* Work on this project was performed as an independent consultant to The Energy Foundation. For identification purposes, the author is senior lect urer, School of Natural Resources and Environment, and a faculty fellow, Michigan Memorial Phoenix Energy Institute, Univ ersity of Michigan. Contact: DeCicco@umich.edu 

## A Fuel Efficiency Horizon for U.S. Automobiles

John M. DeCicco University of Michigan * September 2010

Report prepared for The Energy Foundationi 

### Table of Contents

E XECUTIVE S UMMARY iii Harnessing Real-World Progress iii Getting from Here to There v Costs and Savings Estimates vi Large Benefits; Cost and Consumer Interest Challenges vii

A CKNOWLEDGEMENTS viii

I NTRODUCTION 1 Auto Efficiency and GHG Emissions 1 Efficiency in the Context of Other Options 3 Overview of Report 4

P AST E FFICIENCY -R ELATED T RENDS 5 Steady Gains in Technical Efficiency 6 Performance's Efficiency Penalty 7

E FFICIENCY O PPORTUNITIES 8 Compatible as well as Technical Solutions 9 Toward Virtual Performance 10 The Challenge of Performance Expectations 10 Technologies for Efficiency 12 Tractive Load Reduction 12 Powertrain Efficiency 15

S YNTHESIS OF T ECHNOLOGY A SSESSMENTS 19 Incremental vs. Fundamental Analysis 20 Incremental Approach fo r Justifying Regulations 20 Fundamental Analysis for Future Horizons 21 Rates of Technology Change 24 New Fleet Scenarios 27

C OST OF E FFICIENCY I MPROVEMENT 30 Interpreting Cost Estimates 30 Cost Curves 32 Net Present Value Results 35 Automakers Invest, Consumers Save 37 Sensitivity Analysis 38

C ONCLUSIONS 41

A PPENDIX : C OST C URVE D ERIVATIONS 43 Adjusting the Cost of Weight Reduction 46 Cost Curve Shifts Through Time 47 Costs of Percentage Changes in Fleet Average Energy Intensity 48

R EFERENCES 49ii 

### List of Tables

1. Projected horizon for fuel e fficiency, fuel savings and CO 2 reductions for a U.S. light duty gasoline and diesel vehicle fleet v

2. Design strategies for automotive energy efficiency 9 3. PNGV concept car fuel economy and consumption results 23 4. Light duty fleet average energy intensity and related metrics: reference levels and future targets based on fuel efficiency horizon scenario 29

5. Cost and benefit results for fuel efficiency horizon scenario 36 6. Sensitivity analysis results 39



### List of Figures

1. New fleet efficiency horizon compared to history and recent regulations iv 2. Technology costs and fuel savings lead ing to tripled efficiency by 2035 vii 3. GHG emissions breakdown for an average U.S. light vehicle 1
4. Per-vehicle incremental costs and GHG reductions for automotive technologies in a 2035 time horizon, based on MIT (2008) 3

5. PSFI for new cars and light trucks, 1977-2005 6 6. Trends in engine specific power and vehicle power-to-weight ratio 15 7. Costs of energy savings implied by the MY2012-16 Joint Rule 20 8. Potential gasoline vehicle energy inte nsity reductions achievable by 2030 22 9. Notable historical rates of technolog y utilization with logistic fits 25 10. Hypothetical market adoption curve fo r hybrid drive compared to logistic curves based on historical auto motive technology adoption rates 26

11. Scenarios for new fleet energy intensity toward tripled efficiency target 28 12. Transaction prices and pr ice index for new vehicles in the United States 31 13. Average costs of light vehicle energy consumption reduction from quadratic approximations to results of recent studies 33

14. Cost curves for horizons of auto motive efficiency improvement 35 15. Acceleration performance and effect on the cost of consumption reduction 40 A-1. Cost increase vs. consumption decrease for selected studies 43 A-2. Costs and quadratic fits for cars and light trucks based on MIT study 44 A-3. Normalized plots of cost incr ease vs. consumption decrease 45iii 

## A Fuel Efficiency Horizon for U.S. Automobiles

E XECUTIVE 

### S UMMARY Improving automotive fuel efficiency is an important way to address tr ansportation oil demand and greenhouse gas (GHG, "carbon") emissions. In th e United States, new light duty vehicle fuel economy rose nearly 70% after the 1970s energy crisis but th en changed little over the 1985- 2005 period. Recently, rises in oil prices and concerns about cl imate disruption have led to stricter fuel economy and automotive GHG emissi ons standards. The single national program finalized in April 2010 targets a 38% increase in fuel economy, fo r a projected new fleet average of 34 miles per gallon (lab-test mpg) by 2016 or a 27% decrease in energy intensity (energy use per distance driven) and ta ilpipe carbon dioxide (CO 2 ) emissions compared to 2005.

Fuel efficiency is only one element of a comp lete transportation sector climate policy, which must also address travel demand and the net carbon balance of fuel supply. Nevertheless, in light of its important role, a key question is just howfar fuel efficiency can progress over the coming decades. Given a stock turnover period of roughly 15 years, the new fleet efficiency horizon for 2035 is relevant for helping meet mid-century GHG emissions targets. This study examines how far automobile efficiency can be taken if it is pur sued with determination, using an analysis that assumes success in technology and design strategi es that offer "revolution by evolution."

Many analyses highlight alternat ive technologies for re placing internal co mbustion engines and petroleum-based fuels, such as grid-based vehi cle electrification, biof uels, hydrogen or natural gas. Others point to radical ch anges in vehicle materials and de sign. Proponents of these options marshal technological optimism in scenarios that show how their alternat ives will revolutionize the automobile. While such options mature, it will be crucial to pursue evolutionary advances in existing systems, which can be greatly improved at cost, but at much less cost and with none of the other barriers faced by alternative technologies.

Harnessing Real-World Progress This study focuses the lens of technological op timism on evolutionary changes in automobiles that still rely on internal combustion engines a nd largely steel-based structures. Technologies considered include turbocharged gasoline direct-i njection engines, low-emission diesels, a range of hybrid drive options (but not"plugging in"), advances in li ghtweight steel and other massefficient material and design solutions. Assume d rates of progress are based on what has been observed historically in the auto market, where innovation is relentless ev en when not focused on fuel efficiency. The analysis assumes competitivel y driven advances in engineering, with cost reductions achieved through budget- constrained product developmen t and productivity gains as witnessed in all aspects of vehicle manufacturing.

Automotive engineering has always s een continuous progress; at issu e is the extent to which it is harnessed for fleetwide efficiency as opposed to other market outco mes. It is this factor -- the emphasis on reducing fuel consumption relative to different goals -- thatmore than any other will influence the direction of the fleet over the first half of the 21 st century. In short, improving auto efficiency is as much a matter of desi gn priority as it is on e of technology change.iv Many studies analyze the ways to improve vehi cles along energy-relate d dimensions including powertrain efficiency and load reduction. This report does not perform ne w detailed engineering analysis, but rather synthesizes published results under an assumption that efficiency gains from evolutionary technology changes are maximized on a fleetwide basis. New features of this study include highlighting the role of efficiency-compa tible product planning strategies in addition to the technology options usually anal yzed; fitting historical technol ogy changes to logistic curves to identify rapid but feasible adoption rates for key new technol ogies; and modeling costs using quadratic functions of energy consumpti on reduction that sh ift through time.

The conclusion is that a tripling of new fleet fu el economy is an ambitious but defensible horizon for 2035. Figure 1 shows the resulting trajectory interms of new fleet average energy intensity (kilojoules per meter, kJ/m, similar to Btu/mile), with historical averages and the levels implied by recent regulations shown for comparison. Th e right-hand axis give s corresponding nominal fuel consumption rates (liters per 100 kilometers, L/100km, based on unadjusted lab tests), which fall from 9.5 L/100km to 3.2 L/100km over the 25 year horizon 2005-2035. Historically, a drop of comparable magnitude (roughly 6 L/100km) happened over 12 years, 1975-1987. In terms of nominal, lab-test fuel economy, this trajectory would take the ne w U.S. car and light truck fleet from 25 mpg in 2005 to 74 mpg in 2035, implying an improvement from 20 mpg to 60 mpg in terms of estimated on-road (" real world") fuel economy.

The assumed rates of technological progress, particularly for the de gree of hybridization likely to be needed, are in line with those of some ma jor automotive technology changes in the past. The challenge is cost, which rises steadily as greater use of adva nced technology is required. Nevertheless, projected fuel savi ngs greatly exceed upfront cost s and this evolutionary horizon remains less costly than revolu tionary changes in vehicles ba sed on electric grid connection, hydrogen or other alternatives that en tail extensive new infrastructure.

Figure 1. New fleet efficiency horizon in terms of energy in tensity and fuel consumption rate compared to historical fleet averages and recent U.S. regulations

Energy intensity is estimated on-road value in kilojoules per meter (kJ/m) assuming 20% fuel economy shortfall for all years. Right axis gives nominal (lab-test) fuel c onsumption rate in liters per hundred kilometers (L/100km); convert to miles per gallon (mpg) by dividing L/100km into 235.2. See Table 1 for further details.v Although it may seem high, a tripled efficiency hori zon can be viewed as having been validated by the Partnership for a New Generation of Ve hicles (PNGV) program. The PNGV concept cars shown a decade ago demonstrated a factor of 2.6 incr ease in fuel efficiency with prototype diesel hybrid powertrains less advanced than today's. Since then many technologies have not only been developed, but also progressed into commercialization and its atte ndant stages of competitively driven refinement and cost reduction. Although performance has risen, so has technological capability. Thus, tripled efficiency is indeed attainable by 2035, a time frame more than three decades after the PNGV delivered proof-of- concept cars approaching that goal.

Getting from Here to There

Table 1 summarizes the results of a scenario l eading to tripled effici ency by 2035. The analysis works forward from a 2005 base year fleet, with new fleet average energy in tensity declining at a compound rate of 3.6% per year (a 3.7% per ye ar fuel economy increase). By comparison, the recent Joint Rule for 2012-16 requires a fuel econo my increase averaging 4% per year between now and 2016. By 2025, the trajectory shown here reaches a nominal 52 mpg, or a doubling of fuel economy compared to the 2005 fleet. For fl eet average GHG emissions rates, given here after subtracting the reductions likely to be f easible with improved air conditioning systems, the trajectory implies 157 CO 2 -equivalent grams per mile (g/mi) in 2025 and 104 g/mi in 2035.

To project this horizon, this analys is uses results from studies th at assess particular technologies such as advanced engines and hybrid drive. As interpreted here, however, these options are used only as an existence proof of the potential for hi gh fuel efficiency rather than as a literal technology pathway to be implemented. Thus, the view is technologicall y agnostic and reflects opportunities identified through engineering fundamentals. Table 1. Projected horizon for fu el efficiency, fuel savings and CO 2 emissions reductions for a U.S. light duty gasoline and diesel vehicle fleet

Base & Current Year Trajectory by Model Year New Light Duty Fleet Averages and Per-Vehicle Estimates 2005 2010 2020 2025 2030 2035 Energy intensity (on-road kJ /m) 3.81 3.52 2.20 1.83 1.53 1.27 reduction in energy intensity 0% 8% 42% 52% 60% 67% Fuel economy (unadjusted mpg) 25 27 43 52 62 74 on-road (adjusted) mpg 20 22 34 41 50 60
CO 2 emissions (g/mi, with A/C) 358 330 192 157 128 104 Lifetime fuel savings (gallons) 590 3,264 4,039 4,696 5,254
Lifetime CO 2 reduction (tons) 5 29 36 42 47 Fuel savings benefits (201 0$) 5,600 6,900 8,000 8,800 Technology costs (2010$) 2,600 3,400 4,000 4,200 Net Present Value (2010$) 3,000 3,500 4,000 4,600
Assumptions: shadow price of fuel $2.88/gal (2010$); 20% fuel economy shortfall; 7% discount rate; 10% rebound effect. The CO 2 emissions rate reflects subtraction of potential ai r conditioning (A/C) reductions starting in 2020. Lifetime fuel savings are undiscounted ga llons of gasoline equivalent (gge) and CO 2 reductions are direct (tailpipe only) metric tons, both relative to the 2005 base year fleet; do llar values are rounded; see Table 5 for further details.vi That being said, extensive use of hybrid drive appears necessary for tr ipling fleet efficiency. High utilization of hybrids could be reached by 2 035 if their adoption rate is greatly increased, with associated implications for increased cost. The hybrid adoption rate assumed here is faster than in MIT's studies but still mid-range am ong those observed historically (see Figure 10). Reliance on hybrids could be lower if greater prog ress is seen in mass reduction, other ways to reduce loads and recover energy, use of start-stop, additional refinements of gasoline and diesel engines (including lean emissi ons control) and other advan ced combustion techniques.

A central assumption is that going forward, e ssentially all en ergy-related technology advances are focused on fuel efficiency rather than further boosting aver age acceleration performance. A sensitivity analysis shows that if performance instead rises in line with past trends, then the 2035 horizon drops to doubled rather than tripled fuel efficiency. Performance enhancement consumes technological capability and so achieving greater progress on fuel efficiency means redirecting market trends and customer preferences away from ever more mass and muscle.

Mindful of this challenge, it wi ll be valuable for automakers to pursue what this study terms efficiency-compatible design strategies in addition to th e technical efficiency improvements commonly considered. Compatible strategies ar e approaches for both individual vehicles and product plans that enhance customer value by empha sizing features that ar e not inherently fuel consumptive. Such options include intelligent systems content, matching performance to realworld driving needs, creative downs izing, interior packaging and styl ing -- in short, appealing to consumers in ways that are in line with, as opposed to work against, fuel efficiency.

Costs and Savings Estimates To estimate the costs of pursuing the efficiency horizon given here, estimates from recent studies were synthesized into cost curves (Figures 13 and 14 in the main text). These curves were chosen to be quadratic in energy inte nsity reduction, reflecting an increas ing marginal cost for each step of additional fuel savings. They were also mode led to shift rightward as time goes on, reflecting progress toward lower costs for achieving a given level of fuel savings overall.

The near-term curve was derived from costs es timated for the Model Year 2012-2016 Joint Rule. Long-term cost estimates were derived using a recent study by MIT, which reflects steady gains in efficiency by optimizing internal combustion engines and hybrid driv e along with declining costs for advanced powertrain components. Thos e findings imply a long-term (2035) cost curve much lower than the near-term curve. Both nea r- and long-term curves were adjusted downward based on studies by the steel indus try and Lotus Engineering. In interpreting those findings, this report assumes that evolutionary mass reducti on (reaching 20% by 2035) canbe obtained at no net cost, in contrast to a presumption that saving weight al ways adds cost.

Figure 2 shows the incremental technology costs and discounted lifetime benefits for the new fleet by model year. Benefits related to fuel sa vings mainly depend on the pre-tax price of fuel, assumed to average $2.50 per gallon. Benefits also include avoided CO 2 emission and oil import externalities valued at $22/ton CO 2 and $7.50 per barrel, respectively. The difference between benefits and costs is the net pres ent value (NPV) listed in the la st row of Table 1. NPV grows as time goes on, reaching $3,500 in 2025 and $4,600 by 2035 (all costs are given in 2010$).

Base case technology costs are $3,400 in 2025 and $4,200 in 2035. Sens itivity cases with more and less optimistic assumptions yield cost s ranging from $3,200 to $4,000 respectively in 2025vii and ranging from $3,800 to $5,700 in 2035. The NPV results vary accordingly, though lifetime savings still significantly exceed costs in all cases (see Table 6 for details).

Large Benefits; Cost and Consumer Interest Challenges The technology costs of achieving the fuel efficiency horizon projected here are large even though they are exceeded by the energysavings and also provide substantial nonmonetary value in terms of reduced GHG emissions. Pursuing this trajectory may require ways to better reconcile the upfront costs, largely borne by automakers, with the substantial but diffuse benefits that accrue to consumers over vehicle lifetimes.

The more that consumers can be interested in on going efficiency gains -- or at least have their interests pulled toward amenities that conflict less with fuel efficiency -- the easier it will be for automakers to invest in the technology and de sign changes needed. New efforts to engage consumers in fuel efficiency in spite of the vagari es of fuel prices will therefore be crucial. The auto market cannot expect to "have it all," th at is, to see significant ongoing gains in power performance and other energy-impactful vehicle amenities while affordably achieving high levels of fuel efficiency.

A key implication of this analysis is that a grea t deal of progress can be made with evolutionary design and technology changes even though the exact mix of vehicle designs and technologies involved cannot be projected. Therefor e, public efforts to foster inte rest in higher fuel efficiency should be technology neutral rather than promoting politically popular alternatives that are likely to be much less cost effective for reducing petroleum demand and limiting GHG emissions from the automotive fleet.

Attaining this fleetwide fuel effi ciency horizon will entail a sustained effort to make efficiency improvement a high priority bothin individual vehicle design a nd in automakers' overall product strategies as well as by consumers and policymak ers. Policy guidance is clearly needed, although identifying the best set of policies is left for other work. Effective climate protection will also require greatly reducing the net GHG impact of fuel supply as well as atte ntion to travel demand and other factors within the tran sportation sector and across the ec onomy as a whole. In any case, if the fuel efficiency horizon identified here is achieved or even approached, the benefits will be substantial for both protecting the clim ate and reducing of oil dependence.  Figure 2. Technology costs and fuel savings benefits leading to a new fleet of tripled efficiency by 2035viii 

### A CKNOWLEDGEMENTS
The author wishes to thank the individuals w ho provided peer-review comments and suggestions on earlier drafts of this report. Review ers included: Randy Armstrong, Roland Hwang, David Friedman, John German, Jack Jordan, Walt Kreuch er, Therese Langer, Michael Love, Nicholas Lutsey, Don MacKenzie, Sue Zielinski and Ma rty Zimmerman. Comments reflected diverse views, not all of which were fully accommodated, and so the author retains full responsibility for the results and conclusions contained in this repo rt. The author is also most grateful to The Energy Foundation for its support of the work.1 

## A Fuel Efficiency Horizon for U.S. Automobiles

Introduction

Perennial concerns about ener gy security and growing concer ns about climate disruption motivate measures to transfor m transportation energy use. Be cause it reduces fuel demand, improving vehicle energy efficiency is one way to address such issues. In the United States, automobiles (cars and light trucks) account for 60% of transportation energy use (Davis et al. 2009). Their dominance has made them a focus of efforts to improve efficiency and develop alternatives. Corporate Average Fuel Economy (CAF E) standards established in response to the 1973 oil embargo contributed to a near doubling of new car fuel economy by 1985.

Market forces then worked against efficiency fo r most of the next two decades. The new fleet mix shifted from passenger cars to less fuel-effici ent light trucks, which came to comprise half the fleet by 2004. Nevertheless, measured by esti mated on-road fuel economy, the U.S. light vehicle stock was roughly 50% more efficient in 2005 than it had been in 1975. The new fleet in 2005 averaged 19.9 miles per gallon (mpg) according to EPA's adjusted estimates, barely more than the on-road stock average of 19.7 mpg estimated by FHWA (2006). In nominal (unadjusted lab-test) terms as used for regulatory purposes , the 2005 new light duty vehicle fleet stood at 24.8 mpg and 358 g/mi CO 2 (EPA 2009).

Fuel economy is the inverse of fuel consumption, which in turn maps to a travel activity-based measure of vehicle energy intensity such as Btu/ mile or MJ/km. Important impacts of concern -- whether petroleum demand, CO 2 emissions or consumer operati ng cost -- scale with energy intensity, making it an ideal metric for analys is. Although fuel economy is commonly used in the United States, its inverse relationship to energy-re lated impacts can distort one's view. Thus, this report presents key results in terms of energy in tensity, or "tank to wheels" consumption rate.



### A UTO 

### E FFICIENCY AND 

### GHG E MISSIONS
The overall GHG emissions associated with motor vehicles result from two intersectingproduct lifecycles. One is the "cradle-tograve" materials cycle for production and disposal of the vehicle and its components. The other is the "well-to -wheels" lifecycle for production and consumption of motor fuel. Figure 3 shows the breakdown for a typical U.S. light duty vehicle of today running on gasoline. About 70% of the combined lifecycle GHG emissions is from the tailpipe during vehicle operation (end use); 20% occurs upstream during fuel production; and 10% is associated with the materials cycle for the vehicle and its components. Figure 3. GHG emissions breakdown for an average U.S. light vehicle

70% Tailpipe

10%

20% "Cradle to Grave" Vehicle Materials

"Upstream" Fuel Production

Source: Rounded values based on 20 mpg in-use fuel economy, 150,000 miles lifetime usage, and lifecycle estimates from Burnham et al. (2006).2 For a given fuel, end-use em issions (largely tailpipe CO 2 ) depend on fuel chemistry and so are directly proportional to vehicle energy intensity. Upstream ("well to tank") emissions also scale with the rate of fuel cons umption. Thus, 90% of the CO 2 emissions for today's vehicle-fuel system are tied to vehicle energy intensity and so represent the potential reductions that can be leveraged by improving automotive end-use efficiency.

This report gives on-road ("real world") estimates for energy inte nsity in kilojoules per meter (kJ/m = MJ/km = 1.525 kBtu/mile). It also report s gasoline-equivalent fuel economy (mpg), fuel consumption (L/100 km) and tailpipe CO 2 emissions rates (g/mi), with these latter three metrics given using nominal values (as derived from EPA unadjusted 55% cit y, 45% highway driving cycle laboratory tests) unless ot herwise stated. The historical fuel economy gain noted above took the new U.S. car and light truck fleet from 15.3 mpg in 1975 to 25.9 mpg by 1987 in terms of nominal fuel economy (EPA 2009, Table 1). The entire light duty stock saw a net gain from 13.3 mpg in 1975 to 19.7 mpg by 2005 in terms of es timated on-road fuel economy (DeCicco et al. 2007, Table A-1), corresponding to the 38% decrease in ener gy intensity from 6.2 kJ/m in 1975 to 3.8 kJ/m in 2005 as illustrated earlier in Figure 1.

Modest efficiency improvements may be acceptable, as they historically have been, in an energy security context. Energy conservation is part of a U.S. policy response that otherwise emphasizes measures oriented to energy supply, including military and diplomatic activities to secure access to foreign sources of petroleum and facil itate oil imports; expanding domestic petroleum production; tapping remote and unco nventional fossil resources; and promoting alternatives such as biofuels, electricity and hydr ogen. However, as climate protection rises in importance as a policy concern, the magnitude of the challenge related to unmitigated consumption of carbonbased fuels is amplified. Science-based target s for limiting GHG concentration imply ambitious goals, such as an 80% reduction in U.S. G HG emissions relative to recent levels by 2050 (USCAP 2007; White House 2009). Such goals indi cate a need for much gr eater change in the transportation energy system along all dime nsions, including vehicle efficiency.

To date, regulation to improve vehicle efficiency has proven to be both politically acceptable (though of uneven implementation) and measurab ly effective among the various options for addressing transportation energy us e. NRC (2002) found that CAFE standards contributed to fuel savings that reached 2.8 million barrels per day (6.3 EJ) by 2000. These savings amounted to 25% of U.S. automobile fuel demand (the policy' s direct target) and 12% of total U.S. petroleum demand. CAFE standards have also proven to be co st effective. As a leading analyst concludes, "although numerous hypothetical and theoretical objections to CAFE have been raised, tangible evidence of significant ne gative effects is lackin g" (Greene 1998: 595).

As energy-related concerns returned to prominen ce in recent years, policym akers again turned to regulation, as seen in the recent California and Federal initiatives on GHG emissions and fuel economy respectively. Efforts converged in a single national program, the EPA & NHTSA (2010) Joint Rule for model years 2012-16, which requires a 27% average reduction in new light vehicle energy intensity from the 2005 level. The agencies recently announced a plan to develop a next round of standards to cover model years 2017-2025 (EPA 2010).3 

### E FFICIENCY IN THE 

### C ONTEXT OF 

### O THER 

### O PTIONS While clearly important, auto efficiency repres ents an incomplete focus for addressing energy and climate issues. Automobiles are but one mode of transporta tion, which is in turn but one sector of the economy. Efficien cy is but one factor that determines oil demand and GHG emissions, others being travel demand and th e fuel supply system. Most analyses include automobile efficiency improvement as one part of a broader set of opport unities to be pursued (Greene & Shafer 2003; Cambridge Systematics 2009) . Particularly for climate protection, it is important to address regulations and other measures that target vehicle efficiency within a policy framework capable of linking vehicle efficien cy needs to overall GHG limitation goals while balancing its benefits and costs against those of the other factors that determine emissions (DeCicco 2010).

In terms of the vehicle-fuel system, reducing th e net carbon impact of fuel supplied is crucial. Biofuels encompass many options, bu t just what their net benefits are and even how to properly evaluate them in light of globally coupled ener gy and terrestrial systems remain open questions. Some analysts advocate all-electric drive, which is also seen as a basis for "reinventing" the car (Sperling 1996; Mitchell et al. 2010). Although e xpensive because of inherently high materials costs for batteries and other components, electri fication improves end-use efficiency relative to current vehicles because thermodynamic losses are either shifted to the electric supply system (for grid-connected electric vehicles) or a voided (for fuel cell ve hicles, though other losses occur). Such "carbon free" energy carriers are no w more carbon intensive than petroleum-based

Figure 4. Projected per-vehicle incremen tal costs and GHG re ductions for future automotive technologies in a 2035 ti me horizon, based on MIT (2008)

Source: MIT (2008), Table ES-1 and Figure ES-1(b). Area shaded (by author) covers options based on vehicle efficiency only that do not require major changes in fu el supply system. Technologies are: NA SI = naturally aspirated spark ignition; Turbo SI = turbocharged spark ignition; Diesel = turbocharged compression ignition; HEV = hybrid-electric vehicle with NA SI engine; PHEV = plug-in hybrid-electric vehicle with NA SI engine; FCV = hydrogen fuel cell vehicle; BEV = battery electric vehicle. All future options assume a streamlined, 20% mass-reduced vehicle platform. Costs (retain-price equiv alent) and full vehicle+fuel lifecycle GHG reductions (metric tonnes) are shown relative to a 2006 midsize car (origin of axes).4 fuels per unit of energy supplied. For current vehicles the efficiency of electric drive more than compensates. However, this advantage greatly narr ows if internal combustion engine vehicles -- including grid-free hybrids -- see their efficiency greatly impr ove, as seen above in Figure 4 and further elaborated in this report.

Vehicle-fuel systems are commonly analyzed on a "well-to-wheels" basis, reflecting the effects of both vehicle efficiency and fu el carbon (GHG) intensity. For gaso line, a typical "well-to-tank" carbon intensity is 94 gCO 2 eq/MJ, reflecting the 70% + 20% ta ilpipe + upstream portion of the breakdown shown in Figure 3. For electricity, th e emissions all occur upstream during power distribution and generation. U.S. electricity remains half based on coal, resulting in a current average carbon inte nsity of 177 gCO 2 eq/MJ (2010 extrapolation based on data from eGrid and Electric Power Annual [EPA 2010b; EIA 2010]). A lthough electricity is now nearly twice as carbon intensive as gasoline per unit of energy supplied, its GHG emissions intensity has been trending downward as generation efficiency im proves and coal's share slowly declines. In contrast, the GHG emissions for gasoline produc tion are creeping upward as supply turns to heavy crudes, oil sands and other unconventi onal resources (Brandt & Farrell 2007).

The MIT (2008) analysis applies assumptions for fuel cycle GHG emissions that are cautious and consistent across sectors, using EIA (2007) reference-case project ions for electricity generation mix and natural-gas-based hydroge n production over the next 30 y ears. Over this horizon, MIT analysts see the potential for substantial (yet non-breakthrough-dependent ) progress in gasoline internal combustion vehicles (including grid-f ree hybridization). Figur e 4 plots the resulting estimates of incremental vehicle costs agains t lifetime reductions in GHG emissions. The point of reference (origin of graph) is a 2006 mids ize car rated at 26.4 mpg (8.9 L/100km unadjusted; 3.6 kJ/m on-road) having lifetime GHG emissions of 69 tonnes (metric) and a retail price of $20,500 (2010$). Thus, the hybrid-electric vehicle (HEV) estimate, for example, is that a lifetime reduction of 42 tonnes (61%) would be achie ved at an added vehicle cost of $4,700.

The shaded band in the lower portion of the grap h highlights an evolutionary pathway that does not require major investments in fuel supply an d distribution infrastruc ture. MIT's "apples-toapples" comparison finds that impr oving efficiency is the least costly way to cut transportation GHG emissions for the foreseeable future. As seen in Figure 4, BEVs and FCVs offer no advantage over optimized grid-free HEVs as l ong as both the liquid and electric energy supply systems remain largely based on unmitigated use of fossil fuels. These results buttress this paper's focus on vehicle efficiency while l eaving for other discussions the many issues surrounding alternatives (such as electricity, hyd rogen and biofuels), which their proponents believe are essential for the future ev en if more costly at present.



### O VERVIEW OF 

### R EPORT The report provides context for the issue by review ing past trends in fuel economy and related attributes. The achieved level of fleetwide efficiency results as much from design choices made about how to harness innovation as it is about technology innovation itself. Progress in automotive engineering is ongoing but often used to offer features other than higher fuel economy. This realization suggests a broader way to view options for improving efficiency. The concept of "efficiency compatible" solutions is introduced to compleme nt the evaluation of technical solutions, emphasizing the perspective fact that vehicle efficiency improvement is as much a matter of design priority as it is of technology adoption.5 With this perspective in mind, the report turns to a synthesis of previously published technology assessments in order to project a long-ter m (2035) efficiency horizon. The discussion distinguishes incremental versus fundamental a pproaches to analysis, relying on the latter to identify a large potential for efficiency gain through evolutionary te chnology change with a proviso that further gains in fl eet-average performance are foregone . The analysis then examines rates of technology adoption, finding that the rate of hybridizati on likely to be needed is comparable to the rates at which some major technology changes occurred in the past. Although high utilization of hybrid drive is a part of the efficiency horizon "existence proof," the interpretation given here is technology neutral. While many st udies identify specific technology "pathways," this study emphasizes that the techno logy mix should not be taken literally and that automotive engineering offers a range of solutions for realizing fundamental efficiency gain.

Turning to cost issues, the report draws on recen t studies that reflect the reductions in cost available through evolutionary strategies. In keeping with th e fundamental, technologically agnostic engineering assessment, co sts are treated at a hi ghly aggregate level using quadratic cost curves. Such curves fit previously published detailed cost estimates reasonably well while reflecting an assumption that marginal costs grow linearly as each next step of energy intensity reduction is sought. Technology costs are compared to benefits based on fuel savings evaluated over the lifetime of the new fleet. Although the lifetim e benefits exceed costs, the fact that costs grow steadily raises questions regarding the exte nt to which automakers can recoup the upfront costs of technology improvement. A final section examines the sensitivity of the results to differing assumptions.

Compared to the prior literature on automotive effi ciency, this report presen ts three new ways of thinking about the issue. One is highlighting the primacy of design priority in shaping the future of the fleet. If high priority is given to efficiency improveme nt, then efficiency-compatible product planning strategies should be considered in addition to the technology strategies usually analyzed, and high fuel efficiency levels identif ied by fundamental engine ering analysis can be realized. Another contribution is applying logistic curves based on fits to historical technology change in order to develop ambitious but plausi ble rates of adoption for hybrid drive. A third new approach is modeling fleetwide costs using quadratic functions of energy savings that shift through time. The development of these curves is detailed in the report's Appendix.



### Past Efficiency-Related Trends
It is now well appreciated that until very recently, most of the past two decades' advances in automotive engineering went toward delivering fast er performance, greater carrying capacity and numerous amenities in addition to lower tail pipe emissions of non-GHG pollutants and better crashworthiness. EPA's Fuel Economy Trends reports (EPA 2009 and earlier editions) document how car and light truck fleet ch aracteristics evolved with incr easing horsepower while new fleet fuel economy slowly declined from its 1987 p eak until recent improvements started in 2005. Historical U.S. new fleet fuel economy was shown in Figure 1; more detailed charts of the trends are published by EPA (2009) and so are not repeated here.6 

### S TEADY 

### G AINS IN 

### T ECHNICAL 

### E FFICIENCY This report uses fuel efficiency synonymously with fuel economy. The
term technical efficiency is used here to denote the broader notion of how efficiently a vehicle provides other amenities such as carrying capacity and acceleration performance. While fuel efficiency did not improve for many years, technical efficiency did improve and historical data reveal that such improvements have been quite steady. To measure technical efficiency gains, An &DeCicco (2007) developed a PerformanceSize-Fuel-economy Index (PSFI), which provided a strong line ar correlation to historical data for these variables.

Figure 5 shows PSFI results for the new car and new light truck fleets. Relative to a 1977 base year, the index shows average rates of increase of 5.3% per year for cars and 3.1% per ye ar for light trucks. These estimates average out to a historical technical efficien cy gain of 4.2% per year for the light duty fleet as a whole.

The PSFI trends can be interpreted as reveali ng an intrinsic rate of technological progress on vehicle energy efficiency. Automakers appear to adopt efficiency-relat ed technology at a steady rate whether or not that progress is used to ach ieve net gains in fuel economy. Such findings are in line with other analyses that examine the tr ade-offs between reducing fuel consumption and increasing other vehicle attributes such as we ight and performance (Cheah et al. 2008).

EPA (2009) documents an ongoing gain in a weight -fuel economy metric (ton miles per gallon) and also examines the effects of weight, perf ormance and vehicle size mix on potential fuel economy. For example, using 1981 weight and perf ormance distributions, EPA calculated that 2005 cars and light trucks would have had fu el economy levels 26% and 34% higher, respectively, than actual 2005 values. Examining changes in new fleet mix, size, performance and weight, Greene & Fan (1994) found nearly li near technological progress equivalent to 0.6 mpg per year for cars, correspondi ng to a 3.8% per year rate of t echnical efficiency gain relative to a 1975 base. Referenced to 1980 levels and co ntrolling for horsepower , torque and weight, Knittel (2009) estimated that light vehicle fu el economy could have been 50% higher in 2006 than it actually was.

These overall technical efficiency trends correspond to observed ga ins in engineering figures of merit. A key metric is engine sp ecific power, or peak power per unit of engine displacement as measured in kilowatts per liter (kW/L), for example. It also exhibits a stri kingly linear trend (see Figure 6 later in this report), wi th estimated rates of gain of 5. 5% per year for cars and 4.3% per year for light trucks (An & DeCi cco 2007). Lutsey & Sperling (2005) identified steady trends in progress for other efficiency-rela ted engineering metrics, includi ng aerodynamic drag and rolling resistance as well as engine performance. Figure 5. PSFI for new cars and light trucks, 1977-2005

CARS y = 3.557x - 6960

r 2 = 0.992

LIGHT TRUCKS y = 2.028x - 3949

r 2 = 0.974 50 70 90 110 130 150 170

1975 1980 1985 1990 1995 2000 2005PSFI (hp/lb)·size·mpg

Source: An & DeCicco (2007); PSFI = Performance - Size - Fuel Economy Index, with size measured as interior volume for cars and wheelbase for light trucks.7 An & DeCicco (2007) concluded that one implicati on of a linear PSFI trend is that higher levels of performance and size imply a lower level of potential fuel economy gai n, at least as long as the intrinsic rate of progress is maintained at the historically observed rate. Mathematically, letting P = performance, S = size, F = fuel ec onomy, and t = time, a linear PSFI trend can be represented as

PSFI(t) = P(t)·S(t)·F(t) = α t + β

where α and β are the slope and intercept of the trend line. Partial differen tiation then yields

∂ F/ ∂ t = α/(P·S)

which implies that the rate of trend-consistent progress on fuel economy is inversely proportional to achieved levels of performance and size. In other words, ongoing gains in performance and size "use up" technical progress on vehicle efficien cy, and so a significant portion of the intrinsic potential for improving fuel economy is lost.



### P ERFORMANCE 

### ' S 

### E FFICIENCY 

### P ENALTY The foregoing analysis underscores the efficiency-p erformance trade-off likely to be faced in the years ahead. For example, although the potential for fuel economy gain at constant size and performance was 4.2% per year relative to the 1977 new fleet, it falls to 2% per year relative to 2005 fleet levels of size and performance (based on An & DeCicco 2007, Table A1).

This effect is dominated by acceleration performa nce. Either technological progress more rapid than historically observed or a re laxation of fleet average performan ce levels is needed to realize fuel economy gains at rates similar to those based on past trends. Increasing the rate of technological progress suggests incr easing costs, an inference borne out later in th is report by the rising costs projected for adva ncing the fleet toward the e fficiency horizon identified. Conversely, cost impacts would be moderated if performance expectations are moderated.

Rising acceleration performance penalizes potentia l fuel efficiency gain because it prevents advances in powertrain engineering from being utilized in ways that significantly improve the part-load efficiency of the engine-transmission syst em. Hybrid drive mitigates this trade-off, but also comes at a greater price than conventional powertrain refinements. Thus, speeding up the rate of fleetwide fuel economy gain through hybridi zation is consistent with the cost implications just noted.

The performance penalty can be seen in the lack of progress in automobile tractive efficiency, which is the fraction of fuel energy ultimately delivered to the wheels for overcoming the basic forces of vehicle motion. These forces -- in energy terms called tractive loads -- are aerodynamic drag, tire rolling resistance a nd the dissipation of kinetic en ergy through braking. The tractive efficiency of a typical late-model U.S. passe nger car is only about 16% (for a 2006 midsize sedan; MIT 2008). A similar value of 17% is obt ained for a late-model midsize SUV (derived from graphic in Ponticel 2010). These recent es timates match the 17% estimate made for an average car of the early 1990s (DeCicco & Ross 1994). As the EPA data show, the 1990-2006 period saw little gain in average fuel economy but a 35% increase in average power-to-weight ratio. Tractive efficiency had increased significan tly in earlier years; it was estimated at 12% for8 an average 1980 car (Gray and von Hippel 1981). From 1980-1990, average new car fuel economy increased 18% and average powe r-to-weight ratio increased 25%.

A point of comparison is the brake efficiency of an engine, referring to the energy available at the engine's output shaft (input to the transmission) relative to the energy in the fuel. It reflects thermodynamic losses and other heat losses. Peak brak e efficiency is attained at an engine's ideal operating conditions of wide-open throttle and relatively low RPM. For current gasoline engines peak efficiency is around 35%, or about double the average tractiv e load efficiency. If engine efficiency is improved through e ngineering refinements that reduc e frictional losses under partload conditions but performance continues to gr ow, the engine still largely operates at points well away from peak efficiency and little progress is made on tractive efficiency. That has been the situation since new fleet fuel economy peaked in 1987. Unless ongoing performance gains are moderated it will be difficult to increase fu el economy without either incurring much higher technology costs or making other trade-offs (such as a smaller vehicle mix).

Hybrid drive enables an advance in tractive e fficiency by using the electric drive system to handle a much greater fraction of part-load operation, reserving the combustion engine for operating conditions closer to its peak e fficiency. As MIT researchers point out,

The difference in relative fuel consump tion benefit from hyb ridization between different vehicles depends mainly on how efficiently the original engine was operating. A vehicle with a lower average engine load in its non-hybrid version will have lower average engine efficiency and will thus benefit more from using a hybrid powertrain. (Kasseris & Heywood 2007: 11)

Higher performance vehicles operate at lower aver age engine loads in ordinary driving. Kasseris & Heywood found that hybrid drive reduced fu el consumption by nearly 60% for a higher performance sedan versus 50% for a lower performance sedan.

Hybrid drive also enables recovering kinetic energy through regenerative braking. Therefore a more basic measure of tractive efficiency might count only the inherently dissipative forces of aerodynamic drag and rolling resistance. With su ch a convention, end-use efficiency would appear even lower; for example, only 9% of th e fuel energy ultimately gets applied to overcome those two tractive loads for a current average car. Without de lving further into possible definitions of vehicle energy efficiency, suffice it to say that the tractive efficiency metric noted above suggests ample opportunity for improvement , and that minimizing increases in power performance is likely to help minimize th e costs for making such improvements.



### Efficiency Opportunities
The basic principles for improving internal comb ustion engine vehicle fuel economy have been analyzed extensively since the issue was elevat ed by the 1970s oil crises. It comes down to minimizing the energy loads that must be met while maximizing the efficiency with which the powertrain (engine-transmission system) converts fuel energy into useful work for meeting the loads. Even as technology has been expended to improve technical effi ciency as seen above, innovation yields new options to address the same basic challenge s of decreasing vehicle loads and increasing powertrain efficiency.9 The main development of significance in recent years is hybrid drive. While long known as an R&D opportunity, Toyota's 1997 introduction of the Prius placed hybrid drive squarely on the list of solutions available for evolutionary impr ovement of vehicle fuel efficiency. However, technology for efficiency -- which hybridization ha s come to epitomize -- cannot be evaluated in isolation from the market and design context th at determines not only which technologies are used but also what benefits they deliver.



### C OMPATIBLE AS WELL AS 

### T ECHNICAL 

### S OLUTIONS
Table 2 offers a holistic look at the ways that automotive produc t planners can address vehicle efficiency, identifying what are termed efficiency- compatible design strategies in addition to the technical strategies commonly analyzed.

Technical strategies entail engin eering modifications that raise fu el efficiency while preserving other vehicle attributes. Compatible strategies involve rethinking design goa ls for both individual vehicles and the product mix while exploiting features to enhance customer value in ways that are less inherently fuel consumptive. Through pr oduct planning choices thatavoid conflict with fuel efficiency, or facilitate the ability to appl y engineering progress to efficiency rather than performance, compatible strategies can help automakers minimize the difficult-to-recover costs of adding technology to improve fuel efficiency beyond the levels that customers might choose on their own.

Most fuel economy assessments focus on techni cal options and assume that other vehicle attributes are constant. However, attributes have not been constant. Indeed, many years of changes in capacity and performance have been incompatible with fuel economy improvement. Such attribute changes increase energy loads or prevent powertrain refinements from closing the gap between peak and part-load engine efficienc y. If improving efficiency becomes a priority -- expressed as a shared sense of direction among c onsumers, automakers and policymakers -- then all strategies are implicitly on the table even if there are ample technical opportunities to increase fuel economy without changing ot her attributes. While the fuel efficiency analysis horizon developed in this report adheres to the cons tant performance convention, this discussion highlights these issues in r ecognition of the challenges invol ved and just how much of a departure from past patterns it would be to redirect technologi cal progress toward raising fuel efficiency instead of any further incr ease in fleet average performance.

Table 2. Design strategies for automotive energy efficiency

C OMPATIBLE

• Creative downsizing and styling

• Intelligent systems content

• Performance matching

• Interior packaging efficiency T ECHNICAL

• Load reduction

• Engine improvements

• Transmission improvements

• Hybrid drive10 Although his subject is very small vehicles, desi gner Robert Q. Riley of fers a useful insight when he states that developing new paradigms through innovative design is "closer to the grasp of available technique th an the technical solutions needed to double or triple the fuel economy of conventional vehicles" (Riley 1994: 356). Approaches that fight less against fuel efficiency are not just a matter of tech nological feasibility, but also of afford ability. Large fuel efficiency gains in line with the need to limit GHG emissions will be less costly to achieve if the mix of vehicles sold is less massive than what resulted from design priorities that gave little importance to reducing consumption-related impacts. A resurgen ce in small car classes, the relative success of products such as the Mini Cooper (perhaps antici pated a decade ago by the Chrysler PT Cruiser, which sold even when gas prices were low and large SUVs were still on the upswing), and the proliferation of segment-defying designs on co mpact and midsize platforms: such trends illustrate the roles that creative downsizing and st yling can play in product strategies compatible with the need to improve fuel efficiency.

Toward Virtual Performance From a business perspective, degrading customer appeal is of course counterproductive. The challenge is finding ways to emphasize features , such information tec hnology (IT) and styling, that might appeal to buyers w ith less recourse to design re quirements that degrade fuel efficiency. The fact that IT is an ever-e xpanding area of opportunity for automobiles (as throughout the economy) opens up nu merous possibilities for opti ons compatible with greater fuel efficiency. What can be termed "virtual pe rformance," measured by th e types and quality of IT features as well as by technical metrics suchas memory capacity and bandwidth, is beginning to compete for consumer dollars with traditional physical power metrics such as horsepower and engine displacement.

This trend will accelerate as demographics shif t toward millenials ("Gen Y") and successive cohorts. Millenials now exceed one-quarter of the U.S. driving population and "have an insatiable appetite for digital technology," accord ing to Ford President of the Americas Mark Fields (2008). Automakers are pursuing these opportunities as customer s show an increasing willingness to pay. The average transaction price of the new Ford Focus, for example, was increased by $750 through optional purchase of the Sync ® IT package, which was approaching 50% take rate (Fields 2008).

While caution is needed to avoid compromising safety when adding IT features, the category also includes driving and safety enhancements such as adaptive cruise control, stability control, parking assist, rear-view cameras and emerging crash-avoidance techno logies. These virtual performance features help driv ers deal with everyday road c onditions better than performance capabilities than can often only be exercised on a race track. Such IT options are migrating from luxury segments into mainstream cars (Kranz 20 06), providing automakers with opportunities to offer customer value in ways that displace strategies based on size and horsepower.

The Challenge of Performance Expectations As new, efficiency-compatible content expands, au tomakers may have a chance to put traditional performance metrics into better balance with th e concerns that motivate policy. In Table 2, performance matching refers to a pragmatic approach on power instead of ongoing power gains. Such a shift is yet to be seen statistically; at least for now, customers seem to value more of both virtual and physical performance. The view in automotive circles is that the horsepower war is11 far from over and that the market will see trade-offs in other attributes, such as size, before relinquishing further performance enhancement (Winter 2009a).

These sentiments underpin the prevailing approachon engine sizing as new, high-specific output options, such as turbocharged direct-injection ga soline engines, are scal ed to exceed the power performance requirements of larger engines they replace. In th e impressionistic world of car sales, automakers see enough challenge in c onvincing consumers that they will get good performance with, say, a 6- rather than 8-cylinder engine, let alone trying to convince them that they can have a great driving experien ce without a high powe r-to-weight ratio.

At least to some extent, the horsepower war is a cultural phenomenon, an interplay between car journalists and other market intermediaries a ttuned to subtleties of performance and handling. This dynamic promotes products that advance such metrics each year and at least implicitly pans products that lag in performance, even if this ye ar's laggards perform at levels that would have earned them high marks in previous years. Pr aiseworthy performance gets benchmarked, for example, in a recent review of the Land Rover LR4 luxury SUV, which with its 5.0L, 375 hp V8 and 5,700 pound curb weight delivers "smooth and effortless acceleration" and "towed our 5,000-pound test trailer to 60 mph inan excellent 12.8 seconds" ( Consumer Reports, April 2010: 55). A 12.8 second zero-to-sixty (Z60) time was the es timated average for all light duty vehicles -- unladen and trailerl ess -- in 1988, when average curb weight was under 3,000 pounds, gasoline was 91¢/gal ($1.66 in 2010$), sales were a robust 15 million units and the market was widely seen as having recovered from the detuned doldrums of the post-oil-shock early 1980s.

The customer experience of good performance is imme diate and even visceral. This is in contrast to the benefits of fuel efficien cy which -- unless fuel prices are high and awareness of higher fuel prices is strong -- are either uns een and societal (e.g., lower envi ronment impacts) or take the form of deferred gratification th rough fuel savings experienced periodically when refueling and with marginally lower expenses over the period of ownership. The ge nerally tepid interest in fuel economy in spite of the cumulative savings it offers can be explained by consumers' loss aversion and the perceived uncertainty of the fuel-s aving benefits (Greene et al. 2009). Thus, it is no surprise that under most conditions to date, performance has trumped efficiency. While there are signs that fuel efficiency is becoming more of a selling point, how strong and durable that trend will be is yet to be seen.

In contrast, promotion of class-leading performance can be found in any market segment and featuring it is a time-honored automotive marketi ng and journalism idiom. Those attempting to advance green technologies, for ex ample, electric cars such as th e Tesla Roadster or promised Fisker Karma, feel compelled to market high pe rformance and prove thattheir products are not underpowered "golf carts." The estimated new fl eet average Z60 time has fallen to 9.5 seconds (EPA 2009) but exotic sports cars now clock in at under 4 seconds, indicating that the limit of fleetwide performance remains a long way off. Pursuing an efficiency-compatible performance matching product strategies would be a profound para digm shift. However, failing to rethink the extent of design priority placed on performance enhancement may consign the industry to slower and more costly progress on higher fuel effi ciency than might otherwise be necessary.

The last compatible strategy listed in Table 2 is packaging, which i nvolves designing vehicle interiors to provide a given amount of usable , comfortable space within a given size overall structure. It is often menti oned among the technical options for mass reduction (as by Duleep12 2010 and previous studies), but is distinguished here from engin eering approaches that use new materials or structural and asse mbly techniques. Of course, bounda ries blur among strategies for reducing mass; the larger point is that designers can find ways to meet spatial requirements while minimizing the amount of material needed for safety and amenity.



### T ECHNOLOGIES FOR 

### E FFICIENCY Technical options for fuel efficiency include tw o broad approaches: tractive load reduction and powertrain improvements. Both can increase fuel economy without significantly changing size, performance, carrying capacity and other customer amenities. Such strategies are the traditional focus of technology assessments as reviewed by Greene and DeCicco (2000), examined in the NRC (1992, 2002, 2008, 2010) studies and applied in regulatory analyses up to and including EPA & NHTSA (2010). Improving efficiency thr ough technology was successful historically, with an estimated 80% of U.S. light vehicl e fuel economy improvement since 1975 having been technological in nature (DOE 1995).

Fuel efficiency measures evolve continuously. Technologies already in production can be used more widely, implemented more optimally and see reductions in cost. Emerging technologies now in late stages of development are introduced and join the production refinement process as time goes on. Advanced technologies now in earlie r stages of R&D will become available over the course of the 25 year time horizon considered here. Cost is always a critical factor: a technology is not ultimately availa ble unless it is affordable. Of course, what cost levels are considered affordable is a key issue. The more expensive options, such as hybrid powertrains, may find their utilization limited by upfront costs even if the net present value of their fuel saving benefits minus costs is strongly positive.

Tractive Load Reduction Reducing tractive loads, which refer to the energyneeded to overcome inertia and the resistance due to aerodynamic drag and tire friction, is a highly leveraged option for improving vehicle fuel efficiency. Given that today's av erage powertrains have a net effi ciency over U.S. driving cycles of 16%-17% (about one-sixth), a given reduction in tractive energy requirem ents can translate to a sixfold reduction in fuel energy requirements assuming that the powertrain is reoptimized for constant performance.

Mass reduction A cornerstone of technical efficiency improve ment is mass reduction without downsizing. In general, a 10% reduction in mass yields a 6.5% reduction in fuel consumption when the powertrain is reoptimized (EPA & NHTSA 2010 TSD: 3-76). The sensitivity is somewhat lower for hybrid vehicles where regenerative braking enab les recovery of inertial energy (An & Santini 2004; Pagerit et al. 2007). Materials substitution and improved technique with existing materials have roles to play in redu cing vehicle mass. Engine downs izing and the associated mass decompounding can also provide additional weight reduction.

The potential for mass reduction and its cost-e ffectiveness for improving efficiency are both substantial (Lutsey 2010). However, it has been in adequately treated in some studies such as NRC (2002 and its updates) and of ten underappreciated in policymak ing circles. At issue have13 been the contentious and sweeping claims -- ulti mately unsubstantiated scientifically -- about safety risks from mass reduction. Such allegations sparked a trenchant dissent in the NRC (2002) study; that discussion provides a rigorous, well -considered treatment of the topic (Appendix A by Greene & Keller in NRC 2002). It is prudent to leave unchanged an earlier conclusion that "concern for safety should not be allowed to para lyze the debate on the desirability of enhancing the fuel economy of the light -duty fleet" (NRC 1992: 7).

Some concept cars and a few produc tion vehicles demonstrate majo r changes in materials use. Examples include aluminum-intensive structures of several Audi and Jaguar models, compositebased structures such as Chrysler’s ESX con cepts and the ultralightcarbon-fiber bodies as proposed by Lovins et al. (1993). Aluminum a nd magnesium have seen steady increases in component level applications for several de cades and competition among materials for costeffective mass-saving solutions is ongoing (Ward' s 2008; Winter 2009b). Plastics continue to see new uses and nanotechnology is enabling new fo rmulations for saving weight while offering other benefits (Miel 2004, 2008). Pl astics are beginning to make in roads for weight reduction in vehicle glazing (Weernink 2007).

Carbon fiber is perhaps the ultimate material for high strength plus low mass. BMW recently broke ground on a joint venture facility to produce carbon fiber for its upcoming Megacity vehicle (BMW & SGL 2010). Top carbon fiber pr oducers in Japan, now largely oriented to supplying non-automotive uses, ar e steadily expanding their capacityfor automotive applications (Greimel 2008). Nevertheless, although carbon fibe r is seeing expanded use for components in mainly high-end vehicles, the materials revolution promoted by Lovins et al. (1993) has yet to materialize, as adoption remains slow due to still much higher cost (LeGault 2008).

For mainstream, high-volume production over the foreseeable future, most automakers will continue to take an evolutionary, "menu" appr oach to materials. A lthough the prospect of a major shift in materials has long been identified as a possibility, it still remains true that automotive materials changes are "distinctly evolutionary in na ture" even though "the cumulative impact of a long series of minor innovations could allow an eventual thorough redesigning of vehicles" (Amendola 1990: 498). Automakers assess the range of options th at are available and cost effective for given applic ations at a given time and se lect materials accordingly.

Established materials continue to evolve, narrowing the gap withalternative materials and doing so with little or no net cost impact (DeCicco 2005). Responding to PNGV work that aimed for substantial (order of 40%) mass reduction by em phasizing aluminum and composites, American Iron and Steel Institute (AISI) programs have dem onstrated large reductions using advanced steel technologies. That work found that high-stre ngth steels plus innovative design, forming and joining techniques can reduce auto body mass by 35%–40% (AISI 2002; Krupitzer 2008). Recent and near-term programs are obtaining 22% –30% weight savings in major structural assemblies, chasses and closures at no addi tional cost (Krupitzer 2009). Although the current fleet incorporates many such advances, AISI 's Advanced Vehicles Concepts (AVC) and FreedomCAR-oriented Future Steel Vehicle anal yses show that evolutionary progress using steel-based technologies still ha s a large potential for ongoing progr ess, targeting as much as 50% mass reduction at no additi onal cost (Krupitzer 2009).14 Evolutionary change without higher cost Under an evolutionary process, progressive ma ss reduction can be achieved over regular product development cycles at little or no added program cost if saving weight is made a high enough priority. A recent comprehensive study of structur al, chassis, interior and ancillary components found that improved materials along with componen t integration and assembly techniques offer a near-term mass reduction potential of 21% at a 2% cost decrease a nd 38% at a 3% cost increase (Lotus 2010). The higher reduction target (which Lotus termed a "high development vehicle") involved a greater degree of materials change, bu t the aforementioned AISI studies suggest that similar reductions can be achieved still using largely steel-based structures.

Choices automakers make within cost-constrai ned vehicle programs will draw upon a range of options and exploit the competition among material s to hold costs down even as additional mass reduction is sought as long as the rate of reduction falls within the range of evolutionary change. This picture is quite different th an that assumed in the NAS studies and recent regulations such as EPA & NHTSA (2010), which assumed a fixed cost of roughly $1.50 per pound of mass saved. That implies an RPE impact of r oughly $1200 for 20% mass reduction in an average vehicle, a 4% increase in contrast to the 2% cost decrease found by Lotus (2010) and the cost savings implied by the AISI studies.

A 20% mass reduction, as assumed by the MIT ( 2008) study on which this report's efficiency horizon is based, would lower light duty fleet average curb wei ght by 760 lbs relative to the recent average of 3800 lbs. That would amount to trimming an average of 30 lbs per year over the 25 year horizon between now and 2035. By co mparison, the light duty fleet gained an average of 44 lbs per year from 1987 to 2007 (weight gain has greatly slow ed over the past three years; see EPA 2009, Table 2). Thus, the mass reduc tion assumptions used here fall well within an envelope of evolutionary change. Note th at mass reduction need not, and perhaps should not, be uniform across the fleet: heavier vehicles can st and to lose more weightand vehicles that are already relatively small and light might not lose any or could even see incremental gains.

Aerodynamic and tire improvements Similar themes apply to aerodynamic drag and ti re rolling resistance. Drag coefficients (C D ) can be cut by 10% or more during redesign and ha ve been declining steadily. However, when vehicles are upsized the increase in frontal ar ea at least partly offsets the decrease in C D and a net reduction in drag force may not be fully rea lized. Similarly, while tire compounding techniques enable lower rolling resistance (C R ), other design priorities for improving handling, off-road capability and performance -- as well as styling objectives that use large tires for reasons of appearance -- can negate the potential for net gains in tire efficiency.

On C D , recent benchmark averages are 0.33 for sedans, 0.43 for SUVs and 0.48 for pickup trucks (Yang and Khalighi 2005). Given that passenger C D levels on the order of 0.20 have already been demonstrated, and assuming a comparable degree of relative progre ss for other types of vehicles as well as a rebalancing of the fleet mix away from very boxy truck-like designs to crossovers, sport wagons and othe r larger-size body styles amenable to streamlining, it is reasonable to assume a similar rate of progress across the fleet. Recent studies have shown that even challenging body styles such as pickups can see significant drag reduction (Yang and Khalighi 2005; Monaghan 2008). Over a 30 year horizon, that would enable an average C D reduction rate on the order of 1.5% per year, mo re rapid than the 1% per year assumed by15 Kasseris & Heywood (2007). That study also assumed the feasibility of a 1.65% per year rate of tire C R reduction. These assumptions are among t hose underpinning the future new vehicle efficiency levels on which the scenarios here are based.

Although not reviewed here, additional incremen tal load reductions can be obtained by improving the efficiency of various vehicle ac cessories as discussed by the NRC reports, the EPA & NHTSA (2010) TSD, and other detailed technology assessments.

Powertrain Efficiency The largest potential for efficiency gain is found in the powertrain, involving the enginetransmission system and including various forms of hybrid drive. The distinctions "diesel" vs. "hybrid" vs. "advanced gasoline" are in many wa ys artificial for the purposes of a public assessment, even though they represent choices th at individual automakers must make privately during product development. The options availa ble are best viewed as a continuum, with different versions of advanced engines, transm issions and hybrid confi gurations likely to be selected in different ways as automakers de cide which combinations of technology are best suited to deliver the right balance of fuel efficiency, performance and cost for a given vehicle in a given segment.

In addition to different types of powertrains, other opportunities for optimizing vehicle system energy efficiency may also emerge. Strategies for energy harvesting (e.g., using thermoelectric or other technologies) can recover additional por tions of the substantial low-grade heat now dissipated in vehicles. Su ch options are not reviewed here, bu t could offer alternative ways to help reach ambitious efficiency targets.

Engines The specific power (kW/L) of engines has been steadily increasing as shown here in Figure 6. Those gains have been applied to increases in vehicle power-toweight ratio (W/kg, equal to kW/tonne), shown on the right axis of the figure, which in turn provide increased acceleration performance. Increases in specific power can instead provide increases in fuel economy if engines are downsized to maximi ze the allocation of the technological bene fit to reducing fuel consumption as opposed to enhancing performance. Ongoing advances in engine technology enable a continuation of specific power gains for the foreseeable future.

Exploiting this progress through engine downsizing is especially valuable for the larger, high-consumption segments of Figure 6. Trends in engine specific power and vehicle power-to-weight ratio

Source: Trend lines derived from EPA (2009) for combinedU.S. car and light truck fleet. The individual points are powerto-weight ratios for the 2005 reference vehicles analyzed by Kasseris & Heywood (2007):  for average performance sedan;  for pickup truck, and  for high performance sedan.16 the market, where efficiency gains are well leve raged for reducing fleet average energy intensity. EPA (2009) documents how the specific power of li ght trucks significantly lags that of cars. Although noise and vibration concerns may lim it engine downsizing in smaller vehicles, substantial gains can be had by displacement reduc tions on the order of one-third that will be possible for V6 and V8 engines (Duleep 2010). Iconic large-displacement engines traditionally featured by some automakers wi ll have diminishing market ro les in the future (Wernie 2008). Instead, exploiting a range of engine refinement s to enable small engine displacements while maintaining performance is widely seen in the industry as the key near - and mid-term strategy for efficiency gain (Lewin 2008).

One such set of refinements invol ves the valvetrain. Variable valv e control (VVC) refers to a set of approaches for optimally controlling intake and exhaust valves. VVC use has been increasing since its earliest U.S. introduction by Honda tw o decades ago. Cam phasing and other forms of variable valve timing are already widely used as part of the recent technical efficiency gains that went largely to performance enhancement. A ll major automakers have now commercialized some form of VVC (Simanaitis 2007 provides an accessible overview). Variable valve lift and other VVC refinements still have a potential fo r greater use. Among other benefits, valvetrain strategies can reduce throttling lo sses, enable operation with more efficient engine cycles and generally improve efficiency (Duleep 2010). Valve control can also enable cylinder deactivation. Future possibilities include electromechanical va lve actuation, which can lead to throttleless operation and so even greater re ductions in frictional losses.

A key technology now seeing greater use is direct injection spark ignition, also known as gasoline direct injection (GDI). In general, GDI offers a higher compression ratio than portinjection and enables efficiency gains due to im proved cold start performance; mixture control including charge air cooling, improved scavengi ng, improved charge stratification; and other benefits including lower emissions and reduced heat loss. Combined with turbo-charging, GDI greatly increases specific power and torque, wh ich is crucial for good re sponsiveness and realworld performance while reaping the efficiency be nefit of significant engine downsizing (Lake et al. 2004). Turbocharged GDI can enable the use of 3-cylinder engines and extend the reach and efficiency benefits of downsizing to sma ller vehicle segments (Kirwan et al. 2010).

While efficiency gains in the European market relied on dieselization enabled by historically lenient NOx standards, automakers there have recently been turni ng to GDI as a way to continue progress on fuel efficiency while meeting tight er emissions constrai nts (Weernink 2005). GDI provides significant benefits even with stoichio metric operation compatible with established 3- way catalytic controls that achieve near-zero (e .g., PZEV) tailpipe emissions. Lean GDI offers higher fuel efficiency but is still inhibited by tailpipe emissions cons traints, although differing GDI modes can enable adequately low emissions with higher effi ciency as lean rather than stoichiometric operation is used fo r an increasing share of operation.

Modern diesel engines with direct injection a nd turbocharging are common in Europe and seeing some use in the U.S. market, mainly by Eur opean manufacturers or in heavy-duty pickups. Diesels' emissions hurdles and higher costs compar ed to advanced gasoline engines still inhibit more widespread use. Nevertheless, with diesel fuel sulfur levels now much lower (15 ppm) and aftertreatment a very active area of R&D, diesels offer a parallel pathway for efficiency gain well suited to some market segments. Although the scen arios developed below do not rely explicitly17 on diesels, reductions in aftertreatment cost and other advances may make them competitive within the fundamentals-based technology so lution set for a high efficiency fleet.

Even more sophisticated technolog ies are emerging that further ex tend the efficiency of internal combustion engines. One such option (also not explicitly assumed for the scenarios here) is homogeneous charge compression ignition (HCCI). This approach achie ves uniform combustion throughout a cylinder, avoiding th e NOx-forming temperature peaks of a flame front and enables a combination of high efficiency and very lo w engine-out emissions (Ashley 2001; Sherman 2004). Versions are applicable to either gasoline a nd diesel fuel, and for diesel in particular hold promise for avoiding NOx problems. Mixed-mode, HCCI-capable engines have been put into production by Nissan and Toyota among others; a barrier has been extending HCCI operation beyond limited load conditions. Whether such very advanced approaches become widely applicable, ongoing gains from engine optimizati on and friction reduction will extend efficiency improvements well into the future.

Transmissions Engine efficiency over a given driving cycle is a function of the engine-transmission system (collectively the powertrain), incl uding the choice of the shift schedule that determines which gear is used under what operating conditions. Tr ansmission design and shift optimization balance fuel economy against performance and driving re sponsiveness. Manual transmissions were once more efficient than automatics because they avoide d a torque converter and other internal losses. Now however, with torque convert er lockup and other improvements in automatics plus the fact that a well-optimized automatic shift schedule can be more efficient than a typical driver's use of a manual, the distinction is less important thanit once was. In additi on, steady refinements of materials and lubricants enable ongoing fricti on reduction throughout the entire driveline.

A transmission with a greater number of gears enab les the engine to operate more frequently at efficient points of its map (lower RPM, more open throttle), minimizing pumping losses and other forms of engine friction. The trade-off is performance, and so the design priority placed on efficiency again determines how much benef it can be found. Because shift schedules are now under programmable control, an essentially no-c ost gain can be found by choosing a more fuelefficient shift schedule while sacrificing responsiveness levels likely to be unnoticeable in ordinary driving. This option is demonstrated ina number of vehicles on the market that offer their drivers a choice of sh ift schedules (e.g., an "eco" vs. performance modes).

The ultimate increase in number of gears leads to the continuously variable transmission (CVT), which enables a smoothly varying range of ratio s between engine speed and final drive speed. However, CVTs have higher internal friction thanthe best fixed-gear automatics and to date have also been load limited, restricting their use to smaller vehi cles. Nevertheless, CVTs remain among the options for improving efficiency fleetwide.

The two most significant recent advances in transmission tech nology are designs based on what is known as the Lepelletier gearse t and designs enabling automated direct gear shifts in what is known as the dual-clutch transmission (DCT). The former has enabled a jump to six or more gears at low cost with multiple benefits includ ing higher torque capacity and better acceleration, reduced transmission size and weight as well as higher efficiency. Introduced by transmission supplier ZF Friedrichshafen with BMW a decad e ago, Lepelletier-based designs are now in18 production throughout the industry a nd likely to become a dominan t transmission type in the coming years. The DCT, pioneered in Eur opean mass production by Volkswagen and BorgWarner, uses two clutches and automated contro ls to select gears us ing two shafts (for alternating even and odd gears), avoiding the n eed for a torque convert er. Design and control refinements are enabling DCTs to have the smoothness customary to traditional automatics and so provide another competitive option for tr ansmission-based efficiency improvement.

Collectively, these transmission technologies in co mbination with optimized shift schedules will enable maximum efficiency gains from internal combustion engines, with particular transmission types suited for different applications. The vari ous choices can be broke n out with particular levels of efficiency gain, as tabulated by the NRC studies and EPA-NHTSA rulemaking documents, for example. However, the potential benefits are ultimately best viewed more holistically for enabling better optimization for the engine-tra nsmission system, as modeled by Kasseris & Heywood (2007), for example.

Hybrid Drive Hybridization combines a combustion engine with another type of motor and an energy storage device capable of buffering tractive power. The hybr id electric vehicle (HEV) is being widely commercialized. Other forms of hybrid drive, suchas those using hydraulic motors and storage, are being demonstrated for commercial vehicles . The efficiency benefits of hybrid propulsion include enabling the engine to run more often at its most ef ficient operating points, thereby allowing it to be downsized and be turned off when not needed; recovering braking energy; and providing ample power for electrifi cation of vehicle accessories. A rich literature exists on hybrid technology and so it is no t discussed here in depth. G ood sources are the overview by German (2004), the analysis by Kromer & He ywood (2007), and the acce ssible, continually updated information on www.hybridcars.com

. HEVs have captured the popular imagination as a breakthrough enabling much greater gains in vehicle efficiency. From an engineering pers pective, however, grid-f ree (non-plug-in) hybrids are better viewed as a step in the evolution of internal comb ustion engine vehicles, extending their potential for higher efficiency at higher cost . As shown earlier in Fi gure 4, the cost-benefit ratio for HEVs largely follows the same increasi ng marginal cost line as improved conventional technology. Thus, HEVs do not offer higher efficiency gains at lowe r costs as would be expected of a "game-changing" breakthrough. In short, grid-free hybrids are no more and no less than a valuable advance in the sophisticated and mature field of automotive engineering.

Hybrid vehicles entered the global market withthe December 1997 launch of the Toyota Prius in Japan. In the United States, the original Honda In sight hybrid went on sale in early 2000 and the Prius began U.S. sales in July 2000. In 2009, new U.S. hybrid sales tallied to 290,000, or 2.8% of the new U.S. light vehicle market, and globa l sales approached 600,000 (Hybridcars.com 2010). The United States and Japan are the two largest hybrid markets by far, and the Toyota Prius has been the top selling hybrid nameplate. All ma jor automakers have hybrid programs underway and the number of offerings is ri sing steadily. Even though hybridization entails an inherent cost increase compared to non-hybrid powertrains, it is now seen as a foundation for automotive technology in the decades ahead. As one industry expert put it, "hybrid technology is the core technology that drives us toward a sustainable future" (Ward 2010).19 Although analysts classify hybrids in various ways (series vs. parall el, single vs. two mode, mild vs. full, etc.), the technology is really a spectrum of options. Ev en a standard alternator can recapture a small amount of braking energy and gr eater regeneration is possible with start-stop systems, particularly in the more sophisticated fo rm of an integrated starter-generator (ISG). The term hybrid drive is generally reserved for when the electrical machine provides at least some degree of tractive power. Projecting which hybrid configurations will dominate is difficult and not really needed for public policy. The technology will see variations and improvements at all levels, from components such as batteries, moto rs and controllers to systems integration. The rapid pace of development is all the more reason to rely on engi neering fundamentals rather than second guessing the creativity that will unfold dur ing competitively driven product development by automakers and their suppliers.

Thus, even though the fuel efficiency of hybrids is often reported with distinct estimates for different configurations, the pot ential gains are best viewed al ong a cost-benefit continuum. Well optimized, fully capable hybrid systems can cu t energy intensity by as much as 50% when controlling for performance. However, even star t-stop systems that fall short of providing a tractive boost, as well as a variety of single-mo tor/generator designs, yield benefits that offer cost-effective fuel efficiency solutions. The indus try is exploring a range of approaches as seen in recent announcements by General Motors, Hyundai and others. Because much of the potential benefit of hybridization is tied to its ability to optimize engine operation, the efficiency of a hybrid vehicle also depends on the ba lance of the powertrain system.

Although hybridization can further in crease of efficiency of high-out put engines such as direct injection turbocharged designs, it can also be very effective with an advanced low output engine. That very approach is seen in the Toyota Prius and other vehicl es that mate hybrid drive to a naturally aspirated, port-injected Atkinson cycl e engine, which uses an extended expansion stroke to extract more work from the charge. Th at limits torque and therefore limits peak engine power, for which the electric motor-generator components compensa te. Kasseris & Heywood (2007) simulate hybrid drive with an advanced, lo w-friction naturally aspirated engine, which is less costly than the GDI and diesel engines theyseparately analyze and therefore helps balance the higher costs of electric driv e components. Their results by no means represent the limit of the potential efficiency gains for hybridi zed internal combustion powertrains.



### Synthesis of Technology Assessments
Numerous studies have been publis hed that draw by varying degrees on the technical options just summarized. This section starts w ith an overview, contrasting methods that can be classified as either assuming incremental application of detailed technology speci fications or assuming optimal application of capabiliti es based on engineering fundament als. The issue of how quickly the necessary technology, particularly extensiv e use of hybrid drive, might be adopted is addressed by applying logistical functions derived from histor ical technology adoption rates. These results are then used to construct an amb itious but still evolutionary scenario for the new fleet fuel efficiency horizon through 2035.20 

### I NCREMENTAL VS



### . F UNDAMENTAL 

### A NALYSIS The technology assessments done to support standa rds setting are cautious by nature, working from make- and model-level data characterizi ng a baseline fleet and specifying technology modifications in detail. In cont rast, a fundamental approach pos its "clean sheet" vehicle designs, which have specified size and performance levels but need not be modified versions of current vehicles, and so is less restrictive of the e fficiency potential that can be identified.

Incremental Approach for Justifying Regulations An advantage of incremental analysis is its ability to develop detailed cost estimates tied to the use of specified technologies. Working from kno wn vehicle platforms, it enables regulators to identify stepwise changes automakers can make intheir products to yield a fleet of higher fuel economy. Identifying a detailed compliance pathwa y is considered important for justifying standards that impact near-term cycle plans (5-10 years).

A detailed incremental analysis was used fo r developing the MY2012-16 single national program (EPA & NHTSA 2010 Joint Rule). Its per-vehicle retail price equivalent (RPE) cost estimates are plotted as a function of en ergy consumption reduction in Fi gure 7. The rule requires annual increases in fuel economy and cuts in GHG emissions (includi ng air-conditioning related offcycle and trace gas emissions) leading to a projected 2016 new fleet average of 250 g/mi CO 2 - equivalent. The fuel economy part of the proposal aims for 34.1 mpg by 2016, a 25% increase over the projected 2011 level and 38% higher than the 2005 combined fleet average of 24.8 mpg. In consumption terms, the rule implies a 27% re duction relative to the 2005 fleet, which had an average annual fuel use rate of 74 GJ/yr (605 gal/yr of gasoline).

An aggregate incremental approach was used by the NRC fuel economy studies. NRC (2002) reported a midrange estimate of 26% fuel ec onomy gain (21% energy intensity reduction) without significant weight reduc tion and with cost-effectivene ss based on $1.50/gallon gasoline. The NRC (2010) update estimated energy intensity reductions from 29% for non-hybrid gasoline

Figure 7. Costs of energy savings implied by the MY2012-16 Joint Rule21 vehicles to 44% for HEVs as achievable with in 15 years. The King Review (2007) drew on incremental assessments for its estimate of a potential 30% energy intensity reduction for the UK light vehicle fleet. Ricardo ( 2008) simulated incremental powertrain improvements without mass reduction and projected near-term energy inte nsity reductions of 23%–29% over a set of representative vehicles.

Duleep (2010) also uses an incremental approach , at a high level of aggr egation but consistent with the more detailed analyses that his firm ha s conducted for past agency studies. Relative to a 2005 baseline, such studies support the 32% fuel economy improvement (24% energy intensity reduction) required by the EPA & NHTSA (2010) rule. By 2025, Duleep (2010) estimates a potential 50% fuel economy gain (33% energy intensity reduction) from maximum use of conventional (non-hybrid) technologies.

Fundamental Analysis for Future Horizons A fundamental approach analyzes future designs of specified characteristics and determines the ability to maximize efficiency based on engineering principles. It identifies technologies usable for future designs but does not detail a techno logy pathway for modifying today's vehicles to reach future efficiency levels. It can be consider ed a more theoretical analysis in that regard. Nevertheless, as long as it is based on sound engin eering analysis, it yields results to which the fleet can evolve given enough lead time and an abili ty to execute the necessary design changes. Such a timeframe extends beyond near-term cycle plans and may require automakers to rethink mid- and long-term (10-20 year) product strategies.

Fundamental analyses can be carried out usi ng engineering simulation models (e.g., Kasseris & Heywood 2007) or by using simplified physical models (e.g., EEA 1991, OTA 1995, and one of the approaches used by DeCicco et al. 2001). The opportunities iden tified by such analyses are based on physical principles related to the tractive and other ener gy loads that must be met and the efficiency with which the powertrain supplie s these loads. By way of historical note, a fundamental approach was used in the "120 Day Study" carried out by DOT & EPA (1974) to support the original CAFE sta ndards, which required a near doubling of passenger car fuel economy within a decade.

With a 2005 baseline and a 2030 time horizon, th e Kasseris & Heywood (2007) results average out to a 45% energy intensity reduction from effi cient use of conventional technologies and a 67% reduction from a combination of advanced e ngine technologies and optimized hybrid drive. Their results for gasoline vehicles are summari zed in Figure 8, converted to on-road energy intensity in kJ/m. They also analyzed diesel versions of the same vehicles, finding energy intensity reductions somewhat better than tur bocharged gasoline engine s but short of hybrids. Kasseris & Heywood assumed fixed performance (i .e., Z60 times matching those of the model year 2005 reference vehicles), a 20% reduction in curb weight, and decreases in aerodynamic drag and tire rolling resistance well within the bounds of historical rates of progress.

For averaging their modeled vehicles to represen t the overall fleet, weighting factors of 50% low performance, 30% high performance and 20% pic kup truck characteristics were assumed. Using the values shown in Figure 8 w ould imply that an all-hybrid fl eet would achieve tripled fuel economy, corresponding to the 67 % consumption reduction noted above. As applied here, however, these efficiency projections are combined with adjusted cost projections to develop aggregate cost curves as explained in the next section of the report; the fleet efficiency horizon22 scenario is in turn based on the cost curves. Thus , the future tripled efficiency fleet need not be an all-hybrid fleet, but rather a fleet involving so me combination of technology and design changes that realize similar fuel efficiency levels at similar costs.

Those results confirm the potential for large re ductions in fuel consumption with internal combustion engine vehicles fo reseen by earlier work. For example, the EEA (1991) boundary analysis for 2010 identified a potential to impr ove fuel economy by a factor of 2.5 (60% energy intensity reduction) using what was then characte rized as higher risk te chnology involving either hybrid drive or small-displacement direct inje ction diesel engines (but not both). For a 2015 horizon, OTA (1995) projected a potential fuel economy improvement by a factor of 1.9 for advanced conventional (non-hybrid ) cars and up to a factor of 2.6 for hybrids, both based on reduced-mass aluminum-intensive body structures . DeCicco et al. (2001) modeled an advanced sport wagon (with capacity and performance similar to a midsize SUV or fullsize minivan) achieving a 55% consumption reduc tion (factor of 2.2 in fuel economy). This gain was projected without the need for hybrid drive by using a direct-injection gasolin e engine with an integrated starter-generator (enabling st art-stop) and an automatic, direct shift transmission in a streamlined, 40% reduced mass structure. These ol der analyses all worked from older baselines having weight and performance levels less than those of recent model years.

Fundamental analyses also guided the Partners hip for a New Generati on of Vehicles (PNGV). Announced in September 1993, that program had three main goals. One was to improve the competitiveness of U.S. manufacturing; the second was to improve fuel economy and reduce emissions for conventional vehicl es; and the third was developing vehicles with a fuel economy three times (3x) that of contemporary midsize cars. The partnership invol ved the then-Big Three automakers General Motors, Ford and Chrysler. Th e baseline vehicles were 1994 versions of the

Figure 8. Potential gasoline vehicle energy intensity reductions achievable by 2030

3.04

1.89 4.64

3.71

2.95

2.25

1.67 2.49
2.04

1.04 1.61
1.10

0.0 1.0 2.0 3.0 4.0 5.0

LoPerf Sedan HiPerf Sedan Pickup TruckEnergy Intensity kJ/m Reference NA SI Turbo Hybrid

Source: Derived from Kasseris & Heywood (2007), using a 55% city and 45% highway (CAFE) cycle weighting adjusted for on-road consumption assuming 20% fuel econo my shortfall. Reference vehicles are 2005 models, including a low-performance midsize sedan (currently with a 4-cylinder engine), a high-performance sedan (currently 6-cylinder) and a full-size pickup truck (currently6-cylinder). Future vehicles all assume a 20% massreduced streamlined platform. NA SI = naturally aspirate d, possibly direct-injection spark ignition engine; Turbo = turbocharged version thereof; Hybrid = optim ized hybrid drive with NA SI engine.23 Chevrolet Lumina, Ford Taurus and Chrysler Concorde, which had a nominal average fuel economy of 26.6 mpg. In energy inte nsity terms, the 3x goal meant reducing from a baseline 2.84 kJ/m down to 0.95 kJ/m, corresponding to the wi dely cited target of 80 mpg (nominal). The PNGV (1994) program plan sketched a solution space dependent on three key factors: vehicle mass, powertrain efficiency and degree of regene rative braking. Both regenerative braking and the high powertrain efficiency of either hybridization or all-elect ric drive were presumed to be necessary for reaching the 3x goal.

Although the program was terminated before re aching its objective of having pre-production prototypes ready in 2004, PNGV concept cars were shown p ublicly in January 2000 (NRC 2000; Lynn 2004). All of the concepts used diesel hyb rid powertrains with lightweight streamlined bodies. Table 3 lists their specifi cations; the average fuel economy of 76.5 mpg on diesel, or 68.6 mpg gasoline-equivalent, was not triple the base line, but did demonstrat e a factor of 2.6 gain, corresponding to a 61% energy inte nsity reduction. The concept cars had an average curb weight 29% lower than the baseline, also short of th e program's internal 40% mass reduction target.

Although the PNGV concepts did not meet affordab ility objectives at the time, their technical achievements provide a tangible marker for the efficiency levels proj ected on the basis of fundamental analysis. Subsequent engineering progress -- including significant evolutionary mass reduction potential, ongoing advances in engine s and hybrid drive -- makes a 3x goal more attainable and affordable than it was a decade ago. Such a target is consistent with Kasseris & Heywood (2007), whose projections for what is possible by 2030 average to a 3x fuel economy gain over 2005 reference vehicles, a baseline that reflects many improved features and better performance than the PNGV's 1994 baseline.

In any case, fundamental engineering considera tions imply that hybrid drive is a key technology for attaining these efficiency levels. Some may view hybridization as a stepping stone across the

Table 3. PNGV concept car fuel economy and consumption results

F UEL E CONOMY AND C ONSUMPTION R ATE
Model year 1994 reference cars mpg kJ/m PNGV Concepts diesel gasolineequiv MPG gain relative consmp kJ/m

Chrysler Concorde 26.7 3.55 Chry sler ESX3 72 64.6 2.42 0.41 1.46

Ford Taurus 27.1 3.48 Ford Prodigy 78 69.9 2.58 0.39 1.35
Chevrolet Lumina 26.1 3.63 GM Precept 80 71.7 2.75 0.36 1.32 Average 26.6 3.55 Averag e 76.5 68.6 2. 58 0.39 1.38

3x Goal 79.9 3.00 0.33 1.18

C URB W EIGHT

Reference cars kg lb PNGV concepts kg lb reduct Chrysler Concorde 1594 3515 Chrysler ESX-3 1021 2251 36% Ford Taurus 1508 3325 Ford Prodigy 1083 2388 28% Chevrolet Lumina 1533 3380 GM Precept 1176 2593 23% Average 1545 3407 Average 1093 2410 29%
Source: PNGV (1994) and NRC (2000).24 divide from internal combustion en gines to all-electric vehicles. Regardless of that eventuality, hybrid drive greatly extends the efficiency horiz on for vehicles whose prime movers remain internal combustion engines operated on liquid hydrocarbon fuels and which therefore do not require major changes in the ener gy distribution system. Hybrid uti lization is still low, however, and so an important factor is how quicklyit might be adopted across the fleet.



### R ATES OF 

### T ECHNOLOGY 

### C HANGE Automobiles are continually redesigned and t echnology continually improves, though not always with fuel efficiency as a design priority. Due to constraints of financing, facilities, human resources and supplier capabilities , "it is difficult for automakers to do too much too fast" (CAR 2007, viii). That being said, the questions are wh ether the 25 year horizon considered here is sufficient for tripling new fleet efficiency and wh at intermediate levels are possible between now and that long-term target.

For incremental progress toward maximum use of cost-effective near-term technologies, typical lead time estimates are 10-15 years (DeCi cco et al. 2001; NRC 2002; CARB 2004a). Such a period is partly based on the time it would take an automaker to systematically redesign all of the vehicles in its fleet (which might include dr opping some models and introducing new ones). A quicker time frame is implied by state-of-the-art practices, as driven by competitive pressure and enabled by advances in design and manufacturing that entail round-the-cl ock global engineering work. Standard product development times are no w 2-3 years and typical product cycles are 5-6 years (CAR 2007), suggesting thatfleetwide changes can be made in less than a decade.

However, major new technologies cannot be advanced from low to high levels of utilization over the course of a single redesign cycle. An empiri cal basis for rates of technology change can be found in historically observed di ffusion of automotive technologi es and design changes. Such data are given in EPA's Fuel Economy Trends report s and reveal a wide range for rates of change (EPA 2009). The MIT study notes that "even very cost-effective technologies such as variable valve timing have taken 10-15 years to penetrate half of new vehicles," and that automatic transmissions "having reached half of the market by 1950, required 20 more years to be available in 90% of vehicles" (MIT 2008: 109).

On the other hand, the same EPA database reveal s rapid progress for some major changes. Frontwheel-drive was quickly adopted across the passenger car fleet, a major modification that also involved a conversion to unibody construction of the older bo dy-on-frame designs on which most rear-drive vehicles were bui lt. Even more rapid adoption occurr ed as fuel injection replaced carburetors. Figure 9 shows the ut ilization rates (U) fo r these technology changes along with fits to growth curves based on a logistic function:

U(t) = U max (1 + Ae -bt ) -1

The function's parameters imply th e maximum rate of change (U' max , the slope of the logistic at its inflection point) and the time (t 90 ) needed to reach 90% of maximum utilization (U max ). These values are given by U' max = b/4, where b is the exponen tial rate parameter, and t 90 = ln(9A)/b (derived from the logistic; s ee, e.g., Draper & Smith 1981: 508). Using the fits illustrated, t 90 was 9 years for fuel injection and 14 years for conversion to front-wheel drive, with U' max values of 17%/yr and 11%/yr, respectiv ely. These were very rapid rates of change.25 In fact, the rate of adoption for a given technol ogy is tied to the need for the technology. In keeping with a key theme of this paper, the type of technology targeted for progress itself can be considered a matter of design pr iority. In both cases of rapid adoption shown in Figure 9 there was a strong driver for change. In the early 1980s, automakers faced sudden pressure to improve fuel economy in the wake of the oil crisis and ramp-up of CAFE standard s for cars. Some frontwheel drive substitution occurr ed through redesign of existin g models and some occurred competitively, as imported cars displaced domestic models even as the Detroit fleet was modernizing. As shown in Figure 9(b), by 1987 U. S. new cars were 78% front-wheel drive (90% of the plateau value of 87%), up from less than 10% as of 1978. The even more rapid adoption of fuel injection was driven by emissions regulations , which required precise mixture control for 3- way catalysts to operate properl y. Fuel injection also offere d performance and efficiency benefits, enabling engineers to sidestep the trad e-offs encountered whenrecalibrating carbureted vehicles for lower tailpipe emissions.

Moreover, those changes were not trivial. The conversions to front-wh eel drive and unitized body structures had substantial im pacts on design and engineeri ng, involving major architectural changes in both vehicle structures and drivelines. It involv ed new tooling and new supplier relationships, as well as new de sign, engineering, fabrication a nd assembly techniques. Although more restricted in the scope of its impact on th e whole vehicle, fuel in jection was a significant change, involving many new components and the need for computerized engine controls.

Although slower rates of adoption occurred for au tomatic transmissions and valve train changes, neither technology met needs as compelling as thos e that drove fuel injection and front-wheel drive. Many consumers continued to prefer ma nual transmissions (and some, though a declining number, still do), and relative to manuals, auto matics once had a fuel eco nomy penalty. Detroit automakers generally eschewed overhead cams and the valvetrain refinements they facilitated, instead continuing to adapt pushrod (over-head valv e) engines; moreover, they did not need the benefits of more sophisticated valv etrains until relatively recently.

Figure 9. Notable historical rates of change in automotive technology utilization, with fits to logistic functions

(a) Fuel injection in light duty vehicles (b) Front-wheel drive in passenger cars

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

1970 1980 1990 2000 2010Share of New LDVs

Logistic model: U(t) = U max (1 + Ae -bt ) -1

Parameter estimates: A = 40 b = 0.68 for U max= 100%

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

1970 1980 1990 2000 2010Share of New Cars

Logistic model: U(t) = U max (1 + Ae -bt ) -1

Parameter estimates: A = 55 b = 0.44 for U max = 87%

Source: author's fits to data from EPA (2009), Table 13. Fuel injection includes all forms (throttle body, port and direct); front-wheel drive includes all-wheel drive variants of front-wheel drive models.26 Thus, achievable rates of change depend to a large extent on the pressure to change. Of course, if technology development is shaped inparticular directions, progre ss in other directions may be foregone. It is difficult to pred ict how cars would have evolved without the fuel economy and emissions control pressures of the early 1980s. Nevertheless, it seems likely that many other comfort, performance and capability amenities would have been seen (or seen sooner) if there had not been the prioritization of fuel economy and emissions control that drove the rapid adoption of front-wheel drive and fu el injection duri ng the 1980s.

The most challenging future change needed is lik ely to be extensive use of hybrid drive. Hybrid powertrains are used in less than3% of new light vehicles to date. Nevertheless, given the announced introductions and the spread of hybrid capability from pioneers such as Toyota and Honda to all automakers and major suppliers, the technology could soon enter a phase of rapid adoption, should the need arise. That again will depend on the degree of design priority -- as jointly determined by consumers, automakers and policymakers -- placed on achieving much higher fuel efficiency, which is a strength of hybrid drive.

The complexity of the change, in terms of design a nd engineering needs, is comparable to that of the conversion to front-wheel drive unibody car platforms in parallel to the rise of fuel injection a generation ago. Automakers have successfully ma naged complex changes in vehicles within relatively short periods of time. However, those pa st changes did not entail the inherently higher costs seen for some electric drive components. Thus, the rate of hybr id adoption is more likely to be limited by its expense than by the ability to rapidly execute technol ogy change, a situation reflected in the cost estimates incorporat ed into the scenarios developed below.

If a significant adoption phase st arts soon, hybrid utilization even tually plateaus at 90% of the fleet (allowing 10% traditionalist non-hybrid hold- outs), reaches 90% of it s plateau in 20 years

Figure 10. Hypothetical market adoption curv e for hybrid drive compared to logistic curves based on historical auto motive technology adoption rates27 (t 90 =20, i.e., by roughly 2030) and has a logistic para meter similar to that for front-wheel drive (A=55), then the rate parameter given by ln(9A)/t 90 is b = 0.31. The maximum rate of increase (the steepest part of the adoption curve) is then 7.6% per year. The resulting hypothetical diffusion curve is shown in Figure 10, with the fits from Figure 9 and some slower technology adoption curves shown for comparison. The conclusi on is that an extensiv e conversion to hybrid drive could happen with adoption rates within the range of those observed historically for other major technology changes.

This discussion is not meant to suggest thathybridization per se should follow the trajectory illustrated. A tripled efficiency fleet will undoubtedl y see a high utilization of hybrid drive, but the level could be lower than that assumed here if greater progress is seen in any number of other areas including mass reduction, other aspects of load reduction, use of start-stop and new energy harvesting technologies with turbo GDI engines, diesel emissions control and very advanced combustion technologies such as HCCI. As emphasi zed throughout this repor t, the intent is not to advocate any particular technol ogical pathway, but rather to identify a solution set for a highefficiency future fleet while remaining agnostic a bout the ultimate mix of technologies needed to achieve it, which is best left for the market to determine.



### N EW 

### F LEET 

### S CENARIOS It is common for analysts to posit scenarios base d on projected shares of advanced or alternative powertrains. Technologies are distinguished by treat ing, say, a turbocharged gasoline engine as distinct from a diesel or hybrid powertrain or from a naturally aspirated engine. Automakers themselves need to decide what technologies to use on any given product. But for policy analysis, these "named technology" categories cr eate unnecessary di stinctions. This degree of technological specificity result s in scenarios called "hybrid" and "diesel" for powertrains, for example, or "steel-based" and "a luminum-intensive" for vehicle bodies, fostering lively debates about hybrids vs. diesels a nd aluminum vs. steel.

Although perhaps fascinating, such debates cannot be resolved outsi de the confidential confines of automaker product planning. That is the onlycontext where realistic technology choices can be made, as program directors face the trade-o ffs due to budget constraints and the numerous parameters that must be considered for defi ning a successful product. These decisions depend on not only technology readiness and co st, but also its appeal to a product's target customers and how well it leverages a firm's strengths, its brands, and market trends in th e particular segments in which products must compete.

Here we avoid such technological specificity and define scenarios strictly based on average fleet energy intensity outcomes, which ultimately depend on performance and size mix as well as on technology. This relegates the role of specific te chnologies to that of demonstrating feasible levels of efficiency while a llowing that other design and tech nology combinations may also work. It reflects the fact that it is impossible to specify which of today's named technologies will offer the most workable solutions tomorrow, or whether some options that now seem promising will fall by the wayside as new options emerge. An agnostic perspective is also in keeping with fundamental engineering analysis in that many technologies tack le one or more of the same underlying sources of inefficiency.28 The factor of three identified above as attainab le through an evolutionaryprocess that exploits known powertrain technologies wi th moderate degrees of mass reduction and streamlining is adopted as the long-term efficiency horizon for the new U.S. light duty vehicle fleet in 2035. Figure 11 plots trajectories toward that target starting from the 2005 fleet and its on-road average energy intensity of 3.8 kJ/m. The 2035 efficien cy horizon is 1.27 kJ/m on-road, corresponding to a nominal (unadjusted) fuel economy of 74 mpg. Also plotted in th e figure is the fleet average achieved to date (EPA 2009) and projected thro ugh 2016 for the recently promulgated Joint Rule (EPA & NHTSA 2010). Refer back to Figure 1 in th e Executive Summary to see this scenario in comparison to the historical datafor new fleet energy intensity.

In terms of improvement paths, Figure 11 show s an upper bounding trajectory based on a linear decline in energy intensity (the same as a linear decline in efficiency-related CO 2 emissions rate), representing a slower near-term path toward the long-term target. A lower bounding trajectory is based on a linear increase in fuel economy, which would represent more rapid near-term progress toward the same long-term goal. Here, a middle path is assumed that uses a constant compound rate of change starting in 2005 and ending in 2035. That trajectoryentails an average 3.6% per year decline in energy intensity or a 3.7% per year rate of increase in fuel economy.

The implied 5-year targets for 2020-35 are listed in Table 4 along with the 2005 baseline level and projections for 2010 and 2016 based on standa rds recently promulgated. Estimated on-road fuel economy (adjusted as for average label values ) is shown in the row below the nominal levels as used for CAFE compliance. For CO 2 emissions, the first line is a tailpipe-only rate computed using a constant gasoline-equiva lent carbon coefficient of 8.887 kg/ga l. The last line is a fleet average CO 2 -equivalent GHG emissions rate after subtracting 15 g/mi for likely reductions of GHG emissions related to automotive air conditioners ; it is less than the potential reductions of 21.2 g/mi identified in EPA & NHT SA (2010 Joint Rule: 25427-28).

Figure 11. Scenarios for decline of new fleet energy intensity toward long-term target based on tripled fuel economy

Left axis shows estimated on-road energy intensity adjusted for 20% fuel economy shortfall; right axis is gasolineequivalent fuel consumption rate in nominal terms (as would be derived from unadjusted lab tests).29 For the intermediate year of 2025, the implication is a 52% reduction in ne w fleet average energy intensity, from 3.81 kJ/m in 2005 down to 1.83 kJ/m. The corresponding nominal fuel economy level is 52 mpg in nominal terms (41 mpg on-road ), or just over a doubling of the 2005 level.

As one industry-based point of reference, Hyunda i recently announced a goal for its overall U.S. car and light truck fleet of "a t least 50 mpg by 2025" (Hyundai 2010). Hyundai's fleet is already more efficient than average (its combined fleet of 30.9 mpg was 17% higher than the 26.3 mpg market average in 2008; EPA 2009, Table A-7), so th e relative improvement needed is less than average. Therefore, its announcement falls short of corroborating the more ambitious trajectory suggested here. Nevertheless, remarks made by a Hyundai executive capture the spirit of the approach that this repo rt takes in analyzing the efficiency horizon. In an interview following the announcement, Hyundai Motor America president John Krafcik said:

... at Hyundai, often what we do is set rea lly stretch targets, where at the time we don't necessarily have a specific roadmap, we don't know in detail how to get there. But for this minimum 50 mpg by 2025 we have an awful lot of confidence that we can get there. ... It will align the whol e company; it will align our engineering teams, our manufacturing and supplier partners; we'll find a way to get it done. And now we'll get into the really hard work, like what are our implementation plans.

-- Krafcik (2010b) interview by Automotive News

Mr. Krafcik continued the interview by describi ng how Hyundai's plans will lean heavily on optimizing non-hybrid internal combustion engine powertrains, which would still account for 75%-80% of Hyundai's mix in 2025. Achieving that degree of efficiency gain without hybridization is consistent withthe Kasseris & Heywood (2007) esti mates for the capabilities of turbocharged gasoline engines (again, a ssuming progress on mass reduction and foregoing significant performance gains).

Thus, efficiency levels approaching a doubling of fuel economy do not seem likely to require extensive use of hybrids. However, reaching tr ipled efficiency for the long-term 2035 horizon will almost certainly require extensive use of hybr id drive. This raises a conundrum automakers might face regarding their long-term powertrain stra tegies. If the ultimate horizon is on the order

Table 4. Light duty fleet average energy inte nsity and related metric s: reference levels and future targets based on fuel efficiency horizon scenario

New Fleet Average 2005 2010 2016 2020 2025 2030 2035 kJ/m (on road) 3.81 3.52 2.77 2.20 1.83 1.53 1.27 mpg (nominal/CAFE) 24.8 26.9 34.1 43.0 51.6 62.0 74.4 (on-road adjusted) 20 22 27 34 41 50 60 L/100km (nominal) 9.48 8. 74 6.90 5.48 4.56 3.80 3.16 g/mi CO 2 (nominal) 358 330 261 207 172 143 119 (with A/C reductions) 250 192 157 128 104
N.B. These projections are for gasoline and diesel v ehicles (including non-plug-in hybrids) only.30 of doubled fuel economy, then adoption of hybrid dr ive can proceed more slowly than illustrated here in Figure 10 for a trajectory that aims fo r tripled fuel economy. On the other hand, if the long-term target is likely to be much higher, then even though extensiv e hybridization may not be needed by 2025, investing in it sooner may be important for gaining experience and reducing costs as needed for a more ambitious long-term go al. This issue of path dependence is one that may merit further analysis and discussion.



### Cost of Efficiency Improvement
This study adapts previously published cost estima tes rather than developing a new cost analysis. Near-term values are derived from recent rulema king analyses. Long-term values draw mainly on MIT (2008), which analyzed costs for a few re presentative vehicle types. Those estimates were adjusted downward under an assumption of modest mass reduction at zero net cost as discussed earlier. After discus sing interpretation issues, this section describes a methodology for modeling aggregate (fleetwide) cost s as quadratic functions that sh ift rightward (down) over time to reflect cost reductions likely to be achieved through ongoing engineering progress.

Most studies state the cost of m odifying vehicles in terms of reta il price equivalent (RPE). Those values are calculated by marking up manufacturi ng cost estimates with multipliers as described by EPA & NHTSA (2010 TSD), for example. RPE es timates should be inte rpreted carefully, an issue worth some discussion before pres enting the cost estimates themselves.



### I NTERPRETING 

### C OST 

### E STIMATES It is common to equate RPE with a higher price that consumers will face for new vehicles. For example, the EPA & NHTSA (2010) Joint Rule char acterizes its average $ 950 per vehicle cost as an increase in the consumer price of a 2016 new vehi cle. That notion leads in turn to estimates of how long it takes consumers to recoup the price increase through fuel savings. Although this assumption and related cost discounting calculati ons are a straightforward way to analyze the situation, it is unclear that it well represents the realities of vehicle pricing and sales.

Some assumptions from economic theory suggest that automakers largel y pass costs through to consumers. Crandall et al. (1986) found that the costs of safety and emissions standards appeared to be fully passed on after a short lag. However, their analysis covered a period when the car market was sustaining annual real price increases. Considering more recent conditions, several factors make it difficult for automakers to pass co sts through to consumers. This seems likely for changes not fully valued by consumers, such as policy-driven fuel economy increases beyond the level that consumers would choose based on fuel prices alone. Also, the auto market has a scale (of total revenue) determined by overall economic conditions, and it is unclear that total vehicle expenditures increase proportionately simply because automakers incur additional costs.

The average price elasticity of new automobile sale s is considered to be close to unity (-1); a commonly cited value is -0.87 as estimated by Mc Carthy (1996). Unit elastic ity means that total revenue does not change as prices rise, so that automakers would sell fe wer vehicles at a higher price but not gain any revenue. However, th e actual market response is complex and not reflected only in sales volume. The impact of a pr ice increase may be partly seen in reduction of content (expenditures by trim level or optional features per unit sold), not just reduction of31 volume (number of units sold). Either way, auto makers may gain little additional revenue by raising prices. Given the industry's high fixed cost structure, a contraction in volume is harmful. For the relatively large cost impacts (upwards of 15% of baseline price) estimated for the longterm efficiency horizons identified here, it seem s unlikely that automakers would pass all of the cost on to consumers and suffer the loss of sales volume that it might imply.

The auto market is highly competitive and at least until recently has suffered from chronic overcapacity. Both of these fact ors also hold prices down. The difficulty automakers have in raising prices, at least over the past 15 years, is apparent in Figure 12. Chart (a) shows average new car transaction prices in both nominal and infl ation-adjusted terms. Real prices peaked in 1998 and have been trending downward ever since. Th ese data are for cars only; light truck data are not as readily available and might show less drop-off, but for much of the past two decades the general trend in transaction prices was flat at best even before the current recession. Chart (b) compares the consumer price index for new auto mobiles with that for all goods and services. New vehicle prices have long lagged general inflation due to productivity gains in manufacturing as well as competition from imports. The recent up tick in the new vehicle index may indicate the start of a pricing recovery, but by how much is yet to be seen. In any case, the cost pass-through question is one that need s further investigation.

To the extent that the ability to raise prices is restricted, financial constraints are more severe. Automakers must develop vehicles on fixed budgets determined by pric es actually commanded, the sales volume achieved and the needed return on investment. This economic fundamental of constrained revenues -- scrutinized by financial analysts in bothcar companies and investment firms from which they raise capital -- di sciplines automakers' product planning budgets:

Every automotive program has an internal busin ess case that is ultimately controlled by vehicle prices and sales volumes; all aspects of a program plan must meet the resulting budget constraint. (CAR 2007: v) Figure 12. Transaction prices and price inde x for new vehicles in the United States

Source: Ward's (2010) for new car transaction prices; BLS (2010) for price indices.32 As a result, managing costs means making trade-o ffs among features in order to stay on budget. Failing to do so (or failing to use realistic assump tions about pricing and sa les) results in severe financial risks. Budget constraint s affect vehicle attributes and content whether or not they are desired by customers, and so the costs of effici ency improvement are confronted by automakers in that context. Regarding policy requirements:

Regulations represent things that have to be done—for safety, emissions, and fuel economy—regardless of how they are valued by customers. ... In short, product requirements that are not valued by customers force trade-offs in features that would otherwise be designed or included on the basis of customer value. (CAR 2007: 21)

That report goes on to say:

Some such requirements can be handled at a systems level while fitting within budget constraints. Such a systems-level strategy is the ideal way to a ddress fuel economy, for example. Because it affects nearly every vehi cle system, a holistic approach is needed; if this is done well, fuel economy can be ha ndled without incurring additional costs and while staying within bud get. (CAR 2007: 21)

These considerations imply that cost estimates for technology change should be interpreted mainly as an indicator of the impact on automakers, only a portion of which gets passed through as an impact on consumer prices. Automakers ar e likely to have to manage the cost impact by trading off other features to st ay within budget and avoid hiking pr ices to an extent that overly depresses sales. In other words, incremental RP E values can be viewed as measuring the tradeoffs that need to be made when improving effi ciency rather than im proving a product along other dimensions. Much of the cost impact may take the form of foregone spending on other features that otherwise would have been offered.

Thus, it is incorrect to simply equate RPE estim ates with price increases to be experienced by consumers. A more correct interpretation is that they are retail price equivalent estimates for the opportunity cost of technology changes made w ithin a constrained product development budget. This interpretation suggests that policy-driven en ergy efficiency gains may be an even better deal for consumers than they are held out to be by presenting the simple payback estimates given by many studies (including some earlier studies by this author). It also means that the challenges are compounded for automakers, who must invest intechnology but may be unable to fully recoup those costs in the marketplace.



### C OST 

### C URVES To synthesize cost estimates into aggregate valu es for assessing the efficiency horizon, they can be distilled into curves that enable different re sults to be readily compared. Such aggregation is well suited for a high-level assessment in light of the uncertainties involved and the fact that the efficiency gains are based on engineering f undamentals rather than detailed technology specifications. Cost curves based on key recent studies are shown in Figure 13, as described below and derived in the Appendix.

It is convenient to use a quadrat ic form for several reasons, not the least of which is parsimony, i.e., that no more than one or two coefficients are needed. A greater number of parameters is33 unlikely to be meaningful in light of the uncer tainties in the underlying "data," which are in reality judgment-based estimates of cost. Other advantages are that quadratic total costs imply linear marginal costs, making cost-benefit equations tractable and transparent, and that a quadratic can be seen as a 2 ndorder approximation to any func tion near the origin, which is appropriate when calculating co sts relative to a baseline (Greene & DeCicco 2000). Finally, inspection of scatter plots sugge sts as good a fit as is likely to be provided by any function.

In contrast to curves that are quadratic in fuel economy, as used by Greene & DeCicco (2000) and NRC (2002), this analysis repr esents costs as quadratic in fuel savings (energy intensity reduction). The main reason is that benefits are linear in fuel consumption, as opposed to fuel economy for which successive increments provide a diminishing benefit. The quadratic form itself represents increasing costs, and no particular insight provides a rationale for a quadratic in fuel economy, which would imply costs rising bo th hyperbolically and quadratically as a function of benefit. For fits within a limited rang e of incremental improvements, either approach basis is probably suitable. But given this study' s objective of characterizing long-term horizons involving large changes in efficien cy, a quadratic function of fuel savings seems a more prudent choice. Of course, these curves should not be us ed to extrapolate beyond the range of technically feasible improvements (otherwise, a quadratic proj ects a finite cost for reducing consumption to zero, which is not physically possible).

The quadratic approximations to the costs from several studies are shown in Figure 13 based on regressions described in the Appendix. Thes e curves use a single-parameter form, Δ C = A( Δ E) 2

, where Δ C is the projected retail-price e quivalent (RPE) cost increase and Δ E is the incremental

Figure 13. Average costs of lig ht vehicle energy consumptio n reduction from quadratic approximations to results of recent studies

Source: derived from EPA & NHTSA (2010) "Joint Ru le," MIT (2008) and NESCCAF (2004). The quadratic coefficients are: Joint Rule 4.0, NESCCAF 4.3, MIT cars 2.7 and MIT trucks 1.4 [2010$ per (GJ/yr) 2 ]. For common units, 1 GJ = 8.22 gallons of gasoline equivalent (gge, on-road LHV basis); e.g., 40 GJ/yr ≈ 330 gge/yr.34 energy savings. RPE value from the studies were all inflated to 2010$ to facilitate comparison. The resulting quadratic coefficient estimat es (Â) are given in the figure caption.

These curves show how a long-term analysis ti ed to engineering fundamentals paints a more optimistic picture than seen wh en extrapolating from near-term studies. As shown in Figure 6, the EPA & NHTSA (2010) Joint Rule reflects a relatively limited range of energy use reduction, about 16 GJ/yr relative to a MY2011 baseline cons umption of 67 GJ/yr. Qu adratic extrapolation to about twice that level of reduction (as illustrated in Figur e 13) shows how these near-term estimates are close to those of NESCCAF (2004), which is not surprising si nce that study was a basis for California's AB 1493 proposal whic h in turn influenced the Joint Rule.

Notably lower costs are inferred from MIT (2008). That study's 2035 horizon is two decades beyond that of the NESCCAF study and it assume s steady progress over time, allowing greater cost reductions in new technologies as well as ongoing progress in exis ting technologies. The MIT-based curve for light trucks is lower than th at for cars mainly because trucks have a higher base level of consumption, and so proportionally higher savings, while the estimated costs of technology change are not that much highe r for light trucks than for cars.

To project costs over the horizon of this study, ad justed quadratic coefficients were derived by assuming that incremental mass reduction does not entail additional cost as it is phased in to a 20% reduction level by 2035. These assumptions plus the lower long-term costs of advanced powertrains imply a steady rightwa rd (falling cost) shift in the curves. The declining costs are modeled by making the quadratic coefficient time dependent:

A(t) = A 0 (1-d) t

where t is number of years from t 0 = 2015, A 0 is the coefficient for the near-term curve, and d is an annual rate of decline derived so that A( t) reaches the long-ter m coefficient by 2035 (see Appendix for details). The rate of decline calculated for the shift projected here is d = 2.9% per year, which corresponds to the cost of achieving a given level of efficiency improvement falling 44% over 20 years. The resulting near-, mid- and long-term cost curves, applicable for 2015, 2025 and 2035 respectively, are plotted in Figure 14.

This substantial reduction of costover time may seem optimistic relative to othe r analyses of automotive technology change. A key point is that the efficiency horizon pr ojected here depends only on refinement of technologies already commercialized. It therefore benefits from both competitive and collaborative dynamic s within the industry that intently focus on cutting costs to maximize profits. A similar view is seen in Moody's (2008) outlook, which concluded that the costs of complying with the European Union's proposed 130 g/km standards could fall to within
€ 300- € 1000 per car, notably lower than EU agency estimates of € 1300 per car and industry (ACEA) estimates of € 3650 per car.

As described in the Appendix, the coefficient of a quadratic cost curve speci fied as a function of percent decrease in fuel consumption can be related to the "halving cost ," or cost of a 50% reduction in vehicle energy intensity. That is th e form illustrated in Figure 14. For the mid-term (2025) time frame, an average RPE cost of about $3,200 per vehicle is implied for cutting the rate of fuel consumption in half (i.e., doubling fuel economy) relative to the 2005 baseline. By35 2035 the halving cost falls to an average of $2,400 per vehicle. The coefficients in terms of consumption reduction (GJ/yr) are al so listed in the figure caption.

Besides technology cost, the other costs to cons ider are those of ex ternalities (congestion, accidents and noise) associated with the addi tional driving (rebound effect) induced by higher vehicle efficiency. The parameters for representin g those driving-related co sts are also discussed in the Appendix.



### N ET 

### P RESENT 

### V ALUE 

### R ESULTS This report does not attempt to analyze benefits and costs using economic welfare theory. Although it tallies changes in drivin g-related externalities tied to higher vehicle efficiency, its cost-benefit results are limited to an accounting analysis that estim ates the net present value of fuel efficiency gains based largely on the diffe rence between fuel savings and technology costs. Whether or not or to what exte nt consumers value the fuel savi ngs, the avoided monetary costs of fuel are real and dom inate such a calculation.

As discussed earlier, it is not clear that consumers will see significantly higher vehicle prices. Moreover, from a policy perspective, the reason to pursue higher auto efficiency is not to save
consumers money per se , but rather to address GHG emissi ons and the costs of oil imports. Issues surrounding benefits to consumers and ho w to characterize them have been broadly debated, as discussed in the EPA & NHTSA (2010) rule and supporting st udies. At the crux of the issue -- one of longtime disagreement betw een proponents of government intervention and industries regulated by such inte rvention -- is a question about the extent to which consumers make socially optimal decisions given the right pr ice signals. In this case, the question is whether

Figure 14. Cost curves for horizons of automotive efficiency improvement

Quadratic cost curves for Δ C = 4hx 2 where x is percent decrease in fleet av erage energy intensity from the 2005 level of 3.81 kJ/m (corresponding to 24.8 mpg CAFE or 9.5 L/100km) and the coefficients (h) are "halving costs." These h values are ~$4,300 near-term (2015), ~$3, 200 mid-term (2025) and ~$2,400 long-term (2035). The corresponding coefficients for quadratics given in terms of consumption reduction (simil ar to those in Figure 13) are 3.15 near-term, 2.35 mid-term and 1.76 long-term [2010$ per (GJ/yr) 2 ].36 consumer choices based on the price of fuel yi eld market outcomes with the "right" amount of fuel economy in light of societal concerns about transportation energy use. Windows into other sides of this debate are given by Cranda ll et al. (1986) and Kl eit & Lutter (2004).

The narrowly framed results given here are deri ved by applying the cost functions shown in Figure 14 to the new fleet energy intensity traj ectory shown in Figure 11. The resulting cost estimates are given in Table 5. Costs are compared to benefits based on the present discounted value of fuel consumption avoided over the life time of the fleet. These values can be computed using a present value function and assumed lifetime, as done by N RC (2002), or calculated from vehicle travel and survival stat istics as given by NHTSA (2006). Th is analysis uses the present value method, with base case results assuming a 7% discount rate, a 4.5% per year drop-off in surviving miles as vehicles age and a 14 year vehicle lifetime (similar to NRC 2002). The resulting discounted lifetime travel value (denoted D L ) is 108,000 miles. Due to discounting, this distance is 65% of the average U.S. light duty vehicle total lifetime operation of 166,000 miles given by NHTSA (2006).

To evaluate the societal benefits, a shadow price (P E ) for vehicle energy consumption is used. It depends on three factors: the expected pre-tax price of fuel, assumed to be $2.50/gal (based roughly on $80/bbl crude oil); the social cost of carbon, assumed to be $22/tonCO 2 or $0.20/gal (from the EPA & NHTSA 2010 Joint Rule); and th e economic benefits of reduced oil imports, assumed to be $0.18/gal (also from the Joint Ru le). These numbers are in 2010$, inflated from Table 5. Cost and benefit results fo r fuel efficiency horizon scenario

Base & Current year Projections by Model Year Per-vehicle Averages for the New Light Duty Fleet 2005 2010 2020 2025 2030 2035 Energy intensity reduction 0% 8% 42% 52% 60% 67% Fuel economy (unadjusted mpg) 24.8 26.9 43.0 51.6 62.0 74.4
CO 2 (unadjusted tailpipe g/mi) 358 330 207 172 143 119

with A/C reductions (g/mi) 192 157 128 104 Lifetime Values (on-road) Energy Consumption (gge) 6,803 6,298 4,037 3,393 2,851 2,396 Energy Savings (gge) 590 3,264 4,039 4,696 5,254
CO 2 reduction (metric tons) 5 29 36 42 47
Discounted Lifetime Costs Total benefit (avoided cost, $) 6,113 7,566 8,798 9,842 Rebound externalities ($) 489 657 829 1,005 Net benefit (avoided cost, $) 5,625 6,908 7,968 8,838 Technology costs ($ RPE) 2,635 3,436 3,960 4,229 Net Present Value (2010$) 2,990 3,472 4,008 4,608
Assumptions: $2.88/gal (2010$) for lifet ime average shadow price of fuel; to tal lifetime driving of 166,000 miles, 7% discount rate; 10% rebound effect; 20% fuel economy shortfall; energy given as gallons of gasoline equivalent (gge) based on LHV of 115.4 kBtu /gal; unadjusted (tailpipe) CO 2 is test-cycle end-use only based on 8,887 g/gal; the second CO 2 line is CO 2 -equivalent GHG emissions after subtracting 15 g/mi for likely A/C-related reductions; technology costs are RPE values based on the curves shown in Figure 14.37 their respective sources. P E then amounts to $2.88/gal gasoli ne energy equivalent ($23.62/GJ). Note that 87% of this sh adow price is based on the pre-tax price of fuel.

Table 5 summarizes the costs and benefits for the trajectory given in Figure 11 and Table 4. The net present value increases through time because be nefits grow in proportion to declining vehicle energy intensity while the relative cost of a given level of energy intensity declines. These results are also shown in Figure 2 of the Executive Summary.

The efficiency horizon identified here has monetary benefits that exceed monetary costs by a large margin. By the 2025 model year, for example, the trajectory leads to a 52% reduction in new fleet average energy intensity from the 2005 level, roughly equivalent to doubling fuel economy (a nominal average of about 52 mpg) and halving CO 2 emissions rate (to an average of 172 g/mi, tailpipe only). This report does not an alyze the added incremental costs of improved automotive air conditioning systems, which are assumed to reduce CO 2 -equivalent GHG emissions by an additional 15 g/mi as noted in Tables 4 and 5. Those costs are relatively small, amounting to less than $75 per vehicle (EPA& NHTSA 2010 RIA, Chapter 2). For the efficiency improvements analyzed here using the mid-term cost curve in Figure 15, the projected 2025 RPE technology cost is roughl y $3,400 per vehicle (rounded 2010$) . That is a substantial cost, equivalent to 16% of a 2005 baseline light vehicle price of $21,000 or 12% of recent new vehicle transaction pr ices averaging $28,000.

Nevertheless, the lifetime avoided costs from such a halving of energy intensity are even more substantial, amounting to $6,900 per vehicle after netting out drivin g-related externalities. The result is a strongly positive net present value of $3,500 per new vehicle in 2025. For the tripled efficiency horizon in 2035, the RPE technology co st is $4,200 per vehicle and the net present value reaches $4,600. Recall that most of the benef it is based on fuel cost savings, which account for 87% of the total avoided cost with th e shadow price assumptions used here.

Automakers Invest, Consumers Save A distributional and timing disparityexists between the upfront co sts that automakers bear, and can probably only partly pass on, and the benefits that accrue to consumers over the lifetime of the improved vehicles. The problem is a clas sic one in public polic y including au tomobile regulation. What is notable in this case is that the costs of an e fficiency horizon such as that projected here are much larger than those of recent regulation s, such as the 2010 Joint Rule's estimate of $950 (RPE) for average per-v ehicle costs by model year 2016.

Looking back historically, the auto market has absorbed large costs for emissions and safety requirements. The Bureau of Labor Statistics estimated the price difference between a 2005 new car with and without safety and emissions equi pment at $5,100 relative to a comparable vehicle from the pre-regulatory era (Ward's 2008: 69, ad justed to 2010$). A difference between the situation for conventional polluti on control and safety and that for fuel economy is that the former policies did not yield large direct consum er savings but rather re ductions in external costs, which indirectly save money through a voided mortality, morbidity and monetary costs associated with those impacts (although some tailp ipe pollution controls had small ancillary fuel savings benefits). In the case of fuel efficiency, automakers are as ked to make an investment (or at least invest differently) on beha lf of society while direct financ ial benefits accrue to consumers in addition to the benefits that accrue to society.38 Thus, an issue these results raise is the extentto which the high incremental technology costs projected here deviate from a histor ical trend regarding the automobile market's ability to absorb the costs of regulatory requirements. To the exte nt that costs exceed the trend, a further question becomes one of what measures are available fo r mitigating the impact on automakers so that society can obtain the benefits that high levels of vehicl e efficiency would provide.

Sensitivity Analysis The results in Table 5 depend on assumptions that can be varied to yi eld different cost and benefit values. The set of sensitivity cases described below is summarized in Table 6, where each case represents changing a single assumption (the combined effects of changing more than one parameter at a time are not shown). The values are tabulated as calculated by the spreadsheet model used for the analysis, but in the follo wing narrative are rounded to the nearest $100 under a presumption of no more than two digits of significance.

The first sensitivity case lowers the discount rate from 7% to 3%. This change raises the 2025 net present value (NPV) from $3,500 to $5,200 and raises the 2035 NPV from $4,600 to $6,800. In the Joint Rule, a 3% rate was used for the main an alysis while 7% was used as a sensitivity case. A higher discount rate is considered appropriate for policies that al ter private use of capital, an assumption in line with the present study's view th at automakers have a poor ability to recoup technology costs (see discussion in EP A & NHTSA 2010 Joint Rule: 25596).

The rebound effect also influences the net benefits. Lowering the assumed rebound elasticity from 10% to 5% raises the net present value from $3,500 to $4,100 in 2025 and from $4,600 to $5,400 in 2035. A lower rebound effect becomes more appropriate as time goes on, at least if the fuel price level does not rise greatly. The higher net benefits calculated using a lower rebound assumption may better reflect the value of the long-term efficiency horizon in 2035.

As discussed in the section on mass reduction, an argument can be made that evolutionary mass reduction can be achieved with net cost savings. Assuming that the 20% mass reduction used as the basis of the efficiency horiz on identified here can be achieved with a 2% cost savings, as suggested by Lotus (2010), would lower the costs as seen in the quadratic curves (Figure 14). Phasing such a reduction in by 2035 would result inthe cost curve coeffi cient declining by 3.4% per year instead of 2.9% per year as in the base case. It would decrease the 2025 coefficient from 2.35 to 2.24 and decrease the 2035 coefficient from 1.76 to 1.59 [2010$ per (GJ/yr) 2 ]. Projected technology costs would fall fr om $3,400 to $3,300 in 2025 and from $4,200 to $3,800 in 2035.

Conversely, one can use less optimistic assumptions regarding cost reduction. If costs fall at only half the rate assumed in the base scenario -- i.e., th e quadratic coefficient decreases by 1.4% per year rather than 2.9% per year -- then the cost s of achieving the efficiency horizon would rise accordingly. Such an assumption yields the values for the last sensitivity case in Table 6, with technology costs of $4,000 in 2025 and $5,700 in2035, and NPV falling to $3,000 in 2025 and $3,200 in 2035.

The underlying price assumed for fuel, largely dete rmined by the world oil price, dominates any results. A conservative fixed-price assu mption of $2.50/gal (roughly corresponding to $80/bbl crude oil) was used. The benefits calculation can be easily scaled by a diffe rent fixed price and it is also straightforward to run th e calculations with other fuel pr ice projections. Such analyses are39 routine and their implications ar e obvious, and so creating fuel pr ice scenarios is left as an exercise for others.

Another important factor, which this study is no t able to fully analy ze quantitatively, is the sensitivity to power performance, e.g., as expres sed by zero-to-sixty (Z60) time. The cost curves used here all assume fixed fleet average performance. If performance rises, the cost of achieving a given level of efficiency improve ment will also rise, resulting ina leftward (higher cost) shift of the cost curves. Calculating the degree of sh ift for a given change in acceleration ability requires estimation of three relationships: (1) th at between fuel consumption and acceleration; (2) that between acceleration (or its correlate, po wer-to-weight ratio) and engine displacement; (3) and that between engine displacement and cost.

The first relationship is reas onably well known (see, e.g., Cheah et al. 2008; MacKenzie 2009), with correlations that take the form of z = pf -q where z is Z60 time, f is fuel consumption rate, and p and q are empirically determined coefficients. The former ( p ) relates to the technology level of the fleet and so changes through time, e.g., reflecting a decrease in fuel consumption rate at fixed performance as tec hnology improves. The latter ( q ) reflects the underlying trade-off and appears fairly stable (which is consistent with the PSFI relationship shown in Figure 5). A recent estimate for q is 0.865 (MacKenzie 2009: 43). Inverting the preceding equation and expressing fuel consumption as a function of average actual acceleration (e.g., m/s 2 ) yields an equation of the form f = ka (1/q) where k depends on p and unit conversions. Using the above cited value yields 1/q = 1.16, which is slightly greater than one and so indicates a strong, slightly superlinear sensitivity of fuel consumption to acceleration performance.

The estimated light duty fleet average Z60 in2005 was 9.9 seconds (EPA 2009, Table 2), which is the constant performance le vel on which the efficiency hor izon projection is based. That implies an average accel eration rate of 2.7 m/s 2 (a value that could be expressed dimensionlessly as 0.27 g relative to the grav itational acceleration g = 9.8 m/s 2 ). As shown here in Figure 15(a), the historical trend has been fairly linear since acceleration ability bottomed out in 1982. Note that these EPA estimates are based on a fixed co rrelation of Z60 time to power-to-weight ratio, and so do not reflect other forms of progress that can further enhance acceleration ability, such as improvements in aerodynamics, tires, driveline friction, vehicle body struct ures and suspensions. Table 6. Sensitivity analysis results

Technology costs Net present value retail price equivalent (RPE) in 2010$ 2025 2035 2025 2035
Base case 3,436 4,229 3,472 4,608 Lower discount rate to 3% (unchanged) 5,186 6,802

Lower rebound effect to 5% (unchanged) 4,098 5,436

Mass reduction at 2% costsavings 3,269 3,828 3,639 5,010

Half the rate of cost reduction 3,981 5,677 2,927 3,161

Base case results are from Table 5 and sensit ivity cases are as described in the text.40 In any case, a fit to th e 1982-2009 data in Figure 15(a) imp lies an average increase of 0.036 m/s 2 per year (r2 = 0.995).

Extrapolating this trend to 2035 yields a fleet average acceleration of 3.8 m/s 2 and corresponding Z60 time of 7.1 seconds. Such a number is not im plausible given that to day’s high performance cars readily exceed that level of capability. Applyi ng the sensitivity factor of 1.16 noted above to the resulting 40% gain in accel eration performance yields a 47% increase in average energy intensity, or a 32% loss in fuel economy, for the future fleet. Thus, such a performance gain would reduce the horizon from one of trip led fuel economy down to roughly doubled fuel economy by 2035. The implied rate of increase in ne w fleet average effici ency would fall from 3.7% per year to 2.4% per year, for 2025 fleet of roughly 40 mpg instead of 52 mpg. Note that this is an approximate calculation based on simply sc aling by a sensitivity f actor and so is subject to verification through direct modeling of a higher performance fleet. Nevertheless, it is broadly in line with the trade-offs modeled by Cheah et al. (2008), which examined efficiency levels less ambitious than those projected here.

The other two relationships needed to tie changes in performance assumptions to changes in cost can also be examined empiricall y, with some existing work on wh ich to draw, but doing so was not possible within the scope of this study. Note that the cost impact of engine downsizing may be at least partly quantized in that little savi ngs may accrue when changing displacement with a fixed number of cylinders, but a significant savi ngs can result when decreasing the number of cylinders. For example, NESCCAF (2004) reports savings of $550 for replacing a 6-cylinder engine with a 5-cylinder and $700 for replacing a 6 with a 4.

Evaluating the impact of such changes (cost pos itive or negative depending on whether engines are upsized or downsized) would be a rather involved effort that requires analyzing the projected engine mix in the fleet and assessing how variou s changes in displacemen t would affect cylinder counts. In any case, a qualitative viewis illustrated in Figure 15(b).

Figure 15. Light duty fleet average acceleratio n performance and effect on the cost of achieving a given level of consumption reduction

Source: (a) derived from EPA (2009), Table 2; (b) qualitat ive illustration using mid-term curve from Figure 14.41 

### Conclusions
Important perspective is provide d by viewing technology in the c ontext of other factors that influence vehicle design. A review of the past several decades' automotive trends shows how technological progress was harnesse d largely to objectives other than improving fuel efficiency. This trade-off spotlights the cha llenge of directing future progre ss toward measurable reductions in energy intensity as opposed to increasing the technical ener gy efficiency with which other vehicle amenities are provided. Ther efore, auto efficiency should be seen as a matter of design priority -- that is, an outcome of a process that emphasizes multiple objectives, only one of which may be fuel efficiency -- rather than mainly as a matter of technology change.

Overall fleet efficiency also depends on high-level choices of what to prioritize in the multilevel, competitive process that determines not only te chnology use in individual models but also the product strategy of a company and the directions of the industry as a whole. Sensitivity analysis of fleetwide performance indicates that the fuel efficiency level attainable using a given set of technologies varies greatly depending on how the technological capabilitie s are used. Thus, this report distinguishes what are termed compatible strategies for efficiency improvement -- those that emphasize features that avoid conflict with a need to reduce energy intensity -- from the purely technical strategies commonly considered.

The technological opportunities are substantial and were reviewed here with reference to the existing automotive technology literature. Synthesi zing such results yields a projection of the fuel efficiency levels feasible if technology applications are fullyprioritized for reducing vehicle energy intensity. Because the goal was to identif y an efficiency horizon, the analysis draws on studies using a fundamental engi neering approach rather than specifying detailed technology pathways. Nevertheless, extensive use of hybrid dr ive appears necessary fo r realizing high levels of fleetwide fuel efficiency.

The rate at which technologies might be adopted was examined by reviewing historical adoption rates as represented by logistic cu rves. Cost estimates were distil led into quadratic cost curves, which present an aggregate viewof likely impacts and are conve nient for modeling reductions in cost expected through engineering progress. These estimates were us ed to construct a scenario of energy intensity reduction for the light duty flee t as it could evolve between now and 2035. Even though costs rise, they are greatly exceeded by fuel savings over the lifetime of the fleet, resulting in a positive net presen t value that rises over time. Neve rtheless, automakers bear any technology costs upfront; the extentto which they are passed on to consumers is unclear.

In summary, tripling new fleet fuel efficiency is an ambitious but defensible horizon for 2035. Reaching it will entail rising costs that are best seen as opportunitycosts for features that might otherwise appear in cars if high fuel efficiency levels are not sought. Achieving this horizon at the costs projected means foregoing further gains in average acceleration performance, a marked departure from past trends. If in stead performance rises in line with its historical trend, the 2035 horizon drops from tripled down to roughly doubled fuel efficiency. Nevertheless, improving the fleet toward such a horizon through evolutionary ch ange remains less costly than "revolutionary" alternatives requiring extensive new infrastructure . In short, attaining or even approaching the fuel efficiency horizon projected here will yield substantial benefits for reducing oil consumption and limiting GHG emissions from the transportation sector. 4243 

### Appendix: Cost Curve Derivations

A quadratic form is useful for approximating technology cost as a function of vehicle energy intensity decrease. It represents costs as a function of the benefits delivered, which are proportional to avoided fuel consumption. Figure A-1 compares estimates from some of the studies reviewed here (MIT 2008, NESCCAF 2004, and EPA 2004) along with the quadratic fits used for the summary curves that were shown in Figure 13.

Although different studies analyze different reference vehicles, all use relatively recent base years (2002-2006). Those years, which were prior to the recent changes induced by high fuel prices and strengthened CA FE standards, saw little change in aver age efficiency-related characteristics of the fleet. Given this study's long-term horizon, the baseline differences are small compared to the magnitude of changes projected.

As seen in Figure A-1, total incremental cost (given as retail price equivalent, RPE) is plotted against a decrease in energy consumption calculated assuming 12,000 miles of annual driving and a 20% fuel economy shortfall for all vehicles. Estimates from the studies were fit to the function:

TC T = A·Q 2 (A1)

where TC T is the total co st of technology improvement and Q is the quantity of energy savings on an annualized basis (GJ/yr). The resulting coefficient estimates Â are shown with each graph, with the units of Â being 2010$ per (GJ/yr) 2

. Figure A-1. Cost increase (retail price equivalent) vs. consumption decrease for selected studies

(a) MIT estimates for midsize sedan

(b) NESCCAF estimates for midsize sedan

(c) EPA ATD estimates for large SUV 0 2,000 4,000 6,000 8,000 10,000

0 10 20 30 40 50 Consumption decrease (GJ/yr)RPE Increase (2010$)

0 2,000 4,000 6,000 8,000 10,000

0 10 20 30 40 50 Consumption decrease (GJ/yr)Price increase ($RPE) 0 2,000 4,000 6,000 8,000 10,000

0 10 20 30 40 50 Consumption Decrease (GJ/yr)RPE Increase (2010$) Â = 2.68 (±0.25) adj r 2 = 0.72

Â = 4.28 (±0.30)

adj r 2 = 0.86

Â = 2.08 (±0.18)

adj r 2 = 0.8244 The NESCCAF fit yields a quadra tic coefficient (A) of 4.28, somewh at larger than the estimate of 4.02 implied by the cost of reaching the light duty fleet average CAFE level in 2016 projected by the EPA & NHTSA (2010) Joint Rule. As was s hown in Figure 13, those two curves are very close in any case. The latter coefficient, A 0 = 4.02 [2010$ / (GJ/yr) 2 ], is chosen for defining a near-term technology cost curve.

Shown for comparison are results from the EP A (2004) Advanced Technology Division (ATD) study, which provides an optimistic bounding analys is. The estimates plotted in Figure A-1(c) are for a large SUV using various configuratio ns of hydraulic hybrid drive with advanced gasoline or diesel engines. That study estimat ed costs of hydraulic hybrids as markedly lower than those of electric hybrids, a nd the hardware is suited to larg er vehicles (demonstrations to date have been in SUVs or delivery trucks). Not shown here are the EPA ATD estimates for a midsize sedan, which are somewhat more cos tly per unit energy savings because a sedan's baseline consumption is lower than an SUV's and some technology configurations for sedans cost a bit more than those for SUVs.

Figure A-2 plots MIT's estimates for cars and light trucks. The truck values again appear less costly because the relative energy savi ng is significantly higher than that for cars while the technology cost is only a bit higher. The resulting quadratic coefficient (Â) estimate for light trucks is 1.41 (±0.09) compared to an estimate of 2.68 (±0.25) for cars [2010$ per (GJ/yr) 2 ]. A composite LDV cost curve can be derived from a weighted average of these two curves, with a 60% car share implying a coefficient of 2.17. The resulting composite curve is not plotted here but this coefficient is used as aninput for deriving the adjusted longterm cost curve as described below.

Note that the 60% share used for cars is not inte nded to reflect a legal car-truck distinction, but rather an assumption that 60% of the future fleetwill have characteristics that are more car-like than truck-like in terms of f unctional requirements. The market has already more than moved in that direction, with truck-based vehicles dr opping from 31% in 2007 to 23% through the first half of 2010, and is very unlikel y to shift back to the higher fr actions of truck-based products seen at the peak of the SUV boom (Snyder 2010) . EPA & NHTSA already assume cost to a 67%-33% car-truck split for the fleet mix going forward. However, a higher car weighting for the composite curve would not reflect the fact that much of the baseline fleet falls under a light truck classification and so has a greater potential for lower cost consumption reduction.

These plots also enable discussion of the general shape of the costcurve in light of the scatter involved. Some of the cost estimates might just as well be fit by a straight line, or perhaps a twoparameter quadratic with a dominant linear te rm. In particular, points from EPA & NHTSA Figure A-2. Cost estimates and quadratic fits for cars and light trucks based on MIT (2008)45 (2010) shown earlier in Figure 6 appear more linear than quadr atic for the limited range of reductions assessed for near-term policy. A linear relationship is not surprising for near-term estimates because what drives such an analysis is largely greater utilization of a static set of technologies that are modeled with costs that are either constantor slightly declining. This situation is unlike that for a long-term assessment, which involves tapping more expensive technologies as less costly options are exhausted.

The NESCCAF (2004) estimates, which incorporate both nearer- and longer-term technologies, show a more quadratic trend as seen in Figure A-1(b). The EPA (2004) ATD estimates could readily be represented as linear as could the MIT (2008) estimates. The latter have just a few points in any case; although not shown here, a linear fit to the MIT estimates yields an adjusted r 2 of 0.73, compared to 0.72 for the quadratic.

It is difficult to be conclusive given the scatter involved. This analysis adopts a quadratic for the reasons noted in the maintext, particularly the desire to represent increasing marginal costs as seems appropriate for a fundamentals-based long-term efficiency horizon.

For comparing studies, it is useful to normalize one or both of the cost and fuel savings variables, expressing them in percentage terms relative to a base vehicle or base fleet (Santini et al. 2002). Curves normalized in both cost and fuel consumption enable comparisons in dimensionless form, obtained by fitting:

Δ C/C 0 = c( Δ E/E 0 ) 2 (A2)

Here C 0 and E 0 are the base cost (RPE) and energy consumption (GJ/yr) of the reference vehicle used in a given study. The relationships to the parameters of Equation (A1) are given by Δ C = TC T and Δ E = Q. Figure A-3 shows normalized cost and savings values and their respective curve fits.

Recall that although its units are RPE, TC T should not be viewed as an actual price increase likely to be experienced by consumers, but rather as a retail equivalent estimate for the impact of the technology changes on automakers' product Figure A-3. Normalized plots of cost increase vs. consumption decrease

SUMMARY OUTPUT

Regression Statistics Multiple R 0.995781 R Square 0.99158 Adjusted R 0.74158
Standard E 0.018656 Observati o 5

ANOVA (a) MIT (2008), relative to a 2005 midsize sedan

(b) NESCCAF (2004), relative to a 2002 midsize sedan

(c) EPA (2004), relative to a 2002 large SUV 0% 10% 20% 30% 40% 50%

0% 20% 40% 60% 80% Consumption decreaseCost increase ĉ = 0.940 (±0.070) adj r2 = 0.85 0% 10% 20% 30% 40% 50%

0% 20% 40% 60% 80% Consumption decreaseCost increase

0% 10% 20% 30% 40% 50%

0% 20% 40% 60% 80% Consumption decreaseCost increase ĉ = 0.387 (±0.030) adj r2 = 0.84 ĉ = 0.707 (±0.052) adj r2 = 0.7346 development budgets. As discussed in the main text, automakers probably cannot fully pass costs on to consumers; rather, the cost impact is likel y to be reflected as fo regone spending on other features that might have been offered instead of higher fuel economy. Thus, the percent cost increases shown in Figure A-3 should be interprete d as measuring such op portunity costs relative to the retail price of th e reference vehicle.

The normalized coefficient ( ĉ ) is readily adaptable for calcula ting the relative cost of halving vehicle energy intensity (doubling fuel economy). In this case, Δ E/E 0 = 1⁄2 and the percent change in cost is given by ĉ (1⁄2) 2 = ĉ /4. Relative halving costs on the or der of, say, 18% as suggested for a 50% decrease in consumption along the MIT-ba sed curve, clearly have a large impact on an automaker's product development budget, and if pa ssed through to consumers, would represent a major impact on retail price as well. The projected "halving cost" (h = ĉ /4) is an intuitive way to present the quadratic coefficients, as illustrated in Figure 14.

Cutting vehicle energy intensity by two-thirds (a 67% reduction) is an extrapolation beyond the range of cost estimates from most studies. Howe ver, it is not much beyond MIT's results for a hybrid-electric vehicle, estimated as achieving a 61% reduction in fuel consumption rate. In normalized quadratic terms, the cost of such a tripling of fuel economy can be extrapolated as ĉ ( 2⁄3 ) 2 = 0.444 ĉ . For the MIT passenger car curve, this im plies a relative RPE impact of 31%. As for any application of this curve, such es timates are for a long-term time horizon of 2035.

Another thing to note when inte rpreting these estimates is that the RPE normalization was done relative to vehicles from non-luxury segments. Th at is, it estimates impacts based on reference vehicles from high-volume segments that do not command premium prices. Thus, these normalized estimates should not be applied relati ve to the price of a vehicle carrying a price premium that reflects many features, or even intangibles related to brand status, that would not see proportional cost increases for tec hnology to improve fuel efficiency.

Adjusting the Cost of Weight Reduction As noted in the main text, mass reduction up to if not beyond the 20% level assumed in the MIT (2008) analysis is within the range achievable by ongoing evolutionary refinements in materials and technique. This analysis therefore assu mes that fleet average mass reductions incur no additional cost at the incrementally increasing levels entailed in the efficiency horizon identified here. Although the Lotus (2010) study estimates the potential for a 21% mass reduction at 2% cost savings even for a relatively near-term ( 2017) program, reviewers raised concerns about the ability to achieve that de gree of optimization in all vehicles. Th at more optimistic view is treated here as a sensitivity case that results in lowe r cost estimates for 2020-2035. The cost curves derived above were adjusted to reflect these assumptions as explained below.

Adjusting the near-term cost curve involves subtracti ng the costs of mass reduction used in the recent Joint Rule. EPA & NHTSA (2010 TSD: 3-79) applied an average mass reduction cost of $1.48 per pound (2007$, which inflates to $1.55/lb 2010$, or $3.42/kg). They assumed varying amounts of mass reduction ranging 3.5%–8.5% for various segments of th e fleet (with some vehicles seeing no reduction). Applying the averag e of this range (6%) to the average 2005 light duty vehicle curb weight of 3800 lbs yields $350. A ssuming further that this cost applies to about half of the fleet suggests a downward adjustment of $175. This value is subtracted at the average level of the 2016 CAFE rule to lower the near-term cost curve at this point. Recomputing the47 near-term quadratic coefficient (A 0 ) accordingly yields a value of 3.15, compared to the value of 4.02 derived earlier; units are 2010$/(GJ/yr) 2

. The long-term (2035) curve is adjusted by subtra cting costs from the composite curve derived from MIT (2008). That study assumed a constantcost per unit of mass reduction for new vehicles in 2035, which they project to be 20% lighter than the 2005 reference vehicle (assuming 100% emphasis on reducing fuel consumption). They assume that a 14% mass reduction through materials substitution costs $3/kg and that the rest of the reduction to the 20% level is obtained from secondary weight savings at no cost. Th e resulting estimates are $700 per car and $900 per light truck (2007$; MIT 2008: 36- 37), which average to $816 assuming a 60%-40% car-truck split and inflating to 2010$. This cost is subtract ed from the MIT-based composite curve at the point of 60% normalized reduction in energy in tensity. The resulting adjusted quadratic coefficient of 1.76 [2010$ per (GJ/yr) 2 ] defines the long-term (2035) cost curve shown in Figure 14. For years between now and 2035, with intermedia te levels of mass reduction also assumed at zero net cost, cost curves are defined by the shift function described below.

A sensitivity case examining the likelihood of ev en lower mass reduction costs is based on the Lotus (2010) study's "low development program" es timate of 21% mass reduction with a 2% cost savings. Based on detailed component- and vehicl e subsystem-level analyses, the study argues that such savings can result from a synergistic , whole-vehicle strategy for cutting mass. Lotus (2010) based its cost savings estimate on the no n-powertrain portion of a base vehicle, which accounts for 77% of vehicle cost. Using the MIT (2008) base vehicle costs, which average to $21,100 (inflated to 2010$) and ta king (2%)(77%) implies an av erage savings of $325. This value is combined with the $816 calculated a bove for a total downward adjustment of $1,141. Subtracting this value from the MIT-based com posite curve at the 60% consumption reduction point yields a quadratic coeffi cient of 1.59 [2010$ per (GJ/yr) 2 ], which is used for the lower cost sensitivity case given in Table 6.

Cost Curve Shifts Through Time The drop in costs as projected in the curves deri ved above can be viewed as a rightward shift in the cost ("supply") curve for energy conservati on through vehicle efficiency improvement. To interpolate cost curves for intervening years, a shift through time can be modeled by making the quadratic coefficient (A) a function of time (t):

A(t) = A 0 (1-d) t (A3)

where d represents a rate of decline in cost. If t 0 is 2015, a rightward shif t over the 20 year period until 2035 can be represented by starting with A 0 = 3.15, as derived above by adjusting costs from the EPA & NHTSA (2010) Joint Rule, and ending with A 20 = 1.76, as derived above by adjusting costs from the MIT (2008) study. The result ing decline in the quadratic cost coefficient is d = 2.9% per year. This shift function is usedto derive the mid-term curve (for a 2025 time frame) shown in Figure 14. A greater rate of sh ift is implied by the lower-cost mass reduction sensitivity case, which implies a decline (d) of 3.4% per year in the quadratic coefficient. In this case, which can be seen as reflecting greater productivity gains in s upplying technologies for vehicle efficiency, the cost of achieving a given fleet average energy intensity reduction drops by 50% between 2015 and 2035, compared to a 44% drop in th e base case.48 Costs of Percentage Changes in Fleet Average Energy Intensity It is useful to parametize the cost function us ing percent reduction of annual energy consumption relative to the base year (2005), a variable that is also useful for calculating the other costs and benefits that are proportional to the change in vehicle energy use. Therefore, let x represent the percent reduction in energy intensity relative to the base fleet:

x = Δ E/E 0 = Q/E 0 (A4)

where E 0 is the consumption level (GJ/yr) of the base year fleet. The time-varying total cost of technology from Equations A1 and A3 is then given by:

TC T = A(t)·Q 2 = A(t)E 0 2 x 2 (A5)

The other costs to be considered are the externalities associated with additional driving induced by the rebound effect. Small and Van Dender (2 007) found that the rebound effect for U.S. conditions is small (about 10%) and likely dec lining as time goes on; it is denoted here by δ and a fixed value of 10% is assu med. The total shadow price (P D ) of these externalities (congestion, accidents and noise) is assumed to be $0.08 per mile, a car-truck average from EPA & NHTSA (2010) inflated to 2010$. Their total cost is then given by

TC D = P D D L (1-x) - δ (A6)

where D L is the discounted lifetime travel distance of the average vehicle as discussed in the main text. These functions were used to calculate the values in Table 5.

The total cost (TC) is th e sum of the technology cost plus the ex ternal costs of induced driving. It can be given in terms of pe rcent reduction in energy inte nsity (x) by year (t) as:

TC(x,t) = TC T + TC D = A(t)E 0 2 x 2 + P D D L (1-x) - δ (A7)

The marginal cost is then:

MC(x,t) = 2A(t)E 0 2 x - δ P D D L (1-x) - δ -1 (A8)

Although a formal cost-benefit anal ysis is not attempte d here, this equation could be used to derive so-called "cost-efficient" levels of en ergy intensity reduction by equating the marginal benefit to the marginal cost (MB = MC). MB is essentially a linear function of intensity reduction and so can also be parametized by x. MB is dominated by the fuel price projection, which can vary as a function of time (t) but of course remains highly uncertain.49 

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