人工智能的能源悖论：平衡挑战与机遇

AIGovernanceAlliance

IncollaborationwithAccenture

TransformationofIndustriesintheAgeofAI

ArtificialIntelligence’s

EnergyParadox:BalancingChallengesandOpportunities

WHITEPAPERJANUARY2025

Images:GettyImages

Contents

Readingguide3

Foreword4

Executivesummary

5

Introduction6

1ElectricityconsumptionofAI

7

1.1TheAIlifecycle

7

1.2Theroleofdatacentres

8

1.3OpportunitiestoreduceAIsystemelectricityconsumption

9

2AI-enabledenergytransition

11

2

.1Non-exhaustiveexampleopportunitiesforAI-enabled11

electricityreduction

2

.2Sampleusecases

12

3

Primarychallengesandecosystemenablers14

3.1Infrastructurechallenges

14

3.2Environmentalchallenges14

3.3Overviewofecosystemenablers15

3.4Regulatoryandpolicyenablers16

3.5Financialincentiveenablers16

3.6Technologicalinnovationenablers17

3.7Marketdevelopmentenablers17

4

FutureoutlookofAIenergyimpact18

4.1Thedeploymentandcollaborationlandscape18

4.2AIandenergy–2024to2025outlook22

Conclusion23

Contributors

24

Endnotes

26

Disclaimer

ThisdocumentispublishedbytheWorldEconomicForum

asacontributiontoaproject,insightareaorinteraction.The

findings,interpretationsandconclusionsexpressedhereinare

aresultofacollaborativeprocessfacilitatedandendorsedby

theWorldEconomicForumbutwhoseresultsdonotnecessarilyrepresenttheviewsoftheWorldEconomicForum,northe

entiretyofitsMembers,Partnersorotherstakeholders.

©2025WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinany

formorbyanymeans,includingphotocopyingandrecording,orbyanyinformationstorageandretrievalsystem.

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities2

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities3

Readingguide

TheWorldEconomicForum’sAITransformationofIndustriesinitiativeseekstocatalyseresponsible

industrytransformationbyexploringthestrategic

implications,opportunitiesandchallengesof

promotingartificialintelligence(AI)-driveninnovationacrossbusinessandoperatingmodels.

ThiswhitepaperseriesexploresthetransformativeroleofAIacrossindustries.Itprovidesinsights

throughbothbroadanalysesandin-depth

explorationsofindustry-specificandregionaldeepdives.Theseriesincludes:

Crossindustry

Impactonindustrialecosystems

AIGovernance

Alliance

IncollaborationwithAccenture

TransformationofIndustriesintheAgeofAI

AIinAction:Beyond

Experimentationto

TransformIndustry

FLAGSHIPWHITEPAPERSERIES

JANUARY2025

AIGovernance

Alliance

IncollaborationwithAccenture

TransformationofIndustriesintheAgeofAI

ArtificialIntelligence’s

EnergyParadox:Balancing

ChallengesandOpportunities

WHITEPAPER

JANUARY2025

AIinAction:Beyond

Experimentationto

TransformIndustry

Leveraging

GenerativeAIforJob

Augmentationand

WorkforceProductivity

ArtificialIntelligence’s

EnergyParadox:

BalancingChallenges

andOpportunities

PwC

Incollaborationwith

LeveragingGenerativeAI

forJobAugmentationand

WorkforceProductivity:

andaFrameworkforAction

Scenarios,CaseStudies

INSIGHTREPORT

NOVEMBER2024

ArtificialIntelligence

andCybersecurity:

BalancingRisks

andRewards

ArtificialIntelligenceandCybersecurity:BalancingRisks

andRewards

WHITEPAPERJANUARY2025

AIGovernanceAlliance

IncollaborationwiththeGlobalCyberSecurityCapacityCentre,UniversityofOxford

TransformationofIndustriesintheAgeofAI

Regionalspecific

AIGovernance

Alliance

IncollaborationwithAccenture

TransformationofIndustriesintheAgeofAI

BlueprinttoAction:

China’sPathtoAI-Powered

IndustryTransformation

WHITEPAPER

JANUARY2025

BlueprinttoAction:

China’sPathto

AI-PoweredIndustry

Transformation

Impactonregions

Industryorfunctionspecific

Impactonindustries,sectorsandfunctions

Advanced

manufacturing

andsupplychains

IncollaborationwithBostonConsultingGroup

TransformationofIndustriesintheAgeofAI

FrontierTechnologies

inIndustrialOperations:TheRiseofArtificial

IntelligenceAgents

WHITEPAPERJANUARY2025

FrontierTechnologies

inIndustrial

Operations:The

RiseofArtificial

IntelligenceAgents

Financialservices

AIGovernance

Alliance

IncollaborationwithAccenture

TransformationofIndustriesintheAgeofAI

ArtificialIntelligence

inFinancialServices

WHITEPAPER

JANUARY2025

ArtificialIntelligence

inFinancialServices

Media,

entertainment

andsportHealthcareTransport

Incollaborationwith

McKnsey&Company

TransformationofIndustresntheAgeofAI

IntelligentTransport,

GreenerFuture:

AIasaCatalystto

Decarbonize

GlobalLogistics

WHITEPAPER

JANUARY2025

AIGovernance

Alliance

IncollaborationwithAccenture

TransformationofIndustriesintheAgeofAI

ArtificialIntelligenceinMedia,

EntertainmentandSport

WHITEPAPER

JANUARY2025

IncollaborationwithBostonConsultingGroup

TransformationofIndustriesintheAgeofAI

TheFutureofAI-EnabledHealth:

LeadingtheWay

WHITEPAPER

JANUARY2025

TheFutureof

AI-EnabledHealth:

LeadingtheWay

ArtificialIntelligencein

Media,Entertainment

andSport

IntelligentTransport,

GreenerFuture:

AIasaCatalyst

toDecarbonize

GlobalLogistics

Telecommunications

Upcoming

industryreport:

Telecommunications

Consumergoods

Upcoming

industryreport:

Consumergoods

Additionalreportstobeannounced.

AsAIcontinuestoevolveatanunprecedented

pace,eachpaperinthisseriescapturesauniqueperspectiveonAI–includingadetailedsnapshotofthelandscapeatthetimeofwriting.Recognizingthatongoingshiftsandadvancementsarealreadyinmotion,theaimistocontinuouslydeepenand

updatetheunderstandingofAI’simplicationsandapplicationsthroughcollaborationwiththecommunityofWorldEconomicForumpartners

andstakeholdersengagedinAIstrategyandimplementationacrossorganizations.

Together,thesepapersofferacomprehensiveviewofAI’scurrentdevelopmentandadoption,aswellasaviewofitsfuturepotentialimpact.

Eachpapercanbereadstand-aloneoralongsidetheothers,withcommonthemesemerging

acrossindustries.

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities4

January2025

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities

Foreword

RobertoBocca

Head,CentreforEnergyandMaterials;Member,ExecutiveCommittee,

WorldEconomicForum

JeremyJurgens

ManagingDirector,WorldEconomicForum

CathyLi

Head,AI,DataandMetaverse;

DeputyHead,Centre

fortheFourthIndustrialRevolution;Member,

ExecutiveCommittee,WorldEconomicForum

JamesMazurek

ManagingDirector,USUtilitiesStrategyLead,Accenture

Intodayseconomy,artificialintelligence(AI)systemsofferbothchallengesandopportunities.Asintegralcomponentsofdigitalinfrastructure,thedatacentresthatenableAIsupportavarietyofapplications,

fromcloudcomputingtocomplexdataprocessing.AIsrapidexpansion,however,isaccompaniedby

growingelectricitydemand,withthelargestfacilitiesintheworldusingthesameamountofpoweras

smallcitiestoensureuninterruptedoperation.Datacentrescomeinvaryingsizeshowever,rangingfromlarge,hyperscalefacilitieswithmorethan1gigawatt(GW)ofpowercapacity,tosmaller,microedge

deploymentsthatmaydrawlessthan10kilowatts(kW)ofpower.1

Oneestimatenowexpectsdata-centre-related

electricityconsumptiontogrowfromapproximately1%ofglobalelectricitydemandtoover2%by

2026,potentiallyreaching3%by2030ifforecastedgrowthcontinues.2Suchprojectionshaveraised

concernsaboutsupportingthisdemandwhilealsomeetingnet-zerocommitments.Simultaneously,AIcanbeapowerfultooltopositivelysupportwiderenergysystemtransformation.Forexample,itis

alreadybeingusedtoimproveenergyefficiencyacrossindustries,acceleraterenewableenergyintegrationandmakepowergridsmoreresilient.

ThisistheAIenergyparadoxbalancingthesechallengesagainstAI-enabledopportunities.

However,currentestimatesofAIsenergyimpactvary,andthemagnitudeofelectricitydemand

growthremainsunclear.Otherissuesincludealackofstandardizedtaxonomiesanddefinitions.

Theextenttowhichelectricitydemandgrowthwill

beoffsetbyefficiencygainsfromadvancements

intechnologies(e.g.chips,algorithmsetc.),data

centredesignandchangingregionaldynamics

isalsouncertain.Whileanear-termriseinAIs

electricityconsumptionisexpected,thefuture

magnitudeofthisgrowthmaydeclineduetothe

achievementofefficiencygains.Toachievethis,

itspivotaltounderstandinnovativemitigation

strategiesandsolutionsthatcaneffectivelyfacilitatethisbalance.

Overthepastyear,theWorldEconomicForums

AIGovernanceAlliancehasunitedindustryand

governmentwithcivilsocietyandacademia,

establishingaglobalmultistakeholdereffortto

ensureAIservesthegreatergoodwhilemaintainingresponsibility,inclusivityandaccountability.PlayersfromacrosstheAIvaluechainareconvenedto

cultivatemeaningfuldialogueonemergingAIissues.

WithAccentureasaknowledgepartner,the

alliancesAIEnergyImpactCommunity(composedofover40globalmembers)hasfacilitatedcross-industrydiscoursetowardsconsensusand

surfacedappliedusecasesonAIsenergyimpact.

Thispaperhighlightscross-industryinsightsfromadiversestakeholdergrouptooutlinemitigationstrategies:

IdentifyingelectricityusereductionstrategiesforAIsystems

TouchinguponAIspotentialforthewiderenergytransition

Outliningkeypartnerships,frameworksandpoliciestosupportsustainableAIadoption

TheincreaseinAIadoption,alongsideothermarketfactorsiscontributingtoincreasedelectricityuse.

Annualglobalelectricitydemandgrowthisnow

forecastedtoreachnearly3.5%inthecoming

years.3,4Thischallengeisamplifiedbyglobal

competitionforAIprojectsacrossregions.This

willrequirestakeholdersacrossthevaluechainto

navigatemarketpressuresforcomputingpower,

whilebalancingsustainabilitytargets,gridconstraintsandcommunityimpacts.

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities5

Executivesummary

Artificialintelligencepresentsenergyopportunitiesandchallenges–strategicmitigationcanhelp

tomaximizebenefitswhilereducingburdens.

Artificialintelligence(AI)isfacilitatinganewera

ofinnovation,withnearlythreeinfourcompaniesusingAIforatleastonebusinessfunction.5

Thisinnovationbringsmanybenefits,including

enhancedproductivity,newwaysofworkingand

revenuegrowth.AI-relatedelectricityconsumption

isexpectedtogrowbyasmuchas50%annually

from2023to2030.AIdatacentreconsumption,

whilegrowingrapidly,isprojectedtoremainasmallfractionofglobalelectricitydemand,startingat

just0.04%in2023(seeFigure4).However,when

combinedwithothermarketfactors(suchasgrowingelectricitydemandfortransport,buildingsandmore),AI’sacceleratedadoptioncouldpotentiallyincreasethestrainonpowergridsandelectricityproviders.

However,suchprojectionscanvary.6Uncertainty

remainsaroundhowprofoundAI’soverallenergy

impactwillbeandwhichstrategiescouldmitigatechallengesthatariseorenablenewsolution

opportunities.Inthiscontext,it’sessentialtoassesshowAIcouldacceleratetheenergytransitioninlinewithnet-zerogoals,aswellaswhichsupporting

ecosystemenablerscansupportthis.Thispaper

focusesonAI’selectricityimpactswhileaddressingthebroaderenergylandscape,includinggenerationandfuelsourcessupportingAI.

WorkundertheAIGovernanceAlliance(AIGA)

AIEnergyImpactInitiative

hassurfacedkeyinsightsonthesetopics.Theinitiativecollaborateswith

over40globalorganizationsacrossmorethannineindustriesdrivingAIadoption.

ThisanalysishighlightskeyfindingsrelevanttothreedistinctareasrelatedtoAI’sroleintransforming

energysystems:

1.ElectricityconsumptionofAI:ReviewingtheAIlifecycle,strategiesforreducingitsconsumptionandnewopportunitiesforprocessdigitalization

–AIadoptionvariesbysector,withelectricitydemandexpectedtorisesharply.However,projectionsremainuncertain,underscoringaneedforongoingassessment.

–OptimizingAI’sconsumptionincludes

harnessingtechnologicalinnovationssuchasenergy-efficientAIchiphardwareandAI-optimizedcoolingsolutions.

–Companiesarereducingdata

centreelectricityconsumptionthroughoperationalstrategieslikeAI-driven

environmentalcontrols,servervirtualizationandworkloaddistribution.

2.AI-enabledenergytransition:Exploring

innovative,emergingcompanyusecases

andthepotentialforscalingacrossindustries

–Existingusecasesdemonstratereducedenergyconsumptionof10-60%in

someinstances,withpotentialforfurtheroptimization.

–AIishelpingelectricityprovidersoptimizeoperationsviaenergystorage,enhancedbatteryefficiencyandsmartgrid.

–AIcansupportdecarbonization,helpingtoloweremissions,reducewasteandimproveresourceuse.

3.Primarychallengesandecosystem

enablers:Analysingregulation,policyandpartnershipsnecessaryforsustainableAIadoptionatscale

–EnablingsustainableAIrequiresa

multifacetedapproachspanning:regulationandpolicy,financialincentives,technologicalinnovationandmarketdevelopment.

–Regulatory,policyandfinancial

enablerscanincentivizeresponsibleAIthroughcomplianceframeworksandfundingmechanisms.

–Technologicalinnovationandmarket

developmentfosterresearch,collaborationandsustainableAIadoption.

ThiswhitepaperisapreliminaryexplorationofAI’senergy-relatedimpact,andoutlinesthekeychallengesandopportunitiesthatemergeasAIadoptiongrowsacrossindustries.ItconcludesbysharingfourareastomonitorforcontinuedunderstandingofAI’sevolvingenergyimpact:

–AIdeploymentfordecarbonization

–TransparentandefficientAIelectricityuse

–Innovationintechnologyanddesign

–Effectiveecosystemcollaboration

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities6

Introduction

AIisrevolutionizingindustries,resulting

ingrowingelectricitydemand,butpredictingAI-specificenergyimpactsremainscomplex.

GrowingdemandforAIOverallelectricitydemand

acrossindustriesgrowthdrivers

Artificialintelligence(AI)istransformingseveralSeveralmarketfactorscontributetoincreased

aspectsofdailylife.Fromautomatingsimpletasksglobalelectricitydemand.AsidefromAIand

toenablingcomplexproblem-solving,AIisdrivingtheelectrificationofbothtransportandbuildings,

innovation,increasingefficiencyandchanginghowothergrowthdriversincludeindustrialshiftstowards

societyoperates.Inparticular,generativeAIhaselectricmotors,urbanization,populationgrowth

emergedasapowerfultransformationalcatalystandtherisingadoptionofdigitaleconomysolutions.capableofautomatingtasksandreinventing

processesacrossvaluechains,therebyenhancingProjectingAI-specificgrowthischallenging,

performanceandcompetitiveness.7however,astechnologicaladvancementsand

differingadoptionratescomplicatepredictions.

WhileFigure1givessomeindication,further

researchisneededtoelucidatetherolethat

AI-relatedelectricitydemandgrowthplaysinthecontextofglobalenergytrends.

anddatacentresensitivitycases

OtherHeavyindustry

Otherbuildings

Otherindustry

Electricitydemand

growth,2023-30

Datacentres6760TWh

Spaceheating

Desalination

Electricvehicles

Spacecooling

Othertransport

Source:InternationalEnergyAgency(IEA).(2024).WorldEnergyOutlook.

FIGURE1ElectricitydemandgrowthbyenduseintheStatedPoliciesScenario(STEPS)2023-2030,

1

ElectricityconsumptionofAI

ModeldeploymentisAIsmostenergy-intensivestage(accountingforapproximately60%)

innovativestrategiescanmitigateconsumption.

TheAIlifecycle

1.1

TheAIlifecyclebeginswithplanninganddata

collection,duringwhichdataisgathered,processedandstored.8Next,themodeldevelopmentphase

includesdesign,problemanalysisanddata

preparation.Modeltrainingthenoptimizesthemodelthroughiterativedataexposure.Modeldeploymentsubsequentlyopensthemodelforreal-worldapplication.Lastly,monitoringandmaintenancesupportongoingrefinement.

Furtherresearchisneededtoestimateconsumptionforstages1and5,howeverestimatesexistforstages2-4.Withinthesethreestages,modeldeployment

isthemostenergy-intensive(approximately60-70%ofcombinedelectricityconsumption),butwilllikely

continuegrowinginthelongterm.Modeltrainingis

thenextmostenergy-intensive,accountingfor20-

40%ofconsumption,followedbymodeldevelopmentatupto10%.9Theseestimateshowever,willlikely

varyacrossdifferingAImodeltypes.

ElectricityconsumptionacrosstheAIlifecycle

FIGURE2

Stage1:

Planninganddata

collectionson

nature*

C

1

Stage5:

Monitoringandmaintenance*

5

3

4

2

Stage2:

Modeldevelopment

10%

</>

Stage4:

Deployment

60%

Stage3:

Modeltraining

30%

*Insufficientdataavailableforestimation

Source:ElectricPowerResearchInstitute(EPRI).(2024).PoweringIntelligence:AnalyzingArtificialIntelligenceandDataCenterEnergyConsumption.International

EnergyAgency(IEA).(2023).TrackingDataCentresandDataTransmissionNetworks.

/energy-system/buildings/data-centres-and-data-

transmission-networks

;D.Pattersonetal.(2022).TheCarbonFootprintofMachineLearningTrainingWillPlateau,ThenShrink.Computer,vol.55,no.7,pp.18-28.

/document/9810097

.

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities7

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities8

1.2

Theroleofdatacentres

–Coolingsystems(30-40%)tomaintainoptimaltemperatures.

–Auxiliarycomponents(10-30%),includingpowersupplies,securityandlighting.

NotethattheseproportionswillevolveovertimeasAIusebecomesmoreprevalent.

Harnessingpowerfulservers,specializedhardwareandadvancednetworkingcapabilities,datacentresenablethehigh-speedcomputationsanddata

processingrequiredforAI.

Withindatacentres,electricityconsumptionincludesthreemaincomponents:10

–ITequipment(40-50%),includingservers,storageandnetworksystems.

FIGURE3

Exampledatacentrelayout

UPS*

Holdandcoldaisles

Racks

Security

Enginegenerators

D

Cooling

Firesystem

*Uninterruptiblepowersupply

Source:Vianova.(n.d.).DataCenteroffer.

https://www.vianova.it/en/data-center/

.

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities9

1.3OpportunitiestoreduceAI

systemelectricityconsumption

DatacentreconsumptionincludesbothAI

Thisincreasedenergyintensity,however,is

accompaniedbytheadditionalbenefitsthat

capabilitieslikegenerativeAIcanprovide,includingtheabilitytoperformmorecomplexworkandto

enableexpandedvalueopportunities.

andnon-AIelements.AIprocessing,particularlyforgenerativeAI,ismoreenergy-intensive

duetolargemodelcomplexity,longertrainingdurationsandsubstantialdataprocessing.

FIGURE4Datacentredemandovertime

Datacentredemand(TWh):Non-AIversusAI

1400

1200

1000

800

600

400

200

0

2024202520262027202820292030

2023

Non-AIdemand(TWh)AIdemand(TWh)

Note:ThisisanextrapolatedscenariothatextendstheIEA’sforecastfrom2023to2026through2030usingacombinationof2021-2023historicalgrowthandtheirproposedgrowthratefrom2023-2026.

Source:InternationalEnergyAgency(IEA);Goldman;Accenture.

EnablingamoreenergyefficientAIsystemincludes

exploringopportunitieswithindatacentrestoreduceelectricityconsumption.Accordingly,anon-exhaustiveinventoryofexamplestrategiesareexploredbelow.

Datamanagementstrategies

WithinAI’sfirststage(planninganddatacollection),“digitaldecarbonization”techniquescanaddress

“darkdata”,whichoccupiesserverspaceandconsumeselectricitywithoutprovidingvalue.

Forsomeorganizations,darkdatamayaccountforasmuchas60-75%ofstoreddata.11

Digitaldecarbonizationstrategiescanidentifyandeliminatedarkdata,reducingstorageandelectricityconsumption.Opportunitiesmayalsoexistto

repurposedarkdatatogeneratevalue.

TABLE1Featureddatamanagementusecase

LoughboroughUniversity:automotiveindustrycollaboration:unlockingdarkdataforsustainableindustrialmaintenance

Approach

Aknowledgemanagementsystemwith

datascrapingandenrichmenttechniques

wasdevelopedtointegrateandstructure

darkdata,organizingitintovaluabledatasetsfordecision-making,andwastecategories

fordisposal.

Results

Intotal,10-20%ofdarkdatawas

transformedintoactionableknowledge,

Situation/context

“Darkdata”remainedinstorage,underusedduetopoorlystructuredformats.

improvingfaultanalysisandmaintenance,

enhancingdatareliability,reducingdowntime,loweringtheenvironmentalfootprintand

highlightingwastedata.

Source:Communityconsultation.

ArtificialIntelligence,sEnergyParadox:BalancingChallengesandOpportunities10

–Datacentreinfrastructuremanagement

softwareoptimizeselectricityuse,improvingsystemoperationandmaintenance.

–Advancedcoolingtechniquescanreduce

consumption,comparedtotraditionalmethods.

Technologicalstrategies

SeveraltechnologicalstrategiescanhelpenablesustainableAI:

–Energy-efficienthardware(e.g.chips)

andmodelsreduceelectricityconsumptionthroughouttheAIlifecycle.

–Innovative,insulatedbuildingmaterialsreducetheneedforheating,ventilationandcooling(HVAC)efforts.

TABLE2Featuredtechnologicalusecase

VirginMediaO2:AI-poweredcoolingoptimization

Approach

VirginimplementedEkkoSense’sAI-

enabledapproachtooptimizethermal,powerandcapacityperformanceacross20datacentres.

Results

Benefitsincludedcoolingsavingsworthover£1millionperyear,a15%coolingelectricityreductionanda760tonnesofCO2saving.

Situation/context

VirginMediaO2partneredwithEkkoSensetoimprovedatacentreefficiency.

Source:Communityconsultation.

–Virtualizationtechniquesreducephysicalserverrequirementsandconsumption.

–Temperatureoptimizationandhumiditymanagementreduceovercooling

andconsumption.

–Dynamicpowermanagementadjustsprocessingbasedonworkload,reducingconsumption.

Operationalstrategies

SeveraloperationalstrategiescanalsosupportsustainableAI:

–Incorporatingtargetenduse(model

developmentversustrainingversusdeployment)intositeselectionhelpsoptimizeefficiency

basedonworkload.

–Usingscalablebuildingdesignsthatgrowasdemandincreasesmitigatesoversizing.

TABLE3Featuredoperationalusecase

SAP:Aimingfor“green”datacentres

Situation/context

GreendatacentresarekeytoSAP’ssustainabilitystrategy.

Approach

SAPdatacentrestrackresourceuseand

minimizewastebyusingthermalcamerastooptimizeairflowandinsulation,whilealsoimplementingcool/hotaislecontainmenttosaveenergy.

Results

In2023,SAPachievedcarbonneutralityandisnowontracktoachievenetzeroalongitsvaluechainby2030.

Source:Communityconsultation.

2

AI-enabled

energytransition

AIsolutionscandriveenergyefficiencyacrosssectors,offeringdecarbonizationopportunitiesbyoptimizingoperationsandreducingresourceconsumption.

Extensivedecar