Discussion Paper Series
2014-03
The effectiveness of vehicle emission control policies:
Evidence from Japanese experience
Kazuyuki Iwata, Toshi H. Arimura and Tetsuya Shimane
May 19, 2014

Discussion Papers can be downloaded:
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp14-03

The effectiveness of vehicle emission control policies:
Evidence from Japanese experience
Kazuyuki Iwata 1 (Takasaki City University of Economics)
Toshi H. Arimura (Waseda University)
Tetsuya Shimane (Tokyo Institute of Technology)

Abstract
Governments in developed countries have implemented various regulations to manage air pollutantsfrom automobiles, such as emission standards and subsidies for low emission vehicles. Japan is aunique example of a country that has overcome the severe air pollution of nitrogen oxides (NOx) andparticulate matter (PM) through the mandatory retirement of old, high emission vehicles inmetropolitan areas. To date, however, it is not clear which policy instruments have been effective in
mitigating the air pollution from automobiles. The purpose of this paper is to empirically examine
the effectiveness of policy instruments in attaining cleaner air in Japanese metropolitan areas. Using
data from 1990 to 2005, we estimate the concentration functions of NOx and PM using a spatial
econometric model. We find that most regulations and subsidies decreased the concentration levelsof both pollutants. Traditional emission standards were found to be more effective than other policy
instruments. Vehicle replacement subsidies were more cost-effective than those for PM-removal
equipment. Furthermore, the empirical results indicate that the effect of subsidies for vehicle
replacement on one municipality’s air had spillover effects by improving the pollutant concentrationsin the surrounding municipal areas.

Keywords
Air pollution, Automobile, Regulation, Subsidies and Spillover

Corresponding author
Address: 1300 Kaminamie, Takasaki, Gunma, 3700801 JAPANE-mail: iwata.kazuyu@gmail.com
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1. Introduction
Air pollution presents health risks, such as cardiovascular and respiratory diseases andlung cancer. The World Health Organization (WHO) reports that urban outdoor air pollution killed
1,152,000 people globally in 2004. 2 This human suffering occurred not only in developing countries
but also in developed countries, such as in Europe, where the estimated death toll from urban air
pollution in 2004 was 225,000. Numerous governments are facing the urgent need to implement
effective countermeasures against this externality.
The two major sources of air pollution are stationary sources, such as factories, and mobile
sources, such as automobiles. Governments in developed countries have implemented various
regulations to control emissions from stationary sources and have dramatically reduced theseemissions. However, it has been more difficult to control emissions from mobile sources, for two
reasons. First, mobile sources are more numerous than stationary sources. Second, due to themobility of these sources, it is difficult for regulators to identify and control air pollution fromvehicles at the appropriate level of marginal external cost.
A traditional approach to this problem is the introduction of emission standards (ESs).
Although ESs seem to be effective, they face several problems. First, even with a stringent ES, theincreasing number of vehicles makes it difficult to achieve the ambient standard in metropolitan
areas. Second, ESs apply to new vehicles only, which comprise only a small portion of all vehicles.
Consequently, ESs have limited impacts on the ambient air quality, at least in the short term.
Since 1990, the Japanese government has taken a unique approach to overcoming thisproblem. Although the Japanese government has continuously intensified ESs since theirintroduction in 1966, the concentrations of nitrogen oxide (NOx) and particulate matter (PM) 3 inmetropolitan areas remained high in the 1990s. The government introduced a mandatory
replacement of old, high emission vehicles with new, cleaner vehicles in designated metropolitan
areas. The first replacement policy (RP) was introduced in 1992 to manage NOx. This policy wasamended in 2001 to reduce PM emissions as well as NOx. Although there have been several
voluntary programs to promote the retirement of old vehicles, mandatory retirement is a unique2
3

Refer to the WHO database, http://www.who.int/healthinfo/global_burden_disease/en/index.html.
In this study, PM indicates PM10, which has a particulate diameter less than 10 µm.

2policy. Therefore, this replacement policy is worthy of empirical analysis.
In addition to regulations by the national government, local governments in Japanese
metropolitan areas introduced a technical regulation in 2003, when local governments beganrequiring the installation of PM removal equipment (DPFM) if vehicles did not satisfy a certain
emission standard. Furthermore, local governments have offered two types of subsidies since 2000
in an attempt to control emissions. The first type promotes the replacement of older, high emission
vehicles with new, cleaner vehicles, and the second type of subsidy encourages the installation of
PM removal equipment.
As of 2008, the Japanese emissions policy comprises five parts: an ES, an amended
replacement policy (ARP), DPFM and two types of subsidies. These regulations and subsidies
appear to be effective because the NOx and PM concentrations at the monitoring stations have been
decreasing (see Figures 1 and 2). However, this effectiveness is inferred from the relationshipbetween the regulations/subsidies and the concentration levels over the years. Rigorous empiricalanalysis is necessary to clarify the impacts of various policy instruments on air quality.
Several studies have examined the effectiveness of Japanese regulations for vehicle
emission control as an ex-ante policy evaluation. Using a theoretical model, Iwata and Arimura(2009) found that the amended replacement policy (ARP) could provide a positive net social benefit,
and they suggested that this benefit could be significantly increased through the design of optimal
policies. Iwata (2011) conducted an ex-ante cost-benefit assessment of DPFM with a theoreticalframework and concluded that DPFM was a valid policy for attaining cleaner air. These studies
showed that ARP and DPFM theoretically improved social welfare. Iwata (2011) also suggested thatsubsidies should be spent on low emission vehicles (LEVs) because the marginal abatement cost is
higher to subsidize PM removal equipment than to subsidize LEVs. These studies, however, areex-ante theoretical analyses, which cannot guarantee that the policy improved air quality.
Although the air quality has been monitored, the effectiveness of individual regulations
and subsidies has not been evaluated from an ex-post perspective. Using ambient air quality data on
NOx and PM concentrations from 1990 to 2005, this paper examines the effectiveness of individual
regulations and subsidies by conducting an ex-post evaluation of past and current regulations.

3

Specifically, we examine the effectiveness of ES, RP, ARP, DPFM and two types of subsidies.
Because metropolitan areas are primarily affected by vehicle air pollution, we focus on the fourlargest metropolitan areas in Japan (Tokyo, Nagoya, Osaka, and Fukuoka) (see Figure 3).
Although this paper is one of the first studies to conduct an ex-post analysis of theeffectiveness of vehicle emission policies in Japan, several studies have reported similar cases inother countries. In the United States, the federal government requires counties to provide
improvement plans for reducing ozone (O3) levels if they do not satisfy emissions targets. Using O3
data at air pollution monitoring stations in the United States from 1977 to 1987, Henderson (1996)
suggested that these controls decreased O3 concentrations by 3 to 8 percent.
Auffhammer et al. (2009) also examined the impact of regulation on air pollutant
concentrations in the United States. They focused on the 1990 Clean Air Act Amendments and used
monitoring data from 1990 to 2005 to examine whether these amendments contributed to the
reduction of PM10 levels. These authors concluded that that the amendments reduced concentrations
by 11 to 14 percent.
Using data from air pollution monitoring stations in Delhi, India from 1990 to 2005,
Narain and Krupnick (2007) found that the conversion of commercial passenger vehicles (buses,
three-wheelers, and taxis) to compressed natural gas helped to reduce PM, CO, and SO2
concentrations. These studies used data from monitoring stations managed by governments.
Following these studies, the current study also uses data from governmental air pollution monitoring
stations.
This study attempts to contribute to the literature in the following four ways. First, this is
the first study to examine the effectiveness of the mandatory replacement of old vehicles. Althoughvoluntary programs to replace old vehicles have been implemented in several US states, such as
California (Dill, 2004) and Delaware (Alberini et al., 1996), research on the mandatory replacement
program has yet to be scrutinized. In addition, an ex-ante study (Iwata & Arimura, 2009) showedthat the cost of this replacement policy is significant. Thus, the effectiveness of RP is worthy of
research.
Second, this study identifies the effectiveness of each emission control policy. Although an

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increasing number of regulations and subsidies have been introduced, it is not clear which policies
have helped to reduce specific air pollutant and to what extent these policies have been effective.
The Japanese experience can provide useful implications for policymakers in both developed andemerging countries. For example, Korea introduced a regulation requiring the installation ofpollutant elimination equipment in in-use vehicles in the Seoul metropolitan area, which is similar to
the Japanese DPFM. 4 Many cities in these countries and states are experiencing severe air pollution
and have high population densities similar to Japanese metropolitan cities.
Third, this study adopts a spatial econometric approach to evaluate the air pollution
regulations of mobile sources. The concentrations of pollutants at neighboring monitoring points canbe correlated if they are geographically close because the omitted variables are often spatiallycorrelated. Because it is difficult to control all omitted variable problems, we adopt an econometricapproach to the spatial correlation issue, in contrast to previous studies (Henderson, 1996;
Auffhammer et al., 2009; Narain & Krupnick, 2007).
Finally, this paper explores the spillover effect of subsidies. One municipality’s subsidy
may contribute to the reduction of emissions in neighboring municipalities. Furthermore, firms may
not necessarily use their vehicles only within a single municipality. If firms purchase LEVs with aparticular municipality’s subsidy and drive across municipalities, the subsidy will effectively reduce
emissions not only in the municipalities with the subsidy but also in the municipalities where thevehicles are used. However, air pollutants may spread to neighboring municipalities by wind, and thespillover effect may exist even when a firm’s vehicles remain within a single municipality.
The rest of the paper is organized as follows. In the next section, we explain the details of
the Japanese vehicle emission control policies. Our estimation model is described in Section 3.
Section 4 explains the dataset, and Section 5 provides the estimation results. Section 6 concludes thepaper.

4

This regulation is based on the Special Act on Seoul Metropolitan Air Quality Improvement.
5

2. Overview of the Japanese NOx and PM Regulations

2.1. Emission Standards (ES)The first air pollution regulation on the vehicle was emission standards (ES) introduced in
1966. The ES sets the upper limit value of emission intensity and applies to new cars only. The typeof this regulation is implemented by many countries such as United States, Canada, Mexico, EU,
China, India and so on. Unless automakers comply with the standard, they cannot sell their vehicles
in Japan. At first, target pollutant was only Carbon Oxide. And then the ES on NOx was announced
in 1972 (enforced in 1973) and gradually has become stringent overtime. For example, the standard
for the standard diesel truck in 1973 was 5.28g/km, and 0.33g/km in 2005. The standard on PM wasintroduced much later in 1993. The standard on PM on diesel standard truck was 0.033g/km. Later, it
was revised to 0.002g/km for the 2005 vehicles. The transition of tightening of these emission
intensitys is shown in Figure 4. This standard covers all regions of Japan.

2.2. Replacement Policy (RP)Due to the ES on vehicle, the air quality improved dramatically since 1970s. The annual
average NOx concentration level in Japan dropped from 0.073 ppm in 1972 to 0.039 ppm in 1990.
The level in the two largest metropolitan areas (Tokyo and Osaka) in 1990, however, was still high,0.059 ppm (see Figure 1).
One limitation of the ES is that it applies to new vehicles only. Vehicles in use were notaffected by ES. Facing the high NOx concentration in the areas, the national government introduced
Automobile NOx Law in 1992. Unlike ES, this law covers only the two largest metropolitan areas,
which the national government designated as non-attainment area. The law regulates vehicles in use
in the non-attainment areas and is unique in the sense that it sets the terminal years of the vehicle
usages in the non-attainment areas. Thus, it enforces replacement of the old-dirty vehicles with new
ones. The main objective was to reduce NOx concentration from diesel trucks. The terminal years
depends on vehicle type such as trucks, buses and passenger vehicles.

62.3. Amended Replacement Policy (ARP)
Despite the introduction of RP, the NOx concentration in the Tokyo and Osaka
metropolitan areas was still higher than the other areas (see Figure 1). The PM concentration waseven worse. In response to this situation, the Automobile NOx Law was amended in 2001 into
Automobile NOx-PM Law to improve the ambient air quality of both NOx and PM more effectively.
This time, Nagoya metropolitan area, the third largest metropolitan area in Japan, was also added as
non-attainment area. The amended law expanded target vehicles more than before by tightening thecriteria of regulation. We refer this amended regulation as “ARP”.

2.4. Diesel Particulate Filter Mandate (DPFM)
Apart from the national government regulation such as ES and RP, in 2003 municipalities
in the Tokyo metropolitan area independently implemented another regulation, diesel particulate
filter mandate (DPFM), in order to reduce PM concentration. Specifically, they mandated theinstallation of diesel particulate filter (DPF) to dirty diesel trucks in use unless they satisfy the 1999
emission intensity under ES. DPF is a kind of PM emission control technology (Lopez et al., 2009).
The official equipments approved by municipalities can remove PM emission by between 30 and 70
percent (8Capital Pref City Aozora Network, 2007). Trucks without DPFs are prohibited to operatein Tokyo metropolitan area unless they comply with the 1999 standard.

2.5. DPF and LEV Subsidies
Some municipalities in non-attainment areas provided two types of subsidies to reduce
emission from vehicles since late 1990’s. One is a subsidy for promoting the DPF installation (DPF
subsidy). Another is for purchase of LEVs such as natural gas and hybrid vehicles (LEV subsidy).
The amounts of subsidies are different from each municipality. For example, municipalities in Tokyo
area spent 27.5 billion yen for DPF installation from 2002 to 2005. On the contrary, same subsidy inOsaka area was only 0.27 billion yen. There was no subsidy in Fukuoka metropolitan area that is the
fourth biggest metropolitan area (see Table 1). Fukuoka metropolitan area is assigned as attainmentarea, unlike the top three metropolitan areas. This Fukuoka area is applies as a control group because7only ES is introduced in this area..

3. Econometric ModelNOx and PM concentration levels (ppm or mg/m3) have been measured as ambient air
quality. Thus, vehicle emission regulations are expected to reduce their levels. Their concentrationlevels also depend on other factors including socio-economic and climate/weather conditions.
Following Henderson (1996), we thus specify the concentration function as follows 5:

AC p ,ijt = X1, p ,ijt β1, p + X 2,ijt β 2, p + X 3,ijt β 3, p + ε p ,ijt

(1)where AC p ,ijt is the concentration levels of air pollutant p (NOx or PM) at monitoring station i inmunicipality j at year t. X1 is the vector of variables for vehicle emission control policies, X2 is thevector of social structure, and X3 is the vector of climate/weather condition. The parameters βｋ (k =1,2,3) are to be estimated and ε is an error term. We examine the effectiveness of the regulations by
estimating coefficients in equation (1) for each pollutant and looking particularly at the estimatedcoefficients of X1. Further, by comparing the size of the coefficients, we identify to what extent theexisting regulations have helped to reduce NOx and/or PM concentrations.
Considering unobserved station specific effects, we employ two specifications for the error
term; fixed effects and spatial random effects (Leggett & Bockstael, 2000). Additionally, we assume
autocorrelation between current error term and last year’s error term as we use panel dataset. The
error terms for fixed effect model and spatial random effect model are thus described as following
equation (2) and (3), respectively.

ε p ,ijt = λε p ,ijt −1 + η p ,i + ν p ,ijt

ε p ,ijt = ρ ∑ Wil ε p ,ljt + lε p ,ijt −1 + η p ,i + ν p ,ijt

(2),i ≠ ll5(3)Sigman (2002), Greenstone (2003), and Greenstone (2004) estimate concentration/emission function similar to thisstudy. Our concentration function uses flow concentration at a certain point in time with the assumption that theconcentration levels at a monitoring station in the previous year do not influence the levels in the next year.

8where λ and ρ are parameters in the both specifications. W is a spatial weighting matrix for
distances between any two stations.

3.1. Vector of Emission Control Policies

The vector of policies, X1, includes the following six variables (and eight for PM
concentration function). The first variable is to capture the effectiveness of ES, i.e., the upper limit
value of emission intensity imposed by the regulation. NOx regulation began in 1973 and PM
regulation in 1993. Therefore, the values used for NOx and PM concentration functions are different.
NOx regulation has seventeen categories and PM regulation has seven categories based on theweight and vehicle type. This study uses the average value of those categories. The standard valuesof NOx and PM are set to 100, respectively. The standard value of NOx is that in 1972, i.e., the year
before the regulation started. The standard value of PM is that in 1993, that is, the year when theregulation started. PM values before 1993 are also set to 100 because information on PM emissions
before 1993 is not available.
The second is the RP dummy, i.e., a variable that captures the effectiveness of theAutomobile NOx law. This law is the forerunner of the ARP under Automobile NOx-PM law. RP
introduced in 1992 is the nation’s first regulation that focused on vehicles in use. The law wasamended in 2001 as its effectiveness was said to be insufficient (Sano, 2008). In order to estimate theeffectiveness of the RP, we use the RP dummy where an air pollution monitoring station takes one if
it is located in the Tokyo and Osaka metropolitan areas designated as non-attainment area by the RP
between 1992 and 2002 (i.e., from the year the regulation was introduced until the year it was takenover by the NOx-PM law) and takes zero otherwise.
The third is the ARP dummy, i.e., a variable that captures the effectiveness of theAutomobile NOx-PM Law. This dummy takes one if a monitoring station is located in the Tokyo,
Osaka and Nagoya metropolitan areas, which were designated as non-attainment area by the law
after 2003 and takes zero otherwise. Although the law was amended in 2001, the ARP had teeth

9since 2003. It should be noted that the designated areas by this law are different from those by its
forerunner, the Automobile NOx Law. Because we use the data from 1990 to 2005, the effectivenessof the ARP can be measured only for the period of three years, from 2003 to 2005.
The fourth is the DPFM dummy. This variable captures the effectiveness of the DPFM thatstarted in the Tokyo metropolitan area in October 2003. This variable takes one if a monitoring
station was built in the designated areas after 2004 and takes zero otherwise. While DPFM makes it
mandatory to install PM removal equipment, some firms observe the regulation by replacing theirvehicles to new ones instead of installing the equipment (Iwata, 2011). This may lead to help reduce
NOx concentration in addition to PM concentration, as the two pollutant emission intensities of newvehicles are better than those of old ones because of the ES. Thus, we use DPFM dummy variable
for NOx concentration function as well as PM concentration function.
The fifth is the amount of the LEV subsidies, i.e., a variable that captures the costeffectiveness of the subsidies. Lately, many municipalities offer subsidies and encourage a switch to
LEVs. Amount of LEV subsidies differ by municipality and year (see Table 1). In 2007, for example,
Kanagawa prefecture in Tokyo metropolitan area subsidized one-eighth of the cost for either
switching to LEVs to firms in the prefecture that own freight weighing over 3.5 tons. Because asubsidy affects air quality not only of the year when the subsidy is offered but also in the subsequent
years (i.e., subsidized firms that purchased LEVs will continue using the vehicles in the later years),
we consider the subsidy as flow and revised the stock based on the assumption that the depreciation
rate is 3 percent.
The sixth variable captures the spillover effect of LEV subsidies. Vehicles in a
municipality may be used outside of the municipality and thus, a municipality’s subsidy may help
improving ambient air quality in its neighboring municipalities. To capture this spillover effect, wegrouped the areas under study into four metropolitan areas (Tokyo, Nagoya, Osaka and Fukuoka)
and use the sum total of subsidies in the region to which each municipality belongs. For example,
there are 8 municipalities in Tokyo metropolitan area. We set all subsidies except Tokyo municipality
in the Tokyo metropolitan area as the value for spillover effect of Tokyo municipality.
The seventh variable captures the cost effectiveness of DPF subsidies. The equipment is10

effective to reduce PM emissions and not NOx. Hence, DPF subsidies are used only for PM
concentration function. Just like LEV subsidies, the effectiveness of DPF subsidy lasts after the year
in which it was offered and thus, its stock value was revised in accordance with the 3 percent
depreciation rate.
The eighth variable captures the spillover effect of DPF subsidy. Just like LEV subsidies, amunicipality’s DPF subsidy may lead to improving ambient air quality not only in that municipality
but also in the surrounding municipalities. Thus, we applied the same procedure to this variable asthat for the LEV subsidy.
All eight variables are expected to contribute to reducing air pollutants; that is, theircoefficients are likely to be negative. This study looks at the size of the coefficients to compare theeffectiveness between/among the regulations.

3.2. Vector of Social Structure

NOx and PM concentration levels are influenced by economic activities aroundmonitoring stations as well. To capture the size of economic activity, we use the following threevariables. As a variable for traffic volume around stations, we use the freight tonnage in themunicipality to which each station belongs. We use freight tonnage because most NOx and PM
pollutions are caused by diesel trucks. To understand traffic activities around stations in more details,
we use highway dummy. “Highways” in this study refer to the “major regional roads” as defined in
the census of transportation as well as the roads larger than them. The highway dummy takes one if astation is near a highway and takes zero otherwise.
Ambient air quality is also affected by industry activities at factories and thus, we use adummy for industrial areas to capture productive activities near stations. This dummy takes one if astation is located either at “semi-industrial area,” “industrial area,” or “industrial exclusive area” asdefined in The Japanese City Planning Law. The dummy takes zero otherwise.
All these variables capture economic activities. The coefficients of these variables are
expected to be positive, as ambient air quality deteriorates as economic activities become active.

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3.3. Vector of Climate

Climate conditions are also included into the model as explanatory variables. As pointed
by Henderson (1996) and Narain & Krupnick (2007), meteorological conditions significantly affectambient air quality. Wind and rain disperse air pollutants while reducing their concentration levels.
In winter if warm air interacts with cold air, dispersion of pollutants occurs even when winds are
weak around stations (Goyal, 2002). Therefore, factors such as wind speed, rainfalls, or temperatures
are also likely to affect NOx and PM levels. We thus use three variables to capture climate
conditions: the average annual temperature, the average annual wind speed, and the average annual
rainfall.

4. DataThis study uses data derived from air pollution monitoring stations in four metropolitan
areas (Tokyo, Nagoya, Osaka, and Fukuoka) from 1976 and 2005. Based on “Air Pollution
Monitoring Stations Data” published by the Japanese Ministry of the Environment (JEPA), there are
1,003 and 944 monitoring stations for NOx and PM in the four metropolitan areas between 1976 and2005, respectively. 6 The numbers of stations, however, change every year as new stations open andexisting stations either close down or withdraw. To ensure computational feasibility for spatial
estimation described in next section, we extract balanced data from the unbalanced one. In addition,
to control for effects of non-transportation policies on air pollution from industrial sectors, we only
use the data of load side monitoring station.
The period of extracted balanced panel data is from 1990 to 2005 as the data provides
largest numbers of observations for PM which is 1,184 observations (87 stations for each year).
Many PM monitoring stations were constructed since late 1980s. For example, when we use
balanced panel data from 1982 to 2005, the number of PM monitoring stations for each year is only

6

The data is located at the National Institute for Environmental Studies (NIES) website,
http://www.nies.go.jp/igreen/index.html. The institution is established under the Japanese Ministry of theEnvironment.

1215. On the contrary, the number of NOx monitoring stations did not change so much over years. The
numbers of NOx monitoring stations for each year are 111 and 147 with 1982-2005 and 1990-2005
balanced panel data, respectively. Hence, to obtain large observations on PM, we use data from 1990
to 2005.
The data contains information including the location of each station, environment aroundthe station, as well as the types of air pollutants it monitors. Using this data of the location, wedevelop industrial-area dummy and highway dummy.
The annual average concentrations and hourly maximum concentrations of NOx (unit:ppm) and PM (unit: mg/m3) for each monitoring station are obtained from “Monthly and Annual
Levels of Air Quality” published by the JEPA, and then we merge the station data and pollution data.
The database on NOx and PM concentration levels at monitoring stations is then mergedwith the following data. NOx and PM values for ES are created from Osaka Prefecture (2006).
Because the upper limit of emission intensity under ES is identical across all regions in Japan, thevariable changes only by year. As mentioned earlier, we have standardized the value to 100 for theyears before the regulation was introduced, which means that if the regulation becomes more strict,the value becomes smaller than 100.
The annual gross amount of LEV and DPF subsidies are obtained from the municipalities.
These values are identical for stations within the same municipality. As shown in Table 1, mostmunicipalities introduced the subsidies after 2001 because the Automobile NOx-PM Law was
introduced that year. Installation of DPF is mandatory under the DPFM in the Tokyo metropolitan
area. Thus, the total amount of DPF subsidies in the area is larger than that in the other areas where
the installation is not mandatory. Neither of these subsidies is available in Fukuoka area where RP,
ARP and DPFM are not implemented.
In regard with the other data for control variables, we use vehicle/freight tonnage bymunicipality available at “Land and Transportation Statistics Directory” published by the Japanese
Ministry of Land, Infrastructure, Transport and Tourism. We obtain climate/weather data from theweather station nearest to each monitoring station. These climate data are available in “Weather
Statistics Information” published by Japan Meteorological Agency. Table 2 shows descriptive

13

statistics of our data.

5. Estimation Results

We estimate the concentration function (1) with two specifications on the error terms;
fixed effects and spatial random effects. First, considering monitoring station’s unobserved specificfixed effect, we conducted Hausman test to examine which model is suitable – a fixed effects modelor a random effects model –. The former one was found to be more suitable. When we conducted
Serial test (Wooldridge, 2002), a serial correlation was observed. Thus, treating the effect at
individual stations as fixed effect, we estimated AR1 fixed effects model.
Next, we consider spatiality, that is, the location of monitoring stations. If pollutant
concentration level at a station is high, the level at other neighboring station is also likely to be highbecause socio-economic or geological characteristics between the two stations may not be different
from each other. To consider this spatial autocorrelation between any two stations in same
metropolitan area, we employ feasible generalized least squares with AR1 disturbance. We set spatial
weighing matrix for each area, that is, we assume that error term in a monitoring station in a
metropolitan area is correlated with that in same areas’ station according to distance between the twostations. Weighting matrix for each area is assumed to be an inverse function of squared distance
between two stations in same area, and value in the matrix is zero when distance between two
stations is more than 10km. 7

5.1. NOx Concentration Function

Table 3 exhibits estimation results by fixed effect model, column (1) and (2), and spatial
random effect model, column (3) and (4), respectively. 8 Over viewing the estimation results of fixedand spatial random effect models, we do not find remarkable difference.

7

Though we use other functional forms for the weighting matrix, estimation results are not different.
These results are also available for the cases using annual average and hourly maximum concentration levels as
dependent variable.

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The coefficient of ES in all estimation results is positive and significant at the 1 percent
level both for the annual average and hourly maximum concentrations. This means that NOx
concentration levels decrease as the maximum allowable limit of emission intensity for NOx under
ES becomes severe. It is found that the average concentration decreases by 0.0003~0.0004 ppm andthe maximum concentration by 0.0017~0.0024 ppm if the limit is reduced to 1 percent below 1972
levels.
In all the estimation results, the RP dummy variable is negative and significant. Thismeans that RP helps reducing NOx concentration levels. The reduction is about 0.0020 ppm for the
average concentration and 0.0111 to 0.0208 ppm for the maximum concentration. Although theeffectiveness of the law was said to be restrictive (Sano, 2008), it was indeed effective.
ARP by the Automobile NOx-PM Law also reduces the average NOx concentration by
0.0022 to 0.0028 ppm and the maximum concentration by 0.0418 to 0.0432 ppm (statisticallysignificant at the 1 percent level). All the estimation results show that the size of reduction is greater
under the ARP than the RP for the average and maximum concentrations That is, the law amendment
was effective in reducing concentration levels.
The effectiveness of DPFM is statistically significant only in the spatial random effectmodel at 10 percent. Hence, we can roughly conclude that DPFM improved NOx concentration levelin Tokyo metropolitan area. The absolute values of the coefficient are smaller than those of RP and
ARP. Although some firms may have switched to LEVs to comply with the regulation, DPFM did
not seem to have much contribution in improving the concentration levels (i.e., the change broughtby the regulation is not statistically significant), compared with the other regulations.
The coefficient of the LEV subsidies is also negative and statistically significant. That is,
the more subsidies are given, the more NOx concentration reduced. Some firms may have purchased
LEVs with the subsidies simply because they were planning to renew their vehicles anyway. In thatcase the subsidies do not have actual effect to reduce concentration levels. The results, however,show that the kind of free ride behavior is limited.
The spillover effects of a municipality’s LEV subsidy on other municipalities are
significant in all the estimation results. That is, NOx concentration levels in a municipality improve

15

even without subsidizing LEVs if LEV subsidies are offered in the surrounding municipalities. This
result suggests that an environmental subsidy using a municipality’s own budget generates positive
external economy effect. The size of reduction brought by the spillover is about a third of that
achieved by a municipality that offers its own subsidy. 9 Therefore, the spillover effect of the subsidy
is neither small nor negligible.
The coefficient of traffic volume, used as the vector of social structure, is positive andsignificant at the 1 percent level (insignificant in fixed effect model). This means that ambient air
quality deteriorates as the volume of freight traffic increases. It is also found that the average
concentration levels are significantly high in industrial areas and both the average and maximum
concentration levels are significantly high in the areas near highways. In fixed effect model, thesedummy variables are dropped because the values do not vary over years.
The average concentration levels are higher in industrial areas whereas the maximum
levels are the same as in other areas. The reason is because massive industrial production may nottake place only at particular hours (which would lead to raise the hourly maximum concentrationlevels).
The average temperature, i.e., the vector of climate/weather conditions, negatively
influences the concentration levels. That is, NOx concentration levels rise as the temperature lowers.
This is consistent with the results in Goyal (2002). It is also found that wind speed and rain fallsignificantly affect NOx concentration levels.

5.2. PM concentration function

This section discusses the estimation results of the PM concentration function described inTable 4. The sample size is 8,384. The PM concentration function is different from the NOx
concentration function in that there are eight variables for the category “emission control policies”;
DFP subsidies and spillover effect have been added.
Similar to that of the NOx concentration function, the coefficient of ES is positive and9
On spillover effect and free-riding, see Sigman (2002) that explores the context of the European Union andexamines the spillover effect of pollution in international rivers as well as countries’ free-riding.

16

statistically significant at the 1 percent level in all estimation results. That is, PM concentration
decrease as the regulation becomes stringent both at the average and maximum concentration levels.
The average concentration decreases by 0.0001 mg/m3 and the maximum concentration decreases by
0.0010 mg/m3 if the regulation is reinforced to 1 percent above 1993 levels.
Both the coefficients of the RP dummy and the ARP dummy are found negative andstatistically significant in all estimation models, meaning that these renewal policies havecontributed to reducing PM levels by 0.0019 to 0.0031 mg/m3 for the average concentration and by
0.0120 to 0.0224 mg/m3 for the maximum concentration. The results are slightly different from thoseof the NOx concentration function when their coefficients are compared at the absolute values. In
estimation results for the annual average concentration function, the coefficient of the RP is greaterthan that of the ARP, suggesting that the reduction was larger before the regulation amendment took
place. This indicates that while the RP under the Automobile NOx Law was meant to reduce NOx
emissions exclusively, it actually reduced PM emissions as well.
The coefficient of DPFM dummy is negative and statistically significant in one estimation
result only. Hence, the DPFM is not robustly effective in reducing PM concentration level, unlike
the ES and RP. The reduction brought from DPFM is not as strong as those of the RP and ARP
Therefore, it is concluded that the municipal DPFM helped reducing neither NOx nor PM
concentrations well.
The effectiveness of LEV subsidies is not found to be significantly negative except theresult shown in column (1) by fixed effect estimation. The negative coefficient of DPF subsidies issignificant by fixed effect model, but not by spatial random effect model. Hence, we cannot
conclude that these subsidies brought reduction of PM concentration. Though the both coefficientsof DFP and LEV subsidies are significantly negative in column (1), the value of DPF subsidies iseight times lower than that of LEV subsidies. Thus, it may be more cost effective to subsidize LEVs
than DPFs. This is consistent with Iwata’s (2011) simulation study. 10 Tables 1 shows that themunicipalities in the Tokyo metropolitan area spent their subsidies more on the equipment than on

10

The designated equipment can reduce PM emissions by 30 to 70 percent. However, switching to low emission
vehicles is more cost effective than installing the equipment, because PM emissions from vehicles had been reduced
significantly after the implementation of PM unit regulation in 1993 (Iwata, 2011).

17

vehicles, though the opposite is the case in other areas. It may be more effective for themunicipalities in the Tokyo metropolitan area to shift their focus on subsidizing cleaner vehicles.
The spillover effects of LEV subsidies are statistically significant in most estimation
results. The spillover effects of DPF subsidies are insignificant or significantly positive. That is,unlike the NOx concentration function, neither subsidies have robust spillover effects in mitigating
PM concentration in other municipalities.
Unlike the NOx concentration function, obvious differences between estimation results for
subsidies from fixed and spatial random effect model are found. If we take spatial correlation among
monitoring stations into account, the significant effects of the subsidies and their spillovers in
improving ambient air quality do not tend to be observed. This fact shows that spatiality plays animportant role in considering the PM concentration function.
Moving to the variables for social structure, we can find link between PM concentrationlevels and traffic volume. Also, the effect of highways near monitoring stations is statisticallysignificant. The coefficient of industrial-area dummy is not statistically significant.
Although some are not statistically significant, most coefficients of meteorologicalconditions are negative. That is, PM concentration levels decrease as the average temperature
becomes higher, the wind speed becomes faster, or rainfall increases. This is consistent with theresults in the past studies.

5.3. The Effectiveness of Policies After the 1990s

This section discusses how ambient air quality was improved by the each policy, namely,
ES, RP, ARP, DPFM, DPF and LEV subsidies. Our analysis here focuses on the areas in the Tokyo
metropolitan area where all policies are effective. Using the values from the spatial random effectestimation results in Tables 3 and 4, we estimated predicted values of concentration levels both forthe cases where vehicle emission control policies exist and do not exist, while holding all otherfactors fixed. Figures 5 to 8 present NOx and PM concentration reductions from each policy,
respectively, specifically, by showing the change of the means at monitoring stations.

18For the purpose of comparing the effectiveness of the policies, we set 1991 as an initial
year in simulation because the RP started in 1992. The value of ES in 1991 is assumed to unchange
afterward. In other words, as the baseline of our computation we conditioned on the upper limits
under ES before 1991.
The line, “Without policy”, in each figure, shows the change of concentration levels whennone of the policies exists. “With ES” is the case when only ES exists. That is, the gap between“Without policy” and “With ES” indicates the concentration reduction brought by ES after 1992.
Similarly, the gap between “With ES, RP & ARP” and “With ES” is the concentration reduction
brought by RP and ARP, and the gap between “With ES, RP & ARP” and “With ES, RP, ARP &DPFM” is the concentration reduction brought by the DPFM carried out in municipalities in theTokyo metropolitan region. The gap between “With ES, RP, ARP & DPFM” and “With all policies”
means the concentration reduction brought by the two types of subsidies.
Even when no policy is imposed, both NOx and PM concentration levels change because
traffic volume and weather/climate condition have changed. The amount of traffic volume in Japan
dramatically dropped because of great recession in 1994, therefore the NOx and PM concentrationlevels in 1994 decrease even without policy.
In the case when only ES is effective, the PM concentration levels decrease especially after
1997 as the regulation became more stringent (see Figure 4). The effectiveness of RP between 1992
and 2002 is attributed to the Automobile NOx Law, and that of ARP between 2003 and 2005 to the
Automobile NOx PM Law. NOx and PM concentration reductions by DPFM are limited as shown infigures, compared to other policies.
Table 5 presents the annual average percentage of concentration reduction by each type of
regulation. The contribution of subsidies is excluded because variables representing regulations in
the table are binary. The baseline is when the respective regulation is absent (though we conditioned
on regulations that were implemented prior to the studied period). That is, for example, thepercentages in the column “ES” show the percentages of concentration reductions compared to thecase where the ES regulation is not implemented. The percentages in the column “ARP” show theconcentration reductions compared to the case where ARP is not implemented while RP and ES are19

effective.
ES is quite effective for reducing NOx and PM concentration levels. It is found that PMconcentrations reduced greatly in particular, apparently because upper allowable limit of PMemission intensity under ES was reinforced rapidly in a short period of time (see Figure 4). Although
the regulation cannot improve the environmental performance of vehicles that are currently being
used, the size of its effectiveness is as large as those of RP and ARP.
Although the RP targeted NOx concentrations, it is found to be also effective to decrease
PM concentration levels. Presumably, this is due to the combination of two reasons: 1) ES for PM
concentrations has been reinforced rapidly since 1993 and thus PM concentrations from newvehicles decreased more greatly than NOx concentrations, and 2) high emission vehicles wereencouraged to be renewed under the RP. The change from the RP to the ARP seems to havecontributed to improving ambient air quality; this replacement policy is more effective with the law
amendment, resulting in more reductions both in NOx and PM concentrations. With regard to DPFM,
NOx and PM concentration reduction percents are small and thus their effectiveness is limited.

6.

Conclusion

Using data from 1990 to 2005 on NOx and PM concentration levels at air pollution
monitoring station, this study examines the effectiveness of the Japanese past and current vehicle
emission control policies. Our results indicated that most policies are effective to decrease both NOx
and PM concentration levels. Thus, it is concluded that Japanese authority succeeded in attaining
cleaner air by emission control policies.
In all the estimation results, the improvement on ambient air quality by ES is great, and the
RP and ARP are also effective to attain cleaner air. However, it is not clear if the DPFM had effects
on decreasing emission concentration levels. LEV subsidies reduce NOx concentration level,
whereas LEV and DPF subsidies do not robustly decreased PM concentration level.
From these findings, we can derive the following three policy implications. First, it issuggested that the emission intensity regulation like the ES is most important among available

20

emission control policies. The reasons are following two; 1) robust improvement of emission
concentrations are found only by the ES and RP. 2) replacement policy from old vehicles into newones such as RP does not work as an emission control policy unless emission intensity of newvehicles is better than that of old ones. When an authority implements ES type regulation, it
gives/enforces automakers an incentive/duty to produce cleaner vehicles. Therefore, in order toobtain further social welfare by mitigating air pollution from automobiles, emission intensity
regulation for new vehicles should be firstly introduced in a country where any emission control
policies have not been implemented yet.
Second, subsidies should be spent on LEVs rather than PM removal equipment because
the later subsidy does not significantly robustly improve PM concentration level. In the Tokyo
metropolitan area, more subsidies are spent on the equipment perhaps because they have imposed an
ordinance to make its installation mandatory. Subsidizing the equipment is found to be costineffective both in the ex-ante (Iwata, 2011) and ex-post (this study) evaluations. Thus, it is advisedthat the municipalities, particularly those in the Tokyo metropolitan area, shift their target tosubsidizing LEVs. In addition, if policymakers intend to subsidize LEVs, emission intensity
regulation is necessary. This is because replacement by the subsidy is not environmentally friendly ifenvironmental performance of new vehicles is equal to that of old ones.
Third, authority should consider presence of the spillover effect in subsidizing LEVs. The
LEV subsidies are found to have spillover effect for improving NOx concentration on otherneighboring municipalities, and the effect is not negligible. If neighboring municipalities are keen to
reduce emissions, it is not necessary for a municipality to keep intensifying its own regulation. To
avoid appearance of free-riding on environmental aggressive municipalities, co-operation or
negotiation among municipalities is needed.
Due to data restriction, this study estimated the effectiveness of vehicle emission
regulations exclusively. As part of the ex-post analysis, it is important to estimate the cost of theregulations. This issue is left for future studies.

21

Acknowledgement

Toshi H. Arimura appreciates financial support from Multi-level Environmental Governance for
Sustainable Development by the Ministry of Education, Culture, Sports, Science & Technology.
Kazuyuki Iwata is grateful for the financial support from Sonpo Japan Environmental Foundation
and also from Japan Society for the Promotion of Science. We thank comments from Akira Hibiki
and Kenji Takeuch.

References

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Amendments on Ambient PM10 concentrations: The critical importance of a spatiallydisaggregated analysis, Journal of Environmental Economics and Management, Vol.58, pp.15-26.
Alberini, A., Harrington, W., McConnell , V., (1996) Estimating an Emissions Supply Function fromAccelerated Vehicle Retirement Programs, Review of Economics and Statistics, Vol.78(2),pp.251–265.
Dill, J., (2004) Estimating Emissions Reductions from Accelerated Vehicle Retirement Programs,
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Goyal, S. P., (2002) Effect of Winds on SO2 and SPM Concentrations in Delhi. Atmospheric
Environment, Vol.36(17), pp.2925-2930.
Greenstone, M., (2003) Estimatinig Regulation-Induced Substitution: The Effect of the Clean Air
Act on Water and Ground Pollution, The American Economic Review, Vol.93(2), pp.442-448.
Greenstone, M., (2004) Did the Clean Air Act cause the remarkable decline in sulfur dioxide
concentrations?, Journal of Environmental Economics and Management, Vol.47(3), pp.585-611.
Henderson, J. V., (1996) Effects of Air Quality Regulation, The American Economic Review,
Vol.86(4), pp.789-813.

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Iwata, K., (2011) Cost-Benefit Analysis of Enforcing Installation of Particulate Matter Elimination
Devices on Diesel Trucks in Japan, Environmental Economics and Policy Studies, Vol.13(1),pp.1-19.
Iwata, K. and Arimura, T. H., (2009) Economic analysis of Japanese air pollution regulation: An
optimal retirement program under the vehicle type regulation in the NOx-particulate matter law,
Transportation Research Part D, Vol.14(3), pp.157-167.
Leggett, C. G. and Bockstael, N. E. (2000) Evidence of the Effects of Water Quality on Residential
Land Prices. Journal of Environmental Economics and Management, Vol.39(2), pp.121-144.
Lopez, J. M., Jimenez, F., Aparicio, F. and Flores, N. (2009) On-road emissions from urban buseswith SCR plus Urea and EGR plus DPF systems using diesel and biodiesel. Transportation
Research Part D, Vol.14(1), pp.1-5.
Narain, U. and Krupnick, A., (2007) The Impact of Delhi’s CNG Program on Air Quality, Resources
for the Future Discussion Paper 07-06.
Osaka Prefecture, (2006) 2006 Osaka Environmental White Book, Planning Section, Environmental
Pollution Control Center, Osaka. (in Japanese)
Sano M, (2008). Genealogy of Automotive Technology Innovation and Technology Policy in Japan(in Japanese). In: Miyoshi H, Tanishita M (eds) Technological Innovation in the Automotive
Industry and Economic Welfare. Hakuto Shobo, Tokyo, pp 1-36.
Sigman, H., (2002) International Spillovers and Water Quality in Rivers: Do Countries Free Ride?,
The American Economic Review, Vol.92(4), pp.1152-1159.
Wooldridge J. M., (2002) Econometric Analysis of Cross Section and Panel Data, Massachusetts
Institute of Technology, MA.
8Capital Pref City Aozora Network (2007) Guideline on Designating Diesel Particulate Filter in
8Cpalital Pref City (in Japanese).
http://www.8taiki.jp/regulatory/pdf/youkou.pdf (accessed in 22.09.10)

23

Tables & Figures

Figure 1. Transition of NOx Concentration Levels (unit: ppm)

Figure 2. Transition of PM Concentration Levels (unit: mg/m3)

24

Figure 3. Biggest four metropolitan area in Japan

Osaka Area
1973 ES(NOx)1992 RP
1993 ES(PM)2003 ARP
Fukuoka Area
1973 ES(NOx)1993 ES(PM)Tokyo Area
1973 ES(NOx)1992 RP
1993 ES(PM)2003 ARP
2004 DPFM
Nagoya Area
1973 ES(NOx)1993 ES(PM)2003 ARP

Figure 4. Transition of tightening of NOx and PM Emission intensitys

Note: The values are average because emission intensitys are set by vehicle type. The standard
values are set to 100. The standard value of NOx and PM is in 1972 and 1993, respectively.

25

Figure 5 Annual average NOx reductions by policy (Unit: ppm)

0.043

0.038
With all policies
With ES, RP, ARP & DPFM
0.033

With ES, RP & ARP
With ES
Without policy
2003

2004

2005

2003

2004

2005

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

0.028
1991

Annual Average NOx Concentration

0.048

0.42
0.4
0.38
0.36
0.34
0.32

With all policies

0.3

With ES, RP, ARP & DPFM

0.28

With ES, RP & ARP
With ES

0.26

Without policy
2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

0.24
1991

Hourly Maximum NOx Concentration

Figure 6 Hourly maximum NOx reductions by policy (Unit: ppm)

Figure 7. Annual average PM reductions by policy (Unit: mg/m3)

26

Annual Average PM Concentration

0.041
0.039
0.037
0.035
0.033
0.031

With all policies

0.029

With ES, RP, ARP & DPFM

0.027

With ES, RP & ARP
With ES

0.025

Without policy
2003

2004

2005

2003

2004

2005

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0.023

0.35

0.3

0.25

With all policies
With ES, RP, ARP & DPFM
With ES, RP & ARP

0.2

With ES
Without policy

27

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

0.15
1991

Hourly Maximum PM Concentration

Figure 8. Annual average PM reductions by policy (Unit: mg/m3)

Table 1

Annual amount of subsidies by metropolitan area (unit: 10,000 yen)
Subsidy for Low Emission Vehicle
Year Tokyo Area Osaka Area Nagoya Area Fukuoka Area
1999
0
2146
0
0
2000
0
2465
0
0
2001
52205
2228
0
0
2002
60564
22210
4139
0
2003
149391
28743
8749
0
2004
27230
30822
23510
0
2005
33518
25652
25619
0
Total
32290711426662017
0Subsidy for DPF instration
1999
0
0
0
0
2000
0
0
0
0
2001
60588
5500
0
0
2002
527792
2689
0
0
2003
1798329
10000
4691
0
2004
234255
3321
2586
0
2005
127320
5600
0
0
Total
2748284
27109
7278
0

Note: Tokyo, Osaka, Nagoya and Fukuoka metropolitan areas are consist of 8, 4, 2 and 3
municipalities, respectively.

Table 2

Descriptive Statistics
Pollutant
Number of Observations

NOx
9696
Mean
S.D.

Concentration level
Annual Average NOx Concentration (ppm)
Hourly Maximum NOx Concentration (ppm)
Annual Average PM Concentration (mg/m3)
Hourly Maximum PM Concentration (mg/m3)
Emission Control Policies
ES (NOx)
ES (PM)
RP
ARP
DPFM
LEV subsidy (10,000 yen)
LEV spillover (10,000 yen)
DPF subsidy (10,000 yen)
DPF spillover (10,000 yen)Social Structure
Industrial-Area Dummy
Highway Dummy
Freight Tonnage (1,000 tonnages)
Climate Conditions
Average Temperature (℃)
Wind Speed (m)Rain Fall (mm)

28

0.046
0.414

PM
8384
Mean
S.D.

0.032
0.190
0.035
0.285

0.011
0.115

63.49
0.420
0.151
0.065
7951
46035
52693
325341

34.12
0.494
0.358
0.246
21542
109718178272968666

22.56

6.91

0.421
0.150
0.061
8175
44278

0.494
0.357
0.240
22120
1070970.071
0.221
246417

0.258
0.415
54651

0.073
0.128
246706

0.259
0.334
55062

15.722
2.419
1440

0.985
0.912
351

15.701
2.409
1440

0.981
0.930
347

Table 3

Estimation results of NOx concentration function
(2)

(1)
Model
Dependent Variables

Annual Average
Coef.

Emission Control Policies
ES
RP
ARP
DPFM
LEV subsidy
LEV spillover
Social Structure
Industrial-Area Dummy
Highway Dummy
Freight Tonnage
Climate Conditions
Average TemperatureWind Speed
Rain Fall
Constant term

Note:

(3)

Fixed Effect

0.0004-0.0020-0.00280.0005-0.0314-0.0123t-value

Hourly Maximum
Coef.

15.65 ***
-5.64 ***
-6.63 ***
1.35
-4.45 ***
-6.73 ***

(4)
Spatial Random Effectt-value

0.0024-0.0111-0.04180.0067-0.3787-0.1423

Annual Average
Coef.

t-value

Hourly Maximum
Coef.

t-value

10.30 ***
-2.55 **
-8.20 ***
1.26
-5.82 ***
-8.03 ***

0.0003-0.0020-0.0022-0.0008-0.0252-0.0076

8.30 ***-5.67 ***
-4.60 ***
-1.77 *
-2.96 ***
-3.52 ***

0.0017-0.0208-0.0432-0.0188-0.2060-0.0853

3.56 ***-2.68 ***
-4.63 ***
-1.76 *
-1.76 *
-2.51 **

7.90 ***
80.52 ***
17.61 ***

-0.0020
0.16630.5297-0.19
21.13 ***
6.38 ***-0.49
4.39 ***
2.18 **
4.32 ***

0.00140.0154-13.7270
0.1581

0.56
3.31 ***-2.53 **
3.02 ***

0.00601.45

0.06311.17

0.00850.05780.0596-0.0008-0.00141.05480.0517-7.56 ***
-3.78 ***
5.36 ***
65.02 ***

-0.00860.0010-6.46230.5053-5.41 ***
0.22
-2.14 ***
18.44 ***

-0.0001
0.0011
0.46820.0096***, **, and * indicate the significance at the 1%, 5%, and 10% levels, respectively. Unit

for rain fall was modified to 1 kilometer, for traffic volume to one billion tonnes, and for subsidies
and spillover effect (both low emission vehicles and PM removal equipment) to ten billion yen.

Table 4

Estimation results of PM concentration function
(1)
Model
Dependent Variables

Annual Average
Coef.

Emission Control Policies
ES
RP
ARP
DPFM
LEV subsidy
LEV spillover
DPF subsidy
DPF spillover
Social Structure
Industrial-Area Dummy
Highway Dummy
Freight Tonnage
Climate Conditions
Average TemperatureWind Speed
Rain Fall
Constant term

Note:

(2)

(3)

Fixed Effect

0.0001-0.0031-0.0028-0.0006-0.0195-0.0145-0.00240.0002t-value
26.79 ***
-9.18 ***
-7.23 ***
-1.56
-2.80 ***
-3.65 ***
-2.76 ***
0.44

(4)
Spatial Random EffectHourly Maximum
Coef.

t-value0.0010-0.0120-0.0169-0.00530.0451-0.4186-0.0272
0.0333

Annual Average
Coef.

t-value

Hourly Maximum
Coef.

t-value

20.21 ***
-2.43 **
-3.03 ***
-0.88
0.48
-7.33 ***
-2.20 **
5.31 ***

0.0001-0.0027-0.0019-0.0013-0.0127-0.0080-0.0010-0.0001

12.63 ***
-6.43 ***
-3.42 ***
-2.35 **
-1.27
-1.32
-0.71
-0.18

0.0010-0.0174-0.0224-0.0100
0.0136-0.4355-0.0261
0.037713.92 ***
-3.29 ***
-3.14 ***
-1.17
0.11
-5.32 ***
-1.48
4.19 ***

0.54
19.64 ***
5.22 ***-0.00190.02770.1062-0.26
4.81 ***
1.94 *

0.85-1.14-1.77 *
9.47 ***-0.00520.0012-1.91840.3013-2.62 ***
0.41
-0.43
8.16 ***-0.0034-0.85

0.13732.24 **

0.00040.01050.0208-0.0004-0.0008-0.09110.0398-3.34 ***
-2.25 **
-0.45
29.59 ***

-0.0079-0.0066-3.01200.3499-4.12 ***
-1.35
-0.85
10.45 ***

0.0001-0.0003-0.46390.0248***, **, and * indicate the significance at the 1%, 5%, and 10% levels, respectively. Unit

for rain fall was modified to 1 kilometer, for traffic volume to one billion tonnes, and for subsidies
and spillover effect (both low emission vehicles and PM removal equipment) to ten billion yen.

29

Table 5

Reductions of annual average NOx/PM levels by regulation
ES

RP

ARP

DPFM

Annual average NOx concentration level

5.92%

3.97%

4.80%

2.16%

Hourly maximum NOx concentration level

4.47%

4.42%

10.35%

5.96%

Annual average PM concentration level13.23%

6.69%

5.21%

4.07%

Hourly maximum PM concentration level

18.23%

5.05%

7.74%

4.16%

Note: The numbers are the percentages of reductions compared to the case where the respective
regulations are absent. In each case, we conditioned on regulations that were imposed prior to therespective regulations.

30

The Society of Regional Policy, Takasaki City University of Economics
1300, Kaminamie-machi, Takasaki-city, Gunma 370-0801 Japan+81-27-344-6244
c-gakkai@tcue.ac.jp
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp14-03