Discussion Papers In Economics
And Business

Environmental Kuznets Curve on Country Level:
Evidence from China
Junyi Shen
Yoshizo Hashimoto

Discussion Paper 04-09

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANEnvironmental Kuznets Curve on Country Level:
Evidence from China
Junyi Shen
Yoshizo Hashimoto

Discussion Paper 04-09

May 2004

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANEnvironmental Kuznets Curve on Country Level:
Evidence from China

Junyi Shen*
Yoshizo Hashimoto**
May 2004
Abstract

The Environmental Kuznets Curve (EKC) hypothesis proposes that there is aninverse-U-shape relationship between environmental degradation and per capita
income. This evidence has been manifested to be existed in most air pollutants andseveral water pollutants by estimating on cross-country data. Different from mostearlier empirical studies, this paper uses the cross-province panel data of sevenpollutants from China to investigate whether the EKC hypothesis may even exist on acountry level. The estimated results find out that the EKC hypothesis exists in five of
these pollutants, while the other two show a N-shape relationship between pollutant
emission and per capita income. Moreover, this paper suggests some problems of thisregression as being remained for future study.

Keywords: Environmental Kuznets Curve (EKC), pollution emission, economic growth,
GDP per capita* Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama,

Toyonaka, Osaka 560-0043, Japan. E-mail: junyi@osipp.osaka-u.ac.jp** Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama,
Toyonaka, Osaka 560-0043, Japan. E-mail: yoshizo@osipp.osaka-u.ac.jp
11. Introduction
Fully understanding the impact of economic development on the environmental
quality is becoming increasingly important as general environmental concerns aremaking their ways into main public policy agenda. A number of recent studies have
explored the relationship between economic development and environmental quality
both on theoretical and empirical level. Theoretical analysis by John and Pecchenino [1],Seldon and Song [19], Stocky [13], and Andreoni and Levinson [12] have derived eachtransition path for pollution, abatement effort and development under alternative
assumptions about social welfare functions, pollution damage, the cost of abatement,
and the productivity of capital. Empirical studies (Shafik and Bandyopadhyay [17],Seldon and Song [20], Grossman and Krueger [10], and Holtz-Eakin and Selden [8])have searched for systematic relationships by regressing cross-country measures ofambient air and water quality on various polynomial specifications of income per capita.
There is evidence that the level of environmental degradation measured by many formsof pollution and per capita income follow an inverse-U-shaped pattern, first rising andthen falling as income increases. Due to its similarity to the time series of income
inequality described by Kuznets [18], the environmental pattern has been labeled theEnvironmental Kuznets Curve (Selden and Song [20]).
Most of the empirical studies focus on using the cross-country panel data toestimate the relationship between per capita income and various environmental
indicators. There are just a few literatures based on individual countries. However,moving fromcross-country to a single-country study is a new try for EKC researches,

since the study of a single country eliminates the problems associated with datacomparison from different countries (Carson et al [4]) and more could be learned froman examination of the experiences of individual countries (de Bruyn and Heitz [7]).
Moreover, there is no any empirical study that has tested the EKC hypothesis by using
data from China. Thus, the main contribution of this paper is that it employs Chinese
cross-province panel data and a common methodology to investigate the relationshipbetween the scale of economic activity and environmental quality for a comparative
broad set of environmental indicators. The second contribution of this paper is that it
includes share of the secondary industry in the regression to investigate the industrial
structural effect on environmental quality empirically, which has been seldom done inearlier empirical studies.
The remainder of this paper is organized as follows. The next section introduces themodel specification and data description. Section 3 describes the methodological issues
and empirical results, and Section 4 draws the conclusion and some suggestions for2

future study.

2. Model specification and data
2.1 Model specification
Earlier empirical analyses used various specifications to estimate the relationshipbetween pollution and income level. This paper presents the model as below1:(1) ln p it = β 0i + β 1 ln y it + β 2 (ln y it ) + β 3 (ln y it ) + β 4 ind it + β 5 ln pd + β 6 trend + ε it2

where p :3

per capita pollutant emission

y : per capita GDP
ind : share of the secondary industry
pd : population density
trend : time trend
i : a province(metropolitan city) indext : a time index

ε : an error term.
Table 1 lists the conditions for different relationships between pollution emissions
and per capita income, presented by different estimated coefficient signs of per capita
GDP and its quadratic and cubic terms. We notice that emissions can be said to exhibita meaningful Kuznets relationship with per capita GDP if

β 1 > 0 , β 2 < 0 ―and if theturning point − β 1 / 2 β 2 is a “reasonably” low number (Seldon and Song [20])2.
As for the other explanatory variables in addition to per capita GDP, we include
shares of the secondary industry, population density and time trend into the models toimprove the estimation. The sign for share of the secondary industry is expected to bepositive, which shows the positive relationship between pollutant emission andsecondary industry. Meanwhile, population density is expected to enter in equation (1)with a positive sign, since aggregate emission ought to increase with population growth.
The estimated coefficient for time trend means the average growth rate of estimatedpollutant emissions, which to some extent reflects the efectiveness of current Chinese
environmental policy.

1

In the cases of Arsenic, Cadmium, Mercury and Industrial waste stock, we useln( pit + 0.000000001) as the dependent variable since the samples of these pollutants
include zero.
Since we include cubic terms of per capita GDP in the regression, therefore, β 3 must2

be zero for the EKC’s existence.
3

Table 1
Coefficient signs of per capita GDP and its quadratic and cubic terms

Coefficient signs

Relationship between pollution and income

β1 > 0, β 2 < 0, β 3 = 0
β 1 < 0, β 2 > 0, β 3 = 0
β 1 > 0, β 2 < 0, β 3 > 0
β 1 < 0, β 2 > 0, β 3 < 0

Inverse-U-shaped curve (EKC)
U-shaped curve
N-shaped curve
Inverse-N-shaped curve

An issue concerns that besides the explanatory variables presented in theestimation model, there are also some other exogenous factors that affect emissions. For
instance, climate and geography vary widely among provinces in China and may well becorrelated with emissions. Insofar as these factors cause the error terms in model to becorrelated

among provinces for a given period, OLS estimates that ignore thiscorrelation will be inefficient. Moreover, if these omitted variables are correlated withper capita GDP, then random-effects estimates will also yield biased and inconsistent
results. For details of this issue, we will present in the next section.

2.2. Data
Due to the limitation of data, this paper uses the panel data of two air pollutants(SO2 and Dust Fall) from 1990-2001, four water pollutants (COD: Chemical Oxygen
Demand, Arsenic in water, Cadmium in water and Mercury in water) and one solid
pollutant (Industrial waste stock) from 1993-2001 in China’s 31 provinces andmetropolitan cities. Table 2 lists the descriptive statistics of per capita emissions of SO2,
Dust Fall, COD, Arsenic, Cadmium, Mercury and Industrial waste stock, per capita
GDP, share of the secondary industry, and population density.

3. Methodological issues and empirical results
3.1 Methodological issues
A first methodological issue concerns the degree of the polynomial. We first use t
test to check whether the cubic terms of per capita GDP are statistically different fromzero or not. If the cubic terms are found not to be significantly different from zero evenat the 10% level, we estimate the equation (1) without the cubic terms. In contrast, if
they are found to be significantly different from zero, we present the estimates ofequation (1) with cubic terms.

4

TABLE 2
Descriptive Statistics of Variables

SampleSampleSampleSampleStandard

Number ofmeanmedianmaximum

minimum

deviationobservations

Per capita SO2 (kg)14300.47

12530.63

48067.47

279.0879103.715

372

Per capita Dust

10618.39

7991.571

42124.54

36.0368061.554

372

Per capita COD (kg)5.5669785.024658

31.521330.246667

3.838718

279

Per capita Cadmium

0.000117

1.34E-05

0.0016402.36E-05279

0.001291

0.000182

0.031921

00.004116

279

1.09E-05

2.28E-06

0.0001602.28E-05

279

5314.438

3637.265

39689.99

06096.802279

342.8924

244.6974

2640.171.80723412.9402

372

46.1828

46.25

81.00

9.00

10.6263372

5213.784091.00

37382.00

778.004966.51372

Fall(kg)(kg)
Per capita Arsenic(kg)
Per capita Mercury(kg)
Per capita Industrial
Waste Stock (kg)Population density(person/km2)
The secondary
industry share (%)
Per capita GDP (yuan)aData source: Chinese Statistical Yearbook (1990-2001)
bNumbers of observation of per capita GDP, population density and the secondary industry share are

279 in COD, Arsenic, Cadmium, Mercury and Industrial waste stock cases.

The second issue is that we employ F test to check the homogeneity of the province
effects. If province effects are found to be existed, then estimating equation (1) by OLS
which omit the province effects will lead biased and inefficient estimators.
The third issue concerns choosing between fixed-effects model and random-effects
model. An important criterion here is whether the province effects are correlated withthe explanatory variables. In the absence of such correlation, random-effects estimationis consistent and efficient. In contrast, if such correlation exists, there may be omitted
variable bias, necessitating fixed-effects estimation (Selden and Song [20]). The
disadvantage of fixed-effects model is that there are too many parameters to be
estimated and hence loss of degrees of freedom which can be avoided if we either

5assume the same intercept for all the cross sectional units, or assume β 0i to be randomvariables. Nevertheless, the random-effects model is not totally free from problems. In
case where the province effects are correlated with explanatory variables, therandom-effects model is similar to an omitted variable

specification which leads tobiased parameter estimates, making a fixed-effects model a more appropriate choice. In
order to help choose between these two approaches, we employ the Hausman test in thispaper.

3.2 Empirical results
Tables 3-4 present estimated results. A first issue concerns the homogeneity of theprovince effects. In all cases, the null hypothesis of homogeneity is rejected by a wide
margin (see F statistics reported in the tables). This suggests that OLS estimators
are inefficient at best and may yield biased coefficient estimates.
The Hausman test suggests that in all the cases except Arsenic, random-effects
estimation is preferred to be accepted3.
The most important issue concerns the curvature of the relationship between percapita pollutant emissions and per capita GDP. The results of the estimation indicatethat in all the water pollutants and SO2 cases, the inverse-U-shaped EKC relationships
are presented, while in other two pollutants (Dust Fall and Industrial waste stock)cases, a N-shaped curve are presented since that the null hypothesis of which the cubic
terms of per capita GDP is not different from zero can not be rejected even at 10% level4.
Turning to the other explanatory variables issue, in all the cases except SO2, theestimated signs of share of the secondary industry are consistent with the expected
positive one. For the signs of population density, in air pollutants and solid pollutant
cases, they show the expected positive one, although two of them are not statisticallysignificant. But for water pollutants, only in COD case, the estimated sign shows theconsistency with the expectance, while in other three cases it shows the negative sign.
Comparing the estimated income turning points with per capita GDP data in 2001, we
find that SO2 and Mercury emissions have already been on the decreasing transition
path, while Arsenic emission is still on the increasing transition path. In Cadmium and
COD cases, there are still 5 and 15 provinces respectively being on the emission
increasing transition path. In Dust Fall and Industrial waste stock cases, the emissions
We only list the estimated results for the accepted models according to Hausman test.
The other estimated results are available for request.
4 The N-shaped curve indicates the deteriorating environmental performance at theinitial phases of growth which is followed by a phase of improvement and then a further
deterioration once a critical level of income is reached.
3

6

TABLE 3
Estimated Results for Water Pollutants(t statistics in parentheses)
Arsenic

COD

Cadmium

Mercury

721.8124***63.71565*

253.5605

475.0844**(4.51)(1.70)(1.37)(2.45)

8.441154***4.601456***

8.188167**

5.528574(2.83)(5.51)(2.01)(1.17)-0.3914011**-0.2630823***-0.4795596**-0.3723443(-2.20)(-5.40)(-2.02)(-1.36)The secondary

0.0767181*

0.0211059**

0.0807817*

0.0718029

industry share(1.89)(2.29)(1.76)(1.59)-10.96644***

0.1634534**-0.1690892-0.0085571(-3.29)(2.28)(0.46)(-0.03)-0.3606806***-0.0420511**-0.1523534*-0.2562206**(4.23)(-2.20)(-1.61)(-2.58)t-test for cubic term

0.79

-1.05

1.36

0.48

Adjusted R2

0.786

0.770

0.714

0.562

Hausman test

877.45

5.05

4.59

4.34

(5)

(5)

(5)

(5)

Fixed-effects

Random-effects

Random-effects

Random-effects

Turning point (yuan)

48,207

6,298

5,101

(1,691)

Sample size

279

279

279

279

Constantln(per capita GDP)(ln(per capita GDP))2ln(population density)

Time trend

Accepted modelaOne, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at

10%, 5% or 1% level, respectively.
b

F statistics for the null hypothesis of homogeneity is 29.29, 20.04, 18.11 and 8.69 for Arsenic, COD

Cadmium and Mercury, respectively.

are currently on the decreasing transition path in most provinces5, however after per
capita GDP reaches 24,847 yuan (in Industrial waste stock case) and 69,071 yuan (inDust Fall case), they will increase again. This result may give Chinese government
more incentive to make much more efforts on abating environmental pollution while
keeping continuous rapid economic growth.
Only except for Guizhou province with per capita GDP at 2,895 yuan in 2001 which isstill on the emission increasing transition path in Industrial waste stock case.

5

7

TABLE 4
Estimated Results for Air and Solid Pollutants(t statistics in parentheses)
SO2

Dust Fall

Industrial waste stock

10.8022-102.58**-222.925**(0.47)(-2.18)(-2.23)1.225114***20.04458***

41.97864**(4.19)(2.78)(2.07)-0.084602***-2.215366***-4.667846**(-4.91)(-2.59)(-1.98)―0.0787306**

0.1708688*(2.34)(1.88)-0.0063048***0.0197386***

0.0484451**(-2.72)(4.16)(2.36)

0.3131878***

0.1575465*

0.1397132(3.92)(1.80)(0.84)-0.0070271

0.0221726

0.0519431(-0.59)(0.99)(1.30)

t-test for cubic term

-1.21

2.34

1.88

Adjusted R2

0.939

0.812

0.975

Hausman test

7.09

10.59

7.20

(5)

(6)

(6)

Random-effects

Random-effects

Random-effects

1,395

2,031

3,268

69,072

24,847372

279

Constantln(per capita GDP)(ln(per capita GDP))2(ln(per capita GDP))3

Share of the secondary
industryln(population density)

Time trend

Accepted model
Turning point (yuan)

Sample size

372

aOne, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at

10%, 5% or 1% level, respectively.
b

F statistics for the null hypothesis of homogeneity is 124.82, 30.39 and 24.84 for SO2, Dust Fall andIndustrial waste stock, respectively.

In most pollutants estimated in this paper, the income turning points estimated byusing cross-country panel data in earlier empirical studies were much higher than the
results presented in this paper. We think the reasons may probably be existed in three
ways. First, largely using of efficient anti-pollution machinery in recent years in China

8

shifts the turning points of EKC towards the origin. Second, the subsidies fromdeveloped countries help China spend much on pollution abatement, which also makes
the EKC lower and flatter and thus decreases the turning points. Third, the data usedin this paper is from 1990-2001 (for water and solid pollutants, from 1993-2001), maybe
it is too short for the estimation of long-run effects. Therefore, although the estimatedturning points for these pollutants in this paper are comparatively lower and several
pollutants have already been on the decreasing transition path, there still remains one
point to be noticed, that is the absolute aggregate emission level of China is muchhigher than the international average level, which indicates that the low turning points
do not mean Chinese government can stop investing on pollutant abatements. In
contrast, if the abatements are abandoned, the curve may be changed to another shapewhich results in the continuing increase or re-increase of these pollutants.
Finally, we notice that these five pollutants that have shown inverse-U-shaped
curve should not be viewed as representative of all pollutants in China. In fact, it seems
likely that the combination of important own-province pollution effects and relatively
low abatement costs makes these pollutants to exhibit inverse-U-shape relationships
with income. Thus, we can only say that in these five pollutants, the EKC hypothesis isfound to be existed in China. It is likely that some other pollutants, such as CO2, which
are costly to be abated, may appear to rise monotonically with income6.

4. Conclusion
This paper uses the cross-province panel data for seven pollutants from China toinvestigate the relationship between economic growth and environmental quality in
China. The estimated results indicate that there exists an Environmental Kuznets
Curve in five pollutants. The other two pollutants show an N-shaped curve. Meanwhile,
a positive effect caused by share of the secondary industry on emissions has been
empirically found in most pollutants.
Finally, in most empirical EKC literatures including this paper, EKC are always
estimated in the form of a single equation. However, as we know, pollution emission
may reduce production through the restriction of environmental input’s supply via
environmental degradation or the loss of workdays due to health problem caused bypollution, therefore influence the level of per capita income. Thus, generally, theeconomy and its environment are jointly determined (Perrings, [14]) and estimating
single equation relationships by ordinary least squares where simultaneity is existed
Holtz-Eakin and Selden [8] used cross-country panel data and found the CO2 emission
appears to have a monotonic EKC.
69

produces biased and inconsistent estimates (Stern, [5]). From this view, it may be
therefore more appropriate to use a simultaneous-equation model for regression7.

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