technical change theory and learning curves: patterns of … · 2007. 11. 23. · 52 / the energy...

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Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies

Tooraj Jamasb*

Better understanding of the role of learning in technical progress is important for the development of innovation theory and technology policy. This paper presents a comparative analysis of the effect of learning and technical change in electricity generation technologies. We use simultaneous two-factor learning and diffusion models to estimate the effect of learning by doing and learning by research on technical progress for a range of technologies in four stages of development. We find learning patters broadly in line with the perceived view of technical progress. The results generally show higher learning by research than learning by doing rates. Moreover, we do not find any development stage where learning by doing is stronger than learning by research. We show that simple learning by doing curves overstate the effect of learning in particular for newer technologies. Finally, we find little substitution potential between learning by doing and research for most technologies.

1. iNTRODUCTiON

Theimportanceoftechnologicalprogressasamajorforcebehindfactorproductivityandeconomicgrowthiswellestablishedintheliterature.Thefocusofearlyliteratureonscienceandtechnologywas,however,ontheeffectandmea-surementoftechnicalchangeonoutputandgrowth.Technicalchangewastreatedasanexogenousphenomenontotheeconomyaviewwhichposedclearlimita-tionsforpolicyanalysis.Sincethe1960s,thefocusoftheliteraturehasshiftedto

The Energy Journal,Vol.28,No.3.Copyright©2007bytheIAEE.Allrightsreserved.

* Corresponding author. University of Cambridge, Faculty of Economics, Sidgwick Avenue,AustinRobinsonBuilding,CambridgeCB39DE,UK.Phone:+44-(0)1223-335271,Fax:+44-(0)1223-335299,Email:[email protected].

TheauthorwishestoextendspecialthankstotheSAPIENTproject(DGResearch)attheLEPII-EPE,Grenoble,Franceforgeneroushelpandprovidingaccesstodataforthiswork.HewouldalsoliketothankBruceChenforresearchassistanceandanonymousrefereesandacknowledgethefinancialsupportoftheUKResearchCouncils’(ESRCandEPSRC)SupergenandTSECprojects.

tia

Note

Article from 2007, Volume 28, Number 3

52/The Energy Journal

theroleofeconomicfactorsintechnicalchange(ThirtleandRuttan,1987).Thenewparadigmviewstechnicalchangeasanendogenousfactorandthat itmaybeinduced.Thisviewisreflectedintheincreasedinterestintheuseoflearningcurvesintechnologyanalysis.Recently,thenotionofinducedtechnicalchangehasbeenadoptedinanalysisofenergyandenvironmentaltechnologies(Criquietal.,2000;Grubbetal.,2002).

Innovation theoryandcross-technologyanalysisusing learningcurvescanshedlightonthecharacteristicsandthestagesoftechnicalchangeprocess.It isalsoofinteresttoimprovetheprocessoflearningandidentifythosetech-nologiesthatarelikelytoachievemostprogressduringagivenperiod.Further,itisimportanttodeterminewhetherresourcesallocatedtopromotionofagiventechnologyarebetterspentonresearchanddevelopment(R&D)oroncapacitypromotionpolicies.

Inrecentyears,learningcurveshavebeenappliedtoanalysisofinducedtechnicalchangeinenergytechnologies.Themostcommonlyusedformsaresin-gle-factorlearningcurvesthatestimatetheeffectofcumulativecapacityorpro-ductiononreducingthecostoftechnology(learningbydoing).ThisframeworkoverlookstheeffectofR&Dasaninfluentialfactorandpolicytool(learningbyresearch).Asaresult,notonlytheeffectofR&Dontechnicalprogresscannotbedeterminedbuttheestimatesoflearningbydoingcanalsobeaffected.Inaddi-tion,understandingtherelativeimportanceoflearningbydoingandlearningbyresearchontechnicalprogressisimportantforimprovinginnovationtheoryandenergytechnologypolicy.

Thispaperpresentsacomparativeanalysisoftechnicalchangeinarangeof electricity generation technologies at different stages of development usingextendedlearningcurvesthatreflectthemaintenetsofinnovationtheory.Weusesimultaneoustwo-factorlearningcurvesanddiffusionmodelstoestimatetheef-fectoflearningbydoingandlearningbyresearchontechnicalprogress.WethenexaminetherelativeimportanceofR&Dandcapacitydeploymentfordifferenttechnology categories. The results generally show higher learning by researchthan learning by doing rates. We do not find any technological developmentstage where learning by doing is the dominant driver of technical change.Wealsocomparethelearningbydoingresultsfromourmodelwiththoseofsinglefactorlearningbydoingmodels.Finally,wecalculatetheelasticityofsubstitu-tionbetweenR&Dandcapacitydeploymentforthetechnologiesexamined.Thenextsectionreviewstherelevantliteratureandconceptsoftechnicalchangeandtechnologylearningcurves.Section3describesthemethodologyanddatausedfortheanalysisinthepaper.Section4presentstheresultsoftheanalysis.Section5summarizesandconcludesthepaper.

2. iNDUCED TECHNiCAL CHANGE AND LEARNiNG CURVES

Technicalchangeisgenerallyconceptualizedasagradualprocessthatinvolvesdifferentstagesofprogress.Theprocessanditsstageshavebeende-

scribedinvariousways.Themostestablishedof these isSchumpeter’s inven-tion-innovation-diffusion paradigm (Schumpeter, 1934; 1942). Briefly, withinthis framework, invention is viewed as the generation of new knowledge andideas.Intheinnovationstage,inventionsarefurtherdevelopedandtransformedintonewproducts.Finally,diffusionrefers towidespreadadoptionof thenewproducts.Therelationshipbetweenthestagesoftechnicalprogressisnolongerthought tobelinearbutanon-linearprocesswithfeedbacksbetweenitscom-ponents(Stoneman,1995).However,thisprocessisnotwellunderstoodandacoherenttheoryoftechnicalchangeremainsillusive.Theconceptsandcharac-teristicsof thestagesandprocessof technicalchangealsoapply toelectricitygenerationtechnologiesasthesegenerallyevolvethroughsimilarstagesofprog-ress(seeJensen,2004).

R&Dandcapacitydeploymentarethemaindriversofchangeinenergytechnologies(Skytteetal.,2004;Criquietal.,2000).R&Dhasaroleinallstagesoftechnicalprogressalthoughthenatureofitcanchange.Thereisabroadcorre-spondencebetweentheprocessoftechnicalchangeandthemainR&Dactivities–i.e.basicresearch,appliedresearch,anddevelopment.Basicresearchisrelatedtotheinventionandearlystagesofconceptionoftechnology.Asthetechnologymatures,appliedresearchanddevelopmentareassociatedwiththeinnovationanddiffusion stagesof technicalprogress. Inaddition, theknowledgeand learningbydoinggained frommanufacturing, scaleofproduction,andutilization isanimportantsourceoftechnicalprogress.

Theperceivedviewoftheprocessoftechnicalprogressisthattherela-tive importanceofR&Dand capacitydeployment varies in different stagesofdevelopmentofatechnology.Atearlystagesofdevelopment,technicalprogressis mainly achieved through R&D and, in the absence of commercial viability,growthincapacityislimited.Gradually,diffusionofinstalledcapacitybeginstogrowascostreductionsandtechnologysupportpoliciesimprovecommercializa-tion of a technology.While capacity deployment is constrained, R&D plays aleading role inachieving technicalprogress.As the technologymaturesand isadoptedtheeffectofcapacitydeploymentincreases.

Itis,therefore,importanttostudytherelativeimportanceoftechnologypushandmarketpull and, inparticular, their role indifferent stagesof techno-logicaldevelopment(seeGrübleretal.,1999).Thiswillenhanceourunderstand-ingoftheprocessandstagesoftechnicalchangesandwillhelpinthedesignofmoreeffectivepoliciesandallocationoftechnologypromotionresourcesbetweenR&Dandcapacitydeployment.However,ittakesalongtimebeforeatechnologyevolvesfrominventiontoinnovationstageandultimatelybecomesfullycommer-cialized.Thetransitionfrominventionandinnovationtodiffusionstageiscrucialfortechnologicalprogress.TheoryinformedpoliciesandempiricalevidencecouldimprovetheprocessandcontributetobetterallocationoftechnologypromotionfundsbetweenR&Dandcapacitydeploymentacrosstechnologies.

Technical Change Theory and Learning Curves/53


2.1 R&D and Technology Policy

Thereisarangeofelectricitygenerationtechnologiesatdifferentstagesofprogress.Meanwhile,thenotionofinducedtechnicalchangeimpliesthattheprocessofinnovationcanbeinfluenced.Thelogicalextensionofthisisthatpoli-ciescanbedevisedtomitigatemarketfailurefornewtechnologies.Atypologyofsuchpolicies,consistentwiththeinvention-innovation-diffusionparadigm,di-videstheseintosupplypushanddemandpullmeasures.

R&Dactivitiescanbesubject to three typesofmarket failurenamelyindivisibility,uncertainty,andexternalities(FergusonandFerguson,1994).Theaimoftechnologypushmeasuresistoovercomesuchbarriersandtoenhancetheknowledgebaseanddevelopmentoftechnologies.Inturn,marketordemand-pullmeasurespromotetechnicalchangeandlearningbycreatingdemandanddevel-opingmarketsfornewtechnologies.GovernmentR&Dandpromotionschemesaremoreimportantatthebasicresearchanddevelopmentstage.Asthetechnol-ogymaturespoliciessupportingdemandpullwillgraduallybecomeeffectiveinpromotingtechnicalprogress.

2.2 Learning Curves

One approach to measure technical change that has recently receivedrenewedattention isbasedon thenotionof learningcurvesand theestimationoflearningrates.Learningcurvesareusedtoestimatetechnicalchangeasare-sultofinnovativeactivities.TheconceptoflearningeffectasadistinctsourceoftechnicalchangewaspresentedinWright(1936)andArrow(1962)andisoftentermedas“learning-bydoing”.Technicalchangethroughlearningeffectisgener-allyderivedfromlearningcurveswhereprogressistypicallymeasuredintermsofreductionintheunitcost(orprice)ofaproductasafunctionofexperiencegainedfromincreaseincumulativecapacityoroutput.

Theconceptoflearningcurveshasbeenknownforsometime.However,early applicationsof these, between1930s and1960s,were related to productmanufacturing(Wright1936;Alchian,1963;Arrow,1962;Hirsh,1952).In1970sand1980s,theywereappliedinbusinessmanagement,strategy,andorganisationstudies(BCG,1970;DuttonandThomas,1984;HallandHowell,1985;Lieber-man,1987;Spence,1986;ArgoteandEpple,1990).Since1990s, thepressingneedforeconomicandpolicyanalysisofenergy technologieshasbeenan im-portantsourceofinterestinapplicationoflearningcurvestothisarea(Papineau,2006;McDonaldandSchrattenholzer,2001;Criquietal.,2000;IEA,2000).

Inthemostcommonform,learningcurvesdefinethecostorpriceofatechnologyasapowerfunctionofalearningsourceincumulativeformsuchasinstalledcapacity,output,orlabour.ThelearningcurveisdefinedasinEquation(1).Thelearningeffectofcapacityincreaseonthecostoftechnologyisexpressedas“learningrate”LRmeasuredintermsofthepercentagecostreductionforeachdoublingofthecumulativecapacityorproductionasinEquation(2).

c=a*Cape (1)

LR=1–2–e (2)

where:c unitcostCap cumulativecapacity(orproduction,etc.)e learningelasticitya constantLR learningrate

Some Issues with Single-factor Learning Curves

TheusefulnessofthesimplespecificationoflearningcurvesinEquation(1),originallydevelopedin thecontextofmanufacturingandmature industries,totechnicalchangeinevolvingandemergingtechnologiesisuncertain.Theen-dogenousviewofandproactiveapproachtotechnicalprogressimpliesthatbothpushandpullinstrumentscaninducetechnicalprogress.Therefore,single-factorlearningcurvesinenergytechnologyanalysisposesomeknownlimitations.Animportantshortcomingofsingle-factorcurvesisthatthattheydonottaketheeffectofR&Doncostreductionintoaccount.Fromapolicypointofview,single-factorlearningcurvesonlyleadtocapacity-orientedrecommendationswhileignoringtheroleofR&Dintechnicalchange.Inaddition,intheabsenceofR&D,single-factorcurvesarelikelytoproduceinaccurateestimatesoflearningbydoingrates.

Moreover,inthecontextoftechnologyanalysis,therecanbeadegreeofendogeneitybetweencostreductionandcapacityexpansion-i.e.reductioninthecostofatechnologyisalsolikelytoincreasedeploymentofthattechnology.Therefore,withintheframeworkoftheinvention-innovation-diffusionparadigm,single-factorcurvesamounttoleavingoutthemainaspectoftechnologydiffu-sion.Byusingcumulativecapacityonly,single-factorlearningbydoingcurvesarejustapartialmodelofthediffusionaspectofthetechnicalchangeprocess.Consequently, single-factor curves arenot appropriate for analysis of evolvingandemergingtechnologieswheretheinnovationstageofthetechnologicalpro-cessisgenerallyofmostinterest.

2.3 Two-factor Learning Curves

Insomerecentstudies,thenotionoflearningeffecthasbeenextendedtoinclude“learning-by-researching”whereR&Disassumedtoenhancethetech-nologyknowledgebase,whichinturnleadstotechnicalprogress.ThelearningeffectofR&Disaccountedforin“two-factorlearningcurves”thatincorporatecumulativeR&Dspendingornumberofpatentsasproxiesforstockofknowledge(Kouvariatakisetal.,2000).Asapolicyanalysistool,two-factorlearningcurves



acknowledgearoleforR&Dand,thus,ineffect,fortechnologypolicyinpromot-ingandachievinginducedtechnicalprogress.

Theconceptoftwo-factorlearningcurveswasfirstproposedinKouvari-atakisetal.(2000)wherecumulativeR&Dandcumulativeproductionareassumedtobethemaindriversoftechnologycostimprovement.Despitetheirrelativeadvan-tages,however,thereareonlyfewexamplesofapplicationoftwo-factortechnologylearningcurves.Klassenetal.(2005)andCoryetal.(1999)haveappliedtwo-factorlearningcurvesinanalysisofinnovationinwindpower.Also,MiketaandSchrat-tenholzer (2004)andBarretoandKypreos (2004)haveused two-factor learningcurvesinlargescalebottom-upoptimizationmodelsofenergytechnologies.

3. METHOD AND DATA

3.1 Method

All electricity generation technologies produce a homogenous energyoutput.However,theunderlyingtechnicalcharacteristicsandknowledgebaseofthesecanvarygreatly.Therearealsodifferencesincontextualfactorssuchasinmarketconditions,policy,andregulatoryframeworkwithinwhichthetechnolo-giesevolve.Inaddition,technologiescanbeatdifferentstagesofmaturityandexhibitdifferencesintheirprogress.Consequently,itisunlikelythatthereexistsasinglelearningmodelandspecificationforalltechnologiesthatproducesthebestestimatesoflearningrates.

Estimatesoflearningratesarecontext-dependentanddrivenbymodelspecification,variablesused,andaggregationlevel.Indeed,thereisconsiderablevariationintheempiricalestimatesoflearningratesforsomeenergytechnolo-gies(McDonaldandSchrattenholzer,2001;Ibenholt,2002).Moreover,estimatedlearning rates canvarydependingon the timeperiod forwhich they aremea-sured(ClaesonColpierandCornland,2002).Therefore,thereisnotanabsoluteoruniquelearningrateforagiventechnology.Also,duetotheunderlyingdifferenc-es,estimationsoflearningratesfordifferenttechnologiesmaylendthemselvestodifferentmodelsandspecifications.Thisisexpectedasthecharacteristicsofdifferenttechnologiescanvary.

Modelsused for estimationof learning rates should take theeffectofR&Donreducingthecostoftechnologyintoaccount.AssuggestedinSöderholmandSundqvist(2003),inclusionofR&DspendinginlearningcurvemodelsaddsacontrollablepolicyvariableandreducestheproblemofomittedvariablesbiasthatwouldattributesomecostreductionachievedbyR&Dtocumulativecapacity.Inaddition,modelsoflearningneedtotakeintoaccounttheendogeneityofthecapacityandcostoftechnology–i.e.whilehigherinstalledcapacitycanresultinunitcostreduction,thecostreductioncanstimulatemarketdiffusionandpoliciesfavoringcapacitydeployment.

Figure1summarizesthetheoretical,practical,andpolicyconceptualisa-tionoftechnicalchangeandhowtheserelatetodifferentmodelsoftechnology

learning.Asshown,single-factorlearningcurves(1-FLC)onlyreflectaparticularaspectoftechnicalchangeprocess–i.e.theeffectofdiffusionormarketpullontechnologycost.Thetwo-factorlearningcurvemodel(2-FLC)incorporatestheeffectsofbothR&D(technologypush)andcapacitydeployment(marketpull)ontechnicalchange.However,thediffusionandinstalledininstalledcapacityofatechnologycaninturnincreaseasaresultofreductioninthecostofthattechnol-ogyandwithtime.The2-FLCmodelcanbeextendedtoalsoincludetheseeffectson the uptake of technology.The extended model (2-FLDC) therefore reflectsboth thecausal relationshipbetweencostanddiffusionandendogeneityof in-novative activities and diffusion as policy instruments.Therefore, the 2-FLDCmodeldepictedinFigure1capturesthemainfeaturesoftheSchumpeterianpara-digmoftechnicalchangeasdepictedindashedarrows.

AsystemofsimultaneousequationsincorporatingR&Dandendogene-ityofcapacityoncost,transformssingle-factorlearningbydoingcurvesfromapartialmodeltoatheory-informedlearning-innovation-diffusionmodelthatre-flectsthemainelementsandfeedbackintheinvention,innovation,anddiffusionparadigm.Toourknowledge,theonlyexampleofsuchapproachisreportedinSöderholmandKlassen(2007)whichusessimultaneouslearning-diffusionequa-tionstoestimatetheeffectofpromotionpoliciesforwindpowerintheUK,Spain,Denmark,andGermany.Thestudyfindsevidenceofdiffusioni.e.significantpos-itiveeffectfromcostreductiononcumulativecapacityaswellaseffectofpolicytypeanddesignoncostdevelopmentofwindpower.

Figure 1. Technical Change Concepts and Models of Learning Curves

Basedontheconceptualmodeloftechnicalchangeandinnovationout-lined in Figure 1, we estimate the two-factor simultaneous learning-diffusion



model (Model-I) as specified in Equations (3) and (4).We use the three-stageleast squares (3SLS) method to estimate the model for each technology sepa-rately.Equation(3)isatwo-factorlearningcurveestimatingthelearningeffectofcumulativecapacityandR&Dontheunitcapacity–capacity–i.e.thediffusionoruptakeofatechnology.Equation(4)reflectstheeffectofcostreductionandtimeoncumulativecapacityi.e.thediffusionofatechnology.

TheunitcostoftechnologycandthecumulativeinstalledcapacityCaparetreatedasendogenous variables–i.e.theyaredeterminedwithinthemodel.Othervariables such as cumulativeR&DspendingRD, timevariableT (whensignificant),andcumulativenumberofpatentsPatforeachtechnologyareusedasexogenousvariables.Theexogenousvariables(e.g.numberoftechnologypatent)donotneedtobepartofthestructuralvariablesandareusedinthefirststageoftheSLSestimationtoregresstheendogenousvariablesonexogenousvariablesofthesystem.Inaddition,thecumulativenumberofpatentsPatis,whereappropri-ate,usedasinstrumentalvariable.

Two-factorlearningequation:

Logcnt=a

n+b

n*LogRD

nt+k

t*LogCap

nt (3)

Capacitydiffusionequation:

LogCapnt=µ

n+w

n*Logc

nt+χ

t*LogT

nt (4)

Endogenous variables: Logcnt, LogCap

nt

Exogenous variables: LogRDnt, LogPat

nt, LogT

nt

where:c unitcostofgenerationcapacity(€1999/KW)RD cumulativeprivateandpublicR&Dspending(mill.€1999)Cap cumulativeinstalledgenerationcapacity(MW)T timevariable(years)Pat cumulativenumberoftechnologypatentsn technologyt learningperiod(1, …, t, …w)

Ageneral issue inestimationof learningmodels is toseparate theef-fectoflearningontechnicalchangefromthatofexogenousprogressthatoccursovertime.Therefore,atimevariableTisincludedintheEquation(4)inordertoseparatetheeffectoftimeontechnicalchange.Althoughitsgenerallypreferabletoincludeatimevariableinthetechnologylearningmodels,incaseswhereinclu-sionofthisresultsinwrongsignorinsignificantcoefficient,wedropthisvariablefromtheestimation.Themainreasonforthisisthatthesignandsignificanceofthelearningbydoingandlearningbyresearchcoefficientsareimportantforthe

reliabilityof the learning ratesandelasticityof substitution thatarecalculatedfromthese.Itispossiblethatsometechnologiesarelessinfluencedbyexogenoustechnicalchangeorthatinthefuturelongertimeseriesmayshedmorelightontheroleoftimeandexogenouseffects.

Thenatureandactualprogresspathof some technologies candifferfromtheabovegeneralmodel.Thefirstpreferenceistoapplythemorecom-pleteModel-Itoalltechnologies.However,fortechnologiesthatModel-Idoesnot find correct sign or statistically significant coefficients we use a simplersingle-equationtwo-factorspecification(Model-II)asinEquation(2)instead.Model-II uses two-stage least squares (2SLS) estimation method and, wherepossible,withcumulativenumberofpatentsPatortimevariableTasexogenousvariables. Some studies of technology learning rates have used time lags orsomemeasuresofknowledgedepreciation.Suchextensionsoflearningmodelsareconceptuallycorrectandusefulbutcareshouldalsobetakenintheunder-lyingassumptionsandapplicationtoindividualtechnologies.Forthepurposeofthisstudywhichinvolvesarangeofdifferenttechnologiesthiscouldnotbehandledproperly.

WealsocalculatetheelasticityofsubstitutionbetweencumulativeR&Dspendingandcapacitydeploymentforthetechnologiesstudiedhere.Elasticityofsubstitutionisaunit-neutralmeasureoftheeasewithwhichinputfactorsi.e.inthiscaseinstalledcapacityandR&Dcansubstituteeachother.InaCob-Douglasspecification,elasticityofsubstitutioncanthenbecalculatedfromEquation(5).

bn Cap

ntσ=——*——– (5) k

n RD

nt

Asubstitutionelasticityequal tounity represents thecaseof constantreturnstoscale.Theextenttowhichthemeasuredelasticitydeviatesfromunityindicates thedegreeofdifficultywithwhichthemaintwolearningfactorsandsourcesoftechnicalchangecansubstituteeachother.

3.2 Data

Anyattempttoestimatetechnologylearningratesisfacedwiththechoiceofproperlevelofdataaggregation.Theappropriatelevelofanalysisisdependentonthepurposeofthestudy.Forexample,countryorregional-levelstudiesallowforexaminationoftheeffectofpoliciesandlocalcircumstancesontechnologycost.Asthispaperaimstoexaminehigh-levelpatternsoftechnicalchange,weuseaggregateglobaldatainordertoobtainabroadviewoftechnologicalprog-ress.Anadvantageofusinggloballeveldataforthisanalysisisthattheycapturetheeffectofunobservablefactorssuchasspillovereffectsoftechnicalprogresswhichoccuratnationalandregionallevels.Apotentialdrawbackofusinggloballeveldataisthattheaccuracyofsomeofdatamaydecrease.Forexample,bestavailabletechnologyorcostimprovementscanreachdevelopingcountrieswitha



delay.Inaddition,someinformationmaybelostinconversionofcostsindifferentcurrenciesintoasinglemonetaryunit.

Table1summarisesthetechnologiesandtimeperiodsexaminedinthisstudy.ThedataforthetechnologiesusedwerecompiledfromtheinformationinthedatabaseofthePOLESmodel.1TheunitcostandR&Dspendingfiguresareexpressed inconstant1999USdollars.TheR&Ddatacomprise theestimatedgovernmentandprivatespendingonresearchanddevelopment.Thepatentdataused is the cumulative number of patents for the technology being examined.MargolisandKammen(1999)haveshownthatthereisapositiveandstrongcor-relationbetweenthenumberoftechnologypatentsandR&Dspending.ThedataisbasedonthecountoftheelectricitytechnologypatentssubmittedbyOECDcountriestotheEuropeanPatentOffice.Aconsistentprocedurehasbeenusedinordertotransformtheprimarydatainthepatentofficeclassificationcodingsys-temintospecificelectricitygenerationtechnologiescategoriesforthemodel.

Thedata enablesus to estimate the learningeffect for avaried setofelectricitygenerationtechnologies.Thechoiceoftechnologiesstudiedherehasbeendrivenbyavailabilityofsuitabledatai.e.thekeyvariablesthatallowderiva-tionoflearningratesusingsimultaneoustwo-factorlearninganddiffusionmod-els.However,thispreferredmodelspecificationhashadtheeffectoflimitingthenumberofyearsforwhichsomeofthesetechnologiescouldbeanalyzed.Forthecombinedcyclegasturbinetechnology,thepost-1990periodwasseparateddue

1.TheTECHPOLdatabasehasbeenkindlyprovidedbytheLEPII-EPE,Grenoble,France.ThisdatabasehasbeenassembledintheframeworkoftheSAPIENTproject(DGResearch)toinformtheworldenergysimulationmodelPOLES.

Table 1. Technologies and Data Summary (Mean Values) Cumulative Cumula- Cumula- Unit cost installed tive R&D tive of capacity capacity (mill. patents Technology Year ($1999/kW) (MW) $99) (number)

1 Pulverizedfuelsupercriticalcoal 1990–1998 1,493 19,034 7,461 495

2 Coalconventionaltechnology 1980-1998 1,308 650,512 35,452 -

3 Ligniteconventionaltechnology 1980-2001 1,275 105,120 7,877 -

4 Combinedcyclegasturbines 1980-1989 573 1,524 15,438 3,324

1990-1998 509 62,301 25,448 7,634

5 Largehydro 1980-2001 3,426 452,558 17,881 -

6 Combinedheatandpower 1980-1998 920 31,084 14,913 47

7 Smallhydro 1988-2001 2,431 23,708 1,171 -

8 Wastetoelectricity 1990-1998 3,528 11,338 18,928 5,407

9 Nuclearlightwaterreactor 1989-2001 3,015 334,266 100,729 -

10 Wind–onshore 1980-1998 2,094 2,913 7,099 1,634

11 Solarthermalpower 1985-2001 4,990 256 4,498 -

12 Wind–offshore 1994-2001 2,066 82 261 -

toanapparentstructuralbreakinthedata.Thiscoincides(thoughitmaynotfullyexplainthebreak)withthestartofliberalizationofthesectorintheUKandlaterinothercountrieswheregaswasthetechnologyofchoiceinderegulatedmarketsandresultedinsignificantexpansionoftheinstalledcapacity.

4. RESULTS

Asdiscussedpreviously,movingfromsimplesingle-factorlearningcurvesto two-factor learning-diffusion models is conceptually preferable. However, insomecases, thismaycausepracticalestimationissues.Whiletheformermodelsalwaysreturnsomesignificantresult,thelattermodelsmaynotnecessarilydoso.Technologylearningratesoftenrelyoneconometricestimationsofrelativelyshorttime-seriesdatathatalsoexhibitstrongtrends.Theresultsofregressionanalysismay,therefore,bespuriousandtheR-squarescanoverestimatetherelationshipbe-tweenthedependentandindependentvariables.Moreover,someestimatedcoef-ficientscanbecomestatisticallyinsignificantormayevenshowthewrongsign.2

Therearesignificantdifferencesintheunderlyingtechnicalandknowl-edgepropertiesoftheelectricitygenerationtechnologies.Thiscanresultindif-ferentmodelsbeingsuitableforestimationofthelearningratesforthese.Weusetwomodelspecificationstothesetoftechnologiesinthefollowingorderofpref-erence.Wefirstestimatesimultaneoustwo-factorlearning-diffusionmodelswithexogenousvariables (Model-I).Where this approachdoesnotyield significantand reasonable results, we use the simpler single-equation two-factor learningcurves(Model-II).

Theresultsareorganisedbyplacingthetechnologiesinfourcategories(mature,reviving,evolving,andemerging)thatarebroadlyinlinewiththeirper-ceived levelofdevelopment. Inaddition,wecalculateelasticityofsubstitutionbetween learningbydoingand learningbyresearching in reducing thecostofdifferenttechnologies.

4.1 Mature Technologies

Thefirstcategoryoftechnologiesconsistsofthemorematureandestab-lishedgenerationsources.Thetechnologiesinthiscategoryhavebeendevelopedandutilizedoveralongperiodoftimeandhavehadamajorshareoftheexpansionofelectricitysectorsworldwide(Table2).Column1ofTable2indicateswhetherafull two-factor learning-diffusionora two-factor learningmodelproducedthebestresults.Columns2and3showtheestimatedelasticitiesofcumulativecapac-ity(andsignificancelevel)andthecorrespondinglearningbydoingrateforthe

2.Coryetal.(1999)estimatetwo-factorlearningcurvesforwindturbinesintheUnitedStatesbetween1981and1995andfirstobtainpositivesignforthecoefficientofthenumberofturbines.Theyattributethistolargechangesinmarketgrowthinpartoftheperiodofstudyandfindplausibleestimatesaftersplittingthedataintotwoseparateperiods.


62/The Energy JournalT

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“learningequation”respectively.Similarly,columns4and5showtheestimatedlearningbyresearchelasticitiesandratesrespectively.Columns6and7showthecoefficientsofdiffusionandtimeforthe“diffusionequation”respectively–i.e.theeffectofreductioninthecostoftechnologyandtimeoncumulativecapacity.Column8showstheinstrumentalvariablesusedinthelearning-diffusionmodels.

Theestimatedelasticitieshavealltheexpectedsigns–i.e.anincreaseincumulativecapacityorR&Dspendreducesthecostoftechnologies.Likewise,are-ductioninthecostofthettechnologiesandanincreaseintimeresultsinhigherup-takeandcumulativecapacity.Theresultsshowthatthematuretechnologiesexhibitfairlycomparablelearningcharacteristics–i.e.theyshowlowlearningbyresearchandlearningbydoingrates.Thesetechnologies,duetotheirmainstreampositionandwidespreaduse,have faced littlemarket constraints in termsofcommercialandexpansionopportunitiesthanothertechnologies.Maturetechnologiesarealsocomparativelylesscapitalintensivethanthenewertechnologiesowingtoalongerprocessoftechnologicalimprovementandrelativelylargersizeoftheunits.

Anotableexceptionis theconventionalcoal technology,whichshowsa somewhat higher learning by doing rate.While the learning coefficients arestatisticallysignificant,thereasonsforthisarenotimmediatelyclear.However,inpractice,itshouldbenotedthat,giventhehighlevelsofexistingcapacityforestablishedtechnologies,adoublingofcapacityandfurthercostimprovementscanonlytakeplaceoveralongperiodoftime.

4.2 Reviving Technologies

Thenextcategoryoftechnologiescomprisesasetof“reviving”genera-tionsources.Thesetechnologieshavebeenutilisedforalongtimeandassucharenotradicalinnovations(Table3).Theresultsshowthattherearesomecom-monlearningcharacteristicsamongthetechnologiesinthiscategoryintheformoflowlevelsoflearningbydoingwhileshowingafairlyhighdegreeoflearningbyresearch.

The low learning by doing rates for these technologies suggest limitedscopeforfuturecostreductionsthroughcapacitydeployment.Moreover,theexist-inghighlevelsofinstalledcapacityforthesetechnologiessuggestthattheyhavelimitedscopeforcostreductions–i.e.intermsoflearningratesittakesalongertimefortheirinstalledcapacitytodouble.Atthesametime,thelearningbyresearchratesshowconsiderablepotentialforfurthercostreductions.Although,theextenttowhichthehighlearningbyresearchratescansustaininthefutureisuncertain.

Duringtheperiodsstudiedhere,therevivingtechnologieshaveachievedtechnicalprogressandduetotheirenvironmentaladvantages,havebenefitedfromfavorablepoliciesandmarketopportunities.Asaresult,marketuptakeofthesetechnologieshasbeenunconstrainedandthesehaverealizedmuchoftheirlearn-ingbydoingpotential.Smallhydropowerbenefitedfromincreasedresearch inrenewableenergysources.Availabilityof smallerandmoreefficientcombinedheatandpowerunitshaveexpandedthemarketforthistechnologybyfacilitat-



ing industrial and commercial applications of it. Combined cycle gas turbinesachievedtechnicalprogressmainlybyincreasingtheefficiencyandreducingthecosteffectivesizeoftheturbines.

AnothersharedcharacteristicoftherevivingtechnologiesisthatR&Dand technical change has lowered the cost efficient size of generation plants.Moreover, similar to mature technologies, reviving technologies are not com-parativelycapitalintensive.Therefore,therequiredcapitalinvestmentsinthesetechnologieshavedecreasedwhich,inliberalisedelectricitymarkets,amountstoacomparativeadvantage.

4.3 Evolving Technologies

Thethirdcategoryoftechnologiescomprises“evolving”generationre-sources.Thetechnologiesinthiscategoryconsistofnuclearpower(lightwaterreactor),waste toelectricity, andwindpower.These technologieshaveexistedeitherforashortertimeand/orhaveexperiencedmoreconstraints,duetoenvi-ronmentalconcernsorplanningissues,intheircapacityexpansionthanrevivingtechnologiesduringtheperiodunderconsideration.

Theestimatedlearningratesforthesetechnologiesshowhighlevelsoflearningbydoingaswellaslearningbyresearch(Table4).Nuclearpowerhasnotbeenapriorityareainenergypolicyandenvironmentalconcernswithaccidentsand radioactivewastehave significantly reduced itsmarketopportunities.Windpowerhas enjoyed a favorablepolicy environment inmanycountries and, as aresult of capacity deployment and promotion schemes, has shown considerablegrowthinrecentyears.However,duetorelianceonpublicsubsidiesandlackoffullcostcompetitivenessinrelationtoestablishedconventionaltechnologies,windtechnologyfacesmarketconstraintsinreachingasignificantshareofgenerationresourcemix.Wastetoelectricityisinamiddleposition.Environmentalconcernswithemissionsandsitingconstraintshavemeantthatthistechnologyhasnotexpe-riencedanexpansivemarketgrowth.Moreover,liberalisationofthesectorinmanycountrieshas further limited themarketpotential for theevolving technologies.Withoutgovernmentsupportthesetechnologieswillnotbetheobviouschoicesforprivateinvestorsoperatingincompetitivemarkets.

Asnoted,theevolvingtechnologieshavefacedsomemarketconstraintsthathavelimitedtheirgrowthpotential.Itis,therefore,plausiblethatthesetech-nologies still have significant potential for further cost improvement throughlearningbydoing,forexample,throughtincreaseinmanufacturingscaleandde-signstandardisation.Theirmoderateorlowlevelsofinstalledcapacityalsosug-gestthatthesetechnologiesstillpossessscopeforsignificantcapacityincreasesandcostreductionsthroughlearningbydoing.

Moreover, the estimated learningby research rates showconsiderablepotentialforcostreduction.Nuclearandwindpowerarecapitalintensivetechnol-ogiesandtheinitialcapitalinvestmentsrequiredinthesetechnologiesarecom-parativelyhigherthanthoseoffossilfuelbasedtechnologies.Asaresult,muchof


Tab

le 4

. Lea

rnin

g E

last

icit

ies

and

Rat

es fo

r “E

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s

1

2 3

4 5

6 7

8

Cap

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Tab

le 5

. Lea

rnin

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last

icit

ies

and

Rat

es fo

r “E

mer

ging

” T

echn

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ies

in

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8

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futurecostimprovementsinthesetechnologiescanalsocomefromlearningbyresearchresultinginlowercapitalinvestmentrequirements.

4.4 Emerging Technologies

Thefinalcategoryof technologiesexaminedis“emerging”generationsourcesandincludesthermalsolarpowerandoffshorewindpower.Theemergingtechnologieshaveexistedforarelativelyshortertimeandshowalesserdegreeoftechnicalprogressduringtheperiodunderconsideration.Theestimatedlearn-ing rates forboth technologies indicate low learningbydoingand learningbyresearchrates(Table5).

Bothofthesetechnologieshaveenvironmentaladvantagesandtheyhavebenefitedfrompromotionpoliciesforrenewableenergytechnologies.However,limitedprogressintechnicalchangeandcostrelativetoothertechnologieshasconstrainedtheirmarketopportunityanddiffusion.Thisisalsoreflectedinourresultsas,duetoalackofcapacityresponsivenesstocostimprovement,learn-ing-diffusionmodelsdidnotreturnacceptableresults.Asaresultofmarketcon-straintsandlackofcostcompetitiveness,diffusionofemergingtechnologieshasbeenslowandtheyareyettogainanoticeableshareofenergymix.

Similartoevolvingtechnologies,electricitysectorliberalisationhasin-creased thedependenceof theemerging technologiesonpublicR&Dandpro-motionschemes.Inthelightoftheexistinglowlevelsofinstalledcapacityandpresenceofmarketconstraints,theemergingtechnologiesarelikelytohavesig-nificantpotentialforcost improvement throughlearningbydoingandlearningby research.Anothersimilarity toevolving technologies is thatemerging tech-nologiesarecapitalintensiveandasaresult,themainpotentialforfurthercostimprovementsinthesetechnologies,fromlearningbyresearchandlearningbydoing,needtocomeintheformofreductionsininvestmentrequirements.

Single-factorlearningcurvesdonotreflecttheeffectofR&Dontechni-calchangeandoverstate theeffectof learningbydoing.Table6compares thelearningbydoingratesfromtwo-factorlearningcurveswiththoseofsimplesin-gle-factorlearningcurvesasspecifiedinEquations(1)-(2).Asshowninthetable,the learning by doing rates from single-factor learning curves are higher thanthoseestimatedbytwo-factorlearning-diffusioncurves.Moreover,theoverstate-ment is larger forevolvingandemerging technologies,whichareofparticularinteresttothecurrentenergytechnologypolicydebate.AnimplicationofdevisingpoliciesbasedonoverestimatedlearningbydoingratesisthattheycanshiftthescarceresourcesearmarkedforinnovationresourcesfrommoreproductiveR&Dactivitiestolessproductiveandmorecostlycapacitydeploymentpolicies.

ThemainresultsandcharacteristicsforthefourtechnologycategoriesaresummarizedinTable7.TheresultsindicatethatemergingtechnologiescaninitiallyexperiencearelativelylongperiodduringwhichtheyrespondslowlytoR&Def-fortsandcapacitydeployment.Inthenextdevelopmentstage,asevolvingtechnolo-gies,theyexhibitbothhighlearningbydoingandlearningbyresearchrates.

Table 6. Learning by Doing Rates Using Single-factor Curves Learning Learning by Doing Rate by Doing Rate Technology Two-Factor Curves Single-Factor Curves

1 Pulverizedfuelsupercriticalcoal 3.75% 4.8%2 Coalconventionaltechnology 13.39% 15.1%3 Ligniteconventionaltechnology 5.67% 7.8%4 Combinedcyclegasturbines(1980-89) 2.20% 2.8% Combinedcyclegasturbines(1990-98) 0.65% 3.3%5 Largehydro 1.96% 2.9%6 Combinedheatandpower 0.23% 2.1%7 Smallhydro 0.48% 2.8%8 Wastetoelectricity 41.5% 57.9%9 Nuclearlightwaterreactor 37.6% 53.2%10 Wind-onshore 13.1% 15.7%11 Solarthermalpower 2.2% 22.5%12 Wind–offshore 1.0% 8.3%

Table 7. Development Stage, Learning Rate, Capital intensity, and Market Opportunity for the Technology Categories

Learning Learning Capital Market by Doing by Research intensity Opportunity

Maturetechnologies Low Low Low HighRevivingtechnologies Low High Low HighEvolvingtechnologies High High High LowEmergingtechnologies Low Low High Low

Moreover,itisnoteworthythatrevivingtechnologiesshowconsiderablepotential for technical improvement through learningby researchalthough theydonotfacesignificantmarketconstraints.Atthefinaldevelopmentstage,maturetechnologiesexhibitrathersimilarlearningcharacteristicstoemergingtechnolo-giesintheformoflowlearningbydoingandbyresearchrates.Inaddition,therevivingandmaturetechnologiesarerelativelylesscapitalintensivethanevolv-ingandemergingtechnologies.Astechnicalprogressismainlyembodiedinthestockofcapital,emergingandevolvingtechnologieshaveasignificantpotentialforachievingfurthercostreductionsthatneedtoberealized.Furthermore,matureandrevivingtechnologieshaveaccountedforthebulkofgenerationcapacityoffer-ingthemlargemarketsharesandthuspotentialforlearningbydoingeffect.Ontheotherhand,evolvingandemergingtechnologiesstillfacemarketconstraintsandneedpublicsupportthatlimitstheirpotentialbenefitsfromlearningbydoing.

Asexpected,someoftheresultsshowthatunitcostreductionstendtoin-creasemarketdiffusionandadoptionoftechnologies.However,weonlyfindhighratesoflearningbydoingintheevolvingtechnologies.Withaviewtoastylizedtechnicalprogressanddiffusionpath,highcapitalintensityandlimitedmarketop-portunitiescanslowthepaceofprogressinemergingandevolvingtechnologies.



4.5 Elasticity of Substitution Between R&D and Capacity

Aninterestingtechnologypolicyquestionfollowingfromthediscussionoflearningbydoingversuslearningbyresearchistheextenttowhichthesemaysubstituteeachotherandwhetherthismaybedependentonthestageofthedevel-opmentoftechnologies.Thisknowledgewouldbeparticularlyusefulinalloca-tionofpublicfundsfortechnologypromotionbetweentechnologypush(learningbyresearch)andmarketpullanddeployment(learningbydoing)measures.Table8showstheaverageelasticityofsubstitutionforthetechnologiesduringthefirstandsecondhalfoftheperiodsofthestudy.

Asshownin the table,whilesometechnologieshavemovedcloser tounity(fullsubstitution)othershavemovedfurtherawayfromthis.Itisdifficulttodeterminethecauseofthisfromtheanalysishere.However,itisimportanttonotethatthediversenatureofthetechnologiesmaypartlyexplainthis.Formostofthetechnologiesthesubstitutionelasticitiesdeviatefromunitythusindicatingonlyweaksubstitutionpossibilitybetweenlearningbydoingandlearningbyresearch.Notableexceptionsare,however,onshorewind,offshorewind,andsolarthermalpowertechnologieswherewefindsomeevidenceofrelativeeaseofsubstitutionbetweenR&Danddeploymentasthemaininnovationinputfactors.Inaddition,conventional coal and CCGT (1990-98) technologies show some indication ofsubstitutionpossibility.

Table 8. Elasticity of Substitution – R&D and Capacity Technology First half-period Second half-period

1 Pulverizedfuelsupercriticalcoal -0.308 -0.127

2 Coalconventionaltechnology -0.361 -0.481

3 Ligniteconventionaltechnology -0.180 -0.205

4 Combinedcyclegasturbines(1980-89) -121.68 -14.78

Combinedcyclegasturbines(1990-98) -1.142 -0.644

5 Largehydro -0.015 -0.007

6 Combinedheatandpower -0.093 -0.009

7 Smallhydro -0.040 -0.015

8 Wastetoelectricity -0.248 -0.227

9 Nuclearlightwaterreactor -0.427 -0.320

10 Wind-onshore -9.044 -1.097

11 Solarthermalpower -2.593 -1.624

12 Wind–offshore -1.441 -1.648

Itshouldbenotedthat,asseenfromEquation(5), thecoefficientsforcumulativecapacity(b)andR&D(k)areconstantandthetimevariationintheelasticities is due to changes in values of cumulative capacity Cap and R&DspendingRDovertime.Therefore,thechangesintheelasticitiesovertimeshould

beinterpretedwithsomecare.Forexample,mostofthetechnologiesanalyzedhereexhibitadeclineinsubstitutionelasticityfromthefirsttothesecondhalfoftheperiods.ThiscanbeduetorelativelyhigherincreaseininstalledcapacityinrelationtoR&Dspendingwhichhasbeennegativelyaffectedbyaglobaldeclinesincethe1980s(seeJamasbandPollitt,2005).

5. CONCLUSiONS

Abetterunderstandingoftheroleoflearningintechnicalchangeandatdifferentstagesoftechnologicalprogressisimportantfordevelopingbettertheo-riesofinnovationanddesigningmoreeffectivetechnologypolicies.Thispaperpresentsacomparativeempiricalanalysisofprogressandlearninginelectricitygenerationtechnologiestowardsthisaimusingtheinvention-innovation-diffusionparadigmoftechnicalchange.Weestimatethelearningbydoingandlearningbyresearchratesforarangeofgenerationtechnologiesindifferentstagesofprog-ressusingtwo-factormodelsoftechnologylearning.Theestimatedlearningratesofthetechnologiesbroadlyreflecttheirexpectedstageofdevelopment.

Wefind thatemerging technologiesexperienceaperiodduringwhichthey respond slowly to R&D and capacity deployment. Evolving technologiesexhibitbothhighlearningbydoingandlearningbyresearch.Revivingtechnolo-giesshowconsiderablepotentialfortechnicalimprovementthroughlearningbyresearchalthoughtheydonotfacesignificantmarketconstraints.Finally,maturetechnologiesexhibitsimilarlearningcharacteristicstoemergingtechnologies.

TherelativeeffectivenessandtherelationshipbetweenR&Dandcapac-ityexpansionisan importantpolicyrelatedmatterand,at thesametime, littleunderstoodaspectoftechnicalchange.Theresultsgenerallypointtotherelativeimportance of R&D in technological progress.We find higher learning by re-searchthanlearningbydoingrates(althoughnotalwaysstatisticallysignificant).Moreover,wedidnotfindanystageoftechnologicaldevelopmentwherelearningbydoingalonewasthedominantdriverofprogress.

Inaddition,theresultsshowthatsingle-factorlearningcurvesoveresti-matetheeffectoflearningbydoingandinparticularforemergingandnewtech-nologies.Atthesametime,wegenerallyfindlittlescopeforpotentialsubstitutionbetweenlearningbydoingandlearningbyresearchformostofthetechnologies.TheeffectsofR&Dandcapacitydeploymentontechnologycostimprovementcanthusberegardedaslargelyindependentfromeachother.

Acrucial policyquestion ishow technologiespass fromone stageofdevelopment toanother.This is inparticular important in thepassagefromthe“emerging” to “evolving” technology stage. There remains an ample need formoreextensiveandaccuratedataondifferenttechnologies.Betterdatawillen-able more elaborate models of technology learning. This will in turn enhancethe contribution of empirical studies towards improving innovation theory andtechnologypolicy.



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