Spatial Distribution of Disposal Sites:
Empirical Evidence from Japan

Yuichi Ishimura
Kenji Takeuchi
June 2015
Discussion Paper No.1523

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANSpatial Distribution of Disposal Sites:
Empirical Evidence from Japan
Yuichi Ishimura and Kenji Takeuchi∗
Graduate School of Economics, Kobe University

June 17, 2015

Abstract
This study is an empirical investigation of the location of industrial waste disposalsites in Japan. We found some evidence of spatial concentration of industrial waste
disposal sites in area with other waste-related facilities. In addition, we found a higher
number of industrial waste disposal sites per capita in municipalities that had notexperienced conﬂict relating to the construction of disposal sites. Our results suggestthat companies may decide to locate disposal sites in areas in which other waste related
facilities already exist and/or where there is less citizen conﬂict over their construction.
This would explain why there is a spatial concentration of unwanted facilities in someareas.

Keywords: Disposal site; Industrial waste; Spatial econometrics; NIMBY.
JEL Classiﬁcation Numbers: D72, Q53, R39.

∗

E-mail: ishimura0601@yahoo.co.jp (Ishimura), takeuchi@econ.kobe-u.ac.jp (Takeuchi).

11Introduction
Deciding where to locate facilities is of capital importance for ﬁrms. For those dealing

with waste disposal, such decisions can be extremely complicated. While ﬁrms hope to ﬁnd
an optimal location for their operations, waste disposal sites are generally considered unwanted facilities. Nearby residents often oppose the construction of such facilities, and local
governments worry about conﬂict related to NIMBY (not in my back yard) and LULU (locally unwanted land use) issues. These issues raise several questions: How are site locations
determined? Are these locations concentrated in municipalities with speciﬁc characteristics?
To what extent do residents’ opinions aﬀect location decisions? Previous empirical studies
on the location decisions of ﬁrms have tended to focus on geographical endowment, relative
price, transport cost, or agglomeration beneﬁts (Wheeler and Mody 1992; Head et al. 1995,
Devereux et al. 2007). In cases of unwanted facilities, however, the local community may
put pressure on ﬁrms and ultimately aﬀect their location decisions. Thus, residents’ welfare
loss might be a signiﬁcant factor in the location decisions for waste facilities.
The purpose of this paper is to understand the spatial distribution of unwanted facilities.
Using spatial econometric techniques, we investigate the characteristics of communities inJapan that currently host industrial waste disposal sites. We also examine the characteristics
of communities in which conﬂict over the location of disposal sites is likely to occur andexamine the impact of conﬂict and policy on these location decisions.
Previous studies on the location of waste treatment facilities have mainly focused on site
selection procedures. Kunreuther and Kleindorfer (1986) proposed a sealed-bid mechanism
for eliciting citizens’ willingness to accept facilities. Minehart and Neeman (2002) presented
a modiﬁed second-price auction procedure for choosing a site location. Swallow et al. (1992)
proposed a three-phased approach that integrates the technical, economic, and politicaldimensions related to the landﬁll-siting process. While these studies relate to the normative
aspect of site selection, few studies have investigated the empirical aspect of location decision
making for disposal sites or waste-related facilities. One exception is Laurian and Funderburg2(2014), who focused on the location of waste incinerators in France from the viewpoint ofenvironmental justice.
This study contributes to the existing literature in two ways. First, we investigate theconcentration of disposal sites empirically. Since it is diﬃcult to identify municipalities thatare most likely to accept disposal sites, waste management companies tend to choose speciﬁcmunicipalities with certain characteristics. Japan provides an excellent setting to test thishypothesis because of its high population density and the scarcity of land for refuse disposal.
Second, we examine whether conﬂict with residents aﬀects the location decision. While itis reasonable to expect that conﬂict reduces the number of disposal sites per capita, it isunclear to what extent this opposition is successful. For example, conﬂicts may not reduce
the number of disposal sites if legal procedures and regulations do not strictly require theagreement of local residents regarding disposal site selection.
The next section describes the process of locating disposal sites for industrial waste inJapan. Section 3 introduces the method used for estimation as well as the study’s empiricalstrategy. Section 4 explains the model speciﬁcation and data, and Section 5 explains theresults of the spatial concentration analysis of disposal sites and conﬂict. Finally, Section 6
concludes and discusses policy implications.

2

Background and Hypothesis Development
Figure 1 classiﬁes the types of disposal sites in Japan. Even though the annual volume

of industrial waste is ten times greater than that of municipal solid waste, Japan has fewer
disposal sites for industrial waste (688) than for municipal solid waste (1,185). The available
capacity of industrial waste disposal sites is only 15 years because of the diﬃculty of siting.
Figure 2 shows the locations of the industrial waste disposal sites currently in operation in
Japan. Most disposal sites for industrial waste are constructed and operated by private waste
management companies. While these sites are distributed throughout the country, there are3particularly high concentrations in some areas, and 82 percent of Japanese municipalities
have no industrial disposal sites at all.
What is the mechanism behind the locational concentration of disposal sites? As the
economy expands and demand for waste disposal grows, suitable space for disposal becomesscarce. The shortage of disposal sites leads to longer transport distances for waste as well as
illegal dumping. This increases anxiety and concern among residents over the constructionof disposal sites and waste-related facilities. Although such anxiety and concerns can be
mitigated by tighter regulations on the management of disposal sites, such regulations may
increase the cost of construction and lead to a further shortage of disposal sites, creating a
vicious cycle. Therefore, waste management companies have limited possibilities for new site
locations, and they tend to choose areas with existing disposal sites instead of developing
new areas. Our ﬁrst hypothesis can be summarized as follows.
Hypothesis 1: Waste management companies tend to locate disposal sites inareas in which construction is easier―that is, areas with an existing disposalsite. Therefore, there are likely to be some areas in which disposal sites andother waste management facilities are spatially concentrated.
The typical siting process for disposal sites involves ﬁve steps: planning, environmental
assessment, permission, construction, and operation. At the planning stage, the waste management company chooses candidate locations based on various factors such as the potential
waste supply to the location, transportation costs, and ﬁxed costs, including the land price.
The Japanese Environmental Impact Assessment Act and the Waste Management and Public Cleansing Act require constructors of disposal sites to conduct environmental assessmentsof air, water, and soil pollution. The waste management company must submit an environmental assessment report when applying prefectural permission to construct the disposalsite. Permission is granted only if conditions relating to the disposal method and facility
equipment standards are met. Although the agreement of nearby residents is not required ina strict legal sense, there are many cases in which opposition by residents has impeded the4construction process. Therefore, companies try to obtain the agreement of residents prior toconstruction, which can lead to signiﬁcant delays to oﬃcial operation of the sites. Figure 3
shows an example of a location process with no opposition from residents. Even in such acase, it takes 12 years from the planning stage to operation.
Residents oppose the construction of industrial waste disposal sites for four main reasons.
The ﬁrst is the risk of environmental pollution. Inhabitants of an area that uses a signiﬁcantamount of groundwater or is prone to landslides may be more anxious about the risks ofpollution from disposal sites. Residents may also be anxious about future environmental
pollution since there have been several cases in which environmental contamination was
discovered after the closure of a site (e.g., Love Canal in New York, USA). The second
reason is the economic eﬀects of waste disposal sites on land and housing prices (Kiel and
McClain 1995; Farber 1998; McCluskey and Rausser 2003; Ihlanfeldt and Taylor 2004).
Proximity to unwanted facilities decreases property values even though such facilities often
bring oﬀsetting employment opportunities. In many cases, the devaluation due to nearbychanges in land use is an uninsured risk of homeownership (Fischel 2001). The third reason
is distrust of waste management companies and local governments. Even when the required
environmental assessment has been completed, some residents are suspicious of the results
and of the information provided by the companies and government, and they doubt the safetyof the waste being disposed of or the disposal methods being employed. Public participationin the siting process should be an important component of information disclosure (Ishizaka
and Tanaka 2003; Hsu 2006), as this participatory approach may improve residents’ opinions
regarding the necessity of the facility, thereby increasing the possibility of acceptance (Loberand Green 1994). The fourth reason is a feeling of unfairness at having to treat waste
generated by other communities. The idea of accepting waste from other municipalities
often brings antipathy from local residents (Ferreira and Gallagher 2010). To mitigate such
antipathy, a fair siting procedure is important in increasing residents’ willingness to host anoxious facility (Frey and Oberholzer-Gee 1996).

5

According to Taguchi (2003), 278 Japanese municipalities experienced protests by residents against the construction of industrial waste disposal sites between 1990 and 2000.1
Inhabitants opposed construction plans by collecting signatures and submitting petitions totheir municipality and prefecture to prevent construction approval. In addition, residents
participated in public demonstrations against the waste management companies. As partof such opposition, inhabitants can ﬁle lawsuits against the waste management companies;
however, the companies can sue the residents as well as the prefecture over the postponement of construction and operation. In many cases, the court recommends that both parties
accept a reconciliation plan. This line of reasoning suggests the next hypothesis.
Hypothesis 2: Residents worry about environmental or economic damage
from disposal site, and strongly oppose to the construction of disposal sites.
Therefore, the conﬂict likely to be caused in area where is susceptible to environmental or economic damage from disposal site.
Even if residential opposition is strong, the prefecture must approve the construction
as long as the company’s application fulﬁlls all the legal conditions. This means that awaste management company has the right to construct its facility as long as it meets thelicensing standards. There have been some cases in which the waste disposal company
received approval despite signiﬁcant local opposition. For example, in the town of Umikami
in Chiba Prefecture, a waste facility was built even though 97.6 percent of voters opposed
the construction of the site in a local referendum in 1998. While this case suggests that theeﬀect of such conﬂict might not be strong enough to change the construction plan, a waste
management company might wish to avoid a municipality that had already experienced
conﬂict as a candidate for the construction of a new site. This leads to the last hypothesis.
Hypothesis 3: Waste management companies tend to avoid areas in which
conﬂicts with residents over construction have taken place. Therefore, the1Japanese local government is divided into two tiers: prefectural governments and municipalities (cities,
towns, and villages). The nation comprises 47 prefectures. The number of municipalities was 1,718 in total
as of April 2015.

6

more conﬂicts over waste facilities there have been in an area, the less likely
it is for disposal sites to be constructed there.

3

Spatial Dependency in Disposal Site Location
To test our hypothesis 1, we ﬁrstly estimate the Moran’s I statistic which is a measureused to test spatial dependency (Anselin 1988). For industrial waste disposal sites, Moran’s I
is 0.122, with a P value of 0.000. This suggests that spatial dependency exists within the site
location data at the 1% level of statistical signiﬁcance. We also tested spatial dependence
among intermediate waste processing facilities as well as among hazardous waste facilities.
For the intermediate waste processing facilities, Moran’s I is 0.073 with a P value of 0.000,
while for hazardous waste facilities, Moran’s I is -0.001 with a P value of 0.051. These
results show that industrial waste disposal sites have the highest spatial dependency among
the waste management facilities. In addition, we examined spatial dependency with regard
to citizen conﬂicts over the construction of industrial waste disposal sites between 1990
and 2000 as reviewed by Taguchi (2003).2 We found that spatial dependency exists within
the conﬂict data, as Moran’s I is 0.109 with a P value of 0.000. Considering these results,
aspatial statistical modeling approaches lead to signiﬁcant model misspeciﬁcation and biased
parameter estimates (Anselin 1988). To detect local patterns of spatial association, we
calculated the Anselin local Moran’s I statistic (Anselin 1995) and the Getis-Ord Gi* statistic(Getis and Ord 1992; Ord and Getis 1995). The speciﬁc test results and a description of theclusters are presented in the Figures 4 and 5.
We estimate a spatial Tobit model to investigate the location of disposal sites and a
spatial probit model to investigate the characteristics of communities in which conﬂict overthe location of sites is likely to occur, respectively. The data are for 1,693 municipalities in
2

The data on conﬂict related to construction plan of disposal sites is not readily available. For instance,
according to the report of the Japanese Ministry of the Environment (1996), there were 221 conﬂicts related
to waste disposal sites between 1990 and 1996. However, the data are at the country level rather than themunicipality level, and it is not possible distinguish conﬂicts over plans for industrial waste disposal sites
from those over the operation of such sites.

7

Japan that share a border with at least one other municipality. A spatial Tobit model isdeﬁned as follows:

yi =



 y∗
i

if yi∗ ≥ 0 0

if yi∗ < 0,yi∗ = (In − ρW)−1 Xβ + (In − ρW)−1 ϵ,
where yi∗ is the number of disposal sites for industrial waste per 10,000 residents inmunicipality i, which takes a value of zero if there is no disposal site in the municipality; X
is a vector of characteristics describing the site; W is spatial weight matrix; and β and ρ areparameters. The negative values from vector yi∗ is set to zero to reﬂect sample truncation atzero. The spatial weight matrix W is generated using randomly located coordinates basedon each municipality’s ﬁve nearest neighbors. The conditional distribution for the latentvariable thus takes the following form:yi∗ ∼ T M V N (µ, Ω),s.t. µ = (In − ρW)−1 Xβ,and Ω = [(In − ρW)(In − ρW)]−1 .

To estimate the spatial Tobit model, we rely on the Bayesian strategy of the Markov
chain Monte Carlo (MCMC) sampling method (LeSage 1999, 2000; LeSage and Pace 2009).
The parameters speciﬁed are estimated using the Gibbs sampling method based on 1,000
retained draws from a sample of 1,100.
In the spatial probit model, yi is the binary dependent variable for municipality i. If
there is any conﬂict over the industrial waste disposal site in the municipality, the dependent
variable takes a value of one, and if there is no conﬂict, it takes a value of zero. The model8

is represented by:

yi =



 1

if yi∗ ≥ 0 0

if yi∗ < 0,yi∗ = (In − ρW)−1 Xβ + (In − ρW)−1 ϵ.

4Explanatory Variables
A disposal site requires a considerable amount of land, and in many cases, it is diﬃcult toredevelop such sites after the completion of operation. This suggests that location decisions
regarding disposal sites may be more complex than those for other NIMBY and LULU
facilities. We hypothesize that ﬁve groups of variables can inﬂuence the location of disposalsites: conﬂicts, the existence of other waste-related facilities, local community characteristics,
geography, and input markets. The summary statistics for all the variables are presented inTable 1.
As discussed in Section 2, there are many cases in which local residents have campaigned
against the construction of a disposal site. It is reasonable to assume that the existence of
conﬂict reduces the number of disposal sites per capita within a municipality. The dummy
variable for conﬂict takes a value of one when there has been any conﬂict regarding an
industrial waste disposal site within a municipality and a value of zero when there has notbeen any conﬂict. We use this variable to examine the inﬂuence of conﬂict on disposal site
location decisions. We also analyze the characteristics of local municipalities that have had
more incidences of conﬂict over the construction of disposal sites in order to investigate
the conditions that may determine residents’ acceptance of location planning. The data
related to conﬂicts are drawn from a survey by Taguchi (2003). On the basis of a studyof articles published in Japanese newspapers, he identiﬁes 1,218 conﬂicts over plans for andthe operation of waste disposal sites as well as illegal dumping in Japanese municipalities
9between 1990 and 2000. We use the data for 220 of these conﬂicts that are related to plansfor constructing industrial waste disposal sites. Other conﬂicts, such as conﬂicts over theoperation of waste disposal sites and waste incineration sites, plans for the construction ofmunicipal solid waste disposal sites, and illegal dumping, are not included in our sample.
To measure the impact of other waste-related facilities, three variables are employed: thenumber of intermediate processing facilities for industrial waste per capita, the number ofdisposal sites for hazardous waste per capita, and a dummy variable of disposal sites forindustrial waste with public sector involvement. Intermediate processing facilities and hazardous waste facilities are also unwanted land uses. Intermediate waste processing facilities
include incineration plants, recycling plants, crushing plants, and separation plants for industrial waste. This variable could also be interpreted as access to input markets since their
output is, ultimately, waste that requires ﬁnal disposal. Hazardous waste facilities accept
oil, acid, alkali, infectious waste, polychlorinated biphenyls (PCBs), and asbestos, so it isdiﬃcult for a waste management company to ﬁnd a suitable disposal site location for such afacility since there might be even stronger opposition from residents than for an industrial
waste facility. It is expected that the higher the number of other waste-related facilities
there are per capita, the higher the number of industrial waste disposal sites there will be
per capita. The location data for disposal sites for industrial waste, intermediate processing
facilities, and hazardous waste facilities are derived from the 2012 Geographic Information
Systems (GIS) database of the Geospatial Information Authority of Japan (2014).
In contrast to the above two variables for waste-related facilities, the existence of industrial waste disposal sites founded with public sector involvement is expected to reducethe concentration of industrial waste disposal sites managed by private company within amunicipality. To facilitate the disposal of waste, some prefectures play an active role in theconstruction of disposal sites for industrial waste. The main reason for this involvement isto reduce possible resident objections that may arise out of anxiety that the public interest
may not be an upheld by a private company in pursuit of economic proﬁt. We expect that10

disposal sites founded with public sector involvement are substitutes for private disposal sites
and lead to a lower number of disposal sites by private companies in a municipality. The
location data for the publicly supported industrial waste disposal sites are taken from theSurvey Report on Administrative Organizations for Industrial Waste by Japanese Ministryof the Environment (2013).
Local community characteristics include variables such as the land price, unemployment
rate, population density, percentages of agricultural and manufacturing workers, municipal
ﬁnancial stability index, and city dummy. Land price is a signiﬁcant part of the ﬁxed cost for
the waste management company. We use the average land price in each municipality, whichis reported in the Annual Survey of Price Guidelines for Property Values by the JapaneseLand Appraisal Committee. The data are drawn from the 2014 Land Price Guidelines on
the website of the Land Information Center3 and the investigation of land prices within eachprefecture. The unemployment rate captures the community’s political power. By using
the unemployment variable, we can test the hypothesis that industrial waste disposal sites
might be spatially concentrated in municipalities with more disadvantaged populations. Highpopulation density means that there are potentially many inhabitants who would oppose
the construction of a site. Agricultural workers might fear that the disposal site will leakpollutants and damage their produce. Conversely, a higher ratio of manufacturing workers
and a high municipal ﬁnancial stability index value indicate greater economic activity, anda municipality that has such characteristics is more likely to support the location decision.
We use the city dummy variable to test whether the industrial waste disposal site is locatedin a rural area4 . The unemployment, population density, percentages of agricultural andmanufacturing workers, and city dummy data are drawn from the 2010 National Census(Japanese Ministry of Internal Aﬀairs and Communications 2010). The municipal ﬁnancial
stability index is drawn from the ﬁnancial indicators of local governments for 2012 (Japanese3

http://www.lic.or.jp/landinfo/ (Accessed March 16, 2015)
In Japan, a city is deﬁned as a local municipality with more than 50,000 inhabitants, 60% or more ofwhich live in a central area.
411

Ministry of Internal Aﬀairs and Communications 2012).
The geographic factor is also important in the location decision of a disposal site. To begin
construction, companies are required to follow environmental assessment procedures. Amongthe various aspects that are investigated during this process, we include three representative
variables as factors that might strongly aﬀect the location of disposal sites: (1) the numberof landslide hazard spots at the prefecture level, (2) the prefectural nature reserve dummy(1 = the disposal site is located in a prefectural nature conservation area, 0 = other), and(3) the amount of groundwater usage at the prefecture level. By including these variables,
we can examine whether the location decision is inﬂuenced by consideration of disasterrisk aversion and the natural environment. The data regarding the number of landslide
hazard spots are drawn from the website of the Japanese Ministry of Land, Infrastructure,
Transport and Tourism.5 The data for nature reserves are based on prefectural nature
conservation areas as of 2014. Under the Japanese Nature Conservation Law, areas for nature
conservation are classiﬁed as wilderness areas, nature conservation areas, and prefectural
nature conservation areas. The prefectural nature conservation areas are designated by theprefectural governments, and the regulations on land use in such areas are less strict thanfor the other two types. The data regarding groundwater usage are drawn from the Fifth
Survey on the Usage of Groundwater for Agriculture (Japanese Ministry of Agriculture, andFisheries 2011).
The last group of explanatory variables is input markets. Empirical studies suggest that
input markets have a signiﬁcant eﬀect on plant location (Fortenbery et al. 2013). In the caseof industrial waste, the input for disposal sites is the waste generated by industrial activities.
This study uses three variables for the input markets: (1) the amount of industrial waste
generated at the prefectural level, (2) the revenue from production by the local manufacturing sector measured at the municipal level, and (3) the total length of highway infrastructure
measured at the prefectural level. The generation of industrial waste captures the supply
5

http://www.mlit.go.jp/river/sabo/link20.htm (Accessed March 16, 2015)12

of waste processed for disposal within the prefecture.6 The total revenue of production by
the local manufacturing sector captures the demand for waste disposal in the municipality. The total length of the municipality’s highways reﬂects the amount of transportation
infrastructure, and a greater length should be associated with lower transportation costs.
Data on industrial waste generation are drawn from the 2012 Survey on Industrial Waste
Emissions and Disposal (Japanese Ministry of the Environment 2012). The data regarding
manufacturing production are drawn from the 2012 census of manufacturers (Japanese Ministry of Economy, Trade and Industry 2012). The data regarding the total length of highway
infrastructure in each municipality are drawn from the Annual Report of Road Statistics
2010 (Japanese Ministry of Land, Infrastructure, Transport and Tourism 2010).

5

Results

5.1

Spatial Concentration of Disposal Sites

We estimate models that take the number of industrial waste disposal sites per capita as
the dependent variable. There is a high correlation between the amount of industrial waste
output variable and the length of highway infrastructure variable, so we do not include themin the same model. The results of the Bayesian spatial Tobit models and spatial probit
models are presented in Table 2. For completeness, we also report the estimation results by
the aspatial Tobit and probit models. The spatial parameter ρ is statistically signiﬁcant in
the four models estimated using spatial methods. The results show that the disposal sites
for industrial waste are spatially concentrated. Thus, we found strong support for our ﬁrst
hypothesis, in addition to the result of Moran’s I statistics.
The results in Table 2 indicate that the number of intermediate processing facilities forindustrial waste per capita and the number of hazardous waste disposal sites per capita havepositive and statistically signiﬁcant coeﬃcients. A higher number of these waste-related
6

The data on the amount of industrial waste generated at municipality level is unavailable.

13

facilities is associated with a higher number of industrial waste disposal sites. This result
suggests that waste management companies may ﬁnd a suitable place for construction inmunicipalities that have a higher number of other waste-related facilities per capita.
Contrary to our expectations, the location of publicly supported disposal sites is positively
associated with the number of disposal sites per capita. The public site variable in Table 2 has
positive and signiﬁcant coeﬃcients, which means that the private disposal sites per residents
is higher in municipalities that have publicly supported disposal sites. If the construction ofa public site increases the acceptability of waste facilities in general, it might also encourage
the construction of private disposal sites as well, similar to the eﬀect of other waste-related
facilities.
Land price and population density are statistically signiﬁcant and negative. These results
are plausible, since higher land price and higher population density means higher costs oflocating disposal sites. Unemployment is positive but not statistically signiﬁcant. The result
is in contrast to that of Laurian and Funderburg (2014), who found that towns in France withlarge vulnerable populations are more likely to host the construction of waste incineration
facilities. The percentage of agricultural workers is positive but not statistically signiﬁcant,while the percentage of manufacturing workers is negative and has statistically signiﬁcantcoeﬃcients. The city dummy is statistically signiﬁcant with positive coeﬃcients, although
this result might come from either stronger demand for disposal of waste or from signiﬁcanceof the transportation costs of waste.
Among the three geographical variables, only the level of groundwater usage is statistically signiﬁcant with negative coeﬃcients. This suggests that the municipalities with higher
levels of groundwater usage are less likely locations for disposal sites. Since higher dependence to groundwater means higher possibility of pollution damage, the negative coeﬃcientof the variable is in line with our expectation.
Input market factors are highly signiﬁcant in every speciﬁcations. The amount of industrial waste and total revenue from manufacturing production are positive and signiﬁcant.

14

These results suggest that the location of industrial waste disposal sites is sensitive to themarket where waste for disposal is generated. The total length of highway infrastructure
within a municipality is statistically signiﬁcant and positive, suggesting an important rolethat the level of infrastructure for transportation might play in determining the locations ofdisposal sites.

5.2

Spatial Concentration of Conﬂicts

Next, we investigate a model that explains the number of conﬂicts per capita to test
our hypothesis 2. We use as a dependent variable a dummy variable that takes a value of
one if a municipality had a conﬂict over the construction of an industrial waste disposal site
between 1990 and 2000. The explanatory variables in this model are similar to those used in
the models for the location of disposal sites, except with the data period being 2000 insteadof 2012. Since the data of landslide hazard spots and groundwater usages are not available
for the year 2000, we use the data for the years 2002 and 1996, respectively.
The estimation results from the spatial probit model are presented in Table 3. The
coeﬃcients of unemployment are negative and statistically signiﬁcant in model 2-2 and model
2-4. This is in contrast to the result shown in Table 2 that unemployment has positive andstatistically insigniﬁcant coeﬃcients on location of disposal sites. These results suggest thata municipality with high unemployment has a lower potential for conﬂict, although thismight not be associated with a signiﬁcant increase in the number of disposal sites. The city
dummy is positive and statistically signiﬁcant, which can be interpreted as that residents
living in urban areas tend to oppose the construction of disposal sites. The agricultural
worker and manufacturing worker variables are positive but not statistically signiﬁcant.
The number of landslide hazard spots and the amount of groundwater usage are positiveand signiﬁcant. These results supports our hypothesis 2 that inhabitants in municipalities
with vulnerable environment are more likely to worry about environmental damage and tostrongly oppose to the construction of disposal sites.
15

The variable for the amount of industrial waste is negative and statistically signiﬁcant.
Higher industrial waste generation in a municipality is associated with a lower potential for
conﬂict over disposal sites. To put diﬀerent way, conﬂict is higher where industrial waste
generation is lower. This is in line with the result by Ferreira and Gallagher (2010) thatthe treatment of local waste at a local facility is important driver for mitigating the protest
response toward accepting compensation to host waste disposal facility.

5.3

Impact of Conﬂict on the Concentration of Disposal Sites

To test our hypothesis 3 on the impact of conﬂict on the location of disposal sites forindustrial waste, we cannot simply include the conﬂict variable in a regression model of thelocation decision making. Since conﬂicts take place only when there is a plan for a disposal
site, conﬂict might positively correlate with location, although this does not necessarily meanthat conﬂict increases disposal site concentration. Therefore, the analysis in this section
restricts the sample to municipalities that had any construction plans for a disposal site.
Since an oﬃcial list of construction plans for industrial waste disposal sites is not available,
we constructed the sample as follows. If a municipality had a completed disposal site, itmeans there was a plan to build it. If a municipality had a conﬂict case, it also means
there was a plan to build a disposal site. We combine these two sets of municipalities
and assume these are the municipalities with construction plans. Although this procedureomits municipalities that had plans for disposal sites that were never completed while neverexperienced conﬂict, it is probably reasonable to think that such cases are few.
Conﬂict is measured by a dummy variable that takes a value of one if a municipality had
more than one conﬂict over the construction of disposal sites. We use 220 municipality-level
conﬂicts over plans for the construction of industrial waste disposal sites between 1990 and2000 (Taguchi 2003). While the data are conﬂict between 1990 and 2000, location data are
as of 2012. It is probable that the sites in operation in 2012 were built before 2000 since
the typical operation length of disposal sites is longer than 15 years. Thus, we consider that16

conﬂicts between 1990 and 2000 had some impact on the location decisions of the disposalsites that were in operation in 2012. We expect that conﬂict is negatively related to site
location for two reasons: (1) the transaction costs become higher for waste managementcompanies to negotiate with inhabitants, and (2) the opportunity costs also become higher
as the construction and operation of the site is delayed.
The estimation results from the Tobit model are presented in Table 4. The conﬂict
dummy is negative and statistically signiﬁcant, which means that the construction of disposalsites per capita is higher in a municipality that has experienced conﬂict with inhabitants.
This ﬁnding supports our third hypothesis that conﬂict with inhabitants can decrease thepossibility of site location because of higher transaction costs and opportunity costs for
waste management companies. The results regarding to other independent variables arequite similar to the estimation results shown in Table 2.

6

Conclusions
This study investigated the relationship between the location of disposal sites and thecharacteristics of local communities. We found that disposal sites for industrial waste wereconcentrated in particular area. In addition, we focused on conﬂict with residents over theconstruction of these sites and analyzed the factors related to the occurrence of conﬂict aswell as the impact of conﬂict on site location.
Our results revealed the characteristics of areas in Japan in which industrial waste disposal sites are spatially concentrated. The results showed that the existence of other wasterelated facilities is associated with a higher number of disposal sites for industrial waste percapita. While the signiﬁcance of groundwater usage suggests the importance of environmental factors in location decision making, input market factors such as the amount of industrial
waste and highway length also played signiﬁcant roles.
We further analyzed the impact of conﬂict on the location decisions. The results show that17

the occurrence of conﬂict has a negative relationship with location decisions, which suggeststhat waste management companies tend to avoid municipalities with a high probability of
conﬂict despite the fact that these companies have the legal right to construct their sites.
The results of this study have important implications for environmental policy. Firstly,
concentration of waste-related facilities suggest that the environmental impact of wasterelated facilities can be extremely high in these areas. Secondly, while the existence of
conﬂict in a given municipality might reduce the number of industrial waste disposal sites
per capita, it might also result in a shift in the site location to another municipality. In
that sense, the concentration of disposal sites in some areas might be attributable to theweak bargaining power of certain local communities. Thirdly, if residents want to avoid
the construction of local disposal sites and other waste-related facilities, it is important
for them to reduce the generation of waste within their municipality. However, it is noteasy for a single municipality to implement a practical intervention to reduce the volume
of industrial waste. Waste disposal sites are often unwanted by the local communities inwhich they operate, while it is inevitably important service for society as a whole. It is thus
important to understand how to mitigate the concentration of these sites and conﬂict overtheir construction.

18

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21

Table 1: Descriptive statistics
Unit

Mean

Min

Max

SD

Disposal site for industrial waste

sites/10,000 persons

0.125

0

6.485

0.496

Disposal site for hazardous waste

sites/10,000 persons

0.068

0

15.0380.474Intermediate site for industrial waste

sites/10,000 persons

1.306

0

64.088

2.264

0.027

0

1

0.163

10,000 yen

6.111

0.162

546.7

22.711

Unemployment

%

6.327

0

22.718

2.123

Population density

2

4.127

0.001

218.81518.701Agricultural workers

%

4.94

0

52.333

5.187

Manufacturing workers

%

8.131

0.492

23.831

4.094

points

0.5

0.07

2.13

0.282

0.461

0

1

0.499

22.0252.064

63.97411.241

0.203

0

1

0.402

1 = yes,Publicly supported disposal site

0 = otherwise
Land price

1,000 persons/km

Municipal ﬁnancial stability index

1= yes,City0= otherwise
Number of landslide

1,000 spots
1 = yes,Nature reserve

0 = otherwise
Amount of groundwater usage

million m3

6.219

0.21

69.087

10.204

Total manufacturing revenue

billion yen

17.01301208.88648.038Amount of industrial waste

10 million ton

1.178

0.123

3.576

1.017

km

266.661

18.2

894.4

236.344

0.129

0

1

0.336

Length of highway infrastructure

1 = yes,

Conﬂict

0 = otherwise

22

Table 2: Determinants of locations for industrial waste disposal site
Spatial Tobit
Aspatial Tobit
Model 1-1 Model 1-2 Model 1-3 Model 1-4
Disposal site for hazardous waste
0.329∗∗∗
0.329∗∗∗
0.306∗∗∗
0.308∗∗∗(0.010)(0.096)(0.085)(0.083)
Intermediate site for industrial waste
0.111∗∗∗
0.105∗∗∗
0.099∗∗∗
0.096∗∗∗(0.025)(0.021)(0.020)(0.020)Publicly supported disposal site
0.534∗
0.456∗
0.481∗
0.411∗(0.296)(0.268)(0.253)(0.249)Land price−0.068∗∗∗ −0.054∗∗∗ −0.071∗∗∗ −0.051∗∗∗(0.014)(0.012)(0.017)(0.016)
Population density−0.028∗−0.032∗∗−0.036∗−0.029(0.015)(0.014)(0.020)(0.019)
Unemployment
0.020
0.042
0.023
0.041
(0.028)(0.028)(0.026)(0.026)
Agricultural workers
0.018
0.017
0.018
0.019
(0.016)(0.015)(0.013)(0.013)
Manufacturing workers−0.027∗−0.027∗−0.020−0.021(0.016)(0.016)(0.015)(0.014)
Municipal ﬁnancial stability index
0.035
0.275
0.061
0.250
(0.293)(0.306)(0.279)(0.272)
City
0.989∗∗∗
0.907∗∗∗
0.759∗∗∗
0.770∗∗∗(0.178)(0.151)(0.142)(0.140)
Number of landslides
0.000−0.0010.001−0.001(0.005)(0.005)(0.005)(0.005)Nature reserve
0.036−0.0010.009−0.010(0.134)(0.137)(0.121)(0.120)
Amount of groundwater usage−0.025∗∗∗ −0.020∗∗∗ −0.027∗∗∗ −0.021∗∗∗(0.007)(0.007)(0.008)(0.007)
Total manufacturing revenue
0.004∗∗∗
0.004∗∗∗
0.003∗∗∗
0.003∗∗∗(0.001)(0.001)(0.001)(0.001)
Amount of industrial waste
0.266∗∗∗
0.312∗∗∗(0.052)(0.047)
Length of highway infrastructure
0.002∗∗∗
0.002∗∗∗(0.000)(0.000)ρ
0.221∗∗∗
0.195∗∗∗(0.051)(0.055)σ22.378∗∗∗
2.172∗∗∗(0.306)(0.237)
Intercept−1.925∗∗∗ −2.252∗∗∗ −1.946∗∗∗ −2.338∗∗∗(0.420)(0.424)(0.372)(0.383)Observations
1,693
1,693
1,693
1,693
Note: Standard errors are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

23

Table 3: Determinants of conﬂict over the construction plan for disposal site
Spatial Tobit
Aspatial Tobit
Model 2-1 Model 2-2 Model 2-3 Model 2-4
Disposal site for hazardous waste
4.572−9.183
5.170−13.250∗∗(5.278)(5.709)(6.151)(6.530)Intermediate site for industrial waste −0.080−0.036−0.063−0.012(0.066)(0.067)(0.076)(0.073)Publicly supported disposal site
0.229
0.239
0.277
0.273
(0.247)(0.262)(0.245)(0.247)
Land price−0.007−0.006−0.001−0.001(0.009)(0.007)(0.008)(0.007)
Population density−0.144∗∗−0.139∗∗−0.208∗∗∗ −0.017(0.058)(0.057)(0.062)(0.012)
Unemployment−0.014−0.016∗−0.014−0.207∗∗∗(0.012)(0.009)(0.012)(0.060)Agricultural workers
0.005
0.005
0.006
0.005
(0.009)(0.009)(0.010)(0.010)
Manufacturing workers
0.006
0.005
0.009
0.007
(0.009)(0.009)(0.010)(0.010)
Municipal ﬁnancial stability index
0.008−0.0850.045−0.072(0.199)(0.200)(0.218)(0.220)City
0.681∗∗∗
0.677∗∗∗
0.671∗∗∗
0.675∗∗∗(0.096)(0.092)(0.094)(0.094)
Number of landslides
0.012∗
0.011∗
0.018∗∗
0.015∗∗(0.006)(0.006)(0.007)(0.008)Nature reserve
0.036
0.027
0.017
−0.007(0.100)(0.104)(0.101)(0.102)
Amount of groundwater usage
0.005∗
0.007∗∗
0.006∗
0.009∗∗∗(0.003)(0.003)(0.003)(0.003)
Total manufacturing revenue−0.001−0.001−0.001−0.001(0.001)(0.002)(0.001)(0.002)
Amount of industrial waste−0.011∗∗−0.014∗∗(0.005)(0.006)
Length of highway infrastructure
0.001∗
0.001∗∗(0.000)(0.001)ρ
0.355∗∗∗
0.351∗∗∗(0.075)(0.074)
Intercept−0.963∗∗∗ −1.166∗∗∗ −1.462∗∗∗ −1.690∗∗∗(0.225)(0.214)(0.233)(0.226)
Observations
1,693
1,693
1,693
1,693
Note: Standard errors are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

24

Table 4: Impact of conﬂict on the concentration of disposal sites
Conﬂict
Disposal site for hazardous waste
Intermediate site for industrial waste
Publicly supported disposal site
Land price
Population density
Unemployment
Agricultural worker
Manufacturing worker
Municipal ﬁnancial stability index
City
Number of landslides
Nature reserve
Amount of groundwater usage
Total manufacturing revenue
Amount of industrial waste

Model 3-1
−0.925∗∗∗(0.089)0.207∗∗∗(0.056)0.032∗∗(0.014)0.000(0.170)0.006(0.016)0.012(0.022)0.000(0.000)0.040∗∗∗(0.013)−0.008(0.013)−0.545∗∗(0.239)−0.290∗∗(0.117)−0.005(0.004)−0.027(0.096)−0.013∗∗(0.005)0.000(0.001)0.210∗∗∗(0.039)Aspatial Tobit
Model 3-2
−0.917∗∗∗(0.089)0.208∗∗∗(0.056)
0.033∗∗(0.013)−0.032(0.169)0.013(0.016)0.025(0.022)0.000(0.000)0.044∗∗∗(0.013)−0.010(0.012)−0.404∗(0.240)−0.268∗∗(0.117)−0.006(0.004)−0.050(0.096)−0.011∗∗(0.005)0.000(0.001)0.001∗∗∗(0.000)Intercept
0.756∗∗
0.491(0.321)(0.339)Observations
462
462
Note: Standard errors are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Length of highway infrastructure25

Figure 1: Classiﬁcation of disposal site in Japan

Municipal solid waste (N=1,185)
Private (N=643)
Final disposal site

Industrial waste (N=688)(N=2,574)

Public (N=45)
Toxic waste (N=701)

Source: Japanese Ministry of the Environment (2014), Geospatial Information Authority of Japan (2014)26

Figure 2: Locations of industrial waste disposal sites throughout Japan27

Figure 3: Example of the siting process without conﬂict

Agreement

Environmental
assessment

Committee

Application for
construction
OperationPlan
2 years

4 years

3 years

Source: Sugiyama (2012)28

3 years

Figure 4: Spatial clustering of ﬁnal disposal sites via the Anselin Local Moran’s I

29

Figure 5: Spatial clustering of ﬁnal disposal sites via Getis-Ord Gi*30