DocumentationGlobalEnergyandClimateModelDocumentationGlobalEnergyandClimateModelTheIEAexaminesthefullspectrumofenergyissuesincludingoil,gasandcoalsupplyanddemand,renewableenergytechnologies,electricitymarkets,energyefficiency,accesstoenergy,demandsidemanagementandmuchmore.Throughitswork,theIEAadvocatespoliciesthatwillenhancethereliability,affordabilityandsustainabilityofenergyinits31membercountries,11associationcountriesandbeyond.Pleasenotethatthispublicationissubjecttospecificrestrictionsthatlimititsuseanddistribution.Thetermsandconditionsareavailableonlineatwww.iea.org/t&c/Thispublicationandanymapincludedhereinarewithoutprejudicetothestatusoforsovereigntyoveranyterritory,tothedelimitationofinternationalfrontiersandboundariesandtothenameofanyterritory,cityorarea.Source:IEA.InternationalEnergyAgencyWebsite:www.iea.orgIEAmembercountries:AustraliaAustriaBelgiumCanadaCzechRepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyJapanKoreaLithuaniaLuxembourgMexicoNetherlandsNewZealandNorwayPolandPortugalSlovakRepublicSpainSwedenSwitzerlandRepublicofTürkiyeUnitedKingdomUnitedStatesTheEuropeanCommissionalsoparticipatesintheworkoftheIEAIEAassociationcountries:INTERNATIONALENERGYAGENCYArgentinaBrazilChinaEgyptIndiaIndonesiaMoroccoSingaporeSouthAfricaThailandUkraineIEA.CCBY4.0.TableofContents1TableofContents1Overviewofmodelandscenarios........................................................................................................................51.1GECModelscenarios.........................................................................................................................61.2Selecteddevelopmentsin2022......................................................................................................101.3GECModeloverview.......................................................................................................................122Cross-cuttinginputsandassumptions...............................................................................................................172.1Populationassumptions..................................................................................................................172.2Macroeconomicassumptions..........................................................................................................182.3Prices...............................................................................................................................................192.4Policies.............................................................................................................................................222.5Techno-economicinputs.................................................................................................................233End-usesectors..................................................................................................................................................253.1Industrysector.................................................................................................................................253.2Transportsector..............................................................................................................................303.3Buildingssector...............................................................................................................................393.4Hourlyelectricitydemandanddemand-sideresponse...................................................................424Electricitygenerationandheatproduction.......................................................................................................454.1Electricitygeneration.......................................................................................................................454.2Value-adjustedLevelizedCostofElectricity....................................................................................504.3Electricitytransmissionanddistributionnetworks.........................................................................534.4Hourlymodel...................................................................................................................................564.5Mini-andoff-gridpowersystems...................................................................................................574.6Renewablesandcombinedheatandpowermodules....................................................................574.7Hydrogenandammoniainelectricitygeneration...........................................................................594.8Utility-scalebatterystorage............................................................................................................605Otherenergytransformation.............................................................................................................................615.1Oilrefiningandtrade.......................................................................................................................615.2Coal-to-liquids,Gas-to-liquids,Coal-to-gas.....................................................................................625.3Hydrogenproductionandsupply....................................................................................................625.4Biofuelproduction...........................................................................................................................656Energysupply.....................................................................................................................................................696.1Oil....................................................................................................................................................696.2Naturalgas.......................................................................................................................................736.3Coal..................................................................................................................................................746.4Bioenergy.........................................................................................................................................752InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION7Criticalminerals..................................................................................................................................................797.1Demand...........................................................................................................................................807.2Supplyrequirements.......................................................................................................................808Emissions............................................................................................................................................................818.1CO2emissions..................................................................................................................................818.2Non-CO2greenhousegases.............................................................................................................818.3Airpollution.....................................................................................................................................828.4Globaltemperatureimpacts...........................................................................................................828.5Oilandgasmethaneemissionsmodel............................................................................................829Investment.........................................................................................................................................................899.1Investmentinfuelsupplyandthepowersector.............................................................................899.2Demand-sideinvestments...............................................................................................................919.3Financingforinvestments...............................................................................................................929.4Emissionsperformanceofinvestments..........................................................................................9310EnergyandCO2decomposition........................................................................................................................9510.1Methodology...................................................................................................................................9611Energyaccess...................................................................................................................................................9711.1Definingmodernenergyaccess.......................................................................................................9711.2Outlookformodernenergyaccess.................................................................................................9812Employment.....................................................................................................................................................9912.1Definitionandscopeofemployment..............................................................................................9912.2Estimatingcurrentemployment....................................................................................................10012.3Outlookforemployment...............................................................................................................10113Assessinggovernmentspendingoncleanenergyandenergyaffordability..................................................10313.1Governmentspendingpolicyidentificationandcollection...........................................................10313.2Assessingtheimpactonoverallcleanenergyinvestment............................................................104AnnexA:Terminology........................................................................................................................................107Definitions.................................................................................................................................................107Regionalandcountrygroupings...............................................................................................................114Acronyms..................................................................................................................................................118AnnexB:References..........................................................................................................................................121TableofContents3ListoffiguresFigure1.1⊳GlobalEnergyandClimateModelOverview13Figure2.1⊳Componentsofretailelectricityend-useprices21Figure3.1⊳Generalstructureofdemandmodules25Figure3.2⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinindustry26Figure3.3⊳Industrysectormodelinternalmodulestructureandkeydataflows28Figure3.4⊳Structureofthetransportsector32Figure3.5⊳Illustrationofscrappagecurveandmileagedecaybyvehicletype33Figure3.6⊳Theroleofpassenger-LDVcostmodel34Figure3.7⊳Illustrationofanefficiencycostcurveforroadfreight35Figure3.8⊳Refuellinginfrastructurecostcurve(illustrative)36Figure3.9⊳Structureofthebuildingssector39Figure3.10⊳Majorcategoriesoftechnologiesbyend-usesubsectorinbuildings41Figure3.11⊳IllustrativeloadcurvesbysectorforaweekdayinFebruaryintheEuropeanUnioncomparedtotheobservedloadcurvebyENTSO-Efor201443Figure4.1⊳Structureofthepowergenerationmodule45Figure4.2⊳Loaddurationcurveshowingthefourdemandsegments47Figure4.3⊳Examplemeritorderanditsintersectionwithdemandinthepowergenerationmodule48Figure4.4⊳Exampleelectricitydemandandresidualload49Figure4.5⊳Exemplaryelectricitydemandandresidualload50Figure4.6⊳MovingbeyondtheLCOE,tothevalue-adjustedLCOE51Figure4.7⊳ElectricitynetworkexpansionperunitofelectricitydemandgrowthbyGDPpercapita54Figure5.1⊳Schematicofrefiningandinternationaltrademodule61Figure5.2⊳Schematicofmerchanthydrogensupplymodule63Figure6.1⊳Structureoftheoilsupplymodule71Figure6.2⊳Evolutionofproductionofcurrentlyproducingconventionaloilfieldsfromafield-by-fielddatabaseandfromtheGECModel73Figure6.3⊳Schematicofbiomasssupplypotentials75FigureA.1⊳GECModelregionalgroupings115ListoftablesTable1.1⊳DefinitionsandobjectivesoftheGECModel2022scenarios6Table2.1⊳Populationassumptionsbyregion17Table2.2⊳RealGDPaveragegrowthassumptionsbyregionandscenario18Table2.3⊳Fossilfuelpricesbyscenario19Table2.4⊳CO2pricesforelectricity,industryandenergyproductioninselectedregionsbyscenario20Table2.5⊳Capitalcostsforselectedtechnologiesbyscenario24Table6.1⊳Remainingtechnicallyrecoverablefossilfuelresources,end-202174Table7.1⊳Criticalmineralsinscope79Table8.1⊳CategoriesofemissionsourcesandemissionsintensitiesintheUnitedStates83Table8.2⊳ScalingfactorsappliedtotheUnitedStatesemissionintensities83Table8.3⊳Equipment-specificemissionssourcesusedinthemarginalabatementcostcurves84Table8.4⊳Abatementoptionsformethaneemissionsfromoilandgasoperations85Table9.1⊳Sub-sectorsandassetsincludedinfuelsupplyinvestment90Table9.2⊳Sub-sectorsandassetsincludedinpowersectorinvestment91Table9.3⊳Sub-sectorsandassetsincludedinend-useenergyinvestment924InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONListofboxesBox1.1⊳AnintegratedapproachtoenergyandsustainabledevelopmentintheNetZeroEmissionsby2050Scenario9Box4.1⊳Long-termpotentialofrenewables58Box6.1⊳GECModeldifferencesinmethodologycomparedwiththeMedium-TermOilMarketReport70Box6.2⊳Methodologytoaccountforproductiondeclineinoilandgasfields72Section1Overviewofmodelandscenarios5Section11OverviewofmodelandscenariosSince1993,theIEAhasprovidedmedium-tolong-termenergyprojectionsusingacontinually-evolvingsetofdetailed,world-leadingmodellingtools.First,theWorldEnergyModel(WEM)–alarge-scalesimulationmodeldesignedtoreplicatehowenergymarketsfunction–wasdeveloped.Adecadelater,theEnergyTechnologyPerspectives(ETP)model–atechnology-richbottom-upmodel–wasdeveloped,foruseinparalleltotheWEM.In2021,theIEAadoptedforthefirsttimeanewhybridmodellingapproachrelyingonthestrengthsofbothmodelstodeveloptheworld’sfirstcomprehensivestudyofhowtotransitiontoanenergysystematnetzeroCO2emissionsby2050.Sincethen,theIEAhasworkedtodevelopanewintegratedmodellingframework:IEA’sGlobalEnergyandClimate(GEC)Model.Asof2022,thismodelistheprincipaltoolusedtogeneratedetailedsector-by-sectorandregion-by-regionlong-termscenariosacrossIEA'spublications.TheGECModelbringstogetherthemodellingcapabilitiesoftheWEMandETPmodels.Theresultisalarge-scalebottom-uppartial-optimisationmodellingframeworkallowingforauniquesetofanalyticalcapacitiesinenergymarkets,technologytrends,policystrategiesandinvestmentsacrosstheenergysectorthatwouldbecriticaltoachieveclimategoals.IEA’sGECModelcovers26regionsindividuallythatcanbeaggregatedtoworld-levelresultsandallsectorsacrosstheenergysystemwithdedicatedbottom-upmodellingfor:◼Finalenergydemand,coveringindustry,transport,buildings,agricultureandothernon-energyuse.Thisisdrivenbydetailedmodellingofenergyserviceandmaterialdemand.◼Energytransformation,includingelectricitygenerationandheatproduction,refineries,theproductionofbiofuels,hydrogenandhydrogen-derivedfuelsandotherenergy-relatedprocesses,aswellasrelatedtransmissionanddistributionsystems,storageandtrade.◼Energysupply,includingfossilfuelsexploration,extractionandtrade,andavailabilityofrenewableenergyresources.TheGECModelisaverydata-intensivemodelcoveringthewholeglobalenergysystem.Muchofthedataonenergysupply,transformationanddemand,aswellasenergypricesisobtainedfromtheIEA’sowndatabasesofenergyandeconomicstatistics(http://www.iea.org/statistics)andthroughcollaborationwithotherinstitutions.Italsodrawsdatafromawiderangeofexternalsourceswhichareindicatedintherelevantsectionsofthisdocument.ThedevelopmentoftheGECModelbenefitedfromexpertreviewwithintheIEAandbeyond,andtheIEAcontinuestoworkcloselywithcolleaguesintheinternationalmodellingcommunity.TheGECModelisdesignedtoanalyseadiverserangeofaspectsoftheenergysystem,including:◼Globalandregionalenergyprospects:theseincludetrendsindemand,supplyavailabilityandconstraints,internationaltradeandenergybalancesbysectorandbyfuelintheprojectionhorizon.◼Environmentalimpactofenergyuse:thisincludesCO2emissionsfromfuelcombustion,processemissionsandfromflaring,methaneemissionsfromtheoilandgassectorandcoalmining,CH4andN2Oemissionsfromfinalenergydemandandenergytransformationlocalairpollutants,andtemperatureoutcome.◼Effectsofpolicyactionsandtechnologicalchanges:scenariosanalysetheimpactofarangeofpolicyactionsandtechnologicaldevelopmentsonenergydemand,supply,trade,investmentsandemissions.◼Investmentintheenergysector:thisincludesinvestmentrequirementsinthefuelsupplychaintosatisfyprojectedenergydemandanddemand-sideinvestmentrequirements.◼Modernenergyaccessassessments:theseincludetrendsinaccesstoelectricityandcleancookingfacilities,andtheadditionalenergydemand,investmentsandCO2emissionsduetoincreasedenergyaccess.◼Energyemployment:thisincludestheimpactofthescenariosonemploymentinvariousenergysectors6InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION1.1GECModelscenariosTheIEAmediumtolong-termoutlookpublications–theWorldEnergyOutlook(WEO)andtheEnergyTechnologyPerspectives(ETP)-useascenarioapproachtoexaminefutureenergytrendsrelyingontheGECModel.TheGECModelisusedtoexplorevariousscenarios,eachofwhichisbuiltonadifferentsetofunderlyingassumptionsabouthowtheenergysystemmightrespondtothecurrentglobalenergycrisisandevolvethereafter.Bycomparingthem,thereaderisabletoassesswhatdrivesthevariousoutcomes,andtheopportunitiesandpitfallsthatliealongtheway.Thesescenariosarenotpredictions–GECModelscenariosdonotcontainasingleviewaboutwhatthelong-termfuturemighthold.Instead,whatthescenariosseektodoistoenablereaderstocomparedifferentpossibleversionsofthefutureandtheleversandactionsthatproducethem,withtheaimofstimulatinginsightsaboutthefutureofglobalenergy.TheWEO-2022andETP-2023–basedontheintegratedGECmodellingcycle–explorethreescenarios,allofwhicharefullyupdatedtoincludethelatestenergymarketandcostdata.TheNetZeroEmissionsby2050Scenario(NZEScenario)isnormative,inthatitisdesignedtoachievespecificoutcomes–anemissionstrajectoryconsistentwithkeepingthetemperaturerisein2100below1.5°C(witha50%probability),universalaccesstomodernenergyservicesandmajorimprovementsinairquality–andshowsapathwaytoreachit.TheAnnouncedPledgesScenario(APS),andtheStatedPoliciesScenario(STEPS)areexploratory,inthattheydefineasetofstartingconditions,suchaspoliciesandtargets,andthenseewheretheyleadbasedonmodelrepresentationsofenergysystems,includingmarketdynamicsandtechnologicalprogress.The2022GECmodellingcycledoesnotincludetheSustainableDevelopmentScenario(SDS),whichisanothernormativescenariousedinpreviouseditionstomodela“wellbelow2°C”pathwayaswellastheachievementofothersustainabledevelopmentgoals.TheAPSoutcomesareclose,insomerespects,tothoseintheSDS,inparticularintermsofthetemperatureoutcome.ButtheyaretheproductofadifferentmodellingapproachandsoaslongaspolicyambitiondoesnotfullycaptureallSDSoutcomes,theAPSfallsshortofachievingthose.Table1.1⊳DefinitionsandobjectivesoftheGECModel2022scenariosNetZeroEmissionsby2050ScenarioAnnouncedPledgesScenarioStatedPoliciesScenarioDefinitionsAscenariowhichsetsoutapathwayfortheglobalenergysectortoachievenetzeroCO2emissionsby2050.Itdoesnotrelyonemissionsreductionsfromoutsidetheenergysectortoachieveitsgoals.Universalaccesstoelectricityandcleancookingareachievedby2030.Ascenariowhichassumesthatallclimatecommitmentsmadebygovernmentsaroundtheworld,includingNationallyDeterminedContributions(NDCs)andlonger-termnetzerotargets,aswellastargetsforaccesstoelectricityandcleancooking,willbemetinfullandontime.Ascenariowhichreflectscurrentpolicysettingsbasedonasector-by-sectorandcountrybycountryassessmentofthespecificpoliciesthatareinplace,aswellasthosethathavebeenannouncedbygovernmentsaroundtheworld.ObjectivesToshowwhatisneededacrossthemainsectorsbyvariousactors,andbywhen,fortheworldtoachievenetzeroenergyrelatedandindustrialprocessCO2emissionsby2050whilemeetingotherenergy-relatedsustainabledevelopmentgoalssuchasuniversalenergyaccess.Toshowhowclosedocurrentpledgesgettheworldtowardsthetargetoflimitingglobalwarmingto1.5°C,ithighlightsthe“ambitiongap”thatneedstobeclosedtoachievethegoalsagreedatParisin2015.Italsoshowsthegapbetweencurrenttargetsandachievinguniversalenergyaccess.Toprovideabenchmarktoassessthepotentialachievements(andlimitations)ofrecentdevelopmentsinenergyandclimatepolicy.Section1Overviewofmodelandscenarios7Thescenarioshighlighttheimportanceofgovernmentpoliciesindeterminingthefutureoftheglobalenergysystem:decisionsmadebygovernmentsarethemaindifferentiatingfactorexplainingthevariationsinoutcomesacrossourscenarios.However,wealsotakeintoaccountotherelementsandinfluences,notablytheeconomicanddemographiccontext,technologycostsandlearning,energypricesandaffordability,corporatesustainabilitycommitments,andsocialandbehaviouralfactors.However,whiletheevolvingcostsofknowntechnologiesaremodelledindetail,wedonottryandanticipatetechnologybreakthroughs(e.g.,nuclearfusion).Aninventoryofthekeypolicyassumptionsavailablealongwithalltheunderlyingdataonpopulation,economicgrowth,resources,technologycostsandfossilfuelpricesareavailableintheMacroDriversandTechno-economicinputspages.Forthefirsttime,theprojectionsweregeneratedbyaunifiedmodelthatintegratesthestrengthsthepreviousWorldEnergyModel(WEM)andtheEnergyTechnologyPerspectives(ETP)model.Combiningthedetailedfeaturesofthetwopreviousmodelsallowsustoprepareauniquesetofinsightsonenergymarkets,investment,technologiesandthepoliciesthatwouldbeneededforthecleanenergytransition.NetZeroEmissionsby2050ScenarioTheNetZeroEmissionsby2050Scenario(NZE)isanormativeIEAscenariothatshowsapathwayfortheglobalenergysectortoachievenetzeroCO2emissionsby2050,withadvancedeconomiesreachingnetzeroemissionsinadvanceofothers.Thisscenarioalsomeetskeyenergy-relatedUnitedNationsSustainableDevelopmentGoals(SDGs),inparticularbyachievinguniversalenergyaccessby2030andmajorimprovementsinairquality.Itisconsistentwithlimitingtheglobaltemperatureriseto1.5°Cwithnoorlimitedtemperatureovershoot(witha50%probability),inlinewithreductionsassessedintheIPCCinitsSixthAssessmentReport.TherearemanypossiblepathstoachievenetzeroCO2emissionsgloballyby2050andmanyuncertaintiesthatcouldaffectanyofthem;theNZEScenarioisthereforeapath,notthepathtonetzeroemissions.Muchdepends,forexample,onthepaceofinnovationinnewandemergingtechnologies,theextenttowhichcitizensareableorwillingtochangebehaviour,theavailabilityofsustainablebioenergyandtheextentandeffectivenessofinternationalcollaboration.TheNetZeroEmissionsby2050Scenarioisbuiltonthefollowingprinciples:◼Theuptakeofalltheavailabletechnologiesandemissionsreductionoptionsisdictatedbycosts,technologymaturity,policypreferences,andmarketandcountryconditions.◼Allcountriesco-operatetowardsachievingnetzeroemissionsworldwide.Thisinvolvesallcountriesparticipatingineffortstomeetthenetzerogoal,workingtogetherinaneffectiveandmutuallybeneficialway,andrecognisingthedifferentstagesofeconomicdevelopmentofcountriesandregions,andtheimportanceofensuringajusttransition.◼Anorderlytransitionacrosstheenergysector.Thisincludesensuringthesecurityoffuelandelectricitysuppliesatalltimes,minimisingstrandedassetswherepossibleandaimingtoavoidvolatilityinenergymarkets.Inrecentyears,theenergysectorwasresponsibleforaroundthree-quartersofglobalgreenhousegas(GHG)emissions.Achievingnetzeroenergy-relatedandindustrialprocessCO2emissionsby2050intheNZEScenariodoesnotrelyonactioninareasotherthantheenergysector,butlimitingclimatechangedoesrequiresuchaction.WethereforeadditionallyexaminethereductionsinCO2emissionsfromlandusethatwouldbecommensuratewiththetransformationoftheenergysectorintheNZEScenario,workingincooperationwiththeInternationalInstituteforAppliedSystemsAnalysis(IIASA).8InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONAnnouncedPledgesScenarioTheAnnouncedPledgesScenariointroducedin2021aimstoshowtowhatextenttheannouncedambitionsandtargets,includingthemostrecentones,areonthepathtodeliveremissionsreductionsrequiredtoachievenetzeroemissionsby2050.ItincludesallrecentmajornationalannouncementsasofSeptember2022for2030targetsandlonger-termnetzeroandotherpledges,regardlessofwhetherthesehavebeenanchoredinimplementinglegislationorinupdatedNDCs.IntheAPS,countriesfullyimplementtheirnationaltargetsto2030and2050,andtheoutlookforexportersoffossilfuelsandlowemissionsfuelslikehydrogenisshapedbywhatfullimplementationmeansforglobaldemand.Forthefirsttime,theAPSassumesthisyearthatallcountry-levelaccesstoelectricityandcleancookingtargetsareachievedontimeandinfull.ThewaythesepledgesareassumedtobeimplementedintheAPShasimportantimplicationsfortheenergysystem.AnetzeropledgeforallGHGemissionsdoesnotnecessarilymeanthatCO2emissionsfromtheenergysectorneedtoreachnetzero.Forexample,acountry’snetzeroplansmayenvisagesomeremainingenergy-relatedemissionsareoffsetbytheabsorptionofemissionsfromforestryorlanduse.Itisnotpossibletoknowexactlyhownetzeropledgeswillbeimplemented,butthedesignoftheAPS,particularlywithrespecttothedetailsoftheenergysystempathway,hasbeeninformedbythepathwaysthatanumberofnationalbodieshavedevelopedtosupportnetzeropledges.PoliciesincountriesthathavenotyetmadeanetzeropledgeareassumedtobethesameasintheSTEPS.Non-policyassumptions,includingpopulationandeconomicgrowth,arethesameasintheSTEPS.StatedPoliciesScenarioTheSTEPSprovidesamoreconservativebenchmarkforthefuture,becauseitdoesnottakeitforgrantedthatgovernmentswillreachallannouncedgoals.Instead,ittakesamoregranular,sector-by-sectorlookatwhathasactuallybeenputinplacetoreachtheseandotherenergy-relatedobjectives,takingaccountnotjustofexistingpoliciesandmeasuresbutalsoofthosethatareunderdevelopment.TheSTEPSexploreswheretheenergysystemmightgowithoutamajoradditionalsteerfrompolicymakers.AswiththeAPS,itisnotdesignedtoachieveaparticularoutcome.ThepoliciesassessedintheStatedPoliciesScenariocoverabroadspectrum.TheseincludeNationallyDeterminedContributionsundertheParisAgreement,butmuchmorebesides.Inpractice,thebottom-upmodellingeffortinthisscenariorequiresalotofdetailatthesectorallevel,includingpricingpolicies,efficiencystandardsandschemes,electrificationprogrammesaswellasspecificinfrastructureprojects.ThescenariotakesintoaccountthepoliciesandimplementingmeasuresaffectingenergymarketsthathadbeenadoptedasofendofSeptember2022,togetherwithrelevantpolicyproposals,eventhoughspecificmeasuresneededtoputthemintoeffecthaveyettobefullydeveloped.Thesortsofannouncementsmadebygovernmentsincludesomefar-reachingtargets,includingaspirationstoachievefullenergyaccessinafewyears,toreformpricingregimesand,morerecently,toreachnetzeroemissionsinsomecountriesandsectors.AswithallthepoliciesconsideredintheStatedPoliciesScenario,theseambitionsarenotautomaticallyincorporatedintothescenario:fullimplementationcannotbetakenforgranted,sotheprospectsandtimingfortheirrealisationarebaseduponourassessmentofcountries’relevantregulatory,market,infrastructureandfinancialcircumstances.Wherepoliciesaretime-limited,theyaregenerallyassumedtobereplacedbymeasuresofsimilarintensity,butwedonotassumefuturestrengthening–orweakening–offuturepolicyaction,exceptwheretherealreadyisspecificevidencetothecontrary.Section1Overviewofmodelandscenarios9TheSTEPSshowsthatinaggregate,currentcountrycommitmentsareenoughtomakeasignificantdifference.However,thereisstillalargegapbetweentheprojectionsintheSTEPSandatrajectoryoftheothertwoscenarios.Box1.1⊳AnintegratedapproachtoenergyandsustainabledevelopmentintheNetZeroEmissionsby2050ScenarioTheNetZeroEmissionsby2050Scenario(NZEScenario)integratesthreekeyobjectivesoftheUN2030AgendaforSustainableDevelopment:universalaccesstomodernenergyservicesby2030(embodiedinSDG7),reducinghealthimpactsofairpollution(SDG3.9),andactiontotackleclimatechange(SDG13).Asafirststep,weusetheGECModeltoassesshowtheenergysectorwouldneedtochangetodeliveruniversalaccesstomodernenergyservicesby2030.Toanalyseelectricityaccess,wecombinecost-optimisationwithnewgeospatialanalysisthattakesintoaccountcurrentandplannedtransmissionlines,populationdensity,resourceavailabilityandfuelcosts.Second,weconsiderambientandhouseholdairpollutionandclimategoals.ThepoliciesnecessarytoachievethemultipleSDGscoveredintheNZEScenarioareoftencomplementary.Forexample,energyefficiencyandrenewableenergysignificantlyreducelocalairpollution,particularlyincities,whileaccesstocleancookingfacilitatedbyliquefiedpetroleumgasalsoreduceshouseholdairpollutionandoverallgreenhousegasemissionsbyreducingmethaneemissionsfromincompletecombustionofbiomassaswellasbyreducingdeforestation.Trade-offscanalsoexist,forexamplebetweenelectricvehiclesreducinglocalairpollutionfromtraffic,butatthesametimeincreasingoverallCO2emissionsifthereisnotaparallelefforttodecarbonisethepowersector.Ultimately,thebalanceofpotentialsynergiesortrade-offsdependsontheroutechosentoachievetheenergytransition,makinganintegrated,whole-systemapproachtoscenariobuildingessential.TheemphasisoftheNZEScenarioisontechnologieswithshortprojectleadtimesinthepowersectorinparticular,suchasrenewables,whilethelonger-termnatureofclimatechangeallowsforothertechnologychoices.ModernusesofbiomassasadecarbonisationoptionisalsolessrelevantintheNZEthaninasingle-objectiveclimatescenario.Thisisbecausebiomassisacombustiblefuel,requiringpost-combustioncontroltolimitairpollutantemissionsand–dependingontheregioninquestion-makingitmorecostlythanalternatives.Sinceitslaunchin2021,theNZEScenario,alsolooksattheimplicationsfortheenergysectorforachievingseveralofthetargetsunderUnitedNationsSustainableDevelopmentGoal6(cleanwaterandsanitationforall)andwhatpolicymakersneedtodotohitmultiplegoalswithanintegratedandcoherentpolicyapproach.Inordertoreflectinourmodellingtheannouncementsmadebyseveralcountriestoachievecarbonneutralityby2050andalsoallowsustomodelthepotentialfornewtechnologies(suchashydrogenandrenewablegases)tobedeployedatscale,thetimehorizonofthemodelis2050.TheinterpretationoftheclimatetargetembodiedintheNZEScenarioalsochangesovertime,asaconsequenceofbothongoingemissionsofCO2aswellasdevelopmentsinclimatescience(refertothe8Emissionssectionformoredetails).DespitethefundamentalchangesacrossallsectorstheNZEscenariostillensuresanorderlytransition.Thisincludesensuringthesecurityoffuelandelectricitysuppliesatalltimes,minimisingstrandedassetswherepossibleandaimingtoavoidvolatilityinenergymarkets.10InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION1.2Selecteddevelopmentsin2022InadditiontotheoverallmergeprocessofthepreviousWEMandETPmodelsandtheirdatapipelines,sectoralandtopic-specificdevelopmentsthisyear,undertakenaspartoftheGECModeldevelopment,includethefollowing:FinalenergyconsumptionBehaviouralanalysis◼Severalnewspecificbehaviouralchangeshavebeenmodelledindetail,includingmeasurestomanagegrowthinaviationdemand,suchasfrequentflyerlevies,andtheimpactofmeasurestoreducethesalesanduseofSUVs.Inaddition,themodellingofthepotentialforride-sharingtoimpactdemandwascoveredindetail.◼Theregionalgranularityofmodellinghasbeenimprovedtoreflectdifferencesinthepotentialscope,scaleandspeedofadoptionofbehaviouralchanges.Inputsintothismodellingincludetheabilityofexistinginfrastructuretosupportsuchchangesanddifferencesingeography,climate,urbanisation,socialnormsandculturalvalues.Buildingsmodule◼Thebuildingsmoduleunderwentsignificantupdatesforthe2022modellingcycle,themodulenowfullycombinesthestrengthsofthepre-existingWEOandETPmodellingframeworks,allowingformoredetailedrepresentationofthestockofbuildingsandtechnologies.Thenewmergedframeworknotablyincludes:•Astockaccountingmodelusedtodescribetheevolutionofbuildings,trackingthevintageofeachbuilding,itsenergyservicedemand,energyperformance,lifetimeandwhetherthebuildinghasundergonearetrofittoimproveitsenergyefficiency.Uponconstruction,buildingsareclassifiedintothreecategories:non-complianttobuildingenergycodes,complianttobuildingenergycodesandzero-carbon-readybuilding.Constructedbuildingscanthenberetrofittoimproveenergyefficiency,andarecategorisedas:retrofittocompliant,orretrofittozero-carbon-ready.Improvedrepresentationofthebuildingstockallowsforbetterrepresentationoftheimpactofchangestobuildingenergycodesandotherpolicyactions,theevolutionofbuildingfloorareabyvintage,thegainsthatcanbeachievedbyretrofittingbuildings,includingtheabilitytotargetretrofitstowardtheleastefficientbuildings.•Buildinguponlocalclimatedata,populationdensitymappingandregionalestimatesofenergydemandbyend-useandsectorprovideabasisfordistributingheatingandcoolingdemandatthelocallevelandassessingcleantechnologydeploymentstrategies.Forinstance,theassessmentofheatandcolddemanddensitiesatthecityordistrictleveliskeytomakingsoundjudgementcallsonthedecarbonisationpotentialofdistrictenergysystems(togetherwithothervariablessuchastheshareofvariablerenewablesintheelectricitymixandtheavailabilityofwasteheatsources).Localclimateandpopulationdataarealsousedtoderiveheatpumpenergyperformance.Industrymodule◼TheindustrymodulewentthroughacompleteoverhaultotakethebestofbothWEOandETPframeworks.Thenewmoduleenablesapreciserepresentationofheavyindustries(chemicals,ironandsteelandcement)andlightindustries(construction,food,machineryequipment,miningandquarrying,textileandleather,andwoodandwoodproducts),industrialcapacityprojectionsandrelatedlock-inemissionsanalysis.ThepreviousTIMESmodelsforheavyindustryareretainedassatellitemodulesthatcanbeusedforexploratoryanalysisinordertoinformtheGECmoduleparameters,forexampletestingtheimpactofaparticularshock,newtechnologyorotherimportantchangeinthesystem.Section1Overviewofmodelandscenarios11Transportmodule◼ThetransportmoduleintegratedtheframeworkofWEOandETPmodules,toallowforimprovedsectoralrepresentationacrossallmodes:road,aviation,navigationandrail.TheintegratedmodelutilisesmainlyVensim,aswellasdedicatedmodulesdevelopedinJavaandR.•Forroad,scrappagefunctionsareextendedacrossallvehicletypestoimprovesectoralrepresentation,anddynamicscrappagefunctionisimplementedbasedonacorrelationofaveragelifetimewitheconomicgrowth.Mileagecurveshavebeenupdatedtotakeintoaccountthatoldvehiclesaredrivenless.◼AviationmodellinghasintegratedmainfeaturesoftheAviationIntegratedModelling(AIM)tooldevelopedbyUniversityCollegeLondon(UCL)including:•Operationalandtechnicalpotentialforenergyintensityimprovementsbasedoniterativecostminimisationmodellingacrossdifferentairframe-propulsionsystemsandstockaccounting.Electricitygeneration◼Thestructureofthegridscomponentofthemodulehasbeensignificantlyexpandedtoincludeincreaseddetailonlineandcabletypes.Thisincludesbyvoltagelevel,overheadlineorundergroundcable,andACorDClinesandcables.Inaddition,costinputsforbothnewandreplacementlineshaveincreasedingranularitybylinetypeaswellasbyregion.Finally,theimpactsofintegratinghighsharesofrenewableshavebeenfurtherdevelopedintermsoftransmissiongridreinforcementsandgridformingrequirements.Energysupply◼Againstthebackdropofanincreasinglyfragmentedworld,theoilandgassupplymodulesaccountthisyearforawiderangeoffinancialrisks(e.g.,geopolitics,ruleoflaw,regulatoryoversight).Thisimprovestherepresentationofdecisionsmadebycompanieslookingtoinvestinoilandnaturalgasfieldsindifferentcountries.OthertransformationHydrogenmodule◼Thetemporalresolutionofthehydrogenmodulehasbeenenhancedbyintroducingsub-annualtimeslicestocapturethevariabilityindedicatedrenewableelectricitygeneration(solarPV,onshorewind,offshorewind)fortheproductionofhydrogenandhydrogen-basedfuelsandtoenablethemodellingofhydrogenstorage.◼Atooltoanalysetheregionalhydrogeninfrastructureneedsandrelatedinvestmentsforpipelines,ships,portterminalsandstoragehasbeendeveloped.Biofuelproductionmodule◼Themodellingoftradeinliquidbiofuelsbetweenthe26GECModelregionshasbeenexpandedbyaddingbiojetkerosenetothealreadyexistingtrademodellingforethanolandbiodiesel.Criticalminerals◼Thecriticalmineralsmodulehasbeenupdatedwithamoregranulartechnologyrepresentation(e.g.,batterychemistry,gridtypeandvoltagelevels,typesofEVmotors)andmineralintensityinputs,whilealsobeingfullylinkedtoexistingmodulesoftheGECmodel(transport,electricitygenerationandhydrogentransformation).12InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONEnergyaccess◼Inpreviousyears,energyaccesswasassessedintwodifferentscenarios:STEPSlookingattheimpactsofaccesspolicies(forelectricityandcleancooking),andtheachievementofSDG7(universalaccesstoelectricityandcleancooking).ThiseditionofAPSnotonlyincludeallcurrentannouncedenergyandclimatecommitmentsbutalsoelectricityandcleancookingcountriestargets.TheAPSassumesthatallthesetargetsareimplementedinfullandontime.Employment◼Currentemploymentnowreflectsthelabourrequiredforfutureprojectsinthepipeline.◼ValuechainsegmentshavebeenalignedwiththeInternationalStandardIndustrialClassification(revision4).◼Themodelnowincorporatesparametersestimatinglabourproductivityimprovements.◼Globaltradeisreflectedbyanewcalculationreflectingtheregionaldistributionsofmanufacturingcapacityforkeycleanenergytechnologies.◼Thegranularityforfossilfuelsupplyandpowergenerationhasbeenimproved.Assessinggovernmentspendingoncleanenergyandenergyaffordability◼TheIEAhasextendedthescopeofitsgovernmentspendingmonitoringtocoverbothcleanenergyinvestmentsupportandenergyaffordabilityforconsumersinresponsetotheenergycrisis.◼Mobilisationeffectsonprivateinvestmenthavealsobeenupdatedsincelastyear.1.3GECModeloverviewModellingmethodologyTheGECModelisabottom-uppartial-optimisationmodelcoveringenergydemand,energytransformationandenergysupply(Figure1.1).Themodelusesapartialequilibriumapproach,integratingpricesensitivities.Itshowsthetransformationofprimaryenergyalongenergysupplychainstomeetenergyservicedemand,thefinalenergyconsumedbytheend-user.Thevarioussupply,transformationanddemandmodulesofthemodelaredynamicallysoft-linked:consumptionofelectricity,hydrogenandhydrogen-relatedfuels,biofuels,oilproducts,coalandnaturalgasintheendusesectormodeldrivesthetransformationandsupplymodules,whichinturnfeedenergypricesbacktothedemandmoduleinaniterativeprocess.Inaddition,energysystemCO2,CH4andN2Oemissionsareassessed.Themodelcontainsanumberofadditionalanalysisfeaturesevaluatingfurthersystemimplicationssuchasinvestments,criticalminerals,employment,temperatureoutcomes,land-use,andairpollution(seemoredetailsbelow).Themainexogenousdriversofthescenariosareeconomicgrowth,demographicsandtechnologicaldevelopments.Energyservicedemanddrivers,suchassteeldemandinindustryornumberofapplianceswithinhouseholds,areestimatedeconometricallybasedonhistoricaldataandonthesocioeconomicdrivers.Interactionsbetweenenergyservicedemanddriversarealsoaccountedfor,suchastheinfluenceofthenumberofvehiclessalesonmaterialsdemand.Thisservicedemandismetbyexistingandnewtechnologies.Allsectormodules(seesubsequentsectionsformoredetailsonthesemodules)basetheirprojectionsontheexistingstockofenergyinfrastructure(e.g.,thenumberofvehiclesintransport,productioncapacityinindustry,floorspaceareainbuildings),throughdetailedstock-accountingframeworks.Toassesshowtheservicedemandismetinthevariousscenarios,themodelincludesawiderangeoffuelsandtechnologies(existingandadditions).Thisincludescarefulaccountingofthecurrentenergyperformanceofdifferenttechnologiesandprocesses,andpotentialtoimproveefficiency.Section1Overviewofmodelandscenarios13Figure1.1⊳GlobalEnergyandClimateModelOverviewIEA.CCBY4.0.Thesectoralandcross-sectoralenergyandemissionbalancesarecalculatedbasedonthefinalenergyenduses–theservicedemand–bydeterminingfirstthefinalenergydemandneededtoserveit,thentherequiredtransformationstoconvertprimaryenergyintotherequiredfuels,andfinallytheprimaryenergyneeds.Thisisbasedonapartialequilibriumapproachusingforsomeelementsapartialoptimisationmodel,withinwhichspecificcostsplayanimportantroleindeterminingtheshareoffuelsandtechnologiestosatisfytheenergyservicedemand.Indifferentpartsofthemodel,LogitandWeibullfunctionsareusedtodeterminetheshareof14InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONtechnologiesbasedupontheirspecificcosts.Thisincludesinvestmentcosts,operatingandmaintenancecosts,fuelcostsandinsomecasescostsforemittingCO2.Incertainsectors,suchashydrogenproduction,speciallydesignedandlinkedoptimisationmodulesareused.Whilethemodelaimstoidentifyaneconomicalwayforsocietytoreachthedesiredscenariooutcomes,theresultsdonotnecessarilyreflecttheleast-costwayofdoingso.Thisisbecauseanunconstrainedleast-costapproachmayfailtotakeaccountofalltheissuesthatneedtobeconsideredinpractice,suchasmarketfailures,politicalorindividualpreferences,feasibleramp-uprates,capitalconstraintsandpublicacceptance.Instead,theanalysispursuesaportfoliooffuelsandtechnologieswithinaframeworkofcostminimisation,consideringtechnical,economicandregulatoryconstraints.Thisapproach,tailoredtoeachsectorandincorporatingextensiveexpertconsultation,enablesthemodeltoreflectasaccuratelyaspossibletherealitiesofdifferentsectors.Italsooffersahedgeagainsttherealrisksassociatedwiththepathways:ifonetechnologyorfuelfailstofulfilitsexpectedpotential,itcanmoreeasilybecompensatedbyanotherifitsshareintheoverallenergymixislow.Allfuelsandtechnologiesincludedinthemodelareeitheralreadycommerciallyavailableoratarelativelyadvancedstageofdevelopment,sothattheyhaveatleastreachedaprototypesizefromwhichenoughinformationaboutexpectedperformanceandcostsatscalecanbederived.Costsfornewcleanfuelsandtechnologiesareexpectedtofallovertimeandinformedinmanycasesbylearningcurveapproaches,helpingtomakeanetzerofutureeconomicallyfeasible.Besidesthismainfeedbackloopbetweensupplyanddemand,therearealsolinkagesbetweenthetransformationandsupplymodules.Furtherlinkagesbetweenenergysectorsarecapturedinthemodel,e.g.,materialflowsorbiogenicoratmosphericCO2viaDirectAirCaptureforsyntheticfuelproduction.Primaryenergyneedsandavailabilityinteractwiththesupplymodule.CompleteenergybalancesarecompiledataregionallevelandtheCO2emissionsofeachregionarethencalculatedusingderivedCO2factors,takingintoaccountreductionsfromCO2removaltechnologies.TheGECModelisimplementedinthesimulationsoftwareVensim(www.vensim.com),butmakesuseofawiderrangeofsoftwaretools,includingTIMES(https://iea-etsap.org/index.php/etsap-tools/model-generators/times).DatainputsTheGECModelisadata-intensivemodelcoveringthewholeglobalenergysystem.Muchofthedatatocalibratetohistoricalenergysupply,transformationanddemand,aswellasenergyprices,isobtainedfromtheIEA’sowndatabasesofenergyandeconomicdata.Additionaldatafromawiderangeofoftensector-specificexternalsourcesisalsousedinparticulartoestablishhistoricsizeandperformanceofenergy-consumingstocks.Themodeliseachyearrecalibratedtothelatestavailabledata.Theformalbaseyeariscurrently2020,asthisisthelastyearforwhichacompletepictureofenergydemandandproductionisinplace.However,wehaveusedmorerecentdatawhereveravailable,andweinclude2021and2022estimatesforenergyproductionanddemand.EstimatesarebasedonupdatesoftheGlobalEnergyReviewreportswhichreliesonanumberofsources,includingthelatestmonthlydatasubmissionstotheIEAEnergyDataCentre,otherstatisticalreleasesfromnationaladministrations,andrecentmarketdatafromtheIEAMarketReportSeriesthatcovercoal,oil,naturalgas,renewablesandelectricity.Forasummaryofselectedkeydatainputs–includingmacrodriverssuchaspopulation,economicdevelopmentsandpricesaswellastechno-economicinputssuchasfossilfuelresourcesortechnologycosts–pleaseviewtheGlobalEnergyandClimateModelkeyinputdataset(https://www.iea.org/data-and-statistics/data-product/global-energy-and-climate-model-2022-key-input-data).Section1Overviewofmodelandscenarios15RegionalcoverageandtimehorizonTheGECModelcoverstheenergydevelopmentsinthefullglobalenergysystemupto2050,withthecapacitytoextendbeyond2050forsomeregions.Simulationsarecarriedoutonanannualbasis.Thecurrentversionofthemodelprovidesresultsfor26regionsoftheglobe,ofwhich12areindividualcountries.Severalsupplycomponentsofthemodelhavefurtherregionaldisaggregation:theoilandgassupplymodelhas113regionsandthecoalsupplymodel32regions.CapabilitiesandfeaturesIEA’sGECModeloffersunparalleledscopeanddetailontheenergysystem.Itsraisond'êtreisevaluatingenergysupplyanddemand,aswellastheenvironmentalimpactsofenergyuseandtheimpactsofpolicyandtechnologydevelopmentsontheenergysystem.Throughlong-termscenarioanalysis,themodelenablesanalysisofpossiblefuturesrelatedtothefollowingmainareas:◼Globalandregionalenergytrends:thisincludesassessmentofenergydemand,supplyavailabilityandconstraints,internationaltradeandenergybalancesbysectorandbyfuel.◼Environmentalimpactofenergyuse:CO2emissionsfromfuelcombustionarederivedfromtheprojectionsofenergyconsumption.CO2processemissionsarecalculatedbasedontheproductionofindustrialmaterialsandCH4andN2Oemissionsareassessedforfinalenergydemandaswellasforenergytransformation.Methanefromoilandgasoperationsareassessedthroughbottom-upestimatesanddirectemissionsmeasurements(seeMethaneTracker).ThisallowstopublishtheCO2-equivalentemissionsfortheentireenergysector.LocalairpollutantsarealsoestimatedlinkingtheGECModelwiththeGAINSmodeloftheInternationalInstituteforAppliedSystemsAnalysis(IIASA)andthetemperatureoutcomesofmodelledscenariosareassessed.◼Policyandtechnologydevelopments:alternativescenariosanalysetheimpactofarangeofpolicyactionsandtechnologicaldevelopmentsonenergydemand,supply,trade,investmentsandemissions.Additionally,theGECModelhasmultipledetailedfeaturesthateitherunderlyingorbuildfromanalysisofthebroaderenergytrends.Theseincludethefollowing:◼Technologies:Detailedtechno-economiccharacterisationofcleanenergytechnologiesunderdevelopment(eitheratprototypeordemonstrationstage)includingdifferentapplicationsinheavyindustries,longdistancetransportandcarbondioxideremovaltechnologiesamongmorethan800hundredtechnologiescovered.◼People-centred:Detailedmodellingofbehaviouralchanges,energysectoremploymentandenergyaffordabilityamongotherimplicationsforcitizens.◼Criticalminerals:Comprehensiveanalysisofprojecteddemandandsupplyofcriticalmineralsfortheenergysector’stransition.◼Infrastructure:Detailedmodellingandanalysisonenablingenergyinfrastructuredevelopmentneedsandstrategiesincluding:electricitysystems,fossilfuels,hydrogen-relatedfuelsdistributionandCO2transportoptions.◼Variablerenewablespotential:Detailedgeospatialanalysisofvariablerenewablespotentialsacrosstheglobeandmodellingoftheirimpactofexploitingthoseforhydrogenproduction.◼Modernenergyaccess:Comprehensivemodellingoftheimplicationsandopportunitiestoprovideenergyaccesstoallcommunities.Thisincludesaccesstoelectricityandcleancookingfacilities,andanevaluationofadditionalenergydemand,investmentsandrelatedgreenhousegasemissions.16InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION◼Materialefficiency:Granularmodellingofstrategiesalongsupplychainstomakemoreefficientuseofmaterialslikesteel,cement,aluminium,plasticsandfertilisers,andtheirresultingimpactonmaterialsdemand.◼Investments:Detailedmodellingofoverallenergysectorandcleanenergyinvestmentsbysub-sectorandtechnologyareas,andcomprehensiveanalysisoneffectivefinancingstrategies.Thisincludesinvestmentrequirementsinfuelsupplychainstosatisfyprojectedenergydemandandfordemand-sidetechnologiesandmeasures(e.g.,energyefficiency,electrification).Governmentspendingisalsotracked.◼Decomposition:Detailedmathematicalframeworktoanalysesystematicallythespecificcontributionofdifferentstrategiestoemissionsorenergysavingsbetweenscenariosandovertime.ConnectionswiththeinternationalenergymodellingcommunityThedevelopmentoftheGECModelbenefitsfromexpertreviewwithintheIEAandbeyondandtheIEAworkscloselywithcolleaguesintheglobalmodellingcommunity.Forexample,theIEAparticipatesinandregularlyhoststheInternationalEnergyWorkshop,andtheanalysisfortheNetZeroEmissionsby2050Scenariowasinformedbydiscussionswithmodellingteamsfromacrosstheworld,includingfromChina,theUnitedStates,Japan,theUnitedKingdom,theEuropeanUnionandtheIPCC.TheIEAalsohasalong-standinghistoryofworkingwithresearchersandmodellersaroundtheworldaspartofitsTechnologyCollaborationProgrammes(TCP)network.TheTCPssupporttheworkofindependent,internationalgroupsofexpertsthatenablegovernmentsandindustriesfromaroundtheworldtoleadprogrammesandprojectsonawiderangeofenergytechnologiesandrelatedissues.TheEnergyTechnologySystemsAnalysis(ETSAP)TCP,establishedin1977,isamongthelongestrunningTCPs.Itsmissionistosupportpolicymakersinimprovingtheevidencebaseunderpinningenergyandenvironmentalpolicydecisionsthroughenergysystemsmodellingtoolsandcapabilitythroughauniquenetworkofnearly200energymodellingteamsfromapproximatelyseventycountries.TheETSAPTCPdevelops,improvesandmakesavailabletheTIMESenergysystemsmodellingplatform.IEA’sGECModelalsointeractscloselywithotherinternationallyrecognisedmodels:◼TheIEAusestheModelfortheAssessmentofGreenhouseGasInducedClimateChange(MAGICC),developedandmaintainedbyClimateResourceandoftenusedbyIPCCforkeypublications,toinformitsanalysisontheimpactofdifferentgreenhousegasesbudgetsontheaverageglobaltemperaturerise.◼IEAmodellingresultsarecoupledwiththeGreenhouseGas–AirPollutionInteractionsandSynergies(GAINS)modeldevelopedandmaintainedbyInternationalInstituteforAppliedSystemsAnalysis(IIASA).ThisallowsfordetailedanalysisontheimpactonairpollutionofdifferentIEAscenarios.◼IEAresultsarecoupledwiththeGlobalBiosphereManagementModel(GLOBIOM)developedandmaintainedbyIIASAtocomplementIEA’sanalysisonbioenergysuppliesandeffectiveusestrategies.◼TheAviationIntegratedModel(AIM)developedbyUniversityCollegeLondon(UCL)formsthebasisforourmodellingoftheaviationsector.◼IEAmodellingresultshavebeenlinkedtotheGlobalIntegratedMonetaryandFiscal(GIMF)modeloftheInternationalMonetaryFund(IMF)toassesstheimpactsofchangesininvestmentandspendingonglobalGDP.◼TheOpenSourceSpatialElectrificationTool(OnSSET),aGIS-basedoptimisationtooldevelopedoutofacollaborationamongseveralorganisation,isusedtoinformtheIEA’senergyaccessmodelling.Section2Cross-cuttinginputsandassumptions17Section22Cross-cuttinginputsandassumptionsTheGlobalEnergyandClimateModel(GECModel)usesmacrodrivers,techno-economicinputsandpoliciesasinputdatatodesignandcalculatethescenarios.EconomicactivityandpopulationarethetwofundamentaldriversofdemandforenergyservicesinGECModelscenarios.Unlessotherwisespecified,thesearekeptconstantacrossallscenariosasameansofprovidingastartingpointfortheanalysisandfacilitatingtheinterpretationoftheresults.Energypricesareanotherimportantinput.Theprojectionsconsidertheaverageretailpricesofeachfuelusedinfinaluses,powergenerationandothertransformationsectors.Theseend-usepricesarederivedfromprojectedinternationalpricesoffossilfuelsandsubsidy/taxlevelsandvarybycountry.2.1PopulationassumptionsTable2.1⊳PopulationassumptionsbyregionCompoundaverageannualgrowthratePopulation(million)Urbanisation(Shareofpopulation)2000-212021-302021-50202120302050202120302050NorthAmerica0.9%0.6%0.5%50253258082%84%89%UnitedStates0.8%0.5%0.4%33535238183%85%89%CentralandSouthAmerica1.1%0.7%0.5%52355960181%83%88%Brazil1.0%0.5%0.2%21422422987%89%92%Europe0.3%0.0%-0.1%70070169076%78%84%EuropeanUnion0.2%-0.1%-0.2%45144842975%77%84%Africa2.5%2.3%2.1%13721686248744%48%59%MiddleEast2.1%1.5%1.1%25228934873%76%81%Eurasia0.4%0.3%0.2%23724425365%67%73%Russia-0.1%-0.2%-0.2%14414213475%77%83%AsiaPacific1.0%0.6%0.4%42504496473450%55%65%China0.5%0.2%-0.1%14231443138363%71%80%India1.3%0.8%0.6%13931504163935%40%53%Japan-0.1%-0.5%-0.6%12512010592%93%95%SoutheastAsia1.2%0.8%0.6%67472679251%56%66%World1.2%0.9%0.7%78358507969257%60%68%Source:UNDESA(2018,2019);WorldBank(2022a);IEAdatabasesandanalysis.RatesofpopulationgrowthforeachGECModelregionarebasedonthemedium-fertilityvariantprojectionscontainedintheUnitedNationsPopulationDivisionreport(UNDESA,2019)1.Inthe2022GECmodellingcycle,populationrisesfrom7.8billionin2021tomorethan9.6billionin2050.Populationgrowthslowsovertheprojectionperiod,inlinewithpasttrends:from1.2%peryearin2000-2021to0.9%in2021-2030,dueinlargeparttofallingglobalfertilityratesasaverageincomesrise.1TheWorldPopulationProspects2022fromUNDESAwaspublishedatatimewhenthemodellingwasalreadywelladvancedforthiscycle.18InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONMorethanhalfoftheincreaseintheglobalpopulationto2050isinAfrica,underliningtheimportanceofthiscontinenttotheachievementoftheworld’ssustainabledevelopmentgoals.Indiaaccountsforalmost15%ofthegrowthandbecomestheworld’smostpopulouscountryintheneartermasChina’spopulationgrowthstalls.Estimatesoftherural/urbansplitforeachGECModelregionhavebeentakenfromUNDESA(2018).Thisdatabaseprovidesthepercentageofpopulationresidinginurbanareasbycountrywithannualgranularityovertheprojectionhorizon.BycombiningthisdatawiththeUNpopulationprojectionsanestimateoftherural/urbansplitmaybecalculated.In2021,about57%oftheworldpopulationisestimatedtobelivinginurbanareas.Thisisexpectedtoriseto68%by2050.2.2MacroeconomicassumptionsTable2.2⊳RealGDPaveragegrowthassumptionsbyregionandscenarioCompoundaverageannualgrowthrate2010-20212021-20302030-20502021-2050NorthAmerica1.9%2.0%2.0%2.0%UnitedStates2.0%2.0%2.0%2.0%CentralandSouthAmerica0.9%2.4%2.4%2.4%Brazil0.7%1.8%2.5%2.3%Europe1.6%2.0%1.4%1.6%EuropeanUnion1.2%1.9%1.2%1.4%Africa2.7%4.1%4.2%4.1%SouthAfrica1.1%1.6%2.8%2.4%MiddleEast2.0%3.2%3.2%3.2%Eurasia2.1%0.1%1.4%1.0%Russia1.7%-1.1%0.7%0.1%AsiaPacific4.9%4.7%3.1%3.6%China6.8%4.7%2.8%3.4%India5.5%7.2%4.4%5.2%Japan0.5%0.9%0.6%0.7%SoutheastAsia4.1%5.0%3.3%3.8%World2.9%3.3%2.6%2.8%Note:CalculatedbasedonGDPexpressedinyear-2021USdollarsinpurchasingpowerparityterms.Source:IEAanalysisbasedonOxfordEconomics(2022)andIMF(2022).EconomicgrowthassumptionsfortheshorttomediumtermarearebroadlyconsistentwiththelatestassessmentsfromtheIMFandOxfordEconomics.Overthelongterm,growthineachGECModelregionisassumedtoconvergetoanannuallong-termrate.Thisisdependentondemographicandproductivitytrends,macroeconomicconditionsandthepaceoftechnologicalchange.InGECModel2022scenarios,thegrowthtrajectoryremainspositive,butmuchlesssothanayearagowhenglobalaggregatedemandwasexperiencingnearrecordgrowthinresponsetotheremovalofpandemiclockdownsandrestrictionsbeingeasedinmanycountries.Theglobaleconomyisassumedtogrowby2.8%peryearonaverageovertheperiodto2050,withlargevariationsbycountry,byregionandovertime(Table2.2).Thisgrowthisprimarilydrivenbyemergingmarketanddevelopingeconomies.Overthenearterm,thegrowthtrajectoryincludestheimpactofRussia’sinvasionofUkraineandrisinginflation.Thereare,however,downsideSection2Cross-cuttinginputsandassumptions19risksfortheoutlookto2030resultingfromhigherinterestrates,amoodofinsecurityholdingbackinvestmentdecisionsandspendingonhouseholddurables,anduncertaintyastowhethermacroeconomicauthoritiesareabletocontaininflationandavoidaprice-wagespiral.Theassumedratesofeconomicgrowthareheldconstantacrossthescenarios,whichallowsforacomparisonoftheeffectsofdifferentenergyandclimatechoicesagainstacommonbackdrop.Thewaythateconomicgrowthplaysthroughintoenergydemanddependsheavilyonthestructureofanygiveneconomy,theexposureandresiliencetoshocks,thebalancebetweendifferenttypesofindustry,servicesandagriculture,andonpoliciesinareassuchaspricingandenergyefficiency.2.3PricesInternationalfossilfuelpricesTable2.3⊳FossilfuelpricesbyscenarioNetZeroEmissionsby2050AnnouncedPledgesStatedPoliciesRealterms(USD2021)20102021203020502030205020302050IEAcrudeoil(USD/barrel)9669352464608295Naturalgas(USD/MBtu)UnitedStates5.33.91.91.83.72.64.04.7EuropeanUnion9.09.54.63.87.96.38.59.2China8.010.16.15.18.87.49.810.2Japan13.310.26.05.19.17.410.910.6Steamcoal(USD/tonne)UnitedStates6344221742244644EuropeanUnion113120524262536064Japan132153594674599172CoastalChina142164584873628974Notes:MBtu=millionBritishthermalunits.TheIEAcrudeoilpriceisaweightedaverageimportpriceamongIEAmembercountries.Naturalgaspricesareweightedaveragesexpressedonagrosscalorific-valuebasis.TheUSnaturalgaspricereflectsthewholesalepriceprevailingonthedomesticmarket.TheEuropeanUnionandChinanaturalgaspricesreflectabalanceofpipelineandLNGimports,whiletheJapangaspricesolelyreflectsLNGimports.TheLNGpricesusedarethoseatthecustomsborder,priortoregasification.Steamcoalpricesareweightedaveragesadjustedto6000kilocaloriesperkilogramme.TheUSsteamcoalpricereflectsminemouthpricesplustransportandhandlingcosts.CoastalChinasteamcoalpricereflectsabalanceofimportsanddomesticsales,whiletheEuropeanUnionandJapanesesteamcoalpricesaresolelyforimports.Source:IEAGECModel2022.Internationalpricesforcoal,naturalgasandoilintheGECModelreflectthepricelevelsthatareneededtostimulatesufficientinvestmentinsupplytomeetprojecteddemand.Theyareoneofthefundamentaldriversfordeterminingfossilfueldemandandsupplyprojectionsinallsectorsandarederivedthroughiterativemodelling.Thesupplymodulescalculatetheproductionofcoal,naturalgasandoilthatisstimulatedunderagivenpricetrajectory,takingintoaccountthecostsofvarioussupplyoptionsandtheconstraintsonresourcesandproductionrates.Ifpricesaretoolowtoencouragesufficientproductiontocoverglobaldemand,thepricelevelisincreasedandenergydemandisrecalculated.Thenewdemandresultingfromthisiterativeprocessisagainfedbackintothesupplymodulesuntilabalancebetweendemandandsupplyisreachedforeachprojectedyear.20InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONThepricetrajectoriesdonotattempttorepresentthefluctuationsandpricecyclesthatcharacterisecommoditymarketsinpractice.Thepotentialforvolatilityiseverpresent,especiallyinsystemsthatareundergoinganecessaryandprofoundtransformation.Fossilfuelpricepathsvaryacrossthescenarios(Table2.3).Forexample,intheStatedPoliciesScenario,althoughpoliciesareadoptedtoreducetheuseoffossilfuels,demandisstillhigh.ThatleadstohigherpricesthanintheAnnouncedPledgesScenarioandtheNetZeroEmissionsby2050Scenario,wherethelowerenergydemandmeansthatlimitationsontheproductionofvarioustypesofresourcesarelesssignificantandthereislessneedtoproducefossilfuelsfromresourceshigherupthesupplycostcurve.CO2pricesTable2.4⊳CO2pricesforelectricity,industryandenergyproductioninselectedregionsbyscenarioUSD(2021)pertonneofCO2203020402050StatedPoliciesScenarioCanada546277Chile,Colombia132129China284353EuropeanUnion9098113Korea426789AnnouncedPledgesScenarioAdvancedeconomieswithnetzeroemissionspledges1135175200Emergingmarketanddevelopingeconomieswithnetzeroemissionspledges240110160Otheremergingmarketanddevelopingeconomies-1747NetZeroEmissionsby2050ScenarioAdvancedeconomieswithnetzeroemissionspledges140205250Emergingmarketanddevelopingeconomieswithnetzeroemissionspledges90160200Otheremergingmarketanddevelopingeconomies25851801IncludesallOECDcountriesexceptMexico.2IncludesChina,India,Indonesia,BrazilandSouthAfrica.Note:Thevaluesarerounded.Source:IEAGECModel2022.CO2priceassumptionsareoneoftheinputsintoGECModelasthepricingofCO2emissionsaffectsdemandforenergybyalteringtherelativecostsofusingdifferentfuels.Therewere68directcarbonpricinginstrumentsexistingasofMay2022:32emissionstradingsystemsand38carbontaxesonfuelsaccordingtotheirrelatedemissionswhencombusted,coveringmorethan40countries.Manyothershaveschemesunderdevelopmentorareconsideringtodoso.TheStatedPoliciesScenariotakesintoconsiderationallexistingorannouncedcarbonpricingschemes,atnationalandsub-nationallevel,coveringelectricitygeneration,industry,energyproductionsectorsandend-usesectors,e.g.,aviation,roadtransportandbuildings,whereapplicable.IntheAnnouncedPledgesScenario,higherCO2pricesareintroducedacrossallregionswithnetzeroemissionspledges.Inaddition,severaldevelopingeconomiesareassumedtoputinplaceschemestolimitCO2emissions.Allregionalmarketshaveaccesstooffsets,whichisexpectedtoleadtoaconvergenceofprices.Noexplicitpricingisassumedinsub-SaharanAfrica(excludingSouthAfrica),theCaspianregionandOtherAsiaregions.Instead,theseregionsrelyondirectpolicyinterventionstodrivedecarbonisationintheAPS.IntheNetZeroEmissionsby2050Scenario,CO2pricescoverallregionsandriserapidlyacrossalladvancedeconomiesaswellasinemergingeconomieswithnetzeroSection2Cross-cuttinginputsandassumptions21emissionspledges,includingChina,India,Indonesia,BrazilandSouthAfrica.CO2pricesarelower,butnevertheless,risinginotheremergingeconomiessuchasNorthAfrica,MiddleEast,RussiaandSoutheastAsia.CO2pricesarelowerinallotheremergingmarketanddevelopingeconomies,asitisassumedtheypursuemoredirectpoliciestoadaptandtransformtheirenergysystems(Table2.4).End-userpricesFuelend-usepricesForeachsectorandGECModelregion,arepresentativeprice(usuallyaweightedaverage)isderivedtakingintoaccounttheproductmixinfinalconsumptionanddifferencesbetweencountries.Internationalpriceassumptionsarethenappliedtoderiveaveragepre-taxpricesforcoal,oil,andgasovertheprojectionperiod.Excisetaxes,valueaddedtaxrates,subsidiesandCO2prices(whereapplicable)aretakenintoaccountincalculatingaveragepost-taxpricesforallfuels.Inallcases,theexcisetaxesandvalueaddedtaxratesonfuelsareassumedtoremainunchangedovertheprojectionperiod.Weassumethatenergy-relatedconsumptionsubsidiesaregraduallyreducedovertheprojectionperiod,thoughatvaryingratesacrosstheGECModelregionsandthescenarios.IntheAnnouncedPledgesScenarioandtheNetZeroEmissionsby2050Scenario,theinternationaloilpricedropsincomparisontotheStatedPoliciesScenarioduetolowerdemandforoilproducts.Inordertocounteractareboundeffectinthetransportsectorfromlowergasolineanddieselprices,anincreaseoffueldutyontopofCO2priceisappliedwheneverisnecessaryforensuringthatend-userpricesarekeptatleastatthesamelevelasintheStatedPoliciesScenario.AllpricesareexpressedinUSdollarsandassumenochangeinexchangerates.Electricityend-usepricesThemodelcalculateselectricityend-usepricesasasumofthewholesaleelectricityprice,systemoperationcost,transmission&distributioncosts,supplycosts,andtaxesandsubsidies(Figure2.1).Figure2.1⊳Componentsofretailelectricityend-usepricesIEA.CCBY4.0.Thereisnosingledefinitionofwholesaleelectricityprices,butintheGlobalEnergyandClimateModelthewholesalepricereferstotheaveragepricepaidtogeneratorsfortheiroutput.Foreachregion,wholesaleelectricitypricearederivedundertheassumptionthatallplantsoperatinginagivenyearrecoverthefullcosts–fixedcostsaswellasvariablecosts–ofelectricitygenerationandstorage.Thekeyregion-specificfactorsaffectingwholesalepricesaretherefore:22InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION◼Theupfrontcapitalinvestmentandfinancingcostsofelectricitygenerationandstorageplants;◼Theoperationandmaintenancecostsofelectricitygenerationandstorageplants;and◼Thevariableofcoal,naturalgas,oilandotherfuelsinputsand,ifapplicable,CO2costofgenerationplants’output.Systemoperationcostsaretakenfromexternalstudiesandareincreasedinthepresenceofvariablerenewablesinlinewiththeresultsofthesestudies.Transmissionanddistributiontariffsareestimatedbasedonaregulatedrateofreturnonassets,assetdepreciationandoperatingcosts.Supplycostsareestimatedfromhistoricdata,andtaxesandsubsidiesarealsotakenfromthemostrecenthistoricdata,withsubsidyphase-outassumptionsincorporatedovertheOutlookperiodinlinewiththerelevantassumptionsforeachscenario.SubsidiestofossilfuelsTheIEAmeasuresfossilfuelconsumptionsubsidies2usingaprice-gapapproach.Thiscomparesfinalend-userpriceswithreferenceprices,whichcorrespondtothefullcostofsupply,or,whereappropriate,theinternationalmarketprice,adjustedforthecostsoftransportationanddistribution.Theestimatescoversubsidiestofossilfuelsconsumedbyend-usersandsubsidiestofossil-fuelinputstoelectricitygeneration.Theprice-gapapproachisdesignedtocapturetheneteffectofallsubsidiesthatreducefinalpricesbelowthosethatwouldprevailinacompetitivemarket.However,estimatesproducedusingtheprice-gapapproachdonotcapturealltypesofinterventionsknowntoexist.They,therefore,tendtobeunderstatedasabasisforassessingtheimpactofsubsidiesoneconomicefficiencyandtrade.Despitetheselimitations,theprice-gapapproachisavaluabletoolforestimatingsubsidiesandforundertakingcomparativeanalysisofsubsidylevelsacrosscountriestosupportpolicydevelopment(Koplow,2009).2.4PoliciesInordertounderpinscenarioanalysisoftheGECModel,anextensiveeffortismadetoupdateandexpandthelistofenergyandclimate-relatedpoliciesandmeasuresthatfeedintoourmodelling.Assumptionsaboutgovernmentpoliciesarecriticaltothisanalysisandarethemainreasonforthedifferencesinoutcomesacrossthescenarios.TwonotableIEApolicytrackingeffortsinputintothescenarios:◼PoliciesandMeasuresdatabase:TheIEA’sPoliciesandMeasuresDatabaseprovidesaccesstoinformationonpast,existingorplannedgovernmentpoliciesandmeasurestoreducegreenhousegasemissions,improveenergyefficiencyandsupportthedevelopmentanddeploymentofrenewablesandothercleanenergytechnologies.ThisuniquepolicydatabasebringstogetherdatafromtheIEA’sSustainableRecoveryTracker,IEA/IRENARenewableEnergyPoliciesandMeasuresDatabase,theIEAEnergyEfficiencyDatabase,theAddressingClimateChangedatabase,andtheBuildingEnergyEfficiencyPolicies(BEEP)database,alongwithinformationonCCUSandmethaneabatementpolicies.Thispolicyinformationhasbeencollectedsince1999fromgovernments,partnerorganisationsandIEAanalysis.Governmentshaveanopportunitytoreviewthepolicyinformationperiodically.◼SDG7database:TheInternationalEnergyAgencyisattheforefrontofglobaleffortstoassessandanalysepersistentenergyaccessdeficit,providingannualcountry-by-countrydataonaccesstoelectricityandcleancooking(SDG7.1)andthemaindatasourcefortrackingofficialprogresstowardsSDGtargetsonrenewables(SDG7.2)andenergyefficiency(SDG7.3).TheIEAisoneoftheappointedco-custodiansfortrackingglobalprogressonSDG7alongsideIRENA,UNSD,theWorldBank,andWHO.2https://www.iea.org/topics/energy-subsidiesSection2Cross-cuttinginputsandassumptions23Intotal,newpoliciesandmeasuresgloballyhavebeenconsideredduringthemodelpreparation,includingrecentannouncementssuchastheInflationReductionAct(UnitedStates),Fitfor55(EuropeanUnion),ClimateChangeBill(Australia),andGXGreenTransformation(Japan)aswellasgovernmentalspendingasareactiontothecurrentenergycrisis.ThenationalnetzeroemissionspledgesannouncedbyIndiaandIndonesiaarealsoimportantchangescomparedtolastyear.AsummaryofsomeofthekeypolicytargetsandmeasuresfordifferentsectorsbyselectedcountriesandregionscanbefoundintheAnnexBofWEO-2022.Theconsideredpoliciesareadditiveacrossscenarios:measureslistedundertheAnnouncedPledgesScenario(APS)supplementthoseintheStatedPoliciesScenario(STEPS).Inaddition,separatepolicyassumptionsaregivenfortheNetZeroEmissionsby2050Scenario(NZE)whichprovideindicativepolicymakinganddecarbonisingmilestonesthatwouldsteerglobalenergysystemstotheseoutcomes.Thepublishedtablesbeginwithbroadcross-cuttingpolicyframeworks,followedbymoredetailedpoliciesbysector:power,industrybuildings,andtransport.Thetableslistonlythe“newpolicies”enacted,implementedorrevisedsincethelastpublicationcycle2021.Someregionalpolicieshavebeenincludediftheyplayasignificantroleinshapingenergyataglobalscale(e.g.regionalcarbonmarkets,standardsinverylargeprovincesorstates).Thetablesdonotincludeallpoliciesandmeasures,rathertheyhighlightthepoliciesmostshapingglobalenergydemandtoday,whilebeingderivedfromanexhaustiveexaminationofannouncementsandplansincountriesaroundtheworld.2.5Techno-economicinputsIncorporationofadiverserangeoftechnologiesisakeyfeatureoftheGECModel.Extensiveresearchisundertakentoupdatetherangeoftechnologiesinthemodel,aswellastheirtechno-economicassumptions.TheGECModelincludesthebreadthoftechnologiesthatareavailableonthemarkettoday.Additionally,themodelintegratesinnovativetechnologiesandindividualtechnologydesignsthatarenotyetavailableonthemarketatscalebycharacterisingtheirmaturityandexpectedtimeofmarketintroduction.Foreachsectorandtechnologyarea,newprojectannouncementsandimportanttechnologicaldevelopmentsaretrackedindatabasesthatareregularlypublished.Themodelledscenariosareinformedbysuchdetailedtechnologytrackingprocess.Forinstance,theprojectplanningfinancingstatusisanimportantconsiderationforwhetherprojectsarereflectedinSTEPSorratherinAPS.Fortechnologydevelopmentprogressandthetimetobringnewtechnologiestomarkets,thescenariosassumedifferentpaceofprogressasthesupportanddegreeofinternationalcooperationoncleanenergyinnovationincreaseswiththeambitionindecarbonisation.Thefollowingdatabasesareparticularlyrelevantforthedefinitionofthedifferentscenarios:◼Cleaninnovativetechnologiestracking:•CleanTechnologyGuide:interactivedatabasethattracksthetechnologyreadinesslevel(TRL)ofover500individualtechnologydesignsandcomponentsacrossthewholeenergysystemthatcontributetoachievingthegoalofnet-zeroemissions.TheGuideisupdatedeveryyear.•CleanEnergyDemonstrationProjectsDatabase:newlylaunchedin2022,thatprovidesmoredetailedtrackingofthelocation,status,capacity,timingandfunding,ofover400demonstrationprojectsacrosstheenergysector.•TrackingCleanEnergyProgress:annualtrackingofdevelopmentsfor55componentsoftheenergysystemthatarecriticalforcleanenergytransitionsandtheirprogresstowardsshort-term2030milestonealongthetrajectoryoftheNetZeroby2050Scenario.24InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION◼HydrogenProjectsDatabase:coversallprojectscommissionedworldwidesince2000toproducehydrogenforenergyorclimate-change-mitigationpurposes.◼GlobalEVOutlook:annualpublicationthatidentifiesanddiscussesrecentpolicyandmarketdevelopmentsinelectricmobilityacrosstheglobe.ItisdevelopedwiththesupportofthemembersoftheCleanEnergyMinisterialElectricVehiclesInitiative(EVI).Technologycostsareanimportantinputtothemodel.Allcostsrepresentfullyinstalled/deliveredtechnologies,notsolelytheequipmentcost,unlessotherwisenotedasforfuelcells.Installed/deliveredcostsincludeengineering,procurementandconstructioncoststoinstalltheequipment.Someillustrativeexamplesincludethefollowing:◼Industrycostsreflectaverageironandsteelproductioncostsforagiventechnologyanddifferentiatebetweenconventionalandinnovativeproductionroutes.◼ElectricVehiclecostsreflectproductioncosts,notretailprices,tobetterreflectthecostdeclinesintotalcostofmanufacturing,whichmoveindependentlyoffinalmarketpricesforelectricvehiclestocustomers.Fortheglobalaveragebatterypacksize,historicalvaluesin2021havebeenused.Inhybridcars,thefuturecostincreaseisdrivenbyregionalfueleconomyandemissionsstandards.◼Electrolysercostsreflectaprojectedgloballyweightedaverageofinstalledelectrolysertechnologies(excludingChina,wherelowercostsareassumed),includinginverters.◼Fuelcellcostsarebasedonstackmanufacturingcostsonly,notinstalled/deliveredcosts.Thecostsprovidedareforautomotivefuelcellstacksforlight-dutyvehicles.◼Utility-scalestationarybatterycostsreflecttheaverageinstalledcostsofallbatterysystemsratedtoprovidemaximumpoweroutputforafour-hourperiod.Table2.5⊳CapitalcostsforselectedtechnologiesbyscenarioStatedPoliciesAnnouncedPledgesNetZeroEmissionsby20502021203020502030205020302050Primarysteelproduction(USD/tpa)Conventional640650660650670650680Innovativen.a.1400105013309801020910Vehicles(USD/vehicle)Hybridcars16122146861486114528147181446014638Batteryelectriccars21322157721418515265136181478313251BatteriesandhydrogenHydrogenelectrolysers(USD/kW)1505575445390265315230Fuelcells(USD/kW)100604050354530Utility-scalestationarybatteries(USD/kWh)285185135185135180135Notes:kW=kilowatt;tpa=tonneperannum;kWh=kilowatt-hour;n.a.=notapplicable.AllvaluesareinUSD(2021).Sources:IEAanalysis;Jameset.al.(2018);Thompson,etal.(2018);FinancialTimes(2020);BNEF(2021);Coleetal.(2020);Tsiropoulosetal.(2018);Section3End-usesectors25Section33End-usesectorsAll26regionsaremodelledinconsiderablesectoralandend-usedetail.Specifically:◼Industryiscomposedoffiveenergy-intensiveandeightnon-energy-intensivesub-sectors;◼Buildingsisseparatedintoresidentialandservicesbuildings,withelevenend-usesmodelledseparately;◼Transportisseparatedintoninemodeswithconsiderabledetailforroadtransport;◼Agriculturemodellingreflectstherangeoffuelsandenergyconsumingapplicationsinthesector.Totalfinalenergydemandisthesumofenergyconsumptionineachfinaldemandsector.Ineachsub-sectororend-use,atleastseventypesofenergyareshown:coal,oil,gas,electricity,heat,hydrogenandrenewables.Themainoilproducts–liquefiedpetroleumgas(LPG),naphtha,gasoline,kerosene,diesel,heavyfueloil(HFO)andethane–aremodelledseparatelyforeachfinalsectors.Demand-sidedrivers,suchassteelproductioninindustryorhouseholdsizeindwellings,areestimatedeconometricallybasedonhistoricaldataandonsocioeconomicdrivers(GDPandpopulation).Allend-usesectormodulesbasetheirprojectionsontheexistingstockofenergyinfrastructure.Thisincludesthenumberofvehiclesintransport,productioncapacityinindustry,andfloorspaceareainbuildings.Totakeintoaccountexpectedchangesinstructure,policyortechnology,awiderangeoftechnologiesareintegratedinthemodelthatcansatisfyeachspecificenergyservice.End-userfuelpricesandtechnologycostsplayanimportantroleindeterminingthedistributionoftechnologiesandfuels,althoughreal-worldnon-costinfluencesalsoplayarole.Respectingtheefficiencylevelofallend-usetechnologiesgivesthefinalenergydemandforeachsectorandsub-sector(Figure3.1).Figure3.1⊳GeneralstructureofdemandmodulesIEA.CCBY4.0.3.1IndustrysectorTheoriginsoftheGECindustrysectormodelaretheindustrysectormodulesoftheformerWEM(simulation)andtheETP(TIMESoptimisation)models,bothnowsupersededbytheGECframework.TheGECindustrysectormodelcombinesthestrengthsofeachoftheseformermodelsintoasinglesimulationframework,withitsconstraintsandinputparametersinformedby,amongotherthings,periodicmodelrunsoftheformerETPTIMESoptimisationframework.Theresultofthesedevelopmentsin2022isatechnology-rich,optimisation-informed,simulationmodel,fullyintegratedintothebroaderGECModelframework.TheGECindustrymodelisimplementedinVensim,usingthe26GECmodelregions(activitymodellingisconductedatthecountrylevel),inannualtime-steps.IndustrymodelcoverageandapproachForthepurposesoftheGECindustrymodel,theindustrialsectorincludesInternationalStandardIndustrialClassification(ISIC)Divisions7,8,10-18,20-32and41-43,andGroup099,coveringminingandquarryingDriversEconometricanalysisEnergyservicedemand(demandforusefulenergy)Least-costapproachTechnology/fuelallocationEfficiencylevelsFinalenergydemand26InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION(excludingminingandextractionoffuels),construction,andmanufacturing.ThiscoveragefollowsthestructureoftheIEAEnergyBalances,coveringalloftheindustrycomponentsoftotalfinalconsumption.Chemicalfeedstock(acomponentofnon-energyuse)andblastfurnaceandcokeovenenergyuse(bothtransformationandownuse)arealsoincludedwithintheboundariesofindustry.Asidefrompetrochemicalfeedstock,othernon-energyuseisnotincludedintheGECModel’sindustrysectorboundary,butratherismodelledasaseparatecategoryinthesameframework.Figure3.2⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinindustryIEA.CCBY4.0.Theindustrysectorismodelledusingahybridapproach(Figure3.2).Technology-richsimulationmodels,informedbyperiodicmodelrunsoftheformerETPTIMESoptimisationframework,areusedforfiveenergy-intensivesub-sectorscomponentsthereof(ironandsteel;primarychemicalswithinchemicalsandpetrochemicals;cementwithinnon-metallicminerals;aluminiumwithinnon-ferrousmetals;paper,pulpandCCUSoptions(cross-cutting)Technology-richenergy-intensivesub-sectormodelsIronandsteelChemicalsandpetrochemicalsNon-metallicmineralsNon-ferrousmetalsPaper,pulpandprintingCross-sectoralconversiondevicesimulationmodelMaterialandfuelpreparation•Cokeovens(cokedryquenchingoption)•Sintering•PelletisingIronproduction•Blastfurnaces(topgasrecovery,toppressurerecovery,hydrogenamplification,charcoalandhydrogen/biomassblendingoptions)•Smeltreduction•Directreducediron(electrolysisoption)Steelproduction•Basicoxygenfurnace•Openhearthfurnace•Electricarcfurnace•InductionfurnaceRawmaterialandfuelgrinding•Ballmill•Rollerpress&ballmill•VerticalmillClinkerproduction•Drykilns•Wetkilns•Verticalshaftkilns•ElectrickilnsFinishedcementgrinding•Ballmill•Rollerpressandballmill•VerticalmillAluminarefining•Bayerprocess•Bayer-Sinterprocess•SinterprocessAluminiumproduction•Hall-Héroultsmelting(inertanodeoption)•Soderbergsmelting•Secondaryfurnaces(inductionfurnaceandreverbatoryfurnaceoptions)Finishing•Coldrolling•Extrusion•Hotrolling•ShapecastingPulpproduction•Conventionalboilers(e.g.coal,oil,gas)•Barkboiler•Blackliquorrecovery•Pulping•Pulpbleaching•PulpdryingPaperproduction•Conventionalboilers(e.g.coal,oil,gas)•Barkboiler•Paper-makingprocessesHighvaluechemicalproduction•Steamcracking•Electricseamcracking•Bioethanoldehydration•Naphthacatalyticcracking•Propanedehydrogenation•Methanoltoolefins•MethanoltoaromaticsMethanolproduction•Fossilfuel-based•Biomass-based•Electrolysis-basedAmmoniaproduction•Fossilfuel-based•Biomass-based•Electrolysis-based•Pyrolysis-basedSectorsOtherindustry•Transportequipment•Machinery•Miningandquarrying•Foodandtobacco•Textileandleather•Woodandwoodproducts•Construction•Non-specifiedindustryEquipment•Coolingandrefrigeration•Boilers•Heatpumps•Solar/geothermalheating•Resistanceheating•Electro-magneticheating•Motors•MotordrivensystemsGECindustryhybridmodellingapproachMerchanthydrogenandsynthetichydrocarbonoptions(cross-cutting)Othernon-metallicmineralproduction•FuelelasticitysimulationOthernon-ferrousmetalproduction•FuelelasticitysimulationOtherchemicalproduction•FuelelasticitysimulationSemi-finishingandfinishingprocesses•FuelelasticitysimulationPrintingandfinishingprocesses•FuelelasticitysimulationSection3End-usesectors27printing).Theremainingnon-energy-intensiveindustrysub-sectors(construction,miningandquarrying,transportequipment,machinery,foodandtobacco,woodandwoodproducts,textileandleatherandindustrynot-elsewherespecified)aremodelledusingacross-cuttingconversiondevicesimulationapproach.Fortheresidualcomponentsofthefiveenergy-intensivesub-sectors(chemicalsbesidesprimarychemicals,non-metallicmineralsbesidescement,non-ferrousmetalsbesidesaluminium,downstreamfinishingprocessesintheironandsteelandpaper,pulpandprintsectors),thesamecross-cuttingapproachisappliedastothenon-energy-intensivesub-sectors.Thefiveenergy-intensivesub-sectormodelscharacterisetheenergyperformanceofprocesstechnologiesattheprocessunitlevel(e.g.coalblastfurnace,naphthasteamcracker).Thecross-cuttingsimulationmodelfortheremainingindustrysub-sectorscharacterisesthestockofthemainconversiondevices(e.g.motors,heatingequipment)usedtoprovidevariousenergyservicesrequiredduringtheproductionofthousandsofmaterialsandproducts.Seesections3.1.3and3.1.4formoreinformationontheapproachestakenforeachofthesemaincomponentsoftheGECindustrymodel.Energy-intensivesub-sectorsForeachofthefiveenergy-intensityindustrysub-sectors,themodellingframeworkconsistsofaseriesofinteractingsub-modulesandacoretechnologymodel(seeFigure3.3).Thesub-modulesconsistofanactivitymodel,astockmodelandacapacitymodel.Theactivitydriversforeachsub-sectoroftheGECindustrymodelaretonnagesofmaterialproducedinagivenscenarioatagivenpointintime.Activitymodellingishandledinasimilarmannerforallenergy-intensiveindustrysub-sectors.Demandformaterialsisprojectedthroughinteractionbetweenanactivitymodelandastockmodel,togetherwithmodellingofmaterialefficiencystrategies.Theactivitymodelusescountry-levelhistoricaldataonmaterialconsumptiontocalculatedemandpercapita,thenprojectsforwardtotaldemandusingpopulationprojectionsandindustryvalue-addedprojections.Theindustryvalue-addedprojectionsinformtherateofchangeindemandpercapita.Theresultsoftheactivitymodelondemandprojectionsfeedintothestockmodel,whichusesbottom-upmaterialdemandinputsfromthebuildings,transportandsupplymodulesandcomplementaryassumptionsaboutotherend-productsharesandlifetimestocalculatetheimpliedbuild-upofmaterialstocks.Stocksaturationinthestockmodelinturninformspercapitamaterialdemandsaturationintheactivitymodelthroughaseriesofiterations.Materialefficiencystrategiesacrossvaluechainsalsoaremodelled.ThismodellingworkbuildsmainlyontheliteratureandpreviousIEApublicationsrelatingtomaterialefficiency(IEA2019a).Strategiesconsideredinclude:◼Designstage:light-weighting(producethesameproductwithaloweraveragemassperproduct),designforfuturematerialsavings(modulardesigntoenablereduce,designforrecyclability)◼Constructionandmanufacturing:increasedyields(reducethelossesinsemi-manufacturingandmanufacturing),reducedmaterialswaste(morecarefulconstructionpracticesandmaterialhandling)◼Use:longerlifetimes(refurbishingbuildingsforotheruses,re-usingcomponentsforparticularproducts),moreintensiveuseofproducts(forexamplecarsharingorusingabuildingforahighershareoftheday),◼End-of-life:directmaterialsre-use(useofpost-consumermaterials–withoutre-meltinginthecaseofmetals–forthesameorotherapplications),recycling(increasedcollectionandimprovedsorting).Thosestrategiesoccurringintheotherend-usesectors(e.g.buildinglifetimeextension,vehiclelight-weighting)arefedintothestockmodelviathebottom-updemandestimates,whilematerialefficiencystrategieswithintheindustryboundary(e.g.manufacturingyieldimprovements,directreuseandrecycling)aremodelledwithinthestockmodel.Thesestrategiesleadtoreducedmaterialdemand,whichisfedintotheactivitymodelviaamaterial28InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONefficiencyfactor.Theresultingactivityprojectionsfromtheactivitymodelandscrapavailability(includingsemi-manufacturing,manufacturingandpost-consumerscrap)fromthestockmodelfeedintothemaintechnologymodel.Materialtradebetweenmodelregionsisnotmodelledendogenouslyinthetechnologymodel,butratherisreflectedintheactivityprojectionsdevelopedintheactivityandstockmodels.Apartfromspecificinstanceswhereannouncedpoliciesorprojectedenergypricesignalsproviderelevantevidencetothecontrary,tradepatternsinmaterialproductionandconsumptionareprojectedtofollowcurrenttrends.Globaltotalmaterialdemandisthusallocatedintoregionalproductionbasedonthesecurrenttrends.Thecapacitymodelcontainsdataonhistoricandplannedplantcapacityadditionsandretrofitsbyplanttype.Usingassumptionsaboutinvestmentcycles,itcalculatesplantrefurbishmentsandretirements.Theresultingremainingcapacityinformsthemaintechnologymodel.Thecapacitymodelalsoprovidesprojectionsontheaverageageofplantsatagiventime.Figure3.3⊳IndustrysectormodelinternalmodulestructureandkeydataflowsIEA.CCBY4.0.Notes:Internalindustrymodelflows:1)Historicproduction,populationprojection,industryvalue-addedprojection,2)End-usedemand,productlifetimes,processyields,recyclingandre-userates,3)Energyandrawmaterialintensities,energyprices,CAPEXandOPEX,lifetimes,technologydeploymentconstraints,CO2emissionsreductiontrajectory,4)Historicandplannedcapacity,lifetimes,refurbishments,5)Consumptionprojections,6)Materialstockssaturation,materialefficiencyfactors,7)Productionprojections,8)Scrapavailability,9)Residualcapacity.Modelresults:A)Materialproduction,B)Materialstockssaturation,C)Energyconsumption,CO2emissions,technologyshares,investments,D)Capacityinstalled,addedandretired.Themaintechnologymodelofeachsectorconsistsofadetailedrepresentationofprocesstechnologiesrequiredforrelevantproductionroutes.Energyuseandtechnologyportfoliosforeachcountryorregionarecharacterisedinthebaseyearusingrelevantenergyuseandmaterialproductionstatistics.Throughoutthemodellinghorizon,demandformaterials(asdictatedbytheactivitymodeloutputs)ismetbytechnologiesandfuels,whosesharesareinformedbyreal-worldtechnologyprogressandthepreviousETPTIMESoptimisationmodel.Thatmodelusedaconstrainedoptimisationframework,withtheobjectivefunctionsettomakechoicesthatminimiseoverallsystemcost(comprisedofbothenergycostsandinvestments).Changesinthetechnologyandfuelmix,aswellasefficiencyimprovements,areinpartdrivenbyacombinationofexogenousassumptionsonthepenetrationandenergyperformanceofbestavailabletechnologies,InputdataModelresultsActivitymoduleCapacitymoduleTechnologymodelStockmodule5234D61789BCASection3End-usesectors29constraintsontheavailabilityofrawmaterials(suchasscrapavailabilityaccordingtothestockmodeloutputs),techno‑economiccharacteristicsoftheavailabletechnologiesandprocessroutes,andassumedprogressondemonstratinginnovativetechnologiesatcommercialscale.Theresultsaresensitivetoassumptionsabouthowquicklyphysicalcapitalisturnedover(includingretirementsofexistingcapacityaccordingtothecapacitymodeloutputs)andabouttherelativecostsofthevarioustechnologyoptionsandfuels.AgivenscenariocanalsobesubjecttoaCO2emissiontrajectorythatthemodelmustadhereto.Modeloutputsincludeenergyconsumption,fuelcombustionandprocessCO2emissionsbothemittedandcaptured,technologyshares,rawmaterialsandintermediateindustrialmaterialsflowsandinvestmentrequirements.Someindustrialsectorshavetheparticularitytoproduceanduse“on-site”hydrogenwithintheindustrialfacilityasforspecificammonia,methanolorprimarysteelproductionprocesses.Thishydrogenisnotreportedinthestandardenergybalancebutitisreportedasfossilfuelorelectricitydependingonwhetheritisproducedviasteamreformingorwaterelectrolysis.Accountingofthishydrogen,necessarytobuildtheglobalhydrogenaccounting,isperformedinadedicatedhydrogenmodule.Outputsofthismodulearehydrogenquantitiesproducedonsite(low-emissionsornot),electrolysercapacityandrelated-investmentsrequirements,energyinputandrelatedCO2emissionsemittedaswellascapturedandstored.Non-energyintensivesub-sectorsActivitymodellingforthenon-energy-intensivesub-sectorsfollowsadifferentapproachtotheenergy-intensivesectors.Thesesub-sectorsproducealargerangeoffinalproductswithoutaclearcommonintermediateinmanycases.Thiscontraststotheenergy-intensivesub-sectors,whichhavealargerangeoffinalproductsbutaclearcommonintermediateproductforwhichproductioninphysicaltermscanbeclearlyprojected(e.g.crudesteelintheironandsteelsector).Assuch,macro-economicindicators(e.g.industrialvalue-added)areusedastheactivitydriversfornon-energyintensivesub-sectors,ratherthanphysicalproduction.Usinghistoricrelationshipsbetweenmacro-economicindicatorsandindustrialenergydemand,togetherwithdemandsignalsfromtheotherend-usemodels(e.g.vehiclesalesfromthetransportmodelforthetransportequipmentsector)andmaterialefficiencyconsiderations(basedontheresultsoftheenergy-intensivesub-sectoranalyses)whererelevant,projectionsofenergyservicedemandaremadeacrossthefollowingcategories:◼Heatdeliveredatfivetemperaturebands(0-60°C,60-100°C,100-200°C,200-400°Candabove400°C);◼Mechanicalworktobedeliveredbyelectricmotors;◼Otherenergyservicesinaggregate(cooling,lightingetc.).Theseenergyservicedemandsformthefinalactivitydriversforthenon-energy-intensiveindustrysub-sectormodels.Arangeoftechnologiesarecharacterisedformeetingeachcategoryofactivitydemand,includingarangeofdifferentheatingtechnologiesusingdifferentfuels(fossilfuels,solarthermal,geothermal,electro-magneticheating,electricresistanceheating,heatpumps,hydrogen,bioenergy)andarangeofmotoroptions(differingefficienciesofthemotordrivensystem,efficienciesofthemotoritself,variablespeeddriveoption).Thesharesofenergyservicedemandmetbyeachofthesetechnologiesareinformedbytheirlevelisedcost(includingtheimpactofanyCO2prices),constraintsonfuelavailability(e.g.,bioenergyresources),technologyreadiness(e.g.,electro-magneticheatingforlargenon-conductivemedianotcommerciallyavailabletoday),limitsonpotential(e.g.,industrialheatpumppenetrationinmediumandhightemperatureheatbands)andanyCO2emissionsconstraintsofthescenario.Thesharesoffuels(andassociatedemissions)usedtomeettheremainingenergyservicedemandofmultifuelprocessesorprocessesthatarenotcoveredbythebottom-uptechnologymodellingacrossthenon-energy-intensivesectors(andresidualportionsoftheenergy-intensivesectorsnotcoveredintheenergy-intensivesub-30InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONsectormodels)ismodelledbyfuelusingaWeibullfunction.Thisfunctionisinformedbypreviousyear’sfuelshare,thefuelpricechange(includingtheimpactofanyCO2prices)andthepricechangeinthepreviousyear.AnyCO2constraintsspecifiedbythescenarioarealsorespected.IndustrysectorinvestmentsTheboundariesforinvestmentsreportingincludecapitalexpenditure(CAPEX),andengineering,procurementandconstructioncosts.Forcarboncapture,utilisationandstorage(CCUS)technologies,CO2transportandstoragecostsarealsoincluded.Formaterialefficiency,investmentsarebasedondataonCO2abatementcostsformaterialefficiencystrategies,convertedintocostsformaterialsavings.Fixedoperatingandmaintenanceexpenditures(OPEX)arenotincludedunderreportedinvestments,thoughtheyareconsideredinthecontextoftheeconomiccharacterisationoftechnologiesinthemodel.EnergysysteminvestmentsdonotincludecoreindustrialequipmentCAPEX,butdoincludetheadditionalinvestmentrequiredtoincrementally(e.g.,energyefficiencyimprovementsthroughadoptionofBAT)orsubstantially(e.g.electrolyserandcarboncaptureequipment)adjusttheenergyoremissionsperformanceofatechnology.Otherinvestmentsincoreindustrialequipmentarealsoaccountedfor,butnotreportedwithintheboundaryofenergysysteminvestments.InputdataInputdatatothemodelcomesfromawidevarietyofsources.SourcesforhistoricalproductionandconsumptionusedintheactivitymodellingincludetheWorldSteelAssociation,theInternationalFertilizerAssociation,theUnitedStatesGeologicalSurvey,theInternationalAluminiumInstituteandanumberofproprietarysources.Dataontheenergyintensitiesofprocessescomefromavarietyofindustrysources(e.g.theGettingtheNumbersRightpublicationoverseenbytheGlobalConcreteandCementAssociation),academicliteratureandindustrycontacts.CAPEXandOPEXsimilarlycomefromacombinationofindustryandacademicsources.Population,economicindicators(e.g.valueaddedbyindustry),fuelcosts–i.e.end-useenergyprices,andCO2pricesareprovidedbythemainGECModel(seeSection2).OtherkeyinputsfromtheGECmodellingframeworkandassociatedworkstreamsincludethehydrogenandCCUSprojectsdatabasesandthetechnologyreadinessassessmentsthatformpartoftheCleanTechnologyGuideandDemonstrationProjectsDatabase.Techno-economicparametersareperiodicallyreviewed,bothasacomponentofaforementionedworkstreams,andduringthecourseofpreparing‘deep-dive’analysesonspecificsectorortechnologyareas(e.g.theIEA’sIronandSteelTechnologyRoadmap,theAmmoniaTechnologyRoadmap,TheFutureofPetrochemicals).3.2TransportsectorTheGECtransportmodelcombinesstrengthsofbothformerWorldEnergyModel(WEM)andMobilityModel(MoMo),andconsistsofdedicatedsectoralmodelforroadtransport,aviation,maritimeandrail.TheHistoricalDatabaseOnekeyfoundationfortransportmodellingworkistheroadtransportdatabase,adatabasethatisupdatedannuallybasedprimarilyonpubliclyavailabledataonroadvehiclesales,stocks,activity,andoperations.TheroaddatabasefurtherbenefitsfromdataandanalyticalworkfortheElectricVehiclesInitiative1andtheGlobalFuelEconomyInitiative2.Similarhistoricaldatabasesformthebasisformodellingrail,internationalmaritime,andcommercialpassengeraviation.1https://www.iea.org/programmes/electric-vehicles-initiative2https://www.iea.org/reports/global-fuel-economy-initiative-2021Section3End-usesectors31Eachregionischaracterisedonthebasisofinformationthatincludes,foreachroadtransportmode,vehiclesales,mileage,andenergyintensitybyvintage,aswellastheoverallvehiclestock,loadfactorsandfuelefficiency.Thedatabaseallowslinkinghistoricaldataonseveralinterconnectedvariables,tryingtoassureinternalconsistencyacrossindicators,accordingtotheASIFframework,whereinActivity,Structure,andIntensitydetermineestimatesofFueluse):𝐹=∑𝐹𝑖=𝐴∑(𝐴𝑖𝐴)(𝐹𝑖𝐴𝑖)=𝐴∑𝑆𝑖𝐼𝑖𝑖=𝐹𝑖𝑖FtotalFueluseAvehicleActivity(expressedinvkm)Fifuelusedbyvehicleswithagivensetofcharacteristics(i)(e.g.segmentsbyservice,mode,vehicleandpowertrain)Ai/A=SisectoralStructure(samedisaggregationlevel)Fi/Ai=IiEnergyIntensity,i.e.averagefuelconsumptionpervkm(samedisaggregationlevel)Theparametersmonitoredincludeincludingsales/newregistrationsofvehicles,secondhandimports,survivalages,stock,mileages,vehicleactivity(vehicle-kilometresorvkm),loads/occupancyrates,passengerandfreightactivity(passenger-kilometresorpkmandtonne-kilometresortkm),fueleconomiesandenergyuse(basedontheIEAdataonenergydemandbycountry).Thefollowingparametersarecollectedandcalibrated/validatedagainsttheroadenergybalancesonanannualbasis:◼Sales/newvehicleregistrationdataaretakenfrompubliclyavailabledatasources(e.g.ACEA,USBureauofTransportationStatistics,andothers).◼Fueleconomydataforpassengerlight-dutyvehiclesarebasedonaggregateddatafromaproprietarydatabase,plusconversions(basedonanexternalresearchreport)acrossregionalvehicletestcyclestotheWorldLight-DutyTestCycle(WLTC),plusestimatesforthegapbetweenthistestcycleandreal-worldspecificfuelconsumption(again,basedonexternalresearchreports).◼Fueleconomydataforbuses,trucks,two/three-wheelersaretakenfromvariousacademic,governmentandindustryreportsortechnicalcalculations,overthecourseofnearly20years.◼Stocksarebasedonourestimatesofhowlongdifferentvehicletypesarekeptinthefleet(i.e.scrappagefunctions),andwhenreliableexternalestimatesareavailable(asisthecase,forinstance,intheUnitedStatesandEurope),thesearecalibratedtoofficialdata(e.g.ACEA,USBureauofTransportationStatistics).Incountrieswhereacademicorindustrystudiesexistontheagedistributionoftheon-roadfleet,scrappagefunctionsarecompared/calibratedwiththese.◼Occupancy(averagepeoplepervehicle)andLoadFactors(averagecargoweightpervehicle)arebasedonofficialstatistics(e.g.,Eurostat),academicreportsorsurveys,oraredevelopedbyanalogy/regression-basedestimateswhennodataareavailable.◼AverageMileage(i.e.,annualkilometresdriven)estimatesaresimilarlytakenfromorcompared/calibratedtoofficialdataandliterature◼Scrappageandmileagearethenadjusted,acrossallvehiclecategories(e.g.,two/three-wheelers,cars,buses,lightcommercialvehicles,medium-andheavy-trucks)andacrossallfuel/powertraintypes(e.g.gasoline,diesel,conventionalhybrid,plug-inhybrid,batteryandfuel-cellelectric,etc.)tomatchthecountry-/regionaltimeseriesofroadgasoline,diesel,electricity,naturalgasandLPGconsumptionasreportedintheIEAenergybalances.32InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONThetransportmoduleThetransportmoduleoftheGECModelconsistsofseveralsub-modelscoveringroad,aviation,railandnavigationtransportmodes(Figure3.4).TheGECModelfullyincorporatesadetailedbottom-upapproachforthetransportsectorinallGECModelregions.Figure3.4⊳StructureofthetransportsectorIEA.CCBY4.0.Note:‘Other’includespipelineandnon-specifiedtransport.Foreachregion,activitylevelssuchaspassenger-kilometresandtonne-kilometresareestimatedeconometricallyforeachmodeoftransportasafunctionofpopulation,GDPandend-userprice.Transportactivityislinkedtopricethroughelasticityoffuelcostperkilometre,whichisestimatedforallmodesexceptpassengerbusesandtrainsandinlandnavigation.Thiselasticityvariableaccountsforthe“rebound”effectofincreasedcarusethatfollowsimprovedfuelefficiency.Energyintensityisprojectedbytransportmode,takingintoaccountchangesinenergyefficiencyandfuelprices.RoadtransportRoadtransportenergydemandisbrokendownamongpassengerlightdutyvehicles(PLDVs),lightcommercialvehicles(LCVs),buses,mediumtrucks,heavytrucksandtwo-andthree-wheelers.Themodelallowsfuelsubstitutionandalternativepowertrainsacrossallsub-sectorsofroadtransport.Thegapbetweentestandon-roadfuelefficiency,i.e.,thedifferencebetweentestcycleandreal-lifeconditions,isalsoestimatedandprojected.Asthelargestshareofenergydemandintransportcomesfromoiluseforroadtransport,theGECModelcontainstechnology-detailedsub-modelsofthetotalvehiclestockandthepassengercarfleet.ThestockprojectionmodelisbasedonanS-shapedGompertzfunction,proposedinDargayetal.(2006).Thismodelgivesthevehicleownershipbasedonincome(derivedfromGDPassumptions)and2variables:thesaturationlevel(assumedtobethemaximumvehicleownershipofacountry/region)andthespeedatwhichthesaturationlevelisreached.Theequationusedis:𝑉𝑡=𝑦𝑒𝑎𝑒𝑏𝐺𝐷𝑃𝑡RoadtransportRailNavigationOtherPassenger-kilometresTonne-kilometresActivityvariablesPopulationGDPAviationSub-sectorsEnd-useenergypricesHistoricaltrendsSection3End-usesectors33whereVisthevehicleownership(expressedasnumberofvehiclesper1,000people),yisthesaturationlevel(expressedasnumberofvehiclesper1,000people),aandbarenegativeparametersdefiningtheshapeofthefunction(i.e.,thespeedofreachingsaturation).Thesaturationlevelisbasedonseveralcountry/regionspecificfactorssuchaspopulationdensity,urbanisationandinfrastructuredevelopment.Usingtheequationabove,changesinpassengercarownershipovertimearemodelled,basedontheaveragecurrentglobalpassengercarownership.Bothtotalvehiclestockandpassengervehiclestockprojectionsarethenderivedbasedonourpopulationassumptions.Projectedvehiclestocksandcorrespondingvehiclesalesarethenbenchmarkedagainstactualannualvehiclesalesandprojectedroadinfrastructuredevelopments.TheresultingvehiclestockprojectionscanthereforedifferfromthosethatwouldbederivedbytheuseoftheGompertzfunctionalone.Toimprovethestockevolutionofthevehiclefleet,adynamicscrappagefunctionhasbeendevelopedwherededicatedscrappagecurvesareestimatedbyregionbasedonacorrelationofaveragelifetimewitheconomicgrowth.Dynamicscrappagefunctionallowstoevaluatepolicymeasures,suchasearlyretirementofvehicle(Figure3.6).Totakeintoaccountthatoldervehiclesareusedless,anextensiveliteraturereviewhasbeencarriedouttoidentifymileagecurvespervehicletype.Thisenablesamoregranularassessmentofhoweachvehicletypepervintage(purchaseyear)iscontributingtothetotalroadactivity.Figure3.5⊳IllustrationofscrappagecurveandmileagedecaybyvehicletypeIEA.CCBY4.0.Theanalysisofpassengerlight-dutyvehicle(PLDV)usesacosttoolthatguidesthechoiceofdrivetraintechnologiesandfuelsasaresultoftheircost-competitiveness.Thetoolactsonnewpassenger-LDVsalesasdepictedinFigure3.6,anddeterminestheshareofeachindividualtechnologyinnewpassengerLDVssoldinanygivenyear.Thepurposeofthecosttoolistoguidetheanalysisoflong-termtechnologychoicesusingtheircost-competitivenessasoneimportantcriterion.ThetoolusesalogitfunctionforestimatingfuturedrivetrainchoicesinpassengerLDV.3TheshareofeachPLDVtypejallocatedtothepassengerlightdutyvehiclemarketisgivenby𝑆ℎ𝑎𝑟𝑒𝑗=𝑏𝑗𝑃𝑃𝐿𝐷𝑉𝑗𝑟𝑝∑(𝑏𝑗𝑃𝑃𝐿𝐷𝑉𝑗𝑟𝑝)𝑗3Originallydevelopedtodescribethegrowthofpopulationsandautocatalyticchemicalreactions,logitfunctionscanbeappliedtoanalysethestockturnoverindifferentsectorsoftheenergysystem.Here,itusesthecost-competitivenessoftechnologyoptionsasanindicatorforthepaceofgrowth.0%20%40%60%80%100%0510152025SurvivalprobabilityPassengercarsHeavy-dutytrucksScrappagecurve0510152025MileageYearsMileagedecay34InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONWhere:•PPLDVjistheannualcostofavehicle,includingannualisedinvestmentandoperationandmaintenancecostsaswellasfueluse•rpisthecostexponentthatdeterminestherateatwhichaPLDVwillenterthemarket•bjisthebaseyearshareorweightofPLDVjThecostdatabaseinthecosttoolbuildsonananalysisofthecurrentandfuturetechnologycostsofdifferentdrivetrainsandfueloptions,comprisingthefollowingtechnologyoptions:◼conventionalinternalcombustionengine(ICE)vehicles(sparkandcompressionignition)◼hybridvehicles(sparkandcompressionignition)◼plug-inhybrids(sparkandcompressionignition)◼batteryelectriccarswithdifferentdriveranges◼hydrogenfuelcellvehiclesFigure3.6⊳Theroleofpassenger-LDVcostmodelIEA.CCBY4.0.Themodeltakesintoaccountthecostsofshort-andlong-termefficiencyimprovementsinpersonaltransportdistinguishingnumerousoptionsforengine(e.g.reducedenginefriction,thestarter/alternator,ortransmissionimprovements)andnon-enginemeasures(e.g.tyres,aerodynamics,downsizing,light-weightingorlighting).Inaddition,itusesprojectionsforthecostsofkeytechnologiessuchasbatteries(NiMHandLi-Ion)andfuelcells.Thepaceoftechnologycostreductionsisthencalculatedusinglearningcurvesattechnology-specificlearningrates.Thecostanalysisbuildsonacomprehensiveanddetailedreviewoftechnologyoptionsforreducingfuelconsumption.Thedatabasewasreviewedbyapanelofselectedpeer-reviewers,andfeedsintothecosttool.Thecostdatabaseisconstantlyreviewedandtakesaccountofrecentresearch.CostcurvesassumptionsacrossallvehicletypesarebasedonworkbytheJointResearchCentre(JRC)(Krauseetal,2017;KrauseandDonati,2018).RegionalcharacteristicsandeconomicfactorshavebeentakenintoaccountinordertoexpandcostcurvescoverageforallGECModelregions.Projectedsalesofalternativepowertrains(andfocusingprimarilyonelectricvehicleswithinlight-dutyvehicles,andelectricandfuel-cellelectricvehicleswithinheavy-dutysectors)forthetop20globalautomakersisregularlyupdatedoverthecourseofeachyear.Thisanalysispermitsustoassesswhethervehiclemanufacturers’commitmentsforlaunchingnewelectrifiedcarmodelsarefallingbehindthenecessaryEVsrolloutformeetingSection3End-usesectors35fueleconomygoalsandZeroEmissionVehiclesmandates.VehiclemanufacturersandnationalandstatejurisdictionswithICEphaseoutcommitmentsforacertainyeararealsopartofthisanalysis.Projectionsofbatteryandplug-inelectricvehiclesarematchedtosimpleprojectionsofbatterycapacityand(cathodeandcell)chemistry,andtheseprojectionsarelinkedtobottom-upanalysesofbatterycosts(to2030),andcriticalmineralrequirements.TheseprojectionsinformIEA'son-goingworktoassessthecriticalmineralsandvaluechainimplicationsofashifttoelectromobility.Regardinghydrogenfuel-cellelectricvehiclesprojections,theytakeintoaccounttherecentcarmarketdevelopments,policyannouncementsandthekeyoutcomesfromIEA’sGlobalHydrogenReview2022(IEA,2022).Roadfreighttransportvehiclescanbebroadlyclassifiedintolight-commercialvehicles(<3.5t),mediumtrucks(3.5tto15t)andheavytrucks(>15t).Forthelattertwocategories,GECModelcomprisestwodetailedsub-modelstoguidethedevelopmentofaveragefueleconomyimprovementsontheonehand,andtechnologychoicesontheotherhand.Fortheformer,themodelendogenizesthedecisionofinvestmentsinenergyefficiencybytakingtheviewofrationaleeconomicagentsonthebasisthatminimisingcostsisakeycriterionforanyinvestmentdecisioninthissector.UsingtheefficiencycostcurvesofJRC,themodelcalculatestheundiscountedpaybackperiodofaninvestmentintomorefuel-efficienttrucksandheavytrucks.Themodelthenallowsforinvestmentswherethecalculatedpaybackperiodisshorterthananassumedminimumpaybackperiodthatisrequiredbyfleetoperators(generallyassumedbetween1and3years,dependingontheregion).Theproblemissolvedinaniterativemannerasthemodelseekstodeploythenextefficiencystepontheefficiencycostcurveasdeterminedbyliterature,butmayuseefficiencyimprovementlevelsinbetweenindividualstepsontheefficiencycostcurve(Figure3.7).Figure3.7⊳IllustrationofanefficiencycostcurveforroadfreightIEA.CCBY4.0.Inasecondstep,themodelsimulatesthecosteffectivenessofaconventionalinternalcombustionenginevehicleagainstothercompetingalternativeoptions.ThesimulationisguidedbytheuseofaWeibullfunction.Alternativepowertrainsformedium-andheavy-dutytruckshavebeenimplementedintheGECModel:fuelcell,batteryelectricandplug-inhybridelectric.Inordertoassesstheproblemscreatedduetochicken-and-egg-typeofsituationswhenitcomestothedeploymentofthosealternativefuelsintransportthatrequireadedicatedrefuellinginfrastructure,andtobetterreflectpotentialspill-overeffectsoftheuseofsuchalternativefuelsinothersectorsoftheenergysystem,theGECModelhastwodedicatedsub-models,onecoveringnaturalgasinfrastructureandtheotherelectricity-Fuelconsumptionimprovementrate(litres/100km)USD/vehicleShort-termLong-term36InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONrelatedrefuellinginfrastructure.Inprinciple,bothmodulesseektoquantifythecostsandbenefitsofincreasedinfrastructureavailabilityfortransmissionanddistributionofthesealternativefuels.Inessence,therelationshipofthesespill-overbenefitscanbeillustratedasinFigure3.8.Figure3.8⊳Refuellinginfrastructurecostcurve(illustrative)IEA.CCBY4.0.Forthecaseofelectricvehicles,availabilityoftransmissionanddistributiongridislessofanissue,especiallyinadvancedeconomies,thankstothealreadyexistingwidespreaduseofelectricityindifferentendusesectors(especiallybuildings).However,theavailabilityofelectricrecharginginfrastructureisoneoftheimportantconstraintsinthiscase,andhenceitisimportanttodeterminehowareductioninrefuellingcostscouldinfluencethepossibilityforoilsubstitutioninroadtransport.Therefore,theelectricvehicle(EV)sub-moduleassessesthecascadingeffectofanincreasedshareofelectricvehiclesinoverallvehiclesalesonbringingdowntherefuellingcosts.Detailedcostcurveswerepreparedoutliningthereductionofrefuellingcostswiththeincreaseinoverallvehiclestockofelectricvehicles.Thesecostcurveswereprovidedasanexogenousinputtothemodel,soastocontinuouslyadjusttherefuellingcostsastheshareofEVsalesrisesinthefuture.Electricvehiclesupplyequipment(EVSEorEVcharger)stockisalsoprojectedbyvehiclecategory.Forlight-dutyvehicles,thenumberofpublicchargersiscalibratedtostartfromthehistoricaltrendsofEVSE/EV,whererelevant.Theshareofslowandfastchargersisalsocalibratedtohistoricdata,whereavailable.Thepaceofthedeploymentofprivateandpubliccharginginfrastructureisinformedbydataontheshareofhouseholdslivinginsingle-familyhouses,theavailabilityofEVcharginginfrastructureinprivateandmulti-familydwellings,andthecurrentprovisionandlevelofpubliclyavailablechargingportsandstations.Ingeneral,thepublicEVSE-to-EVratioisprojectedsuchthatastheEVstockshareincreases,therequiredkWofpublicchargingcapacityperEVdecreases.Forbusesandtrucks,theshareofelectricitydemandmetthroughopportunityorpublicchargersisprojectedbysegment.Urbanbusesareassumedtochargestrictlyatdepots,whileintercitybusesareassumedtorequiresomeshareoftheirelectricitydemandtobeprovidedoutsideofthedepot.Therequirednumberofpublicchargersisthenestimatedbasedonanassumedmixofchargerswithdifferentchargingcapacities.Hydrogenfuelconsumptionisusedtoestimatethenumberofhydrogenrefuellingstations(HRS)neededtomeetdemand.Stationcapacitiesaremodelledtoevolve(grow)overtime,withdifferentsizelimitssetbasedonthetargetvehiclesbeingserved.Forexample,hydrogenrefuellingstationsfortruckshavehighermaximumcapacitiesthanstationstoservethelight-dutyvehiclemarket.Though,ofcourse,somestationswillhavedualpressuredispensingandservedifferentvehiclemarkets.However,themodellingalsodifferentiatesutilisationRefuellingCost=F(NGVshareinVehicleSales)DistributionCost=F(gasshareinfinalconsumption)TransmissionCost=F(gasshareinprimaryenergy)DrivingfactorforspilloverbenefitsInfrastructurecostsSection3End-usesectors37ratesbytargetvehiclecategory,wherestationsforfleetsofbusesandtrucksareexpectedtohavehigheraverageutilisationratesthanthoseforlight-dutyvehicles.Thus,thestockofHRSrequiredtoserveFCEVsis:𝐻𝑅𝑆𝑆𝑡𝑜𝑐𝑘=∑𝐹𝑖𝐶𝑖×𝑈𝑖Where:•irepresentsthevehiclecategory•Firepresentsthehydrogenfueldemand(kgH2/year)ofvehiclecategoryi•Cirepresentstheaveragenameplatecapacity(kgH2/year)ofhydrogenrefuellingstationsservingprimarilycategoryi•Uirepresentstheaverageutilisationrate(%)ofhydrogenrefuellingstationsservingprimarilycategoryiFinally,basedonprojectionsoftheaveragefuelconsumptionofnewvehiclesbyvehicletype,theroadtransportmodelcalculatesaveragesalesandstockconsumptionlevels(on-roadandtestcycle)andaverageemissionlevels(ingrammesofCO2perkilometre)overtheprojectionperiod.ItfurtherdeterminesincrementalinvestmentcostsrelativetootherscenariosandcalculatesimplicitCO2pricesthatguideoptimalallocationofabatementintransport.AviationAviationvehicleandpassengeractivitycalibratedatacountry/regionalleveltomatchdomesticandinternationalenergydemandforjetkerosene.AviationmodellingbuildsuponcollaborationwithresearchersatUniversityCollegeLondon(UCL),whohavedevelopedandmaintaintheopen-sourceAviationIntegratedModel(AIM)4.KeyfeaturesofAIMpreservedinIEAmodellinginclude:◼Operationalandtechnicalpotentialforenergyintensityimprovementsbasedondetailed,origin-destinationmodellingofaircraftandairportoperationsandairframe-propulsionsystems,withstock-modellingandtechno-economicmodelling,intheframeworkofiterativecostminimisation.◼Regionalandairport-resolutionlong-termpriceandGDPdemandelasticitiesalignedwithIATAandotherauthoritativestudiesenablingcredibleandhigh-resolutionactivityprojections.ProjectionsintegratethemainfeaturesofdetailediterativecostminimisationmodellingusingtheAviationIntegratedModel(AIM)with“top-down”projectionsoffuelconsumptionbyotheraviationactivities(dedicatedcargo,generalaviation).FurtherelaborationbuildsuponIEAtechno-economicmodellingofenergysupplyandfuelstransformationmodelling,aswellaselaborationsofpolicytargetsanddemand-sidemanagementstrategies.InternationalMaritimeThebottom-upmodellingofinternationalshippingisbasedontheASIF(Activity,Structure,IntensityandFueluse)framework(Schipper,2010)toassessenergydemandandCO2emissionsbyregionandshiptype.ActivityprojectionsaredevelopedincoordinationwithOECD(EnvironmentDirectorate)andtheInternationalTransportForum,whoprovidetradeprojectionsbyvalueandweightofdifferentcommoditycategories.Basedontheoriginanddestination,distanceestimatesareusedtocalculatethetonne-kmofeachcommoditytype.Ashareofeachcommoditytypeisthenallocatedtooneofthefollowingfivecategoriesofships:◼Liquidbulkcarriers(includingoiltankers)◼Drybulkcarriers◼Containerships4https://www.ucl.ac.uk/energy-models/models/aim38InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION◼Generalcargoships◼OthershipsThemodellingbuildsuponexternaldataonvesselstockandsales(UNCTADandBloomberg);speed,daysatsea,deadweighttonnageandcapacityfactor(IMO);andfueleconomy(TechnicalUniversityofDenmark).Thestructurevariableisinterpretedastheloadfactor,i.e.,theaveragecapacityutilisationpershippertrip,whichallowsderivingthevehicle-kilometresprojectedforeachregionandforeachshiptype.Loadfactorprojectionsarebasedonhistoricallyobservedgrowthratesoftheaveragesizeofthedifferentshiptypes,whicharepublishedbyUNCTAD.Fueleconomyisbasedonshiptype,deadweighttonnage,andcapacityfactor.MultiplyingfuelconsumptionbytheCO2emissionfactorsofthedifferentfuelsmodelled(heavyfueloil,marinedieseloilandLNG)givesthetotalCO2emissions.RailRailvehicleandpassenger/cargoactivityarecalibratedacrossurban(metroandlight-rail)andnon-urban(conventionalandhigh-speedpassengerrail,andfreightrail),andfordieselandelectricity,tomatchtheenergybalancesatthecountryandregionallevel.Railmodellingbuildsupondatabasesofurbanrailactivity(metroandlight-rail)fromtheInternationalAssociationforPublicTransport(UITP)andtheInstituteforTransportationDevelopmentandPolicy(ITDP),includingdatabasesofgreenfieldraildevelopmentsplannedforthecoming~5years.ItfurtherbuildsupondatafromtheInternationalRailwayUnion(UIC)onintercity(conventional),high-speed,andfreightrailactivity,includingplansforrailnetworkextensionandelectrification.An“avoid-shift”modelthatallocatespassenger-kilometreactivityacrossmodesofsurfacetransport(i.e.,2-and3-wheelers,cars,buses,andrail),accordingtoregulatory,fiscal,andinvestmentpoliciesthatvarybyscenario.BehaviourchangeanalysisSeveralanalysesregardingbehaviourchangeintransporthavebeencarriedout:◼Ex-postanalysisfortheimpactofbehaviourchangeonaviationsectorhasbeendeveloped.Historicaldata(OAG,AIMfromUCL)hasbeenusedtodisaggregateaviationactivityperpersonanddistance.Changesinoccupancyfactorshavebeenassumedtoassesstheimpactofbehaviourchangeinoildemand.◼Bothcommercial(IHSMarkit,JatoDynamics,Marklines)andin-house(GlobalFuelEconomyInitiative,MoMoDatabase)datasethavebeenusedtoperformanin-depthanalysiswasperformedontheriseofsportutilityvehicles(SUVs)atacountrylevel.Basedonananalysisofhistorictrends,amoderategrowthofSUVsisanticipatedintheSTEPSonaglobalscale.◼Thecarmarketisanalysedusingmultiplesources(Marklines,EVVolumesetc.),estimatingcarsalesrecoverypattern.Basedonastockmodel,achangeincarsalesvolumeduetobothnewpurchasesanddelayedreplacementisestimated.Econometricfunctionshavebeenappliedtoprojectthefuturetrend,assumingthatthecarmarketwillreturntonormalby2030.◼Historicaldatashowashiftfrompublictransporttoprivatevehiclesduetohealthconcerns.Publiclyavailablereports(i.e.surveybyIpsos)wereusedtoestimatethemobilityneedsthathavetobecoveredeitherbybicyclesorprivatecars.DifferentassumptionshavebeenmadefordifferentGECModelregions,dependingontheaccessibilitytobikes(i.e.lowaccessibilityintheUnitedStates,highaccessibilityintheNetherlands),andtheimpactonoildemandduetothismodalshiftwasestimated.Regardingtheanalysisontheimpactofteleworking,aliteraturereviewonthedistanceofacommutebymodeforkeyGECModelregionshasbeencarriedout.Thesedatahavebeenexpandedtoallregionsandtheoildemandforeachmodehavebeenestimated.Afterassumingthemaximumpotentialofteleworkingoftheworkforce(i.e.20%by2030),theimpactofteleworkingonoildemandwasassessed.Section3End-usesectors393.3BuildingssectorThebuildingssectormoduleoftheGECModelissubdividedintotheresidentialandservicessectors,bothhavingasimilarstructure(Figure3.9).Population,GDP,climateanddwellingoccupancydrivetheactivityvariables,suchasfloorspace,applianceownership,numberofhouseholds(residentialsector)andvalueadded(intheservicessector).Figure3.9⊳StructureofthebuildingssectorIEA.CCBY4.0.Intheresidentialandservicessectors,energydemandisfurthersubdividedintosixstandardendusesinbuildings,namelyspaceandwaterheating,appliances(dividedintofourdifferentcategories:refrigeration–fridgeandfreezer;cleaning–washing,dryingmachinesanddishwashers;browngoods–TVsandcomputers;andotherappliances),lighting,cookingandspacecooling–airconditionersandfans.Alllistedenduseswithineachsubmodulearemodelledindividually,withfinalenergyconsumptionprojectedfromthebaseyearovertheprojectionhorizonforeachend-useinthreesteps.Inafirststep,thedemandforanenergyservice,i.e.theusefulenergydemand,isdetermined,basedontheactivityvariables.𝐸𝑛𝑑𝑢𝑠𝑒𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑑𝑒𝑚𝑎𝑛𝑑=𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒×𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦Here,activityreferstothemaindriveroftheenergyservicedemand–fortheresidentialsectoritisfloorspacearea,peopleperhousehold,andappliancesownership;andforservices,itisvaluedaddedandfloorareabytheservicesector.Intensityreferstotheamountofenergyservice(e.g.spaceheating)neededperunitofactivityvariable(e.g.floorspace).Theactivityvariablesareprojectedeconometrically,basedonhistoricaldataandlinkingtosocio-economicdriverssuchasGDP,population,urbanisationandaccesstoenergy.Foreachenduse,theintensityvariableisprojectedusingthehistoricalintensityandadjusting,foreachprojectionyear,tothechangeinaverageend-userfuelprices(usingpriceelasticity)andchangeinaveragepercapitaincome(usingincomeelasticity).Inthespecificcaseofspaceheatingandcooling,theintensityprojectionsarealsoadjustedforhistoricalvariationsintemperature.Historicalenergydemandforspaceheating/spacecoolingandhistoricalHeatingSpaceheatingWaterheatingCookingLightingSpacecoolingNumberofhouseholdsAppliancesownershipServicesvalueaddedFloorspacePopulationGDPDwellingoccupancyUrbanisationAppliancesSub-sectorsRefrigerationWashingmachinesBrowngoodsOtherHistoricaltrendsEnd-useenergypricesDishwashersDryers40InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONDegreeDay(HDD)/CoolingDegreeDay(CDD)dataiscombinedtonormaliseprojectionsofspaceheating/spacecoolingenergydemand,removingtheimpactofyearonyearvolatilityinenergyserviceneeds.Theimpactofclimatechangeonspaceheatingandcoolingdemandisincludedaswell.Basedontheanticipatedchangeinheatingandcoolingdegreedaysduetoclimatechangeineachregionandundereachscenario’stemperaturepathway,theincreaseinheatingandcoolingdemandisquantified.TheseprojectionsarebasedonIEAownanalysisderivedfromNCARGISProgram(2012).ClimateChangeScenarios5,June2004,version3.0.wasusedtoderivedataproducts.Forthisanalysis,outcomesfromRCPs2.6and4.5(anomalies,multi-yearmeanofmonthlydataoffutureclimatesimulations)havebeenassociatedtotheNZEandSTEPSscenariorespectively.6Thespaceheatingandcoolingservicedemandiscomputedforbuildingsupontheirconstructionbasedonthebuildingsenergyefficiencyperformanceatthetimeofconstruction.Newbuildingsinthemodelareconstructedaseithernon-compliantwithbuildingenergycodes,compliant,orzero-carbon-readybuildings,thischoice,aswellastheregionandtheyearofconstruction,willinfluencethebuildingsenergyservicedemand.Theenergyservicedemandofabuildinginthemodelcanalsobeinfluencedbyretrofitting,anexistingbuildingcanberetrofittoimproveitsenergyperformance,bringingthebuildingtoacompliantstatus,whilethemoststringentretrofitsallowexistingbuildingenvelopestobecomezero-carbon-ready.Theprojectionsofthesharesofeachtypeofretrofitdependontheircostsandimplementedpoliciesineachscenario.Retrofittingabuildingwillextenditslifetime,influencingtheneedfornewconstructions.Thetotalenergyservicedemandtobemetbyheatingorcoolingequipmentisthenthesumoftheservicedemandacrossthedifferentvintagesofbuildingsandacrossthefivecategoriesofbuilding:non-compliant,compliant,zero-carbon-ready,retrofittocompliant,retrofittozero-carbon-ready.Improvementsintheperformanceofthebuildingenvelope(eitherviamoreefficientnewconstructionsorviaretrofits)shiftbuildingsfromonecategorytoanotherandtherebyreducethetotalenergyservicedemandforspaceheatingandspacecoolingthatremainstobemetbyheatingorcoolingequipment.Inasecondstep,thetechnologiestosupplytheend-useservicedemandarechosen.Foreachenduse,thereisadetailedsetoftechnologiesavailabletothemodel(Figure3.10).Withineachtechnologyoption,forexampleagasboiler,thereareseveraltypes,representingthevaryinglevelsofefficiencyandtheassociatedinvestmentcost.Additionally,thereisapossibilitytoswitchfuelsandtechnologies,wherebyheat-pumpscouldbeusedforspaceheating,insteadofgasboilers.Withintheresidentialsector,additionaldetailregardingbioenergyallowsformoreaccuratemodellingofthehistoricalandprojecteduseofbiogasdigesterstomeethomeenergyneeds,aswellastheuseofbioethanolandotherliquidsincookingstovesandhouseholdheatingequipment.Thetechnologychoiceismadebasedonrelativecosts,efficienciesofthetechnologiesandpolicyconstraints,ifany.TheshareoftechnologiesisallocatedbyaWeibullfunctionbasedontheirspecificcostsperunitofservicedemandsupplied,whichincludesinvestmentcosts,operatingandmaintenancecosts,andfuelcosts.Forexample,therelativeeconomiccompetitivenessofaheatpumpversusagasboilerforspaceheatingwilldifferdependingonthebuildingservicedemandforheating,whichimpacttheimportanceofinvestmentcostsrelativetooperationalcosts.Thisservicedemandisinfluencedbythebuildingtype,andthereforeitsefficiency,aswellasclimate.Theroutineallocatesthedifferenttechnologiestosatisfythenewservicedemandforeveryyearoverthemodelhorizon.Thisallocationissubjecttoupperandlowerboundaries,reflectingreal-worldconstraintssuchastechnologyavailabilityandadoption,policies,andmarketbarriers.Toassessandupdateequipmentandapplianceefficiency,andrelatedcosts,alargenumberofcompanies,expertsandresearchinstitutionsatthenationalandinternationallevels,includingIEATechnologyCollaboration5http://www.cesm.ucar.edu/models/ccsm3.0/6Seealsothiscommentary:https://www.iea.org/commentaries/is-cooling-the-future-of-heatingSection3End-usesectors41Programmes,areregularlycontacted.Theassessmentwasalsosupportedbyaninitialextensiveliteraturereviewtocataloguetechnologiesthatarenowusedindifferentpartsoftheworldandtojudgetheirprobableevolution(Anandarajah,etal.,2011;Econoler,etal.,2011;IEA,2010;IEA,2011;IEA,2012b;Kannan,etal.,2007;Waide,2011;IEA,2013a;IEA2014b).TheefficiencypotentialforelectricalapplianceshasbeendeterminedusingtheBUENAS(Bottom-UpEnergyAnalysisSystem)model,aninternationalappliancepolicymodeldevelopedbyLawrenceBerkeleyNationalLaboratory(LBNL).BUENAScoversthirteeneconomiesthattogetheraccountfor77%ofglobalenergyconsumption,andtwelvedifferentend-uses,includingairconditioning,lighting,refrigeratorsandindustrialmotors(LBNL,2012).TheassessmentofefficiencypotentialintheservicessectorbuildingsalsobenefitedfrompreliminaryestimatesavailablefromGBPN(GlobalBuildingsPerformanceNetwork)andCEU(CentralEuropeanUniversity)studyonbuildings(GBPNandCEU,2012).Figure3.10⊳Majorcategoriesoftechnologiesbyend-usesubsectorinbuildingsIEA.CCBY4.0.Inathirdstep,totalfinalenergyconsumptionintheresidentialandservicesectorisobtainedbasedontheefficienciesofexistingandnewbuildingequipment.Efficiencyrepresentstheamountofenergyneededtomeetaunitofservicedemand,andthusrepresentsthetechnicalperformanceoftheequipmentorappliances.Finalenergyconsumptioninthebuildingssectorisasummationofthesub-sectoralenergyconsumedbythetotaltechnologystock,whichincludesthehistorical(declining)stockofappliancesandequipment,andthenewtechnologiesaddedeveryyearoverthemodelhorizonbythetechnologyallocationroutine.𝐹𝑖𝑛𝑎𝑙𝑒𝑛𝑒𝑟𝑔𝑦𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛=1𝜂×𝐸𝑛𝑑𝑢𝑠𝑒𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑑𝑒𝑚𝑎𝑛𝑑Theimpactofbehaviouralchangeisintegratedatthispoint,withboththeenergyuseandenergyservicedemandpertechnologyadjustedtoreflectscenarioassumptionsonthebreadthanddepthofbehaviouralchangeinthebuildingssector.SpaceandwaterheatingFossilfuel-based(coal/oil/gas)-Conventional-CondensingElectricboilerHeatradiatorHeatpump(airorgroundsource)Renewables(solarheaters,geothermal,bioenergy)HydrogenandfuelcellboilersAppliancesMinimumavailablelevelofefficiencyMediumlevelofefficiencyBestavailabletechnologyCoolingRoomairconditionerSplit-airconditionerCentralairconditionerGroundsourceheatpumpDistrictcoolingSolarcoolingGaschillersLightingIncandescentHalogenFluorescentFluorescentrapidstartLEDCookingFossilfuel-based(LPG/gas/coal)ElectricSolidBioenergyBiofuelsBiogas42InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONThebuildingsmoduleisdirectlylinkedtotheenergyaccess(electricityaccessandcleancookingaccess)moduletotakeintoaccountthegrowthofelectricityandofalternativefuelsorstovesforcooking.Modeloutputsintermsofenergydemandbytechnology,thenumberoftechnologyunitsdeployed,buildingsconstructedorretrofit,areallusedtocalculateinvestmentsandenergyexpenditure.CO2,otherGHGemissionsandmaterialneeds(steel,cementandaluminium)relatedtothebuildingssectorarealsocalculated.Thebuildingsmoduleisdirectlylinkedtotheaccess(electricityaccessandcleancookingaccess)moduletotakeintoaccountthegrowthofelectricityandofalternativefuelsorstovesforcooking(seeSection11).BehaviourchangeBehaviouralchangesmodelledwithinthebuildingsmoduleincludelowerindoorairtemperaturesettings,loweruseofairconditioning,useofline-dryingandcoolwashing,aliteraturereviewwascarriedouttoassesstheimpactonenergyconsumption.AssumptionsregardingthepotentialallowedustoassessthetotalimpactandtheresultingdecreaseinCO2emissions.Regardingtheanalysisontheimpactofteleworking,aliteraturereviewontheimpactofworkingforhomeontheincreaseinresidentialconsumptionforkeyGECModelregionshasbeencarriedout.Thesedatahavebeenexpandedtoallregionsandthetotalincreaseforeachfuelhasbeenestimated.Themaximumpotentialforteleworkingwasassessedonacountry-by-countrybasisandbasedonthis,theimpactofteleworkingonresidentialconsumptionwasassessed.3.4Hourlyelectricitydemandanddemand-sideresponseUnderstandingthehourly,dailyandseasonalevolutionofelectricitydemandiscriticaltoaccuratemodellingofelectricitysystems,includingassessingelectricitysystemflexibilityneedsandtheroleofdemand-sideresponse.Modellingofhourlyelectricitydemandisundertakenatanend-uselevel.End-uselevelmodellingallowsthemodeltoreflecttheimpactofthefullscopeofdemandsideintegrationmeasures:electrificationandenergyefficiencyimpacttheannualdemandforend-useswhiledemand-sideresponse,includingloadshiftingandshedding,impactsdemandatamoretemporallygranularlevel.Modellinghourlyloadrequiresassessmentofthehourlyloadprofileforeachend-usewithineachsector,residentialandservices(e.g.spaceheating,waterheating.),industry(e.g.steel,chemicalsindustry),transport(e.g.roadandrail)andagriculture.Hourlyloadcurvesareassessedforevery24hoursof36typicaldays(aweekday,SaturdayandSundayofeachmonth).Hourlyloadcurvesforend-usesareinformedbyresearchandsurveydatawhereavailable.Detailonmodellingofhourlyheating,coolingandlightingelectricitydemandacrosstheyearisincluded,withdeeplearningalgorithmsusedtopredictspaceheatingandcoolingdemandforbothresidentialandservicesbuildingsbasedontemperature,buildingoccupancyratesandhistoricaldemand.Lightinghourlyelectricitydemandisprojectedbasedonbuildingactivityandoccupationrates,daylighttimesandinsolationlevels.Theaggregateelectricitydemandofeachend-useorsubsectoristhenmatchedtothetotalhistoricalhourlyloadprofileofagivencountry.7AnexampleoftheloadaggregationisdisplayedinFigure3.11.Modellingtheroleandpotentialofdemand-sideresponserequiresassessmentoftheshareofdemandthatisflexibleineachend-use.Thisshareistheproductofthreeflexibilityfactors,sheddability,controllabilityandacceptability(OokieMa,2013):◼Sheddability:Shareoftheloadofeachend-usethatcanbeshed,shiftedorincreasedbyatypicalDSRstrategy.7DatafromENTSO-E,PJM,ERCOT,MISO,NEISO,NYISOwereusedtoreplicaterespectivelytheoverallloadcurvesofEuropeanUnion,UnitedStatesandIndia.Section3End-usesectors43◼Controllability:Shareoftheloadofeachend-usewhichisassociatedwithequipmentthathasthenecessarycommunicationsandcontrolsinplacetotriggerandachieveloadsheds/shifts.◼Acceptability:Shareoftheloadforagivenend-usewhichisassociatedwithequipmentorserviceswheretheuseriswillingtoacceptthereducedlevelofserviceinademand-responseeventinexchangeforfinancialincentives.Thisframeworkenablesscenariostoconsiderdemandflexibilityfromvarioustechnologiesandatvaryinglevelsofsocialacceptability.Figure3.11⊳IllustrativeloadcurvesbysectorforaweekdayinFebruaryintheEuropeanUnioncomparedtotheobservedloadcurvebyENTSO-Efor2014IEA.CCBY4.0.Note:ENTSO-Erepresentstheaggregatedloadcurveforthe28EuropeanUnioncountries.Sources:(ENTSO-E,2016);IEAanalysis.TotalOtherindustryAluminiumPaperCementChemicalsIronandsteelOthertransportRailRoadAgricultureSpaceheatingLightingRefrigerationBrowngoodsCleaningOtherAppliancesWaterheatingResidential0h8h16h24hIndustryAgricultureandtransport0h8h16h24hGW0h8h16h24hGWServices0h8h16h24h0h8h16h24hGW4h12h20hCookingCoolingIndustryAgricultureTransportServicesResidentialENTSO-ESection4Electricitygenerationandheatproduction45Section44ElectricitygenerationandheatproductionBasedonelectricitydemand,whichiscomputedinallend-usesectors(describedinsection3)andotherenergytransformationsectors–notablyhydrogenproducedviaelectrolysis(section5),thepowergenerationmodulecalculatesthefollowing:◼Amountofnewgeneratingcapacityneededtomeetdemandgrowthandcoverretirementsandmaintainsecurityofsupply.◼Typeofnewplantstobebuiltbytechnology.◼Amountofelectricitygeneratedbyeachtypeofplanttomeetelectricitydemand,covertransmissionanddistributionlossesandownuse.◼Fuelconsumptionofthepowergenerationsector.◼CO2emissionsfromthecombustionoffossilfuelsandnon‐renewablewastes,includingreductionsfromtheuseofcarboncapture,utilisationandstorage(CCUS)technologies.◼Transmissionanddistributionnetworkinfrastructureneededtomeetnewdemandandreplaceretiringnetworkassets.◼Wholesaleandend-useelectricityprices.◼Investmentassociatedwithnewgenerationassetsandnetworkinfrastructure.4.1ElectricitygenerationThestructureofthepowergenerationmoduleisoutlinedinFigure4.1.Thepurposeofthemoduleistoensurethatenoughelectricalenergyisgeneratedtomeettheannualvolumeofdemandineachregion,andthatthereisenoughgeneratingcapacityineachregiontomeetthepeakelectricaldemand,whileensuringsecurityofsupplytocoverunforeseenoutages.Figure4.1⊳StructureofthepowergenerationmoduleIEA.CCBY4.0.KeyresultsGenerationbyplantRefurbishments/Retrofitting/MothballingofexistingplantsExistingcapacitybyplantRetirementsofexistingplantsWholesalepricePlantCapacityFactorsElectricitydemand+losses+ownuseAdditionsofnewplantsbytechnologyLoadcurveFuelandCO2pricesInvestmentassumptionsEfficienciesFuelconsumptionbyplantCO2emissionsbyplanttypeInvestmentneedsEnd-userpricesMeritorder46InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONThemodelbeginswithexistingcapacityineachregion,whichisbasedonadatabaseofallworldpowerplants.Thetechnicallifetimesofpowerplantsareassumedtorangebetween45and60yearsforexistingfossil-fuelplantsandnuclearplants(unlessotherwisespecifiedbygovernmentpolicies).ThelifetimesofwindandsolarPVinstallationsareassumedtohaveadistributioncentredaround25years,rangingfrom20to30years;hydropowerprojects50years;andbioenergypowerplants25years.CapacityadditionsThemodeldetermineshowmuchnewgenerationcapacityisrequiredannuallyineachregionbyconsideringthechangeinpeakdemandcomparedtothepreviousyear,retirementsofgenerationcapacityduringtheyear,andanyincreaseinrenewablecapacitybuiltastheresultofgovernmentpolicy.Installedgeneratingcapacitymustexceedpeakdemandbyasecurity-of-supplymargin;ifthismarginisnotrespectedafterchangesindemand,retirements,andrenewablesadditions,thenthemodeladdsnewcapacityintheregion.Inmakingthiscalculation,themodeltakesintoaccountlossesintransmissionanddistributionnetworksandelectricityusedbygenerationplantsthemselves.BecauseofthestochasticnatureoftheoutputofvariablerenewablessuchaswindandsolarPV,onlyaproportionoftheinstalledcapacityofthesetechnologiescanbeconsideredtocontributetotheavailablegenerationmargin.Thisisreflectedinthemodellingbytheuseofacapacitycreditforvariablerenewables.Thiscapacitycreditisestimatedfromhistoricaldataonhourlydemandandhourlygenerationfromvariablerenewablesinanumberofelectricitymarkets,anditreflectstheproportionoftheirinstalledcapacitythatcanreliablybeexpectedtobegeneratingatthetimeofpeakdemand.Whennewplantsareneeded,themodelmakesitschoicebetweendifferenttechnologyoptionsonthebasisoftheirregionalvalue-adjustedlevelisedcostofelectricity(VALCOE),whicharebasedonthelevelisedcostofelectricity(LCOE),alsoreferredtoasthelong-runmarginalcost(LRMC).TheLRMCofeachtechnologyistheaveragecostofeachunitofelectricityproducedoverthelifetimeofaplant,andiscalculatedasasumoflevelisedcapitalcosts,fixedoperationandmaintenance(O&M)costs,andvariableoperatingcosts.Variableoperatingcostsareinturncalculatedfromthefuelcost(includingaCO2pricewhererelevant)andplantefficiency.Ourregionalassumptionsforcapitalcostsaretakenfromourownsurveyofindustryviewsandprojectcosts,togetherwithestimatesfromNEA/IEA(2010).Theweightedaveragecostofcapital(pre-taxinrealterms)isassumedtobe8%intheOECDand7%innon-OECDcountriesunlessotherwisespecified,forexamplewithrevenuesupportpolicies,onshorewindandutility-scalesolarPVat3-6%(seefinancingcostssectionbelow),andoffshorewindat4-7%dependingontheregion.TheLRMCcalculatedforanyplantispartlydeterminedbytheirutilisationrates.Themodeltakesintoaccountthefactthatplantswillhavedifferentutilisationratesbecauseofthevariationindemandovertime,andthatdifferenttypesofplantsarecompetitiveatdifferentutilisationrates.(Forexample,coalandnucleartendtobemostcompetitiveathighutilisationrates,whilegasandoilplantsaremostcompetitiveatlowerutilisationrates).Thespecificnumericalassumptionsmadeoncapitalcosts,fixedO&Mcosts,andefficiencycanbefoundontheGECmodelwebsite:https://www.iea.org/reports/global-energy-climate-model/techno-economic-inputs.ThelevelisedcostmodulecomputesLRMCs(orLCOEs)forthefollowingtypesofplant:◼Coal,oilandgassteamboilerswithandwithoutCCUS(carboncapture,utilisationandstorage);◼Combined-cyclegasturbine(CCGT)withandwithoutCCUS;◼Open-cyclegasturbine(OCGT);◼Integratedgasificationcombinedcycle(IGCC);◼Oilandgasinternalcombustion;◼Fuelcells;◼BioenergywithandwithoutCCUS;Section4Electricitygenerationandheatproduction47◼Geothermal;◼Windonshoreandoffshore;◼Hydropower(conventional);◼Solarphotovoltaics;◼Concentratingsolarpower;◼Marine;and◼Utility-scalebatterystorage.RegionalLRMCsarealsocalculatedfornuclearpowerbutadditionsofnuclearpowercapacityaresubjecttogovernmentpolicies.GenerationvolumesForeachregion,themodeldeterminesthegenerationfromeachplantbasedonthecapacityinstalled,themarginalcosttoproduceelectricityandthelevelofelectricitydemand.Demandisrepresentedasfoursegments:◼baseloaddemand,representingdemandwithadurationofmorethan5944hoursperyear;◼low-midloaddemand,representingdemandwithadurationof3128to5944hoursperyear;◼high-midloaddemand,representingdemandwithadurationof782to3128hoursperyear;and◼peakloaddemand,representingdemandwithadurationoflessthan782hoursperyear.Thisresultsinasimplifiedfour-segmentload-durationcurvefordemand(Figure4.2).Thisdemandmustbemetbytheavailablepowercapacityofeachregion,whichconsistsofvariablerenewables–technologieslikewindandsolarphotovoltaics(PV)withoutstoragewhoseoutputisdrivenbyweather–anddispatchableplants(generationtechnologiesthatcanbemadetogenerateatanytimeexceptincasesoftechnicalmalfunction).Inordertoaccountfortheeffectofvariablerenewablesonwholesaleprices,themodelcalculatestheprobablecontributionofvariablerenewablesineachsegmentofthesimplifiedload-durationcurve.Subtractingthecontributionofrenewablesfromeachsegmentinthemeritorderleavesaresidualload-durationcurvethatmustbemetbydispatchablegenerators.Figure4.2⊳LoaddurationcurveshowingthefourdemandsegmentsIEA.CCBY4.0.PeakMid1Mid2Basetpeaktmid1tmid2tbase=8760Time(sorted)Load(GW)48InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONThemodelsubtractsfromthedemandineachsegmentanygenerationcomingfromplantsthatmustrun–suchassomeCHPplantsanddesalinationplants–andalsogenerationfromrenewables.Forgenerationfromvariablerenewables,theamountofgenerationineachdemandsegmentisestimatedbasedonthehistoricalcorrelationbetweengenerationanddemand.Theremainderofthedemandineachsegmentmustbemetbyproductionfromdispatchableplants.Themodeldeterminesthemixofdispatchablegenerationbyconstructingameritorderoftheplantsinstalled–thecumulativeinstalledgenerationcapacityarrangedinorderoftheirvariablegenerationcosts–andfindingthepointinthemeritorderthatcorrespondstothelevelofdemandineachsegment(Figure4.3).Asaresult,plantswithlowvariablegenerationcosts–suchasnuclearandlignite-burningplantsintheFigure4.3example–willtendtooperateforahighnumberofhourseachyearbecauseevenbaseloaddemandishigherthantheirpositioninthemeritorder.Ontheotherhand,someplantswithhighvariablecosts,suchasoil-firedplants,willoperateonlyduringthepeakdemandsegment.Figure4.3⊳ExamplemeritorderanditsintersectionwithdemandinthepowergenerationmoduleIEA.CCBY4.0.Note:Demandheremeansdemandnetofgenerationby“mustrun”plantssuchasdesalinationandsomeCHPplants,andnetofgenerationbyrenewables.CalculationofthecapacitycreditandcapacityfactorofvariablerenewablesPowergenerationfromweather-dependentrenewablessuchaswindandsolarpowervariesovertimeandthecharacteristicsofthepowersupplyfromvariablerenewableshavetobetakenintoaccountforthedecisionsondispatchandcapacityadditionsoftheremaining,mostlydispatchablepowerplants.Theeffectofallvariablerenewables(solarPV,solarCSPwithoutstorageandwindon-andoffshore)istakenintoaccountviathecapacitycreditandthecapacityfactorineachloadsegment.Thecapacitycreditofvariablerenewablesreflectstheproportionoftheirinstalledcapacitythatcanreliablybeexpectedtobegeneratingatthetimeofhighdemandineachsegment.Itdeterminesbyhowmuchnon-variablecapacityisneededineachloadsegment.Thecapacityfactorgivestheamountofenergyproducedbyvariablerenewablesineachloadsegmentanddetermineshowmuchnon-variablegenerationisneededineachsegment.Both,capacitycreditandcapacityfactorarecalculatedbasedthecomparisonbetweenthehourlyloadprofileandthewindandsolarsupplytime-series,derivedfrommeteorologicaldata.Toquantifytheeffectsofvariablerenewables,thehourlyloadprofileiscomparedtothehourlyresidualload,beingtheelectricityloadafter$/MWh050100150200MWhCHPLigniteandsteamcoalGasCCGTGasGTOilSteamandGTNuclearBaseloaddemandLow-midloaddemandHigh-midloaddemandPeakdemandSection4Electricitygenerationandheatproduction49accountingforpowergenerationfromvariablerenewables(seeFigure4.4a).Bysortingtheresidualload,thelevelsofaverageandmaximaldemandperloadsegmentcanbedetermined.Thedifferencebetweentheloadlevelsofthenormalloadandtheresidualloadgivestheimpactofvariablerenewablesonthepowergenerationandcapacityneeds(seeFigure4.4b).Figure4.4⊳Exampleelectricitydemandandresidualloada)Loadandresidualloadforselecteddaysb)LoadandresidualloaddurationcurveforoneyearIEA.CCBY4.0.Thecapacityfactorofvariablerenewables(varRE)perloadsegmentcanbecalculatedgenerationperloadsegmentsoftheresidualload.𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑓𝑎𝑐𝑡𝑜𝑟𝑠=𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑁𝑒𝑒𝑑𝑠𝑛𝑜𝑛−𝑣𝑎𝑟,𝑠𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸=GenerationvarREs𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸Forcapacityadditions,thepeakloadsegmentisrelevant.Thecapacitycreditisestimatedbasedonthedifferencebetweenmaximalloadandmaximalresidualload:𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑐𝑟𝑒𝑑𝑖𝑡𝑝𝑒𝑎𝑘=𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑁𝑒𝑒𝑑𝑠𝑛𝑜𝑛−𝑣𝑎𝑟𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸=maxt(𝐿𝑜𝑎𝑑(𝑡))−maxt(𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝐿𝑜𝑎𝑑(𝑡))𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸Meteorologicaldata(windspeedandsolarirradiation)forseveralyearswasusedforthecapacitycreditcalculation.Inaggregatingtheresultsofcapacitycreditobtainedfromdifferentyearsofmeteorologicaldata,asfirstorderapproachitwasassumedthattheannualpeakresidualdemandisnormally-distributedandcalculatedthecapacitycreditbasedonthedifferencebetweenpeakdemandandthepointonestandarddeviationabovetheresidualpeakdemand(Figure4.5).Themeteorologicaldatawindandsolardatastemsfromthefollowingre-analysisdatasets◼WorldWindAtlas(Sander+PartnerGmbH):Globaldatasetofhourlywindspeedsat10mheight,1979-2009,derivedfromreanalysisdatabasedonclimatemodelling(Suraniana,2010)◼Windsupplytime-seriesforWestandEasternUSasderivedbyWWITS(2010)andEWITS(2011).◼Windandsolarsupplytime-seriesforEurope-27asprovidedbySiemensAG(Heide,2010)foreachmajorRegioninEurope.OriginalmeteorologicalwindspeedstemsfromReanalysisdata(WEBROG,2008).◼HourlysolarirradiationdatafromsatelliteobservationsfortheUS(NREL,2010)◼Estimationofsolarirradiationbasedonsolarheight(Aboumahboub,2010)0102030405060708090100125497397121145169193217241265289313337GWhour0102030405060708090100110012001300140015001600170018001GWhours(sorted)RenewableenergygenerationLoadResidualload50InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONFigure4.5⊳ExemplaryelectricitydemandandresidualloadIEA.CCBY4.0.4.2Value-adjustedLevelizedCostofElectricityMajorcontributorstotheLevelizedCostofElectricity(LCOE)includeovernightcapitalcosts;capacityfactorthatdescribestheaverageoutputovertheyearrelativetothemaximumratedcapacity(typicalvaluesprovided);thecostoffuelinputs;plusoperationandmaintenance.Economiclifetimeassumptionsare25yearsforsolarPV,onshoreandoffshorewind.Foralltechnologies,astandardweightedaveragecostofcapitalwasassumed(7-8%basedonthestageofeconomicdevelopment,inrealterms).Thevalue-adjustedLCOE(VALCOE)isametricforcompetitivenessforpowergenerationtechnologies,buildingonthecapabilitiesoftheGECModelhourlypowersupplymodel.ItisintendedtocomplementtheLCOE,whichonlycapturesrelevantinformationoncostsanddoesnotreflectthedifferingvaluepropositionsoftechnologies.WhileLCOEhastheadvantageofcompressingallthedirecttechnologycostsintoasinglemetricwhichiseasytounderstand,itneverthelesshassignificantshortcomings:itlacksrepresentationofvalueorindirectcoststothesystemanditisparticularlypoorforcomparingtechnologiesthatoperatedifferently(e.g.variablerenewablesanddispatchabletechnologies).VALCOEenablescomparisonsthattakeaccountofbothcostandvaluetobemadebetweenvariablerenewablesanddispatchablethermaltechnologies.TheVALCOEbuildsonthefoundationoftheaverageLCOE(orLRMC)bytechnology,addingthreeelementsofvalue:energy,capacityandflexibility.Foreachtechnology,theestimatedvalueelementsarecomparedagainstthesystemaverageinordertocalculatetheadjustment(eitherupordown)totheLCOE.Afteradjustmentsareappliedtoalltechnologies,theVALCOEthenprovidesabasisforevaluatingcompetitiveness,withthetechnologythathasthelowestnumberbeingthemostcompetitive(Figure4.6).TheVALCOEisapplicableinallsystems,asenergy,capacityandflexibilityservicesareprovidedandnecessaryinallsystems,eventhoughtheymaynotberemuneratedindividually.Inthisway,ittakestheperspectiveofpolicymakersandplanners.Itdoesnotnecessarilyrepresenttheperspectiveofinvestors,whowouldconsideronlyavailablerevenuestreams,whichmayalsoincludesubsidiesandothersupportmeasures,suchasspecialtaxprovisions,thatarenotincludedintheVALCOE.Theimpactofthevalueadjustmentvariesbytechnologydependingonoperatingpatternsandsystem-specificconditions.DispatchabletechnologiesthatoperateonlyduringpeaktimeshavehighcostsperMWh,butalsorelativelyhighvalueperMWh.Forbaseloadtechnologies,valuetendstobeclosetothesystemaverageandthereforetheyhaveasmallvalueadjustment.Forvariablerenewables,thevalueadjustmentdependsmainlyonσPeakdemandGWProbabilitydensityfunctionofresidualdemand-ExpectedpeakresidualdemandCapacitycreditcalculationbasedonthisdifferencepSection4Electricitygenerationandheatproduction51theresourceandproductionprofile,thealignmentwiththeshapeofelectricitydemandandtheshareofvariablerenewablesalreadyinthesystem.DifferentoperationalpatternscanbeaccountedforintheVALCOE,improvingcomparisonsacrossdispatchabletechnologies.Figure4.6⊳MovingbeyondtheLCOE,tothevalue-adjustedLCOEIEA.CCBY4.0.TheVALCOEiscomposedofLCOEandenergy,capacityaswellasflexibilityvalue.Itscalculationgoesasfollows:𝑉𝐴𝐿𝐶𝑂𝐸𝑥=𝐿𝐶𝑂𝐸𝑥+[𝐸̅−𝐸𝑥]⏞𝐸𝑛𝑒𝑟𝑔𝑦𝑣𝑎𝑙𝑢𝑒+[𝐶̅−𝐶𝑥]⏞𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒+[𝐹̅−𝐹𝑥]⏞𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒⏞𝑉𝑎𝑙𝑢𝑒𝑎𝑑𝑗𝑢𝑠𝑡𝑚𝑒𝑛𝑡𝑠Theadjustmentforenergyvalue[𝐸𝑥]ofatechnologyx(orgenerationunit)isthedifferencebetweentheindividualunittothesystemaverageunit[𝐸̅].[𝐸𝑥]iscalculatedasfollows:𝐸𝑛𝑒𝑟𝑔𝑦𝑣𝑎𝑙𝑢𝑒𝑥($𝑀𝑊ℎ)=∑[𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒𝑃𝑟𝑖𝑐𝑒ℎ($𝑀𝑊ℎ)×𝑂𝑢𝑡𝑝𝑢𝑡𝑥,ℎ(𝑀𝑊)]8760ℎ∑𝑂𝑢𝑡𝑝𝑢𝑡𝑥,ℎ(𝑀𝑊)8760ℎWholesaleelectricitypricesandoutputvolumesforeachtechnologyxineachhourhoftheyeararesimulated.Wholesalepricesarebasedonthemarginalcostofgenerationonlyanddonotincludeanyscarcitypricingorothercostadders,suchasoperatingreservesdemandcurvespresentinUSmarkets.HourlymodelsareappliedfortheUnitedStates,EuropeanUnion,ChinaandIndia.Forotherregions,wholesalepricesandoutputvolumesaresimulatedforthefoursegmentsoftheyearpresentedinsection4.1.2.Theadjustmentforcapacityvalue[𝐶𝑥]ofagenerationunitiscalculatedasfollows:𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑥($𝑀𝑊ℎ)=𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑐𝑟𝑒𝑑𝑖𝑡𝑥×𝐵𝑎𝑠𝑖𝑠𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒($/𝑘𝑊)(𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑓𝑎𝑐𝑡𝑜𝑟𝑥×ℎ𝑜𝑢𝑟𝑠𝑖𝑛𝑦𝑒𝑎𝑟/1000)Thecapacitycreditreflectsthecontributiontosystemadequacyanditisdifferentiatedfordispatchableversusrenewabletechnologies:◼Dispatchablepowerplants=(1-unplannedoutageratebytechnology)◼Renewables=analysisoftechnology-specificvaluesbyregionwithhourlymodelling52InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONTheBasiscapacityvalueisdeterminedbasedonsimulationofcapacitymarket,setbythehighest“bid”forcapacitypayment.Positivebidsreflectthepaymentneededtofillthegapbetweentotalgenerationcosts(includingcapitalrecovery)andavailablerevenue.Thecapacityfactorisdifferentiatedbytechnology:◼Dispatchablepowerplants=modelledassimulatedoperationsinpreviousyear◼WindandsolarPV=alignedwithlatestperformancedatafromIRENAandothersources,improvingovertimeduetotechnologyimprovements◼Hydropowerandotherrenewables=alignedwithlatestperformancedatabyregionandlong-termregionalaveragesTheflexibilityvalue[𝐹𝑥]ofagenerationunitiscalculatedasfollows:𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑥($𝑀𝑊ℎ)=𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟𝑥×𝐵𝑎𝑠𝑒𝑓𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒($𝑘𝑊)(𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑓𝑎𝑐𝑡𝑜𝑟𝑥×ℎ𝑜𝑢𝑟𝑠𝑖𝑛𝑦𝑒𝑎𝑟/1000)◼TheFlexibilityvaluemultiplierbytechnologyisbasedonavailablemarketdataandheldconstantovertime.Targetedchangesintheoperationsofpowerplantstoincreaseflexibilityvaluearenotrepresented.◼TheBaseflexibilityvalueisafunctionoftheannualshareofvariablerenewablesingeneration,informedbyavailablemarketdataintheEUandUS.TheflexibilityvalueisassumedtoincreasewithrisingVREshares,uptoamaximumequaltothefullfixedcapitalrecoverycostsofapeakingplant.AdvantagesandlimitationsoftheVALCOEVALCOEhasseveraladvantagesovertheLCOEalone:◼Itprovidesamoresophisticatedmetricofcompetitivenessincorporatingtechnology-specificinformationandsystem-specificcharacteristics◼Itreflectsinformation/estimationsofvalueprovidedtothesystembyeachtechnology(energy,capacity/adequacyandflexibility)◼Itprovidesarobustmetricofcompetitivenessacrosstechnologieswithdifferentoperationalcharacteristics(e.g.baseloadtopeaking,ordispatchabletovariable)◼ItprovidesarobustmetricofcompetitivenesswithrisingsharesofwindandsolarPVHowever,networkintegrationcostsarenotincluded,norareenvironmentalexternalitiesunlessexplicitlypricedinthemarkets.Fueldiversityconcerns,acriticalelementofelectricitysecurity,arealsonotreflectedintheVALCOE.TheVALCOEapproachhassomeparallelselsewhere,inotherapproachesusedforlong-termenergyanalysis,aswellsomereal-worldapplications.TheVALCOEismostcloselyrelatedtotheSystemLCOE,whichprovidesacomprehensivetheoreticalframeworkforassessingsystemvaluebeyondtheLCOE(Ueckerdt,Hirth,Luderer,&Edenhofer,2013).TheVALCOEandSystemLCOEaresimilarinscope,andre-arrangingtermscanalignsignificantportionsofthecomputations.Optimisationmodelsimplicitlyrepresentthecostandvalueoftechnologiesthroughstandardprofitabilitymetrics,suchasnetpresentvalueandinternalratesofreturn,butmaybelimitedbythescopeofcostsincluded,suchasthoserelatedtoancillaryservices.Otherlong-termenergymodellingframeworkshaveincorporatedcostandvaluemetricsincapacityexpansiondecisions,suchastheLevelisedAvoidedCostofElectricity(LACE)built-intheNEMSmodelusedbytheUSEnergyInformationAdministration.Inpolicyapplications,inthe2017cleanenergyauctionschemesinMexico,averageenergyvaluesforprospectiveprojectshavebeensimulatedandusedtoadjustthebidprices,seekingtoidentifythemostcost-effectiveprojects.Ascleanenergytransitionsprogressaroundtheworld,experiencewithhighersharesofwindandsolarPVinlargesystemswillincreaseandprovideopportunitiestorefinetheVALCOEandothermetricsofcompetitiveness.Section4Electricitygenerationandheatproduction53Financingcostsforutility-scalesolarPVThedecliningcostsofsolarPVhavebeenimpressive,withinnovationdrivingdownconstructioncostsby80%from2010to2019(IRENA,2020).Costreductionshavebeencomplementedbyimprovedperformanceresultingfromhigherefficiencypanelsandgreateruseoftrackingequipment.Financingcosts,however,havereceivedlittleattentiondespitetheirimportance.Theweightedaveragecostofcapital(WACC)canaccountforuntilhalfofthelevelisedcostofelectricity(LCOE)ofutility-scalesolarPVprojects.WEO-2020focusedonfinancingcostthroughanextensiveworkbasedondatafromfinancialmarketsandacademicliterature,andontheanalysisofauctionresultsandpowerpurchaseagreements(PPAs),complementedbyalargenumberofconfidentialinterviewswithexpertsandpractitionersaroundtheworld.Theanalysisfoundthatin2019,WACCsfornewutility-scalesolarPVprojectswithrevenuesupportstoodat2.4-4.5%inEuropeandtheUnitedStates(inrealterms,pre-tax),3.4-3.6%inChinaand5.0-6.6%inIndia.Theanalysisofbusinessmodelsdrawsonthekeyrevenueriskcomponents–price,volumeandoff-takerrisk–andtheirimplicationsforthecostofcapital.Itfocusesonmodelswherepricespaidforsolargenerationaredefinedlargelybypolicymechanisms,whichsupportthevastmajorityofdeploymentworldwide.ThefindingsofthisanalysisontheprevailingaveragecostsofcapitalinmajorsolarPVmarketsunderpintheprojectionsintheIEAGlobalEnergyandClimateModel.Fullmerchantprojects(withoutanyformofpriceguaranteeexternaltomarkets)wereconsideredasapointofcomparisonandanindicativeWACCprovided,thoughtodatethismodelremainssomewhattheoreticalforsolarPV.Inthelongerterm,thistypeofinvestmentmaybecomemorecommon.4.3ElectricitytransmissionanddistributionnetworksThemodelcalculateselectricitytransmissionanddistributionnetworkexpansionandreplacementalongwithassociatedinvestmentperregion.Transmissionnetworkstransportlargevolumesofelectricityoverlongdistancesathighvoltage.Mostlargegeneratorsandsomelarge-scaleindustrialusersofelectricityareconnecteddirectlytotransmissionnetworks.Distributionnetworkstransformhigh-voltageelectricityfromthetransmissionnetworkintolowervoltages,forusebylight-industrial,commercial,anddomesticend-users.ElectricitynetworksintheGECModelaredividedintoseveralcategories:representedasfivedistinctvoltageranges,overheadlineorundergroundcable,andbyalternatingcurrent(AC)ordirectcurrent(DC),creating20possiblelineorcabletypes.Thisallowsforincreasedgranularityonequipmentcosts,materialneeds,andregionaldifferences.Thisinformationisthenusedinthemodeltounderstandcurrentandprojectedcompositionofnetworks,aslineexpansionprojectionscarrythesamelevelofdetailonlineandcabletype.Becauseofthis,coststhatareregion-andline-specificcanbepairedwithlineandcabletypetocreateamodelrepresentationofinvestmentneedsforthatparticulargrid.Inaddition,thisdetailedviewoflineandcabletypeisthenpairedwithmaterialsuseperkm,notablywithcriticalminerals,toformprojectionsofmaterialsdemandduetothegrowingelectricitynetwork.Theneedfornewelectricitynetworklinelengthsisdrivenbythreefactors:toreplaceexistinglinesnearingtheendoftheirtechnicallifetime,tosupportincreasingelectricitydemand,andtointegrateadditionalrenewablesinthepowersector.LinereplacementduetoageinginfrastructureAssuminganaveragelifetimeof40yearsforlinesandcables,themodelcalculatesannualreplacementsaccordingly.Whilethisdoesnotincreasetheoverallsizeofthenetwork,itaddsmillionsofkmofnewlinesandcableseachyearthatmustbeaccountedforintermsofinvestmentsandmaterialdemand,aswellasforprojectplanning.54InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONLinelengthexpansionduetoelectricitydemandgrowthNetworkexpansionincreasesalongsidegrowthinelectricitydemand.Inordertorepresentthis,adynamicrelationshipbetweennetworkexpansionperunitofdemandgrowthwascreatedthatrelatestoGDPpercapita.Inthis,thekmoflinelengthperTWhofdemandforeachregionisusedinconjunctionwiththeGDPpercapitaforthegivenregioninordertoproduceanequationthatrepresentsthisgloballevelrelationship.Asthenetworkgrowthratesdifferbetweenthedistributionandtransmissionlevel,thisrelationshipwasdoneforeach,yieldingtwosetsofalphaandbetaparametersthatcanbeusedaccordingly.Figure4.7⊳ElectricitynetworkexpansionperunitofelectricitydemandgrowthbyGDPpercapitaIEA.CCBY4.0.LinelengthexpansionduetorenewablesAconsiderableamountofthecapacityadditionsprojectedoverthemodellinghorizonperiodisfromrenewables.Thegeographicallocationofthesetechnologiesisoftenstronglyinfluencedbythelocationoftheunderlyingresource(e.g.areaswherethewindisstrongorinsolationishigh),whichmaynotbeclosetoexistingcentresofdemand.Inaddition,someofthesetechnologies,mainlysolarPV,areconnectedattheend-usersideofthegridinfrastructure.Thismodulardeploymentofgenerationcapacitycanleadtoincreasedistributioncapacityneeds.Becausetheintroductionoflargequantitiesofremoteorvariablerenewableswasnotamarkedfeatureofthehistoricdevelopmentofelectricitynetworks(withtheexceptionofregionswhereremotehydroelectricityrepresentsalargeproportionofthegenerationmix),theadditionofmorerenewablesislikelytoincreasetheaveragelengthofnetworkadditions.Lineexpansionisbeingdrivenbytwofactors:thetransmissionlinesthatconnectsolarandwindfarmstothegrid,andenforcementrequirementswithinthegrid.Afactorfortheaverageconnectinglinelengthwasderivedfromtheaveragelinelengthconnectingpastutility-scalesolarPV,windonshoreandwindoffshoreprojects.Theaddedcapacityfromtheserenewableenergytechnologiesismultipliedbythehistoricalrelationshiptoobtainrelatedlineextensions.Thegridenforcementsarebasedonastudyconductedincountrieswithhighrenewableenergydevelopment.Uptoathresholdoftheshareofrenewablegeneration,thereisnoneedforgridimprovements.Anincreaseinthesharebeyondthisthresholdleadstoadditionallengthstoreinforcethegrid,basedontheliteratureandprojectedsharesofrenewablesbyregionandscenario.Theestimationofdistributiongridextensionsforrenewablescontainsalotmoreuncertaintiesthanthetransmissiongrid,aslessdataorstudiesareavailableonthetechnicallycomplexdistributionnetworkisavailable2000400060008000100001200001020304050607080GDPpercapitaDistributionkmperTWh10020030040050060070001020304050607080TransmissionSection4Electricitygenerationandheatproduction55andownuseofdistributedgenerationcaninturnleadtoareducedneedfordistributiongridinfrastructure.Therefore,weassume,thatadditionalnetworkinvestmentisrequiredonlyiftheelectricitygeneratedfromdistributedgeneration,suchassolarPVinbuildings,exceedslocaldemandandisfedbacktothesystem.ElectricitynetworkinvestmentInvestmentsforelectricitynetworksarecomposedofthoserelatedtothethreemaindriversoflinelengthexpansion;increasingdemand,replacements,andincreaseinrenewables.Inaddition,theyalsoincludeinvestmentsduetonon-line-lengthcomponentssuchasgridformingrequirementsandtransmissionlevelreinforcement.Forthelinelengthcomponentsoftheinvestment,whichcomprisethemajorityofoverallnetworkinvestments,themodelcalculatesthisasthenetworkexpansioninkmduetoagivendrivermultipliedbytheunitcostforeachlineandcabletype.Gridformingrequirementsarealsoincorporatedintotheelectricitynetworkrepresentation,relatedtotheextentoftheshifttovariablerenewablesintheprojections.Variablerenewableslackmechanicalinertiaasitconnectstothenetworkviaaconverter.Inertiacomesfromthelargerotatingmassesinthegeneratorsinpowerplantsandisnecessarytokeepthenetworkstabilizedespeciallyincaseoffaultevents.WiththerisingshareofvariablerenewablesthenetworkneedsgridformingstabilizingtechnologyfromtheFACTsfamily.Thecalculationforthisinvestmentisbasedondeploymentneedsrealizedincountrieswithhighshareofrenewables.Theinvestmentisdrivenbytheexpansionofrenewablesgenerationaboveaminimumlevel,andincreasesbasedonassessedneedsineachregion.Belowtheminimumlevel,thegridremainsstablewithoutadditionalmeasures.Eachoftheelectricitynetworkequipmentunitcostshavebeencreatedusinganaverageofprojectandnationallevelcosts,collectedfrompublicationsthatdetailcostsperkmbasedoncorrespondinglineandcabletype.Theyrepresentcostsfromseveralregionsglobally,allowingforabalancedviewofregion-specificcosts.Thesecostsarethentailoredfurtherperregion,creatingaseriesof20differentcostsperkmforeachregion.Similarly,replacementcostsarealsolineandregionspecific.Foralltypesandregions,replacementcostsarelowerthanthatofnewlines,aspermitting,land,andmuchofthecapitalcostsdonotneedtoberedone.However,region-specificdiscountersareusedtodifferentiatebetweenmaterialuseperregionaswellaslabourcostsperregion,twofactorsthatcangreatlyinfluencecostsperkm.Bringingallofthesecostsanddriversfornetworkexpansion,themodelcalculatesoverallnetworkinvestmentwiththefollowingequationforeachofthe20lineandcabletypes:𝐴𝑛𝑛𝑢𝑎𝑙𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑏𝑦𝑟𝑒𝑔𝑖𝑜𝑛=∑[𝑐𝑜𝑠𝑡𝑛𝑒𝑤𝑙𝑖𝑛𝑒𝑠𝑉,𝑃,𝐶𝑉,𝑃,𝐶∗(∆𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝑑𝑒𝑚𝑎𝑛𝑑∗(𝛼∗ln(𝐺𝐷𝑃𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎)+𝛽)∗𝑔𝑟𝑖𝑑𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑉,𝑃,𝐶+∑(𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑅𝑅∗𝛾𝑉,𝑃,𝐶)+∆𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠∗𝜓∗𝑡𝑜𝑡𝑎𝑙𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑙𝑒𝑛𝑔𝑡ℎ𝑉,𝑃,𝐶)+𝑐𝑜𝑠𝑡𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶∗𝑙𝑖𝑛𝑒𝑠𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶]+𝑐𝑜𝑠𝑡𝑆𝑇𝐴𝑇𝐶𝑂𝑀∗𝜙∗∆𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑∗𝑡𝑜𝑡𝑎𝑙𝑝𝑜𝑤𝑒𝑟𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝐴𝑛𝑛𝑢𝑎𝑙𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑏𝑦𝑟𝑒𝑔𝑖𝑜𝑛=∑[𝑐𝑜𝑠𝑡𝑛𝑒𝑤𝑙𝑖𝑛𝑒𝑠𝑉,𝑃,𝐶∗(∆𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝑑𝑒𝑚𝑎𝑛𝑑∗(𝛼∗ln(𝐺𝐷𝑃𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎)+𝛽)∗𝑔𝑟𝑖𝑑𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑉,𝑃,𝐶+∑(𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑅𝑅∗𝛾𝑉,𝑃,𝐶))𝑉,𝑃,𝐶+𝑐𝑜𝑠𝑡𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶∗𝑙𝑖𝑛𝑒𝑠𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶]56InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONWhere•𝑉isvoltagelevelband•𝑃isposition(overhead,underground)•𝐶iscurrent(ACorDC)•𝑅istherenewableenergytechnology•𝑔𝑟𝑖𝑑𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑉,𝑃,𝐶isthehistoricalsharesofthegridbyvoltage,position,andcurrent•𝛼,𝛽aredimensionlessvariablesintheequationrelatingdemandgrowthtoGDPpercapita,derivedfromhistoricaldatabyregion•𝛾istheadditionallinelengthsrequiredtoconnectnewrenewablescapacityadditions,measuredinkmperGW,byvoltage,position,andcurrent•𝜓isthedimensionlessfactorofadditionaltransmissionnetworkrequirementsduetohighsharesofvariablerenewables,whereitexceedsaminimumthreshold•∆𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝑑𝑒𝑚𝑎𝑛𝑑istheannualincreaseinelectricitydemandintheregion•∆𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠istheannualincreaseinshareofvariablerenewablesintotalinstalledcapacity•𝑙𝑖𝑛𝑒𝑠𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶arethelinestobereplaced,inkm,definedasthosereaching40yearsofuse•𝑐𝑜𝑠𝑡𝑆𝑇𝐴𝑇𝐶𝑂𝑀isthecostofSTATCOMdevices(staticsynchronouscompensators)•𝜙isthedimensionlessfactorofadditionalgridformingrequirementsduetohighsharesofvariablerenewables,giventhattheshareofrenewablesexceedsaminimumthreshold4.4HourlymodelToquantifythescaleofthechallengearisingfromtheintegrationofhighsharesofVREandtoassesswhichmeasurescouldbeusedtominimisecurtailment,anhourlymodelwasdevelopedforWEO-2016,toprovidefurtherinsightsintotheoperationsofpowersystems.ThemodelbuildsupontheannualprojectionsgeneratedintheGECModelandmakesitpossibletoexploreemergingissuesinpowersystems,suchasthosethatariseastheshareofVREcontinuestorise.ThemodelthenfeedsthemainGECModelwithinformationaboutadditionalconstraintsontheoperationsofdifferentpowerplants.Themodelisaclassicalhourlydispatchmodel,representingallhoursintheyear,settingtheobjectiveofmeetingelectricitydemandineachhourofthedayforeachdayoftheyearatthelowestpossiblecost,whilerespectingoperationalconstraints.1All106powerplanttypesrecordedintheGECModelandtheirinstalledcapacitiesarerepresentedinthehourlymodel,includingexistingandnewfossil-fuelledpowerplants,nuclearplantsand16differentrenewableenergytechnologies.ThefleetofpowerplantsthatisavailableineachyearisdeterminedinGECModelanddiffersbyscenario,dependingontheprevalentpolicyframework.Theseplantsarethenmadeavailabletothehourlymodelandaredispatched(orchosentooperate)onthebasisoftheshort-runmarginaloperatingcostsofeachplant(whicharemainlydeterminedbyfuelcostsasprojectedinGECModel)totheextentrequiredtomeetdemand.Thedispatchoperatesunderconstraints:thereareminimumgenerationlevelstoensuretheflexibilityandstabilityofthepowersystemandtomeetotherneeds(suchascombinedheatandpower);thevariabilityofrenewableresources(suchaswindandsolar)determinestheavailabilityofvariablerenewablesand,hence,themaximumoutputatanypointintime;andrampingconstraintsapply,derivedfromthelevelofoutputintheprecedinghourandthecharacteristicsofdifferenttypesofpowerplants.Thehourlydispatchmodeldoesnotrepresentthetransmissionanddistributionsystem,norgridbottlenecks,cross-borderflowsortheflowofpowerthroughthegrid.ItthereforesimulatessystemsthatareabletoachievefullintegrationacrossbalancingareasineachGECModelregion(e.g.UnitedStates,EuropeanUnion,ChinaandIndia).1Themodelworksonanhourlygranularity,andthereforeallintra-hourvaluesofdifferentdevices(e.g.ofstoragetechnologies)arenotcaptured.Section4Electricitygenerationandheatproduction57KeyinputstothemodelincludedetailedaggregatehourlyproductionprofilesforwindpowerandsolarPVforeachregion,whichweregeneratedbycombiningsimulatedproductionprofilesforhundredsofindividualwindparksandsolarPVinstallations,distributedacrosstherelevantregion.2Theindividualsiteswerechosentorepresentabroaddistributionwithinaregion,allowingthemodeltorepresentthesmoothingeffectachievedbyexpandingbalancingareas.Onthedemandside,themodelusesadetailedanalysis,withhourlydemandprofilesforeachspecificend-use(suchasforlightingorwaterheatingintheresidentialsector),coupledwiththeannualevolutionofelectricitydemandbyspecificend-useoverthemodelhorizonfromthemainGECModel(seeSection3.4).Thehourlymodelaccountsforgrid,flexiblegenerationandsystem-friendlydevelopmentofVRE,inthreesteps:first,itassessestheamountofcurtailmentofvariablerenewablesthatwouldoccurwithoutdemand-sideresponseandstorage.Second,itdeploysdemand-sideresponsemeasures,basedontheavailablepotentialineachhourforeachelectricityend-use.Andthird,itusesexistingandnewstoragefacilitiestodeterminetheeconomicoperationsofstoragebasedonthepricedifferentialacrosshoursandcharge/dischargeperiods.Ittherebyenablestheintegrationneedsarisingfromgrowingsharesofrenewablestobeassessed.Amongtheotherimportantmodeloutputsistheresultinghourlymarketprice,whichcandroptozerointhehourswhengenerationfromzeromarginalcostgenerators(suchasvariablerenewables)issufficienttomeetdemand.Bymultiplyingthemarketpricebygenerationoutputineachhour,themodelcalculatestherevenuesreceivedfortheoutputineachhourbyeachtypeofplant,creatingabasisforcalculatingthevalueofVRE.Naturally,themodelalsoincludeshourlyoperationinformationforeachplanttype,includingfuelcostsandassociatedgreenhouse-gasandpollutantemissions.4.5Mini-andoff-gridpowersystemsSincetheAfricaEnergyOutlookin2014,therepresentationofmini-andoff-gridsystems,relatedtothosegainingaccesstoelectricity,hasbeenimprovedandbetterintegratedintotheGECModel.Inlinewiththeapproachforon-gridpowersystems,tomeetadditionalelectricitydemand,themodelchoosesbetweenavailabletechnologiesformini-andoff-gridsystemsbasedontheirregionallong-runmarginalcosts,andusingdetailedgeospatialmodellingtotakeintoaccountseveraldeterminingfactors.FortheAfricaEnergyOutlook2019,theIEArefineditsanalysisusingup-to-datetechnologycosts,demandprojections,andthelatestversionoftheOpenSourceSpatialElectrificationTool(OnSSET)3developedbyKTH,tocoverindetail44countriesinsub-SaharanAfrica.Thetechnologiesarerestrictedbytheavailableresourcesineachregion,includingrenewableenergyresourcessuchasriversystems,biomassfeedstocks(e.g.forestsandagriculturalresidues),windandthestrengthofsolarinsolation.Back-uppowergenerationforthosewithaccesstothegrid,typicallygasolineordieselfuelled,wasalsorepresentedtothemodel,withitsprojectedusetiedtothequalityoftheon-gridpowersupply.4.6RenewablesandcombinedheatandpowermodulesTheprojectionsforrenewableelectricitygenerationandcombinedheatandpower(CHP)arederivedinseparatesub-modules.CombinedheatandpoweranddistributedgenerationTheCHPoptionisconsideredforfossilfuelandbioenergy-basedpowerplants.TheCHPsub-moduleusesthepotentialforheatproductioninindustryandbuildingstogetherwithheatdemandprojections,whichareestimatedeconometricallyinthedemandmodules.2WindandsolarPVdataarefromRenewables.ninja(https://beta.renewables.ninja/)andUeckerdt,F.,et.al.(2016).3FormoredetailsontheOpenSourceSpatialElectrificationTool,seewww.onsset.org.ForthelatestOnSSETmethodologyupdaterefertoKorkovelos,A.etal.(2019).58InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONRenewableenergyTheprojectionsofrenewableelectricitygenerationarederivedintherenewablessub-module.Thedeploymentofrenewablesismodelledbasedonpolicytargets,technologycompetitivenessandresourcepotential,specifiedforeachtechnology(bioenergy,hydropower,solarPV,concentratingsolarpower,geothermalelectricity,wind,andmarine)ineachofthe26GECModelregions.4Policytargetsareoftenforspecifictechnologies,forexample,over130countrieshavesupportpoliciesinplacetoexpandtheuseofsolarPVandwindasof2020.Thoughothersmayspecifythetotalcontributionofrenewableenergy,theshareofrenewablesintotalelectricitygeneration,orthelowemissionsshareofgenerationincludingrenewables.Incaseswherepoliciesspecifyabroadtargetthatincludesrenewables,technologycompetitivenessandresourcepotentialsdrivetherelativecontributions.Technologycompetitivenessisbasedonthevalue-adjustedLCOE(seesectionabove)andappliesequallytocomparisonsamongstrenewableenergytechnologiesandabroadersetoftechnologies.Resourcepotentialisconsideredonaregionalbasisforeachrenewableenergytechnology(seeBox2).Beyondthereachofpolicytargets,technologycompetitivenessandresourcepotentialsarethecriticalconsiderationsforrenewablesdeployment.Marketconstraints,includingadministrativeones,andtechnicalbarrierssuchasgridconstraintswhereapplicableareconsidered,andaremostimportantintheneartermastechnologiesmature.Electricitygenerationfromnewlybuiltrenewablesiscalculatedbasedonanassessmentofhistoricaloperationsandevolvingtechnologydesigns.Forexample,windturbinedesignshaveimprovedoverthepastdecade,achievinghigherperformanceunderavarietyofwindconditions.Assumedcapacityfactorsfornewrenewableenergyprojectsaretechnology-andregion-specific.Totalelectricitygenerationfromarenewabletechnologyisthesumofallprojectsinoperationwithinagivenyear.Overnightinvestmentneedsforrenewablesarecalculatedbasedonthedeploymentofrenewablesandevolvingtechnologycosts.Ourmodelling,inallscenarios,incorporatesaprocessoflearning-by-doingforprojectedcapitalcostsforrenewables(andothertechnologiesnotyetmature).Learningratesareassumedbydecadeforspecifictechnologies.TheoverallevolutionofthetechnologycostsarecommonlyexpressedthroughtheLCOE.Whiletechnologylearningisintegraltotheapproach,theGECModeldoesnottrytoanticipatetechnologybreakthroughs.Box4.1⊳Long-termpotentialofrenewablesThestartingpointforderivingfuturedeploymentofrenewablesistheassessmentoflong-termrealisablepotentialsforeachtypeofrenewableandforeachregion.Theassessmentisbasedonareviewoftheexistingliteratureandontherefinementofavailabledata.Itincludesthefollowingsteps:◼Thetheoreticalpotentialsforeachregionarederived.Generalphysicalparametersaretakenintoaccounttodeterminethetheoreticalupperlimitofwhatcanbeproducedfromaparticularenergy,basedoncurrentscientificknowledge.◼Thetechnicalpotentialcanbederivedfromanobservationofsuchboundaryconditionsastheefficiencyofconversiontechnologiesandtheavailablelandareatoinstallwindturbines.Formostresources,technicalpotentialisachangingfactor.Withincreasedresearchanddevelopment,conversiontechnologiesmightbeimprovedandthetechnicalpotentialincreased.Long-termrealisablepotentialisthefractionoftheoveralltechnicalpotentialthatcanbeactuallyrealisedinthelongterm.Toestimateit,overallconstraintsliketechnicalfeasibility,socialacceptance,planningrequirementsandindustrialgrowtharetakenintoconsideration.4Anumberofsub-typesofthesetechnologiesaremodelledindividually,asfollows.Biomass:smallCHP,mediumCHP,electricityonlypowerplants,biogas-fired,waste-to-energyfiredandco-firedplants.Hydro:large(≥10MW)andsmall(<10MW).Wind:onshoreandoffshore.SolarPV:large-scaleandbuildings.Geothermal:electricityonlyandCHP.Marine:tidalandwavetechnologies.Section4Electricitygenerationandheatproduction59WindoffshoretechnicalpotentialIncollaborationwithImperialCollegeLondon,adetailedgeospatialanalysiswasundertakenforWEO-2019toassessthetechnicalpotentialforoffshorewindworldwide.Thestudywasamongthefirsttousethe“ERA-5”reanalysis,whichprovidesfourdecadesofhistoricglobalweatherdata.“Renewables.ninja”extrapolateswindspeedstothedesiredhubheightandconvertsthemtooutputusingmanufacturers’powercurvesforturbinemodels.ResultscanbefoundontheIEAwebsite.DataTheavailabilityofhigh-resolutionsatellitedataandcomputinggainshassignificantlyimprovedthegranularityandaccuracyofwindresourceassessmentsinrecentyears.Emergingwindturbinedesignsarealsocausetoupdatepotentialassessments,astheyincreaseperformanceinwell-establishedareasandmakelowerqualityresourcesmoresuitableforenergyproduction.ExclusionsCommerciallyavailableoffshorewindturbinesarecurrentlydesignedforwindspeedsofmorethan6m/s.Somecompaniesarealsolookingintoturbinedesignsforlowerwindspeeds.FollowingtheInternationalUnionforConservationofNature’s(IUCN)classificationofmaritimeprotectionareas,thosecategorisedasIa,Ib,IIandIIIwereexcludedfromthestudy(IUCN,2013).However,ateachprojectlevelotherenvironmentalconsiderationsmustalsobetakenintoaccountandafullenvironmentalimpactassessmentconductedasmandatedbypublicauthorities.Bufferzoneswerealsoexcludedforexistingsubmarinecables(within1kilometre[km]),majorshippinglanes(20km),earthquakefaultlines(20km)andcompetingusessuchasexistingoffshoreoilandgasinstallationsandfisheries.TurbinedesignsInordertoassesstheglobaltechnicalpotential,best-in-classturbineswerechosenwithspecificpowerof250,300and350wattpersquaremetre(W/m2)thatcorrespondstolow-medium,mediumandhighwindspeeds.Thepowercurvesoftheseturbineswereusedinconjunctionwiththeglobalcapacityfactorsofeach5kmby5kmcellselectedfortheanalysistoderivethetechnicalpotentialofoffshorewindintermsofcapacityandgeneration.Newpowercurvesweresynthesisedfornext-generationturbineswithratedcapacityofupto20MW,forwhichdataarenotyetavailable(Saint-Drenanetal.,2019).Furthertothis,theanalysistakesintoaccountfurtherconsiderationssuchasoffshorewindfarmdesigns,distancefromshoreandwaterdepth,offshorewindcostdevelopmentsandthetechnicalpotential.4.7HydrogenandammoniainelectricitygenerationLow-carbonhydrogenandammoniaarefuelsthatcanprovidealowemissionsalternativetonaturalgas-andcoal-firedelectricitygeneration-eitherthroughco-firingorfullconversionoffacilities.IntheGECModel,blendinglevelsofhydrogeningas-firedplantsandammoniaincoal-firedplantsarespecifiedinlinewithpolicyandemissionstargets.Aspartofthescenarios,thesharesofhydrogenand/orammoniablendingincreaseovertime,representingbothadvancesinthecapabilitytoretrofitexistingfacilitiestoco-firehighersharesofhydrogenand/orammonia,andtheuptakeofnewdesignsthataredesignedtohighersharesofhydrogenorammonia,orplantsthatarepurposelydesignedtorunentirelyonhydrogenorammonia.IncreasedlevelsofhydrogenandammoniablendingintheGECModelincuradditionalcapitalexpenditureduetotheneedformoreextensiveretrofittingofexistingnaturalgas-andcoal-firedpowerplants.Electricitysectordemandforhydrogenandammoniaisusedbythehydrogensupplymoduletoinformtheoveralldemandforhydrogenproduction.60InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION4.8Utility-scalebatterystorageUtility-scalebatterystorageintheGECModelprovidesanimportantsourceofpowersystemflexibility,particularlyimportantwhereflexibilityneedsincreaseduetoevolvingelectricitydemandpatternsandrisingsharesofvariablerenewables.Lithium-ionbatteriesdispatchinthehourlymodel,wherebybatteriescharginganddischargingpatternsareoptimisedbasedonpricearbitrageopportunities(i.e.chargingwhenpricesarelowanddischargingwhenpricesarehigh).Utility-scalebatterystoragerangefromonetoeighthoursinduration(i.e.numberofhoursatmaximumoutput).Batteriesoperateonlywhenthedifferencebetweenthepricereceivedfordischargingandpricepaidforchargingwithina24-hourperiodisgreaterthanathreshold,whichissetbasedonfactorssuchasupfrontcapitalcosts,expectedlifetimecyclesandround-tripefficiency.Similarlytootherelectricitysectortechnologies,batteriesinvestmentdecisionsarebasedonVALCOE,withbatteriesassumedtohavedifferentlevelsofcapacitycreditdependingontheirduration–contributingtosystemadequacyandflexibility.Utility-scalebatterystoragecaneitherbestand-aloneprojectsorpairedwithpowerplants,suchaswindandsolarPV.Inthe2022GECmodellingcycle,utility-scalebatterystoragecapitalcostsdeclinefrom285USD/kWhin2021onaveragegloballyto180USD/kWhin2030and135USD/kWhintheNZE(forsystemsratedtoprovidemaximumpoweroutputforafour-hourperiod).Historicalcapitalcostsforutility-scalebatterystorageareupdatedregularlybasedonreportedindustrycosts(BloombergNewEnergyFinance,2020;Coleetal.2021).Thedegreeoftechnologycostreductionsisthencalculatedbasedonlearningratesfromexistingliterature,appliedforthebatterypackandforauxiliarycomponentssuchasinvertorsandoverheadcosts.5Forbatterypacks,projectedcostsaredrivenbythedemandforbatteriesacrossallsectors,withthelargestvolumerelatedtotheglobaldeploymentofelectricvehicles.Forothercomponentsofutility-scalebatterystorage,projectedcostsarerelatedtotheglobaldeploymentwithintheelectricitysector.5BasedonSchmidtetal.(2017)andTsiropoulosetal.(2018)Section5Otherenergytransformation61Section55Otherenergytransformation5.1OilrefiningandtradeTherefineryandtrademodulelinksoilsupplyanddemand.Itisasimulationmodel,withcapacitydevelopmentandutilisationmodelledfor134individualcountries,withtheremainingcountriesgroupedinto11regions.Thismodulehasseveralauxiliariesthatstretchintosupplyanddemanddomainstobetterlinkboth:◼Naturalgasliquidsmoduletodetermineyieldsofvariousproductsaswellascondensate.◼Extra-heavyoilandbitumenmoduletomodelsyntheticcrudeoiloutputanddiluentrequirementsforbitumen.◼Splitofoildemandintodifferentproductioncategoriesforallsectorsexceptroadtransportandaviation.ThelatterareprovidedbyGECModel’stransportdemandmodel.Projectionsforrefiningsectoractivityarebasedprimarilyonrefiningcapacityandutilisationrates.Refiningcapacityconsistsofcrudedistillationunits(CDU)andcondensatesplitters.Refiningcapacityisbasedon2022datafromtheIEA.Capacityexpansionprojectsthatarecurrentlyannouncedareassessedindividuallytoidentifyonlytheprojectsthatarelikelytogoahead.Someofthesearedelayedfromtheirannouncedstart-updatestoallowforamorerealistictimeline.Themodelalsotakesintoaccountrefineryclosuresthathavebeenannounced.Beyond2026,newcapacityexpansionisprojectedbasedoncrudeavailabilityandproductdemandprospectsforeachoftheregionsspecifiedbelow.Capacityatriskisdefinedasthedifferencebetweenrefinerycapacityandrefineryruns,withthelatterincludinga14%allowancefordowntime.Projectedshutdownsbeyondthosepubliclyannouncedarealsocountedascapacityatrisk.Utilisationratesaredeterminedbydomesticdemand,productyieldsandrefineryconfiguration(e.g.,complexity).Amongoil-importingregions,prioritycalloninternationalsupplyofcrudeoilisgiventothosewheredemandisgrowing:robustdomesticdemandiseffectivelyaproxyforrefinerymarginsthatarenotexplicitlycalculatedorusedbythemodel.Figure5.1⊳SchematicofrefiningandinternationaltrademoduleIEA.CCBY4.0.CrudeoilmarketproductsproductsproductsproductscrudeoiloutputrefiningcapacityregionaldemandRegionAcrudeoiloutputrefiningcapacityregionaldemandRegionBcrudeoiloutputrefiningcapacityregionaldemandRegionCcrudeoiloutputrefiningcapacityregionaldemandRegionD62InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONOiloutputanddemandprojectionsareprovidedbyGECModel’sfossil-fuelsupplyandfinalenergyconsumptionmodules.Refineriesdonotprovidefor100%ofoilproductdemand.Forthepurposesofthisanalysis,weshowthenetcallonrefineriesaftertheremovalofbiofuels,liquefiedpetroleumgas(LPG),ethaneandlightnaphthafromnaturalgasliquids(NGL),syntheticliquidsfromcoal-to-liquids(CTL)andgas-to-liquids(GTL)andadditives.Thesupply-sidenomenclaturefortherefiningmodelisslightlydifferentfromtheoilsupplymodel.Theterm“crudeoil”usedinthemodeldescribesallcrudeoilsthathaveconventional-typequalityforprocessingpurposes.Thisincludesconventionalcrudeoilfromthesupplymodel,someextraheavyoilsthatarenotdilutedorupgraded,tightoilandsyntheticcrudefrombitumenupgradingprocesses.Dilutedbitumenandcondensatearerepresentedasseparatestreamsforintakeandtrademodellingpurposes.Yields,outputandtradearedefinedforthefollowingproductcategories:ethane,LPG,naphtha,gasoline,kerosene,diesel,heavyfueloilandotherproducts(whichincludepetroleumcoke,refinerygas,asphalt,solvents,wax,etc).CrudeoiltradepositionandrefinedproductstradebalancesfollowGECModel’sdemandmodelgranularityof26individualcountriesorregions(Figure5.1).5.2Coal-to-liquids,Gas-to-liquids,Coal-to-gasCoal-to-liquids(CTL),Gas-to-liquids(GTL)andCoal-to-gas(CTG)technologieschemicallyconvertcoalandnaturalgasintootherliquidandgaseoushydrocarbons.TheFischer-Tropschprocessforinstanceturnscoalandnaturalgasintosyntheticfuelsthroughaseriesofchemicalreactions.Tothatend,anessentialfirststepinthisprocessistotransformcoalandnaturalgasintosyntheticgas(alsocalledsyngas).Syngasisamixtureofcarbonmonoxideandhydrogenobtainedbycoalgasificationandthedryreformingofmethane.SyngascanalsobeusedtoproducemethanethroughtheSabatierreactionandisthereforeameansofconvertingcoalintogas.Countrieswithlargecoalornaturalgasresources(e.g.,China)typicallyresorttoCTL,GTLand/orCTGtobolstertheirenergysecurityandsovereignty.However,becausethesetechnologiesarecapital-intensive,low-costcoalornaturalgasisessentialtomakethefinalproductscompetitive.Forthisreason,thefewexistingandplannedprojectsremainsconcentratedinahandfulofcountries.IntheGECModel,projectionsareconsistentwiththestatusoftheprojects(e.g.,underconstructionorplanned)andareupdatedeveryyearonaproject-by-projectbasis.Energy-relatedCO2emissionsareaccountedforandtechnologiescanbefittedwithCCUS.5.3HydrogenproductionandsupplyHydrogenintoday’senergysystemispredominantlyusedasafeedstockratherthanafuel,especiallyinallthesituationsinwhichitisusedasapurifiedhydrogengas.TheseexistingapplicationsaremostlyintherefiningandchemicalssectorsandarepartoftheindustryandrefiningmodulesoftheGECModel.MosthydrogenfortheseexistingapplicationsistodayproducedonsitebysteammethanereformingofnaturalgasorcoalgasificationwithoutCCUS,whileinthescenariosanincreasingshareofthishydrogenisproducedovertimeusingtechnologiesthathaveverylowCO2intensities,includingelectrolysisandconversionoffossilfuelsequippedwithCCUS.ThisonsiteproductionofhydrogenismodelledwithintheindustryandrefiningmodulesoftheGECModel.ThehydrogenproductionandsupplymoduleoftheGECModelcoverstheproductionofmerchanthydrogenandhydrogen-derivedfuels.Today,thismerchanthydrogenproductioniscomplementingonsitehydrogenproductioninthechemicalsandrefiningsectors.Inthescenarios,theuseofmerchanthydrogenproducedfromtechnologieswithlowCO2emissionsexpandsfromverylowlevelstodaytonewapplications–includingtransport,powergeneration,buildingsandindustrialheat–contributingtoCO2emissionreductionsinthesesectorsbyreplacingunabatedfossilfueluse.Thislow-emissionsupplyissettobecomeakeypartofthefutureenergytransformationsector,alongsidepowergenerationandheatandcoolingsupply.Section5Otherenergytransformation63Themerchanthydrogensupplymoduleusesacost-optimisationmodellingframeworkcalledTIMES,atechnology-richmodellingplatformdevelopedandfurtherimprovedbytheETSAPTechnologyCollaborationProgrammeoftheIEA.Thehydrogenmoduledepictsvarioustechnologyoptionstoproducehydrogenandhydrogenderivedfuels(ammonia,syntheticliquidhydrocarbonfuels,syntheticmethane)intermsofexistingcapacities,conversionefficiencies,fuelcosts,operatingandmaintenancecosts,CO2emissionsaswellasCO2captureratesforfossilfuelbasedtechnologiesandcapitalcostsfornewcapacityadditions.Electrolysercapitalcostsrepresentaweightedaverageoflikelydeploymentsharesofdifferentelectrolysertechnologies,whichfuturecostreductionsbeingderivedbycomponent-wiselearningcurves.Capitalcostsforalltechnologiesalsoincludeallbalance-of-plantandengineering,procurementandconstruction(EPC)costs,whichcanrepresentahighshareoftotalinstalledcosts.Figure5.2⊳SchematicofmerchanthydrogensupplymoduleIEA.CCBY4.0.Basedondemandsformerchanthydrogenandhydrogen-derivedfuelsfromtheend-usesectors,electricityandheatgenerationsector,refineriesandbiofuelproduction,thehydrogensupplymoduledeterminesaleast-costtechnologymixtocoverthesedemands.Besidesthesedemandsandthetechnicalandeconomiccharacteristicsoftechnologies,themoduletakesintoaccountannouncedhydrogenproductionortradeprojects(usingforexampletheIEA’sHydrogenProjectDatabase)aswellaspolicyconstraints,suchasCO2pricesorhydrogendeploymenttargets.Afocusofthemodelanalysisisonlow-emissionshydrogenproduction,i.e.,hydrogenisproducedinawaythatitdoesnotcontributetoanincreaseinatmosphericCO2concentrations.Emissionsassociatedwithfossilfuel-basedhydrogenproductionarepermanentlypreventedfromreachingtheatmosphereandthenaturalgassupplychainmustresultinverylowlevelsofmethaneemissions,ortheelectricityinputtohydrogenproducedfromwatermustbefromrenewableornuclearsources.Thereareseveralcomplementarypathwaystoproducelow-emissionshydrogen,someofwhicharematuretechnologiesandsomeofwhichareatearlierstagesofdevelopment.ThetwodominantpathwaysinGECModelarealreadydemonstratedatcommercialscales:◼FossilfuelswithCCUS.Thetypicaltechnologyforproducinglow-carbonhydrogenfromfossilfuelswithCCUSissteammethanereforming(SMR)ofnaturalgasequippedwithCO2captureunitthatcapturestheoverwhelmingmajorityoftheCO2generatedbytheSMRprocess.Thehydrogenyieldcanbeimprovedwithwatergasshift(WGS)reactiontoproducecarbondioxideandadditionalhydrogenfromcarbonmonoxide64InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONandwater.AdaptationstotheSMRprocess,includingautothermalreformingandpartialoxidation,canachievecaptureratesabove95%.AswithothertechnologiesintheGECModel,costandperformanceimprovementsareassumedtoarisefromhigherdeploymentlevels.TheGECModelaccountsforthesafetransportandpermanentgeologicalstorageofallofthecapturedCO2.◼ElectrolysisofwaterusingelectricitywithverylowCO2intensity.Electrolysersareawell-establishedtechnologytosplitwaterintohydrogenandoxygen.Thereareseveraltechnologiesunderdevelopmenttodaythatcanimproveexistingprocesses,andtheseincludevariationsofalkalineelectrolysers,polymerelectrolytemembrane(PEM)electrolysersandsolidoxideelectrolysercells.ElectrolysercapitalcostsinGECModelaimtorepresentaweightedaverageoflikelydeploymentsharesofthesetechnologies,whichallimprovewithincreaseddeployment,capturedbyusingcomponent-wiselearningcurveapproaches,andalsoincludeallbalance-of-plantandengineering,procurementandconstruction(EPC)costs,whichcanrepresentahighshareoftotalinstalledcosts.Themoduleallowstheuseofgridelectricityforhydrogenproduction,whichdependinggridelectricitymix,however,maynotnecessarilybealow-emissionselectricitysource.DedicatedrenewableelectricitygenerationfromsolarPV,onshoreandoffshorewindismodelledasalow-emissionselectricitysourceforhydrogenproduction.Thecorrespondingrenewableelectricitygenerationtechnologiesarecharacterisedbytheircostdata,capacityfactorsandresourcepotentials.Thelattertwoarederivedusinggeospatialanalyses,characterisingtherenewablepotentialbycapacityfactorrangesforthemodelregions.ToreflectthevariabilityofsolarPVandwindforhydrogenproduction,thehydrogenmoduledividesayearinfourtypicaldays,whichareagaindividedintoeighttimeslicesof3-hourduration.Sincethisresolutionisstilltoocoarsetofullyreflectthevariability,aseparatehourlyTIMESmodelforhydrogenproductionhasbeendeveloped,whichforaspecificlocationanditshourlysolarPVandonshorewindcapacityfactorsdeterminesthecost-optimalcapacitiesforsolarPV,windandtheelectrolyseraswellastheneedforflexibilityoptions,suchashydrogenstorage,batterystorageorcurtailment.Thishourlyanalysisforasingleyearcantakeintoaccountoperationalconstraintsofsubsequentsynthesisprocesses,suchasminimumloadconstraintsforHaber-BoschorFischer-Tropschsynthesisprocesses.ApplyingthemodelforagridofrasterpointsinaregionandtakingintoaccountexclusionzonesnotavailableforelectricitygenerationfromsolarPVandwindallowstoderiveregionalsupplycostcurvesforhydrogenproduction.ThesecurvesareusedtoinformtheregionalpotentialsforhydrogenproductionfromsolarPVandwindintheGECModel.Theproductionoflow-emissionhydrogen-basedfuels–includingsyntheticliquidfuelslikesynthetickeroseneormethanol,ammoniaandsyntheticmethane–becomesakeyadditionalcomponentofenergytransformationinGECModelscenarios.Therelativeeaseoftransportinghydrogen-basedliquidfuelscomparedwithgaseoushydrogenmeansthatdemandcanbesatisfiedbyimportswherethisiscost-effective,andinsomecasesdemandforgaseoushydrogencanbemetbyimportinghydrogen-basedfuelsratherthangaseoushydrogen.Inthecaseofammonia,itcaninsomecasesbe“cracked”atthepointofdeliverytoregenerategaseoushydrogen.TheGECModeltakesthesedynamicsandoptionsintoaccounttomodelthetradeofgaseoushydrogenviapipelinesandofliquidhydrogen,ammoniaandsyntheticliquidhydrocarbonfuelsviaships,withtheenergyneedsandcostsfortheconversionprocessesandtransportoptionsbeingconsideredinthecost-optimisationapproachofthehydrogenmodule.Forcarbon-containinghydrogen-basedfuels,thecarboninputhastocomefromsourcesthatarecompatiblewithverylowCO2intensitythroughoutthesupplychain,includingco-products,withoutoffsets.IntheGECModel,directaircapture(DAC)andbiogeniccarboncapturedatbioenergyconversionplantsareconsideredascarbonsources.ThehydrogensupplymoduleinterfaceswithseveralothersectorsoftheGECModel.Themostnotableoftheseistheelectricitygenerationmodule,whichisbothaconsumerofhydrogenandhydrogen-basedfuels,andalsoprovidingelectricity(alongsidenaturalgas)tosatisfyinghydrogenproductionneedsatlowestcost.TheresultsSection5Otherenergytransformation65fordedicatedrenewableelectricitygenerationofthehydrogenmoduleareintegratedintheelectricitygenerationmodule,andfeedbacksacrossthisinterfaceareperformediteratively.Demandforhydrogenandhydrogen-basedfuelsineachsectorisdeterminedwithineachsectoralmodule,withiterationstoupdatehydrogensupplycostsbasedonoveralldemandwhererelevant.Tounderstandthehydrogeninfrastructureneedsandrelatedinvestmentrequirements,aninfrastructuretoolhasbeendeveloped,whichcomplementstheinfrastructureneedsforinternationalhydrogentradefromthehydrogenmodulebyanalysingthedomesticinfrastructureneedswithinregions,inparticularforpipelines(neworrepurposednaturalgaspipelines)andstorage.5.4BiofuelproductionBioenergyisanimportantrenewableenergyoptioninallofitsforms:solid(biomass),liquid(biofuels)andgas(biogasandbiomethane).Thebioenergysupplymoduledeterminesprimarybioenergyavailability(seeSection6.4).Forliquidandgaseoususes,bioenergyistransformedpriortofinaluseintheliquidbiofuelsandbiogasandbiomethanesupplymodules.TheliquidbiofuelssupplymodulebuildsuponpreviousmodellingworkfortheWEMandETPmodelsandisdesignedtoassessthedeploymentofliquidbiofuelconversiontechnologiesrequiredtomeetdemandintheend-usesectorsoftransport,industry,buildingsandagriculturefromavariantofbiomassfeedstocksthatarecoherentwithboththebioenergysupplymoduleandthebiogasandbiomethanesupplymodule.Themodulecalculatesconversionlosses,energyinputrequirements,investmentspending,andassessestheamountofliquidbiofuelsproductionassociatedwithcarboncaptureforuseandstorage.ThebiogasandbiomethanesupplymoduleisdesignedtoassessthesustainabletechnicalpotentialandcostsofbiogasandbiomethaneforalltheGECModelregions.Thisanalysisincludesfeedstocksthatcanbeprocessedwithexistingtechnologies,thatdonotcompetewithfoodforagriculturalland,andthatdonothaveanyotheradversesustainabilityimpacts(e.g.,reducingbiodiversity).Feedstocksgrownspecificallytoproducebiogas,suchasenergycrops,arealsoexcluded.Themoduleexcludesinternationaltradeofbiogasandbiomethane.LiquidbiofuelsupplymoduleLiquidbiofuelstodayaremainlyproducedusingcommerciallyavailabletechnologiesthatconvertfood-basedenergycropsintoso-calledconventionalbiofuels.Technologiesincludeethanolproductionfromstarchandsugar,fattyacidmethylester(FAME)biodiesel,andhydrotreatedvegetableoil(HVO)renewablediesel.Inthemodelledscenarios,anincreasingshareofliquidbiofuelsareproducedfromadvancedconversiontechnologies(suchasbiomassgasificationandFischerTropschsynthesisorcellulosicethanolproduction)andfromadvancedfeedstockssuchaswasteoils,forestryresidues,cropresidues,andnon-foodenergycropsgrownonnonarable,marginalland.Advancedfeedstocksdonotcompetewithfood,andminimisenegativeenvironmentalimpactsonsoilhealthandwater.Theliquidbiofuelssupplymoduleusesacost-optimisationmodellingframeworkcalledTIMES,atechnology-richmodellingplatformdevelopedandfurtherimprovedbytheETSAPTechnologyCollaborationProgrammeoftheIEA.Theliquidbiofuelsmoduledepictsvarioustechnologyoptionstoproduceliquidbiofuels(ethanol,biodieselandrenewablediesel,biojetkerosene)withandwithoutcarboncapture,intermsofexistingcapacities,conversionefficiencies,fuelcosts,operatingandmaintenancecosts,CO2emissionsaswellasCO2captureratesandcapitalcostsfornewcapacityadditions.Liquidbiofuelcapitalcostsrepresentthelatestdataavailablefromindustryandacademia,withfuturecostreductionsassessedusinglearningcurves.Avarietyofbiomassfeedstocksareincludedinthemodel,suchasforestryresidues,cropresidues,andnon-foodenergycrops.Thesebiomassfeedstocksarecoherentwiththebioenergysupplymoduleandthebiogasandbiomethanesupplymodule.Theliquidbiofuelsmodulealsomodelsliquidbiofueltradeforethanol,biodieselandbiojetkerosenebetweenthe26GECModelregions(seeSection6.4).66InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONBasedondemandsforliquidbiofueldemandfromtheend-usesectors,theliquidbiofuelsupplymoduledeterminesaleast-costtechnologymixtocoverthesedemands.Besidesthesedemandsandthetechnicalandeconomiccharacteristicsoftechnologies,themoduletakesintoaccountannouncedbiofuelproductionandtradeprojectsasassessedbytheIEA’sRenewableEnergyMarketreports,aswellaspolicyconstraints,suchasCO2prices,biofuelssubsidiesortargetsforadvancedbiofuelsproduction.Theliquidbiofuelsmoduleincludesthefollowingconversionpathwaysforeachliquidbiofuelproduct:◼Ethanolisproducedfromconventionalfermentationprocessesusingstarch(e.g.corn)orsugar(e.g.sugarcane)crops,orfromanadvancedfermentationprocessusingcellulosicfeedstocks(e.g.cornstover),inwhichthefeedstockmustfirstundergoaprocesstobreakdownthefeedstockandreleasethesugarspriortofermentation.◼Biodieselandrenewablediesel.ConventionalbiodieselisproducedfromtheFAMEconversionprocess,whileadvancedrenewabledieselisproducedfromtheHVOprocessaswellasthethermochemicalprocessofbiomassgasificationfollowedbyFischer-Tropschsynthesis.◼BiojetkeroseneisproducedfromeithertheHVOprocess(alsoknownashydroprocessedestersandfattyacids,orHEFA)orthermochemicallyfrombiomassgasificationandFischer-Tropschsynthesis.Additionally,severalliquidbiofuelproductionpathwayscanbedeployedwithcarboncaptureforuseorstorage.Theseincludeconventionalandadvancedethanolroutes,andrenewabledieselandbiojetkerosenefrombiomassgasificationandFischer-Tropschsynthesis.CapturedCO2iseitherstoredcreatingso-calledorusedfortheproductionofsynthetichydrocarbonfuelsinthehydrogenmodule.BiogasandbiomethanesupplymoduleBiogasandbiomethanesupplypotentialhasbeenassessedconsideringawidevarietyoffeedstock,groupedinsixcategories:cropresidues,animalmanure,municipalsolidwastes(MSW),forestproductresidues,wastewaterandindustrialwastes.ThefeedstocksupplypotentialsarebuiltonawiderangeofdataoriginatinglargelyfromtheFoodandAgricultureOrganizationoftheUnitedNations(FAO)databaseandOECD-FAOstudy(OECD/FAO,2018)forwheat,maize,rice,othercoarsegrains,sugarbeet,sugarcane,soybean,andotheroilseeds,cattle,pig,poultryandsheep,logfellingresidues,woodprocessingresiduesanddistillerdriedgrains(DDGs),aby-productofethanolproductionfromgrainsandfromaWorldBankstudy(WorldBank,2018)fordifferentcategoriesoforganicmunicipalsolidwastesuchasfoodandgreenwaste,paperandcardboard,andwood.WastewaterincludesonlymunicipalwastewaterandisbasedontheoutputdatafromtheWatermodulepreviouslydevelopedbytheWorldEnergyOutlookteam.Biogasisproducedbyanaerobicdigestion.Fivetechnologiesofcentralisedbiogasproductionplantsaremodelled:landfillgasrecoverysystem,digesterinmunicipalwastewatertreatmentplantandthreecentralisedco-digestionplants(small-,mediumandlarge-scale).Inaddition,twotypesofhousehold-scaledigesteraremodelledintheresidentialsectoroftheGECModel,toaccountforruralanddecentralisedbiogasproductioninruralareasofdevelopingeconomies.Forbiomethane,twoproductionpathwaysareconsidered:upgradingofbiogasproducedbyanaerobicdigestionandthermalgasificationandmethanationoflignocellulosicbiomass.Foreachtechnologytechnicalandeconomicparameters,e.g.efficiency,lifetime,overnightcapitalcostoroperationalcostsarecollectedtoassesstheproductioncosts.Thecombinationoftheassessmentofthesupplypotentialandtheeconomicevaluationofthedifferentbiogasandbiomethaneprocesseswereusedtoassessbiogasandbiomethanesupplycostcurves.Foragivenyear,itisSection5Otherenergytransformation67madeoftheaggregationofbiomethanepotentialandassociatedlevelisedcostofproductionforeveryregion,feedstockandtechnology.Informationprovidedbysupplycurvesisthenusedtoassessthecost-competitivenessofthetwomainusesofbiogasandbiomethane:electricityandheatgenerationandinjectioninthegasgrid.SupplycurvesareusedtocalculateGHGemissionspotentialsavingsandrelatedabatementcosttounderstandthefutureroleofcarbonpricingonbiogasandbiomethanedevelopment.Section6Energysupply69Section66Energysupply6.1OilThepurposeofthismoduleistoprojectthelevelofoilproductionineachcountrythroughabottom-upapproach1buildingon:◼thehistoricalseriesofproductionbycountries;◼standardproductionprofilesandestimatesofdeclineratesatfieldandcountrylevelsderivedfromthedetailedfield-by-fieldanalysisfirstundertakeninWEO-2008andupdatedsince;◼anextensivesurveyofupstreamprojectssanctioned,plannedandannouncedovertheshortterminbothOPECandNon-OPECcountries,includingconventionalandnon-conventionalreserves,asperformedbytheIEAOilMarketReportteam;thisisusedtodriveproductioninthefirst5yearsoftheprojectionperiod(asummaryofthedifferencesinmethodologybetweenGECModelandtheMedium-TermOilMarketReportisincludedasBox3);◼amethodology,whichaimstoreplicateasmuchaspossiblethedecisionmodeoftheindustryindevelopingnewreservesbyusingthecriteriaofnetpresentvalueoffuturecashflows;◼asetofeconomicassumptionsdiscussedwithandvalidatedbytheindustryincludingthediscountrateusedintheeconomicanalysisofpotentialprojects,findinganddevelopmentcosts,andliftingcosts;◼anextensivesurveyoffiscalregimestranslatingintoanestimateofeachgovernment’stakeinthecashflowsgeneratedbyprojects;◼acomprehensiveassessmentofvariousfinancialrisks(e.g.geopolitics,ruleoflaw,regulatoryoversight)torepresenttheattractivenessofinvestmentinoilandnaturalgasfields;and◼valuesofremainingtechnicallyrecoverableresources(Table6.1)calculatedbasedoninformationfromtheUnitedStatesGeologicalSurvey(USGS),BGRandothersources.TheparagraphsbelowdescribehowtheUSGSdataareusedintheGECModel.USGSpublishesitsWorldPetroleumAssessment,athoroughreviewofworldwideconventionaloil(andgas)resources.Init,USGSdividedtheresourcesintothreeparts:◼Knownoil,whichcontainsbothcumulativeproductionandreservesinknownreservoirs.◼Undiscoveredoil,abasin-by-basinestimateofhowmuchmoreoiltheremaybetobefound,basedonknowledgeofpetroleumgeology.◼Reservesgrowth,anestimateofhowmuchoilmaybeproducedfromknownreservoirsontopoftheknownreserves.Asthenameindicates,thisisbasedontheobservationthatestimatesofreserves(includingcumulativeproduction)inknownreservoirstendtogrowwithtimeasknowledgeofthereservoirandtechnologyimproves.Forthe2000assessment,reservegrowthasafunctionoftimeafterdiscoverywascalibratedfromobservationinUSfields,andthiscalibrationappliedtotheknownworldwidereservestoobtainanestimateofworldwidereservesgrowthpotential.Sincethe2000assessment,USGShasregularlypublishedupdatesonundiscoveredoilinvariousbasins,andthesewereconsideredintheGECModel.In2012,USGSpublishedanupdatedsummaryofworldwideundiscoveredoil,aswellasarevisedestimateforreservesgrowthbasedonanewfield-by-fieldmethodfocusedonthelargefieldsintheworld.PreviouslytheknownoilestimatesusedbytheUSGSwhengeneratingitsreservegrowthestimateshadnotbeenreleasedpublicly.However,arecentreportprovidesitsassumptions,albeitaggregatedatagloballevel(USGS,2015).TheUSGSestimateofcumulativeproductionandreservesoutsidethe1“Bottom-up”inthiscontextmeans“basedonfield-by-fieldanalysis”.70InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONUnitedStatesis2060billionbarrels,whichisinclosealignmentwiththeIEAequivalentestimateof2050billionbarrels.Forconventionaloil,theUSGSestimatesofundiscoveredoilandreservesgrowthpublishedin2012providethekeyfoundationforthevaluesusedinGECModel.TheGECModelestimatesofremainingtechnicallyrecoverableresourcescombineUSGSundiscovered,USGSreservesgrowthandIEAestimatesforknown.Asimilaranalysis,basedonthesameUSGSpublications,feedsintotheIEANGLsandnaturalgasresourcesdatabase,whichallowslookingattotalconventionalliquidhydrocarbonsresourcesandconventionalgasresources.Box6.1⊳GECModeldifferencesinmethodologycomparedwiththeMedium-TermOilMarketReportTheIEApublishesannuallyprojectionsofoilsupplyanddemandforthenextfiveyearsintheMediumTermOilMarketReport(MTOMR),andforthenexttwoandhalfdecadesintheGECModel.Thosetwosetsofprojectionsusedifferentmethodologiesthatevolveeveryyear.Thismakescomparisonsnotstraightforwardforsomereaders.Thisboxsummarizesthekeydifferences.AveryimportantdifferencebetweenMTOMRandtheGECModelistheoilpriceassumption.MTOMRassumesthattheoilpricefollowsthefuturesmarketcurveatthetimeofpublication;thisisthenusedforthedemandprojection,andsupplyisassumedtofollow,withOPECfillingthegapbetweenfield-by-fieldprojectionsofnon-OPECsupplyanddemand.TheGECModeldeterminestheequilibriumpricethatbringssupplyanddemandinbalance.However,toavoidgeneratinginvestment/pricecycleswhichwouldobscurepolicyeffectsandlongtermtrends,thisequilibriumisperformedasatrendandnotyear-by-year.TheGECModelreliesonthefield-by-fieldanalysisofMTOMRtoguideproductionbycountryinthefirstfiveyearsoftheprojectionperiod.ThecountrybycountrymethodologyisalsoextendedtoOPECcountries,soOPECisnottreatedastheswingproducer,thoughconstraintsthoughttorepresentpossibleOPECpoliciesareincorporatedintheGECModeloilsupplymodule.Resultsarealsooftenpresentedslightlydifferentlyinthetworeports.ConventionalandunconventionaloilmaybegroupeddifferentlywiththeGECModelincludingallofCanadianoilsandsandVenezuelanOrinocoproductioninunconventional,whileMTOMRgenerallycountsonlyupgradedbitumenorextra-heavyoilasunconventional.Inanalysingandprojectingoildemand,theGECModelandMTOMRhavemethodologicaldifferences.SincetheGECModelisconcernedwithprojectionsofsupplyanddemandofallenergysourcesandprojectsaworldenergybalanceinthefuture,itincorporatesalldemandcomponents.Duetothenatureofthesecomponents,theycanbewithaplusoraminussign(i.e.increasingordecreasingthedemandfigure).Therefore,whiletheGECModelincorporatesstatisticaldifferencesandrefinerytransformationlossesintohistoricaldemandvaluesandprojectsthoseintothefuture,MTOMR’sdemanddefinitiondoesnotincludethesetwocategoriesinitshistoricalvaluesandprojections.TheGECModelalsosplitsbiofuelsfromhistoricaloildemandandprojectsoildemandandbiofuelsdemandseparately.OMRdoesnotseparatebiofuelsfromthehistoricaloildemand,andtheoildemandisprojectedwithamixofbiofuels.Asaresult,onebarrelofoilfromMTOMRprojectionshaslowerenergycontentthanthatoftheGECModelifbiofuelsareprojectedtogrow.AdirectcomparisonoftheGECModelandOMRresultsisthusonlypossibleifbiofuelsarestrippedoffMTOMRvaluesofoildemand.Thedifferencesinrefiningmainlyconcerntheinterpretationofinstalledcapacity.TheGECModeldiscountsmostofidledcapacityofChineseteapotandsmallerrefineriesthatrunbelow30%utilizationrates.Italsodiscardsthemothballedcapacityinentirety,eveniftheowneroftherefineryhasannouncedthatitisatemporaryeconomicshutdown.MTOMRandtheGECModelmayalsodifferintheirprojectionoffirmcapacityadditionswithinthesametimeframe.Section6Energysupply71Eachcountry’sprojectedoilproductionprofileismadeofsixcomponents.Conventionalcrudeoilfieldsarealsodistinguishedbywaterdepth(onshore,shallow[waterdepthlessthan450metres],deepwater[between450-1500metres]andultra-deepwater[greaterthan1500metres]).Forunconventionaloil,extra-heavyoilandbitumenisalsodistinguishedbyminingorinsitutechnologiesandtightoilbyplayproductivity.◼Productionfromcurrentlyproducingfieldsasofanestimatedend-2021:theprojecteddeclineratesineachcountryarederivedfromtheanalysissummarisedinBox6.1;◼Productionfromdiscoveredfieldswithsanctioned,plannedandannounceddevelopments;◼Productionfromdiscoveredfieldsawaitingdevelopment;◼Productionfromfieldsyettobediscovered;◼Productionofnaturalgasliquids;and◼Productionofunconventionaloil.Trendsinoilproductionaremodelledusingabottom-upmethodology,makingextensiveuseofourdatabaseofworldwideultimatelytechnicallyrecoverableresources.Themethodologyaimstoreplicateinvestmentdecisionsintheoilindustrybyanalysingtheprofitabilityofdevelopingreservesattheprojectlevel(Figure6.1).Figure6.1⊳StructureoftheoilsupplymoduleIEA.CCBY4.0.ProductionrequiredtobebroughtonstreamRankingofNPVsProjectedproductionbroughtonstreampercountry,withinlogisticalandURRconstraintsNPVfordevelopingproductionincountryXExistingProductionCostsDeclineRatesGlobalDemand72InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONIntheGECModeloilsupplymodule,productionineachcountryorgroupofcountriesisseparatelyderived,accordingtothetypeofassetinwhichinvestmentsaremade:existingfields,newfieldsandnon-conventionalprojects.Standardproductionprofilesareappliedtoderivetheproductiontrendforexistingfieldsandforthosenewfields(bycountryandtypeoffield)whicharebroughtintoproductionovertheprojectionperiod.Theprofitabilityofeachtypeofprojectisbasedonassumptionsaboutthecapitalandoperatingcostsofdifferenttypesofprojects,andthediscountrate,representingthecostofcapital.Thenetpresentvalueofthecashflowsofeachtypeofprojectisderivedfromastandardproductionprofile.Projectsareprioritisedbytheirnetpresentvalueandthemostpotentiallyprofitableprojectsaredeveloped.Constraintsonhowfastprojectscanbedevelopedandhowfastproductioncangrowinagivencountryarealsoapplied.Thesearederivedfromhistoricaldataandindustryinputs.Whendemandcannotbemetwithoutrelaxingtheconstraints,thissignalsthatoilpricesneedtobeincreased.UStightoilmodelAtightoilmoduleispartofGECModel,originallydevelopedforWEO-2016,anditexploresthesensitivityofproductionoftightoilintheUnitedStatestochangesinpriceandresourceavailability.Themoduleprojectspossiblefutureproductionacross23shaleplaystakingintoaccounttheestimatedultimaterecovery(EUR),initialproduction,rateofdeclineanddrillingcostsofwellsdrilledandcompletedacrossdifferentareasofeachplay.Existingproductionismodelledbyestimatingdeclineparametersofwellsbasedonlatestproductioninformationavailable,andthetimewhenthesewellswerecompleted.Pricedynamicsaffectthenumberofrigsthatareavailabletodrillnewwells,withalagbetweenincreasesinpricesandincreasesinthenumberofrigsoperating(asobservedempirically).Technologyincreasesboththespeedatwhichnewwellscanbedrilledandcompleted(thenumberofwellsperrig)andtheamountofproductionfromeachwell(theEUR/well).Conversely,theEUR/wellofagivenareainagivenplayisassumedtodegradeasthatareaisdepletedovertime.Box6.2⊳MethodologytoaccountforproductiondeclineinoilandgasfieldsTheWorldEnergyOutlookhaspreviouslypresentedanalysesofdeclineratesinoilfieldsonanumberofoccasions,basedonlookingatactualproductiondatatimeseriesforalargenumberoffields.Theoutcomeofthisworkisavalueforobserveddeclineratesbytypeoffield,geographicallocationandphaseofdecline,aswellasanestimateforthedifferencebetweenobserveddeclineratesandnaturaldeclinerates(thedeclineratethatwouldbeobservedintheabsenceoffurtherinvestmentinproducingfields).Inprinciplethisprovidestheelementstoprojectthefutureproductionofallfieldsindeclineamongthesetoffieldsused.Themethodologycouldbeasfollows:◼Foreachfieldinthedatabase,assignatype(super-giant,giant…onshore,offshore,deepwater)anddeterminethecurrentdeclinephase.◼ProjectfutureproductionforeachfieldaspercorrespondingdeclinerateprovidedinWEO-2013,updatingdeclineratesasthefieldchangesphase.Butthisdoesnotallowtheprojectionofworldproductionfromallcurrentlyproducingfields,asonealsoneedstoprojectproductionfromfieldscurrentlyrampingup(i.e.,oneneedstoknowtheirfuturepeakyearandpeakproduction)andfromdecliningfieldsnotinthedatabase.Thisisdoneusingaproprietarycommercialdatabasethatcontainsarepresentationofpossiblefutureproductionforallfieldsintheworld.Basedonthismorecompletedataset,theGECModeloilsupplymoduleusesacountry-by-countryparameterisationofnaturaldeclinerates(foreachresourcestype)andaproductionprofileforresourcesdevelopedineachcountryduringtheprojectionperiod(i.e.,resourcesdevelopedinagivenyearthenprovidearamping-upofproduction,followedbypeakanddecline).AsshowninFigure6.2,thisparameterizationSection6Energysupply73givesagoodmatchwiththeresultsoftheproprietarydatabase(asthetwodatabaseshaveslightlydifferentbaseproductions,botharenormalizedtoallowaclearercomparisonofdecline)forthelongtermdecline;intheshortterm,theIEAfield-by-fieldanalysis(comingfromtheMediumTermOilMarketReport)ismoreconservativethatthecommercialdatabase,asitaccountsforexpectedfieldmaintenanceandweatherdisruptions.Figure6.2⊳Evolutionofproductionofcurrentlyproducingconventionaloilfieldsfromafield-by-fielddatabaseandfromtheGECModelIEA.CCBY4.0.Sources:RystadEnergyAS,IEAanalysisanddatabases.Rigsaredistributedacrossplaysbasedoncurrentactivity,andtheexpectedcosteffectivenessofnewwellsthataredrilled.Itisassumedthatwhileoperatorswouldaimtodrillonlyintheirmostproductiveareas,somewellswillinevitablybelocatedinregionswithlowerEUR/wellorhigherdeclinerates.Theproductofnumbersrigs,wells/rig,andproduction/wellthengivesthenewproductionthatcomesonlineineachplayineachmonthstartinginJanuary2020.ResultsfromthismodulearedirectlyfedintoGECModelforeachofthescenariosimplemented.AsimilarmodelwasdevelopedforshalegasproductionintheUnitedStates.6.2NaturalgasNaturalgasproductionandtradeprojectionsarederivedfromahybridGECModelgassupplymoduleinvolvingbottom-upandtop-downapproaches.Themodulehassimilarinputs,logicandfunctionalityastheoilsupplymoduledescribedabove.However,contrarytooilwhichisassumedtobefreelytradedglobally,gasisassumedtobeprimarilytradedregionally,withinter-regionaltradeconstrainedbyexistingorplannedpipelines,LNGplantsandlongtermcontracts.Firstthetop-downmoduleisrunfor20regions(seeAnnex1),forwhichindigenousproductionismodelledonthebasisofremainingtechnicallyrecoverableresources(Table6.1)anddepletionrates,takingaccountofproductioncosts,taxes,pricesandvariousrisksintheregion.Subtractingdomesticproductionfromdemand,inaggregateforeachimportingregionalblock,yieldsgasimportrequirements.Foreachgasnet-exportingregionalblock,aggregateproductionisdeterminedbythelevelofdomesticdemandandthecallonthatregion’sexportableproduction(whichisdeterminedbytheimportneedsofthenetimportingregionsandsupplycosts).Longtermcontracts(current,orassumedforthefuture)areservedfirst,thenexportingregionscompeteonthebasisofmarginalproductioncostsplustransportcosts,74InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONwithincurrentandassumedfutureLNGandpipelinecapacities.Thisprovidesinter-regionalgastrade.Theeffectsofpricingpolicies(currentorassumedforthefuture)ofexportingregionscanalsobetakenintoaccount.Inthebottom-upmodule,productionwithineachregionisallocatedtoindividualcountriesaccordingtoremainingtechnicallyrecoverableresources,depletionratesandrelativesupplycosts,withalogicsimilartothatoftheoilsupplymodule,butwith“demand”beingprovidedbytherespectiveregionalproductionderivedfromthetop-downmodule.6.3CoalThecoalmoduleisacombinationofaresourcesapproach(Table6.1)andanassessmentofthedevelopmentofdomesticandinternationalmarkets,basedontheinternationalcoalprice.Production,importsandexportsarebasedoncoaldemandprojectionsandhistoricaldata,onacountrybasis.Fourmarketsareconsidered:cokingcoal,steamcoal,ligniteandpeat.Worldcoaltrade,principallyconstitutedofcokingcoalandsteamcoal,isseparatelymodelledforthetwomarketsandbalancedonanannualbasis.Table6.1⊳Remainingtechnicallyrecoverablefossilfuelresources,end-2021Oil(billionbarrels)ProvenreservesResourcesConventionalcrudeoilTightoilNGLsEHOBKerogenoilNorthAmerica23724242372171727981000CentralandSouthAmerica29185625357494933Europe1511257192836Africa12544430454842-MiddleEast8871139887291791430Eurasia146940228855755218AsiaPacific502771227265316World17526192208853363318651073Naturalgas(trillioncubicmetres)ProvenreservesResourcesConventionalgasTightgasShalegasCoalbedmethaneNorthAmerica161485010817CentralandSouthAmerica884281541-Europe546185185Africa191015110400MiddleEast81120101911-Eurasia69168130101017AsiaPacific2113844215320World2198064228025449Coal(billiontonnes)ProvenreservesResourcesCokingcoalSteamcoalLigniteNorthAmerica2578389103158401519CentralandSouthAmerica146033225Europe137982164415403Africa15343452970MiddleEast141366-Eurasia1912015387996632AsiaPacific4608974173758091428World1075208033401133954007Notes:NGLs=naturalgasliquids;EHOB=extra-heavyoilandbitumen.ThebreakdownofcoalresourcesbytypeisanIEAestimate.CoalworldresourcesexcludeAntarctica.Source:IEAGECModel2022.Section6Energysupply756.4BioenergyBioenergyisanimportantrenewableenergyoptioninallofitsforms:solid(biomass),liquid(biofuels)andgas(biogasandbiomethane).Bioenergyprovidesasignificantportionofrenewables-basedelectricityandtransportfuelsinallscenariosoftheGECModelandasgasitcanalsocontributetodecarbonisethegasnetwork.Manyregionsorcountrieshaveorareconsideringpoliciesthatwillincreasethedemandforbioenergyinthepowerandtransportsectorsfurtherinthefuture.TheBioenergysupplymoduleisdesignedtoassesstheabilityofGECModelregionstomeettheirdemandforbioenergyforpowergenerationandbiofuelswithdomesticresources.Wheretheyarenotabletodoso,themodulealsosimulatestheinternationaltradeofsolidbiomassandbiofuels.Theavailabilityofbioenergyisrestrictedtorenewablesourcesofbiomassfeedstockthatisnotincompetitionwithfood.Thebioenergysupplydeterminesprimarybioenergyavailability,andforliquidandgaseoususesfeedsintotheliquidbiofuelsandbiogasandbiomethanesupplymodulesfortransformationpriortofinaluse(seeSection5.4).BioenergysupplymoduleBiomasssupplypotentialsbyregionThefeedstocksupplypotentialsarebuiltonawiderangeofdatarelatedtoland,cropsandfooddemand,originatinglargelyfromthedatabaseoftheFoodandAgricultureOrganizationoftheUnitedNations(FAO),aswellasacademicliteratureandtheGlobalAgro-EcologicalZones(GAEZ)system,acollaborativeprojectinvolvingFAOandtheInstituteforAppliedSystemsAnalysis(IIASA).Totalsupplypotentialsbyregioninthebioenergysupplymodulearethesumofthepotentialsupplyforfourcategoriesoffeedstocks:forestryproducts,forestryresidues,agriculturalresiduesandenergycrops(Figure6.3).Startingfromcurrentactivitylevels,rampingupcollectionanddeliveryoftheseoftendiffusefeedstocksrequiressignificantleadtimesbeforemaximumpotentialsupplylevelscanbereached.Thepotentialsupplyofforestryandagriculturalresiduesisreducedbyindustrialandresidentialusetoproduceheat,aswellasdemandfortraditionaluses.Figure6.3⊳SchematicofbiomasssupplypotentialsIEA.CCBY4.0.Forestryproductsincludeonlyforestryactivities,suchasharvestingtreesandcomplementaryfellings,fortheprimarypurposeofproducingpowerortransportbiofuels.Themaximumpotentialavailabilityofforestryproductsislimitedtotheexpectedgrowthintotalforestareaperyear,afterotherforestrydemandsaremet,ineachregion,therebyavoidingdirectdeforestation.Forestryresiduesarethosematerials,orsecondaryproducts,producedfromforestryactivitieswheretheprimarymotivationissomethingotherthantoproducebioenergy.Theseincludeforestryscraps,barkleftoverfromthetimberindustry,industrialby-productsandwastewood.Themaximumpotentialavailabilityislimitedbytheleveloftherelatedactivitiesandtheusableshareoftheleftovermaterials.76InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONAgriculturalresiduesaretheleftovermaterialsafterharvestingcrops,suchascornstover,strawandbagassefromsugarcaneprocessing.Dataforharvestsbyregionincludethefollowingcrops:barley,maize(corn),oats,rice,sorghum,wheat,othercereals,rapeseed,soybeans,sunflowerseed,andsugarcane.Themaximumpotentialavailabilityislimitedbytheamountofcropsharvestedandbytherecoverableshareoftheresidues.Itisimportantforaportionoftheresiduestoremaininfieldstoreplenishsoilnutrientsandmaintainyieldsforfutureharvests,byhelpingreducesoilerosionandmaintainingwaterandtemperatureinthesoils.Thepercentageoftheseresiduesthatcanbemadeavailableforenergyproductioninasustainablemannerisregion-andcrop-specific,andisstillbeinginvestigatedactively.Energycropsarethosegrownspecificallyforenergypurposes,includingsugarandstarchfeedstockforethanol(e.g.,corn,sugarcane,andsugarbeet),vegetable-oilfeedstockforbiodiesel(e.g.,rapeseed,soybeanandoilpalmfruit)andlignocellulosicmaterial(e.g.,switchgrass,poplarandmiscanthus)foradvancedbiofuels.Themaximumpotentialavailabilityisdeterminedbytheavailablearableland,aftertakingintoaccountfood-relateddemandforland,cropchoiceandrisingyieldsovertime.Thepotentialsupplyfromenergycrops(milliontonnes)iscalculatedasfollows:𝑃𝑡,𝑟=∑(𝑥𝑡,𝑟,𝑙,𝑔,𝑐×𝑦𝑡,𝑟,𝑙,𝑔,𝑐×𝑠𝑡,𝑟,𝑐)𝑙,𝑔,𝑐where,foragivenyeartandregionr,•𝑃𝑡,𝑟isthepotentialbiomassfeedstocksupplyfromenergycrops;•𝑥𝑡,𝑟,𝑙,𝑔,𝑐istheavailablelandbytypel,gradeg,andcropc;•𝑦𝑡,𝑟,𝑙,𝑔,𝑐isthecropyield;and•𝑠𝑡,𝑟,𝑐istheshareofavailablelandforeachcrop.Availablelandisdividedintothreegradesoflandquality(prime,goodandmarginal)andthreetypesofland(cultivated,unprotectedgrasslandandunprotectedforestland).Lowerqualitygradesoflandprovidelowercropyields.Inthisassessment,unprotectedforestlandisnotallowedtobeconvertedtocroplandsandsoisunavailableforbioenergypurposes.Cropyieldsaredefinedbyregion,reflectingtheaveragegrowingconditionsinaregion,andareassumedtocontinuetoimprovemoderatelythrough2035.Cropchoiceisinfluencedbycurrentlyfavouredcropsforbioenergy,thechangingeconomicsoffeedstock(throughincreasedyieldsandrelativeattractivenesscomparedtothefossilfuelalternative),andpolicydevelopment.Forexample,policygoalsforadvancedbiofuelswillincreasedemandforlignocellulosicenergycrops,decreasingtheshareoflanddevotedtoconventionalfeedstock.SupplytomeetdemandDemandforbiomassfeedstockisbasedondemandprojectionsforboththepowerandtransportsectors(demandforothersectorsisassumedtobemetfromdomesticresources).Tomeetdemand,domesticsuppliesaregivenpriority;theremainderiscoveredthroughinternationalmarkets.Themodeliscalibratedtomeetexistingtradeflowsreportedinarangeofindustryreports,includingtheF.O.Lichtseries“WorldEthanol&BiofuelReport”,andgovernmentreports,suchasregionalGlobalAgriculturalInformationNetwork(GAIN)reportsonbiofuelsbytheUSDepartmentofAgriculture.DomesticsupplyBiomassfeedstockcompetestomeetdemandonthebasisofconversioncosts,includingfeedstockpricesandtheenergycontentsoffeedstock.Severalbiomassfeedstocktypescanbeusedforbothpowergenerationandtheproductionofbiofuels.Theseincludeforestryproducts,forestryresiduesandagriculturalresidues.Wherethisisthecase,thenetpresentvaluesforbothusesarecomparedandranked,basedontechnologycostdataSection6Energysupply77fromtheGECModelandIEA’sMobilityModel.Accordingtorank,availablebiomassfeedstocksuppliesareallocated.Domesticsupplyofbiofuelsislimitedbyrefiningcapacity.Inthenearterm,thisisrestrictedbyexistingrefineriesandthosealreadyunderconstructionorplanned.GlobaltradeThemodelusesaglobaltradematrixtomatchunsatisfieddemandwithavailablesupplyonaleast-costbasis,includingtransportationcosts.Transportationcostsbetweenregionsincludebothaverageover-landandby-seacosts.Threeproductsaretraded:ethanol,biodiesel,biojetkeroseneandsolidbiomasspellets.Thelatterarehigh-densityuniformproductsthatcanbemadefromresiduesandotherfeedstock,andtheiruniformityanddensitymakehandlingandtransportationeasierandlessexpensiveoverlongdistancescomparedwithotherbioenergyresources.Theconversionofbiomassfeedstocktobiofuelsoccursintheexportingregion,thereforeconversioncostsarecalculatedbasedonthetechnologycostsintheexportingregion.Importingregionschoosesuppliersbasedonleast-costavailablesupplies(includingtransportationcosts).Exportingregionsmakesuppliesavailabletoimportingregionswillingtopaythehighestprice.Section7Criticalminerals79Section77CriticalmineralsScopeThecriticalmineralsmodel,addedasapermanentmoduleintheGECModelduringthe2022modellingcycle,assessesthemineralrequirementsforthefollowingcleanenergytechnologies:◼SolarPV(utility-scaleanddistributed)◼Wind(onshoreandoffshore)◼Concentratingsolarpower(parabolictroughsandcentraltower)◼Hydropower◼Geothermal◼Bioenergyforpower◼Nuclearpower◼Electricitynetworks(transmission,distribution,andtransformer)◼Electricvehicles(batteryelectricandplug-inhybridelectricvehicles)◼Batterystorage(utility-scaleandresidential)◼Hydrogen(electrolysersandfuelcells).Alloftheseenergytechnologiesrequiremetalsandalloys,whichareproducedbyprocessingmineral-containingores.Ores–theraw,economicallyviablerocksthataremined–arebeneficiatedtoliberateandconcentratethemineralsofinterest.Thosemineralsarefurtherprocessedtoextractthemetalsoralloysofinterest.Processedmetalsandalloysarethenusedinend-useapplications.Whilethisanalysiscoverstheentiremineralandmetalvaluechainfromminingtoprocessingoperations,weuse“minerals”asarepresentativetermforthesakeofsimplicity.Wefocusspecificallyontheuseofmineralsincleanenergytechnologies,giventhattheygenerallyrequireconsiderablymoremineralsthantheirfossilfuelcounterparts.Ourmodelalsofocusesontherequirementsforbuildingaplant(ormakingequipment)andnotonoperationalrequirements(e.g.uraniumconsumptioninnuclearplants).OurmodelconsidersawiderangeofmineralsusedincleanenergytechnologieslistedinTable7.1.Theyincludechromium,copper,majorbatterymetals(lithium,nickel,cobalt,manganeseandgraphite),molybdenum,platinumgroupmetals,zinc,rareearthelements,silicon,silverandothers.Table7.1⊳CriticalmineralsinscopeFocusmineralsOtherminerals•Cobalt•Copper•Lithium•Nickel•Rareearthelements(Neodymium,Dysprosium,Praseodymium,Terbium,others)•Arsenic•Boron•Cadmium•Chromium•Gallium•Germanium•Graphite•Hafnium•Indium•Iridium•Lead•Magnesium•Manganese•Molybdenum•Niobium•Platinum•Selenium•Silicon•Silver•Tantalum•Tellurium•Tin•Titanium•Tungsten•Vanadium•Zinc80InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONSteelandaluminiumarewidelyusedacrossmanycleanenergytechnologies,butwehaveexcludeditfromthescopeofthisanalysis.Steeldoesnothavesubstantialsecurityimplicationsandtheenergysectorisnotamajordriverofgrowthinsteeldemand.Aluminiumdemandisassessedforelectricitynetworksonlyastheoutlookforcopperisinherentlylinkedwithaluminiumuseingridlines,butisnotincludedintheaggregatedemandprojections.7.1DemandForeachofthecleanenergytechnologies,weestimateoverallmineraldemandusingfourmainvariables:◼cleanenergydeploymenttrendsunderdifferentscenarios;◼sub-technologyshareswithineachtechnologyarea;◼mineralintensityofeachsub-technology;and◼mineralintensityimprovements.CleanenergydeploymenttrendsundertheStatedPoliciesScenario(STEPS),AnnouncedPledgesScenario(APS)andNetZeroEmissionsby2050Scenario(NZE)aretakenfromtheprojectionsfromthe2022GECModellingcycle.Sub-technologyshareswithineachtechnologyarea(e.g.solarPVmoduletypes)aretakenfromthe2022GECModellingCycle,complementedbytheGlobalEVOutlook2022andothersources.Mineralintensityassumptionsweredevelopedthroughextensiveliteraturereview(seeIEA(2021)fordetails)andexpertandindustryconsultations,includingwithIEATechnologyCollaborationProgrammes.Thepaceofmineralintensityimprovementsvariesbyscenario,withtheSTEPSgenerallyseeingminimalimprovementovertimeascomparedtomodestimprovement(around10%inthelongerterm)assumedintheAPSandNZE.Inareasthatmayparticularlybenefitfromeconomiesofscaleortechnologyimprovement(e.g.siliconandsilveruseinsolarPV,platinumloadinginfuelcells,rareearthelementsuseinwindturbines),specificimprovementrateshavebeenappliedbasedonthereviewofunderlyingdrivers.7.2SupplyrequirementsForthefivefocusminerals(cobalt,copper,lithium,nickelandrareearthelements),totaldemandandprimarysupplyrequirementshavebeenassessed.Consumptionoutsidethecleanenergysectorhasbeenestimatedusinghistoricalconsumptionbyend-useapplications,relevantactivitydrivers(e.g.GDP,industryvalueadded,steelproduction,etc.)andmaterialintensities.Primarysupplyrequirementshavebeenassessedbydeducingprojectedsecondarysupplyfromprojectedtotaldemand.Secondaryproductionisestimatedwithtwoparameters:theaveragerecyclingrateandthelifetimeofeachend-usesector.Therecyclingrateisthecombinationoftheend-of-lifecollectionrate(theamountofacertainproductbeingcollectedforrecycling)andtheyieldrate(theamountofmaterialarecyclingprocesscanactuallyrecover).Forexistingwastestreams(e.g.industrialapplications),weassumeonlymarginalimprovementincollectionrates,whileforemergingtechnologiessuchaslithium-ionbatteries,weassumecollectionratesincreaseatafasterpace.Forbatteries,thecollectionratesgraduallyincreasefromaround45%intheearly-2020sto80%by2040.Forbatteries,theyieldrateisassumedtovaryaccordingtothetechnicallimitationsfortheextractionofeachmineralusingthecurrentlyavailablerecyclingmethods.Thereuseratesaremuchlowerthanthecollectionrateforrecyclingastheuseofsecond-lifebatteriesfacesmanytechnicalandregulatoryobstacles.Section8Emissions81Section88Emissions8.1CO2emissionsAsenergy-relatedCO2emissionsaccountforthelion'sshareofglobalgreenhousegasemissions,oneoftheimportantoutputsoftheGECModelisregionbyregionCO2emissionsfromfuelcombustionandfromindustrialprocesses.Carbondioxideemissionsfromfuelcombustionandfromindustrialprocessesdonotincludefugitiveemissionsfromfuels,flaringorCO2fromtransportandstorage.Unlessotherwisestated,CO2emissionsreportedfromtheGECModelrefertocombustionoffossilfuelsandnon-renewablewaste,industrialprocessCO2emissions,andfugitiveemissionsfromflaring.GECModelCO2emissionsaccountingalsoconsidercarbondioxideremovalfromtheatmospherethroughdirectaircapture(DAC)andstoredpermanentlyinundergroundreservoirs.ForeachGECModelregion,sectorandfuel,CO2emissionsfromfuelcombustionarecalculatedbymultiplyingenergydemandbyanimpliedCO2contentfactor.TheimpliedCO2contentfactorsforcoal,oilandgasdifferbetweensectorsandregions,reflectingtheproductmixandefficiency.TheyhavebeencalculatedasanaverageofthepastthreeyearsfromIEAenergy-relatedsectoralapproachCO2dataforallGECModelregionsandareassumedtoremainconstantovertheprojectionperiod.FortheWEOSpecialReportEnergyandClimateChange1,adetailedanalysisofprocess-relatedCO2emissionsfromvariousindustrialsourcesbyGECModelregionwasconducted.FortheestimationaTier1orTier2methodhasbeenused,whichingeneralmeansthatemissionshavebeenestimatedbasedontheproductionofindustrialmaterialsandanemissionsfactorfromthe2006IPCCGuidelinesforNationalGreenhouseGasInventories.Sofartheanalysisislimitedtothemostimportantsourcesofindustrialprocessemissions:◼Mineralindustry:clinker,lime,limestoneuse,sodaashuse◼Metalindustry:primaryaluminium◼Chemicalindustry:ammonia,methanol,ethylene,sodaash◼Non-energyproducts:lubricantsandparaffin◼Transformation:coal-to-liquids,coal-to-gasandgas-to-liquids,hydrogenproduction,biofuelsproduction(whichcanbringCarbonDioxideRemoval).TheGECModelalsoaccountsforcarboncapture,utilisationandstorage(CCUS).CCUStechnologiescanbedeployedintheelectricityandheat,industryandtransformationsectors.Inthemodel,capturedCO2emissionscanbestoredinundergroundgeologicalformations,onshoreoroffshoreorusedasafeedstockinmanufacturingofsyntheticfuelsinparticular.ThetrajectoryofCO2emissionsfromlanduse,land-usechangeandforestry(LULUCF)havebeenassessedusingtheGLOBIOMmodelinconjunctionwithIIASAasneeded(e.g.nationallydeterminedcontributionassessments).8.2Non-CO2greenhousegasesTheGECModelmodelsallenergy-relatedGHGemissions,bothCO2andnon-CO2.TheCO2andnon-CO2emissionsmodelledwithinGECModelarebenchmarkedagainstscenariosusedinthedatabaseofscenariosfromtheIPCCSpecialReportonWarmingof1.5°CtoprovidecommensurateprojectionsforallotherGHGs(IPCC,2018).Thisincludesprojectionsforotherbiogenicmethaneemissions,nitrousoxide(N2O),andF-gases.Thelastcategoryincludeshydrofluorocarbons(HFCs),perfluorocarbons(PFCs)andsulphurhexafluoride(SF6)fromseveralsectors,mainlyindustry.1https://www.iea.org/reports/energy-and-climate-change82InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION8.3AirpollutionEmissionsofmajorairpollutantsresultingfromtheGECModelenergyscenarioshavebeenestimatedinco-operationwiththeInternationalInstituteforAppliedSystemsAnalysis(IIASA).UsingtheIIASAGAINSmodel,estimateshavebeenmadeforthefollowinglocalairpollutants:sulphurdioxide(SO2),nitrogenoxides(NOx),blackcarbonandPM2.5.2MoreinformationcanbefoundintheWEOSpecialReportonEnergyandAirPollution3aswellasinapreviousdetailedreportoutliningtheapproach,resultsandinformationabouthealthimpacts,aswellaspollutioncontrolcosts.8.4GlobaltemperatureimpactsTheaverageglobalsurfacetemperaturerisethatwouldresultfromgreenhousegasandaerosolemissionsinGECModelscenarioshasbeencarriedoutincloseco-operationwithClimateResourcePtyLtdusingtheModelfortheAssessmentofGreenhouseGasInducedClimateChange(“MAGICC”),4anddrawingonothertoolsusedbytheglobalscientificcommunity.TheMAGICCclimatemodelshavebeenusedextensivelyinassessmentreportswrittenbytheIntergovernmentalPanelonClimateChange.MAGICC7,theversionusedinthisanalysis,isusedintheIPCC’sSixthAssessmentReport(IPCC,2021)anddescribedinCross-ChapterBox7.1therein.Emissionsofallenergy-relatedgreenhousegasesfromtheGECModelscenariosaresupplementedwithcommensuratechangesinnon-energy-relatedemissionstakenfromthescenariodatabasepublishedaspartoftheIPCCSpecialReportonGlobalWarmingof1.5°C(IPCC,2018).8.5OilandgasmethaneemissionsmodelGlobalestimateofmethaneemissionsfromoilandgasoperationsOurapproachtoestimatingmethaneemissionsfromglobaloilandgasoperationsreliesongeneratingcountry-specificandproductiontype-specificemissionintensitiesthatareappliedtoproductionandconsumptiondataonacountry-by-countrybasis.OurstartingpointistogenerateemissionintensitiesforupstreamanddownstreamoilandgasintheUnitedStates(Table8.1).The2020USGreenhouseGasInventoryisusedforthisalongwitharangeofotherdatasources,includingoursurveyofcompaniesandcountries.Thehydrocarbon-,segment-andproduction-specificemissionintensitiesarethenfurthersegregatedintofugitive,ventedandincompleteflaringemissionstogiveatotalof19separateemissionintensities.TheUSemissionsintensitiesarethenscaledtoprovideemissionintensitiesinallothercountries.Thisscalingisbaseduponarangeofauxiliarycountry-specificdata.Fortheupstreamemissionintensities,thescalingisbasedontheageofinfrastructureandtypesofoperatorwithineachcountry(namelyinternationaloilcompanies,independentcompaniesornationaloilcompanies).Fordownstreamemissionintensities,country-specificscalingfactorswerebasedupontheextentofoilandgaspipelinenetworksandoilrefiningcapacityandutilisation.Thestrengthofregulationandoversight,incorporatinggovernmenteffectiveness,regulatoryqualityandtheruleoflawasgivenbytheWorldwideGovernanceIndicatorscompiledbytheWorldBank(2021a),affectsthescalingofallintensities.Someadjustmentsweremadetothescalingfactorsinalimitednumberofcountriestotakeintoaccountotherdatathatweremadeavailable(wherethiswasconsideredtobesufficientlyrobust).2Fineparticulatematterisparticulatematterthatis2.5micrometresindiameterandless;itisalsoknownasPM2.5orrespirableparticlesbecausetheypenetratetherespiratorysystemfurtherthanlargerparticles.3https://www.iea.org/reports/energy-and-air-pollution4InformationsourcedtoClimateResourceinWEO-2021wascontributedbyClimateResourcePtyLtdusingMAGICC7.NeitherClimateResourcenoranyofitsofficers,employees,contractorsoraffiliatesmakeanywarrantyorguaranteeabouttheaccuracy,completenessorreliabilityoftheclimatedataprovidedandanyliabilityresultingfromitsuseisthesoleresponsibilityofthereader.Section8Emissions83Table8.1⊳CategoriesofemissionsourcesandemissionsintensitiesintheUnitedStatesIntensity(massCH4/massoilorgas)HydrocarbonSegmentProductionTypeVentedFugitiveOilUpstreamOnshoreconventional0.34%0.03%Offshore0.23%0.02%Unconventionaloil0.86%0.08%Downstream0.004%0.001%GasUpstreamOnshoreconventional0.41%0.18%Offshore0.24%0.11%Unconventionalgas0.70%0.31%Downstream0.12%0.23%Table8.2providestheresultantscalingfactorsinthetopoilandgasproducers(thecountrieslistedcover95%ofglobaloilandgasproduction).ThesescalingfactorsaredirectlyusedtomodifytheemissionsintensitiesinTable8.1.Forexample,theventedemissionintensityofonshoreconventionalgasproductioninRussiaistakenas0.41%×1.6=0.66%.Theseintensitiesarefinallyappliedtotheproduction(forupstreamemissions)orconsumption(fordownstreamemissions)ofoilandgaswithineachcountry.Table8.2⊳ScalingfactorsappliedtotheUnitedStatesemissionintensitiesCountryOilandgasproductionin2020(mtoe)OilGasUpstreamDownstreamUpstreamDownstreamUnitedStates15041.01.01.01.0Russia11161.81.81.61.7SaudiArabia6120.80.70.80.7Canada4040.80.60.90.6Iran3373.23.22.83.1China3651.41.41.31.4Iraq2203.64.03.44.0UnitedArabEmirates2201.10.81.10.8Qatar2291.10.91.10.9Norway1950.10.00.00.0Kuwait1501.21.21.21.2Brazil1731.31.51.31.5Algeria1383.42.92.52.9Nigeria1292.62.62.22.6Mexico1201.71.41.31.4Kazakhstan1181.41.31.41.3Australia1431.10.60.70.6Indonesia891.61.41.31.4Malaysia851.50.90.90.9UnitedKingdom850.60.20.50.2Egypt792.42.11.92.1Oman791.61.11.21.1Venezuela437.26.75.36.7Turkmenistan815.56.04.86.0Angola732.22.72.12.784InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONMarginalabatementcostcurvesToconstructthemarginalabatementcostcurves,the19emissionssourceslistedinTable8.1werefurtherseparatedinto86equipment-specificemissionssources(Table8.3).Theallocationofemissionsfromeachofthe19emissionssourcestothese86equipment-specificsourceswasgenerallybasedonproportionsfromtheUnitedStates.However,anumberofmodificationsweremadeforcountriesbasedonotherdatasourcesanddiscussionswithrelevantstakeholders.Someofthelargestchangesmadewerefortheproportionofemissionsfrom:pneumaticcontrollers(whicharelessprevalentinmanycountriesoutsideNorthAmerica),LNGliquefaction(whichwereassumedtobelargerinLNGexportingcountries),andassociatedgasventing.Table8.3⊳Equipment-specificemissionssourcesusedinthemarginalabatementcostcurvesHydrocarbonSegmentEquipmentsourceOilUpstream•LargeTanksw/Flares•LargeTanksw/VRU•LargeTanksw/oControl•SmallTanksw/Flares•SmallTanksw/oFlares•MalfunctioningSeparatorDumpValves•PneumaticDevices,HighBleed•PneumaticDevices,LowBleed•PneumaticDevices,IntBleed•ChemicalInjectionPumps•VesselBlowdowns•CompressorBlowdowns•CompressorStarts•AssociatedGasVenting•WellCompletionVenting(lessHFCompletions)•WellWorkovers•HFWellCompletions,Uncontrolled•HFWellCompletions,Controlled•PipelinePiggingDownstream•Tanks•TruckLoading•MarineLoading•RailLoading•PumpStationMaintenance•PipelinePigging•UncontrolledBlowdowns•AsphaltBlowing•ProcessVents•CEMSGasUpstream•ProductionCompressorVented•GasWellCompletionswithoutHydraulicFracturing•GasWellWorkoverswithoutHydraulicFracturing•HydraulicFracturingCompletionsandWorkoversthatvent•HydraulicFracturingCompletionsandWorkoverswithRECs•WellDrilling•PneumaticDeviceVents(LowBleed)•PneumaticDeviceVents(HighBleed)•PneumaticDeviceVents(IntermittentBleed)•ChemicalInjectionPumps•KimrayPumps•DehydratorVents•LargeTanksw/VRU•LargeTanksw/oControl•SmallTanksw/oFlares•MalfunctioningSeparatorDumpValves•GasEngines•WellCleanUps(LPGasWells)-VentUsingPlungers•WellCleanUps(LPGasWells)-VentWithoutUsingPlungers•VesselBD•PipelineBD•CompressorBD•CompressorStarts•G&BStationEpisodicEvents•PressureReliefValves•Mishaps•Recip.Compressors•CentrifugalCompressors(wetseals)•CentrifugalCompressors(dryseals)•DehydratorsoAGRVentsoPneumaticDevicesDownstream•Blowdowns/VentingoReciprocatingCompressoroCentrifugalCompressor(wetseals)oCentrifugalCompressor(dryseals)oReciprocatingCompressoroDehydratorvents(Transmission)oDehydratorvents(Storage)•PneumaticDevices(HighBleed)•PneumaticDevices(IntermittentBleed)•PneumaticDevices(LowBleed)•PipelineventingoStationVentingTransmissionoStationVentingStorage•LNGReciprocatingCompressorsVented•LNGCentrifugalCompressorsVented•LNGStationventingoPressureReliefValveReleasesoPipelineBlowdownoMishaps(Dig-ins)Section8Emissions85TheabatementoptionsincludedinthemarginalabatementcostcurvestoreduceemissionsfromthesesourcesarelistedinTable8.4.WeareunabletoprovidethespecificcostsandapplicabilityfactorsfortheseasitisbasedonproprietaryinformationgatheredbyICF(althoughseeICF(2016a)andICF(2016b)fordatathathasmadeavailablepublicly).CostswereagainbaseduponinformationfromtheUnitedStates.However,labourcosts,whethertheequipmentisimportedormanufactureddomestically(whichimpactsthecapitalcostsandwhetherornotimporttaxesarelevied),andcapitalcostsweremodifiedbasedoncountry-specificorregion-specificinformation.Similarly,theapplicabilityfactorsaremodifiedbasedonotherdatathatisavailablepublicly(forexamplethatsolar-poweredelectricpumpscannotbedeployedaswidelyinhigh-latitudecountries).Table8.4⊳AbatementoptionsformethaneemissionsfromoilandgasoperationsAbatementoptions•BlowdownCaptureandRoutetoFuelSystem(perCompressor)•BlowdownCaptureandRoutetoFuelSystem(perPlant)•Earlyreplacementofhigh-bleeddeviceswithlow-bleeddevices•Earlyreplacementofintermittent-bleeddeviceswithlow-bleeddevices•InstallFlares-Completion•InstallFlares-Portable•InstallFlares-PortableCompletionsWorkoversWOHF•InstallFlares-PortableWOPlungerLifts•InstallFlares-StrandedGasVenting•InstallFlares-Venting•InstallNewMethaneReducingCatalystinEngine•InstallNonMechanicalVaporRecoveryUnit•InstallPlungerLiftSystemsinGasWells•Installsmallflare•InstallVaporRecoveryUnits•LDARGathering•LDARLDC-Large•LDARLDC-MRR•LDARProcessing•LDARReciprocatingCompressorNon-seal•LDARTransmission•LDARWells•MechanicalPumpingforLiquidsUnloading•PipelinePump-DownBeforeMaintenance•RedesignBlowdownSystemsandAlterESDPractices•ReducedEmissionCompletion•ReplaceKimrayPumpswithElectricPumps•ReplacePneumaticChemicalInjectionPumpswithElectricPumps•ReplacePneumaticChemicalInjectionPumpswithSolarElectricPumps•ReplacewithInstrumentAirSystems•ReplacewithElectricMotor•ReplacewithServoMotors•ReplacewithSolenoidControls•ReplacementofReciprocatingCompressorRodPackingSystems•Routetoexistingflare-LargeDehydrators•Routetoexistingflare-LargeTanks•Routetoflare-SmallDehydrators•Routetoexistingflare-SmallTanks•RouteVentVaporstotank•WetSealDegassingRecoverySystemforCentrifugalCompressors•WetSealRetrofittoDrySealCompressor•Microturbine•Mini-LNG•Mini-GTL•Mini-CNGLeakdetectionandrepair(LDAR)programmesarethekeymechanismtomitigatefugitiveemissionsfromtheproduction,transmissionordistributionsegmentsofthevaluechain.Thecostsofinspectiondifferdependingonthesegmentinquestionsinceittakeslongertoinspectacompressoronatransmissionpipelinethaninaproductionfacility.Itisassumedthatinspectionscanbecarriedoutannually,twiceayear,quarterlyormonthly,witheachoptionincludedasaseparatemitigationoptioninthemarginalabatementcostcurves.Annualinspectionsareassumedtomitigate40%offugitiveemissions,biannualinspectionsmitigateanadditional20%,quarterlyinspectionsmitigateanadditional10%,andmonthlyinspectionsmitigateanadditional5%.ImplementingamonthlyLDARprogrammethereforereducesfugitiveemissionsby85%;theremaining15%cannotbeavoided.Asthefrequencyofimplementingeachprogrammeincreases,sodoesthecostperunitofmethanesaved.Forexample,whiletheincrementalcostofabiannualinspectionprogrammeisthesameasthatofanannualinspection,theincrementalvolumeofmethanesavedislower(20%ratherthan40%).Nevertheless,86InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONLDARprogrammesremainsomeofthemostcost-effectivemitigationoptionsavailable,i.e.theytendtocomprisealargeproportionofthepositivenetpresentvalueoptionsincountries.Well-headpricesusedinnetpresentvaluecalculationSincenaturalgasisavaluableproduct,themethanethatisrecoveredcanoftenbesold.Thismeansthatdeployingcertainabatementtechnologiescanresultinoverallsavingsifthenetvaluereceivedforthemethanesoldisgreaterthanthecostofthetechnology.Well-headpricesareusedineachcountrytodeterminethevalueofthemethanecaptured.Themarginalabatementcostcurvesexaminethisissuefromaglobal,societalperspective.Thecreditobtainedforsellingthegasisthereforeappliedregardlessofthecontractualarrangementsnecessaryandthepricesassumethattherearenodomesticconsumptionsubsidies(asthegascouldbesoldontheinternationalmarketatagreaterprice).Thewell-headgaspricesusedcouldthereforebesubstantiallydifferentfromsubsidiseddomesticgasprices.Representativeaveragenaturalgasimportpricesseenfrom2017to2021arethestartingpointforthewell-headpriceswithineachcountry.Toestimatewell-headpricesovertime,eachcountryisassignedtobeeitheranimporteroranexporterbasedonthetrendsseenintheStatedPoliciesScenario.Forimportingcountries,anygasthatwouldbesavedfromavoidingleakswoulddisplaceimports.Thewell-headpriceisthereforetakenastheimportpriceminusthecostoflocaltransportandvarioustaxesthatmaybelevied(assumedtobearound15%oftheimportprice).Forexportingcountries,therelevantwell-headpriceistakenastheimportpriceintheirlargestexportmarketnet-backedtotheemissionssource.Forthenet-back,allowanceismadefortransportcosts(includingliquefactionandshippingorpipelinetransport),feesandtaxes.Forexample,inRussiatheexportpriceistakenastheimportpriceinEurope($7.4/MBtuaverage2017-2021price).Exporttaxesof40%arethensubtractedalongwithafurther$0.5/MBtutocoverthecostoftransportbypipeline.Thisgivesawell-headgaspriceinRussiaofabout$4.0/MBtu.IntheUnitedStatesandCanada,thewell-headpriceistakenastheHenryHubpriceminus15%(tocoverthecostoflocaltransportationandfees).Thecostsandrevenueforeachtechnologyorabatementmeasureisconvertedintonetpresentvalueusingadiscountrateof10%anddividedbythevolumeofemissionssavedtogivethecostindollarspermillionBritishthermalunits(MBtu).OthernotesonmarginalabatementcostcurvesToaidvisualisationofthemarginalabetmentcostcurves,thecostsandsavingsfrommultipletechnologiesareaggregatedtogether.Withineachcountry,theabatementoptionsthatcouldbeappliedtoeachofthe19emissionsourceslistedinTable8.1areaggregatedintothreecoststeps.Thesestepsroughlyrepresentthecheapest50%ofreductions,thenext30%ofreductionsandthefinalof20%reductions.MethaneTrackerupdateThereareseveralemergingtechnologiesandapproachestomeasurementthatappearpromisingtoelevatedataavailableonoilandgasmethaneemissions–amongthemaresatellitesandotheraerialdetectioninstrumentsutilisedduringmeasurementcampaigns.Confirmingandreconcilingbottom-upestimateswithdirectemissionsmeasurements,viaaerialinstrumentsorotherwise,isthebestoptiontohoneaccurateemissionsinformationandovercomeshortcomingsassociatedwithanysingleapproach.Inthisregard,usingstationarymonitors,groundvehicles,oraerialinstrumentssuchassatellites,drones,andplanes,canreducetheriskthatbottom-upestimatessignificantlyunderestimateemissionsfromasite.Inordertohavethegreatestimpactonimprovingestimationtechniques,site-levelstudiesshouldbesufficientlyrepresentativegeographicallyandtemporally,publiclyreported,andindependentlyverified.Section8Emissions87TheIEAhasstayedabreastofinstancesoftheseemergingmeasurementstrategiesandworkedtointegrateresultsfromcrediblesourcesintotheMethaneTrackerwheredatahasbecomeavailable.The2020Trackerupdatereflectsmajordownwardrevisionstoemissionslevelsinahandfulofjurisdictions–notablyNorwayandtheNetherlands.ThiswastheresultofaseriesofmeasurementcampaignsofmethaneemissionsfromoilandgasproductionintheNorthSea–theseeffortsyieldedimprovementstotheprocessofinventoryingemissionsandconfirmedestimatesgeneratedandreportedbyNorwegianandDutchindustryoperators.The2021updatetotheMethaneTrackerincorporatedemissionsdetectedbysatellitesforthefirsttime.Changesintheatmosphericconcentrationofmethanecanbeusedtoestimatetherateofemissionsfromasourcethatwouldhavecausedsuchachange.ThiswasdonebasedondataprocessingbyKayrros,anearthobservationfirm,toconvertreadingsofconcentrationstoidentifylargesourcesofemissionsfromoilandgasoperations.Reportedemissionsencompassindividualmethanesourcesabove5tonnesperhouraswellasclustersofsmallersourcesindenseareas(e.g.shaleplays).Largeemissionsfromoilandgasoperationsthatweredetectedbysatellitesin2020areincludedintheMethaneTrackerforonshoreareasin:Algeria,Kazakhstan,Iraq,RussianFederation,Turkmenistan.EmissionsdetectedbysatellitesarereportedasaseparateitemwithintheMethaneTrackerexceptfortheUnitedStates.FortheUnitedStates,theemissionsdetectedfromthePermianandMarcellusshaleplaysareintegratedwithintotalestimatesforunconventionaloilandgasproduction.Inallcountries,emissionsareassignedeithertoupstreamordownstreamoperationsbasedonthegeographiclocationofdirectly-observedemissionsevents.Thesereadingsarealsousedtoinformestimatesofemissionsthatmaybeoccurringincountriesthatcannotcurrentlybeobserveddirectlybysatellites.Theincreasingamountofdataandinformationfromsatelliteswillcontinuetoimproveglobalunderstandingofmethaneemissionslevelsandtheopportunitiestoreducethem.However,satellitesdohavesomelimitations:◼Existingsatellitesdonotprovidemeasurementsoverequatorialregions,northernareasorforoffshoreoperations.Thismeansthattherearealargenumberofmajorproductionareas(e.g.inareasthatareoftencoveredwithsnow)whereemissionscannotbedirectlydetectedbysatellites.TheemissionsdetectedbysatellitesthatareincludedintheMethaneTrackercomefromareasthatprovidearoundonequarterofglobaloilandgasproductionin2020.◼Existingsatellitesshouldbeabletoprovidemethanereadingsgloballyonadailybasisbutthisisnotalwayspossiblebecauseofcloudcoverandotherweatherconditions.Sentinel5Preadingsfor2020werealsoaffectedbyadataoutagethatreducedthenumberofdirectobservationsthatarecurrentlyavailable(theseshouldbeavailableatalaterdate).TheemissionsincludedintheMethaneTrackeraretheestimateafteranupwardrevisionofdirectlyobservedleaksin2020toaccountforthelackofperfectcoverage.◼Satellitesprovidedataforlargeemittingsources.Theymayfailtocapturesmall-scaleemissionssourcessuchasfaultycomponents,whichcouldadduptoalargeoverallquantityofemissions.◼Theprocessofusingchangesintheatmosphericconcentrationofmethanetoestimateemissionsfromaparticularsourcecanrelyonalargelevelofauxiliarydataandbesubjecttoahighdegreeofuncertainty.Thecountry-by-countryemissionslevelsinMethaneTrackerincludeestimatesforemissionsfromlarge-emittingsources,eveniftheyhavenotbeendirectlyobservedbysatellite.Thisis,ofcourse,subjecttoahighdegreeofuncertainty,butwedosotoensurethatourcountry-by-countyestimatesprovideacomprehensivepictureofallmethaneemissionssources.Asadditionaldatabecomesavailablefrommeasurementcampaigns–whetherrecordedfromgroundoraerialprocessesorbysatellites–wewillincorporatetheseintotheMethaneTrackerandadjustestimatesaccordingly.88InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONFlaringcombustionefficiencyupdateGlobalflaringestimatesarebasedonreporteddatafromtheWorldBank’sGlobalGasFlaringReductionPartnershipusingdatagatheredandmadeaccessiblebytheNationalOceanicandAtmosphericAdministration(NOAA)andthePayneInstitute(WorldBank,2021b).Contributionstocountrytotalproductionarebinnedbysupplytype(unconventionalonshore,conventionalonshoreandoffshore)andproductionstart-upyearusingRystadEnergyUCubedesignations.Flaringdesignstandards,API521andAPI537,wereutilisedtoguidanceflarestacksizingassumingbest-casedesignandoptimalflareparametersduringearlyproductiontime(API,2014;API,2017).Combustionefficienciescanreduceasaresultoflowerproductionrates,highandvariablewinds,andpoormaintenanceresultingfromlackofregulatorypolicy,enforcementorcompanypolicy(Johnson,2001;Kostiuk,2004).TheimpactofwindspeedwasincorporatedusingNASA’sPredictionofWorldwideEnergyResources(POWER)MeterologyDataAccessViewer(NASA,2021).Onshorewindspeedswereassessedat10mandoffshorewindspeedsat50mtoreflectclosestheightofflarestacksinactualfacilitydesign.Windspeedvariabilityanditsimpactoncombustionefficiencywasincorporatedcorrespondingtothelocationofproduction.Flarevolumeoperatorshipweresegregatedbycompanytype:Majors(ExxonMobil,Chevron,BP,RoyalDutchShell,EniSpA,TotalEnergies,andConocoPhillips),NationalOilCompanies(NOCs)andOther(e.g.Independent,PrivateEquity)utilisingoperatorshipassessmentfromRystadEnergyUCube.MaintenancelevelstoimproveflaringcombustionefficiencieswereappliedseparatelybycompanytypeassumingthatmorescrutinyfrominvestorsandthepublicisplacedontheMajorsascomparedtoNOCsorOther.TheWorldBank’sWorldwideGovernanceIndicatorsdatabase(2021a)wasusedasthebasistoassessthegeneralstrengthofregulatoryoversight.Countrieswithstrongerflaringregulationandstrongregulatoryoversightwerecalibratedassumingcompaniesweremandatedtoquicklyinspectandrepairanymalfunctioningorpoorperformingflaresites.Countrieswithweakflaringregulationandlowlevelsofoversightwereassumedtoperformlittletonoadditionalmaintenance.CarbondioxideandmethaneemissionsarefurthercalibratedtothelocalhydrocarboncontentusingtheIEA’sWorldSupplyModel.CarbondioxideequivalentemissionsfromthecombustionofthehydrocarbonfluidstreamsareestimatedinaccordancewithIPCC(2006)recommendedvalues.Onetonneofmethanereleasedisassumedtobeequalto30tonnesofCO2-eq,basedonthe100yearglobalwarmingpotential.Section9Investment89Section99Investment9.1InvestmentinfuelsupplyandthepowersectorInvestmentismeasuredastheongoingcapitalexpendituresinfuelproductionandpowergenerationcapacity,aswellasinfrastructure.ProjectionsofinvestmentrequirementsbyscenarioarederivedfromtheGECModelenergysupplyanddemandmodules.Thecalculationoftheinvestmentrequirementsforpowergenerationandfuelsupplyinvolvedthefollowingstepsforeachregion:◼Newcapacityneedsforproduction,transportationand(whereappropriate)transformationwerecalculatedonthebasisofprojecteddemandtrends,futuresupplyrequired,estimatedratesofretirementoftheexistingsupplyinfrastructureanddeclineratesforoilandgasproduction.◼Unitcapitalcostestimateswerecompiledforeachcomponentinthesupplychain.Thesecostswerethenadjustedforeachyearoftheprojectionperiodusingprojectedratesofchangebasedonadetailedanalysisofthepotentialfortechnology-drivencostreductionsandoncountry-specificfactors.◼Incrementalcapacityneedsweremultipliedbyunitcoststoyieldtheamountofinvestmentneededasiftheassetswereconstructedandbecameoperationalonanovernightbasis.◼Finally,usingtechnologyandcountry/region-specificspendingprofiles,overnightinvestmentneedswerethendistributeduniformlyacrossconstructionleadtimesestimatedforeachasset,whatwerefertoas‘investmentspending’.Theestimatesofinvestmentinthecurrentdecadetakeaccountofprojectsthathavealreadybeendecidedandexpendituresthatarealreadyongoing.Thisapproachbasedoncapitalspendingcandifferacrosssupplyareas.Forsomesectors,suchaspowergeneration,theinvestmentisspreadoutfromtheyearinwhichanewplantorupgradeofanexistingonebeginsitsconstructiontotheyearinwhichitbecomesoperational.Forothersources,suchasupstreamoilandgasandliquefiednaturalgas(LNG)projects,investmentreflectsthecapitalspendingprofilestypicallyincurredasproductionfromanewsourcerampsuportomaintainoutputfromanexistingasset.ForthepurposesofoutlooksusingtheGECModel,investmentisdefinedascapitalexpenditureonly.Itdoesnotincludespendingthatisusuallyclassifiedasoperations,maintenance,orspendingdevotedtoservicingfinancingcosts.Short-termoilandnaturalgasupstreaminvestmentProjectionsofupstreaminvestmentarebasedonacombinationofbottom-upandtop-downapproaches.Theformerinvolvesadetailedanalysisoftheplansandprospectsforoilandgasindustryinvestmentinthefuture,withtheaimofdetermininghowmuchtheindustryisplanningtoinvestinresponsetocurrentpricesandtotheneedfornewcapacityandofassessingtheresultingadditionstoproductioncapacity.Thisanalysisisbasedonasurveyofthecapital-spendingprogrammesofover80leadingupstreamoilandgascompanies(nationalandinternationalcompaniesandpureexplorationandproductioncompanies),coveringactualcapitalspendingfrom2000to2020andtheirplansorforecastsofupcomingspendingwhenavailable.Companieswereselectedonthebasisoftheirsizeasmeasuredbytheirproductionandreserves,thoughgeographicalspreadanddataavailabilityalsoplayedarole.Thesurveyedcompaniesaccountforoverthree-quartersofworldoilandgasproduction.Totalindustryinvestmentwascalculatedbyadjustingupwardsthespendingofthecompanies,accordingtotheirshareofworldoilandgasproductionforeachyear.Datawasobtainedfromcompanies’annualandfinancialreports,corporatepresentations,pressreports,tradepublicationsanddirectcontactsintheindustry.90InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONTable9.1⊳Sub-sectorsandassetsincludedinfuelsupplyinvestmentSub-SectorAssetsOilandGas•Upstreamoil•Upstreamgas•Midstreamoil(pipelines)•Midstreamgas(pipelinesandLNG)•Refining(greenfield)•Refining(upgradeandmaintenance)Coalsupply•Coalmining•CoaltransportationLow-emissionsfuels•Biogases•Liquidbiofuels•Hydrogenandhydrogen-basedfuelsproduction•HydrogeninfrastructureLong-terminvestmentinfuelsupplyProjectionsoflong-termoil,gas,coalandlow-emissionsfuelsinvestmentrequirementsaregeneratedintherespectivesupply-sidemodules.Thelevelofinvestmentissettomeetthelevelofdemandprojectedinagivencountry,regionandyear.Themethodologyestablishesadirectlinkovertimebetweennewproductioncapacitybroughtonstream,thecashflowgeneratedandtheinvestmentsrequired.Thecostofnewcapacityisestimatedfromasetofvariables:sizeofthereserves,degreeofdepletion,locationtypeofresource,technologyemployed,technologylearning,andunderlyingassumptionsforcostchanges(whichareafunctionofoilpricesintheoilandgassupply-sidemodules).Amoredetailedprojectionwasmadeforinvestmentsassociatedwithhydrogen-basedsupply,includingproductionoflow-carbonhydrogenfromelectrolysis,fossilfuels(fittedwithcarboncaptureutilisationandstorage[CCUS]andinfrastructure).PowersectorinvestmentLargeinvestmentsinthepowersectorwillbeneededovertheOutlookperiodtomeetrisingelectricitydemand,achievedecarbonisationgoalsandtoreplaceorrefurbishobsoletegeneratingassetsandnetworkinfrastructure.Theovernightinvestmentsingeneratingassetsareastraightforwardcalculationmultiplyingthecapitalcost($/kW)foreachgeneratingtechnologybythecorrespondingcapacityadditionsforeachmodelledregion/country.Investmentoutlaysarethenspreadovertimebasedonspendingprofilesthatbeginatthestartofconstructionandfinishwhenanassetbecomesoperational.Thecapitalcostsassumedinthepowergenerationsectorarebasedonareviewofthelatestcountrydataavailableandonassumptionsoftheirevolutionovertheprojectionperiod.Theyrepresentovernightcostsforalltechnologies.ForrenewablesourcesandforplantsfittedwithCCUSfacilities,theprojectedinvestmentcostsresultfromthevariouslevelsofdeploymentinthedifferentscenarios.IndicativeovernightcostsandotherrelevantinvestmentassumptionsforalltechnologiesbyregionmaybefoundontheGECModelkeyinputdatapage1.Forinvestmentintransmissionanddistributionnetworks,pleaserefertosection4.3.1https://www.iea.org/data-and-statistics/data-product/global-energy-and-climate-model-2022-key-input-dataSection9Investment91Table9.2⊳Sub-sectorsandassetsincludedinpowersectorinvestmentSub-SectorAssetsFossil-fuelbasedpowergeneration•Coal-firedpower•Coal-firedpowerwithCCUS•Gas-firedpower•Gas-firedpowerwithCCUS•Oil-firedpowerNuclearpowergeneration•Nuclearpowerplants(greenfield)•Refurbishmentsandupgradesforlong-termoperationsRenewablepowergeneration•Bioenergy•Hydropower•Wind(onshoreandoffshore)•Geothermal•SolarPV(utility-scale;residential,commercialandotherdistributed)•Solarthermal•MarineElectricitygrids•Transmission•Distribution•PublicEVchargersBatterystorage•Utility-scaleandbuildings9.2Demand-sideinvestmentsDemand-sideinvestmentsareconsumeroutlaysforthepurchaseofend-useequipment.Ongoingspendingassociatedisassumedtooccurinthesameyearaswhenassetsbecomeoperational.Forefficiency,thisdoesnotincludeallofthespending,onlytheamountthatisspent(includingtaxesandfreightcosts)toprocureequipmentthatismoreefficientthanabaseline.Theinvestmentcostincludeslabourcoststhataredirectlyrelatedtoaninstallation,whileadditionalcostscanarisefromadministrativeprocedures,legalprotectionandborderclearances,whicharealsoincludedinthecostestimate.Inotherwords,thiscalculationreflectstheadditionalamountthatconsumershavetopayforhigherenergyefficiencyovertheprojectionperiod.AcrosstheGECModelregionsandforeachend-usesector(industry,transportandbuildings),theinvestmentneededtomovetogreaterefficiencylevelshavebeenanalysed.Theanalysisisbasedoninvestmentcost,stockturnoverandtheeconomicreturnrequiredacrosssub-sectorsinindustry,acrossmodesoftransportandacrossend-usesinbuildings.Forexample,intheroadtransportsector,thecostsofefficiencyimprovementsandofaswitchtoalternativefuelvehiclesareusedasaninputtothemodeltodetermineeachoption’scost-competitiveness.Basedontheoutcomeofthisanalysis,theinvestmentneedsarethendeterminedbymultiplyingthenumberofvehiclessoldineachyearbythecostsofeachvehicle.Inadditiontoenergyefficiency,end-useinvestmentsincludedirectuseofrenewables,electricvehicles,electrificationinbuildings/industry,useofhydrogenandhydrogen-basedfuels,andCCUSinindustry.Demandmodeloutputsincludetheadditionalannualcapitalneedsforeachregionandend-usesector.Theimpactoftheenergysavingsonconsumers’billsisalsoanalysed.Thesectoralend-userprices(includingtaxes)havebeenusedtoassesstheoverallimpactofthepoliciesonconsumersovertime.Theresultsalsoincludetheimpactonmainimportingcountries.92InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONTable9.3⊳Sub-sectorsandassetsincludedinend-useenergyinvestmentSectorSub-sectorBuildings•Energyefficiency(includingbuildingenvelopesandretrofits)•Electrification•Renewablesuse•Hydrogen-baseduseIndustry•Energyefficiency•Electrification•Renewablesuse•CCUS•Hydrogen-baseduse•Fossilfuel-basedindustrialfacilitiesTransport•Energyefficiencyofroadtransport•Electrificationofroadtransportandinternationalmarinetransport•Hydrogenandhydrogen-basedroadtransportandshippingOther•Directaircarboncaptureandstorage9.3FinancingforinvestmentsSourcesoffinanceBuildinguponanalysiscarriedoutin2021andtheFinancingCleanEnergyTransitionsinEmergingandDevelopingEconomiesreport,anupdatedassessmentofthesourcesoffinanceassociatedwithinvestmentswascarriedout.Whileprojectdevelopersactastheprimaryactorsinvestinginenergyassets,theirsuccessdependsonahavingrobustinter-connectedsystemoffinancialsourcesandintermediaries,diverseinvestmentvehiclestofacilitateflowsandclearsignalsforaction,basedonprofitexpectationsandriskprofiles.Thesourcesoffinancearecharacterisedacrossfourbroadparameters:◼typeoffinancingstructure(off-balancesheet[projectfinance]oron-balancesheet[corporatefinance]);◼typeofprovider(privateorpublic[publicfinanceinstitutionsandstate-ownedenterprises]);◼typeofinstrument(accordingtocapitalstructure-debtorequity);◼originofprovider(internationalordomesticsources).Forfurtherdetailsonestimationapproach,pleaseseetheWorldEnergyInvestmentReport2022MethodologyAnnex.CostoffinanceTheGECModelincorporatesdifferentiatedassumptionsonthecostofcapitalacrossregionswithinthesupply,powerandend-usesectors.Forexample,assomecountriespursueeffortstominimiseemissionsfromoilandgasoperationsintheAPS,thisincreasestheirproductioncostsrelativetootherproducersandinmanycasesalsoinvolvesadditionalfinancingcosts(comparedtothoseassumedintheSTEPS).AsexplainedinSection4,adetailedanalysishasbeenundertakentoreflectthereductioninfinancingcostsforsolarPVandwindacrossGECModelcountries/regions.Investmentdecisionsinenergyefficiencyreflecttheestimatesfortheprevailingdebtandequityfinancecostsfacedbyconsumers(forresidentialbuildingsandvehicles),businessesintherealSection9Investment93estatesector(forcommercialbuildings)andcompaniesfromdifferentindustrialsectorsacrossGECModelregions.Financingcostsareexpressedinpre-taxtermscalculatedusingtheweightedaveragecostofcapital(WACC):𝑊𝐴𝐶𝐶𝑟𝑒𝑎𝑙,𝑝𝑟𝑒−𝑡𝑎𝑥=1+(𝐶𝑒×𝑤𝑒+𝐶𝑑×𝑤𝑑)1+𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛−1Where:𝐶𝑒Costofequity𝐶𝑑Costofdebt𝑤𝑖shareofdebtorequityinthecapitalstructureForsectorswherepricesandunderlyingcontractsarelargelydenominatedininternationalcurrencies(e.g.USD),asintheoilandgasindustry,costcomponentswereestimatedusingmaturemarketrisk-freeratesadjustedforcountryandsectoralrisks.Forsectorswherepricesandunderlyingcontractsaredenominatedinlocalcurrencies,suchasinpowerandend-use,costcomponentswereestimatedusinglocalmarketrisk-freeratesadjustedforcountryandsectoralrisks.NominaldataareconvertedintorealtermsusingtheFischerEquation.EstimatingtheWACCcomponentsforthedifferentenergysectorsreflectsdatafromfinancialmarketsandacademicliterature,complementedbyinterviewswithmarketexpertsandpractitioners.Inaddition,differentiatedWACCsforthepowersectoroutlookincludeanalysisofauctionresultsandPPApricing.9.4EmissionsperformanceofinvestmentsMeasuringtheperformanceandtargetingofcapitalflowsagainsttheinvestmentneedsoflong-termnetzeroemissionsgoalsisacomplextask.Someinvestmentswillunequivocallyhelptoreduceemissions;othersaresuretoincreasethem.Thescenariosrevealalargenumberofgradations:alargeportionofinvestmentsgotowardssectors,technologiesandinfrastructurethatdonotimmediatelydeliverzeroemissionsenergyorenergyservices,butdoenablesuchinvestmentsorprovideincrementalemissionsreductions;someoftheseinvestmentscanalsodeliverzeroemissionsenergyovertime,butarecontingentonactionselsewhereinthesystem,notablythoseconcernedwithdecarbonisingthepowersector.Toillustrate,theGECModeldividesthetotalinvestmentrequirementinthescenariosintofourcategories:◼Lowemissions:Investmentsthatprovidezeroemissions(orverylowemissions)energyorenergyservices,regardlessofhowtheenergysystemevolves.◼Contingent:Investmentsthatcouldprovideorenablezeroemissionsenergyorenergyservicesbutonlywithchangeselsewhereintheenergysystem.◼Transition:Investmentsthatprovideemissionsreductionsbutdonotthemselvesdeliverzeroemissionsenergyorenergyservices.◼Unabatedfossilfuelsthatdonotenableemissionsreductions:Investmentsincoal,oilandnaturalgasthatdonotprovideanyemissionsreductionsfromtoday.Theallocationofinvestmentincertainassetsortechnologiesvariesacrossregionsandovertime.Forfurtherdetails,pleaseseeBox1.3ofWEO-2021.Section10EnergyandCO2decomposition95Section1010EnergyandCO2decompositionTheGECModelincludesamodule–thedecompositionmodule–toquantifythedifferenceofenergyandCO2emissionsbetweentwoscenariosorinonescenarioovertime.Decompositionanalysisisappliedtoallend-usesectors(industry,transport,buildings,agriculture)andthetransformationsector(electricitygenerationandheatproduction,refineries,biofuels,hydrogenandhydrogen-derivedfuels,otherenergysectors)ex-posttotheGECModelusingthefinalresultsforthedecomposedscenarios.Thedifferencebetweenscenariosorpointsintimeisapportionedouttoseveral“levers”thatrepresentimportantstrategiestoreduceenergyconsumptionandemissionswithintheenergysystem.Theseinclude:◼Activity:differenceinenergyoremissionsfromeconomicactivityandchangeinservicedemand,e.g.,increaseinindustrialvalueadded,travelledkilometresorusedfloorspace.◼Avoideddemand–resourceefficiency:differenceinenergyoremissionsfromefficiencyimprovementsintheuseofresources,e.g.,extensionofbuildinglifetimesleadingtolesssteelorcementdemand.◼Avoideddemand–behaviour:differenceinenergyoremissionsfromavoideddemandduetobehaviouralshifts.PleaseseethebehavioursectionsintheEnergydemandsection(Section3)formoredetails.◼Economicstructure:differenceinenergyoremissionsfromstructuralchangesintheindustrysectorduetochangingimportanceofcertainindustrialsubsectors.◼Energyefficiency:differenceinenergyoremissionsfromefficiencyimprovementsofdeployedtechnologies.◼Fuelshifts:differenceinenergyoremissionsfromchangingthefuelused,includingthroughusingdifferenttechnologiesthatmayhavehigherefficiency,e.g.,theshifttoelectricvehiclesfromcombustionenginesortheshifttoheatpumpsfromgasboilers.Thiseffectisfurtherbrokendowntospecificfuels:•Electrification:assessingthechangesforelectricity,e.g.,useofelectricvehiclesordirectelectrificationinindustry.Foremissions,thiscanbedoneinadirectdecomposition(excludingemissionsfromtheelectricityandheatsector)orinanindirectdecomposition(includingemissionsfromtheelectricityandheatsector).•Bioenergy:assessingthechangesforbioenergy,e.g.,inpowergenerationorasafuelinbuildingsorindustry.•Otherrenewables:assessingthechangesforotherrenewables,e.g.,useofsolarPVandwindforpowergenerationorsolarthermalinbuildings.•Hydrogen:assessingthechangesfortheuseofhydrogenandhydrogen-derivedfuels,e.g.,inthetransportsectororinenergy-intensiveindustries.•Otherfuelshifts:assessingthechangesforotherfuels,e.g.,switchesbetweenfossilfuelsornuclear.◼CCUS:differenceinenergyoremissionsfromthedeploymentofcarboncaptureutilisationandstorage.Thedecompositionmodulealsohasthecapabilitytoapportionemissionandenergychangesaccordingtotechnologyreadinesscategory,usingthetechnologyreadinesslevel(TRL)ofeachmodelledtechnologyorstrategy.TheassessmentisbasedontheETPCleanEnergyTechnologyGuideandclassifiestechnologiesbeingdeployedinagivenyearintofourtiers:as“mature”,at“marketuptake”,under“demonstration”orstilla“prototype”ingivenyears.TheTRLbreakdownmakesitpossibletoallocatethecontributionofleverssuchasfuelswitchingorenergyefficiencytodifferenttechnologicalmaturities,andthustohighlightwherethereisneedforprogressininnovationtoclosethegapbetweenscenariosorovertimewithinascenario.96InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION10.1MethodologyThedecompositionmoduleadherestotheLogarithmic-Mean-Divisia-Index(LMDI)approachtobreakdownthedifferencebetweenareferenceandacomparisonpoint(eitheranotherscenarioorthepreviousyear)foragivenyearbythekeylevers.TheapproachmakesuseoftheKayaequationthatsinglesoutdifferenteffectsandseparatestheevaluatedlevers.TheKayaequationcanvarybysectorbutcanbedescribedasanexamplefortheCO2emissionswiththeactivity(A)andtheenergy(E)foreachtechnology(t)asfollows:𝐶𝑂2𝑡=𝐴∗𝐴𝑡𝐴∗𝐸𝐴𝑡∗𝐶𝑂2𝐸Inthisequation,themultipliersrepresentbyordertheactivity(A),structuralchanges(S),energyintensity(I)andtheCO2intensity(C).Thosemultiplierscanbeprocessedandfurtherbrokendowntocalculatetheabove-mentionedkeyleversthatareassessed.Applyingthelogarithmfunctiontothedifferencebetweenareference(ref)andacomparisonpoint(comp),leadstothefollowingdifferenceforemissionsbetweenthesescenarios:𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−𝐶𝑂2𝑡,𝑟𝑒𝑓=𝛼𝑡∗{ln(𝐴𝑐𝑜𝑚𝑝𝐴𝑟𝑒𝑓)+ln(𝑆𝑐𝑜𝑚𝑝𝑆𝑟𝑒𝑓)+ln(𝐼𝑐𝑜𝑚𝑝𝐼𝑟𝑒𝑓)+ln(𝐶𝑐𝑜𝑚𝑝𝐶𝑟𝑒𝑓)},with𝛼𝑡=𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−𝐶𝑂2𝑡,𝑟𝑒𝑓ln𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−ln𝐶𝑂2𝑡,𝑟𝑒𝑓Theseformulasaredefinedinaverysimilarwayforanenergydecomposition.Foradecompositionbetweenscenarios,thetwoscenarios,comparisonandreference,arethecomparedpoints,e.g.,NZEandSTEPS.Foradecompositionofonescenarioovertime,thecomparisonandreferencepointsusethesameGECModelscenariobutjustwithadelayofoneyearbetween(e.g.,comparingvaluesin"t"withvaluesin"t-1"asareference)tocalculatetheleversforeachyearstep.Forthecalculationofeffectsinatargetyear,annualeffectsareaccumulatedfortheperiodafterthebaseyear.Thedecompositionmodulecalculatestheeffectsconsideringhightechnologicalresolution,whichmeansbyend-usetechnologyandfuelforeachmodelledregion.Thisframeworkmakesitpossibletocalculateinterlinkagesbetweeneffects,suchastheindirectordirectdecompositionreflectingemissionsfrompowergenerationorenergyefficiencyimprovementsfromfuelswitching,e.g.electrification.Resultsatthesectoral,regionalorgloballevelareobtainedbysummingrelevantcontributions.Section11Energyaccess97Section1111Energyaccess11.1DefiningmodernenergyaccessThereisnosingleinternationally-acceptedandinternationally-adopteddefinitionofmodernenergyaccess.Yetsignificantcommonalityexistsacrossdefinitions,including:◼Householdaccesstoaminimumlevelofelectricity◼Householdaccesstosaferandmoresustainable(i.e.minimumharmfuleffectsonhealthandtheenvironmentaspossible)cookingandheatingfuelsandstoves◼Accesstomodernenergythatenablesproductiveeconomicactivity,e.g.mechanicalpowerforagriculture,textileandotherindustries◼Accesstomodernenergyforpublicservices,e.g.electricityforhealthfacilities,schoolsandstreetlightingAlloftheseelementsarecrucialtoeconomicandsocialdevelopment,asareanumberofrelatedissuesthataresometimesreferredtocollectivelyas"qualityofsupply",suchastechnicalavailability,adequacy,reliability,convenience,safetyandaffordability.ThedataandprojectionsfromtheGECModelfocusontwoelementsofenergyaccess:ahouseholdhavingaccesstoelectricityandtocleancookingfacilities.TheIEAdefinesenergyaccessas"ahouseholdhavingreliableandaffordableaccesstobothcleancookingfacilitiesandtoelectricity,whichisenoughtosupplyabasicbundleofenergyservicesinitially,andwiththelevelofservicecapableofgrowingovertime".ThisenergyaccessdefinitionservesasabenchmarktomeasureprogresstowardsgoalSDG7.1andasametricforourforward-lookinganalysis.Accesstoelectricityentailsahouseholdhavinginitialaccesstosufficientelectricitytopowerabasicbundleofenergyservices–ataminimum,severallightbulbs,phonecharging,aradioandpotentiallyafanortelevision–withthelevelofservicecapableofgrowingovertime.Inourprojections,theaveragehouseholdwhohasgainedaccesswillhaveintimeenoughelectricitytopowerfourlightbulbsoperatingatfivehoursperday,onerefrigerator,afanoperating6hoursperday,amobilephonechargerandatelevisionoperating4hoursperday,whichequatestoanannualelectricityconsumptionof1250kWhperhouseholdwithstandardappliances,and420kWhwithefficientappliances.Thisservice-leveldefinitioncannotbeappliedtothemeasurementofactualdatasimplybecausethelevelofdatarequireddoesnotexistinalargenumberofcases.Asaresult,ourelectricityaccessdatabasesfocusonasimplerbinarymeasureofthosethathaveaconnectiontoanelectricitygrid,orhavearenewableoff-ormini-gridconnectionofsufficientcapacitytodelivertheminimumbundleofenergyservicesmentionedabove.Accesstocleancookingfacilitiesmeansaccessto(andprimaryuseof)modernfuelsandtechnologies,includingnaturalgas,liquefiedpetroleumgas(LPG),electricityandbiogas,orimprovedbiomasscookstoves(ICS)thathaveconsiderablyloweremissionsandhigherefficienciesthantraditionalthree-stonefiresforcooking.Currently,veryfewICSmodelsattainthisloweremissionstarget,particularlyunderreal-worldcookingconditions.Therefore,ourcleancookingaccessdatabasereferstohouseholdsthatrelyprimarilyonfuelsotherthanbiomass(suchasfuelwood,charcoal,treeleaves,cropresiduesandanimaldung),coalorkeroseneforcooking.Forourprojections,onlythemostimprovedbiomasscookstovesthatdeliversignificantimprovementsareconsideredascontributingtoenergyaccess.ThemainsourcesaretheWorldHealthOrganisation(WHO)HouseholdEnergyDatabaseandtheIEAEnergyBalances.98InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION11.2OutlookformodernenergyaccessOutlookforelectricityaccessTheIEA’selectricityaccessdatabase1providesvaluableinformationaboutthecurrentelectrificationratesinalargenumberofcountries.Inordertoprovideanoutlookforelectricityaccessinthenextdecades,amodelabletogenerateprojectionsofelectrificationratesbyregionhasbeendeveloped.Theprojectionsarebasedonaneconometricpanelmodelthatregresseshistoricelectrificationratesofdifferentcountriesovermanyvariables,totesttheirlevelofsignificance.Variablesthatweredeterminedstatisticallysignificantandconsequentlyincludedintheequationsareper-capitaincome,demographicgrowth,urbanisation,fuelprices,levelofsubsidies,technologicaladvances,energyconsumption,andenergyaccessprogrammes.ToidentifythemorefeasibleaccesstoelectricitypathwaystheIEAusesthelatestavailablecountry-by-countrygeospatialdatatoidentifytheleastcostpathwayprovidingconnectionstoun-electrifiedpopulations.Thisassessment,usingthepubliclyavailableOnSSETmodel,takesintoaccountdistancestothegrid,expecteddemand,thepopulationdensityandavailableresourcestoselecttheleastcostsolutionsforeachsettlement.Itthenfactors-inotherimportantindicatorsasthepotentialspeedatwhichgridandoff-gridsystemscanprovideaccess,thepotentialforsimultaneouslyelectrifyingothersectorssuchasindustry,agricultureortransport,theoptimalsolutionformaximisingreliability,resilienceandqualityofsupply,andtheattractivenessofinvestmenttodifferentinvestorsandvendors.OutlookforcleancookingaccessOurbaselinedataonthetraditionaluseofbiomassforcookingisbasedontheWorldHealthOrganization’s(WHO)GlobalHealthObservatoryestimatesofrelianceonsolidfuels.2Toprovideanoutlookforthenumberofpeoplerelyingonthetraditionaluseofbiomassinthenextdecades,aregionalmodelwasdevelopedunderdifferentassumptions.Relianceonbiomassratesofdifferentcountriesisprojectedusinganeconometricpanelmodelestimatedfromahistoricaltimeseries.Variablesthatweredeterminedstatisticallysignificantandconsequentlyincludedintheequationsareper-capitaincome,demographicgrowth,urbanisationlevel,levelofpricesofalternativemodernfuels,subsidiestoalternativemodernfuelconsumption,technologicaladvancesandcleancookingprogrammes.Forfurtherdetailontheenergyaccessanalysisandmethodologyseethededicatedwebsite:https://www.iea.org/topics/energy-access.AffordabilityofbasicelectricityservicesStartingin2020,anewanalysiswasconductedontheimpactoftheCovid-19pandemicontheaffordabilityofbasicelectricityservicesforhouseholdsinAfricaandDevelopingAsia.Thisanalysishasbeenupdatedforin2021and2022,wherealsopre-pandemicaswellcookingLPGaffordabilityhavebeenestimatedincludingtheuseofrecentenergypricespikes.UsingpovertydatafromLakneretal.(2021),aswellascountryelectricityandLPGprices,weanalysedtheextenttowhichpovertyandtheimpactofCovid-19couldbringaboutenergyaffordabilityifhouseholdsareunabletoaffordbasicelectricityservices.Weconsideredtwobundlesofelectricityservices:anessentialbundle(includingfourlightbulbsoperatingfourhoursperday,afanthreehoursperdayandatelevisiontwohoursperday;equatingto500kilowatt-hours(kWh)perhouseholdperyearwithstandardappliances),andanextendedbundle(includingtheessentialbundleplusonerefrigerator,anddoublehoursforthefanandthetelevision;equatingto1250kWhperhouseholdperyearwithstandardappliances).Thenumberofpeopleatriskoflosingbasicelectricityserviceswasestimatedbycombiningdataonthecostsofthesebundlesindifferentcountrieswithdataonthenumberofadditionalhouseholdspushedacrossdifferentpovertylines($1.90/day,$3.20/dayor$5.50/day)asaresultofthecrisis.Weconsideredahouseholdatriskoflosingabilitytopaywhenitrepresentsover5%ofthehouseholdspending.1https://www.iea.org/reports/sdg7-data-and-projections/access-to-electricity2Formoreinformation,seewww.who.int/gho/phe/indoor_air_pollution/en/index.htmlSection12Employment99Section1212EmploymentTheIEAaddedanenergyemploymentmodulein2020andcompletedafullerintegrationandtransfertoVensimwiththeGECModelframeworkin2022.Employmentmodellingnowcovers40energysubsectorsin26regionsunderdifferentIEAscenarios.Themodelcurrentlyanalyses:◼Thenumberofpeoplecurrentlyemployedinfuelsupply(includingcoal,oil,gas,bioenergy,andhydrogen),thepowersector(generation,transmission,distribution,andstorage),aswellasmajorendusesectors(vehiclesmanufacturing,andenergyefficiencyforbuildingsandindustry);and◼Thenumberofjoblossesandgainsinthesesectorsasadirectresultofshiftinginvestmentsinnewinfrastructure,theproductionofenergycommodities,andtheoperationofenergyassets.12.1DefinitionandscopeofemploymentEmploymentliteraturetypicallyclassifiesjobcreationimpactsbythefollowingschema:◼Direct:Jobscreatedtodeliverafinalprojectorproduct.◼Indirect:Supplychainjobscreatedtoprovideinputstoafinalprojectorproduct.◼Induced:Jobscreatedbywagesearnedfromtheprojectsandspentinotherpartsoftheeconomy,therebycreatingadditionaljobs.Ouremploymentanalysisincludesalldirectjobsandtheindirectjobsfromsuppliersthatmanufactureimmediateinputstotheenergysector.Otherindirectjobs,aswellasinducedjobsarenotincluded.Inemploymentliterature,indirectjobssometimesincludejobs“supported”bythepurchasewheretheequipmentisakeyenablerforanotherjob.Forexample,automobilemanufacturingisakeyenablerfordeliveryandtaxidrivingjobs.These“supported”jobsarenotincludedinouranalysis.Thissetsaclearboundaryaroundthejobsthatenergyinvestmentcreatestodelivernewproject,orthejobsrequiredtooperateexistingenergyfacilities.Jobsarenormalisedtofull-timeemployment(FTE)forconsistentaccounting.OneFTEjobrepresentsoneperson’sworkforoneyearatregulatednorms(e.g.,40hoursaweekfor52weeksayear,excludingholidays).Forexample,twoseparate,six-monthjobsarecountedasoneFTEjob.Wheredataisavailableforhoursworkedweekly,wecalculatepart-timeworkerswiththecorrespondingproportion.Otherwise,part-timeemploymentisassumedas0.5FTE.Employmentnumbersincludeourbestestimateofthenumberofinformalworkers,withthehopethatournumbersreflectthescopeofenergypolicyimpactmorecompletely.InalignmentwithInternationalLabourOrganisation(ILO)guidelines,informalemploymentincludesallremunerativeworkthatisnotregistered,regulated,orprotectedbyexistinglegalorregulatoryframeworks.Thiscomprisesown-accountworkersandworkersemployedininformalsectorenterprises;contributingfamilyworkers;employeesholdinginformaljobs;membersofinformalproducers’cooperatives;andown-accountworkersengagedintheproductionofgoodsexclusivelyforownfinalusebytheirownhousehold.EstimatesarebasedonILOdataandaliteraturereviewofinformalityratesbyregionandsector.CategorisationbyvaluechainstepEmploymentiscategorisednotonlybyenergyindustries,butalsobyvaluechainstepsoreconomicsectorsasdefinedbytheInternationalStandardIndustrialClassification(ISIC)revision4,withsignificantnumbersofworkersinthefollowingfivegroupings:◼Rawmaterials:Agriculture(codeA)forbioenergyproductionandMiningandquarrying(codeB)forfossilfuelproduction100InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATION◼Manufacturing:ISICcodeC◼Construction:ISICcodeF◼Professionalsandutilities:Electricity,gas,steam,andairconditioningsupply(codeD)aswellasProfessional,scientific,andtechnicalactivities(codeM)◼Wholesaleandtransport:Wholesaleandretailtrade(codeG)plusTransportationandstorage(codeH)Whereverpossible,weprovideacomprehensivemappingofjobsacrossalloftheabovesectors.CategorisationbyassetlifestageEmploymentisalsocategorisedaccordingtowhetherthejobisassociatedwithbuildinganewprojectoroperatingandmaintainingexistingenergyinfrastructure.ThissplitisbasedonIEAenergybalancesandrelateddata.Forexample,theratiobetweencapacityadditionsandinstalledtotalpowercapacityinformsthesplitbetweenpowersectorworkersworkingonnewprojectsversusexistingpowerplants.Thewording“Operationsandmaintenance”(O&M)isused,torefertotheworkersinexistingenergyinfrastructureorassets,asanindicationofallongoingjobsrequiredtosupporttheproperoperationofanenergyproject.12.2EstimatingcurrentemploymentOurmodelusesIEAenergyinvestmentandspendingdata,dataonenergyproductionandconsumption,powercapacityandelectricitygeneration,technologystocksandsalesasthebasistoestimateglobalemployment.Thesedatapointsaremultipliedbyemploymentmultiplierstailoredtoeachenergysub-sectortoestimatetotalemploymentinthebaseyear.Multipliersareproducedviaacomprehensiveliteraturereviewandusingwagedataforeachsubsectorandregionwhereavailable.Theyarealsoinformedbyliteraturereviewandcalibratedagainstexternallysourcedemploymentdatarelevanttoenergysub-sectors.MultipliersandemploymentestimateshavebeentestedwithcompanieswithinIEA’sEnergyBusinessCouncil,peerreviewers,academics,industrygroupsandinternationalorganisations(suchastheInternationalMonetaryFundandILO).EstimatingjobmultipliersTwotypesofmultipliersareusedinthemodel,basedoninvestment(jobspermillionUSdollarsinvested)andvolumetricdata(forexample,jobsperGWcapacityorjobspertonnesproduced).Multipliersvarybyregiontoreflectdifferencesinthelocalcostoflabourandworkersproductivity.Theyalsovarybyenergysubsector,reflectingdifferentprojectcostbreakdowns,inotherwordshowmuchofeachmillionUSdollarsinvestedisallocatedtospendingonlabourversusmaterials.Theprimarysourcesusedtoestimatemultipliersinclude:◼Wagedatafromnationalstatisticsandinternationaldatabases,forinvestmentmultipliers◼Legalfinancialfilingsthatprovideinformationonemploymentandrevenue,costbreakdownsforprojectsandaveragewages◼Academic,intergovernmentalresearchandmodelledestimates◼IndividualcompanyandindustrygroupestimatesGovernmentsurveysofbusinesseswereprioritised,whenavailablewithsufficientdetail,tosupportthesub-sectoralanalysis.Employmentandfinancialinformationwereextractedfromtheannualreportsofmajorcompaniesineachsector,thoughthismethodcouldonlybeusedforsectorswithahighdegreeofconsolidationinmajorfirmsthatarepubliclylisted.Materialfromacademicandindustrysourceswasscreenedtoensureharmoniseddefinitionsandreferencevalueswereadjustedtoadheretotheframeworkdescribed.Wherevaluesfromthesesourceswereunavailable,estimateswerebasedonemploymentmultipliersforsimilartechnologies.Wherewagedataspecifictoenergyindustriesisnotavailable,generalisedwagedatabyregionisused.Section12Employment101GatheringemploymentdataArichcollectionofemploymentdatafromexternalsourcesiscollectedannually,toserveasbenchmarksforthecalibrationofmultipliers.Thesedatasourcesincluded:◼Nationalstatisticsforallmajorcountries◼InternationalLabourOrganization(ILO)employmentdatabases◼UnitedNationsIndustrialDevelopmentOrganization(UNIDO)IndStatandMinStatdatabases◼Reportsbyinternationalorganisationsandindustryassociations◼Academicliterature◼Annualreportsofmajorcompanies◼CompanyinterviewsWheredataiscollectedfrombroadlabourdatabases,wefocusoncategoriesrelevanttoenergy,includingthecompletelistofISICcodespresentedintheUnitedNations’InternationalRecommendationsforEnergyStatistics.Ourscopeincludescodessuchas0510(miningofhardcoal),0610(extractionofcrudepetroleum,0620(extractionofnaturalgas),1920(manufactureofrefinedpetroleumproducts),2910(manufactureofmotorvehicles),3510(electricpowergeneration,transmissionanddistribution),4322(plumbing,heatandairconditioninginstallation),and4930(transportviapipeline),andmanyothers.AmappingbetweenISICandotherclassificationssuchastheNorthAmericanIndustryClassificationSystem(NAICS)ortheEuropeanNomenclatureofEconomicActivities(NACE)enabledaharmonisedapproachtocollectingofficialstatisticsfromdifferentcountries.Dataofthehighestgranularityavailable(mostdigitsoncodes)isusedineachcase.AllocatingemploymentacrossglobalsupplychainsForenergytechnologieswithhighlyglobalisedsupplychains,employmentestimatesreflectwhereintheworldupstreammanufacturingcapacityislocated,ratherthanwheretherecorrespondingtechnologiesaredeployed.Dataaboutthemanufacturingcapacityforspecifictechnologies(suchassolarPVpanels,windturbines,gasturbines,etc.)wasgatheredbycountryorregion,andtheglobaltotalofmanufacturingjobswasredistributedacrossGECModelregionsaccordingly.Fortechnologiesthathaveverylocalisedproduction,suchasbuildingmaterialsandbiofuels,allmanufacturingjobswereassumedtobecreatedlocally.12.3OutlookforemploymentProjectionsbyscenarioarebasedonIEAscenarioresultsforallofthesameinputsthatwereusedtoestimatebaseyearemployment.Thesearemultipliedbythecorrespondingjobmultipliers–thataredifferentiatedbyregionandenergyindustry-inordertoestimatetotaljobsincomingyearsuntil2030,andtherebyestimatechangesinjobgainsandlossesrelativetothebaseyear,aswellaswhatportionofexistingjobsaremaintained.ModellinglabourproductivityimprovementsMultipliersevolveovertimetoreflectassumptionsaboutlabourproductivityimprovements.Whereindustry-specifichistorictimeseriesofemploymentandcorrespondingproduction(oranotherrelevantmetric)areavailable,thehistoricrateofchangeisextendedforward.Wherespecifictimeseriesarenotavailable,datafromUNandILOonvalueaddedbyeconomicactivityandemploymentbyeconomicactivityareusedtocomputehistoriclabourproductivityimprovementratesbyregion,andappliedtofuturemultiplierimprovements.TimingemploymentfornewprojectsinthepipelineSinceouremploymentestimatesforanygivenyearcomprisebothjobsintheoperationsofexistingassetsandjobsinthebuildoutofnewprojects,investmentovernightvaluesarespreadacrossthepreviousyearstoreflect102InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONwhenjobcreationwouldoccur,basedontypicalprojectdeliverytimelines.Inotherwords,weconsiderforhowlonganinvestmentcreatesjobsandinwhichyearrelativetotheprojectdelivery.Forinstance,investmentinanewhydroelectricdamwouldcreatesomejobsintheplanningandpreparationphasepriortotheinvestment.Whenfinancialcloseoccurs,thesejobsdisappear,butconstructionandequipmentmanufacturingjobsarecreated.Whenconstructioniscompleted,thesejobsdisappear,thenO&Mjobsbegin.Jobsareassignedtotherelevantyearstounderstandtotalemploymentonanannualbasis.Section13Assessinggovernmentspendingoncleanenergyandenergyaffordability103Section1313AssessinggovernmentspendingoncleanenergyandenergyaffordabilityTheIEAhasbeenmonitoringgovernmentspendingdedicatedtocleanenergysectorsfromApril2020,intheframeworkofitsSustainableRecoveryTrackerwhichassessestheimpactofsustainablerecoverypoliciesenactedbygovernmentsinresponsetotheCovid-19andenergycrises.Morerecently,theAgencyhasenlargedthescopeofitstrackingtomeasuresaimedatcushioningdomesticconsumersfromtheimpactofthecurrentglobalenergypricecrisis.TheIEAassessmentoftheimpactofgovernmentspendingoncleanenergyandenergyaffordability:◼collectstheamountofgovernmentspendingenactedtowardcleanenergyinvestmentsupportorconsumerenergyaffordabilitymeasures;and◼estimatestheamountofprivatespendingmobilisedthankstothecleanenergyinvestmentsupportandincorporatesintheGECmodellingfortheSTEPS.Inthefollowingsection,wedescribethepolicycollectionprocessandhowtheimpactontotalcleanenergydeploymentisassessed.13.1GovernmentspendingpolicyidentificationandcollectionSustainablerecoverypoliciesSustainablerecoverypoliciesaredefinedaspoliciesdrivingspendingoncleanenergyinvestmentsupportincludedingovernmenteconomicrecoveryplansinresponsetotheCovid-19pandemicorthesubsequentglobalenergycrisis.Commonsustainablerecoverypoliciesincludeconsumerorproducersubsidiestodevelopelectricvehiclemarkets,directspendingorpublic-PrivatePartnershipforbuildinglow-carbonandefficienttransportinfrastructures,grantsforemergingenergytechnologypilotprogrammes,ortaxincentivesforenergy-efficientbuildingrenovations.QuantitativeestimatesintheSustainableRecoveryTrackerarebasedonnational-levelcleanenergysectorpoliciesenactedbygovernmentsfromthesecondquarterof2020untilApril2022aspartofCovid-19relatedrecoverymeasures,anddirectedtowardlong-termprojectsandmeasurestoboosteconomicgrowth.Thefollowingtypeofspendingareconsideredintheanalysis:◼Totalfiscalsupport:allgovernmentspendingdisbursedfrom2020inresponsetotheCOVID-19crisis,intheformofadditionalspendingand/orforgonerevenue,aspertheIMFFiscalMonitordefinition.Thisincludesshort-termeconomicreliefpaymentstocitizensandfirmstoweathertheeffectsofthepandemic.◼Economicrecoveryspending:governmentspendingdirectedtolong-termprojectsandmeasurestoboostgrowth,asubsetoftotalfiscalsupport.Examplesincludeinfrastructureprojectslikeroads,broadbandinternet,publichousingupgrades,incentivesforbusinessimprovementsetc.Manygovernmentstendedtoturntotheselong-termperspectivepoliciesfromthesecondquarterof2020,afterhavingprecedentconcentratedonemergencyeconomicandhealthsupport.Thisdoesnotincludeeconomicreliefpaymentstocitizensandfirms;andonlyincludesspendingthatisdirectedspecificallytonewinvestments.◼Governmentspendingonsustainablerecoverymeasures:governmentspendingtargetingcleanenergyinvestmentsupport,asubsetofeconomicrecoveryspending.Thisincludesconsumerorproducersubsidies,taxbreaks,publicprocurement,loanguarantees,PPPcontractsandotherco-fundingschemesfavouredbygovernments.Onlydirectgovernmentfiscalspendingfromthesecondquarterof2020isconsidered,spendingdirectedbyregulatorstostate-ownedenterprises(SOEs)orpubliclyregulatedentitiesbeingsetaside.104InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONThelasttwocategories,whichencompassgovernmentandtotalmobilisedsustainablerecoveryspendingwerecompared,onasectoralandregionalbasis,insixkeysectors:low-carbonelectricity,electricitynetworks,low-carbonandefficienttransport,energyefficientbuildingsandindustry,cleanerfuelsandemerginglow‐carbontechnologies.Onlyadditionalrecoveryspendingaimedatcreatingnewassetsorextendingthelifeofexistinglow-carboninfrastructureisconsidered.Accordingly,Covid-19relatedliquiditymeasuresforenergycompaniesorenergyintensiveindustriesarenotdirectlyincorporated,sincetheydonotsupportadditionallow-carbonactivities.However,assupportingenergyfirmsthroughthepandemicpreservestheirabilitytoattractinvestment,thisbenefitiscapturedincalibratingsectoralfactorsassessingmobilisedprivatespending,togetherwithpoliciesgenerallyamelioratingtheinvestmentenvironment(seeSection1.2,Assessingmobilisationfactors).EnergycrisisresponsepoliciesAffordabilitysupportincludesemergencyconsumersupportenactedbygovernmentsinresponsetotheinternationalpricerisethatmaterialisedinthefourthquarterof2021andwasfurtheraggravatedbyRussia’sinvasionofUkraine.Themostcommonpolicyinstrumentsincludetemporaryconsumersubsidiesortaxalleviation/exemption,state-backedloansorpriceregulationmechanism,oftenenactedastemporarymeasures.Thespendingisassessedfromthegovernment’sperspective,asdirectbudgetallocation,foregonetaxrevenuesetc.QuantitativeestimatesfromenergycrisisresponsepoliciesarebasedonpoliciesenactedbygovernmentsfromtheSeptember2021toSeptember2022,andarederivedexclusivelyfromofficialgovernmentestimatesofthetotaldirectcostofsupportingthosemeasuresbornebygovernments.Accordingly,itdoesnotcaptureotherformsofsub-marketpricesubsidiesthatmaybechannelledthroughutilitiesandotherenergy-relatedstate-ownedenterprises.CollectionprocessTheIEAindependentlycollectsrecoverypolicies,incooperationwithitsmembers,aswellasG20members.ThefulllistofpoliciesconsideredintheSustainableRecoveryTracker,includingbudgetinformation,isavailableontheIEAPoliciesandMeasures(PAMS)Database,auniquerepositorythathasaggregatedenergypoliciesoverthelast20years,bringingtogetherdatafromtheIEAEnergyEfficiencyDatabase,theAddressingClimateChangedatabase,andtheBuildingEnergyEfficiencyPolicies(BEEP)database,theIEA/IRENARenewableEnergyPoliciesandMeasuresDatabase,alongwithinformationonCCUSandmethaneabatementpolicies.Thesepolicyrecordsincludeconcisesummariesofthepolicy,linkstotheoriginalsource,andrelevanttaggingforpolicytype,technologiesandsectors.Inadditiontothetensofthousandsofpoliciesincludedinthedatabase,over1000sustainablerecoveryandenergyaffordabilitypoliciescanbeaccessedonline,coveringover50countries.Governmentsustainablerecoveryspendingisrecordedandattributedtotimelinesofficiallyannounced,accordingtoavailableinformation.Totalmobilisedsustainablerecoveryspendingisafterwardsspreadevenlyacrossallannouncedyears.Eachbudgetitemisalsotaggedwiththesustainablerecoverymeasureittargets.13.2AssessingtheimpactonoverallcleanenergyinvestmentTheimpactofgovernmentrecoveryspendingonoverallcleanenergydeploymentwasassessedusingmobilisationfactorspersectorandgeography.Thisassessmentisusedtoassesstheimpactofthelatestpolicies,butisnotusedasanestimatefortotalcleanenergyinvestment,whichinsteadflowsfromthemainGECModeloutputs.Section13Assessinggovernmentspendingoncleanenergyandenergyaffordability105Theabilityforgovernmentspendingtocrowd-inprivateinvestmentvariesgreatlyacrosscontexts,anddependsonmanydifferentfactors,rangingfromthetype,scaleandtemporalityofthefiscalinterventiontoaspectsinherenttolocaleconomicandfinancialcontextsand,increasingly,globalcommercialtrends.Theapproachchosenseekstoapproximatethismobilisationeffectbasedonalimitednumberofknownfactors,partlydrawnfromhistoricaltrends.Theevaluationwillbecomplementedandenhancedasdatabecomesavailable,notablyontheevolutionoftheeconomiccrisisindifferentregionsaswellasontheex-postassessmentsofCovid-19recoverypolicies.TheIEAaimsatrefiningthismodellingapproach,inparticulartotryandassessbetterhowaspecificpolicytypeimprovesefficacyofpublicdollarsmobilisedandcalibratingtheapproachbasedonrealinvestmentseeninthefield.AssessingmobilisationfactorsforcleanenergyinvestmentsupportPastmobilisationfactors(onepertechnologyperregion)werederivedfromhistoriclevelsofinvestmentandgovernmentsupport,drawingfromtheIEA’senergyinvestmentdatabase.Thesehistoricmobilisationfactorswerethencalibratedtoreflectchanginginvestmentconditions.TheIEAusedaseriesofindices,pulledfromIEAdataorglobalfinancialsources,tohelpcalibratethemobilisationfactors.Theseindicescanuserawdatapoints(e.gGDPgrowth),Binaryvariable(e.g.isthissupportingpolicyavailableintheregion),andexpertratingvariables(e.g.onascaleof1-5,howmatureistheXXmarketinregionYY).Theindicesusedforthiscalibrationinclude:◼Macroeconomicfactors:GDPgrowthrate,costofcapital,creditriskratingofthecountry/region;◼Energyindustryhealth:whetherliquiditysupportwasmadeavailable,maturityofthemarketforthespecificcleanenergymeasureinquestion◼Supportingpolicyenvironment:thepresenceofsupportingnon-fiscalpolicies(e.g.priorityparkingforelectricvehicles),marketorpricingmechanismssupportiveofdeployment(e.g.specialall-electricutilityrates),degreeofadministrativesupport/burden(e.g.typicaltimelinesforpermittingapproval),effectiveness/maturityofpolicymechanismsdeployed(e.g.howmanyyearshasthepolicybeeninplace)◼Cost-effectiveness:paybackperiodforthemeasuresorcost-competitivenessagainstalternatives(e.g.LCOEs)EnsuringadditionalitytoglobaltotalcleanenergyinvestmentTheIEAgovernmentspendinganalysiseffectivelyaimsatidentifyingadditionallevelsofinvestmentsontopoftheIEA’sStatedPoliciesScenario,whichisupdatedannuallytoconsidertheeffectofnewlyannouncedpoliciesonthetrajectoryforenergydemand.Whiledifficulttodisentangleandproduceaclearcounterfactual,theanalysisidentifieswhichportionofthepublicspendingcontributestoelevatinginvestmentoverpreviousIEAprojectionsforfutureinvestment.Somecasesareclear,suchasanewinnovationfundordirectgovernmentspendingonenergyefficiencyinpublicbuildingsrecentlyvoted,bothexplicitlytiedtoaspecificnationalrecoveryoraffordabilityplan.Someofthemobilisedspendingishowevernotalwaysadditivetopreviousprojections,notablybecausepartofthepublicspendinginevitablygoestosupportprojectsalreadyinthepipelinethatwouldhaveoccurredwithoutthesupport(forinstance,newEVpurchasethatwouldhavehappenedwithoutasubsidy)Asecondsetofdiscountingfactorsareusedtoreducetheimpactofthehistoricmobilisationratios,toreflectwhatportionisexpectedtobeadditionaltotheSTEPS.Thiscalibrationofthediscountingfactorsconsiderstheeffectsdiscussedaboveaspossible.Examplesinclude:extensionoftaxcreditsforprojectsalreadyinthepipelinethatmaynowbeeligibleorincentiveselectricvehicles,whichmaygotopeoplealreadyplanningtobuyEVsormaybringdeferredcarpurchasesforwardafewyears,firmingupinvestmentsdelayedbyCovid-19.106InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONDeterminingimplementationtimelinesManysustainablerecoverypoliciesaretargetingprojectsorinvestmentsthatwillnotmaterialiseinthenear-term(e.g.offshorewindprojectswithlongleadtimes,orCCUSpilots).Italsoconsidershowsomespendingismeanttolaythegroundworkforincreasedlong-termprivatesectorspendingorinvolvement(e.g.portandfuellinginfrastructure,andsupporttoinnovation).Theanalysisdetermineswhenthetotalsustainablerecoveryspendingmobilisedactuallymaterialised-in-the-groundbytakingintoaccountthreespecificstepsandassociateddelays:◼averagetimefrompolicyannouncementtodisbursementforviableprojects(frompolicyassessmentsconductedattheIEA);◼averagetimefromfinancialclosuretoeffectiveoperation(fromourWorldEnergyInvestmentdata);◼averagedelayforcertaingovernmentsupports(e.g.supportinginfrastructure,innovationfunding,research,marketreforms)tomaterialisetheirimpacts(estimatedbasedonlargeinfrastructureprojecttimelines),Thefirsttwoarereflectedbydelayingtheyearwhenthoseinvestmentscomeonrelativetotheyearthefundingisannounced.Thelastisbyincreasingtheprivatespendingmobilisationfactorforsubsequentyears.Whilethelatterdoesnotprominentlyeffectestimatesinthetracker(onlycovers2021-23),itwillfigureinforthcomingIEApublicationsandtracking.AnnexATerminology107AnnexAAnnexA:TerminologyThisannexprovidesgeneralinformationonterminologyusedthroughoutthisreportincluding:definitionsoffuels,processesandsectors;regionalandcountrygroupings;andabbreviationsandacronyms.DefinitionsAdvancedbioenergy:Sustainablefuelsproducedfromnon-foodcropfeedstocks,whicharecapableofdeliveringsignificantlifecyclegreenhousegasemissionssavingscomparedwithfossilfuelalternatives,andwhichdonotdirectlycompetewithfoodandfeedcropsforagriculturallandorcauseadversesustainabilityimpacts.Thisdefinitiondiffersfromtheoneusedfor“advancedbiofuels”inUSlegislation,whichisbasedonaminimum50%lifecyclegreenhousegasreductionandwhich,therefore,includessugarcaneethanol.Agriculture:Includesallenergyusedonfarms,inforestryandforfishing.Agriculture,forestryandotherlanduse(AFOLU)emissions:Includesgreenhousegasemissionsfromagriculture,forestryandotherlanduse.Ammonia(NH3):Isacompoundofnitrogenandhydrogen.Itcanbeuseddirectlyasafuelindirectcombustionprocesses,aswellasinfuelcellsorasahydrogencarrier.Tobealowemissionsfuel,ammoniamustbeproducedfromlow-carbonhydrogen,thenitrogenseparatedviatheHaberprocesswithelectricitygeneratedfromlow-carbonsources.Aviation:Thistransportmodeincludesbothdomesticandinternationalflightsandtheiruseofaviationfuels.Domesticaviationcoversflightsthatdepartandlandinthesamecountry;flightsformilitarypurposesareincluded.Internationalaviationincludesflightsthatlandinacountryotherthanthedeparturelocation.Back-upgenerationcapacity:Householdsandbusinessesconnectedtothemainpowergridmayalsohavesomeformofback-uppowergenerationcapacitythat,intheeventofdisruption,canprovideelectricity.Back-upgeneratorsaretypicallyfuelledwithdieselorgasoline.Capacitycanbeaslittleasafewkilowatts.Suchcapacityisdistinctfrommini-gridandoff-gridsystemsthatarenotconnectedtoamainpowergrid.Batterystorage:Energystoragetechnologythatusesreversiblechemicalreactionstoabsorbandreleaseelectricityondemand.Biodiesel:Diesel-equivalent,processedfuelmadefromthetransesterification(achemicalprocessthatconvertstriglyceridesinoils)ofvegetableoilsandanimalfats.Bioenergy:Energycontentinsolid,liquidandgaseousproductsderivedfrombiomassfeedstocksandbiogas.Itincludessolidbioenergy,liquidbiofuelsandbiogases.Biogas:Amixtureofmethane,CO2andsmallquantitiesofothergasesproducedbyanaerobicdigestionoforganicmatterinanoxygen-freeenvironment.Biogases:Includebothbiogasandbiomethane.Biomethane:Biomethaneisanear-puresourceofmethaneproducedeitherby“upgrading”biogas(aprocessthatremovesanyCO2andothercontaminantspresentinthebiogas)orthroughthegasificationofsolidbiomassfollowedbymethanation.Itisalsoknownasrenewablenaturalgas.Buildings:Thebuildingssectorincludesenergyusedinresidential,commercialandinstitutionalbuildingsandnon-specifiedother.Buildingenergyuseincludesspaceheatingandcooling,waterheating,lighting,appliancesandcookingequipment.Bunkers:Includesbothinternationalmarinebunkersandinternationalaviationbunkers.108InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONCapacitycredit:Proportionofthecapacitythatcanbereliablyexpectedtogenerateelectricityduringtimesofpeakdemandinthegridtowhichitisconnected.Carboncapture,utilisationandstorage(CCUS):TheprocessofcapturingCO2emissionsfromfuelcombustion,industrialprocessesordirectlyfromtheatmosphere.CapturedCO2emissionscanbestoredinundergroundgeologicalformations,onshoreoroffshoreorusedasaninputorfeedstockinmanufacturing.Carbondioxide(CO2):Isagasconsistingofonepartcarbonandtwopartsoxygen.Itisanimportantgreenhouse(heat-tapping)gas.Cleanenergy:Inpower,cleanenergyincludes:generationfromrenewablesources,nuclearandfossilfuelsfittedwithCCUS;batterystorage;andelectricitygrids.Inefficiency,cleanenergyincludesenergyefficiencyinbuildings,industryandtransport,excludingaviationbunkersanddomesticnavigation.Inend-useapplications,cleanenergyincludes:directuseofrenewables;electricvehicles;electrificationinbuildings,industryandinternationalmarinetransport;useofhydrogenandhydrogen-basedfuels;CCUSinindustryanddirectaircarboncaptureandstorage.Infuelsupply,cleanenergyincludeslowemissionsfuelsliquidbiofuelsandbiogases,low-carbonhydrogenandhydrogen-basedfuels.Cleancookingsystems:Cookingsolutionsthatreleaselessharmfulpollutants,aremoreefficientandenvironmentallysustainablethantraditionalcookingoptionsthatmakeuseofsolidbiomass(suchasathree-stonefire),coalorkerosene.Thisrefersprimarilytoimprovedsolidbiomasscookstoves,biogas/biodigestersystems,electricstoves,liquefiedpetroleumgas,naturalgasorethanolstoves.Coal:Includesbothprimarycoal(i.e.lignite,cokingandsteamcoal)andderivedfuels(e.g.patentfuel,brown-coalbriquettes,coke-ovencoke,gascoke,gasworksgas,coke-ovengas,blastfurnacegasandoxygensteelfurnacegas).Peatisalsoincluded.Coalbedmethane(CBM):Categoryofunconventionalnaturalgas,whichreferstomethanefoundincoalseams.Coal-to-gas(CTG):Processinwhichminedcoalisfirstturnedintosyngas(amixtureofhydrogenandcarbonmonoxide)andthenintosyntheticmethane.Coal-to-liquids(CTL):Transformationofcoalintoliquidhydrocarbons.Itcanbeachievedthrougheithercoalgasificationintosyngas(amixtureofhydrogenandcarbonmonoxide),combinedusingtheFischer-Tropschormethanol-to-gasolinesynthesisprocesstoproduceliquidfuels,orthroughthelessdevelopeddirect-coalliquefactiontechnologiesinwhichcoalisdirectlyreactedwithhydrogen.Cokingcoal:Typeofcoalthatcanbeusedforsteelmaking(asachemicalreductantandasourceofheat),whereitproducescokecapableofsupportingablastfurnacecharge.Coalofthisqualityisalsocommonlyknownasmetallurgicalcoal.Concentratingsolarpower(CSP):Solarthermalpowergenerationtechnologythatcollectsandconcentratessunlighttoproducehightemperatureheattogenerateelectricity.Conventionalliquidbiofuels:Fuelsproducedfromfoodcropfeedstocks.Commonlyreferredtoasfirstgenerationbiofuelsandincludesugarcaneethanol,starch-basedethanol,fattyacidmethylester(FAME),straightvegetableoil(SVO)andhydrotreatedvegetableoil(HVO)producedfrompalm,rapeseedorsoybeanoil.Decompositionanalysis:Statisticalapproachthatdecomposesanaggregateindicatortoquantifytherelativecontributionofasetofpre-definedfactorsleadingtoachangeintheaggregateindicator.TheGECModelusesanadditiveindexdecompositionofthetypeLogarithmicMeanDivisiaIndex(LMDI).Demand-sideintegration(DSI):Consistsoftwotypesofmeasures:actionsthatinfluenceloadshapesuchasenergyefficiencyandelectrification;andactionsthatmanageloadsuchasdemand-sideresponse.AnnexATerminology109Demand-sideresponse(DSR):Describesactionswhichcaninfluencetheloadprofilesuchasshiftingtheloadcurveintimewithoutaffectingtotalelectricitydemand,orloadsheddingsuchasinterruptingdemandforashortdurationoradjustingtheintensityofdemandforacertainamountoftime.Directaircarboncapture,utilisationandstorage(DAC):TechnologytocaptureCO2fromtheatmosphereandpermanentlystoreitindeepgeologicalformationsortobeusedintheproductionoffuels,chemicals,buildingmaterialsorotherproductsthatuseCO2.WhentheCO2isgeologicallystoreditispermanentlyremovedfromtheatmosphereresultinginnegativeemissions.Dispatchablegeneration:Referstotechnologieswhosepoweroutputcanbereadilycontrolled,i.e.increasedtomaximumratedcapacityordecreasedtozero,inordertomatchsupplywithdemand.Electricitydemand:Definedastotalgrosselectricitygenerationlessownusegeneration,plusnettrade(importslessexports),lesstransmissionanddistributionlosses.Electricitygeneration:Definedasthetotalamountofelectricitygeneratedbypoweronlyorcombinedheatandpowerplantsincludinggenerationrequiredforownuse.Thisisalsoreferredtoasgrossgeneration.End-usesectors:Includesindustry(i.e.manufacturing,mining,chemicalproduction,blastfurnacesandcokeovens),transport,buildings(i.e.residentialandservices)andother(i.e.agricultureandothernon-energyuse).Energy-intensiveindustries:Includesproductionandmanufacturinginthebranchesofironandsteel,chemicals,non-metallicminerals(includingcement),non-ferrousmetals(includingaluminium),andpaper,pulpandprinting.Energy-relatedandindustrialprocessCO2emissions:Carbondioxideemissionsfromfuelcombustionandfromindustrialprocesses.Notethatthisdoesnotincludefugitiveemissionsfromfuels,flaringorCO2fromtransportandstorage.Unlessotherwisestated,CO2emissionsreportedfromtheGECModelrefertoenergy-relatedandindustrialprocessCO2emissions.Energysectorgreenhousegas(GHG)emissions:Energy-relatedandindustrialprocessCO2emissionsplusfugitiveandventedmethane(CH4)andnitrousdioxide(N2O)emissionsfromtheenergyandindustrysectors.Energyservices:Seeusefulenergy.Ethanol:Referstobio-ethanolonly.Ethanolisproducedfromfermentinganybiomasshighincarbohydrates.Currently,ethanolismadefromstarchesandsugars,butsecondgenerationtechnologieswillallowittobemadefromcelluloseandhemicellulose,thefibrousmaterialthatmakesupthebulkofmostplantmatter.Fischer-Tropschsynthesis:Catalyticproductionprocessfortheproductionofsyntheticfuels.Naturalgas,coalandbiomassfeedstockscanbeused.Fossilfuels:Includecoal,naturalgas,oilandpeat.Gases:Includenaturalgas,biogases,syntheticmethaneandhydrogen.Gaseousfuels:Includenaturalgas,biogas,biomethane,hydrogenandsyntheticmethane.Gas-to-liquids(GTL):Processfeaturingreactionofmethanewithoxygenorsteamtoproducesyngas(amixtureofhydrogenandcarbonmonoxide)followedbysynthesisofliquidproducts(suchasdieselandnaphtha)fromthesyngasusingFischer-Tropschcatalyticsynthesis.Theprocessissimilartothatusedincoal-to-liquids.Geothermal:Geothermalenergyisheatderivedfromthesub-surfaceoftheearth.Waterand/orsteamcarrythegeothermalenergytothesurface.Dependingonitscharacteristics,geothermalenergycanbeusedforheatingandcoolingpurposesorbeharnessedtogeneratecleanelectricityifthetemperatureisadequate.110InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONHeat(end-use):Canbeobtainedfromthecombustionoffossilorrenewablefuels,directgeothermalorsolarheatsystems,exothermicchemicalprocessesandelectricity(throughresistanceheatingorheatpumpswhichcanextractitfromambientairandliquids).Thiscategoryreferstothewiderangeofend-uses,includingspaceandwaterheatingandcookinginbuildings,desalinationandprocessapplicationsinindustry.Itdoesnotincludecoolingapplications.Heat(supply):Obtainedfromthecombustionoffuels,nuclearreactors,geothermalresourcesandthecaptureofsunlight.Itmaybeusedforheatingorcooling,orconvertedintomechanicalenergyfortransportorelectricitygeneration.Commercialheatsoldisreportedundertotalfinalconsumptionwiththefuelinputsallocatedunderpowergeneration.Heavyindustries:Ironandsteel,chemicalsandcement.Hydrogen:Inthisreport,hydrogenreferstolow-carbonhydrogenunlessotherwisestated.Tobelow-carbonhydrogen,eithertheemissionsassociatedwithfossilfuel-basedhydrogenproductionmustbeprevented(e.g.bycarboncapture,utilisationandstorage)ortheelectricityforhydrogenproductionfromwatermustbelow-carbonelectricity.Hydrogenisusedintheenergysystemtorefinehydrocarbonfuelsandasanenergycarrierinitsownright.Itisalsoproducedfromotherenergyproductsforuseinchemicalsproduction.Inthisreport,totalhydrogendemandincludesgaseoushydrogenforalluses,includingtransformationintohydrogen-basedfuelsandbiofuels,powergeneration,oilrefining,andonsiteproductionandconsumption.Finalconsumptionofhydrogenincludesgaseoushydrogeninend-usesectors,excludingtransformationintohydrogen-basedfuelsandbiofuels,powergeneration,oilrefiningandonsiteproductionandconsumption.Hydrogen-basedfuels:Includeammoniaandsynthetichydrocarbons(gasesandliquids).Hydrogen-basedisusedinthefiguresinpublicationsusingtheGECModeltorefertohydrogenandhydrogen-basedfuels.Hydropower:Theenergycontentoftheelectricityproducedinhydropowerplants,assuming100%efficiency.Itexcludesoutputfrompumpedstorageandmarine(tideandwave)plants.Industry:Thesectorincludesfuelusedwithinthemanufacturingandconstructionindustries.Keyindustrybranchesincludeironandsteel,chemicalandpetrochemical,cement,aluminium,andpulpandpaper.Usebyindustriesforthetransformationofenergyintoanotherformorfortheproductionoffuelsisexcludedandreportedseparatelyunderotherenergysector.Thereisanexceptionforfueltransformationinblastfurnacesandcokeovens,whicharereportedwithinironandsteel.Consumptionoffuelsforthetransportofgoodsisreportedaspartofthetransportsector,whileconsumptionbyoff-roadvehiclesisreportedunderindustry.Internationalaviationbunkers:Includesthedeliveriesofaviationfuelstoaircraftforinternationalaviation.Fuelsusedbyairlinesfortheirroadvehiclesareexcluded.Thedomestic/internationalsplitisdeterminedonthebasisofdepartureandlandinglocationsandnotbythenationalityoftheairline.Formanycountriesthisincorrectlyexcludesfuelsusedbydomesticallyownedcarriersfortheirinternationaldepartures.Internationalmarinebunkers:Coversthosequantitiesdeliveredtoshipsofallflagsthatareengagedininternationalnavigation.Theinternationalnavigationmaytakeplaceatsea,oninlandlakesandwaterways,andincoastalwaters.Consumptionbyshipsengagedindomesticnavigationisexcluded.Thedomestic/internationalsplitisdeterminedonthebasisofportofdepartureandportofarrival,andnotbytheflagornationalityoftheship.Consumptionbyfishingvesselsandbymilitaryforcesisexcludedandinsteadincludedintheresidential,servicesandagriculturecategory.Investment:Investmentismeasuredastheongoingcapitalspendinginenergysupplycapacity,energyinfrastructureandenergyend-useandefficiency.Allinvestmentdataandprojectionsreflectspendingacrossthelifecycleofaproject,i.e.thecapitalspentisassignedtotheyearwhenitisincurred.Fuelsupplyinvestmentsincludeproduction,transformationandtransportationforoil,gas,coalandlowemissionsfuels.Powersectorinvestmentsincludenewbuildsandrefurbishmentsofgeneration,electricitygrids(transmission,distributionAnnexATerminology111andpublicelectricvehiclechargers),andbatterystorage.Energyefficiencyinvestmentsincludethosemadeinbuildings,industryandtransport.Otherend-useinvestmentsincludedirectuseofrenewables;electricvehicles;electrificationinbuildings,industryandinternationalmarinetransport;useofhydrogenandhydrogen-basedfuels;fossilfuel-basedindustrialfacilities;CCUSinindustryandDACCUS.Investmentdataarepresentedinrealtermsinyear-2020USdollarsunlessotherwisestated.Light-dutyvehicles(LDVs):Includespassengercarsandlightcommercialvehicles(grossvehicleweight<3.5tonnes).Lightindustries:Includesnon-energy-intensiveindustries:foodandtobacco,machinery,miningandquarrying,transportationequipment,textile,woodharvestingandprocessingandconstruction.Lignite:Typeofcoalthatisusedinthepowersectormostlyinregionsnearligniteminesduetoitslowenergycontentandtypicallyhighmoisturelevels,whichgenerallymakeslong-distancetransportuneconomic.DataonligniteintheGECModelincludespeat,asolidformedfromthepartialdecompositionofdeadvegetationunderconditionsofhighhumidityandlimitedairaccess.Liquidbiofuels:Liquidfuelsderivedfrombiomassorwastefeedstockandincludeethanol,biodieselandbiojetfuels.Theycanbeclassifiedasconventionalandadvancedbiofuelsaccordingtothecombinationoffeedstockandtechnologiesusedtoproducethemandtheirrespectivematurity.Unlessotherwisestated,biofuelsareexpressedinenergy-equivalentvolumesofgasoline,dieselandkerosene.Liquidfuels:Includesoil,liquidbiofuels(expressedinenergy-equivalentvolumesofgasolineanddiesel),syntheticoilandammonia.Low-carbonelectricity:Includesrenewableenergytechnologies,hydrogen-basedgeneration,nuclearpowerandfossilfuelpowerplantsequippedwithcarboncapture,utilisationandstorage.Lowerheatingvalue:Heatliberatedbythecompletecombustionofaunitoffuelwhenthewaterproducedisassumedtoremainasavapourandtheheatisnotrecovered.Lowemissionsfuels:Includeliquidbiofuels,biogasandbiomethane,hydrogen,andhydrogen-basedfuelsthatdonotemitanyCO2fromfossilfuelsdirectlywhenusedandalsoemitverylittlewhenbeingproduced.Marine:Representsthemechanicalenergyderivedfromtidalmovement,wavemotionoroceancurrentsandexploitedforelectricitygeneration.Middledistillates:Includejetfuel,dieselandheatingoil.Mini-grids:Smallelectricgridsystems,notconnectedtomainelectricitynetworks,linkinganumberofhouseholdsand/orotherconsumers.Modernenergyaccess:Includeshouseholdaccesstoaminimumlevelofelectricity;householdaccesstolessharmfulandmoresustainablecookingandheatingfuels,andstoves;accessthatenablesproductiveeconomicactivity;andaccessforpublicservices.Moderngaseousbioenergy:Seebiogases.Modernliquidbioenergy:Includesbio-gasoline,biodiesel,biojetkeroseneandotherliquidbiofuels.Modernrenewables:Includeallusesofrenewableenergywiththeexceptionoftraditionaluseofsolidbiomass.Modernsolidbioenergy:Referstotheuseofsolidbioenergyinimprovedcookstovesandmoderntechnologiesusingprocessedbiomasssuchaspellets.Naturalgas:Comprisesgasesoccurringindeposits,whetherliquefiedorgaseous,consistingmainlyofmethane.Itincludesbothnon-associatedgasoriginatingfromfieldsproducinghydrocarbonsonlyingaseousform,and112InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONassociatedgasproducedinassociationwithcrudeoilaswellasmethanerecoveredfromcoalmines(collierygas).Naturalgasliquids,manufacturedgas(producedfrommunicipalorindustrialwaste,orsewage)andquantitiesventedorflaredarenotincluded.Gasdataincubicmetresareexpressedonagrosscalorificvaluebasisandaremeasuredat15°Candat760mmHg(StandardConditions).Gasdataexpressedintonnesofoilequivalent,mainlyforcomparisonreasonswithotherfuels,areonanetcalorificbasis.Thedifferencebetweenthenetandthegrosscalorificvalueisthelatentheatofvaporizationofthewatervapourproducedduringcombustionofthefuel(forgasthenetcalorificvalueis10%lowerthanthegrosscalorificvalue).Naturalgasliquids(NGLs):Liquidorliquefiedhydrocarbonsproducedinthemanufacture,purificationandstabilisationofnaturalgas.NGLsareportionsofnaturalgasrecoveredasliquidsinseparators,fieldfacilitiesorgasprocessingplants.NGLsinclude,butarenotlimitedto,ethane(whenitisremovedfromthenaturalgasstream),propane,butane,pentane,naturalgasolineandcondensates.Networkgases:Includenaturalgas,biomethane,syntheticmethaneandhydrogenblendedinagasnetwork.Non-energyuse:Fuelsusedforchemicalfeedstocksandnon-energyproducts.Examplesofnon-energyproductsincludelubricants,paraffinwaxes,asphalt,bitumen,coaltarsandoilsastimberpreservatives.Nuclear:Referstotheprimaryenergyequivalentoftheelectricityproducedbyanuclearpowerplant,assuminganaverageconversionefficiencyof33%.Off-gridsystems:Stand-alonesystemsforindividualhouseholdsorgroupsofconsumers.Offshorewind:Referstoelectricityproducedbywindturbinesthatareinstalledinopenwater,usuallyintheocean.Oil:Includesbothconventionalandunconventionaloilproduction.Petroleumproductsincluderefinerygas,ethane,liquidpetroleumgas,aviationgasoline,motorgasoline,jetfuels,kerosene,gas/dieseloil,heavyfueloil,naphtha,whitespirits,lubricants,bitumen,paraffin,waxesandpetroleumcoke.Otherenergysector:Coverstheuseofenergybytransformationindustriesandtheenergylossesinconvertingprimaryenergyintoaformthatcanbeusedinthefinalconsumingsectors.Itincludeslossesbygasworks,petroleumrefineries,coalandgastransformationandliquefaction.Italsoincludesenergyownuseincoalmines,inoilandgasextractionandinelectricityandheatproduction.Transfersandstatisticaldifferencesarealsoincludedinthiscategory.Fueltransformationinblastfurnacesandcokeovensarenotaccountedinotherenergysector.Otherindustry:Acategoryofindustrybranchesthatincludesconstruction,foodprocessing,machinery,mining,textiles,transportequipment,woodprocessingandremainingindustry.Passengercars:Aroadmotorvehicle,otherthanamopedoramotorcycle,intendedtotransportpassengers.Itincludesvansdesignedandusedprimarilytotransportpassengers.Excludedarelightcommercialvehicles,motorcoaches,urbanbuses,andmini-buses/mini-coaches.Powergeneration:Referstofueluseinelectricityplants,heatplantsandcombinedheatandpowerplants.Bothmainactivityproducerplantsandsmallplantsthatproducefuelfortheirownuse(auto-producers)areincluded.Processemissions:CO2emissionsproducedfromindustrialprocesseswhichchemicallyorphysicallytransformmaterials.Anotableexampleiscementproduction,inwhichCO2isemittedwhencalciumcarbonateistransformedintolime,whichinturnisusedtoproduceclinker.Productiveuses:Energyusedtowardsaneconomicpurpose:agriculture,industry,servicesandnon-energyuse.Someenergydemandfromthetransportsector(e.g.freight)couldbeconsideredasproductive,butistreatedseparately.AnnexATerminology113Renewables:Includesbioenergy,geothermal,hydropower,solarphotovoltaics(PV),concentratingsolarpower(CSP),windandmarine(tideandwave)energyforelectricityandheatgeneration.Residential:Energyusedbyhouseholdsincludingspaceheatingandcooling,waterheating,lighting,appliances,electronicdevicesandcooking.Roadtransport:Includesallroadvehicletypes(passengercars,two/three-wheelers,lightcommercialvehicles,busesandmediumandheavyfreighttrucks).Self-sufficiency:Correspondstoindigenousproductiondividedbytotalprimaryenergydemand.Services:Energyusedincommercialfacilities,e.g.offices,shops,hotels,restaurants,andininstitutionalbuildings,e.g.schools,hospitals,publicoffices.Energyuseinservicesincludesspaceheatingandcooling,waterheating,lighting,appliances,cookinganddesalination.Shalegas:Naturalgascontainedwithinacommonlyoccurringrockclassifiedasshale.Shaleformationsarecharacterisedbylowpermeability,withmorelimitedabilityofgastoflowthroughtherockthanisthecasewithinaconventionalreservoir.Shalegasisgenerallyproducedusinghydraulicfracturing.Shipping/navigation:Thistransportsub-sectorincludesbothdomesticandinternationalnavigationandtheiruseofmarinefuels.Domesticnavigationcoversthetransportofgoodsorpeopleoninlandwaterwaysandfornationalseavoyages(startsandendsinthesamecountrywithoutanyintermediateforeignport).Internationalnavigationincludesquantitiesoffuelsdeliveredtomerchantships(includingpassengerships)ofanynationalityforconsumptionduringinternationalvoyagestransportinggoodsorpassengers.Solar:Includessolarphotovoltaicsandconcentratingsolarpower.Solarphotovoltaics(PV):Electricityproducedfromsolarphotovoltaiccells.Solidbioenergy:Includescharcoal,fuelwood,dung,agriculturalresidues,woodwasteandothersolidwastes.Solidfuels:Includecoal,modernsolidbioenergy,traditionaluseofbiomassandindustrialandmunicipalwastes.Steamcoal:Typeofcoalthatismainlyusedforheatproductionorsteam-raisinginpowerplantsand,toalesserextent,inindustry.Typically,steamcoalisnotofsufficientqualityforsteelmaking.Coalofthisqualityisalsocommonlyknownasthermalcoal.Syntheticmethane:Low-carbonsyntheticmethaneisproducedthroughthemethanationoflow-carbonhydrogenandcarbondioxidefromabiogenicoratmosphericsource.Syntheticoil:Low-carbonsyntheticoilproducedthroughFischer-Tropschconversionormethanolsynthesisfromsyngas,amixtureofhydrogen(H2)andcarbonmonoxide(CO).Tightoil:Oilproducedfromshaleorotherverylowpermeabilityformations,generallyusinghydraulicfracturing.Thisisalsosometimesreferredtoaslighttightoil.TightoilincludestightcrudeoilandcondensateproductionexceptfortheUnitedStates,whichincludestightcrudeoilonly(UStightcondensatevolumesareincludedinnaturalgasliquids).Totalenergysupply(TES):Representsdomesticdemandonlyandisbrokendownintoelectricityandheatgeneration,otherenergysectorandtotalfinalconsumption.Totalfinalconsumption(TFC):Isthesumofconsumptionbythevariousend-usesectors.TFCisbrokendownintoenergydemandinthefollowingsectors:industry(includingmanufacturing,mining,chemicalsproduction,blastfurnacesandcokeovens),transport,buildings(includingresidentialandservices)andother(includingagricultureandothernon-energyuse).Itexcludesinternationalmarineandaviationbunkers,exceptatworldlevelwhereitisincludedinthetransportsector.114InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONTotalfinalenergyconsumption(TFEC):Isavariabledefinedprimarilyfortrackingprogresstowardstarget7.2oftheUnitedNationsSustainableDevelopmentGoals.Itincorporatestotalfinalconsumptionbyend-usesectorsbutexcludesnon-energyuse.Itexcludesinternationalmarineandaviationbunkers,exceptatworldlevel.Typicallythisisusedinthecontextofcalculatingtherenewableenergyshareintotalfinalenergyconsumption(indicator7.2.1oftheSustainableDevelopmentGoals),whereTFECisthedenominator.Totalprimaryenergydemand(TPED):Seetotalenergysupply.Traditionaluseofbiomass:Referstotheuseofsolidbiomasswithbasictechnologies,suchasathree-stonefire,oftenwithnoorpoorlyoperatingchimneys.Transport:Fuelsandelectricityusedinthetransportofgoodsorpeoplewithinthenationalterritoryirrespectiveoftheeconomicsectorwithinwhichtheactivityoccurs.Thisincludesfuelandelectricitydeliveredtovehiclesusingpublicroadsorforuseinrailvehicles;fueldeliveredtovesselsfordomesticnavigation;fueldeliveredtoaircraftfordomesticaviation;andenergyconsumedinthedeliveryoffuelsthroughpipelines.Fueldeliveredtointernationalmarineandaviationbunkersispresentedonlyattheworldlevelandisexcludedfromthetransportsectoratadomesticlevel.Trucks:Includesallsizecategoriesofcommercialvehicles:lighttrucks(grossvehicleweightlessthan3.5tonnes);mediumfreighttrucks(grossvehicleweight3.5to15tonnes);andheavyfreighttrucks(>15tonnes).Unabatedcoal:ConsumptionofcoalinfacilitieswithoutCCUS.Unabatedfossilfuels:ConsumptionoffossilfuelsinfacilitieswithoutCCUS.Unabatedgas:ConsumptionofnaturalgasinfacilitieswithoutCCUS.Usefulenergy:Referstotheenergythatisavailabletoend-userstosatisfytheirneeds.Thisisalsoreferredtoasenergyservicesdemand.Asresultoftransformationlossesatthepointofuse,theamountofusefulenergyislowerthanthecorrespondingfinalenergydemandformosttechnologies.Equipmentusingelectricityoftenhashigherconversionefficiencythanequipmentusingotherfuels,meaningthatforaunitofenergyconsumed,electricitycanprovidemoreenergyservices.Variablerenewableenergy(VRE):Referstotechnologieswhosemaximumoutputatanytimedependsontheavailabilityoffluctuatingrenewableenergyresources.VREincludesabroadarrayoftechnologiessuchaswindpower,solarPV,run-of-riverhydro,concentratingsolarpower(wherenothermalstorageisincluded)andmarine(tidalandwave).Zerocarbon-readybuildings:Azerocarbon-readybuildingishighlyenergyefficientandeitherusesrenewableenergydirectlyoranenergysupplythatcanbefullydecarbonised,suchaselectricityordistrictheat.Zeroemissionsvehicles(ZEVs):VehiclesthatarecapableofoperatingwithouttailpipeCO2emissions(batteryelectricandfuelcellvehicles).RegionalandcountrygroupingsInseveraltablesofthismethodologydocument,aswellasintheflagshippublications,resultsfromtheGECModelareoftenpresentedwiththebelowregionalgroupings:Advancedeconomies:OECDregionalgroupingandBulgaria,Croatia,Cyprus1,2,MaltaandRomania.Africa:NorthAfricaandsub-SaharanAfricaregionalgroupings.AsiaPacific:SoutheastAsiaregionalgroupingandAustralia,Bangladesh,DemocraticPeople’sRepublicofKorea(NorthKorea),India,Japan,Korea,Mongolia,Nepal,NewZealand,Pakistan,People’sRepublicofChina(China),SriLanka,ChineseTaipei,andotherAsiaPacificcountriesandterritories.3AnnexATerminology115FigureA.1⊳GECModelregionalgroupingsIEA.CCBY4.0.Note:Thismapiswithoutprejudicetothestatusoforsovereigntyoveranyterritory,tothedelimitationofinternationalfrontiersandboundariesandtothenameofanyterritory,cityorarea.Caspian:Armenia,Azerbaijan,Georgia,Kazakhstan,Kyrgyzstan,Tajikistan,TurkmenistanandUzbekistan.CentralandSouthAmerica:Argentina,PlurinationalStateofBolivia(Bolivia),Brazil,Chile,Colombia,CostaRica,Cuba,Curaçao,DominicanRepublic,Ecuador,ElSalvador,Guatemala,Haiti,Honduras,Jamaica,Nicaragua,Panama,Paraguay,Peru,Suriname,TrinidadandTobago,Uruguay,BolivarianRepublicofVenezuela(Venezuela),andotherCentralandSouthAmericancountriesandterritories.4China:Includesthe(People'sRepublicof)ChinaandHongKong,China.DevelopingAsia:AsiaPacificregionalgroupingexcludingAustralia,Japan,KoreaandNewZealand.Emergingmarketanddevelopingeconomies:Allothercountriesnotincludedintheadvancedeconomiesregionalgrouping.Eurasia:CaspianregionalgroupingandtheRussianFederation(Russia).Europe:EuropeanUnionregionalgroupingandAlbania,Belarus,BosniaandHerzegovina,NorthMacedonia,Gibraltar,Iceland,Israel5,Kosovo,Montenegro,Norway,Serbia,Switzerland,RepublicofMoldova,RepublicofTürkiye,UkraineandUnitedKingdom.EuropeanUnion:Austria,Belgium,Bulgaria,Croatia,Cyprus1,2,CzechRepublic,Denmark,Estonia,Finland,France,Germany,Greece,Hungary,Ireland,Italy,Latvia,Lithuania,Luxembourg,Malta,Netherlands,Poland,Portugal,Romania,SlovakRepublic,Slovenia,SpainandSweden.IEA(InternationalEnergyAgency):OECDregionalgroupingexcludingChile,Iceland,Israel,Latvia,LithuaniaandSlovenia.LatinAmerica:CentralandSouthAmericaregionalgroupingandMexico.MiddleEast:Bahrain,IslamicRepublicofIran(Iran),Iraq,Jordan,Kuwait,Lebanon,Oman,Qatar,SaudiArabia,SyrianArabRepublic(Syria),UnitedArabEmiratesandYemen.116InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONNon-OECD:AllothercountriesnotincludedintheOECDregionalgrouping.Non-OPEC:AllothercountriesnotincludedintheOPECregionalgrouping.NorthAfrica:Algeria,Egypt,Libya,MoroccoandTunisia.NorthAmerica:Canada,MexicoandUnitedStates.OECD(OrganisationforEconomicCo-operationandDevelopment):Australia,Austria,Belgium,Canada,Chile,CostaRica,CzechRepublic,Colombia,Denmark,Estonia,Finland,France,Germany,Greece,Hungary,Iceland,Ireland,Israel,Italy,Japan,Korea,Latvia,Lithuania,Luxembourg,Mexico,Netherlands,NewZealand,Norway,Poland,Portugal,SlovakRepublic,Slovenia,Spain,Sweden,Switzerland,RepublicofTürkiye,UnitedKingdomandUnitedStates.OPEC(OrganisationofthePetroleumExportingCountries):Algeria,Angola,RepublicoftheCongo(Congo),EquatorialGuinea,Gabon,theIslamicRepublicofIran(Iran),Iraq,Kuwait,Libya,Nigeria,SaudiArabia,UnitedArabEmiratesandBolivarianRepublicofVenezuela(Venezuela).SoutheastAsia:BruneiDarussalam,Cambodia,Indonesia,LaoPeople’sDemocraticRepublic(LaoPDR),Malaysia,Myanmar,Philippines,Singapore,ThailandandVietNam.ThesecountriesareallmembersoftheAssociationofSoutheastAsianNations(ASEAN).Sub-SaharanAfrica:Angola,Benin,Botswana,Cameroon,RepublicoftheCongo(Congo),Côted’Ivoire,DemocraticRepublicoftheCongo,Eritrea,Ethiopia,Gabon,Ghana,Kenya,Mauritius,Mozambique,Namibia,Niger,Nigeria,Senegal,SouthAfrica,SouthSudan,Sudan,UnitedRepublicofTanzania(Tanzania),Togo,Zambia,ZimbabweandotherAfricancountriesandterritories.6Countrynotes1NotebytheRepublicofTürkiye:Theinformationinthisdocumentwithreferenceto“Cyprus”relatestothesouthernpartoftheisland.ThereisnosingleauthorityrepresentingbothTurkishandGreekCypriotpeopleontheisland.TürkiyerecognisestheTurkishRepublicofNorthernCyprus(TRNC).UntilalastingandequitablesolutionisfoundwithinthecontextoftheUnitedNations,Türkiyeshallpreserveitspositionconcerningthe“Cyprusissue”.2NotebyalltheEuropeanUnionMemberStatesoftheOECDandtheEuropeanUnion:TheRepublicofCyprusisrecognisedbyallmembersoftheUnitedNationswiththeexceptionoftheRepublicofTürkiye.TheinformationinthisdocumentrelatestotheareaundertheeffectivecontroloftheGovernmentoftheRepublicofCyprus.3Individualdataarenotavailableandareestimatedinaggregatefor:Afghanistan,Bhutan,CookIslands,Fiji,FrenchPolynesia,Kiribati,Macau(China),Maldives,NewCaledonia,Palau,PapuaNewGuinea,Samoa,SolomonIslands,Timor-LesteandTongaandVanuatu.4Individualdataarenotavailableandareestimatedinaggregatefor:Anguilla,AntiguaandBarbuda,Aruba,Bahamas,Barbados,Belize,Bermuda,Bonaire,BritishVirginIslands,CaymanIslands,Dominica,FalklandIslands(Malvinas),FrenchGuiana,Grenada,Guadeloupe,Guyana,Martinique,Montserrat,Saba,SaintEustatius,SaintKittsandNevis,SaintLucia,SaintPierreandMiquelon,SaintVincentandGrenadines,SaintMaarten,TurksandCaicosIslands.5ThestatisticaldataforIsraelaresuppliedbyandundertheresponsibilityoftherelevantIsraeliauthorities.TheuseofsuchdatabytheOECDand/ortheIEAiswithoutprejudicetothestatusoftheGolanHeights,EastJerusalemandIsraelisettlementsintheWestBankunderthetermsofinternationallaw.6Individualdataarenotavailableandareestimatedinaggregatefor:BurkinaFaso,Burundi,CaboVerde,CentralAfricanRepublic,Chad,Comoros,Djibouti,KingdomofEswatini,Gambia,Guinea,Guinea-Bissau,Lesotho,Liberia,Madagascar,Malawi,Mali,Mauritania,Réunion,Rwanda,SaoTomeandPrincipe,Seychelles,SierraLeone,SomaliaandUganda.FossilfuelsupplyregionsAsnotedinthemodeldescription,thefossilfuelsupplymoduleshaveadifferentregionalbreakdownrelativetothe26regionsusedintherestoftheGECModel.Thisenablesthesupplymodulesinordertomostaccuratelyreflecttheparticularitiesoffossilfuelproducingcountriesandregions.Theregionalbreakdownforthesemodulesareasfollows:AnnexATerminology117OilandnaturalgassupplymoduleTheGECModeloilandnaturalgassupplymoduleconsistsof113regions,ofwhich102countriesaremodelledonanindividualbasis.Tradevolumesbrokendownybypipelineandliquefiednaturalgasaremodelledforthefollowing20regions:Canada,Mexico,UnitedStates,Brazil,OtherCentralandSouthAmerica,EuropeanUnion,OtherEurope,OthertransitioneconomiesinEurope,NorthAfrica,WestAfrica,EastAfrica,Russia,Caspian,MiddleEast,JapanandKorea,AustraliaandNewZealand,China,India,SoutheastAsia,andOtherAsiaPacific.The102countriesmodelledindividuallyintheoilandnaturalgasmodulearecategorisedintothe20naturalgastraderegionsinthefollowingmanner:Canada:Canada.Mexico:Mexico.UnitedStates:UnitedStates.Brazil:Brazil.OtherCentralandSouthAmerica:Argentina,Bolivia,Chile,Colombia,Cuba,Ecuador,Guyana,Paraguay,Peru,TrinidadandTobago,Uruguay,andVenezuela.EuropeanUnion:Denmark,Estonia,France,Germany,Italy,Netherlands,Poland,Romania,Slovenia,andSweden.OtherEurope:Greenland,Israel,Norway,andtheUnitedKingdom.OthertransitioneconomiesinEurope:Ukraine.NorthAfrica:Algeria,Libya,Egypt,Tunisia,andMorocco.WestAfrica:Angola,Benin,Cameroon,CentralAfricanRepublic,Chad,Congo,DemocraticRepublicofCongo,EquatorialGuinea,Gabon,Gambia,Ghana,Guinea,GuineaBissau,IvoryCoast,Liberia,Mauritania,Niger,Nigeria,Senegal,SierraLeone,andTogo.EastAfrica:Botswana,Eritrea,Ethiopia,Kenya,Madagascar,Mozambique,Namibia,Seychelles,Somalia,SouthAfrica,SouthSudan,Sudan,Tanzania,andUganda.Russia:Russia.Caspian:Azerbaijan,Kazakhstan,Turkmenistan,andUzbekistan.MiddleEast:Bahrain,Iran,Iraq,Jordan,Kuwait,Lebanon,Oman,Qatar,SaudiArabia,Syria,UnitedArabEmirates,andYemen.DataforSaudiArabiaandKuwaitinclude50%eachofproductionfromtheNeutralZone.JapanandKorea:JapanandKorea.AustraliaandNewZealand:AustraliaandNewZealand.China:China.India:India.SoutheastAsia:BruneiDarussalam,Indonesia,Malaysia,Philippines,Thailand,andVietNam.OtherAsiaPacific:BangladeshandPakistan.Coalsupplymodule19countriesaremodelledonanindividualbasisintheGECModelcoalsupplymodule:Australia,Brazil,Canada,Chile,China,Colombia,India,Indonesia,Japan,Korea,Mexico,Mongolia,Mozambique,NewZealand,Russia,SouthAfrica,theUnitedStates,VenezuelaandVietNam.118InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONAcronymsAPECAsia-PacificEconomicCooperationAPSAnnouncedPledgesScenarioASEANAssociationofSoutheastAsianNationsBECCSbioenergyequippedwithCCUSBEVbatteryelectricvehiclesCAAGRcompoundaverageannualgrowthrateCAFEcorporateaveragefueleconomystandards(UnitedStates)CBMcoalbedmethaneCCGTcombined-cyclegasturbineCCUScarboncapture,utilisationandstorageCDRcarbondioxideremovalCEMCleanEnergyMinisterialCH4methaneCHPcombinedheatandpower;thetermco-generationissometimesusedCNGcompressednaturalgasCOcarbonmonoxideCO2carbondioxideCO2-eqcarbon-dioxideequivalentCOPConferenceofParties(UNFCCC)CSPconcentratingsolarpowerCTGcoal-to-gasCTLcoal-to-liquidsDACdirectaircaptureDACCUSdirectaircapturewithcarboncapture,utilisationandstorageDERdistributedenergyresourcesDRIdirectreducedironDSIdemand-sideintegrationDSOdistributionsystemoperatorDSRdemand-sideresponseEHOBextra-heavyoilandbitumenEORenhancedoilrecoveryEPAEnvironmentalProtectionAgency(UnitedStates)ESGenvironmental,socialandgovernanceEUEuropeanUnionEUETSEuropeanUnionEmissionsTradingSystemEVelectricvehicleFAOFoodandAgricultureOrganizationoftheUnitedNationsFCEVfuelcellelectricvehicleFDIforeigndirectinvestmentFiTfeed-intariffFOBfreeonboardGDPgrossdomesticproductGECModelGlobalEnergyandClimateModelGHGgreenhousegasesGTLgas-to-liquidsHEFAhydrogenatedestersandfattyacidsHFOheavyfueloilIAEAInternationalAtomicEnergyAgencyICEinternalcombustionengineAnnexATerminology119ICTinformationandcommunicationtechnologiesIEAInternationalEnergyAgencyIGCCintegratedgasificationcombined-cycleIIASAInternationalInstituteforAppliedSystemsAnalysisIMFInternationalMonetaryFundIMOInternationalMaritimeOrganizationIOCinternationaloilcompanyIPCCIntergovernmentalPanelonClimateChangeLCOElevelisedcostofelectricityLCVlightcommercialvehicleLDVlight-dutyvehicleLEDlight-emittingdiodeLNGliquefiednaturalgasLPGliquefiedpetroleumgasLULUCFlanduse,land-usechangeandforestryMEPSminimumenergyperformancestandardsMERmarketexchangerateNDCsNationallyDeterminedContributionsNEANuclearEnergyAgency(anagencywithintheOECD)NGLsnaturalgasliquidsNGVnaturalgasvehicleNOCnationaloilcompanyNPVnetpresentvalueNOXnitrogenoxidesN2OnitrousdioxideNZENetZeroEmissionsby2050ScenarioOECDOrganisationforEconomicCo-operationandDevelopmentOPECOrganizationofthePetroleumExportingCountriesPHEVplug-inhybridelectricvehiclesPLDVpassengerlight-dutyvehiclePMparticulatematterPM2.5fineparticulatematterPPApowerpurchaseagreementPPPpurchasingpowerparityPVphotovoltaicsR&DresearchanddevelopmentRD&Dresearch,developmentanddemonstrationSDGSustainableDevelopmentGoals(UnitedNations)SDSSustainableDevelopmentScenarioSMEsmallandmediumenterprisesSMRsteammethanereformationSO2sulphurdioxideSTEPSStatedPoliciesScenarioT&DtransmissionanddistributionTEStotalenergysupplyTFCtotalfinalconsumptionTFECtotalfinalenergyconsumptionTSOtransmissionsystemoperatorUAEUnitedArabEmiratesUNUnitedNations120InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONUNDPUnitedNationsDevelopmentProgrammeUNEPUnitedNationsEnvironmentProgrammeUNFCCCUnitedNationsFrameworkConventiononClimateChangeUSUnitedStatesUSGSUnitedStatesGeologicalSurveyVALCOEvalue-adjustedlevelisedcostofelectricityVREvariablerenewableenergyWACCweightedaveragecostofcapitalWEOWorldEnergyOutlookWHOWorldHealthOrganizationZEVzeroemissionsvehicleZCRBzerocarbon-readybuildingAnnexBReferences121AnnexBAnnexB:ReferencesAboumahboubetal.(2010):OptimalConfigurationofaRenewable-basedElectricitySupplySector,WSEASTransactionsonPowerSystems(ISSN:1790-5060),2,p.120-129,http://www.wseas.us/e-library/transactions/power/2010/89-612.pdf,accessed28June2011AirbusSAS(2004),GlobalMarketForecast2004-2023,AirbusSAS,Blagnac,France.AIM(AviationIntegratedModel),developedatUCL,https://www.ucl.ac.uk/energy-models/models/aimAllenetal.(2016),Newuseofglobalwarmingpotentialstocomparecumulativeandshort-livedclimatepollutants,NatureClimateChange(6).Anandarajah,G.etal.(2011),TIAM-UCLGlobalModelDocumentation,UKERCWorkingPaperUKERC/WP/ESY/2011/001,UKEnergyResearchCentre,London.API.(2014).APIStandard521Pressure-relievingandDepressuringSystems.Washington,DC:AmericanPetroleumInstitute.API.(2017).API537FlareDetailsforPetroleum,Petrochemical,andNaturalGasIndustries.WashingtonDC:AmericanPetroleumInstitute.APP(Asia-PacificPartnershiponCleanDevelopmentandClimate)(2009),EnergyEfficiencyandResourceSavingTechnologiesinCementIndustry,APP,WashingtonD.C.,http://www.asiapacificpartnership.org/english/cement_tf_docs.aspxBGR(GermanFederalInstituteforGeosciencesandNaturalResources)(2014),Energiestudie2014,Reserven,RessourcenundVerfügbarkeitvonEnergierohstoffen,(EnergyStudy2014,Reserves,ResourcesandAvailabilityofEnergyResources),BGR,Hannover,Germany.BloombergNewEnergyFinance(2020),EnergyStorageSystemCostsSurvey2020Boeing(2005),BoeingCurrentMarketOutlook2005,Boeing,Chicago.Breiman,L.etal.(1984),ClassificationandRegressionTrees,WadsworthInternational,Belmont,UnitedStates.CNI(Confederaçãonacionaldaindústria),IEL(InstitutoEuvaldoLodi),Eletrobras(2010a),Oportunidadesdeeficiênciaenergéticaparaaindústria–setorcimenteiro[Opportunitiesforenergyefficiencyforindustry–cementsector],CNI,Brasilia,Brazil−(2010b),Oportunidadesdeeficiênciaenergéticaparaaindústria–setorpapelecelulose[Opportunitiesforenergyefficiencyforindustry–pulpandpapersector],CNI,Brasilia,BrazilCSI(CementSustainabilityInitiative)(2013),ExistingandPotentialTechnologiesforCarbonEmissionsReductionsintheIndianCementIndustry,CSI,Geneva.CSI(CementSustainabilityInitiative)andECRA(EuropeanCementResearchAcademy)(2009),DevelopmentofStateoftheArt-TechniquesinCementManufacturing:TryingtoLookAhead,CSI/ECRATechnologyPaper,CSI,Geneva.Cole,Wesley,A.WillFrazier,andChadAugustine.(2021).CostProjectionsforUtilityScaleBatteryStorage:2021Update.Golden,CO:NationalRenewableEnergyLaboratory.NREL/TP-6A20-79236.https://www.nrel.gov/docs/fy21osti/79236.pdf.Dargay,J.,D.GatelyandM.Sommer(2006),“VehicleOwnershipandIncomeGrowth,Worldwide:1960-2030”,EnergyJournal,Vol.28,No.4,Elsevier,Amsterdam.122InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONEC(Eur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