Stand.earth2026AI气候宣传争议研究报告科技巨头AI环境影响漂绿行为分析

FEBRUARY2026

TheAIClimateHoax:BehindtheCurtainofHowBigTechGreenwashesImpacts

Contents

ExecutiveSummary

4

Introduction

6

DatacentreexpansionisworseningclimatechangeTheexplosionofAIcompanies’energyuse

6

7

Bigtechgreenwashing

10

WhattypeofAI?GenerativeversustraditionalAI

12

Methodology

14

Results

15

AItype

15

Evidencestrength

16

Conclusion

18

Anewgreenwashingtactic

18

Appendices

20

AppendixA-Methodologydetails

20

AppendixB-Caveats

23

AppendixC-Companyemissionstargetsadjustmentexplanations

23

AppendixD-ClimateAdaptationandresilienceclaims

24

References

25

Credits29

3AUTHOR+ORGANIZATIONS

Author+Organisations

KetanJoshiisananalyst,writerandcommunicatorspecialisingincleanenergy,corporateaccountabilityand

advocacy.Hehasworkedwithrenewableenergycompaniesindataanalysisandadvocacy,governmentrenewableenergyagencies,theCommonwealthIndustrialandScientificResearchOrganisation(CSIRO,Australia)inthe

Data61unit,andwrote"Windfallunlockingafossilfreefuture"withtheUniversityofNewSouthWalespress,publishedin2020.

BEYOND

FOSSILFUELS

STAND

ClimateAction

AgainstDisinformation

GreenScreen

Coalition

FriendsoftheEarth

GREENWEBFOUNDATION

E×ECUTIVESUMMARY

ExecutiveSummary

Theexpansionofdatacentres-whichisdrivenin

largepartbyAlgrowth-iscreatingashockingnew

demandforfossilfuels.ThetechcompaniesdrivingAlexpansiontrytodownplayAI'sprovenclimateimpactsbyclaimingthatAlwilleventuallyhelpsolveclimate

change.Ouranalysisoftheseclaimssuggeststhat

ratherthanrelyingoncredibleandsubstantiateddata,

thesecompaniesarewritingthemselvesablankcheque

topolluteontheemptypromiseoffuturesalvation.

WhilethecurrentnegativeeffectsofAlontheclimateareclear,provenandgrowing,thepromiseoflarge-

scalesolutionsisoftenbasedonwishfulthinking,andalmostalwayspresentedwithscantevidence.

Keyfindings:Bait-and-switchandweakevidence

ThisanalysiscollectedthemostprominentAlclimateclaimsanddetermineda)whattypesofAlwere

referredtoandb)whatevidencewaspresentedtobackupthoseclaims.Itfindsthat:

1.Mostclaimsofclimatebenefitrelateto'traditional'AI,whichhasamuchlowerenvironmentalimpactthanconsumergenerativeAltools.Evenifthese

benefitsarereal,theyareunrelatedto-and

dwarfedby-themassiveexpansionofenergyusefromthegenerativeAlindustry.

2.WhereclaimsoftraditionalAlclimatebenefits

aremade,theytendtorelyonweakerformsofevidence,suchascorporatewebsites,ratherthanpublishedacademicresearch.Only26%cited

publishedacademicresearchwhile36%didnotciteanyevidenceatall.

Oldermachinelearningtoolsthatperformnarrowtasks,suchasclassifyingimages,arenotlinkedto

rapidgrowthinAlinfrastructuretoday,nortothe

correspondingclimateandenvironmentalimpacts.Rather,studiesfindmostoftheclimateimpacts

willcomefromconsumergenerativetools,such

asCopilot,GeminiorChatGPT.Atnopointdid

thissearchoranalysisuncoverexamplesinwhichconsumergenerativesystemswereleadingtoa

material,verifiableandsubstantiallevelofemissions

reductions.BymuddlingthesetwotypesofAlintooneumbrellaterm,purportedclimatesolutionsarecoupled

toextremepollutionandpresentedasapackagedeal.

E×ECUTIVESUMMARY

ENERGYCONSUMPTION

Projectedenergyconsumptionwillbe13xhigherforgenerativeAIthantraditionalAl,intheyear2030

Consumer

TraditionalAl

generativeAl

CLIMATEBENEFITCLAIMS

..uttheclaimsofclimatebenefitscentrealmostexclusivelyon'traditionalAI

Consumer

TraditionalAl

generativeAl

FIGURE1-CLIMATEBENEFITversusDAMAGE

ThisgraphicillustrateshowmostclimatebenefitclaimsrelatetotraditionalAl,whereasmostharmwillrelatetoconsumergenerativeAI.Thefirstcomparestheprojectedelectricityconsumptioninacentral2030scenario,alsorepresentedbytheareaofeachsquare.ThesecondcomparisonshowsthenumberofclaimsbyAltype,representedbytheareaofeachsquare.

Thisdeceptivebait-and-switchisanewformofgreenwashingusedtojustifyastampedeof

unprecedenteddigitalbloatrunningroughshodoverclimateandlocalcommunitiesthroughtheformofdatacentres,mostofwhichwillnotbeprocessingclimate-beneficialcomputationontheirservers.

ClaimsthatdeploymentofAl,regardlessoftype,

canbringaboutgigatonne-scalereductionsinglobalemissionsare,atbest,deservingofmorescrutiny

andatworstcatastrophicallyoverstated.Putsimply,theevidenceformassiveclimatebenefitsofAlis

weak,whilsttheevidenceofsubstantialharmisstrong.

TheoverstatementofthepotentialforAltotackle

climatechangemisdirectsfromtheimmediatecostsofgiantenergyandwaterintensivedatacentresbeingimposedoncommunitiesworldwide.

ThenoveltyofgenerativeAlandtheintensityof

datacentreexpansionhavelentthemselvestonewgreenwashingtactics.Thisanalysisshowsthatto

bringthedeploymentofdigitalservicesinbounds

withthephysicallimitsoftheplanet,techcompaniesinvestinginAlshouldimplementactualsustainability

measuresratherthanmaskingever-worsening

damagetotheclimateandenvironmentwithvague

termsandweakevidence.

INTRODUCTION

Introduction

Anenvironmentallydestructiveheavyindustryhas

emergedintheblinkofaneye.Therapidexpansionofdatacentrestodrivethedeploymentoftechnologiesmarketedas“artificialintelligence”(Al)rescues

prospectsforfossilfuelsbyboostingdemand

andtriggeringpanickeddeploymentofnewfossil

infrastructure.Thiscorrosivetrendisoftenjustifiedonthegroundsthat"AI"willultimatelyundothesesinsbyservingasanetbenefittoclimateaction.

ThisreportinterrogatesforthefirsttimewhetherthisclaimhasmeritbyexaminingthetypesofAlreferencedintheseclaims,andthestrengthoftheevidence

putforthalongsidethem.Itfindsthattheclimate

impactofenergy-hogginggenerativeAlisobfuscated

anddownplayedthroughtheuseofgeneralised'Al

sustainability'claims,creatingvaguenessaround

completelydifferenttypesoftechnologiesandcoupling

harmfulgenerativeAltoolder'traditionalAI.

Italsofindsthemajorityofclaimedclimatebenefits

comewithremarkablyweakevidence,ornoevidenceatall.Thisisanewformofgreenwashingusedbythetechindustry;onethatmustbechallengedurgently.

Datacentreexpansionisworseningclimatechange

TheInternationalEnergyAgency(IEA),provides

acomprehensiveoverviewoftheuniquenessof

growthininformationtechnology.Datacentre

demandaccountedfor1.5%oftheworld'selectricityconsumptionin2024andhasgrownatfourtimes

therateofbroaderelectricityconsumptionsince

2017.TheIEAprojectsadoublingoftotaldatacentreconsumptionby2030,muchofitdrivenbythegrowthofAl.BloombergNewEnergyFinanceestimates²thatthiswillincreasetotalglobalpowersectoremissionsby10%overthecomingdecade.

Theconstructionandoperationofthedatacentresthemselvesseeabroadsuiteofdirectimpactson

greenhousegasemissions:

■Construction:Theemissionsassocatedwith

thebuildingofdatacentres.Thisincludesthe

manufacturingofcomputerchips³,asector

whereemissionshavebeenincreasinglysteadilythankstoAl⁴.Italsoincludesconcrete,steeland

machinery,alongsidetheincreasedextractionofrawmaterials5.

Gridfossilunitdispatch:Theincreaseddispatchoffossil-fuelledpowergenerationinresponsetonewdatacentredemand,suchasinTexas6orIreland7,8

Gridfossilplantpersistence:Theconstructionofnewlong-lastingcoalandgas9,10powerstationsonpowergrids,alongwiththere-energisation

ofrecentlyretiredfossilassetsandthelifespanextension11,12offossilassetspreviouslyduetoretire.

Off-gridfossilplantconstruction:Ariseindirectly-connectedorlocally-createdfossil-fuelledpowergeneration,primarilyfossilgas,suchasthoseat

Meta13,OpenAI14andxAI15sites.

Resourcediversion:Thediversionofresources

previouslyearmarkedfortheenergytransition

towardsthedatacentreboom,suchasfinance16,batteryproduction17,windturbinesandsolar

panels.Thecapacityforpowergridstoabsorbnewloadfromdatacentrescompetesinparticularwiththelarge-scaleelectrificationofhomes,buildings,transportandheavyindustry.

Variousend-usesofdatacentresoftwareservicesfacilitatethefossilfuelindustry,forexample,AI

isusedbyfossilfuelcompaniestostreamline

explorationandincreaseextraction18.Similarly,

applicationssuchasthegenerationoftextarebeingusedbyclimatedenialgroupstofacilitatemass

disinformationcampaigns19.Thesefacilitiesalso

causeacutenon-climateimpacts,suchasharmingbiodiversityorsequesteringvastvolumesofwaterinwater-stressedregions20.

Agrowingtrendofweakened,missedorignored

climatetargetsandambitionsforcompaniesand

regionsdemonstratestheimpactsofdatacentre

growth.UtilitiesintheUS,forinstance,nowexplicitlycitenewdatacentredemandasakeycauseof

missedtargetsandcoalplantextensions21,22.

Australia’sindependentClimateChangeAuthority

weakenedrecommended2035targetspartlyonthegroundsofdatacentregrowthandthegovernment’senvironmentagencyciteditasakeyreasonfor

7INTRODUCTION

morepessimisticprojectionsofpowersector

emissions23,24.Recentwork25byresearcherHannahDalylaysoutthatIreland’sdatacentreexpansion‘posesamaterialrisktoIreland’sabilitytomeetitscarbonbudgets’.

Mostmajortechnologycompaniesareveeringawayfromtheclimatetargettheysetforthemselves.For

mostthisistrueevenwhenyouaccountforclaims

they’reoffsettingtheiremissionsusingcertificatesorpurchasingdealsmadedirectlywithrenewableenergycompanies.Thisisdepictedinthefigurebelow.

Thesignificantincreaseingreenhousegasemissionsassociatedwithdatacentreexpansion,strikingly

manifestedinthetrajectoriesoflargetechnology

companyemissions,isoccurringforakeyreason:theincreaseddeploymentofAItechnologies.

TheexplosionofAIcompanies’energyuse

Thefundamentalnatureofthegenerationoftext,

imagesandvideosrequiresboththeingestionof

largevolumesofdataintoamachine-learningmodel,theongoing“training”ofthatmodelwithnewdata,andthecomputationally-intensive‘inference’of

informationwhenuserssubmitqueriesthatrequireasynthesisedoutput.

AcorebeliefwithinvariousAIdevelopmentcircleshasbeentheideaof‘scalinglaws’,whichtheorisemoreefficacyonlystemsfromincreasinglylarge

andenergy-intensivemodels.Recently,OpenAICEOSamAltmansaid“Idoguessalotoftheworldgetscoveredindatacentresovertime”27;anextremely

unlikelyscenariobutillustrativeofthescalebeingconsideredbykeyplayersintheindustry.Another

reasonforgrowthisthetrendofAIbecoming

embeddednon-optionallyintomostdigitalservices,beingtriggeredwithouttheexpressintentofusers.Arecentstudy28examinedexamplesofthis,suchasGoogle’s“AIOverviews”insearchresults.

Whileprecisesharesaredifficulttopindown,

itisclearAIcomputationisgrowing,alongside

pre-existingfunctionssuchasfilestorage,cloud

computing,videostreamingandcryptocurrency.TheIEA’s“EnergyandAI”reportestimatesapproximately

INTRODUCTION

Amazon

Google

25

35

30

Unadjusted

20

25

15

20

Adjusted

15

10

10

Target

5

5

0

2019

2020

2021

2022

2023

2024

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Microsoft

Meta

30

7

25

6

5

20

4

15

3

10

2

5

1

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

FIGURE2-COMPANYEMISSIONSGOALS

Thesechartsshowcompiledemissionsdataforfourmajortechcompaniescomparedtothetrajectoryof

targetstheysetinrecentyears."Unadjusted"emissionsestimatethevolumeoffossilfuelsusedintheirpower

consumptiononagrid."Adjusted"emissionsaccountforcertificatesfromrenewableenergycompanies,orpowerpurchasingdeals.Forsomecompanies,theseadjustmentsincludearbitraryexclusionsofcertaincategoriesof

emissions.Moredetailcanbefoundintheappendixofthisreport.Allrawdataarepubliclyavailableatauthor'swebsite.Allvaluesareinmegatonnesofcarbondioxideequivalent.

15%ofexistingdatacentredemandisattributabletoAlapplications,notingthatestimateishighlyuncertain.

ResearcherAlexDeVries-Gaoestimated²9thatglobaldemandforAlsystemswasbetweenseventotwelvegigawattsin2024(11-20%ofdatacentres),basedonthedeploymentofAl-specifichardwareunits.Anotherpaper³⁰byDeVries-Gaoestimatesthetotalglobal

footprintofAltobe"between32.6and79.7milliontonsofC02emissionsin2025”.ThisrangeispartlycorroboratedinaNovember2025paper³1published

inNature,whichestimatesanadditional24-44milliontonnesofCO2annuallybetween2024and2030,

purelyfortheUnitedStates.

However,theproportionofAlinthegrowthofdatacentredemandseemshigher.TheIEA's“Energy

andAl"reportpinshalfthegrowthinthecominghalf-decadeto'acceleratedservers',whichtheIEAsaysisthecomputationhardwaremostassociatedwithAlservices.TheIEAalsocautionsthiscouldbeanunderestimateoftheportionofgrowth

relatedtoAl.

INTRODUCTION

Figure2.10Breakdownofthefactorsdrivingelectricitydemandgrowthin

datacentresintheBaseCase,2024-2030

■Serverstock

Serverwattage

■Utilisationrate

■Idlepower

■OtherITequipment

■Infrastructure

200…

Conventionalservers

50

20242030

Accelerated

servers

100

IEA.CCBY4.0.

Themaindriversofgrowthinelectricityconsumptionfromdatacentres

aretheincreasesinthestockandwattageofservers

FIGURE3-IEASERVERGROWTH

Figure2.10fromtheIEA's"EnergyandIA”report,illustratingthelargeproportionofgrowthrelatingto'accelerated'servers

Australia'sCleanEnergyFinanceCorporation

recentlyestimated³284%ofnewhyperscalefacilitiesinthecountrywillrelatetoAl

applications.

ItisclearthatAlalreadyaccountsforasubstantial

proportionoftotalglobaldatacentredemand,

andwillbethekeydriverofgrowthinthecominghalf-decade.Thisgrowthiscausingaworseningofgreenhousegasemissions.

InDeVries-Gao'smostrecentpaper³³quotes³4areincludedfromMeta,MicrosoftandGoogleeach

specifyingthatAlhasbeenaprimarycauseoftheirgrowingenergyconsumptionandemissions.

However,noneofthesecompaniesdisclosematerialestimatesofenergydemandgrowth

thatrelatesspecificallytocomputationforAl

applications.Notably,companieslikeGoogle

haveshown³5theycanestimatetheenergy

andemissionsimpactofsinglegenerativeAl

interactions,suggestingpublishingapproximationsoftheabsolute,company-wideclimateimpactofAlcouldbepossible.

Bigtechgreenwashing

Astheimpactsofrapiddatacentregrowthhave

emerged,sotoohaveeffortstoerase,distractfromormasktheseimpacts.Thishasoftenreliedon

traditional‘greenwashing’tactics,including:

Hidingemissions:Manycompaniesartificially

lowertheirreportedemissionsusingclever

accountingstrategies,suchasbuyingcertificatesforrenewableenergywhiletheiractualoperationsarepoweredbyfossilfuels36.Somecompanies

aretryingtoworsenthispracticeandpreventreformsoftheaccountingstandards37.

Emptypromises:Thepromiseduseofnuclearfusion38,carboncaptureandstorage39,40,space-baseddatapairedwithsolar

directair.

cases,keyplayersclaim“superintelligent”AIwillfindasolutiontoclimatechange.Theclaims

aresuperficiallyplausible,incrediblyexpensiveandunlikelytowork.Emptytechpromises

distractfrom-

offossilfuelsinfrastructure.

Downplaying:Manytechnologycompanies

havebegundrip-feedingsmallandrelatively

meaningless“disclosures”ofAI-related

sustainabilityinformation.OnerecentexampleisGooglesharingthecarbonemissionsofa

singlechatbotquery,butnotdisclosingtheentiresystem’scarbonfootprint,northeimpactsof

images,videosorlongerqueries44.

Tacticalfatalism:Prominentvoicesintech

declareclimategoalsessentiallyunachievable,suchasGoogle’sformerCEOEricSchmidt45,

CrusoeEnergy’sCEO46,ortheformerCEOof

Ireland’sgridoperator(alsoaprominentvoiceinthetechnologysector)47.

Emptyambition:Thesettingofambitioustargetspairedwithnotablyinsufficientactiontoachievethemintheshortorlongterm,withaparticular

relianceonthetechno-solutionsmentionedaboveandoftenframedasa‘moonshot’48.

Asidefromthese‘traditional’tactics,themost

thegrowthoftheAIindustryhasthatitwillserveasamajor

action,andoffsetitsemissionsthrough.

10INTRODUCTION

Googleusedthisclaiminapolicyroadmap50targetingtheEuropeanUnion.

However,subsequentinvestigationbythis

authortracingtheclaimsbackthroughthe

linksshowedGooglewererelyingona2021blogpostbyconsultingfirmBCG,whichhadextrapolatedthefigurefromtheir‘experiencewithclients’51.Thisquestionableextrapolationofmassiveglobalclimatebenefitsjustified

onseeminglyanecdotalevidencewasthefirstclearinstanceofwhathasbecomealonger-termtrendofoverstatingtheclimatebenefitsofAI.

BOX1-GOOGLE'SHOLLOWAIEMISSIONSCLAIM

AnearlyclaimofthismannercanbefoundfromGoogle’srepresentatives,whichclaimsthat

“researchshowsthatbyscalingcurrentlyprovenapplicationsandtechnology,AIcouldmitigate5to10%ofglobalgreenhousegasemissionsby2030–theequivalentofthetotalannual

emissionsoftheEuropeanUnion”49.Thestatisticwaswidelyrepeated,includedinpublished

papersandinFortuneMagazine,alongside

frequentusageacrossGoogle’ssustainabilitytalksandmaterials.AsrecentlyasApril2025,

TheclaimthatthegrowthofAlwilldirectlyresult

inalarge-scaleglobalreductioninemissionshas

emergedfromothersources.TheIEA'sApril2025

"EnergyandAl"report(peerreviewedbymembersofGoogle,Amazon,Nvidia,MetaandMicrosoft

amongothers)calculatedestimatedbenefitsof

Al,aswellasestimateddownsides(particularly

duetodatacentreexpansion).TheIEAconcluded

inavideopresentationofthereportthat⁵2“evenif

[reboundeffectsoccur],ifweweretoputinplacetherightconditionstohavewidespreaduseofAlintheenergysector,thepotentialforemissionreductions,cuttingcost,making[the]energymoreaffordable

faroutweighthedirectemissionthatwewouldhavefromtheelectricitysector”.

Asimilarconclusionwasreachedinananalysis⁵³

publishedinNature,byLordNicholasStern,whichclaimedAldeployment'snetbenefitwouldbea36%reductioninmeasuredglobalemissions,towards

climategoalsandawayfrom'businessasusual,by2035.DrAmyLuers,headofSustainabilityScience

andInnovationatMicrosoft,publishedapaperin

Nature⁵4citingbothSternandtheIEAinclaimingAI'sdevelopmentwillnotonlyyiedanetclimatebenefitbutserveasamajorglobalaccelerantinachievementofglobalclimategoals.Mostsustainabilityreports

fromthecoretechnologycompanies,suchasGoogleandMicrosoft,prominentlyfeaturethisconceptof

'climate-beneficialAI,oftendedicatinglargesectionstoit,listingalargenumberofcasestudiesandexamplestobuttresstheoverallclaimsofaclimatebenefit.

Theseclaims,whichappeartoogoodtobetrue,begthequestion:isthedeploymentofAllikelytobring

aboutthesedeep,rapidandsustainedglobalcuts

inclimatepollution?Theanswerhastwoparts.The

firstiswhattypeofAlwe'rereallytalkingabout.The

secondiswhethertheseclaimsarebeingmadebasedonstrongformsofevidence.

AIEMISSIONSTRADE-OFF

Widespreaddeploymentofartificialintelligence(Al)

toolsintheenergysectorcouldcut1.4gigatonnesof

annualgreenhouse-gasemissionsby2035-more

thantwicetheamountprojectedtobeproducedby

useinelectricity,transportandfoodsectors.

datacentres.FurtherreductionscouldcomefromAl

Alroll-out

■Limited

ZAccelerated

Projectionsby2035

■Energysectoralone

■Electricity,foodandtransport

EmissionsfromEmissions

data-centrereductions

operationsenabledbyAl

1…………

-2……………

-3·………………

-4……………

5……………

Chart1-Leursetal

MtCO

Emissionsfromdatacentres

1000

500

0

-500

-1500

Datacentres

●Lift-Off

EstimatedimpactofAladoption

Widespread

&dootlonCsetr

end-usesectors

Lowrebound

effects

effects

Chart2-InternationalEnergyAgency

—BAUscenario

--Ambitiousemissionsreductionscenario

28

24

GtCO2e

peryearreduction

16

20232024202520262027203020312033

Time(years)

Yearlyglobal

emissions

(GtCO2e/y

20

18

Chart3-Sternetal

INTRODUCTION

FIGURE4-GRAPHICALCLAIMSOFNETCLIMATEBENEFIT

Thesecharts,drawnfromthereferencesabove,showhowestimatedclimateimpactsofAlarepresentedagainsttheassumedbenefitstosuggestalikelynetbenefit.NotethattheSternEtAlgraphicincludesanestimateofdatacentreemissions,andshowsa'net'resultofbothdecreaseandincrease.

WhattypeofAl?Generativeversus

traditionalAI

Thephrase“artificialintelligence”,orAl,was

originallycoinedasamarketingterm⁵5inthe1950sbyresearchersfocusedonmachineautomation.AsexploredinjournalistKarenHao'sbook“Empireof

AI”,thetermhasbeenusedtodescribeabroadsetoftechnologies.AsHaonoted56inarecentinterview:

"PeoplethinkthattogetanybenefitfromAlin

general,whichisahugecategoryoftechnologies,theysomehowhavetoacceptthemost

environmentallyegregiousformofAI.Ireallyrejectthatpremise.ThetermAlissovaguethatit'slike

thetermtransportation.Youcouldbetalkingaboutabicycleorarocket,andthosearefundamentallydifferentformsoftransportation.AndinAl,we

mishmashabicycleintherocketintothesame,broadcategory.Butthatdoesn'tmeanthatthebenefitfromusingthebicyclealsojustifiesusbuildingtherocket”

In2019,researcherDavidRolnickpublished“TacklingClimateChangewithMachineLearning"57andco-

foundedthe“ClimatechangeAI”non-profit,which

"catalysesimpactfulworkattheintersectionof

climatechangeandmachinelearning”.Theseminal2019paperlistsalargenumberofpotentialclimatebeneficialapplicationsformachinelearning.RolnickrecentlyexpressedasimilarfrustrationtoHaoonsocialmedia58:

"I'msotiredofthissalespitch:"Alisusingalotof

energybutit'salsohelpingtheclimate”.TheAlthathelpstheclimateisgenerallyverydifferentfromtheAlthatusesalotofenergy(e.g.LLMslikeChatGPT).Wecanhavetheformerwithoutthelatter”

TheincreasedfossilfueluseofAlhasbeenjustified

bycorporationsandinstitutionsasworthwhileonthe

groundsthatAlasageneraltechnologycandeliveraclimatenetbenefit.However,muchoftheprojectedenergyconsumptionofAlwillstemspecificallyfrom“generativeAl”,ratherthanmoretraditionalformsofmachinelearning.

ThisdistinctionwaslaidoutindetailinarecentreportpublishedbySchneiderElectric⁵9,where

variousscenariosoffutureAlgrowthallshowaclear

differencebetweendiscreteAltechnologiesintermsofenergygrowth:

INTRODUCTION

212

416

GenerativeAlconsumes,andwill

consume,significantlymoreenergy

than'traditional'Al

导

SustainableAILimitsToGrowthAbundanceEnergyCrisis

■Generative(training)■Generative(inference)

■Traditional(training)■Traditional(inference)

FIGURE5-ENERGYCONSUMPTIONBYAITYPE

ThischartshowstheestimatedglobalenergyconsumptionofAlbyapplicationtypein2030.Acrossthe

scenarios,generativeAlconsumesbetweensixtofourteentimesmorethantraditionalAI.Valuesareexpressedinterawatthours.

Ifthemajorityofclimatebenefitclaimsrelateto

traditionalAI,theseareseparableincorporate

decisionmakingfromthedeploymentofclimate-

damaging,resource-intensiveandconstantly-scalinggenerativesystems.Suchclaimscannotbeusedasadefenseofmissedclimatetargetsornewfossil

fuelincentivisation.Itmatters,then,whetherthemajorityofclimateclaimsrelatetotraditionalorgenerativeAI.

13INTRODUCTION

Thisprojectaimstoapproximatethedistribution

ofclaimsofAIclimatebenefitsacrossthevariousdiscreteformsofAItechnologiesbyanalysingthekeydocumentsinwhichthoseclaimsaremadeandassigningthe‘type’ofAItheclaimrelatesto.

Giventhesignificanceofclaimingoverallclimate

benefit,thisprojectalsorecordsthecharacteristicsofevidenceputforthwiththeaimofestimatingthestrengthofclimatebenefitclaimsregardlessofAItype.

METHODOLOGY

Methodology

Todeterminethe'types'ofAlbeingusedtopresentthenarrativeofclimatebenefits,andthestrengthofevidence,thisanalysisfirstsetadefinition

ofaclimateclaim:statementsfrominstitutions

onthepublicrecord,assertingthatan“artificial

intelligence”or"Al"technologywillleadtoamaterialnetdecreaseingreenhousegasemissionsthroughitsdeployment.

Basedonthisdefinition,aninternetsearchsurfacedseveramajorinstitutionalreports,academicpapersandcorporatepublicationsrepeatedregularlyin

mediacoverage.Asetofdefinitionsofdifferent

Altypeswascreated,usingtheIEA's“Energyand

Al"reportasreference,withanadditionalsub-

categoryofgenerativeAlrelatingeitherto'narrow'generativeAl(suchastrainingalanguagemodelonntergovernmentalPanelonClimateChange(IPCC)reports)and'consumer'generativeAl(largepublic

toolstrainedonamassivecorpusofpublicdigital

informationfromtheinternet,suchasChatGPT).A

fulllistofsourcesanddefinit