2°CPORTFOLIOASSESSMENTDOCUMENTATION

version1:10/11/2016

1. BACKGROUND 3

2. SCENARIOS 4

2.1 OVERVIEWOFSCENARIOS 42.2 OVERVIEWOFSCENARIOINDICATORSUSEDINTHEMODEL 52.3 APPLICATIONOFECONOMICSCENARIOSTOASSETCLASSES 6

3. DATA 7

3.1 OVERVIEW 73.2 ASSETDATA 73.3 PORTFOLIODATA 83.4 FINANCIALDATA 83.5 MODELLEDDATA 8

4. FINANCIALASSETALLOCATIONRULES 10

4.1 OVERVIEW 104.2 PRIVATEANDLISTEDEQUITY 104.3 BONDSANDOTHERCREDITINSTRUMENTS(EXCEPTFORSOVEREIGNBONDS) 104.4 SOVEREIGNBONDS 104.5 REALESTATEANDINFRASTRUCTURE 104.6 OTHERASSETCLASSES 10

5. MODELASSUMPTIONS 11

5.1 OVERVIEW 115.2 PRINCIPLESAROUNDAPPLYINGMACROECONOMICTRENDSANDSHOCKSTOFINANCIALPORTFOLIOSAND

COMPANIES 115.3 DEFININGTHE2°CMARKETBENCHMARK 135.4 2°CALIGNMENTTEST–TECHNOLOGYEXPOSURE 145.5 2°CALIGNMENTTEST–FORWARD-LOOKINGCARBONFOOTPRINT 145.6 2°CALIGNMENTTEST–INVESTMENTGAPANALYSIS 145.7 2°CALIGNMENTTEST–REVENUEGAPANALYSIS 14

6. CAVEATSANDFURTHERRESEARCH 16

7. BIBLIOGRAPHY ERROR!BOOKMARKNOTDEFINED.

8. ANNEX1–DATA 17

8.1 POWERDATATHROUGHGLOBALDATA 178.2 AUTOMOTIVEDATATHROUGHWARDSAUTOFORECASTSOLUTIONS 198.3 OIL&GASDATATHROUGHGLOBALDATA. 19

9. ANNEX2–MODELLEDDATA 20

1. BackgroundThefollowingprovidesdocumentationonthe2°Cportfolioassessmentdevelopedbythe2°InvestingInitiativeinthecontextoftheSustainableEnergyInvestingMetricsproject,fundedbytheEuropeanCommissionandinvolvingWWFGermany,WWFEuropeanPolicyOffice,CDP,ClimateBondsInitiative,UniversityofZurich,FrankfurtSchoolofFinance,Cired,andKepler-Cheuvreux.

Theobjectiveoftheassessmentframeworkistomeasurethealignmentoffinancialportfolioswith2°Cdecarbonisationpathways.Specifically,theframeworkquantifiesafinancialportfolio’sexposureto a 2°C benchmark in relation to a series of climate-related technologies. The result is thus amisalignment indicator thatmeasures theextent towhichcurrentandplannedassets,productionprofiles,investments,andGHGemissionsarealignedwitha2°Ctrajectory.

The model involves a number of key features and distinguishing characteristics that are brieflysummarizedbelow:

• The model does not pre-define macroeconomic trends or shocks, but rather creates a‘translation software’ thatmaps forecastedmacroeconomic trendsand shocks to financialportfolios.Itthusdoesn’trelyondevelopingtheseeconomictrendsthemselvesandcanbeusedtotestanymacroeconomicassumption.

• Themodel assesses the 2°C alignment of financial portfolioswith a 5-year time horizon /forecastperiod.Thetimehorizonislimitedtothetimehorizonofcapitalexpenditureplanningforwhichdatacanbetrackedatameaningfullevel.Morelong-termassessmentsareplannedas the model gets extended to risk-related indicators, requiring a set of additionalassumptions.

• Themodelassessesthe‘technologyexposure’ofportfoliosacrossarangeofclimate-relevantbusinesssegmentsandsectors.Atthisstage,itcoversfossilfuels,power,andtransport(light-passengerdutyvehicles,airplanes).Indicatorsareconsideredeitherin‘aggregateexposure’terms or ‘trajectory terms’ (i.e. investments, asset additions / retirements, changes inproductionprofiles).

• Themodelsources,wherepossible,forward-lookingasset-leveldataforkeytechnologiesinordertoprovideforgeography-specificassessmentsforspecificbusinesssegments. It thusbypasses wherever possible backward-looking, corporate level reporting, although suchreportingcanbeusedforvalidatingforward-lookingparameters(e.g.GHGemissions).

• The model develops financial market specific, science-based benchmarks that compareportfolioperformancenot just toexistingmarketbenchmarks,butbenchmarksassociatedwithdecarbonisationpathways.

• Themodelfocusesonassessingspecificassetclasses,withtheassessmentatthisstagelimitedtocredit instruments(inparticularcorporatebonds)andequity(inparticular listedequity,althoughitcanbeappliedtoprivateequity).

• Givenitsemphasisontechnologiesandclimate,theanalysisislimitedtothosepartsoftheportfoliowithdirectexposuretotherelevanttechnologies.Itthusonlycoversaround20-30%of theaverageportfolio in termsofAUM,althougharound70-80%of theportfolio’sGHGimpacts.

2. Scenarios

2.1 OverviewofscenariosAsoutlinedabove,thebenchmarkappliedinthemodelreliesonatranslationofanenergy-technologyscenarios into benchmarks for financial portfolios. Themodel by itself does not prescribe specificenergy-technologyscenarios,butallowsforarangeofscenariostobeusedasbenchmarks.Thechoiceofwhichisusedcancriticallyinfluencetheresultsoftheassessmentandthusanassessmentcanbemade against both individual scenarios or all available scenarios to show a range of possibledecarbonisationpathways.

2°Cassessmentframeworksrequirescenariosthatcommentonthetrajectoryofproduction,assets,and investment in the real economy. The granularity of the scenario will then determine thegranularityoftheassessment.Thus,scenariosthatprovidedatapointsbyregionallowforassessmentatregionallevel.

A rangeoforganizationshavedevelopedenergy-technology scenarios that canbeusedas inputs.NotableexamplesincludetheInternationalEnergyAgency450Scenarioand2Dscenario,aswellasGreenpeaceandIRENEA.Thesescenariosprovidefora50%probabilityofachievingtheglobalpolicyobjectiveoflimitingglobalwarmingto2°Cabovepre-industriallevels.Thescenariosareglobalwithatimehorizonof2040 (450Scenario) and2050 (ETP) respectively. The450 scenario coverselectricpowerandfossilfuels,withthe2Dscenarioprovidingformoredetailonothersectors(e.g.transport,cement, etc.). Both scenarios are thus used in the assessment as the ‘default’ scenarios in theassessment,withalternativescenariosbeingintegratedintotheassessmenttool(e.g.Greenpeace,Cired,etc.).

Scenariosdifferinthefollowingelements:

• Scenarios will reflect different levels of ambition regarding the decarbonisation of theeconomy;

• Scenarioswillinvolvedifferentspeedsaroundwhichdecarbonisationtakesplace,withsomeassumingamoreaccelerated,linear,andshort-termadjustmentsandothersassumingmorelong-termdisruption;

• Scenarioshavedifferentcoverageintermsofgeographiesassessed,bothintermsofabsolutecoverageandtheresolutionofgeographicspecificities;

• Scenariosreflectdifferentprogressincertaintechnologies(e.g.nuclear,carboncaptureandstorage,etc.);

• Scenariosprovide fordifferent timehorizons,withsomescenariosasshortas5yearsandotherscalculatingdecarbonisationpathwaysoverseveraldecades.

2.2 Overviewofscenarioindicatorsusedinthemodel2.2.1 Overview

The indicators extracted from the scenarios to inform the model at this stage are either assetindicators (e.g. installed capacity) or production indicators. In theory, indicators could also beextendedtoinvestmentindicators,althoughthelackofannuallyupdated,technology-specific,globalinvestmentroadmapscreateabarriertousingtheseasbenchmarks.Inaddition,investmentfiguresareassociatedwithhigher levelsofuncertaintygiven theuncertaintybotharound the technologypathwayitselfandthecostsassociatedwithdifferenttechnologydeploymentswithinthesepathways.

2.2.2 Regions&TimehorizonData for the scenario pathways are extracted with a 25-year time horizon. For the 2°C portfolioassessment, the actual assessment is limited to a 5 year timeperiod (see p. 11). Scenario data isextractedfor theregionsprovidedbythescenarioproviderandthenaggregated into fiveregions:Global,OECD,Non-OECD,USA,andEurope.Furtherdetailispossibleandcanbeappliedtothemodel.

2.2.3 CompletingmissingdataDatapointsfrompubliclyavailablescenariosusuallyarepresentedin5yearintervals.Missingdataisinterpolatedusingalinearfunction.Afunctionwithmoredegreesoffreedomcouldbeappliedasanalternativemodellingdecisionto‘smooth-out’thetransitionbetweendatapoints.

2.2.4 ElectricPowerTheextracteddatafromthescenariosforelectricpowerisinstalledcapacityandCO2emissionsbyfuel/technology.Thedifferentfuelcategoriesarecoal,gas,oil,nuclear,hydropower(largeandsmall-scale),andrenewables.RenewablesisanaggregatedcategoryinvolvingsolarPV,CSP,windpower,biofuels,andgeothermal.Theaggregationdecision isa functionof reducingthecomplexityof theresultswhilestillmaintainingresolutiononhydropowergivenitsdifferentlevelofsocietalacceptancein some countries. Annex 1 shows how different fuels aremapped to these categories, althoughscenariosdon’tusuallyprovidethisgranularity.

2.2.5 AutomobileTheextracteddatafromthescenariosforautomobileatthisstageislimitedtolightpassengerdutyvehicledatabydrivetrain.Itdistinguishesthreecategories:electricvehicle(whichincludeextendedrange electric vehicles), hybrid (which includes plug in and conventional hybrids), and internalcombustion engine (which includes diesel, gasoline/petrol, compressed natural gas, and liquefiedpetroleumgasvehicles).Whilefuelcellandothertypesofdrivetraindataisalsoextracted,thecurrentmarginalproductiondoesnotallowforameaningfulassessment.Inaddition,whereavailable,fuelefficiencyestimatesarealsoextractedthatcanbeintegratedintotheassessment.

2.2.6 Oil&gasTheextracteddatafromthescenariosforoil&gasareproductionprofilesbyregion.

2.2.7 CoalproductionTheextracteddatafromthescenariosforcoalisglobalcoalproduction.

2.2.8 OthersectorsAtthisstage,themodeldoesnotextractdataforothersectors.

2.3 Applicationofeconomicscenariostoassetclasses

2.3.1 OverviewThe basic framework of themodel is an application of economic scenarios to asset classes. Thisrequiresatranslationofthesescenariosforeachspecificassetclasses.Thistranslationisbasedondefining the market portfolio for the financial portfolio being assessed, which encompasses theinvestable universe for that portfolio manager (e.g. US listed equities, etc.). At this stage, thisinvestable universe is only constrained by the asset class and the region inwhich the investor isinvestedinanddoesnotprovideforfurtherconstraints(e.g.largecap,investmentgrade,etc.).

2.3.2 ListedEquityThemodeltakesasthebasisthecapacitymixandabsoluteexposureintheassetleveldatabases(seep.7)forthemarketportfoliorelatedtotheportfoliounderassessment.Thisiscalculatedintermsoftherelativeweight/absoluteexposureofthemarketportfoliotoeachindicatorinthemodel(e.g.fuelmix for power, oil production, etc.). The economy level build-out and decline rates are thenappliedtothatstartingpointtodefinethe2°Cbenchmarkforprivateequityportfoliosin2020.

2.3.3 PrivateequityThemodeltakesasthebasisthenon-listedcapacityintheassetleveldatabases(seep.7)tocalculatethestartingpointforprivateequityexposure.Theeconomylevelbuild-outanddeclineratesarethenappliedtothatstartingpointtodefinethe2°Cbenchmarkforprivateequityportfoliosin2020.

2.3.4 CorporatebondsThebenchmarkforcorporatebondsasanassetclassismoredifficulttoidentifysincebothlistedandnon-listed entities issue corporate bonds. At this stage, the benchmark thus simply reflects thecompany benchmark. More precise iterations will seek to model the corporate bonds universeexposure.

2.3.5 SovereignbondsThemodelcurrentlydoesnotcoveranassessmentofsovereignbonds.Foradiscussionofoptionsaroundassessingtheclimatefriendlinessand2°Calignmentofsovereignbonds,seeBeyondRatings/ 2° Investing Initiative (2016) “Sovereign bonds and the transition to a low-carbon economy:Measurementoptionsforinvestors”.

2.3.6 RealestateThe model currently does not cover an assessment of real estate. Analysis on this is underdevelopment in partnership with the Climate Bonds Initiative as part of the Sustainable EnergyInvestingmetricsproject.

2.3.7 InfrastructureThemodeltakesasthebasisthe‘utility-scale’intheassetleveldatabases(seep.7)tocalculatethestartingpointfortheinfrastructureexposureforthemarketinwhichtheinvestoris investedin, intermsoftherelativeweight/exposureoftheassetclasstoeachindicatorinthemodel(e.g.fuelmixforpower,oilproduction,etc.).Theeconomylevelbuild-outanddeclineratesarethenappliedtothatstartingpointtodefinethe2°Cbenchmarkforprivateequityportfoliosin2020.

2.3.8 HouseholdloansThemodelcurrentlydoesnotcoveranassessmentofhouseholdloans.Analysisisplannedfor2017.

2.3.9 OtherassetclassesAtthisstage,themodeldoesnotintegrateotherassetclasses.

3. Data

3.1 OverviewInadditiontothescenariodata(seep.4),themodelreliesonthreeadditionaldata inputsfortheanalysis.Thesearetransitiondataonentitiesexposuretoassets/production/investments,portfoliodataonwhichthemodelisapplied,andfinancialdata.

3.2 Assetdata3.2.1 Overview

The‘transitiondata’inthemodelrelies,whereverpossible,onbottom-up,physicalassetleveldata.Thesephysicalassetleveldatabasesaresourcedsector-specific.Foreachsector/technology,they,canbesourcedfromarangeofdifferentdataproviders.Thefollowingsummarizesthedatapointssourcedineachofthesedatabases.

3.2.2 ElectricpowerThe power data relies on current and planned installed power capacity by fuel. Current installedcapacityonlyconsidersactivecapacity.Themodelalsotracksplannedcapacitythatfallsintooneofthe following categories: "Announced", "Financed", "Partially Active", "Permitting", "TemporarilyShutdown", "Under Construction", "Under Rehabilitation &Modernization". The data is currentlysourcedfromGlobalData.PlattsWEPPisonepotentialalternative.

Thefuels identified inthedatabasearecategorized intothecategory“coal”,“gas”,“hydropower”,“oil”,“nuclear”,and“renewables”.SeeAnnex1foracategorizationofdifferentfuels

Dual-fuelpowerplantsarecategorizedaccordingtotheprimaryfuel.Thedatadoesnothaveaself-containedtimehorizon,butcoversroughlya5yeartimehorizonatameaningfullevel,afterwhichthenumberofprojectsinthepipelinedropssignificantly.

3.2.3 AutoTheautomobiledataatthisstagefocusesonpassengerlight-dutyvehicles.Datacanbesourcedfromanumberofdifferentdataproviders,withthecurrentiterationofthemodelrelyingonWardsAutoForecastSolutions.Thedataprovidescurrentandfutureproductionwithaneight-yeartimehorizon.Theforecastingisdonethroughmonitoringofcurrentandfutureproductionplantcapacity,vehiclepart shipments from suppliers, and vehiclemodel producing life cycles to givemonthly estimatedproductionatthemodelandplantlevel.

Themodelcurrentlypresentstheresultsusingfourcategories:Internalcombustionengine(includingpetrol,diesel,LPGandCNGvehicles),hybridvehicles(includingplug-inhybrid),fuelcell,andelectricvehicles.The resultsusuallyomit fuel cell vehicles in thepresentation,given themarginal tonon-existentpipelineofproductionofcarspoweredbyfuelcells.Additionaldatathat istrackedbythedataprovidercanbefoundinAnnex1.

3.2.4 Oil&gasTheoil&gasdataatthisstagefocusesonupstreamoil&gasproduction.Alternativeassessmentsextendingtoproductionassociatedwithcostcurves(seeLeatonetal.2015),typesofoilplays(e.g.tarsands,deepwater,etc.)couldbeenvisioned,butarecurrentlynotconsidered,giventhattheyarenotexplicitlyrepresentedinthescenarios.

While therearealsoanumberofdataproviders foroil&gasproductionGlobalData’splatform iscurrentlyused.Theplatformcoversupstream,midstreamanddownstreamproduction.Howeveronlyupstreamiscurrentlyassessedinthemodel.Theproductiondataisgatheredatthefieldlevelandis

aggregatedtotheglobalparentownerviaindividualequitystakesineachfieldbyGlobalData.Theproductionincludesabaselineforecastandanestimatedhighandrangeproductionforecast.Onlythebaselinescenario isusedfortheassessment. Inaddition,commoditypricedata isextractedtoinformtheadditionalmodellingfeatures.

GlobalData’supstreamanalytics includesdetailsonglobal fields including forecasts forcapitalandoperatingexpenditures,cashflows,andproductionscenariosbyfieldatcompany level.AdditionaldatathatistrackedbythedataprovidercanbefoundinAnnex1.

3.2.5 CoalThecoalproductiondataatthisstagesourcescompany-levelreportingprovidedonBloombergandislimited to coalproductionas reported in2015. This isobtained through theBloombergdata fieldFS377-Mining-TotalCoalMinedProduction(MINING_COAL_PRODUCTION_ACTUAL). Inaddition,commoditypricedataisextractedtoinformtheadditionalmodellingfeatures.

3.2.6 OthersectorsThemodelatthisstagedoesnotintegrateothersectors.

3.3 PortfoliodataTheportfoliodatarequiredfortheanalysisisthelistofuniquesecurityIDforeachhelpsharealongwiththetotalquantityheld.CurrentlyISINsareusedastheprimaryID,butotherIDssuchasCUSIPandSEDOLcanalsobeused.

Alternatively,tobenchmarkafund,theweightofeachsecurityinthefund,andthedatetheweightswerecalculatedcanalsobeused,althoughtheultimateportfolioownershipassumptionthendependsontheassumptionaroundthesizeoftheportfolio.Thisinformationallowsforareverse-engineeringaroundtheinformationrelatedtonumberofsharesintheportfolio.

3.4 FinancialdataThemodelextractsanumberofindicatorsfromBloomberginordertoinformthemodel’sfinancialassetallocationrules(p.10)andthemodelcalculationrules(p.11).Specificallythemodelrequiresextracting the followingdata fields for each security: ID_ISIN (alternatively ID_SEDOL1, ID_CUSIP),SECURITY_NAME, COUNTRY_ISO, EQY_FUND_TICKER, TICKER_AND_EXCH_CODE,ICB_SUBSECTOR_NAME,ADR_ADR_PER_SH,EQY_SH_OUT,EQY_FLOAT,PX_LAST. Inaddition to thisthetotalnumbersharesneedtobetakenoverallshareclassesthisisdonethroughtheBloombergExcelAPI,byusingthefollowingcommand:

{=SUM(BDH(BDS( “RDSA LN Equity","MULTIPLE_SHARE_INFO","Endcol=1","array=true")&"EQUITY","EQY_SH_OUT","30/12/2015","30/12/2015"))}.

HereRoyalDutchShellisusedasanexample,forthelasttradingdayof2015.

Toaccessthegloballistedmarketthatownerproductionintheselectedtechnologiesrequiresaccesstoasignificantnumberofsecurities.Forthisreason,thefinancialdataisupdateperiodicallyandiscurrentlyvalidasof30.06.16.

3.5 ModelleddataIn addition to the primary data sourced fromasset level databases, financial databases, companyreporting, andenergy technology scenarios, themodel also integrates ‘modelleddatapoints’ thatinformthefinalassessment.Thesedatapointsaresourcedfromeconomicdatabasesormodelledaspartofseparateanalysis.Modelleddataisnotpartofthebasic2°Calignmentanalysis,butmayinformsomeoftheadditionalelementscomputedinthecontextofthemodelapplication.Theuseofthese

datapointsthusdependsonthescopeoftheanalysisandtheextenttowhichallthepotentialfeaturesofthemodelareapplied.Specifically,atthisstagethemodelreliesonmodelledGHGemissionsdataappliedtoassets,doneinpartnershipwithEYandinvolvingopen-sourcecarbonfactorsappliedtoassetleveldata,investmentestimatesderivedfromcapitalexpendituredataavailableinasset-leveldatabasesor through thirdparty sources, revenueestimatesbasedoneconomic costsandprices.Annex2(p.20)providesanoverviewofthesedatabases.

Whiletheapplicationbythe2°InvestingInitiativeoftheassessmenttoolinvolvestheselectionofaparticularsetofmodelleddatasources,thesecouldofcoursebereplacedbydatapointsofsimilarqualityandscopeorexpandedupontoincludeadditionaldatapoints.

3.6 ConsolidationrulesDatasets forproductionandownershipof assets can report thisdata atdifferent levels along theownershiptree.Inmostinstancesthedataprovideraggregatesproductionfromallsubsidiariestoaglobalparentowner,anddoesthisbasedoftheequityshareprinciple.However,thisisnotalwaysthecase.

Forthepowersectorthedataisaggregatedtothesubsidiary level,ownershipofphysicalassets isattributedtoasubsidiarybasedontheirrespectiveequitystakeineachphysicalplant.Thisownershipis thenaggregated to theparentof the subsidiary as identifiedby thedataprovider, in this case,GlobalData,dependingonifthesubsidiaryisprivateorpubliclylisted.Forprivatesubsidiaries,100%ofassetsownershipattributedtothesubsidiaryisrolluptotheparentcompany.Forpubliclylistedsubsidiaries, the ownership of physical assets is allocated over both the floating and non-floatingportionofthecompany’stotalequity.Thenon-floatingportionoftheequityisattributedtotheparentcompany,andthefloatingportiontotherespectivestockexchange.

FortheAutomotivesectortheproductionvaluesareaggregatedtobrandofautomotiveproducer,aswellasuptotheultimateglobalbrandowner.Inthecaseofjointventures,whicharemostprevalentintheChinesemarket,theproductionfromthesemanufacturesisdistributedtothejointventure’sownersbytheirrespectiveequityshare.ThisdataforjointventureequityshareisobtainedthroughBureauvanDijk’sdatabase,Orbis.

Using industrydatabasesallocationofownershipandclassificationof companies listing statuscancreateatimelagfortheallocationofcapitaltothecorrectcurrentowner.Forthisreason,linkingtheindustrydatabasetoa3rdpartyplatform,suchasOrbis,couldincreasetheaccuracyoftheanalysis.Theaccuracyof industrydatabases reportingonequity sharesand listing status is currentlybeinginvestigatedbytheUniversityofZurich.

4. Financialassetallocationrules

4.1 OverviewInordertoassessfinancialportfolios,therealeconomicactivityidentifiedintheassetlevelorotherdatabases(p.7)needstobematchedtofinancialassets.Thisrequiresfinancialassetallocationrules,whichmaybedifferent fordifferentasset classes.The followingbriefly summarizes theallocationruleschosenbytheassessmentframework.

4.2 PrivateandlistedequityThemodelreliesonthe‘equityownership’approachtoallocateproductiontoindividualstocks.Therelativeownershipinaportfolioisthuscalculatedbasedonownershipofshares/#ofoutstandingshares.Thisusesstandardfinancialaccountingpracticesmodelledafterthepracticesdevelopedaspartoffinancedemissionsmethodologies.1

Illustrationcase:Ifportfolioowns1%ofonelistedutility’stotalequitywhointurnowned50GWofinstalledpowercapacityinEurope,theportfoliowouldbeallocated500MWofthiscapacity.Iffromthetotal50GW,theutilityowns7GWofrenewablepowercapacity,theportfoliowouldbeallocated70MWofrenewablepower.Ifthelistedutilityplanstobuildout1GWby2020thentheportfoliowillbeallocatedatotalof10MWofrenewablecapacityby2020.

4.3 Bondsandothercreditinstruments(exceptforsovereignbonds)Therearethreeoptionstoallocatingeconomicactivitytocorporatebonds:

• The'portfolioweightapproach’weightseachbond’sexposuretoacertaincompanybasedonthe weight the bond has in the portfolio (e.g. 1% weight in the portfolio will lead to anallocationof1%ofthecompany).

• The‘equity+debtownershipapproach’weightseachbond’sexposuretoacertaincompanybasedontheshareofthebondintotaloutstandingequityanddebt,thustreatingdebtandequityequallyinallocation.

• The‘financingapproach’seekstoquantifyeachbond’sexposuretoacertaincompanybasedon the estimated financing the bon provides to the company’s specific expenditures. Thisapproachthusseekstomapexpendituresdirectlywithabond,which,givendatalimitations,doesn’t,requiresasignificantsetofassumptions.

Themodelapplicationatthisstagecanbedoneusingeitherthefirstorthesecondapproach,withnocompletemethodologythatexiststhatwouldaccompanythethirdapproach.

4.4 SovereignbondsThe model currently does not directly integrate assessments for sovereign bonds. For furtherinformationaroundallocation rules, seeBeyondRatings / 2° Investing Initiative (2016) “Sovereignbondsandthetransitiontoalow-carboneconomy:Measurementoptionsforinvestors”.

4.5 RealestateandinfrastructureTheallocationofrealestatetofinancialsecuritiesreplicatestheprivateandlistedequityallocationrulesintermsoftherelativeownershipof

4.6 OtherassetclassesAtthisstage,themodeldoesnotintegrateassessmentsforotherassetclasses.

12°ii2013‘Fromfinancedemissionstolong-terminvestingmetrics.State-of-the-artreviewofGHGemissionsaccountingforthefinancialsector’

5. Modelassumptions

5.1 OverviewThe2°Cportfolioassessmentdevelopedbythe2°InvestingInitiativeinthecontextoftheSustainableEnergyInvestingMetrics(SEIM),2EnergyTransitionRisk&Opportunity(ETRisk),3andTragedyoftheHorizons4projects.Thefollowingsectionssummarizethekeymodellingassumptionsandcalculationrulesforeachtypeofassessmentperformedaspartofthe2°Cportfoliotest.

5.2 Principles around applying macroeconomic trends and shocks to financial portfolios andcompanies

Thecoremodellingchallengeassociatedwitheachtypeofassessment ismappingmacroeconomictrendsandshockstofinancialportfoliosandcompanies.Themodelsdevelopedherearethusdifferentinsofar as their key role is to map the exposure to and impact of economic trends to financialportfolios.

Themodel uses a simple ‘fair share’ assumption tomap these trends to companies and financialportfolios. This fair share assumption stipulates that economic impacts are mapped to financialportfoliosandunderlyingcompaniesbasedonthemarketsharetheseportfoliosandcompanieshaveinthetechnologyormarketthataffectedbythisimpact.

The future market share is calculated depending on whether the production exposure is set todecreaseorincreaseinthenext25yearsaccordingtothemacroeconomictrend.Iftheproductionismeanttoincrease,thefairshareiscalculatedbasedonthetotalmarketshareoftheproduct(e.g.installedcapacity,etc.).Thisapproachiscalledthe‘marketfairshare’.Iftheproductionismeanttodecrease,thefairshareiscalculatedbasedonthetotalmarketshareofthespecificfuel/technology(e.g.coalproduction,coalinstalledpowercapacity).Thisapproachiscalledthe‘technologyfairshare’.This distinction was chosen since applying market fair share to declining technologies can yieldnegativeresultseventually(sincethemarketsharecouldbehigherthanthetechnologyfairshare)andbecauseportfoliosthathave‘lagged’productionincreasesinthepastshouldn’tbeassumedtodosointhefuture.Intheory,themodelcouldapplythetechnologyfairshareforbothincreasinganddecreasingtechnologies,amodellingchoicenotmadeinthecurrentiteration.

Theuseofthefairshareapproachcouldbecontestedsinceitignoresimportantmarketrealitiesthatwill dictate how each individual company performs under different macro scenarios. Alternativeapproaches involve bottom-up assessments of each individual company. While this is technicallyfeasible, it ismuchmoreexpensive and technically complex.Analternativeoption foroil and gascompaniesistousecostcurvestomapimpactstolow-costandhigh-costproducers.Thechallengewith thisapproach isboth thequalityof thedataand the logicofassumingcostsare theprimarydrivers.Nevertheless,suchacostcurveapproachislikelytobemoreaccuratethanasimplefairshare2TheEUHorizons2020theSustainableEnergyInvestingMetrics(SEIM)projectiscurrentlyintheprocessofprovidingaframeworkforinvestorsandpolicymakerstotranslatehigh-levelclimatepolicygoals(e.g.limitingglobalwarmingto2°C)intoabenchmarkthatcaninformportfolioallocationtargets.Theconsortiumisledby2°iiandconsistsofCIRED,WWFGermany,KeplerCheuvreux,ClimateBondsInitiative,FrankfurtSchoolofFinance&Management,CDP,WWFEuropeanPolicyOfficeandtheUniversityofZurich.(Grantnumber:649982)3TheEUHorizons2020EnergyTransitionRisk(ETRisk)projectiscurrentlyintheprocessofprovidingstandardizedtoolsforassessingcarbonriskbytranslatingtheeconomicriskindicatorsaroundcapitalmisallocationintheeconomyintofinancialriskindicatorsforfinancialmarketactors.Theconsortiumisledby2°iiandconsistsofUniversityofOxford,KeplerCheuvreux,CarbonTrackerInitiative,InstituteforClimateEconomics(I4CE),TheCO-FirmandStandard&Poor´s.42°ii&TheGenerationFoundationhaveformedapartnershipwithaprojectentitled‘TragedyoftheHorizons’toexploreandaddressthenotionofthe'tragedyofthehorizon',describingthepotentialsuboptimalallocationofcapitalforthelong-termduetotheinabilityofthefinancesectortocapturelong-termriskswithshort-termrisk-assessmentframeworks.

assumption and can be applied to scale with given datasets where they include production costinformation.Itwouldhoweverbelimitedtofossilfuelcompaniesinitsapplication.

5.3 Definingthe2°CmarketbenchmarkThemodelinvolvesthreetypesofassessments.

• An‘absolute’2°Calignmentassessment,basedontheabsoluteweightofthetechnologyinaportfoliorelativetowhatwouldbeexpectedundera2°Ctransitionforthatportfolio’smarket;

• A‘relative’2°Calignmentassessmentbasedontheexposureoftechnologiesrelativetoeachotherintheportfolio;and

• A ‘trend’ 2°C alignment assessment that ignores the starting point exposure and onlymeasuresincrease/declinerates.

Eachoftheseassessmentsrequiresabenchmarkagainstwhichmisalignmentcanbetested.Thekeydistinctionforeachoftheseapproachesthenrelatestohowthemarketbenchmark’sstartingpointisdefined.Thebenchmarkconsistsoftwoelements,namelythecurrentpoint-in-timestartingpointandthe increase / decrease of the exposure over a 5-year time horizon. The 2°C benchmark is thencalculatedforthemarketportfolioandscaledtothesizeoftheportfoliobeingassessed.

Thestartingpointiscalculatedinthreedifferentways:

• Forthe‘absolute’2°Calignmenttest,thestartingpointcalculatesthetotalexposureofthemarketportfolio(i.e.investableuniverse)toeachtechnologyandscalesitonthebasisofthesize of the portfolio being assessed relative to themarket portfolio. Themodel currentlyappliestheabsolutealignmenttestforfossilfuels,forwhicharelative2°Calignmenttestisdifficultgiventhelackof‘green’technologiestobuildratioswithinthefossilfuelsector.Example:Ifthemarketportfoliocontains100GWofrenewablepowerandtheportfoliobeingassessedis1%ofthesizeofthemarketportfolio,thestartingpointtowhichtheincreases/decreasesareappliedusingthefairshareprincipleis1GW.

• For the ‘relative’2°Calignment test, the startingpoint is calculatedbasedon the relativeweight of different technologies in the market portfolio within one sector. The modelcurrentlyappliesthe‘relative’2°Calignmenttestforelectricpowerandautomobile.Example:Ifthemarketportfolio’spowermixatthestartingpointoftheassessmentincludes20%renewablepower,the20%shareisappliedtothetotalpowerexposureintheportfoliobeingassessed,independentofwhattheabsoluteexposuremaybe.

• Forthe‘trend’2°Calignmenttest,thestartingpointoftheportfoliobeingassessedisusedasthebasisandonlytheincrease/decreasebasedonthefairshareprincipleisapplied.Example: If the portfolio has only 1% renewable power rather than20%as in themarketportfolio,thestartingpointis1%forthebenchmark,buttheincreaserateisappliedbasedonthetotalshareinelectricpowerinthemarket.

5.4 2°Calignmenttest–TechnologyexposureThe 2°C alignment test – Technology exposure assessment involves themodelling steps outlinedbelow, supported visually by the associated figure applied as an example to calculating the 2°Calignment test – technology exposure to renewable power. The example used here applies the‘relative’assessment,couldhoweverbeadaptedfortheothertwotypesoftests.

First,themodelassessestheexposuretorenewablepoweratthestartingpointoftheassessment,withtheportfoliorepresentingtheblueline①andthenmeasurestheplannedincreasedexposurebasedonbottom-upassetleveldatabases②.The2°Cbenchmark(thegreenline)isthencalculatedbyscalingtherelativeexposurestotheportfoliosize.Thus,ifthetotalportfolioownershipofelectricpoweris16GWandtheweightofrenewablepowerinthemarketportfolioin2015is20%,thestartingpointis3.2GW(20%of16GW)③.(NB:Asanillustrativeexamplefortheabsoluteassessment,iftheportfoliowas3.2%ofthemarketportfolioandtherenewablepowerownershipbythemarketportfoliowas100GW,thenthestartingpointwouldbe3.2GW).Theslopeofthegreenlinecontinuingfromthatstartingpointisthenbasedonthefairshare‘marketprinciple’④.Thedifferencebetweenthegreen line and the blue line in 2020 informs the 2°C alignment test⑤. This difference can beexpressedineconomicunits(e.g.GW)orpercentageterms.Theeconomicunitsarehelpfultoinformonsometypeof‘real’impactwhereasthepercentagetermshelpcreatecomparabilityacrossfinancialportfoliosofdifferentsizes.Theresultscanbecalculatedataggregateportfoliolevelandforregionalexposureswithintheportfolio.

5.5 2°Calignmenttest–Forward-lookingcarbonfootprintThis test applies the results calculated in economicunits and translates them intoGHGemissionsindicatorsusingtheforward-lookingGHGemissionsfactorsdevelopedbythe2°InvestingInitiativeinpartnershipwithEY.

5.6 2°Calignmenttest–InvestmentgapanalysisThistestappliestheresultscalculatedineconomicunitsandtranslatesthemintoinvestmentgap/over-investmentfiguresusinginvestmentfactorssourcedfromasset-levelandeconomicdatabases.

5.7 2°Calignmenttest–RevenuegapanalysisThemodelappliestheresultscalculatedineconomicunitsandtranslatesthemintorevenueimpactfiguresusingrevenuefactorssourcedfromasset-leveldataandeconomicdatabases.

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CaveatsandfurtherresearchThefollowingbrieflysummarizessomeofthemaincaveats

• Powerplantcapacityisallocatedbasedonthebasisofequityshareintheplantwheremultipleownersexist.Wheredataisavailable,productionandcapacityindicatorswereallocatedtoownersofsubsidiariesbasedontheequityshareprinciple.Forpowercapacity,GlobalData’sinternal listofplantownersandsubsidiarieswereutilized,with100%ofcapacityallocatedfrom subsidiary to parent. For the automotive data there a number of joint ventures,particularlybetweenOECDandChinesefirms.Theproductioncapacityisdividedbytheequityshare. Oil and Gas production is allocated based on equity stake at field level, and isaggregatedtoparentcompaniesbyGlobalData,andcoalproductionistakenfromcompanyreporting.

• The planned renewable capacity additions captured in the GlobalData database decreasestartingin2019.Therearethuslikelytobeanumberofprojectsthatutilitieswillplanoverthe time horizon that are not yet captured in the database. The results thus under-staterenewableexposure.Moreover,around10%ofthecurrentdataiscurrentlynotallocatedtoanowner/developer.

• Retirements are currently not forecasted for coal, given data constraints. Thus, 2020estimatesofcoalpowercapacitymaybeoverestimatedtotheextentthatsuchcapacitywillberetiredbythen.

• Another challenge relates to the fair share assumption for renewables. Listed utilitiescurrently have a lower share of renewable capacity than the total market. This can beinterpretedtobeahistoricalfluke(i.e.listedutilitiesarelate-comers)orassomeformoflistedutilitybiasthatwillremainevenina2°Ceconomy.Themodelappliedhereassumesthefirst.Alternatively, themodel could be developed assuming the second, where the 2°C equitybenchmarkiscalculatedbyapplyingthe‘fairsharetechnology’approachtoalltechnologies.Bothoptionswillbereviewedinfurtherdetailinthenextiterationsofthemodel.Atthisstage,the ‘fairmarket share’was chosen given that there is no scientific basis to assume listedutilitiesshouldbelessinvolvedinbuildingoutrenewables,whereindeedsomelistedutilitiesexceedtheir‘fairmarketshare’approach.Moreover,thefairmarketshareapproachshowsmore accurately potentialmisalignmentwith broadermarket trends,whichwould impactmarketshareoverall.

6. A listof the ISINs from the testedportfolio thathavebeencapturedby themodelingandallocatedproductionvaluesbasedontheaforementioneddatabases,canbeprovideduponrequest.2iicannotdoesnotguaranteethatallapplicableISINswithinthetestportfoliohavebeenmatchedtotheirassociatedproductionvalues,orthatallsubsidiarieshavebeenmatedtoparentcompanies.

Annex1–Data

6.1 PowerdatathroughGlobaLData

ThefollowingsummarizesthematchingofdataforfuelsusedforpowerproductiontocategoriesforGlobalData:

Coal Gas Hydro Nuclear

Oil Renewables

Anthracite BlastFurnaceGas

Hydropower

Nuclear

Asphalt Agricultural By-Product

Anthracite,Bituminous

CityGas AutomotiveDieselOil

AnimalWaste

Asphaltite CoalBedMethaneGas Automotive GasOil

Bagasse

Bituminous CoalMineGas Bitumen Bio-WasteBituminous,Subbituminous

CoalMineMethane BunkerOil IndustrialWaste

BlackCoal CoalSeamGas CorexGas LandFillGasBrownCoal CoalSeamMethaneGas CrudeOil Landfill-GasCoal CoalbedMethane Diesel Municipal Solid

WasteCoal,Lignite CokeGas DieselOil SewageGasHardCoal CokeOvenGas DistillateFuelOil WasteWaterLignite CompressedNaturalGas DistillateOil WoodBy-ProductLowSulphurCoal FlareGas DistilledFuelOil (blank):MiddlingCoal FlueGas FuelOil BiopowerPeat FuelGas FuelOil,Diesel BiopowerPetCoke FuelOil FurnaceOil GeothermalPetroleumCoke Gas GasOil GeothermalPulverizedcoal GasOil HeatingOil OceanPulverizedLignite IndustrialGas HeavyCrudeOil OceanThermalRawLignite IndustrialWasteGas HeavyFuelOil TidalRefinedCoal LiquefiedNaturalGas Heavy Fuel Oil,

DieselWave

SoftCoal LiquefiedNaturalGas,NaturalGas Heavy FurnaceOil

Solar

SpongeCoke LiquefiedPetroleumGas HeavyOil SolarCPVSubbituminous LiquifiedNaturalGas High Speed

DieselSolarCSP

Sub-Bituminous/Bituminous

LiquifiedPetroleumGas High SpeedFurnaceOil

SolarPV

Syngas LiquifiedPropaneGas HighSulphurFuelOil

Wind

WasteCoal Methane JetFuel Wind MethaneGas Kerosene

Naptha LightCrudeOil NaturalGas LightDieselFuelOil NaturalGas,Diesel LightDieselOil NaturalGas,FurnaceOil LightDistillateOil NaturalGas,PetroleumGas LightFuelOil NaturalGas,Propane LightOil NaturalGas,Diesel LiquefiedFuelOil NaturalGas,Oil LiquifiedFuelOil NaturalGas/Diesel LowPourFuelOil Off-Gas LowSulfurDistillateFuelOil OtherGas LowSulfurHeavyFuelOil PermeateGas LowSulphurFuelOil PetroleumGas LowSulphurHeavyFuelOil Propane LowSulphurHeavyStock RefineryGas LowSulphurHeavyStockFuelOil RegasifiedLiquefiedNaturalGas LowSulphurHeavyStockOil ResidualGas LowæSulphuræFurnaceOil Syngas Low-sulfurKerosene WasteCoalMineGas MarineDieselFuelOil WasteGas MarineDieselOil MarineFuelOil Mazout Naphtha Naptha Oil OilShale Oil,Coal Orimulsion PetCoke ResidualFuelOil ResidualFurnaceOil ResidualOil Syngas Ultra-lowSulfurKerosene WasteOil

Thedatafieldsretrievedfromthedatabaseare listedbelow.Thefields inboldarecurrentlybeingusedwithintheanalysis.Technology,PowerPlantName,SubsidiaryAssetName,Fuel,PrimaryFuelSecondaryFuel,Region,Country,StateOrProvince,County,CityorTown,TotalCapacity(MW),ActiveCapacity(MW),PipelineCapacity(MW),DiscontinuedCapacity(MW),TypeofPlant,Owner,OwnerStake(%),OwnershipYear,Operator,Developer, EPC Constructor, Power Purchaser, CapexUSD, Efficiency, Latitude, Longitude, CCS/CCUSTechnology,AnnualOutput,CapacityFactor,CO2Emissions ( Tonnes per annum), NOx Emissions (Tonnes per annum), SOx Emissions (Tonnes perannum),NOxControlSystem,NOxControlSystemManufacturer,SOxControlSystem,SOxControlSystemManufacturer,Status,YearOnline.

6.2 AutomotivedatathroughWardsAutoForecastSolutionsThedataplatformincludes,butisnotlimitedto,thefollowingfieldswiththefieldsinboldcurrentlybeing usedwithin the analysis:monthly production volume, vehiclemanufacturer,brand owner,brand,vehicleplatform,vehicleprogram,stateofproduction,enddateofproduction,nameplate,vehiclesegment,vehicleassemblyplant,plantcountry,powertraintype,primaryfueltype,numberof cylinders, aspiration, displacement,max voltageand transmission type.Oil&Gasdata throughGlobalData.

Thedataplatformincludes,butisnotlimitedto,thefollowingfieldswiththefieldsinboldcurrentlybeingusedwithintheanalysis:AnnualProduction,FieldName,Region,Country,ConstituentEntity,FieldTerrain,FieldStatus,ResourceType,Basin,Participants,Operator,AssociatedBlock,AssociatedLicense,ReserveStatus,1pReserves(boe), 2p Reserves (boe), 3p Reserves (boe), RecoverableReserves (boe),RemainingReserves (boe),PrimaryTrapType,Gravity, SulphurContent,HydrogenSulfideContent,Formation,FormationRockType,FacilityType,CalorificValue,County,RemainingNPV,IRR%,CapitalExpenditureper(boe),GasOilRatio,Paybackyears,OperatingExpenditure(perboe),NPV(perboe),Remainingbreakevenoilprice(perbbl),Remainingbreakevengasprice(permcf),Fullcyclebreakevenoilprice(perbbl),Fullcyclebreakevengasprice(permcf),TotalCapitalExpenditure,FullCycleNPV,RecoveryFactor,CarbonDioxide.

7. Annex2–ModelleddataTypeofdatapoint Source UseFutureemissions 2°ii/EY Oilandgasprices International Energy

Agency