A logit analysis of vehicle emissions using inspection and maintenance testing data

A logit analysis of vehicle emissionsusing inspection and maintenance testing data

Okmyung Bin *

Department of Economics, East Carolina University, Greenville, NC 27858-4353, USA

Abstract

Many states use vehicle inspection and maintenance (I/M) programs to identify high polluting vehicles

and ensure that they operate in accordance with standards. While I/M programs are generally regarded as avaluable means to curb urban air pollution, they have been often criticized for their cost-ineffectiveness.

One criticism has been centered on the blanket approach that requires all vehicles within the program

boundaries to participate regardless of their emission conditions. This paper explores the basis for a se-

lective sampling of vehicles most likely to be pollution violators. Using I/M testing data from Portland,

Oregon, it estimates logit equations for the likelihood of carbon monoxide and hydrocarbon emission

violations given a set of vehicle characteristics. The results indicate that vehicle age, engine size, and

odometer reading all play a significant role in determining the probability of emission test failure.

� 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Vehicle emissions; Inspection and maintenance testing

1. Introduction

According to the US Environmental Protection Agency (EPA), motor vehicles are a significantsource of air pollution in the US, contributing one-third of emissions of nitrogen oxide, one-quarter of the emissions of the volatile organic compounds, and more than one-half of carbonmonoxide emissions (US Environmental Protection Agency, 2000). Despite the advent of ad-vanced emission control systems, overall vehicle emissions remain high for two basic reasons.First, the number of vehicles on the road and the number of miles driven per vehicle haveincreased substantially. Second, in most vehicle emission control systems begin to function

Transportation Research Part D 8 (2003) 215–227www.elsevier.com/locate/trd

* Tel./fax: +1-252-328-6820.

E-mail address: [email protected] (O. Bin).

1361-9209/03/$ - see front matter � 2003 Elsevier Science Ltd. All rights reserved.doi:10.1016/S1361-9209(03)00004-X

mail to: [email protected]

improperly while the vehicles are still being driven. As a result, used and aging vehicles havebecome a major problem of air pollution in many metropolitan areas. It has been frequentlyreported that the majority of vehicle emissions come from roughly 10% to 30% of used vehiclesthat are poorly maintained or that have malfunctioning emission control systems (Bishop et al.,1997; Calvert et al., 1993).Since inspection and maintenance (I/M) programs were established in 64 cities in 1983, many

states have implemented these programs to identify high-polluting used vehicles and ensure thatthey meet appropriate emission standards (US Environmental Protection Agency, 1994). In Or-egon, the Department of Environmental Quality (DEQ) administers the I/M program that re-quires most vehicles within the Portland and Rogue Valley areas to have a certificate ofcompliance for registration every two years. The program ensures that emission control equip-ment is working properly by analyzing the amount of pollutants coming from a vehicle. If avehicle fails the emission tests, it must be re-inspected for registration, possibly after repairs.While I/M programs are generally regarded as a valuable means to curb urban air pollution,

they have been criticized on the following grounds. First, it has been argued that I/M programsare an inefficient use of resources to achieve air quality objectives. A recent cost-effectivenessanalysis for the Arizona�s Enhanced I/M program found that the program was not as cost-effective as expected by the EPA (Harrington et al., 2000). The unattractiveness was partly ex-plained by the technical difficulties of finding a relatively small number of high-polluting vehiclesamong the mass of clean ones. Second, I/M programs are not the most effective way to identifyhigh polluting vehicles. Actual vehicle emissions on the road have been discovered to be, onaverage, one and a half to two times higher than the design values to which the vehicles werecertified, with some vehicles having emissions 50 times higher (Ramsden, 1997). Many I/M testprocedures do not account for the real world driving conditions such as acceleration and decel-eration cycles, and thus vehicles passing the emission tests may still be gross polluters in real worlddriving conditions (Washburn et al., 2001). Third, I/M programs have failed to provide driverswith incentives to minimize their vehicle emissions (Hubbard, 1997). Drivers only need to passperiodically scheduled emission inspections without regard to improving in-use emission condi-tions. 1 Recent studies found that California drivers who failed initial emission tests ultimatelypassed re-tests without purchasing durable repairs (Lawson, 1993, 1995).Given the growing skepticism about the cost-effectiveness of I/M programs, this paper explores

the basis for a selective sampling of vehicles most likely to be pollution violators. Analysis of I/Mtesting data provides a means to identify the characteristics that are most likely to signify thatvehicles are high polluters. Using I/M testing data from Portland, Oregon, this study estimateslogit equations for the likelihood of carbon monoxide and hydrocarbon emission violations givena set of vehicle characteristics. This methodology isolates the separate effects of characteristicssuch as make, age, engine size, odometer reading, and number of cylinders on the likelihood ofemission test failure. Information from this study can be used as a groundwork for the selective

1 Recognizing this in-use emission problem, Congress, as part of the 1990 Clean Air Act Amendments, required that

I/M programs be updated. Accordingly, many states adopted new I/M test procedures such as remote sensing emission

tests.

216 O. Bin / Transportation Research Part D 8 (2003) 215–227

sampling of vehicles which might substantially improve the cost-effectiveness of I/M programs bysaving millions of dollars spent by taxpayers and clean vehicles drivers.The results indicate that vehicle age, engine size, and odometer reading all play a significant role

in determining the likelihood of emission test failure. The probability of emission test failure ishigher as vehicles become older, more driven, and smaller in engine size. Vehicles manufacturedby foreign make have in general lower probability of emission violations than domestic vehicles.Passenger vehicles are also less likely to fail the emission test than non-passenger vehicles.

2. Data

The I/M testing records from September 1997 from various test centers in Portland, Oregon areused. The Oregon DEQ administers the I/M program which requires most vehicles within itsprogram boundaries to have an emission test certificate as part of the biennial registration renewalprocess. Cars, trucks, vans, motor homes and buses powered by gasoline or alternative fuels, anddiesel powered vehicles with manufacturer�s gross weight rating of 8500 lbs or less are subject tothe test. The data come from a two-speed idle test: the vehicle is placed in neutral and idles for30 seconds, and the taken to 2500 revolutions per minute for 30 seconds and idles for another30 seconds. Vehicles are monitored for various pollutants such as carbon monoxide, hydrocar-bons, smoke and excessive noise. If a vehicle fails the test, it must be repaired or adjusted and thenre-tested to be registered. Table 1 provides the vehicle emission control standards for carbonmonoxide and hydrocarbons, which are the most significant sources of urban air pollution.The data contain information on various characteristics of tested vehicles, such as make, model,

model year, odometer reading, engine size, and emission readings. Observations with completeinformation total 20,428. The variable abbreviations and definitions are displayed in the first twocolumns of Table 2. The two dependent variables for the regression analysis are constructed fromthe emission readings for carbon monoxide in percent (CO) and hydrocarbons in parts per million(HC). The dependent variables are binary indicators of vehicle emission test failure. Anotherbinary variable, PASS, indicates passenger vehicles. Non-passenger vehicles include light duty(gross vehicle weight rating less than 6000) and medium duty (gross vehicle weight rating between6001 and 8000) cars and trucks.The last three columns of Table 2 show the variable means and standard deviations for the total

20,428 vehicles, the 19,115 vehicles that have both CO and HC below the control standards, and

Table 1

Oregon motor vehicle emission control standards

Model year Carbon monoxide (percent of emission volume) Hydrocarbons (parts per million)

Pre-1975 No check No check

1975–1980 (non-catalyst) 2.5 300

1975–1980 (catalyst) 1.0 220

1981 and newer 1.0 220

Source: Oregon Department of Environmental Quality, Vehicle Inspection Program.

Notes: Standards vary across vehicles depending on fuel type and gross vehicle weight rating. Reported are the

standards for passenger, light (gross vehicle weight rating less than 6000) and medium (gross vehicle weight rating

between 6001 and 8500) duty gasoline vehicles.

O. Bin / Transportation Research Part D 8 (2003) 215–227 217

the 1,313 vehicles that have either CO or HC above the control standards. About 5.8% and 2.1%of the sample vehicles have respectively, the emission readings for carbon monoxide and hy-drocarbons above the cut points. 2 For those failing vehicles, the average CO and HC are about17 and 9 times higher than those for the passing vehicles, respectively. About 40% of the vehiclesare foreign made and about 64% are passenger vehicles. On average, tested vehicles are 9 years oldand have been driven 89,000 miles. About half of the vehicles have four cylinders and about 60%have automatic transmission. About 75% of the vehicles have fuel injection and about 40% havean air pump.Table 3 shows the number of vehicles tested and the failure rates by manufacturer. The sample

data reveal that Chrysler and Nissan vehicles have relatively higher failure rates while Ford andToyota vehicles have relatively lower failure rates.

Table 2

Variable definitions, means, and standard deviations

Variable name Definition Total (N ¼ 20; 428) Pass (N ¼ 19; 115) Fail (N ¼ 1313)CO Carbon monoxide measured

by the percent of total volume

of emission gas while revved

at 2500 rpm

0.346 0.170 2.911

(0.922) (0.253) (2.998)

HC Hydrocarbon measured by

parts per million while revved

at 2,500 rpm

44.499 29.916 256.805

(117.953) (41.353) (378.806)

IMPORT ¼ 1 if foreign make vehicle; 0.406 0.405 0.432

¼ 0 otherwise (0.491) (0.491) (0.496)

PASS ¼ 1 if passenger vehicle; 0.639 0.640 0.615

¼ 0 otherwise (0.481) (0.480) (0.487)

AUTO ¼ 1 if automatic transmission; 0.583 0.589 0.495

¼ 0 otherwise (0.493) (0.492) (0.500)

FUELINJ ¼ 1 if vehicle with fuel injec-tion;

0.732 0.753 0.439

¼ 0 otherwise (0.443) (0.432) (0.496)

AIRPUMP ¼ 1 if vehicle with air pump; 0.447 0.434 0.634

¼ 0 otherwise (0.497) (0.496) (0.482)

CYLINDER Number of engine cylinders 5.326 5.344 5.054

(1.476) (1.478) (1.409)

AGE Model year subtracted from

1997

8.665 8.447 11.841

(5.187) (5.244) (2.742)

ENGINE Engine size measured by cubic

centimeter displacement

2990.950 3016.100 2764.760

(1262.940) (1264.760) (1212.480)

ODOMETER Vehicle odometer reading in

thousands of miles

88.892 86.710 120.664

(51.319) (50.730) (49.313)

Note: In each cell, the first row indicates the sample means and the second row indicates the standard deviations.

2 The actual failure rate for the two speed idle test is about 10 percent, since vehicles fail the test for various reasons

including excessive nitrogen oxide and tailpipe smokes as well as excessive CO and HC.


3. Empirical methods

This study uses logit regressions to examine the likelihood of CO and HC emission violationsgiven a set of vehicles� characteristics. It is assumed that the probability of emission test failuredepends on a set of vehicle characteristics according to a logistic cumulative distribution functionas follows:

PðF ¼ 1Þ ¼ Kðb0XÞ ¼ expðb0XÞ½1þ expðb0XÞ� ð1Þ

where PðF ¼ 1Þ is the probability that the vehicle fails the emission test given a vector of vehiclecharacteristics, X , and K represents the logistic cumulative distribution function. The parametersb are estimated by the method of maximum likelihood.Unlike the linear regression model, the parameter estimates are interpreted as the rate of change

in the log-odds of test failure as vehicle characteristics change, which is not very intuitive.Therefore, the marginal effects of the vehicle characteristics on the probability of test failure arealso calculated, as follows (Greene, 1997):

oPoxi

¼ Kðb0xiÞ½1� Kðb0xiÞ�b: ð2Þ

The marginal effects are evaluated at the means of the characteristics.For carbon monoxide and hydrocarbon emission violations, two alternative specifications are

estimated to detect collinearity among the vehicle characteristics. In particular, regressions areestimated with the binary variables for automatic transmission, fuel injection, and air pump ex-cluded, and then compared to the estimates with these binary variables included. Because theeffects of vehicle characteristics on the likelihood of emission test failure might differ by make orclass, separate regressions are estimated for domestic and imported vehicles, and passenger andnon-passenger vehicles.

Table 3

Emission test failure by vehicle manufacturer

Vehicle manufacturer Number of vehicles tested

(percent in parentheses)

Number of failures

(percent in parentheses)

GM 4793 (23.5%) 302 (6.3%)

Ford 4609 (22.6%) 211 (4.6%)

Chrysler 2725 (13.3%) 233 (8.5%)

Toyota 2602 (12.7%) 139 (5.3%)

Honda 2124 (10.4%) 152 (7.2%)

Nissan 1591 (7.8%) 153 (9.6%)

European 1064 (5.2%) 55 (5.2%)

Other Asian 920 (4.5%) 68 (7.4%)

Total 20,428 1313


4. Estimation results

Table 4 reports the regression estimates that include all makes and classes. The overall coef-ficient signs are unchanged across the columns, but the magnitude of marginal effects for AGE,IMPORT, PASS are slightly higher in the models without AUTO, FUELINJ, and AIRPUMP.The coefficient signs and marginal effects for ENGINE and ODOMETER do not change acrossalternative specifications at all. The results indicate little evidence of collinearity among the vehiclecharacteristics. Bottom of Table 4 provides the likelihood ratio statistic for testing the joint sig-nificance of the three excluded variables. Given the critical chi-squared value of 7.82, the joint nullhypothesis is rejected for both CO and HC failure models.Table 4 shows that AGE, ENGINE, and ODOMETER significantly affect the probability of

CO and HC failures in the expected directions which are consistent with previous analyses ofvehicle emissions (Kahn, 1996; Washburn et al., 2001). The coefficient estimates for CYLINDERand AUTO are insignificant in all specifications. Vehicles with fuel injection are less likely to failemission tests than ones without. The probability of CO failure is significantly lower for importedand passenger vehicles, but the negative effect of passenger vehicles on the probability of HCfailure falls by about 40% and becomes insignificant when AUTO, FUELINJ, and AIRPUMPvariables are added.

Table 4

Logit estimates for vehicle emission test failure

Variable CO failure CO failure HC failure HC failure

AGE 0.1060a 0.0536a 0.0727a 0.0446a

[0.0044] [0.0021] [0.0011] [0.0007]

ENGINE )0.0004a -0.0004a )0.0005a )0.0005a

[)0.00002] [)0.00001] [)0.00001] [)0.00001]ODOMETER 0.0062a 0.0062a 0.0086a 0.0086a

[0.0003] [0.0002] [0.0001] [0.0001]

CYLINDER 0.0169 )0.0059 0.1302 0.1224

[0.0007] [)0.0002] [0.0020] [0.0019]

AUTO 0.0462 )0.1541[0.0018] [)0.0024]

FUELINJ )0.6691a )0.3064b

[)0.0267] [)0.0047]AIRPUMP 0.4565a 0.3598a

[0.0182] [0.0055]

IMPORT )0.5193a )0.4639a )0.6645a )0.6749a

[)0.0216] [)0.0185] [)0.0104] [)0.0104]PASS )0.4015a )0.3153a )0.2587b )0.1639

[)0.0176] [)0.0126] [)0.0041] [)0.0025]

Observations 20,428 20,428 20,428 20,428

Log likelihood )4144.220 )4066.445 )1972.818 )1959.629Likelihood ratio 155.550 26.378

Notes: In each cell, the first row indicates the coefficient estimates and the second row indicates the marginal effects. The

superscripts a and b indicate significance at the 1% and 5% levels, respectively.


The marginal effects of vehicle characteristics on the probability of emission test failures appearin brackets. The estimates predict that aging one more year from 9 to 10 years old raises theprobability of CO and HC failures by about 0.2% and 0.1% points, respectively. Meanwhile, a1000 cc decrease in engine size also raises the probability of CO and HC failures by about 1%each, which represents a substantive change given the mean CO and HC failure rates of 5.8% and2.1% for the sample. Similarly, evaluated at the mean level, a 10,000 mile increase in odometerreading raises the likelihood of CO and HC failures by about 0.2% and 0.1% points, respectively.Vehicles with fuel injection are 2.7% points less likely to fail the CO test, and 0.5% point less likelyto fail the HC test, while vehicles with air pump are 1.9% points more likely to fail the CO test and0.6% point more likely to fail the HC test. Imported vehicles are about 1.9% and 1.0% pointsless likely to fail the CO and HC tests, respectively, representing a substantive decrease in fail-ure probability. The likelihood of CO failure is also about 1.3% points lower for passenger ve-hicles.The estimates for IMPORT and PASS indicate that emission test failure patterns might differ

across vehicle make or class. Hence, the CO and HC failure logits are estimated separately fordomestic and import vehicles, and passenger and non-passenger vehicles (Tables 5 and 6). Inaddition, Figs. 1–3 display the predicted and actual probability of emission test failures by vehicleage, engine size and odometer reading.

Table 5

Logit estimates for vehicle CO emission test failure

Variable Domestic Import Passenger Non-passenger

AGE 0.0392a 0.0652a 0.0741a 0.0154

[0.0014] [0.0029] [0.0030] [0.0007]

ENGINE )0.0004a )0.00002 )0.0007a )0.0001[)0.00001] [)0.000001] [)0.00003] [)0.00005]

ODOMETER 0.0056a 0.0073a 0.0061a 0.0064a

[0.0002] [0.0003] [0.0002] [0.0002]

CYLINDER )0.0256 )0.0547 0.2472a )0.2965a

[)0.0009] [)0.0024] [0.0099] [)0.0013]AUTO 0.0960 )0.0158 0.1144 0.0378

[0.0034] [)0.0007] [0.0046] [0.0014]

FUELINJ )0.9334a )0.5023a )0.4003a )1.2136a

[)0.0331] [)0.0222] [)0.0160] [)0.0460]AIRPUMP 0.8253a 0.0158 0.4106a 0.5267a

[0.0292] [0.0007] [0.0164] [0.0200]

IMPORT )0.3681a )0.8371a

[)0.0147] [)0.0318]PASS )0.4277a )0.0696

[)0.0152] [)0.0031]

Observations 12,127 8301 13,043 7385

Log likelihood )2282.940 )1747.910 )2569.411 )1471.010


superscript a indicates significance at the 1% level.


4.1. The likelihood of CO failure

The results for domestic and passenger vehicles in Table 5 show that most vehicle character-istics significantly influence the probability of CO failure. The signs of significant estimates are thesame as in Table 4. As vehicles age an additional year, increases in failure probability are twice ashigh for imported vehicles as domestic ones. Similarly, as vehicles are driven more, increases infailure probability are higher for imported vehicles than domestic ones. Combined with the resultsfrom Table 4, these findings imply that vehicles manufactured by foreign makes, in general, emitless carbon monoxide than domestic vehicles. However, as the vehicles become older and moredriven, emissions from foreign make vehicles increase at a higher rate than emissions from do-mestic vehicles. Evaluated at the mean, a decrease in engine size by 1000 cc raises the probabilityof CO failure by about 1% for imported vehicles and 3% points for passenger vehicles. The co-efficient estimates of ENGINE are insignificant for imported and non-passenger vehicles. Forpassenger and non-passenger vehicles, the coefficient estimates of CYLINDER become significantand have opposite signs, indicating that as the number of engine cylinders increases, the likelihoodof CO failure increases for passenger vehicles but decreases for non-passenger vehicles. The co-efficient estimate AIRPUMP loses significance for imported vehicles. PASS is significant fordomestic vehicles but insignificant for imported ones.The top of Fig. 1 displays the probability of CO failure by vehicle age, holding other char-

acteristics constant. The predicted failure rates mirror the actual rates, especially for vehicle age

Table 6

Logit estimates for vehicle HC emission test failure

Variable Domestic Import Passenger Non-passenger

AGE 0.0417b 0.0340 0.0344c 0.0608b

[0.0006] [0.0021] [0.0005] [0.0008]

ENGINE )0.0004a )0.0009a )0.0008a )0.0001[)0.00001] [)0.00001] [)0.00001] [)0.00001]

ODOMETER 0.0071a 0.0111a 0.0096a 0.0070a

[0.0001] [0.0002] [0.0001] [0.0001]

CYLINDER 0.0029 0.4427b 0.4851a )0.3463b

[0.00004] [0.0065] [0.0076] [)0.0047]AUTO )0.1036 )0.2032 )0.2235 0.0953

[)0.0016] [)0.0030] [)0.0035] [0.0013]

FUELINJ )0.4852b )0.0989 )0.2681 )0.3550[)0.0073] [)0.0267] [)0.0042] [)0.0048]

AIRPUMP 0.6621a )0.0386 0.2016 0.6854a

[0.0100] [)0.0006] [0.0031] [0.0093]

IMPORT )0.6677a )1.0260a

[)0.0104] [)0.0139]PASS )0.2101 )0.3141

[)0.0032] [)0.0046]

Observations 12,127 8301 13,043 7385

Log likelihood )1163.160 )783.273 )1258.819 )685.768


superscripts a, b, and c indicate significance at the 1%, 5% and 10% levels, respectively.


between 6 and 9. The graph shows that a selective sampling of vehicles older than 10 years wouldsubstantially increase the likelihood of finding high polluting vehicles. In Fig. 2, the predicted andactual probability of CO failure by engine size follow a very similar pattern. It reveals that vehicleswith engine size smaller than 2000 cc are more likely to be pollution violators. Fig. 3 presents thatthe vehicles driven more than 90,000 miles have a higher probability of CO test failure thanoverall vehicles.

Fig. 1. Actual vs. predicted emission test failure rates by vehicle age: (a) carbon monoxide failure rate and (b) hy-

drocarbon failure rate.


4.2. The likelihood of HC failure

Regarding the HC failure, Table 6 shows that increases in failure probability as vehicles age arehigher for non-passenger vehicles than for passenger ones. Consistent with the results from Table4, the coefficient estimates on ODOMETER are positive and significant in all columns, but the

Fig. 2. Actual vs. predicted emission test failure rates by vehicle engine size: (a) carbon monoxide failure rate and (b)

hydrocarbon failure rate.


magnitudes of the marginal effects are about half of those for CO failure. A decrease in engine sizeby 1000 cc raises the probability of HC failure by about one percentage point for domestic,imported, and passenger vehicles. The coefficient estimates of ENGINE are insignificant for non-passenger vehicles. AUTO is insignificant again, implying the types of transmission do notinfluence emission test failures. The estimation results of CYLINDER for passenger and

Fig. 3. Actual vs. predicted emission test failure rates by vehicle Odometer reading: (a) carbon monoxide failure rate

and (b) hydrocarbon failure rate.


non-passenger vehicles are similar to those in Table 5. They imply that the increase in the numberof engine cylinders raises the likelihood of HC failure for passenger vehicles but lowers thelikelihood for non-passenger vehicles. While vehicle classes do not have significant impacts on theHC test results, imported vehicles decrease the probability of HC failure by about 1% point forboth passenger and non-passenger vehicles.The bottom of Fig. 1 displays the probability of HC test failure by vehicles age. The sample

mean of HC failure rate is about 2.1%. Similar to the result for CO, a selective sampling for thevehicles older than 10 years would increase the likelihood of finding high HC polluters. Fig. 2 alsoreveals that the probability of HC violation is higher for vehicles with engine size smaller than2000 cc than those of overall vehicles. Fig. 3 predicts that the vehicles driven more than 90,000miles have a higher chance of being high polluters than others. Adding or deleting some variablesdoes not change the patterns of these graphs.In sum, the probability of the emission test failure is higher as vehicles become older, have

smaller engine size, and are driven more. These results are quite consistent across differentspecifications. Moreover, the vehicle make and class are important factors in understandingpatterns of emission test results. Foreign vehicles in general have a lower probability of failure forboth CO and HC tests. Similarly, passenger vehicles are less likely to fail the emission tests thannon-passenger vehicles. As the number of engine cylinders increases, the likelihood of emissiontest failure increases for passenger vehicles, but decreases for non-passenger vehicles. The types oftransmission do not have a significant effect on the likelihood of emission test failure.

5. Conclusions

This paper uses logit regressions on I/M testing data from Portland, Oregon to identify thecharacteristics of vehicles that are significantly associated with carbon monoxide and hydrocar-bon emission test failures. The findings indicate that vehicle age, engine size and odometer readingall play a significant role in determining I/M test results, as do vehicle make and class. Informationfrom this study can be used as a groundwork for the selective sampling of vehicles which mightimprove the cost-effectiveness of I/M programs. For example, targeting vehicles of more than10 years old, engine size smaller than 2000 cc, and odometer reading over 100,000 would sub-stantially increase the likelihood of finding high polluting vehicles. This kind of selectivity canimprove the cost-effectiveness of traditional I/M programs as long as the financial savings infocusing on specific categories outweigh the costs of missing polluters in other categories.

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Documents

A logit analysis of vehicle emissions using inspection and maintenance testing data