ICTI 2012

ISSN: 16941225

Energy use, Emissions, Economic growth and Trade: Evidence from Mauritius

Seetanah Boopen University of Mauritius [email protected]

Neeliah Harris Mauritius Research Council [email protected]

Abstract This paper investigates the relationship among energy, emissions and economic growth inMauritius in the presence of trade activities, with capital and labour as other control variables. Using annual data from 1960 to 2011, it is found that the variables are non-stationary and cointegrated. The relationship among the various variables are thus examined in a dynamic VECMframework. Ourempirical results comply with the growth hypothesis. Output elasticities of 0.17, 0.25 and 0.43 show that increases in energy consumption cause increases in economic growth, capital accumulation and trade in the long run. We also found that CO2 negatively affects output, but has no significant effect on trade. Findings for the long-run generally tend to tally with those in the short-run. Interestingly we found that energy consumption has a 1

ICTI 2012

ISSN: 16941225

significant impact on CO2 emissions. Our results tend to suggest that implementing energy conservation strategies to mitigate the negative impact of CO2 emissions can dent economic growth, and that promoting cleaner energy production could be a better alternative for Mauritius.

Keywords: Energy; Emissions; Economic growth; Export; VECM; JEL classification: C32; Q43; Q50

2

ICTI 2012

ISSN: 16941225

1.0 Introduction Unprecedented economic growth over the last four decades, fuelled by increased energy use has lead to increasing green house gases (GHG) emissions. This has concomitantly resulted in global warming. The increasing threat of global warming and subsequent climate change has been a major global issue, and their impact on the world economy has been intensively studied. The Kyoto protocol under the United Nations Framework Convention on Climate Change (UNFCCC) has the objective to reduce GHG over specific periods of time. Among the numerous environmental pollutants carbon dioxide is considered the main one. GHGs result mostly from electricity generation, manufacturing activities, transport and consumption of goods and services. There is a relationship between emissions and economic activities, therefore any GHG emission abating measures should be thoroughly planned so that they do not negatively impact on the above-mentioned economic activities and thus the economy.

Mauritius is an energy-dependent economy, and GHG are emitted as a result of fuel combustion. It is therefore crucial to investigate the effects of curbing emissions through energy reduction measures and its related impact on economic activity. Furthermore Mauritius is an open economy where trade significantly contributes to socio-economic development. Trade entails the movement of goods and services produced in Mauritius to be consumed in the rest of the world, and goods imported to be consumed in Mauritius. Pollution is generated in both the production and consumption of such goods and services. Carbon dioxide emissions are embodied in the trade of such goods and services. We therefore posit that there are 3

ICTI 2012

ISSN: 16941225

dynamic relationships among economic growth, energy consumption, carbon dioxide emissions and trade. The dynamic relation among these variable have not been systematically studied for Mauritius. The empirical findings from this study can inform policy makers about possible appropriate measures to consider to curb emissions from intensive industries that produce goods and services for export, without endangering economic growth. This paper attempts tofill this gap in the literature and aims at investigating this nexus for Mauritius in a multivariate framework.

The remainder of this paper is organized as follows: section 2.0 briefly reviews the literature in the relationships among economic growth, energy consumption, carbon dioxide emissions and trade. Section 3.0 describes the empirical model used investigate the relationship and section 4.0 presents the findings. The last section concludes.

2.0 Review of literature The interaction among energy consumption, economic growth and environmental quality is so important that is has motivated studies from the theoretical, empirical and policy perspectives.Over the past three decades, a plethora of studies have been conducted to empirically identify, understand and quantify this nexus. There are three global events that have prompted economists to study this intricately complex issue. These are; the oil shock in 4

ICTI 2012

ISSN: 16941225

the 1970s; the adoption of the Kyoto Protocol in late 1997; and the recent increase in energy prices. The studies around this nexus have been extensively reviewed by Payne (2010) and Ozturk (2010). These authors have showed that the majority of the studies generally focused on causal relationship between energy use, economic growth and environmental qualityGiven the primary focus of the current empirical analysis, the literature can be classified into four categories. The first group analyzes the causal relationship between economicgrowth and environmental pollutants, referred to as the growth-environment nexus (e.g., Grossman and Krueger, 1991; Shafik, 1994; Agras and Chapman, 1999; Heil and Selden, 1999; Friedl and Getzner 2003; Dinda and Coondoo, 2006; Managi and Jena, 2008). These studies have typically concentrated on identifying the existence of environmental Kuznets curve (EKC). The EKC hypothesizes an inverted U-shaped relationship between (per capita) income and pollution levels; that is, environmental quality first deteriorates and then improves with per capita income. Dinda (2004) provides a thorough review of the literature on the EKC. In their seminal work, for example, Grossman and Krueger (1991) find that the EKC hypothesis holds for North American countries. Agras and Chapman (1999) use a cross-section panel of countries to examine the EKC hypothesis and on the other hand find little evidence of the inverted relationship between income and the environment.

The second strand of study turns its attention to investigate the relationship between income growth and energy consumption, referred to as the growth-energy nexus (e.g., Kraft and Kraft 5

ICTI 2012

ISSN: 16941225

1978; Yu and Choi 1985; Glasure and Lee 1997; Soytas et al. 2001; Soytas and Sari 2003; 2006; Akinlo 2008; Tsani, 2010; Eggoh et al., 2011; Wang et al., 2011). The central hypothesis here is whether economic growth stimulates energy consumption or vice. Kraft and Kraft (1978), for example, examine the causal linkages between energy consumption and economic growth in the United States; they find that the causal relationship runs from economic growth to energy, but the reverse does not hold. Soytas et al. (2001) analyze income-energy causality in Turkey; they show that economic growth depends on energy consumption, and a decrease in energy consumption may restrain economic growth. Constantini and Martini (2010) adopted a VECM on non-stationary and cointegrated panel data across four different energy sectors in 71 developing and developed countries to study the linkage between the economic performance and the energy sector. Ozturk (2010) provides a detailed literature review on this strand of study and based on the interpretation of statistical signs and causal directions, categorized the above linkage under three types of energy-economic growth types, namely (i) unidirectional causality either the causality is running from energy to economic growth which is called growth hypothesis or from economic growth to energy (conservation hypothesis), (ii) bi-directional causality between energy and economic growth or called feedback hypothesis, and (iii) nocausal relationship between energy and economic growth (neutral hypothesis).

Thirdly, there has recently been a growing body of literature that has combined the first and second approaches as noted earlier in order to analyze dynamic relationships among economic growth, energy consumption and the environment, referred to as the growth-energy6

ICTI 2012

ISSN: 16941225

environment nexus (e.g., Soytas et al., 2007; Zhang and Cheng, 2009; Soytas and Sari, 2009; Jalil and Mahmud, 2009; Menyah et al., 2010;. Tiwari, 2011, Wang et al., 2011). Zhang and Cheng (2009), for example, examine the dynamic interrelationships between energy consumption, output and carbon emission in China; they find that, in the long-run, CO2 emissions tend to increase as income and energy consumption increase.Tiwari (2011) examined the causality in both static and dynamic framework between energy consumption, carbon dioxide emissions and economic growth in India using Granger causality approach in a VAR framework. The main finding was that energy Granger-causes economic growth and not vice versa and also impacted in carbon dioxide emissions.Wang et al. (2011) used panel data cointegraton and panel vector error correction modeling based on 28 provinces in China over the period 1995-2007 and showed that carbon dioxide emissions, energy consumption and economic growth were cointegrated, and that there existed bidirectional causality between carbon dioxide emissions and energy consumption, and also between energy consumption and economic growth.

A fourth line of inquiry has emerged over the last couple of years, that focusses on the above triumvirate within a trade context. Proponents of trade liberalization claim that, given the fact that environmental quality is a normal good, income growth induced by trade can lead individuals to increase their demand for environmental quality; to increasingly enforce environmental regulations, thus encouraging firms to shift towards cleaner production techniques. This can in fact positively impact the environment and income growth. On the other hand opponents of trade liberalization, argue that if production techniques do not 7

ICTI 2012

ISSN: 16941225

change, then globalization could aggravate environmental damage through more protracted economic activity, rapid growth of pollution-intensive industries, thereby having a detrimental effect on the environment. Given the above set-up trade should be accounted for when the economic growth-carbon dioxide-energy consumption nexus is studied. The last few years have seen an emergence of studies that have included trade. Weber et al., (2008) and Yan and Yang (2010) have concluded that increasing the openness to trade has resulted to more carbon dioxide emissions in China. On the other hand, Jalil and Mahmud (2009)found no significant relationship between trade and carbon dioxide. Baek et al. (2009) that has attempted to study this nexus. They use the Johansen cointegration analysis in investigating the effect of trade openness on environment quality (i.e., SO2 emissions) for developed and developing countries. They found that trade and income growth tend to improve environmental quality in developed countries, while they have detrimental effects on the environment in developing countries. However, their cointegration analysis included only three variables, namely trade openness, income and sulphur dioxide emissions. It is a fact that emission of GHGs is mainly as a result of the combustion of fossil fuels. This is accelerated by trade-induced economic growth. Therefore an indicator for trade should be included as an explanatory variable. Jayanthakumaran et al. (2012) used the Bounds testing approach to cointegration and the ARDL methodology to test the long- and short-run relationships among economic growth, trade, carbon dioxide and energy use between China and India. Carbon emissions in China were influenced by income, structural changes and energy consumption, whereas a similar connection could not be established for India.Other studies that have attempted to study the above relationship empirically examined the dynamic causal relationships between carbon dicoide emissions, 8

ICTI 2012

ISSN: 16941225

energy consumption, income and foreign trade include Halicioglu (2009), Hossain (2011) and Baek and Seok Kim (2011).

Different empirical approaches have been used to study the above nexus. First generation studies assumed that time series examined were stationary and causality was analysed within a VAR framework (e.g. Sims, 1972). Subsequent studies acknowledged the non-stationarity of the data and used the cointegration and vector error correction model (VECM) techniques have been applied to explain the causal relationship between energy consumption and economic growth(e.g.Soytas and Sari, 2007; Masih and Masih, 1997). In the presence of cointegration the VECM can distinguish between along run and a short-run relationship among the variables. This also allows the identification of sources of causation that are not usually picked up by the conventional Granger causality tests. The methods used to investigate this nexus have also evolved from a bivariate framework to a multivariate one. Employing bivariate approach is subjected to severe weaknesses, as they tend to focus on the direction of causality and may also suffer from omitted variable bias problem. Concentrating on two variables may diminish the relevance of the findings for policy recommendation. Multivariate estimators on the other hand facilitate the estimation of systems where the restrictions on cointegration relations can be tested and the shor-run and/or long-run adjustment can be simultaneously investigated. Empirical research within the multivariate framework hassubsequently branched into two segments, namely the demand and the production sides. For the purpose of this paper we will

9

ICTI 2012

ISSN: 16941225

focus on the production side. The production side model takes energy, capital and labour as inputs in a production framework.

The previous sections have shown that various studies have focused on the relationship between energy consumption, carbon dioxide emissions and economic growth. But few of them have examined the relationship among trade liberalization, income growth, energy consumption and environmental quality. These have employed different econometric methodologies, different data sets, different countries, different functional forms and different time periods and thus have found conflicting results. According to Baek et al (2009) it is important to also note that, since individual countries experience different levels of income and trade openness, energy consumption relative to their process of development, the true form of the pollution-income-openness-energy relationship mainly depends on where an economy is currently located in a development trajectory. This coupled to the reasons elicited earlier point to the fact that results from the literature cannot therefore be directly transposed to Mauritius. The contribution of our paper is twofold. We first contribute to the burgeoning body of knowledge by studying the economic growth, energy consumption, carbon dioxide emissions and trade for Mauritius. Secondly we simultaneously attempt to address the econometric issue of omitted variables bias by using a multivariate causality framework where the model can be augmented to also include carbon dioxide emission, trade, energy and other production inputs like capital andlabour.

10

ICTI 2012

ISSN: 16941225

3.0Energy, Co2 emissions, trade and economic growth in Mauritius Mauritius is a middle income small island development state with no oil, natural gas or coal reserves and therefore exclusively depends on imported petroleum products to meet most of its energy requirement. In 2010, imported fossil fuels accounted for about 83% of total primary energy requirement, contrasting with 66% in 1993. Over the same period energy from local sources as a percentage of primary energy requirement decreased from 34% to 17%. In 2010, imported fossil fuels accounted for about 83.4% of total primary energy requirement, coming from gasoline (9.0% of primary energy requirement), diesel oil (15.1%), kerosene (9.3%), fuel oil (16.4%), LPG (4.5%) and coal (29.2%). The remaining 16.6% came from local energy sources, namely bagasse and hydro respectively representing 15.9% and 0.63% of primary energy requirement (CSO, 2010). Energy is a crucial input in economic growth, but at the same time its generation and use cause air pollution. In Mauritius fuel combustion by the energy industries remained the largest source of GHG emissions and carbon dioxide accounted for 61% (2,206 thousand tonnes) of the total GHG emissions in 2011 (CSO, 2012). The transport sector accounted for up to 25% (922 thousand tonnes) of total GHG emissions and the manufacturing industries making up another 9% (337 thousand tonnes).

Figure 1.0 shows a plot of that energy consumption, carbon dioxide emissions and real GDP. It shows that there seems to be co-movement and trending in the variables. These tend to point 11

ICTI 2012

ISSN: 16941225

towards a long-run relationship in the variables.But there is no systematic investigation to elicit such relationships in Mauritius.

Real GDP (million MRU)

Energy (Ktoe)

CO2 emissions (Kt)

4000 3500 3000 2500 2000 1500 1000 500 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

Figure 1.0: Real GDP, carbon dioxide emissions and energy trends for Mauritius

4.0 Econometric methodology 4.1 The model To derive an estimated model, a production function is presented as a function of capital stock and labour, as follows: (1) 12

ICTI 2012

ISSN: 16941225

Previous studies include energy, as a third factor of production, thus Equation (1) isaugmented into: (2)

The Cobb-Douglas functional form is adopted to represent the production function. (2) is therefore transformed into: (3)

where

to represent constant elasticity of substitution. Takinglogarithms for

equation (3), and where the lowercase variables are the natural log of the respective uppercase variable and t stands for time: y t     1 stockt   2 labourt   3 energyt   4 co 2 t   5 exp ortt   t

(4)

Two additional variables are included in (4) to account for the issue under study here. Firstly co2 (representing carbon dioxide emissions)is plugged intothe model to measure the environmental effects of the economy. As mentioned in section 1.0, we use carbon dioxide to as a proxy for GHG emissions as it is the major contributor. Next we also include a trade

13

ICTI 2012

ISSN: 16941225

variable (proxied by the value of export) into (4) to account for the particulareffects of trade on emissions and economic growth in Mauritius.

4.2 Data sources Output is measuredby the gross domestic product (GDP). Capital is the stock of capital of the island and has been constructed using the Perpetual inventory approach(See Seetanah, 2008 for additional information).Energy use is measured in Ktoe.Emissions are proxied by CO2 emissions measured by Kt.Export is the total export value of goods andservices exported from Mauritius and the variable labour is proxied b the employment level. Output, capital, employment level, export and energy are annual data retrieved from Statistics Mauritius covering the period from 1960 to 2010, while data for GDP are taken from the World Bank Indicators 2012.

4.3 The Econometric Model and preliminary tests To determine the order, unit roottests are implemented. The tests include the augmented Dickey-Fuller (ADF) test, Phillips-Perron (PP) test, and the (KPSS) test.Test for stationarity shows that all our variables are integrated of order 1 (I(1) and thus stationary in difference).

14

ICTI 2012

ISSN: 16941225

4.4 Co-Integration Issues Stock still (1987) argued that even when variables are non stationary but stationary in first difference they may be co-integrated3. The Johansen (1988) procedure is thus used to determine the presence of cointegration in a vector-error-correction model (VECM) of capita, labour, energy, carbon dioxide, and trade. The VECM can be expressed in a vector for of the first difference as follows:

Zt    1Zt 1   2 Z t 2 .......   k 1Zt k 1   Zt 1  t

(5)

, where Z= [stock, labour, energy, co2, export]. The lag order of the VECM is selected on the basis of the Schwarz Information Criterion. The trace test and the maximal eigenvalue test are considered while determining the number of cointegrating vectors in the system. In the presence of cointegration the matrix  has non-zero but less-than-full rank and can be decomposed into  / , where  is a matrix of long-run parameters and  is a matrix of shortrun adjustment parameters.The VECM also allows the examination of the direct and the potential indirect effects. A test for cointegration is undertaken using the Johansen procedure. At the 10% level, trace value and maximum eigen-value test both shows there is one cointegrating vector. The coefficients attached to the different explanatory variables are all significant with the required theoretical sign. Table 1.0 presents the long-run estimates.

15

ICTI 2012

ISSN: 16941225

Table 1.0: Estimated Cointegrating Vector and Error-Correction Equations

 (output Stock equation)

equation

Labour equation

Output

1

0.45*

0.63**

0.633*

0.57*

0.38*

Stock

0.65*

1

0.53*

0.53*

0.42*

0.28*

Labour

0.36**

0.23*

1

0.32*

0.22**

0.21*

Energy

0.17**

0.25**

0.15*

1

0.65*

0.43*

CO2

-0.39*

-0.12

0.19

0.11

1

0.05

Trade

-1.55*

0.46*

0.45*

0.45*

0.49*

1

Variable [as in Equation (4)]

Energy equation

CO2 equation

Trade equation

Note: *significant at 10% level, ** significant at 5% level

5.0 Determinants of Growth The general results here show that stock, labour, energy, carbon dioxide emissions and trade are all significant growth determinants in Mauritius. More interestingly energy has an output elasticity of 0.17. The long run findings supports the so-called growth hypothesis, that is energy consumption stimulates economic growth and trade implying that restrictions on the use of energy may adversely affect economic growth, while increase in energy contributes to economic growth. On the other hand carbon dioxide emission has an output elasticity of -0.39. This implies that a

1% increase in carbon dioxide emissions would lead to a 0.39% decrease in output. CO2emmision is seen to have a negative impact on growth and this result is in line with that of Seetanah and Sannassee(2010) for Mauritius. As expected trade, labour and capital stock have significant positive effect in economic performance with the latter exercising a relatively higher effect on output.

16

ICTI 2012

ISSN: 16941225

5.1 CO2 and growth Referring to the 6th column, CO2 emissions are found to have a positive long-run relationship with income, suggesting that economic growth causes significant environmental degradation. These findings could be explained using the term emission intensity (ratio), which is defined as the ratio of per capita CO2 emissions to per capita income (Baek et al. 2009). Deterioration in emissions intensity (or increase in the ratio), on the other hand, can be interpreted to mean that, since an economy has not reached the EKC turning point, CO2 emissions increases as income rises; hence, CO2 emissions have a positive relationship with income. This concurs with Seetanah and Sannassee (2010) who reported that the country did not reach income levels high enough to derive the EKC turning point so that emission level tends to increase with higher income growth. They found that Mauritius could not curb its carbon

dioxide emissions in the last three decades. Thus as hypothesized the cost of degradation associated with GDP grows over time and it suggests that the economic and human activities are having increasingly negative environmental impacts on the country relative to their economic prosperity. The results contrast that of Sotyas et al. (2007) who examined energy, economic growth and carbon emission in the United States (US). Their study finds that that income does not Granger cause carbon emission in the long run. Similar finding is also found by Zhang and Cheng (2009) in China and Sotyas and Sari (2009) in Turkey. This finding tends to indicate that environmental conservation can be implemented without hurting economic growth.

17

ICTI 2012

ISSN: 16941225

5.2 Energy and growth Our finding complies with growth hypothesis, the positive sign means that the increases in energy cause increases in economic growth and capital accumulation.It is noteworthy that the relatively small coefficient of energy implies that the economy requires less energy consumption as the economy moves to less-energy intensive service sectors. This relationship can be explained by the development focus of Mauritius which has focussed on expanding its services sector since the mid 1980’s. Thus the contribution of manufacturing sector which aremore energy-intensive,has decreased in importance. Growth is also seen to positively affect energy, thus confirming a bidirectional relationship or feedback hypothesis confirming an interdependence of energy and economy growth such that an increase in energy consumption causes increases in economic growth and vice versa. Under this feedback hypothesis, conservation policies are most welcomed as the policies will not be harming the economy but also increase the economy further, instead.Yoo (2006) and Shuyun and Donghua (2011) also found evidence of bidirectional relationship betweent energy consumption and economic growth in Korea and China respectively.

5.3 CO2and trade CO2 emissions are found to have a positive but insignificant long-run relationship with openness, suggesting that air pollution trends did not seem to have an effect with a higher degree of trade and openness over the period of study. Though it is believed that openness of 18

ICTI 2012

ISSN: 16941225

the Mauritian economy could have added to pollution in the early stage of export development which was mostly related to sugar and textile production, (relying on heavy energy), however (export mainly v/s Import, or cleaner product) since the mid 1980s, Mauritius is a net exporter of services (using less energy as compared to manufacturing). But more importantly one could argue that trade expansionmay have witnessedthe gains-from-trade hypothesis; iewith trade induced income growth, countries tend to be more willing and able to channel resources into environmental protection through the enforcement of environmental regulations and the investment on cleaner production technologies, thereby improving environmental quality. Thus the combined effect may have resulted in an insignificant effect, at least over the period of study.

5.4 C02 and energy Finally, a positive coefficient of energy consumption on CO2 emissions indicates that an increase in energy consumption results in more CO2emissions thus contributing to environmental deteriorationThis finding is according to a-priori expectation. It suggests that over the past four decades energy consumption has had a significant detrimental effect on environmental quality, and this result thus could be interpreted to support the contention that among various greenhouse gases, CO2 emissions through the combustion of fossil fuels (e.g., coal, petroleum and natural gas) seems to be the major contributor of global warming. It is also observed that CO2emission have no significant impact on energy use.

19

ICTI 2012

ISSN: 16941225

5.5 VECM estimates The formulation and estimation of the VECM (see Engle and Granger, 1987) was then carried out. The estimated error-correction equations are not subject to residual autocorrelation at the 5% significance level and the results are presente in Table 2.0. The variables in the system are all endogenous, given that the lagged error-correction terms in all the equations of the VECM are significant.

Table 2.0: Estimated Error-Correction Equations

Independent Variables

Dependent Variable Δoutputt

Δstockt

Δlabourt

Δenergyt

ΔCO2t

Δexportt

outputt 1

0.41*

0.231*

0.275*

0.23*

0.39**

0.12

stockt 1

0.31**

0.685*

0.19*

0.23*

0.28*

0.18*

labourt 1

0.15**

0.102

0.45*

0.114

0.089

0.16*

energyt 1

0.077**

0.125*

0.12*

0.521*

0.429**

0.18*

co 2 t 1

-0.06**

0.114

0.083

0.065

0.627*

0.02

 exp ortt 1

0.27*

0.173*

0.25**

0.21*

0.319*

0.634*

 t 1

-0.471**

-0.494**

-0.41

-0.435*

-0.521**

-0.32*

R2

0.65

0.63

0.49

0.23

0.39

0.46

20

ICTI 2012

ISSN: 16941225

To check for model specification, four diagnostic tests are run. First, the LM test forresidual serial correlation finds no evidence of serial correlation eventhough the lag isextended to 8. The residuals are also multivariate normal. Null hypothesis of normality fails to be rejected for skewness, kurtosis and Jarque-Bera tests. Besides that, thehomoscedasticity of the residuals also failed to be rejected for both joint and individual tests.

6.0 Discussion, conclusion and policy implication The VECM results generally obtained in the long-run and the short-run tend to validate each other, even thoughrelatively lower coefficients are obtained (or at times even insignificant but with the same size) in the shor run. It may be argued that it takes time for some effect to have their full effects.For instance, with respect to the relationship between energy and growth, in the long run we could interpret that a one percent increase in energy would increase GDP by 0.17 and VECM short term estimates show that for a similar 1 percentage-point increase in the growth rate of energyleads to a 0.077 percentage-point increase in the growth rate of GDP after one year. Such interpretation can be extended to the other explanatory variables.

Interestingly VAR modeling also allows us to investigate the possibility of other indirect effects, for instance from the long run estimates energy also have indirect effects on growth through the capital accumulation channel since energy is positively and significantly related to capital stock. Such is also the case with CO2emissions. Similar linkages can be traced from both the 21

ICTI 2012

ISSN: 16941225

long run and VECM estimates. It is important to note that the significant and positive coefficient of the lagged dependent variables in each respective equations from the VECM represent the existence of dynamism.

So to reduce the cumulative emissions of carbon dioxide in Mauritius, policy makers should take more care for clean or environment friendly energy production as well as appropriate technology and adapt some policies regarding the reduction of carbon dioxide emission rather than to solely focus on growing the GDP.

Therefore, we recommend to focus on improving the energy-economic growth relationship through developing internal economic capacity through utilizing energy. Additionally such energy should increasingly be sourced from cleaner (renewable) sources this tackling the negative impacts of CO2 emissions. The government should also focus on promoting energy saving practice by unproductive sectors. The practice should not be seen as part of the energy use reduction policy but as a policy to restructure energy supply to energy intensive industries.

References

22

ICTI 2012

ISSN: 16941225

Ang, J. (2008). conomic development, pollutant emissions and energy consumption in Malaysia.Journal of Policy Modeling, 30: 271-278. Aslanidis, N. (2009) Environmental Kuznets curves for carbon emissions: A critical survey. FondazioneEni Enrico Mattei Working Papers 336, 1-33. Baek, J. and Seok Kim, H. (2011) Trade liberalization, economic growth, energy consumption and the environment: Time series evidence from G-20 economies. Journal of East Asian Economic Integration, 15(1): 3-32. Bank, W. (2011).World Development Indicators, ESDS International, University of Manchester.September 2011. Bo, S. (2011). A literature survey on environmental Kuznets curve.Energy Procedia, 5: 13221325. Chen, S., Kuo, H., Chen, C. (2007). The relationship between GDP and electricity consumption in 10Asian countries.Energy Policy 35: 2611-2621. Chiou-Wei, S., Chen, C., Zhu, Z. (2008). Economic growth and energy consumption revisited Evidence from linear and nonlinear Granger causality. Energy Economics, 30: 3063-3076. Chontanawat, J., Hunt, L., Pierse, R. (2008). Does energy consumption cause economic growth?:Evidence from a systematic study of over 100 countries. Journal of Policy Modeling,30: 209-220.

23

ICTI 2012

ISSN: 16941225

Constantini, V. and Martini, C. (2010) The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data. Energy Economics, 32: 591-603. Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey.Ecological Economics 49: 431-

Eggoh, J., Bangake, C. and Rault, C. (2011) Energy consumption and economic growth revisited in African countries.Energy Policy, 39; 7408-7421.

Elliott, G., Rothernberg, T., Stock, J. (1996). Efficient tests for an autoregressive unit root.Econometrica 64: 813-836. Glynn, J., Perera, N., Verma, R. (2007) Unit root tests and structural breaks: A survey withapplications. Revista De MetodosCuantitativos Para La Economia Y La Empresa 3, 63-79 Halicioglu, F. (2009).An econometric study of CO2 emissions, energy consumption, income andforeign trade in Turkey.Energy Policy, 37: 1156-1164. Hamit-Haggar, M. (2012). Greenhouse gas emissions, energy consumption and economic growth: Apanel cointegration analysis from Canadian industrial sector perspective. Energy Economics, 34:358-364. Hossain, M. S. (2011) Panel estimation for CO emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialised countries. Energy Policy, 39: 6991-6999. 24

ICTI 2012

ISSN: 16941225

Huang, B., Hwang, M., Yang, C. (2008). "Causal relationship between energy consumption and GDPgrowth revisited: A dynamic panel data approach." Ecological Economics 67: 41-54. Jalil, A. and Mahmud, S. F. (2009) Environment Kuznets curve for carbon dioxide: A cointegration analysis for China. Energy Policy, 37(12): 5167-5172. Jinke, L., Hualing, S., Dianming, G. (2008). "Causality relationship between coal consumption andGDP: Difference of major OECD and non-OECD countries." Applied Energy 85: 421-429. Johansen, S. (1995).Likelihood-Based Inference in Cointegrated Vector Autoregressive Models.Oxford, Oxford University Press. Kijima, M., Nishide, K., Ohyama, A. (2010). "Economic models for the environmental Kuznets curve:A survey." Journal of Economic Dynamics and Control 34: 1187-1201. Koop, G., Pesaran, M., Potter, S. (1996). "Impulse response analysis in nonlinear multivariatemodels."Journal of Econometrics 74: 119-147. Kwiatkowski, D., Phillips, P., Schmidt, P., Shin, Y. (1992). "Testing the null hypothesis of stationarityagainst the alternative of a unit root: How sure are we that economic time series have a unit root?"Journal of Econometrics 54: 159-178. Lean, H., Smyth, R. (2010). "CO2 emissions, electricity consumption and output in ASEAN."AppliedEnergy 87: 1858-1864. Lee, C., Chang, C. (2008). "Energy consumption and economic growth in Asian countries: A morecomprehensive analysis using panel data." Resource and Energy Economics 30: 50-65. 25

ICTI 2012

ISSN: 16941225

Lee, C., Chang, C., Chen, P. (2008). "Energy-income causality in OECD countries revisited: The keyrole of capital stock." Energy Economics 30: 2359-2373. Mahadevan, R., Asafu-Adjaye, J. (2007). "Energy consumption, economic growth and prices: Areassessment using panel VECM for developed and developing countries." Energy Policy 35: 2481-2490. Masih, A., Masih, R. (1996). "Energy consumption, real income and temporal causality: results from amulti-country study based on cointegration and error-correction modelling techniques." EnergyEconomics 18: 165-183. Masih, A. and Masih, R. (1997) On the temporal causal relationship between energy consumption, real income, and prices; some new evidence from Asian dependent NICS based on a multivariate cointegration/vector error-correction approach. Journal of Policy Modelling 19: 417-440. Menyah, K. and Wolde-Rufael, Y. (2010) Energy consumption, pollutant emissions and economic growth in South Africa.Energy Economics, 32: 1374-1382. Narayan, P., Prasad, A. (2008). "Electricity consumption-real GDP causality nexus: Evidence from abootstrapped causality test for 30 OECD countries." Energy Policy 36: 910-918. Ozturk, I. (2010). "A literature survey on energy-growth nexus."Energy Policy 38: 340-349. Payne, J. (2009). "On the dynamics of energy consumption and output in the US."Applied Energy 86:575-577. 26

ICTI 2012

ISSN: 16941225

Payne, J. (2010). "Survey of the international evidence on the causal relationship between energyconsumption and growth."Journal of Economic Studies 37: 53-95. Payne, J., Taylor, J. (2010). "Nuclear energy consumption and economic growth in the U.S.: Anempirical note." Energy Sources 5: 301-307. Perron, P. (1989). "The Great Crash, the oil price shock, and the unit root hypothesis." Econometrica57: 1361-1401. Reynolds, D., Kolodziej, M. (2008). "Former Soviet Union oil production and GDP decline: Grangercausality and the multi-cycle Hubbert curve." Energy Economics 30: 271-289. Sari, R., Ewing, B., Sotyas, U. (2008). "The relationship between disaggregate energy consumptionand industrial production in the United States: An ARDL approach." Energy Economics 30: 2302-2313. Seetanah and Sannassee (2010)

Shuyun, Y. and Donghua, Y. (2011) The causality between energy consumption and economic growth in China: Using panel method in a multivariate framework. Energy Procedia, 5: 808-812. Sims, C. A. (1972) Money, income and causality.American Economic Review, 62: 540-552. Song, T., Zheng, T, Tong, L. (2008). "An empirical test of the environmental Kuznets curve in China: Apanelcointegration approach." China Economic Review 19: 381-392. Sotyas, U., Sari, R. (2009). "Energy consumption, economic growth, and carbon emissions:Challenges faced by an EU candidate member." Ecological Economics 68: 1667-1675. 27

ICTI 2012

ISSN: 16941225

Sotyas, U., Sari, R., Ewing, B. (2007). "Energy consumption, income, and carbon emissions in theUnited States."Ecological Economics 62: 482-489. Stern, D. (2004). "The rise and fall of the environmental Kuznets curve." World Development 32:1419-1439. Tiwari, A. K. (2011) energy consumption, CO2, emissions and economic growth: A revisi of the evidence from India. Applied Econometrics and International Development, 11-2: 165-189. Tsani, S. Z. (2010) Energy consumption and economic growth: A causality analysis for Greece. Energy Economics, 32: 582-590. Wang, S. S., Zhou, D. Q., Zhou, P. and Wang, Q. W. (2011) CO2 emissions, energy consumption and economic growth in China: A panel data analysis. Energy Policy, 39: 4870-875. Wang, Y., Wang, Y., Zhou, J., Zhu, X. and Lu G. (2011) Energy consumption and economic growth in China: A multivariate causality test. Energy Policy, 39: 4399-4406. Weber, C. L., Peters, G. P., Guan, D., Hubacek, K. (2008) The contribution of Chinese exports to climate change. Energy Policy, 36(9): 3572-3577. Yan, Y. F. and Yang, L. K. (2010) China’s foreign trade and climate change: A case study of CO2 emissions. Energy Policy, 38(1): 350-356. Yamada, H., Toda, H. (1998). "Inference in possibly integrated vector autoregressive models: Somefinite sample evidence." Journal of Econometrics 86: 55-95.

28

ICTI 2012

ISSN: 16941225

Yoo, S. (2006)."The causal relationship between electricity consumption and economic growth inthe ASEAN countries."Energy Policy 34: 3573-3582. Yuan, J., Kang, J., Zhao, C., Hu, Z. (2008). "Energy consumption and economic growth: Evidence fromChina at both aggregated and disaggregated levels." Energy Economics 30: 3077-3094. Zhang, X., Cheng X. (2009)."Energy consumption, carbon emissions, and economic growth in China."Ecological Economics 68: 2706-2712. Zivot, E., Andrews, D. (1992). "Further evidence on the Great Crash, the oil price shock, and the unitroothypothesis."Journal of Business and Economic

29