2010-61
Estimation of Social Costs of Transportation in Japan(Revised Version)
Fumitoshi Mizutani Yusuke Suzuki

Hiroki Sakai

Estimation of Social Costs of Transportation in Japan(Revised Version)

byFumitoshi MIZUTANI , Yusuke SUZUKI, Hiroki SAKAI

Kobe University,
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501
Japan

Correspondent Author: Fumitoshi Mizutani
toshi@kobe-u.ac.jp[Abstract]:
Using a data set of 111 Japanese cities from the year 2005, we aim in thispaper to estimate the social costs of car transport and to analyze the structure of the components ofand the relationship between social costs and city size. We obtain the following major results.
First, the social costs of vehicular transport increase at an accelerated pace as city size becomeslarger. Second, while the construction of roads does not work to decrease the social costs ofvehicular transport, public transportation has a tendency to decrease such costs, though with minimal
effect. Third, the traffic congestion component represents more than 45% of the total social cost ofvehicular transport. Cost due to global warming accounts for 5 to 11% of the total. Fourth, thesocial costs of vehicular transport are about 8.0% of GDP. Fuel tax for car transport in Japan
covers only 16.3% of the social costs of regular car use.
[JEL Classification] Q51, Q52, R41[Key Words] Social costs, External costs, Vehicular transport, Environmental protection,
Global warming

1

1. Introduction
With more people concerned about protecting the environment at both local and global
levels, dependence on vehicular transportation in cities has brought about problems.

First,

dependence on autos causes air pollution, which has detrimental health effects. Second, traffic
congestion resulting from the excessive use of automobiles wastes time, money, gasoline, andeconomic and social opportunities.

Furthermore, the noise from traffic congestion makes it hard

to maintain a peaceful, attractive urban environment.

What many people are perhaps mostconcerned about, however, is the idea that continued dependence on car use is likely exacerbating
the problem of global warming.

Policy makers in cities seek to address all these problems andworries by looking for constructive ways to regulate car use with the goal of improving or protecting
the environment, while at the same time maintaining healthy economic conditions.

In order toimplement policies conducive to creating a sustainable environment, it is necessary to measurecorrectly the social costs of vehicular transport, that is, the external costs of such phenomena as
traffic accidents, air pollution, noise, global warming, and traffic congestion.

By using a data set

from Japan, this paper aims to estimate the social costs of vehicular transport and to analyze thestructure of the components of the total cost, as well as to examine the relationship between social
costs and city size.
Many studies have estimated social costs.

For example, in the U.S., by analyzing a data

set from Los Angeles, Small and Kazimi (1995) and McCubbin and Delucchi (1999) estimate thesocial costs of air pollution from both gasoline cars and diesel trucks.

Forkenbrock (1999)

estimates not only the social cost of air pollution but also that of traffic accidents, noise, and global
warming.

In Europe, there has been a considerable amount of research.

For example, by using adata set from Brussels for a study focusing on the social costs of accidents, air pollution, noise, andtraffic congestion, Mayeres et al. (1996) estimate the social costs of various types of vehicles, suchas gasoline cars, diesel cars, buses, and trucks. Moreover, they distinguish between social costs at
peak and off-peak periods.

ECMT (1998) estimates for 17 European countries the social costs ofsuch phenomena as traffic accidents, air pollution, noise, and global warming.

Other studies from

Europe include Eyre et al. (1997) for the UK, and Danielis and Chiabai (1998) for Italy.

Since

2000, UNITE (2003) and INFRAS/IWW (2004) have estimated various kinds of social costs,
including that of global warming.

In addition, studies from places other than the U.S. andEuropean countries have estimated social costs: Koyama and Kishimoto (2001) in Japan, Deng
(2006) in Beijing, China, and Jakob et al. (2006) in Auckland, New Zealand.
Our study has several distinguishing characteristics, the most important of which is that2

we base our estimate of each individual city’s social costs on the entire city’s average speed at thepeak period, and the total traffic volume of each city according to its types of roads (i.e. regularroads or highways) and its types of vehicular transport. We consider each city’s traffic conditions
and urban structure when estimating the social costs of vehicular transport.

Second, this study

focuses on five kinds of social costs of vehicular transport, of which we give estimates for individual
cities in Japan: traffic accidents, air pollution, noise, global warming, and traffic congestion.

Ofprevious studies, only those of Koyama and Kishimoto (2001) and UNITE (2003) estimate these fivesocial costs.

Third, by using a data set for 111 cities in Japan in 2005, we analyze the relationshipbetween the social costs of vehicular transport and city size, investigating whether city size is
proportional to social cost.

This study is perhaps the first empirical analysis to estimate how social

costs increase along with city size.

Fourth, with regression analysis, we evaluate the effects ofurban infrastructure on the social costs of vehicular transport, determining to what extent
infrastructure and public transportation reduces the social costs of a city’s vehicular transport.

Last,by comparing the degree of GDP, the fuel tax level, and other factors, we assess the magnitude ofsocial costs.
The structure of our study is as follows.

First, we summarize previous studies, withattention to the following points: the kinds of social costs and sub-items considered to estimatesocial costs, the aggregate level, the method of estimation, and the magnitude of the social costs ofvehicular transport.

Second, we explain our method for estimating social cost, describing specificequations for the five main categories of social costs. Third, based on these equations, we conduct
an empirical analysis using a data set of 111 cities, to estimate the social costs of vehicular transport.
By using comparisons of the cities’ GDP and fuel tax levels, we evaluate the results regarding suchmatters as the relationship between social costs and city size, the effect of urban structure andinfrastructure on social costs, and the magnitude of social costs.

Finally, we summarize our major

findings.

2. Previous Studies of the Social Costs of Transportation
2.1 Kinds of Social Costs
First, we deal with air pollution, the main issue concerning most researchers who haveaimed to estimate transportation’s social costs, examined previously by Small and Kazimi (1995),
Eyre et al. (1997) Danielis and Chiabai (1998), WHO (1999) and McCubbin and Delucchi (1999).
Also widely considered as social costs of transportation are traffic accidents, noise, andtraffic congestion, all of which are used, in addition to air pollution estimates, by Mayeres et al.

3(1996), Levinson et al. (1998), and Beuthe et al. (2002).
More recently, global warming believed to be caused by transportation has been added to
the list of its social costs, as can be seen in such previous studies as ECMT (1998), Forkenbrock(1999), Koyama and Kishimoto (2001), UNITE (2003) and INFRAS/IWW (2004).
Other social costs include damage to the landscape caused by transportation facilities, thecost of separating pedestrian from motor traffic, and the problem of space scarcity as land isallocated for roads.

There are also costs associated with the impediments to cycling caused bytransportation facilities.

Though few studies take these factors into account when calculating thesocial costs of transportation, INFRAS/IWW (2004), for example, includes them.
In this study, we take for our estimation of the social costs of transportation those mostcommonly used: traffic accidents, noise, air pollution, global warming and traffic congestion.

2.2 Aggregation Unit
Most previous studies use whole countries as the aggregation unit of social costs. For
example, ECMT (1998), UNITE (2003) and INFRAS/IWW (2004) use whole countries in Europe,
although these studies choose to focus on slightly different country groupings, employing
cross-sectional data sets. On the other hand, some studies estimate the social costs of a single
country by using a data set for the individual country. Examples of these studies include Eyre et al.
(1997) for the UK, Levinson et al. (1998) and Forkenbrock (1999) for the USA, Danielis andChiabai(1998) for Italy, Beuthe et al. (2002) for Belgium, and Koyama and Kishimoto (2001) forJapan.
Studies using cities as aggregation units are few, although some such studies do estimate
the social costs of transportation.

Included in this category are Small and Kazimi (1995), Mayeres

et al. (1996), McCubbin and Delucchi (1999), Gibbons and O’Mathony (2002), Deng (2006) andJakob et al. (2006).

While it should be noted that these studies estimate social costs oftransportation for only one city, there are no studies using cross-sectional data to estimate social
costs for different cities.

2.3 Estimation Approach of Social Costs
Traffic Accidents
The most commonly used measures of the social costs of traffic accidents are deaths,
injuries, and damaged goods, of which the former two predominate, as shown in Persson andÖdegaard (1995), ECMT (1998), Koyama and Kishimoto (2001), Beuthe et al. (2002), and UNITE

4

(2003).

Some studies further subdivide injuries according to the level of seriousness, as in Beuthe

et al. (2002), UNITE (2003) and Koyama and Kishimoto (2001).

Damaged goods as a measure areused in Levinson et al. (1995) and Beuthe et al. (2002).
Previous studies generally estimate traffic accidents’ social costs in two steps: firstestimating the number of accidents related to vehicular transport and evaluating the resulting damage,and second calculating the monetary value of damage resulting from accidents.
The first step can be approached in two ways.

One, seen in studies such as ECMT(1998), UNITE (2003), and INFRASS/IWW (2004), is the direct use of accident numbers fromavailable data sources.

This approach is accurate and convenient if data can be directly obtained,

but infeasible if accident data are unavailable. The second approach, used in studies such as
Persson and Odegaard (1995), Mayeres et al. (1996) and Beuthe et al. (2002), is to estimate thenumber of accidents by building equations describing the relationship between accidents and factors
like road conditions.

This approach is useful if accident data are unavailable, but is contingent

upon the construction of a reasonable model for predicting accidents.
For the second step, calculating the monetary value of traffic accident damage, mostprevious studies use the number of fatalities, the number of severely injured persons, and the numberof accidents resulting in damage to property.

Also used as social costs in previous studies, such as

ECMT (1998) and INFRASS/IWW (2004), are WTP (willingness to pay) for avoiding traffic
accidents, other economic costs such as loss of production due to accidents, administrative costs forambulances, and medical costs.

Air Pollution
The most widely used measure for the social costs of air pollution is PM10, as can be seenin Small and Kazimi (1995), Eyre et al. (1997), McCubbin and Delucchi (1999), WHO (1999),
Koyama and Kishimoto (2001) and the European Commission (2005).

In addition to PM10, ozone,

NOX, SOX and VOC are sometimes used, as shown in, for example, Small and Kazimi (1995), Eyreet al. (1997) and McCubbin and Delucchi (1999). Most studies, such as Small and Kazimi (1995),
WHO (1999), and McCubbin and Delucchi (1999), consider the effects of pollutants on humanhealth.

In addition to human health effects, Eyre et al. (1997), UNITE (2003) and INFRAS/IWW(2004) consider pollutants’ effects on agriculture and forests.
Of the two different approaches to air pollution, one uses air pollution’s unit social costs.
By multiplying the total volume of pollutants in a given area by the costs per pollutant produced by
vehicular transport, the total social costs of air pollution are obtained.

5

Mayeres et al. (1996), Eyre

et al. (1997), and Levinson et al. (1998) use this approach. The second approach is to obtain themarginal social costs of air pollution in a given area, information descriptive of the relationshipbetween the degree of air pollution and the traffic volume under given city conditions.

Examplesusing this approach are Small and Kazimi (1995), WHO (1999), and INFRAS/IWW (2004).
Each approach has advantages and disadvantages.

The former is a convenient way toestimate social costs, but if city conditions are different from those considered in previous studies,
the estimates’ reliability might decrease. On the other hand, while the latter approach describes therelationship between air pollution and traffic volume in city conditions, and thus would provide
more reliable estimates of social costs, this approach requires more information about population,road conditions, and so on.

Noise
The social costs of noise are estimated generally in two steps.
effect on areas are estimated.

First, noise level and its

Second, the monetary values of noise levels are obtained.

The first step can be carried out in two ways, one using available information fromprevious studies to show the percentage of the population exposed to vehicular transport noise.
Studies using this approach are ECMT (1998), UNITE (2003) and INFRAS/IWW (2004). The
second approach, a more sophisticated method describing the relationship between noise level andtraffic volume, can be seen in Mayeres et al. (1997) and Levinson et al. (1998).
The first approach is a convenient method of estimating social costs, but because thenoise level and the affected areas vary according to environmental conditions, it might be difficult tofind among previous studies conditions similar to those under present consideration.
By considering the traffic volume of each vehicle type, traffic speed, city conditions suchas population density and so on, the second approach establishes a model describing the relationshipbetween traffic and noise. The second approach enables a more precise estimation of noise than thefirst approach.

Noise produced by vehicular transport depends on many factors. The estimation

model for congestion must take into account such factors as traffic volume, speed of vehicles, kindsof vehicles, and city conditions.

But this approach requires extensive data, which cities often do

not record, making it difficult for independent researchers to prepare adequate information.

Global Warming
As for global warming measures, CO2 is most commonly used, for example in studies by
Mayeres et al. (1996), Eyre et al. (1997), Forkenbrock (1999) and Koyama and Kishimoto (2001)

6

INFRAS/IWW (2004).

Some studies, such as Mayeres et al. (1996) and Eyre et al. (1997), use CO

in addition to CO2.
The social cost of global warming is generally obtained in by first estimating the volumesof pollutants causing global warming, and second estimating the monetary values of the global
warming damage resulting from pollution.
There are in general two approaches to estimating the pollutants causing global warming.
Some studies, for example UNITE (2003) and INFRAS/IWW (2004), use the first, which relies onprevious studies to estimate the volume of CO2 as pollutants emitted by cars.

The second approachis to use the volume of CO2 per traffic volume by taking into account kinds of vehicle types and roadconditions. This approach is used in Mayeres et al. (1996), Eyre et al. (1997) and Forkenbrock(1999).
In the second step, the monetary values of CO2 emissions are estimated using unit social
costs of CO2 from previous studies.

For example, INFRAS/IWW (2004) and UNITE (2003) use

the unit costs for countermeasures as discussed in the Kyoto Protocol at the United Nation’s
Framework Convention on Climate Change in 1997.1

Congestion
Traffic congestion is measured as time lost in traffic congestion.
costs of traffic congestion, the following procedure is generally used.
transport is obtained from traffic volume.

To estimate the social

First, the speed of vehicular

Estimation models are obtained from previous studies

formulating the relationship between flow speed and traffic volume.

For example, Mayeres et al.

(1996) use a study by Kirman et al. (1995) formulating such a relationship.

Other studies such as

Levinson et al. (1998) and UNITE (2003) use similar kinds of models.
In the second step, time loss by congestion is calculated based on the flow speed obtainedin the first step.

This step involves taking into account variations in traffic conditions.

For

example, Mayeres et al. (1996) estimate time loss from congestion by distinguishing vehicle types aswell as time differences (peak and off-peak periods).

Furthermore, INFRAS/IWW (2004)

considers travel purpose when estimating time loss due to congestion.
In the last step, the social costs of traffic congestion are obtained from unit social costs
by multiplying time loss by congestion.
1

In INFRAS/IWW (2004), the lower level of unit cost of CO2 and the higher level of unit costs of CO2
are used. The lower level of unit costs is obtained from information regarding the Kyoto Protocol. On
the other hand, the higher unit costs are obtained by setting up a target of achieving a 50% reduction of
CO2 by 2030.

7

2.4 Degree of Social Costs
From the social costs of vehicular transport, we select five items: traffic accidents, air
pollution, noise, global warming, and traffic congestion.

These five items cannot include all social

costs of transportation, but we think they comprise a large proportion of total costs.

For example,

while we do not make a separate category for “health,” we calculate certain health-related costs
under the heading of air pollution.

We do not include items such as undesirable effects on thelandscape, the cost of protecting pedestrians from motor traffic, or the loss of space for cyclists andothers due to the encroachment of roads. We believe these costs are less important, both in terms ofquantity and measurability, than the costs we have chosen and which most studies cited in ourliterature review have focused on.

Table 1 is a summary of the five social costs. To the greatest

extent possible we show the unit social costs of each item (i.e. US dollar per vehicle-km, yen per
vehicle-km).

********************
Table 1********************
This table shows the nominal values in each currency unit.

Because it cannot evaluate

the range of the social costs of transportation, for each previous study we have converted each cost
into 2005 US currency values based on the exchange rate for the year in which the study was done.
As a result, we find that the social costs of vehicular transport are about 6 to 15 for traffic accidents,
0.7 to 5.3 for air pollution, 0.6 to 3.2 for noise, 0.6 to 1.9 for global warming and 4.9 to 6.4 fortraffic congestion, all in US cents per vehicle-km for gasoline cars in general.

3. Method of Estimation of Social Costs
3.1 Major Characteristics of this StudyIn this section, we explain our method of estimating the social costs of vehiculartransport.

First, we estimate the social costs of vehicular transport in each city by considering thetypes of vehicular transport such as cars, buses, small trucks, and trucks.

The total social costs ofvehicular transport are the sum of the costs of the five items mentioned above. Thus, the social
costs of vehicular transport in city-a (SCa) are defined as follows.

8SC a  C acc,a  C air ,a  C dB ,a  C war ,a  C con,a(1)

SCa：Social costs of vehicular transport in city-a,WhereCacc,a ：Social costs of traffic accidents in city-a,Cair,a ：Social costs of air pollution in city-a,
CdB,a ：Social costs of noise in city-a,Cwar,a ：Social costs of global warming in city-a,Ccon,a ：Social costs of traffic congestion in city-a.

Second, in this study, we estimate the whole city’s average speed at the peak period andthe total traffic volume of each city, using actual measured information from government documents.
We separate types of roads (i.e. regular roads or highways) and types of vehicular transport in ourestimation of the city’s speed at the peak period, using as our data source “2005 Road Transportation
Census (2005 Doro Kotsu Sensasu),” issued by the Ministry of Land, Infrastructure and Transport.
Last, the social costs are obtained by the estimation formula of each item of social costs,
constructed on the basis of results from previous using studies.

As Japanese data sets are used in

the estimation of social costs in this study, previous studies’ estimation formulae based on cities in
Europe and the US are not always appropriate, in which cases we make modifications in order toavoid estimation bias related to city structure.

3.2 Key Variables for the Estimation of Social Costs
City’s Traffic Volume
Traffic volume of vehicle type-b in city-a ( Qab ) is the sum of traffic volumes on trunkbbroads ( Qa,trunk) and those on city roads ( Qa,city), as equation-(2) shows.

Equation-(3) shows theestimation of traffic volume on trunk roads and equation-(4) is for the traffic volume on city roads.
Thus, the basic formula for the estimation of traffic volume is obtained from results of daily traffic
volume on the observed road section.
bb
Qab  Qa,trunk Qa,city

(2)





b
b
b
Qa,trunk d   DISa,c  CARa,c,w

(365

d)

DIS

CAR



 a,c
a,c,h 
,
 c

 c


(3)

b
b
b
Qa,city d  DISa,city  CARa,city,w (365  d)  DISa,city  CARa,city,h

(4)

where

b
a ：Annual traffic volume of vehicle type – b in city-a (vehicle-km),Qb：Annual traffic volume of vehicle type-b on the trunk road in city-a (vehicle-km),
Qa,trunk

9b
Qa,city：Annual traffic volume of type-b on the city road in city-a (vehicle-km),

DISa,trunk ：Length of observed road section-c on trunk road in city-a (km),DISa,city ：Total length of city road in city-a (km),b
CARa,c,k：Daily traffic volume of vehicle type-b in the road section-c on trunk road incity-a for day type-k (vehicle/day),ba,city,k ：Daily traffic volume of vehicle type-b on city road in city-a for day type-k

CAR(vehicle/day),a: city , b: type of vehicle (1= car, 2 = bus, 3 = small truck, 4 = truck), c: road section on
trunk road, d: number of weekdays (d=246 days), k: day type (w = weekday, h =weekends).
Observed daily traffic volume is shown in the 2005 Road Transport Census, carried out
mainly on bigger roads and trunk roads.

A comparison of total traffic volume appearing in the2005 Road Transport Census with traffic volumes recorded in other sources showed that those fromthe Census are about 30% smaller than from other sources, which include smaller city roads. We
include estimates of traffic volume on city roads, as shown in equation-(4).

Speed on Roads
We distinguish two kinds of roads: general roads and highways.

For general roads, weestimate the average traffic speed of a city based on traffic speed data obtained from given
observation points.

The estimation formula for speed shows the relationship between vehicle speed

and traffic volume.

Taking into consideration variations in city conditions, the formula includes theratio of roads passing through DID (Densely Inhabited District) areas.

The estimated average

traffic speed for general roads is as follows2.

Va ,lane  38 .1274  0 .0059 q a ,lane  19 .9388 DID a ,lane( 43 .81)(  3 .95 )

adjR

where

(5)

(  19 .81)

2

 0 .465

Va ,lane ：Vehicle speed (km/h) per lane in city – a,qa ,lane ：Traffic volume (vehicle/h) per lane,
DIDa ,lane ：The ratio of roads passing through DID (Densely Inhabited District) areas.

2

According to Doll and Jansson (2005), there are two kinds of model specifications in the speed-flow
model: the logit model and the linear model. From the sample distribution, the linear model seems more
appropriate than the logit model. Therefore, we specify the linear model.

10

As for highway speed, we distinguish highways in large metropolitan areas from those in
non-large metropolitan areas.

Data from Road Traffic Census (Doro Kotsu Sensasu) shows traffic

congestion at peak periods on highways in large metropolitan areas.

The majority of social costs

described here are caused at peak period, so that in the large metropolitan areas only, we distinguishpeak and off-peak average speed. The average speed for highways at the peak period in the large
metropolitan areas is the observed speed at the peak period3.

On the other hand, the speeds ofothers (i.e. the highways in large metropolitan areas at the non-peak period, and the highways in
non-large metropolitan areas at both the peak and the off-peak periods) are the legal speed limits4.

3.3 Estimation Models of Individual Social Costs
In this section, we will explain the formula for estimating five items of social costs
produced by vehicular transport on a city-base.

Traffic Accidents
First, the social costs of traffic accidents are estimated by multiplying the unit social costof traffic accidents by the number of victims resulting from traffic accidents, as equation-(6) shows.
As these social costs vary according to type of damage, we distinguish types of victims.

Cacc,a   (Pacc,e  POPacc,a,e )

(6)

e

where

Cacc,a：Social costs of traffic accident in city – a (yen),Pacc,e：Unit social cost of traffic accident type – e (yen),
POPacc,a,e：Number of victims of type- e caused by traffic accidents (persons),
e：Type of victims (1: death, 2 : seriously injured, 3: lightly injured)

Statistics on type of victims caused by accidents are obtained from the Annual Report ofTraffic Accidents (Kotsu Jiko Tokei Nenpo).

The unit costs are obtained on the basis of WTP(Willingness to Pay), as was the case in previous studies such as INFRAS/IWW (2004) and
Forkenbrock (1999).

Based on reported statistics on traffic accidents in Japan from the Cabinet

3

The large metropolitan areas are cities of more than 1 million in population. The peak period for
highways in the large metropolitan areas is assumed to be 6 hours per day.
4
Although the legal speed limit for highways varies among routes and section, we use a uniform value
assumed to be 80km/h.

11Office (2007), we define unit social costs as 229,032 thousand yen for the death of a victim, 84,810
thousand yen for a seriously injured victim, and 846 thousand yen for a lightly injured victim.

Air Pollution
There are two approaches to estimating the social costs of air pollution, as classified by
INFRAS/IWW (2004): top down and bottom up.

The top down approach, used in WHO (1999)

and INFRAS/IWW (2004), applies the unit costs of air pollution obtained from previous studies,while the bottom up approach calculates the social costs by examining, in order, pollutants produced
by vehicular transport.

If data for air pollution and vehicular transport traffic are available, thebottom up approach is better because the method itself is precise. In this study, we employ thebottom up approach to estimate social costs, as shown in equations-(7) to (11).

Cair,a   (Pair,g  POPair,a,g )(7)g

POPair ,a , g  f air , g ( ATM PM ,a )(8)

ATMPM ,a  TranPM  atmPM ,a  atmPM ,nntr,a (9)

atmPM ,a   air,PM  EMIPM ,a

(10)

b
b
EMIPM ,a    (ERPM,i  Qa,i )

(11)

i

where

b

Cair,a: Social costs of air pollution in city – a caused by vehicular transport (yen),
Pair,g: Unit social cost of air pollution type- g (yen),
POPair,a,g：Number of victims of type – g caused by air pollution (person),fair,g(・)：Exposed function by air pollution for type – g,
ATMPM,a ：Annual concentration level of air pollution substances of PM10 caused by
vehicular transport in city – a,
TranPM：Adjustment coefficient for the difference between Japan and Europe in PM10(h=1.07),
atmPM,a：Annual concentration level of PM10 (SPM) in city – a,
atmPM,nntr,a：Annual concentration level of PM10 (0.012mg/m3) caused by non-vehiculartransport emissions in city – a,aair,PM：Parameter for the impact of PM10 by vehicular transport pollutants on surroundingarea per 1km2,
EMIPM,a：Annual amount of PM10 per area of 1km2 of vehicular transport pollutants in city
– a (g/year),

12

ERbPM,i：Coefficient for pollution by PM10 in road type-i by vehicular transport type-b(g/km)5,
Qba,i: Traffic volume of vehicular transport type-b on road type-i in city-a (vehicle-km),
b：Vehicular transport type (b=1: passenger car, 2: bus, 3: small truck, 4: truck),
g：Type of health damage by air pollution6,
i：Road type (i = 1: highway,

2: ordinary roads).

The essence of these equations is as follows. First, equation-(7) obtains the social costs
from the number of victims of air pollution.

Second, equation-(8) shows the relationship between

the number of air pollution victims and the level of concentration of air pollutants PM10.

Called thedose-response function, this relationship has often been used in previous studies such as Mayeres etal. (1996), WHO (1999) and the European Commission (2005). We employ the dose-response
function based on the European Commission (2005).

Equation-(9) and (10) show methods ofcalculating the concentration level of air-polluting substances PM10 caused by vehicular transport.
These equations exclude air pollution substances produced by sources other than cars.

Last,equation-(11) shows the method of estimating air pollution substances from traffic volume.

Noise
The social costs of noise are estimated in two steps, the first of which is to estimate noisecaused by vehicular transport, as shown in equation-(13), which shows how noise affects people insurrounding areas.

In the second step, as equation-(12) shows, noise is transformed into monetary

value.
*C dB ,a   PdB  EMI dB ,a ,i  EMI dB
 POP dB ,a ,i

(12)

i

POP dB ,a ,i   DIS a ,i,h  R dB  PD a ,h

(13)

h

Where

CdB,a：Social costs of noise by vehicular transport in city-a,
PdB：Unit social cost of noise (yen/dB),
EMIdB,a,i：Noise level caused by vehicular transport on road type-i in city-a (dB(A))7,5

The coefficient for pollution by PM10 is obtained by using the calculation method of the Ministry of theEnvironment.
6
Health damage caused by air pollution is classified into two major categories: mortality and morbidity,
as in the previous study by the European Commission (2005).
7 Noise level is estimated with equations based on the Doro Toshi no Hyoka ni Kansuru Shishin Kento
Iinkai (1998).

13

EMIdB*：Standard noise level (50dB),
POPdB,a,i：Exposed population to noise level EMIdB,a,i (person),
DISa,i,h：Road length of road type-i in surrounding area type-h in city-a (km),
RdB：Affected areas exposed to noise (extension from the roadside = 10m),
PDa,h：Population density in surrounding area-h in city-a (person/km2),
i：Road type (i = 1: highway, 2: ordinary road),h：Surrounding area (h=1: DID area, 2: non-DID area,

DID (Densely Inhabited District)

Theoretically, noise caused by vehicular transport is affected by the speed of traffic,
traffic volume, composition of vehicule type, distance from roads, and so on.
these factors are not available for selected roads8.

However, data on all

Therefore, we modify the equation shown in the

Doro Toshi no Hyoka ni Kansuru Shishin Kento Iinkai (1998).
There are many variations in the unit social costs per noise level.

These results are

obtained with two different approaches: the hedonic approach and the CVM (Contigent Valuation
Method).

The hedonic approach, as in Yashima and Kanemoto (1992) and Hidano et al. (1996),

obtains unit social costs by analyzing the relationship between noise level and land prices.

The

CVM approach, as in Kashima and Imanaga (2004) and Matsui et al. (2005), estimates the unit
social costs of noise by analyzing the willingness to pay for protection against noise.

We use 5,000

yen /dB, obtained from the result of Koyama’s (2004) result, considered moderate because it isobtained from several previous studies.

Global Warming
The social costs of global warming are calculated in two steps, the first being to estimate
annual emission of CO2 from vehicular transport.

In the second step, the monetary values of thesocial costs of CO2 emissions are calculated. Equation-(14) and (15) show the formulae of these
two steps.

Cwar,a  Pwar  EMICO 2,a  TranCO 2

(14)

b
b
EMICO2,a   ERCO2,a,i
 Qa,i

(15)

b

where

i

Cwar,a：Social cost of global warming caused by vehicular transport in city-a,
Pwar：Unit social cost of emission of CO2（yen/t- CO2）,8

For example, Koyama (2004) considers the attenuation effects of noise due to types of buildings.
However, because we lack this information, the equation in this study is an approximation of noise level.

14EMIco2,a：Annual emission of CO2 in city-a (g- CO2/year),TransCO2：Transformation coefficient for ton unit of emission of CO2,
ERCO2,a,b,i：Coefficient for emission of CO2 by vehicular transport type-b on road type-i incity-a(g-CO2/km),

Qba,i: Traffic volume of vehicular transport type-b on road type-i in city-a (vehicle-km),
b：Vehicular transport type（b=1：passenger car,2：bus, 3：small truck,

4：truck）,

i：Road type (i=1: highway, 2: ordinary road)

Oshiro et al. (2001) devised a formula for CO2 emission according to vehicle type, and we
employ their results in our own estimation.9
Last, we use previous studies to fix the unit social cost of CO2 emission at 14,000 yen/ tCO2.

The value of unit social costs for this item varies among previous studies, the lowest being

about 1,296 yen in Mayeres et al. (1996) and the highest about 274,349 yen in Koyama andKishimoto (2001).

Other studies such as Watkiss (2005) show that unit social costs are about 35 to

140 pounds in the case of the UK, which had a value of about 14,000 yen in 2005.

We adopt here

moderate values similar to those of INFRAS/IWW (2004).

Traffic Congestion
The social costs of traffic congestion are estimated in three steps.
due to traffic congestion is estimated.

Time loss is specified as a function of traffic volume androad length and speed, as shown in equation-(18).
congestion is calculated.

First, daily time loss

Second, annual time loss due to traffic

As equation-(17) shows, congestion conditions on weekdays andweekends are different, so we obtain the time loss separately.

Last, the monetary value of traffic

congestion is obtained, as equation-(16) shows.
b
C con , a   P con
 TL ba(16)b

TLba  tlab, k  weekday  d w  tlab, k  weekend  d h

(17)

9

The results of Oshiro et al. (2001) are as follows:
ERCO2,a,b,i= /Va,i –  Va,i +  V2a,i + 
Where ERCO2,a,b,i : coefficient for emission of CO2 by vehicular transport type-b on road type-i in city-a ,(g-CO2/km),
Va,i: average speed (km/h) on road type-i
Oshiro et al. (2001) estimate individually the parameters, , ,,  for cars, buses, small trucks, andtrucks.

15

DIS
DISa,i  bbtla,k  a,i  Qa,i,k
Va,i
Vi* 
i 
where

(18)

Ccon,a：Social cost of congestion in city-a,b

P con：Unit social cost of time loss caused by congestion in vehicle type-b in city-a(yen / minute-vehicle),
TLba：Annual time loss caused by congestion in vehicle type-b in city-a(minute-vehicle/year),tlba,k：Daily time loss caused by congestion of day type-k in vehicle type-b in city-a(minute-vehicle/day),dk：Number of weekdays and weekends per year, k = w(weekday), h(weekend)（dw＝246 days, dh＝119 days）,
DISa,i：Road length of road type-i in city-a (km),Va,I : Average speed on road type-i in city-a (km/h),
Vi*：Legally permitted speed on road type-i (km/h),
Qba,i,k：Traffic volume of vehicle type-k on road type-i in city-a (vehicle/day)b：Vehicular transport type（b=1：passenger car,2：bus, 3：small truck,

4：truck）

i：Road type (i = 1: highway, 2: ordinary road),
k：Weekday and weekend (k = w: weekday, h: weekend)

Time loss caused by congestion is calculated based on the value of time (VOT).

Previous

studies, such as INFRAS/IWW (2004), evaluate the VOT for each vehicle type, as VOT varies
according to kinds of people, and vehicle types could serve as proxy variables for kinds of people.
Furthermore, Mayeres et al. (1996) distinguish the VOTs for the peak and off-peak periods.
Unfortunately, however, data on the VOT for peak and off-peak periods is not available to us.

For

the unit social costs of traffic congestion, we use results from the Ministry of Land, Infrastructure
and Transport (2003): 62.86 for cars, 519.74 for buses, 56.81 for small trucks, and 87.44 for trucks(yen/minute per vehicle).

4.

Empirical Analysis of Social Costs

4.1 Sample Selection of Cities and Major Assumptions
Because metropolitan areas have not been officially defined in Japan, we base our
calculations of the social costs of vehicular transport and related variables on city information.
total sample size for this analysis is 111 cities for the year 2005.

16

The

Because the social costs ofvehicular transport are highly affected not only by urban structure, infrastructure conditions, andeconomic environment, but also by geographical and climate conditions, samples are selected withattention to regional balance and variation in city size.
It is important to consider the following points when calculating the social costs of vehiculartransport.

As the equations shown in the previous section indicate, the social costs of vehiculartransport are calculated according to their individual five subcategories.

Second, social costs arecalculated separately according to type: passenger cars, buses, small trucks, and trucks. Last,
traffic volumes and speed of vehicular transport vary among cities.

Therefore, individual social

costs vary among cities.
The data are based on the year 2005, because census data and main traffic data such as
cities’ speed and traffic volumes are available for that year.

However, data regarding the unit costsof social costs are drawn from many sources at different times.

Our unit costs for each item arebased on the following sources: the Cabinet Office (2007) for accidents, the European Commission(2005) and the Cabinet Office (2007) for air pollution, Koyama (2004) for noise, Watkiss (2005) for
global warming and the Ministry of Land, Infrastructure and Transport for traffic congestion.

The

unit costs used are summarized in Table 2.

********************
Table 2********************

4.2 Estimation Results of Social Costs
This section describes the structure of the estimated social costs of vehicular transport
regarding the following points: (i) the relationship between social costs and city size, (ii) the
relationship between unit social costs and population density, (iii) the structure of social costs, and(iv) the magnitude of social costs.
First, Figure 1 shows the relationship between the social costs of vehicular transport andthe city size measured as population. The most important finding is the fact that social costs
increase at an accelerated rate as the city size increases, indicating that a city with a larger population
produces larger unit social costs.

As the fitted line by the quadratic curve shows, the coefficient of

the term of the square of population shows positive with statistical significance.
terms of the social costs of transportation, a smaller size city is desirable.

17

Therefore, in********************
Figure 1********************

Second, however, there are variations in the details of social costs. For example, Figure
2 shows that the relationship between population density and unit social costs per traffic is not linear.
As the fitted line by the quadratic curve shows, the coefficient of the term of the square of
population density shows a negative sign.

Therefore, the unit social costs per traffic increase but

the rate of increase decreases as population density increases, suggesting that public transportationand infrastructure conditions might affect social costs, a city with higher population density having
in general more convenient public transportation and perhaps less car usage.

********************
Figure 2********************

Third, the structure of social costs in a city is shown in Table 3. We divided city size
into four categories and discovered the following facts.

The largest component of vehiculartransport’s social costs is traffic congestion, accounting for more than 45%, with social costs oftraffic congestion comprising a larger percentage in large cities.

In cities with populations of morethan 1 million, traffic congestion’s social costs comprise more than 62%.
component is air pollution, accounting for 18 to 21%.

The second large

Global warming is the fourth largest

component after accidents, accounting for around 5 to 11%.

********************
Table 3********************

4.3 Regression Analysis of the Effect of Urban Structure on Social Costs
Empirical Models
In this section, we will analyze the effect of urban structure on the social cost oftransportation.

No previous studies directly examine this point but there are related studies.

18

For

example, Stewart and Bennett (1975) investigate the relationship between urban structure andgasoline consumption as a proxy variable of transportation demand.

Schimek (1996) studies how

characteristics of urban structure such as population density affect car dependency.

Newman and

Kenworthy (1989) and Mindahi et al. (2004) investigate how urban structure and economic
conditions affect car dependency, which is associated with gasoline consumption.
on the social costs of, rather than the demand for, vehicular transport.
interested in urban size.

Here we focus

Furthermore, we are more

If the social costs of vehicular transport increase more sharply than theincreasing rate of urban size, then from a city planner’s point of view, a large city is undesirable.
However, as city size increases, more public transportation is provided, likely suppressing the use of
cars.

There is the large city’s merit of agglomeration economies, diminishing car dependency perperson.

Thus, economic activities, infrastructure, and transportation conditions should all beconsidered when estimating costs.
Based on this argument, we specify the regression model to explain the social costs ofvehicular transport, as equation-(19) shows.

Urban structure is summarized in four components:

city size, infrastructure conditions, economic activity level, and condition of public transportation.

lnSC =  + POP lnPOP + RDlnRD + MPlnMP + BDlnBD + SDlnSD
where

(19)

SC: social costs of vehicular transport in a city,
POP: city population (+),
RD: road density (+),
MP: total sales of products per person (-/+),
BD: bus network density (-),
SD: railway station density (-).

City size is explained as city population (POP).

Road density in a city (RD) is included

as affecting traffic congestion, and is measured by total road length per city area.

Included in the

category of economic conditions is the item of total sales of products per person in a city (MP).
Regarding public transportation conditions, both bus network density (BD) and railway station
density (SD) are used. Bus network density is obtained by dividing total bus route length by cityarea.

Railway station density is also obtained by dividing the number of stations by city area.

We

acknowledge the shortcoming that this study does not include public transportation’s service level,such as the number of trains and buses, but unfortunately data for this variable are unavailable to us
on a city basis.

19

As equation-(19) shows, the regression formula is specified as the log-linear function.
Therefore, the coefficient of each explanatory variable shows elasticity to social costs.

The

expected signs of these variables are positive for city population and road density but negative forbus density and railway station density. The sign of the coefficient of the total sales of products perperson is an empirical question, but we expect it to be negative because the larger a city is, the more
likely it is to have a strong city center less dependent on vehicular transport than a smaller city islikely to have.

Definition of Variables
The basic data are collected on a city basis for the year 2005, with total observations of
111 cities.

Statistics of variables used for the regression analysis are summarized in Table 4.

Variables are defined as follows.

First, the social costs of vehicular transport (SC) are the sum of

five external costs produced by vehicular transport: traffic accidents, air pollution, noise, global
warming and traffic congestion.

********************
Table 4********************We define explanatory variables as follows.
population registered in a city.
city by total city area.

City population (POP) is the total

Road density (RD) is obtained by dividing total road lengths of a

The total sales of products per person (MP) are obtained by dividing thetotal sales of products in a city by the total population.

Numbers to obtain the variables (i.e. city

population, road length, city area, total sales of products) are collected from Statistical Observationsof Municipalities (Tokei de Miru Shichouson no Sugata), issued by the Ministry of Internal Affairs
and Communications.

Bus network density (BD) is obtained by dividing the total route-km of thebus network by the city area, which is taken from the Census of Road Transportation (Doro Kotsu
Sensasu) issued by the Ministry of Land, Infrastructure and Transport.

Last, railway station density(SD) is defined by dividing the number of stations by the city area, the main source for which
information is the Annual Report of Regional Transportation (Chiiki Kotsu Nenpo) issued by the
Ministry of Land, Infrastructure and Transport for major cities, and individual city maps for smaller
cities.

20

Results
We apply regressions to equation-(19). Because we use a cross-section data set, themain estimation method is the OLS (Ordinary Least Square) method.
that the error term has a common variance.

The OLS method assumes

However, in order to avoid the heteroskedasticity

problem, which is that the error term is a non-constant variance, we also estimate the regressions byusing the OLS with the HCSE (Heteroskedasticity-Consistent Standard Error) and the ML(Maximum Likelihood) methods.

Table 5 shows the estimation results, which indicate that there is

not much difference among methods.

Furthermore, the coefficients of the explanatory variables

show a reasonable sign, with the goodness to fit of regression being reasonably high.

********************
Table 5********************

These results produced interesting findings.

First, the social costs of vehicular transport

increase at a higher rate as city population increases.

The coefficient of city population (POP) is

1.136, indicating that social costs increase by 11.36% when the population increases by 10%.
Therefore, a smaller city is better in terms of social costs.
Second, the construction of roads does not work to decrease the social costs of vehiculartransport.

As road density (RD) increases, social costs tend to increase.

Presumably, theconstruction of roads increases car usage by increasing its perceived convenience, paradoxicallyexacerbating traffic congestion.
Third, public transportation has a tendency to decrease social costs, although the effectsof the decrease are minimal because the coefficients of both bus network density (BD) and railway
station density (SD) are small, -0.132 and -0.027 respectively.

These results suggest that we should

not rely too heavily on public transportation to reduce the social costs of vehicular transportation.

4.4 Magnitude of the Social Costs of Vehicular TransportComparison with GDP
Table 6 is a comparison of the magnitude of vehicular transport’s social costs with theGDP.

For better understanding, we include results from previous studies.

21

********************
Table 6********************
It can be seen that the social costs of vehicular transport represent about 8.0% of GDP.
This result is very similar to that of INFRAS/IWW (2004).

In the case of ECMT (1998), the ratio

is smaller than in our estimation because it does not include the social costs of congestion.
Koyama and Kishimoto’s (2001) estimation results are from a whole country’s data, and our results
are close to their higher values.

Based on a data set from Germany, only UNITE’s (2003) results

are rather low and may reflect less harsh, more smooth traffic conditions, causing lower externalcosts.
Our conclusion is that social costs due to vehicular transport have a magnitude of about
8% of GDP.

These costs are certainly high enough for the government to consider ways of

reducing them.

One idea might be to impose more fuel taxes.

Comparisons regarding a Fuel Tax for Vehicle Users
Finally, we evaluate to what extent a fuel tax on vehicular transport can cover social costs.
We classify the social costs of vehicular transportation, obtained in section 4.2, into the four types ofvehicles with which they are associated. The results of social costs classified into vehicle type canbe shown in Table 7.

These monetary values are for the year 2005.

This table shows that trucks’

social costs per vehicle-km are 4 times higher than those of regular cars.

********************
Table 7********************
With these findings in mind, we evaluate fuel tax coverage.

Kanemoto (2007) shows

the fuel tax level by vehicle type, with the total fuel tax for regular cars 58.9 yen per liter forgasoline, and 36.1 yen per liter for diesel oil.

Based on Kanemoto’s (2007) information, the fueltax for regular cars covers only 16.3% (=58.9/361.5) of social costs and for trucks covers only 7.5%(=36.1/481.1).10

These results show that the fuel tax coverage ratio to social costs is very small, at

10

Kanemoto’s (2007) study shows that the social costs are about 124.0 yen per liter for cars, smaller thanour estimated social costs. Consequently, his result shows that the fuel tax covers about 47.5%(=58.9/124.0).

22

most about 16%.

Furthermore, the fuel tax coverage ratio to social costs of heavy vehicles such astrucks is much smaller than for regular cars.

From an environmental point of view, therefore,

keeping fuel taxes on heavy vehicles at the current level does not make sense.

5. Conclusion
The main purpose of this paper was to estimate the social costs of vehicular transport,
and to analyze the structure of the components of social costs and the relationship between social
costs and city size.

The main characteristics of our study were as follows.

First, it investigated

the social costs of vehicular transport with a city data set including such items as traffic volume, theflow speed of cars, and so on.

Second, this study considered five kinds of social costs of vehiculartransport: traffic accidents, air pollution, noise, global warming and traffic congestion.
studies estimating these five social costs were few.

Previous

Third, our study investigated the relationshipbetween the social costs of vehicular transport and city size, attempting to determine whether bigger
city size entails higher social costs than smaller city size. Our study was probably the first such
empirical investigation.

Last, with regression analysis, we evaluated the effects of an urban

structure’s infrastructure on the social costs of vehicular transport.
The most important findings are as follows.

First, social costs increased at anaccelerated rate as city size increased. Thus a city with a larger population produced larger unit
social costs.

Therefore, in terms of transportation’s social costs, a smaller size city is desirable.

On the other hand, when we checked the relationship between population density in a city and theunit social costs per traffic, the relationship was not linear.
Second, from the regression results, it was proved that the social costs of vehiculartransport increased at an accelerated pace as city size increased.

Furthermore, the construction ofroads did not work to decrease the social costs of vehicular transport.

However, public

transportation did have a tendency to decrease the social costs of vehicular transport, even though
the effects of the decrease were minimal.
Third, the largest component of social costs was traffic congestion, at more than 45% of
the total.
cities.

Furthermore, social costs due to traffic congestion reached a larger percentage in large

The second large component was air pollution, accounting for 18 to 21% of total social costs.

Although global warming has been recently viewed with great concern, global warming caused bytransportation accounted for only 5 to 11% of total social costs.
Last, the magnitude of the social costs of vehicular transport seemed large, at about 8.0%

23

of GDP.

Furthermore, the fuel tax for vehicular transport in Japan covered only 16.3% of the social

costs of regular car use. The ratio for heavy vehicles such as trucks was smaller, so that from anenvironmental point of view, the current fuel tax on heavy vehicles is too low.
Our results suggest that the best cities are compact cities, and that policy makers should
work toward the goal of creating them.

[2010.11.18 1011]

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26

（billion yen）
2,000
1,800
1,600
1,400
1,200
SC 1,000
800
600
400
200
0
0

500,000

1,000,000

1,500,000

2,000,000
Pop(Note): The fitted line is as follows:
SC = 908.469 + 0.0357(Pop) + 0.155x10-8 (Pop)2(9.72)(11.79)(26.13)
R2: Coefficient of determination
Numbers in the parentheses are t-statistics

Figure 12,500,000

3,000,000

R2=0.939

Relationship Between Social Costs and City Size

27

3,500,000

4,000,000(person)（yen/v-ｋｍ）
160.0

140.0

120.0

100.0

SC per Tr 80.0

60.0

40.0

20.0

0.0
0

2000

4000

6000

8000

10000

12000

Pop Dens(Note):(1)“PopDens” refers to population density in a city. “SC per Tr” refers to social costs per traffic.
(2) Unit: population density (person/km2), social costs per traffic (yen/vehicle-km)(3) The fitted line is as follows:
SC per Tr = 17.954 + 0.0134(Pop Dens) - 0.386x10-6 (Pop Dens)2 R2=0.876(7.94)(10.06)(-2.92)
R2: Coefficient of determination
Numbers in the parentheses are t-statistics

Figure 2 Relationship Between Unit Social Costs and Population Density

28

14000
（person/km2)

Table 1

Study
Small
and
Kazimi (1995)

Place(Year)
Los Angeles(1992)

Mayeres et al.
(1996)

Brussels(1991)

Eyre et(1997)al.

UK (1995)

Levinson et al.
(1998)ECMT(1998)

USA (1995)

Danielis
andChiabai(1998)

Forkenbrock(1999)WHO (1999)

McCubbin and
Delucchi(1999)

15
EU
countries,
Switzerland,
Norway(1991)
Italy (1992)

USA(1994)
Austria,
France,Switzerland(1995)
Los Angeles(1991)

Koyamaand
Kishimoto(2001)

Japan (1999)

Beutheetal(2002)
Gibbons
andO’Mathony(2002)UNITE(2003)

Belgium(1995)
Dublin(2005)
Germany(1998)

Summary of Social Costs of Vehicular Transport

Mode

Unit

Accident

Congestion-

GlobalWarming-

Gasoline Car
Dieseltruck(heavy-duty)
Small gasoline
car (peak)
Small diesel car(peak)
Small gasoline
car (off-peak)Bus (peak)Bus (off-peak)
Truck (peak)Truck(off-peak)Petrol (urban)Gas(urban)
Diesel(urban)
Petrol (rural)Gas(rural)
Diesel(rural)
Highways

Cents/vehicle-mileCents/vehicle-mile

0.036

0.002

-

0.269

0.079

0.056

0.002

-

0.269

ECU/vehicle-km

0.110

0.035

0.007

-

0.002

ECU/vehicle-km
ECU/vehicle-km
ECU/vehicle-km
ECU/vehicle-km

0.699
0.896
0.216
0.300

0.406
0.345
0.788
0.294

0.019
0.073
0.019
0.073

-

0.537
0.004
0.537
0.004

pence/km
pence/km
pence/km
pence/km
pence/km
pence/km
dollar/vehicle-km

0.03

1.060
0.375
2.717
0.500
0.211
0.723
0.00560.0068-0.0069Cars

ECU/1000
vehicle-km

60

13

5

10

-

Petrol vehicle
Diesel vehicle(Light-duty)
Diesel vehicle(Heavy-duty)truck

cents/vehicle-km
cents/vehicle-km

-

1.5
27.0

-

-

-

cents/vehicle-km

-

170.9

-

-

-

Cents/ton-mile

0.59

0.08

0.04

0.15

-

Road transport
for
whole
country

Million Euro-

49,715---Gasoline(Light-duty)
Diesel(Light-duty)
Diesel(Heavy-duty)
Car
Bus
Truck
Small Truck
Road(Truck)Cents/vehicle-mile(high)
Cents/vehicle-mile(high)
Cents/vehicle-mile(high)
Yen/vehicle-km
Yen/vehicle-km
Yen/vehicle-km
Yen/vehicle-km
ECU/ton-km

-

23.2

-

-

-

-

64.4

-

-

-

-

324.0

-

-

-

7.1
7.4
7.9
4.9
0.00937

1.8
69.2
59.1
13.8
0.01820

3.6
35.6
35.6
3.6
0.00665

2.2
9.4
7.8
3.1

7.3
14.6
14.6
7.3
0.02108

Car
petrol)(small,EURO/
passenger-km

0.5
0.09

(peak)(off-peak)

Private Vehicles

EURO/1000
vehicle-km
EURO/1000
vehicle-km
EURO/1000
vehicle-km
EURO/1000
vehicle-km

Bus/coach
LGV
HGV

Noise

-

Air
Pollution
3.28
52.70

ECU/vehicle-km

0.079

ECU/vehicle-km29-

129.51

7.70

4.6

4.91

44

111.05

96.40

40.8

18.01

132

28.18

16.10

53.1

7.46

67

22.82

71.80

35.0

19.60

133

INFRAS/IWW(2004)

17
EU
countries(2000)

Car

Car
Bus
Truck
Small Truck
Road transport

EURO/1000
passenger-km
EURO/1000
passenger-km
EURO/1000
passenger-km
EURO/1000
ton-kmEURO/1000
ton-km
Yen/vehicle-km
Yen/vehicle-km
Yen/vehicle-km
Yen/vehicle-km
Million USD

Public transport
Private car

NZ$/ vehicle-km
NZ$/ vehicle-km

Bus
Motor cycle
LDV
HDV

Koyama (2004)

Japan (1999)

Deng (2006)

Beijing(2000)
Auckland(2001)

Jacobs
(2006)

et

al.

30.9

10.1

5.2

17.6

-

2.4

16.9

1.3

8.3

-

188.6

3.3

16.0

11.7

-

35.1

77.6

32.4

57.4

-

4.75

34.0

4.9

12.8

-

-

974

1.10
2.95
3.46
1.34
-

-

-

0.73
0.062

(Note):(1) Numbers for WHO (1999) are averages for three countries, Austria, France and Switzerland.
(2) Gibbons and O’Mathony (2002) do not distinguish each individual social cost. In this table, thetotal social costs of all items are shown.
(3) UNITE (2003) estimated the social costs of 18 countries for 1994, 1995, and 2005. However,
we show results only for Germany in 1998 because the results are described in detail in
UNITE(2003).
(4) Jacobs et al. (2006) do not distinguish each individual social cost. In this table, the total social
costs of all items are shown.

30

Table 2 Values of Unit Costs and Parameters Used for the Estimation of Social Costs
Kinds ofSocial Costs
Traffic
Accident
Air Pollution

Symbol Sub-item
Pacc,ePair,gUnit cost

Death
Seriously injured
Lightly injured
Mortality
Hospital admissions
General Practitioner visits:
AsthmaGeneral Practitioner visits:
Respiratory symptoms
Respiratory symptoms inasthmatics: Adults
Respiratory symptoms inasthmatics: Children
Respiratory medication
use-adults and children
Cough day
Symptom day
Chronic bronchitis

Noise
GlobalWarmingTraffic
Congestion

Unit (thousand yen)
232,742
86,184860
142,064270.08per person
per person
per person
per person
per admission

7.16 per consultation
10.56 per consultation
17.56 per event
37.81 per event
0.14 per day
5.13 per day
5.13 per day
25,657.96 per case

PdB-

4.935 per dB per m2

Pwar

-

12.962 per ton-CO2

Pbcon

Passenger car
Bus
Small truck
Truck

0.061420.50785
0.055510.08544(Note): These numbers are all year 2005 values.

31

per minute-vehicle
per minute-vehicle
per minute-vehicle
per minute-vehicle

Table 3 Estimation Results of Social Costs of Vehicular Transport

City Size(Population)
More than
1 million
1 million to
500 thousand
500 to 300
thousandLess than
300 thousand

Total

Accident

750.4(100.0%)226.8(100.0%)119.2(100.0%)54.8(100.0%)94.9(12.7%)
45.5(20.1%)
23.7(19.9%)
11.7(21.2%)

Air
pollution
133.9(17.8%)
46.9(20.7%)
24.6(20.6%)
11.2(20.4%)

Noise
17.0(2.3%)
5.2(2.3%)
2.7(2.3%)
1.2(2.2%)

Globalwarming
40.8(5.4%)
16.6(7.3%)
9.6(8.0%)
5.8(10.6%)

Trafficcongestion
463.8(61.8%)
112.2(49.6%)
58.7(49.2%)
25.0(45.6%)(Note):(1) Unit: billion yen(2) These numbers are sample means of all observations (cities) in each category.

Table 4 Statistics of Variables Used for Regressions
Variables

Unit

Mean

SC
(social costs of vehicular transport)
POP(city population)
RD(road density)MP(total sales of products per person)

billionyen
personBD(bus network density)SD(railway station density)

Maximum

Minimum

167.8

Standard
deviation
250.1

1,615.8

19.3

482,329

518,375

3,579,628

103,652

km/km213.082

3.649

21.0984.852

millionyen per
personkm/km2

2.236

2.991

26.782

0.093

0.954

0.323

2.142

0.365

stations
per km2

0.137

0.136

0.851

0.000

32

Table 5 Estimation Results of Regressions
Variables
Estimation method(constant)Case-1
OLS-8.823***(0.400)1.136***(0.030)0.467***(0.074)-0.018(0.023)-0.132**(0.063)-0.027**(0.013)
0.951
-

POP

(city population)

RD(road density)

MP(total sales of products per person)

BD(bus network density)

SD(railway station density)
adjR2
Log likelihood

Case-2
OLS with HCSE
-8.828***(0.395)1.136***(0.028)0.467***(0.061)-0.018*(0.022)-0.132**(0.062)-0.027(0.018)
0.951-

Case-3ML-8.718***(0.402)
1.128***(0.031)0.467***(0.070)-0.015(0.022)-0.124**(0.060)-0.026**(0.012)23.986(Note):(1) Numbers in parentheses are standard error.
(2) Symbols are statistically significant at 1% (***), 5% (**) and 10% (*).

Table 6 The Percentage of Social Costs to GDP

Study
ECMT
(1998)
Koyama andKishimoto (2001)
UNITE (2003)
INFRAS/IWW(2004)
This study(2009)

Total

AccidentAir
pollution

Noise

GlobalwarmingTrafficcondition

3.9%

2.5%

0.6%

0.3%

0.5%

-

5.6 – 11.3%

1.0%

1.7 – 2.4%

1.2 – 1.7%

3.7%

2.5%

1.9%

0.1%

0.4%

0.3%

0.2%

0.9%

7.3%

-

-

-

-

-

8.0%

1.4%

1.6%

0.2%

0.6%

4.4%

(Note):(1) The sample in this study is based on city data, and GDP information is not available for cities.
Taxable income for whole country is about one third of GDP of whole country in Japan. Data are
available regarding taxable income for each city. Therefore, we estimate GDP from the taxable
income by multiplying by three.
Table 7 The Social Costs and Vehicle Type

Items
Social
costs
per
liter
(yen/liter)
Social costs per vehicle-km(yen/vehicle-km)

Car
361.5

Bus
1,111.4

Small truck
358.7

Truck
481.1

38.5

362.0

43.6

131.1

33