2025年人工智能在脱碳领域的应用状况研究报告（英文版）-

StateofAIforDecarbonisation2025January2026144

Improving

Manufacturing

Process

Efficiency53

Optimising

Soil

Management

62

Minimising

Methane

inAgriculture

70

Optimising

EV

Infrastructure

and

Charging

78

Decarbonising

Freightand

Fleets3

Introduction10

Unlocking

DomesticDecarbonisation

18

Enabling

NetZero

Infrastructure

27

Maximising

Flexibilityin

Energy

Networks

36

Decarbonising

Manufacturing

InputsContents2TherealityThisannualreportanswersthatquestionwithareview

ofhowUKapplicationsofAIfordecarbonisationhave

maturedoverthelastyear.Thisrangesfromapplicationsthatarereachingscaleandmeaningfully

contributingtonationaldecarbonisation,throughtoearlierstageresearchthathasmadenotableprogress.Therehavebeentangiblestepsforwardinsomeareasthisyear.AI-poweredEVchargingisalreadyplayinga

significantroleinmanagingourlowcarbonelectricity

grid.Heatpumpinstallationsarequickerandcheaper

becauseofAI.Steelfurnacesandcementplantsare

reducingemissionsthroughAI-optimisedoperations.Butinotherareasprogresshas

beenslow

or

hindered

bygenerativeAIhype.LotsmoreworkisrequiredtofullyrealisethebenefitsofAIfordecarbonisation.ThehypeAIhasbeenconstantlyintheheadlinesthisyear.Jaw-droppinglylargeinvestmentsinAIdatacentresunderpinnedUSeconomicgrowth.Securingenoughenergyforthosedatacentresbecameamajorconcernandtechcompaniesstartedfundingnuclearpowerplants

andgridreinforcement.BusinessesaroundtheworldstartedintegratinggenerativeAIintotheirsoftwareand

processes,withmixedresults.Debatesaboutthefuture

ofworkintensified,withpeopleeitherworriedorexcited

aboutAI’spotentialtoautomate

jobs.EvennationalcarbonbudgetsstartedincludingassumptionsthatAI

couldsavemillionsoftonnesofCO2

emissions.Butamidstthattorrentofnewsandhypeitcanbehardto

findtheanswertoanimportantquestion:howeffectively

isAIbeingappliedtokeysocietalchallenges

likedecarbonisation?2025:lots

of

hype,some

tangible

progress3Inouroriginalreport

ADViCEidentifiedsevendecarbonisationGrandChallenges

whereAIcouldmosthaveimpact.Thisyearwehavealsoaddedtwo

transport-relatedGrandChallenges.EachGrandChallengeisbrokendown

into

morespecificchallengesforAIto

tacklein

theDecarbonisationChallengeCards.ToidentifyprogressthisyeartheADViCEteamofdomainexpertsreviewedpotential

examplesofprogressineachspecificchallenge(sourcedusingAI-basedresearch

toolsandconversationswithstakeholders).

ForeachGrandChallengetheexpertsthen

identifiedkeythemesandexamplesofwhereAIhasmadeprogressin2025.Measuring

progress

on

the

nine

Grand

Challenges4In2025AI

hada

measurable

impactonthe

UK’sabilitytooperate

a

low

carbon

electricity

grid.

AI-

poweredsolarnowcasting

reducedemissionsbyan

estimated

300,000

tonnes,

smart

EV

charging

lowered

peakelectricityusagefrom

EVsby42%

andvirtual

power

plants

helped

balancethe

grid.Thisyearsawa

notablestepforward

inAIapplicationstodomestic

decarbonisationwith

the

national

rolloutoftwoAItools

lookingtostreamlinedifferent

partsofthe

heat

pump

installation

journey.Inmanufacturingandtransport,adoptionofAIforprocessefficiencyandoptimisationcontinuedto

grow.Largerorganisationsstartedtomovefromdemonstrationtodeployment,thoughsmallerorganisationstypicallylaggedbehind.AIforsoilmanagementprogressedfromtheoreticalpilotingtoearlycommercialisation,while

applicationstoagriculturalmethanereductionmadesomeprogressattheresearchstage.LessprogresswasmadeonapplyingAItodecarbonisingmanufacturinginputsorelectrifyingfreight,

partlyduetothehighcapitalcostsinvolved.ThesemayneedadditionalinterventionstoaccelerateAI

adoption.Overall

progress

in

20255Shown

belowisanassessmentofhowfarsolutionsforeachGrandChallengeprogressed

between

2024

and

2025,

alongwith

a

rough

estimateofpotentialprogressin2026basedontheenablersand

blockers

identified

in

this

report.No

significant

Early

exploratory

Major

research

Proof

of

concept

Pilots

in

some

Meaningful

impact

for

Growing

impact

National-scale

Maximum

decarbonisationEnabling

Net

Zero

infrastructure

2024

一

2025

2026

Maximising

flexibility

in

energy

networks

202420252026*

DecarbonisingmanufacturinginputsImprovingmanufacturingprocessefficiencyOptimisingsoil

managementMinimising

methaneinagricultureOptimising

EVinfrastructureandchargingDecarbonisingfreightandfleets2026

---

-

-

-

-

-

-

-

-

-

-

-

-⃞

-

-

-

-

-

-

-

-

-

-

-

-

-

-⃞

2024KeyMaturityattheend

of2024Maturityattheend

of2025Potentialmaturityattheend

of2026(estimate)20252026I

2024

2025

-----------⃞

2026

*Note:shorterarrowsfor2026in‘National-scaledecarbonisationimpact’arenotanindicatorprogressisexpectedtoslow,butbecause

thefigureisfocusedonthegrandchallengesinearlierstages.

AI’sdecarbonisation

impactfor

themorematurechallengesisexpectedtogrowsteadilyin2026andfuturereportsmaystarttotrack

thatmoreclearly.work

research

programmes

demonstrators

organisations

individual

orgs

across

sectorVisualsummaryofprogressdecarbonisation

impact

impactUnlockingdomesticdecarbonisation2024

—一

20252024

202520242026*2026202620262026202520242025202420252024

2025--------

-

-

-

-

-⃞61500farms

usingautonomousdroneflightstoinspectcrops50%reductionin

heatpumpinstallationtimeusingAItools2%reductionin

CO2fromcementproductionusingAI79%ofEV

ownershavea

smartcharger300,000tonnesofCO2avoidedeachyearusing

solarnowcastingSome

key

numbers

from

20257The

UKAIfordecarbonisationecosystem

hasshownsteadygrowth

in

recent

years.

Most

companies

are

inthe

seed

andventurestage,which

broadly

mirrorstheoverallAIecosystem

inthe

UK. ActiveCompanies7%12%45%36%366343307268236Thestartupecosystemcontinuestogrow8*DatasourcedfromBeauhurstcompanydatabaseonaselectionof380companiesidentifiedthroughacustomquery.DatacollectedupuntilQ3of2025.CategoriesofevolutionstagesofcompaniescanbefoundhereSeedVentureGrowthEstablished2020

2021

2022

2023

2024Q3*2025379*ADViCE

existsto

joinuptheAIfordecarbonisationecosystem.We’realways

keentohearaboutwhatyou’redoinginthis

space.Thisisanannualreportandforfutureeditionswewillbeworkingwiththeecosystem

tocurateacontinuallyupdated

databaseofAIfordecarbonisationsolutionsandtheirprogress.We’dlovetohearfromyouwhetheryou:•

haveadecarbonisationchallengeyou

thinkismissing•areworkingonanAIsolutionthatis

deliveringdecarbonisation•

or

justknowaboutsomethinginteresting

wehaven’tcovered.YoucancontactusatADViCE@turing.ac.uk,orsignuptoourmailinglistto

benotifiedofthewebinars,

workshopsandinpersoneventsthatwehost.More

detailsaboutthelaunchof

thedatabaseofAIfordecarbonisationsolutions

willbeannouncedshortly.

Wealsohavea

knowledge

basewithkeyresources.AboutADViCEAIforDecarbonisation’sVirtualCentreofExcellence

(ADViCE)isaprogramme

fundedbytheDepartment

forEnergySecurityand

Net

Zero.It

isa

partnershipbetweenDigitalCatapult,

EnergySystemsCatapult

andTheAlanTuringInstitute.Isthisreportmissingsomething?9GrandChallenge

1UnlockingDomesticDecarbonisation10Residentialheatingisresponsibleformorethan

13%ofgreenhousegasemissionseachyear,andsoisessentialindecarbonisingtheUKeconomy.However,decarbonisinghomesrequireschangestobothheatingsystemsand

consumerbehavioursineveryhomeintheUK.

Engagingconsumersinthatprocess,financing

it,anddeliveringitat

pace

are

all

majorchallenges.ThisyearsawanotablestepforwardinAIapplicationstothisareawiththenationalrollout

oftwoAItoolslookingtostreamlinedifferentpartsoftheheatpumpinstallationjourney.TherearesomewellstudiedAIapplicationareaswhichhavebothacademicresearchanda

handfulofearly-stagecommercialofferings,but

fewexamplesofsuccessatscaleyet.Bothdataavailabilityandthepotentialmarket

forAIaregrowingasdomesticdecarbonisation

picksupspeed.Therehavebeena

numberof

large

publicsectorinnovationfundingprogrammesinthis

areaoverthelastfewyears,includingtheNet

ZeroHeatprogramme.Overview:Unlocking

Domestic

Decarbonisation11AIapplicationsthatcouldaddressthischallenge12UseofAIinacceleratingheatingsystemdesignhasmovedfrompilots(e.g.

Geo’sAISmartHeat

Pathway

in2022-24)tonational-scaleproductlaunches(HeatGeek’sZeroDisruptAI)thatarehelpingtosignificantlyreduce

heatpumpinstallationcosts.Frictionintheadminprocessforlowcarbontechnology(LCT)installshasbeen

reduced

nationwide,withthe

abilitytoprovidesame-dayauto-approvalsforLCTinstallsusingENA’sConnectDirect.Specialistchatbotsaimedatsupportinginstallers

andconsumers

withheatpumpinstallationshavebeen

developedandtrialled-includingapublic-facingrollout-but

have

notyetseensignificanttraction.AdoptionofAItoimproveidentificationofvulnerableenergyconsumers

isnowwidespread,fromcallanalysisby

ScottishPower,tofuelpovertyriskmapping

tobetter

targetgrantsupport,toSSENforecastingfuturevulnerability

atalocal

level.AItoautomaticallybreaksmartmeterusageintodifferentappliances

(knownasnon-intrusive

load

monitoring,

NILM)remainsanactiveareaofacademicresearch.Itisalreadyemployedinanumberofconsumer-facingapps

includingLoop,whichhashelped>150kusersreduceenergyusagebyanaverageof15%.12345ThemesinAIadoptionfordomesticdecarbonisation13HeatGeek

haveusedAIextensivelyinautomatingpartsof

theheatingsystemdesignprocess.Thisincludes:•LiDAR-basedautomatedinternalsurveyingofhomes•

Computervision-basedheatpumpsitingassessment•

Automationofformpopulationandcommunications•

AI-basedselectionofoptimaldesignparameters

to

achievetargetefficiencyatminimumcostIntrialsthishasreducedcosttocustomers*by~75%and

installationtimeby~50%,andhasnowbeenrolledoutnationallyandisinusebyall

HeatGeek

installers.Thisdirectlyaddresses

thecostanddisruptionchallenges

thatareslowingheatpumpadoptionandislikelytosignificantlyacceleratetherateofheatpumpinstallations.Heat

pump

design:

Heat

Geek

Zero

Disruptupgradeschemegrantsof£7.5karetakenintoaccount14

*after

boilerTheEnergyNetworkAssociation

haveintegratedAIintothe

nationalconnectionapplicationservicefordomesticlowcarbontechnologies(LCTs)likeheatpumpsandEV

chargers.Itutilisescomputervisiontoreviewphotosofcut-outs(essentiallythefusebetweenahomeandthegrid)toremovetheneedforahumantorevieweveryphotoand

enableinstantapprovalofapplicationswhereitwasclear

nocut-outupgradewasrequired.Ithasreducedthetimeandcostofcompletingpaperwork

forinstallersandhasbeenusedtospeedupover185k

LCT

approvals

forconsumers.Connection

approval:ENA

Connect

Direct15Enablers

for

the

next12

monthsHeatpumpinstallations

increasingHeatpumpinstallationratesareincreasingandtheUKnow

hasover300,000heatpumps

installed.ThismeansthereisbothmoredataavailablefortrainingAI

(includingfreedatafromtrials)andalargermarkettodrive

revenuesforAIinnovators.Smartmeterdatastartstobecome

more

availableSeveralongoinginitiatives

tomakeaccesstosmartmeter

dataeasierareunlikelytohaveanimpactin2026,butsyntheticsmartmeterdata

isavailablenow.GenerativeAItoolingmaturesAsgenerativeAImodels(andassociatedtooling)mature,it

becomesincreasinglyfeasiblefor

themtobeusedatscale

tosupportconsumersindecarbonisingtheirhomes.16Electricityremainsmoreexpensivethan

gasDespiterecentgovernmentmovestoreducepolicycosts

onelectricity,electricityremainsmore

than4xasexpensiveasgas.Thismakesitextremelydifficultforheatpumpstobecostcompetitivewithgasboilers(even

thoughtheyare3-4xmoreefficient).ThisincreasestheimportanceofusingAItoimproveheat

pumpoperation-bothimprovingefficiencyandshifting

usagesohouseholdscanbenefitfrom

time-of-usetariffs.InteroperabilityisacontinuingchallengeLackofopenAPIsandinteroperablestandardsfordata

andcontrolsremainsasignificantbarriertoapplyingAI

todomesticheating.Remaining

gaps

and

barriers17GrandChallenge2EnablingNetZeroInfrastructure18Electrificationofheatingandtransportation,combinedwithincreasedrenewables,meansweneedbothsignificantexpansionofourelectricitynetworksandwaystomanagenetworkconstraints.

Deliveringattherequiredscale-andpace-isarealchallenge,withnewrenewableprojectsheldup

bydelaysoruncertaintyin

networkconnections.Despitesignificantattentionfromgovernmentand

industry,therehasbeenlittleprogressonapplying

AItothegridconnectionqueuethisyear.ApplicationsofAIforreal-timeoptimisationand

controlhavereachedreal-world

pilot

stage

inoffshorewindanddistributionnetworks.SomeestablishedareasofAIusage,particularlyin

optimisingroutesandlayoutsfornewassets,

have

continuedtoseesteadygrowthinadoption.ContinuedpoliticalfocusonthisareameansitislikelywewillcontinuetoseestrongfundingforAIapplicationsinthisareaoverthenexttwelvemonths

(includingviatheStrategicInnovationFundandGreenIndustriesGrowthAccelerator),butthepotentialforbreakthroughremainsconstrainedbychallengesintegratingwithhard-to-changebureaucraticprocesses,aswellasinsufficientdata.Overview:

Enabling

NetZero

Infrastructure19AIapplicationsthatcouldaddressthischallenge20BigpromisesaboutAIbeingabletostreamlinegridconnectionsandaccelerateapprovalshaveyettodeliver,with

thegovernment-announcedConnect

tool(matchingcapacitytodemand)havingbeenpaused.AItoolstooptimiseinfrastructureplacementarerelativelymatureandwidelyadopted

(e.g.

Continuum

Optioneer,

KinewellEnergy’scable

andturbine

layoutoptimisation),reducingprojectdevelopmenttimelinesandcosts.AIforreal-timecontrolandoptimisationofassetshasseennoticeableprogress,includingoperational

pilotsofreinforcementlearningforwindfarmcontrol(AIOLUS)anddistributedcoordinationandcontrolofnetworkassets

(Constellation).TheNationalEnergySystemOperator’sVOLTA

programmeisscopingoutadoptionofAIwithinthe

nationalcontrolroom.Cross-sectorandmulti-scaleplanningremainsakeychallengethatisstartingtobeaddressed.Thereare

notable

data-sharinganddigitaltwininitiativesinthisspace(ENSIGN

&CReDo+),buttheenablingconditionsforAItohave

alargeimpactarenotyetinplace.GenerativeAIhaslargelybeenlimitedtodataqualityenablers(e.g.publicsentimentanalysis

anddatadiscovery)

andisnotcurrentlydisplacingcoreengineering/optimisationworkflows.12345ThemesinAIadoptionforNetZeroinfrastructure21tocover

thescreeningphase

ofrenewabledevelopment.Deployingnewenergyinfrastructureisslowandcostlybecauseplannersmustmanuallyevaluate

thousandsof

routeoptionsagainstengineering,environmental,andpermittingconstraints.ContinuumIndustries

isworkingwithmajornetworkoperators,includingNationalGrid,SSENTransmission,SGN,

andNationalGasTransmission,

tosolve

thiswithOptioneer.TheplatformusesAI-drivengeospatialoptimisationandconstraint-basedsearchevaluatemillionsofroutingNGG’spipelinestudy.Thisreducescostsandacceleratesinfrastructureneededfor

electrificationandhydrogen

transition.In2025

thetoolhasalsobeenextendedContinuumarecurrentlyscalingwithliveUKdeploymentsand£8.2m

SeriesA

funding.RouteOptimisation:Continuum

IndustriesIt

delivered

~60%reduction

in

programme

time

for

SSEN’s132kV

extension

and

~93%reduction

forscenariosinminutes,balancingcost,environmentalimpact,andengineering

feasibility.22IntelligentWind

FarmControl:AIOLUSWindfarmslose10-20%oftheirpotentialoutput

to"wakeeffects"

whereupstream

turbinesslowthewindfordownstream

turbines,

butcurrentcontroltechnologiescan'toptimise

thewholefarm.University

of

Warwick

has

developed

AIOLUS,the

first

Europeandeepreinforcementlearningsystemforwholewindfarmcontrol.

Itusesreinforcementlearning

tooptimise

turbinesettingsinreal-

time

tominimise

wakeeffectsandmaximisefarm-wideoutput.In

thelastyear

thishasmoved

fromlate-stagedevelopment(ManchesterPrizefinalistinMay2024and£415kEPSRCgrant)

intoareal-worldpilotwithoperationalcontrolofawindfarm.Thiscoulddelivera3-5%increaseinannualenergyoutput-equivalent

topowering1millionUKhouseholdsfromexistingwindcapacitywithoutnewinfrastructure.Byoptimisingexisting

assets,itreducesneedfornewlandandoffshoredevelopments.23UKPowerNetworkspartneredwithABB,GeneralElectric/GEVernova,UniversityofStrathclydePNDC,andmore

todevelop

the

world’sfirstsmartsubstations

capableofanalysingmillionsofdatapointsandreconfiguringnetworksettingsinreal-time.Smartsubstationsforecastandanalyselocalpowerflowsand

communicatewitheachother(rather

thanrelyingoncentralcontrol)

tofreeupcapacityandincreaseresilience.MLmodelsare

trainedcentrally

thendistributed

tosubstationsfor

autonomousoperation,providingresiliencewhencommunications

fail.ThefirstsmartsubstationwasinstalledJan2025inMaidstone,

withfivemore

tobeinstalledbySeptember2026.Thesolutions

trialledaspartofConstellationcouldsavecustomersinGB£132m

by2030.Constellationestimates

theycanalsosave17m

tCO2by2050

if

fully

rolled

out.Localgridoptimisation:Constellation24Enablers

for

the

next12

monthsPoliticalappetiteis

highPressuretobothkeepenergybillsdownandspeedupconnections,particularlyfordatacentres,meanspolitical

supportforAIapplicationsinthisspaceisextremelystrong.The

AIEnergyCouncil

focusesonspeedingupgridconnectionsandissupportedbyworkatDESNZand

NESO.TheCleanPower2030

targetcreatesaharddeadline,incentivisingexperimentationwithAIsolutions.AIcompaniesneedenergyinfrastructure

nowAccesstoenergyfordatacentresisbecomingabinding

constraintonlargeAIcompanies.Theywillinvestbothcashandtalentinunlockingthat,andwillbepredisposed

toAI-basedsolutions.Thisislikelytoincludecreationof

newrevenuestreams-e.g.Piclo’sdatacentreconnection

accelerationprogramme

in

theUSwhichisexchangingenergyflexibilityservicesforfasterconnection.25ChangingprocessesrequiresmorethantechnologyManyoftheprocessesinvolvedininfrastructuredevelopmentareformalisedunderlegislationorregulation,whichrequiresfocusedpoliticalwilltochange

quickly.AImayhelpspeedupcertainelementsbutcannotstreamlineentireprocesses(orchangecultures)

inhighlyregulatedareas.Planningdataremainsfrustratinglypatchyand

opaqueThecomplexity(andhistoricallymanualnature)oftheplanningprocessmeansconsistent,goodqualitydatais

rarelyavailable.ThismakesitchallengingtobuildAItoolsinthisarea(seeYottar’sdevelopmentdiary).Therehasbeengradualprogressonthis,andOfgem’s

latestreview

proposesimportantactionsfornetworks

thatwouldfurtherclosethisgap.Remaining

gaps

and

barriersGrandChallenge3MaximisingFlexibility

inEnergy

Networks27Ahighrenewablesfuturerequires

energydemandto

flexsoweconsumeandstoreenergywhenthewindis

blowingandthesunisshining.Thisisaradicalchangeinnetworkandmarketoperation,and

isfundamentallydependentonusingAItoforecastand

optimisedemandandsupplyatmuchmoregranular

levelsthaneverbefore.2025hasseenanaccelerationinthedeploymentof

flexibilityonthenetwork,withAIplayinganessential

roleinthat.MostoftheimpacthascomefromagrowingmarketusingexistingMLtools,ratherthan

newAI-driventechnologicalbreakthroughs.However,akeydevelopmentisthatdeeplearning

basedsolarforecastswerefullyoperationalised

bythesystemoperator,savinghundredsofthousandsof

tonnesofemissionsandtensofmillionsofpounds.AIadoptionforforecastingandoptimisationremains

mixed.Manyorganisationcontinuetorelysolelyonstatisticalforecastingandmathematicaloptimisation

techniques,butanincreasingnumber(particularlybatteryoptimisers)areutilisingMLandReinforcement

Learningforcompetitiveadvantage.Inthenext12monthsweexpecttoseeAIadoptionat

scaledeliveringincreasinglylargeimpacts

(bothenvironmentalandfinancial)across

thisGrand

Challengeduetotherapidlygrowingmarket,and

strongfitforAIcapabilities.Overview:

Maximising

Flexibilityin

Energy

Networks28AIapplicationsthatcouldaddressthischallengeAI-basedvirtualpowerplantorchestrationishelpingbalancethegridatscale.Thereare

now

multipleorganisations(includingKraken,Kaluza,Flexitricity,andArenko)usingAItoaggregatedistributedenergy

resourceslikeEVchargers,batteries,andindustrialloadsintovirtualpowerplantsat

uptoGWscale.AIisalsobeingusedtomatchrenewablegeneratorstodemandatalocal

level,

increasing

margins

for

renewablesandmakingthemmoreeconomicallyviable.Solarnowcasting

(forecastingforthenextfewhours)ismateriallyimprovingcontrol-roomdecisions,

savingtensofmillionsongridoperatingcostsandhundredsofthousandsoftonnesofCO2

peryear.Forecastingremainsthefoundationforflexibility.ManyorganisationsrelyonMLforforecastsofdemand,

generationandprice.Thelastyearhasseensomeprogress

inresearch

onfoundationmodels

forforecastingbutthereal-worldimpactoftheseremainstobeproven.AIisstartingtomakebuildingsflexiblegridassets.CompanieslikeGridEdge

and

Carbon

Laces

areautomatingreal-timeloadshiftinganddemandresponsewithAI-ledoptimisationofbuildingenergydeliver

15-34%reductionsinpeakdemandintrialsduring2025.12345ThemesinAIadoptionforenergyflexibility30Thetechnologyachieved40%improvement

overpreviousforecastsandisfullyoperationalinNESO'scontrolroom,saving300,000tonnesofCO2

and£30millionper

year.Adeepneuralnetworkcombinesmeasuredsolargeneration,numericalweatherpredictionandsatellite

imagerytopredictcloudmomentsandsolargeneration

acrosstheUKoverthenextfewhours.Solargeneration'sunpredictabilityforcesgridoperators

tomaintainexpensivefossilfuelbackupcapacity,driving

upcosts.OpenClimateFixpartneredNESOtodeployQuartzSolar

inbalancingcosts,withpotentialto

reach£150m

by2035.Solar

forecasting:Open

Climate

Fix31Directpeer-to-peerdomesticenergysupplycanmakeenergycheaperbydecouplingrenewablesfromvolatilewholesalegasmarkets,butisnotyetpermittedintheUK.Instead,UrbanChainactsasaregulatedenergysupplier

butusesAIandblockchaintocreatep