2012 Cambridge Business & Economics Conference
ISBN : 9780974211428
Halimahton Binti Borhan
Faculty Business Management, Universiti Teknologi Mara, Malaysia
012-2951627; hali@bdrmelaka.uitm.edu.my, halimahton2001@yahoo.com
Mizan Bin Hitam
Faculty of Architecture, Planning and Surveying, Universiti Teknologi Mara, Malaysia
06-2857001; drmizan@melaka.uitm.edu.my
Rozita Naina Mohamed
Faculty Business Management, Universiti Teknologi Mara, Malaysia
012-9741676; azarozi_naina@yahoo.com
Mazzini Muda
Faculty Business Management, Universiti Teknologi Mara, Malaysia
012-9151964; mazzini.muda@gmail.com
INCOME AND CO2 IN CHINA AND MALAYSIA FROM ENVIRONMENTAL
KUZNETS CURVE (EKC) PERSPECTIVE
ABSTRACT
Pollution causes not only physical disabilities but also psychological and behavioral disorders in
people. This kind of pollution will also effect economic development in a country. The first to
model the relationship between environmental quality and economic growth was Grossman and
Krueger (1991) by converting the original Kuznets Curve to the Environmental Kuznets Curve.
The objective of the study is to test the relationship between economic growth and air pollution
(CO2) in China and Malaysia from the year 1965 to 2010. This study makes a comprehensive
investigation into this relationship by using simultaneous equation model. This study employs a
Hausman specification test and two stage least square (2SLS) method to approximate the
simultaneous equations models. The EKC relationship is found in the case of Malaysia and
China.
Keywords: Environmental Kuznets Curve, Hausman, Income, Pollution, Simultaneous,
ACKNOWLEDGEMENT
We are heartily thankful to all our colleagues who have made the completion of this research
paper. Lastly, we offer our regards and blessings to all of those who supported us in any respect
during the completion of the paper.
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1. INTRODUCTION
Pollution causes not only physical disabilities but also psychological and behavioral
disorders in people. This kind of pollution will also effect economic development in a country. A
common problem unique to developing countries is depletion and destruction of natural resource,
environmental degradation, and resulting social and economic effects. Environmental
degradation is caused by the following key factors such as industrialization, transportation,
population, poverty, soil erosion, congestion and traffic and exploitation of open access resource
due to ill-defined property rights. There are numerous current studies of the Environmental
Kuznets Curve (EKC) that have attempted to reply to this question. Former studies such as
Shafik (1992), Panayoutou (1993), and Grossman (1995) showed initial sign that some pollutants
applied an EKC shape. It was thought that economic growth was by nature the cure to
environmental problems. An opposite view was revealed by a later study by de Bruyn (2000).
Problems concentrated on the consequence of employing various indicators of a bigger range of
explanatory variables than income alone. The United Nations Development Programme (1997)
reported that Malaysia’s rapid economic growth has caused environmental degradation. Air
pollution occurs due to urbanization, industrial activities and motor vhicles. In 1995, 75 per cent
of air pollution came from vehicles, power stations and industrial fuels led about 20 per cent and
5 per cent cam from burning of household and industrial wastes. Transboundary atmospheric
pollution has added to critical haze troubles. Primary environmental pollution issues faced by
Malaysian today include air pollution, mainly from industry and vehicular emissions, and water
pollution, as a result of raw sewage disposal and deforestation. The problem of high
environmental pollution is mostly due to the new industrial revolution. An International
agreement has been made by the Government in order to minimize pollution (Rhoda, 1995).
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Lakes, rivers and the air in many places in China are still polluted, some seriously, in
spite of continuous efforts to control pollution. Zhang Lijun, deputy minister of environmental
protection, said environmental protection departments across the country should press enterprises
harder on pollution control (Charles, 2011). Economic growth of China will be affected by these
environmental issues. Therefore, this study attempts to test the relationship between income to
air pollution (CO2) in Malaysia and China by using the environmental Kuznets curve analysis.
The specific objectives are as follows:
i.
To test the endogeneity of CO2 and income in Malaysia and China.
ii.
To test whether the EKC really fits to CO2 in Malaysia and China.
2. LITERATURE REVIEW
During the period 1965-1990 Newly industrializing countries were among the highest
growing economies; and in order of performance they can be listed as, Singapore (1), Korea (2),
Taiwan (5), Hong-Kong (6), China (7), Indonesia (8), Japan (10), Malaysia (11), Thailand (18),
Brazil (19) and Yugoslavia (20) (Carlos et al, 2008). Unfortunately however, the byproduct of
rapid industrialization in these countries has been a concurrent increase in environmental
pollution. This relationship between environmental degradation and income has been tested by
earlier empirical studies and the curve of this relationship has been analyzed by Grossman and
Krueger (1991). The first to model the relationship between environmental quality and economic
growth was Grossman and Krueger (1991) by converting the original Kuznets Curve to the
Environmental Kuznets Curve. In cross-country analysis most previous studies derived an
inverted U shape curve depicting the relationship, which was named the Environmental Kuznets
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Curve (EKC), whereby it slopes upwards at the lower income range and conversely slopes
downwards at the higher income range. The assumption behind the inverted U-shaped EKC is
that across the transformation of economic development, most environmental degradation
variables experience two stages, increasing during the first stage and decreasing at the latter
stage. The postulation of the inverted U-shaped EKC has received mixed responses in previous
studies with some acceptance and also some rejections.
Heilbroner and Thurow (1987) stated that economic growth is a function of population
and per-capita consumption, manifested by an increase in supply and demand for goods and
services. However, as every successful economic development is accompanied by various
problems the question which arises is on whether the process of environmental degradation
which invariably seems to accompany economic development can be averted. The hypothesis of
the environmental Kuznets curve (EKC) is a good starting point in this debate. According to
Toru Iwami (2001), the EKC assumes that up to a certain turning point, growth in income per
capita runs concurrently with a decline in environmental quality. Subsequently, beyond the
threshold point, the relationship is reversed with income growth being accompanied by a
reduction in environmental degradation. Viewed positively, validation of this hypothesis will
result in endorsement of the development policies undertaken. Further if proven valid, there is a
necessity to relook at the various factors which affect the environmental conditions in the
countries concerned.
Brajer et al (2008) tested for the hypothesis of an EKC for China’s annual ambient levels
of SO2 pollution applying a city-specific panel data set. They use SO2 air quality measurements
1990-2004 for about 100 cities. They come up with two statistically equivalent models that have
distinctly different policy implications. One is a traditional bell-shaped Environmental Kuznets
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curve. The other is an N-shaped curve with a second turning point at about 33,000 Yuan, about
twice the current GDP per capita level in China. The relationship between various air and water
pollutants and per capita income, in Malaysia, has been examined by Vincent (1997) using data
for the period 1987 to 1991. The study found no indication of an inverted-U relationship between
income and any of the pollutants. Elias et al (2010) investigated on the relationship between
income and environmental degradation in Malaysia. The study found the existence of the
Environmental Kuznets Curve. There exists an EKC type relationship between several pollutants
and total real GDP which has additional contribution to environmental Kuznets curve hypothesis.
3. RESEARCH METHODOLOGY
In estimating the relationship between per capita income and respective environmental
indicators, most of the former EKC studies concentrated on employing the cross country panel
data. But use of individual country data is a new line for EKC researches nowadays. However,
only a few studies estimated the EKC by employing individual country data. There is an
empirical study in Malaysia by Vincent (1997) and Elias et al (2010) that attempted to estimate
the EKC by using individual country data. Many scholars in Environmental Kuznets Curve
(EKC) studies used linear and quadratic as well as cubic equations (Shafik 1994, Moomaw and
Unruh 1997, Wu 1998, Friedl and Getzer 2003). The quadratic equation (Y2) means at the initial
stage of development when GDP increases environmental degradation increases, and later with
further increases in GDP environmental degradation decreases. The cubic equation (Y3) means
with further increases in GDP environmental degradation decreases. Therefore for the EKC to
exist, in cubic equation, Y must have the positive coefficient, Y2 must have the negative
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coefficient and Y3 must have the negative coefficient. In case of quadratic equation, Y must have
the positive coefficient and Y2 must have the negative coefficient.
This study uses individual country data and both single equation method and a
simultaneous equation method with a structure of two equations. Equations of the model are:
Equation 1:
Air Pollution = f (Income, Population density, Time)
Equation 2:
Income (Y) = f (Air Pollution, Fixed capital, Foreign Direct Investment, Labour,
Net Export, Time)
Equations (1) and (2) designate the simultaneous equations for this model.
log CO2 tiQj = α0 + α1 Iog Y tiQj + α2 (IogY tiQj )2 + α3 log PD tiQj + α6T2 + α7T3 + α8T4 + e tiQj
Iog Y tiQj = β0 + β2 Iog CO2 tiQj + β3 Iog K tiQj + β4 log FDI tiQj + β5 log L tiQj + β6 NX + β7 T2 +
β8 T3 + β9 T3 + € tiQj
i:
46 years
t:
time
The data used in this study have been collected in the form of secondary sources. This is
including the East Asian 8 Air Quality Data Reports 1965 – 2010 and East Asian 8 Statistics
Data Reports. Besides, World Development Indictors online database; International Labour
Origination and all the sources have been referred throughout the findings and analysis of the
research. In this regard the monetary terms with regard to GDP, Physical capital, Foreign Direct
Investment (FDI)
and government expenditure are deflated by the Consumer Price Index
(CPI) with the base year of 1987.
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In order to ascertain the econometric explanation of the model specification, in the
simultaneous equation method a Hausman test is used for income endogeneity. Holtz-Eakin and
Selden (1995) and Cole et al. (1997) used this test in their studies.
The analysis starts by testing the normality or goodness-of-fit. The Jacque-Bera (JB)
Normality Test is used to examine whether the residuals of the final estimation regression are
normally distributed. The reported probability of the JB statistic and a 5% significance level are
used for making a decision whether to reject the null hypothesis or not. A probability that is
greater than the significance level leads to a failure to reject the null hypothesis of a normal
distribution. Next, the study tests for stationarity of the available data using conventional time
series unit root test. The two unit root tests that will be used are Dickey Fuller (DF) or
Augmented Dickey Fuller (ADF) unit root test and Phillip-Perron unit root test. Then,
cointegration test will be used once the stationarity of all data is detected. The Johansen-Juselius
cointegration test has been used in order to see if there exists a long run relationship between the
variables. The optimum lag length is selected before the Johansen-Juselius cointegration test is
conducted once all the residual free from autocorrelation. The Lagrange Multiplier (LM) Serial
Correlation Test is used to test for the first-order and the second-order residual serial correlation
to confirm that the error terms (et) residuals (ut) have been used in place of errors in the analysis
of the estimated regressions do not exhibit autocorrelation.
Next, in order to examine the existence of multicollinearity and heteroscedasticity this
study executes the following diagnostic-check:
1) Multicollinearity correlation test will be used in order to test the multicollinearity problem
2) White test will be used in order to test the heteroscedasticity problem
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The exogeneity of the log form of per capita GDP and its quadratic term, per capita
government pollution abatement expense and per capita population density in Equation (1) is the
next issue the study is interested in. The single polynomial equation estimation may generate
unfair and not consistent forecasts if an explanatory variable is an endogenous variable.
Therefore, this study necessitates an Instrument Variable (IV) method. Thus use of two-stage
least square (2SLS) method is essential. To assure the exogeneity of these four right-hand
variables in Equation (1), this study employs a Hausman specification test. The final issue is that,
the two-stage least square (2SLS) method is employed to approximate these simultaneous
equations models whenever the Hausman specification test rejects the hypothesis that per capita
Gross Domestic Product, its quadratic term, per capita pollution abatement expense, and
population density are exogenous variables.
4. ANALYSIS AND INTERPRETATION OF RESULTS
In order to examine the relationship between water pollution and income in Malaysia,
both single and simultaneous equation methods has been adopted by the study. Based on the
Hausman test, air pollutant CO2 was found having simultaneous relationship with income for
both cases of Malaysia and China.
To test the normality of the residuals, the study used Jacque-Bera (JB) Normality Test to
examine whether the residuals of the final estimation regression are normally distributed. The
result indicates that a probability is lesser than the significance level. This leads to reject the null
hypothesis of a normal distribution.
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To test the stationarity of the available data, a unit root test by using the conventional
Augmented Dickey-Fuller (ADF) unit root test and Phillips-Perron (PP) unit root test has been
used. Table 4.1 and Table 4.2 presents the results of unit root at level and after first difference.
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Table 4.1: Unit Root Test for Malaysia
MALAYSIA
log CO
Iog Y
log NX
log PD
Iog L
(IogY)2
log FDI
log K
DF/ADF Unit Root Test
Level
First Difference
No Trend
With Trend
No Trend
With Trend
-3.022068 (9)
-3.292210
-7.353248
-7.241739
(9)
(9)***
(9)***
0.557013 (9)
-2.200347
-4.756360
-4.918133
(9)
(9)**
(9)**
-0.371258 (9)
-2.303392
-3.727737
-3.937671
(9)
(9)**
(9)**
-1.260739 (9)
-1.166623
-6.258719
-6.391704
(9)
(9)***
(9)***
-2.511766 (9)
-2.807611
-10.36940
-10.23899
(9)
(9)***
(9)***
0.557013 (9)
-2.200347
-2.756360
-4.918133
(9)
(9)*
(9)**
-1.913368 (9)
-4.062863
-11.04183
-11.03195
(9)
(9)***
(9)***
-2.897551 (9)
-2.913068
-5.625608
-5.592981
(9)
(9)***
(9)***
PP Unit Root Test
Level
First Difference
No Trend
With Trend
No Trend
With Trend
-3.173014 (9) -3.358529 (9)
-7.435155
-7.314398
(9)***
(9)***
-1.180566 (9) -2.620569 (9)
-9.010324
(9)***
11.45953(9)***
-1.448366 (9) -2.541586 (9)
-6.111009
-6.600924
(9)***
(9)***
-1.287638 (9) -1.166623 (9)
-6.258712
-6.391182
(9)***
(9)***
-2.460849 (9) -2.807611 (9)
-18.34788
-18.55401
(9)***
(9)***
-1.180566 (9) -2.620569 (9)
-9.010324
-11.45953
(9)***
(9)***
-2.735569 (9) -4.081312 (9)
-15.17223
-26.67061
(9)***
(9)***
-2.141588 (9) -2.376980 (9)
-6.505356
-6.684979
(9)***
(9)***
Notes: Lag length selected by using Schwarz Info Criterion. A maximum of 9 lags are used for Malaysia as listed above. The null hypothesis is
that the series is non-stationary, or contain unit root. Figures within parentheses indicate the number of lag structure for DF/ADF Test and lag
truncation selected automatically by Newey and West Bandwidth using Barlett Kernal Spectral estimation method for PP Test. All variables are
transformed by taking their natural logarithm. *** represents P<0.01; **, P<0.05; *, P<0.1.
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Table 4.2: Unit Root Test for China
DF/ADF Unit Root Test
Level
First Difference
No Trend
With Trend
No Trend
With Trend
-2.175750 (9)
-2.322282
-6.036648
-6.036398
(9)
(9)***
(9)***
0.557013 (9)
-2.200347
-4.756360
-4.918133
(9)
(9)**
(9)**
-2.015850 (9)
-2.290658
-6.333842
-6.255791
(9)
(9)***
(9)***
-1.260739 (9)
-1.166623
-6.258719
-6.391704
(9)
(9)***
(9)***
-2.511766 (9)
-2.807611
-10.36940
-10.23899
(9)
(9)***
(9)***
0.557013 (9)
-2.200347
-2.756360
-4.918133
(9)
(9)*
(9)**
-1.913368 (9)
-4.062863
-11.04183
-11.03195
(9)
(9)***
(9)***
-2.897551 (9)
-2.913068
-5.625608
-5.592981
(9)
(9)***
(9)***
MALAYSIA
log CO
Iog Y
log NX
log PD
Iog L
(IogY)2
log FDI
log K
PP Unit Root Test
Level
First Difference
No Trend
With Trend
No Trend
With Trend
-2.921410 (9) -2.892191 (9)
-6.326172
-6.309360
(9)***
(9)***
-1.180566 (9) -2.620569 (9)
-9.010324
(9)***
11.45953(9)***
-2.135208 (9) -2.462740 (9)
-6.333842
-6.255791
(9)***
(9)***
-1.287638 (9) -1.166623 (9)
-6.258712
-6.391182
(9)***
(9)***
-2.460849 (9) -2.807611 (9)
-18.34788
-18.55401
(9)***
(9)***
-1.180566 (9) -2.620569 (9)
-9.010324
-11.45953
(9)***
(9)***
-2.735569 (9) -4.081312 (9)
-15.17223
-26.67061
(9)***
(9)***
-2.141588 (9) -2.376980 (9)
-6.505356
-6.684979
(9)***
(9)***
Notes: Lag length selected by using Schwarz Info Criterion. A maximum of 9 lags are used for Malaysia as listed above. The null hypothesis is
that the series is non-stationary, or contain unit root. Figures within parentheses indicate the number of lag structure for DF/ADF Test and lag
truncation selected automatically by Newey and West Bandwidth using Barlett Kernal Spectral estimation method for PP Test. All variables are
transformed by taking their natural logarithm. *** represents P<0.01; **, P<0.05; *, P<0.1.
.
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The result indicates that all of the data for Malaysia and China are stationary after the
first difference for both DF/ADF and PP Unit root test. These result confirmed that the model
meet the requirement to proceed with panel cointegration test. Once all series are confirmed to be
categorizing as stationary, the Johansen-Juselius test is used to test whether the dependent
variable and all the independent variables in all the equations exhibit fundamental long-run
relationship among each other. The entire residual are free from autocorrelation (The Lagrange
Multiplier (LM) Serial Correlation Test) therefore the optimum lag length is selected. The results
for Johansen-Juselius cointegration test can be seen in Table 4.3 and Table 4.4. It is shown that
the value of trace statistic and max-eigen value for Malaysia are larger than the 5% critical value.
Therefore, we reject the null hypothesis of no cointegrating vector found in the long run. This
indicates that at least one cointegrating vectors that offers a stable relationship among variables.
Table 4.3: Cointegration Test for Malaysia
Lag
Equation
1( CO )
Equation
2
Equation
3
7
8
9
Hypothesis Eigen Value
Trace
Statistic
Critical
Value
(5%)
Max-Eigen
Value
Critical
Value
(5%)
0.589968**
0.488439**
0.452178**
111.0066
74.45427
46.97241
79.34145
55.24578
35.01090
36.55236
27.48186
24.67397**
37.16359
30.81507
24.25202
0.694201**
0.450518
0.280353
103.9169
55.33901
30.78907
95.75366
69.81889
47.85613
48.57791**
24.54994
13.48879
40.07757
33.87687
27.58434
0.810173**
0.793182**
0.736206**
265.6834
197.5561
132.9437
159.5297
125.6154
95.75366
68.12724**
64.61248**
54.63605**
52.36261
46.23142
40.07757
None
At most 1
At most 2
None
At most 1
At most 2
None
At most 1
At most 2
Notes: ** denote rejection of the hypothesis at 5% critical values. The optimum lag length is selected once all the
residual free from autocorrelation. None that r indicates the number of cointegrating vectors where none represent
r=0, at most 1 represent r  1, and at most 2 represent r  2.
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Table 4.4: Cointegration Test for China
Lag
Equation
1( CO2)
Equation
2
Equation
3
Hypothesis Eigen Value
8
None
9
At most 1
At most 2
None
8
At most 1
At most 2
None
At most 1
At most 2
Trace
Statistic
Critical
Value
(5%)
Max-Eigen
Value
Critical
Value
(5%)
0.586977**
0.516466**
0.479565**
108.3578
72.10350
42.31151
79.34145
55.24578
35.01090
36.25433
29.79199
26.77673**
37.16359
30.81507
24.25202
0.857936**
0.834564**
0.637234**
261.3416
181.3310
107.5649
159.5297
125.6154
95.75366
80.01058**
73.76609**
41.57394**
52.36261
46.23142
40.07757
0.645607**
0.536856**
0.428681
115.4086
72.87723
41.31879
95.75366
69.81889
47.85613
42.53134**
31.55843
22.95210
40.07757
33.87687
27.58434
Notes: ** denote rejection of the hypothesis at 5% critical values. The optimum lag length is selected once all the
residual free from autocorrelation. None that r indicates the number of cointegrating vectors where none represent
r=0, at most 1 represent r  1, and at most 2 represent r  2.
To determine the existence of multicollinearity correlation test was done. None of the
independent variables were found to be correlated with each other. For estimating the
heteroscedasticity, a White test was performed and no heteroscedasticity was found (Table 4.5 Table 4.6). The error terms for all of the variables in the model have a constant variance or
homoscedasticity. The coefficients were not found significant as at 5% level. In respect of the
exogeneity of the log form of per capita Gross Domestic Product, its quadratic term, and the
population density, the null hypothesis of exogeneity of these variables is statistically rejected.
This means that in the data set of both Malaysia and China, the simultaneous relationship
between per capita income and per capita pollutant emission does exist. We have used the F test
as there are more than one endogenous regressors involved (Gujarati, 1995).
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Table 4.5 Estimated Regression Results for CO2 and Income (Eq-1)
Dependent variables
Simultaneous equations
Independent variables
Intercept
log Y(per capita GDP)
log Y2(per capita GDP)2
log PD (population
density)
Time trend, T2
Time trend, T3
Time trend, T4
Adjusted R-square
Hausman Test for
exogeneity (F-statistic)
China
CO2
-31113 (-9.3580)***
Malaysia
CO2
8971.853 (0.5991)
59.3396 (2.1968)*
White 2.5339
-0.0006 (-0.5583)
White 1.3052
4424.549 (12.3723)***
White 1.4526
0.6892
(2.9856)**
White 2..2865
0.8562
(2.6896)**
White 1.0897
0.9637
(3.2341)**
White 1.1154
0.960405
0.4633 (5.2485)***
White 2.0586
-3.0907 (-3.1176)***
White 1.2356
-745.5508 (-0.5334)
White 2.0043
-
-
07526 (2.5785)**
White 2.1033
0.6234
(2.0590)*
White 2.5236
0.8925
(3.0026)**
White 1.7823
0.977593
note: 1. t-statistics in parentheses.
2. *** represents P<0.01; **, P<0.05; *, P<0.1.
This estimation was done under simultaneous equation method.
(1) The anticipated EKCs are found to exist in pollutants CO2 (coefficient of log Y is
+59.3396 and log Y2 is -0.0006) for China and (coefficient of log Y is +0.4633 and log
Y2 is -3.0907) for Malaysia.
(2) Population density has a positive and significant effect on pollution in China (coefficient
of 4424.549 and t-statistic of 12.3723). This is following the economic theory that as
population density increases pollution also increases. Different result shows for Malaysia
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in which population density has a negative and not significant on pollution (coefficient of
-745.5508 and t-statistic of -0.5334).
Contribution of endogeneous variables on CO2:
Income contribution: As income increases by 1%, air pollution indicator CO2 increases by
59.3396 % and after that decreases by 0.0006 % (in line with the theory of EKC where at an
early stage as income increases pollution also increases and at the latter stage as income
increases pollution decreases)(Grossman and Krueger, 1992; Toru Iwami, 2001; Cole , 2004), for
China and increases by 0.4633% and after that decreases by 3.0907% for Malaysia. This is
following the theory that as income increases pollution also increases at the early stage and
decreases at the latter stage.
Population density contribution: Population density does not contribute to a higher pollution in
Malaysia in fact as population density increases pollution decreases for the indicator of air
pollution. Besides, in the case of China population density does contribute to a higher pollution
in which as population density increases pollution also increases for CO2.
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Table 4.6 Estimated Regression Results for Income (Eq-2)
Independent variables
Intercept
log CO2
log L (labor)
log K (physical capital)
log NX (net export)
log FDI (foreign direct
investment)
Time trend, T2
Time trend, T3
Time trend, T4
Adjusted R-square
Dependent variable
China
log Y (GDP)
475.9710 (0.733259)
-0.000635 (-2.691768)**
White 01355
0.001979 (1.069175)
White 0.1895
2.035087 (22.98243)***
White 1.4925
-0.004171 (-1.306574)
White 1.7855
0.039366 (4.718005)***
White 0.3298
0.9628 (3.8936)***
White 1.0458
0.8085 (3.9635)***
White 1.2266
0.8563 (4.5662)***
White 1.0398
0.997261
Dependent variable
Malaysia
log Y (GDP)
15101.66 (4.383679)***
-0.629119 (-2.501090)**
White 0.0985
2.355599 (2.339944)**
White 0.1897
2.534014 (13.03908)***
White 1.0863
2.479752 (16.08341)***
White 1.4229
0.179987 (1.629069)
White 1.5859
0.7421 (3.1255)***
White 1.1163
0.8334 (3.0021)***
White 1.3652
0.6542 (2.9585)***
White 1.0855
0.995241
note: 1. t-statistics in parentheses.
2. *** represents P<0.01; **, P<0.05; *, P<0.1.
The indicator of pollutant emissions, CO2 (coefficient of -0.000635 for China and
coefficient of -0.629119 for Malaysia) is negatively associated to the GDP and CO2 (t-statistic of
-2.691768 for China and t-statistic of -2.501090 for Malaysia) indicate significance on income.
This is coherent with the theory that as pollution level increases income reduces. Thus, this study
can conclude that there is a significant effect of air pollutant on income in Malaysia and China. It
could be due to CO2 as the primary shares of air pollutant that decrease income in Malaysia and
China which primarily comes from industrial activities.
Most of the approximated coefficients are coherent with the anticipated signs and
significant. The normal inputs such as labor (coefficient of 0.001979 for China and coefficient of
2.355599 for Malaysia) and physical capital (coefficient of 2.035087, t-statistic of 22.98243 for
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China and coefficient of 2.534014, t-statistic of 13.03908 for Malaysia) are positively and
significantly related to the Gross Domestic Product in the income equation. In the case of China,
the share of human capital in production is not significant (t-statistic of 1.069175) on income in
the model although labor is an important factor in production.
The coefficient of net export shows a positive relationship with income (coefficient of
2.479752) and is highly significant effect on income (t-statistic of 16.08341) in Malaysia but not
in China. Foreign direct investment has a positive relationship with income (coefficient of
0.039366 for China and coefficient of 0.179987 for Malaysia) and has a significant effect on
income (t-statistic of 4.718005) only for China but not for Malaysia.
5. CONCLUSION
In the course of fulfilling the research objectives of this study, various econometric tests
have been employed such as Hausman test. For each of the study objectives, this study has
identified a specific method. the study summarizes the findings of the objectives as follows.
Endogeneity of Different Indicators of Pollution and Economic Growth
An analysis was carried out to measure the relationship between economic growth and CO2 in
Malaysia and China for the period from 1965 to 2010. First, to examine the exogeneity of
income in the pollution equations this study employed a Hausman test. The test shows that there
exist simultaneous relationship between CO2 and income for both Malaysia and China in the
estimated pollution equations in the model. Thus, the two stage least square method will be
employed.
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The EKC Really Fits to Air Pollution
This study examines whether or not the EKC hypothesis applies to Malaysia. For the indicator of
air pollution, CO2 the coefficients of log Y are positive, and that all the coefficients of the
squared term, (log Y) 2, are negative as can be observed. Thus, the EKC hypothesis is supported
by the cases of air pollution CO2 in Malaysia and China.
Based on the findings, the following conclusions can be drawn:
From previous literature, the economy and its environment are jointly determined. As a first step
towards better understanding of the income-environment relationship, this study incorporates
explicitly the simultaneity between income and pollution. By using the existing theoretical
framework, this study uses the theory that economic growth and pollution are jointly determined.
According to Shen (2006), if simultaneity between income and pollution does exist, examining
the relationship between these two variables only by an ordinarily employed single polynomial
equation produces biased and not consistent estimates. Concerning this issue, this study firstly
constructs a simultaneous equations model. Secondly, in order to measure the presence of
simultaneous relationship between income and pollution exists; a Hausman test is employed. In
CO2 pollutants indicator, the outcomes show that the simultaneity between income and pollutant
emission do exists in both countries Malaysia and China. Therefore, to estimate the simultaneous
equations model, the two stage least square method is applied. This issue indicates that in future
EKC works, the requirement of examining the simultaneity between income and pollution should
be considered. In CO2 pollution indicators emissions in Malaysia and China, the EKC
relationship is found.
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