Economic Growth and Air Quality in China
Daigee Shaw*, Arwin Pang**, Ming-Feng Hung***, Wei Cen****
* Institute of Economics, Academia Sinica,Taiwan
** Department of Public Finance, Chengchi University, Taiwan
** * Department of Industrial Economics, Tamkang University, Taiwan
**** Jin He Economic Research Center, XiAn JiaoTong University, XiaXi, China
Abstract
This paper investigates the relationship between economic development and air
quality by examining Environmental Kuznets Curves from 1992 to 2001 for Mainland
China. We construct simultaneity models for three air pollutants (SO2, NOX and TSP)
and income. We then compile a panel data set of air quality, production, income and
environmental policy variables for 99 cities for these years to estimate the
simultaneity models. The regression results indicate that the EKC hypothesis is
supported in the cases of SO2 and TSP. The only one pollutant that does not reflect
the EKC relationship is NOx, probably due to the fact that it is the most
expensive-to-abate pollutant.
Keywords: Air quality; China; Environmental Kuznets curve; Latecomer
JEL classification: O13; Q53; Q56
1
1. Introduction
Switching from planned to market economy has brought about Mainland China’
s
economic miracle. Since the economic reform in 1978, China has experienced rapid
economic growth. During past years the average annual economic growth rate is close
to ten percent. In 2001 China’
s gross domestic product is 1,207,924 million RMB (at
1978’
s price), while it was only 362,410 million RMB in 1978. The GDP has become
tripled.
Although the per capita income in China before 1978 was very low, a series of
measures designed to protect the environment were implemented. After taking part in
the first United Nations Conference on the Human Environment (UNCHE), which
was held in Stockholm, Sweden in 1972, China started to concern itself with
environmental issues. In 1973, the Chinese government held the first national
conference on environmental protection, set up a national environmental protection
organization a
nds
t
i
pul
a
t
e
dt
he“
three s
y
nc
hr
oni
z
a
t
i
ons
”system.1 However, in actual
pr
a
c
t
i
c
e
,t
he “
three s
y
nc
hr
oni
z
a
t
i
ons
” system was said to have been rarely
implemented (Goldman and Tsuru, 1985). In 1978, the Chinese Constitution adopted
1
The organization is the Environmental Protection Office under the State Council. This “
three
s
y
n
c
h
r
on
i
z
a
t
i
ons
”system entailed (1) designing antipollution measures simultaneously, (2)
constructing antipollution equipment simultaneously with the construction of industrial plants, and (3)
operating antipollution equipment simultaneously with the operation of industrial plants.
2
Article 9 that states: “
t
heSt
a
t
eshall protect the environment and natural resources,
and shall a
l
s
o pr
e
ve
nta
nd e
l
i
mi
na
t
e pol
l
ut
i
o
na
nd ot
he
rpubl
i
c nui
s
a
nc
e
s
.
”In
accordance with this ne
wa
r
t
i
c
l
e
,“
t
he PRC Environmental Protection Law for Trial
Implementation”was promulgated by the National People’
s Congress in 1979 which
adopted the Environmental Impact Assessment (EIA) System and the Polluter Pays
Principle.2 A pollution levy system, which was based on the Polluter Pays Principle,
was implemented nationally in 1982. Observers seem to agree that the real
improvements in environmental protection started to come only after the
promulgation of the Environmental Protection Law in 1979 (Goldman and Tsuru,
1985).
In 1983 environmental protection was declared as one of the two Chinese
3
“
national fundamental policies”
.
More environmental laws and ambient standards
were gradually established in the 1980s. An important one wast
he“
environmental
responsibility system”i
nwhi
c
hg
ove
r
nor
sa
nd ma
y
or
swould be responsible for
overall environmental quality in their jurisdictions. In the 1990s, the cleaner
production program and the discharge permit system were applied. Under the
discharge permit system, pollution sources were required to register with local
Environmental Protection Bureaus (EPB) and apply for a discharge permit. These
2
The Environmental Protection Law was revised and officially enacted in 1989.
3
The other national fundamental policy is aimed at controlling the growth of population.
3
EPBs then allocated the allowable pollution loads to sources, issued discharge permits,
and enforced permit conditions. Thus, a quite complete system of environmental
management regulations and institutions was developed along with the rapid
economic growth over the last twenty years.
This is quite different from what had happened in the high-income countries such
as Japan, the U.K. and the U.S. during the last century. These countries by contrast all
followed t
hepa
t
t
e
r
nof“
pollute first and clean up later.”It thus needs to be asked
whether a latecomer such as China could have been capable of avoiding this pattern
by adopting the policy that economic growth could not continue without consideration
being given to its impact upon on the environment from the earliest days of
development.4 We would not expect to see any deterioration in the environmental
quality, or even an improvement in it along with the rapid economic development, if
such policies and regulations were to be completely enforced. That is, we would not
be able to find an inverted U-shaped relationship between income and environmental
quality, i.e. the famous Environmental Kuznets Curve (EKC), as has been observed
for many different kinds of environmental quality indices in the high-income
countries.
This paper is intended to answer this question by investigating the relationship
4
Compared with Japan and “
dragons”of East Asia (South Korea, Taiwan, Singapore and Hong Kong),
China is a latecomer economy.
4
between economic growth and air quality in China using a panel data set comprising
ninety-nine cities. If the results support the EKC hypothesis, this means that China
has failed to enforce its policy of “
three synchronizations.”
The air pollution indicators used in our study are sulphur dioxide (SO2), nitrogen
oxides (NOX) and total suspended particulates (TSP). All of them are major local
non-uniformly mixed assimilative pollutants. SO2 mainly comes from burning fuel
containing sulphur such as coal and oil. Exposure to an environment with high
concentration of SO2 will damage the respiratory system and aggravate the bronchitis.
NOX is produced during high temperature combustion in power generators and car
engines. It would irritate the lungs and decrease the respiratory system’
s resistance to
infection. In the long run, it will cause the pathological changes of lung. TSP comes
from roads, construction works, industrial procedures or in nature. The harms to our
health of TSP are respiratory problems and even long-term damage to lung.
Although there is a large body of literature on the EKC phenomenon for many
countries and for many environmental quality indices, little is known about China.
There is only one previous study about China.5 Groot, Withagen, and Zhou (2004),
5
The three other papers studied the Chinese income-pollution relationships (Wu, et al., 2002, Yuan and
Yang, 2002, Wu and Chen, 2003). However, they all focused on one city or province using time series
data and their regression analyses of the EKC relationship were not performed properly. Wu, et al.
(2002) used R2 to determine whether the estimated regressions support the EKC hypothesis. Yuan and
Yang (2002) used the correlation between pollution and GDP to examine EKC. Wu and Chen (2003)
5
studied EKC for China by a sample of thirty regions from 1982 to 1997. Their models
are standard EKC regression models. The pollution has three forms, emissions in
absolutely levels, per capita terms and per unit of Gross Regional Product terms.
However, all these three indices of emission are not good measures of environmental
quality because they ignore the dispersion effect that transforms emissions into
ambient environmental quality. They also ignored the fact that bad environmental
quality would have some impacts on the economy. Thus, the paper was unable to
answer the question we have asked.
We first explore the relationship between China’
s air quality and economic
development by examining its EKCs following Hung and Shaw’
s(2004) simultaneous
EKC model to test China’
s data. Then, we use the empirical results to evaluate the
performance of China’
s environmental policy and its implementation. Finally, we
conclude and draw some policy implications about the real forces behind China’
s
environmental performance.
2. Methods and Procedures
2.1 The EKC Hypothesis
First, we inquire into the empirical relationship between income and
didn’
t show the significance of variables in their model.
6
environmental quality. Grossman and Krueger (1991) initially estimated the
relationship between air quality and economic growth using reduced-form regression
models based on cross-sectional data for various countries. They found that two
pollutants, sulfur dioxide and smoke, exhibited an inverted U-shaped relationship.
When national income was low, these two concentrations increased with per capita
GDP. However, after reaching a certain maximum, they started to decrease at higher
levels of national income. The curve followed a similar pattern to the income
inequality pointed out by Kuznets (1955), and so this inverted U-shaped trend
between income and pollution was referred to as the “
Environmental Kuznets Curve.”
The relationship depicted by this curve is that pollution is inevitable in the early
stages of development, but that there will be improvements as the economy reaches a
certain point.
Since then, there have been a considerable number of empirical studies on the
EKC hypothesis. The issue of environmental quality has been broadened to
encompass the toxic intensity of industrial production (Hettige, Lucas, and Wheeler,
1992), water quality in river basins (Grossman and Krueger, 1995), industrial
pollution (Hettige, Muthukumara, and Wheeler, 2000), deforestation (Bhattarai and
Hamming, 2001), the percentage of protected areas within a national territory
(Bimonte, 2002), a composite environmental degradation index (Jha and Murthy,
7
2003), and so on. Not all of the studies, however, have resulted in consistent “
inverted
U-shaped”income-pollution relationships.
On the other hand, numerous attempts have been made to identify the forces
behind the EKC relationship (Grossman and Krueger, 1991 and 1995; Selden and
Song, 1994; Dinda, Coondoo and Pal., 2000; Gangadharan and Valenzuela, 2001;
Copeland and Taylor, 2003). The first force relates to the scale effect. It means that the
total amount of pollution will rise along with economic activity provided that the
economic activity does not change. This scale effect explains the rising part of the
EKC curve. The second is concerned with the composition effect.6 When a country
becomes significantly richer, the clean parts within the economic structure become
larger. The third has to do with the technique effect in which case the richer country
will invest more in research and development, and will adopt more advanced
production and pollution control technologies that generate less pollution. The fourth
is the income effect. Higher income means not only increased affordability of
advanced technologies, but also higher demand on the part of the people for a cleaner
environment because environmental quality is usually a luxury good. These last three
effects improve the environment along with the increase in income and explain why
part of the EKC actually decreases.
6
Copeland and Taylor (2003) presented “
sources of growth”to highlight the composition effect.
8
In addition to the above factors, political systems (Selden and Song, 1994; Torras
and Boyce, 1998; Dinda, Coondoo and Pal, 2000; Magnani, 2000), income
distribution (Torras and Boyce, 1998; Magnani, 2000; Bimonte, 2002), threshold
limits (Copeland and Taylor, 2003), increasing returns to scale in the abatement
technology (Andreoni and Levinson, 2001; Copeland and Taylor, 2003) and the
“
latecomer’
s advantage”have been invoked to explain the shape of the EKC.7
2.2 The Model
In this paper, we specify a simultaneity model to investigate the relationship
between urban air pollution and economic development in China. Since the economy
and the environment are determined in combination, the relationship must thus be
estimated simultaneously (Perrings, 1987; Lopez, 1994; Stern, 1998; Borghesi, 1999).
Hung and Shaw (2004) first set up a simultaneity model to study the income-pollution
relationship for Taiwan. In this paper, we use the simultaneity model to examine this
issue for China.
The basic simultaneity model is as follows:
2
ln Pit t 
lnX it it
1 (lnYit ) 
2 (lnYit ) 
7
(1)
The “
latecomer’
s advantage”means that the latecomer may have advantages such as learning from
the lessons of developed countries, more availability of technology and lower abatement cost. The peak
of the EKC may drop and the EKC pattern may occur at an earlier stage of economic development for
such latecomers (Iwami, 2004).
9
ln Yit t 1 ln Pit 2lnFI it 3lnK it 4lnLit 5lnGit 6lnH it it
(2)
where subscript i denotes city and t year. Equations (1) are the pollution equations.
Pti denotes air pollution in year t for city i. The intercepts ( t , t and t ) denote
the time-specific effects in the fixed-effects model that we use to analyze the panel
data. X is a vector of exogenous variables. Y is per capita GDP. Equation (1) is a
quadratic model with both linear term and squared term of income. If the subscript i
of the coefficients of the lnY term and its squared term are positive and negative,
respectively, then the EKC hypothesis holds.8
Equations (2) is a Cobb-Douglas production function. The independent variables
are air pollution, foreign indirect investment (FI), fixed capital investment (K), labor
(L), government spending (G), human capital (H). We can expect that the production
factors apart from pollution would have a positive effect on production.9
A special feature of this fixed-effects model specification is that there is only
time-specific effects and no city-specific effects. We choose this model specification
after we closely examine the estimated cross-sectional relationships of air quality and
8
Actually, during regression analysis we have tried both linear models and cubic models in addition to
the quadratic models. After close examination, we find the most appropriate model for SO2 and TSP is
quadratic and for NOX is linear.
9
Barro (1990) studied the issue of whether different allocations of government spending will have
different effects on economic growth. He found that government expenditure on consumption may
decrease the economic growth rate.
10
economic development for each year as in Appendix 3. This will be discussed in
depth in the estimation section.
The vector of exogenous variables, X, used in the regression includes population
(POP), the contribution of secondary industry to GDP (TWO) and a policy variable,
i.e., the cumulative number of years that a city has been included in the Two Control
Areas (TC). 10 These variables can capture the forces behind EKC. POP is
corresponding to the scale effect, TWO to the composition effect, TC to the policy
effect, and Y to the income effect.
2.3 Data
We compile a panel data set for the ninety-nine cities of the national monitoring
network (see the Appendix). 11 These cities are selected to represent different
characteristics of Chinese cities such as per capita income, environmental quality,
pollution size, location, etc. The data comes from different sources including the
China Environment Yearbooks from 1992 to 2001, Chinese Statistical Yearbook,
10
In 1995, the National Environmental Protection Agency marked out two control areas, which
included an acid rain control zone and an SO2 control zone.
11
From 1980 China National Environmental Monitoring Center is responsible for managing and
guiding the work of the national environmental monitoring system, and providing technical support to
State Environmental Protection Administration of China in carrying out environmental supervision and
management. It is the center of network, technology, information and training for the national
environmental monitoring. The center also ensures the environmental monitoring data to be reliable.
11
Cities of China 1949-1998, and Urban Statistical Yearbook of China from 1993 to
2002. The summary statistics and the sources of these data are presented in Table
Ⅰ.12
Among these variables, monetary terms as Y, FI, K and G are deflated by the
indices for the gross domestic product with the base year being 2000.13 H is the
number of persons with a senior high school education and above as a percentage of
the total population. In accordance with the availability of data, the yearly numbers of
persons with a senior high school education and above are linearly interpolated using
the two values for the two census years 1990 and 2000.14
3. Estimation
In the beginning, we estimate cross-sectional relationships of air quality and
economic development for each year as shown in Appendix 3. We find that the overall
impacts of income and other variables as measured by the regression coefficients are
largely the same for the three kinds of air quality measures, although the quantitative
12
Since some of the data are unavailable, e.g., NOX for 2001, our model forms an unbalanced panel.
Greene (2000, pp. 566-567) suggested no other changes are necessary for the one factor LSDV (least
squares dummy variable) estimator in the case of unbalanced fixed effects panels.
13
Data are obtained from the Chinese Statistical Yearbook (2002).
14
The number of persons with a senior high school education and above is obtained from China’
s
population census that was conducted every ten years.
12
estimates vary.
Based on these findings, we specify the model for the panel data using
time-specific fixed effects and without city-specific fixed effects. The estimation of
cross-section data reflects only inter-city differences inherent in comparisons of
different cities. By following giving cities over time as they grow, we can study the
relationship between income and pollution. It is also widely recognized the panel data
model has many advantages. It can construct and test more complicated behavioral
models than purely cross-sectional or time-series data. It gives researchers a large
number of data points, increases the degrees of freedom and reduces the collinearity
among explanatory variables and hence improves the efficiency of econometric
estimates (Hsiao, 2002, p.5).
The time-specific effect may represent the effects of technological changes and
changes in government regulatory policies in different years. This is especially true
for China because the evolution of environmental protection policies for every city
has been tightly controlled by the central government.
We presents the results of our panel data analysis and tests in Table II. Some tests
are performed before running regressions and the results are listed in Table II.15 First,
15
We also consider the problem of unit roots in our model. Levin, Lin and Chu (2002, p. 3) studied the
unit root in panel data. They noted the following: “
On the other hand, if the time series dimension of
the panel is very small, and the cross-section dimension is very large, then existing panel data
13
we consider the endogeneity of income in the pollution equations by means of the
Wu-Hausman test. We see that the test results of NOX and TSP indicate that the null
hypothesis of income exogeneity are rejected at the 1% significance level.16 Second,
we find that the order and rank conditions in our two simultaneity models are both
satisfied. Third, the group-effects models rather than the classical regression models
with no group specific effects are chosen on the basis of the Lagrange multiplier (LM)
test. Fourth, each result from using the Hausman test shows that the null hypothesis of
the random-effects model is rejected. The adoption of the fixed-effects model also
accords with the view put forward by Hsiao (2002, p. 43).17
We use the two-stage least squares method to estimate this fixed-effects model
for NOX and TSP, and the least squares dummy variable model for SO2. The
regression results are presented in Table II. The table contains three models, of three
air pollutants (SO2, NOX and TSP) and income.
The robust estimates of heteroscedasticity are presented in Table II as white (1)
procedures will be appropriate.”So it is not necessary to test the hypothesis of unit roots in our model.
16
Regarding the causality problem, Maddala (1992, p. 394) suggests that, if the periods in the panel
data are not numerous, “
Granger noncausality is neither necessary nor sufficient for exogeneity as
understood
17
in the usual simultaneous equations literature.”
See Hsiao (2002,p.
43)
:“
The situation to which a model applies and the inferences based on it are
the deciding factors in determining whether we should treat effects as random or fixed. When
inferences are going to be confined to the effects in the model, the effects are more appropriately
considered to be fixed.”
14
and white (2).18 We find that the estimates of white (1) and white (2) are smaller than
the least squares results and the significances are not changed.19 The estimates of
autocorrelation are also presented in Table II. We find no evidence of autocorrelation.
First, we examine whether or not the EKC hypothesis applies to Mainland China.
We can observe that the coefficients of lnY are positive, and that all the coefficients of
the squared term, (lnY)2, are negative for SO2 and TSP. All of the coefficients of lnY
and (lnY)2 in the two models are significantly different from zero. Thus, the EKC
hypothesis is supported by the cases of SO2 and TSP). The coefficients of lnY for NOX
is positive and significant. The result is reasonable since NOX is the most expensive of
the three pollutants in terms of the air pollution abatement activities.
The turning points, 
1 2
2 , for TSP is about 90,000 RMB ($11,524 in 2000 US
dollars) , and for SO2 it is about 60,000 RMB ($7,877 in 2000 US dollars) (see Figure
1-2).20 In 2001, the minimum, average and maximum per capita GDP of these 99
cities was 3,645, 17,865 and 127,529 RMB respectively. This shows that the pollution
18
The row of t-statistics labeled white (1) are the estimates based on the usual calculation, white (2)
are the estimates based on the groupwise heteroscedasticity model.
19
Greene (2000, p. 315) said that it is unclear which computation is preferable.
20
World Bank (2001, p 79) analyzed the time-series monitoring data in urban areas between 1991 and
1998 and showed similar ambient air quality trends. It stated that there are different trends among three
pollutants. First, ambient SO2 levels declined significantly, Second, ambient TSP levels decreased
slightly, but remained high in most urban areas. Third, NOX level worsened, reflecting the growing
impact of vehicular emissions.
15
is increasing for most cities in 2001. In addition, SO2 is the pollutant that passed the
turning point the earliest. This can be attributed to reduced coal consumption and
successful promotion of use of gaseous fuels in urban residential and commercial
sectors, especially in large cities. (World Bank, 2001) While TSP is more difficult to
control due to its various sources and it still is China’
s most significant air pollutant.
Let us now examine the effects of exogenous variables in the pollution equations.
First, the total population has a positive and significant influence on all pollutants.
This means that the more urbanization there is in a city, the more serious the pollution
is. The coefficients for secondary industry are positive in relation to air pollution,
which indicates that a higher degree of industrialization will result in more air
pollution. In China, secondary industries mainly rely upon coal burning for their
energy needs.
TC is the cumulative number of years a city has been included in the Two
Control Areas which includes Acid Rain Control Zones and SO2 Control Zones. In our
regression, the coefficients of this variable are significantly positive. It indicates the
fact that the problems of SO2 pollution are more serious in the Two Control Areas. In
1995, Two Control Areas contributed 60% of total SO2 emissions while they only
occupied 11.4% of China’
s territory. Although the central government has established
SO2 emission standards for these cities,, its ineffectiveness in enforcement has
16
resulted in an unsuccessful outcome.
We now turn to the income equations. Basically, pollutants have two contrasting,
i.e. positive and negative, effects on income. They might on the one hand increase
income by acting as production inputs, but they might also decrease income by
harming human health and increasing production costs when firms are asked to
control pollution. Among air pollutants, only SO2 is negatively insignificant in
relation to income. It may be due to the fact that the above-mentioned two effects on
income offset each other. NOX and TSP are significantly positive in the income
equation. The estimated income equations indicate that in general China has sacrificed
air quality for the sake of economic progress in this period.
Traditional production factors such as fixed capital, labor and government
expenditure perform as expected, and all have significantly positive effects on income.
Foreign direct investment also has a positive effect on income. During the decade
considered, there was a huge influx of foreign direct investment into China which
gave rise to significant economic achievements. Although labor is an important factor
in production, the contribution of human capital in production is not consistent in the
two models. On the one hand, owing to the limitation of data which is stated in
section 2.3, the representation of human capital is not good. On the other hand, this
indicates that the economic development in China still relies primarily on
17
labor-intensive industries.
Coming back to the questions we posed in the introduction, we can draw three
conclusions. First, since the EKC hypothesis is supported in the cases of SO2 and TSP,
there exists a pattern of “
pollute first and control pollution later”in China, even
t
houg
ht
hec
e
nt
r
a
lgove
r
nme
ntr
e
pe
a
t
e
dl
ys
a
i
d“
no”t
osuch a development pattern
since 1974. There is much evidence indicating that the local governments’
enforcement and implementation of policies to protect the environment has been
rather uneven. There have been many reports in the news and in journals that have
documented serious incidences of pollution in cities or villages. Xia (2001, p. 153-159)
cites several news reports that show that in reality local governments put economic
growth and industrial production first, and do not seriously enforce regulations to
abate pollution.
Second, the turning points in regard to the Chinese EKC occurred at an earlier
stage of economic development. O’
Connor (1994) pointed out that there may be four
reasons for the “
latecomer’
s advantage”
: learning from experience, increased
availability of technology, lower unit abatement costs, and increased exposure to
international environmental pressures. The timing of these turning points reveals that
China has this advantage. Several articles can support this finding (Vermer, 1998;
Mao, 2001; World Bank, 2001). Chinese environmental laws, law-making bodies and
18
other related regulations have been built upon foreign countries’experiences.
Pollution treatment techniques, clean production processes and the evaluation of
environmental monitoring have also been mostly imported. Foreign technical and
financial assistance agencies such as the World Bank and the Asian Development
Bank have played important roles in improving China’
s environment. International
conventions have played a significant role in promoting environmental protection in
China. Besides, the variables of key environmental cities in the regression are
significant. The effects of the central government’
s environmental policies have also
resulted in a lowering of the peak in the environmental Kuznets curve.
Third, the income effect in relation to the EKC still exists even though the
political system is not open and democratic in China yet. During the early stages of
economic development, the scale effect contributed to more environmental pollution.
Then the income effect brought about an improvement in the environment when the
economy became richer in addition to the composition and technique effects. The
demand on the part of the people for a clean environment can curtail the inconsistency
between
the central
government’
s directives
implementation of them.
4. Conclusions
19
and
the local
governments’
In recent years, Mainland China has been successful in its economic
accomplishments and has built up a fairly complete system of environmental
management regulations and institutions. While the EKC hypothesis has been
examined since the 1990s for a number of countries, there is only one application to
China. In this paper, we use a simultaneity model to examine the relationship between
China’
s economic growth and its air quality by panel data .
By testing the exogeneity hypothesis, we find that the income and air pollution
must be determined jointly for NOX and TSP. The regression results lead us to
conclude that two of the air pollutants (SO2 and TSP) confirm the EKC hypothesis. It
can be seen that China did not achieve economic growth and environmental protection
simultaneously. In practice, local governments sought economic development first
and then cleaned up. From the Chinese experience, the income effect in relation to
EKC theory is still important even in a politically tightly-controlled country if there is
to be an improvement in the environment, i.e. the implementation of environmental
policies in China depends on the public demand for them.
The turning points in terms of per capita GDP occurred $7,877 for SO2 and
$11,524 for TSP in 2000 US dollars. Compared with other countries’experiences,21
China entered this decreasing part of the EKC at an earlier stage of development. This
21
For example, Selden and Song (1994) estimated turning point for SO2 exceed $8,000 in 1985 dollars.
Kaufmann et. al (1998) estimated turning point for SO2 exceed $125,000 in 1985 dollars
20
may be explained in terms of the “
latecomer’
s advantage”and the effects of the
central government’
s environmental policies. China has learned from other countries’
experiences in managing its environmental problems, as Grossman (1995, p.44)
pointed out: “
The low-income countries of today have a unique opportunity to learn
from this history and thereby avoid some of the mistakes of earlier growth episodes.”
On the other hand, pollution also affects income. In our regression results, we
find that China achieves economic benefits in exchange for enhanced environmental
quality.
Variables such as the share of clean energy in the overall energy source, the
expenditure on the environment and specialists for each city, and the numbers of
motor vehicles are lacking in the statistics. In addition, the environment exerts an
all-round influence. Apart from air pollution, water quality, solid waste treatment,
hazardous waste and noise in the city are all important to residents. A further
extension could be made in any of these directions.
21
TableⅠ
Variable Statistics
Variables
Description
Mean
Standard
Minimum
Maximum
Source
Deviation
3
SO2 (0.001mg/m )
Sulfur dioxide concentrations (annual mean)
73.16
65.06
10
463
China Environment
NOX
Nitrogen oxides concentrations (annual mean)
47.59
24.85
4
164
Yearbook
3
(1993-2002)
(0.001mg/m )
TSP (0.01mg/m3)
Total suspended particulates concentrations
29.89
14.65
5.5
143.3
Per capita GDP
18.09
15.24
2.48
133.30
Cities of China 1949-1998
POP (10 person)
Total population at the end of the year
174.37
186.62
10.93
1262.41
(National Bureau of
TWO (％)
The contribution of secondary industry as a
53.53
11.14
16.07
92.84
Statistics of China)
2.108
2.747
0
10
Urban Statistical Yearbook
(annual mean)
3
Y (10 yuan)
4
share of on GDP
TC (year)
Cumulative number of years a city has been
of China (1993-2002,
included in the Two Control Areas
FI (10 US dollar)
Per capita foreign direct investment for
33.37
60.07
0.02
539.78
of China)
employed persons at the end of the year
K (103 yuan)
Per capita fixed capital investment for employed
12.9
11.62
0.55
85.42
49.93
24.85
6.14
248.49
persons at the end of the year
L (％)
Employed persons as a percentage of total
National Bureau of Statistics
population at the end of the year
22
G (102 yuan)
Per capita local government budgetary
14.95
19.25
0.50
190.76
36.71
11.62
0.55
85.42
expenditure
H (％)
The number of persons with a senior high school
Chinese Statistical Yearbook
education or above as a percentage of total
(2002, National Bureau of
population at the end of the year
Statistics of China)
Note: 1. The city boundaries in our sample do not include prefectures under the muni
c
i
pa
l
i
t
y
’
s control.
2. mg/m3 = 10-3 grams per cubic m
3.1 US$= 8.2775 Yuan. (Year 2000)
23
Table Ⅱ
Regression Results (Panel Data)
1.
Model of SO2
Variable
SO2
Y
Y
0.96***
(4.39)
White(1) (2.95)
White(2) (3.428)
SO2
-0.001
(-0.15)
White(1) (-0.098)
White(2) (-0.099)
Y2
-0.256*** FI
(-6.624)
White(1) (-4.752)
White(2) (-5.711)
0.062***
(7.169)
White(1) (4.327)
White(2) (4.834)
POP
0.328***
(9.745)
White(1) (7.083)
White(2) (7.406)
K
0.319***
(13.753)
White(1) (7.691)
White(2) (7.785)
TWO
0.472***
(3.827)
White(1) (2.423)
White(2) (2.542)
L
0.57***
(12.391)
White(1) (4.609)
White(2) (4.686)
TC
0.122***
(10.072)
White(1) (4.311)
White(2) (4.546)
G
0.229**
(10.284)
White(1) (5.379)
White(2) (5.472)
H
0.101**
(2.934)
White(1) (2.23)
White(2) (2.256)
Adj R2
0.349
Adj R2
0.769
LM test
270
LM test
13.83
Hausman test
15.24
Hausman test
10.62
Autocorrelation
0
Autocorrelation
0
Wu-Hausman test
10.51
Turning point (RMB) 65,208
No. of observations
725
No. of observations
656
No. of cross-sections
99
No. of cross-sections
99
No. of time periods
10
No. of time periods
10
24
2.
Model of NOX
Variable
NOX
Y
Y
0.06*
(1.764)
White(1) (1.66)
White(2) (1.671)
NOX
0.176***
(4.075)
White(1) (4.361)
White(2) (4.43)
POP
0.358*** FI
(15.408)
White(1) (15.458)
White(2) (15.65)
0.707***
(7.042)
White(1) (6.026)
White(2) (6.114)
TWO
0.432*** K
(5.022)
White(1) (4.264)
White(2) (4.309)
0.303***
(11.776)
White(1) (7.134)
White(2) (7.264)
L
0.588***
(10.855)
White(1) (9.939)
White(2) (10.058)
G
0.199***
(8.009)
White(1) (6.029)
White(2) (5.983)
H
0.06
(1.509)
White(1) (1.524)
White(2) (1.569)
Adj R2
0.356
Adj R2
0.759
LM test
0.04
LM test
22.29
Hausman test
7.7
Hausman test
11.42
Autocorrelation
-0.10
Autocorrelation
-0.026
Wu-Hausman test
24.61***
No. of observations
521
No. of observations
521
No. of cross-sections
99
No. of cross-sections
99
No. of time periods
9
No. of time periods
0
25
3. Model of TSP
Variable
TSP
Y
Y
1.448***
(4.595)
White(1) (4.278)
White(2) (5.152)
TSP
0.597***
(4.36)
White(1) (2.506)
White(2) (2.559)
Y2
-0.321*** FI
(-5.817)
White(1) (-5.253)
White(2) (-6.315)
0.137***
(6.186)
White(1) (3.43)
White(2) (3.534)
POP
0.129***
(5.514)
White(1) (5.797)
White(2) (6.084)
K
0.365***
(10.186)
White(1) (7.88)
White(2) (8.253)
TWO
0.511***
(6.375)
White(1) (5.442)
White(2) (5.663)
L
0.684***
(9.229)
White(1) (3.501)
White(2) (3.582)
G
0.279***
(7.672)
White(1) (6.89)
White(2) (6.924)
H
-0.168**
(-2.048)
White(1) (-1.103)
White(2) (-1.129)
Adj R2
0.255
Adj R2
0.5
LM test
11.37
LM test
20.92
Hausman test
6.89
Hausman test
9.21
Autocorrelation
0
Autocorrelation
0
Wu-Hausman test
42.3***
617
Turning point (RMB) 95,396
No. of observations
617
No. of observations
No. of cross-sections
99
No. of cross-sections 99
No. of time periods
10
No. of time periods
26
10
Notes:
(1)
All variables are in the form of natural logarithms except for three variables, TC. In order to keep
scale ,
(2)
* Significant at the 10% confidence level.
** Significant at the 5% confidence level.
*** Significant at the 1% confidence level.
(3)
t-statistics are shown in parentheses.
4. Time effect
pollutant
SO2
NOX
TSP
-0.0362631
0.0427546
0.122882
-0.054672
-0.219219
-0.382406
-0.658909
-0.820363
-0.996625
-1.05699
0.0647994
0.15045
0.159394
0.145011
0.188215
0.182731
0.167595
0.262951
0.122207
-0.932981
-0.843767
-0.702696
-0.726798
-0.783471
-0.741516
-0.896742
-0.904181
-0.729395
-0.880852
year
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
27
SO2(1000mg/m3)
Per capita income (103 RMB)
Figure 1. The relationship estimated between income and SO2
Note: On the 95% confidence level, the confidence interval for turning point of SO2 is 64,099 to
66,317 RMB.
NOX(100mg/m3)
Per capita income (103 RMB)
Figure 2. The relationship estimated between income and NOX
28
TSP(100mg/m3)
Per capita income (103 RMB)
Figure 3. The relationship estimated between income and TSP
Note: On the 95% confidence level, the confidence interval for turning points of TSP is 94,287 to
96,505 RMB.
Appendix 1: Cities in the Study
1
ANQING
34
HEGANG
67
SHANGHAI
2
ANSHAN
35
HENGYANG
68
SHENYANG
3
ANYANG
36
HUAIHUA
69
SHENZHEN
4
BAISE
37
HUHEHAOTE
70
SHIJIAZHUANG
5
BAODING
38
HULUDAO
71
SHIZUISHAN
6
BAOJI
39
JIAN
72
SIPING
7
BAOTOU
40
JIAOZUO
73
SUZHOU
8
BEIJING
41
JIAYUGUAN
74
TAIYUAN
9
CHANGCHUN
42
JILIN
75
TANGSHAN
10
CHANGDAO
43
JINAN
76
TIANJIN
11
CHANGDU
44
JINGDEZHEN
77
TUMEN
12
CHANGHAI
45
JIUJIANG
78
WANXIAN
13
CHANGSHA
46
KAIFENG
79
WENZHOU
14
CHENGDU
47
KUNMING
80
WUHAN
15
CHONGQING
48
LANZHOU
81
WULUMUQI
16
DALIAN
49
LASA
82
WUZHOU
17
DAQING
50
LESHAN
83
XIAMEN
29
18
DATONG
51
LIANYUNGANG
84
XIAN
19
FUZHOU
52
LIUPANSHUI
85
XIANGFAN
20
GANZHOU
53
LUOYANG
86
XINING
21
GEERMU
54
NANCHANG
87
XUZHOU
22
GEJIU
55
NANCHONG
88
YAN AN
23
GUANGZHOU
56
NANJING
89
YIBIN
24
GUILIN
57
NANNING
90
YICHANG
25
GUIYANG
58
NANTONG
91
YICHUN
26
HARBIN
59
NINGBO
92
YINCHUAN
27
HAIKOU
60
PINGDINGSHAN
93
YUNCHENG
28
HAILAER
61
PINGXIANG
94
ZHANJIANG
29
HAMI
62
QINGDAO
95
ZHENGZHOU
30
HANGZHOU
63
QINGHUANGDAO
96
ZHUHAI
31
HANZHONG
64
QITAIHE
97
ZHUZHOU
32
HECHI
65
RIKEZE
98
ZIBO
33
HEFEI
66
SANMING
99
ZIGONG
Appendix 2: The Acid Rain Control Zone and SO2 Control Zone
ph＜5.6
acid rain
control zone
● SO2
control zone
Source: The acid rain control zone and the SO2 control zone, State Environmental Protection
Administration of China.
Note: The acid rain control zone covers the areas where the ph value of precipitation is lower than 4.5,
sulfur deposition exceeds the critical load and the SO2 emissions are large. The SO2 control zone
30
includes areas whose average sulfur dioxide concentration exceeds the standard level. The SO2 control
zone is mainly located in the north and the acid rain control zone is mostly in the south
Appendix 3
Regression Results (Cross–sectional)
1.
Model of SO2
Year
Variable
Y
Y2
POP
TWO
TC
Constant
Adj R2
Hausman test
Durbin-watson
test
Variable
SO2
FI
K
L
G
H
Constant
Adj R2
Durbin-watson
test
(to be continued)
Year
Variable
Y
1992
SO2
-2.867
(-1.09)
0.359
(0.79)
0.576***
(3.99)
1.501**
(2.09)
0.794***
(3.37)
-0.067
(-0.02)
0.189
11.22*
1.914
1993
SO2
1.414
(1.4)
-0.376**
(-2.16)
0.389***
(3.75)
0.439
(0.72)
0.331**
(2.89)
-0.529
(-0.24)
0.356
9.93
2.131
1994
SO2
-1.414*
(-1.93)
0.103
(0.85)
0.427***
(4.1)
1.239**
(2.58)
0.189**
(2.52)
0.305
(0.16)
0.349
16.48**
1.91
1995
SO2
1.169
(1.42)
-0.314**
(-2.34)
0.256**
(2.7)
0.993**
(2.36)
0.221**
(2.89)
-2.012
(-0.97)
0.38
5.13
2.045
1996
SO2
1.087
(1.26)
-0.322**
(-2.38)
0.414***
(3.92)
1.285**
(2.47)
0.189**
(2.98)
-3.779
(-1.53)
0.471
4.14
2.083
1997
SO2
0.969
(1.58)
-0.28**
(-3.02)
0.361***
(4.37)
0.773**
(2.2)
0.149**
(3.16)
-1.617
(-0.95)
0.428
4.49
2.167
Y
0.246**
(2.48)
0.058*
(1.79)
0.538**
(2.21)
1.325***
(4.81)
0.09
(0.61)
-0.176
(-1.07)
-7.09***
(-4.62)
0.702
2.227
Y
0.018
(0.33)
0.001
(0.01)
0.379**
(2.35)
0.498**
(2.42)
0.358***
(3.62)
0.107
(0.61)
-3.19**
(-3.2)
0.722
1.993
Y
0.121
(1.34)
0.032
(0.76)
0.358*
(1.88)
0.809***
(4.37)
0.236**
(2.07)
0.079
(0.49)
-4.345**
(-3.17)
0.689
2.339
Y
-0.001
(-0.08)
0.099***
(3.47)
0.136**
(2.54)
0.714***
(5)
0.188***
(4.42)
0.255**
(2.36)
-2.62***
(-3.42)
0.821
1.882
Y
-0.01
(-0.22)
0.089**
(2.28)
0.271**
(2.37)
0.859***
(5.17)
0.104*
(1.98)
0.136
(1.3)
-3.409**
(-3.45)
0.769
1.701
Y
-0.168
(-0.39)
0.046
(1.29)
0.315**
(3.33)
0.619**
(3.22)
0.158*
(1.67)
0.188**
(2.26)
-2.927**
(-3.01)
0.787
1.854
1998
SO2
1.381**
1999
SO2
1.215*
2000
SO2
0.289
31
2001
SO2
1.143*
(2.1)
Y
-0.339**
(-3.18)
POP
0.411***
(3.93)
TWO
0.307
(0.71)
TC
0.15**
(3.21)
Constant
-1.006
(-0.52)
2
Adj R
0.441
Hausman test
9.86
Durbin-watson test 2.029
Variable
Y
SO2
-0.03
(-0.54)
FI
0.07**
(2.23)
K
0.381***
(3.73)
L
0.619***
(4.66)
G
0.131*
(1.78)
H
0.107
(1.19)
Constant
-2.979**
(-3.42)
2
Adj R
0.784
Durbin-watson test 1.95
2
Model of NOX
Year
1992
Variable
NOX
Y
0.112
(0.78)
POP
0.327***
(4.33)
TWO
0.426
(1.04)
Constant
0.161
(0.09)
Adj R2
0.272
Hausman test
7.18
Durbin-watson 2.26
test
Variable
Y
(1.93)
-0.276**
(-2.72)
0.277**
(2.92)
-0.025
(-0.07)
0.138***
(3.79)
0.763
(0.44)
0.35
7.26
1.892
Y
-0.016
(-0.43)
0.064**
(2.79)
0.254**
(2.37)
0.36**
(3.02)
0.445***
(5.17)
-0.004
(-0.04)
-2.589*
(-1.8)
0.836
1.745
(0.41)
-0.108
(-0.99)
0.239**
(2.76)
0.124
(0.36)
0.1**
(3.26)
1.566
(0.86)
0.245
7.13
1.853
Y
-0.014
(-0.27)
0.04
(1.25)
0.221*
(1.67)
0.298**
(2.39)
0.488***
(5.81)
0.179*
(1.86)
-1.854**
(-2.09)
0.803
1.596
(1.7)
-0.228**
(-2.11)
0.178**
(2.05)
0.121
(0.43)
0.065**
(2.46)
0.646
(0.43)
0.205
2.7
1.943
Y
0.034
(0.59)
0.097*
(1.78)
0.427**
(3.07)
0.539**
(3.45)
0.208**
(2.52)
0.082
(0.85)
-3.519**
(-3.24)
0.787
1.786
2.
1993
NOX
0.056
(0.51)
0.318***
(5.12)
0.488
(1.29)
0.161
(0.1)
0.274
5.06
2.28
1994
NOX
0.127
(0.91)
0.412***
(6.34)
0.616*
(1.91)
-1.009
(-0.77)
0.42
8.75*
2.15
1995
NOX
0.07
(0.56)
0.415**
(5.27)
0.698**
(2.47)
-1.195
(-1.02)
0.38
10.43**
2.514
1996
NOX
0083
(0.27)
0.341***
(5.34)
0.328
(1.26)
0.815
(0.72)
0.389
2.38
2.183
1997
NOX
-0.043
(-0.5)
0.395***
(8.02)
0.285
(1.29)
0.882
(0.92)
0.49
3.87
1.931
Y
Y
Y
Y
Y
32
NOX
0.081
0.096
0.211*
0.197*
(1.05)
(1.22)
(1.97)
(1.83)
FI
0.033
0.011
0.054
0.085**
(1.06)
(0.24)
(1.25)
(2.3)
K
0.467**
0.37**
0.328*
0.23**
(2.56)
(2.06)
(1.88)
(2.12)
L
1.057*** 0.466** 0.799*** 0.76***
(4.61)
(2.54)
(5.46)
(6.06)
G
0.06
0.353*** 0.162*
0.184***
(0.5)
(3.41)
(1.71)
(4.37)
H
0.034
0.061
0.023
0.112
(0.024)
(0.37)
(0.15)
(0.73)
Constant
-5.449*** -3.142** -4.088** -3.62***
(-5.18)
(-2.92)
(-4.39)
(-4.04)
Adj R2
0.777
0.73
0.726
0.759
Durbin-watson 2.251
2.02
2.299
2.114
test
(to be continued)
Year
1998
1999
2000
Variable
NOX
NOX
NOX
Y
0.055
0.027
0.178**
(0.46)
(0.5)
(2.41)
POP
0.388*** 0.189*** 0.235**
(7.02)
(4.13)
(3.68)
TWO
0.412*
-0.327
-0.289
(1.85)
(-1.63)
(-0.93)
Constant
0.11
4.205*** 3.219**
(0.11)
(5.05)
(2.49)
2
Adj R
0.446
0.329
0.37
Hausman test
3.5
4.44
9.54**
Durbin-watson test 2.04
1.959
1.993
Variable
Y
Y
Y
NOX
0.091
-0.05
-0.08
(0.98)
(-0.57)
(-0.37)
FI
0.075** 0.104*** 0.028
(2.16)
(5.77)
(0.59)
K
0.361*** 0.143
0.295*
(2.76)
(1.24)
(1.66)
L
0.575*** 0.27*
0.296
(4)
(1.74)
(1.48)
G
0.104*
0.428*** 0.468**
(1.76)
(4.38)
(3.25)
H
0.106
0.06
0.337*
(1.13)
(0.55)
(1.31)
Constant
-3.099** -0.623
-2.6*
(-3.28)
(-0.76)
(-1.82)
2
Adj R
0.75
0.855
0.836
Durbin-watson test 1.944
1.641
1.764
33
0.038
(0.44)
0.093**
(2.3)
0.26**
(2.06)
0.883***
(6.04)
0.104*
(1.99)
0.11
(1.09)
-3.555***
(-4.08)
0.778
1.765
0.001
(0.1)
0.047
(1.21)
0.319**
(2.97)
0.637**
(3.32)
0.159*
(1.68)
0.182**
(2.18)
-3.112**
(-3.22)
0.787
1.887
Model of TSP
Year
1992
1993
1994
1995
1996
Variable
TSP
TSP
TSP
TSP
TSP
Y
2.684
0.979
0.903** 0.414
0.533
(1.43)
(0.69
(2.68)
(1.11)
(1.37)
2
Y
-0.669* -0.298** -0.224** -0.139** -0.152**
(-1.66) (-1.12)
(-3.21)
(-2.02)
(-2.05)
POP
0.173* 0.212** 0.12**
0.118*
0.098
(1.99)
(2.6)
(2.01)
(1.88)
(1.42)
TWO
0.864** 0.887** 0.427** 0.544*
0.433
(2.48)
(2.52)
(2.08)
(1.79)
(1.56)
Constant
-3.48
-1.731
0.388
0.651
0.919
(-1.59) (-1)
(0.38)
(0.49)
(0.72)
Adj R2
0.269
0.214
0.171
0.285
0233
Hausman test 14.05** 14.15** 6.05
5.65
5.07
Durbin-watson 2.208
1.842
1.923
1.943
1.812
test
Variable
Y
Y
Y
Y
Y
TSP
0.465* 0.991** -0.057
-0.076
-0.053
(1.85)
(2.33)
(-0.45)
(-0.62)
(-0.65)
FI
0.056
0.169** 0.026
0.055
0.084*
(1.3)
(2.07)
(0.57)
(1.05)
(1.92)
K
0.614** 0.669** 0.338** 0.223** 0.256**
(2.39)
(2.12)
(2.13)
(2.26)
(2.17)
L
0.613
-0.13
0.747** 0.744*** 0.834***
(0.83)
(-0.21)
(2.17)
(5.86)
(5.74)
G
0.101
0.439** 0.196
0.188*** 0.114**
(0.59)
(2.68)
(1.48)
(4.42)
(2.02)
H
-0.039
-0.4
0.12
0.232*
0.157
(-0.16) (-1.29)
(0.99)
(1.96)
(1.48)
Constant
-5.67** -4.38
-3.29** -2.86*** -3.169***
(-2.28) (-1.5)
(-2.18)
(-3.69)
(-3.84)
Adj R2
0.5
0.148
0.665
0.773
0.772
Durbin-watson 1.698
1.6
2.167
1.814
1.7
test
(to be continued)
Year
1998
1999
2000
2001
Variable
TSP
TSP
TSP
TSP
Y
2.867** 2.086** 0.465
0.693
(3.06)
(2.07)
(1.21)
(1.4)
2
Y
-0.583** -0.427** -0.153** -0.185**
(-3.04)
(-2.2)
(-2.32)
(-2)
POP
0.089
0.125** 0.122** 0.092
(1.16)
(2.01)
(2.54)
(1.62)
TWO
0.633** 0.429*
0.512** 0.334**
(2.42)
(1.81)
(2.79)
(2.12)
Constant
-2.849
-1.471
0.486
1.011
(-1.63)
(-0.82)
(0.52)
(1.01)
2
Adj R
0.08
0.08
0306
0.263
3.
34
1997
TSP
0.706*
(1.79)
-0.186**
(-2.6)
0.084
(1.51)
0.635**
(2.6)
-0.049
(-0.05)
0.283
4.5
1.961
Y
-0.094
(-1.05)
0.032
(0.77)
0.318**
(3.34)
0.587**
(3)
0.152*
(1.63)
0.233**
(2.7)
-2.683**
(-2.81)
0.793
1.823
Hausman test
Durbin-watson test
Variable
TSP
15.06**
2.291
Y
0.726
(1.59)
FI
0.174**
(2.23)
K
0.512**
(2.4)
L
0.73**
(3.26)
G
0.263**
(2.11)
H
-0.349
(-1.05)
Constant
-5.834**
(-2.33)
Adj R2
0.436
Durbin-watson test 2.125
9.33*
2.068
Y
0.071
(0.41)
0.08**
(2.78)
0.263**
(2.15)
0.383**
(2.87)
0.408***
(3.46)
-0.01
(-0.12)
-1.914*
(-1.79)
0.815
1.92
3.98*
1.874
Y
-0.207**
(2.34)
0.017
(0.53)
0.23*
(1.91)
0.289**
(2.2)
0.447***
(5.4)
0.238**
(2.45)
-1.334
(-1.6)
0.821
1.453
5.14
1.936
Y
-0.262*
(-1.82)
0.048
(0.89)
0.463**
(3.36)
0.713**
(3.45)
0.118
(1.18)
0.143
(1.18)
-3.259**
(-3.31)
0.817
1.616
Notes:
(1) All variables are in the form of natural logarithms except for three variables, TC.
(2)* Significant at the 10% confidence level. ** Significant at the 5% confidence level. *** Significant
at the 1% confidence level.
(3) t-statistics with robust standard errors are shown in parentheses.
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