"The impact of foreign direct investments on the CO2 emissions in Southeast Asian countries"

The impact of foreign direct investments on VUONG DUC HOANG QUAN Ho Chi Minh City Institute for Development Studies – quanv.biz@gmail.com BUI HOANG NGOC University of Labour Social Affa

The impact of foreign direct investments on

VUONG DUC HOANG QUAN

Ho Chi Minh City Institute for Development Studies – quanv.biz@gmail.com

BUI HOANG NGOC

University of Labour Social Affairs HOANG THI BICH DIEN

University of Labour Social Affairs

Abstract

The pressure to increase the national income per capita sometimes puts the Government of developing countries into a dilemma between economic growth and environmental protection

On the basis of the original model developed by Kuznets (1955) regarding the relationship between a country’s CO 2 emissions to the environment and its average income, an empirical test applying the System Generalized Method of Moments (S-GMM) and Pooled Mean Group (PMG) regression to data from 7 Southeast Asian countries during a period of twenty years from 1995

to 2014 is conducted The results find strong statistical evidence for the existence of an

inverted-U effect as the theory assumes Moreover, the impact of the factor of FDI attraction on the environment which is added to the Kuznets’ original model is also confirmed by the study

Keywords: FDI; Environmental Kuznets Curve (EKC); CO2 emission; Southeast Asian countries

1 Introduction

The capital needed for investment in infrastructure, social security, education, health care, national defense, and so on is always great in the process of development Economic theory has shown that capital for developing countries or developing territories is of particular important significance in the early stages of development, which satisfies immediate needs, also helps to promote the efficiency of other sources of

capital such as resource capital, human capital, scientific and technical capital, etc So then, foreign direct investment (FDI) is considered as a priority capital source

In addition, the positive contribution of economic growth based on the overall assessment of the World Economic Forum (WEF) 2010/2011, the FDI capital has also had negative impacts on the host countries The typical negatives are: Natural resources being over-exploited; causing the difficulties for the development of domestic enterprises; a national identity being abrasive; the risk reducing the quality of life caused

by excessive environmental pollution

The Association of SouthEast Asian Nations (ASEAN) which is abbreviated as Asean was established in 1967 The five original members have now expanded to eleven countries including Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste and Vietnam According to the International Monetary Fund (IMF), the amount of FDI being poured into ASEAN countries is still increasing Asean is considered a fairly attractive destination for foreign investors with annual FDI capital exceeding $ 100 billion which not many regions in the world are able

to do If only considering per capita income, the economic development model of Singapore and Malaysia are the models worth learning However, considering the quality of life is another matter because the per capita CO2 emissions of Singapore and Malaysia are more than twice the per capita CO2 emissions of the world

There have been empirical studies on the inverted-U letter effect in the Asean region, but some challenges need still to be studied further: specifically finding the answer to the bending point of the U figure (ie, the point transferring gradually from the polluted environment to improved environment) This study is divided into 5 sections After the introduction, section 2 presents the summary theoretical background and overview of previous studies on the topic of this study Section 3 introduces research models, data sources, and methods of analysis and data processing The results of the study, discussion of the results and some management policy implications will be presented in section 4 The final section will be the conclusions and limitations of the study and some suggestions for the next research

2 Theoretical background and overview of previous studies

2.1 Theoretical background

Describing the relationship between economic growth and environmental quality (represented by the level of environmental pollution), Kuznets (1995) [1] has given the idea of an inverted U curve (EKC - Environmental Kuznets Curve) He said that in the first stage of economic growth, the Government has tended to loosen environmental regulations to attract FDI because of the high growth pressure and the capital accumulation scale of the whole economy limited Thanks to FDI, the average income has improved; however, with the increase in average income, the environmental pollution has also increased At this stage, the large fuel consumption, has resulted in huge amounts of CO2 emissions into the environment because countries have mainly exploited natural resources in raw form, production technology has been backward, management level has been weak

As the average income increases to some extent and life is improved, people begin to perceive the importance of quality of life and the quality of the surrounding ecological environment In addition, along with the improved economic conditions, the economic integration and the advantage of the later developed countries will help nations, businesses and citizens to be able to choose green, clean technologies and friendly with environment Environmental pollution will decelerate, reverse and then reduce, so then environmental quality will be enhanced

2.2 Overview of previous studies

The relationship between economic growth and environmental quality has been considered and studied in many countries/ regions around the world Pham Xuan Hoan

et al., (2014) [2] have used balance panel data with two variables including average GDP and average GDP2, regression estimation using the Fixed Effect Model (FEM) and the Random Effect Model (REM) to test the Kuznets curve of environment for the ten Asean countries during the period 1985-2010 confirmed the existence of an inverted-U effect However, this study has ignored one important factor that is the relationship between the CO2 emissions of the current year and the CO2 emissions of previous years

Tran Thi Tuan Anh (2016) [3] extended the model to include four variables under the study: average GDP, average GDP2, trade openness, and density population Applying

the Spatial Regression to Dynamic Panel Data (DPD) to the data of eight Asean countries during the period 1994-2011, the author found the statistical evidence of the environmental existence of the inverted-U shape effect in the Southeast Asian countries, and the impact of CO2 emissions last year to the present year was credible However, both the studies ignored the impact of FDI and the industrial share of GDP, according to Anis Omri et al (2014) [4] and Huiming Zhu et al., (2016) [5], it is really unfortunate that the sector that emits the most of CO2 is the industry, and in the early stages of economic growth, the industry is mainly dominated by foreign invested enterprises

In other regions of the world, the study of Shafik & Bondyupadhya (1997) [6] was performed for 149 countries during the period 1960-1990, with time-series data and cross-sectional data, these authors found that the deterioration of the environmental quality as rising average income, and the trends of the environmental quality to be better when the countries are richer Galeotti & Lanza (1999) [7] used panel data for 110 countries from 1970 to 1996 finding the existence of an inverted-U shape effect for the global environment They asserted that global pollution was still rising by the causes from the pressures of the rapidly growing per capita income of developing countries, and two factors of income and population played a decisive role in the amount of CO2

emissions

The study by Anis Omri et al (2014) used panel data for fifty-four countries around the world, and the study by Shenggang Ren et al., (2011) [8] for the Chinesse economy proved that CO2 emissions would be increased by FDI which was the second strongest impact factor after the previous year's CO2 emissions The study by Huiming Zhu et al., (2016) for five Asean countries including Indonesia, Malaysia, Philippines, Singapore, Thailand, and the study by Pao & Tsai (2010) [9] for the four BRIC countries including Brazil, Russian Federation, India and China which both of these studies confirmed to have the relationship between CO2 emissions, FDI and economic growth However, Dijkagraff & Vollebergh (2005) using data of OECD countries during the period

1960-1997 found evidences to deny the existence of the inverted-U shape effect of EKC “A new look” was the expression of Jungho Baek (2015) when testing the effect of EKC for five Asean countries including Indonesia, Malaysia, Philippines, Singapore and Thailand during the period 1981-2010 He did not find the inverted-U shape effect, but found the

U shape effect in the long-term cointegration relationship between GDP, GDP2, energy consumption and FDI and CO2 emissions into the environment This difference in the

results of the above studies on the existence of the inverted-U shape effect and the impact

of FDI on CO2 emissions in EKC curve testing has explained the necessity for having more experimental evidence of this relationship

3 Research methodology

3.1 Research models

To test the inverted-U shape effect or the relationship between CO2 emissions and per capita income, Ang (2008), Sharma (2011), and Anis Omri (2013) using the Cobb-Douglas production function has been suitable Especially, Anwar & Nguyen (2010) [14], and Anwar & Sun (2011) also added the FDI factor to the production function So, the Cobb-Douglas function is written as:

Where: Y is total production (the real value of all goods produced in a year (GDP)), A is total factor productivity, E is total Energy consumption, K is the capital input of the economy (domestic capital and FDI), L is labor input (the total number of person-hours worked in a year) α, β, λ is the contribution proportion of factors to the actual output When further research on the production function, Pereira & Pereire (2010) [15] proposed E = b.CO2, while Anis Omri et al (2014) suggested that K = c.FDI, so that Equation 1 can be rewritten into:

2 ( ) ( D )

Assumed that the economy is constant in scale (ie α + β + λ = 1), then divide both sides of the equation 2 for L to find the per capita income, Equation 2 is written:

2 D

b c e A

 (Equation 3)

Take the logarithm both sides of Equation 3 as follows:

Set a =log( b c A  ) and move the side, Equation 4 is represented in the form of panel data as follows:

0 1 2

L   L  L  (Equation 5)

To test the inverted-U shape effect, variable (GDP/L) squared have to be added, and

the control variables are increased into Equation 5 Tran Thi Tuan Anh (2016), using the

control variable as the openness of the economy and population density Huiming Zhu

et al., (2016), beside the openness variable, population density is also used the variable

of the industry ratio in GDP and total capitalization of the stock market Inheriting the

above studies, this paper proposes the model for this study is as follows:

2

LnCO capit     v  LnG Pcapit   LnG Pcapit   LnControl  e

(Model A) According to Bond (2002), data of CO2 emissions is often a persistent data series,

meaning that the current year's emissions are strongly correlated with previous years’

emissions Ignoring this effect may cause model A to be endogenous by omitting the

variable, so the paper uses Model B to study the inverted-U shape effect as follows:

2

2 ait ( 0 i) 2 ai t, 1 1 D ait 2 D ait it it

LnCO capit    v LnCO capit  LnG Pcapit LnG Pcapit LnControl e

(Model B)

Where: i = 1,2,.,7 denotes the country: Cambodia, Indonesia, Malaysia, Philippines,

Singapore, Thailand, and Vietnam

vi: denotes individual characteristics of each country It means it   vi eit

t: denotes the time period (from 1995 to 2014)

Controlit: denotes control variables respectively other factors impacting on CO2

emissions (including FDI variable, Hour variable, Open variable and Industry variable)

The paper uses more the Hour variable (average number of working hours)

because in addition to the mainly factor emitting CO2 is the industrial sector and the FDI

sector, increasing the number of working hours will also create emissions higher than

normal Moreover, due to the characteristics of sectors, the industry sector will often

have to increase the number of working hours than other sectors, so it is reasonable to

add more the average working hour variable to be a control variable

Table 1

Conventions of Variables

GDPbq Per capita income (calculated by the fixed

GDP2bq Per capita income squared Million people WB FDI Number of FDI per capita (calculated by FDI

Hour The average number of working hours by an

3.2 Research methodology and data

Most studies on EKC before 2000 used time-series and cross-sectional data This is only suitable at that time because time-data requires a long observation, and cross-sectional data is not a reflection of the continuity of observations by having to cut at certain points Jodson (1995) argues that if a study does not utilize all of the time and spatial aspect of data, the study wastes a lot of information possibly provided by data4

Panel data that later evolved to address those disadvantages, but the lumped impact

estimate (Pooled) did not account for the differences in special characteristics Estimated

by fixed effects (FE), random effects (RE) will be dictated when the model has a short time series t and large space i (Judson et al., 1996)

According to Bond (2002) [10], data of FDI in terms of CO2 emissions are usually persistent time series, ie, the amount of the attracted FDI, CO2 emissions in the following years are often the very powerful relationship with data from previous years, so the synchronism of this factor should not be neglected in this study model After Hansen [11] published the Generalized Method of Moments (GMM) in 1982, Arellano & Bond

4 Tran Tho Dat (2011), The Role of Human Capital in Growth Models, Economic Research No 393

(1991) [12] applied GMM to the dynamic table model to improve the firmness and the effectiveness of the DPD (Dynamic Panel Data) model However, the GMM method also has limitations5: (i) the slope coefficients vary by panel unit; (ii) the short-term dynamic characteristics and long-term coherence are not shown

Therefore, in this study, the authors used both the Generalized Method of Moments (GMM) and the Pooled Mean Group Regression (PMG) estimation method based on balanced panel data for seven countries including Cambodia, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam for 20 years from 1995 to 2014 to determine the inverted-U shape effect of the Kuznets curve that actually exists in the long term ? And in the short term, what factors impact on the CO2 emissions of the seven Southeast Asia countries? Data collected from three reliable sources are the International Energy Agency (IEA) and the World Bank (WB), the United Nations Conference on Trade and Development (UNCTAD) Myanmar, Laos, Brunei, and Timor-Leste have to be eliminated because the data of these four countries is lacking, especially the lack of data on working time Laos and Timor-Leste also lack import and export data,

so it is impossible to calculate the openness of the economy

4 Results and discussion

4.1 Descriptive statistics

According to the International Energy Agency (IEA), the annual emissions of the world are relatively stable and rising slightly However, in Southeast Asia, the average level of CO2 emissions of the countries varies considerably Singapore and Malaysia have been two times higher than the world average of CO2 emissions, in the last 20 years, Singapore has been trying to decrease greenhouse gases, but CO2 emissions of Malaysia has been still rising In Cambodia, Indonesia, Philippines, Thailand and Vietnam, though

CO2 emissions have tended to rise, but remain below the world average However, this does not mean that these five countries are not threatened by air pollution, which has occurred in some concentrated industrial parks and large urban areas where average statistics can not reflect the details

5 Nguyen Minh Tien (2015), DGMM and PMG Regression with Panel Data, Foreign Economic Relations Journal, No

11, 40-48

Figure 1 Average CO2 Emissions of the World and 7 ASEAN Countries

Table 2 describes the average value of variables in the model by each country, and Singapore is the country with the highest GDP per capita at $ 62,500 per year and also the country that attracts the highest average FDI per capita reaching $ 6,000 per year Cambodia and Vietnam are the two countries with the lowest average income, but also the two countries in which labor have to work with the most of the average hours per year

Table 2

Average Values by Country

Variables Indonesia Cambodia Malaysia Philippines Singapore Thailand Vietnam

4.2 Research results

The time series properties of the variables in Model B are checked through five types

of panel unit root test: LLC, Breitung, IPS, ADF and PP tests Both LLC and Breitung test assume that there is a common unit root process across the cross-sections For these tests, the null hypothesis is that there is a unit root, while the alternative hypothesis is

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

Average CO 2 Emissions of the

World

5.00 10.00 15.00

Average CO 2 Emissions of ASEAN Countries

Vietnam

that there is no unit root The other tests assume that there are individual unit root process across the cross-sections The null hypothesis is that there is a unit root On balance, the results of table 3 demonstrade that all of the series in Model B appear to contain a panel unit root in their levels but are stationary in their first differences, indicating that they are integrated at order one, i.e., 1st

Table 3

Unit Test Results of Each Variable

Variable

Names

Common Unit Root Individual Unit Root

Level 1 st diff Level 1 st diff Level 1 st diff Level 1 st diff Level 1 st diff

CO 2 bp@ 1.131 -2.77*** 1.395 -0.405 3.004 -4.84*** 4.080 49.97*** 3.463 994.71*** GDPbp@@ 4.598 -3.30*** -0.510 -3.71*** 7.695 -2.70*** 0.132 32.11*** 0.035 45.80*** GDPbq 2 @ 7.734 -8.71*** 6.419 -1.459* 9.155 -6.57*** 0.045 21.29* 0.011 36.32*** FDI@@ -2.178** -4.13*** 0.542 0.1142 -1.437* -5.77*** 23.22* 57.75*** 29.77*** 107.67*** Hour@ -1.860** -2.72*** 1.497 -0.461 -0.467 -4.22*** 19.43 45.69*** 21.75* 143.63*** Open@@ -1.359* -3.61*** 0.668 -2.44*** -0.777 -3.95*** 16.36 40.30*** 19.63 97.61*** Industry@ -1.660** -1.77** 1.661 -3.41*** 0.291 -2.77*** 17.47 30.18*** 17.80 86.07***

Note: @ not trending, @@ trending *,**,*** respectively with the significance level of 10%, 5%, 1%

However, Anis Omri et al (2014), testing the U-curve shape inverted effect of the EKC for 54 countries in the world during the period 1990-2011 found that the stop test was only important for regression result, and it was difficult to ensure the stop of the actual data series because of large geographic distribution In order to make this study reliable, the paper uses three models simultaneously

Model 1 uses the original data, and it can be estimated using the OLS method

(Pooled), fixed-effect (FE), and random-effect (RE) with balance panel data

Model 2 uses the original data; the regression results are estimated by using the

System Generalized Method of Moments (S-GMM)

Model 3 uses 1st differential data, using PMG (Pooled Mean Group Regression) to estimate