Bilgi Ekonomisi ve Yönetimi Dergisi / 2013 Cilt: VIII Sayı: II

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1 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II CO2 EMISSIONS, RENEWABLE ENERGY CONSUMPTION, POPULATION DENSITY AND ECONOMIC GROWTH IN G7 COUNTRIES Abstract Fatma Fehme AYDIN 1 Ths study ams nvestgatng the relatonshp between CO2 emssons, renewable energy consumpton, economc growth, and populaton densy n G7 countres for perod. In ths study, Levn, Ln and Chu; Breung; Im, Pesaran and Shn; ADF- Fsher Ch-square; ADF- Cho Z-stat; PP- Fsher Ch-square and PP- Cho Z-stat panel un root tests, Johansen-Fsher panel contegraton test, panel Granger causaly test, mpulse-response test and Panel OLS, fxed effects, random effects tests were employed. As a result of the study we can say that from country to country the relatonshp between our varables may show dfference, but ultmately we have presented evdence that economc growth, renewable energy consumpton and populaton densy are the causes of CO2 emssons. Keywords: Carbondoxde emssons, renewable energy consumpton, populaton densy, economc growth, G7. Introducton As one of the man problems of economcs, economc growth s one of the man objectves of most of the countres for many years. Income growth s val for achevng economc, socal, and even polcal development. Countres that grow strongly for sustaned perods of tme are able to reduce ther poverty levels sgnfcantly, strengthen ther democratc and polcal stably, mprove the qualy of ther natural envronment, and even dmnsh the ncdence of crme and volence (Loayza and Soto 2002). Untl the 1970s economc growth and development focused on only ncreasng per capa ncomes and mprovng the welfare levels,.e. only on read-economc growth. After ths year, startng to expressng the opnon that socal development should not lmed wh only economy, should also cover envronment, nature and the needs of future generatons, has led to an ncrease n the crcsms of the tradonal development model (Acar 2002). Carbon doxde (CO2) emssons come n at the begnnng of the factors that negatvely effect the envronment and the nature. CO2 emssons accumulate n the atmosphere and create costly changes n regonal clmates throughout the world. Due to these losses of CO2 emssons, researchers have nterested more n the factors ncreasng and decreasng CO2 emssons. In ths regard, ths study ams nvestgatng the relatonshp between CO2 emssons and renewable energy consumpton, populaton densy and economc growth n G7 countres for perods. The reason for choosng G7 countres as sample s that, G7 economes have caused 27.7% of World's total CO2 emssons n 2009 (WDI, World Development Indcators 2013). The paper s organzed as follows: Next secton s devoted to the lerature. Secton 3 presents the data, methodology and results. Fnally, Secton 4 concludes. 1 Bngol Unversy Faculty of Economcs and Admnstratve Scences Bngol/TURKEY [email protected] Tüm hakları BEYDER e atr 89 All rghts reserved by The JKEM

2 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL 1. Lerature Revew The relatonshp between CO2 emssons, economc growth, renewable energy consumpton and populaton densy has been treated n the lerature usng dfferent methodologcal approaches. The results have dffered sgnfcantly dependng on the country, perod, varables and method used for the analyss. The studes examnng the relatonshp between economc growth and CO2 emssons have reached three dfferent conclusons. Km et al. (2010, for lnear causaly), Ozturk and Acaravc (2010), Jayanthakumaran et al. (2012, for Inda), Saboor et al. (2012, for short run) concluded that there s no causal relatonshp between economc growth and CO2 emssons. Lotfalpour et al. (2010), Jayanthakumaran et al. (2012, for Chna), Saboor et al. (2012, for long run) concluded that there s a undrectonal causaly from economc growth to CO2 emssons. Km et al. (2010, for nonlnear causaly), Shahbaz et al. (2013), Park and Hong (2013) and Wang (2013) concluded that there s bdrectonal causaly between economc growth and CO2 emssons. Table 1 Summary of recent lerature revew for economc growth and CO2 emssons Study Perod Country Methodology Confrmed hypothess Km, Lee and Nam (2010) Ozturk and Acaravc (2010) Lotfalpour, Falah and Ashena (2010) Jayanthakumaran, Verma and Lu (2012) Saboor, Sulaman and Mohd (2012) Arour, Youssef, M Henn and Rault (2012) Shahbaz, Hye, Twar and Leao (2013) Park and Hong (2013) Wang (2013) Korea Turkey Smooth transon autoregressve model, lnear and nonlnear Granger causaly tests ARDL contegraton analyss, Engle Granger causaly Lnear causaly: no causaly; nonlnear causaly: twoway causaly Long run relatonshp; No causaly Iran Un root, Toda-Yamamoto causaly Undrectonal causaly from economc growth to CO2 emssons Chna and Inda Malaysa 12 MENA countres Bounds testng approach to contegraton and the ARDL methodology ARDL methodology, VECM Granger Causaly Panel un root and contegraton tests Indonesa Un root, ARDL bounds, VECM Granger causaly, nnovatve accountng approach South Korea Regresson analyss, Markov swchng model 138 countres Panel data analyss, quantle regresson analyss, short run error correcton model In Chna: growth CO2 emssons In Inda: no causal relatonshp U shape relatonshp Short run: no causaly Long run: Undrectonal causaly from economc growth to CO2 emssons Quadratc relatonshp Bdrectonal causaly Very close correlaton, varables are movng dentcally Absolute decouplng Relatve decouplng Feedback The studes examnng the relatonshp between renewable energy consumpton and CO2 emssons have reached three dfferent conclusons. Menyah and Wolde-Rufael (2010) have used Granger causaly test and generalzed mpulse response approach for the perod Tüm hakları BEYDER e atr 90 All rghts reserved by The JKEM

3 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II 2007 n US. They concluded that there s no causal relatonshp between renewable energy consumpton and CO2 emssons. Sadorsky (2009), Marques et al. (2010), Shafe and Salm (2012) and Farhan (2013, for long run) concluded that there s a undrectonal causaly from CO2 emssons to renewable energy consumpton. Twar (2011), Shabbr et al. (2011), Slva et al. (2012), Kulons (2013) and Farhan (2013, for short run) concluded that there s a undrectonal causaly from renewable energy consumpton to CO2 emssons. Table 2 Summary of recent lerature revew for renewable energy consumpton and CO2 emssons Study Perod Country Methodology Confrmed hypothess Sadorsky (2009) Menyah and Wolde-Rufael (2010) Marques et al. (2010) Twar (2011) Shabbr et al. (2011) Slva et al. (2012) Shafe and Salm (2012) Kulons (2013) Farhan (2013) G7 Vector auto regresson technques Undrectonal causaly from CO2 emssons to renewable energy consumpton US Granger Causaly test, generalzed No causaly mpulse-response approach 24 EU countres Panel regresson technques Undrectonal causaly from CO2 emssons to renewable energy consumpton Inda Pakstan USA, Denmark, Portugal and Span 29 OECD countres Denmark 12 MENA countres Structural Vector Auto Regresson Analyss Clemente-Montanes-Reyes detrended structural break un root test, ARDL bounds test Un root, mpulse-response functon STIRPAT model, panel un root, panel contegraton, panel DOLS and panel causaly tests Un root, Toda-Yomamoto Granger causaly, contegraton, mpulse response functon Panel un root, panel contegraton, panel causaly, panel FMOLS and DOLS tests Undrectonal causaly from renewable energy consumpton to CO2 emssons Undrectonal causaly from renewable energy consumpton to CO2 emssons Undrectonal causaly from renewable energy consumpton to CO2 emssons Undrectonal causaly from CO2 emssons to renewable energy consumpton Undrectonal causaly from renewable energy consumpton to CO2 emssons Short run: undrectonal causaly from renewable energy consumpton to CO2 emssons Long run: undrectonal causaly from CO2 emssons to renewable energy consumpton The studes examnng the relatonshp between populaton and CO2 emssons have reached four dfferent conclusons. Knapp and Mookerjee (1996) used contegraton analyss, granger causaly and ECM causaly for the perod They concluded that there s a undrectonal causaly from CO2 emssons to populaton. Detz and Rosa (1997) and Sh (2001) concluded that there s a undrectonal causaly from populaton to CO2 emssons. Lantz and Feng (2006) used fve regon panel data analyss for the perod n Canada. They concluded that there s an nverted U-shaped relatonshp between populaton and CO2 emssons. Martnez- Zarzoso et al. (2007) used STIRPAT model, panel OLS, fxed effects and random effects model and Generalzed method of moments (GMM) test for the perod n 23 EU countres. Ther results show that the mpact of populaton growth on emssons s more than proportonal for recent accesson countres whereas for old EU members, the Tüm hakları BEYDER e atr 91 All rghts reserved by The JKEM

4 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL elastcy s lower than uny and non sgnfcant when the propertes of the tme seres and the dynamcs are correctly specfed. Jorgenson and Clark (2010) used cross natonal panel study for the perod n 86 countres. They concluded that there s a large and stable posve assocaton between populaton and CO2 emssons. Table 3 Summary of recent lerature revew for Populaton and CO2 emssons Study Perod Country Methodology Confrmed hypothess Knapp and Mookerjee (1996) Detz and Rosa (1997) Sh (2001) Lantz and Feng (2006) Martínez-Zarzoso et al. (2007) Jorgenson Clark (2010) and World Contegraton analyss, Granger causaly, Undrectonal causaly 1989 ECM causaly from CO2 emssons to populaton countres Impact=Populaton Affluence Technology Undrectonal causaly (IPAT) model from populaton to CO2 emssons 93 countres Descrptve analyss, fxed effects model Undrectonal causaly from populaton to CO2 emssons Canada Fve-regon panel data analyss Inverted U-shaped relatonshp 23 EU STIRPAT model, OLS, fxed effects and The elastcy of countres random effects model, Generalzed emssons-populaton s method of moments (GMM) much lower for old EU members then recent accesson countres 86 countres Cross-natonal panel study a large and stable posve assocaton Ths paper analyzes the relatonshp between CO2 emssons, economc growth, renewable energy consumpton and populaton densy. 2. Data, Methodology and Results 2.1. Data In our study carbon doxde emssons are n metrc tons per capa, representng economc growth GDP s n current US dollars, renewable energy consumpton s share of renewable n prmary consumpton (%), and populaton densy s people per square klometer of land area. Data set covers perod n G7 countres and attaned from Enerdata energy statstcal yearbook 2013 and World Bank. In our study the man model we examne s: ln CO 2 1 ln gdp 2reru 3 ln popdens e 2.2. Panel Un Root Test In panel data models, the leadng studes proposed un root test are Levn, Ln and Chu (2002), Breung (2000), Im, Pesaran and Shn (2003), Maddala and Wu (1999) and Cho (2001). In our study these un root tests are appled. Levn, Ln and Chu (LLC) and Breung tests assume that there s a common un root process. And these tests employ a null hypothess of a un root. LLC and Breung tests consder the followng basc ADF specfcaton: Tüm hakları BEYDER e atr 92 All rghts reserved by The JKEM

5 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II y p 1 jy j X j1 y. Here y ndcates the seres to be done un root test, Δ ndcates the frst order dfference processor, ndcates cross secton uns or seres, t ndcates perods, X ndcates the exogenous values n the model and ndcates errors. The null and alternatve hypotheses for the tests may be wrten as: H H 0 1 : 0 : 0 Under the null hypothess there s a un root, under the alternatve hypothess there s no un root (Levn et al., 2002; and Breung, 2000). The Im, Pesaran and Shn (IPS) and the Fsher ADF and PP tests assume that there s an ndvdual un root process. These tests are characterzed by the combnng of ndvdual un root tests to derve a panel-specfc result. The null and alternatve hypotheses for the IPS test may be wrten as: H H 0 1 : 0, for all 0, for 1,2,, N1 : 0, for N 1, N 2,, N whch may be nterpreted as a non-zero fracton of the ndvdual processes s statonary (Im et al., 2003). Maddala and Wu (1999) used the Fsher (1932) test results whch are based on combnng the p-values of the test statstc for a un root n each cross secton. If we defne as the p-value from any ndvdual un root test for cross secton, that are U[0,1] and ndependent, and 2 2log e has a dstrbuton wh 2 degrees of freedom. The null and alternatve hypotheses are the same as n the IPS test. Applyng the ADF estmaton equaton n each cross-secton, we can compute the ADF t-statstc for each ndvdual seres, fnd the correspondng p-value from the emprcal dstrbuton of ADF t-statstc, and compute the Fsher-test statstcs and compare wh the approprate McNown, 2006). 2 crcal value (Hoang and Tüm hakları BEYDER e atr 93 All rghts reserved by The JKEM

6 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL Table 4 Panel Un Root Tests Results (Level and 1st Dfferences) ln CO 2 Intercept t-stat I(0) Prob I(0) t-stat I(1) Prob I(1) Levn, Ln&Chu ** Breung Im, Pesaran&Shn *** ADF- Fsher Ch-square *** ADF- Cho Z-stat *** PP- Fsher Ch-square *** PP- Cho Z-stat *** ln gdp Intercept t-stat I(0) Prob I(0) t-stat I(1) Prob I(1) Levn, Ln&Chu ** ** Breung Im, Pesaran&Shn *** ADF- Fsher Ch-square *** ADF- Cho Z-stat *** PP- Fsher Ch-square ** PP- Cho Z-stat ** reru Intercept t-stat I(0) Prob I(0) t-stat I(1) Prob I(1) Levn, Ln&Chu *** Breung Im, Pesaran&Shn *** ADF- Fsher Ch-square *** ADF- Cho Z-stat *** PP- Fsher Ch-square *** PP- Cho Z-stat *** ln popdens Intercept t-stat I(0) Prob I(0) t-stat I(1) Prob I(1) t-stat I(2) Prob I(2) Levn, Ln&Chu *** Breung Im, Pesaran&Shn *** ADF- Fsher Ch-square *** ADF- Cho Z-stat *** PP- Fsher Ch-square *** *** PP- Cho Z-stat *** ***, **, * ndcate sgnfcance at the level of 1, 5 and 10 percent, respectvely. Optmal lag length s chosen accordng to the Schwarz nformaton creron. In LLC and PP tests Bartlett Kernel method s used and the wdth of Bandwdth s determned by Newey-West method. As can be seen from table 1, accordng to the un root tests results, appled to the levels of varables, t stats and probably results ndcate that CO2 emssons seres that wll be used n the econometrc analyss s not statonary n s level [I(0)]. For ths reason, the frst dfference of the seres s researched, and lookng at the frst dfference of CO2 emssons seres, s seen that s frst dfference [I(1)] s statonary accordng to all of the un root tests results except Breung. It s seen that economc growth seres s statonary n s level [I(0)] accordng to the LLC test, but accordng to the other un root tests results s not statonary. For ths reason, the frst dfference of the seres s researched, and lookng at the frst dfference of economc growth seres, s seen that s frst dfference [I(1)] s statonary accordng to all of the un root tests results except Breung. It s seen that renewable energy consumpton seres s not statonary n s level [I(0)] accordng to un root tests. For ths reason, the frst dfference of the seres s researched, and lookng at the frst dfference of renewable energy consumpton seres, s seen that s frst dfference [I(1)] s statonary accordng to all of the un root tests results except Breung. Tüm hakları BEYDER e atr 94 All rghts reserved by The JKEM

7 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II And lastly, s seen that populaton densy seres s statonary n s level [I(0)] accordng to the PP-Fsher Ch-Square test, but accordng to the other un root tests results s not statonary. For ths reason, the frst dfference of the seres s researched, and lookng at the frst dfference of populaton densy seres, s seen that s frst dfference [I(1)] s not statonary accordng to all of the un root tests results. Then the second dfference of the seres s researched, and lookng at the second dfference of populaton densy seres, s seen that s second dfference [I(2)] s statonary accordng to all of the un root tests results except Breung Panel Contegraton Test In our study Johansen Fsher panel contegraton analyss was used after nvestgatng un roots n order to nvestgate f n the long term there s a mutual relaton between the seres. Johansen Fsher panel contegraton test s developed by Maddala and Wu (1999). As an alternatve test for contegraton n panel data, Maddala and Wu used Fsher's result to propose a method for combnng tests from ndvdual cross-sectons to obtan a test statstc for the panel data. Two knds of Johansen Fsher tests have been developed: the Fsher test from the trace test and the Fsher test from the maxmum egen-value test (Shegeyuk and Yoch, 2009). We dd not use populaton densy varable n contegraton analyss because whle other varables are statonary n ther frst level, populaton densy varable s not statonary n s frst level. Table 5 Johansen Fsher Panel Contegraton Test Results ln CO 2 ln 1 gdp 2 reru e Johansen Fsher Panel Contegraton Test Results Hypotheszed No. of CE(s) Fsher Stat.* (from trace test) Prob. Fsher Stat.* (from max-egen test) Prob. None 157.2*** *** At most *** *** At most ***, **, * ndcate sgnfcance at the level of 1, 5 and 10 percent, respectvely. Lag nterval s chosen as 1 to 2. Accordng to the table can be sad that both the hypothess of there s no contegraton and the hypothess of there s at most one contegraton are rejected. And the hypothess of there s at most two contegraton s accepted. So the concluson to be drawn here s there s a contegraton relatonshp between CO2 emssons, economc growth and renewable energy consumpton. In ths context, n the long term n G7 countres CO2 emssons, economc growth and renewable energy consumpton seres move together. Tüm hakları BEYDER e atr 95 All rghts reserved by The JKEM

8 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL Table 6 Johansen Fsher Panel Contegraton Test Indvdual Cross Secton Results Indvdual cross secton results Trace Test Max-Egn Test Cross Secton Statstcs Prob.** Statstcs Prob.** Hypothess of no contegraton Hypothess of at most 1 contegraton relatonshp Hypothess of at most 2 contegraton relatonshp **MacKnnon-Haug-Mchels (1999) p-values When we look at the ndvdual cross secton results, accordng to both the trace test and maxegen test, n all the countres there s at most two contegraton relatonshps between economc growth, CO2 emssons and renewable energy consumpton Panel Granger Causaly Test Fndngs and Evaluaton In our study Panel Granger causaly test s used to examne f there s causaly between CO2 emssons, economc growth, renewable energy consumpton and popdens varables. Panel Granger causaly test s developed by Granger (1969) for the queston of whether x causes y. Granger s method ams to see how much of the current y can be explaned by past values of y and then to see whether addng lagged values of x can mprove the explanaton. If x helps n the predcton of y or f the coeffcents on the lagged x s are statstcally sgnfcant then y s sad to be Granger-caused by x. There can be also b-drectonal causaly, x Granger causes y and y Granger causes x (Granger, 1969). There are many ways to examne for Granger causaly because of the assumptons of heterogeney across countres and tme (Chen et al., 2013). The smple two-varable causal model s as follows: X Y t t j t j j1 j1 m m c a X X m m j t j j1 j1 b Y d j j Y t j t j t t Tüm hakları BEYDER e atr 96 All rghts reserved by The JKEM

9 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II Here X t and Y t are two statonary tme seres wh zero means. uncorrelated whe-nose seres. t and t are two The null hypothess s that x does not Granger-cause y n the frst regresson and that y does not Granger-cause x n the second regresson (Granger, 1969). Table 7 Parwse Granger Causaly Test Results Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO *** LNCO2 does not Granger Cause LNGDP LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU *** ndcate sgnfcance at the level of 1 percent. Lag length s chosen as 6. As can be seen from table, accordng to the Panel Granger Causaly Test Results, economc growth s Granger Cause of CO2 emssons at the 1% sgnfcance level, but there s no causal relatonshp between other varables. Country base Granger Causaly s presented n the followng tables. Table 8 Granger Causaly Test Results for Canada Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO ** LNCO2 does not Granger Cause LNGDP RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS ** ndcate sgnfcance at the level of 5 percent. Lag length s chosen as 5. In Canada, accordng to the Granger Causaly Test Results, economc growth s Granger Cause of CO2 emssons at the 5% sgnfcance level, but there s no causal relatonshp between other varables. Table 9 Granger Causaly Test Results for France Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO LNCO2 does not Granger Cause LNGDP ** RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS ** ndcate sgnfcance at the level of 5 percent. Lag length s chosen as 5. In France, accordng to the Granger Causaly Test Results, CO2 emssons s Granger Cause of economc growth at the 5% sgnfcance level, but there s no causal relatonshp between other varables. Tüm hakları BEYDER e atr 97 All rghts reserved by The JKEM

10 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL Table 10 Granger Causaly Test Results for Germany Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO LNCO2 does not Granger Cause LNGDP RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS *** *** ndcate sgnfcance at the level of 1 percent. Lag length s chosen as 5. In Germany, accordng to the Granger Causaly Test Results, CO2 emssons s Granger Cause of populaton densy at the 1% sgnfcance level, but there s no causal relatonshp between other varables. Table 11 Granger Causaly Test Results for Italy Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO ** LNCO2 does not Granger Cause LNGDP RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO *** LNCO2 does not Granger Cause LNPOPDENS *** and ** ndcate sgnfcance at the level of 1 and 5 percent, respectvely. Lag length s chosen as 5. In Italy, accordng to the Granger Causaly Test Results, economc growth s Granger Cause of CO2 emssons at the 5% sgnfcance level, and populaton densy s granger cause of CO2 emssons at the %1 sgnfcance level, but there s no causal relatonshp between CO2 emssons and renewable energy consumpton varables. Table 12 Granger Causaly Test Results for Japan Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO LNCO2 does not Granger Cause LNGDP RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS Lag length s chosen as 5. In Japan accordng to the Granger Causaly Test Results, there s no causal relatonshp between our varables. Table 13 Granger Causaly Test Results for Uned Kngdom Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO LNCO2 does not Granger Cause LNGDP RERU does not Granger Cause LNCO LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS Lag length s chosen as 5. In Uned Kngdom, accordng to the Granger Causaly Test Results, there s no causal relatonshp between our varables. Tüm hakları BEYDER e atr 98 All rghts reserved by The JKEM

11 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II Table 14 Granger Causaly Test Results for Uned States Null Hypothess: Obs F-Statstc Prob. LNGDP does not Granger Cause LNCO LNCO2 does not Granger Cause LNGDP RERU does not Granger Cause LNCO * LNCO2 does not Granger Cause RERU LNPOPDENS does not Granger Cause LNCO LNCO2 does not Granger Cause LNPOPDENS * ndcate sgnfcance at the level of 10 percent. Lag length s chosen as 5. In Uned States, accordng to the Granger Causaly Test Results, renewable energy consumpton s Granger Cause of CO2 emssons at the 10% sgnfcance level, but there s no causal relatonshp between other varables Impulse-Response Test Impulse-response functon shows the effect of shocks on the varables and shows n whch tme and how a change occurs n mpulse. Wh mpulse-response analyss s examned that n whch varable shocks have occurred and how other varables wll react to these shocks (Hamlton, 1994). In order to determne how the shocks wll occur, the movements of varables for 10 perods are analyzed. The responses of other varables aganst a one un change n shocks occurs n the seres used n ths study are shown n the followng graphs. Graph 1 Impulse-Response Functon Tests Response to Cholesky One S.D. Innov atons Response of LNCO2 to LNCO2 Response of LNCO2 to LNPOPDENS Response of LNCO2 to LNGDP Response of LNCO2 to RERU Response of LNPOPDENS to LNCO2 Response of LNPOPDENS to LNPOPDENS Response of LNPOPDENS to LNGDP Response of LNPOPDENS to RERU Response of LNGDP to LNCO2 Response of LNGDP to LNPOPDENS Response of LNGDP to LNGDP Response of LNGDP to RERU Response of RERU to LNCO2 Response of RERU to LNPOPDENS Response of RERU to LNGDP Response of RERU to RERU Tüm hakları BEYDER e atr 99 All rghts reserved by The JKEM

12 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL The mpact of a shock of one standard devaton n economc growth on CO2 emssons nally ncreases up to , then becomes negatve n thrd perod, and begnnng from the fourth perod contnuously fluctuates between and The mpact of a shock of one standard devaton n populaton densy and renewable energy consumpton on CO2 emssons monors a negatve course and gradually decreases OLS, Fxed Effects Model and Random Effects Model In our study three dfferent models for panel data are used to estmate the coeffcents of relatonshp between female labor force partcpaton and natonal competveness. Frst model s ordnary least squares. If z contans only a constant term, then ordnary least squares provdes consstent and effcent estmates of the common α and the slope vector β. But f z s unobserved, but correlated wh x, then the least squares estmator of β s based and nconsstent as a consequence of an omted varable. However, n ths nstance, fxed effects model provdes consstent and effcent estmatons. Fxed effects model can be wrten as follows: y x Here z embodes all the observable effects and ndcates an estmable condonal mean. Fxed effects approach takes model. as a group-specfc constant term n the regresson If the unobserved ndvdual heterogeney can be assumed to be uncorrelated wh the ncluded varables then random effects model provdes consstent and effcent estmatons. Random effects model may be formulated as follows: y x E x u z z Ez Ths formulaton shows that as a lnear regresson model wh a compound dsturbance that may be consstently estmated by least squares. Random effects model ndcates that u s a group-specfc random element, smlar to except that for each group, there s a sngle draw that enters the regresson dentcally n each perod (Greene, 2010). Our model s ln CO 2 1 ln gdp 2reru 3 ln popdens e Tüm hakları BEYDER e atr 100 All rghts reserved by The JKEM

13 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II Table 15 OLS, cross secton fxed effects and cross secton random effects tests results OLS Cross Secton Cross Secton Fxed Effects Random Effects Constant (0,3751) (0.0000) (0.0000) LNGDP (0.0000) (0.6676) (0.5305) RERU (0.0000) (0.0000) (0.0000) LNPOPDENS (0.0000) (0.2439) (0.0001) R F (0.0000) (0.0000) (0.0000) Accordng to table, all three models gave statstcally sgnfcant results. To nvestgate whch one of these models s approprate, we employed Hausman (1978) and Lkelhood Rato Tests. Under the null hypothess that the unobservable, ndvdual-specfc effects and the regressors are orthogonal, Hausman specfcaton test s based on the dea that the set of coeffcent estmates obtaned from the fxed-effects estmaton should not dffer systematcally from the set obtaned from random-effects estmaton. If the test results suggest rejectng the equaly of both coeffcent sets, then can be sad that fxed effects estmaton results s more approprate than random effects estmaton results. If ths s the case than random effects estmatons are gnored (Frondel and Vance, 2010). In panel data models, to test the valdy of the classc model (OLS);.e. there s whether the un and/or tme effects, lkelhood rato test can be appled. Lkelhood rato test, that s used to test classcal model aganst the fxed effects model, s appled to determne n whch model framework the equaton wll be estmated. Lkelhood rato test research f standard errors of un effects are equal to zero; n other words, f the basc hypothess that classcal model s approprate ( H : 0). If H 0 s rejected than can be sad that classcal model s not 0 approprate (Gern et al., 2012). Lkelhood rato and Hausman tests have been appled to fnd the ftest of these models. Lkelhood rato test has been appled to fnd the approprate one of the OLS model and fxed effects model. Hausman test has been appled to decde to use whch one of the fxed effects and random effects models. It s examned f the dfference between the two model s parameters s statstcally sgnfcant. Accordngly the results of the lkelhood rato test under the null hypothess of the OLS estmator s correct and the Hausman test under the null hypothess of the random effects estmator s correct are shown n the followng table. Table 16 Lkelhood Rato and Hausman Test Results Test Summary Statstc d.f. Prob. Cross-Secton F Cross-Secton Ch-Square ,0000 Cross-Secton Random Tüm hakları BEYDER e atr 101 All rghts reserved by The JKEM

14 The Journal of Knowledge Economy & Knowledge Management / Volume: VIII FALL When we look at the lkelhood rato test results, H 0 hypothess s rejected because the probably s less than 0. Because of ths, fxed effects model s more favorable for ths dataset. And f the Hausman test results are taken nto account, as the probably s hgher than 0.05, H 0 hypothess s accepted. So the random effects model s more approprate for the dataset. Accordng to both Hausman and lkelhood rato tests, random effects model s more approprate. Accordng to the cross secton random effect model, R 2 s lower than average and the equaton s lke that: ln CO ln gdp reru ln popdens e The coeffcents except economc growth are statstcally sgnfcant at the 1%, 5% and 10% sgnfcance level. A one category ncrease n renewable energy consumpton leads to a decrease of % n CO2 emssons, and a one category ncrease n populaton densy leads to a decrease of % n CO2 emssons. Concluson Ths study ams nvestgatng the relatonshp between CO2 emssons, renewable energy consumpton, economc growth, and populaton densy n G7 countres for perod. In ths study, Levn, Ln and Chu; Breung; Im, Pesaran and Shn; ADF- Fsher Chsquare; ADF- Cho Z-stat; PP- Fsher Ch-square and PP- Cho Z-stat panel un root tests, Johansen-Fsher panel contegraton test, panel Granger causaly test, mpulse-response test and Panel OLS, fxed effects, random effects tests were employed. Accordng to the un root tests results, appled to the levels of varables, t stats and probably results ndcate that CO2 emssons, GDP and renewable energy consumpton seres are not statonary n ther level [I(0)]. Lookng at the frst dfference of these seres, s seen that CO2 emssons, GDP and renewable energy consumpton s frst dfference [I(1)] s statonary accordng to all of the un root tests results except Breung. But also s seen that populaton densy s frst dfference [I(1)] s not statonary but second dfference [I(2)] s statonary accordng to all of the un root tests results except Breung. Accordng to Johansen Fsher panel contegraton test results there s a contegraton relatonshp between CO2 emssons, economc growth and renewable energy consumpton. In ths context, n the long term n G7 countres CO2 emssons, economc growth and renewable energy consumpton seres move together. Accordng to Parwse Granger Causaly Test Results, economc growth s Granger Cause of CO2 emssons at the 1% sgnfcance level, but there s no causal relatonshp between other varables. Lookng at the country base Granger Causaly test s seen that n Canada economc growth s Granger Cause of CO2 emssons at the 5% sgnfcance level, but there s no causal relatonshp between other varables. In France CO2 emssons s Granger Cause of economc growth at the 5% sgnfcance level, but there s no causal relatonshp between other varables. In Germany, CO2 emssons s Granger Cause of populaton densy at the 1% sgnfcance level, but there s no causal relatonshp between other varables. In Italy, economc growth s Granger Cause of CO2 emssons at the 5% sgnfcance level, and Tüm hakları BEYDER e atr 102 All rghts reserved by The JKEM

15 Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II populaton densy s granger cause of CO2 emssons at the %1 sgnfcance level, but there s no causal relatonshp between CO2 emssons and renewable energy consumpton varables. In Japan and Uned Kngdom, there s no causal relatonshp between our varables. In Uned States, accordng to the Granger Causaly Test Results, renewable energy consumpton s Granger Cause of CO2 emssons at the 10% sgnfcance level, but there s no causal relatonshp between other varables. Accordng to mpulse-response test, the mpact of a shock of one standard devaton n economc growth on CO2 emssons nally ncreases up to , then becomes negatve n thrd perod, and begnnng from the fourth perod contnuously fluctuates between and The mpact of a shock of one standard devaton n populaton densy and renewable energy consumpton on CO2 emssons monors a negatve course and gradually decreases. Lastly panel OLS, fxed effects and random effects tests were employed. And lkelhood rato and Hausman tests have been appled to fnd the ftest of these models. Accordng to both Hausman and lkelhood rato tests, random effects model s more approprate. Accordng to the results of cross secton random effect model, a one category ncrease n renewable energy consumpton leads to a decrease of % n CO2 emssons, and a one category ncrease n populaton densy leads to a decrease of % n CO2 emssons. To sum up, we can say that from country to country the relatonshp between our varables may show dfference, but ultmately we have presented evdence that economc growth, renewable energy consumpton and populaton densy are the causes of CO2 emssons. References Acar Y İktsad Büyüme ve Büyüme Modeller, Generalzed 4 th press. Vpaş Publcatons, Publcaton Number: 67, Bursa. Arour MEH, Youssef AB, M henn H and Rault C Energy Consumpton, Economc Growth and CO2 Emssons n Mddle East and North Afrcan Countres. Dscusson Paper Seres, IZA, DP No: Breung J The local power of some un root tests for panel data. In Nonstatonary Panels, Panel Contegraton, and Dynamc Panels. Baltag B. (ed.). Advances n Econometrcs 15 Amsterdam: JAI Press; Chen W, Clarke JA and Roy N Health and wealth: Short panel granger causaly tests for developng countres. The Journal of Internatonal Trade & Economc Development: An Internatonal and Comparatve Revew. Date of Access: Cho I Un Root Tests for Panel Data. Journal of Internatonal Money and Fnance 20(2): Detz T and Rosa EA Effects of populaton and affluence on CO 2 emssons. Proceedngs of the Natonal Academy of Scences 94: Farhan S Renewable energy consumpton, economc growth and CO2 emssons: evdence from selected MENA countres. Energy Economcs Letters: Fscher RA Statstcal methods for research workers. Ednburg: Olver & Boyd. Frondel M and Vance C Fxed, random, or somethng n between? A varant of Hausman's specfcaton test for panel data estmators. Economcs Letters 107: Gern M, Emsen ÖS, Özdemr D and Buzdağlı Ö Determnants of corrupton and ther relatonshp to growth. Internatonal Conference on Eurasan Economes: Sesson 1B: Growth and Development I October 2012, Almaty, Kazakhstan. Granger CWJ Investgatng causal relatons by econometrc models and crossspectral methods. Econometrca. 37: Grene W Models for panel data, Econometrc Analyss. Date of Access: Hamlton JD Tme Seres Analyss, Prnceton Unversy Pres, U. K. Hausman JA Specfcaton Tests n Econometrcs, Econometrca 46 (6), Hoang NT and McNown RF Panel data un roots tests usng varous estmaton methods. Workng paper. Department of Economcs, Unversy of Colorado at Boulder. Tüm hakları BEYDER e atr 103 All rghts reserved by The JKEM

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