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Global Energy and

Climate Model

Documentation - 2023

The IEA examines the

full spectrum

of energy issues

including oil, gas and

coal supply and

demand, renewable

energy technologies,

electricity markets,

energy efficiency,

access to energy,

demand side

management and

much more. Through

its work, the IEA

advocates policies that

will enhance the

reliability, affordability

and sustainability of

energy in its

31 member countries,

13 association

countries and beyond.

This publication and any

map included herein are

without prejudice to the

status of or sovereignty over

any territory, to the

delimitation of international

frontiers and boundaries and

to the name of any territory,

city or area.

Source: IEA.

International Energy Agency

Website: www.iea.org

IEA member

countries:

Australia

Austria

Belgium

Canada

Czech Republic

Denmark

Estonia

Finland

France

Germany

Greece

Hungary

Ireland

Italy

Japan

Korea

Lithuania

Luxembourg

Mexico

Netherlands

New Zealand

Norway

Poland

Portugal

Slovak Republic

Spain

Sweden

Switzerland

Republic of Türkiye

United Kingdom

United States

The European

Commission also

participates in the

work of the IEA

IEA association

countries:

Argentina

Brazil

China

Egypt

India

Indonesia

Kenya

Morocco

Senegal

Singapore

South Africa

Thailand

Ukraine

INTERNATIONAL ENERGY

AGENCY

Table of Contents

1 Overview of model and scenarios ........................................................................................................................ 5

1.1 GEC Model scenarios ......................................................................................................................... 6

1.2 Selected developments in 2023 ........................................................................................................ 9

1.3 GEC Model overview ....................................................................................................................... 12

2 Cross-cutting inputs and assumptions ............................................................................................................... 17

2.1 Population assumptions .................................................................................................................. 17

2.2 Macroeconomic assumptions .......................................................................................................... 18

2.3 Prices ............................................................................................................................................... 19

2.4 Policies ............................................................................................................................................. 22

2.5 Techno-economic inputs ................................................................................................................. 23

3 End-use sectors .................................................................................................................................................. 25

3.1 Industry ............................................................................................................................................ 25

3.2 Transport ......................................................................................................................................... 30

3.3 Buildings .......................................................................................................................................... 39

3.4 Hourly electricity demand and demand-side response ................................................................... 42

4 Electricity generation and heat production ....................................................................................................... 45

4.1 Electricity generation ....................................................................................................................... 45

4.2 Value-adjusted Levelised Cost of Electricity .................................................................................... 50

4.3 Electricity transmission and distribution networks ......................................................................... 53

4.4 Hourly model ................................................................................................................................... 57

4.5 Mini- and off-grid power systems ................................................................................................... 59

4.6 Renewables and combined heat and power modules .................................................................... 59

4.7 Hydrogen and ammonia in electricity generation ........................................................................... 61

4.8 Utility-scale battery storage ............................................................................................................ 61

5 Other energy transformation ............................................................................................................................. 63

5.1 Oil refining and trade ....................................................................................................................... 63

5.2 Coal-to-liquids, Gas-to-liquids, Coal-to-gas ..................................................................................... 64

5.3 Hydrogen production and supply .................................................................................................... 64

5.4 Biofuel production ........................................................................................................................... 67

6 Energy supply ..................................................................................................................................................... 71

6.1 Oil .................................................................................................................................................... 71

6.2 Natural gas ....................................................................................................................................... 75

6.3 Coal .................................................................................................................................................. 76

6.4 Bioenergy ......................................................................................................................................... 77

IEA. CC BY 4.0.

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GlobalEnergyandClimateModelDocumentation-2023INTERNATIONALENERGYAGENCYTheIEAexaminestheIEAmemberIEAassociationfullspectrumcountries:countries:ofenergyissuesincludingoil,gasandAustraliaArgentinacoalsupplyandAustriaBrazildemand,renewableBelgiumChinaenergytechnologies,CanadaEgyptelectricitymarkets,CzechRepublicIndiaenergyefficiency,DenmarkIndonesiaaccesstoenergy,EstoniaKenyademandsideFinlandMoroccomanagementandFranceSenegalmuchmore.ThroughGermanySingaporeitswork,theIEAGreeceSouthAfricaadvocatespoliciesthatHungaryThailandwillenhancetheIrelandUkrainereliability,affordabilityItalyandsustainabilityofJapanenergyinitsKorea31membercountries,Lithuania13associationLuxembourgcountriesandbeyond.MexicoNetherlandsThispublicationandanyNewZealandmapincludedhereinareNorwaywithoutprejudicetothePolandstatusoforsovereigntyoverPortugalanyterritory,totheSlovakRepublicdelimitationofinternationalSpainfrontiersandboundariesandSwedentothenameofanyterritory,Switzerlandcityorarea.RepublicofTürkiyeUnitedKingdomUnitedStatesTheEuropeanCommissionalsoparticipatesintheworkoftheIEASource:IEA.InternationalEnergyAgencyWebsite:www.iea.orgTableofContents1Overviewofmodelandscenarios........................................................................................................................51.1GECModelscenarios.........................................................................................................................61.2Selecteddevelopmentsin2023........................................................................................................91.3GECModeloverview.......................................................................................................................122Cross-cuttinginputsandassumptions...............................................................................................................172.1Populationassumptions..................................................................................................................172.2Macroeconomicassumptions..........................................................................................................182.3Prices...............................................................................................................................................192.4Policies.............................................................................................................................................222.5Techno-economicinputs.................................................................................................................233End-usesectors..................................................................................................................................................253.1Industry............................................................................................................................................253.2Transport.........................................................................................................................................303.3Buildings..........................................................................................................................................393.4Hourlyelectricitydemandanddemand-sideresponse...................................................................424Electricitygenerationandheatproduction.......................................................................................................454.1Electricitygeneration.......................................................................................................................454.2Value-adjustedLevelisedCostofElectricity....................................................................................504.3Electricitytransmissionanddistributionnetworks.........................................................................534.4Hourlymodel...................................................................................................................................574.5Mini-andoff-gridpowersystems...................................................................................................594.6Renewablesandcombinedheatandpowermodules....................................................................594.7Hydrogenandammoniainelectricitygeneration...........................................................................614.8Utility-scalebatterystorage............................................................................................................615Otherenergytransformation.............................................................................................................................635.1Oilrefiningandtrade.......................................................................................................................635.2Coal-to-liquids,Gas-to-liquids,Coal-to-gas.....................................................................................645.3Hydrogenproductionandsupply....................................................................................................645.4Biofuelproduction...........................................................................................................................676Energysupply.....................................................................................................................................................716.1Oil....................................................................................................................................................716.2Naturalgas.......................................................................................................................................756.3Coal..................................................................................................................................................766.4Bioenergy.........................................................................................................................................77TableofContents1IEA.CCBY4.0.7Criticalminerals..................................................................................................................................................817.1Demand...........................................................................................................................................827.2Supplyrequirements.......................................................................................................................828Emissions............................................................................................................................................................838.1CO2emissions..................................................................................................................................838.2Methaneemissions.........................................................................................................................838.3Othernon-CO2greenhousegasemissions......................................................................................848.4Airpollution.....................................................................................................................................848.5Globaltemperatureimpacts...........................................................................................................849EnergyandCO2decomposition..........................................................................................................................8510Investment.......................................................................................................................................................8710.1Investmentinfuelsupplyandthepowersector.............................................................................8710.2Demand-sideinvestments...............................................................................................................8910.3Financingforinvestments...............................................................................................................9011Energyaccess...................................................................................................................................................9311.1Definitionofmodernenergyaccess................................................................................................9311.2Outlookformodernenergyaccess.................................................................................................9412Employment.....................................................................................................................................................9512.1Definitionandscopeofemployment..............................................................................................9512.2Estimatingcurrentemployment......................................................................................................9712.3Outlookforemployment.................................................................................................................9813Governmentspendingoncleanenergyandenergyaffordability....................................................................9913.1Policyidentificationandcollection..................................................................................................9913.2Assessingtheimpactonoverallcleanenergyinvestment............................................................100AnnexA:Terminology........................................................................................................................................103Definitions.................................................................................................................................................103Regionalandcountrygroupings...............................................................................................................111Acronyms..................................................................................................................................................114AnnexB:References..........................................................................................................................................1192InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONListoffiguresGlobalEnergyandClimateModelOverview..........................................................................13Componentsofretailelectricityend-useprices.....................................................................21Figure1.1⊳Generalstructureofdemandmodules...................................................................................25Figure2.1⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinindustry.....................................26Figure3.1⊳Industrysectormodelinternalmodulestructureandkeydataflows....................................27Figure3.2⊳Structureofthetransportdemandmodule...........................................................................32Figure3.3⊳Illustrationofscrappagecurveandmileagedecaybyvehicletype........................................33Figure3.4⊳Theroleofpassenger-light-dutyvehiclecostmodel..............................................................34Figure3.5⊳Illustrationofanefficiencycostcurveforroadfreight...........................................................35Figure3.6⊳Refuellinginfrastructurecostcurve(illustrative)....................................................................36Figure3.7⊳Structureofthebuildingsdemandmodule............................................................................39Figure3.8⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinbuildings....................................41Figure3.9⊳Thermosensitivityanalysisforhourlyloadcurveassessment................................................43Figure3.10⊳IllustrativeloadcurvesbysectorforaweekdayinFebruaryintheEuropeanUnionFigure3.11⊳comparedtotheobservedloadcurvebyENTSO-Efor2014..................................................44Figure3.12⊳Structureofthepowergenerationmodule............................................................................45Loaddurationcurveshowingthefourdemandsegments.....................................................47Figure4.1⊳Examplemeritorderanditsintersectionwithdemandinthepowergenerationmodule....48Figure4.2⊳Exampleelectricitydemandandresidualload.......................................................................49Figure4.3⊳Exemplaryelectricitydemandandresidualload....................................................................50Figure4.4⊳MovingbeyondtheLCOEtothevalue-adjustedLCOE...........................................................51Figure4.5⊳ElectricitynetworkexpansionperunitofelectricitydemandgrowthbyGDPpercapita......54Figure4.6⊳Schematicofrefiningandinternationaltrademodule...........................................................63Figure4.7⊳Schematicofmerchanthydrogensupplymodule..................................................................65Figure5.1⊳Schematicofliquidbiofuelsmodel.........................................................................................68Figure5.2⊳Structureoftheoilsupplymodule.........................................................................................73Figure5.3⊳EvolutionofproductionofcurrentlyproducingconventionaloilfieldsfromaFigure6.1⊳field-by-fielddatabaseandfromtheGECModel...................................................................75Figure6.2⊳Schematicofbiomasssupplypotentials.................................................................................77GECModelregionalgroupings..............................................................................................112Figure6.3⊳FigureA.1⊳ListoftablesDefinitionsandobjectivesoftheGECModel2023scenarios...................................................6Populationassumptionsbyregion..........................................................................................17Table1.1⊳RealGDPaveragegrowthassumptionsbyregionandscenario.............................................18Table2.1⊳Fossilfuelpricesbyscenario...................................................................................................19Table2.2⊳CO2pricesforelectricity,industryandenergyproductioninselectedregionsbyscenario..20Table2.3⊳Capitalcostsforselectedtechnologiesbyscenario...............................................................24Table2.4⊳Remainingtechnicallyrecoverablefossilfuelresources,end-2021.......................................76Table2.5⊳Criticalmineralsinscope........................................................................................................81Table6.1⊳Sub-sectorsandassetsincludedinfuelsupplyinvestment....................................................88Table7.1⊳Table10.1⊳TableofContents3IEA.CCBY4.0.Table10.2⊳Sub-sectorsandassetsincludedinpowersectorinvestment................................................89Table10.3⊳Sub-sectorsandassetsincludedinend-useenergyinvestment............................................90Table12.4⊳Skilllevelsofemploymentestimatesbyassociatededucationlevelsandoccupations.........96ListofboxesAnintegratedapproachtoenergyandsustainabledevelopmentintheNetZeroEmissionsby2050Scenario.......................................................................................................................8Box1.1⊳Long-termpotentialofrenewables........................................................................................60MethodologicaldifferencesbetweentheGECModelandtheIEAMedium-TermOilBox4.1⊳MarketReport.........................................................................................................................72Box6.1⊳Methodologytoaccountforproductiondeclineinoilandgasfields....................................74Box6.2⊳4InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection11OverviewofmodelandscenariosSince1993,theIEAhasprovidedmedium-tolong-termenergyprojectionsusingacontinuallyevolvingsetofdetailed,world-leadingmodellingtools.First,theWorldEnergyModel(WEM)–alarge-scalesimulationmodeldesignedtoreplicatehowenergymarketsfunction–wasdeveloped.Adecadelater,theEnergyTechnologyPerspectives(ETP)model–atechnology-richbottom-upmodel–wasdevelopedforuseinparalleltotheWEM.In2021,theIEAadoptedforthefirsttimeanewhybridmodellingapproachrelyingonthestrengthsofbothmodelstodeveloptheworld’sfirstcomprehensivestudyofhowtotransitiontoanenergysystematnetzeroCO2emissionsby2050;thisanalysishasbeenupdatedin2023.Overthepasttwoyears,theIEAhasworkedtodevelopanewintegratedmodellingframework:theIEA’sGlobalEnergyandClimate(GEC)Model.Thismodelisnowtheprincipaltoolusedtogeneratedetailedsector-by-sectorandregion-by-regionlong-termscenariosacrossIEA'spublications.TheGECModelbringstogethertheuniquemodellingcapabilitiesoftheWEMandETPmodels.Theresultisalarge-scale,bottom-uppartial-optimisationmodellingframeworkallowingforauniquesetofanalyticalcapacitiesinenergymarkets,technologytrends,policystrategiesandinvestmentsacrosstheenergysectorthatwouldbecriticaltoachieveclimategoals.TheIEA’sGECModelcovers29regionsthatcanbeaggregatedtoworld-levelresults,andcoversallsectorsacrosstheenergysystemwithdedicatedbottom-upmodellingfor:◼Finalenergydemand,coveringindustry,transport,buildings,agricultureandothernon-energyuse.Thisisdrivenbydetailedmodellingofenergyserviceandmaterialdemand.◼Energytransformation,includingelectricitygenerationandheatproduction,refineries,theproductionofbiofuels,hydrogenandhydrogen-basedfuelsandotherenergy-relatedprocesses,aswellasrelatedtransmissionanddistributionsystems,storageandtrade.◼Energysupply,includingfossilfuelsexploration,extractionandtrade,andtheavailabilityofrenewableenergyresources.TheGECModelisahighlydata-intensivemodelcoveringthewholeglobalenergysystem.Muchofthedataonenergysupply,transformationanddemand,aswellasenergyprices,isobtainedfromtheIEA’sowndatabasesofenergyandeconomicstatistics(http://www.iea.org/statistics).Italsodrawsondatafromcollaborationwithotherinstitutionsandfromawiderangeofexternalsources,whichareindicatedintherelevantsectionsofthisdocument.ThedevelopmentoftheGECModelbenefitedfromexpertreviewwithintheIEAandbeyond,andtheIEAcontinuestoworkcloselywithcolleaguesintheinternationalmodellingcommunity.TheGECModelisdesignedtoanalyseadiverserangeofaspectsoftheenergysystem,including:◼Globalandregionalenergyprospects:includingtrendsindemand,supplyavailabilityandconstraints,internationaltradeandenergybalancesbysectorandbyfuelintheprojectionhorizon.◼Environmentalimpactofenergyuse:includingCO2emissionsfromfuelcombustion,industrialprocessesandflaring;methane(CH4)emissionsfromfossilfueloperations;CH4andnitrousoxide(N2O)emissionsfromfinalenergydemandandenergytransformation,localairpollutants,andtemperatureoutcomes.◼Effectsofpolicyactionsandtechnologicalchanges:includingtheimpactofarangeofpolicyactionsandtechnologicaldevelopmentsonenergydemand,supply,trade,investmentsandemissions.◼Investmentintheenergysector:includinginvestmentrequirementsinfuelandtechnologysupplychainstosatisfyprojectedenergydemandanddemand-sideinvestmentrequirements.◼Modernenergyaccessassessments:includingtrendsinaccesstoelectricityandcleancooking,aswellastherelatedadditionalenergydemandandinvestments,andchangesingreenhousegasemissions.◼Energyemployment:includingtheimpactoftheenergysector’sevolutiononemploymentineachscenario.Section1Overviewofmodelandscenarios5IEA.CCBY4.0.1.1GECModelscenariosTheIEA’smedium-tolong-termoutlookpublications–includingtheWorldEnergyOutlook(WEO)andEnergyTechnologyPerspectives(ETP)–useascenarioapproachrelyingontheGECModeltoexaminefutureenergytrends.TheGECModelisusedtoexploremultiplescenarios,eachofwhichisbuiltonadifferentsetofunderlyingassumptionsabouthowtheenergysystemmightevolveovertime.Bycomparingthem,readerscanassesswhatdrivesthevariousoutcomes,andtheopportunitiesandpitfallsthatliealongtheway.Thesescenariosarenotpredictions,anddonotcontainasingleviewaboutwhatthelong-termfuturemighthold.Instead,thescenariosseektoenablereaderstocomparedifferentpossibleversionsofthefuture,andtheleversandactionsthatproducethem,andtogaininsightsintothefutureofglobalenergy.TheWorldEnergyOutlook,EnergyTechnologyPerspectivesandtheirrelatedreportsexploredifferentaspectsofthreescenarios,allofwhicharefullyupdatedtoincludethelatestenergymarketandcostdata.TheNetZeroEmissionsby2050Scenario(NZEScenario)isnormative,inthatitisdesignedtoachievespecificoutcomes–netzeroemissionsfromtheenergysectorby2050withoutoffsetsfromothersectors,anemissionstrajectoryconsistentwithkeepingthetemperaturerisein2100below1.5°C(withatleasta50%probability)withlimitedovershoot,universalaccesstomodernenergyservicesby2030andmajorimprovementsinairquality–andshowsapathwaytoreachthem.TheAnnouncedPledgesScenario(APS)andtheStatedPoliciesScenario(STEPS)areexploratory,inthattheydefineasetofstartingconditions,suchaspoliciesandtargets,andseewheretheyleadbasedonmodelrepresentationsofenergysystemsthatreflectmarketdynamicsandtechnologicalprogress.Table1.1⊳DefinitionsandobjectivesoftheGECModel2023scenariosDefinitionsNetZeroEmissionsby2050AnnouncedPledgesStatedPoliciesScenario(NZEScenario)Scenario(APS)Scenario(STEPS)AscenariowhichsetsoutaAscenariowhichassumesthatallAscenariowhichreflectscurrentpathwayfortheglobalenergyclimatecommitmentsmadebypolicysettingsbasedonasector-sectortoachievenetzeroCO2governmentsandindustriesby-sectorandcountry-by-countryemissionsby2050.Itdoesnotaroundtheworldbytheendofassessmentoftheenergy-relatedrelyonemissionsreductionsAugust2023,includingNationallypoliciesthatwereinplacebythefromoutsidetheenergysectorDeterminedContributionsendofAugust2023,aswellastoachieveitsgoals.Universal(NDCs)andlonger-termnetzerothosethatareunderaccesstoelectricityandcleantargets,aswellastargetsfordevelopment.Thescenarioalsocookingareachievedby2030.accesstoelectricityandcleantakesintoaccountcurrentlyThescenariowasfullyupdatedincooking,willbemetinfullandplannedmanufacturing2023.ontime.capacitiesforcleanenergytechnologies.ObjectivesToshowwhatisneededacrossToshowhowclosecurrentToprovideabenchmarktothemainsectorsbyvariouspledgesgettheworldtotheassessthepotentialactors,andbywhen,forthetargetoflimitingglobalwarmingachievements(andlimitations)ofworldtoachievenetzeroto1.5°C.Thedifferencesrecentdevelopmentsinenergyenergy-relatedandindustrialbetweentheAPSandtheNZEandclimatepolicy.TheprocessCO2emissionsby2050Scenariohighlightthe“ambitiondifferencesbetweentheSTEPSwhilemeetingotherenergy-gap”thatneedstobeclosedtoandtheAPShighlighttherelatedsustainabledevelopmentachievethegoalsoftheParis“implementationgap”thatneedsgoalssuchasuniversalenergyAgreementadoptedin2015.Ittobeclosedforcountriestoaccess.alsoshowsthegapbetweenachievetheirannouncedcurrenttargetsandachievingdecarbonisationtargets.universalenergyaccess.Thescenarioshighlighttheimportanceofgovernmentpoliciesindeterminingthefutureoftheglobalenergysystem:decisionsmadebygovernmentsarethemaindifferentiatingfactorexplainingthevariationsinoutcomesacrossourscenarios.However,wealsotakeintoaccountotherelementsandinfluences,notablytheeconomic6InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONanddemographiccontext,technologycostsandlearning,energypricesandaffordability,corporatesustainabilitycommitments,andsocialandbehaviouralfactors.Whiletheevolvingcostsofknowntechnologiesaremodelledindetail,wedonottrytoanticipatetechnologybreakthroughs(e.g.nuclearfusion).Aninventoryofthekeypolicyassumptionsalongwiththeunderlyingdataonpopulation,economicgrowth,resources,technologycostsandfossilfuelpricesareavailableinchapter2.NetZeroEmissionsby2050ScenarioTheNZEScenarioisanormativescenariothatshowsapathwayfortheglobalenergysectortoachievenetzeroCO2emissionsby2050,withadvancedeconomiesreachingnetzeroemissionsinadvanceofothers.Thisscenarioalsomeetskeyenergy-relatedSustainableDevelopmentGoals(SDGs),inparticularuniversalenergyaccessby2030andmajorimprovementsinairquality.Itisconsistentwithlimitingtheglobaltemperatureriseto1.5°C(withatleasta50%probability)withlimitedovershoot,inlinewithreductionsassessedintheIntergovernmentalPanelonClimateChange(IPCC)’sSixthAssessmentReport.TherearemanypossiblepathstoachievenetzeroCO2emissionsgloballyby2050andmanyuncertaintiesthatcouldaffectanyofthosepathways;theNZEScenarioisthereforeapath,andnotthepathtonetzeroemissions.The2023NetZeroEmissionsby2050Scenario:◼DescribesapathwayfortheglobalenergysectortoreachnetzeroemissionsofCO2by2050bydeployingawideportfolioofcleanenergytechnologies,withoutoffsetsfromland-usemeasures,andwithdecisionsabouttechnologydeploymentdrivenbycosts,technologymaturity,marketconditions,availableinfrastructureandpolicypreferences.◼RecognisesthatachievingnetzeroenergysectorCO2emissionsby2050dependsonfairandeffectiveglobalco-operation.Thepathwaytonetzeroemissionsby2050isverynarrow.Allcountrieswillneedtocontributetodeliverthedesiredoutcomes;advancedeconomiestaketheleadandreachnetzeroemissionsearlierintheNZEScenariothanemergingmarketanddevelopingeconomies.Globalaccesstoelectricityandcleancookingisachievedby2030inlinewithestablishedSDGs.Rapidandmajorreductionsinmethaneemissionsfromtheoil,gasandcoalsectorshelptobuysometimeforlessabruptCO2reductionsinemergingmarketanddevelopingeconomies.Globalcollaborationfacilitatesthedevelopmentandadoptionofambitiouspolicies,drivesdowncleantechnologycosts,andscalesupdiverseandresilientglobalsupplychainsforcriticalmineralsandcleanenergytechnologies.Enhancedfinancialsupporttoemergingmarketanddevelopingeconomiesplaysacriticalpartinthiscollaboration.◼Prioritisesanorderlytransitionthataimstosafeguardenergysecuritythroughstrongandco-ordinatedpoliciesandincentivesthatenableallactorstoanticipatetherapidchangesrequired,andtominimiseenergymarketvolatilityandstrandedassets.Thescenarioisunderpinnedbydetailedanalysisofprojectleadtimesformineralssuppliesandcleanenergytechnologiesaspartofeffortstoensurethefeasibilityofthedeployment.Inrecentyears,theenergysectorwasresponsibleforaroundthree-quartersofglobalGHGemissions.Achievingnetzeroenergy-relatedandindustrialprocessCO2emissionsby2050intheNZEScenariodoesnotrelyonactionbeyondtheenergysector,butlimitingclimatechangedoesrequiresuchaction.WethereforeadditionallyexaminethereductionsinCO2emissionsfromlandusethatwouldbecommensuratewiththetransformationoftheenergysectorintheNZEScenario,workinginco-operationwiththeInternationalInstituteforAppliedSystemsAnalysis(IIASA).Section1Overviewofmodelandscenarios7IEA.CCBY4.0.Box1.1⊳AnintegratedapproachtoenergyandsustainabledevelopmentintheNetZeroEmissionsby2050ScenarioTheNetZeroEmissionsby2050Scenario(NZEScenario)integratesthreekeyobjectivesoftheUnitedNations(UN)2030AgendaforSustainableDevelopment:universalaccesstomodernenergyservicesby2030(SustainableDevelopmentGoal[SDG]7.1),reducinghealthimpactsofairpollution(SDG3.9),andactiontotackleclimatechange(SDG13).Asafirststep,weusetheGECModeltoassesshowtheenergysectorwouldneedtochangetodeliveruniversalaccesstomodernenergyservicesby2030.Toanalyseelectricityaccess,wecombinecost-optimisationwithnewgeospatialanalysisthatconsiderscurrentandplannedtransmissionlines,populationdensity,resourceavailabilityandfuelcosts.Second,weconsiderambientandhouseholdairpollutionandclimategoals.ThepoliciesneededtoachievetheSDGscoveredintheNZEScenarioareoftencomplementary.Forexample,energyefficiencyandrenewableenergysignificantlyreducelocalairpollution,particularlyincities.AccesstocleancookingreducesindoorairpollutionandyieldsanetreductioninGHGemissions(byreducingemissionsfromtheincompletecombustionofbiomassaswellasbyreducingdeforestation).However,trade-offsalsoexist.Forexample,electricvehiclesreducelocalairpollutionfromtraffic,butcanincreaseoverallCO2emissionsifthereisnotaparallelefforttodecarbonisethepowersector.Ultimately,thebalanceofpotentialsynergiesortrade-offsdependsontheroutechosentoachievetheenergytransition,makinganintegrated,whole-systemapproachtoscenariobuildingessential.TheemphasisoftheNZEScenarioisontechnologieswithshortprojectleadtimesinthepowersectorinparticular,suchasrenewables,butgiventhelong-termnatureofclimatechange,othertechnologychoiceswillcomeintoplayinthefuture.ModernuseofbiomassasadecarbonisationoptionisalsolessrelevantintheNZEScenariothaninasingle-objectiveclimatescenario,becausebiomassisacombustiblefuel,requiringpost-combustioncontroltolimitairpollutantemissions,makingitmorecostlythanitsalternativesincertainregions.TheNZEScenarioalsolooksattheimplicationsfortheenergysectorofachievingtargetsunderSDG6(cleanwaterandsanitationforall)andwhatpolicymakersneedtodotoachievemultiplegoalswithanintegratedandcoherentpolicyapproach.Thetimehorizonofthemodelis2050,toenableustoreflectinourmodellingtheannouncementsmadebyseveralcountriestoachievecarbonneutralityby2050,andthepotentialfornewtechnologies(suchashydrogenandrenewablegases)tobedeployedatscale.TheinterpretationoftheclimatetargetembodiedintheNZEScenarioalsochangesovertime,asaconsequenceofbothongoingGHGemissionsaswellasdevelopmentsinclimatescience(refertosection8onemissionsformoredetail).AnnouncedPledgesScenarioTheAPS,introducedin2021,aimstoillustratetheextenttowhichannouncedambitionsandtargetsareabletodelivertheemissionsreductionsneededtoachievenetzeroemissionsby2050.ItincludesallrecentmajornationalannouncementsasoftheendofAugust2023,forboth2030targetsandlonger-termnetzeroorcarbonneutralitypledges,regardlessofwhethertheseannouncementshavebeenanchoredinlegislationorinupdatedNDCs.IntheAPS,countriesfullyimplementtheirnationaltargets,andtheoutlookforexportersoffossilfuelsandlow-emissionsfuelssuchashydrogenisshapedbywhatfullimplementationofalltargetsmeansforglobaldemand.TheAPSalsoassumesthatallcountrytargetsforaccesstoelectricityandcleancookingareachievedontimeandinfull.ThewaythesepledgesareassumedtobeimplementedintheAPShasimportantimplicationsfortheenergysystem.Anetzeropledgeforeconomy-wideGHGemissionsdoesnotnecessarilymeanthatCO2emissionsfrom8InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONtheenergysectorneedtoreachnetzero.Forexample,acountry’snetzeroplansmayenvisagethatsomeremainingenergy-relatedemissionsareoffsetbytheabsorptionofemissionsfromforestryorlanduse.Itisnotpossibletoknowexactlyhownetzeropledgeswillbeimplemented,butthedesignoftheAPS,particularlywithrespecttothedetailsoftheenergysystempathway,hasbeeninformedbythepathwaysthatanumberofnationalbodieshavedevelopedtosupportnetzeropledges.Forcountriesthathavenotyetmadeanetzeropledge,policiesareassumedtobethesameasintheSTEPS.Non-policyassumptions,includingpopulationandeconomicgrowth,arethesameasintheSTEPS.StatedPoliciesScenarioTheSTEPSprovidesamoreconservativebenchmarkforthefuture,bynottakingforgrantedthatgovernmentswillreachallannouncedgoals.Instead,itprovidesamoregranular,sector-by-sectorevaluationofthepoliciesthathavebeenputinplacetoreachthestatedgoalsofthesepoliciesandotherenergy-relatedobjectives,takingaccountnotonlyofexistingpoliciesandmeasuresbutalsoofthosethatareunderdevelopment.TheSTEPSexploreswheretheenergysystemmightgowithoutamajoradditionalsteerfrompolicymakers.SimilarlytotheAPS,itisnotdesignedtoachieveaparticularoutcome.ThepoliciesassessedintheSTEPScoverabroadspectrum,includingNDCsundertheParisAgreementandmuchmore.Inpractice,thebottom-upmodellingeffortinthisscenariorequiresextensivedetailatthesectorallevel,includingpricingpolicies,efficiencystandardsandschemes,electrificationprogrammesandspecificinfrastructureprojects.ThescenariotakesintoaccounttherelevantpoliciesandimplementationmeasuresadoptedasoftheendofAugust2023,aswellaspolicyproposals,eventhoughspecificmeasuresneededtoputthemintoeffecthaveyettobefullydeveloped.Governmentannouncementsincludesomefar-reachingtargets,suchasaspirationstoachievefullenergyaccessinafewyears,toreformpricingregimesand,morerecently,toreachnetzeroemissions.AswithallthepoliciesconsideredintheSTEPS,theseambitionsarenotautomaticallyincorporatedintothescenario.Fullimplementationcannotbetakenforgranted,sotheprospectsandtimingfortheirrealisationarebaseduponourassessmentofcountries’relevantregulatory,market,infrastructureandfinancialcircumstances.Wherepoliciesaretime-limited,theyaregenerallyassumedtobereplacedbymeasuresofsimilarintensity.Wedonotassumefuturestrengthening–orweakening–offuturepolicyaction,exceptwheretherealreadyisspecificevidencetothecontrary.Forthefirsttimein2023,theSTEPStakesaccountofindustryaction,includingmanufacturingcapacityforcleanenergytechnologies,andtheimpactsofthiscapacityonmarketuptakebeyondpoliciesinforceorannounced.TheSTEPSshowsthatinaggregate,currentcountrycommitmentsareenoughtomakeasignificantdifference.However,thereisstillalargegapbetweentheSTEPSprojectionsandthetrajectoriesoftheAPSandtheNZEScenario.1.2Selecteddevelopmentsin2023Theprimarysectoralandtopic-specificmodeldevelopmentsundertakenthisyearincludethefollowing:Cross-cutting◼Thenumberofregionsincludedforthe2023modellingincreasedfrom26to29,withtheadditionofcountry-specificregionsforChile,Colombia,CostaRicaandArgentinaforthiscycle’sLatinAmericaEnergyOutlook.◼TheNZEScenariounderwentasignificantupdatein2023,retainingthesamedesignprincipleswhiletakingintoaccountkeychangesthathaveoccurredsince2021inenergypolicies,technologies,marketsandsupplychains.Section1Overviewofmodelandscenarios9IEA.CCBY4.0.FinalenergyconsumptionBehaviouralanalysis◼Acomprehensiveassessmentofbehaviouralchangesfeaturinginannouncedclimatepledges(NDCsandlong-termstrategies)hasbeencarriedout,toallowpoliciesrelatedtobehaviouralchangestobeincorporatedintotheAPS.Buildings◼Activitydriversincludingbuiltfloorarea,applianceownershipbyappliancetypeandairconditionerownershiphavebeenupdatedwithmorerecentdatabycountry.◼Inputsincludingbuildingdemolitionratesandincomeelasticitieshavebeenalignedtothelatestliterature.ProjectionsofheatingandcoolingdegreedayshavealsobeenupdatedfollowingthereleaseoftheIPCCSixthAssessmentReport.◼Theservicessub-sectormodelhasbeenenhanced,allowingforhighertechnologygranularityinspaceheating,waterheatingandspacecooling.◼Theheatpumpssalesandstockmodulehasbeenenhanced,allowingforhighertechnologydisaggregationtobetterrepresentcurrentmarkettrends.Industry◼Foraluminium,thetechnologygranularityhasincreased,forexamplebyincorporatingdifferentprocesseswithinaluminarefineries.◼Forthesteelsub-sectormodel,ironandsteelproductionhavebeenseparatedmoreclearly,incorporatingthetradeofironandcombinationsofdifferentproductionroutes.◼Thestockmodellingfunctionalityhasbeenmorecloselyintegratedwithinthecoretechnologymodel,allowingforbettertrackingofscrapmetalandplasticwasteresourcesandimprovedrepresentationofsecondaryproductiondynamics.Transport◼Fortheroadmodule,costcurvesoflow-emissionstruckswereupdatedbasedonrecentpublications.Theroadfreightmodulewasenhanced,improvingthefreightactivityprojectionsforalltrucksizes.◼TheconnectionbetweentheaviationmoduleandthelatestversionoftheAviationIntegratedModelwasrefined.Hydrogenaircraftenergyintensityandoccupancyfactoranalysiswasalsoupdated.◼Methanolwasincorporatedintotheshippingmodule.Policyresearchwasconductedtoupdatethefuelandtechnologyprojections.◼Acomprehensiveupdateoftherailmodulewascarriedouttoimproverailactivityandenergydemandprojectionsandprovideresolutionbycountry,includinganewframeworkforhistoricaldataaccommodatingfiverailtypesandthreefuels;anewmethodologytoderivehistoricalactivity,energyintensity,mileageandotherkeymetrics;andanewprojectionmethodologyforhigh-speedrailactivity.Hourlyelectricitydemand◼Anewhourlyloadcurvemodelhasbeendeveloped.Statisticalanalysisofhistoricaldemandallowsforthesimulationofaregion’selectricityloadcurvewithaveryhighlevelofconfidence.Thermosensitivityreflectstheimpactofvariationsinweatherandenablesthesimulationofelectricitydemandacrossmanyweatheryears.10InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONElectricitygeneration◼Anewhourlydispatchmodelwasdevelopedtoassesstheimpactofweather-inducedvariabilityonpowersystemoperationsandlong-termflexibilityneeds.◼Demandbyend-useandproductionprofilesforwind,solarPVandrun-of-riverhydro,aswellasinflowprofilesforreservoirhydroweregeneratedfor30weatheryears1inordertoassessthevariabilityofsystemoperationsandflexibilityneeds.Themodelincludesadetailedrepresentationofreservoirandpumpedstoragehydro,aswellastemperature-sensitivedemandanddemandresponse,hydrogenelectrolysersandhydrogenstorage.Energysupply◼Newemissionsintensitiesdataofupstreamoilandgasoperationswereintegratedintotheoilandgasmodel.◼Adetailedgeospatialanalysisoftheelectrificationpotentialofupstreamoilandgasoperationswasconducted.◼Revisedmethodstoestimatenetincomeandinvestmentfromoilandgasoperationswereimplemented.OthertransformationHydrogenmodule◼Globalmethanoltradinghasbeenincorporatedintothehydrogenmodule(TIMES[TheIntegratedMARKAL-EFOMSystem]model).◼AnewhourlyanalysishasbeendevelopedusingtheETHOS(EnergyTransformationPatHwayOptimizationSuite)modelsuiteoftheInstituteofEnergyandClimateResearch-3atResearchCentreJülichtoderivetheregionalproductioncostcurvesforhydrogenproductionfromrenewableelectricity.Biofuelproductionmodule◼Alcohol-to-jetbiojetkeroseneproductionroutehasbeenaddedtotheliquidbiofuelsmodel.Biomethanolproduction(withandwithoutcarboncapture,utilisationandstorage[CCUS])wasalsoaddedtotheliquidbiofuelsmodel.◼Biomethanoltradehasbeenaddedtothemodel.Criticalminerals◼TheGECmodelnowmakesuseofthedataavailableinthenewinteractiveCriticalMineralsDataExplorer,providingglobaldemandprojectionsfor37criticalmineralsacrossthethreemainIEAscenariosand12technology-specificcases.◼Updatesweremadetointegratenewbatterychemistrydevelopmentsobservedsince2022.Analternativecasenowexploresimpactsoffurtherhighlithiumironphosphateandsodium-ionsharesinbatteries.Emissions◼Mine-leveldataonthetypeofcoal,minedepthandmethanegascontentwereintegratedtoimproveestimatesofmethaneemissionsofcoalsupply.◼Satellite-data,measurementstudies,governanceindicatorsandrelateddatausedtoestimatemethaneemissionsfromfossilfuelsupplyhavebeenupdated.1Aweatheryearisasetofweatherparameterssuchastemperature,solarradiation,windspeedandprecipitationcompiledfromhistoricalrecordstocreatecurvesofhourlyloadsandrenewablesoutput.Section1Overviewofmodelandscenarios11IEA.CCBY4.0.Employment◼Thescopeofthemodelhasbeenexpandedtoincludeemploymentinnuclearfuelsupply,andgranularityhasbeenintroducedonemploymentincriticalmineralsbymineral.Governmentspendingoncleanenergyandenergyaffordability◼Thegranularityofthegovernmentenergyspendingdatahasbeenenhanced,notablywithregardstothetimelinefordisbursementofgovernmentfundingearmarkedbothforcleanenergyinvestmentsupportandenergyaffordabilityforconsumers.1.3GECModeloverviewModellingmethodologyTheGECModelisabottom-uppartial-optimisationmodelcoveringenergydemand,energytransformationandenergysupply(Figure1.1).Themodelusesapartialequilibriumapproach,integratingpricesensitivities.Itshowsthetransformationofprimaryenergyalongenergysupplychainstomeetenergyservicedemand,thefinalenergyconsumedbytheend-user.Thesupply,transformationanddemandmodulesofthemodelaredynamicallysoft-linked:consumptionofelectricity,hydrogenandhydrogen-relatedfuels,biofuels,oilproducts,coalandnaturalgasintheend-usesectormodeldrivesthetransformationandsupplymodules,whichinturnfeedenergypricesbacktothedemandmoduleinaniterativeprocess.Inaddition,energysystemCO2,methane(CH4)andnitrousoxide(N2O)emissionsareassessed.Themodelalsocomprisesadditionalmodulesevaluatingsystemimplicationssuchasinvestments,criticalminerals,employment,temperatureoutcomes,landuse,andairpollution.Themainexogenousdriversofthescenariosareeconomicgrowth,demographicchange,andtechnologicaldevelopments.Energyservicedemanddrivers,suchassteeldemandinindustryorthenumberofappliancesownedbyeachhousehold,areestimatedeconometricallybasedonhistoricaldataandonthesocio-economicdrivers.Interactionsbetweenenergyservicedemanddriversarealsoaccountedfor,suchastheinfluenceofthenumberofvehiclesalesonmaterialsdemand.Thisservicedemandismetbyexistingandnewtechnologies.Allsectormodules(seesubsequentsectionsformoredetailsonthesemodules)basetheirprojectionsontheexistingstockofenergyinfrastructure(e.g.theproductioncapacityinindustry,floorspaceinbuildings,numberofvehiclesintransport),throughdetailedstock-accountingframeworks.Toassesshowservicedemandismetinthevariousscenarios,themodelincludesawiderangeoffuelsandtechnologies(existingandadditions).Thisincludescarefulaccountingofthecurrentenergyperformanceofdifferenttechnologiesandprocesses,andthepotentialforenergyefficiencyimprovements.Thesectoralenergyandemissionbalancesarecalculatedbasedonthefinalenergyenduses–theservicedemand–bydeterminingfirstthefinalenergydemandneededtoserveit,thentherequiredtransformationstoconvertprimaryenergyintotherequiredfuels,andfinallytheprimaryenergyneeds.Thisisbasedonapartialequilibriumapproachusingforsomeelementsapartialoptimisationmodel,withinwhichspecificcostsplayanimportantroleindeterminingtheshareoffuelsandtechnologiestosatisfyenergyservicedemand.Indifferentpartsofthemodel,logitandWeibullfunctionsareusedtodeterminetheshareoftechnologiesbasedupontheirspecificcosts.Thisincludesinvestmentcosts,operatingandmaintenancecosts,fuelcostsandinsomecasescostsforemittingCO2.Incertainsectors,suchashydrogenproduction,speciallydesignedandlinkedoptimisationmodulesareused.Whilethemodelaimstoidentifyaneconomicalwayforsocietytoreachthedesiredscenariooutcomes,theresultsdonotnecessarilyreflecttheleast-costpathway.Thisisbecauseanunconstrainedleast-costapproachmayfailtotakeaccountofalltheissuesthatneedtobeconsideredinpractice,suchasmarketfailures,political12InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONorindividualpreferences,feasibleramp-uprates,capitalconstraintsandpublicacceptance.Instead,theanalysispursuesaportfoliooffuelsandtechnologieswithinaframeworkofcostminimisation,consideringtechnical,economicandregulatoryconstraints.Thisapproach,tailoredtoeachsectorandincorporatingextensiveexpertconsultation,enablesthemodeltoreflectasaccuratelyaspossibletherealitiesofdifferentsectors.Italsooffersahedgeagainsttherealrisksassociatedwiththepathways:ifonetechnologyorfuelfailstofulfilitsexpectedpotential,itcanmoreeasilybecompensatedbyanotherifitsshareintheoverallenergymixislow.Figure1.1⊳GlobalEnergyandClimateModelOverviewSection1OverviewofmodelandscenariosIEA.CCBY4.0.IEA.CCBY4.0.13Allfuelsandtechnologiesincludedinthemodelareeitheralreadycommerciallyavailableoratarelativelyadvancedstageofdevelopment,andthereforehaveatleastreachedaprototypesizefromwhichenoughinformationaboutexpectedperformanceandcostsatscalecanbederived.Costsfornewcleanfuelsandtechnologiesareexpectedtofallovertimeandareinformedinmanycasesbylearningcurveapproaches,helpingtomakeanetzerofutureeconomicallyfeasible.Besidesthismainfeedbackloopbetweensupplyanddemand,therearealsolinkagesbetweenthetransformationandsupplymodules,andfurtherlinkagessuchasmaterialflowsorbiogenicoratmosphericCO2viadirectaircapture(DAC)forsyntheticfuelproduction.Primaryenergyneedsandavailabilityinteractwiththesupplymodule.CompleteenergybalancesarecompiledataregionallevelandtheCO2emissionsofeachregionarethencalculatedusingderivedCO2factors,takingintoaccountreductionsfromCO2removaltechnologies.TheGECModelisimplementedinthesimulationsoftwareVensim(https://vensim.com/),butmakesuseofawiderrangeofsoftwaretools,includingTIMES(https://iea-etsap.org/index.php/etsap-tools/model-generators/times).DatainputsTheGECModelisadata-intensivemodelcoveringthewholeglobalenergysystem.Muchofthedataonhistoricalenergysupply,transformationanddemand,aswellasenergyprices,isobtainedfromtheIEA’sownenergyandeconomicdata.Additionaldatafromawiderangeofoftensector-specificexternalsourcesisalsoused,inparticulartoestablishthehistoricalsizeandperformanceofenergy-consumingstocks.Themodelisrecalibratedannuallytothelatestavailabledata.Theformalbaseyearforthisyear’sprojectionsis2021,asthisisthemostrecentyearforwhichafullenergybalancebycountryisavailable.However,wehaveusedmorerecentdatawhereveravailable,andincluding2022and2023estimatesforenergyproductionanddemand.Estimatesfortheyear2022arebasedontheIEA’sCO2Emissionsin2022report,inwhichdataarederivedfromanumberofsources,includingthelatestmonthlydatasubmissionstotheIEAEnergyDataCentre,otherstatisticalreleasesfromnationaladministrations,andrecentmarketdatafromtheIEAMarketReportSeriesthatcovercoal,oil,naturalgas,renewablesandelectricity.Investmentestimatesincludedatafortheyear2022,basedontheIEAWorldEnergyInvestment2023report.Dataondeploymentandtechno-economicperformanceoftechnologiesusedindifferentsectormodelsinclude2022andestimatesfor2023,suchasdatainTrackingCleanEnergyProgress2023,theGlobalHydrogenReview2023,andtheGlobalElectricVehicleOutlook2023.Forasummaryofselectedkeydatainputs–includingmacrodriverssuchaspopulation,economicdevelopmentsandpricesaswellastechno-economicinputssuchasfossilfuelresourcesandtechnologycosts–pleaseseetheGECModelkeyinputdataset(https://www.iea.org/data-and-statistics/data-product/global-energy-and-climate-model-2023-key-input-data).RegionalcoverageandtimehorizonTheGECModelcoverstheenergydevelopmentsinthefullglobalenergysystemupto2050,withthecapacitytoextendbeyond2050forsomeregions.Simulationsarecarriedoutonanannualbasis,withhourlymodellingforthepowersector.Thecurrentversionofthemodelprovidesresultsfor29regionsoftheglobe,ofwhich16areindividualcountries.Severalsupplycomponentsofthemodelhavefurtherregionaldisaggregation:theoilandgassupplymodelhas113regionsandthecoalsupplymodel32regions.14InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONCapabilitiesandfeaturesTheIEA’sGECModeloffersunparalleledscopeanddetailabouttheenergysystem.Itsessentialpurposeisevaluatingenergysupplyanddemand,aswellastheenvironmentalimpactsofenergyuseandtheimpactsofpolicyandtechnologydevelopmentsontheenergysystem.Throughlong-termscenarioanalysis,themodelenablesanalysisofpossiblefuturesrelatedtothefollowingmainareas:◼Globalandregionalenergytrends:Assessmentofenergydemand,supplyavailabilityandconstraints,internationaltradeandenergybalancesbysectorandbyfuel.◼Environmentalimpactofenergyuse:CO2emissionsfromfuelcombustionarederivedfromtheprojectionsofenergyconsumption.CO2processemissionsarebasedontheproductionofindustrialmaterialsandCH4andN2Oemissionsareassessedforfinalenergydemandaswellasforenergytransformation.Methanefromoilandgasoperationsisassessedthroughbottom-upestimatesanddirectemissionsmeasurements(seeMethaneTracker).LocalairpollutantsarealsoestimatedlinkingtheGECModelwiththeGAINSmodelofIIASAandthetemperatureoutcomesofmodelledscenariosareassessedusingtheModelfortheAssessmentofGreenhouseGasInducedClimateChange(MAGICC).◼Policyandtechnologydevelopments:theimpactofpolicyactionsandtechnologicaldevelopmentsonenergydemand,supply,trade,investmentsandemissionscanbeinvestigatedbycomparingbetweenscenarios.Additionally,theGECModelhasmultipledetailedfeaturesthateitherunderlieorbuildfromtheanalysisofbroaderenergytrends.Theseinclude:◼Technologies:Detailedtechno-economiccharacterisationofmorethan800cleanenergytechnologies,includingthosestillunderdevelopment(eitheratprototypeordemonstrationstage)fordifferentapplicationsinheavyindustries,long-distancetransportandcarbondioxideremovaltechnologiesamongothersectors◼People-centredtransitions:Detailedmodellingofbehaviouralchanges,energysectoremployment,equityoutcomesandenergyaffordability,amongotherimplicationsforcitizens.◼Criticalminerals:Comprehensiveanalysisofprojecteddemandandsupplyofcriticalmineralsneededfortheenergysector’stransition.◼Infrastructure:Detailedmodellingandanalysisofenergyinfrastructuredevelopmentneedsandstrategiesincludingelectricitysystems,fossilfuels,hydrogen-relatedfuelsdistributionandCO2transportoptions.◼Variablerenewablespotential:Detailedgeospatialanalysisofvariablerenewablespotentialsacrosstheglobeandmodellingoftheimpactofexploitingthemforhydrogenproduction.◼Modernenergyaccess:Comprehensivemodellingoftheimplicationsandopportunitiestoprovideenergyaccesstoallcommunities.Thisincludesaccesstoelectricityandcleancookingfacilities,andanevaluationofadditionalenergydemand,investmentsandrelatedGHGemissions.◼Materialefficiency:Granularmodellingofstrategiesalongsupplychainstomaketheuseofmaterialsincludingsteel,cement,aluminium,plasticsandfertilisersmoreefficient.◼Investments:Detailedmodellingofoverallenergysectorandcleanenergyinvestmentsbysub-sectorandtechnologyareas,andcomprehensiveanalysisoneffectivefinancingstrategies.Thisincludesinvestmentrequirementsinfuelsupplychainstosatisfyprojectedenergydemandandfordemand-sidetechnologiesandmeasures(e.g.energyefficiency,electrification).Governmentspendingisalsotracked.◼Decomposition:Detailedmathematicalframeworktosystematicallyanalysethespecificcontributionofdifferentstrategiestoemissionsorenergysavingsbetweenscenariosandovertime.Section1Overviewofmodelandscenarios15IEA.CCBY4.0.ConnectionswiththeinternationalenergymodellingcommunityThedevelopmentoftheGECModelbenefitsfromexpertreviewwithintheIEAandbeyond,andtheIEAworkscloselywithcolleaguesintheglobalmodellingcommunity.Forexample,theIEAparticipatesinandregularlyhoststheInternationalEnergyWorkshop,andregularlyinteractswiththeIntegratedAssessmentModellingConsortium.TheinitialNetZeroEmissionsby2050Scenarioin2021wasinformedbydiscussionswithmodellingteamsfromacrosstheworld,includingfromChina,theEuropeanUnion,Japan,theUnitedKingdom,theUnitedStates,andtheIPCC.TheIEAalsohasalong-standinghistoryofworkingwithresearchersandmodellersaroundtheworldaspartofitsTechnologyCollaborationProgrammes(TCP)network.TheTCPssupporttheworkofindependent,internationalgroupsofexpertsthatenablegovernmentsandindustriesfromaroundtheworldtoleadprogrammesandprojectsonawiderangeofenergytechnologiesandrelatedissues.TheEnergyTechnologySystemsAnalysisProgramme(ETSAP)TCP,establishedin1977,isamongthelongest-runningTCPs.TheETSAPTCPsupportspolicymakersinimprovingtheevidencebaseunderpinningenergyandenvironmentalpolicydecisionsthroughenergysystemsmodellingtoolsincludingtheTIMESmodellingplatform,andbringstogetherauniquenetworkofnearly200energymodellingteamsfromapproximately70countries.IEA’sGECModelalsointeractscloselywithotherinternationallyrecognisedmodels:◼TheIEAusestheModelfortheAssessmentofGreenhouseGasInducedClimateChange(MAGICC),developedandmaintainedbyClimateResourceandoftenusedbytheIPCCforkeypublicationstoinformitsanalysisoftheimpactofdifferentgreenhousegasesbudgetsontheaverageglobaltemperaturerise.◼IEAmodellingresultsarecoupledwiththeGreenhouseGas–AirPollutionInteractionsandSynergies(GAINS)modeldevelopedandmaintainedbyIIASA.ThisallowsfordetailedanalysisontheimpactonairpollutionofdifferentIEAscenarios.◼IEAresultsarecoupledwiththeGlobalBiosphereManagementModel(GLOBIOM)developedandmaintainedbyIIASAtocomplementtheIEA’sanalysisonbioenergysuppliesandeffectiveusestrategies.◼TheAviationIntegratedModel(AIM)developedbyUniversityCollegeLondonformsthebasisforourmodellingoftheaviationsector.◼IEAmodellingresultshavebeenlinkedtotheGlobalIntegratedMonetaryandFiscal(GIMF)modeloftheInternationalMonetaryFund(IMF)toassesstheimpactsofchangesininvestmentspendingonglobalGDP.◼TheOpenSourceSpatialElectrificationTool(OnSSET),aGIS-basedoptimisationtooldevelopedasaresultofacollaborationamongseveralorganisations,isusedtoinformtheIEA’senergyaccessmodelling.16InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection22Cross-cuttinginputsandassumptionsTheGECModelusesmacrodrivers,techno-economicinputsandpoliciesasinputdatatodesignandcalculatethescenarios.EconomicactivityandpopulationarethetwofundamentaldriversofdemandforenergyservicesinGECModelscenarios.Unlessotherwisespecified,thesearekeptconstantacrossallscenariosasameansofprovidingastartingpointfortheanalysisandfacilitatingtheinterpretationoftheresults.Energypricesareanotherimportantinput.Theprojectionsconsidertheaverageretailpricesofeachfuelusedinfinaluses,powergenerationandothertransformationsectors.Theseend-usepricesarederivedfromprojectedinternationalpricesoffossilfuelsandsubsidy/taxlevelsandvarybycountry.2.1PopulationassumptionsTable2.1⊳PopulationassumptionsbyregionCompoundaverage2022Population2050Urbanisationannualgrowthrate(million)(shareofpopulation)2000-222022-302022-502030202220302050NorthAmerica0.9%0.6%0.4%50552856583%84%89%UnitedStates0.7%0.5%0.4%336350372C&SAmerica1.0%0.7%0.5%52955960183%85%89%Brazil0.9%0.5%0.2%215224231Europe0.3%0.0%-0.1%69569668282%83%88%EuropeanUnion0.2%-0.1%-0.2%449446426Africa2.6%2.3%2.0%14251708248288%89%92%MiddleEast2.2%1.4%1.1%265297364Eurasia0.4%0.3%0.2%23824325376%78%84%Russia-0.1%-0.3%-0.3%143140132AsiaPacific1.0%0.6%0.3%42954489473475%77%83%China0.5%-0.1%-0.3%142014101307India1.3%0.8%0.6%14171515167044%48%59%Japan-0.1%-0.6%-0.6%125119105SoutheastAsia1.2%0.8%0.5%67972378773%75%81%World1.2%0.9%0.7%79508520968165%67%73%75%77%83%50%55%64%64%71%80%36%40%53%92%93%95%51%56%66%57%60%68%Notes:C&SAmerica=CentralandSouthAmerica.Seeannexforcompositionofregionalgroupings.Sources:UNDESA(2018,2022);WorldBank(2023);IEAdatabasesandanalysis.RatesofpopulationgrowthforeachGECModelregionarebasedonthemedium-fertilityvariantprojectionscontainedintheUnitedNationsPopulationDivisionreport(UNDESA,2022).Inthe2023GECmodellingcycle,populationrisesfromslightlylessthan8billionin2022toaround9.7billionin2050.Populationgrowthslowsovertheprojectionperiod,inlinewithpasttrends:from1.2%peryearin2000-2022to0.9%in2022-2030,dueinlargeparttofallingglobalfertilityratesasaverageincomesrise.Aroundthree-fifthsoftheincreaseintheglobalpopulationto2050isinAfrica,underliningtheimportanceofthiscontinenttotheachievementoftheworld’ssustainabledevelopmentgoals.AroundafurtherquarterisintheAsiaPacificregion,whereIndiaaloneaccountsforalmost15%ofthegrowthandbecomestheworld’smostpopulouscountryintheneartermasChina’spopulationgrowthstallsandreverses.Section2Cross-cuttinginputsandassumptions17IEA.CCBY4.0.Estimatesoftherural/urbansplitforeachGECModelregionhavebeentakenfromUNDESA(2018).Thisdatabaseprovidesthepercentageofpopulationresidinginurbanareasbycountrywithannualgranularityovertheprojectionhorizon.BycombiningthisdatawiththeUNpopulationprojectionsanestimateoftherural/urbansplitmaybecalculated.In2022,about57%oftheworldpopulationisestimatedtobelivinginurbanareas.Thisisexpectedtoriseto68%by2050.2.2MacroeconomicassumptionsTable2.2⊳GDPaveragegrowthassumptionsbyregionCompoundaverageannualgrowthrate2010-20222022-20302030-20502022-20501.9%NorthAmerica2.0%1.8%2.0%1.9%UnitedStates2.4%2.1%1.9%1.9%2.1%1.5%CentralandSouthAmerica1.2%2.3%2.4%1.3%Brazil4.0%0.9%1.8%2.3%2.3%3.0%Europe1.7%1.8%1.4%1.3%EuropeanUnion0.4%1.5%1.6%1.1%3.3%2.8%Africa2.9%3.8%4.0%4.9%0.6%SouthAfrica1.2%1.3%2.7%3.7%2.6%MiddleEast2.5%3.0%3.1%Eurasia1.9%1.0%1.4%Russia1.4%0.1%0.6%AsiaPacific4.8%4.1%2.9%ChinaIndia6.5%3.9%2.4%JapanSoutheastAsia5.7%6.4%4.3%0.6%0.7%0.5%4.3%4.6%3.3%World3.0%3.0%2.5%Note:CalculatedbasedonGDPexpressedinyear-2022USdollarsinpurchasingpowerparityterms.Source:IEAanalysisbasedonOxfordEconomics(2023)andIMF(2023).EconomicgrowthassumptionsfortheshorttomediumtermarearebroadlyconsistentwiththelatestassessmentsfromtheIMFandOxfordEconomics.Overthelongterm,growthineachGECModelregionisassumedtoconvergetoanannuallong-termrate.Thisisdependentondemographicandproductivitytrends,macroeconomicconditionsandthepaceoftechnologicalchange.InGECModel2023scenarios,theglobaleconomyisassumedtogrowby2.6%peryearonaverageovertheperiodto2050,withlargevariationsbycountry,byregionandovertime(Table2.2).TheinitialyearsintheOutlookareshapedbycountries’exposureandresiliencetoshocksandbywheretheyarecurrentlypositionedintheeconomiccycle.Thereverberationsfromthepandemicandtheglobalenergycrisisarebeingfeltacrossthebroadereconomyashouseholdpurchasingpoweriserodedbyhigherinflationandasbusinessinvestmentisrestrainedbyrisingborrowingcosts(althoughcleanenergyappears,insomecases,tobebuckingthistrend).Notwithstanding,labourmarketconditionsremainrelativelybuoyant:theunemploymentrateisatornearitslowestlevelinhalfacenturyinmostcountries,andthisishelpingtosupporthouseholdincomeandeconomicactivity.GlobalGDPgrowthovertheperiodto2030isprojectedtoaverage3%.Partlyreflectingthesecyclicalfactors,therangeincountryandregionalgrowthratesiswiderintheperiod2022-2030(6.3percentagepoints)thanitisintheperiod2030-2050(3.8percentagepoints).18InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTheassumedratesofeconomicgrowthareheldconstantacrossthescenarios,whichallowsforacomparisonoftheeffectsofdifferentenergyandclimatechoicesagainstacommonbackdrop.Thewaythateconomicgrowthplaysthroughintoenergydemanddependsheavilyonthestructureofanygiveneconomy,theexposureandresiliencetoshocks,thebalancebetweendifferenttypesofindustry,servicesandagriculture,andonpoliciesinareassuchaspricingandenergyefficiency.2.3PricesInternationalfossilfuelpricesTable2.3⊳FossilfuelpricesbyscenarioSTEPSAPSNZERealterms(USD2022)20102022203020502030205020302050IEAcrudeoil(USD/barrel)10398858374604225Naturalgas(USD/MBtu)5.85.14.04.33.22.22.42.0UnitedStatesEuropeanUnion9.932.36.97.16.55.44.34.1ChinaJapan8.813.78.47.77.86.35.95.314.615.99.47.88.36.35.55.3Steamcoal(USD/tonne)6753464143262723UnitedStatesEuropeanUnion122290676968535743JapanCoastalChina142336987780596547153205968079626449Notes:MBtu=millionBritishthermalunits.TheIEAcrudeoilpriceisaweightedaverageimportpriceamongIEAmembercountries.Naturalgaspricesareweightedaveragesexpressedonagrosscalorific-valuebasis.TheUSnaturalgaspricereflectsthewholesalepriceprevailingonthedomesticmarket.TheEuropeanUnionandChinanaturalgaspricesreflectabalanceofpipelineandLNGimports,whiletheJapangaspriceissolelyLNGimports.TheLNGpricesusedarethoseatthecustomsborder,priortoregasification.Steamcoalpricesareweightedaveragesadjustedto6000kilocaloriesperkilogramme.TheUSsteamcoalpricereflectsminemouthpricesplustransportandhandlingcosts.CoastalChinasteamcoalpricereflectsabalanceofimportsanddomesticsales,whiletheEuropeanUnionandJapanesesteamcoalpricesaresolelyforimports.Source:IEAGECModel2023.Internationalpricesforcoal,naturalgasandoilintheGECModelreflectthepricelevelsthatareneededtostimulatesufficientinvestmentinsupplytomeetprojecteddemand.Theyareoneofthefundamentaldriversfordeterminingfossilfueldemandandsupplyprojectionsinallsectorsandarederivedthroughiterativemodelling.Thesupplymodulescalculatetheproductionofcoal,naturalgasandoilthatisstimulatedunderagivenpricetrajectory,consideringthecostsofvarioussupplyoptionsandtheconstraintsonresourcesandproductionrates.Ifpricesaretoolowtoencouragesufficientproductiontocoverglobaldemand,thepricelevelisincreased,andenergydemandisrecalculated.Thenewdemandresultingfromthisiterativeprocessisagainfedbackintothesupplymodulesuntilabalancebetweendemandandsupplyisreachedforeachprojectedyear.Thepricetrajectoriesdonotattempttorepresentthefluctuationsandpricecyclesthatcharacterisecommoditymarketsinpractice.Thepotentialforvolatilityiseverpresent,especiallyinsystemsthatareundergoinganecessaryandprofoundtransformation.Fossilfuelpricepathsvaryacrossthescenarios(Table2.3).Forexample,intheStatedPoliciesScenario(STEPS),althoughpoliciesareadoptedtoreducetheuseoffossilfuels,demandisstillhigh.ThatleadstohigherpricesSection2Cross-cuttinginputsandassumptions19IEA.CCBY4.0.thanintheAnnouncedPledgesScenario(APS)andtheNetZeroEmissionsby2050Scenario(NZEScenario),wherethelowerenergydemandmeansthatlimitationsontheproductionofvarioustypesofresourcesarelesssignificantandthereislessneedtoproducefossilfuelsfromresourceshigherupthesupplycostcurve.CO2pricesTable2.4⊳CO2pricesforelectricity,industryandenergyproductioninselectedregionsbyscenarioUSD(2022)pertonneofCO2203020402050StatedPoliciesScenarioCanada130150155ChileandColombia132129China284353EuropeanUnionKorea120129135AnnouncedPledgesScenario426789Advancedeconomieswithnetzeroemissionspledges1Emergingmarketanddevelopingeconomieswithnetzeroemissionspledges2135175200Otheremergingmarketanddevelopingeconomies40110160NetZeroEmissionsby2050Scenario-Advancedeconomieswithnetzeroemissionspledges1747Emergingmarketanddevelopingeconomieswithnetzeroemissionspledges140Selectedemergingmarketanddevelopingeconomies(withoutnetzero90205250emissionspledges)25160200Otheremergingmarketanddevelopingeconomies1518085Note:Valuesarerounded.35551IncludesallOECDcountriesexceptMexico.2IncludesChina,India,Indonesia,BrazilandSouthAfrica.Source:IEAGECModel2023.CO2priceassumptionsareoneoftheinputsintotheGECModelasthepricingofCO2emissionsaffectsdemandforenergybyalteringtherelativecostsofusingdifferentfuels.Thereare73directcarbonpricinginstrumentsexistingtoday,coveringaround40countriesandover30subnationaljurisdictions.Manyothershaveschemesunderdevelopmentorareconsideringdoingso.AllscenariosconsidertheeffectsofotherpolicymeasuresalongsideCO2pricing,suchascoalphase-outplans,efficiencystandardsandrenewabletargets.Thesepoliciesinteractwithcarbonpricing;therefore,CO2pricingisnotthemarginalcostofabatementasisoftenthecaseinothermodellingapproaches.TheSTEPStakesintoconsiderationallexistingorscheduledcarbonpricingschemes,atnationalandsub-nationallevel,coveringelectricitygeneration,industry,energyproductionsectorsandend-usesectors,e.g.aviation,roadtransportandbuildings,whereapplicable.IntheAPS,higherCO2pricesareintroducedacrossallregionswithnetzeroemissionspledges.Inaddition,severaldevelopingeconomiesareassumedtoputinplaceschemestolimitCO2emissions.Allregionalmarketshaveaccesstooffsets,whichisexpectedtoleadtoaconvergenceofprices.Noexplicitpricingisassumedinsub-SaharanAfrica(excludingSouthAfrica)andOtherAsiaregions.Instead,theseregionsrelyondirectpolicyinterventionstodrivedecarbonisationintheAPS.IntheNZEScenario,CO2pricescoverallregionsandriserapidlyacrossalladvancedeconomiesaswellasinemergingeconomieswithnetzeroemissionspledges,includingChina,India,Indonesia,BrazilandSouthAfrica.CO2pricesarelower,butneverthelessrising,inotheremergingeconomiessuchasinNorthAfrica,MiddleEast,RussiaandotherSoutheastAsia.CO2pricesarelowerinallotheremergingmarketanddevelopingeconomies,asitisassumedtheypursuemoredirectpoliciestoadaptandtransformtheirenergysystems(Table2.4).20InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONEnd-userpricesFuelend-usepricesForeachsectorandGECModelregion,arepresentativeprice(usuallyaweightedaverage)isderivedtakingintoaccounttheproductmixinfinalconsumptionanddifferencesbetweencountries.Internationalpriceassumptionsarethenappliedtoderiveaveragepre-taxpricesforcoal,oil,andgasovertheprojectionperiod.Whereapplicable,excisetaxes,value-addedtaxrates,subsidiesandCO2pricesarecalculatedintheaveragepost-taxpricesforallfuels.Inallcases,theexcisetaxesandvalue-addedtaxratesonfuelsareassumedtoremainunchangedovertheprojectionperiod.Weassumethatenergy-relatedconsumptionsubsidiesaregraduallyreducedovertheprojectionperiod,thoughatvaryingratesacrossregionsandscenarios.IntheAPSandtheNZEScenario,theinternationaloilpricedropsincomparisontotheSTEPSduetolowerdemandforoilproducts.Tocounteractareboundeffectinthetransportsectorfromlowergasolineanddieselprices,anincreaseoffueldutyontopofCO2priceisappliedwheneverisnecessaryforensuringthatend-userpricesarekeptatleastatthesamelevelasintheSTEPS.AllpricesareexpressedinUSdollarsandassumenochangeinexchangerates.Electricityend-usepricesThemodelcalculateselectricityend-usepricesasasumofthewholesaleelectricityprice,systemoperationcost,transmissionanddistributioncosts,supplycosts,andtaxesandsubsidies(Figure2.1).Figure2.1⊳Componentsofretailelectricityend-usepricesIEA.CCBY4.0.Thereisnosingledefinitionofwholesaleelectricityprices,butintheGECModelthewholesalepricereferstotheaveragepricepaidtogeneratorsfortheiroutput.Foreachregion,wholesaleelectricitypricesarederivedundertheassumptionthatallplantsoperatinginagivenyearrecoverthefullcosts–i.e.fixedcostsaswellasvariablecosts–ofelectricitygenerationandstorage.Thekeyregion-specificfactorsaffectingwholesalepricesaretherefore:◼Theupfrontcapitalinvestmentandfinancingcostsofelectricitygenerationandstorageplants.◼Theoperationandmaintenancecostsofelectricitygenerationandstorageplants.◼Thevariablefuelcostofcoal,naturalgas,oilandotherinputfuelsand,ifapplicable,theCO2costofgenerationplants’output.Systemoperationcostsaretakenfromexternalstudiesandareincreasedinthepresenceofvariablerenewablesinlinewiththeresultsofthesestudies.Transmissionanddistributiontariffsareestimatedbasedonaregulatedrateofreturnonassets,assetdepreciationandoperatingcosts.Supplycostsareestimatedfromhistoricdata,Section2Cross-cuttinginputsandassumptions21IEA.CCBY4.0.andtaxesandsubsidiesarealsotakenfromthemostrecenthistoricdata,withsubsidyphase-outassumptionsincorporatedovertheOutlookperiodinlinewiththerelevantassumptionsforeachscenario.FossilfuelsubsidiesTheIEAmeasuresfossilfuelconsumptionsubsidies1usingaprice-gapapproach.Thiscomparesfinalend-userpriceswithreferenceprices,whichcorrespondtothefullcostofsupply,or,whereappropriate,theinternationalmarketprice,adjustedforthecostsoftransportationanddistribution.Theestimatescoversubsidiestofossilfuelsconsumedbyend-usersandsubsidiestofossil-fuelinputstoelectricitygeneration.Theprice-gapapproachisdesignedtocapturetheneteffectofallsubsidiesthatreducefinalpricesbelowthosethatprevailinacompetitivemarket.However,estimatesproducedusingtheprice-gapapproachdonotcapturealltypesofinterventionsknowntoexist.Therefore,theytendtounderstatetheimpactofsubsidiesoneconomicefficiencyandtrade.Despitetheselimitations,theprice-gapapproachisavaluabletoolforestimatingsubsidiesandcomparingsubsidylevelsacrosscountriestosupportpolicydevelopment(Koplow,2009).2.4PoliciesUnderpinningthescenarioanalysis,anextensiveeffortismadetoupdateandexpandthelistofenergyandclimate-relatedpoliciesandmeasuresthatfeedintoourmodelling.Assumptionsaboutgovernmentpoliciesarecriticaltothisanalysisandarethemainreasonforthedifferencesinoutcomesacrossthescenarios.TwonotableIEApolicytrackingeffortsprovideinputintothescenarios:◼PoliciesandMeasuresDatabase:TheIEA’sPoliciesandMeasuresDatabaseprovidesaccesstoinformationonpast,existingorplannedgovernmentpoliciesandmeasurestoreduceGHGemissions,improveenergyefficiencyandsupportthedevelopmentanddeploymentofrenewablesandothercleanenergytechnologies.ThisuniquepolicydatabasebringstogetherdatafromtheIEA/IRENARenewableEnergyPoliciesandMeasuresDatabase,theIEAEnergyEfficiencyDatabase,theAddressingClimateChangedatabase,theBuildingEnergyEfficiencyPolicies(BEEP)database,andtheIEA’sGovernmentEnergySpendingTracker,alongwithinformationoncarboncapture,utilisationandstorage(CCUS),methaneabatement,hydrogenandcriticalmineralspolicies.Thispolicyinformationhasbeencollectedsince1999fromgovernments,partnerorganisationsandIEAanalysis.Governmentshaveanopportunitytoreviewthepolicyinformationperiodically.◼SDG7database:TheIEAisattheforefrontofglobaleffortstoassessandanalysepersistentenergyaccessdeficit,providingannualcountry-by-countrydataonaccesstoelectricityandcleancooking(SDG7.1)andthemaindatasourcefortrackingofficialprogresstowardsSDGtargetsonrenewables(SDG7.2)andenergyefficiency(SDG7.3).TheIEAisoneoftheappointedco-custodiansfortrackingglobalprogressonSDG7alongsideIRENA,UNSD,theWorldBank,andWHO.Newpoliciesandmeasuresgloballyhavebeenconsideredduringthemodelpreparation,includingrecentannouncementssuchastheInflationReductionAct(UnitedStates),Fitfor55(EuropeanUnion),ClimateChangeBill(Australia),andGXGreenTransformation(Japan).AsummaryofkeypolicytargetsandmeasuresbysectorinselectedcountriesandregionscanbefoundinAnnexBofWorldEnergyOutlook-2023.Theconsideredpoliciesareadditiveacrossscenarios:measureslistedundertheAPSsupplementthoseintheSTEPS.AdditionalpolicyassumptionsareincorporatedintheNZEScenario,presentedasindicativepolicy-makinganddecarbonisationmilestonesthatwouldsteerglobalenergysystemstotheseoutcomes.1https://www.iea.org/topics/energy-subsidies22InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONThepublishedtablesbeginwithbroadcross-cuttingpolicyframeworks,followedbymoredetailedpoliciesbysector:power,industry,buildingsandtransport.Thetablesonlylistpoliciesthathavebeenenacted,implementedorrevisedsincethelastpublicationcycle.Someregionalpolicieshavebeenincludediftheyplayasignificantroleinshapingenergyataglobalscale(e.g.regionalcarbonmarketsandstandardsinverylargeprovincesorstates).Thetablesdonotincludeallpoliciesandmeasures;rathertheyhighlightthepoliciesprincipallyshapingglobalenergydemandtoday,beingderivedfromanexhaustiveexaminationofannouncementsandplansincountriesaroundtheworld.2.5Techno-economicinputsIncorporationofadiverserangeoftechnologiesisakeyfeatureoftheGECModel.Extensiveresearchisundertakentoupdatetherangeoftechnologiesinthemodel,aswellastheirtechno-economicassumptions.TheGECModelincludesthebreadthoftechnologiesthatareavailableonthemarkettoday.Additionally,themodelintegratesinnovativetechnologiesandindividualtechnologydesignsthatarenotyetavailableonthemarketatscalebycharacterisingtheirmaturityandexpectedtimeofmarketintroduction.Foreachsectorandtechnologyarea,newprojectannouncementsandimportanttechnologicaldevelopmentsaretrackedindatabasesthatareregularlypublished.Themodelledscenariosareinformedbyasimilarlydetailedtechnologytrackingprocess.Forinstance,theprojectplanningfinancingstatusisanimportantconsiderationforwhetherprojectsarereflectedinSTEPSorratherinAPS.Fortechnologydevelopmentprogressandthetimetobringnewtechnologiestomarkets,thescenariosassumeadifferentpaceofprogressasthesupportanddegreeofinternationalco-operationoncleanenergyinnovationincreaseswithambitionondecarbonisation.Thefollowingdatabasesareparticularlyrelevantforthedefinitionofthedifferentscenarios:◼Cleaninnovativetechnologiestracking:◼CleanTechnologyGuide:interactivedatabasethattrackstheTechnologyReadinessLevel(TRL)ofover550individualtechnologydesignsandcomponentsacrossthewholeenergysystemthatcontributetoachievingthegoalofnet-zeroemissions.TheGuideisupdatedeveryyear.◼CleanEnergyDemonstrationProjectsDatabase:newlylaunchedin2022andupdatedin2023,thisprovidesmoredetailedtrackingofthelocation,status,capacity,timingandfundingofover350demonstrationprojectsacrosstheenergysector.◼TrackingCleanEnergyProgress:annualtrackingofdevelopmentsforover50componentsoftheenergysystemthatarecriticalforcleanenergytransitionsandtheirprogresstowardsshort-term2030milestonesalongthetrajectoryoftheNZEScenario.◼HydrogenProjectsDatabase:coversallprojectscommissionedworldwidesince2000toproducehydrogenforenergyorclimate-change-mitigationpurposes.◼GlobalEVOutlook:annualpublicationthatidentifiesanddiscussesrecentpolicyandmarketdevelopmentsinelectricmobilityacrosstheglobe.ItisdevelopedwiththesupportofthemembersoftheCleanEnergyMinisterialElectricVehiclesInitiative(EVI).Technologycostsareanimportantinputtothemodel.Allcostsrepresentfullyinstalled/deliveredtechnologies,notsolelytheequipmentcost,unlessotherwisenotedasforfuelcells.Installed/deliveredcostsincludeengineering,procurementandconstructioncoststoinstalltheequipment.Someillustrativeexamplesincludethefollowing:Section2Cross-cuttinginputsandassumptions23IEA.CCBY4.0.◼Iron-basedsteelproductioncostsdisplayarangeconsideringtechnologyandregionaldifferencesanddifferentiatebetweenconventionalandinnovativeproductionroutes.Conventionalroutesareblastfurnace-basicoxygenfurnaceanddirectreducediron-electricarcfurnace(DRI-EAF).TheinnovativeroutesareinnovativesmeltingreductionwithCCUS,DRI-EAFwithCCUS,electrolytichydrogen-basedDRI-EAFandironoreelectrolysis.◼Vehiclecostsreflectproductioncosts,notretailprices,tobetterreflectthecostdeclinesintotalcostofmanufacturing,whichmoveindependentlyoffinalmarketpricesforelectricvehiclestocustomers.Historicalvaluesin2022havebeenusedfortheglobalaveragebatterypacksize.Inhybridcars,thefuturecostincreaseisdrivenbyregionalfueleconomyandemissionsstandards.◼Electrolysercostsreflectaprojectedweightedaverageofinstalledelectrolysertechnologies(excludingChina,wherethemodelledcostsarelower),includinginverters.◼Fuelcellcostsarebasedonstackmanufacturingcostsonly,notinstalled/deliveredcosts.Thecostsprovidedareforautomotivefuelcellstacksforlight-dutyvehicles.◼Utility-scalestationarybatterycostsreflecttheaverageinstalledcostsofallbatterysystemsratedtoprovidemaximumpoweroutputforafour-hourperiod.Table2.5⊳Capitalcostsforselectedtechnologiesbyscenario2022StatedPoliciesAnnouncedPledgesNetZeroEmissions2030205020302050by205020302050Iron-basedsteelproduction(USD/tpa)340-500340-450360-490380-630490-690440-650590-740Conventional590-770570-730590-780540-700600-760570-720InnovativeVehicles(USD/vehicle)16800153001540015200153001510015200Hybridcars20500166001470016100141001560013700BatteryelectriccarsBatteriesandhydrogen1505575445390265315230Hydrogenelectrolysers(USD/kW)Fuelcells(USD/kW)115614152344530Utility-scalestationarybatteries(USD/kWh)315185140180135175130Notes:kW=kilowatt;tpa=tonneperannum;kWh=kilowatt-hour;n.a.=notapplicable.AllvaluesareinUSD(2022).Sources:IEAanalysis;Jameset.al.(2018);Thompson,etal.(2018);FinancialTimes(2020);BNEF(2021);Coleetal.(2020);Tsiropoulosetal.(2018);JatoDynamics(2021).24InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection33End-usesectorsAll29regionsaremodelledinconsiderablesectoralandend-usedetail.Specifically:◼Industryiscomposedoffiveenergy-intensiveandeightnon-energy-intensivesub-sectors.◼Buildingsisseparatedintoresidentialandservicesbuildings,withsixend-usesmodelledseparately.◼Transportisseparatedintoninemodeswithconsiderabledetailforroadtransport.◼Agriculturemodellingreflectstherangeoffuelsandenergy-consumingapplicationsinthesector.Totalfinalenergydemandisthesumofenergyconsumptionineachfinaldemandsector.Ineachsub-sectororend-use,atleastseventypesofenergyareshown:coal,oil,gas,electricity,heat,hydrogenandrenewables.Themainoilproducts–liquefiedpetroleumgas(LPG),naphtha,gasoline,kerosene,diesel,heavyfueloil(HFO)andethane–aremodelledseparatelyforeachfinalsector.Demand-sidedrivers,suchassteelproductioninindustryorhouseholdsizeinbuildings,areestimatedeconometricallybasedonhistoricaldataandonsocioeconomicdrivers(suchasGDPandpopulation).Allend-usesectormodulesbasetheirprojectionsontheexistingstockofenergyinfrastructure.Thisincludesthenumberofvehiclesintransport,productioncapacityinindustry,andfloorspaceareainbuildings.Totakeintoaccountexpectedchangesinstructure,policyortechnology,awiderangeoftechnologiesthatcansatisfyeachspecificenergyserviceareintegratedinthemodel.End-userfuelpricesandtechnologycostsplayanimportantroleindeterminingthedistributionoftechnologiesandfuels,althoughreal-worldnon-costinfluencesalsoplayarole.Respectingtheefficiencylevelofallend-usetechnologiesgivesthefinalenergydemandforeachsectorandsub-sector(Figure3.1).Figure3.1⊳GeneralstructureofdemandmodulesDriversEconometricEnergyserviceLeast-costTechnology/EfficiencyFinalenergyanalysisdemandapproachfuelallocationlevelsdemand(demandforusefulenergy)IEA.CCBY4.0.3.1IndustryIndustryisthemostenergy-consumingandCO2-emittingend-usesector.Itaccountsfor38%oftotalfinalenergyconsumptionand47%ofCO2emissions(includingemissionsfromelectricityandheat).Theindustrymodelcoversfiveenergy-intensivesectors–accountingfor70%ofglobalindustryenergydemand:◼ironandsteel,withtechnology-richmodellingofironandsteelproduction◼chemicals,withtechnology-richmodellingofammonia,methanolandhigh-valuechemicalsproduction◼non-metallicminerals,withtechnology-richmodellingofcementproduction◼non-ferrousmetals,withtechnology-richmodellingofaluminaandaluminiumproduction◼paper,pulpandprinting.Italsocoverseightnon-energy-intensivesectors:construction;foodandtobacco;machinery;miningandquarrying;transportationequipment;woodandwoodproducts;andotherindustrynotspecifiedelsewhere.Theindustrysectormodelcombinesthestrengthsofbothsimulationandoptimisationmodelsintoasinglesimulationframework,withitsconstraintsandinputparametersinformedbyperiodicmodelrunsoftheformerETPTIMESoptimisationframework,amongotherthings.Section3End-usesectors25IEA.CCBY4.0.IndustrymodelcoverageandapproachForthepurposesoftheGECindustrymodel,theindustrialsectorincludesInternationalStandardIndustrialClassification(ISIC)Divisions7,8,10-18,20-32and41-43,andGroup099,coveringminingandquarrying(excludingminingandextractionoffuels),construction,andmanufacturing.ThiscoveragefollowsthestructureoftheIEAEnergyBalances,coveringalltheindustrycomponentsoftotalfinalconsumption.Chemicalfeedstock(acomponentofnon-energyuse)andblastfurnaceandcokeovenenergyuse(bothtransformationandownuse)arealsoincludedwithintheboundariesofindustry.Asidefrompetrochemicalfeedstock,othernon-energyuseisnotincludedintheGECModel’sindustrysectorboundary,butratherismodelledasaseparatecategoryinthesameframework.Figure3.2⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinindustryTechnology-richenergy-intensivesub-sectormodelsGECindustryhybridmodellingapproachIronandsteelChemicalsandNon-metallicNon-ferrousPaper,pulpandpetrochemicalsmineralsmetalsprintingMaterialandfuelHighvaluechemicalRawmaterialandfuelAluminarefiningPulpproductionpreparationproductiongrinding•Bayer&Sinterprocesses•Conventionalboilers•Cokeovens(cokedry•Steamcracking•Ballmillincluding:(e.g.coal,oil,gas)quenchingoption)•Electricseamcracking•Rollerpress&ballmill•Calcinationprocesses•Barkboiler•Bioethanoldehydration•Verticalmill•Digestionprocesses•Blackliquorrecovery•Sintering•Naphthacatalytic•Pulping•PelletisingClinkerproductionAluminium•Pulpbleachingcracking•Drykilnsproduction•PulpdryingIronproduction•Propane•Wetkilns•Hall-Héroultsmelting•Blastfurnaces(topgas•Verticalshaftkilns(inertanodeoption)Paperproductiondehydrogenation•Electrickilns•Soderbergsmelting•Conventionalboilersandtoppressure•Methanoltoolefins•Secondaryfurnacesrecovery,hydrogen•MethanoltoaromaticsFinishedcement(inductionfurnaceand(e.g.coal,oil,gas)amplification,charcoalgrindingreverbatoryfurnace•Barkboilerandhydrogen/biomassMethanolproductionoptions)•Paper-makingprocessesblendingoptions)•Fossilfuel-based•Ballmill•Smeltreduction•Biomass-based•RollerpressandballmillFinishingPrintingandfinishing•Directreducediron•Electrolysis-based•Verticalmill•Coldrollingprocesses(electrolysisoption)•Extrusion•IronoreelectrolysisAmmoniaproductionOthernon-metallic•Hotrolling•Fuelelasticitysimulation•Fossilfuel-basedmineralproduction•ShapecastingSteelproduction•Biomass-based•Fuelelasticitysimulation•Basicoxygenfurnace•Electrolysis-basedOthernon-ferrous•Openhearthfurnace•Pyrolysis-basedmetalproduction•Electricarcfurnace•Fuelelasticitysimulation•InductionfurnaceOtherchemicalproductionSemi-finishingandfinishingprocesses•Fuelelasticitysimulation•FuelelasticitysimulationCCUSoptions(cross-cutting)Merchanthydrogenandsynthetichydrocarbonoptions(cross-cutting)Cross-sectoralconversiondevicesimulationmodelOtherindustrySectorsEquipment•Textileandleather•Transportequipment•Woodandwoodproducts•Coolingandrefrigeration•Resistanceheating•Machinery•Construction•Boilers•Motors•Miningandquarrying•Non-specifiedindustry•Heatpumps•Motordrivensystems•Foodandtobacco•Solar/geothermalheating•LightingIEA.CCBY4.0.26InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTheindustrysectorismodelledusingahybridapproach(Figure3.2).Technology-richsimulationmodels,informedbyperiodicmodelrunsoftheformerETPTIMESoptimisationframework,areusedforfiveenergy-intensivesub-sectorscomponentsthereof(ironandsteel;primarychemicalswithinchemicalsandpetrochemicals;cementwithinnon-metallicminerals;aluminiumwithinnon-ferrousmetals;paper,pulpandprinting).Theremainingnon-energy-intensiveindustrysub-sectors(construction,miningandquarrying,transportequipment,machinery,foodandtobacco,woodandwoodproducts,textileandleather,andindustrynot-elsewherespecified)aremodelledusingacross-cuttingconversiondevicesimulationapproach.Fortheresidualcomponentsofthefiveenergy-intensivesub-sectors(chemicalsbesidesprimarychemicals,non-metallicmineralsbesidescement,non-ferrousmetalsbesidesaluminium,downstreamfinishingprocessesintheironandsteelsector,andpaper,pulpandprintsector),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-intensiveindustrysub-sectors,themodellingframeworkconsistsofaseriesofinteractingsub-modulesandacoretechnologymodel(seeFigure3.3).Thesub-modulesconsistofanactivitymodel,astockmodelandacapacitymodel.Figure3.3⊳IndustrysectormodelinternalmodulestructureandkeydataflowsInputdata12345StockmoduleCapacitymoduleActivitymodule68DB97TechnologymodelACModelresultsIEA.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;and9)Residualcapacity.Modelresults:A)Materialproduction;B)Materialstockssaturation;C)Energyconsumption,CO2emissions,technologyshares,investments;andD)Capacityinstalled,addedandretired.Section3End-usesectors27IEA.CCBY4.0.Theactivitydriversforeachsub-sectoroftheGECindustrymodelaretonnagesofmaterialproducedinagivenscenarioatagivenpointintime.Activitymodellingishandledinasimilarmannerforallenergy-intensiveindustrysub-sectors.Demandformaterialsisprojectedthroughinteractionbetweenanactivitymodelandastockmodel,togetherwithmodellingofmaterialefficiencystrategies.Theactivitymodelusescountry-levelhistoricaldataonmaterialconsumptiontocalculatedemandpercapita,thenprojectsforwardtotaldemandusingpopulationprojectionsandindustryvalue-addedprojections.Theindustryvalue-addedprojectionsinformtherateofchangeindemandpercapita.Theresultsoftheactivitymodelondemandprojectionsfeedintothestockmodel,whichusesbottom-upmaterialdemandinputsfromthebuildings,transportandsupplymodules,andcomplementaryassumptionsaboutotherend-productsharesandlifetimestocalculatetheimpliedbuild-upofmaterialstocks.Stocksaturationinthestockmodelinturninformspercapitamaterialdemandsaturationintheactivitymodelthroughaseriesofiterations.Materialefficiencystrategiesacrossvaluechainsarealsomodelled.ThismodellingworkbuildsmainlyontheliteratureandpreviousIEApublicationsrelatingtomaterialefficiency(IEA2019a).Strategiesconsideredinclude:◼Designstage:light-weighting(producingthesameproductwithaloweraveragemassperproduct),designforfuturematerialsavings(modulardesigntoenablereduction,designforrecyclability)◼Constructionandmanufacturing:increasedyields(reducingthelossesinsemi-manufacturingandmanufacturing),reducedmaterialswaste(morecarefulconstructionpracticesandmaterialhandling)◼Use:longerlifetimes(refurbishingbuildingsforotheruses,re-usingcomponentsforparticularproducts),moreintensiveuseofproducts(forexamplecarsharingorusingabuildingforalargershareoftheday)◼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,whichisfedintotheactivitymodelviaamaterialefficiencyfactor.Theresultingactivityprojectionsfromtheactivitymodelandscrapavailability(includingsemi-manufacturing,manufacturingandpost-consumerscrap)fromthestockmodelfeedintothemaintechnologymodel.Materialtrade(forfinalorintermediateproducts)betweenmodelregionsisnotmodelledendogenouslyinthetechnologymodel,butratherisreflectedintheactivityprojectionsdevelopedintheactivityandstockmodels.Apartfromspecificinstanceswhereannouncedpoliciesorprojectedenergypricesignalsproviderelevantevidencetothecontrary,tradepatternsinmaterialproductionandconsumptionareprojectedtofollowcurrenttrends.Globaltotalmaterialdemandisthusallocatedintoregionalproductionbasedonthesecurrenttrends.Thecapacitymodelcontainsdataonhistoricandplannedplantcapacityadditionsandretrofitsbyplanttype.Usingassumptionsaboutinvestmentcycles,itcalculatesplantrefurbishmentsandretirements.Theresultingremainingcapacityinformsthemaintechnologymodel.Thecapacitymodelalsoprovidesprojectionsontheaverageageofplantsatagiventime.Themaintechnologymodelofeachsectorconsistsofadetailedrepresentationofprocesstechnologiesrequiredforrelevantproductionroutes.Energyuseandtechnologyportfoliosforeachcountryorregionarecharacterisedinthebaseyearusingrelevantenergyuseandmaterialproductionstatistics.Throughoutthemodellinghorizon,demandformaterials(asdictatedbytheactivitymodeloutputs)ismetbytechnologiesandfuels,whosesharesareinformedbyannouncedprojects,real-worldtechnologyprogressandthepreviousETPTIMESoptimisation28InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONmodel.Thatmodelusedaconstrainedoptimisationframework,withtheobjectivefunctionsettomakechoicesthatminimiseoverallsystemcost(comprisedofbothenergycostsandinvestments).Changesinthetechnologyandfuelmix,aswellasefficiencyimprovements,areinpartdrivenbyacombinationofexogenousassumptionsonthepenetrationandenergyperformanceofbestavailabletechnologies,constraintsontheavailabilityofrawmaterials(suchasscrapavailabilityaccordingtothestockmodeloutputs),techno‑economiccharacteristicsoftheavailabletechnologiesandprocessroutesandassumedprogressondemonstratinginnovativetechnologiesatcommercialscale.Theresultsaresensitivetoassumptionsabouthowquicklyphysicalcapitalisturnedover(includingretirementsofexistingcapacityaccordingtothecapacitymodeloutputs)andabouttherelativecostsofthevarioustechnologyoptionsandfuels.AgivenscenariocanalsobesubjecttoaCO2emissiontrajectorythatthemodelmustadhereto.Modeloutputsincludeenergyconsumption,fuelcombustionandprocessCO2emissionsbothemittedandcaptured,technologyshares,rawmaterialsandintermediateindustrialmaterialsflowsandinvestmentrequirements.Someindustrialsectorshavetheparticularityofproducingandusing“on-site”hydrogenwithintheindustrialfacility,suchasforspecificammonia,methanoloriron-basedsteelproductionprocesses.Thishydrogenisnotreportedinstandardenergybalancesbutitisreportedasfossilfuelorelectricitydependingonwhetheritisproducedviasteamreformingorwaterelectrolysis.Accountingofthishydrogen,necessarytobuildtheglobalhydrogenaccounting,isperformedinadedicatedhydrogenmodule.Outputsofthismodulearehydrogenquantitiesproducedonsite(low-emissionsornot),electrolysercapacityandrelated-investmentrequirements,energyinputandrelatedCO2emissionsemittedaswellascapturedandstored.Non-energy-intensivesub-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.Usinghistoricalrelationshipsbetweenmacro-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).◼Mechanicalworktobedeliveredbymotors.◼Otherenergyservicesinaggregate(cooling,lightingetc.)Theseenergyservicedemandsformthefinalactivitydriversforthenon-energy-intensiveindustrysub-sectormodels.Arangeoftechnologiesarecharacterisedformeetingeachcategoryofactivitydemand,includingarangeofdifferentheatingtechnologiesusingdifferentfuels(fossilfuels,solarthermal,geothermal,electricheating,heatpumps,hydrogen,bioenergy)andarangeofmotoroptions(differingefficienciesofthemotor-drivensystem,efficienciesofthemotoritself,variablespeeddriveoption).ThesharesofenergyservicedemandmetbyeachofthesetechnologiesismodelledusingaWeibullfunction.Thisfunctionisinformedbyeachtechnology’slevelisedcost(includingfuelpriceevolutionandtheimpactofanyCO2prices),constraintsonfuelavailability(e.g.bioenergyresources),technologyreadiness,limitsonpotential(e.g.industrialheatpumppenetrationinmediumandhightemperatureheatbands)andanyCO2emissionsconstraintsofthescenario.Section3End-usesectors29IEA.CCBY4.0.Thesharesoffuels(andassociatedemissions)usedtomeettheremainingenergyservicedemandofmultifuelprocessesorprocessesthatarenotcoveredbythebottom-uptechnologymodellingacrossthenon-energy-intensivesectors(andresidualportionsoftheenergy-intensivesectorsnotcoveredintheenergy-intensivesub-sectormodels)ismodelledbyfuelusingaWeibullfunction.Thisfunctionisinformedbytheevolutionoffuelprices(includingtheimpactofanyCO2prices).AnyCO2constraintsspecifiedbythescenarioarealsorespected.IndustrysectorinvestmentsTheboundariesforinvestmentsreportingincludecapitalexpenditure(CAPEX),andengineering,procurementandconstructioncosts.Forcarboncapture,utilisationandstorage(CCUS)technologies,CO2transportandstoragecostsarealsoincluded.Formaterialefficiency,investmentsarebasedondataonCO2abatementcostsformaterialefficiencystrategies,convertedintocostsformaterialsavings.Fixedoperatingandmaintenanceexpenditures(OPEX)arenotincludedunderreportedinvestments,thoughtheyareconsideredinthecontextoftheeconomiccharacterisationoftechnologiesinthemodel.EnergysysteminvestmentsdonotincludecoreindustrialequipmentCAPEX,butdoincludetheadditionalinvestmentrequiredtoincrementally(e.g.energyefficiencyimprovementsthroughadoptionofbestavailabletechnologies)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).3.2TransportTheGECtransportmodelcombinesthestrengthsofboththeformerWorldEnergyModel(WEM)andtheMobilityModel(MoMo),andconsistsofdedicatedsectoralmodelforroadtransport,aviation,maritimeandrail.ThehistoricaldatabaseOnekeyfoundationfortransportmodellingworkistheroadtransportdatabase,whichisupdatedannuallybasedprimarilyonpubliclyavailabledataonroadvehiclesales,stocks,activityandoperations.TheroaddatabasefurtherbenefitsfromdataandanalyticalworkfortheElectricVehiclesInitiative.1Similarhistoricaldatabasesformthebasisformodellingrail,internationalshippingandcommercialpassengeraviation.1https://www.iea.org/programmes/electric-vehicles-initiative30InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONEachregionischaracterisedonthebasisofinformationthatincludes—foreachroadtransportmode—vehiclesales,mileage,andenergyintensitybyvintage,aswellastheoverallvehiclestock,loadfactorsandfuelefficiency.Thedatabaseallowslinkinghistoricaldataonseveralinterconnectedvariables,tryingtoassureinternalconsistencyacrossindicators,accordingtotheASIFframework,whereinActivity,StructureandIntensitydetermineestimatesofFueluse):𝐴𝑖𝐹𝑖𝐹=∑𝐹𝑖=𝐴∑(𝐴)(𝐴𝑖)=𝐴∑𝑆𝑖𝐼𝑖=𝐹𝑖𝑖𝑖FtotalFueluseAvehicleActivity(expressedinvehicle-kilometres[vkm])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-kilometres[vkm]),loads/occupancyrates,passengerandfreightactivity(passenger-kilometres[pkm]andtonne-kilometres[tkm]),fueleconomiesandenergyuse(basedontheIEAdataonenergydemandbycountry).Thefollowingparametersarecollectedandcalibrated/validatedagainsttheroadenergybalancesonanannualbasis:◼Sales/newvehicleregistrationdataaretakenfrompubliclyavailabledatasources(e.g.theEuropeanAutomobileManufacturers'Association[ACEA],USBureauofTransportationStatistics,andothers).◼Fueleconomydataforpassengerlight-dutyvehiclesarebasedonaggregateddatafromaproprietarydatabase,plusconversions(basedonanexternalresearchreport)acrossregionalvehicletestcyclestotheWorldLight-DutyTestCycle,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-orregional-timeseriesofroadgasoline,diesel,electricity,naturalgasandLPGconsumptionasreportedintheIEAenergybalances.Section3End-usesectors31IEA.CCBY4.0.ThetransportmoduleThetransportmoduleconsistsofseveralsub-modelscoveringroad,aviation,railandnavigationtransportmodes(Figure3.4)andincorporatesadetailedbottom-upapproachinallmodelregions.Figure3.4⊳StructureofthetransportdemandmoduleEnd-useActivityvariablesSub-sectorsenergypricesPassenger-RoadtransportkilometresHistoricalAviationtrendsTonne-kilometresRailGDPNavigationPopulationOtherIEA.CCBY4.0.Note:‘Other’includespipelineandnon-specifiedtransport.Foreachregion,activitylevelssuchaspassenger-kilometresandtonne-kilometresareestimatedeconometricallyforeachmodeoftransportasafunctionofpopulation,GDPandend-userprices.Transportactivityislinkedtopricethroughelasticityoffuelcostperkilometre,whichisestimatedforallmodesexceptpassengerbusesandtrainsandinlandnavigation.Thiselasticityvariableaccountsforthe“rebound”effectofincreasedcarusethatfollowsimprovedfuelefficiency.Energyintensityisprojectedbytransportmode,takingintoaccountchangesinenergyefficiencyandfuelprices.RoadtransportRoadtransportenergydemandisbrokendownamongpassengerlightdutyvehicles(PLDVs),lightcommercialvehicles(LCVs),buses,mediumtrucks,heavytrucksandtwo-andthree-wheelers.Themodelallowsfuelsubstitutionandalternativepowertrainsacrossallsub-sectorsofroadtransport.Thegapbetweentestandon-roadfuelefficiency,i.e.thedifferencebetweentest-cycleandreal-lifeconditions,isalsoestimatedandprojected.Asthelargestshareofenergydemandintransportcomesfromoiluseforroadtransport,theGECModelcontainstechnology-detailedsub-modelsofthetotalvehiclestockandthepassengercarfleet.ThestockprojectionmodelisbasedonanS-shapedGompertzfunction,proposedinDargayetal.(2006).Thismodelgivesthevehicleownershipbasedonincome(derivedfromGDPassumptions)and2variables:thesaturationlevel(assumedtobethemaximumvehicleownershipofacountry/region)andthespeedatwhichthesaturationlevelisreached.Theequationusedis:𝑉𝑡=𝑦𝑒𝑎𝑒𝑏𝐺𝐷𝑃𝑡whereVisthevehicleownership(expressedasnumberofvehiclesper1000people),yisthesaturationlevel(expressedasnumberofvehiclesper1000people),aandbarenegativeparametersdefiningtheshapeofthe32InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONfunction(i.e.thespeedofreachingsaturation).Thesaturationlevelisbasedonseveralcountry/regionspecificfactorssuchaspopulationdensity,urbanisationandinfrastructuredevelopment.Usingtheequationabove,changesinpassengercarownershipovertimearemodelled,basedontheaveragecurrentglobalpassengercarownership.Bothtotalvehiclestockandpassengervehiclestockprojectionsarethenderivedbasedonourpopulationassumptions.Projectedvehiclestocksandcorrespondingvehiclesalesarethenbenchmarkedagainstactualannualvehiclesalesandprojectedroadinfrastructuredevelopments.TheresultingvehiclestockprojectionscanthereforedifferfromthosethatwouldbederivedusingtheGompertzfunctionalone.Toimprovethestockevolutionofthevehiclefleet,adynamicscrappagefunctionhasbeendevelopedwherededicatedscrappagecurvesareestimatedbyregionbasedonacorrelationofaveragelifetimewitheconomicgrowth.Dynamicscrappagefunctionallowstoevaluatepolicymeasures,suchasearlyretirementofvehicle(Figure3.6).Totakeintoaccountthatoldervehiclesareusedless,anextensiveliteraturereviewhasbeencarriedouttoidentifymileagecurvespervehicletype.Thisenablesamoregranularassessmentofhoweachvehicletypepervintage(purchaseyear)iscontributingtothetotalroadactivity.Figure3.5⊳IllustrationofscrappagecurveandmileagedecaybyvehicletypeScrappagecurveMileagedecay100%Survivalprobability80%Mileage60%40%510152025051015202520%0%0PassengercarsHeavy-dutytrucksYearsIEA.CCBY4.0.Theanalysisofpassengerlight-dutyvehicle(PLDV)usesacosttoolthatguidesthechoiceofdrivetraintechnologiesandfuelsasaresultoftheircost-competitiveness.ThetoolactsonnewPLDVsalesasdepictedinFigure3.6,anddeterminestheshareofeachindividualtechnologyinnewPLDVssoldinanygivenyear.Thepurposeofthecosttoolistoguidetheanalysisoflong-termtechnologychoicesusingtheircost-competitivenessasoneimportantcriterion.ThetoolusesalogitfunctionforestimatingfuturedrivetrainchoicesinPLDV.2TheshareofeachPLDVtypejallocatedtothePLDVmarketisgivenby:𝑆ℎ𝑎𝑟𝑒𝑗=𝑏𝑗𝑃𝑃𝑟𝐿𝑝𝐷𝑉𝑗𝑟∑𝑗(𝑏𝑗𝑃𝑃𝐿𝑝𝐷𝑉𝑗)Where:◼PPLDVjistheannualcostofavehicle,includingannualisedinvestmentandoperationandmaintenancecostsaswellasfueluse;2Originallydevelopedtodescribethegrowthofpopulationsandautocatalyticchemicalreactions,logitfunctionscanbeappliedtoanalysethestockturnoverindifferentsectorsoftheenergysystem.Here,itusesthecost-competitivenessoftechnologyoptionsasanindicatorforthepaceofgrowth.Section3End-usesectors33IEA.CCBY4.0.◼rpisthecostexponentthatdeterminestherateatwhichaPLDVwillenterthemarket;and◼bjisthebaseyearshareorweightofPLDVj.Thecostdatabaseinthecosttoolbuildsonananalysisofthecurrentandfuturetechnologycostsofdifferentdrivetrainsandfueloptions,comprisingthefollowingtechnologyoptions:◼Conventionalinternalcombustionengine(ICE)vehicles(sparkandcompressionignition).◼Hybridvehicles(sparkandcompressionignition).◼Plug-inhybrids(sparkandcompressionignition).◼Batteryelectriccarswithdifferentdriveranges.◼Hydrogenfuelcellvehicles.Figure3.6⊳Theroleofpassenger-light-dutyvehiclecostmodelIEA.CCBY4.0.Note:LDVs=light-dutyvehicles.Themodeltakesintoaccountthecostsofshort-andlong-termefficiencyimprovementsinpersonaltransportdistinguishingnumerousoptionsforengine(e.g.reducedenginefriction,thestarter/alternator,ortransmissionimprovements)andnon-enginemeasures(e.g.tyres,aerodynamics,downsizing,light-weightingorlighting).Inaddition,itusesprojectionsforthecostsofkeytechnologiessuchasbatteries(nickelmetalhydrideandlithium-ion)andfuelcells.Thepaceoftechnologycostreductionsisthencalculatedusinglearningcurvesattechnology-specificlearningrates.Thecostanalysisbuildsonacomprehensiveanddetailedreviewoftechnologyoptionsforreducingfuelconsumption.Thedatabasewasreviewedbyapanelofselectedpeer-reviewers,andfeedsintothecosttool.Thecostdatabaseisconstantlyreviewedandtakesaccountofrecentresearch.CostcurvesassumptionsacrossallvehicletypesarebasedonworkbytheJointResearchCentre(JRC)(Krauseetal,2017;KrauseandDonati,2018).RegionalcharacteristicsandeconomicfactorshavebeentakenintoaccountinordertoexpandcostcurvescoverageforallGECModelregions.Projectedsalesofalternativepowertrains(andfocusingprimarilyonelectricvehicleswithinlight-dutyvehicles,andelectricandfuel-cellelectricvehicleswithinheavy-dutysectors)forthetop20globalautomakersareregularlyupdatedoverthecourseofeachyear.Thisanalysispermitsustoassesswhethervehiclemanufacturers’commitmentsforlaunchingnewelectrifiedcarmodelsarefallingbehindthenecessaryEVrolloutformeetingfueleconomygoalsandzero-emissionsvehiclemandates.VehiclemanufacturersandnationalandstatejurisdictionswithICEphase-outcommitmentsforacertainyeararealsopartofthisanalysis.34InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONProjectionsofbatteryandplug-inelectricvehiclesarematchedtosimpleprojectionsofbatterycapacityand(cathodeandcell)chemistry,andtheseprojectionsarelinkedtobottom-upanalysesofbatterycosts(to2030),andcriticalmineralrequirements.TheseprojectionsinformIEA'songoingworktoassesscriticalmineralsdemandandvaluechainimplicationsofashifttoelectromobility.Hydrogenfuel-cellelectricvehiclesprojectionstakeintoaccounttherecentcarmarketdevelopments,policyannouncementsandthekeyoutcomesfromIEA’sGlobalHydrogenReview2023(IEA,2023).Roadfreighttransportvehiclescanbebroadlyclassifiedintolight-commercialvehicles(<3.5t),mediumtrucks(3.5tto15t)andheavytrucks(>15t).Forthelattertwocategories,theGECModelcomprisestwodetailedsub-modelstoguidethedevelopmentofaveragefueleconomyimprovementsontheonehand,andtechnologychoicesontheotherhand.Fortheformer,themodelendogenizesthedecisionofinvestmentsinenergyefficiencybytakingtheviewofrationaleconomicagentsonthebasisthatminimisingcostsisakeycriterionforanyinvestmentdecisioninthissector.UsingtheefficiencycostcurvesofJRC,themodelcalculatestheundiscountedpaybackperiodofaninvestmentintomorefuel-efficienttrucksandheavytrucks.Themodelthenallowsforinvestmentswherethecalculatedpaybackperiodisshorterthananassumedminimumpaybackperiodthatisrequiredbyfleetoperators(generallyassumedtobebetween1and3years,dependingontheregion).Theproblemissolvedinaniterativemannerasthemodelseekstodeploythenextefficiencystepontheefficiencycostcurveasdeterminedbyliteraturebutmayuseefficiencyimprovementlevelsinbetweenindividualstepsontheefficiencycostcurve(Figure3.7).Figure3.7⊳IllustrationofanefficiencycostcurveforroadfreightShort-termLong-termUSD/vehicleIEA.CCBY4.0.Fuelconsumptionimprovementrate(litres/100km)IEA.CCBY4.0.Asasecondstep,themodelsimulatesthecosteffectivenessofaconventionalICEvehicleagainstothercompetingalternativeoptions.ThesimulationisguidedbytheuseofaWeibullfunction.Alternativepowertrainsformedium-andheavy-dutytruckshavebeenimplementedintheGECModel:fuelcell,batteryelectricandplug-inhybridelectric.Theroadfreightmoduleutilisesregionalanalysistoassessfreightactivity,takingintoaccountthegeographicalandeconomiccharacteristicsofthecountriesunderexamination.Thisenhancedmoduleprovidesprojectionsbyfactoringineconomicindicatorsandthedynamicsoffuelprices.Itprovidesvaluableinsightsintoboththefuturetrajectoryoffreightactivityandtheevolvingoccupancyfactorsfordifferenttrucksizecategories.Toassesstheproblemscreatedduetochicken-and-egg-typeofsituationswhenitcomestothedeploymentofthosealternativefuelsintransportthatrequireadedicatedrefuellinginfrastructure,andtobetterreflectpotentialspill-overeffectsoftheuseofsuchalternativefuelsinothersectorsoftheenergysystem,theGECSection3End-usesectors35Modelhasdedicatedsub-modelsforcoveringrefuellinginfrastructure.Inprinciple,themodulesseektoquantifythecostsandbenefitsofincreasedinfrastructureavailabilityfortransmissionanddistributionofthesealternativefuels.Inessence,therelationshipofthesespill-overbenefitscanbeillustratedasinFigure3.8.Figure3.8⊳Refuellinginfrastructurecostcurve(illustrative)RefuellingCost=F(NGVshareinvehiclesales)DistributionCost=F(gasshareinfinalconsumption)TransmissionCost=F(gasshareinprimaryenergy)InfrastructurecostsDrivingfactorforspilloverbenefitsIEA.CCBY4.0.Note:NGV=naturalgasvehicle.Inthecaseofelectricvehicles,availabilityofarobusttransmissionanddistributiongridislessofanissue,especiallyinadvancedeconomies,thankstothealreadyexistingwidespreaduseofelectricityindifferentend-usesectors(especiallybuildings).However,theavailabilityofelectricrecharginginfrastructureisoneoftheimportantconstraintsinthiscase,andhenceitisimportanttodeterminehowareductioninrefuellingcostscouldinfluencethepossibilityforoilsubstitutioninroadtransport.Therefore,theelectricvehicle(EV)sub-moduleassessesthecascadingeffectofanincreasedshareofelectricvehiclesinoverallvehiclesalesonbringingdowntherefuellingcosts.Detailedcostcurveswerepreparedoutliningthereductionofrefuellingcostswiththeincreaseinoverallvehiclestockofelectricvehicles.Thesecostcurveswereprovidedasanexogenousinputtothemodel,soastocontinuouslyadjusttherefuellingcostsastheshareofEVsalesrisesinthefuture.EVsupplyequipment(EVSEorEVcharger)stockisalsoprojectedbyvehiclecategory.Forlight-dutyvehicles,thenumberofpublicchargersiscalibratedtostartfromthehistoricaltrendsofEVSE/EV,whererelevant.Theshareofslowandfastchargersisalsocalibratedtohistoricdata,whereavailable.Thepaceofthedeploymentofprivateandpubliccharginginfrastructureisinformedbydataontheshareofhouseholdslivinginsingle-familyhouses,theavailabilityofEVcharginginfrastructureinprivateandmulti-familydwellings,andthecurrentprovisionandlevelofpubliclyavailablechargingportsandstations.Ingeneral,thepublicEVSE-to-EVratioisprojectedsuchthatastheEVstockshareincreases,therequiredkWofpublicchargingcapacityperEVdecreasestoreflectthatthesystembecomesbetteroptimisedasthemarketmatures.Forbusesandtrucks,theshareofelectricitydemandmetthroughopportunityorpublicchargersisprojectedbysegment.Urbanbusesareassumedtochargestrictlyatdepots,whileintercitybusesareassumedtorequiresomeshareoftheirelectricitydemandtobeprovidedoutsideofthedepot.Therequirednumberofpublicchargersisthenestimatedbasedonanassumedmixofchargerswithdifferentchargingcapacities.Hydrogenfuelconsumptionisusedtoestimatethenumberofhydrogenrefuellingstations(HRS)neededtomeetdemand.Stationcapacitiesaremodelledtoevolve(grow)overtime,withdifferentsizelimitssetbasedonthetargetvehiclesbeingserved.Forexample,HRSfortruckshavehighermaximumcapacitiesthanstationstoservethelight-dutyvehiclemarket.Though,ofcourse,somestationswillhavedualpressuredispensingandservedifferentvehiclemarkets.However,themodellingalsodifferentiatesutilisationratesbytargetvehiclecategory,36InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONwherestationsforfleetsofbusesandtrucksareexpectedtohavehigheraverageutilisationratesthanthoseforlight-dutyvehicles.Thus,thestockofHRSrequiredtoserveFCEVsis:𝐹𝑖𝐻𝑅𝑆𝑆𝑡𝑜𝑐𝑘=∑𝐶𝑖×𝑈𝑖Where:◼irepresentsthevehiclecategory◼Firepresentsthehydrogenfueldemand(kgH2/year)ofvehiclecategoryi◼Cirepresentstheaveragenameplatecapacity(kgH2/year)ofhydrogenrefuellingstationsservingprimarilycategoryi◼Uirepresentstheaverageutilisationrate(%)ofHRSservingprimarilycategoryiFinally,basedonprojectionsoftheaveragefuelconsumptionofnewvehiclesbyvehicletype,theroadtransportmodelcalculatesaveragesalesandstockconsumptionlevels(on-roadandtestcycle)andaverageemissionlevels(ingrammesofCO2perkilometre)overtheprojectionperiod.ItfurtherdeterminesincrementalinvestmentcostsrelativetootherscenariosandcalculatesimplicitCO2pricesthatguideoptimalallocationofabatementintransport.AviationAviationvehicleandpassengeractivitycalibratedatacountry/regionalleveltomatchdomesticandinternationalenergydemandforjetkerosene.AviationmodellingbuildsuponcollaborationwithresearchersatUniversityCollegeLondon(UCL),whohavedevelopedandmaintaintheopen-sourceAviationIntegratedModel(AIM).3KeyfeaturesofAIMpreservedinIEAmodellinginclude:◼Operationalandtechnicalpotentialforenergyintensityimprovementsbasedondetailed,origin-destinationmodellingofaircraftandairportoperationsandairframe-propulsionsystems,withstock-modellingandtechno-economicmodelling,intheframeworkofiterativecostminimisation.◼Regionalandairport-resolutionlong-termpriceandGDPdemandelasticitiesalignedwithIATAandotherauthoritativestudiesenablingcredibleandhigh-resolutionactivityprojections.ProjectionsintegratethemainfeaturesofdetailediterativecostminimisationmodellingusingAIMwith“top-down”projectionsoffuelconsumptionbyotheraviationactivities(dedicatedcargo,generalaviation).FurtherelaborationbuildsuponIEAtechno-economicmodellingofenergysupplyandfuelstransformationmodelling,aswellaselaborationsofpolicytargetsanddemand-sidemanagementstrategies.Thebottom-upmodellingofinternationalshippingisbasedontheASIFframework(Schipper,2010)toassessenergydemandandCO2emissionsbyregionandshiptype.Activityprojectionsaredevelopedinco-ordinationwiththeOECD(EnvironmentDirectorate)andtheInternationalTransportForum,whoprovidetradeprojectionsbyvalueandweightofdifferentcommoditycategories.Basedontheoriginanddestination,distanceestimatesareusedtocalculatethetonne-kmofeachcommoditytype.Ashareofeachcommoditytypeisthenallocatedtooneofthefollowingfivecategoriesofships:◼Liquidbulkcarriers(includingoiltankers)◼Drybulkcarriers◼Containerships◼Generalcargoships◼Otherships3https://www.ucl.ac.uk/energy-models/models/aimSection3End-usesectors37IEA.CCBY4.0.Themodellingbuildsuponexternaldataonvesselstockandsales(UNCTADandBloomberg);speed,daysatsea,deadweighttonnageandcapacityfactor(InternationalMaritimeOrganization[IMO]);andfueleconomy(TechnicalUniversityofDenmark[DTU]).Thestructurevariableisinterpretedastheloadfactor,i.e.theaveragecapacityutilisationpershippertrip,whichallowsderivingthevehicle-kilometresprojectedforeachregionandforeachshiptype.Loadfactorprojectionsarebasedonhistoricallyobservedgrowthratesoftheaveragesizeofthedifferentshiptypes,whicharepublishedbyUNCTAD.Fueleconomyisbasedonshiptype,deadweighttonnageandcapacityfactor.MultiplyingfuelconsumptionbytheCO2emissionfactorsofthedifferentfuelsmodelled(heavyfueloil,marinedieseloil,LNGandmethanol)givesthetotalCO2emissions.RailTherailmodulebuildsoffahistoricaldatabasethatcoversfivedifferentrailtypesandthreefuelsacrossover130countries.Keyparametersincludeenergyintensity,activity,mileage,stock,load/occupancyfactorandtracklengthandutilisation.Threedifferentscrappagecurvesareusedtoderivehistoricaltrainsalesnumbersperrailtypeandfuel.Inthedatabase,railvehicleandpassenger/freightactivityarecalibratedacrossurban(metroandlight-rail)andnon-urban(conventionalandhigh-speedpassengerrail,andfreightrail),fordiesel,electricityandcoal,tomatchtheenergybalancesatthecountrylevel.Railmodellingbuildsupondatabasesofurbanrailactivity(metroandlight-rail)fromtheInternationalAssociationforPublicTransportandtheInstituteforTransportationDevelopmentandPolicy,includingdatabasesofurbanrailinfrastructure.ItfurtherbuildsupondatafromtheInternationalRailwayUnionandtheJRConintercity(conventional),high-speed,andfreightrailactivity,stock,andinfrastructure,includingplansforrailnetworkextension,primarilyhigh-speedrail.Electrificationandhydrogenpenetrationratesinconventionalpassengerandfreightrailisinformedbycurrentandannouncedrailprojectsandtargets,aswellasregulatory,fiscal,investmentandclimatepoliciesthatvarybyscenario.Energyintensity,occupancy/loadfactor,mileageandtrackutilisationareprojectedwithconsiderationofGDP/capitaandthehistoricalperformanceandvarybyscenario.Activityprojectionsperrailtypearecalibratedwiththepreviousrailmodule.Behaviourchangeimpactintherailmoduleisdifferentiatedbetweenhigh-speedrail(HSR)andnon-HSR,alsovaryingbyscenario.ForHSR,behaviourimpactedactivitygrowthconsidersaviation-to-railshiftinformedbycountries’NationallyDeterminedContribution(NDC).Fornon-HSR,activitygrowthduetobehaviouralchangeisbasedonhistoricaltrackutilisationandrailpassengeractivitypercapitadata,aswellastheroleofrailintheNDCs.BehaviouralchangeanalysisSeveralanalysesregardingbehaviouralchangeintransporthavebeencarriedout:◼Ex-postanalysisontheimpactofbehaviouralchangeontheaviationsectorhasbeendeveloped.Historicaldata(OAG,AIMfromUCL)hasbeenusedtodisaggregateaviationactivityperpersonanddistance.Changesinoccupancyfactorshavebeenassumedtoassesstheimpactofbehaviouralchangeinoildemand.◼Bothcommercial(IHSMarkit,JatoDynamics,Marklines)andin-house(GlobalFuelEconomyInitiative,historicalroaddatabase)datasetshavebeenusedtoperformin-depthanalysisbycountryontheriseofsportutilityvehicles(SUVs).Basedonananalysisofhistorictrends,amoderategrowthofSUVsisprojectedintheSTEPSonaglobalscale.◼Thecarmarketisanalysedusingmultiplesources(Marklines,EVVolumes,etc.),estimatingthefutureevolutionofcarsales.Basedonastockmodel,achangeincarsalesvolumeduetobothnewpurchasesandreplacementsisestimated.Econometricfunctionshavebeenappliedtoprojectthefuturetrend.38InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATION◼DuringtheCovid-19pandemic,datashowedashiftfrompublictransporttoprivatevehiclesduetohealthconcerns.Publiclyavailablereports,includingasurveybyIpsos,wereusedtoestimatethemobilityneedsthatneedtobecoveredbybicyclesorprivatecars.AssumptionsdifferbyGECModelregion,dependingontheaccessibilityofbikes,andtheimpactonoildemandduetothismodalshiftwasestimated.◼Regardingtheimpactofworkingfromhome,aliteraturereviewhasbeencarriedoutontheaveragecommutingdistancebytransportmodeforkeyGECModelregions.Thesedatahavebeenextrapolatedtoallregions.Assumingthemaximumpotentialfortheworkforcetoworkfromhome(i.e.20%by2030),theimpactofworkingfromhomeonoildemandwasassessed.3.3BuildingsThebuildingssectormoduleoftheGECModelissubdividedintotheresidentialandservicessectors,withbothhavingsimilarstructures(Figure3.9).Population,GDP,climateanddwellingoccupancyinformtheactivityvariables,whichincludefloorspace,applianceownership,numberofhouseholds(fortheresidentialsector)andvalueadded(fortheservicessector).Figure3.9⊳StructureofthebuildingsdemandmoduleEnd-useenergypricesActivityvariablesBuildingenvelopesUrbanisationPopulationFloorspaceSub-sectorsNumberofhouseholdsDwellingoccupancyApplianceownershipSpaceheatingGDPServicesvalueaddedSpacecoolingHistoricaltrendsWaterheatingCookingLightingAppliancesRefrigeratorsWashingmachinesDishwashersDryersBrowngoodsOtherappliancesIEA.CCBY4.0.Withintheresidentialandservicessectors,energydemandissubdividedintosixstandardend-usesinbuildings,namelyspaceheating,waterheating,appliances,lighting,cookingandspacecooling.Appliancesaredividedintofourmaincategories:refrigeration(refrigeratorsandfreezers),cleaning(washingmachines,dryingmachinesanddishwashers),browngoods(televisionsandcomputers);andotherappliances.Spacecoolingcomprisesairconditionersandfans.Alllistedend-useswithineachsectoraremodelledindividually,withfinalenergyconsumptionbeingprojectedfromthebaseyearforeachend-useinthreesteps.Thefirststepiscalculatingthedemandforanenergyservice,i.e.,theusefulenergydemand,basedonactivityvariables.Thebasicconceptforthisstepis:𝐸𝑛𝑑𝑢𝑠𝑒𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑑𝑒𝑚𝑎𝑛𝑑=𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒×𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦Section3End-usesectors39IEA.CCBY4.0.Activityvariablesrefertothemaindriversofenergyservicedemand–fortheresidentialsector,theseincludefloorspace,peopleperhouseholdandappliancesownership;andfortheservicessector,thisincludesvalueaddedandfloorspace.Intensityreferstotheamountofenergyservice(e.g.spaceheating)neededperunitoftheactivityvariable(e.g.floorspace).Theactivityvariablesareprojectedeconometrically,basedonhistoricaldataandlinkingtosocio-economicdriversincludingGDP,population,urbanisationrates,andaccessratestomodernenergy.Foreachend-use,theintensityvariableisprojectedusinghistoricalintensityandadjusting,foreachprojectionyear,tothechangeinaverageend-userfuelprices(basedonpriceelasticities)andthechangeinaveragepercapitaincome(basedonincomeelasticities)overtime.Inthecaseofspaceheatingandspacecooling,intensityprojectionsarealsoadjustedforhistoricalvariationsintemperature,andtheimprovementsinbuildingsenergyperformanceassociatedwithnewconstructionorrenovationstandards.Historicalenergydemandforspaceheatingandspacecooling,aswellashistoricalheatingandcoolingdegree-daydataiscombinedtonormaliseprojectionsofspaceheatingandspacecoolingenergydemand,removingtheimpactsofyear-on-yearvolatilityonenergyservicedemand.Theimpactofclimatechangeonspaceheatingandspacecoolingdemandisaccountedforinthemodelaswell,basedontheanticipatedchangeinheatingandcoolingdegree-daysduetoclimatechangeineachregionandundereachscenario’stemperaturepathway.TheseprojectionsarebasedontheIEA’sanalysisthatisderivedfromrelevantprojectionspublishedintheIPCCWorkingGroupIInteractiveAtlas.Spaceheatingandspacecoolingservicedemandiscomputedforbuildingsupontheirconstruction,basedonthebuilding’senergyefficiencyperformanceatthetimeofconstruction.Newbuildingsinthemodelareconstructedaseithernon-compliantwithbuildingenergycodes,compliant,orzero-carbon-readybuildings.Thischoice,aswellastheregionandtheyearofconstruction,influencesthebuilding’senergyservicedemand.Theenergyservicedemandofabuildinginthemodelcanalsobeinfluencedbyretrofitting:anexistingbuildingcanberetrofittedtoimproveitsenergyperformance,sothatthebuildingcomplieswithbuildingenergycodesorbecomeszero-carbon-ready.Theprojectionsofthesharesofeachtypeofnewbuildingandretrofitdependonthepolicyassumptionsunderlyingeachscenario.Retrofittingabuildingextendsitslifetime,influencingboththeneedfornewconstructionsandtheassociateddemandforconstructionmaterials.Thetotalenergyservicedemandtobemetbyspaceheatingorcoolingequipmentisthereforethesumoftheservicedemandacrossthedifferentvintagesofbuildings,determinedbytheyearofconstruction,andacrossthefivecategoriesofbuildings(non-compliant,compliant,zero-carbon-ready,retrofittocompliant,retrofittozero-carbon-ready).Improvementsintheperformanceofbuildingenvelopes(eitherviamoreefficientnewconstructionsorviaretrofits)shiftthemfromonecategorytoanotherandtherebyreducethetotalenergyservicedemandforspaceheatingandspacecoolingthatremainstobemetbyheatingorcoolingequipment.Thesecondstepischoosingthetechnologiestosupplytheend-useservicedemand.Foreachend-use,thereisadetailedsetoftechnologiesavailableinthemodel,asshowninFigure3.10⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinbuildings.Mostofthetechnologyoptionsaremodelledintiers,representingvaryinglevelsofenergyefficiencyandassociatedinvestmentcosts.Additionally,thereisapossibilitytoswitchfuelsandtechnologies,wherebyheatpumpscouldbeusedforspaceheatinginsteadofgasboilers,forexample.Withintheresidentialsector,additionaldetailregardingbioenergyallowsforthemoreaccuratemodellingofthehistoricalandprojecteduseofbiogasdigesterstomeethomeenergyneeds,aswellastheuseofbioethanolandotherliquidsincookstovesandhouseholdheatingequipment.Overtheprojectionhorizon,thetechnologychoiceisbasedontechnologies’relativecosts,theirefficiencies,andanyrelevantpolicyconstraintsinthatregion.TechnologysharesareallocatedbyaWeibullfunctionthataccountsforeachtechnology’scostsperunitofservicedemandsupplied,whichincludesinvestmentcosts,operationandmaintenancecostsandfuelcosts.Forexample,therelativeeconomiccompetitivenessofaheatpumpversusa40InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONgasboilerforspaceheatingdiffersdependingonthebuilding’sservicedemandforheating,whichimpactstheimportanceofinvestmentcostsrelativetooperationalcosts.Themodelroutineallocatesdifferenttechnologiestosatisfytheadditionalservicedemandeachyearoverthemodelhorizon.Thisallocationissubjecttoupperandlowerboundaries,reflectingreal-worldconstraintssuchastechnologyavailability,policiesandmarketbarriers.Toassessandupdateequipmentandapplianceefficiency,andrelatedcosts,alargenumberofcompanies,expertsandresearchinstitutionsatthenationalandinternationallevels,includingIEATechnologyCollaborationProgrammes,areregularlycontacted.Theassessmentwasalsosupportedbyaninitialextensiveliteraturereviewtocataloguetechnologiesthatarenowusedindifferentpartsoftheworldandtojudgetheirprobableevolution(Anandarajah,etal.,2011;Econoler,etal.,2011;Kannan,etal.,2007;Waide,2011;LBNL,2012;GBPNandCEU,2012).Figure3.10⊳Majorcategoriesoftechnologiesbyend-usesub-sectorinbuildingsSpaceandwaterAppliancesCoolingLightingCookingheatingMinimumlevelofRoomairIncandescentFossilfuel-basedFossilfuel-basedefficiencyconditionerstoves(LPG,gas,(coal,oilorgas)Halogen-ConventionalAveragelevelofSplit-airFluorescentorcoal)-CondensingefficiencyconditionerElectricCompactSolidbioenergyElectricboilersandBestavailableCentralairfluorescentBiofuelsresistancetechnologyconditionerLight-emittingBiogasdiode(LED)HeatradiatorsGround-sourceIEA.CCBY4.0.heatpumpHeatpumps(air-orground-source)DistrictcoolingRenewablesSolarcooling(solarheaters,geothermal,bioenergy)GaschillersHydrogenboilersandfuelcellsThethirdstepiscalculatingtotalfinalenergyconsumptionintheresidentialandservicesectorbasedontheefficienciesofexistingandnewbuildingequipment.Efficiencyrepresentstheamountofenergyneededtomeetaunitofservicedemand,andthusrepresentsthetechnicalperformanceoftheequipmentorappliances.Finalenergyconsumptioninthebuildingssectorisasummationofthesub-sectoralenergyconsumedbythetotaltechnologystock,whichincludesthehistorical(declining)stockofappliancesandequipment,andthenewtechnologiesaddedeveryyearoverthemodelhorizonbythetechnologyallocationroutine.1𝐹𝑖𝑛𝑎𝑙𝑒𝑛𝑒𝑟𝑔𝑦𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛=𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦×𝐸𝑛𝑑𝑢𝑠𝑒𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑑𝑒𝑚𝑎𝑛𝑑Theimpactofbehaviouralchangeisintegratedatthispoint,withboththeenergyuseandenergyservicedemandpertechnologyadjustedtoreflectscenarioassumptionsonthebreadthanddepthofbehaviouralchangeinthebuildingssector.Section3End-usesectors41IEA.CCBY4.0.Modeloutputsintermsofenergydemandbytechnology,thenumberoftechnologyunitsdeployed,buildingsconstructedorretrofitted,areallusedtocalculateinvestmentsandenergyexpenditure.CO2,otherGHGemissionsandmaterialneeds(steel,cementandaluminium)relatedtothebuildingssectorarealsocalculated.Thebuildingsmoduleisdirectlylinkedtotheenergyaccessmoduleinordertotakeintoaccountthegrowthofelectricityandofalternativefuelsorstovesforcooking(seeSection11).BehaviouralchangeBehaviouralchangesmodelledwithinthebuildingsmoduleincludelowerindoorairtemperaturesettings,loweruseofairconditioning,useofline-drying,efficientuseoflightingandappliances,optimisedboilersettingsandcoolwashing.Aliteraturereviewwascarriedouttoassesstheimpactonenergyconsumption.Thepotentialisestimatedtoassessthetotalimpactandtheresultingdecreaseinbuildingssectoremissions.TheimpactofworkingfromhomeisanalysedbasedonaliteraturereviewonhowmuchworkingfromhomeincreasesresidentialenergyconsumptioninkeyGECModelregions.Datafromtheliteraturereviewhavebeenextrapolatedtoallregionsandtheimpactsonenergydemandestimatedbyfuel.Themaximumpotentialforworkingfromhomewasassessedonacountry-by-countrybasistoinformtheimpactonresidentialenergyconsumption.3.4Hourlyelectricitydemandanddemand-sideresponseUnderstandingthehourly,dailyandseasonalevolutionofelectricitydemandiscriticalforaccuratemodellingofelectricitysystems,includingtheassessmentofelectricitysystemflexibilityneedsandtheroleofdemand-sideresponse.Modellingofhourlyelectricitydemandisundertakenatanend-uselevel.End-uselevelmodellingallowsthemodeltoreflecttheimpactofthefullscopeofdemandsideintegrationmeasures:electrificationandenergyefficiencyimpacttheannualdemandforend-useswhiledemand-sideresponse,includingloadshiftingandshedding,impactsdemandatamoretemporallygranularlevel.Modellinghourlyloadrequiresassessmentofthehourlyloadprofileforeachend-usewithineachsector,residentialandservices(e.g.spaceheating,waterheating),industry(e.g.steel,chemicals),transport(e.g.road,rail)andagriculture.Loadcurvesareassessedforafullyearatthehourlyresolution.Loadcurveparametersarederivedfromhistoricaldata.Astatisticalanalysisisconductedonhistoricalhourlydemand(IEA,2022a)andtemperaturetimeseries(IEA,2023).Thehourlyloaddependencytocalendarvariablesandtemperatureisextractedfromregressionparameters.Theaveragedailytemperatureisweightedbypopulation,andtemperaturethresholdsactivatingcoolingandheatingdemandaredeterminedbasedontheloadresponsetotemperaturevariations(Figure3.11).Thestatisticalanalysisisconductedonasmanyhistoricalyearsasavailable,consideringthestructuralchangesinelectricitydemandovertimeandtemporarychangesinelectricityconsumptionpatterns,suchasduringpandemiclockdowns.Averageweeklyprofilesforheating,coolingandnon-thermosensitiveloadarederivedfromthisanalysis,alongwithadditionalparameterssuchastheaveragedemandperweekoftheyear.Thisthermosensitivityanalysisallowsforthesimulationofaregion’selectricityloadcurvewithaveryhighlevelofconfidence,consideringtheimpactofvariationsinweather,mostnotablytemperature.42InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONFigure3.11⊳ThermosensitivityanalysisforhourlyloadcurveassessmentAnnualhourlyloadprofileAveragewinterweekhourlyloadprofileAveragesummerweekhourlyloadprofileCAveragedailyloadagainstaveragedailytemperatureIEA.CCBY4.0.Individualhourlyloadcurvesforeachelectricityendusearegeneratedbasedonthethermosensitivityanalysis,research,andsurveydatawhereavailable.Totalspaceheatingandcoolingprofilesaresplitbetweenresidentialandservicesendusesdependingonthehourlyactivityineachsector.Loadcurvesforotherendusesareinformedbyliteratureandadjustedtomatchthetotalnon-thermosensitiveprofileoftheregion.Lightinghourlyelectricitydemandisprojectedbasedondaylighttimesandsolarinsolationlevels.AnexampleoftheloaddisaggregationperenduseisdisplayedinFigure3.12.Eventually,loadprofilesarescaledtotheannualendusedemandintheprojectedyear,assimulatedintheGECmodel.Thehourlyloadprofilecanbegeneratedfordifferentweatherconditions,byvaryingthehistoricaltemperaturetimeseriesusedasaninput.Thisallowsforanassessmentoftheimpactofweatheronpeakdemandandflexibilityneedsovermultipleweatheryears.ThehourlyloadgenerationiseitherperformedatthecountrylevelorattheGECregionlevel.Thehourlyloadmodelcoversmorethan75%ofworldelectricityconsumptionin2022,including,amongothers,China,India,theEuropeanUnion,theUnitedStatesandJapan.Modellingtheroleandpotentialofdemand-sideresponserequiresassessmentoftheshareofdemandthatisflexibleineachenduse.Thisshareistheproductofthreeflexibilityfactors,shiftability,controllabilityandacceptability(Olsen,2013):◼Shiftability:Shareoftheloadofeachend-usethatcanbeshed,shiftedorincreasedbyatypicaldemandresponsestrategy.◼Controllability:Shareoftheloadofeachendusewhichisassociatedwithequipmentthathasthenecessarycommunicationsandcontrolsinplacetotriggerandachieveloadsheds/shifts.Section3End-usesectors43IEA.CCBY4.0.◼Acceptability:Shareoftheloadforagivenend-usewhichisassociatedwithequipmentorserviceswheretheuseriswillingtoacceptthereducedlevelofserviceinademand-responseeventinexchangeforfinancialincentives.Thisframeworkenablesscenariostoconsiderdemandflexibilityfromvarioustechnologiesandatvaryinglevelsofsocialacceptability.Demand-responseisincludedinanhourlyelectricitymodel,thatjointlysimulatesthedispatchofgenerationassets,storage,interconnections,anddemandresponsetominimisetotaloperatingsystemcosts(seesection4.4foradescriptionofthehourlymodel).Demandresponseactivationconsiderstheflexibilitypotential(inGW),themaximumshiftingorsheddingduration(inhours)andtheflexibilityactivationcost.Figure3.12⊳IllustrativeloadcurvesbysectorforaweekdayinFebruaryintheEuropeanUnioncomparedtotheobservedloadcurvebyENTSO-Efor2014IndustryAgricultureandtransportGW0h8h16h24h0h8h16h24hIronandsteelChemicalsCementAgricultureRoadPaperAluminiumOtherindustryRailOthertransportResidentialServicesGW0h8h16h24h0h8h16h24hWaterheatingOtherAppliancesRefrigerationCleaningBrowngoodsCookingLightingSpaceheatingCoolingTotalGW0h4h8h12h16h20h24hIndustryENTSO-EAgricultureTransportServicesResidentialIEA.CCBY4.0.Note:EuropeanNetworkofTransmissionSystemOperatorsforElectricity(ENTSO-E)representstheaggregatedloadcurveforallcountriesintheEuropeanUnion.Sources:IEAanalysisbasedonENTSO-E,2016data.44InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection44ElectricitygenerationandheatproductionBasedonelectricitydemand,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⊳StructureofthepowergenerationmoduleElectricitydemand+losses+ownuseLoadcurvePlantCapacityFactorsInvestmentassumptionsRetirementsofexistingMeritorderAdditionsofnewplantsbyplantstechnologyExistingcapacitybyplantFuelandCO2pricesRefurbishments/RetrofittingEfficiencies/MothballingofexistingplantsEnd-userpricesWholesalepriceGenerationbyplantFuelconsumptionbyplantCO2emissionsbyplanttypeKeyresultsInvestmentneedsSection4ElectricitygenerationandheatproductionIEA.CCBY4.0.IEA.CCBY4.0.45Themodelbeginswithexistingcapacityineachregion,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,togetherwithestimatesfromtheNuclearEnergyAgency(NEA)andtheIEA(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.◼Combined-cyclegasturbine(CCGT)withandwithoutCCUS.◼Open-cyclegasturbine(OCGT).◼Integratedgasificationcombinedcycle(IGCC).◼Oilandgasinternalcombustion.◼Fuelcells.◼BioenergywithandwithoutCCUS.46InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATION◼Geothermal.◼Windonshoreandoffshore.◼Hydropower(conventional).◼Solarphotovoltaics.◼Concentratingsolarpower.◼Marine.◼Utility-scalebatterystorage.RegionalLRMCsarealsocalculatedfornuclearpowerbutadditionsofnuclearpowercapacityaresubjecttogovernmentpolicies.GenerationvolumesForeachregion,themodeldeterminesthegenerationfromeachplantbasedonthecapacityinstalled,themarginalcosttoproduceelectricityandthelevelofelectricitydemand.Demandisrepresentedasfoursegments:◼baseloaddemand,representingdemandwithadurationofmorethan5944hoursperyear◼low-midloaddemand,representingdemandwithadurationof3128to5944hoursperyear◼high-midloaddemand,representingdemandwithadurationof782to3128hoursperyear◼peakloaddemand,representingdemandwithadurationoflessthan782hoursperyear.Thisresultsinasimplifiedfour-segmentload-durationcurvefordemand(Figure4.2Figure4.2).Thisdemandmustbemetbytheavailablepowercapacityofeachregion,whichconsistsofvariablerenewables–technologieslikewindandsolarphotovoltaics(PV)withoutstoragewhoseoutputisdrivenbyweather–anddispatchableplants(generationtechnologiesthatcanbemadetogenerateatanytimeexceptincasesoftechnicalmalfunction).Toaccountfortheeffectofvariablerenewablesonwholesaleprices,themodelcalculatestheprobablecontributionofvariablerenewablesineachsegmentofthesimplifiedload-durationcurve.Subtractingthecontributionofrenewablesfromeachsegmentinthemeritorderleavesaresidualload-durationcurvethatmustbemetbydispatchablegenerators.Figure4.2⊳LoaddurationcurveshowingthefourdemandsegmentsLoad(GW)PeakMid1Mid2Basetpeaktmid1tmid2tbase8760Time(sorted)Section4ElectricitygenerationandheatproductionIEA.CCBY4.0.IEA.CCBY4.0.47Themodelsubtractsfromthedemandineachsegmentanygenerationcomingfromplantsthatmustrun–suchassomecombinedheatandpower(CHP)plantsanddesalinationplants–andalsogenerationfromrenewables.Forgenerationfromvariablerenewables,theamountofgenerationineachdemandsegmentisestimatedbasedonthehistoricalcorrelationbetweengenerationanddemand.Theremainderofthedemandineachsegmentmustbemetbyproductionfromdispatchableplants.Themodeldeterminesthemixofdispatchablegenerationbyconstructingameritorderoftheplantsinstalled–thecumulativeinstalledgenerationcapacityarrangedinorderoftheirvariablegenerationcosts–andfindingthepointinthemeritorderthatcorrespondstothelevelofdemandineachsegment.Asaresult,plantswithlowvariablegenerationcosts–suchasnuclearandlignite-burningplantsintheexampleofFigure4.3–willtendtooperateforahighnumberofhourseachyearbecauseevenbaseloaddemandishigherthantheirpositioninthemeritorder.Ontheotherhand,someplantswithhighvariablecosts,suchasoil-firedplants,willoperateonlyduringthepeakdemandsegment.Figure4.3⊳Examplemeritorderanditsintersectionwithdemandinthepowergenerationmodule200$/MWh150IEA.CCBY4.0.100OilSteam50GasandGTGTGasCCGT0NuclearCHPLigniteandsteamcoalLow-midloadHigh-midloadPeakMWhBaseloaddemanddemanddemanddemandIEA.CCBY4.0.Notes:CCGT=Combined-cyclegasturbine;CHP=combinedheatandpower;GT=gasturbine.Demandheremeansdemandnetofgenerationby“mustrun”plantssuchasdesalinationandsomeCHPplants,andnetofgenerationbyrenewables.CalculationofthecapacitycreditandcapacityfactorofvariablerenewablesPowergenerationfromweather-dependentrenewablessuchaswindandsolarpowervariesovertimeandthecharacteristicsofthepowersupplyfromvariablerenewableshavetobetakenintoaccountforthedecisionsondispatchandcapacityadditionsoftheremaining,mostlydispatchablepowerplants.Theeffectofallvariablerenewables(solarPV,concentratingsolarpower[CSP]withoutstorageandwindon-andoffshore)istakenintoaccountviathecapacitycreditandthecapacityfactorineachloadsegment.Thecapacitycreditofvariablerenewablesreflectstheproportionoftheirinstalledcapacitythatcanreliablybeexpectedtobegeneratingatthetimeofhighdemandineachsegment.Itdeterminesbyhowmuchnon-variablecapacityisneededineachloadsegment.Thecapacityfactorgivestheamountofenergyproducedbyvariablerenewablesineachloadsegmentanddetermineshowmuchnon-variablegenerationisneededineachsegment.Bothcapacitycreditandcapacityfactorarecalculatedbasedthecomparisonbetweenthehourlyloadprofileandthewindandsolarsupplytime-series,derivedfrommeteorologicaldata.Toquantifytheeffectsofvariablerenewables,thehourlyloadprofileiscomparedtothehourlyresidualload,beingtheelectricityloadafter48InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONaccountingforpowergenerationfromvariablerenewables(Figure4.4).Bysortingtheresidualload,thelevelsofaverageandmaximaldemandperloadsegmentcanbedetermined.Thedifferencebetweentheloadlevelsofthenormalloadandtheresidualloadgivestheimpactofvariablerenewablesonthepowergenerationandcapacityneeds.Figure4.4⊳Exampleelectricitydemandandresidualloada)Loadandresidualloadforselecteddaysb)Loadandresidualloaddurationcurveforoneyear1001009080706050403020100GW90Renewable12580energy497370generation9712160Load1451695019321740Residual24126530load2893132033710GW1010012001300140015001600170018001hourhours(sorted)IEA.CCBY4.0.Thecapacityfactorofvariablerenewables(varRE)perloadsegmentcanbecalculatedbasedongenerationperloadsegmentsoftheresidualload:𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑓𝑎𝑐𝑡𝑜𝑟𝑠=𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑁𝑒𝑒𝑑𝑠𝑛𝑜𝑛−𝑣𝑎𝑟,𝑠GenerationvarREs=𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸Forcapacityadditions,thepeakloadsegmentisrelevant.Thecapacitycreditisestimatedbasedonthedifferencebetweenmaximalloadandmaximalresidualload:𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑐𝑟𝑒𝑑𝑖𝑡𝑝𝑒𝑎𝑘=𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑁𝑒𝑒𝑑𝑠𝑛𝑜𝑛−𝑣𝑎𝑟maxt(𝐿𝑜𝑎𝑑(𝑡))−maxt(𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝐿𝑜𝑎𝑑(𝑡))=𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑟𝑅𝐸Meteorologicaldata(windspeedandsolarirradiation)forseveralyearswasusedforthecapacitycreditcalculation.Inaggregatingtheresultsofcapacitycreditobtainedfromdifferentyearsofmeteorologicaldata,asfirstorderapproachitwasassumedthattheannualpeakresidualdemandisnormally-distributedandcalculatedthecapacitycreditbasedonthedifferencebetweenpeakdemandandthepointonestandarddeviationabovetheresidualpeakdemand(Figure4.5).Themeteorologicaldatastemfromthefollowingre-analysisdatasets:◼WorldWindAtlas(Sander+PartnerGmbH):Globaldatasetofhourlywindspeedsat10-metreheight,1979-2009,derivedfromreanalysisdatabasedonclimatemodelling(Suraniana,2010)◼Windsupplytime-seriesforthewesternandeasternUnitedStatesasderivedbyWWITS(2010)andEWITS(2011).◼Windandsolarsupplytime-seriesforEurope-27asprovidedbySiemensAG(Heide,2010)foreachmajorRegioninEurope.OriginalmeteorologicalwindspeedstemsfromReanalysisdata(WEPROG,2010).◼HourlysolarirradiationdatafromsatelliteobservationsfortheUnitedStates(NREL,2010).◼Estimationofsolarirradiationbasedonsolarheight(Aboumahboub,2010).Section4Electricitygenerationandheatproduction49IEA.CCBY4.0.Figure4.5⊳ExemplaryelectricitydemandandresidualloadpCapacitycreditcalculationbasedonthisdifferenceProbabilitydensityfunctionofresidualdemand-σExpectedpeakPeakGWresidualdemanddemandIEA.CCBY4.0.4.2Value-adjustedLevelisedCostofElectricityMajorcontributorstotheLevelisedCostofElectricity(LCOE)includeovernightcapitalcosts;capacityfactorthatdescribestheaverageoutputovertheyearrelativetothemaximumratedcapacity(typicalvaluesprovided);thecostoffuelinputs;plusoperationandmaintenance.Economiclifetimeassumptionsare25yearsforsolarPV,onshoreandoffshorewind.Foralltechnologies,astandardweightedaveragecostofcapitalwasassumed(7-8%basedonthestageofeconomicdevelopment,inrealterms).Thevalue-adjustedLCOE(VALCOE)isametricforcompetitivenessforpowergenerationtechnologies,buildingonthecapabilitiesoftheGECModelhourlypowersupplymodel.ItisintendedtocomplementtheLCOE,whichonlycapturesrelevantinformationoncostsanddoesnotreflectthedifferingvaluepropositionsoftechnologies.WhileLCOEhastheadvantageofcompressingallthedirecttechnologycostsintoasinglemetricwhichiseasytounderstand,itneverthelesshassignificantshortcomings:itlacksrepresentationofvalueorindirectcoststothesystem,anditisparticularlypoorforcomparingtechnologiesthatoperatedifferently(e.g.variablerenewablesanddispatchabletechnologies).VALCOEenablescomparisonsthattakeaccountofbothcostandvaluetobemadebetweenvariablerenewablesanddispatchablethermaltechnologies.TheVALCOEbuildsonthefoundationoftheaverageLCOE(orLRMC)bytechnology,addingthreeelementsofvalue:energy,capacityandflexibility.Foreachtechnology,theestimatedvalueelementsarecomparedagainstthesystemaveragetocalculatetheadjustment(eitherupordown)totheLCOE.Afteradjustmentsareappliedtoalltechnologies,theVALCOEthenprovidesabasisforevaluatingcompetitiveness,withthetechnologythathasthelowestnumberbeingthemostcompetitive(Figure4.6).TheVALCOEisapplicableinallsystems,asenergy,capacityandflexibilityservicesareprovidedandnecessaryinallsystems,eventhoughtheymaynotberemuneratedindividually.Inthisway,ittakestheperspectiveofpolicymakersandplanners.Itdoesnotnecessarilyrepresenttheperspectiveofinvestors,whowouldconsideronlyavailablerevenuestreams,whichmayalsoincludesubsidiesandothersupportmeasures,suchasspecialtaxprovisions,thatarenotincludedintheVALCOE.Theimpactofthevalueadjustmentvariesbytechnologydependingonoperatingpatternsandsystem-specificconditions.Dispatchabletechnologiesthatoperateonlyduringpeaktimeshavehighcostspermegawatt-hour(MWh),butalsorelativelyhighvalueperMWh.Forbaseloadtechnologies,valuetendstobeclosetothesystemaverageandthereforetheyhaveasmallvalueadjustment.Forvariablerenewables,thevalueadjustment50InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONdependsmainlyontheresourceandproductionprofile,thealignmentwiththeshapeofelectricitydemandandtheshareofvariablerenewablesalreadyinthesystem.DifferentoperationalpatternscanbeaccountedforintheVALCOE,improvingcomparisonsacrossdispatchabletechnologies.Figure4.6⊳MovingbeyondtheLevelisedCostofElectricitytothevalue-adjustedLevelisedCostofElectricityIEA.CCBY4.0.Note:LCOE=LevelisedCostofElectricity.TheVALCOEiscomposedofLCOEandenergy,capacityaswellasflexibilityvalue.Itscalculationgoesasfollows:𝑉𝑎𝑙𝑢𝑒𝑎𝑑𝑗𝑢𝑠𝑡𝑚𝑒𝑛𝑡𝑠𝐸⏞𝑛𝑒𝑟𝑔𝑦𝑣𝑎𝑙𝑢𝑒𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑉𝐴𝐿𝐶𝑂𝐸𝑥=𝐿𝐶𝑂𝐸𝑥+[⏞𝐸̅−𝐸𝑥]+[⏞𝐶̅−𝐶𝑥]+[⏞𝐹̅−𝐹𝑥]Theadjustmentforenergyvalue[𝐸𝑥]ofatechnologyx(orgenerationunit)isthedifferencebetweentheindividualunittothesystemaverageunit[𝐸̅].[𝐸𝑥]iscalculatedasfollows:$∑8ℎ760[𝑊ℎ𝑜𝑙𝑒𝑠𝑎𝑙𝑒𝑃𝑟𝑖𝑐𝑒ℎ(𝑀𝑊$ℎ)×𝑂𝑢𝑡𝑝𝑢𝑡𝑥,ℎ(𝑀𝑊)]𝐸𝑛𝑒𝑟𝑔𝑦𝑣𝑎𝑙𝑢𝑒𝑥(𝑀𝑊ℎ)=∑8760𝑂𝑢𝑡𝑝𝑢𝑡(𝑀𝑊)ℎ𝑥,ℎWholesaleelectricitypricesandoutputvolumesforeachtechnologyxineachhourhoftheyeararesimulated.Wholesalepricesarebasedonthemarginalcostofgenerationonlyanddonotincludeanyscarcitypricingorothercostadders,suchasoperatingreservesdemandcurvespresentinUSmarkets.HourlymodelsareappliedfortheUnitedStates,EuropeanUnion,ChinaandIndia.Forotherregions,wholesalepricesandoutputvolumesaresimulatedforthefoursegmentsoftheyearpresentedinSection4.1.2.Theadjustmentforcapacityvalue[𝐶𝑥]ofagenerationunitiscalculatedasfollows:$𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑐𝑟𝑒𝑑𝑖𝑡𝑥×𝐵𝑎𝑠𝑖𝑠𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒($/𝑘𝑊)𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑥(𝑀𝑊ℎ)=(𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑓𝑎𝑐𝑡𝑜𝑟𝑥×ℎ𝑜𝑢𝑟𝑠𝑖𝑛𝑦𝑒𝑎𝑟/1000)Thecapacitycreditreflectsthecontributiontosystemadequacyanditisdifferentiatedfordispatchableversusrenewabletechnologies:◼dispatchablepowerplants=(1-unplannedoutageratebytechnology)◼renewables=analysisoftechnology-specificvaluesbyregionwithhourlymodelling.Section4Electricitygenerationandheatproduction51IEA.CCBY4.0.Thebasiscapacityvalueisdeterminedbasedonsimulationofcapacitymarket,setbythehighest“bid”forcapacitypayment.Positivebidsreflectthepaymentneededtofillthegapbetweentotalgenerationcosts(includingcapitalrecovery)andavailablerevenue.Thecapacityfactorisdifferentiatedbytechnology:◼dispatchablepowerplants=modelledassimulatedoperationsinpreviousyear◼windandsolarPV=alignedwithlatestperformancedatafromIRENAandothersources,improvingovertimeduetotechnologyimprovements◼hydropowerandotherrenewables=alignedwithlatestperformancedatabyregionandlong-termregionalaverages.Theflexibilityvalue[𝐹𝑥]ofagenerationunitiscalculatedasfollows:$𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟𝑥×𝐵𝑎𝑠𝑒𝑓𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒(𝑘$𝑊)𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑣𝑎𝑙𝑢𝑒𝑥(𝑀𝑊ℎ)=(𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑓𝑎𝑐𝑡𝑜𝑟𝑥×ℎ𝑜𝑢𝑟𝑠𝑖𝑛𝑦𝑒𝑎𝑟/1000)◼TheFlexibilityvaluemultiplierbytechnologyisbasedonavailablemarketdataandheldconstantovertime.Targetedchangesintheoperationsofpowerplantstoincreaseflexibilityvaluearenotrepresented.◼TheBaseflexibilityvalueisafunctionoftheannualshareofvariablerenewablesingeneration,informedbyavailablemarketdataintheEuropeanUnionandUnitedStates.TheflexibilityvalueisassumedtoincreasewithrisingVREshares,uptoamaximumequaltothefullfixedcapitalrecoverycostsofapeakingplant.AdvantagesandlimitationsoftheValue-adjustedLevelisedCostofElectricityVALCOEhasseveraladvantagesovertheLCOEalone:◼Itprovidesamoresophisticatedmetricofcompetitivenessincorporatingtechnology-specificinformationandsystem-specificcharacteristics.◼Itreflectsinformation/estimationsofvalueprovidedtothesystembyeachtechnology(energy,capacity/adequacyandflexibility).◼Itprovidesarobustmetricofcompetitivenessacrosstechnologieswithdifferentoperationalcharacteristics(e.g.baseloadtopeaking,ordispatchabletovariable).◼ItprovidesarobustmetricofcompetitivenesswithrisingsharesofwindandsolarPV.However,networkintegrationcostsarenotincluded,norareenvironmentalexternalitiesunlessexplicitlypricedinthemarkets.Fueldiversityconcerns,acriticalelementofelectricitysecurity,arealsonotreflectedintheVALCOE.TheVALCOEapproachhassomeparallelselsewhere,inotherapproachesusedforlong-termenergyanalysis,aswellsomereal-worldapplications.TheVALCOEismostcloselyrelatedtotheSystemLCOE,whichprovidesacomprehensivetheoreticalframeworkforassessingsystemvaluebeyondtheLCOE(Ueckerdt,etal.,2013).TheVALCOEandSystemLCOEaresimilarinscope,andre-arrangingtermscanalignsignificantportionsofthecomputations.Optimisationmodelsimplicitlyrepresentthecostandvalueoftechnologiesthroughstandardprofitabilitymetrics,suchasnetpresentvalueandinternalratesofreturn,butmaybelimitedbythescopeofcostsincluded,suchasthoserelatedtoancillaryservices.Otherlong-termenergymodellingframeworkshaveincorporatedcostandvaluemetricsincapacityexpansiondecisions,suchastheLevelisedAvoidedCostofElectricitybuiltintotheNationalEnergyModellingSystemusedbytheUSEnergyInformationAdministration.Inpolicyapplications,inthe2017cleanenergyauctionschemesinMexico,averageenergyvaluesforprospectiveprojectshavebeensimulatedandusedtoadjustthebidprices,seekingtoidentifythemostcost-effectiveprojects.Ascleanenergytransitionsprogressaroundtheworld,experiencewithhighersharesofwindandsolarPVinlargesystemswillincreaseandprovideopportunitiestorefinetheVALCOEandothermetricsofcompetitiveness.52InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONFinancingcostsforutility-scalesolarPVThedecliningcostsofsolarPVhavebeenimpressive,withinnovationdrivingdownconstructioncostsby80%from2010to2019(IRENA,2020).Costreductionshavebeencomplementedbyimprovedperformanceresultingfromhigherefficiencypanelsandgreateruseoftrackingequipment.Financingcosts,however,havereceivedlittleattention,despitetheirimportance.Theweightedaveragecostofcapital(WACC)canaccountforuntilhalfoftheLCOEofutility-scalesolarPVprojects.WEO-2020focusedonfinancingcostthroughextensiveworkbasedondatafromfinancialmarketsandacademicliterature,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.ThefindingsofthisanalysisontheprevailingaveragecostsofcapitalinmajorsolarPVmarketsunderpintheprojectionsintheGECModel.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,itaddsmillionsofkilometres(km)ofnewlinesandcableseachyearthatmustbeaccountedforintermsofinvestmentsandmaterialdemand,aswellasforprojectplanning.Section4Electricitygenerationandheatproduction53IEA.CCBY4.0.LinelengthexpansionduetoelectricitydemandgrowthNetworkexpansionincreasesalongsidegrowthinelectricitydemand.Inordertorepresentthis,adynamicrelationshipbetweennetworkexpansionperunitofdemandgrowthwascreatedthatrelatestoGDPpercapita.Inthis,thekmoflinelengthperterawatt-hour(TWh)ofdemandforeachregionisusedinconjunctionwiththeGDPpercapitaforthegivenregion,inordertoproduceanequationthatrepresentsthisglobal-levelrelationship.Asthenetworkgrowthratesdifferbetweenthedistributionandtransmissionlevel,thisrelationshipwasdoneforeach,yieldingtwosetsofalphaandbetaparametersthatcanbeusedaccordingly.Figure4.7⊳ElectricitynetworkexpansionperunitofelectricitydemandgrowthbyGDPpercapitaDistributionTransmission700kmperTWh120006001000050040080003006000200400010020000102030405060708001020304050607080GDPpercapitaIEA.CCBY4.0.LinelengthexpansionduetorenewablesAconsiderableamountofthecapacityadditionsprojectedoverthemodellinghorizonisfromrenewables.Thelocationofthesetechnologiesisoftenstronglyinfluencedbythelocationoftheunderlyingresource(e.g.areaswherethewindisstrongorinsolationishigh),whichmaynotbeclosetoexistingcentresofdemand.Inaddition,someofthesetechnologies,mainlysolarPV,areconnectedattheend-usersideofthegridinfrastructure.Thismodulardeploymentofgenerationcapacitycanleadtoincreaseddistributioncapacityneeds.Becausetheintroductionoflargequantitiesofremoteorvariablerenewableswasnotamarkedfeatureofthehistoricdevelopmentofelectricitynetworks(exceptforregionswhereremotehydroelectricityrepresentsalargeproportionofthegenerationmix),theadditionofmorerenewablesislikelytoincreasetheaveragelengthofnetworkadditions.Lineexpansionisbeingdrivenbytwofactors:thetransmissionlinesthatconnectsolarandwindfarmstothegrid,andreinforcementrequirementswithinthegrid.Afactorfortheaverageconnectinglinelengthwasderivedfromtheaveragelinelengthconnectingpastutility-scalesolarPV,windonshoreandwindoffshoreprojects.Theaddedcapacityfromtheserenewableenergytechnologiesismultipliedbythehistoricalrelationshiptoobtainrelatedlineextensions.Thegridreinforcementsarebasedonastudyconductedincountrieswithhighrenewableenergydevelopment.Uptoathresholdoftheshareofrenewablegeneration,thereisnoneedforgridimprovements.Anincreaseinthesharebeyondthisthresholdleadstoadditionallengthstoreinforcethegrid,basedontheliteratureandprojectedsharesofrenewablesbyregionandscenario.54InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTheestimationofdistributiongridextensionsforrenewablescontainsmoreuncertaintiesthanthetransmissiongrid,aslessdataorstudiesareavailableonthetechnicallycomplexdistributionnetworkandownuseofdistributedgenerationcaninturnleadtoareducedneedfordistributiongridinfrastructure.Therefore,weassume,thatadditionalnetworkinvestmentisrequiredonlyiftheelectricitygeneratedfromdistributedgeneration,suchassolarPVinbuildings,exceedslocaldemandandisfedbacktothesystem.TransformercapacityexpansionduetoelectricitydemandandsupplygrowthTransformercapacityisbasedonhistoricaldataassociatedwiththepowergenerationcapacityineachGECregion.Forthecalculationoftransformercapacitygrowth,newpowercapacityistakenintoconsiderationwhereasallsmall-scalesolarPVisdeductedasitisassumedthatmostoutputwillbeusedlocally.AnotherportionofsolarPVwillconnectedtothedistributionnetworkandisaccountedfor,andpartofadditionalbatterystoragecapacityisdeductedasitwillreducetheflowofpowerthroughthegridandthereforetheneedfortransformercapacity.TransformerreplacementduetoageingAssuminganaveragelifetimeof40yearsfortransformers,annualreplacementsarecalculatedaccordingly.Whilethisdoesnotincreasetheoverallcapacityinthenetwork,itaddstransformercapacityeachyearthatmustbeaccountedforintermsofinvestmentandmaterialdemand.ElectricitynetworkinvestmentInvestmentsforelectricitynetworksarecomposedofthoserelatedtothethreemaindriversoflinelengthexpansion;increasingdemand,replacementsandincreaseinrenewables.Inaddition,theyalsoincludeinvestmentsduetonon-line-lengthcomponentssuchasgridformingrequirementsandtransmissionlevelreinforcement.Forthelinelengthcomponentsoftheinvestment,whichcomprisethemajorityofoverallnetworkinvestments,themodelcalculatesthisasthenetworkexpansioninkmduetoagivendrivermultipliedbytheunitcostforeachlineandcabletype.Gridformingrequirementsarealsoincorporatedintotheelectricitynetworkrepresentation,relatedtotheextentoftheshifttovariablerenewablesintheprojections.Variablerenewableslackmechanicalinertiaastheyconnecttothenetworkviaaconverter.Inertiacomesfromthelargerotatingmassesinthegeneratorsinpowerplantsandisnecessarytokeepthenetworkstabilised,especiallyincaseoffaultevents.WiththerisingshareofvariablerenewablesthenetworkneedsgridformingstabilisingtechnologyfromtheFlexibleACTransmissionSystems(FACTs)family.Thecalculationforthisinvestmentisbasedondeploymentneedsrealisedincountrieswithahighshareofrenewables.Theinvestmentisdrivenbytheexpansionofrenewablesgenerationaboveaminimumlevel,andincreasesbasedonassessedneedsineachregion.Belowtheminimumlevel,thegridremainsstablewithoutadditionalmeasures.Eachoftheelectricitynetworkequipmentunitcostshavebeencreatedusinganaverageofprojectandnationallevelcosts,collectedfrompublicationsthatdetailcostsperkmbasedoncorrespondinglineandcabletype.Theyrepresentcostsfromseveralregionsglobally,allowingforabalancedviewofregion-specificcosts.Thesecostsarethentailoredfurtherperregion,creatingaseriesof20differentcostsperkmforeachregion.Similarly,replacementcostsarealsolineandregionspecific.Foralltypesandregions,replacementcostsarelowerthanthatofnewlines,aspermitting,land,andmanyofthecapitalcostsdonotneedtoberedone.However,region-specificdiscountersareusedtodifferentiatebetweenmaterialuseperregionaswellaslabourcostsperregion,twofactorsthatcangreatlyinfluencecostsperkm.Bringingtogetherallofthesecostsanddriversfornetworkexpansion,themodelcalculatesoverallnetworkinvestmentwiththefollowingequationforeachofthe20lineandcabletypes:Section4Electricitygenerationandheatproduction55IEA.CCBY4.0.𝐴𝑛𝑛𝑢𝑎𝑙𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑏𝑦𝑟𝑒𝑔𝑖𝑜𝑛=∑[𝑐𝑜𝑠𝑡𝑛𝑒𝑤𝑙𝑖𝑛𝑒𝑠𝑉,𝑃,𝐶𝑉,𝑃,𝐶∆𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝑑𝑒𝑚𝑎𝑛𝑑∗(𝛼∗ln(𝐺𝐷𝑃𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎)+𝛽)∗𝑔𝑟𝑖𝑑𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑉,𝑃,𝐶∗+∑(𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑅∗𝛾𝑉,𝑃,𝐶)𝑅(+∆𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠∗𝜓∗𝑡𝑜𝑡𝑎𝑙𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑙𝑒𝑛𝑔𝑡ℎ𝑉,𝑃,𝐶)+𝑐𝑜𝑠𝑡𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶∗𝑙𝑖𝑛𝑒𝑠𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶]+𝑐𝑜𝑠𝑡𝑆𝑇𝐴𝑇𝐶𝑂𝑀∗𝜙∗∆𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑∗𝑡𝑜𝑡𝑎𝑙𝑝𝑜𝑤𝑒𝑟𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝐴𝑛𝑛𝑢𝑎𝑙𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑏𝑦𝑟𝑒𝑔𝑖𝑜𝑛=∆𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝑑𝑒𝑚𝑎𝑛𝑑∗(𝛼∗ln(𝐺𝐷𝑃𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎)+𝛽)∗𝑔𝑟𝑖𝑑𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑉,𝑃,𝐶∑[𝑐𝑜𝑠𝑡𝑛𝑒𝑤𝑙𝑖𝑛𝑒𝑠𝑉,𝑃,𝐶∗(+∑(𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑅∗𝛾𝑉,𝑃,𝐶))𝑉,𝑃,𝐶𝑅+𝑐𝑜𝑠𝑡𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶∗𝑙𝑖𝑛𝑒𝑠𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶]Where:◼𝑉isvoltagelevelband◼𝑃isposition(overhead,underground)◼𝐶iscurrent(ACorDC)◼𝑅istherenewableenergytechnology◼𝑔𝑟𝑖𝑑𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑉,𝑃,𝐶isthehistoricalsharesofthegridbyvoltage,position,andcurrent◼𝛼,𝛽aredimensionlessvariablesintheequationrelatingdemandgrowthtoGDPpercapita,derivedfromhistoricaldatabyregion◼𝛾istheadditionallinelengthsrequiredtoconnectnewrenewablescapacityadditions,measuredinkmperGW,byvoltage,positionandcurrent◼𝜓isthedimensionlessfactorofadditionaltransmissionnetworkrequirementsduetohighsharesofvariablerenewables,whereitexceedsaminimumthreshold◼∆𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝑑𝑒𝑚𝑎𝑛𝑑istheannualincreaseinelectricitydemandintheregion◼∆𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒𝑠istheannualincreaseinshareofvariablerenewablesintotalinstalledcapacity◼𝑙𝑖𝑛𝑒𝑠𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑉,𝑃,𝐶arethelinestobereplaced,inkm,definedasthosereaching40yearsofuse◼𝑐𝑜𝑠𝑡𝑆𝑇𝐴𝑇𝐶𝑂𝑀isthecostofSTATCOMdevices(staticsynchronouscompensators).◼𝜙isthedimensionlessfactorofadditionalgridformingrequirementsduetohighsharesofvariablerenewables,giventhattheshareofrenewablesexceedsaminimumthreshold.ThecurrentannualexpendituresofboththeDistributionSystemOperator(DSO)andTransmissionSystemOperator(TSO)undergoexaminationacrossvariousregions,andthesefiguresarelinkedtothecalculatedinvestment.Itisimportanttonotethattheinvestmentingridinfrastructuredoesnotadheretoaspendingmodelthatspreadstheinvestmentovertime,incontrasttotheapproachusedforcalculatinginvestmentsinpowergeneration.56InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATION4.4HourlymodelToquantifythescaleofthechallengearisingfromtheintegrationofhighsharesofVREandtoassesswhichmeasurescouldbeusedtominimisecurtailment,anhourlymodelwasdevelopedforWEO-2016,toprovidefurtherinsightsintotheoperationsofpowersystems.ThemodelbuildsupontheannualprojectionsgeneratedintheGECModelandmakesitpossibletoexploreemergingissuesinpowersystems,suchasthosethatariseastheshareofVREcontinuestorise.ThemodelthenfeedsthemainGECModelwithinformationaboutadditionalconstraintsontheoperationsofdifferentpowerplants.Themodelisaclassicalhourlydispatchmodel,representingallhoursintheyear,settingtheobjectiveofmeetingelectricitydemandineachhourofthedayforeachdayoftheyearatthelowestpossiblecost,whilerespectingoperationalconstraints.1All106powerplanttypesrecordedintheGECModelandtheirinstalledcapacitiesarerepresentedinthehourlymodel,includingexistingandnewfossil-fuelledpowerplants,nuclearplantsand16differentrenewableenergytechnologies.ThefleetofpowerplantsthatisavailableineachyearisdeterminedintheGECModelanddiffersbyscenario,dependingontheprevalentpolicyframework.Theseplantsarethenmadeavailabletothehourlymodelandaredispatched(orchosentooperate)onthebasisoftheshort-runmarginaloperatingcostsofeachplant(whicharemainlydeterminedbyfuelcostsasprojectedintheGECModel)totheextentrequiredtomeetdemand.Thedispatchoperatesunderconstraints:thereareminimumgenerationlevelstoensuretheflexibilityandstabilityofthepowersystemandtomeetotherneeds(suchascombinedheatandpower);thevariabilityofrenewableresources(suchaswindandsolar)determinestheavailabilityofvariablerenewablesand,hence,themaximumoutputatanypointintime;andrampingconstraintsapply,derivedfromthelevelofoutputintheprecedinghourandthecharacteristicsofdifferenttypesofpowerplants.Thehourlydispatchmodeldoesnotrepresentthetransmissionanddistributionsystem,norgridbottlenecks,cross-borderflowsortheflowofpowerthroughthegrid.ItthereforesimulatessystemsthatareabletoachievefullintegrationacrossbalancingareasineachGECModelregion(e.g.UnitedStates,EuropeanUnion,ChinaandIndia).KeyinputstothemodelincludedetailedaggregatehourlyproductionprofilesforwindpowerandsolarPVforeachregion,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.Themodelalsoprovideshourlyoperationinformationforeachplanttype,includingfuelcostsandassociatedGHGandpollutantemissions.1Themodelworksonanhourlygranularity,andthereforeallintra-hourvaluesofdifferentdevices(e.g.ofstoragetechnologies)arenotcaptured.2WindandsolarPVdataarefromRenewables.ninja(https://beta.renewables.ninja/)andUeckerdtet.al.(2016).Section4Electricitygenerationandheatproduction57IEA.CCBY4.0.Modellingseasonalvariabilityandlong-termstorageToassesstheimpactofweather-inducedvariabilityonpowersystemoperationsandlong-termflexibilityneedsinsystemscharacterisedbyrisingsharesofvariablerenewablesandtemperature-sensitiveend-usessuchaselectricheatingandcooling,anewhourlydispatchmodelwasdevelopedfortheWEO-2023.BuildingontheannualprojectionsoftheGECModel,itisappliedtoquantifypowersystemflexibilityneedsontimescalesrangingfromhoursoverdaysandweekstoseasonsandidentifyhowtheseneedscanbemetinacost-optimalmanner.Itrepresentsallhoursinayear,settingtheobjectiveofmeetingelectricitydemandineachhouroftheyearatthelowestpossiblecost,whilerespectingoperationalconstraints.ThemodelwasbuiltinPythonusingthePyPSAopen-sourcepythonenvironmentforenergysystemmodelling3andissolvedusinglinearoptimisation.Theoptimisationensuresthatpowerplants,energystoragetechnologies,demandresponseandelectrolysersareoperatedinawaythatminimisesthetotalsystemcost(thusmaximisingtheirutilitytothesystem).Productionprofilesforwind,solarPVandrun-of-riverhydro,aswellasinflowprofilesforreservoirhydroweregeneratedusingtheAtliteopen-sourcePythonlibrary,whichprovidesfunctionsthatconvertweatherdatasuchaswindspeeds,solarirradiance,temperatureandrunoffintohourlywindpower,solarpower,run-of-riverhydropower,hydroreservoirinflowandheatingdemandprofiles(Hoffmanetal.,2021).Toassessthepotentialvariabilityofweather-dependentrenewablesandtemperature-dependentdemandacrossyearsandcaptureextremeevents,weatherdatafor30historicalweatheryears4(1987-2016)wasobtainedfromtheERA5reanalysisdatasetofEuropeanCentreforMedium-RangeWeatherForecasts(ECMWF),whichcoverstheentireglobeat30-kmresolution.5Tomodelthelong-termimpactofweather-relatedvariabilityinsystemsdominatedbyrenewables,themodelincludesadetailedrepresentationofreservoirandpumpedstoragehydro,aswellastemperature-sensitivedemandanddemandresponse(seeSection3.4),hydrogenelectrolysersandhydrogenstorage.Hydroreservoirandpumpedstoragedispatchisconstrainedbywaterlevelsinthereservoir,withnaturalinflowsderivedbasedonrunoffsandhydrologicalbasinsforeachhydropowerplant.Tomodelthepossibleinteractionbetweentheelectricityandhydrogensystems,themodeloptimisestheoperationofgrid-connectedelectrolysers,hydrogenstorageandthermalpowerplantsusinghydrogen,whileconsideringhydrogenproductionfromoff-gridelectrolysersconnectedtodedicatedrenewablesaswellasdemandprofilesforotherusesofhydrogen.Toreflecttheimpactofconstraintsinthetransmissionsystem,themodelledGECregionsaredisaggregatedintoseveralnodesthatcanexchangeelectricitybetweeneachother,withtheexchangeslimitedbytheoverallcapacityofthetransmissionsystembetweeneachofthenodes.AssessingflexibilityneedsFlexibilitycanbedefinedastheabilityofapowersystemtoreliablyandcost-effectivelymanagethevariabilityanduncertaintyofsupplyanddemandacrossallrelevanttimescales.Flexibilityneedscanbeseenasthebalancingeffortrequiredtosmoothentheresidualloadoveragiventimescale(whichcouldthenbesatisfiedwithbaseloadcapacity).Toaccountforspecificflexibilityneedsofthesystemdependingonthetimescale,wedistinguishbetweenshort-termandseasonalflexibilityneedsintheWEO-2023.Short-termflexibilityneedsarecalculatedastheaveragehourlyramp(differenceintheresidualloadbetweenagivenhourandtheprevioushour)oftheresidualloadoverthetop-100hourswiththehighestupwardramps,dividedbytheaveragehourlyelectricitydemandfortheyear(electricitydemandinthiscasedoesnotincludebatterycharging,pumpedstoragepumpingornetexports).Seasonalflexibilityneedsareassessedbycomputingthedifferencebetweentheweeklyandannualaverageoftheresidualload,dividedbytheannualelectricitydemand.3https://pypsa.org/4Aweatheryearisasetofweatherparameterssuchastemperature,solarradiation,windspeedandprecipitationcompiledfromhistoricalrecordstocreatecurvesofhourlyloadsandrenewablesoutput.5https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v558InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONShort-termflexibilityneedsarecomputedasfollows:𝑅𝑎𝑚𝑝(𝑡)=𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝐿𝑜𝑎𝑑(𝑡)−𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝐿𝑜𝑎𝑑(𝑡−1ℎ)∑𝑡∈{𝑡1,𝑡2…𝑡100}𝑤𝑖𝑡ℎ𝑅𝑎𝑚𝑝(𝑡𝑖)≥𝑅𝑎𝑚𝑝(𝑡𝑖+1)𝑅𝑎𝑚𝑝(𝑡)100𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑁𝑒𝑒𝑑𝑠𝑦𝑒𝑎𝑟,𝑠ℎ𝑜𝑟𝑡−𝑡𝑒𝑟𝑚=𝐴𝑛𝑛𝑢𝑎𝑙𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝐷𝑒𝑚𝑎𝑛𝑑𝑦𝑒𝑎𝑟8760Seasonalflexibilityneedsarecomputedasfollows:𝐹𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑁𝑒𝑒𝑑𝑠𝑦𝑒𝑎𝑟,𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙=∑𝑡∈𝑦𝑒𝑎𝑟𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝐿𝑜𝑎𝑑𝑊𝑒𝑒𝑘𝑙𝑦𝐴𝑣𝑔(𝑡)−𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝐿𝑜𝑎𝑑𝐴𝑛𝑛𝑢𝑎𝑙𝐴𝑣𝑔(𝑡)𝐴𝑛𝑛𝑢𝑎𝑙𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦𝐷𝑒𝑚𝑎𝑛𝑑𝑦𝑒𝑎𝑟4.5Mini-andoff-gridpowersystemsSincetheAfricaEnergyOutlookin2014,therepresentationofmini-andoff-gridsystems,relatedtothosegainingaccesstoelectricity,hasbeenimprovedandbetterintegratedintotheGECModel.Inlinewiththeapproachforon-gridpowersystems,tomeetadditionalelectricitydemand,themodelchoosesbetweenavailabletechnologiesformini-andoff-gridsystemsbasedontheirregionallong-runmarginalcosts,andusingdetailedgeospatialmodellingtotakeintoaccountseveraldeterminingfactors.FortheAfricaEnergyOutlook2019,theIEArefineditsanalysisusingup-to-datetechnologycosts,demandprojections,andthelatestversionoftheOpenSourceSpatialElectrificationTool(OnSSET)6developedbytheKTHRoyalInstituteofTechnology,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.RenewableenergyTheprojectionsofrenewableelectricitygenerationarederivedintherenewablessub-module.Thedeploymentofrenewablesismodelledbasedonpolicytargets,technologycompetitivenessandresourcepotential,specifiedforeachtechnology(bioenergy,hydropower,solarPV,concentratingsolarpower,geothermalelectricity,wind,andmarine)ineachoftheGECModelregions.7Policytargetsareoftenforspecifictechnologies,forexample,over130countrieshavesupportpoliciesinplacetoexpandtheuseofsolarPVandwindasof2020.Otherpoliciesmayspecifythetotalcontributionofrenewableenergy,theshareofrenewablesintotalelectricitygeneration,6FormoredetailsontheOpenSourceSpatialElectrificationTool,seewww.onsset.org.ForthelatestOnSSETmethodologyupdate,refertoKorkovelos,A.etal.(2019).7Anumberofsub-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.Section4Electricitygenerationandheatproduction59IEA.CCBY4.0.orthelowemissionsshareofgenerationincludingrenewables.Incaseswherepoliciesspecifyabroadtargetthatincludesrenewables,technologycompetitivenessandresourcepotentialsdrivetherelativecontributions.TechnologycompetitivenessisbasedontheVALCOE(seesection4.2)andappliesequallytocomparisonsamongstrenewableenergytechnologiesandabroadersetoftechnologies.Resourcepotentialisconsideredonaregionalbasisforeachrenewableenergytechnology(seeBox4.1).Beyondthereachofpolicytargets,technologycompetitivenessandresourcepotentialsarethecriticalconsiderationsforrenewablesdeployment.Marketconstraints,includingadministrativeones,andtechnicalbarrierssuchasgridconstraints,whereapplicable,areconsidered,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,overallconstraintssuchastechnicalfeasibility,socialacceptance,planningrequirementsandindustrialgrowtharetakenintoconsideration.WindoffshoretechnicalpotentialIncollaborationwithImperialCollegeLondon,adetailedgeospatialanalysiswasundertakenforWEO-2019toassessthetechnicalpotentialforoffshorewindworldwide.Thestudywasamongthefirsttousethe“ERA-5”reanalysis,whichprovidesfourdecadesofhistoricglobalweatherdata.“Renewables.ninja”extrapolateswindspeedstothedesiredhubheightandconvertsthemtooutputusingmanufacturers’powercurvesforturbinemodels.ResultscanbefoundontheIEAwebsite.DataTheavailabilityofhigh-resolutionsatellitedataandcomputinggainshassignificantlyimprovedthegranularityandaccuracyofwindresourceassessmentsinrecentyears.Emergingwindturbinedesignsarealsocauseto60InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONupdatepotentialassessments,astheyincreaseperformanceinwell-establishedareasandmakelowerqualityresourcesmoresuitableforenergyproduction.ExclusionsCommerciallyavailableoffshorewindturbinesarecurrentlydesignedforwindspeedsofmorethan6m/s.Somecompaniesarealsolookingintoturbinedesignsforlowerwindspeeds.FollowingtheInternationalUnionforConservationofNature’s(IUCN)classificationofmaritimeprotectionareas,thosecategorisedasIa,Ib,IIandIIIwereexcludedfromthestudy(IUCN,2008).However,ateachprojectlevelotherenvironmentalconsiderationsmustalsobetakenintoaccountandafullenvironmentalimpactassessmentconductedasmandatedbypublicauthorities.Bufferzoneswerealsoexcludedforexistingsubmarinecables(within1km),majorshippinglanes(20km),earthquakefaultlines(20km)andcompetingusessuchasexistingoffshoreoilandgasinstallationsandfisheries.TurbinedesignsInordertoassesstheglobaltechnicalpotential,best-in-classturbineswerechosenwithspecificpowerof250,300and350wattpersquaremetre(W/m2)thatcorrespondstolow-medium,mediumandhighwindspeeds.Thepowercurvesoftheseturbineswereusedinconjunctionwiththeglobalcapacityfactorsofeach5kmby5kmcellselectedfortheanalysistoderivethetechnicalpotentialofoffshorewindintermsofcapacityandgeneration.Newpowercurvesweresynthesisedfornext-generationturbineswithratedcapacityofupto20MW,forwhichdataarenotyetavailable(Saint-Drenan,etal.,2020).Furthertothis,theanalysistakesintoaccountfurtherconsiderationssuchasoffshorewindfarmdesigns,distancefromshoreandwaterdepth,offshorewindcostdevelopmentsandthetechnicalpotential.4.7HydrogenandammoniainelectricitygenerationLow-carbonhydrogenandammoniaarefuelsthatcanprovidealowemissionsalternativetonaturalgas-andcoal-firedelectricitygeneration-eitherthroughco-firingorfullconversionoffacilities.IntheGECModel,blendinglevelsofhydrogeningas-firedplantsandammoniaincoal-firedplantsarespecifiedinlinewithpolicyandemissionstargets.Aspartofthescenarios,thesharesofhydrogenand/orammoniablendingincreaseovertime,representingbothadvancesinthecapabilitytoretrofitexistingfacilitiestoco-firehighersharesofhydrogenand/orammonia,andtheuptakeofnewdesignsthataredesignedtoblendhighersharesofhydrogenorammonia,orplantsthatarepurposelydesignedtorunentirelyonhydrogenorammonia.IncreasedlevelsofhydrogenandammoniablendingintheGECModelincuradditionalcapitalexpenditureduetotheneedformoreextensiveretrofittingofexistingnaturalgas-andcoal-firedpowerplants.Electricitysectordemandforhydrogenandammoniaisusedbythehydrogensupplymoduletoinformtheoveralldemandforhydrogenproduction.4.8Utility-scalebatterystorageUtility-scalebatterystorageintheGECModelprovidesanimportantsourceofpowersystemflexibility,particularlywhenflexibilityneedsincreaseduetoevolvingelectricitydemandpatternsandrisingsharesofvariablerenewables.Inthehourlymodel,charginganddischargingpatternsforutility-scalebatteriesareoptimisedbasedonpricearbitrageopportunities(i.e.chargingwhenpricesarelowanddischargingwhenpricesarehigh).Utility-scalebatterystoragevolumesrangefromonetoeighthoursinduration(referringtothenumberofhoursafullbatterytakestodischargefullyatmaximumoutput).Batteriesoperateonlywhenthedifferencebetweenthepricereceivedfordischargingandpricepaidforchargingwithina24-hourperiodisgreaterthanathreshold,whichissetbasedonfactorssuchasupfrontcapitalcosts,theexpectednumberofSection4Electricitygenerationandheatproduction61IEA.CCBY4.0.cyclesoverthebattery’slifetimeandround-tripefficiency.Similartootherelectricitysectortechnologies,batteryinvestmentdecisionsarebasedontheVALCOE,withbatteriesassumedtohavedifferentlevelsofcapacitycreditdependingontheirduration–contributingtosystemadequacyandflexibility.Utility-scalebatterystoragecaneitherbestand-aloneorpaireddirectlywithpowerplants,suchaswindandsolarPV.Utility-scalebatterystoragecapitalcostsareprojectedtodeclineovertime.Thedegreeoftechnologycostreductionsiscalculatedbasedonlearningratesfromexistingliterature,appliedtothebatterypackandtoauxiliarycomponentssuchasinverters,aswellasotheroverheadcosts.8Forbatterypacks,projectedcostsaredrivennotonlybythedeploymentofutility-scalebatteriesinthepowersector,butthedemandforbatteriesacrossallsectors,withbyfarthelargestvolumesusedinelectricvehicles.Fortheothercomponentsofutility-scalebatteries,thedeclineincostisestimatedbasedonthecumulativecapacityadditionsofutility-scalebatteryenergystoragesystemsthemselves.8BasedonSchmidtetal.(2017)andTsiropoulosetal.(2018)62InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection55Otherenergytransformation5.1OilrefiningandtradeTherefineryandtrademodulelinksoilsupplyanddemand.Itisasimulationmodel,withcapacitydevelopmentandutilisationmodelledfor134individualcountries,withtheremainingcountriesgroupedinto11regions.Thismodulehasseveralauxiliariesthatstretchintosupplyanddemanddomainstobetterlinkboth:◼Naturalgasliquidsmoduletodeterminethesupplyoflightoilproductsaswellascondensate.◼Extra-heavyoilandbitumenmoduletomodelsyntheticcrudeoiloutputanddiluentrequirementsforbitumen.◼Splitofoildemandintodifferentproductcategoriesforallsectorsexceptroadtransportandaviation.ThelatterareprovidedbyGECModel’stransportdemandmodel.Projectionsforrefiningsectoractivityarebasedprimarilyonrefiningcapacityandutilisationrates.Refiningcapacityconsistsofcrudedistillationunits(CDU)andcondensatesplitters.Refiningcapacityisbasedon2022datafromtheIEA.Capacityexpansionprojectsthatarecurrentlyannouncedareassessedindividuallytoidentifyonlytheprojectsthatarelikelytogoahead.Someofthesearedelayedfromtheirannouncedstart-updatestoallowforamorerealistictimeline.Themodelalsotakesintoaccountrefineryclosuresthathavebeenannounced.Beyond2026,newcapacityexpansionisprojectedbasedoncrudeavailabilityandproductdemandprospectsforeachoftheregionsspecifiedbelow.Capacityatriskisdefinedasthedifferencebetweenrefinerycapacityandrefineryruns,withthelatterincludinga14%allowancefordowntime.Projectedshutdownsbeyondthosepubliclyannouncedarealsocountedascapacityatrisk.Figure5.1⊳SchematicofrefiningandinternationaltrademoduleRegionARegionBregionalproductsregionaldemanddemandrefiningrefiningcapacitycapacitycrudeoilcrudeoiloutputoutputproductsproductsCrudeoilmarketregionalproductsregionaldemanddemandrefiningrefiningcapacitycapacitycrudeoilcrudeoiloutputoutputRegionCRegionDIEA.CCBY4.0.Utilisationratesaredeterminedbydomesticdemand,productyieldsandrefineryconfiguration(e.g.complexity).Amongoil-importingregions,prioritycalloninternationalsupplyofcrudeoilisgiventothosewheredemandisgrowing:robustdomesticdemandiseffectivelyaproxyforrefinerymarginsthatarenotexplicitlycalculatedorusedbythemodel.Section5Otherenergytransformation63IEA.CCBY4.0.OilsupplyanddemandprojectionsareprovidedbytheGECModel’sfossil-fuelsupplyandfinalenergyconsumptionmodules.Refineriesdonotprovidefor100%ofoilproductdemand.Forthepurposesofthisanalysis,weshowthenetcallonrefineriesaftertheremovalofbiofuels,LPG,ethaneandlightnaphthafromnaturalgasliquids(NGLs),syntheticliquidsfromcoal-to-liquids(CTL)andgas-to-liquids(GTL)andadditives.Thesupply-sidenomenclaturefortherefiningmodelisslightlydifferentfromtheoilsupplymodel.Theterm“crudeoil”usedinthemodeldescribesallcrudeoilsthathaveconventional-typequalityforprocessingpurposes.Thisincludesconventionalcrudeoilfromthesupplymodel,someextraheavyoilsthatarenotdilutedorupgraded,tightoil,andsyntheticcrudefrombitumenupgradingprocesses.Dilutedbitumenandcondensatearerepresentedasseparatestreamsforintakeandtrademodellingpurposes.Yields,outputandtradearedefinedforthefollowingproductcategories:ethane,LPG,naphtha,gasoline,kerosene,diesel,heavyfueloilandotherproducts(whichincludepetroleumcoke,refinerygas,asphalt,solvents,wax,etc).CrudeoiltradepositionandrefinedproductstradebalancesfollowtheGECModel’sdemandmodelgranularityof29individualcountriesorregions(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-Tropschprocess,forinstance,turnscoalandnaturalgasintosyntheticfuelsthroughaseriesofchemicalreactions.Tothatend,anessentialfirststepinthisprocessistotransformcoalandnaturalgasintosyntheticgas(alsocalledsyngas).Syngasisamixtureofcarbonmonoxideandhydrogenobtainedbycoalgasificationandthedryreformingofmethane.SyngascanalsobeusedtoproducemethanethroughtheSabatierreactionandisthereforeameansofconvertingcoalintogas.Countrieswithlargecoalornaturalgasresources(e.g.China)typicallyresorttoCTL,GTLand/orCTGtobolstertheirenergysecurityandsovereignty.However,becausethesetechnologiesarecapital-intensive,low-costcoalornaturalgasisessentialtomakethefinalproductscompetitive.Forthisreason,thefewexistingandplannedprojectsremainconcentratedinahandfulofcountries.IntheGECModel,projectionsareconsistentwiththestatusoftheprojects(e.g.underconstructionorplanned)andareupdatedeveryyearonaproject-by-projectbasis.Energy-relatedCO2emissionsareaccountedforandtechnologiescanbefittedwithcarboncapture,utilisationandstorage(CCUS).5.3HydrogenproductionandsupplyHydrogenintoday’senergysystemispredominantlyusedasafeedstockratherthanafuel,especiallyinallthesituationsinwhichitisusedasapurifiedhydrogengas.TheseexistingapplicationsaremostlyintherefiningandchemicalssectorsandarepartoftheindustryandrefiningmodulesoftheGECModel.MosthydrogenfortheseexistingapplicationsistodayproducedonsitebysteammethanereformingofnaturalgasorcoalgasificationwithoutCCUS,whileinthescenariosanincreasingshareofthishydrogenisproducedovertimeusingtechnologiesthathaveverylowCO2intensities,includingelectrolysisandconversionoffossilfuelsequippedwithCCUS.ThisonsiteproductionofhydrogenismodelledwithintheindustryandrefiningmodulesoftheGECModel.ThehydrogenproductionandsupplymoduleoftheGECModelcoverstheproductionofmerchanthydrogenandhydrogen-basedfuels.Today,thismerchanthydrogenproductioniscomplementingonsitehydrogenproductioninthechemicalsandrefiningsectors.Inthescenarios,theuseofmerchanthydrogenproducedfromtechnologieswithlowCO2emissionsexpandsfromverylowlevelstodaytonewapplications–includingtransport,powergeneration,buildingsandindustrialheat–contributingtoCO2emissionreductionsinthesesectorsbyreplacingunabatedfossilfueluse.Thislow-emissionssupplyissettobecomeakeypartofthefutureenergytransformationsector,alongsidepowergenerationandheatandcoolingsupply.64InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONThemerchanthydrogensupplymoduleusesacost-optimisationmodellingframeworkcalledTIMES,atechnology-richmodellingplatformdevelopedandfurtherimprovedbytheETSAPTechnologyCollaborationProgramme.Thehydrogenmoduledepictsvarioustechnologyoptionstoproducehydrogenandhydrogen-basedfuels(ammonia,syntheticmethaneandsyntheticliquidhydrocarbonfuelssuchasdiesel,keroseneandmethanol)intermsofexistingcapacities,conversionefficiencies,fuelcosts,operatingandmaintenancecosts,CO2emissionsaswellasCO2captureratesforfossilfuel-basedtechnologiesandcapitalcostsfornewcapacityadditions.Electrolysercapitalcostsrepresentaweightedaverageoflikelydeploymentsharesofdifferentelectrolysertechnologies,withfuturecostreductionsbeingderivedbycomponent-wiselearningcurves.Capitalcostsforalltechnologiesalsoincludeallbalance-of-plantandengineering,procurementandconstruction(EPC)costs,whichcanrepresentahighshareoftotalinstalledcosts.Figure5.2⊳SchematicofmerchanthydrogensupplymoduleIEA.CCBY4.0.Basedondemandsformerchanthydrogenandhydrogen-basedfuelsfromtheend-usesectors,electricityandheatgenerationsector,refineriesandbiofuelproduction,thehydrogensupplymoduledeterminesaleast-costtechnologymixtocoverthesedemands.Besidesthesedemandsandthetechnicalandeconomiccharacteristicsoftechnologies,themoduletakesintoaccountannouncedhydrogenproductionortradeprojects(using,forexample,theIEA’sHydrogenProjectDatabase)aswellaspolicyconstraints,suchasCO2pricesorhydrogendeploymenttargets.Afocusofthemodelanalysisisonlow-emissionshydrogenproduction,i.e.hydrogenproducedinawaythatdoesnotcontributetoanincreaseinatmosphericCO2concentrations.Emissionsassociatedwithfossilfuel-basedhydrogenproductionarepermanentlypreventedfromreachingtheatmosphereandthenaturalgassupplychainmustresultinverylowlevelsofmethaneemissions,ortheelectricityinputtohydrogenproducedfromwatermustbefromrenewableornuclearsources.Thereareseveralcomplementarypathwaystoproducelow-emissionshydrogen,someofwhicharematuretechnologiesandsomeofwhichareatearlierstagesofdevelopment.ThetwodominantpathwaysintheGECModelarealreadydemonstratedatcommercialscale:◼FossilfuelswithCCUS.Thetypicaltechnologyforproducinglow-emissionshydrogenfromfossilfuelswithCCUSissteammethanereforming(SMR)ofnaturalgasequippedwithaCO2captureunitthatcapturestheoverwhelmingmajorityoftheCO2generatedbytheSMRprocess.Thehydrogenyieldcanbeimprovedwithwatergasshift(WGS)reactiontoproduceCO2andadditionalhydrogenfromcarbonmonoxideandwater.Section5Otherenergytransformation65IEA.CCBY4.0.AdaptationstotheSMRprocess,includingautothermalreformingandpartialoxidation,canachievecaptureratesabove95%.AswithothertechnologiesintheGECModel,costandperformanceimprovementsareassumedtoarisefromhigherdeploymentlevels.TheGECModelaccountsforthesafetransportandpermanentgeologicalstorageofthecapturedCO2.◼ElectrolysisofwaterusingelectricitywithverylowCO2intensity.Electrolysersareawell-establishedtechnologytosplitwaterintohydrogenandoxygen.Thereareseveraltechnologiesunderdevelopmenttodaythatcanimproveexistingprocesses,andtheseincludevariationsofalkalineelectrolysers,polymerelectrolytemembrane(PEM)electrolysersandsolidoxideelectrolysercells(SOEC).ElectrolysercapitalcostsintheGECModelaimtorepresentaweightedaverageoflikelydeploymentsharesofthesetechnologies,whichallimprovewithincreaseddeployment,capturedbyusingcomponent-wiselearningcurveapproaches,andalsoincludeallbalance-of-plantandEPCcosts,whichcanrepresentahighshareoftotalinstalledcosts.Themoduleallowstheuseofgridelectricityforhydrogenproduction,whichdependingonthegridelectricitymixineachregion,however,maynotnecessarilybealow-emissionselectricitysource.DedicatedrenewableelectricitygenerationfromsolarPV,onshoreandoffshorewindismodelledasalow-emissionselectricitysourceforhydrogenproduction.Thecorrespondingrenewableelectricitygenerationtechnologiesarecharacterisedbytheircostdata,capacityfactorsandresourcepotentials.Thelattertwoarederivedusinggeospatialanalyses,characterisingtherenewablepotentialbycapacityfactorrangesforthemodelregions.ToreflectthevariabilityofsolarPVandwindforhydrogenproduction,thehydrogenmoduledividesayearinfourtypicaldays,whichareagaindividedintoeighttimeslicesofthree-hourduration.Sincethisresolutionisstilltoocoarsetofullyreflectthevariability,theETHOSmodelsuiteoftheInstituteofEnergyandClimateResearch-3atResearchCentreJülich,withmoredetailedtimeresolution(30typicalperiodswith24typicaltimeslices),hasbeenused.TheETHOSmodelsuitedetermines,foreachlocationanditshourlysolarPVandonshorewindcapacityfactors,thecost-optimalcapacitiesforsolarPV,windandelectrolysersaswellastheneedforflexibilityoptions,suchashydrogenstorage,batterystorageorcurtailment.Thishourlyanalysisforasingleyearcantakeintoaccountoperationalconstraintsofsubsequentsynthesisprocesses,suchasminimumloadconstraintsforHaber-BoschorFischer-Tropschsynthesisprocesses.ApplyingthemodelforagridofrasterpointsinaregionandtakingintoaccountexclusionzonesnotavailableforelectricitygenerationfromsolarPVandwindallowstoderiveregionalsupplycostcurvesforhydrogenproduction.ThesecurvesareusedtoinformtheregionalpotentialsforhydrogenproductionfromsolarPVandwindintheGECModel.Theproductionoflow-emissionshydrogen-basedfuels–includingsyntheticliquidfuelslikesynthetickerosene,diesel,methanol,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.ThehydrogensupplymoduleinterfaceswithseveralothermodulesoftheGECModel.Themostnotableoftheseistheelectricitygenerationmodule,whichisbothaconsumerofhydrogenandhydrogen-basedfuels,andalso66InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONprovideselectricity(alongsidenaturalgas)tosatisfyhydrogenproductionneedsatlowestcost.Theresultsfordedicatedrenewableelectricitygenerationofthehydrogenmoduleareintegratedintheelectricitygenerationmodule,andfeedbacksacrossthisinterfaceareperformediteratively.Demandforhydrogenandhydrogen-basedfuelsineachsectorisdeterminedwithineachsectoralmodule,withiterationstoupdatehydrogensupplycostsbasedonoveralldemandwhererelevant.Tounderstandthehydrogeninfrastructureneedsandrelatedinvestmentrequirements,aninfrastructuretoolhasbeendeveloped,whichcomplementstheinfrastructureneedsforinternationalhydrogentradefromthehydrogenmodulebyanalysingthedomesticinfrastructureneedswithinregions,inparticularforpipelines(neworrepurposednaturalgaspipelines)andstorage.5.4BiofuelproductionBioenergyisanimportantrenewableenergyoptioninallitsforms:solid(biomass),liquid(biofuels)andgas(biogasandbiomethane).Thebioenergysupplymoduledeterminesprimarybioenergyavailability(seeSection6.4).Forliquidandgaseoususes,bioenergyistransformedpriortofinaluseintheliquidbiofuelsandbiogasandbiomethanesupplymodules.TheliquidbiofuelssupplymodulebuildsuponpreviousmodellingworkfortheWEMandETPmodelsandisdesignedtoassessthedeploymentofliquidbiofuelconversiontechnologiesrequiredtomeetdemandintheend-usesectorsoftransport,industry,buildingsandagriculturefromavarietyofbiomassfeedstocksthatarecoherentwithboththebioenergysupplymoduleandthebiogasandbiomethanesupplymodule.Themodulecalculatesconversionlosses,energyinputrequirementsandinvestmentspending,andassessestheamountofliquidbiofuelsproductionassociatedwithCCUS.ThebiogasandbiomethanesupplymoduleisdesignedtoassessthesustainabletechnicalpotentialandcostsofbiogasandbiomethaneforallGECModelregions.Thisanalysisincludesfeedstocksthatcanbeprocessedwithexistingtechnologies,thatdonotcompetewithfoodforagriculturalland,andthatdonothaveanyotheradversesustainabilityimpacts(e.g.reducingbiodiversity).Feedstocksgrownspecificallytoproducebiogas,suchasenergycrops,arealsoexcluded.Themoduleexcludesinternationaltradeofbiogasandbiomethane.LiquidbiofuelsupplymoduleLiquidbiofuelstodayaremainlyproducedusingcommerciallyavailabletechnologiesthatconvertfood-basedenergycropsintoso-calledconventionalbiofuels.Technologiesincludeethanolproductionfromstarchandsugar,fattyacidmethylester(FAME)biodiesel,andhydrotreatedvegetableoil(HVO)renewablediesel.Inthemodelledscenarios,anincreasingshareofliquidbiofuelsareproducedfromadvancedconversiontechnologies(suchasbiomassgasificationandFischer-Tropschsynthesisorcellulosicethanolproduction)andfromadvancedfeedstockssuchaswasteandresidueoils,forestryresidues,cropresidues,andnon-foodenergycropsgrownonnon-arable,marginalland.Advancedfeedstocksdonotcompetewithfoodandfeed,andminimisenegativeenvironmentalimpactsonsoilhealth,waterresourcesandbiodiversity.Theliquidbiofuelssupplymoduleusesacost-optimisationmodellingframeworkcalledTIMES,atechnology-richmodellingplatformdevelopedandfurtherimprovedbytheETSAPTechnologyCollaborationProgrammeoftheIEA.Theliquidbiofuelsmoduledepictsvarioustechnologyoptionstoproduceliquidbiofuels(ethanol,biodieselandrenewablediesel,biojetkeroseneandbiomethanol)withandwithoutcarboncapture,intermsofexistingcapacities,conversionefficiencies,fuelandfeedstockcosts,operatingandmaintenancecosts,CO2emissionsaswellasCO2captureratesandcapitalcostsfornewcapacityadditions.Liquidbiofuelcapitalcostsrepresentthelatestdataavailablefromindustryandacademia,withfuturecostreductionsassessedusinglearningcurves.Avarietyofbiomassfeedstocksareincludedinthemodel,suchasforestryresidues,cropresidues,andnon-foodenergycrops.Thesebiomassfeedstocksarecoherentwiththebioenergysupplymoduleandthebiogasandbiomethanesupplymodule.Theliquidbiofuelsmodulealsomodelsliquidbiofueltradeforethanol,biodieselandrenewablediesel,biojetkeroseneandbiomethanolbetweeneachGECModelregion(seeSection6.4).Section5Otherenergytransformation67IEA.CCBY4.0.Basedondemandforliquidbiofuelsfromtheend-usesectors,theliquidbiofuelsupplymoduledeterminesaleast-costtechnologymixtocoverthesedemands.Besidesthesedemandsandthetechnicalandeconomiccharacteristicsoftechnologies,themoduletakesintoaccountannouncedbiofuelproductionandtradeprojectsasassessedbytheIEA’sRenewableEnergyMarketreports,aswellaspolicyconstraints,suchasCO2prices,biofuelssubsidiesortargetsforadvancedbiofuelsproduction.Figure5.3⊳SchematicofliquidbiofuelsmodelElectricityNaturalgasHydrogenStorageCO2UseBlackliquorBagasseAnaerobicBiogasupgradingCCUSBiogasDemandMSW(renewable)digestionBiomethaneDemandCCUSBio-SNGManurePelletising/CCUSSolidbiomassDemandAgriculturaltorrefactionDemandBiomethanolresiduesBiomethanolTradeWood/forestBiojetDemandBio-FTCCUSresiduesBioethanolTradeCellulosicCCUSDemandWoodyenergyfermentationBiodieselcropsandTradeATJDemandStarchcropsrenewableSugarcropsFermentationCCUSdieselTradeOilseedcropsHVO/HEFADemandFeedstockSupplyFAMETransformationIEA.CCBY4.0.Notes:ATJ=alcohol-to-jet;CCUS=carboncapture,utilisationandstorage;FAME=fattyacidmethylester;FT=Fischer-Tropsch;HEFA=hydroprocessedestersandfattyacids;HVO=hydrotreatedvegetableoil;MSW=municipalsolidwaste;SNG=syntheticnaturalgas.Bio-hydrogenproductionisincludedinmerchanthydrogenmodule(seeSection5.3).Theliquidbiofuelsmoduleincludesthefollowingconversionpathwaysforeachliquidbiofuelproduct:◼Ethanolisproducedfromconventionalfermentationprocessesusingstarch(e.g.corn)orsugar(e.g.sugarcane)crops,orfromanadvancedfermentationprocessusingcellulosicfeedstocks(e.g.cornstover),inwhichthefeedstockmustfirstundergoaprocesstobreakdownthefeedstockandreleasethesugarspriortofermentation.◼Biodieselandrenewablediesel.ConventionalbiodieselisproducedfromtheFAMEconversionprocess,whileadvancedrenewabledieselisproducedfromtheHVOprocessaswellasthethermochemicalprocessofbiomassgasificationfollowedbyFischer-Tropschsynthesis.◼BiojetkeroseneisproducedfromeithertheHVOprocess(alsoknownashydroprocessedestersandfattyacids,orHEFA),thermochemicallyfrombiomassgasificationandFischer-Tropschsynthesis,orfromconventionalandadvancedethanolusingthealcohol-to-jet(ATJ)pathway.◼Biomethanolisproducedthermochemicallyfromthebiomassgasificationandmethanolsynthesispathway.Additionally,severalliquidbiofuelproductionpathwayscanbedeployedwithcarboncaptureforuseorstorage.Theseincludeconventionalandadvancedethanolroutes,renewabledieselandbiojetkerosenefrombiomass68InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONgasificationandFischer-Tropschsynthesis,andbiomethanolfrombiomassgasificationandmethanolsynthesis.CapturedCO2iseitherstored,creatingso-calledcarbonremovals,orusedfortheproductionofsynthetichydrocarbonfuelsinthehydrogenmodule.BiogasandbiomethanesupplymoduleBiogasandbiomethanesupplypotentialhasbeenassessedconsideringawidevarietyoffeedstock,groupedinsixcategories:cropresidues,animalmanure,municipalsolidwastes,forestproductresidues,wastewaterandindustrialwastes.ThefeedstocksupplypotentialsarebuiltonawiderangeofdataoriginatinglargelyfromtheFoodandAgricultureOrganizationoftheUnitedNations(FAO)databaseandOECD-FAOstudy(OECD/FAO,2018)forwheat,maize,rice,othercoarsegrains,sugarbeet,sugarcane,soybean,andotheroilseeds,cattle,pig,poultryandsheep,logfellingresidues,woodprocessingresiduesanddistillerdriedgrains,aby-productofethanolproductionfromgrainsandfromaWorldBankstudy(WorldBank,2018)fordifferentcategoriesoforganicmunicipalsolidwastesuchasfoodandgreenwaste,paperandcardboard,andwood.WastewaterincludesonlymunicipalwastewaterandisbasedontheoutputdatafromtheWatermodulepreviouslydevelopedbytheWorldEnergyOutlookteam.Biogasisproducedbyanaerobicdigestion.Fivetechnologiesofcentralisedbiogasproductionplantsaremodelled:landfillgasrecoverysystem,digesterinmunicipalwastewatertreatmentplantandthreecentralisedco-digestionplants(small-,mediumandlarge-scale).Inaddition,twotypesofhousehold-scaledigesteraremodelledintheresidentialsectoroftheGECModel,toaccountforruralanddecentralisedbiogasproductioninruralareasofdevelopingeconomies.Forbiomethane,twoproductionpathwaysareconsidered:upgradingofbiogasproducedbyanaerobicdigestionandthermalgasificationandmethanationoflignocellulosicbiomass.Foreachtechnology,technicalandeconomicparameters,e.g.efficiency,lifetime,overnightcapitalcostoroperationalcosts,arecollectedtoassesstheproductioncosts.Thecombinationoftheassessmentofthesupplypotentialandtheeconomicevaluationofthedifferentbiogasandbiomethaneprocesseswereusedtoassessbiogasandbiomethanesupplycostcurves.Foragivenyear,itismadeoftheaggregationofbiomethanepotentialandassociatedlevelisedcostofproductionforeveryregion,feedstockandtechnology.Informationprovidedbysupplycurvesisthenusedtoassessthecost-competitivenessofthetwomainusesofbiogasandbiomethane:electricityandheatgenerationandinjectioninthegasgrid.SupplycurvesareusedtocalculateGHGemissionspotentialsavingsandrelatedabatementcosttounderstandthefutureroleofcarbonpricingonbiogasandbiomethanedevelopment.Section5Otherenergytransformation69IEA.CCBY4.0.IEA.CCBY4.0.Section66Energysupply6.1OilThepurposeofthismoduleistoprojectthelevelofoilproductionineachcountrythroughabottom-upapproach1thatbuildson:◼Thehistoricaltimeseriesofproductionbycountries.◼Standardproductionprofilesandestimatesofdeclineratesatfieldandcountrylevelsderivedfromthedetailedfield-by-fieldanalysisfirstundertakeninWEO-2008andupdatedsince.◼Anextensivesurveyofupstreamprojectssanctioned,plannedandannouncedovertheshortterminbothOPECandnon-OPECcountries,includingconventionalandnon-conventionalreserves,asperformedbytheIEAOilMarketReportteam;thisisusedtoderiveproductioninthefirst5yearsoftheprojectionperiod(asummaryofthedifferencesinmethodologybetweentheGECModelandtheMedium-TermOilMarketReportisexplainedinBox6.1).◼Amethodology,whichaimstoreplicateasfaraspossiblethedecisionmodeoftheindustryindevelopingnewreservesbyusingthecriteriaofnetpresentvalueoffuturecashflows(Figure6.1).◼Asetofeconomicassumptionsdiscussedwithandvalidatedbytheindustryincludingthediscountrateusedintheeconomicanalysisofpotentialprojects,findinganddevelopmentcosts,andliftingcosts.◼Anextensivesurveyoffiscalregimestranslatingintoanestimateofeachgovernment’stakeinthecashflowsgeneratedbyprojects.◼Acomprehensiveassessmentofvariousfinancialrisks(e.g.geopolitics,ruleoflaw,regulatoryoversight)torepresenttheattractivenessofinvestmentinoilandnaturalgasfields.◼Valuesofremainingtechnicallyrecoverableresources(Table6.1)calculatedbasedoninformationfromtheUnitedStatesGeologicalSurvey(USGS),BGRandothersources.TheparagraphsbelowdescribehowtheUSGSdataareusedintheGECModel.USGSpublishesitsWorldPetroleumAssessment,athoroughreviewofworldwideconventionaloilandgasresources.Init,USGSdividestheresourcesintothreeparts:◼Knownoil,whichcontainsbothcumulativeproductionandreservesinknownreservoirs.◼Undiscoveredoil,abasin-by-basinestimateofhowmuchmoreoiltheremaybetobefound,basedonknowledgeofpetroleumgeology.◼Reservesgrowth,anestimateofhowmuchoilmaybeproducedfromknownreservoirsontopoftheknownreserves.Asthenameindicates,thisisbasedontheobservationthatestimatesofreserves(includingcumulativeproduction)inknownreservoirstendtogrowwithtimeasknowledgeofthereservoirandtechnologyimproves.Forthe2000assessment,reservegrowthasafunctionoftimeafterdiscoverywascalibratedfromobservationinUSfields,andthiscalibrationappliedtotheknownworldwidereservestoobtainanestimateofworldwidereservesgrowthpotential.Sincethe2000assessment,USGShasregularlypublishedupdatesonundiscoveredoilinvariousbasins,andthesewereconsideredintheGECModel.In2012,USGSpublishedanupdatedsummaryofworldwideundiscoveredoil,aswellasarevisedestimateforreservesgrowthbasedonanewfield-by-fieldmethodfocusedonthelargefieldsintheworld.Previously,theknownoilestimatesusedbytheUSGSwhengeneratingitsreserve1“Bottom-up”inthiscontextmeans“basedonfield-by-fieldanalysis”.Section6Energysupply71IEA.CCBY4.0.growthestimateshadnotbeenreleasedpublicly.However,arecentreportprovidesitsassumptions,albeitaggregatedatagloballevel(USGS,2015).TheUSGSestimateofcumulativeproductionandreservesoutsidetheUnitedStatesis2060billionbarrels,inclosealignmentwiththeIEAequivalentestimateof2050billionbarrels.Forconventionaloil,theUSGSestimatesofundiscoveredoilandreservesgrowthpublishedin2012providethekeyfoundationforthevaluesusedinGECModel.TheGECModelestimatesofremainingtechnicallyrecoverableresourcescombineUSGSundiscovered,USGSreservesgrowthandIEAestimatesforknown.Similaranalysis,basedonthesameUSGSpublications,feedsintotheIEANGLsandnaturalgasresourcesdatabase,whichallowsanevaluationoftotalconventionalliquidhydrocarbonsresourcesandconventionalgasresources.Box6.1⊳MethodologicaldifferencesbetweentheGECModelandtheIEAMedium-TermOilMarketReportEveryyear,theIEApublishesprojectionsofoilsupplyanddemandforthenextfiveyearsintheMedium-TermOilMarketReport(MTOMR),andforthenexttwoandhalfdecadesintheGECModel.Thesetwosetsofprojectionsusedifferentmethodologiesthatevolveovertime,suchthatcomparisonsarenotnecessarilystraightforward.Thisboxsummarisesthekeydifferences.AkeydifferencebetweentheMTOMRandtheGECModelistheoilpriceassumption.TheMTOMRassumesthattheoilpricefollowsthefuturesmarketcurveatthetimeofpublication.Thisisthenusedfordemandprojections,andsupplyisassumedtofollow,withOPECfillingthegapbetweenfield-by-fieldprojectionsofnon-OPECsupplyanddemand.Bycontrast,theGECModeldeterminestheequilibriumpricethatbringssupplyanddemandinbalance.(Thisequilibriumisperformedasatrendandnotyear-by-yeartoavoidgeneratinginvestment/pricecycleswhichwouldobscurepolicyeffectsandlong-termtrends.)TheGECModelreliesonthefield-by-fieldanalysisunderlyingtheMTOMRtoguideproductionbycountryinthefirst5yearsoftheprojectionperiod.Thecountry-by-countrymethodologyisalsoextendedtoOPECcountries,sothatOPECisnottreatedastheswingproducer,thoughconstraintsthoughttorepresentpossibleOPECpoliciesareincorporatedintheGECModeloilsupplymodule.Resultsarealsooftenpresentedslightlydifferentlyinthetworeports,includingintermsofthegroupingsforconventionalandunconventionaloil.TheGECModelincludesallCanadianoilsandsandVenezuelanOrinocoproductionasunconventionaloil,whiletheMTOMRgenerallycountsonlyupgradedbitumenorextra-heavyoilasunconventional.Inanalysingandprojectingoildemand,theGECModelandMTOMRhavemethodologicaldifferences.SincetheGECModelisconcernedwithprojectionsofsupplyanddemandofallenergysourcesandprojectsaworldenergybalanceinthefuture,itincorporatesalldemandcomponents.WhiletheGECModelincorporatesstatisticaldifferencesandrefinerytransformationlossesintohistoricaldemandvaluesandprojectsthoseintothefuture,MTOMR’sdemanddefinitiondoesnotincludethesetwocategoriesinitshistoricalvaluesandprojections.TheGECModelalsosplitsbiofuelsfromhistoricaloildemandandprojectsoildemandandbiofuelsdemandseparately.OMRdoesnotseparatebiofuelsfromhistoricaloildemand,andoildemandisprojectedwithamixofbiofuels.Asaresult,onebarrelofoilfromMTOMRprojectionshaslowerenergycontentthanonebarrelintheGECModelifbiofuelsareprojectedtogrow.AdirectcomparisonofGECModelandOMRresultsisthusonlypossibleifbiofuelsarestrippedfromMTOMRoildemandvalues.Thedifferencesinrefiningmainlyconcerntheinterpretationofinstalledcapacity.TheGECModeldiscountsmostoftheidledcapacityofChineseteapotandsmallerrefineriesthatrunbelow30%utilisationrates.Italsodiscardsmothballedcapacityinitsentirety,eveniftheowneroftherefineryhasannouncedthatitisatemporaryeconomicshutdown.TheGECModelandtheMTOMRmayalsodifferintheirprojectionoffirmcapacityadditionswithinthesametimeframe.72InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONEachcountry’sprojectedoilproductionprofileismadeofsixcomponents.Conventionalcrudeoilfieldsarealsodistinguishedbywaterdepth(onshore,shallow[waterdepthlessthan450metres],deepwater[between450and1500metres]andultra-deepwater[greaterthan1500metres]).Forunconventionaloil,extra-heavyoilandbitumenisalsodistinguishedbyminingorinsitutechnologiesandtightoilbyplayproductivity.◼Productionfromcurrentlyproducingfieldsasofanestimatedend-2021:theprojecteddeclineratesineachcountryarederivedfromtheanalysissummarisedinBox6.1.◼Productionfromdiscoveredfieldswithsanctioned,plannedandannounceddevelopments.◼Productionfromdiscoveredfieldsawaitingdevelopment.◼Productionfromfieldsyettobediscovered.◼Productionofnaturalgasliquids.◼Productionofunconventionaloil.Trendsinoilproductionaremodelledusingabottom-upmethodology,makingextensiveuseofourdatabaseofworldwideultimatelytechnicallyrecoverableresources.Themethodologyaimstoreplicateinvestmentdecisionsintheoilindustrybyanalysingtheprofitabilityofdevelopingreservesattheprojectlevel(Figure6.1).Figure6.1⊳StructureoftheoilsupplymoduleIEA.CCBY4.0.IntheGECModeloilsupplymodule,productionineachcountryorgroupofcountriesisderivedseparately,accordingtothetypeofassetinwhichinvestmentsaremade:existingfields,newfieldsandnon-conventionalprojects.Standardproductionprofilesareappliedtoderivetheproductiontrendforexistingfieldsandforthosenewfields(bycountryandtypeoffield)whicharebroughtintoproductionovertheprojectionperiod.Theprofitabilityofeachtypeofprojectisbasedonassumptionsaboutthecapitalandoperatingcostsofdifferenttypesofprojects,andthediscountrate,representingthecostofcapital.Thenetpresentvalueofthecashflowsofeachtypeofprojectisderivedfromastandardproductionprofile.ProjectsareprioritisedbytheirnetpresentSection6Energysupply73IEA.CCBY4.0.valueandthemostpotentiallyprofitableprojectsaredeveloped.Constraintsonhowfastprojectscanbedevelopedandhowfastproductioncangrowinagivencountryarealsoapplied.Thesearederivedfromhistoricaldataandindustryinputs.Whendemandcannotbemetwithoutrelaxingtheconstraints,thissignalsthatoilpricesneedtobeincreased.UStightoilmodelAtightoilmoduleispartofGECModel,originallydevelopedforWEO-2016,anditexploresthesensitivityofproductionoftightoilintheUnitedStatestochangesinpriceandresourceavailability.Themoduleprojectspossiblefutureproductionacross23shaleplays,takingintoaccounttheestimatedultimaterecovery(EUR),initialproduction,rateofdeclineanddrillingcostsofwellsdrilledandcompletedacrossdifferentareasofeachplay.Existingproductionismodelledbyestimatingdeclineparametersofwellsbasedonlatestproductioninformationavailable,andthetimewhenthesewellswerecompleted.Pricedynamicsaffectthenumberofrigsthatareavailabletodrillnewwells,withalagbetweenincreasesinpricesandincreasesinthenumberofrigsoperating(asobservedempirically).Technologyincreasesboththespeedatwhichnewwellscanbedrilledandcompleted(thenumberofwellsperrig)andtheamountofproductionfromeachwell(theEUR/well).Conversely,theEUR/wellofagivenareainagivenplayisassumedtodegradeasthatareaisdepletedovertime.Rigsaredistributedacrossplaysbasedoncurrentactivityandtheexpectedcosteffectivenessofnewwellsthataredrilled.Itisassumedthatwhileoperatorsaimtodrillonlyintheirmostproductiveareas,somewellsareinevitablylocatedinregionswithlowerEUR/wellorhigherdeclinerates.Theproductofthenumberofrigs,numberofwellsperrig,andproductionperwellthengivesthenewproductionthatcomesonlineineachplayineachmonthstartinginJanuary2020.ResultsfromthismodulearedirectlyfedintotheGECModelforeachofthescenariosimplemented.AsimilarmodelwasdevelopedforshalegasproductionintheUnitedStates.Box6.2⊳MethodologytoaccountforproductiondeclineinoilandgasfieldsTheWorldEnergyOutlookhaspreviouslypresentedanalysesofdeclineratesinoilfieldsonanumberofoccasions,basedontimeseriesofactualproductiondataforalargenumberoffields.Theoutcomeofthisworkisavalueforobserveddeclineratesbytypeoffield,geographicallocationandphaseofdecline,aswellasanestimateforthedifferencebetweenobserveddeclineratesandnaturaldeclinerates(i.e.thedeclineratethatwouldbeobservedintheabsenceoffurtherinvestmentinproducingfields).Inprinciple,thisprovidestheelementstoprojectfutureproductionofallfieldsindeclineamongthesetoffieldsused.Themethodologyisasfollows:◼Foreachfieldinthedatabase,assignatype(e.g.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,thisparameterisation74InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONgivesagoodmatchwiththeresultsoftheproprietarydatabase(asthetwodatabaseshaveslightlydifferentbaseproductions,botharenormalisedtoallowaclearercomparisonofdecline)forthelong-termdecline;intheshortterm,theIEAfield-by-fieldanalysis(comingfromtheMTOMR)ismoreconservativethanthecommercialdatabase,asitaccountsforexpectedfieldmaintenanceandweatherdisruptions.Figure6.2⊳Evolutionofproductionofcurrentlyproducingconventionaloilfieldsfromafield-by-fielddatabaseandfromtheGECModelIEA.CCBY4.0.Sources:RystadEnergy,IEAanalysisanddatabases.6.2NaturalgasNaturalgasproductionandtradeprojectionsarederivedfromahybridGECModelgassupplymoduleinvolvingbottom-upandtop-downapproaches.Themodulehassimilarinputs,logicandfunctionalitytotheoilsupplymoduledescribedabove.However,contrarytooil,whichisassumedtobefreelytradedglobally,gasisassumedtobeprimarilytradedregionally,withinter-regionaltradeconstrainedbyexistingorplannedpipelines,LNGplantsandlong-termcontracts.Firstthetop-downmoduleisrunfor20regions(seeAnnex1),forwhichindigenousproductionismodelledfromvariousfactorsincludingremainingtechnicallyrecoverableresources(Table6.1),depletionrates,productioncosts,taxes,pricesandvariousrisksintheregion.Subtractingdomesticproductionfromdemand,inaggregateforeachimportingregionalblock,yieldsgasimportrequirements.Foreachgasnet-exportingregionalblock,aggregateproductionisdeterminedbythelevelofdomesticdemandandthecallonthatregion’sexportableproduction(whichisdeterminedbytheimportneedsofthenetimportingregionsandsupplycosts).Long-termcontracts(current,orassumedforthefuture)areservedfirst,thenexportingregionscompeteonthebasisofmarginalproductioncostsplustransportcosts,withincurrentandassumedfutureLNGandpipelinecapacities.Thisprovidesinter-regionalgastrade.Theeffectsofpricingpolicies(currentorassumedforthefuture)ofexportingregionscanalsobetakenintoaccount.Inthebottom-upmodule,productionwithineachregionisallocatedtoindividualcountriesaccordingtoremainingtechnicallyrecoverableresources,depletionratesandrelativesupplycosts,withalogicsimilartothatoftheoilsupplymodule,butwith“demand”beingprovidedbytherespectiveregionalproductionderivedfromthetop-downmodule.Section6Energysupply75IEA.CCBY4.0.6.3CoalThecoalmoduleisacombinationofaresourcesapproach(Table6.1)andanassessmentofthedevelopmentofdomesticandinternationalmarkets,basedontheinternationalcoalprice.Production,importsandexportsarebasedoncoaldemandprojectionsandhistoricaldata,onacountrybasis.Fourmarketsareconsidered:cokingcoal,steamcoal,ligniteandpeat.Worldcoaltrade,principallyconstitutedofcokingcoalandsteamcoal,isseparatelymodelledforthetwomarketsandbalancedonanannualbasis.Table6.1⊳Remainingtechnicallyrecoverablefossilfuelresources,2022OilProvenResourcesConventionalTightNGLsEHOBKerogen(billionbarrels)reservescrudeoiloiloilNorthAmerica23922352151467971000CentralandSouthAmerica22085424757494973Europe3031115619286Africa45131254833-MiddleEast1487829230Eurasia1251122224851711418AsiaPacific900937120725855216World1462752071531643107318685161426001760NaturalgasProvenResourcesConventionalTightShaleCoalbed(trillioncubicmetres)reservesgasgasgasmethaneNorthAmerica14750CentralandSouthAmerica17842810817Europe946181541-Africa551185MiddleEast1011015400Eurasia191211291011-AsiaPacific83167441017World69138421953202180310253492222180CoalProvenResourcesCokingSteamLignite(billiontonnes)reservescoalcoalNorthAmerica2578389111957511519CentralandSouthAmericaEurope146033225AfricaMiddleEast137982164414403EurasiaAsiaPacific1534346296-141365-19120153869976324608974173658101428World1074208043490133064007Notes:NGLs=naturalgasliquids;EHOB=extra-heavyoilandbitumen.ThebreakdownofcoalresourcesbytypeisanIEAestimate.CoalworldresourcesexcludeAntarctica.Sources:BGR,2021;BP,2022;CEDIGAZ,2023;OGJ,2022;USDOE/EIA,2022;USGS,2012a;USGS,2012b;IEAdatabasesandanalysis.76InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATION6.4BioenergyBioenergyisanimportantrenewableenergyoptioninallitsforms:solid(biomass),liquid(biofuels)andgas(biogasandbiomethane).Bioenergyprovidesasignificantportionofrenewables-basedelectricity,heat,andtransportfuelsinallscenariosoftheGECModeland–asbiomethane–itcanalsocontributetodecarbonisingthegasnetwork.Manyregionsorcountrieshaveorareconsideringpoliciesthatwillincreasethedemandforbioenergyinthepowerandtransportsectorsfurtherinthefuture.TheBioenergysupplymoduleisdesignedtoassesstheabilityofGECModelregionstomeettheirdemandforbioenergyforpowergeneration,biofuelsandbiogaseswithdomesticresources.Wheretheyarenotabletodoso,themodulealsosimulatestheinternationaltradeofliquidbiofuels.Theavailabilityofbioenergyisrestrictedtorenewablesourcesofbiomassfeedstockthatisnotincompetitionwithfoodandfeed.Thebioenergysupplydeterminesprimarybioenergyavailability,which–forliquidandgaseoususes–feedsintotheliquidbiofuelsandbiogasandbiomethanesupplymodulesfortransformationpriortofinaluse(seeSection5.4).BioenergysupplymoduleBiomasssupplypotentialsbyregionThefeedstocksupplypotentialsarebuiltonawiderangeofdatarelatedtoland,cropsandfooddemand,originatinglargelyfromthedatabaseofFAO,aswellasacademicliteratureandtheGlobalAgro-EcologicalZones(GAEZ)system,acollaborativeprojectinvolvingFAOandIIASA.Totalsupplypotentialsbyregioninthebioenergysupplymodulearethesumofthepotentialsupplyforfourcategoriesoffeedstocks:forestryproducts,forestryresidues,agriculturalresiduesandenergycrops(Figure6.3).Startingfromcurrentactivitylevels,rampingupcollectionanddeliveryoftheseoftendiffusefeedstocksrequiressignificantleadtimesbeforemaximumpotentialsupplylevelscanbereached.Thepotentialsupplyofforestryandagriculturalresiduesisreducedbyindustrialandresidentialusetoproduceheat,aswellasdemandfortraditionalusesofbioenergy.Figure6.3⊳SchematicofbiomasssupplypotentialsTotalbiomassfeedstocksupplypotentialForestryForestryEnergyCropMunicipalLivestockWastewaterproductsresiduescropsresiduessolidwasteManureAdditionalForestryUsableAvailableCropCropyieldsUsableBiogasforestactivitiessharearablelandchoice/yieldsharepotentialgrowthIEA.CCBY4.0.Note:Onlytheorganicfractionofmunicipalsolidwasteisincludedinthebiomasssupply.Forestryproductsincludeonlyforestryactivities,suchasharvestingtreesandcomplementaryfellings,fortheprimarypurposeofproducingpowerortransportbiofuels.Themaximumpotentialavailabilityofforestryproductsislimitedtotheexpectedgrowthintotalforestareaperyear,afterotherforestrydemandsaremet,ineachregion,therebyavoidingdirectdeforestation.Forestryresiduesarethosematerials,orsecondaryproducts,producedfromforestryactivitieswheretheprimarymotivationissomethingotherthantoproducebioenergy.Theseincludeforestryscraps,barkleftoverfromthetimberindustry,industrialby-products,wastewoodandsawdustleftoverafterwoodprocessing.TheSection6Energysupply77IEA.CCBY4.0.maximumpotentialavailabilityislimitedbytheleveloftherelatedactivitiesandtheusableshareoftheleftovermaterials.Cropresiduesaretheleftovermaterialsafterharvestingcrops,suchascornstover,strawandbagassefromsugarcaneprocessing.Dataforharvestsbyregionincludethefollowingcrops:barley,maize(corn),oats,rice,sorghum,wheat,othercereals,rapeseed,soybeans,sunflowerseedandsugarcane.Themaximumpotentialavailabilityislimitedbytheamountofcropsharvestedandbytherecoverableshareoftheresidues.Itisimportantforaportionoftheresiduestoremaininfieldstoreplenishsoilnutrientsandmaintainyieldsforfutureharvests,byhelpingreducesoilerosionandmaintainingwaterandtemperatureinthesoils.Thepercentageoftheseresiduesthatcanbemadeavailableforenergyproductioninasustainablemannerisregion-andcrop-specific,andisstillbeinginvestigatedactively.Otherwasteandresiduesourcesmodelledarelivestockmanure,theorganicfractionofmunicipalsolidwaste(MSW)andwastewaterforbiogasproduction.Livestockmanureincludescattle,poultry,pigandsheep.Similarlytocropresidues,thesefeedstockpotentialsarebuiltonawiderangeofdataoriginatinglargelyfromtheFAOdatabaseandOECD-FAOstudy(OECD/FAO,2018)forpigs,poultryandsheep.Theorganicfractionofmunicipalsolidwasteincludesfoodandgreenwaste,paperandcardboard,andwood,andiscalculatedfromaWorldBankstudy(WorldBank,2018).WastewaterincludesonlymunicipalwastewaterandisbasedontheoutputdatafromtheWatermodulepreviouslydevelopedbytheWorldEnergyOutlookteam.Thesebiomasspotentialsareincludedinthemodelasbiogaspotential.Energycropsarethosegrownspecificallyforenergypurposes,includingfoodsugarandstarchfeedstockforethanol(e.g.corn,sugarcane,andsugarbeet),vegetableoilfeedstockforbio-baseddiesels(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.78InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSupplytomeetdemandDemandforbiomassfeedstockisbasedondemandprojectionsforpower,industry,buildingsandtransportsectors.Tomeetdemand,domesticsuppliesaregivenpriority;theremainderiscoveredthroughinternationalmarketsformodelledbioenergytradeflows,namelyliquidbiofuels.ThemodeliscalibratedtomeetexistingtradeflowsasassessedbytheIEA’sRenewableEnergyMarketreports.DomesticsupplyBiomassfeedstockcompetestomeetdemandonthebasisofconversioncosts,includingfeedstockpricesandtheenergycontentsoffeedstock.Severalbiomassfeedstocktypescanbeusedforbothpowergenerationandtheproductionofliquidbiofuelsandbiogases.Theseincludeforestryproducts,forestryresiduesandagriculturalresidues.Wherethisisthecase,thenetpresentvaluesforbothusesarecomparedandranked,basedontechnologycostdatafromtheGECModelandIEA’sMobilityModel.Accordingtorank,availablebiomassfeedstocksuppliesareallocated.Domesticsupplyofliquidbiofuelsislimitedbyrefiningcapacity.Inthenearterm,thisisrestrictedbyexistingrefineriesandthosealreadyunderconstructionorplanned.GlobaltradeThemodelusesaglobaltradematrixtomatchunsatisfieddemandwithavailablesupplyonaleast-costbasis,includingtransportationcosts.Transportationcostsbetweenregionsincludebothaverageover-landandby-seacosts.Fourproductsaretraded:ethanol,biodiesel,biojetkeroseneandbiomethanol.Theseliquidbiofuelsarehigh-densityuniformproductsthatcanbemadefromresiduesandotherfeedstock,andtheiruniformityanddensitymakehandlingandtransportationeasierandlessexpensiveoverlongdistancescomparedwithotherbioenergyresources.Theconversionofbiomassfeedstocktoliquidbiofuelsoccursintheexportingregion,thereforeconversioncostsarecalculatedbasedonthetechnologycostsintheexportingregion.Importingregionschoosesuppliersbasedonleast-costavailablesupplies(includingtransportationcosts).Exportingregionsmakesuppliesavailabletoimportingregionswillingtopaythehighestprice.CollaborationIEAbioenergydemandandsupplyresultsarecoupledwiththeGlobalBiosphereManagementModel(GLOBIOM)developedandmaintainedbyIIASAtocomplementtheIEA’sanalysisonbioenergysuppliesandeffectiveusestrategies,particularlyonbiomassfeedstocksupply,landuseandemissionsfromtheagriculture,forestryandotherlanduse(AFOLU)sectors.Section6Energysupply79IEA.CCBY4.0.IEA.CCBY4.0.Section77CriticalmineralsScopeThecriticalmineralsmodel,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•Arsenic•Hafnium•Niobium•Tin•Boron•Indium•Platinum•Titanium•Copper•Cadmium•Iridium•Selenium•Tungsten•Chromium•Lead•Silicon•Vanadium•Lithium•Gallium•Magnesium•Silver•Zinc•Germanium•Manganese•Tantalum•Nickel•Graphite•Molybdenum•Tellurium•Rareearthelements(Neodymium,Dysprosium,Praseodymium,Terbium,others)Steelandaluminiumarewidelyusedacrossmanycleanenergytechnologies,butwehaveexcludedthemfromthescopeofthisanalysis.SteeldoesnothavesubstantialsecurityimplicationsandtheenergysectorisnotaSection7Criticalminerals81IEA.CCBY4.0.majordriverofgrowthinsteeldemand.Aluminiumdemandisassessedforelectricitynetworksonlyastheoutlookforcopperisinherentlylinkedwithaluminiumuseingridlines,butisnotincludedintheaggregatedemandprojections.7.1DemandIn2023,theIEApublishedtheinteractiveCriticalMineralsDataExploreronitswebsite.Thisonlinetoolprovidesglobaldemandprojectionsfor37criticalmineralsneededforcleanenergytransitionsacrossthethreemainIEAscenariosand12technology-specificcases.Foreachofthecleanenergytechnologies,weestimateoverallmineraldemandusingfourmainvariables:◼cleanenergydeploymenttrendsunderdifferentscenarios◼sub-technologyshareswithineachtechnologyareabasedontechnology-specificcases◼mineralintensityofeachsub-technology◼mineralintensityimprovements.CleanenergydeploymenttrendsundertheStatedPoliciesScenario(STEPS),theAnnouncedPledgesScenario(APS),andtheNetZeroEmissionsby2050Scenario(NZEScenario)aretakenfromtheprojectionsfromthe2023modellingcycle.Sub-technologyshareswithineachtechnologyarea(e.g.solarPVmoduletypesorEVbatterychemistries)aretakenfromthe2023GECModellingCycle,complementedbytheGlobalEVOutlook2023andothersources.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.82InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection88Emissions8.1CO2emissionsAsenergy-relatedCO2emissionsaccountforthelion'sshareofglobalgreenhousegasemissions,oneoftheimportantoutputsoftheGECModelisregion-by-regionCO2emissionsfromfuelcombustionandfromindustrialprocesses.Carbondioxideemissionsfromfuelcombustionandfromindustrialprocessesdonotincludefugitiveemissionsfromfuels,flaringorCO2fromtransportandstorage.Unlessotherwisestated,CO2emissionsreportedfromtheGECModelrefertocombustionoffossilfuelsandnon-renewablewaste,industrialprocessCO2emissions,andfugitiveemissionsfromflaring.GECModelCO2emissionsaccountingalsoconsidercarbondioxideremovalfromtheatmospherethroughcapturingCO2fromtheair(throughdirectaircapture[DAC])orfrombiogenicsources(Bioenergywithcarbon,capture,andstorage[BECCS])forpermanentstorageinundergroundreservoirs.ForeachGECModelregion,sectorandfuel,CO2emissionsfromfuelcombustionarecalculatedbymultiplyingenergydemandbyanimpliedCO2contentfactor.TheimpliedCO2contentfactorsforcoal,oilandgasdifferbetweensectorsandregions,reflectingtheproductmixandefficiency.TheyhavebeencalculatedasanaverageofthepastthreeyearsfromIEAenergy-relatedsectoralapproachCO2dataforallGECModelregionsandareassumedtoremainconstantovertheprojectionperiod.Process-relatedCO2emissionsfromvariousindustrialsourcesareestimatedbyGECModelregion.FortheestimationaTier1orTier2methodhasbeenused,whichingeneralmeansthatemissionshavebeenestimatedbasedontheproductionofindustrialmaterialsandanemissionsfactorbasedonthe2006IntergovernmentalPanelonClimateChange(IPCC)GuidelinesforNationalGreenhouseGasInventories.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.CO2emissionsfromlanduse,land-usechangeandforestry(LULUCF)consistentwithcurrentpolicysettings(fortheSTEPS)andannouncedpledges(fortheAPS),aswellasbioenergydemandinIEAscenarioshavebeenassessedbyIIASAusingtheGLOBIOMmodel(IIASA,2022).8.2MethaneemissionsTheGlobalMethaneTrackerwithintheGECModelframeworkisusedtoproduceIEAestimatesformethaneemissionsfromthesupplyoruseoffossilfuels(coal,oilandnaturalgas)andfromtheuseofbioenergy(suchassolidbioenergy,liquidbiofuelsandbiogases).Themethodologyforthemainsegmentsofmethaneemissionsisasfollows:◼Upstreamanddownstreamoilandgas-Ourapproachtoestimatingmethaneemissionsfromglobaloilandgasoperationsreliesongeneratingcountry-specificandproductiontype-specificemissionintensitiesthatareappliedtoproductionandconsumptiondataonacountry-by-countrybasis.Section8Emissions83IEA.CCBY4.0.◼Coalminemethane-Estimatesforcoalminemethane(CMM)emissionsarederivedfrommine-specificemissionsintensitiesforthreemajorcoalproducingcountries.Themine-levelCMMestimatesgeneratedinthiswayarethenaggregatedandverifiedagainstcountry-levelestimatestakenfromsatellite-basedmeasurements.Basedonthesedata,coalquality(e.g.theashcontentorfixedcarboncontentofcoalproducedbyindividualmines),minedepthandregulatoryoversightareusedaskeyfactorstoestimateCMMemissionintensitiesforminesinothercountriesforwhichtherearenoreliabledirectestimates.Emissionsfromfuelcombustion(enduse)-Estimatesformethaneemissionsfromtheuseoffuels(includingbioenergy)instationaryandmobileapplicationsarefromtheIEAGreenhouseGasEmissionsfromEnergy.TheTier1methodologyfromthe2006IPCCGuidelinesforNationalGreenhouseGasInventorieshavebeenadoptedforthepurposeofestimatingthenon-CO2emissionsfromfuelcombustion.Formoreinformationonthemethodologyusedtodevelopestimatesformethaneemissionsfromthesupplyoruseofenergy,pleaserefertotheGlobalMethaneTrackerDocumentation.8.3Othernon-CO2greenhousegasemissionsMostenergy‐relatedgreenhousegas(GHG)emissions,bothCO2andnon-CO2,inIEAscenariosaremodelledusingtheIEA’sGECModel.SignificantsourcesofotherGHGs,e.g.blackcarbon,aswellasGHGsrelatedtolanduseandagricultureconsistentwithIEAscenarios,suchasbiogenicmethane,aremodelledbytheIIASAusingtheGAINSmodel(IIASA,2023)andtheGLOBIOMmodel.ProjectionsforallremainingtypesandsourcesofGHGemissions,suchasF-gasesusedmainlyinindustrialapplications,aresupplementedusingthescenariodatabasepublishedaspartoftheIPCCSpecialReportonGlobalWarmingof1.5°C(IPCC,2018)8.4AirpollutionEmissionsofmajorairpollutantsresultingfromtheGECModelenergyscenarioshavebeenestimatedinco-operationwithIIASA.UsingtheIIASAGAINSmodel,estimateshavebeenmadeforthefollowinglocalairpollutants:sulphurdioxide(SO2),nitrogenoxides(NOx),blackcarbonandPM2.5.1MoreinformationcanbefoundintheWEOSpecialReportonEnergyandAirPollution2aswellasinapreviousdetailedreportoutliningtheapproach,resultsandinformationabouthealthimpacts,aswellaspollutioncontrolcosts.8.5GlobaltemperatureimpactsTheaverageglobalsurfacetemperaturerisethatwouldresultfromGHGandaerosolemissionsinGECModelscenarioshasbeencarriedoutincloseco-operationwithClimateResourcePtyLtdusingtheModelfortheAssessmentofGreenhouseGasInducedClimateChange(“MAGICC”),3anddrawingonothertoolsusedbytheglobalscientificcommunity.TheMAGICCclimatemodelshavebeenusedextensivelyinassessmentreportswrittenbytheIPCC.MAGICC7,theversionusedinthisanalysis,isusedintheIPCC’sSixthAssessmentReport(IPCC,2021)anddescribedinCross-ChapterBox7.1therein.1Fineparticulatematterisparticulatematterthatis2.5micrometresindiameterandless;itisalsoknownasPM2.5orrespirableparticlesbecausetheypenetratetherespiratorysystemfurtherthanlargerparticles.2https://www.iea.org/reports/energy-and-air-pollution3InformationsourcedtoClimateResourceinWEO-2021wascontributedbyClimateResourcePtyLtdusingMAGICC7.NeitherClimateResourcenoranyofitsofficers,employees,contractorsoraffiliatesmakeanywarrantyorguaranteeabouttheaccuracy,completenessorreliabilityoftheclimatedataprovidedandanyliabilityresultingfromitsuseisthesoleresponsibilityofthereader.84InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection99EnergyandCO2decompositionTheGECModelincludesamodule–thedecompositionmodule–usedtoquantifythedifferenceofenergyandCO2emissionsbetweentwoscenariosorinonescenarioovertime.Decompositionanalysisisappliedtoallend-usesectors(industry,transport,buildingsandagriculture)andthetransformationsectors(electricitygenerationandheatproduction,refineries,biofuels,hydrogenandhydrogen-basedfuels,otherenergysectors)ex-posttotheGECModelusingthefinalresultsforthescenariosbeinganalysed.Thedifferencebetweenscenariosorpointsintimeisapportionedtoseveral“levers”thatrepresentimportantmitigationmeasurestoreduceenergyconsumptionandemissionswithintheenergysystem.Theseinclude:◼Activity:differenceinenergyoremissionsfromeconomicactivityandchangeinservicedemand,e.g.increaseinindustrialvalueadded,travelledkilometresorusedfloorspace.◼Avoideddemand–resourceefficiency:differenceinenergyoremissionsfromefficiencyimprovementsintheuseofresources,e.g.extensionofbuildinglifetimesleadingtolesssteelorcementdemand.◼Avoideddemand–behaviour:differenceinenergyoremissionsfromavoideddemandduetobehaviouralshifts.PleaseseethebehaviouralchangesectionsintheEnergydemandsection(Section3)formoredetails.◼Climateeffect:differenceinenergyoremissionsinthebuildingssectorcausedbyclimatechangeimpactsontemperaturechanges.IPCCscenariotemperatureprojectionsareusedtodeterminetheshiftedenergyuse.◼Energyefficiency:differenceinenergyoremissionsfromtechnicalefficiencyimprovementsofdeployedtechnologies.Examplesincludeimprovedinsulationofbuildings,thedeploymentofapplianceswithhigherefficiencystandards,improvedfueleconomyormoreefficientmotors.◼Fuelshifts:differenceinenergyoremissionsfromchangingthefuelused,includingthroughusingdifferenttechnologiesthatmayhavehigherefficiency,e.g.theshifttoelectricvehiclesfromcombustionenginesortheshifttoheatpumpsfromgasboilers.Thiseffectisfurtherbrokendowntospecificfuels:◼Electrification:assessingthechangesforelectricity,e.g.useofelectricvehiclesordirectelectrificationinindustryincludingefficiencygains.Foremissions,thiscanbedoneinadirectdecomposition(excludingemissionsfromtheelectricityandheatsector)orinanindirectdecomposition(includingemissionsfromtheelectricityandheatsector).◼Bioenergy:assessingthechangesforbioenergy,e.g.inpowergenerationorasafuelinbuildings,transportorindustry.◼Otherrenewables:assessingthechangesforotherrenewables,e.g.useofsolarPVandwindforpowergenerationorsolarthermalinbuildings.◼Hydrogen:assessingthechangesfortheuseofhydrogenandhydrogen-basedfuels,e.g.inthetransportsectororinenergy-intensiveindustries.On-sitehydrogenuse,suchaselectrolytichydrogen-basedsteelorammoniaproduction,arealsoaccountedtothislever.◼Otherfuelshifts:assessingthechangesforotherfuels,e.g.switchesbetweenfossilfuelsornuclear.◼CCUS:differenceinenergyoremissionsfromthedeploymentofcarboncaptureutilisationandstorage.Thedecompositionmodulealsohasthecapabilitytoapportionemissionandenergychangesaccordingtotechnologymaturitycategory,usingthetechnologyreadinesslevel(TRL)ofeachmodelledtechnologyorstrategy.TheTRLassessmentisbasedontheETPCleanEnergyTechnologyGuideandclassifiesthetechnologiesbeingdeployedinagivenyearin4tiersbasedontheircurrentTRLstatus:asmature,atmarketuptake,underdemonstrationorstillaprototype.TheTRLbreakdownmakesitpossibletoallocatethecontributionofleversSection9EnergyandCO2decomposition85IEA.CCBY4.0.suchasfuelswitchingorenergyefficiencytodifferenttechnologicalmaturities,andthustohighlightwherethereisneedforfurtherprogressininnovationtoclosethegapbetweenscenariosorovertimewithinascenario.OnlysavingsfrombehaviouralmeasuresarenotallocatedtoaTRLsincethesearenotprimarilydrivenbythetechnologiesdeployedbutbyshiftedbehaviourofend-users.ThedecompositionmoduleadherestotheLogarithmic-Mean-Divisia-Index(LMDI)approachtobreakdownthedifferencebetweenareferenceandacomparisonpoint(eitheranotherscenarioorthepreviousyear)foragivenyearbythekeylevers(Ang,2004).TheapproachisbasedontheKayaequationthatsinglesoutdifferenteffectsandseparatestheevaluatedlevers.TheKayaequationcanvarybysectorbutcanbedescribedasanexampleforCO2emissionswiththeactivity(A)andtheenergy(E)foreachtechnology(t)asfollows:𝐴𝑡𝐸𝐶𝑂2𝐶𝑂2𝑡=𝐴∗𝐴∗𝐴𝑡∗𝐸Inthisequation,themultipliersrepresenttheactivity(A),structuralchanges(S),energyintensity(I)andtheCO2intensity(C).Thesemultiplierscanbeprocessedandfurtherbrokendowntocalculatealltheabove-mentionedkeyleversthatareassessed.ApplyingtheLMDIfunctiontothedifferencebetweenareference(ref)andacomparisonpoint(comp),leadstothefollowingdifferenceforemissionsbetweenthesescenarios:𝐴𝑐𝑜𝑚𝑝𝑆𝑐𝑜𝑚𝑝𝐼𝑐𝑜𝑚𝑝𝐶𝑐𝑜𝑚𝑝𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−𝐶𝑂2𝑡,𝑟𝑒𝑓=𝜔𝑡∗{ln(𝐴𝑟𝑒𝑓)+ln(𝑆𝑟𝑒𝑓)+ln(𝐼𝑟𝑒𝑓)+ln(𝐶𝑟𝑒𝑓)},with𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−𝐶𝑂2𝑡,𝑟𝑒𝑓𝜔𝑡=ln𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−ln𝐶𝑂2𝑡,𝑟𝑒𝑓Theseformulasaredefinedinasimilarwayforanenergydecomposition.Foradecompositionbetweenscenarios,thetwoscenarios,comparisonandreference,arethecomparedpoints,e.g.theNZEScenarioandtheSTEPS.Foradecompositionofonescenarioovertime,thecomparisonandreferencepointsusethesameGECModelscenariobutjustwithadelayofoneyearbetween(e.g.comparingvaluesin"t"withvaluesin"t-1"asareference)tocalculatetheleversforeachyearstep.Forthecalculationofeffectsinatargetyear,annualeffectsareaccumulatedfortheperiodafterthebaseyear.Thedecompositionmodulecalculatestheeffectsconsideringhightechnologicalresolution,whichmeansbyend-usetechnologyandfuelforeachmodelledregion.Thisframeworkmakesitpossibletocalculateinterlinkagesbetweeneffects,suchastheindirectordirectdecompositionreflectingemissionsfrompowergenerationorenergyefficiencyimprovementsfromfuelswitching,e.g.electrification.Resultsatthesectoral,regionalorgloballevelareobtainedbysummingrelevantcontributions.86InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection1010Investment10.1InvestmentinfuelsupplyandthepowersectorInvestmentismeasuredastheongoingcapitalexpendituresinfuelproductionandpowergenerationcapacity,aswellasinfrastructure.ProjectionsofinvestmentrequirementsbyscenarioarederivedfromtheGECModelenergysupplyanddemandmodules.Thecalculationoftheinvestmentrequirementsforpowergenerationandfuelsupplyinvolvedthefollowingstepsforeachregion:◼Newcapacityneedsforproduction,transportationand(whereappropriate)transformationwerecalculatedonthebasisofprojecteddemandtrends,futuresupplyrequired,estimatedratesofretirementoftheexistingsupplyinfrastructureanddeclineratesforoilandgasproduction.◼Unitcapitalcostestimateswerecompiledforeachcomponentinthesupplychain.Thesecostswerethenadjustedforeachyearoftheprojectionperiodusingprojectedratesofchangebasedonadetailedanalysisofthepotentialfortechnology-drivencostreductionsandoncountry-specificfactors.◼Incrementalcapacityneedsweremultipliedbyunitcoststoyieldtheamountofinvestmentneededasiftheassetswereconstructedandbecameoperationalonanovernightbasis.◼Finally,usingtechnologyandcountry/region-specificspendingprofiles,overnightinvestmentneedswerethendistributeduniformlyacrossconstructionleadtimesestimatedforeachasset,whichwerefertoas‘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),coveringactualcapitalspendingfrom2000to2022andtheirplansorforecastsofupcomingspendingwhenavailable.Companieswereselectedonthebasisoftheirsizeasmeasuredbytheirproductionandreserves,thoughgeographicalspreadanddataavailabilityalsoplayedarole.Thesurveyedcompaniesaccountforoverthree-quartersofworldoilandgasproduction.Totalindustryinvestmentwascalculatedbyadjustingupwardsthespendingofthecompanies,accordingtotheirshareofworldoilandgasproductionforeachyear.Datawasobtainedfromcompanies’annualandfinancialreports,corporatepresentations,pressreports,tradepublicationsanddirectcontactsintheindustry.Section10Investment87IEA.CCBY4.0.Table10.1⊳Sub-sectorsandassetsincludedinfuelsupplyinvestmentSub-sectorOilandgasAssets•Upstreamoil•Upstreamgas•Midstreamoil(pipelines)•Midstreamgas(pipelinesandLNG)•Refining(greenfield)•Refining(upgradeandmaintenance)Coalsupply•Coalmining•CoaltransportationLow-emissionsfuels•Biogases•Liquidbiofuels•Hydrogenandhydrogen-basedfuelsproduction•HydrogeninfrastructureNote:LNG=liquefiednaturalgas.Long-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(USD/kW)foreachgeneratingtechnologybythecorrespondingcapacityadditionsorreplacement/refurbishmentforeachmodelledregion/country.Investmentoutlaysarethenspreadovertimebasedonspendingprofilesthatbeginatthestartofconstructionorfinancialcloseandfinishwhenanassetbecomesoperational.Thecapitalcostsassumedinthepowergenerationsectorarebasedonareviewofthelatestcountrydataavailableandonassumptionsoftheirevolutionovertheprojectionperiod.Theyrepresentovernightcostsforalltechnologies.ForrenewablesourcesandforplantsfittedwithCCUSfacilities,theprojectedinvestmentcostsresultfromthevariouslevelsofdeploymentinthedifferentscenarios.IndicativeovernightcostsandotherrelevantinvestmentassumptionsforalltechnologiesbyregionmaybefoundontheGECModelkeyinputdatapage.88InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTable10.2⊳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-scaleandbuildingsNote:CCUS=carboncapture,utilisationandstorage;PV=photovoltaic;EV=electricvehicle.10.2Demand-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.Section10Investment89IEA.CCBY4.0.Table10.3⊳Sub-sectorsandassetsincludedinend-useenergyinvestmentSectorSub-sectorBuildings•Energyefficiency(includingbuildingenvelopesandretrofits)•Electrification•Renewablesforenduse•Hydrogen-baseduseIndustry•Energyefficiency•Electrification•Renewablesforenduse•CCUS•Hydrogen-baseduse•Fossilfuel-basedindustrialfacilitiesTransport•Energyefficiencyofroadtransport•Electrificationofroadtransportandinternationalmarinetransport•Hydrogenandhydrogen-basedroadtransportandshipping10.3FinancingforinvestmentsSourcesoffinanceBuildinguponanalysiscarriedoutin2021andtheFinancingCleanEnergyTransitionsinEmergingandDevelopingEconomiesreport,anupdatedassessmentofthesourcesoffinanceassociatedwithinvestmentswascarriedout.Whileprojectdevelopersactastheprimaryactorsinvestinginenergyassets,theirsuccessdependsonahavingrobustinter-connectedsystemoffinancialsourcesandintermediaries,diverseinvestmentvehiclestofacilitateflowsandclearsignalsforaction,basedonprofitexpectationsandriskprofiles.Thesourcesoffinancearecharacterisedacrossfourbroadparameters:◼typeoffinancingstructure(off-balancesheet[projectfinance]oron-balancesheet[corporatefinance])◼typeofprovider(privateorpublic[publicfinanceinstitutionsandstate-ownedenterprises])◼typeofinstrument(accordingtocapitalstructure-debtorequity)◼originofprovider(internationalordomesticsources).Forfurtherdetailsonestimationapproach,pleaseseetheWorldEnergyInvestment2022MethodologyAnnex.CostoffinanceTheGECModelincorporatesdifferentiatedassumptionsonthecostofcapitalacrossregionswithinthesupply,powerandend-usesectors.Forexample,assomecountriespursueeffortstominimiseemissionsfromoilandgasoperationsintheAnnouncedPledgesScenario(APS),thisincreasestheirproductioncostsrelativetootherproducersandinmanycasesalsoinvolvesadditionalfinancingcosts(comparedtothoseassumedintheStatedPoliciesScenario[STEPS]).AsexplainedinSection4,adetailedanalysishasbeenundertakentoreflectthereductioninfinancingcostsforsolarPVandwindacrossGECModelcountries/regions.Investmentdecisionsinenergyefficiencyreflecttheestimatesfortheprevailingdebtandequityfinancecostsfacedbyconsumers(forresidentialbuildingsandvehicles),businessesintherealestatesector(forcommercialbuildings)andcompanies90InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONfromdifferentindustrialsectorsacrossGECModelregions.Financingcostsareexpressedinpre-taxtermscalculatedusingtheweightedaveragecostofcapital(WACC):𝑊𝐴𝐶𝐶𝑟𝑒𝑎𝑙,𝑝𝑟𝑒−𝑡𝑎𝑥=1+(𝐶𝑒×𝑤𝑒+𝐶𝑑×𝑤𝑑)−11+𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛Where:𝐶𝑒Costofequity𝐶𝑑Costofdebt𝑤𝑖shareofdebtorequityinthecapitalstructureForsectorswherepricesandunderlyingcontractsarelargelydenominatedininternationalcurrencies(e.g.USD),asintheoilandgasindustry,costcomponentswereestimatedusingmaturemarketrisk-freeratesadjustedforcountryandsectoralrisks.Forsectorswherepricesandunderlyingcontractsaredenominatedinlocalcurrencies,suchasinpowerandend-use,costcomponentswereestimatedusinglocalmarketrisk-freeratesadjustedforcountryandsectoralrisks.NominaldataareconvertedintorealtermsusingtheFischerEquation.EstimatingtheWACCcomponentsforthedifferentenergysectorsreflectsdatafromfinancialmarketsandacademicliterature,complementedbyinterviewswithmarketexpertsandpractitioners.Inaddition,differentiatedWACCsforthepowersectoroutlookincludeanalysisofauctionresultsandpowerpurchaseagreement(PPA)pricing.𝐴𝑡𝐸𝐶𝑂2𝐴𝑐𝑜𝑚𝑝𝑆𝑐𝑜𝑚𝑝𝐼𝑐𝑜𝑚𝑝𝐶𝑐𝑜𝑚𝑝𝐶𝑂2𝑡=𝐴∗𝐴∗𝐴𝑡∗𝐸𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−𝐶𝑂2𝑡,𝑟𝑒𝑓=∗{ln(𝐴𝑟𝑒𝑓)+ln(𝑆𝑟𝑒𝑓)+ln(𝐼𝑟𝑒𝑓)+ln(𝐶𝑟𝑒𝑓)},=𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−𝐶𝑂2𝑡,𝑟𝑒𝑓ln𝐶𝑂2𝑡,𝑐𝑜𝑚𝑝−ln𝐶𝑂2𝑡,𝑟𝑒𝑓Section10Investment91IEA.CCBY4.0.IEA.CCBY4.0.Section1111Energyaccess11.1DefinitionofmodernenergyaccessThereisnosingleinternationallyacceptedandinternationallyadopteddefinitionofmodernenergyaccess.Yetsignificantcommonalityexistsacrossdefinitions,including:◼Householdaccesstoaminimumlevelofelectricity◼Householdaccesstosaferandmoresustainable(i.e.minimumharmfuleffectsonhealthandtheenvironmentaspossible)cookingandheatingfuelsandstoves◼Accesstomodernenergythatenablesproductiveeconomicactivity,e.g.mechanicalpowerforagriculture,textileandotherindustries◼Accesstomodernenergyforpublicservices,e.g.electricityforhealthfacilities,schoolsandstreetlightingAlltheseelementsarecrucialtoeconomicandsocialdevelopment,asareanumberofrelatedissuesthataresometimesreferredtocollectivelyas"qualityofsupply",suchastechnicalavailability,adequacy,reliability,convenience,safetyandaffordability.ThedataandprojectionsfromtheGECModelfocusontwoelementsofenergyaccess:householdshavingaccesstoaminimumlevelofelectricityandtocleancookingfacilities.TheIEAdefinesenergyaccessas"ahouseholdhavingreliableandaffordableaccesstobothcleancookingfacilitiesandtoelectricity,whichisenoughtosupplyabasicbundleofenergyservicesinitially,andwiththelevelofservicecapableofgrowingovertime".ThisdefinitionofenergyaccessservesasabenchmarktomeasureprogresstowardsgoalSDG7.1andasametricforourforward-lookinganalysis.Accesstoelectricityentailsahouseholdhavinginitialaccesstosufficientelectricitytopowerabasicbundleofenergyservices–attheminimum,severallightbulbs,phonecharging,aradioandpotentiallyafanortelevision–withthelevelofservicecapableofgrowingovertime.Inourprojections,theaveragehouseholdwhohasgainedaccesswillhaveintimeenoughelectricitytopowerfourlightbulbsoperatingatfivehoursperday,onerefrigerator,afanoperatingsixhoursperday,amobilephonechargerandatelevisionoperatingfourhoursperday,whichequatestoanannualelectricityconsumptionof1250kWhperhouseholdwithstandardappliances,and420kWhwithefficientappliances.Thisservice-leveldefinitioncannotbeappliedtothemeasurementofactualdatasimplybecausethelevelofdatarequireddoesnotexistinalargenumberofcases.Asaresult,ourelectricityaccessdatabasesfocusonasimplerbinarymeasureofthosethathaveaconnectiontoanelectricitygrid,orhavearenewableoff-ormini-gridconnectionofsufficientcapacitytodelivertheminimumbundleofenergyservicesmentionedabove.Forexample,inthecaseofSolaroff-grid,onlySolarHomeSystemsofcapacityabove10Wpareincludedinaccessrates.SeetheIEA“GuidebookforImprovedElectricityAccessStatistics”formoredefinitions.Accesstocleancookingfacilitiesmeansaccessto(andprimaryuseof)modernfuelsandtechnologies,includingnaturalgas,LPG,electricityandbiogas,orimprovedbiomasscookstoves(ICS)ofISOTier>2thathaveconsiderablyloweremissionsandhigherefficienciesthantraditionalthree-stonefiresforcooking.Currently,veryfewICSmodelsattainthisloweremissionstarget,particularlyunderreal-worldcookingconditions.Therefore,ourcleancookingaccesshistoricdatabasereferstohouseholdsthatrelyprimarilyonfuelsotherthanbiomass(suchasfuelwood,charcoal,treeleaves,cropresiduesandanimaldung),coalorkeroseneforcooking.Forourprojections,onlythemostimprovedbiomasscookstovesthatdeliversignificantimprovementsareconsideredascontributingtoenergyaccess.ThemainsourcesforthehistoricdataaretheWorldHealthOrganisation(WHO)HouseholdEnergyDatabaseandtheIEAEnergyBalances.Section11Energyaccess93IEA.CCBY4.0.11.2OutlookformodernenergyaccessOutlookforelectricityaccessTheIEA’selectricityaccessdatabase1providesvaluableinformationaboutthecurrentelectrificationratesinalargenumberofcountries.Inordertoprovideanoutlookforelectricityaccessinthenextdecades,amodelabletogenerateprojectionsofelectrificationratesbyregionhasbeendeveloped.Theprojectionsarebasedonaneconometricpanelmodelthatregresseshistoricelectrificationratesofdifferentcountriesovermanyvariables,totesttheirlevelofsignificance.Variablesthatweredeterminedstatisticallysignificantandconsequentlyincludedintheequationsareper-capitaincome,demographicgrowth,urbanisation,fuelprices,levelofsubsidies,technologicaladvances,energyconsumption,energyaccessprogrammesandpolicies.ToidentifythemorefeasibleaccesstoelectricitypathwaystheIEAusesthelatestavailablecountry-by-countrygeospatialdatatoidentifytheleastcostpathwayprovidingconnectionstoun-electrifiedpopulations.Thisassessment,usingthepubliclyavailableOnSSETmodel,takesintoaccountdistancestothegrid,expecteddemand,thepopulationdensityandavailableresourcestoselecttheleastcostsolutionsforeachsettlement.Itthenfactors-inotherimportantindicatorsasthepotentialspeedatwhichgridandoff-gridsystemscanprovideaccess,thepotentialforsimultaneouslyelectrifyingothersectorssuchasindustry,agricultureortransport,theoptimalsolutionformaximisingreliability,resilienceandqualityofsupply,andtheattractivenessofinvestmenttodifferentinvestorsandvendors.Investment’srequirementsinelectricityaccessaremodelledonabaseofthepopulationgainingaccessbytechnologyandlatesttechnologycost.Investmentsincludesupplyinfrastructurescostforpopulationgainingfirstaccessaselectricitygenerationunits(forgrid,mini-gridsandstand-alonesystems),transmission(forgrids)anddistribution(forgridsandmini-grids)lines.OutlookforcleancookingaccessOurbaselinedataonthetraditionaluseofbiomassforcookingisbasedontheWorldHealthOrganization’s(WHO)GlobalHealthObservatoryestimatesofrelianceonsolidfuels.2Toprovideanoutlookforthenumberofpeoplerelyingonthetraditionaluseofbiomassinthenextdecades,aregionalmodelwasdevelopedunderdifferentassumptions.Relianceonbiomassratesofdifferentcountriesisprojectedusinganeconometricpanelmodelestimatedfromahistoricaltimeseries.Variablesthatweredeterminedstatisticallysignificantandconsequentlyincludedintheequationsareper-capitaincome,demographicgrowth,urbanisationlevel,levelofpricesofalternativemodernfuels,subsidiestoalternativemodernfuelconsumption,technologicaladvancesandcleancookingprogrammesandpolicies.Forfurtherdetailontheenergyaccessanalysisandmethodologyseethededicatedwebsite:https://www.iea.org/topics/energy-access.Investment’srequirementsinaccesstocleancookingaremodelledonabaseofthepopulationgainingaccessbytechnologyandlatesttechnologycosts.Investmentsincludeend-useequipmentsuchasstovesandbiodigestersaswellasinfrastructuresforLPG(primarystorageunits,refillingandsecondarystorage,cylinders…)andelectricity(additionalgenerationandlinestopowerelectric-cooking).1https://www.iea.org/reports/sdg7-data-and-projections/access-to-electricity2Formoreinformation,seewww.who.int/gho/phe/indoor_air_pollution/en/index.html94InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection1212EmploymentTheIEAaddedanenergyemploymentmodulein2020andcompletedafullerintegrationandtransfertoVensimwiththeGECModelframeworkin2022.Employmentmodellingnowcovers42energysub-sectorsforeachGECModelregionunderdifferentIEAscenarios.Themodelcurrentlyanalyses:◼Thenumberofpeoplecurrentlyemployedinresourcesupply(includingcoal,oil,gas,bioenergy,nuclearfuelsupply,criticalminerals,andhydrogen),thepowersector(generation,transmission,distribution,andstorage),aswellasmajorend-usesectors(vehiclemanufacturing,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.InalignmentwithInternationalLabourOrganization(ILO)guidelines,informalemploymentincludesallremunerativeworkthatisnotregistered,regulated,orprotectedbyexistinglegalorregulatoryframeworks(ILO,2023).Thiscomprisesown-accountworkersandworkersemployedininformalsectorenterprises;contributingfamilyworkers;employeesholdinginformaljobs;membersofinformalproducers’cooperatives;andown-accountworkersengagedintheproductionofgoodsexclusivelyforownfinalusebytheirownhousehold.EstimatesarebasedonILOdataandaliteraturereviewofinformalityratesbyregionandsector.CategorisationbyvaluechainstepEmploymentiscategorisednotonlybyenergyindustries,butalsobyvaluechainstepsoreconomicsectorsasdefinedbytheInternationalStandardIndustrialClassification(ISIC)revision4(UNDESA,2008),withsignificantnumbersofworkersinthefollowingfivegroupings:◼Rawmaterials:Agriculture(codeA)forbioenergyproductionandMiningandquarrying(codeB)forfossilfuelproductionSection12Employment95IEA.CCBY4.0.◼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.CategorisationbyskilllevelEmploymentisalsocategorisedbyskilllevel,inharmonywiththeInternationalStandardClassificationofOccupations2008(ISCO-08)laidoutbytheILO(ILO,2023a).SkilllevelisdefinedbytheILOas“afunctionofthecomplexityandrangeoftasksanddutiestobeperformedinanoccupation,”considering:◼Thenatureofworkperformed.◼Thelevelofformaleducationrequiredforcompetentperformance,asdefinedbytheInternationalStandardClassificationofEducation(ISCED-97)(UNESCO,2006).◼Theamountofworkexperienceand/oron-the-jobtrainingrequiredforcompetentperformance.Table12.4⊳SkilllevelsofemploymentestimatesbyassociatededucationlevelsandoccupationsSkilllevelILOSTATAssociatedISCED-97levelsAssociatedISCO-08skillleveloccupations“High”3-4ISCEDLevel5b:1-3yearsofstudyatahighereducational1.Managersinstitutefollowingcompletionofsecondaryeducation.2.ProfessionalsISCEDLevel5aorhigher:3-6yearsofstudyatahigher3.Techniciansandassociateeducationalinstituteleadingtotheawardofafirstdegreeorprofessionalshigherqualification;formalqualificationsmayberequiredforentrytotheoccupation.“Medium”2ISCEDLevel2:Completionofthefirststageofsecondary4.Clericalsupportworkerseducation.5.ServiceandsalesworkersISCEDLevel3:Completionofthesecondstageofsecondary6.Skilledagricultural,forestryeducation,whichmayincludeasignificantcomponentofandfisheryworkersvocationaleducationand/oron-the-jobtraining.7.CraftandrelatedtradesISCEDLevel4:Completionofvocation-specificeducationworkersundertakenaftercompletionofsecondaryeducation.8.Plantandmachineoperators,andassemblers“Low”1ISCEDLevel1:Completionofprimaryeducationorthefirst9.Elementaloccupationsstageofbasiceducationmayberequired,alongwithpossibleon-the-jobtraining.96InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTable12.4illustratestheoccupationsandeducationlevelstypicallyobservedateachskilllevel.Inmanycases,formaleducationisnotanidealmethodforapproximatingskilllevel,andassuchtheISCED-97levelassignedisindicativeofhowworkersofthatskilllevelgenerallyobtaintheknowledgeandskillsrequiredforcompetentperformance.Itisalwayspossiblethattheappropriatedegreeofworkexperienceand/oron-the-jobtrainingmaysubstituteforthelevelofformaleducationindicated.12.2EstimatingcurrentemploymentOurmodelusesIEAenergyinvestmentandspendingdata,dataonenergyproductionandconsumption,powercapacityandelectricitygeneration,technologystocksandsalesasthebasistoestimateglobalemployment.Thesedatapointsaremultipliedbyemploymentmultiplierstailoredtoeachenergysub-sectortoestimatetotalemploymentinthebaseyear.Multipliersareproducedviaacomprehensiveliteraturereviewandusingwagedataforeachsub-sectorandregionwhereavailable.Theyarealsoinformedbyliteraturereviewandcalibratedagainstexternallysourcedemploymentdatarelevanttoenergysub-sectors.MultipliersandemploymentestimateshavebeentestedwithcompanieswithinIEA’sEnergyBusinessCouncil,peerreviewers,academics,industrygroupsandinternationalorganisationssuchastheIMFandILO.EstimatingjobmultipliersTwotypesofmultipliersareusedinthemodel,basedoninvestment(jobspermillionUSdollarsinvested)andvolumetricdata(forexample,jobsperGWcapacityorjobspertonnesproduced).Multipliersvarybyregiontoreflectdifferencesinthelocalcostoflabourandworkerproductivity.Theyalsovarybyenergysub-sector,reflectingdifferentprojectcostbreakdowns,inotherwordshowmuchofeachmillionUSdollarsinvestedisallocatedtospendingonlabourversusmaterials.Theprimarysourcesusedtoestimatemultipliersinclude:◼Wagedatafromnationalstatisticsandinternationaldatabases,forinvestmentmultipliers◼Legalfinancialfilingsthatprovideinformationonemploymentandrevenue,costbreakdownsforprojectsandaveragewages◼Academic,intergovernmentalresearchandmodelledestimates◼IndividualcompanyandindustrygroupestimatesGovernmentsurveysofbusinesseswereprioritised,whenavailablewithsufficientdetail,tosupportthesub-sectoralanalysis.Employmentandfinancialinformationwereextractedfromtheannualreportsofmajorcompaniesineachsector,thoughthismethodcouldonlybeusedforsectorswithahighdegreeofconsolidationinmajorfirmsthatarepubliclylisted.Materialfromacademicandindustrysourceswasscreenedtoensureharmoniseddefinitionsandreferencevalueswereadjustedtoadheretotheframeworkdescribed.Wherevaluesfromthesesourceswereunavailable,estimateswerebasedonemploymentmultipliersforsimilartechnologies.Wherewagedataspecifictoenergyindustriesisnotavailable,generalisedwagedatabyregionisused.GatheringemploymentdataArichcollectionofemploymentdatafromexternalsourcesiscollectedannually,toserveasbenchmarksforthecalibrationofmultipliers.Thesedatasourcesincluded:◼Nationalstatisticsforallmajorcountries◼InternationalLabourOrganization(ILO)employmentdatabases(ILO,2023b)◼UnitedNationsIndustrialDevelopmentOrganization(UNIDO)IndStatandMinStatdatabases(UNIDO,2023aand2023b)Section12Employment97IEA.CCBY4.0.◼Reportsbyinternationalorganisationsandindustryassociations◼Academicliterature◼Annualreportsofmajorcompanies◼CompanyinterviewsWheredataiscollectedfrombroadlabourdatabases,wefocusoncategoriesrelevanttoenergy,includingthecompletelistofISICcodespresentedintheUnitedNations’InternationalRecommendationsforEnergyStatistics(UNStat,2011).Ourscopeincludescodessuchas0510(miningofhardcoal),0610(extractionofcrudepetroleum,0620(extractionofnaturalgas),1920(manufactureofrefinedpetroleumproducts),2910(manufactureofmotorvehicles),3510(electricpowergeneration,transmissionanddistribution),4322(plumbing,heatandairconditioninginstallation),and4930(transportviapipeline),andmanyothers.AmappingbetweenISICandotherclassificationssuchastheNorthAmericanIndustryClassificationSystem(NAICS)ortheEuropeanNomenclatureofEconomicActivities(NACE)enabledaharmonisedapproachtocollectingofficialstatisticsfromdifferentcountries.Dataofthehighestgranularityavailableisusedineachcase.AllocatingemploymentacrossglobalsupplychainsForenergytechnologieswithhighlyglobalisedsupplychains,employmentestimatesreflectwhereintheworldupstreammanufacturingcapacityislocated,ratherthanwheretherecorrespondingtechnologiesaredeployed.Dataaboutthemanufacturingcapacityforspecifictechnologies(suchassolarPVpanels,windturbines,gasturbines,etc.)wasgatheredbycountryorregion,andtheglobaltotalofmanufacturingjobswasredistributedacrossGECModelregionsaccordingly.Fortechnologiesthathaveverylocalisedproduction,suchasbuildingmaterialsandbiofuels,allmanufacturingjobswereassumedtobecreatedlocally.12.3OutlookforemploymentProjectionsbyscenarioarebasedonIEAscenarioresultsforallofthesameinputsthatwereusedtoestimatebaseyearemployment.Thesearemultipliedbythecorrespondingjobmultipliers–thataredifferentiatedbyregionandenergyindustry-toestimatetotaljobsincomingyearsuntil2030,andtherebyestimatechangesinjobgainsandlossesrelativetothebaseyear,aswellaswhatportionofexistingjobsaremaintained.ModellinglabourproductivityimprovementsMultipliersevolveovertimetoreflectassumptionsaboutlabourproductivityimprovements.Whereindustry-specifichistorictimeseriesofemploymentandcorrespondingproduction(oranotherrelevantmetric)areavailable,thehistoricrateofchangeisextendedforward.Wherespecifictimeseriesarenotavailable,datafromUNandILOonvalueaddedbyeconomicactivityandemploymentbyeconomicactivityareusedtocomputehistoriclabourproductivityimprovementratesbyregionandappliedtofuturemultiplierimprovements.TimingemploymentfornewprojectsinthepipelineSinceouremploymentestimatesforanygivenyearcomprisebothjobsintheoperationsofexistingassetsandjobsinthebuildoutofnewprojects,investmentovernightvaluesarespreadacrossthepreviousyearstoreflectwhenjobcreationwouldoccur,basedontypicalprojectdeliverytimelines.Inotherwords,weconsiderforhowlonganinvestmentcreatesjobsandinwhichyearrelativetotheprojectdelivery.Forinstance,investmentinanewhydroelectricdamwouldcreatesomejobsintheplanningandpreparationphasepriortotheinvestment.Whenfinancialcloseoccurs,thesejobsdisappear,butconstructionandequipmentmanufacturingjobsarecreated.Whenconstructioniscompleted,thesejobsdisappear,thenO&Mjobsbegin.Jobsareassignedtotherelevantyearstounderstandtotalemploymentonanannualbasis.98InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSection1313GovernmentspendingoncleanenergyandenergyaffordabilityTheIEAhasbeenmonitoringgovernmentspendingdedicatedtocleanenergysectorssinceApril2020,intheframeworkofitsGovernmentEnergySpendingTracker(formerlytheSustainableRecoveryTracker)whichassessestheimpactofsustainablerecoverypoliciesenactedbygovernmentsinresponsetotheCovid-19pandemicandenergycrisis.In2022,theAgencyenlargedthescopeofitstrackingtomeasuresaimedatcushioningdomesticconsumersfromtheimpactofthecurrentglobalenergypricecrisis.TheIEAassessmentoftheimpactofgovernmentspendingoncleanenergyandenergyaffordability:◼Collectstheamountofgovernmentspendingenactedtowardcleanenergyinvestmentsupportorconsumerenergyaffordabilitymeasures.◼EstimatestheamountofprivatespendingmobilisedthankstothecleanenergyinvestmentsupportandincorporatesitintheGECmodellingfortheSTEPS.Inthefollowingsection,wedescribethepolicycollectionprocessandhowtheimpactontotalcleanenergydeploymentisassessed.13.1PolicyidentificationandcollectionSustainablerecoverypoliciesSustainablerecoverypoliciesaredefinedaspoliciesdrivingspendingoncleanenergyinvestmentsupportincludedingovernmenteconomicrecoveryplansinresponsetotheCovid-19pandemicorthesubsequentglobalenergycrisis.Commonsustainablerecoverypoliciesincludeconsumerorproducersubsidiestodevelopelectricvehiclemarkets,directspendingorpublic-privatepartnership(PPP)forbuildinglow-carbonandefficienttransportinfrastructures,grantsforemergingenergytechnologypilotprogrammes,ortaxincentivesforenergy-efficientbuildingrenovations.QuantitativeestimatesintheSustainableRecoveryTrackerarebasedonnational-levelcleanenergysectorpoliciesenactedbygovernmentsfromthesecondquarterof2020untilApril2022aspartofCovid-19relatedrecoverymeasuresanddirectedtowardlong-termprojectsandmeasurestoboosteconomicgrowth.Thefollowingtypesofspendingareconsideredintheanalysis:◼Totalfiscalsupport:allgovernmentspendingdisbursedfrom2020inresponsetotheCovid-19crisis,intheformofadditionalspendingand/orforgonerevenue,aspertheIMFFiscalMonitordefinition.Thisincludesshort-termeconomicreliefpaymentstocitizensandfirmstoweathertheeffectsofthepandemic.◼Economicrecoveryspending:governmentspendingdirectedtolong-termprojectsandmeasurestoboostgrowth,asubsetoftotalfiscalsupport.Examplesincludeinfrastructureprojectslikeroads,broadbandinternet,publichousingupgrades,incentivesforbusinessimprovementsetc.Manygovernmentstendedtoturntotheselong-termperspectivepoliciesfromthesecondquarterof2020,afterhavingprecedentsconcentratedonemergencyeconomicandhealthsupport.Thisdoesnotincludeeconomicreliefpaymentstocitizensandfirms;andonlyincludesspendingthatisdirectedspecificallytonewinvestments.◼Governmentspendingonsustainablerecoverymeasures:governmentspendingtargetingcleanenergyinvestmentsupport,asubsetofeconomicrecoveryspending.Thisincludesconsumerorproducersubsidies,taxbreaks,publicprocurement,loanguarantees,PPPcontractsandotherco-fundingschemesfavouredbygovernments.Onlydirectgovernmentfiscalspendingfromthesecondquarterof2020isconsidered,spendingdirectedbyregulatorstostate-ownedenterprises(SOEs)orpubliclyregulatedentitiesbeingsetaside.Section13Governmentspendingoncleanenergyandenergyaffordability99IEA.CCBY4.0.Thelasttwocategories,whichencompassgovernmentandtotalmobilisedsustainablerecoveryspending,werecomparedonasectoralandregionalbasis,insixkeysectors:low-carbonelectricity,electricitynetworks,low-carbonandefficienttransport,energyefficientbuildingsandindustry,cleanerfuelsandemerginglow‐carbontechnologies.Onlyadditionalrecoveryspendingaimedatcreatingnewassetsorextendingthelifeofexistinglow-carboninfrastructureisconsidered.Accordingly,Covid-19-relatedliquiditymeasuresforenergycompaniesorenergyintensiveindustriesarenotdirectlyincorporatedsincetheydonotsupportadditionallow-carbonactivities.However,assupportingenergyfirmsthroughthepandemicpreservestheirabilitytoattractinvestment,thisbenefitiscapturedincalibratingsectoralfactorsassessingmobilisedprivatespending,togetherwithpoliciesgenerallyamelioratingtheinvestmentenvironment.EnergycrisisresponsepoliciesAffordabilitysupportincludesemergencyconsumersupportenactedbygovernmentsinresponsetotheinternationalpricerisethatmaterialisedinthefourthquarterof2021andwasfurtheraggravatedbyRussia’sinvasionofUkraine.Themostcommonpolicyinstrumentsincludetemporaryconsumersubsidiesortaxalleviation/exemption,state-backedloansorpriceregulationmechanisms,oftenenactedastemporarymeasures.Thespendingisassessedfromthegovernment’sperspective,asdirectbudgetallocation,foregonetaxrevenuesetc.QuantitativeestimatesfromenergycrisisresponsepoliciesarebasedonpoliciesenactedbygovernmentsbetweenSeptember2021andSeptember2022,andarederivedexclusivelyfromofficialgovernmentestimatesofthetotaldirectcostofsupportingthosemeasuresbornebygovernments.Accordingly,theydonotcaptureotherformsofsub-marketpricesubsidiesthatmaybechannelledthroughutilitiesandotherenergy-relatedstate-ownedenterprises.CollectionprocessTheIEAindependentlycollectsdataonrecoverypolicies,inco-operationwithitsmembers,aswellasG20members.AllpoliciesconsideredintheSustainableRecoveryTracker,alongsidetheircorrespondingbudgets,areavailableintheIEAPoliciesandMeasures(PAMS)Database,auniquerepositorythathasaggregatedenergypoliciesoverthepasttwodecades,bringingtogetherdatafromtheIEAEnergyEfficiencyDatabase,theAddressingClimateChangedatabase,theBuildingsEnergyEfficiencyPoliciesdatabaseandtheIEA/IRENARenewableEnergyPoliciesandMeasuresDatabase,alongwithinformationonCCUS,criticalmineralsandmethaneabatementpolicies.Eachrecordincludesaconcisesummaryofthepolicyandlinkstotheoriginalsource,andistaggedbypolicytype,technologyandsector.AmongthethousandsofpoliciesinthePAMSdatabaseare1500sustainablerecoveryandenergyaffordabilitypoliciescoveringaround70countries.Governmentsustainablerecoveryspendingisrecordedandattributedtothetimelinesofficiallyenacted,accordingtoavailableinformation.Totalmobilisedsustainablerecoveryspendingisspreadevenlyacrossallannouncedyears.Eachbudgetitemisalsotaggedwiththesustainablerecoverymeasureittargets.13.2AssessingtheimpactonoverallcleanenergyinvestmentTheimpactofgovernmentrecoveryspendingonoverallcleanenergydeploymentisassessedusingmobilisationfactorspersectorandgeography.Thisassessmentisusedtoassesstheimpactofthelatestpoliciesbutisnotusedasanestimatefortotalcleanenergyinvestment,whichinsteadflowsfromthemainGECModeloutputs.100InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTheabilityforgovernmentspendingtocrowd-inprivateinvestmentvariesgreatlyacrosscontexts,anddependsonmanydifferentfactors,rangingfromthetype,scaleandtemporalityofthefiscalinterventiontoaspectsinherenttolocaleconomicandfinancialcontextsand,increasingly,globalcommercialtrends.Theapproachchosenseekstoapproximatethismobilisationeffectbasedonalimitednumberofknownfactors,partlydrawnfromhistoricaltrends.Theevaluationiscontinuouslycomplementedandenhancedasdatabecomesavailable,notablyontheevolutionoftheeconomiccrisisindifferentregionsaswellasontheex-postassessmentsofCovid-19recoverypolicies.TheIEAaimsatrefiningthismodellingapproach,inparticulartotrytobetterassesshowaspecificpolicytypeimprovesefficacyofpublicdollarsmobilised,andcalibratingtheapproachbasedonrealinvestmentseeninthefield.AssessingmobilisationfactorsforcleanenergyinvestmentsupportPastmobilisationfactors(onepertechnologyperregion)werederivedfromhistoricallevelsofinvestmentandgovernmentsupport,drawingfromtheIEA’senergyinvestmentdatabase.Thesehistoricalmobilisationfactorswerethencalibratedtoreflectchanginginvestmentconditions.TheIEAusedaseriesofindices,pulledfromIEAdataorglobalfinancialsources,tohelpcalibratethemobilisationfactors.Theseindicescanuserawdatapoints(e.g.GDPgrowth),binaryvariables(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).DeterminingimplementationtimelinesManysustainablerecoverypoliciesaretargetingprojectsorinvestmentsthatwillnotmaterialiseinthenear-term(e.g.offshorewindprojectswithlongleadtimes,orCCUSpilots).Italsoconsidershowsomespendingismeanttolaythegroundworkforincreasedlong-termprivatesectorspendingorinvolvement(e.g.portandfuellinginfrastructure,andsupporttoinnovation).Theanalysisdetermineswhenthetotalsustainablerecoveryspendingmobilisedactuallymaterialisedon-the-groundbytakingintoaccountthreespecificstepsandassociateddelays:◼averagetimefrompolicyannouncementtodisbursementforviableprojects(frompolicyassessmentsconductedattheIEA).◼averagetimefromfinancialclosuretoeffectiveoperation(fromourWorldEnergyInvestmentdata).◼averagedelayforcertaingovernmentsupports(e.g.supportinginfrastructure,innovationfunding,research,marketreforms)tomaterialisetheirimpacts(estimatedbasedonlargeinfrastructureprojecttimelines).Thefirsttwoarereflectedbydelayingtheyearwhenthoseinvestmentscomeonrelativetotheyearthefundingisenacted.Thelastisbyincreasingtheprivatespendingmobilisationfactorforsubsequentyears.Section13Governmentspendingoncleanenergyandenergyaffordability101IEA.CCBY4.0.IEA.CCBY4.0.AnnexAAnnexA:TerminologyThisannexprovidesgeneralinformationonterminologyusedthroughoutthisreportincluding:definitionsoffuels,processesandsectors;regionalandcountrygroupings;andabbreviationsandacronyms.DefinitionsAdvancedbioenergy:Sustainablefuelsproducedfromwastesandresiduesandnon-foodcropfeedstocks(excludingtraditionalusesofbiomass),whicharecapableofdeliveringsignificantlifecyclegreenhousegasemissionssavingscomparedwithfossilfuelalternativesandofminimisingadversesustainabilityimpacts.Advancedbioenergyfeedstockeitherdonotdirectlycompetewithfoodandfeedcropsforagriculturallandorareonlydevelopedonlandpreviouslyusedtoproducedfoodcropfeedstocksforbiofuels.Thisdefinitiondiffersfromtheoneusedfor“advancedbiofuels”inUSlegislation,whichisbasedonaminimum50%lifecyclegreenhousegasreductionand,therefore,includessugarcaneethanol.Agriculture:Includesallenergyusedonfarms,inforestryandforfishing.Agriculture,forestryandotherlanduse(AFOLU)emissions:Includesgreenhousegasemissionsfromagriculture,forestryandotherlanduse.Ammonia(NH3):Acompoundofnitrogenandhydrogenthatcanbeusedasafeedstockinthechemicalsector,asafuelindirectcombustionprocessesinfuelcells,orasahydrogencarrier.Tobealow-emissionsfuel,ammoniamustbeproducedfromhydrogenthatisitselfproducedusingelectricitygeneratedfromlow-emissionssources,andnitrogenseparatedviatheHaberprocessusingelectricitygeneratedfromlow-emissionssources.Aviation:Thistransportmodeincludesbothdomesticandinternationalflightsandtheiruseofaviationfuels.Domesticaviationcoversflightsthatdepartandlandinthesamecountryaswellasflightsformilitarypurposes.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.Bioenergywithcarbon,capture,andstorage(BECCS):TechnologyinvolvinganyenergypathwaywhereCO2iscapturedfromabiogenicsource(e.g.biofuelplant)andpermanentlystored.Biogas:Amixtureofmethane,CO2andsmallquantitiesofothergasesproducedbyanaerobicdigestionoforganicmatterinanoxygen-freeenvironment.Biogases:Includebothbiogasandbiomethane.Biojetkerosene:Kerosenesubstituteproducedfrombiomass,viaconversionroutessuchashydroprocessedestersandfattyacids(HEFA)andbiomassgasificationwithFischer-Tropsch.Itexcludessynthetickeroseneproducedfrombiogeniccarbondioxide.AnnexATerminology103IEA.CCBY4.0.Biomethane:Biomethaneisanear-puresourceofmethaneproducedeitherby“upgrading”biogas(aprocessthatremovesanyCO2andothercontaminantspresentinthebiogas)orthroughthegasificationofsolidbiomassfollowedbymethanation.Itisalsoknownasrenewablenaturalgas.Buildings:Includesenergyusedinresidentialandservicesbuildings.Servicesbuildingsincludecommercialandinstitutionalbuildingsandothernon-specifiedbuildings.Buildingenergyuseincludesspaceheatingandcooling,waterheating,lighting,appliancesandcookingequipment.Bunkers:Includesbothinternationalmarinebunkersandinternationalaviationbunkers.Capacitycredit:Proportionofthecapacitythatcanbereliablyexpectedtogenerateelectricityduringtimesofpeakdemandinthegridtowhichitisconnected.Carboncapture,utilisationandstorage(CCUS):TheprocessofcapturingCO2emissionsfromfuelcombustion,industrialprocessesordirectlyfromtheatmosphere.CapturedCO2emissionscanbestoredinundergroundgeologicalformations,onshoreoroffshore,orusedasaninputorfeedstockinmanufacturing.Carbondioxide(CO2):Agasconsistingofonepartcarbonandtwopartsoxygen.Itisanimportantgreenhouse(heat-trapping)gas.Carbondioxideremoval(CDR):ProcessresultinginpermanentremovalofCO2fromtheatmosphere.IntheGECmodel,thiscanbeachievedthroughpermanentlystoringCO2capturedfrombiogenicsources(BECCS)orfromtheair(DACS).Cleancookingsystems,fuelsstovesandtechnologies:Cookingsolutionsthatreleaselessharmfulpollutants,aremoreefficientandenvironmentallysustainablethantraditionalcookingoptionsthatmakeuseofsolidbiomass(suchasathree-stonefire),coalorkerosene.Thisrefersprimarilytoimprovedsolidbiomasscookstoves(ISOTier>2),biogas/biodigestersystems,electricstoves,liquefiedpetroleumgas,naturalgasorethanolstoves.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.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.104InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONConcentratingsolarpower(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.Demand-sideresponse(DSR):Describesactionswhichcaninfluencetheloadprofilesuchasshiftingtheloadcurveintimewithoutaffectingtotalelectricitydemand,orloadsheddingsuchasinterruptingdemandforashortdurationoradjustingtheintensityofdemandforacertainamountoftime.Directaircapture(DAC):AtypeofCCUSthatcapturesCO2directlyfromtheatmosphereusingliquidsolventsorsolidsorbents.ItisgenerallycoupledwithpermanentstorageoftheCO2indeepgeologicalformationsoritsuseintheproductionoffuels,chemicals,buildingmaterialsorotherproducts.WhencoupledwithpermanentgeologicalCO2storage,DACisacarbonremovaltechnology,anditisknownasdirectaircaptureandstorage(DACS).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:Includestheproductionandmanufacturingofironandsteel,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.AnnexATerminology105IEA.CCBY4.0.Gaseousfuels:Includenaturalgas,biogases,syntheticmethane,andhydrogen.Gases:Seegaseousfuels.Gas-to-liquids(GTL):Processfeaturingreactionofmethanewithoxygenorsteamtoproducesyngas(amixtureofhydrogenandcarbonmonoxide)followedbysynthesisofliquidproducts(suchasdieselandnaphtha)fromthesyngasusingFischer-Tropschcatalyticsynthesis.Theprocessissimilartothatusedincoal-to-liquids.Geothermal:Geothermalenergyisheatderivedfromthesub-surfaceoftheearth.Waterand/orsteamcarrythegeothermalenergytothesurface.Dependingonitscharacteristics,geothermalenergycanbeusedforheatingandcoolingpurposesorbeharnessedtogeneratecleanelectricityifthetemperatureisadequate.Heat(end-use):Canbeobtainedfromthecombustionoffossilorrenewablefuels,directgeothermalorsolarheatsystems,exothermicchemicalprocessesandelectricity(throughresistanceheatingorheatpumpswhichcanextractitfromambientairandliquids).Thiscategoryreferstothewiderangeofend-uses,includingspaceandwaterheatingandcookinginbuildings,desalinationandprocessapplicationsinindustry.Itdoesnotincludecoolingapplications.Heat(supply):Obtainedfromthecombustionoffuels,nuclearreactors,geothermalresourcesandthecaptureofsunlight.Itmaybeusedforheatingorcooling,orconvertedintomechanicalenergyfortransportorelectricitygeneration.Commercialheatsoldisreportedundertotalfinalconsumptionwiththefuelinputsallocatedunderpowergeneration.Heavyindustries:Ironandsteel,chemicalsandcement.Hydrogen:Hydrogenisusedintheenergysystemasanenergycarrier,asanindustrialrawmaterial,orcombinedwithotherinputstoproducehydrogen-basedfuels.Unlessotherwisestated,hydrogenreferstolow-emissionshydrogen.Hydrogen-basedfuels:Seelow-emissionshydrogen-basedfuels.Hydropower:Theenergycontentoftheelectricityproducedinhydropowerplants,assuming100%efficiency.Itexcludesoutputfrompumpedstorageandmarine(tideandwave)plants.Improvedcookstoves:Intermediateandadvancedimprovedbiomasscookstoves(ISOtier>2).Itexcludesbasicimprovedcookstoves(ISOtier0-2).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/internationalsplitisdeterminedonthebasisoftheportofdepartureandportofarrival,andnotbytheflagornationalityoftheship.Consumptionbyfishingvesselsandbymilitaryforcesisexcludedandinsteadincludedintheresidential,servicesandagriculturecategory.106InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONInvestment:Investmentismeasuredastheongoingcapitalspendinginenergysupplycapacity,energyinfrastructureandenergyend-useandefficiency.Allinvestmentdataandprojectionsreflectspendingacrossthelifecycleofaproject,i.e.thecapitalspentisassignedtotheyearwhenitisincurred.Fuelsupplyinvestmentsincludeproduction,transformationandtransportationforoil,gas,coalandlowemissionsfuels.Powersectorinvestmentsincludenewbuildsandrefurbishmentsofgeneration,electricitygrids(transmission,distributionandpublicelectricvehiclechargers),andbatterystorage.Energyefficiencyinvestmentsincludethosemadeinbuildings,industryandtransport.Otherend-useinvestmentsincludedirectuseofrenewables;electricvehicles;electrificationinbuildings,industryandinternationalmarinetransport;useofhydrogenandhydrogen-basedfuels;fossilfuel-basedindustrialfacilities;CCUSinindustryandDACS/DACU.Investmentdataarepresentedinrealtermsinyear-2023USdollarsunlessotherwisestated.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.Low-emissionsfuels:Includemodernbioenergy,low-emissionshydrogenandlow-emissionssyntheticmethane.Low-emissionshydrogen-basedfuels:Includeammonia,methanol,andothersynthetichydrocarbons(gasesandliquids)madefromlow-emissionshydrogen.Anycarboninputs,e.g.fromCO2,arenotfromfossilfuelsorprocessemissions.Hydrogen-basedisusedinthefiguresinpublicationsusingtheGECModeltorefertohydrogenandhydrogen-basedfuels.Low-emissionshydrogen-basedliquidfuels:Asubsetoflow-emissionshydrogen-basedfuelsthatincludesonlyammonia,methanolandsyntheticliquidhydrocarbons,suchassynthetickerosene.Marineenergy:Representsthemechanicalenergyderivedfromtidalmovement,wavemotionoroceancurrentsandexploitedforelectricitygeneration.Middledistillates:Includejetfuel,dieselandheatingoil.Mini-grids:Smallelectricgridsystems,notconnectedtomainelectricitynetworks,linkinganumberofhouseholdsand/orotherconsumers.AnnexATerminology107IEA.CCBY4.0.Modernenergyaccess:Includeshouseholdaccesstoaminimumlevelofelectricity;householdaccesstolessharmfulandmoresustainablecookingandheatingfuels,andstoves;accessthatenablesproductiveeconomicactivity;andaccessforpublicservices.Moderngaseousbioenergy:Seebiogases.Modernliquidbioenergy:Includesbio-gasoline,biodiesel,biojetkeroseneandotherliquidbiofuels.Modernrenewables:Includeallusesofrenewableenergywiththeexceptionoftraditionaluseofsolidbiomass.Modernsolidbioenergy:Includesallsolidbioenergyproducts(seesolidbioenergydefinition)exceptthetraditionaluseofbiomass.Italsoincludestheuseofsolidbioenergyinintermediateandadvancedimprovedbiomasscookstoves(ISOtier>2)requiringfueltobecutinsmallpiecesoroftenusingprocessedbiomasssuchaspellets.Naturalgas:Comprisesgasesoccurringindeposits,whetherliquefiedorgaseous,consistingmainlyofmethane.Itincludesbothnon-associatedgasoriginatingfromfieldsproducinghydrocarbonsonlyingaseousform,andassociatedgasproducedinassociationwithcrudeoilaswellasmethanerecoveredfromcoalmines(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.Nearzeroemissioncapablematerialproductioncapacity:Capacitythatwillachievesubstantialemissionsreductionsfromthestart–butfallshortofnearzeroemissionmaterialproduction(seefollowingdefinition)initially–withplanstocontinuereducingemissionsovertimesuchthattheycouldlaterachievenearzeroemissionproductionwithoutadditionalcapitalinvestment.Nearzeroemissionmaterialproduction:Forsteelandcement,productionthatachievesthenearzeroemissionGHGemissionsintensitythresholdsdefinedintheIEA’s‘AchievingNetZeroHeavyIndustrySectorsinG7Members’(2022b);thethresholdsdependonthescrapshareofmetallicsinputforsteelandtheclinker-to-cementratioforcement.Forotherenergy-intensivecommoditieslikealuminium,fertilisersandplastics,productionthatachievesreductionsinemissionsintensityequivalenttotheconsiderationsfornearzeroemissionsteelandcement.Nearzeroemissionmaterialproductioncapacity:Capacitythat,onceoperational,willachievenearzeroemissionmaterialproduction(seeprecedingdefinition)fromthestart.Networkgases:Includenaturalgas,biomethane,syntheticmethaneandhydrogenblendedinagasnetwork.Non-energyuse:Fuelsusedforchemicalfeedstocksandnon-energyproducts.Examplesofnon-energyproductsincludelubricants,paraffinwaxes,asphalt,bitumen,coaltarsandoilsastimberpreservatives.Non-renewablewaste:Non-biogenicwastesuchasplasticsinmunicipalorindustrialwaste.Nuclear:Referstotheprimaryenergyequivalentoftheelectricityproducedbyanuclearpowerplant,assuminganaverageconversionefficiencyof33%.108InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONOff-gridsystems:Stand-alonesystemsforindividualhouseholdsorgroupsofconsumers.Offshorewind:Referstoelectricityproducedbywindturbinesinstalledinopenwater,usuallyintheocean.Oil:Includesbothconventionalandunconventionaloilproduction.Petroleumproductsincluderefinerygas,ethane,liquidpetroleumgas,aviationgasoline,motorgasoline,jetfuels,kerosene,gas/dieseloil,heavyfueloil,naphtha,whitespirits,lubricants,bitumen,paraffin,waxesandpetroleumcoke.Otherenergysector:Coverstheuseofenergybytransformationindustriesandtheenergylossesinconvertingprimaryenergyintoaformthatcanbeusedinthefinalconsumingsectors.Itincludeslossesintheproductionoflow-emissionshydrogenandhydrogen-basedfuels,bioenergyprocessing,gasworks,petroleumrefining,coalandgastransformationandliquefaction.Italsoincludesenergyownuseincoalmines,inoilandgasextractionandinelectricityandheatproduction.Transfersandstatisticaldifferencesarealsoincludedinthiscategory.Fueltransformationinblastfurnacesandcokeovensarenotaccountedforinthiscategory.Otherindustry:Acategoryofindustrybranchesthatincludesconstruction,foodprocessing,machinery,mining,textiles,transportequipment,woodprocessingandremainingindustry.Itissometimesreferredtoasnon-energy-intensiveindustries.Passengercars:Aroadmotorvehicle,otherthanamopedoramotorcycle,intendedtotransportpassengers.Itincludesvansdesignedandusedprimarilytotransportpassengers.Itexcludeslightcommercialvehicles,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.Rareearthelements(REEs):Agroupof17chemicalelementsintheperiodictable,specificallythe15lanthanidesplusscandiumandyttrium.REEsarekeycomponentsinsomecleanenergytechnologies,includingwindturbines,EVmotorsandelectrolysers.Renewables:Includesbioenergy,geothermal,hydropower,solarphotovoltaics(PV),concentratingsolarpower(CSP),windandmarine(tideandwave)energyforelectricityandheatgeneration.Residential:Energyusedbyhouseholdsincludingspaceheatingandcooling,waterheating,lighting,appliances,electronicdevicesandcooking.Roadtransport:Includesallroadvehicletypes(passengercars,two/three-wheelers,lightcommercialvehicles,busesandmediumandheavyfreighttrucks).Services:Energyusedincommercialfacilities,e.g.offices,shops,hotels,restaurants,andininstitutionalbuildings,e.g.schools,hospitals,publicoffices.Energyuseinservicesincludesspaceheatingandcooling,waterheating,lighting,appliances,cookinganddesalination.Shalegas:Naturalgascontainedwithinacommonlyoccurringrockclassifiedasshale.Shaleformationsarecharacterisedbylowpermeability,withmorelimitedabilityofgastoflowthroughtherockthanisthecasewithinaconventionalreservoir.Shalegasisgenerallyproducedusinghydraulicfracturing.AnnexATerminology109IEA.CCBY4.0.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):Thesumofconsumptionbythevariousend-usesectors.TFCisbrokendownintoenergydemandinthefollowingsectors:industry(includingmanufacturing,mining,chemicalsproduction,blastfurnacesandcokeovens),transport,buildings(includingresidentialandservices)andother(includingagricultureandothernon-energyuse).Itexcludesinternationalmarineandaviationbunkers,exceptatworldlevelwhereitisincludedinthetransportsector.Totalfinalenergyconsumption(TFEC):Avariabledefinedprimarilyfortrackingprogresstowardstarget7.2oftheUnitedNationsSustainableDevelopmentGoals.Itincorporatestotalfinalconsumptionbyend-usesectorsbutexcludesnon-energyuse.Itexcludesinternationalmarineandaviationbunkers,exceptatworldlevel.Typically,thisisusedinthecontextofcalculatingtherenewableenergyshareintotalfinalenergyconsumption(SustainableDevelopmentGoal7.2.1),whereTFECisthedenominator.Totalprimaryenergydemand(TPED):Seetotalenergysupply.Traditionaluseofbiomass:Theuseofsolidbiomasswithbasictechnologies,suchasathree-stonefireorbasicstoves(ISOTier0-2),oftenwithnoorpoorlyoperatingchimneys.Transport:Fuelsandelectricityusedinthetransportofgoodsorpeoplewithinthenationalterritoryirrespectiveoftheeconomicsectorwithinwhichtheactivityoccurs.Thisincludesfuelandelectricitydeliveredtovehiclesusingpublicroadsorforuseinrailvehicles;fueldeliveredtovesselsfordomesticnavigation;fueldeliveredtoaircraftfordomesticaviation;andenergyconsumedinthedeliveryoffuelsthroughpipelines.Fueldeliveredtointernationalmarineandaviationbunkersispresentedonlyattheworldlevelandisexcludedfromthetransportsectoratadomesticlevel.110InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONTrucks:Includesallsizecategoriesofcommercialvehicles:lighttrucks(grossvehicleweightlessthan3.5tonnes);mediumfreighttrucks(grossvehicleweight3.5to15tonnes);andheavyfreighttrucks(>15tonnes).Unabatedcoal:ConsumptionofcoalinfacilitieswithoutCCUS.Unabatedfossilfueluse:ConsumptionoffossilfuelsinfacilitieswithoutCCUS.Unabatedgas:ConsumptionofnaturalgasinfacilitieswithoutCCUS.Usefulenergy:Theenergythatisavailabletoend-userstosatisfytheirneeds.Thisisalsoreferredtoasenergyservicesdemand.Asresultoftransformationlossesatthepointofuse,theamountofusefulenergyislowerthanthecorrespondingfinalenergydemandformosttechnologies.Equipmentusingelectricityoftenhashigherconversionefficiencythanequipmentusingotherfuels,meaningthatforaunitofenergyconsumed,electricitycanprovidemoreenergyservices.Value-adjustedlevelisedcostofelectricity(VALCOE):Incorporatesinformationonbothcostsandthevalueprovidedtothesystem.BasedontheLCOE,estimatesofenergy,capacityandflexibilityvalueareincorporatedtoprovideamorecompletemetricofcompetitivenessforpowergenerationtechnologies.Variablerenewableenergy(VRE):Referstotechnologieswhosemaximumoutputatanytimedependsontheavailabilityoffluctuatingrenewableenergyresources.VREincludesabroadarrayoftechnologiessuchaswindpower,solarPV,run-of-riverhydro,concentratingsolarpower(wherenothermalstorageisincluded)andmarine(tidalandwave).Zero-carbon-readybuildings:Azero-carbon-readybuildingishighlyenergyefficientandeitherusesrenewableenergydirectlyoranenergysupplythatcanbefullydecarbonised,suchaselectricityordistrictheat.Zeroemissionsvehicles(ZEVs):VehiclesthatarecapableofoperatingwithouttailpipeCO2emissions(batteryelectricandfuelcellvehicles).RegionalandcountrygroupingsResultsfromtheGECModelareoftenpresentedwiththebelowregionalgroupings: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.3Caspian:Armenia,Azerbaijan,Georgia,Kazakhstan,Kyrgyzstan,Tajikistan,TurkmenistanandUzbekistan.CentralandSouthAmerica:Argentina,PlurinationalStateofBolivia(Bolivia),Brazil,Chile,Colombia,CostaRica,Cuba,Curaçao,DominicanRepublic,Ecuador,ElSalvador,Guatemala,Guyana,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).AnnexATerminology111IEA.CCBY4.0.FigureA.1⊳GECModelregionalgroupingsIEA.CCBY4.0.Note:Thismapiswithoutprejudicetothestatusoforsovereigntyoveranyterritory,tothedelimitationofinternationalfrontiersandboundariesandtothenameofanyterritory,cityorarea.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.LatinAmericaandtheCaribbean:CentralandSouthAmericaregionalgroupingandMexico.MiddleEast:Bahrain,IslamicRepublicofIran(Iran),Iraq,Jordan,Kuwait,Lebanon,Oman,Qatar,SaudiArabia,SyrianArabRepublic(Syria),UnitedArabEmiratesandYemen.Non-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).112InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONSoutheastAsia:BruneiDarussalam,Cambodia,Indonesia,LaoPeople’sDemocraticRepublic(LaoPDR),Malaysia,Myanmar,Philippines,Singapore,ThailandandVietNam.ThesecountriesareallmembersoftheAssociationofSoutheastAsianNations(ASEAN).Sub-SaharanAfrica:Angola,Benin,Botswana,Cameroon,RepublicoftheCongo(Congo),Côted’Ivoire,DemocraticRepublicoftheCongo,EquatorialGuinea,Eritrea,Ethiopia,Gabon,Ghana,Kenya,KingdomofEswatini,Madagascar,Mauritius,Mozambique,Namibia,Niger,Nigeria,Rwanda,Senegal,SouthAfrica,SouthSudan,Sudan,UnitedRepublicofTanzania(Tanzania),Togo,Uganda,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,SintEustatiusandSaba,BritishVirginIslands,CaymanIslands,Dominica,FalklandIslands(Malvinas),Grenada,Montserrat,SaintKittsandNevis,SaintLucia,SaintPierreandMiquelon,SaintVincentandGrenadines,SaintMaarten(Dutchpart),TurksandCaicosIslands.5ThestatisticaldataforIsraelaresuppliedbyandundertheresponsibilityoftherelevantIsraeliauthorities.TheuseofsuchdatabytheOECDand/ortheIEAiswithoutprejudicetothestatusoftheGolanHeights,EastJerusalemandIsraelisettlementsintheWestBankunderthetermsofinternationallaw.6Individualdataarenotavailableandareestimatedinaggregatefor:BurkinaFaso,Burundi,CaboVerde,CentralAfricanRepublic,Chad,Comoros,Djibouti,Gambia,Guinea,Guinea-Bissau,Lesotho,Liberia,Malawi,Mali,Mauritania,SaoTomeandPrincipe,Seychelles,SierraLeoneandSomalia.FossilfuelsupplyregionsAsnotedinthemodeldescription,thefossilfuelsupplymoduleshaveadifferentregionalbreakdownrelativetotheregionsusedintherestoftheGECModel.Thisenablesthesupplymodulesinordertomostaccuratelyreflecttheparticularitiesoffossilfuelproducingcountriesandregions.Theregionalbreakdownforthesemodulesareasfollows:OilandnaturalgassupplymoduleregionsTheGECModeloilandnaturalgassupplymoduleconsistsof113regions,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.AnnexATerminology113IEA.CCBY4.0.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.Coalsupplymoduleregions19countriesaremodelledonanindividualbasisintheGECModelcoalsupplymodule:Australia,Brazil,Canada,Chile,China,Colombia,India,Indonesia,Japan,Korea,Mexico,Mongolia,Mozambique,NewZealand,Russia,SouthAfrica,theUnitedStates,VenezuelaandVietNam.AcronymsEuropeanAutomobileManufacturers’AssociationalternatingcurrentACEAagriculture,forestryandotherlanduseACAviationIntegratedModelAFOLUAnnouncedPledgesScenarioAIMAssociationofSoutheastAsianNationsAPSactivity,structure,intensityandfueluseASEANalcohol-to-jetASIFbioenergyequippedwithCCUSATJbatteryelectricvehiclesBECCScapitalexpenditureBEVcoalbedmethaneCAPEXcombined-cyclegasturbineCBMcarboncapture,utilisationandstorageCCGTcarbondioxideremovalCCUSmethaneCDRCH4114InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationIEA.CCBY4.0.ModelDOCUMENTATIONCHPcombinedheatandpower;thetermco-generationissometimesusedCOcarbonmonoxideCO2carbondioxideCO2-eqcarbon-dioxideequivalentCSPconcentratingsolarpowerCTGcoal-to-gasCTLcoal-to-liquidsDACdirectaircaptureDACUdirectaircaptureandutilisationDACSdirectaircaptureandstorageDCdirectcurrentDRIdirectreducedironDSIdemand-sideintegrationDSOdistributionsystemoperatorDSRdemand-sideresponseEAFelectricarcfurnaceEHOBextra-heavyoilandbitumenEORenhancedoilrecoveryEPCEngineering,procurementandconstructionESGenvironmental,socialandgovernanceETPEnergyTechnologyPerspectivesETSAPEnergyTechnologySystemsAnalysisProgramEUEuropeanUnionEUETSEuropeanUnionEmissionsTradingSystemEVelectricvehicleEVSEElectricvehiclesupplyequipmentFAMEfattyacidmethylesterFAOFoodandAgricultureOrganizationoftheUnitedNationsFCEVfuelcellelectricvehicleFDIforeigndirectinvestmentFTEFull-timeemploymentGAINSGreenhouseGas-AirPollutionInteractionsandSynergiesGDPgrossdomesticproductGECModelGlobalEnergyandClimateModelGHGgreenhousegasesGIMFModelGlobalIntegratedMonetaryandFiscalModelGISGeographicInformationSystemGLOBIOMGlobalBiosphereManagementModelGTLgas-to-liquidsH2hydrogenHEFAhydrogenatedestersandfattyacidsHFOheavyfueloilHRShydrogenrefuellingstationsHSRhigh-speedrailHVOhydrotreatedvegetableoilIAEAInternationalAtomicEnergyAgencyICEinternalcombustionengineICSimprovedbiomasscookstovesIEAInternationalEnergyAgencyIGCCintegratedgasificationcombined-cycleAnnexATerminology115IEA.CCBY4.0.IIASAInternationalInstituteforAppliedSystemsAnalysisIEA.CCBY4.0.ILOInternationalLabourOrganizationIMFInternationalMonetaryFundIMOInternationalMaritimeOrganizationIPCCIntergovernmentalPanelonClimateChangeIRENAInternationalRenewableEnergyAgencyISCEDInternationalStandardClassificationofEducationISICInternationalStandardIndustrialClassificationISOInternationalOrganizationforStandardizationJRCJointResearchCentreLCOElevelisedcostofelectricityLCVlightcommercialvehicleLDVlight-dutyvehicleLEDlight-emittingdiodeLMDIlogarithmicmeandivisiaindexLNGliquefiednaturalgasLPGliquefiedpetroleumgasLRMCLong-runmarginalcostLULUCFlanduse,land-usechangeandforestryMAGICCModelfortheAssessmentofGreenhouseGasInducedClimateChangeMoMoMobilityModelMTOMRMedium-TermOilMarketReportNAICSNorthAmericanIndustryClassificationSystemNACEEuropeanNomenclatureofEconomicActivitiesNDCsNationallyDeterminedContributionsNEANuclearEnergyAgencyNGLsnaturalgasliquidsNGVnaturalgasvehicleNH3ammoniaNOXnitrogenoxidesN2OnitrousoxideNZENetZeroEmissionsby2050ScenarioO&MOperationsandmaintenanceOECDOrganisationforEconomicCo-operationandDevelopmentOnSSETOpenSourceSpatialElectrificationToolOPECOrganizationofthePetroleumExportingCountriesOPEXoperationalexpenditurePEMpolymerelectrolytemembraneorprotonexchangemembranePLDVpassengerlight-dutyvehiclePMparticulatematterPM2.5fineparticulatematterPPApowerpurchaseagreementPPPpublic-privatepartnershipPVphotovoltaicSDGSustainableDevelopmentGoalSDSSustainableDevelopmentScenarioSMRsteammethanereformationSO2sulphurdioxideSOECSolidoxideelectrolysercellsSTEPSStatedPoliciesScenario116InternationalEnergyAgencyGlobalEnergyandClimateModelDocumentationModelDOCUMENTATIONSUVsSportutilityvehiclesSVOStraightvegetableoilT&DtransmissionanddistributionTCPTechnologyCollaborationProgrammeTEStotalenergysupplyTFCtotalfinalconsumptionTFECtotalfinalenergyconsumptionTIMESModelTheIntegratedMARKAL-EFOMSystemModelTRLtechnologyreadinesslevelTSOtransmissionsystemoperatorUCLUniversityCollegeLondonUNUnitedNationsUNIDOUnitedNationsIndustrialDevelopmentOrganizationUSUnitedStatesUSGSUnitedStatesGeologicalSurveyVALCOEvalue-adjustedlevelisedcostofelectricityVREvariablerenewableenergyWACCweightedaveragecostofcapitalWEMWorldEnergyModelWEOWorldEnergyOutlookWHOWorldHealthOrganizationZEVzero-emissionvehicleZCRBzero-carbon-readybuildingAnnexATerminology117IEA.CCBY4.0.IEA.CCBY4.0.AnnexBAnnexB:ReferencesAboumahboubetal.(2010):OptimalConfigurationofaRenewable-basedElectricitySupplySector,WSEASTransactionsonPowerSystems(ISSN:1790-5060),2,p.120-129,http://www.wseas.us/e-library/transactions/power/2010/89-612.pdf,accessed28June2011.AIM(AviationIntegratedModel),developedatUCL,https://www.ucl.ac.uk/energy-models/models/aimAnandarajah,G.etal.(2011),TIAM-UCLGlobalModelDocumentation,UKERCWorkingPaperUKERC/WP/ESY/2011/001,UKEnergyResearchCentre,London.Ang(2004),Decompositionanalysisforpolicymakinginenergy:whichisthepreferredmethod?,EnergyPolicy,vol.32(9),p.113-1139.BGR(GermanFederalInstituteforGeosciencesandNaturalResources)(2014),Energiestudie2014,Reserven,RessourcenundVerfügbarkeitvonEnergierohstoffen,(EnergyStudy2014,Reserves,ResourcesandAvailabilityofEnergyResources),BGR,Hannover,Germany.−(2021).Energiestudie-DatenundEntwicklungenderdeutschenundglobalenEnergieversorgung.Hannover,Germany:FederalInstituteforGeosciencesandNaturalResources.https://www.bgr.bund.de/DE/Themen/Energie/Downloads/energiestudie_2021.htmlBNEF.(2022).Top10EnergyStorageTrendsin2023.Vonhttps://about.bnef.comBP.(2022).StatisticalReviewofWorldEnergy.London,UK:BP.https://www.bp.com/en/global/corporate/energy‐economics/statistical‐review‐of‐world‐energy.htmlCEDIGAZ.(2023).CountryindicatorsfromCedigazdatabases.Rueil-Malmaison,France:Cedigaz.https://www.cedigaz.org/databases/Cole,W.(2021).CostProjectionsforUtilityScaleBatteryStorage:2021Update.https://www.nrel.gov/docs/fy21osti/79236.pdfDargay,J.,D.GatelyandM.Sommer(2006),“VehicleOwnershipandIncomeGrowth,Worldwide:1960-2030”,EnergyJournal,Vol.28,No.4,Elsevier,Amsterdam.Econoler,etal.(2011),CoolingBenchmarkingStudyReport,CollaborativeLabelingandApplianceStandardsProgram(CLASP),Brussels.ENTSO-E.(2016).ConsumptionData.Retrievedfromhttps://www.entsoe.eu/data/data-portal/consumption/Pages/default.aspx.EVVolumes(2020),https://www.ev-volumes.com/EWITS(2011):EasternWindIntegrationandTransmissionStudy,preparedfor:TheNationalRenewableEnergyLaboratory,preparedby:EnerNexCorporation,http://www.nrel.gov/wind/systemsintegration/pdfs/2010/ewits_final_report.pdf,accessed28June2011.Dataavailableonhttp://www.nrel.gov/wind/systemsintegration/ewits.html,accessed28June2011.FinancialTimes.(2020).Electriccarcoststoremainhigherthantraditionalengines.https://www.ft.com/content/a7e58ce7-4fab-424a-b1fa-f833ce948cb7GBPN(GlobalBuildingsPerformanceNetwork)andCEU(CentralEuropeanUniversity)(2012),BestPracticePoliciesforLowCarbon&EnergyBuildings-BasedonScenarioAnalysis,GBPNandCEU,ParisandBudapest.AnnexBReferences119IEA.CCBY4.0.Heideetal.(2010):Seasonaloptimalmixofwindandsolarpowerinafuture,highlyrenewableEurope.RenewableEnergyVol.35,p.2483-2589,http://www.mng.org.uk/gh/resources/Heide_et_al2.pdf,accessed28June2011Hofmannetal.,(2021).atlite:ALightweightPythonPackageforCalculatingRenewablePowerPotentialsandTimeSeries.JournalofOpenSourceSoftware,6(62),3294,https://doi.org/10.21105/joss.03294IAI(InternationalAluminiumInstitute)(2019),PrimaryAluminiumProduction,London,http://www.world-aluminium.org/statistics/IATA(InternationalAirTransportAssociation)(2012),TechnologyRoadmapReport4thedition,IATA,Geneva.IEA(InternationalEnergyAgency)(2022a),Real-TimeElectricityTracker,https://www.iea.org/data-and-statistics/data-tools/real-time-electricity-tracker.−(2022b),AchievingNetZeroHeavyIndustrySectorsinG7Members,OECD/IEA,Paris.−(2023),WeatherforEnergyTracker,https://www.iea.org/data-and-statistics/data-tools/weather-for-energy-tracker.IIASA(InternationalInstituteofAppliedSystemsAnalysis)(2023),GAINSTechnicalReports,https://gains.iiasa.ac.at/models/gains_tech_reports.htmlILO(InternationalLabourOrganization)(2023a),InternationalStandardClassificationofOccupations(ISCO-08),https://www.ilo.org/public/english/bureau/stat/isco/docs/publication08.p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