4 sem 2sls ekc china

China Economic Review 17 (2006) 383–394

A simultaneous estimation of Environmental KuznetsCurve: Evidence from China

Junyi SHEN *

Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama, Toyonaka,Osaka, 5600043, Japan

Received 25 November 2004; accepted 1 March 2006

Abstract

This paper uses Chinese provincial data from 1993 to 2002 to examine the existence of an EnvironmentalKuznets Curve (EKC) relationship between per capita income and per capita pollutant emission.Acknowledging the theoretical framework that economic growth and pollution are jointly determined, thispaper starts from formulating a simultaneous equations model (SEM) to investigate the relationship betweenincome and pollutant emission. A Hausman test is applied for income exogeneity and a two-stage leastsquares (2SLS) method is used to estimate the SEM. There are three main differences found between singlepolynomial equation estimators commonly used in EKC literatures and simultaneous equationa estimators.Since these differences tend to cause different policy implications, therefore, this paper suggests that thesimultaneity between income and pollution should be considered before regressing the model in future EKCstudies. In addition, this paper also investigates the determinants of income and government pollutionabatement expense. Negative impact of pollution on income, and positive effects of physical and labor onincome are found in income equation. Whilst positive effects of pollutant emissions, physical capital and thesecondary industry share on pollution abatement expenses are also found in abatement equation.© 2006 Elsevier Inc. All rights reserved.

JEL classification: O13; Q25; Q53Keywords: Environmental Kuznets Curve (EKC); Simultaneous equations model (SEM); Two-stage least squares(2SLS); Economic growth; Pollutant emission

1. Introduction

There has been a large body of literature on the relationship between economic growth andenvironment in the last decade. Substantial empirical evidence suggested that the relationships

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384 J. Shen / China Economic Review 17 (2006) 383–394

between many forms of pollution and national income follow an inverse-U-shaped pattern, risinginitially, peaking, and then declining.1 Due to its similarity to the time-series pattern of incomeinequality described by Kuznets (1955), this environmental pattern has been called anEnvironmental Kuznets Curve (EKC). The findings of EKC then naturally lead to some ideathat economic growth is the remedy to environmental problems even without strict environmentalpolices. However, in many cases, if a wider range of explanatory variables other than income isincluded in the model, the EKC results are not reproduced. This issue suggests that the EKChypothesis may also depend on some other factors, such as industrial structures, technologicalprogresses and environmental policies, etc. Therefore, estimating the EKC hypothesis withouttesting other important determinants of pollution probably leads to a biased result.2

One important critique for the existing empirical EKC studies is that, although inmany theoreticalmodels pollution is assumed as both an input and byproduct of production, these studies are based ona single polynomial equation where there is no feedback effect from pollution to economic growthand therefore pollution is viewed only as the outcome of economic growth. The validity of ignoringthis feedback effect should depend on that there is no simultaneous relationship between these twovariables. However, as we know, in real world, pollutant emission may reduce production eitherthrough the restriction of environmental input's supply via environmental degradation or the loss ofworkdays due to health problem caused by pollution. Thus, the economic growth and theenvironmental quality are jointly determined, and estimating the relationship only by a singlepolynomial equationmight probably produce biased and inconsistent estimates. From this view, it istherefore more appropriate to use a simultaneous equations model (SEM) for the estimation.

However, limited to my knowledge, there are seldom empirical studies that estimate the EKC byusing SEM. The first possible reason may be due to the difficulty in model specification. Therefore,the first purpose of this paper is to formulate a simultaneous equationsmodel between per capitaGDPand per capita pollutant emission. Based on the theoretical implication, a three-equation model isconstructed. The first equation (pollution equation) is a commonly used polynomial equation in EKCempirical literatures. Different from the others, I add two extra important variables— the secondaryindustry share and government pollution abatement expense into pollution equation to explain theimpacts of industrial structure and environmental policy on pollution. The second equation (incomeequation) manipulates the pollutant emission as an input in an extended Cobb–Douglas productionfunction to control the feedback impact of pollution on income. Since adding government pollutionabatement expense into pollution equation may cause another source of simultaneous error into themodel, i.e. pollution abatement expense and the emission level are also jointly determined, therefore,a third equation (abatement equation) is introduced to explain abatement expense. After the SEM isconstructed, the second purpose of this study is consequently to investigate the difference betweensingle polynomial equation estimators and simultaneous equations estimators.

The second reason for few studies on simultaneous relationship between income and pollutionis probably due to the difficulty in satisfying data requirement. Recently, abundant provincialmacro-data comes to be available in the Chinese Statistical Yearbook and the ChineseEnvironmental Statistical Yearbook. Thus, this study uses Chinese provincial data from 1993 to2002 for the empirical analysis. Most of the previous EKC studies focus on using the cross-country panel data to estimate the relationship between per capita income and variousenvironmental indicators. However, moving from a cross-country study to an individual country's

1 See, for example, Grossman and Krueger (1995), Selden and Song (1994), Shafik and Bandyopadhyay (1992), Hiltonand Levinson (1998), etc.2 Theoretical explanations of EKC hypothesis are well discussed in Copeland and Taylor (2003).

385J. Shen / China Economic Review 17 (2006) 383–394

cross-region study is a new trend for EKC researchers, since the latter one can eliminate theproblems associated with cross-country data and allow more being learned from an examinationin an individual country. Meanwhile, two air pollutants (SO2 and Dust Fall) and three waterpollutants (Chemical Oxygen Demand: COD, Arsenic in water, and Cadmium in water) areexamined here. The relative abundance of pollutant variety makes income–pollution relationshipcaused by different pollutants comparable.

With the income equation and abatement equation being constructed, the third purpose of thispaper is then to investigate the influence of pollutant emission on output and the determinants ofpollution abatement expense, which has not been yet examined empirically before in China.

The remainder of this paper is organized as follows. The next section introduces the modelspecification and data description. Section 3 describes the methodological issues and empiricalresults, and Section 4 draws the conclusion.

2. Model specification and data description

2.1. Model specification

Based on the discussions in the previous section, I combine a single polynomial equation ofincome affecting pollution with an extended Cobb–Douglas production function and an equationexplaining the determinants of environmental policy (government pollution abatement expense),under the consideration of feedback effect of pollution on production and the simultaneousrelationship between pollution and abatement expense. Then, the simultaneous equations modelcan be given as Eqs. (1), (2), and (3).3

ln pit ¼ a0 þ a1ln yit þ a2ðln yitÞ2 þ a3ln abateit þ a4ln indit þ a5ln pdit þ a6T þ eit ð1Þ

ln yit ¼ b0 þ b1ln kit þ b2ln lit þ b3ln pit þ b4T þ lit ð2Þ

ln abateit ¼ g0 þ g1ln kit þ g2ln indit þ g3ln pit þ g4T þ mit ð3Þwhere p, y, abate, ind, pd, k, l and T denote per capita pollutant emission, per capita GDP adjustedby CPI (Consumer Price Index), per capita government pollution abatement expense adjusted byCPI, the secondary industry share, population density, per capita physical capital adjusted by CPI,labor (expressed by employed persons as percentage of total population) and time trend,respectively. ε, μ, ν are error terms and i, t denote province index and time index.4

Eq. (1) represents the pollution equation. An EKC relationship can be said to be exhibited withthe condition that α1N0 and α2b0. There are two reasons for me to take the log form of all thevariables except the time trend. First, both log forms of the per capita pollutant emission and the

3 I employed a t test to check the statistical significance of the cubic terms of ln(per capita GDP) in all the pollutantsand found that all of them are not significantly different from zero even at 10% level. Therefore, I omit the cubic terms inEq. (1).4 A possible concern here is that the considerable correlation between per capita GDP and per capita pollution

abatement expense may cause the problem of multicollinearity in Eq. (1). However, the correlation coefficient betweenthese two variables is only 0.2943 in my dataset. Since the primary purpose of this paper is to investigate the EKChypothesis by applying a SEM, therefore I do not deal with the issue of income and abatement due to their lowcorrelation.

Table 1Expected signs for the estimated coefficients in Eqs. (1), (2), and (3)

Equation (1) Equation (2) Equation (3)

Explanatory variables Signs Explanatory variables Signs Explanatory variables Signs

ln(per capita GDP) + ln(physical capital) + ln(physical capital) +(ln(per capita GDP))2 − ln(labor) + ln(secondary ind. share) +ln(abatement expense) − ln(pollutant emission) − ln(pollutant emission) +ln(secondary ind. share) +ln(population density) +/−

Note: The expected signs of per capita GDP and its quadratic term are based on EKC's existence.


per capita GDP terms can generally help me to construct a SEM with an extended Cobb–Douglasproduction function in Eq. (2) (income equation). For the same reason, the government pollutionabatement expense is also taken in a log form to be consistent with a Cobb–Douglas typepollution abatement function in Eq. (3) (abatement equation). Secondly, the distribution of thesesix variables in Eq. (1) with positive skewness can be easily corrected due to the taking in of a logform.5 In addition, Eq. (3) is applied into the model to control the simultaneous relationshipbetween the per capita pollutant emission and the per capita pollution abatement expense.

Following the theoretical implication, Table 1 lists the expected signs of all the explanatoryvariables in Eqs. (1), (2), and (3). The sign of the government pollution abatement expense in Eq.(1), controlling the impact of environmental policy on pollution6, is expected to be negativebecause the more the investments are placed in pollution abatement, the less emissions areexpected to be emitted. Meanwhile, I expect that the secondary industry share in both Eqs. (1) and(3) has a positive sign, since the heavier the weight of the secondary industry is, the morepollution is likely emitted, therefore, the more abatement expenses are required. Anotherexplanatory variable in Eq. (1), population density, is expected to enter with either a positive signor a negative sign. Due to the fact that emission will be generated more with population growth, itfollows a positive relationship between population density and pollutant emission. However, asSelden and Song (1994) stated that insofar as sparsely populated provinces are likely to be lessconcerned about reducing per capita emissions, at every level of income, than more denselypopulated ones. Therefore, a negative sign is also permitted.

The existing theoretical frameworks help me to construct Eq. (2) as an income equation. In thisextended Cobb–Douglas production function, output is a function of physical capital, labor, andpollutant emission. Both signs of physical capital and labor are expected to be positive. Incontrast, pollutant emission is expected to negatively contribute to production due to the reasonsthat I mentioned in the previous section. Concerning the role of pollutant emission, physicalcapital and the secondary industry share being played in Eq. (3), all are expected to be positivelyrelated to pollution abatement expense. Finally, a time trend is added into each equation to controlthe time effect on each dependent variable.

5 After being taken by log form, the skewness of per capita GDP and its quadratic term, per capita pollution abatementexpense, the secondary industry share, population density, per capita SO2 emission, per capita Dust Fall emission, percapita COD emission, per capita Arsenic emission and per capita Cadmium emission are improved from 2.605, 5.152,3.243, 2.132, 3.379, 2.246, 4.449, 2.596, 5.759 and 3.241 to 0.539, 0.705, 0.366,0.335, 0.317, −0.325, −0.265, −0.817,−0.797 and −0.189, respectively.6 Honestly speaking, the pollution tax rate should reflect the stringency of the environmental policy more accurately

than the government pollution abatement expense. However, the data on the provincial pollution tax rates have not beenfound in China.

Table 2Descriptive statistics of variables

Samplemean

Samplemedian

Samplemaximum

Sampleminimum

Standarddeviation

Samples

Per capita SO2 (kg) 12,038.66 9373.286 82,028.3 72.84737 11,497.43 310Per capita Dust Fall(kg) 310.3019 123.4056 4874.385 2.184488 593.9046 310Per capita COD (kg) 5.407049 4.889379 31.52133 0.246667 3.783135 310Per capita Cadmium(kg) 0.000114 0.0000131 0.0016398 0 0.0002584 310Per capita Arsenic (kg) 0.0012178 0.0001597 0.0319208 0 0.0039425 310Per capita GDP (yuan) 5046.91 3699.679 28,302.74 977.6873 4033.244 310Population density (person/km2) 351.1363 248.3523 2640.17 1.888636 425.8857 310The secondary

industry share (%)43.54794 43.9 59.6 17.4 7.906297 310

Per capita physicalcapital (yuan)

2379.511 1695.031 10547.21 422.1859 1995.102 310

Employed persons as percentageof total population (%)

49.7915 50.0869 66.82909 34.93507 5.352708 310

Per capita government pollutionabatement expense (yuan)

77.44734 38.85194 753.3143 3.561133 114.2643 310

Note: Per capita GDP, per capita physical capital and per capita government pollution abatement expense are adjusted byConsumer Price Index (CPI), setting CPI in 1993=100.Source: the Chinese Statistical Yearbook (1993–2002) and the Chinese Environmental Statistical Yearbook (1993–2002).


2.2. Data description

This paper uses the data of two air pollutants (SO2 and Dust Fall) and three water pollutants(Chemical Oxygen Demand(COD), Arsenic in water, and Cadmium in water) from 1993 to 2002in China's 31 provinces and metropolitan cities. Table 2 lists the descriptive statistics of all thevariables used in this paper.

There are two issues to be noted here. First, there are two GDP measures listed in the ChineseStatistical Yearbook. The first one is measured by the value-added method. The alternative one ismeasured by the expenditure method. As noted by Keidel (2001), for official GDP growthstatistics, China relies on value-added measures. The reason why China uses value-added ratherthan the expenditure method is because of its inherited output reporting system, which reliesheavily on the direct enterprise reporting of Gross Value Output, intermediate inputs and incomecomponents. Considering that the provincial GDP is published by each province at the beginningof a year and provincial officials have the incentive to exaggerate the provincial GDP and itsgrowth rate, I believe that the expenditure accounts are probably truer measures of provincialoutput. Therefore, I apply the expenditure measures of provincial GDP in this paper.7 The secondissue is how to deal with inflation and/or deflation in the GDP, physical capital stock and pollutionabatement expense. As it is well known, during the period from 1993 to 2002 there are seriousinflations as well as deflations in China, and inflation/deflation rates vary across provinces.However, all the provincial data for the above three variables reported in Chinese StatisticalYearbook are calculated at current prices. Therefore, adjustments must be made for thesevariables. Since there is only the official data for provincial Consumer Price Index (CPI)available, I am compelled to adjust GDP, physical capital and pollution abatement expense by CPI(setting CPI in 1993=100).

7 For detail discussion on China's GDP expenditure measures, see Keidel (2001).


3. Methodological issues and empirical results

3.1. Methodological issues

The first methodological issue concerns the exogeneity of the log form of per capita GDP, itsquadratic term and per capita government pollution abatement expense in Eq. (1). It follows that ifan explanatory variable is an endogenous variable, the single polynomial equation estimation mayyield biased and inconsistent estimates, therefore necessitating an Instrumental Variable (IV)method. For this reason, I employ a Hausman specification test to check the exogeneity of thesethree right-hand variables in Eq. (1).

The second issue is that, if the Hausman specification test rejects the hypothesis that per capitaGDP, its quadratic term and per capita pollution abatement expense are exogenous variables,which the IV method should be applied to estimate this SEM. Before choosing an estimationmethod, the identification problem and choice of instruments of this nonlinear SEM should bementioned. The identification problem for simultaneous equations models that are nonlinear insome endogenous variables is well discussed in Wooldridge (2002). The critical issue here is thechoice of instruments for the quadratic term of per capita GDP. If the coefficient α2 in Eq. (1)equals zero, then the model of Eqs. (1), (2), and (3) turns into a linear simultaneous equationssystem. In most linear simultaneous equations system, it is common to use all the exogenousvariables in the system to be the instruments for all the endogenous variables. It is to say, in thesystem here, the instruments for per capita GDP, per capita government pollution abatementexpense and per capita pollutant emission are per capita physical capital, labor, the secondaryindustry share, population density and time trend. From this view, the instruments for thequadratic term of per capita GDP are therefore chosen as the above five exogenous variables,interaction terms of these five variables and their quadratic terms. After solving the instrumentsissue, the system in Eqs. (1), (2), and (3) can be studied using the usual rank and order conditions.It is obvious that all the three equations are over-identified, thus, a two-stage least square (2SLS)method, which is the most common method used for estimating simultaneous equations models,may be the best simple estimation method for the model in this paper. Consequently, the nextissue concerns the validity of all the instruments used in this model. In order to check that all theinstruments are valid in the sense that they are uncorrelated with error terms, I apply anoveridentification restriction test.8 The usefulness of this test is that, if the null hypothesis isrejected, then the logic of choosing the instruments must be reexamined. In contrast, if the null isnot rejected, then I can have some confidence in the overall set of instruments used.

The third issue is to choose the estimation method for panel data, i.e. the choice among pooledOLS, random-effects model and fixed-effects model. I first employ the F test to check thehomogeneity of the province effects. Upon the result that province effects are found to exist, thena Hausman specification test will be employed to help choose between the random-effectsestimation and the fixed-effects estimation.

3.2. Empirical results

Tables 3–6 present the empirical results. In all the pollutants, the null hypothesis of thehomogenous province effect is strongly rejected at a wide margin (see F statistics in the tables).This evidence suggests that OLS estimators are inefficient and may yield biased estimates. In

8 For details on overidentification restriction test, see Wooldridge (2002).

Table 3Estimated results for air pollutants (Eq. (1)) a (t statistics in parentheses)

Single polynomial equation Simultaneous equations

SO2 Dust Fall SO2 Dust Fall

Intercept 32.6875⁎⁎⁎ (3.20) −9.7688 (−0.89) 86.5774⁎⁎⁎ (4.88) 9.5399⁎⁎⁎ (3.52)ln(per capita GDP) −7.6945⁎⁎⁎

(−4.78)4.6376⁎⁎⁎ (2.67) −15.5328⁎⁎⁎ (−5.62) 0.6490 (0.23)

(ln(per capita GDP))2 0.4762⁎⁎⁎ (4.98) −0.2520⁎⁎ (−2.44) 0.9054⁎⁎⁎ (5.45) 0.0576 (0.34)ln(abatement expense) 0.0314 (0.43) −0.1510⁎⁎ (−1.98) −0.8217⁎⁎⁎ (−3.26) −0.9386⁎⁎⁎ (−3.63)ln(secondary industry share) −0.8986⁎ (−1.80) 1.5532⁎⁎⁎ (2.90) 1.3154⁎⁎⁎ (3.13) 1.3331⁎⁎⁎ (2.89)ln(population density) 1.3709 (1.27) −1.0695 (−0.93) −1.8529 (−1.21) −2.0949⁎⁎⁎ (−3.34)Time trend −0.0480 (−1.37) −0.0359 (−0.96) 0.2282⁎⁎⁎ (3.14) 0.0198⁎⁎⁎ (4.27)R−square (overall) 0.158 0.506 0.131 0.367Hausman Test for exogeneity – – 16.83⁎⁎⁎ (6) 19.25⁎⁎ (6)Overidentification test – – 12.862 (14) 16.350 (14)Hausman Test for random

or fixed effects21.66⁎⁎⁎ (6) 30.08⁎⁎⁎ (6) 16.36⁎⁎ (6) 23.78⁎⁎⁎ (6)

F statistics 26.61⁎⁎⁎ (30,273) 22.78⁎⁎⁎ (30,273) 16.39⁎⁎⁎ (30,273) 16.65⁎⁎⁎ (30,273)Turning pointb (3229) 9913 (5313) –Samples 310 310 310 310a One, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at 10%, 5% or 1%

level, respectively.b Turning points in parentheses indicate that the relationship between pollution and income is an U-shaped curve.


addition, the results of the Hausman test for selecting between random-effects or fixed-effectsestimation all reject the assumption of the random-effects model. Therefore, to save space, OLSand Random-effects estimators are not reported here.

The next issue concerns the exogeneity of the log form of per capita GDP, its quadratic termand per capita pollution abatement expense. The results of the Hausman test for exogeneity listedin Tables 3 and 4 show that the null hypothesis of exogeneity of these variables is statisticallyrejected in all cases, which suggest that the simultaneous relationship between per capita incomeand per capita pollutant emission does exist in the dataset of China, therefore necessitating the IV(2SLS) method for estimating the simultaneous equations model. Meanwhile, in all pollutants, theoveridentification restriction tests support that the instruments used in this nonlinear simultaneousequations system are reliable, since the null hypothesis that these instruments are valid in all casescannot be rejected (see overidentification test in Tables 3 and 4).

Turning to the comparison between the single polynomial equation model estimators and thesimultaneous equations model estimators, three main differences are found:

(1) In the single polynomial equation model, estimated results suggest that in all pollutantsexcept SO2, the expected EKCs are found to exist, whilst the 2SLS estimators indicate thatthe EKC relationships only exist in water pollutants, i.e. the EKC evidence for Dust Fallfound in single polynomial equation estimator failed to be generated in the simultaneousequations estimator. The 2SLS estimators probably well reflect the fact that air pollution isrelatively serious than water pollution in China, since China has had an extensive waterpollution levy system in place for many years, therefore, causing all the water pollutantsexamined in this paper to generate the EKC.

(2) Turning points of these inverse-U-shaped curves in water pollutants are estimated nearly1.68–2.49 times larger in magnitude after applying the 2SLS method (see Table 4). This

Table 4Estimated results for water pollutants (Eq. (1)) a (t statistics in parentheses)

Single polynomial equation Simultaneous equations

COD Arsenic Cadmium COD Arsenic Cadmium

Intercept −15.1579(−1.59)

−72.9876⁎⁎

(−2.42)−138.5327⁎⁎⁎

(−3.01)−57.8825⁎⁎⁎

(−3.77)−232.936⁎⁎⁎

(−4.20)−344.3816⁎⁎⁎

(−4.46)ln(per capita GDP) 6.5740⁎⁎⁎

(4.36)23.6145⁎⁎⁎

(4.96)26.1151⁎⁎⁎

(3.60)12.5506⁎⁎⁎

(5.25)48.1684⁎⁎⁎

(5.57)56.4921⁎⁎⁎

(4.70)(ln(per capita GDP))2 −0.3868⁎⁎⁎

(−4.31)−1.3251⁎⁎⁎

(−4.33)−1.5837⁎⁎⁎

(−3.67)−0.6958⁎⁎⁎

(−4.85)−2.4649⁎⁎⁎

(−5.13)−3.0852⁎⁎⁎

(−4.55)ln(abatement expense) −0.0673

(−0.99)0.3511⁎

(1.83)−0.0749(−0.23)

−0.5098⁎⁎

(−2.34)−3.3492⁎⁎⁎

(−4.25)−3.4039⁎⁎⁎

(−3.10)ln(secondary industry

share)1.2594⁎⁎⁎

(2.71)3.5225⁎⁎

(2.39)7.1764⁎⁎⁎

(3.22)0.7593⁎⁎

(2.27)2.9553⁎⁎

(2.35)4.3841⁎⁎

(2.12)ln(population density) −2.0998⁎⁎

(−2.07)−8.8007⁎⁎⁎

(−2.77)4.1390 (0.86) 0.4992 (0.38) 0.5558 (0.12) 14.2789⁎⁎

(2.15)Time trend 0.01245

(0.38)−0.4039⁎⁎⁎

(−4.54)−0.0435(−0.28)

−0.1853⁎⁎⁎

(−2.75)−1.0901⁎⁎⁎

(−4.43)−0.8829⁎⁎

(−2.60)R-square (overall) 0.148 0.026 0.053 0.217 0.046 0.007Hausman Test for

exogeneity– – – 17.47⁎⁎⁎ (6) 16.95⁎⁎⁎ (6) 18.77⁎⁎ (6)

Overidentification test – – – 15.273 (14) 12.120 (14) 16.392 (14)Hausman Test for random

or fixed effects17.36⁎⁎⁎ (6) 16.33⁎⁎ (6) 24.13⁎⁎⁎ (6) 15.58⁎⁎ (6) 23.22⁎⁎⁎ (6) 18.67⁎⁎⁎ (6)

F statistics 17.74⁎⁎⁎

(30,273)33.77⁎⁎⁎

(30,273)19.20⁎⁎⁎

(30,273)13.44⁎⁎⁎

(30,273)19.68⁎⁎⁎

(30,273)13.58⁎⁎⁎

(30,273)Turning point 4905 7409 3808 8257 17516 9465Samples 310 310 310 310 310 310a One, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at 10%, 5% or 1%

level, respectively.


evidence indicates that if we ignore the simultaneous relationship between income andpollution, and estimate the impact of income on pollution directly by a single polynomialequation model, the turning points would be underestimated. These different turning pointssurely lead to different policy implications. For example, in the case of COD, if we believe thatthere is no simultaneity between income and pollution, then the result indicates that after theper capita GDP reaches 4905 yuan level, the per capital emission should be decreased asincome increases, whilst according to the simultaneous equations model, the per capitaemission starts to decrease after the per capita GDP reaches the 8257 yuan level. For thoseprovinces with per capita GDP between 4905 and 8257 yuan, different estimated turningpoints probably mean different policies that they will implement. From another view,comparing the turning points estimated by bothmethods with the per capita GDP data in 2002,it is found that after controlling the simultaneity between income and pollution, the number ofprovinces that are still on the pollution increasing path of EKC (i.e. the left part of inverse-U-shaped curve) changes from14 to 22 inCOD emission, from 22 to 29 inArsenic emission, andfrom 2 to 23 in Cadmium emission. These changes caused by different estimation methodsmay not only cause different policies for each individual province, but also correspond todifferent economical and environmental policies implemented by the central government ofChina. This is because the situation described by a single polynomial equation methodindicates thatmore than half of the provinces have already been on the decreasing path of EKC

Table 5Estimated results for income equation (Eq. (2)) a (t statistics in parentheses)

ln(GDP) ln(GDP) ln(GDP) ln(GDP) ln(GDP)

ln(SO2) −0.0272⁎⁎⁎

(−3.56)– – – –

ln(Dust Fall) – −0.0225⁎⁎⁎

(−4.18)– – –

ln(COD) – – −0.0306⁎⁎⁎

(3.48)– –

ln(Arsenic) – – – −0.0189⁎⁎⁎

(3.47)–

ln(Cadmium) – – – – −0.0064⁎⁎⁎

(−5.22)Intercept 5.858⁎⁎⁎

(14.49)5.5590⁎⁎⁎

(15.88)5.6779⁎⁎⁎

(15.61)6.0864⁎⁎⁎

(16.15)5.2934⁎⁎⁎

(12.91)ln(physical capital) 0.1961⁎⁎⁎

(6.63)0.1983⁎⁎⁎

(6.84)0.1880⁎⁎⁎

(6.11)0.1717⁎⁎⁎

(5.78)0.2094⁎⁎⁎

(7.17)ln(labor) 0.2299⁎⁎⁎

(2.75)0.2113⁎⁎

(2.51)0.2162⁎⁎⁎

(2.57)0.1906⁎⁎⁎

(2.33)0.2702⁎⁎⁎

(3.10)Time trend 0.0657⁎⁎⁎

(22.88)0.0667⁎⁎⁎

(21.36)0.0671⁎⁎⁎

(21.42)0.0714⁎⁎⁎

(21.59)0.0639⁎⁎⁎

(21.80)R-square (overall) 0.564 0.504 0.571 0.461 0.587Hausman Test for random or

fixed effects105.20⁎⁎⁎ (4) 77.38⁎⁎⁎ (4) 139.44⁎⁎⁎ (4) 157.49⁎⁎⁎ (4) 670.84⁎⁎⁎ (4)

F statistics 54.68⁎⁎⁎

(30,275)56.46⁎⁎⁎

(30,275)51.07⁎⁎⁎

(30,275)55.96⁎⁎⁎

(30,275)55.40⁎⁎⁎

(30,275)Samples 310 310 310 310 310a One, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at 10%, 5% or 1%



(i.e. the right part of inverse-U-shaped curve), while a simultaneous equations methodindicates that only a few provinces have been on the right part of EKC.

(3) Another source of difference between these two methods is found in the estimatedcoefficients of several other explanatory variables. First, for per capita government pollutionabatement expense, the first difference is the coefficients’ sign and statistical significance. InDust Fall, COD and Cadmium cases, statistical significance is achieved or improved due tothe application of the 2SLS method, while in the SO2 and Arsenic cases, application of the2SLS changes the coefficient's positive sign into a significantly negative sign. All thesechanges due to the 2SLS method make these coefficients significantly consistent withtheoretical expectation. The second difference for per capita pollution abatement expense isthat its elasticities on per capita emission in all pollutants are estimated to be larger due to the2SLS method. This evidence is significant to give the policy makers a higher incentive toinvest more on pollution abatement in order to reduce pollutant emissions.

Secondly, for the secondary industry share, in nearly all the cases, both methods estimate apositive and statistically significant coefficient9, which is consistent with the expectation,indicating that the heavier the weight of the secondary industry is, the more pollution will beemitted. However, the impact of the secondary industry share on per capita pollutant emission issmaller in 2SLS estimators. The 2SLS method provides us a way to investigate industrial

9 Only the coefficient in single polynomial equation of SO2 is estimated with a negative and weakly significant sign.

Table 6Estimated results for abatement equation (Eq. (3))a (t statistics in parentheses)

ln(Abatement) ln(Abatement) ln(Abatement) ln(Abatement) ln(Abatement)

ln(SO2) 0.2261⁎⁎⁎

(1.85)– – – –

ln(Dust Fall) – 0.4136⁎

(1.86)– – –

ln(COD) – – 0.0338⁎⁎

(2.01)– –

ln(Arsenic) – – – 0.0781⁎⁎⁎

(2.50)–

ln(Cadmium) – – – – 0.0115⁎⁎⁎

(3.43)Intercept 2.8417

(1.36)1.4261(0.84)

2.2684(1.30)

5.4754⁎⁎⁎

(2.76)3.0107(1.51)

ln(physical capital) 0.4262⁎⁎⁎

(2.90)0.4397⁎⁎⁎

(2.86)0.4390⁎⁎⁎

(2.93)0.3401⁎⁎

(2.22)0.4198⁎⁎⁎

(2.82)ln(secondary

industry share)0.8179⁎

(1.88)0.0446⁎

(1.73)0.7282⁎

(1.62)0.6546⁎⁎⁎

(2.73)0.8710⁎

(1.88)Time trend 0.1722⁎⁎⁎

(13.22)0.1428⁎⁎⁎

(9.18)0.1699⁎⁎⁎

(11.85)0.1967⁎⁎⁎

(12.09)0.1742⁎⁎⁎

(12.34)R-square (overall) 0.532 0.519 0.539 0.381 0.509Hausman Test for random

or fixed effects8.12⁎

(4)13.49⁎⁎⁎

(4)12.58⁎⁎⁎

(4)14.50⁎⁎⁎

(4)15.23⁎⁎⁎

(4)F statistics 25.85⁎⁎⁎

(30,275)24.81⁎⁎⁎

(30,275)25.92⁎⁎⁎

(30,275)25.70⁎⁎⁎

(30,275)25.95⁎⁎⁎

(30,275)Samples 310 310 310 310 310a One, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at 10%, 5% or 1%



structural impact by two sources: direct impact measured by the coefficient in Eq. (1) and indirectimpact measured by the coefficient of the secondary industry share in Eq. (3) multiplying thecoefficient of per capita pollution abatement expense in Eq. (1), therefore the net impact should becalculated as the net values of these two impacts. For example, for Arsenic, the direct impactindicates that a 1% increase in the secondary industry share causes an increase of 2.9553% in percapita emission, while the indirect impact via pollution abatement expense shows that a 1%increase in the secondary industry share causes an increase of 0.6546% of per capita pollutionabatement expense, and a 1% increase in pollution abatement expense reduces per capita emissionby 3.3492%, therefore, a reduction of 2.1924 % (=3.3492 ⁎0.6546) of per capita emission.Consequently, the net impact is that a 1% increase in the secondary industry share causes a netincrease of 0.7629% (=2.9553−2.1924) in per capita emission, which is only one fifth of that oneestimated in single polynomial equation.10

Thirdly, for the remaining variables in Eq. (1), by applying the 2SLS method, time effect turnsto be significant in all pollutants. However, population density is not significant anymore in CODand Arsenic.

From the above discussions, I find that the differences between the single polynomial equationmodel and the simultaneous equations model do exist, and these differences will certainly lead to

10 The net impact of the secondary industry share on per capita emission in SO2, Dust Fall, COD and Cadmium due to2SLS method is calculated as 0.6433, 1.2912, 0.3881 and 1.4913, respectively.


different policy implications. Therefore, I confirm that it is necessary to consider the simultaneitybetween income and pollution before directly regressing EKC in future studies.

Finally, turning to the estimated results of income and abatement equations in Tables 5 and 6,most of the estimated coefficients are highly significant and consistent with the expected signs. Inthe income equation, the normal inputs such as physical capital and labor contribute positively tothe GDP, and all the pollutant emissions are negatively related to the GDP. Besides these,pollutant emissions are also important determinants of pollutant abatement expense in all cases(see Table 6). In the abatement equation, another two critical determinants of the pollutionabatement expense are physical capital and the secondary industry share. More physical capitalleads to more pollution abatement expense available. Whilst, the heavier the weight of thesecondary industry, the more pollution might be emitted, therefore, the more pollution abatementexpense would be needed. These evidences from income and abatement equations stronglysuggest that pollution reduces income in China, thus, more pollution abatement investment arerequired to keep sustainable growth in the long run for the Chinese economy.11

4. Conclusion

I acknowledge by the existing theoretical framework inwhich economic growth and pollution arejointly determined. Subsequently, if simultaneity between income and pollution does exist,investigating the relationship between these two variables only by a commonly used singlepolynomial equation produces biased and inconsistent estimates. Concerning about this issue, in thispaper, I first construct a simultaneous equations model and then employ a Hausman test to checkwhether or not a simultaneous relationship between income and pollution exists in the dataset ofChina. The results confirm that the simultaneity between income and pollutant emission exists in allthe pollutants (SO2, Dust Fall, COD,Arsenic andCadmium). Applying the 2SLSmethod to estimatethe simultaneous equations model, I find several different results between a single polynomialequation model and a simultaneous equations model, which may lead to different policyimplications. This issue indicates that in future EKC studies, the necessity of investigating thesimultaneity between income and pollution should be considered before regressing the model.

An EKC relationship is found in COD, Arsenic and Cadmium emissions in China. Meanwhile,SO2 shows a U-shaped curve and Dust Fall indicates no relationship with income level. Governmentpollution abatement expense has a significant and negative effect on pollution, suggesting thatenvironmental policy impact is quite strong in China, at least for the air and water pollutants'emissions examined in this paper. The net impacts from the secondary industry share on per capitapollutant emission are all positive and significant. This evidence claims that the industrial structurealso plays an important role in determining pollution. Therefore, the evidence from the theoreticalframework that environmental policy and industrial structure as well as economic growth haveimportant effects on pollution is empirically verified in China. As a result, environmentalimprovement does not depend exclusively on income growth. Poor provinces need not thereforewait passively to become wealthy before doing something else to improve their environment.

In addition, most of the estimated coefficients in income and abatement equations are highlysignificant and consistent with our expectation. The evidences from these two equations and thediscussions in the previous section suggest that the Chinese government ought to invest more onpollution abatement expense to achieve sustainable growth in the long run.

11 It is also very possible that the pollution abatement expense may slow down economic growth rate in the short run,because the opportunity cost of abatement expense is measured by less direct investment available to the final products.


Acknowledgement

The author would like to thank the coeditor, two anonymous referees, Yoshizo Hashimoto andTsunehiro Otsuki for their helpful comments on this research.

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4 sem 2sls ekc china

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