Income and Co2 in China and Malaysia from Environmental Kuznets Curve (Ekc) Perspective

2012Cambridge Business & Economics ConferenceISBN : 9780974211428

Halimahton Binti Borhan

Faculty Business Management, Universiti Teknologi Mara, Malaysia

012-2951627; ,

Mizan Bin Hitam

Faculty of Architecture, Planning and Surveying, Universiti Teknologi Mara, Malaysia

06-2857001;

Rozita Naina Mohamed

Faculty Business Management, Universiti Teknologi Mara, Malaysia

012-9741676;

Mazzini Muda

Faculty Business Management, Universiti Teknologi Mara, Malaysia

012-9151964;

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 growthwas 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.

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).

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:

To test the endogeneity of CO2 and income in Malaysia and China.
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 growthwas 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 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 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 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.

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

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.

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.

June 27-28, 2012

Cambridge, UK

2012Cambridge Business & Economics ConferenceISBN : 9780974211428

Table 4.1: Unit Root Test for Malaysia

DF/ADF Unit Root Test / PP Unit Root Test
MALAYSIA / Level / First Difference / Level / First Difference
No Trend / With Trend / No Trend / With Trend / No Trend / With Trend / No Trend / With Trend
log CO / -3.022068 (9) / -3.292210 (9) / -7.353248 (9)*** / -7.241739 (9)*** / -3.173014 (9) / -3.358529 (9) / -7.435155 (9)*** / -7.314398 (9)***
Iog Y / 0.557013 (9) / -2.200347 (9) / -4.756360 (9)** / -4.918133 (9)** / -1.180566 (9) / -2.620569 (9) / -9.010324 (9)*** / -11.45953(9)***
log NX / -0.371258 (9) / -2.303392 (9) / -3.727737 (9)** / -3.937671 (9)** / -1.448366 (9) / -2.541586 (9) / -6.111009 (9)*** / -6.600924 (9)***
log PD / -1.260739 (9) / -1.166623 (9) / -6.258719 (9)*** / -6.391704 (9)*** / -1.287638 (9) / -1.166623 (9) / -6.258712 (9)*** / -6.391182 (9)***
Iog L / -2.511766 (9) / -2.807611 (9) / -10.36940 (9)*** / -10.23899 (9)*** / -2.460849 (9) / -2.807611 (9) / -18.34788 (9)*** / -18.55401 (9)***
(IogY)2 / 0.557013 (9) / -2.200347 (9) / -2.756360 (9)* / -4.918133 (9)** / -1.180566 (9) / -2.620569 (9) / -9.010324 (9)*** / -11.45953 (9)***
log FDI / -1.913368 (9) / -4.062863 (9) / -11.04183 (9)*** / -11.03195 (9)*** / -2.735569 (9) / -4.081312 (9) / -15.17223 (9)*** / -26.67061 (9)***
log K / -2.897551 (9) / -2.913068 (9) / -5.625608 (9)*** / -5.592981 (9)*** / -2.141588 (9) / -2.376980 (9) / -6.505356 (9)*** / -6.684979 (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.

Table 4.2: Unit Root Test for China

DF/ADF Unit Root Test / PP Unit Root Test
MALAYSIA / Level / First Difference / Level / First Difference
No Trend / With Trend / No Trend / With Trend / No Trend / With Trend / No Trend / With Trend
log CO / -2.175750 (9) / -2.322282 (9) / -6.036648 (9)*** / -6.036398 (9)*** / -2.921410 (9) / -2.892191 (9) / -6.326172 (9)*** / -6.309360 (9)***
Iog Y / 0.557013 (9) / -2.200347 (9) / -4.756360 (9)** / -4.918133 (9)** / -1.180566 (9) / -2.620569 (9) / -9.010324 (9)*** / -11.45953(9)***
log NX / -2.015850 (9) / -2.290658 (9) / -6.333842 (9)*** / -6.255791 (9)*** / -2.135208 (9) / -2.462740 (9) / -6.333842 (9)*** / -6.255791 (9)***
log PD / -1.260739 (9) / -1.166623 (9) / -6.258719 (9)*** / -6.391704 (9)*** / -1.287638 (9) / -1.166623 (9) / -6.258712 (9)*** / -6.391182 (9)***
Iog L / -2.511766 (9) / -2.807611 (9) / -10.36940 (9)*** / -10.23899 (9)*** / -2.460849 (9) / -2.807611 (9) / -18.34788 (9)*** / -18.55401 (9)***
(IogY)2 / 0.557013 (9) / -2.200347 (9) / -2.756360 (9)* / -4.918133 (9)** / -1.180566 (9) / -2.620569 (9) / -9.010324 (9)*** / -11.45953 (9)***
log FDI / -1.913368 (9) / -4.062863 (9) / -11.04183 (9)*** / -11.03195 (9)*** / -2.735569 (9) / -4.081312 (9) / -15.17223 (9)*** / -26.67061 (9)***
log K / -2.897551 (9) / -2.913068 (9) / -5.625608 (9)*** / -5.592981 (9)*** / -2.141588 (9) / -2.376980 (9) / -6.505356 (9)*** / -6.684979 (9)***

June 27-28, 2012

Cambridge, UK

2012Cambridge Business & Economics ConferenceISBN : 9780974211428

The result indicates that all of the data for Malaysiaand Chinaare 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 / Hypothesis / Eigen Value / Trace Statistic / Critical Value (5%) / Max-Eigen Value / Critical Value (5%)
Equation 1( CO ) / 7 / None / 0.589968** / 111.0066 / 79.34145 / 36.55236 / 37.16359
At most 1 / 0.488439** / 74.45427 / 55.24578 / 27.48186 / 30.81507
At most 2 / 0.452178** / 46.97241 / 35.01090 / 24.67397** / 24.25202
Equation 2 / 8 / None / 0.694201** / 103.9169 / 95.75366 / 48.57791** / 40.07757
At most 1 / 0.450518 / 55.33901 / 69.81889 / 24.54994 / 33.87687
At most 2 / 0.280353 / 30.78907 / 47.85613 / 13.48879 / 27.58434
Equation 3 / 9 / None / 0.810173** / 265.6834 / 159.5297 / 68.12724** / 52.36261
At most 1 / 0.793182** / 197.5561 / 125.6154 / 64.61248** / 46.23142
At most 2 / 0.736206** / 132.9437 / 95.75366 / 54.63605** / 40.07757

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.