An analysis on long term emission benefitsof a government vehicle fleet replacement planin northern illinois

Jie Lin Æ Cynthia Chen Æ Debbie A. Niemeier

Published online: 4 December 2007� Springer Science+Business Media, LLC. 2007

Abstract There have been a number of studies of the effectiveness of vehicle scrappage

programs, which offer incentives to accelerated scrappage of older vehicles often thought

to be high emitters. These programs are voluntary and aimed at replacement of household

vehicles. In contrast, there is a gap in knowledge related to the emissions benefits of

government fleet replacement (retirement) programs. In this study, the efficacy of a fleet

replacement program for a local government agency in Northern Illinois, the Forest Pre-

serve of DuPage County (FPDC), is examined using a probabilistic vehicle survival model

that accounts for time-varying covariates such as vehicle age and gasoline price. The

vehicle lifetime operating emissions are calculated based on the estimated vehicle survival

probabilities from the survival model and compared with those derived using the EPA

default fleet used in MOBILE6 and the fleet represented by the Oak Ridge National

Laboratory (ORNL) survival curve. The results suggest that while there may be short term

emission benefits of the FPDC fleet replacement plan, the long-term emission benefits are

highly sensitive to economic factors (e.g., future gasoline price) and exhibit a decreasing

trend. This indicates that an adaptive multi-stage replacement strategy as opposed to a

fixed one is preferable to achieve optimal cost effectiveness.

Keywords Vehicle scrappage � Local government fleet � Light duty vehicle �Survival probability � Lifetime emissions

J. Lin (&)Department of Civil and Materials Engineering, Institute for Environmental Science and Policy,University of Illinois at Chicago, 842 W. Taylor St. (MC246), Chicago, IL 60607, USAe-mail: janelin@uic.edu

C. ChenDepartment of Civil Engineering, City College of New York, 140th Street and Convent Avenue,New York, NY 10031, USAe-mail: c.chen@ccny.cuny.edu

D. A. NiemeierDepartment of Civil and Environmental Engineering, University of California at Davis,One Shields Avenue, Davis, CA 95616, USAe-mail: dniemeier@ucdavis.edu

123

Transportation (2008) 35:219–235DOI 10.1007/s11116-007-9149-1

Introduction

Older vehicles constitute a small proportion of the entire vehicle fleet, and yet, con-

tribute to a disproportionate amount of motor vehicle emissions. A recent National

Research Council report finds less than 10% of the vehicles contribute to more than

50% of the emissions (National Research Council 2001). Trends suggest that a higher

percentage of older vehicles (15 years and older) operate in today’s fleet than 30 years

ago. For example, the share of 15-year and older automobiles in operation was 16.1%

in 2001 versus 2.9% in 1970 (Oak Ridge National Laboratory 2006). Some of the

increased longevity may be attributable to improvements in vehicle technologies that

have improved vehicle durability.

Vehicle scrappage programs offer incentives to accelerated scrappage of older vehicles

and thus are generally perceived as an alternative to further reducing regional vehicle

emissions. One of the earliest vehicle scrappage program was launched by the Unocal

Corporation (known as the South Coast Recycled Auto Program or SCRAP) in 1990 and

has since been implemented in a number of cities, particularly in Southern California

(Hahn 1995). Most of the existing vehicle scrappage programs are voluntary programs and

have aimed at replacement of household vehicles.

Early studies of vehicle scrappage programs have examined various operational

aspects including determinants of program participation (Alberini et al. 1995), vehi-

cle-type choice and utilization (Kavalec and Setiawan 1997; Mannering and Winston

1985; Berkovec and Rust 1985), emission reductions of scrapped vehicles (Alberini

et al. 1996), economic trade-offs between the reduced emissions and the cost of

vehicle scrappage (Alberini et al. 1995; Hahn 1995), and cost-effectiveness in

reducing ozone precursor emissions (Deysher and Pickrell 1997). Others have

investigated the effect of vehicle life expectancy variations on emission reductions

(Hsu and Sperling 1994) and the factors that impact the accurate estimation of

emissions reductions (Dill 2004).

Most studies thus far have focused on the short-term (1–3 years) effects or benefits of

a scrappage program. Because vehicles are durable goods, the short-term approach tends

to oversimplify the complexity of the scrappage program without taking into account the

dynamics of vehicle retirement process involving factors like future purchasing deci-

sions, which are highly influenced by gasoline price and economic growth. It is

recommended in the Guide to Good Practice by the European Conference of Ministers of

Transport (ECMT) that vehicle scrappage programs be analyzed in the mid to long term

(ECMT 1999).

Little has been studied regarding the benefits of government fleet replacement

(retirement) programs. Government fleet replacement could produce high emission

benefits more cost-effectively than household vehicle scrappage programs, owing to both

the government fleet characteristics and purchase decision processes. Government fleets

have on average higher annual mileage than household vehicles and operate one third of

the truck population, which has even higher annual mileage (Oak Ridge National Lab-

oratory 2006). Compared with privately owned vehicles, government fleets are more

politically compliant (Nesbitt and Sperling 2001). For example, the Energy Policy Act

(EPAct) mandates federal and state government fleet replacement with more fuel-effi-

cient vehicles. Thus, targeting this group of vehicles may prove more cost-effective than

household vehicles.

This paper evaluates the long-term emission benefits of a fleet replacement program

implemented by a local government agency, the Forest Preserve of DuPage County

220 Transportation (2008) 35:219–235

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(FPDC) in Northern Illinois1. With a probabilistic model specified to capture the agency’s

fleet replacement pattern (represented by vehicle survival probabilities over time), the

forecasted survival probabilities are used to calculate the vehicle lifetime operating

emissions. The plan’s long-term emission benefits are derived by comparing lifetime

operating emissions of the FPDC fleet against those of the U.S. Environmental Protection

Agency (EPA)’s default national vehicle fleet and the Oak Ridge National Laboratory

(ORNL)’s national fleet. The study findings will provide insight in developing proactive,

long-term strategic plans to optimally reduce vehicle lifetime operating emissions, which

account for over 80% of the vehicle lifecycle energy consumption and emissions (Sullivan

et al. 1998)2.

Existing vehicle scrappage modeling

Assessment of the effectiveness of a vehicle scrappage program must properly account for

vehicle survival probabilities of future years. This can be accomplished by employing a

probabilistic survival model to directly estimate the probability that a vehicle will survive

for a certain period given such attributes as vehicle age and mileage. Application of

survival models includes estimation of the probability that a vehicle will stay in the fleet

(Chen and Niemeier 2005; Chen and Lin 2006; Yamamoto et al. 2004) or in the household

(Gilbert 1992). Alternatively, a vehicle scrappage decision can be captured with a disag-

gregate vehicle holding model (e.g., Berkovec and Rust 1985) or a disaggregate vehicle

type-choice and utilization model (e.g., Mannering and Winston 1985). Compared with

these two types of models, probabilistic survival models relax the assumption that

households make frequent vehicle transactions and always maintain an optimal number of

vehicles (De Jong and Kitamura 1992) and require less data.

Survival modeling has also been employed in the EPA MOBILE6 emissions model

(U.S. Environmental Protection Agency 2001). In MOBILE6 future year vehicle popula-

tions by vehicle class and age are determined by setting the vehicle counts for the xth year

equal to the vehicle counts for the (x-1)th year multiplied by (1-scrappage rate for the xth

year) plus the new sales for the xth year determined based on the estimated growth rates

(e.g., 0.5%). The 1996 vehicle population was used as the baseline for 1997 and forward

estimates. Vehicle scrappage rates were adopted from the 1996 World Vehicle Forecasts

and Strategies’ Report (Pemberton 1996) for both passenger cars (see Fig. 1) and com-

mercial vehicles for different time periods. All light duty vehicles’ scrappage rates were set

equal to the rates for passenger cars, i.e., 5.77%, 5.7%, 6.01%, 6.34%, and 6.56%,

1 The FPDC fleet replacement plan started in 2001. It is a 10-year plan to replace or convert its entire fleet toalternative fuel vehicles. The FPDC fleet is currently comprised of over 180 active on-road vehiclesincluding passenger cars, light, medium, and heavy-duty vehicles. The functions of these vehicles includedtransporting staff between the Districts many locations, police patrolling of the 24,000 acres, refuse removal,and other maintenance support activities. The replacement plan is based on a ‘‘Total Cost’’ ranking eval-uation method. Each vehicle was manually given a numerical point value based on nine criteria, includingprojected maintenance expense (not including major component replacement cost), projected major com-ponent life, safety, structural and body integrity, reliability, downtime, productivity, appearance, structuraland driver’s acceptability. Vehicles that were scored the highest were the best candidates for replacement.2 Some may argue that the emissions associated with the upstream and downstream lifecycle of a vehicleshould be accounted for. While this may be appropriate if the scrappage program is permanent, it is notnecessary for a one time program, which affects the vehicle demand and production by bringing forward thedemand to the course of the program. The long term vehicle demand and production are not likely to beaffected by a one time program.

Transportation (2008) 35:219–235 221

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respectively, for the periods 1995–1999, 2000–2004, 2005–2009, 2010–2014, and 2015–

2020. Age is the only variable included in the EPA’s light duty and heavy duty vehicle

models. Implicit within this method is that vehicles of the same age, regardless their

condition, have the same scrappage rate.

The ORNL model, originally developed by Greenspan and Cohen (1999), defines two

types of scrappage, ‘‘engineering’’ scrappage and ‘‘cyclical’’ (or non-engineering) scrap-

page. Engineering scrappage reflects physical deterioration of a vehicle as it ages; a vehicle

is scrapped due to natural physical failure. Age is the only variable considered in the

engineering scrappage procedure (in a nonlinear form of age). Cyclical scrappage is due to

non-engineering causes including unemployment rate, the prices of new vehicles, repairs,

and gasoline, and attributes of new vehicles. The two procedures were modeled separated

by fitting regression lines to the vehicle stock data published by R.L. Polk & Co. and the

American Automobile Manufacturers Association (AAMA). ORNL adopted the model and

estimated survival probabilities for the 1970, 1980, and 1990 model year light duty

vehicles (LDVs). The curve shown in Fig. 1 is of the 1990 survival probabilities.

These two models predict quite different survival probabilities. The ORNL’s survival

probabilities are lower than the EPA’s for vehicles under 13 years of age (i.e., faster

vehicle retirement) and significantly higher than the EPA’s for vehicles older than 13 years

(i.e., slower vehicle retirement). These differences may reflect that contrasting scrappage

decisions (e.g., engineering and cyclical) dominate in different vehicle age groups, in

addition to the different models and data used.

A delicate issue that arises when a survival model is used for vehicle scrappage or

transaction prediction is the handling of time-varying variables such as vehicle age and

gasoline price. In its most basic form, a survival model assumes that attributes recorded at

the study starting time do not change during the study period. While this may be true for

some attributes, the assumption does not hold for vehicle age and gasoline price. Changes

in these time-varying attributes during the study time period may affect the vehicle’s

survival probability.

Even though both vehicle age and gasoline price change over time, the nature of the

change is different. Vehicle age is a deterministically time-varying variable, meaning that

the change over time is fixed (i.e., vehicle age increases by 1 year for every 12 months).

Gasoline price is a stochastically time-varying variable (Kalbfleisch and Prentice 1980),

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25

age (year)

Su

rviv

alR

aet

ORNL LDV

EPA LDV

Fig. 1 MOBILE6 and ORNL LDV survival curves

222 Transportation (2008) 35:219–235

123

suggesting that the change is not fixed. The incorporation of deterministically time-varying

covariates is relatively straightforward. It can be done either by a Cox proportional hazard

model (Blossfeld et al. 1989; Chen and Niemeier 2005) or by integrating on the time-

varying covariate (Chen and Lin 2006). The incorporation of stochastically time-varying

covariates is much more complex and may involve maximizing partial likelihood function

of a complex form (Kalbfleisch and Prentice 2002).

Method

To assess the long-term emission benefits of the FPDC vehicle fleet replacement plan,

the total emissions of a ‘‘representative’’ vehicle of the fleet are estimated over its

operating lifetime and then compared with a predefined baseline, which in this study is a

representative vehicle from the EPA default fleet. That is, to assume that the FPDC

lifetime fleet emissions would have been the same as the EPA default fleet if FPDC had

not implemented its vehicle replacement plan. A representative vehicle is one whose

annual vehicle miles traveled (VMT) is the average fleet VMT weighted by the fleet’s

survival probabilities. Only light duty vehicles (LDVs) are considered in the study due to

the small number of heavy-duty vehicles (HDVs)—39 HDVs in contrast to 245 LDVs—

in FPDC’s fleet inventory. The ORNL scenario is retained in the analysis for comparison

purposes.

It is assumed that the maximum expected lifetime of the representative vehicle is

25 years and that the vehicle will be retired after that regardless of its condition3. Our

analysis applies this maximum age assumption to all three fleets to warrant consistent

comparison.

Our model estimation is conducted in two stages: first, the probability of a vehicle

surviving for at least another year (i.e., survival curve) is estimated for the next 25 years

since 2004, which then gives the expected VMT over the life of the vehicle, and second,

the lifetime operating emissions are calculated and compared across the FPDC’s, EPA’s

and ORNL’s representative vehicles.

Prediction of survival probabilities over lifetime

The key element in survival models is the specification of the hazard rate, the rate at which

the vehicle survives in the fleet after time t, given that it has lasted at least until t (Greene

2003). The hazard rate function k(t) can be expressed as:

kðtÞ ¼ limDt!0

Prðt\T\t þ DtjT � t; xðtÞÞDt

;

where T is the random variable for the survival duration of a vehicle, x(T) is a set of

independent variables, some of which may be functions of T. Survival probability function

3 A post-model sensitivity analysis confirmed that extending a LDV’s age from 25 to 30 years in FPDC hada negligible effect on total life-time emissions by pollutant. The largest increase was 0.2% in PM2.5 from tirewear-and-tear. The main reasons for these very small differences are (1) that vehicles become much cleanerin the future and emission factors tend to flat out after 20 years (see for example Fig. 6); and (2) survivalprobabilities after 25 years are quite small. Probably not by coincidence, the 25-year vehicle life is themaximum vehicle age considered in MOBILE6.2.

Transportation (2008) 35:219–235 223

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S(t) and hazard rate function k(t) have the following relationship: kðtÞ ¼ �ðd ln SðtÞ=dtÞ; or

equivalently,

SðtÞ ¼ exp �Z t

0

kðsÞds

0@

1A:

The following Weibull form specification of hazard rates is used: k(t)=ka ata-1, where

k[ 0 and a[ 0. If the scale parameter a [ 1, it suggests that the hazard rate is positively

related to the survival time; If 0 \ a \ 1, it suggests that the hazard is inversely related to

the survival time. Assuming that the location parameter k is related to the covariate vector

log-linearly, i.e., k = exp(XK), then the Weibull form hazard rate function is:

kðtÞ ¼ ½expðxjÞ�a a ta�1 ¼ expðxbÞa ta�1;

where b = aK. The Weibull form hazard rate function for vehicle survival modeling has

been used for the EPA’s survival curve presented earlier (in Fig. 1), using vehicle age as

the only covariate for estimating hazard rates (U.S. Environmental Protection Agency

2001). Other applications of the Weibull form hazard rate function in vehicle fleet turnover

include Chen and Lin (2006) and Zachariadis et al. (1995). These studies found that the

Weibull form was a good fit with real vehicle data. The Weibull model assumes monotonic

hazard rates over time. It may be possible, although highly unlikely, that hazard rates are

not monotonic in an accelerated vehicle retirement program because bad performing

vehicles (the ‘‘lemons’’) would retire much earlier than they otherwise would have. Since

all regression models estimate an average trend, this should not be an issue, given the bad

performing vehicles make up a relatively small proportion.

There are different methods in the literature to incorporate time-varying covariates in

the survival model. The Cox Proportional model is a well-established semi-parametric

method to incorporate time-varying covariates. While the estimation of the Cox Propor-

tional model is straightforward, the application of the model for forecasting purposes can

be complicated when the forecasting duration goes beyond the observation period. Para-

metric survival models can also accommodate time-varying covariates via integration

(Chen and Lin 2006). This study adopts a numerical approximation approach (Petersen

1986) to incorporate time-varying covariates.

In the Weibull form, kðtÞ ¼ expðx1b1 þ b2x2ðtÞ þ b3x3ðtÞÞa ta�1; x1 is a vector of time-

invariant independent variables; x2 represents a deterministically time-varying variable and

x3 a stochastically time-varying variable. Thus, the probability of surviving beyond tj given

that the vehicle has survived tj - 1 is

Pr½T � tjjT � tj�1� ¼ exp �Ztj

tj�1

kðsÞds

8><>:

9>=>;

¼ exp �Ztj

tj�1

½expðx1b1 þ b2x2ðsÞ þ b3x3ðsÞÞa sa�1�ds

8><>:

9>=>;:

If the interval between j - 1 and j is sufficiently small, it can be assumed that x2(s) and

x3(s) stay constant between tj - 1 and tj. Hence, the probability of surviving beyond tj can

be rewritten as

224 Transportation (2008) 35:219–235

123

SðtÞ ¼ exp �Zt1

0

½expðx1b1 þ b2x2ð0Þ þ b3x3ð0ÞÞa sa�1�ds

8<:

9=;� � � �

� exp �Ztj

tj�1

½expðx1b1 þ b2x2ðtj�1Þ þ b3x3ðtj�1ÞÞa sa�1�ds

8><>:

9>=>;;

or simplified to

SðtÞ ¼ exp �Xj

j¼1

Ztj

tj�1

½expðx1b1 þ b2x2ðtj�1Þ þ b3x3ðtj�1ÞÞa sa�1�ds

8><>:

9>=>;:

Lifetime emission estimation

The potential air quality benefits of the FPDC vehicle replacement plan are quantified by

comparing the total operating emissions of a representative FPDC LDV, a representative

EPA LDV, and a representative ORNL LDV for a horizon of 25 years. The pollutants

considered are CO, NOx, VOC, PM2.5, benzene, and CO2. The evaluation period begins in

2004 when the data was acquired and extends to 2028.

It is worth noting that in calculating the emissions the following assumption must be

made given the data constraints, which is that vehicles scrapped would be replaced by a

new vehicle of a similar type (e.g., passenger cars will be replaced by passenger cars). In

other words, the fleet’s LDV mix remains constant. This is not unreasonable because a

government fleet is maintained to accomplish specific service tasks and functionality. In

theory, this assumption could be relaxed by modeling vehicle-type choice and utilization as

a utility maximization problem such as in the models proposed by Mannering and Winston

(1985) and Alberini et al. (1995). These random utility models evaluate the effect of

individual households’ socioeconomic characteristics on vehicle utilization. They are not

immediately applicable to government fleets as fleet purchase is an organizational (in

contrast to household) behavior and the decisions are at times motivated by factors beyond

utility. The lack of detailed fleet and organizational information is another hindrance to the

adoption of such models.

Data

The fleet data used in the study were acquired from the FPDC fleet management team, who

are responsible for recording the daily activity for each individual vehicle in the fleet using

a computerized fleet inventory management system called CFA developed by the Com-

puterized Fleet Analysis, Inc. The dataset contains four main categories of information

about the 1701 vehicles owned by the department between 2000 and 2005. General vehicle

information includes age, make and model, purchase price, engine type, fuel type, and

routine maintenance availability. Vehicle status (whether the vehicle is in service, inactive,

disposed, auctioned, or dead) and service period related information are crucial informa-

tion to our study. The vehicle repair history documents each repair date/period, reason and

Transportation (2008) 35:219–235 225

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vehicle mileage at the time of repair, labor hours, labor costs and fuel costs4. The vehicle

fuel consumption history contains data for each fueling transaction, including fueling date,

vehicle mileage at the time of fueling, type of fuel, amount of fuel at the pump, and fuel

costs.

Not all variables in the dataset can be used for this analysis due to incomplete, missing

or obviously wrong entries. Table 1 lists the variables considered in the survival model.

Among them, there are three dummy variables (1 if yes and 0 otherwise): reformulated

unleaded gasoline5, minivan, and any one of the three brands—Chevrolet, Dodge, or Ford.

Inclusion of these dummy variables in the survival model may capture such unobserved

heterogeneity in relation to vehicle-use patterns (e.g., VMT) and durability as found in

Chen and Lin (2006). Assuming one homogenous constant term for all vehicle brands and

types proves to be inappropriate as seen later in the paper.

The analysis dataset contains 245 LDV vehicles6, including passenger cars, minivans,

pickups, SUVs, and 14-seat vans between July 1, 2004 and February 28, 2005—the sur-

vival model study period7. Among these 245 vehicles, 225 vehicles are reformulated

gasoline powered and 191 are vehicles of Ford, Dodge, or Chevrolet; 157 were still in

operation through February 28, 2005 (active vehicle set) and 88 vehicles were either

Table 1 Descriptive analysis of variables included for model estimation (n = 245)

Mean Std. error Minimum Maximum Median

Active vehicles (n = 157)

Survival time (mo/10) 0.81 0.05E-1 0.70 0.90 0.81

Vehicle age (mo/10) 8.24 0.50 1.3 28.9 6.1

# of road calls (/100) 0.02 0.02E-1 0 0.13 0

Reformulated unleaded gasoline 0.88 0.03 0 1 1

# of repairs (/100) 0.64 0.05 0.01 2.88 0.4

Minivan 0.11 0.02 0 1 0

Brand of Chevrolet, Dodge, or Ford 0.73 0.44 0 1 1

Disposed or auctioned vehicles (n = 88)

Survival time (mo/10) 0.22 0.01 0.1 0.4 0.2

Vehicle age (mo/10) 19.3 0.52 6.1 31.3 19.3

# of road calls (/100) 0.05 0.04E-1 0 0.14 0.04

Reformulated unleaded gasoline 0.97 0.02 0 1 1

# of repairs (/100) 1.36 0.07 0.07 3.31 1.34

Minivan 0.11 0.03 0 1 0

Brands of Chevrolet, Dodge, or Ford 0.86 0.34 0 1 1

4 However, these labor hours, labor costs, and fuel costs are found unreliable. This information is not usedin this study.5 DuPage County is within the EPA designated 8-hr ozone and PM2.5 nonattainment area. Reformulatedunleaded gasoline is mandatory for the region.6 Heavy-duty trucks, non-road vehicles (e.g., farm equipment) and other unknown equipment are notincluded in our analysis. Non-road vehicles were not part of the FPDC replacement plan. They are regulateddifferently from on-road vehicles. Although outside the scope of this study, it would be worthwhile to studythe effect of non-road vehicles’ turnover on emissions in future research.7 This is not to be confused with the evaluation period of 2004 through 2028 for the replacement program.

226 Transportation (2008) 35:219–235

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disposed or auctioned off8 (inactive vehicle set) between July 1, 2004 and February 28,

2005.

The average vehicle age of inactive vehicles is about 2.3 times the average age of active

vehicles. As might be expected, the number of road calls and repairs experienced by active

vehicles are fewer than those experienced by inactive vehicles. A greater share of active

vehicles uses alternative fuels than that of inactive vehicles. The share of minivans is about

the same between the active and inactive vehicles. The proportion of the Chevrolet, Ford,

and Dodge vehicles in the active vehicles is smaller than that in the inactive vehicles.

In estimating survival probabilities, effects of economic factors such as gasoline price

and gross domestic product (GDP) were considered. Weekly gasoline prices, as proxy for

gasoline costs, for the Midwestern region of the United States were obtained from the

Energy Information Administration (EIA) of the U.S. Department of Energy (DOE) for the

survival modeling study period (U.S. Department of Energy/Energy Information Admin-

istration 2007). Quarterly GDP data for the entire United States were obtained from the

U.S. Department of Commerce (DOC)’s Bureau of Economic Analysis for the same period

(Bureau of Economic Analysis 2007).

The 2001 National Household Travel Survey (NHTS) data were used to derive LDV

annual miles of travel by age. The 2001 NHTS consists of information on households,

persons, travel day trips, vehicles, and long-distance trips. There were a total of 69,817

household observations nationally with 139,382 reported household vehicles, which were

classified into eight types, namely automobile/car/station wagon, van, SUV, pickup truck,

other truck, RV, motorcycle, and other. A vehicle identified as one of the first four types in

NHTS was reclassified as a LDV in this study. The vehicle file was used to derive a LDV’s

annual mileage rate.

Using the national household VMT to approximate local government fleet VMT

essentially assumes the two are similar. This could be problematic when trying to quantify

the absolute amount of emissions from the scrapped vehicles or to compare total emissions

between the scrapped and replacement vehicles where their emission rates (in grams/mile)

are different (Dill 2004). This assumption, however, should not alter the final conclusions

of this comparative study. That is, the actual shape of VMT distribution by age should not

affect the relative differences of lifetime emissions from the three representative vehicles

(i.e., FPDC, EPA, and ORNL) when the same distribution is applied. The differences in

total emissions between the three representative vehicles are the direct result of the dif-

ferent age distributions (caused by the different survival probabilities as shown later).

Considering there is a lack of government fleet data, using the 2001 NHTS gives some

degree of reality to the fleet VMT assumption. Figure 2 is the curve-fit annual mileage

distribution of LDVs by age derived from the 2001 NHTS.

Lastly, local emission estimation inputs, except for the vehicle age distribution, to

MOBILE6.2 were provided by the Illinois Environmental Protection Agency for Northern

Illinois non-attainment area including DuPage County. The inputs include fuel type,

oxygenated fuel parameters, I/M programs, VMT fractions by facility type and speed, etc.

These parameters are used to determine emission rates.

8 The original dataset does not distinguish between vehicles that are disposed and auctioned. There is noinformation about the status of those vehicles after they were auctioned. Strictly speaking, the term ‘‘vehiclescrappage’’ is not accurately used but is retained in the rest of paper to maintain consistency. However, itshould be understood as vehicle disposal in the rest of the paper.

Transportation (2008) 35:219–235 227

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Results

Survival model

Table 2 contains the estimation results of the survival model development. The developed

model represents a 27% improvement over the constant-only model. The signs of the

parameter estimates are directly associated with the probability of vehicle scrappage. A

positive sign means that as the value of a variable increases the vehicle scrappage prob-

ability also increases; a negative sign means that as the value of a variable becomes larger

the probability of vehicle scrappage declines.

As seen from Table 2, the signs of all estimates agree with our expectations. As a

vehicle ages or the number of repairs increases, its probability of being disposed or auc-

tioned increases. Being a Chevrolet, a Dodge, or a Ford vehicle increases the probability of

vehicle scrappage; if the vehicle is a minivan or there is an increase in the gasoline price,

the probability of vehicle scrappage decreases. The scale parameter of the Weibull model

Table 2 Model estimation (dependent variable: survival time in months)

Coefficientsa Std. error t-Stat

Explanatory variables

Intercept -1.0462 2.9546 -0.354

Vehicle age (month) 0.0157 0.0031 5.031

Number of repairs/100 0.4366 0.1865 2.340

Minivan -0.9419 0.3449 -3.731

Vehicle brand of Chevrolet, Dodge, or Ford 1.1433 0.2881 3.967

Gasoline price -0.0282 0.0143 -1.972

Scale parameter (a) 1.2156 0.1333 9.119

Goodness of fit measures (n = 245)

Log-likelihood (constant only, no covariates) -334.39

Log-likelihood (model) -241.74

Log-likelihood ratio 185.30

a All but the intercept are significant at the 0.05 level

16000

14000

12000

10000

8000

6000

4000

2000

00

5 10 15 20 25

Age (years)

NH

ST

DL

VA

un

nal

m il

es

Fig. 2 LDV annual mileage by age from NHTS

228 Transportation (2008) 35:219–235

123

is estimated to be 1.22, representing a positive relationship between vehicle scrappage

probability and time progression.

All five explanatory variables are statistically significant. The finding that high gasoline

prices are associated with low scrappage probabilities is interesting. Although reasons are

not fully understood, it is possible that an increase in gasoline price suppresses travel

demand and thus reduces vehicle mileage, which is expected to be positively related to

scrappage probability. Another economic variable, GDP, was insignificant and dropped

from the final model because of considerable correlation between gasoline price and GDP.

The significant negative relationship between gasoline prices and vehicle scrappage

probabilities is consistent with the findings of past studies such as Kahn (1986), Johansson

and Schipper (1997), and Greenspan and Cohen (1999). On the other hand, some studies

have argued that increasing fuel prices served as an incentive to replacing older, less

efficient vehicles with more efficient new vehicles (Eskeland and Feyzioglu 1997). Others

have found no significant relation between fuel prices and vehicle depreciation rates (e.g.,

Storchmann 2004).

Vehicle brand and type also significantly influence survival probabilities. Chevrolet,

Ford and Dodge vehicles are more likely to be disposed or auctioned than other makes at

the same age; minivans tend to have a higher survival probability than other types. These

findings represent correlations rather than causality, as the underlying manufacturing

process of these vehicles must be understood before these observed correlations could be

fully explained.

Predicted long-term survival probabilities

The probabilities of the representative FPDC LDV to survive at least one more year given

it has survived the current year were computed for a 25-year period between 2004 and

2028. Because the survival probabilities are affected by gasoline price, the future gasoline

prices are crucial to the prediction. The projection of future gasoline prices published in the

DOE/EIA report ‘‘Annual Energy Outlook 2007 with Projections to 2030’’ (U.S. Depart-

ment of Energy/Energy Information Administration, 2007) were adopted for our analysis.

The projection is shown in Fig. 3, where the gasoline price would go up initially and peak

in 2006–2007 and then come down to the 2004 level of $2.17 per gallon in 2014, followed

by gradual increase to $2.50 per gallon in 20309.

Figure 4 displays eight selected survival curves10 of the representative FPDC LDV

along with the previous EPA and ORNL LDV survival curves. The general trend in the

FPDC curves is closer to the ORNL’s than to the EPA’s. The FPDC LDV starts with

consistently lower survival probabilities than the EPA’s or the ORNL’s in years 2004 and

2005, indicating accelerated scrappage rates in the FPDC vehicle fleet. After that, higher

survival probabilities are predicted in years 2006, 2007, and 2008 due to higher predicted

gasoline prices, indicating a slower scrappage pace in those years. Starting in year 2010,

the FPDC survival probabilities would drop again below both the EPA’s and the ORNL’s

9 Undoubtedly there are uncertainties associated with the future gasoline price projection. The uncertaintiescould be addressed by running different gasoline price scenarios or sensitivity analysis, which are outsidethe scope of this paper.10 Due to the space limitation, not all survival curves were presented in Fig. 4. The curves of 2004 through2008 (i.e., before and after the gasoline price peak) were included. The curves of the years after 2008 wererandomly selected.

Transportation (2008) 35:219–235 229

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and the similar trend would persist through 2028. These curves indicate that while the

FPDC replacement plan may result in accelerated scrappage at times, gasoline costs may

significantly slow down the replacement process. This is known as the ‘‘take-back’’ or

‘‘rebound effect’’ in energy literature, first identified by Khazzoom (1980) and widely

discussed later on. For example, Greene (1992) gives extensive discussion about the

rebound effect in relation to vehicle use and fuel economy.

Predicted annual VMT over lifetime

The predicted annual VMT on the three representative vehicles were calculated using the

VMT by age curve in Fig. 2 weighted by the survival probabilities derived from the

survival curves. Figure 5 shows the predicted VMT by year as compared with the EPA and

the ORNL baselines. There was a continuous decline in annual VMT between 2004 and

2007 and a rebound afterwards, which corresponds to the incline and decline in gasoline

prices in the same time periods. The reason may be attributed to the rebound effect

discussed earlier, i.e., an increase in gasoline price was linked to the slow-down of the

vehicle replacement process due to suppressed vehicle usage in response to increased fuel

costs.

Survival Rate Survival Rate

0

0.10.2

0.30.4

0.5

0.60.7

0.80.9

1

0

0.10.2

0.30.4

0.5

0.60.7

0.80.9

1

0 5 10 15 20 25 0 5 10 15 20 25

Age (year) Age (year)

ORNL LDV EPA LDV 20042018 2028 2010

ORNL LDV EPA LDV 20052008 2007 2006

Fig. 4 LDV survival curves

Gasoline price

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

2004 02

602

008

0201

2102

0241

2106

0281 02

02 0222 02

42 0226 02

82 0230

In2 00

5d

oallrs

p er

g a

olln

Fig. 3 Gasoline prices between 2004 and 2030 (source: DOE/EIA, 2007)

230 Transportation (2008) 35:219–235

123

Vehicle lifetime emissions

Lifetime emissions from the representative FPDC vehicle were estimated based on the

MOBILE6.2 emission rates and the predicted annual VMT as shown in Fig. 5. The

MOBILE6.2 emission rates (in g/mile) of the criteria pollutants and benzene were gen-

erated separately for the EPA, ORNL and FPDC scenarios, taking into account the

different vehicle age distributions over the years. Emission rates are generally trending

down between 2004 and 2028 (e.g., in Fig. 6) because newer models are equipped with

better technologies including emission control technology. This implies that newer model

year vehicles are presumed to be cleaner than the older model year vehicles when com-

pared at the same age. Interestingly, the emission rates increase between 2004 and 2006 as

a result of the low scrappage rates in those years.

The CO2 emission rates were also estimated using MOBILE6.2. Different from other

pollutants, CO2 emission rates are directly linked to vehicle fuel economy (U.S.

VOC

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

2004 02

60 0280 02

0120

1220

1420

16 0281

2020 02

22 0242 02

6220

28

year

Em

sisi

r n

oat

e( g

m/)i

EPA

ORNL

FPDC

Fig. 6 VOC emission rates by year

0

2000

4000

6000

8000

10000

12000

14000

2004 02

50 0206

2070 02

8020

0920

1020

1120

1220

1320

1420

1520

1620

1720

1820

1920

2020

2120

2220

3220

2420

52 026220

27 0282

EPA

ORNL

nA

nu

alV

MT

Fig. 5 Predicted annual VMT of the FPDC LDV, EPA’s and ORNL’s baseline LDVs

Transportation (2008) 35:219–235 231

123

Environmental Protection Agency 2002). In our estimation, a default MOBILE6.2 fuel

economy value of 24.1 mpg for calendar years 2004 through 2028 was adopted. Consid-

ered that the Corporate Average Fuel Economy (CAFE) standards have not changed in

decades, this assumption gives a conservative estimation over the next 25 years.

The final lifetime operating emissions of the representative FPDC vehicle are shown in

Table 3. The predicted lifetime operating emissions of the representative FPDC LDV for

all of the pollutants of interest are consistently higher, between 1.5% and 16.8%, than those

by the EPA LDV emissions. The ORNL LDV emissions are generally the highest, 6.8–

16.7% higher than the FPDC LDV emissions (with the exception of CO2 and PM pollu-

tants, which are about 5% lower than the FPDC LDV’s). These results indicate no clear

long-term emission benefits for the FPDC vehicle replacement plan. This finding may not

be welcome but hardly surprising due to the rebound effect on emissions as further

demonstrated in Fig. 7. The initial increase in VOC emissions between 2004 and 2006

Table 3 Predicted lifetime LDVemissions

Pollutant FPDC EPA ORNL

VOC (kg) 129.98 111.28 151.67

CO (kg) 1846.43 1818.60 1972.18

NOx (kg) 89.55 84.75 101.13

CO2 (ton) 1166.82 1147.19 1107.86

GasPM2.5 (g) 1116.08 1095.96 1064.16

PM2.5(Brake) (g) 1608.93 1581.86 1527.64

PM2.5(Tire) (g) 607.14 596.93 576.47

Benzene (g) 2886.31 2551.79 3205.21

VOC

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

0240

2005

0260 02

70 0280 02

90 0201 02

11 0221 02

31 0241 02

51 0261 02

71 0281 02

91 0202 02

12 0222

2203

0242 02

52 0262 02

72 0282

Year

An

lau

nV

OC

me ssi

i)

g( sn

o

FPDC

EPA

ORNL

Fig. 7 VOC emission trend between 2004 and 2028

232 Transportation (2008) 35:219–235

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corresponds to the initial increase in gasoline price during the same period. The other

pollutants of concern show a similar pattern.

Figure 7 also demonstrates the diminishing emission benefits in the long run as vehicle

technologies advance over time, if the same replacement plan is carried through. This

points to an adaptive multi-stage fleet replacement plan over a fixed strategy when the

implementation duration is more than 2–3 years so that optimal cost-effectiveness can be

achieved during the entire program.

Conclusions

In this paper, the lifetime operating emission benefits were evaluated of a vehicle

replacement program for a local government agency, the Forest Preserve of DuPage

County. The results show that while the vehicle replacement program has generally

resulted in accelerated vehicle scrappage rates compared to the national curves form EPA

and ORNL, increase in operating costs (e.g., due to increase in gasoline price) can slow

down the scrappage process significantly, which could result in reduced and even negative

vehicle emission benefits of the replacement program. Our analysis indicates no clear long-

term emission benefits if the replacement plan is carried out unchanged over the next

25 years.

These findings must, however, be understood within the context of the assumptions

already discussed in the paper. They are necessary for long-term emission analysis given

the scope of the available fleet data. For example, it was assumed that vehicles disposed/

auctioned would be replaced by a new vehicle of a similar type (e.g., passenger cars will be

replaced by passenger cars), considered that a government fleet tends to maintain a con-

stant vehicle mix for consistent service and functionality. In predicting vehicle survival

probabilities, the replacement plan was assumed to be carried out consistently over time.

This also implied that no drastic technological change would take place in new vehicles,

which could lead to unexpected early retirement of older vehicles during the program life.

Gasoline price plays a crucial role. A different long-term gasoline price projection could

alter the long-term emission profile.

Nevertheless, this study represents one of the first attempts to quantify the long-term

emission benefits of a government fleet replacement plan. This study also demonstrates the

usefulness of probabilistic modeling of vehicle retirement decisions influenced by both

vehicles’ physical attributes and external factors that are stochastic in nature. This

approach is superior to the EPA and the ORNL models by developing a stochastic mul-

tivariate survival model of a vehicle retirement procedure.

As noted earlier, targeting government fleets could be effective in further reducing the

regional vehicle emissions. In actual planning and implementation, the environmental

objective must be balanced with the implementation costs. On one hand, long-term vehicle

emissions are expected to decline but at a decreasing rate as improvements in vehicle

technologies continue. On the other hand, replacing older vehicles with newer and much

more technologically advanced vehicles may be costly. Choosing the wrong timing for

vehicle retirement can cost even more. Therefore, long-term cost-effectiveness is of fun-

damental importance to the program sustainability for financially constrained local

agencies. Furthermore, given different factors are changing over time, it is sensible to

develop an adaptive multi-stage plan so the local resources can be best allocated. Such a

plan adjusts the replacement strategy at various stages of the program life to ensure the

optimal cost-effectiveness is achieved in every stage.

Transportation (2008) 35:219–235 233

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Acknowledgements We thank the FPDC for providing the fleet inventory data and Sam Long at IEPA forhis inputs to MOBILE6.2. We also thank the anonymous reviewers and the editor for their insightfulcomments that helped improve the paper.

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Author Biographies

Dr. Jie Lin (Jane) is an assistant professor in Department of Civil and Materials Engineering and aresearcher with the Institute for Environmental Science and Policy at University of Illinois at Chicago. Hercurrent research is focused on transportation sustainability through holistic modeling of energy consumptionand emissions associated with private, freight, and public transportation activities.

Dr. Cynthia Chen is an assistant professor in the civil engineering department at City College of New York.Her research expertise and interests cover travel behavior analysis, land use and transportation, transpor-tation safety, and environmental analysis.

Dr. Deb Niemeier is a professor at UC Davis and her current research focus is on the nexus betweentransportation, land use and climate change, particularly how land use and transportation decisions affectenergy consumption and contribute to climate change. She is considered an expert on transportation-airquality modeling and policy and sustainability.

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