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A logit analysis of vehicle emissions using inspection and maintenance testing data
Transportation Research Part D 8 (2003) 215–227 www.elsevier.com/locate/trd
A logit analysis of vehicle emissions using 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 a valuable 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 selective 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. Ó 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 significant source of air pollution in the US, contributing one-third of emissions of nitrogen oxide, onequarter of the emissions of the volatile organic compounds, and more than one-half of carbon monoxide emissions (US Environmental Protection Agency, 2000). Despite the advent of advanced 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 have increased substantially. Second, in most vehicle emission control systems begin to function
*
Tel./fax: +1-252-328-6820. E-mail address: [email protected] (O. Bin).
1361-9209/03/$ - see front matter Ó 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1361-9209(03)00004-X
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improperly while the vehicles are still being driven. As a result, used and aging vehicles have become a major problem of air pollution in many metropolitan areas. It has been frequently reported that the majority of vehicle emissions come from roughly 10% to 30% of used vehicles that 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 that they meet appropriate emission standards (US Environmental Protection Agency, 1994). In Oregon, the Department of Environmental Quality (DEQ) administers the I/M program that requires most vehicles within the Portland and Rogue Valley areas to have a certificate of compliance for registration every two years. The program ensures that emission control equipment is working properly by analyzing the amount of pollutants coming from a vehicle. If a vehicle 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 programs are an inefficient use of resources to achieve air quality objectives. A recent cost-effectiveness analysis for the ArizonaÕs Enhanced I/M program found that the program was not as costeffective as expected by the EPA (Harrington et al., 2000). The unattractiveness was partly explained by the technical difficulties of finding a relatively small number of high-polluting vehicles among the mass of clean ones. Second, I/M programs are not the most effective way to identify high polluting vehicles. Actual vehicle emissions on the road have been discovered to be, on average, one and a half to two times higher than the design values to which the vehicles were certified, with some vehicles having emissions 50 times higher (Ramsden, 1997). Many I/M test procedures do not account for the real world driving conditions such as acceleration and deceleration cycles, and thus vehicles passing the emission tests may still be gross polluters in real world driving conditions (Washburn et al., 2001). Third, I/M programs have failed to provide drivers with incentives to minimize their vehicle emissions (Hubbard, 1997). Drivers only need to pass periodically scheduled emission inspections without regard to improving in-use emission conditions. 1 Recent studies found that California drivers who failed initial emission tests ultimately passed 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/M testing data provides a means to identify the characteristics that are most likely to signify that vehicles are high polluters. Using I/M testing data from Portland, Oregon, this study estimates logit equations for the likelihood of carbon monoxide and hydrocarbon emission violations given a set of vehicle characteristics. This methodology isolates the separate effects of characteristics such as make, age, engine size, odometer reading, and number of cylinders on the likelihood of emission 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.
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sampling of vehicles which might substantially improve the cost-effectiveness of I/M programs by saving 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 is higher as vehicles become older, more driven, and smaller in engine size. Vehicles manufactured by 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 are used. The Oregon DEQ administers the I/M program which requires most vehicles within its program boundaries to have an emission test certificate as part of the biennial registration renewal process. Cars, trucks, vans, motor homes and buses powered by gasoline or alternative fuels, and diesel powered vehicles with manufacturerÕs gross weight rating of 8500 lbs or less are subject to the test. The data come from a two-speed idle test: the vehicle is placed in neutral and idles for 30 seconds, and the taken to 2500 revolutions per minute for 30 seconds and idles for another 30 seconds. Vehicles are monitored for various pollutants such as carbon monoxide, hydrocarbons, smoke and excessive noise. If a vehicle fails the test, it must be repaired or adjusted and then re-tested to be registered. Table 1 provides the vehicle emission control standards for carbon monoxide 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 complete information total 20,428. The variable abbreviations and definitions are displayed in the first two columns of Table 2. The two dependent variables for the regression analysis are constructed from the 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. Another binary 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 between 6001 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 1975–1980 (non-catalyst) 1975–1980 (catalyst) 1981 and newer
No check 2.5 1.0 1.0
No check 300 220 220
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.
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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 Hydrocarbon measured by parts per million while revved at 2,500 rpm ¼ 1 if foreign make vehicle; ¼ 0 otherwise ¼ 1 if passenger vehicle; ¼ 0 otherwise ¼ 1 if automatic transmission; ¼ 0 otherwise ¼ 1 if vehicle with fuel injection; ¼ 0 otherwise ¼ 1 if vehicle with air pump; ¼ 0 otherwise Number of engine cylinders
0.346 (0.922)
0.170 (0.253)
2.911 (2.998)
44.499 (117.953)
29.916 (41.353)
256.805 (378.806)
0.406 (0.491) 0.639 (0.481) 0.583 (0.493) 0.732
0.405 (0.491) 0.640 (0.480) 0.589 (0.492) 0.753
0.432 (0.496) 0.615 (0.487) 0.495 (0.500) 0.439
(0.443) 0.447 (0.497) 5.326 (1.476) 8.665 (5.187) 2990.950 (1262.940) 88.892 (51.319)
(0.432) 0.434 (0.496) 5.344 (1.478) 8.447 (5.244) 3016.100 (1264.760) 86.710 (50.730)
(0.496) 0.634 (0.482) 5.054 (1.409) 11.841 (2.742) 2764.760 (1212.480) 120.664 (49.313)
HC
IMPORT PASS AUTO FUELINJ
AIRPUMP CYLINDER AGE ENGINE ODOMETER
Model year subtracted from 1997 Engine size measured by cubic centimeter displacement Vehicle odometer reading in thousands of miles
Note: In each cell, the first row indicates the sample means and the second row indicates the standard deviations.
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 hydrocarbons above the cut points. 2 For those failing vehicles, the average CO and HC are about 17 and 9 times higher than those for the passing vehicles, respectively. About 40% of the vehicles are foreign made and about 64% are passenger vehicles. On average, tested vehicles are 9 years old and 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% have an 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 and Toyota vehicles have relatively lower failure rates.
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.
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Table 3 Emission test failure by vehicle manufacturer Vehicle manufacturer
Number of vehicles tested (percent in parentheses)
Number of failures (percent in parentheses)
GM Ford Chrysler Toyota Honda Nissan European Other Asian
4793 (23.5%) 4609 (22.6%) 2725 (13.3%) 2602 (12.7%) 2124 (10.4%) 1591 (7.8%) 1064 (5.2%) 920 (4.5%)
302 (6.3%) 211 (4.6%) 233 (8.5%) 139 (5.3%) 152 (7.2%) 153 (9.6%) 55 (5.2%) 68 (7.4%)
Total
20,428
1313
3. Empirical methods This study uses logit regressions to examine the likelihood of CO and HC emission violations given a set of vehiclesÕ characteristics. It is assumed that the probability of emission test failure depends on a set of vehicle characteristics according to a logistic cumulative distribution function as follows: P ðF ¼ 1Þ ¼ Kðb0 XÞ ¼
expðb0 XÞ ½1 þ expðb0 XÞ
ð1Þ
where P ðF ¼ 1Þ is the probability that the vehicle fails the emission test given a vector of vehicle characteristics, X, and K represents the logistic cumulative distribution function. The parameters b 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 are also calculated, as follows (Greene, 1997): oP ¼ Kðb0 xi Þ½1 Kðb0 xi Þb: oxi
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 are estimated with the binary variables for automatic transmission, fuel injection, and air pump excluded, and then compared to the estimates with these binary variables included. Because the effects of vehicle characteristics on the likelihood of emission test failure might differ by make or class, separate regressions are estimated for domestic and imported vehicles, and passenger and non-passenger vehicles.
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Table 4 Logit estimates for vehicle emission test failure Variable AGE ENGINE ODOMETER CYLINDER
CO failure a
0.1060 [0.0044] )0.0004a [)0.00002] 0.0062a [0.0003] 0.0169 [0.0007]
AUTO FUELINJ AIRPUMP IMPORT PASS Observations Log likelihood Likelihood ratio
)0.5193a [)0.0216] )0.4015a [)0.0176] 20,428 )4144.220 155.550
CO failure a
0.0536 [0.0021] -0.0004a [)0.00001] 0.0062a [0.0002] )0.0059 [)0.0002] 0.0462 [0.0018] )0.6691a [)0.0267] 0.4565a [0.0182] )0.4639a [)0.0185] )0.3153a [)0.0126] 20,428 )4066.445
HC failure a
0.0727 [0.0011] )0.0005a [)0.00001] 0.0086a [0.0001] 0.1302 [0.0020]
)0.6645a [)0.0104] )0.2587b [)0.0041] 20,428 )1972.818 26.378
HC failure 0.0446a [0.0007] )0.0005a [)0.00001] 0.0086a [0.0001] 0.1224 [0.0019] )0.1541 [)0.0024] )0.3064b [)0.0047] 0.3598a [0.0055] )0.6749a [)0.0104] )0.1639 [)0.0025] 20,428 )1959.629
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.
4. Estimation results Table 4 reports the regression estimates that include all makes and classes. The overall coefficient 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 across alternative specifications at all. The results indicate little evidence of collinearity among the vehicle characteristics. Bottom of Table 4 provides the likelihood ratio statistic for testing the joint significance of the three excluded variables. Given the critical chi-squared value of 7.82, the joint null hypothesis 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 of vehicle emissions (Kahn, 1996; Washburn et al., 2001). The coefficient estimates for CYLINDER and AUTO are insignificant in all specifications. Vehicles with fuel injection are less likely to fail emission tests than ones without. The probability of CO failure is significantly lower for imported and passenger vehicles, but the negative effect of passenger vehicles on the probability of HC failure falls by about 40% and becomes insignificant when AUTO, FUELINJ, and AIRPUMP variables are added.
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The marginal effects of vehicle characteristics on the probability of emission test failures appear in brackets. The estimates predict that aging one more year from 9 to 10 years old raises the probability of CO and HC failures by about 0.2% and 0.1% points, respectively. Meanwhile, a 1000 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% and 2.1% for the sample. Similarly, evaluated at the mean level, a 10,000 mile increase in odometer reading 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 likely to fail the HC test, while vehicles with air pump are 1.9% points more likely to fail the CO test and 0.6% point more likely to fail the HC test. Imported vehicles are about 1.9% and 1.0% points less likely to fail the CO and HC tests, respectively, representing a substantive decrease in failure probability. The likelihood of CO failure is also about 1.3% points lower for passenger vehicles. 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 for domestic and import vehicles, and passenger and non-passenger vehicles (Tables 5 and 6). In addition, Figs. 1–3 display the predicted and actual probability of emission test failures by vehicle age, engine size and odometer reading.
Table 5 Logit estimates for vehicle CO emission test failure Variable
Domestic a
Import a
0.0392 [0.0014] )0.0004a [)0.00001] 0.0056a [0.0002] )0.0256 [)0.0009] 0.0960 [0.0034] )0.9334a [)0.0331] 0.8253a [0.0292]
0.0652 [0.0029] )0.00002 [)0.000001] 0.0073a [0.0003] )0.0547 [)0.0024] )0.0158 [)0.0007] )0.5023a [)0.0222] 0.0158 [0.0007]
PASS
)0.4277a [)0.0152]
)0.0696 [)0.0031]
Observations Log likelihood
12,127 )2282.940
8301 )1747.910
AGE ENGINE ODOMETER CYLINDER AUTO FUELINJ AIRPUMP IMPORT
Passenger a
Non-passenger
0.0741 [0.0030] )0.0007a [)0.00003] 0.0061a [0.0002] 0.2472a [0.0099] 0.1144 [0.0046] )0.4003a [)0.0160] 0.4106a [0.0164] )0.3681a [)0.0147]
0.0154 [0.0007] )0.0001 [)0.00005] 0.0064a [0.0002] )0.2965a [)0.0013] 0.0378 [0.0014] )1.2136a [)0.0460] 0.5267a [0.0200] )0.8371a [)0.0318]
13,043 )2569.411
7385 )1471.010
Notes: In each cell, the first row indicates the coefficient estimates and the second row indicates the marginal effects. The superscript a indicates significance at the 1% level.
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Table 6 Logit estimates for vehicle HC emission test failure Variable
Domestic b
Import
0.0417 [0.0006] )0.0004a [)0.00001] 0.0071a [0.0001] 0.0029 [0.00004] )0.1036 [)0.0016] )0.4852b [)0.0073] 0.6621a [0.0100]
0.0340 [0.0021] )0.0009a [)0.00001] 0.0111a [0.0002] 0.4427b [0.0065] )0.2032 [)0.0030] )0.0989 [)0.0267] )0.0386 [)0.0006]
PASS
)0.2101 [)0.0032]
)0.3141 [)0.0046]
Observations Log likelihood
12,127 )1163.160
8301 )783.273
AGE ENGINE ODOMETER CYLINDER AUTO FUELINJ AIRPUMP IMPORT
Passenger c
Non-passenger
0.0344 [0.0005] )0.0008a [)0.00001] 0.0096a [0.0001] 0.4851a [0.0076] )0.2235 [)0.0035] )0.2681 [)0.0042] 0.2016 [0.0031] )0.6677a [)0.0104]
0.0608b [0.0008] )0.0001 [)0.00001] 0.0070a [0.0001] )0.3463b [)0.0047] 0.0953 [0.0013] )0.3550 [)0.0048] 0.6854a [0.0093] )1.0260a [)0.0139]
13,043 )1258.819
7385 )685.768
Notes: In each cell, the first row indicates the coefficient estimates and the second row indicates the marginal effects. The superscripts a, b, and c indicate significance at the 1%, 5% and 10% levels, respectively.
4.1. The likelihood of CO failure The results for domestic and passenger vehicles in Table 5 show that most vehicle characteristics significantly influence the probability of CO failure. The signs of significant estimates are the same as in Table 4. As vehicles age an additional year, increases in failure probability are twice as high for imported vehicles as domestic ones. Similarly, as vehicles are driven more, increases in failure probability are higher for imported vehicles than domestic ones. Combined with the results from Table 4, these findings imply that vehicles manufactured by foreign makes, in general, emit less carbon monoxide than domestic vehicles. However, as the vehicles become older and more driven, emissions from foreign make vehicles increase at a higher rate than emissions from domestic vehicles. Evaluated at the mean, a decrease in engine size by 1000 cc raises the probability of CO failure by about 1% for imported vehicles and 3% points for passenger vehicles. The coefficient estimates of ENGINE are insignificant for imported and non-passenger vehicles. For passenger and non-passenger vehicles, the coefficient estimates of CYLINDER become significant and have opposite signs, indicating that as the number of engine cylinders increases, the likelihood of CO failure increases for passenger vehicles but decreases for non-passenger vehicles. The coefficient estimate AIRPUMP loses significance for imported vehicles. PASS is significant for domestic vehicles but insignificant for imported ones. The top of Fig. 1 displays the probability of CO failure by vehicle age, holding other characteristics constant. The predicted failure rates mirror the actual rates, especially for vehicle age
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Fig. 1. Actual vs. predicted emission test failure rates by vehicle age: (a) carbon monoxide failure rate and (b) hydrocarbon failure rate.
between 6 and 9. The graph shows that a selective sampling of vehicles older than 10 years would substantially increase the likelihood of finding high polluting vehicles. In Fig. 2, the predicted and actual probability of CO failure by engine size follow a very similar pattern. It reveals that vehicles with engine size smaller than 2000 cc are more likely to be pollution violators. Fig. 3 presents that the vehicles driven more than 90,000 miles have a higher probability of CO test failure than overall vehicles.
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Fig. 2. Actual vs. predicted emission test failure rates by vehicle engine size: (a) carbon monoxide failure rate and (b) hydrocarbon failure rate.
4.2. The likelihood of HC failure Regarding the HC failure, Table 6 shows that increases in failure probability as vehicles age are higher for non-passenger vehicles than for passenger ones. Consistent with the results from Table 4, the coefficient estimates on ODOMETER are positive and significant in all columns, but the
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Fig. 3. Actual vs. predicted emission test failure rates by vehicle Odometer reading: (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 size by 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 nonpassenger vehicles. AUTO is insignificant again, implying the types of transmission do not influence emission test failures. The estimation results of CYLINDER for passenger and
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non-passenger vehicles are similar to those in Table 5. They imply that the increase in the number of engine cylinders raises the likelihood of HC failure for passenger vehicles but lowers the likelihood for non-passenger vehicles. While vehicle classes do not have significant impacts on the HC test results, imported vehicles decrease the probability of HC failure by about 1% point for both 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 the vehicles older than 10 years would increase the likelihood of finding high HC polluters. Fig. 2 also reveals that the probability of HC violation is higher for vehicles with engine size smaller than 2000 cc than those of overall vehicles. Fig. 3 predicts that the vehicles driven more than 90,000 miles have a higher chance of being high polluters than others. Adding or deleting some variables does 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 different specifications. Moreover, the vehicle make and class are important factors in understanding patterns of emission test results. Foreign vehicles in general have a lower probability of failure for both CO and HC tests. Similarly, passenger vehicles are less likely to fail the emission tests than non-passenger vehicles. As the number of engine cylinders increases, the likelihood of emission test failure increases for passenger vehicles, but decreases for non-passenger vehicles. The types of transmission 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 the characteristics of vehicles that are significantly associated with carbon monoxide and hydrocarbon emission test failures. The findings indicate that vehicle age, engine size and odometer reading all play a significant role in determining I/M test results, as do vehicle make and class. Information from this study can be used as a groundwork for the selective sampling of vehicles which might improve the cost-effectiveness of I/M programs. For example, targeting vehicles of more than 10 years old, engine size smaller than 2000 cc, and odometer reading over 100,000 would substantially increase the likelihood of finding high polluting vehicles. This kind of selectivity can improve the cost-effectiveness of traditional I/M programs as long as the financial savings in focusing on specific categories outweigh the costs of missing polluters in other categories.
References Bishop, G., Aldrete, P., Slott, R., 1997. On-road evaluation of an automobile emission test program. Environmental Science and Technology 31, 927–931. Calvert, J., Heywood, J., Sawer, R., Seinfeld, J., 1993. Achieving acceptable air quality: some reflections on controlling vehicle emissions. Science 261, 37–45. Greene, W., 1997. Econometric Analysis. Prentice-Hall, New Jersey. Harrington, W., McConnell, V., Ando, A., 2000. Are vehicle emission inspection programs living up to expectations? Transportation Research D, 5,153–172.
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Hubbard, T., 1997. Using inspection and maintenance programs to regulate vehicle emissions. Contemporary Economic Policy 15, 52–62. Kahn, M., 1996. New evidence on trends in vehicle emissions. Rand Journal of Economics 27, 183–196. Lawson, D., 1993. Passing the test Human behavior and CaliforniaÕs smog check program. Journal of the Air and Waste Management Association 43, 1567–1575. Lawson, D., 1995. The costs of ‘‘M’’ in I/M Reflections on inspection/maintenance programs. Journal of the Air and Waste Management Association 45, 465–476. Ramsden, T., 1997. Vehicle inspection/maintenance. In: Morgenstern, R. (Ed.), Economic Analysis at EPA, Resource for the Future, Washington. US Environmental Protection Agency, 1994. Milestones in auto emissions control. EPA 400-F- 92-014, Washington. US Environmental Protection Agency, 2000. National air pollutant emission trends, 1900–1998. EPA 454-R-00-002, Washington. Washburn, S., Seet, J., Mannering, F., 2001. Statistical modeling of vehicle emissions from inspection/maintenance testing data: an exploratory analysis. Transportation Research D 6, 21–36.
A logit analysis of vehicle emissions using 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 a valuable 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 selective 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. Ó 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 significant source of air pollution in the US, contributing one-third of emissions of nitrogen oxide, onequarter of the emissions of the volatile organic compounds, and more than one-half of carbon monoxide emissions (US Environmental Protection Agency, 2000). Despite the advent of advanced 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 have increased substantially. Second, in most vehicle emission control systems begin to function
*
Tel./fax: +1-252-328-6820. E-mail address: [email protected] (O. Bin).
1361-9209/03/$ - see front matter Ó 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1361-9209(03)00004-X
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improperly while the vehicles are still being driven. As a result, used and aging vehicles have become a major problem of air pollution in many metropolitan areas. It has been frequently reported that the majority of vehicle emissions come from roughly 10% to 30% of used vehicles that 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 that they meet appropriate emission standards (US Environmental Protection Agency, 1994). In Oregon, the Department of Environmental Quality (DEQ) administers the I/M program that requires most vehicles within the Portland and Rogue Valley areas to have a certificate of compliance for registration every two years. The program ensures that emission control equipment is working properly by analyzing the amount of pollutants coming from a vehicle. If a vehicle 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 programs are an inefficient use of resources to achieve air quality objectives. A recent cost-effectiveness analysis for the ArizonaÕs Enhanced I/M program found that the program was not as costeffective as expected by the EPA (Harrington et al., 2000). The unattractiveness was partly explained by the technical difficulties of finding a relatively small number of high-polluting vehicles among the mass of clean ones. Second, I/M programs are not the most effective way to identify high polluting vehicles. Actual vehicle emissions on the road have been discovered to be, on average, one and a half to two times higher than the design values to which the vehicles were certified, with some vehicles having emissions 50 times higher (Ramsden, 1997). Many I/M test procedures do not account for the real world driving conditions such as acceleration and deceleration cycles, and thus vehicles passing the emission tests may still be gross polluters in real world driving conditions (Washburn et al., 2001). Third, I/M programs have failed to provide drivers with incentives to minimize their vehicle emissions (Hubbard, 1997). Drivers only need to pass periodically scheduled emission inspections without regard to improving in-use emission conditions. 1 Recent studies found that California drivers who failed initial emission tests ultimately passed 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/M testing data provides a means to identify the characteristics that are most likely to signify that vehicles are high polluters. Using I/M testing data from Portland, Oregon, this study estimates logit equations for the likelihood of carbon monoxide and hydrocarbon emission violations given a set of vehicle characteristics. This methodology isolates the separate effects of characteristics such as make, age, engine size, odometer reading, and number of cylinders on the likelihood of emission 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.
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sampling of vehicles which might substantially improve the cost-effectiveness of I/M programs by saving 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 is higher as vehicles become older, more driven, and smaller in engine size. Vehicles manufactured by 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 are used. The Oregon DEQ administers the I/M program which requires most vehicles within its program boundaries to have an emission test certificate as part of the biennial registration renewal process. Cars, trucks, vans, motor homes and buses powered by gasoline or alternative fuels, and diesel powered vehicles with manufacturerÕs gross weight rating of 8500 lbs or less are subject to the test. The data come from a two-speed idle test: the vehicle is placed in neutral and idles for 30 seconds, and the taken to 2500 revolutions per minute for 30 seconds and idles for another 30 seconds. Vehicles are monitored for various pollutants such as carbon monoxide, hydrocarbons, smoke and excessive noise. If a vehicle fails the test, it must be repaired or adjusted and then re-tested to be registered. Table 1 provides the vehicle emission control standards for carbon monoxide 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 complete information total 20,428. The variable abbreviations and definitions are displayed in the first two columns of Table 2. The two dependent variables for the regression analysis are constructed from the 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. Another binary 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 between 6001 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 1975–1980 (non-catalyst) 1975–1980 (catalyst) 1981 and newer
No check 2.5 1.0 1.0
No check 300 220 220
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.
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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 Hydrocarbon measured by parts per million while revved at 2,500 rpm ¼ 1 if foreign make vehicle; ¼ 0 otherwise ¼ 1 if passenger vehicle; ¼ 0 otherwise ¼ 1 if automatic transmission; ¼ 0 otherwise ¼ 1 if vehicle with fuel injection; ¼ 0 otherwise ¼ 1 if vehicle with air pump; ¼ 0 otherwise Number of engine cylinders
0.346 (0.922)
0.170 (0.253)
2.911 (2.998)
44.499 (117.953)
29.916 (41.353)
256.805 (378.806)
0.406 (0.491) 0.639 (0.481) 0.583 (0.493) 0.732
0.405 (0.491) 0.640 (0.480) 0.589 (0.492) 0.753
0.432 (0.496) 0.615 (0.487) 0.495 (0.500) 0.439
(0.443) 0.447 (0.497) 5.326 (1.476) 8.665 (5.187) 2990.950 (1262.940) 88.892 (51.319)
(0.432) 0.434 (0.496) 5.344 (1.478) 8.447 (5.244) 3016.100 (1264.760) 86.710 (50.730)
(0.496) 0.634 (0.482) 5.054 (1.409) 11.841 (2.742) 2764.760 (1212.480) 120.664 (49.313)
HC
IMPORT PASS AUTO FUELINJ
AIRPUMP CYLINDER AGE ENGINE ODOMETER
Model year subtracted from 1997 Engine size measured by cubic centimeter displacement Vehicle odometer reading in thousands of miles
Note: In each cell, the first row indicates the sample means and the second row indicates the standard deviations.
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 hydrocarbons above the cut points. 2 For those failing vehicles, the average CO and HC are about 17 and 9 times higher than those for the passing vehicles, respectively. About 40% of the vehicles are foreign made and about 64% are passenger vehicles. On average, tested vehicles are 9 years old and 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% have an 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 and Toyota vehicles have relatively lower failure rates.
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.
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Table 3 Emission test failure by vehicle manufacturer Vehicle manufacturer
Number of vehicles tested (percent in parentheses)
Number of failures (percent in parentheses)
GM Ford Chrysler Toyota Honda Nissan European Other Asian
4793 (23.5%) 4609 (22.6%) 2725 (13.3%) 2602 (12.7%) 2124 (10.4%) 1591 (7.8%) 1064 (5.2%) 920 (4.5%)
302 (6.3%) 211 (4.6%) 233 (8.5%) 139 (5.3%) 152 (7.2%) 153 (9.6%) 55 (5.2%) 68 (7.4%)
Total
20,428
1313
3. Empirical methods This study uses logit regressions to examine the likelihood of CO and HC emission violations given a set of vehiclesÕ characteristics. It is assumed that the probability of emission test failure depends on a set of vehicle characteristics according to a logistic cumulative distribution function as follows: P ðF ¼ 1Þ ¼ Kðb0 XÞ ¼
expðb0 XÞ ½1 þ expðb0 XÞ
ð1Þ
where P ðF ¼ 1Þ is the probability that the vehicle fails the emission test given a vector of vehicle characteristics, X, and K represents the logistic cumulative distribution function. The parameters b 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 are also calculated, as follows (Greene, 1997): oP ¼ Kðb0 xi Þ½1 Kðb0 xi Þb: oxi
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 are estimated with the binary variables for automatic transmission, fuel injection, and air pump excluded, and then compared to the estimates with these binary variables included. Because the effects of vehicle characteristics on the likelihood of emission test failure might differ by make or class, separate regressions are estimated for domestic and imported vehicles, and passenger and non-passenger vehicles.
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Table 4 Logit estimates for vehicle emission test failure Variable AGE ENGINE ODOMETER CYLINDER
CO failure a
0.1060 [0.0044] )0.0004a [)0.00002] 0.0062a [0.0003] 0.0169 [0.0007]
AUTO FUELINJ AIRPUMP IMPORT PASS Observations Log likelihood Likelihood ratio
)0.5193a [)0.0216] )0.4015a [)0.0176] 20,428 )4144.220 155.550
CO failure a
0.0536 [0.0021] -0.0004a [)0.00001] 0.0062a [0.0002] )0.0059 [)0.0002] 0.0462 [0.0018] )0.6691a [)0.0267] 0.4565a [0.0182] )0.4639a [)0.0185] )0.3153a [)0.0126] 20,428 )4066.445
HC failure a
0.0727 [0.0011] )0.0005a [)0.00001] 0.0086a [0.0001] 0.1302 [0.0020]
)0.6645a [)0.0104] )0.2587b [)0.0041] 20,428 )1972.818 26.378
HC failure 0.0446a [0.0007] )0.0005a [)0.00001] 0.0086a [0.0001] 0.1224 [0.0019] )0.1541 [)0.0024] )0.3064b [)0.0047] 0.3598a [0.0055] )0.6749a [)0.0104] )0.1639 [)0.0025] 20,428 )1959.629
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.
4. Estimation results Table 4 reports the regression estimates that include all makes and classes. The overall coefficient 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 across alternative specifications at all. The results indicate little evidence of collinearity among the vehicle characteristics. Bottom of Table 4 provides the likelihood ratio statistic for testing the joint significance of the three excluded variables. Given the critical chi-squared value of 7.82, the joint null hypothesis 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 of vehicle emissions (Kahn, 1996; Washburn et al., 2001). The coefficient estimates for CYLINDER and AUTO are insignificant in all specifications. Vehicles with fuel injection are less likely to fail emission tests than ones without. The probability of CO failure is significantly lower for imported and passenger vehicles, but the negative effect of passenger vehicles on the probability of HC failure falls by about 40% and becomes insignificant when AUTO, FUELINJ, and AIRPUMP variables are added.
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The marginal effects of vehicle characteristics on the probability of emission test failures appear in brackets. The estimates predict that aging one more year from 9 to 10 years old raises the probability of CO and HC failures by about 0.2% and 0.1% points, respectively. Meanwhile, a 1000 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% and 2.1% for the sample. Similarly, evaluated at the mean level, a 10,000 mile increase in odometer reading 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 likely to fail the HC test, while vehicles with air pump are 1.9% points more likely to fail the CO test and 0.6% point more likely to fail the HC test. Imported vehicles are about 1.9% and 1.0% points less likely to fail the CO and HC tests, respectively, representing a substantive decrease in failure probability. The likelihood of CO failure is also about 1.3% points lower for passenger vehicles. 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 for domestic and import vehicles, and passenger and non-passenger vehicles (Tables 5 and 6). In addition, Figs. 1–3 display the predicted and actual probability of emission test failures by vehicle age, engine size and odometer reading.
Table 5 Logit estimates for vehicle CO emission test failure Variable
Domestic a
Import a
0.0392 [0.0014] )0.0004a [)0.00001] 0.0056a [0.0002] )0.0256 [)0.0009] 0.0960 [0.0034] )0.9334a [)0.0331] 0.8253a [0.0292]
0.0652 [0.0029] )0.00002 [)0.000001] 0.0073a [0.0003] )0.0547 [)0.0024] )0.0158 [)0.0007] )0.5023a [)0.0222] 0.0158 [0.0007]
PASS
)0.4277a [)0.0152]
)0.0696 [)0.0031]
Observations Log likelihood
12,127 )2282.940
8301 )1747.910
AGE ENGINE ODOMETER CYLINDER AUTO FUELINJ AIRPUMP IMPORT
Passenger a
Non-passenger
0.0741 [0.0030] )0.0007a [)0.00003] 0.0061a [0.0002] 0.2472a [0.0099] 0.1144 [0.0046] )0.4003a [)0.0160] 0.4106a [0.0164] )0.3681a [)0.0147]
0.0154 [0.0007] )0.0001 [)0.00005] 0.0064a [0.0002] )0.2965a [)0.0013] 0.0378 [0.0014] )1.2136a [)0.0460] 0.5267a [0.0200] )0.8371a [)0.0318]
13,043 )2569.411
7385 )1471.010
Notes: In each cell, the first row indicates the coefficient estimates and the second row indicates the marginal effects. The superscript a indicates significance at the 1% level.
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Table 6 Logit estimates for vehicle HC emission test failure Variable
Domestic b
Import
0.0417 [0.0006] )0.0004a [)0.00001] 0.0071a [0.0001] 0.0029 [0.00004] )0.1036 [)0.0016] )0.4852b [)0.0073] 0.6621a [0.0100]
0.0340 [0.0021] )0.0009a [)0.00001] 0.0111a [0.0002] 0.4427b [0.0065] )0.2032 [)0.0030] )0.0989 [)0.0267] )0.0386 [)0.0006]
PASS
)0.2101 [)0.0032]
)0.3141 [)0.0046]
Observations Log likelihood
12,127 )1163.160
8301 )783.273
AGE ENGINE ODOMETER CYLINDER AUTO FUELINJ AIRPUMP IMPORT
Passenger c
Non-passenger
0.0344 [0.0005] )0.0008a [)0.00001] 0.0096a [0.0001] 0.4851a [0.0076] )0.2235 [)0.0035] )0.2681 [)0.0042] 0.2016 [0.0031] )0.6677a [)0.0104]
0.0608b [0.0008] )0.0001 [)0.00001] 0.0070a [0.0001] )0.3463b [)0.0047] 0.0953 [0.0013] )0.3550 [)0.0048] 0.6854a [0.0093] )1.0260a [)0.0139]
13,043 )1258.819
7385 )685.768
Notes: In each cell, the first row indicates the coefficient estimates and the second row indicates the marginal effects. The superscripts a, b, and c indicate significance at the 1%, 5% and 10% levels, respectively.
4.1. The likelihood of CO failure The results for domestic and passenger vehicles in Table 5 show that most vehicle characteristics significantly influence the probability of CO failure. The signs of significant estimates are the same as in Table 4. As vehicles age an additional year, increases in failure probability are twice as high for imported vehicles as domestic ones. Similarly, as vehicles are driven more, increases in failure probability are higher for imported vehicles than domestic ones. Combined with the results from Table 4, these findings imply that vehicles manufactured by foreign makes, in general, emit less carbon monoxide than domestic vehicles. However, as the vehicles become older and more driven, emissions from foreign make vehicles increase at a higher rate than emissions from domestic vehicles. Evaluated at the mean, a decrease in engine size by 1000 cc raises the probability of CO failure by about 1% for imported vehicles and 3% points for passenger vehicles. The coefficient estimates of ENGINE are insignificant for imported and non-passenger vehicles. For passenger and non-passenger vehicles, the coefficient estimates of CYLINDER become significant and have opposite signs, indicating that as the number of engine cylinders increases, the likelihood of CO failure increases for passenger vehicles but decreases for non-passenger vehicles. The coefficient estimate AIRPUMP loses significance for imported vehicles. PASS is significant for domestic vehicles but insignificant for imported ones. The top of Fig. 1 displays the probability of CO failure by vehicle age, holding other characteristics constant. The predicted failure rates mirror the actual rates, especially for vehicle age
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Fig. 1. Actual vs. predicted emission test failure rates by vehicle age: (a) carbon monoxide failure rate and (b) hydrocarbon failure rate.
between 6 and 9. The graph shows that a selective sampling of vehicles older than 10 years would substantially increase the likelihood of finding high polluting vehicles. In Fig. 2, the predicted and actual probability of CO failure by engine size follow a very similar pattern. It reveals that vehicles with engine size smaller than 2000 cc are more likely to be pollution violators. Fig. 3 presents that the vehicles driven more than 90,000 miles have a higher probability of CO test failure than overall vehicles.
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Fig. 2. Actual vs. predicted emission test failure rates by vehicle engine size: (a) carbon monoxide failure rate and (b) hydrocarbon failure rate.
4.2. The likelihood of HC failure Regarding the HC failure, Table 6 shows that increases in failure probability as vehicles age are higher for non-passenger vehicles than for passenger ones. Consistent with the results from Table 4, the coefficient estimates on ODOMETER are positive and significant in all columns, but the
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Fig. 3. Actual vs. predicted emission test failure rates by vehicle Odometer reading: (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 size by 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 nonpassenger vehicles. AUTO is insignificant again, implying the types of transmission do not influence emission test failures. The estimation results of CYLINDER for passenger and
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non-passenger vehicles are similar to those in Table 5. They imply that the increase in the number of engine cylinders raises the likelihood of HC failure for passenger vehicles but lowers the likelihood for non-passenger vehicles. While vehicle classes do not have significant impacts on the HC test results, imported vehicles decrease the probability of HC failure by about 1% point for both 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 the vehicles older than 10 years would increase the likelihood of finding high HC polluters. Fig. 2 also reveals that the probability of HC violation is higher for vehicles with engine size smaller than 2000 cc than those of overall vehicles. Fig. 3 predicts that the vehicles driven more than 90,000 miles have a higher chance of being high polluters than others. Adding or deleting some variables does 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 different specifications. Moreover, the vehicle make and class are important factors in understanding patterns of emission test results. Foreign vehicles in general have a lower probability of failure for both CO and HC tests. Similarly, passenger vehicles are less likely to fail the emission tests than non-passenger vehicles. As the number of engine cylinders increases, the likelihood of emission test failure increases for passenger vehicles, but decreases for non-passenger vehicles. The types of transmission 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 the characteristics of vehicles that are significantly associated with carbon monoxide and hydrocarbon emission test failures. The findings indicate that vehicle age, engine size and odometer reading all play a significant role in determining I/M test results, as do vehicle make and class. Information from this study can be used as a groundwork for the selective sampling of vehicles which might improve the cost-effectiveness of I/M programs. For example, targeting vehicles of more than 10 years old, engine size smaller than 2000 cc, and odometer reading over 100,000 would substantially increase the likelihood of finding high polluting vehicles. This kind of selectivity can improve the cost-effectiveness of traditional I/M programs as long as the financial savings in focusing on specific categories outweigh the costs of missing polluters in other categories.
References Bishop, G., Aldrete, P., Slott, R., 1997. On-road evaluation of an automobile emission test program. Environmental Science and Technology 31, 927–931. Calvert, J., Heywood, J., Sawer, R., Seinfeld, J., 1993. Achieving acceptable air quality: some reflections on controlling vehicle emissions. Science 261, 37–45. Greene, W., 1997. Econometric Analysis. Prentice-Hall, New Jersey. Harrington, W., McConnell, V., Ando, A., 2000. Are vehicle emission inspection programs living up to expectations? Transportation Research D, 5,153–172.
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Hubbard, T., 1997. Using inspection and maintenance programs to regulate vehicle emissions. Contemporary Economic Policy 15, 52–62. Kahn, M., 1996. New evidence on trends in vehicle emissions. Rand Journal of Economics 27, 183–196. Lawson, D., 1993. Passing the test Human behavior and CaliforniaÕs smog check program. Journal of the Air and Waste Management Association 43, 1567–1575. Lawson, D., 1995. The costs of ‘‘M’’ in I/M Reflections on inspection/maintenance programs. Journal of the Air and Waste Management Association 45, 465–476. Ramsden, T., 1997. Vehicle inspection/maintenance. In: Morgenstern, R. (Ed.), Economic Analysis at EPA, Resource for the Future, Washington. US Environmental Protection Agency, 1994. Milestones in auto emissions control. EPA 400-F- 92-014, Washington. US Environmental Protection Agency, 2000. National air pollutant emission trends, 1900–1998. EPA 454-R-00-002, Washington. Washburn, S., Seet, J., Mannering, F., 2001. Statistical modeling of vehicle emissions from inspection/maintenance testing data: an exploratory analysis. Transportation Research D 6, 21–36.