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The decoupling of affluence and waste discharge under spatial correlation: Do richer communities discharge more waste?*

Published online by Cambridge University Press:  28 May 2014

Daisuke Ichinose
Affiliation:
College of Economics, Rikkyo University, Tokyo, Japan. E-mail: d.ichinose@rikkyo.ac.jp
Masashi Yamamoto
Affiliation:
Center for Far Eastern Studies, University of Toyama, 3190 Gofuku, Toyama-shi, Toyama-ken, 930-8555, Japan. Tel: +81 76 445 6455. Fax: +81 76 445 6510. E-mail: myam@eco.utoyama.ac.jp
Yuichiro Yoshida
Affiliation:
Graduate School for international Development and Cooperation, Hiroshima University, Higashi-Hiroshima, Japan. E-mail: yuichiro@hiroshima-u.ac.jp
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Abstract

A number of developing countries have come to face the growing problems of municipal solid waste management caused by rapid economic growth. Although there are many studies on the environmental Kuznets curve, very few address the issue of municipal solid waste, and there is still controversy concerning the validity of the waste version of the Kuznets curve hypothesis. It is demonstrated that the turning point for household municipal solid waste is approximately 3.7 million yen per person, which is far less than the maximum income in the sample and valid evidence for absolute decoupling. The success of our study partially stems from our highly disaggregated data and use of spatial econometrics. The former aspect indicates that distinguishing between household and business waste reveals the waste–income relationship, whereas the latter indicates the importance of peer effects when municipal governments formulate waste-reduction policies.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2014 

1. Introduction

The compatibility of economic growth with environmental protection has become one of the most important research questions in the field of environmental economics, and a number of researchers have devoted considerable efforts to developing a solution to this problem. One hypothesis that seems to have won a consensus regarding this compatibility is the environmental Kuznets curve (EKC) hypothesis. This hypothesis claims that an economy tends to degrade its environmental quality during its initial move toward economic growth but that beyond a certain threshold its environmental quality begins to improve as per capita income continues to grow. Many researchers support the EKC hypothesis based on measures of environmental quality, such as the sulfur dioxide and suspended particulate matter generated per capita. However, there are still several environmental indicators that challenge the validity of the EKC hypothesis.

One such indicator is municipal solid waste (MSW). When environmental quality is measured in terms of waste generation per capita, the EKC hypothesis is specifically called the waste Kuznets curve (WKC) hypothesis. Although waste is a serious environmental issue for many countries with high economic activity and is becoming a more acute challenge in many rapidly developing countries, there is a lack of solid empirical evidence that demonstrates that per capita waste generation follows the path predicted by the WKC hypothesis. Using a spatial econometric analysis of highly disaggregated data from Japan, this paper provides empirical evidence that MSW and per capita income follow the relationship predicted by the WKC.

The contribution of our paper is twofold. First, we use highly disaggregated municipality-level data (from 1,798 municipalities in Japan) and consider the spatial dependence across municipalities. As mentioned by Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009), one of the obstacles in the examination of the WKC hypothesis is the use of spatially aggregated data, such as country-level data. Often, the definition of waste varies from country to country, thereby inevitably making the results of cross-country analyses biased. Moreover, studies that employ country-level data inadvertently neglect the heterogeneity among municipalities in the same country, such as between Beverly Hills, California and Kodiak Island, Alaska. Such disparities can be more significant than cross-country differences between, for example, the US and Canada. We therefore focus on spatial disaggregation within one country using municipality-level data instead of country-level data. Furthermore, there is increasing evidence that the waste management of a municipality is highly affected by that of its neighbors. For example, Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) and De Jaeger (Reference De Jaeger2011) found waste-price mimicking behavior among municipalities using Flemish municipality-level data. Hage et al. (Reference Hage, Sandberg, Soderhölm and Berglund2008) and Ham (Reference Ham2009) also found spatial dependence between municipalities within the context of waste management.Footnote 1 To capture such spatial autocorrelations, we introduce a spatial econometric approach to the analysis of WKC. In particular, we estimate the WKC trend using a spatial autoregressive model (SAR) and a spatial error model (SEM). Our method is therefore closely related to that of Maddison (Reference Maddison2006), who performed an empirical EKC analysis using SAM and SEM. The greatest difference from Maddison (Reference Maddison2006) is that our results include Bayesian estimation, which is highly popular in the spatial econometric literature. Within the context of WKC analysis, only Mazzanti et al. (Reference Mazzanti, Montini and Nicolli2012) considered the spatial issue, but they only employed Moran's I statistics and did not estimate the WKC trend using SAR or SEM.

Our second contribution is that in our data we classify MSW into two different types: household solid waste and business solid waste (hereafter, household MSW and business MSW, respectively). The former type is the waste that is generated by households, whereas the latter is the waste that is generated by small businesses, offices, restaurants and schools. It is generally understood that the amount of household waste is directly related to the income of the residents in the municipality, whereas the same relationship does not seem to hold for business waste. In fact, the amount of business waste is affected not only by the residents of the municipality but also by the behavior of restaurant patrons or commuters to offices or schools from distant municipalities. Thus, businesses and households employ different decision-making processes when discharging waste. Using aggregate data that combine the two different types of MSW into a single index to study the WKC may therefore make it difficult to identify a robust relationship between income levels and the amount of waste generated. To our knowledge, no other study of WKC has introduced the idea of separating MSW into household and business MSW.

We believe the lack of spatial econometric approaches and disaggregation of the waste type in previous literature makes it difficult to examine the WKC hypothesis accurately. Thus, the purpose of the present study is to reexamine the WKC hypothesis by including these two features in the analysis. Although this study uses the data of a developed country, Japan, to examine the WKC hypothesis, it will be highly informative for devising an effective waste management policy for developing countries because the future situations of these countries will likely be similar to the status quo in developed countries. In fact, a number of developing countries are now confronting a growing problem of MSW disposal. Zhang et al. (Reference Zhang, Tan and Gersberg2010), for example, report that the total amount of MSW generation in China, a leader of the developing countries that has achieved impressive economic growth, increased from 31.3 million tons in 1980 to 212 million tons in 2007. In India, the amount of discharged waste in Delhi is expected to rise from approximately 7,000 tons per day in 2001 to 17,000–25,000 tons per day in 2021 (DUEIIP, 2001). In addition to the problem of increasing waste generation, many developing countries suffer from the lack of a sanitary MSW disposal system or of MSW regulations, which are particularly important for proper waste disposal management.

In developing countries, the issue of waste disposal management is an especially important environmental problem that must be solved immediately. Examining the relationship between economic growth and the amount of discharged waste in those countries is considered appropriate for the development of an effective waste management policy because the policy developed through such procedures is expected to have wide application to future waste generation in developing countries. The present study examines the effects of specific measures of waste management policy, such as the introduction of unit pricing for waste disposal or the number of waste separation categories enacted by the municipality. This study will also be beneficial for the establishment of valid waste management policies in developing countries.

One of the first studies regarding WKC is Cole et al. (Reference Cole, Rayner and Bates1997). The researchers used OECD panel data for 1975–1990 and examined the validity of the EKC hypothesis for several environmental indicators, including MSW. They found evidence to support the EKC hypothesis for indicators such as sulfur dioxide and suspended particulate matter but not for municipal waste. In later studies, Fischer-Kowalski and Amann (Reference Fischer-Kowalski and Amann2001) and Karousakis (Reference Karousakis, Mazzanti and Montini2009) examined the WKC hypothesis using more recent panel data from OECD countries, but they also failed to find evidence that supported the WKC hypothesis for MSW generation.Footnote 2 Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009) examined EU panel data and found overall evidence in favor of the EKC hypothesis for landfill waste but not for MSW generation.Footnote 3 By conducting a country-level analysis, Mazzanti and Zoboli (Reference Mazzanti and Zoboli2005) and Mazzanti (Reference Mazzanti2008) examined European countries but did not find evidence in favor of the EKC hypothesis for MSW generation. In addition to such country-level analyses, there were several studies that used province-level data, such as that of Managi and Kaneko (Reference Managi and Kaneko2009), who employed data from 29 Chinese provinces; Mazzanti et al. (Reference Mazzanti, Montini and Nicolli2012) employed data from 103 Italian provinces. None of these studies, however, found evidence of absolute delinking for MSW generation.Footnote 4 Raymond (Reference Raymond2004) used international cross-sectional data and found evidence that supported the WKC hypothesis. However, because Raymond used a waste/consumption indicator as his dependent variable, the results cannot be applied directly to the case of MSW, which is the focus of our study. Berrens et al. (Reference Berrens, Bohara, Gawande and Wang1997) and Wang et al. (Reference Wang, Bohara, Berrens and Gawande1998) examined the EKC hypothesis for hazardous waste using county-level, cross-sectional data in the US, and both found evidence that supported the EKC hypothesis; however, these studies did not examine any environmental indicators related to MSW generation.

A few limited studies that support the EKC hypothesis for waste generation are Mazzanti et al. (Reference Mazzanti, Montini and Zoboli2008) and Mazzanti et al. (Reference Mazzanti, Montini and Zoboli2009). Although both studies used disaggregated data at the province level in Italy and found some evidence of WKC, they found that only a few of the richer provinces exhibit delinking between economic growth and the amount of waste discharge. Abrate and Ferraris (Reference Abrate and Ferraris2010), using data for selected municipalities in Italy from 2004 to 2006, provide partial evidence of WKC.Footnote 5

There is little evidence that suggests the validity of the WKC hypothesis. All the positive evidence concerns cases of hazardous waste, relies on a waste/consumption indicator, finds that only a few of the richer provinces exhibit delinking between income level and waste generation, and/or examines only selected jurisdictions. In contrast, we present reliable evidence of WKC, especially for household MSW. We do so by disaggregating the data regarding MSW discharge according to the different types of waste generators and by employing a spatial econometric approach.

The remainder of this paper is structured as follows. Section 2 describes the current state of Japan's solid waste management system and presents the data used in the estimation. The econometric models and our definition of spatial dependence are explained in section 3. Section 4 provides the estimation results and presents related policy implications. Section 5 summarizes the discussion.

2. Practical background and data

2.1. The state of municipal solid waste in Japan

In Japan, waste is generally separated into two categories: industrial waste and domestic waste. The Waste Disposal and Public Cleansing Law defines certain types of waste generated by industrial activity as industrial waste and the rest as domestic waste. A typical example of industrial waste is waste generated by a factory, whereas a typical example of domestic waste is waste generated by households, small businesses, restaurants, convenience stores or office buildings. Thus, in Japan, domestic waste corresponds to MSW, as is typically understood in studies of the WKC hypothesis. In what follows, we refer to domestic waste as MSW to remain consistent with the previous literature.

Japanese MSW can be classified into two types: MSW from households and MSW from business activities. As defined in the previous section, the former type of waste is classified as household MSW and the latter as business MSW.Footnote 6 The Japanese Ministry of the Environment (2008) reported that a total of approximately 49.7 million tons of MSW was disposed of in Japan in 2005, of which, 33.5 million tons (67 per cent) was household MSW, and 16.2 million tons (33 per cent) was business MSW.

Although there are differences in the waste disposal systems used across municipalities, as noted below, the waste generator associated with each type of MSW is in many cases obliged to purchase disposal bags as designated by the municipality and to bring the MSW to the appointed location on the designated day. In Japan, most collected waste is incinerated at an intermediate waste treatment facility before it is landfilled. For example, in 2005, 77.4 per cent of disposed waste was directly incinerated, 5.1 per cent was directly recycled, and 2.9 per cent was directly landfilled. The remaining 14.6 per cent underwent intermediate processing with the aim of recycling the waste or reducing its weight. Overall, 14.7 per cent ended up in landfills, and 14.1 per cent was eventually recycled.Footnote 7 The amount of waste that is landfilled greatly depends on the method of intermediate waste disposal employed by each municipality and thus is not decided at the household level. Because the volume of landfilled waste reflects a different type of result in our empirical analysis based on the per capita income level of households, we decided not to include this amount in this paper.

In the past, the priority of the MSW management policy in Japan was to provide acceptable levels of sanitary waste disposal. For this purpose, incineration has become widely used as the waste disposal method in Japan. However, because of the increased amount of MSW in recent years, waste reduction is now a major aim of the waste management policy. In fact, the Japanese government promotes what is called the ‘3Rs’ principle and aims to build a sustainable society. 3Rs is an acronym for ‘Reduce, Reuse, Recycle’, and the order of words indicates the hierarchy of waste management strategies.Footnote 8 In accordance with this principle, several types of waste management legislation, such as the Basic Act for the Promotion of the Recycling-Oriented Society, the Act on Promoting Green Purchasing and the Containers/Packaging Recycling Act have been introduced in Japan. Whereas national legislation establishes the national strategy for waste management, the practical operation of MSW disposal services is planned and conducted by each individual municipality. In fact, chapter four of the Waste Disposal and Public Cleansing Law stipulates that each municipality is responsible for creating its own plan for disposing of the MSW generated in its region. Thus, the waste management policies for MSW differ widely across municipalities.Footnote 9 For example, some municipalities collect plastics as combustible waste, whereas other municipalities collect plastics as incombustible refuse. When processing recyclable waste, some municipalities pick up only packaging materials, whereas others collect waste paper or used textiles in addition to packaging materials. Waste collection systems vary across municipalities. For example, some municipalities have set up waste-collection points, whereas others have introduced door-to-door collection schemes that are similar to the curbside collection systems used in Europe and the United States.

Moreover, there are municipalities that simultaneously use both types of collection systems. A number of municipalities have adopted the waste-collection points system for household MSW while introducing a door-to-door collection system for business MSW. Because the Waste Disposal and Public Cleansing Law permits municipalities to outsource waste collection to the private sector, the operating body responsible for waste collection is not the same across municipalities. Although there are several municipalities that provide waste collection services themselves, a number of municipalities outsource all or a part of their operations. The same is true of waste disposal operations.

Thus, a municipality can be considered an independent decision-making entity within the context of waste management. Consequently, aggregating the data (at the national level, for example) is problematic because it may obscure the effects of disparate waste management policies that differ across municipalities. We therefore use data at the municipality level in our empirical analysis.

2.2. Data

In the following empirical analysis, we developed a municipality-level, cross-sectional data set for Japan in 2005. The waste-related data were obtained from the Japanese Ministry of the Environment (2008), whereas other socio-economic data, such as information regarding income per capita and population density, were obtained from the Ministry of Internal Affairs and Communications (2008).

There are two main reasons why we use cross-sectional data rather than panel data. The most important reason is the large number of municipality mergers led by the national government from the late 1990s to the mid-2000s. In fact, the total number of municipalities was reduced by more than half as a result. The mergers caused an attrition problem that seriously weakens the reliability of the related panel data.Footnote 10 The other reason we use cross-sectional data is the availability of socio-economic data. Because crucial socio-economic data (e.g., information regarding household composition) are only released every five years, we could not develop a panel data set that included sufficient longitudinal information. For these reasons, we employ the latest available cross-sectional data, those from 2005, in the following analysis.

Table 1 presents descriptive statistics for the variables we use. In table 1, waste is the total MSW generation per capita (unit: grams per day per capita) in a municipality. These data can be disaggregated into two classes: wasteh and wasteb. The former is the household MSW, and the latter is the business MSW. As described above, because the relationship between income level and the amount of waste discharged will be different for households and businesses, it is important for us to separate waste into household MSW and business MSW when analyzing the WKC hypothesis.

Table 1. Summary statistics

Notes: The first three variables are dependent variables, all of which are on a per capita basis. See the text for sources and units.

The most important non-waste variable in this study is income. This variable is defined as the total taxable gains (unit: million yen) in a municipality. perinc (million yen per capita) is simply calculated by dividing income by the number of income tax payers. Thus, perinc is considered the income per household rather than the income per capita. perinc2 is the square of perinc.

In analyzing the effect of waste disposal policies, we define hprice, bprice and sorting. hprice(bprice) is a dummy variable that equals one if a municipality introduces unit pricing for the disposal of household MSW (business MSW). To avoid the endogeneity problem with regard to waste policy, a one-year lag is adopted for both hprice and bprice.Footnote 11 However, this approach cannot be directly employed with the new municipalities that came into being as a result of the mergers in 2005. In these cases, we use the weighted average of the one-year lagged policy variables for each municipality in the pre-merger period.Footnote 12 Under the assumption that both household and small-business behavior are rational, we expect less waste generation if unit pricing is introduced. Although studies such as Kinnaman and Fullerton (Reference Kinnaman and Fullerton2000) and Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) considered the effect of charges for waste collection, they examined charges for total MSW rather than distinguishing between household MSW and business MSW.

sorting is the number of categories of waste that the municipality sets and requires the waste discharger to separate. Each municipality can use its own discretion in setting this number. For instance, one municipality might separately collect combustible waste, noncombustible waste, used paper, used plastics and metals, whereas another municipality might collect most of these waste types (or recyclables) together. Like hprice and bprice, to avoid the endogeneity problem, we calculate the weighted average of the policy variables for those municipalities that constitute a new post-merger municipality.Footnote 13 To our knowledge, among the studies of WKC, none has used sorting as an explanatory variable. We hypothesize that sorting has a negative sign because those who employ more time-consuming sorting practices (namely, with greater sorting) will be aware of the reduction in waste generation. Note that MSW policies pricing policies and sorting practices are quite different across municipalities.

We also use other socio-economic variables that affect waste generation. shousehold is the ratio of single-person households to total households. We expect that there will be less per capita MSW generated if there are more than two people in a household. elderly is the ratio of households composed of elderly couples to the total,Footnote 14 and we expect that an elderly household will generate less per capita MSW than a younger household because the amount of goods consumed by elderly people is less than that consumed by younger people. The effect of household size was also considered in Kinnaman and Fullerton (Reference Kinnaman and Fullerton2000), Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009) and Abrate and Ferraris (Reference Abrate and Ferraris2010). Kinnaman and Fullerton (Reference Kinnaman and Fullerton2000), Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009) and Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) examined the effect of elderly households.

commutein indicates the ratio derived by dividing the number of commuters from areas outside the municipality by the number of people who commute from the municipality to elsewhere. We believe this variable indicates the level of economic activity because economically growing municipalities provide employment opportunities for more people and, thus attract people who live outside the municipality. Although studies such as Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) and Mazzanti et al. (Reference Mazzanti, Montini and Nicolli2011, Reference Mazzanti, Montini and Nicolli2012) considered the effect of population inflow as defined by the inflow of tourists, no study has focused on the influence of commuters from areas outside the municipality. We expect commutein to be positively related to the amount of waste discharged and the volume of landfill waste. Finally, popden denotes the population density (the population per 1,000 m2). This indicator was used in a number of studies, including Hage et al. (Reference Hage, Sandberg, Soderhölm and Berglund2008), Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009) and Abrate and Ferraris (Reference Abrate and Ferraris2010). Because population density tends to be high in economically significant municipalities, we expect popden to be positively related to the amount of waste discharged.

3. Econometric models

There are two different measures of waste generation in our data set: household MSW and business MSW. We separately test for each of these waste generation measures whether the WKC hypothesis holds.

3.1. The need for spatial consideration

One caveat here regards our choice of econometric methods. Our initial conjecture that the behavior of Japanese municipal governments tends to ‘mimic’ the waste-collection policy employed by their neighbors requires that we assume spatial correlation in our econometric model.Footnote 15 Thus, we test the WKC hypothesis by adopting two different spatial econometric models: a spatial autoregressive model (SAR) and a spatial error model (SEM).Footnote 16

The SAR model assumes that the dependent variable, i.e., the amount of waste, is spatially correlated across municipalities, whereas the SEM model assumes that the errors are spatially correlated. In Japan, each municipality belongs to one of 47 prefectures. In this two-tiered system, municipalities in the same prefecture tend to have the same information and regulations, which are provided by their prefectural government. They also tend to implement similar waste management policies. Thus, municipal governments in Japan tend to mimic each other more with regard to their waste-reduction efforts than in their actual waste reduction. Given that these efforts are latent and act as omitted variables in our analysis, we suspect that the errors rather than the amount of waste are spatially correlated.

To reflect this two-tiered regional government system, we design the spatial weight matrix such that before row standardization, its ij element is one if municipality j (≠i) is in the same prefecture as the municipality i and zero otherwise.Footnote 17 In what follows, we refer to this matrix as spatial weight matrix I (SWM1). This matrix captures the administrative relationship between municipalities rather than focusing on more orthodox geographic relationships (with the elements of the matrix as the inverse of the distance between the municipalities squared.Footnote 18 ) We refer to the latter matrix as SWM2, and we use these two matrices alternatively in estimating all the models.

3.2. OLS and the Lagrange multiplier tests

We begin by specifying the following quadratic relationship between waste generation and per capita income, which is standard in the EKC literature:

(1) $$Y=\beta_{0}+X\beta_{1}+X^{2}\beta_{2}+Z\gamma+\epsilon$$

where Y is the n × 1 vector of waste generation per capita; X is the n × 1 vector of the per capita income of the municipalities; and Z is the n × k matrix of the exogenous variables, where β0, β1, β2 and γ are the corresponding parameters. In our model, parameters that satisfy β1 > 0 and β2 < 0 imply evidence for the WKC hypothesis.

The above simple OLS model is considered the model under the null hypothesis such that ρ = λ= 0. In a more general model,

$$\eqalign{& \quad y=\beta_{0}+\rho W^{L}Y+X\beta_{1}+X^{2}\beta_{2}+Z\gamma+\mu \cr & \quad \mu = \lambda W^{E}+\epsilon}$$

where the parameters ρ and λ are spatial autocorrelation coefficients; W L (W E ) as the spatial weight matrix defined above, and ε is the error, which is assumed to be independent and identically distributed (iid) with mean 0. We compute the Lagrange multipliers for both lags and errors under the null of ρ = λ= 0 and the robust Lagrange multipliers for lags and errors under the null of ρ = 0 and λ = 0 without any restrictions on the values of λ and ρ.Footnote 19 Comparing these figures indicates whether it is more appropriate to assume SAR or SEM. We will elaborate on these findings in the results section.

3.3. Spatial autoregressive and spatial error models

Following Anselin (Reference Anselin and Baltagi2001) and others, we consider two alternative spatial econometric models: the SAR and the SEM. The spatial lag model is

(2) $$Y=\beta_{0}+\rho WY+X\beta_{1}+X^{2}\beta_{2}+Z\gamma+\epsilon$$

Again, the parameter ρ is a spatial autocorrelation coefficient, and W is the spatial weight matrix defined above. We assume here that ε is iid with mean 0.

Another econometric specification is the SEM, in which the spatial interdependence occurs through the error terms. Formally, this model is represented as follows:

(3) $$Y = \beta_{0}+X\beta_{1}+X^{2}\beta_{2}+Z\gamma+\mu$$
(4) $$\mu = \lambda W \mu+\epsilon$$

The behavioral assumption in the SAR model is that municipalities care about the actual amount of waste generated by their neighbors; we consider this assumption to be unlikely. Instead, we expect SEM to be more appropriate than SAR because municipalities in the same prefecture are expected to be consistent with regard to waste reduction efforts that are largely unobservable, such as educational programs and public advertisements encouraging waste reduction.Footnote 20 Should the contiguity effect exist, it will appear in the error term as the omitted variable, which yields the SEM.

3.4. The estimation methods

The field of spatial econometrics has rapidly developed since the seminal textbook by Anselin (Reference Anselin1988) was written. This development enables us to expand the scope of spatial econometrics. It is inappropriate to estimate (2) using ordinary least squares (OLS) because of the endogeneity problem with regard to the spatial lag term. Given the large sample (n = 1, 798) in our work, it would be standard to use maximum likelihood estimation. However, the results of the Jaque–Bera test (reported in tables 2 and 3) cause us to reject the normality of the error distribution. To address this problem, we use the generalized spatial two-stage least squares approach developed by Kelejian and Prucha (Reference Kelejian and Prucha1998) for the estimation of SAR and GMM in the manner of Kelejian and Prucha (Reference Kelejian and Prucha1999) for SEM.

Table 2. Estimation results of wasteh (above) and wasteb (below) with SWM1

Notes: **1%, *5%, 10%.

Table 3. Estimation results of wasteh (above) and wasteb (below) with SWM2

Notes: **1%, *5%, 10%.

Another method to address relaxing the assumption of constant variance normal disturbances is to use Bayesian estimation. The introduction of Bayesian estimation has a very significant impact. One of the reasons for this impact is that ‘Bayesian models allow for the direct estimation of the influence of heteroskedasticity and outliers’ (Ross, Reference Ross2013: 458). With the help of the development of numerical computation techniques, the Bayesian method has been widely applied in the spatial econometrics literature.

In light of these advances, we have added Bayesian estimation results. For our purposes, being able to check the variance of the linear combination of the estimates without using the linear approximation (delta method) is an additional advantage of using Bayesian models. The difference between the conventional and Bayesian approaches is the use of prior information in Bayesian estimation.Footnote 21 Our assumption regarding the prior distribution (π(·)) is as follows.

(5) $$\pi({\bf \beta})\sim N(c,\sigma^{2}T)$$
(6) $$\pi(\sigma^{2})\sim NIG (a,b) ={b^{a}\over \Gamma (a)} (\sigma^{2})^{-(a+1)} \exp (-b/\sigma^{2})$$
(7) $$\pi(\rho)=U(\lambda^{-1}_{min}, \lambda^{-1}_{max} ),$$

where a, b, c, T are parameters. N(·) is the normal distribution, whereas NIG (·) denotes the normal inverse-gamma distribution. We assume non-informative priors for ρ and U(·), namely, that they are uniformly distributed, and that λ min max ) denotes the minimum (maximum) eigenvalue of the spatial weight matrix. By combining this prior information into (2), (3) and (4), we can derive the posterior distribution of each spatial econometric model. To solve the models, we use Metropolis–Hastings sampling, which is one of the Markov Chain Monte Carlo (MCMC) approaches. We set the number of samplings to 150,000 and discard the first 5,000 as the ‘burn-in’.Footnote 22

4. Results

Through our empirical analysis, we found solid evidence in favor of the WKC hypothesis for household MSW. The estimation results are summarized in tables 2 and 3. Note that each variable in the estimation (except for the dummy variables) is in the form of a natural logarithm. Each table contains estimates from different econometric models for each of the alternative spatial weight matrices.Footnote 23 The dependent variables are indicated in the top-left corner of each table.

4.1. Evidence for spatial interdependence

All Moran's I tests and LM tests (LM err , LM lag , RLM err and RLM lag ) are statistically significant at the 5 per cent level or better for all models, which indicates the existence of spatial correlation and thus supports our tests of spatial models in investigating the WKC hypothesis. Our initial conjecture was that neighboring municipalities' mimicking behavior is mostly not measurable and, hence, appears in the error term as a missing variable. Indeed, as indicated by table 2, the values obtained from the robust LM tests reveal that RLM err is always significant and consistently larger than RLM lag .Footnote 24

First, we focus on the results of the household MSW. Through the GS2LS and GMM estimation methods of spatial models, the lag coefficient ρ is insignificant, whereas λ for the spatial error is significant for household MSW under SWM2, the distance-based spatial weight matrix. These results seem to support our view that municipal governments care more about their neighbors' efforts to reduce waste than about their neighbors' actual performance at waste reduction. However, the results for the contiguity-based spatial weight matrix (SWM1) presented in table 2 and the Bayesian estimation results demonstrate that both ρ and λ are significant, thereby implying that municipalities follow the actual waste generation performance of others on top of the above-mentioned mimicking behavior.Footnote 25

The results for business MSW are not necessarily consistent with the results for household MSW. For business MSW, whereas the spatial lag coefficient ρ is insignificant and RLM err is greater than RLM lag for both spatial weight matrices, it is only with SWM2 that the spatial error coefficient λ is significant. However, the magnitude of λ is still less than that of household MSW. For example, according to the result from SEM with SWM2, it is 0.504 in the case of business MSW. Therefore, our initial conjecture regarding the intangible mimicking behavior of these municipalities is only weakly supported for business MSW.

The validity of SEM implies that the spatial correlation stems not from the strategic behavior of particular municipalities but rather from the unobservable characteristics of each municipality. The SAR model is considered a more appropriate model in most previous studies that used data from European countries, such as Hage et al. (Reference Hage, Sandberg, Soderhölm and Berglund2008), Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) and De Jaeger (Reference De Jaeger2011). However, the significance of the SEM in our study reflects the particular attributes of waste management in Japan, which is quite different from its European counterpart. One of the reasons could be that red-tapism, or a focus on justification of the waste management policy rather than the actual outcome, is more pervasive and dominant in local Japanese governments compared with the consequentialism that is prevalent among European countries. To ensure that a waste management policy attains the designated aim, we should not be satisfied with just implementing a proper policy package; rather, we must monitor the consequences on a regular basis. Although it would be interesting to examine these differences between Europe and Japan, this issue is one for future research.

4.2. Evidence for WKC

We summarize the results for the tests of the WKC hypothesis. To confirm the WKC hypothesis, we must obtain a positive sign for the estimated coefficient for per capita income and a negative sign for the estimated coefficient for its squared term. For household MSW, these coefficients satisfy the sign requirements and are statistically significant at the 1 per cent level in all spatial models, thus demonstrating WKC. This result is robust regardless of which of the two alternative spatial weight matrices we employ, SWM1 or SWM2.

However, as indicated in tables 2 and 3, WKC does not hold as expected for business MSW because a number of people commute to offices or schools from distant municipalities; hence, business MSW is not directly related to the per capita income of the residents of the municipality.Footnote 26 These results imply that the dependence of waste generation on income growth depends on the type of MSW. Thus, to demonstrate WKC, we must distinguish household MSW from business MSW. To ignore this factor as previous studies have done makes it difficult to demonstrate WKC.

4.3. Turning points on WKC: absolute decoupling

Next, we investigate the actual income distribution over the observed WKC to determine whether the turning point of the curve falls within the observed income range. Based on the definition provided by OECD (2002), we conclude that absolute decoupling occurs if the turning point is within the range of the observed income levels; otherwise, relative decoupling occurs.

Table 4 presents a summary of the turning points obtained through our analyses.Footnote 27 As indicated in the table, absolute decoupling is observed, with the turning point being less than the maximum of the observed income. Furthermore, the quadratic term of per capita income is estimated to be consistently strictly negative for the household MSW. In particular, the results of SEM for household MSW demonstrate that the turning point is at a log per-capita income of approximately 1.3, or 3.7 million Japanese yen,Footnote 28 recognizing that the standard errors for SAR in table 4 seem to be sufficiently small, while the turning points for SEM have much larger standard errors. Because the results with SWM2 are nearly the same for both SAR and SEM, the results demonstrate the robustness of the absolute decoupling, at least for the SAR model.

Table 4. Turning points based on GS2LS and GMM (household MSW)

Note: Standard errors are computed by the delta method.

For further robustness, we compute the same turning points using the coefficients derived by Bayesian estimation and summarize the results in table 5. By computing the stationary point $\lpar ={\hat{\beta}_{1}}/{2\hat{\beta}_{2}}\rpar $ of each of 150,000 sampling processes, and storing them, we can replicate the probability density function for the turning points. Figure 1 shows the histogram of the turning points for SAR and SEM. The figure indicates that 95 per cent of the sample is within the range of 1.1 to 1.4. Given that the log of the average per capita income is 1.09, with a minimum of 0.746 and a maximum of 1.78, absolute decoupling is observed with 5 per cent significance.

Figure 1. Distribution of the turning points

Table 5. Turning points (household MSW)

Note: The quantile figures above are based on sample generated during MCMC procedure.

4.4. Implication of policy variables

Regarding the policy variables, it is noteworthy that the signs of hprice and sorting are significantly negative for household MSW. This result implies that charges for garbage collection and increases in the number of waste separation categories significantly decrease the amount of waste. This tendency is also observed in online Appendix tables A1 and A2, where we run the spatial Durbin model (SDM), which incorporates the neighbors, effects on explanatory variables.Footnote 29

In contrast, the sign of unit pricing for business MSW (bprice) is significantly positive, and it is inconsistent with our expectation. Unlike the unit pricing scheme for household MSW, bprice has already been introduced in numerous municipalities, and the value of bprice does not vary significantly among municipalities. This lack of difference may make estimation of the effect of bprice difficult. Because the effect of unit pricing schemes for business MSW is ambiguous, additional strategies (e.g., setting physical targets for business MSW reduction) are necessary to further reduce the amount of business MSW.

4.5. Implication of socio-economic variables

We also find interesting results with respect to the socio-economic variables examined here. First, the variable commutein is positively and significantly related to all the waste-generation measures. As noted above, this variable indicates the level of economic activity. Therefore, it is quite natural that the amount of waste discharged increases as commutein increases.

The population-related data also yield several implications. First, the sign of shousehold is significantly positive in all the models. This result indicates that an increase in the ratio of single-person households to total households significantly increases the amount of both household MSW and business MSW. Although Kinnaman and Fullerton (Reference Kinnaman and Fullerton2000) considered the effect of household composition on waste discharge by using family size as an explanatory variable, they did not find statistically significant results. As was the case for elderly, the results are rather unclear. The results are significantly negative for business MSW; however, the results are positive but not statistically significant for household MSW. Because a household's economic activity is thought to decrease as its members age, business MSW will decrease as the percentage of elderly couple households increases. This result is consistent with that of Kinnaman and Fullerton (Reference Kinnaman and Fullerton2000), whereas Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009) and Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) reached the opposite conclusion. Finally, the variable popden is positive and statistically significant in almost all the models. This result implies that the amount of waste discharged significantly increases as population density increases. Mazzanti and Zoboli (Reference Mazzanti and Zoboli2009) and Mazzanti et al. (Reference Mazzanti, Montini and Nicolli2012) obtained similar findings, whereas Kinnaman and Fullerton (Reference Kinnaman and Fullerton2000), Hage et al. (Reference Hage, Sandberg, Soderhölm and Berglund2008), Abrate and Ferraris (Reference Abrate and Ferraris2010) and Eyckmans et al. (Reference Eyckmans, De Jaeger and Verbeke2009) did not.

5. Conclusions

In this paper, we found strong evidence of WKC when analyzing the MSW discharged by municipalities in Japan. Disaggregating the data for MSW discharge based on the types of waste generators at the municipal level, we extended the literature by demonstrating that the data for household MSW support the WKC hypothesis but that those for business MSW do not. We demonstrated that the relationship between income level and the amount of waste discharged is different for households and businesses. The amount of household MSW is directly affected by the behavior of the residents of the municipality, whereas the amount of business MSW is significantly affected by the behavior of the people who come to the municipality from distant municipalities. Thus, distinguishing between these two types of waste may be the key to confirming the WKC hypothesis for MSW generation.

In recent years, a number of developing countries have faced growing MSW management problems because of rapid economic growth. To address this problem, authorities are enhancing MSW management policies, including continuous construction of new waste disposal facilities. However, our result demonstrates the possibility that this increasing tendency in the total amount of MSW will come to an end in the not-so-distant future. Thus, policy makers should account for this probability when they develop future waste management strategies.

Our results also suggest the importance of diverse MSW management policies that are tailored to each region's income level. As is well known, there is considerable wealth disparity between urban and rural areas in developing countries. Some parts of urban areas will enter the declining period of waste generation earlier than rural areas. Therefore, it may be appropriate for urban authorities to ease waste regulations in anticipation of the possibility of a decrease in the amount of MSW. In fact, in the decreasing period of waste generation, it may be beneficial to spend resources not on waste reduction policy but rather on the policies aimed at the solution of other serious environmental problems, such as air pollution and water contamination.

Supplementary materials and methods

The supplementary material referred to in this paper can be found online at journals.cambridge.org/EDE/.

Footnotes

*

The original version of this article was published with the incorrect title. A notice detailing this has been published and the error rectified in the online and print PDF and HTML copies.

1 In particular, Ham (Reference Ham2009) applied several different spatial econometric models to the UK's municipality-level data and found evidence of spatial clustering among municipalities with similar recycling rates and evidence of the regional convergence of recycling rates. Hage et al. (Reference Hage, Sandberg, Soderhölm and Berglund2008) investigated the determinants of the household plastic packaging collected per resident using Swedish municipal-level data in 2005 and found that the amount of plastic waste collection in a municipality is positively related to that of neighboring municipalities.

2 Although Fischer-Kowalski and Amann (Reference Fischer-Kowalski and Amann2001) could not find evidence of the WKC hypothesis for MSW generation, they found that the hypothesis holds for landfilled waste.

3 They only found evidence of a relative delinking between income and MSW generation.

4 Absolute delinking occurs if the turning point is within the range of the observed income levels, whereas relative delinking indicates a positive but decreasing relationship between economic growth and waste discharge.

5 Because there are no publicly available data regarding the waste collected in each municipality, these researchers used the data from an extraction survey provided by the company EcoCerved.

6 An illustration using a simple figure is available in an online Appendix at http://journals.cambridge.org/EDE.

7 We excluded the recyclable waste that is collected through ‘group collection’ when we calculated the recycle ratio. This scheme is employed by citizen groups and private recyclers and is independent of municipality-level waste collection systems.

8 Similar principles have been introduced in several countries and regions. For example, the EU has introduced a five-stage hierarchy of waste management strategy: first comes the prevention of waste, followed by reuse, recycling, other recovery and disposal.

9 In contrast, regarding industrial waste, the law stipulates that the waste should be disposed of by the generator itself.

10 See Wooldridge (Reference Wooldridge2002, chapter 17) for details.

11 In this manner, we follow Mazzanti et al. (Reference Mazzanti, Montini and Nicolli2012).

12 For further details regarding this variable, see the online Appendix available at http://journals.cambridge.org/EDE.

13 See online Appendix for details.

14 An elderly couple household is defined as a household that is composed of a husband of age 65 or over and a wife of age 60 or over.

15 Our Moran's I test results indicate the existence of spatial interdependency among municipalities for all types of waste generation measures. See tables 2 and 3 for details.

16 To check the robustness of our evidence, we also estimate the spatial Durbin model (SDM). The assumptions and results of the SDM are summarized in the online Appendix, and they support the conclusions derived using SAR and SEM.

17 For example, it is block diagonal with all diagonal elements being zero. Row standardization then divides all the numbers in the ith row by N i  − 1, where N i is the total number of municipalities in the prefecture to which municipality i belongs. Note that each row sums to unity. See online Appendix for more details.

18 Each element is 1/dij 2 for all i ≠ j, where d ij is the distance between two municipalities i and j, and the diagonal elements are again zero. Row normalization is employed in this case as well such that each row sums to unity.

19 See Anselin et al. (Reference Anselin, Bera, Florax and Yoon1996) for details.

20 In Japan, there are several types of educational programs related to waste reduction that are provided by municipalities. For example, some municipalities organize seminars for residents or businesses that promote an understanding of how to reduce waste generation or how to sort waste. Furthermore, there are several municipalities that set up tours of waste disposal facilities or recycling facilities so that residents can learn about the municipality's waste management scheme.

21 There are philosophical discussions between conventional frequentists and Bayesians regarding the use of prior information. Thus, we estimate in both ways to compare the results. Poirier (Reference Poirier1988) provides an excellent survey of Bayesian methods and the frequentist method.

22 See LeSage and Pace (LeSage and Pace Reference LeSage and Pace2009: 133–141) for details of the computational methodology.

23 Recall that SWM1 captures administrative proximity, with the ij element being non-zero if municipalities i and j (≠i) are in the same prefecture and zero otherwise. In contrast, SWM2 is a more orthodox spatial weight matrix based on the geographical distance between the municipalities.

24 Note that we have

$$LM_{err}+RLM_{lag}=LM_{lag}+RLM_{err}$$

Direct comparison of RLM err and RLM lag as a model-specification strategy is outlined in Anselin and Rey (Reference Anselin and Rey1991), Maddala (Reference Maddala1992), Florax and Folmer (Reference Florax and Folmer1992), Anselin and Florax (Reference Anselin, Florax, Anselin and Florax1995) and Florax et al. (Reference Florax, Folmer and Rey2003).

25 Results of the spatial Durbin regression including WZ. The spatial correlation of the policy variables fortifies this view, with both ρ and λ being statistically significant. The spatial Durbin regression is conducted via a Bayesian approach for the technical reason that G2SLS uses WZ as an instrument. See online Appendix and tables A1 and A2 for details.

26 Another potential reason why WKC is not observed for business MSW is that the amount of business MSW processing is to some extent substitutive to that of household MSW with greater income elasticity. This point should be investigated in future research.

27 These results are obtained by calculating the stationary point of the estimated equations (2) and (3) with respect to per capita income and applying the delta method for the standard deviations. The delta method computes the standard deviation of the turning points through a linear approximation around the point estimates of the slope and second-order coefficients of income. Therefore, we provide its intervals via results from Bayesian estimation below.

28 This value is approximately US $ 37,000 per household. Recall that perinc is taxable gain (not actual figures on a payroll).

29 As shown table A3, the indirect effect on household MSW is positive, whereas the direct effect is negative for both policy variables, with the total effect being negative. See online Appendix E for details.

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Figure 0

Table 1. Summary statistics

Figure 1

Table 2. Estimation results of wasteh (above) and wasteb (below) with SWM1

Figure 2

Table 3. Estimation results of wasteh (above) and wasteb (below) with SWM2

Figure 3

Table 4. Turning points based on GS2LS and GMM (household MSW)

Figure 4

Figure 1. Distribution of the turning points

Figure 5

Table 5. Turning points (household MSW)

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