Envronmental Kuznets Curve for carbon doxde emssons: lack of robustness to heterogenety? Thomas Jobert a, b c,d,*, Fath Karanfl, Anna Tykhonenko a a Nce Sopha Antpols Unversty, Nce, France. b CREDEG CNRS,Valbonne, France. c Department of Economcs, Galatasaray Unversty, Istanbul, Turkey. d GIAM-Galatasaray Unversty Economc Research Center, Istanbul, Turkey. Abstract: Ths paper focuses solely on the energy consumpton, carbon doxde ( CO ) emssons and economc growth nexus applyng the teratve Bayesan shrnkage procedure. The envronmental Kuznets curve (EKC) hypothess s tested usng ths method for the frst tme n ths lterature and the results obtaned suggest that: frst, the EKC hypothess s rejected for 49 out of the 51 countres consdered when heterogenety n countres energy effcences and cross-country dfferences n the CO emssons trajectores are accounted for; second, a classfcaton of the results wth respect to countres development levels reveals that an overall nverted U-shape curve s due to the fact that ncrease n gross domestc product (GDP) n the hgh-ncome countres decreases emssons, whle n the low-ncome countres t ncreases emssons. Keywords: Envronmental Kuznets curve; Bayesan shrnkage estmator; Heterogenety JEL classfcaton: O13; O44; Q56 * Correspondng author at Galatasaray Unversty, Department of Economcs. Cragan Cad. No:36, 34357 Besktas, Istanbul, Turkey. Tel.:+90174480-44 Fax:+9015883. E-mal addresses: thomas.jobert@unce.fr (T. Jobert), fkaranfl@gsu.edu.tr (F. Karanfl), anna.tykhonenko@unce.fr (A. Tykhonenko). 1
1. Introducton and theoretcal background Snce the poneerng study of Grossman and Krueger (1991), debates about the exstence of an envronmental Kuznets curve (EKC; an nverted U-shaped relatonshp between polluton and development) have resulted n numerous studes. 1 In recent years, scholars begun to queston the necessty of further research on the EKC and to clam that the lterature on the EKC s very large and why, ndeed, do we need another paper? (Johansson and Krström, 007, p. 78). But others argue, as does Stern (004), that the research challenge now s to revst some of the ssues addressed earler n the EKC lterature usng the new decomposton and fronter models and rgorous panel data and tme-seres statstcs (Stern, 004, p. 1435). As ndcated by Wagner (008), the seres of per capta gross domestc product (GDP) and per capta carbon doxde ( CO ) emssons are often non-statonary, and ths problem has not been suffcently addressed n the EKC lterature. The author made a survey on panel unt root tests, dstngushng between so-called frst generaton tests desgned for cross-sectonally ndependent panels and second generaton tests that allow accountng for cross-sectonal correlaton. In fact, these unt root tests are not wthout a number of problems. Indeed, although, under the alternatve hypothess of statonarty, some tests can be employed to release the constrant on the coeffcent homogenety, ther use may have further shortcomngs. In partcular, Im et al. (003) develop several unt root tests for the model wth random coeffcents, n whch they loosen the homogenety constrant mposed on the autoregressve structure under the alternatve hypothess. So far, snce the unt root tests developed for panel data have been based on ndvdual tme-seres unt root tests, we can 1 Due to the avalablty of excellent survey artcles (see for nstance, Dasgupta et al. 00; Dnda, 004; Carson, 010), we wll not elaborate n detal on the state of the art n ths feld of research.
stress about the nterpretaton of the unt root test results n panel data; that s, t s not because the null hypothess of unt root s rejected for the whole sample of countres that the varables are all statonary. It s suffcent to have some seres that are statonary, and others not (the seres contan a unt root) to reject the null hypothess. Furthermore, sometmes, ntroducng one atypcal country n the sample may be suffcent for the analyss to fal to assess the statonarty propertes of the entre sample of countres. Usng recently developed tests for unt roots and contegraton n panel data, some scholars test for contegraton consderng that the EKC estmates wll be spurous f the regressons do not contegrate. However, panel contegraton technques do not take nto account the heterogenety n the coeffcents of the long-term relatonshp. These coeffcents are assumed to be dentcal for all countres n the sample, whch mples, n consequence, a turnng pont ncome (descrbed below) common to all countres. However, ths assumpton s not reasonable. It s thus necessary to nvestgate the EKC hypothess n a way that the heterogenety n countres energy effcences and cross-country dfferences n the CO emssons trajectores can be accounted for. On the other hand, recent emprcal panel studes ponted out the problem of nconsstent estmators caused not by non-statonary seres but rather by the nsuffcent consderaton of cross-country heterogenety (Baltag et al., 008; Baltag and Kao, 000; Maddala et al., 1997). Accordng to Maddala et al. (1997), n the panel data analyss, t s customary to pool the observatons, wth or wthout ndvdual-specfc dummes. These dummy varables are assumed to be fxed (fxed-effects models, named FE models) or random (random-effects or varance-components models, named RE models). In RE models, heterogenety s modeled through the random effects (ndvdual and temporal) absorbed nto the regresson resdual term. Recently, Stern (010) uses the between estmator, whch, despte the restrctve assumptons assocated wth ts use (ncludng more specfcally the lack of correlaton between the specfc effects and the explanatory varables), may be seen as a consstent 3
estmator of the long-run relatonshp. But stll, ths specfcaton mposes the restrcton that the slope coeffcents of ths relatonshp are common to all countres. Ths problem was already dscussed by Maddala et al. (1997) who argued that the realty s probably stuated between complete homogenety and complete heterogenety. The parameters are not perfectly dentcal, but there s a certan smlarty between them. One way to take nto account ths smlarty s to admt that the parameters are assumed to come from a common dstrbuton, from the same mathematcal expectaton, and from the non-zero varance-covarance matrx. The authors show that the resultng parameter estmates are a weghted average of the overall pooled estmate and the separate tme-seres estmates based on each cross-secton. Each ndvdual estmator s thus shrunk toward the pooled estmator (.e. shrnkage estmators ). The authors also show that the shrnkage estmator gves much more reasonable parameter values. Hsao et al. (1999) confrmed that n the case of panel data model wth coeffcent heterogenety, the Bayesan approach performs farly well, even when the tme dmenson s small. Maddala and Hu (1996) have also presented some Monte Carlo evdence to suggest that the teratve procedure gave better estmates (n the mean squared sense) for panel data models. To conclude, n the Bayesan framework, the panel data models rase other problems than ndvdual tme seres (such as a correct consderaton of crosscountry homogenety/heterogenety). Ths s the reason why the Bayesan shrnkage estmator can be consdered as an alternatve estmaton method capturng cross-sectonal heterogenety n the economy-energy-envronment relatonshp. In ths way, the soluton reles on the use of random-coeffcent model n whch the parameters are assumed to come from a common dstrbuton. In our study the ndvdual dmenson (N=51) s more mportant than the tme dmenson (T=39). 4
The outlne of the remanng part of ths paper s as follows: n Secton we ntroduce the data sets used n the study and perform some descrptve analyses to provde an overvew of energy consumpton and CO emsson trends; detals of the emprcal methods employed and the results obtaned are presented n Secton 3; and n Secton 4, we draw our conclusons and further dscuss the results.. Data and prelmnary analyss.1. Data descrpton The varables consdered n ths study are per capta CO emssons, real per capta GDP and per capta energy consumpton. Both CO emssons and prmary energy consumpton data (n mllons tones of CO (MtC) and n mllon tones of ol equvalent, respectvely) are taken from BP (010) 3. Furthermore, data for per capta GDP (n real terms,.e. n US dollars at constant 1990 prces and exchange rates) and the data for total populaton (n thousand) are taken from UNCTAD (009). All data s annual and covers the years 1970 to 008, and t extends to 55 countres. The countres studed wth the abbrevatons that tables and fgures use throughout the present paper are as follows: Algera (ALG), Argentna (ARG), Australa (AUS), Austra (AUT), Belgum & Luxembourg (BEL), Brazl (BRZ), Bulgara (BLG), Canada (CND), Chle (CHL), Chna (CHN), Chna Hong Kong SAR (CHK), Colomba (CLB), Czech Republc (CZE), Denmark (DNK), Ecuador (ECD), Egypt (EGP), Fnland (FIN), France (FRA), Germany (DEU), Greece (GRC), Hungary (HUN), Iceland (ICL), Inda 3 BP (010) uses standard global average converson factors to estmate carbon emssons. The Internatonal Energy Agency (IEA) provdes also data for CO emssons from fuel combuston, whch are calculated usng the ntergovernmental panel on clmate change (IPCC) method. Consequently, these two data sets have very smlar trends and magntudes, therefore, workng wth ether BP or IEA data set does not have a sgnfcant mpact on the estmaton results of ths study. 5
(IND), Indonesa (INA), Iran (IRN), Italy (ITL), Japan (JPN), Kuwat (KUW), Malaysa (MLS), Mexco (MEX), Netherlands (NLD), New Zealand (NZL), Norway (NRW), Pakstan (PKS), Peru (PER), Phlppnes (PHI), Poland (POL), Portugal (PRT), Qatar (QTR), Republc of Ireland (IRL), Romana (ROM), Saud Araba (SAR), Sngapore (SGP), South Afrca (AFR), South Korea (KOR), Span (ESP), Sweden (SWE), Swtzerland (SWZ), Tawan (TWN), Thaland (TAI), Turkey (TRK), Unted Arab Emrates (EMT), Unted Kngdom (GBR), Unted States of Amerca (USA), and Venezuela (VEN). We should menton here that although ths sample of 55 countres covers nearly 90% of global CO emssons, because of the unavalablty of data, some countres (more mportantly, Eastern European and ex-sovet countres) have been excluded from the analyss. To gve some examples of the magntude of ths excluson, n 009, Russan CO emssons represented 4.9% of global CO emssons whle ts prmary energy consumpton was 5.7% of global prmary energy consumpton, whch s roughly equal to the total prmary energy consumed n Mddle-Eastern countres. Smlarly, prmary energy consumpton n both Ukrane and Australa represent 1% of global consumpton, and Ukranan emssons account for 0.9% of global CO emssons due to fossl fuel combuston. Some summary statstcs on the varables of nterest for the countres under analyss are provded n the Appendx A (Table A.1)... A frst look From Fg. 1 one can see the frst sgn of the exstence of an EKC for a sample of 55 countres n the perod consdered. Representng per capta CO emssons as a functon of per capta GDP seems to create an nverted U-shape curve. Naturally, such a relatonshp s not surprsng, and t has smlar (but not dentcal) representatons n the lterature. 6
Fg. 1. Scatter plot of per capta CO emssons (n kg of CO ) and per capta GDP (n constant 1990 US dollars): full sample of 55 countres. Data sources: BP (010), UNCTAD (009). A more nterestng pont may be made, n Fg.1, by representng the outlers wth a damond shape and representng the data for all the other countres wth a damond-on-square shape. We then see clearly that an nverted U-shape curve exsts for the CO -GDP relatonshp, both wth and wthout the outlers, although t s much more evdent n the frst case. In fact, the relatve share of the outlers (.e. Qatar, Unted Arab Emrates, Kuwat and Sngapore) prmary energy consumpton and CO emssons s not that hgh. It represents roughly only 1.7% of global energy consumpton and emssons. To provde a further prelmnary analyss, let us now examne ths relatonshp n a more analytcal manner. In the standard EKC hypothess testng procedure, the equaton to be estmated s n the followng form: e t = c + b y t + a ( y ) + ε (1) t t 7
where e t s an ndcator of envronmental degradaton (n general per capta CO emssons), y t denotes ncome per capta (per capta GDP) and ε t and c represent respectvely the stochastc error term and the constant. The shape of the curve s determned by the parameters b and a. The dea s that the relatonshp between per capta CO emssons and per capta GDP may have an nverted U-shape curve f b > 0 and a < 0. On the other hand, the turnng pont ncome (henceforth TP), where per capta CO emssons reach ther maxmum level, can smply be calculated by b y t =. a In the related lterature, Eq. (1) s also used to test the same hypothess n the case of energy consumpton. So on the left-hand sde of Eq. (1), one would ntroduce energy data nstead of CO data (e.g. Luzzat and Orsn, 009). However, n general, Eq. (1) s modfed by ntroducng, as an addtonal covarate, energy data on the rght-hand sde (e.g. Apergs and Payne; 010). In our case, per capta prmary energy consumpton s ncluded as an addtonal varable, that s, we have: CO = c + b GDP + a ( GDP ) + d NRJ + ε () t t t t t where NRJ represents per capta prmary energy consumpton. Note that other varables are also n per capta terms. Table 1. OLS estmaton results Wth outlers R² = 0.91 Wthout outlers R² = 0.77 Varables Coeffcent Std.-Error T-Stat. Coeffcent Std.-Error T-Stat. Constant 94 101.90 635 100 6.3 GDP 187 0 9.35 300 1.1 14.5 GDP^ -9.05 0.58-15.41-11.08 0.59-18.7 NRJ.44 0.0 116.8 1.96 0.039 49.9 8
Table 1 gves the estmaton results when an ordnary least squares (OLS) regresson s appled to Eq. () usng our data set. Both Fg. 1 and the results gven n Table 1 gve confrmaton of the exstence of an EKC for both 55- and 51-country samples, snce all varables are found to be sgnfcant wth expected sgns. Furthermore, as predcted from Fg. 1, the EKC hypothess seems to be supported more strongly (havng greater R² value) when the outlers are ncluded. Moreover, one may calculate the turnng pont ncome of the EKC from the estmated coeffcents, whch s 10.33 wth the outlers, and 13.53 wthout the outlers. Evdently ths analyss gnores two crucal facts. Frst, t s assumed that all the countres nvolved n the analyss are homogenous and second, the dstrbuton of test statstcs generated by the pooled OLS regresson model s based on the assumpton that the data s statonary. In lght of ths, t s clear that f ether or both of these assumptons do not hold, based estmates may result. In consequence, ths frst look brngs us to the queston asked n the ttle of ths paper, that s, s there a lack of robustness to heterogenety n the EKC analyss? In what follows, we extend the EKC analyss to the Bayesan shrnkage framework whch allows the queston of nterest to be addressed rgorously and the heterogenety between countres to be accounted for. 3. Specfcaton and estmaton of the model Before we get nto the estmaton method and provde the estmaton results, let us dscuss very brefly the possble shapes that the CO -GDP nexus can take. For ths purpose, consder Eq. (). The sgn of the parameter a determnes whether the CO -GDP nexus has a concave, convex, or lnear relatonshp. More specfcally, we have three possble cases: 9
If a<0, we have an nverted U-shape relatonshp and the curve s concave. Dependng on the TP (.e. b a ) the curve may be: ncreasng (the TP has not yet been reached); ncreasng and decreasng (the TP has been reached and passed); or decreasng (the TP has been passed and ncreases n per capta GDP decrease per capta emssons). If a>0, we have a U-shape relatonshp and the curve s convex. The curve may be decreasng; decreasng and ncreasng; ncreasng for the three cases of TP gven above, respectvely. If a=0 the relatonshp s lnear. Dependng on the sgn of the parameter b, the lne may be ncreasng (b>0); decreasng (b<0); or horzontal (b=0). On the other hand, the parameter d measures envronmental effcency of energy use. Its magntude reflects whether, n a gven country, energy consumpton s more or less carbonntensve. 3.1. Estmaton method Consder once agan Eq. () whch can be rewrtten n the framework of the randomcoeffcents model, wth followng specfcaton: y = X γ + u (3) where y contans CO tme seres, X s the matrx wth explanatory varables, and γ slope coeffcents. In the Bayesan framework, the pror dstrbuton of γ s gven by: γ N ( µ, Σ) where the parameters µ (mean of γ ), Σ (varance of γ ) and σ (resdual varance) are unknown. That s why some assumptons have to be made on the pror specfcaton of these parameters. Then we can derve the posteror dstrbuton for the parameters γ. On the other 10
hand, f µ, Σ and σ are all known, the posteror dstrbuton of γ s normal and calculated by: γ 1 * = 1 µ 1 σ* X +Σ 1 X * 1 X X ˆ γ +Σ* * σ* (4) where γˆ s the OLS estmator of γ. The posteror dstrbuton mean of γ and ts varance are shown n Eqs. (5) and (6) respectvely. N µ * = 1 γ * (5) N 1 = 1 1 1 V[ γ *] = X +Σ* * X (6) σ Snce n general, Σ and σ are unknown parameters, one needs to specfy prors for them. For ths purpose, Smth (1973) suggested usng the mode of the jont posteror dstrbuton gven by the followng equatons: σ * = 1 + ς λ ( y X γ *) ( y X γ T + + *) (7) ς and N Σ* = 1 R+ + ( γ * µ *)( γ * µ *) T k δ = 1 (8) where the parameters ς, λ, δ and R arse from the specfcaton of the pror dstrbutons. Moreover, Smth (1973) proposed the approxmaton of these parameters by settng ς = 0, δ =1 and R as a dagonal matrx wth small postve entres (e.g., 0.001). By dong so, the estmators take the followng forms: σ * = 1 ( y X γ *) ( y X γ *) (9) T + 11
N Σ* = 1 R+ ( γ * µ *)( γ * µ *) (10) T k 1 = 1 γ 1 * = 1 µ 1 σ* X +Σ 1 X * 1 X X ˆ γ +Σ* * σ* (11) and N µ * = 1 γ * (1) N 1 = 1 1 1 V[ γ *] = X +Σ* * X (13) σ Then Eqs. (9-13) should be solved teratvely, wth the ntal teraton usng the OLS estmator γˆ to compute µ *, Σ * and σ *. The second teraton s based on the emprcal teratve Bayes estmator γ *. The thrd and followng teratons are dentcal to the second one. The emprcal Bayes estmator was proposed by Maddala et al. (1997). The only dfference wth Smth s estmator les n the computaton of the parameters s, we have: σ * and Σ *, that 1 σ* = ( y Xγ *) ( y Xγ *) (14) T k N Σ* = 1 R+ ( γ * µ *)( γ * µ *) (15) N 1 = 1 3.. The results The estmated parameters usng Bayesan shrnkage estmators for the model gven n Eq. () and correspondng T-Statstcs are reported n Table A. n Appendx A. 1
In order to make the estmaton results more readable and easer to nterpret we present them also n a graphcal form (see Fg. ). On the top horzontal axs, countres are arrayed accordng to the shape of the CO -GDP nexus: countres at the top of Fg. are those that have a nonlnear relatonshp (concave or convex) and symmetrcally, countres at the bottom have a lnear relatonshp. On the other hand, the vertcal axs reports the value of the coeffcent assocated wth the varable of prmary energy consumpton, NRJ, whch s always postve. From ths perspectve, a country closer to zero (upwards as well as downwards) uses prmary energy sources that are relatvely less carbon ntensve. Fg.. Classfcaton of countres based on shrnkage estmators Countres havng non lnear relatonshp are separated by a vertcal axs that may be nterpreted as an axs of decrease. Accordngly, countres on the left sde have a standard concave (nverted U-shape) relatonshp. Furthermore, the top horzontal axs measures the 13
decreasng part of the curve as a percentage of the entre curve. For each country separately, ths percentage s calculated n the followng way: frst, from the estmated parameters a and b (see Table A.) we calculate the TP. Then takng nto account the sgn of the coeffcents (n order to determne the form of the curve), we count the number of per capta GDP data ponts before and after the TP, whch s then used to compute the proporton of ncreasng and decreasng parts of the curve. 4 As a result, the further on the left sde of ths axs a country s stuated, the larger the ncreasng part of the EKC t has. On the rght sde of the same axs, countres have a non-lnear convex relatonshp. In ths case, the top horzontal axs measures n percentage the ncreasng part of the curve. Hence, symmetrcally, countres stuated more on the rght sde are those who have relatvely larger ncreasng part n the EKCs. Countres n the lower part of the fgure have a lnear relatonshp. For these countres, the bottom horzontal axs reports T-Statstcs values (coeffcent dvded by standard devaton) of the coeffcent assocated wth per capta GDP. Thus, the sgn of the T-Statstcs s the same as the coeffcent. Therefore, countres on the left sde have a decreasng relatonshp and those on the rght sde have an ncreasng relatonshp. At a confdence nterval of 5%, the tabulated Student statstcs value beng equal to 1.96, countres postoned n the vertcal band between -1.96 and 1.96 are those for whch ths coeffcent s not sgnfcant. Ths mples that economc growth does not appear to be an explanatory varable for CO emssons. 4 At ths pont we note that ths method works well for all countres but one, Egypt, for whch the TP s found to be negatve. Snce such a result s nconsstent wth the nature of the relatonshp, Egypt s excluded from the later analyss. 14
To gve an analytcal descrpton of the dstrbuton of countres based on shrnkage estmators, the nformaton provded n Fg. makes t possble to classfy seven types of countres: 1. Northwest quadrant: Countres wth a standard (concave) EKC. These countres may be qualfed as ecologst (or envronmentally frendly) 5. (ecologsts).. North-central quadrant (close to 0): Countres wth a decreasng convex curve 3. Northeast quadrant: Countres wth an exponentally ncreasng (convex) relatonshp. These countres can be qualfed as polluter. 4. Southwest quadrant: Countres wth a lnear decreasng relatonshp (.e. ecologsts). 5. South-central quadrant (close to 0): Countres havng no CO -GDP relatonshp, but usng less pollutant energy sources (ecologsts). 6. South quadrant (close to the bottom horzontal axs): Countres wthout CO -GDP relatonshp, but usng relatvely more carbon ntensve energy sources (polluters). 7. Southeast quadrant: Countres havng ncreasng CO emssons wth ncreasng GDP (polluter). We wll dscuss these fndngs n the followng fnal secton of ths paper. 5 It should be mentoned that the term ecologst should be nterpreted here wth some cauton because of the fact that we ntroduce at ths pont a dynamc vson of the evoluton of the CO -GDP nexus and that, rrespectve of ther CO emsson levels, countres are qualfed as ether ecologst or polluter dependng on ther emsson trends. 15
4. Dscusson and conclusons Snce the EKC hypothess s made to test the dependence of envronmental degradaton on the level of economc development, we wll analyze the results takng nto account the development level of each country. Accordng to the standard classfcaton of countres by levels of economc development, countres fall nto fve dfferent categores: developed countres (group 1), transton economes (group ), newly ndustralzed countres of Asa (group 3), new emergng markets and ol exportng countres (group 4) and least developed countres (group 5). Frst, we wll consder the ecologsts. Not very surprsngly, from our results t appears that the countres n group 1 are found to be the most ecologst countres. These countres ether dversfy ther prmary energy sources (Norway, Swtzerland, Fnland, Sweden, Iceland, Austra, Belgum, Luxembourg, Germany, Canada, France and Unted Kngdom), or they consume ther fossl fuels, but reduce ther CO emssons (Denmark and USA). On the other hand, the transton economes (countres n group,.e. Hungary, Czech Republc, Bulgara, Poland, and Romana) are the countres that faced a major transton after the dsntegraton of the Sovet Unon n 1991, whch lead to a decrease n ther CO emssons. Recently, Jobert et al. (010) argued that durng the transformaton of the economc structure, these countres reduced the ndustral share of ther GDP and that therefore, they mght be qualfed as ecologsts despte themselves. The results of the present study gve further support to ths nterpretaton. The countres n group 3 (Chna Hong Kong SAR and Tawan), havng smlar economc growth paths as some European countres n the catch up process (such as Republc of Ireland and Span), may be consdered as ecologsts snce these countres have drected ther 16
development towards low-pollutng ndustres (hgh technology, servce, fnance and toursm). Dversfcaton of energy sources allowed the countres of group 4 (Argentna, Venezuela and Colomba) to be more envronmental frendly. In addton, an unexpected result has been obtaned for the case of Pakstan. For ths country, whch s n group 5, CO emssons have found to be decreasng lnearly wth ncreasng GDP. For the case of pollutng countres, those n group 1 have nether dversfed ther energy sources nor decreased ther CO emssons (Netherlands, Australa, New Zealand, Greece, Portugal and Italy). In group 3, the South Korea can be consdered as a pollutng country snce the steel ndustry and automoble ndustry are among the country s man economc actvtes. In other countres from both group 4 (Saud Araba, Chle, Malaysa, Brazl, Mexco, Turkey, South Afrca, Algera, Thaland, Iran, Peru, Chna and Ecuador) and group 5 (Phlppnes and Inda), t seems that the energy mx has been somewhat stable over tme. Therefore these countres appear n our analyss as pollutng countres. 6 Fnally, for some other countres, such as Japan and Indonesa, the results are somewhat ndecsve as to whether these countres would be qualfed ecologst or polluter. We hope to have clarfed how to nterpret the fact that the EKC hypothess does not hold for ndvdual countres, but emerges from the overall pcture (see Fg. 1). Keepng n mnd the results found above, f one looks at the typology of countres wth respect to per capta GDP, one can see that: (1) hgh-ncome countres can be qualfed as ecologsts snce they have 6 The reports of the Internatonal Energy Agency consttute a very useful source of nformaton about energy ndcators and emsson trends. For detaled statstcs and further analyss see IEA (010a, b, c). 17
decreasng emsson paths, () mddle-ncome countres are ether ecologsts or polluters and they have an horzontal emsson trends (dfferences n level rather than the slope of the relatonshp) and (3) low-ncome countres are polluters snce they have ncreasng per capta CO emssons. To make the pont concrete, consder as a fnal llustraton, Fg. 3 whch provdes CO emsson trends wth respect to GDP n some selected countres havng dfferent levels of development. Fg. 3. Selected countres havng dfferent trends n both per capta CO emssons (vertcal axs; n thousand tones of CO ) and per capta GDP (horzontal axs; n constant 1990 US dollars) From Fg. 3 t s qute clear that dependng on the development stage, countres have varous per capta CO paths, and that channg ndvdual paths together shows the emergence of dfferent EKCs n dfferent per capta CO and GDP levels, and combnng those gves an 18
overall EKC. However, the queston arses whether hgh-ncome countres reduce ther CO emssons va envronmental polces, measures and practces (such as regulatons, more effcent use of energy, nvestments n abatement technologes, fuel swtchng or renewable energy facltes) or by changng the composton of domestc economc actvtes by producng hgh-value added green products and movng ther pollutng producton to lowncome countres, by means of polluton haven based nvestment relocatons. We hope that further research wll contnue to explore factors nfluencng the shape of the EKC. References Apergs, N., Payne, J.E., 010. The emssons, energy consumpton and growth nexus: Evdence from the Common wealth of ndependent states. Energy Polcy 38, 650-655. Baltag, B.H., Bresson, G. and Protte, A., 008. To pool or not to pool? n The Econometrcs of Panel Data: Fundamentals and Recent Developments n Theory and Practce, (L. Mátyás and P. Sevestre eds.). Seres: Advanced Studes n Theoretcal and Appled Econometrcs 33. Sprnger-Verlag, New York. Baltag, B.H., Kao, C., 000. Nonstatonary Panels, Contegraton n Panels and Dynamc Panels: a Survey. In: Baltag, B.H. (Eds.), Advances n Econometrcs 15, Elsever Scence, 7-51. BP, 010. Statstcal Revew of World Energy 010.http://www.bp.com/statstcalrevew Carson, R.T., 010. The Envronmental Kuznets Curve: Seekng Emprcal Regularty and Theoretcal Structure. Revew of Envronmental Economcs and Polcy 4, 3-3. Dasgupta, S., Laplante, B., Wang, H., Wheeler, D., 00. Confrontng the Envronmental Kuznets Curve. The Journal of Economc Perspectves 16, 147-168. 19
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Appendx A Table A.1. Summary statstcs of the full sample of 55 countres Years 1970 1990 008 Percentage of the world populaton 76.8 75.6 73.8 Percentage of the world GDP 9.6 94.1 97.6 Percentage of global CO emssons 80.8 78.9 87.5 Percentage of global prmary energy consumpton 81.5 78.6 86.3 Data sources: BP (010), UNCTAD (009)
Table A.. Shrnkage estmators state by state (number of teratons: 5) Varable Country Coeff. T-Stat Country Coeff. T-Stat Country Coeff. T-Stat Const ALG 169.09 0.98 FIN 9654.79 7.9 PER -448.40-1.74 GDP -16.56-0.88-953.38-5.53-95.78-0.34 GDP^ 35.4 1. 13.68 5.8-39.07-0.55 NRJ.54 165.86 3.47 7.10 3.90 7.31 Const ARG 189.53.7 FRA 15331.47 6.4 PHI -37.48-1.05 GDP 18.88 0.57-11.55-4.85-116.75-1.6 GDP^ 16.07 0.57 18.95 3.9 54.86 0.95 NRJ -0.08-0.41.4 4.00 3.9 69.70 Const AUS -36.51 -.19 DEU 13585.95 5.74 POL 355.49 3.78 GDP 153.68 1.00-1580.19-6.4-660.05-8.0 GDP^ -.50-0.80 9.74 5.3 79.3 4.49 NRJ 3.6 0.81 4.60 19.65 3.88 89.51 Const AUT 5454.15 7.54 GRC 380.00 0.75 PRT -1197.87 -.54 GDP -580.4-6.49-110.89-1.00 78.84 1.84 GDP^ 11.73 6.56-1.39-0.31-9.83-1.03 NRJ.59 8.3 3.58 41.48.57 7.53 Const BEL 11573.89 5.89 HUN 35.3.54 IRL 6.41 0.19 GDP -1141.59-9.16-1164.19 -.34-63.75 -.07 GDP^ 0.03 5.80 57.79 0.91 0.64 1.13 NRJ 3.4 13.31 3.1 7.77 3.31.99 Const BRZ -19.54-0.10 ICL 5075.51 1.69 ROM -603.7-3.65 GDP 37.64.43-136.36-0.66-380.7-1.94 GDP^ -36.9-1.61 6.04 1.35 64.1 1.07 NRJ 1.3 8.3 0.48 3.9 3.16 84.93 Const BLG 171.44 4.97 IND -6.79-0.85 SAR -198.97 -.38 GDP -564.81-9.8-7.69-0.33 704.56 4.4 GDP^ 365.30 6.08 6.41 0.14-33.6-4.08 NRJ 3.55 31.95 3.31 33.57.55 84.40 Const CND 17368.41 6.80 INA 98.94 5.83 AFR 146.9 0.19 GDP -1553.96-4.9-481.03-6.99-0.10-0.04 GDP^ 3. 4.38 47.4 8.38 0.40 0.8 NRJ.08 4.75 3. 40.89 3.57 90.0 Const CHL 173.45 0.63 IRN 415.51.11 KOR 458.81 4.07 GDP -161.64-0.63-64.65-0.34-49.50-0.63 GDP^ 8.19 0.9 18.69 0.43 -.95-1.45 NRJ.67 5.91.50 153.73.90 0. Const CHN -16.94-1.31 ITL -17.7-0.3 ESP 58.60 4.50 GDP -165.65-6.07 49.8 1.44-499.39-4.43 GDP^ -49.3-4.74-1.88-1.7 10.55 3.53 NRJ 3.75 10.90.74 18.48 3.55 13.00 Const CHK -739.65 -.86 JPN 5177.09 6.9 SWE 3441.49 13.07 GDP 148.96 3.5-380.17-6.83-1901.88-7.4 GDP^ -4.57 -.91 7.30 5.05 7.9 6.15 NRJ 3.14 13.75.59 14.08 0.89.0 Const CLB 804.36 6.7 MLS -54.31-0.57 SWZ 6548.44 1.99 GDP -1009.19-4.91 185.48 1.81-6.39-0.1 GDP^ 130.35 1.99 -.78-0.6-0.80-0.5 NRJ.86 19.41.38 4.9 0.41 1.8 Const CZE -859.07-0.97 MEX -735.46-3.05 TWN 33.73 3.63 GDP -106.07-3.9 430.94.53-414.54-8.46 GDP^ -.36-0.05-75.36-3.48 13.88 1.33 NRJ 4.44 48.36.84 30.79 3.63.50 Const DNK -5381.8-5.6 NLD 415.15 3.10 TAI -53.11 -.55 GDP 505.78 7.5-16.35-1.9 11.99 3.8 GDP^ -9.83-7.63 4.09.58-5.17-0.45 NRJ 3.01 6.69.5 6.08.75 43.33 Const ECD -69.9-0.75 NZL 5556.06 1.83 TRK 00.90 0.71 GDP 91.69 1.86-71.1-1.53-6.67-0.11 GDP^ 18.3 0.30 3.09.05-10.14-0.37 NRJ 1.98 4.5 1.51 8.95.88 11.7 Const EGP -9.35-1.50 NRW 5101.85 6.50 GBR 7570.06 6.5 GDP -9.56-0.70 173.1 1.88-769.75-8.6 GDP^ -148.4-3.15-1.41-1.05 16.64 6.73 NRJ.97 5.4-0.08-0.53 3.04 11.94 Const USA 88.47 3.7 PKS 59.39.75 VEN 197.5 1.64 GDP -185.04-3.77-385.80 -.86 405.13 0.91 3
GDP^ 3.06.81 88.76 1.58 34.74 0.43 NRJ.76 35.15.85 17.07 1.15 7.47 4