U.S. Department Of Transportation National Highway Traffic Safety Administration



# Final Regulatory Impact Analysis



# Corporate Average Fuel Economy for MY 2012-MY 2016 Passenger Cars and Light Trucks

Office of Regulatory Analysis and Evaluation National Center for Statistics and Analysis

March 2010

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### TABLE OF CONTENTS

EXECUTIVE SUMMARY ............................................................................................................ 1

I. INTRODUCTION

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II. NEED OF THE NATION TO CONSERVE ENERGY

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III. BASELINE AND ALTERNATIVES

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IV. IMPACT OF OTHER FEDERAL MOTOR VEHICLE STANDARDS ON FUEL

ECONOMY

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V. FUEL ECONOMY ENHANCING TECHNOLOGIES AND THE VOLPE MODEL ........ 86

VI. MANUFACTURER CAFE CAPABILITIES ..................................................................... 86

VII. COST IMPACTS

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VIII. BENEFITS FROM IMPR OVED FUEL ECONOMY ................................................... 380

IX. IMPACT OF WEIGHT REDUCTION ON SAFETY

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X. NET BENEFITS AND SENSITIVITY ANALYSES ........................................................ 455

XI. FLEXIBILITIES IN MEETING THE STANDARD

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XII. PROBABILISTIC UNCERTAINTY ANALYSIS

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XIII. REGULATORY FLEXIBILITY ACT AND UNFUNDED MANDATES REFORM ACT

ANALYSIS

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## EXECUTIVE SUMMARY This assessment examines the costs and benefits of improving the fuel economy of passenger cars and light trucks for model years (MY) 2012 through MY 2016. It includes a discussion of the technologies that can improve fuel economy, an alysis of the potential impact on retail prices, safety, lifetime fuel savings and their value to consumers, and other societal benefits such as improved energy security and reduced emissions of pollutants and greenhouse gases. 1



### As required by the Energy Independence and Security Act of 2007 (EISA), NHTSA sets attribute-based corporate average fuel economy (CAFE) standards that are based on a mathematical function. For purposes of MYs 2012-2016, as for MY 2011 passenger cars and MYs 2008-2011 light trucks, the CAFE standards have been based on vehicle footprint. 2 

### The mathematical function or “curve” representing the footprint-based standards is a constrained linear function (as compared to the constrained logistic function used for the MY 2011 standards), that provides a separate fuel economy ta rget for each vehicle footprint, generally with more stringent targets for smaller vehicles and less stringent targets for larger vehicles. Different parameters for the continuous mathematical function are derived. Individual manufacturers will be required to comply with a single fuel economy level that is based on the distribution of its production among the footprints of its ve hicles. Although a manufacturer’s compliance obligation is determined in the same way for both passenger cars and light trucks, the footprint target curves for the different fleets are established with different continuous mathematical functions that are specific to the vehicles’ design capabilities.

The baseline assumptions for this rulemaking differ from previous analyses in support of previous CAFE standards. For previous analyses, NHTSA developed a baseline using confidential product plans for each model year provided by the manufacturers regulated by the rule. For purposes of this analysis, in contrast, in the interest of improving transparency, the baseline was developed using each manufacturer’s MY 2008 fleet as represented in CAFE certification data available to EPA. In order to conduct this analysis, we assume that similar vehicles will be produced through MY 2016 and technologies are added to this baseline fleet to determine what mpg levels could be achieved by the manufacturers in the MYs 2012-2016 timeframe. NHTSA has examined a variety of alternatives to the final standards for MYs 2012- 2016. The eight alternatives examined include five that are annual percentage improvements over the baseline – 3%/year, 4%/year, 5%/year, 6%/year, and 7%/year. In addition to those five are what NHTSA has called the “Preferred Alternative,” the “Maximum Net Benefits” alternative, and the “Total Costs Equal Total Benefits” alternative. The “Preferred Alternative” would require fuel economy levels that are generally between the 4 and 5 percent annual increase alternatives, although the percentage increase varies from year to year. The “Maximum Net Benefits” alternative is based upon availability of technologies and a marginal cost/benefit analysis. In this case the model continues to include technologies until marginal cost of adding the next technology exceeds the marginal benefit. The “Total Costs Equal Total Benefits” alternative represents an increase in the standard to a point where essentially total costs of the

1 This analysis does not contain NHTSA’s assessment of the potential environmental impacts of the final rule for purposes of the National Environmental Policy Act (NEPA), 42 U.S.C. 4321-4347. 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 width (the distance between the centerline of the tires) of the vehicle (in square feet).

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### technologies added together over the baseline added equals total benefits over the baseline. In this analysis, for brevity, at times it is labeled “TC = TB.”

Table 1a shows the agency’s projection of the estimated actual harmonic average that would be achieved by the manufacturers, assuming that some manufacturers will pay fines rather than meet the required levels. Table 1b shows the estima ted required levels. All of the tables in this analysis compare an adjusted baseline to the projected achieved harmonic average.

Costs : Costs were estimated based on the specific technologies that were applied to improve each manufacturer’s fuel economy up to their achieved level under each alternative or fines that would be assessed. Table 2 provides the cost and fine estimates on an average per-vehicle basis, and Table 3 provides those estimates (without counting fines) on a fleet-wide basis in millions of dollars.

Benefits : Benefits are determined mainly from fuel savings over the lifetime of the vehicle, but also include externalities such as reductions in criteria pollutants. The agency uses a 3 percentand 7 percent discount rate to value intra-generational future benefits and costs. Intergenerational 3 

### benefits from future carbon dioxide reductions are discounted at 3 percent in the main analysis, even when intra-generational benefits are discounted at 7 percent. Sensitivity analyses in Chapter X analyze other inter-generational discount rates that accompany alternative estimates of the social cost of carbon. Table 4 provides th ose estimates on an industry-wide basis at a 3 percent discount rate and Table 6 provid es the estimates at a 7 percent discount rate.

Net Benefits : Tables 5 and 7 compares societal costs and societal benefits of each alternative at the 3 percent and 7 percent discount rates, respectively.

Fuel Savings : Table 8 shows the lifetime fuel savings in millions of gallons.

3 Inter-generational benefits, which include reductions in the expected future economic damages caused by increased global temperatures, a rise in sea levels, and other projected impacts of climate change, are anticipated to extend over a period from approximately fifty to two hundred or more years in the future, and will thus be experienced primarily by generations that are not now living.

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### Table 1a Alternative CAFE Levels Projected Achieved Harmonic Average for the Fleet, in mpg

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Passenger Cars Preferred Alternative 32.8 34.4 35.3 36.3 37.2 3% Annual Increase 32.2 33.2 33.7 34.3 35.2 4% Annual Increase 32.3 33.6 34.6 35.5 36.7 5% Annual Increase 32.6 34.4 35.8 36.9 38.3 6% Annual Increase 32.8 34.9 36.6 38.0 39.7 7% Annual Increase 33.0 35.4 37.4 38.9 40.7
Max Net Benefits 33.0 35.5 37.3 38.4 39.8 Total Cost = Total Benefit 33.1 35.7 37.7 39.0 40.5
Light Trucks Preferred Alternative 25.1 26.0 27.0 27.6 28.5 3% Annual Increase 24.4 25.0 25.8 26.4 26.9 4% Annual Increase 24.7 25.5 26.5 27.2 28.1 5% Annual Increase 24.9 26.0 27.4 28.3 29.3 6% Annual Increase 25.0 26.5 28.1 29.4 30.6 7% Annual Increase 25.2 26.9 28.8 30.1 31.4
Max Net Benefits 25.5 27.3 28.8 29.9 30.6 Total Cost = Total Benefit 25.5 27.4 29.1 30.2 31.1
Passenger Cars & Light Trucks Preferred Alternative 29.3 30.6 31.7 32.6 33.7 3% Annual Increase 28.6 29.5 30.3 31.0 31.8 4% Annual Increase 28.8 30.0 31.1 32.1 33.2 5% Annual Increase 29.1 30.6 32.1 33.3 34.6 6% Annual Increase 29.3 31.2 32.9 34.4 36.0 7% Annual Increase 29.4 31.6 33.7 35.2 36.9
Max Net Benefits 29.6 31.8 33.6 34.9 36.1 Total Cost = Total Benefit 29.7 32.0 34.0 35.4 36.7



### Preferred Alternative CAFE Levels Projected Achieved Harmonic Average for the Fleet, in gallons per 100 miles
MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

PC 3.0517 2.9111 2.8321 2.7581 2.6877

LT 3.9894 3.8417 3.7059 3.6206 3.5048

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### Table 1b Alternative CAFE Levels Estimated Required Average for the Fleet, in mpg

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Passenger Cars Preferred Alternative 33.3 34.2 34.9 36.2 37.8 3% Annual Increase 31.7 32.6 33.5 34.4 35.4 4% Annual Increase 32.1 33.3 34.5 35.8 37.2 5% Annual Increase 32.4 33.9 35.5 37.1 39.0 6% Annual Increase 32.7 34.5 36.5 38.6 40.9 7% Annual Increase 33.0 35.2 37.6 40.1 42.9
Max Net Benefits 33.0 36.1 38.1 39.4 40.9 Total Cost = Total Benefit 33.4 36.7 39.2 40.7 42.3
Light Trucks Preferred Alternative 25.4 26.0 26.6 27.5 28.8 3% Annual Increase 24.1 24.8 25.5 26.2 27.0 4% Annual Increase 24.4 25.3 26.3 27.2 28.3 5% Annual Increase 24.6 25.8 27.0 28.3 29.7 6% Annual Increase 24.9 26.3 27.8 29.4 31.1 7% Annual Increase 25.1 26.8 28.6 30.5 32.6
Max Net Benefits 26.3 27.7 29.1 30.3 31.1 Total Cost = Total Benefit 26.3 28.0 29.7 30.7 31.8
Passenger Cars & Light Trucks Preferred Alternative 29.7 30.5 31.3 32.6 34.1 3% Annual Increase 28.3 29.1 30.0 31.0 32.0 4% Annual Increase 28.6 29.7 30.9 32.2 33.6 5% Annual Increase 28.8 30.3 31.8 33.4 35.2 6% Annual Increase 29.1 30.8 32.7 34.7 36.9 7% Annual Increase 29.4 31.4 33.7 36.0 38.7
Max Net Benefits 30.0 32.3 34.2 35.6 36.9 Total Cost = Total Benefit 30.3 32.8 35.0 36.5 38.0



### Estimated Required Preferred Alternative CAFE Levels Projected Achieved Harmonic Average for the Fleet, in gallons per 100 miles

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

PC 2.9988 2.9277 2.8624 2.7628 2.6483

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### Table 2 Average Incremental Cost or Fines Per Vehicle (2007 Dollars)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Passenger Cars Preferred Alternative $505 $573 $690 $799 $907
3% Annual Increase $191 $248 $317 $394 $493
4% Annual Increase $254 $366 $500 $640 $764
5% Annual Increase $362 $558 $749 $918 $1,088
6% Annual Increase $526 $758 $1,064 $1,321 $1,546
7% Annual Increase $616 $952 $1,361 $1,634 $1,941
Max Net Benefits $612 $954 $1,282 $1,460 $1,628
Total Cost = Total Benefit $675 $1,065 $1,440 $1,653 $1,878 Light Trucks Preferred Alternative $322 $416 $621 $752 $961
3% Annual Increase $1 $78 $234 $348 $484
4% Annual Increase $166 $293 $506 $646 $830
5% Annual Increase $417 $633 $1,036 $1,186 $1,361
6% Annual Increase $516 $943 $1,394 $1,706 $2,007
7% Annual Increase $575 $1,222 $1,716 $2,181 $2,549
Max Net Benefits $761 $1,249 $1,665 $1,948 $2,082
Total Cost = Total Benefit $780 $1,344 $1,806 $2,157 $2,366 Passenger Cars & Light Trucks Preferred Alternative $434 $513 $665 $782 $926
3% Annual Increase $117 $183 $287 $378 $490
4% Annual Increase $220 $338 $502 $642 $787
5% Annual Increase $384 $587 $855 $1,013 $1,182
6% Annual Increase $522 $829 $1,185 $1,457 $1,705
7% Annual Increase $600 $1,055 $1,492 $1,828 $2,150
Max Net Benefits $670 $1,066 $1,423 $1,633 $1,784
Total Cost = Total Benefit $716 $1,171 $1,575 $1,831 $2,046

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### Table 3 Incremental Total Costs by Societal Perspective 4 

### , by Alternative (Millions of 2007 Dollars)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative $4,148 $5,411 $6,855 $8,221 $9,534 $34,170
3% Annual Increase $1,622 $2,341 $3,142 $4,047 $5,222 $16,375
4% Annual Increase $2,148 $3,455 $4,944 $6,561 $8,031 $25,138
5% Annual Increase $3,074 $5,288 $7,426 $9,410 $11,403 $36,601
6% Annual Increase $4,504 $7,196 $10,567 $13,546 $16,130 $51,943
7% Annual Increase $5,263 $8,985 $13,451 $16,627 $19,898 $64,224
Max Net Benefits $5,217 $8,837 $12,535 $14,930 $17,050 $58,568
TC = TB $5,674 $9,779 $13,898 $16,673 $19,403 $65,427 Light Trucks Preferred Alternative $1,754 $2,479 $3,657 $4,318 $5,369 $17,578
3% Annual Increase $8 $463 $1,371 $2,009 $2,719 $6,570
4% Annual Increase $968 $1,747 $2,975 $3,714 $4,647 $14,051
5% Annual Increase $2,407 $3,791 $6,103 $6,833 $7,614 $26,749
6% Annual Increase $2,950 $5,646 $8,157 $9,813 $11,226 $37,793
7% Annual Increase $3,260 $7,298 $10,020 $12,478 $14,074 $47,130
Max Net Benefits $4,149 $7,370 $9,698 $11,163 $11,648 $44,028
TC = TB $4,247 $7,891 $10,464 $12,330 $13,218 $48,149 Passenger Cars & Light Trucks Preferred Alternative $5,903 $7,890 $10,512 $12,539 $14,904 $51,748
3% Annual Increase $1,630 $2,804 $4,513 $6,057 $7,941 $22,944
4% Annual Increase $3,116 $5,202 $7,919 $10,275 $12,678 $39,189
5% Annual Increase $5,482 $9,079 $13,529 $16,243 $19,017 $63,350
6% Annual Increase $7,455 $12,842 $18,724 $23,359 $27,356 $89,736
7% Annual Increase $8,524 $16,283 $23,471 $29,104 $33,972 $111,354
Max Net Benefits $9,366 $16,207 $22,233 $26,092 $28,698 $102,597
TC = TB $9,921 $17,670 $24,362 $29,003 $32,620 $113,577

4 Includes technology costs and societal costs, but does not include fines.

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### Table 4 Present Value of Lifetime Societal Benefits 5



### , by Alternative (3% Discount Rate) (Millions of 2007 Dollars)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative $6,826 $15,155 $21,626 $28,677 $35,200 $107,483
3% Annual Increase $3,397 $8,374 $12,331 $16,760 $23,122 $63,984
4% Annual Increase $4,186 $11,006 $17,315 $24,469 $32,309 $89,286
5% Annual Increase $6,152 $15,404 $24,075 $32,114 $40,905 $118,649
6% Annual Increase $7,071 $18,062 $28,137 $37,552 $47,754 $138,576
7% Annual Increase $8,038 $20,627 $32,225 $42,010 $52,606 $155,507
Max Net Benefits $8,019 $20,896 $31,683 $39,863 $48,228 $148,689
TC = TB $8,666 $22,374 $33,916 $42,737 $51,659 $159,352 Light Trucks Preferred Alternative $5,110 $10,684 $15,506 $19,364 $24,310 $74,974
3% Annual Increase $687 $3,920 $7,635 $11,604 $14,940 $38,786
4% Annual Increase $2,590 $7,361 $12,580 $17,089 $21,830 $61,450
5% Annual Increase $4,003 $10,407 $17,686 $23,206 $28,324 $83,626
6% Annual Increase $4,893 $13,933 $22,031 $28,987 $34,727 $104,571
7% Annual Increase $5,634 $16,326 $25,550 $32,714 $38,229 $118,453
Max Net Benefits $7,528 $18,302 $25,913 $31,563 $34,835 $118,141
TC = TB $7,631 $18,954 $27,294 $33,381 $37,262 $124,522 Passenger Cars & Light Trucks Preferred Alternative $11,936 $25,840 $37,132 $48,040 $59,509 $182,457
3% Annual Increase $4,085 $12,294 $19,966 $28,364 $38,062 $102,770
4% Annual Increase $6,776 $18,367 $29,895 $41,559 $54,139 $150,735
5% Annual Increase $10,155 $25,811 $41,760 $55,320 $69,229 $202,275
6% Annual Increase $11,964 $31,995 $50,168 $66,539 $82,481 $243,147
7% Annual Increase $13,672 $36,953 $57,776 $74,724 $90,835 $273,960
Max Net Benefits $15,547 $39,198 $57,596 $71,426 $83,063 $266,830
TC = TB $16,297 $41,328 $61,209 $76,118 $88,922 $283,874

5 These benefits are considered from a “societal perspective” because they include externalities. They are distinguished from a consumer perspective, because cons umers generally would not think about the value of carbon dioxide, etc.

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### Table 5 Present Value of Net Total Benefits 6 

### by Alternative (Millions of 2007 Dollars) (3% Discount Rate)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative $2,677 $9,745 $14,770 $20,455 $25,665 $73,313
3% Annual Increase $1,776 $6,033 $9,188 $12,713 $17,900 $47,609
4% Annual Increase $2,038 $7,551 $12,371 $17,909 $24,278 $64,147
5% Annual Increase $3,077 $10,116 $16,649 $22,704 $29,502 $82,048
6% Annual Increase $2,567 $10,866 $17,570 $24,005 $31,624 $86,633
7% Annual Increase $2,775 $11,642 $18,775 $25,383 $32,709 $91,283
Max Net Benefits $2,802 $12,059 $19,148 $24,933 $31,179 $90,121
TC = TB $2,992 $12,595 $20,017 $26,064 $32,257 $93,925 Light Trucks Preferred Alternative $3,356 $8,205 $11,849 $15,045 $18,940 $57,396
3% Annual Increase $679 $3,458 $6,264 $9,595 $12,222 $32,217
4% Annual Increase $1,622 $5,614 $9,605 $13,375 $17,183 $47,399
5% Annual Increase $1,596 $6,616 $11,582 $16,373 $20,710 $56,877
6% Annual Increase $1,943 $8,287 $13,874 $19,174 $23,501 $66,779
7% Annual Increase $2,373 $9,028 $15,531 $20,236 $24,155 $71,324
Max Net Benefits $3,379 $10,932 $16,215 $20,400 $23,186 $74,112
TC = TB $3,384 $11,063 $16,830 $21,050 $24,045 $76,373 Passenger Cars & Light Trucks Preferred Alternative $6,033 $17,950 $26,619 $35,501 $44,606 $130,709
3% Annual Increase $2,455 $9,490 $15,453 $22,307 $30,121 $79,826
4% Annual Increase $3,660 $13,165 $21,976 $31,284 $41,461 $111,546
5% Annual Increase $4,673 $16,732 $28,231 $39,076 $50,213 $138,925
6% Annual Increase $4,509 $19,154 $31,444 $43,180 $55,125 $153,412
7% Annual Increase $5,148 $20,670 $34,305 $45,619 $56,864 $162,606
Max Net Benefits $6,181 $22,991 $35,363 $45,333 $54,365 $164,233
TC = TB $6,377 $23,658 $36,847 $47,114 $56,301 $170,297

6 This table is from a societal perspective, thus, fines are de leted from the costs because they are a transfer payment.

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### Table 6 Present Value of Lifetime Societal Benefits 7



### , by Alternative (7% Discount Rate) (Millions of 2007 Dollars)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative $5,474 $12,255 $17,499 $23,235 $28,567 $87,031
3% Annual Increase $2,727 $6,778 $9,980 $13,585 $18,774 $51,844
4% Annual Increase $3,356 $8,904 $14,015 $19,838 $26,241 $72,353
5% Annual Increase $4,941 $12,472 $19,493 $26,030 $33,185 $96,122
6% Annual Increase $5,667 $14,612 $22,763 $30,402 $38,735 $112,180
7% Annual Increase $6,448 $16,692 $26,080 $34,028 $42,669 $125,917
Max Net Benefits $6,134 $16,378 $25,041 $31,517 $38,120 $117,191
TC = TB $6,957 $18,112 $27,453 $34,625 $41,897 $129,044 Light Trucks Preferred Alternative $4,015 $8,427 $12,243 $15,302 $19,225 $59,212
3% Annual Increase $545 $3,099 $6,035 $9,178 $11,823 $30,679
4% Annual Increase $2,035 $5,802 $9,927 $13,500 $17,260 $48,524
5% Annual Increase $3,129 $8,189 $13,929 $18,300 $22,365 $65,913
6% Annual Increase $3,823 $10,966 $17,349 $22,842 $27,385 $82,366
7% Annual Increase $4,404 $12,838 $20,108 $25,767 $30,132 $93,248
Max Net Benefits $5,736 $12,761 $18,525 $22,485 $25,290 $84,797
TC = TB $6,039 $14,926 $21,502 $26,237 $29,295 $97,999 Passenger Cars & Light Trucks Preferred Alternative $9,490 $20,682 $29,742 $38,538 $47,791 $146,243
3% Annual Increase $3,272 $9,877 $16,014 $22,763 $30,597 $82,523
4% Annual Increase $5,390 $14,706 $23,942 $33,338 $43,500 $120,877
5% Annual Increase $8,070 $20,661 $33,422 $44,330 $55,551 $162,035
6% Annual Increase $9,490 $25,579 $40,111 $53,245 $66,120 $194,545
7% Annual Increase $10,852 $29,530 $46,187 $59,795 $72,801 $219,165
Max Net Benefits $11,870 $29,140 $43,566 $54,002 $63,410 $201,988
TC = TB $12,997 $33,037 $48,955 $60,862 $71,193 $227,044

7 These benefits are considered from a “societal perspective” because they include externalities. They are distinguished from a consumer perspective, because cons umers generally would not think about the value of carbon dioxide, etc.

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### Table 7 Present Value of Net Total Benefits 8 

### by Alternative (Millions of 2007 Dollars) (7% Discount Rate)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative $1,326 $6,844 $10,644 $15,014 $19,032 $52,861
3% Annual Increase $1,106 $4,436 $6,838 $9,537 $13,552 $35,469
4% Annual Increase $1,208 $5,449 $9,071 $13,277 $18,210 $47,215
5% Annual Increase $1,867 $7,184 $12,067 $16,620 $21,782 $59,521
6% Annual Increase $1,163 $7,416 $12,196 $16,856 $22,605 $60,237
7% Annual Increase $1,185 $7,707 $12,629 $17,401 $22,771 $61,693
Max Net Benefits $1,170 $7,894 $12,838 $16,969 $21,583 $60,454
TC = TB $1,283 $8,333 $13,555 $17,952 $22,495 $63,617 Light Trucks Preferred Alternative $2,261 $5,948 $8,586 $10,984 $13,855 $41,635
3% Annual Increase $537 $2,636 $4,664 $7,169 $9,104 $24,110
4% Annual Increase $1,067 $4,055 $6,952 $9,786 $12,613 $34,473
5% Annual Increase $722 $4,398 $7,826 $11,467 $14,751 $39,164
6% Annual Increase $872 $5,321 $9,192 $13,029 $16,159 $44,573
7% Annual Increase $1,143 $5,540 $10,088 $13,289 $16,058 $46,119
Max Net Benefits $1,647 $6,581 $10,195 $12,936 $15,123 $46,482
TC = TB $1,579 $7,463 $10,988 $13,982 $16,229 $50,241 Passenger Cars & Light Trucks Preferred Alternative $3,587 $12,792 $19,230 $25,998 $32,888 $94,495
3% Annual Increase $1,642 $7,073 $11,501 $16,706 $22,656 $59,579
4% Annual Increase $2,274 $9,504 $16,023 $23,064 $30,822 $81,688
5% Annual Increase $2,589 $11,583 $19,893 $28,087 $36,534 $98,685
6% Annual Increase $2,035 $12,737 $21,387 $29,885 $38,764 $104,810
7% Annual Increase $2,328 $13,247 $22,717 $30,690 $38,829 $107,812
Max Net Benefits $2,818 $14,475 $23,033 $29,904 $36,706 $106,936
TC = TB $2,863 $15,795 $24,543 $31,933 $38,724 $113,858

8 This table is from a societal perspective, thus, fines are deleted from the costs because they are a transfer payment.

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### Table 8 Savings in Millions of Gallons of Fuel Undiscounted Over the Lifetime of the Model Year

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative 2,396 5,153 7,233 9,446 11,433 35,660 3% Annual Increase 1,197 2,845 4,120 5,509 7,490 21,161 4% Annual Increase 1,476 3,740 5,787 8,046 10,475 29,524 5% Annual Increase 2,157 5,230 8,066 10,630 13,381 39,463 6% Annual Increase 2,520 6,200 9,530 12,589 15,770 46,609 7% Annual Increase 2,855 7,086 10,933 14,080 17,419 52,374
Max Net Benefits 2,848 7,159 10,731 13,324 15,893 49,956
TC = TB 3,071 7,673 11,492 14,295 17,086 53,619 Light Trucks Preferred Alternative 1,805 3,698 5,281 6,504 8,061 25,350 3% Annual Increase 234 1,349 2,589 3,882 4,935 12,988 4% Annual Increase 916 2,557 4,298 5,751 7,251 20,773 5% Annual Increase 1,434 3,631 6,076 7,856 9,463 28,460 6% Annual Increase 1,758 4,869 7,584 9,859 11,677 35,747 7% Annual Increase 2,019 5,718 8,813 11,139 12,866 40,555
Max Net Benefits 2,688 6,395 8,919 10,735 11,700 40,437
TC = TB 2,724 6,621 9,392 11,348 12,507 42,591 Passenger Cars & Light Trucks Preferred Alternative 4,201 8,851 12,514 15,950 19,494 61,010 3% Annual Increase 1,431 4,193 6,709 9,391 12,425 34,149 4% Annual Increase 2,391 6,296 10,085 13,798 17,726 50,297 5% Annual Increase 3,590 8,860 14,142 18,486 22,845 67,923 6% Annual Increase 4,279 11,069 17,114 22,448 27,447 82,356 7% Annual Increase 4,875 12,804 19,746 25,219 30,285 92,929
Max Net Benefits 5,536 13,555 19,650 24,059 27,593 90,392
TC = TB 5,795 14,294 20,884 25,643 29,593 96,210

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### Breakdown of costs and benefits for the preferred alternative

Tables 9 and 10 provides a breakdown of the costs and benefits for the preferred alternative using a 3 percent and 7 percent discount rate, resp ectively, safety estimates are not included.

Table 9 Preferred Alternative Cost and Benefit Estimates Passenger Cars and Light Trucks Combined 3% Discount Rate

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Total Technology Costs $5,695 $9, 295 $12,454 $15,080 $17,633 $60,157

Benefits

Lifetime Fuel Expenditures $10, 197 $22,396 $32,715 $41,880 $50,823 $158,012

Consumer Surplus from Additional Driving $751 $1,643 $2,389 $3,029 $3,639 $11,451

Refueling Time Value $776 $1,551 $2,198 $2,749 $3,277 $10,550

Petroleum Market Externalitie s $559 $1,194 $1,700 $2,129 $2,538 $8,121 Congestion Costs ($460) ($934) ($1,332) ($1, 657) ($1,991) ($6,376)
Noise Costs ($7) ($14) ($21) ($26) ($31) ($99)
Crash Costs ($217) ($437) ($625) ($776) ($930) ($2,985)

CO2 $1,028 $2,287 $3, 382 $4,376 $5,372 $16,446

CO $0 $0 $0 $0 $0 $0

VOC $41 $80 $108 $131 $156 $518

NOX $82 $132 $155 $174 $200 $744

PM $220 $438 $621 $771 $904 $2,956

SOX $161 $345 $490 $613 $731 $2,341
Total $13,132 $28,680 $41, 781 $53,394 $64,687 $201,676

Net Benefits $7,044 $18, 759 $27,090 $34,710 $41,386 $128,992

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### Table 10 Preferred Alternative Cost and Benefit Estimates Passenger Cars and Light Trucks Combined 7% Discount Rate

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Total Technology Costs $5,695 $9, 295 $12,454 $15,080 $17,633 $60,157

Benefits

Lifetime Fuel Expenditures $7, 991 $17,671 $25,900 $33,264 $40,478 $125,305

Consumer Surplus from Additional Driving $590 $1,301 $1,896 $2,412 $2,904 $9,102

Refueling Time Value $624 $1,249 $1,770 $2,215 $2,642 $8,500

Petroleum Market Externali ties $448 $960 $1,367 $1,712 $2,043 $6,531 Congestion Costs ($371) ($753) ($1,074) ($1, 335) ($1,606) ($5,138)
Noise Costs ($6) ($12) ($16) ($21) ($24) ($80)
Crash Costs ($173) ($352) ($503) ($626) ($749) ($2,403)

CO2 $797 $1,781 $2,634 $3,410 $4,189 $12,813

CO $0 $0 $0 $0 $0 $0

VOC $33 $65 $87 $106 $125 $416

NOX $60 $99 $120 $135 $156 $570

PM $170 $344 $492 $613 $721 $2,339

SOX $129 $278 $394 $493 $588 $1,882
Total $10,292 $22,631 $33, 066 $42,380 $51,468 $159,837

Net Benefits $4,281 $12, 832 $18,818 $24,414 $29,293 $89,638

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## I. INTRODUCTION

The purpose of this study is to analyze the effects of changes in the fuel economy standards for passenger cars and for light trucks for MY 2012 - 2016. It includes a discussion of the technologies that can improve fuel economy, the potential impacts on reta il 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), setting 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 light trucks for MYs 2008 to 2011 under a new “CAFE Reform” structure.

In December 2007, Congress passed the Energy Independence 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 manufacturers in the U.S. in model year 2020 equals or exceeds 35 miles per gallon. EISA additionally gave NHTSA auth ority to reform passenger car CAFE, allowing the agency to set standards for those vehicles according to an attribute-based mathematical function.

In mid-October 2008, the agency completed and re leased a final environm ental impact statement in anticipation of issuing standards for those years. Based on its consideration of the public comments and other available information, including information on the financial condition of the automotive industry, the agency adjusted its analysis and the standards and prepared a final rule and Final Regulatory Impact Analysis (FRI A) for MYs 2011-2015. On November 14, the Office of Information and Regulatory Affairs (OIRA) of the Office of Management and Budget concluded review of the rule and FRIA. 9 

### However, issuance of the final rule was held in abeyance. On January 7, 2009, the Department of Transportation announced that the final rule would not be issued, writing:

The Bush Administration will not finalize its rulemaking on Corporate Fuel Economy Standards. The recent financial difficulties of the automobile industry will require the next administration to conduct a thorough review of matters affecting the industry, including how to effectively implement the Energy Independence and Security Act of 2007 (EISA). The National Highway Traffic Safety Administration has done significant work that will position the next Transportation Secretary to finalize a rule before the April 1, 2009 deadline. 10



### In light of the requirement to prescribe standards for MY 2011 by March 30, 2009 and in order to provide additional time to consider issues concerning the analysis used to determine the appropriate level of standards for MYs 2012 and beyond, the President issued a memorandum on January 26, 2009, requesting the Secretary of Transportation and Administrator of the National

9 Record of OIRA’s action can be found at http://www.reginfo.gov/public/do/eoHistReviewSearch (last visited March 4, 2010). To find the report on the clearance of the draft final rule, select “Department of Transportation” under “Economically Significant Reviews Completed” and select “2008” under “Select Calendar Year.”
10 The statement can be found at http://www.dot.gov/affairs/dot0109.htm (last accessed March 4, 2010).

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### Highway Traffic Safety Administration NHTSA to divide the rulemaking into two parts: (1) MY 2011 standards, and (2) standards for MY 2012 and beyond.

The request that the final rule establishing CAFE standards for MY 2011 passenger cars and light trucks be prescribed by March 30, 2009 was based on several factors. One was the requirement that the final rule regarding fuel economy standards for a given model year must be adopted at least 18 months before the beginning of that model year (49 U.S.C. 32902(g)(2)). The other was that the beginning of MY 2011 is considered for the purposes of CAFE standard setting to be October 1, 2010. As part of that final rule, the President requested that NHTSA consider whether any provisions regarding preemption are consistent with the EISA, the Supreme Court's decision in Massachusetts v. EPA and other relevant provisions of law and the policies underlying them.

The President requested that, before promulgating a final rule concerning the model years after model year 2011, NHTSA

[C]onsider the appropriate legal factors under the EISA, the comments filed in response to the Notice of Proposed Rulemaking, the relevant technological and scientific considerations, and to the extent feasible, the forthcoming report by the National Academy of Sciences mandated under section 107 of EISA.

In addition, the President requested that NHTSA further consider whether any provisions regarding preemption are appropriate under applicable law and policy.

On March 20, 2009 (74 FR 14196) NHTSA issued a final rule for MY 2011 passenger cars and light trucks, superseding the previously issued fi nal rule for MY 2011 light trucks. Similar to this report, a Final Regulatory Impact Analysis accompanied that final rule. 11



### In keeping with the President’s remarks on January 26 for new national policies to address the closely intertwined issues of energy independen ce, energy security and climate change, and for the initiation of serious and sustained domestic and international action to address them, NHTSA and EPA proposed standards for MY 2012 - 2016 after collecting new information, conducting a careful review of technical and economic inpu ts and assumptions, and standard setting methodology, and completing new analyses. The NPRM was issued on September 22, 2009 (74 FR 48192) and it was accompanied by a Preliminary Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2012-MY 2016 Passenger Cars and Light Trucks, (Docket No. 2009-0059-0016.1).

The goal of the review and re-evaluation was to ensure that the approach used for MY 2012 and thereafter produces standards that contribute, to the maximum extent possible under EPCA/EISA, to meeting the energy and environm ental challenges and goals outlined by the President. We seek to craft our program with the goal of creating the maximum incentives for innovation, providing flexibility to the regulated parties, and meeting the goal of making substantial and continuing reductions in the consumption of fuel.

11 “Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2011 Passenger Cars and Light Trucks”, March 2009, Docket No. NHTSA-2009-0062-0004.1.DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### 17

The dual fuel incentive program, through which manufacturers 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. 12



### Throughout this analysis, unless otherwise noted, the agency has not considered the ability of manufacturers to use credits or credit trading in achieving the alternative fuel economy levels. This is also a statutory requirement. 13



### Throughout this document, confidential information is presented in brackets [ ].

12 See 49 U.S.C. § 32902(h)
13 Id

.

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## II. NEED OF THE NATION TO CONSERVE ENERGY

The Energy Policy and Conservation Act (EPCA) states that:

“When deciding maximum feasible average fuel economy ... the Secretary of Transportation shall consider technological 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.” 14

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 energy prices and supply on national economic wellbeing that led to the enactment of EPCA persist today. The demand for petroleum grew in the U.S. up through the year 2005 and is now declin ing slowly averaging approximately 19.4 million barrels per day in 2008. 15 World demand, however, is expect ed to continue to rise until 2030. 16



### Since 1970, there have been a series of events that suggest that the behavior of petroleum markets is a matter for public concern.

• Average annual crude oil prices rose from $68 per barrel in 2007 to $99 per barrel in 2008, having peaked at $129 per barrel in July 2008. Prices declined to $49 per barrel in April 2009, but then rose to $75 per barrel in February 2010. 17 

### As recently as 1998, crude prices averaged about $13 per barrel. 18 

### Gasoline prices more than doubled during this ten-year period, from $1.22 in 1998 to $3.32 in 2008, declining to $2.75 in early March 2010. 19



### • U.S. domestic petroleum production stood at 10 million barrels per day in 1975, rose slightly, then declined to 6.7 million barrels per day in 2008. Between 1975 and 2008, U.S. petroleum consumption increased from 16.3 million barrels per day to 20.8 million barrels per day. In 2008, net petroleum imports accounted for 57 percent of U.S. domestic petroleum consumption. 20



### • Worldwide oil demand is fairly inelastic: declining prices do not induce large increases in consumption, while higher prices do not significantly restrain consumption. For example, the price of unleaded regular gasoline rose from an average of $2.59 in 2006 to $2.80 in 2007 (an 8.1 percent increase) and vehicle miles traveled decreased by 0.6

14 49 U.S.C. § 32902(f)
15 U.S. Department of Energy, Energy Information Administration , Petroleum Basic Statistics, October 2009. See http://www.eia.doe.gov/emeu/international/oilconsumption.html (last accessed March 4, 2010).
16 U.S. Department of Energy, Energy Information Administration , International Energy Outlook 2009. See http://www.eia.doe.gov/oiaf/ieo/highlights.html (last accessed March 4, 2010).
17 U.S. Department of Energy, Energy Information Administration, World Crude Oil Prices. See http://tonto.eia.doe.gov/dnav/pet/pet_pri_wco_k_w.htm (last accessed March 4, 2010).
18 U.S. Department of Energy, Energy Information Administration, Petroleum Marketing Monthly
, July 2009
, Table 1. See http://www.eia.doe.gov/oil_gas/petroleum/data_publications/petroleum_marketing_monthly/pmm.html (last accessed March 4, 2010).
19 U.S. Department of Energy, Energy Information Administration, Weekly Retail and Gasoline Diesel Prices

. See http://tonto.eia.doe.gov/dnav/pet/pet_pri_gnd_dcus_nus_w.htm (last accessed March 4, 2010).
20 U.S. Department of Energy, Energy Information Administration, Monthly Energy Review, February 2010

. See http://www.eia.doe.gov/emeu/mer/pdf/pages/sec3_7.pdf (last accessed March 4, 2010).

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### carbon dioxide emissions.

es a

bove, U.S. energy consumption has increasingly been outstripping U.S. energy production.

ile imports of etroleum have been increasing to meet the rising U.S. demand for petroleum.

y security and can reduce the flow f oil profits to certain states now hostile to the U.S.

lting in more fuel-efficient vehicles, i.e. , vehicles requiring less fuel consumption per unit mile. percent. 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 Asia and worldwide in the future resulting in upward oil cost pressure.

• Foreign oil production facilities, refineries , and supply chains have been disrupted from time to time, either by wars, political acti on by oil producers, civil unrest, or natural disasters.

• High oil prices, sometimes induced by disruptions in oil markets, have often coincided with rising inflation and subsequent economic recessions.

• Greenhouse gas emissions from the consumption of petroleum have become a subject of increasing public policy concern, both in the United States and internationally. Greenhouse gases in general and carbon dioxide in particular have not thus far been subject to national regulation. Studies by multiple sources suggest that rising atmospheric concentrations of greenhouse gases will damage human health and welfare. 21 

### There is a direct linkage between the consumption of fossil energy and emissions of the greenhouse gas carbon diox ide, as essentially all of the carbon in hydrocarbon fuels is oxidized into carbon dioxide when the fuel is combusted. Reducing U.S. fossil petroleum consumption will generally induce a proportional reduction in

Energy is an essential input to the U.S. economy, and having a strong economy is essential to maintaining and strengthening our national securi ty. Secure, reliable, and affordable energy sources are fundamental to economic stability and development. Rising energy demand pos challenge to energy security, given increased reliance on global energy markets. As noted

a

Table II-1 presents trend data on the production and consumption of petroleum for transportation. Domestic petroleum production has been decreasing over time, wh
p

Conserving energy, especially reducing the nation’s dependence on petroleum, benefits the U.S. in several ways. Improving energy efficiency has benefits for economic growth and the environment, as well as other benefi ts, such as reducing pollution and improving security of energy supply. More specifical ly, reducing total petroleum use decreases our economy’s vulnerability to oil price shocks. Reducing dependence on oil imports from regions with uncertain conditions enhances our energ

o

This CAFE final rule encourages conservation of petroleum for transportation by the application of broader use of fuel saving technologies, resu

21 IPCC 2007: Climate Change 2007: Synthesis Report: Contributions of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Pane l 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 /, (last accessed March 4, 2010).

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### Table II-1 Petroleum Production and Supply (Million Barrels per Day) 22



### Domestic Petroleum Production Net Petroleum

Imports U.S. Petroleum Consumption World Petroleum Consumption Net Imports as a Share of U.S. Consumption 1975 10.0 5.8 16.3 56.2 35.8% 1985 10.6 4.3 15.7 60.1 27.3% 1995 8.3 7.9 17.7 70.1 44.5% 2005 6.9 12.5 20.8 84.0 60.3% 2008 6.7 11.0 19.4 N/A 56.9% DOE Predictions 2015 7.6 9.7 20.2 90.6 49% 2025 9.1 8.0 20.8 101.1 40% 2030 9.3 8.4 21.7 106.6 41% Note: DOE predictions are based on petroleum demand.

Table II-2

Petroleum Transportation Consumption by Mode (Thousand Barrels per Day) 23



### Passenger Cars Light Trucks Total Light Vehicles Total Transportation Light Vehicles as % of Trans. 1975 4,836 1,245 6,081 8,474 72% 1985 4,665 1,785 6,450 9,538 68% 1995 4,440 2,975 7,415 11,347 65% 2005 5,050 3,840 8,890 13,537 66% 2007 4,850 4,032 8,883 13,710 65%

22 U.S. Department of Energy, EIA , Monthly Energy Review
, February 2010 , Table 3.1. Available at http://www.eia.doe.gov/emeu/mer/petro.html (last accessed March 4, 2010). U.S. Department of Energy, EIA, Annual Energy Outlook 2009 , Table 20. Available at http://www.eia.doe.gov/oiaf/archive/aeo09/pdf/0383(2009).pdf (last accessed March 4, 2010).
23 U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Transportation Energy Data Book , Table 1.14. Available at http://cta.ornl.gov/data/chapter1.shtml (last accessed March 4, 2010).

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### f

odel year 2008 data. The baseline fleet is created in order to track th volumes and types of fuel economy-improving technologies which are already present

ards.

. How was the baseline vehicle fleet developed?

data

t required

SD. 

## III. BASELINE AND ALTERNATIVES

A. The baseline vehicle fleet

1. Why establish a baseline vehicle fleet?

In order to calculate the impacts of the final rule, it is necessary to estimate the composition o the future vehicle fleet absent the final CAFE standards in order to conduct comparisons. EPA in consultation with NHTSA developed a comparison fleet in two parts. The first step was to develop a baseline fleet based on m

e in today’s fleet. Creating a baseline fleet helps to keep, to some extent, the agencies’ models from adding technologies to vehicles that already have these technologies, which would result in “double counting” of technologies’ costs and benefits. The second step was to project the baseline fleet sales into MYs 2011-2016. This is called the reference fleet, and it represents the fleet that would exist in MYs 2011-2016 absent any change from current regulations. The third step was to add technologies to that fleet such that each manufacturer’s average car and truck CO 2 

### levels are in compliance with their MY 2011 CAFE standards. This final “reference fleet” is the light duty fleet estimated to exist in MY s 2012-2016 without the final CAFE stand All of the agency’s estimates of fuel economy improvements, costs, and societal impacts are developed in relation to the respective reference fleets.

2

At proposal, EPA and NHTSA developed a baseline fleet comprised of model year 2008 gathered from EPA’s emission cer tification and fuel economy database. MY 2008 was used as the basis for the baseline vehicle fleet because it was the most recent model year for which a complete set of data is publicly available. This remains the case. Manufacturers are no to submit final sales and mpg figures for MY 2009 until April 2010, 24 

### after the CAFE standard’s mandated promulgation date. Consequently, in this final rule, EPA and NHTSA made no changes to the method or the results of the MY 2008 baseline fleet used at proposal, except for some specific corrections to engineering inputs for some vehicle models reflected in the market forecast input to NHTSA’s CAFE model. More details about how the agencies constructed this baseline fleet can be found in Chapter 1.2 of the Joint TSD. Corrections to engineering inputs for some vehicle models in the market forecast input to NHTSA’s CAFE model are discussed in Chapter 2 of the Joint T

3. How was the projected MY 2011-2016 vehicle fleet developed?

EPA and NHTSA have based the projection of total car and total light truck sales for MYs 2011- 2016 on projections made by the Department of Energy’s Energy Information Administration (EIA). EIA publishes a mid-term projection of national energy use called the Annual Energy Outlook (AEO). This projection utilizes a num ber of technical and ec onometric models which are designed to reflect both economic and regulator y conditions expected to exist in the future. In support of its projection of fuel use by light-duty vehicles, EIA projects sales of new cars and

el Year Report 24 40 CFR 600.512-08, Mod

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### in

f

. Although e total level of sales of 16.5 million units is similar to pre-2008 levels, the fraction of car sales

rage

el

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

In addition to a shift towards more car sales, sales of segments within both the car and truck markets have been changing and are expected to continue to change. Manufacturers areintroducing more crossover models which offer much of the utility of SUVs but use more carlike designs. The AEO 2010 report does not, however, distinguish such changes within the car and truck classes. In order to reflect these changes in fleet makeup, EPA and NHTSA considered several other available forecasts. EPA purchased and shared with NHTSA forecasts from two well-known industry analysts, CSM Worldwide (CSM), and J.D. Powers. NHTSA and EPA decided to use the forecast from CSM, modified as described below, for several reasons presented in the NPRM preamble 26 

### and draft Joint TSD. The changes between company market share and industry market segments were most significant from 2011-2014, while for 2014-2015 the changes were relatively small. Noting this, and lacking a credible forecast of company and segment shares after 2015, the agencies assumed 2016 market share and market segments to be the same as for 2015.

CSM Worldwide provides quarterly sales forecasts for the automotive industry. In the NPRM, the agencies identified a concern with the 2 nd 

### quarter CSM forecast that was used as a basis for light trucks. In the proposal, the agencies used the three reports published by EIA as part of the AEO 2009. We also stated that updated versions of these reports could be used in the final rules should AEO timely issue a new version. EIA published an early version of its AEO 2010 December 2009, and the agencies are making use of it in this final rulemaking. The differences in projected sales in the 2009 report (used in the NPRM) and the early 2010 report are very small, so NHTSA and EPA have decided to simply scale the NPRM volumes for cars and trucks (in the aggregate) to match those in the 2010 report. We thus employ the sales projections from the scaled updated 2009 Annual Energy Outlook, which is equivalent to AEO 2010 Early Release, for the final rule. The scaling factors for each model year are presented in Chapter 1 o the Joint TSD for this final rule.

In the AEO 2010 Early Release, EIA projects th at total light-duty vehicle sales will gradually recover from their currently depressed levels by around 2013. In 2016, car sales are projected to be 9.4 million (57 percent) and truck sales are pr ojected to be 7.1 million (43 percent) 25



### th is projected to be higher than that existing in the 2000-2007 timeframe. This projection reflects the impact of higher fuel prices, as well as EISA’s requirement that the new vehicle fleet ave at least 35 mpg by MY 2020. The agencies note that AEO does not represent the fleet at a lev of detail sufficient to explicitly account fo r the reclassification—promulgated as part of NHTSA’s final rule for MY 2011 CA

the projection. CSM projections at that time were based on an industry that was going through a significant financial transition, and as a result the market share forecasts for some companies

25 EPA and NHTSA have evaluated the differences between the AEO 2010 (early draft) and AEO 2009 and found

refore conclude that EIA has made no significant changes to little difference in the fleet projections (or fuel prices). This analysis can be found in the memo to the docket: (Docket EPA-HQ-OAR-2009-0472). The agenci es the its projections.
26 See , e.g., 74 FR 49484.

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### were impacted in surprising ways. As the indus ation has settled somewhat over the past
y fold increase in sales for Chrysler compared t cant loss of market share shown for Chrysler in the 2 nd 

### quarter p on. Additionally ch as GM gnized that the fleet appea d all be ck agencies discovere at i es below 14,00 in eav cle ted by this fina he r ht d ns at propos re full size truc ue ta ty versions hic g ed at filtered the th 

### er ese ve d this filtere r f A m anufacturer can n D b c nd rd


### 4 th 

### quarter CSM a e le

ted eca t cars a s t egment onto A.3 he resulting p o ce el m sales that occurred in baselin 008 model ar. Both ta es show sales using the tr itional n of cars an ks. tr y’s situ ear, the 4 th 

### quarter projection appears to address this issue – for example, it shows nearly a twoo signifi rojecti , some commenters, su , reco 27 

### red to inclu d (indepen e an unusu ntly of the y high num mments) th r of large pi CSM’s stan up trucks. rd forecast In fact, the cluded all de 0 GVWR, l rule. 28 

### co cluding cla

commen da y duty vehi ect that li n s, which ar uty refer vehicl regula ss 2b and 3 h rs were thus e not nce fleet T al had mo of those te

les. The cor ks and vans d encies request g to the mis
a separa e ken inclusio data foreca projectio heavy du n of the t from ve ir 4 quart d 4 th 

### qua a projection orecast f te heavy duty detailed s hicles. Th parison o CSM th then use to exclude th the final rule e agencies the market rte be found i forecasts h or the final TS ve been plac . . For the pu d in the dock co lic’s referen et for this ru f e, copies of t making. 29 

### by m and he 2 , 3 ,

We then projec the CSM for sts for rela ive sales of nd truck by manufac urer and by market s show t the total sales rojections f estimates of r the referen AEO 2010. Tab 2016 mod les III. year and co -1 and III.A pare these t .3-2 actual o ad e 2 d light truc ye bl definitio

usually large 27 GM argued that the un vo quire As discussed below, the agencies’ analysis for the al rule corrects the number of l e pickups. W this tion and other up e o r t c ng e final stand ev e rm f are very similar to those from the NPRM, especially for light trucks.
28 These include the Ford F-250 & F-350, Econoline E-250, & E-350; Chevy Express, Silverado 2500, & 3500; GMC Savana, Dodge 2500, & 3500; among others.
29 The CSM Sales Forecast Excel file (“CSM North America Sales Forecasts 2Q09 3Q09 4Q09 for the Docket”) is available in the docket (Docket EPA-HQ-OAR-2009-0472). lume of large pickups led to highe r overall re ments for th ose vehicles. fin ncies’ market f ing the averag arg analytical inpu ance levels de ith s, the target fun ining the nation correc defini dates to the ag ards (and achi recast and othe required perfo tions al program) th

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### Cars Light Trucks Total Table III-1. Annual Sales of Light-Duty Vehicles by Manufacturer in 2008 and Estimated for 2016

Y 2 8 MY 2016 MY Y 2008 M 2016 MY 00 2008 M 2016 MY

BMW 1, 0 291,796 424,923 61,324 17 560 353,12 596,482

Chrysler 5, 5 537,808 340,908 1,119,397 52 128 1,657,20 866,037

Daimle r 126, 7 208,052 272,252 79,135 880 287,18 399,133

Ford 3, 8 709,583 1,118,727 1,158,805 1,36 256 1,868,38 2,481,983

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

Honda 899,498 1,283,937 85, 9 612,281 1,5 828 1,511,77 2,869,766

Hyundai 270,293 811,214 671, 7 120,734 437 391,02 1,482,651

Kia 145,863 401,372
, 2 135,589 211 996 281,45 613,368

Mazda 191,326 455,643 210, 6 111,220 717 302,54 666,360

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

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

N 20

issan 653,121 33,471 370,294 16,749 1,023,415 50,2

S 876,677 49,211 457,114 198,581 1,333,790

ubaru 149,370

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

T 1

ata 9,596 65,806 55,584 42,695 65,180 108,50

T 002

oyota 1,143,696 2,069,283 1,067,804 1,249,719 2,211,500 3,319,

V 710,011

olkswa en 290,385 586,011 26,999 124,703 317,384 g

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

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### Table III-2. Annual S ty Vehicles b in 2008 and Estimated for 2016 Cars Light Trucks ales of Light-Du y Market Segment

2008 M 2008 MY 2016 MY Y 2016 MY

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

Mid 0 -Size Car 2,166,849 2,550,561 Full-Size Van 33,384 65,65

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

Sma ,077 ll Car 1,912,736 2,503,566 Mid-Size MAV* 110,353 116
Spe 4 cialty Car 459,273 769,679 Small MAV 231,265 62,51

9 Full-Size SUV* 559,160 232,61
,502 Mid-Size SUV 436,080 162
Small SUV 196,424 108,858
Full-Size CUV* 264,717 260,662
Mid-Size CUV 923,165 1,372,200

6 Small CUV 1,548,288 2,181,29

Tota 23 l Sales** 7,034,997 9,468,365 6,806,367 7,079,3 * MAV – Multi-Activity Vehicle, SUV – Sport Utility Vehicle, CUV – Crossover Utility Vehicle **Total Sales are based on the classic Car/Truck definition.



### Determining which traditionally-defined trucks will be defined as cars for purposes of this analysis using the revised definition established by NHTSA for MYs 2011 and beyond requires more detailed information about each vehicle model. This is described in greater detail in Chapter 1 of the final TSD.

The forecasts obtained from CSM provided estimates of car and truck sales by segment and by manufacturer, but not by manufacturer for each market segment. Therefore, NHTSA and EPA needed other information on which to base these mo re detailed projected market splits. For this task, th agencies used as a starting point each manufacturer’s sales by market segment from e model year 2008, which is the baseline fleet. Because of the larger number of segments in the truck market, the agencies used slightly diff erent methodologies for cars and trucks.

The first step for both cars and trucks was to break down each manufacturer’s 2008 sales according to the market segment definitions used by CSM. For example, the agencies found that Ford’s 30 

### cars sales in 2008 were broken down as shown in Table III-3:

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

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

EPA and NHTSA then adjusted each manufacturer’s sales of each of its car segments (and truck segments, separately) so that the manufacturer’s total sales of cars (and trucks) matched the total estimated for each future model year based on AEO and CSM forecasts. For example, as

for cars, the agencies first adjusted sales of luxury,
, any other

s

rs

341 units in 2008.

four

e

change over the remaining three segments, the agencies assigned the entire change to mid-size of indicat Table III-1, Ford’s total car sales in 2008 were 709,583 units, while the agencies project that they will increase to 1,113,333 units by 2016. This represents an increase of 56.9 percent. Thus, the agencies increased the 2008 sa les of each Ford car segment by 56.9 percent. This produced estimates of future sales which matched total car and truck sales per AEO and the manufacturer breakdowns per CSM. However, the sales splits by market segment would not necessarily match those of CSM (shown for 2016 in Table III-2).

In order to adjust the market segment mix ed in

specialty and other cars. Since the total sales of cars for each manufacturer were already set changes in the sales of one car segment had to be compensated by the opposite change in an segment. For the luxury, specialty and other car segments, it is not clear how changes in sale would be compensated. For example, if luxury car sales decreased, would sales of full-size ca increase, mid-size cars, and so on? The agencies have assumed that any changes in the sales of cars within these three segments were compensated for by proportional changes in the sales of the other four car segments. For example, for 2016, the figures in Table III.A.3-2 indicate that luxury car sales in 2016 are 1,548,242 units. Luxury car sales are 1,048, Howev ter adjusting 2008 car sales by the change in total car sales for 2016 projected by EIA and a change in manufacturer market share per CSM, luxury car sales decreased to 1,523,171 units. Thus, overall for 2016, luxury car sales had to increase by 25,071 units or 6 percent. The agencies accordingly increased the luxury car sales by each manufacturer by thispercentage. The absolute decrease in luxury car sales was spread across sales of full-size, midsize, compact and subcompact cars in proportion to each manufacturer’s sales in these segments in 2008. The same adjustment process was used for specialty cars and the “other cars” segment defined by CSM.

The agencies used a slightly different approach to adjust for changing sales of the remaining car segments. Starting with full-size cars, the agencies again determined the overall percentag change that needed to occur in future year full-s ize car sales after 1) adjusting for total sales per AEO 2010, 2) adjusting for manufacturer sales mix per CSM and 3) adjusting the luxury, specialty and other car segments, in order to meet the segment sales mix per CSM. Sales of each manufacturer’s large cars were adjusted by this percentage. However, instead of spreading this er, af

vehicles. The agencies did so because the CSM data followed the trend of increasing volumes smaller cars while reducing volumes of larger cars. If a consumer had previously purchased a

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### -size cars

n opposite change in compact car sa les. This process was repeated one more time for compact car sales, with changes in sales in this segment being matched by the opposite l

e

er vehicles to smaller vehicles would have been possible. However, the CSM forecasts indicated large changes in total

er’s left the need to adjust truck s es by segment to match the CSM forecast for each model year.

o

t

s

tion and CSM’s market segment forecast. However, after this step, sales by manufa turer no longer met CSM’s forecast. Thus, we repeated step three and adjusted each

d AEO’s full-size car, we thought it unlikely that their next purchase would decrease by two size categories, down to a subcompact. It seemed more reasonable to project that they would drop one vehicle size category smaller. Thus, the change in each manufacturer’s sales of full was matched by an opposite change (in absolute units sold) in mid-size cars.

The same process was then applied to mid-size cars, with the change in mid-size car sales being matched by a

change in the sales of subcompacts. The overall result was a projection of car sales for mode years 2012-2016--the reference fleet--which matc hed the total sales projections of the AEO forecast and the manufacturer and segment splits of the CSM forecast. These sales splits can be found in Chapter 1 of the Joint TSD for the final rule.

As mentioned above, the agencies applied a slightly different process to truck sales, because th agencies could not confidently project how the change in sales from one segment preferentially went to or came from another particular segment. Some trend from larg

sport utility vehicle, multi-activity vehicle and cross-over sales which could not be connected. Thus, the agencies applied an iterative, but straightforward process for adjusting 2008 truck sales to match the AEO and CSM forecasts.

The first three steps were exactly the same as for cars. EPA and NHTSA broke down each manufacturer’s truck sales into the truck segmen ts as defined by CSM. The agencies then adjusted all manufacturers’ truck segment sales by the same factor so that total truck sales in each model year matched AEO projections for tr uck sales by model year. The agencies then adjusted each manufacturer’s truck sales by segment proportionally so that each manufactur percentage of total truck sales matched that forecas t by CSM. This again al

In the fourth step, the agencies adjusted the sales of each truck segment by a common factor s that total sales for that segment matched the combination of the AEO and CSM forecasts. For example, projected sales of large pickups across all manufacturers were 1,286,184 units in 2016 after adjusting total sales to match AEO’s forecast and adjusting each manufacturer’s truck sales to match CSM’s forecast for the breakdown of sales by manufacturer. Applying CSM’s forecas of the large pickup segment of truck sales to AEO’s total sales forecast indicated total large pickup sales of 1,379,036 units. Thus, we increa sed each manufacturer’s sales of large pickup by 7 percent. 31 

### The agencies applied the same type of adjustment to all the other truck segments at the same time. The result was a set of sales projections which matched AEO’s total truck sales projec

c manufacturer’s truck sales so that they met CSM’s forecast. The sales of each truck segment (by manufacturer) were adjusted by the same factor. The resulting sales projection matche total truck sales projection and CSM’s manufactur er forecast, but sales by market segment no

31 Note: In the NPRM this example showed 29 percent instead of 7 percent. The significant decrease was due tohad too many fullusing the filtered 4th quarter CSM forecast. Commenters, such as GM, had commented that we size trucks and vans, and this change addresses their comment.

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### e .1 percent of our goal, which is well within the needs of this nalysis.

The ne ithin each

anufacturer-segment combination. In large part, this was based on the characterization of the

limited to vehi cle models known to satisfy consumer demands in tility, performance, safety, and other vehicle attributes.

otive

segment. If the combination of AEO and CSM forecasts indicated an increase in a given manufacturer’s sales of a particular market

les
ct

e average footprint to remain constant. First, as the number of subcompacts increases, so do the longer met CSM’s forecast. However, the differen ce between the sales projections after this fifth step was closer to CSM’s market segment forecast than it was after step three. In other words, the sales projection was converging to the desired result. The agencies repeated these adjustments, matching manufacturer sales mix in one step and then market segment in the next a total of 19 times. At this point, we were able to match the market segment splits exactly and th manufacturer splits were within 0

a

xt step in developing the reference fleets was to characterize the vehicles w

m specific vehicle models sold in 2008 -- i.e. , the vehicles comprising the baseline fleet. EPA and NHTSA chose to base our estimates of detailed vehicle characteristics on 2008 sales for several reasons. One, these vehicle characteristics are not confidential and can thus be published here for careful review by interested parties. Two, because it is constructed beginning with actual sales data, this vehicle fleet is light of price, u

As noted above, the agencies gathered most of the information about the 2008 baseline vehicle fleet from EPA’s emission certification and fuel economy database. The data obtained from this source included vehicle production volume, fuel economy, engine size, number of engine cylinders, transmission type, fuel type, etc. EPA’s certification database does not include a detailed description of the types of fuel economy-improving/CO 2 

### -reducing technologies considered in this final rule. Thus, the agencies augmented this description with publicly available data which includes more complete technology descriptions from Ward’s Autom Group. 32 

### In a few instances when required vehicle information (such as vehicle footprint) was not available from these two sources, the agen cies obtained this information from publicly accessible internet sites such as Motortrend.com and Edmunds.com. 33



### The projections of future car and truck sales de scribed above apply to each manufacturer’s sales by market segment. The EPA emissions certification sales data are available at a much finer level of detail, essentially vehicle configuration. As mentioned above, the agencies placed each vehicle in the EPA certification database into one of the CSM market segments. The agencies then totaled the sales by each manufacturer for each market

segment, then the sales of all the individual vehicle configurations were adjusted by the same factor. For example, if the Prius represented 30 percent of Toyota’s sales of compact cars in 2008 and Toyota’s sales of compact cars in 2016 was projected to double by 2016, then the sa of the Prius were doubled, and the Prius sales in 2016 remained 30 percent of Toyota’s compa car sales.

The projection of average footprint for both cars a nd trucks remained virtually constant over the years covered by the final rulemaking. This occurrence is strictly a result of the CSM projections. There are a number of trends that occur in the CSM projections that caused th

32 Note that WardsAuto.com is a fee-based service, but all information is public to subscribers.
33 Motortrend.com and Edmunds.com are free, no-fee internet sites.

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### nd in any segment that can be linked to the unchanging footprint, but there is a trend that non-pickups’ volumes will move from truck segments that are ladder frame to those that are

s

ly

he proposal discusses many of the advantages and disadvantages of the market forecast

e

this could be advantageous in the development of a reference fleet. First, some known changes to the fleet may not be captured by the approach of solely using publicly

dge

to

ing

em , whose product plans specify which vehicles and how many are planned to have this technology. Chrysler Group LLC has announced plans to number of 2-wheel drive crossover vehicles (that are regulated as cars). Second, truck volumes have many segment changes during the rulemaking time frame. There is no specific footprint related tre

unibody-type vehicles. A table of the footprint pr ojections is available in the TSD as well as further discussion on this topic.

4. How is the development of the baseline fleet for this rule different from NHTSA’ historical approach, and why is this approach preferable?

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

T approach used by the agencies, including the agencies’ interest in examining product plans as a check on the reference fleet developed by the agencies for this rulemaking. One of the primary reasons for the request for data in 2009 was to obtain permission from the manufacturers to mak public their product plan information for model years 2010 and 2011. There are a number of reasons that

available information. For example, the agencies’ current market forecast includes some vehicles for which manufacturers have announced plans for elimination or drastic production cuts such as the Chevrolet Trailblazer, the Chrysler PT Cruiser, the Chrysler Pacifica, the Do Magnum, the Ford Crown Victoria, the Mercury Sable, the Pontiac Grand Prix, the Pontiac G5 and the Saturn Vue. These vehicle models appear explicitly in market inputs to NHTSA’s analysis, and are among those vehicle models included in the aggregated vehicle types appearing in market inputs to EPA’s analysis. However, al though the agencies recognize that these specific vehicles will be discontinued, we continue to include them in the market forecast because they are useful as a surrogate for successor vehicles that may appear in the rulemaking time frame replace the discontinued vehicles in that market segment. 34



### Second, the agencies’ market forecast does not include some forthcoming vehicle models, such as the Chevrolet Volt, the Ford Fiesta and several publicly announced electric vehicles, includ the announcements from Nissan regarding the Leaf. Nor does it include several MY 2009 or 2010 vehicles, such as the Honda Insight, the Hyundai Genesis and the Toyota Venza, as our starting point for defining specific vehicle models in the reference fleet was Model Year 2008. Additionally, the market forecast does not account for publicly announced technology introductions, such as Ford’s EcoBoost syst

offer small- and medium-sized cars using Fiat powertrains. Were the agencies to rely on

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

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### e

duction f

r

n- and intellig ce-sharing agreement with GM that lasts through the introduction of redesigned Saab 9-

sis

y

gins. Thus, in fact, the omission only reflects e reference fleet, rather than the agencies’ conclusions regarding how stringent the

at

that f ’ analysis, and the counterbalancing aspects of potentially omitted technology in the referen fleet, the agencies believe their determination of the stringency of new standards has

t manufacturers’ product plans (that were submitte d), the market forecast would account for not only these specific examples, but also for simila r examples that have not yet been announced publicly.

Additionally, NHTSA acknowledges that Pontiac and Saturn lines are being discontinued. W also are aware that as of October, General Motors Corp. and Sichuan Tengzhong Heavy Industrial Machinery Co., Ltd had tentatively reached an agreement for Tengzhong to acquire GM's Hummer truck brand. As we understand it, GM was planning to continue to build the trucks and provide business services to the buyer during a transitional period. Two GM assembly plants that produce the H2, H3 and H3T models were supposed to continue pro of those trucks until June 2011, with the option of a one year extension until June 2012. As o late February, the agencies were made aware that the deal to sell Hummer to Sichuan Tengzhong Heavy Industrial Machinery Co., Ltd. had fallen through because Sichuan Tengzhong Heavy Industrial Machinery Co., Ltd. failed to win Chinese government approval of the sale. It is believed that GM is planning to wind down the Hummer brand, while at the same time being open to other potential bidders for Hummer.

We are also aware that as of late February, GM completed the sale of Saab to Dutch sports ca maker Spyker Cars NV. The purchase created a new company, called Saab Spyker Automobiles NV. According to the CEO of Saab Automobile, Jan Ake Jonsson, Saab has a productio

en 3 in 2012.

Some commenters, such as CBD and NESCAUM, suggested that the agencies’ omission of known future vehicles and technologies in the reference fleet causes inaccuracies, which CBD further suggested could lead the agencies to set lower standards. On the other hand, CARB commented that “the likely impact of this omission is minor.” Because the agencies’ analy examines the costs and benefits of progressively adding technology to manufacturers’ fleets, the omission of future vehicles and technologies pr imarily affects how much additional technolog (and, therefore, how much incremental cost and benefit) is available relative to the point at which the agencies’ examination of potential new standards be th standards should be. This is discussed further below. The agencies believe the above-mentioned comments by CBD, NESCAUM, and others are based on a misunderstanding of the agencies’ approach to analyzing potential increases in regulatory stringency. The agencies also note th manufacturers do not always use technology solely to increase fuel economy, and that use of technology to increase vehicles’ acceleration pe rformance or utility woul d probably make technology unavailable toward more stringent standards. Considering the incremental nature o the agencies

ce not been impacted by any such omissions.

Moreover, EPA and NHTSA believe that not including such vehicles after MY 2008 does no significantly impact our estimates of the technology required to comply with the standards. If included, these vehicles could increase the extent to which manufacturers are—in the reference case—expected to over-comply with the MY 2011 CAFE standards, and could thereby make the

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### rojections of the vehicle fleet generally shift vehicle designs towards those more likely to be typic l of newer vehicles. Compared to using product plans that show continu fuel continue to inc nefits of the new standards, as the costs and benefits of all fuel economy improvements beyond those required by the MY 2011

ing

nology

o urate production intent information in thes e releases for reasons such as: (a) competitors ill closely examine their information for data in their product planning decisions; (b) the press

overa uncements is not uniform, meaning highly anticipated models ave more coverage and materials than models that may be less exciting to the public and

date

at

ir press releases, both on projec ted date of release of new models and on sales

al new standards appear to cost less and yield less benefit relative to the reference case. However, in the agencies’ judgment, production of the most advanced technology vehicles, such as the Chevy Volt or the Nissan Leaf (for example), will most likely be too limited during MY 2011 through MY 2016 to significantly impact manufacturers’ compliance positions. While we are projecting the characteristics of the future fleet by extrapolating from the MY 2008 fleet, the primary difference between the future fleet and th e 2008 fleet in the same vehicle segment is the use of additional CO 2 

### -reducing and fuel-saving technologies. Both the NHTSA and EPA models add such technologies to evaluate means of comply ing with the standards, and the costs of doing so. Thus, our future p

a ed economy increases planned based on expectations that CAFE standards will rease, this approach helps to clarify the costs and be

CAFE standards are being assigned to the final rules. In some cases, the “actual” (vs. projected or “modeled”) new vehicles being introduced into the market by manufacturers are done so in anticipation of this rulemaking. On the other hand, manufacturers may plan to continue us technologies to improve vehicle performance and/or utility, not just fuel economy. Our approach prevents some of these actual technological improvements and their associated cost and fuel economy improvements from being assumed in the reference fleet. Thus, the added tech will not be considered to be free (or having no benefits) for the purposes of this rule.

In this regard, the agencies further note that manufacturer announcements regarding forward models (or future vehicle models) need not be accepted automatically. Manufacturers tend t limit acc

w c ge of forward model anno h consistency and uniformity cannot be ensured with the usage of press information; and (c) these market projections are subject to change (sometimes significant), and manufacturers may not want to give the appearance of being indecisive, or under/over-confident to their shareholders and the public with premature release of information.

NHTSA has evaluated the use of public manufacturer forward model press information to up the vehicle fleet inputs to the baseline and referenc e fleet. The challenges in this approach are evidenced by the continuous stream of manufac turer press releases throughout a defined rulemaking period. Manufacturers’ press releases su ffer from the same types of inaccuracies th many commenters believe can affect product plans. Manufacturers can often be overly optimistic in the volumes.

More generally and more critically, as discussed in the proposal and as endorsed by many of the public comments, there are several advantages to the approach used by the agencies in this fin rule. Most importantly, today’s market forecast is much more transparent. The information sources used to develop today’s market forecast are all either in the public domain or available commercially. Another significant advantage of today’s market forecast is the agencies’ ability to assess more fully the incrementa l costs and benefits of the propos ed standards. In addition, by

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### e s final rule, the baseline market data comes from actual vehicles (on the road today) which have actual fuel economy test data (in contrast to manufactu rer estimates of future product fuel economy) – so there is no question what is the basis for the fuel economy or CO 2 

### performance of the baseline market data as it is.

5. How is this baseline different quantitatively from the baseline that NHTSA used for the MY 2011 (March 2009) final rule?

As discussed above, the current baseline was developed from adjusted MY 2008 compliance data and covers MYs 2011-2016, while the baseline that NHTSA used for the MY 2011 CAFE rule was developed from confidential manufacturer product plans for MY 2011. This section describes, for the reader’s comparison, some of the differences between the current baseline and the MY 2011 CAFE rule baseline. This compar ison provides a basis for understanding general characteristics and measures of the difference, in this case, between using publicly (and commercially) available sources and using manufa cturers’ confidential product plans. The current baseline, while developed using the same methods as the baseline used for MYs 2012- 2016 NPRM, reflects updates to the underlying commercially-available forecast of manufacturer and market segment shares of the future light vehicle market. These changes are discussed above.

Estimated vehicle sales developing baseline and reference fleets from common sources, the agencies have been able to avoid some errors—perhaps related to interpretation of requests—that have been observed in past responses to NHTSA’s requests. An additional advantage of the approach used for this proposal is a consistent projection of the change in fuel economy and CO 2 

### emissions across th various vehicles from the application of new technol ogy. With the approach used for thi

:

The sales forecasts, based on the Energy Information Administration’s (EIA’s) Annual Energy Outlook 2010 (AEO 2010), used in the current baseline indicate that the total number of light vehicles expected to be sold during MYs 2011-2015 is 77 million, or about 15.4 million vehicles annually. NHTSA’s MY 2011 final rule forecast, based on AEO 2008, of the total number of light vehicles likely to be sold during MY 2011 through MY 2015 was 83 million, or about 16.6 million vehicles annually. Light trucks are expected to make up 41 percent of the MY 2011 baseline market forecast in the current baseline, compared to 42 percent of the baseline market forecast in the MY 2011 final rule. These changes in both the overall size of the light vehicle market and the relative market shares of passenger cars and light trucks reflect changes in the economic forecast underlying AEO, and changes in AEO’s forecast of future fuel prices.

The figures below attempt to demonstrate graphica lly the difference between the variation of fuel economy with footprint for passenger cars under the current baseline and MY 2011 final rule, and for light trucks under the current baseline an d MY 2011 final rule, respectively. Figures III1 and III-2 show the variation of fuel economy with footprint for passenger car models in the current baseline and in the MY 2011 final rule, while Figures III-3 and III-4 show the variation of fuel economy with footprint for light truck m odels in the current baseline and in the MY 2011 final rule. However, it is difficult to draw meaningful conclusions by comparing figures from the current baseline with those of the MY 2011 final rule. In the current baseline the number of

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### make/models, and their associated fuel economy rint, are fixed and do not vary over time compared to the number of data points in the MY 2011 final rule baseline. In contrast, the baseline fleet used in the MY 2011 final rule vari es over time as vehicl es (with different fuel economy and footprint characteristics) are added to and dropped from the product mix. and footp —this is why the number of data points in the current baseline figures appears smaller as

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### Figure III-1. Planned Fuel Economy vs. Foot print, Passenger Cars in Current Baseline

10 15 20 25 30 35 40 45 50 55 60 65 70

35 40 45 50 55 60 65 70 75

Footprint (sf)Fuel Economy(mpg)

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### Fig le ure III-2. Planned Fuel Economy vs. Footprint, Passenger Cars in MY 2011 Final Ru

10 15 20 25 30 35 40 45 50 55 60 65

35 40 45 50 55 60 65 70 75 80 Footpri 70

f)Fuel Economy (mpg)

nt (s

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### Figure III-3. Planned Fuel Economy vs. Footprint, Light Trucks in Current Baseline

10 15 20 25 30 35 40 45 50 55 60 65 70

35 40 45 50 55 60 65 70 75 80
Footprint (sf)Fuel Economy (mpg)

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### Figure III-4. Planned Fuel Economy vs. Footprint, Light Trucks in MY 2011 Final Rule

10 15 20 25 30 35 40 45 50 55 60 65

35 70

50 55 65 70 80 FootpriFuel Economy (mpg)

40 45 60 (sf) 75

nt

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### Estimated manufacturer market shares :

NHTSA’s expectations regarding manufacturers’ market shares (the basis for which is discussed below) l rule, given that the agency is relying on

ifferent sources of material for these assumptions as discussed above and in Chapter 1 of the have also changed since the MY 2011 fina d joint TSD. These changes are reflected below in Table III-4, which shows the agency’s sales forecasts for passenger cars and light trucks under the current baseline and the MY 2011 final rule. 35



### Table III-4. Sales Forecasts (Production for U.S. Sale in MY 2011, Thousand Units) Current Baseline MY 2011 Final Rule Manufacturer Passenger Nonpa ssenger Passenger Nonpassenger Chrysler 326 737 707 1,216 Ford 1,344 792 1,615 1,144 General Motors 1,249 1,347 1,700 1,844 Honda 851 585 1,250 470 Hyundai 382 46 655 221 Kia 306 88 Nissan 612 331 789 479 Toyota 1,356 888 1,405 1,094 Other Asian 664 246 441 191 European 833 396 724 190 Total 7,923 5,458 9,286 6,849

Dual-fueled vehicles :

Manufacturers have also, during and since MY 2008, indicated to the agency that the y intend to

ell more dual-fueled or flexible-fuel vehicles (F FVs) in MY 2011 than indicated in the current b to petroleum-based ga mpliance with CAFE standards, the fuel eco to lim account for this potential. 36 

### However, is precluded from “taki it” for the complia by acco bilit d ting the leve tandards.” 37 

### Some facturers pl produce siderably gr share of FFV earn full credit un CA. The cted avera V share of arket in MY 7 percent for the curr seline, versus 17 percent e MY 2011 rule. NHTSA at in MY 2008 (the l year prov the vehic pon which today’s ased), th e U.S.-bas Ms produ e F offered s aseline of adjusted MY 2008 compliance data. FFVs create a potential market for alternatives soline and diesel fuel. For purposes of determining co nomy of a FFV is , subject NHTSA itations, adjusted upward to ng cred nce flexibility unting for m anufacturers’ a y to earn an use credits in set l of the s manu an to a con eater s than can der EP proje ge FF the m 2011 is ent ba for th final

notes th mode iding le models u market forecast is b e thre ed OE ced most of th FVs

35 As exp although NHTSA n ed each manufacturer’s overal et share to pro realistica fleet, the product mix for anufacturer bmitted pro ns was preserved. The agency h ed manufacturers’ produ in detail, an rstands that manufacturers do not sell the same mix of vehicles in every model year. lained b elow, ormaliz l mark duct pla duce a lly-sized as review each m ct plans that su d unde

36 See 49 U.S.C. 32905 and 32906.
37 49 U.S.C..32902(h).

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### for sale ., yet these OEMs nt are pro to account for a smaller share of the future m he forecast the age s used to d p and an today’s rule in the forecas used to develop and analyze the 1 stand

achieved fuel eco levels in the U.S accou jected

arket in t ncy ha evelo alyze than t the agency MY 201 ards.

Estimated nomy :

Becaus ers’ product plans al reflect simultaneous cha in fleet mix and other vehicle ristics, the relations tween incr technolo lization and fuel eco nnot be isolated wit certainty. To do so wo ul uire an apple apples “counte of vehicles th ex cept for ology an economy, i al— for exam fleet mix an icle perfor and util he current b ne

arket forecast shows industry-wide average fuel economy levels somewhat lower in MY 2011 than shown in the MY 2011 final rule and the MYs 2012-2016 NPRM. Under the current b the MY 2011 final rule, and 26 s 2012-2016 NPRM. The 0.3 mpg change relative to th 6 th s in manufacturer and market segment shares o ar

These differences are sh eater detail below able III-5, w ows manufacturerspecific CAFE levels (n ing FFV credits th e manufactu xpect to earn) from the current baseline ver Y 2011 final rule ne (from ma turers’ 2008 product plans) for passenger car . Table I ows the com averages of these planned CAFE levels in ective baseline fle These tables nstrate that, while the difference at the industr ot so large, ther significant di ces in CAFE at the manufacturer level betw e current baseline an MY 2011 fi le baseline. For example, while Volksw is essentially the sam er both, Toyota and Nissan show increased combined CA ls under the curren line (by 1.9 and 0.7 mpg respectively), while Chrysler, Ford, a ow decreased com d CAFE leve er the current baseline (by 1.4, 1.1, and 0.8 mp ela tive to Y 2011 fina baseline.

Table III-5. Current B lanned CAFE Le n MY 2011 versus MY 2011 Final Rule P AFE Levels (Passenger and Nonpass ) e manufactur so nges characte hip be eased gy uti increased
nomy ca h any d req s-torfactual” fleet at are, techn d fuel dentic

ple, in terms of d veh mance ity. T aseli

m

aseline, average fuel economy for MY 2011 is 26 .4 mpg, versus 26.5 mpg under the baseline in .7 mpg under the baseline in the MY e MYs 2012-201 f the MY 2011 m baseline is ket. e result of change

own in gr in T hich sh
ot count at som rers e sus the M baseli nufac s and light trucks II-6 sh bined the resp ets. demo y level is n e are fferen een th d the nal ru
agen e und FE leve t base nd GM sh bine ls und g, respectively) r the M l rule

aseline P vels i lanned C enger rent baseline CAFE levels MY 2011 planned CAFE ls Cur leve Manufacturer Passenger Nonpas Passen Nonpassenger senger ger BMW 27.2 0 2 23.0 23. 7.0 Chrysler 27.8 8 2 23.1 21. 8.2 Ford 28.0 21.0 29.3 22.5 Subaru 29.2 26.1 28.6 28.6 General Motors 28.2 21.2 30.3 21.4 Honda 33.5 25.0 32.3 25.2 Hyundai 32.5 24.3 31.7 26.0 Tata 24.6 19.6 24.7 23.9 Kia 31.7 23.7 38

38 Again, Kia is not listed in the table for the MY 2011 final rule because it was considered as part of Hyundai f purposes of that analysis ( i.e. , Hyundai-Kia). or

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### Mazda 39 

### 30.6 26.0 Daimler 26.4 21.0 

### 25.2 20.6 Mitsubishi 29.4 23.6 29.3 26.7 Nissan 31.7 21.7 31.3 21.4 Porsche 26.2 20.0 27.2 20.0 Ferrari 40 

### 16.2 Maserati 41 

### 18.2 Suzuki 30.9 23.3 28.7 24.0 Toyota 35.1 23.7 33.2 22.7 Volkswagen 29.1 20.2 28.5 20.1 Total/Average 30.3 22.2 30.4 22.6

Table III-6. Current Baseline Planned C MY 2011 versus MY 2011 Final Rule ed C Lev om AFE Levels in Plann AFE els (C bined)

Manu facturer rren selin MY Final Rule baseline Cu t ba e 2011

BMW 2 26.0 5.0 Ch rysler 2 24.7 3.3 Ford 24.9 26.0 Subar u 2 28.6 7.9 Gene ral Motors 2 24.9 4.1 Honda 2 30.0 9.5 H yundai 3 30.0 1.3 Tata 2 24.4 1.4 Kia 2 9.5 Mazd a 2 9.8 Daiml er 2 23.6 4.4 Mitsu bishi 2 29.1 7.4 Nissa n 2 26.6 7.3 Po rsche 2 22.0 3.7 Ferra ri 16.2 Maserati 18.2 Suzuk i 29.7 27.8 Toyota 2 27.6 9.5 Volkswagen 27.0 27.1 Total/Average 26.4 26.5

39 Mazda is not l the MY nal rule it was part of Ford for purposes of that analysis.
40 EPA did not in urrent b based o nclusio ncluding them would not impact the results, and t t listed ble for t t base
41 EPA did not in urren ne based conclu t including them would not impact the result ti is n d in the t r the cur seline. isted in the table for 2011 fi because considered as

clude Ferrari in the c aseline n the co n that i herefore Ferrari is no in the ta he curren line. clude Maserati in the c s, and therefore Masera t baseli ot liste on the able fo sion tha rent ba

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### Tables III-7 through III-9 summarize other differ ces between the current baseline and manufacturers’ product plans submitted to NHTSA in 2008 for the MY 2011 final rule. These

dustry. The tables containing product plan data do not identi manufacturers by name, and do not present them in

Tables III-7a and II at the cu asel ect ht decrease in overall average passenger v relative t anu s’ This is a reflection of the market segment shif ing the sa cast cu aseline.

Tabl s ver 2 hicle Footprint are F

er en

tables present average vehicle footprint, curb weight, and power-to-weight ratios for each manufacturer represented in the current baseline and of the seven largest manufacturers represented in the product plan data used in that rulemaking, and for the overall in fy the same sequence.

I-7b show th rrent b ine refl s a slig ehicle size o the m facturer plans. ts underly les fore s of the rrent b

e III-7a. Current Ba eline A age MY 011 Ve (Squ eet) Manufactur PC LT Avg. BM W 45.4 49.9 47.5 Chrysle r 46.8 52.8 50.9 Daimler 47.1 53.3 49.0 Ford 46.3 56.1 49.9 General Motors 46.4 58.2 52.5 Honda 44.3 49.1 46.3 Hyun dai 44.4 48.7 44.8 Kia 45.2 51.0 46.5 Mazda 44.4 47.3 44.9 Mitsubis hi 43.8 46.5 44.6 Nissan 45.3 53.9 48.3 Porsche 38.6 51.0 42.8 Subaru 43.1 26.6 36.8 Suzuki 40.8 47.2 41.6 Tata 50.3 47.8 48.8 Toyota 44 .0 53.0 47.6 Volkswag en .5 43 52.6 45.1 Industry Average 45 .2 53.5 48.6

Table III-7b. Rule A Plan 201 cle Footprint (Square Feet) MY 2011 Final verage ned MY 1 Vehi PC LT Avg. Manufacturer 1 46.7 58.5 52.8 Manufacturer 2 46.0 5.4 47.1 Manufacturer 3 44.9 52.8 48.4 Manufacturer 4 45.4 55.8 49.3 Manufacturer 5 45.2 57.5 50.3 Manufacturer 6 48.5 54.7 52.4 Manufacturer 7 45.1 49.9 46.4 Industry Average 45.6 55.1 49.7

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### Tables III-8a and I at the curr selin cts e in overall average vehicle weight rela manufacture s. As above, st likely a reflection of the market segmen derlying the rec e t baseline.

Table III-8 ne Av eh rb Weight (Pounds) facturer P LT A II-8b show th ent ba e refle a decreas tive to the rs’ plan this is mo t shifts un sales fo asts of th curren

a. Current Baseli erage MY 2011 V icle Cu Manu C vg. BMW 3 4 4 ,535 ,648 ,055 Chry sler 3 4 4 ,572 ,469 ,194 Daimler 3 5 4 ,583 ,127 ,063 Ford 3 4 3 ,526 ,472 ,877 General Mo tors 3 4 4 ,528 ,978 ,281 Honda 3 4 3 ,040 ,054 ,453 Hyunda i 3 4 3 ,014 ,078 ,129 Kia 3 4 3 ,035 ,007 ,252 Mazd a 3 3 3 ,258 ,803 ,348 Mitsubish i 3 3 3 ,298 ,860 ,468 Nissan 3 4 3 ,251 ,499 ,689 Porsche 3 4 3 ,159 ,906 ,760 Subaru 3,176 2,001 2,727 Suzuki 2,842 3,843 2,965 Tata 3,906 5,171 4,627 Toyota 3,10 321 3,589 9 4, Vo swagen 3,445 3 lk 5,672 ,839 Industry Average 3,313 4 3,7 ,499 97

Table III-8b. le Av lanned MY 2011 le Curb Weight (Pounds) P L Avg. MY 2011 Final Ru erage P Vehic C T Manufacturer 1 3, 4, 3 197 329 ,692 Manufacturer 2 3, 4, 4 691 754 ,363 Manufacturer 3 3,293 4,038 3,481 Manufacturer 4 3,254 4,191 3,510 Manufacturer 5 3,547 5,188 4,401 Manufacturer 6 3,314 4,641 3,815 Manufacturer 7 3,345 4,599 3,865 Industry Average 3,380 4,687 3,935

ance st likely a
at Tables III-9a and III-9b show that the current baseline reflects a decrease in average perform relative to that of the manufacturers’ product plans. This decreased performance is mo reflection of the market segment shifts underlying the sales forecasts of the current baseline, th is, an assumed shift away from higher performance vehicles.

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### Table III-9a. Current Baseline Average MY 2011 Vehicle Power-to-Weight Ratio (hp/lb) Manufacturer PC LT Avg. BMW 0.072 0.061 0.067 Chrysler 0.055 0.052 0.053 Daimler 0.068 0.056 0.064 Ford 0.058 0.054 0.056 General Motors 0.057 0.056 0.056 Honda 0.056 0.054 0.056 Hyundai 0.052 0.055 0.052 Kia 0.050 0.056 0.051 Mazda 0.052 0.055 0.052 Mitsubishi 0.053 0.056 0.054 Nissan 0.059 0.057 0.058 Po 5 73 4 rsche 0.10 0.0 0.09 S ubaru 0.060 0.030 0.048 S 0

uzuki 0.049 0.062 .051 T 0 0

ata 0.077 .057 .065 T 0

oyota 0.053 .062 0.056 V 0 0

olkswagen 0.057 .052 .056 Industry Average 0.057 0.056 0.056

Table III-9b. MY 2 al Rule Avera lanned 2011 icle Power-to-Weight Ratio p/lb) PC 011 Fin ge P MY Veh (h LT Avg. Manu 0. 8 60 facturer 1 065 0.05 0.0 Manu 0. 5 62 facturer 2 061 0.06 0.0 Manu 0 9 56 facturer 3

. 053 0.05 0.0 Manu 0 8 59 facturer 4 .060 0.05 0.0 Manu 0 7 59 facturer 5

. 060 0.05 0.0 Manu 0 5 65 facturer 6 .063 0.06 0.0 Manu 0 5 53 facturer 7 .053 0.05 0.0 Industry Average 0.060 0.059 0.060

As discussed above, the agencies’ market forecast for MY 2012-2016 holds the performance and other characteristics of individual vehicle models constant, adjusting the size and composition of the fleet from one model year to the next.

Refresh and redesign schedules (for application in NHTSA’s modeling) :

Expected model years in which each vehicle m odel will be redesigned or freshened constitute another important aspect of NHTSA’s market forecast. As discussed in Chapter V below, NHTSA’s analysis supporting the current rulemaking times the addition of nearly all technologies to coincide with either a vehicle redesign or a vehicle freshening. Product plans submitted to NHTSA preceding the MY 2011 final rule contained manufacturers’ estimates of vehicle redesign and freshening schedules and NH TSA’s estimates of the timing of the five-year

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### redesign cy e plans. In the current baseli contrast, estimates o timing e refresh and redesign cycles were based on histo counting fo from k n rede ing in or prior to MY 2008 fo the fleet an igning h and ign years accordingly. After timates, the s a rers’ senger car and light truck estimated to b Y 2011 wer summarized below e current baseline and the MY 2011 fi 10 below e es o manufacturer’s fleets expected to b Y 2011 for urrent ne. Ta I-11 presents corresponding estim rket foreca d by N in th ysis supporting the MY 2011 final rule protect confidenti rmation nufac are not identified

y name). cle and the two- to three-year refresh cycle were made with reference to thos ne, in f the of th rical dates— i.e.
, rward now signs occurr r each vehicle in d ass refres redes applying these es hares of m nufactu pas e redesigned in M e as for th nal rule. Table III- shows the p rcentag f each e redesigned in M the c baseli ble II

ates from the ma st use HTSA e anal (again, to al info , ma turers b

Table III-10. Current Baseline, Share of Fleet Redesigned in MY 2011 Manufacturer PC LT Avg. BMW 32% 37% 34% Chrysler 0% 13% 9% Daimler 0% 0% 0% Ford 12% 8% 11% General Motors 17% 3% 9% Honda 29% 26% 28% Hyundai 26% 0% 23% Kia 38% 83% 48% Mazda 0% 0% 0% Mitsubishi 0% 59% 18% Nissan 5% 25% 12% Porsche 0% 100% 34% Subaru 0% 42% 16% Suzuki 4% 21% 6% Tata 28% 100% 69% Toyota 5% 15% 9% Volkswagen 16% 0% 13% Industry Average 13% 15% 14%

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### PC LT Avg. Table III-11. MY 2011 Final Rule, Share of Fleet Redesigned in MY 2011

Manufacturer 1 19% 0% 11% Manufacturer 2 34% 27% 29% Manufacturer 3 5% 0% 3% Manufacturer 4 7% 0% 5% Manufacturer 5 19% 0% 11% Manufacturer 6 34% 28% 33% Manufacturer 7 27% 28% 28% Overall 20% 9% 15%

We continue, therefore, to estimate that manufacturers’ redesigns will not be uniformly distributed across model years. This is in keeping with standard industry practices, and reflects

ct MY ation that

advantages. NHTSA plans to consider these advantages and disadvantages further in

e what m nufacturers actually do–NHTSA has observed that manufacturers in fact do redesign more vehicles in some years than in others. NHTSA staff have closely examined manufacturers’ planned redesign schedules, contacting some manuf acturers for clarification of some plans, and confirmed that these plans remain unevenly distributed over time. For example, although the table above shows that NHTSA expects Company 2 to redesign 34 percent of its passenger car models in MY 2011, current information indicates that this company will then redesign only (a different) 10 percent of its passenger cars in MY 2012. Similarly, although the table above shows that NHTSA expects four of the larges t seven light truck manufacturers to redesign virtually no light truck models in MY 2011, current information also indicates that these four manufacturers will redesign 21-49 percent of their light trucks in MY 2012.

6. How does manufacturer product plan data factor into the baseline used in this final rule?

While the agencies received updated product plans in Spring and Fall 2009 in response to NHTSA’s requests, the baseline data used in this final rule is not informed by these produ plans, except with respect to specific engineering characteristics ( e.g. , GVWR) of some 2008 vehicle models, because these product plans contain confidential business inform a

the age cies are legally required to protect from disclosure, and because the agencies have concluded that, for purposes of this final rule, a transparent baseline is preferable.

For the NPRM, NHTSA conducted a separate analysis that did make use of these product plans. NHTSA performed this separate analysis for purposes of comparison only. For the final rule which this FRIA accompanies, NHTSA used the publicly-available baseline for all analysis related to the development and evaluation of the new CAFE standards. As discussed above, while a baseline developed using publicly and commercially available sources has both advantages and disadvantages relative to a baseline developed using manufacturers’ product plans, NHTSA has concluded for today’s analysis that the advantages outweigh the n

dis

onnection with future rulemakings, taking into account changes c in the market, changes in th scope and quality of publicly and commercia lly available data, and any changes in manufacturers’ willingness to make some produ ct planning informati on publicly available.

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### wide se to comments received in the last round of rulemaking, in our March 2009 notice of intent to prepare an environmental impact statement, the agency selected a

. Thus, centage

ch

In the N RM, we noted that each of the listed alternatives represents, in part, a different way in

e

as

2 

### emissions, and fuel savings by consumers, as well as other relevant factors such as afety. For example, the 7% Alternative weighs energy conservation and climate change

e

ed s) will

ized

g a to illustra the similarities and dissimilarities. To that end, we included and analyzed two

ich total spect B. Alternatives examined by the agency, and why NHTSA selected the Preferred Alternative

In developing the proposed MY 2012-16 standards, the agency developed and considered a variety of alternatives. In respon

range of candidate stringencies that increased annually, on average, 3% to 7%. 42 

### That same approach has been carried over to this final ru le and to the accompanying FEIS and FRIA the majority of the alternatives considered in this rulemaking are defined as average per increases in stringency—3 percent per year, 4 percent per year, 5 percent per year, and so on. NHTSA believes that this approach clearly communicates the level of stringency of ea alternative and allows us to identify alternativ es that represent different ways to balance NHTSA’s statutory requirements under EPCA/EISA.

P which NHTSA could conceivably balance different policies and considerations in setting th standards. We were mindful that the agency needs to weigh and balance many factors, such technological feasibility, economic practicability, including lead time considerations for the introduction of technologies and impacts on the auto industry, the impacts of the standards on fuel savings and CO

s considerations more heavily and technological feasibility and economic practicability less heavily. In contrast, the 3% Alternative, the least stringent alternative, places more weight on technological feasibility and economic practicability. We recognized that the “feasibility” of th alternatives also may reflect differences and uncertainties in the way in which key economic ( e.g. , the price of fuel and the social cost of carbon) and technological inputs could be assess and estimated or valued. We also recognized that some technologies ( e.g. , PHEVs and EV not be available for more than limited commercial use through MY 2016, and that even those technologies that could be more widely commercialized through MY 2016 cannot all be deployed on every vehicle model in MY 2012 but require a realistic schedule for more widespread commercialization to be within the realm of economically practicability.

In addition to the alternatives that increase evenly at annual rates ranging from 3% to 7%, NHTSA also included alternatives developed using benefit-cost criteria. The agency emphas benefit-cost-related alternatives in its rule makings for MY 2008-2011 and, subsequently, MY 2011 standards. By including such alternatives in its current analysis, the agency is providin degree of analytical continuity between the two appr oaches to defining alternatives in an effort
te additional alternatives, one that sets standards at the point where net benefits are maximized (labeled “MNB” in the table below), and another that sets standards at the point at wh costs are most nearly equal to total benefits (labeled “TCTB” in the table below). 43 

### With re

42 Notice of intent to prepare an EIS, 74 FR 14857, 14859-60, April 1, 2009.
43 The stringency indicated by each of these alternatives depends on the value of inputs to NHTSA’s analysis. Results presented here for these two alternatives are based on NHTSA’s reference case inputs, which underlie the central analysis of the proposed standards. In the acco mpanying FRIA, the agency presents the results of that

n ology cost estimates), our analysis often exhausts analysis to explore the sensitivity of results to changes in key economic inputs. Because of numerous changes i model inputs ( e.g. , discount rate, rebound effect, CO 2 value, techn

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### at least some vehicle , market forecasts and economic assumptions, NHTSA used the Volpe model

statuto criteria, given the difficulties confro nting the industry and the economy, and other relevan on and achieving a nationally harmoni zed and coordinated program for regulating fuel economy and GHG emissio

Part of that a t c sa achie both Federal and State GHG emission reduc tion go specially those of California and ates that have adopted its GHG emission standard for motor vehicles. Given that EPCA trib as nda HT d E ter the w a nati G on ndar ld n be ach he s missi n orn the forni G p m. as by e ting a e C ir tan for M 16 oul y ac e c y and e of emissions reduction which Californ ia standards would require for the subset of les sold lif nd Ca ia st ds f 2009-2016 ( n as “ y n s ncy e C ia P 1 p m w alua ut for nd Fo mber of rea i scussed in th l ru asse nt wa oped of national new vehicle fleet-wide CO 2 

### performance standards for model year 2016 result in the new light-duty vehicle fleet in the State of California having CO rmance o rf f e C nia 1 s rds. level iv to pg if the G and re m clus by conom nt d t era lt is ode 20 ls o Nat Progr

the of en r the nal am f 25 i C n be m th

on t e” vem w the G-re on r nt h a re ant l e re ns. E st ds, cusse

t al ru , canno e m impro ents t canno e accoun ed for o e to the first of those alternatives, we note that Executive Order 12866 focuses attention on an approach that maximizes net benefits. Further, since NHTSA has thus far set attribute-based CAFE standards at the point at which net benefits are maximized, we believed it would be useful and informative to consider the potential impact s of that approach as compared to the new approach for MYs 2012-2016.

After working with EPA in thoroughly reviewing and in some cases reassessing the effectiveness and costs of technologies (most of which are al ready being incorporated in

s ) extensively to assess the technologies that the manufacturers could apply in order to comply with each of the alternatives. This allowed us to assess the variety, amount and cost of the technologies that could be used to enable the manufacturers to comply with each of the alternatives. NHTSA estimated how the applic ation of these and other technologies could increase vehicle costs, reduce fuel consumption, and reduce CO 2 

### emissions.

The agency then assessed which alternative would represent a reasonable balancing of the
ry t goals and priorities. Those priorities and goals include maximizing energy conservati

ns.

ssessme nt of alt ernatives entailed a n evalua ion of the als, e stringen ies neces ry to

ve the St requires at ute-b ed sta rds, N SA an PA de mined level at hich onal attribute-base i d GH emissi s sta d wou eed to set to ieve t ame e on reductions id Calif lean A ia as Act s Cali dard a GH Y 20 rogra that w This w d appl done ross th valua
ountr requir nationw the levels vehic in Ca ornia u er the liforn andar or MY know Pavle 1”). In esse ce, the tringe of th aliforn avley rogra as ev ted, b a national sta ard. r a nu sons d e fina le, an ssme s devel which would perfo 2 

### , 250 equal t the pe ormance rom th alifor Pavley tanda That g/mi, is equ alent 35.5 m HG st ard we et ex ively fuel e y improveme s – an he ov ll resu the m l year 16 goa f the ional am.

However, level string cy fo Natio Progr goal o 0 g/m O 2 

### ca et wi both fuel ec omy “ ailpip impro ents as ell as o r GH ducti elated improveme elsewhere in s, suc his fin s A/C le friger t b eakag et by ductio

vem CAF hat andar t b as dis
t d n th FTP/HFET tests. Thus, setting CAFE standard s at 35.5 mpg would require more tailpipe technology (at more expense to manufacturers) than would be required under such a CAA

e austs all available technologies is considered. all available technologies before reaching the point at which total costs equal total benefits. In these cases, th stringency that exh

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### rd. To obtain an equivalent CAFE standard, we determined how much tailpipe technology ecess in or et an mpg level if ufactur also loyed w n ge amount o “c r (lea d enc each 0 le his ts gure .1 m the pri unte CAFE erential giv nuf rs th ortu o re 5.5 quiva under t CAA in ways that would significantly reduce their costs. Were NHTSA instead to

pg by MY 20

new the statutory factors and other relevant considerations. For the reader’s reference, the estimated required levels of stringency for each alternative in each model year are presented below:

Table III-12. Estimated Required Fuel Ec onomy Level for Regulatory Alternatives. 44

Alt. 1 Alt. 2 Alt. 3 Alt. 4 Alt. 5 Alt. 6 Alt. 7 Alt. 8 Alt. 9 

### standa would be n EPA ary to be a der to m

avera e of 35.5 edits” man kage an ers effici emp y) to r hat the 25 deemed g/mi equiva f A/C nt. T resul in a fi of 34 pg as appro ate co rpart standard. Th is diff es ma acture e opp nity t ach 3 mpg e lent he establis its standard at the same level, manufacturers would need to make substantially greater expenditures on fuel-saving technologies to reach 35.5 mpg under EPCA.

Thus, as part of the process of considering all of the factors relevant under EPCA for setting standards, in a context where achieving a harmonized National Program is important, for the proposal we created a new alternative whose annual percentage increases would achieve 34.1 h

m 16. That alternative is one which increases on average at 4.3% annually. This alternative, like the seven alternatives presented above, represents a unique balancing of

No Action 3%/year Increase 4%/year Increase ~4.3%/year Increase 5%/year Increase ~6.0%/year Increase

MNB 6%/year Increase 7%/year Increase ~6.6%/year Increase TCTB
2012 Passenger Cars 30.5 31.7 32.1 33.4 32.4 33.0 32.7 33.0 33.4 Light Trucks 24.4 24.1 24.4 25.3 24.6 26.3 24.9 25.1 26.3
Combined 27.8 28.3 28.6 29.7 28.8 30.0 29.1 29.4 30.3
2013 Passenger Cars 30.5 32.6 33.3 34.2 33.9 36.1 34.5 35.2 36.7 Light Trucks 24.4 24.8 25.3 25.9 25.8 27.7 26.3 26.8 28.0
Combined 27.8 29.1 29.7 30.5 30.3 32.3 30.8 31.4 32.8
2014 Passenger Cars 30.5 33.5 34.5 35.0 35.5 38.1 36.5 37.6 39.2 Light Trucks 24.5 25.5 26.3 26.6 27.0 29.1 27.8 28.6 29.7
Combined 28.0 30.0 30.9 31.3 31.8 34.2 32.7 33.7 35.0
2015 Passenger Cars 30.5 34.4 35.8 36.2 37.1 39.4 38.6 40.1 40.7 Light Trucks 24.4 26.2 27.2 27.5 28.3 30.3 29.4 30.5 30.7
Combined 28.0 31.0 32.2 32.6 33.4 35.6 34.7 36.0 36.5

44 Also, the “MNB” and the “TCTB” alternatives depend on the inputs to the agencies’ analysis. The sensitivity analysis presented in the Chapter X documents the response of these alternatives to changes in key economic inputs. For example, the combined average required fuel economy under the “MNB” alternative is 36.9 mpg under the reference case economic inputs presented here, and ranges from 32.0 mpg to 37.2 mpg under the alternative economic inputs.

### 51

2016 Passenger Cars 30.5 35.4 37.2 37.8 39.0 40.9 40.9 42.9 42.3 Light Trucks 24.4 27.0 28.3 28.7 29.7 31.1 31.1 32.6 31.8
Combined 28.1 32.0 33.6 34.1 35.2 36.9 36.9 38.7 38.0



### The following this same inform t in a diff ay, co ing estimated average fuel ec required of manufa s under the regula lternatives in MYs 2012, 20 levels for 2013 and MY 2015 fall een those for MYs 2012 and d 2016, re ively. Req evels f ese interim years are not pr following figure si imit the lexity ure.

Figure III ed onomy ( 012, 2 nd 2016) figure presents at u ion b erent w mpar onomy levels ct urer eight tory a 14, and 2016. Required s 2014 an MY betw 2014 and MY spect uired l or th esented in the mply to l comp of the fig

-1. Average E stimated Requir Fuel Ec MYs 2 014, a

45

20 25 30 35 40

A v e r a g e R e q u i r e d F u e l E c o n o m y ( m p g )

PC, 2012
LT, 2012 Comb., 2012
PC, 2014 LT, 2014 Comb., 2014 PC, 2016
LT, 2016
Comb. 2016

3%/y 4%/y Final 5%/y 6%/y Max
NB 7%/y TC=TB



### As this figure illustrates, the final standards involve a “faster start” toward increased stringency than do any of the alternatives that increase steadily ( i.e. , the 3%/y, 4%/y, 5%/y, 6%/y, and 7%/y alternatives). However, by MY 2016, the string ency of the final standa rds reflects an average annual increase of 4.3%/y. The final standards, therefore, represent an alternative that could be se.”

s d referred to as “4.3% per year with a fast start” or a “front-loaded 4.3% average annual increa

For each alternative, including today’s final st andards, NHTSA has estimated all corresponding effects for each model year, including fuel savings, CO 2 

### reductions, and other effects, as well a the estimated societal benefits of these effects. The accompanying FRIA presents a detaile

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### analysis of these results. Table III-13 presents fuel savings, CO reductions, and total industry 2 

### cost outlays for model year 2012 – 2016 for the eight alternatives.

Table III-13. Fuel Savings, CO 2 

### Reductions, and Technology Costs for Regulatory Alternatives Regulatory Alternative Fuel Savings (b. gal) CO 2 

### Reductions (mmt) Cost ($b) 3% per Year 34 373 23 4% per Year 50 539 39 Final (4.3% per Year) 61 655 52 5 % per Year 68 709 63 6% per Year 82 840 90 Maximum Net Benefit 90 925 103 7% per Year 93 945 111 Total Cost = Total Benefit 96 986 114

As noted earlier, NHTSA has used the Volpe model to analyze each of these alternatives based on analytical inputs determined jointly with EPA. For a given regulatory alternative, the Volpe model estimates how each manufacturer could apply technology in response to the MY 2012 standard (separately for cars and trucks), carri es technologies applied in MY 2012 forward to MY 2013, and then estimates how each manufactur er could apply technology in response to the MY 2013 standard. When analyzing MY 2013, the model considers the potential to add “extra” technology in MY 2012 in order to carry that technology into MY 2013, thereby avoiding the use of more expensive technologies in MY 2013. The model continues in this fashion through MY 2016, and then performs calculations to estimate the costs, effects, and benefits of the applied technologies, and to estimate any civil penalties owed based on projected noncompliance. For each regulatory alternative, the model calculates incremental costs, effects, and benefits relative to the regulatory baseline ( i.e. , the no-action alternative), under which the MY 2011 CAFE standards continue through MY 2016. The model calculates results for each model year, because EPCA requires that NHTSA set its standards for each model year at the “maximum feasible average fuel economy level that the Secretary decides the manufacturers can achieve in that model year” considering four statutory factors. Pursuant to EPCA’s requirement that NHTSA not consider statutory credits in establishing CAFE standards, NHTSA did not consider FFV credits, credits carried forward and backward, and transferred credits in this calculation 45
, 46 

### In addition, the analysis incorporates fines for some manufacturers that have traditionally paid fines ply with the standards. Because it entails year-by-year examination of eight rather than com regulatory alternatives for, separately, passenger cars and light trucks, NHTSA’s analysis involves a large amount of information. Detailed results of this analysis are presented separately in Chapter XI below.

45 NHTSA has conducted a separate analysis, discussed in Chapter XI, which accounts for EPCA’s provisions regarding FFVs.
46 For a number of reasons, the results of this modeling differ from EPA’s for specific manufacturers, fleets, a model years. These reasons include representing every model year explicitly, accounting for estimates of when vehicle model redesigns will occur, and not considering those compliance flexibilities where EPCA forbids such consideration in setting CAFE standards. It should be noted, however, that these flexibilities in fact provide manufacturers significant latitude to manage their compliance obligations. nd

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### es

s for additional technology in each model year. The following figure shows cu ulative results for MYs 2012-2016 for industry as a whole oyota. This figure focuses on these manufacturers as they currently (in MY 2010) represent three large U.S.- headquartered and three large foreign-headquartered full-line manufacturers.

Figure III-2. Incremental Technology Outlays (MYs 2012-2016) In reviewing the results of the various alternatives, NHTSA confirmed that progressive increas in stringency require progressively greate r deployment of fuel-saving technology and corresponding increases in technology outlays and related costs, fuel savings, and CO 2 

### emission reductions. To begin, NHTSA estimated total incremental outlay

m and Chrysler, Ford, General Mo tors, Honda, Nissan, and T

- 20 40 60 80 100

- 5 10 15 20 25

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB I n c r e m e n t a l T e c h n o l o g y O u t l a y s ( $ b ) 120

30

Chrysler
Ford

General Motors
Honda

Nissan Toyota Industry



### As part of the incremental technology outlays, NHTSA also analyzes which technologies manufacturers could apply to meet the standards. In NHTSA’s analysis, manufacturers achieve compliance with the fuel economy levels through application of technology rather than through

anges in the mix of vehicles produced for sale in the U.S. The accompanying FRIA presents ailed estimates of additional technology penetration into the NHTSA reference fleet ssociated with each regulatory alternative. The fo llowing four charts illustrate the results of this

alysis, considering the application of four technologies by six manufacturers and by the ch det

a

an industry as a whole. Technologies include gasoli ne direct injection (GDI), engine turbocharging and downsizing, diesel engines, and strong HEV systems (including CISG systems). GDI and turbocharging are presented because they are among the technologies that play an important role

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### in achiev d strong HEVs are presented because they re ologies involving significant cost and related lead time challenges for widespread use through MY 2016. These figures focus on Chrysler, Ford, General Motors, Honda, Nissan, and Toyota, as above. For each alternative, the figures show additional application of technology by MY 2016.

Figure III-3. Additional Application of GDI (MY 2016) ing the fuel economy improvements shown in NHTSA’s analysis, and diesels an
presen t techn

0% 20% 40% 60% 80%

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A d d i t i o n a l A p p l i c a t i o n ( % P r o d u c t i o n ) 100%

Chrysler
Ford

General Motors
Honda

Nissan Toyota Industry

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### Figure III-4. Additional Application of Engine Turbocharging & Downsizing (MY 2016)

0% 20% 40% 60% 80%

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A d d i t i o n a l A p p l i c a t i o n ( % P r o d u c t i o n ) 100%

Chrysler
Ford

General Motors
Honda

Nissan Toyota Industry

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### Figure III-5. Additional Application of Diesel Engines (MY 2016)

0% 20% 40% 60% 80%

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A d d i t i o n a l A p p l i c a t i o n ( % P r o d u c t i o n ) 100%

Chrysler
Ford

General Motors
Honda

Nissan Toyota Industry

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### Figure III-6. Additional Application of CISG and Strong HEV Systems (MY 2016)

0% 20% 40% 60% 80% 100%

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A d d i t i o n a l A p p l i c a t i o n ( % P r o d u c t i o n )

Chrysler
Ford

General Motors
Honda

Nissan Toyota Industry



### The modeling analysis demonstrates that applying these technologies, of course, results in fuel savings. Relevant to EPCA’s requirement that NHTSA consider, among other factors, economic practicability and the need of the nation to conserve energy, the following figure compares the incremental technology outlays and related cost presented above for the industry to the corresponding cumulative fuel savings.

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### Figure III-7. Incremental Technology Outlays and Fuel Savings (MYs 2012-2016)

- 20 40 60 80 100 120 120

- 20 40 60 80 100

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB F u e l S a v i n g s ( b . g a l . )

T e c h n o l o g y O u t l a y s ( $ b )

Cost Fuel Savings

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### These incremental technology outlays (and corresponding fuel savings) also result in corresponding increases in incremental cost per vehicle, as shown below. The following five figures show industry-wide average incremental ( i.e. , relative to the reference fleet) per-vehicle costs, for each model year, each fleet, and the combined fleet. Estimates specific to each manufacturer are shown in Chapter VII.

Figure III-8. Average Incremental Per-Vehicle Costs (MY 2012)

- 500 1,000 1,500 2,000

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A v e r a g e T e c h n o l o g y C o s t ( $ / V e h i c l e ) 2,500

PC

LT

Comb.

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### Figure III-9. Average Incremental Per-Vehicle Costs (MY 2013)

- 500 1,000 1,500 2,000

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A v e r a g e T e c h n o l o g y C o s t ( $ / V e h i c l e ) 2,500

PC

LT

Comb.

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Figure III-10. Average Incremental Per-Vehicle Costs (MY 2014)

- 500 1,000 1,500 2,000

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A v e r a g e T e c h n o l o g y C o s t ( $ / V e h i c l e ) 2,500

PC

LT

Comb.

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Figure III-11. Average Incremental Per-Vehicle Costs (MY 2015)

- 500 1,000 1,500 2,000

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A v e r a g e T e c h n o l o g y C o s t ( $ / V e h i c l e ) 2,500

PC

LT

Comb.

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Figure III-12. Average Incremental Per-Vehicle Costs (MY 2016)

2,000 2,500

i c l e )

- 500 1,000 1,500

3%/y 4%/y Final 5%/y 6%/y Max. NB 7%/y TC=TB A v e r a g e T e c h n o l o g y C o s t ( $ / V e h

PC

LT
Com



### As discussed in the NPRM, the agency began th e process of winnowing the alternatives by determining whether any of the lower stringency alternatives should be eliminated from consideration. To begin with, the agency needs to ensure that its standards are high enough enable the combined fleet of passenger cars and light trucks to achieve at least 35 mpg not later than MY 2020, as required by EISA. Achieving that level makes it necessary for the chosen alternative to increase at over 3 percent annually. Additionally, given that CO 2 

### and fuel savings are very closely correlated, the 3%/y and 4%/y alternative would not produce the reductions in fuel savings and CO 2 

### emissions that the Nation needs at this time. Picking either of those alternatives would unnecessarily result in foregoin g substantial benefits, in terms of fuel savings and red ced CO b.



### to

2 

### emissions, which would be achievable at reasonable cost. And finally, neither vital
ent. u the 3%/y nor the 4%/y alternatives would lead to the regulatory harmonization that forms a core principle of the National Program that EPA and NHTSA are jointly striving to implem These alternatives would give inadequate we ight to other standards of the Government, specifically EPA’s and CARB’s. Thus, the agency concluded that alternatives less stringent than the proposed standards would not yield the em issions reductions required to produce a harmonized national program and would not produce corresponding fuel savings, and therefore would not place adequate emphasis on the nation’s need to conserve energy. NHTSA has therefore concluded that it must reject the 3%/y and 4%/y alternatives.

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### ion

e

SA

r technologically feasible, and thus concludes that it would result in standards that would be beyo

For the ot es, NH t w a t f the other mo t alternatives du oncer lead nd ec c prac ity. There are ld technological ono on s whi due to the sho me available for t ly y his p , in p ar fo 2012 and 2013. es more st t than nal st s beg ccrue considera re rapidly than they fue ngs and ons tions, a levels that are in ly economically b some cially ering ed to underlyin tments ( e.g. , for en ring an ling) adva actual tion. As shown s III-2 to III-6 a while nal st s alre e sive applicatio hnologies, more s t stan would ire m r (includin ubstantial impleme on of ced te gies s s stoichiometric gasoline jection engines, di ngines stron s), uld r ious issues of y of lead time, no o mee stand t to te s nificant changes w nufacturers’ redesig cles. T ency m ains, as s histo , that there is a distinction bet ons id ns of logica ibility economic bility, both of wh ter int agency termin of th um feasible levels of stringency. A given level of pe nce may be technologically feasible ( i.e.
, setting aside economic constraints) for a given v icle model. However, it would not be

econ vehicle will immediately ( i.e. , with omulgation) perform at its highest te feasib ec fa n li ss to the financial or the time requ hire e h eng build ac and install en ling. The lead tim sonab ded t capit stme to devote th and time to d nd pr r co al p on of fuel efficient portant ele that N takes onsid n e hing the standards

In additio igures presented ab eveal creasi ingenc ond t l standards ail significant ad al app on o ology the more stringent ives, the one close tringen the s s be lized is the alternativ which combined CA stringe increase an . As i ted above, th ative would yield ving O 2 

### r ns ab % an igher, respectiv the final standards. wev ared final ds, rnative NHTSA then considered the “environmentally-preferable” alternative. Based on the informat provided in the FEIS, the environmentally-preferable alternative would be that involving stringencies that increase at 7% annually. 47 

### NHTSA notes that NEPA does not require that agencies choose the environmentally-preferable alternative if doing so would be contrary to th choice that the agency would otherwise make under its governing statute. Given the levels of technology and cost required by the environmentally-preferable alternative and the lack of lead time to achieve such levels between now and MY 2016, as discussed further below, NHT concludes that the environmentally-preferable alternative would not be economically practicable

o nd the level achievable for MYs 2012-2016.

her alternativ TSA determ ined that i ould be in ppropriate o choose any o re strin gen

r e and

to c ns over mic tim ti
st me a o ch

nomi t icabil real-wo d ti ec

r e c t raint must be considered rt lea he ea ears of rogram articul r MYs

The alternativ ringen the fi andard in to a costs bly mo accrue l savi emissi reduc nd at creasing urden , espe consid the ne make g inv es g inee d too w ell in n ce of produc in Figure bove, the fi andard ady requir aggres n of tec tringen dards requ ore widesp ead use
g more s ntati advan chnolo uch a direct in esel e , and g hybrid and wo aise ser adeq uac t only t t the ar ds bu co ordina uc h sig ith ma n cy he ag aint it ha rically n important ween c eratio techno l feas and practica ich en o the ’s de ation e maxim rforma eh omically practicable to require a level of fleet average performance that assumes every in 18 months of the rule’s pr chnologically le level, b ause manu cturers do ot have un mited acce

resources ired to noug i

o m

neers, al i enough f il
nts

ities, ough too

urces e rea ly nee ake nve and

e reso esign a epare fo mmerci roducti a more vehicle is an im ment HTSA into c eration i stablis

.
n, the f ove r dition that in li ng str y bey he fina would ent cati f techn . Among alternat st in s cy to tandard ing fina today e under FE ncy s at 5% nually ndica is altern fuel sa s and C eductio out 11 d 8% h ely, t han Ho er, c omp to t he stan dar thi s alte

47 See, e.g. 2, p. 18, which sh that 7%/y rnative greates lative e n global mean temp , FEIS, figure S-1 ows alte yields t cumu ffect o
erature.

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### would increase outlays for new technologies dur MY 2012-2016 by about 22%, or $12b. Ave $1,152 when stringency increases at 5% an s represents a 28% increase in per-vehicle cost for o ease in er (o -p sis fuel savings ar rtional). Addition he 5% ernativ ly burd e light truck flee g a nearly $400 ( rcent) ncrea Y 20 pare e final standards llowing three tabl ma imate factu el av increment 5%/y alter nd erage passe nd lig k fleets:

Tab verage Increm osts ( icle) the 5% terna FE S r enger
MY 2 MY 2 MY MY MY 

### ing rage MY 2016 cost increases would, in turn, rise from $903 under the final standards to nually. Thi nly a 3% incr average p formance n a gallon er-mile ba to which
e prop o

n a 4 lly, t /

c y alt e dispropo

se r 1 tional ens th t requiri fo 2 pe ost i in M 6 com d to th . The es sum rize est
t d manu rer-lev erage al costs for the native a he av of the nger a ht truc

le III-14. A ental C $/veh under /y Al tive CA tandards fo Pass Cars Manufacturer 012 013 2014 2015 2016 BMW 3 4 24 184 585 Chrysler 734 1,303 1,462 1 ,653 1 ,727 Daimler - - 410 801 1,109 Ford 74 3 1,245 1,261 1,583 1,923 Gener al Motors 448 823 1 ,187 1 ,425 1 ,594 Honda 50 109 271 375 606 Hyundai 747 877 1 1 1 ,057 ,052 ,124 Kia 49 128 197 261 369 Mazda 555 718 1 1 ,166 ,407 1,427 Mitsubishi 534 7 2,534 3,213 3,141 50 Nissan 294 491 965 1,064 1,125 Porsche 68 (52) (51) (50) (49) Subaru 292 324 1,372 1,723 1,679 Suzuki - 959 1,267 1,316 1,540 Tata 111 93 183 306 710 Toyota 31 29 52 129 212 Volkswagen 145 428 477 492 783 Average 337 540 726 886 1,053



### Table III-15. Average Incremental Costs ($/vehicle) under the 5%/y Alternative CAFE Standards for Light Trucks
Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 BMW 169 160 201 453 868 Chrysler 360 559 1,120 1,216 1,432 Daimler 60 55 51 52 51 Ford 1,207 1,663 1,882 2,258 2,225 General Motors 292 628 866 968 1,136 Honda 258 234 611 750 1,047 Hyundai 711 685 1,923 1,909 1,862 Kia 47 293 556 782 1,157 Mazda 248 408 419 519 768 Mitsubishi - - 1,037 1,189 1,556 Nissan 613 723 2,142 2,148 2,315 Porsche - (0) (1) 469 469 Subaru 1,225 1,220 1,365 1,374 1,330 Suzuki - 1,998 1,895 1,837 2,096 Tata - - - - 503 Toyota 63 187 594 734 991 Volkswagen - - 514 458 441 Average 415 628 1,026 1,173 1,343

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### Table III-16. Average Incremental Costs ($/vehicle) under the 5%/y Alternative CAFE Standards
Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 BMW 72 64 84 265 666 Chrysler 499 870 1,272 1,414 1,569 Daimler 20 20 281 554 773 Ford 914 1,407 1,498 1,838 2,034 General Motors 371 726 1,033 1,205 1,379 Honda 135 157 396 518 769 Hyundai 742 838 1,237 1,186 1,235 Kia 49 168 273 355 506 Mazda 500 667 1,053 1,272 1,330 Mitsubishi 371 352 1,973 2,386 2,506 Nissan 399 565 1,344 1,387 1,467 Porsche 52 (39) (35) 130 124 Subaru 617 628 1,369 1,597 1,553 Suzuki - 1,134 1,381 1,404 1,630 Tata 61 56 101 182 629 Toyota 43 82 239 333 466 Volkswagen 117 333 486 486 723 Average 367 573 836 987 1,152



### These cost increases derive from increased application of advanced technologies as stringency increases past the levels in the final standards. For example, under the final standards, addition diesel application rates average 1.6% for the industry and range from 0% to 3% among Chrysler, Ford, GM, Honda, Nissan, and Toyota. Under standards increasing in combined stringenc 5% annually, these rates more than triple, aver aging 6.2% for the industry and ranging fr to 21% for the same six manufacturers.

These technology and cost increases are significant, given the amount of lead-time between no and model years 2012-2016. In order to achieve the levels of technology penetration for the fin standards, the industry needs to invest significant capital and product de al

y at om 0%

w al velopment resources right aw in particular for the 2012 and 2013 model year, which is only 2-3 years from now.

It is important to note, and as discussed in the final rule, as well as in the Joint Technical Support

re ay, For the 2014-2016 time frame, significant product development and capital investments will need to occur over the next 2-3 year in order to be ready for launching these new products for those model years. Thus a major part of the required capital and resource investment will need to occur now and over the next few years, under the final standards. NHTSA believes that the final rule requires significant investment and pr oduct development costs for the industry, focused on the next few years.

Document and later in this FRIA, the average model year 2016 per-vehicle cost increase of mo than $900 includes an estimate of both the increase in capital investments by the auto companies and the suppliers as well as the increase in pr oduct development costs. These costs can be significant, especially as they must occur over the next 2-3 years. Both the domestic and transplant auto firms, as well as the domestic and world-wide automotive supplier base, are experiencing one of the most difficult markets in the U.S. and internationally that has been seen

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### d

The ag y concludes that the levels of technology penetration required by the final standards

ent

the most approp balance of the various statutory factors.

at

”

osts

of ld

d in the past 30 years. One major impact of the global downturn in the automotive industry an certainly in the U.S. is the significant reduction in product development engineers and staffs, aswell as a tightening of the credit markets which allow auto firms and suppliers to make the nearterm capital investments ne cessary to bring new technology into production.

enc are reasonable. Increasing the standards beyond those levels would lead to rapidly increasing dependence on advanced technologies with higher costs—technology that, though perhaps technologically feasible for individual vehicle m odels, would, at the scales involved, pose too great an economic burden given the state of the industry, particularly in the early years of the rulemaking time frame. 48



### Therefore, the agency concluded that these more stringent alternatives would give insuffici weight to economic practicability and related le ad time concerns, given the current state of the industry and the rate of increase in stringency that would be required. Overall, the agency concluded that among the alternatives considered by the agency, the proposed alternative contained the maximum feasible CAFE standards for MYs 2012-2016 as they were riate

Some commenters argued that the agency should select a more stringent alternative than th proposed in the NPRM. The Union of Concer ned Scientists (UCS) commented that NHTSA should set standards to produce the “maximum envi ronmental benefit” available at “reasonable cost, and at least at the stringency maximizing net benefits. Students from the University of California at Santa Barbara commented that the agency should have based standards not just on technologies known to be available, but also on technologies that may be available in the future—and should do so in order to force manufacturers to “reach” to greater levels of performance. Also, the Center for Biological Diversity (CBD) commented that, having conducted an unbiased cost-benefit analysis show ing benefits three times the magnitude of c for the proposed alternative, the agency should select a more stringent alternative. CBD also argued that the agency should have evaluated the extent to which manufacturers could deploy technology more rapidly than suggested by a five-year redesign cycle.

Conversely, other commenters argued that NHTSA should select a less stringent alternative, either in all model years or at least in the earlier model years. Chrysler, VW, and the Alliance Automobile Manufacturers commented that the stringency of NHTSA’s CAFE standards shou be further reduced relative to that of EPA’s GHG emissions standards, so that manufacturers would not be required by CAFE to add any tailpipe technology beyond what they thought would be necessary to meet an mpg level of 35.5 mi nus the maximum possible A/C credits that coul be obtained under the EPA program. Also, Chrysler, Daimler, Toyota, Volkswagen, and the

48 Although the final standards are projected to be slightly more costly than the 5% alternativ e in MY 2012, that alternati tandard becomes progressively more costly than the final standards in the remaining model years. See

significantly increased economic burden on the manufacturer s in the next few calendar years to prepare for these the ve s Figures III.B.8 through III.B.10 above. Moreover, as discussed above, after MY 2012, the 5% alternative standard yields less incremental fuel economy benefits at increased cost (both industry-wide a nd per vehicle), directionally the less desirable result. These increased costs incurred to increase fuel economy through MY 2016 would impose

future model years. In weighing the statutory factors, NHTSA accordingly rejected this alternative in favor of final standard.

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### ss

he

osed standards, as well as the agencies’ underlying technical analysis and weighi of statutory factors. CARB further co mmented that the stringency increases in the

e finds that alternatives less stringent that the one adopted today would leave too much technology “on

ference,

y MY

difficulty Alliance argued that the agency should reduce the rate of increase in stringency to produce steadier and more “linear” increases between MY 2011 and MY 2016. In addition, the Heritage Foundation commented that the proposed standards would, in effect, force accelerated progre toward EISA’s “35 mpg by 2020” requirement, causing financially-stressed manufacturers to incur undue costs that would passed along to consumers.

However, most commenters supported the agency’s selection of the proposed standards. T American Chemical Society, the New York De partment of Environmental Conservation, the Washington State Department of Ecology, and several individuals all expressed general support for the levels of stringency proposed by NHTSA as part of the joint proposal. General Motors and Nissan both indicated that the proposed standards are consistent with the National Program announced by the President and supported in letters of commitment signed by these companies’ executives. Finally, the California Air Re sources Board (CARB) strongly supported the stringency of the prop
ng earlier model years are essential to providing envir onmental benefits at least as great as would be achieved through state-level enforcement of CARB’s GHG emissions standards. 49



### The agency has considered these comments an d all others, and having considered those comments, believes the final standards best balance of all relevant factors that the agency considers when determining maximum feasible CAFE standards. As discussed below, having updated inputs to its analysis and correspondingl y updated its definition and analysis of these regulatory alternatives, the agency continues to conclude that manufacturers can respond to th proposed standards with technologies that will be available at reasonable cost. The agency

the shelf” unnecessarily, thereby failing to deliver the fuel savings that the nation needs or to yield environmental benefits nece ssary to support a harmonized national program. In response to some manufacturers’ suggestion that NHTSA’s CAFE standards should be made even less stringent compared to EPA’s GHG emissions standards, NHTSA notes that the dif consistent with the underlying Notice of Intent, is based on the agencies’ estimate of the average amount of air conditioning credit earned, not the maximum theoretically available, and that NHTSA’s analysis indicates that most manufacturers can achieve the CAFE standards b 2016 using tailpipe technologies. This is fully co nsistent with the agency’s historical position. As NHTSA explained in the NPRM, the Conference Report for EPCA, as enacted in 1975, makes clear, and applicable law affirms, “a determination of maximum feasible average fuel economy should not be keyed to the single manufacturer which might have the most achieving a given level of average fuel economy.” CEI-I , 793 F.2d 1322, 1352 (D.C. Cir. 1986). Instead, NHTSA is compelled “to weigh the benefits to the nation of a higher fuel economy standard against the difficulties of individual automobile manufacturers.” Id . Thus, the law permits CAFE standards exceeding the projected capability of any particular manufacturer as long as the standard is economically practicable for the industry as a whole.

While some manufacturers may find greater A/C improvements to be a more cost-effective way of meeting the GHG standards, that does not mean those manufacturers will be unable to meet

49 Generally speaking, the cumulative benefits (in terms of fuel savings and GHG reductions) of front-loaded standards will be greater than standards that increas e linearly.

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### gies. “Econo ic practicability” means just that, practicability, and need not always mean what is

s. TSA

e

aking

ed to

dards. Therefore, the agency believes that alternatives less stringent than the one adopted today would not give sufficient emphasis to the nation’s need to

t ent ndence on the most expensive logies and those which are technical ly more demanding to implement, with

are not ec racticab th e di ie e. The agency al rees with CBD’s a on th SA a A hav overl conserva suming an averag sign f 5 y ere e m urers who appl cycles (such as man ers d d abo ere ar s who have shor ome of the ucts, ere a e pro g. , c ans) that tend to be gned on longer cycl HTSA eves th re are ll line manufact o can maintain sig nt red s of ve (with ve larg s) in 1 or 2 year BD has provided n ence ting th uld be icable. mplete redesign e U.S. light-dut t by m year 2 clear asible HTSA and EPA at several mode additi ad t ces orde e manufact meet the most strin and e gr ed incr the cy of the CAFE standards with tailpipe technologies. NHTSA’s analysis has demonstrated a feasible path to compliance with the CAFE standards for most manufacturers using those technolo

m “cheapest” or “most cost-effective” for a specific manufacturer. Moreover, many of the A/C improvements on which manufacturers intend to rely for meeting the GHG standards will reduce GHG emissions, specifically HFC emissions, but they will not lead to greater fuel saving 50 

### The core purpose of the CAFE standards under EPCA is to reduce fuel consumption. NH believes that less stringent standards would allo w tailpipe fuel economy technologies to be left on the table that can be feasibly and economically applied, and failing to apply them would lead to a loss in fuel savings. This would not pl ace appropriate emphasis on the core CAFE purpos of conserving fuel. For this reason, we decline to reduce the stringency of our standards as requested by some manufacturers. Similarly, we decline to pursue with EPA in this rulem the suggestion by one commenter that that agency ’s calculation authority under EPCA be us provide A/C credits.

With respect to some manufacturers’ concerns regarding the increase in stringency through MY 2013, the agency notes that stringency increases in these model years are especially important in terms of the accumulation of fuel savings and em ission reductions over time. In addition, a weakening would risk failing to produce emission reductions at least as great as might be achieved through CARB’s GHG stan

conserve energy. The requirement to set standards that increase ratably between MYs 2011 and 2020 must also be considered in the context of what levels of standards would be maximum feasible. The agency believes that the rate of increase of the final standards is reasonable.

On the other hand, the agency disagrees with comments by UCS, CBD, and others indicating tha more stringent standards would be appropriate. As discussed above, alternatives more string than the one adopted today would entail a rapidly increasing depe techno commensurately rapid increases in costs. In the agency’s considered judgment, these alternatives onomically p le, nor do ey provid correspon ngly suffic nt lead tim so disa g ss

e r

erti at NHT cycl nd

ea EP e

a been y tive in as

er ede e o rs. Th re som anufact y long smaller i ufactur escribe ve), th e other ter cycles for s r prod and th re som ducts ( e. argo v redesi es. N beli at the no fu urers wh nifica esign hicles relati e sale s, and C o evid indica is wo pract A co of the entir th y flee odel 012 is im ly infe

s , and N

r believe l years onal le e is ne ary in for th
urers to gent st ards. Th aduat ease in stringen

50 This is n TSA means, in ay, to d anufacturers from emp A/C tec ies to meet EPA’ t simply to say th A’s i ndent ob n to set um feas AFE standards t rough application of e techn alone m fulfille recogn the flexibilities offered in another regulatory program. ot to say that NH s standards, bu at NHTS any w e ndepe ter m loying hnolog ligatio maxim ible C o be met th tailpip ologies ust be d, while izing

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### the standards from MYs 2012 through 2016 acco ts for the economic necessity of timing the

a

In contra alysis of the ative r shown e, as w e omplete and detail s presented in thi ysis, N A has ed th fina rds are technolog asible and econom prac e. The and ill re manufact siderabl tional ology, ng wit signif investme hnology design, de ment apital i ment c for in t xt few years. A HTSA cannot pr how m e respo he fi rds, the agenc lysis indicates that andards could lead gnifica reate f advanced d transmission t logies hown e, the a y’s an shows considera eases in the applic of SGDI systems a gine hargi downsizi ugh not presented ab , the a y’s anal also sh ilar e increases se of dual-clutch ted m trans ns (A How he agency’s s does not suggest tha e ad app th nol response nal standards woul nd be levels achievable b indust hese technolog to be applied t least so xtent in the ce of n AFE standards tion, the agency’ s in s that manufa u ly to a limited extent on the most costly technologi ch as diesel engines and advanced technologies, such as strong HEVs.

As shown i t t an ld l era ental costs rang $303 per vehicle light tru in MY ) to $9 r veh r light trucks in ), increasing stea rom $ er vehi r all hicle 2012 to $903 f ht vehicle in MY NH timat these woul considera nufacturer ould excee 00 p cle. wing three tabl arize estimated m cturer averag ent sts for nal standards average of the pas r and ruck f

Table III ge Incremental ($/ve under Final Pass ar C tandards
MY 2 MY 2 MY 2 MY MY 

### un pplication of many major technologies to co incide with scheduled model redesigns.

st, thro ugh an
t illustr esults abov ell as th m l st

ore c ed resul y fe s anal H TS conclud a t the anda icall ically ticabl final st ards w quire urers to apply con e addi techn starti h very icant nt in tec velop and c nvest alled he ne lthough N edict anufactur rs will nd to t nal standa y’s ana the st ech to si ntly g r u se o engine an no . A s s abov genc alysis ble incr ation nd en turboc ng and ng. Tho ove genc ysis ows sim ly larg in the u automa anual missio MTs). ever, t analy si t th ditio nal licati on of ese tech og ies in to the fi d exte yond

m y the ry. T ies are likely o at e e even absen ew C . In addi s analysi dicate most cturers wo ld rely on
es, su

below, NHTSA est mates tha he final st dards cou ead to av ge increm ing fro m 6 (for cks 2012 47 pe ic

s le (fo MY 201 lig d ily f 39 6 p cle fo lig ht ve in MY or all 2016.

s TSA es es that costs d vary bly among ma , but w ra rely d $1,8 er vehi The follo

es summ anufa -level e increm al co the fi and the senge light t leets:

-17. Avera

turer 

### Costs hic le) enger C
2 

### AFE S
2 Manufac 012 013 014 015 016 BMW 3 4 24 184 585 Chrysler 1 1 734 ,043 ,129 1,270 1,358 Daimler - - 410 801 1,109 Ford 1, 1 1 1 619
, 537 ,533 ,713 1 ,884 General Motors 1 1 1 448 896 ,127 ,302 ,323 Honda 33 98 205 273 456 Hyundai 559 591 768 744 838 Kia 110 4 14 177 235 277 Mazda 555 6 799 854 923 65 Mitsubishi 534 460 1,588 1,875 1,831 Nissan 119 323 707 723 832 Porsche 68 (52) (51) (50) (49) Subaru 292 324 988 1,385 1,361 Suzuki - 625 779 794 1,005 Tata 111 93 183 306 710 Toyota 31 29 41 121 126 Volkswagen 145 428 477 492 783 Average 455 552 670 774 880

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### andards

Table III-18. Average Incremental Costs ($/vehicle) under Final Light Truck CAFE St
Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 BMW 252 239 277 281 701 Chrysler 360 527 876 931 1,170 Daimler 60 51 51 52 51 Ford 465 633 673 1,074 1,174 General Motors 292 513 749 807 986 Honda 233 217 370 457 806 Hyundai 693 630 1,148 1,136 1, 113 Kia 400 467 582 780 1, 137 Mazda 144 2 41 250 354 480 Mitsubishi - - 553 686 1,371 Nissan 398 489 970 1,026 1,362 Porsche - (1) (1) 469 469 Subaru 1,036 995 1,016 1,060 1,049 Suzuki - 1,797 1,744 1,689 1,732 Tata - - - - 503 Toyota 130 150 384 499 713 Volkswagen - - 514 458 441 Average 303 411 615 741 947



### Table III-19. Average Incremental Costs ($/vehicle) under Final CAFE Standards

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 BMW 106 94 110 213 618 Chrysler 499 743 989 1,084 1,257 Daimler 20 18 281 554 773 Ford 1,195 1,187 1,205 1,472 1,622 General Motors 371 705 946 1,064 1,165 Honda 116 144 266 343 585 Hyundai 577 599 847 805 879 Kia 176 221 263 334 426 Mazda 482 587 716 778 858 Mitsubishi 371 319 1,200 1,389 1,647 Nissan 211 376 792 813 984 Porsche 52 (39) (35) 130 124 Subaru 551 552 998 1,267 1,248 Suzuki - 823 954 946 1,123 Tata 61 56 101 182 629 Toyota 67 70 159 248 317 Volkswagen 117 333 486 486 723 Average 396 498 650 762 903



### In summary, NHTSA has considered eight regulator y alternatives, including the final standards, examining technologies that could be applied in response to each alternative, as well as corresponding costs, effects, and benefits. The agency has concluded that alternatives less stringent than the final standards would not produce the fuel savings and CO 2 

### reductions necessary at this time to achieve either the overarching purpose of EPCA, i.e. , energy conservation, or an important part of the regulatory harmonization underpinning the National Program, and would forego these benefits even though there is adequate lead time to implement reasonable and feasible technology for the vehicles. Conversely, the agency has concluded that

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### s uld provide inadequate lead time, ustry, would not be economically erefore, having considered these eight regulatory alternatives, and the statutorilyotor more tringent standards would involve levels of additional technology and cost that would be economically impracticable and, correspondingly, wo considering the economic state of the automotive ind practicable. Th relevant factors of technological feasibility, economic practicability, the effect of other m vehicle standards of the Government on fuel economy, and the need of the United States to conserve energy, along with other relevant factors such as the safety impacts of the final standards, NHTSA concludes that the final standards represent a reasonable balancing of all of these concerns, and are the maximum feasible average fuel economy levels that the manufacturers can achieve in MYs 2012-2016.

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## V. IMPACT OF OTHER MOTOR VEHICLE STANDARDS OF THE

troduction 

## I GOVERNMENT ON FUEL ECON OMY

In The Energy Policy and Conservation Act ( Act) requires that fuel economy standards be set a eria: (1)
tec ibility, (2) e impact of o t stan on fuel economy, and (4) n to con as a ne, this section discusses overnme (M 2-2016 passenger car and . Th inc the Volpe model at this on MY EPCA or the t the maximum feasible level after considering the following crit hnological feas conomic practicability, (3) the ther Governmen

dards the need of the Natio serve energy. Using MY 2008 baseli the effect s of other g nt regulations on model year Y) 201 light truck fuel economy ese effects have not been luded in time, which is based 2008 vehicles.

The Impact on Weight of Safety Standards and 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 vehi cle. The agency’s estimates are

ased on cost and weight tear-down studies of a few vehicles a nd cannot possibly cover all the variations in the manufacturers’ fleets. NH ted, and various manufacturers provided, confidential estimates in 2009 o safety improvements. Those incr ases are shown in subsequent ta

We wn our analysis of the i of safety standards that mig ct the MY 201 to three parts: 1) those N final rules with known effec ates, 2)

rop r soon to be proposed rule HTSA, without final effectiv tes, and 3) urrently voluntary safety improvements planned by the manufacturers. b TSA reques f increases in we ight resulting from

e bles.

have broken do mpact ht affe 2-16 fleets in osed rules o HTSA s by N tive d e da p

c

Weight Impacts of Required Safety Standards (Final Rules) The National Highway Traffic Safety Administration (NHTSA) has issued several safety standards that become effective for passenger cars and light trucks between MY 2009 and MY 2016. We will examine the potential impact on passenger car and light truck weights for MY

012-2016, using MY 2008 as a baseline. 2

1. FMVSS 126, Electronic Stability Control 2. FMVSS 206, Door Latches for Sliding Doors 3. FMVSS 20 8, 35 mph Belted Testing of 5 th 

### Female 4. FMVSS 214, Side Impact Oblique Pole Test 216, Roof Crush 5. FMVSS

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### FMVSS 126, Electronic Stability Control Th e phase-in schedule for vehicle manufacturers is:

Electronic Stability Control Effective Dates Phase-in Schedule Table IV-1

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 vehicles

The final rule requires all light vehicles to meet the ESC requirem ents by MY 2012. In nce was estimated as shown in Table IV-2. All

Tab co mparison, the MY 2008 voluntary complia light vehicles must meet the requirements by MY 2012.

le IV-2 untary Comp MY 2008 Vol liance Passenger Cars Lig ht Trucks AB S and ESC 36% 64% ABS alone 46% 35% No systems 18% 1%

Th 51 

### f .8 lbs.
pe 2 ry in 2 nt

wo h enger

ca s. (0.82

Th t fo line is 3.0 cars lbs.) for th

MVSS 206, Door locks

e agency’s analysis o r vehicle for a total of 1 stallation of ESC in M weight impacts found that ABS adds 10.7 lbs. and ESC adds 1 .5 lbs. Based on confidential manufacturers’ plans for volunta Y uld have ESC. Thus, t rs would be 9.42 lb 008, 82 percent of passenger cars would have ABS and 36 perce e MY 2008 weight added by the manufacturers’ plans for pass

*10.7 + 0.36*1.8).

r the period of MY 2012-2016 compared to the MY 2008 base e incremental weigh 8 lbs. for passenger e ESC requirements. (12.5 – 9.42 lbs) and 0.75 lbs. for light trucks (12.5 – 11.75

F

s were 13.2 percent of light truck sales in MY 2007, it is estimated that in MY 200 9, average light truck we ight would be increased for A new door lock test for sliding doors took effect in MY 2009. This test was expected to force those sliding doors that used a latch/pin mechanism to change to two latches to help keep sliding doors closed during crashes. The increase in weight is estimated to be 1.0 lbs. Several van models had two sliding doors. Out of 1.4 million MY 2003 vans an estimated 1.2 million door needed to be changed to the two latch system. Given that vans

by 0.11 lbs. for sliding door latches (1.2/1.4 million * 0.132 * 1 lb.). The incremental weight

51 “Final Regulatory Impact Analysis, FMVSS 126, Electronic Stability Control Systems”, March 2007, NHTSA, Docket No. 2007-27662-2.

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### each year of MY 2012-2016 compared to the MY 2008 baseline is 0 lbs. for passenger cars and 0.11 lbs. for light trucks for the sliding door latch requirements.

FMVSS 208, Occupant Crash Protection – 35 mph belted 50 th 

### percentile male and 5 th 

### percentile female testing The agency phased-in requirements for 35 mph belted testing with the 50 th 

### percentile male w 35 percent for MY 2008, 65 percent for MY 2009 ere , and 100 percent for MY 2010. The agency phased-in requirements for 35 mph belted testing with the 5 th 

### percentile female were 35 percent r MY 2011, and 100 percent for MY 2012. Several different for MY 2010, 65 percent fo technologies could be used to pass this test, bu t the agency’s analysis of these countermeasures showed no increase in weight was needed. Only one of the manufacturers’ confidential submissions showed a small weight increase for FMVSS 208.

FMVSS 214, Oblique Pole Side Impact Test The phase-in requirements for the side impact test are as shown below in Table IV-3:

Table IV-3 FMVSS 214 Final Rule Phase-In Schedule Phase-in Date Percent of each man ight vehicles that must comply ufacturer’s l duri ng the producti on period Septemb Aug er 1, 2010 to ust 31, 2011 20 p bs.) ercent (excluding vehicles GVWR > 8,500 l

September 1, 2011 to August 31, 2012 40 percent vehicles (excluding vehicles GVWR > 8,500 lbs.)

September 1, 201 2 to August 31, 2013 60 percent v ehicles (excluding vehicles GVWR > 8,500 lbs.)

September 1, 201 3 to August 31, 2014 80 percent v ehicles (excluding vehicles GVWR > 8,500 lbs.)

On or after September 1, 2014 All vehicles GVWR > 8,5 including limited line vehicles, except vehicles with 00 lbs., alterers, and multi-stage manufacturers On or after September 1, 2015 All vehicles, including vehicles with GVWR > 8,500 lbs., excluding alterers and multi-stage manufacturers On or after All vehicles, including vehicles wi th GVWR > 8,500 lbs., alterers stage manufacturers September 1, 2016 and multi-

A teardown study of five thorax air bags resulted in an average weight increase per vehicle of 4.77 pounds (2.17 kg). 52 

### A second study 53 

### performed teardowns of 5 window curtain systems. One of the window curtain systems was very 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 figure which is assumed to be average for all vehicles in the future.

52 Khadilkar, et al. “Teardown 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.
53 Ludtke & Associates, “Perform Cost and Weight Analysis, Head Protection Air Bag Systems, FMVSS 201”, page 4-3 to 4-5, DOT HS 809 842.

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### t of passenger cars and 85.4 percent of light

ucks and torso protection was projected at 92.1 percent of passenger cars and 50.1 percent of rmation indicates that on average the MY 2012 phase-in Based on MY 2008 Buying a Safer Car data supplied by the manufacturers, the projected number of side air bags with head protection was 98.5 perc en tr light trucks. Combined this info requirement would be already be met voluntarily in MY 2008 and that the weight increases for MY 2013 would be 0 for passenger cars and 0.47 lbs. for light trucks, MY 2014 would be 0 for passenger cars and 1.43 lbs. for light trucks, MY 2015 and MY 2016 would be 0.48 lbs. for passenger cars and 3.37 lbs. for light trucks.

FMVSS 216, Roof Crush On May 12, 2009, NHTSA issued a final rule amending the roof crush standard from 1.5 times the vehicle weight to 3.0 times the vehicle weight for passenger cars and light trucks of 6,000 lbs. GVWR or less. 54 

### Vehicles over 6,000 lbs. and less than 10,000 lbs. GVWR will be requir to meet the same test bu ed t at 1.5 times the vehicle weight. In the FRIA, the average passenger car nd light truck weight was estimated to increase weight by 7.9 to 15.4 lbs. The average weight -4 lbs.

-4.

Table IV-4 FMVSS 216 Final Rule Phase-In Schedule a of 11.65 lbs. will be used in later tables and will be multiplied by the percentages in Table IV to get incremental weights by model year (2.91 lbs. in MY 2013, 5.83 lbs. in MY 2014, 8.74 in MY 2015, and 11.65 lbs. in MY 2016). The final rule effective dates are shown in Table IV

Phase-in Date Percent of each m anufacturer’s light vehicles that must comply during the production period September 1, 2012 to August 31, 2013 25 percent

September 1, 2013 to August 3 50 percent 1, 2014 September 1, 2014 to 75 percent August 31, 2015 On or after All vehicles September 1, 2015

FMVS S 301 Fuel System Integrity t test procedure to a 50 mph offset test. The , 70 percent for MY 2008, and 100 percent r MY 2009. Thus, an incremental 30 percent of the fleet needs to meet the standard in omparison to the MY 2008 baseline. Several di ferent countermeasures could be used to meet the standard. Averaging the most likely two res ed 55 

### in an estimated 3.7 lbs. to passenger cars N HTSA issued a final rule changing the rear impac phase-in effective dates are 40 percent for MY 2007 fo

c f ult

54 Final Regulatory Impact Analysis, FMVSS 216 Upgrade Roof Crush Resistance, (Docket No. 2009-0093-4) (May 12, 2009) (74 FR 22347)
55 Improvements in the fuel filler neck and redesigning areas around the fuel tank shield, for example a deformed gusset plate punctured the fuel tank wall.

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### and light trucks. Assuming an incremental f the fleet for MY 2009 at 3.7 lbs., results in an increase of 1.11 lbs. for the average vehicle.

NPRM on Ejection M 30 percent o

itigation
56 

### The agency has published an NPRM on ejection mitigation . The likely result of the planned pro is for window curtai bags (li e used to meet the FM pol es) to b for a sor d. cy esti ere will incre und c pounds for light trucks (wh acc ut 50 f ligh third row of seats that need d). T effect s and the phase-in schedule are 20% in MY 2014 in MY 201 5% in resulting in weight incr of 0.34 lbs. in MY 2 68 lb. in M 5, and 1.2 MY 2016 enger car eight increases of 0. . in MY 2014 3 lb. in M , and 3.24 MY 201 ght trucks.

In a n, advanced glazing lternative anufactur t pursue fo fic win pplications for eject gation (po for fixed w for third r app r more broadly. nced glazin ely to hav ht implicat he age made an estim he likeliho advanced g might be r its

we ns.

NH on Pedestrian Protection posal n side air e larger and be a weight h takes int kely to b

ro llover sen

a se of 1.7 po ount that abo VSS 214 oblique Preliminary agen e test in all vehicl mates are that th to be installe s for passenger percent o ars and 4.32 t trucks have a ic o to be covere , 40% he proposed 5, and 7 ive date MY 2016,

eases 014, 0. 8 s Y 201 .7 8 lbs. in for pass s and w 6 for li 6 lb , 1 Y 2015 lbs. in

dditio is one a i i that m ers migh

s r speci dow a licatio on mit Adva ssibly g is lik indow e weig ow ions. T ns) o ncy has not ight implicatio ate of t od that glazin used o

TSA initiative The has started to anal costs and of a Gl hnical Reg on

edestrian protection. The effective dates have not been decided, however, it is possible that a ule on pedestrian protection could start to be phased in by the end of the period of this proposed rulemaking. Potential weight increases for pedestrian head and leg protection have not yet been identified.

Summary – Overview of Anticipated Weight Increases agency yze the benefits obal Tec ulation
p

r

Table IV-5 summarizes estimates made by NHTSA regarding the weight added by the above discussed standards or likely rulemakings. NHTS A estimates that weight additions required by final rules and likely NHTSA regulations effective by MY 2016, compared to the MY 2008 fleet, will increase passenger car weight by at least 17. 59 lbs. and light truck weight by at least 20.23 lbs.

Table IV-6 shows the distribution by model year.

56 74 FR 63180, December 2, 2009, (Docket No. NHTSA-2009-0183.0002.1)

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### Table IV-5


### Weight Additions Due to Final Ru les or Likely NHTSA Regulations Compa fleet ring MY 2016 to the MY 2008 Baseline



### Standard No. Added Weight in

pounds
P assenger Car Added Weight in k ilograms Passenger Car Adde d Weight in

pounds Light Trucks Added Weight in kilograms Light trucks

126 0.34 3.08 1.40 0.75
206 0.05 0 0 0.11
214 0.48 0.22 3.37 1.53
216 11.65 5.28 11.65 5.28
301 1.11 0.50 1.11 0.50 Ejection Mitigation 1.28 0.58 3.24 1.47



### Pedestrian Protection ? ? ? ?



### Total 17.59 7.98 20.23 9.18

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### Table IV-6



### Weight Additions by Model Year Due to Final Rules or Likely NHTSA Regulations Compared to a MY 2008 Baseline

Added Weight in

pounds Passenger Car A dded W eight in kil ograms Pa ssenger Car A dded W eight in

p ounds Light T rucks Added Weight in kilograms Light trucks

MY 2012 4.19 0.89 1.90 1.97
MY 2013 7.10 5.35 2.43 3.22
MY 2014 6 1 4.58 10.3 4.70 0.09
MY 2015 9 1 7.17 14.0 6.39 5.81
MY 2016 9 2 9.18 17.5 7.98 0.23

nomy al gorithms, a 3-4 pound increase in weight of 3.5 pounds increase in

s the results for final Based on NHTSA’s weight-versus-fuel-eco equates to a loss of 0.01 mpg in fuel economy. Assuming an average weight equates to a loss of 0.01 mpg in fuel economy, Table IV-7 show rules or likely future safety standards.

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### Table IV-7



### Estimated mpg Impact of Weight Additions by Model Year
Due to Fin gulations
C to Ba al Rules or Likely NHTSA Re ompared a MY 2008 seline

MPG Impact of

Added Weight

Passenger Car MPG Impact of

Added Weight

Light Trucks

MY 2012 0.012 0.006
MY 2013 0.020 0.015
MY 2014 0.030 0.029
MY 2015 0.040 0.045
MY 2016 0.050 0.058

CONFIDENTIAL SUBMISSIONS Weight Impacts of Potential Future Voluntary Safety Improvements

At the time the agency requested information 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 manufacturers provided confidential information in 2009 about plans they had to meet final rules, proposed safety st andards, or to voluntarily increase safety for the years 2012-2016. The plans are compared to a MY 2008 baseline fleet. The areas covered above and the regulatory areas described as final and proposed, and voluntary safety initiatives from manufacturers that have confidential increases for the period after MY 2008 are shown in the following tables. [

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### GM Estimates o Impact on mp

MY 2012 Y 2015 Table IV-8

f g

M 2013 MY 2 014 MY MY 20 16

Domestic P C

I m p o r t P C

T rucks

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### [ Table IV-9a Confidential Submissions of Weight Impacts compared to a Baseline of MY 2008

Ford
C ar MY Light Truc k MY

Final and Propose d2015201220132014201520162012201320142016



### 1 C



### 2

6 ES



### 2 th Female Belted 

### 08 5



### 2 e Impact



### 14 Sid 

### 2 Crush



### 16 Roof



### 2 n itigation 

### 26 Ejectio M 

### 3 el System



### 01 Fu

Total Final and Rule Increme Proposed nts

Voluntary and Other R u s le



### 20 2a Head Restra ints 

### T B ion 

### D Ped. Protect



### T B ility 

### D Compatib EDR part 563 

### N/ ther Voluntary



### A O

T ry an Rule Incremen otal Volunta d Othe ts r

Total by Year

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### [ Table IV-9b Confidential Submissions of Weight Impacts compared to a Bas 08

otors eline of MY 20

General M Car MY Light Truck MY

F inal and Proposed2012201320141520162020130141516201222020



### 126 ESC

2 B

08 5 th 

### Female e d

lte 2 S

14 ide Impact 2 R

16 oof Crush

2 E M 26 jection itigation 3 F

01 uel System

Tot P a l Final and

roposed Rule Increments Vol untary and Other Rules

202a H traints ead Re s T n

BD P ed. Protectio

TBD Compatibility

E DR part 563

N/A Voluntary O ther

Total V d Ot oluntary an her Rule Increments

r Total by Yea

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### [ Table IV-9c Confidential Submissions of Weight Impacts compared to a Baseline of MY 2008

Chrysler Car MY Light Truck MY

Final2016 

### and Proposed201220132014201520162012201320142015



### 126 E SC

208 5 B

th 

### Female elted 214 S ide Impact 216 Roof Crush

226 Ejection Mitigation 301 Fuel System Total Final and Proposed Rule

Increments Voluntary and Other Rules

202a Head Restraints TBD Ped. Protection

TBD Compatibility

EDR part 563

N/A Other Voluntary Total Voluntary and Other Rule Increments Total by YearDELI O NOT RELEASE 

### 85

Fue The ehicles and (MS GV s in M imp duri

new on fuel

BERATIVE DOCUMENT – D 

### l Economy Impacts of Government Emission Standards only program EPA has that has been finalize d but is not yet in-force for light-duty v MDPVs is the new cold hydrocarbon standard finalized under the Mobile Source Air Toxics AT) rule. For <6,000 lb. vehicles the standard begins in MY 2010. But for 6,000-8,500 lb. WR vehicles and for MDPVs, the standard has a phase-in that starts with MY 2012 and end

Y 2015. EPA estimated the new standard c ould have a small, but unquantified, impact on roving fuel consumption during cold start co nditions. However, in the temperature range ng which the CAFE test procedures are performed (68 - 86 deg. F), EPA does not believe the cold hydrocarbon standard will have any impact on fuel economy. Therefore, the impact economy is expected to be zero for both passenger cars for light trucks.

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### on do the agencies use, and why?

eased CO 2 

### standards.

,

turers to the

phasized the impact of th is non-harmonization on manufacturers who sell vehicles globally, and asked the agencies to consider these effects. NADA supported the

in

account

agencies to consider a multi-attribute approach incorporating curb weight, maximum engine power or torque, and/or engine displacement, as it had requested in the previous dual, Mr. Kenneth Johnson, commented that 

## V. FUEL ECONOMY ENHANCING TECHNOLOGIES AND THE VOLPE MODEL

A. What attribute and mathematical functi

In the NPRM, NHTSA and EPA proposed to set attribute-based CAFE and CO 2 

### standards that are defined by a mathemati cal function for MYs 2012-2016 passenger cars and light trucks. EPCA, as amended by EISA, expressly requires that CAFE standards for passenger cars and light trucks be based on one or more vehicle attributes related to fuel economy, and be expressed in the form of a mathematical function. 57 

### The CAA has no such requirement, though in past rules, EPA has relied on both universal and attribut based standards ( e.g. , for nonroad engines, EPA uses the attribute of horsepower). However, given the advantages of using attr ibute-based standards and given the goal of coordinating and harmonizing CO 2 

### standards promulgated under the CAA and CAFE standards promulgated under EPCA. EPA also proposed to issue standards that are attribute-based and defined by mathematical functions. There was consensus in the public comments that EPA should develop attribute-b

Comments received in response to the agenci es’ decision to base standards on vehicle footprint were largely supportive. Seve ral commenters (BMW, NADA, NESCAUM) expressed support for attribute-based (as opposed to flat or universal) standards generally and agreed with EPA’s decision to harmonize with NHTSA in this respect. Many commenters (Aluminum Association, BM W, ICCT, NESCAUM, NY DEC, Schade, Toyota) also supported the agencies’ decision to continue setting CAFE standards, and begin setting GHG standards, on the basis of vehicle footprint, although one commenter (NJ DEP) opposed the use of footprint due to concern that it encourages manufac upsize vehicles and undercut the gains of the standard. Of the commenters supporting use of footprint, several focused on the benefits of harmonization—both between EPA and NHTSA, and between the U.S. and the rest of the world. BMW commented, forexample, that many other countries use weight-based standards rather than footprintbased. While BMW did not object to NHTSA’s and EPA’s use of footprint-based standards, it em

use of footprint, but cautioned that the agencies must be careful in setting the footprint curve for light trucks to ensure that manufacturers can continue to provide functionality like 4WD and towing/hauling capacity.

Some commenters requested that the agencies consider other or more attributes addition to footprint, largely reitera ting comments submitted to the MYs 2011-2015 CAFE NPRM. Cummins supported the agencies using a secondary attribute to for towing and hauling capacity in large tr ucks, for example, while Ferrari asked the

round of CAFE rulemaking. An indivi

57 49 U.S .C. 32902(a)(3)(A).

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### does not necessa ily guarantee safety the way the agencies say it does, and because weight-based standards would be fairer to manufacturers.

In response, EPA and NHTSA continue to believ e that the benefits of footprint-attributeed standards outweigh any potential drawbacks raised by commenters, and that ization between the two agen cies should be the overriding goal on this issue. As ed by NHTSA in the MY 2011 CAFE final rule, 58 

### the agencies believe that the ossibi s, as oppo that inc pability, and/or off-road capability in additio ntly more complex, but by providing degrees e it less cer CO 2 

### le 2016 C

The ag manufacturers if

ere w harmonization of fuel ec onomy and GHG standards, but

attributes appropr iate for basing standards, depending on the particular challenges they face – from fuel prices, to family size and land use, to safety concerns, to fleet composition and consumer preference, to other environmental challenges besides climate change. Given differences in fleet composition, identical attribute-based standards would produce diff erent levels of compliance burden in different countries. The agencies anticipate working more closely with other countries and regions in the future to consider how to mitigate these issues in a way that least burdens manufacturers while respecting each co untry’s need to meet its own particular challenges.

Under an attribute-based standard, every ve hicle model has a performance target (fuel economy and CO 2 

### emissions for CAFE and CO 2 

### emissions standards, respectively), the level of which depends on the vehicle’s attr ibute (for this rule, footprint). The manufacturers’ fleet average performance is determined by the production-weighted 59 

### average (for CAFE, harmonic average) of those targets. NHTSA and EPA are promulgating CAFE and CO 2 

### emissions standards defined by constrained linear functions and, equivalently, piecewise linear functions. 60 

### As a possible option for future rulemakings, the constrained linear form was introduced by NHTSA in the 2007 NPRM weight-based standards would be preferable to footprint-based ones, because weight correlates better with fuel economy than foot print, because the use of footprint

r

bas h armon discuss
p lity of gaming attribute-based standards is lowest with footprint-based standard sed to weight-based or multi-attribut e-based standards. Specifically, standards

a orporate weight, torque, power, towing c n to footprint would not only be significa of freedom with respect to more ea sily-a djusted attributes, they would mak tain that the future fleet would ac tually achieve the average fuel economy an d vels projected by the agencies. The agencies are therefore finalizing MYs 2012- AFE GHG standards based on footprint. and

encies also recognize that there could be benefits for a number of as greater international th th is is largely a question of how stringent standards are and how they are enforced. It is entirely possible that footprint-based and weight-based systems can coexist internationally and not present an undue burden for manufacturers. Different countries or regions may find different

58 See 74 Fed. Reg. at 14359 (Mar. 30, 2009).
59 Production for sale in the United States.
60 The equations are equivalent but are specified differently due to differences in the agencies’ respective models.

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### proposing CAFE standards for MY 2011-2015. Described mathematically, the proposed constrained linear function was defined according to the following formula: 61



### 1 TAR



### where

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

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

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

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

Because the format is linear on a gallons-per-m ile basis, not on a miles-per-gallon basis, it is plotted as fuel consumption below. Gr aphically, the constrained linear form appears as shown in Figure V-1. 1 1

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

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

### 89700.0556065Figure V-1 The Shape of the Constrained Linear FormIMITUPPER

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### del

g 1 e form )

ng all

etter

In fittin the curves, NHTSA and EPA also continued to fit the sloped portion of the ued

e

e

afety conside ations that support the provision of a disincentive for downsizing as a

consistent with safety considerations.

nd

the footprint ranges covering most vehicle models, but that the single (as opposed to pie cewise) linear form does not perform well in The specific form and stringency for each fleet (passenger car and light trucks) and mo year are defined through specific values for the four coefficients shown above.

For purposes of the final rules, NHTSA and EPA developed the basic curve shapes usin methods similar to those applied by NHTSA in fitting the curves defining the MY 201 standards. The first step involved defining the relevant vehicle characteristics in th used by NHTSA’s CAFE model ( e.g. , fuel economy, footprint, vehicle class, technology described in Section II.B of the Preamble a nd in Chapter 1 of the joint TSD. However, because the baseline fleet utilizes a wide range of available fuel saving technologies, NHTSA used the CAFE model to develop a fleet to which all of the technologies discussed in Chapter 3 of the joint TSD 62 

### were applied, except dieselization and stro hybridization. This was accomplished by ta king the following steps: (1) treating manufacturers as unwilling to pay civil pena lties rather than applying technology, (2) applying any technology at any time, irrespec tive of scheduled vehicle redesigns or freshening, and (3) ignoring “phase-in caps” that constrain the overall amount of technology that can be applied by the model to a given manufacturer’s fleet. These steps helped to increase technological parity am ong vehicle models, thereby providing a b basis (than the baseline or reference fleets) for estimating the statistical relationship between vehicle size and fuel economy.

g function to vehicle models between the footprint values at which the agencies contin to apply constraints to limit the function’s value for both the smallest and largest vehicles. Without a limit at the smallest footprints, the function—whether logistic or linear—can reach values that would be unfa irly burdensome for a manufacturer that elects to focus on the market for small vehicles; depending on the underlying data, an unconstrained form, could result in stringency levels that are technologically infeasibl and/or economically impracticable for those manufacturers that may elect to focus on th smallest vehicles. On the other side of the function, without a limit at the largest footprints, the function may provide no floor on required fuel economy. Also, the s

r compliance strategy apply weakly, if at all, to the very largest vehicles. Limiting the function’s value for the largest vehicles leads to a function with an inherent absolute minimum level of performance, while remaining

Before fitting the sloped portion of the constrained linear form, NHTSA and EPA selected footprints above and below which to apply constraints (i.e., minimum a maximum values) on the function. The agencies believe that the linear form performs well in describing the observed relationship between footprint and fuel consumption or CO 2 

### emissions for vehicle models within

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

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### describing this relationship for the smallest and largest vehicle models. For passenger cars, the agency noted that several manufact urers offer small, sporty coupes below 41 square feet, such as the BMW Z4 and Mini , Honda S2000, Mazda MX-5 Miata, Porsche Carrera and 911, and Volkswagen New Beetle. Because such vehicles represent a small portion (less than 10 percent) of the passenger car market, yet often have performance, utility, and/or structural characteristics that could make it technologically infeasible and/or economically impracticable for manuf acturers focusing on such vehicles to achieve the very challenging average requirements that could apply in the absence of a constraint, EPA and NHTSA proposed to “cut off” the linear portion of the passenger car function at 41 square feet. For consistency, the agency proposed to do the same for the light truck function, although no light trucks ar e currently offered below 41 square feet. The agencies further noted that above 56 square feet, the only passenger car model present in the MY 2008 fleet were four luxury vehicles with extremely low sales volumes—the Bentley Arnage and three versions of the Rolls Royce Phantom. NHTSA and EPA therefore also proposed to “cut off” the linear portion of the passenger car function at 56 square feet. Finally, the agen cies noted that although public information is limited regarding the sales volumes of the many different configurations (cab designs and bed sizes) of pickup trucks, most of the largest pickups (e.g., the Ford F-150, GM Sierra/Silverado, Nissan Titan, and Toyota Tu ndra) appear to fall just above 66 square feet in footprint. EPA and NHTSA therefore proposed to “cut off” the linear portion of the light truck function at 66 square feet.

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

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

The result of this methodology is shown be low in Figures V-2 and V-3 for passenger cars and light trucks, respectively. The fitte d curves are shown with the underlying “maximum technology” passenger car and light truck fleets. For passenger cars, the mean absolute deviation of the sloped portion of the function was 14 percent. For trucks, the corresponding MAD was 10 percent.

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Fuel Consumption (gpm)Figure V-2 “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function0.060

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Fuel Consumption (gpm)0.060506080re V-3 imum Technology” Light Truck with Fitt Constra e ar Function7565 70ed in d Line55Footprint (sf)35 40 45Figu “Max

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### rd) light

ari

rt for the application of limits on the functions’ values, support for a generally less steep passenger car curve compared to MY 2011, and support for the supported thods

n , students from UC Santa Barbara commented that the passeng nd light truck curves should be flatter and should converge over time in

),

idual

to of

e

e point defining the smallest 10 percent of the fleet, both for consistency with the passenger car cut-off point, and to provide a greater incentive for manufacturers to downsize the

ger ssed The agencies used these functional forms as a starting point to develop mathematical functions defining the actual proposed standards as discussed above. The agencies then transposed these functions vertically (i.e., on a gpm or CO 2 

### basis, uniformly downwa to produce the same fleetwide fuel economy (and CO 2 

### emission levels) for cars and trucks described in the NPRM.

A number of public comments generally supp orted the agencies’ choice of attributebased mathematical functions, as well as the methods applied to fit the function. Ferr indicated support for the use of a constrained linear form rather than a constrained logistic form, suppo

inclusion of all manufacturers in the analysis used to fit the curves. ICCT also the use of a constrained linear form. Toyot a expressed general support for the me and outcome, including a less-steep passenger car curve, and the application of limits on fuel economy targets applicable to the smallest vehicles. The UAW commented that the shapes and levels of the curves are reasonable.

Other commenters suggested that changes to the agencies’ methods and results would yield better outcomes. GM suggested that steeper curves would provide a greater incentive for limited-line manufacturers to apply technology to smaller vehicles. GMargued that steeper and, in their view, fairer curves could be obtained by using salesweighted least-squares regression rather th an minimization of the unweighted mea absolute deviation. Conversely

er car a order to encourage the market to turn, as the agencies’ analysis assumes it will, away from light trucks and toward passenger cars.

NADA commented that there should be no “cut-o ff” points (i.e., lower limits or floors because these de facto “backstops” might limit consumer choice, especially for light trucks—a possibility also suggested by the Alliance. The Alliance and several indiv manufacturers also commented that the cut-off point for light trucks should be shifted 72 square feet (from the proposed 66 square feet), arguing that the preponderance high-volume light truck models with footprints gr eater than 66 square feet is such that a 72 square foot cut-off point makes it unduly challenging for manufacturers serving th large pickup market and thereby constitutes a de facto backstop. Also, with respect to the smallest light truck models, Honda commented th at the cut-off point should be set at th

smallest light truck models (which provide greater functionality than passenger cars).

Other commenters focused on whether the agencies should have separate curves for different fleets or whether they should have a single curve that applied to both passen cars and light trucks. This issue is related, to some extent, to commenters who discu whether car and truck definitions should change. CARB, Ford, and Toyota supported separate curves for cars and trucks, generally stating that different fleets have different

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### ing the current definitions of car and truck the same. CBD, ICCT, and NESCAUM supported a single

have made is decision considering that, while EISA mandates that CAFE standards be defined by a

r

a erati ons, as discussed below and in the Joint Techni cument.

ares

n the

l relationship between fuel economy and footprint, given comparable levels of technology, and this supports weighting discrete vehicle models equally. On the other hand, sales functional characteristics and these characteristics are appropriately addressed by separate curves. Likewise, AIAM, Chrysler, and NADA supported leav

curve, based on concerns about manufacturers gaming the system and reclassifying passenger cars as light trucks in order to obtain the often-less stringent light truck standard, which could lead to lower benefits than anticipated by the agencies.

In addition, the students from UC Santa Ba rbara reported being unable to reproduce the agencies’ analysis to fit curves to the passenger car and light truck fleets, even when using the model, inputs, and external analysis files posted to NHTSA’s web site when the NPRM was issued.

Having considered public comments, NHTSA and EPA have re-examined the development of curves underlying the standards proposed in the NPRM, and are promulgating standards based on the same unde rlying curves. The agencies th mathematical function in terms of one or more attributes related to fuel economy, neithe EISA nor the CAA require that the mathematical function be limited to the observed or theoretical dependence of fuel economy on the selected attribute or attributes. As a means by which CAFE and GHG standards are specified, the mathematical function can and does properly play a normative role. Therefore, NHTSA and EPA have concluded that, as supported by comments, the mathematical function can reasonably be based on blend of analytical and policy consid cal Support Do

With respect to GM’s recommendation that NHTSA and EPA use weighted least-squ analysis, the agencies find that the market forecast used for analysis supporting both the NPRM and the final rule exhibits the two key characteristics that previously led NHTSA to use minimization of the unweighted Mean Absolute Deviation (MAD) rather than weighted least-squares analysis. First, pr ojected model-specific sales volumes i agencies’ market forecast cover an extremely wide range, such that, as discussed in NHTSA’s rulemaking for MY 2011, while unweighted regression gives low-selling vehicle models and high-selling vehicle models equal emphasis, sales-weighted regression would give some vehicle models considerably more emphasis than other vehicle models. 63 

### The agencies’ intention is to fit a curve that describes a technica

63 For example, the agencies’ market forecast shows MY 2016 sales of 187,000 units for Toyota’s 2WD Sienna, and shows 27 model configurations with MY 2016 sales of fewer than 100 units. Similarly, th agencies’ market forecast shows MY 2016 sales of 268,000 for the Toyota Prius, and shows 29 model configurations with MY 2016 sales of fewer than 100 units. Sales-weighted analysis would give the Toyota Sienna and Prius more than a thousand times the consideration of many vehicle model configurations. Sales-weighted analysis would, therefore, cause a large number of vehicle model configurations to be virtually ignored. See discussion in NHTSA’s final rule for MY 2011 passenger ca and light truck CAFE standards, 74 FR 14368 (Mar. 30, 2009), and in NHTSA’s NPRM for that rulemaking, 73 FR 24423-2442 e

r

9 (May 2, 2008).

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### gical

is a

on,

energy savings and environmental benefits would be lower than projected, because the steeper curves would provide a greater

les.

the range of d

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

Second, even after NHTSA’s “maximum techno logy” analysis to increase technolo parity of vehicle models before fitting curv es, the agencies’ market forecast contains many significant outliers. As discussed in NHTSA’s rulemaking for MY 2011, MAD statistical procedure that has been demons trated to produce more efficient parameter estimates than least-squares analysis in the presence of significant outliers. 64 

### In additithe agencies remain concerned that the steeper curves resulting from weighted leastsquares analysis would increase the risk that

incentive to increase sales of larger vehicles with lower fuel economy levels. Based on these technical considerations and these concerns regarding potential outcomes, the agencies have decided not to re-fit curves using weighted least-squares analysis, but note that they may reconsider using least-s quares regression in future analysis.

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

NHTSA and EPA have also considered the comments by the students from UC Santa Barbara indicating that the passenger car and light truck curves should be flatter and should converge over time. The agencies co nclude that flatter curves would reduce the incentives intended in shifting from “flat” CAFE standards to attribute-based CAFE and GHG standards—those being the incentive to respond to attribute-based standards in ways that minimize compromises in vehicle safety, and the incentive for more manufacturers (than primarily those selling a wider range of vehicles) across the attribute to have to increase the applica tion of fuel-saving technologies. With regar to whether the agencies should set separate cu rves or a single one, NHTSA also notes th EPCA requires NHTSA to establish standard s separately for passenger cars and light trucks, and thus concludes that the standards for each fleet should be based on the

64 Id. In e case of a dataset not drawn from a sample with a Gaussian, or normal, distribution, there is

l

,

e

2, MIT Press, Cambridge, MA). th often a need to employ robust estimation methods rather than rely on least-squares approach to curve fitting. The least-squares approach has as an underlying assumption that the data are drawn from a norma 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 their distance from the fitted curve, and, hence, distort the resulting fit. With outliers in the sample, the typical solution is to use a robust method such as a minimum absolute deviation, rather than a squared term, to estimate the fit ( see , e.g., ‘‘AI Access: Your Access to Data Modeling,’’ at http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_O_Pa.htm#Outlier). Th effect on the estimation is to let the presence of each observation be felt more uniformly, resulting in a curve more representative of the data ( see , e.g., Peter Kennedy, A Guide to Econometrics , 3 rd edition, 199

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### d

cs

ends

d was more apparent when car-like WD SUVs were classified as light trucks, it seems likely to diminish for the

suggestion that there should be no such cut-off points, the agencies conclude that curves lacking maximum fuel economy targets (i.e., minimum CO 2 

### targets) would result in average fuel economy and GHG requirements that would not be technologically feasible or economically practicable for manufacturers concentrating on those market segments. In addition, minimum fuel economy targets (i.e., maximum CO 2 

### targets) are important to mitigate the risk to energy and environmental benefits of potential market shifts toward large vehicles. The agencies also disagree with comments by the Alliance and several individual manufacturers that the cut-off point for light trucks should be shifted to 72 square feet (from the proposed 66 square feet) to ease compliance burdens fa cing manufacturers serving the large pickup market. Such a shift would increase the risk that energy and environmental benefits of the standards would be compromised by induced increases in the sales of large pickups, in situations where the increased compliance burden is feasible and appropriate. Also, the agencies’ market forecast suggests that most of the light trucks models with footprints larger than 66 square feet have curb weights near or above 5,000 pounds. This suggests, in turn, that in terms of highway safety, there is little or no need to discourage downsizing of light trucks with footprints larger than 66 square feet. Based on these energy, environmental, technological feasibi lity, economic practicability, and safety considerations, the agencies conclude that the light truck curve should be cut off at 66 square feet, as proposed, rather than at 72 squa re feet. The agencies also disagree with Honda’s suggestion that the cut-off point for the smallest trucks be shifted to a larger footprint value, because doing so could potenti ally increase the incentive to reclassify vehicles in that size range as light trucks, and could thereby increase the possibility that energy and environmental benefits of the rule would be less than projected.

Finally, considering comments by the UC Santa Barbara students regarding difficulties reproducing NHTSA’s analysis, NHTSA reexamined its analysis, and discovered some erroneous entries in model inputs underlying the analysis used to develop the curves proposed in the NPRM. These errors are disc ussed in NHTSA’s final Regulatory Impact Analysis (FRIA) and have since been corrected. They include the following: incorrect valvetrain phasing and lift inputs for many BMW engines, incorrect indexing for some Daimler models, incorrectly enabled valvetra in technologies for rotary engines and Atkinson cycle engines, omitted baseline appl ications of cylinder deactivation in some characteristics of vehicles in each fleet. In other words, the passenger car curve shoul be based on the characteristics of passenger cars, and the light truck curve should be based on the characteristics of light trucks—thus to the extent that those characteristi are different, an artificially-forced conve rgence would not accurately reflect those differences. However, such convergence could be appropriate depending on future tr in the light vehicle market, specifically further reduction in the differences between passenger car and light truck characteristics. While that tren 2model year vehicles subject to these rules as the truck fleet will be more purely “trucklike” than has been the case in recent years.

NHTSA and EPA have also considered comments on the maxima and minima that the agencies have applied to “cut off” the linear function underlying the proposed curves for passenger cars and light trucks. Contrary to NADA’s

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### Honda nd GM engines, incorrect valve phasing codes for some 4-cylinder Chrysler engines, omitted baseline applications of advanced transmissions in some VW models, incorrectly enabled advanced electrification te chnologies for several hybrid vehicle models, and incorrect DCT effectiveness estimates for subcompact passenger cars. These errors, while not significant enough to impact the overall analysis of stringency, did affect the fitted slope for the passenger car curve and would have prevented precise replication of NHTSA’s NPRM analysis by outside parties.

After correcting these errors and repeating the curve development analysis presented the NPRM, NHTSA obtained the curves shown below in Figures V-4 and V-5 for passenger cars and light trucks, respectively. The fitted curves are shown with the underlying “maximum technology ” passenger car and light truck fleets. For passeng cars, the mean absolute deviation of the sloped portion of the function was 14 percen For trucks, the corresponding MAD was 10 percent. a

in

er t.

### 990.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.0550.06035 40 45 50 55 60 65 70 75 80Footprint (sf)

Fuel Consumption (gpm)Figure V-4 Revised “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function

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### 1000.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.0550.06035 40 45 50 55 60 65 7075 80FunctionFuelConsumption(gpm)Footprint (sf)Figure V-5 Revised “Maximum Technology” Light Truck with Fitted Constrained Linear

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### This refitted passenger car curve is similar to that presented in the NPRM, and the refitted light truck curve is nearly identical the corresponding curve in the NPRM. However, the slope of the refitted passenger car curve is about 27 percent steeper (on a gpm per sf basis) than the curve presented in the NPRM. For passenger cars and light trucks, respectively, Figures V-6 and V-7 show the results of adjustment—discussed in the t section—of the above curves to yield the average required fuel economy levels corresponding to the final standards. nex

### 10220253035404535 40 45 50 55 60 65 70 75 80Target(mpg)Footprint (sf)NPRMFinalRe-FittedFigure V-6 MY 2016 Passenger Car Targets: NPRM, Final Rule, and if Using Re-Fitted Curve

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### 10375 80

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### se

lso, the agencies note that the refitted passenger car curve produces only a slightly

rt the

.

e ions may merit updating or vision in future analysis to support CAFE and CO 2 

### standards or for other purposes.

functions in the context of future rulemakings.

prices could

is is often described as a While the resultant light truck curves are visually indistinguishable from one another, the refitted curve for passenger cars would increas e stringency for the smallest cars, decrea stringency for the largest cars, and provide a greater incentive to increase vehicle size throughout the range of footprints within wh ich NHTSA and EPA project most passenger car models will be sold through MY 2016. The agencies are concerned that these changes would make it unduly difficult for manufacturers to introduce new small passenger cars in the United States, and undul y risk losses in energy and environmental benefits by increasing incentives for the passenger car market to shift toward larger vehicles.

A closer fit to the corrected fleet than would the curve estimated in the NPRM; with respect to the corrected fleet (between the “cut off” footprint values, and after the “maximum technology” analysis discussed above), the mean absolute deviation for the refitted curve is 13.887 percent, and that of a refitted curve held to the original slope is 13.933 percent. In other words, the data support the original slope very nearly as well as they suppo refitted slope.

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

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

Thus, in summary, the agencies’ approach to developing the attribute-based mathematical functions for MY 2012-2016 CAFE and CO 2 

### standards represents the agencies’ best technical judgment and consider ation of potential outcomes at this time, and we are confident that the conclusions have resulted in appropriate and reasonable standards. Th agencies recognize, however, that aspects of these decis

re Consistent with best rulemaking practices, the agencies will take a fresh look at all assumptions and approaches to curve fitting, appropriate attributes, and mathematical

The agencies also recognized in the NPRM the possibility that lower fuel lead to lower fleetwide fuel economy (and higher CO 2 

### emissions) than projected in this rule. One way of addressing that concern is through the use of a universal standard—that is, an average standard set at a (single) absolute level. Th

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### , but

e

nt

beyond

ssociation, alifornia Air Resources Board (CARB), Envi ronment America, Environment Defense

private citizens. Commenters in favor of additional backstop standards for all fleets for
65



### ize, and 2) lower enger cars encourage manufacturers to find ways to

rers

r “backstop standard.” The agencies explained that under the CAFE program, EISA requires such a minimum average fuel economy standard for domestic passenger cars is silent with regard to similar backstops for imported passenger cars and light trucks, while under the CAA, a backstop could be adopted under section 202(a) assuming it could be justified under the relevant statutor y criteria. NHTSA and EPA also noted that the flattened portions of the curves at the largest footprints directionally address the issu of a backstop (i.e., the mpg “floor” or gpm “ceiling” applied to the curves provides a universal and absolute value for that range of footprints). The agencies sought comme on whether backstop standards, or any othe r method within the agencies’ statutory authority, should and can be implemented in order to guarantee a level of CO 2 

### emissions reductions and fuel savings under the attribute-based standards.

The agencies received a number of comments regarding the need for a backstop NHTSA’s alternative minimum standard. Comme nts were divided fairly evenly between support for and opposition to additional backstop standards. The following organizations supported the need for EPA and NHTSA to ha ve explicit backstop standards: American Council for an Energy Effi cient Economy (ACEEE), American Lung A C Fund, Massachusetts Department of Envi ronmental Protection, Natural Resources Defense Council (NRDC), Northeast States for Coordinated Air Use Management (NESCAUM), Public Citizen and Safe C limate Campaign, Sierra Club, State of Washington Department of Ecology, Union of Concerned Scientists, and a number of

both NHTSA and EPA generally stated that the emissions reductions and fuel savings expected to be achieved by MY 2016 depended on assumptions about fleet mix that might not come to pass, and that various kinds of backstop standards or “ratchet mechanisms” 66 

### were necessary to ensure that those re ductions were achieved in fact. In addition, some commenters 67 

### stated that manufacturers might build larger vehicles or more trucks during MYs 2012-2016 than the agen cies project, for example, because 1) s encourages manufacturers to ups any amount of slope in target curve

ass targets for light trucks than for p reclassify vehicles as light trucks, such as by dropping 2WD versions of SUVs and offering only 4WD versions, perhaps spurre d by NHTSA’s reclassification of 2WD SUVs as passenger cars. Both of these mechanisms will be addressed further below. Some commenters also discussed EPA authority under the CAA to set backstops, 68 

### agreeing with EPA’s analysis that section 202 (a) allows such standards since EPA has wide discretion under that section to craft standards.

The following organizations opposed a backstop : Alliance of Automobile Manufactu (AAM), Association of International Automobile Manufacturers (AIAM), Ford Moto

65 ACEEE, American Lung Association, CARB, Christopher Lish, Environment America, EDF, MA DEP, NRDC, NESCAUM, Public Citizen, Sierra Club et al. , SCAQMD, UCS, WA DE
66 Commen ters generally defined a “ratchet mechanism” as an automatic re-calculation of stringency to

e ensure cumulative goals are reached by 2016, even if emissions reductions and fuel savings fall short in th earlier years covered by the rulemaking.
67 CBD, MA DEP, NJ DEP, Public Citizen, Sierra Club et al. , UCS
68 CARB, Public Citizen, Sierra Club et al.

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### ps

ess’ silence regarding backstops r imported passenger cars and light trucks shou ld be construed as a lack of authority for

ia, dard

ld

eet Company, National Automobile Dealers A ssociation (NADA), Toyota Motor Company, and the United Auto Workers Union. Commenters stating that additional backsto would not be necessary disagreed that upsizing was likely, 69 

### and emphasized the antibacksliding characteristics of the target curves. Others argued that universal absolute standards as backstops could restrict consum er choice of vehicles. Commenters making legal arguments under EPCA/EISA 70 

### stated that Congr fo NHTSA to create further backstops. Commenters making legal arguments under the CAA 71 

### focused on the lack of clear authority under the CAA to create multiple GHG emissions standards for the same fleets of ve hicles based on the same statutory criter and opposed EPA taking steps that would redu ce harmonization with NHTSA in stan setting. Furthermore, AIAM indicated that EISA’s requirement that the combined (car and truck) fuel economy level reach at least 35 mpg by 2020 itself constitutes a backstop. 72 

### One individual 73 

### commented that while additional backstop standards might be necessary given optimism of fleet mix a ssumptions, both agencies’ authorities wou probably need to be revised by Congress to cl arify that backstop standards (whether for individual fleets or for the national fleet as a whole) were permissible.

In response, EPA and NHTSA remain confident that their projections of the future fl mix are reliable, and that future changes in the fleet mix of footprints and sales are not likely to lead to more than modest changes in projected emissions reductions or fuel savings. 74 

### Both agencies thus remain confident in these fleet projections and the

69 For example, the Alliance and Toyota said that upsizing would not be likely because (1) it would not necessarily make compliance with applicable standards easier, since larger vehicles tend to be heavier and heavier vehicles tend to achieve worse fuel economy/emissions levels; (2) it may require expensive platform changes; (3) target curves become increasingly more stringent from year to year, which redu the benefits of upsizing; and (4) the mpg floor and gpm ceiling for the largest vehicles (the point at which the curve is “cut off”) discourages manufacturers from continuing to upsize beyond a point because doing so makes it increasingly difficult to meet the flat standard at that part of the curve.
70 AIAM, Alliance, Ford, NADA, Toyota
71 Alliance, Ford, NADA, UAW
72 NHTSA and EPA agree with AIAM that the EISA 35 mpg requirement in MY 2020 has a backstop-like function, in that it requires a certain level of achieved fleetwide fuel economy by a certain date, although it is not literally a backstop standard. Considering that NHTSA’s MY 2011 CAFE standards increased projected average fuel economy requirements (relative to the MY 2010 standards) at a significantly faster rate than would be required to achieve the 35-in-2020 requirement, and considering that the standards being finalized today would increase projected average combined fuel economy requirements to 34.1 mpg in MY 2016, four years before MY 2020, the agencies believe that the U.S. vehicle market would have to shift in highly unexpected ways in order to put the 35-in-2020 requirement at risk, even despite the fact that due to the attribute-based standards, average fuel economy requirements will vary depending on the mix of vehicles produced for sale in the U.S. in each model year. The agencies further emphasize that both NHTSA and EPA plan to conduct and document retrospective analyses to evaluate how the market’s ces

evolution during the rulemaking timeframe compares with the agencies’ forecasts employed for this rulemaking. Additionally, we emphasize that both agencies have the authority, given sufficient lead time,

e 45.6 square feet (sf) and 55.1 sf, respectively, such that , respectively, for passenger to revise their standards upwards if necessary to avoid missing the 35-in-2020 requirement.
73 Schade
74 For reference, NHTSA’s March 2009 final rule establishing MY 2011 CAFE standards was based on a forecast that passenger cars would represent 57.6 percent of the MY 2011 fleet, and that MY 2011 passenger cars and light trucks would averag average required CAFE levels would be 30.2 mpg, 24.1 mpg, and 27.3 mpg

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### the

sistent

the
g ssenger between the two flattened portions of the corresponding curve, the actual slope of fuel economy with respect to footprint, if fit to that data by

t the same time, adding another backstop standard would have virtually no effect if the

ore y

r

for

ile. resulting emissions reductions and fuel savi ngs from the standards. As explained in Section II.B of the Preamble, the agencies’ pr ojections of the future fleet are based on most transparent information currently available to the agencies. In addition, there are only a relatively few model years at issue. Mo reover, market trends today are con with the agencies’ estimates, showing shifts from light trucks to passenger cars and increased emphasis on fuel ec onomy from all vehicles.

Finally, the shapes of the curves, including the “flattening” at the largest footprint values, tend to avoid or minimize regulatory incentive s for manufacturers to upsize their fleet to change their compliance burden. Given the way the curves are fit to the data points (which represent vehicle m odels’ fuel economy mapped against their footprint), agencies believe that there is little real benefit to be gained by a manufacturer upsizin their vehicles. As discussed above, the agencies’ analysis indicates that, for pa car models with footprints falling

itself, is about 27 percent steeper than the curve the agencies are promulgating today. This difference suggests that manufacturers would, if anything, have more to gain by reducing vehicle footprint than by increasing vehicle footprint. For light trucks, the agencies’ analysis indicates that, for models with footprints falling between the two flatted portions of the corresponding curve, the slope of fuel economy with respect to footprint is nearly identical to the curve the agencies are promulgating today. This suggests that, within this range, manufacturers would typically have little incentive to either incrementally increase or reduce vehicle footprint.

A standard was weak, but a more stringent backstop could compromise the objectives served by attribute-based standards – that they distribute compliance burdens m equally among manufacturers, and at the sa me time encourage manufacturers to appl fuel-saving technologies rather than simply downsizing their vehicles, as they did in past decades under flat standards. This is why Congress mandated attribute-based CAFE standards in EISA. This compromise in objectives could occur for any manufacture whose fleet average was above the backstop, irrespective of why they were above the backstop and irrespective of whether the indus try as a whole was ac hieving the emissions and fuel economy benefits projected for the fi nal standards, the problem the backstop is supposed to address. For example, the proj ected industry wide leve l of 250 gm/mile MY 2016 is based on a mix of manufacturer levels, ranging from approximately 205 to 315 gram/mile 75 

### but resulting in an industry wide ba sis in a fleet average of 250 gm/m Unless the backstop was at a very weak level, above the high end of this range, then some percentage of manufacturers would be above the backstop even if the performance of the entire industry remains fully consistent with the emissions and fuel economy levels

cars, light trucks, and the overall light-duty fleet. Based on the agencies’ current market forecast, even as soon as MY 2011, passenger cars will comprise a larger share (59.2 percent) of the light vehicle market; passenger cars and light trucks will, on average, be smaller by 0.5 sf and 1.3 sf, respectively; and average

ndards presented in tables III.B.1-1 and III.B.1-2. required CAFE levels will be higher by 0.2 mpg, 0.3 mpg, and 0.3 mpg, respectively, for passenger cars, light trucks, and the overall light-duty fleet.
75 Based on estimated sta

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### ers be

pose

to be imposed on manufacturers only when there is a failure to achieve the projected reductions across the

about

e new

Given r of model years covered by this rulemaking and the short lead-time lready before the 2012 model year, a ratchet mechanism in this rulemaking that would utomatically tighten the standards at some point after model year 2012 is finished and would fail to provide equate lead time for any new, more stringent standards

be more expensive and, other ings being equal, have inherently lower fuel economy than their 2WD equivalent models ere might shift fro les if 4WD becomes available at little or no
extra c an

concern ou many manufac models. Additi rm changes, which lly r urs on average once every 5 years for most models). Alternatively, turning many 2WD SUVs

r projected for the final standards. For these manufacturers and any other manufactur who were above the backstop, the objectives of an attribute based standard would compromised and unnecessary costs would be imposed. This could directionally im increased costs for some manufacturers. It would be difficult if not impossible to establish the level of a backstop standard such that costs are likely

industry as a whole. An example of this kind of industry wide situation could be when there is a significant shift to larger vehicles across the industry as a whole, or if there is a general market shift from cars to trucks. The problem the agencies are concerned in those circumstances is not with respect to any single manufacturer, but rather is based on concerns over shifts across the fleet as a whole, as compared to shifts in one manufacturer's fleet that may be more than offset by shifts the other way in another manufacturer's fleet. However, in this respect, a traditional backstop acts as a manufacturer specific standard.

The concept of a ratchet mechanism recogni zes this problem, and would impose th more stringent standard only when the problem arises across the industry as a whole. While the new more stringent standards would enter into force automatically, any such standards would still need to provide adequate lead time for the manufacturers. the limited numbe

a

a apply the new more stringent standards fo r model years 2016 or earlier, ad

Additionally, we do not believe that the ri sk of vehicle upsizing or changing vehicle offerings to “game” the passenger car and light truck definitions is as great as commenters imply for the model years in question. 76 

### The changes that commenters suggest manufacturers might make are neither so simple nor so likely to be accepted by consumers. For example, 4WD versions of vehicles tend to th . Th fore, although there is a market for 4WD vehicles, and some consumers m 2WD vehicles to 4WD vehic ost, m y consumers still may not desire to purchase 4WD vehicles because of

s ab t cost premium and additiona l maintenance requirements; conversely, turers often require the 2WD opt ion to satisfy demand for base vehicle onally, increasing the footprint of vehicles requires platfo usua equires a product redesign phase (the agencies estimate that this occ

into 2WD light trucks would require manufacturers to squeeze a third row of seats in o significantly increase their GVWR, which also requires a significant change in the

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

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### t lead time is provided.

MY 2016 fleet emissions and fuel economy goals of 250 g/mi and 34.1 mpg for EPA and NHTSA resp ectively are estimates and not standards

economy and emissions goals exists, but is not likely Given this, and given the potential

re ards

s,

developing today’s final CAFE standards, NHTSA has made significant use of results produc th CAFE nsportation System nte he model, which CAFE standar

(1) estimating how manufacturers could apply technologies in response to new fuel economy standards,

, vehicle. 77 

### The agencies are confident that the anticipated increases in average fuel economy and reductions in average CO 2 

### emission rates can be achieved without backstops under EISA or the CAA. As noted above, the agencies plan to conduct retrospective analysis to monitor progress. Both agencies have the authority to revise standards if warranted, as long as sufficien

The agencies acknowledge that the

(the MY 2012-2016 curves are the standards). Changes in fuel prices, consumer preferences, and/or vehicle survival and mile age accumulation rates could result in either smaller or larger oil and GHG savings. As explained above and elsewhere in the rule, the agencies believe that the possibility of not meeting (or, alternatively, exceeding) fuel

complexities in designing an appropriate backstop, the agencies believe the balance he points to not adopting additional backstops at this time for the MYs 2012-2016 stand other than NHTSA’s finalizing of the on es required by EPCA/EISA for domestic passenger cars. If, during the timeframe of this rule, the agencies observe a significant shift in the manufacturer’s product mix resultin g in a relaxation of their estimated target NHTSA and EPA will reconsider options, both for MYs 2012-2016 and future rulemakings.

B. How does NHTSA use the assumptions in its modeling analysis?

In ed by e CAFE Compliance and Effects Model (commonly referred to as “the model” or “the Volpe model”), which DOT’s Volpe National Tra s Ce r developed specifically to support NHTSA’s CAFE rulemakings. T has been constructed specifica lly for the purpose of analyzing potential ds, integrates the following core capabilities:

(2) estimating the costs that would be incurred in applying these technologies (3) estimating the physical effects resulting from the application of these technologies, such as changes in tr avel demand, fuel consumption, and emissions of carbon dioxide and criteria pollutants, and (4) estimating the monetized societal benefits of these physical effects.

An overview of the model follows below. Separate model documentation provides a detailed explanation of the functions the mode l performs, the calculations it performs in

77 Increasing the GVWR of a light truck (assuming this was the only goal) can be accomplished in a number of ways, and must include consideration of: (1) redesign of wheel axles; (2) improving the vehicle suspension; (3) changes in tire specification (which will likely affect ride quality); (4) vehicle dynamics development (especially with vehicles equipped with electronic stability control); and (5) brake redesig Depending on the vehicle, some of these changes may be easier or more difficult than others. n.

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### doing s , and how to install the model, construct inputs to the model, and interpret the

ource on is

m

perate?

IV.C.3 of the Preamble and Chapter 4 of the ) inputs defining baseli ne and potential new CAFE standards. For

s the

technology. o model’s outputs. Documentation of the model, along with model installation files, s code, and sample inputs are available at NHTSA’s web site. The model documentati also available in the docket for today’s final rule, as are inputs for and outputs fro analysis of today’s final CAFE standards.

1. How Does the Model O

As discussed above, the agency uses the Volpe model to estimate how manufacturers could attempt to comply with a given CAFE standard by adding technology to fleets that the agency anticipates they will produce in future model years. This exercise constitutes a simulation of manufacturers’ decisions regarding compliance with CAFE standards.

This compliance simulation begins with the following inputs: (a) the baseline market forecast discussed in Section IV.C.1 of the Preamble and Chapter 1 of the TSD, (b) technology-related estimates discussed in Sect ion IV.C.2 of the Preamble and Chapter 3 of the TSD, (c) economic inputs discussed in Section TSD, and (d each manufacturer, the model applies technologies in a sequence that follows a defined engineering logic (“decision trees” discussed in the MY 2011 final rule and in the model documentation) and a cost-minim izing strategy in order identify a set of technologie manufacturer could apply in response to new CAFE standards. The model applies technologies to each of the projected individu al vehicles in a manufacturer’s fleet, until one of three things occurs:

(1) the manufacturer’s fleet achieves compliance with the applicable standard; (2) the manufacturer “exhausts” 78 

### available technologies; or (3) for manufacturers estimated to be willing to pay civil penalties, the manufacturer reaches the point at wh ich doing so would be more costeffective (from the manufacturer’s perspective) than adding further
79

78 In a given model year, the model makes additional technologies available to each vehicle model within several constraints, including (a) whether or not the technology is applicable to the vehicle model’s technology class, (b) whether the vehicle is undergoing a redesign or freshening in the given model year, (c) whether engineering aspects of the vehicle make the technology unavailable ( e.g. , secondary axle

an ce enough technology is added to a given manufacturer’s fleet in a

udes the possibility of he agency believes is disconnect cannot be applied to two-wheel drive vehicles), and (d) whether technology application remains within “phase in caps” constraining the overall share of a manufacturer’s fleet to which the technology c be added in a given model year. On given model year that these constraints make further technology application unavailable, technologies are “exhausted” for that manufacturer in that model year.
79 This possibility was added to the model to account for the fact that under EPCA/EISA, manufacturers must pay fines if they do not achieve compliance with applicable CAFE standards. 49 U.S.C. 32912(b). NHTSA recognizes that some manufacturers will find it more cost-effective to pay fines than to achieve compliance, and believes that to assume these manufacturers would exhaust available technologies before paying fines would cause unrealistically high estimates of market penetration of expensive technologies such as diesel engines and strong hybrid electric vehicles, as well as correspondingly inflated estimates of both the costs and benefits of any potential CAFE standards. NHTSA thus incl manufacturers choosing to pay fines in its modeling analysis in order to achieve what t

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### technologies between model years. CAFE model accoun ts explicitly for each model year because
ye ar determination of the appropriate level of strin ency and then set the standard at that level, while ensuring ratable increases in

s applying .C.2 of the Preamble, and by accounting for the number of affected vehicles. It accounts for effects such as changes in vehicle teria

on

ying

s

evelopment of a CAFEstandar . It can be used to fit a mathematical function forming the basis for an attributebased C As discussed below, the model has also been modified in order to apply additional technology in early model years if doing so will facilitate compliance in later model years. This is designed to simulate a manufacturer’s decision to plan for CAFE obligations several years in advance, which NHTSA believes better replicates manufacturers’ actual behavior as compared to the year-by-year evaluation which EPCA would otherwise require.

The model accounts explicitly for each model year, applying most technologies when vehicles are scheduled to be redesigned or freshened, and carrying forward TheEPCA requires that NHTSA make a year-byg average fuel economy. 80 

### The multi-year planning capa bility mentioned above increases the model’s ability to simulate manufacturer s’ real-world behavior, accounting for the fact that manufacturers will seek out compliance paths for several model years at a time, while accommodating the year-by-year requirement.

The model also calculates the costs, effects, and benefits of technologies that it estimate could be added in response to a given CAFE standard. 81 

### It calculates costs by the cost estimation techniques discussed in Section IV

travel, changes in fuel consumption, and changes in greenhouse gas and cri pollutant emissions. It does so by applying the fuel consumption estimation techniques also discussed in Section IV.C.2 of the Pr eamble, and the vehicle survival and mileage accumulation forecasts, the rebound effect es timate and the fuel properties and emissi factors discussed in Section IV.C.3 of the Preamble. Considering changes in travel demand and fuel consumption, the model es timates the monetized value of accompan benefits to society, as discussed in Section IV.C.3 of the Preamble. The model calculate both the undiscounted and discounted value of benefits that accrue over time in the future.

The Volpe model has other capabilities that facilitate the d d AFE standard, following the steps described below. It can also be used to

bilities. 20.
81 As for all of its other rulemakings, NHTSA is required by Executive Order 12866 and DOT regulations to analyze the costs and benefits of CAFE standards. Executive Order 12866, 58 FR 51735 (Oct. 4, 1993); olicies and Procedures,” 1979, available at gov/rulemakingrequirements.htm (last accessed February 21, 2010). a more realistic simulation of manufacturer decision-making. Unlike flex-fuel and other credits, NHTSA is not barred by statute from considering fine-payment in determining maximum feasible standards under EPCA/EISA. 49 U.S.C. 32902(h).
80 49 U.S.C. 32902(a) states that at least 18 months before the beginning of each model year, the Secretary of Transportation shall prescribe by regulation average fuel economy standards for automobiles manufactured by a manufacturer in that model year, and that each standard shall be the maximum feasible average fuel economy level that the Secretary decides the manufacturers can achieve in that year. NHTSA has long interpreted this statutory language to require year-by-year assessment of manufacturer capa 49 U.S.C. 32902(b)(2)(C) also requires that standards increase ratably between MY 2011 and MY 20

DOT Order 2100.5, “Regulatory P http://regs.dot.

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### d

stringe which net benefits to society are maximized, the stringency at which a specifie

nt

re ecified probabil ity distributions, such that the uncertainty of key ures ( e.g. , fuel consumption, costs, benefits) can be evaluated.

uld final

ility to

e Volpe t and evaluation of potential CAFE standar s. Also, although some observers have criticized analyses the agency has

ficiently transparent. However, in NHTSA’s view, the complexity of the Volpe model has evolved in response

model transparency relate to the use of confidential business information, not to the as discussed below in Section V.B.5, NHTSA and the Volpe Center evaluate many ( e.g. , 200 per model year) potential leve ls of stringency sequentially, an identify the stringency at which specific criteria are met. For example, it can identify the ncy at

d total cost is reached, or the stri ngency at which a given average required fuel economy level is attained. This allows the agency to compare more easily the impacts in terms of fuel savings, emissions reductions, and costs and benefits of achieving differe levels of stringency according to different criteria. The model can also be used to perform uncertainty analysis ( i.e. , Monte Carlo simulation), in which input estimates a varied randomly according to sp

meas

2. Has NHTSA Considered Other Models?

Nothing in EPCA requires NHTSA to use the Volpe model. In principle, NHTSA co perform all of these tasks through other means. For example, in developing today’s standards, the agency did not use the Volpe model’s curve fitting routines; rather, as discussed in Section II of the Preamble, the agency fitted curves outside the model (as for the NPRM) but elected to retain the curve shapes defining the proposed standards. In general, though, these model capabilities have greatly increased the agency’s ab rapidly, systematically, a nd reproducibly conduct key analyses relevant to the formulation and evaluation of new CAFE standards.

During its previous rulemaking, which led to the final MY 2011 standards promulgated earlier this year, NHTSA received comments from the Alliance and CARB encouraging NHTSA to examine the usefulness of other models. As discussed in that final rule, NHTSA, having undertaken such consideration, concluded that th model is a sound and reliable tool for the de velopmen
82 

### d conducted using the Volpe model, those criticisms have largely concerned inputs to the model (such as fuel prices and the estimated economic cost of CO 2 

### emissions), not the model itself. In comments on the NPRM preceding today’s final rule, one of these observers, the Center for Biological Diversity (CBD), suggested that the revisions to such inputs have produced an unbiased cost-benefit analysis. 83



### One commenter, the International Council on Clean Transportation (ICCT) suggested that the Volpe model is excessively complex and insuf

to the complex analytical demands surrounding very significant regulations impacting a large and important sector of the economy, and ICCT’s own comments illustrate some of the potential pitfalls of mode l simplification. Furthermore, ICCT’s assertions regarding

Volpe model itself; have taken pains to make the Volpe model transparent by releasing the model and

82 74 FR 14372 (Mar. 30, 2009).
83 CBD, p. 2.

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### comments.

el

PCA, NHTSA has traditionally assessed the ann al costs and benefits of the standards. The first (2002) version of DOT’s model

technology in MY 2007. GM (and other manufacturers) argued that this completely failed to represent

allowed

e recent rulemaking for MY 2011 passenger cars and light trucks, DOT further modified the CAFE model to account for cost re ductions attributable to “learning effects” supporting documentation, along with the underlying source code and accompanying model inputs and outputs. Therefore, the agen cy disagrees with these ICCT

In reconsidering and reaffirming this conclusion for purposes of this rule, NHTSA notes that the Volpe mo del not only has been formally peer-reviewed and tested through three rulemakings, but also has some features especially important for the analysis of CAFE standards under EPCA/EISA. Among these are the ability to perform year-byyear analysis, and the ability to account for engineering differences between specific vehicle models.

EPCA requires that NHTSA set CAFE stan dards for each model year at the level that would be “maximum feasible” for that year. 84 

### Doing so requires the ability to analyze each model year and, when developing regulations covering multiple mod years, to account for the interdependency of model years in terms of the appropriate levels of stringency for each one. 85 

### Also, as part of the evaluation of the economic practicability of the standards, as required by E

u treated each model year separate ly, and did not perform this type of explicit accounting. Manufacturers took strong exception to these shortcomings. For example, GM commented in 2002 that “although the table suggests that the proposed standard for MY 2007, considered in isolation, promises benefits exceeding costs, that anomalous outcome is merely an artifact of the peculiar Volpe methodology, which treats each year independently of any other...” In 2002, GM also criticized DOT’s analysis for, in some cases, adding a technology in MY 2006 and th en replacing it with another

true manufacturer product-development cycles, and therefore could not be technologically feasible or economically practicable.

In response to these concerns, and related concerns expressed by other manufacturers, DOT modified the CAFE model in order to account for dependencies between model years and to better represent manufacturers’ planning cycles, in a way that still NHTSA to comply with the statutory requirement to determine the appropriate level of the standards for each model year. This was accomplished by limiting the application of many technologies to model years in whic h vehicle models are scheduled to be redesigned (or, for some technologies, “freshened”), and by causing the model to “carry forward” applied technologies fr om one model year to the next.

During th

related to volume ( i.e. , economies of scale) and the passage of time ( i.e. , time-based

84 49 U.S.C. 32902(a).
85 For example, the CAFE model “carries forward” technologies applied in earlier model years and, when evaluating standards in later model years, evaluates the potential to add “extra” technology in earlier mode years if doing so will sufficiently facilitate compliance in later model years. However, because EPCA doe not allow NHTSA to consider manufacturers’ potential use of CAFE credits, NHTSA’s analysis does no attempt to account for manufacturers’ ability to earn credits in one ye l

s t ar and apply them toward compliance in a different model year, even though this is an important flexibility actually allowed by EPCA.

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### learnin , both of which evolve on year-by-year basis. These changes were implemented

s ers are most likely to broadly apply generic technology “packages,” and the Volpe model does tend to form “packages” dynamically, s, and schedules for increases in CAFE standar s. For example, under the final CAFE standards for passenger cars, the CAFE with Y

rbochargers.

el-byces

reliable means of predicting when the driver will change gears. By retaining cylinder

s

ultimately present at the conclusion of modeling. For each vehicle, the same file shows resultant changes in vehicle weight, fuel ec onomy, and cost. This provides for efficient g) in response to comments by environmental groups and other stakeholders.

The Volpe model is also able to account fo r important engineering differences between specific vehicle models, and to thereby reduce the risk of applying technologies that may be incompatible with or already present on a given vehicle model. Some commenter have previously suggested that manufactur

based on vehicle characteristics, redesign sche dule d model estimated that manufacturers could apply turbocharged SGDI engines mated dual-clutch AMTs to 2.4 million passenger cars in MY 2016, about 22 percent of the M 2016 passenger car fleet. Recent modificatio ns to the model, discussed below, torepresent multi-year planning, increase the model’s tendency to add relatively costeffective technologies when vehicles are estim ated to be redesigned, and thereby increase the model’s tendency to form such packages.

On the other hand, some manufacturers have indicated that especially when faced with significant progressive increases in the stringency of new CAFE standards, they are likely to also look for narrower opportunities to appl y specific technologies. By progressively applying specific technologies to specific ve hicle models, the CAFE model also produces such outcomes. For example, under the final CAFE standards for passenger cars, the CAFE model estimated that in MY 2012, some manufacturers could find it advantageous to apply SIDI to some vehicle models without also adding tu

By following this approach of combining technologies incrementally and on a mod model basis, the CAFE model is able to account for important engineering differen between vehicle models and avoid unlikely technology combinations. For example, the model does not apply dual-clutch AMTs (or strong hybrid systems) to vehicle models with 6-speed manual transmissions. Some vehicle buyers prefer a manual transmission; this preference cannot be assumed away. The model’s accounting for manual transmissions is also important for vehicles with larger engines: for example, cylinder deactivation cannot be applied to vehicles w ith manual transmissions, because there is no

deactivation as a specific technology rather than part of a pre-determined package and by retaining differentiation between vehicles with different transmissions, DOT’s model i able to target cylinder deactivation only to vehicle models for which it is technologically feasible.

The Volpe model also produces a single vehicl e-level output file that, for each vehicle model, shows which technologies were pres ent at the outset of modeling, which technologies were superseded by other t echnologies, and which technologies were

identification, analysis, and correction of er rors, a task with which the public can now assist the agency, since all inputs and outputs are public.

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### s, as well as those related to the efficiency with which the Volpe model is able to analyze attribute-base d CAFE standards and changes in vehicle el analysis such as stringency estimation (to meet predetermined criteria), sensitivity analysis, an d uncertainty analysis, lead the agency to

e

plexity pe model, each model year is parately and explicitly, the model could not be modified to add this

s, duces more realistic estimates of the costs and benefits of such standards.

amount ot

e Such consideration

classification, and to perform higher-lev

conclude that the model remains the best available to the agency for the purposes of analyzing potential new CAFE standards.

3. What Changes Has DOT Made to the Model?

As discussed in the NPRM preceding toda y’s final rule, the Volpe model has been revised since the 2011 CAFE rule to make some minor improvements, and to add one significant new capability: the ability to simulate manufacturers’ ability to engage in “multi-year planning.” Multi-year planning re fers to the fact that when redesigning or freshening vehicles, manufacturers can anticipate future fuel economy or CO 2 

### standards, and add technologies accounting for these standa rds. For example, a manufacturer might choose to over-comply in a given model year when many vehicle models are scheduled for redesign, in order to facilitate compliance in a later model year when standards will b more stringent yet few vehicle models are scheduled for redesign. 86 

### Prior comments have indicated that the Volpe model, by not representing such manufacturer choices, tended to overestimate compliance costs. However, because of the technical com involved in representing these choices when, as in the Vol accounted for se capability prior to the statutory deadline for the MY 2011 final standards.

The model now includes this capability, and NHTSA has applied it in conducting analysis to support the NPRM and in analyzing the standards finalized today. Consequently, this new capability often produces results indicating that manufacturers could over-comply in some model years (w ith corresponding increases in costs and benefits in those model years) and thereby “carry forward” technology into later model years in order to reduce compliance costs in those later model years. NHTSA believes this better represents how manufacturers would actually respond to new CAFE standard and thereby pro

The Volpe model has also been modified to accommodate inputs specifying the of CAFE credit to be applied to each manuf acturer’s fleet. Although the model is n currently capable of estimating manufacturers’ decisions regarding the generation and use of CAFE credits, and EPCA does not allow NHTS A, in setting CAFE standards, to tak into account manufacturers’ potential use of credits, this additional capability in the Volpe model provides a basis for more accurately estimating costs, effects, and benefits that may actually result from new CAFE standards. Insofar as some manufacturers actually do earn and use CAFE credits, this provides NHTSA with some ability to

86 Although a manufacturer may, in addition, generate CAFE credits in early model years for use in later model years (or, less likely, in later years for use in early years), EPCA does not allow NHTSA, when setting CAFE standards, to account for manufacturers’ use of CAFE credits.

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### xamine outcomes more realistically than EPCA allows for purposes of setting new

ards are set. Although the model has been programmed in previous rulemakings to estimate at what stringency net it

erned
sets

econom cability, the effect of other standards of the Government on fuel

actors, nd

form and contribute to the agency’s conside ation of the “need of the United States to conserve energy,” as well as the other

what standards to propose and finalize, but the model does not determine the standards. e CAFE standards.

4. Does the Model Set the Standards?

Since NHTSA began using the Volpe model in CAFE analysis, some commenters have interpreted the agency’s use of the model as the way by which the agency chooses the maximum feasible fuel economy standards. This is incorrect. Although NHTSA currently uses the Volpe model as a tool to inform its consideration of potential CAFE standards, the Volpe model does not determine the CAFE standards that NHTSA proposes or promulgates as final regulation s. The results it produces are completely dependent on inputs selected by NHTSA, based on the best available information and data available in the agency’s estimation at the time stand

benefits are maximized, it was not the model’s decision to seek that level of stringency, was the agency’s, as it is always the agency’s decision what level of CAFE stringency is appropriate. Ultimately, NHTSA’s selection of appropriate CAFE standards is gov and guided by the statutory requirements of EPCA, as amended by EISA: NHTSA the standard at the maximum feasible average fuel economy level that it determines is achievable during a particular model year, considering technological feasibility, ic practi economy, and the need of the nation to conserve energy.

NHTSA considers the results of analyses conducted by the Volpe model and analyses conducted outside of the Volpe model, including analysis of the impacts of carbon dioxide and criteria pollutant emissions, analysis of technologies that may be available in the long term and whether NHTSA could expedite their entry into the market through these standards, and analysis of the extent to which changes in vehicle prices and fuel economy might affect vehicle production and sa les. Using all of this information—not solely that from the Volpe model—the agency considers the governing statutory f along with environmental issues and other rele vant societal issues such as safety, a promulgates the standards based on its best judgment on how to balance these factors.

This is why the agency considered eight regulatory alternatives, only one of which reflects the agency’s final standards, based on the agency’s determinations and assumptions. Others assess alternative standards, some of which exceed the final standards and/or the point at which net benefits are maximized. 87 

### These comprehensive analyses, which also included scenarios with different economic input assumptions as presented in the FEIS and FRIA, are intended to in

r statutory factors. 49 U.S.C. 32902(f). Additionally, the agency’s analysis considers the need of the nation to conserve energy by accounting for economic externalities of petroleum consumption and monetizing th e economic costs of incremental CO 2 

### emissions in the social cost of carbon. NHTSA uses information from the model when considering

87 See Section IV.F of the Preamble for a detailed discussion of the alternatives.

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### Model documentation, which is publicly available in the rulemaking docket and on model inputs (all of hich are available to the public) and outputs are structured, and how the model is ft

ng

in lties

i a at Santa Barbara, though successful in installing and running the model, reported being unable to reproduce NHTSA’s results

y

tation notes that some of the develop ent and operating environment used by the Volpe model ( e.g. , the software

is,

errors 5. How Does NHTSA Make the Model Available and Transparent?

NHTSA’s web site, explains how the model is installed, how the
88 

### w used. The model can be used on any Windows- based personal computer with Microso Office 2003 or 2007 and the Microsoft .NET framework installed (the latter available without charge from Microsoft). The execut able version of the model and the underlyi source code are also available at NHTSA’s web site. The input files used to conduct the core analysis documented in this final rule are available in the public docket. With the model and these input files, anyone is capable of independently running the model to repeat, evaluate, and/or modify the agency’s analysis.

NHTSA is aware of two attempts by commenters to install and use the Volpe model connection with the NPRM. James Adcock, an individual reviewer, reported difficu installing the model on a computer with Microsoft® Office 2003 installed. Also, students from the University of Californ

underlying the development of the shapes of the passenger car and light truck curves.

Regarding the difficulties Mr. Adcock reporte d encountering, NHTSA staff is aware of no attempts to contact the agency for assistance locating supporting material related to the MYs 2012-2016 CAFE rulemaking. Further, the model documentation provides specific minimum hardware requirements a nd also indicates operating environment requirements, both of which have remained materially unchanged for more than a year. Volpe Center staff members routinely inst all and run the model successfully on new laptops, desktops, and servers as part of normal equipment refreshes and interagenc support activities. We believe, therefore, that if the minimum hardware and operating environment requirements are met, inst alling and running the model should be straightforward and successful. The mode l documen

m environment rather than the hardware on wh ich that software environment operates), particularly the version of Microsoft® Excel used by the model, is Microsoft® Office 2003. We recognize that some users may have more recent versions of Microsoft® Office. However, as in the case of other large organizations, software licensing decisions, including the version of Microsoft® Office, is centralized in the Office of the Chief Information Officer. Nonetheless, th e Volpe Model is proven on both Microsoft® Office version 2003 and the newer 2007 version.

As discussed in Section II.C of the Preamble to today’s final rule, considering comments by the UC Santa Barbara students regarding difficulties reproducing NHTSA’s analys NHTSA reexamined its analysis, and discovered some erroneous entries in model inputs underlying the analysis used to develop the curves proposed in the NPRM. These

88 We note, however, that files from any supplemental analysis c onducted that relied in part on confidential manufacturer product plans cannot be made public, as prohibited under 49 CFR Part 512.

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### re discussed in the FRIA and have since been corrected. Updated inputs and outputs ave been posted to NHTSA’s web site, and should enable outside replication of the

ce.

egarding the possible effe cts of potential new CAFE standards. An

er that estimat uel

econom low. However, NHTSA has ing such a model. sensitiv lues of these c n published studies.

the N ways to improve

S,

n

this

omments received in connection earlier experimental version of the Volpe model included a at estimated changes in sales resulting from CAFE-induced

the model. The tter does not have the capabilities discussed below. a h analysis documented in today’s noti

6. Estimating Market Effects Induced by New CAFE Standards

In comments on recent NHTSA rulemakings, some reviewers have suggested that the Volpe model should be modified to estimate the extent to which new CAFE standards would induce changes in the mix of vehicles in the new vehicle fleet. NHTSA agrees that a “market shift” model, also called a c onsumer vehicle choice model, could provide

seful information r u earli experimental version of the Volpe model included a multinomial logit model changes in sales resulting from CAFE-induced increases in new vehicle f ed y and prices. A fuller description of this attempt can be f ound be thus far been unable to develop credible coefficients specify In addition, as discussed in Sect ion II.H.4 of the Preamble, such a model is e to the coefficients used in it, and there is great variation over some key va oefficients i

PRM preceding today’s final rule, NHTSA sought comment on In on this earlier work and develop this capability effectively. Some comments implied that the agency should continue work to do so , without providing specific recommendations. The Alliance of Automobile Manufacturers identified consumer choice as one of several factors outside the industry’s control yet influential with respect to the agencies’ analysis. Also, the University of Pennsylvania Environmental Law Project suggested that the rule would change consumers’ vehicle purchasing decisions, and the California Air Resources Board expressed support for continued consideration of consumer choice modeling. On the other hand, citing concerns regarding model calibration, handling of advanced technologies, and applicability to the future light vehicle market, ACEEE, ICCT, UC and NRDC all expressed opposition to the possibility of using consumer choice models i estimating the costs and benefits of new standards.

Notwithstanding comments on this issue, NHTSA has been unable to further develop capability in time to include it in the analysis supporting decisions regarding final CAFE standards. The agency will, however, continue efforts to develop and make use of this

apability in future rulemakings, taking into account c c with today’s final rule. An

ultinomial logit model th m increases in new vehicle fu el economy and prices, as well as an accompanying cost allocation algorithm to estimate how manufactu rers might allocate compliance costs. However, the agency has thus far been unable to develop credible coefficients specifying such a model. The agency intends to continue seeking to develop such methods, and documents its prior attempts here in the inte rest of providing an overview of how they might be formulated and applied. The following description applies to an earlier

xperimental version of the Volpe model, not to the current version of e la

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### a. Cost Alloc

s

r

As-Incurred 

### ation Assumptions

At the compliance simulation’s conclusion, each represented vehicle model has some incurred technology cost (potentially zero), a nd each represented manufacturer has some zero or positive incurred CAFE fines ( i.e. , civil penalties). We consider several cost allocation assumptions to distribute these compliance costs across each manufacturer’ product line, following one of the following four strategies as specified as a user input fo each manufacturer:

: Based on the total technology costs incurred by each vehicle.

Price-Based : Based on the initial price (MSRP) of each vehicle.

Elasticity-Based : Based on the inverse of each vehicle’s price elasticity of demand.

Uniform : Based on uniform allocation across all vehicles.

A review of relevant literature did not reveal published studies that focus specifically on the relationship between CAFE compliance costs and vehicle prices. However, this review did reveal studies that generically a ddress automotive price elasticities of demand and their influence on pricing decisions, as we ll as production costs and pricing strategies

r some categories of automotive powertrain components. Interviews with selected experts suggest that m iance costs between vehicle odels in order to maintain or improve comp etitiveness in profitable market segments.

n

and

influencing their choices to purchase a new vehicle during a specific model year. fo industry anufacturers may shift compl

m Specific information regarding the pricing strategies followed by individual manufacturers is unavailable. The pricing strategies provided by the cost allocation assumption portion of the model are intended to realistically bracket the potential range of strategies.

At the conclusion of the cost allocation assumption part of the system, each vehicle model is assigned a regulatory cost, which is reported as a price increase and used whe applying the market share model discussed below.



### b. Market Share Model

To provide the capability to analyze the market response to changes in vehicle prices other attributes resulting from manufacturers’ efforts to comply with CAFE regulation, we developed a statistical model to analyze the factors influencing new car buyers’ choices among vehicle models. Our mode l focuses on buyers’ decisions to choose specific vehicle types individual models, but does not analyze the factors

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i. Market Share Model Structure

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### two-step process. First, a

onsumer chooses a type of vehicle, for example, a mid-size premium automobile, a mall pickup r a large sport-utility vehicle. 89 

### Conditioned on that decision, a uyer then selects an individual vehicle mo del from among those making up the chosen The model uses a nested logit model to represent buyers’ decisions about the type of vehicle to purchase and their choices among co mpeting models of that type. As Figure V-8 illustrates, buyers are assumed to make decisions using a

c s truck, o b “market segment”.

Buyer

Small - Budget Midrange - Standard Other Market Segm ents SUV - Midrange

GMC Trailblazer Toyota 4Runner Isuzu Trooper Other Midrange S UVs Choice of Segment:

Choice of model:

Figure V Other Midrange S UVs

-8. Nested Logit Model



### , al

make individual vehicl e models attractive to potential buyers have

f similar body type or style, overall size, luxury level, and performance. This model relies on several underlying assumptions; most important, that buyers derive utility from the attributes offered by different vehicle models, including characteristics such as its passenger- and cargo-carrying cap acity, driving performance, fuel economy comfort level, transmission and drive type (two- versus four-wheel drive). Individu buyers are assumed to choose the specific vehicle model whose purchase price and combination of attributes offers the maximum level of utility. Many of the attributes or

haracteristics that c been well documented, and some of these can be readily measured and compared.

However, other characteristics that lead buy ers to view particular models as closely competitive may be difficult to quantify, or may simply be unknown. The presence of these unobserved attributes means that vehicl es are likely to form groups or market segments, and that models within each segment compete more closely with one another than with models belonging to other market segments. Our model uses the common assumption in automotive marketing that market segments consist of vehicle models o

89 Our model employs the market segmentation presented in 2002 Automotive News Car Market Classifications , [ne ed website address]

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### ii. Factors Affecting Vehi cle Buyer’s Behavior

se Using the subscript s to designate market segments, k to designate individual vehicle models, and n to designate buyers, the probability that a representative buyer will choo a vehicle of type and luxury or performance level s is simply 

## ( )

n 

### P s (0.1)



### In turn, the probability that buyer n 

### will choose to purchase a specific brand and model k 

### from within market segment, or P n 

### ( sk 

### ), is


## ( ) 

# ( ) ( )

n nn 

### P sk P k s P s = (0.2)



### Here, P n 

### ( k 

### | s 

### ) represents the conditional probability that the representative bu yer will

elect model o purchase a vehicle of the body type and luxury or perfor segment s



### . In choosing a market segment and a specific vehicle model, the probability that a buyer

ill choose a specific alternative depends on how the utility or benefits it provides

or example, the probability uyer will select a specific vehicle model from a given market segment depends on how the utility its attributes offer compares to the utility

vels offered by other vehicle models within that same market segment. Similarly, a choice of the vehicle type, size, l uxury, and performance level to shop for ends on how the composite utility of th e various models making up that market egment compares to the composite offered by the vehicles included in the other arket segments.

able or measu

l

k 

### , having already decided t mance level represented by s

w compare to those supplied by the competing choices. Since buyers are assumed to choose the alternative that offers the maximum utility, the likelihood that any specific alternative will be chosen depends on the probability that it offers the maximum utility level among the choices available.

F that a b

le buyer’s dep

s utility

m

The observ rable component of utility offered by each vehicle model depends on the particular features or attributes it provides, such as its driving performance, fuel economy, and seating or luggage-carrying capacity, as well as on its purchase price. 90 

### The unobserved component of utility that each model offers arises partly from uncertainty about which observabl e attributes are important to buyers, as wel as about the relationship between a vehicle’s co mbination of attributes and the utility it offers to prospective buyers. Other sources of unobserved utility include errors in

90 It may also be affected by characteristics of the buyers who choose the market segment containing tha model, since certain characteristics of buyers may affect their preferences for or valuation of specific vehicle attributes. t

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### ttp://www.epa.gov/QUALITY/informationguidelines/



### ply

likely to

erred

icle nction of its measuring or describing these attributes, and the potential existence of attributes that, though valued by buyers, are unknown or difficult to measure.

h 

### By making a specific assumption about the probability distribution of these unobserved components of utility, the probability that a representative buyer will select a specific vehicle model can be expressed as a function of the utility it’s measured attributes sup and of how it compares to the utility levels offered by competing models. 91 

### One common assumption is that the unobserved components of utility follow a specific probability distribution in which large values are rare (a Type I extreme value distribution, which somewhat resembles a normal distribution), and are thus un be sufficiently large to offset any differen ce in observed utilities between the pref model and other competing choices.

Under this assumption, the probability that a representative buyer will purchase a veh model ( k 

### ) from among those within a market segment ( s 

### ) is an exponential fu utility as well as those offered by the other models in that market segment:

sk 

# ( )

s k n

k s 

### P ks U 

# e ′

′ ∈ 

### = 

# ∑ (0.3) 

### U 

# e

where U sk



### represents the level of utility provided by the attributes of vehicle model k 

### . In turn, the probability that a representative buyer will decide to purchase a vehicle from

arket segment s 

### can be expressed as m 

## ( ) s

s

s

s n

s s 

### U P s U 

# e

e

μ

μ

′

′ ∈ 

### = 

# ∑ (0.4)



### where 

# ( ) log U s k
s 

# U ′ 

# = ∑ k s 

# e ′ ∈ (0.5)



### he term U 

### T s k 

# e ∑ , often referred to as the expected maximum utility 

### provided by

that market es that the probability a buyer will k s

′ ∈ 

### the choices available in market segment s, is a measure of the composite utility – i.e. 

### , the overall attractiveness to potential buyers – offered by all of the vehicle models making up ′



### segment. Thus, Equation (0.4) stat purchase a vehicle from market segment s 

### – say a small economy car – depends on how the composite utility (or combined attractiveness) of the models making up that category

91 The specific probability distribution assumed for the unobserved utility components determines the form of the expression for the probability that an individual model will be chosen, because it determines the probability that a vehicle model offering the m aximum observed or measured level of utility to a buyer

lected in the decision. would still represent that buyer’s utility-maximizing choice if the unobserved component of utility were also ref

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### is f me market segment relative to that of the remaining nobserved component of utility, which differs for each vehicle model. This parameter

e

ur model assumes that the utility offered by an individual vehicle model is a linear function of the offers, including its driving

erformance, seating capacity, fuel econom y, transmission and drive type, and its compares to the composite utility measures for each of the other market segments (sports cars, large automobiles, midsize sport-utility vehicles, etc.), the sum of which appears in the denominator.

Equation (0.4) also shows that the expected maximum utility of each market segment scaled by the parameter μ s 

### , which measures the variance in the unobserved component o utility shared by models in the sa

u (sometimes referred to as the nesting coefficient) has the convenient property that th value of [1 - ( μ s 

### ) 2 

### ] measures how similarly buyers view the various vehicle models included within each market segment, thus indicating how closely the market segmentation used in the model matches shoppers’ views of model groupings or segmentation in the new vehicle market. 92



### O levels of various attribut es that it
p purchase price. Denoting these attributes X 1 

### , X 2 

### , ..., X n 

### , vehicle model k 

### within market segment s 

### provides a utility level

1 2 1 2 kk n k s k s s k n 

# U XX X β β β ε ε = + ++ + +

K (0.6)



### where, for example, X 1 k 

### denotes the level of attribute 1 – say, the ratio of horsepower to weight, a widely used index of driving performance – provided by vehicle model k



### . 93



### he relative importance or T weight that buyers attach to each vehicle attr ibute is

s 

### d component of utility that is red by all vehicle models in market segments, that implies the hierarchical structure of buyers’ decisions.

iii. Statistical Estimation of Model Parameters

in an input file define this model based on any of several candidate summarized by the value of its coefficient ( β 1 

### , β 2 

### , ... β n 

### ), while the terms ε s 

### and ε sk 

### respectively represent the unobserved components of utility shared by all vehicles in market segment s 

### and unique to vehicle model k 

### . As discussed previously, it is the

resence of the term ε , which represents the unobserve p sha

Parameters specified

a ttributes. These parameters can be estimated statistically by using the market shares of

92 Specifically, [1- ( μ s ) 2 ] measures the correlation between the utility levels offered by any two vehicle models that are included in the same market segment. The value of μ s is theoretically restricted to the

nge from 0 to 1; values close to 0 indicate that the utilities offered by models in the same m re closely correlated, and thus that the market segmentation used in the model accurately re views about how closely different vehicle types and models compete with one another. In contrast, values loser to 1 indicate that the utilities of models in the same segment are not closely correlated, and thus that ra arket segment

a flects buyers’

c the market segmentation may be inaccurate.

93 Thus in this model, the parameter μ s in Equation (0.4) measures the variance in ε s relative to the variance in ε sk .DELIBERA 

### 124

.

d

e assembled data on suggested retail and actual sales prices, horsepower, vehicle

tics

Using the Market Share Model

escribing the attributes and prices of vehi cles that manufacturers will offer for sale during re m be used to simulate how sales

r market shares in future years would change in response to changes in attributes or



## ( ) total sales accounted for by each individual vehicle model during a recent model year to approximate the probabilities that a “typical” vehicle buyer would choose each model We estimated the model’s parameters, including the coefficients ( β 1 

### , β 2 

### , ... β n 

### ) in Equation (0.6) and the nesting parameter μ s 

### , using market share and attribute data for the approximately 1,300 automobile and light truck models that were produced and sol during model year 2002. Total automobile a nd light truck sales during that model year were about 17 million vehicles.

W weight, seating capacity, fuel economy, fuel tank capacity, transmission and drive type, continent of origin, and brand name for each vehicle model produced and sold during model year 2002. These attributes were used to define additional vehicle characteris such as the ratio of horsepower to vehicle weight and refueling range, and the resulting set of attributes was used to test a variety of different specifications for Equation (0.6).

iv.

With a sufficiently large number of new vehicle sales, the model’s predicted probabilities that a representative buyer will choose each vehicle model can be interpreted as the share or fraction of total sales it is likely to account for. Thus the model can be used to estimate how the market shares of individual vehicle models would have differed during that period if one or more attributes of a specific model had been different. If data d futu odel years are available, this m odel can also

o prices for some models.

The change in the probability that an individual vehicle model k 

### would have been chosen by a representative buyer – or in the aggregate, its market share of total new vehicle sales – in response to a change in one of its attributes X i,k



### is:

TIVE DOCUMENT – DO NOT RELEASE 

# ( ) ( )



# ( ) ( ) ( ) 

# ( ) () () , ,, 2 i k i k i k

s 

## ( ) P ks

n n n 

### P sk P s

X XX

1 1 i n n n in n n n 

### P ks P ks P s P ks P s P s P β μ β =− + − ⎡ ⎤ ⎣ ⎦ ⎣ ⎦ (0.7) 

### s ∂ ∂ ∂



### Normalizing vehicle’s m the elasticity = + ∂ ∂∂ ⎡ ⎤



### Equation (0.7) to measure the proportional (rather than absolute) change in a arket share in response to a proportional change in one of its attributes gives

of its market share: 

## ( ) ( ) 

# ( ) ( ) ( )
,

, , 

### 1 1 i k s i ik n i ik n n 

### X P sk X P ks X P ks P s

X P sk β μ β ⎡ ⎤ ⎡ ⎤ =− + −

, n

ik n 

### ∂ ⎡ ⎤ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ (0.8) 

### ∂ ⎣ ⎦

The computed values of these elasticities, which depend on the estimated parameters (the β i 

### s), the values of the attributes that change (the X i,k 

### s), and the initial market shares of

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### individual vehicles ( the elasticities of vehicle models’ market shares with respect to their own selling prices can be used to implement the cost-sharing calculation that apportions a manufacturer’s technology costs for improving the fuel economy of its fleet in inverse proportion to th e price elasticity of demand for each of its models. Second, th ey can be used to estimate the resulting changes in market shares for individual models that results when these technology costs are “spread” among a manufacturer’s fleet us ing this or any other cost allocation assumption.

However, certain attributes of at least some vehicle models – notably fuel economy, and possibly weight and performance – will also change as part of manufacturers’ efforts to comply with stricter fuel economy standards. When prices and other attributes of a ber of vehicle models change simultaneously, it is often simpler to estimate t e new

eflected in vehicle prices. This revised CAFE level and

le sing price and four othe r attributes. Other NML form ulations may be specified, the values of P sk 

### ), can be used in two ways. First,

num

m h

arket shares that will result by inserting the changed prices and attribute values in the utility expression for these models and recalculate the new market shares of all models directly.

These new market shares can then be used to recalculate how each manufacturer’s salesweighted CAFE level would have changed on ce the technology costs for improving some of its models’ fuel economy were r

c an then be used to assess each manufacturer’s compliance with the revised standard, thus its need to apply additional fuel economy technology to its vehicle models.

v. NML (Market Share) Model Specification



### The system uses a 2-level nested multinomial logit (NML) model to recalculate market shares and sales volumes of different vehicle models after compliance costs have been estimated and allocated. Table V-1 lists the attributes accommodated by the system, and shows the inclusive value parameter the coefficients used in Equation (0.6) for a samp model u subject to the following constraints:

• The inclusive value parameter must be between 0 and 1.

• Coefficients must apply to attributes measured in the indicated units.

• The number of market segments must correspond to the vehicles input file.

### 126

Table V-1. Market Share Model Coefficients (Sample) Inclusive Value Parameter 0.579638

Attribute Units Coefficient Effective Price dollars ( -0.000061 Fuel Economy mpg Seating Capacity (Max.) number of seat belts 0.175729 2003)

Curb Weight pounds 4 Wheel Drive 1=present 0.075382 Automatic Transmission 1=present Power horsepower Power/Weight horsepower/pound 10.046800 Range miles Weight-Specific Fuel Economy pound-miles per gallon



### W hen developing an input file defining the initial state of the MY2002 fleet based on the

th

ng the resulting data file was missing some or all attribute ata for certain vehicle models. Wherever po ssible, we filled these gaps by collecting racteristics and related data uch as Edmunds.com. As part of this process, we also obtained the Manufacturer’s Suggested Retail Price (MSRP) for each vehicle model produced during model year 2002.

Because actual purchase prices for most vehi cle models typically differ significantly from their suggested retail process, we adjusted each vehicle model’s MSRP for model year 2002 by the ratio of its nationwide average “True Market Value” (TMV) during model year 2004, as estimated by Edmunds.com, to its MSRP during model year 2004. 94 

### This adjustment provided an estimate of its nationwide average actual selling price during model year 2002. For vehicle models produce d in model year 2002 but no longer offered for sale during model year 2004, we used the ratio of Edmunds’ estimated TMV to MSRP for the vehicle model in the same mark et segment we judged to be most similar (and where possible, produced by the same manufacturer).

To calculate an “effective price” that takes into account fuel costs, we combined this with the estimated value to the consumer of fuel outlays during a specific payback period. We structure shown in Table V-1 we estimated the annual sales volumes for the 1,355 individual vehicle models produced during model year 2002 using production data reported to NHTSA by manufacturers for the purpose of determining their CAFE compliance, supplemented with confidential a nd commercial data regarding vehicles wi curb weights over 8,500 pounds.

As discussed above, we developed the vehicle attribute, price, and other data used to estimate the market share model using several sources. We initially obtained some vehicle attribute data through information requests to the automotive manufacturers, but because of inconsistent reporti d supplemental information from online sources of vehicle cha

s

DELIBERATIVE DOCUMENT – DO NOT RELEASE ket share model. 94 Edmunds’ estimates of vehicles True Market Values for model year 2002 were no longer available at the time we developed the mar

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### calculated this value using the same methodology used in the compliance simulation model. The model-specific form applied here is as follows:



## ( ) 0.5 0 

### (1 ) 1 FUEL v v 

### LUE FE gap r v PB v v MY v 

### SURV MI FUELPRICE VA = +

+ = 

### = − +



# ∑ (0.9)



### is the number of miles driven during the year when a vehi where MI v 

### cle produced in model year MY reaches age v , SURV v 

### is the probability that a vehicle of that vintage (model year) will remain in service through age v , FE is the vehicle’s fuel economy, FUELPRICE MY+v 

### is the price of fuel in year MY+v , and PB is a “payback period”, or number of years in the future the consumer is assumed to take into account when considering fuel savings. Payback periods of three and five years produced similar results.

Table V-2 lists the vehicle attributes for wh ich we were able to obtain complete data using the combination of sources discussed above. We used the estimated market shares and attribute data for individual vehicle mode ls to develop a two-level nested logit model of each vehicle model’s market share. In this model, buyers first choose one of the 23

ury

ls m arket segments developed by Automotive News to represent the new vehicle market, each of which represents one combination of vehicle type (automobile versus light truck), style ( e.g. , sedan, pickup, or utility vehicle), size (small, mid-size, or large), and lux level (standard, “upscale,” etc.). Table V-2 give s examples of vehicles that fall into each of these segments. 95 

### Buyers then choose to purchase one of the specific vehicle mode within that market segment.

Table V-2. NML Market Segments and Example Vehicles

ne 95 When using forward-looking product plans, it will be necessary to assign each new vehicle model to o of these market segments.

### 128

Segment Name Examples

DELIBERATIVE DOCUMENT – DO NOT RELEASE 1 Small - Budget Small - Econ Hyundai Accent, Toyota Echo
omy urn S Series, Toyota Corolla ouring yota MR2 Spyder, Mini Cooper remium che (all), BMW Z3 Exotic prit, Dodge Viper ge - Lowe alibu, Honda Civic, VW Golf

- Standa ota Camry, Honda Accord

rem axima, Saab 9-3 ria, Toyota Avalon Lu 70 Series, Chrysle 30
ry Jaguar XJ, Mercedes-Be ss Upscale - Premium Mercedes-Benz CL600, Rolls-Royce 13 Pickups - Small Chevrolet S, Dodge Dakota, Mazda B-Series Dodge Ram, Ford F-Series, Toyota Tundra Honda Odyssey, Toyota Sienna, Dodge Caravan 16 Vans - Full-Sized Chevrolet Express, Dodge Ram Van, Ford Econoline

ator 2 Dodge Neon, Sat

o 3 Sporty - T - P Mazda Miata, T 4 Sporty 5 Sporty - Audi TT Coupe, Pors Ferrari (all), Lotus Es 6 Mid-Ran r Chevrolet M 7 Mid-Range rd Buick Century, Toy 8 Mid-Range - P itional ium Audi A4, Nissan M 9 Trad Buick LeSabre, Ford Crown Victo , Volvo 10 Upscale - Near 11 Upscale - Luxu xury Acura TL, BMW 3-Series Acura RL, BMW 5-Series, r nz E Cla 0M

12 Bentley (all),

14 Pickups - Full-Sized 15 Vans - Mini

17 SUV - Standard Sport Wagon Honda CRV, Ford Escape, Toyota Highlander 18 SUV - Premium Sport Wagon Acura MDX, BMW X5, Mercedes-Benz M-Class 19 SUV - Small Chevrolet Tracker, Jeep Liberty, Nissan Xterra 20 SUV - Mid-Range Chevrolet Trailblazer, Dodge Durango, Honda Passport 21 SUV - Large Chevrolet Suburban, Ford Expedition, Toyota Sequoia 22 SUV - Premium Cadillac Escalade, Land Rover Range Rover, Mercedes-Benz G Class, Lincoln Navig 23 SUV - Sport-utility pickups Chevrolet Avalanche, Lincoln Blackwood, Cadillac Escalade EXT 24 Hybrid Toyota Prius, Honda Insight



### We used the Gauss Mathematical and Statistical System produced by APTECH System Inc., to estimate the parameters of the nested logit model of vehicle market shares

escrib s,

ed previously in the report. This system uses a conventional maximum-likelihood rocedure to estimate the parameter values for the utility function and the associated inclusive value parameter. As indicated as previously in the text, the value of this parameter provides some indica tion of how accurately the nesting structure used in the model (the Automotive News market segmen tation) reflects buyers’ views of the new vehicle market.

We experimented with a large number of alternative specifications of the utility function shown in Equation (0.6) for individual vehicle models, each using different combinations of the vehicle attributes shown in the table. We selected the combination of attributes to include in the final model on the basis of th e reasonableness of the signs and relative magnitudes of their estimated coefficients, the model’s ability to replicate actual market shares for individual models, and the estimated value of the nesting coefficient or inclusive value parameter. 96 

### Table V-3 indicates the subset of attributes that were included in final model, and reports the estimated values of their coefficients.

Table V-3. NML Model Attributes and Coefficients

Best Model Specification 

### d
p

Attribute Measure Coefficient t-statistic

Equivalent Price Est. sale price plus est. fuel value over 5 years -0.0000556 -847 Performance Ratio of horsepower to curb weight 9.605 285 Weight Curb weight

96 The wide variation in the orders of magnitude of the estimated coefficients for the different attributes reflects similarly wide variation in their measurem ent scales.

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Seating Capacity Number of adults seated 0.171 688 Towing Capacity Maximum trailer weight Payload Maximum cargo weight Luggage Space Enclosed cargo volume Fuel Economy EPA combined MPG rating Fuel Tank Size Capacity in gallons Refueling Range Fuel tank capacity * MPG Transmission Type Automatic =1; manual = 0 Drive Type 2—wheel drive = 0; 4-wheel drive =1 0.054 81 Continent of Origin Asia, Europe, or North America Brand Manufacturer identity



### c. Model Convergence

After the market share model has concluded, the sales volumes of different vehicle models will typically have changed relative to values used to determine compliance with CAFE standards. Because this can cause changes in CAFE levels, the revised sales volumes are used to repeat the compliance simulation, cost allocation, and market share models. This process is repeated until th e model converges, as determined by the magnitude of changes in CAFE levels and market share specific to each manufacturer and regulatory class. The process, for which Figure V-9 provides an overview, terminates if such changes are all less than 1% or if the sequence has been repeated 10 times. 97
, 98

97 This cycling currently leads to “overcompliance” in some cases, which we are attempting to minimize by developing code to selectively “remove” technologies between iterations.
98 A limit of 10 iterations is imposed to guard against indefinite repetition. The system typically converges within 5-6 iterations to changes smaller than 1%.

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Market Share Model

Convergence? Compliance Simulation

No

Yes Calculate Effects

Figure V-9. Model Convergence Process

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### ten

tions.

ts

nical Report and supplemented the review with updated information from the FEV tear-d own studies contracted by EPA, more current rature, new product plans and EPA certification testing data. e fol ness estimates ompleted for technologies deemed to be appropriate in the rulemaking timeframe. The estimates are d he analysis was conducte rs from DOT and EPA and represents what the agencies believe to be the best available estimates for the MY 2012-2016 rulemaking timefra

el

el saving technologies can be ad ded to the baseline fleet, NHTSA researches and dev logies that

input into

fleet in

t to ology “availability” is not determined based simply on whether the technology exists, but depends also on whether the technology has achieved a level of technical viability that C. Technologies – Costs and Effectiveness



### Technology assumptions, i.e., assumptions about their availability, cost, effectiveness, and the rate at which they can be incorporated into new vehicles, are of very controversial as they have a significan t impact on the levels of the standards. Agencies must, therefore, take great care in developing and justifying these assump In developing technology inputs for MY 2012-2016 standards, NHTSA and EPA reviewed, as requested by President Ob ama in his January 26 memorandum, the technology assumptions that NHTSA used in setting the MY 2011 standards, the comments that NHTSA received in response to its May 2008 NPRM and the commen received in response to the NPRM for this rule. In addition, the agencies reviewed the technology input assumptions identified in EPA’s July 2008 Advanced Notice of Proposed Rulemaking and 2008 Staff Tech 99



### lite Th lowing section details the availability, cost and effective

c rawn from an analysis condu cted between NHTSA and EPA in 2009. T d by enginee

me.

A. NHTSA analyzes what technologi es can be applied beyond those in the baseline vehicle fleet



### One of the key statutory factors that NHTSA must consider in setting maximum feasible CAFE standards for each model year is the availability and feasibility of fu saving technologies. The baseline vehicle fleet identifies the technologies already deployed for each vehicle model. The agency uses the baseline vehicle fleet data to ascertain the “baseline” capabilities and average fuel economy of each manufacturer. Given the agency’s need to consider economic practicability in determining how quickly additional fu elops, based on the best available information and data, a list of techno the agencies believe will be ready for im plementation during the model years covered by the rulemaking. This includes developing estimates of the costs and effectiveness of each technology and lead time needs. The resultant technology assumptions form an the Volpe model. The model simulates how manufacturers can comply with a given CAFE level by adding technologies beyond those included in the baseline vehicle a systematic, efficient and reproducible manner. CBD commented that because many of the technologies considered in the NPRM are currently available, manufacturers should be able to attain mpg levels equivalen the MY 2016 standards in MY 2009. In response, as discussed below, techn

99 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologie Duty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, March 2008. s Used to Reduce Light-

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### makes ent

en if a technology is available for applica

ne or

Final Rule



### In two of the a blished light truck AFE standards for MYs 2005-2007 and MYs 2008-2011, NHTSA relied on the 2002 Nation erage

of

f Congress, in order to provide independent and peer viewed estimates of cost and effectivene ss numbers. The NAS list was determined by aational Academy of Sciences, and was then peerreview

er economy technologies that will be available and their costs and effectiveness values. To aid the
agency it appropriate for widespread application. This depends in turn on compon supplier constraints, capital investment and engineering constraints, and manufacturer product cycles, among other things. Moreover, ev tion, it may not be available for every vehicle. Some technologies may have considerable fuel economy benefits, but pres ent significant technica l changes related to NVH and drivability issues when applied to some vehicles within the model years covered by this rule – for example, applying cylinder deactivation to 4-cylinder engi on vehicles with manual transmissions. The agencies have provided for increases over time to reach the mpg level of the MY 2016 standards precisely because of these types of constraints, because they have a real effect on how quickly manufacturers can apply technology to vehicles in their fleets. Th e following sections describe NHTSA’s fuelsaving technology assumptions and methodology for estimating them, and their applicability to MY 2012-2016 vehicles.

B How NHTSA decides which technologies to include

1. How NHTSA did this historically, and how for the MY 2011



### gency’s past CAFE rulemakings, which esta C al Academy of Sciences’ report, “Effectiveness and Impact of Corporate Av Fuel Economy Standards” 100 

### (“the 2002 NAS Report”) for estimating potential fuel economy effectiveness values and associated retail costs of applying combinations technologies in 10 classes of production vehicles. The NAS study was commissioned by the agency, at the direction o

re panel of experts formed by the N ed by individuals chosen for their diverse perspectives and technical expertise in accordance with procedures approved by the Report Review Committee of the National Research. In the 2008 NPRM, NHTSA explained that there has been substantial advancement in fuel-saving automotive tec hnologies since the publication of the 2002 NAS Report. New technologies, i.e. , ones that were not assessed in the NAS report, have appeared in the market place or are expected to appear in the timeframe of the rulemaking. Also, new studies have been c onducted and reports issued by several oth organizations providing new or different information regarding the fuel

in assessing these developments, NHTS A contracted with the NAS to update the fuel economy section, Chapter 3, of th e 2002 NAS Report. However, as NHTSA explained, the NAS update was not available in time for this rulemaking.

100 National Research Council, “Effectiveness and Impact of Corporate Average Fuel Economy (CAFE)

11, 2008). Standards,” National Academy Press, Washington, DC (2002). Available at http://www.nap.edu/openbook.php?isbn=0309076013 (last accessed October

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### Accordingly, NHTSA worked with EPA staff to update the technology s NPRM. EPA staff published a related report a

cal

believed that the assumptions of availability and applica

e



### e

e more information can be found on thes assumptions, and used the results as a basis for it nd submitted it to the NAS committee. 101 

### For the MY 2011 final rule, NHTSA hired an international consulting firm, Ricardo, to aid the agency in analyzing the comments the agency received in response to its 2008 NPRM. Ricardo’s role was as a tec hnical advisor to NHTSA staff. In this capacity, Ricardo helped NHTSA undertake a comprehensive review of the NPRM technology assumptions and all comments rece ived on those assumptions, based on both old and new public and confidential manufacturer information. Relying on the techni expertise of Ricardo and taking into consideration all the information available, NHTSA revised its estimates of the availability and applicability of many technologies. While NHTSA sought Ricardo’s expertise and relied significantly on their assistance as a neutral expert in developing its technical assumptions, it retained responsibility for the final assumptions. The agency bility for the MY 2011 final rule were more accurate than those used in the NPRM, and were the best available for purposes of that rulemaking.

C. What technology assumptions has NHTSA used for the final rule?

1. How do NHTSA’s technology a ssumptions in the final rul differ from those used in the NPRM?



### In developing this final rule, and in working in conjunction with the EPA, NHTSA has revised some of the inputs used by the Volpe model to assess the appropriat stringency for future CAFE standards. The following section discusses the more important changes and revisions, and also ad vises wher e and other changes.

Baseline and Market Data File: As described in detail above in Section III. b. of the Preamble, the agencies relied on CSM forecast data to assist us with establishing the baseline and the market data file. After the NPRM had been published, the agencies noticed that the standard CSM forecast included heavy duty class 2b and class 3 ve hicles. These vehicl es are unregulate CAFE (e.g., Ford F-350). The forecast from CSM used in the NPRM was from quarter of 2009. The agencies requ ested that CSM to make a custom 4 th 

### quarter forecast with these vehicles removed for the final rule making. The customized forecast from CSM has allowed the agencies to correct this error and we now believe the estimates included in the baseline fleet to be accurate for all classes of vehicles.

More information on the advantages and disadvantages of the current approach d by the 2nd

and the agencies’ decision to follow it is available in Section II.B.3 of the Preamble, and Section I of the joint TSD describes in greater detail the process the agencies used in sourcing the data for the baseline fleet and de veloping it into a representation of a future
et. fle

101 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce LightDuty Vehicle Carbon Dioxide Emissions, EPA 420-R-08-008, March 2008.

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### Revisions to Technologies and Their Estimates: Specific to its modeling, NHTSA has revised two technologies used in the final rule analysis from those considered in the NPRM. These revisions were based on

omments received in response to the NPRM and the identification of area to improve accuracy. In th PR not available for small v hicles because it did not appear to be a cost-effective option. However, based on

comme r tion

ss

and

ess

a great extent, the agencies, in reconsidering information from many

sources Lift

al

08

logies

. Thus, NHTSA corrected these effectiveness estimat

HTSA c e N M, a diesel engine option (DSLT or DSLC) was

e nts received in response to the NPRM, the agency added a diesel engine option fo small vehicles. Additionally, in the NPRM, the mass reduction/material substitu technology, MS1, assumed engine downsizing. However, for purposes of the final rule, engine downsizing is no longer assumed for MS1, thus slightly lowering the effectivene estimate, to better reflect how manufacturers might implement small amounts of mass reduction/material substitution. These changes are discussed in greater detail below and Chapter 3 of the joint TSD. Building on NHTSA’s estimates developed for the MY 2011 CAFE final rule EPA’s Advanced Notice of Proposed Rulemaking, which relied on EPA’s 2008 Staff Technical Report, 102 

### the agencies took a fresh look at technology cost and effectiven values and incorporated FEV tear-down study results for purposes of this joint final rule under the National Program. Generally speaking, while NHTSA found that much of the cost information used in the MY 2011 final rule and EPA’s 2008 Staff Report was consistent to , revised the component costs of several major technologies including: turbocharging/downsizing, mild and strong hybr ids, diesels, SGDI, and Valve Train Technologies for purposes of the NPRM. In addition, based on FEV tear-down studies, the costs for turbocharging/downsizing, 6-, 7- , 8-speed automatic transmissions, and du clutch transmissions were revised for this final rule. Most effectiveness estimates used in both the MY 2011 final rule and the 20 EPA Staff Report were determined to be accurate and were carried forward without significant change into this rulemaking. When NHTSA and EPA’s estimates for effectiveness diverged slightly due to differ ences in how the agencies apply techno to vehicles in their respective models, we report the ranges for the effectiveness values used in each model. For purposes of the fi nal rule analysis, NHTSA made only a couple of changes to the effectiveness estimates. Specifically, in reviewing the NPRM effectiveness estimates for this final rule NHTSA discovered that the DCTAM effectiveness value for Subcompact and Comp act subclasses was incorrect; the (lower) wet clutch effectiveness estimate had been used instead of the intended (higher) dry clutch estimate for these vehicle classes 103 

### es. Additionally, as discussed above, the effectiveness estimate for MS1 was revised (lowered) to better represent the impact of reducing mass at a refresh. These revisions are discussed at length in the joint TSD and in this document below. N

102 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Li Duty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, March 2008.
103 A dry clutch DCTAM is more efficient and cheaper than a wet clutch DCTAM because it doesn’t require a wet clutch type hydraulic system, which is used to cool the clutches. However, due to this lack of cooling, a dry clutch DCTAM has a lower torque capacity. Dry clutch DCTAM’s are ideal for smallerghtvehicles (i.e. Subcompact and Compact) with lower engine torque ratings.

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### e

2. How are technologies applied in the model?



### s has its

w the

ented before l

particu s

ology are

ness

ose

other technol

evaluated in an “apples-to-apples” comparison with those used by the CAFE model, since
t and EPA are confident that the thorough revi ew which has been conducted has led to th best available conclusion regarding technology costs and effectiveness estimates for the current rulemaking and resulted in excellent consistency between the agencies’ respective analyses for developing the CAFE and CO 2 

### standards.

As in the MY 2011 final rule and NPRM, each technology is assigned to one of the five following categories based on the system it affects or impacts: engine, transmission, electrification/accessory, hybrid or vehicle. Each of these categorie own decision tree that the CAFE model uses to apply technologies sequentially during the compliance analysis. The decision trees were designed and configured to allo CAFE model to apply technologies in a cost-effective, logical order that also considers ease of implementation. For example, software or control logic changes are implem replacing a component or system with a completely redesigned one, which is typically a much more expensive option. In some cases, and as appropriate, the mode may combine the sequential technologies shown on a decision tree and apply them simultaneously, effectively developing dyna mic technology packages on an as-needed basis. For example, if compliance demands indicate, the model may elect to apply LUB, EFR, and ICP on a dual overhead cam engine, if they are not already present, in one single step. Each technology within the decision trees has an incremental cost and an incremental effectiveness estimate associated with it, and estimates are specific to a lar vehicle subclass. Each technology’ s incremental estimate takes into account it position in the decision tree path. If a technology is located further down the decision tree, the estimates for the costs and effectiven ess values attributed to that techn influenced by the incremental estimates of costs and effectiveness values for prior technology applications. In essence, this approach accounts for “in-path” effective synergies, as well as cost effects that occur between the technologies in the same path. When comparing cost and effectiveness estimates from various sources and th provided by commenters, it is important that the estimates evaluated are analyzed in the proper context, especially as concerns their likely position in the decision trees and ogies that may be present or missing. Not all estimates available in the public domain or offered for the agencies’ consideration during the comment period can be

in some cases the order of application, or included technology content, is inconsisten with that assumed in the decision tree. In the MY 2011 final rule, significant revisions had been made to the sequence of technology applications within the decision trees, and in some cases the paths themselves had been modified and additional paths had been added. These revisions were maintained for this final rule analysis. The additional paths allow for a more accurate application of technology, insofar as the model now considers the existing configuration of the vehicle when applying technology. In this analysis, single overhead camshaft (SOHC), dual overhead camshaft (DOHC) and overhead valve (OHV) configured engines now have separate paths that allow for unique path-dependent versions of certain engine technologies. Thus, the cylinder de activation technology (DEAC) now consists of

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### ve

properly account for technology effec path, the increm al effe g loss reductions have already been accomplished by the preceding technology, CCPS, which reduces or diminishes the effectiveness estimate for DVVLS because part of the efficiency gain associated with the reduc tion of the pumping loss mechanism has already occurred. This accounting approach resolv es this potential double-counting issue. To address any potential confusion, NHTSA would like to draw attention to the retention of previously applied technologies when more advanced technologies ( i.e.
, those further down the decision tree) were applied. In both the MY 2011 final rule and this final rule, as appropriate and feasible, previously-applied technologies are retained in combination with the new technology being applied, but this is not always the case. For instance, one exception to this would be the application of diesel technology, where the entire engine is assumed to be replaced, so gasoline engine technologies cannot carry over. This exception for diesels, along wi th a few other technologies, is documented below es.

sts

ject,

, three unique versions that depend on whether the engine being evaluated is an SOHC, DOHC or OHV design; these technologies are designated by the abbreviations DEACS, DEACD and DEACO, respectively , to designate which engine path they are located on. Similarly the last letter for the Coupled Ca m Phasing (CCP) and Discrete Variable Val Lift (DVVL) abbreviations are used to identify which path the technology is applicable
to. Use of separate valvetrain paths and unique path-dependent technology variations also ensures that the incremental cost and effectiveness estimates ts so as not to “double-coun t.” For example, in the SOHC ctiveness estimate for DVVLS assumes that some pumpin ent

in the detailed discussion of each de cision tree and corresponding technologi As the Volpe model steps through the decision trees and applies technologies, it accumulates total or “NET” cost and effectiveness values. Net costs are accumulated using an additive approach while net effectiveness estimates are accumulated multiplicatively. As with the MY 2011 final rule, the decision trees have been expanded so that NHTSA is better able to track the incremental and net/cumulative cost and effectiveness of each technology, which substa ntially improves the “accounting” of co and effectiveness for this final rule. 104 

### To help readers better understand the accumulation process, and in response to comments expressing confusion on this sub the following examples demonstrate how th e Volpe model calculates net values. Accumulation of net cost is explained first as this is the simpler process. This exampl e uses the Electrification/Accessory decision tree sequentially applying the EPS

104 In addition to the (simplified) decision trees, as published in this document, NHTSA also utilized “expanded” decision trees in this final rule analysis. Expanded decision trees graphically represent each unique path, considering the branch points available to the Volpe model, which can be utilized for applyin fuel saving technologies. For instance, the engine decision tree shown in this document has 20 boxes representing engine technologies, whereas the expanded engine decision tree requires a total of 45 boxe accurately represent all available application variants. Expanded decision trees presented a signi improvement in th staff to a urately g

s to ficant e overall assessment and tracking of applied technologies since they allowed NHTSA view and assess both the incremental and the accumulated, or net cost and effectiveness at any st e of technology application in a decision tree. Because of the large format of the expanded decision s, they could not be included in the Federal Register, so NHTSA refers the reader to Docket No. NHTSA-2009-0059. Expanded decision trees for the engine, electrification/transmission/hybridization, and the vehicle technologies (three separate decision trees) were ket for cc ag tree

developed for each of the 12 vehicle technology application classes and have been placed in the doc the reader’s information.

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### IACC, MH cost and effectiv ness estimates from its input sheet. As seen in Table V-4 below, the input sheet cost estimates have a lower and upper value which may be the same or a different value ( i.e. , a single value or a range) as shown in columns two and three. The Volpe model first averages the values (column 4), and then sums the average values to calculate the net cost of applying each technology (column 5). Accordingly, the net cost to apply the MHEV technology for example would be ($106.00+ $128.00 + $288.00 = $522.00). Net costs are calculated in a similar manner for all the decision trees. Table V-4 Sample Volpe Mo del Net Cost Calculation 

### EV, BISG and CISG technologies to a subcompact vehicle using the

e



### Tech. Abrev. Lower INCR Cost Upper INCR Cost Av g . INCR Cost NET Cost EPS 106.00 $ 106.00 $ 106.00 $ 106.00 $
IACC 128.00 $ 128.00 $ 128.00 $ 234.00 $
MHEV 288.00 $ 288.00 $ 288.00 $ 522.00 $
BISG 286.00 $ 286.00 $ 286.00 $ 808.00 $ CISG 2,791.00 $ 2,791.00 $ 2,791.00 $ 3,599.00 $ 

## Example Net Cost Calculation: Elect./Acc. Path, Subcompact Vehicle Subclass

The same decision tree, technologies, and vehicle are used for the example demonstrating the model’s net effectiveness calculation. Table V-5 below shows averag incremental effectiveness estimates in column two; this value is calculated in the same manner as the cost estimates above (average of lower and upper value taken from the input sheet). To calculate the change in fu el consumption due to application of the EPS technology with incremental effectiveness of 1.5 percent (or 0.015 in decimal form, column 3), when applied multiplicatively, means that the vehicle’s current fuel consumption ‘X’ would be reduced by a factor of (1 – 0.015) = 0.985, e

02, or a net 2.98 percent eness, as shown in the table. Net effectiveness is calculated in a similar manner the all decision trees. It should be noted that all incremental effectiveness estimat were derived with this multiplicative approach in mind; calculating the net effectiv ness using an additive approach will yield a different and incorrect net 105 

### or mathematically 0.985*X. To represent the changed fuel consumption in the normal fashion (as a percentage change), this value is subtracted from 1 (or 100%) to show the net effectiveness in column 5. As the IACC technology is applied, the vehicle’s fuel consumption is already reduced to 0.985 of its original value. Therefore the reduction for an additional incremental 1.5 percent results in a new fuel consumption value of 0.97 effectiv for

es

e effectiveness.

105 A decrease in fuel consumption (FC) means the fuel economy (FE) will be increased since fuel consumption and economy are related by the equ ation FC = 1/FE.

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### Ta -5 s Calculation ble V Sample Volpe Model Net Effectivenes

Tech. Abrev. INCR Eff. % Avg. INCR Eff. (decimal) Mu Cu Avg. ltiplicative FC Reduction rrent FC * (1-Avg INCR) Net Effect. (1 - Red)

18.00% EPS 1.50% 0.0150 1 * (1 - 0.015) = 0.985 1.50% IACC 1.50% 0.0150 0.985 * (1 - 0.015) = 0.9702 2.98% MHEV 2.50% 0.0250 0.9702 * (1 - 0.0250) = 0.9459 5.41% BISG 5.00% 0.0500 0.99459* (1 - 0.0500) = 0.8986 10.14% CISG 8.75% 0.0875 0.8986 * (1 - 0.0875) = 0.8200 

## Example Net Effectiveness Calculation: Elect./Acc. Path, Subcompact Vehicle Subclass

To improve the accuracy of accumulating net cost and effectiveness estimate “path-dependent corrections” were employed in the MY 2011 final rule and are being utilized in this final rule. The prior NPRM analysis (2008) had the potential to overestimate or underestimate net cost a nd effectiveness depending on which decision tree path the Volpe model followed when applying the technologies. For example, i the 2008 NPRM analysis a diesel technology was applied to a vehicle that followed OHV path, the net cost and effectiveness could be different from the net estimates vehicle that followed the OHC path even though the intention was to have the same ne cost and effectiveness. In order to correct this issue path-dependent correction ta were added to s,

either

f in the for a
t bles the input sheets. The model uses these tables to correct net cost and effectiv single which

re for

e l

is into the strong hybrids, the correction tables are used to adjust the estimates from the CVT path. eness estimate differences that occur when multiple paths lead into a technology that is intended to have the same net cost and effectiveness no matter path was followed. 106 

### Path-dependent corrections were used when applying cylinder deactivation (on the DOHC path), turboc harging and downsizing, diesel and strong hybrids. For the engine technologies listed in the preceding sentence, the fuel consumption and cost estimates stated in following sections and the input sheets a an SOHC engine. The correction tables discussed above are then used to adjust th estimates for the different paths (i.e. DOHC or OHV). Similarly, all strong hybrid fue consumption and cost estimates stated in th e following section and the input sheets are relative to a vehicle that is following the CVT path, discussed in the Electrification/Accessory Technology Decision Tree section below. For a vehicle that following the 6-, 7- and 8-speed automatic tran smission path

106 The correction tables are used for path deviations within the same decision tree. However, there is one exception to this rule, specifically that the tables are used to keep the model from double-counting cost and effectiveness estimates when both the CBRST and MHEV are applied to the same vehicle. Both technologies try to accomplish the same goal of reducing fuel consumption, by limiting idle time, but through different means. If either of these technologies exists on a vehicle and the Volpe model applies the other, the correction tables are used to remove the cost and effectiveness estimates for CBRST, thus ensuring that double-counting does not occur.

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### 3. Technology application decision trees



### The following paragraphs explain, in greater detail, the decision tree logic and revisions to the decision trees from the MY 2011 final rule that have been incorporated for this final rule.

Engine Technology Decision Tree



### For the NPRM and this final rule, NHTSA reviewed the engine decision tree and the model’s technology application logic used in the MY 2011 final rule, andconcluded that no revisions were necessary to the engine tree at this time. Figure V10 below shows the decision tree for the engine technology category. As in the MY 2011 final rule, NHTSA does not show Camless Valve Actuation (CVA), Lean-Burn GDI (LBDI), and Homogenous Charge Compression Ignition (HCCI) on the decision trees becaus e these technologies were determined to be in the research phase of development; no new information to suggest these technologies are under development has been received at this time. As also discussed in the MY 2011 final rule, SOHC, DOHC a nd OHV engines have separate paths to allow the model to apply unique path-dependent valvetrain technologies (Variable Valve Timing, Variable Valve Lift, and cylinder deactivation) that are tailored to those specific engine types. This approach also improves the accuracy of accounting for net cost and effectiveness compared to that used in the 2008 NPRM or prior rulemakings. Also as in the MY 2011 final rule, the Turbocharging and Downsize technology (TRBDS) is considered to be a completely new engine that has been converted to DOHC (if not already a DOHC in the baseline vehicle) with LUB, EFR, DCP, SGDI and CBRST applied. Similarly, the conversion to Diesel (DSLC and DSLT) is considered to be a completely new engine that replaces the gasoline engine(although it carries over the LUB and EFR technologies). We note that the pathdependent variations of these three te chnologies (TRBDS, DSLC, and DSLT) all result in the same technology state for the m odified vehicle regardless of the path the model followed to achieve it. Theref ore, in conducting the analysis, the net cost and effectiveness estimates for the different engi ne paths are considered to be the same (regardless of path), and the incremental cost and effectiveness estimates are adjusted as appropriate to account for the path-dependent variationsDE 

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ecision Tree FigureV-10. Engine Technology (EngMod) D

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### ing this decision tree, NHTS A made some revisions from the version used in ) has

Figure V-11. Electric Power Steering (EPS) is the first technology in this decision tree, since it is a primary enabler for both mild and strong hybrids, and is followe as in the MY 2011 final rule. Micro-Hybrid (MHEV , a 12-volt system that offers basic idle stop/start functionality only, continues to follow ge

ents a higher voltage,

.

is

example ially realized version of this te chnology. CISG, which is the most capable of the mild hybrid options, is the final step ne cessary in order to convert the vehicle to a (full) strong hybrid; it replaces BISG when it is applied, but again, the final vehicle state contains both EPS and IACC. All Electrifica tion/Accessory technologies can be applied to both automatic and manual transmission vehicles.

Transmission Technology Decision Tree



### For the NPRM and this final rule, NHTSA reviewed the transmission technology decision tree and the model’s technology app lication logic used in the MY 2011 final rule, and concluded that no revisions to the tran smission tree were necessary at this time. This decision tree, shown in Figure V-11, contains two paths: one for automatic transmissions and one for manual transmissions, that are identical to those used in the MY 2011 final rule. On the automatic path, the decision tree first optimizes the current transmission by improving the control system via the Impr oved Automatic Transmissions Controls and other Externals (IATC) technology before a pplying more expensive technologies. After IATC, the decision tree splits into a “Unibody only” and “Unibody or Ladder Frame” path, both of which result in conversion to new and fully optimized transmission designs. The Unibody only path contains the Continuously Variable Transmission (CVT) technology, while the Unibody or Ladder Frame path has 6/7/8-Speed Automatic Transmission with Improved Internals ( NAUTO). The NAUTO technology is followed by Dual Clutch Transmission/Automated Ma nual Transmission (DCTAM) technology. Dual Clutch Transmission (DCT) designs do not suffer torque interrupt when shifting; a Electrification/Accessory Technology Decision Tree



### After review the MY 2011 final rule. Specifica lly, one of the 2011 technologies (HVIA been incorporated into a new mild hybrid te chnology (BISG), which allows the model to choose from a broader range of mild hybrid options before conversion to a strong hybrid, as shown in

d by Improved Accessories (IACC), ) as the first of the mild hybrid techno logies. However, while the Higher Volta and Improved Alternator (HVIA) technology followed MHEV in the MY 2011 final rule, for purposes of the NPRM and this final rule, HVIA has been incorporated into the next technology, Belt Integrated Starter Generator (B ISG). BISG repres such as 42 volts, mild hybrid system with idle stop/start functionality, but with higher capability than MHEV including limited energy recovery through regenerative braking BISG represents a mid-point option between MHEV and the next level of mild hybrid. BISG replaces the MHEV technology when it is applied, but EPS and IACC remain on the vehicle. Crank Integrated Starter Generator (CISG), the last of the mild hybrids, also a higher voltage system with rege nerative braking and limited motive power, primarily launch assist. Honda’s Integrate Motor Assist (IMA) system is a good of a commerc

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### charact ments from manufacturers expressing concern that torque interrupt will not be acceptable to consumers, the DCTAM technology is intended to use a DCT-type transmission only. The manual transmission path again has only one technology application; conversion to a 6-Speed Manual with Impr oved Internals (6MAN). NHTSA anticipates limited use of manual transmissions with more than 6 speeds within the MY 2012-2016 timeframe.

Hybrid Technology Decision Tree



### NHTSA also reviewed the hybrid technology decision tree and the model’s technology application logic used in the MY 2011 final rule, and concluded that no revisions were necessary for the hybrid tree for the NPRM and this final rule. The model continues to only apply strong hybrid technologies when both the Electrification/Accessory and Transmission (a utomatic transmissions only) technologies have been fully added to the vehicle, as seen in Figure V-11. When the CAFE model applies strong hybrids it takes into account th at some of the fuel consumption reductions have already been included when technologies like EPS or IACC have been previously applied. When strong hybrids are required, the model chooses the most appropriate application of the Two Mode (2MHEV), Power Split (PSHEV) or Plug-in Hybrid Vehicle (PHEV), based on the vehicle’s subclass and/or the most cost-effective application. eristic associated with automated manual transmission (AMT) designs. In response to com

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### gy

Vehicle Te



### After re technology tree fro utilized three Materi Technology Deci the NPRM and this final rule as Mass Redu ction and are discussed in greater detail below. As shown in Figure V-12, this rule uses two technologies, (MS1) and (MS2), and a dedicated path in the Vehicle Technology Decision Tree. Both have a different definition than was used in the prior rule. The Mass Reduction 1 (MS1) technology now represents a 1.5 percent (of vehicle curb weight) weight decrease that can be applied to any subclass of vehicle at the Refresh or Re design cycle. The MS2 technology defines a 3.5 percent to 8.5 percent subclass-dependent mass reduction, which can only be applied at the Redesign cycle, with the lower reductions occurring in the smaller/lighter vehicles. MS2 is incremental to MS1, which means that the model may, subject to subclass and cycle constraints, potentially reduce vehicle weight by a total of 5 to 10 percent (of curb weight) within the rulemaking time frame. To allow manufacturers lead time to Figure V-11. Electrification/Accessory, Transmission and Hybrid Technolo Decision Tree

chnology Decision Tree



### ing this decision tree, NHTSA made some revisions to the vehicle m the version used in the MY 2011 final rule. The MY 2011 final rule

al Substitution (MS) technologies in a dedicated path in the Vehicle sion tree. These technologies have been reconsidered for purposes of view

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### implement larger mas uct 2014. Low Rolling Resistanc B) and Secondary Axle Disconnect (SAX) all ha e the same definition and path as used in the MY 2011 final ru s red ions, the MS2 technology is made unavailable until MY e Tires (ROLL) , Low Drag Brakes (LD

v le, with SAX applied to 4WD vehicles only. Aerodynamic Drag Reduction (AERO) remains a separate path.

Figure V-12. Vehicle T echnology Decision Tree

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### logy



### on using ten vehicle “class es” (4 cars classes and 6 truck classes), 107


### but did not det lass. NAS’s
purpos n sep ” vehicles, i.e. , veh cles similar in size, powertrain confi guration, weight, and consumer use, and for which similar technologies ar e applicable. NHTSA similarly differentiates vehicles, referring to each grouping as a “subclass,” for the purpose of applying technologies to vehicles and assessing their incremental costs and effectiveness. These technology subclasses should not be confused with the regulatory classifications pursuant to 49 CFR Part 523. For this final rule as for the MY 2011 final rule, the CAFE model divides the vehicle fleet into subclasses based on m odel inputs, and applie s subclass-specific estimates, also from model inputs, of the applicability, cost, and effectiveness of each fuel-saving technology. Therefore, the model’s estimates of the cost to improve the fuel economy and the amount of fuel economy improvement of each vehicle model depend upon the subclass to which the vehicle model is assigned. NHTSA’s analysis for the MY 2005-2007 and MY 2008-2011 light truck CAFE standards used the same vehicle classes defined by NAS in its 2002 Report. The 2008 NPRM for MY 2011-2015 also used those same vehicle classes, but included some differentiation in cost and effectiveness numb ers between the various classes to account for differences in technology costs and effectiv eness that are observed when technologies are applied on to different classes and subc lasses of vehicles. The agency found it important to make that differentiation because it estimated that, for example, engine turboch

016 vehicles under consideration. The methodology is as follows: NHTSA examined the car and truck segments separately. First, for the car segment, NHTSA plotted the footprint distribution of vehicles in the baseline vehicle fleet and divided that distribution into four equivalent footprint range segments. The footprint ranges were named Subcompact, Compact, Midsize, and Large classes in ascending order. Cars were then assigned to one of these classes based on their specific footprint size. Vehicles in each range were then manually reviewed by NHTSA staff to 4. Division of vehicles into subclasses based on techno applicability, cost and effectiveness



### As part of its consideration of technological feasibility, the agency evaluates whether each technology could be implemented on all types and sizes of vehicles, and whether some differentiation is necessary in applying certain t echnologies to certain types and sizes of vehicles, and with respect to the cost incurred and fuel consumption and CO 2 

### emissions reduction achieved when doing so. GM commented as to the applicability of technologies based on size and classification of vehicles, NHTSA agrees and applied technologies with this approa ch. The 2002 NAS Report differentiated technology applicati ermine how cost and effectiveness values differ from class to c arating vehicles into these classe s was to create groups of “like e i

i

arging and downsizing would have differe nt implications for large vehicles than for smaller vehicles. However, for purposes of the NPRM and this final rule, NHTSA closely re-examined the subclasses used for the MY 2011 final rule and found that the methodology and subclasses used then, which had been developed in response to comments arguing insufficient differentiation, remain appropriate for the MY 2012-2

107 The NAS classes included subcompact cars, compact cars, midsize cars, large cars, small SUVs, midsize SUVs, large SUVs, small pickups, large pickups, and minivans.

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### ity of size, weight, powertrains, consumer use, etc. Ho wever, each group contained some vehicles that orts or high-perform different technologies and cost and effe ates may be a TSA employed a perf ss within e ac e the accuracy of technology appl rmine which specific cars would be assigned to the performance subc TSA graphed (in t ratio for each veh mp lass plot is shown below in Figure V-1 bpopulation was th ine an appropriate transition point betw e” and “non-performance” models with Figure V-13. Power/Weight R lass 

### evaluate and confirm that they represente d a fairly reasonable homogene

were sp ance models . Since ctiveness estim ppropriate for these type vehicles, NH ormance subcla ication. To dete h car subclass to maximiz

lasses, NH ascending rank order) the power-to-weigh icle in a subclass. An exa le of the Compact subc 3. The su en manually re viewed by NHTSA staff to determ een “performanc in each class.

a tio for Compact Subc

Compact Subclass - P/W Ratio in Ascending OrderP

0 0.02 0.04 0.06 0.08

0.1 0.12 0.14 0.16 0.18

0.2ower (HP) / Curb Wght. (Lbs) Compact

Transition Area

High Perf. Normal Perf.



### A total of eight classes (including performance subclasses) were identified for the car segment: Subcompact, Subcompact Pe rformance, Compact, Compact Performance Midsize, Midsize Performance, Large and , Large Performance. In total, the number of ars that were ultimately assigned to a performance subclass was less than 10 percent. Table V-6 ss. c provides examples of the types of vehicles assigned to each car subcla

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### Table V-6 Passenger Car Subclass es Example (MY 2008) Vehicles

Class Example vehicles Subcompact 

### Chevy Aveo, Hyundai Accent Subcompact Performance 

### Mazda MX-5, BMW Z4

Compact 

### Chevy Cobalt, Nissan Sentra and Altima Compact Performance 

### Audi S4, Mazda RX8 Midsize 

### Chevy Impala, , Toyota Camry, Honda Accord, Hyundai Azera Midsize Performance 

### Chevy Corvette, Ford Mustang (V8), Nissan G37 Coupe Large 

### Audi A8, Cadillac CTS and DTS Large Perfor 

### Daimler CL600 mance 

### Bentley Arnage,

For light trucks, as in the MY 2011 final rule, NHTSA found less of a distinction in the anticipated vehicle fleet during the model years covered by the rulemaking between SUVs and pickup trucks than appear ed to exist in earlier rulemakings. We anticipate fewer ladder-frame and more uni body pickups, and that many pickups wil share common powertrains with SUVs. Thus, SUVs and pickups are grouped in the same subclasses. Additionally, it made sense to carry forward NHTSA’s decision from the MY 2011 final rule to employ a separate minivan class, because minivans ( e.g. , th Honda Odyssey) are more car-like and differ si gnificantly in terms of structural and other engineering characteristics as compared to other vans ( e.g. , Ford’s E-Series—also known as Econoline—vans) intended for more passengers and/or heavier cargo and which are more truck-like. Thus, the remaining vehicles (other vans, pickups, and SUVs) were then segregated into three footprint ranges and assigned a class of Small Truck/SUV, Midsize Truck/SUV, and Large Truck/SUV based on their footprints. l

e

NHTSA staff then manually reviewed each population for inconsis tent vehicles based on engine cylinder count, weight (curb and/or gross), or in tended usage, since these are important considerations for technology application, and reassigned vehicles to classes as appropriate. This system produced four tr uck segment subclasses—minivans and small, medium, and large SUVs/Pickups/Vans. Table V-7 provides examples of the types of vehicles assigned to each truck subclass.

Table V-7. Light Truck Subcla sses Example (MY 2008) Vehicles Class Example vehicles Minivans 

### Dodge Caravan, Toyota Sienna Small SUV/Pickup/Van 

### Ford Escape & Ranger, Nissan Rogue

Midsize 

### Chevy Colorado, Jeep Wrangler, Toyota Tacoma SUV/Pickup/Van Large 

### Chevy Silverado, Ford E-Series, T SUV/Pickup/Van 

### oyota Sequoia

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### e

rulema

e

the

A’s

l

r

M, in

the etermine its components and what they cost.

icle

technologies, upon further joint review and fo r purposes of the MY 2012-2016 standards, the agencies decided that some of the costin g information in that report was no longer accurate due to downward trends in commodity prices since the publication of that report.
ents

nt As mentioned above, NHTSA employed this method for assigning vehicle subclasses for this final rule after reviewing the process used in the MY 2011 final rul and concluding that it continued to be a reasonable approach for purposes of this king. NHTSA believes that this me thod substantially improves the overall accuracy of the results as compared to systems employed previously, due to the close manual review by NHTSA staff to ensure pr oper assignments, the use of performanc subclasses in the car segment, and the condensi ng of subclasses in the truck segment, all of which further refine the system without overly complicating the CAFE modeling process. Nevertheless, NHTSA invites comme nts on the method of assigning vehicles to subclasses for the purposes of technology application in the CAFE model, and on issue of technology-application subclasses generally.

5. How did NHTSA develop technology cost and effectiveness estimates for this final rule?



### Building on NHTSA’s estimates developed for the MY 2011 final rule and EP Advanced Notice of Proposed Rulemaking, which relied on the 2008 Staff Technica Report, 108 

### the agencies took a fresh look at t echnology cost and effectiveness values fo purposes of the joint final rule under the National Program. For costs, the agencies reconsidered both the direct or “piece” costs and indirect costs of individual components of technologies. For the direct costs, the agencies followed a bill of materials (BOM) approach employed by NHTSA in NHTSA’s MY 2011 final rule based on recommendation from Ricardo, Inc. Ricardo was hired by NHTSA, as discussed previously, to aid in the analysis of public comments on its proposed standards for MYs 2011-2015 because of its expertise in the area of fuel economy technologies A BO a general sense, is a list of components that make up a system—in this case, an item of fuel economy-improving technology. The BOM approach is similar in concept to the approach used in tear down studies In order to determine what a system costs, one of first steps is to d NHTSA and EPA estimated these components and their costs based on a number of sources for cost-related information. The objective was to use those sources of information considered to be most credible for projecting the costs of individual veh technologies. For example, while NHTSA and Ricardo engineers had relied considerably in the MY 2011 final rule on the 2008 Mart ec Report for costing contents of some

The agencies reviewed, revalidated or updated cost estimates for individual compon based on new information. Thus while NHTSA and EPA found that much of the cost information used in NHTSA’s MY 2011 final rule and EPA’s staff report was consiste to a great extent, the agencies, in re considering information from many sources, 109

teport, the 2004 study done by NESCCAF, the California Air Resources Board (CARB)

(EEA) for the Department of Energy, a study done by Martec for the Alliance of 108 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Ligh Duty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, March 2008.
109 The 2002 NAS R Initial Statement of Reasons in support of their carbon rulemaking a 2006 study done by Energy and Environmental Analysis

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### sels, SGDI, Valve Train Lift Technologies, 6 speed automatic
transm

e

for additional engine and tra

e revised component costs of several major te chnologies (turbocharging downsizing, mild and strong hybrids, die ission and dual clutch transmission). These are discussed at length below. For some technologies such as turbocharging/downsizing, SGDI, 6 speed automatic transmission and dual clutch transmission, the agencies relied, to the extent possible, on the tear down data available and scaling methodologies used in EPA’s ongoing study with FEV Inc., an independent engine and powertrain systems research, design and development company. This study consists of complete system tear-down to evaluate technologies down to the nuts and bolts to arrive at very detailed estimates of th costs associated with manufacturing them. 110 

### For the NPRM, the agencies used a completed analysis to estimate costs of turbocharging with downsizing for I4 engines only. The NPRM stated that tear-down cost estimates from FEV nsmission technologies be came available shortly before the release of the NPRM, but not in time to be incorporated into the agencies’ cost analysis of the proposed standards. These preliminary results were made available for review and the agencies stated they would consider this informa tion for use in the final rule analysis. 111 

### The NPRM also stated that a detailed report would be submitted to the docket on these additional technologies during the public comment period for this rule. That deadline was not met but all additional technologies for which cost study tear downs have been completed from this study with FEV (studies have now been completed on turbocharging and downsizing for V6 and V8 engines, stoich iometric gasoline direct injection, 6/7/8- speed automatic transmission and dual clut ch transmission technologies) have been considered for the final rule and details are contained in two reports placed in the docket. 112 113 

### EPA and NHTSA reviewed all of the above information in order todevelop the best estimates of availability, cost and effectiveness of these fuelsaving/CO 2 

### -reducing technologies. The confidential information provided by manufacturer under their product plan submi ssions to the agencies or discussed in meetings between the agencies and the manufacturers and suppliers served largely as a check on publicly-available data. For the other technologies, because tear down studies were not yet available, th agencies decided to pursue the (BOM) approa ch considering all sources of information. The agencies worked together intensively during the summer of 2009 to determine component costs for each of the technologies and build up the costs accordingly. Where estimates differ between sources, we have us ed engineering judgment to arrive at what

Automobile Man acturers and the 2008 Martec Report which updated that study, and vehicle fuel
economy arch 2009

ty Technology Cost Analysis PilotStudy,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149. uf certification data and confidential data submitted by manufacturers in response to the M request for product plans.
110 “Draft Report – Light-Duty Technology Cost Analysis Pilot Study,” U.S. Environmental Protection Agency, Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009
111 Memorandum from Don Kopinski, U.S. EPA, Docket No. EPA-HQ-OAR-2009-0472-0217, dated September 11, 2009.
112 U.S. Environmental Protection Agency, “Draft Report – Light-Du

113 Insert docket info for the FEV final report on additional case studies beyond the I4 to I4 (should be available in mid-Feb).

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### we beli at

fferent for

th o reexamined the estimates from NHTSA’s MY 2011 CAFE final rule and EPA’s ANPRM 2

re to ns and other vehicle attributes such as performance, refinem

s

icant

ir

n each m eve to be the best cost estimate ava ilable today, and explained the basis for th exercise of judgment. Once costs were determined, they were adjusted to ensure that they were all expressed in 2007 dollars using a ratio of GDP values for the associated calendar
years, 114 

### and indirect costs were accounted for using the new approach developed by EPA for this rulemaking and explained in the joint TSD, rather than using the traditional Retail Price Equivalent (RPE) multiplier of 1.5. This report can be found in the docket for this notice. NHTSA and EPA also considered how costs should be adjusted by modifying or scaling content assumptions to account for differences across the range of vehicle sizes and functional requirements, and adjusted the associated material cost impacts to account for the revised content, although these adjustments were di each agency due to the different vehicle subclasses used in their respective models. Regarding estimates for technology effectiveness, NHTSA in coordination wi EPA als and Staff Technical Report, which la rgely mirrored NHTSA’s NPRM estimates in the 2008 proposed rule. The agencies also reconsidered other sources such as the 200 NAS Report, the 2004 NESCCAF report and recent CAFE compliance data. Using the BOM framework utilized in MY 2011 CAFE final rule, NHTSA and EPA engineers reviewed effectiveness information from th e multiple sources for each technology. Together, they compared the multiple estimates and assessed their validity, taking ca ensure that common BOM definitio ent, and drivability were taken into account. However, because the agencies’ respective models employ different number s of vehicle subclasses and use different technology decision trees to arrive at the st andards, direct comparison of technologie was somewhat more complicated. To address this and to assure an apples-to-apple comparison, NHTSA and EPA developed mapping techniques, devising technology packages and corresponding incremental technology estimates. This approach helped compare incremental and packaged estimates and derive results that are consistent and could be translated into the respective models of the agencies. In general, most effectiveness estimates used in both the MY 2011 CAFE final rule and the 2008 EPA staff report were determined to be accurate and were carried forward without signif change into this rulemaking. When NH TSA and EPA’s estimates for effectiveness diverged slightly due differences in how agencies apply technologies to vehicles in the respective models, the agencies will report the ranges for the effectiveness values used i odel, as well as the reasons the range is reasonable.

6. Learning curves



### In the MY 2011 CAFE final rule and its related 2008 proposal, NHTSA accounted for the cost reductions manufact urers realized through experiential learning achieved through applying technologies. NHTSA continues to account for these cost reductions in this final rule through the use of two mutually exclusive learning types, “volume-based” and “time-based,” as discussed below.

114 NHTSA examined the use of the CPI multiplier instead of GDP for adjusting these dollar values, bu t found the difference to be exceedingly small – only $0.14 over $100.

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### using

,

e

e 2008 NPRM, NHTSA applied a learn e (thus

as

rring

es.

the

he MY 2011 final rule. Comm n

me olumes that were on the extended, flatter p

ns if fic, creases as the cu rrent time- and volume-based learning curves
appear be

st le In the 2008 NPRM, working in conjunction with the EPA, NHTSA applied learning factors to technology costs for the fi rst time. The factors were developed the three parameters of learning threshold, le arning rate, and the initial technology cost and were based on the “experience curve” concept which describes reductions in production costs as a function of accumulated production volume. The typical curv shows a relatively steep initial decline in cost which flattens out to a gentle downwardly sloping line as the volume increase to large values. In th ing rate discount of 20 percent for each successive doubling of production volum (on a per manufacturer basis), and a learning threshold of 25,000 units was assumed a technology was viewed as being fully lear ned out at 100,000 units). The factor was only applied to certain technologies th at were considered emerging or newly implemented on the basis that significant cost improvements would be achieved economies of scale were realized ( i.e. , the technologies were on the steep part of the curve). In the MY 2011 final rule, NHTSA continued to use this learning factor, refe to it as volume-based learning since the cost reductions were determined by production volume increases, and again only applied it to low volume, emerging technologi However, and in response to comments, NHTSA revised its assumptions on learning threshold, basing them instead on an industry-wide production basis, and increasing threshold to 300,000 units annually (and thus a technology is considered to be fully learned out at 1.2M annual units). However commenters to the 2008 NPRM also described another type of learning factor which NHTSA, working in conjunctio n with its contractor Ricardo, Inc who assisted in finalizing the rule, adopted a nd implemented in t enters described a relatively small negot iated cost decrease that occurred on a annual basis through contractual agreements with first tier component and systems suppliers. These agreements were generally only applicable to readily available, high volume technologies that were commonly in use by multiple OEMs. Based on the sa experience curve principal, however at production v art of the curve (and thus the types of volumes that more accurately represent an annual industry-wide production volume), NHTSA adopted this type learning and referred to it as time-based learning. An annual cost reduction of 3 percent in the second and each subsequent year, which was cons istent with estimates from commenters and supported by work Ricardo conducted for NHTSA, was used in the 2011 final rule. NHTSA received comments from ICCT and Ferrari related to learning curves. ICCT stated the agencies could improve the accuracy of the learning curve assumptio they used a more dynamic or continuous lear ning curve that is more technology-speci rather than using step de to do. ICCT also commented on the appropriate application of volume- versus time-based learning, and stated further that worldwide production volumes should taken into account when developing learning curves. Ferrari commented that is more difficult for small-volume manufacturers to negot iate cost decreases from things like co learning effects with their suppliers, implying that learning effects may not be applicab equally for all manufacturers. NHTSA agrees that a continuous curv e, if implemente d correctly, could potentially improve the accuracy of modeli ng cost-learning effects. To implement a

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### el

of this sis, however, NHTSA

hat

ard to ICCT’s comments on th e appropriate application of volumeersus time-based learning, however, it seems as though ICCT is referencing a study that defines volume- and time-based learning in a different manner than the currentdefinitions used by the agencies. The agencies use “volume-based” learning for nonmature technologies that have the potentia l for significant cost reductions through learning, while “time-based” learning is used for mature technologies that have already had significant cost reductions and only have the potential for smaller cost reductions. For “time-based” learning, the agencies chose to emulate the small year-over-year cost reductions manufacturers realize through de fined cost reductions, approximately 3 percent per year, negotiated into contracts with suppliers. And finally, in response to Ferrari’s comment, NHTSA recognizes that cost negotiations can be differen t for different manufacturers, but believes that on balance, cost learning at the supplier level will generally impact costs to all purchasers. Thus, if cost reductions are realized for a particular technology, all entities that purchase the technology will benefit from these cost reductions. In developing this final rule, NHTSA, taking into account comments received, has reviewed both types of learning factors, and the thresholds (300,000) and cost reduction rates (20 percent for volume, 3 percent for time-based) they rely on, as implemented in the MY 2011 final rule and the NPRM, and has concluded that both learning factors continue to be accurate and appropriate. NHTSA therefore continues to implement both time- and volume-based learning in the analyses that supports this final rule. Noting that only one type of learning can be applied to any single technology, if any learning is applied at all, NHTSA reviewed each tec hnology to determine which if any learning factor was appropriate. Working under the principal that volume-based learning is applicable to lower volume, higher complexity, emerging technologies while time-based learning is appropriate for high volume, established a nd readily available technologies, NHTSA determined the learning factors shown in Tabl e V-8 below. These factors, which were used in this analysis, closely resemble the settings used in the 2011 final rule with the exception of PSHEV which has been revised from time-based to volume-based learning. Note that no learning is applied to technologies which are potentially affected by commodity costs (LUB, ROLL) or that have loosely-defined BOMs (EFR, LDB) in the continuous curve, however, NHTSA would need to develop a learning curve cost mod to be integrated into the agency’s existing model for CAFE analysis. Due to time constraints the agencies were not able to in vestigate fully the use of a continuous costlearning effects curve for each technology, but we will investigate the applicability approach for future rulemakings. For purposes of the final rule analy believes that while more detailed cost learning approaches may eventually be possible, the approach taken for this final rule is valid. Additionally, while the agencies agree that worldwide production volumes can impact learning curves, the agencies do not forecast worldwide vehicle production volumes in addition to the already complex task of forecasting the U.S. market. T said, the agencies do consider current and pr ojected worldwide technology proliferation when determining the maturity of a par ticular technology used to determine the appropriateness of applying tim e- or volume-based learning, which helps to account for the effect of globalized production. With reg

v

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### lumebased learning has been applied, NHTSA has ta ken great care to ensure that the initial costs (before learning is applied) properly reflect low volume, unlearned cost estimates ( i.e ., any high volume cost estimates used in the analysis have been appropriately “reverse learned” so as not to underestimate the final learned costs). Regarding these initial volume-based learning costs, ICCT commented that it would be helpful to clarify the assumed production volumes to better interpret the costs of technologies, which are eligible for “volume-based” learning. The agencies have not defined the specific cumulative production vo lume for technologies that are eligible for volume-based learning. When developing the costs for these technologies it was assumed that cumulative production volumes have not exceeded 300,000 but the agencies did not try to specify the exact production volume. Due to the uncertainty of projected production volumes the agencies did not believe it advantageous to define costs based on a finer level detail and believe the costs developed are the most appropriate for this rulemaking. this analysis, as was also the case in the MY 2011 final rule analysis. Where vo

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### Table V-8 Application of learning-related cost reductions for technologiesDE 

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2-

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### Model Learning Learning
ate

Speed 

### 3%

3%

Mode Hybrid 2MHEV 

### VOLUME 20%

AERO 

### TIME 3% Technology Abbreviation Type R

Low Friction Lubricants LUB Engine Friction Reduction EFR VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 

### TIME 3% Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 

### TIME 3% Cylinder Deactivation on SOHC DEACS 

### TIME 3% VVT - Intake Cam Phasing (ICP) ICP 

### TIME 3% VVT – Dual Cam Phasing (DCP) DCP 

### TIME 3% Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 

### TIME 3% Continuously Variable Valve Lift (CVVL) CVVL 

### TIME 3% Cylinder Deactivation on DOHC DEADD 

### TIME 3% Cylinder Deactivation on OHV DEACO 

### TIME 3% VVT - Coupled Cam Phasing (CCP) on OHV CCPO 

### TIME 3% Discrete Variable Valve Lift (DVVL) on OHV DVVLO 

### TIME 3%

Conversion to DOHC with DCP CDOHC 

### TIME 3%

Stoichiometric Gasoline Direct Injection (GDI) SGDI 

### TIME 3%

Combustion Restart CBRST 

### TIME 3% Turbocharging and Downsizing TRBDS 

### TIME 3% Exhaust Gas Recirculation (EGR) Boost EGRB 

### TIME 3% Conversion to Diesel following CBRST DSLC 

### TIME 3% Conversion to Diesel following TRBDS DSLT 

### TIME 3% Manual/Improved Internals 6MAN 

### TIME 3% Improved Auto. Trans. Controls/Externals IATC 

### TIME Continuously Variable Transmission CVT 

### TIME 3% 6/7/8-Speed Auto. Trans with Improved Internals NAUTO 

### TIME

Dual Clutch or Automated Manual Transmission DCTAM 

### TIME 3% Electric Power Steering EPS 

### TIME 3% Improved Accessories IACC 

### TIME 3% 12V Micro-Hybrid MHEV 

### TIME 3% Belt Integrated Starter Generator BISG 

### VOLUME 20% Crank Integrated Starter Generator CISG 

### VOLUME 20% Power Split Hybrid PSHEV 

### VOLUME 20%

Plug-in Hybrid PHEV 

### VOLUME 20% Mass Reduction 1 (1.5%) MS1 Mass Reduction 2 (3.5% – 8.5%) MS2 Low Rolling Resistance Tires ROLL Low Drag Brakes LDB Secondary Axle Disconnect 4WD SAX 

### TIME 3% Aero Drag Reduction

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### e 5



### ,

on

reased fuel consumption re duction). An example of a positive synergy might b r hicle

e ping

ere pumping losses are less significant, leaving less opportunity for the com

lumped

s

s with an

e

f c. However, regardl 7. Technology synergies



### When two or more technologies are a dded to a particular vehicle model to improve its fuel efficiency, the resultant fu el consumption reduction may sometimes b higher or lower than the product of the individual effectiveness values for those items. 11 

### This may occur because one or more technologies applied to the same vehicle partially address the same source (or sources) of engine, drivetrain or vehicle losses. Alternately this effect may be seen when one technology shifts the engine operating points, and therefore increases or reduces the fuel consumption reduction achieved by another technology or set of technologies. The difference between the observed fuel consumpti reduction associated with a set of technol ogies and the product of the individual effectiveness values in that set is referred to as a “synergy.” Synergies may be positive (increased fuel consumption reduction compared to the product of the individual effects) or negative (dec e a vehicle technology that reduces road loads at highway speeds ( e.g. , lowe aerodynamic drag or low rolling resistance tires), that could effectively extend the ve operating range over which cy linder deactivation may be employed, thus allowing a greater fuel consumption reduct ion than anticipated or predicted by analysis. An exampl of a negative synergy might be a variable valvetrain technology, which reduces pum losses by altering the profile of the engine speed/load map, and a six-speed automatic transmission, which shifts the engine operating points to a portion of the engine speed/load map wh bined technologies to decrease fuel consumption. As the complexity of the technology combinations is increased, and th e number of interacting technologies grows accordingly, it becomes increasingly important to account for these synergies. NHTSA determined synergistic impacts fo r this rulemaking using EPA’s “ parameter” analysis tool, which EPA desc ribed at length in its March 2008 Staff Technical Report. 116 

### The lumped parameter tool is a spreadsheet model that represent energy consumption in terms of average performance over the fuel economy test procedure, rather than explicitly analyzing spec ific drive cycles. The tool begin apportionment of fuel consumption across several loss mechanisms and accounts for th average extent to which different technologies affect these loss mechanisms using estimates of engine, drivetrain and vehicle characteristics that are averaged over the EPA fuel economy drive cycle. Results of this analysis were generally consistent with those o full-scale vehicle simulation modeling performed in 2007 by Ricardo, In ess of a generally consistent set of results for the vehicle class and set of technologies studied, the lumped parameter tool is not a full vehicle simulation and

115 More specifically, the resultant is cal culated as the products of the differences between the numeric alue one (i.e., 1.0) and the technology-specific levels of effectiveness in reducing fuel consumption (expressed as a numeric value also, i.e., 10% = 0.10). For example, not accounting for interactions, if technologies A and B are estimated to reduce fuel consumption by 10% (i.e., 0.1) and 20% (i.e., 0.2) respectively, the “product of the individual effectiveness values” would be (1 – 0.1) times (1 – 0.2), or 0.9 times 0.8, which equals 0.72, corresponding to a combined effectiveness of (1 - .72 = .28) or 28% rather

ghtoxide Emissions; EPA420-R-08-008, March 2008. v

than the 30% obtained by adding 10% to 20%. The “synergy factors” discussed in this section further adjust these multiplicatively combined effectiveness values.
116 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Li duty Vehicle Carbon Di

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### cannot replicat Because NHTSA applies technologies indi vidually in its modeling analysis, NHTSA incorporates synergistic effects betwee n pairings of individual technologies. The use of discrete technology pair incremental synergies is similar to that in DOE’s National Energy Modeling System (NEMS). 117 

### Inputs to the Volpe model incorporate NEMS-identified pairs, as well as additional pairs from the set of technologies considered in the Volpe model. For this final rule, as was the case in the 2011 final rule, NHTSA used the lumped parameter tool to evaluate accurate synergy values. During the 2011 final rule analysis, and with the assistance of Ricardo, NHTSA modified the lumped parameter tool by updating the list of tec hnologies and their asso ciated effectiveness values, and expanding the list of synergy pair ings based on further consideration of the technologies for which a competition for losses would be expected, for the purposes of evaluating appropriate synergy values. Table V-9 below presents the types of losses that were analyzed. NHTSA notes that synergies that occur within a particular decision tree are already accounted for within the incremental effectiveness values assigned for each technology, and therefore additional synergy pairs for these technologies are not required. For example, all engine technologies take in to account the synergies that occur with the preceding/existing engine technologies, a nd all transmission technologies take into account synergies of preceding transmission te chnologies, etc. These synergy factors are accounted for in the fuel c onsumption improvemen t estimates in the input files used by the Volpe model. For applying incremental synergy factors in separate path technologies, i.e., between two or more decision trees, the Volp e model uses an input table (see Tables V10 a-d) which lists technology pairings and incremental synergy factors associated with those pairings (most of which are betwee n engine technologies and transmission/ electrification/hybrid technologies). When a technology is applied to a vehicle by the Volpe model, all instances of that technology in the incremental synergy table which match technologies already applied to the vehicle (either pre-existing or previously applied by the Volpe model) are summed and applied to the fuel consumption improvement factor of the technology bei ng applied. Synergies for the strong hybrid technology fuel consumption reductions are included in the incremental value for the specific hybrid technology since the model applies technologies in the order of the most effectiveness for least cost and also applies all available electrification and transmission technologies before applying strong hybrid technologies. NHTSA received only one comment regarding synergies, from MEMA, who commented that NHTSA’s Volpe model adequately addressed synergistic effects. Having received no information to the contrary, NHTSA finalized the synergy approach and values for the final rule. e the physics of such a simulation.

117 U.S. partment of Energy, Energy Information Administration, Transportation Sector Module of the

ationa ergy Modeling System: Model Documentation 2007 , May 2007, Washington, DC, DOE/EIAM070(2007), at 29-30. Available at http://tonto.eia.doe.gov/ftproot/modeldoc/m070(2007).pdf (last accessed Jul. 6, 2009). De N l En

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### Table V alysis

VEHICLE TRANS ENGINE ENGINE ENGINE ENGINE Tractive Drivetrain Mechanical Pumping Accessory Indicated

Effort Losses Friction Losses Losses Efficiency ENGINE Low Friction Lubricants + Engine Friction Reduction + VVT - Coupled Cam Phasing (CCP) on SOHC -+ + Discrete Variable Valve Lift (DVVL) on SOHC - + Cylinder Deactivation on SOHC + + VVT - Intake Cam Phasing (ICP) -+ + VVT - Dual Cam Phasing (DCP) -+ + Discrete Variable Valve Lift (DVVL) on DOHC - + Continuously Variable Valve Lift (CVVL) - + Cylinder Deactivation on DOHC + + Cylinder Deactivation on OHV + + VVT - Coupled Cam Phasing (CCP) on OHV -+ + Discrete Variable Valve Lift (DVVL) on OHV - + Conversion to DOHC with DCP -+ + Stoichiometric Gasoline Direct Injection (GDI) + Combustion Restart + ++ Turbocharging and Downsizing - + Exhaust Gas Recirculation (EGR) Boos 

### -9 Loss Factors Considered in Synergy An

t + Conversion to Diesel + + TRANSMISSION (MANUAL) 6-Speed Manual/Improved Internals + + TRANSMISSION (AUTOMATIC) Improved Auto. Trans. Controls/Externals + + Continuously Variable Transmission - + 6/7/8-Speed Auto. Trans with Impr. Internals + + Dual Clutch/Automated Manual Transmission + ELECTRIFICATION/ACCESSORY Electric Power Steering + Improved Accessories + 12V Micro-Hybri d + ++ Belt Integrated Starter Generato r + ++ Crank Integrated Starter Generator + ++ (STRONG) HYBRID Power Split Hybri d + + ++ 2-Mode Hybrid + + ++ Plug-in Hybrid + + ++ VEHICLE Mass Reduction 1 (1.5%) + Mass Reduction 2 (3.5% - 8.5%) + Low Rolling Resistance Tires + Low Drag Brakes + Secondary Axle Disconnect - 4WD + Aero Drag Reduction + + Technology has a positive effect on fuel consumption - Technology has a negative e ffect on fuel consumption 

# Lumped Parameter Synergy AnalysisDE 

### 159

Tec

DV DV DV DV DV DV DE DE DE DE DE DE IC IC IC IC IC IC DC DC DC DC DC DC DV DV DV DV DV DV DE DE DE DE DE DE CVV CVV CVV CVV CVV CVV
DE DE DE DE DE DE Synergies Fuel Consumption Improvement Sy nergy values by Vehicle Subclass 

### Table V-10a Synergy pairings and values

LIBERATIVE DOCUMENT – DO NOT RELEASE hnology A Technology B Subcompact PC Subcompact Perf. PC Compact PC Compact Perf. PC Midsize PC Midsize Perf. PC CCPS 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% CCPS IATC -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CCPS CVT -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% CCPS NAUTO -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% CCPS MHEV -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% CCPS BISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9%

VLS 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% VLS IATC -0.5% -0.5% -0.5% -0.5% -0.5% -0.5% VLS CVT -1.4% -1.4% -1.4% -1.4% -1.4% -1.4% VLS NAUTO -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% VLS MHEV -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% VLS BISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% ACS 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% ACS IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% ACS CVT -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% ACS NAUTO -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% ACS MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% ACS BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%
P 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1%
P IATC -0.2% -0.2% -0.2% -0.2% -0.2% -0.2%
P CVT -0.8% -0.8% -0.8% -0.8% -0.8% -0.8%
P NAUTO -0.4% -0.4% -0.4% -0.4% -0.4% -0.4%
P MHEV -0.7% -0.7% -0.7% -0.7% -0.7% -0.7%
P BISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9%
P 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1%
P IATC -0.4% -0.4% -0.4% -0.4% -0.4% -0.4%
P CVT -1.3% -1.3% -1.3% -1.3% -1.3% -1.3%
P NAUTO -0.7% -0.7% -0.7% -0.7% -0.7% -0.7%
P MHEV -0.8% -0.8% -0.8% -0.8% -0.8% -0.8%
P BISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% VLD 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% VLD IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% VLD CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% VLD NAUTO -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% VLD MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% VLD BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% ACD 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% ACD IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% ACD CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% ACD NAUTO -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% ACD MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% ACD BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

L 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% L IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% L CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% L NAUTO -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% L MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% L BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% ACO 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% ACO IATC -0.5% -0.5% -0.5% -0.5% -0.5% -0.5% ACO CVT -1.4% -1.4% -1.4% -1.4% -1.4% -1.4% ACO NAUTO -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% ACO MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% ACO BISG -1.2% -1.2% -1.2% -1.2% -1.2% -1.2% Positive values are positive synergies, negative values are dissynergies.DE 

### 160

Tec

CCP CCP CCP CCP CCP CCP

CB CB CB CB CB CB CB
TR TR TR TR TR TR DS DS DS DS DS DS DS DS DS DS DS DS CCP

IC DCP

CCP

CB
TR DS DS Fuel Consumption Improvement Synergy values by Vehicle Subclass 

### Table V-10b Synergy pairings and values

LIBERATIVE DOCUMENT – DO NOT RELEASE hnology A Technology B Subcompact PC Subcompact Perf. PC Compact PC Compact Perf. PC Midsize PC Midsize Perf. PC O 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% O IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% O CVT -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% O NAUTO -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% O MHEV -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% O BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DVVLO 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% DVVLO IATC -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% DVVLO CVT -2.0% -2.0% -2.0% -2.0% -2.0% -2.0% DVVLO NAUTO -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DVVLO MHEV -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DVVLO BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CDOHC 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CDOHC IATC -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% CDOHC CVT -2.0% -2.0% -2.0% -2.0% -2.0% -2.0% CDOHC NAUTO -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CDOHC MHEV -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% CDOHC BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

RST 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% RST IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% RST CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% RST NAUTO -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% RST BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% RST EPS -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% RST IACC -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% BDS 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% BDS IATC -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% BDS CVT -2.4% -2.4% -2.4% -2.4% -2.4% -2.4% BDS NAUTO -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% BDS MHEV -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% BDS BISG -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% LC 6MAN -0.3% -0.3% -0.3% -0.3% -0.3% -0.3% LC IATC 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% LC CVT -2.9% -2.9% -2.9% -2.9% -2.9% -2.9% LC NAUTO -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% LC MHEV -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% LC BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% LT 6MAN -0.3% -0.3% -0.3% -0.3% -0.3% -0.3% LT IATC 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% LT CVT -2.9% -2.9% -2.9% -2.9% -2.9% -2.9% LT NAUTO -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% LT MHEV -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% LT BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

S CISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% DVVLS CISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DEACS CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

P CISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% CISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DVVLD CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DEACD CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CVVL CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DEACO CISG -1.2% -1.2% -1.2% -1.2% -1.2% -1.2% O CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DVVLO CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CDOHC CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

RST CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% BDS CISG -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% LC CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% LT CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% Synergies Positive values are positive synerg ies, negative values are dissynergies.DE 

### 161

Tec

CCP CCP CCP CCP CCP CCP

DEA DEA DEA DEA DEA DEA IC IC IC IC IC IC DCP DCP DCP DCP DCP DCP

DEA DEA DEA DEA DEA DEA CVV CVV CVV CVV CVV CVV DEA DEA DEA DEA DEA DEA Fuel Consumption Impr ovement Synergy values by Vehicle Subclass 

### Table V-10c Synergy pairings and values

LIBERATIVE DOCUMENT – DO NOT RELEASE hnology A Technology B Large PC Large Perf. PC Minivan LT Small LT Midsize LT Large LT

S 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% S IATC -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% S CVT -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% S NAUTO -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% S MHEV -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% S BISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% DVVLS 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% DVVLS IATC -0.5% -0.5% -0.5% -0.5% -0.5% -0.5% DVVLS CVT -1.4% -1.4% -1.4% -1.4% -1.4% -1.4% DVVLS NAUTO -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% DVVLS MHEV -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% DVVLS BISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% CS 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CS IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% CS CVT -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% CS NAUTO -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% CS MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% CS BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%
P 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1%
P IATC -0.2% -0.2% -0.2% -0.2% -0.2% -0.2%
P CVT -0.8% -0.8% -0.8% -0.8% -0.8% -0.8%
P NAUTO -0.4% -0.4% -0.4% -0.4% -0.4% -0.4%
P MHEV -0.7% -0.7% -0.7% -0.7% -0.7% -0.7%
P BISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% IATC -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% CVT -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% NAUTO -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% MHEV -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% BISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DVVLD 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% DVVLD IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% DVVLD CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% DVVLD NAUTO -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DVVLD MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% DVVLD BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CD 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CD IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% CD CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% CD NAUTO -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% CD MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% CD BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% L 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% L IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% L CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% L NAUTO -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% L MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% L BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CO 6MAN -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% CO IATC -0.5% -0.5% -0.5% -0.5% -0.5% -0.5% CO CVT -1.4% -1.4% -1.4% -1.4% -1.4% -1.4% CO NAUTO -0.8% -0.8% -0.8% -0.8% -0.8% -0.8% CO MHEV -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% CO BISG -1.2% -1.2% -1.2% -1.2% -1.2% -1.2% Synergies Positive values are positive synergies, negative values are dissynergies.DELIBE 

### 162

and values

DS

DE Fuel Consumption Improvement Synergy values by Vehicle Subclass 

### Table V-10d Synergy pairings

RATIVE DOCUMENT – DO NOT RELEASE -1.1% -1.1% -1.1% -1.1%

1% 1% ACO CISG -1.2% -1.2% -1.2% -1.2% -1.2% -1.2%

1% -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% Synergies s.
Technology A Technology B Large PC Large Perf. PC Minivan LT Small LT Midsize LT Large LT

CCPO 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CCPO IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% CCPO CVT -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% CCPO NAUTO -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% CCPO MHEV -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% CCPO BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DVVLO 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% DVVLO IATC -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% DVVLO CVT -2.0% -2.0% -2.0% -2.0% -2.0% -2.0% DVVLO NAUTO -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DVVLO MHEV -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DVVLO BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CDOHC 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CDOHC IATC -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% CDOHC CVT -2.0% -2.0% -2.0% -2.0% -2.0% -2.0% CDOHC NAUTO -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CDOHC MHEV -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% CDOHC BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CBRST 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CBRST IATC -0.6% -0.6% -0.6% -0.6% -0.6% -0.6% CBRST CVT -1.8% -1.8% -1.8% -1.8% -1.8% -1.8% CBRST NAUTO -1.1% -1.1% CBR Positive values are positive synergies, negative values are dissynergie

ST BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CBRST EPS -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% CBRST IACC -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% TRBDS 6MAN -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% TRBDS IATC -0.7% -0.7% -0.7% -0.7% -0.7% -0.7% TRBDS CVT -2.4% -2.4% -2.4% -2.4% -2.4% -2.4% TRBDS NAUTO -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% TRBDS MHEV -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% TRBDS BISG -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% DSLC 6MAN -0.3% -0.3% -0.3% -0.3% -0.3% -0.3% DSLC IATC 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% DSLC CVT -2.9% -2.9% -2.9% -2.9% -2.9% -2.9% DSLC NAUTO -1.7% -1.7% -1.7% -1.7% -1.7% -1.7% DSLC MHEV -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DSLC BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DSLT 6MAN -0.3% -0.3% -0.3% -0.3% -0.3% -0.3% DSLT IATC 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% DSLT CVT -2.9% -2.9% -2.9% -2.9% -2.9% -2.9% DSLT NAUTO -1.7% -1.7% -1. 7% -1.7% -1.7% -1.7%

LT MHEV -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DSLT BISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CCPS CISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% DVVLS CISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DEACS CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% ICP CISG -0.9% -0.9% -0.9% -0.9% -0.9% -0.9% DCP CISG -1.0% -1.0% -1.0% -1.0% -1.0% -1.0% DVVLD CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DEACD CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1. CVVL CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.

CCPO CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% DVVLO CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CDOHC CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% CBRST CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1.1% TRBDS CISG -1.3% -1.3% -1.3% -1.3% -1.3% -1.3% DSLC CISG -1.1% -1.1% -1.1% -1.1% -1.1% -1. DSLT CISG

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### o terms th at describe when technology changes to vehicle

a n or at least two years before a scheduled redesign. For the majority of technol vel

rates at

model
year, an ign

Even within the context of the phase-in caps discussed below, NHTSA considers

ery year. In reality, 9. Refresh and redesign schedule



### Because of the complexities of the automobile manufacturing process, manufacturers are generally only able to add new technologies to vehicles on a specific schedule; just because a technology exists in the marketplace, does not mean that it is immediately available for application on all of a manufacturer’s vehicles. In the automobile industry there are tw s occur: redesign and refresh (i.e., freshening). Vehicle redesign usually refers to significant changes to a vehicle’s appearance, shape, dimensions, and powertrain. Redesign is traditionally associated with the introduction of “new” vehicles into the market, often characterized as the “next generation” of a vehicle, or a new platform. Vehicle refresh usually refers to less extensive vehicle modifications, such as minor changes to a vehicle’s appearance, a moderate upgrade to a powertrain system, or small changes to the vehicle’s feature or safety equipment content. Refresh is traditionally associated with mid-cycle cosmetic changes to a vehicle, within its current generation, to make it appear “fresh.” Vehicle refresh gene rally occurs no earlier than two years after vehicle redesig ogies discussed today, manufacturers will only be able to apply them at a refresh or redesign, because their a pplication would be significant enough to involve some le of engineering, testing, and calibration work. 118 

### Thus, in addition to developing methods that address limitations on the which new technologies can feasibly pene trate manufacturers’ fleets, which NHTSA refers to as phase-in caps, the agency has also developed methods to address the feasible scheduling of changes to specific vehicle models. In the Volpe model, which the agency used to support this final rule, these scheduling-related methods were first applied in 2003, in response to concerns that an earl y version of the model would sometimes add and then subsequently remove some technologies. 119 

### By 2006, these methods were integrated into a new version of the model, one which explicitly “carried forward” technologies added to one vehicle model to succeeding vehicl e models in the next d which timed the application of many technologies to coincide with the redes or freshening of any given vehicle model. 120 

### In the 2008 NPRM and subsequent final rule for the MY 2011 CAFE standards, NHTSA tied the application of the majority of technologies to a vehicle’ s refresh/redesign cycle.

these model-by-model scheduling constraints necessary in order to produce an analysis that reasonably accounts for the need for a period of stability following the redesign of any given vehicle model. If engineering, to oling, testing, and other redesign-related resources were unlimited, every vehicle model could be redesigned ev however, every vehicle redesign consumes resources simply to address the redesign, and thus cost expenditures occur. Phase-in caps, which are applied at the level of a

e 118 For example, applying material substitution through weight reduction, or even something as simple as low rolling-resistance tires, to a vehicle will likely require some level of validation and testing to ensure that the vehicle may continue to be certified as compliant with NHTSA’s Federal Motor Vehicle Safety Standards (FMVSS). Weight reduction might affect a vehicle’s crashworthiness; low rolling-resistanc tires might change vehicle’s braking characteristics or how it performs in crash avoidance tests.
119 68 FR 16874 (Apr. 7, 2003).
120 71 FR 17582 (Apr. 6, 2006).

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### ar basis,

in the analysis that supported the MY ted sis

e in how

TSA

t with

cycle for the majority of light trucks. A fuller discussion of NHTSA’s justification and
125



### ould manufacturer’s entire fleet, do not, by themselves, constrain the scheduling of changes to any particular vehicle model. Conversely , scheduling constraints to address vehicle freshening and redesign do not necessarily yiel d realistic overall penetration rates for a particular technology type (e.g ., for strong hybrids), while phase-in caps do. Thus, the two constraints work together in the model to ensure that the timing and application rate for various fuel-saving technologies is feasib le for manufacturers on a year-by-ye as required by EPCA/EISA. 121 

### For purposes of the analysis supporting this final rule, NHTSA has employed, as inputs to the Volpe model, a redesign cycl e of 5 years for all manufacturers, with a refresh cycle of 2-3 years. This is the schedule employed 2011 final rule, and is consistent with the most recent manufacturer product plans received in response to NHTSA’s March 2009 and September 2009 requests for upda plans. However, the application of the refresh/redesign cycle in the modeling analy has changed in this final rule from the MY 2011 final rule due to the characteristics of th new joint approach for establishing the baseline fleet. The paragraphs below expla NHTSA developed the refresh/redesign cycle, and how its application has changed for this final rule. In the MY 2011 final rule NHTSA develo ped the redesign and refresh schedules based on a combination of manufacturers’ confidential product plans and NHTSA’s engineering judgment. In most instances, NHTSA reviewed manufacturers’ planned redesign and refresh schedules as stated in their confidential submissions and incorporated them into the market data file, as done in past rulemakings. If companies did not provide product plan data, NHTSA used publicly available data to estimate the redesign and refresh schedules for the vehicles produced by these companies. 122 

### Unless a manufacturer submitted plans for a more rapid redesign and refresh schedule, NH assumed that passenger cars would normally be redesigned every 5 years, consisten industry trends over the last 10-15 years. 123 

### NHTSA also projected a 5-year redesign
124 

### rationale for the 5-year redesign cycle can be found in the MY 2011 final rule. Some manufacturers commented in the last round of CAFE rulemaking, even before the economic crisis had reached today’ s levels that their vehicle redesign cycles take at least five years for cars and 6 years and longer for trucks because they rely on those later years to recover investments and earn a profit. They argued that they w

121 49 U.S.C. § 32902(a) requires that NHTSA set CAFE standards at the maximum feasible level for each fleet, for each model year.
122 Sources included but were not limited to manufacturers’ web sites, industry trade publications ( e. Automotive News), and commercial data sources ( e.g. , Ward’s Automotive, etc.).
123 Exceptions were made for high performance vehicles and other vehicles that traditionally had longer than average design cycles due to their unique design characteristics and their evolutionary, as opposed to revolutionary product development practices (e.g., the Porsche 911 has remained the same basic vehicle for many years). 124 NHTSA recognized in the MY 2011 CAFE rulemaking that light trucks are currently redesigned every 5 to 7 years, with some vehicles (like full-size vans) having longer redesign periods. However, in the most competitive SUV and crossover vehicle segments, the redesign cycle currently averages slightly above 5 years. NHTSA concluded that the light truck redesign schedule will be shortened in the future due to competitive market forces. Thus, for almost all light trucks scheduled for a redesign in the early portions o the rulemaking period, NHTSA projected a 5-year redesign cycle.
125 74 FR 14265 (Mar. 30, 2009) g. ,

f

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### n

uing to

market will 5

ri

e

g analysis. NHTSA has conside

nt not be able to sustain their businesses if forced by CAFE standards to a shorter redesig cycle. Expecting that those concerns may be magnified in the current economic climate, NHTSA recognizes that some manufacturers ar e severely stressed and may be delaying or hoping to delay planned vehicle redesigns in order to conserve financial resources. However, manufacturers must balance this concern against their interest in contin provide vehicles that the public wishes to purchase, which may be redesigned or refreshed vehicles. Consistent with its forecast of the overall size of the light vehicle from MY 2011 on, the agency tentatively expects that the industry’s status improve and that manufacturers will typically redesign both car and truck models every years in order to be competitive in the market. NHTSA received comments from the Center for Biological Diversity (CBD) and Ferrari regarding redesign cycles. CBD stated that manufacturers do not necessarily adhere to the agencies’ assumed five-year redesign cycle, and may add significant technologies by redesigning vehicles at more fr equent intervals, albeit at higher costs. CBD argued that NHTSA should analyze the costs and benefits of manufacturers choosing to redesign vehicles more frequently than a 5-year average. Conversely, Ferra agreed with the agencies that major technology changes are introduced at vehicle redesigns, rather than at vehicle freshenings, stating further that as compared to full-lin manufacturers, small-volume manufa cturers in fact may have 7 to 8-year redesign cycles. In response, NHTSA recognizes that not all manufacturers follow a precise five-year redesign cycle for every vehicle they produce, 126 

### but continues to believe that the fiveyear redesign cycle assumption is a reasonable estimate of how often manufacturers can make major technological changes for purposes of its modelin 127 

### red attempting to quantify the increased cost impacts of setti ng standards that rise in stringency so rapidly that manufacturers are forced to apply “usual redesign” technologies at non-redesign intervals, but such an analysis would be exceedingly complex and is beyond the scope of this rulemaking given the timeframe and the curre

126 In prior NHTSA rulemakings, the agency was able to account for shorter redesign cycles on some models ( e.g. , some sedans), and longer redesign cycles on others ( e.g. , cargo vans), but has standardized t redesign cycle in this analysis using the transparent baseline.
127 In the MY 2011 final rule, NHTSA noted that the CAR report submitted by the Alliance, prepared by the Center for Automotive Research and EDF, stat concepti he

ed that “For a given vehicle line, the time from

o ated

pdf).

ferent s),

o

John Murphy, Research Analyst, Bank of America/Merrill Lynch research paper, July 15, 2009. on to first production may span two and one-half to five years,” but that “The time from first production (“Job#1”) to the last vehicle off the line (“Balance Out”) may span from four to five years t eight to ten years or more, depending on the dynamics of the market segment,” The CAR report then st that “At the point of final production of the current vehicle line, a new model with the same badge and similar characteristics may be ready to take its place, continuing the cycle, or the old model may be dropped in favor of a different product.” See NHTSA-2008-0090-0170.1, Attachment 16, at 8 (393 of NHTSA explained that this description, which states that a vehicle model will be redesigned or dropped after 4-10 years, was consistent with other characterizations of the redesign and freshening process, and supported the 5-year redesign and 2-3 year refresh cycle assumptions used in the MY 2011 final rule. See id. , at 9 (394 of pdf). Given that the situation faced by the auto industry today is not so wholly different from that in March 2009, when the MY 2011 final rule was published, and given that the commenters did not present information to suggest that these assumptions are unreasonable (but rather simply that dif manufacturers may redesign their vehicles more or less frequently, as the range of cycles above indicate NHTSA believes that the assumptions remain reasonable for purposes of this final rule analysis. See als “Car Wars 2009-2012, The U.S. automotive product pipeline”, John Murphy, Research Analyst, Merrill Lynch research paper, May 14, 2008 and “Car Wars 2010-2013, The U.S. automotive product pipeline”,

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### ve

feasibil

s.

in the modeling analysis for this

t

lable, ly available. The advanta

cts

ar

prior to or ced M’s Chevy Volt and Chrysler’s anticip
t

e baselin Y 2008 vehicles, using publicly -available data and its own engineering judgment, which required finding the date of most recent redesign for each vehicle. Next, the agency applied 5-year redesign cycles to obtain new redesign dates for each vehicle, starting with the da te of most recent redesign and working forward. Thus, a vehicle that was determined to have been la st redesigned in MY 2008 would be projected to be redesigned again in MY 2013. The assumption here is that future vehicles that are replacements for vehicles currently in the market will tend to follow the same cycles as their predecessors, so it is appr opriate to reflect the MY 2013 da te in the market data file. NHTSA tried to ensure that most if not all vehicles had a redesign scheduled in the analysis during the rulemaking time frame, consistent with the industry’s response in confidential product plans to the estimated levels of stringency announced in the joint NOI preceding the NPRM and this final rule . Manufacturers appear to be redesigning condition of the industry. NHTSA emphatical ly disagrees that the redesign cycle is a barrier to increasing penetration of technol ogies as CBD suggests, but we also belie that standards so stringent that they would require manufacturers to abandon redesign cycles entirely would be beyond the realm of economic practicability and technological ity, particularly in this rulemaking timeframe given lead time and capital constraints. Manufacturers can and will accomplish much improvement in fuel economy and GHG reductions while applying technology consistent with their redesign schedule NHTSA has concluded that the 5-year redesign is still appropriate and is retaining the 5-year redesign with 2-3 year refresh cycle assumptions for the final rule, noting that, for the most part, the cycl e times are supported by manufacturer’s confidential responses to NHTSA’s March and September 2009 product plan requests. With regard to how the refresh/redesign cycle was implemented final rule given the new joint baseline approach, as discussed in Section I of the Preamble.], NHTSA previously used confidential manufacturer produ ct plan information and the refresh and redesign dates contained therein for formulating the market data inpu file used by the Volpe model, or relied on ot her sources of information where that data did not exist. For purposes of this final rule, in contrast, the agencies developed a baseline vehicle fleet data file from MY 2008 CAFE certif ication data. As discussed above, the certification data represents an hist orical data source that is publicly avai which allows NHTSA to make the baseline market data file itself public ge to this approach is the greater tr ansparency provided with a publicly-available baseline market data file as compared to one based on confidential manufacturer data, as also discussed at greater length in Section I of the Preamble. However, using adjusted historical data rather than estimated future data impa how NHTSA is able to model the refresh/redes ign cycle in its analysis of year-by-ye maximum feasible CAFE standards. For example, some vehicles that exist in the MY 2008 certification-data based fleet manufactur ers have indica ted (either publicly or in their product plans) they will be discontinued ( i.e. , no longer produced or sold) within the rulemaking period. Conversely, some vehicle models will be first introdu to the market during the rulemaking time fra me, like G ated new models based on Fiat platfo rms. Since these vehicles were not sold (unavailable) in 2008, they do not exist in the MY 2008 certification data, and thus do no exist in the final rule’s market data file. To address this problem, NHTSA first determined redesign schedules for th e M

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### the vas ajority of today’s vehicles, or replacing them with new models, between now and the end of ilar fashion, based on those of the baseline fleet and using the 2 to 3 year cycle, also working to ensure that all derwent a refr esh h ram akings, NHTSA used manufacturers’ confidential information

e arket file for each unique vehicle model, including new models ( troduced. For the new appro h, which does

n ation to produce the base e vehicle fleet

a ic vehicle that manufactur rrently intend
t time frame, the ag cy had to develop a new method f and subtracti on of ve cles during that time frame from
t 2008 ba ne fleet. the v cle fleet as fo llows. While each

e file, by definition, is a vehicle th was sold in MY 2 certification data), for purposes of projecting that vehicle

m e fleet in the ru lemaking period, each ent so be used
t arket segm t (e.g., subcomp t, SUV/CUV,
p eet. The particular vehicle model shown in the f n the future vehicle fleet, and in fact som odels are

e Y 2016, as discussed above HTSA believes that it is reasona e to expect that anufacturer

w e, or some group of sim r vehicles, to compete in the same

m facturer will of the same vehicl model, a fully

r rsion of that mo , or an entirely n w vehicle or
g del or nameplate of a similar type. his is how N e GM Volt: altho es not app r in the baseline

m as one of the e isting GM models of similar type

a nce it becomes av able. NHTSA also used

m this app nd t m fairly

c et data fi le on NHTSA’s website, ntains the refresh

a veloped by NHTSA for this fi l rule. Table V-1 elow provides

w chnologies are “an ytime” tech ogies, “redesign only”
t ign” technologies, for purposes of th inal rule. t m MY 2016. Finally, the agency determined refresh dates in a sim

vehicles un cycle within t e rulemaking time f e. In previous rulem to stablish entries in the m such as the GM Volt) when they were in ac ot rely on confidential manufacturer inform of the specif lin nd which does not model all s ers cu o produce during the rulemaking en or accounting for the addition hi he pool of vehicles that makes up the MY NHTSA accounts for these changes in seli ehi ntry in the new baseline market data at 008 (based on the MY 2008 odel forward into the futur ry can al o represent a vehicle in that particular m en ac ickup, etc.) of a manufacturer’s future fl ile may or may not be sold i e m xpected to be discontinued well before M

. However, N bl the m ill produce a similar vehicl ila arket segment—whether the manu fer e edesigned but otherwise similar ve del e roup of vehicles, sold as a new mo T HTSA addresses the issue of th ugh it do ea arket data file, it will be considered x nd in the same market segment o ail anufacturers’ product plans as a check on roach, and fou he onsistent with the resulting baseline mark arket data file, availab le. The baseline m co nd redesign dates de hether particular te na 1 b nol echnologies, or “ refresh or redes is f

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### y Refresh and Redesign Application

me 

### Table V-11 Technolog

Technology Redesign only Redesign or Refresh Anyti
Low Friction Lubricants X Engine Friction Reduction X VVT - Coupled Cam Phasing (CCP) on SOHC X Discrete Variable Valve Lift (DVVL) on SOHC X Cylinder Deactivation on SOHC X VVT - Intake Cam Phasing (ICP) X VVT – Dual Cam Phasing (DCP) X Discrete Variable Valve Lift (DVVL) on DOHC X Continuously Variable Valve Lift (CVVL) X Cylinder Deactivation on DOHC X Cylinder Deactivation on OHV X VVT - Coupled Cam Phasing (CCP) on OHV X

Discret e Variable Valve Lift (DVVL) on OHV X
Conversion to DOHC with DCP X Stoichiometric Gasoline Direct Injection (GDI) X
Combustion Restart X Turbocharging and Downsizing X Exhaust Gas Recirculation (EGR) Boost X Conversion to Diesel following CBRST X Conversion to Diesel following TRBDS X 6-Speed Manual/Improved Internals X Improved Auto. Trans. Controls/Externals X Continuously Variable Transmission X 6/7/8-Speed Auto. Trans with Improved Internals X
Dual Cl utch or Automated Manual Transmission X Electric Power Steering X Improved Accessories X 12V Micro-Hybrid X Belt Integrated Starter Generator X Crank Integrated Starter Generator X Power Split Hybrid X 2-Mode Hybrid X Plug-in Hybrid X Mass Reduction 1 (1.5%) X Mass Reduction 2 (3.5% – 8.5%) X Low Rolling Resistance Tires X Low Drag Brakes X Secondary Axle Disconnect 4WD X Aero Drag Reduction X

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### rate of t

rer

nel and

mited vailable resources during the rulemaking time frame, especially in years where
many m

roposed to the

he agency’s analysi

ps h

s 10. Phase-in caps



### Besides the refresh/redesign cycles used in the Volpe model, which constrain the echnology application at the vehicle level so as to ensure a period of stability following any modeled technology applications, the other constraint on technology application employed in NHTSA’s analysis is “phase-in caps.” Unlike vehicle-level cycle settings, phase-in caps constrain technology application at the vehicle manufactu level. 128 

### They are intended to reflect a manufacturer’s overall resource capacity available for implementing new technologies (such as engineering and development person financial resources) thereby ensuring that resource capacity is accounted for in the modeling process. At a high level, phase-in caps and refresh/redesign cycles work in conjunction with one another to avoid the modeling process out-pacing an OEM’s li pool of a odels may be scheduled for refresh or redesign. This helps to ensure technological feasibility and economic practica bility in determining the stringency of the standards. NHTSA has been developing the concept of phase-in caps for purposes of the agency’s modeling analysis over the course of the last several CAFE rulemakings, as discussed in greater detail in the MY 2011 final rule. 129 

### In 2002, when NHTSA p MY 2005-2007 standards for light trucks using a predecessor modeling algorithm Volpe model, manufacturers commented exte nsively on the issue of lead time and the potential for the rapid and widespread applic ation of new technologies in t s. Specifically, GM’s comment pointed to the most significant manufacturer concern, the algorithm’s “application of technologies to all truck lines in a single model year.” 130 

### In response, NHTSA modified the algor ithm to moderate the rates at which technologies were estimated to penetrate manufacturers’ fleets in the MY 2005-2007 CAFE standards. The modeling changes produced more realistic estimates of the technologies manufacturers could apply in resp onse to new standards, and more realistic estimates of the costs of those standards. Explicit phase-in caps were included in the Volpe model analysis for the next rulemaking, establishing standards for MY 2008-2011 light trucks. These phase-in ca constrained the rates at whic h each technology would be es timated to penetrate eac manufacturer’s fleet in response to new CAFE standards. The agency’s final standard for those model years used phase-in caps of up to 25 percent (corresponding to full penetration of the fleet within 4 years) for most technologies, and up to 10 percent (full penetration of the fleet within 10 years) for more advanced technologies such as hybrid

128 While phase-in caps are expressed as specific percentages of a manufacturer’s fleet to which a technology may be applied in a given model year, phase-in caps cannot always be applied as precise limits, and the Volpe model in fact allows “override” of a cap in certain circumstances. When only a small portion of a phase-in cap limit remains, or when the cap is set to a very low value, or when a manufacturer has a very limited product line, the cap might prevent the technology from being applied at all since any ceeded. Therefore, the Volpe model evaluates and enforces each y the application of the technology (as opposed to ich can result in the described overriding of the cap. ) application would cause the cap to be ex phase-in cap constraint after it has been exceeded b evaluating it before application), wh
129 74 FR 14268–14271 (Mar. 30, 2009
130 68 FR 16874 (Apr. 7, 2003).

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### es “that er.” 132



### as nditions and technology de velopments. The agency concluded that many t

of

-in , b)

nal

not consider the

resourc

r of

,

phasized that the MY 2011 p

r individual

years

le for some electric vehicles. 131 

### The agency based these rates on consideration of comments and on the 2002 NAS Committee’s findings that “widespread penetration of even existing technologies will probably require 4 to 8 year s” and that for emerging technologi require additional research and development, this time lag can be considerably long In its 2008 NPRM proposing new CAFE standards for passenger cars and light trucks sold during MYs 2011-2015, NHTSA considered manufacturers’ planned product offerings and estimates of technology availabi lity, cost, and effectiveness, as well broader market co echnologies could be deployed more ra pidly than it had estimated during the prior rulemaking 133 

### and increased some of the estimates as it determined appropriate. However, as in its earlier CAFE rulemaki ngs, the agency continued to recognize that myriad constraints prohibit most technologies from being applied across an entire fleet vehicles within a single year, even if those technologies are readily available in the market. The comments NHTSA received in response to the 2008 proposal asserted three basic concerns with the agency’s adjustments to phase in caps; a) that the hybrid phase caps were much lower than manufacturer announcements would otherwise suggest that the phase-ins were too high in the early years of the rulemaking and did not reflect the very small (from a manufacturing perspec tive) amount of lead- time between the fi rule and the standards taking effect, and/or were too low in the later years of the rulemaking given the increased lead-time, or c) that NHTSA did es (either in terms of capital or e ngineering) required to implement the number (quantities) of technologies implied by the phase-in caps simultaneously. NHTSA responded to these comments in the final rule, 134 

### noting that a numbe factors potentially impact a manufacturer’s ability to implement new technologies including commercial viability, infrastructure requirements, and resource and lead-time considerations. 135 

### The agency explained that evaluating all the factors involved would require an extraordinary effort and that the analysis would likely involve significant uncertainties that would raise questions about its accuracy and usefulness. Nevertheless, the agency concluded that its use of phase-i n caps was still appropriate “to apply the agency’s best judgment of the extent to which such factors combine to constrain the rates at which technologies may feasibly be deployed.” NHTSA em hase-in caps were based on assumptions for the full five year period of the proposal (2011-2015), and stated that it woul d reconsider the phase-in settings for all years beyond 2011 in future rulemaking analysis. Some phase-in caps fo technologies were raised and some were lo wered, and the Volpe model was revised to add the ability to define unique phase-in caps for each model year, allowing non-linear technology application rates throughout the ru lemaking period (lower in the early and increased in later, or vice-versa) if required. Table V-12 below outlines the phase-in caps for the technologies used in this ru by model year. As in the MY 2011 final rule, NHTSA combines phase-ins caps

ar. 30, 2009) 131 71 FR 17572, 17679 (Apr. 6, 2006).
132 Id . at. 17572. See also 2002 NAS Report, at 5.
133 73 FR 24387-88 (May 2, 2008).
134 74 FR 14268-69 (Mar 30, 2009)
135 74 FR 14268 (M

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### from

o technol

ule,

nd

ke

e

tation A

ent, for the most part, a significant departur e from the vehicle archite e 2011 rule, Volpe

nce the

e NPRM analysis (as
compar t groups of similar technologies, such as valve phasing technologies that are applicable to different forms of engine design (SOHC, DOHC, OHV), since they are very similar an engineering and implementation standpoi nt. When the phase-in caps for tw ogies are combined, the maximum total application of either or both to any manufacturers’ vehicle fleet is limited to the value of the cap. 136 

### In contrast to the phasein caps used in the MY 2011 final rule, NHTSA has increased the phase-in caps for most of the technologies, except those for diesels and stronger hybrid technologies, as discussed below. In developing phase-in cap values for purposes of the NPRM and this final r NHTSA initially considered the fact that many of the technologies commonly applied by the model, those placed near the top of the decision trees, such as low friction lubes, valve phasing, electric power steering, improved automatic transmission controls, a others, have been commonly available to manufacturers for several years now. Many technologies, in fact, precede the 2002 NAS Report, which estimated that such technologies would take 4 to 8 years to penetrat e the fleet. Since this final rule will ta effect in MY 2012, nearly 10 years beyond the NAS report, and extends to MY 2016, NHTSA determined that higher phase-in caps were likely justified. Additionally, NHTSA considered the fact that manufacturers, as part of the agreements supporting th National Program, appear to be anticipating higher technology application rates than those used in the MY 2011 final rule. This also supported higher phase-in caps of commonly applied technologies for purposes of this final rule. However, for a few of the more complex and intrusive (from an implemen perspective) technologies, specifically dies elization and stronger hybridization, NHTS has retained the more stringent phase-in levels used in the 2011 final rule since these technologies repres ctures commonly utilized by most OEMs today. As was the case in th these more stringent phase-in caps limit technology application, i.e., due to the modeling process, to 3 percent per annum up to a maximum of 15 percent by the 2016 model year. 137 

### Additionally, for some technologies that are not available in certain model years, a phase-in cap of 0 percent is shown for those model years, such as the combustion restart technology that is not determined to be available until 2014; he values of 0 percent for MYs 2012 and 2013 shown in Table V-12 below. NHTSA received comments from the Alliance and ICCT relating to phase-in caps. The Alliance commented that the higher phase-in caps in th ed to the MY 2011 final rule) “ignore OEM engine architecture differences/limitations,” arguing that the agency must consider manufacturing investmen and lead time implications when defining phase-in caps. The Alliance also commented that it seems the combining of technologies “isn’t being considered” due to most technologies be “phased in at 85%/100% rates by the mid-decade”. ICCT did not raise the issue of phase-in caps directly, but co mmented that the agencies had not provided information in the proposal documents explaining when each manufacturer can

136 See 74 FR 14270 (Mar 30, 2009) for further discussion and examples.
137 A 15 percent maximum application rate should not be confused with the overall penetration of the technology, i.e., the amount of the technology applied by the modeling process plus that which existed in the baseline or was installed at the discretion of the manufacturer. Penetration rates typically exceed application rates.

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### o spread

develop

011 of ements supporting the National Program, appear to be anticipating higher technology application rates during the rulema king timeframe. Additionally, the agencies did not receive any comments fr om manufacturers indicating a concern with the proposed application rates after reviewing the detaile d manufacturer level model outputs. The agencies believe that as manufacturers focus their resources ( i.e. , engineering, capital investment, etc.) on fuel economy-improving te chnologies, many of which have been in production for many years, the application ra tes being modeled are appropriate for the timeframe being analyzed. Regarding the Alli ance’s comments about the consideration of technology combinations, the 85%/100% phase-in caps do not dictate the level of applied technologies. As stated above, phase-in caps are one of the constraints, in combination with other constraints, used to limit technology applications, they are not used to prescribe technology application rates. The model applies different combinations of technologies at differing rates, however most technologies never approach the levels of the phase-in caps. In response to ICCT’s comments, the combination of phase-in caps, refresh/redesign cycles, engineering constr aints, etc., are intended to simulate manufacturers’ technology application decisions, and ultimately define the technology application/implementation rates for each ma nufacturer. NHTSA notes that the PRIA and the FRIA do contain manufacturer-specific application/implementation rates for prominent technologies, and that manufactu rer-specific technology application as employed in the agency’s analysis is available in full in the Volpe model outputs available on NHTSA’s website. The model ou tputs present the resultant application of technologies at the industry, manufa cturer, and vehicle levels. Theoretically, significantly higher phase-in caps, such as those used in the NPRM and this final rule as compared to those used in the MY 2011 final rule, should result in higher levels of technology penetration in the modeling results. Reviewing the modeling output does not, however, indicate unreason able levels of technology penetration as shown in Tables V-48 and V-49. NHTSA believes that this is due to the interaction of the various changes in methodology applied for the NPRM analysis and carried into this final rule--changes to phase-in caps are but one of a number of revisions to the Volpe model and its inputs that coul d potentially impact the rate at which technologies are applied in the NPRM and this final rule as compared to prior rulemakings. Other revisions that could impact application rates include the use of transparent CAFE certification data in baseline fleet formula tion and the use of other data for projecting it implement the different technologies and how lo ng it will take the technologies t across the fleet. ICCT argued that this information was crucial to considering how quickly the stringency of the standards could be increased, and at what cost. In response to the Alliance comments, the phase-in cap constraint is, in fact, exactly intended to account for manufacturing investment and lead time implications, as discussed above: phase-in caps are intended to reflect a manufacturer’s overall resource capacity available for implementing new technologies (such as engineering and ment personnel and financial resources), to help ensure that resource capacity is accounted for in the modeling process. Although the phase-in caps for the analysis supporting these standards are higher than the phase-in caps employed in the MY 2 final rule, as stated above, the agencies considered the fact that manufacturers, as part the agre

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### rward, 138 

### or the use of a multi-year planning programming technique to apply technology retroactively to ear lier-MY vehicles, both of whic h may have a direct impact on the m ther areas that also could impact technology appl ation, such as in the refresh/redesign cycle settings, or the eff s used for chno largely unchang Y 2011 final ru e cha toget ficul
t n caps should be expec cti he n del ces wing the output files, N A be hat her

c tion rates produc the e mo bo
t propriate for th l rul ievi

s ing un istic o hiev e

r fo

odeling process. Conversely the m odel and inputs remain unchanged in o ic ectiveness estimate ed from the M the te e. The logies, both of which remain ke it dif l s ted to fun nges on in t her ma

ew mo t

s. o predict how phase-i ing pro Thus, after revie HTS lieves t the hig phase-in aps, and the resulting technology applica ed by Volp del, at th he industry and manufacturer level, are ap ithout requir is fina e, ach ng a uitable level of stringency w real r unac able pen tration
ates.

138 The baseline fleet sets the starting point, from a te logy point of view, for where the model begins the technology application process, so changes have a irect impact on the net application of technology. chno d

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### Tabl nt Rule

Final Rule Phase-In Caps by Model Year * 

### e V-12 Phase-in Caps from 2011 Final Rule and Curre

Final Rule 2012-2016 Te chno 015 2016 logy MY 2011 2012 2013 2014 2 Low Friction Lubricants 50% 100% 100% 100% 100 % 100% Engine Friction Reduction 20% 85% 85% 85% 100 % 100% VVT - Coupled Cam Phasing (CCP) on SOHC 15% 85% 85% 85% 10 0% 100% Discrete Variable Valve Lift (DVVL) on SOHC 15% 85% 85% 85% 1 00% 100% Cylinder Deactivation on SOHC 9% 85% 85% 85% 8 5% 85% VVT - Intake Cam Phasing (ICP) 15% 85% 85% 85% 100% 100% VVT – Dual Cam Phasing (DCP) 15% 85% 85% 85% 100% 100% Discrete Variable Valve Lift (DVVL) on DOHC 15% 85% 85% 85% 10 0% 100% Continuously Variable Valve Lift (CVVL) 15% 85% 85% 85% 100% 100% Cylinder Deactivation on DOHC 9% 85% 85% 85% 85% 85% Cylinder Deactivation on OHV 9% 85% 85% 85% 85% 85% VVT - Coupled Cam Phasing (CCP) on OHV 15% 85% 85% 85% 100% 100% Discrete Variable Valve Lift (DVVL) on OHV 15% 85% 85% 85% 100% 100% Conversion to DOHC with DCP 9% 85% 85% 85% 85% 85% Stoichiometric Gasoline Direct Injection (GDI) 3% 85% 85% 85% 85% 85% Combustion Res tart 0% 0% 0% 85% 85% 85% Turbocharging and Downsizing 9% 85% 85% 85% 85% 85% Exhaus 85% 85% t Gas Recirculation (EGR) Boost 0% 0% 85% 85% Conversion to Diesel following CBRST 3% 3% 6% 9% 12% 15% Conversion to Diesel following TRBDS 3% 3% 6% 9% 12% 15% 6-Speed Manual/Improved Internals 33% 85% 85% 85% 10 0% 100% Improved Auto. Trans. Controls/Externals 33% 85% 85% 85% 10 0% 100% Continuously Variable Transmission 5% 85% 85% 85% 85 % 85% 6/7/8-Speed Auto. Trans with Improved Internals 50% 85% 100% 100% 100% 100 % Dual Clutch or Automated Manual Transmission 20% 85% 100% 100% 100% 100 % Electric Power Steering 10% 85% 85% 85% 100% 100% Improved Accessories 10% 85% 85% 85% 100% 100% 12V Micro-Hybrid 3% 85% 85% 85% 85 % 85% Belt mounted Integrated Starter Generator n/a 85% 85% 85% 85% 85% Crank mounted Integrated Starter Generator n/a 3% 6% 9% 12 % 15% Power Split Hybrid 0% 3% 6% 9% 12% 15% 2-Mode Hybrid 0% 3% 6% 9% 12% 15% Plug-in Hybrid 0% 3% 6% 9% 12 % 15% Mass Reduction (1.50%) 5% 85% 85% 85% 8 5% 100% Mass Reduction (5% to 10% Cum) 5% 0% 0% 85% 85 % 100% Low Rolling Resistance Tires 20% 85% 85% 85 % 100% 100% Low Drag Brakes 20% 85% 85% 85% 100% 100% Secondary Axle Disconnect - Ladder Frame 17% 85% 85% 85% 100% 100% A 17% 85% 85% 85%

ero Drag Reduction 100% 100% * - a phase-in cap of 0% is shown for the years the technology is unavailabl e

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### s



### ornia

vehicle fuel economy certification data. Bo th agencies also reviewed the published technic nomy, such as ers published by the Society of Automotive Engineers and the American Society of M manufacturers for product plans, and confidential information and NHTSA a cross check

lemaking.

s a contracted study ongoing with FEV that consists of complete ystem tear-downs to evaluate technologies dow n to the nuts and bolts to arrive at very

NHTSA and EPA believe the best way to derive technology cost estimates is to conduct real-world tear down stu sitio d s ICCT an ed no comm tra tu to ns of vehicle stems th ploy the n chnolog or system new te ies. Ana with ex D. Specific technologies consid ered for application and NHTSA’ estimates of their incremental costs and effectiveness

1. What data sources did NHTSA evaluate?



### NHTSA and EPA have done extensive research in identifying the most credible sources of information. These sources included: the 2002 NAS report on the effectiveness and impact of CAFE standards; 139 

### the 2004 study done by NESCCAF; 140 

### the Calif Air Resources Board (CARB) Initial Statemen t of Reasons in support of their carbon rulemaking; 141 

### a 2006 study done by Energy and Environmental Analysis (EEA) for the Department of Energy; 142 

### a study done by the Martec Group for the Alliance of Automobile Manufacturers, and an upda te by the Martec Group to that study; 143 

### and

al literature which addressed the issue of CO 2 

### emission control and fuel eco
pap echanical Engineers. In addi tion, confidential data submitted by vehicle in response to NHTSA’s request 144 

### shared by automotive industry co mponent suppliers in meetings with EPA staff held during the second half of the 2007 calendar year were used as of the public data mentioned above and not as a significant basis for this

ru

EPA also ha

s detailed estimates of the costs associated with manufacturing them. 145 

### As a general matter, dies. This po ents to the con n is supporte
ry. 146 

### These s by commenters dies are based uch as a large d we receiv degree on tear dow s or vehi cle sy at em ew te ies, and of similar vehicles s without th e chnolog lysts pertise

d Impact of Cor Econo FE) Standard ational Re ien

1 “Staff eport: Initial Statement of Reasons for Proposed Rulemaking,” California Environmental Protectio gency, Air Resources Board, Regulations to Control Greenhouse Gas Emissions from Motor Vehicles 139 “Effectiveness an porate Average Fuel my (CA s,” N search Council, National Academy of Sc
140 ces, 2002.

“Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” Northeast States Center f lean Air Future, September 2004. or a C
14 R n A , August 6, 2004.
142 “Technology to Improve the Fuel Economy of Light Duty Trucks to 2015,” Energy and Environmental Analysis, Inc., May 2006.
143 “Variable Costs of Fuel Economy Technologies,” prepared for The Alliance of Automobile Manufacturers, June 1, 2008; and, “Variable Costs of Fuel Economy Technologies,” prepared for The Alliance of Automobile Manufacturers, June 1, 2008, Amended December 10, 2008.
144 74 FR 9185 (Mar. 3, 2009)
145 “Draft Report – Light-Duty Technology Cost Analysis Pilot Study,” U.S. Environmental Protection Agency, Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009
146 See comments from ICCT (EPA-HQ-OAR-2009-0472-7156), CARB (EPA-HQ-OAR-2009-0472- 7189), NESCAUM (EPA-HQ-OAR-2009-0472-7235)

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### dividual n studies

y. , four technologies (downsizing and tu rbocharging, stoichiometric gasoline directocket.

hese recently completed tear-downs include the following technologies:

o GDI

n 

### in automotive design, materials, and manufacturing then compare the tear down components and evaluate the differences. Using databases for materials, labor, manufacturing overhead, and mark-up costs, the overall costs to manufacture in parts are calculated and summed into final results. However, as such, tear dow require a significant amount of time and are very costly. EPA has begun conducting tear down studies to assess the costs of 4-5 technologies under a contract with FEV Inc., an independent engine and powertrain systems research, design and development compan To date injection, dual clutch transmission and 6- speed automatic transmission) have been evaluated. The tear down study has been peer-reviewed and the report for these tear down studies and the peer-review report have been made public in the rulemaking d The agencies have considered these studies and the comments received on them, as practicable and appropriate, in developing tec hnology cost assumptions for this final rule.

T

1. 3.0L V6 port fuel-injected (PFI) downsized and turbocharged to a 2.0L I4 gasoline direct injection (GDI) 2. 5.4L V8 PFI downsized and turbocharged to a 3.0L V6 twin-turb 3. 5-speed automatic transmission to 6-speed automatic transmission 4. 6-speed automatic transmission to 6-speed wet dual-clutch transmission

A comparison between costs reported in the NPRM and the final rule can be found in Table V-1 3 below.

Table V-13. A comparison of NPRM costs and the final rule for five updated technologies ($2007 i 2012).

Technology Incremental To NPRM Direct Manufacturing Cost Final Rule Direct Manufacturing Cost Change

2.0L I4 Turbo GDI 3.0L V6 MPFI $248 $152 -$96 3.0L V6 twin-turbo GDI 5.4L V8 MPFI $1,081 $964 -$117 5-speed auto trans 4-speed auto trans $91 $91 $0 6-speed auto trans 4-speed auto trans $153 $101 -$52 6-speed auto trans 5-speed auto trans $62 * $9 -$53 6-speed dual-clutch trans (wet) 6-speed auto trans $126 -$11 -$137 * Calculated as the difference between the 4 to 6 speed trans ($153) and the 4 to 5 speed trans ($91).



### FEV tear down cost analysis studies were conducted based on the assumption that the analyzed technologies and the manufactu ring for those technologies were both fully “mature,” in that designs and manufacturing processes have been reasonably optimized. The studies also assumed that manufacturing facilities have annual production levels of 450,000 units. EPA and NHTSA recognize that in early implementation years, designs and manufacturing processes may not be optimized to that extent, and investment cost may ex ed those of fully matu re technologies. To account for higher cost in th ce

e e earlier implem ntation years of the rulemaking period, NHTSA and EPA estimated MY 2012 costs as the average of the FEV tear down study cost and the NPRM cost for technologies

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### for whi udies. Time-based learning is used to reflect st in later years. This approach is applied to downsizing and turbocharging for V6 and

on

f

to the best vailable conclusion regarding technology co sts and effectiveness estimates for the current rulemaking and resulted in excellent consistency between the agencies’ respective analyses for dev

tal benefits and cos

n, in which such interactions are analyze ehicle

ailable for this rulemaking. The agencies look forward to considering the results ch there were completed FEV tear dow n st

co V8, stoichiometric gasoline direct-injection for V6 and V8, dual clutch transmission and 6-speed automatic transmission. In the NPRM, the costs for turbocharging, downsizing and SGDI for I4 engines were based on an FEV teardown cost study that was completed prior to release of the NPRM. For the final rule, these costs were carefully reviewed and updated to bett er account for early year implementati
costs. EPA and NHTSA reviewed all this information in order to develop the best estimates o availability, cost and effectiveness of these fuel-saving/CO 2 

### -reducing technologies. NHTSA and EPA are confident that the thorough review conducted, led

a

eloping the CAFE and CO 2 

### standards.

The agencies would also like to note that per the Energy Independence and Security Act (EISA), the Natio nal Academies of Sciences is conducting an updated study to update chapter 3 of their 2002 NAS Report, which presents technology effectiveness estimates. The update will take a fresh look at that list of technologies and their associated cost and effectiveness values. Some of specific tasks that NAS will undertake in updating the technology chapter are to de fine and document specific methodologies and input parameters to account for the sequen tial application and incremen
ts o f technologies, including the methods used to account for variations in vehicle characteristics ( e.g. , size, weight, engine characteristics). Some methodologies might involve simple mathematical relationships ( e.g. , cost per cylinder). Others might involve matrices ( e.g. , of effectiveness versus vehicle catego ry or versus the presence of other technologies) or more complex structural representations ( e.g. , decision trees). In addition, NAS will identify and assess leading computer models for projecting vehicle fuel economy as a function of additional tech nology. These models would include both lumped-parameter (or Partial Discrete Approximation) type models, where interactions between technologies are represented using energy partitioning and/or scalar adjustment factors (aka “synergy” factors), and full vehicle simulatio d using explicit drive cycle and engine cycle simulation, based on detailed v engineering characteristics ( e.g. , including engine maps, transmission shift points, etc.). Finally, NAS will examine the effectiveness and impacts of vehicle weight and engine size/horsepower reductions which will be li mited to advances in structural design and lightweight materials.

The updated NAS report was ex pected to be available on September 30, 2009, but has not been completed and released to th e public. The updated report is currently undergoing various levels of required review. It is anticipated that the final report will be published after the publication of this final rule, thus the results from this study thus are

unav m this study as part of the next round of rulemakin fro g for CAFE standard.

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### or em at the aut

se, tor anufacturing costs, recovers total revenue 147 

### . This multipl n is accurate only in the aggregate; it does not in reality apply to any specific technology. The RPE is a way to estimate indirect costs on the assumption that indirect costs are constant across all technologies and processes in a company. In the MY 2011 CAFE final rule NHTSA utilized a 1.5 RPE multiplier.

In fact, however, the indirect costs of new technologies vary, both with the complexity of the technology and with the time frame. For instance, a hybrid-electric engine is likely to involve greater research and development and marketing costs per dollar of direct costs than low-rolling-resistance tires; the research and development costs of any technology are likely to decrease over time. In recognition of this concern, EPA contracted with RTI International to provide a current estimate of the RPE multiplier and to examine whether the indirect costs of new technologies are likely to vary across technologies. The report “Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers,” by Alex Rogozhin, Michael Gallaher, and Walter McManus, 148 

### calculates The Indirect Cost Methodology (ICM)



### Indirect costs include production-related co sts (research, development, and other engineering), business-related costs (corpor ate salaries, pensions and manufacturer profits), and retail-sales-related costs (dealer support, marketing and dealer profits). F this analysis, direct cost estimates were first developed for each technology or syst o manufacturer level, i.e., the price paid by the manufacturer to a Tier 1 component supplier. To these costs, an indirect cost markup factor was then applied that varied by the best estimate of the particul ar technology’s complexity. This section describes the approach to determining the indirect cost multipliers (ICM) used in this analysis and the specific multipliers used for each piece of technology.

Concept behind and development of indirect cost multipliers

If all desirable data were available, when a new technology is implemented, the costs of that technology would include the di rect and indirect costs particular to that technology. For instance, some changes may involve new tooling, while others may not;

some m ay affect the way the car is marketed, while others are of limited interest to consumers. In a world of full information, the indirect costs of a new technology would be calculated specifically for that technology. In practice, though, it is often difficult, if not impossible, to identify the indirect costs specific to a new technology.

The automotive industry, EPA, and NHTSA have commonly used retail price equivalent (RPE) multipliers to approximate the indirect costs associated with a new technology. The RPE is a ratio of total revenues to direct manufacturing costs. Becau by definition, total revenues = direct costs + indirect costs + profit, the RPE is the fac that, w multiplied by direct m hen icatio

147 Note that unlike the RPE, the ICM does not include profits.
148 Rogozhin, Alex, Michael Gallaher, and Walter McManus, “Automobile Industry Retail Price Equivalentand Indirect Cost Multipliers,” EPA 420-R-09-003, February 2009, http://epa.gov/otaq/ldhwy/420r09003.pdf.

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### the RPE multiplier as 1.46 i ndirect cost (IC) multipliers ry with the complexity frame. While any multiplier an appro tio ue ect co f a ol e IC multipliers in this report m aw the ass mption th the propo n of indirect costs t across al nolo and ta to acc some e variation in these lie velo n this rt are p ted ble V The agencies received comments from The National Automobile Dealers Associ ati at all cost assoc with costs-of sales uding consumer financ be accounted f “deal ” e agen have included dealer cos f-sal sellin ts) in t indirect st mu er (ICM which makes up part of t lo sts—a e ent. e is no compelling reason t clude those s in th e aler pro elem f the I . As for the finance cos id nsu it is i tant to that a soc
persp h e ar ec g sts selve d do not de the f s. While those costs are incurred by consumers, they are merely transfer costs fr m the perspective of regulatory cost analysis. We have included financing costs l n 2007. The report then develops i that va is only of technology and the time xima

ove n of th ay from e tr indir

u sts o at new tec rtio

hn ogy, th

is constan l tech gies ke in ount of th costs. The multip rs de ped i repo resen in Ta -1

.

ation (NADA) st ng th iated deal er , incl e costs, should
ts-o o r in he er profits

co . Th ltipli cies )— es ( g cos he final techno gy co s a uniqu elem Ther

o in ts pa cost
mers, “de
mpor fit”
note ent o from CM ial by co ective, the costs of t ina ce cost e rul e the t hnolo y co them s an inclu

n

o in our consumer welfare analysis since costs there are not social costs but rather persona
costs. The indirect cost multipliers used adjust ment factors, developed by a team of EPA engineers with expertise in the auto industry, which accounted for the differences in complexity of the specific technologies under study. To examine the sensitivity of the results to different technologies of the same complexity, and to provide more detailed documentation of the development of the adjustment factors, EPA convened a second panel, 149 

### with NHTSA’s input, to develop adjustment factors for three different technologies. This latter process allowed fo r estimates of the variation in adjustment factors, and thus in the variation of indirect cost multipliers. These results are also presented in Table V-14.

149 “Memorandum: Documentation of the Development of Indirect Cost Multipliers for Thre Technologies,” Gloria Helfand and Todd Sherwood, Office of Transportation and Air Quality, U.S. Environmental Protection Agency e Automotive

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TECHNOLOGY COMPLEXITY Short Run Long Run STUDY Low Medium High Low Medium High
RTI Report 1.05 1.20 1.45 1.02 1.05 1.26

EPA Memo: Average 1.16 1.29 1.64 1.12 1.20 1.39

Standard Deviation 0.14 0.15 0.21 0.14 0.13 0.15

Median 1.12 1.26 1.66 1.06 1.20 1.40

Max 1.43 1.53 2.15 1.42 1.45 1.69

Min 1.00 1.02 1.37 1.00 1.01 1.12
Multipliers Used in this Analysis 1.11 1.25 1.45 1.64 1.07 1.13 1.26 1.39



### The table shows minor differences in the multipliers for low- and medium-complexity technologies (roughly 0.1), but larger differen ces in the high-complexity technologies. The EPA and NHTSA engineers who reviewed the results believed that the differences reflected actual differences in the technol ogies under study. In particular, for low complexity, low-rolling-resistance tires (the application in the RTI Report) would involve lower indirect costs than aerodynamic improvem ents (the application in the EPA memo); and, for medium complexity, dual-clutch tr ansmissions (the application in the RTI Report) should have a smaller multiplier th an engine downsizing done in conjunction with turbocharging (the application in the EPA Memo). For these two cases, EPA and NHTSA considered these technologies to span the range of technologies assigned to those classes; the costs in this study, then, use the averages of the values of the two reports, as shown in the last line of Table V-1 . For high complexity technologies, the agencies felt the tec hnologies assigned to these categor ies—hybrid-electric vehicles in the RTI Report; plug-in hybrid electric vehicles in the EPA Memo—were sufficiently different that each deserved a different category. This is discussed in more detail in the next section which highlights the multipliers used for each specific technology.

Application of specific indirect cost multipliers to each technology



### As noted in the previous section and in the NPRM a different ICM was applied to each technology’s direct cost to arrive at its compliance cost. These different ICMs were chosen based on the complexity of integrating the technology into the vehicle in the opinions of staff engineers at EPA and NHTS A, most of whom have several years of experience in the auto industry. As shown in Table V-1 , ICMs were developed via two separate processes: that presented in the RTI report; and that presented in the EPA Memo. While all of the ICMs generated via these two processes were in general agreement, some differences did exist. In determining how to deal with these differences, EPA and NHTSA agreed that, for the low and medium complexity technologies, a simple average of the two values would be used. However, for the high complexity technologies, it was decided that two separate high-multipliers should be used. The

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### wer multiplier, deemed high, would be applied to those technologies of high complexity but with some level of use in the marketplace today. Such technologies would . The higher multiplier, deemed high+, would be applied to those technologies of high complexity but with no, or

essen current flee hnolog h

. lo

be power-split and 2-mode hybrid electric vehicles

tially no, use in the t. Such tec ies would be plug-in ybrids and full electric vehicles

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### able V-1 shows the complexity level for each technology considered in this analysi T

s.

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### Table V-15 Complexity Levels of Technologies

LOW COMPLEXITY MEDIUM COMPLEXITY HIGH COMPLEXTIY HIGH+ COMPLEXITY Low friction lubes (LUB) Combustion Restart (CBRST) Continuously variable valve lift (CVVL) Plug-in hybrid

Engine friction reduction (EFR) Exhaust gas recirculation boost (EGRB) 2-mode hybrid (2MHEV) Full electric vehic le

Intake cam phasing (ICP) Belt integrated starter generator (BISG) Power-split hybrid (PSHEV) Coupled cam phasing (CCPO) a nd (CCPS) Turbocharge with Crankshaft integrated downsize (TRBDS) starter generator (CISG) Dual cam phasing (DCP) Conversion to diesel (DSLC) and (DSLT) Cylinder deactivation (DEACS), (DEACD), and (DEACO) Dual clutch transmission (DCTAM) Discrete variable valve lift (DVVLS), (DVVLO) and (DVVLD) Continually variable transmission (CVT) Stoichiometric gasoline direct injection (SGDI) 12 volt micro hybrid (MHEV) Conversio n to DOHC with DCP ( CDOHC) 6 /7/8-speed (NAUTO) auto transmission

Improved auto transmission (IATC) 6-speed manual transmission (6MAN) Improved accessories (IACC) Electric power steering (EPS) Low rolling resistance tires (ROLL) Low drag brakes (LDB) Secondary axle disconnect (SAXU/SAXL)) Improved aerodynamics (AERO) Mass reduction (MS1) 1.5% Mass reduction (MS2) 3.5 - 8.5%



### ICCT commented that the integration aspect of a technology needs to be considered when defining technology co mplexity levels for purposes of ICM applications. While assigning ICM complexity levels to each technology used in the NPRM the agencies took into account the inte gration challenges wi th each technology. After considering the comments and evaluating the complexity levels of each technology relative to assigned complexity levels of other technologies the agencies still believe the complexity levels assigned in the NPRM to be valid. Thus for purposes of this final rule the complexity level assignments will be the same as those in the NPRM. For future rulemakings that agencies will re-evaluate the current list of technologies and emerging technologies to determine the approp riate technology complexity levels.

The estimates of vehicle compliance costs cover the years of implementation of the PA’s analysis, compliance costs have also been

stimated for the years following implementation to shed light on the long term – 2022 and later – cost impacts of the rule. The year 2022 is used by EPA because the shortterm and long-term markup factors described above are applied in five year increments program – 2012 through 2016. In E

e

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### ith th

ty analyses performed by NHTSA for this final rule was to valuate the effects of using the Retail Price Equivalent (RPE) multiplier employed in the MY 20 TSA rulemakings, instead of using the Indirec Cost Multiplier (ICM) developed for this rulemaking. The RPE methodology

ultipl ed

e

er

2. Individual technology descriptions and cost/effectiveness



### he end of the section. Related to costs the Volpe Model handles learning effects within the model itself so that odel year would be lower than those in previous ears. The costs in this section and the su mmary tables are for model year 2012 vehicles

d, th

olumeg the echanical work output as shaft torque and ower delivered to the transmission and to the vehicle’s driving wheels. Vehicle fuel economy is directly proportional to the effi ciency of the engine. Two common terms are used to define the efficiency of an engi ne are (1) Brake Specific Fuel Consumption w e 2012 through 2016 implementation span and the 2017 through 2021 span both representing the short-term.

One of the sensitivi

e 11 CAFE final rule and in other NH
t m ies variable costs by 1.5 to estimate consumer costs. The 1.5 multiplier is a general increase that is applied consistently to every technology and consists of fix costs and profits for the manufacturer and automobile dealers. The indirect cost multiplier is estimated depending upon the complexity of the technology and in the aggregate for the technol ogies utilized in the model results in a multiplier in the 1.2 to 1.25 range and does not include profits. The sensitivity analysis indicates that using th 1.5 RPE multiplier would result in noticeably higher costs compared to the final rule costs incorporating the ICM multiplier, alth ough we note that even with those high costs the 1.5 RPE analysis still resulted in significant net benefits for the rulemaking as a whole 150 

### . The agency plans to investigate this issue further for future rulemakings.

estimates



### The technology cost and effectiveness estimates used in the Volpe Model are defined in this section and are summarized in the tables at t

individual technology costs in the 2016 m
y an erefore, represent fully learned costs in the context of EPA’s analysis. For technologies added in years prior to 2016, EPA has backed out the learning effects relative to the costs shown in the tables. For example, the small car stop-start vehicle cost is $351 in 2016. In the 2012 model year, this cost would be higher since the v based learning reflected in the 2016 cost would not have occurred yet. Backing out two volume-based learning steps (i.e., dividing $351 by 80% twice) would result in a 2012 cost estimate of $548.

(a) Gasoline Engine Technologies

(i) Overview



### Most passenger cars and light trucks in the U.S. have gasoline-fueled spark ignition internal combustion engines. These engines move the vehicle by convertin chemical energy in gasoline fuel to useful m
p

150 See Section X. below for the results of the sensitivity analyses.DELIBE 

### 185

(BSFC), which fuel used to the output mechanical energy; and (2) Bra ciency (BTE), which is the ratio of the fuel chemical energy, known with

50 ) and typically between 70-85 percent of aximum torque output at that speed. At this operating condition, BTE is typically 33- 36 percent. However, at low gine speeds engine operates in most consumer vehicle use and on standardized drive cycles, BTE typically drops t

rk.

gine

tion ion
9 Eng

VV

Co 

### is the ratio of the mass of ke Thermal Effi as calorific value, to th e output mechanical energy. The efficiency of an automotive spark ignition engine varies considerably the rotational speed and torque output demanded from the engine. The most efficient operating condition for most current engine designs occurs around medium speed (30- percent of the maximum allowable engine rpm

m er en and torque outputs, at which the

o 20-25 percent. Spark ignition engine efficiency can be improved by reducing the energy losses that occur between the point of combustion of the fuel in the cylinders to the point where that energy reaches the output crankshaft. Reduction in this energy loss results in a greater proportion of the chemical energy of the fuel being converted into useful wo For improving engine effi ciency at lighter engine load demand points, which are most relevant for CAFE fuel economy, the technologies that can be added to a given en may be characterized by which type of energy loss is reduced, as shown in Table V-16 below.

Table V-16 Technology Characterization by Type of Loss Reduced



### Technology Heat Loss Reduction Exhaust Energy Reduction Gas Exchange Reduction Fric Reduct
Low Friction Lubricants

RATIVE DOCUMENT – DO NOT RELEASE 9

C 9

9

s Recirculation (EGR) Boost 999

n

ine Friction Reduction VVT - Coupled Cam Phasing (CCP) on SOHC 9 Discrete Variable Valve Lift (DVVL) on SOHC 9 Cylinder Deactivation on SOH T - Intake Cam Phasing (ICP) 9 VVT - Dual Cam Phasing (DCP) 9 Discrete Variable Valve Lift (DVVL) on DOHC 9 Continuously Variable Valve Lift (CVVL) 9 Cylinder Deactivation on DOHC 9 Cylinder Deactivation on OHV 9 VVT - Coupled Cam Phasing (CCP) on OHV 9 Discrete Variable Valve Lift (DVVL) on OHV 9 Conversion to DOHC with DCP 9 Stoichiometric Gasoline Direct Injection (GDI) 9 Combustion Restart Turbocharging and Downsizing 9 9
Exhaust Ga version to Diesel 9 99

9 Represents area of primary influence

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### y ar e exhaust energy losses, engine friction losses, gas exchange losses. Converting the gasoline engine to a diesel engine can also reduce heat losses.

three

e cycle

io. The latter is

issipated through the co oling system in the form of heat. Besides via dire e

stion cylinde lift. king it

arget reduced pumping loss will have little additional ffectiveness. Thus, in the revised decision trees, the effectiveness value shown for additional technologies targeting pumping loss depends on the existing technology combination already present on the engine. As Table V-16 shows, the main types of energy losses that can be reduced in gasoline engines to improve fuel econom and

Exhaust Energy Loss Reduction



### Exhaust energy includes the kinematic and thermal energy of the exhaust gases, as well as the wasted chemical energy of unburned fuel. These losses represent approximately 32 percent of the initial fuel chemical energy and can be reduced in ways: first, by recovering mechanical or electrical energy from the exhaust gases; second, by improving the hydrocarbon fuel conversion; and third, by improving th thermodynamic efficiency. Th e thermodynamic effi ciency can be improved by either increasing the engine’s compression ratio or by operating with a lean air/fuel rat not considered to be at th e emerging technology point yet due to the nonavailability of lean NO x 

### aftertreatment, as discussed below. However, the compression ratio may potentially be raised by 1 to 1.5 ratios using stoichiometric direct fuel injection.

Engine Friction Loss Reduction



### Friction losses can represent a significant proportion of the global losses at low load. These losses are d ct reduction measures, friction can also be reduced through downsizing the engin by means of increasing the engine-specific power output.

Gas Exchange Loss Reduction



### The energy expended while delivering the combustion air to the cylinders and expelling the combustion products is known as gas exchange loss, commonly referred to as pumping loss. The main source of pumping loss in a gasoline engine is the use of an inlet air throttle, which regulates engine output by controlling the pre-combu r air pressure, but is an inefficient way to achieve this pressure control. A more efficient way of controlling the cylinder air pres sure is to modify the valve timing or Another way to reduce the average pumping losses is to “downsize” the engine, ma run at higher loads or higher pressures. Several different technologies target pum ping loss reduction, but it is important to note that the fuel consumption reduction fr om these technologies is not necessarily cumulative. Once most of the pumping work has been eliminated, adding further technologies that also t

e

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### a. Engine Techn es



### rces

er

. ,

aft, rod and main bearings and changes to the me ability

wer

at low friction lubricants could have an effectiveness value between 0 to 1 percent. For
purpos

ng a

st e that the $3 cost estimate is appropriate and independent of vehicle class since the eng apply to any engine size. Apply ect cost m complexity technology

Neither volume-based cost reductions nor time-based cost reductions are applied ologi



### NHTSA and EPA have reviewed the engine technology estimates used in NHTSA’s MY 2011 CAFE final rule and EPA’s 2008 Staff Report and available comments to the NPRM. In doing so NHTSA and EPA reconsidered all available sou and updated estimates as appropriate. The se ction below describes each of the engine technologies considered for this rulemaking.

(1) 

### Low Friction Lubricants (LUB)

One of the most basic methods of reducing fuel consumption in gasoline engines is the use of lower viscosity engine lubricants. More advanced multi-viscosity engine oils are available today with improved performance in a wider temperature band and with better lubricating properties. This can be accomplished by changes to the oil base stock ( e.g. , switching engine lubricants from a Group I base oils to lower-friction, low viscosity Group III synthetic) and through changes to lubricant additive packages ( e.g friction modifiers and viscosity improvers). The use of 5W-30 motor oil is now widespread and auto manufacturers are introducing the use of even lower viscosity oils, such as 5W-20 and 0W-20, to improve cold-flow properties and reduce cold start friction. However, in some cases, changes to the cranksh chanical tolerances of engine component s may be required. In all cases, dur testing would be required to ensure that durability is not compromised. The shift to lo viscosity and lower friction lubricants will al so improve the effectiveness of valvetrain technologies such as cylinder deactivation, which rely on a minimum oil temperature (viscosity) for operation. Several manufacturers have previously commented confidentially, th

es of this final rule, NHTSA is using an effectiveness estimate within this range. Therefore 0.5 percent is used. The 2002 NAS study estimated the low friction lubricant RPE at $8 to $11 usi 1.4 markup factor. The NESCCAF study showed an RPE of $5 to $15 with a 1.4 markup. The EEA report to DOE showed ma nufacturer costs of $10 to $20 with no markup. Confidential Business Information ( CBI) data estimates an average incremental cost of $3 for the use of low friction lubric ants. EPA’s 2008 Staff Report also confirms this $3 cost (2006$). NHTSA believes that manufacturer’s estimates are the mo accurate, and thus continues to believ i neering work required should ing an indir ultiplier (ICM) of 1.11, for a low , results in a compliance cost of $3.33 per vehicle (2007$) for a MY 2012 through MY 2016 vehicle. The costs developed for low friction lubricants reflect the costs associated with any engine changes that would be required as well as any durability testing.

to low friction lubricants. This technology is presumed to be significantly dependent on commodity raw material prices and to be pr iced independent of particular design or

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### nd proving the design of engine components and subsyst ples

ce mprove,

more o

orts as ness for engine friction n to be between 1 to 3 percent. NHTSA continues to believe that this range is accurate. B f the increm ded to continue to use the narrower range of 1-2 percent, which was also used in the MY 2011 C

r

ce

s cost is multiplied by the number of engine cylinders for Vol

uf acturers’ comments, NHTSA has maintained s it did n

le manufacturing savings. This technology can be applied to any vehicle class with a phasein of 100 percent starting in MY 2012.

(2) 

### Engine Friction Reduction (EFR)

In addition to low friction lubricants, manufacturers can also reduce friction a improve fuel consumption by im ems. Approximately 10 percent of the energy consumed by a vehicle is lost to friction, and just over half is due to frictional losses within the engine. 151 

### Exam include improvements in low-tension piston rings, piston skirt design, roller cam followers, improved crankshaft design and bearings, material coatings, material substitution, more optimal thermal management, and piston and cylinder surfa treatments. Additionally, as computer-aided modeling software continues to i pportunities for evolutionary friction reductions may become available. All reciprocating and rotating components in the engine are potential candidates for friction reduction, and minute improvements in several components can add up to a measurable fuel economy improvement. The 2002 NAS, NESCCAF and EEA rep well as confidential manufacturer data suggested a range of effective reductio ecause o ental nature of the Volpe model, NHTSA nee

AFE final rule. In the MY 2011 CAFE final rule, NHTSA estimated a range from $13 to $49 using a 1.5 RPE on a per cylinder basis, or $9 to $33 without RPE (2007$). In the 2008 NPRM engine friction reduction was estimated to cost up to $14 without RPE on a per cylinder basis. After review, NHTSA believes that the cost estimate is closer to the lowe end of the MY 2011 CAFE final rule range and thus for this rulemaking has a complian cost of $13 per cylinder (2007$), including the low complexity ICM markup value of 1.11, for a MY 2012 vehicle. Thi pe modeling purposes. Thus a cost of $50 was used for a 4-cylinder engine, $75 for a 6-cylinder engine and $101 for an 8-cylinder engine for this final rule. Engine friction-reducing technologies may be applied to all vehicle classes. No learning factors were applied to costs as the technology has a loosely defined BOM which may in part consist of materials (sur face treatments, raw materials) that are commodity based. As confirmed by man a in the MY 2011 final rule, that engine friction reduction may only be applied i conjunction with a refresh or redesign cycl e. Engine friction has phase-in cap of 85 percent from MY 2012 to 2014 and then increases to 100 percent for the rest of this ru making period.

151 “Impact of Friction Reduction Technologies on Fuel Economy,” Fenske, G. Presented at the March 2009 Chicago Chapter Meeting of the ‘Society of Tribologists and Lubricated Engineers’ Meeting, March 18th, 2009. Available at: http://www.chicagostle.org/program/2008- 2009/Impact%20of%20Friction%20Reduction%20Technologies%20on%20Fuel%20Economy%20- %20with%20VGs%20removed.pdf (last accessed July 9, 2009).

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### Variable va (VVT) clas designs that alter the timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, increas

e timing is in production on most of its engine families. Manufa h

y the level of valvetrain technology already implem et file is

e

a solenoid that controls the oil pressure supplied to the phaser.

e ming or variable valve lift. NHTSA judges

s each of these fa ctors is addressed prior to production implem ntation of the technologies.

(a) Inta (3) 

### Variable Valve Timing (VVT)

lve timing sifies a family of valve-train

e specific power, and control the level of residual gases in the cylinder. VVT reduces pumping losses when the engine is lightly loaded by controlling valve timing closer to the optimum needed to sustain hor sepower and torque. VVT can also improve volumetric efficiency at higher engine speed s and loads. Additionally, VVT can be used to alter (and optimize) the effective compression ratio where it is advantageous for certain engine operating modes ( e.g. , in the Atkinson Cycle). VVT has now become a widely adopted technology: in MY 2007, over half of all new cars and light trucks had engines w ith some method of variable valve timing. 152 

### GM has commented that variable valv cturers are currently using many differe nt types of variable valve timing, whic have a variety of different names and me thods. Therefore, the degree of further improvement across the fleet is limited b ented on the vehicles. Information found in the 2008 baseline vehicle fle used to determine the degree to which VVT technologies have already been applied to particular vehicles to ensure the proper level of VVT technology, if any, is applied. Th three major types of VVT are listed below. Each of the three implementations of VVT uses a cam phaser to adjust the camshaft angular position relative to the crankshaft position, referred to as “camshaft phasing.” The phase adjustment results in changes to the pumping work required by the engine to accomplish the gas exchange process. The majority of current cam phaser applications use hydraulically-actuated un its, powered by engine oil pressure and managed by

In response to the NPRM, NHTSA received comments from GM that included a description of technical considerations, concerns, limitations and risks that need to b considered when implementing variable valve ti that the expressed technical considerations, concerns, limitations and risks are well recognized within the industry and it is standard industry practice to address each during the design and development phases of applying variable valve timing or variable valve lift technologies. Cost and effectiveness estimates used in the final rule are based on analysis that assume

e
ke Cam Phasing (ICP)



### Valvetrains with ICP, which is the simple st of the cam phasing technologies, can modify the timing of the inlet valves by pha sing the intake camshaft while the exhaust valve timing remains fixed. This requires the addition of a cam phaser on each bank o f

152 “Light-Duty Automotive Technology and Fuel Economy Trends: 1975 Through 2007”, EPA420-S-07- 001, September 2007. Available at http://www.epa.gov/oms/cert/mpg/fetrends/fetrends-archive.htm (last accessed July 9, 2009).

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### d so have continued to employ it for this rulemaking. As for costs, NH ($41 nonRPE) c

er

d bined phase-in cap is 85 percent from MY 2012 to 2014 and then increas intake valves on the engine. An in-line 4-cylinder engine has one bank of intake valves, while V-configured engines have two banks of intake valves. NHTSA’s MY 2011 CAFE final rule and EPA 2008 Staff Report estimated an effectiveness of 1 to 2 percent for ICP, which was supported by the NESCCAF report and a majority of confidential manufacturer co mments. NHTSA has found no additional sources to suggest strongly that this estimat e is inaccurate, an

TSA’s MY 2011 CAFE final rule estimated a $61 RPE ost per cam phaser, based on the 2008 Martec Report and confidential manufacturer data. NHTSA believes that this number remains accurate. Using the new indirect cost multiplier of 1.11, for a low complexity technology, the compliance cost p cam phaser would be $45 per bank, yiel ding a $45 cost for and in-line engine configurations and $90 for V-engine configurations for a MY 2012 vehicle. ICP is applicable to all vehicle classes, can be applied at the refresh or redesign cycles and is eligible for time-based learning. For this rulemaking and as it did for the MY 2011 final rule, NHTSA has combined the phase-in caps for ICP, CCPS, CCPO an DCP. This com es to 100 percent for the rest of this rule making period.

(b) Coupled Cam Phasing (CCPS and CCPO)



### Valvetrains with coupled (or coordinated) cam phasing can modify the timing of both the inlet valves and the exhaust valves an equal amount by phasing the camshaft single overhead cam (SOHC) engine or an overhead valve (OHV) engine. For overhead cam engines, this requires the addition of a cam phaser on each bank of th of a

e engine. Thus, an in-li

and

ess for CCPS to be 1 to 3 percent

. ne 4-cylinder engine has one cam phaser, while SOHC V-engines have two cam phasers. For overhead valve (OHV) engines, which have only one camshaft to actuate both inlet and exhaust valves, CCP is the only VVT implementation option available requires only one cam phaser. 153 

### Based on NHTSA’s MY 2011 CAFE final rule, previously-received confidential manufacturer data, and the NESCCAF report, NHTSA estimated the effectiveness of CCP to be between 1 to 4 percent. NHTSA reviewed this estimate for purposes of the NPRM, and continue to find it accurate. Due to the incremental nature and decision tree logic of the Volpe model, NHTSA estimated the effectiven and 1 to 1.5 percent for CCPO. The same cam phaser has been assumed for ICP and CCP applications, thus CCP’s cost per cam phaser is identical to ICP’ s. This results in a cost of $45 for in-line SOHC and OHV engines and $90 for SOHC V-engine configurations for a MY 2012 vehicle with time-based learning applied. CCP is applicable to all vehicle classes a nd can be applied at refresh or redesign For purposes of this rulemaking as in the MY 2011 final rule, NHTSA has combined the phase-in caps for ICP, CCPS, CCCPO and DCP. This combined phase-in cap is 85

153 It is a o noted that coaxial camshaft developments would allow other VVT options to be applied to OHV en nes. However, since they would potentially be adopted on a limited number of OHV engines NHTSA did not include them in the decision tree. ls gi

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### percent from MY 2 nt for the rest of this rule making 012 to 2014 and then increases to 100 perce period. (c) Dual Cam Phasing (DCP)



### The most flexible VVT design is dual (independent) cam phasing, where the intake and exhaust valve opening and closing events are controlled independently. This option allows the option of controlling valve overlap, which can be used as an inte EGR strategy. At low engine loads, DCP cr eates a reduction in pumping losses, resulting in improved fuel consumption. Increased i rnal

nternal EGR also results in lower engine-out O X 

### em at

P’s cost per cam phaser is identical to ICP’s. DCP requires two cam ph

percent

ng

cesses. Variable valve lift control can also be used to induce in-cylinder mixture motion, which improves fuel-air mixing and can result N issions. The amount by which fuel consumption is improved depends on the residual tolerance of the combustion system. Additional improvements are observed idle, where low valve overlap could result in improved combustion stability, potentially reducing idle fuel consumption. In the MY 2011 final rule, NHTSA estimated the effectiveness of DCP to be between 2 to 3 percent relative to an engine with ICP. NHTSA believes that this estimate remains applicable for this rulemaking. As with CCP, the same cam phaser has been assumed for ICP and DCP applications. Thus, DC asers per cylinder bank, one to control the intake valves and one to control the exhaust valves. This results in a cost of $90, relative to an engine without ICP, or $45 relative to an engine with ICP, minus $6 for the removal of the EGR valve, ultimatelyyielding costs of $84 and $39 respectively for in-line DOHC configurations. For Vconfiguration engines, the cost is $180 relative to an engine without ICP, or $90 relative to an engine with ICP, minus $6 for the re moval of the EGR valve, ultimately yielding costs of $174 and $84, respectively. These costs are appropriate for a MY 2012 vehicle application. DCP can be applied to all of the vehicle classes at vehicle refresh. Time-based leaning is applied and NHTSA has combined the phase-in caps of ICP, CCPS, CCPO and DCP with a combined cap of 85 percent for MY 2012 to 2014 and increases to 100 for the rest of this rule making period. In response to the NPRM, NHTSA received comments from a manufacturer that included confidential business information related to the effectiveness of variable valve timing technology. Analysis of the data, that used assumptions similar to those used in the NPRM and final rule, showed that effectiveness values are similar to those used in the NPRM and final rule.

(4) 

### Variable Valve Lift (VVL)

Controlling the lift of the valves provides a potential for further efficiency improvements. By optimizing the valve-lift profile for specific engine operating regions, the pumping losses can be reduced by reducing the amount of throttling required to produce the desired engine power output. By moving the throttling losses further downstream of the throttle valve, the heat transfer losses that occur from the throttli process are directed into the fresh charge-air mixture just prior to compression, delaying the onset of knock-limited combustion pro

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### in impr tially

Discrete Variable Valve Lift (DVVLS, DVVLD, DVVLO)



### s gine. These cam prof iles consist of a low and a high-lift lobe, and may in e an inert or blank lobe to incorporate cylinder deactivation (in the case of a 3- step DVVL system). DVVL is normally applied together with VVT control. DVVL is also known as Cam Profile Switchi technical risk.

he

n inline 4-cylinder engine, $306 for a V6 engine and $396 for a V8 engine

,

is cylinder

ne cylinder head. In the NPRM and final rule, NHTSA limits applica the

ass

oint penetration allowed at 85 p oved thermodynamic effi ciency. Variable valve lift control can also poten reduce overall valvetrain friction. At the same time, such systems may also incur increased parasitic losses associated with their actuation mechanisms. A number of manufacturers have already implemented VVL T into their fleets (Toyota, Honda, and BMW), but overall this technology is still available for most of the fleet. There are two major classifications of variable valve lift, described below:

DVVL systems allow the selection between two or three discrete cam profiles by means of a hydraulically-actuated mechanical system. By optimizing the cam profile for specific engine operating regions, the pumpi ng losses can be reduced by reducing the amount of throttling required to produce the desired engine power output. This increase the efficiency of the en clud

ng (CPS). DVVL is a mature technology with low

NHTSA’s MY 2011 CAFE final rule, previously-received confidential manufacturer data, and the NESCCAF report, all estimate the effectiveness of DVVL to be between 1 to 4 percent above that realized by VVT systems. NHTSA believes this estimate continues to be applicable for the fi nal rule. Taking into account the incremental nature and decision tree logic of Volpe modeling, NHTSA has estimated an incremental reduction in fuel consumption for DVVLS and DVVLD of 1 to 3 percent. On OHV engines, DVVLO is applied following both VVT and cylinder deactivation, therefore t effectiveness estimate is at a slightly lower range of 0.5 to 2.5 percent. In the MY 2011 CAFE final rule, NHTSA estimated an RPE (1.5 markup factor) cost of $201 for a or without RPE $134, $204, $264, resp ectively (all in 2007$). After review NHTSA, in consultation with EPA, has chosen to use the NESCCAF report as the basis for the discrete variable valve lift cost. The NESCCAF estimates we re converted to 2007 dollars, updated for a MY 2012 application, increased by $25 for additional controls hardware and multiplied by the low complexity ICM markup factor or 1.11. For th final rule, NHTSA is using a compliance cost estimate of $141 for an inline 4- engine, $205 for a V6 engine and $293 for a V8 engine. In response to the NPRM, NHTSA received comments from GM that implementation of either 2-step or continuously variable lift must be timed to coincide with changes to the engi tion of these technologies to vehicle re design, which is judged to approximate frequency of engine cylinder head changes. This technology may be applied to any cl of vehicles. NHTSA has combined the phase-in caps for DVVLS, DVVLD, DVVLO and CVVL, as it did in the MY 2011 final rule, and capped the j ercent in MY 2012 to 2014 and increases to 100 percent for the rest of this rule making period with time-based learning applied.

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### ry

m.

intake valve op ening reduction, which improves engine efficien eam

to

1.5 h as VL

ere based on the NESCCAF report, in which

y

facturers’ product lans confirmed that this technology will not be applied by most manufacturers. In the MY 11 CAFE fin continuously variable valve lift to be $ for a V6 engine d $582 for a V8 engine or without RPE $204, $287, $388, respectively. After review the

s

a -based learning applied.

y LS,

penetration allowed at 85 percent in MY 2012 to 2014 and the increases to 100 percent for the rest of this rule making period. 1. Continuously Variable Valve Lift (CVVL) 

### In CVVL systems, valve lift is varied by means of a mechanical linkage, driven by an actuator controlled by the engine control unit. The valve opening and phasing va as the lift is changed and the relation depends on the geometry of the mechanical syste BMW has considerable production experience with CVVL systems and has sold portinjected “Valvetronic” engines since 2001. CVVL allows the airflow into the engine to be regulated by means of cy by reducing pumping losses from th rottling the intake system further upstr as with a conventionally throttled engine. Variable valve lift gives a further reduction in pumping losses compared to that which can be obtained with cam phase co ntrol only, with CVVL providing greater effectiveness than DVVL, since it can be fully optimized for all engine speeds and loads, and is not limited to a two or three step compromise. There may also be a small reduction in valvetrain friction when operati ng at low valve lift, resulting in improved low load fuel consumption for cam phase control with variable valve lift as compared cam phase control only. Most of the fuel economy effectiveness is achieved with variable valve lift on the intake valves only . CVVL is only applicable to double overhead cam (DOHC) engines. NHTSA’s MY 2011 CAFE final rule estimated the effectiveness for CVVL at to 3.5 percent over an engine with DCP, but also recognized that it could go up as hig 5% above and beyond DCP to account for the implementation of more complex CV systems such as BMW’s “valvetronic” engines. This coincides with EPA Staff Report estimates of the contribution of CVVL, wh ich w CVVL could improve effectiveness by 4 percent (minivans) and up to 6 percent (large cars) over dual cam phasing. For this rulemaking, NHTSA has continued to use the 1.5 to 3.5 percent range from the MY 2011 final rule. However, due to the complexit and cost of this technology, the Volpe model projected very limited applications of this technology (i.e., 2 out of 1100 vehicles). The most recent submission of manu
p 20 al rule, NHTSA estimated and RPE (1.5) cost of 306 for an inline 4-cylinder engine, $432

an , NHTSA in consultation with EPA has chosen to use the NESCCAF report as basis for the discrete variable valve lift cost. The NESCCAF estimates were converted to 2007 dollars, updated for a MY 2012 application, increased by $25 for additional controls hardware and multiplied by the low complexity ICM markup factor or 1.45 For thi rulemaking, NHTSA estimated a cost of $277 for an inline 4-cylinder engine, $509 for V6 engine and $554 for a V8 engine with time There are no class specific applications of this technology, although it appears in only the DOHC portion of the decision tree. Due to the changes required to implement CVVL on an engine the Volpe model allows it to be applied at redesign model years onl with time-based learning applied. NHTSA ha s combined the phase-in caps for DVV DVVLD, DVVLO and CVVL, as in the MY 2011 final rule, and capped the joint

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### tion of additional technologies such as 2-step variable lift will be highly depend
t

f technologies to achieve CAFE regulatory compliance, based on their unique circumstances. In response to the NPRM, NHTSA received comments from a manufacturer that include

ACO)

lf of bility – the valves are kept closed, and no fuel is injected – as a result, the trapped air within the deactivated cylinders is simply compressed and expand

Cylinder deactivation control strate gy relies on setting maximum manifold

ed, f

ge of

d applied to

Cylinder deactivation has seen a recent re surgence thanks to better valvetrain

er displacement-to-weight vehicles, operating at lower relative loads r normal driving, have the potential to operate in part-cylinder mode more frequently.

range depending on the engine valvetrain configuration. For example, for DOHC

In response to the NPRM, NHTSA received comments from GM that the introduc ent on the cost-effectiveness of the technology from a fuel economy benefit standpoint. The Volpe model provides one solution that manufacturers could use to mee CAFE regulations to demonstrate the feasibility of regulatory standards. It is expected that in many cases, manufacturers will identify and implement other combinations o

d confidential business information related to the effectiveness of variable valve lift technology. Analysis of the data, that used assumptions similar to those used in the NPRM and final rule, showed that effectiveness values are similar to those used in the NPRM and final rule.

(5) 

### Cylinder Deactivation (DEACS, DEACD, DE

In conventional spark-ignited engines throttling the airflow controls engine torque output. At partial loads, efficiency can be improved by using cylinder deactivation instead of throttling. Cylinder deactivati on (DEAC) can improve engine efficiency by disabling or deactivating (usually) half of the cylinders when the load is less than ha the engine’s total torque capa

ed as an air spring, with reduced friction 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.

absolute pressures or predicted torque within which it can deactivate the cylinders. Noise and vibration issues reduce the operating range to which cylinder deactivation is allow although manufacturers are exploring vehicle changes that enable the possibility o increasing the amount of time that cylinder deactivation might be suitable. Some manufacturers may choose to adopt acti ve engine mounts and/or active noise cancellations systems to address NVH concerns and to allow a greater operating ran activation. Manufacturers have stated that use of DEAC on 4 cylinder engines woul cause unacceptable NVH issues; therefore cylinder deactivation has not been 4-cylinder engines.

designs and engine controls. General Moto rs and Chrysler Group have incorporated cylinder deactivation across a substantial portion of their V8-powered lineups. Honda (Odyssey, Pilot) offers V6 models with cylinder deactivation. Effectiveness improvements scale roughly with engine displacement-to-vehicle weight ratio: the high fo NHTSA reviewed the MY 2011 CAFE estimates and confirmed their appropriateness for this rulemaking. The Vo lpe model, due to its incremental nature,

ses a u

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### so the

ent for DEACS. For OHV e

at

d

n ew ICM multipliers for engines that do not have lost mo

or

r nd

ith

s.

ld

purposes of this rulema ng, NHTSA revised the costs only by identifying this technology as a low complexity technology and applying an indirect cost multiplier of 1.11 resulting in a engines which are already equipped with DCP and DVVLD, there is little benefit that can be achieved from adding cylinder deactivat ion since the pumping work has already been minimized and internal Exhaust Gas Recirc ulation (EGR) rates are maximized, effectiveness range for DEACD is 0.0 to 0.5 percent. For SOHC engines which have CCP and DVVLS applied, effectiveness ranged from 2.5 to 3 perc ngines, without VVT or VVL technologies, the effectiveness for DEACO ranged from 3.9 to 5.5 percent. NHTSA considered a range of $28 to $190 depending on whether an engine already has lost motion devices, oil control valves and camshaft position sensors. This is a departure from NHTSA’s 2011 final rule, which uses a range of $306 to $400. Th range was primarily based on 2008 Martec Report and applied a higher RPE value. In reviewing these assumptions, NHTSA in c onsultation with EPA amended the MY 2011 CAFE estimates and adjusted the estimates to include the new ICM low complexity markup of 1.11. The EPA staff report and NHTSA’s MYs 2011-2015 NPRM showe estimates of a $170 for a 6-cylinder engine and $190 for an 8-cylinder engine whe adjusted for 2007 dollars and using the n tion devices. These numbers were within the ranges described by the 2002 NAS and NESCCAF reports. For Volpe modeling purposes, these costs are appropriate f DEACO on OHV engines. If lost motion devices are on the engine, as is the case fo SOHC and DOHC engines based on the decision tree logic, the cost of DEACS a DEACD ranges from $0 to $56. This $0 to $56 range 154 

### accounts for the potential additional application of active engine mounts on SOHC and DOHC engines and can only be applied on 50 percent of the vehicles. This technology may be applied only to V- 6 and V-8 engines, as discussed above, and so does not apply to vehicle classes with I-4 engines. DEAC can be applied during a redesign or refresh model year with time-based learning. NHTSA has combined the phase-in caps for DEACS, DEACD and DEACO, as it did in MY 2011 final rule, and capped the joint penetration allowed at 85 percent for MY 2012 and beyond.

(6) 

### Conversion to Double Overhead Camshaft Engine w Dual Cam Phasing (CDOHC)

Double overhead camshaft engines achieve increased airflow at high engine speeds, improve volumetric efficiency and redu ctions of the valvetrain’s moving mas Such engines typically develop higher power at high engine speeds. Manufacturers may choose to replace OHV engines with DOHC engine designs with dual cam phasing (DCP). NHTSA continues to use the fuel consumption reduction estimate of 1 to 2.5 percent, as it did in the MY 2011 final rule. As for costs, NHTSA’s MY 2011 CAFE final rule assumed that CDOHC wou have an RPE cost of $746 ($497 non-RPE) for a V8 engine, $590 ($393 non-RPE) for a V6 engine and $373 ($249 non-RPE) for inline 4-cylinder engine. For ki

154 The $28 is an adjustment from the $75 estimate used in the MY 2011 final rule to account for the new ICM markup factor and the fact that it could only be applied on up to 50 percent of the vehicles.

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### ompliance cost of $552 for V8 engine, $436 for a V6 and $276 for an inline 4-cylinder

technology. The phase-in ap for CDOHC has be p p h 16 timeframe. The conversion from OHV t ng h th engine redesign that can be applied in redes ode s onl time- learning applied.

(7) 

### Stoichiometric Gasoline Direct Injection (SGDI)

the cyl

ting and variable valvetrain designs.

Several manufacturers ha recently introduced vehicles with SGDI engines, includi c engine. There are no vehicle class-specific applications of this

c en set at 85 o D C e ercent

c er year for t ite wi e 2012-20 D a major OH ine ar ctu re CP is ign m l year y with based

Gasoline direct injection (GDI) or Spark Ignition Direct Injection (SIDI) engines inject fuel at high pressure directly into the combustion chamber (rather than the intake port in port fuel injection). SGDI requires changes to the injector design, an additional high pressure fuel pump, new fuel rails to hand le the higher fuel pressures and changes to inder head and piston crown design. Direct injection of the fuel into the cylinder improves cooling of the air/fuel charge within the cylinder, which allows for higher compression ratios and increased thermodyna mic efficiency without the onset of combustion knock. Recent injector design advances, improved electronic engine management systems and the introduction of multiple injection events per cylinder firing cycle promote better mixing of the air and fuel, enhance combustion rates, increase residual exhaust gas tolerance and improve cold start emissions. SGDI engines achieve higher power density and match well with other technologies, such as boos

ve ng VW/Audi, BMW, Toyota (Lexus IS 350) and General Motors (Chevrolet Impala and Cadillac CTS 3.6L). BMW, GM , Ford and VW/Audi have announced their plans to increase dramatically the number of SGDI engines in their portfolios.

NHTSA’s MY 2011 CAFE final rule estimated the effectiveness of SGDI to be between 2 and 3 percent. In developing these estimates, NHTSA reviewed estimates from the Auto Alliance of American Manufacturers, which projects 3 percent gains in fuel efficien cy and a 7 percent improvement in torque. The torque increase provides the opportunity to mildly downsize the engine al lowing an increase in efficiency of up to a 5.8 percent. NHTSA also reviewed other published literature, reporting 3 percent effectiveness for SGDI. 155 

### Another source reports a 5 percent improvement on the NEDC drive cycle. 156 

### Confidential manufacturer data reported an efficiency effectiveness range of 1 to 2 percent. In response to the NPRM, NHTSA received

155 Paul

(last

156 Stefan Trampert, FEV Motorentechnik GmbH, “Engine and Transmission Development Trends - Rising Fuel Cost Pushes Technology,” Symposium on International Automotive Technology, Pune, India, January 2007. Whitaker, Ricardo, Inc., “Gasoline Engine Performance And Emissions – Future Technologies and Optimization,” ERC Symposium, Low Emission Combustion Technologies for Future IC Engines, Madison, WI, June 8-9, 2005. Available at http://www.erc.wisc.edu/symposiums/2005_Symposium/June%208%20PM/Whitaker_Ricardo.pdf accessed Nov. 9, 2008).

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### comme

, NHTSA received comments from MECA stating that Gasolin

d downsizing, and cooled EGR along with SGDI, that the 5 percent

with EPA revised the cos for SGDI to take into account the changes required to the engine hardwa e elect c controls, a (NVH) mitigation systems. Through c manufa nts from Porsche stating that Gasoline Direct Injection improves fuel economy up to 3%. NHTSA determined that the range of 2 to 3 percent continues to be appropriate. However, NHTSA notes that combined with other technologies ( i.e. , boosting, downsizing, and in some cases, cooled EG R), SGDI can achieve greater reductions in fuel consumption compared to engines of similar power output. In response to the NPRM e Direct Injection offers CO2 emis sions reductions ranging from 5% to 20% depending on how it is implemented and the base engine to which it is compared. The 5% to 20% range in MECA’s comments is not specific as to the technology level of the base engine for the comparison, nor the ac companying technologies that have been incorporated along with GDI. Therefore, ther e is insufficient information presented in MECA’s comments to enable comparison of effectiveness to the effectiveness range for SGDI in the NPRM and final rule. However, it should be noted that if MECA’s intende to include turbocharging, to 20 percent effectiveness impr ovement range would be consistent with effectiveness estimates in the final rule. In reviewing the MY 2011 estimates, NHTSA in coordination t estimates re, engin roni ncillary and Nois e Vibration and Harshness
ont acts with industry NVH suppliers, and cturer press releases, the agency believes that the NVH treatments will be limited to the mitigation of fuel system noise, specifically from the injectors and the fuel lines

. In the final rule, NHTSA, in coordination with EPA, revised the SGDI costs based on the FEV work that was not yet available for the NPRM. Focusing on direct manufacturing costs, the NPRM estimates, the FEV values, and the final values are shown in

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### Instead, Table V-17 . FEV did not directly estimate the SGDI costs shown here. FEV estimated costs associated with downsiz ing and turbocharging a V8 and V6 engine to a V6 and I4 engine, respectively, and simu ltaneously converting the PFI fuel system to a SGDI fuel system. The agencies, working closely with FEV, then “binned” the costs into three distinct bins: downsize, turbocharge, and SGDI. As such, the FEV results shown in

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### s,

. Table V-17 cannot be found in the FEV reports, but instead are detailed in a memo to the docket which also provides details of this binning process. 157 

### Because the methodology used by FEV presumes high volume production, instead of using the FEV results directly the agencies have averaged those results with the NPRM results to estimate the final values (this is noted above in section V.C.C.5 and in TSD sections 3.3.2.2, 3.4.2.1.9, and 3.4.2.2.5). Note that the costs for the I4 engine has changed since the NPRM but have not been averaged with the NPRM values. The costs changed based on a more rigorous binning process than that conducted for the NPRM, but were not averaged because they are based on the I4 to I4 teardown conducted by FEV which wa in fact, used in the NPRM. While the final value of $213 is lower than the NPRM value of $226, the $13 difference has simply been sh ifted from SGDI to the downsizing bin

15 7 Insert docket info for Mike’s memo to the docket providing details of the FEV binning of costs.

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DELIBERATIVE DOCUMENT – DO NOT RELEASE Table V-17. Direct Manufacturing Costs for SGDI (2007$ in 2012)

Technology NPRM FEV Results Final Rule I4 $226 $213 $213 V6 $293 $321 $307 V8 $318 $386 $352



### For the final rule marked up costs, the agencies estimate SGDI costs at $236 for an inline 4-cylinder and $341 for V6 and $391 for V8 including the low complexity IC M

158



### ed to

onsumption as the engine d

hat

This includes a battery sensor, incremental wiring and high current switching, an markup value of 1.11. As noted above, all of these costs differ slightly from those used in the NPRM analysis. These costs were not changed in response to public comments, but instead were changed due to updated information from the FEV teardown studies. SGDI systems are regarded as mature technology with minimal technical risk and are expected to be increasingly incorporatedinto manufacturers’ product lineups. Timebased learning has been applied to this technol ogy due to the fact that over 1.5 million vehicles containing this technology are now produced annually. Due to the changes to the cylinder head and combustion system and the control system development requir adopt SGDI technology, which are fairly extens ive, SGDI can be applied only at redesignmodel years. There are no limitations on applying SGDI to any vehicle class. The phasein cap for SGDI is applied at an 85 percent rate for MY 2012 and beyond.

(8) 

### Combustion Restart (CBRST)

Combustion restart allows “start-stop” functionality of DI engines through the implementation of an upgraded starter with bi-directional rotation to allow precise crankshaft positioning prior to subsequent fuel injection and spark ignition, allowing engine restart. This method of implementing engine stop/start functionality allows not only the fuel savings from not idling the engine, but also reduces fuel c speeds up to its operational speed. A Direct Injection (DI) fuel system is require for implementation of this technology. NHTSA reviewed the MY 2011 CAFE final rule assumptions and determined t due to technical risks implementation of com bustion restart would likely not be feasible prior to MY 2014. Some of the risks are a ssociated with unresolved issues regarding the impact of very high or very low ambient air te mperatures on the ability to start the engine in the described manner. Although the starter motor can provide fail-safe starting capability in these temperature limited areas, strategies must be developed to manage the transitions. Others relate to production readiness. Additional hardware is required to implement combustion restart, beyond SGDI.

incremental crank position sensor, and, in the case of an automatic transmission applications, a transmission oil pump to allow for torque converter continuity.

158 Insert docket info for Mike’s memo to the docket providing details of the FEV binning of costs.

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### A

T to

m

t

t of 2012 vehicle and will be reduced in future years with the application of time-based learning.

ble to draw air into the combustion chambers. Turbocharging and supercharging (grouped together here as boosti ng) are two methods to increase the intake manifold pressure and cylinder charge-air mass above naturally aspirated levels. Boosting increases the airflow into the engine, thus increasing the specific power level, and with it the ability to reduce engine displacement while maintaining performance. This effectively reduces the pumping losses at lighter loads in comparison to a larger, naturally aspirated engine.

Almost every major manufacturer currently markets a vehicle with some form of boosting. While boosting has been a comm on practice for increasing performance for several decades, turbocharging has considerab le potential to improve fuel economy when the engine displacement is also reduced. Specific power levels for a boosted engine often exceed 100 hp/L, compared to average natura lly aspirated engine power densities of roughly 70 hp/L. As a result, engines can conservatively be downsized roughly 30 percent to achieve similar peak output levels . In the last decade, improvements to turbocharger turbine and compressor desi gn have improved their reliability and BMW has published a 3.5 percent fuel consumption effectiveness over the NEDC drive cycle for combustion restart, 159 

### and AVL a 4.8 percent effectiveness. 160 

### However, these reported effectiveness levels could potentially be reduced significantly on the EP combined drive cycle, as combustion restart does not save fuel on the highway drive cycle. Therefore, NHTSA estimates the fuel consumption effectiveness for CBRS range from 2 to 2.5 percent. Regarding the cost estimate, NHTSA determ ined that the estimate of $118 fro the 2008 Martec Report cost estimates for indivi dual pieces was the best available. The total RPE cost (excluding transmission pump) is $141 at high volumes, which includes $70 for upgrading the starter, $10 for a batter y sensor and wiring, $10 for high curren switch and $4 for crank sensor a totaling $94 (non-RPE) cost. Applying an indirect cost multiplier of 1.25, for a medium complexity technology, results in a compliance cos $118 for a MY

CBRST is first available in MY 2014 and is applicable to all vehicle classes. Confidential product plan da ta indicates CBRST to be at high volume by 2014 so timebased learning is applied. CBRST can be applied a vehicle refresh.

(9) 

### Turbocharging and Downsizing (TRBDS)



### The specific power of a naturally aspirated engine is primarily limited by the rate at which the engine is a

159 Stefan Wolff, Dirk Abendroth, Werner Weigl, Claus-Peter Linner, Rupert Neudecker, Michael Schneider, Wolfgang Huber, and Andreas Rau, BMW, “Introducing The Automatic Start-Stop (ASS) Function In Series Models,” 7th Stuttgart Automotive Vehicle and Engine Symposium, Organised by FKFS, Mar 2007, Vol. 1.
159 G.K. Fraidl, P.E. Kapus, and H. Friedl, AVL List GmbH, “Future Gasoline Engine Technologies for 130

the Working Process of Combustion Engines, TU Graz, g/Km
160 G.K. Fraidl, P.E. Kapus, and H. Friedl, AVL List GmbH, “Future Gasoline Engine Technologies for 130 g/Km CO2,” VKM-THD 11th Symposium on Sept. 2007.DELIB VE DOC E 

### 202

performance across the entire engine operating range. New variable geometry turbines and ball-bearing center cartridges allow fa ster turbocharger spool-up (virtually eliminating the once-common “turbo lag” ) while maintaining high flow rates for increased boost at high engine speeds. However, even with turbocharger improvements, maximum engine torque at very low engine speed conditions, for example launch from standstill, is increased less than at mid and hi gh engine speed conditions. The potential to downsize engines may be less on vehicles with low displacement to vehicle mass ratios in order to provide adequate acceleration from sta , particularly up grades or at high altitudes.

n N om from GM that included a ripti h rn tions and risks that need to be considered when implementing turbocharging and downsizing technologies on full size trucks. T lu in , drivability, control of boost
pre e, n l ehicles that are designed for towing o u a r rs NHTSA judges that the expressed technical co s, conc erns, limitations a are well recognized within the indust a ring the design and dev g nsizing technologies. Cost and effective s tes u in the final rule are n analysis that assumes each of e fa is ressed prior to production im tation of the technologies. In

me m at potential to address knock limit e of higher octane premium fuel /or t ddi ano l in system. For this rulemaking, TSA has ed that either of these ap s is implemented to address knock it co assessment of turbocharging

w ss addition, NHTSA has received fide ss information from a manuf that supports that turbocharging downsi asible on a full size truck p uring the rulemaking period. and air-to-air charge air cooling n ited combustion and allows the of high n r “Ecob wnsized, turbocharged GDI ines intro on MY 2010 vehicles allow ement of V8 engines with V6 engine o fuel economy improvements of up t

ERATI UM EN T – D O N OT R ELE AS 

### ndstill

ments s, limita

e knock ing for v hness. nd risks ddress each du and dow based o plemen ented th clude us jection proache cluded in

. 161 

### In
acturer roduct d engines ock lim oost” do the replac n and with In re desc spo se to the on of tec PR nica M l c , N

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g co

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er compr sy l o

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duced s with impro 2 percent. 162 

### ved in 0- 60 mp h acc ele ra ti o 1

161 Note t es rging and downsizing conducted by FEV I in ed by a 1.6L I4 DOHC SGDI turbochar for the study was a premium octane fuel engine. For th lemaking, NHTSA intends that a t and downsized engine achieve parable perf req ium octane fuel. For the FEV study of tu d ro cification of an engine with a lac m tl g htly larger engine would have small ct on the ve cost analysis used in this rulemaking teardown studies conducted by FEV, both the naturally aspirated engine and the replacem rged and downsized engine were specifi use regula ne fuel.
162 “De ment and e F V6 ombustion System,” Yi,J., Woold , S., Coulso oi paioannou, G., Reiche, D. Shelby, M., hat for one of hich a 2.4L ged engine, t is ru the 4 D he p tea OH arti rdo C n cula wn atu r 1 ana

rall .6L lysi
y as tur s c pir boc ost s ated harg tudi
eng ed en of turbocha e was replac gine chosen urbocharged uiring prem ugh the spe es that a slig . For all other ent turbocha

EcoBoost C lanen, P., Pa , in w

com the 1 disp effe ormanc rbocharge ent of sligh

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eat s i er th eline e

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an ngi uld 1.6 ne be a L. wit ch NH hou iev TSA t ed th

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### systems and more

ggressive engine downsizing targeted toward s reduced fuel consum ption indicate that the may be rcent relative to port-fuel-injected engines. Confidential manufacturer data suggest an incremental range of fuel consumption of 4.8 to 7 e sources suggest asp technic for dow using a NEDC cylinde injectio downsizing to a rbocharged DI engine, again with wall-guided injection. 170 

### These ology

PRM, NHTSA received comments from several sources

re f t

. t they u for t h

o s sho n i to 20 cent y and

a dis nt 30 to 40 perce nomy Recently published data with advanced spray-guided injection

a potential for reducing fuel consumption for turbocharged, downsized GDI engines as much as 15 to 30 pe 163 164 165 166 167



### .5 percent for turbocharging and downsizing. Other publicly-availabl a fuel consumption of 8 to 13 perc ent compared to current-production naturallyirated engines without friction reduction or other fuel economy te chnologies: a joint al paper by Bosch and Ricardo suggesting fuel economy gain of 8 to 10 percent nsizing from a 5.7 liter port injection V8 to a 3.6 liter V6 with direct injection wall-guided direct injection system; 168 

### a Renault report suggesting a 11.9 percent fuel consumption gain for downsizing from a 1.4 liter port injection in-line 4- r engine to a 1.0 liter in-line 4-cylinder engine, also with wall-guided direct
n; 169 

### and a Robert Bosch paper suggesting a 13 percent NEDC gain for
tu reported fuel economy benefits show a wi de range depending on the SGDI techn employed.

In response to the N lated to the effectiveness o methods to estim urbochargi ng and downsizing

ate l econom fits Honeywell commented tha u rging, a sed four the fue y bene
per rbocha nd eac , in f those method ddition, engine wed a mprovement of 15 could be reduced in fuel econom nt. A fuel eco placeme

V B., Weav , Z G., Hinds, B. S . SA cal Paper No - 1 1 

### s et al. , Lot w Cost Solutions for Im Fu my in Gaso

ngines,” Global Powertrain Congress September 27-29, 2005, vol. 33. Available at http:/

anDerWege, er, C. Xu ., Davis, chamel, A E Techni . 2009-01 494, 2009. 63 

### Cairn us, “Lo proved el Econo line E /www.gpc-icpem.org/pages/publications.html 

### (last accessed Nov. 9, 2008). im Lake, John Stokes, Richard Murphy, and Richard Osborne of Ricardo and Andreas Schamel of rke, “Turbocharging Concepts for Downsized DI Gasoline Engines,” VKA/ika Aachen ium 2003. Available at 164 T Ford-We Colloqu http://cat.inist.fr/?aModele=afficheN&cpsidt=16973598 (last accessed 008). rim Report: New Powertrain Technologies and Their Projected Costs,” October 2005, EPA42 Nov. 9, 2
165 “Inte 0-R05-0
166 “ t Trucks,” PA Interim T
167 “Elec Compan
168 D App e at http:/

169 Yves nault Powertrain Division, “The New Renault TCE 1.2L Turbocharged Gasoline Engine,” 28th Vienna Motor Symposium, Apr
170 T , CostOptimal Environment Conference, September 2005. 12. Cost and Fuel Economy Comparison of Diesel and Gasoline Powertrains in Passenger Cars and Ligh submitted by FEV Engine Technology, Inc., April 23, 2003, contained as Appendix I within E echnical Report EPA420-R-04-002. tric Cars: Plugged In, Batteries must be included,” Deutsche Bank Global Markets Research y, June 9, 2008. avid Woldring and Tilo Landenfeld of Bosch, and Mark J. Christie of Ricardo, “DI Boost: lication of a High Performance Gasoline Direct Injection Concept,” SAE 2007-01-1410. Availabl /www.sae.org/technical/papers/2007-01-1410 (last accessed Nov. 9, 2008)

Boccadoro, Loïc Kermanac’h, Laurent Siauve, and Jean-Michel Vincent, Re

il 2007. obias Heiter, Matthias Philipp, Robert Bosch, “Gasoline Direct Injection: Is There a Simplified System Approach for an Attractive Future of Gasoline Engines?” AVL Engine &

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### imp improv

econom

uel economy improvement when engine performance is maintained”. For analysis of the effectiv

d

ine

over a comparable performance natura lly-aspirated SGDI engine taking into account previously applied ich rovement of 15 to 20 percent converts to a fuel consumption effectiveness ement of 13 to 17 percent. Porsch e commented turbocharging improves fuel y up to 1.9%.

Honeywell also commented “that turbo downsizing enables significant f

eness of technologies, NHTSA uses a constraint to maintain ing performance at a level that is as similar to baseline as possible, such that vehicle performance attributes are affected to the smallest possible extent. Effe ctiveness estimates in the final rule are base on use of this constraint. This approach is consistent with the Honeywell comment.

For the NPRM and final rule, NHTSA estimates a turbocharged and downsized eng will improve fuel consumption by 0.3 percent to 6.7 percent incrementally

technologies (e.g., VVT and VVL) as defined on the decision tree. The range of incremental fuel consumption improvement for each engine is also based on wh decision tree path (i.e. SOHC, DOHC or OHV) the engine is following. This is similar to estimates used in the 2011 final rule. This would equate to a 12 to 14 effectiveness improvement over baseline fixed-valve engine, similar to the estimate for Ford’s Ecoboost. When accounting for overall effectiveness for combined engine technologies that may be implemented along with turbocharging and downsizing, as defined in the NPRM and final rule decision trees, the NPRM and final rule estimated cumulative fuel consumption effectiveness is 11.2 to 17.4 percent which converts to 14 to 21 percent on a fuel economy improvement basis. This is similar to the 15 to 20 percent range estimated by Honeywell. The Porsche estimate for fuel economy improvement is consistent with the incremental effectiveness values used in the NPRM and final rule. In response to the NPRM, one manufacturer provided confid ential business information related to turbocharging and downsizing effectiveness wh ich was similar to NPRM and final rule estimates when using similar assumptions for technology application.

As noted above NHTSA, in coordination with EPA, relied on engine teardown analyses conducted by EPA
171 

### , FEV and Munro to develop costs for turbocharged SGDI engines. Teardown studies are the one of the most effective ways to estimate technol
y PRM PRM, mpleted and those results are being used in the final an ogy costs. For the proposal, only the 2.4L I4 DOHC to 1.6L I4 DOHC teardown study had been completed in time for inclusion in the NPRM, and results from that stud were used as the cost in the 2012 model year. For other turbo-downsize costs, the N primarily used values developed for the 2008 EPA staff report. Since issuing the N two more teardown studies have been co alysis with some adjustment. NHTSA, in coordination with EPA, has adjusted the I4 to I4 turbo-downsize costs slightly to reflect an updated burden rate 172 

### employed

171 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis PilotStudy,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149.
172 Burden costs include the following fixed and variable costs: rented and leased equipment; manufact uring equipment depreciation; plant office equipment depreciation; utilities expense; insurance (fire and general); municipal taxes; plant floor space (equipment and plant offices); maintenance of manufacturing equipment - non-labor; maintenance of manufacturing building - general, internal andDELIBERATIVE DOCUMENT – DO NOT RELEASE 

### 205

estimated costs as well. FEV made the assumption that these technologies would be mature when produ large volum that there is potential for near term suppl ier-leve ng, design, and testing (ED&T) costs to be in excess of those considered in the FEV analysis (as existing equipm We

r the OHV applications, the agencies maintained consistency with the EPA 008 S
year

ndby FEV and an updated binning approach employed by the agencies to distinguish turborelated costs from dow nsize-related costs. 173 

### NHTSA and EPA modified the other FEV

ced in es (450,000 units or more). The agencies believe l engineeri

ent and facilities must be converted to production of new technologies). The agencies have therefore decided to average the FEV results with the NPRM values. have also used the FEV results, where possible, to estimate costs for turbo-downsize scenarios that were not done via actual teardown study (e.g., FEV did not conduct a teardown of a V8 DOHC to turbocharged V6 DOHC ). We have also used these values to estimate costs for other camshaft configur ation changes that do not involve engine downsizing.

Fo 2 taff Report and estimate d direct manufacturing costs associated with downsizing to be $50 per cylinder, $10 per valve, and $100 per cam shaft for the 2015 model (2006$). Therefore, these costs have not changed relative to the NPRM.

Table V-18 shows how NHTSA developed the direct cost used by the Volpe model based on NPRM values and the FEV teardown cost study for turbocharging a downsizing technology

173 This “binning” approach simply bins the turbo-downsize-GDI total cost generated by FEV into separate portion, and the GDI portion. external, parts, and labor; operating supplies; perishable and supplier-owned tooling; all other plant wages (excluding direct, indirect and MRO labor); returnable dunnage maintenance; and intra-company shipping costs (see EPA-HQ-OAR-2009-0472-0149).

cost bins for the turbo portion, the downsize206Table V-18. Calculation of Direct Manufacturing Cost for Turbocharging and Downsizing in MY 2012 (2007 dollars)DELIBERATIVE DOCUMENT – DO NOT RELEASEMdFinalNPRFEV TeardownAnalysis BaseCostRuleacfhbdegiTurbocharging and Do(TRBDS)DirectCostTRBDSwnsizingDirectCostTurbochargingDireCosDowns(w/o SDownsicttizeGDIze)DirectCostDownsize(w/SGDIDownsize)Co1.2Tchst with5 ICMurboargingCost1.2ICMDownwith5sizeTotalCost with1.25 ICMforTRBDSDirecCosTRBDttSDirectCostTRBDSV8 SOHC 2v --> V6 DOHC S $878TRBD$681 ($84) ($149)$851 ($112) $740 $592 $735V8 DOHC --> V6 DOHC T$554RBDS$681 ($274) ($339)$851 ($254) $597 $478 $516V8 OHV --> V6 DOHC TRB$990DS$681 $315 $250$851 $313 $1,164 $931 $961V6 SOHC --> I4 DOHC TR$410BDS$404 ($382) ($490)$505 ($368) $137 $110 $260V6 DOHC --> I4 DOHC TR$194BDS$404 ($547) ($655)$505 ($491) $14 $11$102V6 OHV --> I4 DOHC TRB$720DS$404 $270 $162$505 $203 $708 $566 $643I4 SOHC --> I4 DOHC TRB$515DS$404 ($80)($80)$505 ($60) $445 $356 $356I4 DOHC --> I4 DOHC TRB$336DS$404 ($85)($85)$505 ($64) $441 $353$353I4 OHV --> I4 DOHC TRBDS $515$404 $65$65$505 $81 $586 $469 $469* Note that, where downsizing result st savings, the codthcost. The absolute value of the di facturing cost is tothmanufacturing cost of -$149, the com ce cost would be (1dfactor of 1.25 is used for the individu ulations for both tizs in corect manuplianal calcmpliance cost is calculatehensubtracted from that t1.25-1) x│-$149│-│-$urbocharging and downsas the IC markup less 1arrive at the end result.49│= -$112. Note becaing.which is thenmultiFor example, for tuse the Volpe modeplied by the absolute value ofhe V8 SOHC 2v downsized tol combines turbocharging ande direct manufacturinge V6 DOHC at a directownsizing, the ICM

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### Detailed descriptions o

Column a. NPRM cost for turbocharging and downsizing used by NHTSA including

ngine

al

these combinations and downsizing costs are shown in f each colu mn in Table V-18 are listed below.

downsizing of SGDI.

Column b. FEV teardown study direct cost for turbocharging only.

Column c. FEV teardown study direct cost for downsizing, but without downsizing of SGDI components. FEV conducted major teardown cost analysis on three e combinations. For three of the other engine downsizing combinations, FEV conducted an incremental teardown study to determine costs. For three addition combinations, cost was determined through calculation using incremental cost estimates. A summary of Table V-19. More detailed explanation is provided in an EPA memo to the docket. [1]



### Table V-19. 

### Calculated Turbocharging Downsizing Technologies, but Without Downsizing of SGDI Components MY 2012 (2007$)

Technologies Torn-Down by FEV Incremental Studied in FEV Calculated Technologies
Name Value Name Value Name Value V8 SOHC 3v-> V6 DOHC ($155) V8 SOHC 2v -> V8 SOHC 3v V8 SOHC 2v-> $72 V6 DOHC ($84) V8 SOHC 3v-> V6 DOHC ($1 4) 55) V8 SOHC -> V8 DOHC ($119) V8 DOHC -> V6 DOHC ($27 V6 DOHC -> I4 DOHC ($547) OHC -> V6 DOHC $165 V6 SOHC -> I4 DOHC ($382) V6 S



### g with ICM of Column d. FEV teardown study direct cost for downsizing including downsizing of SGDI components. Cost reduction for SG DI downsizing is based on FEV direct manufacturing costs in Table V-17. For V8 to V6 the SGDI cost reduction is $386 -$321 = $65, and for V6 to I4 the cost reduction is $321 - $213 = $108.

Column e. FEV teardown study cost for turbocharging with ICM of 1.25 applied. Because the Volpe model combines turbocharging and downsizing, the ICM factor of 1.25 is used for the individual calculations for both turbocharging and downsizing.

Column f. FEV teardown study cost for downsizing with ICM of 1.25 applied.

Column g. FEV teardown study cost for turbocharging and downsizin 1.25 applied. Column g is the sum of column e and column f.

Column h. FEV teardown study direct cost for turbocharging and downsizing. Column h is equal to column g divided by the ICM factor of 1.25.

[1] Insert Mike’s docket number for bucketing memo.

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### , V teardown based cost (column h).

t a 30 harging.

e ine mented that onside

y

e

t

a

modified from the MY 2011 final rule and are now limited nsidered the Column i. Final Rule direct cost for turbocharging and downsizing including downsizing of SGDI. For V8 to V6 TRBDS and for V6 to I4 TRBDS, column i is the average of the NPRM direct cost (column a) and the FEV teardown study direct cost for turbocharging and downsizing (column h). For I4 to I4 TRBDS Column i is equal to the updated FE

In response to the NPRM, NHTSA received comments from Honeywell tha to 40 percent reduction in engine displacement is possible with the use of turboc Also, if cylinder count is reduced, the cost savings in parts and assembly will offset th costs of turbocharging. For example, replacing a 6-cylinder dual overhead cam eng with a 4-cylinder turbocharged engine may be cost neutral. Honeywell com c ration of the potential to decrease cylinder count and offset costs should be included in calculations for fleet fuel economy and implementation costs.

In the NPRM and final rule, NHTSA uses downsizing ranging from 30 to 40 percent, which is the same as the Honeywell comment. Also, the cost analysis methodology used in the NPRM and final rule account for downsizing and the reduction in cylin der count and parts, and in assembly costs. Based on the FEV teardown stud cost estimates were revised in the final rule. NHTSA estimates the direct cost to replace a 6-cylinder dual overhead cam engine with a 4-cylinder turbocharged engine is $102, compared to Honeywell’s estimate of no cost difference. NHTSA believes that teardown studies are the one of the most effective ways to estimate technology costs. In response to the NPRM, NHTSA received comments from GM related to cost. GM commented that for a full-size truck product, the downsized boosted strategy is expected to yield less fuel economy benefit at higher cost than a direct injection V8 with aggressive usage of cylinde r deactivation. NHTSA analysis show s the same relationship in cost, and the Volpe model applies DEACO prior to TRBDS, recognizing the relationship. The Volp model used for the NPRM and final rule prov ides one solution that manufacturers could use to meet CAFE regulations to demonstrate the feasibility of regulatory standards. It is expected that in many cases, manufacturers will identify and implement other combinations of technologies to achieve CAFE regulatory compliance, based on their unique circumstances.

In response to the NPRM, NHTSA received comments from GM that the engine oil service schedule must be more robust for turbocharged engines to avoid premature wear on oiled engine components. For this rulemaking, NHTSA analysis does not include maintenance costs. NHTSA intends to investigate maintenance costs and consider including them in future rules. NHTSA estimates that the MY 2012 incremental compliance cost, including a medium complexity ICM mark-up of 1.25, for a turbocharged and downsized engine is $445 to downsize from an I-4 SOHC naturally-asp irated engine to a smaller displacemenI-4 DOHC turbocharged engine, $325 for a downsize from a V-6 SOHC naturallyaspirated engine to an I-4 DOHC turbocharg ed engine, and $919 for a downsize from V-8 SO HC naturally-aspirated engine to a V-6 DOHC turbocharged engine.

Phase-in caps have been to 85 percent per year with time-ba sed learning applied. NHTSA co

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### comple e applied at redesign only. There are no subc lass specific limitations on its application.

(EGRB)



### Cooled exhaust gas recirculation (cooled EGR) or EGR Boost is a combustion concept tha stion

mper

f

ss” issues t

mated in cost impact of approximately $173 based o confidential individual component cost data from 2008 Martec describing EGR cooler costs of $75, EGR valve costs of $20 and associated piping costs of $20, totaling $115 (non-RPE). For purposes of this rulemaking, NHTSA found no information to indicate that these estimates were inaccurate. To that end, NHTSA applied an indirect cost multiplier of 1.25, for a medium complexity technology, resulting in a compliance cost of $144 for MY 2012 vehicles with time-based learning applied. EGRB can be xity of implementing this technology and determined that this technology can b

(10) 

### Cooled Exhaust Gas Recirculation/EGR Boost



### t involves utilizing EGR as a charge dilutant for controlling combu te atures and cooling the EGR prior to its introduction to the combustion system. Higher exhaust gas residual levels at part load conditions reduce pumping losses for increased fuel economy. Cooled EGR reduces knock sensitivity which enables the use o more optimal spark advance or enables compression ratio to be increased for improved thermal efficiency, and increased fuel economy. Currently available turbo, charge air cooler, and EGR cooler technologies are sufficient to demonstrate the feasibility of this concept. However, this remains a technology with a number of issues that still need to be addressed and for which there is no production experience. EGR system fouling characteristics could be potentially worse than diesel EGR system fouling, due to the higher HC levels found in gasoline exhaust. Turbocharger compressor contamination may also be an issue for low pressure EGR systems. Additionally, transient controls of boost pressure, EGR rate, cam phasers and intake charge temperature to exploit the cooled EGR combustion concept will require development beyond what has already been accomplished by the automotive industry. These are all “implementation readine hat must be resolved prior to putting EGR Boost into high volume production. NHTSA has concluded that these implementation issues could be resolved and this technology could be brought to production by MY 2013. Supporting this conclusion, MEMA has previously suggested a 5 to 7 percent effectiveness for cooled EGR systems, although without boosting. 174 

### Two public sources indicate a 10 to 20 percent fuel consumption effec tiveness for a downsized DI engine with cooled EGR compared to a naturally aspirated baseline engine i 

### and a 4 percent fuel consumption effectiveness for cooled EG R compared to a conventional downsized DI turbocharged engine. ii 

### Based on the data from these reports, NHTSA estimates the incremental reduction in fuel consumption for EGR Boost to be 4 percent over a turbocharged and downsized DI engine. Thus, if TRBDS precedes EGRB, adding the 12 percent gain from TRBDS to the 4 percent gain from EGRB results in total fuel consumption reduction of 16 percent. This is in agreement with the range suggested in the Lotus report. Regarding costs, the addition of EGR cooler and EGR valve were esti NHTSA’s MY 2011 rule to have an incremental RPE

n

174 Docket No. NHTSA-2008-0089-0193.1

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### learning applied. NHTSA considered the complexity of implementing this technolog ined that this techno at redesign

e T logie

D cteristics that give them rior iency compared ines. Pum sses uch lower du e diesel co on erates at a highe lean air/fuel mixture, and turboch d lightduty dies r torque levels at lower ne sp than equivalen a soline engines. Additionally sel fuel has a hig 5


### D ssions characteristics that presen lenge eeting federal T a significant system ineeri hallenge to mainta tage of the diesel engine while meeting U.S. emission consumption can be negatively imp y em reduction gies employed. Emission

mpliance strategies for diesel vehicles so ld in the U.S. are expected to include a combin

ent. n pressure and multiple-injection capability), advanced controls and sensors to optimize combustion and emissions performance, higher EGR levels and EGR cooling to reduce NO x 

### , and advanced turbocharging systems. On the aftertreatment side, the traditiona l 3-way catalyst aftertreatment found on gasoline-powered vehicles is ineffective due to the lean-burn combustion of a diesel. All diesels will require a diesel particulate filter (DPF) or catalyzed diesel particulate filter (CDPF), a diesel oxidation catalyst (DOC), and a NO x 

### reduction strategy to comply with Tier 2 emissions standards. The most common NO x 

### reduction strategies include the use of lean NO x 

### traps (LNT) or selective catalytic reduction (SCR), which are outlined below. applied to all vehicle classe s starting in MY 2013. Phase-in caps are limited to 85 percent per year with time-base d y and determ logy can be applied only.

(11) 

### Diesel Engin echno s

iesel engines have several c hara supe p lo fuel e ffic to conventional gasoline, spark-ignited eng rottling. Th ing are m e to lack of (or greatly reduced) th , with a very mbusti cycle op r compression ratio arge els typically achieve much highe engi eeds t-displacement naturally-aspirated g , die her energy content per gallon. 17 

### iesel engines have emi t chal s to m ier 2 NO emission x 

### s standards. It is sumption advan s-eng ng c in the fuel con lations. Fuel s regu acted b issions strategies depending on the combination of strate

co ation of combustion improvements a nd aftertreatment. These emission control strategies are being introduced on Tier 2 light-duty diesel vehicles today To achieve U.S. Tier 2 emissions limits, roughly 45 to 65 percent more NO x 

### reduction is required compared to the Euro VI standards. Additionally, as discussed below, there may be a fuel consumption pena lty associated with diesel aftertreatment since extra fuel is needed for the aftertreatment, and this extra fuel is not used in the combustion process of the engine that provides power to propel the vehicle. Light-duty diesel emissions control sy stems capable of meeting Tier 2 Bin 5 emission standards are already in production. Several key advances in diesel technology have made it possible to reduce emissions comi ng from the engine prior to aftertreatm These technologies include improved fuel systems (higher injectio

175 Burning one gallon of diesel fuel produces about 15 percent more carbon dioxide than gasoline due to the higher density and carbon to hydrogen ratio.

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### A lean N erate in by NO 2 exh and stor lkali sorben teria en ntrol d ines mathematical model or a NO x 

### sensor) tha trap is saturated with NO , it switch ing mo ay me ct the m to produce exce o tha a re ag convert the thereby “re ing” NT an nin ore stored. L tia lly sto lfate

talytic rma The mus ergo dic erating at a net-fuel-rich condition at high temperatures in order to

iciency.ded that diese es o ll veh ou LNTe did no de el option for sm hic ause it did st effective so on comments received from A, an e ar ding el o for small l rule, and we the Vo odel oose ther to ould b ted th Volpe model an ysis p es one s coul to m CAFE lation emonstrate andard is exp ted that in many c anu cturers will d implement other co atio technologies to a e CA regulat eir uniqu rcumstances.

Diesel En with tive C tic Red n (SC fteren



### eatment sy us duc pica mo rived that is injected into the ex tr ead o SCR st. s he hardware configuration for an CR system is more complicated than that of an LNT, due to the onboard urea storage ry system (which requires a urea pump and inject or to inject urea into the for NO x 

### reduction,

ma and

cou and to Tier 2, Bin 5 emission standards. Based on

sig

use e Cat ME tha Diesel Engine with Lean NO x 

### Trap (LNT) Catalyst After-Treatment



### O x 

### trap op ing NO2 on a s, in pr
t ma ciple, l. Wh oxidizing the co to NO system in the
eterm a (via es the ust

t the in so x 

### ctly into engine into a rich operat de or m cases inje fuel dire exhaust strea ss hydr carbons t act as ducing ent to stored NO x 

### to N 2 

### and water, locations for NO x 

### to be generat eferen the L

re su d ope
compou g up m nds from th NTs pr e fuel, which can reduce ca perfo n ce. system t und perio desulfurization by op retain NO x 

### trapping eff NHTSA has conclu l engin n sma icles w ld be based. In the NPRM, w not appear to be a co t inclu lution. Based a di es all ve les bec

BorgWarner, ICCT, MEM d Mr. Sc hade w e inclu a dies ption vehicles in the fina are allowing

e no lpe m to ch al whe rovid apply the technology. It sh solution that manufacturer at the d use eet regu s to d feasibility for regulatory st identify an s. It mbin ec ns of ases, m chiev fa FE ory compliance, based on th e ci

gine Selec ataly uctio R) A Treatm t



### An SCR aftertr stem es a re tant (ty lly, a m nia de from urea) haust s eam ah f the cataly Ammonia react with NO x 

### in the SCR catalyst to form N 2 

### and water. T S and delive exhaust stream). While a rich engine -operating mode is not required the urea is typically injected at a rate of approximately 3 percent of the fuel consumed. Manufacturers designing SCR systems intend to align urea tank refills with standard intenance practices such as oil changes. As is the case with LNT-based diesels, EPA NHTSA project that SCR-based diesel en gines will be available within the next ple of years. Mercedes-Benz recently introduced two 2009 model year vehicles R320 GL320, both of which are certified public announcements from several other companies, an increased number of product offerings from multiple companies are expected over the next few years. In response to the NPRM, NHTSA received comments from MECA that nificant criteria emission reductions from diesel vehicles can be achieved through the of several technologies, including: Diesel Particulate Filters (DPFs), and Selectiv alytic Reduction (SCR) and Lean NOx Adso rber Catalysts for Diesel Engines. CA provided a detailed description of these technologies. MECA also commented t these emission control technologies allow all high efficiency powertrains to compete

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### in t pol In nearly all cases, thes e fuel-efficient powertrain designs, combined with appropriate emission controls, can be op timized to either minimize fuel consumption

ove

and reg s during the rulemaking period, and therefore enabling diesel engine

ass

veh around 2.0 liters

wa sys

veh n in-line 4-cylinder diesel engine, with displacement varying around 2.8 liters

mo per Tru arge Car vehicle classe s for the CAFE model. It was assumed

res red NH er data ction of SCR-based diesel system to be between

rec effi

con ME d veh n a fuel

MY
gas per ental to a turbocharged downsized gasoline engine (TRBDS) with EGRB, and a 10.8 to 13.9 percent incremental

n restart (CBRST).

The

• Fuel systems (higher pressures and more responsive injectors); he marketplace by enabling these powertrains to meet current and future criteria lutant standards.

impacts associated with the emission cont rol technology, or, in some cases, improve rall fuel consumption of the vehicle. As the NPRM and final rule, include desc riptions of each of these technologies cite these technologies as enablers fo r diesel engines to meet criteria emission ulation technology to be included as a viable fu el economy improving technology during the rulemaking period, NHTSA views MECA’s comments as supporting NHTSA’s essment of the feasibility of diesel engine technology in the final rule. In order to maintain equivalent performance to comparable gasoline-engine icles, an in-line 4-cylinder diesel engine, with displacement varying was assumed to replace an I4 gasoline base engine for Subcompact, Compact, and Midsize Passenger Car, Performance Subcompact Car and Small Light Truck classes. It s assumed that diesel engines for these classes would utilize LNT aftertreatment
tems. In order to maintain equivalent performance to comparable gasoline-engine icles, a was assumed to replace a V6 gasoline base engine for Performance Compact, Performance Midsize, Large Passenger Car, Minivan, and Midsize Truck for the CAFE del. A V6 diesel engine, with displacement varying around 4.0 liters to meet vehicle formance requirements, was assumed to replace a V8 gasoline base engine for Large ck and Performance L that diesel engines for these classes would utilize SCR aftertreatment systems. Confidential manufacturer and non-conf idential comment data submitted in ponse to NHTSA’s past rulemaking for diesel engines showed a fuel consumption uction in the range of 16.7 percent to 26.7 percent over a baseline gasoline engine. TSA’s MY 2011 CAFE final rule, which wa s supported by confidential manufactur , estimated the fuel consumption redu 20 to 25 percent over a baseline gasoline engine. In response to the NPRM, NHTSA eived comments from MECA that light duty diesel powertrains have higher fuel ciency compared to gasoline engines on the order of 20 percent to 40 percent. This verts to between 16.7 and 28.6 percent on a fuel consumption effectiveness basis. MA commented that compared to a traditional gasoline engine, clean diesel-powere icles average 30 percent better fuel economy. This converts to 23.1 percent o consumption basis. Based on review of all of this information, NHTSA judged that the 2011 estimate of 20 to 25 percent fuel consumption reduction over a baseline oline engine remains the best estimate for this final rule. This equates to a 5.3 to 6.9 cent improvement for DSLT, which is in crem

improvement for DSLC, which is incremental to a gasoline engine with combustio

Diesel engines are more costly than port-injected spark-ignition gasoline engines. se higher costs result from:

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### • Engine design (higher cylinder pressures require a more robust engine, but ent);

201 final rule analysis, NHTSA in consultation with EPA chose to re-analyze

and
rep (LN NESCCAF facturing costs of $1,500 to $1,950. More recently,

cyl SC
sys tho tha

e wit show generally lower than the MY 2011 CAFE final rule

ated. EPA and NHTSA engineers decided that an update to the engine-related costs would • Controls and sensors to optimize combustion and emissions performance;

higher torque output means diesel engines can have reduced displacem

• Turbocharger(s);

• Aftertreatment systems, which tend to be more costly for diesels; Due to a significant decrease in plat inum group metal prices since NHTSA’s MY 1 CAFE diesel costs. In EPA’s 2008 Staff Report, co sts were considered for two types of diesel systems: one using a lean-NO x 

### trap (LNT) along with a diesel particulate filter (DPF); one using a selective catalytic reduction (S CR) system along with a DPF. In that ort, EPA estimated direct manufacturing costs to range from $1,860 for the small car T plus DPF) to $2,710 for the large truck (SCR plus DPF). For comparison, the study showed direct manu NHTSA’s 2011 CAFE final rule showed direct manufacturing costs of $2,670 for a 4- inder engine using a LNT plus DPF system, $3,735 for a 6-cylinder engine using a R plus DPF system, and $4,668 for an 8-cylinder engine using a SCR plus DPF tem. NHTSA noted that es timates in the MY 2011 CAFE fi nal rule were higher than se shown in the proposed rule due largely to the spike in platinum group metal prices t had occurred in the months just prior to issuing the 2011 CAFE final rule.

The following diesel costs were develo ped by EPA, drawing on their experienc h diesel engine and aftertreatment syst ems. A breakdown of the cost estimates is

n in Table V- . These costs are assumptions and were developed by taking a look back at EPA’s 2008 Staff Report, which reveals a couple of factors that resulte d in somewhat misleading costs. First, the engine costs estimated there did not take into account the downsizing that would occur when moving from a gasoline engine to a dies el engine (provided equivalent performance was maintained). Second, the engine costs used in that analysis were actually stated in terms of 2002 dollars rather th an 2006 dollars in which the report was meant to be st

provide a much better cost estimate for conv erting to diesel. This was done by starting with the source for engine costs in the 2008 staff report which was an October 2005 EPA Interim Report iii 

### which, in turn, sourced estimates from a 2003 study done by FEV for EPA contained within a 2004 EPA Interim Technical Report. iv 

### These direct

anufacturing costs are r m eproduced in Table V-20

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### a Baseline Table V-20 

### Diesel Engine Direct Manufacturing Source Costs, Incremental to Gasoline Engine (2002 dollars) Component(s) Large SUV Midsize Gasoline engine (baseline) 5L V8 2.4L I4 Diesel engine 4L V8 2.2L I4 Add high-pressure, common rail diesel fuel injection system $980 $630 Delete gasoli ne fuel injection system -$245 -$165 Add variable geometry turbocharger $175 $126 Delete gasoline ignition system -$120 -$75 Delete fuel pump and other changes to fuel system -$75 -$94 Enhance pow ertrain mounting system $87 $107 Other en gine changes $80 $70 Add air i ntercooler, ducts, and s ensor $80 $55 Larger battery and starter, add g s lo g

w plu $72 $50 Delete exhaust gas oxyge n senso r* -$60 -$30 Add supplemental hea ter $50 $15 Modify transmission $25 $25 Enhance sound insulation package $25 $10 Smaller radiator -$13 -$4 Total $1,0 42 $ 739 Note: Table reproduced from EPA4 -012, Oc 05 20-R-05 tober 20



### Building on the direct manufacturing co sts shown in Table V-20, the agencies

a 4.5L V8 gasoline to a 3L V6

d from a 3.2L V6 gasoline to a 2.8L I4 diesel; and a large truck converted from a 5.6L to a 4 el. T s for ase di n in m T -20 hav ing the G e defl r of 1 Appe ). Sin were develo t ysis co s the c 21 as b pplicab e 2012 ear. used ap propriate scaling to estimate the costs for replacing a baseline gasoline engine with a diesel engine for the following five situations: a small car converted from a 2.4L I4 gasoline to a 2L I4 Diesel; a large car converted from diesel; a medium/large MPV converted from a 3. 2L V6 to a 2.8L I4 diesel; a small truck
converte V8 gasoline e show L V6 dies Table V-21. he result Valu es fro the five b able V gasoline to updated to esel conversions ar 2007 dollars us e been ndix 3.A DP pric ator facto .15 (see ce the
source costs ped in 2003, his anal nservatively c onsider osts shown in Table V- eing a le to th model y

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DELIBERATIVE DOCUMENT – DO NOT RELEASE Tabl gine Di ufactu ed-C , Inc Basel Gaso 07 doll

ponent(s) all

car Me M Sma
truc rge ck e V-21 Diesel En line Engine (20 rect Man ars) ring Scal osts in 2012 remental to ine

Com Sm Large

car d/large PV ll k La

u tr Notes (see text below)

G asoline engine (baseline) 2.4L I4 V8 3.2L V6 3.2L V6 5.6L V8 4.5L

Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 Add high-pressure, common rail diesel fuel injection system $517 $1,026 $724 $724 $1,026 1
Delete gasoline fuel injection system -$52 -$89 -$73 -$73 -$89 2
Add variable geometry turbocharger $145 $173 $145 $145 $201 3
Delete gasoline ignition system -$69 -$138 -$112 -$112 -$138 4 Delete fuel pump and other changes to fuel system -$62 -$108 -$86 -$86 -$108 5
Enhance powertrain mounting system $123 $100 $123 $123 $100 6
Other engine changes $57 $86 $80 $80 $86 7 Add air intercooler, ducts, and

sensor $45 $78 $63 $63 $92 8

L
g arger battery and starter, add

low plu $57 $70 $57 $57 $70 9 gs Delete exhaust gas oxygen sensor* $0 $0 $0 $0 $0 10
Add supplemental heater $17 $37 $17 $17 $57 11 Modify transmission $29 $29 $29 $29 $29 12 E nhance sound insulation package $11 $20 $11 $11 $29 13 Sm a -$15 -$10 -$10 -$15 14 ller radiator -$7 Engi -$185 -$390 -$390 -$185 15 n e downsize credit $0 Tot al $813 $1,085 $580 $580 $1,156 * Note: Oxygen sensor removals are included in aftertreatment costs.



### NOTES: The costs shown in Table V-21 were scaled in the following ways:

1. Large car and large truck cal d 25% of midsize car; medium/ e V-20’s midsize car; small car calculated using rge MPV value of $724 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dol

tems

/fuel culate as 75% the cost of Table V-20’s large SUV and large MPV and small truck calculated as equal to Tabl Table V-21’s Med/la

lars using GDP factor of 1.15.

2. The estimates generated by FEV for eliminating the gasoline fuel injection sys were considerably larger than EPA & NHTSA believed was appropriate. Therefore, for a more accurate estimate, these costs we re estimated, in 2007 dollars as follows: large car and large truck were calculated using incremental costs of $8/injector, $20/fuel rail, and $5 for a pressure damper or $8x8+$20+$5=$89; medium/large MPV and small truck were calculated using incremental costs of $8/injector, $20 rail, and $5 for a pressure damper or $8x6+$20+$5=$73; small car calculated using

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### 3. Large car calculated as the av f Table V size car; medium/large MPV and small truck calculated as equal to Table V-20’s midsize car, and of

’s

f -

arge SUV; small car calculated using Table s Med/larg e ratio of diesel engine sizes (2.0/2.8). Values c onv f 1.1

r;

onverted to 2007 dollars using GDP factor of 1.15.

11. Medium/large MPV and small truck calculated as equal to Table V-20’s midsize car; Large car calculated as the average of Table V-20’s large SUV and midsize car; Table V-21’s Med/large MPV value of -$73 a nd applying the ratio of diesel engine sizes (2.0/2.8).

erage o -20’s large SUV and mid

large truck calculated as equal to Table V-20’s large SUV; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor 1.15.

4. Medium/large MPV and small truck calculated as the average of Table V-20’s large SUV and midsize car; Large car and large truck calculated as equal to Table V-20 large SUV; small car calculated as equal to half of Table V-21’s large truck value o $138. Values converted to 2007 dollars using GDP factor of 1.15.

5. Medium/large MPV and small truck calculated as equal to Table V-20’s midsize car; Lar ar and large truck calculated as equal to Table V-20’s l ge c V-21’ e MPV value of -$86 and applying th erted to 2007 dollars using GDP factor o 5.

6. Medium/large MPV and small truck calculated as equal to Table V-20’s large SUV; Large car and large truck calculated as equal to Table V-20’s midsize car; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

7. Medium/large MPV and small truck calculated as equal to Table V-20’s midsize ca Large car and large truck calculated as the average of Table V-20’s large SUV and midsize car; small car calculated using Ta ble V-21’s Med/large MPV value of $80 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

8. Medium/large MPV and small truck calculated as equal to Table V-20’s midsize car; Large car calculated as the average of Table V-20’s large SUV and midsize car; Large truck calculated as equal to Table V-20’s large SUV; small car calculated using Table V-21’s Med/large MPV value of $63 and applying the ratio of diesel engine sizes (2.0/2.8). Values c

9. Medium/large MPV and small truck calculated as equal to Table V-20’s midsize car; Large car and large truck calculated as the average of Table V-20’s large SUV and midsize car; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

10. Oxygen sensor costs are included in the aftertreatment costs discussed below.

Large truck calculated as equal to Table V-20’s large SUV; small car calculated as

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### equ

14. Medium/large M small tru e SUV and midsize arge car a s large SUV; small car calculated using Table /large MPV value of -$10 and

D

e at al to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

12. Values from Table V-20 converted to 2007 dollars using GDP factor of 1.15.

13. Medium/large MPV and small truck calculated as equal to Table V-20’s midsize car; Large car calculated as the average of Table V-20’s large SUV and midsize car; Large truck calculated as equal to Table V-20’s large SUV; small car calculated as equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

PV and ck calculated as the average of Table V-20’s larg car; L nd large truck calculated as equal to Table V-20’ V-21’s Med applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15.

15. Based on the approach presented in the turbocharging/downsizing section of the TS (section 3.5.2.1.11), the savings associated with downsizing the gasoline engine were calculated by estimating the cost in 2007 dollars of each cylinder at $51, each valv $10, and each cam at $103. 176 

### Therefore, the large car and large truck, which each lose two cylinders (-$102), eight valves (-$82) and no cams realize a $185 savings. The medium/large MPV and small truck would each lose two cylinders (-$102) and eight valves (-$82) and two cams (-$205) for a savings of $390. The small car downsizing credit was left at $0 given the small displacement change and lack of cylinder or valve removals. For the diesel aftertreatment systems, the approach taken is consistent with the approach taken in EPA’s 2007/2010 Highway Diesel rule and EPA’s recent locomotive and marine rule. 177 

### For platinum group metal costs, monthly average prices as of March 2009 as reported by Johnson-Matthey were used. 178 

### Those values were $1,085/troy ounce for platinum and $1,169/troy ounce for rhodium. Aftertreatment devices were sized according to the diesel engine displacement with a 1:1 ratio for both the SCR

176 These are the correct costs for the 2015MY in 2007$. But, they are used here erroneously as 2012MY values. Technically, 3 years of time-based learning should have been backed out to get 2012MY values. So, the 2012MY costs are slightly underestimated by roughly $10. This was true in the proposed a and, because it has no meaningful impact on the analysis (1-2% of the estimated diesel costs), continues to be true in the final analysis.
177 EPA’s 2007/2010 diesel heavy-duty highway final rule at 66 FR 5002; EPA’s Locomotive and Marine final rule at 73 FR 37096.
178 nalysis

http://www.platinum.matthey.com These are the PGM prices used in the NPRM. As precious metals, PGM prices swing widely year-to- year and even month-to-month. Gi ven economic conditions during the past year and the tendency of investors during tough economic times to invest in valuable metals such as gold and PGMs, PGM prices have increased considerably since the NPRM. Were the January 2010 PG prices used, the diesel costs presented here would increase on the order of 10-20 percent. That would s M

erve to make diesels less cost effective options for improving fuel economy or reducing GHGs. Since diesels

ven

s for purposes of this rulemaking. comprise such a small percentage of each agency’s analysis from a technology penetration standpoint, e using the lower March 2009 PGM prices, the choice of what PGM prices to use is of little consequence to the analytical result

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### Also

rge truck 

### catalyst and the DPF, and a 0.5:1 ratio for the DOC (i.e., the DOC is half the displacement of the engine). The end result for aftertreatment devices, including a urea dosing unit, urea tank and necessary br ackets and heaters, are shown in Table V-2

. shown in Table V-2 are the savings associated with removal of the gasoline catalyst. Note that the gasoline catalyst was sized according to the gasoline engine that served as the baseline engine.

Table V-22 Diesel Aftertreatment Direct Manufacturing Costs in 2012 (2007 dollars)

Component(s) Small car Large car Med/large MPV Small truck La

Gasolin L V8 e engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6 Diesel V6 engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L DOC 39 $216 $277 $257 $257 $3 DPF (in OBD & $668 cludes a $20 pressure sensor for sensing) $401 $534 $503 $503
SCR sy OBD & $996 stem (includes a $50 NOx sensor for sensing) n/a $904 $904 $914
LNT Sy OBD a /a s udes $50 NOx sensor for nd sensing $442 n/a n/a n/a n

tem incl

Remov 483 al of gasoline catalysts & sensors -$175 -$401 -$288 -$298 -$ Total $1,520 $883 $1,314 $1,376 $1,376



### The incremental costs to convert from a gasoline to a diesel engine—Table V-21 and Table V-2 combined—are shown in Table V-23.

Table V-23 Direct Manufacturing Costs to Convert from a Gasoline to Diesel System in 20 dollars)
Component(s) Small car Large car Med/large MPV Small truck Large truc 12 (2007

k
Gasoline engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8
Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 Engine-related costs $813 $1,085 $580 $580 $1,156 Aftertreatment $883 $1,314 $1,376 $1,376 $1,5 20 Total $1,697 $2,399 $1,956 $1,956 $2,676



### This analysis applies time-based lear ning to diesel systems and a medium complexity rating of 1.25. Therefore, the MY 2012 compliance costs are as shown in Table V-2

.

Table V-24 Compliance Costs to Convert from a Gasoline to Diesel System in 2012 (2007 dollars)

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G asoline engine 3.2L V (b aseline) 2.4L I4 4.5L V8 3.2L V6 6 5.6L V8

D iesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 T otal $2,121 $2,999 $2,445 $2,445 $3,345



### G
g iven the above analysis, NHTSA estimated that the compliance cost of converting an I4 asoli incremental de de A 012 $24 s estima verting a V6 gasoline eng diesel en s an incremental st of $689 o $1,257 f SLT a 1,226 ,6 estimated fo erting aso e eng This resu s in an incremental c ance c of r ,945 to $2,667 for DSLC. These compliance costs M mark 1.25. I onse t e NPR A

n of the I4 gasoline to .0L Turbcharged that was a the cost estimates this final

engines
t

l



### to lock-up o

Converter Lockup are best optimized simultaneously due to the fact that adding both of them primarily requires only ne engine to a 2.0L diesel engine was $2,121 for MY 2012. This results in an
comp 759 to $907 T and $1,346 to $1,519 for DSLC, liance cost of $ cision tree path.

ine to a 2.8L I4 for DSL cost of gine. This re pending on MY 2 45 wa ults in ted for con

compliance co 2012 cos t or D

r conv nd $ a V8 g to $1 lin 92 for DSLC. A MY V6 t of $3345 was diesel engine. ine to a 4.0L lt ompli ost $1,097 to $1,478 fo DSLT and $1 complexity IC include the medium RB commented that up of n resp o th M, C diesel cost estimates in the NPRM were generally consistent with their own. As costs in the final rule are unchanged from the NPRM, with the exceptio 2 Diesel dded for the final rule, the CARB comment supports used in rule.

The diesel engine technology can be applied to all vehicle classes. Diesel can only be applied at redesign with time-based learning. NHTSA assumed a 3 percen phase-in cap for diesels in MY2012 and increasing 3 percent per year reaching a maximum of 15 percent in MY 2016.

b. Transmission Technologies



### NHTSA has reviewed the transmission tec hnology estimates used in the MY 2011 CAFE final rule and considered or reconsidered all available sources and updated the estimates as appropriate. The section below describes each of the transmission technologies considered for this rulemaking.

(12) Improved Automatic Transmission Contro (IATC) (Aggressive Shift Logic and Early Torque Converter Lockup)



### Calibrating the transmission shift schedule to upshift earlier and quicker, and

r partially lock-up the torque c onverter under a broader range of operating conditions can reduce fuel consumption. However, this operation can result in a perceptible degradation in noise, vibration, and harshness (NVH). The degree to which NVH can be degraded before it becomes noticeable to the driver is strongly influenced by characteristics of the vehicle, and although it is somewhat subjective, it always places a limit on how much fuel consumption can be im proved by transmission control changes. Given that the Aggressive Shift Logic a nd Early Torque

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### minor m

f

er parameters such as temperature. Aggressive shift logic (ASL) can be employed in such a way as to maxim

the

,

el the vehicle, and noise and vibration are not excessive. If the torque converter cannot be fully locked up for maximum efficiency, a partial lockup strategy

Regarding the effectiveness of Improved Automatic Transmission Control, the MY201
to

For the purpose of this rule, NHTSA concluded that the combin odifications to the transmission or calibration software, these two technologies are combined in the modeling.

(13) 

### Aggressive Shift Logic



### During operation, an automatic transmissi on’s controller manages the operation o the transmission by scheduling the upshift or downshift, and locking or allowing the torque converter to slip based on a prepr ogrammed shift schedule. The shift schedule contains a number of lookup table functions, which define the shift points and torque converter lockup based on vehicle speed, throttle position, and oth

ize fuel efficiency by modifying the shift schedule to upshift earlier and inhibit downshifts under some conditions, which reduces engine pumping losses and engine friction. The application of this technology does require a manufacturer to confirm that drivability, durability, and NVH are not significantly degraded.

(14) 

### Early Torque Converter Lockup



### A torque converter is a fluid coupling located between the engine and transmission in vehicles with automati c transmissions and continuously-variable transmissions (CVT). This fluid coupling allows for slip so the engine can run while vehicle is idling in gear (as at a stop light), provides for smoothness of the powertrain and also provides for torque multiplication during acceleration, and especially launch. During light acceleration and cruising, the inherent slip in a torque converter causes increased fuel consumption, so modern automatic transmissions utilize a clutch in the torque converter to lock it and prevent this slippage. Fuel consumption can be further reduced by locking up the torque converter at lower vehicle speeds, provided there is sufficient power to prop 179



### can be employed to reduce slippage. Early torque converter lockup is applicable to all vehicle types with automatic transmissions. Some torque converters will require upgraded clutch materials to withstand additional loading and the slipping conditions during partial lock-up. As with aggressive shift logic, confirmation of acceptable drivability, performance, durability and NVH characteristics is required to successfully implement this technology.

1 CAFE final rule, which was supported by the 2002 NAS and NESCCAF reports as well as confidential manufacturer data, estimated an effectiveness improvement of 1 2 percent for aggressive shift logic and 0.5 percent for early torque converter lockup. These estimates are in agreement with the values stated in the NESCCAF report and confidential manufacturer data. ed estimated effectiveness is 1.5 to 2.5% reduction in fuel consumption.

179 Very aggressive early torque converter lock up may require an adjustment to damper stiffness and hysteresis inside the torque converter.

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### e e based

se

ic

ed with Ys 2012

(NAUTO 

## )

6-, 7-, of

to

n

-
UTO) from a 4- or 5-sp

consu eline 4 or 5 sp ission without IATC em onsumpti would rce TSA wed th fecti acc . In re se to the M, P

ese incremental effectiveness values used in the NPRM and final rule.In e NPRM, For a cost estimate, and for a MY 2012 vehicle, NHTSA updated the MY 2011 CAFE final rule estimate of $59 with a 1.5 RPE to $60 with a low complexity ICM markups value of 1.11. This reflects a revi siting of component costs for the early torqu converter lock-up technology wh ich potentially involves hardwa re changes. Tim learning methods are applied so subsequent MY costs are lower. Given the relative ea of implementation, from a manufacturing perspective, the Volpe model can apply IATC at either the refresh or redesign product cycle, and there are no subclass specif limitations on its application other than that the baseline vehicle must be equipp an automatic transmission. Ph ase-in caps in this rule are set at 85 percent for M to 2014 and 100 percent for the remaining years of the rulemaking.


## (15) Automatic 6-, 7- and 8-Speed Transmissions



### Manufacturers can also choose to replace 4- and 5-speed transmission with or 8-speed automatic transmissions. Additional ratios allow for further optimization engine operation over a wider range of conditions, but this is subject to diminishing returns as the number of speeds increases. As additional planetary gear sets are added (which may be necessary in some cases to achieve the higher number of ratios), additional weight and friction are introduced. Also, the additional shifting of such a transmission can be perceived as bothersome to some consumers, so manufacturers need to develop strategies to minimize the impact of additional shifts. Some manufacturers are replacing 4- and 5-speed automatics with 6-speed automatics, and 7- and 8-speed automatics have also entered production, albeit in lower-volume applications in luxury and performance oriented cars.

As discussed in the MY 2011 CAFE final rule, confidential manufacturer data projected that 6-speed transmissions could in crementally reduce fuel consumption by 0 5 percent from a baseline 4-speed automatic transmission, while an 8-speed transmissio could incrementally reduce fuel consumption by up to 6 percent from a baseline 4-speed automatic transmission. GM has publicly cl aimed a fuel economy improvement of up to 4 percent for its new 6-speed automatic tr ansmissions. The 2008 EPA Staff Technical Report found a 4.5 to 6.5 percent fuel cons umption improvement for a 6-speed over a 4 speed automatic transmission. 180 

### Based on this information, NHTSA estimated in the MY 2011 rule, that the conversion to a 6-,7- and 8-speed transmission (NA eed automatic transmission with IATC would have an incremental fuel mption benefit of 1.4 percen t to 3.4 percent, for all vehicle classes. From a bas eed transm , the incr ental fuel c on benefit be approximately 3 to 6 pe nt. NH revie ese ef veness estimates and concluded that they remain urate spon NPR orsche provided estimate for fuel econom y improvement for 6 to7 speed au tomatic transmission which is consistent with th th NHTSA reviewed the cost estimates from the MY 2011 CAFE final rule

180 Page 17, “EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Light-duty Vehicle Carbon Dioxide Emissions” Environmental Protection Agency, EPA420- R-08- 008, March 2008.

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### Lepelletier gear set) and concluded that some but not all 6-speed automatic transmissions would be equipped with Lepelletier gear set. As such, the estimates were revise stablish the divided between applications using Lepelletier, and applications of a standard planetary
gear se e

e

mance

timates

n work done by F

quire analysis performed in this rule require

Therefore, the $215 va speed to 6 d $91 is subtracted from speed to 5

- Lepelle

ative which used cost estimates from 2008 Martec report and the 2008 EPA Staff Report (which assumed use of a

d to e cost for the 6 speed transmission to be equally

t 6-speed automatic transmission as es timated in the 2008 Martec Report (and th MY 2011 CAFE final rule). The 2008 Martec re port estimated a cost of $323 with RPE adjustment and $215 without RPE adjustment for converting a 4-speed to a 6-speed transmission and a cost of $638 with RPE ad justment or $425 without RPE adjustment for converting a 4-speed to an 8-speed transmission. As a result, the final incremental cost estimate in the NPRM was $170, independent of vehicle type and size and including a low complexity 1.11 ICM (2007$). In the CAFE model, due to the structure of th vehicle classes used, an additional $102 (2007$) was included to account for perfor vehicle classes and for medium and large trucks. This is because for performance classes, additional gear ratios, such as 7 an d 8 speed transmissions may be utilized, and for medium and large trucks heavier duty tr ansmissions are required. These es represented MY 2012 vehicle costs For the final analysis, as noted earlier, NHTSA has considered the teardow EV. In its teardown work, FEV determined the 6 speed automatic transmission to be $106 less costly than a 5 speed automa tic transmission (direct manufacturing cost without markups). This is counterintuitive but can be attributed to the type of gear set employed in the 6 speed automatic transm ission studied by FEV. That 6 speed auto transmission was equipped with a Lepelletier -like gear set and therefore does not re a 1-way clutch that is essential to other designs. The cost s that the cost difference between both a 4 speed and 5 speed automatic transmissions relative to 6 speed automatic transmission be established because there are a substantial number of both 4 speed and 5 speed automatic transmissions used in the baseline fleet. This was done using the NPRM analysis for a 4 speed to a 5 speed and adding the FEV results for the transition from a 5 speed to a 6 speed. The analysis resulted in a cost for a 5 speed auto transmission relative to a 4 speed auto of $91 directmanufacturing cost (2007$ in 2012). As done in the NPRM, NHTSA averaged the nonLepelletier gear set cost with the Lepelletie r gear set cost (FEV tear-down value of - $106). In the 2011 FRM, NHTSA estimated the cost of a 6 speed auto transmission, without a Lepelletier gear set, relative to a 4 speed auto transmission at $215 (2007$). lue (4 speed) from NHTSA’s 2011 FRM is used an that (4 speed) to arrive at a cost of $124 as the non tier cost for a 6 speed automatic transmission relative to a 5 speed automatic transmission. The $124 value is averaged with the FEV value of -$106 (Lepelletier cost) to get an end result of $9 (2007$ in 2012). This $9 cost represents the cost for a 6 speed auto transmission relative to a 5 speed auto transmission in the final analysis. This $9 can then be added to the $91 to get a cost of $101 for a 6 speed auto transmission rel to a 4 speed auto transmission. Error! Not a valid bookmark self-reference. 

### shows the direct manufacturing costs used in the proposed and the final analyses. With the low complexity markup of 1.11, the compliance cost for a 6 sp eed automatic transmission in relative to a 4 speed automatic transmission in 2012 is $1 12 (2007$). With time based learning, the

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### compliance costs in 2016 to move from a 4 speed auto to a 5 speed auto and then from a 5 speed auto to a 6 speed auto transmission would be $90 and $9, respectively (2007$).

Table V-25. Direct Manufacturing Costs for Automatic Transmissions (2007$ in 2012)

Technology Non-Lepelletier Cost Used in 2011

CAFE Analysis NPRM FEV tear down (Lepelletier) Final Rule Comments

5s auto relative to 4s auto $91 n/a $91 Final rule uses NPRM value

6s auto relative to 5s auto -$106 $9 $215-$91=$124 [$124+(-$106)]/2=$9 6s auto relative to 4s auto $215 $153 n/a $101 $91+$9=$101 (values are rounded) Notes: Blank cells represent values not considered in this analysis; n/a means that FEV did not conduct a tear down of the technology; refer to text for more de tail on the comments.



### (16) Dual Clutch Transmissi ons / Automated Manual



### dual- transmission with automated clutch will likely b DCT uses s odd-numbe r faster and s shaft with and five has gear three engaged and is transmitting power. The sh w shift is r engaging th , on the other hand, the driver s change to s made quickl there is no torque being transferred on it.

r Transmissions (DCTAM)



### An Automated Manual Transmission (AMT) is mechanically similar to a conventional manual transmission, but shifting and launch functions are automatically controlled by the electronics. There are two basic types of AMTs, single-clutch and clutch (DCT). A single-clutch AMT is essentially a manual and shifting. Because of shift quality issues with single-clutch designs, DCTs e far more common in the U.S. and are the basis of the estimates that follow. A eparate clutches (and separate gear shafts) for the even-numbered gears and red gears. In this way, the next exp ected gear is pre-selected which allows fo moother shifting. For example, if the vehicle is accelerating in third gear, the gears one, three aft ith gears two, four, and six is id le, but has gear four pre-selected. When a

equired, the controller disengages the odd-gear clutch while simultaneously e even-gear clutch, thus making a smooth shift. If lows down instead of continuing to accelerate, the transmission will have to econd gear on the idling shaft to anticipate a downshift. This shift can be

y on the idling shaft since

In addition to single-clutch and dual-clutch AMTs, there are also wet clutch and dry clutch designs which are used for differen t types of vehicle appl ications. Wet clutch AMTs offer a higher torque capacity that comes from the use of a hydraulic system that cools the clutches. Wet clutch systems are less efficient than the dry clutch systems due to the losses associated with hydraulic pumping. Additionally, wet AMTs have a highe cost due to the additional hydraulic hardware required.

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### ts the

. so

ch performance.

ring

express

e design and development phases of applying DCT

chnologies. Cost and effectiveness estimates used in the final rule are based on analysis that assumes each of t tation of the technologies. As it is stated before, NHTSA believes that DCT offers great potential for improv ical sources such as the NESCCAF study and CBI information NHTSA

a

rd

Subcompact and Compact cars. This results in an incremental effectiveness estimate of Overall, DCTs likely offer the greatest potential for effectiveness improvemen among the various transmission options presented in this report because they offer inherently lower losses of a manual transmission with the efficiency and shift quality advantages of electronic controls. The lower losses stem from the elimination of the conventional torque converter, and a great ly reduced need for high pressure hydraulic circuits to hold clutches or bands to maintain gear ratios (in automatic transmissions) or hold pulleys in position to maintain gear ratio (in Continuously Variable Transmissions) However, the lack of a torque converter will affect how the vehicle launches from rest, a DCT will most likely be paired with an engine that offers sufficient torque at low engine speeds to allow for adequate laun

In response to NPRM, NHTSA received comments from GM that included a description of technical considerations, concerns, limitations and risks that need to be considered when implementing DCT technologie s. These include concerns related to the heat stress/durability of the clutch, ability of achieving a smooth control of torque du launching and shifting, NVH, creep and rollback, packaging in FWD vehicles and the application of DCT on full size trucks. GM stated that there might be an additional gear needed for DCT on full size trucks because of the lack of a torque converter. GM also ed concerns about the US customer's acceptance of DCT due to its loss of smoothness. NHTSA judges that the expre ssed technical considerations, concerns, limitations and risks are recognized within the industry and it is standard industry practice to address each during th
te hese factors is addre ssed prior to production implemen

ing fuel economy and is a valid choice for the future fuel economy improvement according to the techn has received.

GM also expressed concern about the adde d weight for the electronics for DCT, NHTSA believe that DCT is a simpler transmission than automatic transmission and it does not need a torque converter. The overall weight for DCT should not be more than 6 speed automatic transmission. For an example, according to public information, Ford’s dry-clu tch PowerShift is 30 pounds lighter than the existing four speed automatic on Fo Focus. Therefore, NHTSA judges that overall transmission system mass will not be increased with a DCT.

GM commented that some fuel economy improvement technologies, such as dry DCT, are more naturally applicable to smaller footprint vehicles and the smaller sized powert rains that accompany them. NHTSA ha s taken these into consideration when developing this rule and applied dry DCT only to the smallest of vehicle subclasses, Subcompact and Compact cars

For the MY 2011 CAFE final rule, NHTSA estimated a 5.5 to 9.7 percent improvement in fuel consump tion over a baseline 4/5-speed automatic transmission for a wet clutch DCT, which was a ssumed for all but the smallest of vehicle subclasses,

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### CT, NHTSA estimated an 8.2 to 12.9 per cent fuel consumption improvement over a baseline 4/5-speed automatic transmission, which equates to a 5.5 to 7.5 percent incremental improvement over the 6-speed t smission. NHTSA has retained these estimat el economy improvement for t he incremental

ffectiveness values used in the NPRM and final rule. In reviewing the NPRM effectiveness estimates for this final rule NHTSA discovered that the DCTAM effectiveness value used in the Volpe model for Subcompact and Compact subclasses was incorrect; the (lower) wet clutch effectiveness estimate had been used instead of the intended (higher) dry clutch estimate for these vehicle classes. NHTSA has corrected the mistake in this final rule.

In the NPRM analysis, NHTSA estimated costs for 6 speed dual-clutch transmissions relative to 6 speed auto transmissions. For the dry clutch DCT, the agencies estimated a direct manufacturing cost of $59 (2007$ in 2012). For the wet clutch DCT, the estimate was $126 (2007$ in 2012). For the final rule, teardown study done by FEV is considered in which FEV found a wet clutch DCT to be $147 less costly than a 6 speed auto transmission. This cost savings is not unexpected when one considers that the DCT is less complex than an auto transmission. However, such a cost savings presumes that capacity exists to produce dual-clutch transmissions at volumes of 450,000 units, an assumption made by FEV. Since such capacity may not currently exist in the US, the FEV value (-$147) is not used direc tly but instead is averaged with the value used in the NPRM analysis ($126). Therefore, in the final rule, the wet clutch DCT is estimated to save $11 relative to a 6 sp eed auto transmission (2007$ in 2012). To generate a dry clutch DCT cost for the final analysis, the same cost difference from the NPRM for wet clutch versus dry clutch is ap plied. In other words, the NPRM analysis had a c

e 2.7 to 4.1 percent over a 6-speed automatic transmission with IATC. For Subcompact, Compact Cars and Small light truck subclasses, which were assumed to use a dry clutch D

ran es for this rule. In response to the NPRM, Porsche provided an estimate for fu he DCT which is consistent with t

e

ost difference of $67 which was then subtracted from the final wet-clutch DCT cost of -$11 to arrive at a result of -$78 for a dry-clutch DCT relative to a 6 speed auto transmission (2007$ in 2012). Table shows the direct manufacturing costs used in th NPRM and final rule for dual clutch transmissions. Applying the medium complexity ICM of 1.25 results in costs of -$59 for the wet-clutch DCT and -$8 for the dry clutch DCT (2007$ in 2012), both relative to a 6 speed auto transmission. With time based learning, these costs become -$68 and -$9, respectively, in 2016 (2007$).

Table V-26 Direct Manufacturing Costs for Dual-Clutch Transmissions (2007$ in 2012)

Technology NPRM FEV tear down Final Rule Comments 6s wet DCT relative to 6s auto $126 -$147 -$11 [$126+(-$147)]/2=-$11 6s dry DCT relative to 6s auto $59 n/a -$78 -$11-($126-$59) = -$78 Notes: n/a means that FEV did not conduct a tear down of the technology; refer to text for more detail on the comments.


### GM commented that "Producing a DCT transmission in North America requires significant new investment to build new or retool existing transmission manufacturing plants." NHTSA has considered these in NPRM and the final rule by assigning this

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### technol

o Vlic

e

s

or engine optimization. While CVTs by definition are fully continuous, some automakers choose to emulate conventional steppe sensation of the engine speed operating independently of vehicle speed.

-
ss

to their technology that result cycle timin

driver sele efficient tr gear losses actuate the transmissio ratio for fu manual tra that optimizes engine operation more often. Typically, this is achieved through adding ogy a medium complexity ICM factor of 1.25 to cover the cost. This ICM factor is carried to this final rule.

(17) 

### Continuously Variable Transmission (CVT)



### A Continuously Variable Transmission (CVT) is unique in that it does not use gears to provide ratios for operation. In stead, the most common CVT design uses tw shaped pulleys connected by a metal belt. Each pulley is split in half and a hydrau actuator moves the 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 th input to the output shafts. Advantages of the CVT are that the engine can operate at it most efficient speed-load point more of the time, since there are no fixed ratios. However, CVTs are limited by engine power and cannot be applied to high torque applications. Also, CVTs often have a wider range of ratios compared to conventional automatic transmissions which can provide more options f

d automatic operation because some drivers are not used to the

Considering the confidential data togeth er with independent review, NHTSA has estimated the fuel consumption effectiveness for CVTs at 2.2 to 4.5 percent over a 4/5 speed automatic transmission, which translates into a 0.7 to 2.0 incremental effectivene improvement over a planetar y automatic transmission with the IATC technology. NHTSA continues to find these estimates to be accurate.

NHTSA adjusted the original estimates used in MY 2011 CAFE final rule to account for ICM markup of 1.25 for a medium te chnology. For this rule, this results in an incremental compliance cost estimate of $250 for the MY 2012 vehicles. In the Volpe model, this technology was only applie d to vehicles manufactured with unibody
constru ction methods, since ladder frame vehicles are typically unsuitable for CVTs due size and utility requirements. CVTs are an established and readily available so time based learning is applied, and as with other transmission technologies in new installations, CVT are only applied by the Volpe model at redesign g. The phase-in caps are now at 85 percent throughout the rulemaking period.

(18) 6-Speed Manual Transmissions (6MAN)



### Manual transmissions are entirely dependent upon driver input to shift gears: the cts when to perform the shift and which gear to select. This is the most ansfer of energy of all transmissi on layouts, because it has the lowest internal , with a minimal hydraulic syst em, and the driver provides the energy to clutch. From a systems view point, however, vehicles with manual ns have the drawback that the driver may not always select the optimum gear el economy. Nonetheless, incr easing the number of av ailable ratios in a nsmission can improve fuel economy by allowing the driver to select a ratio

overdrive ratios to reduce engine speed at crui sing velocities (which saves fuel through

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### reduced en s the optimu However, if the gear ratio steps are not properly designed, this may require dissatisfac performan

NH increase of 0.5 percent for ch was derived fro te this estima uel consumption for replacing a 5-speed 2011 final an increme the final ru er costs occurring in later MYs due to the application of time based l in the base cycle timin percent for

. H



### ary

ination of the three above mechanisms to reduce ption, available to plug-in hybrids electric vehicles (PHEV) and battery electric vehicles (EV), is by substituting the petroleum fuel energy with energy from another source, such as the ric grid. The effectiveness of fuel consumption reduction depends on the utilization

e and its effect on gine pumping losses) and pushing the torque required of the engine toward m level. the driver to change gears more often in city driving resulting in customer tion. Additionally, if gear ratios are selected to achieve improved launch ce instead of to improve fuel economy, no fuel saving effectiveness is realized.

TSA’s MY 2011 CAFE final rule estimated an effectiveness replacing a 5-speed manual with a 6-speed manual transmission, whi m confidential manu facturer data. NHTSA has found no evidence to dispu te and chosen to use 0.5 percent re duction in f manual with a 6-speed manual transmission for this rule. NHTSA updated the rule costs to reflect the ICM low complexity markup of 1.11 which resulted in ntal compliance cost of $250 for MY 2012 vehicles, as compared to $338 in le, with low earning factors. 6MAN is only applied to vehicles that use a manual transmission line product, and the Volpe model can only apply the technology at redesign g. The phase-in rate has been set to 85 percent for MY 2012 to 2014 and 100 the remaining years of this rule.

ybrid and Electrification/Accessory Technologies c



### A Hybrid is a vehicle that combines two or more sources of propulsion energy, where one uses a consumable fuel (like gasoline), and one is rechargeable (during operation, or by another energy source). Hybrid technology is established in the U.S. market and more manufacturers are adding h ybrid models to their lineups. Hybrids reduce fuel consumption through three major mechanisms:

• The internal combustion engine can be optimized (through downsizing, modifying the operating cycl e, or other cont rol techniques) to operate at or near its most efficient point more of the time. Power loss from engine downsizing can be mitigated by employing power assist from the second
power source.

• Some of the energy normally lost as heat while braking can be captured and stored in the energy storage system for later use.

• The engine is turned off when it is no t needed, such as when the vehicle is coasting or when stopped. Hybrid vehicles utilize some comb fuel consumption. A fourth mechan ism to reduce petroleum fuel consum

elect of the above mechanisms and how aggressively they are pursued. One area where this alent is in th e choice of engine siz variation is particularly prev balancing fuel economy and performance. Some manufacturers choose not to downsize

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### the eng

ly

gnificantly diminished towing performance with a low battery. Although hybrid vehicles using other en ergy storage concepts (flywheel, hydraulic) have been developed, the system s currently in production in the U.S. for passenger cars and light trucks use battery st orage and electric drive systems. Hybrid electric vehicles (HEV) are part of a continuum of vehicles using systems with differing levels of electric drive and electric energy storage. This range of vehicles includes relatively basic engine start/stop systems, HE V systems with varying degrees of electric storage and electric drive system capability, PHEV with differing degrees of all electric range and EV that rely entirely on electric drive and battery electric energy storage. Different HEV, PHEV and EV concepts utilize these mechanisms differently, so they are treated separately for the purposes of this analysis. Below is a discussion of battery energy storage and the major hybrid concepts that were determined to be available in the near term.

i. Batteries for HEV, PHEV and EV Applications



### The design of battery secondary cells can vary considerably between HEV, PHEV and EV applications. MHEV systems will likely continue to use lead-acid batteries due to their lower voltage (12-42 VDC) and relatively low power and energy requirements. However, technology used is expected to be upgraded over conventional (non-MHEV) lead acid batteries to meet the charge cycling demands of MHEV applications, and is likely to include extended-cycle-life flooded (ELF) lead-acid batteries or absorptive glass matt, valve-regulated lead-acid (AGM/VRLA) batteries. HEV applications operate in a narrow, s hort-cycling, charge-sustaining state of charge (SOC). Energy capacity in HEV applications is somewhat limited by the ability of the battery and power electr onics to accept charge and by space and weight constraints within the vehicle design. HEV battery designs tend to be optimized for high power density rather than high energy density, with thinner cathode and anode layers and more numerous current collectors and separators (Figure V-14). EV batteries tend to be optimized for high energy density and are considerablylarger than HEV batteries. PHEV battery designs are intermediate between poweroptimized HEV and energy-optimized EV battery cell designs. PHEV batteries also must provide both charge depleting operation simila r to an EV and charge sustaining operation similar to an HEV. Unlike HEV applications, charge sustaining operation with PHEVs occurs at a relatively low battery state of charge (SOC) which can pose a significant ine when applying hybrid technologies. In these cases, performance is vastly improved, while fuel efficiency improves significantly less than if the engine was downsized to maintain the same performance as the conventional version. While this approach has been used in cars such as th e Honda Accord Hybrid (now discontinued), it is more likely to be used for vehicles like trucks where towing and/or hauling is an integral part of their performance requirements. In these cases, the battery can be quick drained during a long hill climb with a heavy load, leaving only a downsized engine to carry the entire load. Because towing capa bility is currently a heavily-marketed truck attribute, manufacturers are hesitant to o ffer a vehicle with si

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### challen e with respect to attaining acceptable battery cycle life. In the case of the GM Volt, this 30%. 181



### Figure V-14 Schematic representation of power and energy optimized prismatic-layered battery cells 

### g limits charge depleting operat ion to a minimum SOC of approximately

Collector (-)
Cathode (-)
Separator
Anode (+)
Collector (+)

EV Energy-optimized Battery Cell HEV Power-optimized Battery Cell



### em) for the GM Volt PHEV and by AESC, GS-Yuasa and A123 S Power-split hybrid vehicles from Toyota, Ford and Nissan, integrated motor assist hybrid vehicles from Honda and the GM 2-m ode hybrid vehicles currently use nickelmetal hydride (NiMH) batteries. Lithium-ion (Li-ion) batteries offer the potential to approximately double both the energy and pow er density relative to current NiMH batteries, enabling much more electrical-energy-intensive automotive applications such as PHEVs and EVs. Li-ion batteries for hi gh-volume automotive applications differ substantially from those used in consumer electronics applications with respect to cathode chemistry, construction and cell size. Li-ion battery designs currently under development by CPI (LG-Ch ystems (respectively) for the upcomi ng Nissan, Mitsubishi and Chrysler EVs use large-format, layered-prismatic cells assemb led into battery modules. The modules are then combined into battery packs.

181 “Latest Chevrolet Volt Battery Pack and Generator Details and Clarifications.” Lyle Dennis interview of Rob Peterson (GM) regarding the all-electric drive range of the GM Volt, August 29, 2007. Accessed onthe Internet on June 30, 2009 at: http://gm-volt.com/2007/08/29/latest-Chevrolet-volt-battery-pack-andgenerator-details-and-clarifications/

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### ly

s). ion of

ciency

le, is

ate both the hybrid

em and th e complexity for a Crank Integrated

, dai. One estimate from Anderman

er Cathodes for large-format, automotive Li-ion batteries are becoming increasing focused on two chemistries – LiMn2O4-spinel (CPI, GS-Yuasa, AESC) and LiFePO4 (A123 System In addition to the purely hybrid techno logies, which decrease the proport propulsion energy coming from the fuel by increasing the proportion of that energy coming from electricity, there are other steps that can be taken to improve the effi of auxiliary functions ( 

### e.g. , power-assisted steering or air-conditioning) which also reduce fuel consumption. These steps, together with the hybrid technologies, are collectively referred to as “vehicle electrification” because they generally use electricity instead of engine power. In order to achieve consistency between the two modeling techniques, and to improve the number and range of technology offerings, the CAFE model was revised to include one additional mild hybrid technology. The high voltage or improved efficiency alternator (HVIA) technology, which was used in the 2011 ru no longer represented as a separate technology and has instead been incorporated into this new mild hybrid technology, as discussed further below.

ii. Hybrid System Sizing and Cost Estimating Methodology



### NHTSA, in coordination with EPA reviewed estimates of cost and effectiveness for hybrid and related electrical technologies and adjusted them as appropriate. Both agencies found the hybrid technology cost estimating methodology that Ricardo and NHTSA developed during the 2011 final rule to be reasonable and used it to estim hybrid systems costs and account for variation in component sizing across types and vehicle subclasses. That method utilizes four pieces of data: (1) key component sizes for a midsize car by hybrid sy stem type; (2) normalized costs for each key component; (3) component scaling fact ors that are applied to each vehicle class/subclass by hybrid system type; and (4) vehicle characteristics for the subclasses which are used as the basis for the scaling factors. During development of the methodology, NHTSA and Ricardo made several assumptions: 1) Hybrid controls hardware varies with the level of functionality offered by the hybrid technology. Assumed hybrid controls complexity for a 12V micro hybrid (MHEV) and belt integrated star ter generator (BISG) was 25 percent of a strong hybrid controls syst Starter Generator (CISG) was 50 percent. These ratios were estimates based on the directional need for increased functionality as system complexity increases. 2) Li-Ion batteries for hybrid electric vehi cles are currently entering production including a 2010 MY Mercedes and Hyun indicates that Li-ion market penetration will achieve 35 percent by 2015. 182 

### However, as was discussed above, signi ficant development effort is underway by a number of battery producers which could impact cost and overcome oth technical concerns. Therefore it was assumed that mild (MHEV, BISG andCISG) and strong hybrids (PSHEV, 2MHEV and PHEV) will use either LiIon or NiMH batteries, depending on cost considerations. However, plug-in

182 Anderman, Advanced Automotive Battery Conference, May 2008. Proceedings available for purchase at http://www.advancedautobat.com/Proceedings/index.html (last accessed October 17, 2008).

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### hybrids will use Li-ion batteries only. Battery usage is discussed further

3) The plug-in hybrid battery pack w s sized for a mid-sized car by assuming: cle has a 20 mile all nd consum 0 e battery pack c discharged down to 3 epth of

he capacity of battery pack is 20 r than ge on a new battery pack is 24 miles). ncluded a DC conver e electrical ppropri cation in the time frame d. d with vehicle functio of stron id wiring. The s need for se d fun

cluded a su ental heater to pro e is stopped; howe n this rule, it is assumed that only half adapt this technology, as disc included elect conditioning to enable engine stop/start hicle air conditioning w ested b

icardo re i zed that some stro ems

r tional transmission with speci cost offset (i.e., a cost credit) for the remova f a portion of the clutches and gear sets within the transmission. In the MY 2011 rul the transmission cost in Table V-27 below
express on

assume the below.

a the vehi electric range a es an average of 30 W-hr per mile; th an be 0 percent d percent greate discharge; 183 

### and t a new at end of life ( 

### i.e. , ran 4) All hybrid systems i ccommodate vehicl /DC ter which was sized to ate for increased vehicle a electrifi loads a considere 5) High voltage wiring scale hybrid n e ality and could b represented as a fraction based on the directional g hybr increa se ratios were estimate lity as system complex ctiona ity increases. 6) All hybrid systems in pplem vide vehicle heating when the engin ver, i of the vehicles will ussed further below. Only the strong hybrids ric air when ve as requ y the operator.

Furthermore, NHTSA and R cogn ng hybrid syst eplaced a conven a hybrid- fic transmission, resulting in a l o

e , ed hybrid transmission costs as a percentage of traditional automatic transmissicost, as described in the 2008 Martec Report, at $850 direct manufacturing cost (nonRPE/ICM). The method assumed that the mechanical aspect of a power-split transmission with a reduced number of gear sets and clutches resulted in a cost savings of 50 percent ($425) over a conventional transmission with torque converter. For a 2-mode hybrid, the mechanical aspects of the tran smission are similar in complexity to a conventional transmission, so no cost savi ngs was appropriate. The plug-in hybrid d a highly simplified transmission for electric motor drive, thus 25 percent of base vehicle transmission cost was applied (resulting in a $638 credit). The NHTSA MY 2011 CAFE final rule discusses in detail how the hybrid cost estimating methodology uses the information provided in the tables below to calculate costs for each of the strong hybrid systems used in this rule. It also includes a step-bystep ex ample for the midsize vehicle mild hybrid systems used in the MY 2011 CAFE final rule. 184 

### As in that analysis, it is important to understand that the CISG technology replaces existing mild hybrid systems. 185 

### NHTSA and EPA in reviewing the above made the following revisions.

183 The GM Volt operates between 30% DOD and 85% DOD. So there is 55% useable DOD, but charge sustaining operation starts at 30% and cycles between 30 and 35% DOD.
184 74 FR 14291 (Mar. 30, 2009)
185 For the incremental CAFE model, before CISG is applied, the costs for MHEV and BISG are subtracted if they were previously applied.

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### e

to

, First, NHTSA and EPA revalidated the component sizes that were estimated for a midsize car for each type of hybrid system as shown in Table V-27. However, NHTSA and EPA added an additional component-- front engine accessory drive (FEAD), becaus hybridization often involves revision to the FEAD design such that certain devices (belts, pulleys, idlers, etc) as well as other engine components (alternator, A/C compressor, and starter) may no longer be needed and can thus be eliminated, or may be de-specified lower cost alternatives. This is applicable to CISG and the strong hybrid technologies and is intended to account for cost savings associated with items that changed or are no longer required as a result of these technology applications.

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### Table V-27. Component Sizes by Hybrid Type for a Midsize Car

Pr Se Pr Se C Ni Component MHEV BISG CISG PSHEV 2MHEV PHEV
imary Motor power, continuous (kW) 3 11 45 45 45 condary Motor power, continuous (kW) na na 30 45 30 imary Inverter power, continuous (kW) 3 11 45 45 45 condary Inverter power, continuous (kW) na na 30 45 30 ontrols complexity (relative to strong hybrid) 25% 50% 100% 100% 100%
MH Batter y Pack ca p acit y ( kW-hr ) 1 na 1 2 2 na
-Ion Batter Li y Pack ca p acit y ( kW-hr ) 1 na 1 2 2 15 /DC Converter power (kW) 0.7 2 2 2 2 gh Voltage Wiring (relative to strong hybrid) na 50% 100% 100% 100% DC Hi
Su

pp lemental heatin g 2 50% 50% 50% 50% 50% echanical Transmission (relative to baseline vehicle) 100% 100% 50% 100% 25% ectric AC No No Yes Yes Yes Blended Brakes No Yes Yes Yes Yes FEAD Credit No Yes Yes Yes Yes

harger power, continuous (kW) na na na na 3

2 - Implemented through a reduction in component cost (50%) 1 - Assumes the use of either NiMH or Li-Ion, and not both. M El

C Hybrid Type



### Second, the costs estimates of the key components were revised and comment to e cost as they apply to NiMH was provided by ICCT. The MY 2011 CAFE final ru th

as developed at a time when economic conditions were significantly different than those that currently exist, a time when o e hybrid systems were more expensive than toda y. These changes in economic conditions

were o either

e

t per kilowatt hour (kW-hr) fo r Nickel Metal Hydride (NiMH) battery was revised ated,

the marketplace today, as discussed above, and is expecte

g in

r

- study le

w many f the commodity materials used in th

ne of the factors leading to some of the cost revisions EPA and NHTSA jointly discussed and made. Differences in estimates provided by confidential sources to EPA or NHTSA also played a part in the revisions. In addition, the agencies applied the new ICM mark-up factors instead of the RPE th at was used previously. An appropriat ICM factor (1.45 for most mild and strong hybrid technologies) replaces the previous RPE factor (1.5). Specifically, the primary and secondary inverter cost per kilowatt were revised downward from $10 to $7, the controls cost was revised upward from $100 to $115, the DC/DC converter costs were revised from $100 to $88, the blended brake system that was revised from $400 to $310, a nd finally the fully learned, high volume production, cos from $350 to $320. The cost for Lithium Ion (Li-Ion) batteries was also revised. As previously st Li-Ion batteries are being implemented in seri es production in model year 2010. Battery technology is changing rapidly in d to continue along this path throughout the rulemaking period. OEMs are now forming relationships with battery manufacturer s in an effort to research and develop not only new and improved battery technology, but also more efficient manufacturin processes capable of supporting high volume production. Accordingly, as shown Table V-28, the $600 per kW-hr used in the 2011 rule was revised downward to $320 pe kW-hr, matching that of the NiMH technology. The revision downward from $600/kW hr in the 2011 CAFE final rule to $320/kW-hr in this analysis was done based on a

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### the

will not be accomplished during this rulemaking period to achieve this cost. MEMA commented that the cost of $320/kW-hr was t ar exceed $500/kW-hr. The $500/kW-hr used in this rulemaking is a 2012 figure that was adopted from the Electrif

les are based on an assessment of several vehicle models and are specific to those models. For this rule, NHTSA l

e

s

assumpti

technology

lume

lt integrated s r genera fixed cost that, like the automatic transmission pump described later. by Deutsche Bank that estimated Li-Ion battery costs at 300-400 €/kW-hr. v 

### This was converted to $500/kW-hr then learned twice using volume-based learning to arrive at $320/kW-hr. Comments were received from The State of New Jersey Department of Environmental Protection, MEMA , and ICCT on the subject of Lithium-ion battery, and system costs. The State of New Jersey Department of Environmental Protection commented that the battery costs were too low citing an Air Resources Board report which projects $260/kW-hr. Necessary volumes and manufacturing maturity

oo low stating that present costs f

ication Roadmap, November 2009 and then learning, described in the previous paragraph, was applied. ICCT provided examples of charge capacity and power requirements of recently produced vehicle applications. These examp

believes it is appropriate to use an average cost which is representative of al vehicle models using this technology during the 2012 to 2016 model year period. Th examples cited by commenters do, however, further demonstrate the rapid pace of development and innovation in the application of these hybrid technologies. Though the agency believes the cost assumptions used in the Volpe model to be correct at the point in time of their assessment and are accurate for this rulemaking period, the agency agree that further research will be necessary to progress from our current point in time ons on costs and application pa rameters for future rule makings. Li-ion batteries were originally restricted to plug-in hybrids only. Recent vehicle introductions confirm either NiMH or Li-ion battery technology can be used in any mild or strong HEV application. However, manufactu rers are likely to consider cost highly in their selection of battery technol ogy. If Li-ion battery prices de crease to levels similar to NiMH, Li-ion batteries would be the defaul t battery technology for all hybrid electric vehicles. If Li-ion battery prices remain high, NiMH would be the default battery technology for all hybrid electr ic vehicles. For plug-in hybrids Li-ion would continue to be required because plug-in hybrids demand higher energy density than NiMH can provide. Neither the CAFE nor OMEGA mode l predicts a high penetration of plug-in in achieving the standards. Finally, the agencies assessed the cost savings associated with the FEAD credit discussed above. This cost was not previously represented in the hybrid cost model. As shown in Table V-28 below, a $100 credit is used which offsets directly the costs of the other components specified. This is the best approximation of the value of these items, based on NHTSA and EPA engineering assessment. Estimates of each key component are shown in Table V-28 below along with the sources of those estimates. The cost basis estimates assume fully learned, high-vo (greater than 1.2 million units per annum) production, and the costs shown are direct manufacturing costs that are not RPE or ICM adjusted. This table does not show a cost applicable to the be tarte tor system (BISG) since it is a

co st, is not scaled by subclass as

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### s Table V-28 Component Cost Basis at High Volumes and Data Source

COMPONENT COST BASIS DATA SOURCE Primary Motor ($/kW) 

### $15 Secondary Motor ($/kW) 

### $15 Martec 2008 Primary Inverter ($/kW) 

### $7 Secondary Inverter ($/kW) 

### $7 Contr 

### ess Information ols 

### $115 Confidential Busin

NiMH Battery Pack ($/kW-hr.) 

### $320 2011 CAFE FRM (with revision) Li-ion Battery Pack ($/kW-hr.) 

### $320 Deutsche Bank 2008 DC/DC Converter (Size: 2kW) 

### $88 Confidential Business Informatio n High Voltage Wiring 

### $200 Suppleme 

### Martec 2008 ntal Heating 

### $42 Mechanical Transmission 

### $850 Martec 2008 (to 4-spd auto) Elect ric Air Conditioning 

### $450 Blended Brakes 

### $310 Charger 

### $100 Confidential Business Inf ormation

Automatic Transmission Pump 

### $75 Martec 2008 FEAD Credit 

### $(100) Confidential Business Informat ion

Third, NHTSA and EPA also revised component size/scaling assumptions for some vehicles (i.e., large trucks). NHTSA and EPA recognized that some manufacturers may choose not to use supplemental cabin heating opting instead to continue engine operation in the event heat demand occurs; therefore supplemental heating is spec ified for e 50 percent application rate implemented in the hybrid cost estimating methodology reducing the component cost from $84 to $42. N d E A also re hoi ce of a erter as a compone or a midsi epre 250 amp current capability. In retrospect this is a high sp ecification for a midsize vehicle and we revised the estimate to a 2 kW DC/DC converter, as shown in Table V-28 above, which would represent a more reasonable 150 amp current capacity. The scaling factor used for the primary and secondary mo invertors on the large truc V vehicles wa s in the MY 2011 CAFE final rule, a linear extrapolation was used from the eh ed it out to the largest of vehicles, the large truck class. This resulted in projected component sizes th are larger than those used on a commerc lly realized truck in this vehicle class, the Chevrolet Tahoe two-mode (and the agenci approximates t e motor and inverter sizes specified in the Tahoe application. For future analysis, the agencies are considering whether it may be more accurate to use one set of scaling for passenger cars and another different set for light trucks. only half of the vehicles. Table V-28 abov e indicates th

HTSA an P nt size input f viewed the c ze vehicle, which r 3 k W DC/DC conv sented a

tors and k and SU s revised. A midsize v icle and extend
at ia HEV. Accordingly the scalin g factors have been revised for this class s have verified scaling factors for the other classes). This more closely e h

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### p/

se

igher

29

Table V-29 Component Scalin g Factors applied to Vehicle Class for each Hybrid

Li

Ch 

### Another revision involves the addition of a stand-alone higher voltage Start-Sto BISG mild hybrid system. NHTSA and EPA de termined that by applying a cost increa to the MHEV technology to allow for a voltage increase (lead acid batteries) and efficiency improvements to the alternator, the system would then approximate the h voltage Start-Stop /BISG applied by EPA. Based on confidential sources, the estimates provided were first converted to 2007 dolla rs and then reverse learned through two cycles, since volume learning is applicable, to arrive at a non-RPE/ICM incremental compliance cost to be $229. This cost is applicable to all classes that use higher voltage Start-Stop/BISG and is not scal ed by any vehicle attribute. Component scaling factors fo r each type of hybrid system as shown in Table Vbelow

System

Component MHEV CISG PSHEV 2MHEV PHEV
Primary Motor Engine displacement Curb weight Engine power

Secondary Motor na na Curb weight
Primary Inverter Secondary Inverter na na Controls NiMH Battery Pack na Hybrid Type

Engine displacement Curb weight 1



### Primary motor power Secondary motor power Complexity 2



### na -Io

arger all vehicle classes for all vehicle classes Curb weight n Battery Pack na Curb weight DC/DC Converter High Voltage Wiring na Supplemental heating Mechanical Transmission Electric AC na na Blended Brakes na Same Curb weight 3 

### Vehicle footprint

Same for all vehicle classes Vehicle footprint

Vehicle footprint

na na na na Same for

(1) 

### For all vehicle classes except for performance classes which use Engine Torque
(2) 

### Curb weight used as surrogate for vehicle road load
(3) 

### Curb weight used as surrogate for vehicle electrical load



### Regarding the market data file from the MY 2011 CAFE final rule, NHTSA a EPA did not make any revisions to the average vehicle characteristics for each vehicle subclass as shown in Table V-30, which defines the average vehicle characteristics for each vehicle subclass. These characteristics we re used as the basis of the scaling facto in the Volpe model. nd

rs

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### Table V-30 Key Vehicle Characteristics For Each Vehicle Subcl CAFE model
Engine

Su
Co
M La
Pe 

### ass for



## Vehicle Subclass t (lbs) Footpr (ft2) gine Disp. (L) Power (hp) Torque (ft-lb)

bcompac 41 1.9 134 133 mpact C 44 2 166 167 idsize Car 3725 47 9 206 rge Car 4110 50 3.4 258 248

r

318 Curb Weigh int En

t Car

ar 2795
3359 2. 2. 205

formance Subcompact Car 3054 40 2.7 260 260 Performance Compact Car 3516 44 3.0 269 260 Performance Midsize Car 3822 47 3.9 337 Performance Large Car 4189 51 4.8 394 388 Minivan 4090 50 3.3 247 242 Small Truck 3413 45 2.6 178 185 Medium Truck 4260 50 3.6 250 256 Large Truck 5366 63 5.0 323 352

(19) 

### Electrical Power Steering (EPS)



### an

em which may add cost and comple ity. In the 2011 final rule NHTSA estim ss based on the 2002 NAS report, a Sierra Research repo rt, and confidential manufacturer data. NHTSA

ty Electric power steering (EPS) provides a potential reduction in fuel consumption over hydraulic power steering because of reduced overall accessory loads. This eliminates the parasitic losses associated with belt-driven power steering pumps which consistently draw load from the engine to pump hydraulic fluid through the steering actuation systems even when the wheels are not being turned. Additionally EPS is enabler for all vehicle hybridization technol ogies, since it provides power steering when the engine is off, and thus NHTSA places th e technology at the top of the electrification decision tree. While EPS may be implemente d on most vehicles with a standard 12V system, heavier vehicles may require a higher voltage syst

x ated a 1 to 2 percent effectivene

reviewed these effectiveness estimates and found them to be accurate, thus they have been retained for this rule. For costs, in the MY 2011 CAFE final rule, NHTSA estimated EPS at $105 - $120 at a 1.5 RPE markup factor. NHTSA, working in conjunction with EPA, adjusted the EPS cost for this rule based on a review of the specification of the system. Adjustments were made to the potentially higher voltage or heavier duty system operation, such as would be required on some hybrid trucks. Accordingly, higher costs were estimated for EPS due to the system’s higher capability. After accounting for the differences in system capability and applying the ICM markup of low complexi

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### technol

l

p

oil pumps are y driven. A reduction in fuel consumption can be realized by driving

ntarily.

the

e, reduce warm-up fuel enrichment and reduce ed when electrification is combined with an improved higher efficiency engine alter . Ve y heavy payloads, ed for towing have cooling em and n loads, and benefit less r t cooling. Theref intellige oling is n plied to the Large LT su cl FE model, IA refers solely to improv ngine cooling.

NH ed the 1 to 2 percent CC effect eness estima s used in MY 2011 rule an f to be accurate fo s rule. N SA also confirmed the cost assumption ro le and thus o djusted osts to ref e new ICM markup for low complex technology of 1.11; this re lts in a cos rulemakin at MY 2012. Since thes systems are readily available and in production ased learning is applied. Th lpe mode pply IACC at either refresh or e however app to th arge Tru d SUV ubclass is prohibited due to the cooling system requirements of these high utility vehicles. The phase-in rate has been defined as 85 percent in MYs 2012 to 2014, and 100

y ogy of 1.11, the estimated costs for this rulemaking are $106 for a MY 2012 vehicle. As EPS systems are in wide spread usage today, time based learning is also deemed applicable, hence costs will be lower for later MY vehicles. The Volpe mode can apply EPS at refresh or redesign cycles, since it is a reasonably non-intrusive technology. Whereas the 2011 final rule did not apply EPS to the Large Truck and SUV subclass, primarily due to concerns with the system’s capability, there are no subclass specific limitations on its use in this rule for the reasons stated above. The phase-in ca has been set at 85 percent in MYs 2012 to 2014, and 100 percent thereafter,

(20) 

### Improved Accessories (IACC)



### The accessories on an engine, including th e alternator, coolant and traditionally mechanicall them electrically, and only when needed (i.e., “on-demand”).

As the oil pump provides lubrication to the engine’s sliding surfaces such as bearings, pistons, and camshafts, oil flow must be provided whenever the engine is rotating. Because mechanical oil pumps do not operate when the engine is not rotating, there is no efficiency benefit for the ability of an electric oil pump to be switched off when the engine is not rotating. The increased complexity of an electric oil pump system creates greater reliability risk compared to a conventional mechanical oil pump, and increases risk for significant engine damage should the system fail, even mome

Electric water pumps and electric fans can provide better control of engine cooling. For example, coolan t flow from an electric water pump can be reduced and radiator fan can be shut off during engine warm-up or cold ambient temperature conditions which will reduce warm-up tim parasitic losses. Further benefit may be obtain

, nator h h hicles that typ ically carr coo ng fa or that are us ig syst li

fo intelligen ore, nt co ot ap

b ass. In the C A CC ed e

TSA review IA iv te

d ound them r thi HT s f m the final ru ity nly a the c lect th

a su t estimate for this g of $128 e

curr ently time b e Vo l can a redesign cycl lication e L ck an

s

percent thereafter. (21) 

### 12V Micro Hybrid (MHEV)



### The 12V Micro-Hybrid (MHEV) systems ar e the most basic of hybrid systems and offer only the ability to turn the engine off when the vehicle is stopped or potentiall

### 239

DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### st and adaptability to existing powertrains nd latforms can make them ctive f l he convention alternator is repla h a be riven, e ower starteralternator an a d front-end acces drive sy . A conv nal 12V gearreduction st ter ensure relia b old-wea starting. lestop, some er st eering and automatic transmission hydraulic
pressure ar ic power steeri ng d an auxiliary transmission pump may be needed. A he MHEV system hown in re V-1

.
In th 20 effectiven s estimates for this techn y ranged from 2.0 to 4.0 p n whether th hicle is equipped with a 4, 6 or 8 cylinder engine, with he engine having t west ra nd the 8 c der having the highest. Th est the limited ca bility of 1 olt system ich do not er mechanical energy through regenerative braking or provide motive force; sources citing higher estimates typically involve higher voltage systems that have increased capabil

, the use

s,

owever

he

s.

Figure V 

### during deceleration (i.e. idle stop). Their low co

a p attra ced wit or so e app m l d ications. T nha d p al belt-driven t- nce

d redesigne so ry stem entio

ar is retained to ch as pow l c e th r e Also, during id functions su e lost; so electr an

sc matic of the is s Figu

e 1 1 final rule, the es olo g ercent dependent o e e v t 4 cylinder he lo nge a ylin

e imates reflect pa 2 v s wh

recov

ity.

For this rule, the system specifications assumed in the 2011 rule 186 

### were applied (i.e., use of a 3 kW motor and a DC/DC c onverter) and the hybrid technology cost method was used to produce system costs, like was done in 2011 rule. However of revised component costs and new ICM mark ups resulted in costs ranging from a low of $288 for Subcompact subclass to a high of $410 for the Large Performance subclas both of which are for MY 2012 vehicles. This technology is not applied to the Large Truck and SUV subclass due to the higher utility requirements of these vehicles; h this is the only subclass limitation of the MHEV technology. Time based learning is considered applicable, and thus system costs are lower in later MYs. Application by t Volpe model is limited to the redesign cycle since the front engine accessory drive will likely require significant redesign with a phase-in cap of 85 percent for all MY

-15 Schematic of MHEV Type System [Husted, 2003]



### Higher Voltage Stop-Start/Belt Inte grated Starter Generator (BISG)

186 74 FR 14293 (Mar. 30, 2009) 

### En g ine Motor/ Generato r

Trans. Controller Inverte r

Final Drive Wheel + -

Batter y

### 240

DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### at machine and a higher capacity battery, typically 42 volts or above, thus enabling a limited level of re generative braking which is generally not availab e of the

se

or superca

).

on a number of vehicl e characteristics such as engine displacement and vehicle size. Th

e

l, which is incremental to the MHEV technology, adjusts the costs upwards by $286 to reflect the need for additional battery capacity, wiring upgr and a electric machine. The cos

0 volts) and can provide ue for launch as well as genera te current to provide significant levels of brake e ergy recovery. The motor/generator also acts as the starter for the engine and can replace a typical accessory-driven alte rnator. Current CISG systems typically do not full launch the vehicle on electric pow er alone, although some can cruise on electric power; dual-clutch based CISG systems capable of all-electric drive are under Belt Mounted Integrated Starter Genera tor (BISG) systems are higher voltage stop-start similar to a micro- hybrid system, offering idle-stop functionality except th they utilize larger electric

le to 12 volt based systems. The larger electric machine and battery also enables a limited degree of power assist, which MHEV cannot provide. However, becaus limited torque capacity of the belt driven design, these systems have a smaller electric machine, and thus less capability than crank in tegrated or stronger hy brid systems. The systems replace the conventional alternator with a belt-driven starter/alternator. The limited electrical requirements of these systems allow the use of lead-acid batteries pacitors for energy storage. Sche matically BISG is similar to the MHEV technology.

NHTSA did not have an equivalent technology to BISG in the 2011 final rule (the ISG technology used in the 2011 rule was envisi oned to be more capable than BISG Effectiveness estimates for higher voltage stop-start systems found in literature and reports typically range from 3.0 to 7.5 percent, relative to a vehicle without stop-start, and dependent e Volpe model, which applies BISG incrementally to the MHEV technology, uses incremental estimates of 3 to 6 percent in this rule, which makes the net effectiveness comparable to the estimates found in the 2002 NAS and 2004 NESCAFF reports for higher voltage stop-start systems. This estimate applies for all vehicle subclasses except Large Truck and SUV where, due to their high utility requirements, th BISG technology is not considered applicable. Comment received from GM support the exclusion of this technology from Large Truck applications.

For this rule, the cost estimate used by the Volpe mode

ades, larger optimized t estimates reflects volume based learning factors, since these systems are in relatively low usage at this time, and an ICM complexity markup of 1.25 for a medium complexity technology. Like MHEV, BISG can only be applied at redesign cycles times, and a flat 85 percent phase-in setting exists for all MYs.

(22) 

### Crank Integrated Starter Generator (CISG)



### Integrated motor assist (IMA) is a commercially realized system developed and marketed by Honda. This is similar to the CISG technology represented in the Volpe model. They utilize a thin axial electric motor bolted to the engine’s crankshaft and connected to the transmission through a torque converter or clutch. The axial motor is motor/generator that typically operates above 100 volts (but lower than the stronger hybrid systems discussed below, which typi cally operate at about 30 torq

n

y

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### is shown in Figure V-1

.

gure V-16 Schema usted, 2003]



### l

not apply. Note that the net effectiveness assumptions used in this rule do not include engine

the

applicable to all vehicle subclasses. Since significant vehicle modification is required to implem cycle.

ity to move the vehicle on electric power only. It replaces the vehicle’s transmission with a single planetary gear and a motor/generator. A seco

he development. A schematic of the Honda’s IMA system
Fi tic of Honda IMA System [H



### NHTSA did not have an equivalent technology to CISG in the 2011 final rule ( ISG technology used in the 2011 rule was envisioned to be less capable than the CISG technology defined here). For the CISG technology NHTSA estimated a net effectiveness range of 16 to 20 percent, relative to the baseline vehicle and across al vehicle subclasses in this analysis. The Volpe model therefore applies an incremental effectiveness of approximately 8.6 to 8.9 percent relative to the BISG technology and dependent on vehicle subclass except for la rge truck and SUV in which case BISG does the

downsizing, or any other effectiveness gains from engine, transmission, or vehicle technologies added to the vehicle by the modeling process.

Using the hybrid cost estimating method, as discussed above, NHTSA has determined a compliance cost range of $2,791 for a Subcompact vehicle to $5,124 for Large Truck and SUV vehicles, both in MY 2012. These include a high complexity ICM markup factor of 1.45 for this technology. As CISG is still in limited production use, volume based learning is applied which results in lower costs as the model applies sufficient quantities and applies two cycles of 20 percent cost reduction. CISG is

ent this technology the Volpe model only applies CISG during a redesign NHTSA assumed a 3 percent phase-in cap for CISG in MY2012 and increasing 3 percent per year reaching a maximum of 15 percent in MY 2016.

(23) 

### Power Split Hybrid (PSHEV)



### The Power Split hybrid has the abil

nd, more powerful motor/generator is directly connected to the vehicle’s final drive. The planetary gear splits engine power between the first motor/generator and the final drive. The first motor/generator uses power from the engine to either charge t battery or supply power to the wheels. The speed of the first motor/generator determines the relative speed of the engine to the wheels. In this way, the planetary gear allows the engine to operate independently of vehicle speed, much like a CVT. The Toyota Prius and the Ford Hybrid Escape are two ex amples of power split hybrid vehicles. Final Drive Engine Motor/ Generator Trans. + -



### Battery Controller

Inverte r W heel

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### d . The power split system provides good fuel consumption in city driving. During highway cycles, the hybrid

e

otor to provide improved high

ue. n, replacing a with a single pl anetary lectric components are ld hybrid and CISG configura the costs are

lemaking, in conjunction with EPA, anufacturer-supplied information that compared cars and small trucks without a PSHEV hybrid syst em. ata was taken from EPA’s fuel and indicated a combined cycle tailpip CO2 reductions, which are ivalent to fuel consumption reductions , for the PSHEV equipped vehicles compared to the c 32 187



### .

tes that
ve to a CVT.

-26%

404 HEV 2.4L PS 222 Net % difference -36% 

### In addition to providing the functions of idle engine stop, subsequent restart an regenerative braking, this hybrid system allows for pure EV operation

functions of regenerative braking, engine start/stop and optimal engine operation cannot be applied as often as in city driving, and so the effectiveness in fuel consumption is slightly less. Additionally, it is less efficient at highway speeds due to the fact that th first motor/generator must be spinning at a relatively high speed and therefore incurs losses. Newer designs incorporate a gear-reduction m speed efficiency and improv ed matching of motor torque to engine torq The Power Split hybrid also reduces the cost o smissio f the tran conventional multi-speed unit gear. The e bigger than those in mi tions so correspondingly hig her. During development of the joint ru NHTSA reviewed m available with and

conomy test data The

e d

e equ onventional vehicles that ranged from 19 to 36 percent, see Table V- and VConsidering the Volpe model’s incremental ap proach to technology application, where engine downsizing and other vehicle relate d effectiveness improve ments are accounted for on other technology decision trees, NHTSA determined that net effectiveness estimates of 23 to 33 percent were most appropriate for the PSHEV technology in this analysis. These net effectiveness values result in incremental effectiveness estima range from approximately 6 to 12 percent de pending on vehicle subclass and relati

Table V-31 Large Car Power Split Certification Data Tailpipe CO 2 City Hwy 55/45 comb. Nissan Altima

3.5L CVT 444 306 386 HEV 2.5L PS 317 254 286 Net % difference

Toyota Camry

3.0L 5-auto 286 355 234 228

Lexus GS

4.3L 6-auto 493 355 423 HEV 3.5L PS 355 317 341 Net % difference -19%

187 The manufacturer data shows that, for the most part, the PSHEV equipped vehicles in the comparisons utilized engine downsizing, however the data is not intended to identify all other differences that may exist between the hybrid and non-hybrid vehicle versions, such as hybrid-specific powertrain calibrations or other vehicle modifications (tires, mass reductions, etc). Readers should exercise caution in assuming th all of the noted fuel consumption gains can be attributed solely to engine downsizing and the use of the at

PSHEV technology as there may be other modifications or systems that also contributed.

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### tion sign e strong hybrid technologies, NHTSA used phase-in caps of 3 percent per MY, so the maximum application rate occurs in MY 2016 at 15 percent.

or

h initial costs were very high. In addition, virtually all power-split hybrid vehicles have been equipped with NiMH batt battery tec justify the l continue to

(24) 

### 2-Mode Hybrid



### w e capacity and the Table V-32 Small Truck Power Split Certification Data Tailpipe CO 2 City Hwy 55/45 comb. Ford Escape 4X4

3.0L 4-auto 467 386 423 HEV 2.3L PS 277 306 286 Net %

Ford Escape 4X2

3.0L 4-auto 444 370 404 HEV 2.3L PS 247 286 261 Net % difference -35%

Toyota Highlander 4X4

3.3L 5-auto 493 370 423 HEV 3.3L PS 286 329 306 Net % difference -28%



### Using the hybrid cost estimating method NHTSA established overall PSHEV system costs, which include the use of El ectric Power Steering (EPS) and Improve Accessories (IACC) technologies, ranging from $5,509 for the Subcompact subclass to $11,534 for the Performance Large Car subclass for MY 2012 vehicles. In the Volpe model these net costs result in incremental costs ranging from $1,600 to $6,723 depending on vehicle subclass. The costs were determined using a 1.45 ICM for the high complexity PSHEV technology. Volume base d learning is applicable to power split technology, so costs reduce significantly as pe netration levels increase sufficiently. In the Volpe model PSHEV is not applicable to the Large Truck and SUV subclass primarily due to the high utility requirements of these vehicles. PSHEV implementa requires significant vehicle revision, therefor e its application is restricted to rede cycles only. For th

A comment received from ICCT disagreed that volume-based learning is over f the ‘power-split hybrid electric vehicle’ and only time-based learning curve is to be applied. While power-split hybrids have been on the market long enough to achieve hig production volumes, the production ramp up has been very slow and the

eries. NHTSA expects in the future that there will be a shift toward Li-Ion hnology, and that this change in technology will be a sufficiently significant to continued use of volume based learning. Based on this, the Volpe model wil use volume based learning for the final rule.

The 2-Mode Hybrid (2MHEV) uses an adaptation of a conventional stepped-ratio automatic transmission which replaces some of the transmission clutches with two electric motor/generators allowing the transmission to act like a CVT. The motor/generators control the ratio of engine speed to vehicle speed. The clutches allo the motors to be bypassed improving the transmission’s torqu

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### efficiency needed for Chevrolet

In a estart and regene motor/gene motor/gene motor/gene it system due and secondary motor sizing.

the CAFE engine dow ere the 2MHE situations w values rang ive to a CVT.

NH aling factors in t incrementa application 2MHEV te only applie the passeng phase-in cap, so the maxim

Plug-In H ybrid Electric Vehicle b
means to c grid). Sec greater capab at allows

cles ntinuum with HE for improved fuel economy at highway speeds and to meet the requirements towing and high payload capacity. This type of system is used in the Tahoe Hybrid.

ddition to providing the hybrid functions of engine stop and subsequent r rative braking, the 2MHEV allows for pure EV operation. The two rators allow the engine to be run in efficient operating zones. The primary rator is comparable in size to that in the PSHEV system, but the secondary rator is larger. The 2-mode system cost is greater than that for the power spl to the additiona l transmission complexity

For this rule, and for similar reasons as discussed above in the PSHEV section, model considered a net effectiveness range of 23 to 33 percent, assuming no nsizing so as to preserve the util ity nature of medium and large trucks wh V technology is applied (e.g., maintaining full towing capability even in

ith low battery charge). These estimates lead to incremental effectiveness ing from approximately 3 to 9.5 pe rcent for the truck subclasses, and relat

TSA estimated MY 2012 costs using the updated component costs and sc he hybrid cost estimating methodology discussed above and determined l cost estimates ranging from $3 ,521 to $5,779 for the three light duty truck s. These estimates include the 1.45 ICM markup value for high complexity chnology; volume base d learning is applicable. The 2MHEV technology is d by the Volpe model at redesign cycle times, and it is not applicable to any of er car subclasses. NHTSA used a 3 percent per MY um application rate occurs in MY 2016 at 15 percent.

(25) 

### Plug-In Hybrid



### ybrid Electric Vehicles (PHEVs) are very similar to H s, ut with three significant functional differences. The first is the addition of a harge the battery pack from an outsi de source of electricity (e.g. the electric ond, a PHEV would have a larger battery pack with more energy storage, and a ility to be discharged. Finally , a PHEV would have a control system th the battery pack to be significa ntly depleted during normal operation.

Table V-3 below, illustrates how PHEVs compare functionally to both hybrid electric vehicles (HEV) and electric vehicles (EV). These characteristics can change significantly within each class/subclass, so this is simply meant as an illustration of the general characteristics. In reality, the design options are so varied that all these vehi exist on a co Vs on one end and EVs on the other.

### 245

Table V-33 Conventional, HEVs, PHEVs, and EVs Compared
Increasing Electrification

DELIBERATIVE DOCUMENT – DO NOT RELEASE Attribute Conventional HEV PHEV EV Drive Power Engine Blended Engine/Electric Blended Engine/Electric Electric

Engine Size Full Size Full Size or Smaller Smaller or Much Smaller No Engine Electric Range None None to Very Sh ort Short to Medium Medium to Long Battery Charging None On-Board Grid/On-Board Grid Only



### Deriving some of their propulsion energy from the electric grid provides several advantages for PHEVs. PHEVs offer a signif icant opportunity to replace petrole for transportation energy with domestically -produced electricity. The reduction in petroleum usage does, of course, depend on th e amount of electric drive the vehicle is capable of under its duty cycle. PHEVs also provide electric utilities the possibility to increase electric generation during “off-pea k” periods overnight when there is excess generation capacity and electricity prices are lower. Utilities like to increase this load” because it increases overall system efficiency and lowers average costs. Util are also investigating the use of PHEV and EV batteries as a source of grid storag capacity to provide ancillary services for grid stabilization purposes. Unlike most other alternative fuel technologies, PHEVs can in itially use an existing infr um used

“base

ities

e

astructure for refueling (charging and l e reduced.

d some

ower

ond

emissions reductions of PHEVs. This model is based on the PERE (Physical Emission

e but the proportion of electric ener gy that is used to propel the vehicle is

g iquid refueling) so investments in infrastructure may b

PHEVs will be considerably more costly than conventional vehicles an other advanced technologies. To take advantage of their capability, consumers would have to be willing to charge the vehicles ni ghtly, and would need access to electric p where they park their vehicles. For many ur ban dwellers who may park on the street, or in private or public lots or garages, charging may not be practical. Charging may be possible at an owner’s place of work, but that would increase grid loading during peak hours which would eliminate some of the benefits to utilities of off-peak charging vs. peak. Oil savings will still be the same in this case assuming the vehicle can be charged fully.

The effectiveness potential of PHEVs depends on many factors, the most important being the energy storage capacity designed into the battery pack. To estimate the fuel consumption and tailpipe CO 2 

### reduction potential of PHEVs, EPA has develope an in-house vehicle energy model (PEREGRI N) to estimate the fuel consumption/CO 2


### Rate Estimator) physics-based model used as a fuel consumption input for EPA’s MOVES mobile source emissions model.

PHEVs can have a wide variation in the All Electric Range (AER) that they offer. Some PHEVs are of the “blended” type where the engine is on during most of the vehicl operation, significantly higher than that used in a PSHEV or 2MHEV. In this analysis, each PHEV was modeled with enough battery capacity for a 20-mile-equivalent AER and a power requirement to provide similar performance to a hybrid vehicle. 20 miles was selected because it offers a good compromise for vehicle performance, weight, battery packagin

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### e

To calculate the total energy use, the PHEV can be thought of as operating in two distinct n the

and the Utility Factor calcula r

a

smaller, lighter-weight transmission and drive line components. Likewise the and cost. Given expected near-term batter y capability, a 20 mile range represents th likely capability that will be seen in PHEVs in the near-to-mid term.

modes, electric (EV) mode, and hybrid (HEV) mode. During EV operatio fuel consumption is zero. The EV mode fuel economy can then be combined with the HEV mode fuel economy using the Utility Factor calculation in SAE J1711 to determine a total MPG value for the vehicle. (See TableV-3 )

TableV-34. Sample Calculation of PHEV Gasoline-Equivalent CO 2 Reduction Midsize Car EV energy comb (0.55 city / 0.45 hwy) 0.252 kwh/mi EV range (from PEREGRIN) 20 miles SAE J1711 utility factor 0.30 HEV mode comb FE (0.55 city / 0.45 hwy) 49.1 mpg Total UF-adjusted FE (UF*FCEV + (1-UF)*FCHEV) 70.1 mpg Baseline FE 29.3 mpg Percent FE gain 139% Percent CO 2 reduction -58%



### Calculating a total reduction based on model outputs tions results in a 58 percent reduction in fuel consumption for midsize and smalle passenger cars and small trucks and SUVs. This value is used as the net effectiveness estimate for these subclasses in the Volpe model, yielding incremental estimates of approximately 46 percent relative to CVT and independent of engine and other vehicle related effectiveness improvements. Th e CAFE model does not apply the PHEV technology to Large Cars and the Medium and Large Truck and SUV subclasses.

Using the hybrid cost estimating mode l and updated component costs, NHTSA determined MY 2012 incremental cost estimates for the Volpe model ranging from a low of approximately $11,500 for a subcompact car to a high of approximately $19,000 for midsize performance car. This includes the 1.64 ICM markup value for very high complexity technology. Volume based learning lowers the costs in later model years, and a phase-in cap of 3 percent per model year is also applied.

d. Vehicle Technologies (26) 

### Mass Reduction



### Reducing a vehicle’s mass, or down-weighting a vehicle, decreases fuel consumption by reducing the energy demand needed to overcome forces resisting motion, and rolling resistance. Manufacturers empl oy a systematic approach to mass reduction where the net mass reduction is the addition of a direct component or system mass reduction plus the additional mass reduction ta ken from indirect ancillary systems and components, as a result of full vehicle optimization, effectively compounding or obtaining a secondary mass reduction from a primary mass reduction. For example, use of a smaller, lighter engine with lower torq ue output subsequently allows the use of a

compounded mass reductions of the body, engine and drivetrain reduce stresses on the

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### rther

ct ple,

ass

ary weight reduction is whether or not the powertrain components can be included in the mass reduction effort, with th

d a
ents

es, reductions. Mass reductions in suspension components, steering components, wheels, tires and brakes, allowing fu reductions in the mass of these subsystems. The reductions in unsprung masses such as brakes, control arms, wheels and tires furthe r reduce stresses in the suspension mountingpoints. This produces a compound effect of mass reductions, which results in the socalled ripple effect.

Estimates of the synergistic effects of mass reduction and the compounding effe that occurs along with it can vary significantly from one report to another. For exam in discussing its estimate, an Auto-Steel Pa rtnership report states “These secondary m changes can be considerable—estimated at an additional 0.7 to 1.8 times the initial mass change.” 188 

### This means for each one pound reduction in a primary component, up to 1.8 pounds can be reduced from other structures in the vehicle (i.e., a 180% factor). The report also discusses that a primary variable in the realized second

e lower end estimates being applicable when powertrain elements are unavailable for down-weighting. However another repo rt by the Aluminum Association, which primarily focuses on the use of aluminum as an alternative material for steel, estimate factor of 64 percent for secondary mass reduction even though some powertrain elem were considered in the analysis. 189 

### That report also notes that typical values for this factor vary from 50 to 100 percent. Although ther e is a wide variation in stated estimat synergistic mass reductions exist and the effects result in tangible mass a single vehicl e component, for example a door side impact / intrusion system, may actually result in a significantly higher weight savings in the total vehicle, depending on how well the manufacturer inte grates the modification into the overall vehicle design. Accordingly care must be taken when reviewing reports on weight reduction methods and practices to asce rtain if compounding effects have been considered or not.

Manufacturers consider and utilize various methods and options for achieving vehicle mass reductions. Automotive compan ies have largely used weight savings in some vehicle subsystems to offset or mitigate weight gains in other subsystems from increase feature content such as sound insulation, entertainment system, etc. The mass reduction methods can be grouped in four major categories as follows.



### • Material Substitution: One of the more common methods, and one which NHTSA has considered in prior rulemakings, is material substitution, where lower density and/or higher strength materials are utilized in a manner that preserves or improves the function of a component under consideration for

188 “Preliminary Vehicle Mass Estimation Using Empirical Subsystem Influence Coefficients,” M D.E., Reddy, K. Auto-Steel Partnership Report, May 2007. Accessed on the Internet on May 30, 200 http://www.a-sp.org/database/custom/Mass%20Compounding%20-%20Final%20Report.pdf
189 “Benefit Analysis: Use of Aluminum Structures in Conjunction with Alternative Powertrain Technologies in Automobiles,” Bull, M. Chavali, R., Mascarin, A., Aluminum Association Research Report, May 2008. Accessed on the Internet on April 30, 2009 at: alen, 9 at:

http://www.autoaluminum.org/downloads/IBIS-Powertrain-Study.pdf

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### usage of plastic com posite and its strength in automotive industry. NHTSA acknowledge various a pproaches for material substitution and mass reduction. There are increasing numbers of advanced material

ring. These technologies can reduce mass for vehicles components and sub-systems significan example 5 d steel



### • : Computer aided en er ing (CAE ls are anoth portant f improving structural st th and component designs o better duce str and bend nts ap to r optimiz e dimensional aspects e s mass) wh aintaining otentially im ving the grate unique s in a mann at reduces by functions or eliminating separa te faste examp f CAE in e a traditiona fram ehicle whic red uction, where the new design

c

size.

f redesign 190 

### . Aluminum Association commented on the increasing usage of aluminum while American Chemistry C ouncil Plastics Division commented on the increasing

applications in automotive industry, such as high strength steel body structure, aluminum engine block and transmission case, as well as composite floo

tly or an e mil . F , aluminum hoo
counte rt. d o offers 40 t 0 percent mass savings over th rpa
Smart Design gine ) too er im method o reng so as t optimize load paths and re esses ing mome plied them. This allows bette ation of th of th component (and thus it ile m or p pro function, or may inte part er th mass combining ners. An le o the extreme would b l “body on e” v h is esigned with a lighter “unibody” constr optimizes exterior body size, passenger compartment space, powertrain layout and capacity, and the footprint dimension, while giving careful consideration of the utility and market position within the particular segment the vehicle competes in. Vehicle crashworthiness and safety performance must also be considered and at least preserved, if not improved.



### • Content Optimization: Manufacture can achieve mass reduction through content optimization. Some manufactures have replaced spare tire and tire change hardware with tire inflator kits on some of its models, such as Cadilla STS, Mazda MX-5 Miata to reduce weight and gain space.



### • Vehicle Downsizing: Another way of mass reduction is reducing vehicle This is not common currently in US due to many factors, such as consumer choices, attribute-based CAFE standard, gasoline price, etc.

Regardless of how a vehicle’s mass is actually reduced, and what level if any o secondary mass reduction is achieved, the fu el consumption reductions that result are fairly straightforward. A number of rese archers and reports have examined the fuel consumption vs. weight reduction question for a variety of vehicle and engine types. For the most part, one primary variable exists which thereby bounds the two possible alternatives 191 

### , that being whether or not the mass reductions result in: a) improved

190 This includes substitution of high-strength steels, aluminum, magnesium or composite materials for components currently fabricated from mild steel.
191 A third alternative would be to degrade the vehicle such that mass reduction and engine downsizing results in that the lower performance metrics however a primary objective established by NHTSA is modeling process does not perceptibly change the use, function, or utility of the vehicle under consideration, therefore this is not a viable alternative.

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### lished

,

e, a weight is generally expected to reduce fuel consumption by 3 to 4 percent. When appropr iate engine resizing is applied and vehicle perform

e also

he NPRM which, together with response received from P che, MEMA and CARB, agrees with these effectiveness values.

In preparation for this rule, rch st for confidential product plan and other CAFE related technical information from manufa

iven

2009. NHTSA reviewed the responses related to the subject of mass redu

ew tent), in other cases no signific or vehicle performance, such as 0 to 60 times, towing capacity; or power to weight ratio, or alternatively b) performance metrics that re main constant as a result of the weight reduction. This second alternative, with c onstant performance metrics, is accomp through the application of engine resizing (e.g. engines with smaller displacements which consume less fuel) that offsets the performan ce enhancing effects of the weight reduction which from a fuel consumption perspective is obviously the more preferable approach. Thus two fuel consumption effectiveness estimates relating to mass reduction are generally stated in reports and literat ure, one which assumes improved vehicle performance (i.e., the engine displacement is unchanged), and one which assumes constant performance (i.e., the engine is resized). For the improved performance cas 10 percent reduction in vehicle curb

ance is held constant, a 10 percent curb weight reduction results in a 6 to 7 percent fuel consumption savings. Both of th ese estimates are documented in literature and reports on the subject of mass reduction, including the 2002 NAS report, and ar supported by simulation work conducted by Ricardo, Inc. 192 

### , an internationally recognized consultant who, under contract , has assisted both EPA and NHTSA in technical and rulemaking re lated matters. Aluminum A ssociation also presented Ricardo’s study 193 

### in response to t

ors
in Ma 2009, NHTSA made a reque

cturers that produce light vehicles for sale in the U.S. 194 

### Not every manufacturer responded, and those that did in some case s either resubmitted materials previously provided to the agency or submitted truncated responses, which is understandable g the turmoil and uncertainty the industry wa s experiencing at that time. NHTSA also received responses for the latest request for product plans for this final rule which was sent out in September ction and the vehicle weight trends likely to occur in the MY 2012 – 2016 fleet. These responses didn’t show a consistent a pproach. In some cases manufacturers are indicating weight increases (due either to more stringent FMVSS requirements or n model plans that incorporate heavier platfo rms or more con ant change is noted either way, and in some cases the submissions are outdated incomplete (e.g., impacts of the Fiat and Ch rysler relationship and its effects on future product offerings). Although several OEMs have recently made public announcements

192 “Benefit Analysis: Use of Aluminum Structures in Conjunction with Alternative Powertrain Technologies in Automobiles,” Bull, M. Chavali, R., Mascarin, A., Aluminum Association Research Report, May 2008. Accessed on the Internet on April 30, 2009 at: http://www.autoaluminum.org/downloads/IBIS-Powertrain-Study.pdf
193 “Imp

acturers also made submissions to Congress and other governmental agencies where plans regarding future fleet planning, in terms of sales volumes and fleet configuration, were also discussed. act of Vehicle Weight Reduction on Fuel Economy for Various Vehicle Architectures”, ResearchReport, conducted by Ricardo Inc. for the Aluminum Association, 2008-04, Docket number: NHTSA2009-0059
194 This was in addition to similar requests made during the MY 2011 final rule and the 2008 NPRM that preceded it. The request also preceded the President’s May 19, 2009 announcement regarding fuel economy and green house gas standards. NHTSA notes that some manuf

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### indicating their intentions to ease the upcoming years, the confidential product plans, to the extent they are a suitable source for making

gh ’s curb

ses and the

s

e the specific reductions will be achieved (what techniques will be used, etc.), or what effect the changes will have on the vehicle

analysis.

,

s of

establish the future fleet composition, this included the manufacturer’s estimate for a future product’s fuel economy rating. Therefore, in these analyses, technol

s

s will not account for their potential effect on fuel consumption without some further revision, as discussed below.

In the MY 2 inal r technologies that resulted in a maximum 5 hicle curb weight. Materia les with

heaviest of the vehicles in the U.S. fleet repr esented the most safety neutral, or potentially decr light vehicle average fleet weights within

such a determination, do not appear to support this contention.

However one manufacturer did submit what appears to the agency to be a comprehensive response to the March request which does show significant curb weight decreases on the order of 350 to 550 pounds occurring within the timeframe. Althou the stated reductions are sizeable, representing some 5 to 10 percent of the vehicle weight, some notes about the information provide d are appropriate. First off, in all ca these larger reductions are being implemente d at product redesign cycles only, earliest of these occurs in the MY 2014 period. 195 

### Secondly, the affected vehicles are from various vehicle segments, including cars and trucks, which represent high sale volume and a sizeable portion of the manufacturer’s overall production. And lastly th information provided does not describe, in any detail, how

’s physical dimensions, utility, or performance (safety and otherwise) afterwards. So while this information does support the belief that meaningful weight reductions are possible, and that at least one OEM is inte nding to implement them, it does not contain some of the information needed for a more robust

To gain further insight, NHTSA briefly discussed plans for weight decreases on future products with a few vehicle manufacturers. Although discussion of the methods used to achieve the reductions, and their im pacts on dimensions and vehicle performance were not within the scope of the conversation s, the manufacturers did generally indicate their plans for decreasing fleet weights throughout the rulemaking period, with range 5 to 10 percent net curb weight decreases c onsidered potentially realizable by MY 2016. In past rulemakings, where confidential product plan information was, to the extent possible, used to

ogy changes such as weight reductio n would have theoretically been accounted for in the Volpe modeling process. In the current rule, where a baseline MY 2008 fleet i projected forward into a future adjusted fleet, planned techno logy changes such as reductions in vehicle weights, cannot be acc ounted for in this way, since there was no practical way of doing so. So to the extent mass reductions do occur in this rulemakings future fleet, the Volpe modeling proces

011 f ule NHTSA utilized three cumulative material substitution
pe rcent reduction in ve l substitution was intended to be the primary means by which the weight reductions would be achieved. The three tech nologies were only applied to vehic curb weights in excess of 5,000 pounds which effectively limited their applicability to large trucks and SUVs. This was done on the basis that weight reduction from the

195 The reductions might be best characterized as an objective for a new model platform that the company seeks to obtain.

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### t

SA

ass

urb eduction across all vehicle subclasses. This technology is available to the Volpe

lement methods, are envisioned for achieving the weight savings. It is anticipated that this could occur during

aint, which varies the level of reduction by subclass, results in lower levels of mass reduction in the smalle r (and lighter) vehicles , and larger levels

convers t tral rgest ntil

cle, which was what lead NHTSA to set the cycle time for the MS2 technology to redesign only, which is the final constraint used in the modeling process. Table V-35 below summarizes the mas ction , MS2, and the combined effects by each vehicle subclass they are applied to. When both MS1 and MS safety beneficial method of reducing vehicl e weight. Since only large trucks were impacted, where towing and hauling capability is required, NHTSA used a 3.5 percen fuel consumption reduction per 10 percent we ight decrease (i.e., no engine resizing was assumed). NHTSA has revised its approach for mass reduction in the current analysis, as is discussed in the following paragraphs.

In this rule, and in contrast to the 2011 rule, the Volpe model now applies two mass reduction technologies using a tiered a pproach. Mass reduction is intended to encompass a broader spectrum of methods for reducing vehicle mass, such as those discussed above, and those beyond material substit ution, and is intended to be applicable to all vehicle subclasses, regardless of curb weight. Additionally in this analysis NHT considers that vehicle performance metrics are maintained constant as a result of the m reductions, so appropriate levels of engine resizing are assumed.

The first of these technologies is MS1 whic h represents a 1.5 percent vehicle c weight mass r model from the start of the rulemaking period, MY 2012, and may be applied during both the refresh and the redesign cycl e time. For the level of mass reduction required, material substitution techniques, or other relatively easy to imp

the early MYs of the rulemaking period and at the cycle times.

The second mass reduction technology is MS2, which occurs subsequent to, and is cumulative to, the MS1 technology. Since MS2 requires more rigorous mass reduction, additional constraints are utilized in its application. MS2 involves mass reductions of 3.5 to 8.5 percent of curb weight dependent on which vehicle subclass it is applied to. This first constr

ely for the larger (and heavier) vehicles. This is intended to reflect, to the exten possible, the agency’s past practice of reducing vehicle weights in the most safety neu manner; smallest reductions in the smallest vehicles, largest reductions in the la vehicles. Secondly, the MS2 technology is made unavailable to the Volpe model u MY 2014 and thus constrained on the basis that the larger levels of mass reductions required, and the types of methods and techniques needed to achieve them, cannot realistically occur without sufficient leadti me for planning and impl ementation. In all likelihood these levels of mass reduction can only be achieved through a major redesign of the vehi

s redu s, as a percent of curb weight, for the MS1

2 are applied, overall mass reduction of 5 to 10 percent can occur, dependent on vehicle subclass. MEMA commented that "postponing additional mass reduction technologies until MY2014 not only hinder s incorporation of already developed technologies, but also delays the overarching goal of the revised standard". NHTSA encourages any new technology application to comply with the revised standard without hindering vehicle safety. It is the manufacturer's choice to apply the most cost benefit technologies. Applying a phase-in year of 2014 does not mean manufacturers cannot

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### to the th Technologies apply developed technology ahead of that. It just shows that NHTSA thinks that up to 10% mass reduction needs more research, development and integration work, therefore realistically more lead time.

Table V-35. Vehicle Mass (Weight) Reduction as a Percent of Curb Weight Due Application of the MS1, MS2, and th e Combination of Bo

Vehicle Class MS1 (%) Refresh/Redesign MS2 (%)* Redesign only Maximum Total Reduction (%) Subcompact PC 1.5 3.5 5.0 Compact PC 1.5 3.5 5.0 Midsize PC 1.5 6.0 7.5 Large PC 1.5 8.5 10.0 Subcompact Performanc e PC 1.5 3.5 5.0 Compact Performance PC 1.5 3.5 5.0 Midsize Performance PC 1.5 6.0 7.5 Large Performance PC 1.5 8.5 10.0 Small LT 1.5 6.0 7.5 Midsize LT 1.5 6.0 7.5 Large LT and Minivan 1.5 8.5 10.0 

### * - MS2 is unavailable until MY2014 For effectiveness, and as discussed above, NHTSA assumes in this rule that a 10 percent reduction in mass results in a 6.5 pe rcent reduction in fuel consumption for MS2 with appropriate engine downs izing (regardless of reduction technique used or the compounding factor achieved). This approach is intended to yield equivalent vehicle performance (i.e. 0-60 mph time, towing capacity, etc). Based on the Aluminum Association’s response to the NRPM, NHTSA modified the effectiveness for MS1. For MS1, because the amount of the mass reducti on is small, normally no engine downsizing is cons is 3.5 percent reduction in fuel consumption for a 10 percent reduction in mass. In developing costs fo

material substitution by only considering the direct idered for this amount of mass reduction and the appropriate effectiveness

r this rule NHTSA and EPA reviewed three studies of down-weighting/material substitution and the associated cost. The first study, the NAS report, estimated that vehicle weight could be reduced for approxima tely $1.50 per pound. (3-4% reductions in fuel consumption, without engine downsizing, from a 5% reduction in vehicle weight at a cost of $210-$350. This translates into $1.50 per pound, assuming a 3800 pound base vehicle and using the midpoint cost.) Additionally, Sierra Research estimated a 10% reduction, with compounding, could be accomplished for a cost of $1.01 per pound. Finally, MIT estimated that the weight of a vehicle could be reduced by 14%, with no compounding, for a cost of $1.36 per pound. Our final cost estimate is $1.32 per pound and is based on the average of the three referenced studies. Applying an ICM factor of 1.11 for a low complexity technology results in a compliance cost of $1.48 per pound. For the vehicle mass reduction technologies , neither volume-based nor time-based cost reductions are applied since many of the materials under consideration are commodity based and the build of materials is only loosely defined.

The Aluminum Association commented that NHTSA "significantly nderestimated the potential of u

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### compo ction". MEMA also comm ented that light materials can provide secondary mass saving anufacturers can achieve compounding through a systematica sign approach. But as stated before, the magnitude of
compo of

r

e compounding.

that the mass reduction technologies in this rule represent a realisti

B)



### ow drag brakes redu c brake pads on rotors when the brakes disc

up

applied, remains so th nent mass redu s. It is evident that m l rede unding effect varies signi ficantly among different studi es. NHTSA uses a total up to10 percent mass reduction in the Volpe model which includes mass compounding. The effect of mass compounding is also reflecte d in the cost of mass reduction due to the cost saving in the downsizing of some of the components. NHTSA’s cost estimate fo mass reduction also takes mass compounding into consideration by averaging the costs from the three studies mentioned above , in some of which mass compounding is considered. For future rulemaking, NHTSA intends to conduct further study of th magnitude of mass

Lastly, the phase-in cap for MS1 in this rule is 85 percent in MYs 2012 to 2015 and 100 percent in MY 2016, while for MS2 an 85 percent cap exists in MYs 2014 and 2015 followed by a 100 percent in MY 2016. Although a departure from the 2011 rulemaking, NHTSA believes c approach that effectively, and when overall application rates are considered, accurately emulates the weight reductions likely to occur in the U.S. light vehicle fleet within the rulemaking timeframe.

(27) 

### Low Drag Brakes (LD



### L ce the sliding friction of dis are not engaged because the brake pads are pulled away from the rotating either by mechanical or electric methods. While most passenger cars have already adopted this technology with the standardizati on of electronic brake control, there are indications that this technology is still avai lable for body-on-frame trucks and for some large passenger cars.

NHTSA’s MY 2011 CAFE final rule estimated the effectiveness of LDB to be to 1 percent, based on confidential manufa cturer data. NHTSA has reviewed this estimate and believes it to be applicable for this final rule.

NHTSA reviewed the cost estimates from the MY 2011 CAFE final rule and determined that these estimates remain applicable for this final rule. The agency adjusted the costs to apply the ICM indirect cost multiplier of 1.11, for a low complexity technology, instead of the 1.5 RPE factor used in the 2011 final rule. The compliance cost for LDB is therefore $63 for a MY 2012 vehicle, and since no cost learning is roughout the rulemaking timeframe.

T he phase-in cap for LDB in this rule is 85 percent in MYs 2012 to 2014, and 100 percent through the remainder of the rulemaking period. The Volpe model can apply this technology at a vehicle’s refresh or redesign years, and the technology is only applicable to the Large Car, Minivan, and Medium and Large Truck and SUVs since, as mentioned above, it is already largely utilized in most other subclasses.

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### accomp

ehicle. This is based on a cost of $1 per tire per vehicle, includi

e to assess any potential impacts on vehicle dynamics and braking characteristics,

vehicles du

heel



### ing (28) 

### Low Rolling Resistance Tires (ROLL)



### Tire rolling resistance is the frictional loss associated mainly with the energy dissipated in the deformation of the tires under load and thus influences fuel economy and CO 2 

### emissions. Other tire design characteristics ( 

### e.g. , materials, construction, and tread design) influence durability, traction (bot h wet and dry grip), vehicle handling, and ride comfort in addition to rolling resistance. A typical low rolling resistance tire’s attributes would include: increased tire inflation pressure, material changes, tire construction with less hysteresis, geometry changes ( 

### e.g. , reduced aspect ratios), and reduction in sidewall and tread deflection. These changes would generally be anied with additional changes to suspension tuning and/or suspension design. For performance vehicle classifications, due to the increased traction requirements for braking and handling which currently cannot be fully met with low rolling resistance designs, the Volpe model does not apply this technology.

NHTSA estimates a 1 to 2 percent increase in effectiveness with a 10 percent reduction in rolling resistance, which wa s based on the 2002 NAS report findings and consistent with the MY 2011 final rule estimate. NHTSA still believes that this NAS effectiveness estimate is valid for this final rule.

Based on the MY 2011 CAFE final rule and the 2006 NAS/NRC report, NHTSA has estimated the cost r low rolling resistance tires to be $6 per v fo as estimated by NA S/NRC 2006 report, which is $5 ng the spare tire. When applying the ICM low complexity markup factor of 1.11, this results in a compliance cost of $6 per vehicle for a MY 2012 vehicle. 196 

### Lower rolling resistance tires are widely available today however, due to the commodity based nature of the materials used in tire manufac turing, cost learning is not considered applicable.

The phase-in cap for the ROLL technology in this final rule is 85 percent in MYs 2012 to 2014, and 100 percent through the remainder of the rulemaking period. Du the need to the Volpe model can only apply this technology at a vehicle’s refresh or redesign cycle, and as noted above, the model does not apply the technology to the performance subclass e to suitability concerns.

(29) 

### Front or Secondary Axle Disconnect for Four-W Drive Systems (SAX) 

### Energy is required to continually drive the front, or secondary, axle in a four wheel drive system even when the system is not required during most operat conditions. This energy loss directly results in increased fuel consumption and CO 2 

### emissions. Many part-time four-wheel drive systems use some type of front axle

196 Note that the costs developed for low rolling resistance tires for this analysis do not include the increase

e nd to include the lifetime costs in the final analysis. in lifetime costs that would be expected at each tire replacement. Instead, the analysis includes only th upfront increase in costs. The agencies inte

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### disconn rmally

o-

front

ot aware of any manufacturer offerin

an

e MY

e lower and are estimated to be $84 for a MY 2016 vehicle (200

2014,

Drag Reduction (AERO)



### ct of its frontal area and drag coefficient. Reductions in these quantities can therefore reduce drag and lower the vehicle’s fuel consumption and CO 2 

### emissions. Although frontal areas tend to be relatively si milar within a vehicle class (mostly due to market-competitive size requirements), variatio ns in drag coefficient can be observed. ect to provide shift-on-the-fly capabilities. The front axle disconnect is no part of the front differential assembly. As part of a shift-on-the-fly four-wheel drive system, the front axle disconnect serves two basic purposes. First, in two-wheel-drive mode, it disengages the front axle from the front driveline so the front wheels do not turn the front driveline at road speed, saving wear and tear. Second, when shifting from tw to four-wheel drive "on the fly" (while m oving), the front axle disconnect couples the front axle to the 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 a front axle disconnect typically do not have either manual- or automatic-locking hubs. To isolate the front wheels from the rest of the driveline, front axle disconnects use a sliding sleeve to connect or disconnect an axle shaft from the front differential side gear . NHTSA is n g this technology in the U.S. today on unibody frame vehicles; however, it is possible this technology could be introduced by manufacturers within the rulemaking time period.

Based on confidential manufacturer data, the MY 2011 final rule estimated effectiveness improvement of 1 to 1.5 percent for the SAX technology and after thorough review. NHTSA finds this to be an accurate estimate for this final rule.

Regarding costs, NHTSA reviewed the incremental compliance cost from th 2011 final rule and concluded it remains accurate. However a new ICM factor of 1.11, for a low complexity technology, replaces the 1.5 RPE markup factor used previously. Thus, the compliance cost estimate for this rule is $87 for MY 2012 vehicles. As the SAX technology is readily available and in us e today, time based learning is considered applicable, hence the costs for later MYs will b 7$)

The phase-in cap for SAX in this final rule is 85 percent in MYs 2012 to and 100 percent throughout the remainder of the rulemaking period. Due to varying vehicle architecture designs, and thus the potential complexity associated with implementing these systems, the Volpe model can only apply this technology at a vehicle’s refresh or redesign years. SAX is applicable to all vehicle subclasses however an engineering constraint programmed wi thin the Volpe model’s programming code ensures the SAX technology is only applied to vehicles that have (true) four-wheel drive systems in the baseline vehicle (i.e., SAX is not applicable to all-wheel drive equipped vehicles).

(30) 

### Aerodynamic



### Many factors affect a vehicle’s aerodynamic drag and the resulting power required to move it through the air. While th ese factors change with air density and the square and cube of vehicle speed, respective ly, the overall drag effect is determined by the produ

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### Signific ented during a mic reductions, with a somewhat lower effective s, may be achieved through the use of

rev dev fascias, modified front air ar deck lips and underbody panels, and lower aerodynamic drag ex

at a flee o 0 t to ith a cav r ve equates to increm l reductions in fuel consump n of 2 to 3
s are g by confid al fore, N A continues to use le.

so estimated e from $60 to $116 which a 1 5. N A and re ed th 11 uld o t wer en the cost estimat this making is $48 wi ICM mar e fo w complexity lo v d wi crease uture O te logy is conside al ce subclasse re th lpe m e app is

e in cap for AERO his rule 00 pe roug rema t lem es of imp ts en ed i AE echn h or r cycle

d but e e fin e a



### ntified hnolo hat w t be lab e is rulemak hese technologie ile ider E and The e: el ic ve s, lean bur line direct injectio BD mog s I), and assi cha an ser s are likely to become available in small

olo estim by t encies) ith the more conve l techn ies lis eref alysis would preclude election, when e ting cost e ines ently lable i rope eve ese

e t oth eff and the stan s lfu in t ls. T er lo he research phase of de t. A an w ti dustry and system supplier re se o nd ld ailable, consider the e in rul y. ant changes to a vehicle’s aerodynamic performance may need to be implem redesign (e.g. changes in vehicle sh ape). However, shorter-term aerodyna

nes ised exterior components (typically at a model refresh in mid-cycle) and add-on ices that currently being applied. The la tter list would include revised front and rear dams and rear valances, addition of re terior mirro rs.

The MY 2011 final rule estimated th uction is attainable (w t averageeat for “highf 10 to 2 rfo percen

ma tal hicles aerodynamic drag red pe nce” described below) which enta tio percent for both cars and trucks. These n um ber enerally supported enti manufacturer data and public technical lit era ture and t here HTS this estimate for this final ru

The 2011 final rule al a rang used .5 RPE; the non-RPE costs were therefore $ 40 to $7 HTS EPA view e 20 costs and concluded the estimate sho be closer t he lo d of 2011 rulemaking range. Thus, the e used in rule ($43 thout markup), which includes a 1.11 kup valu r a lo techno gy. This compliance cost is for a MY 2012 ehicle an ll de in f years du e to the application of time-based learning. T he AER chno red to ready be in use on most performan s, therefo e Vo odel do s not ly th technology to performance vehicles. Th phase- in t final is 85 percent in MYs 2012 to 2014, and 1 rcent th h the inder of he ru aking period. As noted above, the typ r ovemen vision n the RO t ology are suitable for application at refres edesign

. e. Technologies considere not includ d in th al rul nalysis



### NHTSA and EPA have ide six tec gies t ill no avai le in th time frame considered under th ing. T s, wh cons ed, were not made available in the CAF OMEGA m odels. y ar ectr hicle camless valve actuation (CVA), n ga so n (L I), ho eneou charge compression ignition (HCC hydrauli electric st turbo rging d full ies c hybrids (HHV). While electric v numbers in the near future, the costs for t ehicl e h is techn gy (as ated he ag are non-competitive w ntion a their olog ted abov e. Th ore, the Volpe model an s

are curr stima the s of th rule. Lean Burn direct injection eng vehicles cannot be designed at this ti avai n Eu , how r, th

m in the United States with the current su o be b icien t he fue m he oth

eet NOx techno dard r leve ls gies listed are still in t velopmen
rog NHTS d EPA ill con nue to monitor the in s fo r p

us ss on the

re technol
em ng gies, a a it shou they become more av m for futu aki ctiv More details are described below.

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### i. Electric Vehicle



### ntense interest in Hyb icles a he dev men ybr vehicle battery and motor technology has he ed make Electric Vehicle technology more viable than it has ever been. Electric Vehicles (EVs) require much larger batteries than either HEVs or PHEVs, but the batteries must be of a high-energy and lower-power design to deliver an appropriate amount of power over the useful charge of the battery.These high-energy batteries are generally less expensive per kilowatt-hour than highpower batteries required for hybrids, but the size of the battery pack still incurs a considerable cost. Electric motor and power el ectronics designs are very similar to HEV and PHEV designs, but they must be larger, more powerful, and more robust since they provide the only motive power for the vehicle. On the other hand, the internal combustion engine, fuel system, and possibly the transmission can all be removed for significant weight, complexity and cost savings. While a few manufactures have released public statements indicating that they are planning on producing small volumes of electr ic vehicles within the rulemaking time frame, the agency believes that the applic ation of electric vehicles above and beyond these small volumes will not likely be feasible. Thus for purposes of this final rule, NHTSA has not included electric vehicles in its analysis.

ii. Camless Valve Actuation



### Camless valve actuation relies on electromechanical actuators instead of camshafts to open and close the cylinder valv es. When electromechanical actuators are used to replace cams and coupled with sensors and microprocessor controls, valve timing and lift can be optimized over all conditions. An engine valvetrain that operates independently of any mechanical means provides the increased flexibility for intake and exhaust timing and lift optimization. With it comes increased ability to vary valve overlap, the rapid response required to ch ange between combustion operating modes (such as HCCI and GDI), intake valve throttling, cylinder deactivation, and elimination of the camshafts (reduced friction and rotating mass). This level of control can enable even further incremental reductions in fuel consumption and. This technology has been under research for many decades and although progress is being made, NHTSA has not found evidence to support that the technology can be successfully implemented within the 2012 th rough 2016 timeframe of these regulations. Thus, NHTSA has not estimated cost or effectiveness for this technology at this time.

iii. Lean-Burn Gasoline Direct Injection Technology



### Direct injection, especially with diesel-like “spray-guided” injection systems, enables operation with excess air in a stratified or partially-stratified fuel-air mixture, as a way of reducing the amount of intake throttling. Also, with higher-pressure fuel injection systems, the fuel may be added late enough during the compression stroke so as to delay the onset of auto-ignition, even with higher engine compression ratios or with boosted s



### The recent i rid veh nd t elop t of H id lp

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### engine may achieve high engine thermal effi ncy, which approaches that of a diesel
eng lea Federal Tier 2 em in combustion chamber design and repositioning of ors wed f air/fu g and combustion efficiency. There is a s wall- jection ay guided injection, which improves injec recision targeting ards the sp ombin ith advanc n NO x 

### afte DI e ay e tually be ossibility in North Americ nt a ent is the avai ltra ess than a nic quireme r lean-bur DI engines eet ions standards, Since we do not believe that ULS gasoline will od app le to this final rule, the technology w t

e N , NHTS ed comments from MECA stating t at ine Direct Injection of e pote for lean operation. MECA commen so fuel r limits gasoline ppm avera 80 ppm ca re

Ox a er catalysts to be a v able NOx control strategy fo fuel ur th ploy direct fuel injecti chnology

nsistent with NHTSA’s assessment.

iv. H eou arge C ession I 

### Gasoline homogene e c ression ign e alt e engin ing mode that does no y on m n. Th inciples more close ligned with ch the compressed charge exceeds a temperature and on auto-ignition although it diffe diesel ing tha ing a dif n control ombustion nt. is much shorter in d with h er therma

ag its over ficiency f o main reasons:

he engine is op with gher comp ion ratio, with a short esult a h d e
ated ally e a
hav ease in eng e brake ef ncy (typica 25- greater than 35% a h en the HC erating ra 197


### em s are fa vorab ring HCC eration. L r peratures e to high d ilution) k engine-ou ission a ally bel er 2 le ithout aftertreatment d particulates are us nature of the premixed charge. intake pressure. Taken together, an optimiz ed “lean-burn” direct injection gasoline cie ine. European gasoline direct-injection engines have implemented stratified-charge n-burn GDI, although at higher NOx emissions levels than are allowed under U.S. issions standards. Fuel system improvements, changes the inject have allo or better el mixin currently hift from guided in to spr

tion p and tow ark plug, increasing lean combustion stab ility. C ed w es i rtreatment, lean-burn G ngines m ven a p a. NHTSA’s curre ssessm that lability of u -low sulfur (l 15 ppm sulfur) gasoline is EPA’s Tier 2 N key tech al re nt fo n G to m O x 

### emiss be available during the m el years licab as no applied in the NHTSA anal ysis. In response to th Gasol PRM fers th A receiv ntial h ts al stated the current EPA too hig sulfu dsorb for (30 i ge, p) a

r h to allow lean N efficient, gasoline lean-b n engines at em on te

. MECA’s comments are co

omogen 

### ous charg s Ch


### omp ompr 

### ition (HCCI), a gnition 

### lso referr d to as controlled auto-ignition (CA I), is an ernat e operat t rel a spark event to initiate co bustio e pr are ly a a diesel combustion cycle, in whi pressure necessary for sp taneous rs from by hav a homogenous fuel/air charg e rather n be fusio led c eve The subsequent combustion efficiency. event uration igh l

An HCCI engine has inhere nt advant es in all ef or tw



### • T erated a hi ress and er combustion dura tion, r ing in i gher thermo ynamic effici ncy, and



### • The engine can be oper virtu unthrottled, ven at light lo ds, Combined, these effects 28%) to e shown an t the hig incr d of in CI op ficie
nge. lly

Criteria pollutant peak in-cylinder ission (du very le du
eep I op t NOx em owe s to tem minimum – realistic ow Ti vels w – an low due to the homogeneo

197 “An HCCI Engine Powe Technical Paper No. 2004-01 r Pla rid icle,” Sun Thomas a . Gray, Jr., S

-093

nt for a Hyb

3, 2004. Veh , R., R. nd C AEDELIBERATIVE DOCUMENT – DO NOT RELEASE 

### 259

nherent difficulty in maintaining combustion stability without

knock I is di lt to cont quiring k from nsors a d eng ontrol lo o optimiz bustion t g, ering the t natu f operat onditions in a vehic ue er wh asoline I combustion is stable, t hie a natur lly-aspirated engine is somewhat limi ely y com application would n a “ between HCCI and spark ignition combustion modes, in which HCCI

for best e at ngine and spa tion wou loads and at s ty f dual-mode strategy h ready bee diesel HCCI engin n Europ and A bly the vensis D-Cat

light-duty “ mb diese , gaso CCI te logy was considered t l be in the ufa ers have e public s ents abou H to lig y pass vehicles, have sign nt ration prog imed oducing able produ ithin the n -10 d n as to fuel consumption and reduction in the li . Ba n confi al manufacturer information, it is believed that a gasoline HCCI / GDI dual-mode engine might achieve 10-12% reduction in fuel consumption, compared to a comparable SI engine. Despite its promise, application of HCCI in light duty vehicles is not yet ready for the market. It is not anticipated to be seen in volume for at least e next 5-10 years, which is concurrent with many manufacturers’ public estimates. As noted in MY 2011 CAFE final rule that the technology will not be available within the time frame considered based on a review of confidential product plan information.

v. Electric Assist Turbocharging



### The Alliance commented in NHTSA’s previous rulemaking that global development of electric assist turbocharging has not demonstrated the fuel efficiency effectiveness of a 12V EAT up to 2kW power levels since the 2004 NESCCAF study, and stated that it saw remote probability of its application over the next decade. Whilehybrid vehicles lower the incremental hardware requirements for higher-voltage, higherpower EAT systems, NHTSA believes that significant developmental work is required to demonstrate effective systems and that implementation in significant volumes will not occur in the 2012 to 2016 time frame considered in this rulemaking. Thus, this technology was not included in the final rule.

E. Cost and effectiveness tables 

### The tables representing the Volpe model input files for MY 2012 incremental technology costs by vehicle subclass are presen ted below. The tables have been divided into passenger cars, performance passenger cars, and light trucks to make them easier to read. Due to the i encountering engine , HCC fficu rol, re feedbac incylinder pressure se nd rapi ine c gic t e com imin especially consid ransient re o ing c seen le. D to the highly dilute conditio range of e ns und vable in ich g

a HCC he ted. ngine loads ac Because of this, it is lik mode” strategy that an mer cial operate i dualwould be utilized fficiency light e loads rk igni ld be used at higher idle. Thi pe o as al n employed in and the Nissan es i MK” co e ustion sia (nota ls). Toyota A

Until recently line H chno o stil research phase. However, most man ctur mad tatem t the viability of incorporating CCI in ht-dut enger and ifica vehicle demonst rams a at pr a vi ct w ext 5 years. There is widesprea opinio the CO 2 

### potential for HCCI terature sed o denti

th

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able V-36. Technology Incremental Cost Estimates, Passenger Cars



### VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE SUBCLASS - PASSENGER CAR

Su act Mids rge

bcomp Compact ize La

Car Car Car Car
Nominal Baselin e Engine (Fo t B r Cos asis) I 4 Inline V6 In e 4

lin n e lin 4 Low Friction Lubricants 3 3 3 3 Engine Friction Reduction EF 50 75 R 50 50 VVT - Coupled Cam Phasing (CCP) o n SOH C CC 45 90 PS 45 45 Discrete Variable Valve Lift (DVVL) o n SOHC DV S 142 205 VL 142 14 2 Cylinder D eactivation on SOHC D n.a. 56 EACS n.a. n.a. 0 - VVT - Intake Cam Phasing (ICP) I 45 90 CP 45 45 VVT - Dual Cam Phasing (DCP) DC 38 83 P 38 38 Discrete Variable Valve Lift (DVV L) DV D 142 205 on DOHC VL 142 142 Continuously Variable Valve Lift (CV VL) CV 277 509 VL 277 277 Cylinder De activation on DOHC DE n.a. 0 - 56 ACD n.a. n.a. Cylinder Deactivation on OHV D n.a. 170 EACO n.a. n.a. VVT - Co upled Cam Phasing (CCP ) o CC 45 45 n OHV PO 45 45 Discrete Variable Valve Lift (DVV L) D 142 - 56 on OHV VVLO 142 142 0 Conversion to DOHC with DCP CD 276 436 OHC 276 276 Stoichiometric Gasoline Direct Injecti on (GD I) SG 236 341 DI 236 236 Combustion Restart CBRST 118 118 118 11 8 Turbocha rging and Downsizing T 445 RBDS 445 445 325 Exhaust Gas Recirculation (EGR) Bo EG 144 144 ost RB 144 144
Conversion to Diesel following C BRS D 1,527 556 - 1,612 T SLC 1,527. 1,527 1,

Conversion to Diesel following T RBD D 938 088- 1,143 S SLT 938. 938 1,

6-Speed Manual/Improved Internal s 6M 250 250 AN 250 250 Improv ed Auto. Trans. Controls /Externals IA 60 60 60 60 TC Continuously Variable Transmis sion CVT 250 250 250 250 6/7/8-Spe ed Auto. Trans with Improved NAUTO 112 112 112 112 Internals Dual Clut ch or Automated Manual Tr ion ansmiss DCTAM (59) (5 9) (8) (8) Electric Power Steering EP 106 106 S 106 106 Improved Accessories IA 128 128 CC 128 128 12V Micro-Hybrid MH 342 367 EV 288 31 1 Belt mounted Integrated Starter Generat or BI 286 286 SG 286 286 Crank mounted Integrated Star ter Gen CI 3, 47 erator SG 2,791 3,107 319 3,5 Power Split Hybrid PSHEV 1,600 2,133 2,742 3,261 2-Mode Hybrid 2M n.a. n.a. HEV n.a. n.a. Plug-in Hybrid PH 0 16,21 n.a. EV 11,52 14,135 5 Mass Reduction (1.5%) M 1.5 1.5 S1 1.5 1.5 Mass Reduction (3.5 to 8.5%) M 1.5 1.5 S2 1.5 1.5

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### E able V-37. Technology Incremental Cost Estimates, Performance Passenger C



### VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICL SUBCLASS - PERFORMANCE PASSENGER CARS

Perf Pe orm. orm. Perfo rm. rform. Perf Sub rge comp. Com pact Midsize La Car Car C ar ar C Nominal Baseline Engine (For Cost Basis) In lin V6 V8 e 4 V6 Low Friction Lubrican ts LU 3 3 3 B 3 Engine Friction Reductio n EF 50 75 1 R 75 10 VVT - Coupled Cam Phasing (CCP) on SOHC CC 45 90 PS 90 90

Discrete Variable Valve Lift (DVVL) o n SOHC DV 142 205 3 VLS 205 29
Cylinder Deactivation on SO HC DE n.a. 0 - 56 56 ACS 0 - 56 0 – VVT - Intake Cam Phasing (ICP) ICP 45 90 90 90
VVT - Dual Cam Phasing (DCP) DC P 38 83 83 82

Discrete Variable Valve Lift (DVV L) on DOHC DV 142 205 3 VLD 205 29
Continuously Variable Valve CV 277 509 5 5 Lift (CVVL) VL 09 55
Cylinder Deactivation on DOHC DE n.a. 0 - 5 56 ACD 6 0 - 56 0 –
Cylinder Deactivation on OHV D E n.a. 170 0 ACO 170 19 VVT - Coupled Cam Phasing (CCP) on OHV CC 45 45 PO 45 45

Discrete Variable Valve Lif t DV 142 0 - 5 56 (DVVL) on OHV VLO 6 0 - 56 0 –

Conversion to DOHC with D CP CD 276 436 2 OHC 436 55 Stoichiometric Gasoline Direct Injection (GDI) SG 236 341 2 DI 341 55

Combustion Restart CBRST 118 118 18 118 1 Turbocharging and Downsi zing TR 44 325 3 19 BDS 5 25 9 Exhaust Gas Recirculation (EGR ) Boost EG 144 144 4 RB 144 14
Conversion to Diesel following CBRST DS 1,527 556 – 1,6 1,556 – 2,349 LC 1, 12 – 1,612 2,294

Conversion to D iesel following DS 938 8 – ,08 1,287 TRBDS LT 1,08 1,143 1 8 – 1,143 1,231 –
6-Speed Manual/Improved Internals 6M 250 250 0 AN 250 25
Improved Auto. Trans. Controls/E xternals IAT 60 60 C 60 60
Continuously Variab le CV 250 250 0 Transmission T 250 25
6/7/8-Speed Au to. Trans with NA 12 – 2 2 – 11 214 Improved Internals UTO 1 14 11 214 2 – 214 112 -
Dual Clutch or Automated DCTAM (8) (8) (8) (8)

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12V Micro-Hybr id MH 314 33 10 EV 7 372 4 Belt mounted I ntegrated Starter BIS 286 286 2 286 Generator G 86

Crank moun ted Integrated CISG 839 3,149 3 ,571 Starter Generator 2, ,335 3

Power Split Hybrid PS 3,661 4,018 5, ,723 HEV 287 6

2-Mode Hybrid 2M n.a. n.a. n.a. n.a. HEV
Plug-in Hybrid PH 14,98 16,79

. EV 7 2 19,265 n.a

Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 Low Rolling Resistance Tires ROLL n.a. n.a. n.a. n.a. Low Drag Brakes LDB n.a. n.a. n.a. 63 Secondary Axle Disconnect SAX 87 87 87 87
Aero Drag Reduction AERO n.a. n.a. n.a. n.a.

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able V-38. Technology Incrementa l Cost Estimates, Light Tru



### VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE SUBCLASS - LIGHT TRUCKS

Mini van Sm all Mi dsize Large
L T L

T L T LT
No V6 line 4 V6 minal Baseline En ne (For Cost Basis) gi n I V8 Lo w Friction Lubricants LUB 3 3 3 3 Engine Friction Redu c tion EFR 75 50 75 101 VVT - Coupled Cam Phasing (CCP) on SOHC CCP 90 5 90 S 4 90
Discrete Variable Valve Lift (DVVL) on SOHC DVV 0 4 2 LS 2 5 1 2 05 293
Cylinder Deactivation on SO HC DEA - 56 n.a - CS 0 . 0 56 0 – 56 VVT - Intake Cam Phasing (IC P) ICP 90 9 45 0 90 VVT - Dual Cam Phasing (DCP) DCP 83 8 38 3 83 Discrete Variable Valve Lift (DVVL) on DOHC DVV 0 4 2 LD 2 5 1 2 05 293
Continuously Variable Valve Lift C (CVVL) VV 50 7 5 L 9 2 7 09 555
Cylinder Deactivation on DO HC DEA n.a - CD 0 - 56 . 0 56 0 – 56 Cylinder Deactivation on OHV DEA CO 17 .a 1 0 n

. 70 190 VVT - Coupled Cam Phasing (CCP) on OHV CCP 5 5 45 O 4 4 45

Discrete Variable Valve Lift (DVVL) DVV - 5 4 - on OHV LO 0 6 1 2 0 56 0 – 56

Conversion to DOHC with DCP CDO 43 7 4 HC 6 2 6 36 552 Stoichiometric G asoline Direct SGDI 34 3 3 Injection (GDI) 1 2 6 41 391

Combustion Restart CBR 11 1 1 ST 8 1 8 18 118 Turbocharging and Downsizing TRBD 325 445 32 S 5 919 Exhaust Gas Recirculation (EGR) Boost EGRB 4 44 14 14 1 4 144
Conversion to Diesel following CBRST DSL 6 - 1,61 2,29 C 1,55 2 1,52 7 556 - 1, 1 ,612 4 – 2 ,349

Conversion to Diesel following TRBDS DSLT 088-1 38 1,23 287 1, ,143 9 1,088 – 1,143 1 – 1,

6-Spe ed Manual/Improved Inter nals 6MAN 250 250 250 250 Improved Auto. Trans. Controls/Externals IATC 0 0 60 6 6 60
Continuously Vari ab le Transmission CVT 25 5 2 0 2 0 50 n.a. 6/7/8-Speed Au Improved Intern to. Trans with NAU 11 1 als TO 2 1 2 112 - 214 112 - 214

Dual Clutch or Automated Manual Transmission DC T (8 ( AM ) (8 ) 8) (8)

Electric Power Stee ring EPS 10 6 106 6 10 106 Improved Accessories IACC 128 128 128 n.a. 12V Micro-Hybrid MHEV 367 325 376 n.a.DELIBERATIVE DOCUMENT – DO NOT RELEASE 

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ntegrated Starter BISG 286 286 28 Belt mounted I Generator 6 n.a.
Crank moun ted Integrated Starter CISG 3,547 3,141 3,611 Generator 5,124

Power Split Hybrid PSH 3,261 2,377 3,46 EV 2 n.a.

2-Mode Hybrid 2MH n.a. 3,661 5,90 ,004 EV 4,887 - 4 ,989 2 – 6
Plug-in Hybrid PHE n.a ,7 n V . 14 21 .a. n.a. Mass Reduction (1 .5%) MS1 1.5 1.5 1.5 1.5 Mass Reduction (3.5 to 8.5%) MS2 5 1.5 1.5 1.5 1. Low Rolling Resistance Tires ROLL 6 6 6 6 Low Drag Brakes LDB 63 n.a. 63 63 Secondary Axle Disconnect SAX 87 87 87 87 Aero Drag Reduction AERO 48 48 48 48



### The tables representing the Volpe mode l input files for incremental technology effectiveness values by vehicle subclass are presented below. The tables have been divided into passenger cars, performance passenger cars, and light trucks to make them easier to read.

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VEHI able V-39. Technology Incremental Effectiveness Estimates, Passenger Cars

CLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION (-%) BY VEHICLE SUBCLASS - PASSENGER CAR

Subc C ompact ompact Midsize Large
Car r Car Car Ca 

### Nominal Baseline En gine (For Cost Bas is) Inline I I e 4 4 n 4 line nlin V6
Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 Engine Friction Reduction EFR 1.0 0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2. VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0
Cylinder Deactivation on SOHC DEAC n S .a. n.a. n.a. 2.5 - 3.0 VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 VVT - Dual Cam Phasing (DCP) D CP 0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3. Discrete Variable Valve Lift (DVVL) on DOHC D VVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 Continuously Variable Valve Lift (CVVL) CVVL .5 1. 5 1 - 3.5 5 - 3. 1.5 - 3.5 1.5 - 3.5
Cylinder Deactivation on DOHC DEAC n D .a. n.a. n.a. 0 - 0.5 Cylinder Deactivation on OHV DEAC n O .a. n.a. n.a. 3.9 - 5.5 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 1 .0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5
Discrete Variable Valve Lift (DV VL) DVVL .5 on OHV O 0 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5

Conversion to DOHC with DCP CDOH 1.0 C - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 Stoichiometric Gasol ine Direct SGDI Injection (GDI) 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Combustion Restart CBRS T 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 Turbocharging and Downsizing TRBD 4.2 S - 4.8 4 .2 - 4.8 4.2 - 4 .8 1.8 - 1 .9 Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 3 .0 .0 4.0 .5 - 4.0 3.5 - 4 3.5 - 4
Conversion to Diesel following CBRST DSLC 3.5 – 1 7 1 13.9 3.5 – 13.9 13.5 – 13.9 10.8 - 11. Conversion to Diesel following D TRBDS SLT 5.3 9 – 6.9 5.3 – 6.9 5.3 – 6.9 5.3 - 6.
6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 Improved Auto. Trans. IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 Controls/Externals Continuously V ariable Transmission CVT 0 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 0.7 - 2. 6/7/8-Speed Auto. Trans with NAUT 1.4 4 Improved Internals O - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.

Dual Clutch or Auto mated Manual CTA .5 4.1 Transmission D M 5 – 7.5 5.5 – 7.5 2.7 - 4.1 2.7 -
Electric Power Steering EPS 1.0 .0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2 Improved Accessories IACC 1.0 .0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2 12V Micro-Hybrid MHEV - 2 .0 3.5 2.0 3.0 .0 - 3.0 2.0 - 3 2.5 - Belt mounted Integr ated Starter BISG 4.0 - 4 .0 5 6.0 .0 - 6.0 4.0 - 6 3.5 - 5.

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Generator Crank mounted In Generator tegrated Starter CISG 8.6 - 8 .9 8.9 8.9 .6 - 8.9 8.6 - 8 8.7 -

Power Split Hybrid PSHE 6.3 - 1 6.3 .4 6 .4 - 4 V 2.4 - 12 .3 - 12 6.3 12. 2-Mode Hybrid 2MHE n V .a. n.a. n.a. n.a. Plug-in Hybrid PHEV 45.2 4 - 47.7 5.2 - 47.7 45.2 - 47.7 n.a. Mass Reduction (1.5% ) MS1 0.5 0.5 0.5 0.5 Mass Reduction (3.5 to 8.5%) MS2 2.7 2.7 4.4 6.0 Low Rolling Resistance Tires ROLL 0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.
Low Dr ag Brakes LDB n.a. n.a. n.a. 0.5 - 1.0 Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Aero Drag Reduction AERO 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

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### VEHICL (-%) BY VEHIC R Table V-40. Technology Incremen tal Effectiveness Estimates, Performance Cars

E TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION LE SUBCLASS - PE N RFORMAN CE PASSE GER CA

Perfo rm. . P Perform Perform. erform. Sub comp t . Compac Midsize Large Car Car Car Car Nominal Baseline Engine (For Cost Basis) ne 4 Inli V6 V6 V8 Low F LUB riction Lubricants 0.5 0.5 0.5 0.5 Engin EFR - 2.0 2.0 1 1.0 e Friction Reduction 1.0 1.0 - .0 - 2.0 - 2.0 VVT SOHC CCPS 3.0 1. - Coupled Cam Phasing (CCP) on 1.0 - 1.0 - 3.0 1.0 - 3.0 0 - 3.0

Discre SOHC DVVLS - 3.0 .0 1.0 te Variable Valve Lift (DVVL) on 1.0 1.0 - 3 1.0 - 3.0 - 3.0
Cylind DEACS .a. 2. er Deactivation on SOHC n 2.5 - 3.0 2. .0 5 - 3 5 - 3.0
VVT ICP - 2.0 1.0 - Intake Cam Phasing (ICP) 1.0 1.0 - 2.0 1 .0 .0 - 2 - 2.0
VVT ) DCP 0 .0 2.0 - Dual Cam Phasing (DCP 2.0 - 3. 2.0 - 3 2.0 - 3.0 - 3.0 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0

Contin uously Variable Valve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 Cylind er Deactivation on DOHC DEACD n.a. 0.0 - 0.5 0.0 - 0.5 0.0 - 0.5 Cylinder D eactivation on OHV DEACO n.a. 3.9 - 5.5 3.9 - 5.5 3.9 - 5.5 VVT - Cou pled Cam Phasing (CCP) on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Discrete OHV LO 0. Variable Valve Lift (DVVL) on DVV 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 5 - 2.5

Conve HC - 2.5 - 2.5 1 .5 1.0 rsion to DOHC with DCP CDO 1.0 1.0 .0 - 2 - 2.5 Stoich (GDI) SGDI 0 iometric Gasoline Direct Injection 2.0 - 3. 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Combu RST 2.5 .5 5 2 stion Restart CB 2.0 - 2.0 - 2 2.0 - 2. .0 - 2.5 Turboc TRBDS 4.8 .9 9 1. harging and Downsizing 4.2 - 1.8 - 1 1.8 - 1. 8 - 1.9 Exhau EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 st Gas Recirculation (EGR) Boost

Conve BRST DSLC 5 – 13.9 - 11.7 11.7 11.7 rsion to Diesel following C 13. 10.8 10.8 - 10.8 -

Conve DSLT .9 .9 9 5. rsion to Diesel following TRBDS 5.3 – 6 5.3 - 6 5.3 - 6. 3 - 6.9 6-Spee 6MAN 5 d Manual/Improved Internals 0. 0.5 0.5 0.5 Improv ternals IATC - 2.5 .5 5 1. ed Auto. Trans. Controls/Ex 1.5 1.5 - 2 1.5 - 2. 5 - 2.5 Contin sion CVT - 2.0 0 n uously Variable Transmis 0.7 0.7 - 2.0 .7 - 2.0 .a. 6/7/8-S proved Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 peed Auto. Trans with Im

Dual Clu Transm tch or Automated Manual ission DCTAM 2.7 - 4.1 2.7 - 4.1 2.7 - 4.1 2.7 - 4.1
Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 12V M icro-Hybrid MHEV 2.0 - 3.0 2.5 - 3.5 2.5 - 3.5 3.0 - 4.0 Belt mounted Integrated Starter Generator BISG 4.0 - 6.0 3.5 - 5.5 3.5 - 5.5 3.0 - 5.0 Crank mounted Integrated Starter Generator CISG 8.6 - 8.9 8.7 - 8.9 8.7 - 8.9 8.7 - 8.9
Power Split Hybrid PSHEV 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4

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Plug-in Hybrid PHEV 47.7 47.7 47.7 n.a. 45.2 - 45.2 - 45.2 -

Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 Mass Reduction (3 6.0 .5 to 8.5%) MS2 2.7 2.7 4.4
L R .a. n.a.

ow Rolling Resistance Tires OLL n n.a. n.a. Low Drag Brakes LDB n.a. 0.5 - 1.0 n.a. n.a. Secondary A xle Disconnect SA .0 - 1. .5 1.0 - X 1 5 1.0 - 1 1.0 - 1.5 1.5 Aero Drag Reduction A n.a. n. ERO n.a. n.a. a.

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VEHICL LE SS R ble V-41. Technology Incremental Ef fectiveness Estimates, Light Trucks

E TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION (-%) BY VEHIC SUBCLA - LIGHT T UCKS

Minivan Smal l Mi Large dsize
LT LT LT LT Nominal Baseline Engine (For Cost Ba sis) V6 V8 Inline 4 V6 Low Friction Lubricants L 0.5 0.5 UB 0.5 0.5 Engine Friction Reduction EF .0 - 2.0 .0 1 0 - 2 R 1 1.0 - 2 .0 - 2.0 1. .0 VVT - Coupled Cam Phasi ng (CCP) on CCPS - 3 0 - 3 SOHC 1.0 .0 1.0 - 3.0 1.0 - 3.0 1. .0 Discrete V ariable Valve Lift (DVVL) on D - 3 0 - 3.0 SOHC VVLS 1.0 .0 1.0 - 3.0 1.0 - 3.0 1. Cylinder Deactivation on SO HC DEA 2.5 - 3 5 - 3 CS .0 n.a. 2.5 - 3.0 2. .0 VVT - Intake Cam Phasin g (ICP) ICP 1.0 - 2 1.0 1.0 - 2.0 .0 1.0 - 2.0 - 2.0 VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 Continuously Variable Valve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 Cylinder Deactivation on DOHC DEACD 0 - 0.5 n.a. 0.0 - 0.5 0.0 - 0.5 Cylinder Deactivation on OHV DEACO 3.9 - 5.5 n.a. 3.9 - 5.5 3.9 - 5.5 VVT - Coupled Cam Phasing (CCP) on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 Discrete Variable Valve Lift (DVVL) on OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5

Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 Stoichiometric Gasoline Direct Injection (GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 Turbocharging and Downsizing TRBDS 1.8 - 1.9 4.2 - 4.8 1.8 - 1.9 1.8 - 1.9 Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0
Conversion to Diesel following CBRST DSLC 10.8 - 11.7 13.5 – 13.9. 10.8 - 11.7 10.8 - 11.7 Conversion to Diesel following TRBDS DSLT 5.3 - 6.9 n.a. 5.3 - 6.9 5.3 - 6.9 6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 Improved Auto. Trans. Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 n.a. 6/7/8-Speed Auto. Trans with Improved Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4

Dual Clutch or Automated Manual Transmission DCTAM 2.7 - 4.1 2.7 -4.1 2.7 - 4.1 2.7 - 4.1
Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 n.a. 12V Micro-Hybrid MHEV 2.5 - 3.5 2.0 - 3.0 2.5 - 3.5 n.a. Belt mounted Integrated Starter Generator BISG 3.5 - 5.5 4.0 - 6.0 3.5 - 5.5 n.a.
Crank mounted Integrated Starter Generator CISG 8.7 - 8.9 8.6 - 8.9 8.7 - 8.9 14.1 - 16.3 Power Split Hybrid PSHEV 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 n.a.

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Plug-in Hybrid PHEV 45 4 n.a. n n.a. .2 - 7.7 .a.

Mass Reduction (1.5%) MS1 0 0.5 0.5 0.5 .5 Mass Reduction (3.5 to 8.5%) MS2 4 6. 6.0 4.4 .4 0 Low Rolling Resistance Tires ROLL 1. 1.0 1.0 1.0 0 - 2.0 - 2.0 - 2.0 - 2.0 Low Drag Brakes LDB 0.5 n. 0.5 0.5 - - 1.0 a. - 1.0 1.0 Secondary Axle Disconnect SAX 1.0 1.0 1.0 1.0 - - 1.5 - 1.5 - 1.5 1.5 Aero Drag Reduction AERO 2.0 2.0 2.0 2.0 - - 3.0 - 3.0 - 3.0 3.0



### for a ima (ac ated d be The hav di and light to m them er to .

oximate Net (Accu ted) nolo sts, ger C

HICL BY V LE CL TO K nded t est $1 

### The tables representing the Volpe model inpu technology costs by vehicle subclass are presente t files pprox te net cumul ) low. tables e been vided into passenger cars, performance passenger cars, trucks ake easi read

Table V-42. Appr mula Tech gy Co Passen ars

COSTS PER VE APPROXIMATE ICM NET TECHNOLOGIE E ($) (Rou EHIC

o near ASS 00) EY S
Final technology (As compared to baseline vehicle prior S
to techn ology application) ubcom Car Com C M pact pact

ar idsize Car Large Car Stoichiometric Gasoline Direct In jection 47 4 716 1 (SGDI) 7 77 477 - 77

Turbocharging and Downsizing (TRBDS) 1,03 1,0 1 9 39 ,039 1,158 - 1,213 Diesel Engine (DSLT/DSLC) 2,12 2,1 2 1 21 ,121 2,445
Dual Clutch or Automated Manual Transmission (DCTAM) 113 11 3 164 164
Crankshaft Integrated Starter Generator (CISG) 3,59 3, 4 9 939 ,180 4,434
Power Split Hybrid (PSHEV) 5,509 6,381 7,233 8,005 2-Mo de Hybrid (2MHEV) n.a n

. .a. n.a. n.a. Plug-in Hybrid (PHEV) 15,429 18,384 20,705 n.a.

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### e Net (Accu ted) nolo sts, e Passe Cars

NET COSTS PER VEH L BY V LE CLAS TO KEY TECHNOLOGIES (Rounded to nearest $100) 

### Table V-43. Approximat mula Tech gy Co Performanc nger

APPROXIMATE ICM IC E ($) EHIC S

Final technology (As compared to baseline vehicle prior to technology application) Subcompact Perf Car Compact Perf Car Midsize Perf Car Large Perf Car Stoichiometric Gasoline Direct Injection (SGDI) 477 716 - 771 716 - 771 878 - 934

Turbocharging and Downsizing (TRBDS) 1,039 1,158 - 1,213 1,158 - 1,213 1,914 - 1,970 Diesel Engine (DSLT/DSLC) 2,121 2,445 2,445 3,345 Dual Clutch or Automated Manual Transmission (DCTAM) 164 - 266 164 - 266 164 - 266 164 - 266
Crankshaft Integrated Starter Generator (CISG) 3,673 4,007 4,227 4,500
Power Split Hybrid (PSHEV) 7,645 8,335 9,824 11,534 2-Mode Hybrid (2MHEV) n.a. n.a. n.a. n.a. Plug-in Hybrid (PHEV) 18,970 21,109 23,802 n.a.



### Table V-44. Approximate Net (Accumulate d) Technology Costs, Light Trucks

APPROXIMATE ICM NET COSTS PER VEHICLE ($) BY VEHICLE CLASS TO KEY TECHNOLOGIES (Rounded to nearest $100)
Final technology (As compared to baseline vehicle prior to technology application) Minivan LT Small LT Midsize LT Large LT Stoichiometric Gasoline Direct Injection (SGDI) 716 - 771 477 716 - 771 878 - 934

Turbocharging and Downsizing (TRBDS) 1,158 - 1,213 1,039 1,158 - 1,213 1,914 - 1,970 Diesel Engine (DSLT/DSLC) 2,445 2,121 2,445 3,345
Dual Clutch or Automated Manual Transmission (DCTAM) 164 164 164 - 266 164 - 266
Crankshaft Integrated Starter Generator (CISG) 4,434 3,986 4,507 5,230
Power Split Hybrid (PSHEV) 8,005 6,673 8,279 n.a. 2-Mode Hybrid (2MHEV) n.a. 7,810 9,660 11,398 Plug-in Hybrid (PHEV) n.a. 18,878 n.a. n.a.



### The tables representing the Volpe model inpu t files for approximate net (accumulated) technology effectiveness values by vehicle subclass are presented below. The tables have been divided into passenger cars, performance passenger cars, and light trucks to make them easier to read.

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### Approximate Net Technology Effectiveness, Passenger Cars

MATE NET EFFECTIVENESS ESTIMATES DU ) PE ICL BY CLASS TO KEYTECHNOLOG ound eare ) 

### Table V-45.

APPROXI (FC RE CTION R VEH E (-%) VEHICLE IES (R ed to n st 0.5%

Final technology (As compared to baseli ne vehicle prior Subcompact

r omp Car M rge Car to technology application) Ca C act idsize Car La

Stoichiometric Gasoline Direct Injection (SGDI) 5.4 - 11.0 5. 4 - 11 5.4 0 - 13.7 .0 - 11. 7.8 Turbocharging and Downsizing (TRBD S) 11.2 - 17.4 1.2 - 1 11. .4 - 17.4 1 7.4 2 - 17 11.2 Diesel Engine (DSLT/DSLC) 20.2 - 24.9 .2 - 2 20 9 - 24. 20 4.9 .2 - 24. 20.2 9 Dual Clutch or Automated Manual Transmission (DCTAM) 8.2 - 1 2.9 .2 - 1 5 - 9.7 8 2.9 .5 - 9.7 5.5 Crankshaft Integrated Starter Generator (CISG) 16.0 - 20.0 6.0 - 2 16. .0 - 20.0 1 0.0 0 - 20 16.0 Power Split Hybrid (PSHEV) 23.0 - 33.0 3.0 - 3 23. .0 - 33.0 2 3.0 0 - 33 23.0 2-Mode Hybrid (2MHEV) n.a. n.a. n.a. n.a. Plug-in Hybrid (PHEV) 55.0 - 60.0 5.0 - 6 55. .0 n.a. 5 0.0 0 - 60



### et T logy e Performance Passe Cars

ESTIMATES EDUC ) PER ICLE BY TECHNOLOG Round neares ) 

### Table V-46. Approximate N echno Effectiv ness, nger

APPROXIMATE NET EFFECTIVENESS (FC R TION VEH (-%) VEHICLE CLASS TO KEY IES ( ed to t 0.5%

Final technology (As compared to baseline vehicle prior to technology application) Subcompact Perf Car pa C M Pe r arge rf Car Com Perf ct

ar idsize rf Ca L Pe Stoichiometric Gasoline Direct Injection (SGDI) 5. 4 - 11.0 .8 - 1 7.8 7 - 13.7 7 3.7 - 13. 7.8 Turbocharging and Downsizing (TRBDS) 11.2 - 17.4 .2 - 1 11 4 - 17. 11 7.4 .2 - 17. 11.2 4 Diesel Engine (DSLT/DSLC) 20.2 - 24.9 20.2 - 24.9 20.2 - 24.9 20.2 - 24.9 Dual Clutch or Automated Manual Transmission (DCTAM) 5.5 - 9.7 5.5 - 9 5. 9.7 .7 5 - 9.7 5.5 - Crankshaft Integrated Starter Generator (CISG) 16.0 - 20.0 .0 - 2 16 0 - 20. 16 0.0 .0 - 20. 16.0 0 Power Split Hybrid (PSH V E ) 23.0 - 33.0 .0 - 3 23 0 - 33. 23 3.0 .0 - 33. 23.0 0 2-Mode Hybrid (2MHEV) n.a. n.a. .a. n.a. n Plug-in Hybrid (PHEV ) 55.0 - 60.0 5.0 - 6 55. .0 n.a. 5 0.0 0 - 60

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### et Techno lo ffecti c

T EFFECTIVENESS ESTIMATES EDU ) PE ICL BY LE CLASS TO KEYTECHNOLOG ound eare ) 

### Table V-47. Approximate N gy E veness, Light Tru ks

APPROXIMATE NE VEHIC (FC R IES (R CTION ed to n R VEH st 0.5% E (-%)

Final technology (As compared to baseline vehicle prior to technology application) Minivan LT Small LT Midsize LT Large LT Stoichiometric Gasoline Direct Injection (SGDI) 7.8 - 13.7 5.4 - 11.0 7.8 - 13.7 7.8 - 13.7
Turbocharging and Downsizing (TRBDS) 11.2 - 17.4 11.2 - 17.4 11.2 - 17.4 11.2 - 17.4 Diesel Engine (DSLT/DSLC) 20.2 - 24.9 20.2 - 24.9 20.2 - 24.9 20.2 - 24.9
Dual Clutch or Automated Manual Transmission (DCTAM) 5.5 - 9.7 5.5 - 9.7 5.5 - 9.7 5.5 - 9.7
Crankshaft Integrated Starter Generator (CISG) 16.0 - 20.0 16.0 - 20.0 16.0 - 20.0 15.0 - 18.0
Power Split Hybrid (PSHEV) 23.0 - 33.0 23.0 - 33.0 23.0 - 33.0 n.a. 2-Mode Hybrid (2MHEV) n.a. 23.0 - 33.0 23.0 - 33.0 23.0 - 33.0 Plug-in Hybrid (PHEV) n.a. 55.0 - 60.0 n.a. n.a.



### C. Penetration of Technologies by Alternative

Table V-48 shows the penetration of technologies by alternative for passenger cars and Table V-46 shows the penetration of technologies for light trucks for the alternatives. These tables are for the whole fleet combined, not by specific manufacturers. The application rate only includes technologies that the model applied. The penetration rate includes technologies that the model applies and technologies that were already present in the base fleet/base vehicle. They allow the reader to see the progression of technologies used as the alternatives get stricter. Table V-48. Penetration Rate of New Technologies to Passenger Cars, by Alternative

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### Preferred Alternative

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 76% 78% 78% 78% 77% Engine Friction Reduction 28% 50% 55% 58% 66% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 6% 6% 8% 10% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 8% 9% 10% Cylinder Deactivation on SOHC 1% 1% 1% 0% 1% VVT - Intake Cam Phasing (ICP) 35% 33% 34% 34% 27% VVT - Dual Cam Phasing (DCP) 41% 45% 45% 46% 53% Discrete Variable Valve Lift (DVVL) on DOHC 17% 27% 30% 31% 37% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 3% Cylinder Deactivation on OHV 1% 2% 2% 2% 2% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 5% 5% 5% Discrete Variable Valve Lift (DVVL) on OHV 0% 1% 1% 3% 3% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 18% 26% 31% 35% 38% Combustion Restart 0% 0% 2% 5% 11% Turbocharging and Downsizing 13% 20% 21% 25% 27% Exhaust Gas Recirculation (EGR) Boost 0% 8% 10% 14% 17% Conversion to Diesel following TRBDS 2% 2% 2% 2% 2% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 3% 3% 3% Improved Auto. Trans. Controls/Externals 11% 13% 13% 10% 7% Continuously Variable Transmission 11% 11% 11% 11% 11% 6/7/8-Speed Auto. Trans with Improved Internals 14% 9% 7% 4% 3% Dual Clutch or Automated Manual Transmission 24% 38% 48% 61% 69% Electric Power Steering 40% 54% 57% 59% 72% Improved Accessories 36% 43% 46% 50% 61% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 5% 12% 19% 25% 25% Crank mounted Integrated Starter Generator 4% 3% 3% 3% 3% Power Split Hybrid 5% 5% 6% 5% 5% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 19% 26% 33% 39% 47% Mass Reduction (3.5% to 8.5%) 0% 0% 17% 31% 40% Low Rolling Resistance Tires 50% 60% 66% 75% 78% Low Drag Brakes 8% 9% 11% 11% 13%

### 276

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### 3% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 65% 68% 66% 74% 74% Engine Friction Reduction 26% 44% 46% 48% 57% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 5% 5% 6% 7% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 8% 9% 9% Cylinder Deactivation on SOHC 1% 1% 1% 1% 1% VVT - Intake Cam Phasing (ICP) 39% 40% 40% 42% 34% VVT - Dual Cam Phasing (DCP) 38% 40% 40% 40% 47% Discrete Variable Valve Lift (DVVL) on DOHC 16% 24% 27% 28% 32% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 1% Cylinder Deactivation on OHV 1% 2% 2% 2% 2% VVT - Coupled Cam Phasing (CCP) on OHV 5% 4% 4% 4% 4% Discrete Variable Valve Lift (DVVL) on OHV 0% 0% 0% 0% 0% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 13% 13% 15% 17% 19% Combustion Restart 0% 0% 2% 5% 7% Turbocharging and Downsizing 7% 8% 10% 11% 14% Exhaust Gas Recirculation (EGR) Boost 0% 2% 4% 5% 9% Conversion to Diesel following TRBDS 1% 1% 1% 1% 1% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 2% 2% 2% 2% 2% Improved Auto. Trans. Controls/Externals 10% 11% 8% 5% 5% Continuously Variable Transmission 11% 11% 11% 11% 11% 6/7/8-Speed Auto. Trans with Improved Internals 16% 11% 1 0% 7% 8% Dual Clutch or Automated Manual Transmission 15% 28% 34% 42% 47% Electric Power Steering 39% 49% 52% 55% 63% Improved Accessories 30% 30% 31% 35% 39% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 3% 5% 7% 10% 10% Crank mounted Integrated Starter Generator 1% 1% 1% 1% 1% Power Split Hybrid 4% 4% 4% 4% 4% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 10% 10% 17% 22% 32% Mass Reduction (3.5% to 8.5%) 0% 0% 14% 23% 32% Low Rolling Resistance Tires 47% 55% 57% 59% 73% Low Drag Brakes 8% 8% 9% 9% 9%

### 278

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### 279

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### 4% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 65% 67% 74% 73% 74% Engine Friction Reduction 26% 45% 51% 56% 66% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 6% 6% 8% 10% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 8% 9% 9% Cylinder Deactivation on SOHC 1% 1% 1% 0% 0% VVT - Intake Cam Phasing (ICP) 39% 40% 40% 37% 29% VVT - Dual Cam Phasing (DCP) 38% 39% 40% 44% 53% Discrete Variable Valve Lift (DVVL) on DOHC 15% 24% 27% 27% 32% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 3% Cylinder Deactivation on OHV 1% 2% 2% 2% 2% VVT - Coupled Cam Phasing (CCP) on OHV 5% 4% 4% 4% 4% Discrete Variable Valve Lift (DVVL) on OHV 0% 0% 1% 2% 2% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 17% 22% 27% 31% 36% Combustion Restart 0% 0% 3% 6% 15% Turbocharging and Downsizing 9% 12% 15% 19% 21% Exhaust Gas Recirculation (EGR) Boost 0% 4% 7% 11% 15% Conversion to Diesel following TRBDS 1% 1% 1% 1% 1% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 3% 3% 3% Improved Auto. Trans. Controls/Externals 10% 12% 14% 1 1% 11% Continuously Variable Transmission 11% 11% 11% 11% 11% 6/7/8-Speed Auto. Trans with Improved Internals 19% 15% 1 1 6% 7% 16% Dual Clutch or Automated Manual Transmission 15% 28% 35% 45% 52% Electric Power Steering 39% 55% 62% 64% 79% Improved Accessories 32% 39% 43% 50% 57% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 5% 12% 18% 24% 25% Crank mounted Integrated Starter Generator 1% 1% 1% 1% 1% Power Split Hybrid 4% 4% 4% 4% 4% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 18% 17% 25% 34% 40% Mass Reduction (3.5% to 8.5%) 0% 0% 18% 32% 40% Low Rolling Resistance Tires 48% 58% 66% 74% 88% Low Drag Brakes 8% 9% 11% 11% 13%

### 280

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### 281

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### 5% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 65% 75% 74% 77% 76% Engine Friction Reduction 26% 47% 58% 64% 68% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 6% 9% 10% 10% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 9% 10% 10% Cylinder Deactivation on SOHC 1% 1% 1% 0% 0% VVT - Intake Cam Phasing (ICP) 39% 36% 31% 27% 24% VVT - Dual Cam Phasing (DCP) 38% 41% 47% 51% 53% Discrete Variable Valve Lift (DVVL) on DOHC 18% 30% 34% 35% 38% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 1% Cylinder Deactivation on OHV 1% 2% 2% 2% 2% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 5% 5% 5% Discrete Variable Valve Lift (DVVL) on OHV 0% 0% 1% 2% 2% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 20% 32% 39% 45% 50% Combustion Restart 0% 0% 4% 9% 17% Turbocharging and Downsizing 14% 24% 30% 34% 34% Exhaust Gas Recirculation (EGR) Boost 0% 12% 18% 22% 28% Conversion to Diesel following TRBDS 1% 1% 2% 3% 5% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 3% 4% 4% Improved Auto. Trans. Controls/Externals 10% 13% 14% 9% 2% Continuously Variable Transmission 11% 11% 11% 11% 11% 6/7/8-Speed Auto. Trans with Improved Internals 15% 10% 6% 2% 1% Dual Clutch or Automated Manual Transmission 25% 41% 53% 66% 77% Electric Power Steering 39% 56% 67% 70% 79% Improved Accessories 32% 40% 45% 53% 66% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 11% 21% 28% 34% 37% Crank mounted Integrated Starter Generator 1% 1% 1% 1% 1% Power Split Hybrid 4% 4% 5% 5% 5% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 18% 25% 34% 45% 61% Mass Reduction (3.5% to 8.5%) 0% 0% 19% 33% 45% Low Rolling Resistance Tires 48% 59% 68% 77% 90% Low Drag Brakes 8% 9% 11% 12% 13%

### 282

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### 283

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### ase 6% Annual Incre

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 64% 72% 75% 89% 88% Engine Friction Reduction 25% 48% 60% 69% 83% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 6% 9% 9% 9% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 9% 9% 9% Cylinder Deactivation on SOHC 1% 1% 1% 0% 0% VVT - Intake Cam Phasing (ICP) 37% 33% 25% 18% 6% VVT - Dual Cam Phasing (DCP) 37% 42% 49% 57% 69% Discrete Variable Valve Lift (DVVL) on DOHC 26% 39% 48% 54% 64% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 1% 1% Cylinder Deactivation on OHV 1% 2% 1% 0% 0% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 5% 4% 4% Discrete Variable Valve Lift (DVVL) on OHV 0% 2% 2% 2% 2% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 19% 33% 44% 48% 56% Combustion Restart 0% 0% 3% 7% 14% Turbocharging and Downsizing 15% 27% 37% 43% 49% Exhaust Gas Recirculation (EGR) Boost 0% 14% 25% 31% 41% Conversion to Diesel following TRBDS 4% 4% 6% 9% 9% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 4% 4% 4% Improved Auto. Trans. Controls/Externals 11% 14% 14% 9% 0% Continuously Variable Transmission 11% 11% 10% 10% 11% 6/7/8-Speed Auto. Trans with Improved Internals 14% 9% 5% 2% 0% Dual Clutch or Automated Manual Transmission 25% 44% 56% 67% 77% Electric Power Steering 39% 62% 72% 78% 90% Improved Accessories 32% 41% 46% 55% 71% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 8% 19% 27% 33% 36% Crank mounted Integrated Starter Generator 4% 5% 6% 5% 6% Power Split Hybrid 5% 6% 6% 8% 8% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 18% 27% 36% 47% 61% Mass Reduction (3.5% to 8.5%) 0% 0% 19% 33% 45% Low Rolling Resistance Tires 49% 66% 74% 80% 90% Low Drag Brakes 8% 10% 11% 12% 14%

### 284

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### 285

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### 7% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 71% 74% 90% 88% 86% Engine Friction Reduction 27% 52% 66% 73% 83% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 8% 11% 9% 9% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 9% 9% 9% Cylinder Deactivation on SOHC 1% 1% 1% 0% 0% VVT - Intake Cam Phasing (ICP) 37% 31% 21% 12% 2% VVT - Dual Cam Phasing (DCP) 37% 42% 51% 61% 68% Discrete Variable Valve Lift (DVVL) on DOHC 26% 38% 49% 56% 62% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 1% Cylinder Deactivation on OHV 1% 2% 1% 0% 0% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 4% 4% 4% Discrete Variable Valve Lift (DVVL) on OHV 0% 2% 2% 4% 4% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 25% 40% 52% 61% 71% Combustion Restart 0% 0% 2% 7% 21% Turbocharging and Downsizing 15% 30% 41% 48% 53% Exhaust Gas Recirculation (EGR) Boost 0% 17% 29% 36% 45% Conversion to Diesel following TRBDS 4% 5% 7% 9% 12% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 4% 4% 4% Improved Auto. Trans. Controls/Externals 11% 14% 14% 9% 0% Continuously Variable Transmission 11% 10% 9% 9% 9% 6/7/8-Speed Auto. Trans with Improved Internals 14% 9% 5% 2% 0% Dual Clutch or Automated Manual Transmission 25% 43% 53% 64% 74% Electric Power Steering 40% 67% 81% 87% 90% Improved Accessories 33% 46% 58% 71% 82% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 11% 22% 30% 37% 42% Crank mounted Integrated Starter Generator 4% 4% 5% 5% 5% Power Split Hybrid 5% 8% 10% 11% 12% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 18% 27% 36% 48% 85% Mass Reduction (3.5% to 8.5%) 0% 0% 19% 33% 53% Low Rolling Resistance Tires 50% 66% 75% 80% 90% Low Drag Brakes 8% 10% 12% 13% 14%

### 286

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### Max Net Benefit

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 74% 76% 91% 90% 89% Engine Friction Reduction 27% 55% 70% 75% 85% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 8% 11% 9% 9% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 9% 9% 9% Cylinder Deactivation on SOHC 1% 1% 1% 0% 0% VVT - Intake Cam Phasing (ICP) 37% 28% 19% 14% 10% VVT - Dual Cam Phasing (DCP) 37% 45% 54% 60% 62% Discrete Variable Valve Lift (DVVL) on DOHC 24% 36% 47% 51% 56% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 0% Cylinder Deactivation on OHV 1% 2% 1% 1% 1% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 4% 4% 4% Discrete Variable Valve Lift (DVVL) on OHV 0% 2% 2% 4% 4% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 22% 37% 47% 53% 59% Combustion Restart 0% 0% 3% 8% 16% Turbocharging and Downsizing 1 5% 28% 37% 42% 47% Exhaust Gas Recirculation (EGR) Boost 0% 1 5% 25% 31% 39% Conversion to Diesel following TRBDS 4% 4% 7% 8% 9% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 4% 4% 4% Improved Auto. Trans. Controls/Externals 11% 14% 14% 9% 2% Continuously Variable Transmission 1 1 1% 0% 10% 10% 10% 6/7/8-Speed Auto. Trans with Improved Internals 14% 9% 5% 2% 1% Dual Clutch or Automated Manual Transmission 25% 43% 54% 65% 72% Electric Power Steering 40% 66% 80% 83% 84% Improved Accessories 33% 55% 64% 64% 67% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 1 1% 22% 32% 37% 41% Crank mounted Integrated Starter Generator 4% 5% 5% 5% 5% Power Split Hybrid 5% 8% 9% 10% 10% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 18% 32% 41% 50% 62% Mass Reduction (3.5% to 8.5%) 0% 0% 18% 32% 47% Low Rolling Resistance Tires 50% 66% 75% 80% 90% Low Drag Brakes 8% 12% 14% 14% 14%

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### efit Total Cost = Total Ben

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 75% 78% 90% 89% 87% Engine Friction Reduction 27% 54% 69% 75% 84% VVT - Coupled Cam Phasing (CCP) on SOHC 5% 8% 11% 9% 9% Discrete Variable Valve Lift (DVVL) on SOHC 6% 7% 9% 9% 9% Cylinder Deactivation on SOHC 1% 1% 1% 0% 0% VVT - Intake Cam Phasing (ICP) 35% 27% 17% 12% 6% VVT - Dual Cam Phasing (DCP) 39% 47% 55% 62% 65% Discrete Variable Valve Lift (DVVL) on DOHC 30% 43% 54% 62% 66% Continuously Variable Valve Lift (CVVL) 3% 2% 3% 3% 3% Cylinder Deactivation on DOHC 1% 1% 1% 2% 1% Cylinder Deactivation on OHV 1% 2% 1% 0% 0% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 4% 4% 4% Discrete Variable Valve Lift (DVVL) on OHV 0% 2% 2% 4% 4% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 30% 47% 62% 68% 72% Combustion Restart 0% 0% 5% 9% 17% Turbocharging and Downsizing 1 3 6% 2% 43% 49% 54% Exhaust Gas Recirculation (EGR) Boost 0% 1 9% 31% 37% 46% Conversion to Diesel following TRBDS 4% 5% 7% 8% 11% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 3% 3% 4% 4% 4% Improved Auto. Trans. Controls/Externals 11% 14% 14% 9% 2% Continuously Variable Transmission 1 1 1% 0% 10% 10% 10% 6/7/8-Speed Auto. Trans with Improved Internals 14% 9% 5% 2% 0% Dual Clutch or Automated Manual Transmission 25% 43% 54% 65% 72% Electric Power Steering 40% 66% 81% 84% 84% Improved Accessories 36% 58% 67% 71% 75% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 1 2 3% 3% 33% 39% 44% Crank mounted Integrated Starter Generator 4% 4% 5% 4% 5% Power Split Hybrid 5% 8% 9% 10% 12% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 2 4 1% 0% 51% 58% 66% Mass Reduction (3.5% to 8.5%) 0% 0% 19% 33% 47% Low Rolling Resistance Tires 50% 66% 75% 80% 90% Low Drag Brakes 8% 12% 14% 14% 14%

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### Table V-49. Penetration Rate of New Tec hnologies to Light Trucks, by Alternative

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### Preferred Alternative

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 98% 98% 98% 98% 98% Engine Friction Reduction 57% 78% 79% 82% 94% VVT - Coupled Cam Phasing (CCP) on SOHC 12% 12% 12% 12% 12% Discrete Variable Valve Lift (DVVL) on SOHC 5% 5% 5% 6% 6% Cylinder Deactivation on SOHC 9% 10% 9% 9% 9% VVT - Intake Cam Phasing (ICP) 13% 12% 9% 6% 2% VVT - Dual Cam Phasing (DCP) 38% 41% 49% 49% 53% Discrete Variable Valve Lift (DVVL) on DOHC 25% 29% 35% 34% 35% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 9% 10% 9% 11% 9% Cylinder Deactivation on OHV 17% 17% 15% 17% 22% VVT - Coupled Cam Phasing (CCP) on OHV 5% 6% 5% 5% 18% Discrete Variable Valve Lift (DVVL) on OHV 0% 13% 14% 19% 19% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 22% 27% 34% 35% 42% Combustion Restart 0% 0% 3% 7% 20% Turbocharging and Downsizing 9% 12% 16% 17% 20% Exhaust Gas Recirculation (EGR) Boost 0% 4% 7% 7% 11% Conversion to Diesel following TRBDS 1% 1% 1% 1% 1% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 35% 25% 16% 5% 1% Continuously Variable Transmission 3% 3% 3% 4% 4% 6/7/8-Speed Auto. Trans with Improved Internals 11% 6% 2% 1% 2% Dual Clutch or Automated Manual Transmission 32% 54% 72% 84% 90% Electric Power Steering 72% 76% 75% 84% 91% Improved Accessories 36% 38% 39% 43% 51% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 7% 11% 21% 22% 22% Crank mounted Integrated Starter Generator 0% 0% 0% 0% 0% Power Split Hybrid 3% 2% 2% 3% 3% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 15% 17% 34% 47% 61% Mass Reduction (3.5% to 8.5%) 0% 0% 20% 34% 55% Low Rolling Resistance Tires 92% 95% 95% 98% 99% Low Drag Brakes 33% 52% 50% 51% 60%

### 292

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### 3% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 81% 80% 77% 76% 89% Engine Friction Reduction 39% 54% 56% 59% 64% VVT - Coupled Cam Phasing (CCP) on SOHC 11% 10% 10% 10% 10% Discrete Variable Valve Lift (DVVL) on SOHC 4% 4% 5% 5% 5% Cylinder Deactivation on SOHC 4% 3% 3% 3% 3% VVT - Intake Cam Phasing (ICP) 17% 17% 17% 15% 13% VVT - Dual Cam Phasing (DCP) 33% 35% 41% 43% 44% Discrete Variable Valve Lift (DVVL) on DOHC 17% 20% 23% 22% 23% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 7% 8% 7% 8% 8% Cylinder Deactivation on OHV 17% 16% 14% 15% 18% VVT - Coupled Cam Phasing (CCP) on OHV 3% 2% 2% 3% 3% Discrete Variable Valve Lift (DVVL) on OHV 0% 0% 0% 0% 0% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 11% 13% 14% 14% 15% Combustion Restart 0% 0% 3% 6% 6% Turbocharging and Downsizing 5% 8% 9% 9% 11% Exhaust Gas Recirculation (EGR) Boost 0% 3% 5% 4% 6% Conversion to Diesel following TRBDS 0% 0% 0% 0% 0% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 34% 23% 14% 4% 1% Continuously Variable Transmission 3% 3% 3% 4% 4% 6/7/8-Speed Auto. Trans with Improved Internals 14% 11% 13% 15% 17% Dual Clutch or Automated Manual Transmission 31% 50% 60% 70% 72% Electric Power Steering 59% 60% 61% 66% 74% Improved Accessories 29% 29% 32% 33% 40% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 7% 9% 12% 12% 12% Crank mounted Integrated Starter Generator 0% 0% 0% 0% 0% Power Split Hybrid 1% 1% 1% 2% 2% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 5% 5% 25% 35% 51% Mass Reduction (3.5% to 8.5%) 0% 0% 20% 32% 48% Low Rolling Resistance Tires 80% 82% 81% 92% 94% Low Drag Brakes 19% 20% 22% 26% 45%

### 294

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### 4% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 80% 79% 77% 82% 97% Engine Friction Reduction 44% 60% 62% 69% 84% VVT - Coupled Cam Phasing (CCP) on SOHC 12% 12% 12% 11% 11% Discrete Variable Valve Lift (DVVL) on SOHC 5% 4% 5% 9% 9% Cylinder Deactivation on SOHC 3% 3% 2% 6% 5% VVT - Intake Cam Phasing (ICP) 16% 16% 14% 11% 6% VVT - Dual Cam Phasing (DCP) 34% 35% 44% 46% 51% Discrete Variable Valve Lift (DVVL) on DOHC 24% 27% 33% 31% 36% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 7% 7% 7% 9% 9% Cylinder Deactivation on OHV 17% 16% 14% 15% 21% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 5% 5% 18% Discrete Variable Valve Lift (DVVL) on OHV 0% 13% 14% 18% 18% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 23% 27% 33% 33% 37% Combustion Restart 0% 0% 3% 8% 8% Turbocharging and Downsizing 12% 14% 17% 17% 19% Exhaust Gas Recirculation (EGR) Boost 0% 2% 4% 4% 7% Conversion to Diesel following TRBDS 1% 2% 2% 2% 2% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 34% 24% 18% 8% 3% Continuously Variable Transmission 3% 3% 3% 4% 4% 6/7/8-Speed Auto. Trans with Improved Internals 13% 7% 5% 6% 7% Dual Clutch or Automated Manual Transmission 32% 54% 66% 78% 83% Electric Power Steering 66% 70% 73% 81% 87% Improved Accessories 30% 31% 33% 40% 48% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 10% 15% 24% 27% 28% Crank mounted Integrated Starter Generator 0% 0% 0% 0% 0% Power Split Hybrid 1% 1% 1% 2% 2% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 15% 15% 36% 45% 61% Mass Reduction (3.5% to 8.5%) 0% 0% 21% 32% 50% Low Rolling Resistance Tires 81% 84% 92% 99% 99% Low Drag Brakes 20% 21% 22% 26% 48%

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### 5% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 77% 93% 91% 91% 91% Engine Friction Reduction 42% 63% 73% 81% 86% VVT - Coupled Cam Phasing (CCP) on SOHC 12% 12% 12% 11% 11% Discrete Variable Valve Lift (DVVL) on SOHC 5% 4% 6% 12% 13% Cylinder Deactivation on SOHC 3% 2% 2% 5% 6% VVT - Intake Cam Phasing (ICP) 14% 13% 11% 8% 3% VVT - Dual Cam Phasing (DCP) 34% 36% 40% 41% 45% Discrete Variable Valve Lift (DVVL) on DOHC 22% 29% 34% 34% 39% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 7% 8% 6% 8% 15% Cylinder Deactivation on OHV 17% 16% 15% 16% 22% VVT - Coupled Cam Phasing (CCP) on OHV 5% 5% 7% 7% 18% Discrete Variable Valve Lift (DVVL) on OHV 0% 14% 17% 22% 22% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 20% 37% 43% 52% 59% Combustion Restart 0% 0% 3% 16% 34% Turbocharging and Downsizing 11% 15% 22% 22% 24% Exhaust Gas Recirculation (EGR) Boost 0% 4% 10% 11% 13% Conversion to Diesel following TRBDS 5% 6% 8% 8% 8% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 34% 24% 15% 5% 1% Continuously Variable Transmission 3% 3% 3% 4% 4% 6/7/8-Speed Auto. Trans with Improved Internals 12% 6% 2% 1% 1% Dual Clutch or Automated Manual Transmission 32% 53% 71% 84% 90% Electric Power Steering 69% 77% 83% 88% 90% Improved Accessories 30% 32% 40% 47% 52% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 8% 12% 22% 25% 27% Crank mounted Integrated Starter Generator 2% 2% 4% 4% 4% Power Split Hybrid 3% 4% 4% 4% 4% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 15% 17% 38% 50% 62% Mass Reduction (3.5% to 8.5%) 0% 0% 21% 36% 55% Low Rolling Resistance Tires 81% 84% 92% 99% 99% Low Drag Brakes 25% 32% 38% 42% 65%

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### ase 6% Annual Incre

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 76% 93% 90% 87% 85% Engine Friction Reduction 42% 66% 76% 81% 79% VVT - Coupled Cam Phasing (CCP) on SOHC 12% 12% 12% 12% 12% Discrete Variable Valve Lift (DVVL) on SOHC 5% 4% 6% 12% 13% Cylinder Deactivation on SOHC 3% 3% 2% 2% 1% VVT - Intake Cam Phasing (ICP) 14% 11% 6% 1% 1% VVT - Dual Cam Phasing (DCP) 33% 38% 46% 47% 46% Discrete Variable Valve Lift (DVVL) on DOHC 21% 29% 34% 34% 38% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 7% 8% 6% 7% 5% Cylinder Deactivation on OHV 17% 13% 8% 5% 5% VVT - Coupled Cam Phasing (CCP) on OHV 5% 8% 9% 8% 19% Discrete Variable Valve Lift (DVVL) on OHV 0% 14% 17% 19% 20% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 22% 43% 54% 63% 74% Combustion Restart 0% 0% 7% 16% 38% Turbocharging and Downsizing 15% 29% 40% 47% 57% Exhaust Gas Recirculation (EGR) Boost 0% 14% 25% 34% 47% Conversion to Diesel following TRBDS 5% 6% 9% 12% 14% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 35% 25% 17% 5% 1% Continuously Variable Transmission 3% 3% 2% 2% 2% 6/7/8-Speed Auto. Trans with Improved Internals 11% 6% 2% 1% 0% Dual Clutch or Automated Manual Transmission 30% 51% 68% 82% 89% Electric Power Steering 71% 79% 87% 89% 91% Improved Accessories 31% 36% 45% 51% 53% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 12% 16% 28% 32% 38% Crank mounted Integrated Starter Generator 1% 2% 2% 2% 2% Power Split Hybrid 5% 6% 8% 8% 8% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 15% 18% 40% 56% 82% Mass Reduction (3.5% to 8.5%) 0% 0% 21% 36% 55% Low Rolling Resistance Tires 85% 88% 92% 97% 97% Low Drag Brakes 25% 50% 53% 65% 77%

### 300

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### 7% Annual Increase

Technology MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Low Friction Lubricants 87% 91% 87% 85% 83% Engine Friction Reduction 42% 65% 76% 80% 78% VVT - Coupled Cam Phasing (CCP) on SOHC 12% 12% 13% 13% 13% Discrete Variable Valve Lift (DVVL) on SOHC 5% 4% 6% 12% 13% Cylinder Deactivation on SOHC 3% 3% 2% 2% 1% VVT - Intake Cam Phasing (ICP) 14% 10% 4% 4% 4% VVT - Dual Cam Phasing (DCP) 33% 38% 46% 47% 45% Discrete Variable Valve Lift (DVVL) on DOHC 21% 28% 38% 39% 44% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 7% 8% 8% 9% 2% Cylinder Deactivation on OHV 17% 9% 5% 2% 1% VVT - Coupled Cam Phasing (CCP) on OHV 5% 19% 17% 17% 18% Discrete Variable Valve Lift (DVVL) on OHV 0% 13% 14% 18% 19% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 22% 40% 55% 64% 76% Combustion Restart 0% 0% 10% 22% 39% Turbocharging and Downsizing 15% 34% 50% 57% 67% Exhaust Gas Recirculation (EGR) Boost 0% 20% 35% 46% 60% Conversion to Diesel following TRBDS 6% 9% 12% 15% 17% Conversion to Diesel following CBRST 0% 0% 0% 0% 0% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 35% 25% 17% 5% 1% Continuously Variable Transmission 3% 3% 2% 2% 2% 6/7/8-Speed Auto. Trans with Improved Internals 11% 6% 2% 1% 0% Dual Clutch or Automated Manual Transmission 31% 51% 67% 79% 84% Electric Power Steering 72% 80% 88% 89% 90% Improved Accessories 33% 38% 47% 52% 53% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 13% 16% 27% 29% 36% Crank mounted Integrated Starter Generator 3% 4% 7% 9% 7% Power Split Hybrid 4% 6% 9% 10% 12% 2-Mode Hybrid 0% 0% 0% 1% 1% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 15% 22% 44% 60% 87% Mass Reduction (3.5% to 8.5%) 0% 0% 21% 36% 57% Low Rolling Resistance Tires 92% 95% 97% 97% 97% Low Drag Brakes 29% 56% 60% 69% 77%

### 302

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### Max Net Benefit

Tec hnology 2012 2013 2014 2015 2016 Low Friction Lubricants 93% 91% 88% 85% 85 % Engine Friction Reduction 54% 77% 76% 77% 79% VVT - Coupled Cam Phasing (CCP) on SOHC 13% 13% 13% 13% 13% Discrete Variable Valve Lift (DVVL) on SO HC 5% 5% 6% 12% 13% Cylinder Deactivation on SOHC 9% 9% 8% 4% 4% VVT - Intake Cam Phasing (ICP) 10% 6% 3% 3% 2% VVT - Dual Cam Phasing (DCP) 35% 40% 44% 43% 44% Discrete Variable Valve Lift (DVVL) on DOHC 21% 28% 33% 33% 33% Continuously Variable Valve Lift (CVVL) 4% 4% 4% 4% 4% Cylinder Deactivation on DOHC 7% 8% 8% 9% 6% Cylinder Deactivation on OHV 18% 12% 9% 6% 5% VVT - Coupled Cam Phasing (CCP) on OHV 7% 21% 21% 20% 20 % Discrete Variable Valve Lift (DVVL) on OHV 0% 13% 15% 19% 19% Conversion to DOHC with DCP 0% 0% 0% 0% 0% Stoichiometric Gasoline Direct Injection (GDI) 21% 40% 50% 60% 63% Combustion Restart 0% 0% 9% 18% 28% Turbocharging and Downsizing 16% 34% 44% 51% 54% Exhaust Gas Recirculation (EGR) Boost 0% 18% 28% 35% 42% Conversion to Diesel following TRBDS 7% 9% 12% 14% 15% Conversion to Diesel following CBRST 0% 1% 1% 1% 1% 6-Speed Manual/Improved Internals 1% 1% 1% 1% 1% Improved Auto. Trans. Controls/Externals 35% 25% 17% 5% 1 % Continuously Variable Transmission 3% 3% 2% 2% 2% 6/7/8-Speed Auto. Trans with Improved Internals 11% 6% 2% 1% 0% Dual Clutch or Automated Manual Transmission 30% 50% 67% 80% 88% Electric Power Steering 73% 80% 84% 89% 91% Improved Accessories 37% 42% 47% 53% 52% 12V Micro-Hybrid 0% 0% 0% 0% 0% Belt mounted Integrated Starter Generator 12% 15% 27% 30% 33% Crank mounted Integrated Starter Generator 3% 3% 5% 5% 5% Power Split Hybrid 5% 7% 9% 9% 9% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 29% 51% 64% 71% 81% Mass Reduction (3.5% to 8.5%) 0% 0% 21% 35% 56% Low Rolling Resistance Tires 92% 95% 97% 97% 97% Low Drag Brakes 33% 61% 70% 72% 74%

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Ae ro Drag Reduction 87% 92% 96% 97% 100%

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### otal Cost = Total Benefit

Technology 012 MY 2013 MY 2014 MY 2015 MY 2016 

### T

MY 2 Low Friction Lubricants 91% 87% 85% 84% 93% Engine Friction Reduction 54% 77% 82% 79% 80% VVT - Coupled Cam Phasing (CCP) on 13% 13% 13% 13% SOHC 13% Discrete Variable Valve Lift (DVVL) on 5% 6% 12% 13% SOHC 5% Cylinder Deactivation on SOHC 9% 8% 2% 1% 9% VVT - Intake Cam Phasing (ICP 1% 0% ) 10% 6% 1% VVT - Dual Cam P) 45% 45% Phasing (DC 35% 40% 46% Discrete Variable Lift (D n D 36% 41% Valve VVL) o OHC 21% 28% 34% Continuously Va alve L VL 5% 5% riable V ift (CV ) 4% 4% 5% Cylinder Deacti DOH 9% 8% vation on C 7% 8% 8% Cylinder Deacti OHV 2% 1% vation on 18% 10% 6% VVT - Coupled C ing ( O 20% 20% am Phas CCP) on HV 7% 21% 21% Discrete Variable Valve Lift (D on O 18% 20% VVL) HV 0% 13% 15% Conversion to D ith DC 0% 0% OHC w P 0% 0% 0% Stoichiometric G Direct n 60% 68% asoline Injectio (GDI) 21% 40% 52% Combustion Res 25% 30% tart 0% 0% 12% Turbocharging a nsizin 55% 62% nd Dow g 16% 36% 49% Exhaust Gas Recirculation (EG st 44% 54% R) Boo 0% 20% 32% Conversion to D followin S 15% 15% iesel g TRB D 7% 9% 12% Conversion to D llowin T 1% 1% iesel fo g CBRS 0% 1% 1% 6-Speed Manual/Improved In 1% 1% ternals 1% 1% 1% Improved Auto. ntro rn 5% 1% Trans. Co ls/Exte als 35% 25% 17% Continuously V Transm 2% 2% ariable ission 3% 3% 2% 6/7/8-Speed Auto. Trans with ve Internals 1% 0% Impro d 11% 6% 2% Dual Clutch or Auto Ma Transmission 80% 85% mated nual 30% 49% 67% Electric Power S 90% 92% teering 73% 80% 88% Improved Acces 57% 57% sories 37% 42% 52% 12V Micro-Hybr 0% 0% id 0% 0% 0% Belt mounted In Starte rat 25% 29% tegrated r Gene or 12% 14% 24% Crank mounted Integrated Sta ner 9% 7% rter Ge ator 3% 4% 6% Power Split Hyb 10% 12% rid 5% 7% 10% 2-Mode Hybrid 0% 0% 0% 0% 0% Plug-in Hybrid 0% 0% 0% 0% 0% Mass Reduction (1.5%) 29% 51% 69% 76% 83% Mass Reduction (3.5% to 8.5%) 0% 0% 21% 36% 56% Low Rolling Resistance Tires 92% 95% 97% 97% 97% Low Drag Brakes 33% 61% 70% 72% 77%

### 306

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## VI. MANUFACTURER CAFE CAPABILITIES

Table VI-1 shows the agencies’ forecast of wh nufacturers’ passenger car mpg would be, based on the MY 2008 vehicles exten re. These mpg estimates change for some of the model years, but usually to a ased on chan ges in sale s forecasts between passenger cars and light trucks.

Table VI-2 show TE SEL p ger t hen we do cost and benefit analyses, w e ST S th ut is. The adjusted baseline h ma turer 20 et an kes t the MY 2011 fuel economy standard by adding te y. ju seli me e analysis that each manufacture the M d ap le t an rer, (except BMW, Daimler, Porsche d Vo en) ap hno ac e MY 2011 standard. Our rati is en e b is t co benefits of achieving MY 20 evels alre en a ed a imat e previous analysis. The cos tec ies ima t th no ered part of this rule. We then es e cost ne goi m th ste to the level of the alternatives. 198



### The estimated req ard n in VI pass rs for the preferred alternative. Table VI ide tim hi pg for passenger cars for each of the alt tives. Ta -5 t h V id ame light trucks as Tables VI-1 throu 4 show e rs.

Note that not all m ers um ttem me l es. We assume that BMW, Daimler, Porsche, T d V e d n t th els because, for them, the cost of hese is m an t st of e These manufacturers hav n, in t , th gn ay ies than spend more money to improv econ duc he be it is reasonable to assume that they e th ce n th

The agency has p an a s of anu ers resp changes in the alternative CAFE levels. The a us hn ppl to systematically apply consistent c nd perfor ass ns ent ustr ell as consistent assumptions rega onom ion g by manufa . T lting computer model (the CAFE iance a ct l, o fer s t lpe model”), developed by tec f t T V t ran tion ms Center in consultation with NHTSA staff to stim e o co mpact of the alternative CAFE lpe m anal how impact of the standards in terms of increases in new vehicle pr es on a manufacturer-wide, industry-wide, and average per-vehicle basis. Based on these estim es and corresponding estimates of net ere the ma ded into the futu mini mal extent, b

s the ADJUS e use th D BA INE for A assen cars. No e that w ADJU nufac ED B ’s MY ELINE 08 fle rougho d ma the analys it mee takes eac chnolog Y 20 The ad andar sted ba plicab ne assu o that s for th ufactu r, below 11 st lkswag m logy to , Tata, an onale for th would ply tec hieve th adjustm have t of th ady be aseline nalyz hat the nd est sts and ed in th 11 mpg l ts of these timate th hnolog s and are est fits of ted, bu ng fro ey are e adju t consid d base be line

uired stand levels are show Table -3 for enger ca -4 prov bles V s the es hroug ated ac I-8 prov eved m e the s levels tables for erna I for pass gh VI- nger ca

anufactur are ass ed to a pt to “ et” the a ternativ ata, an levels olkswag ore th n woul he co ot mee paying p ese lev nalties. meeting t

e show e the fuel he past omy of th e willin eir pro ess to p ts, so penalt
agenc rather lieves t e future. y would mak at choi again i

erformed nalysi how m factur could ond to nalysis

manc es a t ec umptio ology a to the ication alg ire ind orithm
y, as w ost a e ic decis rding ec Compl -makin cturers he resu nd Effe he DO s Mode olpe Na ften re ional T red to a sporta he “Vo Syste hnical staff o , is used he Vo help e odel ate th ysis s verall e s the econ nomic i omic standards. T ic
at

198 If the manufacturer’s MY 2008 fleet extended mpg level is above the level of the alternative, their mpg is assumed to remain at that level. Some manufacturer’s levels go slightly above the required mpg mark for them since some technologies are applied to all models of a particular manufacturer so that the exact level for each manufacturer may be slightly higher than the level of the standard and costs and benefits are estimated to that level.

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### economic and other benefits, the agency is der alternatives that are economically practicable and technolog ically feasible.

We note that, as explained above in Chap model has been updated to account for manufacturers’ ability to apply “multi-ye rder to minimize compliance burdens over multiple model years, and to accoun ers’ use of CAFE credits (when specified as a model input). The model h iewed. The model documentation, f the input assumptions and process, as well as peer review reports, was aking docket for the August 2005 NPRM, and updated documentation is also available o TSA bsite.

Our analyses of the potential ef alt e CAFE stan er two major elements: (1) pro s of the ical ter nd olu future product offerings and (2) of th cab d i a and vings associated with different har ange no th ht b ed onse to alternative CAFE standards. able to consi

ter V, the Volpe ar planni ng” in o t for manufactur as been peer rev including a description o made available in the rulem n NH ’s we 199



### fects of techn ernativ chara dards w sales v e founded on

mes o jection c ility an istics a f fuel sa estimates dware ch e appli ncrement l cost s—tech logies— at mig e utiliz in resp

199 See Docket Nos. NHTSA-2005-22223-3, 4, 5.

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### MY ded

Estimated mpg

Pa er C

cturer 

### Table VI-1

2008 Fleet Exten

sseng ars

Manufa MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 27.2 27.2 27.2 27.2 27.2

Chrysler 28.4 28.7 28.7 28.7 28.7

Daiml er 25.9 25.9 25.6 25.2 25.2

Ford 28.3 28.3 28.3 28.1 28.1 Genera Motors l 28.5 28.6 28.5 28.4 28.4

Honda 33.9 34.1 34.1 33.9 34.0

Hyun dai 32.3 31.2 31.1 31.9 32.0

Kia 31.9 32.2 32.5 32.9 33.0

Mazda 30.6 30.9 31.0 30.9 30.9

Mitsubish i 29.5 29.4 29.1 28.9 28.9

Nissan 32.0 32.1 32.0 32.0 32.0

Porsche 26.2 26.2 26.2 26.2 26.2

Subaru 29.4 29.4 29.4 29.4 29.4

Suzuk i 31.2 30.9 30.8 30.8 30.9

Tata 24.6 24.6 24.6 24.6 24.6

Toyota 35.4 35.5 35.4 35.3 35.3

Volkswagen 29.0 29.0 29.0 29.0 29.0

Total/Average 30.5 30.8 30.8 30.7 30.7

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### A

Passenger Cars

Table VI-2

djusted Baseline

Estimated mpg

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.2 30.2 30.2

Chrysler 29.7 29.9 29.9 29.8 29.9

Daimler 27.4 27.9 29.1 29.0 29.0

Ford 30.0 30.0 30.0 30.0 30.0 General Motors 30.4 30.4 30.4 30.4 30.4

Honda 33.9 34.1 34.1 33.9 34.0

Hyundai 32.3 31.2 31.1 31.9 32.0

Kia 31.9 32.2 32.5 32.9 33.0

Mazda 30.6 30.9 31.0 30.9 30.9

Mitsubishi 31.1 31.0 31.1 31.0 31.0

Nissan 32.0 32.1 32.0 32.0 32.0

Porsche 28.3 29.1 29.1 29.2 29.2

Subaru 31.0 31.0 31.0 31.1 31.1

Suzuki 31.4 31.6 31.6 31.6 31.6

Tata 26.0 27.8 28.1 28.1 28.1

Toyota 35.4 35.5 35.4 35.3 35.3

Volkswagen 30.8 30.8 30.8 30.7 30.7

Total/Average 31.6 31.8 31.8 31.8 31.9

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### Estimated Required Fuel for Preferred Alternative

Passenger Cars

Table VI-3

Economy Levels

Estimated mpg

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 33.0 33.7 34.5 35.7 37.3

Chrysler 32.6 33.3 34.1 35.2 36.7

Daimler 32.0 32.7 33.3 34.4 35.8

Ford 32.9 33.7 34.4 35.6 37.1 General Motors 32.7 33.5 34.2 35.4 36.9

Honda 33.8 34.6 35.4 36.7 38.3

Hyundai 33.8 34.3 35.1 36.6 38.2

Kia 33.4 34.2 35.0 36.3 37.9

Mazda 33.8 34.6 35.5 36.8 38.4

Mitsubishi 34.2 35.0 35.8 37.1 38.7

Nissan 33.3 34.1 34.9 36.1 37.7

Porsche 35.9 36.8 37.8 39.2 41.1

Subaru 34.6 35.5 36.3 37.7 39.4

Suzuki 35.8 36.6 37.5 39.0 40.8

Tata 30.7 31.4 32.1 33.3 34.7

Toyota 33.9 34.7 35.5 36.8 38.4

Volkswagen 34.3 35.0 35.9 37.2 38.8

Total/Average 33.3 34.2 34.9 36.2 37.8

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### Estimated Achievable evels, by Alternative

Passenger Cars

Preferred Alternative

cturer 

### Table VI-4

Fuel Economy L

Estimated mpg

Manufa MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.3 31.0 32.4

Chrysler 31.5 34.4 35.1 36.1 36.7

Daiml er 27.4 27.9 30.5 31.8 33.2

Ford 32.5 34.6 34.7 35.9 37.1 Genera Motors l 30.8 33.5 35.0 36.2 36.9

Honda 34.0 34.9 36.0 36.7 38.3

Hyun dai 34.8 34.3 36.1 37.6 38.5

Kia 33.4 34.3 35.1 36.6 37.9

Mazda 32.4 35.1 36.3 36.8 38.4

Mitsubish i 32.2 32.1 37.0 38.7 38.7

Nissan 33.3 34.7 36.6 36.7 37.7

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 37.1 39.4 39.4

Suzuk i 31.4 37.0 39.1 39.5 40.9

Tata 26.3 28.1 28.7 29.2 30.8

Toyota 36.6 36.8 37.0 38.1 38.4

Volkswagen 31.6 33.6 33.9 34.0 35.5

Total/Average 32.8 34.4 35.3 36.3 37.2

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### Estimated Achievable Fuel Economy Levels, by Alternative

Estimated mpg

Passenger Cars

3% Annual Increase

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.3 31.0 32.4

Chrysler 31.1 32.6 32.9 33.6 34.5

Daimler 27.4 27.9 30.5 31.8 33.2

Ford 31.3 33.0 33.1 33.9 34.8 General Motors 30.8 32.2 33.1 33.9 34.7

Honda 33.9 34.1 34.2 34.8 35.9

Hyundai 33.5 32.8 33.9 34.9 35.8

Kia 31.9 32.6 33.6 34.5 35.5

Mazda 32.1 33.4 34.5 34.9 36.4

Mitsubishi 32.2 32.1 35.3 36.2 36.3

Nissan 32.3 32.9 33.8 34.3 35.3

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 36.2 36.9 36.8

Suzuki 31.4 35.6 37.0 37.3 38.5

Tata 26.3 28.1 28.7 29.2 30.8

Toyota 35.4 35.5 35.4 35.3 36.0

Volkswagen 31.6 33.6 33.9 34.0 35.5

Total/Average 32.2 33.2 33.7 34.3 35.2

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### Estimated Achievable Fuel Economy Levels, by Alternative

4% Annual Increase

rer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### Estimated mpg

Passenger Cars

Manufactu

BMW 2 2 3 3 3 9.9 9.9 0.3 1.0 2.4

Chrysler 31.4 34.1 34.8 35.7 36.1

Daimler 27.4 27.9 30.5 31.8 33.2

Ford 31.6 33.8 34.0 35.2 36.5 General Motor s 30.8 32.8 34.2 35.5 36.4

Honda 34.0 34.2 35.3 36.2 37.7

Hyunda i 34.1 33.4 35.2 36.7 37.7

Kia 32.1 33.6 34.8 36.5 37.3

Mazd a 32.4 34.4 36.0 36.4 37.8

Mitsubish i 32.2 32.1 36.7 38.1 38.2

Nissan 32.3 33.2 35.3 36.2 37.1

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 36.5 38.7 38.7

Suzuki 31.4 36.2 38.2 38.6 40.1

Tata 26.3 28.1 28.7 29.2 30.8

Toyot a 35.5 35.7 35.6 36.3 37.8

Volkswa gen 31.6 33.6 33.9 34.0 35.5

Total/Average 32.3 33.6 34.6 35.5 36.7

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### Estimated Achievable Fuel Economy Levels, by Alternative

Estimated mpg

Passenger Cars

5% Annual Increase

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.3 31.0 32.4

Chrysler 31.5 35.0 36.0 37.3 37.8

Daimler 27.4 27.9 30.5 31.8 33.2

Ford 31.9 34.8 35.0 36.5 38.3 General Motors 30.8 33.2 35.3 36.8 38.1

Honda 34.3 35.2 36.9 37.8 40.1

Hyundai 34.5 34.0 36.5 38.5 39.5

Kia 32.4 34.2 35.9 37.3 39.2

Mazda 32.4 34.9 37.0 38.0 39.7

Mitsubishi 32.2 32.1 38.3 40.5 40.6

Nissan 33.3 35.0 37.6 38.3 38.9

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 38.4 40.7 40.7

Suzuki 31.4 38.2 41.4 42.1 43.8

Tata 26.3 28.1 28.7 29.2 30.8

Toyota 36.6 36.8 37.1 38.3 39.7

Volkswagen 31.6 33.6 33.9 34.0 35.5

Total/Average 32.6 34.4 35.8 36.9 38.3

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### Estimated Achievable F Levels, by Alternative

6% Annual Increase uel Economy

Estimated mpg

Passenger Cars

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.3 31.0 32.4

Chrysler 31.5 36.3 38.0 39.5 39.8

Daimler 27.4 27.9 30.5 31.8 33.2

Ford 32.4 35.5 35.8 37.4 39.1 General Motors 30.8 33.8 36.0 38.4 39.9

Honda 34.3 36.3 38.3 39.1 41.8

Hyundai 34.8 34.4 38.4 39.8 41.4

Kia 32.8 34.9 36.8 38.8 41.1

Mazda 32.4 35.4 38.0 39.2 41.6

Mitsubishi 32.2 32.1 39.1 40.8 40.8

Nissan 33.6 35.4 39.2 40.0 40.8

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 39.9 42.8 42.8

Suzuki 31.4 38.5 42.1 42.8 44.5

Tata 26.3 28.1 28.7 29.2 30.8

Toyota 36.9 37.1 37.5 39.3 41.7

Volkswagen 31.6 33.6 33.9 34.0 35.5

Total/Average 32.8 34.9 36.6 38.0 39.7

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### Estimated Achievable F Levels, by Alternative

7% Annual Increase uel Economy

Estimated mpg

Passenger Cars

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.3 31.0 32.4

Chrysler 31.5 37.0 38.7 40.3 40.4

Daimler 27.4 27.9 30.5 31.8 33.2

Ford 32.5 35.7 36.1 37.7 39.4 General Motors 30.8 33.9 36.5 38.9 40.8

Honda 34.3 37.6 40.2 40.9 43.6

Hyundai 35.1 34.8 39.3 41.4 43.7

Kia 33.0 35.6 37.8 40.4 43.1

Mazda 32.4 36.0 38.9 39.7 43.7

Mitsubishi 32.2 32.1 38.7 40.6 40.5

Nissan 34.1 35.9 40.2 40.8 41.3

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 39.4 42.6 42.6

Suzuki 31.4 40.3 45.1 46.4 47.4

Tata 26.3 28.1 28.7 29.2 30.8

Toyota 37.7 38.1 39.1 41.2 43.7

Volkswagen 31.6 33.6 33.9 34.0 35.5

Total/Average 33.0 35.4 37.4 38.9 40.7

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### P

Max Net Benefits

cturer 

### Estimated Achievable Fuel Economy Levels, by Alternative

Estimated mpg

assenger Cars

Manufa MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.9 29.9 30.3 31.0 32.4

Chrysler 31.5 37.1 38.5 39.9 40.0

Daiml er 27.4 27.9 30.5 31.8 33.2

Ford 32.5 36.1 36.4 38.0 39.6 Genera Motors l 30.8 33.9 36.5 38.5 39.9

Honda 34.3 37.3 39.4 40.0 41.6

Hyun dai 35.0 34.7 38.6 39.9 41.5

Kia 33.1 36.4 38.3 39.6 41.1

Mazda 32.4 36.2 38.5 39.6 41.8

Mitsubish i 32.2 32.1 38.7 40.6 40.5

Nissan 34.2 36.1 40.0 40.2 40.8

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 39.7 42.2 42.2

Suzuk i 31.4 38.9 42.5 43.3 44.5

Tata 26.3 28.1 28.7 29.2 30.8

Toyota 37.6 38.0 38.9 40.3 41.7

Volkswagen 31.6 33.6 33.9 34.0 35.5

Total/Average 33.0 35.5 37.3 38.4 39.8

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### Estimated Achievable F Levels, by Alternative

Total Cost=Total Benefit

rer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### uel Economy

Estimated mpg

Passenger Cars

Manufactu

BMW 2 2 3 3 3 9.9 9.9 0.3 1.0 2.4

Chrysler 31.5 37.1 38.7 39.7 39.8

Daimler 27.4 27.9 30.5 31.8 33.2

Ford 32.5 36.2 36.6 38.1 39.6 General Motor s 30.8 33.9 36.5 38.9 40.8

Honda 34.3 38.3 40.8 41.5 43.2

Hyunda i 35.0 34.7 39.4 41.3 43.0

Kia 33.5 36.9 39.2 40.9 42.5

Mazd a 32.4 36.2 38.5 39.6 43.1

Mitsubish i 32.2 32.1 38.7 40.6 40.5

Nissan 34.4 36.4 40.4 40.6 41.3

Porsche 28.5 29.2 29.2 29.2 29.2

Subaru 32.4 32.8 39.7 42.4 42.4

Suzuki 31.4 40.2 43.9 44.6 45.3

Tata 26.3 28.1 28.7 29.2 30.8

Toyot a 38.3 38.7 39.9 41.5 43.1

Volkswa gen 31.6 33.6 33.9 34.0 35.5

Total/Average 33.1 35.7 37.7 39.0 40.5

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### MY ded

E

Light Trucks Table VI-5

2008 Fleet Exten

stimated mpg

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 23.0 23.1 23.1 23.0 23.0

Chrysler 21.9 22.1 22.2 22.2 22.2

Daimler 21.1 21.1 21.0 21.1 21.1

Ford 21.1 21.2 21.3 21.3 21.3 General Motors 21.3 21.4 21.5 21.4 21.4

Honda 25.1 25.1 25.1 25.0 25.0

Hyundai 24.3 24.3 24.3 24.3 24.3

Kia 23.7 23.7 23.8 23.8 23.8

Mazda 26.2 26.6 26.4 26.4 26.4

Mitsubishi 23.7 23.7 23.7 23.6 23.6

Nissan 21.9 22.0 22.1 22.1 22.1

Porsche 20.0 20.0 20.0 20.0 20.0

Subaru 26.2 26.4 26.5 26.8 26.9

Suzuki 23.3 23.3 23.3 23.3 23.3

Tata 19.7 19.8 19.8 19.7 19.7

Toyota 23.9 24.0 24.2 24.2 24.2

Volkswagen 20.2 20.2 20.2 20.2 20.2

Total/Average 22.3 22.5 22.6 22.6 22.6

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### A

Light Trucks Table VI-6

djusted Baseline

Estimated mpg

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 25.7 25.8 25.8 25.7 25.7

Chrysler 24.5 24.7 24.8 24.8 24.8

Daimler 24.6 24.7 24.6 25.2 25.2

Ford 23.8 23.8 23.9 24.0 24.0 General Motors 23.3 23.4 23.5 23.5 23.5

Honda 25.8 25.9 25.9 25.8 25.8

Hyundai 25.9 25.9 25.9 25.9 25.9

Kia 25.3 25.3 25.3 25.3 25.3

Mazda 26.4 26.7 26.6 26.6 26.6

Mitsubishi 27.1 27.1 27.1 27.1 27.1

Nissan 24.5 24.5 24.6 24.7 24.7

Porsche 24.4 24.4 24.4 25.6 25.6

Subaru 26.7 26.8 27.0 27.2 27.3

Suzuki 24.7 26.4 26.4 26.4 26.4

Tata 23.8 23.9 24.1 24.3 26.3

Toyota 24.8 25.0 25.2 25.2 25.2

Volkswagen 21.1 23.8 25.0 25.1 25.1

Total/Average 24.3 24.5 24.7 24.7 24.7

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### Estimated Required Fuel for Preferred Alternative

E

Light Trucks Table VI-7

Economy Levels

stimated mpg

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 26.6 27.3 27.9 28.9 30.2

Chrysler 25.7 26.2 26.8 27.8 29.0

Daimler 25.6 26.3 26.9 27.8 29.1

Ford 24.8 25.4 26.0 27.0 28.1 General Motors 24.2 24.8 25.2 26.1 27.2

Honda 26.9 27.5 28.0 29.1 30.4

Hyundai 27.0 27.6 28.2 29.3 30.7

Kia 26.2 26.7 27.3 28.3 29.5

Mazda 27.6 28.4 28.9 30.1 31.5

Mitsubishi 27.8 28.5 29.1 30.2 31.7

Nissan 25.6 26.2 26.8 27.8 29.1

Porsche 26.3 26.9 27.5 28.5 29.8

Subaru 27.9 28.6 29.2 30.4 31.9

Suzuki 27.5 28.2 28.8 29.9 31.4

Tata 27.4 28.2 28.8 29.9 31.3

Toyota 25.7 26.2 26.8 27.8 29.1

Volkswagen 25.8 26.4 27.0 28.0 29.2

Total/Average 25.4 26.0 26.6 27.5 28.8

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### Estimated Achievable evels, by Alternative

E

Light Trucks

Preferred Alternative

cturer 

### Table VI-8

Fuel Economy L

stimated mpg

Manufa MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 26.6 26.7 26.8 26.7 27.9

Chrysler 24.8 26.2 27.6 27.9 29.0

Daiml er 24.7 24.8 24.7 25.3 25.3

Ford 25.1 25.7 26.0 27.7 28.1 Genera Motors l 23.4 25.0 25.9 26.2 27.2

Honda 27.7 27.7 28.5 29.1 30.5

Hyun dai 28.1 28.0 30.6 30.7 30.7

Kia 26.1 26.7 27.5 28.4 29.7

Mazda 27.8 29.5 29.5 30.1 31.5

Mitsubish i 27.1 27.1 29.7 30.4 33.0

Nissan 25.8 26.2 27.5 27.8 29.1

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 30.3 30.7 31.1 31.8 31.9

Suzuk i 24.7 31.1 31.1 31.1 31.6

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 25.7 26.3 27.6 28.3 29.2

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 25.1 26.0 27.0 27.6 28.5

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### Estimated Achievable Fuel Economy Levels, by Alternative

Estimated mpg

Light Trucks

3% Annual Increase

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 25.6 25.7 25.8 27.2 28.4

Chrysler 24.5 25.1 26.1 26.4 27.2

Daimler 24.7 24.8 24.7 25.3 25.3

Ford 24.0 24.5 25.0 26.6 26.6 General Motors 23.0 24.1 24.7 25.0 25.6

Honda 26.6 26.7 27.1 27.8 28.4

Hyundai 26.6 26.6 28.4 28.6 28.6

Kia 25.3 25.5 26.1 26.8 28.0

Mazda 27.0 28.3 28.3 28.5 29.4

Mitsubishi 27.1 27.1 28.3 28.6 29.5

Nissan 24.9 25.2 26.0 26.5 27.2

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 28.5 28.7 29.4 29.9 30.1

Suzuki 24.7 29.1 29.1 29.1 29.3

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 24.9 25.1 26.0 26.5 27.2

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 24.4 25.0 25.8 26.4 26.9

### 325

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### se Estimated Achievable Fuel Economy Levels, by Alternative

Estimated mpg

Light Trucks

4% Annual Increa

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 25.8 25.8 26.0 27.2 28.4

Chrysler 24.7 25.8 27.2 27.5 28.5

Daimler 24.7 24.8 24.7 25.3 25.3

Ford 24.6 25.2 25.7 27.6 27.7 General Motors 23.3 24.7 25.5 25.8 26.8

Honda 26.8 26.9 28.1 28.9 29.9

Hyundai 27.2 27.2 29.9 30.2 30.2

Kia 25.3 26.0 27.0 27.9 29.1

Mazda 27.1 29.0 29.0 30.0 31.0

Mitsubishi 27.1 27.1 29.4 29.8 32.4

Nissan 25.5 25.9 27.0 27.5 28.6

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 29.4 29.8 30.8 31.6 31.7

Suzuki 24.7 29.6 29.6 29.6 30.8

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 24.9 25.5 26.8 27.5 28.6

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 24.7 25.5 26.5 27.2 28.1

### 326

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### Estimated Achievable Fuel Economy Levels, by Alternative

Estimated mpg

Light Trucks

5% Annual Increase

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 26.1 26.2 26.3 27.2 28.4

Chrysler 24.8 26.1 28.0 28.5 30.0

Daimler 24.7 24.8 24.7 25.3 25.3

Ford 25.0 25.8 26.4 28.7 29.0 General Motors 23.4 25.2 26.2 26.8 28.0

Honda 27.1 27.2 28.9 30.2 31.5

Hyundai 27.5 27.5 31.5 31.8 31.8

Kia 25.4 26.6 27.9 29.0 30.5

Mazda 27.8 30.1 30.1 30.9 32.6

Mitsubishi 27.1 27.1 30.6 31.3 32.8

Nissan 25.9 26.4 28.2 28.7 30.0

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 30.1 30.4 31.8 32.4 32.6

Suzuki 24.7 31.5 31.5 31.5 32.8

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 25.1 26.2 28.2 28.9 30.0

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 24.9 26.0 27.4 28.3 29.3

### 327

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### chievable Fuel Economy Levels, by Alternative

Estimated mpg Estimated A

Light Trucks

6% Annual Increase

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 26.4 26.4 26.6 27.2 28.4

Chrysler 24.8 26.6 28.8 29.6 31.4

Daimler 24.7 24.8 24.7 25.3 25.3

Ford 25.0 25.8 26.2 29.9 30.4 General Motors 23.4 26.2 27.5 28.4 29.3

Honda 28.0 28.0 30.5 31.6 33.3

Hyundai 28.2 28.2 32.0 32.7 32.7

Kia 25.7 27.1 28.6 30.2 32.5

Mazda 28.2 31.5 31.6 32.5 34.4

Mitsubishi 27.1 27.1 31.7 32.5 35.3

Nissan 26.1 26.6 28.6 29.0 31.5

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 30.3 30.8 32.7 33.3 33.5

Suzuki 24.7 32.4 32.4 32.4 33.5

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 25.3 26.8 29.1 29.9 31.5

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 25.0 26.5 28.1 29.4 30.6

### 328

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### Estimated Achievable F Levels, by Alternative

7% Annual Increase

cturer 

### uel Economy

Estimated mpg

Light Trucks

Manufa MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 26.6 26.6 26.8 27.2 28.4

Chrysler 24.8 27.1 29.5 30.2 32.4

Daiml er 24.7 24.8 24.7 25.3 25.3

Ford 25.2 26.0 26.4 30.2 30.8 General Motors 23.4 26.6 28.4 29.6 30.2

Honda 28.4 28.4 31.1 31.7 33.5

Hyun dai 28.5 28.5 32.7 33.4 33.4

Kia 25.9 27.1 28.8 31.3 34.5

Mazda 28.9 34.0 34.0 34.9 36.5

Mitsubish i 27.1 27.1 31.7 32.4 35.3

Nissan 26.2 26.6 28.6 29.0 31.6

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 30.3 30.8 33.1 33.8 33.9

Suzuk i 24.7 32.4 32.4 32.4 33.5

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 25.6 27.4 30.5 31.3 33.0

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 25.2 26.9 28.8 30.1 31.4

### 329

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### Estimated Achievable Levels, by Alternative

ax Net Benefits

cturer 

### Fuel Economy

Estimated mpg

Light Trucks

M

Manufa MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 26.6 26.7 26.8 26.7 27.9

Chrysler 24.8 27.1 29.6 30.3 31.4

Daiml er 24.7 24.8 24.7 25.3 25.3

Ford 25.9 26.7 27.0 30.0 30.5 General Motors 23.4 26.6 28.0 28.8 29.3

Honda 29.0 28.9 31.6 32.1 33.1

Hyun dai 29.5 29.5 33.3 33.5 33.5

Kia 26.1 27.0 28.8 31.2 33.0

Mazda 29.4 33.2 33.0 33.3 34.4

Mitsubish i 27.1 27.1 31.7 32.4 35.3

Nissan 26.6 27.1 29.1 29.2 31.4

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 30.9 31.4 33.0 33.7 33.9

Suzuk i 24.7 33.3 33.3 33.3 34.2

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 26.3 28.2 30.3 31.0 31.5

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 25.5 27.3 28.8 29.9 30.6

### 330

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### Estimated Achievable Fuel Economy Levels, by Alternative

E

Total Cost=Total Benefit

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### stimated mpg

Light Trucks

Manufacturer

BMW 2 2 26 26 27 6.6 6.7 .8 .7 .9

Chrysler 24.8 27.1 29.6 30.3 32.1

Daimler 24.7 24.8 24.7 25.3 25.3

Ford 25.9 26.7 27.0 30.4 31.0 General Motors 23.4 26.9 28.5 29.6 29.9

Honda 29.1 29.1 31.9 32.4 34.1

Hyundai 29.7 29.7 33.6 33.8 33.8

Kia 26.1 27.0 28.8 31.2 33.0

Mazda 29.7 34.2 34.0 34.4 35.4

Mitsubishi 27.1 27.1 31.7 32.4 35.3

Nissan 26.6 27.0 29.0 29.2 31.4

Porsche 24.4 24.4 24.4 26.7 26.7

Subaru 30.9 31.4 33.2 33.9 34.0

Suzuki 24.7 34.2 34.2 34.2 35.1

Tata 23.8 23.9 24.1 24.3 27.9

Toyota 26.3 28.2 30.8 31.4 32.2

Volkswagen 21.1 23.8 26.3 26.2 26.2

Total/Average 25.5 27.4 29.1 30.2 31.1

### 331

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## VII. COST IMPACTS

ion algorith m implemented with the Volpe model was used as the basis fleet. The agency did estimate the costs or fines to bring passenger ar manufacturers up to the MY 2011 standards from their MY 2008 levels, as shown in Table VII-1a and VII-1b for passenger c nd li ks se co ve b stimated, but they are not considere f o g ir sts, and commensurate be e con pa e c d b of ing with previously issued The est s cha om o ye co anges in the manufacturers’ fl on c in t. is st 2012 would be consistent from MY 2 16

Tables VII-2a to w the es o eh i ta sts in millions for the various alt for p r ab 3a imated cost per vehicle and incre otal co ill th s ht trucks. The costs for several manufacturers fin e nuf rs w ave to pay in addition to the te impr ents ve hicl s. W ume that the costs of fines will be p co s. em tota the estimated total manufacturer cos illi doll ate n when we are considering total al costs nefi es ar incl inc are transfer payments and not technology c

In a confidential N 0 9-0 r rese higher cost estimate than show le V ]. The technology applicat for estimating costs for the

c

ars a e costs ght truc meetin . The he requ sts ha ments. Th een e

ese co d to be part o th sidered f rt of th t osts an e enefits

ac nefits, ar rules. comply r ch imate hanges nge fr footprin year t Otherw ar to e the co unt fo shown in MY eet based 012-20

.

2o sho timated c st per v T icle andIIncremen o sho l total co
e est ernatives mental t assenge sts in m cars. ions for les V e variou to 3 alternativ w th es for lig are the

ovem es that th

on an a se ma rage ve acture e basi ould h

e ass chnology assed on to ts and fin nsumer The incr ental l cost tabl

e a es show

, es in m and be ons of ts, fin ars. L
e not r in th uded, s alysis e fines societ
osts.

submission (

n in Tab HTSA-2 09-005 099), Ch ysler p nted a II-2a [

### 332

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### Table a Estimated Incremental Co er Manufacturer’s Plans

acturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### VII-1 sts or Fines ov To get to Adjusted Baseline Average Cost per Vehicle (2007$) Passenger Cars

Manuf

BMW 456 445 489 476 464

Chrysler 8 7 74 72 69 3 6

Daimle r 362 413 570 570 555

Ford 92 91 88 102 97 General Motors 176 173 170 173 168

Honda - - - - -
Hyundai - - - - -

Kia - - - - -

Mazda - - - - -

Mitsu bishi 197 191 177 168 166

Nissan - - - - -

Porsche 661 861 849 834 818

Subaru 215 205 203 198 191

Suzuki 12 46 45 43 42

Tata 361 748 751 731 710

Toyota - - - - -
Volkswag en 171 166 161 157 152

Total/A verage 91 88 92 95 92

### 333

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### Table VII-1b Estimated Incremental C er Manufacturer’s Plans

er MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### osts or Fines ov To get to Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

Manufactur

BMW 2 2 26 25 24 73 72 5 3 9

Chrysler 490 470 463 453 442

Daimle r 729 714 708 805 785

Ford 495 460 455 449 439 General Motors 498 487 497 472 459

Honda 108 124 119 112 110

Hyundai 100 96 93 90 87

Kia 144 136 133 129 126

Mazda 12 10 10 10 9

Mitsu bishi 489 469 463 464 451

Nissan 492 461 458 453 442

Porsche 1,059 1,033 1,009 1,146 1,115

Subaru 63 57 51 42 40

Suzuki 621 717 700 684 668

Tata 903 890 923 949 1,709

Toyota 91 97 91 85 82

Volks wagen 395 1,084 1,387 1,364 1,333

Total/Av erage 367 367 367 356 353

### 334

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### Estimated Incremental er Adjusted Baseline Average Cost per Vehicle (2007$) Passenger Cars

verage Cost per Vehicle

Manufacturer 

### Table VII-2a Costs or Fines ov

Preferred Alternative A

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 157 196 255 443 855

Chrysler 794 1,043 1,129 1,270 1,358

Daim ler 160 198 564 944 1,252

Ford 1,641 1,537 1,533 1,713 1,884 General Motors 552 896 1,127 1,302 1,323

Honda 33 98 205 273 456

Hyund ai 559 591 768 744 838

Kia 110 144 177 235 277

Mazda 632 656 799 854 923

Mitsubish i 644 620 1,588 1,875 1,831

Nissan 119 323 707 723 832

Porsche 316 251 307 390 496

Subaru 413 472 988 1,385 1,361

Suzuki 242 625 779 794 1,005

Tata 243 258 370 532 924

Toyota 31 29 41 121 126

Volkswagen 293 505 587 668 964

Total/Average 505 573 690 799 907

### 335

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### Table VII-2b Estimated Incremental over Adjusted Baseline

Preferred Alternative

otal Incremental Costs

rer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### Costs or Fines Total Incremental Costs in Millions (2007$) Passenger Cars

T

Manufactu

BMW 4 5 9 1 3 5.5 7.7 4.4 82.2 61.4

Chrysler 325.3 444.7 464.5 498.2 542.8

Daimler 33.8 40.1 137.8 249.0 339.3

Ford 2,408.9 2,283.1 2,403.0 2,642.2 2,937.0 General Motor s 875.6 1,383.9 1,637.5 1,936.6 2,003.9

Honda 30.3 104.1 223.1 249.5 424.1

Hyunda i 210.4 233.7 303.7 380.1 434.3

Kia 33.0 47.0 75.8 126.8 151.9

Mazd a 178.8 216.3 302.2 352.8 388.0

Mitsubish i 71.0 64.8 140.0 154.3 151.4

Nissan 97.9 268.7 603.7 669.2 787.1

Porsche 13.0 10.8 10.4 12.7 16.5

Subaru 75.7 82.8 182.3 283.5 281.5

Suzuki 17.5 51.1 70.6 79.9 103.6

Tata 8.8 13.0 18.2 33.9 60.5

Toyot a 50.0 56.6 85.4 263.4 279.5

Volkswa gen 127.3 251.6 304.2 379.2 562.2

Total/Average 4,602.7 5,610.1 7,056.8 8,493.8 9,825.0

### 336

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### Estimated Inc ted Baseline Average (2007$) Passenger Cars

% Annual Increase Average Cost per Vehicle

cturer 3 4 5 6 

### Table VII-2c remental Costs or Fines over Adjus Cost per Vehicle

3

Manufa MY 201 2 MY 201 MY 201 MY 201 MY 201

BMW 69 114 178 349 728

Chrysler 335 445 481 571 695

Daiml er 77 121 492 851 1,137

Ford 292 461 467 621 754 General Motors 470 555 667 781 857

Honda - - 31 90 131

Hyun dai 211 261 412 372 467

Kia 4 26 102 152 205

Mazda 273 308 462 516 538

Mitsubish i 550 582 1,106 1,241 1,218

Nissan 6 53 229 263 361

Porsche 217 178 213 275 342

Subaru 319 379 767 842 824

Suzuk i 143 267 334 342 521

Tata 161 188 298 438 809

Toyota - - - - 24

Volkswagen 200 429 505 563 832

Total/Average 191 248 317 394 493

### 337

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### Table VII-2d Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Passenger Cars

3% Annual Increase Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 20.0 33.5 65.9 143.7 307.9

Chrysler 137.2 189.7 197.7 224.2 278.0

Daimler 16.3 24.5 120.3 224.4 308.0

Ford 429.2 685.6 732.5 957.4 1,175.1 General Motors 744.8 856.7 969.3 1,161.4 1,297.1

Honda - - 33.5 82.1 122.3

Hyundai 79.4 103.4 162.9 190.2 242.3

Kia 1.2 8.5 43.5 82.1 112.1

Mazda 77.3 101.8 174.7 213.1 226.4

Mitsubishi 60.7 60.9 97.5 102.1 100.7

Nissan 4.6 44.0 195.6 243.6 341.7

Porsche 8.9 7.7 7.2 8.9 11.4

Subaru 58.6 66.4 141.4 172.5 170.5

Suzuki 10.3 21.9 30.2 34.5 53.6

Tata 5.8 9.5 14.7 27.9 53.0

Toyota - - - - 53.0

Volkswagen 86.7 213.9 261.4 319.8 485.3

Total/Average 1,741.1 2,427.9 3,248.3 4,188.0 5,338.3

### 338

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### Table VII-2e Estimated Incremental C ver Adjusted Baseline osts or Fines o Average Cost per Vehicle (2007$) Passenger Cars

4% Annual Increase Average Cost per Vehicle

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 86 147 228 421 822

Chrysler 499 860 952 1,082 1,137

Daimler 94 149 542 922 1,219

Ford 425 686 710 955 1,138 General Motors 486 712 932 1,141 1,239

Honda 33 27 134 220 343

Hyundai 506 521 751 734 838

Kia 8 71 169 209 210

Mazda 560 637 860 915 922

Mitsubishi 567 612 1,511 1,706 1,670

Nissan 16 122 554 653 739

Porsche 233 196 274 363 457

Subaru 336 417 827 1,206 1,184

Suzuki 160 412 559 582 798

Tata 177 214 348 510 897

Toyota 21 20 34 122 205

Volkswagen 216 455 560 641 931

Total/Average 254 366 500 640 764

### 339

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### Estimate aseline Total Increme lions (2007$) Passenger Cars

Table VII-2f d Incremental Costs or Fines over Adjusted B ntal Costs in Mil

4% Annual Increase Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 24.8 43.1 84.3 173.2 347.4

Chrysler 204.5 366.8 391.7 424.8 454.3

Daimler 19.8 30.1 132.5 243.2 330.4

Ford 623.3 1,018.5 1,113.0 1,472.5 1,773.7 General Motors 771.0 1,099.3 1,354.0 1,696.7 1,875.6

Honda 30.3 29.3 146.2 200.5 319.5

Hyundai 190.4 206.2 296.9 375.4 434.5

Kia 2.5 23.3 72.2 112.8 115.2

Mazda 158.6 210.1 325.4 378.3 387.5

Mitsubishi 62.5 63.9 133.2 140.4 138.1

Nissan 13.0 101.8 473.1 604.5 699.5

Porsche 9.6 8.5 9.3 11.8 15.2

Subaru 61.6 73.1 152.5 246.8 244.9

Suzuki 11.5 33.7 50.6 58.6 82.2

Tata 6.4 10.8 17.1 32.5 58.7

Toyota 34.2 39.3 71.2 265.1 456.8

Volkswagen 93.9 227.0 290.0 363.6 543.0

Total/Average 2,317.8 3,584.8 5,113.1 6,800.8 8,276.5

### 340

DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### Table VII-2g Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Passenger Cars

5% Annual Increase Average Cost per Vehicle

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 102 180 283 498 915

Chrysler 745 1,303 1,462 1,653 1,727

Daimler 110 182 591 988 1,313

Ford 743 1,245 1,261 1,583 1,923 General Motors 503 823 1,187 1,425 1,594

Honda 50 109 271 375 606

Hyundai 747 882 1,057 1,052 1,124

Kia 49 128 197 261 369

Mazda 577 718 1,166 1,407 1,427

Mitsubishi 583 650 2,534 3,213 3,141

Nissan 294 491 965 1,064 1,125

Porsche 255 234 340 456 578

Subaru 358 456 1,372 1,723 1,679

Suzuki 182 959 1,267 1,316 1,540

Tata 194 247 403 581 990

Toyota 31 29 52 129 212

Volkswagen 233 494 620 723 1,041

Total/Average 362 558 749 918 1,088

### 341

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### Table VII-2h Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Passenger Cars

5% Annual Increase Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 29.6 52.8 104.6 204.9 387.0

Chrysler 305.0 555.4 601.5 648.8 690.2

Daimler 23.3 36.8 144.6 260.7 355.7

Ford 1,091.0 1,849.2 1,977.1 2,441.5 2,998.2 General Motors 797.1 1,271.7 1,723.6 2,119.1 2,413.3

Honda 45.1 115.9 294.3 342.1 563.9

Hyundai 281.1 349.0 418.1 538.0 582.7

Kia 14.7 42.0 84.0 140.5 202.1

Mazda 163.2 236.8 441.1 581.5 599.9

Mitsubishi 64.3 68.0 223.4 264.4 259.7

Nissan 242.2 408.5 823.8 984.9 1,065.0

Porsche 10.5 10.1 11.6 14.8 19.3

Subaru 65.6 79.9 253.1 352.8 347.4

Suzuki 13.1 78.4 114.8 132.3 158.6

Tata 7.0 12.5 19.9 37.1 64.8

Toyota 50.0 56.6 107.7 279.8 472.8

Volkswagen 101.1 246.2 321.3 410.4 607.1

Total/Average 3,303.9 5,469.6 7,664.3 9,753.6 11,787.6

### 342

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### Estimated Inc ted Baseline Average (2007$) Passenger Cars

verage Cost per Vehicle

Manufacturer 

### Table VII-2i remental Costs or Fines over Adjus Cost per Vehicle

6% Annual Increase A

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 119 218 338 575 1,020

Chrysler 761 1,964 2,470 2,800 2,795

Daim ler 127 215 646 1,060 1,406

Ford 1,435 1,749 1,886 2,344 2,707 General Motors 519 1,026 1,457 1,965 2,182

Honda 50 191 386 470 845

Hyund ai 731 845 1,277 1,175 1,315

Kia 39 140 268 386 597

Mazda 593 756 1,534 2,110 2,279

Mitsubish i 605 642 2,688 2,934 2,933

Nissan 679 899 1,755 1,851 1,997

Porsche 277 278 406 550 699

Subaru 374 494 2,093 2,736 2,692

Suzuki 198 1,186 1,589 1,629 1,816

Tata 210 280 452 653 1,084

Toyota 97 93 119 266 404

Volkswagen 249 527 675 806 1,151

Total/Average 526 758 1,064 1,321 1,546

### 343

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### Table VII-2j Estimated Incremental C ver Adjusted Baseline

se osts or Fines o Total Incremental Costs in Millions (2007$) Passenger Cars

6% Annual Increa Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 34.4 64.2 125.0 236.6 431.1

Chrysler 311.7 837.5 1,015.9 1,098.8 1,117.1

Daimler 26.8 43.4 158.0 279.5 381.0

Ford 2,106.8 2,598.6 2,956.3 3,615.3 4,220.7 General Motors 823.3 1,585.1 2,115.6 2,922.7 3,304.9

Honda 45.5 203.8 419.4 428.7 786.5

Hyundai 275.2 334.3 505.1 600.6 681.5

Kia 11.8 45.8 114.7 208.1 326.9

Mazda 167.9 249.3 580.3 872.2 958.1

Mitsubishi 66.8 67.1 236.9 241.5 242.5

Nissan 559.3 747.9 1,498.5 1,712.6 1,890.5

Porsche 11.4 12.0 13.8 17.8 23.3

Subaru 68.7 86.6 386.2 560.2 556.9

Suzuki 14.3 97.0 144.0 163.9 187.0

Tata 7.6 14.1 22.3 41.6 71.0

Toyota 155.1 181.4 247.5 579.6 899.2

Volkswagen 108.2 262.6 349.8 457.3 671.3

Total/Average 4,794.8 7,430.9 10,889.2 14,037.0 16,749.7

### 344

DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### Table VII-2k Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Passenger Cars

7% Annual Increase Average Cost per Vehicle

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 135 251 393 652 1,124

Chrysler 778 2,195 2,696 3,064 3,096

Daimler 138 248 696 1,131 1,500

Ford 1,619 1,902 2,079 2,559 2,990 General Motors 536 1,248 1,912 2,417 2,904

Honda 50 454 737 804 1,181

Hyundai 770 911 1,493 1,464 1,750

Kia 55 251 489 698 963

Mazda 610 1,013 2,097 2,299 2,704

Mitsubishi 622 675 2,758 3,098 3,182

Nissan 867 1,280 2,356 2,482 2,574

Porsche 294 317 472 643 831

Subaru 391 533 1,844 2,629 2,711

Suzuki 220 2,182 3,127 3,294 3,331

Tata 221 313 507 724 1,183

Toyota 295 276 371 574 774

Volkswagen 271 565 736 894 1,267

Total/Average 616 952 1,361 1,634 1,941

### 345

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### Table VII-2l Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Passenger Cars

7% Annual Increase Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 39.1 73.8 145.3 268.3 475.3

Chrysler 318.5 935.8 1,109.0 1,202.4 1,237.7

Daimler 29.1 50.1 170.1 298.4 406.4

Ford 2,376.6 2,825.5 3,259.6 3,946.3 4,662.1 General Motors 849.5 1,927.5 2,777.0 3,594.5 4,398.3

Honda 45.5 483.5 801.3 733.5 1,099.0

Hyundai 289.8 360.4 590.4 748.4 907.0

Kia 16.5 81.8 208.8 375.9 527.5

Mazda 172.6 334.0 793.1 950.0 1,136.8

Mitsubishi 68.6 70.5 243.1 255.0 263.1

Nissan 714.6 1,064.1 2,012.0 2,296.6 2,435.8

Porsche 12.1 13.7 16.0 20.9 27.7

Subaru 71.7 93.4 340.2 538.3 561.0

Suzuki 15.9 178.5 283.3 331.4 343.1

Tata 8.0 15.8 25.0 46.2 77.5

Toyota 470.0 536.5 770.8 1,249.3 1,722.8

Volkswagen 117.8 281.8 381.1 507.2 738.6

Total/Average 5,615.8 9,326.9 13,926.3 17,362.4 21,019.7

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### Table VII-2m Estimated Incremental C ver Adjusted Baseline

Max Net Benefits cle

Manufacturer MY MY MY MY MY 

### osts or Fines o Average Cost per Vehicle (2007$) Passenger Cars

Average Cost per Vehi

2012 2013 2014 2015 2016

BMW 141 301 426 619 1,020

Chrysler 778 2,159 2,452 2,660 2,608

Daimler 143 292 723 1,104 1,406

Ford 1,624 1,962 2,149 2,545 2,891 Genera l Motors 536 1,292 1,939 2,235 2,400

Honda 49 365 562 609 891

Hyundai 757 945 1,304 1,196 1,321

Kia 48 264 422 503 602

Mazda 615 1,219 1,654 2,171 2,437

Mitsubishi 622 730 2,787 3,077 3,072

Nissan 882 1,229 2,080 2,086 2,104

Porsche 299 372 510 605 699

Subaru 396 582 1,961 2,589 2,580

Suzuki 226 1,248 1,649 1,721 1,876

Tata 227 357 535 697 1,084

Toyota 264 242 327 414 464

Volkswagen 271 620 769 855 1,151

Total/Average 612 954 1,282 1,460 1,628

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### Estimate aseline Total Incremen llions (2007$) Passenger Cars

otal Incremental Costs

rer 

### Table VII-2n d Incremental Costs or Fines over Adjusted B tal Costs in Mi

Max Net Benefits T

Manufactu MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 40.7 88.4 157.5 254.7 431.1

Chrysler 318.5 920.5 1,008.7 1,044.1 1,042.3

Daiml er 30.3 59.0 176.8 291.1 381.0

Ford 2,384.6 2,914.3 3,368.6 3,924.9 4,507.0 General Motors 849.5 1,995.5 2,816.9 3,323.3 3,633.9

Honda 44.1 389.1 610.9 555.4 828.4

Hyun dai 284.7 373.8 515.6 611.7 684.6

Kia 14.3 86.2 180.5 271.2 330.0

Mazda 174.1 402.0 625.5 897.2 1,024.7

Mitsubish i 68.6 76.3 245.7 253.2 254.0

Nissan 727.1 1,021.8 1,776.2 1,930.7 1,991.5

Porsche 12.3 16.1 17.4 19.6 23.3

Subaru 72.7 102.0 361.9 530.0 533.7

Suzuk i 16.3 102.0 149.4 173.1 193.2

Tata 8.2 18.0 26.4 44.4 71.0

Toyota 420.2 470.7 679.9 901.0 1,033.5

Volkswagen 117.8 309.2 398.2 485.4 671.3

Total/Average 5,584.0 9,345.0 13,115.9 15,511.1 17,634.6

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### Table VII-2o Estimated Incremental Costs or Fines over Adjusted Baseline Average C e (2007$) Passen

Total Cost = Total Benefit Average Cost per Vehicle

acturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### ost per Vehicl ger Cars

Manuf

BMW 163 334 481 685 1,091

Chrysler 8 2, 2, 2,6 2, 00 057 483 34 639

Daiml er 165 325 773 1,164 1,472

Ford 1,646 1,966 2,198 2,612 2,947 General Motors 558 1,325 1,994 2,455 2,871

Honda 50 630 873 917 1,192

Hyundai 757 984 1,494 1,415 1,569

Kia 131 354 604 750 847

Mazda 637 1,257 1,709 2,248 2,489

Mitsu bishi 649 768 2,847 3,143 3,154

Nissan 958 1,451 2,278 2,288 2,549

Porsche 327 416 576 687 793

Subaru 424 626 2,022 2,726 2,777

Suzuki 253 2,043 2,420 2,496 2,550

Tata 249 390 584 763 1,155

Toyota 496 465 622 727 772

Volk swagen 299 659 829 932 1,228

Total/A verage 675 1,065 1,440 1,653 1,878

### 349

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### Estimate aseline Total Incremen llions (2007$) Passenger Cars

efit Table VII-2p

d Incremental Costs or Fines over Adjusted B tal Costs in Mi

Total Cost = Total Ben Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 47.1 98.1 177.9 281.8 461.4

Chrysler 327 .5 877.2 1,021.3 1,033.6 1,055.0

Daimler 34.9 65.7 188.9 307.1 398.9

Ford 2,416.9 2,920.9 3,445.3 4,029.2 4,595.3 General Motors 884.4 2,046.5 2,896.8 3,651.8 4,348.3

Honda 45.5 670.5 949.5 837.0 1,108.5

Hyundai 284.7 389.1 590.8 723.1 813.5

Kia 39. 1 115.7 257.9 404.2 464.1

Mazda 180. 3 414.7 646.3 929.0 1,046.7

Mitsubishi 71. 6 80.3 251.0 258.7 260.8

Nissan 789.1 1,206. 2 1,945.5 2,117.4 2,412.6

Porsche 13.4 18.0 19.6 22.3 26.4

Subaru 77.7 109.7 373.0 558.2 574.6

Suzuki 18.3 167.0 219.2 251.1 262.6

Tata 9.0 19.7 28.8 48.6 75.6

Toyota 788.5 903.3 1,292.2 1,581.4 1,718.4

Volkswagen 129.7 328.4 429.6 529.1 716.2

Total/Average 6,15 7.9 1 10,431. 14,733.6 17,563.5 20,339.0

### 350

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### Estimated Inc ted Baseline Average C e (2007$) Light Trucks

verage Cost per Vehicle

Manufacturer 

### Table VII-3a remental Costs or Fines over Adjus ost per Vehicl

Preferred Alternative A

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 252 272 338 402 827

Chrysler 409 527 876 931 1,170

Daim ler 98 123 155 189 260

Ford 465 633 673 1,074 1,174 General Motors 336 513 749 807 986

Honda 233 217 370 457 806

Hyund ai 693 630 1,148 1,136 1,113

Kia 406 467 582 780 1,137

Mazda 144 241 250 354 480

Mitsub ishi 39 77 553 686 1,371

Nissan 398 489 970 1,026 1,362

Porsche 44 76 109 568 640

Subaru 1,036 995 1,016 1,060 1,049

Suzuki 66 1,797 1,744 1,689 1,732

Tata 66 110 137 198 690

Toyota 130 150 384 499 713

Volkswagen 44 77 552 557 606

Total/Average 322 416 621 752 961

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### referred Alternative

Manufacturer MY MY MY MY MY 

### Table VII-3b Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Light Trucks

P Total Incremental Costs

2012 2013 2014 2015 2016

BMW 51.4 49.9 64.5 70.5 141.2

Chrysl er 283.4 313.0 451.2 442.5 540.6

Daimler 10.6 14.0 21.2 24.6 32.8

Ford 396.3 595.1 649.5

. 1,006 4 1

. 1,069 Genera l Motors 506.9 787.9 1,001.2 1,113.6 1,322.7

Honda 148.0 146.7 235.0 256.3 439.2

Hyundai 40.3 63.9 119.2 107.4 102.4

Kia 35.6 48.0 66.6 92.4 130.9

Maz da 8.8 15.6 17.0 26.3 34.7

Mitsub ishi 1.9 3.6 29.2 39.0 75.8

Nissan 161.2 191.4 393.9 401.8 518.8

Porsche 0.6 1.1 1.8 9.7 10.7

Subaru 101.5 89.5 100.8 123.0 123.0

Suzuki 0.3 29.8 35.0 34.7 34.6

Tata 2.0 3.7 5.5 8.7 29.3

Toyo ta 115.6 148.6 420.4 552.2 767.5

Volkswag en 4.6 10.9 83.9 71.2 75.4

Total/Average 8 .7 .8 .4 .7 1,868. 2,512 3,695 4,380 5,448

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### Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

3% Annual Incr Average Cost pe cle

Manu 

### Table VII-3c

ease r Vehi

facturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 96 106 179 463 867

Chrysler - 29 242 315 477

Dai mler 49 66 89 112 155

For d 146 295 364 725 720 General

s Motor (213) (52) 94 165 292

Honda 135 121 204 263 452

Hy undai 216 227 607 620 611

Ki a - 19 195 323 675

Mazda 47 62 71 85 215

Mitsubishi - - 154 215 478

Ni ssan 67 111 356 423 632

Porsche (28) 11 43 486 535

Su u

bar 385 396 377 396 397

Suzuki (11) 782 735 708 709

Ta ta (11) 33 66 110 575

Toyota 15 21 214 281 454

Volkswagen (28) 11 514 480 507

Total/Average 1 78 234 348 484

### 353

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### Table VII-3d Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Light Trucks

3% Annual Increase Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 19.7 19.5 34.3 81.4 148.1

Chrysler - 17.4 124.7 149.5 220.6

Daimler 5.3 7.6 12.2 14.6 19.6

Ford 124.5 277.2 351.1 679.0 655.9 General Motors (321.9) (79.3) 125.5 227.3 391.4

Honda 85.4 82.0 129.7 147.5 246.5

Hyundai 12.5 23.1 63.0 58.6 56.2

Kia - 1.9 22.3 38.2 77.7

Mazda 2.9 4.0 4.8 6.3 15.5

Mitsubishi - - 8.1 12.3 26.4

Nissan 27.2 43.4 144.7 165.8 240.6

Porsche (0.4) 0.2 0.7 8.3 8.9

Subaru 37.7 35.6 37.5 45.9 46.5

Suzuki (0.1) 12.9 14.7 14.5 14.2

Tata (0.3) 1.1 2.7 4.8 24.4

Toyota 13.7 20.9 234.8 311.6 488.8

Volkswagen (2.9) 1.6 78.1 61.4 63.1

Total/Average 3.4 469.1 1,388.8 2,027.0 2,744.3

### 354

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### Table VII-3e Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

4% Annual Increase Average Cost per Vehicle

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 103 140 218 524 939

Chrysler 130 287 610 678 867

Daimler 49 94 133 173 232

Ford 512 796 850 1,262 1,266 General Motors - 196 373 432 609

Honda 226 207 407 468 695

Hyundai 554 561 1,201 1,208 1,182

Kia - 136 331 447 798

Mazda 82 179 177 305 383

Mitsubishi - 33 471 532 1,238

Nissan 315 381 741 839 1,153

Porsche (17) 39 87 546 612

Subaru 946 967 981 1,028 1,017

Suzuki - 812 764 736 993

Tata - 60 116 176 663

Toyota 15 30 329 465 729

Volkswagen (11) 39 530 535 579

Total/Average 166 293 506 646 830

### 355

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### Table VII-3f Estimated Incremental C ver Adjusted Baseline

% Annual Increase

Manufacturer MY MY MY MY MY 

### osts or Fines o Total Incremental Costs in Millions (2007$) Light Trucks

4 Total Incremental Costs

2012 2013 2014 2015 2016

BMW 21.1 25.7 41.6 92.0 160.3

Chrysler 90.1 170.8 313.9 322.4 400.8

Daimler 5.3 10.7 18.2 22.4 29.3

Ford 436.1 4 748. .0 .2 6 820. 2 1,18 3 1,15 Genera l 3 2 4 6 Motors - 301. 498. 596. 816.

Honda 143.3 140.2 258.2 262.5 7 378.

Hyunda i 32.2 57.0 124.7 114.3 108.7

Kia - 14.0 37.9 52.9 91.8

Mazda 5.0 11.6 12.0 22.6 27.6

Mitsubi shi - 1.5 24.9 30.2 68.5

Nissan 127.6 149.2 0 301. 328.6 0 439.

Porsche (0.2) 0.6 1.4 9.4 10.2

Subaru 92.7 87.0 97.4 119.2 119.2

Suzuki - 13.4 15.3 15.1 19.8

Tata - 2.0 4.7 7.7 28.2

Toyota 8 4 13.3 29.5 360.4 514. 784.

Volkswage n (1.2) 5.4 80.6 68.4 72.0

Total/Average 8.4 1.0 1.0 965.2 1,76 3,01 3,76 4,708.3

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### % Annual Increase cle

Manufacturer MY MY MY MY MY 

### Table VII-3g Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

5 Average Cost per Vehi

2012 2013 2014 2015 2016

BMW 169 209 316 591 1,028

Chrysler 365 559 1,120 1,216 1,432

Daimler 60 116 177 233 309

Ford 1,207 1,663 1,882 2,258 2,225 Genera l Motors 297 628 866 968 1,136

Honda 258 234 611 750 1,047

Hyundai 711 685 1,923 1,909 1,862

Kia 47 293 556 782 1,157

Maz da 248 408 419 519 768

Mitsubish i - 66 1,037 1,189 1,556

Nissan 613 723 2,142 2,148 2,315

Porsche - 66 131 612 695

Subaru 1,225 1,220 1,365 1,374 1,357

Suzuki 16 1,998 1,895 1,837 2,096

Tata 17 94 165 242 751

Toyo ta 63 187 594 734 991

Volkswag en - 66 574 596 661

Total/Average 417 633 1,036 1,186 1,361

### 357

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### Table VII-3h Estimated Incremental C ver Adjusted Baseline

% Annual Increase

Manufacturer MY MY MY MY MY 

### osts or Fines o Total Incremental Costs in Millions (2007$) Light Trucks

5 Total Incremental Costs

2012 2013 2014 2015 2016

BMW 34.5 38.4 60.4 103.7 175.4

Chrysler 252.9 332.0 5 576. 9 577. 0 662.

Daimler 6.5 13.3 24.2 30.3 39.0

Ford 1,02 8.4 .5 .0 .4 .6 3 1,56 7 1,81 5 2,11 6 2,02 Genera l 5 8.1 6.1 3.8 Motors 448.7 964. 1,15 1,33 1,52

Honda 163.6 158.3 9 387. 4 420. 6 570.

Hyunda i 41.4 69.5 199.7 180.6 171.3

Kia 4.1 30.1 63.6 92.5 133.2

Maz da 15.1 26.5 28.4 38.5 55.4

Mitsubish i - 3.0 54.8 67.6 86.1

Nissan 248.4 283.0 869.7 841.4 9 881.

Porsche - 1.0 2.1 10.5 11.6

Subaru 120.0 109.8 135.5 159.4 159.2

Suzuki 0.1 33.1 38.0 37.7 41.9

Tata 0.5 3.2 6.6 10.6 31.9

Toyota 8 7 6.6 55.8 185.1 650. 812. 1,06

Volkswag en - 9.3 87.3 76.2 82.2

Total/Average 0.0 3.5 60.6 11.7 18.6 2,42 3,82 6,1 6,9 7,7

### 358

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### Table VII-3i Estimated Incremental ver Adjusted Baseline

6% Annual Increase ehicle

Manufacturer MY MY MY MY MY 

### Costs or Fines o Average Cost per Vehicle (2007$) Light Trucks

Average Cost per V

2012 2013 2014 2015 2016

BMW 242 301 411 657 1,115

Chrysler 382 672 1,412 1,665 2,062

Daimler 71 139 221 294 392

Ford 1,149 1,662 1,684 2,596 2,723 Genera l Motors 308 1,268 1,655 1,976 2,163

Honda 786 675 1,368 1,531 2,032

Hyundai 1,517 1,430 2,398 2,442 2,434

Kia 123 979 1,179 1,654 1,990

Maz da 302 622 637 785 1,031

Mitsubish i - 99 2,082 2,130 2,747

Nissan 989 1,136 2,525 2,481 3,194

Porsche 11 93 181 678 777

Subaru 1,254 1,225 1,466 1,507 1,562

Suzuki 33 3,317 2,987 2,928 3,152

Tata 33 127 215 314 844

Toyo ta 98 338 797 952 1,315

Volkswag en 17 99 618 662 744

Total/Average 516 943 1,394 1,706 2,007

### 359

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### se Table VII-3j Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Light Trucks

6% Annual Increa Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 49.4 55.2 78.6 115.3 190.4

Chrysler 264.3 399.2 726.9 791.5 952.8

Daimler 7.7 15.9 30.2 38.1 49.4

Ford 978.7 1,562.8 1,626.1 2,431.8 2,480.1 General Motors 465.3 1,948.3 2,212.4 2,726.4 2,901.6

Honda 498.7 456.5 867.9 858.4 1,107.9

Hyundai 88.2 145.1 249.0 231.1 223.8

Kia 10.8 100.6 134.9 195.8 229.1

Mazda 18.4 40.3 43.1 58.3 74.4

Mitsubishi - 4.6 110.0 121.2 151.9

Nissan 400.6 444.8 1,025.0 972.0 1,216.4

Porsche 0.1 1.4 2.9 11.6 13.0

Subaru 122.8 110.1 145.5 174.9 183.2

Suzuki 0.2 54.9 59.9 60.2 63.0

Tata 1.0 4.3 8.6 13.7 35.9

Toyota 86.8 335.0 873.3 1,054.0 1,415.6

Volkswagen 1.7 14.0 94.0 84.6 92.5

Total/Average 2,994.7 5,693.1 8,288.4 9,938.9 11,381.2

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### verage Cost per Vehicle

Manufacturer 

### Table VII-3k Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

7% Annual Increase A

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 303 376 509 728 1,203

Chrysler 393 1,603 2,145 2,379 3,014

Daim ler 87 167 265 354 474

Ford 1,250 1,694 1,657 2,911 3,263 General Motors 319 1,682 2,247 2,868 3,030

Honda 940 819 1,774 1,943 2,479

Hyund ai 1,601 1,515 2,433 2,514 2,558

Kia 151 1,007 1,289 2,449 3,050

Mazda 985 1,984 1,940 2,027 2,382

Mitsubish i 17 132 2,078 2,262 3,101

Nissan 1,003 1,172 2,297 2,415 3,195

Porsche 27 126 225 744 871

Subaru 1,260 1,230 1,579 1,624 1,740

Suzuki 49 3,442 2,889 2,882 3,146

Tata 50 160 264 391 943

Toyota 164 572 1,221 1,359 1,703

Volkswagen 28 127 662 722 826

Total/Average 575 1,222 1,716 2,181 2,549

### 361

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### Estimate aseline Total Incremen llions (2007$) Light Trucks

otal Incremental Costs

rer 

### Table VII-3l d Incremental Costs or Fines over Adjusted B tal Costs in Mi

7% Annual Increase T

Manufactu MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 61.8 69.1 97.2 127.9 205.5

Chrysler 271.9 952.1 1,104.1 1,130.6 1,392.6

Daim ler 9.4 19.1 36.2 46.0 59.9

Ford 1,064.4 1,592.7 1,599.7 2,726.6 2,972.3 General Motors 482.0 2,583.3 3,003.5 3,957.2 4,064.4

Honda 9 4 8 596.9 554.1 1,125. 1,089. 1,351.

Hyund ai 93.1 153.8 252.7 237.9 235.3

Kia 13.2 103.4 147.4 290.0 351.1

Mazda 59.9 128.5 131.5 150.4 171.9

Mitsubish i 0.8 6.1 109.8 128.7 171.6

Nissan 406.4 459.0 932.5 946.1 1,217.0

Porsche 0.4 1.8 3.6 12.8 14.5

Subaru 123.4 110.7 156.7 188.5 204.1

Suzuki 0.2 57.0 58.0 59.2 62.8

Tata 1.5 5.4 10.6 17.1 40.1

Toyota 145.5 566.3 1,338.3 1,504.4 1,833.8

Volkswagen .7 3 .7 2.9 17.9 100 92. 102

Total/Average 3,333.6 7,380.3 10,208.4 12,705.0 14,451.3

### 362

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### Table VII-3m Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

Max Net Benefits Average Cost per Vehicle

Manufacturer 2012 2013 2014

BMW 314 383 491 567 976

Chrysler 459 1,482 2,154 2,371 2,512

Daimler 153 216 293 343 392

Ford 1,386 1,794 1,738 2,500 2,566 General Motors 380 1,356 1,703 2,021 2,095

Honda 1,043 908 1,883 2,013 2,159

Hyundai 1,183 1,118 2,177 2,183 2,131

Kia 461 964 1,309 2,437 2,692

Mazda 855 1,399 1,321 1,407 1,505

Mitsubishi 94 187 2,100 2,251 2,802

Nissan 1,117 1,229 2,391 2,443 2,925

Porsche 94 175 252 728 783

Subaru 1,366 1,311 1,556 1,594 1,621

Suzuki 127 2,328 2,212 2,151 2,265

Tata 121 214 297 374 850

Toyota 936 1,208 1,498 1,613 1,711

Volkswagen 94 176 690 711 744

Total/Average 761 1,249 1,665 1,948 2,082

### 363

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### emental Costs in Millions (2007$) Light Trucks Table VII-3n Estimated Incremental Costs or Fines over Adjusted Baseline Total Incr

Max Net Benefits Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 64.2 70.3 93.8 99.5 166.6

Chrysler 317.6 880.7 1,109.0 1,126.8 1,160.9

Daimler 16.6 24.7 40.0 44.6 49.4

Ford 1,180.8 1,686.7 1,678.3 2,341.5 2,336.6 General Motors 573.4 2,082.6 2,276.5 2,789.0 2,810.9

Honda 662.1 614.2 1,194.7 1,128.8 1,177.0

Hyundai 68.8 113.5 226.1 206.5 196.0

Kia 40.4 99.0 149.7 288.5 309.8

Mazda 51.9 90.6 89.5 104.4 108.6

Mitsubishi 4.5 8.6 111.0 128.1 155.0

Nissan 452.4 481.1 970.6 957.1 1,114.0

Porsche 1.2 2.6 4.0 12.5 13.0

Subaru 133.8 117.9 154.5 185.0 190.2

Suzuki 0.6 38.5 44.4 44.2 45.2

Tata 3.6 7.2 12.0 16.3 36.1

Toyota 829.9 1,196.7 1,641.8 1,785.8 1,842.4

Volkswagen 9.8 24.9 104.8 90.9 92.5

Total/Average 4,411.6 7,539.9 9,900.7 11,349.5 11,804.4

### 364

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### Table VII-3o Estimated Incremental Costs or Fines over Adjusted Baseline Average Cost per Vehicle (2007$) Light Trucks

Total Cost = Total Benefit Average Cost per Vehicle

Manufacturer 2012 2013 2014 2015 2016

BMW 320 399 530 600 1,020

Chrysler 464 1,504 2,184 2,405 2,713

Daimler 153 233 326 371 430

Ford 1,386 1,811 1,766 2,645 2,942 General Motors 380 1,583 2,035 2,558 2,597

Honda 1,052 927 1,939 2,073 2,390

Hyundai 1,825 1,620 2,670 2,682 2,649

Kia 466 980 1,342 2,464 2,692

Mazda 2,121 3,292 2,849 2,964 3,035

Mitsubishi 99 209 2,139 2,279 3,052

Nissan 1,062 1,197 2,389 2,436 2,929

Porsche 99 197 291 755 821

Subaru 1,366 1,311 1,638 1,697 1,741

Suzuki 127 3,242 2,947 2,885 2,995

Tata 127 237 336 407 894

Toyota 942 1,208 1,605 1,689 1,855

Volkswagen 99 193 723 739 782

Total/Average 780 1,344 1,806 2,157 2,366

### 365

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### Table VII-3p Estimated Incremental Costs or Fines over Adjusted Baseline Total Incremental Costs in Millions (2007$) Light Trucks

Total Cost = Total Benefit Total Incremental Costs

Manufacturer MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW 65.3 73.3 101.1 105.3 174.1

Chrysler 321.4 893.7 1,124.2 1,143.2 1,253.5

Daimler 16.6 26.6 44.5 48.1 54.3

Ford 1,180.8 1,702.2 1,704.8 2,478.0 2,679.8 General Motors 573.4 2,431.0 2,720.6 3,528.8 3,484.3

Honda 667.7 627.1 1,230.7 1,162.4 1,303.1

Hyundai 106.1 164.5 277.3 253.7 243.7

Kia 40.9 100.7 153.5 291.7 309.8

Mazda 128.9 213.2 193.1 220.0 219.0

Mitsubishi 4.8 9.7 113.0 129.6 168.8

Nissan 430.2 468.8 970.2 954.2 1,115.4

Porsche 1.3 2.9 4.7 12.9 13.7

Subaru 133.8 117.9 162.6 197.0 204.2

Suzuki 0.6 53.7 59.1 59.3 59.8

Tata 3.8 8.0 13.5 17.8 38.0

Toyota 834.7 1,196.7 1,759.3 1,869.6 1,996.7

Volkswagen 10.4 27.2 109.9 94.5 97.3

Total/Average 4,520.5 8,117.4 10,742.1 12,566.3 13,415.5

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### direct costs for producing or acquiring each technology to improve fuel onomy, depending on the complexity of the technology and the time frame over which costs

cern is whether achieving the fuel economy improvements required by lternative CAFE standards would require manufacturers to compromise the performance,

e compliance with stricter AFE standards. While exact dollar values of th ese attributes to buyers are extremely difficult

n

e

for

technology costs that include costs for the accompanying changes in vehicle design that are necessary to maintain performance,

Financial Impacts of Raising CAFE Standards the m of HTSA believes s the Technology Costs Table V-5 above provides the technology cost estimates used in this analysis. The technology cost estimates are intended to represent manufacturers’ direct costs for high-volume production of vehicles with these technologies and sufficient experience with their application so that all cost reductions due to “learning curve” effects have been fully realized. Costs are then modified by applying indirect cost multipliers ranging from 1.11 to 1.64 to the estimates of vehicle manufacturers’

ec are estimated. Chapter V also discusses technology cost issues in much more detail.

Potential opportunity costs of improved fuel economy An important con

a carrying capacity, safety, or comfort of their vehicles. If it did so, the resulting sacrifice in the value of these attributes to vehicle buyers would represent an additional cost of achieving th required improvements in fuel economy, and thus of manufacturers’ C to infer from vehicle purchase prices, it is nevertheless clear that changes in these attributes ca affect the utility that vehicles provide to their owners, and thus their value to potential buyers.

The agency has approached this potential problem by developing cost estimates for fuel economy-improving technologies that include any additional manufacturing costs that would be necessary to maintain the performance, comfort, capacity, or safety of any vehicle to which those technologies are applied. Theoretically, opportunity costs could also include any foregone opportunities to enhance these products for consum ers. However, estimating values for foregon opportunities is an even tougher task. So, the agency followed the precedent established by the National Academy of Sciences (NAS) in its 2002 an alysis of the costs and benefits of improving fuel economy by raising CAFE standards. 200 

### The NAS study estimated “constant performance and utility” costs for fuel economy technologies, and the agency has used these as the basis developing the technology costs it employed in analyzing manufacturer’s costs for complying with alternative standards.

NHTSA fully acknowledges the difficulty of estimating

capacity, and utility. A fuller discussion of this issue is presented in Chapter VIII.

The automobile industry is currently experiencing substantial economic hardship, even in absence of new fuel economy standards. Many major firms have announced a steady strea plant closings, layoffs, and employment of new em ployees at reduced wages. N these hardships have much to do with the condition of the national economy and perhap price of gasoline, and little, if anything, to do with the stringency of CAFE standards for the

200 National Academy of Sciences, Costs and Effectiveness of Increasing Corporate Average Fuel Economy Standards , 2002.

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### y

ial

exibility mechanisms in setting fuel economy standards.

policy

f the ered

n the other hand, the agency is mindful that CAFE standards do affect the relative
t

ubstantial financial assistance provided to the automobile industry by the U.S. Government. In addition to the large sums provided to Chrysler and GM,

the

ehicle current or recent model years. We believe that given the scale of the recent decline in industr sales, and the restrictiveness of private credit markets, that near-term developments will be compelled by the industry’s immediate financial situation, rather than by the long-term financ consequences of this rulemaking.

Market forces are already requiring manufacturers to improve the fuel economy of their vehicles, as shown both by changes in product plans reported to NHTSA, and by automaker public announcements. The improvements in fleet fuel economy required by this rule are consistent with the pressure induced by changing consumer preferences.

The various compliance flexibility mechanisms permitted by EISA, including flexible and alternative fuel vehicles, banking, averaging, and trading of fuel economy credits will also reduce compliance costs to some degree. By statute, NHTSA is not permitted to consider the benefits of fl

On May 19, 2009, President Obama announced a National Fuel Efficiency Policy. 201 

### This reflected a consensus among stakeholders (includi ng 14 automobile companies) on desirable and achievable fuel economy standards. We believe that this consensus reflects the view o industry that given current economic conditions, a nd in the light of Federal assistance proff via various means, that the standards finalized here are economically practicable.

O competitiveness of different vehicle manufacturers, and recognizes that standards more stringen than those promulgated here could have a more detrimental effect.

NHTSA’s central problem is to determine what new standards might be economically practicable within the MY 2012-2016 time frame, given the state of both the domestic and the international auto industries. The complexity of an economic practicab ility determination has been materially increased by the s

Congress has appropriated $7.5 billion (to support a maximum of $25 billion in loans under Section 136 of EISA to support the development of advanced technology vehicles and components in the United States. 202 

### On June 23, 2009, the Department of Energy announced first three loans under this program: $5.9 billio n for Ford for advanced vehicle manufacturing, $1.3 billion for Nissan for vehicle and battery manufacturing, and $0.5 billion for electric v start-up Tesla Motors. 203

201 The White House, “President Obama Announces National Fuel Efficiency Policy,” May 19, 2009. Available at http://www.whitehouse.gov/the_press_office/President-Obam a-Announces-National-Fuel-Efficiency-Policy/. (last

s in Sect ion 136 of EISA. This language is amended and funds 0-343). See also the

dvanced Technologies

09). accessed March 4, 2010).
202202 The authorizing language for this provision i are appropriated in the Emergency Economic Stabilization Act of 2008 (H.R. 1424, Pub.L. 11 DOE Advanced Technology Vehicle Manufacturing Loan Program website: http://www.atvmloan.energy.gov/ (last accessed February 6, 2009).
203 US Department of Energy, “Obama Administration Awards First Three Auto Loans for A to Ford Motor Company, Nissan Motors and Tesla Motors,” June 23, 2009. Available at: http://www.atvmloan.energy.gov/ (last accessed July 21, 20

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### “within the financial capability

f the industry, but not so stringent as to threaten substantial economic hardship for the

rimary ways in which the agency seeks to ensure that its standards are within the nancial capability of the industry is to attempt to ensure that manufacturers have sufficient lead

s

each manufacturer based on a 3 percent iscount rate for future fuel savings.

ile l

igher fuel economy standards are expected to increase the price of passenger cars and light

s not

here is a broad consensus in the economic literature that the price elasticity for demand for Given the foregoing, therefore, the agency has decided that in this exceptional situation, economic practicability must be determined based on whether the expenditures needed to achieve compliance with the final MY 2012-2016 standards are

o industry,” no matter who contributes the fu nds. We have set the MY 2012-2016 CAFE standards so that they are both technologically and economically feasible. In principle, most vehicles meeting the standard will provide social benefits to the public at large and private benefits to automobile owners greater than their extra cost.

One of the p fi time to modify their manufacturing plans to comply with the final standards in the model years covered by them. Employing appropriate assumptions about lead time in our analysis helps to avoid applying technologies before they are ready to be applied, or when their benefits are insufficient to justify their costs. It also helps avoid basing standards on the assumption that technologies could be applied more rapidly than practically achievable by manufacturers. NHTSA considers these matters in its analysis of issues including refresh and redesign schedules, phase-in caps, and learning rates.

NHTSA further considers the sales and employment impacts of the final standards on individual manufacturers as part of its efforts to dete rmine whether the standards are economically practicable. The sales analysis looks at a purchasing decision from the eyes of a knowledgeable and rational consumer, comparing the estimated cost increase versus the payback in fuel saving over 5 years (the average new vehicle loan) for each manufacturer. This relationship depends on the cost-effectiveness of technologies available to d

The agency does not have the capability to predict the capital investment needs of the automob industry to install fuel economy technologies, nor the capability to determine the level of capita investments available to specific manufacturers in the future.

The Impact of Higher Prices on Sales and Employment

H trucks. The potential impact of higher vehicle prices on sales was examined on an industry wide basis for passenger cars and light trucks separately. The analysis conducted for this rule doe have the precision to examine effects on individual manufacturers or different vehicle classes.

T automobiles is approximately –1.0. 204
, 205
, 206 

### Thus, every one percent increase in the price of the

204 Kleit, A.N. (1990). “The Effect of Annual Changes in Automobile Fuel Economy Standards,” Journal of Regulat Economics , vol. 2, pp 151-172.
205 Bordley, R. (1994). “An Overlapping Choice Set Model of Automotive Price Elasticities,” Transportation Research B , vol 28B, no 6, pp 401-408. 206 McCarthy , P.S. (1996). “Market Price and Income Elasticities of New Vehicle Demands,” The Review of ory

Economics and Statistics , vol. LXXVII, no. 3, pp. 543-547.

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### costs

ail price increase eeded to cover the fuel economy technology inve stments will manufacturers be able to pass on

nd upon how consumers value the fuel conomy improvements. Consumer valuation of fuel economy improvements often depends

on the price of gasoline, which has recently been very volatile. To the extent that we have ccurately predicted the price of gasoline and consumers reactions, and manufacturers can pass n all of the costs to consumers, then the sales and employment impact analyses are reasonable. manufacturers only increase retail prices to the extent that consumers value these fuel conomy improvements, then there would be no impact on sales.

ales losses are predicted to occur only if consumers fail to value fuel economy improvements at ast as much as they pay in higher prices. If manufacturers are unable to raise prices beyond the level of consumer's valuation of fuel savings, then manufacturer's profit levels would fall but there would be no impact on sales. Likewise, if fuel prices rise beyond levels used in this analysis, consumer's valuation of improved fuel economy could increase to match or exceed their initial investment, resulting in no impact or even an increase in sales levels.

To estimate the average value consumers place on fuel savings at the time of purchase, we assume as a starting point 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. 208 

### The present values of these savings were calculated using a 3 percent discount rate. We used a fuel price forecast (see Table VIII-3) that included taxes, because this is what consumers must pay. Fuel savings were calculated over the first 5 years and discounted back to a present value. The National Auto mobile Dealers Associa tion (NADA) agreed with several of the agency’s assumptions, saying the National Program proposal reasonably assumes vehicle would reduce sales by one percent. Elas ticity estimates assume no perceived change in the quality of the product. However, in this case, vehicle price increases result from adding technologies that improve fuel economy. If consumers do not value improved fuel economy at all, and consider nothing but the increase in price in their purchase decisions, then the estimated impact on sales from price elasticity could be applied directly. However, we believe that consumers do value improved fuel economy, because they reduce the operating cost of the vehicles. How much consumers value fuel economy is an ongoing debate. We know that different consumers value different aspects of their vehicle purchase, and we are trying to get an estimate of how average consumers value fuel economy, but we do not have a reliable consumer survey on this issue. We also believe that consumers consider other factors that affect their and have included these in the analysis. Thus, this analysis makes one set of assumption, but many different assumptions and estimates of the sales impact could be made.

One issue that significantly affects this sales analysis is: How much of the ret

n to consumers? The estimates reported below assume that manufacturers will be able to pass all of their costs to improve fuel economy on to consumers. However, the ability of manufacturers to pass the compliance costs on to consumers will depe
207 

### e up

a

o If

e

S le

207 Gron, Ann and Swenson, Deborah, 2000, “Cost Pass-Through in the U.S. Automobile Market”, The Review of Economics and Statistics , 82: 316-324.

208 National average financing terms for automobile loans are available from the Board of Governors of the Federal Reserve System G.19 “Consumer Finance” release. See http://www.federalreserve.gov/releases/g19/ (last accessed March 4, 2010).

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### uyers will value any fuel savings associated with the purchase of a new motor vehicle over a 5- yr period, rather than over a vehicle's full en at high fuel prices, consumers who view fuel economy as an important essed to make the case for buying a more fuel efficient new vehicle pital costs associated with doing so cannot be recouped in short order. Of course, for purposes of calculation payback, real-world purchaser finance costs, opport costs add ma nce all should be accounted for.

The agency believes that su ay er o r e 5 year horizon when contemplating the h l ge e ctors into the calculation to represent a e o ff mers’ buying considerations.

First, consumers m e to pay at e time purchasing the vehicle. We took s axes i y st a d w d t p n by state to determine a nationa hted-average sales tax of 5.5 percent.

Second, we considered insurance costs over the year period. More expensive vehicles will require more expensive collision and comprehensive ( e.g. , theft) car insurance. The increase in insurance costs is estimated from the average va e of collision plus comprehensive insurance as a proportion of average new vehicle price. comprehensive insurance is the portion of insurance costs that depend on vehicle urance Information Institute 209 

### provides the average value of collision plus c 06 as $448. The average value of a new passenger car in 2006, according partment of Energy, was $22,651. 210 

### Using sales volumes from Ward motiv earbo 0 20 sales and the MY 2008 base vehicle a erage pric et d a ge ger d an average light truck price. The av se r n u m as $26,201 and for all light trucks was $2 l h e s xact price, the ratio of light truck price s ca w e r trations for passenger cars and light s f 6 w pli t l ht vehicle price. 211 

### The result is an average f $ 21 

### 6 ge prices and estimated sales volumes e ca e e y m ow a percent increase in price affects e re al

Dividing the insura st by ag of ve e rtion of comprehensive plus ion insurance as 1.86% of th e price of a vehicle. If we assume that this premium is proport l to the icl i en 1 ent of the new b useful life. Ev purchase crite ria will be hard-pr if the up-front ca

u

nity , and itional intena costs

con mers m consid several ther facto s over th
purc ase of a ne w vehic e. The a ncy add d these fa how n increas in techn logy costs might a ect consu

ight co ales t nsid r the sales tax es they have th of n 2007 b te an eighte hem by opulatio l weig

5

lu Collis ion plus value. The Ins omprehensive insurance in 20 to the U.S. De ’s Auto es, we d e Y ermine ok 20

n avera 8 for MY
passen 07

car an v erage ba $29,678 ( price fo 007 do all pa sse lars). W ger cars il e this m sing this thod doe ethod w not give an e s to pa senger r pr ices as appli d and on- oad regis truck or 200 ere ap ed to ge an overa l new lig price or light ve hicles of 24,033 2 

### for 200 . Avera

are n eded be use pric lasticit is an esti ate of h the p rcent dec ase in s es.

nce co the aver e price a new hicle giv s the propo collis iona new veh e pric e, t repres ts about .86 perc

209 Insurance Information Institute, 2008, “Average Expen ures for Auto Insurance By State, 2005-2006,” available at http://www.iii.org/media/facts/statsbyissue/au / (last accessed March 4, 2010).

U.S. Department of Energy, 2008, “Average Price of a New Car, 1970-2006,” available at ttp://www1.eere.energy.gov/vehiclesandfuels/facts/2008_fotw520.html (last accessed March 4, 2010).
211 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.
212 $29,678/$26,201 = 1.1327 * $22,651 = $25,657 average price for light trucks. In 2006, passenger cars were 54% of the on road fleet and light trucks were 46% of the on road fleet, resulting in an average light vehicle price for 2006 of $24,033. dit
to
210 h

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### ehicle price and insurance is paid each year for the five year period we are considering for payback. Discounting that stream of insurance costs back to present value indicates that the present value of the component of insurance cost at vary with vehicle price is equal to 8.5 percent of the vehicle’s price at a 3 percen

.
Third, we considered that oans to finance their purchase. The average new vehi oan is 5 years at a 6 pe t rat 213 

### t these terms the average person taking a loan wi 16 t m r the icle he 5 years than a consumer paying cash fo e v t o ase. sc the additional 3.2 percent (16 percent o g e ear discount
rate 215 

### results in a disco pr al . n o king a loan. Multiplying that by the 7 r t n th e sumer would pay 10.2 percent more than t t fo n percent discount
rate.

Fourth, we considered th e le c years and expressed this as a tage of the ew vehicle price. In other words, if the price of the vehicle increases due to fue nomy gi res e e go up proportionately. Th age resale ice of a vehicle after 5 years is about 35% 216 

### of the original purchase p Disc e l v c u percent discount rate (35 percent * .8755) gives an effective resid alue at new of 30.6 percent.

We add these four factors together. At a 3 percent discount rate, the consumer considers she

ould get 30.6 percent back upon resale in 5 years, but will pay 5.5 percent more for taxes, 8.5 ercent more in insurance, and 10.2 percent more for loans, results in a 6.48 percent return on the technology (30.6 percent – 5.5 percent - 8.5 percent – 10.2 ice per vehicle is multiplied by 0.9352 (1 – 0.0648) before

nt et

ring that to the $22,651

,202 v

s th t discount rate

70 percent of new vehicle purchasers take out l cle l ll pay ehicle for
percen he tim rcen ir veh
214 

### Di e. A
over t unting ore fo purch r th / 5 years) a per year t e ver the 5 f years usin o nt mid-y a 3 perc unted esent v ue of 14 87 perce t higher f r those ta

0 pe cent that ake a loa , means at the av rage con he re ail price r loans th e consum er discou ted at a 3

e resi dual valu (or resa value) of the vehi le after 5
percen l eco n technolo es, the ale valu of the v hicle will

e aver rice. pr ounting th residua alue ba k 5 years sing a 3 ual v

c p increase in price for fuel economy percent). Thus, the increase in pr subtracting the fuel savings to determine the overall net consumer valuation of the increase of costs on his purchase decision.

A sample calculation for passenger cars under the Preferred Alternative at a 3 percent discou rate in MY 2012 is an estimated retail price increase of $505 which is multiplied by 0.9352 to g a residual price increase of $472. The estimated fuel savings over the 5 years of $310 at a 3 percent discount rate results in a net cost to consumers of $162. Compa average price of a passenger car is a 0.71 percent price increase. Passenger car sales were estimated to be about 9,123,000 passenger cars fo r MY 2012. With a price elasticity of –1.0, a 0.71 percent increase in net cost to consumers co uld result in an estimated loss in sales of 65
passenger cars.

213 New car loan rates in 2007 average about 7.8 percent at commercial banks and 4.5 percent at auto finan companies, so their average is close to 7 percent.
214 Based on www.bankrate.com auto loan calculator for a 5 year loan at 6 percent.
215 For a 3 percent discount rate, 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.
216 216 Consumer Reports, August 2008,“What That Car Really Costs to Own,” available at http://www.consumerreports.org/cro/cars/pricing/what-tha t-car-really-costs-to-own-4-08/overview/what-that-ca ce

rreally-costs-to-own-ov.htm (last accessed March 4, 2010).

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### nd

les

which the sales volumes of different vehicle models might change in response to fuel economy increases, financial outlays for

dditional technology, and increases in civil penalties that could all result from increased CAFE standards.

There are studies that as a result of an increase in price, eve at consumers will purcha e of their improved fuel economy. In general, changes in prices or othe ew vehicles market will also have consequences for the used

ehicle et. Specif t r ew ve nd to crease prices of used c ting them in service for slightly lon r, the age savings over the lifetimes of the new vehicles will exceed the i e , prompting an increase in f new vehicles d odel year le affects. A onsequence, prices for ehicles are also l e, leadin creases in th ate at which used es are retired fro crapped”) d with new ls. In turn, this will

ate the effects o s on fuel co n and GHG ; at the same otal criteria poll ns from th icle fleet m decrease, as newer, ehicle ed vehicles.

General Motors (OAR-472-6953.1) commented that the NHTSA usually does a manufacturer by acturer analysis so far agen only done an y-wide analysis. GM stated that the agencies must complete their analysis of sales and employment impacts of ividua rs before standards. In response, NHTSA does not believe that estimating sales impacts by manufacturer is required, even if the agency may

ve done it in the past. With all the uncertai nty regarding consumer valuation of fuel economy

rovements and consumer surplus, estimating sales impacts is an inexact science, and the gency has even less confidence at this time in estimating sales by manufacturer.

R-472-3651.1) commented on the results of two reports that estimated an increase in sales. The first, ”CAFE and the U.S. Auto Industry Revisited,” Citi Investment Research and Planning, analyzed two regulatory scenarios, specifically, “CAFE 2020,” equivalent to reaching a combined 35 mpg in 2020, and National Pavley, equivalent to reaching on sales, costs, and profits Tables VII-4 a, b, and c show the estimated impa ct on sales for passenger cars, light trucks, and combined, respectively. The Preferred Alternative has the highest combined passenger car a light truck sales increases predicted using this methodology.

Note that there is no feedback loop between this sales analysis and the Volpe model. These sa estimates are not used to determine additional or less mileage traveled or fuel consumed. The Volpe model does not attempt to estimate the extent to

a

estimate that people may hold onto their vehicles longer rything else being held equal. This analysis estimates th se more vehicles becaus

characteristics of the n r mark v in ically, any action tha vehicles, and in turn ger. Howeve ais es prices for n ause owners of exis ncy estimates that the v ncrease in their pric s hicles will also te vehicles to keep alue of fuel

sales o used v uring most m ikely to declin s that the ru g to slight in s a c

e r vehicl
accentu m service (“s f the standard and replace nsumptio mode emissions time, t lower-polluting v utant emissio s replace us e entire veh ay also

manuf of sales, but ci es have industr

standards on ind l manufacture setti ng final

ha imp

a

Two commenters agreed with the agency analysis that sales would go up. The Investor Network on Climate Risk (OAR-0472-7243.1) and the New Jersey Department (NHTSA-2009-0059- 0073) agreed with the findings that project a positive impact on vehicle sales due to reduced fuel costs outweighing the costs of meeting the new emissions and fuel economy standards.

Walter McManus (OA

a combined 35 mpg in 2015. The analysis estimated the impacts relative to a baseline forecast, and concluded that the National Program will help “Detroit 3”

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### el

anufacturing plants’ worth of jobs, and that consumers will save money too despite pump prices of $2.50/gallon, because fuel savings will more than offset vehicle price increases. The second report, ”Fixing Detroit: How Far, How Fast, How Fuel Efficient” was described as analyzing three different fuel economy increases--30% (to 35 mpg), 40% (to 37.7 mpg), and 50% (to 40.4 mpg). The model used in the report estimated the impact of improving fuel economy on vehicle costs, ve hicle prices, and consumer demand. McManus stated that the report found that the Detroit 3 would always be better off increasing fuel economy, and that the more they improved, the better off they would be, because consumers value fuel economy more than it costs manufact urers to add technology. McManus stated that a 30% increase in fuel economy, as with the National Program, is estimated to increase vehicle costs by $1,715, while the fuel savings value to consumers will be $2,578.

In response, the agency notes that these analyses appear to come to similar conclusions as NHTSA’s, that fuel savings should be valued by c onsumers at levels higher than price increases, and that if that occurs, vehicle sales would increase. profits, which will go up by $3 billion per year based on the relative value consumers put on fu costs compared to vehicle price, the future price of fuel, and the combined direct and indirect costs incurred to improve fuel economy. McManus further stated that the report also found that the new standards will save two m

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### Table VII-4a Potent Sales

MY MY MY MY MY 016 

### ial Impact on Passenger Cars

2012 2013 2014 2015 2
Preferred -65,202 46,801 422 168,334 227,039 103,
3% -9,302 54,3 ,838 131,092 196,193 69 91

4% -18,694 54,899 1 162,650 239,959 05, 781

5% -23,655 57,280 1 181,100 253,045 23, 764

6%

- 1 1 66,461 28,276 69, 191 09,779 77,630

7% -82 - ,835 2,203 19,695 53,158 90,246 Max Net

- 1 81,930 946 42,573 89,321 47,282 TC = TB - -1 1 93,933 6,361 16,698 57,137 00,283



### T IPotential Impact on Sales Light rucks

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 

### able VI 4b

T

Preferred 48,561 106,658 139,893 171,920 213,868
3% 11,021 44,784 72,918 111,536 137,306

4% 7,287 52,946 93,466 136,400 175,282
5% -21,608 28,302 62, 836 121,314 169,990
6% -27,282 16,460 55, 058 102,421 139,073

7% -27,556 -7,222 40,806 59,225 81,159 Max Net -36,600 18,512 57, 310 90,736 124,057 TC = TB -38,972 7,428 48, 414 74,060 102,287

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### Table VII-4c Potential Impact on Sales Passenger Cars and Light Trucks Combined

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016
Preferred -16,641 153,459 243, 315 340,255 440,907

3% 1,719 99,153 164,757 242,628 333,499

4% -11,407 107,845 199, 247 299,050 415,241

5% -45,262 85,582 186, 601 302,414 423,036

6% -93,743 44,735 124, 249 212,200 316,703

7% - 110,391 -9,425 60,501 112,383 171,405
Max Net - 118,530 19,458 99,883 180,056 271,339 TC = TB - 132,905 -8,933 65,112 131,196 202,570



### 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 automobile companies and their suppliers to do research and development and testing on new t echnologies to determine their capabilities, durability, platform introduction, etc. The agency does not anticipate a huge number of incremental jobs in the engineering field. Often 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 that new technologies would have on the production line. Again, we do not anticipate a large number of increm ental 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 pa rts and there would be a small increase in the number of production employees, but we do not 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 than the first two areas discussed above. In order to get an estimate of potential job losses per sales loss, we examined recent U.S. employment (original equipment manufacturers and suppliers) and U.S. production. Total employment in 2000 reached a peak in the Motor Vehicle and Parts Manufacturing sector of the economy averaging 1,313,600 workers. Since then there has been a steady decline to

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### ployment in 2008 was about two-thirds of the 2000 level and in the first six months of 2009 employment has been

w ion 1,096,900 in 2006 and more rapid decreases in 2007, 2008, and 2009. Em

around 680,000, averaging about one-half of the peak in the year 2000. Table VII-5 shows ho many vehicles are produced by the average worker in the industry. Averaging the informat shown for 2000-2008, the average U.S. domestic employee produces 11.3 vehicles (the same number as in 2008). Thus, one could assume that projected sales loss divided by 11.3 would give an estimate of the potential employment loss.

Table VII-5

U.S. Light Duty Vehicle Production and Employment

U.S. Light Vehicle Production Motor Vehicle Vehicle and Parts U.S. Employment 217 

### Production per Employee

2000 12,773,714 1,313,600 9.7

2002 13,568,385 1,151,300 11.8

2004 1 3,527,309 1,112,700 12.2

2006 12,855,845 1,096,900 11.7

2008 9,870,473 876,300 11.3

Total/Average 62,595,726 5,550,800

ombining MY 2012-2016, we estimate that the Preferred Altern C ative will result in a small net increase in sales (65,480), and thus in employment (5,795). 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, consumers can elect to defer vehicle purchases by continuing to operate existing vehicles. Eventually, however, the rising maintenance costs for aging vehicles may make replacements look more attractive.

217 U.S. employment data is from the Bureau of Labor Statistics, available at http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?series_id=CES3133600101&data_t ool=XGtable (last accessed

arch 4, 2010). M

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### g existing vehicles in service for somewhat longer. In this ase, the possibility exists that there may be permanent sales losses, compared with a situation in However, vehicle owners may also react to pe rsistently higher vehicle costs by permanently owning fewer vehicles, and keepin

c which vehicle prices are lower.

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### Table VII-6

Impact on Auto Industry Employment by Alternative

Passenger Cars and Light Trucks Combined

(Jobs)

MY 2012 MY 2013 MY 2014 MY 2015 MY 2016
Preferred -1,473 13,580 21,532 30,111 39,018
3% 152 8,775 14,580 21,471 29,513
4% -1,009 9,544 17,632 26,465 36,747

5% -4,006 7,574 16,513 26,762 37,437
6% -8,296 3,959 10,996 18,779 28,027
7% -9,769 -834 5,354 9,945 15,169 Max 1,722 8, 839 15,934 24,012 Net -10,489 TC = TB -11,762 -791 5,762 11,610 17,927



### Scrappage Rates

The effect of this rule on the use and scrappage of older vehicles will be related to its effects on new vehicle prices, the fuel efficiency of new vehicle models, and the total sales of new

v ehicles. If the value of fuel savings resulting from improved fuel efficiency to the typical

replacement by new models – to accelerate slightly, thus accentuating the anticipated effect of th rule on fleet-wide fuel consumption and CO emissions. However, if potential buyers value he

hich potential buyer of a new vehicle outweighs the average increase in new models’ prices, sales of new vehicles will rise, while scrappage rates of used vehicles will increase slightly. This will cause the “turnover” of the vehicle fleet – that is, the retirement of used vehicles and their

e 2 

### future fuel savings resulting from the increased fuel efficiency of new models at less than t increase in their average selling price, sales of new vehicles will decline, as will the rate at w used vehicles are retired from service. This effect will slow the replacement of used vehicles by new models, and thus partly offset the anticipated effects of the final rules on fuel use and emissions.

Because the agencies are uncertain about how the value of projected fuel savings from the final rules to potential buyers will compare to their estimates of increases in new vehicle prices, weDELIBERATIVE DOCUMENT – DO NOT RELEASE 

### 379

have not attempted to estimate explicitly the effects of the rule on scrappage of older vehicles and the turnover of the vehicle fleet.

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## III. BENEFITS FROM IMPROVED FUEL ECONOMY

Improving new vehicles’ fuel efficiency enefits to their buyers and users by reducing fuel consumption an lifetimes, stimulating increased vehicle use through the fuel ect, and increasing vehicles’ driving range so that they require less frequent refueling. At the same time, the reduction in fuel use s from highe y ces fits . onomy by lowe e cost of e ic extern hat resu U.S. p m

umption and cludi cing the f petroleum, lowering ential co disruption in ow of oil s, and po reducing l outlay ure orted oil supplies d cushion ec ono st their po interrup n. el co ion also e econ of en ental e es m fu ction an cluding g the impacts on hum h from issions of crite ollutants educing onomi from tial

ges in the glo imate caus eenhou emission

ese benefits are offset by crease in e that res om add icle use el econom ound effec ell as by costs from increase gestion, es, and noise d by incre ehicle us ould partial et by an es in the utility new vehicles provide to th yers (and quent ow nufacturers cha ther vehicl ibutes as p their strat to comp er

economy req e ss, the tot omic be rom req higher nomy are likel e substanti d the agen s attempte antify f these components caref

’s analys ternative i e standard ld apply to MY 20 6 passenger c light tru ates th be m ado re stringent CAFE standards separate ly for each mo year over its lifespan in the U.S. vehicle fleet, extending from the initial year when a model year is offered for sale through the year when nearly all vehicles from that model year have been retired from service. Each category of benefits resulting from increased fuel economy is measured by comparing the future values of fuel consumption and its associated economic impacts under alternative increases in CAFE standards – and the corresponding improvements in fuel economy – to their value under the baseline alternative, which would extend current CAFE standards to apply to future model years, thus resulting in only minimal improvement in fuel economy.

Because these benefits occur throughout the lifetimes of vehicles whose fuel economy increases in response to higher CAFE standards, their projected values during each future year of their respective lifetimes must be discounted to their present values as of the time each model year is produced and sold in order to facilitate comparison to the costs incurred by vehicle manufacturers for improving fuel economy. 218 

### Thus the selection of an appropriate discount rate 

## V

provides direct b d fuel costs throug hout those vehicles’ economy rebound eff

that result requiring r fuel econom also produ wider bene to the U.S

ec ring th conom alities t lt from etroleu

cons imports, in ng redu pr ice o the pot sts from the fl import ssibly federa s to sec imp an nsumpt the U.S. lowers th my again omic costs tential vironm tio xternaliti Reducing fu resulting fro el produ d use, in reducin an healt

em ria air p , and r future ec c damages poten chan bal cl ed by gr se gas s.

Th partly the in fuel us ults fr ed veh due to the fu y reb t, as w added the d con crash cause ased v e. They w also be subse ly offs y loss that
nge o eir bu art of ners) if ly with high ma fuel e attr egies uirements. Nev rthele a l econ nefits f uiring fuel

eco y to b al, an cy ha d to qu each o ully.

NHTSA is of al ncreases in th CAFE s that wou 12- 201 ars and cks estim e economic nefits fro pting mo del

218 Discounting to the year when each model year was produced allows future economic benefits from improvingeach model year’s fuel economy to be compared to added production costs for making those vehicles more fuelefficient, which are assumed to be incurred at the time those vehicles are manufactured.

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### is also an important issue in the agency’s fits from requiring cars and light trucks to achieve higher fuel economy

This chapter first discusses the forecasts, assump ons, and parameter values that NHTSA uses to analyze m l t g the magnit hese benefits tio inclu detai scussi the fu onomy reboun d the agen ssumption about its magnitu Next, hapter sses the method cy employs stima di rec fits to cle bu resulti om higher my, as we e nat f potential welfar es to rs from nges in other v tics ight pan ovem el economy apter then de es e use tima der s to ec – and in the c ductions in g house missi he glo conom at re om lower f tion and co mption also de es how increa exter osts resultin m added vehicle are cal ted.

Finally presents irical e ates of value o of t benefit at the agency ould resu m est i ng a e C stand or MY 16 passen d light truc hese e s a esented ica its, as t undisco nomic valu f future efits , a iscoun o their ent val sing alterna unt rates.

A. Inputs for Analysis of Economic I cts analysis of bene

.

ti benefits from i proved fue economy. Because i plays a cri tical role in determinin ude of t , this sec n also des a led di on of el ec d effect an cy’s a de. the c discu s the agen

ono to e te the t bene vehi yers ng fr fuel ec ehicle ll as th that ure o

acco e loss
ents buye cha The characteris tails the procedur m that ar m d to es y r te broa

imp in fu benefit . ch
onomy the U.S. ase of re reen gas e ons, t bal e y – th sult fr uel produc nsu . It scrib the ses in nal c g fro use cula

, the chapter

s w emp stim the f each hese s th estimate lt fro ablish lternativ AFE phys ards f 2012-20
ger cars an

unted eco ks. T

es o stimate ben re pr nd d in ted t l un
pres otal

ues u tive disco

Basic mpa

The ma conom alues o se ben and co om in ed fue nomy are infl by a number cast les, ter and ptio se include vel of vehicle s during o affe high rds, the relationship between increases in these vehicles’ easured fuel efficiency and their actual on-road fuel efficiency, assumptions about the lifetimes and usage of future model-year vehicles, the magnitude of the fuel economy rebound effect, future fuel prices and taxes, the values of economic externalities resulting from petroleum consumption and imports, the economic values of environmental externalities re sulting from fuel production, distribution, and use, the value of increased refueling range, and the discount rate applied to future benefits and costs. The following sections discuss the specific forecast s, parameter values, and assumptions NHTSA has employed to estimate benefits and costs from alternative CAFE standards that would require increases in the fuel economy of passenger cars and light trucks produced during model years 2012 through 2016.

Projected Sales of MY 2012-2016 Passenger Cars and Light Trucks A critical variable affecting the total economic benefits from requiring improvements in passenger car and light truck fuel economy is the number of vehicles likely to be produced under stricter CAFE standards. Projections of total pa ssenger car and light truck sales for future years (see Table VIII-1a and VIII-1b) were obtained from the Energy Information Administration’s (EIA) Annual Energy Outlook 2010 Early Release ( AEO 2010 ) , a standard government reference for projections of energy production and consumption in different sectors of the U.S. gnitudes and e ic v f the efits sts fr creas l eco uenced the le of fore ales variab each m parame el year values, cted by assum er CAFE ns. The

standa d EPA-m

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### economy. 219 

### In using these forecasts, NHTSA made the simplifying assumption that projected from 2012 through 2016 represented the

.

light truck sales for the passenger car and light

sts in

PCA)

ctured primarily for use in the transportation f not more than 10 individuals.” Thus there are two general groups of automobiles that qualify

rily”

y

n-passenger automobile s in subsections (a) and (b), respectively, of 9 CFR Part 523.5.

n sales of cars and light trucks during each calendar year likely production volumes for the corresponding model year. The agency did not attempt to establish the exact correspondence between projected sales during individual calendar years and production volumes for specific model years.

NHTSA estimated production volumes of passenger cars and light trucks for individual manufacturers by first calculating their respective shares of total production for each model year These shares were calculated by dividing each manufacturer’s planned car or light truck production volumes by the sum of planned production volumes reported by all manufacturers. 220 

### Next, the resulting estimates of individual manufa cturer’s shares of total car and light truck production during a model year were applied to forecast total car and

orresponding calendar year from AEO 2010 . This produces estimates of c truck production by each manufacturer during each model year from 2012 through 2016. NHTSA employs this process in order to develop production forecasts that are consistent with both the production plans that individual manufacturers reported to the agency, and the foreca of total sales of new cars and light trucks repo rted by the Energy Information Administration AEO 2010

. 221



### Changes in Vehicle Classification Passenger automobiles were defined in the Energy Policy and Conservation Act of 2007 (E as “any automobile (other than an automobile capable of off-highway operation) which the Secretary [ i.e. , NHTSA] decides by rule is manufa

o under EPCA as non-passenger automobiles or light trucks: (1) those defined by NHTSA in its regulations as other than passenger automobiles because they were not manufactured “prima for transporting up to ten individuals; and (2) those expressly excluded from the passenger category by statute due to their capability for off-highway operation, regardless of whether the were manufactured primarily for passenger transportation. NHTSA’s classification rule directly tracks those two groups of no 4

In the final rule for model year 2011, NHTSA tightened the coverage of its regulatory definitio of “light truck” to ensure that 2 wheel drive (2WD ) versions of an SUV are not classified as light trucks under Part 523.5(b) simply because that same SUV model is also available in a 4WD

219 U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2009 , Updated Reference Case (April 2009), Supplemental Table 57, http://www.eia.doe.gov/oiaf/aeo/supplement/stimulus/arra/excel/suptab_57.xls (last accessed July 24, 2 009).
0 These product plans are submitted to NHTSA in response to the agency’s request for information from vehicle manufacturers, and include responses to very detailed questions about vehicle model characteristics that influence

NHTSA’s CAFE compliance 22

fuel economy. The baseline market forecast mix of products (make/model, engines, transmissions, etc.) that NHTSA has used in its analysis is based on the confidenti al product plan information manufacturers submit to the
agency. 221 For manufacturers that did not submit plans, planned production volumes for model years 2012-2016 were assumed to be the same as their model year 2008 production volumes as recorded in database.

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### s

djusted Sales Forecasts

reflect the reasonable assumption that while sales of cars or light trucks produced uring a model year will be distributed over more than one calendar year, production and sales version. 222 

### In addition, 2WD SUVs may not be properly classified as light trucks simply because a manufacturer asserts that their base fo rm has no back seat and thus would “provide greater cargo-carrying than passenger-carrying volume” according to Part 523.5(a)(4). No change in the regulatory definition of a light truck is necessary to implement this clarification. It results in the re-classification of an average of 1,400,000 2WD SUVs from light trucks to passenger cars in each of the five model years th at would be covered by the alternative standard considered in this rulemaking.

A Tables VIII-1a and VIII-1b report forecast produc tion volumes of passenger cars and light trucks for each manufacturer during model years 2012 through 2016. The figures reported in these tables reflect the AEO 2009 Reference Case forecasts of passenger car and light truck sales for 2012-2016, the planned production volumes for model years 2012-2106 reported to NHTSA by individual manufacturers, and the reclassification of certain light truck models as passenger cars. The tables also d for each model year will ultimately be equal.

222 In order to be properly classifiable as a light truck under Part 523, a 2WD SUV must either be over 6,000 lbs GVWR and meet 4 out of 5 ground clearance characteristics to make it off-highway capable under Part 523.5(b), or meet one of the functional characteristics under Part 523.5(a) ( e.g. , greater cargo carrying capacity than passenger carrying capacity). In other words, a 2WD vehicle of 6,000 lbs GVWR or less, even if it has a sufficient number of clearance characteristics, cannot be considered off-highway capable.

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### Sales Projections – Passenger Cars cle

Manufacturer MY 12 Y 20 2 20 MY 2016 

### Table VIII-1a

(1,0 00s of vehi s)

MY 2011 20 M 13 MY 014 MY 15 BMW 2 293.9 70 1.6 422.8 231.0 89.6 3 .0 41 Chrysler 4 426.4 11 2.5 399.7 326.3 09.4 4 .3 39 Daimler 2 202.5 44 3.7 270.9 213.8 11.6 2 .5 26 Ford 1,4 .1 ,485. 56 42. 1,559.2 1,344.4 68 1 7 1, 7.7 1,5 4 General Mo 1, 0 ,544. 45 87. 1,514.4 tors 1,248.9 586. 1 9 1, 2.5 1,4 2 Honda 9 ,064.8 08 2.4 930.3 850.8 06.0 1 1, 7.0 91 Hyundai 511.2 518.4 382.2 376.3 395.5 395.5 Kia 548.0 305.7 299.6 326.6 427.2 538.7 Mazd 420.5 a 292.6 283.1 329.9 378.3 413.3 Mitsu bishi 104.2 110.3 104.5 88.1 82.3 82.7 Nissa n 611.6 824.0 831.6 854.1 925.4 946.5 Porsc he 25.1 41.1 43.3 34.0 32.4 33.3 Suba 6.9 ru 169.7 183.5 175.2 184.5 204.7 20 Suzu 103.0 ki 49.5 72.3 81.8 90.6 100.6 Tata 50.5 49.3 63.7 65.5 23.2 36.4 Toyota 1,355.6 1,591.0 1,941.4 2,078.9 2,176.5 2,226.4 Volk .2 swagen 388.1 434.4 498.6 517.9 567.7 583 Total .7 7,922.7 9,122.7 9,797.1 10,231.3 10,626.4 10,831

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### Table VIII-1b Sales Projections – Light Trucks (1,000s of vehicles)

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 BMW 203 204 184 191 176 171 Chrysler 737 692 594 515 475 462 Daimler 97 108 115 136 130 126 Ford 792 852 940 966 937 911 General Motors 1,347 1,511 1,536 1,337 1,380 1,342 Honda 585 635 677 635 561 545 Hyundai 46 58 102 104 95 92
Kia 88 88 103 114 118 115 Mazda 58 61 65 68 74 72 Mitsubishi 45 48 46 53 57 55
Nissan 331 405 392 406 392 381
P orsche 13 13 15 16 17 17 Subaru 105 98 90 99 116 117 Suzuki 7 5 17 20 21 20
Tata 31 30 34 40 44 42 Toyota 888 887 990 1,096 1,107 1,077 Volkswagen 83 105 141 152 128 124 Total 5,458 5,798 6,038 5,947 5,826 5,669

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### Accounting for the Fuel Ec The fuel economy rebound e of fuel savings expected to result from an increase in vehicle fuel econ e required by the adoption of higher CAFE standards – that is off . The increase in vehicle use occurs because higher fuel econom iving, typically the largest single ent of t tary c and vehicle owners respond to this

in op ts y lowe e ma o h f onomy e number of miles vehicles are driven each d over t imes el economy, this additional driving

s some e fuel gs th w w rd ma to improve fuel economy.

und ef ina ect – es t ti e cte sult fr fuel economy that is offset by additional vehicle use. This measure a y which annual vehicle use increases e fuel c riving onse el economy.

ers typ su la l cl to l ec mile xpr s a e percentage (rather than a dec the us ual convention for expressing elasticity). e the fuel cost of driv el price per gallon divided by fuel

in miles per gallon y this measure declines and vehicle use res incr

de oun rminant of the act result from ado rds, and thus an important parameter ng NHTSA’s evaluati for future model years. Research on the magnitude of the rebound ef se dates to the early 1980s, and almost ously concludes that bound effect occurs when vehicle fuel

improves. 223



### omm ch has b aly e r icle con ices, and other determinants of household travel the respon fuel economy. Several other studies have on econometric analy .S. data on vehicle use, fuel economy, fuel prices, and ariables to identify th cle use to changes in fleet-wide average

omy. Two recent s ation in vehicle ownership and use, fuel

fuel economy am an ex e period in order to

e res o r f onomy Rebound Effect
ffect refers to the fraction omy – particularly an increas set by additional vehicle use y reduces the fuel co st of dr
compon reduction use, improved he mone erating cos uel ec ost of operating a vehicle, by driving slightly more. B leads to an increase in th ring th rginal c st of ve icle

year an

consume CAFE standa heir lifet fuel, so th s require . Even with their higher fu rebound effect reduces the net nufacturers savin at result hen ne

The rebo savings expe fect – orig d to re lly termed the “take back” eff om an increase in ve hicle express he frac on of fu l

lso equals the percentage b each mile declines in resp re the rebound effect by the e onomy itself or fuel cost per imal number, when th Research with respect ost of d ically mea either fue to higher fu sticity of tota

driven, e or averag essed a e vehi positiv e use

Becaus
economy increases in

The magnitu are likely to affecti ing each mile is equal to fu , it is easy to understand wh eased fuel economy.

d effect is an importa nt dete pting stricter CAFE standa on of alternative standards ponse to

of the reb ual fuel sav ings th at

fect in light-duty ve hicle u a statistically significant re

to estimating its magnitude sumption, fuel pr se of vehicle use to higher sis of annual U unanim efficiency

The most c data on veh demand to isolate relied on approa use, fuel een to an ze hous hold su vey

other v fuel econ prices, and measure th e response of average vehi tudies analyzed yearly vari ong individual states over icle use to changing fuel econ tended tim my and othe ponse of veh actors. 224

long-run reb 223 Som e studies estimate that the ound effect is significantly larger than the immediate response to fuel effi lthou ent period required for the rebound effect to reach

magni is l te fo fu s sio

sultin er to future m these t U plying appropriate estimation procedures to data consisting of each year’s average values of these variables for the separate states. increased its long-run reductions re 224 In effect, ciency. A tude vary, th g from strict studies trea gh their estimates of the adjustm ong-run effect is most appropria standards that would apply .S. states as a data “panel” by ap r evaluating the odel years. el saving and emis ns

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### t distinction am fect is whether they assume that the stant, or is likely e in response to the absolute levels of fuel costs, al income, or househo st studies using aggregate annual data for e U.S. assume a constant r ect, although some of these studies test whether the effect using ning

t tes

h is ks, An importan effect is con
person ong studies of the rebound ef to vary over tim ld vehicle ownership. Mo ebound eff th

v aries in response to changes in retail fuel pr ices or average fuel economy. Many studies household survey data estimate significantly di ff rent rebound effects for households ow e varying numbers of vehicles and thus imply that its average value will change over time as vehicle ownership patterns evolve.

However, these studies arrive at differing conclusions about whether the rebound effect is larger among households that own more vehicles, and t hus provide conflicting estimates of changes in its magnitude as the distribution of households by vehicle ownership levels changes. One recent study using state-level data concludes that the rebound effect varies directly in response to changes in personal income and the degree of urbanization of U.S. cities, as well as in response to fuel costs.

In order to arrive at an estimate of the rebound effect for use in assessing the fuel savings, emissions reductions, and other impacts of alternative standards, NHTSA reviewed 22 studies of the rebound effect conducted from 1983 through 2005. The agency then conducted a detailed analysis of the 66 separate estimates of the long-run rebound effect reported in these studies, which is summarized in Table VIII-2 below. 225 

### As the table indicates, these 66 estimates of the long-run rebound effect range from as low as 7 percent to as high as 75 percent, with a mean value of 23 percent. Estimates of the rebound effect reported in the 17 published studies show the same range, but a slightly higher mean value (24 percent). Although this result is not shown in the table, approximately two-thirds of all the estimates reviewed and of all published estimates fall in the range of 10-30 percent.

We are aware of two potential issues involved in these estimates. One, the estimates of total annual car and truck VMT are developed by the states and submitted to FHWA. Each state uses its own definition of a car and a truck. For exampl e, some states classify minivans as cars and some as trucks. Thus, there are known inconsistencies with these estimates when evaluated separately for cars and trucks. Also, total gasoline consumption can be reasonably estimated from excise tax receipts, but separate estimates for cars and trucks are not available. We are no aware of the precise methodology used to develop the distinct on-road fuel economy estima for cars and trucks developed by FHWA. We do not believe that they are based on direct measurements from substantial numbers of vehicles, as no such test programs were found by EPA during its fuel economy labeling rule in 2006. Also, the year-to-year consistency for bot car and truck fuel economy implies some methodol ogy other than direct measurement. For th reason, NHTSA and EPA are not using distinct on -road fuel economy gaps for cars and truc but one common value of 20 percent for both vehicle classes for purposes of estimating the fuel savings of the standards.

225 In some cases, NHTSA derived estimates of the overall rebound effect from more detailed results reported in the

ers computed a weighted average of the reported values using the studies. For example, where studies estimated different rebound effects for households owning different numb of vehicles but did not report an overall value, we reported distribution of households among vehicle ownership categories.

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### Table VIII-2 Summary of Previous Re bound Effect Estimates

Category of Estimates Number of Studies Number of Estimates Low High Median Mean Std. Dev.

All Estimates 22 66 7% 75% 22% 23% 14%
Published Estimates 17 50 7% 75% 22% 24% 14%
U.S. Time-Series Data 7 34 7% 45% 14% 18% 9% Household Survey Data 13 23 9% 75% 31% 31% 16%
Pooled U.S. State Data 2 9 8% 58% 22% 25% 14%
Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16%
Variable Rebound Effect (1) 10 29 10% 45% 23% 23% 10% (1) Three studies estimated both constant and variable rebound effects.



### s Table VIII-2 illustrates, the type of data used and authors’ assumption about wheth th A er e

e last five years of that period (1997-2001). 226 

### These authors

estimating and testing several econometric models of the relationship between vehicle milesrebound effect varies over time have important effects on its estimated magnitude. The 34 estimates derived from analysis of U.S. annual time-series data have a mean of 18 percent, while the mean of 23 estimates based on household survey data is 31 percent, and the mean of 9 estimates based on pooled state data (25 percent) is slightly above that of the entire sample. The average mean is 23 percent for both the 37 estima tes that assume a constant rebound effect and the 29 estimates reported in studies that allow the rebound effect to vary in response to fuel prices, vehicle ownership, or household income.

Recent studies provide some evidence that the rebound effect has been declining over time, and it may decline further over the immediate future if income rises faster than gasoline prices. This result seems plausible, because the responsiveness of vehicle use to variation in fuel costs would be expected to decline as they account for a smaller proportion of the total monetary cost of driving, which has been the case until very recently. At the same time, rising personal incomes would be expected to reduce the sensitivity of vehicle use to fuel costs as the time component of driving costs – which is likely to be related to income levels – accounts for a larger fraction of the total cost of automobile travel. The widely-cited study by Small and Van Dender estimated that the long-run rebound effect averaged 22 percent over the period from 1966-2001, but

eclined to 11 percent over th d subsequently reported that the long-run rebound effect appears to have dropped further to 6 percent over the period from 2000-2004. 227



### To provide additional insight into the rebound effect for the purposes of this rulemaking, NHTSA developed several new estimates of its magnitude. These estimates were developed by

226 Small, K. and K. Van Dender, 2007a. “Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect”, The Ene rgy Journal, vol. 28, no. 1, pp. 25-51.

7 mall, K. and K. Van Dender, 2007b. "Long Run Trends in Transport Demand, Fuel Price Elasticities and 22 S Implications of the Oil Outlook for Transport Policy," OECD/ITF Joint Transport Research Centre Discussion Papers 2007/16, OECD, International Transport Forum.

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### he

latte r’s effect on fuel cost per mile.

HTSA’s analysis used national aggregate data on light-duty vehicle travel covering the period om 1950 through 2006. Several different approaches were used to estimate the effect of fuel efficiency on car and light truck use, and etric procedures were employed to

ac estimated value of the rebound effe A’s analysis are presented in

able VIII-3. For each model that was estimated, the table reports the average value of the rebound effect over the period from 1950-2006, as well as its value during the final year of that period. In addition, the table reports the aver age projected values of rebound effect between 2010 and 2030, which were developed using forecasts of personal income, fuel prices, and fuel efficiency from EIA’s Annual Energy Outlook 2009 Reference Case.

The results of NHTSA’s analysis are broadly consistent with the findings from previous research summarized above. The historical average long- run rebound effect is estimated to range from 16-30%, and comparing these estimates to its calculated values for 2006 (which range from 8- 14%) supports the finding from recent research that it is declining in magnitude. The forecast values of the rebound effect shown in the table, which range from 4-16%, also suggest that this decline is likely to continue through 2030.

Table VIII-3 ound Effect traveled and factors that influence it, including household income, fuel prices, vehicle fuel efficiency, road supply, the number of vehicles in use, vehicle prices, and other factors. As t studies by Small and Van Dender emphasize, it is im portant to account for the effect of fuel prices on vehicle buyers’ demand for fuel effi ciency when attempting to estimate the rebound effect. Failing to incorporate the response of fuel efficiency to fuel prices is likely to cause the rebound effect to be overestimated, because the changes in fuel economy resulting from variation in fuel prices partly offset the

N fr various econom

count for its relationship to fuel prices and co ntrol for the effect of this relationship on the ct. The re sults from NHTS T

Summary of NHTSA Estimates of the Long-Run Reb Using U.S. Annual Data for 1950-2006

Rebound Effects: Model VMT Measure Variables Included in VMT Equation Estimation Technique 1950- 2006 2006 2010- 2030*

Small-Van Dender single VMT equation annual VMT per adult fuel cost per mile, per Capita income, vehicle stock, road miles per adult, fraction of population that is adult, fraction of population living in urban areas, fraction of population living in urban areas with heavy rail, OLS 33.0% 15.8% 8.0%

dummy variables for fuel rationing, time trend Small-VanDender threeequation annual VMT per adult fuel cost per mile, per Capita income, vehicle stock, road miles per adult, fraction of 3SLS 21.6% 5.8% 3.4%

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e V MT model VMT per adult personal income, road miles per Capita, time trend OLS 18.4% 11.7% 9.2%

Singleequation VMT model VMT per vehicle income, road miles per Capita, time trend OLS 17.6% 15.2% 15.7 annual fuel cost per mile, personal %

Singleequation VMT model annual VMT per adult fuel cost per mile, personal income, road miles per Capita, dummy variables for fuel rationing, time trend OLS 34.0% 20.8% 13.6%

Singleequation VMT model annual VMT per vehicle fuel cost per mile, personal income, vehicles per road mile, % of fleet manufactured under CAFE standards, new IV (for fuel cost per

mile) 16.3% 9.2% 7.0%

vehicle prices

Threeequation system for VMT, fuel efficiency, and vehicle stock annual VMT per vehicle fuel cost per mile, personal income, vehicles per capita, vehicles per road mile, fraction of adult population licensed to drive, new vehicle prices, % of fleet manufactured under CAFE standards 2SLS 29.5% 13.4% 15.9%

T hreeiency, vehicle licensed to drive, new vehicle equation system for VMT, fuel annual VMT per income, vehicles per capita, vehicles per road mile, fraction of adult population 3SLS 29.8% 13.7% 16.2% effic and vehicle stock prices, % of fleet manufactured under CAFE standards fuel cost per mile, personal

Threeequation system for VMT, fuel efficiency, and vehicle stock annual VMT per vehicle income, vehicles per capita, vehicles per road mile, fraction of adult population licensed to drive, new vehicle prices, % of fleet manufactured under CAFE standards Vector autoregression 19.9% 10.8% fuel cost per mile, personal

--

Threeequation system for VMT, fuel annual VMT per vehicle fuel cost per mile, personal income, vehicles per capita, vehicles per road mile, fraction of adult population Vector errorcorrection 20.7% 11.2% --

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*Using AEO2009 Reference Case forecasts of fuel prices, fuel economy, and personal income.



### In light of these results, NHTSA has elected to us e a 10 percent rebound effect in its analysis of fuel savings and other benefits from higher CAFE standards for MY 2012-2016 vehicles. The agency’s judgment is that the apparent decline over time in the magnitude of the rebound effect justifies using a value for future analysis that is lower than historical estimates, which average approximately 25 percent. Because the lifetimes of vehicles affected by the alternative CAFE standards considered in this rulemaking will extend from 2012 until approximately 2050, a value that is significantly lower than historical estimates appears to be appropriate. Recognizing the uncertainty surrounding its 10 percent estimate, the agency has analyzed the sensitivity of its benefits estimates to a range of values for the rebound effect from 5 percent to 15 percent.

One possible alternative to attempting to estimate the rebound effect per se would be to use the price elasticity of demand for gasoline, which measures the sensitivity of gasoline consumption to a change in its price, in order to establish a lower bound on its magnitud e. The elasticity of gasoline demand with respect to its price per gallon is likely to provide a reasonable proxy for the rebound effect, since a decline in the price of gasoline has exactly the same effect on the permile cost of driving as an equivalent increase in fuel economy. In the very short run, the only way that people can respond to changes in the price of gasoline is to alter the number of miles they drive. 228 

### Over the relatively short time span of several months, most estimates indicate that the price elasticity of demand for gasoline is approximately -0.1, which corresponds to the shortrun rebound effect of 10 percent used in this FRIA. Over the period of a year, however, the price elasticity of demand is likely to increase somewhat in magnitude, up to a range of -0.3 to -0.4. 229 

### It seems reasonable to assume that the majority of the change in gasoline consumption over such a period results from changes in vehicle use, as distinguished from changes in the fuel economy of new vehicles, since only about 5-10 percent of the fleet would be replaced within one year.

Additionally, NHTSA recognizes that as the world price of oil falls in response to lower U.S. demand for oil, there is the potential for an increase in oil use and, in turn, greenhouse gas emissions outside the U.S. This so called international oil “take back” effect is difficult to estimate. Given that oil consumption patterns vary across countries, there will be different demand responses to a change in the world price of crude oil. In addition, many countries around the world subsidize their oil consumption. It is not clear how oil consumption would change due to changes in the market price of oil given the current pattern of demand and subsidies. Further, many countries, especially in the developed countries/regions ( i.e. , the European Union), already have or anticipate im plementing policies to limit GHG emissions. Further out in the future, it is anticipated that developing countries would take actions to reduce their GHG emissions as well. Any increases in petroleum consumption and GHG emissions in

228 Over the long run, consumers can alter their choice of vehicle (and thus the fuel economy they achieve), in addition to altering their number of miles driven.
229 The long-run price elasticity of demand for gasoline is in the 0.6 to 0.8 range.

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### ther nations that occur in response to a decline in world petroleum prices would be attributed to those nations, and recorded in their respe sions inventories. Thus, including the same increase U.S. would risk double-counting of global em

On-Road Fuel Economy Adjustment Actual fu onomy levels a by vehicles in o g fall sig short of their leve easured under t ory-like test c s used by EPA lish its published el economy ratin fferent models. ing the fuel rom alternativ assenger car and ck CAFE stand ency adjust al fuel economy rformance of eac ger car and ligh odel downwa its rated value to r ect the expected is on-ro ad fuel y “gap.” In D 006, EPA adopted c nges to its regula el economy l ich were in bring vehicles’ d fuel economy oser to their ac oad fuel econo ls. 230



### Supplem l analysis report A as part of its le indicates th on-road fuel economy ight-duty vehic ges 20 percent an published fu omy levels. 231 

### r example, if the EPA fuel econ g of a light tru mpg, the onroad fue nomy actually a y a typical dri at vehicle is ex to be 16 mpg (20*.80) e agency has em revised e of this on-road onomy gap in its analy f the fuel saving g fro m alternat E standards fo 011-2016 passenge rs and light truck

An analy onducted by NH nfirmed that EP of a 20 p ap between test and ad fuel econom -founded. The used data on th er of passenge rs and light truck model year th service (regi or use) during each cale r year from 2000 006, average nomy for passe light trucks pr d during each ar, and estimate erage miles driv year by cars and light ks of different a g each calenda ver that period. data were combine develop estimat ge-weighted e fuel economy e U.S. passenge r and light truck ave achie ing each year from 2000 through 2006 und est conditions.

Table VII 4 compares the agency’s estimates of fleet-w e average fuel econo y under test conditions for 2000 throu tration’ published

stimates of the estimates of actual on-road fuel economy achieved by passenger cars and light ucks during each of those years. As it shows, FHWA’s estimates of actual fuel economy for o ctive GHG emis in emissions as part of the impact of adopting CAFE standards in the issions totals.

el ec chieved n-road drivin nificantly ls m he laborat ondition to estab fu gs for di In analyz savings f
e p light tru ards, the ag s the actu
pe h passen t truck m rd from efl size of th econom ecember 2 ha tions on fu abeling, wh tended to
rate levels cl tual on-r my leve

enta ed by EP Final Ru at actual for l les avera lower th el econ Fo overall omy ratin ck is 20 l eco chieved b ver of th pected . Th ployed EPA’s stimate fuel ec sis o s resultin ive CAF r MY 2

r ca s.

sis c TSA co A’s estimate ercent g

on-ro y is well agency e numb st f r ca s of each at were in ered nda through 2 fuel eco nger cars and oduce model ye s of av en per truc d to ges durin es of the usa r year o averag These that th

r ca fleets would h ved dur
er t

I- id m gh 2006 to the Federal Highway Adminis s (FHWA)

e tr

230 EPA, Fuel Economy Labeling of Motor Vehicles: Revisions To Improve Calculation of Fuel Economy Estima tes; 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

.

231 EPA, Final Technical Support Document: Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates , Office of Transportation and Air Quality EPA420-R-06-017 December 2006, Chapter II, http://www.epa.gov/fueleconomy/420r06017.pdf

.

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### passenger cars ranged from 21 percent to r than NHTSA’s estimates of its fleetwide avera actual fuel economy for light trucks percent lower than NHTSA’s estimates of average light truck fuel economy un er test conditions. These results appear to confirm that the 20% o is n te for

use he f su 2012- 2016 vehicles.

Table VIII

Estima -Wide Fu my of Passe ng ared to eported Fuel E

NHTSA stimated Test MPG F Re Actu Percent
Difference Est st FHWA Reported Actual MPG ent

rence Passen Calendar

Year ght-Dutry Tru 

### 23 percent lowe ge value under test conditions over this period. Similarly, FHWA’s estimates of ranged from 16 percent to 18 d n-road fuel economy d uel savings likely to re count or gap represe lt from alternative CA ts a reasonable estima FE standards for MY in evaluating t

-4 ted Fleet el Econo er Cars and Light Trucks Comp R conomy

E HWA ported al MPG NHTSA imated Te

MPG Perc
Diffe

2000 28.2 -22.2% 17.4 3% 2001 28.2 -21.7% 17.6 5% 2002 28.3 -22.3% 17.5 2% 2003 28.4 -21.9% 17.2 0% 2004 28.5 -21.1% 17.2 3% 2005 28.6 -22.8% 17.7 3% 2006 28.8 -21.8% 17.8 2% Average, 2000-2006 28.4 -22.0% 17.5 7% 21.9 20.8 -16. 22.1 20.8 -15. 22.0 20.9 -16.
22.2 21.0 -18. 22.5 22.1 21.0 21.1 -18. -16. 22.5 21.2 -16.

22.2 21.0 -16. ger Cars Li cks



### B. B fits to Vehicle B om Improving conomy ene uyers fr Fuel E

The mai ce of economic from raisin g CAFE standards is the v the resulting fuel savi over the lifetime cles that are req achieve higher f onomy. The annual fu avings under eac tive CAFE stan measured by th erence between l annual fuel con n by passenge r c ght trucks with el economy they are cted to achieve driving under ernative standard, and their annual fuel cons with the fu els – agai ed for difference n test and actual on riving condit ey would achiev the baseline alt ve. The sum of these al fuel savings o calendar year t or light trucks p d during a model ye re expected to re service represen cumulative lifeti el savings with that rnative CAFE st effect.

Vehicle ival Rates These an fuel sav gs de the number of v that remain in us ing each year of a mode ear’s lifetimes. rucks m ed during a model year that remains in se year is by n sour benefits alue of

c ngs s of vehi uired to uel e el s h alterna dard are e diff
tota sumptio ars or li the fu
expe in on-road that alt umption el economy lev n adjust s betwee -road d ions – th e under ernati

annu ver each hat cars roduce

ar a main in ts their me fu alte andard in

Surv
nual in pend on ehicles e dur

l y The number of passenger cars or light t rvice during each subsequent calendar anufactur estimated

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### er expected to be produced during that model year by the

ort NHTSA’s updated estimates of average car and light truck use. The total number of iles driven by passenger cars or light trucks produced during a model year are driven during

.

6-

m

es VIII-5a and VIII-5b, is important to understand that vehicles are considered to be of age 1 during the calendar year multiplying the original numb proportion of vehicles expected to remain in service to the age they will have reached during that year. The proportions of passenger cars and light trucks expected to remain in service at each age up to their maximum lifetimes (26 and 36 years, respectively) are shown in Tables VIII-5a and VIII-5b. 232 

### These “survival rates,” which are estimated from experience with recent modelyear vehicles, are slightly different than the survival rates used in past NHTSA analyses, since they reflect recent increases in durability and usage of more recent passenger car and light truck models. 233



### Vehicle Use Annual fuel savings during each year of a model year’s lifetime also depend on the number of miles that the remaining vehicles in use are driven. Updated estimates of average annual miles driven by age were developed by NHTSA for MY 2011 rulemaking from the Federal Highway Administration’s 2001 National Household Transportation Survey, and these also differ from the estimates of annual mileage employed in past NHTSA analyses. 234 

### Table VIII-5a and VIII-5b also rep

m each year of its lifetime is estimated by multiplying these age-specific estimates of average car and light truck use by the number of vehicles projected to remain in service during that year

As Tables VIII-5a and VIII-5b also show, the resulting survival-weighted mileage over the 2 year maximum lifetime of passenger cars is 161,847 miles, while that over the 36-year maximu lifetime of light trucks is 190, 066 miles. Fuel savings and other benefits resulting from higher CAFE standards for passenger cars and light trucks are calculated over their respective lifetimes and total expected mileage. It should be noted, however, that survival-weighted mileage is extremely low (less than 1,000 miles per year) after age 20 for cars and after age 25 for light trucks, and thus has little impa ct on lifetime fuel savings or other benefits from higher fuel economy, particularly after discounting those benefits to their present values.

In interpreting the survival and annual mileage es timates reported in Tabl it

232 The maximum age of cars and light trucks was defined as the age when the number remaining in service has declined to approximately two percent of those originally produced. Based on an examination of recent registration data for previous model years, typical maximum ages appear to be 26 years for passenger cars and 36 years for lig trucks.
233 The survival rates were calculated from R.L. Polk, National Vehicle Population Profile (NVPP), 1977-2003; see NHTSA, “Vehicle Survival and Travel Mileage Schedules,” Office of Regulatory Analysis and Evaluation, NCSA January 2006, pp. 9-11, Docket No. 22223-2218. Polk’s NVPP is an annual cens us of passenger cars and light trucks registered for on-road operation in the United States as of Jul 1 each year. NVPP registration data from ht

,

vehicle model years 1977 to 2003 were used to develop the survival rates reported in Tables VIII-5a and VIII-5b. d regression ght

the inistration, Bureau of Transportation Statistics, and National Highway Traffic Safety Survival rates were averaged for the five most recent model years for vehicles up to 20 years old, an models were fitted to these data to develop smooth relationships between age and the proportion of cars or li trucks surviving to that age. 234 See also NHTSA, “Vehicle Survival and Travel Mileage Schedules,” Office of Regulatory Analysis and Evaluation, January 2006, pp. 15-17. The original source of information on annual use of passenger cars and light trucks by age used in this analysis is the 2001 National Household Travel Survey (NHTS), jointly sponsored by Federal Highway Adm Administration.

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### nt ffered for sale in June rough September of the preceding calendar year, depending on manufacturer). Thus virtually

5 

### that coincides with their model year Thus for example, model year 2012 vehicles will be considered to be of age 1 during calendar year 2012. This convention is used in order to accou for the fact that vehicles produced during a model year typical are first o th all of the vehicles produced during a model year will be in use for some or all of the calendar year coinciding with their model year, and they are considered to be of age 1 during that year. 23

235 As an illustration, virtually the entire production of model year 2012 cars and light trucks will have been sold by the end of calendar year 2012, so those vehicles are define d to be of age 1 during calendar year 2012. Model year 2012 vehicles are subsequently defined to be of age 2 during calendar year 2013, age 3 during calendar year 2014, and so on. One complication arises because registration data are typically collected for July 1 of each calendar year, so not all vehicles produced during a model year will appear in registration data until the calendar year when they have reached age 2 (and sometimes age 3) under this convention.

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### Table VIII-5a Survival Rates and Unadjusted Annual Miles Traveled (VMT) by Age for Passenger Cars

V ehicle Age E stimated Survival F r action Estimated Annual VMT Survival-Weighted Annual VMT

1 0 .9950 14,231 14,160 2 0 .9900 13,961 13,821 3 0 .9831 13,669 13,438 4 0 .9731 13,357 12,998 5 0 .9593 13,028 12,497 6 0 .9413 12,683 11,938 7 0 .9188 12,325 11,324 8 0 .8918 11,956 10,662 9 0 .8604 11,578 9,961 10 0 .8252 11,193 9,237 11 0 .7866 10,804 8,499 12 0 .7170 10,413 7,466 13 0 .6125 10,022 6,138 14 0 .5094 9,633 4,907 15 0 .4142 9,249 3,831 16 0 .3308 8,871 2,934 17 0 .2604 8,502 2,214 18 0 .2028 8,144 1,652 19 0 .1565 7,799 1,220 20 0 .1200 7,469 896 21 0 .0916 7,157 656 22 0 .0696 6,866 478 23 0 .0527 6,596 348 24 0 .0399 6,350 253 25 0 .0301 6,131 185 26 0.0227 5,940 135

Estimated Passenger Car Lifetime VMT 161,847

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### Table VIII-5b Survival Rates and Unadjus icle-Miles Traveled (VMT) ted Annua l Veh by Age for Light Trucks

V ehicle Age E stimated Survival Fraction Estimated Annual VMT Survival-Weighted Annual VMT

1 0 .9950 16,085 16,004 2 0 .9741 15,782 15,374 3 0 .9603 15,442 14,829 4 0 .9420 15,069 14,195 5 0 .9190 14,667 13,479 6 0 .8913 14,239 12,691 7 0 .8590 13,790 11,845 8 0 .8226 13,323 10,960 9 0 .7827 12,844 10,053 10 0 .7401 12,356 9,145 11 0 .6956 11,863 8,252 12 0 .6501 11,369 7,391 13 0 .6042 10,879 6,573 14 0 .5517 10,396 5,735 15 0 .5009 9,924 4,971 16 0 .4522 9,468 4,281 17 0 .4062 9,032 3,669 18 0 .3633 8,619 3,131 19 0 .3236 8,234 2,665 20 0 .2873 7,881 2,264 21 0 .2542 7,565 1,923 22 0 .2244 7,288 1,635 23 0 .1975 7,055 1,393 24 0 .1735 6,871 1,192 25 0 .1522 6,739 1,026 26 0 .1332 6,663 887 27 0 .1165 6,648 774 28 0 .1017 6,648 676 29 0 .0887 6,648 590 30 0 .0773 6,648 514 31 0 .0673 6,648 447 32 0 .0586 6,648 390 33 0 .0509 6,648 338 34 0 .0443 6,648 294 35 0 .0385 6,648 256 36 0 .0334 6,648 222

Estimated Lifetime Light Truck VMT 190,066

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### s

use

Release n of and

e percent hose prevailing in 2001 to adjust the estimates f vehicle use derived from the NHTS to reflect the effect of higher future fuel prices. This procedure ual ht

rtance of rowth in average vehicle use, the total number of miles driven by passenger cars increased 35 nd annual growth rate of 1.5 percent. 236



### biles are

rage

HTSA calculated the rate of growth in the mileage schedules shown in Tables VIII-5a and
st

ced Adjusting Vehicle Use The estimates of average annual miles driven by passenger cars and light trucks reported in Table VIII-5a and VIII-5b reflect the historically low gasoline prices that prevailed at the time the 2001 National Household Travel Survey (NHTS) was conducted. To account for the effect on vehicle of subsequent increases in fuel prices, the estimat es of annual vehicle use derived from the NHTS were adjusted to reflect the forecasts of future gasoline prices reported in the AEO 2010 Early Reference Case. This adjustment accounts for the difference between the average price per gallo fuel forecast for each year over the expected lifetimes of model year 2012-2016 passenger cars light trucks, and the average price that prevailed when the NHTS was conducted in 2001. The elasticity of annual vehicle use with respect to fuel cost per mile corresponding to the 10% fuel economy rebound effect used in this analysis (i.e., an elasticity of -0.10) was applied to th difference between each future year’s fuel prices and t

o was applied to the mileage figures reported previously in Tables VIII-5a and VIII-5b to adjust ann mileage by age during each calendar year of the expected lifetimes of MY 2012-2016 cars and lig trucks.

The estimates of annual miles driven by passenger cars and light trucks at each age were also adjusted to reflect projected future growth in average vehicle use. Increases in the average number of miles cars and trucks are driven each year have been an important source of historical growth in total car and light truck use, and are expected to represent an important source of future growth in total light-duty vehicle travel as well. As an illustration of the impo
g percent from 1985 through 2005, equivalent to a compou During that time, however, the total number of passenger cars registered for in the U.S. grew by only about 0.3 percent annually. 237 

### Thus growth in the average number of miles automo driven each year accounted for the remaining 1. 2 percent (= 1.5 percent - 0.3 percent) annual growth in total automobile use. 238 

### Further, the AEO 2010 Reference Case forecasts of total car and light truck use and of the number of cars and light trucks in use suggest that their ave annual use will continue to increase gradually from 2010 through 2030.

In order to develop reasonable estimates of future growth in average car and light truck use, N VIII-5b that would be necessary for total car and light truck travel to increase at the rate foreca in the AEO 2010 Reference Case. This rate was calculated in a manner that is also consistent with future changes in the overall size and age distributions of the U.S. passenger car and light truck fleets that are implied by the agency’s adjusted forecasts of total car and light truck sales reported previously in Tables VIII-1a and VIII-1b, together with the survival rates reported in Tables VIII-5a and VIII-5b. The growth rate in average annual car and light truck use produ

236 Calculated from data reported in FHWA, Highway Statistics, Summary to 1995, Table vm201at http://www.fhwa.dot.gov/ohim/summary95/vm201a.xlw

, and annual editions 1996-2005, Table VM-1 at ttp://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm h (last accessed April 20, 2008).
23 7 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

See supra note [2 above here] ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the agency’s Center for Statistical Analysis.

8 23

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### e

ht 0. Because the effects of both el prices and cumulative growth in average vehicle use vary by year, these adjustments result

es

ect of these two adjustments is to increase xpected lifetime mileage significantly. As an illustration, expected lifetime mileage for

uel

o by this calculation is approximately 1.15% per year. 239 

### This rate was applied to the mileag figures reported in Tables VIII-5a and VIII-5b to estimate annual mileage by age during each year of the expected lifetimes of MY 2012-2016 cars and light trucks.

Tables VIII-5c and VIII-5d report the results of applying the adjustments for both future fuel prices and annual growth in car and light truck use to the figures reported previously in Tables VIII-5a and VIII-5b. Separate adjustments for projected fuel prices and growth in car and lig truck use were made for each calendar year from 2012 through 203 fu in differing VMT schedules for each future year. The adjusted annual VMT estimates reported in Tables VIII-5c and VIII-5d, as well as their lifetime totals, represent averages of the adjusted values of annual car and light truck use by age for calendar years 2012-2030. However, the estimates of fuel savings and other impacts of improved fuel efficiency for individual calendar years over the lifetimes of model year 2012-16 cars and light trucks employ the adjusted valu of car and light truck use by age during those specific calendar years.

While the adjustment for future fuel prices reduces average mileage at each age from the values shown previously, the adjustment for expected futu re growth in average vehicle use increases it. Comparing the mileage estimates in Tables VIII-5c and VIII-5d to those shown previously in Tables VIII-5a and VIII-5b shows that the net eff

e passenger cars rises to 195,264 miles in Table VIII-5c from the 161,847 miles reported previously in Table VIII-5a (or by 21%), while Table VIII-5d shows that expected lifetime mileage for light trucks increases from the 190,066 miles reported previously in Table VIII-5b to 225,865 miles (or by 19%). As previously, howev er, the estimates of survival-weighted mileage decline to less than 1,000 miles per year after age 20 for cars and after age 27 for light trucks. Thus they have relatively little impact on lifetim e fuel savings or other benefits from higher f economy, particularly after discounting the benefits that occur in those distant future years t their present values.

239 It was not possible to estimate separate growth rates in ave rage annual use for cars and light trucks, because of

e significant reclassification of light truck models as passenger cars discussed previously. th

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### Table VIII-5c Survival Rates and Adjusted Annu al Vehicle-Miles Traveled (VMT) by Age for Passenger Cars

Vehicle Age Estimated Survival Fraction Estimated Annual VMT Survival-Weighted Annual VMT 1 0.9950 17,270 17,183 2 0.9900 16,943 16,774 3 0.9831 16,599 16,319 4 0.9731 16,163 15,728 5 0.9593 15,761 15,119 6 0.9413 15,337 14,437 7 0.9188 14,881 13,672 8 0.8918 14,429 12,868 9 0.8604 13,940 11,994 10 0.8252 13,495 11,136 11 0.7866 12,964 10,198 12 0.7170 12,510 8,970 13 0.6125 11,990 7,344 14 0.5094 11,470 5,843 15 0.4142 10,997 4,555 16 0.3308 10,543 3,488 17 0.2604 10,125 2,637 18 0.2028 9,714 1,970 19 0.1565 9,307 1,456 20 0.1200 8,891 1,067 21 0.0916 8,546 783 22 0.0696 8,285 577 23 0.0527 8,136 429 24 0.0399 7,896 315 25 0.0301 7,699 232 26 0.0227 7,530 171 Adjusted Lifetime Passenger Car VMT 195,264

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### Survival R ed (VMT) by Age for Light Trucks Table VIII-5d ates and Adjusted Annu al Vehicle-Miles Travel

Vehicle Age Estimated Survival Fraction Estimate d An nual VM T Su rvi val-Weighted An nu al VMT 1 0.9950 19,2 19 19, 123 2 0.9741 18,7 82 18 ,296 3 0.9603 18,419 17,688 4 46 16,9 0.9420 17,9 05 5 0.919 502 16 0 17, ,085 6 0.8913 16,9 52 15,1 09 7 0.8590 16,4 39 14,1 21 8 0.8226 15,8 29 13,0 21 9 0.7827 15,2 18 11,9 11 10 0.7401 14,6 48 10,8 41 11 0.6956 13,9 92 9,7 33 12 0.6501 13,4 50 8,7 44 13 0.6042 12,8 32 7,7 53 14 0.5517 12,2 12 6,7 37 15 0.5009 11,6 00 5,8 11 16 0.4522 11,0 69 5,0 05 17 0.4062 10,6 17 4,3 13 18 0.3633 10,1 25 3,6 79 19 0.3236 9,65 0 3,1 23 20 0.2873 9,23 8 2,6 54 21 0.2542 8,88 2 2,2 58 22 0.2244 8,66 7 1,9 45 23 0.1975 8,40 0 1,6 59 24 0.1735 8,39 5 1,4 56 25 0.1522 8,19 7 1,2 48 26 0.1332 8,18 8 1,0 91 27 0.1165 8,21 8 95 7 28 0.1017 8,21 6 83 6 29 0.0887 8,21 3 72 8 30 0.0773 8,21 1 63 5 31 0.0673 8,21 0 55 3 32 0.0586 8,20 8 48 1 33 0.0509 8,20 3 41 8 34 0.0443 8,19 6 36 3 35 0.0385 8,18 2 31 5 36 0.0334 8,16 7 27 3 Adjust ed Lifetim ck V 225, e Light Tru MT 865

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### Estimati nual Fuel Con ion NHTSA estimated annual fuel mption dur r of pected life year 201 cars and light with altern AFE sta in effect b ng the total num miles that a m ar’s surviv hicles are by the fu omy that they are ed to achieve u ch alterna ndard. 240 

### me fuel co tion by each mo cars and lig ks is the su e annual the vehicl ed during th el year that are ted to rem ervice du ch year o xpected lifetimes rn, the savings ime fuel co by MY 2012-2016 ca light trucks th ld result from a e increas AFE stan is the diffe between their lifet el use at the fu omy level e project ttain unde djusted Baseline ative, and their e fuel use igher fue my level e

rojected to achieve under each alternative standard.

benefits to vehicle owners and to the U.S. economy that

of

nsumption

ed

. ded from the social value of fuel savings because

e nt

s

t ng An sumpt

consu ing ea ch yea the ex times of model 2-2016 trucks ative C ndards y dividi ber of odel ye ing ve driven el econ expect nder ea tive sta Lifeti nsump del year’s at mod ht truc projec m of th ain in s use by ring ea es produc f their e . In tu in lifet nsumption rs and
at wou lternativ es in C dards rence ime fu el econ they ar ed to a r the A altern lifetim at the h l econo they ar
p

NHTSA’s analysis values the economic result from future fuel savings over the full ex pected lifetimes of MY 2012-2016 passenger cars and light trucks. This reflects the agency’s assumption that while the purchasers of new vehicles might not realize the full lifetime benefits of improved fuel economy, subsequent owners of those vehicles will continue to experience the resulting fuel savings until they are retired from service. Of course, not all vehicles produced during a model year remain in service for the complete lifetimes (26 years for passenger cars or 36 years for light trucks) of each model year. Due to the pattern of vehicle retirements with increasing age, the expected or average lifetimes typical representative cars and light trucks are approximately half of these figures.

conomic Benefits from Reduced Fuel Co E The economic value of fuel savings resulting from alternative CAFE standards is estimated by applying the Reference Case forecast of future fuel prices from the Energy Information Administration’s Annual Energy Outlook 2010 Early Release to each future year’s estimat fuel savings. The AEO 2010 Reference Case forecast of future fuel prices, which is reported in Table VIII-6, represents retail prices per gallon of fuel, including federal, state, and any applicable local taxes. While the retail price of fuel is the proper measure for valuing fuel savings from the perspective of vehicle owners , two adjustments to the retail prices are necessary in order to accurately reflect the economic value of fuel savings to the U.S. economy

First, federal, state, and local taxes are exclu these do not reflect costs of resources used in fuel production, and thus do not reflect resourc savings that would result from reducing fuel consumption. Instead, fuel taxes simply represe resources that are transferred from purchasers of fuel to road and highway users, since fuel taxe primarily fund construction and maintenance of those facilities. Any reduction in State and Federal fuel tax payments by fuel purchasers will reduce government revenues by the same amount, thus ultimately reducing the value of government-financed services by approximately that same amount. The benefit derived from lower ta xes to individuals is thus likely to be offse exactly by a reduction in the value of services funded using those tax revenues.

24 0 The total number of miles that vehicles are driven each year is slightly different under each alternative as a result of the fuel economy “rebound effect,” which is discussed in detail elsewhere in this chapter.

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### nd through 2035, the agency’s

nalysis of the value of fuel savings over the lifetimes of MY 2012-2016 cars and light trucks

ue st

st

l gasoline taxes are currently $0.178 while tate and local gasoline taxes together average $0.231 per gallon, for a total tax burden of $0.401

s eep pace with inflation in nominal terms, and thus to remain constant when
express

r

fuel), and typically require legislation to change.

bal Second, the economic cost of externalities generated by U.S. consumption and imports of petroleum products will be reduced in proportion to fuel savings resulting from higher CAFE standards. The estimated economic value of these externalities, which is discussed in detail in the subsequent section of this Chapter, is conv erted 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 the magnitude of these externalities to be easily compared to the value of the resources saved by reducing fuel production and use, which represents the most important component of the social benefits from saving gasoline.

Table VIII-6 illustrates the adjustment of forecast retail fuel prices to remove the value of fuel taxes and add the value of economic externalitie s from petroleum imports and use. While the Reference Case fuel price forecasts reported in AEO 2010 exte

a requires forecasts extending through calendar year 2050, approximately the last year during which a significant number of MY 20 16 vehicles will remain in service. 241 

### To obtain fuel price forecasts for the years 2036 through 2050,the agency as sumes that retail fuel prices will contin to increase after 2035 at the average rates reported in the AEO 2010 Reference Case foreca over the period from 2025 through 2035 (in constant-dollar terms). 242 

### As Table VIII-6 shows, the projected retail price of gasoline expressed in 2007 dollars rises stea dily over the foreca period, from $2.47 in 2011 to $4.49 in 2050.

The agency has updated its estimates of gasoline taxes, using updated state tax rates reported for January 1, 2006 243 

### Expressed in 2007 dollars, federa

s per gallon. Following the assumptions used by EIA in AEO 2010 , state and local gasoline taxe are assumed to k ed in constant 2007dollars. In contrast, EIA assumes that federal gasoline taxes will remain unchanged in nominal terms, and thus decline throughout the forecast period when expressed in constant 2007 dollars. These differing assumptions about the likely future behavio of federal and state/local fuel taxes are consistent with recent historical experience, which reflects the fact that federal motor fuel taxes as well as most state fuel taxes are specified on a cents-per-gallon basis (some State taxes are levied as a percentage of the wholesale price of

EIA’s AEO 2010 Early Release Reference Case reflects the effects of the American Reinvestment and Recovery Act of 2009, as well as of recent revisions to the U.S. and glo

241 The agency defines the maximum lifetime 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.

242 This projection uses the rate of increase in fuel prices for 2020-2030 rather than that over the complete forecast period (2009-2030) because there is extreme volatility in the forecasts for the years 2009 through approximately 2020. Using the average rate of change over the complete 2009-2030 forecast period would result in projections of declining fuel prices after 2030. 243 FHWA, Highway Statistics 2006 , Section I: Motor Fuel -- Rates and Revenues, Table MF-121T, available at http://www.fhwa.dot.gov/policy/ohim/hs06/pdf/mf121t.pdf

.

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### t that the combined mpg level of U.S. cars and lig be

in

initial release of AEO 2 n economic outlook. In addition, it also reflects the provisions of the Energy Independence and Security Act of 2007 (EISA), including the requirem en ht trucks reach 35 miles per gallon by model year 2020. Because this provision would expected to reduce future U.S. demand for gasoline, and thus lead to a decline in its future price, there is some concern about whether the AEO 2010 forecast of fuel pric es partly reflects the increases in CAFE standards considered in this rule, and whether it is thus suitable for valuing the projected reductions in fuel use.

In response to this concern, the agencies note that EIA issued a revised version of AEO 2008 June 2008, which modified its prev ious December 2007 Early Release of AEO 2008 to reflect the effects of then recently-passed EISA legislation. 244 

### The fuel price forecasts reported in EIA’s Revised Release of AEO 2008 differed by less than one cent per gallon throughout the entire forecast period (2008-230) from those previously issued as part of its 008 . Thus, the agencies are reasonably confident that the fuel price forecasts presented i AEO 2010 and used to analyze the value of fuel savings projected to result from this rule are not unduly affected by the CAFE provisions of EISA.

244 Energy Information Administration, Annual Energy Outlook 2008, Revised Early Release (June 2008), Table 12. Available at http://www.eia.doe.gov/oiaf/archive/aeo08/excel/aeotab_12.xls (last accessed September 12, 2009).

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### Table VIII-6 Adjustment of Forecast Retail Gasoline Prices to Reflect the Economic Value of Fuel Savings

AE0 2010 Forecast of Retail

Gasoline Price Estimated Federal and State Taxes Forecast Gasoline Price

Excluding

Taxes Forecast Gasoline Price

Including

Energy Security Externalities Year

(2007 $/gallon) (2007 $/gallon) (2007 $/gallon) (2007 $/gallon) 2011 $2.47 $0.40 $2.08 $2.24 2012 $2.61 $0.40 $2.21 $2.38 2013 $2.84 $0.40 $2.45 $2.62 2014 $2.95 $0.39 $2.56 $2.73 2015 $3.00 $0.39 $2.61 $2.78 2016 $3.07 $0.39 $2.68 $2.85 2017 $3.13 $0.39 $2.75 $2.92 2018 $3.19 $0.39 $2.80 $2.97 2019 $3.22 $0.38 $2.84 $3.01 2020 $3.27 $0.38 $2.89 $3.06 2021 $3.29 $0.38 $2.92 $3.09 2022 $3.34 $0.37 $2.96 $3.13 2023 $3.37 $0.37 $2.99 $3.16 2024 $3.38 $0.37 $3.01 $3.18 2025 $3.42 $0.37 $3.05 $3.22 2026 $3.46 $0.36 $3.09 $3.26 2027 $3.49 $0.36 $3.13 $3.30 2028 $3.54 $0.36 $3.18 $3.35 2029 $3.59 $0.36 $3.23 $3.40 2030 $3.60 $0.35 $3.25 $3.42 2031 $3.64 $0.35 $3.29 $3.46 2032 $3.69 $0.35 $3.34 $3.51 2033 $3.72 $0.35 $3.37 $3.54 2034 $3.77 $0.35 $3.42 $3.59 2035 $3.83 $0.34 $3.48 $3.65
2036 $3.87 $0.34 $3.53 $3.69
2037 $3.91 $0.34 $3.57 $3.74
2038 $3.95 $0.34 $3.61 $3.78
2039 $3.99 $0.34 $3.66 $3.82
2040 $4.04 $0.33 $3.70 $3.87

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 2041 $4.08 $0.33 $3.75 $3.91
2042 $4.12 $0.33 $3.79 $3.96
2043 $4.17 $0.33 $3.84 $4.01
2044 $4.21 $0.33 $3.89 $4.05
2045 $ 26 4. $0.32 $3.93 $4.10
2046 $4.30 $0.32 $3.98 $4.15
2047 $4.35 $0.32 $4.03 $4.19
2048 $4.39 $0.32 $4.08 $4.24
2049 $4.44 $0.31 $4.12 $4.29
2050 $4.49 $0.31 $4.17 $4.34



### Benefits from Additional Driving The increase in travel associated with the rebound effect produces additional benefits to vehicle owners, which reflect the value to drivers and other vehicle occupants of the added (or more desirable) social and economic opportunities that become accessible with additional travel. As

videnced by the fact that they ele e ct to make more frequent or longer trips when the cost of

erating costs per vehicle-mi le and the resulting increase in the annual

e

driving range before they require refueling. By reducing the frequency with which drivers ically refuel their vehicles and extending the upper limit of the range they can travel before

e ill presumably be flected in lower vehicle sales prices. If manufacturers respond by doing so, this presumably

ase driving declines, the benefits from this added travel exceed drivers’ added outlays for the fuel it consumes (measured at the improved level of fuel economy resulting from stricter CAFE standards). 245 

### The amount by which the benefits from th is increased driving travel exceed its increased fuel costs measures the net benefits they receive from the additional travel, usually are referred to as increased consumer surplus.

NHTSA’s analysis estimates the economic value of the increased consumer surplus provided by added driving using the conventional approximation, which is one half of the product of the

ecline in vehicle op d number of miles driven. Because it depends on the extent of improvement in fuel economy, the value of benefits from increased vehicle use ch anges by model year and varies among alternativ CAFE standards. Under even those alternatives that would impose the highest standards, however, the magnitude of benefits from additional vehicle use represents a small fraction of the total benefits from requiring cars and light trucks to achieve higher fuel economy.

The Value of Increased Driving Range Improving the fuel economy of passenger cars an d light-duty trucks may also increase their

typ requiring refueling, improving fuel economy thus provides some additional benefits to their owners. Alternatively, if manufacturers respon d to improved fuel economy by reducing the siz of fuel tanks to maintain a constant driving range, the resulting cost saving w

re reflects their judgment that the value to economic benefits to vehicle buyers from lower purch prices exceeds that from extended refueling range.

245 These benefits are included in the value of fuel savings reported in Tables VIII-5 through VIII-9.

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### umber of required refueling cycles that results

om improved fuel economy, and applies DOT-recommended values of travel time savings to he

y,

y for all driving trips (1.6 persons), the DOT-recommended value of travel time er vehicle-hour is $24.00 (in 2006 dollars). 249 

### Assuming that locating a station and filling up

re

he agencies’ estimate of benefits from less frequent refueling is subject to several sources of No direct estimates of the value of extended vehicle range are readily available, so the agencies’ analyses calculate the reduction in the annual n fr convert the resulting time savings to their economic value. 246 

### As a coarse illustration of how t value of extended refueling range is estimated, a typical small light truck model has an average fuel tank size of approximately 20 gallons. 247 

### Based on a California Air Resources Board Stud the average fuel purchase is approximately 55% of tank volume. 248 

### Therefore, increasing this model’s actual on-road fuel economy from 24 to 25 mpg would extend its driving range from 216 miles (= 9 gallons x 24 mpg) to 225 miles (= 9 gallons x 25 mpg). Assuming that this vehicle is driven 12,000 miles/year, this reduces the number of times it needs to be refueled each year from 55.5 (= 12,000 miles per year / 216 mile s per refueling) to 53.3 (= 12,000 miles per year / 225 miles per refueling), or by 2.2 refuelings per year.

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and average vehicle occupanc
p requires five minutes, the annual value of time saved as a result of less frequent refueling amounts to $4.40 (calculated as 5/60 x 2.2 x $24.00) . This calculation is repeated for each futu calendar year that light trucks of each model year affected by the alternative fuel economy standards considered in this rule would remain in service. Like fuel savings and other benefits, however, the value of this benefit declines over a model year’s lifetime, because a smaller number of vehicles originally produced during that model year remain in service each year, and those remaining in service are driven fewer miles.

T uncertainty. First, it assumes that manufacturers will not adjust fuel tank capacities downward (from the current average of 19.3 gallons) when they improve the fuel economy of their vehicle models, so that the entire increase in fuel economy will be reflected in increased driving range. Should manufacturers choose to downsize fuel tanks, and all other factors have been estimated with no error, the current estimates of refueling benefits would be overstated. On the other hand, vehicle space, utility and value could increase and vehicle weight will decrease, improving fuel

24 6 Department of Transportation, Guidance Memorandum, “The Value of Saving Travel Time: Departmental Guidance for Conducting Economic Evaluations,” Apr. 9, 1997. http://ostpxweb.dot.gov/policy/Data/VOT97guid.pdf (last accessed October 20, 2007); update available at http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf (last accessed October 20, 2007).

247 Based on the Volpe Model Market Data file for Model Year 2011, average tank volumes for cars and trucks are 16.6 gallons and 23.0 gallons, respectively. This produces a production weighted average of 19.3 gallons.
248 California Environmental Protection Agency, Air Resources Board. Draft Assessment of the Real-World Impact of Commingling California Phase 3 Reformulated Gasoline. August 2003

249 The hourly wage rate during 2006 is estimated t s

o be $24.00. Personal travel (94.4% of urban travel) is valued at 0 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 valued at 70 percent of the wage rate, while business travel (13%) is valued at 100 percent of the wage rate. The resu lting values of travel time are $12.67 for urban travel and 5

$17.66 for intercity travel, and must be multiplied by vehicle occupancy (1.6) to obtain the estimated value of time per vehicle hour.

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### economy and CO 2 

### emissions (all other things being equal). In the context of the rule, this will decrease the cost of compliance with the standards

. Second, the agencies’ analysis assumes that fuel purchases average 55% of fuel tank capacity. However, as shown in the California Air Resource Board (CARB) report, refueling patterns vary. Moreover, the 55% estimate implies that drivers, on average , are either refuel when nearly a half tank of gas remains in their vehicles, or that they are habitually not filling their tanks. Since many drivers only refuel when their tanks are very low, and since many drivers habitually fill up their tan ing

ks, this in turn implies that many drivers in the CARB study are fueling when their tanks are still well above 50% full. Certainly instances of this type of

e

olve

.

avel urposes, and drivers are likely to assign different values to their time when traveling for these
t he

s re behavior occur, but the CARB study implies that it is the norm. Behavior that maximizes the number of fill-ups implies a very low value of time, but it is also possible that the results of th CARB study are not representative of typical behavior across the country. While based on field data, this estimate may thus overestima te the impact of refueling benefits. Third, the agencies’ estimate of refueling benefits assumes that refueling stops inv the same number of vehicle occupants as the overall average for all vehicle trips (1.6 persons) To the extent that drivers refuel while doing other errands or in advance of picking up passengers, this figure may overestimate the typical vehicle occupancy during refueling, and thus the total savings in refueling time. Similarly, the hourly value used to estimate the economic value of savings in refueling time reflects the typical mix of personal and business tr
p different purposes. To the extent that drivers seek to refuel when traveling for purposes tha typically use less valuable time, the hourly value used in the agencies’ analysis may overstate t benefits from saving refueling time. Finally, the agencies assume that both finding and using a refueling station takes, on average, five minutes. There are few, if any, data sources on average refueling time, and thi estimate is subject to significant uncertainty.

NHTSA is planning a nationwide survey of refuel ing patterns at service stations to obtain information to help answer these questions for future rulemakings.

C

. Other Economic Benefits from Reducing U.S. Petroleum Use

Reducing fuel use by requiri ng cars and light trucks to attain higher fuel economy also produces

ider benefits to the U.S. economy by lowering the cost of economic externalities that result

s on human health impacts om emissions of criteria air pollutants, and reducing future economic damages from potential w from U.S. petroleum consumption and imports, including reducing the price of petroleum, lowering the potential costs from disruption in the flow of oil imports, and possibly reducing outlays to support U.S. military activities to se cure the flow of oil imports and to cushion the economy against their possible interruption by maintaining the Strategic Petroleum Reserve. Reducing fuel consumption also lowers the economic costs of environmental externalities resulting from fuel production and use, includin g reducing the impact fr changes in the global climate caused by greenhouse gas emissions.

Economic Externalities from U.S. Petroleum Imports U.S. consumption and imports of petroleum products imposes costs on the domestic economy that are not reflected in the market price for crude petroleum, or in the prices paid by consumers

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### m rice; (2) the risk of isruptions to the U.S. economy caused by sudden redu ctions in the supply of imported oil to the

s

ing

esents an additional economic benefit of raising CAFE standards, over and above e economic value of saving fuel itself.

m

,

cts

r ate benefits to other oil purchasers by reducing these monopsony costs.”

lex of petroleum products such as gasoline. Thes e costs include (1) higher prices for petroleu products resulting from the effect of U.S. oil import demand on the world oil p d U.S.; and (3) expenses for maintaining a U.S. m ilitary presence to secure imported oil supplie from unstable regions, and for maintaining the strategic petroleum reserve (SPR) to cushion against resulting price increases. 250



### Higher U.S. consumption and imports of crude oil or refined petroleum products can raise the magnitude of these external economic costs, thus increasing the true economic cost of supply transportation fuels above the resource costs of producing them. Conversely, reducing fuel consumption by requiring motor vehicles to achieve higher fuel economy will lower U.S. consumption and imports of crude petroleum and refi ned fuels, thus lowering the values of these external costs. Any reduction in their value th at results from requiring improved vehicle fuel
economy repr th

Increased U.S. petroleum consumption can impose higher costs on all purchasers of petroleu products, because the U.S. is a sufficiently large purchaser of foreign oil supplies that changes in U.S. demand can affect the world petroleum price. The effect of U.S. petroleum demand on world oil prices is determined by the degree of OPEC monopoly power over global oil supplies and the degree of monopsony power over world oil demand that the U.S. exercises. The importance of these two factors means that increases in domestic demand for petroleum produ 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 the global petroleum market in excess of the higher prices paid by U.S. consumers. 251 

### Conversely, reducing U.S. oil imports can lowe the world petroleum price, and thus gener

“

Although the degree of current OPEC 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 world oil prices, so that the world oil market does not behave competitively. 252 

### The extent of U.S. monopsony power is determined by a comp

250 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.
251 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 ay ($891 million minus $800 million) is attributable to increasing daily imports by only 1 million barrels. This 1, or $10 more than the newly-increased d means that the incremental cost of importing each additional barrel is $9 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.

252 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/docimages/pdf93/343894_web.pdf (last accessed Dec. 2, 2007).

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### FE standards for model

ears 2005-07 and 2008-11, NHTSA relied on a 199 7 study by Oak Ridge National Laboratories ternalities from petroleum consumption and im rts. More recently, ORNL updated its estimates of the value of these externalities,
cent oil

Fuel

lue

f

U.S.

st

imports to $4.52 to 22.65 per barrel, with a most likely estimate of $12.50 per barrel of h gallon set of factors including the relative 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. 253



### In analyzing benefits from its recent actions to increase light truck CA
y (ORNL) to estimate the value of reduced econom ic ex
254 

### po using the analytic framework developed in its original 1997 study in conjunction with re estimates of the variables and parameters that determine their value. 255 

### These include world prices, current and anticipated future levels of OPEC petroleum production, U.S. oil import levels, the estimated responsiveness of regional oil supplies and demands to prices in different regions of the world, and the likelihood of oil supply disruptions. 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 Standard Rule of 2007 (RFS) 256



### . The updated ORNL study was subjected to a detailed peer review, and its estimates of the va of oil import externalities were subsequen tly revised to reflect their comments and recommendations. 257 

### Specifically, reviewers recommended that ORNL increase its estimates o the sensitivity of oil supply by non-OPEC producers 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 increases in world oil prices. These revisions significantly changed ORNL’s estimates of some components of the external costs of petroleum imports.

At the request of EPA, ORNL further revised its 2008 estimates of external costs from U.S. oil imports to reflect recent changes in the outlook for world petroleum prices and continuing changes in the structure and characteristics of global petroleum supply and demand. These mo recent revisions increase ORNL’s estimates of the monopsony cost associated with U.S. oil

petroleum imported into the U.S. (expressed in 2007$). These estimates imply that eac of fuel saved as a result of adopting higher CAFE standards that is reflected in lower U.S. imports of crude petroleum (or, presumably, refined products) will reduce the monopsony costs

253 Id. , at 18-19.
254 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/docimages/pdf93/343894_web.pdf (last accessed Dec. 2, 2007).

l

, 255 Leiby, Paul N. "Estimating the Energy Security Benefits 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.htm (click on link below “Oil Imports Costs and Benefits”) (last accessed Sept. 10, 2007).
256 Federal Register Vol.72, #83, May 1, 2007 pp.23,900-24,014
257 Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports ICF, Inc., September 2007.

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### ures represent the reduced value of paym ents from U.S. oil purchasers to foreign oil suppliers that results when lower U.S. oil demand reduces the world price of petroleum, beyond

The second component of external economic costs imposed by U.S. petroleum imports arises ported oil

ly

nd to occur suddenly rather than gradua they can also impose costs on business es and households for adjusting their use of

e product of the probability that an oil import disruption will occur and the costs of reduced economic output and abrupt adjustment to sharply higher petroleum prices – is the approp riate measure of their magnitude. Any reduction in the expected value of these costs resulting from a measure that lowers U.S. oil imports represents an additional benefit to the U.S. economy beyond the direct value of savings from reduced purchases of petroleum products. imposed by U.S. oil imports by $0.108 to $0.539 per gallon, with the actual value most likely to be $0.298 per gallon saved (again in 2007$).

These fig

the savings from reduced purchases of petroleum itself. 258 

### Consistency with NHTSA’s use of estimates of the globalbenefits from reducing emissions of CO 2 

### and other greenhouse gases in this analysis, however, requires the use of a global perspective for assessing their net value. From this perspective, reducing these payments simply results in a transfer of resources from foreign oil suppliers to U.S. purchasers (or more properly, in a savings in the value of resources previously transferred from U.S. purchasers to foreign producers), and provides no real savings in resources to the global economy. Thus NHT SA’s analysis of the benefits from adopting higher CAFE standards for MY 2012-2016 cars and light trucks excludes the reduced value of monopsony payments by U.S. oil consumers that might result from lower fuel consumption by these vehicles.

partly because an increase in oil prices triggered by a disruption in the supply of im reduces the level of output that the U.S. economy 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 impo rted oil, as well as on whether and how rapid these prices return to pre-disruption 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 without a disruption in oil supplies.

Because supply disruptions and resulting price increases te lly, petroleum products more rapidly than if the same 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 reached 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 other resources quickly and smoothly in response to rapid changes in prices for petroleum products.

Since future disruptions in foreign oil supplies are an uncertain prospect, 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 costs – th

258 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 occurs entirely within the U.S. economy.

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DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### While e vulnerability of the U.S. economy to oil price shocks is widely believed to depend on total petroleum consumption rather than o il imports, variation in imports is still likely to have some disruption of import supply. I gin quan f pet importe the U.S. may also affect the probability a . th ely price increase or the proba e il imports, the expected value of the mic cos ulting pote pply d also depend on the level o rts.

Businesses and house se a va a ech s, includ l futures markets, energy conservation m es, and ologi t per id fuel switching to “insure” against higher petrole ces and e the ts fo g to price increases. While the availability se mark chan as p educed the potential costs of disruptions to the sup importe ver onsu of petr roducts are unlikely to take accou osts the ose ers, s e costs bably not fully reflected in the price orted oi s ch in oi rt leve bly continue to

ffect the expected co e U.S. ec fr tial ly ns, although this

each ga on of fuel saved that results in a reduction in U.S. petroleum imports (either crude

in its e luation of the economic benefits from adopting higher CAFE standards for MY 2012-

ply of imported oil.

future CAFE standards for light-duty vehicles. U.S. mi litary activities in regions that represent vital sources of oil imports also serve a broader range of security and foreign policy objectives than th n the level of o effect on the magnitude of price increases resulting from a n addition, chan g the tity o roleum d into that such bility that U disruption .S. oil supp will occur lies will b If either disrupted e size of the lik is affected by o

econo ts res from ntial su isruptions will f impo

holds u riety of m rket m anism ing oi

easur techn es tha mit rap um pri of the reduc
et me ir cos is ms h r adjustin robably r sudden

ply of d oil o time, c mers oleum p nt of c y imp on oth o thes are pro of imp st to th l. Thu
onomy anges om poten l impo oil supp ls proba disruptio a component of oil import costs is likely to be significantly smaller than estimated by studies conducted in the wake of the oil supply disruptions that occurred during the 1970s.

ORNL’s most recently updated and revised estimates 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 $3.30 to $11.31 per barrel of imported oil, with a most likely estimate of $7.10 per barrel of imports(all figure s are in 2007$). According to these estimates, ll petroleum or refined fuel) will reduce the expected costs of oil supply disruptions to the U.S. economy by $0.078 to $0.269, with the actual value most likely to be $0.169 per gallon (again in 2007$). Unlike the reduction in monopsony payments that results from lower U.S. petroleum imports, however, the reduction in these expected disruption costs represents a real savings in resources, and thus contributes economic benefits in addition to the savings in resource costs for fuel production that would result from increasing fuel economy. NHTSA employs these values

va 2016 cars and light trucks.

The third component of the external economic costs of importing oil into the U.S. includes government outlays for maintaining a military presence to secure the supply of oil imports from potentially unstable regions of the world and protect against their interruption. Some analysts also include outlays for maintaining the U.S. Strategic Petroleum Reserve (SPR) as an additional cost of U.S. dependence on oil imports, since the SPR is intended to cushion the U.S. economy against the consequences of disruption in the sup

NHTSA currently believes that while costs for U.S. military security may vary over time in response to long-term changes in the actual level of oil imports into the U.S., these costs are unlikely to decline in response to any reduction in U.S. oil imports resulting from raising

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### dards.

either the Congress nor the Executive Branch has ever attempted to calibrate U.S. military ble, or to some calculation of

ed

an

ports in order to investigate its impacts on the standards and fuel savings. Assuming that the

07

mploys

supply disruption may be related to the level of U.S. oil consumption imports.

osts in
not ’s
st

This view which ernment outlays for these purposes are unlikely to result from lting

orts of refined petroleum products among the Refe rence Case, High Economic Growth, and Low s presented in the Energy Information Administration’s Annual simply protecting oil supplies, and as a consequence are unlikely to vary significantly in response to changes in the level of oil imports prompted by higher stan

N expenditures, force levels, or deployments to any oil market varia the projected economic consequences of hostilities in the Persian Gulf. Instead, changes in U.S. force levels, deployments, and thus military spendi ng in that region have been largely govern by political events, emerging threats, and other mil itary and political considerations, rather th by shifts in U.S. oil consumption or imports. NHTSA thus concludes that the levels of U.S. military activity and expenditures are likely to remain unaffected by even relatively large changes in light duty vehicle fuel consumption. As a consequence, the agency’s analysis of alternative CAFE standards for MY 2012-2016 does not include savings in budgetary outlays to support U.S. military activities among the benefits of higher fuel economy and the resulting fuel savings.

Nevertheless, the agency conducted a sensitivity analysis of the potential effect of assuming that some reduction in military spending would result from fuel savings and reduced petroleum im preceding estimate of total U.S. military costs for securing Persian Gulf oil supplies is correct, and that approximately half of these expenses could be reduced in proportion to a reduction in U.S. oil imports from the region, the estimated savings would range from $0.02 to $0.08 (in 20 dollars) for each gallon of fuel savings that wa s reflected in lower U.S. imports of petroleum from the Persian Gulf. If the Persian Gulf region is assumed to be the marginal source of supply for U.S. imports of crude petroleum and refined products, then each gallon of fuel saved might reduce U.S. military outlays by $0.05 per gallon, the midpoint of this range. NHTSA e this estimate in its sensitivity analysis.

Similarly, while the optimal size of the SPR from the standpoint of its potential influence on

omestic oil prices during a d and imports, its actual size has not appeared to vary in response to recent changes in oil Thus while the budgetary costs for maintaining the Reserve are similar to other external c that they are not likely to be reflected in the market price for imported oil, these costs do appear to have varied in response to changes in oil import levels. As a result, the agency analysis of benefits from alternative CAFE st andards for MY 2012-2016 does not include co savings from maintaining a smaller SPR among the external benefits of reducing gasoline consumption and petroleum imports by means of ti ghtening future CAFE standards. concurs with that of the recent ORNL study of economic costs from U.S. oil imports,

oncludes that savings in gov c reductions in consumption of petroleum products and oil imports on the scale of those resu from higher CAFE standards.

The Impact of Fuel Savings on U.S. Petroleum Imports Based on a detailed analysis of differences in fu el consumption, petroleum imports, and imp

Economic Growth Scenario Energy Outlook 2009 , NHTSA estimates that approximately 50 percent of the reduction in fuel

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### reduced ected in U.S. k, while the remaining 10 percent is xpected to reduce U.S. domestic production of crude petroleum. 260 

### Thus on balance, each tal consumption resulting from adopting higher CAFE st andards is likely to be reflected in U.S. imports of refined fuel, while the remaining 50 percent would be expected to be refl reduced domestic fuel refining. 259 

### Of this latter figure, 90 percent is anticipated to reduce imports of crude petroleum for use as a refinery feedstoc

e gallon of fuel saved as a consequence of higher CAFE standards is anticipated to reduce to U.S. imports of crude petroleum or refined fuel by 0.95 gallons. 261



### The Economic Value of Reducing CO 2 

### Emissions

NHTSA has taken the economic benefits of reducing CO 2 

### emission into account in this

lemaking, both in developing alternative CAFE standards and in assessing the economic benefits
terature on

age” of

enefits

ental es in h, ue of usually expressed in dollars per additional metric ton of CO 2 

### missions occurring during a specified year, and is higher for more distant future years because

ual,

lf.

e used by U.S. federal agencies in regulatory evaluations. Under Executive Order 12866, federal agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of antify,

e f the ru benefits of each alternative that was considered. Since direct estimates of the economic from reducing CO 2 

### or other GHG emissions are generally not reported in published li the impacts of climate change, these benefits are typically assumed to be the “mirror im the estimated incremental costs resulting from an increase in those emissions. Thus, the b from reducing CO 2 

### emissions are usually measured by the savings in estimated economic damages that an equivalent increase in emissions would otherwise have caused.

The “social cost of carbon” (SCC) is intended to be a monetary measure of the increm damage resulting from increased carbon dioxide (CO 2 

### ) emissions, including loss agricultural productivity, the economic damages caused by adverse effects on human healt property losses and damages resulting from sea level rise, and changes in the economic val ecosystem services. The SCC is

e the damages caused by an additional ton of emissions increase with larger existing concentrations of CO 2 

### in the earth’s atmosphere. Reductions in CO 2 

### emissions that are projected to result from lower fuel consumption, refining , and distribution during each future year are multiplied by the estimated SCC appropriate for that year, which is used to represent the value of eliminating each ton of CO 2 

### emissions, to determine the total economic benefit from reduced emissions during that year. These benefits are then discounted to their present value as us using a discount rate that is consistent with that used to develop the estimate of the SCC itse

For this final rule, NHTSA has relied on estimates of the SCC developed by a federal interagency working group convened for the specific purpose of developing new estimates to b

the intended regulation and, recognizing that some costs and benefits are difficult to qu propose or adopt a regulation only upon a reas oned determination that the benefits of th intended regulation justify its costs.” The group’s purpose in developing new estimates o

Differences between forecast annual U.S. imports of crude petroleum and refined products among these three

- petroleum, and average 85% over the forecast period spanned by 25 9 scenarios range from 24-89% of differences in projected annual gasoline and diesel fuel consumption in the U.S. These differences average 49% over the forecast period spanned by AEO 2009

. 260 Differences between forecast annual U.S. imports of cr ude petroleum among these three scenarios range from 67 97% of differences in total U.S. refining of crude AEO 2009

. 261 This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95.

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### ide ve small, or “marginal,” pacts on cumulative global emissions, as most federal regulatory actions can be expected to

re the
nerate

d

reflect increasing kno wledge of the science and economics of climate pacts. Technical experts from numerous agencies met on a regular basis to consider public

literature. In

5 percentile SCC estimate across allree models at a 3 percent discount rate, is included to represent the possibility of higher-thanapproximation that is reasonable for policies that have effects on emissions that are small relative SCC was to allow federal agencies to incorporate the social benefits of reducing carbon diox (CO 2 

### ) emissions into cost-benefit analyses of regulatory actions that ha im have.

The interagency group convened on a regular basis to consider public comments, explo technical literature in relevant fields, and discu ss key inputs and assumptions in order to ge SCC estimates. Agencies that actively participated in the interagency process included the Environmental Protection Agency, and the De partments of Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by the Council of Economic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, Office of Energy and Climate Change, and Office of Science and Technology Policy. The main objective of this process was to develop a range of SCC values using a defens ible set of input assumptions that are grounde in the existing literature. In this way, key uncertainties and model differences can more transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group developed its estimat es of the SCC estimates while clearly acknowledging the many uncertainties involved, and with a clear understandi ng that they should be updated over time to im comments, explore the technical literature in re levant fields, and discuss key model inputs and assumptions. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic this way, key uncertainties and model differences transparently and consistently can inform the range of SCC estimates used in the rulemaking process.

The group ultimately selected four SCC values for use in regulatory analyses. Three values are based on the average SCC from three integrated a ssessment models, using discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 9 th


### th expected impacts from temperature change that lie further out in the tails of the distribution of SCC estimates. Table VIII-7 summarizes the interagency group’s estimates of the SCC during various future years. The SCC estimates reported in the table assume that the marginal damages from increased emissions are constant for small departures from the baseline emissions path, an

to cumulative global carbon dioxide emissions.

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### Table VIII-7 Social Cost of CO 2 

### Emissions, 2010 – 2050 (2007 dollars)
Discount Rate 5% 3% 2.5% 3%
Source Average Average Average 95 th 

### Percentile
2010 4.7 21.4 35.1 64.9
2015 5.7 23.8 38.4 72.8
2020 6.8 26.3 41.7 80.7
2025 8.2 29.6 45.9 90.4
2030 9.7 32.8 50.0 100.0
2035 11.2 36.0 54.2 109.7
2040 12.7 39.2 58.4 119.3
2045 14.2 42.1 61.7 127.8
2050 15.7 44.9 65.0 136.2

As Table VIII-7 shows the four SCC estimates selected by the interagency group for use in regulatory analyses are $5, $21, $35, and $65 (in 2007 dollars) for emissions occurring in the year 2010. The first three estimates are base d on the average SCC across models and socioconomic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, respectively. The impacts from temperature change rther out in the tails of the SCC distribution. For this purpose, the group elected to use the

3

e

, the

s ed for e fourth value is included to represent the higher-than-expected fu SCC value for the 95 th 

### percentile at a 3 percent discount rate.

The central value identified by the interagency group is the average SCC across models at the percent discount rate, or $21 per metric ton in 2010. To capture the uncertainties involved in regulatory impact analysis, however, the group emphasized the importance of considering th full range of estimated SCC values. As the table also shows, the SCC estimates also rise over time; for example, the central value increases to $24 per ton of CO 2 

### in 2015 and $26 per ton of CO 2 

### in 2020.

The interagency process is committed to updating these estimates as the science and economic understanding of climate change an d its impacts on society improves over time. Specifically group have set a preliminary goal of revisiting the SCC values within two years or at such time as substantially updated models become available, and to continue to support research in thi area. U.S. federal agencies will periodically review and reconsider estimates of the SCC us

c ost-benefit analyses to reflect increasing knowledge of the science and economics of climate impacts, as well as improvements in modeling.

Details of the process used by the interagency group to develop its SCC estimates, complete results including year-by-year estimates of each of the four values, and a thorough discussion of their intended use and limitations is provided in the document Social Cost of Carbon for

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### s

s

, increase he net effect of stricter CAFE standards on total emissions

emission

ated

the fuel conomy rebound effect.

Emission factors in the MOVES database are expressed in the form of grams per vehicleRegulatory Impact Analysis Under Executive Order 12866 , Interagency Working Group on Social Cost of Carbon, United States Government, February 2010. 262



### Benefits from Reducing Emissions of Criteria Air Pollutants
C ar and light truck use, fuel refining, and fuel distribution and retailing also generate emission of certain criteria air pollutants, including carbon monoxide (CO), hydrocarbon compound (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides (NO x 

### ), fine particulate matter (PM2.5), and sulfur dioxide (SO 2 

### ). While reductions in fuel refining and distribution that result from lower fuel consumption will reduce emissions of criteria pollutants additional vehicle use associated with the rebound effect from higher fuel economy will emissions of these pollutants. Thus t

o f each criteria pollutant depends on the relative magnitudes of reduced emissions in fuel refining and distribution, and increases in emis sions from vehicle use. Because the relationship between emission rates (emissions per gallon refined of fuel or mile driven) in fuel refining and vehicle use is different for each criteria pollutant, the net effect of fuel savings from increased CAFE standards on total emissions of each pollutant is likely to differ.

N HTSA estimates the increase in emissions of each criteria air pollutant from additional vehicle use by multiplying the increase in total miles driven by cars and light trucks of each model year and age by their estimated emission rates per vehicle-mile of each pollutant. These rates differ between cars and light trucks as well as between gasoline and diesel vehicles, and both their values for new vehicles and the rates at which they increase with age and accumul mileage can vary among model years. With the exception of SO 2 

### , NHTSA calculated the increase in emissions of these criteria pollutants from added car and light truck use by multiplying the estimated increases in their vehicles’ use during each year over their expected lifetimes by per-mile emission rates appropriate to each vehicle type, fuel used, model year, and age as of that future year.

These emission rates were developed by U.S. EPA using its recently-developed Motor Vehicle Emission Simulator (MOVES 2010). The MOVES model assumes that the permile rates at which these pollutants are emi tted are determined by EPA regulations and the effectiveness of catalytic after-treatment of engine exhaust emissions, and are thus unaffected by changes in car and light truck fuel economy. As a consequence, the effects of required increases in fuel economy emission s of these pollutants from car and light truck use are determined entirely by the increases in driving that result from

e

hour of operation. To convert these emis sion factors to grams per mile for use in N HTSA’s calculations, MOVES was run for the year 2050, and was programmed to report aggregate emissions from vehicle start and running exhaust. EPA analysts selected the year 2050 in order to generate emission factors that were representative of lifetime

262 This document is available at http://www2.e ere.energy.gov/buildings/appliance_standards/commercial/pdfs/sem_finalrule_appendix15a.pdf (last ccessed March 2, 2010). a

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### ollutant. The resulting emission rates represent average values across the nation, and corporate typical temperature variations over an entire calendar year. These national pecific differences in fuel composition, as well as in the resence and type of vehicle inspection and maintenance programs. 264



### ght trucks that is projected to occur under that alternative. As with other impacts, the

rude oil extraction and transportation, fuel refining, and fuel storage and ansportation. The reduction in emissions during each of these phases depends on the extent to

imported crude oil as a feedstock are assumed to reduce emissions during fuel fining, storage, and distribution, because each of these activities would be reduced. Finally, average emission rates for vehicles meeting the agency’s Tier 2 emission standard. 263 

### Separate estimates were developed for each vehicle type and model year, as well as for each state and month, in order to reflect the effects of regional and temporal variation in temperature and other relevant variables on emissions.

The MOVES emissions estimates were then summed to the model year level and divided by average distance traveled in order to produce per-mile emission factors for each
p in average rates also reflect county-s
p

Emission rates for the criteria pollutant SO 2 

### were calculated by NHTSA using average fuel sulfur content estimates supplied by EP A, together with the assumption that the entire sulfur content of fuel is emitted in the form of SO 2 

### . These calculations assumed that national average gasoline and diesel sulfur levels would remain at current levels. 265 

### Total SO 2 

### emissions under each alternative CAFE standard were calculated by applying the resulting emission rates directly to annual gasoline and diesel fuel use by cars and li changes in emissions of criteria air pollutants resulting from alternative increases in CAFE standards for MY 2012-2016 cars and light trucks were calculated as the difference between emissions under each alternative that would increase CAFE standards and emissions under the baseline alternative, which would extend the MY 2011 standards to apply to future model years.

Emissions of criteria air pollutants also occur during each phase of fuel production and distribution, including c
tr 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 reductions in domestic gasoline refining are reflected in reduced imports of crude oil or in reduced domestic extraction of petroleum. NHTSA’s analysis assumes that reductions in imports of refined fuel would reduce criteria pollutant emissions during fuel storage and distribution only. Reductions in domestic fuel refining using

re

263 Because all light-duty emission rates in MOVES 2010 are assumed to be invariant after MY 2010, a calendar-year 2050 run produced a full set of emission rates that reflect anticipated deterioration in theeffectiveness of vehicles’ emission control systems with increasing age and accumulated mileage for postMY 2010 vehicles.

264 The national mix of fuel types includes county-level market shares of conventional and reformulated gasoline, as well as county-level variation in sulfur content, ethanol fractions, and other fuel properties. Inspection/maintenan ce programs at the county level account for detailed program design elements such as test type, inspection frequency, gram coverage by vehicle type and age.

ams per gallon of diesel. and pro
265 These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively, which produces emission rates of 0.17 grams of SO 2 per gallon of gasoline and 0.10 gr

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### uring all four phases of fuel production and distribution. 266



### ass

e estimates of fuel energy content re ported by GREET. The resulting emission rates ere applied to the agency’s estimates of fuel consumption under each alternative CAFE tandard to develop estimates of total emissions of each criteria pollutant during fuel production
nges in fuel consumption on domestic nd imported sources of fuel supply discussed ab ove were then employed to calculate the effects mestic emissions of ach criteria pollutant.

Finally, NHTSA calculated the net changes in domestic emissions of each criteria pollutant by summing the increases in its emissions projected to result from increased vehicle use, and the reductions in emissions anticipated to result from lower domestic fuel refining and distribution. 269 

### As indicated previously, the effect of adopting higher CAFE standards on total emissions of each criteria pollutant depends on the relative magnitudes of the resulting reduction in emissions from fuel refining and distribution, and the increase in emissions from additional vehicle use. Although these net changes vary significantly among individual criteria pollutants, the agency projects that on balance, adopting higher CAFE standards would reduce emissions of all criteria air pollutants except carbon monoxide (CO).

The net changes in domestic emissions of fine pa rticulates (PM2.5) and its chemical precursors (such as NOx, SOx, and VOCs) are converted to economic values using estimates of the reductions in health damage costs per ton of emissions of each pollutant that is avoided, which were developed and recently revised by EPA. These savings represent the estimated reductions reduced domestic fuel refining using domestica lly-produced crude oil is assumed to reduce emissions d

NHTSA estimated the reductions in criteria pol lutant emissions from producing and distributing fuel that would occur with alternative CAFE standards using emission rates obtained by EPA from Argonne National Laboratories’ Greenhou se Gases and Regulated Emissions in Transportation (GREET) model. 267 

### The GREET model provides separate estimates of air pollutant emissions that occur in four phases of fuel production and distribution: crude oil extraction, crude oil transportation and storage, fuel refining, and fuel distribution and storage. 268 

### EPA modified the GREET model to change cert ain assumptions about emissions during crude petroleum extraction and transportation, as well as to update its emission rates to reflect adopted and pending EPA emission standards. The agency converted these emission rates from the m per fuel energy content basis on which GREET reports them to mass per gallon of fuel supplied using th

w

s and distribution. The assumptions about the effects of cha

a of reductions in fuel use from alternative CAFE standards on changes in do

e

266 In effect, this assumes that the distances crude oil travel s to U.S. refineries are approximately the same regardless of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline travels from refineries to retail stations are approximately the same as those from import terminals to gasoline stations.
267 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 April 20, 2008).
268 Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of volatile organic compounds, or VOCs) are already accounted for in the “tailpipe” emission factors used to estimate the emissions generated by increased light truck use. GREET estimates emissions in each phase of gasoline production and distribution in mass per unit of gasoline energy content; these factors are then converted to mass per gallon of gasoline using the average energy content of gasoline.
269 All emissions from increased vehicle use are assumed to occur within the U.S., since CAFE standards would nly to vehicles produced for sale in the U.S. apply o

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### in the value of damages to human health lower atmospheric concentrations and population expos t that contributes to atmospheric PM2.5 concentrations are red d. The value of reductions in the risk of premature death due to exposure to fine particu pollution (PM2.5) account for a majority of EPA’s es 5 related emissions, although the val avoiding other hea so included in these estimates se values do not i s reduction in re and environm and welfare related to other crit d air toxics. EPA estimates d rent PMrelated pe vehicle use than for reductio missions of that occu and distributi on. NHTSA applies these sepa es to its estimates ction and bution to determine ese pol

EPA projec redu cing emissions of criteria pol om both mobile sou nd stationary sources such as f eries and storage fa flect risin e levels, which are for reduced re to health threa population growth, which incr opulation exposure to

D Add resulting from ure to air pollution that occur when emissions of each pollu tan

uce l ate timated values of reducing PM2. ue of lth impacts related to PM2.5 exposure is al . The nclude a number of unquantified benefits, such a ctions in health the welfa i ental impacts of PM2.5 pollution, or redu mpacts iffe eria pollutants (ozone, NO2, and SO2) an

r- g emissions from

ton values for reducin ns in e r during fuel production rate valu distri of changes in emissions from vehicl e use and fuel produ th mic damages from emissions of th e net change in total econo lutants.

ts that the per-ton values for lutants fr

ue rces (including motor vehicles) a lities will increase over time. Thes l refin g incom ci e projected increases re assumed to increase affected individu als’ willingness to pay exposu es p ts from air pollution , as well as fu ture future levels of air pollution. eas

ed Costs from Congestion, Crashes, and Noise

While it pro d w
economy re ntribute to increased traffic congestion, m e crashes, and n contribute to traffic conge delays by in y-traveled roadways during el periods, d and on where it occurs. B g the number of crashes a nd disabled vehicles, added driving can also increase the ften result from these incidents, although the extent to ally does so agai n when and where the added travel occurs.

In either cas e higher costs on drivers and other vehicle oc the form of in considered as an additiona mic cost associated with the rebound effect. Because drivers do not take these added costs eciding when to make trips or where they travel, th be accounted f y as a cost of the adde d driving associated with the rebou

Increased p truck use due to the rebound effect may also in e costs associated ccount of the potential co ey (and the other oc ssi ity of being involved in a crash when they decide to make additional trips. However, they probably do not consider all of the potential
y vides some benefits to drivers, increased vehicle use associate ith the fuel bound effect can also co otor vehicl highway noise. Additional vehicle use ca stion and creasing recurring congestion on heavil peak trav epending on how the additional travel is distributed over the day y increasin

delays that o which it actu n depends o

e, added delays impos cupants in creased travel time and operating expenses, and these should be l econo into account in d ey must or separatel nd effect.

assenger car and light crease th with traffic crashes. Drivers presumably take a

cupants of their vehicles) face from the po bil sts th

costs they impose on occupants of other vehicles and on pedestrians when crashes occur, so an increase in these “external” crash costs must be c onsidered as another cost of additional reboundeffect driving.

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### Like in eased delay costs, any increase in these external crash costs caused by added driving is likely t epend on the traffic conditions under which it takes place, since crashes are more frequen heavier traffic, but their severity may be reduced by the slower speeds at which heavier Thus estimates of the increase in external crash costs from the

bound effect also need to account for when and where the added driving occurs.

f

nd nomic costs on those it affects, and ese added costs are unlikely to be taken into account by drivers of the vehicles that cause it.

d

in e of cars and light trucks that are borne y persons other than their drivers. FHWA’s “Middle” estimates for congestion, crash, and

s

ee closely with some other recent estimates. For example, recent published search conducted by Resources for the Future

ese cr o d t in traffic typically moves.

re

Finally, added vehicle use from the rebound effect may also increase traffic noise. Noise generated by vehicles causes inconvenience, irritation, and potentially even discomfort to occupants of other vehicles, to pedestrians and other bystanders, and to residents or occupants o surrounding property. Because none of these eff ects are likely to be taken into account by the drivers whose vehicles contribute to traffic noise, they represent additional externalities associated with motor vehicle use.

Although there is considerable uncertainty in estimating its value, the added inconvenience a irritation caused by increased traffic noise imposes some eco th Thus any increase in noise costs resulting from added vehicle use must be included together with other increases in external costs of additional rebound-effect driving.

NHTSA’s analysis uses estimates of the congestion, crash, and noise costs caused by increase travel in automobiles, pickup trucks, and vans developed by the Federal Highway Administration. 270 

### These estimates are intended to measure the increases in external costs – that is, the “marginal” external costs – from added congestion, property damages and injuries traffic crashes, and noise levels caused by additional usag b noise costs imposed by passenger cars are 5.4 cents, 2.3 cents and 0.1 cents per additional vehicle mile when expressed in 2007 dollars. 271 

### For pickup trucks and vans, FHWA’s estimate correspond to 4.8 cents, 2.6 cents, and 0.1 cents per additional vehicle-mile.

The Federal Highway Administration’s estimates of these costs agr

re (RFF) estimates marginal congestion and external crash costs for increased light-duty vehicle use in the U.S. to be 3.9 and 3.4 cents per vehicle-mile when converted to 2007 dollars. 272 

### Th estimates incorporate careful adjustments of congestion 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 severity of motor vehicle crashes.

0 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study

. Federal Highway Administration, 1997 Federal Highway Cost Allocation Study , Tables V-22, V-23, and V-24, http://www.fhwa.dot.gov/policy/hcas/final/index.htm 27

271 . The higher congestion cost for automobiles than for light trucks reflects the larger fraction of auto than of li ght truck use that occurs within congested urban areas.
272 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, March 2002, pp. 19 and Table 1, http://www.rff.org/rff/Documents/RFF-DP02-12.pdf

.

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### FHWA’s estimates of added costs for con s , and noise are multiplied by the estimated increa e affected model years’ lifetimes to yield the estim estion, crash, and noise externality

sts. The resulting yearly estimates are then summed to obtain their lifetime values. The value of these increased costs varies among mode the alternative increases in CAFE stand in this ec es in v depend o improvements in fuel econom uld re pecifi

e Discount R gestion, crashe

ses in passenger car and light truck use due during each year of th ated incr eases in cong

co l years and ards considered analysis, b y that wo ause the increas sult in s ehicle use c model years unde n the r each alternative.

E. Th ate

Discounting future fuel sa their value to society whe immediately. The di vi er benefits is intended to account for the reduction in n ferred u me future da her than receiv
scount s the percent decline in the value of these benefits – as
rspe ch ye y are deferre the future. In ting ative in CAFE standards for MY 2012-2016 passenger cars and as em iscoun f 3% per yea agency has a ed of these benefi t estimates to the use of a 7 percent discount rate.

The primary reason that NH ected 3 percent as the appropriate rate for discounting s from increas tandards at most or all hicle manufact ’ costs ith higher C dards are likely to be reflected in higher sales pr for icle models. B g sale for new ca ight trucks, C ll thus primaril hicle pu and other pr consumption de ons. economic theory and nce on nting indica the future ben nd of regulations that main private c ption should discounted at the ial rate

e preference. 273



### guidance also indic appea count future consumption at an ge at is, adjusted to rem ct of i n) rate of abo ercent when th ce

about its likely le akes i onable estim f the social rate e

ce. 274 

### Thus NHTSA ha mployed t ent rate to t projected f resulting from igher CAFE standards for MY 6 passeng nd ght trucks.

One important exception to the 3% discount rate the rates used to discount benefits from reducing CO 2 

### emissions from the years in which reduced emissions occur, which span the lifetimes of model year 2012-16 cars and light trucks, to their present values. In order to ensure consistency in the derivation and use of the interagency group’s estimates of the unit values of reducing CO 2 

### emissions, the benefits from reducing those emissions during each future year are discounted using the same “intergenerational” discount rates that were used to derive each of the alternative unit values of reducing CO 2 

### emissions. As Table VIII-7 above shows, these rates are 5 percent for the interagency group’s lowest estimate of the SCC, 3 percent for its central and ngs and oth they are de ntil so te, rat ed rate expresse ctive – for ea increases viewed from today’s pe ar the d into evalua the benefits from altern light trucks, NHTSA h the sensitivity ployed a d t and co t rate o r. The lso test

s

TSA has sel future benefit for complying w ed CAFE s AFE stan is th of ve urers ices their new veh regulation wi y increasin y affect ve s prices rchases rs and l ivate AFE cisi Both OMB guida discou te that efits a
costs of tim ly affect onsum be soc

OMB ates that savers r to dis avera real (th ove the effe nflatio ut 3 p ey fa little risk vel, which m t a reas ate o of tim preferen

enefits and costs s e h he 3 perc discoun 2012-201 uture

er cars a b li

273 Id.
274 Office of Management and Budget, Circular A-4, “Regulatory Analysis,” September 17, 2003, 33. Available at http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf (last accessed July 24, 2009).

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### highest estimates, and 5 percent for the esti tween the group’s central and highest estim

Because there is some uncertainty about the extent to which veh nufacturers w to recover their costs for complying with higher CAFE standards by increasing vehicle sales r, NHTSA ha the sensitivity of these benefit and cost estimates to the percent disco B indicates econom l i riate discount rate to apply to future benefits and costs f is “... ace or alter pital in ate s that ently averages about 7 percent. 275 

### Thus the agency has

o nd co ates for alte MY 2012-20 FE a 7 pe discount rate. NHTSA seeks comment on whether it FE stand a disco of 3 perce ercent, or an a ive

mary of Valu timat its mate lying be
ates.

icle ma ill be able

prices, howeve use of a higher s also tested unt rate. OM gui dance that the real y-wide opportunity cost of capita when the primary effect o sector,” and estimate s the approp a regulation to displ the use of ca the priv this rate curr f its benefit a

rcent rea also tested the sensitivity st estim rnative 16 CA standards to the use of should evaluate CA value. l ards using unt rate nt, 7 p lternat

F. Sum es used to Es e Benef

marizes th value estimate b . Table VIII-8 sum e economic s used to enefits

275 Id.

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### T nomic se ompu 2007$)

Fuel Economy Rebound Effect 10% able VIII-8 Eco Values U d for Benefits C tations (

"Ga p" between t -road 20% est and on MPG Valu e of refueling r ($ pe -hour) $ 24 time pe r vehicle .64 Annual growth ehicle 1.15% in average v use Fuel Price s (2012 ge, $/g -50 avera allon) Retail gasoline price $3 .66 Pre-tax gas oline $3 price .29 Economic B enefi du cin ports ($/ga ts from Re g Oil Im llon) "M onopsony" Co $ 0. mponent 00 Price Sho ck Co $ 0.17 mponent Militar y Security ent $ 0 Compon .00 Total Econ omi $ 0.17 c Costs ($/gallon) Em ission Damage eighted $/metric Cost s (w , $/ton or ton) C arbon monox ide $ 0 V olatile organic ds (VO $ 1,3 compoun C) 00 Nitrogen oxide ehicle $ 5,3 s (NOx) – v use 00 Nitrogen oxides fuel produ and distribution $5,100 (NOx) – ction Particulate matter ) – vehicle $ 290,00 (PM 2.5 

### use 0 Particulate ma fuel pr tion and distri n $ 240,00 tter (PM 2.5 

### ) – oduc butio 0 Sulfur dioxide $ 31,00 (SO 2 

### ) 0 Carbon dioxide ions i $ (CO 2 

### ) emiss n 2010 21 Annual Increas age C variab e in CO 2 

### Dam ost le External Costs from Additional bile Use ( - mile) Automo $/vehicle

Congestion $ 0.054 Accidents $ 0.023 Noise $ 0.001 Total External Costs $ 0.078External Costs from Additional Light Truck Use ($/vehiclemile)

Congestion $0.048 Accidents $0.026 Noise $0.001 Total External Costs $0.075 Discount Rate Applie d to Future Benefits 276 

### 3%

276 Future benefits from reducing CO 2 emissions are discounted using the same “intergenerational” discount rates that were used to derive each of the alternative SCC estimates used to value reductions in those emissions. As Table VIII-7 above shows, these rates are 5 percent for the interagency group’s lowest estimate of the SCC, 3 percent for its central and highest estimates, and 5 percent for the estimate lying between the group’s central and highest estimates.

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### G. Benefits Estimates

alculated sep passenger cars and light trucks under each alternative ent for each cove is proposa les VIII-9 10, s nd light truck CAFE standards under the preferred o 12-20 tables in ndiscounted ent val ted 3 percent. They also show changes in the physical odu lues. e values in bles reflect onsumpti ssions and their resulting economic impacts, which om the p hile p alues repre reasing emis ise or crash sev their added costs. The net social benefit from these shown o ine in e.

ative fo cars w ave 35.7 billi llons of fuel an vent 380 million metric tons of ta emissions over the lifetime of the passenger cars sold ears, c the fu gs and emis eductions tha ndards remain adjusted b e for MYs . The pr ative for light trucks 4 b llons of fue event 275 million metric

tailpipe CO 2 

### emission e lifetime e light trucks during those model years, pared to the fuel savings ssions red ns that would o if the standard ed at the adjusted b s 201 6.

total value of societal e prefe lternative fo nger cars and rucks

2 billion 277 

### over the MY Thi ate of societa fits es direct impacts from ons as well as ies, and al s societal costs resulting from the reb ect. Fuel sav account for t nd CO 2 

### emissions account for 8 percent of the societal benefits.

Tables VIII-11 and VIII-12 summarize the societal be nefits for all alternatives for passenger cars and light trucks at the 3 percent and 7 percent discount rates, respectively. As would be expected, benefit levels parallel the increasing stringency of the various alternatives that were examined. The TC=TB scenario produces benefits that exceed the other alternatives because that methodology allows technologies that are cost eff ective to pay for some technologies that are not cost effective. Table VIII-13 summarizes the fuel savings from all alternatives for passenger cars and light trucks. Benefits were c CAFE requirem arately for model year red by th l. In Tab and VIIIthe societal impacts for pa alternative is shown for m well as their net pres senger car a del years 20 16. These clude u values as ues discoun ced these va

on or emi units of measure that pr Negativ these ta net reductions in fuel c represent benefits fr congestion, no roposal, w erity an osi tive v sent inc sions, d n the Total l societal impacts is each tabl

The preferred altern r passenger ilpipe CO ould s on ga d pre

2 

### ompared to

ed at the during those model y occur if the sta el savin aselin sions r 2012-2016 t would eferred altern would save 25. illio n ga l and pr tons of

com s over th and emi of th uctio sold

ccur s remain aseline for MY 2-201

The benefits of th rred a r passe light t is $18 lifetime of the low uel c 2012-16 fleet. s estim l bene includ

ffsetting er f umption ound eff externalit ings so reflect 78 percen o

a

277 The $182 billion estimate is based on a 3% discount rate for valuing future impacts.

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### T ime Be Pr ative b ar -- Pass ars

MY 2012 ffect Undiscou ed Disco lue @ 3% Disco alue @ 7% 

### able VIII-9 Lifet nefits for eferred Altern enger C y Model Ye

Societal E Physical Units nt Value Present unted Va Present unted V

Lifetime Fuel Expenditures 2,395,587 (kgal) $6,573 $5,290 $4,156
Consumer Surplus fro Additional Dr m iving ( 6,826,369

kmiles) $491 $396 $312

Refueling Time Va lue 22,168,301 (hours) $546 $452 $365

Petroleum Marke Externalities t 2,395,587 (kgal) $385 $315 $251

Congestion C osts 6,826,369 (kmiles) -$356 -$292 -$234

Noise Costs 6,826,369 (kmiles) -$7 -$6 -$4

Crash Co sts 6,826,369 -$ -$107 (kmiles) 163 -$1 33

CO2 25 (mmT) $643 $516 $516

CO 183,679 (tons) $0 $0 $0

VOC 21,787 ons) (t $35 $28 $21
N OX 10,764 (tons) $69 $52 $39
P M 530 (tons) $154 $117 $86 SOX 2,975 (tons) $112 $91 $73 Total $8,482 $6,826 $5,474

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### MY 2013 Societal Effect Physical Units Undiscounted Value Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures 5,152,718 (kgal) $14,551 $11,814 $9,353
Consumer Surplus from Additional Driving 13,859,078 (kmiles) $1,075 $874 $693

Refueling Time Value 44,980,471 (hours) $1,108 $920 $746

Petroleum Market Externalities 5,152,718 (kgal) $828 $680 $546

Congestion Costs 13,859,078 (kmiles) -$733 -$603 -$485

Noise Costs 13,859,078 (kmiles) -$14 -$11 -$9

Crash Costs 13,859,078 (kmiles) -$327 -$268 -$215

CO2 54 (mmT) $1,453 $1,174 $1,174

CO 173,993 (tons) $0 $0 $0

VOC 37,362 (tons) $59 $48 $38
NOX 15,020 (tons) $96 $75 $57
PM 1,112 (tons) $324 $257 $199
SOX 6,353 ons) (t $239 $196 $158
T otal $18,660 $15,155 $12,255

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### MY 2014 fect Physical Undiscounted Present Value

Lifetim Expe e Fuel nditures 7,232,873 (kgal) $20,836 $16,914 $13,395
Consumer Surplus from Additional Driving 19,545,328 (kmiles) $1,532 $1,246 $988

Refueling Tim e Value 62,083,457 $1,530 $1,269 $1,029 (hours) Petroleum Mar ket 7 $1,162 $954 $766 Externalities ,232,873 (kgal)
Congestion Costs 19,545,328 -$1,032 -$849 -$682 (kmiles)

Noise Costs 19,545,328 -$20 -$16 -$13 (kmiles)

Crash Costs 19,545,328 -$461 -$379 -$304 (kmiles) CO2 77 (mmT) $2, $1 102 $1,696 ,696

CO 160,287 (tons) $0 $0 $0

VOC 4 ) 8,830 (tons $78 $63 $50 NOX 17,864 (tons) $114 $90 $69
PM 1,547 (tons) $451 $362 $284
SOX $335 $275 $221 8,901 (tons) Total $26,627 $ 6 $17,499 21,62

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### MY 2015 Societal Effect Physical Units Undiscounted Value Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures 9,446,003 (kgal) $27,649 $22,471 $17,816
Consumer Surplus from Additional Driving 25,402,984 (kmiles) $2,010 $1,636 $1,300

Refueling Time Value 80,958,399 (hours) $1,995 $1,656 $1,343

Petroleum Market Externalities 9,446,003 (kgal) $1,517 $1,247 $1,001

Congestion Costs 25,402,984 (kmiles) -$1,343 -$1,106 -$888

Noise Costs 25,402,984 (kmiles) -$25 -$21 -$17

Crash Costs 25,402,984 (kmiles) -$599 -$492 -$395

CO2 101 (mmT) $2,816 $2,274 $2,274

CO 143,844 (tons) $0 $0 $0

VOC 61,141 (tons) $97 $79 $63
NOX 2 0,851 (tons) $133 $106 $82
PM 1,982 (tons) $577 $467 $368
SOX 11,612 ns) (to $437 $359 $288
T otal $35,264 $28,677 $23,235

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### MY 2016 Societal Effect Physical Units Undiscounted Value Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures 11,432,923 (kgal) $33,971 $27,655 $21,958
Consumer Surplus from Additional Driving 30,779,447 (kmiles) $2,449 $1,997 $1,589

Refueling Time Value 96,330,156 (hours) $ 2,374 $1,972 $1,601

Petroleum Market Externalities 11,432,923 (kgal) $ 1,837 $1,511 $1,214

Congestion Costs 30,779,447 (kmiles) -$1,632

- -$1,081 $1,344

Noise Costs 30,779,447 (kmiles) -$31 -$25 -$20

Crash Costs 30,779,447 (kmiles) -$723 -$595 -$478

CO2 123 (mmT) $3,487 $2,820 $2,820

CO 125,778 (tons) $0 $0 $0

VOC 71,893 (tons) $114 $93 $75
NOX 23,524 (tons) $150 $120 $94
PM 2,365 (tons) $689 $561 $447
SOX 14,045 ons) (t $528 $435 $349
T otal $43,214 $35,200 $28,567

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### MY 2012-2016, Passenger Cars Combined Societal Effect Physical Units Undiscounted Value Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures 35,660,103 (kgal) $103,581 $84,145 $66,679
Consumer Surplus from Additional Driving 96,413,205 (kmiles) $7,557 $6,149 $4,882

Refueling Time Value 306,520,784 (hours) $7,553 $6,269 $5,085

Petroleum Market Externalities 35,660,103 (kgal) $5,729 $4,708 $3,779

Congestion Costs 96,413,205 (kmiles) -$5,096 -$4,194 -$3,370

Noise Costs 96,413,205 (kmiles) -$96 -$79 -$64

Crash Costs 96,413,205 (kmiles) -$2,273 -$1,868 -$1,499

CO2 380 (mmT) $10,502 $8,479 $8,479
CO 787,580 (tons) $0 $0 $0
VOC 241,013 (tons) $383 $311 $247
NOX 88,022 (tons) $561 $443 $341
PM 7,536 (tons) $2,195 $1,763 $1,384
SOX 43,887 (tons) $1,651 $1,357 $1,089 Total $132,246 $107,483 $87,031

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### T 0 ime Be Pr ative b ar -- Lig ks

MY 2012 ffect Un d Value Discounted Value Discounted Value 

### able VIII-1 Lifet nefits for eferred Altern ht Truc y Model Ye

Societal E Physical Units discounte Present

@ 3% Present

@ 7%

Lifetime Fuel Expenditures 1,805,043 (kgal) $5,102 $ 3,974 $3,041

Consumer Su rplus from
g 4,054,226 $384 $300 $230 Additional Drivin (kmiles)
Refueling Time Value 1 $315 $255 $202 2,795,747 (hours) Petroleum M arket $290 $231 $181 Externalities 1,805,043 (kgal)
Cong estion Costs 4,054,226 (kmiles) -$195 -$1 55 -$121

Nois e Costs 4,054,226 (kmiles) -$4 -$3 -$3

Crash Costs

- 4,054,226 (kmiles) $105 -$84 -$66

CO2 19 (mmT) $523 $405 $405
CO 3 6,333 ons) (t $0 $0 $0
V OC 11,557 (tons) $18 $14 $11 NOX 3, 686 (tons) $23 $18 $14
PM 388 (tons) $113 $89 $68 SOX 2,217 (tons) $83 $66 $52 Total $6,549 $5,110 $4,015

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### MY 2013 Societal Effect Physical Units Undiscounted Value Present Discounted Value @ 3% Present Discounted Value @ 7%

Lifetime Fuel Expenditures 3,698,295 (kgal) $10,702 $8,364 $6,428
Consumer Surplus from Additional Driving 7,806,595 (kmiles) $804 $630 $486

Refueling Time Value 23,242,410 (hours) $573 $463 $368

Petroleum Market Externalities 3,698,295 (kgal) $594 $473 $371

Congestion Costs 7,806,595 (kmiles) -$375 -$299 -$234

Noise Costs 7,806,595 (kmiles) -$8 -$6 -$5

Crash Costs 7,806,595 (kmiles) -$203 -$162 -$127

CO2 40 (mmT) $1,099 $851 $851
CO 25,256 (tons) $0 $0 $0
VOC 22,147 (tons) $35 $28 $22
NOX 5,894 (tons) $38 $30 $23
PM 767 (tons) $223 $177 $137
SOX 4,537 (tons) $171 $136 $107 Total $13,653 $10,684 $8,427

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### MY 2014 t Physical Undiscounted Present Value Disc

Lifetime Fuel Expe 5,281, (kgal) 15,558 $12,168 $ nditures 256 $ 9,362

Co from A 11,323,656 (kmiles) $1,153 $905 nsumer Surp lus dditional Driving $698

Re 33,659,128 (hours) 9 $670 $533 fueling Time Value $82

Pe Ex 5, 848

troleum Market ternalities 281,256 (kgal) $ $676 $530

Co 11,323,656 ( 544 -$433 ngestion Costs kmiles) -$ -$339

N 11,323,656 (kmiles) oise Costs -$11 -$9 -$7

Cr s 11,323,656 (kmiles) -$294 -$235 ash Cost -$184

CO 57 ) ,60 $1 2 (mmT $1 6 $1,244 ,244
CO 1,07 s) $0 8 (to n $0 $0
VO 30,5 s) $48 C 14 (ton $39 $30
N 7,2 s)

OX 36 (ton $46 $37 $29
PM 1,0 s) 315 82 (ton $ $250 $196
SO 6,4 s) 244 X 76 (ton $ $194 $152 Total ,8 6 $12,243 $19 00 $15,50

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### MY 2015 t Physical Undiscounted Present Value Disc

L ifetime Fuel Expenditures 6,504,296 (kgal) $19,458 $15,229 $11,726

Consumer S urplus from
ng (kmiles) $1,424 $1,118 $863 Additional Drivi 13,804,723

Refueling Time Value 40,605,771 ,001 $643 (hours) $1 $808

Petroleum Market Externalities 6 6 ,04 $653 ,504,29 (kgal) $1 5 $832

Congestion Costs 13,804,723 ( 663 -$528 - kmiles) -$ $414

Noise Costs 13,804,723 ( 14 kmiles) -$ -$11 -$9

Crash Costs 13,804,723 (kmile -$359 -$286 -$22 s) 4

CO2 7 ) ,02 $1 1 (mmT $2 2 $1,566 ,566
CO -38,6 ons) 32 (t $0 $0 $0
VOC 36, ns) 395 (to $58 $46 $36
NOX 7,6 s) 76 (ton $49 $40 $32
PM 1,3 s) 90 $243 38 (ton $3 $310
SOX 7,9 s) 00 $187 73 (ton $3 $239 Total $24 ,71 4 $15,302 1 $19,36

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### MY 2016 Societal Effect Physical Present Present ted Value @ 7% Units Value Discounted Value Discoun Undiscounted

@ 3%

L ifetime Fuel Expenditures 8,061,224 (kgal) $19,169 $14,769 $24,479

Consumer Su rplus from 0 les) 0 Additional Driving 17,154,5 5 $1, (kmi 7 70 $1,3 9 $1,074

Refueling Time Value ,269 rs) $1,2 78 49,147

(hou 11 $9 $778

Petroleum Market Externalities 8,061, 224 l) $1,2 32 (kga 95 $1,0 $809

Congestion Costs 17,154 ,505 les) -$823 -$656 (kmi -$514

Noise Costs 17,154,505 (kmi les) -$1 4 -$11 7 -$1

Crash Costs 17,154,505 s) -$355 -$278 (kmile -$446

CO2 88 (mm T 2 4 ) $ ,561 $1,98 $1,984
CO - (ton 84,105 s) $0 0 $ $0
VOC 4 (tons) $70 6 3,994 $5 $44
NOX 8 (tons) ,618 $55 5 $4 $37
PM 1,661 ( tons $48 85 ) 4 $3 $301
SOX 9,878 (tons) $37 6 2 $2 9 $232 Total $31,0 10 $24,310 $19,225

### 437

DELIBERATIVE DOCUMENT – DO NOT RELEASE Physical Units Undiscounted Value Present Discounted Value Present Discounted Value 

### MY 2012-2016, Combined Light Trucks Societal Effect

@ 3% @ 7%

Lifetime Fuel Expenditures 25,350,115 (kgal) $75,299 $58,903 $45,327
Consumer Surplus from Additional Driving 54,143,706 (kmiles) $5,535 $4,342 $3,351

Refueling Time Value 159,450,326 (hours) $3,929 $3,174 $2,523

Petroleum Market

xternalities 25,350,115 (kgal) $4,072 $3,244 $2,543 E
Congestion Costs 54,143,706 (kmiles) -$2,599 -$2,070 -$1,623

Noise Costs 54,143,706 (kmiles) -$54 -$43 -$34

Crash Costs 54,143,706 (kmiles) -$1,408 -$1,121 -$879

CO2 275 (mmT) $7,812 $6,049 $6,049 CO -60,070 (tons) $0 $0 $0 VOC 144,608 (tons) $230 $183 $144 NOX 33,109 (tons) $211 $170 $134 PM 5,236 (tons) $1,525 $1,210 $945 SOX 31,081 (tons) $1,169 $931 $730
T otal $95,722 $74,974 $59,212

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### Table VIII-11 Present Value of Lifetime Social Benefits by Alternative (millions of 2007 dollars) (3 percent discount rate)

Alternative MY 2012 MY MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative 6,826 15,155 21,626 28,677 35,200 107,483 3% Annual Increase 3,397 8,374 12,331 16,760 23,122 63,984 4% Annual Increase 4,186 11,006 17,315 24,469 32,309 89,286 5% Annual Increase 6,152 15,404 24,075 32,114 40,905 118,649 6% Annual Increase 7,071 18,062 28,137 37,552 47,754 138,576 7% Annual Increase 8,038 20,627 32,225 42,010 52,606 155,507 Max Net Benefits 8,019 20,896 31,683 39,863 48,228 148,689 Total Cost = Total Benefit 8,666 22,374 33,916 42,737 51,659 159,35 2 Light Trucks Preferred Alternative 5,110 10,684 15,506 19,364 24,310 74,974 3% Annual Increase 687 3,920 7,635 11,604 14,940 38,786 4% Annual Increase 2,590 7,361 12,580 17,089 21,830 61,450 5% Annual Increase 4,003 10 ,407 17,686 23,206 28,324 83,626 6% Annual Increase 4,893 13,933 22,031 28,987 34,727 1 04,571 7% Annual Increase 5,634 16,326 25,550 32,714 38,229 118,453 Max Net Benefits 7,528 18,302 25,913 31,563 34,835 118,141 Total Cost = Total Benefit 7,631 18,954 27,294 33,381 37,262 124,522

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### Pre e

(7 percent d rate)

Alternative MY 2012 MY 2013 

### Table VIII-12 sent Value of Lifetime Social Benefits by Alternativ (millions of 2007 dollars) iscount

MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative 5,474 12,255 17,499 23,235 28,567 87,031 3% Annual Increase 2,727 6,778 9,980 13,585 18,774 51,844 4% Annual Increase 3,356 8,904 14,015 19,838 26,241 72,353 5% Annual Increase 4,941 12,472 19,493 26,030 33,185 96,122 6% Annual Increase 5,667 14,612 22,763 30,402 38,735 112,180 7% Annual Increase 6,448 16,692 26,080 34,028 42,669 125,917 Max Net Benefits 6,134 16,378 25,041 31,517 38,120 117,191 Total Cost = Total Benefit 6,957 18,112 27,453 34,625 41,897 129,044 Light Trucks Preferred Alternative 4,015 8,427 12,243 15,302 19,225 59,212 3% Annual Increase 545 3,099 6,035 9,178 11,823 30,679 4% An al Increase 2,035 5,802 9,927 13,500 17,260 48,524 nu 5% Annual Increase 3,129 8,189 13,929 18,300 22,365 65,913 6% Annual Increase 3,823 10,966 17,349 22,842 27,385 82,366 7% Annual Increase 4,404 12,838 20,108 25,767 30,132 93,248 Max Net Benefits 5,736 12,761 18,525 22,485 25,290 84,797 Total Cost = Total Benefit 6,039 14,926 21,502 26,237 29,295 97,999

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### Table VIII-13 Fuel Savings over Lifetimes of Model Year 2012-2016 Passenger Cars

and Light Trucks with Alternativ e Increases in CAFE Standards (million gallons)

Passenger Cars Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Total Preferred Alternative 2,396 5,153 9,446 11,433 35,660 7,233 3% Annual Increase 90 21,161 1,197 2,845 4,120 5,509 7,4 4% Annual In 29,524 crease 1,476 3,740 5,787 8,046 10,475 5% Annual In 39,463 crease 2,157 5,230 8,066 10,630 13,381 6% Annual Increase 2,520 6,200 9,530 12,589 15,770 46,609 7% Annual Increase 2,855 7,086 10,933 14,080 17,419 52,374 Max Net Benefits 2,848 7,159 10,731 13,324 15,893 49,956 Total Cost = Total Benefit 3,071 7,673 11,492 14,295 17,086 53,619 Light Trucks
Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 Total Preferred Alternative 1,805 3,698 5,281 6,504 8,061 25,350 3% Annual Increase 234 1,349 2,589 3,882 4,935 12,988 4% Annual Increase 916 2,557 4,298 5,751 7,251 20,773 5% Annual Increase 1,434 3,631 6,076 7,856 9,463 28,460 6% Annual Increase 1,758 4,869 7,584 9,859 11,677 35,747 7% Annual Increase 2,019 5,718 8,813 11,139 12,866 40,555 Max Net Benefits 2,688 6,395 8,919 10,735 11,700 40,437 Total Cost = Total Benefit 2,724 6,621 9,392 11,348 12,507 42,591



### H

. Potential Impacts of the Final Standards on Consumer Welfare

There are two reasonable viewpoints for evaluating the costs and benefits of the increase in CAFE standards: the private perspective of vehicle buyers themselves on the higher fuel economy levels the final rule would require, an d the economy-wide or “social” perspective on the costs and benefits of requiring higher fuel econ omy. The agency’s analysis of benefits from requiring higher fuel efficiency, presented previously in this Chapter, includes some categories that extend throughout the U.S. economy, such as reductions in the energy security costs associated with U.S. petroleum imports and in the economic damages expected to result from climate change. In contrast, other categories of benefits – principally the economic value of future fuel savings projected to result from higher fuel economy – will be experienced exclusively by the initial purchasers and subsequent owners of vehicle models whose fuelDELIB 

### 441

higher fuel economy represent an imp ant share of the total economic benefits from raising CAFE standards, NHTSA

fuel

s g with increased CAFE standards for those m del years by increasing vehicle sales prices. As the table shows, the agency’s estimates

, economy manufacturers elect to improve as part of their strategies for complying with higher CAFE standards.

Although the economy-wide or “social” benefits from requiring
ort estimates that benefits to vehicle buyers themselves will significantly exceed the costs of complying with the stricter fuel economy standards this rule establishes, as Chapter X shows. Since the agency also assumes that the costs of new technologies manufacturers will employ to improve fuel economy will ultimately be shifted to vehicle buyers in the form of higher purchase prices, NHTSA concludes that the benefits to vehicle buyers from requiring higher fuel efficiency will far outweigh the costs they will be required to pay to obtain it. However, this raises the question of why current purchasing patterns do not result in higher average fuel economy, and why stricter fuel efficiency standards should be necessary to achieve that goal.

As an illustration, Table VIII-14 reports the agency ’s estimates of the average lifetime values of fuel savings for MY 2012-2016 passenger cars and li ght trucks calculated using future retail prices, which are those likely to be used by vehicle buyers to project the value of fuel savings they expect from higher fuel economy. The table compares NHTSA’s estimates of the average lifetime value of fuel savings for cars and light truc ks to the price increases it projects to result a manufacturers attempt to recover their costs for complyin

o of the present value of lifetime fuel savings (discounted using the OMB-prescribed 3% rate) outweigh projected vehicle price in creases for both cars and light trucks in every model year even under the assumption that all of manufactu rers’ technology outlays are passed on to buyers in the form of higher selling prices for new cars and light trucks. By model year 2016, NHTSA projects that average lifetime fuel savings will exceed the average price increase by more than $2,000 for cars, and by more than $2,700 for light trucks.

Table VIII-14 Value of Lifetime Fuel Savings vs. Average Technology Cost Increase/Vehicle

ERATIVE DOCUMENT – DO NOT RELEASE 

### 2012 2013 2014 2015 2016 Value of Fuel Savings $759 $1,469 $1,954 $2,480 $2,932 Average Price Increase $505 $573 $690 $799 $907 Difference $255 $897 $1,264 $1,680 $2,025

Value of Fuel Savin g s $828 $1,634 $2,277 $2,887 $3,700 Average Price Increase $322 $416 $621 $752 $961 Difference $506 $1,218 $1,656 $2,135 $2,739 Light Trucks

Passsenger Cars Model Year Measure Fleet



### Assuming these comparisons are accurate, they raise the question of why current vehicle purchasing patterns do not result in average fuel economy levels approaching those that this ru le would require, and why stricter CAFE standards should be necessary to increase the fuel economy of new cars and light trucks. They also raise the question of why manufacturers do not

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### , since s in Table VIII-14 suggest that doing so would reduce the effective price of purcha ng many new vehicle models, and thus to increase sales of new vehicles. More her arisons

because HTSA has incorrectly estimated some components of the benefits from saving fuel, or because

er

oad

s are evaluated over different time horizons to the estimated increas in its price. This table shows that as reported previously in Table VIII-15, when fuel

d

e out elect to provide higher fuel economy even in the absence of increases in CAFE standards the comparison si specifically, why would potential buyers of new vehicles hesitate to make investments in hig fuel economy that would produce the substantial economic returns illustrated by the comp presented in Table VIII-14? And why would manufacturers voluntarily forego opportunities to increase the attractiveness, value, and competitiv e positioning of their car and light truck models by improving their fuel economy?

One explanation for this apparent paradox is that NHTSA’s estimates of benefits and costs from requiring manufacturers to improve the fuel efficiency of their vehicle models do not match potential vehicle buyers’ assessment of the likely benefits and costs from requiring higher fuel efficiency. This could occur because the agency’s underlying assumptions about some of the factors that affect the value of fuel savings differ from those made by potential buyers, N the agency has failed to account for some potential costs of achieving higher fuel economy.

For example, buyers may not value increased fuel economy as highly as the agencies’ calculations suggest, because they have shorter time horizons than the full vehicle lifetimes assumed by NHTSA and EPA, or because they discount future fuel future savings using high rates than those prescribed by OMB for evaluating federal regulations. Potential buyers may also anticipate lower fuel prices in the future than those forecast by the Energy Information Administration, or may expect larger differen ces between vehicles’ rated and actual on-r MPG levels than the agencies estimate.

To illustrate the first of these possibilities, Table VIII-15 shows the effect of differing assumptions about vehicle buyers’ time horizons for assessing the value of future fuel savings. Specifically, the table compares the average value of fuel savings from purchasing a MY 2016 car or light truck when fuel saving

e savings are evaluated over the entire expected lifetime of a MY 2016 car (approximately 14 years) or light truck (about 16 years), their discounted present value (using the OMB-prescribe 3% discount rate) lifetime fuel savings exceeds the estimated average price increase by more than $2,000 for cars and by more than $2,700 for light trucks.

If buyers are instead assumed to evaluate fuel savings over a 10-year time horizon, however, th present value of fuel savings exceeds the projected price increase for a MY 2016 car by ab $1,300, and by somewhat more than $1,500 for a MY 2016 light truck. Finally, Table VIII-15 shows that under the assumption that buyers value fuel savings only over the length of time for which they typically finance new car purchases (slightly more than 5 years during 2009), the value of fuel savings exceeds the estimated increa se in the price of a MY 2016 car by only about $350, and the corresponding difference is reduced to slightly more than $500 for a MY 2016 light truck.

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### Table VIII-15 Value of Fuel Savings vs. Vehicle Price Increases with Alternative Assumptions about Vehicle Buyer Time Horizons

Expected Lifetime (1) 10 Years Average Loan Term (2) Fuel Savings $2,932 $2,180 $1,254 Price Increase $907 $907 $907

Difference $2,025 $1,273 $347

Fuel Savings $3,700 $2,508 $1,484 Price Increase $961 $961 $9 MY 2016 Passenger Car

MY 2016 Value over Alternative Time Horizons Measure Vehicle

61 Light Truck Difference $2,739 $1,547 $523

(1) Expected lifetimes are approximately 14 years for cars and 16 years for light trucks.

(2) Average term on new-vehicle loans made by auto finance companies during 2009 was 62 months; see Board of Governors of the Federal Reserve System, Federal Reserve Statistical Release G. 19, Consumer Credit, http://www.federalreserve.gov/releases/g19/Current/ last accessed March 1, 2010.



### Potential vehicle buyers may also discount future fuel future savings using higher rates than those typically used to evaluate federal regulations. OMB guidance prescribes that future benefits and costs of regulations that mainly affect private consumption decisions, as will be the case if m anufacturers’ costs for complying with higher fuel economy standards are passed on to vehicle buyers, should be discounted using a consumption rate of time preference. 278 

### OMB

e fuel

2016 passenger cars and light trucks as an example. As Table VIII-14 showed previously, average future fuel savings discounted at the OMB 3% consumer rate exceed the agency’s

t theestimates that savers currently discount future consumption at an average real or inflationadjusted rate of about 3 percent when they face little risk about its likely level, which makes it a reasonable estimate of the consumption rate of time preference. However, vehicle buyers may view the value of future fuel savings that results from purchasing a vehicle with higher fuel economy as risky or uncertain, or they may instead discount future consumption at ratesreflecting their costs for financing the higher capital outlays required to purchase more fuelefficient models. In either case, they may discount future fuel savings at rates well above the 3% assumed in NHTS A’s evaluation.

Table VIII-16 shows the effect of higher discount rates on vehicle buyers’ evaluation of th savings projected to result from the CAFE standards established by this rule, again using MY

estimated price increases by more than $2,000 for MY 2016 passenger cars and by more than $2,700 for MY 2016 light trucks. If vehicle buyers instead discount future fuel savings a average new-car loan rate during 2009 (6.7%), however, these differences decline to slightly

278 Office of Management and Budget, Circular A-4, “Regulatory Analysis,” September 17, 2003, 33. Available at http://www.whitehouse.gov/omb/asset s/regulatory_matters_p df/a-4.pdf (last accessed March 1, 2009).

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### le alternative assumption, because buyers are likely to finance the increases in purchase prices resulting from compliance with higher CAFE standards as part of the process

se more than $1,400 for cars and $1,900 for light trucks, as Table VIII-16 illustrates. This is a particularly plausib

financing the vehicle purchase itself. Finally, as the table also shows, discounting future fuel savings using a consumer credit card rate (which averaged 13.4% during 2009) reduces the differences to less than $800 for a MY 2016 passenger car and less than $1,100 for the typical MY 2016 light truck. Thus even at relatively high discount rates, the higher fuel economy levels required by this final rule would generate significant net benefits to vehicle buyers.

Table VIII-16 Value of Fuel Savings vs. Vehicle Price Increases with Alternative Assumptions about Consumer Discount Rates

OMB Consumer Rate (3%) New Car Loan Rate (6.7%; 1) OMB Investment Rate (7%) Credit Card
Rate (13.4%; 2 Fuel Savings $2,932 $2,336 $2,300 $1,669 Price Increase $907 $907 $907 $907

Difference $2,025 $1,429 $1,393 $762

Fuel Savings $3,700 $2,884 $2,836 $2,030 Price Increase $961 $961 $961 $961

Difference $2,739 $1,923 $1,875 $1,069

(1) Average rate on 48-month new-vehicle m Measure Vehicle

MY 2016 Passenger Car

MY 2016 Light Truck Consumer

)

loans ade by commercial banks during 2009 was 6.72%; Value at Alternative Discount Rates

see Board of Gov ernors of the Federal Reserve System, Federal Reserve Statistical Release G. 19, Consumer Credit, http://www.federalreserve.gov/releases/g19/Current/ last accessed March 1, 2010.

(2) Average rate on consumer credit card accounts at commercial banks during 2009 was13.4%; see Board of Governors of the Federal Reserve System, Federal Reserve Statistical Release G. 19, Consumer Credit, http://www.federalreserve.gov/releases/g19/Current/ last accessed March 1, 2010.



### Combinations of a shorter time horizon and a higher discount rate could further reduce or even eliminate the difference between the value of fuel savings and the agency’s estimates of increases in vehicle prices. One plausible combination would be for b uyers to discount fuel savings over the term of a new car loan, using the interest rate on that loan as a discount rate. Doing so would reduce the amount by which future fuel savings exceed the estimated increase in

ers surveyed by Kubik the prices of MY 2016 vehicles to about $340 for passenger cars and $570 for light trucks. Some evidence suggests directly that vehicle buyers may employ combinations of higher discount rates and shorter time horizons than the agency assumes; for example, consum (2006) reported that fuel savings would have to be adequate to pay back the additional purchase

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### rice of a more fuel-efficient vehicle in less than 3 years to persuade a typical buyer to purchase

l

ables such as future fuel prices or the appropriate discount rate differ sufficiently from the assumptions used by the agency, potential

costs

rrently available models offer. Althou e specific economic values that vehicle buyers attach to indivi dual vehicle attributes of
ges

s of complying with stricter CAFE standards.

at ain

ed the

“constant performance and utility” costs for technologies that manufacturers could employ to increase the fuel efficiency of cars or light trucks. Although NHTSA has revised its estimates of p it. 279 

### As these comparisons and evidence illustrate, reasonable alternative assumptions about how consumers might evaluate the major benefi t from requiring higher fuel economy can significantly reduce its magnitude from the agency’s estimate.

Many plausible combinations of shorter time horizons, higher discount rates, and lower expectations about future fuel prices or annual vehicle use and fuel savings could make potentia buyers hesitant or even unwilling to purchase vehicl es offering the fuel economy levels this rule will require. At the same time, they would al so cause vehicle buyers’ collective assessment of how the benefits from requiring higher fuel economy compare to the costs they will be required to pay for it to differ significantly from NHTSA’s assessment of the aggr4egate benefits and costs of this rule. If consumers’ views about critical vari

vehicle buyers might conclude that the value of fuel savings and other benefits they will experience from higher fuel economy are not sufficient to justify the increase in purchase prices they expect to pay. This would not only ex plain why their current choices among available models do not result in average fuel economy levels approaching those this rule would require, but in extreme cases might also call into question NHTSA’s conclusion that the total – that is, private plus economy-wide – economic benefits from raising CAFE standards exceed the of complying with higher fuel economy requirements.

Another possibility is that achieving the fuel economy improvements required by stricter fuel economy standards might require manufacturers to forego planned future improvements in performance, carrying capacity, safety, or other features of their vehicle models that represent important sources of utility to vehicle owners. In extreme cases, manufacturers might even find it necessary to change the levels of these attributes that some cu gh th such as fuel economy, performance, passenger- and cargo-carrying capacity, and other sources vehicles’ utility are difficult to infer from their purchasing decisions and vehicle prices, chan in vehicle attributes can significantly affect the overall utility that vehicles offer to potential buyers. Compromises in these or other highly-valued attributes would be viewed by potential buyers as an additional cost of improving fuel economy that the agency has failed to acknowledge or include in its estimates of the cost

As indicated in its previous discussion of technology costs, NHTSA has approached this potential problem by developing cost estimates fo r fuel economy-improving technologies th include allowances for any additional manufacturing costs that would be necessary to maint the reference fleet (or baseline) levels of performance, comfort, capacity, or safety of light-duty vehicle models to which those technologies are applied. In doing so, the agency follow precedent established by the 2002 NAS Report on improving fuel economy, which estimated

manufacturers’ costs for some technologies signifi cantly for use in this rulemaking, these revised estimates are still intended to represent costs that would allow manufacturers to maintain the

279 Kubik, M. (2006). Consumer Views on Transportation and Energy. Second Edition. Technical Report: National Renewable Energy Laboratory

.

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### ate allowance for the necessary investments by manufacturers to maintain baselin evels of these critical vehicle attributes. If this is the case, the true economic costs of achievi g higher fuel economy would include the opportunity costs to vehicle owners of any sacrifices in vehicles’ performance, carry utility that accompanied increases in their fu mate the true economic costs of com n standards.

Finally, vehicle buyers may simply prefer the choices of vehicle models they now have available to the combinations of price, fuel economy, and other attributes that manufacturers are likely to offer when required to achieve higher overall fuel economy. If this is the case, their choices among models – and even some buyers’ decisions about whether to purchase a new vehicle – will respond accordingly, and their responses to these new choices will reduce their overall welfare. Some may buy models with combinations of price, fuel efficiency, and other attributes that they consider less desirable than those they would otherwise have purchased, while others may simply postpone buying a new vehicle. It is also possible that manufacturers may discontinue some currently popular vehicle models or styles as part of their efforts to comply with requirements for higher fuel efficiency. Al though the losses in buyers’ welfare associated with these responses cannot be large enough to offset the estimated value of fuel savings reported in the agencies’ analyses, they could significantly reduce the benefits from requiring manufacturers to achieve higher fuel efficiency, particularly in combination with the other possibilities outlined previously.

An entirely different explanation for buyers’ reluctance to invest in higher fuel economy despite the large economic return it appears to promise i that the agency’s assertion that the benefits

n

en performance, carrying capacity, and utility of vehicle models while improving their fuel
economy.

The agency readily acknowledges the difficulty of estimating technology costs that include adequate provision for the accompanying changes in vehicle design that are necessary to maintain performance, capacity, and utility. While NHTSA believe that its cost estimates for fuel economy-improving technologi es are sufficient to prevent significant compromises in other attributes of the vehicle models to which manufacturers apply them, it is possible that these costs do not include adequ e l

n ing capacity, and el economy. In that event, the agen cies’ estimated technology co sts would underesti plying with stricter fuel economy emissio

s buyers will experience from higher fuel economy far outweigh the costs they will pay to acquire it is indeed correct, yet certain plausible – if short-sighted – aspects of normal behavior nevertheless make buyers reluctant to purchase vehicles whose higher fuel economy offers a attractive return. For example, consumers’ understandable aversion to the prospect of losses from making investments that do not produce their expected returns may exaggerate theiruncertainty about the value of future fuel savings sufficiently to make purchasing a more fuelefficient vehicle seem unattractive even when doing so is likely to be a sound economic decision. Greene et al. (2009) find that the expected net present value of increasing the fuel economy of a passenger car from 28 to 35 miles per gallon falls from $405 when calculated using standard net present value calculations, to nearly zero when uncertainty regarding future cost savings is tak

to account. 280 

### in

280 Greene, D., J. German, and M. Delucchi (2009). “Fuel Economy: The Case for Market Failure” in Red Climate Impacts in the Transportation Sector

, Sperling, D., and J. Cannon, eds. Springer Science. Surprising ucing ly, the

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### o ulting fuel savings when those are discounted at 7% ver the vehicle’s expected lifetime. Although the wide variation in these estimates partly ed, her

to its fuel economy, and ompare these two. In either case, although the agency’s estimates of the average value of fuel savings that will result from requiring cars and light trucks to achieve higher fuel economy may be correct, they are not likely to be large enough to lead a sufficient number of buyers to purchase vehicles with higher fuel economy to increase average fuel economy from its current levels.

Defects in the market for cars and light trucks could also lead manufacturers to undersupply fuel economy, even in cases where many buyers were willing to pay the increased prices necessary to provide it. Most obviously, an absence of vigo rous competition among producers of cars and light trucks may lead manufacturers to undersupply attributes that contribute to the overall quality of new vehicles, including fuel economy, because such “imperfect” competition reduces producers’ profit incentive to supply the level of fuel economy that buyers are willing to pay for. Incomplete or “asymmetric” access to information on vehicle attributes such as fuel economy – whereby manufacturers of new vehicles or sellers of used cars and light trucks have more complete knowledge of vehicles’ actual fuel economy levels, or of the value of purchasing higher fuel economy, than do potential buyers – may also prevents sellers of new or used vehicles from capturing its full value. In this situation, the level of fuel efficiency provided in Another possible reconciliation of the agency’s claim that the average vehicle buyer will experience large fuel savings from the higher CAFE standards this rule establishes with the fact that the average fuel economy of vehicles currently purchased falls well short of the new standards is that the values consumers place on the future savings they expect to obtain from higher fuel economy vary widely. As an illustration, one recent review of consumers’ willingness to pay for improved fuel economy found estimates that varied from less than 1% t almost ten times the present value of the res
281 

### o undoubtedly reflects methodologi cal and measurement difference s among the studies survey it probably also reflects the fact that the expected savings from purchasing a vehicle with hig fuel economy vary widely among individuals, because they travel different amounts, have different driving styles, or have different expectations about future fuel prices.

This is likely to be reflected in the fact that buyers with high valuations of increased fuel
economy already purchase vehicle models that offer it, while those with lower values of fuel economy emphasize other vehicle a ttributes in their purchasing d ecisions. A related possibility is that because the effects of differing fuel economy levels are relatively modest when comparedto those provided by other, more prominent features of new vehicles – passenger and cargocarrying capacity, performance, safety, etc. – it is simply not in many shoppers’ interest to spend the time and effort necessary to determine the economic value of higher fuel economy, attempt to isolate the component of a new ve hicle’s selling price that is re lated

c

authors find that uncertainty regarding the future price of gasoline appears to be less important than uncertainty surrounding the expected lifetimes of new vehicles.
281 Greene, David L., “How Consumers Value Fuel Economy: A Literature Review,” Draft report to U.S. Environmental Protection Agency, Oak Ridge National Laboratory, December 29, 2009; see Table 10, p. 37. and Jin-Tan Liu (1988). “Automotive Fuel Economy Im provements and Consumers’ Surplus.” Transportation Research Part A 22A(3): 203-218. The study actually calculated the willingness to pay for reduced vehicle operating costs, of which vehicle fuel economy is a major component.

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### the markets for new or used vehicles mig tently lower than that demanded by potential b

is also possible that deliberate decisions by manufacturers of cars and light trucks, rather than constraints on the combinations of fuel economy, carrying capacity, and performance that manufacturers can offer using current technologies , limit the range of fuel economy available to buyers within individual vehicle market segments, such as full-size automobiles, small SUVs, or minivans. As an illustration, once a potential buyer has decided to purchase a minivan, the range of fuel economy among current models extends only from 18 to 24 MPG. 282 

### Manufacturers might make such decisions if they underestimate the premiums that shoppers in certain market segments are willing to pay for more fuel-efficient versions of the vehicle models thy currently offer to prospective buyers within those segments. If this occurs, manufacturers may fail to supply levels of fuel efficiency as high as those buyers are will ing to pay for, and the average fuel efficiency of their entire new vehicle fleets could remain below the levels that potential buyers demand and are willing to pay for.

Finally, some research suggests that the consumers’ apparent unwillingness to purchase more fuel efficient vehicles stems from their inability to value future fuel savings correctly. For example, Larrick and Soll (2008) find evidence that consumers do not understand how to translate changes in fuel economy, which is denominated in miles per gallon, into resulting changes in fuel consumption, measured in gallons per time period. 283 

### Sanstad and Howarth (1994) argue that consumers appear optimize behavior without full information by resorting to imprecise but convenient rules of thumb, which can cause many buyers to underestimate the value of fuel savings, particularly from significant increases in fuel economy. 284 

### If the behavio identified in these studies is indeed widespread, en the agency’s calculations suggesting that

ing
at estimating the value of fuel savings and omitting potential reductions ht remain persis
uyers.

It

r th the benefits to vehicle owners from requiring hi gher fuel economy significantly exceeds the costs of providing it may indeed be correct, yet the resulting difference is still insufficient to motivate buyers to purchase a mix of car or light truck vehicle models whose average fuel economy approaches those required by this rule.

The agency has been unable to resolve the question of why the apparently large differences between its estimates of benefits from requiring higher fuel economy and the costs of supplying it do not result in higher average fuel economy fo r new cars and light trucks. One explanation is that NHTSA’s estimates are reasonable, and the market for fuel economy is simply not operat efficiently. However, the agency acknowledges that this situation may also reflect the fact th ome combination of over s in the welfare of vehicle buyers means that it has incorrectly characterized the impact of the CAFE standards this rule establishes on consumers. To recognize this possibility, this section presents an alternative accounting of the benefits and costs of CAFE standards for MY 2012- 2016 passenger cars and light trucks and discusses its implications.

282 This is the range of combined city and highway fuel economy levels from lowest (Toyota Siena 4WD) to high (Mazda 5) available for model year 2010; http://www.fueleconomy.gov/feg/bestworstEPAtrucks.htm (last access February 15, 2010). est ed

3 Larrick, R. P., and J.B. Soll (2008). “The MPG illusion

. ” Science 320: 1593-1594. Sanstad, A., and R. Howarth (1994). “’Normal’ Markets, Market Imperfections, and Energy Efficiency.” Energy Policy 22(10): 811-818. 28

284

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### able VIII-17 displays the economic impacts of the rule from the perspective of potential buyers, d by As the table shows, the total enefits to vehicle buyers (line 4) consist of the value of fuel savings at retail fuel prices (line 1),

c g (line 3) . As the zero entries in line 5 of the table uggest, the agency’s estimate of the retail value of fuel savings reported in line 1 is assumed to

ere is no reduction in the total rivate benefits to vehicle owners, so that net private benefits to vehicle buyers (line 6) are equal

s Table VIII-17 also shows, the decline in fuel tax revenues (line 7) that results from reduced
ts conomy-wide or “social” erspective. Thus the sum of lines 1 and 7 is the savings in fuel production costs that was

of Table VIII-17 report the value of ductions in air pollution and climate-related exte rnalities resulting from lower emissions during

rts of crude petroleum and fined fuel. Line 12 reports the costs of increased congestion delays, accidents, and noise that (line

externalities, and increased costs from added driving.

AFE

The net total benefits (line 15 of the table) resulting from the rule consist f the sum of private (line 6) and social (line 13 ) benefits, minus technology costs (line 14); as sly in

vehicle uyers, with only a small share distributed throughout the remainder of the U.S. economy. In T and also reconciles the estimated net benefits of the rule as they are likely to be viewe vehicle buyers with its net benefits to the economy as a whole. b the economic value of vehicle occupants’ savings in refueling time (line 2), and the economi benefits from added rebound-effect drivin

s be correct, and no losses in consumer welfare from changes in vehicle attributes (other than those from increases in vehicle prices) are assumed to occur. Thus th
p to total private benefits (reported previously in line 4).

A fuel purchases is in effect an external cost from the viewpoint of vehicle buyers, which offse part of the benefits of fuel savings when those are viewed from the e
285 

### p reported previously as the value of fuel savings at pre-tax prices in the agency’s usual accounting of benefits and costs (see Chapter X). Lines 8 and 9

re fuel production and consumption, while line 10 reports the savings in energy security externalities to the U.S. economy from reduced consumption and impo

re result from additional driving due to the fuel economy rebound effect; net social benefits 13) is thus the sum of the change in fuel tax revenues, the reduction in environmental and energy security

Line 14 of Table VIII-17 shows manufacturers’ technology outlays for meeting higher C standards for passenger cars and light trucks, which represent the principal cost of requiring higher fuel economy.

o expected, the figures reported in line 15 of the table are identical to those reported previou the agency’s customary format (see Chapter X).

Table VIII-17 highlights several important features of this rule’s economic impacts. First, comparing the rule’s net private (line 6) and ex ternal (line 13) benefits makes it clear that virtually all of the benefits from requiring higher fuel economy are experienced by b turn, the vast majority of private benefits stem from fuel savings, which highlights the importance of the many assumptions the agency uses to estimate and value future fuel savings

es and sfer, omic cost or benefit. Re presenting the change in fuel tax revenues in effect as n economy-wide cost is necessary to offset the portion of fuel savings included in line 1 that represents savings in el tax payments by consumers. This prevents the savings in tax revenues from being counted as a benefit from the economy-wide perspective. 285 Strictly speaking, fuel taxes represent a transfer of re sources from consumers of fuel to government agenci not a use of economic resources. Reducing the volume of fu el purchases simply reduces the value of this tran nd thus cannot produce a real econ a

a fu

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### in its are small because the value of reductions in alities is almost exactly offset by the decline in fuel tax revenues and the increased costs associated w ith added vehicle use via the rebound effect of higher fuel economy. As a consequence, the net economic benefits of the rule mirror closely its benefits to private ve hicle buyers and the technology costs for achieving higher fuel economy, again highlighting the importance of correctly valuing fuel savings from the perspective of those who experience them and accounting for any other effects of the rule on the economic welfare of vehicle buyers.

Table VIII-17 Private, Social, and Total Benefits a nd Costs of MY 2012-16 CAFE Standards: Passenger Cars plus Light Trucks result g from higher fuel economy, as well as of the assumption that the rule has no adverse impacts on vehicle buyers. External benef environmental and energy security extern

2012 2013 2014 2015 2016

1 Value of Fuel Savings (at Retail Fuel Prices) $10.5 $22.9 $32.9 $42.5 $52.7 $161.6 2 Savings in Refueling Time $0.7 $1.4 $1.9 $2.5 $3.0 $9.4
3 4
5 6

7
8 9 10
11 12 13

14

15 Entry Model Year Total, 2012

Consumer Surplus from Added Driving $0.7 $1.5 $2.2 $2.8 $3.4 $10.5 Total Private Benefits (=1+2+3) $11.9 $25.8 $37.0 $47.8 $59.0 $181.5
Reduction in Private Benefits $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 Net Private Benefits (=1+2) $11.9 $25.8 $37.0 $47.8 $59.0 $181.5

Change in Fuel Tax Revenues -$1.3 -$2.7 -$3.8 -$4.8 -$5.9 -$18.5

Reduced Health Damages from Criteria Emissions $0.5 $0.9 $1.3 $1.6 $2.0 $6.4 Reduced Climate Damages from CO2 Emissions $0.9 $2.0 $2.9 $3.8 $4.8 $14.5 Reduced Energy Security Externalities $0.5 $1.2 $1.6 $2.1 $2.5 $8.0 Reduction in Externalities (=8+9+10) $1.9 $4.1 $5.9 $7.6 $9.3 $28.8 Increased Costs of Congestion, etc. -$0.7 -$1.3 -$1.9 -$2.4 -$3.0 -$9.4 Net Social Benefits (=7+11+12) $0.0 $0.1 $0.1 $0.3 $0.5 $1.0

Technology Costs $5.9 $7.9 $10.5 $12.5 $14.9 $51.7

Net Total Benefits (=6+12-14) $6.0 $17.9 $26.6 $35.5 $44.6 $130.7 - 2016



### As discussed in detail previously, it is possible that NHTSA has overestimated the value of fuel

vings to buyers and subsequent owners of the cars and light trucks to which higher CAFE andards will apply. It is also possible that the agency has failed to identify and value ductions in consumer welfare that could result from buyers’ responses to higher vehicle prices or changes in vehicle attributes that manufacturers make as part of their efforts to achieve higher

el economy. To acknowledge these possibiliti es and examine their potential impact on the sa st

re

fu rule’s benefits and costs, Table IV.G.6-5 show s the rule’s cumulative economic impacts for MY 2012-16 passenger cars and light trucks under varying assumptions about the agency’s potential overestimation of fuel savings and the value of po tential changes in vehicle attributes such as performance, carrying capacity, or safety.

Table VIII-18 accounts for both potential overestimation of the value of fuel savings to vehicle buyers and the possible omission of welfare losses from changes in other vehicle attributes in the entry labeled “Reduction in Private Benefits” (line 5). Although the examples reported

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### le overestimation of fuel savings using specific alternatives to the agency’s assumptions, NHTSA has been unable to

fuel

r

ffect of these possibilities using different assumptions bout the fraction of total private benefits to vehicle buyers that might be offset by some ule’s total

f d

set line 6) would be duced to slightly less than half of the agency’s original estimate, while its net total benefits shows, if previously in Tables VIII-15 and VIII-16 illustrated sources of possib

determine exactly how buyers’ time horizons or discount rates might differ from those assumed in its analysis. Nor has NHTSA analyzed how vehicle buyers’ expectations about future prices or differences between fuel economy ratin gs and actual on-road fuel economy might diffe from those it employs to estimate the value of fuel savings. Finally, NHTSA has not attempted to project changes in vehicle attributes other than fuel economy, or to estimate the economic value of resulting losses in vehicle utility.

Instead Table VIII-18 illustrates the e

a combination of these factors. As it shows, if ther e is no offset to private benefits, the r and net private and social benefits are as shown in the last column of Table IV.G.6-4 above. However, if these factors combine to offset even as much as 25% of the agency’s estimate o total private benefits (line 5), the rule’s net private (line 6) and net total (line 15) benefits woul be reduced, but are still substantially positive. The table also shows that if losses in consumer welfare from changes in vehicle attributes or an overestimate of the value of fuel savings off as much as half of the rule’s estimated private benefits, its net private benefits (

re (line 15) would decline to less than one-third of the original estimate. As the table also these factors combined to offset a sufficiently high fraction of the rule’s total private benefits, its net total benefits could decline to zero or even become negative.

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### Table VIII-18 Effect of Overestimation of Fuel Sa ngs or Omission of Welfare Losses

on Net Private and Total Benefits MY 2012-2016 CAFE Standards vi of

N one 2 61.6 $1 5 % uel Saving 1 ueling Time $9.4 $9.4 $9. $9.4
ro Dr $1 $10 10.5 (=1 $18 81. 1.5 5

en $0 $45 90.7 1
1+ $18 36. 90.7

eve $1 1 18.5
ma ri ssi $ $6. 6.4 e Dam C ion $1 4.5 1 y Secur nal $ $8 8.0 1 n in Externalitie 0) $2 8.8
1 s of Congestio tc. -$9.4 -$9.4 -$9.4 $9.4
1 11 $1 $1. .0

1 $5 $51 51.7

1 +12 $13 $85 0.0 vate Benefits Offset by imation of Fuel Savings or Omission

elfare Losses to Vehicle Buyers Entry Fraction of Pri Overest
of W % 50 61.6 $16 75% 1.6 $161.6 1 Value of F s (at Retail F uel Prices) $ 2 Savings in Ref 4 3 Consumer Surplus f m Added iving 0.5 .5 $ $10.5
4 Total Private Benefits +2+3) 1.5 $1 5 $18 $181.
5 Reduction in Private B 6 Net Private Benefits (= efits 2) .0 - 1.5 $1 .4 -$ 1 $ -$136. $45.4

7 Change in Fuel Tax R nues - 8.5 -$ 8.5 -$ -$18.5
8 Reduced Health Da ges from C teria Emi ons 6.4 4 $ $6.4
9 Reduced Climat ced Energ ages from O2 Emiss s 4.5 $14 .5 $1 $14.5
0 Redu 1 Reductio ity Exter s (=8+9+1 ities 8.0 8.8 $28 .0 $ .8 $2 $8.0 $28.8
2 Increased Cost n, e - 3 Net Social Benefits (=7+ +12) .0 0 $1 $1.0

4 Technology Costs 1.7 .7 $ $51.7

5 Net Total Benefits (=6 -14) 0.7 .3 $4 -$5.4



### It ize TS s t ti te e ic im le as ve d t ws pos ex likely. The ag s at to th cu at

va e, and the design of the CA standar (e.g., the f tprint

cu rds d id nu for g wi av n ta e d ve ibu han fu co sed A at to at i es of e p t t at rfor saf att s c nc u ef hi

Ne ncy fe i nt w its f ho are likely to va sav e s n sse performance, utility, or safety of cars and light trucks subject to this rule will occur. As this

nalysis indicates, the implications of such uncer ainty for the magnitude of economic benefits

ely to result from the CAFE standards this rul establishes are significant. NHTSA is ommitted to developing improved methods to estimate the value of improvements in fuel omy, as well as the magnitude and economic consequences of accompanying changes in other vehicle attributes, as part of its future CAFE rulemaking activities.

is important to emphas that NH A view he alterna ve estima s of this rul ’s econom pacts presented in Tab VIII-18 illustrati only, an hat it vie this last sibility as tremely un ency ha tempted develop e most ac rate estim es of the lue of fuel savings that are possibl th FE ds oo rves), the stringency of e standa , the lea time prov

n ed to ma facturers complyin th the new standards h el economy will not be e all bee mpromi ilored to . NHTS sure that ha s also esirable tempted hicle attr ensure th tes other t ts estimat technology costs includ adequate rovisions to preven he degrad ion of pe mance, ety, or other valuable ributes a onseque es of man facturers’ forts to com ply with gher CAFE standards.

vertheless, the age els that it s importa to ackno ledge uncer tainty in estimates o w buyers lue fuel ings, as w ll as in it conclusio that no lo s in the

a lik t

e

c

econ

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## References

Argonne National from Transportation (GREET) Model, Versi 2.

Greene, David L., “Vehicle Use and Fuel Economy: How Big is the Rebound Effect?” The Energy Journal , 13:1 (1992), 11 .

Gree ald W. Jone and Pau . Leiby The Ou for U.S. O epend ence, ORN tio r y

Gree . Ka R u m n Hous Ene n

Gree liya h o n 00 ORN Rid n t

Haug than, and Soum k l s t E r the Unite nergy Journa 17:2, 10 126.

Jones S. er U nd rom Impr Th J 4 )

Leiby Jon n ee, and Rus O ts Asse d Co N , r 1, 1997

Pickr ., “Description F g r r se Forec l Transp ortation Systems Center, U Department of Tran

Pulle ning eh st G g Anal of U ve E c 3

Smal r, K “ to e ce use Gas E il e o n s oa rnia Envi n Age th y s

U.S. Department of Energy, Program Analysis Methodology Office of Transportation Technology: Quality Metrics 2002 , Final Report Office of Tran sportation Technologies, May 9, 2001.

U.S. Environmental Protection Agency, MOBILE Motor Vehicle Emission Factor Model, version 6.2, November 2002 Laboratories, The Greenhouse Gas and Regulated Emissions on 1.6, February 200

7-143

ne, David L., Don s, l N

, tlook il D L-6873, Oak Ridge Na nal Labo atory, Ma 11, 1995.

ne, David L., James R hn, and obert C. G ibson, “F el Econo y Rebou d Effect for ehold Vehicles,” The rgy Jour al, 20:3 (1 999), 1-31

.
ne, David L., and Nata I. Tishc ishyna , C sts of Oil Depende ce: A 20 Update

, L/TM-2000/152, Oak ge Natio al Labora ory, May 2000.

hton, Jona odip Sar ar, “Gaso ine Tax a a Correc ive Tax: stimates fo d States,” The E l, 3-

, Clifton T., “Another Loo k at U. Passeng Vehicle se and th e ‘Rebou ’ Effect f oved Fuel Efficiency,” e Energy ournal, 1 :4 (1993 , 99-110.

, Paul N., Donald W. es, T. Ra dall Curl sell Lee, il Impor : An ssment of Benefits an sts , OR L-6851, O ak Ridge National L aboratory Novembe

.
ell, Don H of VMT orecastin Procedu e for ‘Ca Talk’ Ba Line asts,” manuscript, Volpe Nationa .S. sportation, 1994.

r, S.L., and L.A. Gree ysis Using Nine Years , “Hous .S. Sur old Adju y Data,” ment to nergy E asoline Pr onomics
, ice Chan 21:1, pp. es: An 7-52.

l, K.A. and VanDende . (2005) A Study Evaluat the Effec t of Redu d Greenho missions on Vehicle M es Travel d”, State f Califor ia Air Re ources B rd, Califo ronmental Protectio ncy, and e Californ ia Energ Commis ion.

,

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## X. IMPACT OF WEIGHT REDUCTION ON SAFETY I Placeholder, section forthcoming

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## X. NET BENEFITS AND SENSITIVIT SES

his chapter compares the costs of technologies needed to make improvements in fuel economy ith the potential benefits, expressed in total costs (millions of dollars) from a societal erspective for each model year. The costs do not include CAFE fines estimated to be paid by manufacturers to NHTSA, since these are t ents. Thus, the total costs shown in this section do not matc al costs and benefits compared to the adjusted base payback period is calculated, from the consumer’s perspective. Finall so performed on some of the assumptions made in this analysis.

Table s the total inc al c m f d ) fro cieta
persp provides t otal be fits at a 3 percent t r from a societal
persp ro t 3 disco dol he ed M Table l be a t a pe for all ve le X s r u milli roje t o 1

Total able w r e n techn cturer pp e v F th ired unde aggressive alter T p r the Total enefit alternat e are 1.9 mes tho er t Preferr Alternati r light ts a m r t e altern efe ter

In Ta time l b m igher bene mo si o l th
aggre r the e e a g fits rough the d iv

Table X-5 present the e c iv altern rnati , results a net b efit to s iety. In Table X-3, the c c g i y from % In te i th ost = Total et b fo e r de icle types are $ io n 

## Y ANALY

T

w p ransfer paym h the total costs shown in Chapter VII. These are increment line of MY 2011. A y, sensitivity analyses are al

X-1 provide rement osts (in illions o ollars m a so l ective. Table X-2 he t ne discoun ate ective for all vehicles p duced. Ta ble X-3 sh ows the otal net be nefits at a percent unt rate in millions of lars for t project fleet of s ales for M Y 2012 – Y 2016. X-4 provides the tota nefits at 7 percen discount r ate from societal rspective hicles produced. Tab ons of dollars for the p -5 show cted flee the total ne of sales f t benefits r MY 20 at a 7 pe 2 – MY 2 cent disco 016. nt rate in

costs follow a predict pattern ith costs ising to r flect the m ore expe sive ologies that manufa s must a ly in ord r to achie e the CA E levels at are requ r the more natives. otal com liance co sts for the passenge cars under Cost = Total B iv ti se und he ed ve. Fo trucks, compliance cos

r re 2.7 ti

r es highe under the Total Cos = Total B nefit ative than under the P red Al native.

enefits fo bles X-2 and X-4, life societa llow a si ilar predic table patte rn, with h fits associated with the re expen v e techn logies tha t are enab ed under e more ssive alternatives. Fo combin d fleet, th TC=TB lternative produces ross bene ly 1.55 times those of Preferre Alternat e.

s X-3 and net ben fits to soci ety produ ed by eac h alternat e. Each ative, including the Preferred Alte
pa ve in en oc ombined net benefit for 3 ssenger ars and li ht trucks u nder all f ve model ears ranges $80 billion under the Benefit alternative. N Annual enefits crease al r the Pref rnative to rred Alte $170 bill native (th on under e total un e Total C r both veh and all model years) 131 bill n at the 3 % discou t rate.

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### Incremen spective (Millions of 2007 Dollars)

MY 12 MY 13 

### Table X-1 tal Total Cost – Societal Per

Alternative 20 20 MY 2014 MY 2015 MY 2016 5- Year Total Passenger Cars Preferred Alternative $4,148 $5,411 $6,855 $8,221 $9,534 $34,170
3% Annual Increase $1,622 $2,341 $3,142 $4,047 $5,222 $16,375
4% Annual Increase $2,148 $3,455 $4,944 $6,561 $8,031 $25,138
5% Annual Increase $3,074 $5,288 $7,426 $9,410 $11,403 $36,601
6% Annual Increase $4,504 $7,196 $10,567 $13,546 $16,130 $51,943
7% Annual Increase $5,263 $8,985 $13,451 $16,627 $19,898 $64,224
Max Net Benefits $5,217 $8,837 $12,535 $14,930 $17,050 $58,568
TC = TB $5,674 $9, 9 $13,898 $16 73 $19,403 $65,427 77 ,6 Light Trucks Preferred Alternative $ 1,754 $2,479 $3,657 $4,318 $5,369 $17,578
3% Annual Increase $8 $463 $1,371 $2,009 $2,719 $6,570
4% Annual Increase $968 $1,747 $2,975 $3,714 $4,647 $14,051
5% Annual Increase $ 2,407 $3,791 $6,103 $6,833 $7,614 $26,749
6% Annual Increase $2,950 $5,646 $8,157 $9,813 $11,226 $37,793
7% Annual Increase $3,260 $7,298 $10,020 $12,478 $14,074 $47,130
Max Net Benefits $4,149 $7,370 $9,698 $11,163 $11,648 $44,028
TC = TB $4,247 $7,891 $10,464 $12 30 $13,218 $48,149 ,3 Passenger Cars & Ligh t Trucks Preferred Alternative $5,903 $7,890 $10,512 $12,539 $14,904 $51,748
3% Annual Increase $1,630 $2,804 $4,513 $6,057 $7,941 $22,944
4% Annual Increase $3,116 $5,202 $7,919 $10,275 $12,678 $39,189
5% Annual Increase $5,482 $9,079 $13,529 $16,243 $19,017 $63,350
6% Annual Increase $7,455 $12,842 $18,724 $23,359 $27,356 $89,736
7% Annual Increase $8,524 $16,283 $23,471 $29,104 $33,972 $111,354
Max Net Benefits $9,366 $16,207 $22,233 $26,092 $28,698 $102,597
TC = TB $9,921 $17,670 $29,003 $32,620 $113,577 $24,362

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### Table X-2 s by Alternative 3 % Discount Rate

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total 

### Present Value of Lifetime Societal Benefit

(Millions of 2007 Dollars)

Passenger Cars Preferred Alternative $6,8 26 $15,155 $21,626 $28, 677 $35,200 $107,483
3% Annual Increase $3, 397 $8,374 $12,331 $1 6,760 $23,122 $63,984
4% Annual Increase $4,1 86 $11,006 $17,315 $ 24,469 $32, 309 $89,286
5% Annual Increase $6, 152 $ 075 15,404 $24, $3 2,114 $40,905 $118,649
6% Annual Increase $7,071 $18,062 137 $28, $37,552 $47,754 $138,576
7% Annual Increase 627 $32,225 $8,038 $20, $ 42,010 $52, 606 $155,507
Max Net Benefits $8,019 $20,896 $31,683 $39,8 63 $48,228 $148 ,689
TC = T B $ 22,374 $33,916 8,666 $ $4 2,737 $5 1,659 $1 59,352
Li ght Trucks
Pre ferred Alternative $5,110 $10,684 $15,506 $19,36 4 $24,310 $74,974
3% Ann ual Increase 3,920 $7,635 $1 $687 $ 1,604 $1 4,940 $ 38,786
4% Ann ual Increase $ 7,361 $12,580 2,590 $ $1 7,089 $2 1,830 $ 61,450
5% A nnual Increase $ 10,407 $17,686 4,003 $ $23 ,206 $2 8,324 $8 3,626
6% Annu al Increase $4,893 $13,933 $22,031 $28,987 $34,727 $104,571
7% A nnual Increase $5,634 $16,326 $25,550 $32,714 $38,229 $118,453
Max Net Benefits $ 18,302 $25,913 7,528 $ $31, 563 $34, 835 $11 8,141
TC = T B $7 $27,294 ,631 $18,954 $33, 381 $37, 262 $124 ,522
Passe nger Cars & Light Tru cks
Pr eferred Alternative $11,936 $25,840 $37,132 $48,040 $59,509 $182,457
3% Annu al Increase $4 $19,966 ,085 $12,294 $28, 364 $38, 062 $102 ,770
4% An nual Increase $6 $18,367 $29,895 ,776 $41 ,559 $5 4,139 $1 50,735
5% An nual Increase $10,155 $25,811 $41,760 $55, 320 $69, 229 $202 ,275
6% An nual Increase $11,964 $31,995 $50,168 $66 ,539 $8 2,481 $2 43,147 7% Annual Increase $13,672 $36,953 $57,776 $74 ,724 $9 0,835 $2 73,960
Max Net Benefits $15,547 $39,198 $57,596 $71,42 6 $83,063 $266, 830
TC = T B $16,297 $41,328 $61,209 $76,1 18 $88,922 $283 ,874

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### Table X-3 Net Total Benefits Over the Vehicle’s Lifetime – Present Value 3% Discount Rate (Millions of 2007 Dollars)

Alternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total Passenger Cars Preferred Alternative $2,677 $9,745 $14,770 $20,455 $25,665 $73,313
3% Annual Increase $1,776 $6,033 $9,188 $12,713 $17,900 $47,609
4% Annual Increase $2,038 $7,551 $12,371 $17,909 $24,278 $64,147
5% Annual Increase $3,077 $10,116 $16,649 $ 22,704 $29,502 $82,048
6% Annual Increase $2,567 $10,866 $17,570 $24,005 $31,624 $86,633
7% Annual Increase $2,775 $11,642 $18,775 $25,383 $32,709 $91,283
Max Net Benefits $2,802 $12,059 $19,148 $24,933 $31,179 $90,121
TC = TB $2,992 $12,595 $20,017 $26,064 $32,257 $93,9 25 Light Trucks Preferred Alternative $3,356 $8,205 $11,849 $15,045 $18,940 $57,396
3% Annual Increase $679 $3,458 $6,264 $9,595 $12,222 $32,217
4% Annual Increase $1,622 $5,614 $9,605 $13,375 $17,183 $47,399
5% Annual Increase $1,596 $6,616 $11,582 $16,373 $20,710 $56,877
6% Annua l Increase $1,943 $8,287 $13,874 $19,174 $23,501 $66,779
7% Annual Increase $2,373 $9,028 $20,236 $24,155 $71,324 $15,531
Max Net Benefits $3,379 $10,9 5 $20,400 $23,186 $74,112 32 $16,21
TC = TB 4,045 $76,373 $3,384 $11,063 $16,830 $21,050 $2 Passenger Cars & Light Trucks Preferred Alternative $6,033 $17,950 $26,619 $35,501 $44,606 $130,709
3% Annual Incre $2,455 $9,490 1 $79,826 ase $15,453 $22 ,307 $30,12
4% Annual Incre 65 $21,976 $31,284 1,461 $111,546 ase $3,660 $13,1 $4
5% Annual Incre ,732 $28,231 $39,076 0,213 $138,925 ase $4,673 $16 $5
6% Annual Incre ,154 $31,444 $43,180 5,125 $153,412 ase $4,509 $19 $5
7% Annual Incre ,670 $34,305 $45,619 6,864 $162,606 ase $5,148 $20 $5
Max Net Benefits ,991 $35,363 $45,333 4,365 $164,233 $6,181 $22 $5
TC = TB ,658 $36,847 $47,114 6,301 $170,297 $6,377 $23 $5

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### Table X-4 Present Value of Lifetime Societal Benefits by Alternative 7 % Discount Rate (Millions of 2007 Dollars)

lternative MY 2012 MY 2013 MY 2014 MY 2015 MY 2016 5-Year Total
P assenger Cars
P $5,474 $12,255 $17,499

re ferred Alternative $23,235 $28,567 $87,031
3% Annual Increase $2,727 $6,778 $9,980 $13,585 $18,774 $51,844
4% Annual Increase $3,356 $8,904 $14,015 $19,838 $26,241 $72,353
5 % Annual Increase $4,941 $12,472 $19,493 $26,030 $33,185 $96,122
6% Annual Increase $5,667 $14,612 $22,763 $30,402 $38,735 $112,180
7% Annual Increase $6,448 $16,692 $26,080 $34,028 $42,669 $125,917
M ax Net Benefits $6,134 $16,378 $25,041 $31,517 $38,120 $117,191
T C = TB $6,957 $18,112 $27,453 $34,625 $41,897 $129,044
Li ght Trucks
P referred Alternative $4,015 $8,427 $12,243 $15,302 $19,225 $59,212
3 % Annual Increase $545 $3,099 $6,035 $9,178 $11,823 $30,679
4% Annual Increase $9,927 $13,500 $2,035 $5,802 $17,260 $48,524
5 % Annual Increase $3,129 $8,189 $13,929 $18,300 $22,365 $65,913
6 % Annual Increase $3,823 $10,966 $17,349 $22,842 $27,385 $82,366
7 % Annual Increase $4,404 $12,838 $20,108 $25,767 $30,132 $93,248
M ax Net Benefits $5,736 $12,761 $18,525 $22,485 $25,290 $84,797
T C = TB $6,039 $14,926 $21,502 $26,237 $29,295 $97,999

P asse nger Cars & Light Trucks P ferred Alternative $9,490 $20,682 $29,742

re $38,538 $47,791 $146,243
3% Annual Increase $3,272 $9,877 $16,014 $22,763 $30,597 $82,523
4 % Annual Increase $5,390 $14,706 $23,942 $33,338 $43,500 $120,877
5 ,422

% Annual Increase $8,070 $20,661 $33 $44,330 $55,551 $162,035
6% Annual Increase $9,490 $25,579 $40,111 $53,245 $66,120 $194,545
7% Annual Increase $10,852 $29,530 $46,187 $59,795 $72,801 $219,165
M ax Net Benefits $11,870 $29,140 $43,566 $54,002 $63,410 $201,988
TC = TB $12,997 $33,037 $48,955 $60,862 $71,193 $227,044

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### Over the Vehicle’s Lifetime – Present Value

MY 2012 MY 2013 

### Table X-5 Net Total Benefits

7% Discount Rate (Millions of 2007 Dollars)

Alternative MY 2014 MY 2015 MY 2016 5-Year Total Passenger Car s P referred Alternative $1,326 $6,844 $10,644 $15,014 $19,032 $52,861
3% A nnual Increase $1,106 $4,436 $6,838 $9,537 $13,552 $35,469
4% A nnual Increase $1,208 $5,449 $9,071 $13,277 $18,210 $47,215
5% A nnual Increase $1,867 $7,184 $12,067 $16,620 $21,782 $59,521
6% A nnual Increase $1,163 $7,416 $12,196 $16,856 $22,605 $60,237
7% A nnual Increase $1,185 $7,707 $12,629 $17,401 $22,771 $61,693
Max Net Benefits $1,170 $7,894 $12,838 $16,969 $21,583 $60,454
TC = 2,495 $63,617 TB $1,283 $8,333 $13,555 $17,952 $2 Light Trucks Preferred Alternative $2,261 $5,948 $8,586 $10,984 $13,855 $41,635
3% Annual Increase $537 $2,6 4 $7,169 $9,104 $24,110 36 $4,66
4% Annual Increase $1,067 $9,786 $12,613 $34,473 $4,055 $6,952
5% Annual Increase $7 ,467 3 22 $4 ,398 $7 ,826 $11 $14,751 $ 9,164
6% Ann ease $872 $5, , ual Incr 321 $9 192 $13, 029 $16,159 $44,573
7% Annu al Increase $1,143 $5,54 $10,088 13,289 $16,058 $46,119 0 $
Max Net Benefits $1,647 $6,58 $10,195 12,936 $15,123 $46,482 1 $
TC = TB $1,579 $7,46 $10,988 13,982 $16,229 $50,241 3 $ Passenger Cars & Light T rucks Preferred Alternat ive $3,587 $12,792 $19,230 $25,998 $32,888 $94,495
3% Annual Increase $1,642 $7,07 $11,501 16,706 $22,656 $59,579 3 $
4% Annual Increase $2,274 $9,50 $16,023 23,064 $30,822 $81,688 4 $
5% Annual I ncrease $2,589 $11,583 $19,89 28,087 $36,534 $98,685 3 $
6% Annual Increase $2,035 $12,737 $21,387 $29,885 $38,764 $104,810
7% A nnual Increase $2,328 $13,247 $22,71 30,690 $38,829 $10 7 $ 7,812
Max Net Benefits $2,818 $14,475 $23,033 $29,904 $36,706 $106,936
TC = TB $2,863 $15,795 $24,54 31,933 $38,724 $11 3 $ 3,858

### 461

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### Breakdown of costs and benefits for the preferred alternative

Table X-6 provides a breakdown of the costs and benefits for the preferred alternative using a 3 percent and 7 percent discount rate, respectively.

Table X-6 Preferred Alternative Cost and Benefit Estimates

MY 2012-2016 Combined Passenger Cars and Light Trucks Combined

($ millions)

Undiscounted Discounted 3% Discounted 7% Technol Cos 

### $51,748 $51,748 $51,748 ogy ts

Benefit s Lifetim Exp 

### 143,048 112,005 e Fuel 

### 178,880 enditures Consum Sur Addi Driving 

### ,491 8,233 er 

### 13,092 10 plus from

tional

Refueli

ime V ng alue 

### 11,482 9,443 7,608 T Petroleum Market Externalities 

### 9,801 7,952 6,322

Congestion Costs 

### -7,694 -6,264 -4,993

N oise Costs 

### -151 -122 -97
Crash Costs 

### -3,681 -,2989 -2,378
CO2 

### 18,314 14,528 14,528
CO 

### 0 0 0
VOC 

### 612 494 391
NOX 

### 722 612 475
PM 

### 3,720 2,974 2,329
SOX 

### 2,820 2,288 1,819
Total 

### 227,967 182,457 146,243

Net Benefits $128,992 $89,638

### 462

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### Payback Period

The “payback period” represents the length of time required for a vehicle buyer to through savings in fuel use, the higher cost of purchasing a more fuel-efficient ve Thus, only these two factors are considered (purchase pric vings). When a h AFE standard requires a manufacturer to improve the fuel e y of vehi dels, the manufacturer’s added costs for doing so are general in es f m While buyers of these models pay high e v eir im ved f economy lowers the consu s for hasing pe e may recoup the higher pur s r e f sa s inth requ repay th igher cost ying ls referred to a he buyer’ payback pe d.

s payback n e in e in s p pric in its fuel the r of driv ar, eta e calculate pe ing nom eme ver ated to th ard, t retai ber of cle iv en as t hes atio umer ive tive ly g nefit e e . Th aso es f s a

isco res e, s ers onl er
y a T ack est an for ffe ativ pa ds f 16 a n in

Table X-7 Pa iod 201 Ve (in years)

Pass Li 

### recoup, hicle. igher C cle mo or these
pro r time, buy ving a more fue e and fuel sa

conom ly reflected purchase thes

fuel to o these vehicl e h some of its higher pric

ehicles, th rate them. Ov s in the form o of bu odels. uel

ers er prices to

purc they pay fo

ired to mer’s cost chase price

of time outlays for fuel. The leng efficient vehicle i s t s rio

The length of thi the improvement period depe ds on th itial increas a vehicle’ urchase e, economy, numbe miles it is en each ye and the r il price of fuel. W d payback riods us the fuel eco y improv nt and a age price increase estim result from e stand he future l gasoline pric es, and estimates of the num miles vehi s are dr each year hey age. T e calcul ns are taken from a cons are included on the be ’s perspect s side of th , not a socie quation tal perspec e price of g . Thus, on line includ asoline sav uel taxe ings nd future savings are not d unted to p ent valu ince consum generally y consid and respond to what they pa t the pump. he payb periods are imated as average all manufacturers for the di rent altern es. The yback perio or MY 20 re show Table X-7.

yback Per for MY 6 Average hicles

enger Cars ght Trucks Pref er ativ red Altern e 3.3 2.2 3% An ase nual Incre 2.7 2.1 4% Annua ncrease 3. l I 0 2.5 5% An ase nual Incre 3.4 3.2 6% An ase nual Incre 4.2 3.9 7% An ase nual Incre 4.8 4.6 Max N et Benefits 4.4 4.0 Total Cost Benefi = Total t 4.8 4.3

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### yses performed nsit alys ine i ssu . W ith respect to the following economic parameters:

asoline: The main analysis ( i.e. , the Reference Case) uses the AEO 2010 Refe es or t aso abl ). I al am fe c the Hig Ca r ate d. A case bec AE se ide fere orec

s a 3 pe rcent discount rate e mpg levels. It presents benefi 7 p isco his sho t th ls would be w nefits alue er nt et th he standa

d effect: an e rce jec

es trav co ile eas n l

e eff g a nt o t reb ct in

O 2 

### d m ny: ana $21 on
pe oun qua enef cin is all tify efi t g f pti e sis, we examine the following values and discount rates applied only to l cost of ca lue ben e ar val ich time. These values can be translated into cents per gallon by multiplying 0.0089, givin in s:

2 

### ) = $ er g unte 9 = $0 per ga ount use mai

O = per oun ti f ($64.90 per ton CO 2 

### ) x 0.0089 = $0.5776 per gallon discounted at 3%

5) Military security: The main analysis $0 p r gallon to quantify the military security benefits of reducing fuel consumption. In he sensitivity analysis, we examine the impact of using a value of 5 cents per gallon instead.

6) Consumer Benefit: The main analysis assumes there is no loss in consumer surplus with vehicles that have an increase in price and higher fuel economy. This sensitivity analysis assumes that there is a 25, 50, or 75 percent loss in consumer surplus – equivalent to the Sensitivity Anal The agency has several se ivity an es to exam mportant a mptions e examine sensitivity w

1) The price of g Early Release rence Case timate f he price of g line (see T e VIII-4 n this sensitivity an Low Price Case fo ysis we ex ecast estim ine the ef s instea t of using EO 2009 AEO 2009 s are used h Price ause the se or O 2010 Early Relea only prov s the Re nce Case f ast.

2) Discount rate: The main analysis use to set th also ts using a ercent d unt rate. T sensitivity ws wha e mpg leve levels of t hen be rd. are v d using a 7 p cent discou rate to s e

3) The reboun The main alysis uses a rebound eff ct of 10 pe nt to pro t increased mil eled as the st per m driven decr es. In the se sitivity ana ysis, we examine th ect of usin 5 perce r 15 percen ound effe stead.

4) The values of C benefits an onopso The ma in lysis uses .35 per t discounted at a 3 rcent disc t rate to ntify the b its of redu g CO 2 

### em sions and $0.190 per g on to quan the ben s of reducin uel consum on. In th sensitivity analy the socia rbon to va carbon efits. Thes e the 2010 ues, wh increase over by 286 

### g the follow g value

($4.72 per ton CO x 0.0089 0.042 p allon disco d at 5% ($21.35 per ton CO analysis) 2 

### ) x 0.008 .190 llon disc ed at 3% ( d in the n
($35.06 per ton C 2 

### ) x 0.0089 $0.312 gallon disc ted at 2.5% And a 95 th 

### percen le estimate o

e t

286 The molecular weight of Carbon (C) is 12, the molecular weight of Oxygen (O) is 16, thus 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 2,819 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/gallon

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### assumption that consumers will on alculated benefits they will achieve at 75, 50, or 2

Varying each of the above 6 parameters in isolation results in a variety of economic sc os. These are listed in Table X-8 below along with the preferred alternative, together wit additional scenarios that use values from the first 5 para produce the lowes highest valued benefits.

7) In addition, the agency performed two on an st, in les X 11 and X-12, the agency a e i hat a re factor of 1.5 for all techn n al tead sing odolo ). PE fact lts in h cost fo e technologies. The CM methodology does not include profits, while e RPE tor methodology does. T m ogy results in an overall markup f compared to the ark r fro ble cost of 1.5. Sec 3, we will separat min ensit the ben f redu teria s and vehicle safet ernat es of cal life.

Ta 8 itiv alyse
Fue Pric sc Ra Rebo Ef SCC 

### ly value the c 5 percent, respectively, of their main analysis estimates.

enari h two t and

Tab alent (RPE of u estimates meters that

al sensitivity having the various

or resu additi mpact t d have o The R alyses. Fir tail price equiv ternatives ins igher - )

r nalyzed th ologies woul

gy (ICM the indirect cost meth each of th I th fac 1.2 he ICM RPE m ethodol up facto factor o ond, in to 1.25 Table X-1 m varia ely exa e the s ivity of efits o cing cri pollutant y to alt e valu statisti

ble XSens ity An s

Name l Di

e ount te und fect Monopson y ct tary rity Effe Mili Secu
Reference Refe re 3 10 $20 nce % % 0¢/ gal gal 0¢/ High Fuel Price H igh 3 1 $20 % 0% 0¢/g al al 0¢/g Low Fuel Price L ow 3 1 $20 % 0% 0¢/ gal gal 0¢/ 7% Discount Rate Refere 7 1 $20 nce % 0% 0¢/ gal gal 0¢/ 5% Rebound Effect Refere 3 5 $20 nce % % 0¢/ gal gal 0¢/ 15% Rebound Effect Refere 3 1 $20 nce % 5% 0¢/ gal gal 0¢/ $56/ton CO 2 V alue Refere 3% 10% $56 nc

e 0 ¢/ gal gal 0 ¢/ $10/ton C O 2 Refere 3 10 $10 nce % % 0¢/g al al 0¢/g 5¢/gal M ilitary Security Refere 3 10% $20 Value nce % 0¢/ gal al 5¢/g Lowest Discount ed Low 7 15% $10 Benefits % 0¢/ gal gal 0¢/ Highest Discounted Benefits High 3 5 $56 % % 0¢/ gal gal 5¢/ 25% Consumer Bene fit Refere 3 10 $20 gal al nce % % 0¢/ 0¢/g
50% Consumer Benefit Refere 3 10 $20 gal gal nce % % 0¢/ 0¢/
75% Consumer Benefit Refere 3 10 $20 nce % % 0¢/ gal gal 0¢/



### For these cases, sensitivity an 9 presents the achieved fuel econalyse er n t red A e only e Xom eh ice in total b total cos

s were p

y, per-v formed o icle pr he Prefer
crease, lternativ enefits, . Tabl t,

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### ifetim ctio 2 

### e s that w lt u e mic scena th eved nomy r-vehic ts only the l year 2 results, this m ear showed the nefits, cost savi d C s re s, the ive model rathe thei for MY refl total the various ec nomic assum tions.

nts the percentage es fr Pre lternati nomic he items in Table rom table nclude owin ing rying the economic eter g the ered va

conomic assum s ha ilar ef n the pa r car a t

s. nomic assum s ha st no on achi el eco with narios havin fect e tha pg.

omic assum s ha ost, a impact o cle co l or emissio ion none e effect large

tin vary nom ters i nefits ging to the 09 H orecast bene a her la pac enefi red wit % disc te (- nefits at ent %). rs value the s a d at e mai sis ass ons, l exceed co 6) Changing all economic parameters simultaneously to the lowest or highest values, among the values considered (not including the lue of consumer benefits), changes benefits by at about 56%. However impa cts to other ntities, such as cost, are much smaller, lifetime fuel savings, and the l e redu ns in CO mission ould resu nder th standards from the econo rios. For e achi fuel eco and pe le price increase, the table presen greatest impacts. For be mode s, fuel 016
ngs, an since O emission odel y duction 2 

### r values table presents totals over the f years, r than 2016, to ect the impact of the standards that would result from o p

Table X-10 prese assumptions for t chang X-9. F om the these ferred A
s, we co ve eco the foll g regard the impact of va param s amon consid lues:

1) The various e ption ve sim fects o ssenge nd ligh truck standard 2) Varying the eco none of the sce ption g an ef s almo of mor impact n 0.2 m eved fu nomy,

3) Varying the econ ption s, at m small n vehi sts, tota costs, fuel saved n reduct s with of thes s being r than 10 percent. 4) The largest change resul g from ing ec o ic parame f s on be . Chan the fuel price forecast combined +44.2%. Ot AEO 20 rge im igh Price ts on b Case
ts occur impacts h the 7 fits by
ount ra 19.8%), valuing be 75 perc (-22.3 5) Even if consume benefit chieve 25% of th n analy umpti total benefits stil sts.

va qua resulting in increases or decreases of 7% or less.

Regarding the lower fuel savings and CO 2 

### emissions reductions predicted by the sensitivity analysis as fuel price increases, which initially may seem counterintuitive, we note that there are some counterbalancing factors occurring. As fuel price increases, people will drive less and so fuel savings and CO 2 

### emissions reductions may decrease.

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### (mpg, Per-Vehicle C missions Reduced)

Econo tions MY 201 Achieved
mpg MY 2016PerVehicle

Cost MY 20 Benefits Discounted 3%, in Millions of $ 2012-
ost (Societal rspective) , in Millions of $ MY 2012-

Fu Sav

in Milli of Gallons MY 2012- 2016 CO2 Emissions Reduced, in mmT 

### Table X-9 Sensitivity Analyses ost, Total Benefits, Total Cost, Fuel Saved, & CO2 E

mic Assump 6 2012- 16 MY

, 2016 C

Pe 2016 el ed,

ons

Passenger Cars
Prefe 37. $907 $107,48 $34,170 35,66 380 rred 2 3 0
H 37.4 $960 $154,052 $36,080 34,374 360

igh Fuel Price
Low Fuel Price 37.2 $907 $101,243 $34,170 35,660 380
7% D 37.2 $907 $87,031 $34,170 35,660 380 iscount Rate 5% Rebound Effect 37.2 $907 $112,569 $34,170 37,284 398
15 % Rebound Effect 37.2 $9 $34,170 34,036 362 07 $102,397 $4.72/ton CO2 Value 37.2 4 $34,170 35,660 380 $907 $100,96 $3 380

5.06/ton CO2 Value 37.2 $907 $112,788 $34,170 35,660 $64.90/ton CO2 Value 37.2 $907 $124,921 $34,170 35,660 380 5¢/gal Military Security Value 37.2 $907 $108,875 $34,170 0 380 35,66 Lowest Discounted Benefits 37.0 $858 29 7 358 $46,3 $32,561 33,54 Highest Discounted Benefits 37.4 32 $960 $166,258 $36,080 35,9 377
25% Consumer Benefit 37.2 $907 $35,060 $34,170 35,660 380
5 0% Consumer Benefit 37.2 $907 $59,201 $34,170 35,660 380
75% $83,342 170 Consumer Benefit 37.2 $907 $34, 35,660 380
L ight Trucks
P referred 28.5 $961 $74,974 $17,578 25,350 275
H e

igh Fuel Pric 28.6 $991 $108,996 $19,069 24,826 261
L ow Fuel Price 28.5 $961 $70,544 $17,578 25,350 275
7 % Discoun t Rate 28.5 $961 $59,212 $17,578 25,350 275
5 ffect

% Rebound E 28.5 $961 $78,065 $17,578 26,543 288 15% Rebound Effect 28.5 $961 $71,882 $17,578 24,158 262 $4.72/ton CO2 Value 28.5 $961 $70,311 $17,578 25,350 275 $35.06/ton CO2 Value 28.5 $961 $78,779 $17,578 25,350 275 $ 64.90/ton CO2 Value 28.5 $961 $87,408 $17,578 25,350 275

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7 5% Consumer Benefit 28.5 $961 $58,369 $17,578 25,350 275 Passenger Cars & Light Trucks Combined
Preferred 33.7 $926 $182,457 $51,748 61,010 655 High Fuel Price 33.8 $970 $263,048 $55,149 59,200 621
Low Fuel Price 33.7 $926 $171,787 $51,748 61,010 655 7% Discount Rate 33.7 $926 $146,243 $51,748 61,010 655
5 % Rebound Effect 33.7 $926 $190,634 $51,748 63,826 686 15% Rebound Effect 33.7 $926 $174,279 $51,748 58,194 624 $4.72/ton CO2 Value 33.7 $926 $171,276 $51,748 61,010 655 $35.06/ton CO2 Value 33.7 $926 $191,567 $51,748 61,010 655 $64.90/ton CO2 Value 33.7 $926 $212,329 $51,748 61,010 655
5 ¢/gal Military 655 Security Value 33.7 $926 $184,808 $51,748 61,010 Lowest Discounted Benefits 33.5 $861 $77,998 $47,952 57,293 615 Highest Discounted Benefits 33.8 $970 $283,511 $55,149 61,921 651 25% Consumer Benefit 33.7 $926 $60,219 $51,748 61,010 655
5 0% Consumer Benefit 33.7 $926 $100,965 $51,748 61,010 655 75% Consumer Benefit 33.7 $926 $141,711 $51,748 61,010 65 5

### 468

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mpg MY 2016PerVehicle

Cost MY 2012- 2016 Benefits, Discounted 3%, in Millions of $ MY 2012- 2016 Cost (Societal Perspective),

in Millions of $ MY 2012- 2016 Fuel Saved,

in Millions of Gallons MY 2012- 2016 CO2 Emissions Reduced, in mmT 

### Table X-10 Sensitivity Analyses – Percentage Change from the Reference Case

Passenger Cars
Preferred Base Base Base Base Base Base High Fuel Price 0.4% 5.8% 43.3% 5.6% -3.6% -5.2%
Low Fuel Price 0.0% 0.0% -5.8% 0.0% 0.0% 0.0%
7% Discount Rate 0.0% 0.0% -19.0% 0.0% 0.0% 0.0%
5% Rebound Effect 0.0% 0.0% 4.7% 0.0% 4.6% 4.7%
15% Rebound Effect 0.0% 0.0% -4.7% 0.0% -4.6% -4.7% $4.72/ton CO2 Value 0.0% 0.0% -6.1% 0.0% 0.0% 0.0% $35.06/ton CO2 Value 0.0% 0.0% 4.9% 0.0% 0.0% 0.0% $64.90/ton CO2 Value 0.0% 0.0% 16.2% 0.0% 0.0% 0.0% 5¢/gal Military Security Value 0. 0% 0.0% 1.3% 0.0% 0.0% 0.0%
Lowest Discounted Benefits -0.5 % -5.4% -56.9% -4.7% -5.9% -5.7% Highest Discounted Benefits 0.4% 5.8% 54.7% 5.6% 0.8% -0.7%
25% Consumer Benefit 0.0% 0. 0% -67.4% 0.0% 0.0% 0.0%
50% Consumer Benefit 0.0% 0. 0% -44.9% 0.0% 0.0% 0.0%
75% Consumer Benefit 0.0% 0. 0% -22.5% 0.0% 0.0% 0.0% Light Trucks
Preferred Base Base Base Base Base Base High Fuel Price 0.1% 3.1% 45.4% 8.5% -2.1% -4.9%
Low Fuel Price 0.0% 0.0% -5.9% 0.0% 0.0% 0.0%
7% Discount Rate 0.0% 0.0% -21.0% 0.0% 0.0% 0.0%
5% Rebound Effect 0.0% 0.0% 4.1% 0.0% 4.7% 4.8%
15% Rebound Effect 0.0% 0.0% -4.1% 0.0% -4.7% -4.8% $4.72/ton CO2 Value 0.0% 0.0% -6.2% 0.0% 0.0% 0.0% $35.06/ton CO2 Value 0.0% 0.0% 5.1% 0.0% 0.0% 0.0% $64.90/ton CO2 Value 0.0% 0.0% 16.6% 0.0% 0.0% 0.0% 5¢/gal Military Security Value 0. 0% 0.0% 1.3% 0.0% 0.0% 0.0%
Lowest Discounted Benefits -0.7% -10.0% -57.8% -12.4% -6.3% -6.5%

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ighest Discounted Benefits 0.1% 3.1% 56.4% 8.5% 2.5% -0.2% H
2 5% Consumer Benefit 0.0% -66.4% 0.0% 0.0% 0.0% 0. 0%
50% Cons % 0.0% umer Benefit 0.0% 0. 0% -44.3% 0.0% 0.0
75% Consumer Benefit 0.0% 0. 0% -22.1% % 0.0% 0.0% 0.0 Passenger Cars & Light ombined Trucks C
Pref Base B ase Base erred ase B Base Base
H -5.1%

igh Fuel Price 0.3% 4.8% 44.2% 6.6% -3.0%

L % 0 0.0% 0.0%

ow Fuel Price 0.0% 0.0% -5.8 .0%
7 ate 8% 0 0.0% 0.0%

% Discount R 0% 0. 0.0% -19. .0%
5 ffe 0 4.6% 4.7%

% Rebound E ct 0.0% 0.0% 4.5% .0%
1 5% Rebound Effect 0.0% 0.0% -4.5% 0.0% -4.6% -4.7% $4.72/ton CO2 Value 0.0% 0.0% -6.1% 0.0% 0.0% 0.0% $35.06/ton CO2 Value 0.0% 0.0% 5.0% 0.0% 0.0% 0 .0% $64.90/ton CO2 Value 0.0% 0.0% 16.4% 0.0% 0.0% 0.0% 5¢/gal Military Security Value 0. 0% 0.0% 1.3% 0.0% 0.0% 0. 0%
Lowest Discounted Benefits -0.6 % -7.0% -57.3% -7.3% -6.1% -6.0% Highest Discounted Benefits 0.3% 4.8% 55.4% 6.6% 1.5% -0.5%
25% Consumer Benefit 0.0% 0. 0% -67.0% 0.0% 0.0% 0.0%
50% Consumer Benefit 0.0% 0. 0% -44.7% 0.0% 0.0% 0.0%
7 5% Consumer Benefit 0.0% 0. 0% -22.3% 0.0% 0.0% 0.0%



### In addition, the agency analyzed the impact that having a retail price equivalent (RPE) factor of 1.5 for all technologies would have on the various alternatives instead of using the indirect cost methodology (ICM) resulting in an overall price increase multiplier from variable cost of 1.2 to 1.25, with special attention being paid to the preferred alternative and the maximum net benefit alternative. Table X-11 shows the impact on the MY 2016 achieved mpg level for passenger cars and light trucks by changing the cost markup factors used in the Volpe model. The big difference is in mpg for the maximum net benefit alternative. Having a higher cost for technologies limits how many technologies are cost effective. Table X-12 shows the impacts on costs and benefits for the preferred alternative comparing the preferred alternative using the ICM method to the RPE method.

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### Achieved mpg level

nce Table X-11

Comparing Different Cost Mark-up Methodologies (Achieved mpg levels)

ICM Method (Main analysis) RPE Method (Sensitivity) Differe (mpg) Passenger Car Preferred Al 37.21 37.17 0.04 ternative Passenger Car Maximum Net Benefits Alternative 39.77 39.21 0.56

Light Truck Preferred Alternative 28.53 28.36 .17

Light Trucks Maximum Net Benefits Altern 30.63 30.04 .59

ative

Table X-12 Sensitivity Analyses (mpg, Per-Vehicle Cost, Total Benefits, Total Cost, Fuel Saved, & CO2 Emissions Reduced )

s 2012- 6 CO2 issions duced, in mmT Economic Assumptions MY 2016 Achieved
mpg MY 2016PerVehicle

Cost MY 2012- 2016 Benefits, Discounted 3%, in Millions of $ MY 2012- 2016 Cost (Societal Perspective),

in Millions of $ MY 2012- 2016 Fuel Saved,

in Millions of Gallon MY 201 Em Re

Passenger Cars
Preferred 37.21 $907 $107,483 $34,170 35,660 38 0
RPE method 37.17 $1,136 $102,589 $41,804 34,052 36 3 Light trucks
Preferred 28.53 $961 $74,974 $17,578 25,350 275
RPE method 28.36 $1,141 $74,510 $20,476 25,210 2 73



### Sensitivity Analysis, Value of Statistical Life

### 471

DELIBERATIVE DOCUMENT – DO NOT RELEASE 

### The gulations issued from numerous agencies including DOT, EPA, OSHA, CPSC, etc., and issues guidance for agencies
to u

ensure y various regulatory agencies, OMB has
not ta

recomm assign

The De os for the various

mo , DOT established a VSL of $5.8 million with
sup m $8.4 million in recognition of uncertainty found

ove meas ured in 2007 dollars). Although DOT recently rev d denominated in 2008 dollars, and h

rule would still be highly cost-beneficial using the DOT VSL of $5.8 million.

A for

te is

s noted in the DOT guidance, however, the unc ertainty surrounding the VSL is notable, and should be recognized in regulatory analyses. Accordingly, NHTSA has prepared this sensitivity The value associated with preventing a fatality is measured by the Value of a Statistical Life (VSL), defined as the value of preventing one ra ndom fatality among a po pulation at risk. Office of Management and Budget (OMB) reviews and approves re

se in analyzing the impacts of their regulations. Although OMB guidance generally seeks to a level of consistency in the issues ad dressed b es blished a common VSL for use across all government agencies. Instead, OMB ends that each agency develop and justify its own VSL. As a result, different agencies different values to saving a life in their regulations.

partment of Transportation (DOT) has issued a series of guidance mem des within the department. In February 2008 ple entary calculations at $3.2 million and r a range of studies (these figures are ise its central estimate of VSL to $6.0 million, this figure is w en adjusted to 2007 dollars remains at $5.8 million.

By contrast, EPA uses VSL of $7.6 million (2007 dollars), which is 30% higher than DOT’s central estimate, although still within the upper estimate recommended by DOT to recognize uncertainty. The differing VSLs across agencies should be judged as arising from different conclusions regarding the validity of the various studies on VSL, rather than a function of the different at-risk populations that they represent.

Within the CAFE FRIA, VSL is used for two diff erent purposes, once to value benefits-per-ton from reducing emissions of criteria pollutants in Chapter VIII, and once to value potential safety impacts in Chapter IX. The potential safety impacts calculation is discussed outside the Volpe model, in order to emphasize the uncertainty surrounding this issue. It is examined separately and put in context of the overall net benefi ts derived from the Volpe model. The basic conclusion is that the safety impacts are highly uncertain, but, even under the upper bound estimate, the

The benefits-per-ton values for reducing emissions of criteria pollutants were derived by EP use by both EPA and NHTSA in this rulemaking activity. These estimates were based on an estimate of VSL derived previously by EPA and reported in its Guidelines for Preparing Economic Analyses (see Technical Support Document, Section 4.B.11.b.). 287 

### This estima $6.3 million in 2000 dollars, which corresponds to $7.6 million when expressed in 2007 dollars. NHTSA agreed to use the estimates of per-ton be nefits from reducing air pollutant emissions derived by EPA in this rulemaking, despite their reliance on a VSL estimate higher than that endorsed by DOT.

A

c Analyses. EPA

sf/webpages/Guidelines.html/$file/cover.pdf (last accessed March 4,2010). 287 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economi 240-R-00-003. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available at http://yosemite.epa.gov/ee/epa/eed.n

### 472

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### m analysis, which examines the values of both safe ty mortality impact and mortality benefits fro reducing criteria pollutant emissions under the complete range of DOT VSL values, as well as the EPA value. Table X-13 summarizes these estimates:

### 473

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### Sensitivity Analysis of Alte f 2007$, MYs 2012-2016)

ssumed VSL Source V f Fata Impacts 288 

### Value of Mortality Ben rom R Emissions of Criteria ollutan Table X-13 rnate VSLs (millions o

A (2007$) alue o lity efits f educe d

Air P ts $3.2 million Estim $ 2,055 ,125 DOT Lower ate 182 – $3 $5.8 million l

– 5 DOT Centra Estima te $31 7 3,581 $ ,185 $7.6 million t – $6 EPA Es imate $411 4,637 ,557 $8.4 million DOT Upper Estimate $452 – 5,107 $7,194

As mentioned above, the safety cts ar ly un and sed model. Although t ts be re u e V odel, pac small. Specifically, b r ucin ature ity account r 9 f E estimates of tota ing c emis epen the po DOT’s estimate of er est sed to c e benefits of reducing s, which mea substi DO imate o ould

r benefits estimates by 21% (23% of 90-91%). Our estimates of the total b cing emissions of criteria air po ts would be redu by this e percentage. Since sent 3. % of to enefits the sta ds, ma s change would re e % 3 impa ollutan e high nefits a certain sed in th are not u olpe m in the Volp their im e t is very he criteria p enefits f l benefits from om red

c g pr em

a mortal , d fo

on 0-91% o fic PA’s redu 23% low riteri than the

ns that sions imate u

tuting ding by EPA T’s est speci onstruct th f VSL w llutant. per-ton the VSL is emission educe the per-ton enefits from redu llutan tal b ced ndar sam king these repre 1-3.3 from thi duce total ben fits b y 0.7 (21 % of .0-3.3 %).

288 Note that calculations for safety impacts are based on comprehensive costs, which include economic impacts in addition to VSL estimates, such as medical care costs, legal costs, insurance administrative costs, etc. These costs are based on previous NHTSA studies of motor vehicle crash costs, and add $300,000 to each VSL. However, costs associated with nonfatal injuries are not accounted for in this calculation.

### 474

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## D

t.

g in at the

,

ot ict the extent to which manufacturers might use them, particularly

gic

stantial concerns remain about how to develop a

s, g enefits and costs. Some ma nufacturers make substantial use of the carry

s

the 

## XI. FLEXIBILITIES IN MEETING THE STANDAR

In this context, CAFE credits refer to flexibilities allowed under the Energy Policy and Conservation Act (EPCA) provisions governing use of Alternative Motor Fuels Act (AMFA) credits, the use of credit carry forward and carry back provisions, credit transfers between a manufacturer’s fleets, and credit trades among different manufacturers. Because EPCA prohibits NHTSA from considering these flexibilities when determining the stringency of CAFE standards, NHTSA did not consider these flexibilities when it developed alternatives for this rulemaking.

Under the Energy Independence and Security Act (EISA), AMFA credits are being phased ou The allowable credits are reduced so that by 2020 such credits will no longer be allowed under law. However, AMFA credits are allowed during the years affected by this rulemaking. Manufacturers building dual-fuel vehicles are entitled to a CAFE benefit of up to 1.2 mp 2012 to 2014, 1.0 mpg in 2015, and 0.8 mpg in 2016 for each fleet. NHTSA estimates th impact of the use of AMFA credits could result in an average reduction of almost 1.0 mpg in achieved average fuel economy in model years 2012 through 2016, and a related increase in CO 2 

### emissions.

Regarding credits other than AMFA credits ( e.g. , CAFE credits earned through over-compliance credits transferred between fleets, and credits acquired from other manufacturers), we do n

ave a sound basis to pred h since the credit transfer and credit trading programs have been only recently authorized, and credit transfers could involve complex interactions with multi-year planning. 289



### Such questions are similar to, though possibly le ss tractable than, the behavioral and strate questions that were entailed in representing manufacturers’ ability to “pull ahead” the implementation of some technologies, and that would be involved in attempting to estimate CAFE-induced changes in market shares. Although the Volpe model has been modified to

ccount for multi-year planning effects, sub a credible market share model for integration in to the modeling system NHTSA has used to analyze the costs and effects of credit transfer s and credit trading. The agency continues to consider how a market share model could be integrated into future CAFE analyses.

We believe that some manufacturers are likely to take advantage of these flexibility mechanism hereby reducin b t forward and carry back credit flexibilities today. Many manufacturers make dual fuel vehicle today and earn credits. These vehicles are in their MY 2008 and MY 2011 baselines. Other manufacturers regularly exceed CAFE standards applicable to one or both fleets, and allow corresponding excess CAFE credits to expire. Finally, still other manufacturers regularly pay civil penalties for noncompliance, even when producing dual-fuel vehicles would substantially reduce the magnitude of those penalties.

289 For example, if a manufacturer is planning to redesign many vehicles in M Y 2013, but few vehicles in MY 2015

chnologies in MY 2013 in order to carry them forward to facilitate compliance in MY 2015. when standards will also be significantly more stringent, the benefits (in terms of reducing regulatory burden) of using some flexibilities in MY 2013 ( e.g. , credit transfers) could be outweighed by the benefits of applying extra
te

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### These

ethanol in the fuel and adjusts the engine operating parameters. Fuel System - Plastic Gas Tank, updating components like seals and gaskets to be ethanol d some fuel system materials may be

eased fuel pressure

line powered vehicle. The analysis did not ehicles because for the most part they are already in the MY 2011

be greater under more stringent standards. As r manufacturers to face greater cost increases, and for some, technol ogical implementation, costs could rise substantially. The conomic advantage of employing allowed flexibilities increases and could affect manufacturer . A critical factor in addressing the fuel and emissions impacts of such

ions, the gency has used the Volpe model to estimate costs and effects if every manufacturer is assumed

ing There are vehicle costs to provide the dual fuel capability of using either E85 or gasoline. costs are incremental to the average vehicle costs of a gasoline vehicle. The additional or redesigned components necessary for E85 capability may include:

• Flexible Fuel Sensor or Oxygen Sensor - Determines the amount of

• capable, increased vapor storage capacity, an changed to stainless steel because ethanol in E-85 is corrosive.

• Low Emission Hardware or Improved Evaporative Emission Systems

• Fuel Injectors and Pressure Regulators

• Valve Seat Materials and Rings

• Fuel Rail Changes to allow for incr

• Cold start enhancement Combined, the agency estimates that these needed improvements would increase the consumer cost of a vehicle by $100 to $175 (in $2007), even though the manufacturers are charging the ueled automobile as for a gaso same price for a dual-f include a cost for dual-fueled v

e. adjusted baselin

We expect that use of flexibilities w potential fo ould tend to stringency increases, the

epending on its level of d

e behavior in this regard flexibilities is that the likely extent of utilization cannot be assumed constant across the alternatives.

To gauge the potential upper end of differences that could result from these provis

a to take full advantage of the FFV credit provisions throughout MY 2012-2016, under both the baseline (MY 2011) and preferred alternative. The analysis indicates that full use of the provisions could (a) reduce the average achieved fuel economy by 1.1 in MY 2016, and by 0.6- 1.1 mpg in earlier model years, (b) reduce tech nology outlays by about $14 billion (28%) dur MY 2012-2016, (c) reduce average price increases by $173-$207 during MY 2012-2016, (d) reduce fuel saved during MY 2012-2016 by 2,377 million gallons (3.9%), and (e) increase by 18.7 mmt. (2.9%) CO 2 

### emissions avoided during MY 2012-2016. 290
, 291

290 Estimated differences in costs and prices do not include incremental costs to produce FFVs.
291 With FFV credits, our analysis includes application of diesel engines at lower volumes than when FFV credits ar excluded. Because diesel fuel contains more carbon than gasoline, this difference in diesel application causes a e

slight reduction in CO 2 emissions.

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### s from Table XI-1 shows those potential impacts on passenger cars and light trucks combined. The achieved fuel economy of the fleet and costs are lower if one does not consider the credit dual-fueled vehicles. We could show many tabl es supporting these estimates, but we chose just to show some summary highlights.

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### Preferred Alternative Table XI-1 Potential Impact of Dual-Fueled Vehicle Credits

2012 2013 2014 2015 2016 Total Average Achieved FE (mpg)
With FFVs 28.7 29.7 30.6 31.5 32.6
Without FFVs 29.3 30.6 31.7 32.6 33.7

Differ ence (0.6) (0.9) (1.1) (1.1) (1.1)

Techno logy Outlays ($millions)

With FFVs 3,675 5,123 7,271 9,478 11,921 37,468

Without FFVs 5,903 7,890 10,512 12,539 14,904 51,748

Difference (2,228) (2,767) (3,241) (3,061) (2,983) (14,280)

Price In creases ($)
With FFVs 261 333 458 589 737
Without FFVs 434 513 665 782 926

Difference (173) (180) (207) (193) (189)

Fuel Savings (mil. of gal lons)

With FFVs 4,919 8,236 11,349 15,041 19,092 58,637

Without FFVs 4,201 8,851 12,518 15,950 19,494 61,014

Difference 718 (615) (1,169) (909) (402) (2,377)

Avoided CO2 (m mt)
With FFVs 52.8 89.1 123.1 163.4 207.6 636.0
Without FFVs 43.9 94.4 134.3 171.7 210.4 654.7

Difference 8.9 (5.3) (11.2) (8.3) (2.8) (18.7)

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### e

variety of often conflicting sources. Best estimates were selected based on the

mes, , oil

y

s ilitates a more informed

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 the mathematical model represents an uncertainty factor that would potentially alter the modeling outcomes if its value was changed. We assume that these variables are independent of each other. The confidence intervals around the costs and benefits of technologies reflect independent levels of uncertainty regarding costs and benefits, rather than linked probabilities dependent on higher or lower quality versions of a specific technology.

The uncertainties of these variables are described by appropriate probability distribution functions based on available data. If data are not sufficient or not available, professional judgments are used to estimate the probability distributions of these uncertainty factors. A complete description of the formulas and methods used in the CAFE model is available in the public docket. 293



### 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 

## XII. PROBABILISTIC UNCERTAINTY ANALYSIS

OMB Circular A-4 requires formal probabilistic uncertainty analysis of complex rules wher there are large, multiple uncertainties whose analysis raises technical challenges or where effects cascade and where the impacts of the 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 probability distribution of the benefits, costs, and net benefits of the compliance options selected for the proposed rule for MY 2012-2016 passenger car and light truck CAFE standards. Throughout the course of the main analysis, input values were selected from a preponderance of data and analyses available, but there is inevitably a level of uncertainty in these selections. Some of these inputs contributed less to the overall variations of the outco and, thus, are less significant. Some inputs depend on others or are closely related ( e.g. import externalities), and thus can be combined. With the vast number of uncertainties imbedded in this regulatory analysis, this un certainty analysis identifies only the major independent uncertainty factors having appreciable variability and impact on the end results and quantifies them by their probability distributions. These newly defined values are then randoml selected and fed back into the model to determine the net benefits using the Monte Carlo statistical simulation technique. 292 

### The simulation technique induces the probabilistic outcome accompanied with degrees of probability or plau sibility. This fac decision-making process.

292 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.
293 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. Available at http://www.nhtsa.dot.gov (last accessed March 4, 2010).

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### simulation first randomly selects a value fo certainty factors based on their probability distributions. The sel models to forecast results. In addition to the simulation results e pro al es t gree of certainty (or confidence, credibility). The d of c y e on-maker with an additional piece of important information whi alu for sults.



### Simulation Models an d Un inty s A Monte Carlo simulation was ucte the m tem that was developed to estimate the impacts of high FE en rib evious chapters. The focus of the simulation model was va n ar e c nc parameters and their resulting impact on the key ou ram el , a nefits. Net benefits measure the difference betwee he t r at be saved in fuel and other benefits and (2) the total costs rule

The agency reviewed the inputs and rela t e t E model to determine the factors that are the major sourc unc . S rs entified as potentially contributing to uncertainty to t imat cts er standards, although not all were ultimately selected to be run in the ion (1) Technology costs; (2) Technology effectiveness; (3) Fuel prices; (4) The value of oil consumpti na (5) Greenhouse ga s emissions (6) The rebound effect.



### Technology Costs The costs incurred by manufact to m hei es new CAFE levels are assumed to be passed on to con in m prices. These technology costs are the primary determin the cost of impro el economy.

Thirty-nine different technolog ere d a le s to comply with higher CAFE standards. These techn s w rib ap arlier in this analysis. The expected cost values were used e m ysi e uncertainty analysis the technology complexity ratings that were de ed ate fa ere used to distinguish between levels of uncertainty t e ex rom olo are relatively simple and those that are more uncertain. rat re d d , Medium, and High based on the characteristics of each s c tec y. proach assumes that low complexity technologies would tend to hav re ith well know nderstood supply chains, resource availability, and manu ring u es, which w ply a more narrow range of potential cost variation compar high exi ol hich would have a broader range of uncertainty. In previo alys e og FRIA for MY 2011), cost variation averaging 31% (based on NAS techno l gy estimates) was assumed to represent 3 standard deviations across all tec hnologies. For is analysis, we are assuming that this average r ea ch of the un ected values are then fit into the , th
egree gra m ertain s o estimat provides th he de decis t ch to ev i
ecast re with ate the



### certa Factor
cond d using CAFE odeling sys er CA requirem ts desc ed in pr riatio ound th hosen u ertainty tput pa eters, fu savings nd net be n (1) t otal dolla value th would of the

. tionships hat driv he CAF es of ertainty ix facto were id he est ed impa of high CAFE simulat :

on exter lities; and;

urers odify t r vehicl to meet

sumers the for of higher new car ant of overall ving fu

ies w examine s possib method ologie ere desc ed in Ch ter V e in th ain anal s. For th velop to estim markup ctors w hat ar pected f techn gies that These ings we esignate as Low pecifi
e matu hnolog
costs w This ap n and u factu techniq ould im ed to compl ty techn ogies, w

us an es of the s

o technol ies (see

th

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### tion represents 2 standard deviations, and applying 1 standard deviation for low complexity technologies, 2 for medium complexity te 3 standard deviations for high complexity technologies. This results in ranges 15.5% for low, 31% for medium, and 46.5% for high complexity technologies. The uncertainty model assumes a normal distribution for these cost ranges. Figure XII-1 graphically demonstrates the distributions of a hypothetical sample of three of the technologies.

Figure XII-1 Normal Distributions for 3 Different Technologies

manufacturers to meet the higher CAFE standard s, and will thus determine how much additional varia chno logies, and of

Aero Drag Reduction ($38-59)



### 0.10 0.15 0.20 0.25 0.30

Probabilit Cylinder Deactivation on OHV ($23-34)

Improved Accessories ($76-193)



### y Densit y

0.05

0.00 $0 $20 $40 $60 $80 $100 $120 $140 $160 Retail Price Increase ($)

Technology Effectiveness

The modifications adopted by manufacturers to enab le their vehicles 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

improvement is needed and which additional technologies will be required to achieve full compliance. In selecting the likely path that manufacturers will choose to meet CAFE, the

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### al

y earlier in this nalysis. Chapter V also summarizes the estimated range of effectiveness for these technologies.

Crank mounted Integrated Starter Generator

there

e

he analysis attempts to measure impacts that occur as much as 40 years in the future and

n CAFE model tests the interaction of technology costs and effectiveness to achieve an optim (cost-minimizing) technological solution. Technology effectiveness is thus a primary determinant of the overall cost and benefit of improving fuel economy.

As noted above, thirty-nine different technologies were examined as possible methods to compl with higher CAFE standards. These technologies were described in Chapter V

a The expected values (mid-range values) were used in the main analysis. For the uncertainty analysis, the full range of effectiveness estimates is used except where the specified range was regarded as too narrow by expert opinion. These were adjusted to the ‘default’ range (29%). These technologies are:

Combustion Restart Turbocharging and Downsizing Exhaust Gas Recirculation (EGR) Boost Conversion to Diesel following CBRST Conversion to Diesel following TRBDS Dual Clutch or Automated Manual Transmission 12V Micro-Hybrid Belt mounted Integrated Starter Generator

Plug-in Hybrid

The fuel consumption improvement ranges were regarded as either tight or were non-existent for these technologies because the values developed for them were not done with a mind toward what the average value should be (by vehicle class) and were not done with an eye towards uncertainty analysis.

As was done with costs, the average variation of all technologies where a range is specified was used as 3 standard deviations to be used as the default variation. For all technologies where is no range specified, this default variation was used. The uncertainties model assumes a normal distribution for these values, with each end of the range being three standard deviations from th mean (or expected) value.

Fuel Prices Higher CAFE standards will result in reduced gasoline 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 determinant of the overall social benefit that will result from improving fuel economy.

T 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 2010 Early Releas e (AEO). The main analysis is based o the AEO 2010 Reference Case scenario, which represents EIA’s current best estimate of future fuel prices. For the uncertainty analysis, the Agency examined two other AEO scenarios from

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### only contain the Reference

ase forecast, so the LOP and HOP for 2010 are not yet available. The LOP scenario was ility that the EIA’ s Reference Case predictions could overestimate

ty

g

I-1 trated

ed

ere the 2009 version, the Low Oil Price scenario (LOP) and the High Oil Price scenario (HOP). The 2009 LOP and HOP were employed because AEO Early Releases C chosen to allow for the possib the price of gasoline in the future. However, previous escalation in the price of gasoline resulted in prices that exceeded those estimated by EIA for their reference case. To reflect the possibili of significantly higher prices, the Agency se lected the HOP case, which among the AEO 2009 scenarios comes closest to matching the highest prices seen during the recent gasoline price surge, and which gives the highest gasoline price forecasts among all AEO 2009 scenarios

Each of these scenarios was applied as a discrete input ( i.e. , draws were not made from amon 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 modeled using discrete weights of 50 percent for the Reference Case, and 25 percent for both the LOP and HOP cases. Table XI lists the AEO gasoline price forecasts under each scenario. These same prices are demons graphically (in 2007 economics) in Figure XII-2. Note that these prices include Federal, State, and local fuel taxes. For the uncertainty analysis, taxes were removed because they are view as transfer payments (see discussion in Chapter VIII). Estimated retail prices are shown h because they are a better reference point for most readers.

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Low Re Year ference
2011 94 $2.473 $2.1 $3.361
2012 $2.609 $3.627 $2.103
2013 $2.844 $3.914 $2.08 2
2014 $2.044 $2.954 $4.2 41
2015 $3.004 $2.028 $4.496
2016 $3.072 $4.656 $2.033
2017 5 $3.135 $4.838 $2.03
2018 1 $3.185 $4.935 $2.04
2019 $2.025 $3.222 $5.003
2020 4 $3.268 $5.043 $2.02
2 5 $5.039

021 $2.038 $3.29
2022 $2.061 $3.337 $5.080
2023 3 $3.365 $5.138 $2.08
2 0 $5.145

024 $2.051 $3.38
2025 $2.055 $3.419 $5.1 95
202 $3.457 $5.258 6 $2.054
2027 $2.049 $3.4 91 $5.301
2028 $2.051 $3.541 $5.362
2029 $2.044 $3.585 $5.417
2030 $2.036 $3.602 $5.472
2031 $2.037 $3.644 $5.517
2032 $2.038 $3.690 $5.562
2033 $2.039 $3.723 $5.607
2034 $2.040 $3.768 $5.653
2035 $2.042 $3.829 $5.700
2036 $2.043 $3.866 $5.746
2037 $2.044 $3.904 $5.794
2038 $2.045 $3.942 $5.841
2039 $2.046 $3.981 $5.889
2040 $2.047 $4.020 $5.937
2041 $2.049 $4.059 $5.986
2042 $2.050 $4.099 $6.035
2043 $2.051 $4.139 $6.084
2044 $2.052 $4.180 $6.134
2045 $2.053 $4.221 $6.184
2046 $2.055 $4.262 $6.235
2047 $2.056 $4.304 $6.286
2048 $2.057 $4.346 $6.338
2049 $2.058 $4.389 $6.390
2050 $2.059 $4.432 $6.442

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### Figure XII-2



### Oil Consumption Externalities

Reduced fuel consumption can benefit society by lowering the world market price for oil, reducing the threat of petroleum supply disruptions, and reducing the cost of maintaining military security in oil producing regions and operating the strategic petroleum reserve. These benefits are called “externalities” because they are not reflected directly in the market price of fuel. A full description of these externalities is included in Chapter VIII under “Other Economic Benefits from Reducing Petroleum Use.” These factors 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.

Monopsony costs represent the reduced value of payments from U.S. oil purchasers to foreign oil suppliers that results when lower U.S. oil demand reduces the world price of petroleum, beyond

e savings from reduced purchases of petroleum itself. 294 

### However, consistency w TSA’s se of estimates of the global benefits from reducing emissions of CO 2 

### and other greenhouse ases in this analysis requires the use of a global perspective for assessing their net value. From is perspective, reducing these payments simply results in a transfer of resources from foreign th

u ith NH

g th

294 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 occurs entirely within the U.S. economy.

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### il suppliers to U.S. purchasers (or more properly, in a savings in the value of resources reviously transferred from U.S. purchasers to foreign producers), and provides no real savings resources to the global economy. Thus NHT SA’s analysis of the benefits from adopting higher CAFE standards for MY 2012-201 t trucks excludes the reduced value of monopsony payments consumption by these vehicles, and they are likewise not included in the uncertainty analysis.

The second component of external economic costs imposed by U.S. petroleum imports arises partly because an increase in oil prices triggered by a disruption in the supply of imported oil reduces the level of output that the U.S. economy 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 impo rted oil, as well as on whether and how rapidly these prices return to pre-disruption 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 without a disruption in oil supplies. A more complete discussion of price shock is provided in Chapter V, where is estimated that each gallon of fuel saved that results in a reduction in U.S. petroleum imports (either crude petroleum or refined fuel) will reduce the expected costs of oil supply disruptions to the U.S. economy by $0.078 to $0.269, with the actual value most likely to be $0.169 per gallon. For the uncertainty analysis, this central value is used with a normal di stribution and a standard deviation of $0.06.

A third imported oil externality is military se curity. In Chapter VIII, NHTSA thus concludes that the levels of U.S. military activity and expe n itures are likely to remain unaffected by even relatively large changes in light duty vehicle fuel consumption. As a consequence, the agency’s analysis of alternative CAFE standards fo 6 does not include savings in budgetary outlays to support omy and the resulting fuel savings.

Nevertheless, the agency conducted a sensitivity analysis of the potential effect of assuming that some reduction in military spending would result from fuel savings and reduced petroleum imports in order to investigate its impacts on the standards and fuel savings. Assuming that the preceding estimate of total U.S. military costs for securing Persian Gulf oil supplies is correct, and that approximately half of these expenses could be reduced in proportion to a reduction in U.S. oil imports from the region, the estimated savings would range from $0.02 to $0.08 (in 2007 dollars) for each gallon of fuel savings that wa s reflected in lower U.S. imports of petroleum from the Persian Gulf. If the Persian Gulf region is assumed to be the marginal source of supply for U.S. imports of crude petroleum and refined products, then each gallon of fuel saved might reduce U.S. military outlays by $0.05 per gallon, the midpoint of this range. NHTSA employs this estimate in its sensitivity analysis, and examines it further as part of this uncertainty analysis, assuming a 25% probability for this alternate impact.

Table XII-3 lists the range of values that were examined for oil consumption extern ities. The expected values were used in the main analysis. oth the value of reducing U.S. de and on the world market price for oil and the value of reduced 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 valued in this analysis. A normal distribution was assumed for the o p in 6 cars and ligh by U.S. oil consumers that might result from lower fuel

d

r MY 2012-201 U.S. military activities among the benefits of higher fuel econ

al

m B

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### range of values for oil consumption externalities with the low and high values assumed to be two standard deviations from the me an, based stimates.

Greenhouse Gas Emissions Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the process of producing and distributing transportation fuels, as well as from fuel combustion itself. By reducing the volume of fuel consumed by passenger cars and light trucks, higher CAFE standards will thus reduce GHG emissions generate d by fuel use, as well as throughout the fuel supply cycle. Lowering these emissions is like ly to slow the projected pace and reduce the ultimate extent of future changes in the global climate, thus reducing future economic damages that changes in the global climate are otherwise expected to cause. Further, by reducing the probability that climate changes with potentia lly catastrophic economic or environmental impacts will occur, lowering GHG emissions may also result in economic benefits that exceed the resulting reduction in the expected future economic costs caused by gradual changes in the earth’s climatic systems. In Chapter VIII, a more complete discussion of greenhouse gas emissions is presented along with a variety of estimates. The central estimate used in the analysis is $21.35 per metric ton. Additional scenarios could also be examined. The Low SCC value case assumes $4.52/metric ton. The high value case assumes $35.06/metric ton. In addition, a 95 th 

### percentile case assuming $64.90/me tric ton was examined. SCC wa not included in this uncertainty analysis based on recommendations from the interagenc orking group that produced the SCC values employed i e agency’s main analysis.



### The Rebound Effect By reducing the amount of gasoline used ost of operating a vehicle, higher CAFE standards are exp hicle. This “rebound effect” impacts net societal benefits because the increase in miles driven offsets a portion of the gasoline savings that results from more fuel-efficient vehicles. Although consumers derive some value from this extra driving, it also leads to increases in crash, congestion, noise, and pollution co sts associated with driving. Most recent estimates of the magnitude of the rebound effect for light duty vehicles 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), but studies also show that the rebound effect has been gradually decreasing over time. A more complete discussion of the rebound effect is included in Chapter VIII. The agency employed a rebound effect of 10 percent in the main analysis. For the uncertainty analysis, a range of 0 to 21 percent is used and employed in a slightly skewed Beta distribution which produced a mean of approximately 10.1 percent. The skewed distribution reflects the agency’s belief that the more credible studies that differ from the 10 percent value chosen for the main analysis fall below this value ( i.e. , are more negative) and differ by more substantial margins than the upper range of credible values. Table XII-3 summarizes the economic parameters used in the uncertainty analysis. on the Leiby e

s y w n th

and, thus, the c ected to result in a slight increase in annual miles driven per ve

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### Table XII-3 Monte Car rameters lo Specific Pa
Di scount Rates (%) 0.03, 0.07 Fuel Path Ran domization Parameters Low 25% Reference 50% High 25% Rebound Effect Randomization Parameters Alpha Shape 6.00 Beta Shape 6.50 Scale -0.21 Base 0.00 Monopsony Randomization Parameters Mean $ - Standard Deviation $ - Price Shock Randomization Parameters Mean $0.17 Standard Deviation $0.06 Military Security Randomization Parameters Alternative Cos t $0.05 Alternative Cost Pro ty babili 25%



### Draws

Because of the complexity of the CAFE model, t e computer time required to perform the uncertainty analysis was significant. The uncert nty analysis conducted a total of 40,000 trials (20,000 for each discount rate) Figures X 2 graphically illustrate the draw results for a sample of the 81 varia ess rates, 39 technology costs, the fuel price scenario, oil import externalities, and the rebound effect) that were examined. Modeling Results – Trial

h ai II- 3 through XII-1 bles (39 techno logy effectivenDELIBERATIVE 

### 488

Figure II-3 Monte Carlo Draw Profile, Passenger Car Costs X

Monte Carlo Draw Profile, Passenger Car Effectiveness Figure XII-4

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### 489

Figure X Monte Carlo Draw Profile ger sts II-5 , Passen Cars, Co

Figure XII-6 Monte Carlo Draw Profile, Passenger Cars, Effectiveness

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### 490

Figure XII-7 Monte Carlo Draw Profile, Passenger Cars, Costs

Figure X Profile, Pass ar en II-8 Monte Carlo Draw enger C s, Effectiv ess

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Figure X rlo Dr ile x Fuel P h II-9 Monte Ca

Preta aw Prof rice Pat

Figure XII-10 Monte Carlo Draw Profile

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### Figure XII Monte Carlo Draw -11

Profile

Figure XII Monte Carlo Dra -12 w Profile

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### XII-3

ults, Economic
M 

### Table onte Carlo Draw Res M Economic Inputs 

### Inputs inimum M aximum M t ean S dDev Rebound Effect -0.2071 -0.0058 -0.1012 0.0336 Military Security Cost 0 0.0500 0.0125 NA Price Shock Cost 0.00056 0.4166 0.1695 0.0574

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### nte Carlo Draw Results, Pa g Minim axim ean dDev 

### Table XII-4 Mo ssenger Ca r Technolo y Costs Technology um M um M St
Low Friction Lubricants $ $ $ $0. 2.70 4.21 3.43 18 Engine Friction Reduction $9.90 $1 $1 $0. 5.01 2.57 65 VVT - Coupled Cam Phasing (CCP) on SOHC $ $ $ $2. 35.88 53.93 45.08 31 Discrete Variable Valve Lift (DV VL) on SOHC $1 $16 $13 $6. 07.66 5.79 4.89 93 Cylinder Deactivation on SOHC $ $3 $2 $1. 21.87 4.23 7.75 43 VVT - Intake Cam Phasing (ICP) $ $5 $4 $2. 36.03 6.60 5.11 31 VVT - Dual Cam Phasing (DCP) $ $ $ $1. 30.18 45.89 38.31 98 Discrete Variable Valve Lift (DVVL) on DO HC $ $1 $1 $7 106.30 67.83 35.05 .00 Continuously Variable Valve Lift (CVVL) $1 $43 $27 $42. 10.64 0.11 2.93 48 Cylinder Deactivation on DOHC $ $3 $2 $1. 22.47 3.22 7.76 44 Cylinder Deactivation on OHV $ $3 $2 $1. 22.03 3.57 7.96 46 VVT - Coupled Cam Phasing (CCP) on OHV $ $4 $4 $2. 32.17 9.76 0.85 11 Discrete Variable Valve Lift (DV VL) on OHV $1 $1 $6 $93.29 40.91 17.50 .14
Conversion to DOHC with DCP $2 $32 $26 $13. 15.86 9.32 6.14 72
Stoichiometric Gasoline Direct In jection (GDI) $ $ $ $3 46.24 70.10 58.53 .05
Combustion Restart $67.52 $1 $1 $12 71.46 17.52 .24 Turbocharging and Downsizing $2 $6 $4 $45 55.55 05.14 31.95 .01 Exhaust Gas Recirculation (EGR) Boost $83.19 $214.81 $ $1 144.39 4.75 Conversion to Diesel following TRBDS $561.61 $1 $ $10 ,361.58 973.21 0.70 Conversion to Diesel following CBRS T $ $2,1 $1,5 $160 927.72 68.72 48.84 .15 6-Speed Manual/Improved Internals $2 $29 $25 $12. 03.11 9.79 0.04 96 Improved Auto. Trans. Controls/Exter nals $ $ $3 $47.90 71.87 60.12 .08 Continuously Variable Transmission $ $3 $2 $25. 153.58 61.09 49.91 57 6/7/8-Speed Auto. Trans with Improved Internals $94.85 $1 $11 $6. 48.12 9.38 11
Dual Clutch or Automate d Manual Transmission ($4 ($ ($2 $3. 1.91) 16.84) 9.45) 01 Electric Power Steering $ $1 $1 $5 83.92 27.15 06.45 .50 Improved Accessori es $1 $1 $13 $74.26 89.87 27.58 .13 12V Micro-Hybrid $ $4 $3 $33 197.71 86.70 26.26 .58 Belt mounted Integrated Starter Generator $165.18 $395.40 $ $2 285.53 9.58 Crank mounted Integrated Starter Gene rator $ $5 $3 $48 1,495.44 ,462.30 ,155.31 8.49 Power Split Hybr id $4 $2 $40 $970.47 ,119.34 ,604.72 5.94 2-Mode Hybrid $ $ $ $ 1,680.53 6,684.12 4,123.51 640.63 Plug-in Hybrid $5,56 676 14,432 ,233. 6.05 $22, .11 $ .92 $2 13 Material Substitution (1.50%) $ $1 $1 $0. 1.20 .83 .48 08 Material Substitution (5% to 10 % Cum) $ $1 $1 $0. 1.16 .76 .48 08

### 495

DELIBERATIVE DOCUMENT – DO NOT RELEASE tance Tires $ $ $0. Low Rolling Resis $4.62 6.86 5.72 30 Low Drag Brakes $ $ $6 $3. 49.51 78.70 2.90 27 Secondary Axle Disco nnect - Ladder Frame $ $10 $8 $4. 68.41 3.85 6.82 48 Aero Drag Reduction $37.52 $56.93 $47.62 $2.44

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### I-5

s Table XI Monte Carlo Draw Result s, Passenger Car Fuel Economy Improvement Rate

Technology Minimum Maximum Mean StdDev
Low Friction Lubricants 0.003297 0.006774 0.004995 0.000482 Engine Friction Reduction 0.008521 0.021392 0.014992 0.001665 VVT - Coupled Cam Phasing (CCP) on SOHC 0.006288 0.035075 0.020001 0.003321 Discrete Variable Valve Lift (DVVL) on SOHC 0.006296 0.035190 0.019994 0.003324 Cylinder Deactivation on SOHC 0.024234 0.031422 0.027501 0.000835 VVT - Intake Cam Phasing (ICP) 0.008412 0.021828 0.015010 0.001671 VVT - Dual Cam Phasing (DCP) 0.018200 0.033227 0.025002 0.001670 Discrete Variable Valve Lift (DVVL) on DOHC 0.006924 0.034950 0.019980 0.003372 Continuously Variable Valve Lift (CVVL) 0.012571 0.040441 0.025013 0.003328 Cylinder Deactivation on DOHC 0.000004 0.006493 0.002512 0.000828 Cylinder Deactivation on OHV 0.036372 0.058994 0.047018 0.002676 VVT - Coupled Cam Phasing (CCP) on OHV 0.009095 0.015660 0.012514 0.000839 Discrete Variable Valve Lift (DVVL) on OHV 0.001294 0.031847 0.015009 0.003331
Conversion to DOHC with DCP 0.007506 0.028947 0.017508 0.002507 Stoichiometric Gasoline Direct Inject ion (GDI) 0.018807 0.031099 0.025000 0.001680
Combustion Restart 0.013562 0.031892 0.022523 0.002169 Turbocharging and Downsizing 0.024579 0.054987 0.040107 0.003853 Exhaust Gas Recirculation (EGR) Boost 0.022579 0.051500 0.037481 0.003621 Conversion to Diesel following TRBDS 0.036928 0.088725 0.060763 0.005850 Conversion to Diesel following CBRST 0.082338 0.187326 0.132273 0.012778 6-Speed Manual/Improved Intern als 0.003016 0.006991 0.004994 0.000481 Improved Auto. Trans. Controls/E xternals 0.013181 0.027517 0.019989 0.001670 Continuously Variable Transmission 0.004942 0.021321 0.013475 0.002168 6/7/8-Speed Auto. Trans with Improved Internals 0.010834 0.039515 0.023982 0.003324
Dual Clutch or Automated Manual Transmission 0.028697 0.064629 0.047186 0.004561 Electric Power Steering 0.008688 0.022226 0.014990 0.001664 Improved Accessories 0.008283 0.021049 0.015003 0.001662 12V Micro-Hybrid 0.016744 0.037837 0.026042 0.002524 Belt mounted Integrated Starter Generator 0.029620 0.070075 0.049013 0.004721 Crank mounted Integrated Starter Generator 0.054397 0.118287 0.087782 0.008466 Power Split Hybrid 0.054487 0.135468 0.093148 0.010177 2-Mode Hybrid 0.023122 0.083804 0.051415 0.007172 Plug-in Hybrid 0.297300 0.655074 0.464704 0.045203 Material Substitution (1.50%) 0.003163 0.007165 0.005254 0.000509 Material Substitution (5% to 10% Cum) 0.023032 0.050878 0.037269 0.003621 Low Rolling Resistance Tires 0.008821 0.021629 0.014999 0.001668

### 497

DELIBERATIVE DOCUMENT – DO NOT RELEASE Low Drag Brakes 0.004359 0.011278 0.007508 0.000841 Secondary Axle Disconnect - Ladder Frame 0.009262 0.015969 0.012505 0.000831 Aero Drag Reduction 0.018791 0.030834 0.024981 0.001673

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### ght Truck Technology Costs Table XII-6 Monte Carlo Draw Results, Li

Technology Minimum Maximum Mean StdDev
Low Friction Lubricants $2.70 $4.21 $3.43 $0.18 Engine Friction Reduction $9.90 $15.01 $12.57 $0 .65 VVT - Coupled Cam Phasing (CCP) on SOHC $35.88 $53.93 $45.08 $2.3 1 Discrete Variable Valve Lift (DVVL) on SOHC $99.43 $153.12 $124.58 $6.4 0 Cylinder Deactivation on SOHC $21.87 $34.23 $27.75 $1.4 3 VVT - Intake Cam Phasing (ICP) $36.03 $56.60 $45.11 $2. 31 VVT - Dual Cam Phasing (DCP) $32.07 $48.75 $40.70 $2.11 Discrete Variable Valve Lift (DVVL) on DOHC $98.17 $154.99 $124.73 $6.46 Continuously Variable Valve Lift (CVVL) $107.99 $419.79 $266.38 $41. 46 Cylinder Deactivation on DOHC $22.47 $33.22 $27.76 $1.44 Cylinder Deactivation on OHV $20.85 $31.76 $26.46 $1.3 8 VVT - Coupled Cam Phasing (CCP) on OHV $20.85 $32.25 $26.48 $1.37 Discrete Variable Valve Lift (DVVL) on OHV $28.66 $43.29 $36.10 $1. 89
Conversion to DOHC with DCP $201.34 $307.16 $248.23 $12.8 0 Stoichiometric Gasoline Direct Inj ection (GDI) $43.02 $65.22 $54.45 $2.83
Combustion Restart $67.52 $171.46 $117.52 $12. 24 Turbocharging and Downsizing $325.83 $771.55 $550.73 $57.3 8 Exhaust Gas Recirculation (EGR) Boost $83.19 $214.81 $144.39 $14. 75 Conversion to Diesel following TRBDS $654.05 $1,585.69 $1,133.40 $117. 27 Conversion to Diesel following CBRST $1,094.80 $2,559.29 $1,827.77 $188.9 9 6-Speed Manual/Improved Internals $203.11 $299.79 $250.04 $12.96 Improved Auto. Trans. Controls/Externals $47.90 $71.87 $60.12 $3. 08 Continuously Variable Transmission $153.58 $361.09 $249.91 $25. 57 6/7/8-Speed Auto. Trans with Improved Internals $117.02 $182.75 $147.28 $7.54
Dual Clutch or Automated Manual Transmission ($11.18) ($4.49) ($7.86) $0.80 Electric Power Steering $83.92 $127.15 $106.45 $5.50 Improved Accessories $74.26 $189.87 $127.58 $13.13 12V Micro-Hybrid $218.58 $538.07 $360.70 $37.12 Belt mounted Integrated Starter Generator $165.18 $395.40 $285.53 $29.58 Crank mounted Integrated Starter Ge nerator $1,918.40 $7,007.18 $4,047.71 $626.65 Power Split Hybrid $1,170.26 $4,967.40 $3,140.96 $489.51 2-Mode Hybrid $2,072. 14 $8,241.74 $5,084.42 $789.92 Plug-in Hybrid $6,326. 49 $25,774.17 $16,404. 77 $2,538.23 Material Substitution (1.50%) $1.20 $1.83 $1.48 $0.08 Material Substitution (5% to 10% Cum) $1.16 $1.76 $1.48 $0.08 Low Rolling Resistance Tires $4.62 $6.86 $5.72 $0.30

### 499

DELIBERATIVE DOCUMENT – DO NOT RELEASE Low Drag Brakes 9 $78.70 $62.90 $3.27 $4 .51 Secondary Axle Disc $86.82 $4.48 onnect - Ladder Fra $103.85 me $68.41 Aero Drag Reduction 6.93 7.62 $2.44 $37. 52 $5 $4

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### Tab Monte Carlo ement Rates

Technology m Maximum Mean StdDev 

### le XII-7 Draw Results, Light Truck Fuel Economy Improv
Minimu
Low Friction L ts 3297 774 04995 0.000482 ubrican 0.00 0.006 0.0 Engine Friction Reduct 85 39 2 0.001665 ion 0 .00 21 0.021 2 0.01499 VVT - Coupled HC 2 001 0.003321 Cam Phasing (CCP) on SO 0.00 6 8 35 8 0.0 07 0 5 0.02
Discrete Variab HC 629 519 9994 0.003324 le Valve Lift (DVVL) on SO 0.00 6 0.03 0 0.01 Cylinder Deact 423 42 7501 0.000835 ivation on SOHC 0.02 4 0.031 2 0.02
VVT - Intake C 8412 82 0 0.001671 am Phasing (ICP) 0.00 0 .021 8 .01501 0 VVT - Dual Cam Phasing (DCP) .018200 0.033227 0.025002 0.001670 0
Discrete Variab OHC 9 980 0.003372 le Valve Lift (DVVL) on D 0.006 24 0.034 950 0.019 Continuously V VL) 257 044 5013 0.003328 ariable Valve Lift (CV 0.01 1 0.04 1 0.02 Cylinder Deact 000 64 512 0.000828 ivation on DOHC 0.00 4 0.00 93 0.002 Cylinder Deact 637 89 018 0.002676 ivation on OHV 0.03 2 0.05 94 0.047 VVT - Coupled 9095 .015660 .012514 0.000839 Cam Phasing (CCP) on OHV 0.00 0 0 Discrete Variable Valve Lift (DVVL) on OHV .001294 0.031847 0.015009 0.003331 0
Conversion to D 5 508 0.002507 OHC with DCP 0.007 06 0.028 947 0.017
Stoichiometric I) 880 109 5000 0.001680 Gasoline Direct Inject ion (GD 0.01 7 0.03 9 0.02
Combustion Re 56 18 523 0.002169 start 0.01 3 2 0.03 92 0.022 Turbocharging 402 37 8 0.002199 and Downsizing 0.01 6 .031 0 9 .02288 0
Exhaust Gas Recircul 22579 0.051500 0.037481 0.003621 ation (EGR) Boost 0 .0
Conversion to D 9 763 0.005850 iesel following TRBDS 0.036 28 0.088 725 0.060
Conversion to D T 265 529 6714 0.011275 iesel following CBRS 0.07 3 0.16 1 0.11 6-Speed Manua 01 69 994 0.000481 l/Improved Internals 0.003 6 0.00 91 0.004 Improved Auto ls 0.01318 75 989 0.001670 . Trans. Controls/E xterna 1 0.02 17 0.019 Continuously V 4942 .021321 .013475 0.002168 ariable Transmission 0.00 0 0 6/7/8-Speed Auto. Trans with Improved Intern 010834 0.039515 0.023982 0.003324 als 0.
Dual Clutch or ission 6 009 0.003288 Automated Manual Transm 0.020 83 0.046 581 0.034
Electric Power 868 222 4990 0.001664 Steering 0.00 8 0.02 6 0.01 Improved Acce 28 10 003 0.001662 ssories 0.008 3 0.02 49 0.015 12V Micro-Hy 44 16 682 0.002780 brid 0.0 18 1 0.04 72 0.028
Belt mounted Inte 4 0.066299 0.046372 0.004466 grated Starter Generator 0.02802 Crank mounted Integrated Starter Generator 0.068298 0.148515 0.110214 0.010630 Power Split Hybrid 0.054487 0.135468 0.093148 0.010177 2-Mode Hybrid 0.026160 0.094817 0.058171 0.008115 Plug-in Hybrid 0.297300 0.655074 0.464704 0.045203 Material Substitution (1.50%) 0.003163 0.007165 0.005254 0.000509 Material Substitution (5% to 10% Cum) 0.030551 0.067486 0.049435 0.004803 Low Rolling Resistance Tires 0.008821 0.021629 0.014999 0.001668

### 501

DELIBERATIVE DOCUMENT – DO NOT RELEASE Low Drag Brakes 43 0.011 8 0 8 0.000841 0.00 59 27 .00750 Secondary 0.000831 Axle Disconnect - Ladder Fra 62 0.015969 0.012505 me 0.0092 Aero Drag Redu 1 030834 4981 0.001673 ction 0.01879 0. 0.02



### Modeling Resu

Tables XII-8 an elin ts for fuel saved, total c societal benefits, and net benefits for passenger cars a espectively under a 7% discount rate. They also indic t be d II-11 summarize thes dis T ar ted in Figures XII-13 er c r the Alte 7 percent for MY 2016. Alth en of t g ou ibutions are similar for the l nt disco nd f de well. The humped shape t ene et benefi reflects the three different gasoline price scenarios. About half of all dr elected from the AEO Reference Case, while about one th i were drawn from the High O his produ epar s wh t the increasing impa e p ely h rice The following discu of esen se ta e combined
passenger car an e 7 typic lowe d 3 percent (typically upper

Fuel Savings lts – Output

d XII-9 summarize the mod g re sul osts, nd trucks r ate the probability that ne a 3% nefits excee

cou . zero. Tables he ts XII-10 and X
e tra e same results under through XII-16 for p nt rate ars unde se resul Preferred also illus rnative at asseng ough not shown here, the g eral shape he resultin tput distr ight trucks, the 3 perce unt rate, a or other mo l years as hat occurs for both social b fits and n ts

aws were s quarter were drawn from e Low Oil Pr ce scenario an d one quarter il Price scenario. T ces three s ate hump ich reflec ct on benefits from the thre
range rogressiv igher oil p
t e scenarios. ssions summarize the d light truck across bo results pr percent ( ed in th ally the bles for th r range) an th th range) discount rates. 295



### : T t M e trucks) will exp 5,853 mi 5,297 lion fuel savings over their usefu les c 7,4 illion and 10,722,104 mill ings ov eful l MY icles will experience betw d 14 io f ngs over their useful lifespan. pe een 13, 9,284 and 1 2,026 million gallons of fuel s ifes 16 veh l etween 16,766,243 and ns s e Over the combined lifesp years, bet 4 tril 0.3t lons of fuel will be saved.

Total Costs he analysis indicates tha Y 2012 vehic les (both pass nger cars and light erience between 3,15 llion and ,310 mil gallons of l lifespan. MY 2013 vehic will experien e between 37,996 m ion gallons of fuel sav er their us ifespan. n gallons o 2014 veh fue avi een 10,354,918 million an MY 2015 vehicles will ex ,434,026 mill l s 8,08 rience betw
p an. MY 20 27 avings over their useful l 21,718,166 million gallo icles wil over their us experience b ful lifespan. of fuel saving an of the five model ween 51. lion and 7 rillion gal

: Th ner 2012 pa nger cars and light trucks will pay between $3,838million and $7,968 million i er vehicle prices to purchase vehicles with improved fuel efficiency. MY 2013 owners will etween $5,867 million and $10,731 million more. MY 2014 owners will pay between $7,559 million and $13,840 million more. MY 2015 owners will pay between $9,162 million and $16,536 million more. MY 2016 owners will pay between $11,297 million and $19,262 million more. Owners of all five model years e analysis indicates that ow s of MY sse n high pay b

295 In a few cases the upper range results were obtained fro the 7% rate and the lower range results were obtained from the 3% rate. While this may seem counterintuitive, i rom the random selection process that is inherent in the Monte Carlo technique. m t results f

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### vehicles combined will pay between $37. billion in higher vehicle prices to purchase vehicles w

Societal Benefits: 9 billion and $68.2 ith improved fuel efficiency.

The analysis indicates that ch ges to MY 2012 passenger cars and light trucks to me oposed CAFE standards wil e over ietal be lued between $4,229 million and $22,407 millio 3 vehicles will produce benefits valued between $10 on. e r s valued between $13 3 million. pro s valued between $18 illion. M 6 vehi l prod efits valued between $22 million. e com fespa five model years, societ 69.1 bil $320. n will uced.

Net Benefits an et the pr l produc all soc nefits va n. MY 201 ,126 million and $47,722 milli ,16 MY 2014 v M 5 vehi hicles will p l oduce benefit d efit ,975 million and $66 Y 201 cles wil uce ben ,087 million and $83,333 m ,703 million and $101,174 Y 201 Over th cles wil bined li uce ben n of the al benefits valued betw een $ lion and 8 billio be prod

: The uncertainty analysis indi e net impact of the higher CAFE requirement cars and w e of $2,012 million and 38 million. Th least cent c that changes made to MY he higher tanda prod t benefit. The net imp ement 2013 a ne of between $2,033 milli 286 mill re is rcent that changes mad chie standar ll produ a net benefit. The net impa equire Y 2014 will be a net benefit of between $3,756 milli 4,448 mi i e that changes mad les to achieve th r CAF ards w uce a net benefit. The AFE re q r 5 wi t benefit of between $6, 1 million. T 0 pe rtaint anges made to MY 2015 FE standa produc net benefi he net impact of the higher 2 et bene t of between 51 million and $84,920 rcent cer a to ehicles to achieve the duce a ne ve o higher CAFE stand its rangin 8.5 to $26 on. There is at least cates that th s for MY 2012 passenger light trucks ill be betwe n a net cost a net benefit of $16,1 ere is at a 78 per ertainty 2012 vehicles to achieve t requir CAFE s rds will uce a ne act of the higher CAFE s for MY io e will be a 1 pe t benefit cer inty on and a net benefit of $38, e to MY 2013 vehicles to a n. Th e CAFE 00 ds wi ta

ce ve th ments for M ct of the higher CAFE r on and a net benefit of $5 llion. There s a 100 perc nt certainty e to MY 2014 vehic e highe E stand ill prod net impact of the higher C uirements fo MY 201 ll be a ne 084 million and $69,46 here is 10 rcent ce y that ch vehicles to achieve the CA CAFE requirements for MY rds will

a n e a fi t. T $8,6 016 will be million. There is 100 pe CAFE standards will pro tainty that ch t benefit. O nges made r all five m MY 2016 v del years, the ards will produce ne t benef g fr om $1 billion 3.3 billi a 78 s wil a ne l benefit in each percent certainty that higher CAFE standard l produce t societa of the m this final rule. In most ye prob 100%. odel years covered by ars, this ability is

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### U

Item Mean Low High Table XII-8

NCERTAINTY 

### A NALYSIS 

### R ESULTS 

### , P ASSENGER 

### C ARS 

### (7% Discount Rate)

MY 2 012 Fuel Saved (mill. gall.) 0 1,708,714 2,966,529 2,351,60 T otal Cost ($mill.) $4,183 $2,770 $5,384 S ill.) $ 1

ocietal Benefits ($m 5,576 $2,292 $10,26
N $ -

et Benefits ($mill.) 1,393 $2,168 $6,324 % 0

Certainty Net Ben. > 78% MY 2 013 Fuel Saved (mill. gall.) 1 4,241,046 6,006,703 5,101,26 T otal Cost ($mill.) $5,445 $4,255 $6,779 S ill.) $1 9

ocietal Benefits ($m 2,588 $5,682 $21,96
N $

et Benefits ($mill.) 7,143 $170 $16,280 % 0 1

Certainty Net Ben. > 00% MY 2 014 Fuel Saved (mill. gall.) 6 5,951,278 8,124,710 7,085,20 T otal Cost ($mill.) $6,829 $5,244 $8,408 S ill.) $1 5

ocietal Benefits ($m 7,806 $8,023 $30,96
N $

et Benefits ($mill.) 10,977 $1,249 $23,938 % 0

Certainty Net Ben. > 100% MY 2 015 Fuel Saved (mill. gall.) 9 7,720,755 10,515,156 9,107,02 T otal Cost ($mill.) $8,057 $6,233 $9,990 S ill.) $2 $ 5

ocietal Benefits ($m 3,241 10,574 $40,04
N $

et Benefits ($mill.) 15,184 $2,751 $31,402 % 0

Certainty Net Ben. > 100% MY 2 016 Fuel Saved (mill. gall.) 9 9,840,046 12,649,674 11,268,17 T otal Cost ($mill.) $9,461 $7,356 $12,014 S ill.) $2 $ 4

ocietal Benefits ($m 9,110 13,287 $49,45
N $

et Benefits ($mill.) 19,650 $4,404 $39,532 % 0

Certainty Net Ben. > 100%

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### Table XII-9 U NCERTAINTY 

### A NALYSIS 

### R ESULTS 

### , L IGHT 

### T RUCKS 

### (7% Discount Rate) Item Mean Low High MY 2012 Fuel S aved (mill. gall.) 29 1,4 2,245 1,794,7 47,139 ,130 Total Cost ($mill.) $1,075 $2,584 $1,703 Societal Benefits ($mill.) $7 $4,156 $1,937 ,634 Net Benefits ($mill.) $2,453 $5, $156 448 % Certainty Net Ben. > 0 100%
MY 2013 Fuel Saved (mill. gall.) 3,805,8 50 3,196,950 4,611,44 4 Total Cost ($mill.) $1,708 $3,589 $2,541 Societal Benefits ($mill.) $ $9,011 $4,444 16,318 Net Benefits ($mill.) $6,470 $ $13 1,833 ,010 % Certainty Net Ben. > 0 100%
M Y 2014 Fuel Saved (mill. gall.) 5,246,3 53 4,403,640 6,201,06 5 Total Cost ($mill.) $2,398 $5,096 $3,555 Societal Benefits ($mill.) $ $12,644 $5,952 22,334 Net Benefits ($mill.) $9,089 $ $18 2,507 ,269 % Certainty Net Ben. > 0 100%
M Y 2015 Fuel Saved (mill. gall.) 6,512,0 08 5,558,529 7,441,37 6 Total Cost ($mill.) $2,929 $5,901 $4,292 Societal Benefits ($mill.) $ $15,919 $7,513 28,049 Net Benefits ($mill.) $11,626 $ $23 3,333 ,292 % Certainty Net Ben. > 0 100%
M Y 2016 Fuel Saved (mill. gall.) 7,947,3 35 6,926,197 8,817,02 1 Total Cost ($mill.) $3,941 $6,718 $5,214 Societal Benefits ($mill.) $ $19,600 $9,416 32,950 Net Benefits ($mill.) $14,386 $27 $4,247 ,807 % Certainty Net Ben. > 0 100%

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### U NCERTAINTY 

### A NALYSIS ARS AND 

### L IGHT 

### T RUCKS 

### (7% Discount Rate) Item Mean Low High Table XII-10 R ESULTS 

### , P ASSENGER 

### C

MY 2012 Fuel Saved (mill. gall.) 4,146,329 3,155,853 5,211,659 Total Cost ($mill.) $5,886 $3,845 $7,968 Societal Benefits ($mill.) $9,732 $4,229 $17,895 Net Benefits ($mill.) $3,846 -$2,012 $11,772 % Certainty Net Ben. > 0 94% MY 2013 Fuel Saved (mill. gall.) 8,907,111 7,437,996 10,618,147 Total Cost ($mill.) $7,986 $5,963 $10,368 Societal Benefits ($mill.) $21,599 $10,126 $38,287 Net Benefits ($mill.) $13,613 $2,033 $29,290 % Certainty Net Ben. > 0 100%
MY 2014 Fuel Saved (mill. gall.) 12,3 59 10,354,918 14,325,775 31,5 Total Cost ($mill.) $10,384 $7,642 $13,504 Societal Benefits ($mill.) $3 ,450 $13,975 $53,299 0 Net Benefits ($mill.) $2 ,066 $3,756 $42,207 0 % Certainty Net Ben. > 0 100%
MY 2015 Fuel Saved (mill. gal 279,284 17,956,532 l.) 15,619,037 13, Total Cost ($mill.) 9,162 $15,891 $12,349 $ Societal Benefits ($mill.) $39,160 $18,087 $68,094 Net Benefits ($mill.) $26,810 $6,084 $54,694 % Certainty Net Ben. > 0 100% MY 2016 Fuel Saved (mill. gall.) 19,215,514 16,766,243 21,466,695 Total Cost ($mill.) $14,675 $11,297 $18,732 Societal Benefits ($mill.) $48,710 $22,703 $82,404 Net Benefits ($mill.) $34,036 $8,651 $67,339 % Certainty Net Ben. > 0 100%

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### Table XII-11 U NCERTAINTY 

### A NALYSIS 

### R ULTS 

### , P ASSENGER 

### C ARS 

### (3% Disc nt Rate)

Item Mean Low High ES



### ou

MY 2012 Fuel Saved (mill. gall.) 2,384,741 1,774,115 2,990,158 Total Cost ($mill.) 4,202 2,747 5,379 Societal Benefits ($mill.) 7,041 2,982 12,519 Net Benefits ($mill.) 2,839 -1,316 8,562 % Certainty Net Ben. > 0 91% MY 2013 Fuel Saved (mill. gall.) 5,153,970 4,272,330 6,089,001 Total Cost ($mill.) 5,516 4,115 6,878 Societal Benefits ($mill.) 15,713 7,186 26,921 Net Benefits ($mill.) 10,197 1,842 20,890 % Certainty Net Ben. > 0 100% MY 2014 Fuel Saved (mill. gall.) 7,162,064 5,964,138 8,271,039 Total Cost ($mill.) 6,939 5,163 8,667 Societal Benefits ($mill.) 2,210 10,205 38,008 2 Net Benefits ($mill.) 5,271 3,440 30,520 1 % Certainty Net Ben. > 0 % 100
MY 2015 Fuel Saved (mill. gall.) 9,183,889 7,723,966 10,555,661 Total Cost ($mill.) 6,304 10,405 8,179 Societal Benefits ($mill 13,264 48,823 .) 28,858 Net Benefits ($mill.) 9 39,859 20,679 5,28 % Certainty Net Ben. > 0 100% MY 2016 Fuel Saved (mill. gall.) 11,323,825 9,912,630 12,715,882 Total Cost ($mill.) 9,579 7,470 12,359 Societal Benefits ($mill.) 35,954 16,578 60,044 Net Benefits ($mill.) 26,376 7,779 49,538 % Certainty Net Ben. > 0 100%

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### A NALYSIS 

### R ESULTS 

### , L IGHT 

### T RUCKS



### (3% Discount Rate)

Item Mean Low High Table XII-12 U NCERTAINTY



### MY 2012 Fuel Saved (mill. gall.) 1,813,345 1,447,105 2,307,152 Total Cost ($mill. ) 1,748 1,091 2,494 Societal Benefits ($mill.) 5,342 2,505 9,888 Net Benefits ($mill.) 3,593 762 7,576 % Certainty Net Ben. > 0 100% MY 2013 Fuel Saved (mill. gall.) 3,835,210 3,234,224 4,633,103 Total Cost ($mill.) 2,607 1,752 3,853 Societal Benefits ($mill.) 11,507 5,581 20,801 Net Benefits ($mill.) 8,900 3,069 17,396 % Certainty Net B en. > 0 100% MY 2014 Fuel Saved (mill. gall.) 5,292,250 4,493,060 6,162,987 Total Cost ($mill.) 3,645 2,396 5,173 Societal Benefits ($mill.) 16,128 7,563 28,155 Net Benefits ($mill.) 12,483 4,19 8 23,928 % Certainty Net Ben. > 0 100% MY 2015 Fuel Saved (mill. gall.) 6,560,161 5,690,703 7,526,400 Total Cost ($mill.) 4,388 3,029 6 ,131 Societal Benefits ($mill.) 20,243 9,557 34 ,510 Net Benefits ($mill.) 15,855 5,399 29,6 02 % Certainty Net Ben. > 0 1 00% MY 2016 Fuel Saved (mill. gall.) 7,970,500 6,937,291 9,002,284 Total Cost ($mill.) 5,292 3,945 6,903 Societal Benefits ($mill.) 24,799 11,856 41,130 Net Benefits ($mill.) 19,507 6,724 35,382 % Certainty Net Ben. > 0 100%

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### Item Mean Low High MY 2012 Fuel Saved (mill. gall.) 4,198,086 3,221,220 5,297,310 Total Cost ($mill.) $5,950 $3,838 $7,873 Societal Benefits ($mill.) $12,383 $5,487 $22,407 Net Benefits ($mill.) $6,432 -$554 $16,138 % Certainty Net Ben. > 0 99.9% MY 2013 Fuel Saved (mill. gall.) 8,989,180 7,506,554 10,722,104 Total Cost ($mill.) $8,123 $5,867 $10,731 Societal Benefits ($mill.) $27,220 $12,76 7 $47,722 Net Benefits ($mill.) $19,097 $4,911 $38,286 % Certainty Net Ben. > 0 100% MY 2014 Fuel Saved (mill. gall.) 12,454,314 10,457,198 14,434,026 Total Cost ($mill.) $10,584 $7,559 $13,840 Societal Benefits ($mill.) $38,338 $17,768 $66,163 Net Benefits ($mill.) $27,754 $7,638 $54,448 % Certainty Net Ben. > 0 100% MY 2015 Fuel Saved (mill. gall.) 15,744,050 13,414,669 18,082,061 Total Cost ($mill.) $12,567 $9,333 $16,536 Societal Benefits ($mill.) $49,101 $22,821 $83,333 Net Benefits ($mill.) $36,534 $10,688 $69,461 % Certainty Net Ben. > 0 100% MY 2016 Fuel Saved (mill. gall.) 19,294,325 16,849,921 21,718,166 Total Cost ($mill.) $14,871 $11,415 $19,262 Societal Benefits ($mill.) $60,753 $28,434 $101,174 Net Benefits ($mill.) $45,883 $14,503 $84,920 % Certainty Net Ben. > 0 100%DELIBERATIVE 

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## XIII.

1 2 B 

## REGULATORY FLEXIBILITY ACT AND UNFUNDED MANDATES REFORM ACT ANALYSIS

A. Regulatory Flexibility Act

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 specifies the content of a RFA. Each RFA must contain:

1. A description of the reasons why action 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 estimate of the number of small entities to which the final rule will apply; 4. A description of the projected reporting, recording 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 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 shall also contain a description 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 action 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 update 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 sufficient to ensure that the average fuel economy of the combined fleet of all passenger cars and light trucks sold by all manufacturers in the U.S. in model year 2020 equals or exceeds 35 miles per gallon.

3. Description and estimate of the number of small entities to which the final rule will apply The final rule will affect motor vehicle manufacturers. There are no light truck manufacturers that are small businesses. However, there are six 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 receiv ing Small Business Admi nistration assistance.DELIBERATIVE DOCUMENT – DO NOT RELEASE 

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One of the criteria for determining size, as stated 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 vehicle 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. Those manufacturers that cu rrently don’t meet the 27.5 mpg standard can petition the agency for relief. If the standard is raised, it has no meaningful impact on these manufacturers; they still must go through the same process and petition for relief. Other small manufacturers (Tesla and Fisker) make electric vehicles or hybrid vehicles that will pass the final rule.

Currently, there are six small passenger car motor vehicle manufacturers in the United States. Table X1II-1 provides information about the 6 sm all domestic manufacturers in MY 2007. All are small manufacturers, having much less than 1,000 employees.DELIBERATIVE DOCUMENT – DO NOT RELEASE 

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Table XIII-1 Small Vehicle Manufacturers

Manufacturer Employees Estimated Sales Sale Price Range Est. Revenues*

Fisker Automotive** N/A 15,000 projected $80,000 N/A Mosler Automotive 25 20 $189,000 $2,000,000 Panoz Auto Development Company 50 150 $90,000 to $125,000 $16,125,000

Saleen Inc. 170 1,000 # 

### $39,000 to $59,000 $49,000,000

Saleen Inc. 170 16 ## 

### $585,000 $9,000,000 Standard Taxi*** 35 N/A $25,000 $2,000,000 Tesla Motors, Inc. 250 2,000 $65,000 to $100,000 N/A * Assuming an average sales price from the sales price range. ** Fisker Automotive is a joint venture of Quantum Fuel Systems Technologies Worldwide, Inc. and Fisker Coachbuild, LLC. *** Standard Taxi is a subsidiary of the Vehicle Production Group LLC. 35 employees is the total for VPG LLC.
# 

### Ford Mustang Conversions

The agency has not analyzed the impact of the final rule on these small manufacturers individually. However, assuming those that do not meet the final rule would petition the agency, rather than meet the final rule, the cost is not expected to be substantial.

4. A description of the projected reporting, reco rd 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 necessary for preparation of the report or record. This final rule 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 EPA and NHTSA are proposing joint rules which complement each other. We know of no other Federal rules which duplicate, overlap, or conflict with the final rule.DELIBERATIVE DOCUMENT – DO NOT RELEASE 

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6. A description 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.

There are no other alternatives that can achieve the stated objectives without installing fuel economy technologies into the vehicle.

2 5 B 

### B. Unfunded Mandates Reform Act

Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) requires Federal agencies to prepare a written assessment of the costs, benefits, and other effects of a proposed or final rule that includes a Federal mandate likely to result in the expenditure by State, local, or tribal governments, in the aggregate, or by the private sector, of more than $100 million in any one year (adjusted for inflation with base year of 19 95). Adjusting this amount by the implicit gross domestic product price deflator for 2006 results in $126 million (116.043/92.106 = 1.26). Before promulgating a rule for which a written statemen t is needed, section 205 of UMRA generally requires NHTSA to identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-effective, or least burdensome alternative that achieves the objectives of the rule. The provisions of section 205 do not apply when they are inconsistent with applicable law. Moreover, section 205 allo ws NHTSA to adopt an alternative other than the least costly, most cost-effective, or least burdensome alternative if the agency publishes with the final rule an explanation why that alternative was not adopted.

This final rule will not result in the expenditure by State, local, or tribal governments, in the aggregate, of more than $126 million annually, but it will result in the expenditure of that magnitude by vehicle manufacturers and/or their suppliers. In promulgating this final rule, NHTSA considered a variety of alternative average fuel economy standards lower and higher than those proposed. NHTSA is statutorily required to set standards at the maximum feasible level achievable by manufacturers based on its c onsideration and balancing of relevant factors and has concluded that the final fuel economy standards are the maximum feasible standards for the passenger car and light truck fleets for MYs 2012-2016 in light of the statutory considerations.

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