2025科学领域人工智能（AI）的环境影响研究报告 Considerations on the environmental impact of AI in science

Considerationsonthe

environmentalimpact

ofAIinscience

1

©InternationalScienceCouncil,2025

Tocitethisreport:

InternationalScienceCouncil(2025).ConsiderationsontheenvironmentalimpactofAIinscience.DOI:10.24948/2025.10

Authors:DenisseAlbornozandNataliaNorori

Reviewers:MarcelDorschandGongKe

Projectcoordination:DureenSamandarEweis,VanessaMcBride

Projectchair:DavidCastle

Fundingacknowledgement:Thisworkwascarriedoutwiththeaidofagrantfromthe

InternationalDevelopmentResearchCentre(IDRC),Ottawa,Canada.TheviewsexpressedhereindonotnecessarilyrepresentthoseofIDRCoritsBoardofGovernors.

Design:

MrClinton

Coverphoto:

FrankRamspott

AbouttheInternationalScienceCouncil

TheISCisaninternationalnon-governmentalorganizationwithauniqueglobalmembershipthatbringstogether250internationalscientificunionsandassociations,nationaland

regionalscientificorganizationsincludingscienceacademies,researchcouncils,regionalscientificorganizations,internationalfederationsandsocieties,andacademiesofyoungscientistsandassociations.

TheISCworksatthegloballeveltocatalysechangebyconveningscientificexpertise,adviceandinfluenceonissuesofmajorimportancetobothscienceandsociety.

Contents

KEYTAKEAWAYS

4

ABOUTTHISPAPER

5

INTRODUCTION

6

SECTION1:FOUNDATIONALCONCEPTSANDFRAMEWORKS8

1.1GreenAI 8

1.2SustainableAI 9

1.3EthicalandresponsibleAI 10

SECTION2:ENVIRONMENTALIMPACTOFAIACROSSTHELIFECYCLE11

2.1EnvironmentalimpactsofAI 11

2.2Alifecycleapproachtoassessingenvironmentalimpact 12

-

Softwarelayer12

-

Hardwarelayer13

2.3EstimatingdirectenvironmentalcostsacrosstheAIlifecycle13

-

Estimatingoperationalcosts13

-

Estimatingembodiedcosts14

2.4Transparencyandreporting14

SECTION3:STRATEGIESFORREDUCINGDIRECTENVIRONMENTAL16

COSTSOFAIAPPLICATIONSINSCIENTIFICRESEARCH

3.1Reducingenvironmentalcostsinthesoftwarelifecycle 16

-

Datamanagement16

-

Algorithmicoptimization17

-

CASESTUDY1:InkubaLM:Asmalllanguagemodelforlow-resourceAfrican18

languages

3.2Reducingenvironmentalcostsinthehardwarelifecycle18

-

Energy-efficienthardwareandcomputing18

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CASESTUDY2:TinyMLenablesdataanalysisandresearchinlow-costdevices19

3.3Energy-efficientdatacentres19

-

CASESTUDY3:GreenAIinfrastructuresacrossregions20

3.4Decommissioningandend-of-lifemanagement21

CONCLUSION

22

REFERENCES

23

APPENDIX1:GLOSSARY28

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Keytakeaways

•Thereislimitedawarenessandevidenceabouttheenvironmentalcostsofusing

artificialintelligence(AI)inscientificresearch.Thisarticleoffersframeworksand

toolsscientistsandresearchinstitutionscanconsidertoassesstheenvironmental

impactsoftheirresearchaspartofamoresustainable,ethicalandresponsibleuseofAIinscience.

•AddressingtheenvironmentalimpactofAIrequiresamulti-dimensionalapproach.ScientistsandresearcherswhoareplanningtoincorporateAIintotheirworkflowsneedtoassesstoolsinlightoftheirscientificvalue,socialequityandenvironmentalcostsacrosstheentireAIlifecycle,withattentiontoreboundeffectsandlong-termconsequences.

•Adoptingmoreresource-efficientAImodelshasenvironmentalandsocialbenefits.Smaller,localandfrugalapproachestoAIcanimproveaccessibility,affordability,

transparencyandsocialinclusionaroundtheuseofAI,especiallyindiverseandresource-constrainedresearchcontexts.

Keytakeaways

4

Aboutthispaper

Thispaperexaminestheenvironmentalimplicationsofapplyingartificialintelligence(AI)

inscientificresearch.Itservesasaprimerforscientists,researchinstitutionsandscience

policy-makerswhoseektounderstandvariousapproachestoaddressingtheenvironmentalimpactofAIinscience.Inaddition,itoffersguidanceonhowreducingenvironmental

costscancontributetothebroadergoalsofsustainabilityandethicalAIuseinresearchenvironments.

AlthoughevidenceonthespecificenvironmentalimpactsofAIinscientificresearchisstillemerging,thepaperprovidesconceptualframeworksandpracticaltoolstohelpassesstheenvironmentalimplicationsofthefullAIlifecyclewithinscientificprojects.Thefirstsectionintroduceskeyframeworksforunderstandingenvironmentalimpactsinaholisticway.

Thesecondsectionoutlinesanapproachfordefiningandmeasuringenvironmentalcosts

acrosstheAIlifecycle.Thethirdsectionpresentsconcretestrategiesforreducingthedirectenvironmentalfootprintofscientificprojectsthatuseordependonresource-intensiveAI

applications.

ThispaperreflectsonhowenvironmentalconsiderationsintersectwiththebroaderthemesofAIsustainabilityandethics,thoughanin-depthanalysisoftheseframeworksfallsoutsideitsscope.Similarly,whiletheroleofAIinadvancingtheSustainableDevelopmentGoalsisacknowledged,itisnotexploredindetail.SocialandeconomicimpactsofAIarediscussedonlyinsofarastheyrelatetoenvironmentalcosts.

ThepaperispartofaseriesofthreeprimersthatexplorevarioustechnicaldimensionsofAIanditsimpactonscience.Theotherprimersare“TypesofAIinscience”and“DataforAIinscience”.

Aboutthispaper

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Introduction

Artificialintelligence(AI)isbecomingatransformativetoolacrossscientificfields(LeCun,

2025).Asystematicreviewofover70millionpapersfoundthat,since2015,theinfluenceanduseofAIhasspreadtonearlyeveryareaofnaturalsciences,impactingbiology,chemistry,

geologyandphysics(GaoandWang,2024).NotablebreakthroughsincludehighlyaccurateproteinstructurepredictionswithAlphaFold(Jumperetal.,2021);usingmachinelearningtogeneratethefirst-everphotoofasupermassiveblackhole(Brodericketal.,2022);and

improvedforecastingmodelsforextremeweatherevents(Bodnaretal.,2024).AdvancesinAIareimpactingsocialscienceresearchaswell,transformingmethodsinfieldssuchaslinguistics,politicalscienceandhistory(PennToday,2023).

Muchofthisprogresshasbeendrivenbyadvancesinmachinelearninganddeeplearning

modelsthatcanmoreaccuratelyidentifypatternsandmakepredictionsfromvast

unstructureddata(Choudharyetal.,2022).Inthecaseofsocialscience,generativeAI,

largelanguagemodelsandmultimodalAItools–allofwhicharepoweredbydeeplearning

architectures–arealsobecomingpowerfultoolsfordataanalysis,simulationandhypothesisgeneration(Grossmanetal.,2023).Thedemandforlargedatasets,specializedhardware

andhigh-performancecomputingresourcesrequiredforcomplexAIsystemshasalsoraisedquestionsforresearchinstitutionsaroundtheaccessibility,affordabilityandenvironmentalimpactofincorporatingAIacrossscientificdomains(LeCun,2025).

OneofthekeychallengesistheenvironmentalcostofAIapplications(Luccionietal.,2024b).AgrowingbodyofevidencehasdemonstratedthattrainingcomplexAImodels,suchasdeeplearningorgenerativeAIapplications,involvessubstantialresourceuseandgreenhousegasemissions(OrganisationforEconomicCo-operationandDevelopment,2022).Onestudy

suggeststhattrainingonelargelanguagemodelcangenerateapproximately300,000kgofcarbondioxideemissions,equivalentto125round-tripflightsbetweenNewYorkandBeijing(Dhar,2020).

ThecomputationaldemandsoftrainingAIaredoublingapproximatelyevery100days,withprojectionssuggestingthatby2028,AIcouldconsumemoreelectricitythanIcelanddid

in2021(WorldEconomicForum2024).Reproducibilityalsoremainsachallenge,asmany

deeplearningmodelsareopaqueanddifficulttoreplicate.Thiscreatessituationsinwhich,duetothelackofreportingandtransparency,therearemanyunknownssurroundingthe

trueenvironmentalcostofAItoolsusedforscientificresearch,whichcanalsoleadto

unnecessaryenergyusewhenmodelscannotbereusedorrequirerepeatedexperimentstoreproduceresults(Lannelongueetal.,2023).

Introduction

ThecostsofAIextendbeyondthetrainingphase.Althoughoftenperceivedasanintangibledigitalsystem,AIdependsonextensivephysicalinfrastructurethatconsumesenergyandnaturalresources(OrganizationforEconomicCo-operationandDevelopment,2022).Theproductionofspecializedhardware,suchasgraphicsprocessingunits,whichareessentialfortrainingandrunningAImodels,requirestheextractionandprocessingofrareearth

mineralsandmetals.Thisprocesscontributestoenvironmentaldegradationthrough

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increasedgreenhousegasemissions,addedstrainonoverburdenedwatersystems,and

serioushumancosts,withworkersinvolvedinthesesupplychainsfacingunsafeconditions,exposuretotoxicchemicalsandviolationsoftheirhumanrights(Nayar,2021).

Similarly,theenergydemandofdatacentres,whichpowerAIsystemsbystoringand

processingvastvolumesofdata,isdoublingapproximatelyeveryfouryears(Thangametal.,2024).Thesefacilitiesconsumesubstantialamountsofenergy,whethersourcedfromfossilfuelsorrenewablesources,andrequirelargequantitiesofwaterforcoolingandmaintainingoptimalhardwareperformance.Thisplacesfurtherpressureonlocalenergygridsand

watersupplies,particularlyinregionsfacingresourceconstraints(Mytton,2021).AsAIusecontinuestogrow,theincreasingdemandfordatacentreinfrastructureislikelytoplaceanevengreaterburdenoncountriesintheGlobalSouth,wherechallengingclimateconditions,waterscarcity,limitedconnectivityandfrequentpoweroutagesareongoingconcerns

(UnitedNationsConferenceonTradeandDevelopment,2024).

ThesedevelopmentsraisequestionsabouttheenvironmentalsustainabilityofusingAIforscientificresearch(SamuelandLucassen,2022).AwarenessofAI’senvironmentalcosts

varieswidelyamongresearchers,particularlyinacademicsettingswherecomputational

resourcesmayseem‘free’andtheirenvironmentalimpactlargely‘invisible’(Lannelongueetal.,2021b).Emergingeffortsarebeginningtoquantifythecarbonfootprintofdata-drivenmethodsacrossdisciplinessuchasastrophysics,proteinscience,healthresearchand

computing(LannelongueandInouye,2023;Jahnkeetal.,2020;Lannelongueetal.,2023).Forinstance,astudyfoundthattraininglargedeeplearningmodelssuchasAlphaFoldandESMFoldcanhaveacarbonfootprintofover100tonnes(LannelongueandInouye,2023),theequivalentofpoweringover20homesintheUnitedStatesforayear,demonstrating

thatevenscientificbreakthroughsasimpactfulasproteinpredictioncancomeatsteepenvironmentalcosts.

Theseearlyfindingsprovideevidenceonhow‘sciencecan,andfrequentlydoes,impact

theenvironment’(Lannelongueetal.,2023),raisingquestionsabout‘whoshouldbe

responsiblefortheseimpactsandhowtheseresponsibilitiesshouldbedistributed’(SamuelandLucassen,2022).AsAIandotherdata-intensivetechnologiesbecomemoreembeddedinscientificworkflows,researchersandfundingbodiesareincreasinglyintegrating

sustainabilityandenvironmentalconsiderationsintoresearchethicsframeworks,exploringwaystosupportmoreenvironmentallyresponsiblescientificpractices(Murrayetal.,2023).

Introduction

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Section1:Foundationalconceptsandframeworks

AddressingtheenvironmentalimpactofAIrequiresamulti-dimensionalapproachthat

integratestheprinciplesofsustainableAI,greenAIandresponsibleAI.ThisinvolvesnotonlyreducingthedirectenvironmentalcostsofAItechnologies,suchasenergyconsumption

andcarbonemissions,butalsodevelopingholisticframeworksthatassesstheirlong-termsustainability.Theseeffortsmustbealignedwithbroadercommitmentstoresponsible

andethicalAI,ensuringthattheuseofAIinknowledgeproductionandsocietycontributespositivelyandequitablytohumanandenvironmentalwellbeing.

ThissectionexamineskeyconceptsandframeworkstoconsiderwhenaddressingtheenvironmentalimpactofAIinscientificresearch.

1.1GreenAI

GreenAIisafielddedicatedtounderstandingandreducingtheecologicalfootprintof

computinganddata-driventechnologies.Itpromotesenergyefficiencyandsustainable

computingpracticesthroughouttheentireAIdevelopmentlifecycle(Verdecchiaetal.,

2023).ItaimstolowercarbonemissionsandenvironmentalcostswhilesupportingAI

adoptionthatisalsosociallyinclusive(Schwartzetal.,2020).AcentralconcernofGreenAIistheincreasingenergyuseinhigh-performance“state-of-the-art”research,alsoreferredtoasRedAI.

RedAIemphasizesperformanceandaccuracy,oftenrelyingonenergy-intensivemodelsthatrequirevastdatasets,substantialcomputationalresourcesandrepeatedtraining(Bolón-

Canedoetal.,2024).Thesepractices,largelydrivenbyBigTechcompanies,havefuelledthedevelopmentofincreasinglycomplexandresource-demandingmulti-purposeAIsystems.

Section1:Foundationalconceptsandframeworks

Inacompetitiveresearchlandscape,thepressuretoachievesuperiorresultshasfurther

intensifiedtheenvironmentalandfinancialcostsofAIdevelopment(Luccionietal.,2024a).

Incontrast,GreenAIencouragesmoreenergy-efficientalternativesthatpreservemodelperformancewhilereducingresourceuse(Schwartzetal.,2020).Thisincludesadopting

technicalsolutionstomakealgorithmdesign,hardwareanddatamanagementmore

energy-efficient.GreenAIalsosupportsthedevelopmentofnarrowerandsmallermodelsdesignedforspecifictasks,which,inadditiontoreducingenvironmentalcosts,canbemoreappropriateandeffectiveformanyresearchtasks,domainsandcontexts.ThesepracticesarediscussedinmoredetailinSection3.

Theemphasisonsmaller,task-specificandpurpose-drivenAIsolutionsreflectstheprincipleofenergysufficiency,whichinvolvesintentionallyreducingenergyconsumptionthrough

energy-consciousdesign.Thisleadstomoreaffordableandaccessiblearchitectures

suchasFrugalAI,whichsupportstheuseofAIinsettingswithlimitedcomputingpowerandinfrastructure,whichworkwellinenvironmentswithscarceresources(Yamada,2024).

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Relatedmethods,suchasEdgeAIanddistributedcomputing,prioritizelocaldataprocessingandofferpromisingpathwaysfordevelopingAItechnologiesthatareenvironmentallysustainable,privacy-consciousandcost-effective.

1.2SustainableAI

ThegrowingenvironmentalcostsofAIareoftentiedtoconcernsaboutsustainability.ThefieldofsustainableAIconsiderssustainabilityfromtwoperspectives(VanWynsberghe,2021).

AIforSustainabilityreferstotheapplicationofAIsystemstoaddressSustainable

DevelopmentGoals,includingenvironmentalsustainability,anditsapplicationsinrelevantfieldssuchasclimatescience,environmentalmonitoringornatureprotection.Thesecond,SustainabilityofAI,referstothe‘sustainabledevelopment’ofAIsystems,emphasizingpracticesthatreducetheirenvironmental,economicandsocialcosts.

BothapproachesaregroundedintheUnitedNationsBrundtlandCommission’sdefinitionofsustainability,whichemphasizes‘meetingtheneedsofthepresentwithoutcompromisingtheabilityoffuturegenerationstomeettheirownneeds’(TheWorldCommissionon

EnvironmentandDevelopment,1987).ItrecognizestheinherenttensionofpursuingtheinnovationandefficiencyaffordedbyAIsystems,whileaddressingtheirenvironmental,economicandsocialimpacts.

ApplyingasustainabilitylensrequiresresearcherstoholisticallyaddressdimensionsinAIdevelopmentbeyondperformanceandaccuracy(VanWynsberghe,2021).Areviewof100highlycitedmachinelearningpapersfoundthatonly15percentaddressedsocietalneeds,andjust1percentdiscussednegativeimpacts.Mostpapersemphasizedvaluessuchas

performance,efficiencyandscalabilityasopposedtoaddressingimpactsforunderservedcommunitiesorthereductionofenvironmentalharm(Birhaneetal.,2022).

TherapidlygrowingecologicalfootprintofAItechnologieshasalsoresultedincoordinatedactiontowardsunderstandingandaddressingthesustainabilityofAI(Ramanetal.,2024).

Theseinclude,butarenotlimitedto:

•

CoalitionforsustainableAI

:AninitiativebytheFrenchgovernment,the

UN

EnvironmentProgramme

andthe

InternationalTelecommunicationUnion

,aimedat

promotingtheresponsibledevelopmentanduseofAIforsustainabledevelopment,withanemphasisonthedevelopmentofAIthatisrespectfulofplanetaryboundaries.

•

CoalitionforDigitalEnvironmentalSustainability(CODES)

1:Aglobalhubforpolicy-makers,academics,technologycompanies,andNGOstoleadandcontributetodigitalsustainabilityinitiatives,includingmitigatingthenegativeenvironmentalandsocial

impactsofdigitalization.

•

SustAIn:SustainabilityindexforAI

:Aframeworkandpracticalchecklistsdevelopedtosupportorganizationsinadvancingtheenvironmental,socialandeconomicdimensionsof‘sustainableAI’,understoodasthedevelopmentanddeploymentofAIsystemsthat

respectplanetaryboundaries,donotreinforceproblematiceconomicdynamics,anddonotendangersocialcohesion.

Section1:Foundationalconceptsandframeworks

1TheInternationalScienceCouncilisamemberofCODES.

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Together,theseeffortsreflectagrowingrecognitionthataddressingtheenvironmentalimpactofAIrequiresaholisticandinterdisciplinaryapproach.

1.3EthicalandresponsibleAI

ReducingtheenvironmentalimpactofAIdevelopmentalsorequiresattentiontoitsethicaldimensions.ThegrowingadoptionofethicalAIandresponsibleAIframeworkswithinscientificinstitutions(Banoetal.,2025)reflectsarecognitionthatprinciplessuchas

fairness,accountabilityandtransparencyarekeytobuildingtrustinAIsystems(ISO,n.d.).Thisshiftisalsoevidentacrossdiversecontexts,includingcountriessuchasAustralia,

Uruguay,ChinaandMalaysia,wheretheresponsibleuseofAIinscienceisatthecentreofnationalAIstrategies(Castleetal.,2024).

However,todate,manyoftheseframeworksfailtoframesustainabilityasacomponent

ofethicalandresponsibleAIpractice(Luccionietal.,2025).A2019globalreviewfound

thatonly14of84AIguidelinesmentionedsustainability(Jobinetal.,2019),including

the

EuropeanGuidelinesforTrustworthyAI

and

theUNESCORecommendationontheEthics

ofAI

,consideringbothenvironmentalsustainabilityandsocietalwellbeing.Inresponsetogrowingevidence,AIresearchersarecallingforathirdwaveofAIethicsthatdirectly

addressestheenvironmentalcrisisandplacessustainabledevelopmentatitscentre(Luccionietal.,2025).

Recentinitiativeshavebeguntoaddressthisgap.TheWorkingGrouponResponsibleAIoftheGlobalPartnershiponArtificialIntelligencehasdevelopeda‘

ResponsibleAIStrategyfor

theEnvironment

’tohelpMembercountriesassesstheenvironmentalfootprintofAImodelsandapplications.Researchfundingbodies,suchasthe

WellcomeTrust

,areintegrating

environmentalconsiderationsintofundingrequirements.Newresearchethicsframeworks,suchasenvironmentalresponsibility,arealsoemergingtoguideresearchersinadoptingmoreecologicallyconsciousapproacheswhenconceiving,planning,conductingand

concludingresearch(Murrayetal.,2023).

Applyinganethicallenstosustainabilitycanalsohelpresearchersunderstandthe

implicationsofenvironmentalharmforvulnerablepopulations.ResearchersfromHuggingFaceandtheDistributedAIResearchInstitutehavehighlightedhowtheunevenglobal

distributionofAI’secologicalimpactscanexacerbateexistingsocio-economicdisparitiesandsocial-ecologicaljusticeconcerns(Luccionietal.,2025).Examplesincludehowtheconstructionofdatacentrescontributestowaterscarcityindrought-proneregionsandto

theproliferationofelectronicwasteinnearbycommunities(Barrattetal.,2025).AddressingenvironmentalequitycontributestothedevelopmentofAIsystemsthatarenotonlyenergy-efficientbutalsofair,justandequitable(Lietal.,2023).

Section1:Foundationalconceptsandframeworks

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Section2:EnvironmentalimpactofAIacrossthelifecycle

AssessingthesustainabilityofAIrequiresaholistic,interdisciplinaryapproachthataccountsforenvironmental,economicandsocialcoststhroughouttheAIlifecycle–fromhardware

manufacturingandmodeltrainingtodeploymentanddecommissioning(Luccionietal.,

2024b).Althoughtherearestillnostandardizedmethodsforevaluatingsustainabilityacrossallstages,significantprogresshasbeenmadeinmeasuringenvironmentalimpacts.

2.1EnvironmentalimpactsofAI

Researchersdistinguishbetweentwotypesofenvironmentalimpacts(OrganizationforEconomicCo-operationandDevelopment,2022):

1.Directimpactsarefirst-orderimpactsfromtheAIlifecycleanddescribe‘computing-related’consumptionofresources:energy,waterandmineralresources,aswellas

emissionsande-wastegeneratedfromoperatingAIsystems.Evidenceindicatesthatthesearepredominantlyharmfultotheenvironmentandecosystems.

2.IndirectimpactsareeffectsdrivenbytheimmediateapplicationofAIacrosssectors.Theyaredifficulttopredictandcanbeeitherbeneficialorharmful.Forexample,AImayimproveenergyefficiencyinsmartgridsbutcanalsodriveunsustainableconsumptionpatternsthroughautomation.

Therearetwotypesofindirectimpacts:second-orderimpactsandhigher-orderor

‘system-levelimpacts’.BothareindirecteffectsthatemergeovertimeasAIreshapes

SectionEnvironmentalimpact2:ofAIacrossthelifecycle

productionandconsumptionpatterns,withthelatterpotentiallydrivingstructuralchangesintheeconomyandsociety.Indirectimpactscouldultimatelyoutweightheefficiencies

obtainedbystrategiesfocusedonmitigatingfirst-orderimpacts.

Reboundeffectsareanadditionalcategorythatreferstosituationsinwhichefficiency

gainsleadtogreaterresourceusethatcanoffsetinitialenvironmentalbenefits.Forexample,moreefficientdatacentres,whileconsuminglessenergyperoperation,mightbeusedto

handlemoredataandpowermoreservices,ultimatelyincreasingtotalenergyconsumption.Anticipatingandmeasuringreboundeffectscanillustratehowinitialefficiencygainscanbeoutweighedbynegativeconsequences,challengingthenotionthatenergyefficiencyalonewilldeliverclimatebenefits.

Whilethereareincreasingmethodologiesandmetricstomeasuredirectenvironmental

impactsofAI,limitedconsensusremainsonhowtomeasureindirectsecond-orderand

higher-ordereffects(OECD,2022).GiventhegrowinguseofAIinscienceanditspotentiallarge-scaleeffectonplanetaryhealth,thereisaneedforaresearchagendathataddressestheindirecteffectsofAIsystemsonknowledgeproduction.

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2.2Alifecycleapproachtoassessingenvironmentalimpact

ThissectionexamineskeyapproachestounderstandingandmeasuringdirectenvironmentalimpactsacrosstheAIlifecycle.

AIresearchersandinstitutions,suchastheOrganizationforEconomicCo-operationandDevelopment,theUnitedNationsEnvironmentProgramme,theUNCommissionforTradeandDevelopment(UNCTAD,2024),andtheInternationalTelecommunicationsUnion,

amongothers,proposeusingalifecycleorend-to-endapproachtoassessenvironmentalimpacts(OECD,2022;UnitedNationsEnvironmentProgramme,2024;International

TelecommunicationUnion,2024).ThemostestablishedmethodologyisLifeCycle

Assessment,whichevaluatestheenvironmentalburdensassociatedwithaproductorservice,fromtheextractionofrawmaterialstowasteremoval(Klöpffer,1997).

Alifecycleapproachcanhelpillustratethewiderscopeofdirectenvironmental

costsinvolvedindesigninganddeployingamodel,clarifywhereemissionsoriginate

andconcentrate,andhighlightwheretargetedinterventionsand“sustainableAIpractices”canreduceenvironmentalcosts.TheAIlifecycleistypicallydescribedintwolayers,or

“stacks”:softwareandhardware.

SOFTWARELAYER

Thefirstistheoperationalorsoftwarelayer(UNEP,2024),alsocalledthemodel

developmentcycle(Wuetal.,2022).ThisincludesalldecisionsregardingthedataandAI

modelarchitecturethatinfluenceenergyandresourceuse.Itcoversrelevantdatawork

(datacollectionandpreparation),algorithmicdesign(modelarchitecture),modeltrainingandmodeldeployment(includinginference)(Clemmetal.,2024).

Theenvironmentalcostsofthislayerarecalledoperationalcostsandarecorrelatedto

thesizeofdatasetsandthemodelarchitecture,whichdirectlyaffectcomputetimeandcarbonfootprint(Chenetal.,2022).Trainingandinferenceareusuallythemostsignificantcontributorstolifecycleenergyconsumption.Forexample,researchfoundthroughanAI

inferenceimpactassessmentthatgenerativearchitecturesaremoreenergy-intensivethantask-specificmodelsasaresultofinference(Luccionietal.,2024a).Duetothecommercialdeploymentoflargemulti-purposegenerativeAImodels,likeOpenAI’sGPT-4,inference

nowrepresentsanincreasingmajorityofAI’senergydemands,withanestimated80–90

percentofcomputingpowerinAItra