2026年基于证据的矿业新技术影响研究报告（英文版）

20

YEARS

Evidence-BasedResearchon

theImpactofNewTechnologiesintheMiningIndustry

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©2025InternationalInstituteforSustainableDevelopment

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Development(IGF)supportsitsmorethan80membercountriesin

advancingtheirsustainabledevelopmentgoalsthrougheffectivelaws,

policies,andregulationsfortheminingsector.Wehelpgovernmentstakeactiontodevelopinclusiveandgender-equitablepractices,optimize

financialbenefits,supportlivelihoods,andsafeguardtheenvironment.

Ourworkcoversthefullmininglifecycle,fromexplorationtomineclosure,andprojectsofallsizes,fromartisanalminingtolarge-scaleoperations.

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governmentsofCanadaandtheNetherlands.

Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

December2025

WrittenbytheIGFSecretariat.

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ACKNOWLEDGEMENTS

LeadauthorsofthispublicationareSidiYounga,EgeTekinbas,GrégoireBellois,andIsabelleRamdoo.

X-TWITTERlinkedinfacebook@IGFMining

iii

Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

ExecutiveSummary

Theglobalminingsectorisexperiencingaprofoundtransformationdrivenbytheincreasingdemandforcriticalmineralsessentialtotheenergyanddigitaltransitions,alongside

mountingpressuresfrominvestors,consumers,andcivilsocietyformoresustainable

andequitableminingpractices.Accompanyingandsupportingthistransformation,new

technologiesarebeingrapidlyintegratedintolarge-scaleminingoperations,significantly

impactinglabourmarkets,supplychains,andhostcommunities.Thisreportprovidesan

analysisoftechnologyadoptionandtheirassociatedimpacts,drawingoncasestudiesfromsixlarge-scaleminingoperationsinsixdifferentcountries:Australia,BurkinaFaso,Côte

d’Ivoire,theDemocraticRepublicoftheCongo(DRC),Guinea,andSouthAfrica.

KeyFindings

Thefindingsoutlinedherecomefrombothqualitativeinformationandquantitativedatagatheredfromminingcompanies:

•acceleratedtechnologicaladoption:Miningcompaniesareincreasinglyintegrating

advancedtechnologiessuchasartificialintelligence(AI),autonomousmachinery,anddataanalytics.Thistrendenhancesoperationalefficiencyandsafety,particularly

inundergroundmining,butvariesbyregionduetodifferencesinresourcesandinfrastructure.

•disparitiesbetweeneconomies:AdvancedminingeconomieslikeAustraliaandSouthAfricaareleadingintechnologyadoptionduetobetterfinancialandinfrastructural

resources.Incontrast,emergingminingeconomies,includingBurkinaFasoandthe

DRC,facechallengessuchashighcostsandlimitedconnectivity,whichslowthepaceofadoption.However,decliningtechnologycostsmayeventuallyimproveaccessibilityintheseregions.

•labourmarketshifts:Adoptionofnewtechnologiesisreshapingthelabourmarketbyreducingdemandforlow-skilledjobsandincreasingtheneedforspecializedskillsininformationtechnologies(IT),datamanagement,andengineering.Whileautomationreducesthedemandforcertainroles,italsocreatesnew,higher-paidopportunities.However,thesejobsrequirequalificationsthatareofteninsufficientlyavailable

locally,particularlyindevelopingcountries.

•variedimpactsonjob:Theeffectoftechnologicaladoptiononemploymentvaries

widelyacrosscountriesexaminedinthisreport.Somecompanieshavereduced

theirworkforces,whileothershaveredeployedorretrainedemployeestominimizejoblosses.Themostdisruptivetechnologiesarestillintheearlystagesofadoptioninmostcountriesexamined;therefore,thefullimpactonemploymentisyettobeassessed.

•genderedjobopportunities:Technologiesareopeningupjobopportunitiesforwomen,buttheiruptakeislimitedbytheunderrepresentationofwomeninscience,technology,engineering,andmathematicsfields.Targetedpoliciesandinitiativesareneededto

addressthisimbalanceandincreasewomen’sparticipationinthejobsneededforthedigitalizationofmining.

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Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

•indirectandlocalbenefits:Technologyadoptionisgeneratingindirectjob

opportunitiesandnewbusinessventures,particularlyindigitalservices.However,inemergingeconomies,localbenefitsarelimitedduetotherelianceonimportedtechnologiesandlackoflocalmanufacturingcapacity.

•needforproactivemeasures:Bothminingcompaniesandgovernmentsaretakingstepstoaddressthechallengesposedbythedeploymentofnewtechnologies,butfurtheractionisneeded.Thisincludesimprovingworkforceskillsandcapabilities,fosteringlocaltechnologicalinnovation,andenhancingcommunityengagementtoensurebroad-basedbenefits.

PolicyRecommendations

Tonavigatetheongoingtransformationintheminingsectorandensurethatitsbenefitsarebroadlyshared,governmentsandcompaniesmustadoptforward-looking,data-drivenstrategies.Keyrecommendationsincludethefollowing:

•monitorlabourdynamics:implementsystemstotracktheimpactoftechnologiesonjobsandskills,andtheskillsrequirementsoftheminingsector,allowingfortimely

supportandadaptation;

•adapttrainingprograms:aligneducationalandvocationaltrainingwiththeemergingneedsoftheminingsector,focusingondigitalandtechnicalskills;

•investininfrastructure:enhanceconnectivityanddigitalinfrastructure,especiallyinemergingeconomies,tofacilitatewidertechnologyadoption;

•supportlocalcontent:encouragelocalproductionandserviceprovisionthroughcontentpoliciesandtechnologyhubs;

•engagecommunities:fostercommunityinvolvementintechnologyadoption,ensuringlocalbenefitsandaddressingpotentialsocialimpacts.

Thisreportunderscorestheneedforacollaborativeapproachbetweengovernments,industry,andcommunitiestomanagethecomplexdynamicsoftechnologicalchangeinthemining

sector.Byproactivelyaddressingthechallengesandseizingtheopportunitiespresentedby

newtechnologies,stakeholderscanensurethatthebenefitsofmining'sdigitaltransformationaresharedwidelyandequitably.

Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

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TableofContents

1.0Background,Purpose,andScopeoftheStudy 1

1.1Introduction 1

1.2BackgroundandPurposeoftheStudy 2

1.3CountriesandOperationsSelection 2

1.4ScopeandLimitationsoftheReport 3

2.0OverviewofTechnologicalLandscape 5

2.1TaxonomyofDisruptiveTechnologies 5

2.2AssessmentFramework 8

3.0ImpactofNewTechnologiesonJobs 12

4.0KeyFindings 14

5.0PolicyRecommendations 39

References 43

AppendixA.IndirectOccupationsEmergingFromtheDevelopmentofNew

Technologies 46

ListofFigures

Figure1.Taxonomyofdisruptivetechnologiesintheminingsector 6

Figure2.IllustrationofCAS 8

Figure3.Adoptionrateofnewtechnologiesbyminingcompaniesbasedonthesurvey 15

Figure4.Illustrationofnewtechnologiesembeddedinaminingautonomoustruck 17

Figure5.Adoptionbylevelofcountries1economicdevelopment 19

Figure6.Mapof5GnetworkasofMarch2024 20

Figure7.Simplifiedrepresentationoflabourmarketdynamics 22

Figure8.ShareofIThiringintheminingindustryinAustraliainMarch2022,

byjobclassification 25

ListofTables

Table1.Newtechnologiesassessedinthestudy 9

Table2.Impactscoreoftechnologiesinmininglabourmarket 22

Table3.Newjobscreatedduetonewtechnologies 24

Table4.Destroyedanddecliningjobsduetonewtechnologies 28

Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

vi

Table5.Netimpactonjobsfromnewtechnologies,examplefromcasestudies 30

Table6.Impactofnewtechnologiesonexistingtechnologies 33

Table7.Distributionofnewtechnologiessourcingandmanufacturingperorigin 35

Table8.Summaryofsomeactionsconductedbygovernmentsandminingcompaniesto

improveskillsdevelopment 37

Table9.Policyrecommendations 39

TableA1.Indirectoccupationemergingfromthedevelopmentofnewtechnologies 46

ListofBoxes

Box1.Machineinterfacecontrol:Improvingcollisionavoidanceatminesite 7

Box2.Highlight 17

Box3.Highlight 20

Box4.Highlight 21

Box5.Highlight 26

Box6.Breakingdownroles:Tasksvs.occupations 27

Box7.DescriptionofaremotelyoperatedmineinAustralia 31

Box8.Highlight 32

Box9.Highlight 37

1.0Background,Purpose,andScopeoftheStudy

1.1Introduction

Inrecentyears,theminingindustryhasundergonesignificantchangeswiththeadoptionofnewtechnologies.Buildingonthefindingsofthe2018IntergovernmentalForumonMining,Minerals,MetalsandSustainableDevelopment(IGF)researchprojectNewTechNewDeal

(Ramdoo,2019),whichexploredthepotentialimpactsofemergingtechnologiesonthe

sector,thisreporttakesastepfurtherbyempiricallyanalyzingtherealimpactsofthesetechnologies,notablyonlabourmarkets,supplychains,andhostcommunitiesacrosstheminingvaluechain.

Focusingoncasestudiesfromsixcountries—Australia,BurkinaFaso,Côted’Ivoire,the

DemocraticRepublicoftheCongo(DRC),Guinea,andSouthAfrica—thisreporthighlights

howtheintegrationofadvancedtechnologieslikeAIandautonomousmachineryisreshapingtheindustry.Whiletheseadvancementsareimprovingefficiencyandsafety,particularly

inadvancedeconomies,theyarealsocreatingdisparitiesbetweenregionswithdifferingresourcesandinfrastructures.

Labourmarketdynamicsareshifting,withareduceddemandforlow-skilledjobsanda

growingneedforspecializedskills.Althoughnewopportunitiesareemerging,particularlyinhigher-paidroles,theseareofteninaccessibletolocalcommunitiesindevelopingcountries.

Theimpactoncurrentemploymentalsovaries,withsomecompaniesretrainingemployees,whileothersfacejobreductions.

Thereportalsoexaminestheextenttowhichtechnologyadoptioninemergingeconomies

bringslocalbenefits,whererelianceonimportedtechnologiesmighthamperbroader

economicgrowth.Toaddressthesechallenges,thereportofferspolicyrecommendations

aimedatensuringthatthebenefitsoftechnologicalchangearesharedmoreequitablyacrossthesector.

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Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

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1.2BackgroundandPurposeoftheStudy

Thelarge-scalemining(LSM)sectorisundergoingsignificantstructuralchangesdriven

bytheincreasingdemandforcriticalmineralsarisingfromtheenergyanddigital

transitions,alongsidegrowingpressurefrominvestors,consumers,andcivilsocietyfor

moreresponsibleandequitableminingpractices(IGF,2021,2023).Inresponsetothese

challenges,technologicaladvancementsoffervaluabletoolsforminingcompaniestoimprovetheefficiencyofthesectorbothintermsofproductivityandsustainability,thoughthey

alsointroduceanewsetofchallengestonavigate,forcountries,communities,andminingcompaniesthemselves.

NewtechnologiesarerapidlytransformingtheLSMsector,withsignificantimplications

forlabourmarkets,supplychains,andhostcommunities.In2021,withthesupportofthe

DeutscheGesellschaftfürInternationaleZusammenarbeit(GIZ),theIGFcompleteda

2-yearresearchprojectcalledNewTechNewDeal.Thisresearchmappedthekeydisruptivetechnologiesthatwereexpectedtobeadoptedbytheminingsectorandassessedtheir

potentialimpacts,particularlyinmining-dependentcountries(Ramdoo,2019).Theresearchwasbasedonexpectationsandprospectivescenariosthatpolicy-makerscouldconsider,giventhelikelysocio-economicimplicationsofdisruptivetechnologies.

Sincethisreportcameout,thescaleoftechnologicaladoptionhasbroadened,andthe

paceofdeploymentacrossoperationshasaccelerated.Thisnewreportprovidesexamples

oftheactualimpactsofsometechnologiesonselectedminingcountries.Itcomparesactualimplicationsagainsttheassumptionsandexpectationsthatweremadeinthepreviousreport.

Theresultsofthisreportdrawlessonsforgovernments,industries,andcommunitiestobettermanagerisksandopportunitiesrelatedtotheongoingtransformationintheLSMindustry.

1.3CountriesandOperationsSelection

ThisreportisbasedondatacollectedfromasampleofsixLSMoperationsinsixcountries:BurkinaFaso,Côted’Ivoire,Guinea,Australia,DRC,andSouthAfrica.Inlinewiththepreviousresearch,thecasestudieshavebeenselectedfromasetofcriteriathatconsiderdifferent

levelsofcountries’developmentaswellasthematurityoftheminingindustryinthose

countries,differenttypesofcommoditiesmined,anddifferentmethodsofproduction(suchasundergroundoropen-pitoperations).Countrieswerechosenfromvariousgeographicallocations.Oneofthekeyselectioncriteriaforcompanieswasthewillingnessofmining

companiestosharedata,suchastheimpactoftechnologiesonjobs.

Asthisreportintendstoassessthepaceofadoptionofnewtechnologiesconsidering

countries’economicdevelopment,countrieshavebeendividedintotwomaincategories:

•Advancedminingeconomiesrefertomiddle-andhigh-incomecountriesthathaveanestablishedminingsectorwithasignificantpresenceoflargeminingcompanies.Ofthesixcountriesselected,twocountries—AustraliaandSouthAfrica—fallinto

thiscategory.

•Emergingminingeconomiesrefertoleastdevelopedandlower-middle-income

countriesthatarenonethelessimportantandgrowingminingjurisdictionsthathavereceivedasignificantvolumeofinvestmentintheirminingsectorsinthelastfew

years.Ofthesixcountriesselectedforthisproject,fourcountries—BurkinaFaso,Côted’Ivoire,Guinea,andtheDRC—fallinthiscategory.

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Ascopingdeskstudyhasbeenconductedtoidentifytheindividualoperationsthatwouldbeassessed.Criteriaforsuchidentificationincludethefollowing:

•globalcriteria:

⁰differentcommodities:Theminingofdifferentcommoditiesrequiresdifferentequipment,processes,andsystems.Thiswillhelptoreviewtheimpactof

differentexistingtechnologies.

⁰variouscountries,jurisdictions,languages,andcultures:Jurisdictionaldistinctions(technologicalregulation,taxation,securitycontext,accesstoenergy,network,developmentstage,etc.)haveastrongimpacton

operations.Thiswillhelptoshowtheimpactofnewtechnologiesamongdifferentjurisdictions.

⁰variousminingexploitationmethods(openpitandunderground):Different

miningexploitationmethodsusedifferentminingequipmentandsystems.Thiswillhelpreviewexistingtechnologiesamongdifferentminingexploitationandtheirimpact.

⁰operationsownedbydifferentminingcompanies:Miningcompanieshave

differentvisions,strategicgoals,andoperatingphilosophies.Thiswillhelpinreviewingexistingtechnologiesamongdifferentminingcompanies.

•keyspecificcriteria:

⁰largeworkforce:Thiswillallowafocusonoperationsthathaveahighersocialriskduetothelargenumberofstafftheyemployinthecountry.

⁰engagementinadigitaltransformation:Thiswillhelptofocusonoperations

andkeystakeholderswithanacceptabledigitalmaturityinordertogetrelevantinformationrelatedtotheimpactoftechnologies.

⁰abilitytoaccessrelevantandaccuratedata:Consideringthesocial

responsibilityandcommitmentsofminingcompanies,accesstoinformation

thatcouldbeconsideredsensitive,likeconsiderationsofpotentialjoblossesorreductionoflocalsourcing,iskeytosuccessfullyachievingthisstudy.

⁰operationinamining-dependentcountry(foremergingcountries):Technologymayhavemoreimpactonmining-dependentcountriesduetothemining

sector’sstrongcontributiontonationalbudget,employment,andsocio-economicdevelopments.

1.4ScopeandLimitationsoftheReport

ThisreportfocusesexclusivelyontheimpactsofthedevelopmentofnewtechnologiesintheLSMsectoranddoesnotaddresstheartisanalandsmall-scaleminingsector,whichisthe

subjectofaseparatestudy(IGF,2024a).

ThereportrepresentscasestudiesfromsixLSMoperationsinsixdifferentcountries,

managedbysixdifferentminingcompanies.Itintendstogatherinsightsfromsome

operationsinspecificcircumstancesandisnotaquantitativestudybasedonstatistical

samples.Indeed,althoughthecountrieswereselectedtoensureglobalrepresentation,the

samplesizeisinsufficienttodrawbroadconclusionsabouttheimpactofnewtechnologiesontheminingworkforceglobally.

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Additionally,limiteddataavailabilityinsomeregionspreventsthegeneralizationoffindingsacrosscountrieswithsimilarlevelsofdevelopment.Consequently,thedataanalysis

presentedshouldnotbeinterpretedasabasisforgeneralizationsapplicabletoallmineral-producingcountries.

Instead,thereportidentifieskeytrendsand,whereappropriate,highlightsspecificexamplestounderscoreimportantnuancesandvariationsintheminingsector.

Thisreportshouldalsobereadfromtheperspectivethatoneofthemainchallenges

encounteredduringtheresearchwasthereluctanceofsomeminingcompaniestoshare

datarelatedtoactualorexpectedjoblosses/workforcereductions.Suchdisclosureisindeedconsideredhighlysensitiveandapotentialsourceofmajorsocialtensionsamongthe

workforceandinthevicinityofminingoperations.

2.0OverviewofTechnologicalLandscape

Thetechnologiesreferredtointhisreportareasetofdifferenttypesoftechnologiesthatarebeingadoptedacrossvarioussectors,andnotallofthemarespecifictotheminingindustry.However,whencombinedwithminingequipment,theyimprovetheefficiency,safety,and

overallimpactofminingoperations(IGF,2021).

2.1TaxonomyofDisruptiveTechnologies

InitsTechnologyImpactsReview,theIGF(Ramdoo,2019)mappedkeyemergingtechnologytrendsthatarebeingdevelopedandadoptedinLSMoperations.SuchtechnologiescanbeclassifiedintofourbroadcategoriesasillustratedinFigure1:

•first,usersofbigdata,suchassmartoriginalequipmentmanufacturers(OEMs)

machinesanddevicesembeddedwithtechnologiesthatusebigdatameanttoboosttheefficiencyofmineoperations,suchasautomatedmachineries,digitaltwins,supercomputersforbigdataanalytics,etc.

•second,integrators,suchasadvancedsoftwaretechnologiesthatcollect,analyze,

integrate,andtrackbigdata,whicharethensharedthroughnetworksandhigh-speedconnectivity.ExamplesincludetheInternetofThings(IoT),theuseof5G,virtual

reality,blockchaintechnologies,etc.

•third,enablersofdigitization,whichprovideaninterfacebetweenhumanintelligenceandAI.Examplesincludedrones,sensors,connectedwearables,etc.

•finally,processimprovers,whichareaimedatboostingperformance,improving

footprintsofoperations,andrespondingtoenvironmentalandsocialrequirements.Examplesincludeelectricvehicles,watermanagementtechnologies,renewable

energysourcesetc.

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Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

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FIGURE1.Taxonomyofdisruptivetechnologiesintheminingsector

Usersofbigdata

Analytics

Machinelearning

Automation

Digitaltwin

Integrators/

trackersofbigdata

IoT

Blockchains

Smartcontracts

Enablersofdigitalization

Sensors

Wearables

Drones

Satellites

Processimprovers

TailingrecoveryRenewableenergy

Watermanagement

technologies

Electricvehicles

Source:Ramdoo,2019.1

2.1.1UsersofBigData:DevelopmentofsmartOEMmachinesanddevices

Usingbigdata,certaintechnologiesenablemachinestocomprehend,learn,andrespond

toinformation,adjustingtheiractionsasconditionsevolve(PwC,2017).SmartOEM

machinesembeddedwithmachineinterfacecapabilitiesallowthemachinetobecontrolled(stop,teleoperate,slowdown,etc.)byamachinecontrolsystem.Thesemachinescanalso

beconnectedandexchangedatawithothersmartdevices(cameras,wearables,radar,

etc.)throughacommunicationnetwork(radio,4Gnetwork,wi-fi,etc.).Theyhavesome

automationcapabilities,suchaschangingsomeconsumables,suchasdrillbits,withoutanyhumanintervention.

Theyareoftenassociatedwithenablersofdigitalizationsuchassmartdevices(human-

machineinterface,tablets,wearables,cameras,sensors,drones,robots,etc.),whicharealsoabletoconnecttoanetwork,exchangedatawitheachotherandwithmachinecontrol

systems,etc.Forinstance,acamerainstalledinatruckcandetectahumanpresenceandsendtheinformationtothemachinecontrolsystem,whichcandecidetoslowdownorstopthemachine.

1SeeRamdoo(2019)foradetailedbreakdownofdisruptivetechnologiesandtheirusesintheminingsector.

Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

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2.1.2Integrators-Advancedsoftware(withhighcomputingcapabilities)

Theseadvancedsoftwaretechnologiescancollectandprocesslargeandcomplexamountsofdatareceivedfromdifferentsources(trucks,cameras,sensors,tablets,radar,othersoftware,etc.).ThesetypesofsoftwarecanuseAIcapabilities,suchasmachinelearning,topredict

and/orrecommendsetpoints(explosivequantityforablast),helpbettervisualizeoperationswithaugmentedreality,etc.

2.1.3EnablersofDigitalization

Theseincludetechnologies,suchasnetworkcommunicationtechnologiesandcloud

computing,thatsupportthefirsttwotypesoftechnologies.Theyallowlargeamountsofdatatobeexchangedamongalltheequipment,software,anddevicesinrealtimethroughhigh-

speedconnectivity,allowinginformationtobesharedandprocessedinstantly.Examplesinclude5Garchitecture,wi-fi,4G,cloudcomputing,etc.

Combiningthesetechnologiesenablestheiruseinsuchcasesascollisionavoidancesystems,aspresentedinBox1.

BOX1.MACHINEINTERFACECONTROL:IMPROVINGCOLLISIONAVOIDANCEATMINESITE

Intelligentvehiclecontroltechnologyenhancessafetyonjobsitesbyproviding

automation,remotecontrol,andcollisionavoidancesolutionsforanymobileequipment(RCTGlobal,2022).Advancedcollisionavoidancesystems,illustratedinFigure2,havethefollowingcomponents:

•collisionavoidancesystem(CAS)monitorstheexternalenvironmentwiththeuseoftheGlobalPositioningSystem(GPS)andothersensors(cameras,radar,etc.)todetectobjectsandevaluatecollisionrisk.

•machineinterfacecontrol(MIC)monitorstheoperator’scontrolsandintentionsandpassesthisinformationtotheCAS,whichcomparesitwiththeexternal

environment.

•theCASusesthisinformationtoprovidewarningstotheMIC,whicharethenpassedontoanoperatororanautomaticmachinefunction(e.g.,“slowdown”or“stop”)to

executeinterventionalcollisionavoidanceactions.

Evidence-BasedResearchontheImpactofNewTechnologiesintheMiningIndustry

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FIGURE2.IllustrationofCAS

DangerzoneCautionzoneAlarmzone

Safezone

Source:RCTGlobal,2022.

Machinecontrolsystemsalsoenabletele-remoteguidancecontrolandautomation(drivebywire).

2.1.4ProcessImprovers

Significantinvestmentshavebeenmadeintechnologiesaimedatimprovingmining

processestoaddressenvironmentalchallengesandimprovethesustainabilityofoperations.Thesetechnologiescutacrossvariousopportunities.Examplesinclude(a)thedevelopmentofnewmineralore-processingtechnologies;(b)improvedwasteandtailingsmanagement

techniquestoimprovemineralrecovery;(c)water-andenergy-savingtechnologies;and

(d)theadoptionoflow-carbone-mobility,suchaselectricvehicles.Theseareparticularlyimportanttomitigatetheimpactofminingontheenvironment(suchasgreenhousegas

[GHG]emissions)andtofosterenergytransitionawayfromfossilfuel-poweredoperations.

2.2AssessmentFramework

TechnologiesassessedinthisreportarepresentedinTable1.Theyhavebeenselectedbasedontheirstageofdevelopment,theirexpectedimpactonminingoperationsandjobs,andon

thesocio-economiclandscapearoundminesites.Asaresult,theyrepresentgoodproxiesforassessingglobaltechnologicaldevelopmentintheminingsector.Theyillustratethedegreeoftechnologicalintegrationandcanserveasanindicatorofacompany’stechnologicalmaturity.Theadoptionofthesetechnologiesprovidesacomprehensiveviewofhowmini