Residential Electricity Consumption and Economic Growth in Algeria

18 Pages·2017·0.82 MB·English

energies
Article
Residential Electricity Consumption and Economic
Growth in Algeria
MohammedBouznit1,MaríaP.Pablo-Romero2,3,* ID andAntonioSánchez-Braza2 ID
1 Laboratoired’ÉconomieetDéveloppement,FacultédesSciencesEconomiques,
CommercialesetdesSciencesdeGestion,UniversitédeBejaia,Bejaia06000,Algeria;[email protected]
2 DepartmentofEconomicAnalysisandPoliticalEconomy,FacultyofEconomicsandBusinessSciences,
UniversityofSeville,RamónyCajal1,41018Seville,Spain;[email protected]
3 VicerrectoríadeInvestigaciónyPostgrado,UniversidadAutónomadeChile,PedrodeValdivia425,
Santiago,Chile
* Correspondence:[email protected];Tel.:[phone]
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:11April2018;Accepted:21June2018;Published:26June2018
Abstract:WithintheframeworkoftheCOP21(ConferenceoftheParties)agreement,Algeriasubmitted
itsIntendedNationallyDeterminedContributionpledgingtoreducecarbonemissionsbyatleast7%
by2030. However,itwillbeadifficulttasktoreachthistargetastotalfinalenergyconsumptionhas
increased32%from2010to2014,withthemajorenergyincreasesbeingrelatedtoelectricityusein
theresidentialsector. Inthiscontext,therelationshipbetweenresidentialelectricityconsumption
andincomeisanalyzedforAlgeriaintheperiod1970–2013,byestimatingaresidentialelectricity
consumptionpercapitademandfunctionwhichdependsonGDPpercapita,itssquaredandcubed
terms, the electricity prices, and the goods and services imports. An extended Autoregressive
DistributedLagmodel(ARDL)wasadoptedtoconsiderthedifferentgrowthpatternsregistered
in the evolution of GDP. The estimate results show that the relationships between electricity use
andGDP(inpercapitaterms)presentaninvertedN-shape,withthesecondturningpointhaving
beenreached. Therefore,promotinggrowthinAlgeriacouldbeconvenienttoreducetheelectricity
consumption,asahigherincomelevelmayallowtheuseofmoreefficientappliances.Additionally,
renewable energies may be adequate to increase the electricity production in order to cover the
increasingresidentialdemand.
Keywords: economicgrowth;residentialelectricityconsumption;ARDLmodel;Algeria
1. Introduction
Algeriahasshowngreatinterestinsigningthe2015Parisagreement[1]. Ontheonehand,Algeria
isthethirdlargestCO emitteramongAfricancountries[2]and,in2014,totalemissionsamountedto
2
147MTCO . Ontheotherhand,Algeriaisespeciallyexposedtoclimatechangeeffectsastheannual
2
rainfallhasbeenreducedinrecentyearsbyupto30%,anditscapacitytocapturecarbonissmalldue
tothewideextentofthedesert[3].Therefore,Algeriawasoneofthefirstdevelopingcountriesto
submititsIntendedNationallyDeterminedContribution(INDC),inwhichitiscommittedtoreducing
CO emissionsbyatleast7%,by2030.
2
Nonetheless, Algeria has a difficult mission ahead to comply with this target. Its total final
energyconsumptionhasincreased32%from2010to2014,itsmajorenergyincreasesbeingrelated
to electricity use in the residential sector [4]. Thus, residential energy consumption has increased
43%andtheresidentialelectricityconsumptionincreased50%overthisperiod. AsstatedinBouznit
andPablo-Romero[1],themainenergyincreaseshavebeenespeciallyrelatedtochangesintheway
oflife. Furthermore,electricityconsumptionisestimatedtoincreasemorethandoubleby2030[5].
Energies2018,11,1656;doi:10.3390/en11071656 www.mdpi.com/journal/energies
Energies2018,11,1656 2of18
These energy increases have been accompanied by CO emissions increases, due to the positive
2
evolution of the consumption of non-renewable energy and the insignificant effect of renewable
energyonenvironmentimprovement[6]. Inthisregard,theAlgerianCO EmissionsIntensity−Power
2
sub-index from the Environmental Performance Index has not improved from its baseline score
(referredtotenyearsprevioustocurrent)[7].Thus,iftheAlgerianauthoritieswanttofulfilltheirINDC
targetswhileimprovingthewellbeingofitscitizens,someenergypoliciesoughttobeundertaken
to cushion the probable adverse effects of the increasing electricity demand. In that sense, energy
policiesmaybemoreglobally-effectiveintheresidentialsectorthaninothers[8],asresidentialenergy
consumptionisdifficulttodisplaceoffshore, contrarytowhatishappeninginothersectors[9,10].
Therefore,theanalysisoftheevolutionofresidentialelectricityusebecomesespeciallyinteresting,
andevenmoresoincountriessuchasAlgeria,inwhichsubstantialgrowthisexpected.
Since the seminal study by Kraft and Kraft [11], the energy-growth nexus have been widely
analyzed. However,theempiricalevidenceismixedintermsofthedirectionofcausalitybetween
variables[12].Moststudiesarerelatedtoelectricityenergyanddevelopedcountries[13],thosereferred
specificallytoAlgeriabeingscarce. Toourknowledge,onlytwostudiesspecificallyrefertoAlgeria.
ThestudybySouhilaandKourbali[14]examinedtherelationshipsbetweenenergyandeconomic
growth in the 1965–2008 period, finding unidirectional causality running from GDP to energy
consumption. Likewise, Bélaïd and Abderrahmani [15] examined the causal relationship between
electricity consumption, oil price and economic growth for Algeria over the period of 1971–2010.
Theauthorsfoundbidirectionalcausalrelationshipsbetweenelectricityuseandeconomicgrowth.
Inadditiontothesestudies,thecausalrelationshipsbetweenenergyandeconomicgrowthinAlgeria
havebeenalsoexaminedinotherpapersreferredtoseveralcountries. Amongthese,itmaybecited
thosebyWolde-Rufael[16,17]relatedtoAfricancountries,thosebyOzturkandAcaravci[18]and
Omri[19]referredtoMENAcountries,thosebySqualli[20]referredtoOPECmembersandthose
by Ozturk et al. [18] referred to developing countries. In general, these papers find that causality
runsfromeconomicgrowthtoenergyorelectricityconsumptioninAlgeria,althoughthestudyby
OzturkamdAcaravci[18]doesnotfindcointegrationamongthevariablesandthatbyOmri[19]finds
bidirectionalcausality.
Regarding the analysis of the relationships between the residential energy consumption and
incomegrowth,mostpreviouspapersfocusonthemicroeconomicbehaviorofhouseholds[21–23].
These studies find that growth in residential energy demand depends on the household’s income
level. Fromamacroeconomicperspective,mostofthestudiesthatanalyzetherelationshipbetween
residentialenergyandincome,estimatetheshort-runandthelong-runelasticityofresidentialdemand
forelectricity. Amongthem,SilkandJoutz[24]refertotheUSA,HoltedahlandJoutz[25]toTaiwan,
Hondroyiannis[26]toGreece,FilippiniandPachuari[27]toIndia,NarayanandSmyth[28]toAustralia,
AtakhanovaandHowie[29]toKazakhstan, Halicioglu[30]toTurkey, Nasiretal.[31]toPakistan,
AthukoralaandWilson[32]toSriLanka,Blázquezetal.[33]andRomero-Jordánetal.[34]toSpain,
Atalla and Hunt [35] to the Gulf Cooperation Council countries, and Schulte and Heindl [36] to
Germany. VerylittleattentionhasbeengiventoregionssuchasAfricaandLatinAmerica.
Moreover, these previous studies do not consider how the relationships between residential
electricity use and income may vary as income grows. In this regards, some previous studies
have examined the non-linear relationships between economic growth and energy consumption,
borrowing the idea of the EKC by testing the so called energy-EKC hypothesis [37]. This last
hypothesisstatesthatenergyconsumptionincreaseswitheconomicgrowthuptoacertainincome
level, afterwhichitdeclines. InlinewiththeEKChypothesis, theinverted-Upathforenergyuse
maybeenexplainedintermsofstructuralchangesinthecompositionofeconomicoutputandenergy
efficiency gains [38,39]. The studies by Suri and Chapman [40], Richmond and Kaufmann [38,41],
Nguyen-Van[39],YooandLee[42],andSbiaetal.[43]findevidencefortheexistenceofaninverted-U
curverelationshipsbetweenvariables. Nevertheless,otherstudiesfailtosupporttheinverted-Ucurve
hypothesis. Amongthem,itmaybecitedthestudybyLuzzatiandOrsini[44],ZilioandRecalde[45],
Energies2018,11,1656 3of18
andPablo-RomeroandDeJesús[46]. Mostofthesestudiesreferstointernationalpaneldata,noneof
thembeingfocusedonAlgeria.
Focusingontheresidentialenergyuse,Yinetal.[47]includeincomepercapitaanditssquared
valueasexplanatoryvariableswhenanalyzingtheresidentialelectricityconsumptionelasticityvalues
inChina.Likewise,Pablo-Romeroetal.[48]alsoincludeincomepercapitasquaredandcubedvariables
when analyzing whether joining the Covenant of Mayors is reducing municipalities’ residential
electricity consumption in Andalusia by testing an energy-environmental Kuznets curve. In the
sameway,incomepercapitainsquaredandcubedtermsarealsoconsideredbyPablo-Romeroand
Sánchez-Braza[8]whenestimatingtheenergydemand,consideringresidentialenergyconsumption
andincomefortheEU-28countries.
Followingtheselastpapers,therelationshipbetweenresidentialelectricityuseandincomeis
analyzedforAlgeriaintheperiod1970–2013,byestimatingaresidentialelectricityconsumptionper
capitademandfunctionwhichdependsonGDPpercapita,itssquaredandcubedterms,theelectricity
prices,andthegoodsandservicesimports.
Thisstudyenlargesthepreviousliteraturebyfocusingtheanalysisonelectricityconsumptionof
theresidentialsector. Additionally,toourknowledge,thisisthefirstworktoanalyzethenon-linearity
relationships for residential electricity consumption and income for an African country, Algeria.
Thus,knowledgeoftheserelationshipsisimportanttobetterimplementenergyandenvironmental
policies,especiallyincountriessimilartoAlgeria,whicharedevelopingandincreasingtheirelectricity
demand. Themethodologyappliedistheextendedautoregressivedistributedlagmodel(ARDL)with
breakpoints. Consideringbreakpointsappearstobeconvenient,astheAlgerianGDPgrowthtrend
since1970to2010showsdifferentpatterns,whichmayberelatedtocertainpoliticaldecisions,mainly
developedinthe1980s[15].
Theremainderofthispaperisorganizedasfollows: inSection2themethodologyisexplained.
InSection3adescriptiveanalysisismadeandthestatisticalinformationsourcesusedarespecified.
InSection4theresultsarepresented.InSection5theresultsarediscussed.Finally,themainconclusions
andpolicyimplicationsaregiveninSection6.
2. Methodology
Accordingtothepreviousstudies,theresidentialelectricityuse,measuredasKwhpercapita,
isconsideredtobeafunctionoftheGDPpercapita(constant2010US$), therealelectricityprices
forKwhandtheimportsofgoodsandservicesasashareofGDP[49–51].Additionally,inthispaper,
thesquaredandcubedvaluesofGDPpercapitahavealsobeenconsideredasexplanatoryvariablesof
theelectricityconsumption,asinYinetal.[47]andPablo-Romeroetal.[48].Introducingthesevariables
givesmoreflexibilitytothedemandfunction,allowingthestudyofhowtheelectricityconsumption
variesastheincomevariablegrows. Inthatregard,theelectricityconsumptionelasticitywithrespect
to GDP, may not be constant through time, varying with GDP per capita. Thus, if the estimated
coefficientforGDPpercapitaispositive,andthoserelatedtotheGDPpercapitasquaredandthe
GDPpercapitacubed,arebothnegatives,fromwhichaninvertedN-shapeisobtained. Thesecond
threshold level is reached when the electricity elasticity, with respect to income, becomes zero on
adecreasingelasticitytrend[10]. Nevertheless,iftheestimatedcoefficientsrelatedtoGDPvariables
haveothersigns,alternativetypesofrelationshipswillbedefinedbetweenelectricityconsumption
andGDP[52].
The ARDL cointegration testing approach by Pesaran et al. [53], and extended to introduce
break points, has been used to analyze the long-run and short-run relationships between the
residential electricity consumption and the defined explanatory variables, through the 1970–2013
period. AccordingtoPesaranetal.[53],thismodelcanbeusedevenifvariablesarenotintegrated
to the same order, but being I(1), I(0) or fractionally integrated. It is worth noting that the ARDL
cointegration testing approach by Pesaran et al. [53] holds no feedbacks running from dependent
variabletoindependentvariableatthelevels. Nevertheless,McNownetal.[54]havedevelopedthe
Energies2018,11,1656 4of18
ARDLboundstestwithbootstraptechniqueswhichcansolvetheendogeneityproblems. Therefore,
if there are endogeneity problems, it is convenient to use the ARDL bounds test with bootstrap
techniques. Inthispaper,noendogeneityproblemsaredetected(seeSection4.5). ThereforetheARDL
cointegrationtestingapproachbyPesaranetal.[53]hasbeenused.
The breakpointunit root testhas beenused to test a possiblepresence of structuralbreaks in
thestudiedvariables,asPerron[55]consideredthatmostmacroeconomicseriespresenttransitory
fluctuations. Therefore, if the unit root test does not take into account the structural break point,
selectedexogenously,thedecisiontowardexistsagainstrejectingwillbebiased. FollowingPerron[55]
andZivotandAndrews[56],therearethreetypesofbreakpoint: thoserelatedtochangesinthelevel
ofthetimeseries(changeintheintercept),thoserelatedtothechangeintherateofgrowth(changein
thetrend)andtheresultofboth(changeininterceptandtrend).Inordertoexaminethestationarity
ofeachvariable,thetwo-breakLMunitroottestbyLee-Strazicichisused[57]. Thenullhypothesis
implies that there is a unit root, while the alternative hypothesis implies the series is breakpoint
stationary. Thebreaksaredeterminedendogenously. TheCrashorAmodel,thatcapturesachangein
theleveloftheseries,hasbeenadoptedinthisstudy.
According to previous literature (see for example, Narayan and Smith [58], Belloumi [59],
Charfeddineetal.[60], amongothers), thePesaranetal.[53]modelmaybeimplementedinthree
stepsbysequentiallyestimatingthreefunctions. Thefirstfunctionisdefinedinordertoestimatethe
conditionalerrorcorrectionoftheARDLmodel.Theordinaryleastsquaresmethod(OLS)isused.
Equation(1)maybeexpressedasfollows:
DlogElect =c+α1logElect−1+α2logGDPCt−1+α3(logGDPCt−1)2+α4(logGDPCt−1)3
p−1 p−1
+α5logPt−1+α6logImpt−1+ ∑ β1iDlogElect−i+ ∑ β2iDlogGDPCt−i
i=1 i=0
p−1 p−1 p−1 p−1 (1)
+ ∑ β3iD(logGDPCt−i)2+ ∑ β4iD+ ∑ β3iD(logGDPCt−i)3+ ∑ β5iDlogPt−i
i=0 i=0 i=0 i=0
p−1
+ ∑ β6iDlogImpt−i+∑Ik+γt+εt
i=0
where, log is the natural logarithm, D indicates first difference, Elec is the residential electricity
consumptioninpercapitaterms,GDPCistheGrossDomesticProductpercapita(atconstant2010US$),
Pistherealhouseholdelectricityprice,impistheImportsofgoodsandservices,∑I arethedummy
k
variablesthatcapturetheregimechangesinthemodel,cisaconstant,γisatemporaldummyinyears
forthe1970–2013periodandε istheerrorterm. Inaddition,thelagiscalculatedbyusingtheVAR
t
optimalmodel,minimizingtheAIC,SICandHICinformationcriteria.
The introduction of the GDPC variable and its squared and cubed terms in Equation (1) may
generate multicollinearity problems among the variables [61], which may be analyzed by using
the values of the variance inflation factors (VIFs). Nevertheless, according to Pablo-Romero and
Sánchez-Braza[62],thesemulticollinearityproblemsmaybemitigatedbyconvertingeachexplanatory
variable to deviations from the geometric mean of the sample. These new variables are denoted
respectivelyaselec,y,y2,y3,p,andimpinsteadofLogElec,LogGDPC,(LogGDPC)2,(LogGDPC)3,LogP
andLogImp,respectively. Thus,theEquation(1)mayberewrittenasfollow:
Delect = c+α1 elect−1+α2 yt−1+α3 y2t−1+α4 y3t−1+α5 pt−1+α6 impt−1
p−1 p−1 p−1 p−1
+ ∑ β1iDelect−i+ ∑ β2iDyt−i+ ∑ β3iDy2t−i+ ∑ β4iDy3t−i (2)
i=1 i=0 i=0 i=0
p−1 p−1
+ ∑ β5iDpt−i+ ∑ β6iDimpt−i+∑Ik+γt+εt
i=0 i=0
OncetheEquation(2)hasbeenestimated,theboundstestingapproachtocointegrationshould
beimplementedtotestthepresenceofcointegrationbetweenthestudiedvariables. Tothisend,the
Fisher-test(F-stat)forthelaggedlevelvariablesjointsignificanceisused.Thenullhypothesistobe
Energies2018,11,1656 5of18
testedisH : α =α =α =α =α =α =0,indicatingnocointegrationrelationshipbetweenthe
0 1 2 3 4 5 6
studiedvariables.ThishypothesisisrejectedwhenthecalculatedF-testvalueexceedstheuppercritical
boundsvalue[53].
Ifthenullhypothesisisrejected,then,inasecondstep,theconditionalARDL(q1,q2,q3,q4,q5,
q6,q7)long-runmodelcapturingthelong-rundynamicshouldbeestimated.Thefunctionformmay
bewrittenasfollow[59]:
q1 q2 q3 q4
elect = c+ ∑ α1i elect−i+ ∑ α2i yt−i+ ∑ α3i y2t−i+ ∑ α4i y3t−i
i=1 i=0 i=0 i=0 (3)
q5 q6
+∑ α5i pt−i+ ∑ α6i impt−i+∑Ik+γt+εt
i=0 i=0
ThisequationisestimatedbyOLS,andtheAICandSICinformationcriteriaisusedtodetermine
thelagsoftheARDL(q1,q2,q3,q4,q5,q6)model.
Inaddition,OLSisusedtoestimatetheerrorcorrectionmodelfortheshort-run,andtheAIC
andSICinformationcriteriaareusedtodeterminatetheorderoftheARDL(p1,p2,p3,p4,p5,p6).
Inaddition,itsstabilityischeckedonthebasisofthecumulativesum(CUSUM)testandcumulative
sumofsquares(CUSUMSQ)test. AccordingtoNarayanandSmyth[58],andBelloumi[59],thismodel
maybewrittenas:
p1 p2 p3 p4
Delect = c+ ∑ β1iDelect−i+ ∑ β2iDyt−i+ ∑ β3iDy2t−i+ ∑ β4iDy3t−i
i=1 i=0 i=0 i=0 (4)
p5 p6
+∑ β5iDpt−i+ ∑ β6iDimpt−i+∑Ik+γt+µECTt−1+εt
i=0 i=0
Finally, the vector error-correction based Granger Causality analysis is used to study the
short-runandlong-runGrangercausalitybetweenvariables.Ifevidenceforcointegrationisfound,
thespecificationoftheGrangercausalitytestmaybeexpressed,accordingtoEngleandGranger[63],
asfollows:
p−1 p−1 p−1 p−1
Delect = ∑ β1iDelect−i+ ∑ β2iDyt−i+ ∑ β3iDy2t−i+ ∑ β4iDy3t−i
i=1 i=0 i=0 i=0
p−1 p−1
+ ∑ β5iDpt−i+ ∑ β6iDimpt−i+∑Ik+γt+τECTt−1+εt
i=0 i=0
p−1 p−1 p−1 p−1
Dyt = ∑ β1iDelect−i+ ∑ β2iDyt−i+ ∑ β3iDy2t−i+ ∑ β4iDy3t−i
i=0 i=1 i=0 i=0
p−1 p−1
+ ∑ β5iDpt−i+ ∑ β6iDimpt−i+∑Ik+γt+τECTt−1+εt
i=0 i=0 (5)
p−1 p−1 p−1 p−1
Dpt = ∑ β1iDelect−i+ ∑ β2iDyt−i+ ∑ β3iDy2t−i+ ∑ β4iDy3t−i
i=0 i=0 i=0 i=0
p−1 p−1
+ ∑ β5iDpt−i+ ∑ β6iDimpt−i+∑Ik+γt+τECTt−1+εt
i=1 i=0
p−1 p−1 p−1 p−1
Dimpt = ∑ β1iDelect−i+ ∑ β2iDyt−i+ ∑ β3iDy2t−i+ ∑ β4iDy3t−i
i=0 i=0 i=0 i=0
p−1 p−1
+ ∑ β5iDpt−i+ ∑ β6iDimpt−i+∑Ik+γt+τECTt−1+εt
i=0 i=1
whereECTt−1isone-periodlaggederrorcorrectiontermandτindicatestheadjustmentspeedtoreach
theequilibrium. TheWaldstatisticsofthelaggedexplanatoryvariablescoefficientsinformaboutthe
short-runcausaleffects,whiletheWaldstatisticofτinformsaboutthelong-runcausaleffect.
Energies2018,11,1656 6of18
3. DataandDescriptiveAnalysis
Table1summarizesthemainstatisticsofthevariables. Allvariablesrefertotheperiodfrom1970
to2013. Dataonresidentialelectricityconsumption,GDP,importsofgoodsandservicesandtotal
populationcomefromtheWorldDevelopmentIndicatorsdatabase[64]. Electricitypricedatacome
fromtheinformationprovidedbytheOfficialJournalsoftheAlgerianGovernment[65].Realelectricity
pricehavebeenconsideredinthisstudy. Nevertheless,thenominalpriceisalsoshowninTable1and
Figure1toexplaintherealpricetrend.
Table1.Descriptivestatistics.
Variable Mean Max. Min. Std.Dev. Obs.
Residentialelectricityconsumption(kWhpercapita) 563.22 1277.37 133.08 296.02 44
GDPpercapita(atconstant2010US$) 3646.85 4617.51 2322.06 528.09 44
Importsofgoodsandservices(%ofGDP) 27.78 42.96 18.41 5.58 44
RealElectricityprice(atconstant2010DZD*perkWh) 0.48 7.03 0.016 1.11 44
Priceofelectricity(DZD*perkWh) 1.32 2.97 0.37 1.11 44
DZD*:TheAlgerianDinar(nationalcurrencyofAlgeria).
The top left graph in Figure 1 shows the residential electricity consumption per capita trend.
Acontinuouslypositivegrowthisobservedthroughtheperiod.Thisgrowthisinlinewiththegrowing
trendinAlgerianelectricityconsumptionobservedoverthelastfortyyears. AccordingtoBélaïdand
Abderrahmani[15],thisstrongdemandforelectricitycanbeexplainedbytwofactors: theexpansion
of economic activities and the population growth. Although the Algerian Government had been
establishingseveraldevelopmentplanstoboosteconomicgrowthbydevelopingandmodernizing
the industry sector, which had led to increasing electricity consumption, the growth of Algerian
residentialelectricityconsumptionhasbeenhigherthanthatofindustry,especiallyoverrecentyears.
While,in1990,electricityusebytheindustrysectorwas48.5%oftotalelectricityconsumptionandthe
electricityusebytheresidentialsectorwas49.15%;in2014,theindustrysectorelectricityconsumption
wasonly35%andtheresidentialconsumptionwasmorethan63%. Thisgreaterresidentialelectricity
consumptionmaybeexplainedbyseveralfactors,suchaschangesinlifestyles[15].
ThetoprightgraphinFigure1showstheAlgerianGDPpercapitatrend.Overthesefortyfour
years,Algeriahasnotexperiencedasignificantglobalgrowth,theaverageannualgrowthratebeing
equal to1.25%. Threemain periodsareobserved inits evolution. In thefirst period, from 1970to
1979,theGDPpercapitaexperiencedastrongpositivetrend. From1970,Algeriaadoptedacentrally
plannedcommandeconomy,formulatingtwofour-yearplans: from1970to1973andfrom1974to
1977.Thisorganizationledtoenormousphysicalcapitalinvestmentswhich,accordingtoZouache[66],
couldexplainthenotablegrowthoftheAlgerianeconomy. Theaverageinvestmentratewas28.3%
from 1970 to 1973 and 40.4% from 1973 to 1978 [67]. Over the second period, from 1980 to 1994,
the GDP per capita decreased to $3165.90 in 1994. This situation was the consequence of several
factors,especiallytheoilpricecrashesin1979and1986andthepoliticalinstabilityintheearly1990s.
Finally,from1994,theGDPpercapitaagainstartedtoincreasesignificantly. In1994,Algeriasuffered
fromthefirstIMFstabilizationprogram, andoneyearlaterfromthesecond. Ingeneral, theaims
ofthesestabilizationprogramsweretoliberalizetheeconomy,fromsocialismtoamarket-oriented
economy[66]. Inparticular,theseprogramshadtwomainobjectives: tostronglyincreaseinvestment
inpublicinfrastructuresandtodecreasetheunemploymentratebystimulatingdomesticdemand[68].
Finally, at the end of the nineties, this strategy benefited from a favorable context, due to a better
politicalclimateandrisingoilprices.
The graph at the bottom left of Figure 1 shows the evolution of imports with respect to GDP.
Fourmainstagescanbeobserved. From1970to1975,anotablegrowthofimportsisshown,which
mayberelatedtothedevelopmentsystemimplementedinAlgeria,basedonstrongindustrialization.
Thissystemgeneratednotablesemi-finishedproductsandindustrialequipmentimports. From1975
Energies2018,11,1656 7of18
Energies 2018, 11, x FOR PEER REVIEW   7 of 18 
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increasingdemandsofthepopulation[69].

Figure1.Graphsofstudiedvariables.DZD*:TheAlgerianDinar(nationalcurrencyofAlgeria).

Figure 1. Graphs of studied variables. DZD *: The Algerian Dinar (national currency of Algeria). 
Finally, the graph at the bottom left of Figure 1 shows the evolution of the annual average
householdrealelectricityprice. Overthewholeperiod,theAlgerianGovernmentfixedandsubsidized
Finally, the graph at the bottom left of Figure 1 shows the evolution of the annual average 
the electricity prices. It is worth noting that the production and the distribution of electricity are
household  real  electricity  price.  Over  the  whole  period,  the  Algerian  Government  fixed  and 
completely in the hands of the government company, namely SONELGAZ. From 1970 to 1994,
subsidized the electricity prices. It is worth noting that the production and the distribution of 
thepricesremainquiteconstantaroundconstant2010DZD0.05perkWh. Sincethen,theAlgerian
electricity are completely in the hands of the government company, namely SONELGAZ. From 1970 
Governmentproceededtoincreasethehouseholdelectricityprice. AsfromJune1994,thehousehold
to 1994, the prices remain quite constant around constant 2010 DZD 0.05 per kWh. Since then, the 
electricitypricesbecamedifferentforthoseconsuminglessormorethan500kWhperyear. Inaddition,
Algerian Government proceeded to increase the household electricity price. As from June 1994, the 
gas and electricity quarterly price revision systems were implemented and most controls were
household electricity prices became different for those consuming less or more than 500 kWh per 
year. In addition, gas and electricity quarterly price revision systems were implemented and most 
controls were eliminated, raising electricity prices toward their opportunity cost. Therefore, the
Energies2018,11,1656 8of18
eliminated,raisingelectricitypricestowardtheiropportunitycost. Therefore,theelectricitypricegrew
withthegreaterliberalization[70]. Nevertheless,afterfewyears,theelectricitypricesonlyadjusted
partiallytotheincreasingcostofliving,becomingmuchloweragain. Additionally,therealelectricity
pricesgrowthbetween1995and2000maybealsoexplainedbytheAlgeriancurrencydevaluationon
April1994(40.17%). Thisdevaluationhadremarkablenegativeeffectonthepurchasingpower[70].
4. Results
4.1. BreakPointUnitRootTest(SelectionofStructuralBreakPoint)
Table2showstheresultsoftheLee-Strazicichtwobreaksunitroottest. TheLMunitroottest
rejectstheunitrootnullforyandimp,whiledonotrejecttheunitrootnullfortheothers. Inaddition,
theresultsalsoshowthattherearesixstructuralbreakpointsfrom1987to2002(1987, 1994, 1995,
2000,2001and2002). Indeed,sincetheendofthe1980s,theAlgerianeconomyhasexperiencedreal
mutations. Aftertheoilcrisisof1986,theAlgerianGovernmentstartedtoinitiateeconomicreforms
aimedattransformingfromasocialisttoaliberaleconomy[66]. Lateron,theAlgerianGovernment
adoptedastructuraladjustmentplanin1994. Inordertocapturetheseeconomicchanges,thedummy
variableoftheyear1987andthatrelatedtothestructuraladjustmentplanperiod(1994–2002)have
been introduced into the econometric analysis. The first variable takes the value one for the year
1987 and zero otherwise. The second variable takes the value one for the years 1994 to 2002 and
zerootherwise.
Table2.LMtwobreaksunitroottest(Lee-Strazicichtest[53]).
Variable BreakDates Lag t-Statistic Result
elec 1987/2002 4 −2.730 Notstationary
p 1994/2000 3 −2.784 Notstationary
imp 1995/2001 7 −3.713** BreakpointStationary
y 1987/2002 7 −4.127*** BreakpointStationary
Note: ***and**denotestatisticalsignificanceatthe1%and5%. ThecriticalvaluesfortheLMtestare−4.073,
−3.563,and−3.296atthe1%,5%,and10%levels.
4.2. ARDLBoundsTests
Table 3 shows that the value of F-statistic related to the null hypothesis surpasses the critical
valueoftheboundstest,withunrestrictedinterceptandnotrend,at1%and5%.Therefore,thereis
a cointegration relationship between the variables included in this study, implying that in the
long-run, electricityprices, servicesimportsandGDPpercapita, itssquaredandcubedtermsare
movingtogether.
Table3.Boundstesttocointegration.
CriticalValuesofBoundsTest*at:
1% 5%
F-statvalue I(0) I(1) I(0) I(1) Result
3.91 2.26 3.35 2.62 3.79 Presenceofcointegrationrelationship
*Pesaranetal.[53],tableCI(iii):Unrestrictedinterceptandnotrend.
4.3. Long-RunandShort-RunEstimates
Table4showstheresultsofestimatingtheEquations(3)and(4)whentheordinaryleastsquares
methodisused. ThelagsoftheestimatedARDLmodelwereselectedonthebasisoftheminimum
valuesoftheAkaikeandSchwarzcriteria. Likewise,LMtestsuggestsnoevidenceofserialcorrelation
intheresiduals. Thenullhypothesisofnoserialcorrelationcannotberejected.
Energies2018,11,1656 9of18
Table4.Estimatesoflong-rundynamicandshort-rundynamic.
Long-RunDynamic Short-RunDynamic
DependentVariable:elec(A) DependentVariable:Delec(B)
Variables Coefficients Variables Coefficients
p −0.29***(0.027) - -
imp 0.18***(0.056) Dp −0.13*(0.073)
y 1.35***(0.145) Dimp −0.15***(0.061)
y2 −1.85***(0.541) Dimp(−1) −0.05(0.060)
y3 −6.13***(1.443) Dimp(−2) −0.11**(0.057)
c −1.16***(0.049) Dy 0.79**(0.333)
I −0.13***(0.054) Dy(−1) −0.05(0.184)
87
I 0.15***(0.035) Dy2 −1.03(1.23)
94-02
trend 0.05***(0.002) Dy3 5.60(7.113)
- - C 0.05***(0.009)
- - I −0.14***(0.048)
87
- - ECT(−1) 0.43***(0.166)
LM-test[p-value] 1.38[0.26] LM-test[p-value] 0.085[0.91]
Arch-test[p-value] 1.05[0.31] Arch-test[p-value] 0.10[0.74]
DW 1.47 DW 1.89
- - Normality-test[p-value] 0.78[0.67]
F-Stat 867.58 F-Stat 4.36
Note: Standard errors in brackets. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels,
respectively.AICandSCareusedtoselecttheoptimallagsintheARDL-model.
Column (B) in Table 4 shows the short-run estimate. The coefficient of the estimated error
correctioncoefficient(ECMt−1)appearsnegativeandstatisticallysignificantat1%,withahighvalue
equalto−0.43.
Theshort-runestimateresultsalsoshowthatthecoefficientrelatedtoGDPpercapitaispositive
andsignificant,andthattheresidentialelectricityuseelasticitywithrespecttotheelectricityprices
isnegativeandstatisticallysignificant.Thismeansthatincreasingtheelectricitypriceswillreduce
theresidentialelectricityconsumptioninAlgeria,intheshortterm.Nevertheless,thecoefficientis
lessthanone,andisthereforeconsideredpriceinelastic.Thisresultisinlinewithpreviousstudies
that find negative and less than one short-run price elasticities, as for example in Donatos and
Mergos[71],SilkandJoutz[24],andAthukoralaandWilson[32]. Additionally,theresultsshowthat
theshort-runelectricityuseelasticity,withrespecttoimports,isnegativeandsignificant.Therefore,
increasingimportshavebeenreducingresidentialelectricityconsumptionintheshort-run.Inthat
regard,itmaybepossiblethatimportsofgoodshaveallowed,intheshortterm,thereplacementof
lessenergy-efficientgoodswithothersthataremoreefficient.
Column(A)inTable4showstheresultsobtainedbynormalizingpercapitaresidentialenergy
useinthelongrunTheelasticitywithrespecttoGDPpercapitainthecentralpointofthesampleis
positiveandstaticallysignificant,beingequalto1.35. Theseresultsareinlinewiththoseobtainedby
Zamanetal.[50],BélaïdandAbderrahmani[15],andKamaludin[72]. Nevertheless,thesquaredand
cubedGDPpercapitaestimatedcoefficientsarenegativeandsignificant. Therefore,theresidential
electricityconsumptionelasticitywithrespecttoGDPpercapitaisnotconstantthroughthestudied
period,varyingwithGDPpercapita. Inaddition,asthesecoefficientsarenegative,therelationships
betweenelectricityuseandGDP(inpercapitaterms)presentaninvertedN-shapecurve. However,
inordertodetermineifthesecondturningpointhasbeenreached,itisnecessarytocalculateand
studythefirstderivativeoftheresidentialelectricityconsumptionfunctioninthelong-run,tofindthe
maximumpointofthefunction. Thefirstderivativeistheresidentialelectricityconsumptionelasticity
toGDP.Thereforeiftheelasticityvalueiszero,andthesecondderivativeisnegative(elasticitygoes
frompositivetonegativevalues),thenthesecondthresholdsoftheinvertedN-shapecurveisobtained.
Figure2showstheelasticityvalueswithrespecttotheGDPpercapitalogvalue. Itisworthnotingthat
Energies2018,11,1656 10of18
elasticitybecomeszeroforGDPpercapitavaluesequalto8.39inlogs(about4400constant2010US$).
Therefore,thesecondturningpointoftheinvertedN-shapecurveisreachedwhenGDPpercapita
equals4400constant2010US$. ThisvaluewasreachedfortheAlgerianeconomyin2010.
Energies 2018, 11, x FOR PEER REVIEW   10 of 18 
pita) 1.5
a
er c
p
n
DP (l 1
G
o
ect t
p
with res 0.5
asticity
el
ectricity 0
el
al
esidenti -0.5
R
7.8 8 8.2 8.4
Logarithm of GDP per capita

Figure 2. Residential electricity consumption elasticity with respect to GDP per capita. 
Figure2.ResidentialelectricityconsumptionelasticitywithrespecttoGDPpercapita.
Therefore, the results show that GDP per capita increases in recent years are contributing to 
Therefore, the results show that GDP per capita increases in recent years are contributing to
reduce residential electricity use, which could be related to the fact that the population is replacing 
reduceresidentialelectricityuse,whichcouldberelatedtothefactthatthepopulationisreplacing
their appliances with more efficient ones. In this sense, it is worth highlighting that, in 2012, 100% of 
theirapplianceswithmoreefficientones. Inthissense,itisworthhighlightingthat,in2012,100%of
the population had the most common appliances, with cooking being the highest percentage of 
thepopulationhadthemostcommonappliances,withcookingbeingthehighestpercentageofannual
annual energy consumption [73]. Furthermore, the household electrical equipment consumption 
energyconsumption[73]. Furthermore,thehouseholdelectricalequipmentconsumptionaccountsfor
accounts for 75% of the total electrical energy consumed in the dwellings [74]. 
75%ofthetotalelectricalenergyconsumedinthedwellings[74].
Subsequently, as the income growth does not seem to be the cause of the residential electricity 
Subsequently,astheincomegrowthdoesnotseemtobethecauseoftheresidentialelectricity
growth in the latter years analyzed, there may be some other underlying causes. In this sense, the 
growthinthelatteryearsanalyzed,theremaybesomeotherunderlyingcauses.Inthissense,theresults
results show that the time trend effect is positive and significant, which may be explained by the 
showthatthetimetrendeffectispositiveandsignificant,whichmaybeexplainedbytheincreasing
increasing urbanization and the lifestyle changes. Along this line, Gupta [75] states that the lifestyles 
urbanizationandthelifestylechanges. Alongthisline,Gupta[75]statesthatthelifestylesincitiesof
in cities of the developing countries are becoming energy intensive. Likewise, Karanfil and Li [76] 
thedevelopingcountriesarebecomingenergyintensive. Likewise,KaranfilandLi[76]foundthat
found that urbanization is a relevant factor of electricity use in all income levels, except for the 
urbanizationisarelevantfactorofelectricityuseinallincomelevels,exceptforthehigh-incomelevel,
high‐income level, with it also being the most important driver of electricity use in upper‐middle 
withitalsobeingthemostimportantdriverofelectricityuseinupper-middleincomecountries,such
income countries, such as Algeria [77]. According to the World Bank [64] database, about 70.72% of 
asAlgeria[77]. AccordingtotheWorldBank[64]database,about70.72%oftheAlgerianpopulation
the Algerian population lived in urban regions in 2015, while the urbanized segment of the 
livedinurbanregionsin2015,whiletheurbanizedsegmentofthepopulationwasat39.5%in1970.
population was at 39.5% in 1970. 
Additionally, the import coefficient is positive and significant at a 10% level in the long-run
Additionally, the import coefficient is positive and significant at a 10% level in the long‐run 
estimate. Althoughintheshort-runtheresidentialelectricityconsumptionelasticitywithimportsis
estimate. Although in the short‐run the residential electricity consumption elasticity with imports is 
negative,inthelong-runthesignchangesintopositive.However,althoughimportscouldleadtomore
negative, in the long‐run the sign changes into positive. However, although imports could lead to 
efficientappliancepurchasesintheshort-run,thesecouldalsoprovidemoreappliancepurchasesin
more efficient appliance purchases in the short‐run, these could also provide more appliance 
thehouseholdsovertime,generatingreboundeffectsinthelong-run. Thus,itisworthnotingthat
purchases in the households over time, generating rebound effects in the long‐run. Thus, it is worth 
AlgerianconsumergoodsimportswerevaluedatUS$13.3millionin2015,representing25.65%oftotal
noting that Algerian consumer goods imports were valued at US$13.3 million in 2015, representing 
imports,whilein1992theywerevaluedatUS$1.6million,representingonly19%oftotalimports[78].
25.65% of total imports, while in 1992 they were valued at US$1.6 million, representing only 19% of 
Finally, results in Table 6 also show that the elasticity with respect to residential electricity
total imports [78]. 
prices is negative, significant and lower than one. These results are in line with those obtained in
Finally, results in Table 6 also show that the elasticity with respect to residential electricity 
Kamaludin[72],referringtodevelopingcountries. Theauthorconcludesthatelectricityisassumed
prices is negative, significant and lower than one. These results are in line with those obtained in 
tobeanecessitygoodandthereforeisrelativelyinelastic. Alongthisline,thestudybyArisoyand
Kamaludin [72], referring to developing countries. The author concludes that electricity is assumed 
Ozturk[79]foundlowvaluesforpriceelasticityofresidentialelectricity,implyingthatelectricityis
to be a necessity good and therefore is relatively inelastic. Along this line, the study by Arisoy and 
anecessarygoodforhouseholds. Likewise,priceelasticityhasalsobeenfoundnon-significantwhen
Ozturk [79] found low values for price elasticity of residential electricity, implying that electricity is 
a necessary good for households. Likewise, price elasticity has also been found non‐significant when 
estimating the effect of energy price on transport energy use [10]. In the case of Algeria, it is worth 
noting that residential electricity prices, despite increasing in some periods, are low as they are still 
being greatly subsidized by the Algerian authorities. Therefore, it would be adequate to continue 
increasing them to market prices. Recently, the Algerian energy regulator has augmented electricity