jenn - auto tranistion to sustainable technology

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Automotive Transition to Sustainable Technology Mix, a Response

to CAFE Standards

lan JennCarnegie Mellon University, [email protected] BlancoUniv.of California, Los Angeles,[email protected] ChernicoffToyota Motor North America, Inc., [email protected] zevedoCarnegie Mellon University, [email protected]

Abstract.Corporate Average Fuel Economy, or CAFE, standards were recently passed as ajoint effort between the National Highway Traffic Safety Administration (NHTSA) and theEnvironmental Protection Agency (EPA) to increase fuel efficiency and reduce carbonemissions of passenger cars and light trucks. I examine the effects of this policy on the futurevehicle fleet mix, broken down by vehicle class and vehicle type, specifically improving onearlier works by capturing consumer behavior in the model. Using a non-linear programming(NLP) optimization model to minimize costs, the lowest cost solution to meeting policy mandates

through vehicle sales and adjustments to fuel efficiency are investigated from the perspective ofa social planner. Results indicate that the optimal allocation of vehicles is significantly lowerthan expected growth due to higher prices. Specific aspects of the CAFE regulation have astrong effect on the outcomes, particularly weighting incentives for flex fuel vehicles which resultin high adoption of this technology in 2012 through 2015. While overall gasoline consumptionand emissions are reduced from the mandated standards, CAFE imposes relatively large costson consumers and producers.

Introduction.The vehicle fleet in the United States currently has approximately 250 million carsand trucks on the road, emitting nearly 2 gigatons of carbon dioxide (CO2) and consuming 7trillion barrels (about 60% imported) of oil every year. A number of technological improvementsexist to help increase fuel economy both as evolutionary improvements to existing traditional

internal combustion engine vehicles (ICEV), and revolutionary improvements through non-traditional technologies including hybrid electric vehicles (HEV), flex fuel (FF), fuel cell (FC),plug-in hybrid electric vehicles (PHEV), and fully electric vehicles (EV).

While many of the technologies face technical hurdles and consumer uncertainty, the biggest

issue with widespread adoption is cost. For manufacturers in the vehicle industry, bothincremental improvements and alternative fuel technologies have large premiums associated withtheir development and integration. For consumers, the premiums are reflected in the final price ofthe vehicle and typically are greater than the offset from savings through increased vehicular fuelefficiency. However, in lieu of rising gasoline prices and concerns over greenhouse gasemissions in the transportation sector, the EPA and NHTSA passed the CAFE

Proceedings of the International Symposium on Sustainable Systems and Technologies(ISSN 2329-9169) ispublished annually by the Sustainable Conoscente Network. Melissa Bilec and Jun-ki Choi, co-editors.

[email protected].

Copyright 2013 byAlan Jenn, Christian Blanco, William Chernicoff, Ins Azevedo. Licensed under CC-BY 3.0.

Cite As:

Automotive Transition to Sustainable Technology Mix, a Response to CAFE Standards. Proc. ISSST, Alan Jenn,

Christian Blanco, William Chernicoff, Ins Azevedo. http://dx.doi.org/10.6084/m9.figshare.805148. v1 (2013)


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standards12in the summer of 2010. Under the CAFE mandate, all vehicles sold in the UnitedStates are subject to two sets of standards. EPA sets specific CO2 standards for cars andtrucks which in turn correspond to the standards set by the NHTSA fuel efficiency on cars andtrucks. The first phase of CAFE is referred to as One National Program 1 or ONP1, and hasevolving annual standards from 2012 through 2016 as follows:

Table 1: ONP1 Projected Fleet-Wide Emissions Compliance Levels under the Footprint-Based CO2Standards (g/mi) and Corresponding Fuel Economy (mpg)

Year of Regulation 2012 2013 2014 2015 2016EPA Emission RequirementsPassenger Cars (g/mi) 263 256 247 236 225Light Trucks (g/mi) 346 337 326 312 298Combined Cars & Trucks (g/mi) 295 286 276 263 250NHTSA Fuel Economy Requirements (CAFE Standards)Passenger Cars (mpg) 33.8 34.7 36.0 37.7 39.5Light Trucks (mpg) 25.7 26.4 27.3 28.5 29.8Combined Cars & Trucks (mpg) 30.1 31.1 32.2 33.8 35.5

Non-compliance with the standards results in a penalty of $5.50 per vehicle per 0.1 MPGdifference from the standard for a manufacturers average fuel economy for the vehicles soldunder each class. For example, if a manufacturer sold 1,000 passenger cars that averaged32.8 MPG, they would have to pay a fine of $55,000 (1.0 average MPG beneath than standard,resulting in a $55 fee per vehicle for 1,000 vehicles sold). Following the initial mandates, amuch more aggressive set of standards will be coming online starting in 2017 following a reviewin 2016 to measure how manufacturers are keeping up with the standards. Unlike ONP1, thepenalties proposed by the EPA in the second phase of CAFE (ONP2) are nearly impossible topay, essentially making it illegal to be noncompliant with the standards. ONP1 requires anaverage fuel economy increase of about 7-8 MPG over 5 years while ONP2 requires anaverage fuel economy increase of about 20 MPG over 9 years.

Table 2: ONP2 Projected Fleet-Wide Emissions Compliance Levels under the Footprint-Based CO2Standards (g/mi) and Corresponding Fuel Economy (mpg)

Year of Regulation 2017 2018 2019 2020 2021 2022 2023 2024 2025EPA Emission RequirementsPassenger Cars (g/mi) 213 202 192 182 173 165 158 151 144Light Trucks (g/mi) 295 285 277 270 250 237 225 214 203Combined Cars &Trucks(g/mi)

243 232 223 213 200 190 181 172 163

NHTSA Fuel Economy RequirementsCombined Cars & Trucks

(mpg)

36.6 38.3 39.9 41.7 44.4 46.8 49.1 51.7 54.5

As an incentive to boost the adoption of alternative fuel technologies, the agencies have allowedfor certain credits to help achieve the mandates. For example, plug-in hybrid vehicles and

1Federal Register Vol. 75, No. 88: Light-Duty Vehicle Greenhouse Gas Emissions Standards and

Corporate Average Fuel Economy Standards; Final Rule2Federal Register Vol. 77, No. 199: 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas

Emissions and Corporate Average Fuel Economy Standards


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Methods. In order to predict vehicle fleet mix in the future, I take the approach of a socialplanner and minimize total cost to consumers and producers. Vehicles are allocated and theirfuel efficiencies are improved while trying to minimize cost under constraints such as the CAFEmandated standards. Components of cost include profit loss for producers from decreases in

sales volumes, increased cost for improving fuel efficiencies in ICEVs, increased prices foralternative fuel vehicle technologies, fuel savings, and an artificial penalty for consumersswitching out of their expected vehicle class choice.

The two decision variables in the optimization are the vehicles being allocated and the averagefuel efficiencies for all vehicle classes and types. The vehicle allocations are broken down overthe time period from 2012-2025, with the optimal solution looking over the entire time horizonsimultaneously. Furthermore, the analysis considers vehicles by ten different vehicle classes(by NHTSA classification: cargo van, compact, large, midsize, mini-compact, passenger van,small pickup, standard pickup, subcompact, two seater) and by six different vehicle technologies(ICEV, HEV, PHEV, EV, FF, and FC). The optimization model contains approximately 10,000total decision variables. The complete optimization formulation and variable definitions can be

found in the appendix in A1 and A2 respectively.

The optimization formulation is a non-convex, non-linear programming model. The optimizationis structured in the General Algebraic Modeling System (GAMS) employing BARON as theglobal solver for the non-convex problem and OQNLP as a multi-start tool using CONOPT asthe NLP solver. The OQNLP multi-start is employed as a means of solving the optimizationproblem with a high probability of finding a solution close to the global minimum of the objectivefunction while drastically reducing solve time required to find the global with the BARON solver.

Results. I considered three different scenarios with which to compare model outputs:1. ONP scenario: Standard case with CAFE constraints as currently legislated2. No weighting scenario: Case with CAFE constraints but no included incentives for

alternative fuel vehicles3. Aggressive fuel cell scenario: Same constraints as the ONP scenario but with a large

!parameter for the fuel cell learning curve (steeper reduction in cost as a function ofproduction volume)

The compilations of results are a sample of interesting scenarios run from the optimizationmodel, but a large number of outputs and full sensitivity analyses are not included. All figuresare located in Appendix A4 Figures.

As seen in Figure 1, compared against the exogenous input of vehicle demand, the presence ofany incentive results in a drastic decrease in vehicles allocated due to the higher costs ofadhering to the standard. Another interesting result is that in the years of 2012 through 2015,the two scenarios with CAFE incentives in place are able to keep pace with the exogenous

demand input whereas the no weighting scenario begins to drop off immediately.

Figure 2 shows that manufacturer profit reduction due to decreased sales is quite large due tothe decrease in vehicle sales compared to the base case (exogenous demand input resulting in$0 profit reduction). Despite the large total volume of vehicles being sold in the aggressive FCscenario, the overall producers suffer from a larger profit reduction as fuel cell vehicles offer asmaller (or negative) profit premium. However, this solution is selected by the model due to


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significantly higher savings for consumers due to lower use costs. Note that from the socialplanner perspective, the model is indifferent between costs borne by consumers or producers.

The mandated CAFE standards result in a cumulative reduction in gasoline consumption by2025. The volume decreased is only about 10% for all new vehicles sold over the decade,

though the total amount reduced does not represent future savings due to adoption of the newvehicles at higher fuel efficiencies and is lower than expected due to the high adoption of flex-fuel vehicles assumed to be running on gasoline fuel. Emissions of carbon dioxide do notcorrespond in ranking with gasoline consumption due to the emissions intensity of alternativefuel emissions from generation of fuel (for hydrogen gas) or electricity (for plug-in hybrids andbattery electric hybrids).

In figure 4, the allocations of vehicles broken down by class reveal that under the existing CAFEstandards, the social planner is able to maintain growth of vehicles for all vehicles with theexception of large class vehicles. However, as the constraints on the required fuel efficiencies(and as incentives begin to expire), allocations of many different vehicle classes begin to drop involume. In particular, standard pickup trucks fall drastically in volume after 2022 (following the

grace period of no required fuel efficiency improvements for light-duty trucks). In figure 5,manufacturers are able to take advantage of increased adoption of fuel cell vehicles whichenables them to sell larger numbers of large class vehicles (due to higher profit premiums) whilemeeting the required fuel efficiencies across other vehicle types.

Figures 6 and 7 show the allocation of vehicles broken down by the six vehicle technologies.One of the most noticeable trends is the immediate increase in the allocation of flex fuelvehicles from 2012 through 2016. In actuality, the fuel efficiency of the vehicles are notincreasing but rather manufacturers are taking advantage of the incentives within the CAFEregulations that essentially artificially increase fuel efficiency of flex fuel vehicles by a factor ofsix (to promote adoption of the technology). Due to the cheap premium associated with thistechnology (it only costs several hundred dollars to make an internal combustion engine

compatible with high concentrations of ethanol fuel.

Figures 10-12 demonstrate the optimizations output broken down by both specific vehicletechnology and vehicle class. Within each vehicle class, the proportion of technologies differsdepending on a variety of factors based on each class including the cost of increasing fuelefficiencies, alternative fuel technology costs, profit premiums, and class penalty function. Theresults show that for larger vehicles (e.g. passenger vans in Figure 10), the cost of increasingfuel efficiencies for internal combustion engine vehicles is too steep, causing an adoption of fuelcell vehicles and battery electric vehicles to meet the standard. For compact cars (Figure 11),no technology switch is necessary to meet the constraint while for midsize cars (Figure 12),hybrid electric vehicles are employed to meet the demands of CAFE. As seen before, in allvehicle classes, flex fuel vehicle adoption is prevalent from 2012 through 2016 during the period

of time that the incentive is available.

Conclusions. While the model results do not represent scenarios that are expected to occur inthe real world, a number of insights can be gained from the analysis from a social plannerperspective. One of the most robust trends from the optimization analysis is the deployment offlex fuel vehicles due to incentives tied in with CAFE standards. The low cost of implementingthis technology in existing ICVs means that manufacturers can take advantage of the flex fuelincentives and avoid the associated costs of improving fuel efficiency or switching to otheralternative fuel technologies. While other vehicle technologies such as electric vehicles also


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benefit from similar incentives, there is a long term argument that can be made for promotingadoption of these vehicles to decrease gasoline consumption and emissionsan argument thatcannot be made for flex fuel vehicles under the status quo unless biofuels are also made widelyavailable (nearly all flex fuel vehicles currently consume gasoline fuel, not E-85).

It is immediately apparent from the optimization model that achieving the required standards isinherently difficult. The space of feasible region in the optimization is quite narrow and allscenarios present significantly large costs to consumers and producers. Consumers do notrecover costs of new vehicle purchases from fuel savings, even over the lifetime of the car.Producers are faced with decreased vehicle sales in all scenarios, resulting in significantrevenue and profit reductions from the baseline of no CAFE standard. In addition, manyscenarios are imbalanced with consumers or producers bearing a disproportionate burden ofcosts. As the standards increase in severity towards the end of the decade, certain vehiclesclass sales shrink quite drastically which could in reality be impossible to sustain for specificvehicle manufacturers (particularly those producing higher volumes of larger sized vehicles).Depending on the scenario, the equity of the mandated standards to different vehiclemanufacturing companies is not well balanced.

A number of improvements can be made in future work. Firstly, the cost function used in myanalysis does not incorporate all social costs: the valuation of carbon emissions as well as thebehavior of consumers in the rest of the fleet (understanding the counterfactual: what vehiclesare being driven for individuals who forgo purchasing a new vehicle). A full account of thesefactors could change the optimal results. Secondly, the social planner considers cost to be theonly factor driving consumer decision making, a more accurate consumer preference model canbe used and calibrated to provide a more realistic representation of sales over the next decade.

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University of California Transportation Center, UC Berkeley.Cheah, L., & Heywood, J. (2011). Meeting US passenger vehicle fuel economy standards in 2016 and beyond. Energy Policy, 39(1),

454-466.DeCicco, J. M. (2010). A fuel efficiency horizon for US automobiles. The Energy Foundation.Feldstein, M. S. (1964). The social time preference discount rate in cost benefit analysis. The Economic Journal, 74(294), 360-379.Figliozzi, M. A., Boudart, J. A., & Feng, W. (2011). Economic and Environmental Optimization of Vehicle Fleets: A Case Study of the

Impacts of Policy, Market, Utilization, and Technological Factors: Forthcoming.Greene, D. L. (1991). Short Run Pricing Strategies To Increase Corporate Average Fuel Economy. Economic Inquiry, 29(1), 101-

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McManus, W., & Senter Jr, R. (2009). Market models for predicting PHEV adoption and diffusion.Plotkin, S., & Singh, M. (2009). Multi-path transportation futures study: vehicle characterization and scenario analyses: Argonne

National Laboratory (ANL).Ramage, M. P., Agrawal, R., Bodde, D. L., Friedman, D., Fuhs, S., Greenwald, J., . . . Wu, T. (2010). Transitions to alternative

transportation technologies: plug-in hybrid electric vehicles. National Research Council: National Academy Press.Shiau, C. S. N., Michalek, J. J., & Hendrickson, C. T. (2009). A structural analysis of vehicle design responses to Corporate Average

Fuel Economy policy. Transportation Research Part A: Policy and Practice, 43(9), 814-828.Train, K. E., & Winston, C. (2007). Vehicle Choice Behavior and the Declining Market Share of US Automakers. International

Economic Review, 48(4), 1469-1496.


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AppendixA1. Objective Function

( )n FE

, , , , ,

C n FE dr

, , , , , , , ,,

min ,

, , ,

g j k t g j k

g j k t g j k t g j kx x g j k t

y x x c

g t j k t

! "# $

% &

''''

Minimize CO2emissions,gasoline consumption, and total

costs

n FE

, , , , ,. . . ,

g j k t g j kw r t x x! "# $

With Respect to:

Number of vehicles in ageneration by class and typeover time

Average fuel efficiency in ageneration by class and type

Subject to, over generation g, model class j, model type k, and time t:

1.)

( ) ( )( )

( ) ( )( )

n n

, , , 1, , , 1

p FE p FE

, , , , 1, , 1, , C4 own n

, , , , , ,p FE

, , , , ,

p FE p FE

, , , , 1, , 1, ,

p FE

, , , , ,

j

g j k t g j k tj k j k

g j k g j k g j k g j k

g j k t g j k tipj a k j k g j k g j k

g j k g j k g j k g j k

ip

j k g j k g j k

x r x

y x y xy x

c y x

y x y x

c y x

!

" "

" "

=

" "

# $= % &

# $' (") *+ ,

+

) *+ ,+

- .% &+

"+ +

+

// //

//

C4 cross n

, , , , , ,

, 2012, 2012, ,

a

g j k t j g j k t

j a k

y x

g t g t j k

!

0

# $) *) *) *) *

# $' () *) *+ ,) *) *+ ,) *- .% &% &

1 = > >

/

//

Overall growth rate of vehiclesales accounting for pricechanges from policy mandate

2.)

FE FE

, , 1, , 0

2012, ,

g j k g j kx x

g j k

!! "

# >

Average fuel efficiency does not

decrease

3.)

n MFE g n FE

, , , , , , , , , , ,

, , , 2012

g j k t g j g j k g j k t g j k

j k j k

x c c x x

g t j k g

! " ! "=# $ # $

% = >

&& && Average fuel efficiency adheres

to policy mandate

4.)

n d n

, , , , , , , , 1

, , ,

g j k t g k t g j k tx c x

g t j k t

!=

"

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