On the relationship between energy consumption, CO2 emissions and economic growth in Europe

Energy 35 (2010) 5412e5420

Contents lists available at ScienceDirect

Energy journal homepage: www.elsevier.com/locate/energy

On the relationship between energy consumption, CO2 emissions and economic growth in Europe Ali Acaravci a,1, Ilhan Ozturk b, * a b

Faculty of Economics and Administrative Sciences, Mustafa Kemal University, Antakya-Hatay, Turkey Faculty of Economics and Administrative Sciences, Cag University, 33800, Mersin, Turkey

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 February 2010 Received in revised form 29 June 2010 Accepted 7 July 2010 Available online 24 August 2010

This study examines the causal relationship between carbon dioxide emissions, energy consumption, and economic growth by using autoregressive distributed lag (ARDL) bounds testing approach of cointegration for nineteen European countries. The bounds F-test for cointegration test yields evidence of a long-run relationship between carbon emissions per capita, energy consumption per capita, real gross domestic product (GDP) per capita and the square of per capita real GDP only for Denmark, Germany, Greece, Iceland, Italy, Portugal and Switzerland. The cumulative sum and cumulative sum of squares tests also show that the estimated parameters are stable for the sample period. We found a positive long-run elasticity estimate of emissions with respect to energy consumption at 1% significant level in Denmark, Germany, Greece, Italy and Portugal. Positive long-run elasticity estimates of carbon emissions with respect to real GDP and the negative long-run elasticity estimates of carbon emissions with respect to the square of per capita real GDP at 1% significance level in Denmark and 5% significant level in Italy are also found. These results support that the validity of environmental Kuznets curve (EKC) hypothesis in Denmark and Italy. This study also explores causal relationship between the variables by using error-correction based Granger causality models. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Carbon dioxide emissions Energy consumption Growth Environmental Kuznets curve Europe

1. Introduction The relationship between energy consumption and economic growth, as well as economic growth and environmental pollution, has been the subject of intense research in the last three decades. However, the empirical evidence remains controversial and ambiguous to date. The existing literature reveals that empirical studies differ substantially and are not conclusive to present policy recommendation that can be applied across countries. An assessment of the existing literature suggests that most studies focus either on the nexus of economic growtheenergy consumption or economic growtheenvironmental pollutants where little effort has been made to test these two links under the same framework. Therefore, the aim of study is an attempt to fill this gap. Basically there are three research strands in literature on the relationship between economic growth, energy consumption and environmental pollutants (Zhang and Cheng [1]). The first strand * Corresponding author. Tel./fax: þ90 324 6514828. E-mail addresses: [email protected] (A. Acaravci), [email protected] (I. Ozturk). 1 Tel.: þ90 3262455845; fax: þ90 3262455854. 0360-5442/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2010.07.009

focuses on the environmental pollutants and economic growth nexus which are closely related to testing the validity of the socalled environmental Kuznets curve (EKC) hypothesis. EKC hypothesis postulates an inverted U-shaped relationship between the level of environmental degradation and income growth. That is to say, environmental degradation increases with per capita income during the early stages of economic growth, and then declines with per capita income after arriving at a threshold. In addition, the EKC hypothesis specifies emissions as a function of income which indicates unidirectional causality running from income to emissions. The EKC hypothesis was first proposed and tested by Grossman and Krueger [2]. The studies of Stern [3], Coondoo and Dinda [4], Dinda [5], Luzzati and Orsini [6], Halicioglu [7], among others, provide extensive review surveys of the studies which tested the economic growtheenvironmental pollution nexus and EKC hypothesis. The second strand of the research is related to energy consumption and output nexus. This nexus suggests that economic development and output may be jointly determined, because economic growth is closely related to energy consumption as higher economic development requires more energy consumption (Halicioglu [7]). Following the study of Kraft and Kraft [8], an extensive number of empirical works have assessed the empirical

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

evidence employing Granger causality and cointegration model. Ozturk [9] provides an extensive review survey of the studies on the empirical results from causality tests between energy consumption and economic growth. The third strand is a combined approach of these two methods which investigate the dynamic relationships between economic growth, environmental pollutants and energy consumption altogether. The studies of Richmond and Kaufman [10], Ang [11], Soytas et al. [12], Zhang and Cheng [1], Halicioglu [7], Apergis and Payne [13], Soytas and Sari [14] and Akbostanci et al. [15], among others, investigate growtheenergyepollutant nexus in the same framework. To our knowledge, there is no such study that uses these variables in the same framework for the countries studied in this paper. In this study, we examine the long-run and causal relationships issues between economic growth, carbon emissions and energy consumption for the selected nineteen countries from the European continent by using recently developed autoregressive distributed lag (hereafter ARDL) bounds testing approach of cointegration by Pesaran and Shin [16] and Pesaran et al. [17], and errorcorrection based Granger causality models. The rest of the paper is organized as follows. The next section presents the methodology and data. The third section reports the empirical results. The last section concludes the paper.

5413

employed with their natural logarithms form to reduce heteroskedasticity and to obtain the growth rate of the relevant variables by their differenced logarithms. The parameters b, q and f, indicate the long-run elasticity estimates of carbon emissions per capita with respect to energy consumption per capita, real GDP per capita, and squared real GDP per capita, respectively. The positive long-run elasticity estimates of carbon emissions per capita with respect to energy consumption per capita indicate that increase in energy consumption per capita results in an increase in carbon emissions per capita. Under the EKC hypothesis the long-run elasticity estimates of carbon emissions per capita with respect to real GDP per capita and the square of per capita real GDP per capita are expected to be qi0 and 4h0, respectively. This means there exists an inverted U-shaped pattern that as real GDP per capita increases, carbon emissions per capita increase as well until some threshold level of real GDP per capita is reached after which carbon emissions per capita begin to decline. The long-run and causal relationships between economic growth, carbon emissions, energy consumption and the square of per capita real GDP in European countries will be performed in two steps. Firstly, we will test the long-run relationships among the variables by using the ARDL bounds testing approach of cointegration. Secondly, we will test the causal relationships by using the error-correction based causality models. 2.1. ARDL cointegration analysis

2. Methodology and data To investigate the long-run relationship between carbon emissions per capita, energy consumption per capita and real gross domestic product (GDP) per capita, we employed the following equation:

cot ¼ a þ bect þ qyt þ 4y2t þ 3t

(1)

where, co is the carbon dioxide emissions (measured in metric kilograms per capita), ec is the energy use (measured in kg of oil equivalent per capita), y is per capita real GDP (constant 2000 US$), y2 is the square of per capita real GDP and 3t is the error term. The annual time series data are taken from the World Development Indicators (WDI) online database for the nineteen European countries. The data periods have varied for these countries (see Table 1). All variables are

The ARDL bounds testing approach of cointegration is developed by Pesaran and Shin [16] and Pesaran et al. [17]. The ARDL cointegration approach has numerous advantages in comparison with other cointegration methods such as Engle and Granger [18], Johansen [19], and Johansen and Juselius [20] procedures: (i) no need for all the variables in the system be of equal order of integration, (ii) it is an efficient estimator even if samples are small and some of the regressors are endogenous, (iii) it allows that the variables may have different optimal lags, and (iv) it employs a single reduced form equation. However, if the order of integration of any of the variables is greater than one, for example an I(2) variable, then the critical bounds provided by Pesaran et al. [17] and Narayan [21] are not valid. They are computed on the basis that the variables are I(0) or I

Table 1 Descriptive statistics of variables. Carbon dioxide emissions (metric tons per capita)

Austria Belgium Denmark Finland France Germany Greece Hungary Iceland Italy Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland UK

Energy use (kg of oil equivalent per capita)

Real GDP (2000 US dollars per capita)

Mean

Median

Max.

Min.

SD

Mean

Median

Max.

Min.

SD

Mean

Median

Max.

Min.

SD

7.00 11.11 10.52 9.27 7.17 11.97 5.30 6.37 7.43 6.03 28.58 8.97 6.99 3.20 4.80 7.52 5.82 10.27

7.19 10.47 10.65 10.12 6.88 12.59 5.54 6.04 7.46 6.36 27.28 8.97 7.14 2.91 5.14 6.87 5.93 10.02

8.94 14.24 12.74 13.15 9.50 14.07 8.59 8.12 8.79 7.77 40.17 11.03 11.45 6.01 7.92 11.46 7.07 11.65

4.37 8.78 6.50 3.35 5.85 9.51 1.13 4.54 6.10 2.18 17.31 6.39 3.66 0.92 1.61 4.78 3.64 9.07

1.09 1.49 1.51 2.44 1.14 1.42 2.36 1.02 0.58 1.48 7.13 1.07 1.65 1.65 1.70 1.96 0.68 0.84

2926 4639 3569 4887 3485 3890 1632 2382 7084 2273 9829 4118 4395 1311 1899 4949 3048 3661

3033 4734 3744 4979 3583 4222 1679 2495 7356 2300 9852 4483 4522 1140 1838 5203 3281 3720

4135 6037 4295 7121 4551 4726 2794 2943 12179 3169 13095 5077 7151 2574 3340 5917 3804 4012

1549 2548 1959 2200 1741 1962 303 1428 2961 798 7719 1851 1935 336 531 2731 1406 3044

699 1004 525 1373 838 854 753 434 2765 629 1426 943 1291 732 834 947 700 256

16,429 15,823 21,348 16,019 16,107 18,124 8762 3491 21,879 13,393 27,211 16,561 24,116 6802 9569 20,409 28,708 17,410

16,459 15,668 20,564 16,280 16,400 17,770 9421 3727 23,366 13,312 21,359 15,733 22,954 6377 8867 19,839 28,770 16,092

25,299 23,945 31,464 26,310 23,693 23,707 14,002 5854 36,136 19,636 52,307 25,062 40,618 11,168 15,713 30,873 35,783 27,200

7479 7486 11,387 7305 7633 11,859 3419 1454 9908 5819 13,671 8476 10,697 2343 3716 11,337 18,970 10,480

5435 4865 5835 5366 4740 3812 2611 1149 7165 4381 11807 4871 9189 2784 3301 5071 4665 4889

Notes: Max., Min. and SD are maximum, minimum and standard deviation, respectively. Data period is 1970e2005 for Germany, 1965e2005 for Hungary and 1960e2005 for the rest of countries (see Table 4).

5414

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

Table 2 The null hypotheses for Granger causalities. Short-run

Dco Dec Dy Dy2

Long-run

Dco

Dec

Dy

Dy2

ji

e

p12;1 ¼ . ¼ p12;k ¼ 0

p13;1 ¼ . ¼ p13;k ¼ 0 p23;1 ¼ . ¼ p23;k ¼ 0

p14;1 ¼ . ¼ p14;k ¼ 0 p24;1 ¼ . ¼ p24;k ¼ 0 p34;1 ¼ . ¼ p34;k ¼ 0

j1 j2 j3 j4

p21;1 ¼ . ¼ p21;k ¼ 0 p31;1 ¼ . ¼ p31;k ¼ 0 p41;1 ¼ . ¼ p41;k ¼ 0

e

p32;1 ¼ . ¼ p32;k ¼ 0 p42;1 ¼ . ¼ p42;k ¼ 0

(1). For this purpose, it is necessary to test for unit root to ensure that all the variables satisfy the underlying assumptions of the ARDL bounds testing approach of cointegration methodology before proceeding to the estimation stage. In order to overcome the low power problems associated with conventional unit root tests, especially in small samples, we therefore prefer the weighted symmetric augmented DickeyeFuller (ADF) test (ADF-WS) of Park and Fuller [22]. It requires much shorter sample sizes than conventional unit root tests to attain the same statistical power.

e

p43;1 ¼ . ¼ p43;k ¼ 0

e

¼ ¼ ¼ ¼

0 0 0 0

Leybourne et al. [23] have recently noted that ADF-WS has good size and power properties compared to other tests. Basically, the ARDL bounds testing approach of cointegration involves two steps for estimating long-run relationship. The first step is to investigate the existence of long-run relationship among all variables in the equation. The ARDL model for the standard loglinear functional specification of long-run relationship between carbon emissions per capita, energy consumption per capita and real GDP per capita may follow as:

Table 3 Unit root test results. ADF-WS (levels) Austria co ec y y2 Denmark co ec y y2 France co ec y y2 Greece co ec y y2 Iceland co ec y y2 Luxembourg co ec y y2 Norway co ec y y2 Spain co ec y y2 Switzerland co ec y y2 CV

ADF-WS (1st differences)

1.2487 (0) 0.9567 (0) 0.2815 (0) c 0.1788 (1)

cþt cþt þt cþt

7.7629 5.5412 5.2605 3.4955

(0) (0) (0) (1)

c c c c

0.9703 0.0965 0.6907 1.1812

c c c c

þ þ þ þ

t t t t

7.7629 5.5412 6.8207 4.9540

(0) (0) (0) (1)

c c c c

0.7365 (0) 0.2927 (0) 0.8435 (0) c 0.2902 (0)

cþt cþt þt cþt

7.7629 5.5412 3.7134 2.8774

(0) (0) (0) (1)

c c c c

0.2074 (0) c 0.7286 (0) c 0.2373 (0) c 0.4326 (1)

þt þt þt cþt

7.7629 5.5412 4.2643 3.0519

(0) (0) (0) (1)

c c c c

4.0490 2.3942 2.3721 0.4324

(0) (0) (1) (0)

c c c c

þ þ þ þ

t t t t

5.5412 (0) c 4.7742 (1) c 4.4247 (0) c

2.5764 1.6201 1.4731 0.6500

(1) (0) (0) (1)

c c c c

þ þ þ þ

t t t t

7.7629 5.5412 5.6233 4.2136

(0) (0) (0) (1)

c c c c

1.1490 0.6504 1.2764 0.9637

(1) (0) (1) (0)

c c c c

þ þ þ þ

t t t t

7.7629 5.5412 4.3750 4.0384

(0) (0) (0) (1)

c c c c

0.3829 (0) 0.0586 (0) c 0.1608 (1) 0.8159 (0) c

cþt þt cþt þt

7.7629 5.5412 2.0831 2.6234

(0) (0) (0) (0)

c c c c

0.1131 (0) 0.4897 (0) c 1.4426 (1) 0.7739 (0) 3.3206 (0) 3.2397 (1)

cþt þt cþt cþt

7.7629 5.5412 4.3929 4.4913 2.5188 2.6597

(0) (0) (0) (1) (0) (1)

c c c c

(0) (0) (0) (1)

ADF-WS (levels)

7.7112 (0) c

2.5587 (0)

Belgium co 1.3822 (0) ec 0.8612 (0) y 0.3088 (0) c y2 0.2086 (1) Finland co 0.5044 (0) ec 0.3681 (0) y 1.9995 (1) y2 1.1996 (0) Germany co 0.5740 (0) ec 0.3842 (0) y 5.2583 (0) 2 y 0.9223 (0) Hungary co 0.3008 (0) ec 0.4288 (0) y 1.5081 (1) y2 0.7437 (0) Italy co 1.8083 (0) c ec 1.5682 (0) c y 1.1880 (0) c y2 0.4431 (1) c Netherlands co 1.1781 (0) ec 0.0418 (0) c y 1.5964 (1) 2 y 0.8596 (0) Portugal co 2.0203 (0) ec 1.1252 (0) y 1.3339 (1) 2 y 0.4148 (0) Sweden co 0.9165 (0) ec 0.3982 (0) y 1.1453 (1) y2 0.4898 (0) United Kingdom co 3.2492 (0) ec 1.7881 (0) y 3.9426 (1) y2 2.4068 (0) 3.3206 (0) 3.2397 (1)

ADF-WS (1st differences)

cþt cþt þt cþt

6.3205 5.2205 5.2583 3.2665

(0) (0) (0) (1)

c c c c

c c c c

þ þ þ þ

t t t t

6.2643 3.9135 4.5326 3.6680

(0) (0) (1) (1)

c c c c

c c c c

þ þ þ þ

t t t t

6.2643 3.9135 4.9009 4.9011

(0) (0) (1) (1)

c c c c

c c c c

þ þ þ þ

t t t t

6.2643 3.9135 3.3623 2.8755

(0) (0) (0) (1)

c c c c

t t t t

6.2643 3.9135 4.2411 3.2859

(0) (0) (0) (1)

c c c c

cþt þt cþt cþt

6.2643 3.9135 4.7820 3.5090

(0) (0) (0) (1)

c c c c

c c c c

þ þ þ þ

t t t t

6.2643 3.9135 3.8553 3.9340

(0) (0) (0) (1)

c c c c

c c c c

þ þ þ þ

t t t t

6.2643 3.9135 3.8984 4.0340

(0) (0) (0) (1)

c c c c

c c c c

þ þ þ þ

t t t t

þ þ þ þ

3.9135 (0) c 4.8926 (0) c 2.5188 (0) 2.6597 (1)

Notes: Model c þ t has the DickeyeFuller regressions include an intercept and a linear trend, model c has the DickeyeFuller regressions include an intercept but not a trend. Numbers of lags are in (). CV is the 95% simulated critical value, computed by stochastic simulations for relevant numbers of lags are in () using 1000 replications.

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

Dcot ¼ a1 þ

a1 X

f1i Dcoti þ

i¼1

þ

d1 X

b1 X

b1j Dectj þ

j¼0

c1 X

f2i coti þ

i¼1

p¼0

41q Dy2tq þ d1 cot1 þ d2 ect1 þ d3 yt1

þ

d1 X

b1 X

c1 X

b2j ectj þ

q2p ytp

p¼0

j¼0

42q y2tq þ 32t

(3)

q¼0

q¼0

þ d4 y2t1 þ 31t

ð2Þ

where 31t and D are the white noise term and the first difference operator, respectively. An appropriate lag selection based on a criterion such as Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). The bounds testing procedure is based on the joint F-statistic or Wald statistic that tested the null hypothesis of no cointegration, H0 : dr ¼ 0, against the alternative of H1 : dr s0; r ¼ 1;2;3;4. Two sets of critical values that are reported in Pesaran et al. [17] provide critical value bounds for all classifications of the regressors into purely I(1), purely I(0) or mutually cointegrated. If the calculated F-statistic lies above the upper level of the band, the null hypothesis is rejected, indicating cointegration. If the calculated F-statistic is below the upper critical value, we cannot reject the null hypothesis of no cointegration. Finally, if it lies between the bounds, a conclusive inference cannot be made without knowing the order of integration of the underlying regressors. Recently, Narayan [21] argues that the existing critical values, because they are based on large sample sizes, cannot be used for small sample sizes. Narayan [21] regenerated the set of critical values for the limited data ranging from 30 to 80 observations by using the Pesaran et al.’s [17] GAUSS code. With the limited annual time series for the nineteen European countries data on carbon emissions per capita, energy consumption per capita and real GDP per capita, this study employs the critical values of Narayan [21] for the bounds F-test rather than Pesaran et al. [17]. The second step is to estimate the following long-run and shortrun models that are represented in Equations (3) and (4) if there is evidence of long-run relationships (cointegration) between these variables.

Table 4 Estimated ARDL models and bounds F-test for cointegration.

Dcot ¼ a3 þ

Periods

Models

F

Austria Belgium Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom

1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1970e2005 1960e2005 1965e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005 1960e2005

(1,0,0,0) (1,1,0,0) (1,1,0,0) (1,1,0,0) (1,1,0,1) (1,1,0,0) (1,0,0,0) (1,1,0,0) (1,1,0,0) (0,0,0,0) (1,0,1,0) (1,1,0,0) (1,1,0,0) (1,0,0,0) (1,0,0,0)

14.321* 1.456 2.070 2.235 4.715**

(1,1,0,0) (1,0,0,0) (1,1,0,0)

1.046 10.798* 1.501

I(0) 4.614 3.272 2.676

I(1) 5.966 4.306 3.586

1.762 1.080 6.278* 1.569 1.417 4.013*** 6.971* 1.671 3.886***

Notes: F is the ARDL cointegration test. The critical values for the lower I(0) and upper I(1) bounds are taken from Narayan (2005, Appendix: Case II). *, ** and *** are 1%, %5 and %10 significance levels, respectively.

a1 X

f3i Dcoti þ

i¼1

þ

d1 X

b1 X

b3j Dectj þ

c1 X

q3p Dytp

p¼0

j¼0

43q Dy2tq þ jECTt1 þ 33t

(4)

q¼0

where j is the coefficient of error-correction term (hereafter ECT). ECT, defined as:

ECTt ¼ cot  a2 

a1 X

f2i coti 

i¼1



d1 X

b1 X

b2j ectj 

j¼0

c1 X

q2p ytp

p¼0

42q y2tq

(5)

q¼0

It shows how quickly variables converge to equilibrium and it should have a statistically significant coefficient with a negative sign. 2.2. Causality analysis ARDL cointegration method tests the existence or absence of long-run relationships between carbon emissions per capita, energy consumption per capita, real GDP per capita and the square of per capita real GDP. It does not indicate the direction of causality. Thus, we use the two-step procedures from the Engle and Granger [18] model to examine the causal relationship between carbon emissions per capita, energy consumption per capita, square of real GDP per capita and real GDP per capita. Once estimating the long-run model in Equation (3) in order to obtain the estimated residuals, the next step is to estimate error-correction based Granger causality models. As opposed to the conventional Granger causality method, the error-correction based causality test allows for the inclusion of the lagged error-correction term derived from the cointegration equation (see Odhiambo [24]). Thus, the following models may employ to explore the causal relationships between the variables:

2

Countries

Critical values2 at 1% Critical values2 at 5% Critical values2 at 10%

a1 X

cot ¼ a2 þ

q1p Dytp

5415

3 2 3 2 p11;1 Dcot m1 6 Dec 7 6 m 7 6 p t7 6 6 2 7 6 21;1 6 7 ¼6 7 þ 6 4 Dyt 5 4 m3 5 4 p31;1

Dy2t

p12;1 p13;1 p22;1 p23;1 p32;1 p33;1 p m4 41;1 p42;1 p43;1 2 p11;k p12;k p13;k 6p 6 21;k p22;k p23;k þ.þ6 4 p31;k p32;k p33;k p41;k p42;k p43;k 3 2 2 3 34t j1 63 7 6j 7 6 5t 7 6 27 þ 6 7ECTt1 þ 6 7 4 36t 5 4 j3 5 37t j4

p14;1 32 Dcot1 3 7 6 p24;1 7 76 Dect1 7 76 7 p34;1 54 Dyt1 5 p44;1 Dy2t1 32 p14;k Dcotk 3 7 6 p24;k 76 Dectk 7 7 76 7 p34;k 54 Dytk 5 2 p44;k Dytk ð6Þ

Residual terms, 34t , 35t , 36t and 37t , are independently and normally distributed with zero mean and constant variance. An appropriate lag selection is based on a criterion such as AIC and SBC. Using Equation (6), causal relationships can be examined in two ways: i) Short-run or weak Granger causalities are detected through the F-statistic or Wald test for the significance of the relevant p coefficients on the first differenced series.

5416

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

Fig. 1. Plot of CUSUM and CUSUMSQ tests for the parameter stability.

ii) Another possible source of causation is the ECT in equations. The long-run causalities are examined through the t-test or Wald test for the significance of the relevant j coefficients on the lagged ECT (see Table 2). 3. Empirical results Results of the weighted symmetric ADF test (ADF-WS) are presented in Table 3. The null hypothesis is unit root and the alternative hypothesis is level stationary. The DickeyeFuller regressions include an intercept and a linear trend in the levels, and include an intercept in the first differences. The numbers of optimal lags are based on SBC. Ninety-fivepercent of simulated critical values for several observations are computed by stochastic simulations. The results indicate that only real GDP per capita for Spain is I(2) and then we drop this country from ARDL analysis; on the other hand carbon emissions variable for Iceland and United Kingdom, and real

GDP per capita for United Kingdom are I(0). Thus we can confidently apply the ARDL methodology to our model for all countries except Spain. According to Pesaran and Shin [16], the SBC is generally used in preference to other criteria because it tends to define more parsimonious specifications. With the limited observations, this study used SBC to select an appropriate lag for the ARDL model. Table 4 presents the estimated ARDL models that have passed several diagnostic tests that indicate no evidence of serial correlation and heteroskedasticity. The bounds F-test for cointegration test yields evidence of a long-run relationship between carbon emissions per capita, energy consumption per capita, real GDP per capita and the square of per capita real GDP at 1% significance level in Denmark, Greece, Italy and Switzerland; 5% significance level in Portugal; and 10% significance level in Germany and Iceland (see Table 4). Finally, no long-run relationship has been found for Austria,

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

Fig. 1. (continued).

5417

5418

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

Table 5 Estimated coefficients from ARDL models. Regressors

Denmark

Long-run elasticities ec 0.7243 (5.35)* y 16.9937 (3.70)* 0.8658 (3.78)* y2 Constant 79.9884 (3.63)* ect(1) 0.5379 (4.88)* Short-run elasticities co(1) 0.4621 (4.19)* eu 1.2912 (12.67)* eu(1) 0.9016 (6.33)* y 9.1415 (3.63)* y(1) 0.4658 (3.68)* y2 Constant 43.0282 (3.58)* ARDL() R2 RSS LM HET

(1,1,0,0) 0.9491 0.0433 0.042 [0.836] 1.684 [0.194]

Germany

Greece

Iceland

Italy

Portugal

Switzerland

1.2755 (8.14)* 0.6521 (0.17) 0.0618 (0.32) 1.7758 (0.10) 0.5865 (4.36)*

0.9525 (6.83)* 1.3253 (0.65) 0.0794 (0.32) 3.9119 (0.43) 0.5731 (4.83)*

0.2486 (0.83) 4.5194 (1.50) 0.2100 (1.31) 13.1040 (0.86) 0.4870 (4.08)*

0.8468 (13.47)* 3.3916 (2.19)** 0.1816 (2.26)** 13.6543 (0.43)*** 0.5834 (5.61)*

1.0703 (6.08)* 0.4170 (0.46) 0.0365 (0.59) 0.4115 (0.10) 0.7781 (8.13)*

1.4978 (1.68) 30.4373 (0.48) 1.3839 (0.46) 163.2676 (0.51) 0.3984 (2.59)**

0.4135 (3.08)* 1.0957 (11.34)* 0.3477 (1.95)*** 0.3825 (0.17)

0.4269 (3.60)* 0.5459 (3.39)*

0.4166 (4.00)* 0.4941 (5.62)*

0.2219 (2.32)** 0.8328 (5.20)*

0.6016 (3.92)* 0.5967 (2.94)*

0.7596 (0.68)

0.5130 (4.30)* 0.6838 (3.85)* 0.8048 (4.97)* 2.2010 (0.42)

0.3245 (0.47)

12.1250 (0.57)

0.0362 (0.31) 1.0414 (0.10)

0.0455 (0.75) 2.2421 (0.45)

0.1023 (1.26) 6.3819 (0.85)

2.4597 (2.27)** 0.4909 (2.53)*** 0.1059 (1.70)*** 6.3819 (0.85)

0.3202 (0.10) 6.3819 (0.85)

0.5513 (0.54) 65.0390 (0.61)

(1,1,0,0) 0.9917 0.0051 1.100 [0.294] 0.110 [0.741]

(1,0,0,0) 0.9941 0.0840 0.055 [0.815] 0.329 [0.566]

(1,1,0,0) 0.6193 0.1046 0.086 [0.770] 0.289 [0.591]

(1,0,1,0) 0.9970 0.0102 0.155 [0.694] 1.036 [0.309]

(1,0,0,0) 0.9920 0.0651 0.375 [0.540] 0.265 [0.606]

(1,0,0,0) 0.8683 0.0727 0.727 [0.394] 0.114 [0.736]

Notes: (1) refers one lag of relevant variables. t-statistics for coefficients are in (). RSS is the residual sum of squares; LM and HET are the Lagrange-multiplier statistics for the null hypotheses that are residuals have no serial correlation and heteroskedasticity, respectively. These statistics are distributed as c2 distribution with only one degree of freedom under and their p-values are in []. *,** and *** are 1%, %5 and %10 significance levels, respectively.

Table 6 Granger causality test results. Variables

Short-run (or weak) causalities

Long-run causalities

Dco

Dec

Dy

Dy2

Denmark Dco Dec Dy Dy2

e 1.103 (0.294) 0.118 (0.731) 0.083 (0.773)

1.959 (0.162) e 0.422 (0.516) 0.366 (0.545)

4.922 (0.027)** 5.889 (0.015)** e 0.091 (0.763)

4.734 (0.030)** 5.601 (0.018)** 0.225 (0.635) e

6.656 1.058 1.042 1.001

(0.010)** (0.304) (0.307) (0.317)

Germany Dco Dec Dy Dy2

e 0.755 (0.385) 0.021 (0.885) 0.030 (0.864)

0.726 (0.394) e 0.048 (0.827) 0.069 (0.793)

1.328 (0.249) 0.711 (0.399) e 2.305 (0.129)

1.340 (0.248) 0.763 (0.382) 2.095 (0.148) e

1.879 0.025 0.004 0.008

(0.170) (0.875) (0.950) (0.929)

Greece Dco Dec Dy Dy2

e 0.291 (0.590) 0.001 (0.979) 0.001 (0.979)

0.149 (0.699) e 0.378 (0.539) 0.354 (0.552)

1.935 (0.164) 8.971 (0.003)* e 0.375 (0.540)

1.909 (0.167) 7.985(0.004)* 0.522 (0.470) e

8.531 0.032 0.248 0.191

(0.004)* (0.858) (0.619) (0.662)

Iceland Dco Dec Dy Dy2

e 1.961 (0.161) 0.001 (0.985) 0.005 (0.946)

0.001 (0.981) e 0.476 (0.490) 0.449 (0.503)

2.084 (0.145) 0.192 (0.662) e 0.361 (0.548)

1.963 (0.161) 0.114 (0.735) 0.478 (0.489) e

10.092 1.058 0.102 0.108

(0.001)* (0.304) (0.749) (0.743)

Italy Dco Dec Dy Dy2

e 4.811 (0.028) 0.848 (0.357) 0.769 (0.381)

4.679 (0.031)** e 1.119 (0.290) 1.082 (0.298)

4.740 (0.030)** 6.259 (0.012)** e 1.073 (0.300)

4.807 (0.028) ** 6.429 (0.011)** 1.409 (0.235) e

22.825 9.273 1.848 1.846

(0.000)* (0.002)* (0.174) (0.174)

Portugal Dco Dec Dy Dy2

e 1.465 (0.226) 0.001 (0.991) 0.001 (0.992)

0.163 (0.686) e 0.523 (0.469) 0.476 (0.490)

0.017 (0.890) 0.018 (0.894) e 1.162 (0.281)

0.117 (0.733) 0.004 (0.948) 1.734 (0.188) e

3.593 0.258 2.109 2.191

(0.058)*** (0.612) (0.146) (0.139)

Switzerland Dco Dec Dy Dy2

e 0.536 (0.464) 0.703 (0.402) 0.721 (0.396)

0.906 (0.341) e 3.543 (0.060)*** 3.476 (0.062)***

2.694 (0.101) 2.950 (0.086)*** e 1.123 (0.289)

2.603 (0.107) 2.863 (0.091)*** 1.052 (0.305) e

3.683 1.064 4.434 4.438

(0.055)*** (0.302) (0.035)** (0.035)**

ji ; i ¼ 1; 2; 3; 4

Notes: The null hypothesis is that there is no causal relationship between variables. Values in parentheses are p-values for Wald tests with a c2 distribution. D is the first difference operator. *,** and *** are 1%, %5 and %10 significance levels, respectively.

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

ec y y2

co

y

ec

y2

ec y y2

Denmark

co ec y y2

ec

co

y

ec

y2

Greece

y

y

co ec y y2

y2

y2

ec Italy

Iceland

ec y y2

5419

co

y

ec

y2

Portugal

ec y y2

co

y

ec

y2

Switzerland

co ec y2

co ec y

Long-run Granger causality running from the independent variables to dependent variable, Two-way short-run Granger causality, One-way short-run Granger causality. Fig. 2. Granger causality relationship flows.

Belgium, Finland, France, Hungary, Luxembourg, Netherlands, Norway, Sweden and UK. In addition, no significant ARDL model found for Ireland. In addition, due to the structural changes in these economies it is likely that macroeconomic series may be subject to one or multiple structural breaks. For this purpose, the stability of the short-run and long-run coefficients are checked through the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests proposed by Brown et al. [25]. Unlike Chow test, requires break point(s) to be specified, the CUSUM and CUSUMSQ tests are quite general tests for structural change in that they do not require a prior determination of where the structural break takes place. Fig. 1 presents the plot of CUSUM and CUSUMSQ tests statistics that fall inside the critical bounds of 5% significance. This implies that the estimated parameters are stable over the periods. The long-run elasticity estimates of emissions with respect to energy consumption are expected to be bi0. This means that an increase in energy consumption results in an increase in emissions. We found bi0 at 1% significance level in Denmark, Germany, Greece, Italy and Portugal; bi0 in Switzerland and bh0 in Iceland but statistically insignificant. Under the EKC hypothesis, the long-run elasticity estimates of carbon emissions per capita with respect to real GDP per capita and the square of per

capita real GDP are expected to be qi0 and 4h0. This means as real GDP per capita increases, carbon emissions per capita increase as well until some threshold level of real GDP per capita is reached after which carbon emissions per capita begin to decline. For European countries under the our long-run analysis, we found qi0 and 4h0 at 1% significance level in Denmark, 5% significance level in Italy, and statistically insignificant in Germany, Greece, Iceland and Portugal; qi0 and 4i0 statistically insignificant in Switzerland. These results support the validity of EKC hypothesis in Denmark and Italy. Therefore, beyond a threshold level of real GDP per capita, any increase in real GDP per capita likely reduce the carbon emissions per capita in Denmark and Italy. All the coefficients of estimated ECTs are also negative and statistically significant at 1% confidence level. These values indicate that any deviation from the long-run equilibrium between variables is corrected for each period to return the long-run equilibrium level (see Table 5). This study also explores causal relationship between the variables by using error-correction based Granger causality models which are weak (short-run) Granger causality and long-run Granger causality. The results of both Granger causality models (see Table 6 and Fig. 2) can be summarized as follows:

5420

A. Acaravci, I. Ozturk / Energy 35 (2010) 5412e5420

i) There is an evidence of a long-run unidirectional causal relationship from energy consumption per capita, real GDP per capita and the square of per capita real GDP to carbon emissions per capita in Denmark, Germany, Greece, Iceland, Italy, Portugal and Switzerland. ii) There is an evidence of a short-run unidirectional causal relationship from real GDP per capita and the square of per capita real GDP to carbon emissions per capita in Denmark, and Italy. iii) There is an evidence of a short-run unidirectional causal relationship from real GDP per capita and the square of per capita real GDP to energy consumption per capita in Greece and Italy. iv) There is an evidence of a short-run bidirectional causal relationship from real GDP per capita and the square of per capita real GDP to energy consumption per capita in Switzerland.

4. Concluding remarks This study examines the causal relationship between carbon dioxide emissions, energy consumption, and economic growth by using ARDL bounds testing approach of cointegration for nineteen countries from European continent over the period 1960e2005. The bounds F-test for cointegration test yields evidence of a long-run relationship between carbon emissions per capita, energy consumption per capita, real GDP per capita and the square of per capita real GDP in Denmark, Germany, Greece, Iceland, Italy, Portugal and Switzerland. However, no long-run relationship has been found for the rest of the countries (Austria, Belgium, Finland, France, Hungary, Luxembourg, Netherlands, Norway, Sweden and UK) included in the study. In addition, no significant ARDL model found for Ireland and Spain. Thus, we drop Ireland and Spain from ARDL analysis. Due to the structural changes in these economies, the stability of the short-run and long-run coefficients are checked through CUSUM and CUSUMSQ tests. The CUSUM and CUSUMSQ tests show that the estimated parameters are stable for the sample period. For the European countries under our long-run analysis, we found a positive long-run elasticity estimates of emissions with respect to energy consumption (bi0) at 1% significance level in Denmark, Germany, Greece, Italy and Portugal; bi0 in Switzerland and bh0 in Iceland but it is statistically insignificant. We found a positive long-run elasticity estimates of carbon emissions with respect to real GDP (qi0) and a negative long-run elasticity estimates of carbon emissions with respect to the square of per capita real GDP (4h0) at 1% significance level in Denmark, 5% significance level in Italy, and statistically insignificance in Germany, Greece, Iceland and Portugal; qi0, 4i0 and statistically insignificant in Switzerland. These results support the validity of EKC hypothesis in Denmark and Italy. Thus, beyond a threshold level of real GDP per capita, any increase in real GDP per capita likely reduce the carbon emissions per capita in Denmark and Italy. This study also explores causal relationship between the variables by using error-correction based Granger causality models. The results of Granger causality models can be summarized as follows: (i) There is an evidence of a long-run unidirectional causal relationship from energy consumption per capita, real GDP per capita and the square of per capita real GDP to carbon emissions per capita in Denmark, Germany, Greece, Iceland, Italy, Portugal and Switzerland. (ii) There is an evidence of a short-run unidirectional

causal relationship from real GDP per capita and the square of per capita real GDP to carbon emissions per capita in Denmark and Italy. (iii) There is an evidence of a short-run unidirectional causal relationship from real GDP per capita and the square of per capita real GDP to energy consumption per capita in Greece and Italy. (iv) There is an evidence of a short-run bidirectional causal relationship from real GDP per capita and the square of per capita real GDP to energy consumption per capita in Switzerland. The overall results indicate that energy conservation policies, such as rationing energy consumption and controlling carbon dioxide emissions, are likely to have no adverse effect on the real output growth and EKC hypothesis is not valid for the most countries considered in this study.

References [1] Xing-Ping Zhang, Xiao-Mei Cheng. Energy consumption, carbon emissions, and economic growth in China. Ecological Economics 2009;68:2706e12. [2] Grossman G, Krueger A. Environmental impacts of a North American free trade agreement. In: National Bureau of Economics Research Working Paper, vol. 3194. Cambridge: NBER; 1991. [3] Stern DI. The rise and fall of the environmental Kuznets curve. World Development 2004;32:1419e39. [4] Coondoo D, Dinda S. Causality between income and emissions: a country group-specific econometric analysis. Ecological Economics 2002;40:351e67. [5] Dinda S. Environmental Kuznets curve hypothesis: a survey. Ecological Economics 2004;49:431e55. [6] Luzzati T, Orsini M. Investigating the energy-environmental Kuznets curve. Energy 2009;34(3):291e300. [7] Halicioglu F. An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 2009;37:1156e64. [8] Kraft J, Kraft A. On the relationship between energy and GNP. Journal of Energy and Development 1978;3:401e3. [9] Ozturk I. A literature survey on energyegrowth nexus. Energy Policy 2010;38(1):340e9. [10] Richmond AK, Kaufman RK. Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecological Economics 2006;56:176e89. [11] Ang J. CO2 emissions, energy consumption, and output in France. Energy Policy 2007;35:4772e8. [12] Soytas U, Sari R, Ewing BT. Energy consumption, income and carbon emissions in the United States. Ecological Economics 2007;62:482e9. [13] Apergis N, Payne JE. CO2 emissions, energy usage, and output in Central America. Energy Policy 2009;37:3282e6. [14] Soytas U, Sari R. Energy consumption, economic growth, and carbon emissions: challenges faced by an EU candidate member. Ecological Economics 2009;68:1667e75. [15] Akbostanci E, Turut-Asik S, Tunc GI. The relationship between income an environment in Turkey: is there an environmental Kuznets curve? Energy Policy 2009;37:861e7. [16] Pesaran HM, Shin Y. Autoregressive distributed lag modelling approach to cointegration analysis. In: Storm S, editor. Econometrics and economic theory in the 20th century: the Ragnar Frisch centennial symposium. Cambridge University Press; 1999 [Chapter 11]. [17] Pesaran MH, Shin Y, Smith RJ. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 2001;16:289e326. [18] Engle RF, Granger CWJ. Co-integration and error correction: representation, estimation, and testing. Econometrica 1987;55:251e76. [19] Johansen S. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 1988;12:231e54. [20] Johansen S, Juselius K. Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics 1990;52:169e210. [21] Narayan PK. The saving and investment nexus for China: evidence from cointegration tests. Applied Economics 2005;37:1979e90. [22] Park HJ, Fuller WA. Alternative estimators and unit root tests for the autoregressive process. Journal of Time Series Analysis 1995;16:415e29. [23] Leybourne SJ, Kim T, Newbold P. Examination of some more powerful modifications of the Dickey-Fuller test. Journal of Time Series Analysis 2005;26:355e69. [24] Odhiambo NM. Energy consumption and economic growth nexus in Tanzania: an ARDL bounds testing approach. Energy Policy 2009;37:617e22. [25] Brown RL, Durbin J, Evans JM. Techniques for testing the consistency of regression relations over time. Journal of the Royal Statistical Society 1975;37:149e92.