3.nbim london 2018 - morning -physical...

PhysicalrisksLondon,January19,2018

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Contents

1

BIASINREPORTING2

3 CLIMATEEXTREMES

4 TAILINGSDAMSFAILURES

5 CUMULATIVEWATERPOLLUTIONEFFECTS

WATERUSEANDCOSTS

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1

BIASINREPORTING2

3 CLIMATEEXTREMES

4 TAILINGSDAMSFAILURES

5 CUMULATIVEWATERPOLLUTIONEFFECTS

WATERUSEANDCOSTS

3

WaterUseandCosts

Concern:Increasingscarcity,competitionandconflictà increasinglongrunCAPEXandOPEXforwatermanagementà reducedIRRà assetstranding,especiallyasmetalpricesdropFindings:• Significantvariationsinwateruseandwastewater/tonproduced

• Decliningoregrades=moreprocesswateruse• Trendstowardsre-useanddesalinationinaridregions,andproducedwateruseinhumid

regions• CAPEXandOPEXtypicallyvaryfrom5to10%oftotalproductioncosts,and

efficiency/technologyimprovementssuggestlongruncostcurveswillhold• LongrunCu/Augolddemandcurvestrendupfasterthanprojectedincreaseinwatercosts

asafractionofproductioncosts• Longrunà industrycostcurverisesà newdemand-supplyequilibrium• WRIAqueductScarcityRiskIndexdoesnotpredictNAVorCreditRating

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WaterUseandCosts

CopperPrice(NASDAQ) Notetheover100%variationincopperpricesover5yearsandyearoveryearvariationsof+/-50%

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WaterUseandCosts

• Accordingtothe studybytheChileanCopperCommission,minelevel cashcostsatChile's19largestminesfelltoanaverageof$1.285perpoundduringthefirstthreemonthsoftheyear,down13.3%ornearly20capoundfromthesamequarteroflastyear.

• …improvedminemanagement,lowercostsforelectricity,servicesandshippingandlowertreatmentandrefiningchargedbysmelters.Thetrendoffallinggrades,coupledwithincreasingwatercostsinChilemakesthecostcuttingevenmoreremarkable.

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1

BIASINREPORTING2

3 CLIMATEEXTREMES

4 TAILINGSDAMSFAILURES

5 CUMULATIVEWATERPOLLUTIONEFFECTS

WATERUSEANDCOSTS

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BiasinReporting– ReclamationCostDisclosureAnalysisConcerns:Ifminingcompaniessystematicallyunder-reportreclamationcosts,thenlongterminvestorsmayfacesignificantresidualfinancialandreputationalliabilities.• Companiesmayengageinstrategicbehaviortoavoidcoveringactualreclamationneedssince

theywerenotbudgetedordisclosed.• Dobiasesinthisaspectreflectsystematicbiasesinotherdisclosure?Findings:• Alongitudinaldataonreclamationcost,reserves,productionandothereconomicfactorswas

derivedfromquarterlyreports.• Regressionmodelshowsthatcontrollingforchangesinproduction,reserves,inflationand

otherfactors,the%ofremaininglifeofmineemergesasasignificantpredictorofreportedreclamationcostsà earlyestimatesaresignificantlybiasedlower.

• ComparisonswerealsomadewiththeEPA’srecentlyreleasedmodelwhichonlyusesasingledisclosureofcostsbyacompany,andfocusesonameanvalue.

• Difficulttocompiledataonactualreclamationcostsvsearlierestimates,butwerecommendreclamationbondsreflect90%probabilitycoveragebasedonuncertaintyestimates

DatabaseandRegressionModelDevelopedavailable.Appliedtoestimate/predictdegreeofsystematicunder-reportingofReclamationcosts

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• Miningcompaniesarerequiredtoestimatereclamationcostspriortothecommencementofconstructioninorderto:• Allowmanagementandinvestorstoassesstheoveralleconomicsofagivenprojectandprovideregulators

• Allowlocalstakeholderstheopportunitytoensureassetscanberehabilitatedresponsiblypriortoanymajorimpacttakingplace

• Thesecompany-formulatedestimates(compiledwiththeassistanceofcompany-engagedthirdparties)areincorporatedinto:• feasibilitystudywork(whichareoftenthebasisforprojectsanctioningbymanagementandinvestors)

• environmentalimpactassessments(whichoftenarethebasisforminepermittingapplications)• Dependingonthejurisdiction,reclamationbondsareoftenrequiredtoensuremandatedpost-miningclosureactivitiesarecompliedwith

• Inrecentyears,regulatorshaveattemptedtostandardizeminereclamationplansincludedinfeasibilitystudies(underNI43-101,JORCandSAMREC)toallowformoretransparencyandconsistencyacrosscompany-levelreporting

• Reclamationcostestimatesareamongtheeasiestassumptionstomis-specifygiventhattheyarethefurthestawayfrombecomingareality

BiasinReporting– Background

BiasinReporting– Data

ExampleVariables:

Companylevel:•CompanyOwner•OwnerLocation

Projectlevel:• PrimaryCommodity• Locationofthemine• Minetype

Reportlevel:• Expectedremainingclosurecost

• Expectedremainingminelife• Expectedremainingproduction

• TotalExpectedProduction• TotalExpectedClosureCost• %LifeofMine• %Production• Reserves• Costproductionratio

KeyDataSources

Variable Source

ClosureCostEstimates CompanyTechnicalReports(SEDAR,EDGAR,ASXwebsites)

Mine/CompanySpecificFactors

SNL

MacroeconomicIndicators

Bloomberg,Factset

VariableSummaryAnalysis

Variable Number

NumberofCommodities 43

NumberofProjectsConsidered 74

NumberofReports 157

NumberofCompanies 65

ExampleCompanyClosureCostEstimates

Company Project Original 1st update 2nd update 3rd update 4th update

AsankoGold,Inc. EsaaseGoldProject $20.00201005

$20.00201012

$20.00201102

$29.00201109

$29.60201305

• Comparesthefirstclosureestimateonaprojecttothelastavailableclosurecostestimateonaproject

• Ofthoseprojectswithmorethanoneclosurecostestimate:• 61%showedanincreaseinclosurecosts• 24%showedadecreaseinclosurecosts• 15%remainedthesame

• Nearly20%ofprojectsfromthefirstreporttothelastreportincreasedbymorethan2x

• Otheranalysesperformedwere:• ChangeinClosureCostvs.MineLife• ChangeinClosureCostvs.LOM%• ChangeinClosureCostvs.Production%

BiasinReporting– Methodology/Results

IncreaseinClosureCostNearertoEndofMineLife

BiasinReporting– FittedModel• Producesamodeltoestimateclosurecostestimatesbasedoncompanylevelandminespecificfactors(aswellastemporalfactors)usingregression

• Keyconclusionsare:• Remainingminelife(time.perc)isasignificantvariable,implyingthatestimatesincreaseastheendoftheminelifebecomesnearer

• Minelocation,ownerlocationandprimarycommodityaresignificantvariables– thisislikelyduetomorelaxregulationsandlowerlaborcostsincertainjurisdictions

• Productionandreservesarestatisticallysignificantinpredictors– movingmorematerialrequiresgreateramountsofremediation

Contents

1

BIASINREPORTING2

3 CLIMATEEXTREMES

4 TAILINGSDAMSFAILURES

5 CUMULATIVEWATERPOLLUTIONEFFECTS

WATERUSEANDCOSTS

LOWPROBABILITY/HIGHIMPACTEVENTS

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Databaseandexposureestimation/rankingAppDevelopedandavailable.AppliedtorankcompaniesintermsofVARorcVAR exposure,andforRealoptionsModel

April 17, 2016: “Codelco, the world's top copper producer, said the rains forced the Chilean state-owned miner to suspend production at its century-old underground El Teniente mine, likely leading to the loss of 5,000 tonnes of copper production.”

10 year 1 day event ?

Mine infrastructure is designed for a certain level of flood or drought risk. Insurance may cover the residual risk with a payout limit.Assumption: data used to compute the probabilities is representative of the future.

Unfortunately, climate risk exhibits regime like behavior èDesign risk estimate may be out of phase with operation period risk

Climate risk exposure is also spatially correlated over a business cycle = Elevated Portfolio Risk

Daily Flow of the Mapocho River near Santiago, Chile

ClimateExtremes– MinesiteandPortfolioRisk,anditsChangeoverTime

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ClimateExtremes– RiskClustering

• Regardingwaterandclimate,thisresidualriskisafunctionofclimatecyclesintime,spatialstructureofclimateevents,anddatarecordlength

• Toaddresstimeclusteringlongdatarecordsareneeded• Toaddressspatialclusteringattheportfoliolevelglobaldatasetsarerequired• Oneclassofdatasetscanbeleveraged:NOAAandECMWFreanalysis

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ClimateExtremes– FrameworktoThinkaboutClimateRisk• MinesusestandardstodesignfacilitiesforaT-yearfloodsanddroughts.Often

T=10,100or1000yearssuggestingahighdegreeofprotection• Foraminewitha30(50)yearlifethiscouldmeanafailureprobabilityoverthe

lifeofthemine=0.96(0.995),0.26(0.39),0.03(0.05)respectivelyFailureprobabilitycanbehighoverminelife

• GiventheshortrecordsusedtoestimateT,thereisahighuncertaintyintheestimateofTthatisusedfordesign.• Climateisnon-stationaryandregimelike:

• AnygivennyearsofdatamaygiveahighlybiasedestimateofTforthenextnyears• Highunder/overdesignrisk

• Acrossaportfolioofassets,spatialcorrelationinfailureoccurrenceisaconcernthatisnotaddressedindesign,butisimportantfortheinvestor

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ClimateExtremes– AnalysisSet-up

Measure Riskassociated Potentialsources Variables

x-dayeventwithreturnlevelT - Localizedflooding - Reanalysis - Precipitation

- Storms - IPCC - Windspeed- Heatwaves - Stationnetworks - Temperature

Indices,e.g.PDSI&SPEI,Heatindex - Regionaldrought - Academics- Paleoclimate Data

- Precipitation- Potentialevapotranspiration

- Regionalwetevent - Reanalysis

- Heatwaves - DroughtAtlases - Evapotranspiration

- IPCC - Temperature- Stationnetworks - RelativeHumidity

Sea-leveltrend&cycles - Localizedflooding - IPCC Sea-level

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Choose:eventofinterest:e.g.30-dayprecipitationeventreturn-levelofinterest:e.g.10years,100years

Compute theyearlyextremumtimeseriesateverylocationIdentify thepercentilethresholdforthereturnperiodofinterest

Weight eacheventwithadamagefunctionCompute thetimeseriesofweightedexceedancesattheportfoliolevel

Identify eventsofinterest:allexceedancesofthepercentilethresholdforalldaysintherecordateachlocation

Compute VaR andCVaR-likemeasurestorankportfolios

ClimateExtremes– Analysisset-up– PortfolioLevel

Foragiveneventdurationd,andreturn-levelp,theprocessisthefollowing:- computelocalyearlymaximaandfindthelocalthresholdbasedonp,- foreachsitei,obtain

𝑛",$ 𝑝, 𝑑 and𝐿, 𝑝, 𝑑 = 𝐶 𝑝, 𝑑 𝑉, + 𝐷 𝑝, 𝑑 𝐹,,- defineportfolioexposureas𝑆4 𝑝, 𝑑 = ∑ 𝐿,(𝑝, 𝑑)𝑛,,4(𝑝, 𝑑)�

, or𝑅$ 𝑝, 𝑑 = :; <,=∑ >?�

?- computeVaRq-likemeasureusingquantile(𝑅$ 𝑝, 𝑑 ,q)- computeCVaRq-likemeasureusingtrapezoidalapproximation:

𝐶𝑉𝑅@ 𝑝, 𝑑 = A(ABC)(DEA)

FG <,= EFH <,=I

+ ∑ 𝑅J 𝑝, 𝑑KBAJL@EA

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ClimateExtremes– AnalysisSet-up– PortfolioLevel

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BarrickGold20(14)outof21sitesintheportfolioexperiencedafailureofadesignforthe30dayextremerainfallinthesameyearBasedontheNOAA(ECMWF)datasets(numbersthatneverhappeniftheyearlyexceedanceismodeledwithaPoissonprocessof𝑎 = 𝑝×𝑁PQQR$Q

ClimateExtremes– ResultExample,ExtremeRainfall,T=10years

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Circlesize=AssetNAV

ClimateExtremes– ResultExample– BarrickGold

100year1dayrainfallevent30%NAVExposedwitha1%/yr probability

7%witha5%/yr probability

10year30dayrainfallevent80%ProductionExposedwitha1%/yr probability

45%witha5%/yr probability

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ClimateExtremes– Resultexample- BarrickGoldPortfolioExposure

ExtremeRainfall:40mineRioTintoportfolio.• Highclustering:36exceedancesinoneyearoutof142• Thereisapronouncedtrendanddecadalvariability

BHPBillitonRioTinto

Drought:38mineBHPBillitonportfolio.• Highclustering:24exceedancesinoneyearoutof60

BHPBilliton

RioTinto

Annualexceedancesof10year30dayrainfall

Annualexceedancesof10yeardrought

ClimateExtremes– Resultexample– RioTinto(40assets),BHPBilliton(38assets)

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ForBHPPortfolioforT=100years

Thenumberofeventsexperiencedis5to6xofexpected

=VeryhighresidualriskexposureacrossthePortfolio

Themoreraretheevent(higherT),thehigheristheeffectofclusteringonresidualriskforallportfolios

Fattailriskduetospatialclustering:

ClimateExtremes– ResultExample– FourCompanies– TwoClimateDatasets

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DroughtRiskRankingsbyVARandCVARnormalizedtoportfoliosize

ClimateExtremes– Resultexample– Comparisonacrosscompaniesfordroughtexposure

Contents

1

BIASINREPORTING2

3 CLIMATEEXTREMES

4 TAILINGSDAMSFAILURES

5 CUMULATIVEWATERPOLLUTIONEFFECTS

WATERUSEANDCOSTS

LOWPROBABILITY/HIGHIMPACTEVENTS

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TailingsDamStateIdentification&FailureImpactAnalysisConcern:Tailingsdamsstorehighlytoxicwastes.Theirfailurecanleadtocatastrophic,multi-billion$liabilitiesandpotentiallossoflicensetooperate,assetstranding.• Noglobaldatabaseofdams.Yet,failurerate3-5xofriverdams• Sequentiallyconstructedofearthenfill.Morepronetofailure• Dominantfailuremodes:Overtoppingduetohighrain,GeotechnicalFailure.

MismanagementApproach&Findings:• MachineLearningapproachdevelopedforautomaticidentificationoftailing

damsfromsatelliteimagery• Regressionandindexingbasedapproachforprobabilisticimpactanalysisand

rankingofdamfailureimpact(ecological,population)basedonrunoutfromfailure.

• PredictionprobabilitiesfromthemodelcoveractualSamarco impact• However,hazardratingsformanyotherBraziliantailingdamsintheregionare

muchhigherthanthoseestimatedforSamarco

BARRAGEM

ITABIRUÇU230Mm3

DamFailure, SatelliteImageDatabasesandRiskAppDevelopedandavailable.AppliedtoderiveprobabilistichazardratingsandrankingforMinasGerais dams

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Madewithlocalsoil,rocks,tailings Elevatedinmultiplestages

UpstreamCenterline Downstream

Riskofseepage/stability Riskofseepage/stability/foundation

riskier,$Mediumrisk,$$ Safer,$$$ 29

TailingsDamFacility- Background

TailingsDamFacility- ConceptualRiskProfileasTSFisFilledorRaised

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• Unanticipated/unpricedloss• ValeandBHParepaying$1.2billioneachforSamarco

• thisdoesnotincludelossesinproduction(debtrestructuration)orinternaldamages(onlyforcompensationandrestoration)

• PotentialImpacts:• Lossofproductionandexpenseonrehabilitation• Environmentaldisasterdownstreamofmine+conflict• StrandedAsset

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Samarco, BrasilTailings Dam Failure

TailingsDamFacility– Samarco DamFailureExample

• Noglobalinventoryoftailingsdams• Atleastonepersite?

• Someregionspresenthighrisks,withaconcentrationoflargeinfrastructuresnearpopulationcenters(e.g.MinasGerais)

• Financialriskofatailingsdamfailureisnotreflectedinanypointofthedesignandapprovalprocess.Itisalsonotreflectedintheliabilitiesorintheinsuranceandpotentialimpactsarealmostneverassessedsince:

• either“theyneverfail”(wrongriskassessment)• or“theywon’thaveanyimpact”(actualconsequences)

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• SampleofTSFsaroundtheworld(datafrommultiplesources)

• Manymining-intensivecountriesarenotpictured

TailingsDamFacilities– GlobalPicture

Seriousandveryseriousincidents

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TailingsDamFacilities– HistoricalFailures

• Objective: AssessandcomparethepotentialdamagethatTSFsfailuresmaycausedownstream.• Approach:

• Statisticalmodelforvolumedischargedanddistanceimpacted• Basedontailingsdamheightandstoragecapacity

• ConvolutionwithImpactareainformation• Population,LandUse,HighValueConservationareas

• Result:HazardRatingHR(includinguncertainty)• Application:prioritizewhereitmaybemoreorlessimportanttopursueinquiryintoamoredetailedTSFriskquantificationprocess

• Easytoupdate• Overtoppingisthefailuremechanismin30-40%ofthecases.Theclimatedatacanbeusedtoestimatetheovertoppingprobabilitygivenadditionaltopographicinformation

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TailingsDamFacilities– DevelopmentofaHazardRatingIndex(HR)

• Objective: ExplorehowadditionaldataandanewpredictoronTSFfailuresimpactacceptedrelationshipsbetweenTSFattributes,𝑉S and𝐷DPT

• Approach&Results:• UpdateddatabaseofTSFfailures• Modelusingthepotentialenergyassociatedwiththereleasedvolume𝐻V asopposedtothewholeTSFimpoundmentasthemainpredictorimprovesthevarianceexplained

• Largerdatabaseisneededgiventhevarietyinat-siteconditions,(rheology,failuretype,etc.)toreduceuncertaintyaboutthemean

• CollaborationwithICOLDandStanfordenvisionedtoincreasethedata.

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TailingsDamFacilities– EvolutionofVolumeReleasedandRunoutDistance

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UncertaintybandsestimatedusingBayesianandclassicalregression

TailingsDamFacilities– HazardRatingModel

BARRAGEMITABIRUÇU230Mm3

Samarco Rating:29.3Severaldamsaremuchhigher

TailingsDamFacilities– HazardRatingforMinasGerais,Brazil

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• Objective: BeingabletogloballymapTSFsfromsatelliteimageryindifferenttypesofclimatezones(andperformbasicchangedetection)

• Approach:• GatherhighandmediumresolutionimageryfromGoogleEarthandLandsat• Manuallyidentifyorsegmenttailingsdams• Applypre-trainedneuralnetworksonRGBimages• Application:BuildaglobalmapofTSFsworldwideusingminecoordinates• Easytoupdate

• Imagesources:• Landsat• Sentinel• GoogleEarthPro

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TailingsDamFacilities– TSFAutomaticDetectionandMonitoring

Challenges:

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• DifferenttypesofTSFs,• Differentscales(resolution),• Differentenvironments(climate,nearbyland-use)• Wastemaynotbeveryspectrallydifferentfromsurroundings• Waterbodies• Nopre-trainedANNonmultispectralimages• Laborintensivetodeveloptrainingset

TailingsDamFacilities– TSFAutomaticDetectionandMonitoring

BestResultssofar:classificationthroughANN

• 282imageswith4400X4600pixelswerecollectedfromGoogleEarthPro-spatialresolutionvariesfrom0.5to8m

• Tailingsdamsweremanuallyidentified• Imageswereprocessed,rotated,translatedandtrimmedtogiveatotalof4,781negativeimagesand4,496positiveonesofsize128x128

• Theseimagescapturethecompleteminesandpartofitssurroundings–smallminesweresometimesgroupedintooneimage

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TailingsDamFacilities– TSFAutomaticDetectionandMonitoring

• CNNwith4Layers• Pre-trainedmodelwithnewoutputlayer

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TailingsDamFacilities– TSFAutomaticDetectionandMonitoring

BestResultssofar:classificationthroughANN

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BIASINREPORTING2

3 CLIMATEEXTREMES

4 TAILINGSDAMSFAILURES

5 CUMULATIVEWATERPOLLUTIONEFFECTS

WATERUSEANDCOSTS

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Concerns:Evenifsitelevelregulationofmineeffluentsiseffective,collectiveimpactsfromminingandotherpollutantsourcescancompromisethewatersources,leadingtosocialconflictandlossoflicensetooperate.• Isthereevidencetoquantifytheseeffectsandattributethemtospecificsources?• Docurrentregulatoryprocesseseffectivelyaddresstheserisks?Findings:• Significantlegacywaterpollutioneffectsofminingareidentifiedinallcountries• Datasetstopursuespace-timetrendidentificationandattributionaresparse• Miningcompaniesfaceconsiderablerisksasincreasingwaterscarcityandcompetition

exacerbatetheimpactsofpollutedwaters• EnvironmentalImpactAssessmentsandassociatedbondsarelikelyhighlyinadequateto

addressthesechallenges• Riskquantificationfortheindustryandforamineisconsequentlydifficult.• Anapproachtoregulationthatbuildsinwatershedoutcomesandattributionisneeded.

DatabaseWater qualityandpredictivefactorsdevelopedforbasinsinPeru&USARegressionmodelsillustratetrendsanddependenceonaggregateminingactivity

CumulativeWaterPollutionEffects– RegulatoryEffectivenessandOutcomes

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CumulativePollutionEffects– ProposedVisiontoReduceStrandingPotentials

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