Department of Policy and Planning Sciences
Discussion Paper Series

No.1370

Benefits of Heavy-Duty Diesel Emission Regulations:
Evidence from the World’s Largest Low Emission Zone
by
Cheolmin KANG, Mitsuru OTA, AND Koichi USHIJIMA

September 2020

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPANBenefits of Heavy-Duty Diesel Emission Regulations:
Evidence from the World’s Largest Low Emission Zone
CHEOLMIN KANG1, MITSURU OTA2, AND KOICHI USHIJIMA3
1

Faculty of Political Science and Economics, Waseda University, 1-104

Totsukamachi, Shinjuku, Tokyo, 169-8050, JAPAN (kang@aoni.waseda.jp)
2

Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1

Tennodai, Tsukuba, Ibaraki 305-8573, Japan (ota@sk.tsukuba.ac.jp)
3Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1

Tennodai, Tsukuba, Ibaraki 305-8573, Japan (ushijima@sk.tsukuba.ac.jp).
Ushijima especially thanks Yoshifumi Konishi. We also thank Ryo Kanbayashi,
Kentaro Nakajima, Shin-ichiro Okushima, Ryuichi Tanaka, Morito Tsutsumi, andthe seminar participants at Kyoto University, Nagoya University, and GRIPS for
helpful comments. This study was supported by the Joint Research Program No.
881 at CSIS, University of Tokyo for the use of data. The study was supported by
JSPS KAKENHI (Grant numbers 16K17108 and 20K01602) and by the Murata
Science Foundation.

1

We examined the impacts of heavy-duty diesel emission regulations
on air quality, land prices, and infant health in the Tokyo
metropolitan area. The estimation results show that the greater the
traffic volume of heavy-duty diesel vehicles before the introduction of
the low emission zone, the more all the focal outcomes were improved
even after controlling for the traffic volume of non-regulated gasoline
vehicles. By contrast, the concentrations of non-regulated air
pollutants remained unaffected. Calculations based on the hedonic
approach show that the benefit of air quality improvement in themetropolitan area is about 14 times the cost. (JEL I12, J13, L62, Q52,
Q53, Q58, R23)

Diesel engines have excellent thermal efficiency, durability, and torque (output= torque × engine speed). They are used in trucks and buses, called heavy-duty
diesel vehicles, which are the primary means of land transportation worldwide.1
Diesel vehicle emissions, however, are notorious as a cause of air pollution, because
they contain high levels of particulate matter (PM), including suspended particulatematter (SPM), and nitrogen oxides (NOx).2 These air pollutants cause long-termand short-term health problems.3 Anenberg et al. (2017) suggest that about 174,000
premature deaths could be avoided in the 20 years to 2040 by global elimination of1

In Europe, diesel engines are also used in passenger cars. In the United States and Japan, the majority of passenger cars aregasoline powered.
2
PM is a generic term for grains of various types, properties, and sizes; those that are suspended in the atmosphere are called
SPMs; NOx is a precursor to PM and ozone. Diesel cars produce seven times as many secondary organic aerosols as passenger
cars do (Gentner et al. 2012). Secondary organic aerosols are the main contents of PM.
3
The survey papers are Currie et al. (2014) and Parry, Walls, and Harrington (2007). There are many other studies, such as
Anderson (2019), Arceo, Hanna, and Oliva (2015), Chay and Greenstone (2003), Currie et al. (2009, 2015), Currie,Greenstone, and Meckel (2017), Currie and Neidell (2005), Currie, Neidell, and Schmieder (2009), Currie and Walker (2011),
Gehrsitz (2017), Graff Zivin and Neidell (2018), Greenstone and Hanna (2014), Hanna and Oliva (2015), Isen, Rossin-Slater,and Walker (2017), Jans, Johansson, and Nilsson (2018), Knittel, Miller, and Sanders (2016), Marcus (2017), De la Mataand Garcés (2019), Moretti and Neidell (2011), Neidell (2011), Schlenker and Walker (2016).

2

emissions of diesel-related air pollutants. Indeed, many air pollutants are emitted
by diesel vehicles, with NOx, for example, accounting for 20% of anthropogenic
emissions (Anenberg et al. 2017). Not surprisingly, air pollution from diesel vehicle
emissions is more severe in urban areas with high traffic volumes. 4
Over 250 European cities are working to mitigate health risks from emissions bycreating low emission zones (LEZs) where the passage of some polluting vehicles
is restricted. When implementing traffic regulations, such as LEZ, it is necessary toknow the optimal balance between the positive effect of air quality improvement
due to reduced emissions and the negative impact of curbing economic activities
through logistics. However, as far as we know, there is no evidence to avoid
problems caused by this tradeoff, such as how the intensity of emission regulations
relates to traffic volume or how people feel about the benefits of emission
regulations. Whether LEZs are effective in improving air quality is still an
important research question. Few credible analytical studies have shown that LEZs
in Europe reduce NOx emissions. Many previous studies on this issue conclude thatstronger regulations are needed to reduce NOx (Ellison, Greaves, and Hensher
2013; Santos, Gómez-Losada, and Pires 2019).
Air pollution from vehicle emissions is a significant social problem in the UnitedStates as well. According to the National Emissions Inventory, approximately 32%
of NOx and 29% of carbon monoxide (CO) emissions in the United States in 2018
were from vehicles traveling on highways. These air pollutants cause severe
damage to health. Currie and Walker (2011) find that the introduction of the EZPass at highway tollgates reduced emissions from traffic jams and reduced lowweight births by more than 10% in nearby areas compared to other areas. Knittel,
Miller, and Sanders (2016) determine the health effects of heavy traffic on infants
and young children using data from California between 2002 and 2007.

4

The Case of New York: http://a816-dohbesp.nyc.gov/IndicatorPublic/traffic/index.html (last accessed on Sep. 15, 2020).

3

This study exploits the characteristics of the world’s largest LEZ, the Tokyo LEZ(hereafter, TLEZ), to examine whether the air quality of nearby locations with moreheavy-duty diesel vehicle traffic improves more than those with less heavy-duty
diesel vehicle traffic because of the LEZ setting, and the extent to which residents
evaluate the economic value of the air quality improvement. Moreover, we examine
the impact of emission regulations on infant mortality and low birth weight (LBW)
as other outcomes. Panel data for approximately 20 years are used in the analysisof all outcomes. The world’s largest LEZ is based on two emission regulations thattook effect in the early 2000s in Tokyo and surrounding areas in Japan. This study
uses detailed spatiotemporal data on road traffic, air quality, land prices, and infant
health to measure the effects of the TLEZ. The structure of the dataset enables astrong identification strategy. This study focuses on SPM, NOx, and NO2 as air
pollutants of analysis.
To identify the impact of the LEZ, we exploit the fact that the TLEZ covers a vast
area. London’s LEZ is a famous LEZ covering 1,530 km2. The TLEZ has acoverage area of 13,562 km2, about nine times larger. Because the TLEZ is so vast,
there are two features useful for identification. First, the TLEZ also covers areas
with low traffic volume of heavy-duty diesel vehicles, and the traffic volume isvaried within a narrow range. Second, the magnitude relationship between traffic
volume before the introduction of the TLEZ at any two different points is not likely
to change after the introduction. Because the TLEZ covers the inner-city ring road,the detour cost is high. Owners of heavy-duty diesel vehicles would choose either
to cease operations or to pay the cost of meeting the TLEZ standards. Hence, heavyduty diesel vehicles would rarely change their traffic routes. As shown in
Subsection II.A, there is no evidence that traffic changed after the introduction of
the TLEZ. From these two features, we can exploit the traffic volume of heavyduty diesel traffic before the introduction of the TLEZ as the intensity of the impactof the TLEZ.

4

To identify the causal impact of the TLEZ, we use traffic volume data surveyed
on a road-by-road basis. The land price panel data were surveyed at over 7,500
locations, and the air quality panel data were surveyed at over 250 locations.
According to simple calculations, air quality was surveyed to cover an average
range of 54.2 km2 (= 135,262/250, which is equivalent to a circle with a radius of
4.2 km). In practice, atmospheric stations are located more densely in the areas
analyzed in this study. This rare spatially dense panel survey data allows us tocontribute to the literature based on our strong identification strategies. The
identification strategy of this study could be applied not only to the evaluation of
traffic policy but also to the evaluation of spatially affected policy.
However, there are concerns that the relationship between traffic volume and air
quality or land prices may have confounding factors that change over time due todifferences in the spatial and socioeconomic characteristics of the region and thepreferences of residents. To control for regional time-variant factors, we include in
the analysis interaction terms between non-TLEZ-regulated vehicle traffic and year
fixed effects, and interaction terms between over 300 municipal fixed effects andyear fixed effects. This controlling allows us to avoid, for example, time-variant
factors that are correlated with the structure of the local road infrastructure andmunicipality-specific policy effects.
This study makes two novel contributions in the context of the evaluation ofvehicle emission regulations. First, this study investigates whether diesel emission
regulations worked, and if yes, how well. Many traffic regulations around the world
aim at reducing air pollution in urban areas from car exhaust, but these regulations
often do not work (e.g., Auffhammer and Kellogg 2011; Chen et al. 2013; Davis
2008; Gallego, Montero, and Salas 2013; Zhang, Lin Lawell, and Umansakaya2017). Although many cases of urban traffic regulation with improved air quality
have been identified in relation to LEZs, there is little evidence that LEZs have
improved residents’ health (Mudway et al. 2019). The most reliable previous study5

of LEZs is Wolff (2014), which shows that German LEZs reduce PM10, and theepidemiological benefits are two to five times the cost. However, Wolff (2014) does
not investigate NOx emissions. Like many LEZs, the German LEZ might not have
reduced NOx because German LEZs have not improved infant health (Gehrsitz2017). This study uses a rich and detailed dataset to investigate comprehensive
evidence of the effects of LEZs.
Second, we calculate the economic benefits of improving urban air quality byidentifying the impact of the TLEZ on land prices. Estimating the economic valueof air quality has been recognized as an important research issue since the 1970s,
but Chay and Greenstone (2005) are the first to obtain reliable evidence due to thedifficulty of excluding the effects of unobserved variables. Several studies basedon statistical causal inference have been published since Chay and Greenstone(2005). However, few studies have examined the value of air quality in urban areas.
Clarifying the optimal regulatory levels in urban areas is an important issue because
the economic benefits of improved air quality may be greater in densely populatedareas. This study identifies the effect of air quality on urban land prices by using
difference-in-differences methods based on the structure of the TLEZ.
Our estimation results are as follows. First, although the level of regulation of the
TLEZ is similar to that of the European LEZ, the concentration of air pollutants
subject to regulation has improved in Tokyo. SPM and NOx concentrations
improved by an average of 7.17% and 9.08%, respectively. The concentration of
non-regulated air pollutants did not change. Second, land prices have risen by anaverage of 2.74%. Third, a survey of LBW, infant mortality, and stillbirth as healthindicators for infants showed that almost all indicators improved by more than 10%
on average. Fourth, cost–benefit analysis revealed that the benefits were about 14
times greater than the costs.
The rest of this paper is structured as follows. Section I describes the theoreticalframework and background of the TLEZ, while Section II describes the data. The

6

impacts of the introduction of the TLEZ on air quality are described in Section III,
those on land prices are described in Section IV, and those on infant health aredescribed in Section V. Section VI discusses the results. The conclusions are
summarized in the final section.
I. Conceptual Framework and TLEZ
A. Theoretical Framework
This study adopts a hedonic approach to the cost–benefit analysis of theregulation of diesel vehicle emissions, focusing on residential land prices. There
are few evaluations of the negative externality from mobile emission sources using
the hedonic approach. This is because, for mobile sources, it is difficult to measure
the spatial relationship between the point of origin and the point of exposure toexternalities. We believe that by using the unique characteristics of the TLEZ and
the information of neighborhood traffic volumes, we can assess the impacts of the
TLEZ under a conceptual framework similar to that of Currie et al. (2015). We
describe the partial equilibrium model of land prices under the TLEZ regarding theideas of Currie et al. (2015) in Appendix A.
B. Tokyo Low Emission Zone
This subsection describes the history of the establishment of the TLEZ and thedetails of the system. At the end of the twentieth century, the Tokyo metropolitan
area was among the regions with the most severe air environment in Japan.
Although the level of the environmental SPM standard has remained unchanged
since the 1970s, the environmental standard had an achievement rate of 78%
outside of the Tokyo metropolitan area in 1996, but only 11% in the Tokyo
metropolitan area (Tokyo Metropolitan Government 2000). Approximately 56% of
NO x and 52% of SPM concentration in Tokyo in 2000 were from diesel

7

TABLE 1. REGULATORY OVERVIEW

Automobile NOx/PM Law

Environmental protection
ordinances ofTokyo and three neighboring
prefectures

Administration

Japanese government

Local government

Objective
Revision oflaws/regulations
Introduction toregulation

Reduction of NOx and PM

Reduction of PM

June 2001

September 2002**

October 2002

October 2003

Metropolitan area (including
Tokyo and three prefectures)*

Tokyo and three prefectures(Excluding remote islands)

Vehicles that do not meet the2005 emission standards
Prohibition of registrationin the region

Vehicles that do not meet the 1999
emission standards
No running in the area unlessa DPF is fixed***Grace period

8 to 12 years

7 years

Surveillance

SHAKEN

On-Site/on-road inspections

In the event of aviolation

Unregistrable or fineProhibition order or fineTarget Area
Target Vehicles
Regulations*In addition to the TLEZ, two other metropolitan areas are also regulated. **Since December
2000, local governments have been formulating ordinances at different times, and in September
2002, all local governments formulated ordinances. *** DPF is PM removal equipment.

emissions.5
In this study, the area of the Automobile NOx/PM Law and the heavy-duty diesel
emission regulations of Tokyo and the three neighboring prefectures are called the
TLEZ. Table 1 provides an overview of the two regulations. The Automobile
NOx/PM Law was enacted by the Japanese government in June 2001 andimplemented in October 2002 to reduce air pollutants originating from diesel
vehicles in metropolitan areas. This law aims to set the expiration date for old andhighly polluting vehicles and to encourage replacement with new vehicles with low

5

http://www.nies.go.jp/pmdep/ctype/result/r 203/pdf/3/13.pdf (last accessed on March 20, 2020).

8

pollution levels. Before the introduction of the Automobile NOx/PM Law, existing
regulations set limits on air pollutant emissions for new vehicles only, according to
the government’s safety standards. Although the maximum restrictions under thesafety standards have been tightened over the years, the regulations were only for
new vehicles. Therefore, it was not possible to reduce PM emissions from highly
polluting vehicles in use. The Automobile NOx/PM Law prohibits the registrationof diesel-powered vehicles that do not meet the 2005 exhaust emission standards inthe three major metropolitan areas of Japan, including the Tokyo metropolitan area.
Under this regulation, the expiration date of a car is set between 8 and 12 years.
In October 2003, Tokyo and the three neighboring prefectures introduced
environmental protection ordinances to regulate exhaust emissions from heavyduty diesel vehicles to supplement the Automobile NOx/PM Law. There wereconcerns that the Automobile NOx/PM Law was insufficient because vehicles
registered outside the regulated area would pass through Tokyo and the threeneighboring prefectures. The ordinances of Tokyo and the three neighboring
prefectures have mandated that diesel vehicles that do not meet the 1999 nationalemission standards must be fitted with diesel particulate filters (DPFs) to regulate
emissions from vehicles registered outside the TLEZ. The ordinances set a 7-year
expiration date for diesel vehicles.
Based on this background, there are two standards for TLEZ emissions: theemission standards under the NOx/PM Law to prohibit the registration of storagesites in the area, and heavy-duty diesel emission standards in Tokyo and the threeneighboring prefectures to forbid traffic. The NOx and PM Law is a registration
regulation based on the emission standards in 2005, and the ordinances of one
metropolis and three prefectures are operation regulations based on the emission
standards in 1999. Thus, the emission standards are more stringent under the
NOx/PM Law than under the Environmental Ordinance. Table B.1 in Appendix B
summarizes the emission standards of Japan, Europe, and the United States. The

9

NOx and PM emission standards of Japan are similar to the Euro 4, which is theEuropean LEZ regulation level analyzed in some previous studies, but there is little
evidence that NOx has decreased at the Euro 4 regulation level.
TLEZ has strict enforcement and punishment. The NOx/PM Law and theenvironmental protection ordinances of Tokyo and the three neighboring
prefectures enforce the regulations in different ways. Under the Automobile
NOx/PM Law, the use of heavy-duty diesel vehicles within the TLEZ is controlled
by regularly checking that all vehicles meet safety standards using the vehicle
inspection and registration system called SHAKEN. Trucks and buses that are
subject to the NOx/PM Law are inspected annually. Failure to submit to thisinspection is a criminal offense, punishable by imprisonment and a fine. Moreover,owners are subject to administrative penalties, including suspension of theirdriver’s license.
Meanwhile, under the ordinances of Tokyo and the three neighboring prefectures,
before the regulations started, the people in charge of each local government
inspected offices that owned trucks and buses. After the introduction of an
ordinance, Tokyo and the three neighboring prefectures continued controlling
regulations based on SHAKEN. Vehicles operating within the TLEZ but registeredoutside the TLEZ are audited on the streets and in parking lots. Those who violate
the ordinance are punished by having their names published and a fine imposed of
up to 500,000 yen (about 4,762 US dollars). Additionally, the names of the shippers
are published. In other words, the operator has an incentive to follow the rules ofthe ordinance, not only to avoid administrative penalties but also to avoid damaging
the company’s reputation with customers.
TLEZ has three characteristics. First, it improves air quality at locations with highheavy-duty diesel traffic. Gasoline vehicles are unaffected. Second, air quality
improves over time. The Vehicle NOx/PM Act and the ordinances of Tokyo and thethree neighboring prefectures had a gradual impact immediately after their

10introduction, because the grace period before they became regulated varies
depending on when the vehicle was purchased. Based on these features, the scaleof the improvement in air quality and the increase in land prices in areas where
there was heavy diesel traffic before the introduction of the regulations are expected
to increase year by year.
Third, as discussed in detail in Subsection II.A, the volume of heavy-duty diesel
traffic on each road within the TLEZ changed very little before and after the
restrictions, so that a large part of the amenity improvement can be deemed as an
improvement in air quality.6
Apart from NOx and SPM, other air pollutants like SO2 and CO are also emitted
from diesel vehicles. The TLEZ does not directly regulate these air pollutants. To
confirm the robustness of this result, we examine whether the introduction of the
TLEZ reduced SO2 and CO.
II. Data
We estimated the impact of the TLEZ on air quality, land prices, and infant health.
In this study, the post-regulation period was assumed to be after 2001, when the
Automobile NOx and PM Law was enacted. The analysis used the information on
heavy-duty diesel traffic on arterial roads, six air pollutants, published land prices,
and infant health.
A. Heavy-duty Diesel Traffic
Because the TLEZ intends to reduce emissions from heavy-duty diesel vehicles
passing through the area, more significant changes in air quality are expectedaround roads with higher traffic volumes. In this study, traffic volumes on arterial

6

The reasons for this interpretation are described in Appendix C.

11

FIGURE 1. THE ROAD NETWORK AND HEAVY-DUTY DIESEL TRAFFIC ON A SINGLE DAY IN 1990

information on traffic volumes on each study section of the main roads in the Road
Traffic Census provided by the Ministry of Land, Infrastructure, Transport, andTourism. Information on traffic volumes in 1990 was used since the analysis isbased on outcomes from 1990 to 2010. The Road Traffic Census is a database of
traffic volumes on all highways, national roads, prefectural roads, and municipal
roads in government-designated cities in Japan. In the traffic volume survey,
vehicles that pass through each section are classified into four categories: passenger
cars, small freight cars, large freight cars, and buses. In this study, we regarded thetotal traffic volume of large trucks and buses as the traffic volume of heavy-duty

12diesel vehicles. This assumption is because in Japan, heavy-duty diesel vehicles
account for more than 90% of heavy-duty freight vehicles and buses. By contrast,
large diesel vehicles account for only a small percentage of all other types ofvehicles. The survey was conducted on a weekday from September to November.
In other words, we assumed that the traffic volume of the day is a proxy for theannual traffic volume.
Figure 1 shows the road network and heavy-duty diesel traffic in 1990.7 Twofeatures characterize the road network. First, roads with high heavy-duty diesel
traffic are laid out in a radial or circular pattern around the center of Tokyo. Roads
with relatively high traffic volumes are highways and national roads. Roads withrelatively low traffic volumes are laid to fill the gaps between those roads.
Therefore, the traffic volume within a 2-km radius of a given point varies even in
the central part of Tokyo. Second, the TLEZ covers roads laid out in a circularpattern around the center of Tokyo. In other words, the TLEZ does not have a
bypass to circumvent the regulation.
Since there are no detours in the TLEZ, the distribution of traffic in the TLEZ
may not have changed. As reported in Table D.1 in Appendix D, the correlation
coefficient for heavy-duty diesel traffic in the TLEZ is 0.95 for the values in 1990,
which is 10 years before the introduction of the TLEZ, and for the values in 2005,
which is 5 years after the introduction of the TLEZ. The mean value is 3.9% larger
after the introduction. These statistics suggest that the improvement in air quality
due to the TLEZ is not related to a decrease in traffic volume.

7

To merge information on road traffic volumes in the neighborhood to the observation sites of the outcomes, we obtained
the line information of the road network from Open Street Map, a free road map, added the traffic volume information fromthe road traffic census to it, and then pieced together the road information with the outcome observation points. The OpenStreet Map can be downloaded from https://download.geofabrik.de/asia/japan.html (last accessed on July 13, 2020).

13

B. Air Quality
The air quality information observed by each municipality was used to identify
the impact of the TLEZ on air quality.8 The analysis used the annual average air
pollutant concentrations at stations in Tokyo and the three prefectures from 1991
to 2009.9 The National Institute for Environmental Studies has created a databaseof this data, which is available online. 10 This information includes the latitude,
longitude, and address of the observation points; the monthly average of air
pollution concentrations; the number of days of observation; the time of observation,and other information.
Atmospheric observations are conducted in densely populated plains.11 There are
five substances (SPM, NOx, NO2, CO, and SO2) in diesel vehicle emissions thatcan be used for atmospheric observation data. Although the TLEZ is expected toreduce concentrations of SPM, NOx, and NO2, the concentrations of CO and SO2
are likely to remain unchanged because they are not affected by the TLEZ.
Therefore, to confirm the robustness of the result, we checked whether theconcentration of unregulated substances did not change. Because not all stations
observe the concentrations of the five elements, the number of observations used in
the analysis differs for each air pollutant.
C. Land Price
Public land prices (called kojichika) are used to measure the increase in benefits
to residents due to improvements in air quality. Public land prices are the appraisal

8

Air quality is constantly surveyed by each municipality in accordance with the Basic Environmental Law and the Air
Pollution Control Law.
9
Information from atmospheric stations with fewer days of observation was treated with caution. For example, stations thatstarted their observations in the middle of the year because of the tendency for SPM concentrations to be higher in winterwere excluded from the analysis because they contain structural biases in the annual averages. We excluded them from theanalysis when there were fewer than 274 days of observation.
10
https://www.nies.go.jp/igreen/ (only in Japanese, last accessed on July 13, 2020).
11
See Figure E.1 in Appendix E.

14values assessed by real estate appraisers as of January 1 each year for locations
selected by the Land Appraisal Committee of the Ministry of Land, Infrastructure,
Transport, and Tourism under the Land Price Disclosures Act. Since the calculationof the appraisal value is based on the transaction cases of neighboring land in theprevious year, the public land price is not the actual transaction price, but theamount that reflects the market value of the land. Public land price data are
available online from the Ministry of Land, Infrastructure, Transport, andTourism’s National Land Information Download Service in shapefile format.12 For
the analysis, we employed the information on public land values used as residential
land in Tokyo and the three prefectures from 1991 to 2010.
Public land prices have the following three characteristics. First, they are
individual panel data. The public land price system is a follow-up survey of almost
the same points every year.13 Therefore, the analysis can control for time-invariant
unobserved factors using individual fixed effects. Points where transactions are
infrequent or where transactions take place due to exceptional circumstances are
not included in the evaluation. Second, there is little sample selection bias. For
example, when analyzed using the repeated sales method, sample selection causes
bias in the estimates if there is a tendency for houses with poor air quality and low
prices to be traded more often than those with poor air quality. Public land prices
avoid this problem by surveying the land prices of the points deemed by experts torepresent the area. Third, the measurement error is small. The purpose of the public
land price system is to reduce the information asymmetry between sellers andbuyers in land transactions by making land prices assessed by neutral experts
available to the public. Thus, for example, the prices of unusual transactions thattook place in the neighborhood are not reflected in the public land price calculation.

1213

https://nlftp.mlit.go.jp/ksj/ (only in Japanese, last accessed on July 13, 2020).
Although the survey locations rarely change, they are not balanced panel data.

15

Figure E.1 in Appendix E shows the distribution of the observation points ofpublic land prices. There are many observation points around the high traffic road.
This concentration of observation points suggests that economic activity isrelatively high in the vicinity of public land price points. Additionally, Figure 2
shows that the TLEZ also regulates areas where there is almost no survey point ofpublic land price.
D. Infant health
Using the Vital Statistics, we examined whether infant mortality, LBW, andnatural stillbirth rates were affected by the TLEZ. We continued to focus on
neonatal deaths under 1 week of age, neonatal deaths under 4 weeks of age, andinfant deaths under 1 year of age as infant deaths. Neonatal deaths under 4 weeks
of age and under 1 week of age were included in infant deaths under 1 year of age;and neonatal deaths under 1 week of age were included in neonatal deaths under 4
weeks of age. Analyzing these categories enables us to discuss how air quality
affects the health of newborns at any given time. The results of the Vital Statistics
are available on the government’s website for aggregate data from municipalities.
The analysis in this study used 20 years of complete panel data for 300
municipalities.14 However, since the information on infant health used in this study
is aggregate data, it was estimated based on a weighted average of traffic volume
in the neighborhood of the residents of the municipality.15

14 During the period under analysis, there were many mergers among municipalities in Japan. According to the analysis,
fixed effects are created for each municipality after the merger.
15
Appendix F describes the calculation of the weighted average of the traffic volume.

16

TABLE 2. DESCRIPTIVE STATISTICS

Panel A: Air pollutants
Suspended particulate matter(SPM)
Nitrogen oxide (NOX)
Nitrogen dioxide (NO2)
Sulfur dioxide (SO2)
Carbon monoxide (CO)No. of heavy-duty diesel
vehicles in 1990
No. of gasoline cars in 1990
Panel B: Land priceLand priceNo. of heavy-duty diesel
vehicles in 1990
No. of gasoline cars in 1990
Panel C: Infant health
Number of births
Birth weight < 2500 g
Infant deaths
under 1 year of age
Deaths of newborns
within 4 weeks of birth
No. of early neonatal deaths(within 7 days)
No. of stillbirths
Stillbirths after 22 weeks
of gestation
No. of heavy-duty diesel
vehicles in 1990
No. of gasoline cars in 1990

No. of
obs.

Pre
No.
of
Mean
obs.
points1990–2000

No. of
obs.

Post
No.
of
Mean
obs.
points
2001–2010

Std.
dev.

Std.
dev.

Rate ofchangeof the
mean

3,125

337

48.4

10.2

3,344

358

30.7

7.3

-0.37

3,120
3,121
2,185
1,477

299
299
216
168

63.7
29.8
6.5
1.1

35.6
8.7
2.1
0.6

2,600
2,601
1,633
813

270
270
176
93

44.5
24.6
3.5
0.6

26.8
8.1
2.0
0.2

-0.30
-0.18
-0.46
-0.44

4,721

486

26.3

22.8

4,539

478

26.2

23.2

0.00

4,721

4,539
59,740

478
68.8
77.0
2002–2010
7,639 271152 313284-0.01

65,106

486
69.3
75.1
1991–2001
7,931 401758 613537

65,106

7,931

22.8

18.5

59,740

7,639

23.6

19.9

0.04

65,10651.3

59,740

0.05

1620
30.8

2,267
2,267

7,639
63.7
2001–2010
233
1299
233
93.1

58.8

2,297
2,297

7,931
60.4
1991–2000
231
1271
231
75.5

1689
31.0

0.02
0.23

2,297

231

4.0

5.3

2,267

233

2.9

4.5

-0.28

2,297

231

2.1

3.4

2,267

233

1.4

2.8

-0.32

2,297

231

1.5

2.9

2,267

233

1.0

2.3

-0.34

2,297

231

17.5

29.6

2,267

233

14.0

32.6

-0.20

2,297

231

5.0

20.3

2,267

233

4.4

22.7

-0.11

2,301

231

16.2

16.6

2,270

233

16.4

16.6

0.01

2,301

231

43.4

47.1

2,270

233

43.9

47.3

0.01

-0.33

Notes: All observations in Panel B are restricted to residential use. Panel C reports cross-municipalities mean andstd. dev. As of January 2000, 1 dollar was about 105 yen. The units of measurement of SPM is micrograms per cubic
meter of air (g/m3). The units of measurement of NOX, NO2, SO2, and CO are parts per million(ppm). The unitsof measurement of heavy-duty diesel vehicles and gasoline cars are in thousands. The units of measurement of land
price is Yen per square meter of land. The units of measurement of birth weight < 2500 g, Infant deaths under 1 yearof age, early neonatal deaths (within 7 days), stillbirths, and stillbirths after 22 weeks of gestation are in per thousand
births.

17

E. Summary Statistics
Panel A of Table 2 reports the descriptive statistics for atmospheric stations forpre- and post-regulation. This analysis used information from about 480
atmospheric stations from 1990 to 2010. Air pollutants were studied at a larger
number of atmospheric stations for SPM, NOx, and NO2, which are subject to the
TLEZ regulations. The number of stations does not change much before and after
the introduction of the TLEZ. Meanwhile, the number of atmospheric stations forSO2 and CO was relatively low. In recent years, the number of stations has been
decreasing. The number of heavy-duty diesel vehicles traveling within a 2-km
radius of the atmospheric station averaged 26,000 per day, and the number ofgasoline vehicles averaged 69,300 per day.
Comparison before and after the introduction of the TLEZ shows a decreasing
trend in the concentrations of all air pollutants. SPM, NOx, and NO2 decreased by
37%, 30%, and 18%, respectively. SO2 and CO, which are not regulated, decreasedby 46% and 44%, respectively. In a simple pre-post analysis, the decrease in air
pollution concentration cannot be interpreted as a TLEZ effect because theconcentration of non-regulated substances is also decreasing.
Panel B of Table 2 reports the descriptive statistics for the observation points ofpublic land prices used for residential purposes. The data used for the analysis wereincomplete panel data from 1991 to 2010, with 124,846 observations andapproximately 7,900 survey locations. The average public land price was about
402,000 yen/m2 in the pre-TLEZ period and about 271,000 yen/m2 in the postTLEZ period. During the post-TLEZ period, Japan experienced a significant
economic recession, and land prices fell significantly across the country. The
number of heavy-duty diesel vehicles traveling within a 2-km radius of theobservation points of public land price averaged 22,800–23,600 per day and that ofgasoline vehicles averaged 60,400–63,700 per day.

18

Area 1 (around Bijogi IC; Lat. 35.8)
Area 2 (around Tokyo IC; Lat. 35.63)

Area 3(around Yokohama; Lat. 35.47)

6 km
FIGURE 2. HEAVY-DUTY DIESEL TRAFFIC IN EACH 1-KM MESH
Notes: This figure presents the volume of heavy-duty diesel traffic within a 2-km radius of the center of
each 1-km mesh on a single day in 1990 (see Appendix F for the 1-km mesh). The dotted line frame isapproximately 6-km wide from east to west.

Panel C of Table 2 reports statistics on infant health and estimated traffic volumes
in approximately 230 municipalities. All statistics are cross-municipal averages.
The average number of births did not change significantly before and after the
regulation. The number of LBW infants increased by 23% from 75 to 93 per 1,000.
Regarding infant deaths, an improvement trend can be observed for all indicators.
Specifically, the number of deaths of infants aged under 1 year decreased by 28%,
the number of neonatal deaths of infants under 4 weeks decreased by 32%, thenumber of neonatal deaths of infants under 7 days decreased by 34%, and thenumber of spontaneous stillbirths decreased by 20%. The number of prenatal

19

Panel A: Changes in SPM concentrations (2000 to 2010)

PANEL B : CHANGES IN NOX CONCENTRATIONS (2000 TO 2010)

Figure 3. Changes in air pollution concentration and traffic volume of heavy-duty diesel vehicles in 1990
Notes: We use local polynomial smoothing to depict the difference in air pollution concentrations between 2000 and 2010 and the amount of heavy diesel traffic within
a 2-km radius in 1990 at the values from air stations in the dotted-line frame of Figure 2.

20

stillbirths after 22 weeks of gestation decreased by 11%. Although there is a trend toward
a more significant decrease in postpartum mortality, the rate of LBW is increasing. Thisshows the effect of advances in medical technology. The traffic volumes reported here
are cross-municipal averages of the weighted averages of residents’ neighborhood traffic
volumes (see Appendix F).
Figure 2 shows the traffic volume within a 2-km radius of the center of the 1-km mesh.
The traffic volume is higher around the interchanges of the major highways. For example,
Area 1 (Bijogi IC) has heavy traffic, but immediately to its northwest is an area with little
traffic. The statistics in Table 2 show that the coefficient of variation of diesel traffic foreach panel is between 81.2% and 102.5%. Figure 2 shows that the traffic volume varies
even in spatially close areas within the TLEZ.
Figure 3 shows the traffic volume in 1990 and the change in air pollution concentrationfrom 2000 to 2010 at the air monitoring sites in the frame shown in Figure 2. In the frameof Figure 2, there are three particular traffic nodes, namely, Area 1 (Bijogi IC: 35.80°
north latitude), Area 2 (Tokyo IC: 35.63° north latitude), and Area 3 (Yokohama city:
35.47° north latitude). This simple graph confirms the relationship between the volumeof nearby traffic and changes in air pollution concentrations in 1990. Panel A showschanges in SPM and Panel B shows changes in NOx. The horizontal axis indicates latitude,
the left vertical axis indicates changes in air pollution concentration, and the right verticalaxis indicates the amount of neighboring traffic in 1990.
These graphs indicate whether air quality improves more at sites with heavy diesel
traffic. This shows the essence of the identification strategy in this study. Figure 3 is basedon uncontrolled variables, but even this simple comparison suggests that the higher the21traffic volume, the better the air pollution concentration. To more accurately measure thecausality depicted in Figure 3, the analysis provides further controls.
III. Air Quality Improvement
Our identification strategy exploits the fact that the strength of the policy’s impact
varies between locations with high and low heavy-duty diesel traffic in the neighborhood.
We assume that within a 2-km radius, diesel emissions affect air quality and, consequently,
residential land values and infant health. This distance assumption is based on the findingsof Currie and Walker (2011) and Currie et al. (2015).16
First, we examine the relationship between the amount of diesel traffic in theneighborhood and changes in regulated air pollutants. As discussed in Subsection I.B,
three air pollutants are directly affected by the TLEZ: SPM, NOx, and NO2. Since CO andSO2 are not regulated, they should have no direct impact. In other words, we expect toobserve a decrease in SPM, NOx, and NO2 after the start of the TLEZ, and a different
trend for CO and SO2.
The impacts of the TLEZ are measured by the following linear model, using the merged
data of air quality information and spatial location and traffic volume information of
adjacent roads based on the latitude and longitude of atmospheric stations.
(1) Pollutants𝑗𝑝𝑡 = 𝛼𝑗 + 𝛽(𝐷𝑃𝑜𝑠𝑡 𝑡 × Diesel0−2,𝑗𝑝1990 )+𝛾(𝑌𝑒𝑎𝑟𝐹𝐸 × Cars0−2,𝑗𝑝1990 ) + 𝑌𝑒𝑎𝑟𝐹𝐸 + 𝑃𝑟𝑒𝑓𝐹𝐸𝑗 × 𝑌𝑒𝑎𝑟𝐹𝐸 + 𝜀𝑗𝑝𝑡 ,where 𝑃𝑜𝑙𝑙𝑢𝑡𝑎𝑛𝑡𝑠𝑗𝑝𝑡 is the air pollution concentration at atmospheric station j in
prefecture p in period t. Diesel0−2,𝑗𝑝1990 denotes the volume of heavy-duty diesel traffic

16

The validity of this distance assumption and interpretation of the estimated results is discussed in Appendix G.

22

within a 2km radius of the atmospheric observatory j in 1990, and 𝐷𝑃𝑜𝑠𝑡 𝑡 denotes adummy variable that takes the value of 1 after the introduction of the TLEZ and 0
otherwise. The NOx/PM Law was enacted in June 2001, so that the impact of the TLEZ
was assumed to begin in 2001. Cars0−2,𝑗𝑝1990 is the total traffic volume of passenger cars
(not subject to regulation) within a radius of 2 km of atmospheric observatory j in 1990;
𝛼𝑗 is the individual fixed effect of atmospheric observatory j; 𝑌𝑒𝑎𝑟𝐹𝐸 is the year fixed
effect; 𝑃𝑟𝑒𝑓𝐹𝐸𝑗 is the fixed effect of m prefecture to which atmospheric observatory jbelongs; and 𝜀𝑗𝑝𝑡 is the error term.
In equation (1), we are most interested in the coefficient 𝛽 of the interaction term
between 𝐷𝑃𝑜𝑠𝑡 𝑡 and Diesel0−2,𝑗𝑝1990 . Since the coefficient 𝛽 is a DID estimator, thediscrimination assumption in this estimation is that 𝛽 is not affected by time-varying
omitted variables that correlate with the effect of diesel traffic. Therefore, we draw event
study graphs for each air pollutant before the estimation to ensure that there is no effectof such omitted variables, at least before the introduction of the TLEZ. These graphs are
drawn by exchanging 𝐷 𝑃𝑜𝑠𝑡 𝑡 in equation (1) for 𝑌𝑒𝑎𝑟𝐹𝐸 and plotting the coefficients foreach year. The graph is expected to be as follows. As described in Subsection I.B, since
the TLEZ regulation becomes stronger year by year, the magnitudes of the SPM, NO x,
and NO2 coefficients should become larger year by year.
Figures H.1 and H.2 in Appendix H show the event study graphs for each air pollutant.
The DID estimator is plotted against the base case of 2000, just before the TLEZ policy
was formulated. A 95% confidence interval for each estimator is also provided. In Figure
H.1, the results of SPM in Panel A, NOx in Panel B, and NO2 in Panel C are reported as
regulated air pollutants of the TLEZ. In Figure H.2, SO2 in Panel A and CO in Panel B
are reported as non-regulated air pollutants of the TLEZ.
23

Figure H.1 shows that the three air pollutants affected by the TLEZ, SPM, NOx, andNO2, do not change significantly before 2000. However, after 2000, there is a statisticallysignificant trend of improvement in air quality. A pattern of betterment in air pollution
concentrations of regulated substances can be observed in areas with higher traffic
volumes, and there is a trend for TLEZ regulations to increase in the magnitudes of the
DID estimates from year to year. These results suggest that the common-trend assumption
can be satisfied. Meanwhile, Figure H.2 shows that both CO and SO2, which are notregulated by the TLEZ, improved at a time that is unrelated to the impact of the TLEZ.
SO2 may reflect a decrease in the sulfur content of the fuel due to improvements in oil
refining technology during this period. The atmospheric concentration of CO tended to
improve after the revision of emission standards in 1997. In summary, this study considers
that the DID estimate, β, indicates that the TLEZ’s regulation of the amount of air
pollution in emissions from heavy-duty diesel vehicles improved air quality in areas withhigher traffic volumes.
Table 3 reports the results of estimations of the impact of the TLEZ on air pollution
concentrations of SPM, NOx, and NO2 based on equation (1). The estimation results using
the whole observations are reported in columns (1) to (3), and the estimation results
limiting observations immediately before the introduction of the TLEZ and after 8 to 10
years from the introduction of the TLEZ are reported in columns (4) to (6). All estimates
are controlled by atmospheric station fixed effects. Columns (1) and (4) are further
controlled by a year fixed effect. Columns (2) and (5) add the interaction term between
traffic volume of passenger cars (non-regulated) within the neighborhood and the year
fixed effects as a control to the estimation in columns (1) and (4). This controls local
time- variant factors, for example, those correlated with the structure of nearby roads and24

TABLE 3. THE EFFECT OF TOKYO LEZ ON AIR POLLUTION

Panel A: SPM
1[Post LEZ] × No. of heavy-duty diesel
vehicle (within 2 km)
Year FE
Cars (within 2 km) × Year FE
Prefecture FE × Year FE
No. of observations
No. of groups
Panel B: NOx
1[Post LEZ] × No. of heavy-duty diesel
vehicles (within 2 km)
Year FE
Cars (within 2 km) × Year FE
Prefecture FE × Year FE
No. of observations
No. of groups
Panel C: NO2
1[Post LEZ] × No. of heavy-duty diesel
vehicles (within 2 km)
Year FE
Cars (within 2 km) × Year FE
Prefecture FE × Year FE
No. of observations
No. of groups(1)

1990–2010(2)(3)-0.061-0.122-0.124

1998–2000 & 2008–2010
(4)
(5)
(6)
-0.138-0.132(0.020) (0.036) (0.034) (0.022) (0.036)
YES
YES
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
YES
NO
NO
6,448
6,448
6,448
1,943
1,943
374
374
374
359
359

(0.037)
NO
YES
YES
1,943
359

-0.194-0.211-0.220(0.040) (0.067) (0.064) (0.045) (0.078)
YES
YES
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
YES
NO
NO
5,699
5,699
5,699
1,563
1,563
299
299
299
272
272

(0.076)
NO
YES
YES
1,563
272

-0.022-0.030-0.034(0.011) (0.015) (0.014) (0.011) (0.017)
YES
YES
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
YES
NO
NO
5,701
5,701
5,701
1,563
1,563
299
299
299
272
272

(0.016)
NO
YES
YES
1,563
272

-0.135-0.028-0.143-0.031-0.073-0.202-0.026

Notes: All regressions include individual fixed effects. Standard errors clustered by atmosphericstations are reported in parentheses.

economic activity. Columns (3) and (6) are controlled by replacing the year fixed effectsof columns (2) and (4) with the interaction term of the year fixed effects and the prefecture
fixed effects. Economic activity in Tokyo is very different from that in the threeneighboring prefectures, and this may be correlated with changes in air quality. Estimatesin columns (3) and (6) are derived to consider this possibility.
All the estimated results in Table 3 show that the TLEZ has improved the air pollution
concentration of the regulated substances. Panel A in Table 3 reports the SPM estimation
results. The results in column (1) show that for every 1,000 diesel vehicles in 1990 within25

a 2-km radius of an atmospheric station, SPM concentrations decreased by an average of
0.061 (g/m3). The results for column (2), which add non-regulated vehicle traffic in thevicinity as a control variable, show a decrease in SPM concentration of 0.122 (g/m3).
Furthermore, the results of column (3), which consider the time-variant factors of eachprefecture, are the same as those of column (2). Although the results of columns (4) to(6) should reflect that the atmospheric environment improvement effect of the TLEZ
becomes stronger year by year, there were no significant differences from the results ofcolumns (1) to (3) using the samples for the whole period. In Panel A of Figure H.1, theSPM coefficients from 1990 to 1995 were non-significant but positive. This may haveoverestimated the difference between the 10 years before and the 10 years after the
initiation of the TLEZ using all specimens. Nevertheless, the results from columns (4) to(6) indicate that the TLEZ improved the concentration of SPM. Since the average valueof SPM before 2000 was 48.4 and the average traffic volume in 1990 was 26.3, theaverage atmospheric concentration of SPM at the TLEZ can be said to have improved byabout 7.17% (= 0.132 × 26.3/48.4) by 2010.
Panel B in Table 3 reports the estimated NOx results. The results in column (1) showthat for every 1,000 diesel vehicles in 1990 within a 2-km radius of an atmospheric station,
NOx concentrations decreased by an average of 0.194 parts per million (ppm). Becauseof controlling the amount of traffic in the neighborhood of non-regulated vehicles,
column (2), the NOx concentration decreased by 0.135 (ppm). There is no significant
difference between the results of columns (2) and (3). The magnitude of the DID estimatorof SPM in column (2) is more significant than that in column (1), but the magnitude of
the DID estimator of NOx in column (2) is smaller than that in column (1). These results
suggest that the emissions sources of SPM and NOx other than large diesel vehicles could26

be different. The result in column (6) shows that the NOx concentration decreased by
0.220 ppm, which means that the NOx concentration improved by about 9.08% (= 0.220
× 26.3/63.7) over 10 years. Similarly, Panel C in Table 3 reports lower concentrations of
NO2 in the atmosphere. A comparison between 1998 and 2000, immediately before theintroduction of the TLEZ, and 2008–2010, 10 years after the introduction, showed an
improvement in NO2 air pollution of about 3.00% (= 0.034 × 26.3/29.8).
From the results of the event study graph and estimation, we find that the concentrationof air pollutants subject to regulation improved at locations with a high traffic volume ofheavy-duty diesel vehicles within a 2-km radius before the introduction of the TLEZ. All
estimates also control the volume of traffic in the vicinity of unregulated gasolinepowered passenger cars. Thus, it is unlikely that such factors as an economic activity that
correlates with overall vehicle traffic are confounding factors. As shown in Table D.1 inAppendix D, there is little change in traffic conditions within the TLEZ, and thus, it isunlikely that the total amount of SPM generated from, for example, brakes have changed.
Meanwhile, the effects of the TLEZ on air pollutants not subject to regulation are notconfirmed in the event study graphs. As shown in Appendix G, heavy-duty diesel vehicle
traffic within a 2-km radius well explains the SPM results and may underestimate theNOx results. Therefore, in the analysis that follows, we assume that the volume of diesel
traffic within a 2-km radius is a proxy variable for the strength of the impact of the TLEZ
regulation, although this may be an underestimation.
IV. Land Price
The identification strategy for land prices is essentially the same as the estimation of
the atmospheric environment. However, observation points for public land prices are27

spatially denser than those for atmospheric stations. Moreover, because they contain
information on land attributes, more reliable control variables can be added. In otherwords, the estimation is performed using the following model:(2) LandPrice𝑖𝑚𝑝𝑡 = 𝛼𝑖 + 𝛽(𝐷𝑃𝑜𝑠𝑡 𝑡 × Diesel0−2,𝑖𝑚𝑝1990 )+𝛾1 (𝑌𝑒𝑎𝑟𝐹𝐸 × Cars0−2,𝑖𝑚𝑝1990 ) + 𝛾2 𝑋𝑖𝑚 + 𝑀𝑢𝑛𝑖𝐹𝐸𝑚 × 𝑌𝑒𝑎𝑟𝐹𝐸 + 𝜀𝑖𝑚𝑝𝑡 ,Here, LandPrice𝑖𝑚𝑝𝑡 represents the public land price of observation point i in basemunicipality m of prefecture p in period t. 𝑋𝑖𝑚 represents the attributes (presence orabsence of public water supply, land area, floor-area ratio, building–land ratio, and linear
distance to the nearest railway station) of the observation point of the public land price i.
𝑀𝑢𝑛𝑖𝐹𝐸𝑚 is the fixed effect of the municipality. Other variables are defined in the same
way as the air pollution analysis in equation (1).
Changes in land prices in the TLEZ are based on the hedonic model and are
distinguished from changes in air quality on a theoretical background. Changes in air
quality capture the physical relationship of reduced pollutants from heavy-duty diesel
vehicles, while changes in land prices measure the consequences of people’s choice of
where to live. For example, a residential choice can be influenced by policy changes thatare different from air quality. Therefore, by adding the interaction term between themunicipal fixed effect and the annual fixed effect as control variables, the trend based on
the characteristics of each municipality is controlled more flexibly. In other words, thisanalysis measures the difference in changes in land prices at locations where the traffic
volume of large diesel vehicles differs within the same municipality.
The coefficient of interest in this analysis is β, which represents the relationshipbetween the volume of traffic and the change in land prices after the introduction of the
TLEZ. Because this coefficient is a DID estimator, we draw an event study graph as well28

as an analysis of air quality to meet the common trend assumption. This graph is drawn
by exchanging 𝐷𝑃𝑜𝑠𝑡 𝑡 equation (2) for 𝑌𝑒𝑎𝑟𝐹𝐸 and plotting the coefficients for each year.
Because the official land price is the value as of January 1, the influence of the TLEZ on
land prices starts in 2002, the year following the formulation of the policy. Therefore, theevent study graph is based on 2001. The official land price is reflected in the following
year’s information based on the results of the annual transactions. However, since the
TLEZ policy was formulated in October 2001, only the last three months of 2001
transactions are reflected in public land prices in 2002.Public land prices should fullyreflect the impact of the policy after 2003.
Figure I.1 in Appendix I is an event study graph showing the coefficients for public
land prices in each year. Public land prices tend to rise around roads with higher heavyduty diesel traffic after the introduction of the TLEZ. The timing at which land prices
begin to be affected by the TLEZ is in line with expectations. Before the introduction of
the TLEZ, there is no evidence that public land prices continue to rise with higher heavyduty diesel traffic. The magnitude of the coefficients has been increasing year by year,
which is consistent with the TLEZ regulation being stricter year by year.
Table 4 reports the estimation results based on equation (2) for the impact of TLEZ
enforcement on land prices. Columns (1) to (4) present estimates using observations fromall periods, and columns (5) and (6) together present results using observations for a totalof 5 years, from 1999 to 2001, the 3 years immediately before the start of the TLEZ, in
the case of column (5) and 2009 and 2010, 10 years after the introduction of the TLEZ,
for column (6). From columns (2) to (4), we have more control over regional time-variant
factors. Column (2) is an estimate of column (1) plus a pattern related to the traffic volumeof nearby cars as a control, and column (3) is an estimate of column (2) with time-variant
29

TABLE 4. THE EFFECT OF TOKYO LEZ ON LAND PRICE
1991–2010

1999–2001 & 2009–
2010
(5)
(6)

(1)

(2)

(3)

(4)

0.00160.00110.00120.00110.0012-0.0001(0.0002)(0.0002)(0.0002)(0.0003)(0.0001)(0.0002)

Year FE

YES

YES

NO

NO

NO

NO

Cars (within 2 km) × Year FE

NO

YES

YES

YES

NO

YES

1[Post LEZ] × No. of diesel
vehicles (within 2 km)

Other Controls

NO

YES

YES

YES

YES

YES

Prefecture FE × Year FE

NO

NO

YES

NO

NO

NO

Municipality FE × Year FE

NO

NO

NO

YES

YES

YES

No. of observations

124,846

124,846

124,846

124,846

32,10932,109No. of groups

9,012

9,012

9,012

9,012

7,866

7,866

Notes: Other controls are public water supply, land area, floor-area ratio, building–land ratio, and linear
distance to the nearest railway station. All regressions include individual fixed effects. Standard errors
clustered by point of public land price are reported in parentheses.

factors for each prefecture as a control. Column (4) controls non-linear trends specific toeach municipality by using the interaction term of the year fixed effect and municipalities
fixed effect.
The estimates show that the higher the traffic volume of heavy-duty diesel vehicles in
the neighborhood, the higher the land price after the start of the TLEZ. The results incolumn (1) of Table 4 show a 0.16% increase in land prices for every 1,000 large diesel
vehicles in the neighborhood. The results for column (2) control time-variant factors bytraffic volume of a passenger car, which is non-regulated, and the results for columns (3)and (4) control regional time-variant factors additionally; they show an almost equal
0.11–0.12% increase in land prices. These results suggest that the time-variant factors
specific to the municipality are well controlled. According to the results in column (4),which are the most rigorous, land prices rose by an average of 2.74% (= 22.8 * 0.12). The
column (5) results using a 3-year sample from 1999 to 2001 and a 2-year sample from2009 to 2010 give a coefficient of 0.12%. Therefore, land prices per square meter of30residential land in the TLEZ increased by an average of 11,000 yen (= 0.0012 * 22.8 *401758). Column (6) has the same controls as column (4). Although the coefficient incolumn (6) is different from the results in other columns, the influence of the multiple
collinearities with the traffic volume of the diesel vehicle is considered to strengthen
because of the shortening of the panel in the time direction.
V. Infant Health
Identification strategies in the analysis of infant health are similar to the effects of the
TLEZ on air quality.
(3) InfantHealth𝑚𝑡 = 𝛼𝑚 + 𝛽(𝐷𝑃𝑜𝑠𝑡 𝑡 × ̅̅̅̅̅̅̅̅
Diesel0−2,𝑚1990 ) + 𝑌𝑒𝑎𝑟𝐹𝐸 + 𝜀𝑚𝑡 ,InfantHealth𝑚𝑡 shows the health outcome of infants in municipality 𝑚 in year 𝑡 .
̅̅̅̅̅̅̅̅
Diesel0−2,𝑚1990 shows the neighborhood traffic volume of heavy-duty diesel vehicles ofresidents in municipality 𝑚 . As described in Appendix F, this traffic volume is theaverage value obtained by weighting the traffic volume within a 2-km radius of the centerof the 1-km mesh in the municipality by the population in the mesh. 𝛼𝑚 is the fixed effectof municipality 𝑚 , 𝑌𝑒𝑎𝑟𝐹𝐸 is the year fixed effect, and 𝜀𝑚𝑡 is the error term. Thisestimate uses a generalized linear model to capture the average effect of the TLEZ on
infant health.
We draw event study graphs for each outcome of infant health in the same way as air
quality and land value analysis. Panels A to F in Figure J.1 in Appendix J show thecoefficients (not a marginal effect) and their confidence intervals for outcomes of birthweight less than 2,500 g (LBW), death of an infant less than 1 year old, death of a newborn
less than 4 weeks old, death of a newborn less than 1 week old, stillbirth, and perinatal

31

death (from 22 weeks of pregnancy to delivery). As in the case of air quality, the impactsof the TLEZ are assumed to have started after 2001, with 2000 as the base year.
The LBW graph of Panel A shows that the higher the heavy-duty diesel traffic volume
after the introduction of the TLEZ, the smaller the ratio of LBW year by year. The shapeis very similar to that of SPM and NOx in the event study graphs (Panels A and B in
Figure J.1). None of the pre-TLEZ coefficients tended to be significant. Table 2 showsthat although the ratio of LBW is higher on average after the start of the TLEZ, theprobability of LBW decreases as the traffic volume increases, suggesting that the TLEZ
has had a positive effect on the health of infants. In Panel B, although the significance ofthe coefficient after the initiation of the TLEZ is somewhat equivocal because it is lessfrequent than LBW, infant death under 1 year of age also tends to be similar to LBW.
Although the coefficient after the initiation of the TLEZ is obscure, a pattern similar tothat of infant deaths under 1 year of age is observed in all remaining panels.
In summary, the common trend assumption seems to be satisfied. In particular, LBW
and infant deaths under 1 year of age showed a tendency to improve their indexes year
by year in municipalities in which the traffic volume of heavy-duty diesel vehicles washigh before the TLEZ. Therefore, it is likely that TLEZ has improved the health of infants.
Table 5 reports the marginal effects of the TLEZ on infant health based on theestimation of equation (3). Table 5 shows Panel A, which does not include the interaction
between the average passenger car traffic volume and the year fixed effect as the control,and Panel B, which includes them in the analysis. Different from the analysis of pollution
concentration and land price, the problem of multiple collinearities between the traffic
volume of large diesel vehicles and that of passenger vehicles may arise, since theweighted average traffic volume in each local government is used. Two analyses are
32

TABLE 5. AVERAGE MARGINAL EFFECT OF TOKYO LEZ ON INFANT HEALTH ( PER 1,000)

Panel A
1(Post LEZ) × No. ofheavy-duty diesel vehicles(within 2 km)
Panel B
1(Post LEZ) × No. ofheavy-duty diesel vehicles(within 2 km)

Birthweightless than2500 gInfantdeathsunder 1year ofage

Deaths ofnewborns
within4 weeksof birth

(1)

(2)

(3)

No. ofearly
neonataldeaths(within 7
days)
(4)

-0.00063-0.00004-0.00002

No. ofstillbirths

Stillbirths
after 22
weeks ofgestation

(5)

(6)

-0.00001-0.00012-0.00002(0.00007) (0.00001) (0.00001)(0.00000)(0.00002)(0.00001)-0.00031-0.00001-0.00001-0.00003-0.00001(0.00018) (0.00002) (0.00002)(0.00001)(0.00007)(0.00003)-0.00004

Notes: This table reports the marginal effects of 1(Post LEZ) × No. of heavy-duty diesel vehicles (within
2 km) in each estimation. The number of observations in estimations (1) to (4) is 4564．The number ofobservations in estimations (5) and (6) is 4560．All regressions included individual fixed effects. Eachcoefficient in Panel B is from a regression that also includes controls for cars (within 2 km) × Year FE.
Delta-method standard errors clustered by municipalities are reported in parentheses.

performed to consider this possibility.
According to the results in Panel A of Table 5, marginal effects other than the numberof stillbirths after 22 weeks of perinatal dead pregnancy improved. It is also important tonote that stillbirths might not be the effect of the TLEZ, because it was unclear whether
the common trend assumptions were met. Table 2 shows that the average heavy-duty
diesel vehicle traffic volume was 16,200, indicating that LBW decreased by 10.20 per
1,000 (= -0.00063 * 16.2 * 1,000) in this average traffic volume. This represents a 13.5%
improvement in the rate of LBW. Similarly, the number of infant deaths under 1 year ofage reduced by 0.63 per 1,000 (= -0.00004 * 16.2 * 1,000), which represents a 15.8%
improvement on average. The number of neonatal deaths less than 4 weeks of age reduced
by 0.24 per 1,000 (= -0.00002 * 16.2 * 1,000), which represents an 11.4% improvement
on average. The number of early neonatal deaths within 7 days reduced by 0.16 per 1,00033(= -0.00001 * 16.2 * 1,000), which represents a 10.7% improvement on average. The
number of stillbirths reduced by 1.92 per 1,000 (= -0.00012 * 16.2 * 1,000), which
represents an 11% improvement on average. The number of stillbirths after 22 weeks ofgestation reduced by 0.24 per 1,000 (= -0.00002 * 16.2 * 1,000), which represents a 4.8%
improvement on average.
Panel B of Table 5 also shows a decreasing trend in LBW and mortality under the ageof 1 year. The LBW has a coefficient scale of approximately half that of Panel A. LBW
improved under-1-year mortality by 6.5% at the average traffic volume. The magnitudeof the coefficients for the mortality rate hardly changed. The other coefficients did notchange significantly. These results suggest that the health index for infants improved in
the municipalities with higher traffic volume of heavy-duty diesel vehicles.
VI. Discussion
Our estimation results suggest that the TLEZ has improved the concentration of air
pollutants subject to regulation, resulting in improved infant health and increasing land
prices. We estimate the rough economic value of the TLEZ assessed by residents basedon the rise in land prices. This estimate has several advantages over the epidemiologicalbenefits of the TLEZ. Epidemiological benefits are estimated by accumulating thefinancial value of health improvements, but it might not be possible to list all healthimprovements, and it is not easy to measure improvements in labor productivity. No
matter how significant the epidemiological benefits may be, it is not easy to implement
policies if residents underestimate them. Meanwhile, if the evaluation is based on thehedonic approach adopted in this study, the economic value of the improvement in the34

associated atmospheric environment is estimated by measuring the total value felt by theresidents for the effect of the TLEZ.
First, we estimate the cost of the TLEZ based on previous research. This cost estimation
is based on Iwata and Arimura (2009), who conduct a pre-evaluation of the NOx/PM Law,
and Iwata (2011), who conducts a pre-evaluation of the ordinances of Tokyo and the threeprefectures. The NOx/PM Law mandates the replacement of automobiles that do not meetthe 2005 emission standards, and the ordinances of one metropolis and three prefectures
require the installation of DPFs if they do not meet the 1999 emission standards. Becauseof these characteristics, the cost of the NOx/PM Law is around 521 billion yen, which isoverwhelmingly higher than DPF installation. This cost is an estimate of the replacement
cost only for vehicles registered in the area. Regulated vehicles that pass through the
TLEZ should install DPFs following the ordinances of Tokyo and the three prefectures.
According to Iwata (2011), DPFs cost about 7% as much as the NOx/PM method when
installed on all vehicles subject to the NOx/PM Law. It is not clear how many diesel
vehicles will enter the TLEZ from outside, and thus, it is assumed that 80% of the vehicles
registered with the TLEZ enter from outside the TLEZ.17 The cost is about 29 billion (=521*0.07*0.8) yen. Therefore, the total cost of TLEZ is about 550 billion yen.
Next, the total value of the land price increase by the TLEZ is obtained. It should be
noted that the official land price reflects the transaction price of a house in the TLEZ anddoes not survey the land price in an area in which no transaction takes place. Therefore,
the total benefit cannot be calculated from the sum of the housing land area in the TLEZ.
Then, the average land price of each municipality and the average traffic volume of

17 The Tokyo Metropolitan Government's Environmental Bureau has reported that 80% of the cars running in Tokyo are fromoutside Tokyo.

35

heavy-duty diesel vehicles are calculated based on the information on land priceestimation in Section IV. The total benefit is estimated based on the assumption that therate of land price increase per 1,000 vehicles of traffic volume is equal in the whole area;that is,Total Benefit = 0.0012 × ∑𝑀 𝐴𝑣𝑒𝑃𝐿𝑚 × AveDesels0~2,im1990 × 𝑇𝑜𝑙𝑅𝐿𝐴𝑚 .
Here, 𝐴𝑣𝑒𝑃𝐿𝑚 represents

theaverage

land

price

of

municipality 𝑚 ;𝐴𝑣𝑒𝐷𝑒𝑠𝑒𝑙𝑠0~2,𝑖𝑚1990 represents the average traffic volume of heavy-duty diesel vehicles
of municipality 𝑚 in 1990, based on the information of observation points of the public
land price; and 𝑇𝑜𝑙𝑅𝐿𝐴𝑚 represents the total area of residential land in municipality 𝑚.
0.0012 is the DID estimator value in column (5) of Table 4. This formula gives a total
economic value of about 7.72 trillion yen. The total cost of the TLEZ is 550 billion yen,and thus, the benefit–cost ratio is approximately 14. Therefore, the residents are likely tosupport the TLEZ policy.
To interpret the magnitude of this benefit–cost ratio, we compare it with the results ofprevious studies. A good comparison is Wolff (2014), who evaluates the German LEZ.
Wolff (2014) reports an epidemiological benefit–cost ratio of 1.2–5.3, as underestimatedresults. Although it is not an evaluation of the LEZ, the EPA (2011), which evaluates the
US Clean Air Act, provides useful information as an evaluation of air environment policy.
According to the EPA (2011), the epidemiological benefit of the Clean Air Act from 1990
to 2010 was about 30.8 times its cost. Because Wolff (2014) uses fewer items than the
EPA (2011) does to calculate the epidemiological benefits of the LEZ in Germany, it is
not possible to compare the magnitude of the two epidemiological benefits. However,these studies allow us to interpret that the ratio of economic benefits to costs of the TLEZ
is in the same order of magnitude as the epidemiological benefit to the cost of these36

atmospheric policies. In other words, residents may evaluate the economic benefits of air
environment policies on a scale comparable to the epidemiological benefits.
VII. Conclusion
We examined the impacts of heavy-duty diesel emission regulations on air quality, land
prices, and infant health in metropolitan areas. The estimation results showed that thegreater the traffic volume of heavy-duty diesel vehicles before the introduction of the
TLEZ, the more almost all the outcomes were affected even after controlling for the traffic
volume of non-regulated gasoline vehicles. Although the TLEZ regulation is almost thesame as the European LEZ regulation, this study confirmed that not only SPM and NO2
but also NOx tended to decrease. Land prices rose and the benefits were about 14 times
the cost. Improvement in the health of infants was also confirmed. Although theconclusions of the three outcomes were derived from different estimation models, there
were no inconsistent features. These results suggest that there are permanent health effects
of exhaust gas regulation in cities and that residents recognize and evaluate this benefit.
The benefits of the TLEZ estimated in this study may have been underestimated. The
target of the ordinances of Tokyo and three neighboring prefectures includes heavy-duty
diesel vehicles that are registered outside the TLEZ and pass through the TLEZ. Hence,
it is likely that air quality has improved outside the TLEZ as well as in the vicinity where
those vehicles pass. Since this benefit is not included in the air quality improvement
benefits derived in this study, the original benefits of the TLEZ will be greater. The results
suggest that policies to regulate heavy-duty diesel emissions should be promoted in
metropolitan areas.

37

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42

APPENDIX
Appendix A: Theoretical Framework

The local economy in the LEZ is constructed by agents who choose to reside in two
regions 𝑟 ∈ {𝐻, 𝐿}. Some live in areas with high heavy-duty diesel traffic (𝑟 = 𝐻), whileothers live in areas with low heavy-duty diesel traffic (𝑟 = 𝐿). However, we assume thatthey work in the same labor market regardless of the area in which they reside. Moreover,
we assume that residents of both locations work in the same labor market and, therefore,
earn the same wage w. Residents pay location-specific amenities and costs of living ineach area. Each resident 𝑖 has a specific preference 𝜂𝑖𝑟 for both locations and 𝜂𝑖𝑟 isheterogeneous regarding amenities. We assume that 𝜂𝑖𝑟 is independently and identically
distributed across individuals and has a continuous multivariate distribution with a zeromean value. It is assumed that heavy-duty diesel traffic stimulates neighborhood
economic activity through logistics.
Individuals maximize their utility through residential choice.
𝑈𝑖𝑟 = 𝑚𝑎𝑥{𝑣𝐻 + 𝜂𝑖 𝐻, 𝑣𝐿 + 𝜂𝑖 𝐿}
Here, 𝑣𝑟 represents the average utility of region 𝑟. Individuals live in areas with higher
utility. If there is heterogeneity in individual preferences, for example, the preferences ofindividuals residing in region H satisfy the relationship 𝑣𝐻 − 𝑣𝐿 > 𝜂𝑖 𝐿 − 𝜂𝑖 𝐻 . The
distribution function 𝜀𝑖 ≡ 𝜂𝑖 𝐿 − 𝜂𝑖 𝐻 is defined by 𝑅(∙). Let 𝑆𝐻 ≡ Pr(𝜂𝑖 < 𝑣𝐻 − 𝑣𝐿) bea measure of the individuals in region H.
The social welfare of workers in regions 𝐻 and 𝐿 is calculated as follows:
𝑉 = E[𝑣𝐻 + 𝜂𝑖 𝐻, 𝑣𝐿 + 𝜂𝑖 𝐿]
43

Consider the impact of the LEZ in the region. The establishment of the LEZ has threeeffects. One is to increase the cost of passage in the zone for a diesel vehicle performing
commercial activities. Hence, the traffic volume in each area is reduced. Since this costof passage is uniform within the LEZ and the TLEZ is large, this cost cannot be decreasedby options like detours. Since our model assumes that traffic affects regional productivity,
the first effect of the LEZ is a marginal deterioration of productivity in the local economy.
This productivity deterioration lowers the wages of residents of 𝐻 and 𝐿. Second, theaverage amount of air pollutants emitted from a diesel vehicle is reduced. Thus, theirhealth improves. Third, the decrease in traffic volume alleviates traffic congestion,
reduces traffic accidents, improves noise, and reduces PM generated by the braking of
automobiles. In other words, amenities can be improved based on reduced traffic.
The effect of the introduction of the LEZ on workers’ welfare can be obtained asfollows:

(A.1)

𝑑𝑉
𝑑𝐿𝐸𝑍

=𝑆×

𝜕𝑤

𝜕𝐴𝐻

𝜕𝑤

𝜕𝐴𝐿

= 𝑆𝐻 × [𝜕𝐿𝐸𝑍 + 𝜕𝐿𝐸𝑍] + 𝑆𝐿 × [𝜕𝐿𝐸𝑍 + 𝜕𝐿𝐸𝑍]
𝜕𝑤
𝜕𝐴𝐻
𝜕𝐴𝐿
+ 𝑆𝐻 ×+ 𝑆𝐿 ×
𝜕𝐿𝐸𝑍
𝜕𝐿𝐸𝑍
𝜕𝐿𝐸𝑍

Here, 𝑑𝐿𝐸𝑍 means a marginal effect due to the implementation of 𝐿𝐸𝑍 and 𝑑𝑉 ⁄𝑑𝑣𝑟 =𝑆𝑟. Equation (A.1) summarizes the impact of the LEZ in three terms. Term 1 is the total
wage effect of implementing the LEZ. We assume that all agents are in the same labormarket and are affected equally. Terms 2 and 3 show the changes in amenities due to theimplementation of the LEZ in each region.
The implementation of the LEZ increases marginal welfare in areas with more traffic(𝑆𝐻 × 𝜕𝐴𝐻 ⁄𝜕𝐿𝐸𝑍 > 𝑆𝐿 × 𝜕𝐴𝐿⁄𝜕𝐿𝐸𝑍 ). Some residents of low-traffic areas consider
moving to high-traffic areas for better utility. Because of this migration, residential prices
44

in each region change. The effect of the LEZ implementation on economic welfare may
be approximated by the change in land prices in each region. This study measures theimpact of the LEZ on economic welfare improvement by using difference-in-differences(DID) estimation to measure the relationship between, the difference between 𝑆𝐻 ×𝜕𝐴𝐻 ⁄𝜕𝐿𝐸𝑍 and 𝑆𝐿 × 𝜕𝐴𝐿⁄𝜕𝐿𝐸𝑍 , and traffic volume. For example, we measure thedifference in welfare improvement due to an increase of 1,000 vehicles in traffic volume.
Furthermore, by assuming that the improvement of air quality per diesel vehicle isuniform within the LEZ and the air pollutant reduction improves welfare, it is possible todiscuss the average welfare improvement per diesel vehicle.

45Appendix B: Emission Standards for Heavy-Duty Diesel

TABLE B.1. EMISSION STANDARDS FOR HEAVY-DUTY DIESEL
Year the regulation began

NOx

SPM

1994

6.80

0.96

1999

4.50

0.25

2005

2.70

0.036

Euro III

2000

5.00

0.10

Euro IV

2005

3.50

0.020

2004

5.36

0.13

Japan

EU

US EPA
Tier 2

Notes: All units of pollutants are g/kWh. g/kWh = g/bhp.h × 1.341

46

Appendix C: The Interpretation of TLEZ

In general, because traffic volumes may decrease once traffic restrictions are in place,the change in amenities in region 𝐻 can be expressed as follows:(C.1) 𝜕𝐴𝐻 ⁄𝜕𝐿𝐸𝑍 = 𝑍(Δ𝑇𝐻) + 𝑇𝐻 × ∆𝑃 − Δ𝑇𝐻 × (𝑃 − ∆𝑃),
where 𝑇𝐻 is the traffic volume in 𝐻, Δ𝑇𝐻 is the change in traffic volume in 𝐻, and ∆𝑃 is
the average improvement in air pollution per heavy-duty diesel vehicle by LEZ. 𝑍(∙)
represents the non-air quality amenities (traffic congestion, traffic noise, likelihood of
traffic accidents, etc.) due to traffic volume. The first term in equation (C.1) represents
the change in non-air quality amenities due to changes in traffic volume. The second term
represents the total change in air pollutants emitted by all vehicles passing through theneighborhood before the implementation of the LEZ. The third term represents the
reduction in air pollutants due to a decrease in traffic volume arising from theimplementation of the LEZ, minus the change overlapping the second term. However, if
there is no reduction in traffic (Δ𝑇𝐻 = 0), then equation (2) can be rewritten as follows:𝜕𝐴𝐻 ⁄𝜕𝐿𝐸𝑍 = 𝑇𝐻 × ∆𝑃
In other words, it can be interpreted that improvements in air quality drove the increasein land prices due to the implementation of the TLEZ.

47

Appendix D: Changes in Neighborhood Traffic Volume

Table A reports the statistics of neighborhood traffic based on the road traffic census
for different waves at the locations surveyed for official land prices in 2000. The roadtraffic census was conducted in 1990, 1994, 1997, 1999, and 2005. The survey results ofeach census were merged with road information, and traffic volume on the road within 2
km from the public land price survey point in 2000 was calculated using ArcGIS.
Although the average traffic volume in the vicinity of heavy-duty diesel vehicles
fluctuates every year, the correlation coefficients between the 1990 survey and othersurveys are 0.95–0.97, which is a very high value. Because heavy-duty diesel vehicles
are often driven over long distances, the absence of extensive bypass constructions during
this period may contribute to the high correlation coefficient. This table also reports thecorrelation coefficient between the traffic volume of heavy-duty diesel vehicles and thatof passenger cars, based on the 1990 survey, which is 0.87. Therefore, we believe that we
can control the structure of nearby roads and related factors using passenger car traffic.
TABLE D.1. TRAFFIC STATISTICS AND TRENDS

Correlation with no. of
diesel vehicles in 1990

Correlation with no.
of cars in 1990

(18.7)-

0.87(19.9)

0.97

25.9(20.0)

0.96

6,659

25.3

(20.1)

0.97

6,659

23.8

(19.2)

0.95

No. ofobs.

Mean

Std. dev.

No. of heavy-duty diesel vehicles in 1990 (within 2 km)

6,659

22.9

No. of heavy-duty diesel vehicles in 1994 (within 2 km)

6,659

24.4

No. of heavy-duty diesel vehicles in 1997 (within 2 km)

6,659

No. of heavy-duty diesel vehicles in 1999 (within 2 km)
No. of heavy-duty diesel vehicles in 2005 (within 2 km)

Notes: This table reports the basic statistics of the traffic volume in each traffic census at the survey point of the public land price as of 2000 andtheir correlation. The units of measurement of heavy-duty diesel vehicles and gasoline cars are in thousands.

48

Appendix E: Observation Points of Air Quality and Public Land Price in TLEZ

FIGURE E.1. OBSERVATION POINTS OF AIR QUALITY AND PUBLIC LAND PRICE IN TLEZ

49

Appendix F: Calculation of weighted average of traffic volume

We use 1-km mesh data to obtain the weighted average of the traffic volume of eachmunicipality. A 1-km mesh is the area defined for the report of the Japanese census. In
Japan, an area called the “reference area mesh” divides the entire country into a mesh of
approximately the same size based on latitude and longitude for reporting statistics. A 1km mesh is a reference area mesh, and the demographics in this area are reported.
The weighted average of each municipality is obtained by first measuring thetraffic volume within 2 km from the center of 1-km mesh and then calculating the valueof each municipality with a population of the 1-km mesh as a weight.

50

Appendix G: Validity of distance assumption

This study assumes that the traffic volume of heavy-duty diesel vehicles within 2 km
from the atmospheric observation station affects the atmospheric environment. Unlike theprevious point emission source, this study assesses the effects of mobile emission sources,
and it is not easy to measure the effective distance from a source. Therefore, we explain
the assumption that heavy-duty diesel traffic within 2 km of an atmospheric observatory
has an impact on air quality and the interpretation of the estimated results by comparing
the estimates for the definition of a neighborhood with radii of 1 km, 3 km, 4 km, and 5km.
Consider the following procedure for a radius for measuring traffic that better
explains the change in air quality at a point. Assume that there is an optimal radius formeasuring neighborhood traffic that best describes the change in air quality at a point.
When the radius is other than this optimal radius, the traffic volume used to explain thechange in air quality at a point contains a measurement error. If this measurement error
is random, then the average treatment effect is underestimated. Under the assumption thatthe measurement error is random, we consider a better radius by comparing the average
effect of varying the definition of the neighborhood.
Table B reports the results of the estimates for changing the distance at whichheavy-duty diesel traffic is measured from 2 km in the estimates in Table 4. Columns (1)–(5) report the estimation results when the radius is changed. Column (2) has the samevalue as Column (3) in Table 4. Table B also reports the mean and standard deviation ofeach variable and the average effect based on the estimation results.

51

Panel A reports the estimation results of the SPM changes. The largest average
effect was found for a radius of 2 km. Panel B reports the estimated results for the change
in NOx. The largest average effect for NOx is indicated for a radius of 4 km. This meansthat the NOx estimates reported in Table 4 may be underestimated. Based on these results,
we use traffic volumes within a 2-km radius when assessing the effects of TLEZ on land
prices and infant health.

TABLE G.1. THE EFFECT OF TOKYO LEZ ON AIR POLLUTION (ROBUSTNESS CHECKS)
Radius

1 km

2 km

3 km

4 km

5 km

(1)

(2)

(3)

(4)

(5)

-0.1249-0.1244-0.0654-0.0490-0.0396(0.0342)(0.0338)(0.0178)(0.0132)(0.0101)

Observations

6,340

6,448

6,448

6,448

6,448

Mean of no. of heavy-duty diesel vehicles

14.4

25.4

39.6

53.4

68.6

Std. dev. of no. of heavy-duty diesel vehicles

14.0

22.3

34.4

44.0

53.6

Average effects

-1.79

-3.16

-2.59

-2.62

-2.72

-0.1984-0.1432-0.1064-0.0927-0.0635(0.0624)(0.0640)(0.0372)(0.0261)(0.0201)

Observations

5,594

5,699

5,699

5,699

5,699

Mean of no. of heavy-duty diesel vehicles

14.9

25.8

40.2

54.0

69.3

Std. dev. of no. of heavy-duty diesel vehicles

13.9

23.1

35.2

44.7

53.9

Average effects

-2.95

-3.69

-4.28

-5.00

-4.40

Panel A: SPM
1(Post LEZ) × No. of heavy-duty diesel vehicles

Panel B: NOx
1(Post LEZ) × No. of heavy-duty diesel vehicles

Notes: All regressions include individual fixed effects, year fixed effects, 1(Post LEZ) × No. of gasoline cars (within 2km), and Prefecture FE × Year FE. Standard errors clustered by atmospheric stations are reported in parentheses.

52Appendix H: Event Study Graphs for Each Air Pollutant
0.2

0.25

0.15

0.15

0.1

0.05
-0.05

-0.05

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0
-0.1

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0.05

-0.15
-0.25

-0.15
-0.35

-0.2
-0.25

-0.45

PANEL A: SPM

PANEL B: NOX

0.06
0.04
0.02

-0.02

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0

-0.04
-0.06
-0.08
-0.1

PANEL C: NO2
FIGURE H.1. EVENT STUDY GRAPH OF REGULATED AIR POLLUTANTS
Notes: These figures present DID estimators of the impacts of the TLEZ on each air pollutant. The solid line shows the DID
estimator based on 2000, and the error bar shows the 95% confidence interval.
53

0.08
0.009
0.06

0.007
0.005

0.04

0.003

0.02

0.001

-0.005

-0.02

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

-0.0031990

0-0.001-0.04

PANEL A: SO2

PANEL B: CO

FIGURE H.2. EVENT STUDY GRAPH OF NON-REGULATED AIR POLLUTANTS
Notes: The graphs present DID estimators of the impacts of the TLEZ on each air pollutant. The solid line shows the DID
estimator based on 2000, and the error bar shows the 95% confidence interval.

54

Appendix I: Event Study Graphs for Public Land Price
0.003
0.00250.002
0.00150.001
0.000501991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-0.0005-0.001-0.0015-0.002
FIGURE I.1. EVENT STUDY GRAPH OF PUBLIC LAND PRICE
Notes: These figures present DID estimators of the impacts of the TLEZ on public land prices. The solid line shows the DID
estimator based on 2001, and the error bar shows the 95% confidence interval.

55

Appendix J: Event Study Graphs for Infant Health
0.01

0.02

0.005

0.01

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

-0.01
-0.02

-0.01

-0.03

-0.015

-0.04

-0.02

-0.05

0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
-0.07

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

PANEL A: BIRTH WEIGHT LESS THAN 2500 G

PANEL C: DEATHS OF NEWBORNS WITHIN 4 WEEKS OF BIRTHPANEL B: INFANT DEATHS UNDER 1 YEAR OF AGE

0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
-0.07

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

-0.005

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0

0

PANEL D: NO. OF EARLY NEONATAL DEATHS (WITHIN 7 DAYS)56

0.005-0.005

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0

-0.01
-0.015

0.015
0.01
0.005
0
-0.005-0.01-0.015-0.02-0.025-0.03

PANEL E: NO. OF STILLBIRTHS

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0.01

PANEL F: STILLBIRTHS AFTER 22 WEEKS OF GESTATIONFIGURE J.1. EVENT STUDY GRAPHS OF INFANT HEALTH
Notes: These figures present DID estimators of the impacts of the TLEZ on infant health. The solid line shows the DID estimator
based on 2000, and the error bar shows the 95% confidence interval. The analyses of Panels E and F exclude the sample with Y= 1. The addition of these samples does not significantly affect the interpretation of the results.

57