FDI, Population Density and Carbon Dioxide Emissions: A Case Study of Pakistan

Transcription

1 Iranica Journal of Energy & Environmen 3 (4): , ISSN 79-5 IJEE an Official Peer Reviewed Journal of Babol Noshirvani Universiy of echnology DOI:.589/idosi.ijee BU FDI, Populaion Densiy and Carbon Dioxide Emissions: A Case Sudy of Pakisan Haider Mahmood and A.R. Chaudhary GC Universiy, Kachery Road, Lahore, Pakisan NCBA&E, Gulberg III, Lahore, Pakisan Absrac: he sudy aemps a finding he impac of foreign direc invesmen on carbon dioxide emissions in Pakisan. I akes carbon dioxide emissions as dependen variable and foreign direc invesmen, share of manufacuring value added and populaion densiy as independen variables. ADF, PP, Ng-Perron and Zivo-Andrews Uni roo ess were used o find he uni roo problem. ARDL and is error correcion model were used o find he long run and shor run relaionships. he sudy found he long run relaionship in he model bu shor run relaionship did no exis. Foreign direc invesmen, manufacuring value added and populaion densiy have posiive impac on carbon dioxide emissions. Key words: FDI Carbon Dioxide Emissions Populaion Densiy Coinegraion INRODUCION would also rise. Secondly, beer employmen opporuniies will arac he labor o migrae from rural o Foreign direc invesmen (FDI) can have hree kinds urban areas. I increases he populaion densiy and of effecs on environmenal qualiy of developing economic aciviies in urban area. counries. Firs is a scale effec, which can be posiive if here is economic growh and here has been demand for Lieraure Review: According o Dunning [], he environmenal goods which can help in ackling he culural, poliical and environmenal effecs of FDI environmenal problems. Negaive scale effec occurs depended on he governmen policies. Jaffe e al. [] and when a counry experiences economic growh wihou Beghin[3] found ha he diry indusries moving from he considering environmenal managemen and regulaions. developed counries o he developing counries were Second is echnological effec which is posiive when proof of polluion-heaven hypoheses and in some cases foreign invesors use environmenal friendly echnology he developing counries also purposely relaxed he and also have spillovers on domesic invesmen hrough environmenal policies o arac he foreign invesmen. compeiion. hird is he policy effec which can be In such counries, polluion level would rise wih posiive if hos governmen makes igh regulaions for expansion of foreign invesmen in diry indusries and he proecion of environmen and also enforces he composiion effec would emerge wih increasing share of foreign invesors o follow such regulaions. Negaive diry goods in Gross Domesic Produc (GDP). policy effec can occur when here is compeiion amongs Copeland and aylor [4] found ha capial inensive developing counries o arac FDI and hos governmen counry would produce and rade a polluion inensive relaxes he environmenal regulaions for FDI. produc increasing world s polluion level. Similarly, FDI is usually done in hose counries where here are capial rich counry also did invesmen in poor counries less environmen laws o save he cos of producion. FDI and again increased polluion level. Organizaion of is done in manufacuring secor as well. So, FDI and Economic Cooperaion and Developmen s [5] repor producion of manufacuring secor simulaneously can invesigaed he impac of globalizaion including FDI on affec he environmen of a counry. Populaion growh environmen. Repor claimed ha FDI aciviies generaed and is densiy are responsible for environmenal environmenal degradaion in he hos counries even degradaion. As populaion rises, he demand for energy hough foreign invesors followed greaer environmenal and fuel, demand for indusrial good and ransporaion sandards han local firms. Corresponding Auhor: Haider Mahmood, GC Universiy, Kachery Road, Lahore. el: , haidermahmood@homail.com. 355

2 Iranica J. Energy & Environ., 3 (4): , Kolsad and Xing [6] colleced daa from invesmen was in eriary secor which did no manufacuring indusries locaed in developing and usually conribue in polluan emissions. Baek and developed counries. hey found ha relaxing Koo [8] invesigaed daa of India and China. hey environmenal sandards was he major deerminan of found he long run relaionships amongs FDI, SO FDI. he counries which araced FDI by relaxing emissions and economic growh and a unidirecional environmenal regulaion would have o face heavy causaliy from FDI o economic growh and SO emissions cos in erm of polluion. Goldenman[7] and Zarsky[8] in hese counries. Acharyya [9] used he daa of India saed ha foreign invesors used beer producion for 98-3 and found ha FDI had a posiive and echnologies han local manufacurers. So FDI seemed significan impac on he economic growh and carbon good for he environmen. Dean [9] found ha scale dioxide emissions. effec would emerge wih increasing foreign invesmen and here would be greaer economic aciviies which Model Specificaion and Mehodology: o capure he resuled in depleion of environmenal resources and impac of FDI on polluion level, he sudy uses carbon greaer polluan emissions. dioxide emissions as percenage of Gross Domesic alukdar and Meisner [] used he carbon dioxide Produc (GDP) as dependen variable and uses FDI and emissions as proxy for he environmen and FDI in manufacuring value added as percenage of GDP and developing counries. he sudy found a negaive populaion densiy as independen variable. relaionship beween FDI from developed counries and carbon dioxide emissions. I was an evidence o use Model of sudy is as follows: cleaner echnology by developed counries. Smarzynska and Wei [] colleced daa from 534 Mulinaional COG = f ( FDIG, PD, MVAG ) () Enerprises (MNEs) from differen economies and esed = 97, 973,,5 he Polluion-Heaven Hypohesis of FDI. hey found a posiive bu insignifican relaionship beween FDI and lax where, environmenal regulaions. Bora [] invesigaed he COG = Carbon Dioxide Emissions in kg as percenage polluion inensiy of Unied Saes owned MNEs and of GDP a ime. found ha developed counries had he highes FDIG = Foreign Direc Invesmen inflow in consan proporion of polluion-inensive producion in foreign year US $ as percenage of GDP a ime producion aciviies. Xing and Kolsad [3] saed ha. developing counries used relax environmenal laws o PD = Populaion Densiy, people per square KM a arac FDI from developed counries in diry indusries. ime. hey also found ha US s FDI had bad impac on MVAG = Manufacuring Value Added as percenage of environmenal qualiy of developing counries. GDP a ime. Yang [4] used he provincial level daa of China and found a negaive relaionship beween FDI and sulfur A firs, sudy discusses he Augmened dioxide emissions. Liang [5] used he panel daa of 6 Dickey-Fuller (ADF) es, which was produced by Dickey major ciies of China and esed he environmenal effecs and Fuller [] o check he saionariy in he ime series. of FDI and per capia GDP and found a negaive his es proposed he following equaion wih inercep relaionship beween FDI and sulfur dioxide (SO ) o deec he non-saionariy. emissions. he finding suggesed ha FDI was helpful in reducing sulfur dioxide emissions in China. He [6] Y = + Y + Y + Y m Y m + u claimed ha bad impac of FDI on environmen was due () o lenien environmenal regulaion and also claimed ha environmenal qualiy would improve wih echnical and where, is a difference operaor, refers o he ime knowledge spillovers wih FDI. period and u is a residual a ime period. Y denoes he Merican e al. [7] invesigaed he impac of variable, which is invesigaed for saionariy. Y + FDI on carbon dioxide emissions. he sudy found Y m Y m is used o correc he correlaion ha FDI did no have any impac in enhancing carbon problem among u and regressors of equaion (). dioxide emissions in Indonesia and Singapore. he he equaion () includes inercep and can also be case for Singapore also showed ha major foreign assumed wih inercep and ime-rend as follows: 356

3 Y = + + Y + Y + Y m Y m + u Ng and Perron [] developed efficien and a modified (3) version of PP es by using generalized leas square where is he coefficien of ime-rend (). ADF es derending daa. his procedure is also efficien for large checks he null hypohesis ( =), if is saisically negaive errors and can do beer esimaion han PP es. significan and i is no zero, hen ime series has no uni he efficien and modified PP ess are as follows: roo problem. A ime-series variable is saionary wih wo condiions. A firs, should be saisically non-zero and d d MZ = ( ( y ) f)/k (9) i should be negaive. Phillips and Perron [] developed he uni roo es d MSB = ( k / f ) / () which is differen from ADF ess in dealing wih heeroscedasiciy and serial correlaion. hey ignore he d d d MZ = MZ MSB () Y + Y m Y m from ADF equaion (3) which is for any serial correlaion amongs error erms. d d MP = (( c ) k + ( c) )( y) / f () Phillips-Perron (PP) es removes he serial correlaion by d d giving ranks o he residuals. Equaion of PP es is as where, he saisics MZ and MZ are efficien versions follows: of PP es and Y = + + Y + u u may have heeroscedasiciy, so for correcion of serial correlaion and heeroscedasciy. PP es uses he / ˆ ˆ ˆ. SE( ˆ) Z =. =. ˆ ˆ ˆ Z. SE( ) = ˆ ( ˆ ˆ ) where, SE( ˆ) is he sandard error of ˆ. = is he es ime series is saionary or no. ADF and PP ess do no S = u = = E u = ˆ lim, = E S ˆ lim, = Iranica J. Energy & Environ., 3 (4): , (4) = d k = ( y ) /, c = 3.5. modified saisic Z and Z which are as follows: (3) j= ( ) saisic under he esimaes of ˆ and ˆ, which are given below: where and is he ime-rend. Z and Z of PP µ es follows he same disribuion as he -saisic of ADF es under he null hypohesis ( =). PP es has an advanage over ADF es ha ha PP es robus heeroscedasiciy in he error erm (u ). Secondly, i does no need o specify he lag lengh for is esimaion. f = ( j). k( j/) l where l is a bandwidh parameer (which acs as a (5) runcaion lag in he covariance weighing) and (j) is he j-h sample auo covariance of residuals. Zivo and Andrews [3] modified he PP and ADF uni roo es, which also considers he one-unknown (6) srucural break. he ADF es may fail in idenifying he rue resul in he presence of a srucural break wheher (7) (8) allow for srucural break in daa. Zivo-Andrews es uses he sequenial ADF es o find he break wih he following equaions. A A A A k Y + j Y j + j= Model A : Y = + + DU ( ) + B B A B k Y + j Y j + j= Model B : Y = + + D ( ) + C C C k c C D ( ) + Y + j Y j + j= Model C : Y = + + DU ( ) + (4) (5) (6) 357

4 Iranica J. Energy & Environ., 3 (4): , where DU( ) is and D* ( ) = if >, oherwise. B =, represens a possible break poin. Equaions B (4), (5) and (6)are esed sequenially for =,3,...,-, B where is he number of observaions afer adjusmen of differencing and lag lengh k. Model (A) allows for a change in he inercep of he series, Model (B) allows for a change in he rend of a series, while Model (C) allows changes in boh inercep and rend. Afer esing he uni roo problem in he ime-series variables, he coinegraion es can be used o find he long-run relaionship among he variables. Long-run relaionship saes he long-run equilibrium among variables, which may have he shock of disequilibrium in he shor-run from long-run, bu i will move again in long-run equilibrium Harris and Sollis [4]. Auo-Regressive Disribuive Lag (ARDL) bound esing echnique has been developed by Pesaran e al. [5]. ARDL can be applied if variables have mixed order of inegraion i.e. I() and I(). his approach akes he opimum lag lengh for each variable separaely in he model which helps in he daa generaing process from a general o a specific model. he problems resuling from non-saionariy of daa can also be avoided by using an ARDL approach Laurenceson and Chai [6]. he sudy uses he SBC o find he maximum relevan lag lengh. o find he coinegraion amongs variables of model (), ARDL model is as following: COG = + COG + FDIG + PD + 3 p q MVAG + COG + FDIG + 4 i i i i i= i= r s PD + MVAG + D + 3i i 4i i COG i= i= (7) In equaion (7), firs difference of COG is he dependen variable, he null hypohesis is (H : o= o= o3= o4= ) and alernae hypohesis is ( o o o3 4 ) which shows exisence of long run relaionship in he model, o is a consan and o is error erm. D COG is included in equaion for possible srucural break and o complee informaion in he model. his is also shown as F COG(COG /FDIG,PD,MVAG ). If coinegraion exiss in he model hen long run and shor run coefficiens will be calculaed. Error correcion erm can be used o find he shor-run relaionship in he model. Error correcion model is as follows: p q r COG = + COG + FDIG + PD i i i i 3i i i= i= i= s + MVAHG + D + EC + 4i i COG i= (8) is showing he speed of adjusmen from shor run disequilibrium o long run equilibrium. Aferwards, diagnosic ess are used o check he normaliy, funcional form, heeroscedasiciy and serial correlaion in he model. CUSUM and CUSUMsq saisics are used o ensure he sabiliy of parameers. Daa Source: Daa on carbon dioxide emissions, manufacuring value added, foreign direc invesmen and populaion densiy is aken from World Bank [7] for he years 97 o 5. his sudy used EViews (Version 7) for daa analysis. Empirical Resuls: able shows he resuls of ADF, Phillip-Perron and Ng-Perron ess. he resuls show ha ha all variables of model are non-saionary a level wih all ess. able () shows ha COG is non-saionary wih significan srucural break for he year 979 in inercep, significan break for he year 98 in rend and significan break for he year 979 in boh inercep and rend. FDIG become saionary wih significan srucural break for he year 999 in rend and significan break for he year 995 in boh inercep and rend. PD is non-saionary wih significan srucural break for he year 998 in inercep. MVAG is non-saionary wih significan srucural break for he year 997 in inercep and significan break for he year 98 in boh inercep and rend. able 3 shows ha dcog is saionary a % level of significance in ADF, PP and Ng-Perron (MZ a, MZ and MP) ess and a 5% in Ng-Perron (MSB) es wih inercep. dcog is saionary a % level of significance in ADF, PP and Ng-Perron (MZ and MP) ess and a 5% in Ng-Perron (MZ and MSB) ess wih boh inercep a and rend. dfdig is saionary a % level of significance in ADF, PP and Ng-Perron (Mz ) ess and a 5% in Ng-Perron (MZ, MSB and MP) ess wih inercep. dfdig is saionary a % level of significance in ADF, PP and Ng-Perron (MZ ) ess and a 5% in Ng-Perron (MZ,MSB and MP) ess wih boh inercep and rend. a dpd is saionary a % level of significance in PP ess and a 5% in ADF and Ng-Perron (MZ, MZ, MSB and a MP) ess wih inercep. dpd is saionary a % level 358

5 Iranica J. Energy & Environ., 3 (4): , able : Uni Roo ess a Level Ng-Perron Variable ADF PP MZa MZ MSB MP Model Specificaion: Inercep COG.8().46(5).569() FDIG -.() -.44() -.888() PD.84().63(4).87() MVAG -.87() -.74() () Model Specificaion: Inercep & rend COG -.99() -.875() -.3() FDIG -.668() -.5(3) -.85() PD.65(3).63(5) -6.68() MVAG -.647() -.56() -.8() Noe: * and ** show saionariy a he.5 and. level respecively. Brackes conain he opimum lag lengh. able : Uni Roo es: Zivo-Andrews Variable k Year of Break ype of Model COG A B C FDIG * B * C PD A MVAG A C Noe: * and ** show saionariy a he.5 and. level respecively able 3: Uni Roo ess a Firs Difference Ng-Perron Variable ADF PP MZa MZ MSB MP Model Specificaion: Inercep dcog -8.46**() -8.54**() **() -.64**.89*.763** dfdig **() -8.64**() **() -.546*.88*.7* dpd -3.*() 3.8**() -8.36*() -.7*.94*.8* dmvag -4.98**() **() **() -.77**.73**.73** Model Specificaion: Inercep & rend dcog -8.7**() -8.53**() *() **.5* 4.88* dfdig -7.3**() -.39**(3) *() **.54* 5.84* dpd -3.48*() -3.78*(3) -9.65*() -3.8**.49* 5.4* dmvag -5.7**() **() *() -3.79**.45* 4.488* Noe: * and ** show saionariy a he.5 and. level respecively. Brackes conain he opimum lag lengh. able 4: ARDL Bound es Using ARDL(,,,) A.5 A VARIABLES (when aken as a dependen) F-Saisic I() I() I() I() D(CO ).54** ** Means a 5%, % significan levels rejec he null hypoheses of no coinegraion * Means a % significan level rejec he null hypoheses of no coinegraion able 5: Long Run Resuls: Dependen Variable is COG Regressor Parameer S. E. -Saisic P-value FDIG 5.38* PD.77*** MVAG.77* C 3.55*** DCOG 7.694*** Noe: *, ** and *** show saisical significance of parameers a he.,.5 and. respecively. S. E. is sandard error. 359

6 -6 Iranica J. Energy & Environ., 3 (4): , able 6: Error Correcion Model: Dependen Variable is dcog Regressor Parameer S. E. -Saisic P-value dco-.339* dfdi dfdi * dmva.398* dpd.538** dc 5.47* ddco.6** EC Noe: *, ** and *** show saisical significance of parameers a he.,.5 and. respecively. S. E. is sandard error. able 7: Diagnosic ess LM version P-value Serial Correlaion (x ) Funcional Form (x ).67.3 Normaliy (x ) Heeroscedasiciy (x ).7.46 of significance in Ng-Perron (MZ ) ess and a 5% in ADF, PP and Ng-Perron (MZ a,msb and MP) ess wih boh inercep & rend. dmvag is saionary a % level of significance in all ess wih inercep. dpd is saionary a % level of significance in ADF, PP and Ng-Perron (MZ) ess and a 5% in Ng-Perron (Mz a, MSB and MP) ess wih boh inercep and rend. here is evidence for mix order of inegraion I() and I(). So, ARDL model is suiable o apply here. he sudy finds he opimum lag lengh for ARDL model by using SBC and hen includes dummy variable D COG in he ARDL model o complee he informaion in he model. Opimum lag lengh is for dcog, for dfdig, for dpd and for dmvag. he sudy selecs he year 979 for break period and pus from 97 o 979 and aferward in D COG. he calculaed F-saisic for seleced ARDL model is given in able (4) Fig. (a): CUSUM es C U S U M 5 % S i g n i fi c a n c e Fig. (b): CUSUMsq es C U S U M o f S q u a r e s 5 % S i g n i f i c a n c e able 4 shows ha F-saisic is.54. I is greaer han upper bound boh a % level of significance, so null hypohesis of no coinegraion is rejeced and alernae hypohesis of coinegraion is acceped. he long run relaionships exis amongs variables of he model. able 5 shows ha he coefficiens of FDIG, PD and MVAG are posiive and saisically significan. FDI, populaion densiy and manufacuring value added significanly conribued o carbon dioxide emissions. Inercep (C) is posiive and significan. Coefficien of D is posiive and significan a % level of significance. COG So, inercep has changed afer he year 979. able 6 shows ha coefficiens of all differenced variables a specified lags are saisically significan excep dfdig. he coefficien of EC is negaive and - saisically insignifican. So, here is no evidence for shor run relaionships amongs he variables in he model. Resuls of able (7) show ha P-values of serial correlaion, funcional form, normaliy and heeroscedasiciy are greaer han. so here are no problem of serial correlaion, funcional form, normaliy and heeroscedasiciy. Figure (a) and (b) show ha CUSUM and CUSUMsq did no exceed he criical boundaries a 5% level of significance. his means he model of environmen is correcly specified and long run coefficiens of regressors are reliable. CONCLUSIONS o check he impac of foreign direc invesmen on carbon dioxide emissions, his sudy used FDI and manufacuring value added as percenage of GDP and populaion densiy as independen variables and carbon dioxide emissions as dependen variable. he sudy used ARDL coinegraion echnique and is error correcion model o check he long run and shor run relaionships. he long run relaionship exiss in environmen model and shor run relaionship does no exis in he model. FDI, populaion densiy and manufacuring value added have he posiive impac on carbon dioxide emissions. Resuls give evidence ha all variables in he environmen model conribue o he polluion in Pakisan. 36

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