the marginal external costs of urban transport

Pergamon

Trmpn Res.-D, Vol. I. No. 2, pp. 111-130, 1996 Copyright 0 1996 Elsevier Science Ltd

Printed in Great Britain. All rights reserved l361-9209/96 $15.00 + 0.00

PII:SMl-9209(%)5

THE MARGINAL EXTERNAL COSTS OF URBAN TRANSPORT

INGE MAYERES, SARA OCHELEN and STEF PROOST Katholieke Universiteit Leuven, Center for Economic Studies, Naamsestraat 69, 3000 Leuven, Belgium

(Received 21 February 1996; in revised form I2 June 1996)

Abstract-A necessary input for the analysis of efficient transport policies is the marginal external cost of each transport mode. This paper studies the marginal external costs of urban transportation. These include the marginal external cost of congestion, accidents, air pollution and noise. The costs are computed for cars, buses, trams, metro and trucks. The methodology is described and applied to the urban area of Brussels for the year 2005. Copyright 0 1996 Elsevier Science Ltd

I. INTRODUCTION

Making road users pay for the external costs they cause has become an important principle in transportation economics (Button, 1990). This text improves and updates previous estimates of external costs for cars and trucks presented in Mayeres (1993, 1994). As different concepts of external costs are used in the literature*, it is useful to restate the definition and the use of external cost information in economics. Our main interest here is in the measurement of the marginal external costs of cars, trucks and urban public transport modes. These are the costs caused by an additional car or truck that are borne not by the user himself, but by others. We look for marginal external costs rather than total external costs because they are the necessary ingredient for computing the social marginal cost. This is the sum of private marginal resource costs paid by the user and the marginal external costs. Prices fulfil their allocative function best when they are based on the social marginal cost, rather than on the average social cost. Recently, a number of studies have tried to determine the marginal external costs of (mainly road) transport use. They include, inter alia, the studies by Newbery (1988), Jones-Lee (1990), Mayeres (1993, 1994), Boniver and Thiry (1994), Jansson (1994), Peirson et al. (1994), Small and Kazimi (1995) and Maddison et al. (1996).

Experience in the use of marginal external costs for policy making purposes? has clarified the information needs and the potential for misuse. In this respect, we want to stress three points. First of all, marginal external costs are always computed for a given economic equilibrium. The marginal external costs change if, due to the implementation of social cost pricing, the economic equilibrium changes. Thus, what is needed is a marginal external cost function, rather than a point estimate of the external cost in the present equilibrium.

Secondly, simply charging consumers the ‘equilibrium’ social cost of car and truck use per km is not necessarily the best pricing principle. There are two reasons for this. First, other objectives (equity, tax revenue) or imperfections in the instruments (e.g. the impos- sibility to discriminate between peak and off-peak traffic) or pricing imperfections in related markets (e.g. disequilibrium on the labour market) can require optimal deviations from marginal social cost pricing. In addition, the present equipment of cars and trucks can be inefficient in the sense that additional pollution abatement investments can reduce the external costs per vehicle km drastically. This calls for external cost information expressed as a function of the externality problems themselves (e.g. per gram of a pollutant) rather than per vehicle km.

*For an overview, see Button (1990) and Quinet (1993). tin the TRENEN project (JOULE-II programme of the CEC-DGXII), the objective is precisely to use

marginal external costs in optimal pricing exercises.

111

112

Description

lnge Mayeres et al.

Table 1. The marginal social costs of road transport

Internal External

Private resource costs Average resource costs

Change in resource costs of other vehicles due to the decrease in speed caused by the additional vehicle

Time costs Average time costs The time losses of all other road users due to the decrease in speed caused by the additional vehicle

Accidents Costs associated with average risk (except direct economic costs)

Cost of the increased accident risk + direct economic costs associated with average accident risk

Air pollution

Climate change

Damage to the rest of society

Damage to the rest of society and to future generations

Noise Damage to the vehicle users Damage to the neigbourhood

A third point that needs to be emphasized is that external costs are, by definition, costs borne by others. These others can be the neighbourhood, the rest of the country, the continent, the world or the coming generations. As each government level will, in general, only take into account the welfare of the inhabitants of its constituency, it will only take into account the external costs that are borne by these inhabitants. Therefore splitting the external costs in function of the population they affect is useful information*.

Table 1 gives an overview of the different categories of marginal social costs examined in this paper. They include the marginal external congestion, air pollution, noise and accident costs. All cost functions apply to an urban environment. Their use is illustrated for the urban area of Brussels. The marginal external costs are determined for different transport modes: cars, buses, trams, metro and trucks. The methodology is described in detail and applied for the transport situation of 2005, assuming that there is no policy change. At the end of the paper, a comparison is made between the marginal external costs in 1991 and 2005. A detailed description of the prevailing transport situations is offered in Ochelen and Proost (1995)t. Transferable results of the ExternE projectf have been used in this study as much as possible.

In the next chapters, we discuss the different types of external costs4 one by one.

2. THE MARGINAL EXTERNAL CONGESTION COSTS

In road transport, marginal congestion costs are present whenever an additional vehicle on the road reduces the speed of the other road users. This section focuses on the

*De Borger er al. (1995) examined the pricing policy problems when different levels of government used external costs whose effects were limited to their constituency.

tThe main characteristics of the reference situation for 2005 can be summarized as follows: (i) due to an overall growth of traffic volume between 1991 and 2005 by 22%. the average speed in the peak

period has fallen to 23.5 km/h (in the off-peak period there is much less congestion and speed is 49 km/h);

(ii) all cars comply with the EC emission directives (9lWliEEC); more specifically. we assume that gasoline cars are equipped with a catalytic converter and that diesel cars have improved engine technology (direct injection, turbo charger. charge cooling, exhaust gas recirculation) and an oxidation catalytic converter; and

(iii) taxes, public transport prices and road infrastructure are unchanged with respect to 1991; this means that public transport is subsidized and that car use is taxed mainly via the fuel tax.

SThe ExternE project is a result of the JOULE II programme. The aim of the project was to compute the marginal external costs of different types of electric power plants.

$All costs are given in ECU in prices of 1990. The term mECU refers to 10 s ECU.

Marginal external costs of urban transport 113

time losses of the other road users. In Mayeres (1993) it was shown that the effect of the marginal change in speed on the operating costs and on air pollution costs is of relatively minor importance; therefore, we have decided not to include them. In our calculation, congestion is assumed not to influence the demand of the other road users. We are, therefore, dealing with short run marginal congestion costs. Their magnitude varies according to the transport equilibrium considered.

2.1. Congestion function In principle, congestion is specific to each part of the road network. Adding a car to a

four-lane street has other congestion effects than adding a car to a one-lane street. Here, we compute marginal congestion costs in a more aggregate way. The basic premise is that the urban area has homogeneous traffic conditions and can be represented as if it were a one-link system. Adding one car to the urban traffic then slows down all other cars using the urban network at that moment.

In the calculation of the marginal congestion costs, the speed-flow relationship is of crucial importance. It describes how average speed (s) is influenced by traffic flow (q). Traffic flow is measured in millions of passenger car units (PCU) per hour. PCU are used instead of the number of vehicles to reflect the difference in congestive effect of the vehicle types considered. Generally, a bus or a truck is assumed to correspond to 2 PCU. The aggregate speed-flow relationship has to be derived from simulations with a network model. Such a model is necessary to compute the impact on average speed of a proportional increase in all trips. Kirwan et al. (1995) conclude that an exponential type of aggregate congestion function is the most satisfying.

We estimated the parameters of an exponential congestion function for the transport situation in Brussels, starting from three observation points. The first is the current peak period situation which is characterized by a traffic flow of 0.5337 million PCU per hour and an average speed for cars of 38.2 km/h (cf. Region de Bruxelles-Capitale, 1993, p. 72). In the second observation point, which represents the peak situation in 2005, the traffic flow is 20% higher than in 1991, and the average speed has fallen to 23.7 km/h (Region de Bruxelles-Capitale, 1993). Finally, there is the free-flow situation with a traffic flow equal to zero and an average speed of 50 km/h. The resulting congestion function expresses the minutes needed to drive 1 km in a certain period as a function of the million PCU per hour at that moment in the city:

l/s = 1.194428 + 0.005571 X (exp (7.890545 X q)). (1)

The congestion function for the public transport modes is proportional to that for cars (except for metro whose speed is independent of the volume of other traffic, remaining constant at 30 km/h).

2.2. Time costs The speed-flow relationships enable us to compute the time loss suffered by the other

road users if an additional PCU joins the traffic flow. In order to express this time loss in monetary terms, we use recent value of time (VOT) studies for the Netherlands. Such studies exist for both passenger and freight transport.

For passenger transport, a willingness-to-pay (WTP) study carried out for the Netherlands by the Hague Consulting Group (HCG, 1990) provides empirical evidence about monetary valuations of travel time savings or losses by travellers using private cars and public transport and for different trip purposes. The methodology and the results are discussed extensively by the HCG (1990), Bradley (1990) and Bradley and Gunn (1991)*. Combining their findings with the relative importance of the different trip purposes in Brussels in 1990 (Stratec, 1992), we obtain the results presented in Table 2. This table gives the VOT for the year 2005. They are derived from the values for 1990 using

*For cars, the time-weighted average VOT is 5.3 ECU/h for commuting, 17.5 ECU/h for business trips and 4.2 ECU/h for other trips. For bus users, the values are 4.5 ECU/h, 17.5 ECU/h and 2.6 ECU/h, respectively.

114 Inge Mayeres et al.

Table 2. The VOT in passenger and freight transport (ECU/h)

1991 2005

Peak O&peak Peak Off-peak

Passenger transport: car 6.9 6.6 7.7 7.3 Passenger transport: public 4.9 5.2 5.4 5.1 Freight transport: truck 25.9 25.9 34.2 34.2

Source: own calculations based on HCG (1990) De Jong e( al. (1993) Stratec (1992) and MVA Consulting ef a/. (1987).

Table 3. Marginal external congestion costs for Brussels in 2005

Car Bus and tram Truck

Peak 1.387 2.714 2.774 Off-peak 0.004 0.008 0.008

the relationship between the VOT and income. MVA Consultancy et al. (1987) and HCG ( 1990) have found that the value of time increases with income, but less than proportionally. From the results of the stated preference analysis by HCG (1990), we estimate a linear relationship between VOT and income. According to this relationship, the elasticity of the VOT with respect to income equals 0.368.

The VOT in freight transport is based on De Jong et al. (1993) in which the short and medium term VOT in freight transport are estimated by means of the contextual stated preference method. The average VOT over the different goods categories equals 25.8 ECU/h in 1990. The value for 2005 is obtained by assuming that labour cost, which is one of the main cost components of freight transport, increases by 2% annually between 1990 and 2005.

2.3. Results Table 3 presents the resulting marginal external congestion costs for the reference situ-

ation in 2005*. The marginal external cost are very high, especially in the peak period. The main explanation is that one expects saturation of the urban network in 2005. The expected increase of traffic for 2005 makes the average peak period speed drop from 38 to 23.7 km/h. The marginal external congestion costs of the heavy vehicles (bus, tram and truck) equal twice the cost of passenger cars.

3. THE MARGINAL EXTERNAL AIR POLLUTION COSTS

3.1. Methodology The aim of this section is to determine the costs, to society, of a marginal increase in

the emission of air pollutants by road transport. Road transport is responsible for the emission of nitrogen oxides (NO,), sulphur dioxide (SO?), volatile organic compounds (VOC), carbon monoxide (CO), lead (Pb) and particulate matter with a diameter of less than 10 pm (PM,,,). The effects of these pollutants form the subject of a large number of studies. A good overview is given in, inter alia, Button (1993) Linster (1990) and OECD (1988). Table 4 gives a brief overview of the air pollutants emitted by road transport and of their effects. The effects marked with a double X are considered in this paper. Data problems are the main reason for excluding the others. The focus lies on the health effects of O3 and PM,,,. These are discussed in Section 3.2. Section 3.3 also looks briefly into a number of effects on vegetation. The effects on climate are discussed in Section 3.4.

*We used an occupancy rate of 1 person/vehicle for car solo driving, 2.5 persons/vehicle for car pool, 9 persons/vehicle for off-peak and 40 persons/vehicle for peak public transport.

Marginal external costs of urban transport

Table 4. The external air pollution effects

115

Precursor emissions by road transport

Ambient concentration

Effects

Aquatic Health Vegetation Materials ecosystems Visibility Climate

NO, CO

PM,, NO.\- so, voc SO2 HC Pb

NO, voc NO, SO, co co2

SO2 HC Pb

03

Acid deposition

Global warming

X X

xx xx xx xx X X X

xx xx X X

xx X xx

X X X X

xx

xx X X X X X X X

X X X xx X X

xx

In Mayeres (1993, 1994), the marginal social costs of NO,, VOC and SO2 emissions by road transport were determined on the basis of revealed preference of policy-makers, i.e. on the basis of avoided costs of attaining internationally agreed air pollution objectives. In this paper, however, we follow Small and Kazimi (1995) in using a direct damage estimation approach. We prefer this method because the robustness of the ‘revealed preference of the policy-makers’ method is highly dependent upon some very strong assumptions. It would only be a correct estimate of the social cost if governments were rational, had perfect information and represented the preferences of society perfectly.

We begin by describing, in a general way, the methodology underlying the monetary valuation of the social costs of air pollution. That methodology has been followed as closely as possible in our analysis. However, as will become clear later on, lack of information has often forced us to use a simplified approach.

Valuing marginal road transport emissions generally requires three steps. The first step consists of establishing a relationship between a change in the emissions and the resulting concentration levels of the different primary and secondary air pollutants. This requires atmospheric dispersion models which predict the spread of pollutants from their origin, and chemical transformation models which describe how different pollutants react together to form so-called secondary air pollutants. For some pollutants, these models are relatively simple, while for others, they are extremely complex. We have encountered most problems in this first step of determining the link between emissions and concentrations and have been forced to make many assumptions. There is still a great need for information in the domain of air pollution effects and most particularly for summary information (such as emission-concentration studies).

The second step consists of relating the change in the concentration level to its effects on health, vegetation, materials, visibility and ecosystems. This requires the use of so-called dose-response relationships. ETSU and IER (1994) (hereafter referred to as ExternE 2) give a broad review of existing studies in the field. However, once again, due to information problems, only the effects on public health and some vegetation effects could be included in our study. The final step consists of determining a monetary value for the different effects of air pollution. For this, we have used Metroeconomica (1994) (hereafter referred to as ExternE 9).

3.2. Monetary valuation of the health impacts of road transport emissions 3.2.1. General. Of the health effects summarized in Table 4, we discuss those associated with tropospheric O3 and PMlo. The omission of the other health effects is motivated as

116 Inge Mayeres et (11.

Table 5. The monetary values of different health impacts (ECU)

Health impact Monetary value Monetary value

for 1990 for 2005

Mortality Value of statistical life

Morbidity Respiratory hospital admission, including - hospital admissions for respiratory infections - hospital admissions for chronic obstructive pulmonary disease - hospital admissions for asthma

Emergency room visit, including - ERV for chronic obstructive pulmonary disease - ERV for asthma - hospital visits for childhood croup

Symptoms of chronic bronchitis

Symptoms of chronic cough

Restricted activity day Minor restricted activity day

2.600,OOO 4.100.025

6600 6600 6600

186 273 186 273 186 273

138 203

138 203

62 92 62 92

9700 9700 9700

Asthma attack, including - asthma attack - shortness of breath day in asthmatics

Symptom days

Source: ExternE 9 and own calculations.

31 45 31 45

6 9

follows. We do not consider lead because, in the year 2005, lead emissions by road transport will have become negligible due to the phasing out of leaded gasoline. As for the direct health effects of NO,Y, it is argued, in ExternE 2, that “... the direct effect if any is likely to be small and not quantifiable reliably on the basis of available studies”. As regards the direct effects of SO?, there are a number of studies linking SOI to health effects, but these links disappear when particulates are measured appropriately. We follow ExternE 2 by not including this evidence. The direct health effects of VOC and CO are not included due to lack of information, though they might be important.

The ExternE study proposes monetary values for the different health effects of PM,, and O,, based on a review of the existing literature. These monetary values are summarized in Table 5. (For a detailed description, see ExternE 9.) The estimate for the value of a statistical fife (first row of Table 5) is the average value of a number of European CVM studies (Melinek, 1974; Maclean, 1979 (cited in ExternE 9); Jones-Lee et al., 1985; Persson, 1989; Maier et al., 1989). In our study, we introduce three changes. We add the direct economic effects and the altruistic cost of pain, grief and suffering to relatives and friends*. Finally, since we calculate the marginal external costs for the year 2005, we translate the estimates from 1990 to 2005t. The resulting value of a statistical life is presented in the last column of Table 5.

The values of the morbidity impacts are based on a review of the U.S. literature presented in ExternE 9. The marginal external cost or individual WTP for an illness consists of three components: (i) the value of time lost due to the illness; (ii) the individual value of lost utility due to pain and suffering; and (iii) the expenditures for averting or mitigating

*In Jones-Lee et al. (1985). it is shown that people do not take into account direct economic effects (net output losses) in assessing their willingness to pay. In that case. they should be added to the WTP value of a statistical life used in ExternE 9. For the U.K., Jones-Lee (1990) estimated the net output loss to be equal to f60,OOO in 1989 prices. Converting this into ECU, we get 87,990 ECU in 1990 prices. Secondly, in contrast to ExternE 9, we want our calculations to take into account the altruistic cost of pain, grief and suffering to relatives and friends, which equal 40-50X of the private total valuations (Jones-Lee et II/., 1985).

tin order to derive values for 2005, we have assumed that income increases by 2% annually between 1990 and 2005. We have used the finding of Jones-Lee et ul. (1985) that the elasticity of the marginal rate of substitution between wealth and risk of death with respect to income is 0.3. The net output losses are increased by 2% annually.


the effects of the illness. The first category is estimated directly. In general, it is found to be one-half of the total individual WTP for asthma and one-third for other illnesses. The other parts are estimated by means of CVM methods and models of aversive behaviour. The value of lost utility due to pain and suffering proposed in ExternE 9 does not yet include the altruistic cost. In our analysis, we have taken into account this altruistic cost, and have assumed, as before, that it equals 40% of the pain and suffering component. In addition, we converted the values for 1990 into values for 2005. For the value of time lost, we assumed an annual increase of 2%. For the other components, we applied the income elasticity estimate of Jones-Lee et al. (1985)*. The resulting values for 2005 are summarized in the last column of Table 5.

3.2.2. Tropospheric ozone caused by VOC and NO,v. ExternE 2 presents extensive information on the dose-response relationships for different health effects of tropospheric ozone. These relationships relate the change in annual average 0, concentration (in ppb) to effects on acute mortality and morbidity occurrences. Each time a low, medium and high estimate are given. The morbidity effects include hospital admissions for respiratory infection, chronic obstructive pulmonary disease and asthma, emergency room visits for asthma, minor restricted activity days, asthma attacks and symptom days.

In order to determine the cost, to society, of an increase in the VOC emissions by Belgian road transport, we need to know the relationship between Belgian VOC emissions and the 0, concentration levels in the different European countries. This is analysed in atmospheric dispersion and transformation models (Builtjes, 1991; Simpson, 1991a,b; Zlatev et al., 1991). However, these models are very complex and do not deal explicitly with the question we are interested in. The only study which serves our purpose is that of Simpson (1992) who presents a blame matrix which specifies the contribution of VOC emissions from one country to ozone formation in other countries. For the period April-October 1989, the matrix gives the increase in 6-monthly mean ozone in a number of receiver countries for an increase of the Belgian VOC emissions by 1 ktonne (holding NO, emissions constant).

When combining this information with the dose-response relationships presented in ExternE 2, we are confronted with a number of problems. The main problem is that the blame matrix of Simpson considers changes in the 6-monthly mean of daily maximum Oj concentrations, while the dose-response relationships are expressed in terms of annual average concentrations. We realize that our approach of combining the two is far from perfect. However, at this moment, we do not know any other source of information. Therefore, we apply the dose-response relationships to the results coming from the blame matrix of Simpson, keeping in mind that the approach has to be changed when additional information becomes available.

On the basis of Simpson (1992) we have computed the change in the annual average O3 concentration caused by a reduction in Belgian VOC emissions by 1 ktonnet. Applying the dose-response relationships and the monetary valuation of the health effects to these figures, we have calculated the cost of an increase in Belgian VOC emissions by 1 g for the different countries. The first two columns of Table 6 present the results. A distinction is made between national and transnational health costs of Belgian emissions. We find that the transnational health costs are approximately 11 times as

*Unfortunately, we did not have the information to make the distinction between the second and third component of the individual WTP for an illness. Therefore, to determine the values of 2005, we applied the income elasticity to the sum of the two. A similar procedure was used to derive the altruistic cost. The error made in doing so will not be large if the pain, grief and suffering component is much larger than the third component, which is likely to be the case.

tThe dose-response relationships require the change in annual ozone concentrations as an input. On the basis of Simpson (1992) we only know the change in the 6-monthly ozone concentrations. Therefore, we have to make an assumption about the maximum ozone concentrations in the winter period to translate the changes in 6-monthly ozone concentrations given by Simpson into changes in the annual concentrations. On the basis of Dumont and Roekens (1994) we have assumed the average winter concentration to be 20 ppb. Furthermore, we have assumed that a ktonne increase in VOC emissions only affects the summer concentrations and does not affect those in the winter period.


Table 6. The monetary value of the tropospheric ozone effects due to Belgian VOC and NO,Y emissions

National effects of Transnational effects of National effects of Belgian VOC emissions Belgian VOC emissions Belgian NO,Y emissions

(mECU/g VOC) (mECU/g VOC) (mECU/g NO,) Health cost category Low Mid High Low Mid High Low Mid High

I. Acute mortality 0.055 0.083 0.111 0.621 0.932 1.243 0.190 0.285 0.368 2. Acute morbidity 0.012 0.034 0.076 0.141 0.395 0.862 0.045 0.135 0.296 Hospital admissions for - respiratory infection 0.001 0.001 0.001 0.009 0.012 0.014 0.003 0.004 0.005 - chronic obstructive 0.000 0.001 0.001 0.006 0.009 0.012 0.002 0.003 0.004 pulmonary disease - asthma 0.000 0.001 0.001 0.004 0.008 0.012 0.001 0.003 0.004 Emergency room visits for 0.000 0.000 0.000 0.001 0.001 0.001 0.000 0.000 0.000 asthma Minor restricted activity 0.000 0.014 0.047 0.000 0.158 0.529 0.000 0.059 0.190 days Asthma attacks 0.007 0.012 0.016 0.085 0.135 0.185 0.028 0.046 0.063

Symptom days 0.003 0.006 0.009 0.037 0.072 0.107 0.009 0.019 0.030 Total 0.067 0.117 0.186 0.762 1.327 2.104 0.235 0.420 0.664

large as the national ones. The transboundary dimension is very important in the ozone problem. Furthermore, it is clear that the mortality effects are responsible for the largest part of the marginal social costs. It should be mentioned that there still exists a lot of uncertainty about the mortality effects of ozone. If they turn out to be non-existent, the marginal social health costs of VOC will be much lower than the total values shown in Table 6. The mid-value of the morbidity effects equals 0.034 mECU/g for Belgium and 0.395 mECU/g transnationally.

NO, emissions also contribute to the formation of tropospheric 0,. In contrast to the analysis for VOC, we do not yet have a blame matrix for the contribution of NO, emissions. Therefore, a different methodology has been used. ExternE 2 determines a range of local health costs per kWh of an increase in the ozone concentrations due to the NO,. emissions of a new power plant situated in Lauffen (Germany) and West Burton (U.K.) From this, we derive the national health costs per gram of NO,Y emitted. Our calculations are based on the findings for the Lauffen plant because, of the two plants, it is likely to be more representative for most European countries, especially Belgium. The local area considered for the O3 impact assessment of the Lauffen plant has a surface of 171 X 159 km* which is approximately the surface area of Belgium. The population in the area is 9,039,446. Transferring the findings for the Lauffen plant to emissions caused by the road transport sector in Belgium is not without danger. One of the main objections is that power stations emit their pollutants from high stacks. The behaviour of the emissions ‘differs in both dispersion and chemistry from widespread emissions which are released near the ground surface by traffic’ (I.E.R., 1994). Secondly, atmospheric transport of pollutants emitted in Germany is not necessarily the same as for Belgian emissions. However, until a blame matrix for NO,Y emissions becomes available, we have decided to approximate the costs of NO,. emissions in this way. The results are summarized in the last column of Table 6. We have corrected for the difference in population between Belgium and the area considered in the Lauffen plant study. The results only include national effects. We have no information on the transnational costs. However, if we assume that the ratio between the national and transnational costs is the same for NO, as for VOC, then the transnational costs would equal approximately 4.74 mECU/g NO,.

3.2.3. Concentrations of PM,,,. Small and Kazimi (1995) apply the direct damage estimation approach in order to determine the health costs of the air pollutants generating PM,,, concentrations. Applying the same approach to the Belgian emissions of PM,, has proved to be unfeasible because crucial data are lacking. At this moment, no information


Table 7. The effects on vegetation of Belgian SOI and NO., emissions

119

Marginal social cost (mECU/g)

Pollutant Effect Low Mid High

so2

NO,.

Direct effect of SO1 on wheat, barley rye and oats (national + transnational) Effect of SOz emissions on forests

(national) (transnational)

Effect on 0, on wheat (national)

0.010 0.042

0.236 1.248

1.038 1.127

is yet available on Belgian PMlo concentrations or on the contribution of VOC, NO.,, PM,, and SO, emissions to PM,,, formation in Belgium. Therefore, we have limited ourselves to transferring the estimates of Small and Kazimi (1995) to the Belgian situation. In this process, we have adjusted the values of mortality and morbidity such that they correspond with the values of Table 5. This results in a cost of 83.19 mECU/g for PM,,, 7.55 mECU/g for NO,, 1.51 mECU/g for VOC and 93.70 mECU/g for SO*.

3.3. The effects of pollutants on vegetation ExternE 2 discusses a large number of effects of air pollution on agriculture, forests

and terrestrial ecosystems. However, only three effects are eventually expressed in monetary terms. These include the direct effect of SO* on wheat, barley, rye and oats, the local effect of 0, on wheat, and the effect of SOZ on timber production.

For the first two categories, we transferred the results for the La&en plant of the ExternE 2 study, which presents costs per kWh *, to the emissions by road transport in Belgium. We have used the fact that the Lauffen plant emits 0.83 g SO, and 0.927 g NO, per kWh. Once again, it should be mentioned that our approach is not perfect. Emissions from low level transport sources do not behave in the same way as emissions from high stacks. Moreover, the atmospheric transport of emissions in Germany is not necessarily the same as that of Belgian emissions. The direct effects of SO* on agricul- tural crops include the transnational effects whereas those of 0, only cover an area with a surface comparable to that of Belgium. In the latter case, we have taken into account that the area under cultivation for wheat around the Lauffen plant is smaller than that in Belgium (78,525 ha versus 215,750 ha). The results presented in Table 7 are obtained by using world market prices to value the changes in crop yields.

The effect of SO, on forests is determined on the basis of Nilsson (1991) and IIASA (referred to in ExternE 9). Nilsson has determined the damage to forests in each country per ton of SO, deposited. His estimates include both timber and non-timber values. The IIASA matrix gives the emissions and depositions of sulphur in Europe. On the basis of the IIASA table, we can determine which percentage of Belgian SO* emissions is deposited in the different European countries. The conversion from sulphur to SO? requires that the former is multiplied by 2. The combination of the two data sources allows us to determine the total damage caused by 1 ktonne of Belgian SO, emissions. The results are given in Table 7.

3.4. Global warming Global warming, as a result of increased CO, emissions, is one of the most important, but

also one of the most uncertain external effects of fossil fuel use. Estimates of the marginal costs of 1 tonne of carbon emitted in the period 2000-2010 range between $7/tonne of carbon (C) (Nordhaus, 1993) to over $150/tonne of C (Cline, 1992). These differences are

*For SO*, ExternE produces costs in the range of 8.635 IO-’ mECU/kWh; for NO.,, the range is 0.354.38 10 3 mECU/kWh. These values are based on world market prices.


Table 8. The marginal social costs of Belgian emissions of air pollutants (mECU( 1990/g)

PM,, NO, voc so2 Mid Mid Mid Mid C

Health costs - ozone

- f’M,o

National Transnational

Effects on vegetation Global warming: Belgium

European Union World

Total

83.19

83.19

83.19

12.71 2.95 0.42 0.12 4.74 1.33 1.55 1.51 1.08 n.a.

13.80 2.95

93.10

93.70 1.51

0.0002 1 0.00669 0.02835

95.21 0.02835

n.a.: not available.

the result of differences in basic assumptions on the discount rate and on the rate of technical progress in general and in the development of carbon free energy sources.

We use estimates of Fankhauser (1995). Fankhauser estimates the marginal cost of carbon emissions in the period 2000-2010. This estimate is the expected value of present and future damages resulting from a unit increase in emissions in this period. The damages contain very diverse items: coastal defence, dryland loss, wetland loss, ecosystem loss, agriculture, forestry and fishery losses, gains and losses in the energy and water sup- ply, life and morbidity effects, air pollution damages, migration costs and an estimate of natural hazard damages. The damages do not contain the damages of air pollutants that are directly associated with fossil fuel use like acid rain and ozone formation.

The marginal damage for the world is $22.8 per tonne of carbon. This estimate has been converted into an estimate for the European Union by using the relative share of damages of the EU in world damages for the case of doubling of CO2 concentrations computed by Fankhauser. This gives a share of 23.60% for the marginal cost of carbon emissions at the European level. Finally, estimates for Belgium are obtained by assuming that, within the European Union, damages are distributed proportionally to GDP. Translating the values for 1990 into values for 2005, we obtain a cost of 0.00021 mECU/g carbon at the Belgian level, 0.00669 mECU/g at the European level and 0.02835 mECU/g at the world level.

3.5. The marginal social air pollution costs: summary Table 8 gives a summary of the marginal social costs of Belgian emissions of air pollu-

tants. When using these figures, one should keep in mind the assumptions made in the previous paragraphs and the uncertainties associated with them. This table gives the marginal social cost, per gram, for each pollutant. It can be seen that the dominant cost category is the health costs, especially the health costs caused by PM,, concentrations.

In the first part of Table 9, we give the emission factors of a few car types and of the urban public transport modes. These emission factors are computed with the functions of VIA (1995) in which the travel speeds of 2005 in Brussels are used. The emission rates are computed for the urban driving cycle defined by the EC in the framework of their car emission standards. The urban driving cycle corresponds to one average speed. Emission rates for other average speeds were obtained by homothetically increasing or decreasing the maximum speeds defined in the urban driving cycle. This might be a poor approximation as decreased average speeds originating from increased congestion might result in a more irregular driving pattern. For gasoline cars, the emissions of PM,, and SO, are negligible. The second part of Table 9 gives the external costs of air pollution per vehicle km. Even among private cars, the variation in air pollution costs between different car types and uses is quite important (e.g. the external cost of a large gasoline car in the peak period is twice as high as the external cost of a small diesel car driving in the off peak period). For gasoline vehicles, NO,Y is the dominant cause of air pollution costs. For diesel vehicles, SO,,., PMlo and NO., are the most important costs.


Table 9. The marginal external air pollution costs of different vehicle types in 2005 (Brussels - unchanged policy)

Emission ,fucrors (g per vehicle km (CO, in kg))

NO., co2 voc

Gusoline curs Peak small 1.56 0.17 0.37 Peak large 2.20 0.23 0.51 Off-peak small 1.39 0.14 0.30 OlT-peak large 1.92 0.18 0.42

Diesel cars Peak small 0.20 0.14 0.02 Peak large 0.32 0.17 0.03 Off-peak small 0.16 0.10 0.01 Off-peak large 0.23 0.13 0.02

Public transport Peak diesel bus 18.43 I .oo 1.83 Off-peak diesel bus 14.55 0.75 1.43 Tram 5.04 1.98 0.05 Metro 3.95 1.55 0.04

Freight transport Peak diesel lorry 7.20 0.38 0.48 Off-peak diesel lorry 6.73 0.30 0.34

Monelury valuution mECUper gram 13.80 7.72 2.95

E.uternul cost of uir pollution in mEClJ per vehicle km

Gasoline cars Peak small 21.51 1.29 I .08 Peak large 30.29 1.74 I .52 Off-peak small 19.15 1.10 0.88 0%peak large 26.55 1.35 1.23

Diesel curs Peak small 2.77 1.05 0.04 Peak large 4.44 I .30 0.09 Off-peak small 2.17 0.76 0.03 Off-peak large 3.15 0.97 0.05

Public transport Peak diesel bus 254.33 7.73 5.41 Off-peak diesel bus 200.79 5.79 4.22 Tram 69.55 15.29 0.14 Metro 54.53 1 I .99 0.11

Freight transport Peak diesel lorry 99.32 2.93 I.42 Off-peak diesel lorry 92.92 2.28 0.99

co PM,,

0.00 0.00 0.00 0.00

2.92 4.25 1.80 2.63

0.00 0.00 0.00 0.00

0.33 0.07 0.13 0.67 0.11 0.16 0.26 0.04 0.10 0.56 0.06 0.12

3.00 1.81

1.51 0.95

0.01

0.04 0.05 0.02 0.03

0.00 0.01 0.00 0.01

0.04 0.02

0.02 0.01

0.44 0.95 0.31 0.80 0.51 7.84 0.40 6.14

0.23 0.36 0.15 0.28

83.19 95.2 I

0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

5.49 12.38 21.74 8.98 15.42 30.25 3.41 9.04 15.42 5.24 11.52 20.94

36.35 90.83 394.69 25.71 75.69 312.23 42.18 746. IO 873.26 33.07 584.95 684.65

18.80 34.37 156.86 12.81 26.75 135.77

Total 23.92 33.60 21.15 29.17

4. THE MARGINAL EXTERNAL ACCIDENT COSTS

4.1. Methodology From the economic literature, we know that the accident cost relevant for pricing is

the difference between the marginal social and the marginal private accident cost. This difference is the marginal external accident cost. Following the notation of Jansson (1994) the total accident cost (TAC) can be written as follows:

i (a” + 6” + c”) i r; X, i=l ?!=I /=I

The indices i and j represent the transport modes: car, bus, tram, metro, truck and non- motorized transport. In addition, index j also includes external objects (such as a wall or a tree) as a category. Index n indicates the severity of the accident. A distinction is made between fatal accidents, accidents with serious injuries, accidents with light injuries and accidents with only material damage. X, is the number of vehicle km travelled by transport


mode i. t-l;! is the probability that an accident of severity n occurs between transport modes i and j and in which i is the victim. It is defined as

r; = A.!!/_y ‘I 1 (3) A; = A/ (X;, Xj) V&j;

A; gives the number of accidents between modes i and j in which i is the victim. It is assumed not to depend on the number of vehicle kilometres driven by transport modes other than i and j. a” stands for the willingness-to-pay (WTP) to avoid an accident of type n. b, is the WTP of the relatives and friends of the victim to avoid an accident of type n. a” and b” are the so-called ‘warm-blooded’ costs as opposed to the ‘cold-blooded’ cost category c” which consists of the pure economic costs (net output losses, ambulance costs, medical costs, etc.) which are borne by the rest of society.

The marginal social accident cost (MSAC) of a car is the derivative of the TAC with respect to the number of car km:

MSA Cc,, = i (a” + 6” + 5’) i r&, n=I j=i

4

+ z, (a” + 6” + c”) c - ’ ark,, x

j=l ax,,, car ‘I artcar + C C (an + b” + c”) ax. Xi

iear n=l Cdl-

= MSA CA,, + MSA Cf,, + MSA Cb, (4) The first term gives the social costs of the risk that the car occupants themselves are involved in an accident. The second and third terms give the social cost of the increased accident risk for cars and other road users due to the additional car km. A similar formula can beobtained for the marginal social accident cost of buses, trucks, etc. However, to keep the explanation as simple as possible, we limit ourselves to cars.

In the determination of the marginal external accident costs, there are two main problems. First of all, one needs to determine the relationship between the number of road users and the number of accidents. Secondly, there is the determination of that part of the accident costs which is internalized in each road-user’s decision process.

Part of the marginal social cost is already internalized. From the literature on accident costs, we know that road users already take into account their private marginal costs. These include the insurance premium and two cost categories associated with their own accident risk, namely their own utility loss due to the accident risk (a) and possibly also the utility loss of their relatives and friends* (b). Thus, in the first term of eqn (4), only the c cost category would be external. However, this depends on the type of insurance that is in place. If insurance covering costs of type c is compulsory, then the cold-blooded costs associated with the own accident risk are internalized through the insurance premium. In Belgium, this is the case for car passengers, but not for car drivers. Therefore, the cold- blooded costs are assumed to be external for the car drivers only. For public transport, these costs are assumed to be external for both the driver and the passengers?.

The second term of eqn (4) can be written as:

MSACza, = I? (a” + b” + P) g E;‘,,,~,~~~ r:,_ n= I j=l (5)

~~~~~~~~~~ is the elasticity w.r.t the number of car km driven of the probability of an accident of type n between the car mode and mode j (rg,,J in which car users are the victims. If an additional car km does not increase the risk of accidents of cars with other road

*See the articles of Newbery (1988). Jones-Lee (1990) and Jansson (1994). tThis point needs investigation about the insurance systems used by the public transport firms.


users, the elasticities E;‘,,,,~~~~ = 0 so that eqn (5) reduces to zero. However, if for some transport modes j E;‘,,,,;~~~ is positive, the marginal external costs associated with increased accident risks for cars are non-zero. The value E;,,,~~~~ depends on the relationship between the number of accidents and the traffic flow. In the literature different views are taken on this relationship. For the accidents between two motorized road users an accepted convention* is to assume that the number of accidents is proportional to the traffic volume, such that the elasticities E;,,,~~_~ are zero.

The third term in eqn (5) can be rewritten as:

E;:,,,,~,, is the elasticity w.r.t. the number of car km of the risk of accidents between mode i and the car mode in which the users of mode i are the victim (with i # car). If the number of these accidents is proportional to the number of car km, these elasticities are equal to one. We have assumed this to be the case for all modes except non-motorized transport. In addition we have assumed that the car driver is confronted with the average accident costs he causes to these transport modes, through the insurance premia he pays?. The combination of these two assumptions entails that there are no marginal external accident costs w.r.t. to motorized non-car transport modes.

For the accidents between motorized and non-protected road users we follow Jansson (1994) in assuming that the elasticity E;,,_~~~~ = 0.5. This means that half of the average motorized-non-motorized accident costs (in terms of car km) are external.

4.2. The estimation of marginal external accident costs for Brussels 4.2.1. The monetary valuation of the dIrerent accident cost categories. The empirical estimates of the accident costs can be obtained in different ways. The costs of the direct economic effects are relatively easy to estimate as they are directly observable effects. The warm-blooded costs, on the other hand, are not observable, and the only way to determine them is on the basis of revealed or stated preferences for risk reductions.

In Table 10, an overview is given of all cost components of the different accident types. The value of a statistical life is in line with the estimate used in the valuation of the cost of air pollution in this study. We assume that the costs of accidents with material damage are completely internalized (or that the external part is negligible).

To express these costs in terms of the year 2005, we made the same assumptions as for the valuation of air pollution$.

4.2.2. Determination of the accident risks. We express the risk as the ratio (number of victims/number of vehicle km) of the transport mode considered. Under the assumptions made above, we do not need the risk of accidents between each possible combination of road users, but only an estimate of the accident risk for the occupants of the vehicle and the risk for pedestrians or cyclists.

For the car accidents, we only take into account the driver victims, because, in Belgium, insurance covering the car passengers is compulsory, and their accident costs are thus internalized through the insurance premium. Table 11 gives the risks computed for Brussels in 2005 for all the modes and accident types considered.

*We refer to Vitaliano and Held (199 1), Jones-Lee (I 990). Great Britain, Department of Transport (1987), and U.S. Federal Highway Administration (1982).

tThis is a strong assumption. It can be argued that insurance mainly covers the so-called ‘cold-blooded’ accident costs and only to a minor extent the other cost categories. In this case, the costs associated with the WTP of the victims and their relatives and friends are partially external. Moreover, it can be argued that the insurance premia are perceived as fixed rather than as variable costs by the car users, so that they are not taken into account into their decision whether or not to travel. This implies that the marginal social accident costs associated with the risk to non-car and non-motorized road users are completely external. Both aspects need further analysis.

$We assume that, between 1990 and 2005, real GNP grows by 2% annually. Net output loss and police and medical costs are assumed to grow at the same rate. Since the elasticity between WTP for risk and income is 0.3 (Jones-Lee et al., 1985) the WTP are inflated by a yearly 0.6%.


The data available on the number and type of accidents are limited and not very detailed. The accident risks for 2005 are obtained by extrapolation from 1991, assuming no change in other factors (apart of traffic volumes) which might influence transport safety.

Table 10. The monetary value of the different accident cost categories*

ani b”;

Mortality For 1990t: For 1990’: WTP = 2.6 MECU 40% of own WTP = 1.046

MECU For 2005:

WTP = 2.84 MECU For 2005: 40% of own WTP = I. 137 MECU

Serious injury For 1990’: For 1990: WTP = 0.248 MECU 40% of own WTP = 0.098

MECU For 2005: WTP = 0.27 MECU For 2005:

40% of own WTP = 0.108 MECU

Light injury -4

Material 0 0 damage

For 1990”: output loss = 0.22 MECU(90) police + medical costs = 0.006 MECU(90) - discounted consumption =

0.133 MECU(90) Total = 0.09427 MECU

For 2005: Total = 0.12688 MECU

For 1990**: output loss = 0.22 MECU(90) police + medical costs = 0.006 MECU(90) Total = 0.232 MECU

For 2005: Total = 0.3066 MECU

For 1990tt: 0.0005 MECU For 2005: 0.0007 MECU 0

*All values in ECU or in MECU (million ECU) are in prices of 1990. tThis is the WTP estimate for the value of a statistical life from ExternE 9. $To obtain the WTP for serious injury, we correct the WTP to avoid fatality with the ratio of the marginal

rate of substitution of wealth for risk of serious injury to the marginal rate of substitution of wealth for risk of death. This ratio was found to be 0.095 in O’Reilly et al. (1994).

§No estimate available for the valuation of light injury. Yin Jones-Lee (1990), we find that the WTP of others (family and friends) constitutes about 40% of the WTP

for one’s own safety. lIThe ‘cold-blooded’ cost of mortality consists of the sum of output loss, the police and medical costs minus the

discounted value of the person’s future consumption (Jones-Lee, 1990). **The c-cost of serious injury is the same as for mortality, without subtracting the discounted value of future

consumption (Jones-Lee, 1990). ttNewbery (1987) cites the estimate of light injury used by the Great Britain, Department of Transport. This

estimate is based on the output loss, and amounts to f3 12 (1986).

Table I I. The estimated accident risks* for different types of accidents, Brussels 2005 (in IOE-08)

Car Bus Tram Metro Truck

Peak Off-peak Peak Off-peak Peak Off-Peak Peak Off-peak Peak Off-peak

Motorized accidents? Fatal I .68 I.68 0.00 0.00 0.00 0.00 0.00 0.00 4.14 4.14 Serious injury 9.56 9.56 6.93 6.93 8.03 8.03 0.84 0.84 28.97 28.97 Light injury 170.73 170.73 311.98 311.98 128.49 128.49 3.52 3.52 329.05 329.05

Non-motorized accidentst Fatal 6.25 2.14 0.41 0.29 0.14 0.10 0.00 0.00 0.89 0.30 Serious injury 43.43 14.87 3.09 2.17 1.19 0.84 0.04 0.02 0.97 0.33 Light injury 214.29 73.37 10.91 7.66 4.22 2.96 0.95 0.54 3.61 1.24

Source: Own calculations with data from BIVV (1989). NIS (1989) and Boniver (1993) on the number of accidents and data from Stratec (1992) and STIB (1991) on vehicle km.

*Risk for mode j is defined as the number of accidents in which the occupants of mode j are the victims divided by the number of vehicle km of mode j.

tThese are accidents with victims who are occupants of a motorized vehicle. $These are the risk for non-motorized road users of being victims in an accident with a motorized vehicle.


Table 12. The marginal external accident costs estimated for Brussels 2005 (in mECU per vehicle km)

Car Bus Tram Metro Truck

Peak Off-peak Peak Off-peak Peak Off-peak Peak Off-peak Peak Off-peak

Motorized accidents Fatal 2.13 2.13 0.00 0.00 0.00 0.00 0.00 0.00 5.25 5.25 Serious injury 29.32 29.32 21.26 21.26 24.62 24.62 2.59 2.59 88.84 88.84 Light injury 1.21 1.21 2.21 2.21 0.91 0.91 0.02 0.02 2.33 2.33

Non-motorized accidents Fatal 30.89 59.19 372.77 348.22 247.11 230.84 0.00 0.00 145.99 279.74 Serious injury 34.57 66.23 456.06 426.02 342.64 320.07 5.02 5.79 25.76 49.35 Light injury 0.18 0.35 1.73 1.61 1.30 1.21 0.13 0.15 0.10 0.20

Total All types 98.29 158.43 854.02 799.32 616.58 577.65 7.16 8.55 268.27 425.70

4.2.3. Resulting marginal external accident costs in Brussels. The resulting marginal external accident costs for the transport situation in Brussels in 2005 are summarized in Table 12. The first part of this table gives the marginal external cost of the accident risk with occupants of motorized vehicles as victims. It is simply the accident risk multiplied with the part of the social cost which is external, namely the direct economic costs c”. The accidents causing light injury only have the lowest external cost, despite their high probability. The second part of the table gives the marginal external cost of increased accident risks for non-motorized road users. These accidents, involving pedestrians and cyclists, appear to be the dominant marginal external accident cost for most of the transport modes considered.

5. THE MARGINAL EXTERNAL NOISE COSTS

5.1. Noise function In order to calculate the marginal external noise costs, one needs to determine the

effect on the noise level of an additional car km. The index for noise used is the energy mean sound level, L,,(dB(A)). It gives the average sound level over a given period. The Institut Bruxellois pour la Gestion de 1’Environnement (1995) presents a number of functions that relate the noise level in a street to, inter alia, the traffic flow. We assume that the average street in Brussels has a U-shape, i.e. that it has houses on both sides of the street. In that case, the function is:

L,,(A) = 53.9 + 10 log (Q, + E Q,,) - 10 log I + K, (7)

where L,,(A) is the equivalent noise level at 2 m from the facade. Q,, stands for the flow of light vehicles (~3.5 T) in veh/h. Qp, is the flow of heavy vehicles (>3.5 T) in veh/h. E is an equivalence factor. For slopes smaller than 20/o, one heavy vehicle is assumed to be equivalent to 10 light vehicles. The width between the facades (in metres) is given by 1. K is a correction factor for speed, which means that 1 dB is added for each 10 km/h above a speed of 60 km/h.

The practical problem that we face is that this function is established to compute the noise in a particular street, and not the average city-wide noise level. We have to make several assumptions to deduct an average noise function from this information. More specifically, we have to convert the PCU city-wide to the PCU of an average street. We know that the road network is approximately 2000 km long, taking all types of roads into account. However, the study of the Institut Bruxellois (1995) takes into account only the roads with relatively heavy traffic. This amounts to 500 km. We will assume

126 Inge Mayeres et (11.

that 75% of all traffic is concentrated on this 500 km network, and that this is the only area where noise externalities occur *. This means that expression (7) becomes:

L,,(A) = 53.9 + 10 log [

0.75 (X, + 10 (XZ +x3 + X,)) _ 10 log [ + K 500 1

5.2. Monetary valuation For the monetary valuation, we use the hedonic housing market method, which is the

most widely used method for the valuation of the social costs of noise. The basic idea underlying this technique is that the value of a house not only depends on its intrinsic characteristics, but is also a function of a number of environmental attributes, such as accessibility, proximity to schools, shops and parks and pollution. If the value of a house is, amongst other factors, a function of noise, this means that when individuals buy or rent a house, within their price range, they have the possibility of buying a property in a quiet location rather than a similar property in a noisy location. It is reasonable to expect that, ceteris paribus, houses located in noisy areas are of less value than those located in quiet areas. Therefore, the housing market constitutes a surrogate market for noise (Pearce & Markandya, 1989). For a detailed description of the method and its strengths and weaknesses, we refer to Pearce and Markandya (1989).

Nelson (1982) and Pearce and Markandya (1989) summarize the results of North American hedonic price studies on traffic noise. The majority of the findings correspond with a house value depreciation in the range of 0.4-0.5% per dB(A), giving a mean of 0.4%. The results refer to a standardized house value. This way one tries to eliminate the possibility that higher priced properties may have a greater depreciation than lower priced ones. Traffic noise is expressed in L,, units. According to Alexandre and Barde (1987), as a rule of thumb, a 0.5% house value depreciation per dB(A) constitutes a reasonable guide and is based upon a substantial number of studies. However, they point to the fact that it is probable that this depreciation rate is valid only above a certain noise threshold, say 50 dB(A) L,,, since most surveys show a very low level of annoyance below this level. Furthermore, they mention the possibility that the unit percentage of depreciation increases both with the noise level and with the value of the house.

From the literature, we use the average house depreciation rule of 0.5%. We assume a standardized value of 70,600 ECU per house. Thus, for an increase of 1 dB, which would continue for 50 years, the value of one exposed house decreases with 355 ECU. However, we need to know the value of a short increase of the noise level. We assume an expected house lifetime of 50 years and a discount factor of 5%. This gives a value of 0.003 ECU(90) per dB(A) for one house. Assuming an average of 200 exposed houses per km, we obtain 0.6 ECU per dB per street of 1 km.

We assume that real housing prices remain constant between 1991 and 2005, and the valuation of 1 dB is also supposed to remain the same in the future reference situation.

5.3. The marginal external noise cost The total external noise cost for Brussels is the monetary value per dB, multiplied by

the noise level above the threshold of 50 dB(A), multiplied by the number of road km where a noise externality is generated (we assumed 500 km supra)?.

To compute the resulting marginal external noise cost (MENC) in the reference equilibrium, we derive the total external noise cost function with respect to the number of

*In the 2005 reference peak period, we have a volume of 0.922 million car units per hour (in which bus, tram and truck are equivalent to 10 cars). Assuming that the noise externality is generated by 75% of this total volume on the b&iest 500 km of the road network, we have 1383 car uniis per hour per road km. Assuming an average width between facades of I2 m, we obtain a noise level of 74.5 dB using the noise formula for U-shaped roads. Using the same assumptions in the off-peak period, we have an average flow of 337.5 car units per hour per km, generating a noise level of 68.3 dB.

tFor the reference peak period 2005, this gives (74.5-50 dB) X 0.6 ECU X 500 km, or a total external noise cost of 7350 ECU per peak period hour in Brussels.


Table 13. The marginal external noise cost Brussels 2005 (mECU per vehicle km)

127

Car Bus Truck

Peak 1.41 14.1 14.1 Off-peak 5.8 58 58

Table 14. General overview of marginal external costs, Brussels 2005, in ECU per vehicle km

Congestion Air pollution Noise Accidents Total

Peak small gasoline car 1.387 0.024 0.001 0.098 I.511 Peak small diesel car 1.387 0.022 0.001 0.098 1.508 Off-peak small gasoline car 0.004 0.021 0.006 0.158 0.189

Peak bus 2.774 0.400 0.014 0.854 4.042 OKpeak bus 0.008 0.312 0.058 0.799 1.178 Peak tram 2.774 0.873 0.014 0.617 4.278 Off-peak tram 0.008 0.873 0.058 0.578 I.517 Peak subway 0.000 0.685 0.000 0.008 0.692

Peak truck 2.774 0.157 0014 0.268 3.213 Off-peak truck 0.008 0.136 0.058 0.426 0.627

Table 15. General overview of marginal external costs, Brussels 1991, in ECU per vehicle km

Congestion Air pollution Noise

Peak small gasoline car 0.269 Peak small diesel car 0.269 Off-peak small gasoline car 0.002

Peak bus 0.537 Off-peak bus 0.004 Peak tram 0.537 Off peak tram 0.004 Peak subway 0.000

Peak truck 0.537 Off-peak truck 0.004

0.036 0.002 0.079 0.385 0.056 0.002 0.079 0.405 0.035 0.007 0.110 0.155

0.406 0.019 0.345 0.073 0.784 0.019 0.784 0.073 0.615 0.000

0.788 0.019 0.294 0.073

0.699 1.661 0.896 1.319 0.430 1.771 0.376 I .238 0.006 0.621

0.216 1.560 0.300 0.672

Total

vehicle km. Using the noise formula (8) given above, we find that the MENC of a car km is given by*:

MENC,,, = 0.6 1o 1 lnl0 Xi + 10(X, +X3 + X5) 1 500.

5.4. Results The resulting marginal external noise cost for the 2005 reference situation is given in

Table 13. As expected, the marginal external noise cost generated by a vehicle is much higher in the off-peak period than in the peak period hours of the day. Indeed, an extra vehicle on a quiet moment causes more disturbing noise than an extra vehicle on top of a lot of congestion. By definition, the marginal external noise cost of heavy vehicles is 10 times higher than that of passenger cars.

6. CONCLUSIONS

Table 14 presents expected total marginal external costs in 2005 for different types of modes per vehicle km in Brussels. Table 15 presents analogous estimates for 1991. Table 15 has been constructed using the same methodology as in Table 14. What is different in

*In the reference peak period 2005, the marginal external noise cost of a car amounts to 0.6 ECU X 0.0000047 X 500 = 0.00141 ECU per car km. For a truck or bus, this is IO times higher. The marginal external cost in the off-peak period is 0.0058 ECU per car km.

128 Inge Mayeres et ul.

1991 is the lower level of traffic (and the corresponding higher speed), the use of cars not equipped with three-way catalytic converters, and the lower levels of income.

Marginal external costs vary widely in the function of the volume of traffic (peak vs off-peak) and in the function of the type of vehicle used. Because only results for standardized cars are presented, the variance in the external costs (noise, air pollution) is underestimated in these tables. Cars without a properly working catalytic converter and without noise abatement have marginal external costs that can be a factor of 10 higher. It is also important to note that public transport vehicles entail external costs as well. For meaningful comparisons over modes, the occupancy rates must be taken into account.

In 2005, the external congestion costs dominate the external cost estimates. This holds for peak periods. The absolute level of the marginal air pollution costs has strongly decreased since 1991 due to the use of cleaner cars. Noise costs are of lower importance and actually decrease because of the lower speed levels. The importance of external accident costs increases compared to 1991 because of two reasons: the increased valuation of damages and the increased probability of accidents due to the increased volume of transportation.

It is very important to realize that the marginal external cost estimates given in this table are only valid for the particular transport situation in Brussels in the year 2005 under unchanged policy conditions. A different equilibrium will imply a different level of external costs, hence the importance of external cost functions, rather than point estimates.

These marginal external cost estimates are subject to many uncertainties which depend primarily upon the quality of the data used in the technical relationships, and less upon the quality of the valuation techniques. In the domain of air pollution, for example, the uncertainty is situated mainly in the emission concentration relationships. In the domain of accidents, the relationship between the number of accidents and the traffic flow is hard to establish with any certainty.

Given the constraints on data availability and the limited number of typical European studies, we have sought to establish the best possible marginal external cost estimates. Better data concerning each of the inputs in this valuation study will be warmly welcomed and will certainly merit an update of this paper.

AcknowledgementsThis research is part of the TRENEN project financed by the JOULE-II programme of the EC (DGXII) and of contract G.0263.95 of the Belgian National Fund for Collective Research. S. Proost is Research Fellow of the Belgian National Fund for Scientific Research. We would like to thank K. Van Dender for his helpful comments. All errors remain the authors’.

REFERENCES

Alexandre A. and Barde J.-Ph. (1987) The Valuation of Noise. In Transportation Noise Reference Book (Nelson P. M., Ed., pp. 23/l-23/10). Butterworths, London,

Belgisch Instituut voor de Verkeersveiligheid (BIVV) Nationaal Instituut voor Statistiek (1989) Verkeersongevullen Slutistieken. BNV, Brussels.

Boniver V. (1993) Les cotits marginaux externes du transport public de personnes en milieu urbain, Estimations chiffrees pour la Belgique, Working paper. CIRIEC, Liege.

Boniver V. and Thiry B. (1994) Les cofits marginaux externes du transport public de personnes en milieu urbain, Estimations chilTrees pour la Belgique. Cub. Econ. Bruxelles 192, 203-240.

Bradley M. (1990) Application of the VOT Models to Obtain Representative Average Values of Time. Hague Consulting Group, The Hague.

Bradley, M. A. and Gunn H. F. (1991) Further Application and Validation of the Netherlands Value of Travel Time Study. Paper presented at the 6th International Conference on Travel Behaviour, Quebec.

Builtjes P. (1991) The LOTOS Model Results. In Comparison of Three Models for Long Term Photochemical Oxidants in Europe (Iversen T., Ed.). EMEP/MSC-W, Oslo. EMEP/MSC-W Report 3/91.

Button K. (1990) Environmental Externalities and Transport Policy. Oxford Rev. Econ. Policy 6, 61-75. Button K. (1993) Transport, The Environment and Economic Policy. Edward Elgar, Aldershot. Cline W. R. (1992) The Economies of Globul Warming. Institute for International Economics, Washington. De Borger B., Ochelen, S., Proost, S. and Swysen D. (1995) Regional and Federal Interest in Transport and

Environment Policy Making. Paper presented at the International Symposium on ‘Economic Aspects of Environmental Policy Making in a Federation’, Leuven, June 1995.

De Jong G. C., Gommers M. A. and Klooster J. P. G. N. (1993) De reistijdwaardering in het goederenvervoer. T~dschr(fi vervoerswetenschup 29, 71-85.


Dumont G. and Roekens E. (1994) Fotochemische luchtverontreiniging. In Leren OM re keren. Milieu - en natuurrapporr Vlaanderen (Verbruggen A., Ed., pp. 235-244). Garant, Leuven.

ETSU and IER (1994) Coal Fuel Cycle, Externalities of Fuel Cycles, ‘ExtemE’ Project, Working Document no. 2. European Commission, DG XII, Brussels.

Fankhauser S. (1995) Valuing Climate Change, The Economies of the Greenhouse. Earthscan, London. Great Britain, Department of Transport (1987) COBA 9, Department of Transport, Assessments Policy and

Methods Division. Hague Consulting Group (1990) The Netherlands’ ‘Value of Time’ Study: Final Report. Hague Consulting

Group, The Hague. I.E.R. (1994) Oil Fuel Cycle, Externalities of Fuel Cycles, ‘ExternE’ Project, Working Document No. 4.

European Commission, DG XII, Brussels. Institut Bruxellois pour la Gestion de I’Environnement (1995) Bruit du trafic routier, Region bruxelloise.

I.B.G.E., Bruxelles. Jansson J. 0. (1994) Accident Externality Charges. J. Trans. Econ. Policy 28, 3 143. Jones-Lee M. W., Hammerton, M. and Philips P. R. (1985) The Value of Safety: Results from a National

Sample Survey. The Economic J. %,49-72. Jones-Lee M. W. (1990) The Value of Transport Safety. Oxford Rev. Econ. Policy 6, 39-60. Kirwan K., O’Mahony M. and O’Sullivan, D. (1995) Speed-Flow Relationships for Use in an Urban

Transport Policy Assessment Model, Mimeo (Trinity College Dublin, Dublin). Linster M. (1990) Background Facts and Figures. In ECMT, Transporf Policy and the Environment, ECMT

Ministerial Session, Background Reports. OECD, Paris. Maclean A. D. (1979) The Value of Public Safety: Results of a Pilot Scale Survey. London Home Office

Scientific Advisory Branch. Maddison D., Pearce, D., Johansson, O., Calthrop, E., Litman, T. and Verhoef, E. (1996) The True Costs of

Road Transport. Blueprint 5. CSERGE, Earthscan, London. Maier, G., Gerking, S. and Weiss P. (1989) The Economics of Traffic Accidents on Austrian Roads: Risk

Lovers or Policy Deficit? Mimeo. Wirtschaftsuniversitiit, Vienna. Mayeres I. (1993) The Marginal External Cost of Car Use: with an Application to Belgium. TQdschrift voor

Economic en Managemenr 38,225-258. Maveres I. (1994) The Marginal External Costs of Trucks - An Empirical Exercise for Belgium. Tqdschrift

Vervoerswetens>hap 30, 12?-138. Melinek S. J. (1974) A Method for Evaluating Human Life for Economic Purposes. Accidenr Analysis and

Prevenrion 6, 103-I 14. Metroeconomica (1994) Economic Valuation, Externalities of Fuel Cycles, ExtemE Project, Working

Document No. 9. Euronean Commission, DC XII, Brussels. MVA Consultancy, Institute of Transport Studies University of Leeds, Transport Studies Unit, University of

Oxford (1987) Policy Journals, The Value of Travel Time Savings. Nationaal Instituut voor de Statistiek Belgium (NIS) (1989) Verkeersongevallen op de openbare weg met

doden en gewonden, jaar 1989. Nelson, J. P. (1982) Highway Noise and Property Values. J. Trans. Econ. Policy 16, 117-132. Newbery D. M. (1987) Road User Charges in Great Britain. CEPR Discussion Paper no. 174. Centre for

Economic Policy Research, London. Newbery D. M. G. (1988) Road User Charges in Britain. The Economic J. 98, 161-176. Nilsson S., ed. (1991) European Forest Decline: The Effects of Air Pollution and Suggested Remedial Policies.

IIASA, Laxenburg. Nordhaus W. D. (1993) Optimal Greenhouse Gas Reductions and Tax Policy in the ‘Dice’ model. Am. Econ.

Rev., Papers Proc. 83, 3 13-3 17. O’Reilly D., Hopkin, J., Loomes G. and Jones-Lee, M. (1994) The Value of Road Safety: UK Research on

Injury Valuation. J. Trans. Econ. Policy 28,45-59. Ochelen S. and Proost S. (1995) Optimal Policies for Curbing Congestion and Air Pollution in Urban

Transport, mimeo. OECD (1988) Transport and rhe Environmenf. OECD, Paris. Pearce, D. W. and Markandya A. (1989) Environmental Policy Benefits: Monetary Valuation OECD, Paris. Peirson J., Skinner I. and Vickerman R. (1994) Estimating the External Costs of U.K. Passenger Transport:

The First Step Towards an Efficient Transport Market, Discussion paper 9412. CERTE, Canterbury. Persson U. (1989) The Value of Risk Reduction: Results of a Swedish Sample Survey, Mimeo, The Swedish

Institute of Heath Economics. Quinet E. (1993) The Social Costs of Transport: Evaluation and Links with Internalising Policies. Paper pre-

sented at the joint OECD/ECMT Seminar on ‘Internalising the Social Costs of Transport’, Paris. Region de Bruxelles-Capitale (1993) Plan regional des dtplacements, Plan IRIS, Constat et scenario tendenciel.

Bruxelles. Simpson D. (1991a) The EMEP MSC-W Photo-Oxidant Model. In Comparison of Three Models for Long

Term Photochemical Oxidants in Europe (Iversen T., Ed.). EMEP/MSC-W Report 3/9l. EMEP/MSC-W, Oslo. Simpson D. (199lb) Long Period Modelling of Photochemical Oxidants- in Europe, Calculations for

Aoril-Seotember 1985. Auril-October 1989. EMEP/MSC-W Report 2/91. EMEP/MSC-W. Oslo. Simpson d. (1992) Long Period Modelling of Photochemical Oxidants in Europe, A) Hydrocarbon Reactivity

and Ozone Formation in Europe, B) On the Linearity of Country to Country Ozone Calculations in Europe. EMEP/MSC-W Note l/92. EMEP/MSC-W, Oslo.

Small K. and Kazimi C. (1995) On the Costs of Air Pollution from Motor Vehicles. J. Trans. Econ. Policy 29, 7-32.

STIB (1991) Rapport Annuel 1991. STIB, Brussels. Stratec (1992) Plan regional des d&placements, Analyse de I’enquete ‘Menages’. Region de Bruxelles-Capitale,

Bruxelles.

130 Inge Mayeres CI al

U.S. Federal Highway Administration (1982) Final Report on the Federal Highway Cost Allocation Study. USGPO, Washington.

Verkehrwissenschaftliches Institut der RWTH-Aachen (VIA) (1995) Final Report TRENEN Project, JOULE programme EC.

Vitaliano D. F. and Held J. (1991) Road Accident External Effects: An Empirical Assessment, A&. Eeon. 23. 373-378.

Zlatev Z.. Christensen J. and Frohn T. P. (1991) The Danish Eulerian Model with Non-Linear Chemistry. In Comparison of Three Models for Long Term Photochemical Oxidants in Europe (Iversen T., Ed.). EMEP/MSC-W Report 3191. EMEPIMSC-W, Oslo.

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