GDI – lean burn 2020 Gasoline HCCI dual-mode 2016 Turbocharging & Downsizing 2010 Diesel – Lean NOx trap 2010 Diesel – urea SCR 2010 Aggressive shift logic Present Early torque converter lockup Present 5-speed automatic Present 6-speed automatic Present 6-speed AMT 2010 6-speed manual Present CVT Present Stop-Start with 42 volt system 2014 IMA/ISA/BSG (includes engine downsize) 2014 2-Mode hybrid electric vehicle 2014 Power-split hybrid electric vehicle (P-S HEV) 2014 Full-Series hydraulic hybrid NA Plug-in hybrid electric vehicle (PHEV) NA Full electric vehicle (EV) NA Improved high efficiency alternator & electrification of accessories (12 volt) Present Electric power steering (12 or 42 volt) Present Improved high efficiency alternator & electrification of accessories (42 volt) Present Aero drag reduction (20% on cars, 10% on trucks) Present Low rolling resistance tires (10%) Present Low drag brakes (ladder frame only) Present Secondary axle disconnect (unibody only) 2012 Front axle disconnect (ladder frame only) Present Weight reduction (1%) – above 6000 lbs only Present Weight reduction (2%) – incremental to 1% Present Weight reduction (3%) – incremental to 2% PresentV-40

C. Technology synergies

When two or more technologies are added to a particular vehicle model to improve its fuel efficiency, the resultant fuel consumption reduc tion may sometimes be higher or lower than the product of the individual effectiveness values for those items. This may occur because one or more technologies applied to th e same vehicle partially address the same source or sources of engine or vehicle losses. Alte rnately, this effect may be s een when one technology shifts the engine operating points, and therefore increases or reduces the fuel consumption reduction achieved by another technology or se t of technologies. The difference between the observed fuel consumption reduction associated with a set of technologies and the prod uct of the individual effectiveness values in that se t is sometimes referred to as a “synergy.” Synergies may be positive (increased fuel consumption reduction compar ed to the product of the individual effects) or negative (decreased fuel consumption reduction).

The NAS committee which authored the 2002 Repor t was aware of technology synergies and considered criticisms as part of the peer-r eview process that its analysis was “judgmentsimplified,” but concluded overall that its approach was “sufficiently rigo rous” for purposes of the report. 47 After examining its analysis again, the co mmittee stated that “...the path 1 and path 2 estimate average fuel consumption improveme nts ... appear quite re asonable, although the uncertainty in the analysis grows as mo re technology features are considered.” 48 In essence, as more technology features are cons idered, the features are more li kely to overlap and result in synergies. Because NAS did not expect vehi cle manufacturers to reach “path 3” in the timeframe considered, it did not concern itself deeply with the effect of technology synergies in its analysis.

NHTSA’s rulemaking regarding CAFE standa rds for MY 2008-MY 2011 light trucks made significant use of NAS’ “path 2” estimates of the effectiveness and cost of available technologies. In part because its analysis did not extend to the more a ggressive “path 3,” the agency concluded that the NAS-based multiplicative approach it followed when aggregating these technologies was reasonable. In contrast, the agency’s cu rrent proposal is based on an analysis that includes a broade r range of technologies than wa s considered by NAS in 2001 and 2002. Also, the extent to which technologies ar e included in the current analysis is more consistent with NAS’ prior “path 3” approach. Therefore, the agency’s current analysis uses estimated “synergies” to address the uncer tainties mentioned in the 2002 NAS report.

The Volpe model has been modified to estimate the interactions of tec hnologies using estimates of incremental synergies associated with a number of technology pairs identified by NHTSA, Volpe Center, and EPA staff. The use of disc rete technology pair incremental synergies is similar to that in DOE’s Nationa l Energy Modeling System (NEMS). 49 Inputs to the Volpe model incorporate NEMS-identified pairs, as well as additional pairs from the set of technologies considered in the Volpe model. However, to main tain an approach that was consistent with the

47 NAS Report, p. 151.

48 Id.

49 U.S. Department of Energy, Energy Information Administration, Transportation Sector Module of the National Energy Modeling System: Model Documentation 2007 , May 2007, Washington, DC, DOE/EIAM070(2007), pp. 29-30.V-41

technology sequencing developed by NHTSA, Volpe Center, and EP A staff, new incremental synergy estimates for all pairs we re obtained from a first-order “l umped parameter” analysis tool created by EPA. 50 Results of this analysis were genera lly consistent with those of full-scale vehicle simulation modeling performed by Ricardo, Inc. 51 NHTSA’s analysis applies these incremental synergy values, obtained from the tool using baseline passenger car engine and vehicle inputs, to a ll vehicle classes.

Incremental synergy values are sp ecified in Volpe model input file s in two ways: as part of the incremental effectiveness values table (same path technologies) and in a separate incremental synergies table (separate path technologies). In the case of same path technologies, each technology's incremental effectiveness value was obtained from the tec hnical literature and manufacturers’ submitted information, and then th e sum of all incremental synergies associated with that technology and each technology located higher on the same path was subtracted to determine the incremental effectiv eness. For example, all engine technologies take into account incremental synergy factors of pr eceding engine technologies; all transmission technologies take into account incremental synergy factors of pre ceding transmission technologies. These factors are expressed in the fuel consumption improvement factors in the input files used by the Volpe model.

For applying incremental synergy factors in sepa rate path technologies, the Volpe model uses an input table which lists technology pa irings and incremental synergy factors associated with those pairings, most of which are betw een engine technologies and tr ansmission technologies. When a technology is applied to a vehicl e by the Volpe model, all instan ces of that technology in the incremental synergy table which match technologi es already applied to the vehicle (either preexisting or previously applied by the Volpe model) are summed and applied to the fuel consumption improvement factor of the tec hnology being applied. When the Volpe model applies incremental synergies, the fuel cons umption improvement factors cannot be reduced below zero.

Incremental synergy values were calculated a ssuming the prior application (implying succession in some cases) of all technologies located higher along both paths th an the pair considered. This is usually a true reflection of a given vehicle's equipment at any point in the model run and thus the method is expected to produce reasonable results in most cases.

NHTSA considered other methods for estimati ng interactions between technologies. For example, the agency has considered integrati ng detailed simulation of individual vehicles’ performance into the Volpe model. 52 However, while application of such simulation techniques could provide a useful source of information when developing inputs to the Volpe model, the

50 This tool is a simple spreadsheet model that represents energy consumption in terns of average performance over the fuel economy test procedure, rather than explicitly an alyzing specific drive cycles. The tool begins with an apportionment of fuel consumption across several loss mech anisms, and accounts for th e average extent to which different technologies affect these loss mechanisms

51 EPA contracted with Ricardo, Inc. (an independent consu lting firm) to study the potential effectiveness of carbon dioxide-reducing (and thus, fuel economy-improving) vehicl e technologies. The Ricardo study is available in the docket for this NPRM.

52 In other words, having the Volpe model run a full vehicle simulation every time the Volpe model is evaluating the potential effect of applying a specific technology to a specific vehicle model.V-42

agency believes that applying detailed simulatio n when analyzing the entire fleet of future vehicles is neither necessary nor feasible. NHT SA is charged with setting standards at the maximum feasible level. To understand the pot ential impacts of its st andards, the agency analyzes entire fleets of vehicles expected to be produced in the future. Although some expected engineering characteristics of th ese vehicles are available, the level of detail needed for full vehicle simulation—a level of detail that would be important if NHTSA were actually designing vehicles—is not available.

As another possible alternative to using “syne rgy” factors, NHTSA has also considered modifying the Volpe model to accept as inputs different measures of efficiency for each engine, transmission, and vehicle model in the product plan s. For instance, manufacturers could provide estimates of mechanical and drivetrain efficienci es. Mechanical efficiency (usually between 70 and 90 percent) gives an estimate of the amount of fuel consumed by engine friction and pumping losses. Drivetrain efficiency (usually between 80 and 90 percent) gives an estimate of the amount of fuel consumed by parasitic loads and gearbox friction. From these efficiencies along with other inputs such as compression ra tio, aerodynamic drag, ro lling resistance, and vehicle mass, the model could estimate the fuel consumption associated with each loss mechanism and enforce a maximum fuel consum ption reduction for each vehicle model based on those estimates and the technologies applied. Like the use of incremental synergies, this method could help the model avoid double counting fuel consumption benefits when applying multiple technologies to the same vehicle model. 53 The agency believes that th is approach, like the use of “synergy” factors currently used by the Volpe model, could conceivably provide a means of addressing uncertainty in fuel consumption estimation within th e context of CAFE analysis. However, the agency is not confident that model-by-model estimates of baseline fuel consumption partitioning would be available. Also, partitioned estimates of the effects of all the technologies considered in the analysis of this proposal were not availa ble. If both of these concerns could be addressed, NHTSA believes it would be possible to implement partitioned accounting of fuel consumption. However, the ag ency is unsure whether and, if so, to what extent doing so would represent an improvement over our current approach of using incremental synergy factors.

The agency solicits comments on its use of increm ental synergy factors to address uncertainty in the estimation of the extent to which fuel cons umption is reduced by applying technologies. In particular, the agency solicits comment on (a) th e values of the factors the agency has applied, (b) possible variations across the ten categories of vehicles the agency has considered, and (c) additional technology pairs that may involve such interactions. The proposal of any additional methodologies, such as prototyping and test ing, full vehicle simulation, or partitioned accounting, should address information and resource re quirements, particularly as related to the analysis of entire fleets of future vehicles expected to be produced through MY 2015.

4. Technology cost learning curve

53 This approach was proposed in a paper criticizing NAS’ ap proach to synergies in the 2001-02 peer-review process for the NAS Report. See Patton, et al., “Aggregating Technologies for Reduced Fuel Consumption: A Review of the Technical Content in the 2002 National Research Council Report on CAFE”, SAE 2002-01-0628, March 2002.V-43

In past rulemaking analyses, NHTSA did not explicitly account for the cost reductions a manufacturer may realize thr ough learning achieved from expe rience in actually applying a given technology. NHTSA understood technology co st-estimates to reflect already the full learning costs of technology. EPA felt that for so me of the newer, emerging technologies, cost estimates did not reflect the full impact of lear ning. NHTSA tentatively agreed, but is seeking comment on the impact of learning on cost and the production volumes wh ere it occurs. NHTSA has modified its previous approach in this rule making for that reason. In this rulemaking we have included a learning factor for some of the t echnologies. The “learning curve” describes the reduction in unit incremental production costs as a function of accumul ated production volume and small redesigns that reduce costs.

NHTSA implemented technology learning curves by using three paramete rs: (1) the initial production volume that must be reached before cost reductions begin to be realized (referred to as “threshold volume”); (2) the percent reduction in average unit cost that results from each successive doubling of cumulative production volume (usually referred to as the “learning rate”); and (3) the initial cost of th e technology. Section V below desc ribing the Volpe model contains additional information on learning curve functions.

Figure 2 illustrates a lear ning curve for a vehicle technology with an initial average unit cost of $100 and a learning rate of approxi mately 20 percent. In this hypothetical example, the initial production volume before cost re ductions begin to be realized is set at 12,000 units and the production volume at the cost floor is set at roughly 50,000 units with a cost of $64.

Figure 2. Typical Experience Curve

Most studies of the effect of the learning curv e on production costs appear to assume that cost reductions begin only after some initial volume th reshold has been reached, but not all of these studies specify what this threshold volume is. The rate at which costs decline beyond the initial threshold is usually expressed as the percent reduction in average un it cost that results from each successive doubling of cumulative production volu me, sometimes referred to as the learning rate. Many estimates of learning experience cu rves do not specify a cumulative production volume beyond which cost reductions no longer o ccur, instead depending on the asymptoticV-44

behavior of the above expression of (CQ) for l earning rates below 100 percen t to establish a floor

on costs.

For this analysis, NHTSA has applied learning cu rve cost reductions on a manufacturer-specific basis, and has assumed that learning-based reduct ions in technology costs occur at the point that a manufacturer applies the given technology to 25,000 cars or trucks, and are repeated a second time as it produces another 25,000 cars or trucks for the second learning step (car and truck volumes are treated separately for determining these sales volumes). The volumes chosen represent our best estimate for where learning w ould occur. As such, we believe that these estimates are better suited to this analysis than a more general approach of a single number for the learning curve factor, because each manufactur er would be implementing technologies at its own pace in this rule, rather than assumi ng that all manufacturer s implement identical technology at the same time. NHTSA is aware that some of the cost estimates that is has relied upon were derived from suppliers and has added mu ltipliers so that these costs are reflective of what manufacturers would pay for this technol ogy. NHTSA seeks comments on the estimated level of price markups that manufacturers pay for technologies purchased from suppliers and whether different learning curves should be applied to those types of technologies.

Ideally, we would know the development pr oduction cycle and maturity level for each technology so that we could calcu late learning curves precisely. Without that knowledge, we have to use engineering judgment. After having produced 25,000 cars or trucks with a specific part or system, we believe that sufficient learni ng will have taken place such that costs will be lower by 20 percent for some technologies and 10 percent for others. Af ter another 25,000 units, its expected that, for some technologies, such as 6-speed AMTs, that anot her cost reduction will have been realized.

For each of the technologies, we have considered whether we could project future cost reductions due to manufacturer learning. In maki ng this determination, we considered whether or not the technology was in wide-spread use to day or expected to be by the model year 2011- 2012 time frame, in which case no future learni ng curve would apply because the technology would already be in wide-spr ead production by the automotive industry by that timeframe, e.g., on the order of multi-millions of units per year. (Examples of these include 5-speed automatic transmissions and intake-cam phasing variable va lve timing. These technologies have been in production for light-duty vehicles fo r more than 10 years.) In a ddition, we carefully considered the underlying source data for our cost estimates. If the source data sp ecifically stated that manufacturer cost reduc tion from future learning would occu r, we took that information into account in determining whether we would apply manufacturer learni ng in our cost projections. Thus, for many of the technologies, we have not applied any future cost reduction learning curve.

However, there are a number of technologies wh ich are not yet in mass production for which we have applied a learning curve. As indicated in Table 7 below, we have applied the learning curve beginning in MY 2011 to one set of t echnologies, and for a number of additional technologies we did not apply manufacturer learning until MY 2014. The distinction between MYs 2011 and 2014 is due to our source data for our cost estimates. For those technologies where we have applied manufact urer learning in MY 2011, the s ource of our cost estimate didV-45

not rely on manufacturer learning to develop the initial cost estim ate we have used – therefore we apply the manufacturer learni ng methodology beginning in MY 2011.

Table 7. Learning Curve Application to Technologies Technology First Year of Application Learning Factor

Overhead Cam Branch Cylinder deactivation 2014 20%

Continuous VVLT 2014 20% Camless valvetrain (electromagnetic) 2011 20% GDI – lean burn 2011 20% Gasoline HCCI dual-mode 2011 20% Turbocharging & downsizing 2014 20%

Diesel – Lean NOx trap* 2011 10% Diesel – urea SCR * 2011 10% 6-speed AMT 2011 20% Stop-Start with 42 volt system 2014 20% IMA/ISA/BSG (includes engine downsize) 2014 20% 2-Mode hybrid electric vehicle 2014 20% Power-split hybrid electric vehicle (P-S HEV) 2014 20% Plug-in hybrid electric vehicle (PHEV) 2011 20% Improved high efficiency alternator & electrification of accessories (42 volt) 2011 20%
Secondary axle disconnect (unibody only) 2011 20% Weight reduction (1%) – above 6000 lbs only 2011 20%

Weight reduction (2%) – incremental to 1% 2011 20% Weight reduction (3%) – incremental to 2% 2011 20% * For diesel technologies, l earning is only applied to the cost of the emission control equipment, not the cost for the entire diesel system.

The technologies for which we do not begin ap plying learning until 2014 all have the same reference source, the 2004 NE SCCAF study, for which the sub- contractor was The Martec Group. In the work done for the 2004 NESCCAF report, Martec relied upon actual price quotes from Tier 1 automotive suppliers to develop au tomotive manufacturer cost estimates. Based on information presented by Martec to the Nationa l Academy of Sciences (NAS) Committee during their January 24, 2008, public meet ing in Dearborn, Michigan, 54 we understand that the Martec cost estimates incorporated some element of manu facturer learning. Martec stated that the Tier 1 suppliers were specifically re quested to provide price quotes which would be valid for three years (2009-2011), and that for some components the Tier 1 supplier included cost reductions in years two and three which the supplier anticipa ted could occur, and which they anticipated would be necessary in order for their quote to be competitive with other supp liers. Therefore, for this analysis, we did not apply any learning curve to any of the Martec-sourced costs for the first three years of this proposal (2011-2013). However, the theory of manufactur er learning is that it is a continuous process, though the rate of improvement decreas es as the number of units

54 “Variable Costs of Fuel Economy Technologies” Mart ec Group, Inc Report Presente d to: Committee to Assess Technologies for Improving Light-Duty Vehicle Fuel Economy. Division on Engineering and Physical Systems, Board on Energy and Environmental Systems, the National Academy of Sciences, January 24, 2008.V-46

produced increases. While we were not able to gain access to the detailed submissions from Tier 1 suppliers which Martec relied upon for their estimates, we do believe that additional cost reductions will occur in the future for a number of the technologies for which we relied upon the Martec cost estimates for the reasons stated a bove in reference to the general learning curve effect. For those technologies we applied a learning curve beginning in 2014. Martec has recently submitted a study to the NAS Comm ittee comparing the 2004 NESCCAF study with new updated cost information. Given that this study had just been completed, the agency could not take it into consideration for the NPRM. Ho wever, the agency will review the new study and consider its findings in time for the final rule.

Manufacturers’ actual costs for applying these tec hnologies to specific vehi cle models are likely to include significant additional outlays for accompanying design or engineering changes to each model, development and testing of prototype versions, recalibrat ing engine operating parameters, and integrating the technology with other attributes of the vehicle. Manuf acturers may also incur additional corporate overhead, marketing, or distri bution and selling expenses as a consequence of their efforts to improve the fuel economy of individual vehicle m odels and their overall product lines.

In order to account for these additional costs, NHTSA has applied an indi rect cost multiplier of 1.5 to its estimate of the vehicle manufacturers’ direct costs fo r producing or acquiring each fuel economy-improving technology to arrive at a consum er cost. This estimate was developed by Argonne National Laboratory in a re cent review of vehicle manufactur ers’ indirect costs. The Argonne study was specifically intended to improve the accuracy of future cost estimates for production of vehicles that achie ve high fuel economy by employing many of the same advanced technologies considered in the agency’s analysis. 55 Thus, its recommendation that a multiplier of 1.5 be applied to direct manufact uring costs to reflect manufacturer s’ increased indirect costs for deploying advanced fuel economy technologies appear s to be appropriate for use in the current analysis. Historically, NHTSA has used almost the exact same multiplier , a multiplier of 1.51, as the markup from variable costs or direct manufac turing costs to consumer costs. This markup takes into account fixed costs, burden, manufacturer’s profit, and d ealers profit. Table VII-2 of the PRIA shows the estimated incremental consumer costs for each vehicle type. 56

E. Ensuring sufficient lead time

In analyzing potential technological improve ments to the product offerings for each manufacturer with a substantia l share of the market, NHTSA a dded technologies based on our engineering judgment and expert ise about possible adjustments to the detailed product plans submitted to NHTSA. Our decision whether an d when to add a technology reflected our consideration of the pr acticability of applying a specific t echnology and the necessity for leadtime in its application. NHTSA r ecognizes that vehicle manufactur ers must have sufficient lead time to incorporate changes and new features into their vehicles and henc e added technologies in a cost-minimizing fashion. That is, we genera lly added technologies th at were most costeffective and took into acc ount the year of availability of the technologies.

55 Vyas, Anant, Dan Santini, and Roy Cuenca, Comparison of Indirect Cost Multipliers for Vehicle Manufacturing

, Center for Transportation Research, Argonne National Laboratory, April 2000.

56 PRIA, VII-9.V-47

NHTSA realizes that not all t echnologies will be available im mediately or could be applied immediately and that there are di fferent phase-in rates (how rapi dly a technology is able to be applied across a manufacturer’s fleet of vehicles ) applicable to each technology as well as windows of opportunities when certain technologies could be applied ( i.e. , when a product is redesigned or refreshed).

1. Linking to redesign and refresh

In the automobile industry there are two terms that describe when changes to vehicles occur: redesign and refresh. In projecti ng the technologies that could be applied to specific vehicle models, NHTSA tied the application of the ma jority of the technologies to a vehicle’s refresh/redesign cycle. Ve hicle redesign usually encompa sses changes to a vehicle’s appearance, shape, dimensions, and powertrain and is traditionally associated with the introduction of “new” vehicles into the market, a nd often is characterized as the next generation of a vehicle. In contrast ve hicle refresh usually only encompasses changes to a vehicle’s appearance, and may include an upgraded powertrai n and is traditionally associated with midcycle cosmetic changes to a vehicle within its current generation to make it appear “fresh.” Vehicle refresh traditionally occurs no earlier th an two years after a vehicle redesign or at least two years before a scheduled redesign. Tabl e 8 below contains a complete list of the technologies that were applied and whether NHTSA allowed them to be applied during a redesign year, a refresh year or during any model year is shown in the below table.

Table 8. Technology Refresh and Redesign Application

Technology Abbr. Can be applied during redesign model year only Can be applied during a redesign or refresh model
year Can be applied during any model
year Low Friction Lubricants LUB X X Engine Friction Reduction EFR X Variable Valve Timing (ICP) VVTI X Variable Valve Timing (CCP) VVTC X Variable Valve Timing (DCP) VVTD X Cylinder Deactivation DISP X Variable Valve Lift & Timing (CVVL) VVLTC X Variable Valve Lift & Timing (DVVL) VVLTD X Cylinder Deactivation on OHV DISPO X Variable Valve Timing (CCP) on OHV VVTO X Multivalve Overhead Cam with CVVL DOHC X Variable Valve Lift & Timing (DVVL) on OHV VVLTO X Camless Valve Actuation CVA X Stoichiometric GDI SIDI X Lean Burn GDI LBDI X Turbocharging and Downsizing TURB X HCCI HCCI X Diesel with LNT DSLL XV-48

Diesel with SCR DSLS X 5 Speed Automatic Transmission 5SP X Aggressive Shift Logic ASL X X Early Torque Converter Lockup TORQ X 6 Speed Automatic Transmission 6SP X Automatic Manual Transmission AMT X Continuously Variable Transmission CVT X 6 Speed Manual 6MAN X Improved Accessories IACC X Electronic Power Steering EPS X 42-Volt Electrical System 42V X Low Rolling Resistance Tires ROLL X Low Drag Brakes LDB X Secondary Axle Disconnect – Unibody SAXU X Secondary Axle Disconnect - Ladder Frame SAXL X Aero Drag Reduction AERO X Material Substitution (1%) MS1 X Material Substitution (2%) MS2 X Material Substitution (5%) MS5 X ISG with Idle-Off ISGO X IMA/ISAD/BSG Hybrid (includes engine downsizing) IHYB X 2-Mode Hybrid 2HYB X Power Split Hybrid PHYB X

As can be seen in the above ta ble, most technologies would only be applied by the Volpe model when a specific vehicle was due for a redesi gn or refresh. However, for a limited set of technologies, the model was not restricted to applying them during a re fresh/redesign year and thus they were made available for application at any time. These spec ific technologies are:

• Low Friction Lubricants

• Improved Accessories

• Low Rolling Resistance Tires

• Low Drag Brakes

All of these technologies are very cost-effective, can apply to multiple vehicle models/platforms and can be applied across multiple vehicle models /platforms in one year. Although they can also be applied during a refresh/redesign year, they are not restricted to that timeframe because their application is not viewed as necessitating a ma jor engineering redesign and testing/calibration.

There is an additional technology whose application is not tied to refresh/redesign, which is Aggressive Shift Logic (ASL). ASL is accomp lished through reprogramming the shift points for a transmission to be more like a manual transm ission. Upgrading a transmission to utilize ASL can happen at refresh/redesign, but because it is not a hardware change, it can also occur at other points in a vehicle’s design cycl e. If a model that is sche duled for refresh/redesign has a transmission that is being upgraded to ASL, it is possible that all other vehicles that utilize the same transmission (which is usually produced at the same manufacturing plant) could beV-49

upgraded at the same time to incorporate AS L and that ASL could permeate other vehicle models in years other than a refresh/redesign year.

NHTSA based the redesign rates used in the Volpe Model on a combination of the manufacturers confidential product plans and on NHTSA’s engineering judgment. In most instances, NHTSA has accepted the projected redesign periods from the companies who provided them through MY 2013. If companies di d not provide product plan date, NHTSA used publicly available data about vehicle redesigns to establish the redesign rates for the vehicles produced by these companies.

NHTSA assumes that passenger cars will be rede signed every 5 years, based on the trend over the last 10-15 years for passenger cars to be redesi gned every 5 years. These trends are reflected in the manufacturer production plans that NHTSA received in response to its request for product plan information and was confirmed by many auto makers in meetings held with NHTSA to discuss various issues with manufacturers.

NHTSA believes that the vehicle design proce ss has progressed and improved rapidly over the last decade and these improvements have resulted in the ability of manuf acturers to shorten the design process and to introduce vehicles more frequently to respond to competitive market forces. Almost all passenger cars will be on a 5-year redesign cycle by the end of the decade, with the exception being some high performance vehicles and vehicles’ with specific market niches.

Currently, light trucks are redesigned every 5 to 7 years, with some vehicles having longer redesign periods ( e.g. , full-size vans). In the most competitive SUV and crossover vehicle segments, the redesign cycle currently averages sli ghtly above 5 years. It is expected that the light truck redesign schedul e will be shortened in the future du e to competitive market forces and in response to fuel economy and other regulatory requirements. It is expected that by MY 2014, almost all light trucks will be redesigned on a 5-year cycle. Thus, for almost all vehicles scheduled for a redesign in model year 2014 and la ter, NHTSA estimated that all vehicles would be redesigned on a 5-year cycle. Exceptions we re made for high performance vehicles and other vehicles that traditionally had longer than average design cycles ( e.g. , 2-seater sports cars). For those vehicles, NHTSA attempted to preser ve the historic redesign cycle rates.

2. Technology phase-in caps

In analyzing potential technological impr ovements to the product offerings for each manufacturer with a substantia l share of the market, NHTSA a dded technologies based on our engineering judgment and expert ise about possible adjustments to the detailed product plans submitted to NHTSA. Our decision whether an d when to add a technology reflected our consideration of the pr acticability of applying a specific t echnology and the necessity for leadtime in its application.

NHTSA recognizes that vehicle ma nufacturers must have sufficient lead time to incorporate changes and new features into their vehicles and that these changes cannot occur all at once, but must be phased in over time. As discussed above, our analysis addresses these realities in partV-50

by timing the estimated application of most tec hnologies to coincide wi th anticipated vehicle redesigns and/or freshenings. We have esti mated that future vehicle redesigns can be implemented on a 5-year cycle with mid-cycle freshening, except wher e manufacturers have indicated plans for shorter redesign cycles.

However, the agency further recognizes that engineering, planning and financial constraints prohibit most technologies from being applied across an entire fleet of vehicles within a year. Thus, as for the analysis supporting its 2006 rule making regarding light truck CAFE, the agency is employing overall constraints on the rate s at which each technology can penetrate a manufacturer’s fleet. The Vo lpe model applies these “phase-i n caps” by ceasing to add a given technology to a manufacturer’s fleet in a sp ecific model year once it has increased the corresponding penetration rate by at least amount of the ca p. Having done so, the model proceeds to apply other technologies in lieu of the “capped” technology.

For its regulatory analysis in 2006, NHTSA applied phase-in caps expe cted to be consistent with NAS’ indication in its 2002 report that even existing technologies would require 4 to 8 years to achieve widespread penetration of the fleet. Th e NAS report, which is believed to be the only peer-reviewed source which provides phase-in ra tes, was relied upon for establishing the phasein caps that we used for all technologies, except diesels and hybrids, for which the report didn’t include that information. Most of the phase-i n caps applied by the agency in 2006 ranged from 25 percent (4 year introduction) to 17 percent (approximately 6 years, the midpoint of the NAS estimate). The agency assumed shorter implem entation rates for techno logies that did not require changes to the manufacturing line. Fo r other technologies (e.g., hybrid and diesel powertrains), the agency employed phase-in caps as low as 3 percent, to reflect the major redesign efforts and capital investments re quired to implement these technologies.

Considerable changes have occurred since NHT SA’s 2006 analysis, and even more since the 2002 NAS report. Not only have fuel prices increase d, but official forecasts of future fuel prices have increased, as well. This suggests a market environment in which consumers are more likely to demand fuel-saving technologi es than previously anticipa ted, and it suggests a financial environment in which investors are more likely to invest in companie s developing and producing such technologies. Indeed, some technologies have penetrated the marketplace more quickly than projected in 2006. Confidential product plan information submitted to NHTSA in 2007 and information from suppliers confirm that the ra te of technology penetr ation has increased as compared to 2006.

Also, the statutory environment has changed si nce 2006. With the enactme nt of EISA, Congress has adopted the specific objectiv es of increasing new vehicle fu el economy to at least 35 mpg by 2020 and making ratable progress towa rd that objective in earlier model years. This reduces manufacturers’ uncertainty about the general direction of future fuel economy standards in the United States. Moreover, deve lopments in other regions ( e.g. , Europe) and countries ( e.g. , Canada and China) suggest that the generalized expectation that future vehicles will perform well with respect to energy effici ency is not unique to the Un ited States. Discussions with manufacturers in late 2007 and ear ly 2008 indicate that the industry is highly sensitive to all of these developments and has been anticipating the need to acce lerate the rate of technology deployment in response to the passage of major energy legislation in the U.S.V-51

Considering these developments, the agency revi sited the phase-in caps it had applied in 2006 and determined that it would be a ppropriate to relax many of them. In our judgment, most of the engine technologies could penetrat e the fleet in as quickly as five years—rather than in the six we previously estimated—as long as they are ap plied during redesign. Low friction lubricants are already widely used, and our expectation is that they can qu ickly penetrate the remainder of the fleet. Therefore, we relaxed the 25 percent (4-year) phase-in cap to 50 percent (2 years). Similarly, product plans indicate th at transmissions with 5 or mo re forward gears will widely penetrate the fleet even without the current proposal. Als o, given the technology cost and effectiveness estimates discussed above, th e Volpe model frequently estimates that manufacturers will “leapfrog” past 5-speed transm issions to apply more advanced transmissions ( e.g. , 6-speed or AMT). We have therefore increa sed the phase-in cap for 5-speed transmissions from 25 percent (4 years) to 100 percent (1 yea r). However, in our judgment, phase-in caps of 17 percent (6 years) are curren tly still appropriate for most other transmission technologies.

Although NHTSA has applied phase-in caps of 25 percent (4 years) for most remaining technologies, we continue to anticipate that pha se-in caps of 3 percent ar e appropriate for some advanced technologies, such as hybrids and diesel s. Although engine, vehicle, and exhaust aftertreatment manufacturers have, more recen tly, expressed greater optimism than before regarding the outlook for light vehicl e diesel engines, our expectation is that the phase-in cap that we have chosen is appropriate at this time. We also estimate that a 3 percent rate is appropriate for hybrid technologies, which are very complex, require significant engineering resources to implement, but are just now star ting to penetrate the market.

Table 9 below presents the phase -in caps applied in the current analysis, with rates from the analysis of the 2006 final rule provided for comparison. NHTSA requests comments on the phase-in caps shown here, and on whether slower or faster rates would be more appropriate and, if so, why.

Table 9. Phase-in Cap Application

Technology 2006 Final Rule Current NPRM

Low Friction Lubricants 25% 50% Engine Friction Reduction 17% 20% Variable Valve Timing (ICP) 17% 20% Variable Valve Timing (CCP) 17% 20% Variable Valve Timing (DCP) 17% 20% Cylinder Deactivation 17% 20% Variable Valve Lift & Timing (CVVL) 17% 20% Variable Valve Lift & Timing (DVVL) 17% 20% Cylinder Deactivation on OHV 17% 20% Variable Valve Timing (CCP) on OHV 17% 20% Multivalve Overhead Cam with CVVL 17% 20% Variable Valve Lift & Timing (DVVL) on OHV 17% 20% Camless Valve Actuation 10% 20% Stoichiometric GDI 3% 20% Diesel following GDI-S (SIDI) 3% 3% Lean Burn GDI 20%V-52

Turbocharging and Downsizing 17% 20% Diesel following Turbo D/S 3% 3% HCCI 13% Diesel following HCCI 3% 3% 5 Speed Automatic Transmission 17% 100% Aggressive Shift Logic 17% 25% Early Torque Converter Lockup 25% 6 Speed Automatic Transmission 17% 17% Automated Manual Transmission 17% 17% Continuously Variable Transmission 17% 17% 6 Speed Manual 17% Improved Accessories 25% 25% Electric Power Steering 17% 25% 42-Volt Electrical System 17% 25% Low Rolling Resistance Tires 25% 25% Low Drag Brakes 17% 25% Secondary Axle Disconnect – Unibody 17% 17% Secondary Axle Disconnect - Ladder Frame 17% 17% Aero Drag Reduction 17% 17% Material Substitution (1%) 17% 17% Material Substitution (2%) 17% 17% Material Substitution (5%) 17% 17% ISG with Idle-Off 5% 3% IMA/ISAD/BSG Hybrid (includes engine downsizing) 5% 3% 2-Mode Hybrid 5% 3% Power Split Hybrid 5% 3% Plug-in Hybrid 3%

B. The Volpe model

1. What is the Volpe model?

As it did for the development and analysis of th e April 2006 light truck final rule, in developing this proposal NHTSA made sign ificant use of a peer-reviewed modeling system developed by the Department of Transportation’s Volpe Na tional Transportation Sy stems Center (Volpe Center). The CAFE Compliance and Effects Mode ling System (referred to herein as the Volpe model) serves two fundamental purposes: identi fying technologies each manufacturer could apply in order to comply with a specified set of CAFE standards, and calculating the costs and effects of manufacturers’ a pplication of technologies.

Before working with the Volpe Center to devel op and apply this model, NHTSA had considered other options, including other modeling system s. NHTSA was unable to identify any other system that could operate at a sufficient level of detail with respect to manufacturers’ future products, which involve thousands of unique ve hicle models using hundreds of unique engines and hundreds of unique transmissions. NHTSA was al so unable to identify any other system thatV-53

could simulate a range of diffe rent possible reforms to CAFE standards. The Volpe model provides these and other capabilities, and he lps NHTSA examine potential regulatory options.

2. How does the Volpe model apply t echnologies to manufacturers’ future fleets?

The Volpe model begins with an “initial state” of the domestic vehicle market, which in this case is the market for passenger cars and light truc ks to be sold during the period covered by the proposed rule. The vehicle market is define d on a model-by-model, engine-by-engine, and transmission-by-transmission basis, such that eac h defined vehicle model refers to a separatelydefined engine and a separately-defined transmission.

For the model years covered by the current propos al, the light vehicle (p assenger car and light truck) market forecast included more than 3,000 vehicle models, more than 400 specific engines, and nearly 400 specific transmissions. 57 This level of detail in th e representation of the vehicle market is vital to an accurate analysis of manuf acturer-specific costs and the analysis of reformed CAFE standards, and is much greater than the level of detail used by many other models and analyses relevant to light vehicle fuel economy. Because CAFE standards apply to the average performance of each manufacturer ’s fleets of cars and light tr ucks, the impact of potential standards on individual manufacturers cannot be credibly estimated without analysis of manufacturers’ planned fleets. NHTSA has used this level of detail in CAFE analysis throughout the history of the program. Furtherm ore, because required CAFE levels under an attribute-based CAFE standard depend on manufacturers’ fleet co mposition, the stringency of an attribute-based standard cannot be predicted without performing anal ysis at this level of detail.

Examples of other models and analyses that NHTSA and Volpe Center staff have considered include DOE’s NEMS, Oak Ridge National Laboratory’s (ORNL) Transitional Alternative Fuels and Vehicles (TAFV) model, and the Califor nia Air Resources Boar d’s (CARB) analysis supporting California’s adopted greenhouse gas emissions standards for light vehicles.

DOE’s NEMS represents the light -duty fleet in terms of four “manufacturers” (domestic cars, imported cars, domestic light trucks, and imported light trucks), twelve vehicle market classes ( e.g. , “standard pickup”), and sixteen power train/fuel combinations ( e.g. , methanol fuel-cell vehicle). 58 Therefore, as currently structured, NEMS is unable to estimate manufacturer-specific implications of attribut e-based CAFE standards.

57 The market forecast is an input to the Volpe mode l developed by NHTSA using product plan information provided to the agency by individual vehicle manufacturers in response to NHTSA’s requests. The submitted product plans contain confidential business information (CB I), which the agency is prohibited by federal law from disclosing. As the agency receives ne w product plan information in response to future requests, the market forecast is updated.

58 U.S. Department of Energy, “Transportation Sector Module of the National Energy Modeling System: Model Documentation 2007,” DOE/EIA-M070, May 2007. Available at http://tonto.eia.doe.gov/FTPR OOT/modeldoc/m070(2007).pdf (last accessed Mar. 12, 2008). NEMS’s Manufacturers Technology Choice Submodule (MTCS) is believed to have logical structures similar to those in Energy and Environmental Analysis, Inc.’s (EEA’s) Fuel Economy Regulatory Analysis Model (FERAM). However, FERAM documentation and source code have not been made available to NHTSA or Volpe Center staff.V-54

TAFV accounts for many power train/fuel combina tions, having been originally designed to aid understanding of possible transitions to alternative fueled vehicl es, but it represents the lightduty fleet as four aggregated ( i.e. , industry-wide) categories of vehicles: small cars, large cars, small light trucks, and large light trucks. 59 Thus, again, as currently st ructure, TAFV is unable to estimate manufacturer-specific implications of attribute-based CAFE standards.

CARB’s analysis of light vehicle GHG emissions st andards uses two levels of accounting. First, based on a report prepared for Northeast States Center for a Clean Air Future (NESCCAF), CARB represents the light-duty fleet in terms of five “representative” vehicles. Use of these “representative” vehicles ignores the fact that the engineering characteristics of individual vehicle models vary widely both among manufac turers and within ma nufacturers’ individual fleets. For each of these five vehicles, NESCCA F’s report contains the results of full vehicle simulation given several pre-sp ecified technology “packages.” 60 Second, to evaluate manufacturer-specific regulatory costs, CARB essentially reduces each manufacturer’s fleet to only two average test weights, one for each of California’s two regulatory classes. 61 Even for a flat standard such as considered by Califor nia, NHTSA would not ba se its analysis of manufacturer-level costs on this level of aggregation. Use of CARB’s methods would not enable NHTSA to estimate manufacturer-specific implica tions of the attribute-based CAFE standards proposed today. 62

The Volpe model also uses several additional categories of data and estimates provided in various external input files:

One input file specifies the char acteristics of fuel-saving tech nologies to be represented, and includes, for each technology, the first year in wh ich the technology is expected to be ready for commercial application; upper and lower estimates of the effectiveness an d cost (retail price equivalent) of the technology; co efficients defining the extent to which costs are expected to decline as a result of “learning effects” (d iscussed below); inclusi on or exclusion of the technology on up to three technology “paths”; and constraints (“phase-in caps”) on the annual rate at which manufacturers are estimated to be able to incr ease the technology’s penetration rate. These technology characteri stics and estimates are specified separately for each of the following categories of vehicles: small sport/ utility vehicles (SUVs), midsize SUVs, large SUVs, small pickups, large pickups, minivans, subc ompact cars, compact cars, midsize cars, and

59 Greene, David. “TAFV Alternative Fuels and Vehi cles Choice Model Document ation,” ORNL//TM—2001//134, July 2001. Available at http://www-cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2001_134.pdf (last accessed Mar. 12, 2008).

60 Northeast States Center for a Clean Air Future (NESCCAF), Reducing Greenhouse Gases from Light-Duty Vehicles (2004). Available at http://bronze.nescaum.org/committees/mobile/rpt040923ghglightduty.pdf (last accessed Mar. 12, 2008).

61 California Environmental Protection Agency, Air Resource s Board, Staff Report: Initial Statement of Reasons (CARB ISOR) (2004), at 111-114. Available at http://www.arb.ca.gov/regact/grnhsgas/grnhsgas.htm (scroll down to “Public Hearing Notice and Related Material”) (last accessed Sept. 11, 2007). We note that California has adopted these standards but is currently unable to enforce them, due to EPA’s February 29, 2008 denial of California’s request for waiver of federal preemption under Section 209 of the Clean Air Act. For information on EPA’s decision, see http://www.epa.gov/otaq/ca-waiver.htm . California filed a petition in the 9 th Circuit Court of Appeals challenging EPA’s denial of the waiver on January 2, 2008.

62 Although CARB’s analysis covered a wider range of m odel years than does NHTSA’s analysis, this does not lessen the importance of a detailed representation of manufacturers’ fleets.V-55

large cars. In addition, the i nput file defining tec hnology characteristics can (but need not) contain specified “synergies” be tween technologies—that is, diffe rences in a given technology’s effect on fuel consumption that result from the presence of other technologies.

Another input file specifies vehicular emission rates for the following pollutants: carbon monoxide (CO), volatile organic com pounds (VOCs), nitrogen oxides (NO x ), particulate matter (PM), and sulfur dioxide (SO 2 ). These rates are defined on a model year-by-model year and calendar year-by-calendar year ba sis, and are used to estimate changes in emissions that result from changes in vehicular travel ( i.e. , vehicle-miles traveled or VMT).

A third input file specifies a va riety of economic and other data and estimates. The model can accommodate vehicle survival ( i.e. , percent of vehicles of a given vintage that remain in service) and mileage accumulation ( i.e. , annual travel by vehicles of a given vintage) rates extending as many years beyond the year of sale as for whic h estimates are available and use those for estimating VMT, fuel consumption, and emissions . The model can also accommodate forecasts of price and fuel taxation rates for up to seven fuels ( e.g. , gasoline, diesel) over a similar period. The model uses pump prices ( i.e. , including taxes) to estimate the value manufacturers expect vehicle purchasers to place on saved fuel, becau se they indicate the amount by which the manufacturer is expected to cons ider itself able to increase the retail price of the vehicle based on the purchaser’s considera tion of the vehicle’s increased fuel economy. However, the model uses pretax fuel prices to estimate the monetized societal benefits of reduced fuel consumption, because fuel taxes represent transfers of resour ces from fuel buyers to government agencies rather than real resources that are consumed in the process of supplying or using fuel, so their value must be deducted from retail fuel prices to determine the value of fuel savings to the U.S.
economy.

Other economic inputs include the rebound effect coefficient ( i.e. , the elasticity of VMT with respect to the per-mile cost of fuel); the discount rate; the “payback period” ( i.e. , the number of years manufacturers are estimated to assume ve hicle purchasers consider when taking into account fuel savings); the “gap” between laborat ory and actual fuel economy; the per-vehicle value of travel time (in dollars per hour); the economic costs (in dollars per gallon) of petroleum consumption; various external costs (all in dolla rs per mile) associated with changes in vehicle use; damage costs (all on a dollar per ton basis) for each of the above-mentioned criteria pollutants; and the rate at which noncompliance causes civil penalties. Section V.A.4 below describes in much more detail how these inputs are included and used by the model.

The model also accommodates input data and esti mates addressing the properties of different fuels. These include upstream carbon dioxide and criteria pollutant emission rates ( i.e. , U.S. emissions resulting from the production and distri bution of each fuel), density (pounds/gallon), energy density (BTU/gallon), carbon content, shares of fuel savings leading to reduced domestic refining, and relative shares of different gasolin e blends. These fuel properties and related estimates are used to calculate changes in domestic upstream emissions resulting from changes in fuel consumption.

Coefficients defining the probability distributions to apply when performing sensitivity analysis ( i.e. , Monte Carlo simulation) are also specified in this input file. These coefficients determineV-56

the likelihood that any given value will be select ed when performing this type of analysis ( e.g. , the likelihood that a rebound effect of -0.1 will be tested). High and low fuel price forecasts are also specified in this input file for this purpose.

The final input file contains CAFE scenarios to be examined. The model accommodates a baseline ( i.e. , business-as-usual) scenario and different alternative scenarios. Effects of the alternative scenarios are calculated relative to results for the baseline scenario. Each scenario defines the coverage, structure, and stringency of CAFE standards for each of the covered model
years.

With all of the above input data and estimates, th e modeling system develops an estimate of a set of technologies each manufacturer could apply in response to each specified CAFE scenario. Because manufacturers have many choices regardi ng how to respond to CAFE standards, it is impossible to predict precisely how a given manufacturer would respond to a given set of standards. The modeling system begins with the “initial state” ( i.e. , business-as-usual) of each manufacturer’s future vehicles, and accumulates the estimated costs of progressive additions of fuel-saving technologies. Within a set of specifi ed constraints, the system adds technologies following a cost-minimizing approach, because this is what NHTSA expects a manufacturer would do in real life. At each st ep, the system evaluates the eff ective cost of applying available technologies to individual vehicle models, engines, or transmissi ons, and selects the application of technology that produces the lowest effective cost. The e ffective cost estimated to be considered by the manufacturer is calculated by adding the total incu rred technology costs (in retail price equivalent or RP E), subtracting the reduction in civil penalties owed for noncompliance with the CAFE standar d, subtracting the estimated value 63 of the reduction in fuel costs, and dividing the result by the number of affected vehicles.

In representing manufacturer de cision-making in response to a given CAFE standard, the modeling system accounts for the fact that histor ically some manufactur ers have been unwilling to pay penalties and some have been willing to do so. Thus, the system applies technologies until any of the following conditions are met: th e manufacturer no longer owes civil penalties for failing to meet the ap plicable standard, the manufacturer has exhausted technologies expected to be available in that model year, or the manuf acturer is estimated to be willing to pay civil penalties, and doing so is estimated to be less expensive than continuing to add technologies. The system then progresses to the next model year (if included in the vehicle market and scenario input files), “carrying over” technologies where vehicle models are projected to be succeeded by other vehicle models. 64

In the modeling system, this “compliance simulati on” is constrained in several ways. First, technologies are defined as being applicable or not applicable to each of the ten vehicle categories listed above. The vehicle market forecast input file may also define some

63 The estimated value of the reduction in fuel costs represen ts the amount by which the manufacturer is expected to consider itself able to increase the retail price of the vehi cle based on the purchaser’s consideration of the vehicle’s increased fuel economy. This calculation considers the change in the discou nted outlays for fuel (and fuel taxes) during a “payback period” specifi ed as an input to the model.

64 For example, if Honda is expected to produce the Civic in 2012 and 2013, a version of the Civic estimated to be produced in 2013 may carry over technologies from a version of the Civic produced in 2012 if the latter is identified as a “predecessor” of the former.V-57

technologies as being already pr esent or not applicable to sp ecific vehicles, engines or transmissions. For example, a manufacturer may have indicated it plans to use low-drag brakes on some specific vehicle model, or NHTSA may e xpect that another manufact urer is not likely to apply a 7- or 8-speed transmission after it insta lls a 6-speed transmission on a vehicle. Second, some technologies are subject to specific “engineering constrai nts.” For example, secondaryaxle disconnect can only be appl ied to vehicles with four-wheel (or all-wheel) drive. Third, some technologies ( e.g. , conversion from pushrod valve actu ation to overhead cam actuation) are nearly always applied only when the vehicle is expected to be re designed and others ( e.g. , cylinder deactivation) are applied only when th e vehicle is expected to be refreshed or redesigned, so the model will only apply them at t hose particular points. Fourth, once the system applies a given technology to a pe rcentage of a given manufacturer s’ fleet exceeding a specified phase-in cap, the system instead applies other technologies. The third and fourth of these constraints are intended to produce results co nsistent with manufact urers’ product planning practices and with limitations on how quickly technologies can penetrate the fleet.

One important aspect of this compliance simulati on is that it does not attempt to account for either CAFE credits or intenti onal over-compliance. In the re al world, manufacturers may earn CAFE credits by selling flex-fue led vehicles (FFVs) and/or by exceeding CAFE standards, and may, within limitations, count those credits toward compliance in future or prior model years. However, EPCA and EISA do not allow NHTSA to consider these flexibilities in setting the standards. Therefore, the Volpe model does not attempt to account fo r these flexibilities.

Another possibility NHTSA and Volpe Center sta ff have considered, but do not yet know how to analyze, is the potential that manufacturers might “pull ahead” the implementation of some technologies in response to CAFE standards known to be increasing over time. For example, if a manufacturer plans to redesign many vehicles in MY2011 and not in MY2013, but the standard in MY2013 is considerably higher in MY2013 th an in MY2011, the manufacturer might find it less expensive during MY2011-MY2013 (taken toge ther) to apply more technology in MY2011 than is necessary for compliance with the MY 2011 standard. Under some circumstances, doing so might make sense even setting aside th e potential to earn a nd bank CAFE credits.

NHTSA and Volpe Center staff have discussed the potential to represent th is type of response, but have thus far encountered tw o challenges. First, NHTSA is not certain that in determining the maximum feasible standard in a given mode l year, it would be appropriate to count on manufacturers overcomplying with standards in preceding model years. Second, considering other inter-model y ear dependencies ( e.g. , technologies that carry over between model years, phase-in caps that accumulate across model years, volume-based learning curves), Volpe Center staff currently anticipate that some iterativ e procedure would likely be required. Also, the agency wonders whether trying to represent this type of re sponse would require make undue implicit assumptions regarding manufacturers’ ability to predict future market conditions. Although NHTSA and Volpe Center staff will conti nue to explore the po tential to represent inter-model year timing, it is not yet clear that it will be appropriate and feasible to do so in the

near term.

The agency requests comment on the appropria teness under EPCA of considering (in the standard-setting context) this ty pe of anticipatory application of technology. The agency furtherV-58

requests comment on appropria te methodologies for repres enting such decisions by manufacturers.

3. What effects does the Volpe model estimate?

Having completed this compliance simulation fo r all manufacturers and all model years, the system calculates the total cost of all applied technologies, as well as a variety of effects of changes in fuel economy. The system calculates year-by-year mileage accumulation, taking into account any increased driving estimated to re sult from the rebound effect. Based on the calculated mileage accumulation and on fuel econo my and the estimated gap between laboratory and actual fuel economy, the system calculates year-by-year fuel consumption. Based on calculated mileage accumulation and fuel consum ption, and on specified emission factors, the system calculates future full fuel-cycle domestic carbon dioxide and criteria pollutant emissions. The system calculates total disc ounted and undiscounted national so cietal costs of year-by-year fuel consumption, taking into account estimate d future fuel prices (before taxes) and the estimated economic externalities of fuel consumption. Based on changes in year-by-year mileage accumulation, the system calculates change s in consumer surplus related to additional travel, as well as economic externalities relate d to additional congesti on, accidents, and noise stemming from additional travel. The system calculates the value of time saved because increases in fuel economy produ ce increases in driving range, thereby reducing the frequency with which some vehicles require refueling. The system calculates the monetary value of damages resulting from criteria pollutants. Fi nally, the system accumulates all discounted and undiscounted societal benefits of each scenario as compared to the baseline scenario. For each model year, the system compares total incurred technology costs to the to tal present value of societal benefits for each model year , calculating net societal benefits ( i.e. , discounted societal benefits minus total incurred technology costs) and the benefit-cost ratio ( i.e. , discounted societal benefits divided by total incurred technology costs).

One important effect not currently estimated by the Volpe model is the market response to CAFE-induced changes in vehicle prices and fu el economy levels. NHTSA and Volpe Center staff have considered devel oping and applying a market shar e model capable of estimating changes in sales of individual vehicle models. Doing so would allow estimation of the feedback between market shifts and CAFE requirements. For example, if the relative market share of vehicles with small footprints increases, th e average required CAFE level under a footprintbased standard will also increase.

In an early experimental versi on of the Volpe model, Volpe Center staff included a market share model using a nested multinomial logit specification to calculate model-by-model changes in sales volumes. This allowed the Volpe model to calculate the resulting changes in manufacturers’ required CA FE levels, and to seek iteratively a solution at which prices, fuel economy levels, sales volumes, and required CAFE levels converged to stable values. Although the market share model appeared to operate pr operly (and to converge rapidly), Volpe Center staff suspended its development because of three challenges:

First, Volpe Center staff were not successful in calibrating a logically consistent set of coefficients for the underlying multinomial l ogit model. The analysis, performed usingV-59

information from a known (2002 model year) fleet, c onsistently yielded one or more coefficients that were either directionally incorrect ( e.g. , indicating that some attri butes actually detract from value) or implausibly large ( e.g. , indicating that some attributes were of overwhelming value). Although Volpe Center staff tested many different specifications of the market share model, none produced results that appeared to merit further consideration.

Second, NHTSA and Volpe Center sta ff are not confident that baseline sales prices for individual vehicle models, which would be required by a mark et share model, can be reliably predicted. Although NHTSA requests that manufacturers include planned MSRPs in product plans submitted to NHTSA, MSRPs do not include the e ffect of various sales incentives that can change actual selling prices. The availability and dollar value of such incentives have been observed to vary considerably, but not necessarily predictably.

Finally, before applying a market share mode l, it would be necessary to estimate how manufacturers would allocate compliance cost s among vehicle models. Although one obvious approach would be to assume that all costs woul d be passed through in the form of higher prices for those vehicle models with improved fuel economy, other approaches are perhaps equally plausible. For example, a manufacturer mi ght shift compliance costs toward high-demand vehicles in order to better compete in certain market segments. Although the above-mentioned experimental version of the Volpe model included a “cost allocation” model that offered several different allocation options, NHTSA and Volpe Cent er staff never achieved confidence that these aspects of manufacturer decisions could be reasonably estimated.

NHTSA and Volpe Center staff are continuing to explore options for incl uding these types of effects. At the same time, EPA has contracted with Resources for the Future (RFF) to develop a potential market share model. Depending on the extent to which these efforts are successful, the Volpe model could at some point be modified to include cost allocation and market share models. NHTSA seeks comment on possible met hodologies for incorporating market responses to CAFE-induced changes in vehicle price and fuel economy in the Volpe model. In particular, NHTSA seeks comments addressing the concerns identified above regarding the formulation and calibration of a market share model, the estimation of future vehicle prices, and the estimation of manufacturers’ decision s regarding the allocati on of compliance costs.

4. How can the Volpe model be used to calibrate and evaluate potential CAFE standards?

The modeling system can also be applied in a mo re highly-automated m ode whereby the optimal shape of an attribute-based CAFE standard may be estimated and its stringency may be set at a level that produces a specifie d total technology cost or aver age required CAFE level among a specified set of manufacturers, or that is estimated to maximize net societal benefits. The first step in this operating mode involves identifyi ng manufacturer-by-manufact urer CAFE levels at which societal benefits are estimated to be ma ximized. The second step involves combining the resultant fleets and statistically fitting a constrained logistic curve of the following form:V-60

( )

( ) 1

1 11

1 FOOTPRINT MIDPOINT WIDTH

FOOTPRINT MIDPOINT WIDTH UPPER LOWER UPPER TARGET

e LIMIT LIMIT LIMIT e −

− = ⎛ ⎞

+ −

⎜ ⎟
+ ⎝ ⎠

Here, TARGET is the fuel economy target (in mpg) appl icable to vehicles of a given footprint ( FOOTPRINT , in square feet), LIMIT LOWER and LIMIT UPPER are the function’s lower and upper asymptotes (also in mpg), e is approximately equal to 2.718, 65 MIDPOINT is the footprint (in square feet) at which the inverse of the fuel econo my target falls halfway between the inverses of the lower and upper asymptotes, and WIDTH is a parameter (in square feet) that determines how gradually the fuel economy target transitions fr om the upper toward the lower asymptote as the footprint increases. Figure V-2 below shows an example of a l ogistic target function, where LIMIT LOWER = 20 mpg, LIMIT UPPER = 30 mpg, MIDPOINT = 40 square feet, and WIDTH = 5 square feet:

Figure V-2. Sample Logistic Curve

0 5 10 15 20 25 30 35

0 10 20 30 40 50 60 70 80 90 100
Footprint (Square Feet)Target (Miles per Gallon) L IMIT UPPER

L IMIT L OWER

The lower asymptote is determined by calculating the average fuel economy of the largest vehicles in the “socially optimi zed” fleet discussed above, where th e percentage of the fleet to consider is specified externally. Similarly, the upper asymptote is dete rmined by calculating the average fuel economy of the smallest vehicles in the same fleet. Initial values of the other two

65 The number e is one of the most important numbers in mathematics and statistics. The function has a hockey stick appearance when plotted. The value of e itself is a never ending number whose first 8 digits equal 2.7182818. NHTSA uses it here because it occurs in many natural pro cesses and tends to fit data we ll. In the last light truck rulemaking, NHTSA examined several functional forms that di d not rely on e, but they were judged not to provide as good a fit for the data. We are using the same conclusion here.V-61

coefficients of the logistic function are determined through a standard statistical technique (nonlinear least-square regression), except as disc ussed in sections V and VI below regarding the adjusting of the original curv e for the passenger car function.

Following this initial calibration of the target function, the system ad justs the lower and upper asymptotes uniformly (on a gallon per mile basis) until one of the following externally specified conditions is met: the average CAFE le vel required of the included manufacturers approximately equals an externally specified goal; net societal benefits (i.e., total benefits minus total costs) are maximized, or total benefits are as close as observed (among evaluated stringency levels) to total costs. Due to rounding of fuel economy and CAFE levels, the first condition can only be satisfied on an approximate basis.

The modeling system provides another type of hi gher-level automation—th e ability to perform uncertainty analysis, also referre d to as Monte Carlo simulation. For some input parameters, such as technology costs, values can be test ed over a specified continuous probability distribution. For others, such as fuel prices, discrete scenarios ( e.g. , high, low, and reference cases), each with a specified proba bility, can be tested. The system performs sensitivity analysis by randomly selecting values for parameters to be varied, performing the compliance simulation and effects calculations, repeating these results many times and recording results for external analysis. This operating mode enables the examin ation of the uncertainty of high-level results ( e.g. , total costs, fuel savings, or net societal benefits ), as well as their sensitivity to variations in the model’s input parameters.

5. How has the Volpe model been updated since the April 2006 light truck CAFE final rule?

Several changes were made to the Volpe model between the analysis reported in the April 2006 light truck final rule and the an alysis of the current NPRM. As discussed above, the set of technologies represented was updated, the lo gical sequence for progr essing through these technologies was changed, methods to account for “synergies” ( i.e. , interactions) between technologies and technology cost reductions associated with a manufacturer’s “learning” were added, the effective cost calcu lation used in the technology app lication algorithm was modified, and the procedure for calibrati ng a reformed standard was changed, as was the procedure for estimating the optimal stringency of a reformed standard.

As discussed in Section III above, the set of tech nologies considered by the agency has evolved since the previous light truck CAFE ru lemaking. The set of technologies now included in the Volpe model is shown below in Table V-7, with codes used by the model to refer to each technology.

Table V-7. Revised Technology Set for Volpe Model

Technology Code (for Model)

Low Friction Lubricants LUB
Engine Friction Reduction EFR
Variable Valve Timing (Intake Cam Phasing) VVTIV-62

Variable Valve Timing (Coupled Cam Phasing) VVTC

Variable Valve Timing (Dual Cam Phasing) VVTD
Cylinder Deactivation DISP
Variable Valve Lift & Timing (Continuous VVL) VVLTC

Variable Valve Lift & Timing (Discrete VVL) VVLTD
Cylinder Deactivation on Overhead Valve (OHV ) DISPO
Variable Valve Timing (CCP) on OHV VVTO

Multivalve Overhead Cam with CVVL DOHC

Variable Valve Lift & T iming (DVVL) on OHV VVLTO

Camless Valve Actuation CVA
Stoichiometric Gasoline Direct Injection (GDI) SIDI

Lean Burn GDI LBDI
Turbocharging and Downsizing TURB
Homogeneous Charge Compression Ignition HCCI
Diesel with Lean NOx Trap (LNT) DSLL
Diesel with Selective Catalytic Reduction (SCR) DSLS
5 Speed Automatic Transmission 5SP
Aggressive Shift Logic ASL
Early Torque Converter Lockup TORQ
6 Speed Automatic Transmission 6SP

Automatic Manual Transmission AMT
Continuously Variable Transmission CVT
6 Speed Manual 6MAN
Improved Accessories IACC

Electronic Pow er Steering EPS
42-Volt Electrical System 42V
Low Rolling Resistance Tires ROLL
Low Drag Brakes LDB
Secondary Axle Disconnect – Unibody SAXU
Secondary Axle Disconnect - Ladder Frame SAXL
Aero Drag Reduction AERO
Material Substitution (1%) MS1
Material Substitution (2%) MS2
Material Substitution (5%) MS5
Integrated Starter/Generator (ISG) with Idle-Off ISGOV-63

IMA/ISAD/BSG Hybrid (includes engine downsizi n IHYB
2-Mode Hybrid 2HYB
Power Split Hybrid PHYB
Full Diesel Hybrid DHYB

The logical sequence for progressing between these t echnologies has also been changed. As in the previous version of the Volpe model, technologies are a ssigned to groups ( e.g. , engine technologies) and the model follows a cost-minimizing approach to selecting technologies. However, the model now includes some “branch points” at which it selects from two or mo re technologies within the same group. This enables a more detail ed representation of some technologi es that have multiple variants ( e.g. , variable valve timing) and, as relevant to th e applicability of differe nt technologies, more specific differentiation between technologies that have already been applied to vehicles ( e.g. , single versus dual overhead cam engines). This revised logical sequencing is expected to produce results that are more realistic in term s of the application of technologies to different vehicle models. For example, in this analysis OHV engines a nd OHC engines were considered separately, and the model was generally not allowed to apply multivalve OHC t echnology to OHV engines (except where continuous variable valve timing and lift is applied to OHV e ngines, in which case the model assumes conversion to DOHC valvetrain).

Figure V-3 below shows the resultan t “decision tree” for the group of engine technologies. As an example of the “branching” mentioned above, havi ng applied cylinder deactiv ation and coupled cam phasing to an overhead valve engine, the Volpe model se lects either discrete valve lift or an engine redesign to multivalve overhead cam with continuous variable valve Figure V-4 shows the decision tree for transmission technologies, and Figure V-5 shows the decisi on trees for other technologies.

Figure V-3. Engine Technology Decision TreeV-64

Low-Friction Lubricants

Engine Friction

Reduction

VVT (ICP)

Cylinder Deactivation VVT (Coupled Cam Phasing)

VVLT (Continuous VVL) VVLT (Discrete VVL) VVT (Dual Cam Phasing)

Stoichiometric GDI

HCCI

Diesel Replacing Turbo SI GDI Diesel Replacing

HCCI OHC

DOHC DOHC

DOHC DOHC VVT (Coupled Cam Phasing)

Cylinder Deactivation OHV

DOHC with Continuous VVL VVLT (Discrete VVL)

Turbocharging and Downsizing SOHC

SOHC

Diesel Replacing NA SI GDIV-65

Figure V-4. Transmission Technology Decision Tree

Automatic 5-Speed

Aggressive Shift Logic

AMT 6/7/8-Speed CVT

Early Torque Converter Lockup Unibody

ISG with Idle-Off

IMA/ISAD/BSG Hybrid

2-Mode Hybrid

Power Split

Hybrid Automatic Tran smission Only Unibody Manual 5-Speed

6/7/8-Speed Ladder FrameV-66

Figure V-5. Decision Trees for Other Technologies

Improved Accessories

42-Volt System Electric Power

Steering Low Rolling Resistance Tires

Secondary Axle

Disconnect Low Drag Brakes

Materials Substitution (1%)

Materials Substitution (2%)

Materials Substitution (5%) Aerodynamic Drag Reduction

Each time the model applies a technology to a vehi cle in the fleet, it considers the next available technology on every available path. An available technology is one that is not included in the base vehicle, has not been applied by the model, and is not disqualified due to the vehicle’s characteristics (discussed below). For a given path, the next av ailable technology is the first available item (if no technologies on the path have yet been applied) or the first available item following the most recently applied technology on that path. An available path is any path th at includes available technologies.

The engine and transmission path s contain several forks where the model may choose among two or more same-path items along with items from other path s. At some of these forks, conditions on the connecting arrows require the model to follow a pa rticular branch. Thes e conditions are based on previously applied technologies or vehicle characte ristics. For example, ladder frame vehicles must follow the left branch of the transmission technology path, whereas unibody vehi cles can follow either the right or left branch. The cons equence is that the model considers both aggressive shift logic (ASL)V-67

and CVT for unibody vehicles, but only ASL for ladde r frame vehicles. Conditions along the engine technologies path are based on valvetra in design (OHV, OHC, SOHC, and DOHC). Other conditions require the model to disconti nue considering technolog ies along a given path. For example, 2-Mode Hybrid and Power Split Hybrid drivetrains can be applied only to vehicles equipped with automatic transmissions. If the model has already chosen a manual transmission and IMA/ISAD/BSG Hybrid drivetrain (or if the base vehicle is equipped with these), the hybrid path becomes unavailable and the model must choose subsequent technologies from other paths. a. Technology synergies In some cases, the change in fuel economy achieved by applying a given technology depends on what other technologies are alre ady present. The Volpe model ha s been modified to provide the ability to represent such “synergies” between t echnologies, as discussed a bove. These effects are specified in one of the model’s input files. As shown below in Table V-8, which uses technology codes listed in Table V-7 above, most of the synergies represented in the analysis of this proposal are negative. In other words, most of the interactions are such that a given technology has a smaller effect on fuel economy if some other techno logies have already been applied. The inclusion of such effects in the model is expected to produce more realistic estimates of the benefit of applying various technologies.V-68

Table V-8. "Synergies" from Tec hnology Input File for Volpe Model



### Synergies Synergy Values by Vehicle Class Positive values are synergies, negative values are dissynergies.

Technology A Technology B SUV-Small SUV-Mid SUV-Large Mi nivan Pickup-Small
VVTI 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVTI ISGO -0.50% -0.50% -0.50% -0.50% -0.50%
VVTC 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVTC CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVTC ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DISP 5SP -1.00% -1.00% -1.00% -1.00% -1.00%
DISP CVT -1.00% -1.00% -1.00% -1.00% -1.00%
DISP ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DISP ISGO -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC ASL -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTD CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTD 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DISPO 5SP -1.50% -1.50% -1.50% -1.50% -1.50%
DISPO CVT -1.00% -1.00% -1.00% -1.00% -1.00%
DISPO ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DISPO 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DISPO ISGO -1.00% -1.00% -1.00% -1.00% -1.00%
VVTO CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVTO 6MAN 0.50% 0.50% 0.50% 0.50% 0.50%
DOHC 5SP -1.00% -1.00% -1.00% -1.00% -1.00%
DOHC CVT -1.00% -1.00% -1.00% -1.00% -1.00%
DOHC ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DOHC 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DOHC 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
DOHC ISGO 0.50% 0.50% 0.50% 0.50% 0.50%
VVLTO 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTO CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTO 6SP -0.50% -0.50% -0.50% -0.50% -0.50%V-69



### Synergies Synergy Values by Vehicle Class Positive values are synergies, negative values are dissynergies.

Technology A Technology B SUV- Small SUV-Mid SUV-Large Minivan PickupSmall
CVA 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
CVA CVT -1.00% -1.00% -1.00% -1.00% -1.00%
CVA ASL -0.50% -0.50% -0.50% -0.50% -0.50%
CVA 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
CVA 6MAN 0.50% 0.50% 0.50% 0.50% 0.50%
HCCI CVT -0.50% -0.50% -0.50% -0.50% -0.50%
HCCI 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
TURB 5SP -1.00% -1.00% -1.00% -1.00% -1.00%
TURB CVT -1.00% -1.00% -1.00% -1.00% -1.00%
TURB ASL -0.50% -0.50% -0.50% -0.50% -0.50%
TURB 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
TURB 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
ISGO IACC -0.50% -0.50% -0.50% -0.50% -0.50%
ISGO EPS -1.00% -1.00% -1.00% -1.00% -1.00%
ISGO 42V -1.00% -1.00% -1.00% -1.00% -1.00%
DSLT 5SP 0.50% 0.50% 0.50% 0.50% 0.50%
DSLT CVT 0.50% 0.50% 0.50% 0.50% 0.50%
DSLT ISGO 0.50% 0.50% 0.50% 0.50% 0.50%
DSLT ASL 0.50% 0.50% 0.50% 0.50% 0.50%
DSLH 5SP 0.50% 0.50% 0.50% 0.50% 0.50%
DSLH CVT -0.50% -0.50% -0.50% -0.50% -0.50%
DSLH 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DSLH 6MAN 0.50% 0.50% 0.50% 0.50% 0.50%
DSLH ISGO 0.50% 0.50% 0.50% 0.50% 0.50%
DSLS 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
DSLS CVT -2.50% -2.50% -2.50% -2.50% -2.50%
DSLS 6SP -1.00% -1.00% -1.00% -1.00% -1.00%
DSLS 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
DSLS ISGO 0.50% 0.50% 0.50% 0.50% 0.50%V-70



### Synergies Synergy Values by Vehicle Class Positive values are synergies, negative values are dissynergies.

Technology A Technology B PickupLarge Subcompact Compact Midsize Large
VVTI 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVTI ISGO -0.50% -0.50% -0.50% -0.50% -0.50%
VVTC 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVTC CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVTC ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DISP 5SP -1.00% -1.00% -1.00% -1.00% -1.00%
DISP CVT -1.00% -1.00% -1.00% -1.00% -1.00%
DISP ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DISP ISGO -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC ASL -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTC 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTD CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTD 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DISPO 5SP -1.50% -1.50% -1.50% -1.50% -1.50%
DISPO CVT -1.00% -1.00% -1.00% -1.00% -1.00%
DISPO ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DISPO 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DISPO ISGO -1.00% -1.00% -1.00% -1.00% -1.00%
VVTO CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVTO 6MAN 0.50% 0.50% 0.50% 0.50% 0.50%
DOHC 5SP -1.00% -1.00% -1.00% -1.00% -1.00%
DOHC CVT -1.00% -1.00% -1.00% -1.00% -1.00%
DOHC ASL -0.50% -0.50% -0.50% -0.50% -0.50%
DOHC 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DOHC 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
DOHC ISGO 0.50% 0.50% 0.50% 0.50% 0.50%
VVLTO 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTO CVT -0.50% -0.50% -0.50% -0.50% -0.50%
VVLTO 6SP -0.50% -0.50% -0.50% -0.50% -0.50%V-71



### Synergies Synergy Values by Vehicle Class Positive values are synergies, negative values are dissynergies.

Technology A Technology B Pickup-La rge Subcompact Compact Midsize Large
CVA 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
CVA CVT -1.00% -1.00% -1.00% -1.00% -1.00%
CVA ASL -0.50% -0.50% -0.50% -0.50% -0.50%
CVA 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
CVA 6MAN 0.50% 0.50% 0.50% 0.50% 0.50%
HCCI CVT -0.50% -0.50% -0.50% -0.50% -0.50%
HCCI 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
TURB 5SP -1.00% -1.00% -1.00% -1.00% -1.00%
TURB CVT -1.00% -1.00% -1.00% -1.00% -1.00%
TURB ASL -0.50% -0.50% -0.50% -0.50% -0.50%
TURB 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
TURB 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
ISGO IACC -0.50% -0.50% -0.50% -0.50% -0.50%
ISGO EPS -1.00% -1.00% -1.00% -1.00% -1.00%
ISGO 42V -1.00% -1.00% -1.00% -1.00% -1.00%
DSLT 5SP 0.50% 0.50% 0.50% 0.50% 0.50%
DSLT CVT 0.50% 0.50% 0.50% 0.50% 0.50%
DSLT ISGO 0.50% 0.50% 0.50% 0.50% 0.50%
DSLT ASL 0.50% 0.00% 0.00% 0.50% 0.50%
DSLH 5SP 0.50% 0.50% 0.50% 0.50% 0.50%
DSLH CVT -0.50% -0.50% -0.50% -0.50% -0.50%
DSLH 6SP -0.50% -0.50% -0.50% -0.50% -0.50%
DSLH 6MAN 0.50% 0.50% 0.50% 0.50% 0.50%
DSLH ISGO 0.50% 0.50% 0.50% 0.50% 0.50%
DSLS 5SP -0.50% -0.50% -0.50% -0.50% -0.50%
DSLS CVT -2.50% -2.50% -2.50% -2.50% -2.50%
DSLS 6SP -1.00% -1.00% -1.00% -1.00% -1.00%
DSLS 6MAN -0.50% -0.50% -0.50% -0.50% -0.50%
DSLS ISGO 0.50% 0.50% 0.50% 0.50% 0.50%

b. Technology learning curves

The Volpe model has also been modified to prov ide the ability to account for cost reductions a manufacturer may realize thr ough learning achieved from expe rience in actually applying a given technology. Thus, for some of the tec hnologies, we have includ ed a learning factor. Stated another way, the “learning curve” descri bes the reduction in un it production costs as a function of accumulated production volume and small redesigns that reduce costs.

As explained above, a typical lear ning curve can be described by th ree parameters: (1) the initial production volume before cost re ductions begin to be realized; (2) the rate at which cost reductions occur with in creases in cumulative production be yond this initial volume (usually referred to as the “learning rate”); and (3) th e production volume afte r which costs reach aV-72

“floor,” and further cost reducti ons no longer occur. Over the region where costs decline with accumulating production volume, an expe rience curve can be expressed as C(Q) = aQ -b , where a is a constant coefficient, Q represents cumulative production, and b is a coefficient corresponding to the assumed learning rate. In turn, the learning rate L, which is usually expressed as the percent by wh ich average unit cost declines with a doubling of cumulative production, and is related to the value of the coefficient b by L = 100*(1 – 2 -b ). 66

The new learning curves are described in gr eater detail above in the Proposed Technology Assumptions section, III. We seek co mment on the new proposed learning curves.

c. Calibration of reformed CAFE standards

The procedure used by the Volpe model to develop ( i.e. , calibrate) the initial shape of a reformed standard was also modified. In the version of the model used to analy ze NHTSA’s April 2006 light truck final rule, the asymptotes for the constraine d logistic function defining fuel economy targets were assigned based on the set of vehicles that w ould have been assigned to the lowest and highest bins defined in that rule’s 2005 NPRM. The Volpe model has been modified to accept specified percentages (in terms of either models or sales) of the fleet to include when assigning asymptotes.

The procedure used by the Volpe model to estimate the “socially optimized” stringency of a reformed standard was also modified. In the version of the model used to analyze the 2006 light truck final rule, the shape of the function ( i.e. , the constrained logistic function ) defining fuel economy targets was recalibrated every model year and then shifted up and down to estimate the stringency at which marginal costs begin to exceed marginal benefits or, equivalently, the point at which net societal benefits are maximized. However, analysis cond ucted by the agency to prepare for the current rulemaking revealed several opportunities to refine th e procedure described above before applying it to an action that spans several model years. The firs t refinement is a method for gradually transforming the shape of the continuous functi on between model years and guarding against erratic fluctuations in the shape (though not necessarily the stringency) of the continuous function. The second is the implementation of several anti-backsliding measures that prevents the average required CAFE level from falling between model years and prevents th e continuous function for a given model from crossing or falling below that of the preceding model year. The third, applied to passenger cars only, is an option to specify a fixed relationship between the function’s midpoint and width coefficients. These refinements are discussed in gr eater detail in Section V.B below.

6. What manufacturer information does the Volpe model use?

For purposes of determining and analyzing CAFE standards, NHTSA has historically made significant use of detailed product plan inform ation provided to the agency by individual manufacturers, supplementing this information wh ere appropriate with information from other

66 See, e.g. , Robert H. Williams, “Toward Cost Buydown via Learning-by-Doing for Environmental Energy Technologies,” paper presented at Workshop on Learning-by-Doing in Energy Technologies, Resources for the Future, Washington, DC, June 17-18, 2003, pp. 1-2. Another common but equivalent formulation of the relationship between L and b is (1-L)=2 -b , where (1-L) is referred to as the progress ratio; see Richard P. Rumelt, “Note on Strategic Cost Dynamics,” POL 2001-1.1, Anderson School of Business, University of California, Los Angeles, California, 2001, pp. 4-5.V-73

sources, such as data submitted to the agency in relation to CAFE compliance. Consistent with this practice, the Volpe model uses detailed re presentations of (i.e., model-by-model, linked to specific engines and transmissions) the fleets manuf acturers are expected to produce for sale in the U.S. In preparation for today’s action, the ag ency issued in the spri ng of 2006 a request that manufacturers provide updated product pl ans for passenger cars and light trucks.

NHTSA received product plan information from Chrysler, Ford, GM, Honda, Nissan, Mitsubishi, Porsche and Toyota. The agency di dn’t receive any product plan information from BMW, Ferrari, Hyundai, Mercedes or VW.

Chrysler, Ford, GM, Honda, Nissan, Mitsubishi , Porsche and Toyota provided information covering multiple model years. However, only Chrysler and Mitsubishi provided us with product plans that showed differing production quantities, vehicle introductions, vehicle redesigns/refreshes changes, without any car ryover production quanti ties, from 2007 to MY 2015. The agency incorporated their product plan information as part of the input file to the model without the need to project or carryover any vehi cle production data.

For the other companies which provided data, th e agency carried over production quantities for their vehicles, allowing for growth, starting with the year after their pr oduct plan data showed changes in production quantities or showed the in troduction or redesign/refresh of vehicles. Product plan information was provided until MY 2013 for Ford and Toyota, thus the first year that we started to carry over production quant ities for those companies was MY 2014. Product plan information was provided until MY 2012 for GM and Nissan, thus the first year that we started to carry over producti on quantities for those companies was MY 2013. Product plan information was provided by Honda until MY 2008. Honda asked the agency to carry over those plans and also provided data for the last redesign of a vehicle a nd asked us to carry forward.

Product plan information was provided until MY 2008 for Porsche, thus the first year that we started to carry over pr oduction quantities for Po rsche was MY 2009.

For Hyundai, given that it is one of the largest 7 manufacturers, the agency used the mid-year 2007 data contained in the agency ’s CAFE database to establish the baseline models and production quantities for their vehicles. For the other manufacturers, because of the time constraint the agency was under to meet the statutory deadline, we used the 2005 information from our database, which is the latest information used in the current analysis. To the extent possible , because, the CAFE database doesn’t ca pture all of the product plan data that we request from companies, we supplemented the CA FE database information with information on public websites, from commercial information sources and for Hyundai, from the MY 2008 – 2011 light truck rule.

In all cases, manufacturers’ respective sales vo lumes were normalized to produce passenger car and light truck fleets that reflected manufact urers’ MY2006 market shares and to reflect passenger car and light truck fleets of projected aggregate volume cons istent with forecasts in the EIA’s 2007 Annual Energy Outlook. The agency requests comment on whether alternative methods should be used to estimate manufacturers’ market shares and the overall sizes of the future passenger car and light truck fleets.V-74

In a companion notice, the agency is request ing updated product plan information from all companies, and as in previous fuel economy rulemakings, we will be using those plans for the final rule. These plans will impact the standards fo r the final rule. To that end, the agency is requesting that these plans be as de tailed and as accurate as possible.

7. What economic information does the Volpe model use?

NHTSA’s preliminary analysis of alternative CAFE standards for the model years covered by this proposed rulemaking relies on a range of information, economic estimates, and input parameters. This section describes this info rmation and each assumption and specific parameter values, and discusses the rationale for tentativel y choosing each one. Similar to the product plan information, these economic assumptions play a ro le in the level of the standards, with some having a higher impacts than others. Outside of the cost of technologies and as discussed below, the price of gasoline and discount rate used for discounting future benefits account for the majority of the impacts. The agency seeks comment on the economic assumptions presented below. The agency seeks comment on the economic assumptions presented below. For the reader’s reference, Table V-9 below summarizes th e values used to calculate the impacts of each scenario:

Table V-9. Economic Values for Bene fits Computations (2006$) Rebound Effect (VMT Elasticity w/respect to Fuel Cost per Mile) -0.15 Discount Rate Applied to Future Benefits 7% Payback Period (years) 5.0 "Gap" between Test and On-Road mpg 20% Value of Travel Time per Vehicle ($/hour) $24.00 Economic Costs of Oil Imports ($/gallon) "Monopsony" Component $0.176 Price Shock Component $0.109 Military Security Component $ - Total Economic Costs ($/gallon) $0.285 Total Economic Costs ($/BBL) $11.97 External Costs from Additional Automobile Use Due to "Rebound" Effect ($/vehicle-mile) Congestion $0.047 Accidents $0.025 Noise $0.001 External Costs from Additional Light Truck Use Due to "Rebound" Effect ($/vehicle-mile) Congestion $0.052 Accidents $0.023 Noise $0.001 Emission Damage Costs Carbon Monoxide ($/ton) $ - Volatile Organic Compounds ($/ton) $1,700 Nitrogen Oxides ($/ton) $3,900V-75

Particulate Matter ($/ton) $164,000 Sulfur Dioxide ($/ton) $16,000 Carbon Dioxide ($/metric ton) $ 7.00 Annual Increase in CO2 Damage Cost 2.4%

a. Costs of fuel economy technologies

We developed detailed estimates of the costs of applying fuel econom y-improving technologies to vehicle models for use in analyzing the impact s of alternative standards considered in this rulemaking. The estimates were based on those reported by the 2002 NAS Report analyzing costs for increasing fuel economy, but were modified for purposes of this an alysis as a result of extensive consultations among engi neers from NHTSA, EPA, and the Volpe Center. As part of this process, the agency also developed varying cost estimates for applying certain fuel economy technologies to vehicles of different sizes and body styles. We may adjust these cost estimates based on comments received to this NPRM.

The technology cost estimates used in this anal ysis are intended to represent manufacturers’ direct costs for high-volume production of vehi cles with these techno logies and sufficient experience with their application so that all cost reductions due to “learning curve” effects have been fully realized. However, NHTSA recognize s that manufacturers’ actual costs for applying these technologies to specific vehicle models are likely to include additional outlays for accompanying design or engineering changes to each model, development and testing of prototype versions, recalibrat ing engine operating parameters , and integrating the technology with other attributes of the ve hicle. Manufacturers may also incur additional corporate overhead, marketing, or distribution and selling expenses as a consequence of their efforts to improve the fuel economy of individual vehicle m odels and their overall product lines.

In order to account for these additional costs, NHTSA applies an indirect cost multiplier of 1.5 to the estimate of the vehicle manufacturers’ dir ect costs for producing or acquiring each fuel economy-improving/CO 2 emission-reducing technology. Hi storically, NHTSA has used an almost identical multiplier, 1.51, for the mar kup from variable costs or direct manufacturing costs to consumer costs. This markup takes in to account fixed costs, burden, manufacturer’s profit, and dealers’ profit. NHTSA’s methodol ogy for determining this markup was recently peer reviewed. 67

This estimate was confirmed by Argonne Nationa l Laboratory in a recent review of vehicle manufacturers’ indirect costs. The Argonne st udy was specifically inte nded to improve the accuracy of future cost estimates for production of vehicles that achieve high fuel economy/low CO 2 emissions by employing many of the same advanced technologies considered in our analysis. 68 Thus, we believe that its recommendation that a multiplier of 1.5 be applied to direct

67 See Docket No. NHTSA-2007-27454, Item 4.

68 Vyas, Anant, Dan Santini, and Roy Cuenca, Comparison of Indirect Cost Multipliers for Vehicle Manufacturing

, Center for Transportation Research, Argonne National Laboratory, April 2000. Available at http://www.transportation.anl.gov/pdfs/TA/ 57.pdf (last accesse d Oct. 23, 2007).V-76

manufacturing costs to reflect manufacturers’ increased indirect costs for deploying advanced fuel economy technologies is appropriate for us e in the analysis for this rulemaking.

b. Potential opportunity cost s of improved fuel economy

An important concern is whether achieving the fuel economy improvements required by alternative CAFE standards would require ma nufacturers to compromise the performance, carrying capacity, safety, or comfort of their vehicl e models. If it did so, the resulting sacrifice in the value of these at tributes to consumers would represen t an additional cost of achieving the required improvements in fuel economy, and thus of manufacturers’ compliance with stricter CAFE standards. While exact dollar values of thes e attributes to consumers are difficult to infer from vehicle purchase prices, changing vehicle attr ibutes can affect the utility that vehicles provide to their owners, and thus their value to potential buyers.

NHTSA has approached this potential problem by developing tentative cost estimates for fuel economy-improving technologies that include any additional manuf acturing costs that would be necessary to maintain the performance, comfort, capacity, or safety of any light-duty vehicle model to which those technologies are applied. In doing so, we primarily followed the precedent established by the 2002 NAS Report, although we upda ted its assumptions as necessary for the purposes of the current rulemaking. The NAS study estimated “constant performance and utility” costs for fuel economy technologies, and NHTSA has used these as the basis for their further efforts to develop the technology costs employed in analyzing ma nufacturer’s costs for complying with alternative light truck standards.

NHTSA acknowledges the difficulty of estimating technology costs that include costs for the accompanying changes in vehicle design that are necessary to maintain performance, capacity, and utility. However, we believe that our tentative cost estimates for fuel economy/CO 2 emission-reduction technologies should be genera lly sufficient to prevent significant reductions in consumer welfare provided by vehicle models to which manufacturers apply those technologies. c. The on-road fuel economy “gap”

Actual fuel economy levels achie ved by light-duty vehicles in on -road driving fall somewhat short of their levels measured under the laborato ry-like test conditions used by EPA to establish its published fuel economy ratings for different models. In analyzing the fuel savings from alternative CAFE standards, NHTSA has prev iously adjusted the actual fuel economy performance of each light truck model downward from its rated value to re flect the expected size of this on-road fuel economy “gap.” On December 27, 2006, EPA adopted changes to its regulations on fuel economy labeling, which were intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel economy levels. 69

In its Final Rule, EPA estimated that actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published fuel ec onomy levels. For example, if the overall EPA fuel economy rating of a light tr uck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of that vehicl e is expected to be 16 mpg ( 20*.80). NHTSA has employed EPA’s

69 71 FR 77871 (Dec. 27, 2006).V-77

revised estimate of this on-road fuel economy gap in its analysis of the fuel savings resulting from alternative CAFE standards proposed in this rulemaking.

d. 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 CAFE standards, be cause they determine the value of fuel savings both to new vehicle buyers and to society. NHTSA relied on th e most recent fuel price projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) for this analysis. Specifically, we used the AEO 2008 Ea rly Release forecasts of inflation-adjusted (constant-dollar) retail gasoline and diesel fuel pr ices, which represent the EIA’s most up-to-date estimate of the most likely course of future prices for petroleum products. 70 Federal government agencies generally use EIA’s projec tions in their assessments of future energy-related policies.

The retail fuel price forecasts presented in AEO 2008 span the period from 2008 through 2030. Measured in constant 2006 dollars , the Reference Case forecast of retail gasoline prices during calendar year 2020 is $2.30 per gallon, rising gr adually to $2.49 by the year 2030. However, valuing fuel savings over the 36-ye ar maximum lifetime of light trucks assumed in this analysis requires fuel price forecasts that extend through 2050, the last year dur ing which a significant number of MY 2015 vehicles will remain in service. 71 To obtain fuel price forecasts for the years 2031 through 2050, the agency assumes that re tail fuel prices forecast in the Reference Case for 2030 will remain constant (in 2006 dollars) through 2050.

The value of fuel savings resulting from improved fuel economy/reduced CO 2 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 sa les. Total taxes on ga soline averaged $0.47 per gallon during 2006, while those levi ed on diesel averaged $0.53. State fuel taxes are weighted by sales. Because fuel taxes represent transfers of resource s 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 stringent CAFE st andards to the U.S. economy as a whole.

In estimating the economy-wide or “social” valu e of fuel savings of increasing CAFE/reducing CO 2 emissions levels, NHTSA assumes that current fuel taxes will remain constant in real or inflation-adjusted terms over the lifetimes of the vehicles proposed to be regulated. In effect, this assumes that the average value per gallon of taxes on gasoline and diesel fuel levied by all levels of government will rise at the rate of in flation over that period. This value is deducted from each future year’s forecast of retail gaso line and diesel prices reported in AEO 2008 to determine the social value of each gallon of fuel saved during that year as a result of improved fuel economy/reduced CO 2 emissions. Subtracting fuel taxe s results in a projected value for saving gasoline of $1.83 per gallon during 2020, rising to $2.02 per gallon by the year 2030.

70 Energy Information Administration, Annual Energy Outlook 2008, Early Release Reference Case Table 12. Available at http://www.eia.doe.gov/oiaf/aeo/pdf/aeota b_12.pdf (last accesse d Feb. 2, 2008).

71 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.V-78

In conducting the preliminary uncertainty analysis of benefits and costs from alternative CAFE standards required by OMB, NHTSA also considered higher and lower forecasts of future fuel prices. The results of the sens itivity runs can be found in the PRIA. EIA includes “High Price Case” and “Low Price Case” in AEO analyses that reflect uncertainties regarding future levels of oil production, but those cases are not meant to be probabilistic, and simply illustrate the range of uncertainty that exists. Because AEO 2008 Earl y Release included only a Reference Case of forecast of fuel prices and did not include the High and Low Price cases, the agency estimated high and low fuel prices corresponding to the AEO 2008 Reference Case forecast by assuming that high and low price forecasts would bear the same relationship to the Reference Case forecast as reported in AEO 2007. 72 These alternative scenarios projec t retail gasoline prices that range from a low of $1.94 per gallon to a high of $3.26 per gallon during 2020, and from $2.03 to $3.70 per gallon during 2030. In conjunction with our assumption that fuel taxes will remain constant in real or inflation-adjusted terms over th is period, these forecasts imply social values of saving fuel ranging from $1.47 to $2.79 per gall on during 2020, and from $1.56 to $3.23 per gallon in 2030.

EIA is widely-recognized as an impartial and au thoritative source of anal ysis and forecasts of U.S. energy production, consumption, and prices. The agency has published annual forecasts of energy prices and consumption levels for the U.S. economy since 1982 in its Annual Energy Outlook (AEO) , and these forecasts have been widely relied upon by federal agencies for use in regulatory analysis and for other purposes. Si nce 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 in teraction to determine prices for different forms of energy.

From 1982 through 1993, EIA’s forecasts of world oil prices – the primary determinant of prices for gasoline, diesel, and other transportation 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 corres ponding actual values by 75%.

Since that time, however, EIA’s forecasts of futu re world oil prices show a more mixed record for accuracy. The 1994-2005 editions of AEO reported 91 separate forecasts of world oil prices for the years 1995-2005, of which 33 have subseq uently proven too high while the remaining 58 have underestimated actual prices. The average abso lute error (i.e., regardle ss of its direction) of these forecasts has been 21%, but over- and underes timates have tended to offset one another, so that on average EIA’s more recent forecasts have underestimated actual world oil prices by 7%. Although both its overestimates and underestimates of future world oil prices for recent years have often been large, the most recent editi ons of AEO have significantly underestimated petroleum prices during those years for wh ich actual prices are now available.

72 Energy Information Administration, Annual Energy Outlook 2007 High Price Case, Table 12, http://www.eia.doe.gov/oiaf/aeo/pdf/aeohptab_12.p df and Energy Information Administration, Annual Energy Outlook 2007 Low Price Case, Table 12, http://w ww.eia.doe.gov/oiaf/aeo/p df/aeolptab_12.pdf (last accessed Feb. 2, 2008).V-79

However, NHTSA does not regard EIA’s recent tendency to underestimate future prices for petroleum and refined products or th e high level of current fuel prices as adequate justification to employ forecasts that differ from the Refe rence Case forecast presented in EIA’s Annual Energy Outlook 2008 Revised Early Release. This is particular ly the case because this forecast has been revised upward significantly since the initial release of AEO 2008 , which in turn represented a major upward revision from EIA’s fuel price forecast reported previously in AEO 2007. NHTSA also notes that retail gasoline prices across the U.S. have averaged $2.94 per gallon (expressed in 2005 dollars) for the firs t three months of 2008, slightly below EIA’s recently revised forecast that gasoline prices will average $2.98 per gallon (also in 2005 dollars) throughout 2008.

Comparing different forecasts of world oil prices also shows that EIA’s Reference Case forecast reported in Annual Energy Outlook 2007 (AEO 2007) was actually the highest of all six publicly-available forecasts of world oil prices ov er the 2010-30 time horizon. 73 Because world petroleum prices are the primary de terminant of retail prices for refined petroleum products such as transportation fuels, this suggests that the Refe rence Case forecast of U.S. fuel prices reported in AEO 2007 is likely to be the hi ghest of those projecte d by major forecasting services. Further, as indicated above, EIA’s most recent fuel price forecasts have been revised significantly upward from those previously projected in AEO 2007.

e. Consumer valuation of fuel economy and payback period

In estimating the value of fuel economy improve ments that would result from alternative CAFE standards to potential vehicle buyers, NHTSA assumes that buye rs 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 fi rst five years of a new vehicle’s lifetime, and that buyers behave as if they do not discount the value of these futu re fuel savings. The five-year figure represents the current average term of consumer loans to finance the purchase of new vehicles. We recognize that the period ove r which individual buyers finance new vehicle purchases may not correspond to the time horizons they apply in valuing fuel savings from higher fuel economy. However, NHTSA believes that five years represents a reasonable estimate of the average period over which buyers who finance their purchases of new vehicle receive – and thus must recognize – the monetary value of future fuel savings resulting from higher fuel economy.

The value of fuel savings over the first five year s of a vehicle model’s li fetime that would result under each alternative fuel economy standard is calculated using the projections of retail fuel prices described above. It is then deducted from the te chnology costs incurred by its manufacturer to produce the improvement in that model’s fuel economy estimated for each alternative standard, to determine the increase in the “effective price” to buyers of that vehicle model. The Volpe model uses these estimates of effective costs for increasing the fuel economy of each vehicle model to identify the order in which manufacturers woul d be likely to select models for the application of fuel economy-imp roving technologies in order to comply with stricter standards. The average value of th e resulting increase in effective cost from each

73 See http://www.eia.doe.gov/oiaf/ar chive/aeo07/pdf/forecast.pdf , Table 19, p. 106.V-80

manufacturer’s simulated compliance strategy is al so used to estimate the impact of alternative standards on its total sales for future model years.

However, it is important to recognize that NHT SA estimates the aggregate value to the U.S. economy of fuel savings resulting from alternativ e standards – or their “social” value – over the entire expected lifetimes of vehicles manufactured under those standa rds, rather than over this shorter “payback period” we assume for their buyers . This is discussed directly below in section f on “Vehicle survival and use assumptions.” As indicated previously, the maximum vehicle lifetimes used to analyze the e ffects of alternative fuel economy standards are estimated to be 25 years for automobiles and 36 years for light trucks.

f. Vehicle survival and use assumptions

NHTSA’s preliminary analysis of fuel/CO 2 emissions savings and rela ted benefits from adopting alternative standards for MY 2011- 2015 passenger cars and light truc ks is based on estimates of the resulting changes in fuel use ove r their entire lifetimes in the U.S. vehicle fleet. The first step in estimating lifetime fuel consumption by vehicles produced during a model year is to calculate the number that is expected to remain in servi ce during each future year after they are produced and sold. 74 This number is calculated by multiplying th e number of vehicles originally produced during a model year by the proportion expected to remain in service at the age they will have reached during each subsequent year, often referred to as a “survival rate.”

The agency relies on projections of the number of passenger cars and light trucks that will be produced during future years reported by the EIA in its AEO Reference Case forecast. 75 It uses updated values of age-specific survival rates fo r cars and light trucks estimated from yearly registration data for vehicles produ ced during recent model years, to ensure that forecasts of the number of vehicles in use reflect recent increases in the durability and ex pected life spans of cars and light trucks. 76

The next step in estimating fuel use is to calculate the total number of miles that the cars and light trucks produced in each model year aff ected by the proposed CAFE standards will be driven during each year of their lifetimes. To estimate total miles driven, the number of cars and light trucks projected to remain in use during each future year ( calculated as described above) is

74 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 vehicl es to be the age after which less than 2% 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 athttp://www-nrd.nhtsa.dot.gov/pdf/nrd30/NCSA/Rpts/2006/809952.pdf (last accessed Feb. 2, 2008).

75 The most recent edition is Ener gy Information Administration, Annual Energy Outlook 2008: Early Release

. Available at http://www.eia.doe.gov/oiaf/aeo/index. html (last accessed Feb. 2, 2008).

76 Lu, S., NHTSA, Regu latory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available athttp://www-nrd.nhtsa.dot.gov/pdf/nrd30/NCSA/Rpts/2006/809952.pdf (last accessed Feb. 2, 2008). These updated survival rates suggest that the expected lifetimes of recent-model passenger cars and light trucks are 13.8 and 14.5 years.V-81

multiplied by the average number of miles they ar e expected to be driven at the age they will have reached in that year. The agency estimate d the average number of miles driven annually by cars and light trucks of each age using data from the Federal Highway Administration’s 2001 National Household Trans portation Survey (NHTS). 77

Finally, fuel consumption during each year of a model year’s lifetime is estimated by dividing the total number of miles its surv iving vehicles are driven by the fuel economy they are expected to achieve under each alternative CAFE standa rd. Each model year’s total lifetime fuel consumption is the sum of fuel use by the cars or light trucks produced during that model year that are projected to remain in use during each year of their maximum lif e spans. In turn, the savings in a model year’s lifetime fu el use that will result from each alternative CAFE standard is the difference between its lifetime fuel use at the fuel economy level it attains under the Baseline alternative, and its lifetime fuel use at the higher fuel economy le vel it is projected to achieve under that alternative standard.

To illustrate these calculations, the most recent edition of the AEO projections that 8.52 million light trucks will be produced during 2012, and th e agency’s updated survival rates show that slightly more than half of these – 50.1 percent, or 4.27 million – are projected to remain in service during the year 2027, when they will have r eached an age of 14 years. At that age, light trucks achieving the fuel economy level required under the Baseline altern ative are driven an average of about 10,400 miles, so model year 201 2 light trucks will be driven a total of 44.4 billion miles (= 4.27 million surviving vehicles x 10,400 miles per vehicle) during 2027. Summing the results of similar calculations for each year of their 36-year maximum lifetime, model year 2012 light trucks w ill be driven a total of 1,502 billion miles under the Baseline alternative. Under that alternative, they are projected to achieve a test fuel economy level of 23.8 mpg, which corresponds to actual on-road fuel economy of 19.0 mpg (= 23.8 mpg x 80%). Thus their lifetime fuel use under the Baseline alternative is projec ted to be 79.0 billion gallons (= 1,502 billion miles divided by 19.0 miles per gallon).

g. Growth in total vehicle use

By assuming that the annual number of miles dr iven by cars and light trucks at any age will remain constant over the future, NHTSA’s proce dure for estimating the number of miles driven by cars and light trucks over thei r lifetimes in effect assumes that all future growth in total vehicle-miles driven stems from increases in the number of vehicles in service, rather than from increases in the average number of miles they are driven each year. Similarly, because the survival rates used to estimate the number of cars and light trucks remaining in service to various ages are assumed to remain fixed for future model years, growth in the total number of cars and light trucks in use is effectively assumed to resu lt only from increasing sales of new vehicles. In order to determine the validity of these assumpti ons, the agency conducted a detailed analysis of the causes of recent growth in car and light truck use.

From 1985 through 2005, the total number of miles dr iven (usually referred to as vehicle-miles traveled, or VMT) by passenger cars increased 35 percent, equivalent to a compound annual

77 For a description of the Survey, see http://nhts.ornl.gov/ quickStart.shtml (last accessed Dec. 3, 2007).V-82

growth rate of 1.5 percent. 78 During that time, the total number of passenger cars registered for in the U.S. grew by about 0.3 percent annually, al most exclusively as a re sult of increasing sales of new cars. 79 Thus growth in the average number of miles automobiles are driven each year accounted for the remaining 1.2 percent (= 1.5% - 0.3%) annual growth in total automobile use. 80

Over this same period, total VMT by light truc ks increased much faster , growing at an annual rate of 5.1 percent. In contra st to the causes of growth in au tomobile use, however, nearly all growth in light truck use over these two decades was attributable to rapid increases in the number of light trucks in use. 81 In turn, growth in the size of the nation’s light truck fleet has resulted almost exclusively from rising sa les of new light truc ks, since the fraction of new light trucks remaining in service to various ages has remained stable or even declined slightly over the past two decades. 82

On the basis of this analysis, the agency tenta tively concludes that its projections of future growth in light truck VMT account fully for the primary cause of its recent growth, which has been the rapid increase in sales of new light tr ucks during recent model years. However, the assumption that average annual use of passenger cars will remain fixed over the future appears to ignore an important source of recent growth in their total use, the gradual increase in the average number of miles they are driven. To the exte nt that this factor c ontinues to represent a significant source of growth in fu ture passenger car use, the agen cy’s analysis is likely to underestimate the reductions in fuel use and related envi ronmental impacts resulting from stricter CAFE standards for passenger cars. 83 The agency plans to account explicitly for potential future growth in average annual use of both cars and light trucks in the analysis accompanying its Final Rule establishing CAFE standards for model years 2011-15.

h. Accounting for the rebound e ffect of higher fuel economy

78 Calculated from data reported in FHWA, Highway Statistics, Summary to 1995, Table vm201at http://www.fhwa.dot.gov/ohi m/summary95/vm201a.xlw, and annual editions 1996-2005, Table VM-1 at http://www.fhwa.dot.gov/polic y/ohpi/hss/hsspubs.htm (las t accessed Feb. 2, 2008).

79 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.

80 See supra note [2 above here]

81 FHWA data show that growth in total miles driven by “Two-axle, four-tire trucks,” a category that includes most or all light trucks used as passenger vehicles, averaged 5.1% annually from 1985 th rough 2005. However, the number of miles light trucks are driven each year averaged 11,114 during 2005, almost unchanged from the average figure of 11,016 miles during 1985. Id.

82 Unpublished analysis of R.L. Polk vehicle registration da ta conducted by NHTSA Center for Statistical Analysis, 2005.

83 Assuming that average annual miles driven per autom obile will continue to increase over the future would increase the agency’s estimates of to tal lifetime mileage for MY 2011-18 pa ssenger cars. Their estimated lifetime fuel use would also increase under each a lternative standard considered in this analysis, but in inverse relation to their fuel economy. Thus lifetime fuel use will increase by more under the No Increase alternative than under any of the alternatives that would increase passe nger car CAFE standards, and by progressively less for the alternatives that impose stricter standards. Taking account of this factor would thus in crease the agency’s estimates of fuel savings for those alternatives, and omitting it will cause the agen cy’s analysis to underestimate those fuel savings.V-83

The rebound effect refers to the tendency for owners to increase the number of miles they drive a vehicle in response to an increase in its fuel ec onomy, as would result from more stringent fuel economy standards. The rebound effect occurs b ecause an increase in a vehicle’s fuel economy reduces its owner’s fuel cost for driving each mile, which is typical ly the largest single component of the cost of operating a vehicle. Ev en with the vehicle’s hi gher fuel economy, this additional driving uses some fuel, so the rebound effect will reduce the net fuel savings that result when the fuel economy standards require manufacturers to increase fuel economy. The rebound effect is usually expre ssed as the percentage by whic h annual vehicle use increases when average fuel cost per mile driven decreases in response to a change in the marginal cost of driving an extra mile, due either an increase in fuel economy or a re duction in the price of fuel.

The magnitude of the rebound effect is one of the determinants of the actual fuel savings that are likely to result from adopting stricter standard s, and thus an important parameter affecting NHTSA’s evaluation of alternative standards for future model years. The rebound effect can be measured directly by estimating the elasticity of ve hicle use with respect to fuel economy itself, or indirectly by the elasticity of vehicle use with respect to fuel cost per mile driven. 84 When expressed as a positive percentage, either of thes e parameters gives the fr action of fuel savings that would otherwise result from ad opting stricter standards, but is offset by the increase in fuel consumption that results when vehicles with increased fuel economy are driven more.

Research on the magnitude of the rebound effect in light-duty vehicle use dates to the early 1980s, and almost unanimously concludes that a statistically significan t rebound effect occurs when vehicle fuel efficiency improves. 85 The most common approach to estimating its magnitude has been to analyze statistically household survey data on vehicle use, fuel consumption, fuel prices (often obtained from external sources), and other determinants 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 vehicle use, fuel economy, fuel prices, and other variables to identify the response of total or average vehicle use to changes in fleet-wide average fuel economy and its effect of fuel cost per mile driven. Two recent studies analyzed yearly variation in vehicle ownership and use, fuel prices, and fuel economy among individual states over an extende d time period in order to measur e the response of vehicle use to changing fuel economy. 86

An important distinction among studies of the re bound effect is whether they assume that the effect is constant, or varies over time in respons e to the absolute levels 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

84 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. 85 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. 86 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.V-84

studies using household survey data estimate significantly different rebound effects for households owning varying numbers of vehicles, although they arrive at differing conclusions about whether the rebound effect is larger am ong households that own more vehicles. One recent study using state-level data concludes that the rebound effect va ries directly in response to changes in personal income and the degree of urbanization of U.S. cities, as well as fuel costs.

In order to arrive at a preliminary estimate of the rebound effect for use in assessing the fuel savings, emissions reductions, and other impacts of alternative standard s, NHTSA reviewed 22 studies of the rebound effect conducted from 1983 through 2005. We then conducted a detailed analysis of the 66 separate es timates of the long-run rebound eff ect reported in these studies, which is summarized in the table below. 87 As the table indicates, these 66 estimates of the longrun 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 m ean (24 percent), while focusing on the authors’ preferred estimates from published studies narr ows this range and lowers its average only slightly. The median estimate of the rebound eff ect in all three samples, which is generally regarded as a more reliable indicator of their cen tral tendency than the average because it is less influenced by unusually small and large estimate s, is 22 percent. As Table V-10 indicates, approximately two-thirds of all estimates review ed, of all published estimates, and of authors’ preferred estimates fall in th e range of 10-30 percent.

Table V-10. Summary of Re bound Effect Estimates 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%
Authors' Preferred Estimates 17 17 9% 75% 22% 22% 15%
U.S. Time-Series Estimates 7 34 7% 45% 14% 18% 9% Household Survey Estimates 13 23 9% 75% 31% 31% 16%
Pooled U.S. State Estimates 2 9 8% 58% 22% 25% 14% Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16% Variable Rebound Effect: (1) Reported Estimates 10 29 10% 45% 23% 23% 10% Updated to 2006 (2) 10 29 6% 46% 16% 19% 12% (1) Three studies estimate both consta nt and variable rebound effects. (2) Reported estimates updated to reflect 2006 valu es of vehicle use, fuel prices, fleet fuel efficiency, household income, and household vehicle ownership.

87 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, we computed a weighted average of the reported values using the distribution of households among vehicle ownership categories.V-85

The type of data used and authors’ assumption about whether the rebound effect varies over time have important effects on its estimated magnitude . The 34 estimates derived from analysis of U.S. annual time-series data produce a median estimate of 14 percen t for the long-run rebound effect, while the median of 23 estimates based on household survey data is more than twice as large (31 percent), and the median of 9 estimates based on pooled state data matches that of the entire sample (22 percent). The 37 estimates assuming a constant rebound effect produce a median of 20 percent, while the 29 originally reported estimates of a variable rebound effect have a slightly higher me dian value (23 percent).

In selecting a single valu e for the rebound effect to use in analyzing alternative standards for future model years, NHTSA tentatively attaches greater significance to studies that allow the rebound effect to vary in response to changes in the various factors that have been found to affect its magnitude. However, it is also important to update authors’ original ly-reported estimates of variable rebound effects to reflect current conditi ons. Recalculating the 29 original estimates of variable rebound effects to reflec t current (2006) values for reta il fuel prices, average fuel economy, personal income, and household vehicle ownership reduces their median estimate to 16 percent. 88 NHTSA also tentatively attaches grea ter significance to the recent study by Small and Van Dender (2005), which finds that the reb ound effect tends to decline as average fuel economy, personal income, and suburbanization of U.S. cities increase, but – in accordance with previous studies – rises w ith increasing fuel prices. 89

Considering the empirical evidence on the rebou nd effect as a whole, but according greater importance to the updated estimates from st udies allowing the rebound effect to vary – particularly the Small and Van Dender study – NHTSA has selected a rebound effect of 15 percent to evaluate the fuel savi ngs and other effects of alterna tive standards for the time period covered by this rulemaking. However, we do not believe that evidence of the rebound effect’s dependence on fuel prices or household income is sufficiently convincing to justify allowing its future value to vary in response to forecast changes in these variables. A range extending from 10 percent to at least 20 percent -- and perhaps as high as 25 percent -- app ears to be appropriate

88 As an illustration, Small and Van Dender (2005) allow the rebound effect to vary over time in response to changes in real per capita income as well as average fuel cost per mile driven. While their estimate for the entire interval (1966-2001) they analyze is 22 percent, updating this estimate using 2006 values of these variables reduces the rebound effect to approximately 10 percent. Similarly, updating Greene’s 1992 original estimate of a 15 percent rebound effect to reflect 2006 fu el prices and average fuel economy reduces it to 6 percent. See David L. Greene, “Vehicle Use and Fuel Economy: How Big is the Rebound Effect?” The Energy Journal, 13:1 (1992), 117-143. In contrast, the distribution of households among vehicle ownership categories in the data samples used by Hensher et al. (1990) and Greene et al. (1999) are nearly identical to the most recent es timates for the U.S., so updating their original estimates to current U.S. conditions changes them very little. See David A. Hensher, Frank W. Milthorpe, and Nariida C. Smith, “The Demand for Vehicle Use in the Urban Household Sector: Theory and Empirical Evidence,” Journal of Transport Economic s and Policy, 24:2 (1990), 119-137; and David L. Greene, James R. Kahn, and Robert C. Gibson, “Fuel Economy Rebound Effect for Household Vehicles,” The Energy Journal, 20:3 (1999), 1-21.
89 In the most recent light truck CAFE rulemaking, NHTSA chose not to pref erence the Small and Van Dender study over other published estimates of the value of the rebound effect, stating that since it “remains an unpublished working paper that has not been subjected to formal peer review, ...the agency does not yet consider the estimates it provides to have the same credibility as the published and widely-cited estimates it relied upon.” See 71 FR 17633 (Apr. 6, 2006). The study has subsequently been published and peer-reviewed, so NHTSA is now prepared to “consider it in developing its own estimate of the rebound effect for use in subsequent CAFE rulemakings.”V-86

for the required analysis of the uncertainty surrounding these estimates. While the agency selected 15 percent, it also ran se nsitivity analyses at 10 and 20 pe rcent. The results are shown in the uncertainties analysis.

i. Benefits from increased vehicle use

The increase in vehicle use from the rebound effect provides additional bene fits to their owners, who may make more frequent trips or travel farther to reach more desirable destinations. This additional travel provides benef its to drivers and their passen gers by improving their access to social and economic opportunities away from home . As evidenced by their decisions to make more frequent or longer trips when improved fuel economy reduces 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 amount by which the benefits from this additiona l travel exceed its costs (for fuel and other operating expenses) measures the net benefits th at drivers receive from the additional travel, usually referred to as increased consumer surp lus. NHTSA’s analysis estimates the economic value of the increased consumer surplus pr ovided by added driving using the conventional approximation, which is one half of the product of the decline in vehicle operating costs per vehicle-mile and the resulting incr ease in the annual number of m iles driven. The magnitude of these benefits represents a small fraction of the total benefits from the alternative fuel economy standards considered.

j. Added costs from congestion, crashes and noise

Although it provides some benefits to drivers, increased vehicle use associated with the rebound effect also contribute s to increased traffic congestion, motor vehicle accidents, and highway noise. Depending on how the additi onal travel is distributed over the day and on where it takes place, additional vehicle use can contribute to tr affic congestion and delays by increasing traffic volumes on facilities that are alr eady heavily traveled during peak periods. These added delays impose higher costs on drivers and other vehicle occ upants in the form of increased travel time and operating expenses. Because drivers do not ta ke these added costs into account in deciding when and where to travel, they must be accounted for separately as a cost of the added driving associated with the rebound effect.

Increased vehicle use due to the re bound effect may also increase th e costs associated with traffic accidents. Drivers may take account of the pot ential costs they (and their passengers) face from the possibility of being involved in an accident wh en they decide to make additional trips. However, they probably do not consider all of th e 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 co st of additional rebound-effect driving. Like increased delay costs, any increase in these ex ternal accident costs caused by added driving is likely to depend on the traffic conditions under wh ich it takes place, since accidents are more frequent in heavier traffic (although their severity may be reduced by the slower speeds at which heavier traffic ty pically moves).V-87

Finally, added vehicle use from the rebound eff ect may also increase traffic noise. Noise generated by vehicles causes inconvenience, i rritation, 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 thes e 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.

NHTSA relies on estimates of congestion, acci dent, and noise costs cau sed by automobiles and light trucks developed by the Fe deral Highway Administration to estimate the increased external costs caused by added drivi ng due to the rebound effect. 90 These estimates are intended to measure the increases in costs from added conges tion, property damages a nd injuries in traffic accidents, and noise levels caused by automobile s and light trucks that are borne by persons other than their drivers (or “marginal” exte rnal costs). Updated to 2006 dollars, FHWA’s “Middle” estimates for marginal congestion, acci dent, and noise costs ca used by automobile use amount to 5.2 cents, 2.3 cents, and 0.1 cents per ve hicle-mile (for a total of 7.6 cents per mile), while those for pickup trucks and vans are 4.7 cents, 2.5 cents, and 0.1 cents per vehicle-mile (for a total of 7.3 cents per mile). 91, 92 These costs are multiplied by the annual increases in automobile and light truck use from the rebound effect to yield the es timated increases in congestion, accident, and noise externalit y costs during each future year.

k. 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 petrol eum, or in the prices paid by consumers of petroleum products such as gasoline. In economics literature on this subject, these costs include (1) higher prices fo r petroleum products resulting from the effect of U.S. oil import demand on the world oil price; (2 ) 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 for maintaining the strategic petroleum reserve (SPR) to cu shion against resulting price increases. 93 Higher U.S. imports of crude oil or refined petroleum produc ts increase the magnitude of these external

90 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study ; see http://www.fhwa.dot.gov/policy/ hcas/final/index.htm (last accessed Dec. 3, 2007).

91 See Federal Highway Administration, 1997 Federal Highway Cost Allocation Study

, http://www.fhwa.dot.gov/policy/h cas/final/index.htm, Tables V-22, V-23, and V-24 (las t accessed Dec. 3, 2007).

92 The Federal Highway Administration’s estimates 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 October 20, 2007).

93 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 Security: 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.V-88

economic costs, thus increasing the true economic cost of supplying transportation fuels above the resource costs of producing th em. Conversely, reducing U.S. imports of crude petroleum or refined fuels or reducing fuel consumption can reduce these external costs. Any reduction in their total value that results from improved light truck fuel economy represents an economic benefit of setting more stringent CAFE standards in addition to the value of fuel savings and emissions reductions itself.

Increased U.S. oil imports can impose higher co sts on all purchasers of petroleum products, because the U.S. is a sufficiently large purchaser of foreign oil supplies that changes in U.S. demand can affect the world price. The effect of U.S. petroleum imports on world oil prices is determined by the degree of OPEC monopoly power over global oi l supplies, and the degree of monopsony power over world oil demand exerted by the U.S. The combination of these two factors means that increases in domestic dema nd for petroleum products that are met through higher oil imports can cause the price of oil in the world market to rise, which imposes economic costs on all other purchasers in th e global petroleum market in ex cess of the higher prices paid by U.S. consumers. 94 Conversely, reducing U.S. oil im ports can lower the world petroleum price, and thus generate benef its to other oil purchasers by re ducing these “monopsony costs.”

Although the degree of current OPEC monopoly pow er is subject to debate, the consensus appears to be that OPEC remains able to exer cise some degree of cont rol over the response of world oil supplies to variation in world oil prices , so that the world oil market does not behave completely competitively. 95 The extent of U.S. monopsony power is determined by a complex set of factors including the rela tive importance of U.S. imports in the world oil market, and the sensitivity of petroleum supply and demand to its world price among other participants in the international oil market. Most evidence appears to suggest that variation in U.S. demand for imported petroleum continues to exert some influence on world oil prices, although this influence appears to be limited. 96

The second component of external economic cost s imposed by U.S. petroleum imports arises partly because an increase in oil prices triggere d by a disruption in the supply of imported oil reduces the level of output that the U.S. econom y can produce. The reduction in potential U.S. economic output depends on the extent and duration of the increases in petroleum product prices that result from a disruption in the supply of imported oil, as well as on whether and how rapidly these prices return to pre-disrup tion levels. Even if prices for imported oil return completely to their original levels, however, economic output will be at least temporarily reduced from the level that would have been possible w ithout a disruption in oil supplies.

94 For example, if the U.S. imports 10 million barrels of petroleum per day at a world oil price of $20 per barrel, its total daily import bill is $200 million. If increasing imports to 11 million barrels per day causes the world oil price to rise to $21 per barrel, the daily U.S. import bill rises to $231 million. The resulting increase of $31 million per day ($231 million minus $200 million) is attributable to increasing daily imports by only 1 million barrels. This means that the incremental co st of importing each additional barrel is $3 1, or $10 more than the newly-increased world price of $21 per barrel. This additional $10 per barrel represents a cost imposed on all other purchasers in the global petroleum market by U.S. buyers, in excess of the price they pay to obtain those additional imports.
95 For a summary see Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL-6851, Oak Ridge National Laboratory, November 1, 1997, 17. Available

at

http://dmses.dot.gov/docimag es/pdf93/343894_web .pdf (last accessed Dec. 2, 2007).

96 Id. , 18-19.V-89

Because supply disruptions and resulting price increases tend to occur suddenly rather than gradually, they can also impose costs on busine sses and households for ad justing their use of petroleum products more rapidly than if the sa me price increase had occurred gradually over time. These adjustments impose costs because they temporarily reduce economic output even below the level that would ultimately be reach ed once the U.S. economy completely adapted to higher petroleum prices. The additional costs to businesses and households reflect their inability to adjust prices, output levels, and their use of energy and othe r resources quickly and smoothly in response to rapid changes in prices for petroleum products.

Since future disruptions in forei gn oil supplies are an uncertain pr ospect, each of these disruption costs must be adjusted by the probability that the supply of imported oil to the U.S. will actually be disrupted. The “expected value” of these cost s – the product of the pr obability that an oil import disruption will occur and the costs of re duced economic output and abrupt adjustment to sharply higher petroleum prices – is the appropriate measure of their magnitude. Any reduction in these expected disruption costs resulting from a measure that lowers U.S. oil imports represents an additional economic benefit beyond the direct value of savings from reduced purchases of petroleum products.

While the vulnerability of the U.S. economy to oil price shocks is wi dely thought to depend on total petroleum consumption rather than on the level of oil imports , variation in imports is still likely to have some effect on the magnitude of price increases resulti ng from a disruption of import supply. In addition, changing the quantity of petroleum imported into the U.S. may also affect the probability that such a disruption will occur. If either the size of the likely price increase or the probability that U.S. oil supplie s will be disrupted is affected by oil imports, the expected value of the costs from a supply disruption will also depend on the level of imports.

Businesses and households use a variety of market mechanisms, including oil futures markets, energy conservation measures, and technologies that permit rapid fuel switching to “insure” against higher petroleum prices and reduce their costs for adjusting to sudden price increases. While the availability of thes e market mechanisms has likely reduced the potential costs of disruptions to the supply of imported oil, consum ers of petroleum products are unlikely to take account of costs they impose on others, so these co sts are probably not refl ected in the price of imported oil. Thus changes in oil import levels pr obably continue to affect the expected cost to the U.S. economy from potential oil supply disruptions, although this component of oil import costs is likely to be significantly smaller than estimated by studies conducted in the wake of the oil supply disruptions during the 1970s.

The third component of the exte rnal economic costs of importing oil into the U.S. includes government outlays for maintaining a military presen ce to secure the supply of oil imports from potentially unstable regions of the world and to pr otect against their interruption. Some analysts also include outlays for maintaining the U.S. Strategic Petroleum Reserve (SPR), which is intended to cushion the U.S. economy against the consequences of disruption in the supply of imported oil, as additional costs of protecti ng the U.S. economy from oil supply disruptions.V-90

NHTSA believes that while costs for U.S. military security may vary over time in response to long-term changes in the actual le vel of oil imports in to the U.S., these costs are unlikely to decline in response to any reduction in U.S. o il imports resulting from raising future CAFE standards for passenger cars and light trucks. U.S. military activities in regions that represent vital sources of oil imports also serve a broader range of security and fo reign policy objectives than simply protecting oil supplies, and as a c onsequence are unlikely to vary significantly in response to changes in the level of o il imports prompted by higher standards.

Similarly, while the optimal size of the SPR from the standpoint of its potential influence on domestic oil prices during a suppl y disruption may be related to th e level of U.S. oil consumption and imports, its actual size has not appeared to vary in response to recent changes in oil imports. Thus while the budgetary costs for maintaining the Reserve are similar to ot her external costs in that they are not likely to be reflected in the market price for imported oil, these costs do not appear to have varied in respons e to changes in oil import levels.

In analyzing benefits from its recent actions to increase light truck CAFE standards for model years 2005-07 and 2008-11, NHTSA relied on a 1997 study by Oak Ridge National Laboratory (ORNL) to estimate the value of reduced econom ic externalities from petroleum consumption and imports. 97 More recently, ORNL updated its estimates of the value of these externalities, using the analytic framework developed in it s original 1997 study in conjunction with recent estimates of the variables and para meters that determine their value. 98 These include world oil prices, current and anticipated future levels of OPEC petrol eum production, U.S. oil import levels, the estimated responsiveness of oil supplies and demands to prices in different regions of the world, and the likelihood of oil supply disrup tions. ORNL prepared its updated estimates of oil import externalities for use by EPA in evalua ting the benefits of reductions in U.S. oil consumption and imports expected to result fr om its Renewable Fuel Standard Rule of 2007 (RFS). 99

The updated ORNL study was subjected to a deta iled peer review by experts selected by EPA, and its estimates of the value of oil import external ities were subsequently revised to reflect their comments and recommendations. 100 Specifically, reviewers reco mmended that ORNL increase its estimates of the sensitiv ity of oil supply by non-OPEC pr oducers and oil demand by nations other than the U.S. to changes in the world o il price, as well as reduce its estimate of the sensitivity of U.S. gross domestic product (GDP ) to potential sudden increases in world oil prices.

After making the revisions recommended by peer reviewers, ORNL’s updated estimates of the monopsony cost associated with U.S. oil imports range from $5.22 to $9.68 per barrel, with a

97 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL-6851, Oak Ridge National Laboratory, November 1, 1997. Available at http://dmses.dot.gov/d ocimages/pdf93/34389 4_web.pdf (last accesse d Dec. 2, 2007).

98 Leiby, Paul N. "Estimating the Energy Security Bene fits of Reduced U.S. Oil Imports," Oak Ridge National Laboratory, ORNL/TM-2007/028, Revised July 23, 2007. Available at http://pzl1.ed.ornl.gov/energysecurity.html (click on link below “Oil Imports Costs and Benefits”) (last accessed Sept. 10, 2007).

99 72 FR 23899 (May 1, 2007).
100 Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports

, ICF, Inc., September 2007.V-91

most likely estimate of $7.41 per barrel. These es timates imply that each gallon of fuel saved as a result of adopting higher CAFE standards wi ll reduce the monopsony costs of U.S. oil imports by $0.124 to $0.230 per gallon, with the actual value most likely to be $0.176 per gallon saved. ORNL’s updated and revised estimat es of the increase in the expected costs associated with oil supply disruptions to the U.S. and the resulting rapid increase in prices for petroleum products amount to $4.54 to $5.84 per barrel, although its mo st likely estimate of $4. 59 per barrel is very close to the lower end of this range. According to these estimates, each gallon of fuel saved will reduce the expected costs disrup tions to the U.S. economy by $0.108 to $0.139, with the actual value most likely to be $0.109 per gallon.

The updated and revised ORNL estimates suggest that the combined reduction in monopsony costs and expected costs to the U.S. economy fr om oil supply disruptions resulting from lower fuel consumption total $0.232 to $0.370 per gall on, with a most likely estimate of $0.286 per gallon. This represents the additional economic benefit likely to result from each gallon of fuel saved by higher CAFE standards, beyond the savings in resource costs for producing and distributing each gallon of fuel sa ved. NHTSA employs this midpoint estimate in its analysis of the benefits from fuel savings projected to result from altern ative CAFE standards for model years 2011-15. It also analyzes th e effect on these benefits estimates from variation in this value over the range from $0.232 to $0.370 per gallon of fuel saved.

NHTSA’s analysis of benefits from alternative CAFE standard s does not include cost savings from either reduced outlays for U.S. military operations or maintaining a smaller SPR among the external benefits of reduci ng gasoline consumption and petroleum imports by means of tightening future standards. This view concur s with that of both the original ORNL study of economic costs from U.S. oil imports and its recent update, which conclude that savings in government outlays for these purposes are unlikely to result form reductions in consumption of petroleum products and oil imports on the scale of those likely to result from the alternative increases in CAFE standards cons idered for model years 2011-15. l. Air pollutant emissions

(i) Impacts on criteria air pollutant emissions

This section explains how NHTSA has monetized and modeled air pollutant emissi ons of criteria pollutants and CO 2 in developing the proposed standards usi ng the EPCA factors. However, this section does not contain NHTSA’s assessment of the potential environmental impacts of the proposed standards and reasonable alternatives for purposes of the National Environmental Policy Act (NEPA), 42 U.S.C. 4321-4347. On March 28, 2008, NHTSA published a notice of intent to prepare an environmental impact statement (EIS) and opened the NEPA scoping process (73 FR 16615). NHTSA will consider th e potential environmental impacts of the proposed standards and reasona ble alternatives, including im pacts associated with CO 2 emissions and climate change, through the NEPA process. NHTSA’s NEPA analysis will inform any further action on the proposed standard s, consistent with NEPA and EPCA.

While reductions in domestic fuel refining a nd distribution that result from lower fuel consumption will reduce U.S. emissions of criteria pollutants, additional vehicle use associated with the rebound effect from higher fuel economy w ill increase emissions of these pollutants.V-92

Thus the net effect of stricter CAFE standards on emissions of each criteria pollutant depends on the relative magnitudes of its reduced emissions in fuel refining and distribution, and increases in its emissions from vehicle use. Because the relationship between emission rates (emissions per gallon refined of fuel or mile driv en) in fuel refining and vehicle use is different for each criteria pollutant, the net effect of fuel savings from the proposed standards on total emissions of each pollutant is likely to differ. Criteria air pollutants emitted by vehicles and during fuel production include carbon monoxide (CO), hydr ocarbon compounds (usually referr ed to as “volatile organic compounds,” or VOC), nitrogen oxides (NO x ), fine particulate matter (PM2.5), and sulfur oxides (SO x ).

The increase in emissions of these pollutant s from additional vehicl e use due to the rebound effect is estimated by multiplying the increase in total miles driven by vehicles of each model year and age by age-specific emission rates per vehicle-mile for each pollutant. NHTSA developed these emission rates using EPA’s MOBILE6.2 motor vehicle emissions factor model. 101 Emissions of these pollutants also occur during crude oil extractio n and transportation, fuel refining, and fuel storage and distribution. The reduction in total emissions from each of these sources thus depends on the extent to which fuel savings result in lower imports of refined fuel, or in reduced domestic fuel refining. To a lesser extent, they also depend on whether any reduction in domestic gasoline refi ning is translated into reduced imports of crude oil or reduced domestic extraction of petroleum.

Based on analysis of changes in U.S. gasolin e imports and domestic gasoline consumption forecast in AEO’s 2008 Early Rel ease, NHTSA tentatively estimates that 50 percent of fuel savings resulting from higher CAFE standards will result in reduced imports of refined gasoline, while the remaining 50 percent will reduce domestic fuel refining. 102 The reduction in domestic refining is assumed to leave its sources of crude petroleum unchanged from the mix of 90 percent imports and 10 percent dom estic production projected by AEO.

NHTSA proposes to estimate reductions in crite ria pollutant emissions from gasoline refining and distribution using emission rates obtained from Argonne Na tional Laboratories’ Greenhouse Gases and Regulated Emissions in Transportation (GREET) model. 103 The GREET model provides separate estimates of air pollutant emissi ons that occur in four phases of fuel production and distribution: crude oil extracti on, crude oil transportation and st orage, fuel refining, and fuel distribution and storage. 104 We tentatively assume that reduc tions in imports of refined fuel

101 U.S. Environmental Protection Agency, MOBILE6 Vehicle Emission Modeling Software, available at http://www.epa.gov/otaq/m6.htm#m60 (last accessed Sept. 10, 2007).

102 Estimates of the response of gasoline imports and domestic refining to fuel savings from stricter standards are variable and highly uncertain, but our preliminary analysis indicates that under any reasonable assumption about these responses, the magnitude of the net change in cr iteria pollutant emissions (accounting for both the rebound effect and changes in refining emissions) is extremely low relative to their current total.

103 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Transportation (GREET) Model , Version 1.8, June 2007, available at http://www.transportation.anl.gov/ software/GREET/index.html (last accessed Sept. 10, 2007).

104 Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of volatile organic compounds, or VOCs) ar e 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 averag e energy content of gasoline.V-93

would reduce criteria pollutant emissions during fuel storage and distribution only. Reductions in domestic fuel refining using imported crude oil as a feedstock are tentatively assumed to reduce emissions during crude oil transportation and storage, as well as during gasoline refining, distribution, and storage, because less of each of these activities w ould be occurring. Similarly, reduced domestic fuel refining using domestically -produced crude oil is tentatively assumed to reduce emissions during all phases of gasoline production and distribution. 105

The net changes in emissions of each criteria pol lutant are calculated by adding the increases in their emissions that result from increased vehicl e use and the reductions that result from lower domestic fuel refining and distribution. The net change in emissions of each criteria pollutant is converted to an economic value using estimates of the economic costs per ton emitted (which result primarily from damages to human health) developed by EPA and submitted to the federal Office of Management and Budget for review. Fo r certain criteria pollutants, EPA estimates different per-ton costs for emissions from vehicl e use than for emissions of the same pollutant during fuel production, reflect ing differences in their typi cal geographic distributions, contributions to ambient pollution le vels, and resulting population exposure.

(ii) Reductions in CO 2 emissions

Fuel savings from stricter CAFE standards al so result in lower emissions of carbon dioxide (CO 2 ), the main greenhouse gas emitted as a resu lt of refining, distribution, and use of transportation fuels. 106 Lower fuel consumption reduces carbon dioxide emissions directly, because the primary source of transportation-related CO 2 emissions is fuel combustion in internal combustion engines. NHTSA tentatively estimat es reductions in carbon dioxide emissions resulting from fuel savings by assuming that the entire carbon content of gasoline, diesel, and other fuels is converted to carbon di oxide during the combustion process. 107

Reduced fuel consumption also reduces carbon di oxide emissions that result from the use of carbon-based energy sources during fuel production and distribution. 108 NHTSA currently

105 In effect, this assumes that the dist ances crude oil travels to U.S. refineri es are approximately the same regardless of whether it travels from domestic oilfields or import term inals, and that the distances that gasoline travels from refineries to retail stations are approximately the same as those from import terminals to gasoline stations.

106 Carbon dioxide emissions account for more than 97 percent of total greenhouse gas emissions from the refining and use of transportation fuels; see U.S. Environmental Protection Agency, Draft Inventory of GHG Emissions and Sinks (1990-1999 ), Tables ES-1 and ES-4. Available at http://www.epa.gov/globalwarming/pub lications/emissions/us2001/energy.pd f (last accessed Sept. 10, 2007).

107 This assumption results in a slight overestimate of car bon dioxide emissions, since a small fraction of the carbon content of gasoline is emitted in the forms of carbon monoxide and unburned hydrocarbons. However, the magnitude of this overestimate is likely to be extremely sm all. This approach is consistent with the recommendation of the Intergovernmental Panel on Climate Change for “Tier 1” national greenhouse gas emissions inventories. Cf. Intergovernmental Panel on Climate Change, 2006 Guidelines for National Greenhouse Gas Inventories , Volume 2, Energy, p. 3.16.

108 We note that NHTSA did not, for purposes of this proposed rulemaking, attempt to estimate changes in emissions of greenhouse gases (GHGs) other than carbon dioxide. This was because carbon dioxide itself accounts for more than 95 percent of the total CO 2 -equivalent emissions of fuel production and use, even with other GHGs that result from those activities (principally methane and nitrous oxide) weighted by their higher global warming potentials (GWPs) relative to CO 2 . Calculated from U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990- 2005, EPA430-R-07-02, April 15, 2007. Available at http://www.epa.gov/climatechange/emissions/downloads06/07CR.pdf, Table ES-2.V-94

estimates the reductions in CO 2 emissions during each phase of fuel production and distribution using carbon dioxide emission ra tes obtained from the GREET model, using the previous assumptions about how fuel savings are reflected in reductions in each phase. The total reduction in CO 2 emissions from the improvement in fuel economy under each alternative CAFE standard is the sum of the reductions in emissi ons from reduced fuel use and from lower fuel production and distribution.

iii) Economic value of reductions in CO 2 emissions

NHTSA has taken the economi c benefits of reducing CO 2 emission into account in this rulemaking, both in developing proposed CAFE standards and in a ssessing the economic benefits of each alternative th at was considered. As noted above, the 9th Circuit found in CBD that NHTSA had been arbitrary and capricious in deciding not to m onetize the benefit of reducing CO 2 emissions, saying that the agency had not substantiated the conclusion in its April 2006 final rule that the appropriate course was not to monetize (i .e., quantify the value of) carbon emissions reduction at all.

To this end, NHTSA reviewed published estima tes of the “social cost of carbon emissions” (SCC). The SCC refers to the marginal cost of additional damages caused by the increase in expected climate impacts resulting from the emis sion of each additional metric ton of carbon, which is emitted in the form of CO 2
. 109 It is typically estimated as the net present value of the impact over some time period (100 years or long er) of one additional ton of carbon emitted into the atmosphere. Because accumulated concentrati ons of greenhouse gases in the atmosphere and the projected impacts on global climate are incr easing over time, the economic damages resulting from each additional ton of CO 2 emissions in future years are belie ved to be greater as a result. Thus estimates of the SCC are typically reporte d for a specific year, and these estimates are generally larger for emissions in more distant future years.

There is substantial variation among different aut hors’ estimates of the SCC, much of which can be traced to differences in their underlying assu mptions about several vari ables. These include the sensitivity of global temperatures and othe r climate attributes to increasing atmospheric concentrations of greenhouse gase s, discount rates applied to future economic damages from climate change, whether damages sustained by developing regions of the globe should be weighted more heavily than damages to deve loped nations, how long climate changes persist once they occur, and the economic va luation of specific climate impacts. 110

Taken as a whole, recent estimates of the S CC may underestimate the true damage costs of carbon emissions because they often exclude da mages caused by extreme weather events or

109 Carbon itself accounts for 12/44, or about 27%, of the mass of carbon dioxide (12/44 is the ratio of the molecular weight of carbon to that of carbon dioxide). Thus each ton of carbon emitted is associated with 44/12, or 3.67, tons of carbon dioxide emissions. Estimates of the SCC are typically reported in dollars per ton of carbon, and must be divided by 3.67 to determine their equivalent value per ton of carbon dioxide emissions.

110 For a discussion of these factors, see Yohe, G.W., R.D. Lasco, Q.K. Ahmad, N.W. Arnell, S.J. Cohen, C. Hope, A.C. Janetos and R.T. Perez, 2007: Perspectives on climate change and sustainability. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, pp. 821-824.V-95

climate response scenarios with low probabilit ies but potentially extreme impacts, and may underestimate the climate impacts and damages th at could result from multiple stresses on the global climatic system. At the same time, however, many studies fail to consider potentially beneficial impacts of climate change, and do not adequately account for how future development patterns and adaptations could re duce potential impacts from c limate change or the economic damages they cause.

Given the uncertainty surrounding estimates of th e SCC, the use of any single study may not be advisable since its estimate of the SCC will depend on many assumptions made by its authors. The Working Group II’s contribution to the Fourth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC) 111 notes that: The large ranges of SCC are due in the large part to differences in assumptions regarding climate sensitivity, response la gs, the treatment of risk and equity, economic and non-economic impacts, the in clusion of potentially catastrophic losses, and discount rates. Although the IPCC does not recommend a single estimate of the SCC, it does cite the Tol (2005) study on four separate occasions (pages 17 , 65, 813, 822) as the only available survey of the peer-reviewed literature that has itself been subjected to peer review. Tol developed a probability function using the SCC estimates of th e peer reviewed literat ure and found estimates ranging from less than zero to over $200 per metric ton of carbon. In an effort to resolve some of the uncertainty in reported estimates of climate damage costs from carbon emissions, Tol (2005) reviewed and summarized one hundred and three estimates of th e SCC from 28 published studies. He concluded that when only peer-reviewed studies published in recognized journals are considered, “...climate change impacts may be very uncertain but is unlikely that the marginal damage costs of carbon dioxide emi ssions exceed $50 per [metric] ton carbon [about $14 per metric ton of CO2].” 112 He also concluded that the costs may be less than $14.

Because of the number of assumptions required by each study, the wide range of uncertainty surrounding these assumptions, and th eir critical influence on the re sulting estimates of climate damage costs, some studies have undoubtedly produced estimates of the SCC that are unrealistically high, while others are likely to have estimated valu es that are improbably low. Using a value for the SCC that reflects the cen tral tendency of estimates drawn from many studies reduces the chances of relying on a single es timate that subsequently proves to be biased.

It is important to note that estimates of the SCC almost invariably include the value of worldwide damages from potential climate impacts caus ed by carbon dioxide emissions, and are not confined to damages likely to be suffered within th e U.S. In contrast, the other estimates of costs and benefits of increasing fuel economy included in this prop osal include only the economic values of impacts that occur within the U.S. For example, the economic value of reducing criteria air pollutant emissions from overseas oil refineries is not counted as a benefit resulting

111 Climate Change 2007 – Impacts, Adaptation and Vulner ability, Contribution of Working Group II to the Fourth Assessment Report of the IPCC, 17. Available at http://www.ipcc-wg2.org (las t accessed <Feb. 4, 2008>).

112 Tol, Richard. The marginal dama ge costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33 (2005) 2064–2074, 2072. The summary SCC estimates reported by Tol are assumed to be denominated in U.S. dollars of the year of publication, 2005.V-96

from this rule, because any reduction in dama ges to health and property caused by overseas emissions are unlikely to be experienced within the U.S.

In contrast, the reduced value of transfer paym ents from U.S. oil purchasers to foreign oil suppliers that results when lower U.S. oil de mand reduces the world pr ice of petroleum (the reduced “monopsony effect”) is counted as a benefit of reducing fuel use. 113 If the agency’s analysis was conducted from a worldwide rather th an a U.S. perspective, however, the benefit from reducing air pollution overseas would be incl uded, while reduced payments from U.S. oil consumers to foreign suppliers would not.

In order to be consistent with NHTSA’s use of exclusively domes tic costs and benefits in prior CAFE rulemakings, the appropriate value to be placed on changes climate damages caused by carbon emissions should be one that reflects the change in damages to the United States alone. Accordingly, NHTSA notes that the va lue for the benefits of reducing CO 2 emissions might be restricted to the fraction of t hose benefits that are likely to be experienced within the United States.

Although no estimates of benefits to the U.S. its elf that are likely to result from reducing CO 2 emissions are currently available, NHTSA expect s that if such values were developed, the agency would employ those rather than global be nefit estimates in its analysis. NHTSA also anticipates that if such values were develope d, they would be lower than comparable global values, since the U.S. is likely to sustain only a fraction of total global damages resulting from climate change.

In the meantime, the agency has elected to use the IPCC estimate of $43 per metric ton of carbon as an upper bound on the benefits resulting from re ducing each metric ton of U.S. emissions. 114 This corresponds to approximately $12 per metric ton of CO 2 when expressed in 2006 dollars. This estimate is based on the 2005 Tol study. 115 The Tol study is cited repeatedly as an authoritative survey in various IPCC reports, wh ich are widely accepted as representing the general consensus in the scientific community on climate change science. Since the IPCC estimate includes the worldwide costs of pot ential damages from carbon dioxide emissions, NHTSA has elected to employ it as an upper bound on the estimated value of the reduction in U.S. domestic damage costs that is likely to result from lower CO 2 emissions. 116

113 The reduction in payments from U.S. oil purchasers to domestic petroleum producers is not included as a benefit, since it represents a transfer that occu rs entirely within the U.S. economy.

114 The estimate of $43 per ton of carbon emissions is reported by Tol (p. 2070) as the mean of the “best” estimates reported in peer-reviewed studies (see fn. 4). It thus diffe rs from the mean of all estimates reported in the peerreviewed studies surveyed by Tol. The $43 per ton value is also attributed to Tol by IPCC Working Group II (2007), p. 822.
115 Tol’s more recent (2007) and inclusive survey has been published online with peer-review comments. The agency has elected not to rely on the estimates it reports, but will consider doing so in its analysis of the final rule if the survey has been published, and will also consider any other newly-published evidence.

116 For purposes of comparison, we note that in the rule making to establish CAFE standards for MY 2008-11 light trucks, NRDC recommended a value of $10 to $25 per ton of CO2 emissions reduced by fuel savings and both Environmental Defense and Union of Concerned Scientists recommended a value of $50 per ton of carbon (equivalent to about $14 per ton of CO 2 emissions).V-97

The IPCC Working Group II Fourth Assessment Repor t (2007, p. 822) furthe r suggests that the SCC of carbon is growing at an annual 2.4 percen t growth rate, based on estimated increases in damages from future emissions reported in published studies. NHT SA has also elected to apply this growth rate to Tol’s orig inal 2005 estimate. Thus by 2011, the agency estimates that the upper bound on the benefits of reducing CO 2 emissions will have r eached about $14 per metric ton of CO 2 , and will continue increase by 2. 4 percent annually thereafter.

In setting a lower bound, the agency agrees wi th the IPCC Working Group II (2007) report that “significant warming across the globe and the locations of sign ificant observed changes in many systems consistent with warming is very unlikel y to be due solely to natural variability of temperatures or natural variabil ity of the systems” (pp. 9). Although this finding suggests that the global value of economic benefits from reducing carbon dioxide emissions is unlikely to be zero, it does not necessarily rule out low or zero values for the benefit to the U.S. itself from reducing emissions.

For most of the analysis it performed to devel op this proposal, NHTSA re quired a single estimate for the value of reducing CO 2 emissions. The agency thus elected to use the midpoint of the range from $0 to $14 (or $7.00) per metric ton of CO 2 as the initial value for the year 2011, and assumed that this value would grow at 2.4 pe rcent annually thereafter. This estimate is employed for the analyses conducted using the Vo lpe CAFE model to s upport development of the proposed standards. The agency also conducte d sensitivity analyses of the benefits from reducing CO 2 emissions using both the upper ($14 per metr ic ton) and lower ($0 per metric ton) bounds of this range.

NHTSA seeks comment on its tentative conclusion s for the value of the SCC, the use of a domestic versus global value for the economic benefit of reducing CO 2 emissions, the rate at which the value of the SCC grows over time, the de sirability of and proce dures for incorporating benefits from reducing emissions of greenhouse gases other than CO 2 , and any other aspects of developing a reliable SCC value for purpos es of establishing CAFE standards.

m. The value of increased driving range

Improving vehicles’ fuel economy may also increa se their driving range before they require refueling. By reducing the frequency with whic h drivers typically refuel their vehicles, and by extending the upper limit of the range they can travel before requi ring refueling, improving fuel economy thus provides some additional benefits to their owners. (Alternativ ely, if manufacturers respond to improved fuel economy by reducing the size of fuel tanks to maintain a constant driving range, the resulting cost saving will pr esumably be reflected in lower vehicle sales prices.)

No direct estimates of the value of extended ve hicle range are readily available, so NHTSA’s analysis calculates the reduction in the annual numb er of required refueli ng cycles that results from improved fuel economy, and applies DOT-reco mmended values of travel time savings to convert the resulting time sa vings to their economic value. 117 As an illustration of how the value

117 See Department of Transportation, Guidance Memorandum, “The Value of Saving Travel Time: Departmental Guidance for Conducting Economic Evaluations,” Apr. 9, 1997. Available atV-98

of extended refueling range is es timated, a typical small light truck model has an average fuel tank size of approximately 20 gallons. Assuming that drivers typically refuel when their tanks are 20 percent full ( i.e. , 4 gallons in reserve), increasing this model’s actual on-road fuel economy from 24 to 25 mpg would extend its driv ing range from 384 miles (= 16 gallons x 24 mpg) to 400 miles (= 16 gallons x 25 mpg). A ssuming that it is driven 12,000 miles/year, this reduces the number of times it needs to be refu eled each year from 31.3 (= 12,000 miles per year / 384 miles per refueling) to 30. 0 (= 12,000 miles per year / 400 m iles per refueling), or by 1.3 refuelings per year.

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and average vehicle occupancy for all drivi ng trips (1.6 persons), the DOT-recommended value of travel time per vehicle-hour is $24.00 (in 2006 dollars). 118 Assuming that locating a station and filling up requires ten minutes, the annual value of time save d as a result of less frequent refueling amounts to $5.20 (calculated as 10/60 x 1.3 x $24.00). This calculation is repeated for each future calendar year that vehicles of each model year affected by the altern ative CAFE standards proposed 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, becau se 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. n. Discounting future benefits and costs

Discounting future fuel savings and other benef its 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 decline in th e value of these benefits – as viewed from today’s perspective – for each year th ey are deferred into the future. NHTSA uses a rate of 7 percent per year to discount the value of future fuel savings and other benefits to analyze the potential impacts of al ternative CAFE standards. However, the agency also performs an alternative analysis of benefits from alterna tive increases in CAFE standards using a 3 percent discount rate, and seeks comment on whether the standards should be set using a 3 percent rate instead of a 7 percent rate.

There are several reasons that NHTSA relies prim arily on 7 percent as the appropriate rate for discounting future benefits from increased CAFE standards. First, OMB Circular A-4 indicates that this rate reflects the economy- wide opportunity cost of capital. 119 It also states that this “is the appropriate discount rate whenev er the main effect of a regulat ion is to displace or alter the use of capital in the private sector.” 120 We believe that a substantia l portion of the cost of this

http://ostpxweb.dot.gov/policy/Data /VOT97guid.pd f (last accessed October 20, 2007); update available at http://ostpxweb.dot.gov/policy/Data /VOTrevision1_2-11-03.pdf (l ast accessed October 20, 2007).

118 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 or urban travel) is valued at 100 percent of the hourly wage rate. For intercity travel, personal travel (8 7 percent) is valued at 70 percent of the wage rate, while business travel (13 percent) is valued at 100 percent of th e wage rate. The resulting values of travel time are $12.67 for urban travel 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.

119 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 Feb. 14, 2008).

120 Id.V-99

regulation may come at the expense of other inve stments the auto manufac turers might otherwise make. Several large manufacturers are resource-c onstrained with respect to their engineering and product-development capabilities. As a result, other uses of these resources will be foregone while they are required to be applied to technologies that improve fuel economy.

Second, 7 percent also appears to be an appropriate rate to th e extent that the costs of the regulation come at the expense of consumption as opposed to investment. NHTSA believes that financing rates on vehicle loans represent an appr opriate discount rate, be cause they reflect the opportunity costs faced by consumers when buying vehicles with greater fuel economy and a higher purchase price. Most new and used vehicl e purchases are financed, and because most of the benefits from higher fuel economy standards accrue to vehicle purchasers in the form of fuel savings, the appropriate discount ra te is the interest rate buyers pay on loans to finance their vehicle purchases. 121

According to the Federal Reserve, the interest rate on new car loans made through commercial banks has closely tracked the rate on 10-y ear treasury notes, but exceeded it by about 3 percent. 122 The official Administration forecast is that real (or infl ation-adjusted) interest rates on 10-year treasury notes will average about 3 per cent through 2016, implying that 6 percent is a reasonable forecast for the real interest rate on new car loans. 123 In turn, the interest rate on used car loans made through automobile financing comp anies has closely tracked the rate on new car loans made through commercial banks, but exceeded it by about 3 percent. 124 (We consider rates on loans that finance used car purchases, because some of the fuel savings resulting from improved fuel economy accrue to used car buyers.) Given the 6 percent estimate for new car loans, a reasonable forecast for used car loans is thus 9 percent.

Because the benefits of fuel economy accrue to both new and used car owners, a discount rate between 6 percent and 9 percent is thus appropriate for evaluating future benefits resulting from more stringent fuel economy standards. Assumi ng that new car buyers disc ount fuel savings at 6 percent for 5 years (the average duration of a new car loan) 125 and that used car buyers discount fuel savings at 9 percent for 5 years (t he average duration of a used car loan), 126 the single constant discount rate that yields equivalent present value fuel savi ngs is very close to 7 percent.

121 Some empirical evidence also demonstrates that used car purchasers are willing to pay higher prices for greater fuel economy; see, e.g. , James A. Kahn, “Gasoline Price Expectations and the Used Automobile Market: A Rational Expectations Asset Price Approach,” Quarterly Journal of Economics , Vol. 101 (May 1986), 323-339.

122 See Federal Reserve Bank, Statistical Release H.15, Selected Interest Rates (Weekly) (click on “Historical Data,” then “Treasury constant maturities,” then “10-year, monthly”), available at http://www.federalrese rve.gov/Releases/H15/data/Monthly/H15_TCM NOM_Y10.txt (last accessed February 13, 2008); and Federal Reserve Bank, Statistical Release G.19 , Consumer Credit, (click on “Historical Data,” then “Terms of Credit”) available at http://www.federalreserve.gov/releases /g19/hist/cc_hist_tc.html (last accessed February 13, 2008).

123 See The White House, Joint Press Release of the Counc il of Economic Advisors, the Department of the Treasury, and the Office of Management an d Budget, November 29, 2007, available at http://www.whitehouse.g ov/news/releases/2007/11 /20071129-4.html (last acce ssed February 13, 2008).

124 See supra [2 above here] and Federal Reserve Bank, Sta tistical Release G.20, Finance Companies, (click on “Historical Data,” then “Terms of Credit”) available at http://www.federalreserve.gov/re leases/g20/hist/fc_hist_tc.html (last accessed February 13, 2008).

125 Id

. 126 Id

.V-100

However, NHTSA also seeks comment on whether a discount rate of 3 percent would be more appropriate for this proposed rulemaking. OMB Circular A-4 also states that when regulation primarily and directly aff ects private consumption ( e.g. , through higher consumer prices for goods and services), instead of pr imarily affecting the allocation of capital, a lower discount rate may be appropriate. The alternat ive discount rate that is most appropriate in this case is the social rate of time preference, which refers to the rate at which society discounts future consumption to determine its value at the present time. The rate that savers are willing to accept to defer consumption into the future when there is no risk that borrowers will fail to pay them back offers one possible measure of the social ra te of time preference. As noted above, the real rate of return on long-term government debt, whic h has averaged around 3 percent over the last 30 years, provides a reasonable estimate of this value.

In the context of CAFE standards for motor ve hicles, the appropriate discount rate depends on one’s view of how the costs and benefits of more stringent st andards are distributed between vehicle manufacturers and consumers. Given that the discount rate plays a big role in the level of the standards under a “social optimi zation” context, NHTSA conducted an analysis of what the standards and associated costs a nd benefits would be if the futu re benefits are discounted at 3 percent.

o. Accounting for uncertainty in benefits and costs

In analyzing the uncertainty surrounding its esti mates of benefits and costs from alternative CAFE standards, NHTSA has considered alte rnative estimates of those assumptions and parameters likely to have the largest effect. These include the projected costs of fuel economyimproving technologies and their ex pected effectiveness in reduci ng vehicle fuel consumption, forecasts of future fuel prices, the magnitude of the rebound effect, the reduction in external economic costs resulting from lower U.S. oil impo rts, the value to the U.S. economy of reducing carbon dioxide emissions, and the discount rate applie d to future benefits and costs. The range for each of these variables employed in the uncertainty analysis is presented in the section of this notice discussing each variable.

The uncertainty analysis was conducted by assuming independent normal probability distributions for each of these variables, using th e low and high estimates for each variable as the values below which 5 percent and 95 percent of observed values are believed to fall. Each trial of the uncertainty analysis employed a set of values randomly drawn from each of these probability distributions, assuming that the value of each variable is independent of the others. Benefits and costs of each alternative standa rd were estimated using each combination of variables. A total of 1,000 trials were used to establish the likely pr obability distributions of estimated benefits and costs for each alternative standard.

C. How has NHTSA used the Volpe model to select the proposed standards?

1. Establishing a continuous function standardV-101

NHTSA’s analysis supporting determination of the proposed continuous f unction standard builds on the analysis that supported the determinati on of the standards in NHTSA’s 2006 light truck final rule. That process involved three steps. 127

In “phase one,” NHTSA added fuel saving techno logies to each manuf acturer’s fleet, model by model, for a model year until the net benef it from doing so reached its maximum value ( i.e. , until the incremental cost of improving its fuel economy further just equals the incremental value of fuel savings and other benefits from doing so). This was done for each of the seven largest manufacturers. Data points representing each ve hicle’s size and “optimized” fuel economy from the light truck fleets of those manufacturers we re then combined into a single data set.

In “phase two,” a preliminary continuous function was statisti cally fitted through these data points, subject to constraints at the uppe r and lower ends of the footprint range.

Once a preliminary continuous function was statistic ally fitted to the data for a model year, “phase three” was performed. In that phase, th e level of the function wa s adjusted to maximize net benefits, that is, the preliminary continuous function was raised or lowered until industrywide (limited to the seven largest ma nufacturers) benefits were maximized.

For NHTSA’s 2006 light truck rulemaking, the optimi zation procedure was appl ied in its entirety only for MY 2011. The levels of the functions for MYs 2008-2010 were se t at levels producing incremental costs approximately equivalent to those produced by the alternative Unreformed CAFE standards promulgated for those model years in the same rulemaking.

Analysis conducted by NHTSA to pr epare for the current proposed rulemaking revealed several opportunities to refine the proced ure described above before appl ying it to this action, which spans several model years. The resultant procedure is described below.

2. Calibration of initial continuous function standards

For the optimized standards, the first step in the current procedure involves all three phases described above. Separately, for each of the se ven largest manufacturers, the agency determined the level of additional technology that would maximize net benefits. The agency then combined the resultant fleets and used sta ndard statistical analysis proc edures to specify a continuous function ( i.e. , a function without abrupt changes) with asymptotes 128 set at the average fuel economy levels of the smallest and largest vehicles in this “optimized” fleet. 129

127 See 71 FR 17596-97 (Apr. 6, 2006) for a more complete discussion of this process.

128 Some functions are not bounded. For example, a line that is not flat will increase in one direction without limit and will, in the other direction, decreas e without limit. The continuous functi on applied by the agency is of a form with upper and lower boundaries. Even as vehicle footprint declines or increases, the function’s value (in mpg or grams/mile) will never exceed or fall below a specific value. These upper and lower limits are called asymptotes.

129 Consistent with EPCA, the passenger car and light truck fleets were analyzed separately. For passenger cars, the agency determined the asymptotes of the continuous function by calculatin g the average fuel economy of the smallest 8 percent and the largest 5 percent of the fleet. For light trucks, the agency considered the smallest 11 percent and the largest 10 percent of th e fleet. These cohorts were determined by identifying gaps in the distribution of vehicles according to footprint.V-102

In the 2006 Light Truck Rule, NHTSA created an attribute-based fuel economy standard based upon a continuous function using a logistic curv e. The 2006 rulemaking, and its antecedent advanced notice of proposed rulemaking, cont ains an extended discussion of alternative approaches, including a bin-based syst em and different potential curves. 130 As discussed below, the rulemaking explains NHTSA’s decision to pr omulgate a standard based on a logistic (“S shaped”) curve with constrained asymptotes (upper and lower limits).

Although we did not explicitly recognize it in the MY 2008-2011 light truck rulemaking, NHTSA now wishes to note that any continuous function with upper and lower asymptotes, as was promulgated in the last rulemaking and is proposed in this rulemaking, in fact has an absolute lower limit specified by the lower asympt ote, which guards against the risk of upsizing to a great degree. As vehicle footprint conti nues to increase, decreases in the corresponding target become progressively smaller, such that the target approaches but never reaches the value of the lower asymptote. Because the required leve l of CAFE is the harmon ic average of targets applicable to a manufacturer’s vehicle models, the value of the standard can approach but will never fall to the value of this lower asymptote, no matter how far the manufacturer’s product mix shifts toward larger vehicles. This will prevent loss of fuel savings due to manufacturer decisions to upsize their vehicles.

NHTSA has adopted the same approach for this proposed rulemaking. However, while the principles underlying the Agency’s decision in 2006 continue to be compelling, the application of the logistic curve approach to passenger cars has persuaded us that it may be appropriate to reconsider the choice of the logistic curve for se tting a continuous function, at le ast for passenger

cars.

NHTSA would prefer to let vehicl e data define the shape of th e curve used for the continuous functions. However, NHTSA must also weigh certain practical public policy considerations in establishing the continuous functi on, including for regions where ve hicles are not currently beingbuilt in significant numbers, but might become available in the future. In establishing footprintbased CAFE standards, the agency’s objective is not to reflect a clear engineering relationship between footprint and fuel economy. Other attributes would be more closely correlated with fuel economy. The agency’s objective is to make CA FE regulations more consistent with public policy goals, in particular (1) a rebalancing of re quirements such that full-line manufacturers are not disproportionately burdened and (2) the establishment of an incentive that discourages manufacturers from responding to CAFE standard s in ways that could compromise occupant protection and highway safety. While it is helpfu l that the attribute—in this case footprint—has an observed relationship to fuel economy, it is no t necessary that this relationship be isolated from accompanying relationships (e.g., between we ight and fuel economy) that can be better related to estimable physical processes. Similarl y, it is more important that the functional form for the attribute-based standard yield desirable outcomes than that it ha ve a clear foundation in estimable physical processes.

In general, public policy consid erations and available vehicle da ta combine to suggest that the fuel economy standard should be generally dow nward sloping (on a fuel economy basis) with respect to NHTSA’s chosen attribute, vehicle foot print. The arguments that favor an attribute-

130 FRV-103

based system (maintaining consum er choice, protecting safety, mo re equitable distribution of costs, reducing the cost of regulation) all argue for a downward sloping curve. Larger vehicles should, in principle, have higher drag, weigh more, and therefore have greater inertia than otherwise identical smaller vehicles. Hence, a ll other factors remaining equal, larger vehicles should have lower fuel economy than smaller vehi cles. Therefore, the selection of vehicle footprint as the reference attribute should pr oduce downward sloping curves. Also, the tendency of larger vehicles to have lower fuel econom y than smaller vehicles should provide some disincentive to shift to larger vehicles rather than a dding technology; although doing so would tend to reduce the required CAFE le vel, it would also tend to re duce the achieved CAFE level.

However, vehicle data, by itself, does not necessarily define what functional form that the curve ought to take. In the 2006 light truck rulema king, NHTSA considered a linear, quadratic, exponential, unconstrained logistic, and constrained logistic functi ons as possible alternatives. For light trucks, the various approaches produc ed broadly similar standards through the most commonly used vehicle sizes, but drastically differe nt standards at the high and low ends of the
range.

• Linear functions produced very high fu el economy standards for the smallest vehicles, and low standards for the largest vehicles.

• The quadratic function generated a minimu m at about 75 square feet, and then perversely turned upward for vehicles with larger footprints. The standard for very small vehicles was unreasonably high.

• The exponential and unconstrained logist ic functions produced unreasonably high standards for small vehicles, but fl attened out for larger vehicles.

• The constrained logistic function prov ided a broadly linear downward-sloping through the most commonly used vehicle sizes, along with ba sically flat standards for very large and very small vehicles.

On this basis, NHTSA believed that, while the data did not dictate a particular functional form, public policy considerations made the constrained logistic function particularly attractive. The considerations include:

• A relatively flat standard for larger vehi cles acts as a de facto ‘backstop’ for the standard in the event that future market conditions encourage manufacturers to build very large vehicles. Nothing prevents manu facturers from buildi ng larger vehicles. With a logistic curve, however, vehi cles upsizing beyond some limit face a flat standard that is increasingly difficult to meet.

• A constrained logistic curve doesn’t im pose unachievable fuel economy standards on vehicles that have unusually small footprin ts, thus continuing to keep manufacturing fuel-efficient small vehicles available as a compliance option.V-104

• Infeasible sections of the curve can have ma y be unimportant for the industry at large while having a particular adverse impact on manufacturers that specialize in very large or small vehicles, for example, two-seater sports car.

• The transition from the ‘flat’ portions of th e curve to the ‘slope’ portions of the curve is smooth and gradual, reducing the in centive for manufacturers to achieve compliance through marginal changes in vehicle size.

• The inflection points are set by the data and can potentially vary from year to year, rather than being chosen by NHTSA.

On the other hand, a constrained logistic curve sh ares with other functional forms a risk of an excessively steep or excessively flat slope. The slope of the compliance curve may be considered as ‘too steep’ for public policy purpos es when manufacturers can achieve appreciable reductions in compliance costs by marginally incr easing the size of a vehicle’s footprint—e.g., the cost of compliance from upsizing is lower th an other cost-effective compliance methods open to manufacturers.

A slope is ‘too flat’ for public policy purposes wh en it negates the advantages of an attributebased system: where the standard doesn’t meani ngfully vary with respect to changes in the underlying attribute, it cannot be sa id to be an attribute-based sy stem within the meaning of the

statute.

NHTSA chose footprint as the best attribute for an attribute-base d standard in part because we believed changing a vehicle’s footprint would involve significant costs for manufacturers, probably requiring a rede sign of the vehicle.

While “too steep” or “too flat” inevitably cannot be defined with precision, they need to be kept in mind.

For the proposed standards, the agency define d the continuous function using the following formula:



## ( )

( ) 

# 1

1 11

1 xc d

x c d 

# T

e a ba e −

− 

# = ⎛ ⎞ + − ⎜ ⎟
+ ⎝ ⎠

where

T = the fuel economy target (in mpg)

a = the maximum fuel economy target (in mpg) b = the minimum fuel economy target (in mpg)V-105

c = the footprint value (in square feet) at which the fuel economy target is midway between a and b 131
d

= the parameter (in square feet) defini ng the rate at which the value of targets decline from the largest to smallest values

e = 2.718 132

x

= footprint (in square feet, rounded to the nearest tenth) of the vehicle model

As for analysis of the light truck rule promul gated in 2006, NHTSA constrained this function by determining the maximum and minimum targets ( a and b ) and then holding those targets constant while using statistical technique s to fit the other two coefficients ( c and d ) in this equation.

In the current analysis for passenger cars, the upper and lower asymptotes are based on the smallest three percent and largest four percent, respectively, of the fleet. These reflect footprint values defining distinct cohorts outside the bulk of the fleet, and correspond to footprint values of less than 39.5 square feet ( i.e. , up to the approximate size of a Honda F it) and greater than 52.5 square feet ( i.e. , at least as great as the approximate size of a Toyota Avalon), respectively:

Figure V-6. Passenger Automob ile Footprint Distribution

131 That is, the midpoint.

132 For the purpose of the Reformed CAFE standard, we are carrying e out to only three decimal places. 0% 5% 10% 15% 20% 25%<3535.5-3636.5-3737.5-3838.5-3939.5-4040.5-4141.5-4242.5-4343.5-4444.5-4545.5-4646.5-4747.5-4848.5-4949.5-5050.5-5151.5-5252.5-5353.5-5454.5-5555.5-5656.5-5757.5-5858.5-5959.5-60

Footprint (sf)Percent of Vehicle Models smallest 3%

Honda Fit, [Ford B Car],
and smaller largest 4%

Toyota Avalaon and largerV-106

For light trucks, the upper asymptote ( i.e. , the highest mpg value of th e continuous function defining fuel economy targets) is based on th e smallest (in terms of footprint) eleven percent of the fleet, and the lower asymptote is based on the largest six pe rcent of the fleet. These cohorts correspond to footprint values of less than 44.5 square feet ( i.e. , up to the approximate size of a Honda CR-V) and greater than 72.5 square feet ( i.e. , comprised primarily of extended vans and long-bed pickup trucks), respectively:

Figure V-7. Nonpassenger Automobile Footprint Distribution

0% 5% 10% 15% 20% 25%42.5-4343.5-4444.5-4545.5-4646.5-4747.5-4848.5-4949.5-5050.5-5151.5-5252.5-5353.5-5454.5-5555.5-5656.5-5757.5-5858.5-5959.5-6060.5-6161.5-6262.5-6363.5-6464.5-6565.5-6666.5-6767.5-6868.5-6969.5-7070.5-7171.5-7272.5-7373.5-7474.5-7575.5-76

Footprint (sf)Percent of Vehicle Models smallest 11%

Honda CRV GM Colorado/Canyon
and smaller largest 6%

longbed pickups

NHTSA invites comment on the identification of vehicle cohorts for pur poses of establishing upper and lower limits (asymptotes) bounding the at tribute-based standard. After updating its baseline market forecast in consideration of ne w product plan information from manufacturers, the agency plans to reevaluate these cohorts fo r both passenger cars and light trucks before promulgating a final rule, and notes that cha nges in approach could lead to changes in stringency.

Given the above asymptotes, fitting the above f unctional form to the “ optimized” passenger car fleet resulted in the followi ng initial continuous functions:V-107

Figure V-8. Initial Continuous Function s (Passenger Cars), Before Optimization

20 25 30 35 40 45

35 40 45 50 55 Footprint (sf)Target (mpg) 2011
2012
2013
2014
2015

For each model year, NHTSA then raised or lo wered the resultant continuous function until net benefits were maximized for the seven largest manufacturers (in total). Without subsequent recalibrations discussed below, this produced the following continuous functions for passenger

cars:V-108

Figure V-9. Initial Continuous Function s (Passenger Cars), After Optimization

20 25 30 35 40 45

35 40 45 50 55 Footprint (sf)Target (mpg) 2011 2012 2013 2014 2015

The agency followed the same procedures for set ting light truck standard s and doing so resulted in the following continuous functions:V-109

Figure V-10 Initial Continuous Functions (Light Trucks), After Optimization

20 25 30 35

35 40 45 50 55 60 65 70 Footprint (sf)Target (mpg) 2011
2012
2013
2014
2015

In fitting the continuous function, NHTSA c onsidered a range of statistical estimation techniques. In the 2006 light tr uck rulemaking, NHTSA estimated th e parameters of the logistic function using fuel consumption (measured in ga llons per mile) for each vehicle produced in a particular model year, weighted by sales

For this rulemaking, we observed that estimate d fuel consumption functions for passenger cars were significantly affected by several outliers— a small number of popular vehicles that had significantly higher fuel economy than the fleet as a whole and, even more so, than vehicles of similar footprint. For passenger cars, the func tion, as estimated by weighted ordinary least squares, was exceptionally steep within the range considered. This observation, in turn, led NHTSA to consider alternative approaches to statistically fitting the continuous function.

Among the options considered by NHTSA were th e following: dropping the outlying vehicles from the estimation process, weighted and unweight ed ordinary least squares, and weighted and unweighted mean absolute deviation (MAD). M AD is a statistical procedure that has been demonstrated to produce more efficient paramete r estimates in the presence of significant outliers. 133 As examples, the following two charts show the MY2015 passe nger car and light

133 In the case of a dataset not drawn from a sample with a Ga ussian, or normal, distribution, there is often a need to employ robust estimation methods rather than rely on l east-squares approach to curve fitting. The least-squares approach has, as an underlying assumption, that the data are drawn from a normal distribution, and hence fits a curve using a sum-of-squares 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 theirV-110

truck fleets after the applicati on of technologies to each manuf acturers’ fleet. These charts reveal numerous outliers for the passenger car flee t and, to a lesser extent, the light truck fleet:

Figure V-11. MY2015 Passenger Car Fl eet after Technology Application

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm)

Figure V-12. MY2015 Light Truck Fl eet after Technology Application

distance from the fitted curve, and, hence, distort the resultin g 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_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 , 3rd edition, 1992, MIT Press, Cambridge, MA).V-111

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

35 40 45 50 55 60 65 70 Footprint (sf)Fuel Consumption (gpm)

NHTSA requests comment on the best method for st atistically fitt ing the continuous function.

There are strong theoretical argumen ts for using an unweighted (rath er than weighted) analysis. Although the purpose of the attribute-based stan dard is to discourage downsizing and more equitably distribute compliance bur dens among manufacturers, we e xpect the associated curve to be most successful if it is based on the observe d physical relationship be tween vehicle size (i.e., footprint) and fuel economy. The curve reflects this relationship by de picting the variety of vehicle designs that have been produced in the real world for any given footprint. It is a measure of what is possible, not wh at consumers currently choose to buy.

Consider a scenario in which there are only 2 ve hicle designs with exactly the same footprint, both from the same manufacturer. The manufact urer sells 1,000 units of design A and 100 units of design B. Either design could be produced in any number (no ve hicle-specific resource constraint). The difference in sales is due to a variety of ma rketing considerations including vehicle styling, price, performance, fuel economy, etc., that have made one vehicle model more popular than the other. To desc ribe the potential fuel economy cap ability at any given footprint, we want to recognize that both vehicles represent equally valid and viable designs. If we weight them according to sales, we will set standards that are biased towards existing fleet profiles rather than reflecting design potential.

However, the process by which we select the stringency (as distinct from the form ) of the standard must consider sales volumes because th e standards are based on sales-weighted average performance. Therefore, even if we use unweigh ted analysis develop the form of the standard,V-112

we would continue to evaluate the standard’s stringency (and, th erefore, its costs and benefits) based on sales-weighted averag e calculations done on a manufact urer-by-manufacturer basis.

There is already precedent for using unweighted data to produce curves that are descriptive of engineering relationships. In NHTSA’s Prelim inary Regulatory Impact Analysis for FMVSS 216 roof crush standards, a series of force-versus -deflection curves were produced for individual vehicle models and then averaged together. In that case, the agency was seeking observed relationships that reflect engineering possibilities, rather than a profile of the existing sales fleet.

In terms of relative emphasis on different vehi cle models, the distinction between unweighted and weighted analysis is profound in the light vehicle market, in part because of the way “models” are defined for purposes of CAFE. The highest-selling passenger car model represents 356,000 units, and the lowest-selling model represents only 5 units. As a group, the five lowestselling models represent only 305 units. Thus, we ighted analysis places more than 1,000 times the emphasis on the highest-selling model than on th e five lowest-selling models, and more than 70,000 times the emphasis than on the single lowe st-selling model. The following histograms shows the broader distributions of models and sale s with respect to model-level sales (first for passenger cars, then for light trucks):

Figure XX. Passenger Car Model Sales Volumes

0% 5% 10% 15% 20% 25% 30% 35%<1,0001,001 - 2,0002,001 - 5,0005,001 - 10,00010,001 - 20,00020,001 - 50,00050,001 - 100,000100,001 - 200,000200,001 - 500,000

Annual Sales per ModelShare of Fleet (Models, Sales) Models

Sales

Figure V-13. Light Truck Model Sales VolumesV-113

0% 5% 10% 15% 20% 25% 30% 35%<1,0001,001 - 2,0002,001 - 5,0005,001 - 10,00010,001 - 20,00020,001 - 50,00050,001 - 100,000100,001 - 200,000200,001 - 500,000

Annual Sales per ModelShare of Fleet (Models, Sales) Models

Sales

For purposes of setting the stringency of the corp orate average fuel economy standard, this is vital because enforcement is based on the sales- weighted average. However, for purposes of developing a curve intended to represent fuel ec onomy levels achieved at a given footprint, weighted analysis effect ively ignores many models.

On the other hand, unweighted estimation is depending on the definition of a “model”. Manufacturers will sometimes offer substantially similar vehicles with different badges (i.e., Ford Taurus/Mercury Sable) as two different models. The distinction between differing ‘options packages’ on a single model and two distinct mode ls is inevitably a bit blurry. When estimating fuel economy standards using a sale s-weighted regression, this dist inction is not material, since the estimation process will produce substantially th e same results independently of the number of distribution of those sales into la rger or smaller numbers of models . In unweighted estimation, however, dividing a particular vehicl e family into a larger number of distinct models give that family some extra influence in the analysis. No netheless, considering that such parsing into similar “models” produces far less weighting dispar ity than does sales weighting (as illustrated by the 70,000-to-1 ratio mentioned above), NHTSA has tentatively concluded that unweighted estimation remains preferable to sales-weighted estimation.

The following charts shows, for MY2015 passenger ca rs and light trucks, how the use of salesweighted least-squares estimation compares to the proposed approach, which uses unweighted mean absolute deviation. For passenger cars, the curve resulting from proposed approach is somewhat shallower than the curve resulting from sales-weighted least squares estimation. For light trucks, the curve resulting from pr oposed approach is somewhat steeper:V-114

Figure V-14. Weighted Least Squares Regr ession Compared to Proposed Curve (Using Unweighted Mean Absolute Deviation) fo r MY2015 Passenger Cars with Technologies

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm) MY2015 Fleet Proposed Curve (Before Optimization) Constrained Logistic Usi ng Weighted Least Squares

Figure V-15. Weighted Least Squares Regr ession Compared to Proposed Curve (Using Unweighted Mean Absolute Deviation) for MY2015 Light Trucks with TechnologiesV-115

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

35 40 45 50 55 60 65 70 Footprint (sf)Fuel Consumption (gpm) MY2105 Fleet Proposed Curve (Before Optimization) Constrained Logistic Based on Weighted Least Squares

NHTSA invites comment on the relative merits of unweighted and weighted estimation, as well as on the other curve fitting options (e.g., the use of mean absolute deviation) raised here. The agency plans to reevaluate curve fitting appr oaches for both passenger cars and light trucks before promulgating a final rule, and notes that changes in approach could lead to changes in stringency and impacts on different manufacturers.]

3. Adjustments to address policy considerations

NHTSA believes that the resultant curve character istics discussed above are empirically correct in that they correspond to the footprint and fu el economy values of the fleet obtained by adding fuel saving technologies to each manufacturer’s fl eet until the net benefit from doing so reached its maximum value.

However, there are three issues (described abov e) which may tend to reduce the effectiveness of fuel economy regulation over ti me. These concerns are:

• curve crossings;

• excessive steepness of the passenger car curve;

• risk of upsizing.

In this rule, NHTSA proposes a solution to the curve crossing issu e, requests comment on various methods of reducing the steepness of the passenger car, and examin es the potential for upsizing generally under the provis ions of this proposed rule.

a. Curve crossingsV-116

For both passenger cars and light trucks, NHT SA observed some curve crossings from one model year to the next ( i.e ., for the same footprint, some targets fell below the levels attained in the previous model year), as revealed in the above charts . The upper limit of the MY2012 passenger car curve falls slightly (about 0.1 mpg) below the MY2011 value. For light trucks, the lower asymptote in MY2012 is 0.9 mpg below th e lower asymptote in MY2011. This was not observed during the last round of light truck rulemaking because reformed CAFE was fully implemented only in MY 2011. During the tr ansition period (MYs 2008-2010), the standards were set at levels equivalent in cost to unreformed CAFE. However, for this rulemaking, because the projected fleet composition change s between model years and the fuel economy target function is optimized in every model year , the initial continuous functions do not change monotonically ( i.e. , in only one direction—increasing) from ye ar to year at every footprint value. Given the availability of lead time and the importance of improving fuel economy, NHTSA has decided that, in the setting of the standards, we should ensure that the fuel economy targets do not fall from one year to the next at any footprint value.

To address the year-to-year fluctuations in the functions, which may lead to these curve crossings, NHTSA recalibrated each continuous function to prevent it from crossing the continuous function from any previous model year. In doing so, the agency attempted to avoid continuous functions that would artificially enco urage the product mix to approximate that of earlier years. Instead, the agency recalibrate d by gradually shifting th e initial continuous functions for each model year toward the initial continuous function determined above for the product mix for MY 2015. For both passenger cars a nd light trucks, the agency adjusted each of the four coefficients in the formula determini ng the continuous function su ch that regular steps were taken year by year between the values determined above for MY 2011 and those for MY 2015. For example, the inflection point (the coef ficient determining the footprint at which the target falls halfway between its minimum and ma ximum values) defining the light truck target function was increased by 0.034 square feet annua lly from 51.9 square feet in MY 2011 to 52.1 square feet in MY 2015.

NHTSA also recalibrated the continuous function for each model year by adding, as needed, anti-backsliding constraints that prevent the func tion from either (a) yielding an industry wide average level of CAFE lower than that for the preceding model year, (b) for a given footprint, having targets that fall below the level of previo us year, and (c) having an asymptote lower than that of the preceding model year. The “decisi on tree” for determining for each model year the need for each of these constraints is summarized below in Figure 12.

Figure V-16. Anti-backsliding Decision TreeV-117

PerformManufacturerLevel Optimization

Integrate Optimized Fleets

Fit Initial Target Function for Each

Model Year

Shift Target Function to Maximize Net Benefits in Current Model Year

Compared to Previous Model Year, Has Either Asymptote Declined?

Use Initial Target Function from Previous Model Year Yes

Proceed to Next

Model Year No

Does the Curve Cross Any Other Curves?

No Yes

The industry-wide average CAFE is prevented fr om decreasing between m odel years in order to prevent standards from falling below the level that was determined to be achievable for the model year before. To allow the industry-wid e CAFE level to fall between successive model years would be to promulgate a standard that , notwithstanding maximizing net benefits, falls below what the agency has determined to be feasible in previous years. In a model year in which simple maximization of net benefits would ha ve caused this to occur, NHTSA shifted the resultant curve upward (without changing the curve’s shape) in order to produce an industrywide CAFE equal to that of the preceding model year.V-118

b. Steep curves for passenger cars

NHTSA has developed a set of attribute-based curves for passenger cars for this proposal consistent with the methodology used in the 200 8-2011 light duty truck rule. However, unlike the relatively gradually sloped cu rve relating fuel economy to foot print for trucks, our analysis for cars when utilizing a constr ained logistic curve produces a comparatively steep “S”-shaped curve for passenger cars. This occurs primarily because – unlike trucks – current passenger car sales include vehicles with a wi de range of fuel economy spanni ng a relatively narrow footprint range. Consequently, there is a relatively steep curve applied to the middle range of footprint values with a more rapid change of slope in th e tails to flatten the cu rve and thus satisfy the constrained logistic functional form.

In this Rule, NHTSA is proposing a relatively “s teep” curve. The agency has considered and experimented with several methods of reduci ng the steepness of the pa ssenger car curve. However, each of these approaches has created ch allenges that may potentially be worse than the problem they are trying to cure.

However, any attempt to ‘fix’ the steepness of the passenger car curve a ppears to come at a price: First, flattening the curve by any partic ular method will move th e curve away from the actual vehicle data. Second, flatter curves ar e generally place greater compliance burdens on full-line manufacturers than comparatively st ringent (in terms of average require CAFE) standards. This tends to increase the overall co sts required to achieve a given amount of fuel savings and societal benefits, a nd it increases the risk that NHTSA would need to return to a “least capable manufacturer” approach in order to ensure economic practicability. Doing so would likely reduce stringency, and reduce fuel sa vings. In deciding on a particular approach, NHTSA must balance the certainty of high costs and lost fu el savings through a less “efficient” standard against the risk that the steepness of the curve might stimulate manufacturers to evade the standard over time by redesi gning their vehicles over time.

In proposing steep curve for this rule, NHTSA has te ntatively decided that th e cures that we have identified come at too high a price is lost stri ngency or undesirable side effects. However, NHTSA requests comment on these and other potent ial solutions to reduce the steepness of the proposed car curves.

Some of the approaches consider ed or tested by NHTSA include:

Linear standards . When the fuel consumption of vehicl es with added tec hnologies is plotted against footprint, we note a roughly linear relationship over th e existing range of footprint values. Hence, a simple alternative to the cu rrent constrained logistic function would be to estimate a linear form of the curve with the sales data. However, NHTSA is concerned that such an approach may result in very low fuel econom y standards for the larg est footprint vehicles, very high fuel economy standards for the smallest vehicles, and loss of the inherent backstop properties of the constrai ned logistic function.

In addition, the slope of a line estimated through a ‘cloud’ of data may be very sensitive to the exact characteristics of vehicles with the largest and smallest footprints. It may turn out thatV-119

small changes in vehicle characteristics in the ta ils could shift the slope of a linear estimate. Further, it may be impossible to materially adjust the slope of a linear standard in future years without accepting curve cr ossing. The following two charts co mpare linear regression results for MY2015 to the curves proposed today by NHTSA. Th e result for passenger cars illustrates the concern regarding behavior at larg e and small footprints. Over th e range of footprints in which light trucks are expected to be offered in MY2015, the result for light trucks shows less difference from the proposed curve.

Figure V-17. Linear Fit to MY2015 Passenger Cars with Technologies

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm) MY2015 Fleet Proposed Curve (Before Optimization)

Linear Fit

Figure V-18. Linear Fit to MY2015 Light Trucks with TechnologiesV-120

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

35 40 45 50 55 60 65 70 Footprint (sf)Fuel Consumption (gpm) MY2105 Fleet Proposed Curve (Before Optimization)

Linear Fit

Constrained linear standards . Another possible appr oach would be to retain the flattened tails proposed today but reduce the steep ness of the middle portion by allowing it to directly reflect a linear relationship. This approach could be like ned to a simplification or linearization of the constrained logistic function. The same minima and maxima would be used to bound the vertical extent of the linear form. The following two charts suggest that, at least for the MY2015 passenger car and light truck fleet s considered today, a constrai ned linear standard would, compared to the standard proposed today, likel y result in a similar distribution of compliance burdens among manufacturers (bec ause the stringency at each f ootprint would be similar):

Figure V-19. Constrained Linear Fit to MY2015 Passenger Cars with TechnologiesV-121

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm) MY2015 Fleet Proposed Curve (Before Optimization)

Constrained Linear

Figure V-20. Constrained Linear Fit to MY2015 Light Trucks with Technologies

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

35 40 45 50 55 60 65 70 Footprint (sf)Fuel Consumption (gpm) MY2105 Fleet Proposed Curve (Before Optimization)

Constrained LinearV-122

However, the agency remains concerned that the slope could exhibit greater year-to-year variation than the proposed logis tic form (although further analysis would be require d in order to address this concern). Also, as discussed in the preamble to the 2006 Federal Register notice regarding light truck CAFE standards, the agen cy remains concerned that the upper and lower “kinks” in the function could offe r unexpected incentives for manuf acturers to redesign vehicles with footprints close to the kink-point.

Dual Attribute Approaches . A third possible solution would be to use additional attributebased information to spread out the distribution of passenger cars across the x-axis. In effect, this approach uses a second attribute to normalize the footprint-fuel economy relationship. This second attribute might be horsepower , weight, or horsepower-to-weight.

In analyzing the expected passenger car market , NHTSA observes that the ratio of engine horsepower to vehicle weight ge nerally increases with increasi ng footprint. Higher power-toweight ratios tend to imply lower fuel economy, as the engine is typically larger and operating less efficiently under driving conditi ons applicable to certification. Thus, the fuel consumption versus footprint curves for passenger cars reflect this relationship. For trucks, there does not appear to be a relationship between footprint and the power-to-weight ratio. For passenger cars, then, adjusting fuel consumption values to nor malize for differences in power-to-weight ratio may produce a flatter curve providing less of an upsizing incentive for middle footprint values.

NHTSA has experimented with normalizing footpr int by horsepower-to-weight ratio. The result was a nearly flat standard with respect to footpr int across the most popular size ranges. This did not appear to deliver the benefits of an attribut e-based system. In additi on, it involves significant downward adjustments to the fuel economy of hybrid electric vehicles (such as the Toyota Prius), for which the engine is not the sole source of motive power. Also, it involves significant upward adjustments to the fuel ec onomy of vehicles with high pow er-to-weight ratios (such as the Chevrolet Corvette). Some of these upw ard and downward adjustments are large enough to suggest radical changes in the na ture of the original vehicles. Furthermore, insofar as such normalization implies that NHTSA should adopt a two-attributed standard ( e.g. , in which the target depends on footprint and power-to-we ight ratio), it may be challenging and time consuming to come up with a sufficiently precise vehicle-by-vehicle defi nition of horsepower or horsepower-to-weight to be us ed for regulatory purposes.

Figure V-21. Constrained Logistic Curve afte r Normalization for Differences in Power-toWeight Ratio (MY2015 Passenger Cars with Technologies)V-123

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm) MY2015 Fleet Proposed Curve (Before Optimization) Constrained Logistic after Normalization for Power/Weight

Shape Based on Combined Fleet . A fourth possible solution would be to combine the passenger car and light truck fleet to determine th e shape of the constrained logistic curve, and then determine the stringency (i.e., height) of that curve separately for each fleet. On one hand, this approach would base the curve’s shape on the widest available range of information. On the other, the resultant initial shape for each fleet would be based on vehicles from the other fleet. For example, the initial shape applied to passenge r cars would be based, in part, on large SUVs and pickup trucks, and the initial shape applied to light trucks would be based, in part, on subcompact cars. Stringency would still be dete rmined separately for passenger cars and light trucks, NHTSA invites comments on the consistency of this approach with the requirement in EPCA to establish separate standards for passenger cars and light trucks.

NHTSA performed a preliminary analysis of this approach. Considering the very wide range of fuel consumption levels in the combined fleet , NHTSA developed the asymptotes based on the average fuel consumption of all passenger cars a nd light trucks, respectivel y, rather than on the smallest passenger cars and the largest light tr ucks. The resultant MY2015 curve, shown below, is similar in curvature to the proposed curve for passenger cars and not ably steeper than the proposed curve for light trucks.

Figure V-22. Constrained Logistic Curve Ba sed on Combined Fleet (as Compared to MY2015 Passenger Cars with Technologies)V-124

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm) MY2015 Fleet Proposed Curve (Before Optimization) Constrained Logistic Based on Combined Fleet

Figure V-23. Constrained Logistic Curve Ba sed on Combined Fleet (as Compared to MY2015 Light Trucks with Technologies)

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

35 40 45 50 55 60 65 70 Footprint (sf)Fuel Consumption (gpm) MY2105 Fleet Proposed Curve (Before Optimization) Constrained Logistic Based on Combined FleetV-125

Ignoring Outliers . A fifth possible solution would be to ignore outliers. Lacking an objective means of classifying specific vehicle models as outliers that should be excluded from the analysis, NHTSA explored the possibility of excl uding all hybrid electric vehicles (HEVs). The Japanese government also excluded HEVs for purposes of developing Japan’s light vehicle efficiency standards. However, doing so yield initial curves of shape similar to those proposed, but displaced slightly in the direct ion of lower fuel consumption. The similarity of the shapes of these curves suggests that op timization against the full fl eet (with HEVs) would produce standards of stringency sim ilar to those proposed today.

Figure V-24. Constrained Logistic Curv e Based on MY2015 Passenger Car Fleet with Technologies and Excluding HEVs

0 0.01 0.02 0.03 0.04 0.05

35 40 45 50 55 60 Footprint (sf)Fuel Consumption (gpm) MY2015 Fleet Proposed Curve (Before Optimization) Constrained Logistic Based on no-HEV Fleet

Figure V-25. Constrained Logistic Curv e Based on MY2015 Passenger Car Fleet with Technologies and Excluding HEVsV-126

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

35 40 45 50 55 60 65 70 Footprint (sf)Fuel Consumption (gpm) MY2105 Fleet Proposed Curve (Before Optimization) Constrained Logistic Based on no-HEV Fleet

NHTSA invites comments on the importance of addressing the re lative steepness of the proposed curves for passenger cars, and on the feasibility of , technical basis for, an d implications of any options for doing so. The agency plans to reev aluate standards for both passenger cars and light trucks before promulgating a final rule, and notes that changes in approach—including measures to address the steepness of the pa ssenger car curves—could lead to changes in stringency as well as different impacts on different manufacturers.

c. Risk of upsizing The steepness of the proposed curve for passe nger cars presents a localized risk that manufacturers will respond in ways that compromi se expected fuel savings. That is, although the constrained logistic curve has a steep region, that region does not cover a wide range of footprints. However, any attri bute-based system involves the br oader risk that manufacturers will shift toward vehicles with the lowest fu el economy targets. As mentioned above, the constrained logistic curve propos ed by NHTSA provides an absolute floor. That is, even manufacturers discontinue all bu t the very largest known passenger cars and light trucks, they would still be required to meet CAFE standards no lower than the lower asymptote (on an mpg basis) of the constrained logist ic curve. Also, for domestic passenger cars, EISA establishes a floor or “backstop” equal to 92% of the averag e required CAFE level for passenger cars. This backstop is discussed below in Section VI.

It is difficult to assess the risk that manuf acturers may shift the mix of vehicles enough to approach the EISA floor for dome stic passenger cars, or to appr oach the lower asymptotes for light trucks or imported passenge r cars. However, considering the footprint distribution of vehicles (as indicated by the various histograms and scatter pl ots shown above in this section)V-127

expected to be covered by the proposed rule, NHTSA anticipates that manufacturers would not be able to approach these reduc tions in stringency without dram atically altering product mix. The agency doubts that manufact urers could do so unless consumer preferences also shift dramatically.

NHTSA also notes that under attr ibute-based CAFE standards such as the agency is proposing today, shifts in consumer pref erences could cause manufacturers’ required CAFE levels and, therefore, achieved fuel savings (and perhaps costs) to increase . For example, if changes in fuel prices combine with demographic and/or other factors to cause market preferences to shift significantly toward vehicles with smaller footprints, manufacturers shifting (relative to current estimates) in that direction will face higher required CAFE levels than the agency has estimated.

C. Penetration of Tech nologies by Alternative

Tables V-11a through V-11g show the penetration of technologies by year for passenger cars and Tables V-12a through V-12g show the penetration of technologies by year for light trucks for the alternatives. These tables are for the whole fleet combined, not by spec ific manufacturers. They allow the reader to see the progression of technologies applied as the standa rds get stricter. The baseline shown here is the adjusted baseline for MY 2011.V-128

Table V-11a Penetration Rate of New Technologies to Passenger Cars – 25% Below Optimized

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 78% 81% 100% 100% 100% 100% 2. EFR 14% 20% 26% 38% 58% 71% 3. VVTI 80% 80% 83% 88% 89% 91% 4. VVTC 8% 9% 10% 13% 19% 28% 5. VVTD 27% 26% 29% 31% 32% 34% 6. DISP 4% 4% 7% 10% 15% 16% 7. VVLTC 1% 1% 2% 5% 6% 9% 8. VVLTD 12% 15% 18% 22% 22% 24% 9. DISPO 2% 2% 2% 2% 3% 3% 10. VVTO 6% 6% 6% 5% 5% 5% 11. DOHC 6% 6% 5% 6% 7% 7% 12. VVLTO 0% 0% 0% 0% 0% 0% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 13% 15% 18% 19% 21% 15. DSLS 0% 0% 0% 0% 0% 0% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 8% 8% 9% 9% 9% 11% 18. DSLT 1% 3% 2% 2% 3% 5% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 22% 23% 21% 20% 18% 22. ASL 7% 12% 21% 25% 22% 26% 23. TORQ 4% 5% 6% 10% 16% 14% 24. 6SP 26% 26% 26% 27% 22% 20% 25. AMT 9% 9% 15% 19% 25% 30% 26. CVT 11% 11% 10% 10% 10% 10% 27. 6MAN 7% 8% 8% 8% 9% 9% 28. IACC 20% 27% 27% 38% 39% 44% 29. EPS 14% 16% 20% 27% 28% 34% 30. 42V 0% 2% 1% 1% 2% 8% 31. ROLL 5% 18% 29% 51% 62% 63% 32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 0% 1% 1% 1% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 12% 20% 31% 42% 47% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 2% 3% 3% 8% 10% 40. IHYB 1% 1% 1% 1% 1% 2% 41. 2HYB 0% 0% 0% 0% 1% 1% 42. PHYB 3% 3% 3% 3% 3% 3% 43. PLUG 0% 0% 0% 0% 0% 0%V-129

Table V-11b Penetration Rate of New Technologies to Passenger Cars – Optimized 7%

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 78% 93% 100% 100% 100% 100% 2. EFR 14% 27% 49% 69% 82% 82% 3. VVTI 80% 80% 86% 91% 92% 91% 4. VVTC 8% 9% 20% 28% 34% 34% 5. VVTD 27% 27% 30% 34% 35% 36% 6. DISP 4% 6% 11% 15% 20% 20% 7. VVLTC 1% 1% 1% 4% 7% 15% 8. VVLTD 12% 18% 27% 33% 34% 36% 9. DISPO 2% 3% 4% 3% 3% 3% 10. VVTO 6% 6% 6% 5% 5% 5% 11. DOHC 6% 6% 5% 6% 7% 7% 12. VVLTO 0% 1% 2% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 15% 21% 24% 25% 28% 15. DSLS 0% 0% 0% 0% 0% 0% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 8% 10% 13% 14% 15% 19% 18. DSLT 1% 3% 3% 3% 3% 4% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 21% 22% 19% 16% 15% 22. ASL 7% 17% 29% 29% 21% 19% 23. TORQ 4% 6% 9% 16% 15% 8% 24. 6SP 26% 23% 21% 22% 17% 9% 25. AMT 9% 13% 22% 27% 34% 43% 26. CVT 11% 11% 12% 11% 12% 12% 27. 6MAN 7% 8% 8% 8% 9% 9% 28. IACC 20% 36% 41% 48% 51% 64% 29. EPS 14% 21% 33% 41% 44% 51% 30. 42V 0% 7% 14% 15% 19% 26% 31. ROLL 5% 23% 42% 57% 62% 74% 32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 0% 1% 1% 1% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 18% 31% 40% 52% 56% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 3% 4% 5% 8% 11% 40. IHYB 1% 1% 1% 1% 1% 2% 41. 2HYB 0% 1% 1% 1% 1% 1% 42. PHYB 3% 4% 3% 3% 4% 4% 43. PLUG 0% 0% 0% 0% 0% 0%V-130

Table V-11c Penetration Rate of New Technologies to Passenger Cars – 25% Above Optimized

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015
1. LUB 78% 93% 100% 100% 100% 100%
2. EFR 14% 33% 55% 73% 85% 87%
3. VVTI 80% 79% 84% 88% 88% 87% 4. VVTC 8% 14% 27% 37% 40% 41%
5. VVTD 27% 27% 29% 33% 36% 36% 6. DISP 4% 9% 15% 19% 23% 23% 7. VVLTC 1% 1% 1% 4% 7% 13%
8. VVLTD 12% 19% 29% 36% 38% 38% 9. DISPO 2% 3% 4% 3% 3% 3% 10. VVTO 6% 6% 6% 5% 4% 4% 11. DOHC 6% 6% 6% 7% 8% 8% 12. VVLTO 0% 1% 2% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 14% 25% 29% 32% 34% 15. DSLS 0% 0% 0% 0% 0% 0%
16. LBDI 0% 0% 0% 0% 0% 0%
17. TURB 8% 12% 23% 27% 30% 31% 18. DSLT 1% 5% 6% 6% 8% 9% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 19% 16% 14% 13% 13% 22. ASL 7% 22% 31% 33% 24% 12% 23. TORQ 4% 10% 14% 23% 21% 9% 24. 6SP 26% 26% 25% 25% 18% 5%
25. AMT 9% 14% 27% 29% 38% 50% 26. CVT 11% 13% 13% 12% 12% 12%
27. 6MAN 7% 8% 9% 9% 9% 10% 28. IACC 20% 42% 56% 59% 60% 74% 29. EPS 14% 24% 44% 51% 56% 59%
30. 42V 0% 9% 22% 26% 32% 39% 31. ROLL 5% 23% 48% 63% 86% 88%
32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 0% 1% 2% 2% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 18% 34% 45% 57% 68% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 1% 3% 3% 6% 9%
40. IHYB 1% 1% 1% 1% 2% 2%
41. 2HYB 0% 2% 4% 3% 4% 4%
42. PHYB 3% 5% 5% 6% 7% 7% 43. PLUG 0% 0% 0% 0% 0% 0%V-131

Table V-11d Penetration Rate of New Technologies to Passenger Cars – 50% Above Optimized

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015
1. LUB 78% 93% 100% 100% 100% 100%
2. EFR 14% 33% 63% 83% 91% 91%
3. VVTI 80% 79% 81% 84% 83% 82% 4. VVTC 8% 17% 29% 37% 42% 41%
5. VVTD 27% 26% 26% 30% 34% 34% 6. DISP 4% 9% 17% 21% 25% 25% 7. VVLTC 1% 1% 2% 5% 8% 21%
8. VVLTD 12% 19% 29% 35% 37% 37% 9. DISPO 2% 3% 4% 3% 3% 3% 10. VVTO 6% 6% 6% 5% 4% 4% 11. DOHC 6% 6% 5% 5% 6% 7% 12. VVLTO 0% 1% 2% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 15% 26% 31% 35% 44% 15. DSLS 0% 0% 0% 0% 0% 0%
16. LBDI 0% 0% 0% 0% 0% 0%
17. TURB 8% 12% 24% 27% 31% 38% 18. DSLT 1% 5% 9% 10% 13% 14% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 19% 15% 9% 5% 5% 22. ASL 7% 20% 29% 30% 18% 16% 23. TORQ 4% 8% 12% 21% 18% 5% 24. 6SP 26% 24% 23% 28% 19% 7%
25. AMT 9% 16% 28% 32% 44% 56% 26. CVT 11% 13% 12% 11% 10% 11%
27. 6MAN 7% 8% 9% 9% 10% 10% 28. IACC 20% 45% 64% 71% 72% 74% 29. EPS 14% 26% 50% 62% 66% 67%
30. 42V 0% 10% 26% 30% 38% 54% 31. ROLL 5% 23% 56% 79% 85% 88%
32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 0% 1% 2% 2% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 22% 43% 53% 64% 67% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 1% 2% 2% 5% 7%
40. IHYB 1% 1% 2% 2% 2% 3%
41. 2HYB 0% 2% 4% 4% 5% 5%
42. PHYB 3% 6% 8% 8% 12% 13% 43. PLUG 0% 0% 0% 0% 0% 0%V-132

Table V-11e Penetration Rate of New Technologies to Passenger Cars – Optimized (3%)
Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015
1. LUB 78% 93% 100% 100% 100% 100%
2. EFR 14% 37% 62% 81% 89% 89%
3. VVTI 80% 77% 81% 84% 83% 82% 4. VVTC 8% 19% 31% 38% 42% 42%
5. VVTD 27% 26% 27% 30% 34% 34% 6. DISP 4% 10% 18% 21% 25% 25% 7. VVLTC 1% 1% 2% 5% 12% 25%
8. VVLTD 12% 18% 31% 36% 38% 38% 9. DISPO 2% 3% 4% 3% 3% 3% 10. VVTO 6% 6% 6% 5% 4% 4% 11. DOHC 6% 6% 5% 5% 6% 7% 12. VVLTO 0% 1% 2% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 20% 33% 37% 41% 52% 15. DSLS 0% 0% 0% 0% 0% 0%
16. LBDI 0% 0% 0% 0% 0% 0%
17. TURB 8% 16% 29% 34% 37% 49% 18. DSLT 1% 7% 9% 11% 13% 14% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 12% 5% 3% 1% 1% 22. ASL 7% 33% 30% 32% 17% 4% 23. TORQ 4% 14% 17% 23% 14% 2% 24. 6SP 26% 30% 27% 28% 15% 2%
25. AMT 9% 17% 36% 39% 54% 67% 26. CVT 11% 14% 14% 12% 12% 12%
27. 6MAN 7% 8% 9% 9% 10% 10% 28. IACC 20% 51% 62% 68% 75% 77% 29. EPS 14% 30% 54% 62% 66% 67%
30. 42V 0% 14% 32% 35% 45% 60% 31. ROLL 5% 33% 56% 74% 88% 88%
32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 0% 1% 2% 2% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 26% 41% 50% 58% 61% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 2% 4% 4% 6% 8%
40. IHYB 1% 1% 2% 2% 2% 3%
41. 2HYB 0% 3% 5% 5% 6% 8%
42. PHYB 3% 6% 8% 8% 12% 13% 43. PLUG 0% 0% 0% 0% 0% 0%V-133

Table V-11f Penetration Rate of New Technologies to Passenger Cars – TC = TB

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015
1. LUB 78% 93% 100% 100% 100% 100%
2. EFR 14% 37% 62% 81% 89% 89%
3. VVTI 80% 77% 81% 84% 83% 82% 4. VVTC 8% 19% 31% 38% 42% 42%
5. VVTD 27% 26% 27% 30% 34% 34% 6. DISP 4% 10% 18% 21% 25% 25% 7. VVLTC 1% 1% 2% 5% 12% 25%
8. VVLTD 12% 18% 31% 36% 38% 38% 9. DISPO 2% 3% 4% 3% 3% 3% 10. VVTO 6% 6% 6% 5% 4% 4% 11. DOHC 6% 6% 5% 5% 6% 7% 12. VVLTO 0% 1% 2% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 20% 33% 37% 41% 52% 15. DSLS 0% 0% 0% 0% 0% 0%
16. LBDI 0% 0% 0% 0% 0% 0%
17. TURB 8% 16% 29% 34% 37% 49% 18. DSLT 1% 7% 9% 11% 13% 14% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 12% 5% 3% 1% 1% 22. ASL 7% 33% 30% 32% 17% 4% 23. TORQ 4% 14% 17% 23% 14% 2% 24. 6SP 26% 30% 27% 28% 15% 2%
25. AMT 9% 17% 36% 39% 54% 67% 26. CVT 11% 14% 14% 12% 12% 12%
27. 6MAN 7% 8% 9% 9% 10% 10% 28. IACC 20% 51% 62% 68% 75% 77% 29. EPS 14% 30% 54% 62% 66% 67%
30. 42V 0% 14% 32% 35% 45% 60% 31. ROLL 5% 33% 56% 74% 88% 88%
32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 0% 1% 2% 2% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 26% 41% 50% 58% 61% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 2% 4% 4% 6% 8%
40. IHYB 1% 1% 2% 2% 2% 3%
41. 2HYB 0% 3% 5% 5% 6% 8%
42. PHYB 3% 6% 8% 8% 12% 13% 43. PLUG 0% 0% 0% 0% 0% 0%V-134

Table V-11g Penetration Rate of New Technologies to Passenger Cars – Tech exhaustion

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 78% 93% 100% 100% 100% 100% 2. EFR 14% 37% 66% 88% 96% 96% 3. VVTI 80% 76% 77% 79% 74% 72% 4. VVTC 8% 18% 29% 37% 36% 35% 5. VVTD 27% 26% 26% 29% 33% 34% 6. DISP 4% 10% 18% 22% 26% 27% 7. VVLTC 1% 1% 2% 5% 8% 22% 8. VVLTD 12% 17% 33% 40% 41% 43% 9. DISPO 2% 3% 4% 3% 3% 3% 10. VVTO 6% 6% 6% 5% 4% 4% 11. DOHC 6% 6% 5% 5% 5% 7% 12. VVLTO 0% 1% 2% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 11% 18% 36% 42% 50% 61% 15. DSLS 0% 0% 0% 0% 0% 0% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 8% 15% 32% 38% 46% 58% 18. DSLT 1% 9% 13% 15% 22% 24% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 22% 12% 5% 2% 0% 0% 22. ASL 7% 33% 30% 30% 15% 2% 23. TORQ 4% 14% 17% 24% 15% 2% 24. 6SP 26% 30% 27% 28% 15% 2% 25. AMT 9% 17% 36% 40% 56% 69% 26. CVT 11% 13% 13% 11% 11% 11% 27. 6MAN 7% 8% 10% 10% 12% 12% 28. IACC 20% 59% 75% 84% 88% 88% 29. EPS 14% 34% 66% 76% 87% 88% 30. 42V 0% 14% 40% 46% 65% 81% 31. ROLL 5% 33% 57% 80% 88% 88% 32. LDB 21% 21% 21% 21% 21% 21% 33. SAXU 0% 0% 1% 2% 3% 3% 34. SAXL 0% 0% 0% 0% 0% 0% 35. AERO 7% 26% 41% 53% 60% 69% 36. MS1 0% 0% 0% 0% 0% 0% 37. MS2 0% 0% 0% 0% 0% 0% 38. MS5 0% 0% 0% 0% 0% 0% 39. ISGO 0% 1% 1% 1% 2% 4% 40. IHYB 1% 1% 2% 2% 3% 3% 41. 2HYB 0% 3% 9% 9% 11% 12% 42. PHYB 3% 7% 11% 11% 16% 19% 43. PLUG 0% 0% 0% 0% 0% 0%V-135

Table V-12a Penetration Rate of New Technologies to Light Trucks – 25% Below Optimized

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 73% 85% 100% 100% 100% 100% 2. EFR 59% 71% 79% 93% 92% 90% 3. VVTI 63% 66% 69% 71% 72% 73% 4. VVTC 6% 14% 30% 39% 36% 35% 5. VVTD 17% 18% 20% 25% 30% 30% 6. DISP 13% 19% 31% 43% 44% 45% 7. VVLTC 0% 0% 0% 2% 2% 3% 8. VVLTD 10% 11% 14% 20% 20% 21% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 9% 13% 19% 18% 17% 11. DOHC 11% 11% 18% 19% 21% 22% 12. VVLTO 1% 1% 1% 1% 1% 1% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 12% 19% 23% 26% 30% 15. DSLS 3% 5% 5% 5% 5% 5% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 10% 10% 19% 21% 21% 21% 18. DSLT 0% 0% 0% 0% 0% 0% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 10% 12% 11% 22. ASL 25% 41% 43% 44% 46% 47% 23. TORQ 2% 11% 19% 26% 28% 26% 24. 6SP 36% 40% 40% 36% 36% 38% 25. AMT 11% 19% 33% 47% 45% 44% 26. CVT 4% 4% 3% 3% 3% 3% 27. 6MAN 1% 1% 2% 2% 2% 2% 28. IACC 28% 50% 63% 71% 71% 71% 29. EPS 10% 16% 36% 58% 58% 57% 30. 42V 5% 5% 14% 27% 29% 29% 31. ROLL 7% 28% 31% 34% 34% 34% 32. LDB 26% 32% 35% 36% 36% 36% 33. SAXU 0% 0% 1% 3% 3% 3% 34. SAXL 1% 1% 8% 12% 12% 12% 35. AERO 10% 18% 38% 62% 60% 62% 36. MS1 1% 1% 6% 6% 6% 6% 37. MS2 1% 1% 6% 6% 6% 6% 38. MS5 1% 1% 6% 6% 6% 6% 39. ISGO 2% 2% 3% 5% 5% 5% 40. IHYB 1% 3% 5% 7% 7% 7% 41. 2HYB 0% 1% 1% 1% 1% 1% 42. PHYB 2% 3% 3% 3% 3% 3% 43. PLUG 0% 0% 0% 0% 0% 0%V-136

Table V-12b Penetration Rate of New Technologies to Light Trucks – Optimized 7%

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 73% 85% 100% 100% 100% 100% 2. EFR 59% 71% 79% 92% 91% 93% 3. VVTI 63% 66% 69% 71% 71% 73% 4. VVTC 6% 15% 31% 39% 35% 38% 5. VVTD 17% 18% 22% 26% 30% 31% 6. DISP 13% 22% 35% 46% 47% 49% 7. VVLTC 0% 0% 0% 2% 2% 3% 8. VVLTD 10% 11% 15% 23% 23% 23% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 10% 13% 19% 18% 17% 11. DOHC 11% 11% 18% 19% 21% 22% 12. VVLTO 1% 1% 1% 1% 1% 1% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 12% 24% 32% 34% 39% 15. DSLS 3% 5% 5% 5% 5% 5% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 10% 10% 22% 30% 30% 30% 18. DSLT 0% 0% 0% 0% 0% 0% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 10% 12% 9% 22. ASL 25% 41% 42% 43% 39% 36% 23. TORQ 2% 11% 24% 33% 29% 29% 24. 6SP 36% 40% 38% 36% 29% 28% 25. AMT 11% 19% 36% 48% 53% 57% 26. CVT 4% 4% 3% 3% 3% 3% 27. 6MAN 1% 1% 2% 2% 2% 2% 28. IACC 28% 57% 70% 80% 84% 86% 29. EPS 10% 22% 43% 66% 69% 74% 30. 42V 5% 8% 23% 44% 48% 50% 31. ROLL 7% 28% 31% 34% 34% 34% 32. LDB 26% 35% 37% 37% 37% 37% 33. SAXU 0% 0% 1% 3% 3% 3% 34. SAXL 1% 1% 9% 12% 13% 14% 35. AERO 10% 18% 38% 60% 68% 80% 36. MS1 1% 2% 7% 13% 13% 14% 37. MS2 1% 2% 7% 10% 11% 11% 38. MS5 1% 1% 6% 6% 7% 7% 39. ISGO 2% 2% 3% 4% 5% 7% 40. IHYB 1% 3% 5% 8% 11% 15% 41. 2HYB 0% 1% 1% 1% 1% 1% 42. PHYB 2% 3% 3% 3% 4% 4% 43. PLUG 0% 0% 0% 0% 0% 0%V-137

Table V-12c Penetration Rate of New Technologies to Light Trucks – 25% Above Optimized

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 73% 85% 100% 100% 100% 100% 2. EFR 59% 71% 79% 93% 92% 90% 3. VVTI 63% 66% 69% 71% 72% 73% 4. VVTC 6% 14% 30% 39% 36% 35% 5. VVTD 17% 18% 20% 25% 30% 30% 6. DISP 13% 19% 31% 43% 44% 45% 7. VVLTC 0% 0% 0% 2% 2% 3% 8. VVLTD 10% 11% 14% 20% 20% 21% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 9% 13% 19% 18% 17% 11. DOHC 11% 11% 18% 19% 21% 22% 12. VVLTO 1% 1% 1% 1% 1% 1% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 12% 19% 23% 26% 30% 15. DSLS 3% 5% 5% 5% 5% 5% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 10% 10% 19% 21% 21% 21% 18. DSLT 0% 0% 0% 0% 0% 0% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 10% 12% 11% 22. ASL 25% 41% 43% 44% 46% 47% 23. TORQ 2% 11% 19% 26% 28% 26% 24. 6SP 36% 40% 40% 36% 36% 38% 25. AMT 11% 19% 33% 47% 45% 44% 26. CVT 4% 4% 3% 3% 3% 3% 27. 6MAN 1% 1% 2% 2% 2% 2% 28. IACC 28% 50% 63% 71% 71% 71% 29. EPS 10% 16% 36% 58% 58% 57% 30. 42V 5% 5% 14% 27% 29% 29% 31. ROLL 7% 28% 31% 34% 34% 34% 32. LDB 26% 32% 35% 36% 36% 36% 33. SAXU 0% 0% 1% 3% 3% 3% 34. SAXL 1% 1% 8% 12% 12% 12% 35. AERO 10% 18% 38% 62% 60% 62% 36. MS1 1% 1% 6% 6% 6% 6% 37. MS2 1% 1% 6% 6% 6% 6% 38. MS5 1% 1% 6% 6% 6% 6% 39. ISGO 2% 2% 3% 5% 5% 5% 40. IHYB 1% 3% 5% 7% 7% 7% 41. 2HYB 0% 1% 1% 1% 1% 1% 42. PHYB 2% 3% 3% 3% 3% 3% 43. PLUG 0% 0% 0% 0% 0% 0%V-138

Table V-12d Penetration Rate of New Technologies to Light Trucks – 50% Above Optimized

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 73% 85% 100% 100% 100% 100% 2. EFR 59% 71% 79% 89% 89% 91% 3. VVTI 63% 66% 65% 67% 65% 62% 4. VVTC 6% 15% 30% 33% 29% 34% 5. VVTD 17% 19% 21% 22% 26% 25% 6. DISP 13% 22% 36% 44% 46% 50% 7. VVLTC 0% 3% 2% 5% 6% 5% 8. VVLTD 10% 11% 13% 23% 23% 24% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 10% 13% 19% 18% 17% 11. DOHC 11% 11% 18% 19% 22% 21% 12. VVLTO 1% 1% 1% 3% 3% 3% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 16% 25% 36% 38% 41% 15. DSLS 3% 5% 6% 5% 5% 5% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 10% 10% 22% 32% 33% 37% 18. DSLT 0% 0% 4% 5% 8% 13% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 11% 12% 7% 22. ASL 25% 41% 41% 41% 40% 33% 23. TORQ 2% 11% 22% 28% 22% 23% 24. 6SP 36% 40% 37% 37% 28% 26% 25. AMT 11% 19% 37% 45% 53% 60% 26. CVT 4% 4% 3% 3% 3% 2% 27. 6MAN 1% 1% 2% 2% 2% 2% 28. IACC 28% 61% 78% 85% 96% 97% 29. EPS 10% 26% 53% 73% 78% 86% 30. 42V 5% 12% 30% 51% 62% 71% 31. ROLL 7% 28% 31% 33% 34% 34% 32. LDB 26% 36% 38% 39% 39% 39% 33. SAXU 0% 0% 4% 11% 14% 17% 34. SAXL 1% 3% 10% 14% 14% 14% 35. AERO 10% 18% 38% 56% 64% 79% 36. MS1 1% 3% 8% 13% 17% 17% 37. MS2 1% 2% 7% 13% 17% 17% 38. MS5 1% 2% 7% 13% 14% 15% 39. ISGO 2% 2% 3% 4% 5% 6% 40. IHYB 1% 3% 7% 9% 12% 13% 41. 2HYB 0% 1% 1% 1% 1% 1% 42. PHYB 2% 3% 4% 4% 7% 10% 43. PLUG 0% 0% 0% 0% 0% 0%V-139

Table V-12e Penetration Rate of New Technologies to Light Trucks – Optimized (3%)

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015
1. LUB 73% 85% 100% 100% 100% 100%
2. EFR 59% 71% 79% 95% 94% 95%
3. VVTI 63% 66% 69% 71% 71% 72% 4. VVTC 6% 15% 32% 39% 36% 38%
5. VVTD 17% 19% 23% 26% 31% 31% 6. DISP 13% 22% 36% 49% 50% 53% 7. VVLTC 0% 0% 0% 2% 2% 3%
8. VVLTD 10% 11% 15% 24% 24% 28% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 10% 13% 19% 18% 17% 11. DOHC 11% 11% 18% 18% 20% 21% 12. VVLTO 1% 1% 1% 2% 2% 2% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 12% 23% 33% 35% 41% 15. DSLS 3% 5% 5% 5% 5% 5%
16. LBDI 0% 0% 0% 0% 0% 0%
17. TURB 10% 10% 20% 29% 29% 34% 18. DSLT 0% 0% 0% 1% 3% 4% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 10% 12% 7% 22. ASL 25% 41% 42% 42% 37% 30% 23. TORQ 2% 11% 24% 31% 27% 23% 24. 6SP 36% 40% 38% 33% 26% 24%
25. AMT 11% 19% 36% 50% 56% 63% 26. CVT 4% 4% 3% 3% 3% 2%
27. 6MAN 1% 1% 2% 2% 2% 2% 28. IACC 28% 57% 70% 80% 87% 90% 29. EPS 10% 22% 43% 68% 71% 79%
30. 42V 5% 8% 23% 45% 49% 57% 31. ROLL 7% 28% 31% 34% 34% 34%
32. LDB 26% 35% 37% 37% 38% 38% 33. SAXU 0% 0% 1% 8% 9% 9% 34. SAXL 1% 1% 9% 12% 13% 14% 35. AERO 10% 18% 38% 60% 68% 82% 36. MS1 1% 2% 7% 13% 13% 14% 37. MS2 1% 2% 7% 13% 13% 14% 38. MS5 1% 1% 6% 12% 12% 13% 39. ISGO 2% 2% 3% 4% 5% 7%
40. IHYB 1% 3% 5% 8% 12% 14%
41. 2HYB 0% 1% 1% 1% 1% 1%
42. PHYB 2% 3% 3% 3% 4% 4% 43. PLUG 0% 0% 0% 0% 0% 0%V-140

Table V-12f Penetration Rate of New Technologi es to Light Trucks – TC = TB

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015
1. LUB 73% 85% 100% 100% 100% 100%
2. EFR 59% 71% 79% 93% 94% 94%
3. VVTI 63% 61% 59% 61% 58% 56% 4. VVTC 6% 13% 27% 30% 24% 26%
5. VVTD 17% 16% 19% 22% 25% 27% 6. DISP 13% 23% 37% 43% 44% 48% 7. VVLTC 0% 1% 3% 7% 8% 11%
8. VVLTD 10% 11% 16% 25% 25% 26% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 10% 13% 19% 18% 17% 11. DOHC 11% 8% 16% 23% 27% 30% 12. VVLTO 1% 1% 1% 3% 3% 3% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 15% 27% 45% 50% 58% 15. DSLS 3% 5% 5% 5% 5% 5%
16. LBDI 0% 0% 0% 0% 0% 0%
17. TURB 10% 13% 27% 43% 46% 53% 18. DSLT 0% 4% 10% 12% 16% 19% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 11% 12% 7% 22. ASL 25% 41% 41% 41% 41% 33% 23. TORQ 2% 11% 22% 29% 22% 22% 24. 6SP 36% 40% 37% 37% 29% 25%
25. AMT 11% 19% 37% 45% 53% 61% 26. CVT 4% 4% 3% 3% 3% 2%
27. 6MAN 1% 1% 2% 2% 3% 3% 28. IACC 28% 63% 79% 85% 99% 99% 29. EPS 10% 28% 55% 76% 79% 89%
30. 42V 5% 14% 34% 54% 67% 79% 31. ROLL 7% 28% 31% 33% 34% 34%
32. LDB 26% 36% 38% 39% 39% 39% 33. SAXU 0% 0% 7% 15% 20% 22% 34. SAXL 1% 3% 10% 14% 14% 14% 35. AERO 10% 18% 38% 56% 64% 79% 36. MS1 1% 3% 8% 13% 17% 17% 37. MS2 1% 3% 8% 13% 17% 17% 38. MS5 1% 3% 8% 13% 17% 17% 39. ISGO 2% 2% 2% 2% 3% 5%
40. IHYB 1% 3% 4% 5% 5% 5%
41. 2HYB 0% 1% 1% 1% 1% 1%
42. PHYB 2% 4% 8% 10% 16% 20% 43. PLUG 0% 0% 0% 0% 0% 0%V-141

Table V-12g Penetration Rate of New Technologies to Light Trucks – Tech exhaustion

Baseline MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 1. LUB 73% 85% 100% 100% 100% 100% 2. EFR 59% 71% 79% 95% 96% 95% 3. VVTI 63% 61% 59% 58% 56% 55% 4. VVTC 6% 13% 27% 33% 29% 28% 5. VVTD 17% 16% 19% 22% 25% 27% 6. DISP 13% 23% 37% 51% 52% 55% 7. VVLTC 0% 1% 3% 9% 11% 14% 8. VVLTD 10% 11% 16% 27% 26% 27% 9. DISPO 14% 16% 14% 19% 18% 17% 10. VVTO 9% 10% 13% 19% 18% 17% 11. DOHC 11% 8% 16% 23% 27% 30% 12. VVLTO 1% 1% 1% 3% 3% 3% 13. CVA 0% 0% 0% 0% 0% 0% 14. SIDI 12% 15% 27% 52% 58% 67% 15. DSLS 3% 5% 5% 5% 5% 5% 16. LBDI 0% 0% 0% 0% 0% 0% 17. TURB 10% 13% 27% 51% 56% 63% 18. DSLT 0% 4% 10% 15% 18% 20% 19. HCCI 0% 0% 0% 0% 0% 0% 20. DSLH 0% 0% 0% 0% 0% 0% 21. 5SP 34% 24% 13% 5% 6% 3% 22. ASL 25% 41% 41% 38% 31% 23% 23. TORQ 2% 11% 22% 31% 25% 20% 24. 6SP 36% 40% 37% 34% 26% 21% 25. AMT 11% 19% 37% 55% 63% 71% 26. CVT 4% 4% 3% 2% 2% 2% 27. 6MAN 1% 1% 2% 3% 3% 3% 28. IACC 28% 63% 79% 92% 99% 99% 29. EPS 10% 28% 55% 88% 92% 98% 30. 42V 5% 14% 34% 68% 81% 93% 31. ROLL 7% 28% 31% 34% 34% 34% 32. LDB 26% 36% 38% 39% 39% 39% 33. SAXU 0% 0% 7% 17% 21% 22% 34. SAXL 1% 3% 10% 15% 15% 15% 35. AERO 10% 18% 38% 61% 68% 83% 36. MS1 1% 3% 8% 17% 21% 21% 37. MS2 1% 3% 8% 17% 21% 21% 38. MS5 1% 3% 8% 17% 21% 21% 39. ISGO 2% 2% 2% 2% 2% 3% 40. IHYB 1% 3% 4% 5% 5% 7% 41. 2HYB 0% 1% 1% 1% 1% 1% 42. PHYB 2% 5% 9% 12% 16% 20% 43. PLUG 0% 0% 0% 0% 0% 0%VI-1

VI. MANUFACTURER SPECIFIC CAFE CAPABILITIES

Table VI-1a for passenger cars and Table VI-2a for light trucks shows the CAFE product plans for each of the manufacturers, based on the manuf acturer’s plans without taking into account any alternative or dual fuel vehicle attributes.

Table VI-1b for passenger cars and Ta ble VI-2b for light trucks shows the ADJUSTED BASELINE . Note that when we do cost and benefit analyses, we use the ADJUSTED BASELINE throughout the analysis. The adjusted base line assumes for the analysis that each manufacturer, below the MY 2010 standard applicable to that manufacturer, (except Ferrari, Lotus, Maserati, Mercedes, Porsche and Volksw agen) would apply technology to achieve the MY 2010 standard. Those mpg levels of those manufacturers with produc t plans above the MY 2010 standard, or above their required reform leve l standard in any model year, are retained for the adjusted baseline. Our rationale for this ad justment of the baseline is that the costs and benefits of achieving MY 2010 mpg levels for li ght trucks have already been analyzed and estimated in previous analyses. The methodology in this analysis is to ap ply technologies to the manufacturers’ plans and increase them to the ad justed baseline. The costs of these technologies are estimated, but they are not considered part of this rule. We then estimate the costs and benefits of going from the adjusted base line to the level of the alternatives. 134

The required standard levels for only the proposal are shown in Table V I-1d for passenger cars and VI-2d for light trucks. For the other tables in the group from Tables VI-1c through VI-1j for passenger cars and Tables VI-2 c through VI-2j for light trucks shows what we believe the manufacturers’ fuel economy could be for “meeti ng” the alternative levels analyzed in this analysis. They include in some cases manufacturer s’ plans at levels higher than the alternative standards would require. Note that not all manufacturers are assumed to attempt to “meet” the alternatives. We assume that Ferrari, Lotus, Maserati, Mercedes, Porsche and Volkswagen would not meet these levels because, for them, the cost of meeting these levels is more than the cost of paying penalties. These manufacturers ha ve shown, in the past, the willingness to pay penalties rather than spend more money to im prove the fuel economy of their products.

The agency has performed an analysis of how manufacturers could respond to changes in the proposed CAFE levels. The “Technology Applicati on Analysis” (or the “Volpe Analysis”) uses a technology application algorithm to systemati cally apply consistent cost and performance assumptions to the entire indus try, as well as consistent assumptions regarding economic decision-making by manufacturers. The resultin g computer model (the CAFE Compliance and Effects Model), developed by technical staff of the DOT Volpe National Transportation Systems Center in consultation with NHTSA staff, is used to help estimate the overall economic impact of the alternative CAFE standards. The Volpe anal ysis shows the economic im pact of the standards in terms of increases in new vehicle prices on a manufacturer-wide, indu stry-wide, and average per-vehicle basis. Based on these estimates and corresponding estimates of net economic and

134 Some manufacturer’s plans are above the level of the stan dard already and are assumed to remain at that level. Some manufacturer’s levels go slightly above the proposed mark for them since some technologies are applied to all models of a particular manufacturer so that the exact le vel for each manufacturer may be slightly higher than the level of the standard and costs and benefits are estimated to that level.VI-2

other benefits, the agency is able to consider alternatives that are ec onomically practicable and technologically feasible.

We note that the Volpe model has been updated and refined with re spect to its representation of some fuel-saving technologies, but the model re mains fundamentally unchanged. The model has been peer reviewed. The model documentation, in cluding a description of the input assumptions and process, as well as peer review reports, was made available in the rulemaking docket for the August 2005 NPRM. 135

Our analyses of the potential effects of altern ative CAFE standards were founded on two major elements: (1) projections of the technical char acteristics and sales vo lumes of future product offerings and (2) estimates of the applicability an d incremental cost and fuel savings associated with different hardware changes— technologies—that might be utili zed in response to alternative CAFE standards.

135 See Docket Nos. NHTSA-20005-22223-3, 4, 5.VI-3

Table VI-1a Manufacturers Production Plans Estimated mpg Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 26.9 26.9 26.9 26.9 26.9

Chrysler 28.2 28.2 31.3 31.2 31.3

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 28.2 30.5 30.5 30.5 30.5

Fuji (Subaru) 27.1 27.1 27.1 27.1 27.1

General Motors 28.2 27.8 27.8 27.8 27.8

Honda 34.8 34.8 34.8 34.8 34.8

Hyundai 32.7 32.7 32.7 32.7 32.7

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 25.1 25.1 25.1 25.1 25.1

Mitsubishi 29.8 30.2 30.4 30.6 30.6

Nissan 30.6 31.2 31.2 31.2 31.2

Porsche 24.5 24.5 24.5 24.5 24.5

Suzuki 29.6 29.6 29.6 29.6 29.6

Toyota 34.3 34.3 34.4 34.4 34.4

Volkswagen 28.8 28.8 28.8 28.8 28.8

Total/Average 30.0 30.3 30.5 30.5 30.6VI-4

Table VI-1b Adjusted Baseline

Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 27.5 27.5 27.5 27.5 27.5

Chrysler 28.2 28.2 31.3 31.2 31.3

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 28.2 30.5 30.5 30.5 30.5

Fuji (Subaru) 27.6 27.6 27.6 27.6 27.6

General Motors 28.2 27.8 27.8 27.8 27.8

Honda 34.8 34.8 34.8 34.8 34.8

Hyundai 32.7 32.7 32.7 32.7 32.7

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 27.5 27.5

Mitsubishi 29.8 30.2 30.4 30.6 30.6

Nissan 30.6 31.2 31.2 31.2 31.2

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 29.6 29.6 29.6 29.6 29.6

Toyota 34.3 34.3 34.4 34.4 34.4

Volkswagen 28.8 28.8 28.8 28.8 28.8

Total/Average 30.1 30.4 30.7 30.7 30.7VI-5

Table VI-1c Estimated Achievable Fuel Economy Levels 25% Below Optimized Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 28.2 28.2 31.3 31.2 32.0

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.4 31.4 31.5 32.5 33.7

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5 General Motors 28.6 29.5 30.4 31.9 33.0

Honda 34.8 34.8 34.8 34.8 34.8

Hyundai 32.7 32.7 33.5 34.5 35.6

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 31.2 32.2 33.4 34.7 35.8

Nissan 30.6 31.2 31.9 32.9 34.0

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 39.4

Toyota 34.3 34.3 34.4 34.4 34.4

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 30.5 31.2 31.9 32.8 33.5VI-6

Table VI-1d Required Fuel Economy Levels Proposed Optimized (7%) Standard Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 33.3 35.0 36.0 36.8 37.7

Chrysler 28.7 29.3 32.2 32.6 33.6

Ferrari 30.4 32.0 33.1 33.9 34.9

Ford 31.0 32.7 33.7 34.5 35.5

Fuji (Subaru) 36.9 38.7 39.6 40.1 40.8

General Motors 30.0 31.7 32.8 33.7 34.7

Honda 32.1 33.8 34.8 35.5 36.4

Hyundai 33.4 35.1 36.0 36.7 37.5

Lotus 38.1 40.0 40.8 41.2 41.7

Maserati 28.9 30.6 31.8 32.8 34.0

Mercedes 31.7 33.3 34.4 35.3 36.2

Mitsubishi 33.0 35.1 35.9 37.0 37.9

Nissan 31.2 33.2 34.2 35.0 35.9

Porsche 37.6 39.4 40.3 40.7 41.3

Suzuki 37.3 39.2 40.1 40.6 41.2

Toyota 30.1 31.5 32.7 33.6 34.6

Volkswagen 35.4 37.2 38.2 38.8 39.5

Total/Average 31.2 32.8 34.0 34.8 35.7VI-7

Table VI-1e Estimated Achievable Fuel Economy Levels Proposed Optimized (7%) Standard Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 28.7 29.3 32.3 32.6 33.1

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.5 32.7 33.7 34.5 35.6

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5

General Motors 30.0 31.7 32.3 33.7 34.7

Honda 34.8 34.8 34.8 35.5 36.4

Hyundai 33.4 35.1 36.0 36.7 37.1

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 33.5 35.1 35.9 37.0 37.9

Nissan 31.2 33.2 34.2 35.0 35.9

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 41.8

Toyota 34.3 34.3 34.4 34.4 34.6

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 31.0 32.3 33.1 33.9 34.7VI-8

Table VI-1f Estimated Achievable Fuel Economy Levels 25% Above Optimized Passenger Cars (mpg)

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 30.2 31.4 34.2 34.4 34.1

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.5 33.1 34.3 35.8 37.3

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5 General Motors 30.4 32.9 33.6 35.6 36.4

Honda 34.8 36.4 37.0 37.7 38.3

Hyundai 35.6 37.9 38.5 39.0 39.3

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 34.3 37.3 38.6 39.3 39.6

Nissan 32.8 35.7 36.5 37.0 37.7

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 45.2

Toyota 34.3 34.3 34.6 35.5 36.3

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 31.5 33.3 34.2 35.3 36.1VI-9

Table VI-1g Estimated Achievable Fuel Economy Levels 50% Above Optimized Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 31.0 33.3 36.0 36.1 35.2

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.5 33.1 34.3 35.8 39.2

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5 General Motors 30.4 32.9 33.6 35.9 37.4

Honda 35.5 38.2 39.2 40.6 40.8

Hyundai 37.0 41.2 41.5 42.0 42.4

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 34.3 37.3 41.1 41.3 41.9

Nissan 32.9 35.9 38.0 39.4 40.0

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 49.8

Toyota 34.3 36.1 36.7 37.1 37.7

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 31.7 34.0 35.1 36.4 37.6VI-10

Table VI-1h Estimated Achievable Fuel Economy Levels Optimized (3%)

Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 31.0 34.1 36.7 37.6 36.4

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.5 33.1 34.3 35.8 39.8

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5 General Motors 30.4 32.9 33.6 35.9 37.4

Honda 38.5 40.3 40.6 41.9 42.0

Hyundai 39.6 42.5 42.5 43.5 43.7

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 34.3 37.3 41.1 41.3 41.9

Nissan 32.9 35.9 38.0 39.9 40.5

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 49.8

Toyota 35.6 37.7 37.9 39.0 39.2

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 32.2 34.5 35.5 37.0 38.2VI-11

Table VI-1i Estimated Achievable Fuel Economy Levels Total Cost = Total Benefit Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 31.0 34.4 38.0 38.4 36.8

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.5 33.1 34.3 35.8 39.8

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5 General Motors 30.4 32.9 33.6 35.9 37.4

Honda 39.0 40.6 41.0 43.7 43.8

Hyundai 39.6 44.2 45.0 47.6 47.6

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 34.3 37.3 41.1 41.3 41.9

Nissan 32.9 35.9 38.0 39.9 40.5

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 51.6

Toyota 36.0 40.7 40.9 40.9 41.0

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 32.3 35.0 36.1 37.6 38.8VI-12

Table VI-1j Estimated Achievable Fuel Economy Levels Technology Exhaustion Passenger Cars (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.7 29.0 29.6 30.1 30.1

Chrysler 31.0 34.4 38.0 38.4 36.8

Ferrari 14.6 14.6 14.6 14.6 14.6

Ford 29.5 33.1 34.3 35.8 39.8

Fuji (Subaru) 28.6 28.7 29.8 34.1 37.5 General Motors 30.4 32.9 33.6 35.9 37.4

Honda 39.7 41.4 41.7 44.5 44.7

Hyundai 39.6 44.2 45.0 48.5 48.7

Lotus 29.2 29.2 29.2 29.2 29.2

Maserati 15.9 15.9 15.9 15.9 15.9

Mercedes 26.2 27.0 27.4 29.8 30.1

Mitsubishi 34.3 37.3 41.1 41.3 41.9

Nissan 32.9 35.9 38.0 39.9 40.5

Porsche 25.4 25.5 25.5 26.5 26.5

Suzuki 31.3 34.8 34.8 37.6 51.6

Toyota 36.0 42.0 44.5 46.9 48.7

Volkswagen 29.5 29.7 31.2 31.3 32.3

Total/Average 32.3 35.2 36.6 38.5 39.9VI-13

Table VI-2a Manufacturers Production Plans Estimated mpg

Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 21.3 21.3 21.3 21.3 21.3

Chrysler 23.5 23.8 24.4 25.0 25.4
Ferrari

Ford 23.6 24.1 25.3 25.3 25.3

Fuji (Subaru) 27.1 27.1 27.1 27.1 27.0

General Motors 21.6 21.8 21.8 21.8 21.8

Honda 25.0 25.0 25.0 25.0 25.0

Hyundai 24.5 24.5 24.5 24.5 24.5
Lotus Maserati

Mercedes 18.7 18.7 18.7 18.7 18.7

Mitsubishi 24.9 23.8 26.6 26.7 26.7

Nissan 20.9 21.0 21.0 21.0 21.0

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 22.8 22.8 22.8 22.8 22.8

Toyota 23.3 23.1 23.4 23.4 23.4

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 22.9 23.1 23.5 23.6 23.7VI-14

Table VI-2b Adjusted Baseline Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 23.6 23.9 24.5 25.1 25.6
Ferrari

Ford 23.6 24.1 25.3 25.3 25.3

Fuji (Subaru) 27.5 27.5 27.5 27.5 27.5

General Motors 22.9 23.0 23.0 23.0 23.0

Honda 25.0 25.0 25.0 25.0 25.0

Hyundai 25.2 25.2 25.2 25.2 25.2
Lotus Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 26.7 29.8 29.9 29.9

Nissan 23.7 24.5 24.5 24.5 24.5

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 28.3

Toyota 23.4 23.2 23.5 23.5 23.5

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 23.5 23.7 24.2 24.3 24.4VI-15

Table VI-2c Estimated Achievable Fuel Economy Levels 25% Below Optimized Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.0 25.6 27.7 27.8 28.0

Ferrari

Ford 24.6 25.7 27.7 27.7 27.7

Fuji (Subaru) 27.8 29.0 32.7 32.6 32.8 General Motors 23.0 24.7 26.3 26.3 26.3

Honda 26.0 27.2 28.6 28.6 28.6

Hyundai 26.1 28.7 30.0 30.0 30.3

Lotus

Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 33.1 36.9 37.1 37.2

Nissan 23.8 25.8 27.1 27.1 27.1

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 33.6

Toyota 24.7 25.6 26.9 26.9 26.9

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.3 25.5 27.3 27.3 27.4VI-16

Table VI-2d Required Fuel Economy Levels Proposed Optimized (7%) Standard Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 28.2 29.9 31.2 31.4 31.7

Chrysler 25.2 26.6 28.0 28.5 29.1
Ferrari

Ford 24.7 26.1 28.0 28.3 28.8

Fuji (Subaru) 30.0 31.7 33.1 33.2 33.4

General Motors 23.9 25.4 26.5 27.0 27.4

Honda 26.1 27.7 28.9 29.2 29.6

Hyundai 27.5 29.1 30.4 30.6 31.0
Lotus Maserati

Mercedes 28.4 30.1 31.4 31.6 31.9

Mitsubishi 29.4 30.8 32.2 32.3 32.6

Nissan 24.9 26.2 27.3 27.7 28.2

Porsche 25.9 27.4 28.7 29.0 29.4

Suzuki 30.3 32.1 33.5 33.5 33.7

Toyota 24.9 26.0 27.2 27.6 28.0

Volkswagen 26.2 27.8 29.0 29.3 29.7

Total/Average 25.0 26.4 27.8 28.2 28.6VI-17

Table VI-2e Estimated Achievable Fuel Economy Levels Proposed Optimized (7%) Standard Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.2 25.7 28.0 28.7 29.1
Ferrari

Ford 24.7 26.1 28.0 28.3 28.8

Fuji (Subaru) 27.8 29.0 32.7 33.2 33.5

General Motors 23.0 24.7 26.5 27.0 27.4

Honda 26.1 27.7 28.9 29.2 29.6

Hyundai 26.1 29.1 30.4 30.5 31.0
Lotus Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 33.1 36.9 37.1 37.2

Nissan 23.8 26.2 27.6 27.7 28.2

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 34.8

Toyota 24.9 26.0 27.2 27.6 28.0

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.4 25.8 27.5 28.0 28.4VI-18

Table VI-2f Estimated Achievable Fuel Economy Levels 25% Above Optimized Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.3 25.8 28.2 29.2 30.3

Ferrari

Ford 24.8 26.6 28.2 29.1 29.9

Fuji (Subaru) 27.8 29.0 32.7 35.1 35.1 General Motors 23.0 24.7 26.8 27.4 28.2

Honda 26.3 29.1 29.1 29.9 30.9

Hyundai 26.1 30.2 30.7 30.8 32.4

Lotus

Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 32.9 36.6 36.7 36.7

Nissan 23.8 26.5 27.9 28.1 29.1

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 34.8

Toyota 25.0 26.5 27.4 27.9 29.2

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.4 26.1 27.8 28.5 29.5VI-19

Table VI-2g Estimated Achievable Fuel Economy Levels 50% Above Optimized Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.4 25.9 28.6 29.9 30.6

Ferrari

Ford 25.0 27.0 28.5 29.7 30.5

Fuji (Subaru) 27.8 29.0 32.7 35.0 36.5 General Motors 23.0 24.7 27.0 27.6 28.4

Honda 26.3 29.2 29.6 30.7 32.6

Hyundai 26.1 30.2 31.0 31.1 34.0

Lotus

Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 33.1 36.9 37.1 37.2

Nissan 23.8 27.1 27.9 28.1 29.2

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 34.8

Toyota 25.3 26.9 27.7 28.1 29.8

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.6 26.3 28.0 28.9 30.0VI-20

Table VI-2h Estimated Achievable Fuel Economy Levels Optimized (3%)

Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.2 25.7 28.2 28.9 29.5

Ferrari

Ford 24.7 26.1 28.2 28.7 29.2

Fuji (Subaru) 27.8 29.0 32.7 33.6 33.9 General Motors 23.0 24.7 26.7 27.2 27.7

Honda 26.2 27.7 29.1 29.6 30.1

Hyundai 26.1 29.2 30.3 30.4 31.7

Lotus

Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 33.1 36.9 37.1 37.2

Nissan 23.8 26.2 27.6 27.7 28.7

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 34.8

Toyota 24.9 26.0 27.5 27.9 28.5

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.4 25.8 27.7 28.2 28.8VI-21

Table VI-2i Estimated Achievable Fuel Economy Levels Total Cost = Total Benefit Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.8 26.2 29.1 30.8 31.0

Ferrari

Ford 25.1 27.6 29.0 30.4 31.0

Fuji (Subaru) 27.8 29.0 32.7 36.1 40.9 General Motors 23.0 24.7 27.1 27.7 28.5

Honda 26.3 30.0 30.0 32.1 34.5

Hyundai 26.1 30.3 31.5 31.6 34.6

Lotus

Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 34.4 38.5 38.6 38.7

Nissan 23.8 27.4 28.9 29.1 30.2

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 34.8

Toyota 25.4 26.9 28.2 28.7 30.0

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.7 26.5 28.5 29.5 30.5VI-22

Table VI-2j Estimated Achievable Fuel Economy Levels Technology Exhaustion Light Trucks (mpg)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 22.4 23.8 23.8 23.8 23.8

Chrysler 25.8 26.3 29.1 30.8 31.0

Ferrari

Ford 25.1 27.6 31.1 31.9 32.6

Fuji (Subaru) 27.8 29.0 32.7 36.1 40.9 General Motors 23.0 24.7 27.1 27.7 28.5

Honda 26.3 30.0 32.5 34.7 36.9

Hyundai 26.1 30.3 31.5 31.6 34.6

Lotus

Maserati

Mercedes 19.1 19.6 23.4 23.4 23.4

Mitsubishi 25.5 34.4 38.5 38.7 39.0

Nissan 23.8 27.4 29.1 29.3 30.4

Porsche 17.3 17.3 17.3 17.3 17.3

Suzuki 23.4 23.7 26.4 26.6 34.8

Toyota 25.4 26.9 31.1 31.4 32.5

Volkswagen 20.1 20.1 20.1 20.1 20.1

Total/Average 24.7 26.6 29.4 30.3 31.3VI-23VII-1

VII. COST IMPACTS

Technology Costs Table V-1 provides the technology co st estimates used in this analysis. These are meant to represent consumer costs for high-volume produc tion of these technol ogies after sufficient experience with their applicati on have resulted in all “learn ing curve” effects being fully realized. The method taken to get to this consumer cost estimate st arts with an initial estimate of the incremental manufacturers’ direct costs (or variable costs) for high-volume production of these technologies. In the cas e of some very new technologies, the agency may have only had cost estimates from low volume products and has assumed that the products have not matured in the development production cycle and that a “learn ing curve” will result in a reduction in the variable cost of the product by 20 percent. The technologies to which the learning curve factors were applied are shown in Table V- 3. The variable costs are marked up by a factor of 1.5 to take into account fixed costs of R&D, burden, manufact urer’s profits, and deale r’s profits. The final results are shown in Table V-1.

The variable costs are incremental costs in materi al, labor, and variable bu rden for the product. For example, if a vehicle already has a 4-speed automatic transmission, the cost of applying a 5- speed transmission is assumed to be the incrementa l cost, calculated as the cost of applying a 5- speed transmission less the cost of applying the previously applied 4-speed automatic transmission.

The learning curve For some of the technologies, we have incl uded a learning factor. The “learning curve” describes the reduction in unit production cost s as a function of accumulated production volume and small redesigns that reduce costs.

A typical learning curve can be described by three parameters: (1) the initial production volume before cost reductions begin to be realized; (2) the rate at which cost reductions occur with increases in cumulative production beyond this in itial volume (usually referred to as the “learning rate”); and (3) the produc tion volume after which costs reach a “floor,” and further cost reductions no longer occur. Over the region where costs decline with accumulating production volume, an experience curve can be expressed as C(Q) = aQ -b , where a is a constant coefficient, Q represents cumulative production, and b is a coefficient corresponding to the assumed learning rate. In turn, the learning rate L, which is usually expressed as the percent by which average unit cost declines with a doubling of cumulative production, and is relate d to the value of the coefficient b by L = 100*(1 – 2 -b ) 136

.
Figure VII-1 illustrates a learning curve for a vehicl e technology with an initial average unit cost of $100 and a learning rate of approximately 20 per cent. In this hypothetical example, the initial

136 See, for example, Robert H. Williams, “Toward Cost Buydown via Learning-by-Doing for Environmental Energy Technologies,” paper presented at Workshop on Learning-by-Doing in Energy Technologies, Resources for the Future, Washington, D.C., June 17-18, 2003, pp. 1-2. Another common but equivalent formulation of the relationship between L and b is (1-L)=2 -b , where (1-L) is referred to as the progress ratio; see Richard P. Rumelt, “Note on Strategic Cost Dynamics,” POL 2001-1.1, Anderson School of Business, University of California, Los Angeles, California, 2001, pp. 4-5.VII-2

production volume before cost re ductions begin to be realized is set at 12,000 units and the production volume at the cost floor is set at roughly 50,000 units.

$- $20 $40 $60 $80 $100 $120

0 20000 40000 60000 80000 100000 120000 140000

Cumulative VolumeCost

Figure VII-1 Typical Experience Curve

Most studies of the effect of the learning curv e on production costs appear to assume that cost reductions begin only after some initial volume thre shold has been reached, but not all of these studies specify the threshold volum e. The rate at which costs d ecline beyond the initial threshold is usually expressed 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 learning experience curves do not specify a cumulative production volume beyond which cost reductions no longer occur, in stead depending on the asymptotic behavior of the above expression of (CQ) for learning rates below 100 percent to establish a floor on costs.VII-3

Table VII-1 summarizes estimates of learning rates derived from studies of production costs for various products. 137

Table VII-1 Estimated Learning Rates and Associated Volumes for Various Products

Product(s) Costs Affected Threshold Volume Learning Rate Photovoltaic cells Tota l costs Not reported 20% Wind turbines Total costs 100 MW 20% Gas turbines Total costs 100 MW 10% Semiconductors Total costs Not reported 13-24% Automobile assembly Assembly labor Not reported 16% Truck manufacturing Total costs Not reported 10% Battery-electric LDV Total costs 10,000 units 10% Fuel cell hybrid LDV Total costs 10,000 units 16% Fuel cell LDV powertrain Total costs 10,000 units 19%

In past rulemaking analyses, EPA has used a learning curve factor of 20 percent for each doubling of production volume. For this analys is, however, NHTSA has applied learning curve cost reductions on a manufacturer-specific ba sis, and have assumed that learning-based reductions in technology costs o ccur once during the time that a manufacturer applies the given technology to 25,000 cars or trucks, and are repeat ed a second time as it produces another 25,000 cars or trucks for the second lear ning step (car and truck volu mes are treated separately for determining these sales volumes). The volumes chosen represent our best estimate for where learning would occur. As such, NHTSA believes th at these estimates are better suited to this analysis than the more general approach used by EPA in past rules, because each manufacturer would be implementing technologies at its own pace in this rule, rather than assuming that all manufacturers implement each id entical technology at the same time. The volumes chosen represent our best estimate for where learning would occur.

For this analysis, the agency has used engi neering judgment to estimate the development production cycle and maturity level for each t echnology. After having produced 25,000 cars or trucks with a specific part or system, we belie ve that sufficient learning will have taken place such that costs will be lower by 20 percent fo r some technologies and 10 percent for others. After another 25,000 units for some technologies, a nother cost reduction will have been realized. When we applied a learning curve, we applied a 20 percent learning factor for all newly applied technologies except for diesel engine s. We have applied a 10 percen t factor for diesel costs here because we believe that the di esel technologies being consider ed are reaching their “learned” limit and, therefore, less learning reductions are available. 138

137 Adapted from Williams, Figure 1, p. 14; Rumelt, Exhib it 5, p. 5; Linda Argote and Dennis Epple, “Learning Curves in Manufacturing,” Science , Vol. 247 (1990), pp. 920-924; and Philip Auerswald et al., “The Production Recipes Approach to Modeling Technological Innovation: An Application to Learning by Doing,” Journal of Economic Dynamics and Control, Vol. 24 (2000), pp. 389-450.

138 Importantly, diesel technologies can still be considered to have some lear ning left given recent announcements by General Motors stating potential cost savings associated with their new 4.5 liter Duramax diesel V8 engine (see Automotive News , September 24, 2007).VII-4

For each of the technologies, we have considered whether we could project future cost reductions due to manufacturer learning. In maki ng this determination, we considered whether or not the technology was in wide-spread use to day or expected to be by the model year 2011 time frame, in which case no future learning curve would apply because the technology is already in wide-spread production by the automo tive industry today, e.g ., on the order of multimillions of units per year. (Examples of these include 5-speed automatic transmissions and intake-cam phasing variable valve timing. These technologies have been in production for lightduty vehicles for more than 10 years.) In a ddition, we carefully c onsidered the underlying source data for our cost estimate. If the source da ta specifically stated that manufacturer cost reduction from future learning would occur, we took that information into account in determining whether we would a pply manufacturer learning in our cost projections. Thus, for many of the technologies, we have not applie d any future cost reduction learning curve.

However, there are a number of technologies wh ich are not yet in mass production for which we have estimated the initial cost will be reduced in the time frame of this rule due to manufacturer production learning. As indicated in Table V-3, we have applie d the learning curve beginning in 2011 to one set of technologies, and for a numbe r of additional technologies we did not apply manufacturer learning until 2014. The distinction between 2011 and 2014 is due to our source data for our cost estimates. For those technol ogies where we have appl ied manufacturer learning in 2011, the source of our cost estimate did no t rely on manufacturer le arning to develop the initial cost estimate we have used – theref ore we apply the manufact urer learning methodology beginning in 2011.

The technologies for which we do not begin ap plying learning until 2014 all have the same reference source, the 2004 NES CCAF study, for which the sub- contractor was The Martec Group. In the work done for the 2004 NESCCAF report, Martec relied upon actual price quotes from Tier 1 automotive suppliers to develop au tomotive manufacturer cost estimates. Based on information presented by Martec to the Nationa l Academy of Sciences (NAS) Committee during their January 24, 2008 public m eeting in Dearborn, Michigan [1] , the agency understands that the Martec cost estimates done for the NESCCAF re port incorporated some element of manufacturer learning. Martec informed stated that the Tier 1 suppliers were specifically requested to provide price quotes which would be va lid for three years (2009-2011), and that for some components the Tier 1 supplier included cost reductions in years two and th ree which the supplier anticipated could occur, and which they anticipated would be necessary in order for their quote to be competitive with other suppliers. Therefore, for this analysis , we did not apply any learning curve to any of the Martec-sourced costs for the first three years of th is proposal (2011-2013). However, the theory of manufactur er learning is that it is a conti nuous process, though the rate of improvement decreases as the number of units pr oduced increases. While we were not able to gain access to the detailed submissions from Tier 1 suppliers which Martec relied upon for their estimates, we do believe that additional cost reduc tions will occur in the future for a number of the technologies for which we relied upon the Martec cost estimates for the reasons stated above in reference to the general lear ning curve effect. For those te chnologies we a pplied a learning

[1] “Variable Costs of Fuel Economy Technologies” Martec Group, Inc Report Presented to: Committee to Assess Technologies for Improving Light-Duty Vehicle Fuel Economy. Division on Engineering and Physical Systems, Board on Energy and Environmental Systems, the National Academy of Sciences, January 24, 2008.VII-5

curve beginning in 2014. Martec has recently submitted a study to the NAS Committee comparing the 2004 NESCAF study with new updated cost information. Given that this study had just been completely, the agency could not take it into consideration for the NPRM. However, the agency will review the new study and consider its findings in time for the final rule.

Manufacturers’ actual costs for applying these tec hnologies to specific vehi cle models are likely to include significant additional outlays for accompanying design or engineering changes to each model, development and testing of prototype versions, recalibrat ing engine operating parameters, and integrating the technology with other attributes of the vehicle. Manuf acturers may also incur additional corporate overhead, marketing, or dist ribution and selling expe nses as a consequence of their efforts to improve the fuel economy of individual vehicle m odels and their overall product lines.

In order to account for these additional costs, the agency applies an indirect cost multiplier of 1.5 to its estimate of the vehicle manufacturers’ direct costs for producing or acquiring each fuel economy-improving technology to arrive at a consum er cost. This estimate was developed by Argonne National Laboratory in a re cent review of vehicle manufactur ers’ indirect costs. The Argonne study was specifically intended to improve the accuracy of future cost estimates for production of vehicles that achie ve high fuel economy by employing many of the same advanced technologies considered in the agency’s analysis. 139 Thus, its recommendation that a multiplier of 1.5 be applied to direct manufacturing costs to reflect manufacturers’ increased indirect costs for deploying advanced fuel economy technologie s appears to be appropriate for use in the agency’s current analysis. Historically, NHTSA has used almost the exact same multiplier, a multiplier of 1.51, as the markup from variable costs or direct manufactur ing costs to consumer costs. This markup takes into account fixed co sts, burden, manufacturer ’s profit, and dealers profit. NHTSA’s methodology for developing this markup factor was recently peer reviewed (see Docket No.27453-4).

Potential opportunity costs of improved fuel economy An important concern is whether achieving the fuel economy improvements required by alternative CAFE standards would require ma nufacturers to compromise the performance, carrying capacity, safety, or comfort of their vehicl es. If it did so, the resulting sacrifice in the value of these attributes to vehicle buyers w ould represent an additiona l cost of achieving the required improvements in fuel economy, and thus of manufacturers’ compliance with stricter CAFE standards. While exact do llar values of these attributes to buyers are extremely difficult to infer from vehicle purchase prices, it is nevert heless clear that changes in these attributes can affect the utility that vehicle provide to their ow ners, and thus their value to potential buyers.

The agency has approached this potential pr oblem by developing cost estimates for fuel economy-improving technologies that include any additional manuf acturing costs that would be necessary to maintain the performance, comfort, capacity, or safety of any vehicle to which those technologies are applied. In doing so, the agency followed the precedent established by NAS in

139 Vyas, Anant, Dan Santini, and Roy Cuenca, Comparison of Indirect Cost Multipliers for Vehicle Manufacturing , Center for Transportation Research, Argonne National Laboratory, April 2000.VII-6

its 2002 analysis of the costs and benefits of improving fuel economy by raising CAFE standards. 140 The NAS study estimated “constant pe rformance and utility” costs for fuel economy technologies, and the agency has used th ese as the basis for developing the technology costs it employed in analyzing manufacturer’s cost s for complying with alternative standards.

NHTSA fully acknowledges the difficulty of estim ating technology costs that include costs for the accompanying changes in vehicle design that are necessary to maintain performance, capacity, and utility. However, the agency be lieves its cost estimates for fuel economy technologies are generally sufficient to prevent significant reductions in consumer welfare.

The technology application algorith m implemented with the Volpe model was used as the basis for estimating costs for the fleet. We applied the technology application algorithm described in Chapter VI.

The agency did estimate the costs or fines to bring passenger car manufacturers up to the 27.5 mpg level in place for MY 2010 as shown in Tabl e VII-2. Table VII-3 shows the estimates for those light truck manuf acturers that are not pl anning on meeting the CAFE reform level for MY 2011, without using fuel economy adjustments for alternative fueled vehicles, up to the level required for them for MY 2011. These costs have b een estimated, but they are not considered to be part of the costs of mee ting the proposed requirements. Those costs, and commensurate benefits, are considered part of th e costs and benefits of complying w ith previously issued rules.

Tables VII-4a through 4n for passenger cars a nd Tables VII-5a through 5n show the costs for light trucks (on an average cost-per-vehicle basis and on a total cost basis) of applying technology necessary to move each manufacturer’s planned fuel economy levels up to the level of the alternative. Thus, if a manufacturer’s product plans re sulted in a fuel economy level of 22.2 mpg during each model year, the cost repr esents the cumulative cost of technologies necessary to bring that ma nufacturer’s fleet average up to the le vels of the alternative. The costs for several manufacturers are the fines that these manufacturers woul d have to pay on an average vehicle basis. We assume that the costs of fi nes will be passed on to consumers. The second part of each of these tables shows the estimated total manufacturer costs in millions of dollars. Fines are not included in the sec ond part of these tables, since th ese are transfer payments and not technology costs.

140 National Academy of Sciences, Costs and Effectiveness of Increasi ng Corporate Average Fuel Economy Standards , 2002.VII-7

Table VII-2

Estimated Incremental Costs or Fines over Manufacturer’s Plans To get to Adjusted Baseline - Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 58 59 60 61 61

Chrysler - - - - -

Ferrari 710 710 710 710 710

Ford - - - - -
Fuji (Subaru) 39 40 40 41 41

General Motors - - - - -

Honda - - - - -
Hyundai - - - - -

Lotus - - - - -

Maserati 638 638 638 638 638

Mercedes 255 323 328 356 359

Mitsubishi - - - - -

Nissan - - - - -

Porsche 334 353 355 454 458)

Suzuki - - - - -

Toyota - - - - -
Volkswagen - - - - -

Total/Average 11 13 13 14 14VII-8

Table VII-3

Estimated Incremental Costs or Fines over Manufacturer’s Plans To get to Adjusted Baseline - Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 499 761 745 727 720

Chrysler 9 9 9 9 9
Ferrari

Ford - - - - -
Fuji (Subaru) 35 34 33 32 32

General Motors 647 645 630 612 606

Honda - - - - -
Hyundai 106 103 101 98 97
Lotus Maserati

Mercedes 433 586 1,152 1,124 1,113

Mitsubishi 153 444 419 408 403

Nissan 1,105 1,181 1,147 1,111 1,097

Porsche 363 363 363 363 363

Suzuki 321 377 1,081 1,070 1,101

Toyota 6 5 5 5 5

Volkswagen 220 220 220 220 220

Total/Average 240 244 248 241 239VII-9

Table VII-4a Estimated Incremental Costs or Fines over Adjusted Baseline 25% Below Optimized Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 456 513 611 710 781

Chrysler - - - - 109

Ferrari 77 116 193 248 314

Ford 439 240 239 312 411

Fuji (Subaru) 509 537 912 2,825 3,764 General Motors 42 150 323 453 750

Honda - - - - -
Hyundai - - 45 92 173

Lotus 363 391 462 506 556

Maserati 6 50 127 193 264

Mercedes 138 176 253 439 528

Mitsubishi 109 159 290 533 1,127

Nissan - - 24 116 219

Porsche 435 457 528 578 627

Suzuki 416 615 697 2,087 2,160

Toyota - - - - -
Volkswagen 299 332 440 489 614

Total/Average 126 126 187 294 428VII-10

Table VII-4b Estimated Incremental Costs or Fines over Adjusted Baseline 25% Below Optimized Total Incremental Costs in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler - - - - 61.2

Ferrari - - - - -

Ford 570.2 307.9 304.6 390.5 509.1

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6 General Motors 75.9 270.7 548.2 795.4 1,305.7

Honda - - - - -
Hyundai - - 23.6 47.2 88.0

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 12.5 18.0 32.7 58.9 122.7

Nissan - - 16.4 79.1 147.6

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 162.9

Toyota - - - - -
Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 834.8 818.4 1,253.3 2,152.6 3,208.7VII-11

Table VII-4c Estimated Incremental Costs or Fines over Adjusted Baseline Proposed Optimized (7%) Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 555 672 748 831 891

Chrysler 29 135 151 228 401

Ferrari 160 248 308 352 407

Ford 782 560 627 691 790

Fuji (Subaru) 630 730 1,077 2,968 3,890

General Motors 338 535 644 767 988

Honda - - - 23 55

Hyundai 21 175 390 442 530

Lotus 490 594 638 660 688

Maserati 77 171 237 292 358

Mercedes 231 319 380 554 627

Mitsubishi 1,113 1,585 1,589 1,850 2,303

Nissan 37 164 259 331 575

Porsche 556 655 704 726 759

Suzuki 537 813 868 2,236 2,521

Toyota - - - - 5

Volkswagen 409 508 594 627 735

Total/Average 276 334 404 512 649VII-12

Table VII-4d Estimated Incremental Costs or Fines over Adjusted Baseline Proposed Optimized (7%) Total Incremental Costs in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler 16.8 78.5 87.1 129.0 209.1

Ferrari - - - - -

Ford 908.5 719.3 798.5 863.9 978.9

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6

General Motors 617.6 965.9 1,103.2 1,346.4 1,720.2

Honda - - - 20.5 48.6

Hyundai 11.2 92.5 204.1 226.8 258.3

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 127.7 179.9 178.9 204.3 252.1

Nissan 26.1 114.9 179.6 225.2 387.6

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 190.1

Toyota - - - - 5.9

Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 1,884.4 2,372.6 2,879.1 3,797.5 4,862.2VII-13

Table VII-4e Estimated Incremental Costs or Fines over Adjusted Baseline 25% Above Optimized Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 654 832 886 958 1,001

Chrysler 328 462 738 849 753

Ferrari 248 380 418 462 501

Ford 870 942 1,103 1,492 1,539

Fuji (Subaru) 751 922 1,242 3,122 4,022 General Motors 814 1,510 1,594 1,913 2,028

Honda - 115 148 193 357

Hyundai 245 513 673 704 742

Lotus 622 798 814 820 825

Maserati 154 286 336 391 446

Mercedes 319 462 501 670 732

Mitsubishi 2,091 3,326 3,307 3,633 3,847

Nissan 520 1,065 1,142 1,195 1,394

Porsche 682 858 875 886 891

Suzuki 664 1,011 1,033 2,390 3,945

Toyota - - 7 42 83

Volkswagen 525 690 743 764 856

Total/Average 494 778 871 1,078 1,185VII-14

Table VII-4f Estimated Incremental Costs or Fines over Adjusted Baseline 25% Above Optimized Total Cost in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler 192.2 268.0 424.4 479.1 384.7

Ferrari - - - - -

Ford 908.5 1,062.3 1,300.5 1,817.6 1,907.6

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6 General Motors 1,374.8 2,606.9 2,734.2 3,359.4 3,530.5

Honda - 105.4 133.6 171.3 315.0

Hyundai 130.7 270.9 351.8 361.5 371.7

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 236.7 373.2 372.1 401.2 418.7

Nissan 367.3 744.8 791.4 812.9 939.8

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 297.4

Toyota - - 9.4 54.9 107.0

Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 3,386.6 5,653.1 6,445.3 8,239.5 9,083.9VII-15

Table VII-4g Estimated Incremental Costs or Fines over Adjusted Baseline 50% Above Optimized Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 753 991 1,051 1,117 1,166

Chrysler 1,045 1,478 1,475 1,442 1,137

Ferrari 325 512 539 561 594

Ford 952 1,079 1,230 1,608 2,009

Fuji (Subaru) 872 1,120 1,468 3,364 4,297 General Motors 891 1,636 1,709 2,152 2,505

Honda 19 769 880 946 981

Hyundai 421 1,895 1,916 1,971 2,035

Lotus 754 1,012 1,062 1,100 1,155

Maserati 226 407 435 451 484

Mercedes 407 605 638 791 847

Mitsubishi 2,184 3,485 4,115 4,466 4,765

Nissan 753 1,492 2,005 2,343 2,498

Porsche 814 1,067 1,111 1,150 1,205

Suzuki 790 1,215 1,269 2,648 5,019

Toyota - 115 138 172 223

Volkswagen 635 871 941 973 1,092

Total/Average 620 1,133 1,251 1,501 1,694VII-16

Table VII-4h Estimated Incremental Costs or Fines over Adjusted Baseline 50% Above Optimized Total Incremental Costs in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler 593.5 857.9 848.6 813.9 611.4

Ferrari - - - - -

Ford 908.5 1,062.3 1,300.5 1,817.6 2,489.5

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6 General Motors 1,374.8 2,606.9 2,734.2 3,634.7 4,313.3

Honda 17.9 657.7 772.0 840.9 864.4

Hyundai 224.6 999.7 1,002.0 1,011.6 1,026.8

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 236.7 373.2 461.8 487.1 513.2

Nissan 478.1 951.1 1,355.5 1,593.9 1,684.4

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 378.4

Toyota - 154.4 183.8 225.5 288.5

Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 4,010.5 7,884.7 8,986.1 11,206.8 12,981.4VII-17

Table VII-4i Estimated Incremental Costs or Fines over Adjusted Baseline Optimized (3%) Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 923 1,068 1,089 1,200 1,215

Chrysler 1,182 1,684 1,753 1,982 1,611

Ferrari 473 589 589 671 677

Ford 1,095 1,156 1,274 1,707 2,328

Fuji (Subaru) 1,092 1,225 1,523 3,414 4,308 General Motors 1,028 1,708 1,753 2,256 2,593

Honda 667 1,198 1,229 1,298 1,336

Hyundai 1,875 2,287 2,308 2,401 2,429

Lotus 963 1,095 1,095 1,111 1,122

Maserati 363 457 468 572 583

Mercedes 561 682 682 890 924

Mitsubishi 2,371 3,584 4,176 4,560 4,826

Nissan 907 1,574 2,054 2,661 2,824

Porsche 1,018 1,150 1,150 1,172 1,183

Suzuki 1,010 1,314 1,319 2,687 5,019

Toyota 78 232 234 350 373

Volkswagen 844 970 1,001 1,039 1,120

Total/Average 896 1,284 1,376 1,706 1,915VII-18

Table VII-4j Estimated Incremental Costs or Fines over Adjusted Baseline Optimized 3% Total Incremental Costs in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler 593.5 977.3 1,008.6 1,116.0 861.3

Ferrari - - - - -

Ford 908.5 1,062.3 1,300.5 1,817.6 2,831.4

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6 General Motors 1,374.8 2,606.9 2,734.2 3,634.7 4,313.3

Honda 616.0 1,079.8 1,113.3 1,154.4 1,177.7

Hyundai 976.4 1,206.9 1,206.9 1,232.4 1,235.9

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 236.7 373.2 461.8 487.1 513.2

Nissan 478.1 951.1 1,355.5 1,765.4 1,878.4

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 378.4

Toyota 106.3 312.3 312.2 458.0 483.4

Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 5,466.7 8,791.5 9,820.9 12,447.1 14,484.5VII-19

Table VII-4k Estimated Incremental Costs or Fines over Adjusted Baseline Total Cost = Total Benefit Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 956 1,310 1,331 1,381 1,402

Chrysler 1,204 2,062 2,734 2,766 2,299

Ferrari 490 765 765 776 781

Ford 1,123 1,349 1,466 1,828 2,449

Fuji (Subaru) 1,136 1,538 1,836 3,711 4,611 General Motors 1,050 1,889 1,935 2,355 2,692

Honda 978 1,548 1,594 2,071 2,115

Hyundai 1,891 3,371 3,529 4,003 4,039

Lotus 1,040 1,463 1,463 1,480 1,496

Maserati 385 633 649 660 666

Mercedes 583 886 886 1,022 1,056

Mitsubishi 2,382 3,804 4,396 4,741 5,013

Nissan 929 1,772 2,252 2,793 2,961

Porsche 1,089 1,502 1,502 1,518 1,529

Suzuki 1,060 1,644 1,649 3,006 6,030

Toyota 195 1,143 1,152 1,174 1,187

Volkswagen 871 1,245 1,276 1,287 1,373

Total/Average 966 1,685 1,829 2,159 2,367VII-20

Table VII-4l Estimated Incremental Costs or Fines over Adjusted Baseline Total Cost = Total Benefit Total Incremental Costs in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler 593.5 1,113.5 1,515.9 1,527.5 1,200.0

Ferrari - - - - -

Ford 908.5 1,062.3 1,300.5 1,817.6 2,831.4

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6 General Motors 1,374.8 2,606.9 2,734.2 3,634.7 4,313.3

Honda 903.8 1,224.0 1,274.4 1,797.9 1,815.9

Hyundai 976.4 1,706.0 1,796.7 2,054.8 2,054.8

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 236.7 373.2 461.8 487.1 513.2

Nissan 478.1 951.1 1,355.5 1,765.4 1,878.4

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 440.9

Toyota 265.1 1,537.1 1,535.9 1,535.9 1,538.2

Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 5,913.2 10,795.8 12,302.7 15,402.5 17,397.6VII-21

Table VII-4m Estimated Incremental Costs or Fines over Adjusted Baseline Technology Exhaustion Average Cost per Vehicle (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 1,017 1,475 1,683 1,788 1,969

Chrysler 1,254 2,155 3,026 3,102 2,739

Ferrari 556 919 1,084 1,155 1,287

Ford 1,172 1,503 1,791 2,213 2,961

Fuji (Subaru) 1,213 1,758 2,260 4,156 5,265 General Motors 1,105 2,027 2,243 2,735 3,187

Honda 1,180 1,855 2,083 2,605 2,788

Hyundai 1,968 3,558 3,892 4,728 4,934

Lotus 1,089 1,661 1,870 1,892 2,134

Maserati 435 743 919 1,012 1,133

Mercedes 644 1,045 1,227 1,423 1,595

Mitsubishi 2,470 4,008 4,770 5,165 5,607

Nissan 990 1,932 2,582 3,183 3,484

Porsche 1,139 1,700 1,903 1,936 2,167

Suzuki 1,131 1,864 2,072 3,446 6,684

Toyota 239 2,408 2,913 3,487 3,825

Volkswagen 948 1,449 1,678 1,721 1,994

Total/Average 1,038 2,032 2,406 2,889 3,264VII-22

Table VII-4n Estimated Incremental Costs or Fines over Adjusted Baseline Technology Exhaustion Total Incremental Costs in Millions (2006$) Passenger Cars

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 104.6 117.3 134.8 154.4 156.3

Chrysler 593.5 1,113.5 1,515.9 1,527.5 1,200.0

Ferrari - - - - -

Ford 908.5 1,062.3 1,300.5 1,817.6 2,831.4

Fuji (Subaru) 23.1 23.8 70.5 339.2 472.6 General Motors 1,374.8 2,606.9 2,734.2 3,634.7 4,313.3

Honda 1,069.9 1,389.2 1,444.0 1,964.1 1,982.1

Hyundai 976.4 1,706.0 1,796.7 2,263.1 2,266.7

Lotus - - - - -

Maserati - - - - -

Mercedes - - - 52.1 59.8

Mitsubishi 236.7 373.2 461.8 487.1 513.2

Nissan 478.1 951.1 1,355.5 1,765.4 1,878.4

Porsche - - - - -

Suzuki 16.4 44.7 44.7 157.5 440.9

Toyota 265.1 3,171.3 3,764.8 4,517.6 4,872.6

Volkswagen 32.2 36.1 77.8 78.3 122.7

Total/Average 6,079.4 12,595.2 14,701.1 18,758.8 21,110.1VII-23VII-24

Table VII-5a Estimated Incremental Costs or Fines over Adjusted Baseline 25% Below Optimized Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 149 220 303 297 303 Chrysler 251 396 682 599 563

Ferrari

Ford 147 218 281 273 270 Fuji (Subaru) 160 61 8 2,088 2,031 2,016
General Motors 113 1,030 1,205 1,173 1,160

Honda 141 271 492 478 473 Hyundai 664 898 1,101 1,066 1,067

Lotus

Maserati

Mercedes 149 220 303 297 303
Mitsubishi 149 2,838 2,630 2,558 2,530

Nissan 177 340 1,049 1,016 1,003

Porsche 99 171 248 242 248

Suzuki 121 198 286 281 2,877 Toyota 202 367 477 464 459 Volkswagen 110 182 253 253 253 Total/Average 185 526 738 705 708VII-25

Table VII-5b Estimated Incremental Costs or Fines over Adjusted Baseline 25% Below Optimized Total Incremental Costs in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 0 0 0 0 0 Chrysler 417 618 1,193 1,077 1,023

Ferrari

Ford 230 351 463 463 463 Fuji (Subaru) 5 54 232 232 232 General Motors 140 2,124 2,589 2,589 2,589

Honda 103 204 379 379 379 Hyundai 157 243 304 304 308

Lotus

Maserati

Mercedes 0 0 0 0 0

Mitsubishi 0 107 101 101 101

Nissan 57 149 474 474 474

Porsche 0 0 0 0 0

Suzuki 0 0 0 0 163 Toyota 239 447 594 594 594 Volkswagen 0 0 0 0 0 Total/Average 1,349 4,296 6,329 6,212 6,326VII-26VII-27

Table VII-5c Estimated Incremental Costs or Fines over Adjusted Baseline Proposed Optimized (7%) Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 154 248 319 330 347

Chrysler 329 439 905 838 815
Ferrari

Ford 195 288 332 365 425

Fuji (Subaru) 171 646 2,110 2,061 2,108

General Motors 118 1,052 1,276 1,453 1,487

Honda 175 512 668 700 769

Hyundai 675 1,082 1,243 1,270 1,293
Lotus Maserati

Mercedes 154 248 319 330 347

Mitsubishi 160 2,838 2,630 2,558 2,530

Nissan 182 596 1,283 1,251 1,307

Porsche 110 193 264 281 303

Suzuki 132 231 308 308 3,977

Toyota 262 522 603 774 815

Volkswagen 116 204 270 286 308

Total/Average 224 617 861 924 979VII-28

Table VII-5d Proposed Optimized (7%) Total Incremental Costs in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW - - - - -
Chrysler 546.1 663.6 1,583.3 1,507.1 1,481.1
Ferrari

Ford 305.2 463.5 546.8 618.1 728.7

Fuji (Subaru) 5.2 53.8 231.5 235.0 242.9

General Motors 140.4 2,123.6 2,739.4 3,207.7 3,319.3

Honda 128.2 384.5 514.4 554.3 615.6

Hyundai 157.4 293.1 344.9 360.9 373.1
Lotus Maserati

Mercedes - - - - -

Mitsubishi - 106.8 101.5 101.5 101.5

Nissan 57.0 261.4 579.5 583.6 617.3

Porsche - - - - -

Suzuki - - - - 224.7

Toyota 309.6 635.3 752.1 991.8 1,056.5

Volkswagen - - - - -

Total/Average 1,649.3 4,985.5 7,393.6 8,159.9 8,760.6VII-29

Table VII-5e Estimated Incremental Costs or Fines over Adjusted Baseline 25% Above Optimized Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 171 286 336 374 429

Chrysler 490 610 1,162 1,173 1,541

Ferrari

Ford 233 459 451 797 1,079

Fuji (Subaru) 187 690 2,126 3,316 3,280 General Motors 129 1,080 1,512 1,831 2,125

Honda 228 1,307 1,275 1,306 1,400

Hyundai 686 1,750 1,749 1,804 1,927

Lotus

Maserati

Mercedes 171 286 336 374 429

Mitsubishi 176 3,173 2,880 2,801 2,771

Nissan 193 882 1,551 1,529 1,995

Porsche 121 226 275 319 374

Suzuki 149 275 325 363 4,010

Toyota 312 1,020 1,028 1,335 1,628

Volkswagen 127 237 286 330 380

Total/Average 279 873 1,141 1,352 1,655VII-30

Table VII-5f Estimated Incremental Costs or Fines over Adjusted Baseline 25% Above Optimized Total Cost in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 0 0 0 0 0 Chrysler 813 919 2,032 2,109 2,800

Ferrari
Ford 364 739 744 1,351 1,839 Fuji (Subaru) 5 54 232 378 378 General Motors 140 2,124 3,246 4,018 4,705 Honda 167 982 982 1,035 1,122 Hyundai 157 474 485 505 556

Lotus

Maserati

Mercedes 0 0 0 0 0

Mitsubishi 0 119 111 111 111

Nissan 57 382 700 705 937

Porsche 0 0 0 0 0

Suzuki 0 0 0 0 225 Toyota 369 1,241 1,282 1,690 2,109 Volkswagen 0 0 0 0 0 Total/Average 2,072 7,034 9,815 11,903 14,781VII-31

Table VII-5g Estimated Incremental Costs or Fines over Adjusted Baseline 50% Above Optimized Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 176 314 358 424 501

Chrysler 597 731 1,408 1,657 1,932

Ferrari

Ford 462 770 639 1,144 1,418

Fuji (Subaru) 193 723 2,148 2,982 3,376 General Motors 135 1,102 1,875 2,206 2,518

Honda 257 1,329 1,325 1,506 2,197

Hyundai 691 1,750 1,784 1,866 2,439

Lotus

Maserati

Mercedes 176 314 358 424 501

Mitsubishi 182 2,838 2,630 2,558 2,530

Nissan 199 1,028 1,337 1,355 1,963

Porsche 127 248 292 358 435

Suzuki 154 308 352 418 4,092

Toyota 563 1,279 1,276 1,535 2,033

Volkswagen 132 259 303 369 446

Total/Average 385 1,008 1,347 1,644 2,041VII-32

Table VII-5h Estimated Incremental Costs or Fines over Adjusted Baseline 50% Above Optimized Total Incremental Costs in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 0 0 0 0 0 Chrysler 990 1,097 2,463 2,979 3,432

Ferrari
Ford 723 1,237 1,053 1,939 2,372 Fuji (Subaru) 5 54 232 340 389 General Motors 140 2,124 4,026 4,798 5,485 Honda 184 999 1,021 1,193 1,760 Hyundai 157 474 495 515 704

Lotus

Maserati

Mercedes 0 0 0 0 0

Mitsubishi 0 107 101 101 101

Nissan 57 451 604 609 896

Porsche 0 0 0 0 0

Suzuki 0 0 0 0 225 Toyota 666 1,555 1,591 1,911 2,605 Volkswagen 0 0 0 0 0 Total/Average 2,922 8,098 11,586 14,386 17,969VII-33

Table VII-5i Estimated Incremental Costs or Fines over Adjusted Baseline Optimized (3%) Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 160 253 336 363 380

Chrysler 329 439 1,092 1,022 1,095

Ferrari

Ford 195 288 344 398 468

Fuji (Subaru) 176 651 2,121 2,326 2,362 General Motors 124 1,052 1,435 1,609 1,720

Honda 192 497 711 775 803

Hyundai 675 1,078 1,249 1,288 1,356

Lotus

Maserati

Mercedes 160 253 336 363 380

Mitsubishi 165 2,838 2,630 2,558 2,530

Nissan 182 596 1,283 1,276 1,765

Porsche 110 198 275 303 325

Suzuki 138 237 319 336 3,977

Toyota 262 522 668 837 891

Volkswagen 121 204 281 308 336

Total/Average 227 616 955 1,028 1,145VII-34

Table VII-5j Estimated Incremental Costs or Fines over Adjusted Baseline Optimized 3% Total Incremental Costs in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 0 0 0 0 0 Chrysler 546 664 1,909 1,837 1,991

Ferrari

Ford 305 463 567 675 801 Fuji (Subaru) 5 54 232 265 272 General Motors 140 2,124 3,081 3,552 3,840

Honda 141 374 548 614 644 Hyundai 157 292 341 357 391

Lotus

Maserati

Mercedes 0 0 0 0 0

Mitsubishi 0 107 101 101 101

Nissan 57 261 580 585 834

Porsche 0 0 0 0 0

Suzuki 0 0 0 0 225 Toyota 310 635 832 1,072 1,154 Volkswagen 0 0 0 0 0 Total/Average 1,662 4,974 8,190 9,058 10,253VII-35

Table VII-5k Estimated Incremental Costs or Fines over Adjusted Baseline Total Cost = Total Benefit Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 198 369 391 512 649

Chrysler 857 1,012 1,880 2,348 2,225

Ferrari

Ford 647 1,388 1,201 1,772 1,965

Fuji (Subaru) 215 789 2,187 3,884 5,451 General Motors 151 1,140 2,178 2,475 2,834

Honda 279 2,295 2,238 2,850 3,630

Hyundai 713 1,907 1,984 2,096 2,929

Lotus

Maserati

Mercedes 198 374 391 512 655

Mitsubishi 204 3,743 3,513 3,417 3,380

Nissan 215 1,352 1,977 1,992 2,517

Porsche 143 297 325 435 567

Suzuki 182 374 385 517 4,263

Toyota 705 1,407 1,415 1,700 2,174

Volkswagen 154 308 330 446 578

Total/Average 501 1,325 1,770 2,171 2,509VII-36

Table VII-5l Estimated Incremental Costs or Fines over Adjusted Baseline Total Cost = Total Benefit Total Incremental Costs in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 0 0 0 0 0 Chrysler 1,422 1,529 3,288 4,182 3,773

Ferrari
Ford 995 2,213 1,979 2,948 3,150 Fuji (Subaru) 5 54 232 438 628 General Motors 140 2,124 4,631 5,258 5,944 Honda 184 1,725 1,725 2,258 2,907 Hyundai 157 502 549 564 818

Lotus

Maserati

Mercedes 0 0 0 0 0

Mitsubishi 0 141 136 136 136
Nissan 57 581 893 898 1,129

Porsche 0 0 0 0 0

Suzuki 0 0 0 0 225 Toyota 828 1,657 1,764 2,080 2,653 Volkswagen 0 0 0 0 0 Total/Average 3,788 10,525 15,196 18,762 21,364VII-37

Table VII-5m Estimated Incremental Costs or Fines over Adjusted Baseline Technology Exhaustion Average Cost per Vehicle (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 220 407 638 704 781

Chrysler 961 1,119 2,093 2,479 2,348

Ferrari

Ford 664 1,410 1,912 2,127 2,299

Fuji (Subaru) 242 827 2,467 4,109 5,511 General Motors 168 1,162 2,349 2,585 2,905

Honda 301 2,300 3,049 3,499 4,076

Hyundai 735 1,940 2,220 2,266 3,045

Lotus

Maserati

Mercedes 220 413 644 710 787

Mitsubishi 231 3,743 3,513 3,505 3,755

Nissan 237 1,380 2,287 2,263 2,743

Porsche 165 325 528 578 660

Suzuki 209 413 671 743 4,400

Toyota 722 1,429 2,519 2,595 2,930

Volkswagen 176 341 545 594 677

Total/Average 536 1,364 2,255 2,507 2,785VII-38

Table VII-5n Estimated Incremental Costs or Fines over Adjusted Baseline Technology Exhaustion Total Incremental Costs in Millions (2006$) Light Trucks

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW 0 0 0 0 0 Chrysler 1,567 1,674 3,323 4,180 3,827

Ferrari
Ford 995 2,213 3,025 3,465 3,721 Fuji (Subaru) 5 54 232 438 628 General Motors 140 2,124 4,631 5,258 5,944 Honda 184 1,725 2,299 2,773 3,264 Hyundai 157 502 549 564 818

Lotus

Maserati

Mercedes 0 0 0 0 0

Mitsubishi 0 141 136 137 146
Nissan 57 581 968 973 1,202

Porsche 0 0 0 0 0

Suzuki 0 0 0 0 225 Toyota 828 1,657 3,113 3,263 3,703 Volkswagen 0 0 0 0 0 Total/Average 3,933 10,670 18,275 21,051 23,479VII-39VII-40

Financial Impacts of Raising CAFE Standards The agency does not have the capability to predic t the capital investment needs of the automobile industry to install fuel economy technologies, nor the capability to determine the level of capital investments available to specific manufacturers in the future. Th e analysis estimates the price increases in total for each manuf acturer, under the assumption that prices would be increased and the manufacturer would get back that investment when the vehicles are sold. However, that methodology does not determine whether automob ile manufacturers can pay for research and development, plant changes, and tooling necessary to get the technology into the vehicles in the first place. In essence this is a cash flow questi on. Do they have the cash reserves or can they borrow enough money to fund this process? The im plicit assumption in the analysis is yes.

A significant portion of the capital needs w ill fall upon suppliers to the automobile manufacturers, those companies that develop and sell engines, transmissions, and other fuel economy technologies. So, the capital needs are sp read out to both the suppliers and original equipment manufacturers.

The agency would like to have a more inform ed opinion on the ability of manufacturers to provide the capital investment needs for the vari ous alternatives. In light of these unknowns, the agency is seeking information regarding the manuf acturers financial capabilities in meeting this proposal and the alternatives examined. Specific questions are as follows:

QUESTIONS FOR VEHICLE MANUFACTURERS AND SUPPLIERS 1. For each of the model years 2011-2015, please provide the best possible estimate of the incremental capital investments required for your company to comply with the alternatives discussed in this analysis (25% Below Optimized, Optimized (7%), 25% Above Optimized, 50% Above Optimized, Optimized (3%), TC = TB, and Technology Exhaustion. Capital investment s are defined here by asset cla ss and consist of outlays for property, plant, machinery, equipment, and special tools used in the production process by vehicle manufacturers and s uppliers. Incremental invest ments are defined as those directly attributed to the fuel economy im provements and would not be incurred in the absence of the new requirements.

2. To the degree possible, please provide the above utilizing the elements below for each model year presenting passenger cars and LTV’S separately (suppliers can supply data by model year). NHTSA understa nds that the adoption of fl exible assembly in which production of passenger cars and many LTV’S are integrated onto the same line may make such distinctions infeasible, particularly in the out-years. In such cases, a combined PC/ LTV estimate for each element below will suffice. The agency further acknowledges that estimates of capital requirements for th e out-years must contain, by nature, a high degree of uncertainty.VII-41

Asset Classification Capital Investment Write-off Period (Incremental) New Property $- In years Plant $- In years Machinery& Equipment $- In years Special Tooling $- In years

3. Please discuss whether you anticipate that your firm will to be able to raise the incremental capital investments necessary to meet the levels predicted in answer to the questions above. If the answer is no, what level appe ars likely to be achi evable. What alternatives are available to raise the incremental capital investments necessary?

The Impact of Higher Prices on Sales

Higher fuel economy standards are expected to in crease the price of pass enger cars and light trucks. The potential impact ofhigher vehicle prices on sales was examined on a manufacturerspecific basis, since the estimated cost of improving fuel economy and the fuel economy improvement is different for each manufacturer. There is a broad consensus in the economic literature that the price elasticity for de mand for automobiles is approximately –1.0. 141,142,143 Thus, every one percent increase in the price of the vehicle woul d reduce sales by one percent. Elasticity estimates assume no perceived change in the quality of the product. However, in this case, vehicle price increases re sult from adding technologies that improve fuel economy. If consumers do not value improved fuel economy at al l, and consider nothi ng but the increase in price in their purchase decisions, then the estimat ed impact on sales from price elasticity could be applied directly. However, we believe th at consumers do value improved fuel economy, because they reduce the operating cost of the vehicl es. We also believe that consumers consider other factors that affect their costs and ha ve included these in the analysis.

To estimate the average value consumers place on fuel savings at the time of purchase, we assume that the average purchaser considers the fuel savings they would receive over a 5 year timeframe. We chose 5 years because this is the average length of time of a financing agreement. 144 The present values of these savings were calculated using a 3 percent discount rate, which is more consistent with the real (a fter-inflation) rate that consumers receive from their own personal savings in banks, etc, than th e 7 percent discount factor. We used a fuel price forecast (see Table VIII-3) that included taxe s, because this is what consumers must pay. Fuel savings were calculated over the first 5 ye ars and discounted back to a present value.

141 Kleit, A.N. (1990). “The Effect of Annual Changes in Auto mobile Fuel Economy Standards.” Journal of Regulatory Economics , vol. 2, pp 151-172.
142 Bordley, R. (1994). “An Overlapping Choi ce Set Model of Automotive Price Elasticities,” Transportation Research B , vol 28B, no 6, pp 401-408.
143 McCarthy , P.S. (1996). “Market Price and Income Elasticities of New Vehicle Demands,” The Review of Economics and Statistics , vol. LXXVII, no. 3, pp. 543-547.

144 National average financing terms for automobile loans ar e available from the Board of Governors of the Federal Reserve System G.19 “Consumer Finance” release. See: http://www.federalre serve.gov/releases/g19/VII-42

The agency believes that consumers may consider several other factors over the 5 year horizon when contemplating the purchase of a new vehicl e. The agency added these factors into the calculation to represent how an increase in technology costs mi ght affect consumers’ buying considerations.

First, consumers might consider the sales taxes th ey have to pay at the time of purchasing the vehicle. We took sales taxes in 2007 by stat e and weighted them by population by state to determine a national weighted-average sales tax of 5.5 percent.

Second, we considered insurance costs over the 5 year period. More expensive vehicles will require more expensive collision and comprehensiv e (e.g., theft) car insurance. According to the National Association of Insurance Commissione rs (NAIC) the national average premium for collision + comprehensive insurance in 2000 wa s $389 while the average new car transaction price was $20,600. If we assume that this premium is proportional to the new car price, it represents about 1.9 percent of th e new car price and insurance is paid each year for the five year period we are considering for payback. Discounti ng that stream of insurance costs back to present value using a 3 percent discount rate sugg ests that the present va lue of the component of insurance costs that vary with vehicle price is equal to about 8.0 percent of th e vehicle’s price.

Third, we considered that 70 percent of new ve hicle purchasers take out loans to finance their purchase. The average new vehicle loan is for 5 years at a 6 percent rate 145 . At these terms the average person taking a loan will pay 16 percent mo re for their vehicle over the 5 years than a consumer paying cash for the vehicle at the time of purchase 146 . Discounting the additional 3.2 percent (16 percent / 5 years) per year over the 5 years us ing a 3 percent mid-year discount

rate 147 results in a discounted pres ent value of 14.87 percent highe r for those taking a loan. Multiplying that by the 70 percent that take a lo an, means that the average consumer would pay 10.4 percent more than the retail price for loans.

Fourth, we considered the residu al value (or resale value) of the vehicle after 5 years and expressed this as a percentage of the new vehicle price. In other words, if the price of the vehicle increases due to fuel economy technologies, the resale value of the vehicle will go up proportionately. To estimate that value, we look ed at 138 model year 2002 vehicles to compare their original MSRP values (based on www.nadagui des.com) to their current trade-in values (5 years later in 2007 based on www.edmunds.com). Th e sales weighted average residual value for this group of vehicles was 37.5 percent. Disc ounting the residual value back 5 years using a 3 percent discount rate (37.5 per cent * .8755) gives an effective residual value at new of 32.8 percent.

These four factors together, the consumer considering he could get 32.8 percent back upon resale in 5 years, but will pay 10.4 percent more for loans, 5.5 percent more for taxes and 8.0 percent

145 New car loan rates in 2007 average about 7.8 percent at commercial banks and 4.5 percent at auto finance companies, so their average is close to 7 percent

146 Based on www.bankrate.com auto loan calculato r for a 5 year loan at 6 percent.

147 The summation of 3.2 percent x 0.9853 in year one, 3.2 x 0.9566 in year two, 3.2 x 0.9288 in year three, 3.2 x 0.9017 in year 4, and 3.2 x 0.8755 in year five.VII-43

more in insurance, results in a 8.9 percent re turn on the increase in price for fuel economy technology (32.8 percent – 10.4 percent - 5.5 percen t – 8.0 percent). Thus, the increase in price per vehicle is multiplied by 0.911 (1 – 0.089) before subtracting the fuel savings to determine the overall net consumer valuation the increas e of costs on his purchase decision.

Using sales volumes from Automotive News a nd the Automotive News 2006 Market Data Book for base vehicle average prices for MY 2006, we determined an average passenger car and an average light truck price per manufacturer. Th e average base price fo r all passenger cars using this method was $22,857 and for all light trucks was $26,090. While this method does not give an exact price, the results are reasonabl e and specific to individual manufacturers 148 . These prices are in 2006 dollars. Aver age prices and estimated sales volumes are needed because price elasticity is an estimate of how a percent increase in price affects the percent decrease in sales.

A sample calculation for Ford passenger cars und er the Optimized 7% alternative in MY 2011 is an estimated retail price increase of $782, whic h is multiplied by 0.911 to get a residual price increase of $712. The estimated fuel savings ove r the 5 years of $281 at a 3 percent discount rate results in a net cost to consumers of $431. Co mparing that to the $21,821 average price is 2.39 percent price increase. Ford sales were estimated to be a bout 1,300,000 passenger cars for MY 2011. With a price elasticity of –1.0, a 2.39 percen t increase in sales could result in an estimated loss in sales of 3,104 passenger cars at a 3 percen t discount rate.

Sales increases occur when the value of improve d fuel economy exceeds the consumer cost of added technology. Overall, the 25% Below Optimized and the proposed Optimized (7%) alternatives result in a gain in sales, while th e other alternatives result in almost progressively larger losses in sales. Tables VII-6a th rough 6g show the estimated impact on sales for passenger cars and light trucks combined.

Our projections indicate that CAFE standard s will result in sales increases for some manufacturers under some scenarios. These resu lts rest on several assumptions about consumer behavior, in particular, how consumers valu e fuel economy increases. If consumers are completely unable to perceive any increases in fuel economy, then they would treat the vehicle price increases resulti ng from CAFE standards as pure pri ce increases without any corresponding quality increase. Under those circumstances, one would expect vehi cle sales to fall in accordance with the price elasticity of demand discussed earlier. Our projections of sales increases rest on the assumption that consumers will correctly perceive at least some of the increase in fuel economy and therefore be wil ling to pay somewhat more for a vehicle with greater fuel economy. Even if consumers value only a portion of the resulting fuel savings, there are instances where those fuel savings are noneth eless projected to be large enough to exceed the increased vehicle price, thus leading to an increase in sales. However, this assumption raises the following question: If some fraction of fuel economy improvements (as perceived and valued by vehicle purchasers) is large enough to exceed the in creased vehicle cost (and result in an increase in vehicle sales), then what would be the nature of the market failure such that those levels of fuel economy would not exist but for a CAFE ma ndate? To better understand this issue, NHTSA

148 The base price does not include the more expensive lines of a model or purchased optional equipment; nor does it count discounts given. Thus, it is not an average light truck purchase transaction price, but a price that we can track.VII-44

seeks comment on the following questions: What ev idence or data exist that indicate the extent to which consumers undervalue fuel economy impr ovements? Under what circumstances is it reasonable to expect that a mandated increase in fuel economy would lead to an increase in vehicle sales?

Note that there is no feedback l oop between this sales an alysis and the Volpe model. These sales estimates are not used to determine additional or less mileage traveled or fuel consumed. Also, see the earlier discussion about a mark et share model in Chapter V.VII-45

Table VII-6a Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined 25% Below Optimized

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -1,408 -1,528 -1,520 -1,497 -2,038 Chrysler 17,429 14,748 20,295 12,675 8,732

Ferrari -5 -8 -13 -17 -21

Ford 10,813 16,693 24,406 25,835 26,704 Fuji (Subaru) -1,913 -2,454 -5,948 -11,872 -14,118

General Motors -3,797 -10,067 12,302 23,018 16,005

Honda 4,820 10,573 14,410 14,877 15,049 Hyundai -4,103 1,475 4,404 7,216 9,698

Lotus -32 -34 -40 -43 -46

Maserati 0 -3 -7 -11 -14

Mercedes -864 -1,159 -1,645 -222 -328

Mitsubishi 29 -1,468 -1,456 -1,537 -2,044

Nissan -2,089 1,330 132 2,673 5,279

Porsche -512 -543 -631 -675 -725

Suzuki -321 -50 -373 -1,497 -4,764
Toyota 11,977 20,734 26,551 27,361 27,659 Volkswagen -2,195 -1,979 912 329 980 Total/Average 27,828 46,262 91,779 96,615 86,009VII-46

Table VII-6b Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined Proposed Optimized (7%) Alternative

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -2,258 -2,910 -2,691 -2,540 -3,000 Chrysler 17,331 14,665 10,741 13,423 9,536

Ferrari -11 -17 -21 -24 -27

Ford 8,551 19,365 26,032 29,032 33,226 Fuji (Subaru) -2,629 -3,623 -6,938 -12,359 -14,611

General Motors -2,230 -8,057 14,642 17,547 17,554

Honda 4,764 7,891 11,720 16,296 20,396 Hyundai -2,120 5,283 4,690 5,361 5,893

Lotus -43 -51 -55 -55 -57

Maserati -4 -10 -13 -16 -20

Mercedes -1,290 -1,837 -2,228 -779 -831

Mitsubishi -719 -2,662 -2,588 -2,751 -3,134

Nissan 230 5,842 3,615 5,338 2,525

Porsche -652 -771 -831 -845 -878

Suzuki -485 -349 -616 -1,726 -6,925
Toyota 12,245 19,544 24,826 22,455 26,886 Volkswagen -3,840 -4,587 -1,350 -1,659 -763 Total/Average 26,839 47,716 78,935 86,698 85,769VII-47

Table VII-6c Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined 25% Supra Alternative

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -3,123 -4,309 -3,862 -3,644 -4,025 Chrysler 9,912 6,334 -6,223 -6,195 -24,075

Ferrari -17 -26 -29 -31 -33

Ford 7,370 16,682 19,573 12,529 7,087 Fuji (Subaru) -3,367 -4,857 -7,907 -16,928 -19,038

General Motors -30,508 -61,856 -45,558 -58,594 -66,337

Honda 4,722 1,920 3,970 9,350 9,244 Hyundai 1,088 1,979 830 612 2,889

Lotus -54 -69 -70 -69 -69

Maserati -9 -16 -19 -21 -24

Mercedes -1,711 -2,535 -2,786 -1,355 -1,428

Mitsubishi -1,815 -4,997 -4,703 -5,029 -5,234

Nissan -5,783 -13,221 -16,419 -15,875 -22,833

Porsche -799 -1,006 -1,025 -1,027 -1,033

Suzuki -666 -672 -840 -2,024 -8,197 Toyota 11,809 5,470 10,213 10,327 16,675 Volkswagen -5,569 -7,281 -3,531 -3,651 -2,513 Total/Average -18,519 -68,461 -58,385 -81,627 -118,944VII-48

Table VII-6d Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined 50% Supra Alternative

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -3,972 -5,691 -5,271 -5,028 -5,478 Chrysler -558 -7,331 -20,508 -31,632 -49,142

Ferrari -23 -35 -37 -38 -39

Ford -1,342 4,579 12,174 -1,025 -7,272 Fuji (Subaru) -4,062 -6,079 -9,228 -16,956 -19,945

General Motors -36,279 -72,169 -75,782 -95,110 -114,136

Honda 8,507 -14,076 -12,017 -6,609 -15,842 Hyundai 1,587 -19,379 -18,338 -19,135 -19,963

Lotus -66 -87 -91 -92 -96

Maserati -13 -23 -24 -25 -26

Mercedes -2,113 -3,214 -3,427 -1,965 -2,052

Mitsubishi -1,943 -4,681 -4,943 -5,341 -5,587

Nissan -12,560 -25,212 -34,257 -39,264 -47,405

Porsche -951 -1,247 -1,293 -1,324 -1,386

Suzuki -824 -977 -1,170 -2,439 -9,160 Toyota 6,218 11,832 16,778 11,296 8,569 Volkswagen -7,214 -9,970 -6,437 -6,670 -5,907 Total/Average -55,606 -153,761 -163,872 -221,357 -294,866VII-49

Table VII-6e Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined Optimized (3%)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -5,396 -6,246 -5,557 -5,605 -5,683 Chrysler 10,327 7,841 -7,235 -6,240 -10,436

Ferrari -33 -41 -40 -45 -45

Ford 6,495 16,042 25,196 27,749 30,897 Fuji (Subaru) -5,227 -6,376 -9,419 -15,516 -17,566

General Motors -45,165 -73,656 -53,602 -66,812 -74,776

Honda 37 -9,614 -6,242 -2,122 -840 Hyundai -22,801 -20,020 -20,005 -20,067 -17,556

Lotus -84 -95 -94 -93 -93

Maserati -21 -26 -26 -31 -32

Mercedes -2,772 -3,449 -3,581 -2,279 -2,156

Mitsubishi -2,161 -4,807 -5,020 -5,457 -5,661

Nissan -17,337 -25,420 -36,520 -47,914 -54,454

Porsche -1,182 -1,333 -1,334 -1,340 -1,344

Suzuki -1,043 -933 -1,151 -2,291 -8,889
Toyota 21,822 41,006 46,381 44,740 49,292 Volkswagen -10,333 -11,415 -7,316 -7,598 -6,260 Total/Average -74,873 -98,542 -85,566 -110,920 -125,605VII-50

Table VII-6f Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined Total Costs = Total Benefit Alternative

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -5,733 -8,456 -7,659 -7,327 -7,635 Chrysler -10,937 -23,499 -54,877 -75,745 -73,164

Ferrari -34 -53 -52 -52 -52

Ford -9,599 -21,475 -14,836 -30,982 -35,388 Fuji (Subaru) -5,618 -8,643 -11,398 -21,785 -27,766

General Motors -48,581 -92,408 -109,370 -126,542 -148,076

Honda -10,825 -53,233 -53,413 -66,008 -77,005 Hyundai -23,599 -46,485 -46,833 -53,290 -57,145

Lotus -91 -126 -125 -124 -125

Maserati -22 -36 -36 -36 -36

Mercedes -2,935 -4,557 -4,569 -3,118 -3,226

Mitsubishi -2,226 -5,991 -6,195 -6,590 -6,807

Nissan -18,595 -37,982 -48,329 -58,123 -65,615

Porsche -1,269 -1,746 -1,737 -1,742 -1,761

Suzuki -1,194 -1,612 -1,674 -3,066 -10,532
Toyota 6,846 -26,703 -20,917 -30,747 -38,504 Volkswagen -10,754 -15,514 -11,362 -11,204 -9,951 Total/Average -145,167 -348,520 -393,382 -496,484 -562,788VII-51

Table VII-6g Potential Impact on Sales by Manufacturer Passenger Cars and Light Trucks Combined Technology Exhaustion Alternative

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015

BMW -6,280 -9,901 -10,977 -10,963 -12,440 Chrysler -16,765 -30,229 -71,831 -89,064 -85,899

Ferrari -39 -63 -74 -77 -85

Ford -10,835 -23,723 -30,037 -35,235 -38,953 Fuji (Subaru) -6,152 -10,007 -14,838 -25,105 -31,500

General Motors -53,560 -103,483 -142,788 -160,265 -186,507

Honda -16,012 -62,105 -76,863 -88,495 -101,441 Hyundai -25,559 -50,981 -57,543 -69,209 -75,562

Lotus -95 -144 -160 -159 -178

Maserati -25 -42 -52 -56 -62

Mercedes -3,242 -5,328 -6,511 -5,204 -5,764

Mitsubishi -2,376 -6,250 -6,667 -7,222 -8,018

Nissan -20,935 -43,571 -63,048 -74,077 -84,875

Porsche -1,328 -1,975 -2,219 -2,226 -2,474

Suzuki -1,335 -1,948 -2,798 -4,077 -11,575
Toyota 2,665 -115,685 -147,858 -175,571 -195,117 Volkswagen -11,912 -18,533 -17,317 -17,503 -18,843 Total/Average -173,784 -483,968 -651,580 -764,510 -859,291VII-52

Table VII-6h Potential Impact on Sales Passenger Cars versus Light Trucks by Alternative MY 2015

Passenger Cars Light Trucks Total 25% Below Optimized 22,237 63,773 86,009 Optimized (7%) 21,482 64,288 85,769 25% Above Optimized -48,921 -70,024 -118,944 50% Above Optimized -138,449 -156,417 -294,866 Optimized (3%) -170,031 44,426 -125,605

TC = TB -293,326 -269,462 -562,788 Technology Exhaustion -557,905 -301,386 -859,291VII-53

Potential Impact on Employment There are three potential areas of employment that fuel economy standards could impact. The first is the hiring of additional engineers by au tomobile companies and their suppliers to do research and development and testing on new te chnologies to determin e their capabilities, durability, platform introduction, etc. The agency does not anticipate a huge number of incremental jobs in the engineering field. Of ten people would be diverted from one area to another and the incremental number of jobs might be a few thousand.

The second area is the impact th at new technologies would have on the production line. Again, we don’t anticipate a large number of incremental workers, as for the most part you are replacing one engine with another or one transmission with another. In some instances the technology is more complex, requiring more parts and there would be a small increase in the number of production employees, but we don’t anticipate a large change.

The third area is the potential impact that sales gains or losses could have on production employment. This area is potentially much more sensitive to change th an the first two areas discussed above. In the past, the agency and others have made estimates of the impact of sales losses on employment. In the final rule reducing the light truck fuel economy standard for MY 1985, the agency concluded that sales losses of 100,000 to 180, 000 would result in employment losses of 12,000 to 23,000 (49 FR 41252, October 22, 1984). 149 In the final rule reducing the MY 1986 passenger car fuel economy standard, the agency concluded that while it was difficult to precisely estimate the impacts, “there woul d be a likelihood of sales losses well into the hundreds of thousands of units and job losses we ll into the tens of thousands. Sales and employment losses of these magnitudes would ha ve significant advers e effects on the economy ... ” (50 FR 40538, October 4, 1985). In the fina l rule amending the passenger car standards for MY 1987 and 1988, the agency said that “... do mestic car production may fall by more than 900,000 units. The short employment effects are substantial: over 130, 000 jobs...” (51 FR 35598, October 6, 1986). These estimates imply a ratio between the number of vehicles sales lost and the number of employees laid off in the 1980s of between 6.9 (900,000/130,000) and 8.3 (100,000/12,000).

Certainly productivity has increased since that time. In order to get an estimate of potential job losses per sales loss, we examined more re cent U.S. employment (original equipment manufacturers and suppliers) and U.S. production. Total employment in 2000 reached a peak in the Motor Vehicle and Equipment Manufacturing sector of the economy at 1,313,600. Since then there has been a decline to 1,108,000 in 2003 and to 1,098,000 in 2005 150 . Averaging those three years, the average U.S. domestic empl oyee produces 10.5 vehicles. Thus, one could assume that projected sales loss divided by 10. 5 would give an estimate of the potential employment loss.

149 T he agency’s decision to lower standards based on that am ount of impacts identified in the 1985 rule was upheld by the DC Circuit in Public Citizen v. NHTSA , 848 F.2d 256.

150 Based on “U.S. Automotive Industry Employment Trends ”, Office of Aerospace and Au tomotive Industr ies, U.S. Department of Commerce, March 30, 2005, and Ward’s Automotive Yearbook, 2006, pgs. 215, 222, and 270.VII-54

Table VII-7 U.S. Light Duty Vehicle Production and Employment

U.S. Light Vehicle Production U.S. Employment Production per Employee 2000 12,773,714 1,313,600 9.7 2003 12,087,028 1,108,000 10.9 2005 11,946,653 1,098,000 10.9 Total/Average 36,807,396 3,519,600 10.5

At this time, the agency considers these effects to occur in the short to medium term (meaning up to 5 years). Over the next few years, cons umers can elect to defer vehicle purchases by continuing to operate existing vehicles. Eventu ally, however, the rising maintenance costs for aging vehicles will make replacements look more attractive.

However, vehicle owners may also react to pe rsistently higher vehicle costs by permanently owning fewer vehicles, and keeping existing vehicles in service for somewhat longer. In this case, the possibility exists that there may be permanent sales losses, compared with a situation in which vehicle prices are lower.

The 25% Sub Optimizes and the proposed Optimized (7%) alternative would have positive impacts on employment. The other alternativ es have negative impacts on employment. Combining the sales effect on passenger cars a nd light trucks, the imp act on employment is estimated in the following table.VII-55

Table VII-8 Impact on Auto Industry Em ployment by Alternative (Jobs)

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 25% Below Optimized -409 1,151 1,835 2,632 2,118 Optimized (7%) -327 1,941 2,148 2,689 2,046 25% Above Optimized -25,822 -33,273 -31,777 -31,082 -32,614 50% Above Optimized -5,407 -10,527 -10,782 -11,887 -13,186 Optimized (3%) -9,972 -12,054 -12,381 -14,919 -16,193

TC = TB -11,760 -22,413 -23,420 -26,578 -27,936 Technology Exhaustion -13,587 -34,289 -40,892 -47,829 -53,134

Light Trucks 25% Below Optimized 3,059 3,255 6,906 6,569 6,074 Optimized (7%) 2,883 2,604 5,370 5,568 6,123 25% Above Optimized 1,925 -1,767 -1,021 -3,334 -6,669 50% Above Optimized 111 -4,117 -4,825 -9,195 -14,897 Optimized (3%) 2,841 2,669 4,232 4,356 4,231

TC = TB -2,066 -10,779 -14,045 -20,706 -25,663 Technology Exhaustion -2,964 -11,803 -21,163 -24,982 -28,703

Passenger Cars and Light Trucks Combined 25% Below Optimized 2,650 4,406 8,741 -9,201 8,191 Optimized (7%) 2,556 4,544 7,518 -8,257 8,169 25% Above Optimized -1,764 -6,520 -5,561 7,774 -11,328 50% Above Optimized -5,296 -14,644 -15,607 21,082 -28,082 Optimized (3%) -7,131 -9,385 -8,149 10,564 -11,962

TC = TB -13,825 -33,192 -37,465 47,284 -53,599 Technology Exhaustion -16,551 -46,092 -62,055 72,810 -81,837VII-56

Table VII-9 provides further inform ation relating to the stringency of the different alternatives. It looks at the largest 7 manuf acturers and examines whether or not they run out of technologies that the agency believes they have available. As the alternatives get more stringent, more manufacturers run out of technologies.

Table VII-9 Number of Manufacturers Th at Run out of Technology

Cars: Number of Manufacturers Exhausting Technology 2011 2012 2013 2014 2015 -25% 0 0 1 0 0 MC=MB 1 0 1 0 2 +25% 2 2 2 1 2 +50% 4 4 4 2 3 TC=TB 5 6 6 5 5 Technology Exhaustion 6 6 6 6 7

Trucks: Number of Manufact urers Exhausting Technology 2011 2012 2013 2014 2015 -25% 3 2 0 1 0 MC=MB 3 2 0 1 0 +25% 3 3 0 4 3 +50% 4 3 0 4 5 TC=TB 6 6 2 6 6 Technology Exhaustion 7 6 6 6 6VIII-1

VIII. BENEFITS

Economic Impacts from Higher CAFE Standards

Economic impacts from adopting a more stringent CAFE standard for passenger cars and light trucks were estimated separately for each model year over the lifespan of those vehicles in the U.S. vehicle fleet, extending from the initial ye ar when a model is offered for sale through the year when nearly all vehicles from that model y ear have been retired or scrapped (assumed to be 26 years for passenger cars and 36 year s for light trucks in this analysis). The principal source of the economic and environmental impacts considered in this analysis is the reduction in gasoline use resulting from the improvement in fuel economy of new light-dut y vehicles produced. Reducing gasoline consumption provides consum er benefits through decreased fuel costs, through reduced costs for externalities such as demand price inflation, economic disruption, and military security, through reduced economic and health impacts from criteria pollutants and green house gas emissions, through increased drivi ng ranges for vehicles, and through consumer surplus from added driving. Offsetting a part of these benefits are added costs from congestion, crashes, and noise, as well as some offset to fu el consumption and pollution savings, all due to an increase in driving that results from lower driv ing costs (the rebound effect). Each of these impacts is measured by comparing their value un der each alternative approach to their value under the adjusted baseline. Future impacts ar e estimated after discoun ting to the year the vehicle is sold to determine their present value. 151

Basic Inputs for Analysis of Economic Impacts The variety of impacts discussed above are a fu nction of basic factors which determine their magnitude and define their value. These include the discount rate, the level of vehicle sales, the magnitude of the rebound effect, a nd the relationship between EPA measured fuel efficiency and actual on-road fuel efficiency.

The Discount Rate Discounting future fuel savings and ot her 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 decline in the value of these be nefits – as viewed from today’s perspective – for each year they are deferred into the future. Discount rates are used in a variety of analyses that address different aspects of benefit valuation. These include: 1) selecting a set of standards 2) analyzing the impact of those standards 3) examining the impact of uncertainty surrounding our choice of rates used to analyze impacts, and 4) determining the sensitivity of standards selection to the discount rate. However, the agency must select one specific rate to set the sta ndards. The agency uses a rate of 7 percent per year to discount the value of future fuel savings and ot her benefits when it analyzes the potential impacts of alternative passenger car and light truck CAFE standard s. OMB Circular A-4 requires that the agency examine costs and benefits of proposed standards usi ng discount rates of both 3 percent and 7 percent. The 3 percent rate generally represents the consumer rate of time preference while the 7 percent rate generally represents the economy-wide opportunity cost of capital. Benefits based on both of these rates are presented to value the benefits that are associated with the standards set in this proposal. The agency

151 Discounting to the year when each model year was produced allows future economic benefits from improving each model year’s fuel economy to be compared to added produc tion costs for making thos e vehicles more fuelefficient, which are assumed to be incurred at the time those vehicles are manufactured.VIII-2

uses discount rates ranging from 3 percent to 10 per cent per year to analyze the uncertainty surrounding the future impacts of alternative standards.

There are several reasons for the ag ency’s choice of 7 percent as the appropriate discount rate to determine the standards. First, OMB Circular A-4 indicates that this rate reflects the economywide opportunity cost of capital. The agency believes that a substantial porti on of the cost of this regulation may come at the expense of other inve stments the auto manufac turers might otherwise make. Several large manufacturers are resource-c onstrained with respect to their engineering and product-development capabilities. As a result, other uses of these resources will be foregone while they are required to be applied to technologies that improve fuel economy.

Second, 7 percent also appears to be an appropriate rate to th e extent that the costs of the regulation come at the expense of consumption as opposed to investment. The agency believes that financing rates on vehicle loans represent an appropriate discount rate, because they reflect the opportunity costs faced by consumers when buyi ng vehicles with greater fuel economy and a higher purchase price. Most new and used vehicl e purchases are financed, and because most of the benefits from higher fuel economy standards accrue to vehicle purchasers in the form of fuel savings, the appropriate discount ra te is the interest rate buyers pay on loans to finance their vehicle purchases. 152

According to the Federal Reserve, the interest rate on new car loans made through commercial banks has closely tracked the rate on 10-y ear treasury notes, but exceeded it by about 3 percent. 153 The official Administration forecast is that real intere st rates on 10-year treasury notes will average about 3 percent through 2016, im plying that 6 percent is a reasonable forecast for the real interest rate on new car loans. 154 In turn, the interest rate on used car loans made through automobile financing companies has clos ely tracked the rate on new car loans made through commercial banks, but exceeded it by about 3 percent. 155 (The agency believes it is important to consider rates on loans that finance used car purchases, because some of the fuel savings resulting from improved fuel economy accrue to used car buyers.) Given the 6 percent estimate for new car loans, a reasonable forecas t for used car loans is thus 9 percent.

Because the benefits of fuel economy accrue to both new and used car owners, a discount rate between 6 percent and 9 percent is thus appropriate for evaluating future benefits resulting from higher fuel economy. Assuming that new car buy ers discount fuel savings at 6 percent for 5 years (the average duration of a new car loan) 156 and that used car buyers discount fuel savings at 9 percent for 5 years (the aver age duration of a used car loan) 157 , the single constant discount rate that yields equivalent present value fuel savings is very close to 7 percent.

152 Empirical evidence also demonstrates th at used car purchasers do pay for gr eater fuel economy (Kahn, Quarterly Journal of Economics, 1986).

153 See, http://www.federalreserve.gov/ releases/g20/hist/fc_hist_tc.txt.

154 See, http://www.federalreserve.gov/releas es/h15/data/Monthly/H15_TCMNOM_Y10.txt.

155 See, http://www.federalreserve.gov/ releases/g20/hist/fc_hist_tc.txt.

156 Id.

157 Id.VIII-3

However, the Agency recognizes th at there are arguments for usin g 3 percent as well. Namely that OMB requests benefits to be estimated at both 3 percent as well as 7 percent and that the official Administration forecast is that real inte rest rates on 10-year tr easury notes will average about 3 percent through 2016. Although the agency feels that the arguments for 7% are stronger, we have calculated results und er both 3% and 7% to demonstrate the impact of the lower discount rate on the resulting standards.

Sales Projections

A critical variable affecting th e total economic benefits from improving light truck fuel economy is the number of vehicles likely to be produced un der stricter fuel economy. Projections of total passenger cars and light truck sales for futu re years (see Table VIII-1a and VIII-1b) were obtained from the Energy Info rmation Administration’s (EIA) Annual Energy Outlook 2007 ( AEO 2007 ), a standard government reference for projections of energy production and consumption in different sectors of the U.S. economy. 158 NHTSA estimated the sales by manufacturer, based on their ma rket shares in the NHTSA MY 2006 CAFE data base. These values will be used as multipliers to estimate th e overall impacts (both costs and benefits) of changes in fuel economy standards.

158 U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2007 , Supplemental Table 47, http://www.eia.doe.gov/oiaf/aeo/supplement/suptab_47.xls

.VIII-4

Table VIII-1a Sales Projections – Passenger Cars (1,000s of vehicles)

2011 2012 2013 2014 2015

BMW 187.2 185.2 183.6 180.2 178.6

Mercedes 184.9 182.9 181.3 177.9 176.3

Chrysler 571.9 551.9 569.0 554.0 546.7

Ferrari 1.7 1.7 1.7 1.7 1.6

Ford 1,430.5 1,415.3 1,402.9 1,376.8 1,364.7 Fuji (Subaru) 137.3 135.8 134.7 132.2 131.0 General Motors 2,014.0 2,000.9 1,985.3 1,951.0 1,935.0

Honda 916.6 906.8 898.9 882.2 874.4

Hyundai/Kia 477.5 472.4 468.3 459.6 455.5

Lotus 3.7 3.6 3.6 3.5 3.5

Maserati 2.3 2.3 2.3 2.2 2.2

Mitsubishi 75.7 74.9 74.2 72.8 72.2

Nissan 721.6 713.9 707.7 694.5 688.4

Porsche 16.1 15.9 15.7 15.5 15.3

Suzuki 64.5 63.8 63.2 62.0 61.5

Toyota 1,500.4 1,483.3 1,470.3 1,443.0 1,430.2

Volkswagen 274.0 271.1 268.7 263.7 261.4

Total 8,579.6 8,481.7 8,431.2 8,272.8 8,198.5VIII-5

Table VIII-1b Sales Projections – Light Trucks (1,000s of vehicles)

2011 2012 2013 2014 2015

BMW 66.5 68.4 70.1 72.1 72.9

Mercedes 21.4 22.0 22.6 23.2 23.5

Chrysler 1899.9 1,953.4 2,002.6 2,058.9 2,081.3

Ford 1559.6 1,644.6 1,686.0 1,733.4 1,752.3

Fuji (Subaru) 84.6 86.9 89.0 91.4 92.4

General Motors 2159.2 2,213.6 2,269.3 2,333.0 2,358.4

Honda 570.7 586.8 601.5 618.4 625.2

Hyundai 298.5 306.9 314.6 323.5 327.0

Mitsubishi 40.4 41.5 42.5 43.7 44.2

Nissan 446.3 417.3 429.6 443.6 449.2

Porsche 14.6 15.0 15.4 15.9 16.0

Suzuki 25.0 25.7 26.3 27.1 27.3

Toyota 963.1 990.2 1,015.2 1,043.7 1,055.1

Volkswagen 23.4 24.0 24.6 25.3 25.6

Total 8213.1 8,396.4 8,609.4 8,853.2 8,950.3

The “Rebound Effect”

The rebound effect refers to the tendency for owners to increase the number of miles they drive a vehicle in response to an increase in its fuel ec onomy, as would result from more stringent fuel economy standards. The rebound effect occurs b ecause an increase in a vehicle’s fuel economy reduces its owner’s fuel cost for driving each mile, which is typical ly the largest single component of the cost of operating a vehicle. Ev en with the vehicle’s hi gher fuel economy, this additional driving uses some fuel, so the rebound effect will reduce the net fuel savings that result when the fuel economy standards require manufacturers to increase fuel economy. The rebound effect is usually expre ssed as the percentage by whic h annual vehicle use increases when average fuel cost per mile driven decreases in response to a change in the marginal cost of driving an extra mile, due either an increase in fuel economy or a re duction in the price of fuel.VIII-6

The magnitude of the rebound effect is one of the determinants of the actual fuel savings that are likely to result from adopting stricter standard s, and thus an important parameter affecting NHTSA’s evaluation of alternative standards for future model years. The rebound effect can be measured directly by estimating the elasticity of ve hicle use with respect to fuel economy itself, or indirectly by the elasticity of vehicle use with respect to fuel cost per mile driven. 159 When expressed as a positive percentage, either of thes e parameters gives the fr action of fuel savings that would otherwise result from ad opting stricter standards, but is offset by the increase in fuel consumption that results when vehicles with increased fuel economy are driven more.

Research on the magnitude of the rebound effect in light-duty vehicle use dates to the early 1980s, and almost unanimously concludes that a statistically significan t rebound effect occurs when vehicle fuel efficiency improves. 160 The most common approach to estimating its magnitude has been to analyze statistically household survey data on vehicle use, fuel consumption, fuel prices (often obtained from external sources), and other determinants 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 vehicle use, fuel economy, fuel prices, and other variables to identify the response of total or average vehicle use to changes in fleet-wide average fuel economy and its effect of fuel cost per mile driven. Two recent studies analyzed yearly variation in vehicle ownership and use, fuel prices, and fuel economy among individual states over an extende d time period in order to measur e the response of vehicle use to changing fuel economy. 161

An important distinction among studies of the re bound effect is whether they assume that the effect is constant, or varies over time in respons e to the absolute levels 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 arrive at differing conclusions about whether the rebound effect is larger am ong households that own more vehicles. One recent study using state-level data concludes that the rebound effect va ries directly in response to changes in personal income and the degree of urbanization of U.S. cities, as well as fuel costs.

In order to arrive at a preliminary estimate of the rebound effect for use in assessing the fuel savings, emissions reductions, and other impacts of alternative standard s, NHTSA reviewed 22 studies of the rebound effect conducted from 1983 through 2005. We then conducted a detailed analysis of the 66 separate es timates of the long-run rebound eff ect reported in these studies,

159 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 vehicle’s fuel economy increases. 160 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. 161 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.VIII-7

which is summarized in the table below. 162 As the table indicates, these 66 estimates of the longrun 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 m ean (24 percent), while focusing on the authors’ preferred estimates from published studies narr ows this range and lowers its average only slightly. The median estimate of the rebound eff ect in all three samples, which is generally regarded as a more reliable indicator of their cen tral tendency than the average because it is less influenced by unusually small and large estima tes, is 22 percent. As Table 13 indicates, approximately two-thirds of all estimates review ed, of all published estimates, and of authors’ preferred estimates fall in th e range of 10-30 percent.

Table VIII-1c Summary of Rebound Effect Estimates 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%
Authors' Preferred Estimates 17 17 9% 75% 22% 22% 15%
U.S. Time-Series Estimates 7 34 7% 45% 14% 18% 9% Household Survey Estimates 13 23 9% 75% 31% 31% 16%
Pooled U.S. State Estimates 2 9 8% 58% 22% 25% 14% Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16% Variable Rebound Effect: (1) Reported Estimates 10 29 10% 45% 23% 23% 10% Updated to 2006 (2) 10 29 6% 46% 16% 19% 12% (3) Three studies estimate both consta nt and variable rebound effects. (4) Reported estimates updated to reflect 2006 valu es of vehicle use, fuel prices, fleet fuel efficiency, household income, and household vehicle ownership.

The type of data used and authors’ assumption about whether the rebound effect varies over time have important effects on its estimated magnitude . The 34 estimates derived from analysis of U.S. annual time-series data produce a median estimate of 14 percen t for the long-run rebound effect, while the median of 23 estimates based on household survey data is more than twice as large (31 percent), and the median of 9 estimates based on pooled state data matches that of the entire sample (22 percent). The 37 estimates assuming a constant rebound effect produce a median of 20 percent, while the 29 originally reported estimates of a variable rebound effect have a slightly higher me dian value (23 percent).

162 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, we computed a weighted average of the reported values using the distribution of households among vehicle ownership categories.VIII-8

In selecting a single valu e for the rebound effect to use in analyzing alternative standards for future model years, NHTSA tentatively attaches greater significance to studies that allow the rebound effect to vary in response to changes in the various factors that have been found to affect its magnitude. However, it is also important to update authors’ original ly-reported estimates of variable rebound effects to reflect current conditi ons. Recalculating the 29 original estimates of variable rebound effects to reflec t current (2006) values for reta il fuel prices, average fuel economy, personal income, and household vehicle ownership reduces their median estimate to 16 percent. 163 NHTSA also tentatively attaches grea ter significance to the recent study by Small and Van Dender (2005), which finds that the reb ound effect tends to decline as average fuel economy, personal income, and suburbanization of U.S. cities increase, but – in accordance with previous studies – rises w ith increasing fuel prices. 164

Considering the empirical evidence on the rebou nd effect as a whole, but according greater importance to the updated estimates from st udies allowing the rebound effect to vary – particularly the Small and Van Dender study – NHTSA has selected a rebound effect of 15 percent to evaluate the fuel savi ngs and other effects of alterna tive standards for the time period covered by this rulemaking. However, we do not believe that evidence of the rebound effect’s dependence on fuel prices or household income is sufficiently convincing to justify allowing its future value to vary in response to forecast changes in these variables. A range extending from 10 percent to at least 20 percent -- and perhaps as high as 25 percent -- app ears to be appropriate for the required analysis of the uncertainty surrounding these estimates.

On-Road Fuel Economy Adjustment

Actual fuel economy levels achieved by vehicles in on-road driving fall significantly short of their levels measured under the laboratory-like test conditions used by EPA to establish its

163 As an illustration, Small and Van Dender (2005) allow the rebound effect to vary over time in response to changes in real per capita income as well as average fuel cost per mile driven. While their estimate for the entire interval (1966-2001) they analyze is 22 percent, updating this estimate using 2006 values of these variables reduces the rebound effect to approximately 10 percent. Similarly, updating Greene’s 1992 original estimate of a 15 percent rebound effect to reflect 2006 fu el prices and average fuel economy reduces it to 6 percent. See David L. Greene, “Vehicle Use and Fuel Economy: How Big is the Rebound Effect?” The Energy Journal, 13:1 (1992), 117-143. In contrast, the distribution of households among vehicle ownership categories in the data samples used by Hensher et al. (1990) and Greene et al. (1999) are nearly identical to the most recent es timates for the U.S., so updating their original estimates to current U.S. conditions changes them very little. See David A. Hensher, Frank W. Milthorpe, and Nariida C. Smith, “The Demand for Vehicle Use in the Urban Household Sector: Theory and Empirical Evidence,” Journal of Transport Economic s and Policy, 24:2 (1990), 119-137; and David L. Greene, James R. Kahn, and Robert C. Gibson, “Fuel Economy Rebound Effect for Household Vehicles,” The Energy Journal, 20:3 (1999), 1-21.

164 In the most recent light truck CA FE rulemaking, NHTSA chose not to preference the Small and Van Dender study over other published estimates of the value of the rebound effect, stating that since it “remains an unpublished working paper that has not been subjected to formal peer review, ...the agency does not yet consider the estimates it provides to have the same credibility as the published and widely-cited estimates it relied upon.” See 71 FR 17633 (Apr. 6, 2006). The study has subsequently been published and peer-reviewed, so NHTSA is now prepared to “consider it in developing its own estimate of the rebound effect for use in subsequent CAFE rulemakings.”VIII-9

published fuel economy ratings for different mode ls. In analyzing the fuel savings from alternative passenger car and light truck CAFE st andards, the agency adjusts the actual fuel economy performance of each passenger car and light truck model downward from its rated value to reflect the expected size of this on-ro ad fuel economy “gap.” In December 2006, EPA adopted changes to its regulations on fuel ec onomy labeling, which we re intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel economy levels. 165

Supplemental analysis reported by EPA as part of its Final Rule indicates that actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published fuel economy levels. 166 For example, if the overall EPA fuel econo my rating of a light truck is 20 mpg, the onroad fuel economy actually achieved by a typical dr iver of that vehicle is expected to be 16 mpg (20*.80). The agency has employed EPA’s revised es timate of this on-road fuel economy gap in its analysis of the fuel sa vings resulting from alternative CAFE standards for MY 2011-2018 passenger cars and MY 2012-18 light trucks.

Benefits from Fuel Savings

The main source of economic benefits from a fuel economy standard is the value of the resulting fuel savings over the lifetimes of ve hicles that are required to comply with the stricter standards. These fuel savings for each scenario are measured by the difference between the adjusted baseline fuel economy for each model year and th e fuel economy levels corresponding to that alternative. The sum of these annual fuel savings over each calenda r year that a vehicle remains in service represents the cumulative fuel savi ngs resulting from applying the alternative to vehicles produced during that model year.

As previously noted, actual fuel economy leve ls achieved by vehicles in on-road driving fall significantly short of their levels measured under the laboratory-like test conditions used by EPA to establish its published fuel economy ratings for different mode ls. In analyzing the fuel savings from alternative passenge r car and light truck CAFE sta ndards, the agency adjusts the actual fuel economy performance of each passenger car and light truck model downward from its rated value to reflect the expected size of this on-road fuel economy “gap.” In December 2006, EPA adopted changes to its regulations on fuel ec onomy labeling, which were intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel economy levels. 167

Supplemental analysis reported by EPA as part of its Final Rule indicates that actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published fuel economy

165 EPA, Fuel Economy Labeling of Motor Vehicles: Revisions To Improve Ca lculation of Fuel Economy Estimates; Final Rule, 40 CFR Parts 86 and 600, Federal Register, December 27, 2006, pp. 77872-77969, http://www.epa.gov/fedrgstr/EPA-AIR/2006/December/Day27/a9749.pdf

.

166 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/fue leconomy/420r06017.pdf

. 167 EPA, Fuel Economy Labeling of Motor Vehicles: Revisions To Improve Ca lculation of Fuel Economy Estimates; Final Rule, 40 CFR Parts 86 and 600, Federal Register, December 27, 2006, pp. 77872-77969, http://www.epa.gov/fedrgstr/EPA-AIR/2006/December/Day27/a9749.pdf

.VIII-10

levels. 168 The agency has employed EPA’s revised es timate of this on-road fuel economy gap in its analysis of the fuel sa vings resulting from alternative CAFE standards for MY 2011-2018 passenger cars and MY 2012-18 light trucks.

The number of light vehicles manufactured duri ng each model year that remains in service during each subsequent calendar year is estim ated by multiplying the estimated proportions of vehicles expected to survive to each age up to 26 years for passenger cars (Table VIII-2a) and 36 years for light trucks (Table VIII-2b) by the number of cars and light trucks forecast to be produced during each year. Thes e “survival rates,” which are estimated from experience with recent model-year vehicles, are slightly different than the survival rates used in past NHTSA analyses since they reflect recent increases in durability and usage of mo re recent passenger car and light truck models. 169 Updated estimates of average annua l miles driven by vehicle age were developed from the Federal Highway Administ ration’s 2001 National Hous ehold Transportation Survey, and these also differ from the estimat es of annual mileage employed in past NHTSA analyses. 170 The total number of miles driven by ve hicles of a single model year during each year of its life span in the fleet in effect is estimated by multiplying these age-specific estimates of annual miles driven per vehicle by the number of vehicles projected to remain in service at each age.

Table VIII-2a and VIII-2b provide the new schedules of vehicle miles traveled and survivability based on updated analyses performed by NHTSA. These were developed from registration data for 1977 through 2003, and from a 2001 survey of house hold vehicle use. In this analysis, the maximum vehicle age was defined as the age when the number remaining in service has declined to approximately two per cent of the vehicles originally prod uced. Based on and examination of recent registration data for older model years, typical maximum ages appear to be 26 years for passenger cars and 36 years for light trucks. Using the 36-year estimate of the maximum lifetimes of light trucks results in survival -weighted or “expected” lifetime mileage of 190,066 miles. Fuel savings and other benefits resu lting from higher light truck CAFE standards are calculated over this expected 36 year lifetime and total mileage. In contrast, NHTSA’s previous estimate of lifetime VMT in the 2006 final ru le was 179,954 miles over a 36-year lifetime for light trucks. The resulting survival-weighted mileage over the 26-year maximum lifetime of passenger cars is 161,847 miles, and fuel savi ngs and other benefits resulting from higher passenger car CAFE standards are calculated over this 26-year lifetime and total mileage. It should be noted, however, that survival-weighted VMT is extremely low (less than 1,000 miles per year) after age 20 for cars and age 25 for light trucks, and thus has little impact on lifetime fuel savings or other benefits from higher fuel economy, part icularly after discounting those benefits to their present values.

168 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/fue leconomy/420r06017.pdf

. 169 The survival rates were calculated from R.L. Polk, Na tional Vehicle Population Profile, 1977-2003; see NHTSA, “Vehicle Survivability and Travel Mileage Schedules,” Office of Regulatory Analysis and Evaluation, NCSA, January 2006, pp. 9-11, Docket No. 22223-2218. 170 See also NHTSA, “Vehicle Survivability and Travel M ileage Schedules,” Office of Regulatory Analysis and Evaluation, January 2006, pp. 15-17.VIII-11

The primary source of data for determining ve hicles in operation is the National Vehicle Population Profile (NVPP) compiled by R.L. Po lk and Company. The NVPP is an annual census, as of July 1 of each year, of passenge r cars and light trucks registered for on-road operation in the United States. NVPP registratio n data was used from vehicle model years 1977 to 2003. Survival rates were averaged for the fi ve most recent model years for vehicles up to 20 years old, and regression models were fitted to these data to develop smooth relationships between age and the proportion of ca rs or light trucks surviving to that age. The survival rates predicted by these models are used to deve lop the estimates of annual mileage and fuel consumption used to calculate fuel savings and other impacts of higher fuel economy.

The 2001 National Household Travel Survey ( NHTS) sponsored by the Federal Highway Administration, Bureau of Tran sportation Statistics, and the Na tional Highway Traffic Safety Administration attempted to develop up-to-date information on household vehicle ownership and use. The NHTS is the integration of two previous national travel surveys: the Federal Highway Administration-sponsored Nationwide Personal Tran sportation Survey (NPTS) and the Bureau of Transportation Statistics-sponsored American Travel Survey (ATS). 171 The 2001 NHTS was the source of updated information on annual miles driven by age for passenger cars and light trucks.

Finally, it should be noted that th e estimates of average annual m iles driven by passenger cars and light trucks, while new for NHTSA , are based on data collected during 2001-2002, and reflect the historically low gasoline prices th at prevailed at the time the survey was conducted. To account for the effect on vehicle use of subsequent increases in fuel prices, the estimates of annual vehicle use derived from the NHTS are adjusted to reflect pr ojected future gasoline prices using the rebound effect, which is discussed in detail later in this ch apter. Two factors affect the cost of gasoline per mile driven - fuel prices per gallon, and fuel econo my in miles-per-gallon. Because the intensity of vehicle use depends partly on the cost per mile of driving, the estimates of vehicle use developed from NHTS data reflect both fuel prices and fuel econo my levels that prevaile d during 2001 and 2002, when the survey was conducted. In analyzing the final ru le, the agency adjusted the annual usage estimates derived from the NHTS data to reflect the effect of the higher EIA fuel prices that are forecast over the covered vehicles’ expected lifetimes, whic h exceed those that existed during 2001-2002.

Specifically, the adjustment accounted for the differe nce between the average price per gallon of fuel forecast over the expected lifetimes of model y ear 2011-2015 passenger cars and light trucks and the average price that prevailed during 2000 and 2001. When expressed in percentage terms, this difference was assumed to represent the percent increas e in fuel cost per mile driven between the time the survey was conducted and the time period wh en model year 2011-2015 passenger cars and light trucks would be in service.

The same elasticity of annual vehicl e use with respect to fuel cost pe r mile that was used to estimate the increase in vehicle use resulting from improve d fuel economy (see detailed discussion of the “rebound effect” earlier in this chap ter), assumed to be –0.15, was app lied to this percent difference to adjust the estimates of vehicle use derived from the survey to reflect the eff ect of higher future fuel prices. In contrast, this adju stment reduces model year 2011-2015 passenger cars’ a nd light trucks’ average annual usage at each age to acc ount for the fact that fuel cost per mile driven is expected to be

171 For details on survey coverage and procedures, see http://nhts.ornl.gov/quickStart.shtml

.VIII-12

higher throughout their expected lif etimes than at the time the NHTS was conducted. The results of this adjustment are shown in Table VIII-2c for pa ssenger cars and in Table VIII-2d for light trucks. The unadjusted average lifetime mileage is estimat ed to be 161,847 for passenger cars and 190,066 for light trucks. After adjusting fo r the rebound effect, the average lifet ime mileage is estimated to be 152,274 for passenger cars and 178,824 for light trucks.VIII-13

Table VIII-2a Survival Rates and Unadjusted Annual Vehicle-Miles Traveled (VMT) by Age for Passenger Cars

Vehicle Age Estimated Survivability (1977 to 2002 NVPP) Estimated VMT (2001 NHTS) Weighted Yearly Travel Miles

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 0.5604 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 161,847VIII-14

Table VIII-2b Survival Rates and Unadjusted Annual Vehicle-Miles Traveled (VMT) by Age for Light Trucks

Vehicle Age Estimated Survivability (1977 to 2002 NVPP) Estimated VMT (2001 NHTS) Weighted Yearly Travel Miles

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 190,066VIII-15

Table VIII-2c Survival Rates and Adjusted Annua l Vehicle-Miles Traveled (VMT) by Age for Passenger Cars

Vehicle Age Estimated Survivability Adjusted VMT Weighted Yearly Travel Miles 1 0.9950 13,389 13,322 2 0.9900 13,135 13,004 3 0.9831 12,860 12,643 4 0.9731 12,567 12,229 5 0.9593 12,257 11,758 6 0.9413 11,933 11,232 7 0.9188 11,596 10,654 8 0.8918 11,248 10,031 9 0.5604 10,893 9,372 10 0.8252 10,531 8,690 11 0.7866 10,165 7,996 12 0.7170 9,797 7,025 13 0.6125 9,429 5,775 14 0.5094 9,063 4,617 15 0.4142 8,702 3,604 16 0.3308 8,346 2,761 17 0.2604 7,999 2,083 18 0.2028 7,662 1,554 19 0.1565 7,337 1,148 20 0.1200 7,028 843 21 0.0916 6,734 617 22 0.0696 6,459 450 23 0.0527 6,206 327 24 0.0399 5,974 238 25 0.0301 5,768 174 26 0.0227 5,589 127 Adjusted Lifetime Passenger Car VMT 152,274VIII-16

Table VIII-2d Survival Rates and Adjusted Annua l Vehicle-Miles Traveled (VMT) by Age for Light Trucks

Vehicle Age Estimated Survivability Adjusted VMT Weighted Yearly Travel Miles 1 0.9950 15,133 15,058 2 0.9741 14,849 14,464 3 0.9603 14,529 13,952 4 0.9420 14,178 13,356 5 0.9190 13,799 12,681 6 0.8913 13,396 11,940 7 0.8590 12,974 11,145 8 0.8226 12,535 10,312 9 0.7827 12,084 9,458 10 0.7401 11,625 8,604 11 0.6956 11,161 7,764 12 0.6501 10,697 6,954 13 0.6042 10,235 6,184 14 0.5517 9,781 5,396 15 0.5009 9,337 4,677 16 0.4522 8,908 4,028 17 0.4062 8,498 3,452 18 0.3633 8,109 2,946 19 0.3236 7,747 2,507 20 0.2873 7,415 2,130 21 0.2542 7,117 1,809 22 0.2244 6,857 1,539 23 0.1975 6,638 1,311 24 0.1735 6,464 1,122 25 0.1522 6,340 965 26 0.1332 6,269 835 27 0.1165 6,254 729 28 0.1017 6,254 636 29 0.0887 6,254 555 30 0.0773 6,254 483 31 0.0673 6,254 421 32 0.0586 6,254 367 33 0.0509 6,254 318 34 0.0443 6,254 277 35 0.0385 6,254 241 36 0.0334 6,254 209

Adjusted Lifetime Light Truck VMT 178,824VIII-17

In interpreting the survivability and annual mileage estimates re ported in Tables VIII-2a through VIII-2d, it is important to understand that vehicl es are considered to be of age 1 during the calendar year that coincides with their model year Thus for example, model year 2010 vehicles will be considered to be of age 1 during calendar year 2010. This convention is used in order to account for the fact that vehicles produced during a model year typica l are first offered for sale in June through September of the preceding calendar year (for exam ple, sales of a model year typically begin in June through September of the previous calendar year, depending on manufacturer). Thus virtually a ll of the vehicles produced during a model year will be in use for some or all of the calendar year coinciding with their model year, and they are considered to be of age 1 during that year. 172 As an illustration, virtually the entire production of model year 2008 vehicles will have been sold and placed in se rvice by the end of calendar year 2008, so model year 2008 vehicles are defined to be of ag e 1 during calendar year 2008.Model year 2008 vehicles are subsequently defi ned to be of age 2 during calendar year 2009, age 3 during calendar year 2010, and so on, until they reach their maximum age of 36 years in calendar year 2043 (2008 + 35 = 2043).

To determine the impact of improved CAFE standa rds, fuel consumption is calculated using both current and revised CAFE levels. The differen ce between these estimates represents the net savings from increased CAFE standards. With th e current CAFE standard assumed to remain in effect, total fuel consumption by each model ye ar’s vehicles during ea ch calendar year they remain in service is calculated by dividing the total number of miles they are driven during that year by the average on-road fuel economy level they would achieve under the higher of either the manufacturer-specific standard or their production plans. With the final rule in effect, total fuel consumption by each model year’s vehicles duri ng each future calendar year is calculated by dividing the total number of miles they are dr iven by the higher on-road fuel economy level associated with that stricter CAFE standard. Th e total number of miles that vehicles are driven each year is different under the fi nal rule than with the current st andards remaining in effect as a result of the fuel economy “rebound effect,” which is discussed in detail later in this chapter.

The economic benefits to vehicle owners that resu lt from future fuel savings are valued in this analysis over the complete expected lifetimes of the vehicles affected by the final rule. This reflects the assumption that while the purchaser and first owner of a new vehicle might not realize the full lifetime benefits of improved fu el economy, subsequent owners of that same vehicle will continue to experien ce the resulting fuel savings un til the vehicle is retired from service. It is important to note, however, th at not all vehicles produced during a model year remain in service for the complete lifetime (26-year for passenger cars or 36 –year for light trucks) of each model year assu med in this analysis. Due to the pattern of vehicle retirement over this period, the expected or average lifetime of a representative vehicle is approximately half of that figure.

CAFE’s most immediate impacts are on individual consumers, but regulating fuel economy also has a broader societal impact that must be consid ered. The agency believes that CAFE standards should reflect the true economic value of resources that are saved when less fuel is produced and

172 One complication arises because registration data are ty pically collected for July 1 of each calendar year, so not all vehicles produced during a model year will appear in registration data until the calendar year when they have reached age 2 (and sometimes age 3) under this convention.VIII-18

consumed, higher vehicle prices, and, to the exte nt possible, any externalities that impact the broader society. Consumers’ perceptions of thes e values may differ from their actual impacts, but they will nonetheless experience the full value of actual fuel savings just as they will pay the full increased cost when the vehicle is purchased.

Moreover, the first and any subsequent owners of a vehicle will together realize these savings throughout its entire on-road lifet ime. While a vehicle’s buyer ma y only experience fuel savings for the limited time he or she typically owns that vehicle, any subsequent purchasers and owners of that used vehicle will continue to experience the fuel savings resulting from its higher fuel economy throughout the remainder of its useful life. The agency restricts its analysis of the sales impacts of higher new vehicle pri ces to the length of time the buye rs of new vehicles typically own the vehicles they purchase, under the as sumption that their purchase decisions will be influenced only by the benefits th ey receive during the time they e xpect to own the vehicles they purchase new. The agency estimates the length of this period using the average term of new car loans, which has recently averaged almost exactly 5 years. 173 However, the agency believes that the value of fuel savings resulting from more efficient operation over the entire lifetime of vehicles should be reflected in its analysis of the societal impacts that will determine fuel economy standards.

The economic value of fuel savings resulting fr om the final rule is estimated by applying the forecast of future fuel prices from the Reference Case of the Energy Information Administration’s Annual Energy Outlook 2008 Early Release to each future year’s estimated fuel savings. 174 (The uncertainty analysis reported in Chapter X uses fuel price forecasts from the High and Low Oil Price Scenarios included in AEO 2007 to examine the effects a range of possible fuel price scenarios, since High and Low Oil Price Scenario forecasts for AEO 2008 were not available at the time th is analysis was conducted.) The AEO 2008 Early Release forecast of future fuel prices, which is reported in Table VIII-3, represents retail prices per gallon of fuel, which including Federal, State, and any a pplicable local taxes. While the retail price of fuel is the proper measure for valuing fuel savi ngs from the perspective of vehicle owners, two adjustments to the retail price are necessary in or der to reflect the economic value of fuel savings to society as a whole.

First, Federal and State taxes are excluded from th e social value of fuel savings because these do not reflect costs of resources us ed in fuel production, and thus do not reflect resource savings that would result from reducing fu el consumption. Instead, fuel taxes simply represent resources that are transferred from one segment of the popu lation to another. Any reduction in State and Federal fuel tax payments by consumers will reduce government revenues by the same amount, thus ultimately reducing the value of government -financed services by ap proximately that same amount. The benefit derived from lo wer taxes to individuals is thus likely to be offset exactly by a reduction in the value of servi ces provided to society.

173 This estimate is derived from Federal Reserve Board, Federal Reserve Statistical Release G. 19: Consumer Credit, November 7, 2007, http://www.federalreserve.gov/releases/g19/Current/

. 174 U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2008 Early Release

, Reference Case Table 12, http://www.eia.doe.gov/oiaf/aeo/excel/aeotab_12.xls

.VIII-19

Second, the economic cost of ex ternalities generated by imports and consumption of petroleum products will be reduced in proportion to gasoli ne savings resulting from the final rule. The estimated economic value of these externalities is conve rted into its per-gallon equivalent and added to the pre-tax price of gasoline in order to measure this additional benefit to society for each gallon of fuel saved. This also allows th e magnitude of these exte rnalities to be easily compared to the value of the resources save d from reduced fuel production and use, which represents the most important component of th e social benefits from saving gasoline. A discussion of these externality values is in cluded in the next sec tion of this chapter

Table VIII-3 illustrates the adjustment of forecast re tail fuel prices to remove the value of fuel taxes and add the value of economic externalit ies from petroleum imports and use. The derivation of the estimated value of reduced econo mic externalities from petroleum use shown in the table is explained in detail in the following section. While the Reference Case fuel price forecasts reported in AEO 2008 Early Release extend through 2030, the ag ency’s analysis of the value of fuel savings over th e 26-year maximum lifetimes of MY 2011-15 passenger cars and 36-year maximum lifetimes MY 2011-15 light trucks requires forecasts extending through calendar year 2050. The agency assumes that re tail fuel prices will remain at the 2030 forecast values reported in the AEO 2008 Reference Case forecast over the period from 2030 through 2052 (in constant-dollar terms). As Table VIII-3 shows, the projected retail price of gasoline expressed in 2006 dollars varies over the forecas t period, declining from $2.69 in 2008 to $2.20 in 2016, and then increasing to $2.49 by 2030 and as assumed previously, remaining at that level through 2052.

Since gasoline taxes are a transfer payment and not a societal cost, the value of gasoline taxes is subtracted from the estimated gasoline price to es timate the value to society of saving gasoline. The agency has updated its estimat es of gasoline taxes, using upda ted State tax rates reported for January 1, 2006. Expressed in 2006 dollars, Fede ral gasoline taxes are currently $0.172, while State and local gasoline taxes t ogether average $0.262 per gallon, for a total tax burden of $0.434 per gallon.

Following the assumptions used by EIA in its National Energy Modeling System (NEMS), state and local gasoline taxes are assume d to keep pace with inflation in nominal terms, and thus to remain constant when expressed in constant 2006 dollars. In c ontrast, federal gasoline taxes are assumed to remain unchanged in nominal terms, and thus to decline throughout the forecast period when expressed in constant 2006 dollars. These differing assumptions about the likely future behavior of federal and state/local fuel taxes are consistent w ith recent historical experience, and reflect the fact that Federal motor fuel taxes a nd most State taxes are specified on a cents-per-gallon basis (some State taxes are levi ed as a percentage of the wholesale price of fuel), and typically require legislation to change.

Actual fuel economy levels achie ved by light-duty vehicles in on -road driving fall somewhat short of their levels measured under the laborato ry-like test conditions used by EPA to establish its published fuel economy ratings for different models. In analyzing the fuel savings from alternative CAFE standards, NHTSA has prev iously adjusted the actual fuel economy performance of each light truck model downward from its rated value to re flect the expected size of this on-road fuel economy “gap.” On December 27, 2006, EPA adopted changes to itsVIII-20

regulations on fuel economy labeling, which were intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel economy levels. 175

In its Final Rule, EPA estimated that actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published fuel ec onomy levels. For example, if the overall EPA fuel economy rating of a light tr uck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of that vehicl e is expected to be 16 mpg ( 20*.80). NHTSA has employed EPA’s revised estimate of this on-road fuel economy gap in this analysis of the fuel savings resulting from alternative CAFE standards proposed in this rulemaking.

Table VIII-3 Adjustment of Forecast Retail Gasoline Price to Reflect Social Value of Fuel Savings

Year AE0 2008 Forecast of Retail Gasoline Price (2006 $/gallon) Estimated Federal and State Taxes (2006 $/gallon) Forecast Gasoline Price Excluding Taxes

(2006 $/gallon) Forecast Gasoline Price Including Externalities (2006 $/gallon) 2011 $2.553 $0.420 $2.133 $2.428 2012 $2.477 $0.416 $2.061 $2.356 2013 $2.405 $0.412 $1.993 $2.288 2014 $2.389 $0.409 $1.980 $2.275 2015 $2.316 $0.405 $1.911 $2.206 2016 $2.255 $0.402 $1.853 $2.148 2017 $2.267 $0.399 $1.868 $2.163 2018 $2.293 $0.395 $1.898 $2.193 2019 $2.362 $0.392 $1.970 $2.265 2020 $2.420 $0.388 $2.032 $2.327 2021 $2.386 $0.385 $2.001 $2.296 2022 $2.406 $0.381 $2.025 $2.320 2023 $2.414 $0.378 $2.036 $2.331 2024 $2.409 $0.374 $.2.035 $2.330 2025 $2.425 $0.371 $2.054 $2.349 2026 $2.438 $0.371 $2.067 $2.362 2027 $2.451 $0.371 $2.080 $2.375 2028 $2.474 $0.371 $2.103 $2.398 2029 $2.498 $0.371 $2.127 $2.422 2030-2052 $2.514 $0.371 $2.143 $2.438

175 71 FR 77871 (Dec. 27, 2006).VIII-21

Other Economic Benefits from Reducing Petroleum Use The agency believes that assessing the economic case for increasing the stringency of fuel economy standards requires a comprehensive analysis of the resulting benefits and costs to the U.S economy, rather than simply comparing the di rect costs associated with petroleum use and fuel production to current fuel taxes. The bene fits of more stringent fuel economy standards include the market value of the savings in resour ces from producing less fu el, together with the resulting reductions in the costs of economic externalities associated with petroleum consumption, and of environmental externalitie s caused by fuel consumption and production. Environmental externalities include adverse health impacts associated with criteria pollutants and environmental damage associated with greenhou se gases. The costs imposed on the U.S. economy by more stringent fuel economy regulation include those costs for manufacturing more fuel-efficient vehicles, as well as the increased external costs of congestion, crashes, noise and pollution from added driving cau sed by the rebound effect.

Vehicle buyers value improved fuel economy using retail fuel prices and miles per gallon, but may consider fuel savings only over the time they e xpect to own a vehicle, while the value to the U.S. economy of saving fuel is measured by its pr e-tax price, and includes fuel savings over the entire lifetime of vehicles. Thus, it cannot simply be assumed that the interaction of manufacturers’ costs and vehicle buyers’ dema nds in the private marketplace will determine optimal fuel economy levels, and that these leve ls should only be adjusted by Federal regulation if the external costs of fuel producti on and use exceed current fuel taxes.

The Agency’s analysis estimates the value of eac h category of benefits and costs separately, and it compares the total benefits resulting from each alternative level to its total costs in order to assess its desirability. This more complete acco unting of benefits and costs to the U.S. economy from reducing fuel use is necessary to assess th e case for fuel economy regulation generally, and for increasing the stringency of the current pa ssenger car and light truck fuel economy standards in particular.

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 petrol eum, or in the prices paid by consumers of petroleum products such as gasoli ne. These costs include (1) higher prices for petroleum products resulting from the effect of U. S. oil import demand on the world oil price; (2) 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 ma intaining a U.S. military presence to secure imported oil supplies from unstabl e regions, and for maintaining th e strategic petroleum reserve (SPR) to cushion against resulting price increases . Higher U.S. imports of crude oil or refined petroleum products raise the magnitude of these ex ternal economic costs, thus increasing the true economic cost of supplying transportation fuels above the resource costs of producing them. Conversely, reducing U.S. imports of crude pe troleum or refined fu els or reducing fuel consumption can reduce these external costs. Any reduction in their total value that results from improved vehicle fuel economy represents an economic benefit of raising fuel economy standards in addition to the value of fuel savings and emissions reductions itself.VIII-22

Demand Costs Increased U.S. oil imports can impose higher co sts on all purchasers of petroleum products, because the U.S. is a sufficiently large purchaser of foreign oil supplies that changes in U.S. demand can affect the world price. The effect of U.S. petroleum imports on world oil prices is determined by the degree of OPEC monopoly power over global oi l supplies, and the degree of monopsony power over world oil demand exerted by the U.S. The combination of these two factors means that increases in domestic dema nd for petroleum products that are met through higher oil imports can cause the price of oil in the world market to rise, which imposes economic costs on all other purchasers in th e global petroleum market in ex cess of the higher prices paid by U.S. consumers. 176 Conversely, reducing U.S. oil im ports can lower the world petroleum price, and thus generate benef its to other oil purchasers by re ducing these “monopsony costs.”

Although the degree of current OP EC monopoly power is subject to considerable debate, the consensus appears to be that OPEC remains able to exercise some degree of control over the response of world oil supplies to variation in worl d oil prices, so that the world oil market does not behave competitively. 177 The extent of U.S. monopsony power is determined by a complex set of factors including the rela tive importance of U.S. imports in the world oil market, and the sensitivity of petroleum supply and demand to its world price among other participants in the international oil market. Most evidence appears to suggest that variation in U.S. demand for imported petroleum continues to exert some influence on world oil prices, although this influence appears to be limited. 178

In analyzing benefits from its recent actions to increase light truck CAFE standards for model years 2005-07 and 2008-11, NHTSA relied on a 1997 study by Oak Ridge Na tional Laboratories (ORNL) to estimate the value of reduced econom ic externalities from petroleum consumption and imports. 179 More recently, ORNL updated its estimates of the value of these externalities, using the analytic framework developed in it s original 1997 study in conjunction with recent estimates of the variables and para meters that determine their value. 180 These include world oil prices, current and anticipated future levels of OPEC petrol eum production, U.S. oil import

176 For example, if the U.S. imports 10 million barrels of petroleum per day at a world oil price of $80 per barrel, its total daily import bill is $800 million. If increasing imports to 11 million barrels per day causes the world oil price to rise to $81 per barrel, the daily U.S. import bill rises to $891 million. The resulting increase of $91 million per day ($891 minus $800 million) is attributable to increasing daily imports by only 1 million barrels. This means that the incremental cost of importing each additional barrel is $9 1, or $10 more than the ne wly-increased world price of $81 per barrel. This additional $10 per barrel represents a cost imposed on all other purchasers in the global petroleum market by U.S. buyers, in excess of the price they pay to obtain those additional imports.

177 For a summary see Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL-6851, Oak Ridge National Laboratory, November 1, 1997, at 17. Available at http://dmses.dot.gov/docim ages/pdf93/343894_we b.pdf (last accessed Dec. 2, 2007).

178 Id. , at 18-19.
179 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL-6851, Oak Ridge National Laboratory, November 1, 1997. Available at http://dmses.dot.gov/d ocimages/pdf93/34389 4_web.pdf (last accesse d Dec. 2, 2007).

180 Leiby, Paul N. "Estimating the Energy Security Bene fits of Reduced U.S. Oil Imports," Oak Ridge National Laboratory, ORNL/TM-2007/028, Revised July 23, 2007. Available at http://pzl1.ed.ornl.gov/energysecurity.html (click on link below “Oil Imports Costs and Benefits”) (last accessed Sept. 10, 2007).VIII-23

levels, the estimated responsiveness of oil supplies and demands to prices in different regions of the world, and the likelihood of oil supply disrup tions. ORNL’s prepared its updated estimates of oil import externalities were for use by EPA in evaluating the benefits of reductions in U.S. oil consumption and imports expected to result from its recently-issued Renewable Fuel Standard Rule of 2007 (RFS) 181

.
The updated ORNL study was subjected to a detaile d peer review and its estimates of the value of oil import externalities were subsequen tly revised to reflect their comments and recommendations. 182 Specifically, reviewers recommended th at ORNL increase its estimates of the sensitivity of oil supply by non-OPEC produ cers and oil demand by nations other than the U.S. to changes in the world oil price, as well as reduce its estimate of the sensitivity of U.S. gross domestic product (GDP) to potential sudden in creases in world oil prices. After making the revisions recommended by peer reviewers, ORNL’s updated estimates of the monopsony cost associated with U.S. oil imports range fr om $5.22 to $9.68 per barrel, with a most likely estimate of $7.41 per barrel. These estimates imply that each gallon of fuel saved as a result of adopting higher CAFE standards will reduce th e monopsony costs of U.S. oil imports by $0.124 to $0.230 per gallon, with the actual value most likely to be $0.176 per gallon saved. This represents an economic benefit in addition to th e value of savings in fuel production costs that would result from improving fuel economy.

Disruption and Adjustment Costs The second component of external economic cost s imposed by U.S. petroleum imports arises partly because an increase in oil prices triggere d by a disruption in the supply of imported oil reduces the level of output that the U.S. econom y can produce. The reduction in potential U.S. economic output depends on the extent and duration of the increases in petroleum product prices that result from a disruption in the supply of imported oil, as well as on whether and how rapidly these prices return to pre-disrup tion levels. Even if prices for imported oil return completely to their original levels, however, economic output will be at least temporarily reduced from the level that would have been possible w ithout a disruption in oil supplies.

Because supply disruptions and resulting price increases tend to occur suddenly rather than gradually, they can also impose costs on busine sses and households for ad justing their use of petroleum products more rapidly than if the sa me price increase had occurred gradually over time. These adjustments impose costs because they temporarily reduce economic output even below the level that would ultimately be reach ed once the U.S. economy completely adapted to higher petroleum prices. The additional costs to businesses and households reflect their inability to adjust prices, output levels, and their use of energy and othe r resources quickly and smoothly in response to rapid changes in prices for petroleum products.

Since future disruptions in forei gn oil supplies are an uncertain pr ospect, each of these disruption costs must be adjusted by the probability that the supply of imported oil to the U.S. will actually be disrupted. The “expected value” of these cost s – the product of the pr obability that an oil import disruption will occur and the costs of re duced economic output and abrupt adjustment to

181 Federal Register Vol.72, #83, May 1, 2007 pp.23,900-24,014
182 Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports

, ICF, Inc., September 2007.VIII-24

sharply higher petroleum prices – is the appropriate measure of their magnitude. Any reduction in these expected disruption costs resulting from a measure that lowers U.S. oil imports represents an additional economic benefit beyond the direct value of savings from reduced purchases of petroleum products.

While the vulnerability of the U.S. economy to oil price shocks is wi dely thought to depend on total petroleum consumption rather than on the level of oil imports , variation in imports is still likely to have some effect on the magnitude of price increases resulti ng from a disruption of import supply. In addition, changing the quantity of petroleum imported into the U.S. may also affect the probability that such a disruption will occur. If either the size of the likely price increase or the probability that U.S. oil supplie s will be disrupted is affected by oil imports, the expected value of the costs from a supply disruption will also depend on the level of imports.

Businesses and households use a variety of market mechanisms, including oil futures markets, energy conservation measures, and technologies that permit rapid fuel switching to “insure” against higher petroleum prices and reduce their costs for adjusting to sudden price increases. While the availability of thes e market mechanisms has likely reduced the potential costs of disruptions to the supply of imported oil, consum ers of petroleum products are unlikely to take account of costs they impose on others, so these co sts are probably not refl ected in the price of imported oil. Thus changes in oil import levels pr obably continue to affect the expected cost to the U.S. economy from potential oil supply disruptions, although this component of oil import costs is likely to be significantly smaller than estimated by studies conducted in the wake of the oil supply disruptions during the 1970s.

ORNL’s updated and revised estimat es of the increase in the expected costs associated with oil supply disruptions to the U.S. and the resulting rapid increase in prices for petroleum products amount to $4.54 to $5.84 per barrel, although its mo st likely estimate of $4. 59 per barrel is very close to the lower end of this range. According to these estimates, each gallon of fuel saved will reduce the expected costs disrup tions to the U.S. economy by $0.108 to $0.139, with the actual value most likely to be $0.109 per gallon. Li ke the reduction in monopsony costs, the reduction in expected disruption costs repres ents an economic benefit in addi tion to the value of savings in fuel production costs that would re sult from improving fuel economy.

Military Security and Strategic Petroleum Reserve Costs The third component of the exte rnal economic costs of importing oil into the U.S. includes government outlays for maintaining a military presen ce to secure the supply of oil imports from potentially unstable regions of the world and to pr otect against their interruption. Some analysts also include outlays for maintaining the U.S. Strategic Petroleum Reserve (SPR), which is intended to cushion the U.S. economy against the consequences of disruption in the supply of imported oil, as additional costs of protecti ng the U.S. economy from oil supply disruptions.

NHTSA believes that while costs for U.S. military security may vary over time in response to long-term changes in the actual le vel of oil imports in to the U.S., these costs are unlikely to decline in response to any reduction in U.S. o il imports resulting from raising future CAFE standards for light-duty vehicles. U.S. military activ ities in regions that represent vital sources of oil imports also serve a broader range of secur ity and foreign policy objectives than simplyVIII-25

protecting oil supplies, and as a consequence are unlikely to vary significantly in response to changes in the level of oil impor ts prompted by higher standards.

Similarly, while the optimal size of the SPR from the standpoint of its potential influence on domestic oil prices during a suppl y disruption may be related to th e level of U.S. oil consumption and imports, its actual size has not appeared to vary in response to recent changes in oil imports. Thus while the budgetary costs for maintaining the Reserve are similar to ot her external costs in that they are not likely to be reflected in the market price for imported oil, these costs do not appear to have varied in response to changes in oil import levels. As a result, the agencies’ tentative analysis of benefits from alternativ e CAFE standards does not include cost savings from either reduced outlays for U.S. military operations or maintaining a smaller SPR among the external benefits of reduci ng gasoline consumption and petroleum imports by means of tightening future standards. This view concur s with that of the recen t ORNL study of economic costs from U.S. oil imports, which concludes that savings in government outlays for these purposes are unlikely to result form modest re ductions in consumption of petroleum products and oil imports.

Thus NHTSA has tentatively included only the li kely reductions in monopsony and disruption costs from lower U.S. petroleum imports in its es timate of the savings in external economic costs from reducing fuel consumption. The updated and revised ORNL estimates suggest that the combined reduction in monopsony costs and expected costs to the U.S. economy from oil supply disruptions resulting from lower fuel consumpti on total $0.232 to $0.370 per gallon, with a most likely estimate of $0.286 per gallon. This repr esents the additional ec onomic benefit likely to result from each gallon of fuel saved by higher CAFE standards, beyond the savings in resource costs for producing and distributi ng each gallon of fuel saved. NHTSA tentatively employs this midpoint estimate in its analysis of the benef its from fuel savings projected to result from alternative CAFE standards for model years 2011-15. It also analyzes the effect on these benefits estimates from variati on in this value over the range from $0.232 to $0.370 per gallon of fuel saved.

The Effect of Fuel Savings on Fuel Supply Based on a detailed analysis of differences in fuel consumption, petroleum imports, and imports of refined petroleum products among the Refe rence Case, High Economic Growth, and Low Economic Growth Scenarios presented in the Energy Information Administration’s Annual Energy Outlook 2007 , the agency estimates that approxima tely 50 percent of the reduction in fuel consumption resulting from adopting higher CAFE 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. 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 pr oduction of crude petroleu m. Thus on balance, each gallon of fuel saved as a consequence of higher CAFE standards is anticipated to reduce total U.S. imports of crude petrol eum or refined fuel by 0.95 gallons. 183

183 This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95.VIII-26

Emissions Reductions Result ing from Fuel Savings

NHTSA has estimated emissions reductions resul ting from fuel savings for purposes of this PRIA. However, as indicated previously, NHT SA will consider the potential environmental impacts of the proposed standards and reasonable alternatives for purposes of NEPA through the NEPA process.

Criteria Pollutants While reductions in domestic fuel refining a nd distribution that result from lower fuel consumption will reduce emissions of criteria pollu tants, additional vehicle use associated with the rebound effect of higher fuel economy will increase emissions of these pollutants (see detailed discussion of the Rebound Effect earlier in this chapter). The ne t effect of stricter standards depends on the relative magnitudes of reduced emissions in fuel refining and distribution, and increased emissions from ve hicle use. Because the relationship between emission rates (emissions per gallon refined of fuel or mile driven) from fuel refining and vehicle use differs for each specific crite ria pollutant, the net effect of fuel savings from the proposed standards on total emissions of each pollutant is likely to differ. Predominant criteria pollutants emitted in fuel production and use include carbon monoxide (CO), hydrocarbon compounds (usually referred to as “volat ile organic compounds,” or VOC), nitrogen oxides (NOx), fine particulate matter (PM), and sulfur dioxide (SO2).

For purposes of NHTSA’s PRIA, the increase in emissions of these pollutants from additional vehicle use due to the rebound effect is tentatively estimated by mu ltiplying the increase in total miles driven by vehicles of each model year and age during future calendar years by age-specific emission rates per vehicle-mile for each pollutant. The agencies developed these emission rates using EPA’s MOBILE6.2 motor vehicle emissions factor model, with updated vehicle emission factors for some pollutants. 184 Emissions of these pollutants also occur during crude oil extraction and transportation, ga soline refining, and gasoline storage and distribution. The reduction in total emissions from each of these so urces thus depends on the extent to which fuel savings result in lower imports of refined gaso line, or in reduced domestic gasoline refining. 185

Based on analysis of changes in U.S. gasolin e imports and domestic gasoline consumption forecast in AEO 2007, NHTSA tenta tively estimates that 50 percen t of fuel savings resulting from higher CAFE standards will result in re duced imports of refined gasoline, while the remaining 50 percent will reduce domestic refining. 186 The reduction in domestic refining is assumed to leave its sources of crude petroleu m unchanged from the mix of 90 percent imports and 10 percent domestic production projected by AEO 2007.

184 U.S. Environmental Protection Agency, MOBILE6 Vehicle Emission Modeling Software, available at http://www.epa.gov/otaq/m6.htm#m60 (last accessed Sept. 10, 2007).

185 To a lesser extent, they also depend on whether any reduction in domestic gasoline refining is translated into reduced imports of crude oil or reduced domestic extraction of petroleum.

186 Estimates of the response of gasoline imports and domestic refining to fuel savings from stricter standards are variable and highly uncertain, but our preliminary analysis indicates that under any reasonable assumption about these responses, the magnitude of the net change in cr iteria pollutant emissions (accounting for both the rebound effect and changes in refining emissions) is extremely low relative to their current total.VIII-27

NHTSA proposes to estimate reductions in crite ria pollutant emissions from gasoline refining and distribution using emission rates obtained from Argonne Na tional Laboratories’ Greenhouse Gases and Regulated Emissions in Transportation (GREET) model. 187 The GREET model provides separate estimates of air pollutant emissi ons that occur in four phases of fuel production and distribution: crude oil extracti on, crude oil transportation and st orage, fuel refining, and fuel distribution and storage. 188 We tentatively assume that reduc tions in imports of refined fuel would reduce criteria pollutant emissions during fuel storage and distribution only. Reductions in domestic fuel refining using imported crude oil as a feedstock are tentatively assumed to reduce emissions during crude oil transportation and storage, as well as during gasoline refining, distribution, and storage, simply because less of it would be occurring. Similarly, reduced domestic fuel refining using domestically-produced crude oil is tentatively assumed to reduce emissions during all phases of gaso line production and distribution. 189

The net changes in emissions of each criteria pol lutant are calculated by comparing the increases in their emissions that result from increased vehicle use to the reductions that result from lower domestic fuel refining and distribution. The net change in emissions of each criteria pollutant is converted to an economic value using estimates of the economic costs per ton emitted (which result primarily from damages to human health) developed by EPA and submitted to the federal Office of Management and Budget for review. Fo r certain criteria pollutants, EPA estimates different per-ton costs for increases in emissi ons from vehicle use than for reductions in emissions from fuel refining, reflecting differe nces in their typical geographic distributions, contributions to ambient pollution levels, and resulting population exposure . The per unit costs for each criteria pollutant is summarized in Table VIII-B.

Greenhouse Gases NHTSA has taken the economi c benefits of reducing CO 2 emission into account in this rulemaking, both in developing proposed CAFE standards and in a ssessing the economic benefits of each alternative th at was considered. As noted above, the 9th Circuit found in CBD that NHTSA had been arbitrary and capricious in deciding not to m onetize the benefit of reducing CO 2 emissions, saying that the agency had not substantiated the conclusion in its April 2006 final rule that the appropriate course was not to monetize (i .e., quantify the value of) carbon emissions reduction at all.

To this end, NHTSA reviewed published estima tes of the “social cost of carbon emissions” (SCC). The SCC refers to the marginal cost of additional damages caused by the increase in expected climate impacts resulting from the emis sion of each additional metric ton of carbon,

187 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Transportation (GREET) Model , Version 1.6, April 2005, available at http://www.transporta tion.anl.gov/software/G REET/index.html (last accessed Sept. 10, 2007).

188 Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of volatile organic compounds, or VOCs) ar e 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 averag e energy content of gasoline.

189 In effect, this assumes that the dist ances crude oil travels to U.S. refineri es are approximately the same regardless of whether it travels from domestic oilfields or import term inals, and that the distances that gasoline travels from refineries to retail stations are approximately the same as those from import terminals to gasoline stations.VIII-28

which is emitted in the form of CO 2
. 190 It is typically estimated as the net present value of the impact over some time period (100 years or long er) of one additional ton of carbon emitted into the atmosphere. Because accumulated concentrati ons of greenhouse gases in the atmosphere and the projected impacts on global climate are incr easing over time, the economic damages resulting from each additional ton of CO 2 emissions in future years are belie ved to be greater as a result. Thus estimates of the SCC are typically reporte d for a specific year, and these estimates are generally larger for emissions in more distant future years.

There is substantial variation among different aut hors’ estimates of the SCC, much of which can be traced to differences in their underlying assu mptions about several vari ables. These include the sensitivity of global temperatures and othe r climate attributes to increasing atmospheric concentrations of greenhouse gase s, discount rates applied to future economic damages from climate change, whether damages sustained by developing regions of the globe should be weighted more heavily than damages to deve loped nations, how long climate changes persist once they occur, and the economic va luation of specific climate impacts. 191

Taken as a whole, recent estimates of the S CC may underestimate the true damage costs of carbon emissions because they often exclude da mages caused by extreme weather events or climate response scenarios with low probabilit ies but potentially extreme impacts, and may underestimate the climate impacts and damages th at could result from multiple stresses on the global climatic system. At the same time, however, many studies fail to consider potentially beneficial impacts of climate change, and do not adequately account for how future development patterns and adaptations could re duce potential impacts from c limate change or the economic damages they cause.

Given the uncertainty surrounding estimates of th e SCC, the use of any single study may not be advisable since its estimate of the SCC will depend on many assumptions made by its authors. The Working Group II’s contribution to the Fourth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC) 192 notes that: The large ranges of SCC are due in the large part to differences in assumptions regarding climate sensitivity, response la gs, the treatment of risk and equity, economic and non-economic impacts, the in clusion of potentially catastrophic losses, and discount rates. Although the IPCC does not recommend a single estimate of the SCC, it does cite the Tol (2005) study on four separate occasions (pages 17 , 65, 813, 822) as the only available survey of the peer-reviewed literature that has itself been subjected to peer review. Tol developed a

190 Carbon itself accounts for 12/44, or about 27%, of the mass of carbon dioxide (12/44 is the ratio of the molecular weight of carbon to that of carbon dioxide). Thus each ton of carbon emitted is associated with 44/12, or 3.67, tons of carbon dioxide emissions. Estimates of the SCC are typically reported in dollars per ton of carbon, and must be divided by 3.67 to determine their equivalent value per ton of carbon dioxide emissions.

191 For a discussion of these factors, see Yohe, G.W., R.D. Lasco, Q.K. Ahmad, N.W. Arnell, S.J. Cohen, C. Hope, A.C. Janetos and R.T. Perez, 2007: Perspectives on climate change and sustainability. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, pp. 821-824.

192 Climate Change 2007 – Impacts, Adaptation and Vulner ability, Contribution of Working Group II to the Fourth Assessment Report of the IPCC, 17. Available at http://www.ipcc-wg2.org (las t accessed <Feb. 4, 2008>).VIII-29

probability function using the SCC estimates of th e peer reviewed literat ure and found estimates ranging from less than zero to over $200 per metric ton of carbon. In an effort to resolve some of the uncertainty in reported estimates of climate damage costs from carbon emissions, Tol (2005) reviewed and summarized one hundred and three estimates of th e SCC from 28 published studies. He concluded that when only peer-reviewed studies published in recognized journals are considered, “...climate change impacts may be very uncertain but is unlikely that the marginal damage costs of carbon dioxide emi ssions exceed $50 per [metric] ton carbon [about $14 per metric ton of CO2].” 193 He also concluded that the costs may be less than $14.

Because of the number of assumptions required by each study, the wide range of uncertainty surrounding these assumptions, and th eir critical influence on the re sulting estimates of climate damage costs, some studies have undoubtedly produced estimates of the SCC that are unrealistically high, while others are likely to have estimated valu es that are improbably low. Using a value for the SCC that reflects the cen tral tendency of estimates drawn from many studies reduces the chances of relying on a single es timate that subsequently proves to be biased.

It is important to note that estimates of the SCC almost invariably include the value of worldwide damages from potential climate impacts caus ed by carbon dioxide emissions, and are not confined to damages likely to be suffered within th e U.S. In contrast, the other estimates of costs and benefits of increasing fuel economy included in this prop osal include only the economic values of impacts that occur within the U.S. For example, the economic value of reducing criteria air pollutant emissions from overseas oil refineries is not counted as a benefit resulting from this rule, because any reduction in dama ges to health and property caused by overseas emissions are unlikely to be experienced within the U.S.

In contrast, the reduced value of transfer paym ents from U.S. oil purchasers to foreign oil suppliers that results when lower U.S. oil de mand reduces the world pr ice of petroleum (the reduced “monopsony effect”) is counted as a benefit of reducing fuel use. 194 If the agency’s analysis was conducted from a worldwide rather th an a U.S. perspective, however, the benefit from reducing air pollution overseas would be incl uded, while reduced payments from U.S. oil consumers to foreign suppliers would not.

In order to be consistent with NHTSA’s use of exclusively domes tic costs and benefits in prior CAFE rulemakings, the appropriate value to be placed on changes climate damages caused by carbon emissions should be one that reflects the change in damages to the United States alone. Accordingly, NHTSA notes that the va lue for the benefits of reducing CO 2 emissions might be restricted to the fraction of t hose benefits that are likely to be experienced within the United States.

Although no estimates of benefits to the U.S. its elf that are likely to result from reducing CO 2 emissions are currently available, NHTSA expect s that if such values were developed, the

193 Tol, Richard. The marginal dama ge costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33 (2005) 2064–2074, 2072. The summary SCC estimates reported by Tol are assumed to be denominated in U.S. dollars of the year of publication, 2005.

194 The reduction in payments from U.S. oil purchasers to domestic petroleum producers is not included as a benefit, since it represents a transfer that occu rs entirely within the U.S. economy.VIII-30

agency would employ those rather than global be nefit estimates in its analysis. NHTSA also anticipates that if such values were develope d, they would be lower than comparable global values, since the U.S. is likely to sustain only a fraction of total global damages resulting from climate change.

In the meantime, the agency has elected to use the IPCC estimate of $43 per metric ton of carbon as an upper bound on the benefits resulting from re ducing each metric ton of U.S. emissions. 195 This corresponds to approximately $12 per metric ton of CO 2 when expressed in 2006 dollars. This estimate is based on the 2005 Tol study. 196 The Tol study is cited repeatedly as an authoritative survey in various IPCC reports, wh ich are widely accepted as representing the general consensus in the scientific community on climate change science. Since the IPCC estimate includes the worldwide costs of pot ential damages from carbon dioxide emissions, NHTSA has elected to employ it as an upper bound on the estimated value of the reduction in U.S. domestic damage costs that is likely to result from lower CO 2 emissions. 197

The IPCC Working Group II Fourth Assessment Repor t (2007, p. 822) furthe r suggests that the SCC of carbon is growing at an annual 2.4 percen t growth rate, based on estimated increases in damages from future emissions reported in published studies. NHT SA has also elected to apply this growth rate to Tol’s orig inal 2005 estimate. Thus by 2011, the agency estimates that the upper bound on the benefits of reducing CO 2 emissions will have r eached about $14 per metric ton of CO 2 , and will continue increase by 2. 4 percent annually thereafter.

In setting a lower bound, the agency agrees wi th the IPCC Working Group II (2007) report that “significant warming across the globe and the locations of sign ificant observed changes in many systems consistent with warming is very unlikel y to be due solely to natural variability of temperatures or natural variabil ity of the systems” (pp. 9). Although this finding suggests that the global value of economic benefits from reducing carbon dioxide emissions is unlikely to be zero, it does not necessarily rule out low or zero values for the benefit to the U.S. itself from reducing emissions.

For most of the analysis it performed to devel op this proposal, NHTSA re quired a single estimate for the value of reducing CO 2 emissions. The agency thus elected to use the midpoint of the range from $0 to $14 (or $7.00) per metric ton of CO 2 as the initial value for the year 2011, and assumed that this value would grow at 2.4 pe rcent annually thereafter. This estimate is employed for the analyses conducted using the Vo lpe CAFE model to s upport development of the proposed standards. The agency also conducte d sensitivity analyses of the benefits from

195 The estimate of $43 per ton of carbon emissions is reported by Tol (p. 2070) as the mean of the “best” estimates reported in peer-reviewed studies (see fn. 4). It thus diffe rs from the mean of all estimates reported in the peerreviewed studies surveyed by Tol. The $43 per ton value is also attributed to Tol by IPCC Working Group II (2007), p. 822.
196 Tol’s more recent (2007) and inclusive survey has been published online with peer-review comments. The agency has elected not to rely on the estimates it reports, but will consider doing so in its analysis of the final rule if the survey has been published, and will also consider any other newly-published evidence.

197 For purposes of comparison, we note that in the rule making to establish CAFE standards for MY 2008-11 light trucks, NRDC recommended a value of $10 to $25 per ton of CO2 emissions reduced by fuel savings and both Environmental Defense and Union of Concerned Scientists recommended a value of $50 per ton of carbon (equivalent to about $14 per ton of CO 2 emissions).VIII-31

reducing CO 2 emissions using both the upper ($14 per metr ic ton) and lower ($0 per metric ton) bounds of this range.

NHTSA seeks comment on its tentative conclusion s for the value of the SCC, the use of a domestic versus global value for the economic benefit of reducing CO 2 emissions, the rate at which the value of the SCC grows over time, the de sirability of and proce dures for incorporating benefits from reducing emissions of greenhouse gases other than CO 2 , and any other aspects of developing a reliable SCC value for purpos es of establishing CAFE standards.

Consumer Benefits fr om Additional Driving The increase in travel associated with the rebound effect produces additional benefits to vehicle owners, which reflect the value to drivers and other vehicle o ccupants of the added (or more desirable) social and economic opportunities that become accessibl e with additional travel. As evidenced by the fact that they elect to make more frequent or longer trips when the cost of driving declines, the benefits from this added trav el are at least as large as drivers’ added costs for the fuel it consumes (measured at the improve d level of fuel economy resulting from stricter CAFE standards). 198 The benefits from additional re bound effect travel also include the consumer surplus received by vehicle buyers wh o value the opportunities th at increased travel makes available to them at more than the fuel co st of the additional driving. Because it depends on the improvement in fuel economy, the value of benefits from increased vehicle use changes by model year and alternative CAFE standard, an d is shown in Tables VIII-5 through VIII-9.

Added Costs from Congestion, Crashes, and Noise While it provides some benefits to drivers, increased vehicle use associated with the fuel economy rebound effect can also contribute to increased traffic conge stion, motor vehicle crashes, and highway noise. A dditional vehicle use can contri bute to traffic congestion and delays by increasing recurring c ongestion on heavily-traveled ro adways during peak travel periods, depending on how the addi tional travel is distributed over the day and on where it occurs. By increasing the number of crashes a nd disabled vehicles, a dded driving can also increase the delays that often re sult from these incidents, although the extent to which it actually does so again depends on when and where the added travel occurs. In either case, any added delays impose higher costs on driv ers and other vehicle occupants in the form of increased travel time and operating expenses, and these should be considered as an additional economic cost associated with the rebound effect. Because driv ers do not take these added costs into account in deciding when to make trips or where they travel, they must be accounted for separately as a cost of the added driving associ ated with the rebound effect.

Increased passenger car and light truck use due to the rebound effect may al so increase the costs associated with traffic crashes. Drivers presum ably take account of the potential costs they (and the other occupants of their vehicles) face from th e possibility of being involved in a crash 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 crashes occur, so any increase in these “external” crash costs must be considered as another cost of additional reboundeffect driving. Like increased delay costs, a ny increase in these external crash costs caused by

198 These benefits are included in the value of fuel savings reported in Tables VIII-5 through VIII-9.VIII-32

added driving is likely to depend on the traffic conditions un der which it takes place, since crashes are more frequent in heavier traffic, but their severity may be reduced by the slower speeds at which heavier traffic typically moves. Thus estimates of the increase in external crash costs from the rebound effect also need to accoun t for when and where the added driving occurs.

Finally, added vehicle use from the rebound eff ect may also increase traffic noise. Noise generated by vehicles causes inconvenience, i rritation, 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 none of these effect s are likely to be taken into account by the drivers whose vehicles contribu te to traffic noise, they represent additional externalities associated with motor vehicle use. Although ther e is considerable uncerta inty in estimating its value, the added inconvenience a nd irritation caused by increased traffic noise imposes economic costs on those it affects, and thes e added costs are unlikely to be taken into account by drivers of the vehicles that cause it. Thus any increase in noise costs resulting from added vehicle use must be included together with other increased external co sts from the rebound effect.

Our analysis uses estimates of the congestion cost s, crash costs, and noise costs for pickup trucks and vans developed by the Federal Highway Admini stration to estimate the increased external costs caused by added light truck use from the rebound effect. 199 These estimates are intended to measure the increases in external costs – that is, the marginal external costs – from added congestion, property damages and injuries in traffic crashes, and noise levels caused by additional usage of light trucks that are borne by persons other than their drivers. FHWA’s “Middle” estimates for congestion, crash, and noise costs imposed by passenger cars are 5.22 cents, 2.26 cents and 0.07 cents per vehi cle mile when expressed in 2006 dollars. 200 For pickup trucks and vans these costs are 4.66 cents, 2.51 ce nts, and 0.07 cents per vehicle-mile. These costs are multiplied by the estimated increases in passenger car and light truck use from the rebound effect during each year of the affected model years’ lifetim es in the fleet to yield the estimated increases in congestion, crash, and noi se externality costs dur ing that year. The resulting estimates are discounted to their present values as of th e date each model year is sold and summed to obtain their total values.

The Federal Highway Administration’s estimates of these costs agree closely with some other recent estimates. For example, recent published research conducted by Resources for the Future (RFF) estimates marginal congesti on and external crash costs for increased light-duty vehicle use in the U.S. to be 3.9 and 3.4 cents per ve hicle-mile when converted to 2006 dollars. 201 These estimates incorporate careful adjustments of co ngestion and crash costs that are intended to reflect the traffic conditions under which additional driving is likely to take place, as well as its likely effects on both the frequency and se verity of motor vehicle crashes.

199 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study

.

200 Federal Highway Administration, 1997 Federal Highway Cost Allocation Study , Tables V-22, V-23, and V-24, http://www.fhwa.dot.gov/polic y/hcas/final/index.htm . The higher congestion cost for automobiles than for light trucks reflects the larger fraction of auto than of light truck use that occurs within congested urban areas.

201 Ian W.H. Parry and Kenneth A. Sm all, “Does Britain or the U.S. Have the Right Gasoline Tax?” Discussion Paper 02-12, Resources for the Future, March 2002, pp. 19 and Table 1, http://www.rff.org/rff/Documents/RFF-DP02-12.pdf

.VIII-33

Costs from Increased Air Pollutant Emissions Finally, as noted previously under Emissions Reduc tions Resulting from Fuel Savings, additional passenger car and light truck use associated with the rebound effect will increase emissions of air pollutants that occur as motor vehicles are dr iven. Predominant air pollutants emitted by motor vehicles include hydrocarbon compounds (usually referred to as “volat ile organic compounds,” or VOC), nitrogen oxides (NOx), fine partic ulate matter (PM), and sulfur dioxide (SO 2 ). The increased use of passenger cars and light truc ks that occurs through the rebound effect causes higher emissions of these “criteria” pollutants, since Federal standards limit their permissible emissions by motor vehicles on a per-mile basis. The increase in emissions of these pollutants from additional vehicle use is estimated by multiplying the increase in total miles driven by vehicles of each model year and age during a ca lendar year by age-specific emission rates per vehicle-mile developed using the U.S. Envir onmental Protection Agency’s MOBILE6.2 motor vehicle emissions factor model 202

. The monetized value of changes in criteria pollutant emissions(fine PM, NOx, SO2, VOCs andCO) are derived from EPA estimates of the value of health and welfarerelated damages (incurred or avoided). These estimat es, expressed as dollars per ton, are based on the benefits associated with recently-a dopted regulations that limit emissi ons of air pollutants from mobile sources, a category that includes passenger cars, light trucks, and other highway vehicles

. 203

The Value of Increased Driving Range Improving the fuel economy of passenger cars and light-duty trucks ma y also increase their driving range before they require refueling. By reducing the frequency with which drivers typically refuel their vehicles , and by extending the upper 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 im proved fuel economy by reducing the size of fuel tanks to maintain a constant driving range, the resulting cost saving will presumably be reflected in lo wer vehicle sales prices.)

No direct estimates of the value of extended vehi cle range are readily available, so the agency’s analysis calculates the reduction in the annual numb er of required refueli ng cycles that results from improved fuel economy, and applies DOT-reco mmended values of travel time savings to convert the resulting time sa vings to their economic value. 204 As an illustration of how the value of extended refueling range is es timated, a typical small light truck model has an average fuel tank size of approximately 20 gallons. Assuming that drivers typically refuel when their tanks are 20 percent full (i.e., 4 gallons in reserve), increasing this model’s actual on-road fuel economy from 24 to 25 mpg would extend its driv ing range from 384 miles (= 16 gallons x 24 mpg) to 400 miles (= 16 gallons x 25 mpg). A ssuming that it is driven 12,000 miles/year, this reduces the number of times it needs to be refu eled each year from 31.3 (= 12,000 miles per year / 384 miles per refueling) to 30. 0 (= 12,000 miles per year / 400 m iles per refueling), or by 1.3 refuelings per year.

202 U.S. Environmental Protection Agency, MOBILE6 Vehicle Emission Modeling Software, http://www.epa.gov/otaq/m6.htm#m60

203 EPA, “Mobile Source $ per Ton Estimates,” document provided to NHTSA by EPA Office of Transportation and Air Quality staff, June 26, 2007.

204 See http://ostpxweb.dot.gov/policy/Data/VOT97guid.pdf and http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdfVIII-34

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and average vehicle occupancy for all drivi ng trips (1.6 persons), the DOT-recommended value of travel time per vehicle-hour is $24.00 (in 2006 dollars). 205 Assuming that locating a station and filling up requires ten minutes, the annual value of time save d as a result of less frequent refueling amounts to $5.20 (calculated as 10/60 x 1.3 x $24.00). This calculation is repeated for each future calendar year that light trucks of each model y ear affected by the alternative CAFE standards considered in this rule would remain in service. Like fuel savings a nd other benefits, however, the value of this benefit declines over a mode l year’s lifetime, becau se a smaller number of vehicles originally produced dur ing that model year remain in service each year, and those remaining in service are driven fewer miles.

The following Table summarizes the values used to calculate the impacts of each scenario.

Table VIII-B Economic Values for Benefits Computations (2006$)

Rebound Effect (VMT Elasticity) -0.15 Discount Rate Applied to Future Benefits 7% Payback Period (years) 5.0 "Gap" between Test and On-Road MPG 20% Value of Travel Time per Vehicle ($/hour) $24.00 Economic Costs of Oil Imports ($/gallon)

"Monopsony" Component $0.182 Price Shock Component $0.113 Military Security Component $ - Total Economic Costs ($/gallon) $0.295 Total Economic Costs ($/BBL) $12.38
External Costs from Additional Automobile Use Due to "Rebound" Effect ($/vehicle-mile)

Congestion $0.052 Accidents $0.023 Noise $0.001 External Costs from Additional Light Truck Use Due to "Rebound" Effect ($/vehicle-mile)

Congestion $0.047 Accidents $0.025 Noise $0.001 Emission Damage Costs

Carbon Monoxide ($/ton) $ - Volatile Organic Compounds ($/ton) $1,700

205 The hourly wage rate during 2006 is estimated to be $24.00. Personal travel (94.4% of urban travel) is valued at 50 percent of the hourly wage rate. Business travel (5.6% or urban travel) is valued at 100 percent of the hourly wage rate. For intercity travel, personal travel (87%) is va lued at 70 percent of the wage rate, while business travel (13%) is valued at 100 percent of the wage rate. The resulting values of travel time are $12.67 for urban travel 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.VIII-35

Nitrogen Oxides ($/ton) $3,900 Particulate Matter ($/ton) $164,000 Sulfur Dioxide ($/ton) $16,000 Carbon Dioxide ($/metric ton) $ 7.00

Annual Increase in CO2 Damage Cost 2.4%VIII-36

Summary of Benefits

Benefits were calculated separately for passenge r cars and light trucks under each alternative CAFE requirement for each model year covered by th is proposal. In Tables VIII-5 through VIII9, the societal impacts for passenger car CAFE standards under the proposed Optimized Net Benefits alternative is sh own over the 2011 through 2015 mode l years. Table VIII-10 summarizes the impacts for passenger cars across a ll 5 model years. In Tables VIII-11 through VIII-15 the societal impacts for light truck CAFE standards under the Optimized Net Benefits alternative is shown over th e 2011 through 2015 model years. Table VIII-16 summarizes the impacts for light trucks across all 5 model year s. Table VIII-17 summarizes the impacts across both the passenger car and light truck fleets for the 5 model years combined. These tables include undiscounted values as well as present va lue calculations at 3 percent and 7 percent. They also show changes in the physical units of measure that produced these values. Negative values in these tables reflect net reductions in fuel consumption or emi ssions and their resulting economic impacts, which represent benefits from the proposal, while positive values represent increasing emissions, congestion, no ise or crash severity and their added costs. The net social benefit from these societal impacts is s hown on the Total line in each table.

The proposed standards for passenger cars would sa ve approximately 19 billion gallons of fuel and prevent 178 million metric tons of tailpipe CO 2 emissions over the lifetime of the passenger cars sold during those model years, compared to the fuel savings and emissions reductions that would occur if the standards remained at the adju sted baseline (i.e., the higher of manufacturer’s plans or the manufacturer’s required level of average fuel economy for MY 2010).

The total value of societal benefits of the proposed passenger car standards would be approximately $31 billion 206 over the lifetime of the 5 model y ears combined. This estimate of societal benefits includes direct impacts from lo wer fuel consumption as well as externalities, and also reflects offsetting societal costs result ing from the rebound effect. Direct benefits to consumers, including fuel savings, account for 85% ($29.5 billion) of the roughly $35 billion in

gross consumer benefits 207 resulting from increased passenge r car CAFE. Petroleum market externalities account for roughly 10 % ($3.6 billion). Environmenta l externalities, i.e., reduction of air pollutants accounts for roughl y 5% ($1.8 billion). Over half of this $1.8 billion is the result of greenhouse gas (primarily CO 2 ) reduction ($1.0 billion). Increased congestion, noise and accidents from increased driving will offset roughly $3.8 billion of the $35 billion in gross consumer benefits, leaving total consumer benefits of $31 billion.

The proposed standards for light trucks would save approximately 36 billion gallons of fuel and prevent 343 million metric tons of tailpipe CO 2 emissions over the lifetime of the light trucks sold during those model years, compared to the fuel savings and emissions reductions that would

206 The $31 billion estimate is based on a 7% discount rate for valuing future impacts. NHTSA estimated benefits using both 7% and 3% discount rates. Under a 3% rate, net consumer benefits for passenger car CAFE improvements total $36 billion.

207 Gross consumer benefits are benefits measured prior to accounting for the negative impacts of the rebound effect. They include fuel savings, consumer surplus from additional driving, reduced refueling time, reduced criteria pollutants, and reduced greenhouse gas production. Negative impacts from the rebound effect include added congestion, noise, and crash costs due to additional driving.VIII-37

occur if the standards remained at the adjusted baseline (i.e., the higher of manufacturer’s plans or the manufacturer’s required level of average fuel economy for MY 2010).

The total value of societal benefits of the propos ed light truck standards would be approximately $57 billion 208 over the lifetime of the 5 mode l years combined. This esti mate of societal benefits includes direct impacts from lower fuel consump tion as well as externalit ies, and also reflects offsetting societal costs resulting from the re bound effect. Direct benefits to consumers, including fuel savings, account for 84% ($52.7 billion) of the roughly $63 billion in gross consumer benefits resulting from increased light truck CAFE. Petroleum market externalities account for roughly 10% ($6.5 bil lion). Environmental external ities, i.e., reduction of air pollutants accounts for roughly 6% ($3.5 billion). Ov er half of this $3.5 billion is the result of greenhouse gas (primarily CO 2 ) reduction ($1.9 bill ion). Increased congestion, noise and accidents from increased driving will offset rou ghly $5.4 billion of the $63 billion in gross consumer benefits, leaving total consumer benefits of $57 billion.

Tables VIII-18, 19, and 20 summarize the fuel sa vings from all alterna tives over model years 2011-2015 for passenger cars and light trucks. Each table reports total fuel savings (in millions of gallons) over the lifetime of vehicles manuf actured during each model year that are projected to occur under each scenario. As the tables indica te, there is a steady increase in fuel savings for both passenger cars and light trucks with each su ccessive model year under all 7 scenarios. As would be expected, benefit levels parallel the in creasing stringency of th e various alternatives that were examined. The two Optimized scenar ios pushes technology up to the point where is ceases to be cost effective, but the 3% based s cenario produces more benefits than the 7% based scenario because it places a higher value on benefits experienced in the future. The TC=TB scenario produces benefits that exceed the Optim ized scenario because it allows benefits that accrue from cost-beneficial technologies to offset costs that accrue from technologies that are not cost-beneficial. As might be expected, th e High Technology scenario, which assumes the maximum use of all available technologies in al l vehicles regardless of cost, produces higher savings than any of the 6 other scenarios in all model years. The 25% Below Optimized, 25% Above Optimized, and 50% Above Optimized s cenarios were designed to produce results relative to the Optimized scenario, and their benefits accordingly reflect this.

Tables VIII-21, 22, 23, 24, 25 and 26 summarize the total social benefits from all alternatives over the 2011-2015 model years for passenger cars and light trucks at both 3 percent and 7 percent discount rates. These tables summarize the value of ne t consumer benefits over the lifetime of the vehicles manufact ured during each model year a nd scenario. There is a steady increase in the social value of fuel savings a nd other benefits with each model year under all 7 scenarios, which mirrors the trends in fuel saving s noted above. Likewise, the value of societal benefits mirrors the trends in stri ngency across alternat ive scenarios.

208 The $57 billion estimate is based on a 7% discount rate for valuing future impacts. NHTSA estimated benefits using both 7% and 3% discount rates. Under a 3% rate, net consumer benefits for light truck CAFE improvements total $72 billion.VIII-38

Table VIII-5

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2011, Passenger Cars

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7% Lifetime Fuel Expenditures -1,563,348(kgal) -3,082,877 -2,538,092 -2,074,331 Consumer Surplus from Additional Driving 6,154,197(kmiles) -382,541 -320,292 -261,888 Refueling Time Value -8,064,500 (hours) -193,548 -161,963 -132,232 Petroleum Market Externalities -1,563,348(kgal) -437,683 -366,257 -299,025 Congestion Costs 6,154,197 (kmiles) 321,249 268,824 219,478 Noise Costs 6,154,197 (kmiles) 4,308 3,605 2,943 Crash Costs 6,154,197 (kmiles) 139,085 116,387 95,023 CO2 -15 (mmT) -119,479 -98,360 -78,834 CO 281,949(tons) 0 0 0 VOC -4,589 (tons) -7,801 5,801 -4,102 NOX -7,728 (tons) -30,141 -23,583 -17,773 PM -191 (tons) -31,401 -25,396 -20,088 SOX -2,261(tons) -36,178 -30,274 -24,717 Total -3,807,007 -3,181,201 -2,595,546VIII-39

Table VIII-6

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2012, Passenger Cars

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7% Lifetime Fuel Expenditures -2,967,962 (k gal) -5,819,490 -4,859,768 -3,961,065 Consumer Surplus from Additional Driving 11,771,196(kmiles) -721,598 -603,190 -492,159 Refueling Time Value 14,848,833 (hours) -356,372 -298,216 -243,474 Petroleum Market Externalities -2,967,962 (k gal) -830,928 -695,329 -567,691 Congestion Costs 11,771,196 (kmiles) 614,456 514,183 419,797 Noise Costs 11,771,196 (kmiles) 8,240 6,895 5,629 Crash Costs 11,771,196 (kmiles) 266,029 222,616 181,751 CO2 -28 (mmT) -238,466 -196,315 -157,343 CO 86,340(tons) 0 0 0 VOC 1,086 (tons) 1,851 1,300 840 NOX -6,044 (tons) -23,570 -20,210 -16,942 PM -513(tons) -84,176 -70,096 -56,968 SOX -4,158 (tons) -66,522 -55,667 -45,448 Total -7,250,243 -6,053,796 -4,933,071VIII-40

Table VIII-7

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2013, Passenger Cars

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7% Lifetime Fuel Expenditures -3,716,507 (k gal) -7,272,841 -6,064,046 -4,932,346 Consumer Surplus from Additional Driving 14,947,099 (kmiles) -902,219 -753,173 -613,455 Refueling Time Value -18,783,958 (hours) -450,815 -377,246 -307,997 Petroleum Market Externalities -3,716,507 (k gal) -1,040,492 -870,693 -710,865 Congestion Costs 14,947,099 (kmiles) 780,239 652,911 533,060 Noise Costs 14,947,099 (kmiles) 10,463 8,756 7,148 Crash Costs 14,947,099 (kmiles) 337,804 282,678 230,788 CO2 -36 (mmT) -305,686 -251,653 -201,695 CO 104,544 (tons) 0 0 0 VOC -1,946 (tons) -3,308 -2,545 -1,882 NOX -10,551 (tons) -41,151 -33,835 -27,078 PM -605 (tons) -99,250 -81,737 -65,701 SOX -5,271 (tons) -84,335 -70,572 -57,618 Total -9,071,590 -7,561,156 -6,147,651VIII-41

Table VIII-8

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2014, Passenger Cars

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7% Lifetime Fuel Expenditures -4,771,444(k gal) -9,340,878 -7,780,728 -6,319,874 Consumer Surplus from Additional Driving 18,970,569 (miles) -1,145,324 -955,266 -777,109 Refueling Time Value 24,182,292 (hours) -580,375 -485,663 -396,513 Petroleum Market Externalities -4,771,444(k gal) -1,335,837 -1,117,841 -912,645 Congestion Costs 18,970,569 (miles) 990,264 828,662 676,549 Noise Costs 18,970,569 (miles) 13,279 11,112 9,072 Crash Costs 18,970,569 (miles) 428,735 358,769 292,912 CO2 -46 (mmT) -401,009 -330,127 -264,593 CO -147,821 (tons) 0 0 0 VOC -2,690 (tons) -4,573 -3,520 -2,603 NOX -13,709 (tons) -53,464 -43,909 -35,090 PM -793 (tons) -130,021 -106,690 -85,396 SOX -6,764 (tons) -108,229 -90,567 -73,942 Total -11,667,432 -9,715,770 -7,889,231VIII-42

Table VIII-9

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2015, Passenger Cars

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -5,716,339(k gal) -11,171,665 -9,298,183 -7,542,913 Consumer Surplus from Additional Driving 22,781,264 (kmiles) -1,363,084 -1,135,944 -922,974 Refueling Time Value 28,938,083 (hours) -694,514 -581,176 -474,492 Petroleum Market Externalities -5,716,339 (k gal) -1,600,375 -1,339,209 -1,093,378 Congestion Costs 22,781,264 (kmiles) 1,189,182 995,119 812,450 Noise Costs 22,781,264 (kmiles) 15,947 13,345 10,895 Crash Costs 22,781,264 (kmiles) 517,857 430,847 351,751 CO2 -54 (mmT) -488,493 -402,147 -322,314 CO -273,604 (tons) 0 0 0 VOC -4,634 (tons) -7,878 -6,026 -4,412 NOX -17,657 (tons) -68,864 -56,138 -44,462 PM -958 (tons) -157,158 -127,963 -101,508 SOX -8,104 (tons) -129,669 -108,508 -88,590 Total -13,961,714 -11,615,995 -9,419,948VIII-43

Table VIII-10

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2011- 2015, Passenger Cars

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -18,735,610 (kgal) -36,637, 752 -28,459,291 -24,830,539 Consumer Surplus from Additional Driving 74,624,325 (kmiles) -4,514,765 -3,507,694 -3,067,584 Refueling Time Value -94,817,655 (hours) -2,275, 624 -1,774,730 -1,554,709 Petroleum Market Externalities -18,735,610 (kgal) -5,245,315 -4,090,602 -3,583,605 Congestion Costs 74,624,325 (kmiles) 3,895,390 3,038,066 2,661,335 Noise Costs 74,624,325 (kmiles) 52,237 40,740 35,688 Crash Costs 74,624,325 (kmiles) 1,686,510 1,315,331 1,152,225 CO2 -178 (mmT) -1,553,133 -1,190,956 -1,024,777 CO -721,578 (tons) 0 0 0 VOC -12,770 (tons) -21,709 -15,625 -12,160 NOX -55,689 (tons) -217,189 -165,746 -141,345 PM -3,061 (tons) -502,006 -385,311 -329,661 SOX -26,558 (tons) -424,934 -331,395 -290,316 Total -45,758,291 -35,527,213 -30,985,447VIII-44

Table VIII-11

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2011, Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -2,403,611 (kgal) -4,811,38 3 -3,883,226 -3,087,364 Consumer Surplus from Additional Driving 7,666,300 (kmiles) -595,411 -481,267 -383,161 Refueling Time Value -9,383,882 (hours) -225,213 -182,196 -144,998 Petroleum Market Externalities -2,403,611 (kgal) -672,927 -544,395 -433,248 Congestion Costs 7,666,300 (kmiles) 357,250 289,013 230,007 Noise Costs 7,666,300 (kmiles) 5,366 4,341 3,455 Crash Costs 7,666,300 (kmiles) 192,424 155,670 123,888 CO2 -23 (mmT) -199,178 -156,241 -120,625 CO 84,710 (tons) 0 0 0 VOC 1,859 (tons) 3,161 2,062 1,256 NOX -3,552 (tons) -13,852 -12,277 -10,642 PM -500 (tons) -81,980 -66,292 -52,737 SOX -3,382 (tons) -54,105 -43,771 -34,834 Total -6,095,848 -4,918,579 -3,909,004VIII-45

Table VIII-12

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2012, Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -5,478,272 (kgal) -10,868, 471 -8,754,164 -6,941,594 Consumer Surplus from Additional Driving 17,399,527 (kmiles) -1,327,696 -1,071,130 -850,722 Refueling Time Value -20,525,226 (hours) -492,605 -398,516 -317,152 Petroleum Market Externalities -5,478,272 (kgal) -1,533,724 -1,240,776 -987,452 Congestion Costs 17,399,527 (kmiles) 810,818 655,948 522,026 Noise Costs 17,399,527 (kmiles) 12,180 9,853 7,842 Crash Costs 17,399,527 (kmiles) 436,728 353,311 281,177 CO2 -53 (mmT) -461,762 -362,219 -279,649 CO 761,706 (tons) 0 0 0 VOC 18,561 (tons) 31,554 21,119 13,250 NOX 17,083 (tons) 66,622 37,737 16,756 PM -1,523 (tons) -249,839 -201,709 -160,269 SOX -8,040 (tons) -128,642 -104,069 -82,820 Total -13,704,838 -11,054,616 -8,778,608VIII-46

Table VIII-13

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2013, Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -8,508,804 (kgal) -16,796, 650 -13,508,492 -10,688,849 Consumer Surplus from Additional Driving 27,188,329 (kmiles) -2,054,826 -1,655,395 -1,312,269 Refueling Time Value -32,253,023 (hours) -774,073 -626,221 -498,368 Petroleum Market Externalities -8,508,804 (kgal) -2,382,16 7 -1,927,162 -1,533,702 Congestion Costs 27,188,329 (kmiles) 1,266,976 1,024,978 815,712 Noise Costs 27,188,329 (kmiles) 19,032 15,397 12,253 Crash Costs 27,188,329 (kmiles) 682,427 552,080 439,364 CO2 -82 (mmt) -730,569 -573,079 -442,442 CO 734,617 (tons) 0 0 0 VOC 17,081 (tons) 29,037 19,409 12,103 NOX 9,106 (tons) 35,513 13,286 -2,256 PM -2,232 (tons) -366,080 -295,685 -235,000 SOX -12,319 (tons) -197,096 -159,448 -126,893 Total -21,268,476 -17,120,333 -13,560,347VIII-47

Table VIII-14

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2014, Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -9,391,857 (kgal) -18,376, 166 -14,768,243 -11,671,696 Consumer Surplus from Additional Driving 30,130,353 (kmiles) -2,265,105 -1,823,071 -1,443,287 Refueling Time Value -35,503,043 (hours) -852,073 -689,323 -548,587 Petroleum Market Externalities -9,391,857 (kgal) -2,629,39 1 -2,127,165 -1,692,871 Congestion Costs 30,130,353 (kmiles) 1,404,074 1,135,890 903,980 Noise Costs 30,130,353 (kmiles) 21,091 17,063 13,579 Crash Costs 30,130,353 (kmiles) 756,272 611,820 486,908 CO2 -89 (mmT) -812,842 -637,617 -492,268 CO -5,658 (tons) 0 0 0 VOC -1,090 (tons) -1,853 -2,068 -2,182 NOX -9,088 (tons) -35,445 -36,431 -35,546 PM -2,786 tons) -456,882 -367,050 -290,218 SOX -13,876 (tons) -222,024 -179,614 -142,941 Total -23,470,345 -18,865,810 -14,915,129VIII-48

Table VIII-15

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2015, Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -10,195,498 (kgal) -19,973, 909 -16,040,212 -12,660,941 Consumer Surplus from Additional Driving 32,977,172 (kmiles) -2,454,647 -1,974,101 -1,560,979 Refueling Time Value -38,972,327 (hours) -935,336 -756,682 -602,194 Petroleum Market Externalities -10,195,498 (kgal) -2,854,383 -2,309,182 -1,837,726 Congestion Costs 32,977,172 (kmiles) 1,536,736 1,243,213 989,391 Noise Costs 32,977,172 (kmiles) 23,084 18,675 14,862 Crash Costs 32,977,172 (kmiles) 827,727 669,627 532,912 CO2 -96 (mmT) -902,032 -707,580 -546,283 CO -99,460 (tons) 0 0 0 VOC -3,380 (tons) -5,745 -4,895 -4,158 NOX -13,408 (tons) -52,290 -48,866 -44,507 PM -3,048 (tons) -499,947 -401,659 -317,517 SOX -15,050 (tons) -240,797 -194,802 -155,028 Total -25,531,539 -20,506,465 -16,192,169VIII-49

Table VIII-16

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2011-2015, Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -35,978,042 (kgal) -70,826, 579 -56,954,337 -45,050,444 Consumer Surplus from Additional Driving 115,361,681 (kmiles) -8,697,686 -7,004,965 -5,550,419 Refueling Time Value -136,637,500 (hours) -3,279, 300 -2,652,939 -2,111,299 Petroleum Market Externalities -35,978,042 (kgal) -10,072, 593 -8,148,681 -6,485,000 Congestion Costs 115,361,681 (kmiles) 5,375,854 4,349,041 3,461,116 Noise Costs 115,361,681 (kmiles) 80,753 65,329 51,991 Crash Costs 115,361,681 (kmiles) 2,895,578 2,342,509 1,864,249 CO2 -343 (mmT) -3,106,382 -2,436,737 -1,881,267 CO 1,475,915 (tons) 0 0 0 VOC 33,031 (tons) 56,154 35,627 20,269 NOX 141 (tons) 548 -46,551 -76,195 PM -10,090 (tons) -1,654,728 -1,332,395 -1,055,741 SOX -52,667 (tons) -842,664 -681,704 -542,517 Total -90,071,046 -72,465,802 -57,355,257VIII-50

Table VIII-17

Lifetime Monetized Societal Impacts, Optimized CAFE, MY 2011-2015, Passenger Cars and Light Trucks

Societal Effect Physical Units Undiscounted Value (2006$ k) Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures -54,713,652 (kgal) -107,464, 331 -85,413,628 -69,880,983 Consumer Surplus from Additional Driving 189,986,006 (kmiles) -13,212, 451 -10,512,659 -8,618,003 Refueling Time Value -231,455,155 (hours) -5,554, 924 -4,427,668 -3,666,008 Petroleum Market Externalities -54,713,652 (kgal) -15,317, 908 -12,239,283 -10,068,605 Congestion Costs 189,986,006 (kmiles) 9,271,244 7,387,107 6,122,451 Noise Costs 189,986,006 (kmiles) 132,990 106,069 87,679 Crash Costs 189,986,006 (kmiles) 4,582,088 3,657,841 3,016,475 CO2 -521 (mmT) -4,659,516 -3,627,693 -2,906,044 CO 754,337 (tons) 0 0 0 VOC 20,261 (tons) 34,444 20,001 8,109 NOX -55,549 (tons) -216,641 -212,298 -217,540 PM -13,151 (tons) -2,156,735 -1,717,706 -1,385,402 SOX -79,225 (tons) -1,267,599 -1,013,099 -832,832 Total -135,829,336 -107,993,015 -88,340,704VIII-51

Table VIII-18

Savings in Millions of Gallons of Fuel Undiscounted, over the Lifetime of the Model Year Fleet Passenger Cars

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 708 1261 1946 3135 4151 11201

Optimized Net

Impact -7% 1563 2968 3717 4771 5716 18735

25% Above Optimized 2313 4480 5221 6601 7476 26091

50% Above Optimized 2641 5523 6422 7913 9121 31620

Optimized Net

Impact -3% 3463 6197 6905 8587 9784 34936

TC=TB 3599 6860 7676 9320 10461 37916

Technology Exhaustion 3677 7143 8261 10233 11562 40876

Table VIII-19 Savings in Millions of Gallons of Fuel Undiscounted, over the Lifetime of the Model Year Fleet Light Trucks

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 2157 4933 7902 7799 7808 30599

Optimized Net

Impact -7% 2404 5478 8509 9392 10195 35978

25% Above Optimized 2585 6339 9070 10592 12534 41120

50% Above Optimized 2909 6780 9697 11458 13584 44428

Optimized Net

Impact -3% 2414 5488 8978 9959 11127 37966

TC = TB 3228 7471 10640 12778 14602 48719

Technology Exhaustion 3263 7506 12659 14448 16147 54023VIII-52

Table VIII-20 Savings in Millions of Gallons of Fuel Undiscounted, over the Lifetime of the Model Year Fleet Passenger Cars and Light Trucks

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 2865 6194 9848 10934 11959 41800

Optimized Net

Impact -7% 3967 8446 12226 14163 15911 54713

25% Above Optimized 4898 10819 14291 17193 20010 67211

50% Above Optimized 5550 12303 16119 19371 22705 76048

Optimized Net

Impact -3% 5877 11685 15883 18546 20911 72902

TC = TB 6827 14331 18316 22098 25063 86635

Technology Exhaustion 6940 14649 20920 24681 27709 94899

Table VIII-21 Present Value @3% Discount Rate of Lifetime Social Benefits (Millions of 2006 Dollars), Passenger Cars

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 1418 2581 3977 6399 8387 22762

Optimized Net

Impact-7% 3181 6054 7561 9716 11616 38128

25% Above Optimized 4604 8939 10403 13109 14907 51962

50% Above Optimized 5241 10842 12571 15503 17893 62050

Optimized Net

Impact -3% 6798 12188 13519 16833 19216 68554

TC = TB 7075 13366 14881 18059 20364 73745

Technology Exhaustion 7156 13865 15967 19654 22312 78954VIII-53

Table VIII-22 Present Value @3% Discount Rate of Lifetime Social Benefits (Millions of 2006 Dollars), Light Trucks

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 4414 9959 15910 15715 15745 61743

Optimized Net

Impact -7% 4919 11055 17120 18866 20506 72466

25% Above Optimized 5286 12599 17972 20984 24662 81503

50% Above Optimized 5848 13249 18955 22375 26475 86902

Optimized Net

Impact -3% 4939 11075 17976 19902 22246 76138

TC = TB 6343 14452 20631 24704 28352 94482

Technology Exhaustion 6420 14528 24517 27951 31387 104803

Table VIII-23 Present Value @3% Discount Rate of Lifetime Social Benefits

(Millions of 2006 Dollars), Passenger Cars and Light Trucks

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 5832 12540 19887 22114 24132 84505

Optimized Net

Impact -7% 8100 17109 24681 28582 32122 110594

25% Above Optimized 9890 21538 28375 34093 39569 133465

50% Above Optimized 11089 24091 31526 37878 44368 148952

Optimized Net

Impact -3% 11737 23263 31495 36735 41462 144692

TC = TB 13418 27818 35512 42763 48716 168227

Technology Exhaustion 13576 28393 40484 47605 53699 183757VIII-54

Table VIII-24 Present Value @7% Discount Rate of Lifetime Social Benefits (Millions of 2006 Dollars), Passenger Cars

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 1156 2104 3235 5197 6799 18491

Optimized Net

Impact -7% 2596 4933 6148 7889 9420 30986

25% Above Optimized 3755 7280 8454 10638 12083 42210

50% Above Optimized 4274 8825 10213 12576 14495 50383

Optimized Net

Impact -3% 5543 9922 10983 13654 15569 55671

TC=TB 5769 10878 12087 14644 16492 59870

Technology Exhaustion 5834 11282 12968 15930 18061 64075

Table VIII-25 Present Value @7% Discount Rate of Lifetime Social Benefits (Millions of 2006 Dollars), Light Trucks

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 3508 7910 12603 12432 12441 48894

Optimized Net

Impact - 7% 3909 8779 13560 14915 16192 57355

25% Above Optimized 4201 9990 14236 16587 19457 64471

50% Above Optimized 4642 10507 15011 17687 20892 68739

Optimized Net

Impact -3% 3926 8794 14251 15752 17589 60312

TC = TB 5027 11453 16330 19515 22367 74692

Technology Exhaustion 5088 11513 19395 22074 24759 82829VIII-55

Table VIII-26 Present Value @7% Discount Rate of Lifetime Social Benefits

(Millions of 2006 Dollars), Passenger Cars and Light Trucks

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 All 5 MYs 25% Below Optimized 4664 10014 15838 17629 19240 67385

Optimized Net

Impact -7% 6505 13712 19708 22804 25612 88341

25% Above Optimized 7956 17270 22690 27225 31540 106681

50% Above Optimized 8916 19332 25224 30263 35387 119122

Optimized Net

Impact -3% 9469 18716 25234 29406 33158 115983

TC = TB 10796 22331 28417 34159 38859 134562

Technology Exhaustion 10922 22795 32363 38004 42820 146904VIII-56

References

Argonne National Laboratories, The Greenhous e Gas and Regulated Emissions from Transportation (GREET) Model, Version 1.6, February 2002.

Greene, David L., “Vehicle Use and Fuel Economy: How Big is the Rebound Effect?” The Energy Journal , 13:1 (1992), 117-143.

Greene, David L., Donald W. Jones, and Paul N. Leiby, The Outlook for U.S. Oil Depend ence, ORNL-6873, Oak Ridge National Laboratory, May 11, 1995.

Greene, David L., James R. Kahn, and Robert C. Gibson, “Fuel Economy Rebound Effect for Household Vehicles,” The Ener gy Journal, 20:3 (1999), 1-31.

Greene, David L., and Nataliya I. Tishchishyna , Costs of Oil Dependence: A 2000 Update

, ORNL/TM-2000/152, Oak Ridge Nati onal Laboratory, May 2000.

Haughton, Jonathan, and Soumodip Sarkar, “Gasolin e Tax as a Corrective Tax: Estimates for the United States,” The Energy Journal , 17:2, 103-126.

Jones, Clifton T., “Another Look at U.S. Passe nger Vehicle Use and the ‘Rebound’ Effect from Improved Fuel Efficiency, The Energy Journal , 14:4 (1993), 99-110.

Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL-6851, Oak Ridge Nationa l Laboratory, November 1, 1997.

Pickrell, Don H., “Description of VMT For ecasting Procedure for ‘Car Talk’ Base line Forecasts,” manuscript, Volpe National Transpor tation Systems Center, U.S. Department of Transportation, 1994.

Puller, S.L., and L.A. Greening, “Household Adjustment to Gasoline Price Changes: An Analysis Using Nine Years of U.S. Survey Data,” Energy Economics , 21:1, pp. 37-52.

Small, K.A. and VanDender, K. (2005) “A St udy to Evaluate the Effect of Reduced Greenhouse Gas Emissions on Vehicle Miles Traveled”, State of California Air Resources Board, California Environmental Protection Agency, and the California Energy Commission.

U.S. Department of Energy, Program Anal ysis Methodology Office of Transportation Technology: Quality Metrics 2002, Final Report, Office of Transportation Technologies, May 9, 2001.

U.S. Environmental Protection Agency, MOBI LE Motor Vehicle Emission Factor Model, version 6.2, November 2002.IX-1

IX. NET BENEFITS AND SENSITIVITY ANALYSES

This chapter compares the costs of technologies needed to make improvements in fuel economy to meet the alternatives with the potential bene fits, expressed in total costs (millions of dollars) for each model year. The costs do not include fi nes, since these are transfer payments. Thus, the total costs shown in this section do not ma tch the total costs shown in Chapter VII. The following tables combine the estimated costs and bene fits from a societal perspective. These are incremental costs and benefits compared to an adjusted baseline of manufacturers’ production plans. Tables utilizing a 3 percent and 7 percen t discount rate for benefi ts are presented. Sensitivity analyses are also performed on some of the assumptions made in this analysis. Finally, a payback period is calculated, from the consumer’s perspective.

Table IX-1 provides the total incremental cost s (in millions of dollars) from a societal perspective. Table IX-2a and Ta ble IX-2b provide the total benef its at a 3 percent and 7 percent discount rate from a societal perspective for a ll vehicles produced during each model year to which the standard is applicable. Table IX-3a and Table IX-3b show the total net benefits in millions of dollars at a 3 percent and 7 percent di scount rate for the projected fleet of sales for each model year.IX-2

Table IX-1 Incremental Total Cost (excludes fines) (Millions of 2006 Dollars)

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5
years Passenger Cars 25% Below Optimized 835 818 1,253 2,153 3,209 8,268 Optimized (7%) 1,884 2,373 2,879 3,798 4,862 15,796 25% Above Optimized 3,387 5,653 6,445 8,240 9,084 32,808 50% Above Optimized 4,010 7,885 8,986 11,207 12,981 45,070 Optimized (3%) 5,467 8,791 9,821 12,447 14,484 51,011

TC = TB 5,913 10,796 12,303 15,403 17,398 61,812 Technology Exhaustion 6,079 12,595 14,701 18,759 21,110 73,245

Light Trucks 25% Below Optimized 1,349 4,296 6,329 6,212 6,326 24,512 Optimized (7%) 1,649 4,986 7,394 8,160 8,761 30,949 25% Above Optimized 2,072 7,034 9,815 11,903 14,781 45,606 50% Above Optimized 2,922 8,098 11,586 14,386 17,969 54,961 Optimized (3%) 1,662 4,974 8,190 9,058 10,253 34,136

TC = TB 3,788 10,525 15,196 18,762 21,364 69,635 Technology Exhaustion 3,933 10,670 18,275 21,051 23,479 77,408

Passenger Cars and Light Trucks Combined 25% Below Optimized 2,184 5,114 7,582 8,365 9,534 32,780 Optimized (7%) 3,534 7,358 10,273 11,957 13,623 46,745 25% Above Optimized 5,459 12,687 16,261 20,143 23,865 78,414 50% Above Optimized 6,932 15,983 20,572 25,593 30,950 100,030 Optimized (3%) 7,128 13,765 18,011 21,505 24,737 85,147

TC = TB 9,702 21,321 27,499 34,164 38,761 131,447 Technology Exhaustion 10,013 23,266 32,976 39,810 44,589 150,653IX-3

Total costs follow a predictable pattern with costs rising to reflect the more expensive technologies that manufacturers must apply in orde r to achieve the CAFE levels that are required under the more aggressive alterna tives, with the exception of the Optimized (3%) for light trucks. For the combined fleet, total compliance costs for the Total Cost = Total Benefit alternative is roughly 2.8 times those for the Optimized (7%) alte rnative over the 5 model years. Relative to the proposed Optimized (7%) alternative, Tech nology exhaustion produces costs that are 3.2 times the Optimized cost levels.IX-4

Table IX-2a Present Value of Lifetime Soci etal Benefits by Alternative (Millions of 2006 Dollars) (Discounted 3 % )

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5
years Passenger Cars 25% Below Optimized 1,418 2,581 3,977 6,399 8,387 22,762 Optimized (7%) 3,181 6,054 7,561 9,716 11,616 38,128 25% Above Optimized 4,604 8,939 10,403 13,109 14,907 51,962 50% Above Optimized 5,241 10,842 12,571 15,503 17,893 62,050 Optimized (3%) 6,798 12,188 13,519 16,833 19,216 68,554

TC = TB 7,075 13,366 14,881 18,059 20,364 73,745 Technology Exhaustion 7,156 13,865 15,967 19,654 22,312 78,954

Light Trucks 25% Below Optimized 4,414 9,959 15,910 15,715 15,745 61,743 Optimized (7%) 4,919 11,055 17,120 18,866 20,506 72,466 25% Above Optimized 5,286 12,599 17,972 20,984 24,662 81,503 50% Above Optimized 5,848 13,249 18,955 22,375 26,475 86,902 Optimized (3%) 4,939 11,075 17,976 19,902 22,246 76,138

TC = TB 6,343 14,452 20,631 24,704 28,352 94,482 Technology Exhaustion 6,420 14,528 24,517 27,951 31,387 104,803

Passenger Cars and Light Trucks Combined 25% Below Optimized 5,832 12,540 19,887 22,114 24,132 84,505 Optimized (7%) 8,100 17,109 24,681 28,582 32,122 110,594 25% Above Optimized 9,890 21,538 28,375 34,093 39,569 133,465 50% Above Optimized 11,089 24,091 31,526 37,878 44,368 148,952 Optimized (3%) 11,737 23,263 31,495 36,735 41,462 144,692

TC = TB 13,418 27,818 35,512 42,763 48,716 168,227 Technology Exhaustion 13,576 28,393 40,484 47,605 53,699 183,757IX-5

From Table IX-2a, lifetime societal benefits fo llow a similar predictable pattern, with higher benefits associated with the more expensiv e technologies that are enabled under the more aggressive alternatives. For th e combined fleet, the TC=TB alte rnative produces gross benefits roughly 1.5 times as high as the Optimized (7 %) alternative, and th e Technology exhaustion alternative produces gross benef its that are 1.7 times the Optim ized (7%) alternative.

Similar results occur for benefits discounted at the 7% rate (Table IX-2b). However, while the pattern for benefits is directionally similar to the pattern for costs, the more aggressive technology scenarios do not increas e benefits by as high a ratio as they do for costs. For example, the TC=TB alternative increases total benefits by $46 billion over the Optimized (7%) alternative, but it also increases total costs by $8 5 billion, a net loss to society of $39 billion. This is a function of the more aggressive alternatives relatively unrestrained functions. While the Optimized (7%) alternative adds technology until the marginal cost to society begins to exceed the marginal benefit, the TC=TB scenario and the Technology exhaustion scenario allow for continued investment in technology despite it s negative net return. Thus, while both costs and benefits continue to rise with more aggressi ve technologies, the costs rapidly begin to exceed the benefits that society derives from the added investment.IX-6

Table IX-2b Present Value of Lifetime Soci etal Benefits by Alternative (Millions of 2006 Dollars) (Discounted 7 % )

MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5
years Passenger Car 25% Below Optimized 1,156 2,104 3,235 5,197 6,799 18,491 Optimized (7%) 2,596 4,933 6,148 7,889 9,420 30,986 25% Above Optimized 3,755 7,280 8,454 10,638 12,083 42,210 50% Above Optimized 4,274 8,825 10,213 12,576 14,495 50,383 Optimized (3%) 5,543 9,922 10,983 13,654 15,569 55,671

TC = TB 5,769 10,878 12,087 14,644 16,492 59,870 Technology Exhaustion 5,834 11,282 12,968 15,930 18,061 64,075

Light Trucks 25% Below Optimized 3,508 7,910 12,603 12,432 12,441 48,894 Optimized (7%) 3,909 8,779 13,560 14,915 16,192 57,355 25% Above Optimized 4,201 9,990 14,236 16,587 19,457 64,471 50% Above Optimized 4,642 10,507 15,011 17,687 20,892 68,739 Optimized (3%) 3,926 8,794 14,251 15,752 17,589 60,312

TC = TB 5,027 11,453 16,330 19,515 22,367 74,692 Technology Exhaustion 5,088 11,513 19,395 22,074 24,759 82,829

Passenger Cars and Light Trucks Combined 25% Below Optimized 4,664 10,014 15,838 17,629 19,240 67,385 Optimized (7%) 6,505 13,712 19,708 22,804 25,612 88,341 25% Above Optimized 7,956 17,270 22,690 27,225 31,540 106,681 50% Above Optimized 8,916 19,332 25,224 30,263 35,387 119,122 Optimized (3%) 9,469 18,716 25,234 29,406 33,158 115,983

TC = TB 10,796 22,331 28,417 34,159 38,859 134,562 Technology Exhaustion 10,922 22,795 32,363 38,004 42,820 146,904IX-7

Table IX-3a Net Total Benefits Over the Vehicle’s Lifetime – Present Value (Millions of 2006 Dollars)* (Discounted 3 % )

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5
years 25% Below Optimized 583 1,763 2,724 4,246 5,178 14,494 Optimized (7%) 1,297 3,681 4,682 5,918 6,754 22,332 25% Above Optimized 1,217 3,286 3,958 4,869 5,823 19,154 50% Above Optimized 1,231 2,957 3,585 4,296 4,912 16,980 Optimized (3%) 1,331 3,397 3,698 4,386 4,732 17,543

TC = TB 1,162 2,570 2,578 2,656 2,966 11,933 Technology Exhaustion 1,077 1,270 1,266 895 1,202 5,709

Light Trucks 25% Below Optimized 3,065 5,663 9,581 9,503 9,419 37,231 Optimized (7%) 3,270 6,069 9,726 10,706 11,745 41,517 25% Above Optimized 3,214 5,565 8,157 9,081 9,881 35,897 50% Above Optimized 2,926 5,151 7,369 7,989 8,506 31,941 Optimized (3%) 3,277 6,101 9,786 10,844 11,993 42,002

TC = TB 2,555 3,927 5,435 5,942 6,988 24,847 Technology Exhaustion 2,487 3,858 6,242 6,900 7,908 27,395

Passenger Cars and Light Trucks Combined 25% Below Optimized 3,648 7,426 12,305 13,749 14,598 51,725 Optimized (7%) 4,566 9,751 14,408 16,625 18,499 63,849 25% Above Optimized 4,431 8,851 12,114 13,950 15,704 55,051 50% Above Optimized 4,157 8,108 10,954 12,285 13,418 48,922 Optimized (3%) 4,609 9,498 13,484 15,230 16,725 59,545

TC = TB 3,716 6,497 8,013 8,599 9,955 36,780 Technology Exhaustion 3,563 5,127 7,508 7,795 9,110 33,104IX-8

The impact of the relatively unr estricted technology a pplication that is enabled by the more aggressive scenarios is apparent from Tables IX-3a and IX-3b, which show net total lifetime societal benefits under each alternative. Acro ss all 5 model years the Optimized (7%) or the Optimized (3%) alternative produc es the highest net total benef its to society, as would be expected. Under a 3% discount rate, net benefits produced by the Optimized (7%) alternative exceed those produced by the TC = TB alternat ive by an extra $27 billion, and exceed those produced under the Technology Exha ustion alternative by $30 billion. Under the 7% discount rate, the Technology exhaustion al ternative produ ces a net loss to society of over $3.7 billion.IX-9

Table IX-3b Net Total Benefits Over the Vehicle’s Lifetime – Present Value (Millions of 2006 Dollars) (Discounted 7 % )

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Total 5
years 25% Below Optimized 321 1,286 1,982 3,044 3,590 10,223 Optimized (7%) 712 2,560 3,269 4,091 4,558 15,190 25% Above Optimized 368 1,627 2,009 2,398 2,999 9,402 50% Above Optimized 264 940 1,227 1,369 1,514 5,313 Optimized (3%) 76 1,131 1,162 1,207 1,085 4,660

TC = TB -144 82 -216 -759 -906 -1,942 Technology Exhaustion -245 -1,313 -1,733 -2,829 -3,049 -9,170

Light Trucks 25% Below Optimized 2,159 3,614 6,274 6,220 6,115 24,382 Optimized (7%) 2,260 3,793 6,166 6,755 7,431 26,406 25% Above Optimized 2,129 2,956 4,421 4,684 4,676 18,865 50% Above Optimized 1,720 2,409 3,425 3,301 2,923 13,778 Optimized (3%) 2,264 3,820 6,061 6,694 7,336 26,176

TC = TB 1,239 928 1,134 753 1,003 5,057 Technology Exhaustion 1,155 843 1,120 1,023 1,280 5,421

Passenger Cars and Light Trucks Combined 25% Below Optimized 2,480 4,900 8,256 9,264 9,706 34,605 Optimized (7%) 2,971 6,354 9,435 10,847 11,989 41,596 25% Above Optimized 2,497 4,583 6,429 7,082 7,675 28,267 50% Above Optimized 1,984 3,349 4,652 4,670 4,437 19,092 Optimized (3%) 2,341 4,951 7,223 7,901 8,421 30,836

TC = TB 1,094 1,010 918 -5 98 3,115 Technology Exhaustion 909 -471 -613 -1,806 -1,769 -3,749IX-10

Sensitivity Analyses The agency has performed several sensitivity anal yses to examine important assumptions. The analyses include:

1) The value of CO 2 . We examined a range from $0 per metric ton to $14 per metric ton, with the main analysis usi ng a value of $7.50 per metric ton. These values can be translated into a value for carbon by multiplying by a factor of 3.66, or can be translated into cents per gallon by multiplying by 0.0089 209 , as shown below:

$7.50 per ton CO 2 = $7.50*3.667 = $27.50 per ton C = $7.50*0.0089 = $0.06675 per gallon $14.00 per ton CO 2 = $14.00*3.667 = $51.34 per ton C = $14.00*0.0089 = $0.1246 per gallon

2) The value of externalities. The main analys is uses $0.295 per gallon for externalities. The sensitivity analysis examines $0.120 and $0.504 per gallon.

3) The price of gasoline. The main analysis uses the AEO 2008 reference case estimate for the price of gasoline. The preliminary AEO 2008 estimate does not contain a high price or low price of gasoline case. We assumed for this analysis an estimate of the AEO 2008 high price and low price for gasoline (based on applying the percenta ge increase between the high price estimate and the reference case estimate in the AEO 2007 forecast to the AEO 2008 reference case estimate).

4) The rebound effect. The main analysis us es a rebound effect of 15 percent. The sensitivity analysis examines rebound eff ects of 10 percent and 20 percent.

Sensitivity analyses were performed on just the optimized (7%) alternative. Presented are information on the average mpg expected by mode l year, the price per ve hicle increase for MY 2015, total benefits for MY 2015 ve hicles, the total cost increase for MY 2015, the total fuel saved (all 5 model years combined) and the total CO 2 emissions reduction (all 5 model years combined).

The results of the sensitivity analys es indicate that the value of CO 2, the value of externalities, and the value of the rebound eff ect have almost no impact on the level of the standards. Assuming a higher price of gasoline has the largest impact of the sensitivity analyses examined (raising the MY 2015 passenger car standard leve l by 6.7 mpg and the light truck level by 0.8 mpg). It appears that the light truck levels are not as sensitive as the passenger car levels to changes in the estimated benefits. This can occu r because the technologies that have not been used under the Optimized alternative, and are still available for light trucks, are not that close to being cost effective and it takes a larger increas e in benefits to bring them over the cost-benefit threshold.

209 The molecular weight of Carbon (C) is 12, the molecular we ight of Oxygen (O) is 16, t hus the molecular weight of CO 2 is 44. One ton of C = 44/12 tons CO 2 = 3.67 tons CO 2 . 1 gallon of gas weighs 2,81 9 grams, of that 2,433 grams are carbon. $1.00 CO 2 = $3.67 C and $3.67/ton * ton/1000kg * kg/1000g * 2433g/gallon = (3.67 * 2433) / 1000 * 1000 = $0.0089/gallonIX-11

Note that there are some slight inconsistencie s in the relationships one would expect when comparing the required mpg levels for corres ponding model years in the various sensitivity analyses to those under the proposed CAFE standard s. For example, the level of the standard should increase when CO 2 is assigned a higher value, becaus e this increases the benefit of each gallon of fuel saved, but the optimized st andards for some model years are actually lower with the high CO 2 value than under the proposal. Problems such as this arise when making slight changes in parameter values used by the CAFE model, since the model derives a relationship between net benefits and the stringency level of standards for each model year, and minor changes in parameter values can affect the exact shapes and positions of those curves. In any case, the seemingly anomalous results are mostly small (0.1 mpg or less), and are sometimes the result of rounding. When larger variations are made to the model’s para meters or other inputs, such as substituting EIA’s High gasoline price forecast for the Reference Case forecast, the sensitivity analysis invariable produces the anticipated result.IX-12

Table IX-5a Passenger Car Sensitivity Analyses (mpg) Optimized MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Proposal 31.2 32.8 34.0 34.8 35.7 Low CO 2 31.2 32.7 33.8 34.7 35.3 High CO 2 31.2 32.7 33.8 34.6 35.7 Low Externalities 31.2 32.8 33.9 34.7 35.4

High Externalities 31.1 32.7 34.1 34.9 36.0

Low Fuel Price 31.1 32.8 33.8 34.7 35.9

High Fuel Price 37.4 38.9 40.4 41.3 42.4

10% Rebound 31.1 32.7 33.9 34.6 36.0 20% Rebound 31.2 32.8 33.8 34.8 35.7

Optimized MY 2015 MY 2015 MY 2015 Per Vehicle Cost Total Benefits Total Cost Total Fuel Saved Total CO 2 Emissions Reduced ($) ($ Mill.) ($ Mill.) (Bill. Gal.) (mmt) Proposal 649 9,420 4,862 18.736 178 Low CO 2 571 8,583 4,263 18.129 173 High CO 2 633 9,655 4,731 18.351 174 Low Externalities 596 8,340 4,458 18.307 175

High Externalities 715 10,660 5,367 19.276 182

Low Fuel Price 675 8,273 5,014 18.446 171

High Fuel Price 2,081 24,622 15,477 36.686 319

10% Rebound 714 10,343 5,346 19.730 186 20% Rebound 644 8,833 4,811 17.738 169IX-13

Table IX-5b Light Truck Sensitivity Analyses (mpg) Optimized MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 Proposal 25.0 26.4 27.8 28.2 28.6 Low CO 2 25.0 26.4 27.7 28.2 28.6 High CO 2 25.0 26.4 27.7 28.1 28.6 Low Externalities 25.1 25.4 27.2 27.9 28.2

High Externalities 25.0 26.4 27.8 28.2 28.8

Low Fuel Price 25.1 25.2 26.9 27.6 28.2

High Fuel Price 25.1 26.7 28.1 28.6 29.4

10% Rebound 25.0 26.4 27.7 28.1 28.6 20% Rebound 25.0 26.4 27.8 28.2 28.7

Optimized MY 2015 MY 2015 MY 2015 Per Vehicle Cost Total Benefits Total Cost Total Fuel Saved Total CO 2 Emissions Reduced ($) ($ Mill.) ($ Mill.) (Bill. Gal.) (mmt) Proposal 979 16,192 8,761 35.978 343 Low CO 2 966 15,543 8,646 35.752 340 High CO 2 943 16,587 8,434 35.564 339 Low Externalities 775 13,874 6,927 31.927 304

High Externalities 1,025 18,001 9,172 36.331 346

Low Fuel Price 789 12,887 7,054 30.054 285

High Fuel Price 1,393 27,647 12,468 40.115 376

10% Rebound 943 16,839 8,434 37.262 355 20% Rebound 997 15,603 8,924 34.403 327IX-14

Payback Period

The “payback period” represents the length of time required for a vehicle buyer to recoup, through savings in fuel use, the higher cost of purchasing a more fuel-efficient vehicle. Thus, only these two factors are considered (purchase price and fuel savings). When a higher CAFE standard requires a manufacturer to improve the fuel economy of some of its vehicle models, the manufacturer’s added costs for doi ng so are reflected in higher prices for these models. While buyers of these models pay higher prices to purchase these vehicles, their improved fuel economy lowers the consumer’s costs for purchasi ng fuel to operate them. Over time, buyers may recoup the higher purchase prices they pay fo r these vehicles in the form of savings in outlays for fuel. The length of time required to repay the higher cost of buying a more fuelefficient vehicle is referred to as the buyer’s payback period.

The length of this payback period depends on the in itial increase in a vehicle’s purchase price, the improvement in its fuel economy, the number of miles it is driven each year, and the retail price of fuel. We calculated payback periods using the fuel economy improvement and average price increase estimated to result from the st andard, the future retail gasoline prices, and estimates of the number of miles vehicles are driven each year as they age. These calculations are taken from a consumer’s perspective, not a so cietal perspective. Thus, only gasoline savings are included on the benefits side of the equation. The price of gasoline includes fuel taxes and future savings are not discounted to present value, since consumer s generally only consider and respond to what they pay at the pump. The payback periods was estimated for only MY 2015 vehicles and an average of all manufacturers for the different alternatives. The payback periods for MY 2015 are shown in Table IX-10. Table IX-10 Payback Period for MY 2015 Average Vehicles (in years)

Passenger Cars Light Trucks 25% Below Optimized 4.3 3.9 Optimized (7%) 4.7 4.2 25% Above Optimized 6.7 6.0 50% Above Optimized 8.3 7.0 Optimized (3%) 8.8 4.5

TC = TB 10.4 8.3 Technology Exhaustion Never 8.3X-1

X. PROBABILISTIC UNCERTAINTY ANALYSIS

OMB Circular A-4 requires formal probabilistic uncertainty analysis of complex rules where there are large, multiple uncertainties whose analys is raises technical challenges or where effects cascade and where the impacts of th e rule exceed $1 billion. CAFE meets all of these criteria. This chapter identifies and quantifies the major uncertainties in the preliminary regulatory impact analysis and estimates the probabili ty distribution of the benefits, costs, and net benefits of the compliance options selected for the proposed rule for MY 2011-2015 passenger car and light truck CAFE standards. Throughout the course of the main analysis, input values were selected from a variety of often conflicting sources. Best estimates were selected based on the preponderance of data and analyses available, but there is inevitably a level of uncertainty in these selections. Some of these inputs contribu ted less to the overall vari ations of the outcomes, and, thus, are less significant. Some inputs depe nd on others or are cl osely related (e.g. oil import externalities), and thus can be combined. With the vast number of uncertainties imbedded in this regulatory analysis, this uncertainty analysis identifies only the major independent uncertainty factors having appreciable variability a nd impact on the end results and quantifies them by their probability distributions. These newly defined values are then randomly selected and fed back into the model to determine the net benefits using the Monte Carlo statistical simulation technique. 210 The simulation technique indu ces the probabilistic outcomes accompanied with degrees of probability or plau sibility. This facilitates a more informed decision-making process.

The analysis is based on the actual processes used to derive net benefits as described in the previous chapters. Each variable (e.g., cost of technology) in th e mathematical model represents an uncertainty factor that would potentially alter the modeling outcomes if its value was changed. We assume that these variables ar e independent of each other. The confidence intervals around the costs and bene fits of technologies reflect inde pendent levels of uncertainty regarding costs and benefits, ra ther than linked probabilities dependent on higher or lower quality versions of a specific technology. By c ontrast, there is reason to believe that monopsony costs may be dependent on fuel prices. Howeve r, monopsony costs are only one of several oil import externalities, and the range of monopsony costs is quite narrow. The potential for significant error due to an assumption of indepe ndence for monopsony costs is thus quite low. Given this, the agency has elected to treat monopsony costs as an independent variable.

The uncertainties of these variables are de scribed by appropriate pr obability distribution functions based on available data. If data are not sufficient or not av ailable, professional judgments are used to estimate the probability di stributions of these uncertainty factors. A complete description of the formulas and methods used in the CAFE mode l is available in the public docket. 211

210 See, for example, Morgan, MG, Henrion, M, and Small M, “Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis”, Cambridge University Press, 1990.

211 CAFE Compliance and Effects Modeling System Documentation, Volpe Center, U.S. Dept. of Transportation, July 2005, pp. 27-46 and C-22 to C-35. Docket No. NHTSA 21974-2.X-2

After defining and quantifying the major uncertainty factors, the next step is to simulate the model to obtain probabilistic results rather than single-value estimates. In the uncertainty analysis, CAFE levels were kept constant; in other words, we did not change the CAFE standards for each run based on net benefits. The simulation process was run repeatedly for 20,000 trials under each discount rate scenario. Each complete run is a trial. For each trial, the simulation first randomly selects a value for each of the uncertainty factors based on their probability distributions. The selected values are th en fit into the models to forecast results. In addition to the simulation results, the program also estimates the degree of certainty (or confidence, credibility). The de gree of certainty provides the d ecision-maker with an additional piece of important information with wh ich to evaluate the forecast results.

Simulation Models and Uncertainty Factors A Monte Carlo simulation was conducted using th e CAFE modeling system that was developed to estimate the impacts of higher CAFE requireme nts described in previous chapters. The focus of the simulation model was variation around th e chosen uncertainty parameters and their resulting impact on the key output parameters, fuel savings, and net benefits. Net benefits measure the difference between (1) the total dollar value that would be saved in fuel and other benefits and (2) the total costs of the rule.

The agency reviewed the inputs and relationships that drive th e CAFE model to determine the factors that are the major sources of uncertainty. Five factors were identified as contributing the most uncertainty to the estimated impacts of higher CAFE standards: (1) Technology costs; (2) Technology effectiveness; (3) Fuel prices; (4) The value of oil consum ption externalities; and (5) The rebound effect.

Technology Costs The costs incurred by manufacturers to modify th eir vehicles to meet new CAFE levels are assumed to be passed on to consumers in the form of higher new car prices. These technology costs are the primary determinant of the overall cost of improving fuel economy.

Forty-one different technologies were examined as possible methods to comply with higher CAFE standards. These technologies were summar ized in Chapter V earlie r in this analysis. Chapter V also summarizes the estimated range of costs for these technologies. The expected values (mid-range values) were used in the main analysis. For the uncertainty analysis, the full range of NAS cost estimates is used. The uncer tainty model assumes a normal distribution for these costs, with each end of the range being three standard deviations from the mean (or expected) value. Figure X-1 graphically demonstr ates the distributions of a hypothetical sample of three of the technologies.X-3

Figure X-1 Normal Distributions for 3 Different Technologies

- 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

0 50 100 150 200 250 300 Retail Price Equivalent Increase ($)Probability Density Aero. Drag Reduction ($0-146) Cylinder Deactivation ($116-262) Engine Acc. Improvement ($87-116)

Technology Effectiveness

The modifications adopted by manuf acturers to enable their vehicl es to meet new CAFE levels will improve fuel efficiency and reduce the cost of operating the more efficient vehicles. The effectiveness of each technology determines how la rge an impact it will have towards enabling manufacturers to meet the higher CAFE standard s, and will thus determine how much additional improvement is needed and which additional technologies will be required to achieve full compliance. In selecting the likely path th at manufacturers will choose to meet CAFE, the CAFE model tests the interacti on of technology costs and effectiv eness to achieve an optimal (cost-minimizing) technological solution. Technology effectiveness is thus a primary determinant of the overall cost and benefit of improving fuel economy.

As noted above, forty-one differe nt technologies were examined as possible methods to comply with higher CAFE standards. Th ese technologies were summarized in Chapter V earlier in this analysis. Chapter V also summarizes the estimated range of effectiveness for these technologies. The expected values (mid-range values) were used in the main analysis. For the uncertainty analysis, the full range of effectiveness estimates is used. The uncertainties model assumes a normal distribution for these values, with each end of the range being three standard deviations from the mean (or expected) value.X-4

Fuel Prices Higher CAFE standards will result in reduced ga soline consumption, which will translate into lower vehicle operating costs for consumers. The value of this reduced fuel consumption is a direct function of fuel prices. Fuel prices are thus a primary de terminant of the overall social benefit that will result from improving fuel economy.

The analysis attempts to measure impacts that occur as much as 40 years in the future and estimating gasoline prices this far in advance is an uncertain process. In the main analysis, the agency utilized predicted fuel prices from the Energy Information Administration’s (EIA) publication Annual Energy Outlook 2008 (AEO) Revised Early Release. The main analysis is based on the AEO Reference Case scenario, which re presents EIA’s best estimate of future fuel prices. For the uncertainty analysis, the Agency examined two other AEO scenarios, the Low Oil Price scenario (LOP) and the High Oil Pric e scenario (HOP). The AEO 2008 Early Release did not contain LOP and HOP estimates. The agency therefore estimated these levels by adjusting the 2008 Reference case proportionately to these cases in the AEO 2007 report. The LOP scenario was chosen to allow for the possibi lity that the EIA’s Reference Case predictions could overestimate the price of gasoline in the futu re. However, recent escal ation in the price of gasoline has resulted in prices that have at times exceeded those estimated by EIA for their reference case. It is unclear whether this just reflects a temporary spike in price levels or whether it is an indication of permanently higher price levels. To re flect the possibility of significantly higher prices, the Agency sele cted the HOP case, which among the AEO 2008 scenarios comes closest to matching the highest prices seen during the recent gasoline price surge, and which gives the highest gasoline pric e forecasts among all AEO 2008 scenarios

Each of these scenarios was app lied as a discrete input (i.e., draws were not made from among the three scenarios separately for each future year). Rather, for each draw, one of the three scenarios was chosen and applied across the full vehicle life for each model year. The probability of selection for each of the three scenarios was modele d using discrete weights of 50 percent for the Reference Case, and 25 percent fo r both the LOP and HOP cases. Table X-1 lists the AEO gasoline price forecasts under each scen ario. These same prices are demonstrated graphically (in 2006 economics) in Fi gure X-2. Note that these pri ces include Federal, State, and local fuel taxes. For the uncertainty analysis, taxes were removed because they are viewed as transfer payments (see discussion in Chapter VIII). Estimated retail prices are shown here because they are a better referen ce point for most readers.X-5

Table X-1 AEO 2008 Gasoline Price Scenarios Low Forecast High 2011 $2.302 $2.553 $3.056 2012 $2.151 $2.477 $3.068 2013 $2.069 $2.405 $3.082 2014 $2.076 $2.389 $3.189 2015 $1.986 $2.316 $3.194 2016 $1.931 $2.255 $3.139 2017 $1.892 $2.267 $3.162 2018 $1.940 $2.293 $3.237 2019 $1.991 $2.362 $3.320 2020 $2.047 $2.420 $3.430 2021 $2.027 $2.386 $3.410 2022 $2.021 $2.406 $3.418 2023 $2.057 $2.414 $3.467 2024 $2.039 $2.409 $3.452 2025 $2.040 $2.425 $3.486 2026 $2.057 $2.438 $3.577 2027 $2.031 $2.451 $3.609 2028 $2.029 $2.474 $3.641 2029 $2.040 $2.498 $3.712 2030 $2.052 $2.514 $3.736 2031 $2.052 $2.514 $3.736 2032 $2.052 $2.514 $3.736 2033 $2.052 $2.514 $3.736 2034 $2.052 $2.514 $3.736 2035 $2.052 $2.514 $3.736 2036 $2.052 $2.514 $3.736 2037 $2.052 $2.514 $3.736 2038 $2.052 $2.514 $3.736 2039 $2.052 $2.514 $3.736 2040 $2.052 $2.514 $3.736 2041 $2.052 $2.514 $3.736 2042 $2.052 $2.514 $3.736 2043 $2.052 $2.514 $3.736 2044 $2.052 $2.514 $3.736 2045 $2.052 $2.514 $3.736 2046 $2.052 $2.514 $3.736 2047 $2.052 $2.514 $3.736X-6

Figure X-2

AEO Gasoline Price Forecasts

$0.000 $0.500 $1.000 $1.500 $2.000 $2.500 $3.000 $3.500 $4.0002011201420172020202320262029203220352038204120442047

YearRetail Price Low

Forecast High

Oil Consumption Externalities

Reduced fuel consumption can benefit society by lowering the world market price for oil, reducing the threat of petrol eum supply disruptions, and reduc ing the cost of maintaining military security in oil producing regions and ope rating the strategic petroleum reserve. These benefits are called “externalities” because they ar e not reflected directly in the market price of fuel. A full description of thes e externalities is included in Chapter VIII under “Other Economic Benefits from Reducing Petroleum Use.” These f actors increase the net social benefits from reduced fuel consumption. Although they represen t a relatively small portion of overall social benefits, there is a significant level of uncertainty as to their values. For this reason, they were examined in the uncertainty analysis.

Table X-3 lists the range of values that were examined for oil consumption externalities. The expected values were used in the main analysis. Both the value of reducing U.S. demand on the world market price for oil and the value of reduc ed threat of supply disruptions were derived from a study by Leiby (2008) (see Chapter VIII). For reasons noted in Chapter VIII, military security is not specifically va lued in this analysis. A norma l distribution was assumed for the range of values for oil consumption externalities with the low and high valu es assumed to be two standard deviations from the mean, based on the Leiby estimates. Table X-3 Uncertainty Ranges for Oil Consumption Externalities ($/gallon)

Low Expected High For reducing U.S. demand on world market price $0.028 $0.182 $0.336

For reducing the threat of supply disruptions $0.035 $0.113 $0.191X-7

The Rebound Effect By reducing the amount of gasoline used and, thus , the cost of operating a vehicle, higher CAFE standards are expected to result in a slight increase in annual m iles driven per vehicle. This “rebound effect” impacts net societ al benefits because the increas e in miles driven offsets a portion of the gasoline savings that results from more fuel-efficien t vehicles. Although consumers derive some value from this extra dr iving, it also leads to increases in crash, congestion, noise, and pollution cost s associated with driving. Most recent estimates of the magnitude of the rebound effect for light duty ve hicles fall in the range of 10-20 percent (i.e., increasing vehicle use will offset 10-20 percent of the fuel savings resulting from an improvement in fuel economy). A more complete discussion of the rebound effect is included in Chapter VIII. The agency employed a rebound effect of 15 percent in the main analysis. For the uncertainty analysis, a range of 10 to 20 pe rcent is used and employed in a skewed Beta distribution which produced a m ean of approximately 14 percent. The skewed distribution reflects the agency’s belief that the more credible studies that differ from the 15 percent value chosen for the main analysis fall below this value and differ by more substantial margins than the upper range of credible values. Table X-3 Summarizes the economic parameters used in the uncertainty analysis.

Table X-3 Monte-Carlo Specific Parameters

Alternative Discount Rates (%) 0.03
Rebound Randomization Parameters Rebound Alpha Shape 6.0 Rebound Beta Shape 2.7 Rebound Scale -0.20 Rebound Base -0.05 Monopsony Randomization Parameters Monopsony Mean $0.182 Monopsony Standard Deviation $0.077 Price Shock Randomization Parameters Price Shock Mean $0.113 Price Shock Standard Deviation $0.039 Military Security Randomization Parameters Military Security Mean $0.000 Military Security Standard Deviation $0.000
Total Economic Costs of Petroleum Randomization Parameters (Specified in $/gallon)

Total Economic Costs Alpha Shape 4 Total Economic Costs Beta Shape 2.4118 Total Economic Costs Scale 0.46 Total Economic Costs Base 0.05 Carbon Dioxide Randomization Parameters CO-2 Mean $7.00 CO-2 Standard Deviation $3.25 Default Cost and FC Variations

Cost Variation % 0.37 FC Variation % 0.31X-8

Modeling Results – Trial Draws

Because of the complexity of the CAFE mode l, the computer time required to perform the uncertainty analysis was significan t. The uncertainty analysis conducted a total of 40,000 trials (20,000 for each discount rate) Figures X- 3 thr ough X-14 graphically illustrate the draw results for a sample of the 85 variables (41 technology e ffectiveness rates, 41 technology costs, the fuel price scenario, oil import externalities, the rebound effect, and CO2.) that were examined. Tables X-3 through X-7 list the draw results for each economic input, technology cost, and technology effectiveness.

Figure X-3 Monte Carlo Draw Profile, Passenger Car Costs

Turbocharging and Downsizing

0 200 400 600 800 1000 1200 1400 1600 1800

207.600 283.600 359.600 435.600 511.600 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %X-9

Figure X-4 Monte Carlo Draw Profile, Passenger Car Effectiveness

Turbocharging and Downsizing

0 200 400 600 800 1000 1200 1400 1600 1800

0.014 0.019 0.023 0.027 0.031 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %

Figure X-5 Monte Carlo Draw Profile, Passenger Cars, Costs

Diesel following HCCI

0 200 400 600 800 1000 1200 1400 1600 1800 2000

1,017.20 1,439.20 1,861.20 2,283.20 2,705.20 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %X-10

Figure X-6 Monte Carlo Draw Profile, Passenger Cars, Effectiveness

Diesel following HCCI

0 500 1000 1500 2000 2500

0.05 0.06 0.08 0.10 0.11 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %

Figure X-7 Monte Carlo Draw Profile, Passenger Cars, Costs

Automatic Manual Transmission

0 200 400 600 800 1000 1200 1400 1600 1800

67.800 95.800 123.800 151.800 179.800 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %X-11

Figure X-8 Monte Carlo Draw Profile, Passenger Cars, Effectiveness

Automatic Manual Transmission

0 200 400 600 800 1000 1200 1400 1600 1800

0.041 0.049 0.057 0.065 0.073 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %

Figure X-9 Monte Carlo Draw Profile

Pretax Fuel Price Path

0% 10% 20% 30% 40% 50% 60%

Low Mid HighRelative FrequencyX-12

Figure X-10 Monte Carlo Draw Profile

CO2 Costs

0 200 400 600 800 1000 1200 1400

0.400 3.600 6.800 10.000 13.200 16.400 19.600Number of trials

0% 25% 50% 75% 100% 125%

Cumulative % Frequency

Figure X-11 Monte Carlo Draw Profile

Monopsony Cost

0 200 400 600 800 1000 1200 1400 1600

0.000 0.082 0.163 0.245 0.327 0.408 0.490Number of trials

0% 25% 50% 75% 100% 125%

Cumulative % FrequencyX-13

Figure X-12 Monte Carlo Draw Profile

Rebound Effect

0 200 400 600 800 1000 1200 1400

-0.20 -0.15 -0.10 -0.05Number of trials

0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %

Figure X-13 Monte Carlo Draw Profile

Price Shock Cost

0 200 400 600 800 1000 1200 1400 1600

0.005 0.049 0.092 0.135 0.178 0.221 0.265Number of trials

0% 25% 50% 75% 100% 125%

Cumulative % FrequencyX-14

Figure X-14 Monte Carlo Draw Profile

Total Economic Costs

0 200 400 600 800 1000 1200 1400

0.011 0.099 0.187 0.275 0.363 0.451 0.539Number of trials

0% 25% 50% 75% 100% 125%

Cumulative % Frequency

Table X-3 Monte Carlo Draw Results, Economic Inputs

Economic Inputs Minimum Maximum Mean StdDev

Rebound Effect -0.200 -0.050 -0.139 0.031 Monopsony Cost 2.08E-0 5 0.4758454 0.184348 0.075127 Price Cost Shock 0.0005 87 0.2694044 0.112852 0.038933 Total Economic Costs 0.051902 0.508969 0.328306 0.087728 CO2 Costs 0.003939 19.552248 7.150228 3.110129X-15

Table X-4 Monte Carlo Draw Results, Passenger Car Technology Costs

Passenger Car Technology Costs Minimum Maximum Mean StdDev Low Friction Lubricants $1.49 $4.74 $3.00 $0.37 Engine Friction Reduction $0.33 $119.10 $52.78 $17.42 Variable Valve Timing (ICP) $46.16 $142.36 $89.60 $11.00 Variable Valve Timing (CCP) $0.00 $0.00 $0.00 $0.00 Variable Valve Timing (DCP) $29.90 $93.84 $60.68 $7.50 Cylinder Deactivation $53.49 $165.05 $103.36 $12.82 Variable Valve Lift & Timing (CVVL) $180.04 $553.92 $361.94 $44.52 Variable Valve Lift & Timing (DVVL) $103.16 $327.22 $208.04 $25.65 Cylinder Deactivation on OHV $54.85 $152.41 $103.52 $12.78 Variable Valve Timing (CCP) on OHV $29.20 $94.35 $59.33 $7.33 Multivalve Overhead Cam with CVVL $469.56 $1,444.80 $936.29 $114.36 Variable Valve Lift & Timing (DVVL) on OHV $103.30 $307.42 $208.46 $25.57 Camless Valve Actuation $0.00 $0.00 $0.00 $0.00 Stoichiometric GDI $123.72 $536.56 $318.20 $48.03 Diesel following GDI-S (SIDI) $1,235.43 $3,339.62 $2,219.62 $275.52 Lean Burn GDI $0.00 $0.00 $0.00 $0.00 Turbocharging and Downsizing $204.72 $577.86 $399.36 $49.36 Diesel following Turbo D/ S $946.96 $2,877.83 $1,822.07 $224.84 HCCI $145.97 $440.72 $289.76 $35.85 Diesel following HCCI $991.05 $3,075.29 $1,932.76 $238.19 5 Speed Automatic Transmission $59.29 $180.41 $121.75 $15.09 Aggressive Shift Logic $18.76 $55.53 $38.05 $4.68 Early Torque Converter Lockup $15.13 $47.27 $30.01 $3.68 6 Speed Automatic Transmission $0.00 $0.00 $0.00 $0.00 Continuously Variable Transmission $65.89 $187.47 $120.00 $14.91 6 Speed Manual $54.50 $168.33 $106.43 $13.08 Improved Accessories $116.76 $174.09 $145.25 $6.90 Electronic Power Steering $103.77 $214.40 $157.73 $13.06 42-Volt Electrical System $184.50 $267.97 $226.75 $10.75 Low Rolling Resistance Tires $3.25 $8.69 $6.00 $0.74 Low Drag Brakes $0.00 $0.00 $0.00 $0.00 Secondary Axle Disconnect - Unibody $633.60 $721.41 $675.69 $11.88 Secondary Axle Disconnect - Ladder Frame $0.00 $0.00 $0.00 $0.00 Aero Drag Reduction $0.19 $84.64 $37.59 $12.40 Material Substitution (1%) $0.00 $0.00 $0.00 $0.00 Material Substitution (2%) $0.00 $0.00 $0.00 $0.00 Material Substitution (5%) $0.00 $0.00 $0.00 $0.00 ISG with Idle-Off $299.07 $858.05 $581.96 $71.76 IMA/ISAD/BSG Hybrid (includes engine downsizing $967.35 $3,068.39 $1,958.43 $242.67 2-Mode Hybrid $391.09 $1,163.87 $764.67 $94.39 Power Split Hybrid $309.05 $960.34 $627.41 $76.93X-16

Table X-5 Monte Carlo Draw Resu lts, Passenger Car Fuel Ec onomy Improvement Rates

Passenger Car Fuel Economy Improvement Rates Minimum Maximum Mean StdDev Low Friction Lubricants 0.002908 0.0073035 0.004997 0.000518 Engine Friction Reduction 0.004595 0.0329195 0.01996 0.003341 Variable Valve Timing (ICP) 0.009356 0.0206904 0.014895 0.001545 Variable Valve Timing (CCP) 0.011859 0.0281932 0.020186 0.002083 Variable Valve Timing (DCP) 0.011895 0.0280668 0.02018 0.002085 Cylinder Deactivation 0.013563 0.0348619 0.022915 0.00237 Variable Valve Lift & Timing (CVVL) 0.023194 0.0522346 0.037476 0.003869 Variable Valve Lift & Timing (DVVL) 0.013216 0.0318864 0.022365 0.002324 Cylinder Deactivation on OHV 0.018306 0.044046 0.030546 0.003144 Variable Valve Timing (CCP) on OHV 0.017041 0.0407676 0.027473 0.00283 Multivalve Overhead Cam with CVVL 0.017591 0.0444581 0.030094 0.003111 Variable Valve Lift & Timing (DVVL) on OHV 0.008878 0.0223505 0.014976 0.001546 Camless Valve Actuation 0 0 0 0 Stoichiometric GDI 0.007243 0.0217997 0.014994 0.001682 Diesel following GDI-S (SIDI) 0.094756 0.2441063 0.160479 0.01659 Lean Burn GDI 0.023047 0.0549808 0.037155 0.003851 Turbocharging and Downsizing 0.014385 0.0345359 0.024998 0.002575 Diesel following Turbo D/S 0.078206 0.1893723 0.135461 0.013972 HCCI 0.044389 0.1117523 0.075102 0.007783 Diesel following HCCI 0.049223 0.1261183 0.085368 0.008877 5 Speed Automatic Transmission 0.015092 0.0344635 0.024962 0.002575 Aggressive Shift Logic 0.007926 0.0215958 0.015003 0.001683 Early Torque Converter Lockup 0.002939 0.0074048 0.004999 0.000518 6 Speed Automatic Transmission 0.000918 0.0281055 0.01499 0.003332 Continuously Variable Transmission 0.020615 0.0505527 0.034988 0.003622 6 Speed Manual 0.002992 0.0069901 0.005 0.000518 Improved Accessories 0.007504 0.0212326 0.015009 0.001664 Electronic Power Steering 0.015584 0.0168713 0.016271 0.000147 42-Volt Electrical System 0.008567 0.0217223 0.014992 0.001662 Low Rolling Resistance Tires 0.008928 0.0218487 0.014998 0.001664 Low Drag Brakes 0 0 0 0 Secondary Axle Disconnect - Unibody 0.005908 0.0144799 0.009997 0.001032 Secondary Axle Disconnect - Ladder Frame 0 0 0 0 Aero Drag Reduction 0.017873 0.041923 0.029955 0.003111 Material Substitution (1%) 0.005713 0.0071391 0.006498 0.000168 Material Substitution (2%) 0.00586 0.0071669 0.0065 0.000167 Material Substitution (5%) 0.016592 0.0214113 0.019248 0.000582 ISG with Idle-Off 0.044364 0.1094447 0.075005 0.0078 IMA/ISAD/BSG Hybrid (includes engine downsizing) 0.03486 0.083258 0.059502 0.006136 2-Mode Hybrid 0.010524 0.0267755 0.017813 0.001832 Power Split Hybrid 0.03312 0.0822965 0.057607 0.00598X-17

Table X-6 Monte Carlo Draw Results, Light Truck Technology Costs

Light Truck Technology Costs Minimum Maximum Mean StdDev Low Friction Lubricants $1.49 $4.74 $3.00 $0.37 Engine Friction Reduction $0.42 $155.39 $68.86 $22.73 Variable Valve Timing (ICP) $61.18 $188.69 $118.76 $14.58 Variable Valve Timing (CCP) $0.00 $0.00 $0.00 $0.00 Variable Valve Timing (DCP) $44.25 $138.87 $89.81 $11.10 Cylinder Deactivation $108.70 $335.40 $210.04 $26.04 Variable Valve Lift & Timing (CVVL) $237.53 $730.81 $477.52 $58.74 Variable Valve Lift & Timing (DVVL) $132.02 $418.79 $266.26 $32.82 Cylinder Deactivation on OHV $111.47 $309.70 $210.37 $25.96 Variable Valve Timing (CCP) on OHV $29.20 $94.35 $59.33 $7.33 Multivalve Overhead Cam with CVVL $648.92 $1,996.67 $1,293.93 $158.04 Variable Valve Lift & Timing (DVVL) on OHV $132.21 $393.45 $266.80 $32.73 Camless Valve Actuation $0.00 $0.00 $0.00 $0.00 Stoichiometric GDI $142.88 $619.65 $367.48 $55.47 Diesel following GDI-S (SIDI) $1,470.51 $3,975.08 $2,641.97 $327.94 Lean Burn GDI $0.00 $0.00 $0.00 $0.00 Turbocharging and Downsizing $157.93 $445.79 $308.09 $38.08 Diesel following Turbo D/S $1,214.16 $3,689.84 $2,336.19 $288.28 HCCI $209.73 $633.21 $416.31 $51.51 Diesel following HCCI $1, 143.01 $3,546.83 $2,229.12 $274.71 5 Speed Automatic Transmission $59.29 $180.41 $121.75 $15.09 Aggressive Shift Logic $18.76 $55.53 $38.05 $4.68 Early Torque Converter Lockup $15.13 $47.27 $30.01 $3.68 6 Speed Automatic Transmission $0.00 $0.00 $0.00 $0.00 Continuously Variable Transmission $8.78 $24.99 $16.00 $1.99 6 Speed Manual $54.50 $168.33 $106.43 $13.08 Improved Accessories $116.76 $174.09 $145.25 $6.90 Electronic Power Steering $103.77 $214.40 $157.73 $13.06 42-Volt Electrical System $184.50 $267.97 $226.75 $10.75 Low Rolling Resistance Tires $2.36 $6.31 $4.36 $0.54 Low Drag Brakes $38.97 $122.58 $77.27 $9.63 Secondary Axle Disconnect - Unibody $580.10 $660.49 $618.63 $10.87 Secondary Axle Disconnect - Ladder Frame $55.77 $159.17 $100.75 $12.41 Aero Drag Reduction $0.19 $84.64 $37.59 $12.40 Material Substitution (1%) $0.26 $0.73 $0.50 $0.06 Material Substitution (2%) $0.37 $0.98 $0.67 $0.08 Material Substitution (5%) $0.46 $1.23 $0.83 $0.10 ISG with Idle-Off $308.54 $885.22 $600.39 $74.03 IMA/ISAD/BSG Hybrid (includes engine downsizing $0.00 $0.00 $0.00 $0.00 2-Mode Hybrid $2,121.66 $6,314.06 $4,148.37 $512.05 Power Split Hybrid $0.00 $0.00 $0.00 $0.00X-18

Table X-7 Monte Carlo Draw Resu lts, Light Truck Fuel Economy Improvement Rates

Light Truck Technology Improvement Rates Minimum Maximum Mean StdDev Low Friction Lubricants 0.002908 0.007303 0.004997 0.000518 Engine Friction Reduction 0.004595 0.03292 0.01996 0.003341 Variable Valve Timing (ICP) 0.007989 0.017667 0.012718 0.001319 Variable Valve Timing (CCP) 0.008747 0.020794 0.014888 0.001536 Variable Valve Timing (DCP) 0.007505 0.01771 0.012733 0.001315 Cylinder Deactivation 0.02665 0.068502 0.045026 0.004656 Variable Valve Lift & Timing (CVVL) 0.012312 0.027728 0.019894 0.002054 Variable Valve Lift & Timing (DVVL) 0.00584 0.014089 0.009882 0.001027 Cylinder Deactivation on OHV 0.03597 0.086549 0.060021 0.006178 Variable Valve Timing (CCP) on OHV 0.00783 0.018731 0.012623 0.001301 Multivalve Overhead Cam with CVVL 0.011624 0.029378 0.019886 0.002056 Variable Valve Lift & Timing (DVVL) on OHV 0.005849 0.014726 0.009867 0.001019 Camless Valve Actuation 0 0 0 0 Stoichiometric GDI 0.007243 0.0218 0.014994 0.001682 Diesel following GDI-S (SIDI) 0.106364 0.274009 0.180138 0.018622 Lean Burn GDI 0 0 0 0 Turbocharging and Downsizing 0.014385 0.034536 0.024998 0.002575 Diesel following Turbo D/S 0.089555 0.216855 0.155119 0.015999 HCCI 0.044389 0.111752 0.075102 0.007783 Diesel following HCCI 0.06055 0.155142 0.105014 0.01092 5 Speed Automatic Transmission 0.015092 0.034464 0.024962 0.002575 Aggressive Shift Logic 0.007926 0.021596 0.015003 0.001683 Early Torque Converter Lockup 0.002939 0.007405 0.004999 0.000518 6 Speed Automatic Transmission 0.000918 0.028105 0.01499 0.003332 Continuously Variable Transmission 0.010549 0.025869 0.017904 0.001853 6 Speed Manual 0.002992 0.00699 0.005 0.000518 Improved Accessories 0.007504 0.021233 0.015009 0.001664 Electronic Power Steering 0.019155 0.020736 0.019999 0.000181 42-Volt Electrical System 0.008567 0.021722 0.014992 0.001662 Low Rolling Resistance Tires 0.006489 0.015879 0.010901 0.00121 Low Drag Brakes 0.002886 0.00734 0.004883 0.000504 Secondary Axle Disconnect - Unibody 0.004294 0.010524 0.007266 0.00075 Secondary Axle Disconnect - Ladder Frame 0.004331 0.010856 0.007326 0.000759 Aero Drag Reduction 0.014964 0.0351 0.02508 0.002605 Material Substitution (1%) 0.005713 0.007139 0.006498 0.000168 Material Substitution (2%) 0.00586 0.007167 0.0065 0.000167 Material Substitution (5%) 0.016592 0.021411 0.019248 0.000582 ISG with Idle-Off 0.044364 0.109445 0.075005 0.0078 IMA/ISAD/BSG Hybrid (includes engine downsizing) 0 0 0 0 2-Mode Hybrid 0.035708 0.090851 0.06044 0.006215 Power Split Hybrid 0.027148 0.067457 0.047219 0.004901X-19

Modeling Results – Output

Tables X-8 and X-9 summarize the m odeling results for fuel saved, to tal costs, societal benefits, and net benefits for passenger cars and trucks respectiv ely under a 7% discount rate. They also indicate the probability that net benefits ex ceed zero. Tables X-10 and X-11 summarize these same results under a 3% discount rate. Thes e results are also illustrated in Figures X-15 through X-18 for passenger cars under Optimi zed CAFE at 7 percent for MY 2015. Although not shown here, the general shape of the resulting output distributions are similar for the light trucks, the 3 percent discount rate , and for other model years as well. The humped shape that occurs for both social benefits and net bene fits reflects the three different gasoline price scenarios. About half of all draws were selected from the AEO Reference Case, while about one quarter were drawn from the Low Oil Price scen ario and one quarter were drawn from the High Oil Price scenario. This produ ces three separate humps which reflect the increasing impact on benefits from the three progressi vely higher oil price scenarios. The Low Oil scenario is close enough to the Forecast scenario that the 2 humps visually begin to merge. However, the difference between the High Oil Price scenario a nd the Forecast is typica lly more than double the difference between the Forecast and the Low Oil price scenario, which results in a separate distribution further up the x axis. The followi ng discussions summarize the range of results presented in these tables for the combined passe nger car and light truck across both the 7 percent (typically the lower range) and 3 percent (typically upper range) discount rates 212

.
Fuel Savings : The analysis indicates that MY 2011 vehicles (both passe nger cars and light trucks) will experience between 3,370 million an d 4,735 million gallons of fuel savings over their useful lifespan. MY 2012 vehicles will experience between 7,476 million and 9,639 million gallons of fuel savings over their usef ul lifespan. MY 2013 vehicles will experience between 10,863 million and 13,763 million gallons of fuel savings over their useful lifespan. MY 2014 vehicles will experien ce between 12,568 and 15,664 million gallons of fuel savings over their useful lifespan. MY 2015 vehicles will experience between 14,188 and 17,659 million gallons of fuel savings over their useful lifespan. Over the combined lifespan of the five model years, between 48.5 billion and 61.4 billion gallons of fuel will be saved.

Total Costs : The analysis indicates that owners of MY 2011 passenger cars a nd light trucks will pay between $2,447 million and $5,256 million in highe r vehicle prices to purchase vehicles with improved fuel efficiency. MY 2012 owners will pay between $5,817 million and $10,427 million more. MY 2013 owners will pay be tween $7,942 million and $15,288 million more. MY 2014 owners will pay between $9,338 milli on and $17,189 million more. MY 2015 owners will pay between $10,940 million an d $19,842 million more. Owners of all five model years vehicles combined will pay between $36.5 billion and $67.9 billion in higher vehicle prices to purchase vehicles with improved fuel efficiency.

212 In a few cases the upper range results were obtained from the 7% rate and the lower range results were obtained from the 3% rate. While this may seem counterintuitive, it results from the random selection process that is inherent in the Monte Carlo technique.X-20

Societal Benefits: The analysis indicates that change s to MY 2011 passenger cars and light trucks to meet the proposed CAFE standards will produce overall societal benefits valued between $4,375 million and $13,041 million. MY 2012 vehicles will produce benefits valued between $9,363 million and $28,214 million. MY 2013 vehicles will produce benefits valued between $13,370 million and $41,027 million. MY 2014 vehicles will produce benefits valued between $15,586 million and $47,087 million. MY 2015 vehicles will produce benefits valued between $17,486 million and $53,708 million. Over the combined lifespan of the five model years, societal benefits valued between $60.1 billion and $183.1 billion will be produced.

Net Benefits : The uncertainty analysis indicates th at the net impact of the higher CAFE requirements for MY 2011 passenger cars and light trucks will be a net benefit of between $937 million and $9,678 million. There is at least a 99.3 percent certainty that changes made to MY 2011 vehicles to achieve the higher CAFE standards will produce a net benefit. The net impact of the higher CAFE requirements for MY 2012 w ill be a net benefit of between $283 million and a net benefit of $21,139 million. There is at leas t a 99.6 percent certainty that changes made to MY 2012 vehicles to achieve the CAFE standards will produce a net benefit. The net impact of the higher CAFE requirements for MY 2013 will be a net benefit of between $494 million and a net benefit of $31,311 million. There is at leas t a 99.6 percent certainty that changes made to MY 2013 vehicles to achieve the higher CAFE standards will produce a net benefit. The net impact of the higher CAFE requirements for MY 2014 will be a net benefit of between $711 million and $35,746 million. There is 100 percen t certainty that changes made to MY 2014 vehicles to achieve the CAFE standards will prod uce a net benefit. The net impact of the higher CAFE requirements for MY 2015 will be a ne t benefit of between $654 million and $40,703 million. There is 100 percent certainty that changes made to MY 2015 vehicles to achieve the CAFE standards will produce a net benefit. Over all five model years, the higher CAFE standards will produce net benefits ranging from $3.1 billion to $138.6 billion. There is at least a 99.3 percent certainty that higher CAFE standard s will produce a net societal benefit in each of the model years covered by this final rule. In most years, this probabi lity is 100%.X-21

Table X-8 Uncertainty Analysis Results, Passenger Cars (7% Discount Rate)

MY 2011 Mean Low High Fuel Saved (mill. gall.) 1487 1285 1813 Total Cost ($mill.) 1760 1291 2302 Societal Benefits ($mill.) 2605 1723 4201 Net Benefits ($mill.) 845 435 2574 % Certainty Net Ben. > 0 99.3%

MY 2012 Fuel Saved (mill. gall.) 2870 2618 3403 Total Cost ($mill.) 2271 1676 3237 Societal Benefits ($mill.) 5023 3296 8127 Net Benefits ($mill.) 2753 404 6009 % Certainty Net Ben. > 0 100%

MY 2013 Fuel Saved (mill. gall.) 3585 3237 4283 Total Cost ($mill.) 2769 2021 3970 Societal Benefits ($mill.) 6280 4071 10388 Net Benefits ($mill.) 3511 493 7666 % Certainty Net Ben. > 0 100%

MY 2014 Fuel Saved (mill. gall.) 4613 4200 5372 Total Cost ($mill.) 3707 2793 5109 Societal Benefits ($mill.) 8113 5262 12996 Net Benefits ($mill.) 4407 589 9669 % Certainty Net Ben. > 0 100%

MY 2015

Fuel Saved (mill. gall.) 5513 5025 6389 Total Cost ($mill.) 4713 3773 6339 Societal Benefits ($mill.) 9703 6296 15668 Net Benefits ($mill.) 4989 445 11413 % Certainty Net Ben. > 0 100%X-22

Table X-9 Uncertainty Analysis Results, Light Trucks (7% Discount Rate)

MY 2011 Mean Low High Fuel Saved (mill. gall.) 2312 2085 2922 Total Cost ($mill.) 1696 1156 2954 Societal Benefits ($mill.) 3968 2652 6131 Net Benefits ($mill.) 2272 502 4491 % Certainty Net Ben. > 0 100.0

MY 2012 Fuel Saved (mill. gall.) 5373 4858 6236 Total Cost ($mill.) 5213 4141 7190 Societal Benefits ($mill.) 9128 6067 14300 Net Benefits ($mill.) 3915 -121 9273 % Certainty Net Ben. > 0 99.6%

MY 2013 Fuel Saved (mill. gall.) 8270 7626 9453 Total Cost ($mill.) 7501 5921 11318 Societal Benefits ($mill.) 14053 9299 21967 Net Benefits ($mill.) 6552 -29 15031 % Certainty Net Ben. > 0 99.6

MY 2014 Fuel Saved (mill. gall.) 9071 8381 10258 Total Cost ($mill.) 8216 6545 12080 Societal Benefits ($mill.) 15489 10324 24311 Net Benefits ($mill.) 7273 122 16549 % Certainty Net Ben. > 0 100.0%

MY 2015

Fuel Saved (mill. gall.) 9942 9163 11270 Total Cost ($mill.) 8996 7167 13435 Societal Benefits ($mill.) 17013 11190 26862 Net Benefits ($mill.) 8018 209 18321 % Certainty Net Ben. > 0 100%X-23

Table X-10 Uncertainty Analysis Results, Passenger Cars (3% Discount Rate)

MY 2011 Mean Low High Fuel Saved (mill. gall.) 1503 1285 1798 Total Cost ($mill.) 1782 1309 2295 Societal Benefits ($mill.) 3235 2142 5087 Net Benefits ($mill.) 1454 -29 3544 % Certainty Net Ben. > 0 100%

MY 2012 Fuel Saved (mill. gall.) 2889 2621 3392 Total Cost ($mill.) 2299 1695 3286 Societal Benefits ($mill.) 6215 4050 10036 Net Benefits ($mill.) 3916 1180 7953 % Certainty Net Ben. > 0 100%

MY 2013 Fuel Saved (mill. gall.) 3611 3245 4284 Total Cost ($mill.) 2807 2086 3990 Societal Benefits ($mill.) 7789 5002 12868 Net Benefits ($mill.) 4982 1468 10164 % Certainty Net Ben. > 0 100%

MY 2014 Fuel Saved (mill. gall.) 4639 4220 5374 Total Cost ($mill.) 3754 2877 5050 Societal Benefits ($mill.) 10053 6475 16285 Net Benefits ($mill.) 6299 1847 12783 % Certainty Net Ben. > 0 100%

MY 2015

Fuel Saved (mill. gall.) 5538 5047 6389 Total Cost ($mill.) 4764 3730 6298 Societal Benefits ($mill.) 12025 7770 19419 Net Benefits ($mill.) 7262 1956 15167 % Certainty Net Ben. > 0 100%

Table X-11X-24

Uncertainty Analysis Results, Light Trucks (3% Discount Rate)

MY 2011 Mean Low High Fuel Saved (mill. gall.) 2324 2090 2836 Total Cost ($mill.) 1721 1190 2922 Societal Benefits ($mill.) 5031 3359 7954 Net Benefits ($mill.) 3311 1324 6134 % Certainty Net Ben. > 0 100%

MY 2012 Fuel Saved (mill. gall.) 5371 4855 6234 Total Cost ($mill.) 5212 4200 7046 Societal Benefits ($mill.) 11522 7622 18178 Net Benefits ($mill.) 6310 1853 13186 % Certainty Net Ben. > 0 100%

MY 2013 Fuel Saved (mill. gall.) 8278 7640 9479 Total Cost ($mill.) 7517 6049 11122 Societal Benefits ($mill.) 17791 11729 28159 Net Benefits ($mill.) 10274 2736 21147 % Certainty Net Ben. > 0 100%

MY 2014 Fuel Saved (mill. gall.) 9074 8348 1029 Total Cost ($mill.) 8227 6567 11945 Societal Benefits ($mill.) 19618 12966 30802 Net Benefits ($mill.) 11391 3169 22963 % Certainty Net Ben. > 0 100%

MY 2015

Fuel Saved (mill. gall.) 9942 9175 11264 Total Cost ($mill.) 9008 7297 13544 Societal Benefits ($mill.) 21567 14121 34289 Net Benefits ($mill.) 12559 3551 25536 % Certainty Net Ben. > 0 100%X-25

Figure X-13 Model Output Profile

Cars: Fuel Savin g s for "Optimized Reformed Standards" (2015) at 7% Discount Rate

0 200 400 600 800 1000 1200 1400 1600

5,

052, 314

5,

188, 708

5,325,103 5,461 , 497

5,

597,8 92

5,734 , 286

5,

870, 681

6,007,075 6,143,470

6,

279, 864 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %

Figure X-14 Model Output Profile

Cars: Societal Benefits for "Optimized Reformed Standards" (2015) at 7% Discount Rate

0 200 400 600 800 1000 1200 1400 1600 1800 2000

6,483 7,420 8,358 9,295 10,233 11,17012,108 13,04513,983 14,920 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %X-26

Figure X-15 Model Output Profile

Cars: Total Cost for "Optimized Reformed Standards" (2015) at 7% Discount Rate

0 200 400 600 800 1000 1200 1400 1600 1800 2000

3,821 4,078 4,335 4,592 4,849 5,106 5,363 5,620 5,877 6,134 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %

Figure X-16 Model Output Profile

Cars: Net Benefits for "Optimized Reformed Standards" (2015) at 7% Discount Rate

0 200 400 600 800 1000 1200 1400 1600 1800 2000

660 1,757 2,855 3,952 5,050 6,147 7,245 8,342 9,440 10,537 0% 20% 40% 60% 80% 100% 120%

Frequency Cumulative %XI-1

XI. REGULATORY FLEXIBILITY AC T AND UNFUNDED MA NDATES REFORM ACT ANALYSIS

A. R EGULATORY F LEXIBILITY A CT

The Regulatory Flexibility Act of 1980 (5 U.S.C §601 et seq .) requires agencies to evaluate the potential effects of their proposed and final ru les on small business, small organizations and small Government jurisdictions.

5 U.S.C §603 requires agencies to prepare and make available for public comments initial and final regulatory flexibility analysis (RFA) desc ribing the impact of proposed and final rules on small entities. Section 603(b) of the Act speci fies the content of a RFA. Each RFA must contain:

1. A description of the reasons why acti on by the agency is being considered; 2. A succinct statement of the objectives of, and legal basis for a final rule; 3. A description of and, where feasible, an esti mate of the number of small entities to which the final rule will apply; 4. A description of the project ed reporting, recording k eeping and other compliance requirements of a final rule including an estimate of the classes of small entities which will be subject to the requirement and the type of professional skills necessary for preparation of the report or record; 5. An identification, to the extent practicable , of all relevant Federal rules which may duplicate, overlap or conflict with the final rule; 6. Each final regulatory flexibility analysis sha ll also contain a descri ption of any significant alternatives to the final rule which accomplish the stated objectives of applicable statutes and which minimize any significant economic impact of the final rule on small entities.

1. Description of the reason why act ion by the agency is being considered NHTSA is proposing this action to improve vehicle fuel economy.

2. Objectives of, and legal basis for, the final rule The Energy Policy and Conservation Act requires the agency to set light truck fuel economy standards every year and allows the agency to up date passenger car fuel economy standards. The Energy Independence and Security Act (EISA) ma ndates the setting of separate standards for passenger cars and for light trucks at levels suffici ent to ensure that the average fuel economy of the combined fleet of all passenger cars and light trucks sold by a ll manufacturers in the U.S. in model year 2020 equals or exceeds 35 miles per gallon.

3. Description and estimate of the number of sm all entities to which the final rule will apply The proposal will affect motor vehicle manufactur ers. There are no light truck manufacturers that are small businesses. However, there ar e four domestically owned small passenger car manufacturers.

Business entities are defined as small business using the North American Industry Classification System (NAICS) code, for the purpose of receiving Small Business Administration assistance.XI-2

One of the criteria for determining size, as stat ed in 13 CFR 121.201, is the number of employees in the firm. For establishments primarily enga ged in manufacturing or assembling automobiles, light and heavy duty trucks, buses , motor homes, or motor vehicl e body manufacturing, the firm must have less than 1,000 employees to be classified as a small business.

We believe that the rulemaking would not have a significant economic impact on the small vehicle manufacturers because under Part 525, passenger car manufacturer making less than 10,000 vehicles per year can petition NHTSA to have alternative standards set for those manufacturers. These manuf acturers currently don’t meet th e 27.5 mpg standard and must already petition the agency for relief. If the st andard is raised, it has no meaningful impact on these manufacturers, they still must go through the same process and petition for relief.

Currently, there are four small passenger car moto r vehicle manufacturers in the United States. Table X1-1 provides information about the 4 sma ll domestic manufacturers in MY 2004. All are small manufacturers, having much less than 1,000 employees.

Table XI-1 Small Vehicle Manufacturers Manufacturer Employees Estimated Sales Sale Price Range Est. Revenues* Avanti 22 13 $25,000 to $63,000 $572,000 Panoz 50 150 $90,000 to $125,000 $16,125,000 Saleen 150 1,000 $39,000 to $59,000 $49,000,000 Shelby 44 60 $42,000 to $135,000 $5,310,000 * Assuming an average sales price from the sales price range.

The agency has not analyzed the impact of the proposal on these small manufacturers. However, assuming they would petition the ag ency, rather than meet the propos al, the cost is not expected to be substantial.

4. A description of the projecte d reporting, record keeping and other compliance requirements of a final rule including an estimate of the classes of small entities which will be subject to the requirement and the type of professional skills ne cessary for preparation of the report or record. This proposal includes no new requirements for reporting, record keeping of other compliance requirements.

5. An identification, to the extent practicable, of all relevant Federal rules which may duplicate, overlap, or conflict with the final rule We know of no Federal rules which duplicate, overlap, or conflict with the final rule.

6. A description of any significant alternatives to the final rule which accomplish the stated objectives of applicable statutes and which mi nimize any significant economic impact of the final rule on small entities. There are no other alternatives that can achieve the stated objectives without installing fuel economy technologies into the vehicle.XI-3

B. Unfunded Mandates Reform Act

The Unfunded Mandates Reform Act of 1995 (Public Law 104-4) requires ag encies to prepare a written assessment of the costs, benefits, and othe r effects of proposed or final rules that include a Federal mandate likely to result in the expenditures by States, lo cal or tribal governments, in the aggregate, or by the private sector, of mo re than $100 million annually (adjusted annually for inflation with base year of 1995). Adjusting this amount by the implicit gross domestic product price deflator for 2006 results in $126 million (116.043/92.106 = 1.26). The assessment may be included in conjunction with othe r assessments, as it is here.

C. Market Failure or Other Specific Problem

Executive Order 12866 requires that all new federal regulations specify the market failure or other specific problem that will be addressed by the rulemaking. A market failure occurs when the market fails to allocate scarce resources to their highest-valued uses. This can occur for several reasons, such as market power, externalities, or informa tion problems. (OMB Circular A-4 describes each of these in detail. See http://www.whitehouse.gov/ omb/circulars/a004/a4.pdf). Normally in competitive markets, exch anges between self-interested buyers and sellers will allocate resources to their highest valued uses.

The Energy Policy and Conservation Act (EPCA) directs the Department to balance the technological and economic challenges related to fuel economy with the nation's need to conserve energy. Congress deci ded that the market would not ba lance these cha llenges in the best interest of the nation, and that the Department s hould regulate fuel economy.A-1

APPENDIX A. Required MPG Levels

Table A-1 shows the required levels for the alte rnatives, along with the harmonically weighted average mpg. The Act requires that the agency se t standards that achieve at least an average 35 mpg standard for MY 2020. These tables show th e progress toward that requirement for each of the alternatives. The TC = TB and Technology Exhaustion alternatives already meet 35 mpg by MY 2015. The 2% increase alternative makes some progress toward the 35 mpg requirement, but you need an average annual increase of 4. 5 percent to reach 35 mpg by MY 2020. The optimized alternative makes good progress toward the requirement and needs an average yearly increase of 2.3 percent to reach 35 mpg by MY 2020.

Table A-2 shows the incremental percentage incr ease in the required levels, each year compared to the previous year. The MY 2010 baseline for passenger cars is 27.5 mpg, and the MY 2010 baseline for light truck is the unreformed standard for MY 2010 of 23.5 mpg. The MY 2010 combined harmonically average baseline for pass enger cars and light trucks is 25.34314 mpg, based on a 50/50 split for passenger car and light truck sales for MY 2010. The five year increase is the average yearly increase. In a fo rmula it is (regulation m pg/baseline mpg)^(1/5). In simple terms, for the Optimized (7%) alte rnative, taking the MY 2010 passenger car baseline of 27.5 * 1.054 * 1.054* 1.054* 1.054* 1.054 = 35.77, the mpg needed to be achieved in MY 2015.A-2

Table A-1 Required mpg Levels by Alternative

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 25% Below Optimized 29.6 30.3 31.7 32.7 33.9 Optimized (7%) 31.2 32.8 34 34.8 35.7 25% Above Optimized 32.8 35.3 36.1 36.8 37.5 50% Above Optimized 34.3 37.8 38.5 38.9 39.5 Optimized (3%) 37.1 39 .1 39.3 40.7 40.9

TC = TB 37.5 42.7 43 43.1 43.3 Technology Exhaustion 38.6 45.4 48.9 50.1 52.6

Light Trucks 25% Below Optimized 24.9 26 27.5 27.5 27.5 Optimized (7%) 25 26.4 27.8 28.2 28.6 25% Above Optimized 25.1 26.9 28 28.8 29.8 50% Above Optimized 25.3 27.3 28.3 29.5 30.9 Optimized (3%) 25 26.4 28 28.5 29

TC = TB 25.6 28.1 28.8 30.8 33.1 Technology Exhaustion 25.9 28.6 32.2 33.1 34.7 Harmonic Average mpg

PC+LT MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 25% Below Optimized 27.1 28.0 29.4 29.8 30.2 Optimized (7%) 27.8 29.3 30.6 31.0 31.6 25% Above Optimized 28.5 30.6 31.5 32.2 33.0 50% Above Optimized 29.2 31.7 32.6 33.4 34.5 Optimized (3%) 30.0 31.5 32.6 33.3 33.7

TC = TB 30.6 33.9 34.4 35.7 37.3 Technology Exhaustion 31.1 35.1 38.7 39.6 41.4A-3

Table A-2 Year by Year Percentage Increase In Required mpg and 5 Year Annual Average 213

Passenger Cars MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 5 years 25% Below Optimized 7.6% 2.4% 4.6% 3.2% 3.7% 4.3% Optimized (7%) 13.5% 5.1% 3.7% 2.4% 2.6% 5.4% 25% Above Optimized 19.3% 7.6% 2.3% 1.9% 1.9% 6.4% 50% Above Optimized 24.7% 10.2% 1.9% 1.0% 1.5% 7.5% Optimized (3%) 34.9% 5.4% 0.5% 3.6% 0.5% 8.3%

TC = TB 36.4% 13.9% 0.7% 0.2% 0.5% 9.5% Technology Exhaustion 40.4% 17.6% 7.7% 2.5% 5.0% 13.8%

Light Trucks 25% Below Optimized 6.0% 4.4% 5.8% 0.0% 0.0% 3.2% Optimized (7%) 6.4% 5.6% 5.3% 1.4% 1.4% 4.0% 25% Above Optimized 6.8% 7.2% 4.1% 2.9% 3.5% 4.9% 50% Above Optimized 7.7% 7.9% 3.7% 4.2% 4.7% 5.6% Optimized (3%) 6.4% 5.6% 6.1% 1.8% 1.8% 4.3%

TC = TB 8.9% 9.8% 2.5% 6.9% 7.5% 7.1% Technology Exhaustion 10.2% 10.4% 12.6% 2.8% 4.8% 8.1%

PC+LT 25% Below Optimized 6.9% 3.3% 5.1% 1.2% 1.5% 3.6% Optimized (7%) 9.8% 5.2% 4.4% 1.6% 1.8% 4.5% 25% Above Optimized 12.5% 7.1% 3.1% 2.2% 2.7% 5.4% 50% Above Optimized 15.3% 8.6% 2.6% 2.5% 3.3% 6.4% Optimized (3%) 18.4% 5.2% 3.5% 2.1% 1.1% 5.9%

TC = TB 20.6% 11.1% 1.5% 3.8% 4.4% 8.0% Technology Exhaustion 22.8% 12.8% 10.3% 2.2% 4.7% 10.3%

213 The Baseline mpg levels for MY 2010 are assumed to be 27.5 mpg for passenger cars, 23.5 mpg for light trucks and a harmonic average of 25.34314 mpg for the combined fleet, assuming a 50/50 split in sales between passenger cars and light trucks for MY 2010.