Abstract This study investigates the relationship between CO2

Abstract
This study investigates the relationship between CO2 (carbon dioxide) emissions, energy consumption, economic development and FDI (foreign direct investment) in six QISMUT countries: Qatar, Indonesia, Saudi Arabia, Malaysia, United Arab Emirates and Turkey.

Covering the period between 1990 and 2014. The results indicate that there exists strong bidirectional relat?onsh?p between emission and FDI and unidirectional strong causality running from outturn to FDI. The evidence seems to support the pollution haven and both the halo and scale effects. FDI appears to increase CO2 emissions in some of the countries, while the opposite impact can be observed in others. The most common unidirectional Granger causality relationships run from the other variables to CO2 emissions, with different variables Granger causing CO2 emissions in different countries, and from GDP (gross domestic product) to FDI. Granger causality running to CO2 emissions appears more likely in the countries where the evidence supports the environmental Kuznets curve hypothesis. Otherwise, the causality relationships vary greatly between the countries, making it impossible to give any universal policy recommendations.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Overall, the method of managing both energy demand and FDI and increasing both investment in the energy supply and energy efficiency to reduce CO2 emissions and without compromising a nation’s competitiveness can be adopted by energy-dependent QISMUT countries.

Keywords: Energy Consumption; FDI Inflows; Economic Growth CO2 emissions panel co-integration QISMUT Countries.

CHAPTER ONE
1. Introduction
Economic development today is global. Many companies are participating in the global distribution of investiture, and many nations are encouraging the use of foreign investment to promote their economic growth. However, the environmental problems hidden behind this situation should not be overlooked. Presently, air contamination and global climate change which caused by the greenhouse have become the focus of International concern. The Intergovernmental Panel on Climate Change (IPCC) and the Stern report both demonstrate that the most important environmental problem of our age is global warming. CO2 is considered to be the primary greenhouse gas responsible for global warming, and its regulation has become an important intergovernmental issue. The objective of the 1997 Kyoto protocol was to reduce greenhouse gases (GHG), which cause climate change, and it demanded a reduction of GHG emanation to 5.2% lower berth than the 1990 level during the period from 2008 to 2012. This came into force in 2005. Though QISMUT countries (Qatar, Indonesia, Saudi Arabia Malaysia, Unites Arab Emirates and Turkey) signed the Kyoto protocol to curb emission levels, there are still environmental concerns given the area’s recent economic growth. The relationship between these variables has been a topic of immerse academic work and research over several years. Some of this studies lay emphasis on different countries, time period, and technique of modeling which are used to determine the links between FDI, energy consumption GDP Carbon emission. Base on the FDI inflow and economic growth hypothesis, FDI can stimulate growth for host countries by growing the capital stock, creating new job opportunities and transfer of technology. FDI inflow can also trigger energy consumption through the expansion of industries transportation and manufacturing process. Also FDI can also increase the rate of CO2 emission as a result of increasing in new industries that came into existence. By and large there is a global concern regarding this aspect of the environment. As noted in the year 2015 the global percentage of CO2 emission by Qatar was 0.25%, Indonesia 1.39%, Saudi Arabia 1.40% Malaysia 0.68% UAE 0.55% and Turkey 0.99% this figures where obtain from the world metrological organization. The contribution of this paper to the existing literature is expressed by giving the first integrated approach to examine the three-way linkages between FDI inflows, energy consumption and economic growth in the QISMUT Countries. The model in this paper enable us to examine the impact of FDI on economic growth, energy consumption and CO2 emission. Additionally, most studies discuss environmental pollution and Inflow of foreign direct investment (FDI) across a number of developing as well as developed countries. QISMUT countries are the largest and fast-growing emerging economies; their influence on the world economy is much higher than that of any other smaller developing country. With this trend, the QISMUT have attracted increasingly more FDI inflow and have developed rapidly, beyond the realms of imagination. The six QISMUT economics together have attracted over $790.51 billion as of 2016 data. With Saudi Arabia and Indonesia having a larger chunk of inflow of investment. There is a large amount of literature to explain the causal relationship between energy usage and GDP, FDI and GDP, as well as some of CO2 expelling, energy usage and GDP using a multivariate model over the past decades. However, there is no systematic time series investigation so far analyzing the relationship between pollutant emissions, energy use, outturn and FDI by QISMUT countries. This research is intended to fill the gap in the literature. The dynamic interrelationship in the output-energy-FDI-environment nexus is analyzed by applying a jury co-integration proficiency and a control board link in the short and long-run. The remainder of this theme is organized in the following fashion. Section 2 presents a brief review article of the economic, energy use, environmental pollution and FDI profile of the countries. Section 3 schema the model and estimate methodology, and the econometric results are presented and discussed in the fourth section. Some policy implications and decision are provided in the final section.

CHAPTER TWO
2.1 Literature review.
There is extensive literature on the nexus of Energy use, FDI Inflows and economic growth that have used panel data modeling ideas. Therefore, it should be noted that the modeling techniques with panel data are relatively new compared to modeling techniques based on time series data which has been in use. Generally, the empirical studies on the relationship between FDI inflows energy consumption and economic growth can be divided into two major portion. The first group focal point is on the area of particular studies, while the other group focuses on multi-country studies. Other studies have documented that economic growth attract more FDI inflows in China, India, Singapore and Nigerian. And in the multi country studies Several studies have developed the direction of causality between FDI inflow, energy consumption and economic growing for multi-country studies. For example, Tsai (1994) examined the links between FDI and economic growth for 62 Nation and for 51 Nation and uncovering that FDI promotes economic growth and, in return, economic growth is viewed as a mechanism to attract FDI. Additionally Jensen and the World bank have asserted that although financial progress may enhance economic growth, it may result in more industrial pollution and environmental degradation. Tamazian et al. (2009) found that a higher degree of economic and financial development decreases environmental degradation. In this study, we employed FDI inflows as an indicator of financial development.

The impact of FDI (foreign direct investment) on the host country’s environment has also been a subject of debate. Two conflicting hypotheses have been presented in previous studies: the pollution haven hypothesis and the halo effect hypothesis. According to the halo effect hypothesis, the presence of foreign investors will spur positive environmental spill-overs to the host country because MNCs (multinational companies) have more advanced technology than their domestic counterparts and will tend to disseminate cleaner technology that will be less harmful to the environment. In contrast, the pollution haven hypothesis postulates that MNCs will flock more into countries where environmental regulations are less strict This strategy might harm the environment in the host country if the issue is not taken seriously. The results are both theoretically and empirically mixed. However, there is plenty of evidence suggesting that foreign MNCs tend to relocate the dirt industries in developing countries with lax environmental regulations rather than in developed countries, where the environmental regulations are very strict. Therefore, depending on the nature of and the motives behind the MNCs, FDI can cause more emissions in the host countries. The effect of FDI on GHG emissions in particular has also been a subject of debate in the extant literature. The previous studies see, e.g., Hoffmann et al. and Hassaballa have provided coherent justifications for using GHG (particularly CO2) emissions as a proxy for pollution in general. According to them, CO2 is a primary source of global warming, and the variable is also highly correlated with such local pollutants such as nitrogen oxide and Sulphur dioxide.

The last category of the literature employs a multivariate framework to examine the relationship between the variables by incorporating all the variables of interest in a single equation. The first strand of this literature focuses on the links between CO2 emissions, energy consumption, and economic development, with mixed results Kivyiro, P., ; Arminen, H. (2014) . Another strand in this category adds FDI to the analysis. Some of the previous studies are cross country panel studies, while other studies focus on individual countries.

To summarize our literature review there has been numerous number of research undertaken to examine the relationship and effect of FDI energy consumption and carbon emission and economic growth. However the existing research have failed to provide clear grounds on the centering of causality between these four variables. Therefore, our past literature suggests that the empirical results of the previous studies are inconclusive on the link between FDI inflows, economic increment and energy consumption and CO2 emission. at the same dimension in the QISMUT countries.

2.2 Data

The data set of the six countries have been obtained in this study, annual data covering the period from 1990 to 2014 collected from the world bank data the FDI, CO2, E, and GDP are the variable included in our estimation models and are expressed in their natural logarithmic form

Table 1. Data

Data Source Code

Foreign Direct Investment
World bank data
FDI

Gross Domestic Product
World bank data
GDP

Energy consumption
World bank data
E

Carbon dioxide
World bank data
CO2

Table 2. Descriptive statistics

LFDI LE LGDP LCO2
Mean 22.27 8.21 26.22 2.28
Median 28.81 8.22 26.22 2.23
Max 24.39 9.99 27.58 4.20
Min 16.79 6.60 23.58 0.21
SD 1.50 1.09 0.92 1.16
Jargue Bera 31.59 6.52 11.99 5.13
Probability 0.00 0.038 0.0024 0.076

And we can define the variables used in the studies as follows Foreign direct investment and according to study.com, “Foreign direct investment (FDI) is an investment in a business by an investor from another country for which the foreign investor has control over the company purchased. The Organization of Economic Cooperation and Development (OECD) defines it as control or owning 10% or more of the business. Businesses that make foreign direct investments are often called multinational corporations (MNCs).” Energy consumption the United States energy information administration defines it as the use of energy as a source of heat or power or as a raw material input to a manufacturing process. When we talk about GDP that is “Gross domestic product it is the best way to measure a country’s economy. GDP is the total value of everything produced by all the people and companies in the country. It doesn’t matter if they are citizens or foreign-owned companies.” And our final variable which is CO2 emission can be defined as Carbon dioxide (CO2) makes up the largest share of “greenhouse gases”. It is the release of carbon into the atmosphere. To talk about carbon emissions is simply to talk of greenhouse gas emissions; the main contributors to climate change. “Since greenhouse gas emissions are often calculated as carbon dioxide equivalents, they are often referred to as carbon emissions” when discussing global warming or the greenhouse effect. Since the industrial revolution the burning of fossil fuels has increased, which directly correlates to the increase of carbon dioxide levels in our atmosphere and thus the rapid increase of global warming.

CHAPTER THREE
3. Model and methodology
3.1 Model
The basis of our empirical investigating is on the impact of foreign direct investment (FDI) on the surrounding, as reflected in Carbonic acid gas emissions. Our analysis is carried out in a multivariate circumstance incorporating in one equation variables responsible for Carbon dioxide emissions, such as the Environmental Kuznets curve (EKC) hypothesis and vigor use. Thus, we examine empirically the dynamic relationship between environmental degradation (CO2 emissions) and FDI, energy use and EKC hypothesis and GDP, since the relevant results in the literature appear controversial and ambiguous.

The Environmental Kuznets curve (EKC) hypothesis is a theoretical tool depicting the connection between environmental and economic variables. Following the pioneering work of Grossman and Krueger (1991) who found evidence of an inverted U-shaped relationship between real income and environmental degradation, the empirical evidence provided since then appears to be mixed (Ren et al., 2014; Stern, 2004; Dinda, (2004), although the majority of research points to the cogency of the EKC hypothesis. Foreign Direct Investing and Environmental degrading. Following the empirical literature in energy economics, it is plausible to form a long-run relationship between Carbon dioxide emission, energy consumption, FDI and economic growth in a linear logarithm quadratic form, with a view of testing the validity of the EKC hypothesis, as follows:
+ ………………………………………Equation (1)
Where t denotes the time period I denotes the country and eit is assume to be serially uncorrected error term The variables LFDI, LENG, LCO2, and LGDP represent the natural logarithm of Co2 emission the total energy use FDI net inflows and real GDP respectively. The expected signs of energy consumption and FDI are positive. Because a higher level of energy consumption should result in greater economic process and stimulate CO2 expelling while FDI inflows add local production activities, thereby increasing the use and consumption of resources, and cause more GHG emissions. Under the EKC hypothesis the signal of b3 is expected to be positive, whereas a negative sign is expected for b4. If the sign on the LGDP2 is to be of statistically insignificant, this indicates a monotonic increase in the relationship between per capita CO2 emissions and per capita income. Furthermore since this study is to investigate the interlinkage between foreign direct investment, energy consumption, CO2 emission and economic growth in six QISMUT countries by implementing recently developed panel techniques. D08 variable models were added to control the 2000’s late global economic crisis. Three separate models with different independent variables are employed throughout the study. The models, Equations (2), (3), and (4), are as follows:
where ?it denotes country-specific effects, and eit is the random disturbance error term, representing the deviations running from the long-run linkage toward short-run equilibrium;
i = 1,2,. . ,N are panel members, and t = 1,2,. . .,T stands for the time period.

= + ……………………………………………….. 2
= + …………………………………………………. 3
= + …………………………………………………. 4

In the empirical finding, we test for the availability of a long-run relationship among the time series variable (approximation of Eq. ((one)), and the use of FMOLS to captures the long run dynamics of the variable. The analysis is done in three various ways. The first step is to verify the order of integration for the variables because the various cointegration tests are valid only if the variables have the same order of integration. Five cases of unit root tests, Levin, Lin and Chu (LLC), Breitung, Im, Pesaran and Shinbone (IPS), a Fisher-type test using Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP), are employed. Zapata and Rambaldi have noted that if there is uncertainty as to whether the variables are I(0) or I(1) (integrated of order one).

3.2 Panel Cointegration

In the second step, when all series are integrated into the same order, the Johansen Fisher method are used to test cointegration between these variables, which are based on the estimated residue of Eq. (1). These tests can be classified as either falling within the property (panel testing) or between dimensions (grouping tests). These tests are all based on the residuals from Eq. (1) and are variant of the ADF and PP tests. The within-dimension tests pool the autoregressive coefficient across different variant of the panel. The between dimension tests are less restrictive than the within-dimension tests in the good sense that they allow for the heterogeneity of the parameters across countries. “The Fisher analysis follows the same basic path as the Pedroni tests but specifies cross section specific intercept and homogeneous coefficients during the first stage”. Additionally, the Fisher test is a combined one with Johansen. If cointegration exists among the variables, the ordinary least squares (OLS) method is applied to ensure that the estimate Eq. (1) does not lead to a spurious regression result. Furthermore, the parameters estimated by OLS are super-consistent. The b1, b2, b3 and b4 are the long-run energy consumption elasticity, FDI elasticity, real Gross domestic product elasticity and GDP2 elasticity, respectively.

CHAPTER FOUR
4. Empirical finding
The focal point of this study is to explore the relationship among FDI, energy consumption, real GDP and CO2 emission in QISMUT countries. The annual data are all obtained from the World Bank development indicators covering the period from 2000 to 2014. Per capita CO2 emission in metric tons per capita of carbon dioxide is measured by the consumption of energy. Per capita energy consumption is measured in (Kg of oil equivalent per capita). FDI is measured by the net inflow of foreign direct investment (in current US$ billion). Per capita real GDP is measured in US dollars.

Fig 1. Foreign direct investment (before taking logarithm) Constant US$ Billions

Fig 2. Per capita C02 emission (before taking logarithm) Metric tons of carbon dioxide per capita

Fig 1-4 shows the changing trends for each series of the QISMUT countries in which fig 1 shows that the six countries have shown significant increase in the FDI in recent years. Fig 2-4 shows the per capita level of UAE series for three variables (CO2, energy consumption and GDP) differs from the level of other five countries. In addition the Qatar series shows a different behavior from other countries, while other are on the rise it continue to take a slow pace. The other five countries have shown an almost monotonic increase over the entire time span.

Fig 3. Per capita energy consumption (before taking logarithm) Million Btu per person

Furthermore Turkey series has shown the greatest increase since from 2000. When we evaluate this series Turkey between 2005 to 2008 in GDP while UAE have shown that energy consumption and CO2 emission have been the highest however for FDI and GDP. UAE and Malaysia are moving at the same pace with a slight variation in investment and GDP over the time period.

Fig .4 Per capita real GDP (before taking logarithm) constant US $ Billions

Table 3
4.2 Result of panel unit root tests

Variable common unit root Individual unit root
LCC IPS ADF PP
Level 1st diff Level 1st diff. Level 1st diff. Level 1st diff
LCO2 -2.1392 -4.263 -2.1392 -4.25092 7.98971 40.4080 9.8960 92.6472
LE -2.4434 -1.954 0.36211 -2.61497 8.03159 27.4279 2.76443 89.7219
LFDI -2.9449 -2.541 -1.43833 -1.84766 22.4674 24.8302 17.7156 74.9965
LGDP -3.4928 -1.247 -0.02193 -2.23259 10.9119 23.5227 2.32420 56.2579

Panel unit roots and panel cointegration tests the time series properties of the variables in Eq. (1) are checked through four types of panel unit root tests: LLC, IPS, ADF and PP tests. Both LLC tests 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 that there is no unit root. The other tests assume that there are individual unit root processes across the cross-sections. For these tests, the null hypothesis is that there is a unit root, while the alternative hypothesis is that some cross sections do not have a unit root. On balance, the results of Table 3 demonstrate that all of the series in 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 ie I (1). To have same order of intergration variable I (1) allow us to check cointergration equation among variables therefore we perfumed Johansen Fisher cointergration test.

4.3 Johansen and Fisher combined cointegration test.
The Johansen Fisher test are used to test if variables are cointegrated, it is good practice to always test on the cointegrating vector to establish whether relevant restriction are rejected or not.
Table 4 Johansen and Fisher Cointegration Test

No of CE
Fisher Stat (trace test) Prob. Fisher Stat (from max-eigen test) Prob.

None
78.80
0.0000
65.10
0.0000

At most 1
48.58
0.0000
27.97
0.0005

At most 2
35.52
0.0000
27.11
0.0007

At most 3
23.83
0.0024
23.83
0.0024

None * is the null hypothesis
From the above statistic all the result indicates that there is cointegration among the variables. The P value has a 0.0000 percent which is less than 0.05 therefore we reject the null hypothesis, that there is no cointegration. At most 1, 2 and 3, all indicate no cointegration amongst all the variable meaning there is long run equation.

4.4 FMOLS Test
The fully modify ordinary least square test is carried out investigate the existence of a long run relationship among variables. And here in our result we obtained the following.

GDP is a significant variable to explain Foreign direct investment which has prob. of 0.0000 that is less than 5% and Energy consumption with a negative coefficient and a probability of 0.3791 is not a significant variable to explain foreign direct investment being over 5% and for the CO2 it is significant variable to explain foreign direct investment with a probability of less than 5%. Therefore, we can conclude that if there is an increase in a unit of FDI it will increase the GDP and CO2 emission respectively, however the use of energy cannot be explained.

Table 4 FMOLS Results

Independent Variable
Coefficient
Prob.

LGDP
1.355515
0.0000

LE
-1.862157
0.3791

LCO2
3.564770
0.0409
Dependent variable in the FMOLS model is FDI

As we found the cointegration relationship running from the LGDP, LE and LCO2 to LFDI are in the long run coefficient and we assume variables are non-stationary at level but if we convert them into first difference then they become stationary. This variable is cointegrated meaning there is a long run association they can move together.

CHAPTER FIVE

5. Conclusion and policy implications

In this study we investigate empirically the role of foreign direct investment inflows on environmental quality, as measured by CO2 emissions. Because the sole purpose of FDI is to maximize the amount of profit, investment under such a motive will bring certain negative effects to the host countries in addition to a positive impact on economic growth, of which the most important one is the impact on the environment. This paper has thus attempted to estimate the dynamic relationships between CO2 emission, energy consumption, FDI and economic growth for the QISMUT during the period between 2000 and 2014, using a panel cointegration framework. The main findings of the results indicate the following: (1) all series appear to be non-stationary in levels but stationary in the first differences for logarithmic form, and there exists a long-run equilibrium relationship between emissions, energy consumption, FDI and real output for the panel QISMUT countries; The empirical evidence reveals the integration of all variables at I (1), further con?rmed by panel unit root tests. Panel cointegration tests con-?rmed the long-run relationship between CO2 emissions, FDI, economic growth, and energy consumption. The FMOLS estimation analysis reveals a long run effect of GDP on FDI and effect of CO2 on FDI. And FDI reduces CO2 emissions at every stage of economic growth in QISMUT countries. On conducting the causality analysis, CO2emission and energy consumption were found to be interrelated. For sustaining a clean environment for the coming generations, renewable energy sources such as biomass can be used to reduce CO2 emissions. Demirbas et al. (2009) and Dincer and Rosen (2002) suggested that a renewable energy source such as biomass can contribute signi?cantly to the energy demands of modern developed and developing nations across the world. Indeed, renewable energy technologies and ef?cient energy utilization have been suggested for addressing the present environmental issues. For long-term energy generation, green renewable energy can be introduced to reduce the emission of CO2 from natural energy consumption such as oil, natural gas, and coal, Wind, geothermal heat, and sunlight are green renewable energy components. This ?nding is consistent with Xing and Kolstad (2002), Chang and Wang (2009),Beak and Koo (2009),Lee (2010),Pao andTsai (2011),and Zhang (2011). However, this is in contrast to Tamazianet al. (2009) and Tamazian and Rao (2010), who found that increased FDI reduces CO2emissions. FDI and economic growth can sometimes pro-mote technological innovation, in turn increasing energy ef?ciency with low CO2 emissions (List and Co, 2000; Tamazian et al., 2009).

More environmental preservation efforts are needed as FDI particularly increases pollution. These countries should enforce stringent environmental laws and encourage the use of environment-friendly technologies to enhance domestic production. The governing bodies should also stop licensing polluting industries such as cement and gypsum ?rms and foundries, which emit more CO2 emissions comparatively. Polluting ?rms must be offered more incentives for following legal emissions standards and considering economic and environmental factors during decision making. These polluting ?rms must be regularly assessed for their environmental impact. Increased public awareness on environmental preservation efforts, as well as the effects of hazardous wastes and polluting ?rms, is war-ranted. The social cost of environmental degradation should be included in the total cost of new investment projects in their feasibility studies. Firms must also be up to date with advanced and environment-friendly technologies, which can then be adopted QISMUT countries.

Finally, policies that reduce environmental pollution and regulate the FDI environment relationship should be enforced The negative causality from CO2 emissions to economic growth for all the panels seems to suggest that policymakers should implement policies that encourage environmental friendly energy production and utilization as well as green technologies in order to reduce carbon emissions and to promote economic growth simultaneously.

REFERENCES

Alves DCO, Bueno RD (2003). Short-run, long-run and cross elasticities of gasoline demand in Brazil. Energy Economics.
Al FarraH J,Abu-HijlehB.(2012) The potential role of nuclear energy in mitigating CO2 emissions in the United Arab Emirates Energy Policy;42:272–85.
Alkhathlan K,Javid M (2013). Energy consumption, carbon emissions and economic Growth in Saudi Arabia: an aggregate and disaggregate analysis. Energy Policy 62:1525–32.
Al-Mulali U,Ozturk I (2014).Are energy conservation policies effective without harming economic growth in the Gulf Cooperation Council Countries? Renew Sustain Energy Rev 38:639–50.
Anatasia, V (2015) The Causal Relationship between GDP, Exports, Energy Consumption, and CO2 in Thailand and Malaysia. Int. J. Econ. Perspective. 9, 37–48.
Apergis N, Payne JE (2010). Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy policy38:656–60.
Apergis N, Payne JE. (2011) “On the causal dynamics between renewable and non-Renewable energy consumption and economic growth in developed and developing countries”. Energy Syst 2:299–312.
Apergis N, Payne JE. (2011) “Renewable and non-renewable electricity consumption- Growth nexus: evidence from emerging market economies Apply Energy” 88:5226–30.
Breitung J. (2000) The local power of some unit root tests for panel data. Advances in Econometrics 15:61 -177.
Bank World. (2000) Is globalization causing a ‘race to the bottom’ in environmental standard? PREM economic policy group and development economics group. Washington DC: World Bank;
Carbon Dioxide Information Analysis Center (CDIAC); Boden, T.A.; Marland, G.; Andres, R.J. (2016) Global, Regional, and National Fossil-Fuel CO2 Emissions; Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy: Oak Ridge, TN, USA,
Coondoo D, Dinda S. (2008) The carbon dioxide emission and income: a temporal analysis of cross-country distributional patterns. Ecological Economics 65:375-385.
Choi I (2001) Unit Root tests for panel data. Journal of International Money and Finance 20:249-272.
Dickey, D.A., Fuller, W.A. (1979), Distribution of the Estimators for Auto Regressive Time Series witha Unit Root. Journal of the American Statistical Association, 74(366), 427-431.
Dimitrov, R.S. (2016) The Paris Agreement on Climate Change: Behind Closed Doors. Glob. Environ. Politics 16, 1–11.
Eskeland, G. S., & Harrison, A. E. (2003). Moving to greener pastures? Multinationals and the pollution haven hypothesis. Journal of development economics, 70(1), 1-23.
Friedl, B., Getzner, M.,2003. Determinants of CO2 emissions in a small open economy. Ecological Economics 45,133–148.
Goldman Sachs. (2003) Dreaming with BRICs: the path to 2050. Global Economics Paper; p. 99.
Hansen H, Rand J. (2006) On the causal links between FDI and growth in developing countries. World Economy 29:21-41.
Halicioglu F. (2009) An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37:1156-1164.
Hoffmann, R., Lee, C. G., Ramasamy, B., & Yeung, M. (2005). FDI and pollution: a granger causality test using panel data. Journal of international development, 17(3), 311-317.
Jonsson K. (2011) Testing stationarity in small- and medium-sized samples when disturbances are serially correlated. Oxford Bull Econ Stat 73:669- 690.
Kaplan, M., Ozturk, I., Kalyoncu, H. (2011), Energy Consumption and Economic Growth in Turkey: Cointegration and Causality Analysis. Romanian Journal of Economic Forecasting, 14(2), 31-41
Kraft, J., Kraft, A. (1978), On the Relationship between Energy and GNP. Journal of Energy and Development, 3(2), 401-403.
Kao C. (1999) Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90:1-44.
Kivyiro, P., & Arminen, H. (2014). Carbon dioxide emissions, energy consumption, economic growth, and foreign direct investment: Causality analysis for Sub-Saharan Africa. Energy, 74, 595-606.
Mehrara M. (2007) Energy consumption and economic growth: the case of oil exporting countries. Energy Policy 35:2939-2945.
Ozturk, I., Acaravci, A. (2010), CO2 Emissions, Energy Consumption and Economic Growth in Turkey. Renewable and Sustainable Energy Reviews, 14(9), 3220-3225.
OECD. Greeen growth in cities, OECD green growth studies, OECD publishing towards green growth. Paris: Organisation for Economic Cooperation and Development; 2013. Available at: http://dx.doi.org/10.1787/9789264195325-
Pesaran, M.H., Shin, Y. (1999), Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, 94(446), 621-634.
Pesaran, M.H., Shin, Y., Smith, R.J. (2001), Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289-326.
Selden TM, (1994) Song D. Environmental quality and Development: “Is there a Kuznets curve for air pollution emissions”? J Environ Econ Management 27:147-162.
Talukdar D, Meisner CM.(2001), Does the private sector help or hurt the environment? evidence from carbon dioxide pollution in developing countries. World Development 29(5),827- 840.
Tamazian, A.Chousa,J.P.,Vadlamannati,K.C.,(2009). “Does higher economic and financial development lead to environmental degradation” evidence from BRIC countries. Energy Policy 37,246–253.
The World Bank, (2007a). Growth and CO2 emissions: how do different countries fare. Environment Department, Washington, DC.
The Intergovernmental Panel on Climate Change (IPCC). Climate change (2007): synthesis report, 4th assessment report. Geneva Switzerland.
Usama AM, Che NCS. (2013) Energy consumption, pollution and economic development in 16 emerging countries. J Econ Stud; 40(5):686-698.