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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
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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
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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
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• Comparesthefirstclosureestimateonaprojecttothelastavailableclosurecostestimateonaproject
• Ofthoseprojectswithmorethanoneclosurecostestimate:• 61%showedanincreaseinclosurecosts• 24%showedadecreaseinclosurecosts• 15%remainedthesame
• Nearly20%ofprojectsfromthefirstreporttothelastreportincreasedbymorethan2x
• Otheranalysesperformedwere:• ChangeinClosureCostvs.MineLife• ChangeinClosureCostvs.LOM%• ChangeinClosureCostvs.Production%
BiasinReporting– Methodology/Results
IncreaseinClosureCostNearertoEndofMineLife
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BiasinReporting– FittedModel• Producesamodeltoestimateclosurecostestimatesbasedoncompanylevelandminespecificfactors(aswellastemporalfactors)usingregression
• Keyconclusionsare:• Remainingminelife(time.perc)isasignificantvariable,implyingthatestimatesincreaseastheendoftheminelifebecomesnearer
• Minelocation,ownerlocationandprimarycommodityaresignificantvariables– thisislikelyduetomorelaxregulationsandlowerlaborcostsincertainjurisdictions
• Productionandreservesarestatisticallysignificantinpredictors– movingmorematerialrequiresgreateramountsofremediation
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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
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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$Q
ClimateExtremes– ResultExample,ExtremeRainfall,T=10years
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Circlesize=AssetNAV
ClimateExtremes– ResultExample– BarrickGold
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100year1dayrainfallevent30%NAVExposedwitha1%/yr probability
7%witha5%/yr probability
10year30dayrainfallevent80%ProductionExposedwitha1%/yr probability
45%witha5%/yr probability
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ClimateExtremes– Resultexample- BarrickGoldPortfolioExposure
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ExtremeRainfall:40mineRioTintoportfolio.• Highclustering:36exceedancesinoneyearoutof142• Thereisapronouncedtrendanddecadalvariability
BHPBillitonRioTinto
Drought:38mineBHPBillitonportfolio.• Highclustering:24exceedancesinoneyearoutof60
BHPBilliton
RioTinto
Annualexceedancesof10year30dayrainfall
Annualexceedancesof10yeardrought
ClimateExtremes– Resultexample– RioTinto(40assets),BHPBilliton(38assets)
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ForBHPPortfolioforT=100years
Thenumberofeventsexperiencedis5to6xofexpected
=VeryhighresidualriskexposureacrossthePortfolio
Themoreraretheevent(higherT),thehigheristheeffectofclusteringonresidualriskforallportfolios
Fattailriskduetospatialclustering:
ClimateExtremes– ResultExample– FourCompanies– TwoClimateDatasets
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DroughtRiskRankingsbyVARandCVARnormalizedtoportfoliosize
ClimateExtremes– Resultexample– Comparisonacrosscompaniesfordroughtexposure
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Contents
1
BIASINREPORTING2
3 CLIMATEEXTREMES
4 TAILINGSDAMSFAILURES
5 CUMULATIVEWATERPOLLUTIONEFFECTS
WATERUSEANDCOSTS
LOWPROBABILITY/HIGHIMPACTEVENTS
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TailingsDamStateIdentification&FailureImpactAnalysisConcern:Tailingsdamsstorehighlytoxicwastes.Theirfailurecanleadtocatastrophic,multi-billion$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
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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
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• 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
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Seriousandveryseriousincidents
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TailingsDamFacilities– HistoricalFailures
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• 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)
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• 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
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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
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Challenges:
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• DifferenttypesofTSFs,• Differentscales(resolution),• Differentenvironments(climate,nearbyland-use)• Wastemaynotbeveryspectrallydifferentfromsurroundings• Waterbodies• Nopre-trainedANNonmultispectralimages• Laborintensivetodeveloptrainingset
TailingsDamFacilities– TSFAutomaticDetectionandMonitoring
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BestResultssofar:classificationthroughANN
• 282imageswith4400X4600pixelswerecollectedfromGoogleEarthPro-spatialresolutionvariesfrom0.5to8m
• Tailingsdamsweremanuallyidentified• Imageswereprocessed,rotated,translatedandtrimmedtogiveatotalof4,781negativeimagesand4,496positiveonesofsize128x128
• Theseimagescapturethecompleteminesandpartofitssurroundings–smallminesweresometimesgroupedintooneimage
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TailingsDamFacilities– TSFAutomaticDetectionandMonitoring
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• CNNwith4Layers• Pre-trainedmodelwithnewoutputlayer
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TailingsDamFacilities– TSFAutomaticDetectionandMonitoring
BestResultssofar:classificationthroughANN
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1
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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