Sustainability and Environmental Management Capacity in AsianCountries: Efficiency-based Indicator Development
Katsuya Tanaka
Research Center for Sustainability and Environment
Shiga University
1.

Introduction

The Social Capacity for Environmental Management (SCEM) is the capacity to
manage and solve environmental problems through an endogenous process involving
three actors (governments, firms, and citizens) and their interactions (Matsuoka andKuchiki 2003). Many traditional arguments deal with causal relations between
environmental performance (e.g., SO2 emission) and socioeconomic condition (e.g.,
GDP per capita). The SCEM approach extends such arguments by integrating the
SCEM into a traditional framework. Thus, environmental performance is determinedby socioeconomic condition, SCEM, and their interactions. Figure 1 illustrates thisrelationship.

Figure 1. The SCEM under the Total System
The capacity development approach has gained greater attention from various
environmental perspectives since the 1990s. However, many of the existing relevant
studies are limited in terms of either theory or empirics. Some studies are highly
conceptual and do not demonstrate the empirical application of those concepts using
the existing data. Some other studies present an empirical measurement of capacity
development by developing several indicators. However, many such studies provide
little relationship between such indicators and environmental performance. In order toobtain a valid measurement of capacity, more statistically sound and reliable indicatorsneed to be developed.
This section proposes another approach toward the elicitation of the causal
relationship among environmental performance, socioeconomic condition, and SCEM
capacity indicator development. Our approach is different from existing studies in231

several respects. First, environmental performance is measured in terms of efficiency,
not in terms of a direct measure of pollutant emission. Second, a statisticalinvestigation is conducted on the relationship among three major
components—environmental performance, the socioeconomic condition, and SCEM.
Third, the SCEM indicator is developed using relevant variables and their statisticalsignificance and elasticities. Thus, the indicator we present in this chapter isstatistically valid compared to existing indicators.
Existing Indicators
Before presenting the SCEM indicator framework, we summarize and list several
major indicators dealing with environmental and natural resources. Overall, there arean extensive number of studies to quantify the capacity of environmental performanceand sustainability. Many of these studies either select the relevant indicator variable
and evaluate its progress (selection approach) or aggregate the scores obtained fromchecklists or variables related to the environment of sustainability (aggregation
approach). We introduce MDG-7 (Target 9 Indicator) as the selection approach. In
addition, we introduce Air Quality Management and Assessment Capabilities andEnvironmental Sustainability Index (ESI) as aggregation approaches.
MDG-7, Target 9 Indicators
One of the most important innovations of the MDG approach is its ability to makegovernments more accountable for their performance in improving human well-being.
By defining goals and measuring progress in clear, straightforward language, the MDG
makes it easy for civil society groups to evaluate the progress toward humandevelopment goals and to issue a public ―report card‖ on a government’s success or
failure.
Unfortunately, the lack of clear, comprehensive targets and indicators formeasuring the capacity of ecosystems to provide a sustainable environmental income
for the poor means that the ―accountability effect‖ of the MDG approach is not yet
applicable to the world’s environmental goals. Until the environmental framework of
the MDG is mended, the short-run progress toward the other goals is at a risk of being
unsustainable. For this reason, it is recommended that MDG-7 (Target 9) be updated.
Air Quality Management and Assessment Capabilities
The United Nations Environmental Programme (UNEP) and the World HealthOrganization (WHO) propose the aggregation approach of environmental management
capacity measurement in their report ―Air Quality Management and Assessment
Capabilities in 20 Major Cities‖ (UNEP/WHO 1996). Their approach measuresenvironmental management capacity in 20 major cities by assessing the number ofindicators obtained from four primary categories: (1) Air quality measurement capacity,(2) data assessment and availability, (3) emissions estimate, and (4) management
enabling capabilities. They prepare a checklist for each category and request each city
to provide the current situation in air quality monitoring. The sum of the points ofchecked items under each category is set at 25 points. Thus, this aggregation approachevaluates the capacity of air quality management from the aggregated score ofindicators, which takes a value between 0 and 100.

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Environmental Sustainability Index
The ESI benchmarks the ability of nations to protect the environment over the next
several decades. In addition, in terms of sustainability vision, the ESI provides thefollowing: (1) A powerful tool for situating environmental decision-making on a firmer
analytical footing, (2) an alternative to GDP and the Human Development Index (HDI)
for gauging the progress of a country, and (3) a useful mechanism for benchmarking
environmental performance (Esty, Marc, Tanja, and Alexander 2005). In order toachieve this goal, the ESI is constructed by integrating 76 data sets—tracking natural
resource endowments, past and present pollution levels, environmental management
efforts, and the capacity of a society to improve its environmental performance—into
21 indicators of environmental sustainability. The indicators and variables on which
they are constructed build on the well-established Pressure-State-Response (PSR)
environmental policy model.
These indicators permit comparison across a range of issues that fall into thefollowing five broad categories: (1) environmental systems, (2) reducing
environmental stresses, (3) reducing human vulnerability to environmental stresses, (4)societal and institutional capacity to respond to environmental challenges, and (5)global stewardship.
Overall, the ESI, with its emphasis on relative rankings, provides a mechanism forestablishing context and for understanding what is possible in terms of policy progress.
Indeed, it turns out that comparisons to relevant peer countries are particularly
important in goal setting, identifying best practices in both policymaking andtechnology adoption, and spurring competitive pressure for improved performance.
Outline of This Chapter
This chapter is organized as follows. The next section introduces the manner in which
the SCEM indicator is developed. Each of the three steps involved in the developmentof the SCEM indicator is described in detail. The third section presents two empiricalapplications. These applications reveal how our framework, presented in section 2, isempirically used with the existing data. Although the first application focuses only on
one country (China), the second application compares SCEM development among
eight Asian countries using international panel data. The last section summarizes thischapter and draws conclusions from prior chapters. This section also presents some
limitations of our approach and the manner in which they are resolved using alternative
approaches.

2.

Empirical proceduresThis study develops the SCEM indicator using the three-step modeling approach. The
first step estimates environmental performance by measuring efficiency. We present
two different efficiency measures in this section. The first measure is environmental
efficiency (EE), which focuses only on the emission of pollutants. The second isbalanced growth efficiency (BGE), which estimates the efficiency of sustainableeconomic development by dealing with both output and pollutant emission. The
estimated environmental performance is then used by the second step to identify the
impacts of the relationship between environmental performance, the socioeconomic
condition, and SCEM. Then, we construct the SCEM indicator using variables andstatistical results from the second step. Each of these three steps is described below.

233

Efficiency is measured either in terms of pollutant emission or both output andpollution. This is done by developing the three-step model. The first step estimates thebalanced growth indicator (BGI) for each of the eight Asian countries. Balanced
growth is evaluated in terms of GDP and CO2 emission. This step is empirically carriedout by an output-oriented directional data envelopment analysis (DEA). The estimated
BGI is then used in the second step. We use the Tobit model to identify the factors
affecting BGI. Based on the SCEM framework (figure 1), the three variables
representing the environmental management capacities of governments, firms, andcitizens are selected and used in the Tobit model. These variables and their elasticities
are estimated in the second step and used in the third step to construct the SCEM
indicator. In this section, we describe the details pertaining to each of these three steps.
2.1 First Step: Measuring Environmental Performance
We begin by measuring environmental performance in the first step. There are several
ways to quantify such performance. For example, in the case of sulfur dioxide (SO2),
environmental performance is typically measured in terms of SO2 emissions or SO
concentrations. Although these measures are common and frequently used, they may
underestimate performance in industrial areas and overestimate it in rural areas. In
addition, if two different areas produce the same level of SO2 emission, thenenvironmental performance will be estimated to be the same, even though other factors
such as monetary value of industrial output, size of the workforce, and the value ofcapital are different.
One way to overcome such limitation of conventional performance measurement is
to estimate efficiency as an environmental performance measurement. The basic idea
behind the efficiency approach is presented in figure 2. In this figure, y is the level ofdesirable output (e.g., GDP, firm’s output) and b is the level of undesirable output(e.g., SO2 and CO2 emissions). b can be interpreted as an unavoidable polluting
byproduct resulting from the production of desirable output. Thus, in order to produce
a desirable output, it is necessary to produce a certain amount of undesirable output at
the same time. A production possibility frontier indicates the maximum produciblelevel of desirable output, given the level of undesirable output. Any combination ofdesirable and undesirable outputs  y, b  is possible under this frontier.

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Figure 2. Directional Distance Function
Three different types of efficiencies can be measured in terms of the production
possibility frontier described above. First, assume that the combination of desirable
and undesirable outputs is  yR , bR  , as shown in figure 2. This combination isinefficient because one can increase the desirable output up to ŷ without increasing
the undesirable output. This output-oriented efficiency is known as technical efficiency,
defined by the distance between the observed level of desirable output ( y R ) and the
maximum producible level ( ŷ ). This is denoted as  in figure 2.
Second, efficiency can be measured in terms of the undesirable output. We
continue to assume the same combination of desirable and undesirable outputs
 yR , bR  . In figure 2, one can decrease the level of undesirable output up to b̂ without
decreasing the output. We define this input-oriented efficiency as EE. This is illustrated
by the difference between the observed level of undesirable output ( b̂ ) and theminimum feasible level ( bR ) in figure 2.
Third, efficiency can also be measured in terms of both desirable and undesirable
outputs, represented as  in figure 2. Given the combination of desirable andundesirable outputs  yR , bR  , one can simultaneously increase the desirable output up
to yˆ ' and decrease the undesirable output up to bˆ ' . This efficiency is denoted as thebalanced growth indicator in Watanabe and Tanaka (2006). In order to be consistent
with technical efficiency and EE, we denote the third efficiency as BGE.
Since efficiency is measured in terms of distance between observation and frontier,a small value indicates greater efficiency. The value becomes 0 if observation lies on
the frontier curve. We modified this efficiency score such that it is truncated from low
to high efficiency. Thus, in our modified efficiency score, 0 indicates the least
efficiency and 1 indicates the highest efficiency.
Efficiency can be empirically measured using linear programming software such as
GAMS and OnFront. Technical resources for theoretical foundations and linear
programming algorithm are presented in the appendix.

235

2.2 Second Step: The Relationship between SCEM and Environmental
Performance
Once environmental performance is estimated in the first step, the next step is to
identify the impacts of the relationship among environmental performance, thesocioeconomic condition, and SCEM. Specifically, this step evaluates the impacts ofthe socioeconomic condition and SCEM on environmental performance using thefollowing model:(2.1)
ui t  f  G,i t F,i t C, i t X, i twhere uit is environmental performance for unit i in period t. Environmental
performance can be measured by the environmental efficiency or BGE presented in theprevious section. The unit indicates an economic decision-making unit, depending on
the data. If the data is province-specific aggregated data, the unit will be the province.
If the data is a firm-level survey, the unit will be the firm. Git , Fit , and Cit represent
the capacities of governments, firms, and citizens, respectively. Finally, X it is avector of the socioeconomic condition. In the simplest form, equation (1) can be
estimated by the following linear model:
ui t   0  1G i t   2 F i 
i ,t i t(2.2)
t  3C it  4 X
where  ’s are parameters to be estimated and  it is a disturbance term capturing
random noise.
Since environmental performance, estimated in the first step, has discrete jump at 0
and 1, i.e., the value always falls between 0 and 1, the parameter estimates of equation(2) using the ordinary least square (OLS) results in baseness and inconsistency (Greene2003). Thus, it is necessary to consider the censoring of the dependent variable. One of
the most commonly used applications for such censored data is the Tobit model (alsoreferred to as censored regression). The Tobit model is given as:
i if t  0 RH S 1
  0   1Gi t   F2 it  C3 i 
t X4  i t
uit  0
 i f R H S 0 (2.3)
1
 if RHS 1

where RHS denotes the right-hand side of equation (2). This model has been widelyused in various fields, including economics, and can be estimated using most of the
major econometric software such as Eviews, LIMDEP, and STATA.
Once the Tobit model is estimated and the effects of the socioeconomic condition
and SCEM are found to be statistically significant, we compare the contributions ofthese variables to environmental performance by using elasticity. Elasticity is defined
as an incremental percentage change in one variable—in this case, environmental
performance—due to a 1% increase in another variable. For example, the elasticity ofenvironmental performance with respect to the capacity of a government can beu Gu , where
estimated byis the coefficient for the capacity of the government,
G uGestimated by the Tobit model.
By estimating the elasticity for each of the socioeconomic condition or SCEM
variables, we can reveal the degree of contribution to environmental performance
among SCEM and socioeconomic conditions. Further, the estimated elasticities are236

also used in the next step to construct the SCEM indicator. The following section, will
discuss this issue in detail.
2.3 Third Step: Development of the SCEM Indicator
Once the environmental management capacity variables of the three actors are
estimated and shown to be significant from the Tobit model, these variables andelasticities are used to construct the SCEM indicator. Following Tanaka and Watanabe(2005), the SCEM indicator is calculated by the following weighted average of thecapacities of governments, firms, and citizens: F  , C
(2.4)
SCEM
i t   g Gi
t
f i t
c
i t
~
~
where SCEM it is the value of the SCEM indicator for country i in year t. Git , Fit ,~and Cit are the normalized capacities of governments, firms, and citizens,
respectively. The capacity of governments is normalized by G  G G , where Git
it

it

it

is the maximum value of Git in the data set. Moreover, similar normalization is
performed for firms and citizens.  g ,  f , and  c are the ―weights‖ of the three
actors. The weights are determined by the given estimated elasticities from the Tobit
model. We normalized the weights such that the sum of elasticities take the value of 1
(  g   f   c  1 ), by using the equation  j  e j  eg  e f  ec  for j  G, F , C . By
normalizing all the variables and weights on the right-hand side of equation (4), we
obtain the convenient and intuitive indicator of the SCEM taking a value between 0
and 1.

3.

Applications of the SCEM Indicator Development

Given the framework of the SCEM indicator development presented in section 2, thissection presents two empirical applications. The first application develops the SCEM
indicator in terms of CO2 emissions in eight countries in East and Southeast Asia. Thisapplication reveals how the development process and the contributing actors aredifferent among countries. The second application pertains to the domestic scale: SO2
emissions from industrial sources in China. By focusing only on one country, thisapplication reveals further details in SCEM development.
3.1 Application 1: CO2 Emissions in Eight Asian Countries
In this example, the empirical procedure developed in the previous section is applied toCO2 emissions during 1995–2003 in eight Asian countries—China, Indonesia, Japan,
Malaysia, the Philippines, South Korea, Thailand, and Malaysia. Environmental
performance is measured by BGE (BGE), which is determined by efficiency in termsof both desirable and undesirable outputs. Additional details of BGE are presented in
section 2.2.1.
First Step
We first estimate the BGE for the eight Asian countries during 1995–2003 using a
directional output distance function. In this framework, each country produces two
outputs using three inputs. The two outputs constitute GDP as the desirable output andCO2 as the undesirable output. Labor, capital, and energy consumption are the three
inputs for producing the desirable and undesirable outputs.

237

The data for labor, capital, and GDP are gathered from World Development
Indicators (2005). Data for energy consumption are obtained from Statistics on Energy
Balances by the International Energy Agency/Organization for Economic Cooperation
and Development (IEA/OECD) (2005a, 2005b). Data for CO 2 emission are obtained
from statistics on total CO2 emissions from the Carbon Dioxide Information Analysis
Center (CDIAC) (2005).
Table 1 depicts the estimated BGE in the eight Asian countries during 1995–2003.
Overall, the BGE increases by more than 50% in Asia. As expected, Japan is estimatedto have the highest BGE score. Although many of the countries exhibit significant
increases, the BGE score is low and almost constant for China and Vietnam. Thisimplies that these emerging countries have made little progress toward sustainable
development during the estimation period.
Table 1. The Estimated Balanced Growth Efficiency in Eight Asian Countries(1995–2003)
Balanced growth efficiency
Malaysia The Philippines S. Korea

Year

China

Indonesia

Japan

1995
1996
1997
1998
1999
2000
2001
2002
2003

1.00
0.09
0.08
0.09
0.10
0.10
0.10
0.09
0.09

0.13
0.13
0.13
0.79
1.00
0.53
0.49
0.54
0.70

0.95
0.96
0.99
1.00
0.98
0.98
1.00
1.00
1.00

0.12
0.13
0.13
0.30
0.81
0.20
0.66
0.68
1.00

0.52
0.41
0.37
0.65
0.80
0.62
0.80
0.90
1.00

Mean

0.19

0.49

0.98

0.45

0.67

Thailand

Vietnam

Total

0.25
0.26
0.29
1.00
0.92
0.88
0.89
0.99
1.00

0.13
0.12
0.13
0.71
0.73
0.47
0.40
0.42
0.36

0.19
0.15
0.20
0.19
0.22
0.14
0.12
0.11
0.11

0.41
0.28
0.29
0.59
0.69
0.49
0.56
0.59
0.66

0.72

0.38

0.16

0.51

Second Step
In the second step, given the BGE estimated in the first step, we evaluate theeffects of the socioeconomic condition and actor capacities on environmental
performance by using a censored regression model. Accordingly, EE for province i inyear t is explained by the following model:

B G Eit   0   1 G Oit V 2

F Iit R M
 3

C I itT I Z E N

  4 GDPPER it   5INDONESIA+ MALAYSIA
,
6

(2.5)

  7 THAILAND+ 8D1998+ D1999
9
where the first three variables GOV, FIRM, and CITIZEN are the capacities ofgovernments, firms, and citizens, respectively. GOV is measured by the number ofenvironmental treaties and multilateral agreements per million people. FIRM isrepresented by ISO 14001 certifications over industry value added. CITIZEN is
obtained from the HDI. The socioeconomic indicator is considered to be GDPPER, i.e.,
gross domestic product per capita. The last two variables are dummies to be238constructed from the country and year perspectives.  ’s are the parameter coefficientsto be estimated. All variables are obtained from international organization publications
and databases. Descriptive statistics of the independent variables are summarized intable 2.
Table 2. Variables and Descriptive Statistics for the Tobit Model (Application 1)

Variable

Description

Unit

Mean

Std. Dev.

Min

Max

number of agreements
over million population

1.20

0.98

0.08

3.79

GOV

Capacity of governments,
measured in terms of total
environmental treaties andmultilateral agreements per
million people

FIRM

number of
Capacity of firms, measured incertifications overterms of the number of ISO 14001
billion constant 2000
per billion dollars in value added
US $

2.73

3.03

0.00

11.90

CITIZEN

Capacity of citizens, measured interms of Human Development
Indicator (HDI)

interval between 0 and
1

0.77

0.96

0.56

0.94

GDPPER

billion constant 2000
Gross domestic product per capita US $ per million
population

6.58

11.69

0.30

38.20

INDONESIA
MALAYSIATHAILAND
D1998
D1999

1 if in Indonesia
1 if in Malaysia
1 if in Thailand
1 if in 1998
1 if in 1999

0.13
0.13
0.13
0.13
0.13

0.33
0.33
0.33
0.33
0.33

0.00
0.00
0.00
0.00
0.00

1.00
1.00
1.00
1.00
1.00

The data for environmental treaties are gathered from the Center for International
Earth Science Information Network (CIESIN) and the Environmental Treaties andResource Indicators (ENTRI) database. The data on multilateral environmental
agreements are obtained from Earthtrends Environmental Information searchable
databases under the management of the World Resource Institute (WRI), based on theircollection from each corresponding organization. The data for ISO certification isadopted from annual ISO surveys (2001–2004) conducted by International
Organization for Standardization (ISO). The HDI is collected from annual Human
Development Reports (1994–2005) prepared by the United Nations Development
Programme (UNDP). Since HDI is not reported for the year 1996, we use the datareported in 1995 for the year 1996 and that reported in 1994 for the year 1995, in theestimation. The denominator variables (population and industry value added) and thesocioeconomic background variable—gross domestic product per capita—are obtained
from the same sources as in the first stage estimation, i.e., World Development
Indicators.
Table 3 depicts the estimated coefficients and elasticities for the Tobit model. The
results show that the estimated model fits the data plausibly well. All independent239

variables are statistically significant at either 1%, 5%, or 10% levels. In addition, thejoint null hypothesis is rejected (  2  72.84 ). Results indicate that both socioeconomic
conditions (GDPPER) and SCEM variables (GOV, FIRM, and CITIZEN) aresignificant, implying that they affect environmental performance with statisticalsignificance. The results also indicate that the SCEM variables exert greater influenceon environmental performance than the socioeconomic condition, implying theimportance of social capacity development of environmental management in the eight
Asian countries. Among the three capacities, CITIZEN has the largest elasticity and isconsiderably elastic. GOV and FIRM exhibit nearly the same levels of elasticities.
Table 3. The Estimated Coefficients from the Tobit Model (Application 1)
Independent variable Coefficient Std. Error Elasticity
Intercept

–0.52

0.33

GOV

0.09

***

0.02

0.22

FIRM

0.05

***

0.01

0.24

CITIZEN

0.83

*

0.45

1.26

GDPPER

0.01 ***

0.01

0.16

INDONESIA

0.28 ***

0.09

MALAYSIA

–0.10

0.09

THAILAND–0.18 *

0.09

D1998

0.21 **

0.08

D1999

0.28

0.08

n

72

2

72.84

χ

***

Note: One, two, and three asterisks indicate statistical significance at 1%, 5%, and 10% levels.

Third Step
The third step develops the SCEM indicator based on the results obtained fromprevious steps and from equation (4). Figure 3 reports the estimated SCEM indicator
for the eight Asian countries for the period between 1995 and 2003 and shows asignificant increase in the SCEM for all of the eight countries. Japan is estimated to be
the highest in the SCEM among these countries. The SCEM in Japan increases from0.77 to 0.88, accounting for an increase of 15%. The second highest SCEM is observedin Korea. Overall, the ranking of the SCEM indicator is mostly consistent with thedegree of economic development among these countries. Although China has one of
the lowest SCEM, it exhibits the most rapid increase, which accounts for nearly 40%
during the estimation period.

240

1.0

China
Indonesia
Japan

0.8

Malaysia
The
Philippines
South Korea

SCEM

0.6

Thailand

0.4
Vietnam0.2

0.0
1995 1996 1997 1998 1999 2000 2001 2002 2003
Year

Figure 3. The Estimated SCEM Indicator in Eight Asian Countries (1995–2003)

The estimated capacities of governments, firms, and citizens are shown in figures 4,5, and 6, respectively. These figures reveal the capacity of firms (the number of ISO
certifications per billion dollars) as a primary source of SCEM development. As shownin figure 5, firms in most countries enhanced their capacities rather rapidly during theestimation period. Particularly high increases are observed in Thailand and Japan.
Although the lowest score is observed in China until 2001, the country increases its
capacity rather rapidly after 2002 and keeps pace with other countries. The capacity of
citizens (HDI) is almost constant, particularly after 1997. Although its elasticity isrelatively large, capacity enhancement is slow in all the eight Asian countries
considered in this study. Finally, the capacity of government is observed to be
decreasing in all countries except Japan and Malaysia, albeit rather slowly. This
reflects the recent stagnation of international agreements pertaining to CO2 emission
reduction and other global environmental issues.

241China0.2
Indonesia

Capacity of Government

Japan
Malaysia
The
Philippines
South Korea

0.1
Thailand
Vietnam

0.0
1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

Figure 4. The Estimated Capacity of Governmentin Eight Asian Countries (1995–2003)
China

0.2

Indonesia
Japan

Capacity of Firm

Malaysia
The
Philippines
South Korea
Thailand

0.1

Vietnam

0.0
1995

1996

1997

1998

1999 2000

2001

2002

2003

Year

Figure 5. The Estimated Capacity of Firms
in Eight Asian Countries (1995–2003)

242

China1.0

Indonesia
Japan

Capacity of Citizen

0.8

Malaysia
The
Philippines
South Korea

0.6

Thailand
Vietnam

0.4

0.2

0.0
1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

Figure 6. The Estimated Capacity of Citizen
in Eight Asian Countries (1995–2003)
3.2 Application 2: SO2 Emissions from Industrial Sectors in China
In this example, the empirical procedures presented in the previous section are applied
to SO2 emissions from industrial sectors (electricity, mining, and manufacturingsectors) in China during the period between 1994 and 2002. Environmental
performance is measured by EE in terms of SO2 emission, i.e., the difference between
the minimum feasible level of emission and the observed level of emission, at thegiven level of GDP. This corresponds to the distance  in figure 2.
First Step
We begin by estimating EE, described above, for 30 provinces in China1 For thispurpose, we set up a province-level aggregated production function consisting of two
outputs and three inputs. The outputs include both desirable and undesirable outputs.
The desirable output is measured in terms of the gross industrial output value. The
undesirable output—pollutants as byproducts of industrial operation—is measured interms of the level of SO2 emission from the industrial sectors. With regard to the inputsof the model, we select the size of the labor force, capital, and coal as inputs. Capital ismeasured in terms of the net value of fixed assets. Labor is measured in terms of thenumber of staff and workers. Coal is measured in terms of coal consumption as an
input material. These variables are obtained from China Statistical Yearbook.
Table 4 depicts the estimated EE in China during 1994–2002. The national averageof the efficiency score is estimated to be 0.77, with some fluctuation during theestimation period. Specifically, efficiency gradually decreases until the year 2000.

243

However, the highest efficiency score is achieved in 2002, the last year of ourestimation period.
Table 4. The Estimated Environmental Efficiency in China (1994–2002)
Environmental efficiency
CentralWest
Nation

Year

Coast

1994
1995
1996
1997
1998
1999
2000
2001
2002

0.79
0.71
0.72
0.74
0.69
0.75
0.74
0.79
0.83

0.80
0.79
0.80
0.81
0.74
0.77
0.78
0.79
0.79

0.80
0.80
0.80
0.80
0.77
0.77
0.74
0.78
0.78

0.80
0.76
0.77
0.77
0.73
0.76
0.75
0.79
0.81

Mean

0.75

0.79

0.78

0.77

Second Step
In the second step, using the EE estimated in the first step, we evaluate the effects ofthe socioeconomic condition and SCEM on EE using the censored regression model.
Specifically, EE for province i in year t is explained by the following model:
B G Ei t  0   1 G P PiCI R ATi G
i t O 4V
FIRM
t  2
tI O
3
, i t (2.6)
 5
C I TitI ZE6N
C EN T7 R A L  8W E S T
TIME
where the first two independent variables represent socioeconomic conditions. GPPC
denotes gross provincial product per labor and IRATIO represents the ratio ofindustrial sector. The next three variables GOV, FIRM, and CITIZEN are the capacitiesof governments, firms, and citizens, respectively. GOV is measured in terms of thenumber of monitoring stations. FIRM is represented by SO2 removal rate (ratio of total
SO2 removed to total SO2 emission). CITIZEN is denoted by the number ofenvironmental disputes. CENTRAL and WEST are dummy variables for provinces in
the central and western regions. TIME is a time trend capturing time-varying effects.
 ’s are the parameter coefficients to be estimated. All variables are obtained from the
China Statistical Yearbook and the China Environmental Yearbook between the years
1995 and 2003. Descriptive statistics of the independent variables are summarized inTable 5.

244

Table 5. Variables and Descriptive Statistics for the Tobit Model (Application 2)
Variable

Description

Unit

Mean

Std. Dev.

GPPC

Gross provincial product per labor,
deflated by GDP index

IRATIO

100 million
yuan

0.06

0.03

0.02

0.17

Ratio of industrial sector in terms of
output value

percentage
terms

0.36

0.15

0.08

1.01

GOV

Capacity of government, measured by
the number of monitoring station

counts

1.26

0.60

0.00

4.00

FIRM

Capacity of firm, measured by the rate
percentage
of SO2 removal (ratio of SO2 removal
terms
and SO2 emission)

0.21

0.16

0.01

0.68

counts734.76798.201.00

5530.00

year

0.31
0.24
5.00

0.46
0.43
2.59

0.00
0.00
1.00

1.00
1.00
9.00

CITIZENCENTRAL
WEST
TIME

Capacity of citizen, measured by the
number of cizen groups complainedfor environmental issues= 1 if in Central region= 1 if in Western region
Time trend (1994 = 1)

Min

Max

Table 6 presents the estimated results. Overall, the model fits the data rather well.
All independent variables except CITIZEN are statistically significant at the 1% level.
In addition, a chi-square statistic (123.08) strongly rejects the null hypothesis that all
coefficients are simultaneously zero. As expected, the signs of the socioeconomic
condition (GPPC and IRATIO) and environmental management capacities (GOV,FIRM, and CITIZEN) are shown to be positive. This indicates that both thesocioeconomic condition and SCEM are positive inducements for efficiency in SO2emissions from the industrial sector in China. The capacity of citizens (CITIZEN) is
not significant at any statistical level. This may imply that the citizens’ capacity is notwell developed to induce an increase in environmental efficiency and a consequent
abatement of the SO2 pollution problem in China.

245

Table 6. The Estimated Coefficients from the Tobit Model (Application 2)
Independent variable CoefficientIntercept
GPPC
IRATIOGOVFIRM
CITIZENCENTRAL
WEST
TIME

0.21 ***
4.69 ***
0.68 ***
0.08 ***
0.25 ***< 0.010.16 ***
0.10 ***–0.03 ***

n
2
χ

190
123.08Std. Error Elasticity
0.06
0.53
0.11
0.02
0.07
0.00
0.03
0.03
0.01

0.36
0.32
0.12
0.07
0.08

Note: One, two, and three asterisks indicate statistical significance at 1%, 5%, and 10% levels.

Moreover, Table 6 presents the estimated elasticities of the socioeconomic
condition and SCEM. Two socioeconomic conditions are relatively elastic: theelasticities of GPPC and IRATIO are estimated to be 0.36 and 0.32, respectively. These
indicate that an increase of 1% in GPPC and IRATIO result in an increase in EE by
0.36% and 0.32%, respectively. The elasticities of SCEM variables are relatively small;an increase of 1% in the capacities of governments, firms, and citizens will enhance theEE by 0.12%, 0.07%, and 0.08%, respectively. Among the three actors of the SCEM,
the capacity of governments is the most elastic. This indicates that capacity
development in governments is the most effective in enhancing efficiency in terms of
SO2 emission from the industrial sectors. The capacity of firms is estimated to have thesmallest elasticity among the capacities considered in this study. However, its elasticity
is significant whereas that of citizens is not.
Third Step
The third step develops the SCEM indicator based on the results obtained from the firstand second steps. Specifically, the SCEM indicator is calculated as follows:    ,F
(2.7)
SCEM
i t g Gi t
f i t
 and F are the capacities of governments and firms, respectively, which
where Gitit

are normalized such that they take a value between 0 and 1.  g and  f are theweights of capacities, derived from the elasticities reported in Table 6. These weights
are adjusted such that the sum of the weights is 1. This indicator is a modified versionof equation (4). The only difference is that the capacity of citizens is not included here
due to the insignificance estimated in the Tobit model.
Figure 7 presents the estimated SCEM indicator and observed level of SO 2
emission during 1994–2000. These values are normalized such that they take a value
between 0 and 1 for comparison purpose. As the figure shows, the SCEM indicatorincreases from 0.27 in 1994 to 0.37 in 2002, accounting for a growth of 37%. Thus,
environmental management capacity development has been fairly developed during the246

estimation period. In the same period, SO2 emission decreased for about 10%. It is
modest increase under rapid economic development in the period, and is significantly
due to the SCEM development in the period.
Finally, figure 8 illustrates the changes in the capacities of governments and firms
during the same period. The figure illustrates the SCEM is fairly increasing during theestimation period. The figure illustrates significant development in the capacity of
government. It increases from 0.19 in 1994 to 0.42 in 2002, accounting for an increaseof 118%. In contrast, the capacity of governments is estimated to be nearly constantduring the estimation period. Thus, although the SCEM is developed for nearly 40%
during 1994-2002, such capacity development is mostly due to a rapid enhancement of
the capacity of firms. Further development in the SCEM may require enhancing thecapacity of government as well as that of firms.

0.5

0.4

0.3

0.2

0.1

SO2 emission
SCEM indicator

0.0
1994

1995

1996

1997

1998

1999

2000

2001

2002

Year

Figure 7. SO2 Emissions and SCEM Indicator in China (1994–2002)

247

0.5

0.4

0.3

0.2

0.1

Capacity of government
Capacity of firm

0.0
1994

1995

1996

1997

1998

1999

2000

2001

2002

Year

Figure 8. The Capacities of Government and Firms in China (1994–2002)

4

Summary and Conclusions

This study develops an indicator of SCEM using the three-step modeling approach.
The first step estimates environmental performance by measuring efficiency. We
present two different efficiency measures. The first measure is EE, which focuses only
on the emission of pollutants. The second is BGE (BGE), which estimates theefficiency of sustainable economic development by dealing with both output andpollutant emission. The estimated environmental performance is then used in thesecond step to identify the impacts of the relationship between environmental
performance, the socioeconomic condition, and SCEM. Then, we construct the SCEM
indicator using the variables and statistical results obtained from the second step.
Based on the empirical procedures discussed in section 2.2, we reported two
empirical applications. The first application compared the SCEM and its development
among eight Asian countries during 1995–2003. We evaluated the environmental
performance of each country by the BGE (BGE) and the efficiency measurement interms of GDP and CO2 emission. We observed a significant increase in environmental
performance in the eight Asian countries. The Tobit model revealed both the SCEM
and socioeconomic condition to be a significant source of such performanceenhancements. We then constructed the SCEM indicator for the eight Asian countries.
The indicator revealed that all eight countries develop the SCEM fairly well. Such adevelopment in the SCEM is mostly due to the capacity enhancement of firms. In
contrast, the capacities of governments and citizens remain almost constant during theestimation period.
We also reported another empirical example that focused on industrial SO2
emission in China during 1994–2002. In this example, environmental performance isdefined by EE in terms of SO2 emissions from the industrial sectors in each of the 30
provinces. The directional distance output function reveals that although the EE

248decreases marginally during the estimation period, the highest score is observed in thelast year of the period of estimation (2002). As observed in the first example, theestimated environmental performance is influenced by both the socioeconomic
condition and SCEM. However, the capacity of citizens is estimated not to besignificant at any statistical levels. This may imply that the capacity of citizens is notreasonably developed and therefore has no significant influence on the abatement ofenvironmental pollution in China. Finally, we construct the SCEM using the capacitiesof governments and firms. The capacity of citizens is not included because it isstatistically insignificant. Our results reveal that the SCEM is fairly increasing during
1994–2002. Further, they indicate that this is mostly due to a rapid enhancement of thecapacity of firms. Although this capacity increases by more than 100%, the capacity of
governments is estimated to be nearly constant during the estimation period.
Before concluding this chapter, several limitations need to be identified. First,
because the relationship between environmental performance and the SCEM does notneed to be linear, a more flexible functional form may be appropriate in the Tobit
model. For example, a translog or quadratic form may produce more accurate measuresof such a relationship. Further, interactions among governments, firms, and citizens
may need to be included as interaction terms in the model. Second, this chapter
employed only three variables—governments, firms, and citizens. Although thissimplicity significantly facilitates indicator development, it may be fairly simple toinvestigate each of the three actors for further details. These actors typically consist of
the following three factors: (1) policy and measures, (2) organizational resources, and(3) knowledge and technology. However, the more capacity included in the model, themore likely is the model to have multicollinearity, i.e., correlation among independent
variables. This may result in biased and inefficient estimates.
Thus, the SCEM indicator can be used as a convenient overview of the SCEM
development with readily available data. Further investigation of each actor can be
implemented using another approach with factor-specific data for the three actors. The
next chapter introduces one such methodology, the actor-factor analysis.

249

Appendix: Directional Output Distance Function
This appendix presents the technical details involved in the estimation of thedirectional distance function, derived from Färe and Grosskopf (2004), Watanabe(2004), and Watanabe and Tanaka (2006).
We first denote a vector of inputs by x  ( x1 ,..., x N )   N . There are two types of
outputs—desirable output (e.g., GDP) and undesirable output (e.g., SO2
emission)—which are denoted as y  ( y1 ,..., y M )   M and b  (b1 ,..., bI )   I ,
respectively. The relationship between input and output is represented by an output set:
(A.1)
P( x)  {( y, b) : x can produce ( y, b)} , x   N .
The output set is assumed to have the following properties. The first is ―null-jointness,‖which implies that the desirable output cannot be produced without simultaneously
producing the undesirable output:(A.2)( y, b)  P( x) and b  0 , then y  0 .
The second and third properties relate to the production technology of desirable andundesirable outputs. The second assumption is referred to as the weak disposability of
the undesirable output:( y, b)  P( x) and 0    1, then  ( y, b)  P( x) .
(A.3)
This indicates that it is impossible to reduce the undesirable output without reducing
the desirable output. The third assumption is known as the strong disposability of thedesirable output:( y, b)  P( x) and y 0  y , then ( y 0 , b)  P( x) .
(A.4)
This assumption implies that it is possible to reduce the desirable output without
reducing the undesirable output. Thus, within this model, there exists an asymmetry in
the properties of desirable and undesirable outputs. Figure 2 depicts the output
set, P(x) , for a case comprising one desirable output and one undesirable output. The
output set satisfies the ―null-jointness‖ property as the function passes through theorigin. It is possible to reduce the desirable output, i.e., a vertical downward shift inproduction is possible, although it is not possible to reduce the undesirable output, i.e.,a horizontal leftward shift is not possible for observations on the production frontier.
Given the foundations in equations (A.1)–(A.4), the directional output distancefunction is now defined as follows:(A.5)
Do ( x, y, b;g y , g b )  max{ : ( y  g y , b  g b )  P( x)} .
The value of  represents the distance between the observation ( y, b) and a pointon the production frontier, ( y  g y , b  g b ) . A direction vector, g  ( g y , g b ) ,determines the direction in which efficiency is measured. The direction vectors fordesirable and undesirable outputs are represented by g y and g b , respectively. Given
the production technology ( P(x) ) and direction vector (g), the directional distancefunction yields the maximum feasible expansion of the desirable output and thecontraction of the undesirable output. If the observation is on the production frontier,then the value of the directional output distance function is zero and the observation is
the most efficient. As the value increases, the observation becomes less efficient. Thus,
contrary to the distance function, the efficiency of an observation increases as the valueof the directional distance function approaches zero. It is possible to measure theefficiency in different directions by changing the value of the direction vector.

250

The output directional distance function,  k , for the kth observation is obtained bysolving the following maximization problem:

'
'
'
(A.6)
Do ( x k , y k , b k ; g y , g b )  max  k ''s.t.

 z y  y  g
 z y b  g
K

k'

k 1
K

k

km

k 'm

k 1

k

ki

k 'i

ym

k'

bi

m = 1,…, M(A.7)

i = 1,…, I(A.8)

n = 1,…, N

(A.9)

K

z x  x
k 1
K

k

kn

k 'n

z 1
k 1

(A.10)kzk  0k = 1,…, K,(A.11)
where z k is the weight of the k observation. Note that equations (A.7) and (A.8)
represent the strong disposability of desirable output and the weak disposability ofundesirable output, respectively. Equation (A.10) is omitted if CRS is assumed.
th

251

NOTE
1 Tibet is excluded from this analysis due to data limitation.

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