Guiding European Policy toward a low-carbon economy. Modelling … · Consortium during the GA meeting in Brno (February 2017). Part 2 - Task 3.3.b: Evaluation of monetary fluxes

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GuidingEuropeanPolicytowardalow-carboneconomy.ModellingsustainableEnergy

systemDevelopmentunderEnvironmentalAndSocioeconomicconstraints

Deliverable3.4(D12)AdaptativeScenarios

Version1.0.0

Duedateofdeliverable: 30/04/2017 Actualsubmissiondate: 30/04/2017

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DisclaimerofwarrantiesandlimitationofliabilitiesThisdocumenthasbeenpreparedbyMEDEASprojectpartnersasanaccountofworkcarriedoutwithintheframeworkoftheEC-GAcontractno691287.

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DocumentinfosheetLeadBeneficiary:UniversidaddeValladolid(Uva)

WP:WP3ScenariosandPathways

Task:Deliverable3.4.Adaptivescenarios

Authors:

INSTM:S.Falsini,I.Perissi,U.Bardi

MU:ChristianKimmich,MartinČerný,ChristianKerschner

UVa:IñigoCapellán-Pérez(ICM-CSIC),IgnaciodeBlas,JaimeNieto,LuisJavierMiguel,CarlosdeCastro,MargaritaMediavilla,OscarCarpintero,FernandoFrechoso,SantiagoCáceresandPaulaRodrigo.

Disseminationlevel:Public



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TableofcontentsDISCLAIMEROFWARRANTIESANDLIMITATIONOFLIABILITIES...................................................2

DOCUMENTINFOSHEET..............................................................................................................3

TABLEOFCONTENTS...................................................................................................................4

SCOPEOFDOCUMENT.................................................................................................................7

LISTOFABBREVIATIONSANDACRONYMS...................................................................................8

EXECUTIVESUMMARY...............................................................................................................10

INTRODUCTION.........................................................................................................................12EXERGYANDENERGY...................................................................................................................................................................12Energydefinition.......................................................................................................................................................................12Exergydefinition.......................................................................................................................................................................12

METHODOLOGY........................................................................................................................13METHODOLOGYFORESTIMATINGENERGYANDEXERGYBYSECTOR..................................................................................13Datasource..................................................................................................................................................................................13

METHODOLOGYFORESTIMATINGOT2020ANDMLT2030SCENARIOS........................................................................16METHODOLOGYFORESTIMATINGELECTRICITYSECTORENERGY/EXERGY......................................................................18Datasourceforelectricitysectorenergy.......................................................................................................................18MethodologyforestimatingBAU,OT2020andMLT2030scenarios...............................................................19

RESULTS....................................................................................................................................20RESIDENTIALANDCOMMERCIALSECTOR.................................................................................................................................20Energy/exergyscenariosfortheResidential/Commercialsector......................................................................22

TRANSPORTSECTOR.....................................................................................................................................................................25Energy/exergyscenariosfortransportsector.............................................................................................................25

INDUSTRIALSECTOR.....................................................................................................................................................................28Energy/exergyscenariosforindustrialsector............................................................................................................28

ELECTRICITYSECTOR....................................................................................................................................................................31BAUenergy/exergy..................................................................................................................................................................31OT-2020energy/exergy.........................................................................................................................................................32MLT-2030energy/exergy.....................................................................................................................................................33

CONCLUSIONS...........................................................................................................................34

REFERENCES..............................................................................................................................35

LISTOFTABLES..........................................................................................................................36

LISTOFFIGURES........................................................................................................................37



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PART2-TASK3.3.B:EVALUATIONOFMONETARYFLUXESBETWEENSECTORSNECESSARYTOACHIEVETHEPLANNEDSCENARIOSFOROPTIMALTRANSITION(OT)ANDMID-LEVELTRANSITION(MLT)....................................................................................................................38

INTRODUCTION.........................................................................................................................38

METHODOLOGY........................................................................................................................40Input-outputbasicsandtechnicalcoefficients............................................................................................................44Input-outputbasics..................................................................................................................................................................45Technicalcoefficients,AmatrixandLeontiefinverse..............................................................................................47

DECOMPOSITIONOFSECTORSININPUT-OUTPUTTABLES.....................................................................................................50Structuraldecompositionanalysis....................................................................................................................................51Annualreports...........................................................................................................................................................................52Inputcostshares.......................................................................................................................................................................53

EXPLORINGPOST-CARBONFUTURES.........................................................................................................................................54Approachesbasedonstakeholderparticipation–participatorymodelling..................................................55

RESULTS....................................................................................................................................57HISTORICALTRENDS.....................................................................................................................................................................58Worldlevel...................................................................................................................................................................................58Countrylevelexamples...........................................................................................................................................................65

FUTUREPROJECTIONSOFMONETARYFLUXES.........................................................................................................................71Costsharesofselectedrenewableenergysources.....................................................................................................71Scenarios......................................................................................................................................................................................772030situation:Optimaltransition(OT)scenario......................................................................................................802030situation:Midleveltransition(MLT)scenario.................................................................................................812050situation:Optimaltransition(OT)scenario......................................................................................................822050situation:Midleveltransition(MLT)scenario.................................................................................................83

SUMMARYANDCONCLUSIONS.................................................................................................84Disclaimeronlimitations......................................................................................................................................................86

REFERENCES..............................................................................................................................87

LISTOFTABLES..........................................................................................................................89

LISTOFFIGURES........................................................................................................................90

APPENDIX1:2030MLTIOTABLE(AMATRIXOFTECHNICALCOEFFICIENTS)..............................92

APPENDIX2:2050OTIOTABLE(AMATRIXOFTECHNICALCOEFFICIENTS).................................93

PART3-TASK3.3.C:VARIABILITYOFTHEPROPOSEDSCENARIOSANDPATHWAYSDUETOPHYSICALCONSTRAINTS............................................................................................................94



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INTRODUCTION.........................................................................................................................94

DESCRIPTIONOFSCENARIOS.....................................................................................................99QUALITATIVENARRATIVESMODELLED.....................................................................................................................................99

VARIABILILITYOFTHESCENARIOSDUETOTHEENERGY-ECONOMYFEEDBACK.......................106ENERGY-ECONOMYRELATIONSHIPINTHEINTEGRATEDASSESSMENTMODELS..........................................................106EXPECTEDRESULTSANDMAINMEDEASCONTRIBUTION................................................................................................109

AVAILABILITYOFENERGYRESOURCESINMEDEAS..................................................................111AVAILABILITYOFNON-RENEWABLEENERGY(NRE)RESOURCES....................................................................................111AVAILABILITYOFRENEWABLEENERGYSOURCES(RES)...................................................................................................115RESforheatandbiofuels...................................................................................................................................................116RESforelectricity..................................................................................................................................................................119

MODELLINGOFCLIMATICIMPACTSINMEDEAS......................................................................123

PRELIMINARYRESULTS............................................................................................................127

CONCLUSIONS.........................................................................................................................133

REFERENCES............................................................................................................................134

LISTOFTABLES........................................................................................................................145

LISTOFFIGURES......................................................................................................................146



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ScopeofdocumentThis document is a report of the Task 3.3 on Adaptive scenarios. In this task the variability (orexpected uncertainty) of the proposed scenarios and pathways will be explored using simplecalculationsandphysicalconstraints.Thisapproachwillbeundertakenbymeansofagrossandnetenergyaccountingofeacheconomicsectorconsideredintheselectedscenario.Therequirednetenergyamounttokeepoperatinganeconomicsectorwillalsobeconsideredforthispurpose.Theenergy andmonetary fluxes between socio-economic sectors in each adaptive scenariowill beoutlined.

Thisdeliverableismadeinthreeparts:

Part1 -Task3.3.a:Evaluationofnetenergyandexergyofeacheconomicsector in theOptimalTransition(OT)andMid-leveltransition(MLT).

Part2-Task3.3.b:EvaluationofmonetaryfluxesbetweensectorsnecessarytoachievetheplannedscenariosforOptimalTransition(OT)andMid-leveltransition(MLT).

Part3-Task3.3.c:Variabilityoftheproposedscenariosandpathwaysduetophysicalconstraints.



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ListofabbreviationsandacronymsBAU:Businessasusual

CBM:Coal-bedmethane

CSP:ConcentratedSolarPower

DDEdelaydifferentialequation

CSP:Concentratedsolarpower

EEU:Central&EasternEurope

EROIEnergyreturnonenergyinvested

EL:EnergyLosses

ELF:Energylossfunction

FEC:FinalEnergyConsumption

FED:FinalEnergyDemand

FFFossilFuel

GAINS:GHG-AirpollutionINteractionandSynergies

GCAM:GlobalChangeAssessmentModel

GEA:GlobalEnvironmentalAssessment

GHGGreenhouseGases

GLOBIOM:TheGlobalBiosphereManagementModel

GP:Greenpeace

GP-AdERScenario:GreenpeaceAdvancedEnergyRevolutionScenario

GP-ERScenario:GreenpeaceEnergyRevolutionScenario

IAM:Integratedassessmentmodelling



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IEA:InternationalEnergyAgency

IEA450Scenario:InternationalEnergyAgency450Scenario

IEACPScenario:InternationalEnergyAgencyCurrentPoliciesScenario

IEANPScenario:InternationalEnergyAgencyNewPoliciesScenario

IPCC:Intergovernmentalpanelonclimatechange

IRENA:InternationalRenewableEnergyAgency

MESSAGE:ModelforEnergySupplyStrategyAlternativesandtheirGeneralEnvironmentalImpacts

MLT:Mid-levelTransition

MSW:MunicipalSolidWaste

NPP:NetPrimaryProduction

NRE:Non-renewableenergy

NGLs:NaturalGasLiquids

OT:OptimumTransition

PB:PlanetaryBoundaries

PV:photovoltaics

RERenewableEnergy

RES:RenewableEnergySource

RCP:RepresentativeConcentrationPathway

SRES:TheSpecialReportonEmissionsScenarios

SSP:SharedSocioeconomicPathway

URR:Ultimatelyrecoverableresources

WEU:WesternEurope



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ExecutivesummaryPart1-Task3.3.a:EvaluationofnetenergyandexergyofeacheconomicsectorintheOptimalTransition(OT)andMid-leveltransition(MLT).

Inthefollowingworkweexaminethepossiblerateofchange inthemixofenergyresourcesto2050,takingintoaccountthelimitsofcarbonbudgetsalreadyexploredinthepreviousdeliverableD.3.1andD3.2.

Weexploredpossibletrendsfor:primary,final,andusefulenergyandalsotheexergycontentoftheresources,accordingtothehistoricaldatafromIIASAdatabase.Inparticulartheexergydataanalysishastheaimtoevidencewhichofthepossiblecombinationsofresourcesmantainsthemaximunresidualofavailableworkevenafterthetrasformationfromprimarytousefulenergy.Inthiswork,theOptimalTransitionscenariostartsfrom2020insteadof2016.ThisdecisionwastakenbytheConsortiumduringtheGAmeetinginBrno(February2017).

Part 2 - Task 3.3.b: Evaluation of monetary fluxes between sectors necessary to achieve theplannedscenariosforOptimalTransition(OT)andMid-leveltransition(MLT)

This report is focused on analysing monetary fluxes between sectors considered in transitionscenarios(optimaltransitionandmediumtransitionlevelscenario).Thescenarioparametersaretranslatedintothelogicofinput-outputtables.TwodifferentscenariosareconsideredinwhichtheactionstoreduceGHGemissionsaresupposedtostartfrom2020(Optimaltransitionscenario–OT)or2030(Midleveltransitionscenario–MLT).

Wederivethemonetaryfluxesbyusinginput-ouptutanalysis,basedontheWorldInput-OutputDatabase(WIOD)structure.Theconstructed(monetary)input-outputtablesfulfiltheaimofalow-carboneconomyin2050,inordertostaywithinthe2°Cincreaseoftemperature.TheresultsshowthatthemonetaryfluxesfortheOptimaltransitionscenarioandMidleveltransitionscenarioaresurprisinglyverysimilar.Theincreasingroleofagricultureisobviousinbothscenarios.


OneofthemainchallengesoftheMEDEASprojectisthedevelopmentofasimulationmodelbasedonsystemsdynamicstohelpinthechoiceofenergyandenvironmentalpoliciesthatleadtoalowcarbon economy. In order to be able to approach different future situations, it is common forintegratedassessmentmodelstousescenarios.Scenariomethodologyoffersanapproachtodealwiththelimitedknowledge,uncertaintyandcomplexityofnaturalandsocialsciencesandcanbe



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used to group the variations of policies into coherent andmeaningful scenarios. Each scenariorepresentsanarchetypalandcoherentvisionofthefuture-whichmaybeviewedpositivelybysomepeople and negatively by others. However these commonly used scenarios have been mainlyproposedwithouttakingintoaccountsomeconsiderationsthatincorporatestheMEDEASmodel.Inthistask,thevariabilityoftheproposedscenariosandpathwayshavebeenexploredtakingintoaccountsomephysicalconstraintsandsomefeedbackseffects.Thephysicalconstraintsthatthemodelincorporatesandaredescribedinthisreportarelinkedtothemaximumenergythatcanbeobtained fromnon-renewable and renewable sources in the next years. In addition, themodelincorporates some feedback between final energy availability and the economy, aswell as theeffects of climate change. These feedbacks cause dynamics in the model that are not usuallydescribedintheopen-loopscenarios.Thenewsituationsthataresimulated,takingasastartingpoint the general scenarios described above when introducing the uncertainty due to physicallimitationsandtheir feedback is that ithasbeencalledadaptivescenarios.Thisreportpointstosome of the main causes that can lead to variability on the scenarios commonly described inliterature.SincetheglobalMEDEASmodelhasnotyetbeenfinalizedthisreportonlypointsoutsomeprovisionalresultsthatshouldbereviewedandevaluatedwiththefinalversionofthemodelasofJuly2017.



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Part1-Task3.3.a:Evaluationofnetenergyandexergyofeacheconomicsector intheOptimalTransition(OT)andMid-leveltransition(MLT)

Introduction

ExergyandEnergy

EnergydefinitionFordailylife,peoplehaverequiredillumination,thermalcomfort,mobility,cookedfood,industry.Theseare serviceswhoseenergy is obtainedby the transformationof energy inheat, light andpowermobility. This formof energy is calleduseful energy. It is createdbyend-use conversiondevicessuchasautomobile,lamps,heatingsystemsfromenergycarriers(fuels)soldtoconsumers,calledfinalenergy.Thisenergyisgotbymanyformsofenergyextractedfromnature,forexample,coalandcrudeoilbutalsosunlight,wind,waterfallsoroceantidal.Theyarereferredtoasprimaryenergy(Grubler2012).

Consideringtheenergychainfromthebottominsteadofthetop,primaryenergyisconvertedintofinalenergythatiscontainable,stackableandexchangedinthemarkettransaction;thismeansthatitcouldbeboughtorsold.Someexamplesoffinalenergyareelectricityfromasocket,gasolinefromagaspumpandsteamfromadistrictheatingduct.Appliancesanddevicestransformfinalenergyintousefulenergy:alightbulbtransformselectricityintolight,acarengineconvertsthecombustionof gasoline into mechanical movement of crankshaft allowing the automobile movement andradiatortransfersheatintheairtowarmaspace.

ExergydefinitionGivenanenvironment,exergyrepresentsthecapacityofenergytodophysicalwork(AyresandWarr2010).Itincorporatestherelativequalityoftheenergyform.Exergyanalysisisperformedheretoknowwhichisthebestuseofresourcesinthewaytoproduceanduseenergymoreefficiently.



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Methodology

Methodology for estimating energy and exergy bysector

DatasourceTheonlinedatasetusedinthisworkthatincludestheenergy,aswellas,theexergyseries,ispubliclyavailableonlineandcanbereachedthroughtheTransitiontoNewTechnology(TNT)Programpageon the IIASA website (http://tntcat.iiasa.ac.at/PFUDB/dsd?Action=htmlpage&page=about#intro).SimonDeStarkecreatedthisdatabaseanditsconstructionisexplainedinthedocumentIR-14-013(http://webarchive.iiasa.ac.at/Admin/PUB/Documents/IR-14-013.pdf).

Moreindepth,thedatasetcontainsdataof:

• Primaryenergy;• Primaryexergy;• Finalenergy;• Finalexergy;• Usefulenergy;• Usefulexergy.

for5differenteconomicsectors:

1. Industry;2. ResidentialandCommercial;3. Transport;4. Bunkers;5. Non-energy.

Foreachsector,theonlinedatasetcontainsdataoftheenergy/exergycontributionsby12differentsourceswhichare:

• Coalproducts:allproductsoriginatingfromcoalorpeat,includingmanufacturedgases;• Biomass-waste:bothsolidandliquid,includescharcoalandwaste(includingmunicipaland

otherwaste)(Nakicénovićetal.,1996);



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• Naturalgas:naturalgas,excludingnaturalgasliquids;• Petroleum products: crude oil and refined petroleum products, including liquefied

petroleumgasses.Also,includesnaturalgasliquids.• Nuclear;• Solar:photovoltaicandthermal;Geothermal;• Wind;• Heat;• Electricity;• Hydro:includestide,wave,andoceanenergy;• Other.

Thisdatabasecontainshistoricaldataofenergyandexergyfrom12differentsourcesandforeacheconomicsector,from1900to2014.

Theprojectionsforenergyandexergybysourcesuntil2050areobtainedbythelinearextrapolationofthehistoricaldata.

TheexergytoenergyratiosusedtoestimatetheexergycontentoftheenergyflowsarebasedontheconversionfactorsreportedintheworkbyNakićenovićetal.(1996).Thatstudycontainsfewerthanofthe12energycarriersusedhere,andtheexergyfactorsforthemissingenergycarriersweretakenequaltothoseofcomparableenergycarriersorenergycarrierswithcomparableuse.Thefactorsfornuclear,solarandgeothermalaresetequaltothoseofheat.Hydropowerandwindweretreatedaselectricity.Finally,“other”wasassumedtocorrespondtopetroleumproducts.

Byapplyingthesefactors(seeTable1)totheprimary,finalandusefulenergyseries,anewlayerofexergydataiscreated.



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Table1:Exergyfactors(exergytoenergyratio)basedonNakićenovićetal.(1996).

EnergycarrierPrimary/finalexergyfactor

Usefulexergyfactor

LightHigh Theat

LoW Theat

Stat.power Transport Feedstock Other

CoalProducts 1.06 0.90 0.33 0.10 1.00 0.99 1.06 0.23

Biomass-waste 1.19 0.90 0.26 0.14 1.00 0.99 1.19 0.23

PetroleumProducts 1.04 0.90 0.43 0.23 1.00 0.99 1.04 0.23

NaturalGas 1.03 0.90 0.33 0.10 1.00 0.99 1.03 0.23

Nuclear 1.00 0.90 0.26 0.07 1.00 0.99 0.27 0.23

Hydro 1.00 0.90 0.52 0.15 1.00 0.99 1.00 0.23

Electricity 1.00 0.90 0.52 0.15 1.00 0.99 1.00 0.23

Heat 0.27 0.90 0.26 0.07 1.00 0.99 0.27 0.23

Other 1.04 0.90 0.52 0.15 1.00 0.99 1.04 0.23

Solar 1.00 0.90 0.26 0.07 1.00 0.99 0.27 0.23

Wind 1.00 0.90 0.52 0.15 1.00 0.99 1.00 0.23

Geothermal 1.00 0.90 0.26 0.07 1.00 0.99 0.27 0.23



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Methodology for estimatingOT2020 andMLT 2030scenariosInthiswork,theOptimalTransitionscenariostartsfrom2020insteadof2016.ThisdecisionwastakenbytheConsortiumduringtheGAmeetinginBrno(February2017),mainlybecauseofthisisthefinalyearoftheprojectandthefirstavailableto implementtheMedeas’results.Thusfromnow,weusetheabbreviationOT-2020toindicateOptimalTransitionscenariostartingfrom2020.TheOT-2020,aswellas, theMLT-2030scenarioofenergy foreacheconomicsectorhavebeendesignedtaking intoaccounttheamountofGHGemissions limits, inordertoremainwithintheCarbon Budget and reduce the probability that temperature increase of 2°C, until 2050. Inparticular, in the task 3.1 d,we supposed that in OT-2020 scenario (thatwasMLT-2020), GHGemissionsshouldbereducedfrom66,46GtCO2-eqin2020to17,23GtCO2-eqin2050,whileinMLT-2030scenario,CO2emissionsshouldbereducedfrom81,9GtCO2in2030,to7,97GtCO2in2050.GHGemissionsareproducedmainlybythecombustionofnon-renewableenergies,principallycoal,naturalgasandpetroleumproducts.NotonlyfossilfuelsareresponsibleforCO2emissionsbutalsothe combustion of biomass combinedwithwaste.. Biofuels are far from being neutral carbonemittersduetoIndirectLandUseChanges(ILUC);hence,inaccordancewith(EuropeanCommission,2010;Fargioneetal.,2008;Haberletal.,2012;Searchingeretal.,2008),weassignasimilaremissionlevelofnaturalgas.

Forthisreason,weincludebiomass-wastetogetherwithnuclear,inthegroupoffuelsthatshouldbe reduced in future to cut theGHGemissions. Thisdoesnotmean that thebiomass, that is arenewable energy source, will not have being used anymore, but the emission from biomasscombustionshouldbecompensatedbythecarbonsequestrationduetothebiomassregrowth.Toreachthisgoal,itisnecessarythatnewfuturepoliciesandstrategiestakeintoaccounttherotation

periodofthebiomass(Cherubinietal.2011).Tocalculatetheemissionsonthebasisofthetotalprimaryenergybyresources,weconsiderthefollowingemissionfactors,showedintable2asitisconsideredby IIASA( in reference4).Anyway, regarding the carbonemission factorofbiomass-wastewe consider that theoriginal proposed valueof 29.9 tC/Tj , that refers to biomassmereburningwithouthanypotentialsequestration,mustbemultipliedbyafactor0.6,accordingtoanavaragevalueofemissionimpactforbiomassproposedintheworkofCherubinietal.,obtainingafinalvalueof17,94tC/Tj.



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Table2:Carbonmissionfactors.

Energycarrier Carbonemissionfactor(tC/TJ)

Biomass-waste Inthisstudy17,94

(it was 29,9 as mere biomassburning, without consideringbiomasspotentialsequestretion)

CoalProducts 25,8

NaturalGas 15,3

Other 20,0

PetroleumProducts 20,0

ForOT-2020,wecalculatethetotalprimaryenergyofbiomass-waste,coal,petroleum,otherandnaturalgas,until2050,asaprojectionofthehistoricaldata.Weconsiderthat in2020thetotalenergyfromthesefuelswillbe5,52108TJ,correspondingto45GtCO2eq.Wedecidedtofixin2050adiminutionofGHGemissionsupto17,23GtCO2.Thismeansthatprimaryenergyofthesefuelsshouldbereducedby57.1%until2,50108TJ.

ForMLT-2030,wecalculatethetotalprimaryenergyofbiomass-waste,coal,petroleum,otherandnaturalgasuntil2050,asaprojectionof thehistoricaldata.Weconsider that in2030 the totalenergyfromthesefuelswillbe5,97108TJcorrespondingto49GtCO2eq.Thus,wefix in2050adiminutionofGHGemissionsupto7,97GtCO2.Thismeansthatprimaryenergyofthesefuelsshouldbereducedofabout81,65%until1,16108TJ.



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Methodology for estimating Electricity sectorenergy/exergy

DatasourceforelectricitysectorenergyTheInternationalEnergyOutlook2016(IEO2016)hasbeentakenasthemaindatasourceforworldnet electricity generation, split by 10 different fuels: petroleum, coal, nuclear, natural gas,geothermal,solar,wind,hydropowerandother.Thetermotherincludestheconstributionfrombiomass,wasteandocean.Weconsider that theoceancontribution ismainlydue toprototypeplants, and it is quite low in comparison to biomass and waste, thus it can be , at moment,overlooked.

AccordingtotheIEOreport,theproductionofelectricityisprojectedtoincrease69%by2040,from21.6trillionkWhin2012,to25.8trillionkWhin2020and36.5trillionkWhin2040.Economicgrowthis an important factor in electricity demand growth. In the Reference case, electricity demandcontinuestoincreaseespeciallyamongtheemergingnon-OrganizationforEconomicCooperationand Development (non-OECD) economies. In non-OECD countries world -electricity generationincreasesto61%in2040(IEO2016).Moreover,theIEO2016ReferencecasetakesintoaccountthattherateofgrowthinelectricityconsumptionisgettingsmallercomparedtotherateofgrowthinGDP.From2005to2012,worldGDPincreasedby3.7%/yr,whileworldnetelectricitygenerationrose by 3.2% /yr. Considering a more extensive period, between 2012 and 2040, world GDPincreasesby3.3%/yrandtheworldnetelectricitygenerationonly1.9%/yr.

Figure1:Worldnetelectricitygenerationbyfuel,2012-40(trillionkWh).

0,00

20,00

40,00

2012 2020 2025 2030 2035 2040

Wordnetelectricitygenerationbyfuel,2012-40(trillionkWh)

Petroleum Nuclear NaturalgasCoal Renewables

0,00

5,00

10,00

15,00

2012 2020 2025 2030 2035 2040

Worldnetelectricitygenerationfromrenewablepowerbyfuel,2012-40(trillionkWh)

Other Geothermal Solar Wind Hydropower



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Methodology for estimating BAU, OT 2020 and MLT 2030scenarios.IntheIEO2016Referencecase,newandunanticipatedgovernmentpoliciesorlegislationaimedatlimiting or reducing greenhouse gas or other power-sector emissions,which could substantiallychangethetrajectoriesoffossilandnon-fossilfuelconsumption,werenotincorporated.Forthisreason,we used theworld net electricity generation calculated in IEO 2016 to define the BAUscenario,from2012to2040.TheIEO2016Referencecasecontainsonlyhistoricaldataofenergyfrom1900to2040.

Theprojectionsbysourcesuntil2050areobtainedbythelinearextrapolationofthehistoricaldata.InOT-2020andMLT-2030weconsiderthat,coal,naturalgas,petroleumproductsandotheraretotallyreplacedbyhydro,solar,geothermalandwindenergy.BiomasstoproduceelectricitymightbeusedprovidedthatCO2releasedbyitsburningissequestredbytheregrowthofnewbiomass.



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Results

ResidentialandcommercialsectorInFigure3,forresidentialandcommercialsectorweproposedthefollowinghistoricaldata:

• Primaryenergy;• Primaryexergy;• Finalenergy;• Finalexergy;• Usefulenergy;• Usefulexergy.

Themaindifferenceslieamongprimaryenergy,finalenergyandusefulenergyandanalysingusefulenergyincomparisonwithusefulexergy.

Intheresidentialandcommercialsector,thebiggestcontributionstotheprimaryenergyarecoalproducts,naturalgasandbiomass-waste.Fortheenergycarriershydroenergy,geothermalenergy,nuclearenergy,windand solarenergy, theprimaryenergyequivalent is theenergy required toproduceelectricityfromthesourcewithanefficiencyof35%andheatwithanefficiencyof85%.

The final energy is transformed into useful energy where the contribution of electricity andpetroleumproductsstillremainsignificant.Anotherimportantdifferenceisfoundbetweenusefulenergyandusefulexergy.Inusefulexergy,themajorcontributionstillcomesfromelectricityandaveryloweramountfrompetroleumproductsandfromsolarpanel.



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Figure 2 : Primary energy/exergy, final energy/exergy and useful energy/exergy by fuels forResidentialandCommercialsector,atgloballevel,1900-2050.

-1,00E+08

0,00E+00

1,00E+08

2,00E+08

3,00E+08

4,00E+08

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2014 2017 2020 2030 2040 2050

PrimaryenergyResidentialandCommercialsector(TJ/yr)

Pen_Biomass-waste_Res./Comm._AllUses Pen_CoalProducts_Res./Comm._AllUses

Pen_Electricity_Res./Comm._AllUses Pen_Geothermal_Res./Comm._AllUses

Pen_Heat_Res./Comm._AllUses Pen_Hydro_Res./Comm._AllUses

Pen_NaturalGas_Res./Comm._AllUses Pen_Nuclear_Res./Comm._AllUse

Pen_Other_Res./Comm._AllUses Pen_PetroleumProducts_Res./Comm._AllUses

Pen_Solar_Res./Comm._AllUses Pen_Wind_Res./Comm._AllUses

-1,00E+08

0,00E+00

1,00E+08

2,00E+08

3,00E+08

4,00E+08

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2014 2017 2020 2030 2040 2050

PrimaryexergyResidentialandCommercialsector(TJ/yr)

Pex_Biomass-waste_Res./Comm.AllUses" Pex_CoalProducts_Res./Comm._AllUses"

PEx_Electricity_Res/Comm_AllUses" Pex_Geothermal_Res./Comm._AllUses"

PEx_Heat_Res/Comm_AllUses Pex_Hydro_Res./Comm._AllUses"

Pex_NaturalGas_Res./Comm._AllUses" Pex_Nuclear_Res./Comm._AllUses"

Pex_Other_Res./Comm._AllUses" Pex_PetroleumProducts_Res./Comm._AllUses"

Pex_Solar_Res./Comm._AllUses" Pex_Wind_Res./Comm._AllUses"

0,00E+00

1,00E+08

2,00E+08

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2014 2017 2020 2030 2040 2050

FinalenergyResidentialandCommercialsector(TJ/yr)

Fen_Biomass-waste_Res./Comm._AllUses Fen_CoalProducts_Res./Comm._AllUses

Fen_Electricity_Res./Comm._AllUses Fen_Geothermal_Res./Comm._AllUses

Fen_Heat_Res./Comm._AllUses Fen_Hydro_Res./Comm._AllUses

Fen_NaturalGas_Res./Comm._AllUses Fen_Nuclear_Res./Comm._AllUses

Fen_Other_Res./Comm._AllUses Fen_PetroleumProducts_Res./Comm._AllUses

Fen_Solar_Res./Comm._AllUses Fen_Wind_Res./Comm._AllUses

0,00E+00

1,00E+08

2,00E+08

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2014 2017 2020 2030 2040 2050

FinalexergyResidentialandcommercialsector(TJ/yr)

Fex_Biomass-waste_Res./Comm._AllUses Fex_CoalProducts_Res./Comm._AllUses

FEx_Electricity_Res/Comm_AllUses Fex_Geothermal_Res./Comm._AllUses

FEx_Heat_Res/Comm_AllUses Fex_Hydro_Res./Comm._AllUses

Fex_NaturalGas_Res./Comm._AllUses Fex_Nuclear_Res./Comm._AllUses

Fex_Other_Res./Comm._AllUses Fex_PetroleumProducts_Res./Comm._AllUses

Fex_Solar_Res./Comm._AllUses Fex_Wind_Res./Comm._AllUses

0,00E+00

5,00E+07

1,00E+08

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2014 2017 2020 2030 2040 2050

UsefulEnergyResidentialandCommercialsector(TJ/yr)

Uen_Biomass-waste_Res./Comm._AllUses Uen_CoalProducts_Res./Comm._AllUses

Uen_Electricity_Res./Comm._AllUses Uen_Geothermal_Res./Comm._AllUses

Uen_Heat_Res./Comm._AllUses Uen_Hydro_Res./Comm._AllUses

Uen_NaturalGas_Res./Comm._AllUses Uen_Nuclear_Res./Comm.AllUses

Uen_Other_Res./Comm._AllUses Uen_PetroleumProducts_Res./Comm._AllUses

Uen_Solar_Res./Comm._AllUses Uen_Wind_Res./Comm._AllUses

0,00E+00

2,00E+07

4,00E+07

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2014 2017 2020 2030 2040 2050

UsefulexergyResidentialandcommercialsector(TJ/yr)

UEx_Biomass-waste_Res./Comm._AllUses UEx_CoalProducts_Res./Comm._AllUses

UEx_Electricity_Res./Comm._AllUses UEx_GeothermalRes./Comm.__AllUses

UEx_Heat_Res./Comm._AllUses UEx_Hydro_Res./Comm._AllUses

UEx_NaturalGas_Res./Comm._AllUses UEx_Nuclear_Res./Comm._AllUses

UEx_Other_Res./Comm._AllUses UEx_PetroleumProducts_Res./Comm._AllUses

UEx_Solar_Res./Comm._AllUses UEx_Wind_Res./Comm._AllUses



22

Energy/exergyscenariosfortheResidential/CommercialsectorFigure3focusesonBAU,OT-2020andMLT-2030scenariosfortheprimaryenergybysources.InOT-2020,aswellas,inMLT-2030scenario,thecontributionfromhydroandwindtoprimaryenergybecameprogressivelygreateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfromnaturalgas,biomass-waste,nuclearandpetroleumproducts.

Figure3:BAU,OT-2020andMLT-2030forPrimaryenergyintheResidentialandCommercialsectoratgloballevel.

0,00E+005,00E+071,00E+081,50E+08 2,00E+082,50E+08 3,00E+083,50E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

PrimaryenergyResidentialandcommercialsector(TJ/yr)

Pen_Biomass-waste Pen_CoalProducts Pen_Geothermal Pen_Hydro Pen_NaturalGas

Pen_Nuclear Pen_Other Pen_PetroleumProducts Pen_Solar Pen_Wind

BAU OT-2020 MLT-2030



23

Figure4showsBAU,OT-2020andMLT-2030scenariosforthefinalenergybysources.IntheOT-2020,aswellas,inMLT-2030scenario,thecontributionfromelectricityandheattofinalenergybecomeprogressivelygreateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfromcoalproducts,naturalgas,biomass-wasteandpetroleumproducts.

Figure4:BAU,OT-2020andMLT-2030forfinalenergyinResidentialandCommercialsector.

InFigure5BAU,OT-2020andMLT-2030scenariosfortheusefulenergyareshowed.IntheOT-2020aswellasintheMLT-2030scenario,thecontributionoftheelectricityandheattousefulenergybecameprogressivelygreateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfromcoalproducts,naturalgas,biomass-wasteandpetroleumproducts.

Figure5:BAU,OT-2020andMLT-2030forusefulenergyforResidentialandCommercialsector.

0,00E+002,00E+074,00E+076,00E+078,00E+071,00E+081,20E+081,40E+08 1,60E+08 1,80E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

FinalenergyResidentialandcommercialsector(TJ/yr)

Fen_Biomass-waste Fen_CoalProducts Fen_Electricity Fen_GeothermalFen_Heat Fen_NaturalGas Fen_PetroleumProducts Fen_Solar

0,00E+00

2,00E+07

4,00E+07

6,00E+07

8,00E+07

1,00E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

UsefulenergyResidentialandcommercialsector(TJ/yr)

Uen_Biomass-waste Uen_CoalProducts Uen_Electricity Uen_Geothermal

Uen_Heat Uen_NaturalGas Uen_PetroleumProducts Uen_Solar

OT-2020 MLT-2030 BAU




24

Figure6reportsBAU,OT-2020andMLT-2030scenarios.IntheOT-2020,aswellas,intheMLT-2030scenario,thecontributionoftheelectricityandheattousefulexergybecameprogressivelygreateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfrombiomass-waste,coalproducts,naturalgasandpetroleumproducts.

Figure6:BAU,OT-2020andMLT-2030forusefulenergyinResidentialandCommercialsector.

0,00E+00

5,00E+06

1,00E+07

1,50E+07 2,00E+07

2,50E+07 3,00E+07

3,50E+07

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

UsefulexergyResidentialandcommercial(TJ/yr)

UEx_Biomass-waste UEx_CoalProducts UEx_Electricity UEx_Geothermal

UEx_Heat UEx_NaturalGas UEx_PetroleumProducts UEx_Solar




25

Transportsector

Energy/exergyscenariosfortransportsectorFirst,wecompareBAUscenarioofprimaryenergy,withthoseoffinalenergyandusefulenergy.Wenoticethatthelargestcontributiontotheprimaryenergyintransportsectordependsonpetroleumproducts. Primary energy is transformed into final energywhere the contribution of petroleumproductsstillremainimportant,aswellas,inusefulenergy.

Figure7showsBAU,OT-2020andMLT-2030scenariosofprimaryenergy.IntheOT-2020,aswellasin theMLT-2030 scenario, the contributionofwind, solar andhydro toprimaryenergybecameprogressivelygreateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfrompetroleumproducts.

Figure7:BAU,OT-2020andMLT-2030forprimaryenergyinthetransportsector.

0,00E+00

5,00E+07

1,00E+08

1,50E+08

2,00E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

PrimaryenergyTransportsector(TJ/yr)

Pen_Biomass-waste Pen_CoalProducts Pen_Geothermal Pen_HydroPen_NaturalGas Pen_Nuclear Pen_Other Pen_PetroleumProductsPen_Solar Pen_Wind




26

Figure8reportstheOT-2020andMLT-2030scenariosofprimaryenergyfortransportsector.IntheOT-2020,aswellas,intheMLT-2030scenario,thecontributionoftheelectricitytoprimaryenergybecameprogressivelygraterand it is considered that thesesourcegradually replace theenergyderivedmostlyfrompetroleumproducts.

Figure8:BAU,OT2020andMLT2030forfinalenergyinthetransportsector

Figure9describestheBAU,OT-2020andMLT-2030scenariosofusefulenergy.IntheOT-2020,aswell as, in theMLT-2030 scenario, the contribution of the electricity to useful energy becameprogressivelygreaterinthetransportsectoranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfrompetroleumproducts.

0,00E+00

5,00E+07

1,00E+08

1,50E+08

2,00E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

FinalenergyTransportsector(TJ/yr)

Fen_Biomass-waste Fen_CoalProducts Fen_Electricity Fen_Geothermal

Fen_Heat Fen_NaturalGas Fen_PetroleumProducts Fen_Solar




27

Figure9:BAU,OT-2020andMLT-2030forusefulenergyintransportsector.

Figure10describestheBAU,OT-2020andMLT-2030scenariosofusefulexergy.IntheOT-2020,aswell as, in the MLT-2030 scenario, the contribution of electricity to useful energy becameprogressivelygreaterintrasportsectoranditisconsideredthatthesesourcereplacegraduallytheenergyderivedmostlyfrompetroleumproducts.

Figure10:BAU,OT-2020andMLT-2030forusefulexergyintransportsector.

0,00E+00

1,00E+07

2,00E+07

3,00E+07

4,00E+07

5,00E+07

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

UsefulenergyTransportsector(TJ/yr)

Uen_Biomass-waste Uen_Coalproducts Uen_Electricity Uen_Geothermal


BAU OT-2020

0,00E+001,00E+072,00E+073,00E+074,00E+075,00E+07

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

UsefulexergyTransportsector(TJ/yr)

Uex_Biomass-waste Uex_CoalProducts Uex_Electricity Uex_Geothermal

Uex_Heat Uex_NaturalGas Uex_PetroleumProducts Uex_Solar


MLT-2030



28

Industrialsector

Energy/exergyscenariosforindustrialsectorHere,wecomparebetweenBAUscenariosofprimaryenergy,finalenergyandusefulenergy.Thebiggestcontributiontotheprimaryenergyinindustrialsectordependsalmostcompletelyfromnon-renewable,especiallyoncoalproductsandnaturalgas.Theprimaryenergyistransformedintofinalenergywherethecontributionofcoalproductsstillremainimportant,aswellas,inusefulenergy.Hydro energy, geothermal energy, nuclear energy, the wind and solar energy are required toproduceelectricitywithanefficiencyof35%andheatwithanefficiencyof85%.Inparticularhydro,nuclear, other and wind, which are the principal sources of primary energy are completelytransformed intoelectricitywhichbecameoneofthemajorsourcesof finalenergyandthen, inusefulenergy.Theenergycontributionsdependingfromelectricityishigherinusefulexergythaninusefulenergy.

InFigure11theBAU,OT-2020andMLT-2030scenariosforprimaryenergyintheindustrialsectorarereported.IntheOT-2020,aswellasinMLT-2030scenario,thecontributionsofhydropowertoprimary energy became progressively greater and it is considered that these sources replacegraduallytheenergyderivedmostlyfrombiomass-waste,coalproductsandnaturalgas.

Figure11:BAU,OT-2020andMLT-2030forprimaryenergyintheindustrialsector.

0,00E+005,00E+071,00E+081,50E+08 2,00E+082,50E+08 3,00E+083,50E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

PrimaryenergyIndustrialsector(TJ/yr)

Pen_Biomass-waste Pen_CoalProducts Pen_Geothermal Pen_Hydro

Pen_NaturalGas Pen_Nuclear Pen_Other Pen_PetroleumProducts

Pen_Solar Pen_Wind




29

InFigure12,theBAU,OT2020andMLT2030scenariosforfinalenergyfor industrialsectorarerepresented.IntheOT-2020aswellasintheMLT-2030scenario,thecontributionsoftheelectricitytofinalenergybecameprogressivelygrateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfrombiomass-waste,coalproductsandnaturalgas.

Figure12:BAU,OT-2020andMLT-2030forfinalenergyintheindustrialsector.

In figure 13 BAU, OT-2020 and MLT-2030 scenarios of useful energy for industrial sector aredescribed.IntheOT-2020,aswellas,intheMLT-2030scenario,thecontributionsoftheelectricityandheat touseful energybecameprogressively greater and it is considered that these sourcesreplacegraduallytheenergyderivedmostlyfrombiomass-waste,coalproductsandnaturalgas.

Figure13:BAU,OT-2020andMLT-2030forusefulenergyinindustrialsector.

0,00E+00

5,00E+07

1,00E+08

1,50E+08

2,00E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

FinalenergyIndustrialsector(TJ/yr)

Fen_Biomass-waste Fen_CoalProducts Fen_Electricity Fen_Geothermal

Fen_Heat Fen_NaturalGas Fen_PetroleumProducts Fen_Solar_Industry


0,00E+002,00E+074,00E+076,00E+078,00E+071,00E+081,20E+08

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

UsefulenergyIndustrialsector(TJ/yr)

Uen_Biomass-waste Uen_CoalProducts Uen_Electricity Uen_Geothermal





30

Figure14describesBAU,OT-2020andMLT-2030scenariosofusefulexergy.IntheOT-2020aswellasintheMLT-2030scenario,thecontributionsoftheelectricityandheattousefulenergybecameprogressivelygreateranditisconsideredthatthesesourcesreplacegraduallytheenergyderivedmostlyfrombiomass-waste,coalproductsandnaturalgas.

Figure14:BAU,OT-2020andMLT-2030forusefulexergyinindustrialsector.

0,00E+00

1,00E+07

2,00E+07

3,00E+07

4,00E+07

5,00E+07

6,00E+07

2017 2020 2030 2040 2050 2017 2020 2030 2040 2050 2017 2020 2030 2040 2050

UsefulexergyIndustrialsector(TJ/yr)

Uex_Biomass-waste Uex_CoalProducts Uex_Electricity Uex_Geothermal

Uex_Heat Uex_NaturalGas Uex_PetroleumProducts Uex_Solar




31

Electricitysector

BAUenergy/exergyThenetenergygenerationdependsmostlyonnonrenewableenergiesinparticularoncoal,naturalgas andnuclear. The contribution from renewable energies still remains small if anypolicies toreduceGHGemissionarenottakenintoaccount.

Figure15:Ontherightworldnetelectricityenergybyfuel,2012-50(trillionkWh)whileontheleftnetelectricityexergybyfuel.

01020304050

2012 2017 2020 2025 2030 2035 2040 2045 2050

Worldnetelectricitygenerationbyfuel,2012-50(trillionkWh)

Petroleum Nuclear Naturalgas Coal Other

Geothermal Solar Wind Hydropower

0

20

40

60

2012 2017 2020 2025 2030 2035 2040 2045 2050

Netelectricityexergygenerationbyfuel,2012-50(trillionkWh)





32

OT-2020energy/exergyAsitisshowninfigure16,in2012,thenetenergygenerationdependsmostlyoncoal,naturalgas,nuclearandbiomass.Startingfrom2020,thecontributionfromrenewableenergiesgrowsrapidlyanditissupposedthattheseresources,inparticularhydropowerandwind,replacegraduallyallthefuelsthatareresponsibleforCO2emissions.

Figure16:Ontheright,theOT-2020forworldnetelectricityenergybyfuel,2012-50(trillionkWh)whileontheleft,theOT-2020fornetelectricityexergybyfuel.

0

20

40

60

2012 2017 2020 2025 2030 2035 2040 2045 2050

OT-2020Netelectricityenergy(kWh/yr)

Petroleum Nuclear Naturalgas

Coal Other Geothermal

Solar Wind Hydropower

0

20

40

60

2012 2017 2020 2025 2030 2035 2040 2045 2050

OT-2020Netelectricityexergy(kWh/yr)

Petroleum Nuclear Naturalgas

Coal Other Geothermal

Solar Wind Hydropower



33

MLT-2030energy/exergyFigure17showstheMLT-2030ofworldnetelectricityenergybysources.Inthisscenario,thehydroenergy,solar,windandgeothermalenergyreplaceuntil2050allfossilfuels.

Figure17:Ontheright,theMLT-2030forworldnetelectricityenergybyfuel,2012-50(trillionkWh)whileonthelefttheMLT-2030fornetelectricityexergybyfuel.

01020304050

2012 2017 2020 2025 2030 2035 2040 2045 2050

MLT-2030Netelectricityenergy(kWh/yr)



0

20

40

60

2012 2017 2020 2025 2030 2035 2040 2045 2050

MLT-2030Netelectricityexergy(kWh/yr)





34

ConclusionsIn this report, we assumed that energy/exergy pathways of non-renewable energy for eacheconomicsectorshoulddecrease following thesametrendofGHGemission,except for thenetelectricitygeneration,whererenewableenergiestotallyreplacedthenon-renewableenergies.Weincludedinnon-renewableenergiesalsothebiomassbecauseit isresponsibleforCO2emissionsevenifitisconsideredarenewablessource.

Therefore,consideringtheCO2budgetlimitwedevelopedtwodifferentscenariostheOT-2020andMLT-2030,startingfrom2020and2030upto2050. Inthefirstone,wefoundthatsolar,hydro,geothermal and wind should increase about the 57,1% while in the latter, about 81,65%. Asexpected, theelectricenergy is thehighestqualityofenergycarrier,having the largestvalueofexergyconservedforsubsequentuses.Thepenetrationofelectricitycouldreallycontribute inamassivereductionoftheemissionsintransportationsectors.



35

ReferencesAyres R. U. and Warr B. The economic growth engine: how energy and work drive materialprosperity,EdwardElgarPublishing,2010.

CherubiniF.,PetersG.P.,BernstenT.,.StrØmmanA.H.HertwichE.CO2emissionsfrombiomasscombustionforbioenergy:atmosphericdecayandcontributiontoglobalwarming,GCBBioenergy(2011)3,413–426,doi:10.1111/j.1757-1707.2011.01102.x.

EgglestonS., Buendia L.,MiwaK. 2006 IPCCguidelines fornational greenhousegas inventories,KanagawaJP:InstituteforGlobalEnvironmentalStrategies,2006.

EuropeanCommission,2010.REPORTFROMTHECOMMISSIONonindirectland-usechangerelatedtobiofuelsandbioliquids(No.COM(2010)811final).

Fargione,J.,Hill,J.,Tilman,D.,Polasky,S.,Hawthorne,P.,2008.LandClearingandtheBiofuelCarbonDebt.Science319,1235–1238.doi:10.1126/science.1152747

GrublerA.Energytransitionsresearch:Insightsandcautionarytales,2012,Energypolicy50(0):8-16.

Haberl,H.,Sprinz,D.,Bonazountas,M.,Cocco,P.,Desaubies,Y.,Henze,M.,Hertel,O.,Johnson,R.K.,Kastrup,U.,Laconte,P.,Lange,E.,Novak,P.,Paavola,J.,Reenberg,A.,vandenHove,S.,Vermeire,T., Wadhams, P., Searchinger, T., 2012. Correcting a fundamental error in greenhouse gasaccountingrelatedtobioenergy.EnergyPolicy45,18–23.doi:10.1016/j.enpol.2012.02.051

InternationalEnergyOutlook2016(IEO2016)Chapter5“Electricity”.

NakicénovićN.,GilliP.V.,andKurzR.Regionalandglobalexergyandenergyefficiencies,1996,Energy21(3):223-237.

Searchinger,T.,Heimlich,R.,Houghton,R.A.,Dong,F.,Elobeid,A.,Fabiosa,J.,Tokgoz,S.,Hayes,D.,Yu,T.-H.,2008.UseofU.S.CroplandsforBiofuelsIncreasesGreenhouseGasesThroughEmissionsfromLand-UseChange.Science319,1238–1240.doi:10.1126/science.1151861



36

ListofTablesTable1:Exergyfactors(exergytoenergyratio)basedonNakićenovićetal.(1996).....................15

Table2:Carbonmissionfactors......................................................................................................17



37

ListofFiguresFigure1:Worldnetelectricitygenerationbyfuel,2012-40(trillionkWh).....................................18

Figure 2 : Primary energy/exergy, final energy/exergy and useful energy/exergy by fuels forResidentialandCommercialsector,atgloballevel,1900-2050......................................................21

Figure3:BAU,OT-2020andMLT-2030forPrimaryenergyintheResidentialandCommercialsectoratgloballevel...................................................................................................................................22

Figure4:BAU,OT-2020andMLT-2030forfinalenergyinResidentialandCommercialsector.....23

Figure5:BAU,OT-2020andMLT-2030forusefulenergyforResidentialandCommercialsector.23

Figure6:BAU,OT-2020andMLT-2030forusefulenergyinResidentialandCommercialsector..24

Figure7:BAU,OT-2020andMLT-2030forprimaryenergyinthetransportsector.......................25

Figure8:BAU,OT2020andMLT2030forfinalenergyinthetransportsector.............................26

Figure9:BAU,OT-2020andMLT-2030forusefulenergyintransportsector................................27

Figure10:BAU,OT-2020andMLT-2030forusefulexergyintransportsector..............................27

Figure11:BAU,OT-2020andMLT-2030forprimaryenergyintheindustrialsector.....................28

Figure12:BAU,OT-2020andMLT-2030forfinalenergyintheindustrialsector..........................29

Figure13:BAU,OT-2020andMLT-2030forusefulenergyinindustrialsector..............................29

Figure14:BAU,OT-2020andMLT-2030forusefulexergyinindustrialsector..............................30

Figure15:Ontherightworldnetelectricityenergybyfuel,2012-50(trillionkWh)whileontheleftnetelectricityexergybyfuel............................................................................................................31

Figure16:Ontheright,theOT-2020forworldnetelectricityenergybyfuel,2012-50(trillionkWh)whileontheleft,theOT-2020fornetelectricityexergybyfuel.....................................................32

Figure17:Ontheright,theMLT-2030forworldnetelectricityenergybyfuel,2012-50(trillionkWh)whileonthelefttheMLT-2030fornetelectricityexergybyfuel....................................................33



38

Part2-Task3.3.b:EvaluationofmonetaryfluxesbetweensectorsnecessarytoachievetheplannedscenariosforOptimalTransition(OT)andMid-leveltransition(MLT)

IntroductionChangingourenergysystemwill requireacompleteshift fromfossil fuelenergies to renewable

energies. TheobjectiveofWorkPackage3 is toexploreand studydifferent scenarios and their

temporalevolution foreachselected initialpolicy framework inorder tobeable toachieve the

objectiveofa low-carboneconomy in2050.Theexpectedevolutionof thescenariosduring the

processoftransitionshouldbeanalysed,byconsidering,amongothers,monetaryfluxesbetweensectorsandtheirdevelopmentpathways.

Ourreportprovidesananalysisofexpectedfuturemonetaryfluxesbetweeneconomicsectors(or

industries)duringthetransitiontoalow-carboneconomy.Theanalysisisprovidedonthebasisof

input-output tables from the World Input-Output Database (WIOD) with 35 industries and

addicionalitemsthatarelistedandnumberedbelow.



39

Table1:ListofWIODsectors

Inthefirstpartofthisreport(Methodology),wedescribesomebasicfeaturesofthemethodwe

apply – input-outputmodelling– toprovideexplanationsof the termsweuse andof thebasic

relations between considered aspects of the issue we deal with – monetary fluxes between

industriesduringthetransitiontoalow-carboneconomy.

1-Agriculture,Hunting,ForestryandFishing2-MiningandQuarrying3-Food,BeveragesandTobacco4-TextilesandTextileProducts5-Leather,LeatherandFootwear6-WoodandProductsofWoodandCork7-Pulp,Paper,Paper,PrintingandPublishing8-Coke,RefinedPetroleumandNuclearFuel9-ChemicalsandChemicalProducts10-RubberandPlastics11-OtherNon-MetallicMineral12-BasicMetalsandFabricatedMetal13-Machinery,Nec14-ElectricalandOpticalEquipment15-TransportEquipment16-Manufacturing,Nec;Recycling17-Electricity,GasandWaterSupply18-Construction19-Sale,MaintenanceandRepairofMotorVehiclesandMotorcycles;RetailSaleofFuel20-WholesaleTradeandCommissionTrade,ExceptofMotorVehiclesandMotorcycles21-RetailTrade,ExceptofMotorVehiclesandMotorcycles;RepairofHouseholdGoods22-HotelsandRestaurants

23-InlandTransport24-WaterTransport25-AirTransport26-OtherSupportingandAuxiliaryTransportActivities;ActivitiesofTravelAgencies27-PostandTelecommunications28-FinancialIntermediation29-RealEstateActivities30-RentingofM&EqandOtherBusinessActivities31-PublicAdminandDefence;CompulsorySocialSecurity32-Education33-HealthandSocialWork34-OtherCommunity,SocialandPersonalServices35-PrivateHouseholdswithEmployedPersons60-Totalintermediateconsumption61-Cif/fobadjustmentsonexports62-Directpurchasesabroadbyresidents63-Purchasesonthedomesticterritorybynon-residents64-Valueaddedatbasicprices65-InternationalTransportMargins69-Outputatbasicprices99-taxeslesssubsidiesonproducts



40

MethodologyWe first provide an assessment of historical developments of inter-idustry monetary flows

between1995 and2011. Thepast trends are presented to show changes in relation to various

economic policies and developments. The expected futuremonetary flows are shown for 2030

and2050,andthemonetaryflowsfortheyears inbetweenareinterpolated.Weoperateonthe

globallevelformostofthetime.However,weincludealsotwocountrylevelexamplesforabrief

comparisonand foramoredetailedperspective.For2020weextrapolate thebusinessasusual

scenariofrom2011.For2030,weprovidetwomatrices–onefortheMidleveltransitionscenario

(MLT)andonefortheOptimaltransitionscenario(OT).For2050wethenprovideonlyonetable,

which should describe the state of the economy we are aiming to reach (i.e. the low-carbon

economy that should assure staying below 2°C temperature increase until 2050). For the

modellingneeds,weapproximatethelow-carboneconomyaszero-carbon,orasanalmostzero-

carboneconomy.

Ourapproachistryingtoanswerthequestionatwhatcostisthezero-carboneconomyfeasible(until 2050), andwhich changes in economic structurewould this transition require. The firststepistobetterunderstandhowchangesintheenergymixinfluencereachingthegoal(i.e.how

changesintheenergymixcouldhelptoachievethe“zero-carbon”goalin2050).Themaintaskis

thereforetoincorporatethe(expected)increaseoftherenewableenergyshare,andseehowthe

economic structure changes or might change due to this increase. In other words, we try to

answer(ifthereisnotechnologicalchange),whataneconomyfullybasedonrenewableenergies

would look like, or how feasible such an economy is. As the WIOD IO tables do not have a

disaggregatedelectricitysectoraccordingtodifferentsourcesofenergy,weneedtoinsertanew

sectorintotheinput-outputstructure,inordertotrackthegradualchangesintheenergymix.The

rationale is that the share of energy produced from fossil fuels needs to decrease, and this

productionneedstomovetotherenewableenergiessector(whichneedstoincrease).

Wedecided todisaggregate theelectricityproduction sector (called “Electricity,Gas andWater

Supply”intheWIODstructure)forsolarandwindenergyononehand,andfortherestofenergy

sources(includingfossilfuels,coalbeingamongtheleadingones)ontheother.

The key sector we focus on (Electricity, Gas andWater Supply) is composed of the following,

accordingtotheISIC3standard:



41

TabulationCategory:E-Electricity,gasandwatersupply

Breakdown:

ThisTabulationCategoryisdividedintothefollowingDivisions:

• 40-Electricity,gas,steamandhotwatersupply• 41-Collection,purificationanddistributionofwaterClass:4010-Production,collectionanddistributionofelectricity

This class includes generation, collection, transmission and distribution of electric energy for sale to household,

industrialandcommercialusers.

Electricityproductionmaybehydraulic,conventional,thermal,nuclear,geothermal,solar,tidal,etc.,inorigin.

Included are electric power plants which sell a significant amount of electricity to others, as well as produce

electricity for their parent enterprise andwhich can be reported separately from the other units of the parent

enterprise.

Class:4020-Manufactureofgas;distributionofgaseousfuelsthroughmains

This class includes manufacture of gaseous fuels. Production of gas by carbonation of coal or by mixing

manufacturedgaswithnaturalgasorpetroleumorothergases.

Distributionofgaseousfuelsthroughasystemofmainstohousehold,industrial,commercialorotherusers.

Exclusion: Transportation by pipeline of gaseous fuels, on a fee or contract basis, is classified in class 6030

(Transportviapipelines).

Class:4030-Steamandhotwatersupply

This class includes production, collection and distribution of steam and hotwater for heating, power and other

purposes.

Class:4100-Collection,purificationanddistributionofwater

Thisclass includescollection,purificationanddistributionofwater tohousehold, industrial, commercialorother

users.

Exclusions:Irrigationsystemoperationforagriculturalpurposesisclassifiedinclass0140(Agriculturalandanimal

husbandryserviceactivities,exceptveterinaryactivities).

Treatment of wastewater in order to prevent pollution is classified in class 9000 (Sewage and refuse disposal,

sanitationandsimilaractivities).



42

We decided to focus only onwind and solar energy production. The special nature of biomass

(whichcaninmanycasesrathercontributetoCO2emissionstheneliminatingthem)ledustoskip

thisrenewableenergysource.Problematicaspectsofhydropowerenergy,especiallyofbigdams

projects, ledustoexcludethissourceofenergy, too.Solar (fromphotovoltaicpanels)andwind

energyarealsoamongthemostpromisingenergysourcesforthefuture,asshownbythelatest

IPCCreport–figures1and2below.

Figure 1: Historical development of global primary energy supply from renewable energy from1971to2008.

Source:ClimateChange2014SynthesisReport,2014



43

Figure2:Globalprimaryenergysupply(directequivalent)ofbioenergy,wind,directsolar,hydro,and geothermal energy in 164 long-term scenarios in 2030 and 2050, and grouped by differentcategoriesofatmosphericCO2concentrationlevelthataredefinedconsistentlywiththoseintheAR4.

Thethickblacklinecorrespondstothemedian,thecolouredboxcorrespondstotheinter-quartilerange(25thto75thpercentile)andtheendsofthewhitesurroundingbarscorrespondtothetotalrangeacrossallreviewedscenarios.

Source:ClimateChange2014SynthesisReport,2014

The main challenge is then clear: how to insert these two new sectors into the input-output

structure? The sectoral decomposition should be done in order to allow us modelling the

increasing shareof renewableenergies,and its impact to the restof theeconomy.Thus, in the

secondpartofthischapter,wediscussmethodsofsectoraldecomposition.Apartfromthistask,

theanalysisshouldalsobegroundedinasoundmethodofelicitingfuturedevelopmentpathways.

Weprovideadescriptionofanapproachwethinkmightbeusefultoapplyforestimatingfuture



44

developments in Input-Output economic structure – monetary fluxes between industries in an

economy–furtherbelowaswellasinthethirdpartofthischapter.

Input-outputbasicsandtechnicalcoefficientsThis section is dedicated to an overview of input-output analysis, a method applied to the

modellingofmonetaryfluxes.Aswewillbemostofthetimedealingwiththeso-called“technical

coefficients”,weprovideabriefexplanationofwhatthesetechnicalcoeffientsmeanandwhatis

their role in analysing structural changes in the economy (in this case the transition to a low-

carboneconomy).

The basic input-output transaction table consists of rows showing “Who gives to whom?” and

columns showing “Who receives fromwhom?” in an economy. To identify key features of the

post-carboninput-outputeconomicstructureitisnecessarytofocusonthetechnicalcoefficients

for intermediate inputs. Technical coefficient is a ratio of input to a given sector to its output,

measured in monetary terms (Miller and Blair, 2009). The determinants of the technical

coefficients cover technological progress (Leontief, 1983), but also infrastructure policies,

substitutionduetorelativepricechanges,aswellasindustrialstructure(Peneder,2003).



45

Input-outputbasicsThe following descriptions of input-output basics are based on Miller and Blair (2009). If the

economyisdividedintonsectors,andifwedenotebyXithetotaloutput(production)ofsectoriandbyYithetotalfinaldemandforsectori’sproduct,wemaywritesectori’soutput:

Xi=zi1+zi2+…+zii+…+zin+YiXi…TOTALOUTPUT(PRODUCTION)OFSECTORizi1…PRODUCTSGOINGFROMSECTORiTOSECTOR1Yi…TOTALFINALDEMANDFORSECTORi’sPRODUCT

Thez termson the right-handside represent the interindustry salesby sector i, thus theentireright-hand side is the sumof all sector i’s interindustry sales and its sales to final demand. The

aboveequationrepresentsthedistributionofsectori’soutput.Thefollowingequationreflectstheoutputsofeachofthensectors:

X1=z11+z12+…+z1i+…+z1n+Y1X2=z21+z22+…+z2i+…+z2n+Y2

……………

Xi=zi1+zi2+…+zii+…+zin+Yi……………

Xn=zn1+zn2+…+zni+…+znn+Yn

Considertheinformationintheithcolumnofz’sontheright-handside–thataresalestosectori(i’spurchasesoftheproductsofvariousproducingsectorsintheeconomy):

z1iz2i…zii…zni

These elements are the sales to sector i, that is, i’s purchases of the products of the variousproducing sectors in the country; the column thus represents the sources and magnitudes of

sectori’sinputs.



46

Clearly,inengaginginproduction,thesectoralsopaysforotheritems–forexample,labourand

capital–andusesotherinputsaswell,suchasinventorieditems.Allofthesetogetheraretermed

thevalueaddedinsectori.Inaddition,importedgoodsmaybepurchasedasinputsbysectori.

Alloftheseinputs(valueaddedandimports)areoftenlumpedtogetheraspurchasesfromwhatis

calledthepaymentssector,whereasthez’sontheright-handsideoftheequationservetorecordthe purchases from theprocessing sector, the so-called interindustry inputs. Since each sectorcanalsouseitsoutputasitsowninput,interindustryinputsincludeintraindustryinputsaswell.

Themagnitudesoftheseinterindustryflowscanberecordedinatable,withsectorsoforigin(i.e.sellers) listed on the left, and the same sectors, now “destinations” (i.e. purchasers), listedacross the top. From the columnpointof view, these showeach sector’s inputs; from the row

pointofview,thefiguresareeachsector’soutputs.



47

Technicalcoefficients,AmatrixandLeontiefinverseInthetraditionalLeontiefinput-outputmodel,thesectoraloutputsarederivedfromexogenously

specifiedfinaldemands.Themodelisalsobeingreferredtoas“demand-driven”becauseitisthe

finaldemandvectory(orfinaldemandmatrixY)thatdrivesthemodelentirely.Itdeterminestotal

outputs (x), intermediate inputs (Z) and primary inputs (W) via a set of fixed coefficients. This

approachexamineshowmuch isneededof theoutput frompreceding,verticalstagesorof the

primaryinputs,eitherforfinaluseorforaunitofoutputofsomeindustry(Augustinovics,1970).

The first step in using the information given in IO tables is to calculate the individual technical

inputcoefficients,alsocalledtechnicalcoefficients,i.e.thedirectbackwardlinkages1.

Theinterindustryflowsfromsectoritoj(foragivenperiod–mostly1year)dependentirelyand

exclusivelyonthetotaloutputrequiredfromsectorjforthesameperiod.Forexample,themore

carsproducedinayear,themoresteelwilltheautomobileproducersneedduringthatyear.The

technicalcoefficient–ratioofinputtoagivensectortoitsoutput–definestheexactnatureofthis relationship. Technical coefficient is the ratioof input to sector’s total output,which is the

euro’sworthofinputsfromsectoripereuro’sworthofoutputofsectorj.

Input-outputanalysisworksherewithanassumptionofconstantreturnstoscale,whichmeans

that the coefficient does not depend on (and does not change with) the amount of items

produced.Theassumptionofthesecoefficientstobeconstantmeansthattheinputsacquiredby

sector j from sector i depend only and entirely on the total output of sector j. Technicalcoefficientsaijarecalculatedasshowninequation(1).

(1) orinmatrixform

(2) [ …diagonalisedvectorx]or

1 The termbackward linkages is used to describe the level of interconnection between a particular sector and thesectorsfromwhichitpurchasesinputs.TheelementsoftheAmatrixonlycapturethedirectbackwardlinkages,whilethe elements of the Leontief inverse take account of both, direct and indirect backward linkages (Miller and Blair,2009).

j

ijij x

za =

-1xZA ˆ= x̂



48

(3) ifappliedtotableform

This assumed fixed relationship between a sector’s output and its inputs implies also the

assumptionofa fixedproportionof inputs, i.e.a fixedproduction function.Thismeans, input-

output analysis requires that a sector use inputs in fixedproportions – not onlyaij is assumed

fixed, but also the ratio between total output of a sector and its primary inputs (e.g. labour,

capital,…). Suppose that sector 4 from the example above also buys inputs from sector 2, and

that, for period of observation, z24=$750. Therefore, a24=z24/X4=$750/$15,000=0.05. For

X4=$15,000, inputs from sector 1 and sector 2 were used in the proportion

P12=z14/z24=$300/$750=0.4. This is simply the reflection of the fact that

P12=z14/z24=a14×X4/a24×X4=a14/a24=0.02/0.05=0.4.P–theproportion–istheratioofthetechnical

coefficients,andsincethecoefficientsarefixed(=theproductionhaveconstantreturnstoscale),

thentheinputproportionisfixed.

Technical coefficients development can be used to estimate changes in the productmixwithin

eachsector,aswillbedescribedbelowinthepartonstrucutraldecomposition.

Thisratiobetweentotaloutputofasectoranditsprimaryinputsforaneconomywithsprimary

inputsisgivenbythecoefficientcsi,whichisformalizedinEq.(4).Notethatthesumoftechnicalinput coefficients (A matrix) and primary input coefficients (C) have to amount to unity (i’),becauseof Eq. (9) [compareEq. (6)]. This fixed ratioonly refers tomonetary inputs included in

value added and not any physical ones which are not accounted for in value added (natural

resources).Theassumptionoffixedinputcoefficientscanbeintheoryjustifiedonthebasisofthe

Walras-Leontief production function, where firms are assumed to operate a cost-minimizing

strategy.

(4) orinmatrixannotation

(5)

úúúúúúú

û

ù

êêêêêêê

ë

é

=

3

33

2

32

1

31

3

23

2

22

1

21

3

13

2

12

1

11

xz

xz

xz

xz

xz

xz

xz

xz

xz

A

j

sjsj x

wc =

1ˆ -= xWC



49

(6) i’A+i’C=i’

(7)

Usuallythequestiontobeansweredindemand-sideIOmodelling,isthefollowing:Iffinaldemand

fromoneormoreof theexogenous sectors (e.g.: households, government, etc.) is expected to

increaseordecrease in the future,howwould thisaffect the totaloutputxnecessary tosatisfythis new demand and its effects throughout the economy? In order to get x, Eq. (2) is firstlytransformed intoEq. (8). ReplacingZ in (9) thengives Eq. (10). Bringing all thex ontoone sideequals (11), which can then be transformed into (12) and finally X can be found when pre-multiplyingywiththesocalledLeontief Inverse inEq.(13).This inverse,(I−A)-1,providesanewmatrix which shall be denoted asΩ and its elements asaij. A value of say 0.5 fora23, would

indicatethatinordertosatisfy1dolarworthofincreaseintheservicesector’s(j=3)finaldemand

(Dy3=1),requires0.5dollarworthofincreasedoutputinthemanufacturingsector(i=2),i.e.Dx2=0.5.

(8)

(9) y=Yi

(10)

(11) [Notethat ]

(12)

(13) Leontiefinverse…denotingtheelementsof(I−A)-1asaijthiscanbe

resolvedas:

(14)

÷÷ø

öççè

æ= s

cw

i, az

xsj

sj

ij

ijj allfor allfor Min

ZxA =ˆ

yixAx += ˆ

yAxx =- xix =ˆ

( ) yxAI =-

( ) yAIx 1--=

nnnjnjnnn

ninjijiii

nnjj

yα...yα...yαyαx

yα...yα...yαyαx

yα...yα...yαyαx

+++++=

+++++=

+++++=

2211

2211

112121111

!

!



50

Decompositionofsectorsininput-outputtablesWhenthereisdeeperinterestinlookingatoneofthesectorsinbiggerdetail,analystsareoften

compelledtofindouthowtodisggregatethesectorintosmallercomponentsthatwillbeofuse

for their analysis. This is also the case for inserting our two RES sectors into the input-output

structure. Aswe said above,we take technology as fixed (opposite to some other approaches,

such as endogenizing technological change by Pizer nad Popp (2008)), and we are therefore

interestedintheeffectsofchangingenergymixtothewholestructureoftheeconomy.Thereare

various approaches how to disaggregate the sectors intomore detailed components – someof

whichwepresentbelow. There are varietyofmeansbywhichdata for input-output tables are

compiled.

It should be noted that dealing with technological change and its endogenizing would require

dealingwith theso-called learningcurves.However,although learningcurveshavebeenwidelyadopted in climate-economymodels to endogenoize changes of energy technologies, they are

criticezdbysomeauthors–e.g.byPanandKöhler(2007)–fornotseparatingtheeffectsofprice

and technological change. Thus, they cannot reflect continuous and qualitative change of both

conventional and emerging energy technologies, cannot help to determine the time paths of

technologicalinvestment,andmissthecentralroleofresearchanddevelopmentactivityindriving

technologicalchange(PanandKöhler,2007).



51

StructuraldecompositionanalysisMillerandBlair (2009)provideadescriptionofonemethod fordisggregationof changes in the

sectors in input-outputtables–theso-calledstructuraldecompositionanalysis (SDA).WithSDA,

the total change in gross outputs between two periods can be broken down into that partassociatedwithchangesintechnology(i.e.changesintheLeontiefinversefortheeconomyover

theperiod)andthatpart relatedtochanges in finaldemandover theperiod (MillerandBlair,

2009: 593). At the next level, the total change in the Leontief inverse matrix could be

disaggregated into a part that is associated with changes in technology within each sector (as

reflected inchanges in thedirect inputcoefficientsmatrix)andthatpart that isassociatedwith

changesinproductmixwithineachsector.Especiallythelatter,impactsassociatedwithchanges

inproductmix,willbeofourinterest.

Theinput-outputSDAgenerates,bydefinition,resultsatthesectorallevel.Forann-sectormodel,

each element in the n-element vector of changes will be decomposed into two or more

constituent elements (Miller and Blair, 2009: 596). Alternatives are e.g. grouping sectors into

categoriesandthenfindingaverages–MillerandBlair(2009:596)mentionforexample“fastest

growing sectors”, “fastest declining sectors” and other sectors, or primary (natural resource

related), secondary (manufacturing and processing) and tertiary (supprot and servic oriented)

sectors.Thisapproach,however,runsaseriousriskoflosingthedetailedlookintotheparticular

sectors(incaseofouranalysisintothe35WIODsectors).

Asmentionedabove, theresultingchanges in finaldemandscanbealsoduetoachange in the

relativeproportionsofexpenditureon thevariousgoodsandservices, suchand inourcase the

shiftfromfossilfuelssourcesofelectricitytorenewablesources(mainlywindandsolar).Similarly,

as described above, changes in the Leontief inverse result from changes in the economy’s A

matrix.This, in turn,mayreflectamongotherschanges inproduction recipes (suchas replacing

coalenergyforelectricityproductionbywindorsolarpower),aswellassubstitutionscausedby

relativepricechanges,reductionsinasector’smaterialsinputsperunitofoutputetc.(Millerand

Blair,2009:598).



52

AnnualreportsWhenthereisnotenoughinformationforconstructingthetechnicalcoefficientsinmoredetailed

sectoral levelthanprovidedbytheofficial input-outputdatabases,oneneedstoconstructthese

subsectors“fromthebottom-up”.Thus,adaptationofdatafromothersourcesisamongthemost

formidablechallengesinusinginput-outputanalysis(MillerandBlair,2009:119). Ifdataneeded

for theconstructionofone, twoorseveralparticularsubsectors (suchas inourcase)whichare

notpartofany largerdatacollection(forcollectingdataonnationalaccountsetc.), theyusually

have tobebasedona so-called“ad-hocsurvey”.Thismeansa search forprimaryor secondary

dataontheproducerscostshares.

One possibility is to look into the renewable energy producing companies’ annual reports. The

annual reports should provide information not only on how much and with which inputs

production is realisedby eachRES technology, but also thebalance sheets ofwhatpart of this

energygoeswhere(aselectricity,inourcase).Theannualreportsdescribehowmuchofwhatthe

companybuysfromwhichproducer(supplier)andhowmuchitsellstowhom(forexample,the

company spends X amount ofmoney for buing fuel on trucks, Y amount ofmoney for buying

photovoltaicpanels frommanufactorers, andZ amountofmoney formaintaining thesepanels;

andthensellstheenergyproducedwiththeseinputstoconsumers).



53

InputcostsharesSomewhat similar to looking into companies’ annual reports and balance sheets, albeit a bit

different,istolookinstudiesmappinginputcostsharesforelectricityproductionfromrenewable

energy sources. In practice, thismeans searching for studies on various inputs into production,

managementanddistributionoftheRESenergysources,suchaswindmillsorphotovoltaicpanels.

With this approach, it is possible to open up the input components of the Electricity, Gas and

Water supply sector for the renewableenergypart (namelywindand solarenergyproduction).

Thesecostsharesareusuallyprovidedbysecondarydatasources,suchasreportsandacademic

papersregardingthedistributionorthe“economics”ofrenewableenergyproduction.Thisis,they

providedataonthecostofeachinputtorunarenewableenergysource(windmill,photovoltaic

panel).Forexample,Blanco(2009)providessuchanalysesforwindfarms.

Whenderivingdatafromsuchsources,onehastobecarefulwiththedifferencebetweencapital

costs and variable costs. Basically, capital costs are those needed for the construction of the

turbine/panel (i.e. for theequipmentwhich thenproducesenergy).Usually, theyoccupyonlya

smallproportionoftheoverallnumber,accordingtocapitaldepreciation.Inourcasewecalculate

with a capital cost share of 1/20, which assumes a life-span of such equipment of 20 years.

Variable costs cover the operation andmanagement during the periodwhen the turbine/panel

worksandproduceselectricity.



54

Exploringpost-carbonfuturesAn overview of approches suitable for estimating future changes and developments of input-

outputeconomicstructures isprovided inthissection.First,anapproachbasedonparticipatory

modellingisdiscussed.Weproposethat inordertomaketheanalysisofthemonetaryfluxesas

reliableaspossible,thesetwoapproachesandespeciallytheircombinationseemtobethemost

promising path. As such, both of them should be – ideally – combined in theMEDEASmodel

development.However, for theneedsof this report,amoreconventionalmethodofestimating

possiblefuturemonetaryfluxeswasapplied,asdescribedabove.



55

Approaches basedon stakeholder participation – participatorymodellingAn overview of approches suitable for estimating the future developments of input-output

structuresoftheeconomyisdiscussedbyDuchinandLange(1994).Revealingpotentialpathstoa

post-carbon future requires not only detailed knowledge of the current state and historical

developments,butalsoqualifiedestimatesoffuturedevelopmentinparticularareasofthesocio-

economicsystems,suchasinourcaseoftheenergysector.

Thereareseveralapproachesandmethodsofpredictingfuturechangesinvariousareasofhuman

activityandnaturalsystems.Ourapproach–participatorymodellingforobtainingthenecessary

inputs – is proposed for the MEDEAS model development in order to formulate sectoral

transformation guided by low-carbon transition objectives. Participatory modelling means

incorporating stakeholders such as the general public, decision-makers or experts from the

respectivefieldsintothemodelingprocess.Thisapproachiswidelyappliedinenvironmentaland

natural resourcemodeling to increase the legitimacyofmodel results, guarantee their practical

applicabilityandtriggerlearningeffectsamongparticipantstoallowaco-constructionofpossible

futures(Höltingeretal.,2016).

Thereforewepropose to base themodelling of themonetary fluxes on a participatory process

bringingtogetherpeoplewithexpertknowledgeabout futuredevelopments (organizationaland

technological changes that are supposed to be leading to a low-carbon economy) in particular

economicsectors(namelysectors8and17oftheWIODstructure–Coke,RefinedPetroleumand

Nuclear Fuel, and Electricity, Gas andWater Supply) with people coming up withmultifaceted

proposals, ideas and visions about a low-carbon future thatwill be translated into the logic of

input-outputtablesof2020,2030,2040and2050.

“Experts” (e.g. engineers or researchers) are stakeholders with detailed knowledge of possible

developments inparticulareconomicsectors.Asimilarapproachwasappliede.g.byDuchinand

Lange (1994). People coming up with multifaceted low-carbon transition proposals we call

“engagedcitizens” (weusetheterm“stakeholders”to indicatebothgroups).“Engagedcitizens”

can be e.g. activists (usually represented by citizen initiatives and networks), decision-makers,

NGO analysts, or even researchers. The underlying assumption is that “engaged citizens” have

both(1)knowledgeofthetopic,and(2)deepinterestinshapingthefuturetowardstheirdesires.

For the needs of MEDEAS model development, we propose to frame the research design by

combiningviewsof “experts”with“engagedcitizens”.Suchanapproachshouldassure thatnot



56

only knowledge of development in particular sectors and their interconnections, but also ideas

about ways to shape their development in order to achieve a low-carbon future, are taken

seriouslyintoaccount.

Oncetechnicalcoefficientsandtheirchangesovertimearedefined, it ispossibletobuildfuture

input-output table(s), showing the interacting elements (input-output relations among sectors)

required for a structural representation of the low-carbon economy. Obtaining the technical

coefficients requires detailed quantification of various proposals and ideas about future

developments.Asalreadymentionedabove, themainchallengeof theproposedapproach is to

define technical coefficients – in practical terms, we would propose to ask experts and other

stakeholdershowthey think the inputsandoutputs (=thetechnicalcoefficients)couldevolve in

thefutureandwhy.



57

ResultsFirst,areviewofrecenthistoryofmonetaryfluxesandconsumptionpatternsisprovided.Inthe

secondpart,wefocusonfutureprojectionsforanaggregatedoverviewforallsectorsusedinthe

WIOD tables, modelling the developments in the Electricity, Gas and Water Supply sector.

However, this is of course a non-exhaustive analysis and as such it shouldbe treatedonly as a

thresholdoralineofthinkingfortheMEDEASmodeldevelopment.

In theFigures3,5,7,8,910,15,16,17and18below in this section, the35WIODsectorsare

listedaccordinglymarkedwithdifferentcolours–thefirstbottomcomponentofthechartshows

technicalcoefficientdevelopmentsofthesector1–Agriculture,Hunting,ForestryandFishing;the

secondonefromthebottom(greenbigone)meansdevelopmentsofthetechnicalcoefficientsfor

thesector2–MiningandQuarrying;etc.Thismeansthatthelistofsectorsfrom1to35(seethe

listofsectorsintheintroductorysectionofthisreport)goesfromthebottomup,andsodothe

monetaryflowsdisplayedonthechartnexttothelistofsectors.



58

Historicaltrends

WorldlevelReviewof changes inmonetary fluxesat theworld level for the sector17–Electricity,Gasand

Water supply technical coefficientsand for thesector8–Coke,RefinedPetroleumandNuclear

Fueltechnicalcoefficients.Theresultsforbothsectorsshowoverallincreaseinproductioninthis

period,sloweddownbythe2008worldeconomiccrisis(seeFigure3and5).

Electricity,GasandWaterSupplyThe biggest contributors to the sector’s 17 total output production are Mining and Quarrying

(secondfromthebottom,greenone),thenthesectoritself(Electricity,GasandWatersupply–big

orangeoneinthemiddle),andthenRentingofM&EqandOtherBusinessActivities(6thfromthe

top); Coke, Refined Petroleum andNuclear Fuel (red one, fourth from the bottom); and Inland

Transport (smallerorangeone,13th fromthe top), followedbyothers.MiningandQuarrying is

alsothesectorwhichshowsthebiggestdeclineaftertheworldrecessionin2008.



59

Figure3:Past (1995-2011)developmentsoftechnicalcoefficients (shareof inputs from35WIODindustriestothesector’stotaloutput)forthesector17–Electricity,GasandWatersupplysectoratthegloballevel.

Source:OwncalculationsbasedonWIODdatabase,2013release.

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

PrivateHouseholdswithEmployedPersons

OtherCommunity,SocialandPersonalServices

HealthandSocialWork

Education

PublicAdminandDefence;CompulsorySocialSecurityRentingofM&EqandOtherBusinessActivities

RealEstateActivities

FinancialIntermediation

PostandTelecommunications

OtherSupportingandAuxiliaryTransportActivities;ActivitiesofTravelAgenciesAirTransport

WaterTransport

InlandTransport

HotelsandRestaurants

RetailTrade,ExceptofMotorVehiclesandMotorcycles;RepairofHouseholdGoodsWholesaleTradeandCommissionTrade,ExceptofMotorVehiclesandMotorcyclesSale,MaintenanceandRepairofMotorVehiclesandMotorcycles;RetailSaleofFuelConstruction

Electricity,GasandWaterSupply

Manufacturing,Nec;Recycling

TransportEquipment

ElectricalandOpticalEquipment

Machinery,Nec

BasicMetalsandFabricatedMetal

OtherNon-MetallicMineral

RubberandPlastics

ChemicalsandChemicalProducts

Coke,RefinedPetroleumandNuclearFuel

Pulp,Paper,Paper,PrintingandPublishing

WoodandProductsofWoodandCork

Leather,LeatherandFootwear

TextilesandTextileProducts

Food,BeveragesandTobacco

MiningandQuarrying

Agriculture,Hunting,ForestryandFishing



60

Table2:SummarystatisticsforthetechnologicalcoefficientsofSector17

No Label Mean SD Median Min Max a1 agr 0.001 0.002 0.000 0.000 0.019 a2 min 0.133 0.092 0.114 0.000 0.604 a3 foo 0.001 0.002 0.001 0.000 0.021 a4 tex 0.001 0.001 0.000 0.000 0.011 a5 lth 0.000 0.000 0.000 0.000 0.002 a6 woo 0.002 0.004 0.000 0.000 0.024 a7 ppr 0.003 0.003 0.002 0.000 0.024 a8 ref 0.039 0.054 0.023 0.000 0.440 a9 chm 0.010 0.022 0.005 0.000 0.166 a10 pls 0.002 0.002 0.002 0.000 0.016 a11 nmm 0.002 0.002 0.001 0.000 0.014 a12 met 0.011 0.008 0.009 0.000 0.046 a13 mch 0.011 0.008 0.009 0.000 0.050 a14 ele 0.019 0.018 0.014 0.000 0.112 a15 teq 0.002 0.002 0.001 0.000 0.013 a16 mnf 0.002 0.005 0.001 0.000 0.041 a17 elg 0.153 0.119 0.122 0.001 0.617 a18 cns 0.022 0.020 0.016 0.000 0.105 a19 veh 0.005 0.007 0.004 0.000 0.044 a20 trd 0.029 0.020 0.022 0.002 0.105 a21 ret 0.014 0.012 0.010 0.000 0.073 a22 hnr 0.003 0.011 0.001 0.000 0.110 a23 itr 0.023 0.022 0.014 0.000 0.161 a24 wtr 0.001 0.002 0.000 0.000 0.018 a25 atr 0.001 0.001 0.000 0.000 0.008 a26 otr 0.003 0.003 0.002 0.000 0.016 a27 ict 0.007 0.006 0.006 0.000 0.040 a28 fin 0.020 0.011 0.017 0.002 0.062 a29 est 0.006 0.006 0.004 0.000 0.038 a30 rnt 0.034 0.025 0.028 0.000 0.139 a31 pub 0.004 0.008 0.002 0.000 0.066 a32 edu 0.001 0.001 0.001 0.000 0.008 a33 hlt 0.000 0.001 0.000 0.000 0.003 a34 soc 0.007 0.007 0.004 0.000 0.044 a35 prv 0.000 0.000 0.000 0.000 0.000

AscanbeseenfromthesummarystatisticsinTable2above,thehighestmeaninputcoefficientis

the input from the sector itself, immediately followed by the input frommining and quarrying

(Sector 2). The link to sector 2 is the key link to provide the fossil energy carriers to generate



61

electricity.Noother linkhasacomparablemagnitude.Thereexistsmaller linkstothesectors8,

20,and30.

Figure4:Inputsharefromsector2(MiningandQuarrying)


This histogram shows the fraction of countries with similar coefficient sizes for the input

coefficientfromminingandquarrying(Sector2)intoelectricity,gas,andwatersupply(Sector17)

incolumns.Thehighestshareofcountrieshasan inputcoefficientofapproximately0.1(xaxis),

whichimpliesacostshareof10%.Afewcountrieshavecostsharesthatexceed20%,whilesome

outliersevenhavecostsharesabove40%.

Coke,RefinedPetroleumandNuclearFuelFigure5belowprovidessimilardevelopmentsforsector8–Coke,RefinedPetroleumandNuclear

Fuel, aswas done above for sector 17. The following sectors (afterMining andQuarrying) that

contributemostsignificantlytothesector8(Coke,RefinedPetroleumandNuclearfuel)aresector

8 to itself (red component in Figure 5), followed by sector 23 – Inland Transport (thick orange

one). The refining sector (Sector 8) is, by far, most strongly linked to sector 2 – Mining and

Quarrying,ascanbeseeninFigure5.MiningandQuarryingisthebiggreensectorsecondfrom

thebottom.

0

.05

.1

.15

Frac

tion

0 .2 .4 .6input share from mining and quarrying (sector 2)



62

Figure5:Past (1995-2011)developmentsoftechnicalcoefficients (shareof inputs from35WIODindustriestothesector’stotaloutput)forthesector8–Coke,RefinedPetroleumandNuclearFuelatthegloballevel.


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011



HealthandSocialWork

Education






WaterTransport

InlandTransport





TransportEquipment


Machinery,Nec



RubberandPlastics








MiningandQuarrying

Agriculture,Hunting,ForestryandFishing



63

Table3:SummarystatisticsforthetechnologicalcoefficientsofSector8

No Label Mean SD Median Min Max a1 agr 0.002 0.008 0.000 0.000 0.066 a2 min 0.438 0.191 0.452 0.001 0.834 a3 foo 0.002 0.002 0.001 0.000 0.022 a4 tex 0.001 0.002 0.000 0.000 0.016 a5 lth 0.000 0.000 0.000 0.000 0.001 a6 woo 0.004 0.022 0.000 0.000 0.270 a7 ppr 0.003 0.004 0.001 0.000 0.031 a8 ref 0.064 0.076 0.044 0.000 0.642 a9 chm 0.031 0.054 0.017 0.000 0.376 a10 pls 0.004 0.008 0.002 0.000 0.057 a11 nmm 0.002 0.004 0.001 0.000 0.034 a12 met 0.008 0.010 0.005 0.000 0.075 a13 mch 0.006 0.006 0.004 0.000 0.045 a14 ele 0.003 0.003 0.002 0.000 0.030 a15 teq 0.001 0.002 0.001 0.000 0.025 a16 mnf 0.001 0.003 0.001 0.000 0.026 a17 elg 0.025 0.028 0.016 0.000 0.215 a18 cns 0.005 0.008 0.002 0.000 0.056 a19 veh 0.006 0.007 0.004 0.000 0.050

a20 trd 0.068 0.044 0.060 0.000 0.216 a21 ret 0.022 0.023 0.016 0.000 0.154 a22 hnr 0.002 0.002 0.001 0.000 0.028 a23 itr 0.062 0.052 0.052 0.000 0.300 a24 wtr 0.003 0.005 0.001 0.000 0.042 a25 atr 0.001 0.001 0.000 0.000 0.009 a26 otr 0.007 0.009 0.004 0.000 0.097 a27 ict 0.003 0.003 0.002 0.000 0.019 a28 fin 0.012 0.011 0.010 0.000 0.072 a29 est 0.004 0.007 0.001 0.000 0.048 a30 rnt 0.022 0.023 0.015 0.000 0.125 a31 pub 0.002 0.008 0.001 0.000 0.101 a32 edu 0.001 0.001 0.000 0.000 0.009 a33 hlt 0.000 0.000 0.000 0.000 0.003

a34 soc 0.003 0.003 0.002 0.000 0.017 a35 prv 0.000 0.000 0.000 0.000 0.000



64

The link fromtheMiningandQuarrying sector (sector2)exceeds the linkof the refining sector

(sector8)toitselfbyafactorof8to10,ascanbeseeninFigure5andinTable3.Thefollowing

histogramshowsthedistributionofthestrengthofthiskeylinkbetweencountries:

Figure6:Inputsharefromsector8(Coke,RefinedPetroleumandNuclearFuel)


Thehighest fractionof countrieshas a technological coefficientof 0.6 toMining andQuarrying

(Sector 2), withmost of the countries between 0.35 and 0.7. But there is also a fraction with

relativelylowinputsharesof0.2andinsomecaseseven0.Notethatthemeanandmedianvalues

forthislinkwouldthereforebemisleading.

0

.02

.04

.06

.08

Frac

tion

0 .2 .4 .6 .8input share from mining and quarrying (sector 2)



65

CountrylevelexamplesExamplesofmonetaryfluxespastdevelopmentsinthecaseoftwocountries(Austria,Czechia)are

providedinthissection.Webelievethatthetrendsexploredattheworldlevelcanbemuchbetter

understood from comparison at the country level, which provides a significantlymore detailed

lookinthesenseofdriversofthetechnicalcoefficients(i.e. it ismucheasiertotrackchangesin

thecoefficientsduetovariouspoliciesappliedinacountrythantrytotrackthesamechangesat

thegloballevel,thatcanbecausebyseveraleventsanddrivenbymanydifferenttrends–except

fortheverylargeonessuchastheglobaleconomiccrisisat2008).

Theanalysisare,again,providedfirst forsector17–Electricity,GasandWaterSupply (which is

alsothesectorthatweuseforthemodelingpurposesonthegloballevelfurtherbelow),andfor

sector8–Coke,RefinedPetroleumandNuclearFuel.

AustriaThesameexerciseaswedidattheworldlevelprovidesmuchmoredetailedinformationwhenwe

lookatthecountrylevel.InthecaseofAustria,thereareseveraltraceabletrendspresentinthe

developmentoftechnicalcoefficients inthe1995-2011period.Amongthemostsignificantones

there is an increaseofdeliveries from the sector itself, i.e. themonetary flows from the sector

Electricity,GasandWaterSupplytothesectorElectricity,GasandWaterSupply.Theseincreasing

transactionswithinthesectorcanbeduetodisaggregationofthesector–i.e.increasingnumber

of actors in the sector rather than few big ones. Another significant trend which is worth

mentioning is the development of inputs from theMining and Quarrying sector, which shows

(afterinitialincreasein1998-2000)adecreasesince2000.



66

Figure7:Past (1995-2011)developmentsoftechnicalcoefficients (shareof inputs from35WIODindustriestothesector’stotaloutput)forthesector17–Electricity,GasandWatersupplysectorforAustria.


0,0000

0,1000

0,2000

0,3000

0,4000

0,5000

0,6000

0,7000

0,8000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

PrivateHouseholdswithEmployedPersonsAUTc35OtherCommunity,SocialandPersonalServicesAUTc34HealthandSocialWorkAUTc33

EducationAUTc32

PublicAdminandDefence;CompulsorySocialSecurityAUTc31RentingofM&EqandOtherBusinessActivitiesAUTc30RealEstateActivitiesAUTc29

FinancialIntermediationAUTc28

PostandTelecommunicationsAUTc27

OtherSupportingandAuxiliaryTransportActivities;ActivitiesofTravelAgenciesAUTc26AirTransportAUTc25

WaterTransportAUTc24

InlandTransportAUTc23

HotelsandRestaurantsAUTc22

RetailTrade,ExceptofMotorVehiclesandMotorcycles;RepairofHouseholdGoodsAUTc21WholesaleTradeandCommissionTrade,ExceptofMotorVehiclesandMotorcyclesAUTc20Sale,MaintenanceandRepairofMotorVehiclesandMotorcycles;RetailSaleofFuelAUTc19ConstructionAUTc18

Electricity,GasandWaterSupplyAUTc17

Manufacturing,Nec;RecyclingAUTc16

TransportEquipmentAUTc15

ElectricalandOpticalEquipmentAUTc14

Machinery,NecAUTc13

BasicMetalsandFabricatedMetalAUTc12

OtherNon-MetallicMineralAUTc11

RubberandPlasticsAUTc10

ChemicalsandChemicalProductsAUTc9

Coke,RefinedPetroleumandNuclearFuelAUTc8

Pulp,Paper,Paper,PrintingandPublishingAUTc7

WoodandProductsofWoodandCorkAUTc6

Leather,LeatherandFootwearAUTc5

TextilesandTextileProductsAUTc4

Food,BeveragesandTobaccoAUTc3

MiningandQuarryingAUTc2

Agriculture,Hunting,ForestryandFishingAUTc1



67

Figure8:Past (1995-2011)developmentsoftechnicalcoefficients (shareof inputs from35WIODindustriestothesector’stotaloutput)forthesector8–Coke,RefinedPetroleumandNuclearFuelforAustria.


0,0000

0,1000

0,2000

0,3000

0,4000

0,5000

0,6000

0,7000

0,8000

0,9000

1,0000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

PrivateHouseholdswithEmployedPersonsAUTc35OtherCommunity,SocialandPersonalServicesAUTc34HealthandSocialWorkAUTc33

EducationAUTc32

PublicAdminandDefence;CompulsorySocialSecurityAUTc31RentingofM&EqandOtherBusinessActivitiesAUTc30RealEstateActivitiesAUTc29

FinancialIntermediationAUTc28

PostandTelecommunicationsAUTc27

OtherSupportingandAuxiliaryTransportActivities;ActivitiesofTravelAgenciesAUTc26AirTransportAUTc25

WaterTransportAUTc24

InlandTransportAUTc23

HotelsandRestaurantsAUTc22

RetailTrade,ExceptofMotorVehiclesandMotorcycles;RepairofHouseholdGoodsAUTc21WholesaleTradeandCommissionTrade,ExceptofMotorVehiclesandMotorcyclesAUTc20Sale,MaintenanceandRepairofMotorVehiclesandMotorcycles;RetailSaleofFuelAUTc19ConstructionAUTc18

Electricity,GasandWaterSupplyAUTc17

Manufacturing,Nec;RecyclingAUTc16

TransportEquipmentAUTc15

ElectricalandOpticalEquipmentAUTc14

Machinery,NecAUTc13

BasicMetalsandFabricatedMetalAUTc12

OtherNon-MetallicMineralAUTc11

RubberandPlasticsAUTc10

ChemicalsandChemicalProductsAUTc9

Coke,RefinedPetroleumandNuclearFuelAUTc8

Pulp,Paper,Paper,PrintingandPublishingAUTc7

WoodandProductsofWoodandCorkAUTc6

Leather,LeatherandFootwearAUTc5

TextilesandTextileProductsAUTc4

Food,BeveragesandTobaccoAUTc3

MiningandQuarryingAUTc2

Agriculture,Hunting,ForestryandFishingAUTc1



68

CzechiaIn caseof theCzechRepublic, the trend is verydifferent than inAustria. For the first sight, the

deliveriesfromthesectoritself(largebluearea)decreaseratherthanincreaseasitwasincaseof

Austria.Again,thistrendmightbeexplainedbytheintegration(ratherthandisaggregation,asit

wasincaseofAustria)oftheElectricity,GasandWaterSupplysector.However,averyinteresting

trendisshownintheinputsfromtheMiningandQuarryingsector,whichisfluctuatingovertime,

andwhichshowsanoveralldecreasebytheendoftheperiod.ThistrendisverysimilartoAustria.



69

Figure9:Past (1995-2011)developmentsoftechnicalcoefficients (shareof inputs from35WIODindustriestothesector’stotaloutput)forthesector17–Electricity,GasandWaterSupplyfortheCzechRepublic.


0,0000

0,1000

0,2000

0,3000

0,4000

0,5000

0,6000

0,7000

0,8000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

PrivateHouseholdswithEmployedPersonsCZEc35OtherCommunity,SocialandPersonalServicesCZEc34HealthandSocialWorkCZEc33

EducationCZEc32

PublicAdminandDefence;CompulsorySocialSecurityCZEc31RentingofM&EqandOtherBusinessActivitiesCZEc30RealEstateActivitiesCZEc29

FinancialIntermediationCZEc28

PostandTelecommunicationsCZEc27

OtherSupportingandAuxiliaryTransportActivities;ActivitiesofTravelAgenciesCZEc26AirTransportCZEc25

WaterTransportCZEc24

InlandTransportCZEc23

HotelsandRestaurantsCZEc22

RetailTrade,ExceptofMotorVehiclesandMotorcycles;RepairofHouseholdGoodsCZEc21WholesaleTradeandCommissionTrade,ExceptofMotorVehiclesandMotorcyclesCZEc20Sale,MaintenanceandRepairofMotorVehiclesandMotorcycles;RetailSaleofFuelCZEc19ConstructionCZEc18

Electricity,GasandWaterSupplyCZEc17

Manufacturing,Nec;RecyclingCZEc16

TransportEquipmentCZEc15

ElectricalandOpticalEquipmentCZEc14

Machinery,NecCZEc13

BasicMetalsandFabricatedMetalCZEc12

OtherNon-MetallicMineralCZEc11

RubberandPlasticsCZEc10

ChemicalsandChemicalProductsCZEc9

Coke,RefinedPetroleumandNuclearFuelCZEc8

Pulp,Paper,Paper,PrintingandPublishingCZEc7

WoodandProductsofWoodandCorkCZEc6

Leather,LeatherandFootwearCZEc5

TextilesandTextileProductsCZEc4

Food,BeveragesandTobaccoCZEc3

MiningandQuarryingCZEc2

Agriculture,Hunting,ForestryandFishingCZEc1



70

Figure10:Past(1995-2011)developmentsoftechnicalcoefficients((shareofinputsfrom35WIODindustriestothesector’stotaloutput)forthesector8–Coke,RefinedPetroleumandNuclearFuelfortheCzechRepublic.


0,0000

0,1000

0,2000

0,3000

0,4000

0,5000

0,6000

0,7000

0,8000

0,9000

1,0000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

PrivateHouseholdswithEmployedPersonsCZEc35OtherCommunity,SocialandPersonalServicesCZEc34HealthandSocialWorkCZEc33

EducationCZEc32

PublicAdminandDefence;CompulsorySocialSecurityCZEc31RentingofM&EqandOtherBusinessActivitiesCZEc30RealEstateActivitiesCZEc29

FinancialIntermediationCZEc28

PostandTelecommunicationsCZEc27

OtherSupportingandAuxiliaryTransportActivities;ActivitiesofTravelAgenciesCZEc26AirTransportCZEc25

WaterTransportCZEc24

InlandTransportCZEc23

HotelsandRestaurantsCZEc22

RetailTrade,ExceptofMotorVehiclesandMotorcycles;RepairofHouseholdGoodsCZEc21WholesaleTradeandCommissionTrade,ExceptofMotorVehiclesandMotorcyclesCZEc20Sale,MaintenanceandRepairofMotorVehiclesandMotorcycles;RetailSaleofFuelCZEc19ConstructionCZEc18

Electricity,GasandWaterSupplyCZEc17

Manufacturing,Nec;RecyclingCZEc16

TransportEquipmentCZEc15

ElectricalandOpticalEquipmentCZEc14

Machinery,NecCZEc13

BasicMetalsandFabricatedMetalCZEc12

OtherNon-MetallicMineralCZEc11

RubberandPlasticsCZEc10

ChemicalsandChemicalProductsCZEc9

Coke,RefinedPetroleumandNuclearFuelCZEc8

Pulp,Paper,Paper,PrintingandPublishingCZEc7

WoodandProductsofWoodandCorkCZEc6

Leather,LeatherandFootwearCZEc5

TextilesandTextileProductsCZEc4

Food,BeveragesandTobaccoCZEc3

MiningandQuarryingCZEc2

Agriculture,Hunting,ForestryandFishingCZEc1



71

FutureprojectionsofmonetaryfluxesTheanalysisfor2030and2050fortheglobalaggregatedlevelisprovidedfurtherbelow.Theyare

basedonthe“Inputcostshares”approachdescribedabove,modelledforwindandsolarenergy.

Costsharesofselectedrenewableenergysources

Methodologicalnote:CapitalcostsversusvariablecostsAsmentioned in themethodological part,wehave to dealwith thedifferencebetween capital

costsandvariablecosts.Thesharebetweencapitalcostsandvariablecostscanbeestimatedfrom

the life-span of an item producing the respective energy. In our case, we put 20 years for a

windmillaswellasforaPVpanel,whichmeanstheproportionofcapitalcoststovariablecosts

1/20.Practically,thismeansthat1/20oftotalcostsharesarethecapitalcosts,andthepending

19/20thevariablecosts).Moreover,weexcludeinterestratesfromourcalculations,andassume

justlabourinputsasvalueadded.

WindenergycostsharesThefollowingitemsare,accordingtoBlanco(2009)andanIRENA2012report(RenewablePower

GenerationCostsin2012:AnOverview,2012),partofthecapitalcostsofwindenergy:

• The turbine cost: Including rotor, blades, nacelle, tower and transformer (sector 2 –Mining andQuarrying, sector 12 – BasicMetals and FabricatedMetal and sector 14 –ElectricalandOpticalEquipment)

• Grid connection costs: This can include transformers and sub-stations, as well as the

connection to the localdistributionor transmissionnetwork (sector17–Electricity,GasandWaterSupplyandsector14ElectricalandOpticalEquipment)

• Civilworks: Includingconstructioncosts for sitepreparationandthe foundations for thetowers(sector18–Construction)

• Planning and project costs: These can represent a significant proportion of total costs(sector64–partofValueadded(labourinputs))

• Othercapitalcosts:Thesecanincludetheconstructionofroads,buildings,controlsystems

(sector 18 – Construction), development costs (sector 14 – Electrical and opticalequipment), landcosts (sector1–Agriculture),healthandsafetymeasures (sector33–



72

Healthandsocialwork),taxes(sector99–taxes),licensesandpermits(sector31–Publicadministration),etc.Wesplitthisrestequallytothesectorsjustlisted.

Figure11:EstimatedcapitalcostdistributionofawindprojectinEurope.

Source:Blanco(2009)

Figure12:Estimateofcapitalcostbreakdownforanoffshorewindfarm.

Source:Blanco(2009)



73

Variable costs of wind energy production include, according to Blanco (2009), the followingentries:

• Operation and maintenance, including provisions for repair and spare parts andmaintenanceoftheelectricinstallation(wedividethem50:50intosector2–MiningandQuarrying (repair and spare parts) and 64 – Value added (labour inputs) in theWIODstructure)

• Landandsub-stationrental(wedividethem50:50intosector1–Agricultureandsector17–Electricity,GasandWaterSupplyintheWIODstructure)

• Insuranceandtaxes(wedividethem50:50intosector28–FinancialIntermediationandsector99–taxesintheWIODstructure)

• Management and administration, including audits, management activities, forecasting

services and remote-control measures (sector 64 – Value added (labour inputs) in theWIODstructure)

Figure13:Variablecosts forGermanturbinesdistributed intodifferentcategoriesasanaverageoverthetime-period1997-2001.

Source:Blanco(2009)



74

SolarphotovoltaicenergycostsharesThe capital costof aPV system is composedof thePVmodule cost and thebalanceof system

(BoS)cost.Thecostof thePVmodule–the interconnectedarrayofPVcells– isdeterminedby

raw material costs, notably silicon costs, cell processing/manufacturing and module assembly

costs. The BoS cost includes items such as the cost of the structural system (e.g. structural

installation, racks, sitepreparation andother attachments), theelectrical systemcosts (e.g. the

inverter,transformer,wiringandotherelectricalinstallationcosts)andthecostofthebatteryor

otherstoragesystem,ifany,inthecaseofoff-gridapplications.Forsolarenergy,theIRENA2012report(RenewablePowerGenerationCostsin2012:AnOverview,2012)countswiththefollowing

BoSandinstallationcapitalcosts:

• The inverter, which converts the direct current (DC) PV output into alternating current(AC);

• ThecomponentsrequiredformountingandrackingthePVsystem;

• Thecombinerboxandmiscellaneouselectricalcomponents;• Site preparation and installation (i.e. roof preparation for residential systems, or site

preparationforutility-scaleplants),labour,costsforinstallationandgridconnection;

• Batterystorageforoff-gridsystems;and• System design, management, installer overheads, permit fees, project development

costs,customeracquisitioncosts,andanyup-frontfinancingcosts.

Powelletal.(2012)provideadetailedanalysisofsolarenergyfromPVpanelscapitalcostshares

anditsexpecteddevelopmentsuntil2020–seethetablebelow.



75

Table4:Datatableofcostanalysisresults(capitalcostsharesofsolarPVpanels)inUSdollars

Source:Powelletal.(2012)

Therefore,wehavedecidedtodividethecostshareslistedinthetableaboveaccordinglybetween

capitalcostsandvariablecosts.Thecapitalcoststhusincludethefollowingcounterpartsfromthe

tableabove:

• Siliconfeedstock,Metalpaste(sector2–Miningandquarrying)• Chemicals(sector9–ChemicalsandChemicalProducts)• Encapsulant,Ribbon,Packaging(sector10–RubberandPlastics)• Screens,Glass,Backsheet(sector11–OtherNon-MetallicMineral)• Wiresawing,Ingotcasting,Frame,JBandcable(sector12–BasicMetalsandFabricated

Metal)• Inputelectricity(sector17–Electricity,GasandWaterSupply)



76

• Labour+Maintenance(sector64–Valueadded)

VariablecostsofsolarPVenergyweassumetohavesimilarentriesasthoseofwindenergy,i.e.:

• Operation and maintenance, including provisions for repair and spare parts andmaintenanceoftheelectricinstallation(wedividethem50:50intosector2–MiningandQuarrying(repairandspareparts)and64–Valueadded(labourinputs))

• Landandsub-stationrental(wedividethem50:50intosector1–Agricultureandsector17–Electricity,GasandWaterSupply)

• Insuranceandtaxes(wedividethem50:50intosector28–FinancialIntermediationandsector99–taxes)

• Management and administration, including audits, management activities, forecasting

servicesandremote-controlmeasures(sector64–Valueadded(labourinputs))

For concrete numbers distributing these PV panels variable costs, see Figure 11 from Blanco

(2009)aboveinthewindenergysection.



77

ScenariosAccording to Lane et al. (2016), the installed capacity of renewables without biomass would

amounttoroughly9outof13TWin2050,basedonabusiness-as-usual(BAU)scenarioofenergy

sector growth that stays within the 2 degrees target. In 2030, renewables would contribute

roughly50%totheinstalledcapacity.

Figure14:Assumedinstalledcapacityofrenewables

Source:Laneetal.(2016)

Overview wires.wiley.com/wene

2011350

35,000

30,000

25,000

20,000

15,000

10,000

5,000

4

6

8

10

12

14

0

2

0

400

450

500

550

600

650

700(a)

(b)

(c)

2015 2020

Cha

nge

in d

eman

d (T

Wh/

year

)fo

r fo

ssil-

fired

ele

ctric

ity

2025 2030

Year of reference

Tot

al e

nerg

y de

man

d (E

J/ye

ar)

Inst

alle

d ca

paci

ty (

TW

)

2035 2040 2045 2050

2011 2015 2020 2025 2030 2035 2040 2045 2050

2011 2015 2020 2025 2030 2035 2040 2045 2050

Total BAU

Demand reduction

RenewablesBiomassNuclearNatural gas with CCSNatural gasCoal with CCSCoalOilTotal BAU (high renewables)Total BAUTotal GHG

Shift to renewables

Shift to biomass

Shift to nuclear

Install CCS

Total GHG

Electricity

Transport electrificationGHG scenario in 2050

Transport

Buildings/other

Industry

Non-energy

FIGURE 3 | The changing role for electricity, comparing forecasts for business-as-usual (BAU) energy system growth under a scenario thatachieves greenhouse gas (GHG) mitigation consistent with a 2 ∘C atmospheric temperature increase (ETP2014 study4). (a) Compares total energydemand, showing the portion of overall change attributed to the different sectors. The inset provides the demand profile for the GHG scenario in theyear 2050. (b) Provides a pathway to achieve the GHG mitigation scenario by reducing demand and shifting the generation from fossil fuels tofossil-free technologies. (c) Shows the evolution of the energy generation mix for the GHG mitigation scenario (coloured areas). Also shown is theincrease in capacity needed due to the removal of fossil fuels from the mix, e.g., the total installed capacity (in the year 2050) predicted for the GHGmitigation scenario is 0.7 TW higher than that for the ‘New Policies’ scenario (green dotted line), and 1.9 TW higher than that for the BAU scenario(black dotted line). Those increases represent an additional 17 or 49 GW/year (respectively) of infrastructure that must be constructed (on average)over the forecast period.

costs rise above those of alternative energysources. The current bout of coal plant closuresin the United States is an example of this, insti-gated by a sharp drop in natural gas prices.37

In other cases, concerns over local air qualitymight be the driver for closure of coal plants,such as those occurring in China.

The drivers of demand growth and ‘naturalretirement’ both create the need for construction ofnew infrastructure, regardless of the choice of energysource. Any additional burden introduced by the

transformation to a low-carbon supply mix wouldtherefore be the net difference between the rate ofinstalling that low-carbon supply infrastructure, andthe rate of installing the fossil fuel-based supplies thatwould have otherwise occurred. Quantifying these netdifferences would require a detailed, bottom–up inves-tigation into rate limiting factors for each technologyoption.

Given that the available energy supply alterna-tives share many basic components and subsystems,it may be that the ‘net’ differences involved are notthat great. For example, there are many commonalities

38 © 2015 John Wiley & Sons, Ltd. Volume 5, January/February 2016



78

Giventhatwedonotknowthefutureevolutionoftheplant loadfactors,wedeveloptwobasic

compositionsforthefutureinput-outputstructurestoseetheeffectsoftwoscenarios:

• Inthefirstscenario,weassumeatransformationto100%renewableenergies,andassume

acompositionof50%windandashareof50%solarPV.

• Inthesecondscenario,weassumeatransformationto50%renewableenergies,andagain

assumea compositionof 50%wind and50% solar PV for this share.We keep the input

structurefortherestatthe2011level.

The latter scenario could approximate a 2030 MLT within MEDEAS, while the former could

approximatea2050OTscenario.

MethodAsmentioned above, the 2050 table is provided for 50%wind and 50% PV solar energy based

sector17(Electricity,GasandWatersupply).Thisisatablefor2050OTscenario.The2030tableis

provided with 50% conventional 2011 share and 25%wind and 25% PV. This is (roughly)MLT

scenariofor2030.

First,wedisaggregatedthesector17–Electricity,GasandWaterSupplyinto4parts:

• Electricity fromwind – capital costs (costs necessary to construct and install an energy

producingunit–awindmill)

• Electricity from wind – variable costs (costs necessary to run and maintain the energy

productionduringtheenergyproducingunit’s–windmill–life-span)

• ElectricityfromsolarPVpanels–capitalcosts(costsnecessarytoconstructandinstallan

energyproducingunit–aPVpanel)

• Electricity fromsolarPVpanels–variablecosts (costsnecessary torunandmaintainthe

energyproductionduringtheenergyproducingunit’s–PVpanel–life-span)

InputsfromvariousWIODsectorstothesefourpartsweredistributedaccordingtotheanalyses

byBlanco(2009)forwindenergy,andbyPowelletal.(2012)forenergyfromsolarPVpanels.The

distributionofeachinputisdiscussedabove.

As Blanco provides the input shares already in percentage points, we took these percentage

shares(addingupto100%=1intheAmatrixstructureoftheIOtablewithtechnicalcoefficients)

anddistributedthemaccordinglytothesectorswheretheseinputsshouldcomefrom.Itshould



79

benotedthatthechoiceofsectorswasdonearbitrarilybytheauthorsandcan(andshould)be

adjustedoncemoreconcreteinformationontheinputsharesstructureisavailable.

IncaseofsolarenergyfromPVpanelsbasedontheanalysisbyPoweletal.(2012),wecalculated

thecostsharesaccordingly–dividingcostsofthecomponentsbythetotalcostsoftherespective

itemincaseofcapitalcosts.Incaseofvariablecosts,wehadtroubleswithfindingreliabledata,

andthereforewejustassumedthesamecostsharesasinthecaseofwindenergy.

Thedatawerethen inserted intothedisaggregatedAmatrixtabletothecellswhererespective

sectorscontributetotheoperationofthesector17,whichisintransitionfromtheconventional

one(2011statusquo)tothefullywind-andsolar-basedonein2050.

LimitationsHowever, the method described above has also a plenty of limitations, shortcomings and

simplifications. The results of our analysis can therefore suffer from some inconsistencies and

distortions.

Firstofall, it is far fromrealistic toassumethat therewillbeno technological changeand thus

that thecostshares found in thestudiesbyBlanco (2009)andPowell (2012)will stay thesame

overtheperio(until2050).

Second,thedistributionof inputsderivedfromthecostsharesstudieswouldhavetobedonein

collaborationwithexpertsonwindmills(windturbines,respectively)andsolarPVpanelsexperts,

toestimatetherightcostsharesstructure.Ourestimationsmightbethus flaweddueto lackof

experiencewiththedetailsconcerningtechnologicalconstructionandmaintenanceprocesses.

Third,aswedidnotfindenoughdataonvariablecostsharesforthePVpanels,wejusttook(for

themodellingneeds)thesamestructureasforthewindmills/windturbines.Althoughthishelped

ustofinalizetheanalysisofthefutureexpectedmonetaryfluxesbetweensectors,itmightatthe

sametimedistrorttheresultssignificantly(notethatthevariablecostscountfor19/20ofthetotal

costsforeveryoftheconsideredenergysources).

Lastbutnotleast,ourstudydonottakeintoaccountanybiophysicallimitsofland,mineralsetc.–

bothnecessary(aswillbeseenbelow)sourcesforsuccessfuloperationofalow-carboneconomy,

fullybasedonrenewableenergysources.



80

2030situation:Optimaltransition(OT)scenarioFigure 15: 2011-2030 monetary fluxes projection for Optimal transition scenario – sector 17Electricity,GasandWaterSupplytechnicalcoefficientsdevelopment

Source: Source:OwnelaborationbasedonWIODstructure (2013 release)anddata fromBlanco(2009)andPowell(2012)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1995 2000 2005 2010 2015 2020 2025 2030



HealthandSocialWork

Education






WaterTransport

InlandTransport





TransportEquipment


Machinery,Nec



RubberandPlastics








MiningandQuarrying



81

2030situation:Midleveltransition(MLT)scenarioFigure16:2011-2030monetary fluxesprojection forMiddle level transition scenario– sector17Electricity,GasandWaterSupplytechnicalcoefficientsdevelopment

Source:OwnelaborationbasedonWIODstructure(2013release)anddatafromBlanco(2009)andPowell(2012)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029



HealthandSocialWork

Education






WaterTransport

InlandTransport





TransportEquipment


Machinery,Nec



RubberandPlastics








MiningandQuarrying



82

2050situation:Optimaltransition(OT)scenarioFigure 17: 2011-2050 monetary fluxes projection for Optimal transition scenario – sector 17Electricity,GasandWaterSupplytechnicalcoefficientsdevelopment


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

199520002005201020152020202520302035204020452050



HealthandSocialWork

Education






WaterTransport

InlandTransport





TransportEquipment


Machinery,Nec



RubberandPlastics








MiningandQuarrying



83

2050situation:Midleveltransition(MLT)scenarioFigure18:2011-2050monetary fluxesprojection forMiddle level transition scenario– sector17Electricity,GasandWaterSupplytechnicalcoefficientsdevelopment


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

1995

1998

2001

2004

2007

2010

2013

2016

2019

2022

2025

2028

2031

2034

2037

2040

2043

2046

2049



HealthandSocialWork

Education






WaterTransport

InlandTransport





TransportEquipment


Machinery,Nec



RubberandPlastics








MiningandQuarrying



84

SummaryandconclusionsThisReport3.3bfocusedonanalysingmonetaryfluxesbetweensectorsforthetransitiontolow-

carbon economy until 2050. It also provides – for comparison – past trends at the global and

countrylevel.

Changingourenergysystemwill requireacompleteshift fromfossil fuelenergies to renewable

energies.Weanalysetheexpectedevolutionofthescenariosduringtheprocessoftransition,by

considering,amongothers,monetary fluxesbetweensectors and theirdevelopmentpathways.

Two different scenarios are considered in which the actions to reduce GHG emissions are

supposedtostartfrom2020(OT)or2030(MLT).Wederivethemonetaryfluxesbyusing input-

ouptutanalysis,basedontheWIODstructure.Theconstructued (monetary) input-output tables

fulfil the aim of a post-carbon economy in 2050, in order to stay within the 2°C increase of

temperature.As theWIOD IO tablesdonothaveadisaggregatedelectricity sectoraccording to

different sources of energy, we need to insert new sectors into the input-output structure, in

order to track the gradual changes in theenergymix. The rationale is that the shareof energy

producedfromfossilfuelsneedstodecrease,andthisproductionneedstomovetotherenewable

energiessector(whichneedstoincrease).

Wedecided todisaggregate theelectricityproduction sector (called “Electricity,Gas andWater

Supply” in theWIOD structure) for solar andwindenergyononehand, and for the restof the

energysources(includingfossilfuels,coalbeingamongtheleadingones)ontheother.Wefocus

solelyonwindandsolarenergyproductionduetotheexpectedrequirementsforatransitiontoa

low-carboneconomy.The specialnatureofbiomass (which inmanycases rather contributes to

CO2emissions thaneliminating them) ledus to skip this renewableenergy source.Problematic

aspects of hydropower energy, especially of big damsprojects, ledus to exclude this sourceof

energy,too.Solar(fromphotovoltaicpanels)andwindenergyarealsoamongthemostpromising

energysourcesforthefuture,asshownbytheIPCCreport.

Inthefirstpartofthisreport,wedescribedsomebasicfeaturesofthemethodweapply–input-

output modelling – to provide explanations of the terms we use and of the basic relations

between considered aspects of the issue we deal with – monetary fluxes between industries

duringthetransitiontolow-carboneconomy.

Inthesecondpart,wedepictedthepastevolutionoftheglobalaggregateinput-outputstructure

for the sector “Electricity, Gas andWater Supply”, aswell as the past evolution in Austria and



85

Czechia.Similarly,weshowthepasttrendsforsector“Coke,RefinedPetroleum,andNuclearFuel”

(Sector8).Wealsoshowedsummarystatisticsforthesetwosectorsacrossallcountriescoveredin

WIOD.

Thefollowingobservationscanbemadefromthesepasttrends:Theevolutionattheglobalscale

appearsfairlystableformostcoefficients,butthereareconsiderablechangesintheinputsfrom

miningandquarryingandinrelationsofthesectorto itself.Thesearealsothetwosectorswith

thehighestaverageinputshares.Thisisthecasebothforthemeanaswellasthemediancountry

inWIOD. In both cases, themedian is lower and the input distribution is skewed to the left. A

smallshareofcountrieshasconsiderablyhigherinputcostsfromthesetwosectors.

Forthetwocountrylevelexamples,similarchangescanbeobserved,butthefluctuationismore

pronounced, and, the relationof the sector to itself exhibitsopposing trends inboth countries.

While the inputof thesector to itself increasesconsiderably inAustria, it shrinksalmostby the

samemagnitudeinCzechia.However,theglobalhistoricaltrendisanincrease.

Withregardstothefuelssector,similarpasttrendscanbeobservedontheglobalscale.However,

with amean technological coefficient of 0.438, the input costs from themining and quarrying

sectorarefourtimeslargerthaninthecaseoftheelectricitysector.Inthiscase,theinputcosts

aresomewhatskewedtotheright.Unliketheelectricitysector,thissectorexhibitsasimilartrend

inAustriaandCzechia.

Basedoncostsharedatafrommultiplesources,wecalculatedinputcoefficientsfortheelectricity

sectorcomposedofwindandsolarPV.Thecalculationsarebasedonthe“Inputcostshares”,an

approach based on disaggregated Electricity, Gas and Water supply sector for wind and solar

energy, where wemodelled a 100% share of wind and solar energy (replacing the fossil fuels

basedelectricityproduction).The2050tablewasbasedon50%electricityfromwindsourcesand

50%fromPVsolarenergysources.The2030tablewasbasedon50%fromconventionalsources

(basedon2011data)andfrom25%windand25%PVsolarenergysources.

Surprisingly,theMLTandOTscenariosdonotdiffermuchonthefirstsight.Thismightbedueto

the growing significance of the sector “Agriculture, Forestry and Hunting sector”, which is

necessary toprovide the land forbothwindenergypowerplants and for solar energy fromPV

panels. This is also the most uncertain coefficient, because it depends on the predicted land

scarcity.Othertrendsincludedecreasingdemandfromthesector17toitself,whichmightimplya

growingEROIof therenewableenergysources.Ontheotherhand,unlikeexpected, there isno



86

considerable decrease from the sector 2 (Mining and Quarrying), whichmight be alarming for

long-termsustainabilityofaneconomyfullybasedonRES.

DisclaimeronlimitationsNote that this is an explorative endeavour withmany simplified assumptions and it should be

treatedassuch.Forexample,theresultsdonotyetrespectanybiophysicallimitsofland,minerals

etc.Moreover,wedonotcountwithanychangeof technology,asmentioned in thebeginning.

Especially these twoaspects (andmanyothers)make this studymore a first step in a research

design that can be exploredmore deeply if necessary. For taking such aspects into account, a

much deeper and more exhaustive analysis would be needed, following e.g. the approach we

described in theMethodologysection–participatorymodellingapproachwith includisngexpert

andotherstakeholders’knowledgeontechnologicalchangeetc.



87

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Contributionstoinput-outputanalysis1,249-269.

Blanco,M.I.,2009.Theeconomicsofwindenergy.RenewableandSustainableEnergyReviews13,

1372-1382.

ClimateChange2014SynthesisReport:SummaryforPolicymakers,2014.UnitedNations.

Dietzenbacher,E.,Los,B.,Stehrer,R.,Timmer,M.,DeVries,G.,2013.Theconstructionofworld

input–outputtablesintheWIODproject.Econ.Syst.Res.25,71–98.

Duchin, F., Lange, G.-M., 1994. The Future of the environment: ecological economics and

technologicalchange.OxfordUniversityPress,NewYork.

Höltinger, S., Salak, B., Schauppenlehner, T., Scherhaufer, P., Schmidt, J., 2016. Austria’s wind

energy potential – A participatory modeling approach to assess socio-political and market

acceptance.EnergyPolicy98,49-61.

Lane,J.L.,Smart,S.,Schmeda-Lopez,D.,Hoegh-Guldberg,O.,Garnett,A.,Greig,C.,McFarland,E.,

2016.Understandingconstraintstothetransformationrateofglobalenergyinfrastructure.Wiley

InterdisciplinaryReviews:EnergyandEnvironment5,33-48.

Leontief, W., 1983. Technological advance, economic growth, and the distribution of income.

PopulationandDevelopmentReview,403-410.

Miller, R.E., Blair, P.D., c2009. Input-output analysis: foundations and extensions, 2nd ed. ed.

CambridgeUniversityPress,NewYork.

Pan,H.,Köhler,J.,2007.Technologicalchangeinenergysystems:Learningcurves,logisticcurves

andinput–outputcoefficients.EcologicalEconomics63,749-758.

Peneder,M., 2003. Industrial structure and aggregate growth. Structural Change and Economic

Dynamics14,427-448.

Pizer,W.A., Popp,D., 2008. Endogenizing technological change:Matchingempirical evidence to

modelingneeds.EnergyEconomics30,2754-2770.



88

RenewablePowerGenerationCostsin2012:AnOverview,2012..InternationalRenewableEnergy

Agency(IRENA),AbuDhabi.

Thi, N.B.D., Lin, C.Y., Kumar, G., 2016. Electricity generation comparison of food waste-based

bioenergywithwindandsolarpowers:Aminireview.SustainableEnvironmentResearch26,197-

202.



89

ListofTablesTable1:ListofWIODsectors...........................................................................................................39

Table2:SummarystatisticsforthetechnologicalcoefficientsofSector17....................................60

Table3:SummarystatisticsforthetechnologicalcoefficientsofSector8......................................63

Table4:Datatableofcostanalysisresults(capitalcostsharesofsolarPVpanels)inUSdollars...75



90

ListofFiguresFigure 1: Historical development of global primary energy supply from renewable energy from

1971to2008....................................................................................................................................42

Figure2:Globalprimaryenergysupply(directequivalent)ofbioenergy,wind,directsolar,hydro,

and geothermal energy in 164 long-term scenarios in 2030and2050, and groupedbydifferent

categoriesofatmosphericCO2concentrationlevelthataredefinedconsistentlywiththoseinthe

AR4...................................................................................................................................................43

Figure3:Past(1995-2011)developmentsoftechnicalcoefficients(shareofinputsfrom35WIOD

industriestothesector’stotaloutput)forthesector17–Electricity,GasandWatersupplysector

atthegloballevel.............................................................................................................................59

Figure4:Inputsharefromsector2(MiningandQuarrying)...........................................................61


industriestothesector’stotaloutput)forthesector8–Coke,RefinedPetroleumandNuclearFuel

atthegloballevel.............................................................................................................................62

Figure6:Inputsharefromsector8(Coke,RefinedPetroleumandNuclearFuel)..........................64


industriestothesector’stotaloutput)forthesector17–Electricity,GasandWatersupplysector

forAustria........................................................................................................................................66



forAustria........................................................................................................................................67


industriestothesector’stotaloutput)forthesector17–Electricity,GasandWaterSupplyforthe

CzechRepublic.................................................................................................................................69

Figure10:Past(1995-2011)developmentsoftechnicalcoefficients((shareofinputsfrom35WIOD


fortheCzechRepublic.....................................................................................................................70

Figure11:EstimatedcapitalcostdistributionofawindprojectinEurope.....................................72

Figure12:Estimateofcapitalcostbreakdownforanoffshorewindfarm......................................72



91

Figure13:Variablecosts forGermanturbinesdistributed intodifferentcategoriesasanaverage

overthetime-period1997-2001......................................................................................................73

Figure14:Assumedinstalledcapacityofrenewables......................................................................77

Figure 15: 2011-2030 monetary fluxes projection for Optimal transition scenario – sector 17

Electricity,GasandWaterSupplytechnicalcoefficientsdevelopment...........................................80

Figure16:2011-2030monetaryfluxesprojectionforMiddle leveltransitionscenario–sector17


Figure 17: 2011-2050 monetary fluxes projection for Optimal transition scenario – sector 17


Figure18:2011-2050monetaryfluxesprojectionforMiddle leveltransitionscenario–sector17




92

Appendix 1: 2030 MLT IO table (A matrix oftechnicalcoefficients)

12

34

56

78

910

1112

1314

1516

1718

1920

2122

2324

2526

2728

2930

3132

3334

351

0,1277E-04

0,273

0,067

0,072

0,14

0,022

0,001

0,011

0,0156E-042E-042E-041E-041E-04

0,014

0,064

0,0045E-04

0,003

0,003

0,046

0,0028E-044E-04

0,01

2E-042E-044E-04

0,002

0,002

0,004

0,002

0,0031E-05

20,005

0,179

0,006

0,007

0,005

0,01

0,011

0,496

0,07

0,009

0,131

0,115

0,008

0,003

0,003

0,011

0,19

0,0448E-04

0,001

0,002

0,005

0,013

0,011

0,01

0,006

0,0015E-04

0,002

0,002

0,007

0,004

0,004

0,0062E-05

30,0689E-04

0,157

0,005

0,096

0,003

0,004

0,001

0,014

0,004

0,003

0,002

0,002

0,002

0,001

0,003

0,001

0,002

0,002

0,006

0,005

0,167

0,002

0,007

0,006

0,002

0,0028E-044E-04

0,002

0,007

0,01

0,012

0,0092E-05

40,001

0,001

0,001

0,339

0,059

0,003

0,0074E-04

0,004

0,015

0,004

0,002

0,003

0,002

0,004

0,023

0,001

0,002

0,001

0,003

0,003

0,003

0,002

0,004

0,003

0,002

0,0015E-045E-04

0,002

0,004

0,001

0,004

0,0058E-05

52E-045E-051E-04

0,014

0,2296E-048E-043E-053E-04

0,0012E-041E-042E-045E-04

0,003

0,0067E-051E-043E-043E-042E-041E-042E-041E-042E-041E-042E-043E-051E-051E-041E-031E-042E-044E-043E-06

60,001

0,002

0,001

0,001

0,002

0,246

0,0122E-04

0,001

0,003

0,007

0,003

0,003

0,002

0,002

0,0697E-04

0,0237E-04

0,001

0,001

0,0021E-037E-044E-04

0,0025E-042E-04

0,0028E-04

0,002

0,003

0,002

0,0023E-05

70,002

0,002

0,019

0,009

0,012

0,012

0,219

0,001

0,013

0,014

0,018

0,004

0,006

0,009

0,004

0,017

0,002

0,003

0,006

0,012

0,01

0,007

0,005

0,004

0,005

0,01

0,012

0,01

0,002

0,019

0,015

0,016

0,008

0,0198E-05

80,02

0,011

0,005

0,007

0,005

0,008

0,008

0,067

0,062

0,014

0,025

0,018

0,006

0,004

0,004

0,007

0,033

0,019

0,01

0,011

0,008

0,007

0,09

0,105

0,151

0,041

0,008

0,002

0,002

0,009

0,015

0,005

0,005

0,0092E-05

90,041

0,009

0,009

0,063

0,036

0,041

0,041

0,015

0,248

0,237

0,041

0,014

0,012

0,026

0,013

0,03

0,005

0,013

0,006

0,004

0,002

0,005

0,004

0,007

0,005

0,005

0,0028E-04

0,002

0,006

0,009

0,005

0,063

0,0132E-04

100,004

0,005

0,016

0,009

0,034

0,006

0,016

0,002

0,018

0,156

0,011

0,006

0,022

0,031

0,035

0,034

0,003

0,014

0,013

0,005

0,005

0,004

0,011

0,003

0,005

0,005

0,0035E-04

0,001

0,003

0,002

0,001

0,004

0,0058E-05

110,001

0,003

0,004

0,001

0,001

0,005

0,001

0,001

0,005

0,004

0,108

0,008

0,004

0,011

0,006

0,006

0,006

0,084

0,003

0,0011E-03

0,003

0,0014E-048E-049E-041E-031E-04

0,0029E-04

0,001

0,002

0,002

0,0022E-05

120,004

0,02

0,01

0,005

0,009

0,021

0,01

0,005

0,015

0,025

0,038

0,338

0,193

0,105

0,11

0,092

0,008

0,107

0,016

0,004

0,003

0,004

0,007

0,005

0,005

0,008

0,0046E-04

0,003

0,005

0,004

0,002

0,002

0,0052E-04

130,006

0,014

0,003

0,008

0,006

0,009

0,007

0,004

0,008

0,011

0,017

0,019

0,144

0,02

0,04

0,015

0,008

0,016

0,017

0,003

0,002

0,002

0,005

0,01

0,007

0,006

0,0039E-04

0,001

0,003

0,005

0,002

0,005

0,0033E-04

140,001

0,007

0,002

0,004

0,003

0,004

0,007

0,002

0,008

0,008

0,008

0,01

0,07

0,316

0,047

0,024

0,03

0,028

0,019

0,008

0,006

0,004

0,006

0,003

0,005

0,007

0,043

0,002

0,002

0,02

0,011

0,01

0,014

0,0136E-04

150,003

0,004

0,001

0,002

0,002

0,003

0,0041E-03

0,002

0,005

0,005

0,006

0,015

0,005

0,294

0,007

0,004

0,004

0,065

0,006

0,004

0,002

0,034

0,037

0,054

0,01

0,005

0,0018E-04

0,005

0,013

0,003

0,001

0,0111E-04

166E-047E-048E-04

0,004

0,002

0,003

0,0047E-04

0,002

0,003

0,003

0,007

0,004

0,003

0,005

0,065

0,002

0,003

0,001

0,001

0,001

0,002

0,0047E-04

0,003

0,001

0,0018E-047E-04

0,002

0,003

0,003

0,003

0,004

0,002

170,011

0,034

0,014

0,019

0,008

0,02

0,025

0,016

0,033

0,024

0,049

0,033

0,016

0,011

0,01

0,012

0,15

0,009

0,012

0,01

0,015

0,023

0,018

0,004

0,01

0,014

0,013

0,005

0,007

0,007

0,014

0,02

0,013

0,0161E-04

180,004

0,007

0,002

0,003

0,001

0,003

0,004

0,002

0,003

0,003

0,006

0,003

0,003

0,002

0,002

0,003

0,01

0,056

0,006

0,005

0,006

0,007

0,011

0,002

0,006

0,013

0,013

0,006

0,034

0,006

0,021

0,012

0,01

0,01

7E-04

190,004

0,002

0,006

0,003

0,004

0,005

0,004

0,003

0,004

0,005

0,005

0,004

0,004

0,003

0,005

0,006

0,005

0,004

0,012

0,004

0,004

0,004

0,012

0,002

0,004

0,006

0,004

0,002

0,002

0,004

0,003

0,002

0,003

0,0041E-05

200,031

0,014

0,055

0,042

0,043

0,043

0,044

0,052

0,039

0,04

0,035

0,033

0,04

0,038

0,041

0,043

0,021

0,035

0,031

0,027

0,014

0,04

0,021

0,019

0,023

0,016

0,019

0,004

0,004

0,013

0,015

0,011

0,025

0,0171E-04

210,016

0,006

0,027

0,019

0,022

0,018

0,018

0,016

0,018

0,019

0,016

0,014

0,017

0,015

0,02

0,025

0,008

0,021

0,016

0,007

0,008

0,022

0,015

0,007

0,011

0,008

0,011

0,004

0,003

0,008

0,007

0,005

0,013

0,01

1E-04

220,002

0,004

0,004

0,004

0,004

0,004

0,0067E-04

0,004

0,005

0,006

0,004

0,005

0,005

0,003

0,004

0,003

0,006

0,005

0,01

0,006

0,01

0,008

0,004

0,023

0,026

0,01

0,012

0,003

0,014

0,014

0,012

0,009

0,0124E-05

230,014

0,018

0,024

0,018

0,016

0,022

0,02

0,042

0,02

0,018

0,032

0,016

0,014

0,01

0,013

0,02

0,017

0,022

0,017

0,028

0,016

0,009

0,047

0,009

0,011

0,059

0,013

0,006

0,002

0,007

0,011

0,009

0,007

0,01

3E-04

240,002

0,006

0,004

0,004

0,003

0,006

0,003

0,002

0,004

0,003

0,007

0,005

0,003

0,003

0,003

0,003

0,003

0,004

0,002

0,008

0,004

0,001

0,003

0,135

0,005

0,006

0,0026E-04

0,001

0,001

0,002

0,0017E-04

0,0018E-06

258E-04

0,001

0,001

0,001

0,002

0,002

0,0026E-04

0,002

0,001

0,0019E-04

0,002

0,002

0,001

0,0018E-04

0,001

0,002

0,004

0,002

0,001

0,003

0,005

0,036

0,012

0,006

0,0034E-04

0,004

0,004

0,004

0,001

0,0048E-06

260,003

0,004

0,006

0,003

0,003

0,006

0,009

0,006

0,004

0,005

0,007

0,004

0,004

0,003

0,004

0,004

0,003

0,016

0,012

0,023

0,015

0,006

0,032

0,11

0,091

0,092

0,006

0,0039E-04

0,005

0,004

0,003

0,003

0,0068E-06

270,004

0,004

0,004

0,004

0,005

0,004

0,009

0,002

0,005

0,004

0,004

0,005

0,005

0,006

0,003

0,007

0,005

0,01

0,012

0,016

0,016

0,01

0,011

0,02

0,017

0,016

0,091

0,021

0,003

0,017

0,017

0,009

0,01

0,0152E-04

280,015

0,015

0,014

0,018

0,013

0,015

0,021

0,008

0,016

0,014

0,022

0,016

0,017

0,017

0,016

0,029

0,042

0,017

0,021

0,035

0,035

0,022

0,043

0,022

0,029

0,032

0,024

0,182

0,069

0,032

0,022

0,015

0,021

0,039

0,001

290,003

0,005

0,004

0,006

0,008

0,005

0,009

0,002

0,004

0,006

0,004

0,004

0,005

0,005

0,005

0,008

0,004

0,009

0,025

0,025

0,045

0,026

0,009

0,01

0,01

0,019

0,019

0,022

0,03

0,02

0,014

0,018

0,032

0,0252E-07

300,015

0,025

0,038

0,023

0,019

0,021

0,067

0,013

0,058

0,034

0,03

0,024

0,042

0,046

0,046

0,036

0,028

0,043

0,065

0,085

0,072

0,045

0,041

0,025

0,069

0,065

0,08

0,099

0,031

0,15

0,08

0,035

0,061

0,075

0,003

310,002

0,003

0,0028E-047E-04

0,003

0,005

0,001

0,003

0,002

0,003

0,003

0,002

0,001

0,002

0,003

0,002

0,001

0,004

0,004

0,004

0,004

0,004

0,004

0,007

0,004

0,004

0,002

0,003

0,003

0,009

0,004

0,004

0,0073E-06

328E-047E-045E-044E-045E-045E-047E-044E-041E-036E-046E-045E-04

0,0018E-049E-045E-047E-048E-04

0,001

0,001

0,0018E-04

0,0014E-04

0,004

0,001

0,002

0,0024E-04

0,003

0,006

0,016

0,002

0,0022E-06

330,0019E-046E-046E-04

0,002

0,0018E-042E-048E-044E-04

0,0029E-04

0,002

0,0011E-036E-047E-044E-047E-045E-045E-048E-047E-045E-047E-045E-047E-046E-041E-048E-04

0,006

0,002

0,031

0,0013E-07

340,004

0,006

0,006

0,005

0,006

0,005

0,014

0,002

0,007

0,005

0,007

0,005

0,005

0,004

0,006

0,006

0,007

0,005

0,009

0,013

0,012

0,015

0,017

0,006

0,011

0,018

0,026

0,012

0,008

0,026

0,02

0,013

0,015

0,08

0,001

351E-052E-052E-051E-043E-053E-054E-051E-052E-053E-052E-051E-055E-052E-058E-052E-056E-064E-055E-053E-055E-056E-052E-046E-062E-052E-055E-056E-052E-047E-056E-053E-056E-051E-041E-04

600,417

0,414

0,723

0,72

0,735

0,698

0,635

0,766

0,707

0,715

0,658

0,729

0,68

0,713

0,755

0,637

0,603

0,628

0,413

0,375

0,333

0,508

0,486

0,582

0,628

0,523

0,43

0,407

0,222

0,403

0,369

0,262

0,39

0,444

0,011

610

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

620

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

630

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

640,573

0,581

0,261

0,265

0,248

0,291

0,351

0,195

0,271

0,269

0,328

0,255

0,303

0,271

0,22

0,339

0,384

0,358

0,567

0,617

0,658

0,479

0,491

0,404

0,337

0,464

0,558

0,586

0,773

0,587

0,622

0,732

0,601

0,544

0,988

650,002

0,002

0,003

0,003

0,003

0,004

0,003

0,015

0,006

0,005

0,004

0,008

0,007

0,009

0,01

0,008

0,005

0,003

0,0049E-049E-04

0,001

0,002

0,002

0,004

0,001

0,0022E-043E-049E-04

0,0027E-04

0,002

0,0012E-04

691

11

11

11

11

11

11

11

11

11

11

11

11

11

11

11

11

11

990,009

0,003

0,013

0,011

0,014

0,007

0,011

0,024

0,016

0,011

0,01

0,008

0,01

0,007

0,016

0,017

0,008

0,011

0,015

0,007

0,008

0,012

0,021

0,012

0,031

0,012

0,011

0,007

0,005

0,009

0,006

0,005

0,008

0,0113E-04



93

Appendix 2: 2050 OT IO table (A matrix oftechnicalcoefficients)

12

34

56

78

910

1112

1314

1516

1718

1920

2122

2324

2526

2728

2930

3132

3334

351

0,1277E-04

0,273

0,067

0,072

0,14

0,022

0,001

0,011

0,0156E-042E-042E-041E-041E-04

0,014

0,129

0,0045E-04

0,003

0,003

0,046

0,0028E-044E-04

0,01

2E-042E-044E-04

0,002

0,002

0,004

0,002

0,0031E-05

20,005

0,179

0,006

0,007

0,005

0,01

0,011

0,496

0,07

0,009

0,131

0,115

0,008

0,003

0,003

0,011

0,192

0,0448E-04

0,001

0,002

0,005

0,013

0,011

0,01

0,006

0,0015E-04

0,002

0,002

0,007

0,004

0,004

0,0062E-05

30,0689E-04

0,157

0,005

0,096

0,003

0,004

0,001

0,014

0,004

0,003

0,002

0,002

0,002

0,001

0,003

0,001

0,002

0,002

0,006

0,005

0,167

0,002

0,007

0,006

0,002

0,0028E-044E-04

0,002

0,007

0,01

0,012

0,0092E-05

40,001

0,001

0,001

0,339

0,059

0,003

0,0074E-04

0,004

0,015

0,004

0,002

0,003

0,002

0,004

0,023

0,001

0,002

0,001

0,003

0,003

0,003

0,002

0,004

0,003

0,002

0,0015E-045E-04

0,002

0,004

0,001

0,004

0,0058E-05

52E-045E-051E-04

0,014

0,2296E-048E-043E-053E-04

0,0012E-041E-042E-045E-04

0,003

0,0067E-051E-043E-043E-042E-041E-042E-041E-042E-041E-042E-043E-051E-051E-041E-031E-042E-044E-043E-06

60,001

0,002

0,001

0,001

0,002

0,246

0,0122E-04

0,001

0,003

0,007

0,003

0,003

0,002

0,002

0,0697E-04

0,0237E-04

0,001

0,001

0,0021E-037E-044E-04

0,0025E-042E-04

0,0028E-04

0,002

0,003

0,002

0,0023E-05

70,002

0,002

0,019

0,009

0,012

0,012

0,219

0,001

0,013

0,014

0,018

0,004

0,006

0,009

0,004

0,017

0,002

0,003

0,006

0,012

0,01

0,007

0,005

0,004

0,005

0,01

0,012

0,01

0,002

0,019

0,015

0,016

0,008

0,0198E-05

80,02

0,011

0,005

0,007

0,005

0,008

0,008

0,067

0,062

0,014

0,025

0,018

0,006

0,004

0,004

0,007

0,033

0,019

0,01

0,011

0,008

0,007

0,09

0,105

0,151

0,041

0,008

0,002

0,002

0,009

0,015

0,005

0,005

0,0092E-05

90,041

0,009

0,009

0,063

0,036

0,041

0,041

0,015

0,248

0,237

0,041

0,014

0,012

0,026

0,013

0,03

0,002

0,013

0,006

0,004

0,002

0,005

0,004

0,007

0,005

0,005

0,0028E-04

0,002

0,006

0,009

0,005

0,063

0,0132E-04

100,004

0,005

0,016

0,009

0,034

0,006

0,016

0,002

0,018

0,156

0,011

0,006

0,022

0,031

0,035

0,034

0,003

0,014

0,013

0,005

0,005

0,004

0,011

0,003

0,005

0,005

0,0035E-04

0,001

0,003

0,002

0,001

0,004

0,0058E-05

110,001

0,003

0,004

0,001

0,001

0,005

0,001

0,001

0,005

0,004

0,108

0,008

0,004

0,011

0,006

0,006

0,011

0,084

0,003

0,0011E-03

0,003

0,0014E-048E-049E-041E-031E-04

0,0029E-04

0,001

0,002

0,002

0,0022E-05

120,004

0,02

0,01

0,005

0,009

0,021

0,01

0,005

0,015

0,025

0,038

0,338

0,193

0,105

0,11

0,092

0,007

0,107

0,016

0,004

0,003

0,004

0,007

0,005

0,005

0,008

0,0046E-04

0,003

0,005

0,004

0,002

0,002

0,0052E-04

130,006

0,014

0,003

0,008

0,006

0,009

0,007

0,004

0,008

0,011

0,017

0,019

0,144

0,02

0,04

0,015

0,008

0,016

0,017

0,003

0,002

0,002

0,005

0,01

0,007

0,006

0,0039E-04

0,001

0,003

0,005

0,002

0,005

0,0033E-04

140,001

0,007

0,002

0,004

0,003

0,004

0,007

0,002

0,008

0,008

0,008

0,01

0,07

0,316

0,047

0,024

0,036

0,028

0,019

0,008

0,006

0,004

0,006

0,003

0,005

0,007

0,043

0,002

0,002

0,02

0,011

0,01

0,014

0,0136E-04

150,003

0,004

0,001

0,002

0,002

0,003

0,0041E-03

0,002

0,005

0,005

0,006

0,015

0,005

0,294

0,007

0,004

0,004

0,065

0,006

0,004

0,002

0,034

0,037

0,054

0,01

0,005

0,0018E-04

0,005

0,013

0,003

0,001

0,0111E-04

166E-047E-048E-04

0,004

0,002

0,003

0,0047E-04

0,002

0,003

0,003

0,007

0,004

0,003

0,005

0,065

0,002

0,003

0,001

0,001

0,001

0,002

0,0047E-04

0,003

0,001

0,0018E-047E-04

0,002

0,003

0,003

0,003

0,004

0,002

170,011

0,034

0,014

0,019

0,008

0,02

0,025

0,016

0,033

0,024

0,049

0,033

0,016

0,011

0,01

0,012

0,137

0,009

0,012

0,01

0,015

0,023

0,018

0,004

0,01

0,014

0,013

0,005

0,007

0,007

0,014

0,02

0,013

0,0161E-04

180,004

0,007

0,002

0,003

0,001

0,003

0,004

0,002

0,003

0,003

0,006

0,003

0,003

0,002

0,002

0,003

0,005

0,056

0,006

0,005

0,006

0,007

0,011

0,002

0,006

0,013

0,013

0,006

0,034

0,006

0,021

0,012

0,01

0,01

7E-04

190,004

0,002

0,006

0,003

0,004

0,005

0,004

0,003

0,004

0,005

0,005

0,004

0,004

0,003

0,005

0,006

0,005

0,004

0,012

0,004

0,004

0,004

0,012

0,002

0,004

0,006

0,004

0,002

0,002

0,004

0,003

0,002

0,003

0,0041E-05

200,031

0,014

0,055

0,042

0,043

0,043

0,044

0,052

0,039

0,04

0,035

0,033

0,04

0,038

0,041

0,043

0,021

0,035

0,031

0,027

0,014

0,04

0,021

0,019

0,023

0,016

0,019

0,004

0,004

0,013

0,015

0,011

0,025

0,0171E-04

210,016

0,006

0,027

0,019

0,022

0,018

0,018

0,016

0,018

0,019

0,016

0,014

0,017

0,015

0,02

0,025

0,008

0,021

0,016

0,007

0,008

0,022

0,015

0,007

0,011

0,008

0,011

0,004

0,003

0,008

0,007

0,005

0,013

0,01

1E-04

220,002

0,004

0,004

0,004

0,004

0,004

0,0067E-04

0,004

0,005

0,006

0,004

0,005

0,005

0,003

0,004

0,003

0,006

0,005

0,01

0,006

0,01

0,008

0,004

0,023

0,026

0,01

0,012

0,003

0,014

0,014

0,012

0,009

0,0124E-05

230,014

0,018

0,024

0,018

0,016

0,022

0,02

0,042

0,02

0,018

0,032

0,016

0,014

0,01

0,013

0,02

0,017

0,022

0,017

0,028

0,016

0,009

0,047

0,009

0,011

0,059

0,013

0,006

0,002

0,007

0,011

0,009

0,007

0,01

3E-04

240,002

0,006

0,004

0,004

0,003

0,006

0,003

0,002

0,004

0,003

0,007

0,005

0,003

0,003

0,003

0,003

0,003

0,004

0,002

0,008

0,004

0,001

0,003

0,135

0,005

0,006

0,0026E-04

0,001

0,001

0,002

0,0017E-04

0,0018E-06

258E-04

0,001

0,001

0,001

0,002

0,002

0,0026E-04

0,002

0,001

0,0019E-04

0,002

0,002

0,001

0,0018E-04

0,001

0,002

0,004

0,002

0,001

0,003

0,005

0,036

0,012

0,006

0,0034E-04

0,004

0,004

0,004

0,001

0,0048E-06

260,003

0,004

0,006

0,003

0,003

0,006

0,009

0,006

0,004

0,005

0,007

0,004

0,004

0,003

0,004

0,004

0,003

0,016

0,012

0,023

0,015

0,006

0,032

0,11

0,091

0,092

0,006

0,0039E-04

0,005

0,004

0,003

0,003

0,0068E-06

270,004

0,004

0,004

0,004

0,005

0,004

0,009

0,002

0,005

0,004

0,004

0,005

0,005

0,006

0,003

0,007

0,005

0,01

0,012

0,016

0,016

0,01

0,011

0,02

0,017

0,016

0,091

0,021

0,003

0,017

0,017

0,009

0,01

0,0152E-04

280,015

0,015

0,014

0,018

0,013

0,015

0,021

0,008

0,016

0,014

0,022

0,016

0,017

0,017

0,016

0,029

0,062

0,017

0,021

0,035

0,035

0,022

0,043

0,022

0,029

0,032

0,024

0,182

0,069

0,032

0,022

0,015

0,021

0,039

0,001

290,003

0,005

0,004

0,006

0,008

0,005

0,009

0,002

0,004

0,006

0,004

0,004

0,005

0,005

0,005

0,008

0,004

0,009

0,025

0,025

0,045

0,026

0,009

0,01

0,01

0,019

0,019

0,022

0,03

0,02

0,014

0,018

0,032

0,0252E-07

300,015

0,025

0,038

0,023

0,019

0,021

0,067

0,013

0,058

0,034

0,03

0,024

0,042

0,046

0,046

0,036

0,028

0,043

0,065

0,085

0,072

0,045

0,041

0,025

0,069

0,065

0,08

0,099

0,031

0,15

0,08

0,035

0,061

0,075

0,003

310,002

0,003

0,0028E-047E-04

0,003

0,005

0,001

0,003

0,002

0,003

0,003

0,002

0,001

0,002

0,0036E-04

0,001

0,004

0,004

0,004

0,004

0,004

0,004

0,007

0,004

0,004

0,002

0,003

0,003

0,009

0,004

0,004

0,0073E-06

328E-047E-045E-044E-045E-045E-047E-044E-041E-036E-046E-045E-04

0,0018E-049E-045E-047E-048E-04

0,001

0,001

0,0018E-04

0,0014E-04

0,004

0,001

0,002

0,0024E-04

0,003

0,006

0,016

0,002

0,0022E-06

330,0019E-046E-046E-04

0,002

0,0018E-042E-048E-044E-04

0,0029E-04

0,002

0,0011E-036E-046E-044E-047E-045E-045E-048E-047E-045E-047E-045E-047E-046E-041E-048E-04

0,006

0,002

0,031

0,0013E-07

340,004

0,006

0,006

0,005

0,006

0,005

0,014

0,002

0,007

0,005

0,007

0,005

0,005

0,004

0,006

0,006

0,007

0,005

0,009

0,013

0,012

0,015

0,017

0,006

0,011

0,018

0,026

0,012

0,008

0,026

0,02

0,013

0,015

0,08

0,001

351E-052E-052E-051E-043E-053E-054E-051E-052E-053E-052E-051E-055E-052E-058E-052E-056E-064E-055E-053E-055E-056E-052E-046E-062E-052E-055E-056E-052E-047E-056E-053E-056E-051E-041E-04

600,417

0,414

0,723

0,72

0,735

0,698

0,635

0,766

0,707

0,715

0,658

0,729

0,68

0,713

0,755

0,637

0,74

0,628

0,413

0,375

0,333

0,508

0,486

0,582

0,628

0,523

0,43

0,407

0,222

0,403

0,369

0,262

0,39

0,444

0,011

610

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

620

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

630

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

640,573

0,581

0,261

0,265

0,248

0,291

0,351

0,195

0,271

0,269

0,328

0,255

0,303

0,271

0,22

0,339

0,254

0,358

0,567

0,617

0,658

0,479

0,491

0,404

0,337

0,464

0,558

0,586

0,773

0,587

0,622

0,732

0,601

0,544

0,988

650,002

0,002

0,003

0,003

0,003

0,004

0,003

0,015

0,006

0,005

0,004

0,008

0,007

0,009

0,01

0,008

0,005

0,003

0,0049E-049E-04

0,001

0,002

0,002

0,004

0,001

0,0022E-043E-049E-04

0,0027E-04

0,002

0,0012E-04

691

11

11

11

11

11

11

11

11

11

11

11

11

11

11

11

11

11

990,009

0,003

0,013

0,011

0,014

0,007

0,011

0,024

0,016

0,011

0,01

0,008

0,01

0,007

0,016

0,0176E-04

0,011

0,015

0,007

0,008

0,012

0,021

0,012

0,031

0,012

0,011

0,007

0,005

0,009

0,006

0,005

0,008

0,0113E-04



94


IntroductionMEDEASprojectaimstohelppolicymakersandstakeholderstorankprioritiesinthepromotionofrenewableenergytechnologiesinordertoachieveasocialandeconomiclowcarbontransitioninEurope. To achieve this objective, one of the main tools being developed in the project is asimulation model that relates the main variables of the European energy system, includingsocioeconomicandenvironmentalvariablescloselinkedtoenergy.However,theEuropeanenergy,socialandenvironmentalsituationisstronglyconditionedbytheinternationalcontext.Therefore,prior to the Europeanmodel, it is necessary to develop a global model, which establishes theframeworkofenergy,socio-economicandenvironmentalvariablesinwhichEuropeislocated.Thismodelwillbetheresult linkedtothedeliverable4.1inJune2017.Theeconomic-energy-climatechangeassessmentmodelshavebeenwidelyusedinrecentyearswiththeaimofcontributingtointernationaldecision-makingtocurbclimatechange.Thesemodelssimulatedifferentscenarios.Scenariomethodology offers an approach to deal with the limited knowledge, uncertainty andcomplexityofnaturalandsocialsciencesandcanbeusedtogroupthevariationsofpoliciesintocoherentandmeaningfulscenarios.Eachscenariorepresentsanarchetypalandcoherentvisionofthe future -whichmaybe viewedpositively by somepeople andnegatively by others. This is astandardmethodologyinglobalenvironmentalassessmentstudiessuchastheIPCC’sAssessmentreports (IPCC, 2007a, 2007b, 2001; IPCC SRES, 2000; O’Neill et al., 2014), the UNEP’s GlobalEnvironmentalOutlook(UNEP,2012,2007,2004)ortheMillenniumEcosystemAssessment(MEA,2005).

WiththeaimofstandardizingthescenariostheIPPCpublishedanewsetofscenariosin2000foruseintheThirdAssessmentReport(SpecialReportonEmissionsScenariosSRES).ThesescenariosarecommonlynamedSRESscenarios,andtheyarebasedon“storylines”.Storylineisanarrativedescription of a scenario, highlighting themain scenario characteristics and dynamics, and therelationshipsbetweenkeydrivingforces.(IPCCSRES,2000).FollowingthissamespecialreportoftheIPCC,scenarioisasetofprojectionsofapotentialfuture,basedonaclearlogicandaquantifiedstoryline.



95

Briefly, the four storylines combine two sets of divergent tendencies: one set varying betweenstrong economic values and strong environmental values, the other set between increasingglobalizationandincreasingregionalization.Thestorylinesaresummarizedasfollows(IPCCSRES,2000):

A1storylineandscenariofamily:afutureworldofveryrapideconomicgrowth,globalpopulationthatpeaksinmidcenturyanddeclinesthereafter,andrapidintroductionofnewandmoreefficienttechnologies.

A2storylineandscenariofamily:averyheterogeneousworldwithcontinuouslyincreasingglobalpopulationandregionallyorientedeconomicgrowththatismorefragmentedandslowerthaninotherstorylines.

B1storylineandscenariofamily:aconvergentworldwiththesameglobalpopulationasintheA1storylinebutwithrapidchangesineconomicstructurestowardaserviceandinformationeconomy,with reductions in material intensity, and the introduction of clean and resource efficienttechnologies.

B2storylineandscenariofamily:aworldinwhichtheemphasisisonlocalsolutionstoeconomic,social,andenvironmentalsustainability,withcontinuously increasingpopulation(lowerthanA2)andintermediateeconomicdevelopment.

IntheDeliverable3.3ofthisMEDEASproject,“Pathwaysforthetransition”,asetoffiveStorylinesofSharedSocioeconomicPathways(SSPscenarios)weredescribed.TheseSSPscenarioshavebeenwidelyanalyzed(KrieglerE.etal.,2016;PoppA.etal.,2016;BauerN.etal2016;SamirKCandWolfgangLutz,2014;VanVuurenetal.,2017;RiahiK.etal.,2016;CalvinK.etal,2016).Brieflythesescenariosweredescribedasfollows:

SSP1-Sustainability:Thisisaworldmakingrelativelygoodprogresstowardssustainability,withsustainedeffortstoachievedevelopmentgoals,whilereducingresource intensityandfossil fueldependency.Elementsthatcontributetothisarearapiddevelopmentoflow-incomecountries,areductionofinequality(globallyandwithineconomies),rapidtechnologydevelopment,andahighlevelofawareness regardingenvironmentaldegradation.Rapideconomicgrowth in low-incomecountriesreducesthenumberofpeoplebelowthepovertyline.Theworldischaracterizedbyanopen, globalized economy, with relatively rapid technological change directed towardenvironmentally friendly processes, including clean energy technologies and yield-enhancingtechnologiesforland.Consumptionisorientedtowardslowmaterialgrowthandenergyintensity,



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with a relatively low level of consumption of animal products. Investments in high levels ofeducationcoincidewithlowpopulationgrowth.Concurrently,governanceandinstitutionsfacilitateachievingdevelopmentgoalsandproblemsolving.TheMillenniumDevelopmentGoalsareachievedwithin the next decade or two, resulting in educated populations with access to safe water,improvedsanitation,andmedicalcare.Otherfactorsthatreducevulnerabilitytoclimateandotherglobalchangesinclude,forexample,thesuccessfulimplementationofstringentpoliciestocontrolairpollutantsandrapidshiftstowarduniversalaccesstocleanandmodernenergyinthedevelopingworld.

SSP2-MiddleoftheRoad(orDynamicsasUsual,orCurrentTrendsContinue,orContinuation,orMuddlingThrough): Inthisworld,trendstypicalofrecentdecadescontinue,withsomeprogresstowardsachievingdevelopmentgoals,reductionsinresourceandenergyintensityathistoricrates,and slowly decreasing fossil fuel dependency. Development of low-income countries proceedsunevenly,withsomecountriesmakingrelativelygoodprogresswhileothersareleftbehind.Mosteconomiesarepoliticallystablewithpartiallyfunctioningandgloballyconnectedmarkets.Alimitednumberofcomparativelyweakglobalinstitutionsexist.Per-capitaincomelevelsgrowatamediumpace on the global average, with slowly converging income levels between developing andindustrialized countries. Intra-regional income distributions improve slightly with increasingnationalincome,butdisparitiesremainhighinsomeregions.Educationalinvestmentsarenothighenoughtorapidlyslowpopulationgrowth,particularlyinlow-incomecountries.AchievementoftheMillenniumDevelopmentGoalsisdelayedbyseveraldecades,leavingpopulationswithoutaccesstosafewater,improvedsanitation,medicalcare.Similarly,thereisanonlyintermediatesuccessinaddressingairpollutionorimprovingenergyaccessforthepooraswellasotherfactorsthatreducevulnerabilitytoclimateandotherglobalchanges.

SSP3-Fragmentation(orFragmentedWorld):Theworldisseparatedintoregionscharacterizedbyextremepoverty,pocketsofmoderatewealthandabulkofcountriesthatstruggletomaintainlivingstandards fora stronglygrowingpopulation.Regionalblocksof countrieshave re-emergedwithlittlecoordinationbetweenthem.Thisisaworldfailingtoachieveglobaldevelopmentgoals,andwith little progress in reducing resource intensity, fossil fuel dependency, or addressing localenvironmentalconcernssuchasairpollution.Countriesfocusonachievingenergyandfoodsecuritygoalswithintheirownregion.Theworldhasde-globalized,andinternationaltrade,includingenergyresourceandagriculturalmarkets, isseverelyrestricted.Little internationalcooperationand lowinvestments in technology development and education slow down economic growth in high-,middle-, and low-income regions. Population growth in this scenario is high as a result of theeducation and economic trends. Growth in urban areas in low-income countries is often in



97

unplanned settlements. Unmitigated emissions are relatively high, driven by high populationgrowth, use of local energy resources and slow technological change in the energy sector.Governanceand institutions showweaknessanda lackof cooperationand consensus;effectiveleadershipandcapacitiesforproblemsolvingarelacking.Investmentsinhumancapitalarelowandinequalityishigh.Aregionalizedworldleadstoreducedtradeflows,andinstitutionaldevelopmentisunfavorable,leavinglargenumbersofpeoplevulnerabletoclimatechangeandmanypartsoftheworldwithlowadaptivecapacity.Policiesareorientedtowardssecurity,includingbarrierstotrade.

SSP4-Inequality(orUnequalWorld,orDividedWorld):Thispathwayenvisionsahighlyunequalworldbothwithinandacrosscountries.Arelativelysmall,richglobaleliteisresponsibleformuchoftheemissions,whilealarger,poorergroupcontributeslittletoemissionsandisvulnerabletoimpactsofclimatechange,inindustrializedaswellasindevelopingcountries.Inthisworld,globalenergy corporations use investments in R&Das thehedging strategy against potential resourcescarcityorclimatepolicy,developing(andapplying)low-costalternativetechnologies.Mitigationchallenges are therefore lowdue to some combination of low reference emissions and/or highlatentcapacitytomitigate.

Governanceandglobalizationareeffectiveforandcontrolledbytheelite,butareineffectiveformostof thepopulation.Challengestoadaptationarehighduetorelatively low incomeand lowhumancapitalamongthepoorerpopulation,andineffectiveinstitutions.

SSP5 - Conventional Development (or Conventional Development First): This world stressesconventionaldevelopmentorientedtowardeconomicgrowthasthesolutiontosocialandeconomicproblemsthroughthepursuitofenlightenedself-interest.Thepreferenceforrapidconventionaldevelopmentleadstoanenergysystemdominatedbyfossilfuels,resultinginhighGHGemissionsandchallengestomitigation.Lowersocio-environmentalchallengestoadaptationresultfromtheattainmentofhumandevelopmentgoals,robusteconomicgrowth,highlyengineeredinfrastructurewithredundancytominimizedisruptionsfromextremeeventsandhighlymanagedecosystems.

Howeverthesecommonlyusedscenarioshavebeendevelopedmainlywithouttakingintoaccountsome considerations that incorporates the MEDEAS model. In this task the variability of theproposedscenariosandpathwayswillbeexploredtakingintoaccountsomephysicalconstraintsandsomefeedbackseffects.Thephysicalconstraintsthatthemodelincorporatesandaredescribedinthisreportarerelativetothemaximumenergythatcanbeobtainedfromnonrenewableandrenewable sources in the next years. These limits may be absolute (eg, fossil fuel or uraniumresources)orrelative(egmaximumannualextractioncapacity).Inaddition,themodelincorporatessomefeedbackbetweenfinalenergyavailabilityandtheeconomy,aswellastheeffectsofclimate



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change.Thesefeedbackscausedynamicsinthemodelthatarenotusuallydescribedintheopen-loop scenarios. The new situations that are simulated, taking as a starting point the generalscenariosdescribedabovewhenintroducingtheuncertaintyduetophysicallimitationsandtheirfeedbackisthatithasbeencalledadaptivescenarios.ThisreportpointstosomeofthemaincausesthatcanleadtovariabilityonthescenarioscommonlydescribedinliteratureandthoseusedintheearlyversionsoftheglobalmodelofMEDEAS.Sincethismodelhasnotyetbeenfinalizedthisreportonly points out some provisional results that should be reviewed and evaluated with the finalversionofthemodelasofJuly2017.

Thenextsectionofthisreport"Descriptionofscenarios"brieflyexplainsthescenariosinitiallyusedandtheirrelationshiptothestorylinesdescribedhereandwellknown.Thesection“Variabililityofthe scenariosdue to theEnergy-Economy feedback” isdevoted to theanalysisof theeffectsoffeedbackbetweenenergyandeconomyand thevariability it introduces into the scenarios. Thesection“AvailabilityofenergyresourcesinMEDEAS”describesthephysicalconstraintsduetotheavailabilityofrenewableandnon-renewableenergyresources.Thesection“ModellingofclimaticimpactsinMEDEAS”describesthemethodusedtofeedbacktheeffectofclimatechangeontheenergy system and its consequences on the variability of the scenarios. Finally the section“Preliminaryresults”describessomepreliminaryresultsoftheglobalMEDEASmodel.



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Descriptionofscenarios

QualitativenarrativesmodelledMEDEASmodelneedsassumptionsabouttheworldsocio-economicevolution(suchasexpected

economic growth, population evolution or technological progress) as external inputs. Running

modelscanbeacumbersometaskwhenthemodelshaveseveralparameters,assumptionsand

policiesthatcanbevariedatthesametime.Inordertoestablishthoseinputsinacoherentand

sensibleway,wehaveappliedthescenariomethodology.

Althougheachspecificanalysisdevelopsitsownsetofscenarios,vanVuurenetal.(2012)showed

thatthatthereisactuallyalimitedsetofscenariofamiliesthatformthebasisofmanyscenarios

used indifferent environmental assessments. In this section, a summaryof themost important

qualitativecharacteristicsofthedifferentscenariofamiliesidentifiedinGEAstudiesby(vanVuuren

etal.,2012)isprovided.ABusiness-as-Usual(BAU)scenarioisaddedasreferencethatassumesthat

historicaldynamicswillalsoguidethefuture).1

1Infact(vanVuurenetal.,2012)identified6scenariofamilies.Astheyargueintheirpaper,familyscenario1“Economicoptimism/conventionalmarketsscenarios”and2“Reformedmarketscenarios”areverysimilar.Thus,wedecidedtojointhemforthesakeofsimplicityandminimizethenumberofrepresentativescenarios.



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Scenario1-Economicoptimismwithsomemarketreforming:Strongfocusonthemechanismof

competitive,efficientmarket,freetradeandassociatedrapideconomicgrowth,butincludingsome

additional policy assumptions aimed at correcting some market failures with respect to social

development, poverty alleviation or the environment. The scenario typically assumes rapid

technology development and diffusion and convergence of income levels across the world.

Economicgrowthisassumedtocoincidewithlowpopulationgrowth(givenarapiddropinfertility

levels).Energyandmaterial scarce resourcesareupgraded to reservesor substitutedefficiently

through market signals (price rising). Eventually, everyone will benefit from globalization and

technologicaladvanceswillremedyecologicalproblems(e.g.‘EnvironmentalKuznetsCurve’).



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Scenario2-GlobalSustainableDevelopment:Strongorientationtowardsenvironmentalprotection

andreducinginequality,basedonsolutionsfoundthroughglobalcooperation,lifestylechangeand

technology(moreefficienttechnologies,dematerializationoftheeconomy,serviceandinformation

economy, etc.). Central elements are a high level of environmental and social consciousness

combinedwithacoherentglobalapproachtosustainabledevelopment.Withinthisscenariofamily,

itisassumedthatahighlevelofinternationalgovernmentalcoordinationisnecessaryandpossible

inordertodealwithinternationalproblemslikepovertyalleviation,climateprotectionandnature

conservation.Itentailsregulationofmarketsbutonaglobalscaleandbasedontheconvictionthat

theEarth’slimitsareinsightandthatthereforepro-activepoliciesarenecessary.



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Scenario3-Regionalcompetition/regionalmarkets:Scenariosinthisfamilyassumethatregions

willfocusmoreontheirself-reliance,nationalsovereigntyandregionalidentity,leadingtodiversity

butalsototensionsbetweenregionsand/orcultures.Countriesareconcernedwithsecurityand

protection, emphasizing primarily regional markets (protectionism, deglobalization) and paying

little attention to common goods. Due to the significant reduction in technological diffusion,

technologicalimprovementsprogressmoreslowly.



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Scenario 4- Regional Sustainable Development: this scenario is the “friendly” version of the

previousone,whereglobalizationtendstobedeconstructedandanimportantchangeintraditional

valuesandsocialnormshappensagainstsenselessconsumerismanddisrespectforlife.Citizensand

countriesmust each takeon the responsibilities they canbear, providingaidor setting a green

exampletotherestoftheworld,fromasenseofduty,outofconvictionorforethicalreasonsorto

solveprimarilytheirownproblems.Infact,althoughbarriersforproductsarere-built,barriersfor

information tend to be eliminated. The focus is on finding regional solutions for current

environmental and social problems, usually combining drastic lifestyle changes with

decentralizationofgovernance.

Table1providesaqualitativesummaryofeachscenario’sfeatures.



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Table1:Qualitativesummaryofthemainassumptionsanddriversofeachscenariofollowing(vanVuurenetal.,2012).

BAU 1 2 3 4

Equivalencewith(IPCC

SRES,2000)

- A1 B1 A2 B2

EquivalencewithSSPs

(O’Neilletal.,2014)

SSP2 SSP5 SSP1 SSP3 -

GDPgrowth Historictrends

(~+2%)

VeryHigh High Low Medium

(historic

trends)

Populationgrowth Medium Verylow Medium Medium Medium

NREenergyresource

availability

Medium High Medium Medium Medium

RESdeployment Mediumgrowth Medium

growth

Veryrapid Medium

growth

Veryrapid

Technologydevelopment Medium Rapid Slow Slow Rapid

Mainobjectives Notdefined Variousgoals Security Security Local

sustainability

Environmental

protection

Bothreactiveand

proactive

Mainly

reactive

Reactive Reactive Proactive

Trade Weak

globalization

Globalization Trade

barriers

Trade

barriers

Tradebarriers

NREavailability:weconsiderhighestimatesforconventionaloil,andbestguessestimatesfortherestofresourcesforthescenariosBAU,2,3and4.Forscenario1,weconsiderhighestimatesforunconventionaloilandtotalnaturalgas.



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Table2:Maximumdepletioncurvesconsideredforeachscenario.

Scenario NREavailability

Oil Gas Coal Uranium

BAU Conv oil: (Maggio and Cacciola,2012)High

Unconvoil:(Mohretal.,2015)BG

(Laherrère,2010)

(Mohr, 2012)High

(Zittel,2012)

1 Convoil:asBAU.

Unconvoil:(Mohretal.,2015)High

(Mohr, 2012)BG

AsBAU AsBAU

2 AsBAU AsBAU AsBAU AsBAU





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VariabililityofthescenariosduetotheEnergy-Economyfeedback

Energy-economy relationship in the IntegratedAssessmentModelsMEDEAS is a modular model (Figure 1) in which each module provides feedbacks to other,conditioningthemanddynamicallyadaptingthewholesystem.Theeconomymodule,togetherwiththeenergymodulegeneratesasanoutputtheenergyconsumption.Thisvariable,inturn,istakenasaninputbytheothermodules.Consideringthisinput,eachmodulegeneratesdifferentoutputswhich,directlyorindirectly,enterbackasinputsintheenergymodule.Theresultsobtainedforcestheenergymoduletoadapttothem,providingthefeedbackthatmakestheresultsofMEDEASdynamicallyadaptive.

Figure1:ModulesinMEDEASmodelandtheirfeedbacks.Ownelaboration.

TheEconomymodule is fueledbyaneconometricdemandfunctionwhichprovides“Trend finaldemand”.Itrepresentsthehouseholdfinaldemand,thelevelofinvestmentbytheprivatesectoranddepublic expenditureby sector. The Input-Output Tables (IOT) used come from theMulti-RegionalIOWIOD(Dietzenbacheretal.,2013;M.Timer,2012),astheyincludedataatgloballevel,for40countries(including27memberstatesoftheEuropeanUnionandaRestoftheWorldregion),andenvironmental satelliteaccounts.Themodeluses the InputOutputAnalysis (Leontief1986;MillerandBlair2009) tospreadthedirectand indirecteffectsofdemandchangeandobtaining



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thus, sectorial production. This production leads to an expected energy demand regarding thesectorialenergyintensities–which,inturn,endogenouslyevolve.Inparallel,theenergymodulehastoprovidethenetenergysupplyasanoutputtoconfrontwiththeexpectedenergydemand.

Netenergysupplyavailabilityiscalculatedbyenergyresourceacrosstime.Themodeldisaggregatesnon-renewable energy (NRE) (coal, conventional oil, unconventional oil, conventional gas,unconventionalgasanduranium)andrenewableenergy(RES)(solar,windonshore,windoffshore,hydroelectricity,geothermal,biomass&wasteandoceanic)resources.Theiravailabilitydependsonthebalancebetweentheirlimitsandtechnologicalchange.ForNRE,wetakeintoconsiderationflowlimitsandstocklimits,aswellasotherlimitssuchasenvironmental,economic,social,etc.ForRES, biophysical potential and economic constraints are considered (see section “Availability ofenergy resources inMEDEAS”). Once the net energy supply availability is obtained, themodelcomparesitwiththeexpectedenergydemand.Iftheformerishigherthanthelatter,thereisnoscarcitytofacebytheeconomy.But,ifnot,energyconsumptionwouldonlyreachthelevelallowedby the supply availability. This scarcity is distributed amongst the sectors according to theirrespectiveenergyintensitiesandothercriteria.Thisway,thereisafeedbackbetweenenergyandeconomythatforcesthelattertodynamicallyadapttothelimitsimposedbytheformer,givingamorerealisticapproach.

Most Integrated AssessmentModels (IAMs) –used by IPCC- use to rely on general equilibrium(Scrieciu,Rezai,andMechler2013).Theytendtoassumethatenergysupplyisnosubjecttolimitsor, in the best case, abundant enough for no taking it into account. Nevertheless, other IAMsincluding thoseusingdamage functions, consider climate change impactsover theGDPgrowth.MEDEASdoesnotusethisdamagefunction,mentionedinsection“AvailabilityofenergyresourcesinMEDEAS” as there isa lackof considerationofenergyand resources scarcitywhich leads tounderestimatingtherealeffectsonGDPgrowth.Instead,MEDEASmodelapproachaddressesthislackoffeedbackbetweeneconomyandenergywhichprovidesdynamicadaptivescenarios.Figure2showsaschematicdiagramoftheenergy-economyfeedbackwhich,inadditiontotheconditionsimposedbytheexogenousscenariosframework,endogenouslyadaptsitsresults.

Figure2:Energy-EconomyfeedbackinMEDEAS.Ownelaboration



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In the firstplace, theeconomymoduleprovides–bytheprocessesdescribedabove- theenergydemand (consumption) as an output. Received by the Energy module as an input, it is to beconfrontedwithenergysupplyavailability. Ifsupply ishighenoughtomeetenergydemand,theeconomymodulewouldkeepits‘regular’activitiesandprocesses.But,ifenergyscarcityappears,economicvariables–suchassectorialdemandandproduction-wouldhavetoadapttotheextentof these energy inputs. This approach allows us to achieve a better understanding and amorerealisticviewofhowthewholesystemdynamicallyevolves.Atthemoment,MEDEASproportionallyallocates energy consumption by source amongst sectorswhen a shortage appears. A shortagecoefficient is calculated considering that the scarcer source is theone thatmost conditions thesectorialproductionprocess.Thisshortagecoefficientequals1whenenergyconsumptionsatisfiesdemand,i.e.thereisnosupplyrestriction.Inthiscase–energydemandishigherthanenergysupply-energyconsumptionjustreachestheenergysupplyandtheshortagecoefficientis lowerthan1,reducing(eq.1)theproportionofenergydemandedwhichisactuallyconsumedbyeachsector.

!"#$,& = (ℎ*+,-./1*/22313/4, ∗ !"6$,& (1)!"6$ = ∑!"6$,& (2)

FEC=Finalenergyconsumption

FED=finalenergydemand

FES=finalenergysupply i:finalenergy;j:sector/households



109

ExpectedresultsandmainMEDEAScontributionWithout a feedbackbetweenenergyandeconomy,energydemand shall growexogenouslynottakingintoconsiderationavailabilityofresources(HöökandTang2013;Wangetal.2017;Capellán-Pérezetal.2016).Theunderlyingassumptionhereisthatthisavailabilityofresourcesmatters,andthatthefunctioningoftherealeconomyisnotindependentfromit.Economicgeneralevolutionandsectorialinterdependenciesarenotthesameiftherearenoenergylimitsthanifthereexists.MEDEASpreliminaryresultsillustratethesedifferentperformanceoftheeconomyandthewholesystem.AsFigure3shows,theresultsaresignificantlydifferentwiththefeedbackandwhenweremoveit.Intheexample,weremainundertheconditionsofBusinessasUsual(BAU)scenario.Ascan be seen, the energy-economy feedback provides a result that is not often taken intoconsiderationinotherIAMs,astheytookGDPgrowthasexogenousandenergyresourcesasalwaysabundant. Thus, these models tend to look for an optimum energy mix regardless its supplyavailability–eventhoughtheyusuallytakeintoconsiderationefficiencygains.

In thisexample,whereas theGDPkeepsgrowing–in this case,withanexogenousgrowth rate-depletionofNREspushesdownitssupplyavailability.However,intheNoFeedback(NF)case,withunlimitedresources,bothGPDandtotaloilextractionareabletocontinuegrowing.Asdescribedabove, final energy supply limits energy consumption requirements from the economy. Oilextractionhasbeen selected to illustrate this example, as it is themost important source fromliquids,highlydemandedfortheeconomicprocesses.WhenweaddtheFeedback(F)condition–disabletheunlimitedresources-,thatis,thewayMEDEASisdesigned,GDPhastoadapttotheoilsupplyavailability.Bythesamereasons,thedownsizingofGDPgrowthimposeslesspressuretotheresourcesso,theoilextractionrateislowerthanitwouldbewithNF.



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Figure3:GDPandtotaloilextraction.PreliminaryresultsofglobalMEDEASmodel.Ownelaboration.

Thus,MEDEAScontributestoenhancetheunderstandingofthelinkbetweentheenergyavailabilityandtheeconomyNevertheless,energyrequirementsshallbemodifiedbytechnologicalprogresswithadecreaseinenergyintensitiesortheinputsrequiredbyeachsectorinitsproductionprocess–throughtheevolutionoftechnicalcoefficientsinIOTs.Regardlessthiscircumstances,economicvariablesinMEDEASevolvesubjecttobiophysicallimitsand,simultaneously,pressureonresourcesbytheeconomyisadaptedtothisevolution.Therefore,MEDEASoffersasystemicandholisticpointofvieworientedtoadoptbetterchoicesforsustainabilitytransitions.



111

AvailabilityofenergyresourcesinMEDEAS

Availabilityofnon-renewableenergy(NRE)resourcesTheavailabilityofnon-renewableenergyresourcesinMEDEASdependsupontwoconstraints:

• Stock(availableresourceintheground),ie.energy,

• Flow(extractionrateofthisresource),ie.,energy/time.

Figure 4 illustrates the depletion over time of a non-renewable resource stock (cumulativeextraction,greydashedline)throughflows(depletioncurve,blacksolidline)intheabsenceofnon-geologicrestrictions.Themaximumflowrateisreachedmuchearlierthanthefulldepletionofthestock, athalf the timeassuming that theextraction rate followsa logistic curve (KerschnerandCapellán-Pérez,2017).

Figure4(KerschnerandCapellán-Pérez,2017):Simplifiedrepresentationofthedepletionofanon-renewableresourceintheabsenceofnon-geologicconstraints.Stocksandflowsofenergyrelativetotime.

00-6

energy

energy/tim

e

Time

Flow(extractionrate)

CumulativeextractionStock



112

Theavailablestockofaresourceisusuallymeasuredintermsofultimatelyrecoverableresources(URR),orremainingRURR(RURR)ifreferencedtoagivenyear.TheRURRinagiventimetisdefinedasthedifferencebetweentheURRandcumulativeextractionintimet(seeeq.1):

tt extractioncumulativeURRRURR _-= eq.1

MEDEASconsidersthefollowingnon-renewableprimaryenergyresources:

• Conventionaloil:referstocrudeoilandNGLs.• Unconventionaloil:includesheavyandextra-heavyoil,naturalbitumen(oilsandandtar

sands)andoilshales.Biofuels,CTL,GTLandrefinerygainsaremodeledseparately.• Conventionalgas.• Unconventionalas:includesshalegas,tightgas,coal-bedmethane(CBM)andhydrates.• Coal:includesnthracite,bituminous,sub-bituminous,black,brownandlignitecoal.• Uranium.2

Inordertoestimatethefutureavailabilityoffossilfuels,wehavereviewedthestudiesprovidingdepletion curves for non-renewable energy resources taking into account both stocks and flowlimits.Thesestudiesprovidedepletioncurvesasafunctionoftimebasedondynamicallyestimatingthelikelyextractionrateofwellsandminesglobally(Aleklettetal.,2010;ASPO,2009;EWG,2013,2008,2007,2006;Hööketal.,2010;Laherrère,2010,2006,MaggioandCacciola,2012,2012;Mohr,2012;Mohretal.,2015;MohrandEvans,2011,2009,2009;PatzekandCroft,2010;Zittel,2012).Thesecurvesshouldnotbeinterpretedasprojectionsoftheextractionofagivenfuel,butinsteadrepresentcurvesofmaximumpossibleextractiongiventhegeologicalconstraints(ie.,assumingnodemandorinvestmentconstraints).

Thedepletion curvesofnon-renewableenergies reviewed in the literature representextractionlevels compatiblewith geological constraints as a functionof time. Thus, to be incorporated asinputsinthemodel,thesedepletioncurvesmustbetransformed,sincedemandisendogenouslymodelledforeachresource.Weassumethat,whilethemaximumextractionrate(asgivenbythedepletion curve) is not reached, the extraction of each resource matches the demand. Actualextractionwillthereforebetheminimumbetweenthedemandandthemaximumextractionrate

2Weassume that the technologies that claim they could increase the fisiblematerial by 50 to 100 times, like fastbreedersandtheso-calledfourthgenerationreactors,willnotbeavailableinthenextdecades(Cellier,2009).NuclearfusionisnotconsideredsincetheITERandDEMOprojectsestimatethatthefirstcommercialfusionpowerwouldnotbeavailablebefore2040.



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(seeFigure5a).Todothis,thedepletioncurveshavebeenconverted intomaximumproductioncurvesasafunctionofremainingresources.Inthesecurves,aslongastheremainingresourcesarelarge,extraction isonlyconstrainedbythemaximumextraction level.However,withcumulatedextraction,thereisalevelofremainingresourceswhenphysicallimitsstarttoappearandmaximumextractionratesaregraduallyreduced.Inthisway,themodelusesastockofresources(theRURR)anditstudieshowthisstockisexhausteddependingonproduction,whichisinturndeterminedbydemandandmaximumextraction(seeFigure5b).

Figure5(Mediavillaetal.,2013):Integrationofdepletioncurvesinthemodel.(a)SDmodel.(b)Acurveofmaximumextraction(solid)comparedwiththedemand(dashed).

MEDEAS allows selecting a diversity of depletion curves for each fuel (aswell as considering acustomizedoneorassumingtheunconstrainedextractionofthefuel).

Themaximumextractioncurvedoesnotallowcapturingtheflowconstraintswhenthepeakrateofafuelhasnotbeenreached.Forthisreason,unconventionaloil&gasextractionissubjecttoanadditionalconstraint that limits themaximumannualgrowthextractionratetoavoidunrealisticgrowthextractionrates.

Figure6ashowsthedepletioncurvesasafunctionoftimeandFigure6btheassociatedcurvesofmaximumextractionasafunctionoftheRURRasappliedinthisreport(whicharethesameasin(Capellán-Pérezetal.,2014)).

a b



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Figure 6 (Capellán-Pérez et al., 2014):Non-renewable primary energy resources availability: (a)depletioncurvesasafunctionoftimefromtheoriginalreference;(b)curvesofmaximumextractioninfunctionoftheRURRasimplementedinthemodel.They-axisrepresentsthemaximumachievableextraction rate (EJ/year) in function of the RURR (EJ). For each resource, the extreme left pointrepresentsitsURR.AsextractionincreasesandtheRURRfallbelowthepointwherethemaximumextractioncanbeachieved,theextractionisforcedtodeclinefollowingtheestimationsofthestudiesselected(panel(a)).TheRURRin2007foreachresourceisrepresentedbyarhombus.

0

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Availabilityofrenewableenergysources(RES)Renewable energy is usually considered as a huge abundant source of energy; therefore, thetechnological limits are assumed to be unreachable for decades, and the concern is on theeconomic, political or ecological constraints (de Castro et al., 2011; IPCC, 2011; Kerschner andO’Neill, 2016). However, the large scale deployment of renewable alternatives faces seriouschallengesinrelationtotheirintegrationintheelectricitymixduetotheirintermittency,seasonalityandunevenspatialdistributionrequiringstorage(Lenzen,2010;Smil,2008,p.362;Trainer,2007),theirlowerenergydensity(deCastroetal.,2011,2013b,2014;Smil,2008,pp.383–384),mosthavelowerEROIthanfossilresources(PrietoandHall,2013),theirdependenceonmineralsandmaterialsfortheconstructionofpowerplantsandrelated infrastructuresthatposesimilarproblemsthannon-renewableenergyresourcesdepletion(deCastroetal.,2013b;García-Olivaresetal.,2012),andtheirassociatedenvironmentalimpacts(AbbasiandAbbasi,2012;Danielsenetal.,2009;Keithetal.,2004;Milleretal.,2011),whichalltogethersignificantlyreducetheirsustainablepotential(Capellán-Pérezetal.,2014;deCastroetal.,2011,2013b,2014;Smil,2008;Trainer,2007).

Inthissectionwediscussthetechno-ecologicalpotentialofrenewableenergiesconsideredinthemodel.Specialattention isdevotedtothe landrequirementsofREStechnologiesgiventhatthetransitiontoRESwillintensifythecompetitionforlandglobally(e.g.(Capellán-Pérezetal.,2017a;Scheidel and Sorman, 2012)), in a contextwhere themain drivers of land-use are expected tocontinuetooperateinthenextdecades:populationgrowth,urbanizationtrendsandshifttomoreland-intensivediets(FAO,2009;Kastneretal.,2012;Smithetal.,2010).RESfrombioenergycanbeusedtoobtainheatandbiofuels(section0)andelectricity(section0).



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RESforheatandbiofuelsBiomassislimitedbyatotalterrestrialnetprimaryproductivityofroughly60TW(humansalreadyappropriate indirectly20-50% inanunsustainableway (Crameretal., 1999;Haberletal., 2013,2007;Imhoffetal.,2004;ImhoffandBounoua,2006;Smil,2008;Vitouseketal.,1986).Bioenergyprovidesapproximately10%ofglobalprimaryenergysupplyandisproducedfromasetofsources(dedicatedcrops, residuesandMunicipalSolidWaste (MSW),etc.) thatcanservedifferentuses(biofuels,heat,electricity,etc.).WefollowWBGU(2009)approachanddividebioenergyresourcesinto3categories:traditionalbiomass,dedicatedcropsandresidues:

Theapproachfollowed inMEDEAStoestimatethetechno-ecologicalpotentialofmarginal landsanddedicatedcropsistoexogenouslysetapotentiallandavailability(hectares)foreachone,andsubsequentlyderivetheenergypotentialtakingintoaccountthecorrespondingpowerdensity.Forthosetechnologiesthatcurrentlydonotexistatcommerciallevel,weassumethattheiroutputinthefirstyearswillfollowthehistoricdeploymentratesof2ndgenerationbiofuels(2000-2014).

Thenewlandthatwecouldconverttoagricultureisestimatedin200-500MHa(FAO,2009;Schadeand Pimentel, 2010), or 386MHa in a sustainable way, converting abandoned agricultural land(Campbelletal.,2008;Rockströmetal.,2009).Thismeansthatitmaybenotpossibletomeetthecurrenttrendsofdemandforfoodifthedegradedlandcontinuestogrow,asmorethan350MHawillbelostifpresenttrendscontinue(Foleyetal.,2005;Pimentel,2006).Thus,inviewofthecurrentsituation, and considering that currently almost15%of theworldpopulation isundernourished(FAO,2012),averylargesurfaceforbioenergy(orotherland-intensiveRESsuchassolar)atgloballevelisnotcompatiblewithsustainablefuturescenarios.

Two types of land availability for bioenergy are taken into consideration depending on thecompetitionwithotheruses:

• Marginallands:theydonotimplyacompetitionwithcurrentcrops.Themodelconsiderstheanalysisfrom(Fieldetal.,2008)whofindthat27EJofNPPcanbeextractedfrom386Mhaofmarginal lands avoiding the risk of threatening food security, damaging conservation areas, orincreasingdeforestation.TheyexpectthattheaverageNPPinbiomassenergyplantationsoverthenext50yearsisunlikelytoexceedtheNPPoftheecosystemstheyreplace.

• Landsubjecttocompetitionwithotheruses,whichistobedefinedexogenouslybyeachscenario.Weconsiderthatonlythededicatedcropswouldrequireadditionalland.Relatedtothegross power density of 2nd generation biofuels under land competition, we will consider asreferencetheworldaveragevaluegivenby(UNEP,2009)basedonrealdata(36Mhaoccupiedfor



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1,75 EJ in 2008) that estimates at 0,155W/m2. Assuming a similar energy density for currentproduction, almost 60MHa are nowadays used (BP, 2016).However, the real occupied surfacemightsubstantiallyhighergiventhatthemethodologyappliedbytheUNEPisconservative(see(deCastroetal.,2013a)),thisnumbermightinfactbecloserto100MHa.

Since current conventional bioenergy use for heat (18 EJ/yr harvestable NPP (REN21, 2016))surpassessustainablelevels(deCastroetal.,2013a;Foleyetal.,2005;GFN,2015;Pimentel,2006),weassumethatinthefuturebetterpracticescouldbeadoptedallowingtoincreasethesustainablepotentialto25EJ/yr(NPPharvestable).Aneventualreduceddependenceontraditionalbiomassinthenextdecadesmightalsoallowtousebioenergyresourcesinamoresustainableandefficientway.

There iscurrentlyacontroversialdebateaboutthepotentialofthevaluationofagriculturalandforestry residues, because of its threat to soil fertility preservation in the long run, biodiversityconservation and ecosystem services (Gomiero et al., 2010;Wilhelm et al., 2007).We take theestimationof(WBGU,2009)of25EJNPPtakingintoaccounteconomicrestrictionsandweassumethatmostofitwillbededicatedforheat(50%),andtheremainingforbiofuels(25%)andelectricity(25%).

TwootherRESforheatareconsideredinMEDEAS:solarthermalandgeothermalforheat.Forsolarthermalweconsiderthetechnicalspecificationsfrom(SHC,2016)andthesurfacethatmightbecoveredbysolarcollectorsfrom(Capellán-Pérezetal.,2017b).Duetothethermallosses,thispowerwillbeinstalledclosetotheconsumptionpoints,competinginmanycaseswithPVpanels,mainlyinrooftoplocations.Weassumethatthetechno-ecologicalpotentialestimatedby(deCastro,2012)forgeothermalforelectricity(0.6TWth)is5xtimeslargerforgeothermalforheatgiventhatlowtemperaturescanalsobeutilized(3TWth).

Thus,combiningthedatafromTable3andthegrosstechno-sustainablepotentialofthermalRESconsideredinMEDEASamountstoaround160EJ/yr.



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Table 3: Techno-sustainable potential of RES for heat and liquids in MEDEAS. Other potentialresources,suchas4thgenerationbiomass(algae),arenotconsideredduetothehighuncertaintiesofthetechnologyandthe long-termnatureof itseventualcommercialappearance(Jandaetal.,2012).NPP:NetPrimaryProduction.ThefollowingconversionfactorsfromNPP(harvestable)tofinal(gross)powerareassumed:80%forheat,20%forelectricityand15%forliquids.

Reference Potential UseinMEDEAS NPP

harvestableFinal(gross)

power EJ/yr EJ/yr

Conventionalbioenergy Ownestimation 25 20 Heat

- Marginal lands (nocompetitionwithcurrentuses)

(Fieldetal.,2008) 27 4.1 Biofuels

2ndgen. Dedicated crops (competitionwithcurrentuses)

(de Castro et al.,2013a)

33 4.9 Biofuels

3rd gen.(from2025)

Dedicatedcrops (WBGU,2009) +5.0c +0.7c BiofuelsAgriculture&Forestryresidues (WBGU,2009) 14 11.2 56%heat

4.75 0.95 19%electr.6.25 0.95 25%biofuels

Geothermalforheat (deCastro,2012) - 95 Heat

Solarforheat (Capellán-Pérezetal.,2017b,p.5)

- 22 Heat

TOTAL - 159.8 -



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RESforelectricityThemostpromisingelectricrenewableenergiesaresolarandwind(Smil,2010).However,recentassessmentsusingatop-downmethodologythattakesintoaccountrealpresentandforeseeablefuture efficiencies and surface occupation of technologies find that the potential of theirdeploymentisconstrainedbytechnicalandsustainablelimits(deCastroetal.,2013b,2011).Theevaluation of the global technological onshore wind power potential, acknowledging energyconservation,leadstoapotentialof30EJ/yr(deCastroetal.,2011).Inrelationtooffshorewind,inabackofenvelopeestimation,assumingapowerdensityofnetelectricitydelivered1We/m2andthat 1% of the continental ocean platformsmight be occupied by human infraestructures (thedensityofoccupationbyhumaninfrastructureinlandis1-2%andentireplatformslikeArticandAntarticarenotaccesibletohumanocuppation),aroughpotentialof0,25TWeisconsidered.Theestimationoftherealandfuturedensitypowerofsolarinfrastructures(4-10timeslowerthanmostpublishedstudies)leadstoapotentialofaround65-130EJ/yr(2-4TWe

3)(deCastroetal.,2013b)in60-120MHa.4

WealsoconsiderCSPtechnologywithstorage(moltensalts)withoutback-up.CSPplantsarealessuniversalsolutionthanPV,since(1)theyonlyusethedirectirradiance(PValsousesdiffuse),(2)theyrequirehigherlevelsofirradiancetobeeconomicallyoptimal(+50%),and(3)theyadaptlesswelltoterrainunevenness(Dengetal.,2015;Hernandezetal.,2015).5Thus,weassumealowerlandavailabilityof50MHawhichconsideringthatthistechnologyachievesasimilar landpowerdensity than solar PV (de Castro et al., 2013b)would represent a potential of around 1.7 TWeglobally.

Asdiscussedintheprecedentsection,weassumethatbioenergywillbemainlyusedforproducingheatandliquids.Forelectricityuses,wearbitrarlyassignapotentialforbioenergywaste(agrofuels,woodfuels,etc.)andMSWof2timesthecurrentproduction(~0.1TWe).From2025(thoughthiscanbeadjusteddependingonthescenario),additionalbiomassisavailablethroughthedeploymentofthe3rdgenerationbiomasstechnologies(seeTable3).Theadditionalpotentialwouldamountto

3“TWe”representspowerelectricproduction:8760TWh=1TWe,i.e.inoneyear1TWofcapacityfunctioningwitha100%capacityfactorproduces1TWe.4Thepotentialinurbanareasisgreatlylimitedbythecompetitionwiththesolarthermaltechnologiesandthefactthattheadaptationtotherooftopimplieslowerefficiencies.However,itisplannedtoincluderooftopPVinfutureversionsofMEDEAS.5Additionally,restrictionsonwateruseinthearidregionsthatoftenhavethemostappropriatesolarresourcesforCSPwouldreduceplantefficienciesduetotheimplementationofdry-coolingtechnologies.



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25%ofthetotalNPP(25EJ/yr)consideringanaverageefficiencyconversionof20%(deCastroetal.,2014),6i.e.1.25EJ/yr(0.04TWe).

Hydroelectricity is limitedbya totalgravitationalpowerof rainof25TW(Hermann,2006).Ourestimationoftechnologicalpotentialisaround1TWe;otherstudieshavefoundthattheeconomicpotential is 1-1.5 TWe, being the sustainable potential around 80% of this range (0.8-1.2 TWe)Euroelectric.GiventheconstraintsthatthevariabilityofRESforelectricityimposetothesystem,weassumeanavailablepotentialinMEDEASof1TWe.

Seawavesoncoastsandtidalresourcesarelimitedtoaphysicaldissipationof3TWandgeothermalrenewable resources are limited by a total Earth dissipation of 32 TW (Hermann, 2006).Acknowledgingtheirhighdispersionandroleintheenergeticandmaterialfluxesofecosystems,weestimatethataround1.35TWecouldbeattainedinasustainablewaybyrenewableenergiesotherthansolarandwind.

Following theseconsiderations, theglobal techno-ecologicalpotentialof renewableenergies forelectricity generation is estimatedat around7.5TWe/yrper year (~240EJ/yr, seeTable4). Thetechno-ecologicalpotentialofrenewableenergiesissofaracontroversialsubjectintheliterature,andtheestimationsconsideredinMEDEASareinthelowerrangeoftheliterature.

6 The MSW efficiency reported by the IEA is also in that range (22%): https://www.iea.org/publications/freepublications/publication/essentials3.pdf.



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Table 4: Data of electric renewables considered in MEDEAS.. “TWe” represents power electricproduction:TWh/8760.MSW:Municipalsolidwaste.

Techno-ecologicalpotentialREStechnology TWe

Hydro 1Windonshore 1Windoffshore 0.25(1%ofoceanplatforms)SolarPV 3.3(100MHa)CSP 1.65(50MHa)Biomasswaste&MSW 0.1Geothermal 0.2Oceanic 0.05TOTAL 7.55

Thesesustainablepotentialsrepresentmaximumlevels,howeverinternaldynamicsofthemodelmightpreventtheselevelstobereachedinthetimeframeofthesimulations.Constraintstakeintoaccountreasonabledynamicsofgrowth(unrealisticallyhighleveldeploymentsareprevented),timeforplanningandconstructingpowerplants,therelativelevelofEROIofeachtechnologyinrelationtotheotheravailabletechnologies,theshareofvariabletechnologiesinrelationtothetotal,etc.Additionally, the deployment of capacity follows a logistic behaviour to take into account theslowingdownofthegrowthdynamicswhenapproachingthelimits.Figure7showsanexampletoillustrate the behaviour of exponential growth constrainedby an exogenous limit (upper panel,annualvariationofelectricsolarproduction;lowerpanel,totalelectricitygenerationfromsolar).



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Figure7 :Totalelectricsolarproduction(TWe). Inthis figurewerepresentthedynamicsofsolardeploymentconsideringaveryrapidgrowthofsolar(+19%).Whilebeingfarfromthepotentiallimit,exponential growth drives the growth of new solar power. As the total solar power installedincreases, the depreciation of infrastructures becomes significant. Finally, just 15 years afterreachingthemaximuminstallationrate,95%ofthepotentialisachievedin2065.



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ModellingofclimaticimpactsinMEDEASThescaleofhumanactivitiesworldwidehasgrownsogreatthattheyareincreasinglyaffectingtheregularfunctioningofthebiosphereandcriticallythreateningitsequilibrium:duringthelastfewdecades,humanactionshavebecomethemaindriverofglobalenvironmentalchange.Thescaleofthe anthropogenic disruption of the biosphere can be illustrated by the current level of someindicators,suchastheglobalecologicalfootprint(assessedatover150%oftheglobalbiocapacityratio(GFN,2015))orthe9identifiedPlanetaryBoundaries(PBs)(Steffenetal.,2015).Amongthelatter,itisestimatedthattwo(geneticdiversityandbiogeochemicalflows)havealreadysurpassedtheir PBs and other two (climate change and land-use system change) have been identified ascurrentlylyingintheuncertaintyzone.Moreover,GlobalEnvironmentalAssessments(GEAs)andsimilar analyses conclude that, if current trends are not amended, next decades will see anintensificationofhumanalterationofthebiosphereandthesituationofthecontrolvariablesofthePBswillworsen(e.g.(IPCC,2014;Meadowsetal.,2004;MilleniumEcosystemAssessment,2005;Randers,2012)).Thus, ifnocorrectiveactionsare taken in thenext fewdecades, thedisruptivepotentialoffutureglobalenvironmentalchangewilllikelyescalatetolevelsthatwillpreventlargepartsofthebiospherefrombeinginhabitedbyhumans,thusthreateninghumansocietiesasweknowthemnowadays(Hansenetal.,2016b,2016a,2013;Lelieveldetal.,2015).

Policy-recommendations to propose sustainable alternatives to the current trends are usuallyderivedfromtheapplicationofenergy-economy-environmentmodels,orEnvironmentalIntegratedAssessment Models (IAMs). However, there is a large discrepancy between natural scientists’understandingofecologicalfeedbacksandtherepresentationsofenvironmentaldamage(ifany)found in IAMs (Cumminget al., 2005; Lenton andCiscar, 2013; Pollitt et al., 2010; Stern, 2013;Weitzman, 2012). To date, thesemodels do either not include any impact from environmentaldamages, or just a partial incorporation that translate into practically negligible impacts in thebaselinescenarios(i.e.scenarioswithoutadditionalpolicies)whichprojectincreasesofglobalGDPofseveraltimesthecurrentlevelby2100.WerecallthatGEAsfollowtheconventionaleconomicsapproach where GDP per capita growth and welfare are tightly connected. As a result of notconsideringthecostsofnon-action,recommendationsissuedfrommodelingexercisesusuallyleadtomisguidedpoliticaladvice(e.g.delayedaction,sustainablepoliciesreportedasrequiringnetcostsinsteadofbenefits)(Capellán-Pérez,2016).

Theseshortcomingshaveespeciallybeenpointedoutbysomeauthorsforclimatechange,whichisthemostresearchedPB.Inparticular,theusefulnessoftheapplieddamagefunctions,whichrelatetemperatureincreasewithGDPloss,hasbeenquestionedgiventheirunderestimationofimpacts



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in relation to the forecasts by physical scientists and the fact that they are not calibrated fortemperatureincreasessuchasthelikelyonestobereachedattheendofthiscenturyinbaselinescenarios(i.e.+3.7-4.8°Cabovetheaveragefor1850–1900foramedianclimateresponse).Infact,currentestimationsof impactsof climatechangeorenvironmentaldegradationarebaseduponmonetizeddamages thatomitmany key factors. Thesedeficiencieshave led toquestioning theusefulnessofcurrentIAMsandargueforanewgenerationofmodels(DietzandStern,2015;Giraudetal.,2016;Pindyck,2015,2013;Stern,2013).

However, representations of global environmental change threat to human societies in energy-economy-environmentmodels consistentwith thephysical science literaturehave todatebeenscarce. InMEDEAS,theappliedmethodologybuildsontheaforementionedcritics fromastrongsustainabilityapproach,andfollowsthesubsequentassumptions:

(1) FocusontheclimatechangePBasaproxyofglobalenvironmentaldegradationduetothecurrent development level of the MEDEAS framework. However, some consistency isassuredby the fact that recent findings suggest theexistenceofa two-levelhierarchy inbiosphere processes where climate change is one of the two identified core planetaryboundariesthroughwhichtheotherboundariesoperate(Steffenetal.,2015).

(2) Application of precautionary principle given the high uncertainties and risk of potentialdisruptive environmental/climate change in the next decades as proposed by (Pindyck,2015).

(3) “Energylossfunction”(ELF):a. Environmental/climatechangedamagesaffectnetenergyavailabilitytothesociety,

i.e.affectingthedriversofgrowthinsteadofthelevelofGDPoutput(DietzandStern,2015).

b. Quantitativefunctionwithassociateduncertainty.(4) UseofCO2econcentrationsandtotalradiativeforcingasdriversofclimatechangealteration

insteadoftemperatureincreasessince:a. Globalenvironmentalchangeisnotsolelydrivenbytemperatureincrease(e.g.ocean

acidification is drivenbyCO2 concentration increase); climate is definedbymanyfactorssuchashumidity,winds,solarradiation,etc.

b. Thus,thePBofclimatechangeisdefinedbythesetwovariables(350ppmand+1.0W/m2relativetopre-industriallevels)(Steffenetal.,2015),

c. The large uncertainty on equilibrium climate sensitivity do not affect the policy-makingprocess(focusontargetssuchasthecarbon-budget(IPCC,2014)).

(5) Nodiscountingofimpacts(inter-generationalequity).

Theimplementationofthedamagesfromenvironmental/climatechangesinMEDEASisperformedthrough the integration of an ELF that reduces the overall net energy delivered to the society,assumingthatwhenclimatechangereachesacertainthresholdnotcompatiblewithhumanityasit



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isnowadaysconfigured,theenergylosseswouldreach100%ofthetotalenergysupply.ThereducedavailabilityofenergytranslatesthenintoareducedGDPgrowthinrelationtotheexpectedtrend.

InthecurrentMEDEASversionwehave implementedanELFwitha logisticshapethatusesCO2concentrationsfromthecombustionoffossilfuelsandland-usechangeasclimatechangeindicator,assumesaverylowcontributionofdamagesnowadaysandtakes1,000ppmasthethresholdofclimatechangeincompatiblewithhumanity(seeeq.1andFigure8):

"89:: -; < = 1 − ?

@ABCCDEF

G ;eq.1

Figure8:EnergylossesasafunctionofCO2concentrationsduetoclimateimpactsimplementedinthecurrentversionofMEDEAS.

Theintegrationinthemodeloftheseenergylossesisperformedintwosteps:

1. Firstly,theseadditionallossesareaddedtothetotalfinalenergydemand(FED)fromeconomicsectorsandhouseholds(seesectionVariabililityofthescenariosduetotheEnergy-Economyfeedbackinthisreport).Itisassumedthattheseenergylosses(EL)sharethesameproportionasthetotalfinalenergymix.Thus,thefinalenergydemandbyfinalenergyfuelisincreasedasshownbyeq.2:

!"6(,)$∗ = (!"6(/1,*+((,)$ + !"6ℎ*K(/ℎ*LM((,)$) ∙ (1 + "O(,)) ;eq.2

0.0

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Energy losses due to climate impacts

E loss (600; 50)

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2. Secondly,toaccountfortheimpactsanddamagescausedbyclimatechange,thefinalenergyconsumption(FEC,thusafteraccountingforpotentialenergyavailabilityconstraints),isreducedbytheenergylossesasshownineq.3:

!"#(,)$∗ = !"#(,)$ ∙ (1 − "O(,)) ;eq.3



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PreliminaryresultsAlthoughtheglobalmodelofMEDEASisnotfinalizedorvalidated,sometestswiththedevelopingversionhavebeencarriedouttoobtainsomepreliminaryresults.Thesepreliminaryresultsareonlyindicative.Thefinalresultswillbepresentedinthedeliverable4.1inJune2017.

The experiments have been performed using the scenarios described in the "Description ofscenarios"section.

Thefollowinggraphsshowthebehavioroftwoofthemodel'smaindrivers:GDPandpopulation.ItcanbeobservedthatGDP,althoughitbeginswiththegrowthlevelsdefinedinthescenarios,inthefollowing years begins its decrease, due to the availability of energy and the dynamics of thefeedbacks.

Figure9:GDPandPopulationbehavior.Ownelaboration.

GDP and Population

Population12 B

9 B

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01995 2005 2015 2025 2035 2045

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GDPpc[SCEN1] : Adaptive scenariosGDPpc[SCEN2] : Adaptive scenariosGDPpc[SCEN3] : Adaptive scenariosGDPpc[SCEN4] : Adaptive scenariosGDPpc[BAU] : Adaptive scenarios

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Population12 B

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leGDPpc[SCEN1] : Adaptive scenariosGDPpc[SCEN2] : Adaptive scenariosGDPpc[SCEN3] : Adaptive scenariosGDPpc[SCEN4] : Adaptive scenariosGDPpc[BAU] : Adaptive scenarios



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Figure10:GDPpercapitabehavior.Ownelaboration.

ThisbehaviorofGDPisverysimilartototalprimaryenergydemand.

Figure11:Totalprimaryenergydemandbehavior.Ownelaboration.

GDP and Population

Population12 B

9 B

6 B

3 B

01995 2005 2015 2025 2035 2045

Time (Year)

peop

le


GDP70

52.5

35

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GDPpc8000

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Time (Year)

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ople

GDPpc[SCEN1] : Adaptive scenariosGDPpc[SCEN2] : Adaptive scenariosGDPpc[SCEN3] : Adaptive scenariosGDPpc[SCEN4] : Adaptive scenariosGDPpc[BAU] : Adaptive scenarios

Total primary energy demand700

600

500

400

3001995 2005 2015 2025 2035 2045

Time (Year)

EJ/Y

ear

TPED by fuel[SCEN1] : Adaptive scenariosTPED by fuel[SCEN2] : Adaptive scenariosTPED by fuel[SCEN3] : Adaptive scenariosTPED by fuel[SCEN4] : Adaptive scenariosTPED by fuel[BAU] : Adaptive scenarios



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One of the key factors in this behavior is the availability of oil. In this sense it is important todifferentiatebetweenconventionalandnon-conventionaloil.Whilethelatterisexpectedtogrowstrongly,conventionaloilwillsoondecreaseproduction.

Figure12:Oilextraction.Ownelaboration

The following figure (Figure 13) shows the evolution of electric energy. The electrical energygeneratedfromrenewablesourceshasasignificantgrowthforallscenarios.Incontrasttheelectric

Oil

Conventional_oil_extraction200

100

01995 2010 2025 2040

Time (Year)

EJ

real extraction conv oil EJ[SCEN1] : Adaptive scenariosreal extraction conv oil EJ[SCEN2] : Adaptive scenariosreal extraction conv oil EJ[SCEN3] : Adaptive scenariosreal extraction conv oil EJ[SCEN4] : Adaptive scenariosreal extraction conv oil EJ[BAU] : Adaptive scenarios

Unconventional oil extraction70

35

01995 2010 2025 2040

Time (Year)

EJ

real extraction unconv oil EJ[SCEN1] : Adaptive scenariosreal extraction unconv oil EJ[SCEN2] : Adaptive scenariosreal extraction unconv oil EJ[SCEN3] : Adaptive scenariosreal extraction unconv oil EJ[SCEN4] : Adaptive scenariosreal extraction unconv oil EJ[BAU] : Adaptive scenarios

Total extraction oil200

150

1001995 2010 2025 2040

Time (Year)

EJ/Y

ear

total oil extraction EJ[SCEN1] : Adaptive scenariostotal oil extraction EJ[SCEN2] : Adaptive scenariostotal oil extraction EJ[SCEN3] : Adaptive scenariostotal oil extraction EJ[SCEN4] : Adaptive scenariostotal oil extraction EJ[BAU] : Adaptive scenarios

Oil


100

01995 2010 2025 2040

Time (Year)

EJ



35

01995 2010 2025 2040

Time (Year)

EJ



150

1001995 2010 2025 2040

Time (Year)

EJ/Y

ear


Oil


100

01995 2010 2025 2040

Time (Year)

EJ



35

01995 2010 2025 2040

Time (Year)

EJ



150

1001995 2010 2025 2040

Time (Year)

EJ/Y

ear




130

energy generatedby fossil fuels descends in all the scenarios andalsodescends inmostof thescenariosthenuclearenergy.

Figure13:Evolutionofelectricenergy.Ownelaboration.

Thegrowthofelectricenergywithrenewablesourcesisduetoanevengreaterincreaseofinstalledpowerintheserenewablesources.

Electricity sector

Electricity generation from nuclear3000

1500

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r

FE nuclear Elec generation TWh[SCEN1] : Adaptive scenariosFE nuclear Elec generation TWh[SCEN2] : Adaptive scenariosFE nuclear Elec generation TWh[SCEN3] : Adaptive scenariosFE nuclear Elec generation TWh[SCEN4] : Adaptive scenariosFE nuclear Elec generation TWh[BAU] : Adaptive scenarios

Electricity generation from fossil fuels20,000

10,000

01995 2005 2015 2025 2035 2045

Time (Year)TW

h/Y

ear

FE Elec generation from fossil fuels TWh[SCEN1] : Adaptive scenariosFE Elec generation from fossil fuels TWh[SCEN2] : Adaptive scenariosFE Elec generation from fossil fuels TWh[SCEN3] : Adaptive scenariosFE Elec generation from fossil fuels TWh[SCEN4] : Adaptive scenariosFE Elec generation from fossil fuels TWh[BAU] : Adaptive scenarios

Total electricity generation40,000

20,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r

Total FE Elec generation TWh[SCEN1] : Adaptive scenariosTotal FE Elec generation TWh[SCEN2] : Adaptive scenariosTotal FE Elec generation TWh[SCEN3] : Adaptive scenariosTotal FE Elec generation TWh[SCEN4] : Adaptive scenariosTotal FE Elec generation TWh[BAU] : Adaptive scenarios

Electricity generation from RES30,000

15,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh

FE real tot generation RES elec TWh[SCEN1] : Adaptive scenariosFE real tot generation RES elec TWh[SCEN2] : Adaptive scenariosFE real tot generation RES elec TWh[SCEN3] : Adaptive scenariosFE real tot generation RES elec TWh[SCEN4] : Adaptive scenariosFE real tot generation RES elec TWh[BAU] : Adaptive scenarios

Electricity sector


1500

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r



10,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r



20,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r



15,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh


Electricity sector


1500

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r



10,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r



20,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh/

Yea

r



15,000

01995 2005 2015 2025 2035 2045

Time (Year)

TWh




131

Figure14:Installedpowerinrenewableresources.Ownelaboration.

RES installed capacity by source9

4.5

01995 2005 2015 2025 2035 2045

Time (Year)

TW

installed capacity RES elec TW[wind offshore,SCEN1] : Adaptive scenariosinstalled capacity RES elec TW[wind offshore,SCEN2] : Adaptive scenariosinstalled capacity RES elec TW[wind offshore,SCEN3] : Adaptive scenariosinstalled capacity RES elec TW[wind offshore,SCEN4] : Adaptive scenariosinstalled capacity RES elec TW[wind offshore,BAU] : Adaptive scenariosinstalled capacity RES elec TW[solar PV,SCEN1] : Adaptive scenariosinstalled capacity RES elec TW[solar PV,SCEN2] : Adaptive scenariosinstalled capacity RES elec TW[solar PV,SCEN3] : Adaptive scenariosinstalled capacity RES elec TW[solar PV,SCEN4] : Adaptive scenariosinstalled capacity RES elec TW[solar PV,BAU] : Adaptive scenarios



132

Althoughitisimportanttonotethatonlypreliminaryresultsofthemodelunderdevelopmentandwithoutvalidationareavailable,theresultsonclimatechangearepessimistic.

Figure15:Resultsonclimatechange.Ownelaboration.

Emissions andClimate Change

Annual emissions130

97.5

65

32.5

01995 2005 2015 2025 2035 2045

Time (Year)

GtC

O2/

Yea

r

Total CO2 emissions GTCO2[SCEN1] : Adaptive scenariosTotal CO2 emissions GTCO2[SCEN2] : Adaptive scenariosTotal CO2 emissions GTCO2[SCEN3] : Adaptive scenariosTotal CO2 emissions GTCO2[SCEN4] : Adaptive scenariosTotal CO2 emissions GTCO2[BAU] : Adaptive scenarios

CO2 concentrations500

425

350

275

2001995 2005 2015 2025 2035 2045

Time (Year)

ppm

"CO2 ppm concentrations WoLiM 1.5"[SCEN1] : Adaptive scenarios"CO2 ppm concentrations WoLiM 1.5"[SCEN2] : Adaptive scenarios"CO2 ppm concentrations WoLiM 1.5"[SCEN3] : Adaptive scenarios"CO2 ppm concentrations WoLiM 1.5"[SCEN4] : Adaptive scenarios"CO2 ppm concentrations WoLiM 1.5"[BAU] : Adaptive scenariospre industrial value ppm : Adaptive scenarios

Total radiative forcing4

3

2

1

01995 2005 2015 2025 2035 2045

Time (Year)

wat

t/(m

eter

*met

er)

Total Radiative Forcing[SCEN1] : Adaptive scenariosTotal Radiative Forcing[SCEN2] : Adaptive scenariosTotal Radiative Forcing[SCEN3] : Adaptive scenariosTotal Radiative Forcing[SCEN4] : Adaptive scenariosTotal Radiative Forcing[BAU] : Adaptive scenarios

Temperature change3

2.25

1.5

.75

01995 2005 2015 2025 2035 2045

Time (Year)

Deg

rees

C

Temperature change[SCEN1] : Adaptive scenariosTemperature change[SCEN2] : Adaptive scenariosTemperature change[SCEN3] : Adaptive scenariosTemperature change[SCEN4] : Adaptive scenariosTemperature change[BAU] : Adaptive scenariosTemp change 2C : Adaptive scenarios


Annual emissions130

97.5

65

32.5

01995 2005 2015 2025 2035 2045

Time (Year)

GtC

O2/

Yea

r



425

350

275

2001995 2005 2015 2025 2035 2045

Time (Year)

ppm



3

2

1

01995 2005 2015 2025 2035 2045

Time (Year)

wat

t/(m

eter

*met

er)


Temperature change3

2.25

1.5

.75

01995 2005 2015 2025 2035 2045

Time (Year)

Deg

rees

C



Annual emissions130

97.5

65

32.5

01995 2005 2015 2025 2035 2045

Time (Year)

GtC

O2/

Yea

r



425

350

275

2001995 2005 2015 2025 2035 2045

Time (Year)

ppm



3

2

1

01995 2005 2015 2025 2035 2045

Time (Year)

wat

t/(m

eter

*met

er)


Temperature change3

2.25

1.5

.75

01995 2005 2015 2025 2035 2045

Time (Year)

Deg

rees

C




133

ConclusionsMEDEASmodelneedsassumptionsabouttheworldsocio-economicevolution(suchasexpectedeconomic growth, population evolution or technological progress) as external inputs. Theassignmentofestimatedvaluesforthesevariablesovertimeconsideringsomehypothesesiscalledscenarios. Scenario methodology offers an approach to deal with the limited knowledge,uncertaintyandcomplexityofnaturalandsocialsciencesandcanbeusedtogroupthevariationsof policies into coherent andmeaningful scenarios. Each scenario represents an archetypal andcoherentvisionofthefuture.Nevertheless, thesecommonlyusedprojectionsofvaluesthatarerealized in the scenarios do not contemplate some limitations due to the availability of energyresources and to the feedbacks of the global system. Therefore, it is necessary to analyze thevariability introducedby theseconsiderationson those scenarios.This reportdescribes someoftheseconsiderationsthatwillbesystematicallyanalyzedontheglobalmodelwhenitisfinalizedinJuly 2017. The preliminary results point to significant differences between predicted economicgrowthinthescenariosandtheeconomicgrowthobtainedinthesimulations,takingintoaccounttheenergyavailabilityandtheproposedfeedbacks.Thisdecelerationeffectontheglobaleconomicindicatorscanbeevengreaterifrestrictionsareimposedontheuseoffossilfuelstoguaranteethecarbonbudgetthatavoidstoexceed2ºC.



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ListofTablesTable1:Qualitativesummaryofthemainassumptionsanddriversofeachscenariofollowing(vanVuurenetal.,2012)........................................................................................................................104

Table2:Maximumdepletioncurvesconsideredforeachscenario...............................................105

Table 3: Techno-sustainable potential of RES for heat and liquids in MEDEAS. Other potentialresources,suchas4thgenerationbiomass(algae),arenotconsideredduetothehighuncertaintiesofthetechnologyandthelong-termnatureof itseventualcommercialappearance(Jandaetal.,2012).NPP:NetPrimaryProduction.Thefollowingconversionfactors fromNPP(harvestable)tofinal(gross)powerareassumed:80%forheat,20%forelectricityand15%forliquids...............118

Table 4: Data of electric renewables considered in MEDEAS.. “TWe” represents power electricproduction:TWh/8760.MSW:Municipalsolidwaste...................................................................121



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ListofFiguresFigure1:ModulesinMEDEASmodelandtheirfeedbacks.Ownelaboration..............................106

Figure2:Energy-EconomyfeedbackinMEDEAS.Ownelaboration..............................................107

Figure 3 : GDP and total oil extraction. Preliminary results of global MEDEAS model. Ownelaboration.....................................................................................................................................110

Figure4(KerschnerandCapellán-Pérez,2017):Simplifiedrepresentationofthedepletionofanon-renewableresourceintheabsenceofnon-geologicconstraints.Stocksandflowsofenergyrelativetotime............................................................................................................................................111

Figure5(Mediavillaetal.,2013):Integrationofdepletioncurvesinthemodel.(a)SDmodel.(b)Acurveofmaximumextraction(solid)comparedwiththedemand(dashed).................................113

Figure6 (Capellán-Pérezet al., 2014):Non-renewableprimaryenergy resources availability: (a)depletioncurvesasafunctionoftimefromtheoriginalreference;(b)curvesofmaximumextractionin function of the RURR as implemented in the model. The y-axis represents the maximumachievableextractionrate(EJ/year)infunctionoftheRURR(EJ).Foreachresource,theextremeleftpoint represents its URR. As extraction increases and the RURR fall below the pointwhere themaximumextractioncanbeachieved,theextractionisforcedtodeclinefollowingtheestimationsofthestudiesselected(panel(a)).TheRURRin2007foreachresourceisrepresentedbyarhombus.........................................................................................................................................................114

Figure7:Totalelectricsolarproduction(TWe).Inthisfigurewerepresentthedynamicsofsolardeploymentconsideringavery rapidgrowthof solar (+19%).Whilebeing far fromthepotentiallimit,exponentialgrowthdrivesthegrowthofnewsolarpower.Asthetotalsolarpowerinstalledincreases, the depreciation of infrastructures becomes significant. Finally, just 15 years afterreachingthemaximuminstallationrate,95%ofthepotentialisachievedin2065......................122

Figure8:EnergylossesasafunctionofCO2concentrationsduetoclimateimpactsimplementedinthecurrentversionofMEDEAS......................................................................................................125

Figure9:GDPandPopulationbehavior.Ownelaboration...........................................................127

Figure10:GDPpercapitabehavior.Ownelaboration..................................................................128

Figure11:Totalprimaryenergydemandbehavior.Ownelaboration..........................................128

Figure12:Oilextraction.Ownelaboration...................................................................................129

Figure13:Evolutionofelectricenergy.Ownelaboration.............................................................130



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Figure14:Installedpowerinrenewableresources.Ownelaboration.........................................131

Figure15:Resultsonclimatechange.Ownelaboration...............................................................132