1

II. Joint Technical Work Completed for This Proposa l

A. Introduction

In this section NHTSA and EPA discuss several aspec ts of the joint technical analyses the two agencies collaborated on which are common to th e development of each agency’s proposed standards. Specifically we discuss: the developme nt of the baseline vehicle market forecast used by each agency, the development of the propose d attribute-based standard curve shapes, how the relative stringency between the car and tru ck fleet standards for this proposal was determined, which technologies the agencies evaluat ed and their costs and effectiveness, and which economic assumptions the agencies included in their analyses. The joint Technical Support Document (TSD) discusses the agencies’ join t technical work in more detail.

B. How did NHTSA and EPA Develop the Baseline Mark et Forecast?

1. Why do the Agencies Establish a Baseline Vehicle Fleet?

In order to calculate the impacts of the EPA and NH TSA proposed regulations, it is necessary to estimate the composition of the future vehicle fleet absent these proposed regulations in order to conduct comparisons. EPA a nd NHTSA have developed a comparison fleet in two parts. The first step was to develop a baseline fleet based on model year 2008 data. The second step was to project that fleet into 2011 -2016. This is called the reference fleet. The third step was to modify that 2011-2016 reference f leet such that it had sufficient technologies to

meet the 2011 CAFE standards. This final “referenc e fleet” is the light duty fleet estimated to exist in 2012-2016 if these proposed rules are not adopted. Each agency developed a final reference fleet to use in its modeling. All of the agencies’ estimates of emission reductions, fuel economy improvements, costs, and societal impacts a re developed in relation to the respective

reference fleets.

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

EPA and NHTSA have based the projection of total ca r and total light truck sales on recent projections made by the Energy Information A dministration (EIA). EIA publishes a longterm projection of national energy use annually cal led the Annual Energy Outlook. This projection utilizes a number of technical and econo metric models which are designed to reflect both economic and regulatory conditions expected to exist in the future. In support of its projection of fuel use by light-duty vehicles, EIA projects sales of new cars and light trucks. Due to the state of flux of both energy prices and the economy, EIA published three versions of its 2009 Annual Energy Outlook. The Preliminary 20 09 report was published early (in November 2008) in order to reflect the dramatic inc rease in fuel prices which occurred during 2008 and which occurred after the development of th e 2008 Annual Energy Outlook. The official 2009 report was published in March of 2009 . A third 2009 report was published a month later which reflected the economic stimulus package passed by Congress earlier this year. We use the sales projections of this latest report, re ferred to as the updated 2009 Annual Energy Outlook, here.2

In their updated 2009 report, EIA projects that tot al light-duty vehicle sales will gradually recover from their currently depressed levels by ro ughly 2013. In 2016, car and light truck sales are projected to be 9.5 and 7.1 million units, resp ectively. While the total level of sales of 16.6 million units is similar to pre-2008 levels, the fr action of car sales is higher than that existing in the 2000-2007 timeframe. This presumably reflects the impact of higher fuel prices and that fact that cars tend to have higher levels of fuel econom y than trucks. We note that EIA’s definition of cars and trucks follows that used by NHTSA prior to the MY 2011 CAFE final rule published earlier this year. That recent CAFE rule, which est ablished the MY 2011 standards, reclassified a number of 2-wheel drive sport utility vehicles from the truck fleet to the car fleet. This has the impact of shifting a considerable number of previou sly defined trucks into the car category. Sales projections of cars and trucks for all future model years can be found in the draft Joint TSD for this proposal.

In addition to a shift towards more car sales, sale s of segments within both the car and truck markets have also been changing and are expec ted to continue to change in the future. Manufacturers are introducing more crossover models which offer much of the utility of SUVs but using more car-like designs. In order to refle ct these changes in fleet makeup, EPA and

NHTSA considered several available forecasts. Afte r review EPA purchased and shared with NHTSA forecasts from two well-known industry analys ts, CSM-Worldwide (CSM), and J.D.

Powers. NHTSA and EPA decided to use the forecast from CSM, for several reasons. One, CSM agreed to allow us to publish the data, on whic h our forecast is based, in the public

domain. 1 Two, it covered nearly all the timeframe of great est relevance to this proposed rule (2012-2015 model years). Three, it provided projec tions of vehicle sales both by manufacturer and by market segment. Four, it utilized market se gments similar to those used in the EPA emission certification program and fuel economy gui de. As discussed further below, this

allowed the CSM forecast to be combined with other data obtained by NHTSA and EPA. We

also assumed that the breakdowns of car and truck s ales by manufacturer and by market segment for 2016 model year and beyond were the same as CSM ’s forecast for 2015 calendar year. The changes between company market share and industry m arket segments were most significant from 2011-2014, while for 2014-2015 the changes wer e relatively small. Therefore, we assumed 2016 market share and market segments to be the sam e as for 2015. To the extent that the agencies have received CSM forecasts for 2016, we w ill consider using them for the final rule.

We then projected the CSM forecasts for relative sa les of cars and trucks by manufacturer and by market segment on to the total sales estimat es of the updated 2009 Annual Energy

Outlook. Tables II.B.1-1 and II.B.1-2 show the re sulting projections for the 2016 model year and compare these to actual sales which occurred in 2008 model year. Both tables show sales using the traditional or classic definition of cars and light trucks. Determining which classic trucks will be defined as cars using the revised de finition established by NHTSA earlier this year

1 The CSM data made public includes only the higher level volume projections by market segment and manufacturer. The projections by nameplate and mod el are strictly the agencies’ estimates based on th ese higher level CSM segment and manufacturer distribution.3

and included in this proposed rule requires more de tailed information about each vehicle model which is developed next.

Table II.B.2-1 Annual Sales of Light-Duty Vehicles by Manufacturer in 2008 and Estimated for

2016
Cars Light Trucks Total

2008 MY 2016 MY 2008 MY 2016 MY 2008 MY 2016 MY

BMW 291,796 380,804 61,324 134,805 353,120 515,609
Chrysler 537,808 110,438 1,119,397 133,454 1,657,205 243,891

Daimler 208,052 235,205 79,135 109,917 287,187 345,122

Ford 641,281 990,700 1,227,107 1,713,376 1,868,388 2,704,075

General Motors 1,370,280 1,562,791 1,749,227 1,571,037 3,119,507 3,133,827

Honda 899,498 1,429,262 612,281 812,325 1,511,779 2,241,586
Hyundai 270,293 437,329 120,734 287,694 391,027 725,024

Kia 145,863 255,954 135,589 162,515 281,452 418,469

Mazda 191,326 290,010 111,220 112,837 302,546 402,847

Mitsubishi 76,701 49,697 24,028 10,872 100,729 60,569
Porsche 18,909 37,064 18,797 17,175 37,706 54,240

Nissan 653,121 985,668 370,294 571,748 1,023,415 1,557,416

Subaru 149,370 128,885 49,211 75,841 198,581 204,726

Suzuki 68,720 69,452 45,938 34,307 114,658 103,759

Tata 9,596 41,584 55,584 47,105 65,180 88,689
Toyota 1,143,696 1,986,824 1,067,804 1,218,223 2,211,500 3,205,048
Volkswagen 290,385 476,699 26,999 99,459 317,384 576,158

Total 6,966,695 9,468,365 6,874,669 7,112,689 13,841,364 16,581,055

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

for 2016

Cars Light Trucks

2008 MY 2016 MY 2008 MY 2016 MY

Full-Size Car 730,355 466,616 Full-Size Pickup 1,195,073 1,475,881

Mid-Size Car 1,970,494 2,641,739 Mid-Size Pickup 598,197 510,580
Small/Compact

Car 1,850,522 2,444,479 Full-Size Van 33,384 284,110

Mid-Size Van 719,529 615,349
Subcompact/Mini

Car 599,643 1,459,138 Mid-Size MAV* 191,448 158,930

Small MAV 235,524 289,880
Luxury Car 1,057,875 1,432,162 Full-Size SUV* 530,748 90,636
Specialty Car 754,547 1,003,078 Mid-Size SUV 347,026 110,1554

Others 3,259 21,153 Small SUV 377,262 124,397

Full-Size CUV* 406,554 319,201

Mid-Size CUV 798,335 1,306,770

Small CUV 1,441,589 1,866,580

TotalSales 6,966,695 9,468,365 6,874,669 7,152,470
* MAV – Multi-Activity Vehicle, SUV – Sport Utilit y Vehicle, CUV – Crossover Utility Vehicle

The agencies recognize that CSM forecasts a very si gnificant reduction in market share for Chrysler. This may be a result of the extreme uncertainty surrounding Chrysler in early

2009. We expect to use the most recent forecasts for the final rule. The forecast from CSM used in this proposal is CSM’s forecast from the 2nd qua rter of 2009. CSM also provided to the agencies an updated forecast in the 3rd quarter of 2009, wh ich we were unable to use for this proposal due to time constraints. However, we have placed a copy of the 3rd Quarter CSM forecast in the public docket for this rulemaking, and we will consider its use, and any further updates from CSM or other data received during the comment period when developing the analysis for the final rule. 2 CSM’s forecast for Chrysler for the 3rd quarter o f 2009 was significantly increased compared to the 2nd quarter , by nearly a factor of two increase in projected sales over the 2012-2015 time frame.

The forecasts obtained from CSM provided estimates of car and trucks sales by segment and by manufacturer, but not by manufacturer for ea ch market segment. Therefore, we needed

other information on which to base these more detai led market splits. For this task, we used as a starting point each manufacturer’s sales by market segment from model year 2008. Because of the larger number of segments in the truck market, we used slightly different methodologies for

cars and trucks.

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

cars sales in 2008 were broken down as shown in Tab le II.B.2-3:

Table II.B.2-3: Breakdown of Ford’s 2008 Car Sales
Full-size cars 76,762 units
Mid-size Cars 170,399 units Small/Compact Cars 180,249 units Subcompact/Mini Cars none Luxury cars 100,065 units Specialty cars 110,805 units

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

2 “CSM North America Sales Forecast Comparison 2Q09 3 Q09 For Docket.” 2 nd and 3 rd quarter forecasting results from CSM World Wide (Docket EPA-HQ-OAR-2009-0472).5

indicated in Table II.B.2-1, Ford’s total car sales in 2008 were 641,281 units, while we project that they will increase to 990,700 units by 2016. This represents an increase of 54.5 percent. Thus, we increased the 2008 sales of each Ford car segment by 54.5 percent. This produced

estimates of future sales which matched total car a nd truck sales per EIA and the manufacturer breakdowns per CSM (and exemplified for 2016 in Tab le II.B.1-1). However, the sales splits by market segment would not necessarily match those of CSM (and exemplified for 2016 in Table II.B.2-2).

In order to adjust the market segment mix for cars, we first adjusted sales of luxury, specialty and other cars. Since the total sales of cars for each manufacturer were already set, any changes in the sales of one car segment had to be c ompensated by the opposite change in another segment. For the luxury, specialty and other car s egments, it is not clear how changes in sales would be compensated. For example, if luxury car s ales decreased, would sales of full-size cars increase, mid-size cars, etc.? Thus, any changes i n the sales of cars within these three segments were assumed to be compensated for by proportional changes in the sales of the other four car segments. For example, for 2016, the figures in Ta ble II.B.2-2 indicate that luxury car sales in 2016 are 1,432,162 units. Luxury car sales are 1,0 57,875 units in 2008. However, after adjusting 2008 car sales by the change in total car sales for 2016 projected by EIA and a change in manufacturer market share per CSM, luxury car sa les increased to 1,521,892 units. Thus, overall for 2016, luxury car sales had to decrease by 89,730 units or 6 percent. We decreased the luxury car sales by each manufacturer by this perce ntage. The absolute decrease in luxury car sales was spread across sales of full-size, mid-siz e, compact and subcompact cars in proportion to each manufacturer’s sales in these segments in 2 008. The same adjustment process was used for specialty cars and the “other cars” segment def ined by CSM.

A slightly different approach was used to adjust fo r changing sales of the remaining four car segments. Starting with full-size cars, we aga in determined the overall percentage change that needed to occur in future year full-size cars sales after 1) adjusting for total sales per EIA, 2 ) manufacturer sales mix per CSM and 3) adjustments i n the luxury, specialty and other car segments, in order to meet the segment sales mix pe r CSM. Sales of each manufacturer’s large cars were adjusted by this percentage. However, in stead of spreading this change over the remaining three segments, we assigned the entire ch ange to mid-size vehicles. We did so because, as shown in 2008, higher fuel prices tend to cause car purchasers to purchase smaller vehicles. We are using AEO 2009 for this analysis, which assumes fuel prices similar in magnitude to actual high fuel prices seen in the su mmer of 2008. 3 However, if a consumer had previously purchased a full-size car, we thought it unlikely that they would jump all the way to a subcompact. It seemed more reasonable to project t hat they would drop one vehicle size category smaller. Thus, the change in each manufac turer’s sales of full-size cars was matched by an opposite change (in absolute units s old) in mid-size cars.

The same process was then applied to mid-size cars, with the change in mid-size car sales being matched by an opposite change in compact car sales. This process was repeated one more

3 J.D.Power and Associates, Press Release, May 16, 2 007. “Rising Gas Prices Begin to Sway New-Vehicle Owners Toward Smaller Versions of Trucks and Utility Vehic les.”6

time for compact car sales, with changes in sales i n this segment being matched by the opposite change in the sales of subcompacts. The overall re sult was a projection of car sales for 2012-

2016 which matched the total sale s projections of EIA and the manufacturer and segmen t splits of CSM. These sales splits can be found in Chapter 1 of the draft Joint Technical Support Document for this proposal.

As mentioned above, a slightly different process wa s applied to truck sales. The reason for this was we could not confidently project how t he change in sales from one segment preferentially went to or came from another particu lar segment. Some trend from larger vehicles to smaller vehicles would have been possible. Howe ver, the CSM forecasts indicated large changes in total sport utility vehicle, multi-activ ity vehicle and cross-over sales which could not be connected. Thus, we applied an iterative, but s traightforward process for adjusting 2008 truck

sales to match the EIA and CSM forecasts.

The first three steps were exactly the same as for cars. We broke down each manufacturer’s truck sales into the truck segments as defined by CSM. We then adjusted all manufacturers’ truck segment sales by the same fact or so that total truck sales in each model year matched EIA projections for truck sales by model ye ar. We then adjusted each manufacturer’s truck sales by segment proportionally so that each manufacturer’s percentage of total truck sales matched that forecast by CSM. This again left the need to adjust truck sales by segment to match the CSM forecast for each model year.

In the fourth step, we adjusted the sales of each t ruck segment by a common factor so that total sales for that segment matched the combinatio n of the EIA and CSM forecasts. For example, sales of large pickups across all manufact urer ’ s were 1,144,166 units in 2016 after adjusting total sales to match EIA’s forecast and a djusting each manufacturer’s truck sales to match CSM’s forecast for the breakdown of sales by manufacturer. Applying CSM’s forecast of the large pickup segment of truck sales to EIA’s to tal sales forecast indicated total large pickup sales of 1,475,881 units. Thus, we increased each manufacturer’s sales of large pickups by 29 percent. The same type of adjustment was applied t o all the other truck segments at the same time. The result was a set of sales projections wh ich matched EIA’s total truck sales projection and CSM’s market segment forecast. However, after this step, sales by manufacturer no longer met CSM’s forecast. Thus, we repeated step three a nd adjusted each 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 p rojection matched EIA’s total truck sales projection and CSM’s manufacturer forecast, but sal es by market segment no longer met CSM’s forecast. However, the difference between the sale s projections after this fifth step was closer to CSM’s market segment forecast than it was after ste p three. In other words, the sales projection was converging. We repeated these adjustments, mat ching manufacturer sales mix in one step and then market segment in the next for a total of 19 times. At this point, we were able to match the market segment splits exactly and the manufactu rer splits were within 0.1% of our goal, which is well within the needs of this analysis.

The next step in developing the baseline fleet was to characterize the vehicles within each manufacturer-segment combination. In large part, t his was based on the characterization of the7

specific vehicle models sold in 2008. EPA and NHTS A chose to base our estimates of detailed

vehicle characteristics on 2008 sales for several r easons. One, these vehicle characteristics are not confidential and can thus be published here for careful review and comment by interested parties. Two, being actual sales data, this vehicl e fleet represents the distribution of consumer demand for utility, performance, safety, etc.

We gathered most of the information about the 2008 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, numbe r 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 proposal. Thus, we augmented this description with publicly available data which includes more complete technology descriptions from Ward ’ s Automotive Group

. 4 In a few instances when required

vehicle information was not available from these tw o sources (such as vehicle footprint), we obtained this information from publicly accessible internet sites such as Motortrend.com and

Edmunds.com. 5

The projections of future car and truck sales descr ibed above apply to each manufacturer’s sales by market segment. The EPA em issions certification sales data are available at a much finer level of detail, essentia lly vehicle configuration. As mentioned above, we placed each vehicle in the EPA certification dat abase into one of the CSM market segments. We then totaled the sales by each manufacturer for each market segment. If the combination of EIA and CSM forecasts indicated an increase in a gi ven manufacturer’s sales of a particular market segment, then the sales of all the individua l vehicle configurations were adjusted by the same factor. For example, if the Prius represented 30% of Toyota’s sales of compact cars in 2008 and Toyota’s sales of compact cars in 2016 was projected to double by 2016, then the sales of the Prius were doubled, and the Prius sales in 2 016 remained 30% of Toyota’s compact car

sales.

NHTSA and EPA request comment on the methodology an d data sources used for developing the baseline vehicle fleet for this prop osal and the reasonableness of the results.

3. How is the Development of the Baseline Fleet for this Proposal Different from NHTSA’s Historical approach, and Why is this Approach

Preferable?

NHTSA has historically based its analysis of poten tial new CAFE standards on detailed product plans the agency has requested from manufac turers 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 a nd some smaller manufacturers have voluntarily provided it when requested.

4 Note that Ward sAuto.com s Automotive is a fee-based service, but all information is pub lic to subscribers.

5 Motortrend.com and Edmunds.com are free, no-fee in ternet sites.8

As in this and other prior rulemakings, NHTSA has requested extensive and detailed information regarding the models that manufacturers plan to offer, as well as manufacturers’ estimates of the volume of each model they expect t o produce for sale in the U.S. NHTSA’s recent requests have sought information regarding a range of engineering and planning characteristics for each vehicle model (e.g., fuel economy, engine, transmission, physical dimensions, weights and capacities, redesign schedu les), each engine (e.g., fuel type, fuel delivery, aspiration, valvetrain configuration, val ve timing, valve lift, power and torque ratings), and each transmission (e.g., type, number of gears, logic).

The information that manufacturers have provided i n response to these requests has varied in completeness and detail. Some manufactur ers have submitted nearly all of the information NHTSA has requested, have done so for m ost or all of the model years covered by NHTSA’s requests, and have closely followed NHTSA’s guidance regarding the structure of the information. Other manufacturers have submitted pa rtial information, information for only a few model years, and/or information in a structure less amenable to analysis. Still other manufacturers have not responded to NHTSA’s request s or have responded on occasion, usually with partial information.

In recent rulemakings, NHTSA has integrated this i nformation and estimated missing information based on a range of public and commerci al sources (such as those used to develop today’s market forecast). For unresponsive manufac turers, NHTSA has estimated fleet composition based on the latest-available CAFE comp liance data (the same data used as part of the foundation for today’s market forecast). NHTSA has then adjusted the size of the fleet based on AEO’s forecast of the light vehicle market and n ormalized manufacturers’ market shares based on the latest-available CAFE compliance data.

Compared to this approach, the market forecast the agencies have developed for this analysis has both advantages and disadvantages.

Most importantly, today’s market forecast is much m ore transparent. The information sources used to develop today’s market forecast are all either in the public domain or available commercially. Therefore, NHTSA and EPA are able to make public the market inputs actually used in the agencies’ respective modeling systems, such that any reviewer may independently repeat and review the agencies’ analyses. Previous ly, although NHTSA provided this type of information to manufacturers upon request (e.g., GM requested and received outputs specific to GM), NHTSA was otherwise unable to release market i nputs and the most detailed model outputs (i.e

. , the outputs containing information regarding spec ific vehicle models) because doing so would violate requirements protecting manu facturers’ confidential business information

from disclosure. 6 Therefore, this approach provides much greater op portunity for the public to review every aspect of the agencies’ analyses and c omment accordingly.

Another significant advantage of today’s market for ecast is the agencies’ ability to assess more fully the incremental costs and benefits of th e proposed standards. In the past two years,

6 See 49 CFR Part 512.9

NHTSA has requested and received three sets of futu re product plan submissions from the automotive companies, most recently this past sprin g. These submissions are intended to be the actual future product plans for the companies. In the most recent submission it is clear that many of the firms have been and are clearly planning for future CAFE standard increases for model years 2012 and later. The results for the product plans for many firms are a significant increase in their projected future application of fuel econo my improvement technology. However, for the purposes of assessing the costs of the model year 2 012-2016 standards the use of the product plans presents a difficulty, namely, how to assess the increased costs of the proposed future standards if the companies have already anticipated the future standards and the costs are therefore now part of the agencies’ baseline. This is a real concern with the most recent product plans received from the companies, and is one of th e reasons the agencies have decided not to use the recent product plans to define the baseline market data for assessing our proposed standards. The approach used for this proposal doe s not raise this concern, as the underlying data comes from model year 2008 production. 7

In addition, by developing a baseline fleet from co mmon sources, the agencies have been able to avoid some errors—perhaps related to interp retation of requests—that have been observed in past responses to NHTSA’s requests. Fo r example, while reviewing information submitted to support the most recent CAFE rulemakin g, NHTSA staff discovered that one manufacturer had misinterpreted instructions regard ing the specification of vehicle track width, leading to important errors in estimates of vehicle footprints. Although the manufacturer resubmitted the information with corrections, with this approach, the agencies are able to reduce the potential for such errors and inconsistencies b y utilizing common data sources and procedures.

An additional advantage of the approach used for th is proposal is a consistent projection of the change in fuel economy and CO 2 emissions across the various vehicles from the application of new technology. In the past, compan y product plans would include the application of new fuel economy improvement technology for a ne w or improved vehicle model with the resultant estimate from the company of the fuel eco nomy levels for the vehicle. However, companies did not always provide to NHTSA the detai led analysis which showed how they forecasted what the fuel economy performance of the new vehicle was – that is, whether it came from actual test data, from vehicle simulation mode ling, from best engineering judgment or some other methodology. Thus, it was not possible for N HTSA to review the methodology used by the manufacturer, nor was it possible to review wha t approach the different manufacturers utilized from a consistency perspective. With the approach used for this proposal, the baseline

market data comes from actual vehicles which have a ctual fuel economy test data – 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 actual measured data.

7 However, as discussed below, an alternative approa ch that NHTSA is exploring would be to use only manufacturers’ near-term product plans, e.g., from MY 2010 or MY 2011. NHTSA believes manufacturers’ nearterm plans should be less subject to this concern a bout missing costs and benefits already included in the baseline. NHTSA is also hopeful that in connection with the a gencies’ rulemaking efforts, manufacturers will be willing to make their near-term plans available to the public.10

Another advantage of today’s approach is that futur e market shares are based on a forecast of what will occur in the future, rather t han a static value. In the past, NHTSA has utilized a constant market share for each model yea r, based on the most recent year available, for example from the CAFE compliance data, that is, a f orecast of the 2011-2015 time frame where company market shares do not change. In the approa ch used today, we have utilized the

forecasts from CSM of how future market shares amon g the companies may change over time. 8

The approach the agencies have taken in developing today’s market forecast does, however, have some disadvantages. Most importantly , it produces a market forecast that does not represent some important changes likely to occu r in the future.

Some of the changes not captured by today’s approac h are specific. For example, the agencies’ current market forecast includes some veh icles for which manufacturers have announced plans for elimination or drastic producti on cuts such as the Chevrolet Trailblazer, the Chrysler PT Cruiser, the Chrysler Pacifica, the Dod ge Magnum, the Ford Crown Victoria, the Hummer H2, the Mercury Sable, the Pontiac Grand Pri x, and the Pontiac G5. These vehicle models appear explicitly in market inputs to NHTSA’ s analysis, and are among those vehicle models included in the aggregated vehicle types app earing in market inputs to EPA’s analysis.

Conversely, the agencies’ market forecast does not include some forthcoming vehicle models, such as the Chevrolet Volt, the Chevrolet C amaro, the Ford Fiesta and several publicly announced electric vehicles, including the announce ments from Nissan. 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 vehicle def initions was Model Year 2008. Additionally, the market forecast does not account for publicly a nnounced technology introductions, such as Ford’s EcoBoost system, whose product plans specify which vehicles and how many are planned to have this technology. Were the agencies to rely on manufacturers’ product plans (that were submitted), the market forecast would account for n ot only these specific examples, but also for similar examples that have not yet been announced p ublicly.

The agencies anticipate that including vehicles aft er MY 2008 would not significantly impact our estimates of the technology required to comply with the proposed standards. If they were included, these vehicles could make the standa rds appear to cost less relative to the reference case. First, the projections of sales by vehicle segment and manufacturer include these expected new vehicle models. Thus, to the extent t hat these new vehicles are expected to change consumer demand, they should be reflected in our re ference case. While we are projecting the

characteristics of the new vehicles with MY 2008 ve hicles, the primary difference between the

new vehicles and 2008 vehicles in the same vehicle segment is the use of additional CO 2 - reducing and fuel-saving technology. Both the NHTS A and EPA models add such technology to facilitate compliance with the proposed standards. Thus, our future projections of the vehicle

8 We note that market share forecasts like CSM’s cou ld, of course, be applied to any data used to creat e the baseline market forecast. If, as mentioned above, manufactu rers do consent to make public MY 2010 or 2011 prod uct plan data for the final rule, the agencies could conside r applying market share forecast to that data as we ll.11

fleet generally shift vehicle designs towards those of these newer vehicles. The advantage of our approach is that it helps clarify the costs of this proposal, as the cost of all fuel economy improvements beyond those required by the MY 2011 C AFE standards are being assigned to the proposal. In some cases, the new vehicles being in troduced by manufacturers are actually in response to their anticipation of this rulemaking. Our approach prevents some of these technological improvements and their associated cos t from being assumed in the baseline. Thus, the added technology will not be considered to be f ree for the purposes of this rule.

We note that, as a result of these issues, the mark et file may show sales volumes for certain vehicles during MYs 2012-2016 even though t hey will be discontinued before that time frame. Although the agencies recognize that these specific vehicles will be discontinued, we

continue to include them in the market forecast bec ause they are useful for representing successor vehicles that may appear in the rulemakin g time frame to replace the discontinued vehicles in that market segment.

Other market changes not captured by today’s approa ch are broader. For example, Chrysler Group LLC has announced plans to offer sma ll- and medium-sized cars using Fiat powertrains. The product plan submitted by Chrysle r includes vehicles that appear to reflect these plans. However, none of these specific vehic le models are included in the market forecast the agencies have developed starting with MY 2008 C AFE compliance data. The product plan submitted by Chrysler is also more optimistic with regard to Chrysler’s market share during MYs 2012-2016 than the market forecast projected by CSM and used by the agencies for this proposal. Similarly, the agencies’ market forecast does not reflect Nissan’s plans regarding

electric vehicles.

Additionally, some technical information that manu facturers have provided in product plans regarding specific vehicle models is, at leas t insofar as NHTSA and EPA have been able to determine, not available from public or commercial sources. While such gaps do not bear significantly on the agencies’ analysis, the divers ity of pickup configurations necessitated utilizing a sales-weighted average footprint value 9 for many manufacturers’ pickups. Since our modeling only utilizes footprint in order to estima te each manufacturer’s CO 2 or fuel economy

standard and all the other vehicle characteristics are available for each pickup configuration, this approximation has no practical impact on the projec ted technology or cost associated with compliance with the various standards evaluated. T he only impact which could arise would be if

9 A full-size pickup might be offered with various c ombinations of cab style (e.g., regular, extended, crew) and box length (e.g

. , 51⁄2’, 61⁄2’, 8’) and, therefore, multiple footprint sizes. CAFE compliance data for MY 2008 data does not contain footprint information, and does not con tain information that can be used to reliably ident ify which pickup entries correspond to footprint values estim able from public or commercial sources. Therefore, the agencies have used the known production levels of average va lues to represent all variants of a given pickup li ne (e.g., all variants of the F-150 and the Sierra/Silverado) in order to calculate the sales-weighted average footp rint value for each pickup family. Again, this has no impact on t he results of our modeling effort, although it woul d require reestimation if we were to examine light truck standa rds of a different shape. In the extreme, one sing le footprint value could be used for every vehicle sold by a sin gle manufacturer as long as the fuel economy standa rd associated with this footprint value represented the sales-wei ghted, harmonic average of the fuel economy standar ds associated with each vehicle’s footprint values..12

the relative sales of the various pickup configurat ions changed, or if the agencies were to explore standards with a different shape. This would neces sitate recalculating the average footprint value in order to maintain accuracy.

The agencies have carefully considered these advant ages and disadvantages of using a market forecast derived from public and commercial sources rather than from manufacturers’ product plans, and we believe that the advantages o utweigh the disadvantages for the purpose of proposing standards for model years 2012-2016. NHT SA’s inability to release confidential market inputs and corresponding detailed outputs fr om the CAFE model has raised serious concerns among many observers regarding the transpa rency of NHTSA’s analysis, as well as related concerns that the lack of transparency migh t enable manufacturers to provide unrealistic information to try to influence NHTSA’s determinati on of the maximum feasible standards. Although NHTSA does not agree with some observers’ assertions that some manufacturers have deliberately provided inaccurate or otherwise misle ading information, today’s market forecast is fully open and transparent, and is therefore not su bject to such concerns.

With respect to the disadvantages, the agencies are hopeful that manufacturers will, in the future, agree to make public their plans regarding model years that are very near, such as MY 2010 or perhaps MY 2011, so that this information c an be considered for purposes of the final rule analysis and be available for the public. In any event, because NHTSA and EPA are releasing market inputs used in the agencies’ respe ctive analyses, manufacturers, suppliers, and other automobile industry observers and participant can submit comments on how these inputs should be improved, as can all other reviewers.

4. How Does Manufacturer Product Plan Data Factor i nto the Baseline Used in this Proposal?

In the spring of 2009, many manufacturers submitted product plans in response to NHTSA’s request that they do so. 10 NHTSA and EPA both have access to these plans, an d both agencies have reviewed them in detail. A small amo unt of product plan data was used in the development of the baseline. The specific pieces o f data are:

• Wheelbase

• Track Width Front

• Track Width Rear

• EPS (Electric Power Steering)

• ROLL (Reduced Rolling Resistance)

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

• IACC (Improved Electrical Accessories)

• Curb Weight

• GVWR (Gross Vehicle Weight Rating)

10 74 FR 9185 (Mar. 3, 2009)13

The track widths, wheelbase, curb weight, and GVWR could have been looked up on the internet (159 were), but were taken from the produc t plans when available for convenience. To ensure accuracy, a sample from each product plan wa s used as a check against the numbers

available from Motortrend.com. These numbers will be published in the baseline file since they can be easily looked up on the internet. On the ot her hand, EPS, ROLL, LUB, and IACC are difficult to determine without using manufacturer’s product plans. These items will not be published in the baseline file, but the data has be en aggregated into the EPA baseline in the technology effectiveness and cost effectiveness for each vehicle in a way that allows the baseline for the model to be published without revealing the manufacturers data.

In addition to performing analysis using the baseli ne common to both agencies, NHTSA has conducted a separate analysis that does make us e of these product plans. The analysis is

documented in the Prelimina ry Regulatory Impact Analysis (PRIA) accompanying N HTSA’s Notice of Proposed Rulemaking. However, NHTSA perf ormed this separate analysis for purposes of comparison only. NHTSA used the public ly available baseline for all analysis related to the developme nt and evaluation of the proposed new CAFE standard s.

Considering both the publicly-available baseline us ed in this proposal and the product plans provided recently by manufacturers, however, it is possible that the latter could potentially be used to develop a more realistic forecast of pro duct mix and vehicles characteristics of the near-future light-duty fleet. At the core of conce rns about using company product plans are two concerns about doing so: (a) uncertainty and possib le inaccuracy in manufacturers’ forecasts and (b) the transparency of using product plan data. W ith respect to the first concern, the agencies note that manufacturers’ near-term forecasts (i.e

. , for model years two or three years into the future) should be less uncertain and more amenable to eventual retrospective analysis (i.e

. , comparison to actual sales) than manufacturers’ lon ger-term forecasts (i.e

. , for model years more than five years into the future). With respect to the second concern, NHTSA has consulted with most manufacturers and believes that although few, if any, manufacturers would be willing to make public their longer-term plans, many respondin g manufacturers may be willing to make public their short-term plans. In a companion noti ce, NHTSA is seeking product plan

information from manufacturers for MYs 2008 to 2020, [fill in]

, and the agencies will also continue to consult with manufacturers regarding th e possibility of releasing plans for MY 2010 and/or MY 2011 for purposes of developing and analy zing the final GHG and CAFE standards for MYs 2012-2016. The agencies are hopeful that m anufacturers will agree to do so, and that NHTSA and EPA would therefore be able to use produc t plans in ways that might aid in increasing the accuracy of the baseline market fore cast.

C. Development of Attribute-Based Curve Shapes

NHTSA and EPA are setting attribute-based CAFE and CO 2 standards that are defined by a mathematical function for MYs 2012-2016 passenger cars and light trucks. EPCA, as amended by EISA, expressly requires that CAFE standards for passenger cars and light trucks be based on

one or more vehicle attributes related to fuel econ omy, and be expressed in the form of a14

mathematical function. 11 The CAA has no such requirement, though in past r ules, EPA has

relied on both universal and attribute-based standa rds (e.g., for nonroad engines, EPA uses the attribute of horsepower). However, given the advan tages of using attribute-based standards and given the goal of coordinating and harmonizing CO 2 standards promulgated under the CAA and CAFE standards promulgated under EPCA, as expressed in the joint NOI, EPA is also proposing

to issue standards that are attribute-based and def ined by mathematical functions.

Under an attribute-based standard, every vehicle mo del has a performance target (fuel economy and GHG emissions for CAFE and GHG emission s standards, respectively), the level of which depends on the vehicle’s attribute (for th is proposal, footprint). The manufacturers’ fleet average performance is determined by the prod uction-weighed 12 average (for CAFE, harmonic average) of those targets. NHTSA and EPA are proposing CAFE and CO 2 emissions standards defined by constrained linear functions a nd, equivalently, piecewise linear functions. 13
As a possible option for future rulemakings, the co nstrained linear form was introduced by NHTSA in the 2007 NPRM proposing CAFE standards for MY 2011-2015. Described mathematically, the proposed constrained linear fun ction is defined according to the following

formula: 14

1

1 1

, , TARGET

MIN MAX c FOOTPRINT d a b =  

 

× +        

where

TARGET

= the fuel economy target (in mpg) applicable to v ehicles 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 footprin t of 0 square feet, and

the MIN and MAX functions take the minimum and maximum, respective ly, 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-mile basis, not on a miles-per-gallon basis, it is plotted as fuel consumption below. Graphical ly, the constrained linear form appears as shown in Figure II.C.1-1.

11 49 U.S.C. 32902(a)(3)(A).

12 Production for sale in the United States.

13 The equations are equivalent but are specified dif ferently due to differences in the agencies’ respec tive models.

14 This function is linear in fuel consumption but no t in fuel economy.150.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.05035 40 45 50 55 60 65 70Footprint (sf)

Fuel Consumption (gpm)1/LIMITUPPER1/LIMITLOWERINTERCEPTSLOPEFigure II.C.1-1 The Shape of the Constrained LinearForm16

The specific form and stringency for each fleet (pa ssenger car and light trucks) and model year are defined through specific values for the fo ur coefficients shown above.

EPA is proposing the equivalent equation below for assigning CO 2 targets to an individual vehicle’s footprint value. Although the general model of the equation is the same for each vehicle category and each year, the parameters of the equation differ for cars and trucks. Each parameter also changes on an annual basis, res ulting in the yearly increases in stringency seen in the tables above. Described mathematically , EPA’s proposed piecewise linear function is

as follows:

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

In the constrained linear form applied by NHTSA, th is equation takes the simplified form:

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

where

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

Graphically, piecewise linear form, like the const rained linear form, appears as shown in Figure II.C.1-2.1730 35 40 45 50 55 60 65 70Footprint (sq feet)

CO2 (gram/mile) or Fuel Consumption (gallons/mile)lower limit = lIntercept = dupperlimit = hslope = cmin = amax = bFigure II.C.1-2 The Shape of the Piecewise Linear Form18

As for the constrained linear form, the specific fo rm and stringency for each fleet (passenger car and light trucks) and model year are defined through specific values for the four

coefficients shown above.

For purposes of this rule, NHTSA and EPA developed the basic curve shapes using methods similar to those applied by NHTSA in fittin g the curves defining the MY 2011 standards. The first step is defining the referenc e market inputs (in the form used by NHTSA’s CAFE model) described in Section II.B of this pream ble and in Chapter 1 of the joint TSD. However, because the baseline fleet is technologica lly heterogeneous, NHTSA used the CAFE model to develop a fleet to which nearly all the te chnologies discussed in Chapter 3 of the joint

TSD 15 were applied, by taking the following steps: (1) treating all manufacturers as unwilling to pay civil penalties rather than applying technology , (2) applying any technology at any time, irrespective of scheduled vehicle redesigns or fres hening, 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 increa se technological parity among vehicle models, thereby providing a better basis (than the baseline or reference fleets) for estimating the statistical relationship between vehicle size and f uel economy.

In fitting the curves, NHTSA also continued to appl y 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 value s that would be unfairly burdensome for a

manufacturer that elects to focus on the market for small vehicles; depending on the underlying data, an unconstrained form could apply to the smal lest vehicles targets that are simply unachievable. Limiting the function’s value for th e smallest vehicles ensures that the function remains technologically achievable at small footpri nts, and that it does not unduly burden manufacturers focusing on small vehicles. On the o ther side of the function, without a limit at the largest footprints, the function may provide no floor on required fuel economy. Also, the safety considerations that support the provision of a disincentive for downsizing as a compliance strategy apply weakly—if at all—to the very largest vehicles. Limiting the function’s value for the largest vehicles leads to a function with an in herent absolute minimum level of performance, while remaining consistent with safety consideratio ns.

Before fitting the sloped portion of the constraine d linear form, NHTSA selected footprints above and below which to apply constrain ts (i.e., minimum and maximum values) on the function. For passenger cars, the agency noted that several manufacturers offer small and, in some cases, sporty coupes below 41 square feet, exa mples including the BMW Z4 and Mini, Saturn Sky, Honda Fit and S2000, Hyundai Tiburon, M azda MX-5 Miata, Suzuki SX4, Toyota Yaris, and Volkswagen New Beetle. Because such veh icles represent a small portion (less than 10 percent) of the passenger car market, yet often have characteristics that could make it

15 The agencies excluded diesel engines and strong hy brid vehicle technologies from this exercise (and o nly this exercise) because the agencies expect that manufact urers would not need to rely heavily on these techn ologies in order to comply with the proposed standards. NHTSA and EPA did include diesel engines and strong hybr id vehicle technologies in all other portions of their analyse s.19

infeasible to achieve the very challenging targets that could apply in the absence of a constraint, NHTSA is proposing to “cut off” the linear portion of the passenger car function at 41 square feet. For consistency, the agency is proposing to do the same for the light truck function, although no light trucks are currently offered belo w 41 square feet. The agency further noted that above 56 square feet, the only passenger car m odel present in the MY 2008 fleet were four luxury vehicles with extremely low sales volumes—th e Bentley Arnage and three versions of the Rolls Royce Phantom. NHTSA is therefore proposing to “cut off” the linear portion of the passenger car function at 56 square feet. Finally, the agency 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 th e largest pickups (e.g., the Ford F-150, GM Sierra/Silverado, Nissan Titan, and Toyota Tundra) appear to fall just above 66 square feet in footprint. NHTSA is therefore proposing to “cut of f” the linear portion of the light truck function at 66 square feet.

NHTSA and EPA seek comment on this approach to fitt ing the curves. We note that final decisions on this issue will play an importan t role in determining the form and stringency of

the final CAFE and CO 2 standards, the incentives those standards will pro vide (e.g., with respect to downsizing small vehicles), and the relative com pliance burden faced by each manufacturer.

For purposes of the CAFE and CO 2 standards proposed in this NPRM, NHTSA and EPA recognize that there is some possibility that low f uel prices during the years in which MY 2012- 2016 vehicles are in service might lead to less tha n currently anticipated fuel savings and emissions reductions. One way to assure that emiss ion reductions are achieved in fact is through the use of explicit backstops, fleet average standa rds established at an absolute level. For purposes of the CAFE program, EISA requires a backs top for domestically-manufactured passenger cars—a universal minimum, non-attribute-b ased standard of either “27.5 mpg or 92 percent of the average fuel economy projected by th e Secretary of Transportation for the combined domestic and non-domestic passenger automo bile fleets manufactured for sale in the United States by all manufacturers in the model yea r...,” whichever is greater. 16 In the MY 2011 final rule, the first rule setting standards since EISA added the backstop provision to EPCA, NHTSA considered whether the statute permitted the agency to set backstop standards for the other regulated fleets of imported passenger cars a nd light trucks. Although commenters expressed support both for and against a more permi ssive reading of EISA, NHTSA concluded in that rulemaking that its authority was likely li mited to setting only the backstop standard that Congress expressly provided, i.e

. , the one for domestic passenger cars. A backstop, however, could be adopted under section 202(a) of the CAA as suming it could be justified under the relevant statutory criteria. EPA and NHTSA also no te that the flattened portion of the car curve directionally addresses the issue of a backstop (i. e

. a flat curve is itself a backstop). The agencies seek comment on whether backstop standards, or any other method within the agencies’ statutory authority, should and can be implemented in order t o guarantee a level of CO 2 emissions reductions and fuel savings under the attribute-bas ed standards.

16 49 U.S.C. 32902(b)(4).20

Having developed a set of baseline data to which to fit the mathematical fuel consumption function, the initial values for parame ters c and d were determined for cars and trucks separately. c and d were initially set at the values for which the ave rage (equivalently, sum) of the absolute values of the differences was minimized between the “maximum technology” fleet fuel consumption (within the foot prints between the upper and lower limits) and the straight line 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 t he corresponding straight line.

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

The result of this methodology is shown below in Fi gures II.A.2-2 and II.A.2-3 for passenger cars and light trucks, respectively. The fitted 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 fun ction was 14 percent. For trucks, the corresponding MAD was 10 percent.210.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.0550.06035 40 45 50 55 60 65 70 75 80Footprint (sf)

Fuel Consumption (gpm)Figure II.C.1-3 “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function220.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.0550.06035 40 45 50 55 60 65 70 75 80Footprint (sf)

Fuel Consumption (gpm)Figure II.C.1-4 “Maximum Technology” Light Truck with Fitted Constrained Linear Function23

The agencies used these functional forms as a star ting 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 downward) to produce the relative car and light truck standards described in the next section.

D. Relative Car-Truck Stringency

The agencies have determined, under their respectiv e statutory authorities, that it is appropriate to propose fleetwide standards with the projected levels of stringency of 34.1 mpg or 250 g/mi (as well as the corresponding intermediate year fleetwide standards) for NHTSA and EPA respectively. To determine the relative string ency of passenger car and light truck standards, the agencies are concerned that increasi ng the difference between the car and truck standards (either by raising the car standards or l owering the truck standards) could encourage manufacturers to build fewer cars and more trucks, likely to the detriment of fuel economy and

CO 2 reductions. 17 In order to maintain consistent car/truck standar ds, the agencies applied a constant ratio between the estimated average requir ed performance under the passenger car and light truck standards, in order to maintain a stabl e set of incentives regarding vehicle

classification.

To calculate relative car-truck stringency in this proposal, the agencies explored a number of possible alternatives. In the interest o f harmonization, the agencies agree to use the Volpe model in order to estimate stringencies at wh ich net benefits would be maximized

. Further details of the development of this scenario approach can be found in Section IV of this preamble as well as in NHTSA’s PRIA and DEIS. NHTS A examined passenger car and light truck standards that would produce the proposed com bined average fuel economy levels from Table I.B.2-2 above. NHTSA did so by shifting down ward the curves that maximize net benefits, holding the relative stringency of passen ger car and light truck standards constant at the level determined by maximizing net benefits, such t hat the average fuel economy required of passenger cars remains 34 percent higher than the a verage fuel economy required of light trucks. This methodology resulted in the average fuel econo my levels for passenger cars and light trucks during MYs 2012-2016 as shown in Table I.D.2-1. Th e following chart illustrates this methodology of shifting the standards from the leve ls maximizing net benefits to the levels consistent with the combined fuel economy standards in this rule.

17 For example, since many 2WD SUVs are classified as passenger cars, manufacturers have already warned that high car standards relative to truck standards coul d create an incentive for them to drop the 2WD vers ion and sell only the 4WD version.242025303540452011 2012 2013 2014 2015 2016Model Year

Ave. Required FE (mpg)Combined (National Policy)Cars (Maximizing Net Benefits)Trucks (Maximizing Net Benefits)Combined (Maximizing Net Benefits)Cars (Pending)Trucks (Pending)Combined (Pending)Light TrucksPassenger CarsCombined Ave.Figure II.D.1-1 Shifting the Standards from the Maximizing Net Benefit Levels to the Levels Consistentwith theCombined Fuel Economy Standards in this Rule25

After this analysis was completed, EPA examined two alternative approaches to determine whether they would lead to significantly different outcomes. First, EPA analyzed the relative stringencies using a 10-year payback analy sis (with the OMEGA model). This analysis sets the relative stringencies if increased technol ogy cost is to be paid back out of fuel savings over a 10-year period (assuming a 3% discount rate) . Second, EPA also conducted a technology maximized analysis, which sets the relative stringe ncies if all technologies (with the exception of strong hybrids and diesels) are assumed to be utili zed in the fleet. (This is the same methodology that was used to determine the curve shape as expla ined in the section above and in Chapter 2 of the joint TSD section). Compared to NHTSA’s approa ch based on stringencies estimated to maximize net benefits, EPA staff found that these t wo other approaches produced very similar results to NHTSA’s, i.e

. , similar ratios of car-truck relative stringency ( the ratio being within a range of 1.34 to 1.37 relative stringency of the ca r to the truck fuel economy standard). EPA believes that this similarity supports the proposed relative stringency of the two standards.

The car and truck standards for EPA (Table I.D. 2-4 above) were subsequently determined by first converting the average required fuel economy levels to average required CO 2 emission rates, and then applying the expected air conditioning credits for 2012-2016. These A/C credits are shown in the following table. Fur ther details of the derivation of these factors can be found in Section III of this preamble or in the EPA RIA.

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

Average Technology

Penetration Average

credit

for cars Average

credit for

trucks Average

credit for

combined

fleet

2012 25% 3.0 3.4 3.1

2013 40% 4.8 5.4 5.0

2014 55% 7.2 8.1 7.5

2015 75% 9.6 10.8 10.0

2016 85% 10.2 11.5 10.6

The agencies seek comment on the use of this method ology for apportioning the fleet stringencies to relative car and truck standards fo r 2012-2016.

E. Joint Vehicle Technology Assumptions

Vehicle technology assumptions, i.e

. , assumptions about their cost, effectiveness, and the rate at which they can be incorporated into new veh icles, are often very controversial as they have a significant impact on the levels of the stan dards. Agencies must, therefore, take great care in developing and justifying these assumptions . In developing technology inputs for MY 2012-2016 standards, the agencies reviewed the tech nology assumptions that NHTSA used in setting the MY 2011 standards and the comments that NHTSA received in response to its May 2008 Notice of Proposed Rulemaking. This review is consistent with the request by President Obama in his January 26 memorandum to DOT. In addi tion, the agencies reviewed the technology input estimates identified in EPA’s July 2008 Advanced Notice of Proposed26

Rulemaking. The review of these documents was supp lemented with updated information from more current literature, new product plans and from EPA certification testing.

As a general matter, the best way to derive technol ogy cost estimates is to conduct realworld tear down studies. These studies break down each technology into its respective components, evaluate the costs of each component, a nd build up the costs of the entire technology based on the contribution of each compon ent. As such, tear down studies require a significant amount of time and are very costly. EP A has begun conducting tear down studies to assess the costs of 4-5 technologies under a contra ct with FEV. To date, only two technologies (stoichiometeric gasoline direct injection and turb o charging with engine downsizing for a 4 cylinder engine to a 4 cylinder engine) have been e valuated. The agencies relied on the findings of FEV for estimating the cost of these technologie s in this rulemaking—directly for the 4 cylinder engines, and extrapolated for the 6 and 8 cylinder engines. The agencies request

comment on the use of these estimated costs from th e FEV study. For the other technologies, because tear down studies were not yet available, t he agencies decided to pursue, to the extent possible, the Bill of Materials (BOM) approach as o utlined in NHTSA’s MY 2011 final rule. A similar approach was used by EPA in the EPA 2008 St aff Technical Report. This approach was recommended to NHTSA by Ricardo, an international e ngineering consulting firm retained by NHTSA to aid in the analysis of public comments on its proposed standards for MYs 2011-2015 because of its expertise in the area of fuel econom y technologies. A BOM approach is one element of the process used in tear down studies. The difference is that under a BOM approach, the build up of cost estimates is conducted based o n a review of cost and effectiveness estimates for each component from available literature, while under a tear down study, the cost estimates which go into the BOM come from the tear down study itself. To the extent that the agencies departed from the MY 2011 CAFE final rule estimates , the agencies explained the reasons and provided supporting analyses. As tear down studies are concluded by FEV during the rulemaking process, the agencies will make them ava ilable in the joint rulemaking docket of this rulemaking. The agencies will consider these studi es and any comments received on them, as practicable and appropriate, as well as any other n ew information pertinent to the rulemaking of which the agencies become aware, in developing tech nology cost assumptions for the final rule.

Similarly, the agencies followed a BOM approach for developing its effectiveness estimates, insofar as the BOM developed for the cos t estimates helped to inform the appropriate

effectiveness values derived from the literature re view. The agencies supplemented the

information with results from available simulation work and real world EPA certification testing.

The agencies would also like to note that per the E nergy Independence and Security Act (EISA), the National Academies of Sciences is condu cting an updated study to update Chapter 3 of the 2002 NAS Report, which outlines technology e stimates. The update will take a fresh look at that list of technologies and their associated c ost and effectiveness values.

The report is expected to be available on September 30, 2009. As soon as the update to the NAS Report is received, it will be placed in th e joint rulemaking docket for the public’s review and comment. Because this will occur during the comment period, the public is encouraged to check the docket regularly and provid e comments on the updated NAS Report by27

the closing of the comment period of this notice. NHTSA and EPA will consider the updated NAS Report and any comments received, as practicabl e and appropriate, on it when considering revisions to the technology cost and effectiveness estimates for the final rule. Consideration of this report is consistent with the request by Presi dent Obama in his January 26 memorandum to

DOT.

1. What Technologies do the Agencies Consider?

The agencies considered over 35 vehicle technologie s that manufacturers could use to improve the fuel economy and reduce CO 2 emissions of their vehicles during MYs 2012-2016. The majority of the technologies described in this section are readily available, well known, and could be incorporated into vehicles once production decisions are made. Other technologies considered may not currently be in production, but are beyond the research phase and under development, and are expected to be in production i n the next few years. These are technologies which can, for the most part, be applied both to ca rs and trucks, and which are capable of achieving significant improvements in fuel economy and reductions in CO 2 emissions, at reasonable costs. The agencies did not consider te chnologies in the research stage because the

leadtime available for this rule is not sufficient to move such technologies from research to production.

The technologies considered in the agencies’ analys is are briefly described below. They fall into five broad categories: engine technologi es, transmission technologies, vehicle technologies, electrification/accessory technologie s, and hybrid technologies. For a more detailed description of each technology and their c osts and effectiveness, we refer the reader to Chapter 3 of the joint TSD, Chapter III of NHTSA’s PRIA, and Chapter 1 of EPA’s DRIA. Technologies to reduce CO 2 and HFC emissions from air conditioning systems ar e discussed in Section III of this preamble and in EPA’s DRIA.

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

• Low-friction lubricants – low viscosity and advanced low friction lubrican ts oils are now available with improved performance and better lubrication. If manufacturers choose to make use of these lubricants, they would need to make engine changes and possibly conduct durability testing to accommodate the low-friction lubricants.

• Reduction of engine friction losses – can be achieved through low-tension piston rings, roller cam followers, improved material coat ings, more optimal thermal management, piston surface treatments, and other im provements in the design of engine components and subsystems that improve engin e operation.

• Conversion to dual overhead cam with dual cam phasi ng – as applied to overhead valves designed to increase the air flow with more than two valves per cylinder and reduce pumping losses.28 • Cylinder deactivation – deactivates the intake and exhaust valves and preve nts fuel injection into some cylinders during light-load ope ration. The engine runs temporarily as though it were a smaller engine whic h substantially reduces pumping

losses

• Variable valve timing – alters the timing of the intake valve, exhaust v alve, or both, primarily to reduce pumping losses, increase specif ic power, and control residual

gases.

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

• Continuous variable valve lift – is an electromechanically controlled system in w hich valve timing is changed as lift height is controlle d. This yields a wide range of performance optimization and volumetric efficiency, including enabling the engine to

be valve throttled.

• Stoichiometric gasoline direct-injection technology – injects fuel at high pressure directly into the combustion chamber to improve coo ling of the air/fuel charge within the cylinder, which allows for higher compression r atios and increased thermodynamic efficiency.

• Combustion restart

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

• Turbocharging and downsizing – increases the available airflow and specific pow er level, allowing a reduced engine size while maintai ning performance. This reduces pumping losses at lighter loads in comparison to a larger engine.

• Exhaust-gas recirculation boost – increases the exhaust-gas recirculation used in the combustion process to increase thermal efficiency a nd reduce pumping losses.

• Diesel engines – have several characteristics that give superior fuel efficiency, including reduced pumping losses due to lack of (or greatly reduced) throttling, and a combustion cycle that operates at a higher compress ion ratio, with a very lean air/fuel mixture, relative to an equivalent-performance gaso line engine. This technology requires additional enablers, such as NO

x trap catalyst after-treatment or selective catalytic reduction NO

x after-treatment. The cost and effectiveness estimates for the diesel engine and aftertreatment system utilized in this proposal have been revised from the NHTSA MY 2011 CAFE final rule, and the age ncies request comment on

these diesel cost estimates.

Types of transmission technologies considered inclu de:29 • Improved automatic transmission controls – optimizes shift schedule to maximize fuel efficiency under wide ranging conditions, and minimizes losses associated with torque converter slip through lock-up or modulation

.

• Six-, seven-, and eight-speed automatic transmissio ns – the gear ratio spacing and transmission ratio are optimized for a broader rang e of engine operating conditions.

• Dual clutch or automated shift manual transmissions – are similar to manual transmissions, but the vehicle controls shifting an d launch functions. A dual-clutch automated shift manual transmission uses separate c lutches for even-numbered and odd-numbered gears, so the next expected gear is pr e-selected, which allows for faster and smoother shifting.

• Continuously variable transmission – commonly uses V-shaped pulleys connected by a metal belt rather than gears to provide ratios fo r operation. Unlike manual and

automatic transmissions with fixed transmission rat ios, continuously variable transmissions can provide fully variable transmissi on ratios with an infinite number of gears, enabling finer optimization of transmissi on torque multiplication under different operating conditions so that the engine c an operate at higher efficiency.

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

Types of vehicle technologies considered include:

• Low-rolling-resistance tires – have characteristics that reduce frictional loss es associated with the energy dissipated in the deform ation of the tires under load, therefore improving fuel economy and reducing CO 2 emissions.

• Low-drag brakes – reduce the sliding friction of disc brake pads o n rotors when the brakes are not engaged because the brake pads are p ulled away from the rotors.

• Front or secondary axle disconnect for four-wheel d rive systems – provides a torque

distribution disconnect between front and rear axle s when torque is not required for the non-driving axle. This results in the reductio n of associated parasitic energy

losses.

• Aerodynamic drag reduction – is achieved by changing vehicle shape or reducin g frontal area, including skirts, air dams, underbody covers, and more aerodynamic side

view mirrors.

• Mass reduction and material substitution – Mass reduction encompasses a variety of techniques ranging from improved design and better component integration to application of lighter and higher-strength material s. Mass reduction is further compounded by reductions in engine power and ancill ary systems (transmission,30 steering, brakes, suspension, etc.). The agencies recognize there is a range of diversity and complexity for mass reduction and mat erial substitution technologies and there are many techniques that automotive suppl iers and manufacturers are using to achieve the levels of this technology that the a gencies have modeled in our analysis for this proposal. The agencies seek comments on t he methods, costs, and

effectiveness estimates associated with mass reduct ion and material substitution techniques that manufacturers intend to employ for reducing fuel consumption and

CO 2 emissions during the rulemaking time frame.

Types of electrification/accessory and hybrid techn ologies considered include:

• Electric power steering (EPS) – is an electrically-assisted steering system that has advantages over traditional hydraulic power steerin g because it replaces a continuously operated hydraulic pump, thereby reduc ing parasitic losses from the accessory drive.

• Improved accessories (IACC) – may include high efficiency alternators, electri cally driven (i.e., on-demand) water pumps and cooling fa ns. This excludes other electrical accessories such as electric oil pumps and electric ally driven air conditioner

compressors.

• Air Conditioner Systems – These technologies include improved hoses, connec tors and seals for leakage control. They also include i mproved compressors, expansion valves, heat exchangers and the control of these co mponents for the purposes of improving tailpipe CO 2 emissions as a result of A/C use. These technolo gies are covered separately in the EPA RIA.

• 12-volt micro-hybrid (MHEV) – also known as idle-stop or start stop and common ly implemented as a 12-volt belt-driven integrated sta rter-generator, this is the most basic hybrid system that facilitates idle-stop capa bility. Along with other enablers, this system replaces a common alternator with a bel t-driven enhanced power starteralternator, and a revised accessory drive system.

• Higher Voltage Stop-Start/Belt Integrated Starter G enerator (BISG) – provides idlestop capability and uses a high voltage battery wit h increased energy capacity over typical automotive batteries. The higher system vo ltage allows the use of a smaller, more powerful electric motor. This system replaces a standard alternator with an enhanced power, higher voltage, higher efficiency s tarter-alternator, that is belt driven and that can recover braking energy while the vehic le slows down (regenerative braking).

• Integrated Motor Assist (IMA)/Crank integrated star ter generator (CISG) – provides idle-stop capability and uses a high voltage batter y with increased energy capacity over typical automotive batteries. The higher syst em voltage allows the use of a smaller, more powerful electric motor and reduces t he weight of the wiring harness. This system replaces a standard alternator with an enhanced power, higher voltage, Formatted: Subscript31 higher efficiency starter-alternator that is cranks haft mounted and can recover braking energy while the vehicle slows down (regenerative b raking).

• 2-mode hybrid (2MHEV) – is a hybrid electric drive system that uses an a daptation of a conventional stepped-ratio automatic transmission by replacing some of the

transmission clutches with two electric motors that control the ratio of engine speed to vehicle speed, while clutches allow the motors t o be bypassed. This improves both the transmission torque capacity for heavy-duty app lications and reduces fuel consumption and CO2 emissions at highway speeds rel ative to other types of hybrid electric drive systems.

• Power-split hybrid (PSHEV)– a hybrid electric drive system that replaces the traditional transmission with a single planetary ge arset and a motor/generator. This motor/generator uses the engine to either charge th e battery or supply additional power to the drive motor. A second, more powerful motor/generator is permanently connected to the vehicle’s final drive and always t urns with the wheels. The planetary gear splits engine power between the firs t motor/generator and the drive motor to either charge the battery or supply power to the wheels.

• Plug-in hybrid electric vehicles (PHEV) – are hybrid electric vehicles with the means to charge their battery packs from an outside sourc e of electricity (usually the electric grid). These vehicles have larger battery packs wi th more energy storage and a greater capability to be discharged. They also use a control system that allows the battery pack to be substantially depleted under ele ctric-only or blended mechanical/electric operation.

• Electric vehicles (EV) – are vehicles with all-electric drive and with veh icle systems powered by energy-optimized batteries charged prima rily from grid electricity.

The cost estimates for the various hybrid systems h ave been revised from the estimates used in the MY 2011 CAFE final rule, in particular with respect to estimated battery costs. The agencies request comment on the hybrid cost estimat es detailed in the draft Joint Technical Support Document.

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

Building on NHTSA’s estimates developed for the MY 2011 CAFE final rule and EPA’s Advanced Notice of Proposed Rulemaking, which relie d on the 2008 Staff Technical Report, 18
the agencies took a fresh look at technology cost a nd effectiveness values for purposes of the joint proposal under the National Program. For cos ts, the agencies reconsidered both the direct or “piece” costs and indirect costs of individual c omponents of technologies. For the direct

18 EPA Staff Technical Report: Cost and Effectivenes s Estimates of Technologies Used to Reduce Light-Du ty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, March 2008.32

costs, the agencies followed a bill of materials (B OM) approach employed by NHTSA in

NHTSA’s MY 2011 final rule based on recommendation from Ricardo, Inc. EPA used a similar approach in the 2008 EPA Staff Technical Report. A bill of materials, in a general sense, is a list of components or sub-systems that make up a system— in this case, an item of fuel economyimproving technology. In order to determine what a system costs, one of the first steps is to determine its components and what they cost.

NHTSA and EPA estimated these components and their costs based on a number of sources for cost-related information. The objectiv e was to use those sources of information considered to be most credible for projecting the c osts of individual vehicle technologies. For example, while NHTSA and Ricardo engineers had reli ed considerably in the MY 2011 final rule on the 2008 Martec Report for costing contents of s ome technologies, upon further joint review and for purposes of the MY 2012-2016 standards, the agencies decided that some of the costing information in that report was no longer accurate d ue to downward trends in commodity prices since the publication of that report. The agencies reviewed, then revalidated or updated cost estimates for individual components based on new in formation. Thus, while NHTSA and EPA

found that much of the cost information used in NHT SA’s MY 2011 final rule and EPA’s staff report was consistent to a great extent, the agenci es, in reconsidering information from many

sources, 19,20,21,22,23,24,25 revised several component costs of several major t echnologies: turbocharging with engine downsizing, mild and stro ng hybrids, diesels, stoichiometric gasoline direct injection fuel systems, and valve train lift technologies. These are discussed at length in the joint TSD and in NHTSA’s PRIA.

For two technologies (stoichiometric gasoline direc t injection and turbocharging with engine downsizing), the agencies relied, to the ext ent possible, on the tear down data available and scaling methodologies used in EPA’s ongoing stu dy with FEV. This study consists of complete system tear-down to evaluate technologies down to the nuts and bolts to arrive at very

detailed estimates of the costs associated with man ufacturing them. 26 The confidential information provided by manufacturers as part of th eir product plan submissions to the agencies

19 National Research Council, “Effectiveness and Impa ct of Corporate Average Fuel Economy (CAFE) Standar ds,” National Academy Press, Washington, DC (2002) (the “2002 NAS Report”), available at http://www.nap.edu/openbook.php?isbn=0309076013 (la st accessed August 7, 2009)

20 Northeast States Center for a Clean Air Future (NE SCCAF), “Reducing Greenhouse Gas Emissions from Lig htDuty Motor Vehicles,” 2004 (the “2004 NESCCAF Repor t”), available at http://www.nesccaf.org/documents/rpt040923ghglightd uty.pdf (last accessed August 7, 2009)

21 [add citation for ISOR] “Staff Report: Initial Statement of Reasons for P roposed Rulemaking, Public Hearing to Consider Adoption of Regulations to Control Greenho use Gas Emissions from Motor Vehicles,” California Environmental Protection Agency, Air Resources Boar d, August 6, 2004.

22 Energy and Environmental Analysis, Inc., “Technolo gy to Improve the Fuel Economy of Light Duty Trucks to 2015,” 2006 (the “2006 EEA Report”), Docket EPA-HQ- OAR-2009-0472.

23 Martec, “Variable Costs of Fuel Economy Technologi es,” June 1, 2008, (the “2008 Martec Report”) avail able at

Docket No. NHTSA-2008-0089-0169.1

24 Vehicle fuel economy certification data

25 Confidential data submitted by manufacturers in re sponse to the March 2009 and other requests for pro duct plans

26 U.S. Environmental Protection Agen g cy, “Draft Report – Light-Duty Technology Cost Anal ysis Pilot Study,” Contract No. EP-C-07-069, Work Assignment 1-3, Sept ember 3, 2009. Reference report/draft report of FEVwork.33

or discussed in meetings between the agencies and t he manufacturers and suppliers served largely as a check on publicly-available data.

For the other technologies, considering all sources of information and using the BOM approach, the agencies worked together intensively during the summer of 2009 to determine component costs for each of the technologies and bu ild up the costs accordingly. Where estimates differ between sources, we have used engi neering judgment to arrive at what we

believe to be the best cost estimate available toda y, and explained the basis for that exercise of judgment.

Once costs were determined, they were adjusted to e nsure that they were all expressed in 2007 dollars using a ratio of GDP values for the as sociated calendar years, 27 and indirect costs were accounted for using the new approach developed by EPA and explained in Chapter 3 of the draft joint TSD, rather than using the traditional Retail Price Equivalent (RPE) multiplier approach. A report explaining how EPA developed th is approach can be found in the docket for

this notice. NHTSA and EPA also reconsidered how c osts should be adjusted by modifying or scaling content assumptions to account for differen ces across the range of vehicle sizes and functional requirements, and adjusted the associate d material cost impacts to account for the revised content, although some of these adjustments may be different for each agency due to the different vehicle subclasses used in their respecti ve models. In previous rulemakings, NHTSA has used the Producer Price Index (PPI) to adjust v ehicle technology costs to consistent price levels, since the PPI measures the effects of cost changes that are specific to the vehicle manufacturing industry. For purposes of this NPRM, NHTSA and EPA chose to use the GDP deflator, which accounts for the effect of economy- wide price inflation on technology cost estimates, in order to express those estimates in c omparable terms with forecasts of fuel prices and other economic values used in the analysis of c osts and benefits from the proposed standards. Because it is specific to the automotiv e sector, the PPI tends to be highly volatile from year to year, reflecting rapidly changing bala nces between supply and demand for specific components, rather than longer-term trends in the r eal cost of producing a broad range of powertrain components. NHTSA and EPA seek comment on whether the agencies should use a GDP deflator or a PPI inflator for purposes of deve loping technology cost estimates for the final

rule.

Regarding estimates for technology effectiveness, N HTSA and EPA also reexamined the

estimates from NHTSA’s MY 2011 final rule and EPA’s ANPRM and 2008 Staff Technical Report, which were largely consistent with NHTSA’s 2008 NPRM estimates. The agencies also reconsidered other sources such as the 2002 NAS Rep ort, the 2004 NESCCAF report, recent CAFE compliance data (by comparing similar vehicles with different technologies against each other in fuel economy testing, such as a Honda Civi c Hybrid versus a directly comparable Honda Civic conventional drive), and confidential manufac turer estimates of technology effectiveness. NHTSA and EPA engineers reviewed effectiveness info rmation from the multiple sources for each technology and ensured that such effectiveness estimates were based on technology

27 NHTSA examined the use of the CPI multiplier inste ad of GDP for adjusting these dollar values, but fo und the difference to be exceedingly small – only $0.14 ove r $100.34

hardware consistent with the BOM components used to estimate costs. Together, they compared the multiple estimates and assessed their validity, taking care to ensure that common BOM definitions and other vehicle attributes such as pe rformance, refinement, and drivability were taken into account. However, because the agencies’ respective models employ different numbers of vehicle subclasses and use different modeling te chniques to arrive at the standards, direct comparison of BOMs was somewhat more complicated. To address this and to confirm that the outputs from the different modeling techniques prod uced the same result, NHTSA and EPA developed mapping techniques, devising technology p ackages and mapping them to corresponding incremental technology estimates. Th is approach helped compare the outputs from the incremental modeling technique to those pr oduced by the technology packaging approach to ensure results that are consistent and could be translated into the respective models of the agencies.

In general, most effectiveness estimates used in bo th the MY 2011 final rule and the 2008 EPA staff report were determined to be accurate and were carried forward without significant change into this proposal. When NHTSA and EPA’s es timates for effectiveness diverged slightly due to differences in how agencies apply t echnologies to vehicles in their respective models, we report the ranges for the effectiveness values used in each model. While the agencies

believe that the ideal estimates for the final rule would be based on tear down studies or BOM approach and subjected to a transparent peer-review ed process, NHTSA and EPA are confident that the thorough review conducted, led to the best available conclusion regarding technology

costs and effectiveness estimates for the current r ulemaking and resulted in excellent consistency between the agencies’ respective analyses for devel oping the CAFE and CO 2 standards.

The agencies note that the effectiveness values est imated for the technologies considered in the modeling analyses may represent average valu es, and do not reflect the potentiallylimitless spectrum of possible values that could re sult from adding the technology to different vehicles. For example, while the agencies have est imated an effectiveness of 0.5 percent for low friction lubricants, each vehicle could have a uniq ue effectiveness estimate depending on the baseline vehicle’s oil viscosity rating. Similarly , the reduction in rolling resistance (and thus the improvement in fuel economy and the reduction in CO 2 emissions) due to the application of low rolling resistance tires depends not only on the un ique characteristics of the tires originally on the vehicle, but on the unique characteristics of the t ires being applied, characteristics which must be balanced between fuel efficiency, safety, and perfo rmance. Aerodynamic drag reduction is much the same—it can improve fuel economy and reduce CO 2 emissions, but it is also highly dependent on vehicle-specific functional objectives . For purposes of this NPRM, NHTSA and EPA believe that employing average values for techn ology effectiveness estimates, as adjusted depending on vehicle subclass, is an appropriate wa y of recognizing the potential variation in the specific benefits that individual manufacturers (an d individual vehicles) might obtain from adding a fuel-saving technology. However, the agen cies seek comment on whether additional levels of specificity beyond that already provided would improve the analysis for the final rule, and if so, how those levels of specificity should b e analyzed.

Chapter 3 of the draft Joint Technical Support Docu ment contains a detailed description of our assessment of vehicle technology cost and ef fectiveness estimates. The agencies note that35

the technology costs included in this NPRM take int o account only those associated with the initial build of the vehicle. The agencies seek co mment on the additional lifetime costs, if any, associated with the implementation of advanced tech nologies including warranty costs, and maintenance and replacement costs such as replaceme nt costs for low rolling resistance tires, low friction lubricants, and hybrid batteries

, and maintenance on diesel aftertreatment component s.

F. Joint Economic Assumptions

The agencies’ preliminary analysis of alternative C AFE and GHG standards for the model years covered by this proposed rulemaking rely on a range of forecast information, economic estimates, and input parameters. This sec tion briefly describes the agencies’ preliminary choices of specific parameter values. These proposed economic values play a significant role in determining the benefits of bot h CAFE and GHG standards.

In reviewing these variables and the agency’s estim ates of their values for purposes of this NPRM, NHTSA and EPA reconsidered previous comm ents that NHTSA had received and reviewed newly available literature. As a conseque nce, the agencies elected to revise some economic assumptions and parameter estimates, while retaining others. Some of the most important changes, which are discussed in greater d etail in the agencies’ respective sections below, as well as in Chapter 4 of the joint TSD and in Chapter VIII of NHTSA’s PRIA and Chapter 8 of EPA’s DRIA, include significant revisi ons to the markup factors for technology costs; reducing the rebound effect from 15 to 10 pe rcent; and revising the value of reducing CO 2 emissions based on recent interagency efforts to de velop estimates of this value for governmentwide use. The agencies seek comment on the economi c assumptions described below.

• Costs of fuel economy-improving technologies – These estimates are presented in summary form above and in more detail in the agenci es’ respective sections of this preamble, in Chapter 3 of the joint TSD, and in the agencies’ respective RIAs. The technology cost estimates used in this analysis 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 m odified by applying near-term indirect cost multipliers ranging from 1.11 to 1.64 to the e stimates of vehicle manufacturers’ direct costs for producing or acquiring each techno logy to improve fuel economy, depending on the complexity of the technology and t he time frame over which costs are

estimated.

• Potential opportunity costs of improved fuel econom y – This estimate addresses the possibility that achieving the fuel economy improve ments required by alternative CAFE or GHG standards would require manufacturers to com promise the performance, carrying capacity, safety, or comfort of their vehicle model s. If it did so, the resulting sacrifice in

the value of these attributes to consumers would re present an additional cost of achieving the required improvements, and thus of manufacturer s’ compliance with stricter standards. Currently the agencies assume that thes e vehicle attributes do not change, and include the cost of maintaining these attributes as part of the cost estimates for36

technologies. However, it is possible that the tec hnology cost estimates do not include adequate allowance for the necessary efforts by man ufacturers to maintain vehicle performance, carrying capacity, and utility while i mproving fuel economy and reducing GHG emissions. While, in principle, consumer vehic le demand models can measure these effects, the reliabili ty of measures from these models is not proven these models do not appear to be robust across specifications, sinc e authors derive a wide range of willingness-to-pay values for fuel economy from the se models, and there is not clear guidance from the literature on whether one specifi cation is clearly preferred over

another . Thus, the agencies seek comment on how to estima te explicitly the changes in vehicle buyers’ welfare from the combination of hig her prices for new vehicle models, increases in their fuel economy, and any accompanyi ng changes in vehicle attributes such as performance, passenger- and cargo-carrying capac ity, or other dimensions of utility.

• The on-road fuel economy “gap” – Actual fuel economy levels achieved by light-dut y vehicles in on-road driving fall somewhat short of their levels measured under the laboratory-like test conditions used by NHTSA and E PA to establish compliance with the proposed CAFE and GHG standards The agencies use a n on-road fuel economy gap for light-duty vehicles of 20 percent lower than publis hed fuel economy levels. For example, if the measured CAFE fuel economy value of a light truck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of th at vehicle is expected to be 16 mpg (20*.80). 28 NHTSA previously used this estimate in its MY 201 1 final rule, and the agencies confirmed it based on independent analysis for use in this NPRM

• Fuel prices and the value of saving fuel – Projected future fuel prices are a critical inpu t into the preliminary economic analysis of alternati ve standards, because they determine the value of fuel savings both to new vehicle buyer s and to society. The agencies relied on the most recent fuel price projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) for this analysis, the AEO 2009 (April 2009 release) Reference Case forecasts of in flation-adjusted (constant-dollar) retail gasoline and diesel fuel prices, which represent th e EIA’s most up-to-date estimate of the most likely course of future prices for petroleum p roducts. 29

• Consumer valuation of fuel economy and payback peri od – In estimating the value of fuel economy improvements that would result from alterna tive CAFE and GHG standards to potential vehicle buyers, the agencies assume that buyers value the resulting fuel savings over only part of the expected lifetime of the vehi cles they purchase. Specifically, we assume that buyers value fuel savings over the firs t five years of a new vehicle’s lifetime, and that buyers discount the value of these future fuel savings using rates of 3% and 7%.

28 U.S. Environmental Protection Agency, Final Techni cal Support Document, Fuel Economy Labeling of Moto r Vehicle Revisions to Improve Calculation of Fuel Ec onomy Estimates, EPA420-R-06-017, December 2006.

29 Energy Information Administration, Annual Energy O utlook 2009, Revised Updated Reference Case (April 2009), Table 12. Available at http://www.eia.doe.g ov/oiaf/servicerpt/stimulus/excel/aeostimtab_12.xls (last accessed July 26, 2009). EIA’s Updated Reference Case reflects the effects of the American Reinvestm ent and Recovery Act of 2009, as well as the most recent re visions to the U.S. and global economic outlook.37

The five-year figure represents the current average term of consumer loans to finance the purchase of new vehicles.

• Vehicle sales assumptions – The first step in estimating lifetime fuel consumpt ion by vehicles produced during a model year is to calcula te the number that are expected to be produced and sold. 30 The agencies relied on the AEO 2009 Reference Cas e for forecasts of total vehicle sales, while the baseline market f orecast developed by the agencies (see Section II.B) divided total projected sales into sa les of cars and light trucks.

• Vehicle survival assumptions -- We then applied updated values of age-specific surv ival rates for cars and light trucks to these adjusted f orecasts of passenger car and light truck

sales to determine the number of these vehicles rem aining in use during each year of their expected lifetimes.

• Total vehicle use – We then calculated the total number of miles tha t cars and light trucks produced in each model year will be driven during e ach year of their lifetimes using estimates of annual vehicle use by age tabulated fr om the Federal Highway Administration’s 2001 National Household Transporta tion Survey (NHTS), 31 adjusted to account for the effect on vehicle use of subsequent increases in fuel prices. In order to insure that the resulting mileage schedules imply r easonable estimates of future growth in

total car and light truck use, we calculated the rate of growth in annual car and light truck mileage at each age that is necessary for total ca r and light truck travel to increase at the

rates forecast in the AEO 2009 Reference Case. The growth rate in average annual car and light truck use produced by this calculation is approximately 1.1 percent per year. 32
This rate was applied to the mileage figures derive d from the 2001 NHTS to estimate annual mileage during each year of the expected lif etimes of MY 2012-2016 cars and light trucks. 33

• Accounting for the rebound effect of higher fuel ec onomy – The rebound effect refers to the fraction of fuel savings expected to result fro m an increase in vehicle fuel economy –

30 Vehicles are defined to be of age 1 during the cal endar year corresponding to the model year in which they are produced; thus for example, model year 2000 vehicle s are considered to be of age 1 during calendar yea r 2000, age 2 during calendar year 2001, and to reach their max imum age of 26 years during calendar year 2025. NHTSA

considers the maximum lifetime of vehicles to be th e age after which less than 2 percent of the vehicl es originally produced during a model year remain in service. Ap plying these conventions to vehicle registration da ta indicates that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 y ears

. See Lu, S., NHTSA, Regulatory Analysis and Evaluation D ivision, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). A vailable at http://www-nrd.nhtsa.dot.gov/Pubs/80995 2.pdf (last accessed July 27, 2009)

. 31 For a description of the Survey, see http://nhts.ornl.gov/quickStart.shtml (last acces sed July 27, 2009).

32 It was not possible to estimate separate growth ra tes in average annual use for cars and light trucks , because of the significant reclassification of light truck models as passenger cars discussed previously.

33 While the adjustment for future fuel prices reduces average mileage at each age from the values derive d from the 2001 NHTS, the adjustment for expected future growt h in average vehicle use increases it. The net eff ect of these two adjustments is to increase expected lifetime mi leage by about 18 percent for passenger cars and ab out 16 percent for light trucks.38

particularly an increase required by the adoption o f higher CAFE and GHG standards – that is offset by additional vehicle use. The incr ease in vehicle use occurs because higher fuel economy reduces the fuel cost of driving, typi cally the largest single component of the monetary cost of operating a vehicle, and vehic le owners respond to this reduction in operating costs by driving slightly more. For purp oses of this NPRM, the agencies have elected to use a 10 percent rebound effect in their analyses of fuel savings and other benefits from higher standards.

• Benefits from increased vehicle use – The increase in vehicle use from the rebound eff ect provides additional benefits to their owners, who m ay make more frequent trips or travel

farther to reach more desirable destinations. This additional travel provides benefits to drivers and their passengers by improving their acc ess to social and economic opportunities away from home. The benefits from in creased vehicle use include both the fuel expenses associated with this additional trave l, and the consumer surplus it provides. We estimate the economic value of the consumer surp lus provided by added driving using the conventional approximation, which is one half of the product of the decline in vehicle operating costs per vehicle-mile and the re sulting increase in the annual number of miles driven. Because it depends on the extent of improvement in fuel economy, the value of benefits from increased vehicle use change s by model year and varies among

alternative standards.

• The value of increased driving range – By reducing the frequency with which drivers typically refuel their vehicles, and by extending t he upper limit of the range they can travel before requiring refueling, improving fuel e conomy and reducing GHG emissions thus provides some additional benefits to their own ers. No direct estimates of the value of extended vehicle range are readily available, so the agencies’ analysis calculates the reduction in the annual number of required refuelin g cycles that results from improved fuel economy, and applies DOT-recommended values of travel time savings to convert the resulting time savings to their economic value. 34 The agencies invite comment on the assumptions used in this analysis. Please see the Chapter 4 of the draft Joint TSD for

details.

• See the Chapter 4 of the draft Joint TSD for details.

• Added costs from congestion, crashes and noise – Although it provides some benefits to drivers, increased vehicle use associated with the rebound effect also contributes to increased traffic congestion, motor vehicle acciden ts, and highway noise. Depending on

how the additional travel is distributed over the d ay and on where it takes place,

additional vehicle use can contribute to traffic co ngestion and delays by increasing traffic volumes on facilities that are already heavily trav eled during peak periods. These added delays impose higher costs on drivers and other veh icle occupants in the form of

34 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). Formatted: Bullets and Numbering39

increased travel time and operating expenses, incre ased costs associated with traffic accidents, and increased traffic noise. The agenci es rely on estimates of congestion, accident, and noise costs caused by automobiles and light trucks developed by the Federal Highway Administration to estimate the incr eased external costs caused by added driving due to the rebound effect. 35

• Petroleum consumption and import externalities – U.S. consumption and imports of petroleum products also impose costs on the domesti c economy that are not reflected in the market price for crude petroleum, or in the pri ces paid by consumers of petroleum products such as gasoline. In economics literature on this subject, these costs include (1) higher prices for petroleum products resulting from the effect of U.S. oil import demand on the world oil price (“monopsony costs”); (2) the risk of disruptions to the U.S. economy caused by sudden reductions in the supply o f imported oil to the U.S.; and (3) expenses for maintaining a U.S. military presence t o secure imported oil supplies from unstable regions, and for maintaining the strategic petroleum reserve (SPR) to cushion against resulting price increases. 36 Reducing U.S. imports of crude petroleum or refin ed fuels can reduce the magnitude of these external co sts. Any reduction in their total value that results from lower fuel consumption and petrol eum imports represents an economic benefit of setting more stringent standards over an d above the dollar value of fuel savings itself. The agencies do not include a value for mo nopsony costs in order to be consistent with their use of a global value for the social cos t of carbon. Based on a recently-updated ORNL study, we estimate that each gallon of fuel sa ved that results in a reduction in U.S. petroleum imports (either crude petroleum or refine d fuel) will reduce the expected costs of oil supply disruptions to the U.S. economy by $0 .169 (2007$). The agencies do not include savings in budgetary outlays to support U.S . military activities among the benefits of higher fuel economy and the resulting f uel savings. Each gallon of fuel saved as a consequence of higher standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel by 0.95 gallons. 37

• Air pollutant emissions

o Impacts on criteria air pollutant emissions – While reductions in domestic fuel refining and distribution that result from lower fu el consumption will reduce U.S.

35 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study ; http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed July 29, 2009).

36 See, e.g., Bohi, Douglas R. and W. David Montgomer y (1982). Oil Prices, Energy Security, and Import P olicy Washington, DC: Resources for the Future, Johns Hop kins 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, Pol icy," 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.

37 Each gallon of fuel saved is assumed to reduce imp orts of refined fuel by 0.5 gallons, and the volume of fuel refined domestically by 0.5 gallons. Domestic fuel refining is assumed to utilize 90% imported crude petroleum and 10% domestically-produced crude petroleum as feedst ocks. Together, these assumptions imply that each gallon of fuel saved will reduce imports of refined fuel and crude petroleum by 0.50 gallons + 0.50 gallons*90% = 0.50 gallons + 0.45 gallons = 0.95 gallons.40 emissions of criteria pollutants, additional vehicl e use associated with the rebound effect will increase emissions of these pollutants. Thus the net effect of stricter standards on emissions of each criteria pollutant d epends on the relative magnitudes of reduced emissions from fuel refining and distribution, and increases in emissions resulting from added vehicle use. Criteria air pollutants emitted by vehicles and during fuel production incl ude carbon monoxide (CO), hydrocarbon compounds (usually referred to as “vola tile organic compounds,” or VOC), nitrogen oxides (NO

x ), fine particulate matter (PM2.5), and sulfur oxid es (SO

x ). It is assumed that the emission rates (per mile ) stay constant for future year vehicles.

o EPA and NHTSA estimate the economic value of the hu man health benefits associated with reducing exposure to PM 2.5 using a “benefit-per-ton” method.

These PM 2.5 -related benefit-per-ton estimates provide the tota l monetized benefits to human health (the sum of reductions in premature mortality and premature morbidity) that result from eliminating one ton of directly emitted PM 2.5 , or one ton of a pollutant that contributes to secondarily- formed PM 2.5 (such as NOx, SOx, and VOCs), from a specified source. Chapter 4.2.9 of the Technical Support Document that accompanies this proposal inc ludes a description of these

values. Reductions in GHG emissions – Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the proces s of producing and distributing transportation fuels, as well as from fuel combustion itself. By reducing the volume of fuel consumed by passenger c ars and light trucks, higher standards will thus reduce GHG emissions generated by fuel use, as well as throughout the fuel supply cycle. The agencies est imated the increases of GHGs

other than CO 2 , including methane and nitrous oxide, from additio nal vehicle use by multiplying the increase in total miles driven b y cars and light trucks of each model year and age by emission rates per vehicle-mi le for these GHGs. These emission rates, which differ between cars and light trucks as well as between gasoline and diesel vehicles, were estimated by EPA using its recently-developed Motor Vehicle Emission Simulator (Draft MOVES 2009)

. 38 Increases in

emissions of non-CO 2 GHGs are converted to equivalent increases in CO 2 emissions using estimates of the Global Warming Pot ential (GWP) of methane

and nitrous oxide.

o Economic value of reductions in CO 2 emissions - EPA and NHTSA assigned a

dollar value to reductions in CO 2 emissions using the marginal dollar value (i.e., cost) of climate-related damages resulting from car bon emissions, also referred to as “social cost of carbon” (SCC). The SCC is inten ded to measure the monetary

38 The MOVES model assumes that the per-mile rates at which cars and light trucks emit these GHGs are determined by the efficiency of fuel combustion dur ing engine operation and chemical reactions that oc cur during catalytic after-treatment of engine exhaust, and ar e thus independent of vehicles’ fuel consumption ra tes. Thus MOVES’ emission factors for these GHGs, which are e xpressed per mile of vehicle travel, are assumed to be unaffected by changes in fuel economy.41 value society places on impacts resulting from incr eased GHGs, such as property damage from sea level rise, forced migration due to dry land loss, and mortality changes associated with vector-borne diseases. Publ ished estimates of the SCC vary widely as a result of uncertainties about futu re economic growth, climate sensitivity to GHG emissions, procedures used to mo del the economic impacts of climate change, and the choice of discount rates. EPA and NHTSA’s coordinated proposals present a set of interim SCC values reflecting a federal interagency group’s interpretation of the relevant climate economics literature. Sections III.H.6 and IV.C.3 provide more detail abo ut SCC.

• Discounting future benefits and costs —Discounting future fuel savings and other benefits

is intended to account for the reduction in their v alue to society when they are deferred until some future date, rather than received immedi ately. The discount rate expresses the percent decline in the value of these benefits—as v iewed from today’s perspective—for each year they are deferred into the future. In ev aluating the non-climate related benefits of the proposed standards, the agencies have employ ed discount rates of both 3 percent and 7 percent.

For the reader’s reference, Table II.F.1-1 below su mmarizes the values used to calculate the impacts of each proposed standard. The values presented in this table are summaries of the inputs used for the models; specific values used in the agencies’ respective analyses may be aggregated, expanded, or have other relevant adjust ments. See the respective RIAs for details. The agencies seek comment on the economic assumptio ns presented in the table and discussed

below.

In addition, the agencies have conducted a range of sensitivities and present them in their respective RIAs. For example, NHTSA has conducted a sensitivity analysis on several assumptions including (1) forecasts of future fuel prices, (2) the discou nt rate applied to future benefits and costs, (3) the magnitude of the reboun d effect, (4) the value to the U.S. economy of reducing carbon dioxide emissions, (5) the monopson y effect, and (6) the reduction in external economic costs resulting from lower U.S. oil import s. This information is provided in NHTSA’s PRIA. The agencies will consider additional sensit ivities for the final rule as appropriate, including sensitivities on the markup factors appli ed to direct manufacturing costs to account for indirect costs (i.e., the Indirect Cost Markups (IC Ms) which are discussed in Sections III and IV), and the learning curve estimates used in this analysis.

Table II.F.1-1 Economic Values for Benefits Computa tions (2007$)
Fuel Economy Rebound Effect 10% "Gap" between test and on-road MPG 20% Value of refueling time per ($ per vehicle-hour) $ 24.64
Annual growth in average vehicle use 1.1% Fuel Prices (2012-50 average, $/gallon) Retail gasoline price $3.77
Pre-tax gasoline price $3.40 Economic Benefits from Reducing Oil Imports ($/gall on) "Monopsony" Component $ 0.0042 Price Shock Component $ 0.17 Military Security Component $ 0.00 Total Economic Costs ($/gallon) $ 0.17 Emission Damage Costs (2020, $/ton or $/metric ton)

Carbon monoxide $ 0 Volatile organic compounds (VOC) $ 1,283 Nitrogen oxides (NOx) – vehicle use $ 5,116 Nitrogen oxides (NOx) – fuel production and distrib ution $ 5,339
Particulate matter (PM 2.5 ) – vehicle use $ 238,432
Particulate matter (PM 2.5 ) – fuel production and distribution $ 292,180
Sulfur dioxide (SO 2 ) $ 30,896

Carbon dioxide (CO 2 ) $ 5
$ 10 $ 20
$ 34 $ 56

Annual Increase in CO 2 Damage Cost 3%

External Costs from Additional Automobile Use ($/v ehicle-mile) Congestion $ 0.054

Accidents $ 0.023

Noise $ 0.001

Total External Costs $ 0.078 External Costs from Additional Light Truck Use ($/v ehicle-mile) Congestion $0.048

Accidents $0.026

Noise $0.001

Total External Costs $0.075
Discount Rates Applied to Future Benefits 3%, 7%