Testing the Kuznets Curve hypothesis for Qatar: A comparison between carbon dioxide and ecological footprint

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Testing the Kuznets Curve hypothesis for Qatar: A comparison between carbon dioxide and ecological footprint ⁎

Zouhair Mrabet , Mouyad Alsamara College of Business and Economics, Qatar University, P.O. Box 2713, Doha, Qatar

A R T I C L E I N F O

A BS T RAC T

JEL classification: F18 O13 C23 Q53 C32

This paper explores the validity of the Environmental Kuznets Curve (EKC) using two different environment indicators: the carbon dioxide emissions (CO2) and the ecological footprint (EF) in Qatar over the 1980–2011 period. To this end, we investigate the impact of real gross domestic product (RGDP), the square of RGDP, the energy use, the financial development and the trade openness on the CO2 emissions and the EF. We employ the autoregressive distributed lag (ARDL) model with the presence of unknown structural breaks in order to study short-run and long-run elasticity between the variables. The findings infer that there is a long-run relationship among the selected variables with a shift in the cointegration vector in 1991 and 2000. The empirical result indicates that the inverted U-shaped hypothesis is not valid in Qatar when we use the CO2 emissions, whereas the inverted U-shaped held when using the EF. Furthermore, the error correction results confirm that the convergence towards the long-run equilibrium will mostly occur within one year after a short run shock. Generally, the findings suggest that Qatar should invest more in efficient energy and continue sustained its growth. Moreover, more efforts are needed for diversification particularly in technology-intensive and environment-friendly industries to improve environmental quality.

Keywords: Environment Kuznets Curve (EKC) CO2 emissions Ecological footprint Energy consumption Economic growth ARDL bounds tests with structural breaks

1. Introduction Since the 1960s, Qatar has witnessed an intensive transition from fishing and limited agriculture to an oil and gas based economy. The Qatar economy produces a considerable share of the world's gas and oil production and plays an important role in international energy markets. Moreover, Qatar economy depends mainly on oil and gas revenue to accelerate and sustain their economic growth. Qatar economic growth reached 13% in 2013, making the economy one of the highest incomes in the world [1]. The massive improvements in the economic sectors, the huge population growth, and the progressive increase in producing and consuming carbon resources have renewed the concern about the relationship between economic growth and its linkage to environmental quality in Qatar. Therefore, there is a clear consensus on the fact that the Greenhouse Gas emissions (GHG henceforward) (carbon dioxide CO2, methane CH4, water vapor H2O, nitrous oxides N2O and Ozone O3), are highly correlated with human activity and especially energy production and economic growth [2,3]. In this context economic growth and other macroeconomic indicators, such as energy consumption, financial development, and trade openness, could have a substantial cost on environmental quality. Stern [4] indicated that without reducing GHG emissions, this cost would be

⁎

equal to losing of 5% of global GDP and it could be further reduced to losing of 1% if there is a reduction of GHG emissions. Therefore, the nature and the shape of the relationship between economic growth and environmental degradation have received significant attention in the last few years. This relationship is well-known as the Environmental Kuznets Curve hypothesis, which is represented by a quadratic function and has an inverted U-shaped curve [5–11]. The empirical studies investigating this relationship show mixed results. In one hand, there is evidence supporting the claim that the relationship between environmental degradation and real GDP is an inverted U-shaped curve, and in the other hand findings found different shapes for this relationship (such as U-shape, N-shape, inverted N-shape, and monotonic). These different findings renew the question about the soundness of the EKC hypothesis, since its shape could depend heavily on the environmental quality indicator used, explanatory variables, the econometric estimation techniques, economic features, and the timing of the studied period [6]. Overall, the EKC hypothesis has achieved a particular importance and plays a substantial role in understanding how policy makers should sustain the economic growth and simultaneously attaining a clean green environment. Given the economic and financial features of Qatar's economy, it is important to explore the possible link between the environmental degradation

Corresponding author. E-mail addresses: [email protected] (Z. Mrabet), [email protected] (M. Alsamara).

http://dx.doi.org/10.1016/j.rser.2016.12.039 Received 14 December 2015; Received in revised form 13 November 2016; Accepted 6 December 2016 1364-0321/ © 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Mrabet, Z., Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.12.039

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U-shape by employing different environment indicators such as SO2, N2O, CH4, and EF as an alternative to CO2 emissions. For instance, AL-Mulali et al. [31] have investigated the relationship between EF and economic growth. His empirical results provided strong evidence for the validity of the EKC hypothesis (inverted U-shaped) for a sample of 93 countries over the period 1980–2009. Similarly, Cho et al. [32] have employed SO2, N2O, and CH4 and confirm the validity of the EKC hypothesis in OECD countries. In addition, Fodha and Zaghdoud [8] have used both CO2 and SO2 in a study that provides supporting evidence for the validity of the EKC hypothesis in Tunisia. CavigliaHarris et al. [33] confirmed the relationship between EF and economic development for 146 countries during the 1961–2000 periods. Boutaud et al. [34] propose two aggregated indicators to test the EKC hypothesis: EF and development index. Their cross-country study for 128 countries support the EKC hypothesis only for some components of the EF (Table 1). These studies represent an impressive set of analysis in favor of the EKC hypothesis. In contrast, however, several empirical studies have failed to confirm the EKC hypothesis and have introduced different shapes for the relationship between economic growth and environmental degradation. In this respect, LIorca and Meunie [35] pointed out that EKC hypothesis is not valid when they used SO2 for China. The same results were founded by [36] for Canada when they used both CO2 and SO2. Similarly, Saboori and Suleiman [37], Ozcan [38], Peo et al. [39], and Wang et al. [40] have failed to find evidence for the inverted U-shaped relationship. Recently, Liddle and Messinis [41] examined the relationship between sulfur emissions and per capital GDP for 25 OECD over the period 1950–2005. Their results indicated that 24 countries have either inverted V curve or a decoupling curve where income does not affect the emissions in the long run. Finally the overall EKC empirical studies have provided different findings resulting from the diversity of environmental indicators, the variables used, data availability and econometrics technique (Table 2).

and economic growth. Qatar is especially unique in the evolving use of non-renewable resources since several years (oil and gas). As a result, the balance between economic growth and environmental quality is an issue that is recognized as critical for sustainable development of the country. In this regard, the study of [12] is the only attempt that investigates the validity of the EKC hypothesis in the case of Qatar. To shed more light on this issue, a further empirical investigation of the validity of the EKC hypothesis in Qatar is crucial. This study proposes to investigate the relationship between environmental quality, RGDP, the square of RGDP, the energy use, the financial development and the trade openness in Qatar. To this end, the contributions of this paper are threefold. First, we contribute to the empirical literature on the EKC hypothesis by estimating the EKC in Qatar using two different indicators of environment degradation: CO2 emissions and EF. Second, this paper examines the augmented quadratic function of the EKC hypothesis in the presence of the structural breaks. The augmented EKC model may allow us to reduce the bias of estimation caused by the omitted variables in the basic EKC model specification. Third, this paper accounts for a possible shift in the cointegration vector when investigating the long-run relationship between the selected variables. The rest of the paper is organized as follows, the next section provides literature review and section three refers to the environmental and economic developments in Qatar. The empirical methodology and empirical results are described in section four and five, respectively. Finally, section six provides conclusions and policy implications. 2. Empirical literature review Over the last three decades, the impact of economic and financial development on environmental quality has received a substantial importance in energy and economics literature. The intuition behind such focus is to examine the validity of the EKC hypothesis which was first proposed by [10]. The underpinning of the EKC hypothesis, the inverted U-shaped between environmental degradation and economic development, has been extensively elaborated and investigated by many researchers. The rationale behind the existence of the inverted U-shaped is that economic growth will be compatible with environmental degradation in the first stage of economic development and with environmental improvement in the second stage of economic development, which could be represented by a quadratic (inverted Ushaped) function [5,11]. The recent empirical literature has highlighted several specifications for the EKC hypothesis associated with inverted U shaped of the EKC curve and other different shapes such as U-shaped and N-shaped. Numerous researchers have provided evidence for the validity of the EKC hypothesis and confirmed the inverted U-shaped. For instance, Chow [13], Esteve and Tamarit [14], Hmit-Hagger [15], and Wang [16] have investigated a basic EKC equation using carbon dioxide (CO2) as an indicator for the environment degradation. Their empirical results provided clear evidence of an inverted U-shaped for the EKC hypothesis. Similarly, Saboori et al. [17] and Acaravci and Ozturk [18] found evidence supporting the EKC hypothesis of the inverted U-shaped. In the same vein, several empirical studies have confirmed the linkage between environmental quality and GDP per capita together with energy consumption. In this context, Yavuz [19], Shahbaz et al. [20], Pao and Tsai [21], and Aperjrs and Payne [22] found the existence of inverted U-shaped (Table 1). The validity of the EKC hypothesis is confirmed in other augmented forms of EKC function that include other explanatory variables, such as energy consumption, trade openness and financial development [23– 30]. All these empirical studies confirm the existence of the long term consequences of financial and trade developments in lowering the carbon emissions, as well as the positive role of energy consumption in increasing the carbon emissions (Table 1). Some empirical studies confirmed the existence of the inverted

3. Environmental and economic developments in Qatar Two major aspects related to the environment quality in Qatar over the 1980–2011 period can be shown in Fig. 1. On the one hand, carbon dioxide (CO2) is considered one of the most important indicators of environmental degradation. The evolution of plotting CO2 emissions to GDP ratio witnessed significant changes in the early of 1990s when gas production was boosted, especially in the North field gas project. On the other hand, the EF has been advanced as one of the world's primary measures of human activity demand on nature. It is a global and more comprehensive indicator for the environment quality that measures nature's capacity to meet the entire consumer demand. Given that sustainable development is based on several dimensions that articulate the linkage between economic and environmental objectives, it is crucial to shed light on the relationship between environment degradation, output level, financial matters, and trade developments in Qatar. This relationship is considered to be a milestone for achieving an appropriate balance between the economic development and environment sustainability. Fig. 2 shows an overall snapshot for the main five dimensions that describe the earlier mentioned relationship in Qatar during two sub- periods 1980–1996 and 1997–2011. This figure provides a clear indication on the nature of the relationship between environment degradation (through CO2), economic growth (DRGDP), energy use (EU), financial development (FD), and trade openness indicators (TR). In particularly, the significant increase in CO2 emissions in the second period was associated with a substantial increase in economic growth, trade and financial indicators. In contrast, this environment degradation was not accompanied with a similar increase in local energy consumption, which remained at the same level in the second period. These developments can be explained by the substantial increase in oil and gas production after 1996 in Qatar rather than through oil and gas consumption. 2

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Table 1 EKC hypothesis is valid (inverted U-shaped). Environment indicator

Author

Carbon dioxide emissions (CO2)

Shahbaz et al.[23] 1972–2012 India Tutulmaz[60] 1968–2007 Turkey Yavuz[19] 1960–2007 Turkey Farhani et al.[25] 1971–2008 Tunisia Lau et al.[61] 1970–2008 Malaysia Shahbaz et al.[29] 1971–2010 Tunisia Boutabba[26] 1971–2008 India Baek and Kim[62] 1971–2007 Korea Shahbaz et al.[28] 1980–2010 Romania Kanjilal and Ghosh[54] 1971–2008 India Shahbaz[63] 1971–2009 Pakistan Shahbaz et al.[64] 1975–2011 Indonesia Shahbaz et al.[20] 1971–2011 Malaysia Shahbaz et al.[65] 1971–2011 China Ozturk and Acaravci[29] 1960–2007 Turkey Tiwari et al.[30] 1966–2011 India Ahmed and Long[66] 1970–2008 Pakistan Esteve and Tamarit[14] 1857–2007 Spain Hmit-Hagger[15] 1990–2007 Canada Shahbaz et al.[20] 1971–2009 Pakistan Pao and Tsai[21] Brazil 1980–2007 Acaravci and Ozturk[67] 1960–2005 Europe Fodha and Zaghdoud[12] 1961–2004 Tunisia AL-Mulali et al.[31] 1980–2009 93 countries Cho et al.[32] OECD Fodha and Zaghdoud[12] Tunisia Caviglia-Harris et al.[33] 146 countries Boutaud et al.[34] 128 countries LS (Lee and Strazicich), GH (Gregory–Hansen), GC (Granger Causality)

EF SO2, N2O, CH4 SO2 and CO2 EF EF

Period

Period

Country

Methodology

Begum et al.[68] AL-Mulali et al.[31] Lopez-Menendez et al.[56] Lopez-Menendez et al.[2] Saboori and Suleiman[37] Ozcan[38]

1970–2007 1980–2011 1980–2010

Malaysia Vietnam Venezuela

ARDL ARDL Cointegration technique

1996–2010

27 countries

Panel cointegration

1980–2009

Malaysia

ARDL and VECM

1990–2008

Middle East

Hussain et al.[69]

1971–2006

Pakistan

Wang[16] Peo et al.[43]

1971–2008 1990–2007

98 countries Russia

Wang et al.[39]

1995–2007

China

Akbostanci et al.[59]

1968–2003

Turkey

Halkos, G.E.[70] Azomahou et al.[71] Lise[58] Bimonte and Stabile[11] Dong et al.[4]

36 years 1960–1996 1980–2003 1980–2008

32 countries 100 countries Turkey Italy

Pedroni cointegration, FMOLS and VECM Johansen cointegration and VECM Panel threshold Cointegration, OLS model and VECM Pedroni cointegration and VECM Time series and panel data analyses Panel cointegration tests Panel cointegration ARDL Panel data analysis

1990–2012

Group of countries

Methodology ARDL and LS (2013) Cointegration Cointegration and GH ARDL and VECM GC ARDL and VECM GC ARDL and VECM GC ARDL and VECM GC ARDL ARDL ARDL and Threshold ARDL ARDL and VECM GC ARDL and VECM GC ARDL and VECM GC ARDL ARDL and VECM GC ARDL and VECM GC Threshold VECM model Pedroni VECM model ARDL and VECM GC Gray Model and VECM GC ARDL and VECM GC Cointegration with VECM GC Panel analysis Panel analysis

cross-country

developments, Qatar's economy requires further steps to enhance economic diversification, reduce the use of non-renewable resources, and improve deteriorating environmental conditions. These issues represent a high priority for the Qatar's economy to achieve the 2030 Qatar Vision, which provides a blueprint for future development. Together with economic growth, the financial sector in Qatar has witnessed remarkable progress in the last two decades. Credit to the private sector represents a good indicator for financial development in the economy. Fig. 3 shows the results of this indicator as a percentage of GDP during the 1980–2011 period. It is obvious that the financial sector received significant shrinkage in 1991, which was linked to the first Gulf War. After this regional political event, Qatar's private sector credits to GDP ratio declined steadily from 65% in 1992 to 26% in 2000, but then it started to increase significantly to reach 50% in 2011. This huge increase in the beginning of the 2000s is mostly related to the unprecedented economic and financial developments in Qatar.

Table 2 EKC hypothesis is not valid. Author

Country

4. The data and methodology 4.1. Data and empirical modeling The main selected variables chosen for this study are the EF, CO2, real GDP per capita (Y), energy use per capita (EU), financial development (FD), and trade openness. FD and EX are measured, respectively, by the credit of private sector to GDP ratio and exports of goods and services as percentage of GDP. The annual data on CO2, Y, EU, FD, and EX variables are collected from World Development Indicators (WDI) for Qatar's economy over the years 1980–2011. The data on EF were collected from the Global Footprint Network. Following, Tamazian et al. [42], Haliciogo [43], and Shahbaz et al. [28] our empirical equation is represented in a quadratic form as follow:

Panel data analysis

It is follow that economic development in Qatar has a substantial impact on environmental quality. This economic development was boosted by the oil and gas sector during 1997–2011 (Table 3). The oil and gas sector contributes approximately half of the total economic growth in Qatar during this period. However, other non-oil sectors have also made a significant contribution in the recorded economic growth during the second period (1997–2011) compared to the first period (1980–1996) (Table 3). Although these were far-reaching

EDt =f (Yt , Yt2, EUt ,

FDt ,

EXt )

(1)

All variables are transformed to their logarithmic forms to achieve 3

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Fig. 1. CO2 emissions and Ecological foot print index in Qatar 1980–2011.

Fig. 2. Environmental and economic Snapshot.

Table 3 Real GDP growth in Qatar.

Total real GDP growth

1980–1996 0.022

1997–2011 0.12

Oil and gas sector Non-oil and gas sectors

0.001 0.021

0.056 0.063

Source: Authors calculation.

consistent empirical evidences. It is well known that log-linear equation moderates sharpness in the time series data and allows for better results that control variance as compared to simple specification [20]. The log-linear specification of our empirical equation is modeled as following:

lnEDt =α +β1lnYt +β2lnYt2+β3lnEUt +β4lnFDt +β5lnEXt +εt

Fig. 3. The developments in credits to private sector as a percentage of GDP.

financial development on environmental degradation. If financial development allows easy access to efficient technology then β4 < 0, otherwise β4 > 0. The impact of trade openness on environmental degradation is influenced by the technology transferred to the economy. If an economy benefits from an efficient technology then β5 < 0 otherwise an increase in trade openness will raise environmental degradation i.e.β5 > 0. Since this paper compares the validation of the EKC by using two different indicators for environmental degradation, the empirical analysis will estimate two different specifications derived from Eq. (2): the first specification uses CO2 emissions, whereas the second one uses the ecological footprint as an indicator for the environment degradation.

(2)

where lnEDt , lnYt , lnYt2, lnEUt , lnFDt ,andlnEXt is a natural log of the environmental degradation indicator, which could be either the CO2 emissions per capita or the EF per capita, natural log of per capita real GDP, natural log of squared of real GDP per capita, natural log of the energy use per capita, natural log of FD, and natural log of the EX, respectively. εt is error term which has normal distribution with zero mean and constant variance. The parameters βi,i = 1, 2…..5, indicate the long run elasticity of Yt , Yt2 , EU, FD, and EX, respectively. The EKC hypothesis that designates an inverted U-shape is valid in the quadratic function, if β1>0, β2 <0 . Many others possible forms could be found following the signs of the estimated coefficients β1 and β2 . For instance, when β1>0andβ2=0 the relationship is a monotonically increasing function, and when β1<0, β2 >0 the relationship is U-shaped. The coefficient β3 < 0 implies that efficient use of energy lowers environment degradation otherwise if β3 > 0 energy consumption degrades environmental quality. We can expect positive or negative impact of

ln CO2t =α +β1lnYt +β2lnYt2+β3lnEUt +β4lnFDt +β5lnEXt +εt

ln EFt =β + α1lnYt +α2lnYt2+α3lnEUt +α4lnFDt +α5lnEXt +εt

4

(3) (4)

Z. Mrabet, M. Alsamara

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4.2. Unit root tests

potential explanatory variables, we use the Autoregressive Distributed Lag (ARDL) method based on the unrestricted error correction model (UECM). Despite that this method has several advantages over the other cointegration techniques, it have been criticized because it supposes that the cointegration relationship does not change over the entire period [54]. To tackle this problem, [28] applied the ARDL model by including one structural break based on the finding of [45]. In our empirical analysis, and differently from [28], we will employ the ARDL model that includes more than one structural break based on the unit root test with structural breaks (NP ) and cointegration test with structural breaks (GH and H-J). The ARDL model represents a general dynamic equilibrium specification, which uses the variables, lags of the variables and their dynamics to estimate, simultaneously, the short run effects and the long run cointegration relationship, since the dynamic error correction model (ECM) can be derived from the ARDL [55]. The conditional error correction model (ECM) of ARDL model of Eq. (2) can be written as follow:

The first step of our empirical analysis is to examine the order of integration for all variables. Nowadays, it is well known that the traditional unit root tests1 are not reliable if a structural break is present in the series [44]. In other words, the results of unit root tests will be biased and incorrect if a structural break occurs in the time series. To overcome this problem, Zivot and Andrew (ZA) [45] developed a test allowing the detection of one structural break in time series. Lumsdaine and Papell (LP) [46], Lee and Strazicich (LA) [47] and Narayan and Popp (NP) [48] have extended the ZA [48] test and proposed new unit root tests with two structural breaks. In contrast to LP and LS tests, the NP test chooses the break date by maximizing the significance of the break dummy coefficient. Using Monte Carlo simulations, the NP test has a better size and higher power, and identifies the structural breaks accurately. For this reason, in our empirical analysis we will use the NP test. NP proposed an ADFtype unit root test for innovation-outliners where the problem of spurious rejection could be avoided by formulating a data generating process (DGP) as an unobserved components model. NP used two different specifications for the deterministic component; one allows for two breaks in the level, denoted as model 1 (M1) and the other allows for two breaks in the level as well as slope of the deterministic trend component, denoted as model 2 (M2).

∆ ln EDt =α0+ α1 T + α2 D+ α3lnEDt − i +α4lnYt −1+α5ln (Yt −1)2 +α6lnEUt − i+α7 n

n

n

lnFDt − i +α8lnEXt − i+ ∑ β1i ∆ ln EDt − i + ∑ β2i ∆ ln Yt − i + ∑ β3i i =1

i =0

i =0

∆ ln(Yt −1)2 + n

n

n

∑ β4i ∆ ln EUt −i + ∑ β5i ∆ ln FDt −i + ∑ β6i ∆ ln EXt −i+ε 4.3. Cointegration tests

i =0

i =0

i =0

t

(8) The standard cointegration approaches of [49–51] ignore the presence of any structural break in the cointegration relationship, which might lead to biased results. In order to tackle this problem the cointegration tests of Gregory and Hansen (GH) [52] and Hatemi-J (HJ) [53] will be applied. The first test assumes the presence of just one structural break in the cointegration vector, however, the second test extended the GH cointegration method, and allow for two structural breaks. Both GH and H-J tests have considered three alternative models: Break in the level (Model C: Level shift), break in the level shift with trend (Model C/T) and break in the level and slop (Model C/ S: regime shift) to test for possible shifts in the cointegration vector. Thus the three models with two structural breaks, as proposed by H-J [53] can be specified as follow:

Model C:yt=α 0 +α1D1t +α2 D2t +βxt +ε t

(5)

Model C/T:yt=α 0 +α1D1t +α2 D2t +γt + βxt +ε t

(6)

Model C/S:yt=α 0 +α1D1t +α2 D2t +γt + β1xt +β2 xt D1t +β3xt D2t +ε t

(7)

The ARDL approach starts by the bounds test in order to investigate if a long run cointegration relationship between all variables exists. The ARDL bound test provides the critical values of the F-test statistic for the conditional ECM models. If the computed F-test statistic lies below the 0.05 lower bound, the null hypothesis of no cointegration relationship is not rejected at the 0.05 level. If the statistic falls within the 0.05 bounds, the test is inconclusive and when the F-test statistic lies above the 0.05 upper bound, the null hypothesis of no level relationship is conclusively rejected. Therefore, the null hypothesis of no cointegration relationship H0 : α1=α2=α3=α4=α5=α6=α7=0 is tested against the alternative hypothesis H1 : α1≠α2≠α3≠α4≠α5≠α6≠α7≠0 After choosing the optimal lag order of each variable following Akaike Information Criteria (AIC), the parsimonious ARDL specification is then used to derive the estimated levels relationship as well as the associated ECM model. The Unrestricted Error Correction Model (UECM) can be estimated as follow: n

n

n

∆ ln EDt =φ0 + θECt −1+ ∑ δ1i ∆ ln EDt − i + ∑ δ 2i ∆ ln Yt − i+ ∑ δ3i

Where α 0 refers the intercept before the shift and α1 and α2 indicate the shift in the intercept at the time of the first and the second breakpoints. β1 Indicates the slop coefficient before the shift, β2 and β3 point out the slope change at the time of the first and the second breakpoint. Dit is a dummy variable that equal zero if t ≤ [nτi] and unity if t > [nτi], where the unknown parameter τi∈(0, 1) and indicate to the date of the first breakpoint and [] denotes the integer part. The H-J approach examine for the null hypothesis of no cointegration against the alternative of cointegration in the presence of two potential structural breaks. Since GH and H-J methods check three alternative unit root tests (ADF, Za and Zt), our analysis depends on Zt test statistics as [52] indicate that Zt is the top in terms of size and power.

i =1

∆ ln(Yt −1)2 n

i =0

i =0

+ n

n

∑ δ4i ∆ ln EUt −i + ∑ δ5i ∆ ln FDt −i + ∑ δ6i ∆ ln EXt −i+μ i =0

i =0

i =0

t

(9) Where θ measure the speed of adjustment by which the variables back to the long run equilibrium after a short run shock, and EC is the error correction term. To check the stability of the estimated coefficients in the estimated model, we use the cumulative (CUSUM) and Cumulative sum of squares (CUSUMQ) tests of recursive residuals. 5. Empirical results

4.4. The ARDL estimation

Table 4 provides the descriptive statistics and pair-wise correlations between all variables. The statistics of Jarque-Bera provide clear evidence that EF, CO2 emissions, real GDP per capita, energy use per capita, financial development and trade openness are having zero mean and finite covariance. This means that all variables are normally distributed. The findings of a pair-wise correlation shows that real

To explore the long and short run relationship between the environment degradation indicators, real GDP per capita and other 1 ADF by Dickey and Fuller [72], PP by Phillips and Perron [76], KPSS by Kwiatkowski et al. [74], DF-GLS by Elliot et al. [73] and Ng-Perron by Ng and Perron [75].

5

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Critical values for Model M1=−4.67, −4.08, −3.77 at 1%, 5%, 10%, respectively. Critical values for Model M2=−5.29, −4.69, −4.40 at 1%, 5%, 10%, respectively. TB1 and TB2 are the dates of the structural breaks.

emissions and real GDP, respectively. Financial development and energy use are inversely correlated with real GDP per capita. Trade openness and financial development are negatively linked with energy use and same inference is drawn between financial development and trade openness. As a first step in the empirical part, we conduct the stationarity tests for all selected variables using two different standard unit root tests: the Augmented Dickey fuller test (ADF) and the Philips-Perron test (PP). Table 5 presents the results for the level and the difference of the variables. Our empirical exercise finds that all variables are nonstationary at level and stationary at difference except for the financial development variable which is integrated of order zero i.e. I(0). The critical problem that is raised when using these unit tests is the absence of any information regarding the presence of structural breaks in the time series. This suggests that ADF and PP tests lead to biased results concerning the stationarity of the variables. To deal with this weakness we have used [48] unit root test in the presence of two unknown structural breaks. The results are detailed in Table 6. The empirical results of [48] tests indicate that the null hypothesis of a unit root cannot be rejected for all variables. The variables are found to be stationary at first difference. This means that all variables are integrated of order 1. The structural break dates are found in all variables, and are mainly around two periods: the first period is the early of 1990s and the second period is in the early and mid of 2000s. The fact that most variables are integrated provides a strong rationale to test the existence of cointegration between the variables. In the second step, our empirical study investigates the presence of the cointegration relationship with the presence of unknown structural breaks among the selected variables. The conventional cointegration approach assumes that the cointegration relationship remains stable over time and the results can be misleading.2 To overcome this shortage, we apply GH and H-J cointegration tests with unknown structural breaks for the two specifications determined in Eq. (2) and Eq. (3). Since the critical values of GH and H-J tests are available only for four dependent variables, our empirical analysis considers two models for each specification. The first model includes per capita real GDP, per capita real GDP squared, energy use per capita and financial development, whereas, the second model includes per capita real GDP, per capita real GDP squared, energy use per capita and trade openness variable. GH and H-J cointegration tests are based on three alternative models: (Model C, Model C/T and Model C/S) and three alternative unit root tests (ADF, Za and Zt) to check for possible shifts in the cointegration vector. Since, GH indicates that Zt test statistics is the best in terms of size and power, our empirical analysis will depend only on the Zt test in both specifications. The empirical results of CO2 and EF models (specifications 1 and 2) are reported in Table 7. The results in Table 7 show that the estimated test values are greater than the critical values and the alternative hypothesis of cointegration with unknown structural breaks cannot be rejected. This is true for both tests, given the GH test with one break point and H-J test with two breaks point. Therefore, the empirical results provide evidence about the existence of a long run relationship between the two indicators of environmental degradation, real GDP per capita, energy use per capita, financial development and trade openness in Qatar, in the presence of two structural breaks, mainly in the beginning of 1990s, and 2000s. The next step is to examine the long and short run impact of real GDP per capita, energy use per capita, financial development and trade openness on environmental degradation using ARDL method. The results of the ARDL estimation take into account the breakpoints in 1991 and 2000 based on the finding of the cointegration with structural

GDP per capita and trade openness are positively correlated with EF and CO2 emissions, while negative correlation is found from energy use and financial development to EF and CO2 emissions, respectively. The positive correlation exists from energy use and trade openness to CO2

2 The results of bound test F-statistic without including the structural break show no cointegration in the long run. The results are not reported but available from authors upon request.

Table 4 Descriptive statistics of the selected variables. lnEF

LnCO2

lnY

lnEU

lnFD

lnEX

2.07 2.05 2.44 1.46 0.25 0.10 1.66 2.19 (0.33)

3.84 4.002 4.22 3.207 0.29 −0.62 1.83 3.50 (0.17)

11.99 12.02 12.32 11.66 0.23 0.0017 1.31 3.41 (0.18)

9.75 9.69 10.04 9.52 0.13 −1.53 4.24 3.6 (0.11)

−1.07 −1.02 −0.42 −2.04 0.35 0.018 2.62 0.18 (0.91)

−0.61 −0.6 −0.30 −0.98 0.18 −0.22 1.92 1.63 (0.44)

Pair-Wise Correlations lnEF 1.000 lnCO2 0.36 lnY 0.46 lnEU −0,38 lnFD 0.19 lnEX 0.23

1.000 0.31 0.41 −0.22 0.19

1.000 −0.14 −0.11 0.85

1.000 −0.52 −0.03

1.000 −0.22

1.0000

Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera

Table 5 Unit root tests. Level

First difference

ADF test results

Intercept

Trend

CO2 EF Y Y2 EU FD EX PP test results CO2 EF Y Y2 EU FD EX

−0.34 −2.38 −0.84 −0.82 −0.36 −2.85* −2.59 Intercept −2.31 −2.05 −1.05 −1.03 −0.96 −2.89* −1.51

−1.896 −2.710 −2.12 −2.11 −1.117 −3.64* −2.80 Trend −2.27 −2.47 −1.61 −1.63 −1.10 −3.78* −2.94

ΔCO2 ΔEF ΔY ΔY2 ΔEU ΔFD ΔEX ΔCO2 ΔEF ΔY ΔY2 ΔEU ΔFD ΔEX

Intercept

Trend

−5.50*** −5.79*** −3.62** −3.61** −5.37*** −5.18*** −5.70*** Level −5.35*** −5.93*** −4.56*** −4.57*** −4.89*** −5.12*** −5.05***

−5.81*** −5.76*** −4.23*** −4.23*** −5.64*** −6.89*** −5.59*** Trend −5.27*** −5.84*** −4.45** −4.46** −5.00*** −7.03*** −5.25***

Note: ADF critical values are −3.60, −2.94 and −2.61 at 1%, 5% and 10% respectively with intercept and −4.20, −3.52 and −3.19 at 1%, 5% and 10% respectively with intercept and trend. PP critical values with intercept are −3.53, −2.92, and −2.60 at 1%, 5% and 10%, respectively. PP critical values with intercept and trend are −4.18, −3.51, and −3.18 at 1%, 5% and 10%, respectively. ***, **,* denotes significance at the 1%, 5%, and 10% levels respectively.

Table 6 Narayan and Popp (2010) [48] unit root test results with two structural breaks. Break in intercept (M1)

CO2 EF Y EU FD EX

Break in intercept and trend (M2)

Test statistic

TB1

TB2

Test statistic

TB1

TB2

1.43 −3.4 −3.25 −2.56 −3.25 −3.004

1995 1991 1991 1996 1991 1991

1997 2000 1996 2000 2000 1999

1.62 −3.6 −2.24 −3.74 2.91 −2.40

1991 2000 1991 1991 1991 1989

2000 2004 1996 2003 1989 2000

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Table 7 Cointegration test with unknown structural breaks. CO2: Specification (1) Model

GH:C C/T C/S H-J:C C/T C/S

EF: Specification (2)

CO2t =f (Yt , Yt2,EUt , FDt )

CO2t =f (Yt , Yt2,EUt , EXt )

EFt =f (Yt , Yt2,EUt , FDt )

EFt =f (Yt , Yt2,EUt , EXt )

Zt

Break date

Zt

Break date

Zt

Break date

Zt

Break date

−4.81 −4.89 −4.52 −6.90 −7.97 −6.26

1991 1991 1993 1992–1993 1991–2000 1991–1998

−4.21 −4.82 −4.87 −6.88 −7.49 −9.42

1989 1991 1991 1992–1993 1991–2000 1992–2000

−5.28 −5.08 −5.84 −5.71 −6.10 −8.12

1993 1993 1994 1991–1998 1991–1998 1994–2000

−4.72 −4.91 −5.67 −5.42 −6.14 −8.61

1992 2000 1991 1994–2000 1985–1997 1994–2000

Note: The critical values −8.353, −7.903, −7.705 at 1%, 5% and 10% respectively are collected from Hatemi-J (2008) [53]. Note that m=4 in this application.

development variable has a negative and statistically significant impact on CO2 emissions in the long-run. This result reveals that an increase of 1% increase in financial development will cause a 0.57% reduction in CO2 emissions in Qatar. A possible reason is that the share of capital used by the financial activities is small compared to other industrial activities, and as a result will causes less CO2 emissions. This negative relationship provides evidence that the financial development has reached a level of efficient allocation of resources with less CO2 emissions. This finding is in line with the results of [57] for the case of Malaysia but different from the results of [28] for the case of Malaysia, Bangladesh and Tunisia. Table 8 also provides the short run results for the first specification of the CO2 model which are similar to those in the long-run. The trade openness has a statistically insignificant effect on CO2 emissions in the short run. The error correction term (EC) has an expected negative sign and significant at the 5% level. This result supports the long run relationship between the selected variables and indicates that any adjustment in CO2 emissions from short run towards long-run equilibrium will occur by 0.8% every year. In the case of the second specification (EF model), the long-run estimated coefficients are found statistically significant except for energy use per capita. In this context, per capita real GDP and per capita real GDP squared have a positive and negative impact on EF, respectively. This result shows that the relationship between EF and real GDP per capita is inverted U-shaped, i.e. the EKC hypothesis is valid for Qatar when using the EF as an indicator for environmental degradation. The negative sign of squared term seems to corroborate the delinking of EF and real GDP at the higher level of income per capita. This infers that environment degradation, measured by EF, first increases as GDP per capita increases but then it starts to decrease after a certain turning point of income level. Nowadays, the success of a nation takes into account its contribution to the protection of the environment and natural resources and its efficiency for implementing policies to improve the standards of living without environment degradation. In recent years, the Qatar government has launched a long term strategy to minimize the negative impact of economic development on environment. The other explanatory variables have different effects on the EF compared to the CO2 emissions. On the one hand, the financial development has a long run positive impact on EF per capita and is statistically significant at 10% level. Any increase of 1% in FD causes an increase by 0.36% in EF. Given that EF is a global environment indicator, the net effect of the financial development on EF is positive which is contrary to its effect on the CO2 emissions as a partial environmental indicator. These results confirm that financial development promotes economic activity, which in turns increases the global human demands on nature and contributes to environmental degradation. On the other hand, the trade openness has a negative and significant impact on EF. This indicates that a 1% increases in export to GDP ratio decreases the EF by 2.14%, which suggests that trade

Table 8 ARDL model long and short run parameters estimations. CO2: Specification (1)

EF: Specification (2)

Variable

Coefficient

T-statistic

Coefficient

T-statistic

Y Y2 EU FD EX D2000 D1991

−113.65 4.72 0.51 −0.57 1.23 0.23 0.66

−4.24 4.22*** 11.91*** −6.21*** 3.95*** 3.13** 17.2***

94.07 −3.87 0.08 0.36 −2.14 0.72 0.45

3.18** −3.12** 0.72 2.17* −5.26*** 2.79** 4.88***

***

ECM for ARDL(1,1,2,2,0,2,2,0) model ΔCO2(−1)/ ΔEF(−1)/ ΔY 2 ΔY ΔEU ΔFD ΔEX ΔD2000 ΔD1991 EC (−1)

0.046

0.45

ECM for the ARDL (2,0,2,0,2,1,1) model 0.059 0.27

−21.8 0.93 0.18 −0.30 0.28 −0.50 −0.30 −0.8

−1.25 1.28 4.64*** −3.51** 1.33 −5.45*** −4.05*** −16.2***

82.18 −3.49 0.36 0.32 −0.26 0.052 0.28 −0.9

3.56*** −3.63*** 3.15** 2.09* −1.22 0.52 3.70*** −4.35***

Note: The values in parentheses are the t-ratios. *** Significance at 1% level. ** Significance at 5% level. * Significance at 10% level.

breaks tests in Tables 6 and 7. The results are detailed in Table 8 for the CO2 and the EF model specifications. In the case of the first specification (CO2 model), the results show that all the long-run coefficients are found statistically significant. In particularly, the per capita real GDP and the per capita real GDP squared have a negative and positive impact on CO2, respectively. This result indicates that the relationship between CO2 emission and real GDP per capita is U-shaped i.e. the EKC hypothesis is not valid for Qatar when using CO2 as indicator of environmental degradation. An explanation for this finding is that the CO2 emissions to real GDP per capita ratio (emission intensity) was smaller compared to the same ratio after a certain point of the economic development in Qatar. This reflects the long-run Qatar transition toward more gas and oil production and the significant increase in industrial activities related to the energy sector. This result is consistent with the findings of [56]. Moreover, energy use and export to GDP ratio have a long run positive and significant impact on CO2 emissions. The results show that a 1% increase in energy use combined with a trade openness indicator increase CO2 emissions by 0.51% and 1.23%, respectively. This longrun positive impact is expected as Qatar relies on the production and the exports of gas and oil to sustain its economic growth. The financial 7

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Fig. 4. CSUM and CSUMSQ stability test for CO2 specification.

environment indicators: the CO2 emissions and the EF. The CO2 emissions represent only a small share of total environmental degradation, whereas the EF is considered as a more comprehensive measure of environmental damage. Moreover, we apply the GH and H-J tests with the presence of unknown structural breaks to check if there is a long-run relationship between all variables. The co-integration test results suggest the existence of a long-run relationship among the selected variables with a shift in the cointegration vector in 1991 and 2000. The estimated long-run model indicates a negative impact of per capita real GDP and a positive impact of per capita real GDP squared on the CO2 emissions. In contrast, the long-run effects of per capita real GDP and per capita real GDP squared on EF were found to be positive and negative, respectively. Then, our empirical findings support the validity of EKC hypothesis when we use the EF as an indicator for the environment degradation. This result is consistent with Ozturk and Acaravci [29], Shahbaz et al. [62], and Halicioğlu [43]. On the other hand, the result reveals that the EKC hypothesis is not valid when we use the CO2 emissions as an indicator of environmental degradation. This outcome is consistent with Lise [58], Akbostancı et al. [59], Ozturk and Acaravci [18]. The short-run analysis reveals that the error correction terms have a negative sign for both specifications of CO2 and EF. Moreover, the coefficients of the error correction term in the ECM are significant, which means the existence of a long-run relationship between the variables. Finally, it is worth noting that the shape of the EKC curve and the impact of per capita real GDP on environmental quality are subject to the environmental indicators used by the empirical analysis. The long-run impacts of the other explanatory variables in the CO2 model are different from those in the EF model except the energy use variable, which has a positive impact in both CO2 emissions and EF. The financial development has a negative impact on CO2 emissions but a positive impact on EF which reflects that financial activities increase environmental damage through the increasing of human global demand on nature. On the other hand, the trade openness indicator has positive and negative impacts on CO2 and EF, respectively. These two

openness improves the environmental conditions in the long run in Qatar. This analysis confirms the positive spillover technological effects of trade openness through export activities which reduce the EF in the long run contrary to the positive scale effect captured by the CO2 model. Moreover, the impact of the breakpoints detected by H-J tests in 1991 and 2000 are positive and statistically significant at 1% level in the long run for both specifications. This indicates that the occurred structural economic events in Qatar have a positive impact on environmental degradation. The short run estimations as shown in Table 8 for the second specification of EF model are similar to those in long run. The error correction term (EC) has an expected negative sign and is significant at 5% level. This result supports the cointegration among the selected variables and points out that the speed of adjustment from short run towards long run equilibrium will occur by 0.9% every year. It is worth mentioning that the result of the global environment specification (EF) is inconsistent with that obtained by the partial environment specification (CO2). To this end, the shape of the EKC curve and the long-run impacts of per capita real GDP on the environment quality are subject to the environmental indicators used by the empirical analysis. The stability of the long run specifications is tested by applying the CUSUM and CUSUMSQ tests. The plots of both CUSUM and CUSUMSQ statistics are reported in Figs. 4 and 5. These figures demonstrate that the straight line presents the critical bound at 5% significant. The results of CUSUM and CUSUMSQ for both specification show that the parameters of both CO2 and EF models are stable in the short and the long runs.

6. Conclusion and policy implications The objective of this paper is to investigate the impact of per capita real GDP, the square of per capita real GDP, energy use, financial development, and trade openness on environmental degradation in the case of Qatar over the period 1980–2011, by employing the ARDL approach. To this end, we compare the results for two alternative

Fig. 5. CSUM and CSUMSQ stability test for FP specification CUSUMQ stability tests.

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