Exploring the spatial and temporal determinants of gas central heating adoption

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Contents lists available atScienceDirect

Resource

and

Energy

Economics

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / r e e

Exploring

the

spatial

and

temporal

determinants

of

gas

central

heating

adoption

Daire

McCoy

a,b,∗

_,

_John

_Curtis

b,c

a_The_Grantham_Research_Institute,_London_School_of_Economics,_UK

b_The_Economic_and_Social_Research_Institute,_Ireland

c_Trinity_College_Dublin,_Ireland

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received5December2016 Receivedinrevisedform 20December2017 Accepted26December2017 Availableonline6January2018

JELclassiﬁcation:

Q40 C31 C36

Keywords:

Residentialfuelchoice Spatialeconomics

Instrumentalvariablesestimation

a

b

s

t

r

a

c

t

Inordertobetterunderstandthepotentialforbothpolicyandtechnologicalimprovements toaidcarbonabatement,long-termhistoricalinformationonthetime-pathoftransition frommoretraditionaltocleanerfuelsisuseful.Thisisarelativelyunderstudiedelementof thefuelswitchingliteratureinbothdevelopedandemergingeconomies.Thisresearchadds tothisliteraturebyexaminingtheadoptiontime-pathofnetworkgasasaheatingfuel.We mergeauniquedatasetongasnetworkroll-outovertime,withothergeo-codeddataand employaninstrumentalvariablestechniqueinordertosimultaneouslymodelsupplyand demand.Resultsindicateanon-linearrelationshipbetweentheproportionofhouseholds usinggasastheirprimarymeansofcentralheatingandthelengthoftimethenetwork hasbeeninplaceineacharea.Proximitytothegasnetwork,peatbogs,andareaswhich havebannedtheconsumptionofbituminouscoalalsoaffectgasconnections.Variationsin socioeconomicanddwellingcharacteristicsatarealevelcanalsohelpexplainconnections tothegasnetwork.Abetterunderstandingofthisvariationiscrucialindesigningtargeted policiesandcanaidnetworkexpansiondecisions.

1. Introduction

Policyinterestinresidentialfuelchoiceandconsumptionhasalonghistory(e.g.Halvorsen,1975;Houthakker,1951).

Inrecentyearspolicyfocushascentredonassociatedhealthoutcomesandeconomicgrowthindevelopingcountriesand

moregenerallyonthecontributiontogreenhousegasemissions.WithapproximatelyonequarteroftheEU’stotalprimary

energyrequirementintheresidentialbuildings,1_the_sector_is_a_focal_point_given_the_EU’s_ambition_to_reduce_greenhouse

gasemissions(EuropeanCommission,2014).Fuelswitchingawayfromcarbonintensivefuels,suchaspeatandcoaltoless

carbonintensivefuels,suchasgasorrenewablesisonewaytheresidentialsectorcanreduceemissionsyetsatisfyenergy

servicedemands.

Abodyofresearchwithindevelopmenteconomicsfocusesontheso-called‘energyladder’,inwhichhouseholdstransition

fromtraditionalheatingandcookingfuels,suchasbiomassorwood,tofuelssuchasgasorelectricityastheirincomelevels

increase(HosierandDowd,1987).Ashouseholdswillcontinuetousetraditionalfuelssuchasﬁrewoodalongwithmodern

fuels,switchingbackinresponsetorelativepricesandotherfactors(Wickramasinghe,2011;VanderKroonetal.,2013)

∗ Correspondingauthor.Presentaddress:GranthamResearchInstitute,LondonSchoolofEconomicsandPoliticalScience,LondonWC2A2AE,UK.

E-mailaddress:[email protected](D.McCoy).

1 _See_{http://ec.europa.eu/eurostat/web/energy/data/energy-balances}_for_details. https://doi.org/10.1016/j.reseneeco.2017.12.004

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somehavearguedthatamultiplefuelmodelismoreappropriate(Maseraetal.,2000).Amongthekeydeterminantsof

fuelchoiceamonghouseholdsindevelopingcountriesarefuelprices,income,andeducation,aswellassecurityofsupply

considerationsforfuelssuchasgasandelectricity(Alemetal.,2016;Beheraetal.,2016;MensahandAdu,2015;Zhangand

Hassen,2017).

Residentialfuelchoice2 _and_fuel_switching,_are_also_a_research_focus_in_developed_economies._For_example,_there_has

beenaparticularinterestinrecentyearsintothedecisiontoadoptrenewableormoreefﬁcientresidentialheatingsystems

(MahapatraandGustavsson,2008;SophaandKlockner,2011;MichelsenandMadlener,2012).Acrossnumerousstudiesand

countriesthereisageneralconsensusontherangeoffactorswhichdetermineresidentialfuelchoice.Thesearedescribed

indetailinSection2.Ourresearchaddstoboththisliteratureandtheliteratureexaminingtheacquisitionofenergyusing

assets.3

OurfocusinthispaperisontheadoptionofgascentralheatinginIreland.Irelandprovidesaveryinterestinglensthrough

whichtoexaminethediffusionofanenergy-usingassetovertime.Aculturallegacyofsolidfuelusage,drivenbyplentiful

localendowmentsofpeat,createdareluctancetoswitchtomoremodernheatingsystems.Thiscontrastswithastrong

policypushinrecentyearstoencouragegreaterusageofrenewableenergy,andrecentlegislationprohibitingthesaleand

useofbituminouscoalfordomesticheatinginurbanareas.Accesstonetworkgashasbeenavailableinsomelocationsin

Irelandformorethancentury,however,networkconnectionscanstillberelativelylowinsomelocationsadjacenttothe

gasnetwork.

Weareparticularlyconcernedwithunderstandingmoreabouttheadoptiontime-pathofnetworkgasasadomestic

heatingfuel.Theremaybeseveralreasonswhyatime-lagintheadoptionofmoreefﬁcientheatingmethods,once

avail-able,exists.Therangeoffactorsincludeﬁnancialbarriers,spatialproximitytoalternativeenergysources,culturallegacies

resultinginpreferencesforcertainfuels,misinformationoralackofinformationonalternatives,oruncertaintyaboutfuture

energyprices.Heterogeneityofpreferencesinthepopulationcanalsoexplainvariationsinthetimingofadoption,evenin

caseswherethenewtechnologyisqualitativelybetterthantheexistingone.

Thetime-laginadoptionisarelativelyunderresearchedaspectoffuelswitching,whichgenerallyconsidernetwork

accessasabinaryvariableatapointintime.Ourfocusisenabledbyaccesstoararedatasetcomprisingdetailedinformation

onthelocationandtimingoftheexpansionofeachindividualsegmentoftheoftheIrishgasnetworkover100years.Thisis

linkedtoinformationonthelocationofeveryresidentialdwellinginthecountryandcombinedwithspatialcross-sectional

dataonarea-levelfuelchoicealongwithinformationondwellingattributesandthesocio-demographiccharacteristicsof

households.Suppliersarelikelytoextendthegasnetworktoareasofhighdensity,orthosewithahigherprobabilityof

adoption,andonlythosehouseholdsincloseproximitytothegasinfrastructurecanadopt.Nottakingaccountofthiscould

potentiallybiasourestimates.Toaccountforthissimultaneity,weestimateatwo-stageleastsquaresspeciﬁcation,allowing

ustoidentifythetime-pathofnetworkroll-outinthesecond-stagegasadoptionequation.

Resultsindicateanon-linearrelationshipbetweenthelengthoftimethenetworkhasbeeninplaceandtheproportion

ofgasusersineacharea.Eachyearthenetworkhasbeeninplaceisassociatedwitha3percentagepointincreaseingas

connectionsonaverage,andthiseffectdecreasesovertime.Variationindistancetothenetworkisasigniﬁcantdeterminant

ofconnections,evenforareasincloseproximitytothenetwork.Proximitytopeatsources,suchasbogsisnegatively

associatedwithgasconnections,whileabanonthesaleandburningofbituminouscoalwhichwasinplaceinvariousurban

locationsinIrelandin2011,ispositivelyassociatedwithgasadoption.Oureconometricapproachallowsustoalsoprovide

somescenarioanalysiswhichsimulatesgasnetworkexpansionsyettobeundertakenandthepotentialimpactoftheseon

uptakeandtheassociatedchangesinCO2emissions.

Thelayoutoftheremainderofthepaperisasfollows.Thenextsectionplacesourresearchinaninternationalcontext.

FollowingthisinSection3weprovidesomebackgroundonthehistoricaldevelopmentofresidentialfuelusageinIreland,

includingthegrowthinnetworkgasusage.Section4outlinesthemodelandestimationstrategywepropose,whichis

followedbyanoverviewofthedataused.Estimationresultsandascenarioanalysissimulatinggasnetworkexpansionare

presentedinSections6and7.Section8outlinesarangeofrobustnesschecksundertaken.Section9concludesandprovides

someinsightsforpolicy.

2. Relatedliterature

Asmentioned above,ofprimaryrelevancetothisresearchistheliteratureconcerningfuelchoiceandswitching.In

additiontothis,wealsodrawonotherresearchexaminingtheacquisitionofenergyusingassets.Withregardtotheformer,

thekeydeterminantsareconsideredinturninthefollowingparagraphs.

Buildingattributes,particularlypropertyageandtypeaffectfuelchoice.MichelsenandMadlener(2016)ﬁndthatolder

homesarelesslikelytoswitchtorenewableheatingsystems,possiblyreﬂectingunsuitableexistingheatinginfrastructure.

Theinhabitantsofolderpropertiesaremorelikelytouseoil,ﬁrewoodandcoal,whereasthoseinmorerecentlybuilt

propertiesaremorelikelytousegasorheatpumps(LauretiandSecondi,2012;MichelsenandMadlener,2012),though

2_The_term_{‘residential}_fuel_choice’_is_used_{interchangeably}_with_{‘residential}_heating_system’,_as_some_of_the_literature_focuses_on_particular_heating technologies,e.g.heatpumps,ratherthanthefueltypes.

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Lillemoetal.(2013)ﬁndthatdwellingsizeandtypeimpactonheatingsystemchoicebutnottheproperty’sage.Larger

sizedpropertiesaremorelikelytousegasforheatinginsteadofsolidfuels(Lillemoetal.,2013;MichelsenandMadlener,

2012).Determinantsoffuelorheatingsystemsinnewlybuiltpropertiesgenerallydifferstothatfortheexistinghousing

stock.MichelsenandMadlener(2012)concludethatchoiceofaheatingsysteminnewlybuilthomesishighlyinﬂuenced

bytheoccupants’environmentalpreferences.

Occupants’socio-economiccharacteristicsalsoimpactonfuelchoice,withincome,age,educationandeconomicstatus

beingparticularlyrelevant.Anumberofstudiesﬁndthatlowerincomesareassociatedwithoilandsolidfuels,whichare

moreemissionsintensive(Fuetal.,2014;LauretiandSecondi,2012;Özcanetal.,2013)thoughtherearemanyotherstudies

thatﬁndonlyaminorincomeeffectornone(e.g.Braun,2010;Lillemoetal.,2013;Coutureetal.,2012).Theeffectsofhigher

educationandeconomicstatusonfuelchoicearegenerallysimilartothoseassociatedwithincome.InthecaseofageÖzcan

etal.(2013)ﬁndthathouseholdheadsaged50andabovearemorelikelytochoosegas,oilandelectricitycomparedtocoal

andothersolidfuelsforreasonsofeaseofuseandforhealthconcerns.Onthecontrary,DeckerandMenrad(2015)ﬁndthat

neitherage,educationnorincomeareimportantvariablesinexplainingchoiceofresidentialheatingsystemsinGermany.

Inertia,peereffectsandmotivational impactshavealsobeenfoundtoimpactonfuelchoice.Householdsareoften

reluctanttoadoptmoreenergyefﬁcientoptions,evenifitisﬁnanciallyadvantageousfor themtodo so.This

energy-efﬁciencygapalsocharacterisesthereluctancetoadoptothertypesofenergyefﬁcientappliancesthatofferseemingly

positivebeneﬁt(AllcottandGreenstone,2012;Blumsteinetal.,1980;JaffeandStavins,1994).Theinﬂuenceofpeersisan

importantdeterminantofdecisionsrelatingtoheatingsystemchoice(DeckerandMenrad,2015;MichelsenandMadlener,

2013).Otherimportantmotivationalfactorsincludeattitudetoparticularheatingsystemsorfuels,personalcomfortand

externalthreats,thelaterofwhichreferstoeitheranapprehensionrelatingtodependencyonfossilfuelsorclimaterelated

environmentalconcerns.

Regionalorculturaldifferences,includingthelocalavailabilityofparticularfuelssuchasﬁrewood,canimpactonfuel

choice(Braun,2010;Fuetal.,2014;LauretiandSecondi,2012).Whileweatherisfrequentlyincludedasacovariatein

modellingenergyconsumptionitalsohasanimpactonfuelchoice,similartoaregionaleffect.Inanumberofcases30-year

meanweatherdataisfoundtohaveastronginﬂuenceonfuelchoice,withhighertemperaturelocationslesslikelytouse

oilorsolidfuels(Fuetal.,2014;Mansuretal.,2008).

Fuelpricesandheatingsystemcapitalcostshavesubstantialimpactsonhomeheatingdecisions.Thecapitalcostof

heatingsystemequipmentcanactasabarrierinfuelchoicedecisionsduetobudgetconstraints,however,itisdifﬁcultto

captureempiricalevidenceinrevealedbehaviourdata.MichelsenandMadlener(2016)ﬁndthatcapitalcostsratherthan

fuelpricesareanimportantmotivationalfactorinsuchdecisions.Inanumberofstatedpreferencestudiescapitalcosts

areanimportantattributeorpotentialbarrierassociatedwithresidentialheatingsystemchoicedecisions(Rouvinenand

Matero,2013;ScarpaandWillis,2010).Therearerelativelyhighimplicitdiscountratesassociatedwithelectricityandoil

basedheatingsystemscomparedtodistrictheating,geothermalorwood-basedsystems.Fuelpricesarecertainlyimportant

considerationsinhouseholdfuelchoicedecisionsindevelopingcountries(Alemetal.,2016;MensahandAdu,2015;Zhang

andHassen,2017)butthereismixedevidenceindevelopedcountries.Inthestated-preferencestudiesfuelpriceshavea

signiﬁcantimpact(RouvinenandMatero,2013;ScarpaandWillis,2010)butonlyasmallnumberofotherempiricalstudies

includefuelpricesasapotentialdeterminantoffuelchoice.Mansuretal.(2008)ﬁndclearown-priceandcross-priceeffects

onfuelchoicedecisions,whileCoutureetal.(2012)ﬁndapriceeffectassociatedwithﬁrewood,theonlyfuelpricethey

consider.Numerouspapers examiningdeterminantsofhouseholdfuelchoicedonotincludefuelpricesasexplanatory

variables,thoughthatmayreﬂectdifﬁcultyofacquiringsuchinformationforcross-sectionaldatasets(e.g.Fuetal.,2014;

MichelsenandMadlener,2012;Özcanetal.,2013;LauretiandSecondi,2012).

Accesstothenaturalgasnetworkisalsoanimportantfactorthataffectsresidentialfuelchoice,thoughtheissuehas

receivedrelativelylittleattentionintheliterature.Mansuretal.(2008)ﬁndthatUShouseholdswithnetworkaccessmake

differentconsumptionchoices comparedtothose withoutaccess.Theyareunabletodetermineifthosedifferencesin

consumptionchoicesaresolelyduetonetworkaccessandconsequentlyanalysefuelchoice(andconditionaldemand)

separatelyforhouseholdswithandwithoutnetworkaccess.Coutureetal.(2012)takeadifferentapproachandinclude

networkaccessasacovariatewithinamultinomiallogitmodeloffuelchoiceintheMidi-PyrénéesregionofFrance.Grid

accessincreasesthelikelihoodthatapropertyusesgasastheprimarysourceofenergyby8percentagepoints,withoil

beingthefuelthatisdisplacedtothegreatestextent.InIrelandFuetal.(2014)ﬁndthatthelikelihoodofsolidfuelsbeing

theprimaryresidentialheatingsourcedeclinesby4percentagepointsinareaswithinathresholddistanceofthenatural

gasnetwork.

Inadditiontotheliteratureonfuelchoicedecisionsthereisaparallelliteratureontheacquisitionofenergy-usingassets,

e.g.aresidentialheatingsystem,thatisalsorelevant.OnesideofthatliteraturehasitsorigininBassdiffusionmodels

(Bass,1969)whereadoptionismodelledasasigmoidalfunctionovertime,withadoptionslowatﬁrst,thenaccelerating

beforereachingaplateau.Applicationsincludemodellinghouseholds’adoptionofheatpumpsandphoto-voltaicpanelsasa

functionofage,education,information,andﬁnancialincentives(Hlavinkaetal.,2016;Islam,2014).Energyassetacquisition

isalsostudiedinthecontextofenergyconsumptionwithDubinandMcFadden(1984)amongtheﬁrsttohighlightthat

assetownershipisendogenousinanenergydemandmodel.RecentapplicationsincludeDavisandKilian(2011)whomodel

naturalgasdemandintheUSandalsoMansuretal.(2008),whichmodelsfuelchoiceratherthanheatingsystemchoicein

thecontextofmodellinghouseholdfuelconsumption.Gertleretal.(2016)havemodelledtheeffectofhouseholds’income

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Fig.1. ResidentialfuelsharesinMtoe1990–2014.

Source:Datafromhttp://statistics.seai.ie/

theyﬁndthatcreditconstrainedhouseholdsaremorelikelytopurchaseenergyassetsoncetheirincomepassesathreshold

levelandfurthermorethatthethresholdlevelvariesdependingonthetimingofacquisition.Thissuggeststhattheimpact

ofnetworkgasavailabilityonheatingsystemorfuelchoiceisnon-linearandcannotbeadequatelycapturedwithadummy

variableindicatingavailabilityofanetworkconnection.

3. Background:residentialfuelusageinIreland

Irelandhasalonghistoryofsolidfuelusage,andinparticularpeatusageintheresidentialsector.Mokyr(2013)cites

reportsfromthe1830’sdescribingthegeographicalubiquityofpeatandtheintensityofitsusage.Whilecertainplaces,

suchasSouthAntrimandLimerickhaddepletedtheirreservesbythispoint,itwassoplentifulthroughouttherestofthe

countrythatitwastakenforgranted,and“peoplelivingaslittleas4milesawayfromasourceofturfalreadyconsidered

themselvesinconvenienced”.Peatcontinuedtobetheprimarysourceoffuelforhomeheatinguntilrelativelyrecentlyand

thegeographicalrelationshipbetweenthelocationofsolidfuelresourcesanditsusagepersists(Fuetal.,2014).Peatisstill

commonlyharvestedfrompeatbogsbythepublicandalsosoldaspeatbriquettes.

Asrecentlyas1990,theproportionofhouseholdsusingsolidfuelastheirprimarymeansofspaceheatingwasashighas

60%.Thishadfallento16%by2014.4_However,_62%_of_households_continue_to_use_a_stove,_range_or_open_ﬁre_as_a_secondary

heatingsource,andthemajorityoftheseusesolidfuel.5

Comparableresidentialfuelusagetrendsinkilotonnesofoilequivalentfrom1990–2014aredemonstratedinFig.1.The

fallingshareofsolidfuelisevident,whichhasbeenreplacedbyariseingas,oilandelectricityusageprimarily.Renewable

energyhasnotyetestablisheditselfdirectlyindomesticheating,howeverrenewablesourcesaccountedfor14.5%ofenergy

inputstoelectricitygenerationby2014(SEAI,2015).

IntermsofCO2emissions,eventhoughﬁnalenergyuseinthedomesticsectorincreasedby26%between1990and2011,

energy-relatedCO2emissionsfellby2.7%,reﬂectingthedecreasingshareofsolidfuelusageandtheimprovedefﬁciencyof

oilandgascentralheatingboilers(SEAI,2013).

3.1. Gasusage

Roganetal.(2012)provideacomprehensivesummaryofgasnetworkexpansionandusagetrendsinIrelandbetween

1990and2008.Thegastransmissioninfrastructurehadextendedtoanumberoflargetownsandcitiesby1990,however

90%ofgascustomerswerestillresidentinthetwolargestcitiesofDublinandCork.Thatdecadesawanexpansionofthe

transmissioninfrastructureoutwardfromDublin,alongbothnortheastandsoutheastcoastsandwesttothefast-growing

commutertownsinthegreaterDublinarea.Themid-2000ssawanextensionwestwardslinkingDublinwithGalway,from

hereitwasfurtherextendedtothenorthwestbythelate2000s.Thisextensionresultedinaconstantannualcustomergrowth

4_{http://statistics.seai.ie/}

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rateof9%overtheperiod1990–2008.Thereissigniﬁcantspatialvariationhowever,andby2014,naturalgascustomers

werestillaslowas5%insomewesternareas(CSO,2016).

Overthisperiodconsumptionincreasedby470%(Roganetal.,2012).Thiswasmainlythroughagrowingcustomerbase,

changesinthedwellingstock,andchangingintensityofusage.Weathereffectsarealsoimportant.Fromamicroeconometric

pointofview,Conniffe(1996)andHaroldetal.(2015)alsoﬁndweatherastrongpredictorofseasonaldemand.Thisresearch

alsotiesinwithinternationalresearchofgasconsumptionandmoregeneralspaceheating,whichﬁndthatdwelling

char-acteristicsandthesocioeconomiccharacteristicsofinhabitantshaveasigniﬁcantimpactondemand(Rehdanz,2007;Meier

andRehdanz,2010;Wyatt,2013).

Thefollowingsectionoutlinesourmethodologyandsomeempiricalconsiderationsonemustconsiderwhenmodelling

adoptionatarealevel.

4. Methods

Theutilityconsumersreceivefromadoptinggascentralheatingislikelytobeafunctionofarangeoffactorssuchas

therelativepriceofgascomparedwithalternatives,alongwiththeirsocioeconomicanddwellingcharacteristics.Physical

constraintsonadoptionexistandwillrelatetoeachhousehold’sproximitytothegasinfrastructure.Thekeypricevariable

ataspatiallevelistheconnectioncost.Thisisafunctionofdistancetothenetworkandiscapturedbyavariablewhich

measurestheaveragedistanceofalldwellingsineachareatothenearestpointonthenetwork.Unfortunatelyrelativefuel

pricedatadoesnotexistatacross-sectionallevel.However,providedthisdoesnotvaryacrossareasforagivenperiodit

willbeincludedinourintercept,andasdiscussedaboverelativefuelpricesmayplaylessofarolethanotherfactorsin

developedeconomics.

Economictheorysuggeststhathouseholdswilladoptmainsgascentralheatingifthebeneﬁtsderivedfromadoption

exceedthecostsandthereisanexpectedutilityincreasefromdoingso.However,innovationstaketimetodiffuse,and

householdsregularlymakesuboptimalchoices.Thiscanberelatedarangeoffactors,suchasuncertaintyabouttherelative

costsorbeneﬁtsofadoption,indifference,heterogeneityinconsumerpreferencesorlackofaccesstoﬁnancing.

Inordertoestimatethedeterminantsofgasconnectionsatalocalarealevel,itisnecessarytoconsiderdemandand

supplysimultaneously.Suppliersarelikelytoextendthegasnetworktoareaswithahigherprobabilityofadoption,and

onlythosehouseholdsincloseproximitytothegasinfrastructurecanadopt.Previousresearchhasindicatedthatdwellings

withpipedgasinIrelandhavehigherincomes,partlyduetotheirurbanlocation(Watsonetal.,2003).Thisendogeneity

couldpotentiallyleadtoourcoefﬁcientsbeingbiasedifwesimplyestimateademandequation.Therefore,weﬁrstestimate

asupplyequationinatwo-stageleastsquareregression.Thechoiceofinstrumentandidentiﬁcationaredescribedindetail

inSection4.3andinstrumentvalidityinSection4.4.

Weassumethattheproportionofgasusersinanyareajwillbeafunctionoftheaggregatesocioeconomiccharacteristics

ofthatareaXj,aggregatedwellingcharacteristicsDj,spatialfactorswhichwillvarybylocationSjandthelengthoftimethe

gasnetworkhasbeenlocatedinanareat−t0_j.Thiscanbesummarisedasfollows:

Nj

j=1Gijt

Nj =

f(Xj;Dj;Sj;t−tj0) (1)

whereGijtisabinaryvariableequaltooneifhouseholdiinareajusesgasattimetandequaltozerootherwise.Njisthe

numberofhouseholdsineacharea.

4.1. Supplyequation

Asadoptionmighthaveanon-linearrelationshipwiththelengthoftimethenetworkhasbeeninplace,weestimatetwo

supplyequations.Intheﬁrstequation,thedependentvariableisthelengthoftimethenetworkhasbeeninplaceineach

area,theseconddependentvariableisthesquaredlengthoftimethenetworkhasbeeninplaceineacharea.

Ourﬁrst-stagesupplyequationsaresummarisedbelow:

Tj=˛+ˇZZj+ˇXXj+ˇDDj+ˇSSj+

(2)

T2

j =+ZZj+XXj+DDj+SSj+ı (3)

WeregresstimeandtimesquaredonourinstrumentsetZjconsistingofhouseholdcount,householdcountsquared,area

andareasquared.

Thisgeneratespredictedvaluesfortimeandtimesquaredwhichwecanusetoidentifytheeffectofthesefactorsin

oursecond-stagedemandequation.Allothervariablesfromthesecondstagearealsoincludedintheﬁrststageregression.

Weimplementatwo-stage, generalisedmethodofmomentsspeciﬁcation(GMM),withcommonintercepts(˛,)and

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4.2. Demandequation

Thedependentvariableinthisregressionistheproportionofhouseholdsineachareathatusegasastheirprimarysource

ofcentralheating.Whencompletingthe2011Census,householdswereaskedtoselectfromarangeofoptionstheonethat

bestdescribestheirprimarymeansofcentralheating.ThisissummarisedinTable1inSection5.1.

Thedemandequationtakestheestimatedtimeandtimesquaredfromthesupplyequations,alongwitharangeof

socioeconomicanddwellingcharacteristics,somespatialvariablesrepresentingtheproximitytothegasnetwork,proximity

toalternatefuelsourcesandpolicyvariablesprohibitingthesaleandburningofbituminouscoal.

Nj

j=1Gijt

Nj =

+ı_T_ˆTˆj+ı_Tˆ2Tˆ

2

j +ıXXj++ıDDj+ıSSj+ (4)

Weincludearangeofsocioeconomicfactorsatarealevel,whichmightinﬂuencethedecisiontoadoptgascentralheating.

Thesearerelatedtoeconomicstatus,age,educationlevelsandtenuretype.Dwellingcharacteristicsincludehousetype,

ameasureofenergyefﬁciency(BuildingEnergyRating–BER),anddwellingage.Allofthesevariablesareexpressedas

proportionsforeachSmall-Area.

4.3. Identiﬁcation

Asdescribedabove,weusehouseholdcount,areasizeandtheirsquaredtermsinourﬁrst-stagesupplyregressionto

generatepredictedvaluesfortimeandtimesquaredinthesecondstagedemandregression.Thisisbecausethenetwork

operatorislikelytoexpandthenetworkﬁrsttothoseareaswithahigherprobabilityofadoption.Thismightbiasourresults

unlessaccountedfor.

Therationalebehindthisinstrumentisthatthetotalcostsofextendingthenetworktoanareashouldbeinverselyrelated

tothenumberofcustomersinanarea.Ifdiminishingeconomiesofscaleexist,anegativerelationshipwillalsoexistwith

thesquareofthenumberofcustomers.Inadditiontothis,thedensityofhouseholdswillalsobeanimportantfactorin

drivingnetworkextensions.6_This_instrument_captures_the_key_element_being_the_utilities’_decision_to_extend_the_network

tocertainareasbasedonlocaleconomiesofscale.ThisempiricalstrategydrawsfromLyons(2014)inhisestimationofthe

timinganddeterminantsoflocalbroadbandadoptioninIreland.

Asthenetworkhasbeendevelopedoveralongperiodoftime(approx.100years)usingpopulationdatafrom2011isnot

aperfectmeasure.HoweverwedonothavehistoricalseriesforpopulationatSmall-Arealevel,andthegeographicspread

ofpopulationinthecurrentperiodislikelytobehighlycorrelatedwithpastperiods.

Onthedemandside,thekeyfactordrivingadoptionwillbethecostandavailabilityofthenetworkconnection,thisis

capturedbyoursupply-sideinstrumentsandthevariablemeasuringdistancetothenetwork.Onecouldarguethatuptake

mayalsobeaffectedbyneighbourhoodspillovers,forexampleifahouseholdobservesanumberofneighboursconnecting

tothenetworkandthendecidestoconnect.Further,imperfectinformation,neighbourhoodeffectsorotherfactorsmay

affectthetimingofadoption.Thisunderlinestheimportanceofexaminingthetimelaginadoption.Whilewecanmeasure

themagnitudeofthetime-lagandhowisvariesbyarea,ourdatadonotallowustounpicktheunderlyingreasonsbehindit.

4.4. Instrumentvalidity

Regardinginstrumentrelevance,Baumetal.(2007)suggestusingKleibergen–Paaprkstatistictotestfor

underidentiﬁca-tionwhenusingarobustcovarianceestimator,andthecorrespondingWaldFstatisticwhentestingforweakidentiﬁcation.

Inbothcasestheresultsofthesetestsfailtorejectthenullhypothesisthatourinstrumentsareunderidentiﬁedandweakly

identiﬁed,asperTableB1.Thisislikelytobethecasebecauseweareincludinginteractionsofendogenousvariables(linear

andquadraticterms)inourestimationsandthesearehighlycorrelated.Wooldridge(2010)suggeststhatwhenthisisthe

case,oneshouldcheckwhetherthemostgenerallinearversionofthemodelisidentiﬁedandifthisisnotthecase,

pro-ceedwithcaution.Inourcaseboththelinearandquadraticendogenousvariablesarestronglyidentiﬁedwhenestimated

separately,TableB1,andweproceedonthatbasis.

TheresultfromtheHansenJtestofoveridentifyingrestrictionssuggeststhatwedonotrejectthenullhypothesisthat

theoveridentifyingrestrictionsarevalidforthe100yearsample.Ata5%levelwewouldrejectthenullforthe20year

sample.However,somedoubthasbeencastontheabilityofthistesttoprovideinformationonthevalidityofthemoment

conditionsimpliedbytheunderlyingeconomicmodel(Deaton,2010;ParenteandSilva,2012).ParenteandSilva(2012)in

particularsuggestthatthisshouldmoreaccuratelybeconsideredatestofinstrumentcoherence,asopposedtovalidity.

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5. Data

Thedatainthispapercomefromarangeofsources.Theproportionofnaturalgasuserswithineacharea,alongwitharea

proportionsofsocio-demographicanddwellingcharacteristicswereobtainedfromtheCentralStatisticsOfﬁce(CSO)Census

ofPopulation,Small-AreaPopulationStatistics2011.GasNetworksIreland(GNI)provideddetailedGISmaps,includingthe

timingandgeographiclocationofthehigh-pressure(HP),mediumpressure(MP)andlowpressure(LP)gasnetwork.The

EnvironmentalProtectionAgency’s(EPA)websiteprovideGISmapsofsoiltypesinIreland,fromthiswecalculatedthe

averagedistancetobogsforalldwellingsineachlocation.TheEPAalsoprovideinformationonthetimingandlocationof

smoky-coalbansinIrishurbanareas7_._For_descriptive_statistics_of_all_variables_used_in_estimations,_please_see_Appendix_A.1_.

TheanalysisisconductedatSmall-Arealevel.Thisisthemostdisaggregatedunitforwhichonecanobtainpublicly

availableCensusdatainIreland.Theserangeinpopulationfrom8to549dwellings.Thereareover18,000Small-Areasin

Ireland.Oursampleconsistsof9638Small-Areaswhichareallincloseproximitytothegasnetwork.

5.1. Dependentvariable

ThedependentvariableistheproportionofgasuserswithineachSmall-Area.Thiswasself-reportedbyhouseholdsas

perTable1.Naturalgasusageaccountedforalmostathirdofallprimarycentralheatinginthenationalpopulationin2011.

Weexplorehowthisvariesbyrecalculatingtheproportionsofeachfuelusedastheaveragedistanceofalldwellingsin

aSmall-Areagetclosertothegasnetwork.Theaverageproportionofgasusersjumpsto57.5%inareaswithin1000mof

thenetwork(oursample),andincreasesastheaveragedistancetothenetworkfalls.Themainfueldisplacedisoil,and

electricityisincreasingusedasanalternative.Thisreﬂectsthegreaterproportionofelectricheatinginurbanapartment

buildingsclosetothegasnetwork.

However,evenwithintheseareas,considerablevariationexistsintheproportionofusers.Fig.2illustratesthatevenfor

areasinwhichtheaveragedistanceofallhouseholdstothenearestpointonthelowormediumpressurenetworkisless

than100m,asigniﬁcantproportiondonotusegasastheirprimarymeansofheating.

ThisisillustratedgeographicallyinFig.3.AsexampleswechoosefourmetropolitanareasinIreland,allofwhichhave

accesstothegasnetwork.OutsideofDublin,Corkhasboththegreatestnumberandhighestproportionofhouseholdsusing

naturalgasastheirprimarymeansofcentralheating,howeverthereisstillsigniﬁcantlocalvariation.Galwayhasarelatively

lowproportionofgasusersinmostareas,reﬂectingtherecentextensionofthenetworktothiscity.

5.2. Gasnetwork

ThelocationofthegasinfrastructureinIrelandisdisplayedinFig.4.AsdescribedinSection3.1,thenetworklocation

wasconcentratedmainlyinlargercitiessuchasDublinandCorkuntilrelativelyrecently.Thehigh-pressurenetworkwas

expandedtolinkLimerickandGalwayintheearly2000s.

Detailednetworkmaps,whichalsocontainthedateeachindividualsegmentwaslaid,wereobtainedforeachsegment

ofthegasnetwork.FromthiswecalculatewhenthegasnetworkwasputinplaceforeachSmall-Area.

5.2.1. MeandistancetoLPorMPnetwork

ThisdistancevariablewasgeneratedbycalculatingthedistanceofeverydomesticresidenceintheCSO2011Censusto

thenearestpointontheLPorMPgasnetwork(Krahetal.,2016).WethenaggregatedbySmall-Area,tocalculatetheaverage

Table1

Census2011primarycentralheatingproportions.

Whatisthemaintypeoffuelusedbythe

centralheatinginyouraccommodation?

Nationalpopulation Within1000m Within500m Within100m Within50m Within10m

Nocentralheating 1.6% 1.3% 1.3% 1.3% 1.3% 2.8%

Oil 43.1% 23.9% 22.3% 18.2% 16.2% 3.2%

Naturalgas 33.4% 57.5% 59.2% 64.0% 67.1% 69.7%

Electricity 8.5% 11.5% 11.7% 11.8% 11.1% 19.5%

Coal(includinganthracite) 4.8% 2.5% 2.4% 2.0% 1.8% 1.5%

Peat(includingturf) 4.8% 0.8% 0.7% 0.4% 0.2% 0.1%

LiquidPetroleumGas(LPG) 0.6% 0.3% 0.3% 0.2% 0.2% 0.2%

Wood(includingwoodpellets) 1.3% 0.3% 0.3% 0.2% 0.2% 0.1%

Other 0.5% 0.3% 0.3% 0.2% 0.2% 0.3%

Notstated 1.4% 1.6% 1.6% 1.7% 1.7% 2.7%

Notes:Author’scalculationsbasedonCSOCensus2011data.

Datapresentedfornationalpopulationandforvaryingdistancesfromgasnetwork.

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Fig.2. Proportionofhouseholdsusinggasastheirprimaryfuelincloseproximitytothelowpressuregasnetwork.

Source:Author’scalculationusingCensus2011data.

Fig.3.SpatialvariationingasconnectionsatSmall-ArealevelinfourIrishmetropolitanareas.

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Fig.4. LocationofIrishgasnetworkinfrastructure2011.

Source:DataprovidedbyGasNetworksIreland–pleaseseethedisclaimerattheendofthisdocument.

distanceforeacharea.Thisvariablewillreﬂecttherelativeeaseofconnectionforvariousareas.Thiscanvaryevenwithin

closeproximitytothenetwork–ascanbeseenfromFig.5.

5.2.2. Datenetworkwaslaid

Eachsegmentofthegasnetwork8_has_a_date_identiﬁer_marking_the_day_that_portion_of_the_network_was_laid._Using

GISsoftware,wemapeachnetworksegmenttoanySmall-Areaitisfullywithinorintersectsatanypoint,illustratedin

Fig.6.ThisgeneratesadistributionofdatevariablesforeachSmall-Area.Asthe2011Census(fromwhichwetakeourgas

proportionsdata)tookplaceonApril10th2011,weconsiderthisastimet.Fromthiswecalculatethelengthoftimein

yearssinceeachsegmentwaslaidast−t0

j,wheret0j isthedateeachsegmentwaslaid.Thisgeneratesadistributionof

year-lengthvariablesforeachSmall-Area.Asaproxyforthelengthoftimegaswasavailabletohouseholdsineachareawe

choosemaximumtimelength,i.e.thedatetheﬁrstsegmentwaslaidineacharea.Howeverwealsorunestimationswith

variousothertimevariables,suchastheaveragetimeandlatesttimegasbecameavailableineacharea.9

8 _The_low_pressure_network_contains_135,195_separate_segments,_the_medium_pressure_network_contains_123,048_segments.

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Fig.5.Variationinhouseholddensityandlocationincloseproximitytothelowpressuregasnetwork.

Source:DataprovidedbyCSOPopulationCensus;GasNetworksIreland–pleaseseethedisclaimerattheendofthisdocument.

Fig.6.ExampleofSmall-Areaboundariesandgasnetwork.

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Fig.7. Locationofpeatbogsandareaswherepeatburningistheprimarymeansofcentralheating.

Source:DataprovidedbyCSOPopulationCensus;EPAGISportal.

5.3. Spatialfuelsourceandpolicyvariables

FromCensus20114.8%ofhouseholdsinIrelandusepeatastheirprimaryheatingsource,howeverasizeableproportion

alsohaveanopenﬁreorpeatburningstoveasasecondarysource.AscanbeseenfromFig.7,thispatternishighlycorrelated

withthelocationofpeatbogs.UsingGISsoftwarewecalculatethedistanceofeveryhouseholdtothenearestraisedand

blanketbog.Again,weaggregatethesevariablestoSmall-Arealevel,allowingustodeterminetherelativeproximityof

dwellingsineachareatodifferentbogtypes.

Abanonthemarketing,saleanddistributionofbituminousfuel(or“smokycoalban”)wasintroducedinDublinin1990.10

Thiswasinresponsetosevereinstancesofwintersmog.Thisbanwasextendedtoanincreasingnumberoftownswitha

populationinexcessof15,000peoplebetween1990and2013,andaprohibitiononburningwasintroducedinadditionto

thebanonmarketing,saleanddistribution.By2011thiswasinplacein19townsinIreland.Informationonthelocation

ofthesebansallowustooverlaythisontoourSmall-Areas.Dummyvariablesarethencreatedfortheseareas.Whilewe

cannotinferacausalrelationshipbetweenthispolicyandgasusage,wecanexaminethecorrelation,holdingotherfactors

constant.

5.4. Censusandotherdata

Supplementingthespatialandtemporaldataonfuelsourcesandpolicyvariables,weincludearangeofsocioeconomic,

demographicanddwellingvariablesfromtheCensusinourestimations.ThesevariablesareallatSmall-Arealevelandthus

willreﬂecttheaggregatecharacteristicsofeacharea.Wealsoincludeinformationontheenergyefﬁciencyofdwellings.

ThisdatawasestimatedusingtheSEAIBERdatabaseandtheCensusofpopulation2011.FormoreinformationseeCurtis

etal.(2015).Weusetheproportionoflow-rated(E,F,G)dwellingsineacharea.

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6. Results

Weestimateageneralisedmethodofmoments(GMM)instrumentalvariablesspeciﬁcation,withhouseholdcount,area

andtheirsquaredtermsasinstruments.Thelengthoftimethenetworkhasbeeninplacemightaffecttheproportionof

usersinanon-linearmanner.Forexample,torunmainsgastocertainhousingestatesadjacenttotheexistingnetwork

GNIrequireaminimumproportionofhouseholdswithinthatareatoadoptimmediately.11_This_would_result_in_a_large

initialuptakewhichmitigatesovertime.Alternativelyforone-offconnections,certainhouseholdsmightbeslowtoswitch

tomainsgaswhenitﬁrstbecomesavailable,duetosunkcostsrelatedtotheircurrentheatingsystem.Thismightresultin

aslowinitialuptake,followedbymorerapidswitching.Toaccommodatethis,wespecifytwoﬁrststageregressions,with

timeandtimesquaredasthedependentvariables.Standarderrorsarerobusttoheteroskedasticity.Areasareweightedby

populationinallspeciﬁcations.Werestrictouranalysistoareasinwhichtheaveragedistanceofalldwellingsislessthan

1kmfromthenearestpointonthelowormediumpressuregasnetwork.Otherareasarenotrelevantforouranalysis,as

itwouldnotbefeasibleforhouseholdswithinthemtoconnecttomainsgas.12_We_report_the_results_from_our_ﬁrst_stage

supplyequationsﬁrst,followedbythesecondstagedemandequation.

6.1. Supplyequations

Theseequationsareprimarilyusedtoidentifythelengthoftimethenetworkhasbeeninplaceinourdemandequation.

Theinstrumentsareallsigniﬁcantandhavetheexpectedsigns.Thegasnetworkwaslocatedﬁrstinareasofhighdensity.We

includeallothercovariatesfromoursecondstageintheﬁrststageregressions,asthereisnoefﬁciencylossfromdoingthis.

However,asmanyofthem,particularlythoserelatedtosocioeconomiccharacteristics,reﬂectcurrentfactorsandthegas

networkwasconstructedovermanyyears,theirinterpretationissubjecttocaution.TheresultsarereportedinSectionA.1

oftheappendix.

Onevariableofinterestthoughistheproportionofhousesbuiltinvarioustimeperiodsineacharea.Thiswillreﬂect

changesinthehousingstockovertime.Asonemightexpectthecoefﬁcientsonthesetermsarehighestforthoseareas

withhighproportionsofpre1945dwellings,decreasesforareaswithhigherproportionsofbuildingconstructedbetween

1945–1980,andrisesagainforbuildingsconstructedbetween1980–2000.Thiseffectisindicativeoftheoutwardsprawlof

networkinfrastructurefromareasofhistoricallyhighdensityovertime.

6.2. Demandequation

Predictedvaluesforlengthoftimeandlengthoftimesquaredaregeneratedfromtheﬁrststageestimation.Theproportion

ofgasconnectionsineachareaisthenregressedontheseandothervariables.Wereportresultsforboththewhole100

yearsampleandthemorerecent20yearsampleinTable2.Theresultsindicatethateachadditionalyearthenetworkhas

beeninplaceresultsina3.2percentagepointincreaseintheproportionofhouseholdswithinthatareawhousegasastheir

primaryfuel.Thiseffectmitigatesovertime,asindicatedbythenegativeeffectonthesquaredterm.Wecangraphically

illustratethetime-pathtoadoptionincludingbothlinearandsquaredterms,aspertheleft-handpanelofFig.8.Bothof

theseeffectsarehighlystatisticallysigniﬁcant.Onaverage,forallareasinourestimationthereisanincreasingadoption

uptoabout25yearsinthefullsample.Thelimitingfactorisduetocertainareashavinghadaccesstothegasnetworkfor

uptoacentury,butwhichstilldonothaveaveryhighproportionofconnections.Whentheanalysisisrestrictedtomore

recentperiodstherateofadoptionappearstobemuchfaster.Thisisgraphedintheright-handpanelofFig.8.Whenthe

sampleisrestrictedtotheprevious20years,eachadditionalyearisassociatedwithapproximatelya12percentagepoint

increaseingascustomers,againthiseffectappearstoreduceovertime.ThisisbroadlyinlinewithRoganetal.(2012),

whoreportedanannualincreaseof9%between1980and2010.Onaverage,penetrationratesarereachingabout50%after

8–10years.

Theresultsforboth100yearand20yearsamplearebroadlysimilarformostoftheremainingvariables.Wewilldiscuss

bothtogether,unlessotherwisestated,withthe20yearresultsinparenthesis.Thecoefﬁcientonthevariablerepresenting

distancetothegasnetworkissigniﬁcantatthe1percentlevel.Eveninareasthatarerelativelyclosetothenetwork,distance

stillmatters.Interpretingthisresultimpliesthata1percentincreaseinaveragedistancetothenetworkisassociatedwith

a12(13)percentagepointreductionintheproportionofusersinanarea.Thisreﬂectsthecostofdomesticconnections.

Forhouseswithin15mofthenetworkconnectioncostsareD 220,withachargeofD 45foreachadditionalmetrebeyond

this.13

Thedistancetoacutbogs(thisincludesbothraisedandblanketbogs)hasapositivecoefﬁcient,indicatingthatthefurther

awayanareaisfromacutbog,allelsebeingequal,thehighertheproportionofgasusersinthatarea.Thecoefﬁcienton

11_See_for_details_{http://www.cer.ie/document-detail/Gas-Networks-Ireland-Connections-Policy-Review/1007} 12_We_test_the_sensitivity_of_this_parameter_to_various_distances_from₁₀₀_m_upwards.

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Table2

Secondstagedemandequation.

DepVar:proportionofgasusersbySAin2011

Variablecategory Variable 100yearsample 20yearsample

Coefﬁcient RobustSE Coefﬁcient RobustSE

Spatialfuelandpolicy

variables

Maxlengthyearshat 0.032*** _(0.004) _0.119*** _(0.022)

Maxlengthyearssquaredhat −0.001*** _(0.000) ₋_0.006*** _(0.002)

log(distancetocutbog) 0.019*** _(0.004) _0.038*** _(0.006)

log(distancetobktbog) −0.006 (0.005) −0.020*** _(0.005)

log(meandistancetogasnetwork) −0.120*** _(0.006) ₋_0.129*** _(0.007)

Coalbandummy 0.063*** _(0.011) _0.106*** _(0.016)

Socioeconomic EconWorking [REF] [REF]

EconLookingforﬁrstjob −0.289 (0.313) 0.076 (0.243) EconUnemployed −0.427*** _(0.123) ₋_0.659*** _(0.128)

EconStudent −0.398*** _(0.130) ₋_0.240** _(0.103)

EconHome −0.303* _(0.155) ₋_0.438*** _(0.127)

EconRetired −0.774*** _(0.198) ₋_0.564*** _(0.144)

EconDisabled −0.710*** _(0.175) ₋_0.438*** _(0.121)

EconOther −0.150 (0.198) −0.415 (0.275)

Age25–44 [REF] [REF]

Age0–14 0.560*** _(0.117) _0.405*** _(0.084)

Age15–24 0.188 (0.156) 0.106 (0.118)

Age45–64 −0.293*** _(0.099) ₋_0.039 _(0.109)

Age65plus 1.046*** _(0.239) _0.458*** _(0.165)

Socioeconomic

EduSecondary [REF] [REF]

EduPrimary 0.187** _(0.082) _0.400*** _(0.076)

EduTechnical −0.124 (0.100) −0.225*** _(0.086)

EduDegreeplus 0.253*** _(0.059) _0.198*** _(0.042)

EduRefused −0.029 (0.128) −0.041 (0.086)

TenOwnmortgage [REF] [REF]

TenOwnNomortgage −0.351*** _(0.064) ₋_0.536*** _(0.069)

TenRentland −0.153*** _(0.044) _0.001 _(0.046)

TenRentlocal 0.119** _(0.049) _0.073** _(0.031)

TenRenvol −0.009 (0.081) 0.078 (0.078)

TenRentfree −0.252 (0.203) 0.177 (0.271)

Dwelling DwellBungalow [REF] [REF]

DwellFlat −0.219*** _(0.026) ₋_0.166*** _(0.020)

DwellBedsit −0.192 (0.358) −0.847*** _(0.120)

DwellOther −0.317** _(0.133) _0.285 _(0.181)

ProportionEFG −0.499*** _(0.036) ₋_0.602*** _(0.034)

AgePost2006 [REF] [REF]

AgePre1945 0.446*** _(0.075) _0.713*** _(0.108)

Age1945–60 0.363*** _(0.056) _0.577*** _(0.072)

Age1960–80 −0.126*** _(0.033) _0.235** _(0.101)

Age1980–2000 −0.126*** _(0.025) _0.156* _(0.082)

Constant 0.840*** _(0.097) _0.592*** _(0.108)

Diagnostics N 9638 7965

F(34,9603),(34,7930) 461.38 (0.00) 416.62 (0.00)

Overid–HansenJ 1.341 (0.512) 6.836 0.0328

Notes:ResultsfromIV-GMMspeciﬁcation.Cluster-robuststandarderrorsinparenthesis.

* _p_<_0.1. ** _p_<_0.05. ***_p_<_0.01.

uncutblanketbogsisnegative,butnotsigniﬁcant.14_This_is_likely_to_be_the_case_because_the_current_proportion_of_households

usingsolidfuelinanareawillreﬂectpastincentivesinthatarea.Thereforeproximitytocutbogsmightbeabetterindicator

offuelusageasthiswillreﬂectareaswherepeathasbeenharvestedovermanyyears.Thebanonthesaleandburningof

bituminousfuelappearstoalsohavehadaneffect.Allelsebeingequal,theseareashavea6(11)percentagepointhigher

proportionofgasusers.Wecannotinfercausalityhowever.

Consideringthesocioeconomicanddwellingvariablesnext,ourreadingofthecoefﬁcientschanges.Foreachsetof

vari-ables,weinterprettheeffectrelativetothereferencecategory.Allofthesevariablesareareaproportions.Theemployment

statusvariableindicatesthat,allelseequal,comparedtoareaswithhigherproportionsofpeopleinemployment,allother

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Fig.8.ProportionofgasadoptersatSmall-Arealevelovertime.

categorieshavereducedgasconnections,althoughnotallcoefﬁcientsarestatisticallysigniﬁcant.Takingthe

“EconUnem-ployed”variableasanexample,ourinterpretationisthatallelseequal,a10percentincreaseintheproportionunemployed,

relativetothereferencecategory(thoseinemployment),isassociatedwitha4.27(6.59)percentagepointdecreaseinthe

proportionusingnaturalgas.

Areaswithhighproportionsofyoungfamiliesandelderlypeoplearealsoassociatedwithgreatergasconnections,

com-paredtothosewithhighproportionsof25–44yearolds.Consideringtenuretypenext,thoseareaswithhigherproportions

ofoutrighthomeownersandprivaterentersarelesslikelytohavegasconnectionsthanthosewithhighproportionsof

mortgageholders.However,localauthorityareashavehigherproportionsofgasconnections.

Areaswithhighproportionsofhouses,asopposedtoﬂatsorbedsits(studioapartments)aremorelikelytousegas.

ThisreﬂectsthelargeproportionofelectricalheatinginapartmentcomplexesinIreland.Theproportionoflow-ratedBER

dwellingsinanareaisstronglynegativelyassociatedwithgasconnections.15_Finally,_when_looking_at_the₁₀₀_year_sample_we

canseethatbothverynew(post2000)andveryold(pre1960)constructedhousesaremorelikelytohavehighproportions

ofgasconnections.Thislikelyreﬂectstheurbanlocationofahighproportionoftheolderbuildingstock.Thecoefﬁcients

differslightlyforthe20yearsample,withmorerecentnetworkexpansionsextendingtoahigherproportionofdwellings

builtfrom1960–1980.

Thereisahighdegreeofcollinearitybetweensomeofthesocioeconomicanddemographicvariables.Forexample,areas

withhighproportionsofretiredpeoplealsohavehighproportionsofpeopleagedover65,andhaveahighproportionof

owneroccupierswithoutanyremainingmortgageobligations.Whileeachofthesevariablesiscomparedwiththereference

categoryineachclass,cautionisadvisableininterpretingsomeofthesecoefﬁcients.Forexample,theresultsindicatethat

areaswithgreaterproportionsofretiredhouseholdsarelesslikelytohavehighconnectionstothegasnetworkthanareas

withgreaterproportionsemployed.However,areaswithgreaterproportionsagedover65aremorelikelytohavehighgas

connectionsthanareaswithgreaterproportionsof25–44yearolds.Thisresultseemscontradictory,butisdrivenbythe

referencecategorychangingineachcase,andasmallnumberofareas,withveryhighgasconnections,whichalsohave

householdsagedover65onaverage,thatarenotinretirement.

7. Scenarioanalysis–gasnetworkexpansion

Themodelmaybeusedasatooltopredictresidentialuptakeoffuturegasnetworkexpansionaswellasassessthe

associatedimpactongreenhousegasemissions.Networkexpansionisstillongoingwithanumberofprovincialtowns

earmarkedforconnection.Wexfordtown,whichislocatedinthesouth-eastofthecountry,isonetownwherethenetwork

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Table3

Projectedproportionsofgasnetworkconnections.

After: SmallAreas %Householdsconnected Minimum% Maximum% changeemissionstCO2/year

2years 74 0.19 0.01 0.21 −23,065

4years 74 0.36 0.17 0.38 −44,617

6years 74 0.48 0.29 0.50 −61,651

8years 74 0.55 0.36 0.57 −67,614

10years 74 0.57 0.38 0.59 −70,815

hasrecentlyexpandedandisausefulcasestudyformodelsimulations.Thetowncomprises74SmallAreas,whichare theobservationunitintheestimatedmodel.Basedonthe2011populationcensusthereare17,684peoplelivinginthese SmallAreaswithinahousingstockof8437residentialunits.Theseareasincludeaspectrumofbuildingtypes,aswellas socio-demographiccharacteristicsoftheoccupants.Housesarethemostfrequentresidentialunit,withameanacrossthe 74smallareasof87%,thoughthisvariesfromaminimumof19%toamaximumof100%acrossSmallAreas.Themean shareofolderpre-1945propertiesis17%rangingfromaminimumof0%toamaximumof79%,whichreﬂectsboththeolder centralpartsoftownandmorerecentlybuildareasontheperiphery.Whilethepropertytype,andagemaypresentdifferent engineeringchallengesconnectingtothegasnetworkanimportantadditionalconsiderationistheproperty’soccupants. Themodelincorporatedfoursocio-demographicvariablescoveringtheheadofhousehold’ssocio-economicstatus,ageand education,aswellasdetailsonpropertytenure.Theproportionofhouseholdswithaworkingheadofhouseholdvaries between22%and70%acrossSmallAreas;thosewithauniversitydegreerangefrom6–42%withsimilarlybroadvariations inage.Approximately25–32%ofpropertiesareeitherowner-occupied(with/withoutamortgage)orrentedfromaprivate landlordwhile10%arerentedfromapubliclandlord.Themaximumproportionofeachofthosetenurecategoriesisbetween 63–73%,whiletheminimumvariesbetween0–6%.Theadoptionofgasasafuelislikelytodiffersubstantiallygiventhewide variationbothinbuildingcharacteristicsandtheiroccupants.Theestimatedmodelisanidealtooltopredictgasconnections bySmallAreawiththepassageoftime,whichshouldaidinplanningnetworkexpansion.

Tocompletethesimulationwemakeanumberofassumptions.First,weusethemodelestimatesbasedongasnetwork connectionsinthepast20years,asthisislikelytohavemorerelevanceforpredictingnetworkconnectionsoverashort-term horizon.Tocalculatetheimpactonemissions,dataonthefuelusedpriortogasconnection(i.e.coal,oil,etc.)aswellasthe quantityconsumedisrequired.Meanhouseholdfuelconsumptionbyheatingsourcetypearebasedonﬁguresreportedin

LeahyandLyons(2010).Thepre-switchingfueltypeassumptionisbasedonananalysisofthecompositionoffuelsconsumed

inSmallAreaswithnetworkgasconnectionsat5%incrementsinshareofgasnetworkconnections.

TheprojectednetworkconnectionsarereportedinTable3.Withthemodel’sestimatedinﬂectionpointoccurringat

approximately10years,implyingthatthelevelofconnectionsreachesaplateauafterthattime,wedonotreportpredictions

beyond10years.Within2yearsthemeanshareofconnectionsis19%,whichisrelativelyhighbutastheSmallAreasdifferin

sizethemeanshareisnotequaltotheproportionofallhouseholdsconnected.Thelevelofconnectionsdiffersconsiderably

withtheconnectionsharebeingaslowas1%orashighas21%insomeSmallAreas.Thelevelofconnectionsincreasesquite

rapidlyovertheﬁrst8yearsreachingaplateaujustbelow60%meanshareofgasconnectionsbySmallArea,thoughthe

shareofconnectionsissubstantiallylowerinsomeplaceswith12%ofSmallAreasnotexceedinga50%connectionrate.The

switchtogasisprimarilyfromoil(mostlykerosene)andalsosolidfuelssuchascoal.Theprojectedreductioninemissions

associatedwithfuelswitchingisalsoreportedinTable3.Emissionsreductionsreaches70ktCO2perannumaftertenyears,

whichisapproximately1%ofemissionsfromtheentireIrishresidentialsectorin2015.16_Based_on_these_projections_the

furthergasiﬁcationofresidentialheatingrepresentsamajoropportunitytosubstantiallyreducegreenhousegasemissions

inthecomingyears.

8. Robustnessandsensitivityanalysis

Thissectionexploresarangeofalternativemodelspeciﬁcations.Forreasonsofbrevitythefocusisonourbaseline100

yearsample,butresultsholdforbothunlessotherwisestated.Reportedsecondstagedemandequationresultsarereported

intheAppendix.

8.1. Alternativemodelspeciﬁcations

Theﬁrstsetofadditionalestimationsexaminealternativemodelspeciﬁcations.Column1inTableC1presentsresults

wherehouseholddensityandhouseholddensitysquaredareusedinsteadofhouseholdcountandhouseholdcountsquared

fortheinstruments.Theresultsremainquitestablecomparedtoourmainestimation.Thisislikelybecausebyinstrumenting

withhouseholdcount,areaandtheirsquaretermsinthemainestimationweimplicityaccountfordensity.

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Anothersourceofconcernwithourmainestimationsisthatwedonotexplicitlyaccountforhouseholdincome.

Informa-tiononincomesisnotavailableatSmall-Arealevelandalthoughwecaptureawiderangeofsocioeconomicfactorscorrelated

withincomesomebiasmayexistduetoitsomission.Toaccountforthisweestimatetwoadditionalmodelswhichinclude

proxiesforincome.Column2presentsresultsusingtheTrutzHaaseHPDeprivationIndex(HaaseandPratschke,2012)

foreachSmall-Area.ThisisacompositemeasurecreatedbycombiningarangeofCensusvariables,someofwhichwe

hadincludedinourmainspeciﬁcation.Toavoidpotentialmulticollinearityweomitthesevariablesfromthissetofresults

(employmentstatus,age,educationandtenuretype).Column3includesavariablewhichcapturesaveragerelative

employ-mentcompensationforeachcountyfrom1995–2011.Standarderrorsareclusteredatcountylevelfortheseestimations.

Inbothcasesthesevariablesaresigniﬁcantandhavetheexpectedsign.Coefﬁcientsonthepredictedlengthoftime

vari-ablesremainstableinbothestimations.ThestatisticalsigniﬁcancereducesforsomeofthedistancevariablesinColumn

3–otherwiseresultsremainquitestable.WhiletheTrutzHaaseeisausefulmeasureinitsownright,itisessentiallyan

aggregationofvariablesalreadyincludedinourmodelanddoesnotprovidemuchadditionalinformation.Includingcounty

levelrelativeincomeisausefulmeasure,andgiventhatcountiesareadministrativeboundaries(asopposedtoSmall-Areas)

clusteringthestandarderrorsatthislevelwouldmakesensetocontrolforanyfactorsthataffectgroupsofobservations

uniformlywithineachcounty.However,wearelessconﬁdentabouttheaccuracyofthisspeciﬁcationaswhenclusteringat

countylevelthenumberofclustersareinsufﬁcienttocalculatearobustcovariancematrix.

ToaccountforthisinColumn4weprovideafurtherrobustnesscheckinwhichweclusterstandarderrorsatElectoral

Division(ED)level.17_This_allows_for_a_calculation_of_a_robust_covariance_matrix._However_EDs_are_an_arbitrary_aggregation_of

Small-AreasusedforCensuspurposesanditisnotclearwhyintra-groupcorrelationwouldexistatthislevel.Giventhemain

resultsarequitestableacrossalladditionalmodelsestimatedourmainreportedmodelsremainourpreferredspeciﬁcation.

8.2. Sensitivityanalysisonmissinggassegmentdateidentiﬁers

Aspreviouslydescribedadateidentiﬁerismissingfor20%oftheLPandMPnetworksegments.Whilewemitigate

thisproblemthroughaggregationattheSmall-Arealevel,measurementerrormaystillbiasourresults.Toaccountforthis

weconductsensitivityanalysisonvarioussub-setsofthedata.Columns1–4ofTableC2presentresultswherewesetan

acceptablethresholdofmissingdateidentiﬁersforeachSmall-Areaat0%,5%,10%and20%.TakingColumn1forexample

weomitanySmallAreawithamissingdateidentiﬁer.Thisisquiterestrictiveandreducesoursampleto5072Small-Areas.

Aswemoveacrossthecolumnsthesamplesizeincreasesandresultsconvergetowardsourmainestimates.However,in

allcasestheyarequitestable.Ourmainmodelremainsthepreferredspeciﬁcationasitprovidesaconservativeestimateof

theeffectoftimeongasheatingadoption.

8.3. Sensitivityanalysisonaveragedistancefromgasnetwork

ForthemainestimationsweexamineuptakeofgascentralheatinginSmall-Areasinwhichtheaveragedistanceofall

dwellingsiswithin1000mofthegasnetworkinfrastructure.Thisthresholdischosenasitwouldbeprohibitivelyexpensive

toconnectoverdistancesmuchlongerthanthis.AnotherissueisthatasmallproportionofdwellingsuseLPGandwhile

thisisconsideredseparatelybytheCSOandshouldnotbeincludedinourdependentvariable,somehouseholdsmayhave

answeredthisquestionincorrectly–particularlyiftheystatetheyareusingnaturalgasbutarefarfromthenetwork.Results

ofarangeofsensitivitychecksarereportedinTableC3.Asonemightexpect,resultsarequiteunstableatdistancesfarfrom

thenetwork.Householdsintheselocationswouldhavenorealisticchanceofconnecting.Asthethresholdmovescloserto

thenetworkresultsconvergetowardsthemainestimations.Again,ourmainmodelremainsthepreferredspeciﬁcationand

itprovidesaconservativeestimateoftheeffectoftimeongasheatingadoption.

9. Conclusion

We haveexaminedthedeterminantsofgascentralheatingadoptionatSmall-ArealevelinIreland, simultaneously

modellingsupplyanddemandinordertoaccountforpotentialendogeneityinnetworkinfrastructureroll-outandadoption.

Weexplicitlymodelthetime-pathindiffusion,whichisimportantinordertobetterunderstandthepotentialforbothpolicy

andtechnologicalimprovementstoaidcarbonabatement.Irelandisinterestingfromaninternationalperspectiveasithas

alegacyandcultureofpeatusageforhomeheating.Thegasnetworkhasbeeninplaceinthetwolargestcitiesforacentury,

butonlyrecentlyextendedtootherpartsofthecountry.Ouruniquetimeandlocationcodeddataallowustoexamine

adoptionoveranextendedperiod.

Onaveragetheresultsshowthatoverthepastcentury,eachyearthenetworkhasbeeninplaceisassociatedwitha3%rise

inconnections.Whenmorerecentperiodsareexamined,theconnectionrateismuchhigher,about12%riseperyearover

thepasttwentyyears.Thereappearstobeanon-linearityintheseestimatesandthiseffectdiminishesovertime.Proximity

tothenetworkisalsoanimportantdeterminantofconnections,andreﬂectsthecostofconnectionforalldwellingsinthat

area.

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Thewidespreadavailabilityofpeatasasourceoffuelhasclearlyinhibitedthetransitiontocleanerfuels.Aspeatusage

ishighlycorrelatedgeographicallywiththelocationofpeatbogs,itisusefultoseehowgasnetworkroll-outinteractswith

theproximityofotherfuelsourcesindetermininggascentralheatingadoption.Proximitytopreviouslycutpeatbogsis

negativelyassociatedwithgasconnections.Recentpolicydevelopmentssuchasthebanonthesaleandconsumptionof

bituminouscoalisassociatedwitha6percentagepointhigherproportionofgasconnectionsintheseareas,allelsebeing

equal.Wecan’tattributecausalityherehowever,asthisbanwasﬁrstintroducedinurbanareas,whichwouldalreadyhave

hadhigherproportionsofgasconnectionsbeforethebanswereintroduced.

Inthecontextoffuturenetworkgasexpansiontheanalysisprovidesanumberofusefullessons.Asnotedabove,domestic

gasnetworkconnectionsareneitheruniformnorinstantaneousfollowingnetworkexpansion.However,connectionsdo

occurrelativelyrapidlyreachingaplateauwithin10years.Thereisalsoconsiderableheterogeneitybysocio-demographic

characteristicsandbuildingattributesacrossSmallAreasintermsofnetworkconnections.Thisinformationisusefulfor

networkplannersindecidingwheretonextextendthenetwork,andalsoforcommercialsuppliersofgasindeterminingwhy

certainareasincloseproximitytothenetworkhavelowlevelsofconnections.Areasthataremoresociallydeprived,with

fewer‘working’householdsorlowerlevelsofeducation,havinglowerratesofnetworkgasconnection.Networkexpansion

insuchareasmaybeunproﬁtableorhavelongerpay-backperiods.Ifgasnetworkexpansionisconsideredsociallydesirable

insuchareaspublicsubventionmaybenecessary.

Onereasonwhygasnetworkexpansioncouldbeconsideredapublicpolicyobjectiveisbecauseitcancontributeto

thede-carbonisationoftheresidentialsector.Thecasestudysimulationdemonstratestheshorttermbeneﬁtsofnetwork

expansionforgreenhousegasemissionreductionsassociatedwithfuelswitching.Expansionofthenaturalgasnetworkis

alsoconsistentwiththelongertermambitionsofreducingEUgreenhousegasemissionsby2050byover80%(European

Commission,2011),aslongertermambitionstoinjectbiomethaneintothenaturalgasnetworkhasthepotentialtoreduce

emissionsby74%comparedtonaturalgas(O’Sheaetal.,2017).

Tofullyexaminethefactorsinﬂuencingthechoiceofhome-heatingsystem,wewouldideallyhavehadaccessto

indi-vidualhouseholdleveldata,asevenaggregatingtoSmall-Arealevelcanmaskimportantheterogeneity.Also,asidefromthe

networkroll-outdata,weonlyhavedataforonepointintime.Apaneldatasetonhowgasproportionsandvarious

charac-teristicschangeovertime,wouldhavegivenusgreaterabilitytoidentifyeffects.Similarly,theinclusionofotherspatially

codedinformation,suchasrelativepricesofalternatefuels,orthelocationofkerosenesuppliers,forexample,wouldhave

signiﬁcantlybeneﬁtedthispaper.Thesearealllimitationsoftheresearch.However,ourabilitytoexaminetime-trendsin

adoptionisquitenovelandmakesauniquecontributiontothewiderfuelswitchingliterature.

Disclaimer

“GasNetworksIreland(GNI),itsafﬁliatesandassigns,acceptnoresponsibilityforanyinformationcontainedinthis

document(“theInformation”).GNImakesnorepresentationsorwarrantiesofanykind,expressorimplied,inrelationto

theInformationandherebyexcludesallsuchrepresentationsorwarrantiesinrelationtotheInformationtothefullest

extentpermittedbylaw.Noliabilityshallbeacceptedforanylossordamageincluding,withoutlimitation,direct,indirect,

special,incidental,punitiveorconsequentiallossincludinglossofproﬁts,arisingoutoforinconnectionwiththeuseofthe

Information.”

Acknowledgements

ThismaterialisbaseduponworkssupportedbytheScienceFoundationIrelandunderGrantNo.12/RC/2302.Theresearch

waspartfundedbyGasNetworksIrelandthroughtheGasInnovationGroupandbyScienceFoundationIreland(SFI)through

MaREI–MarineRenewableEnergyIrelandresearchcluster.FundingfromtheESRI’sEnergyPolicyResearchCentreisalso

gratefullyacknowledged.ThisresearchhasalsobeensupportedbytheGranthamInstituteforClimateChangeandthe

EnvironmentandtheESRCCentreforClimateChangeEconomicsandPolicyundergrantnumberES/K006576/1.Wearealso

gratefultotheCentralStatisticsOfﬁce,GasNetworksIrelandandTheEnvironmentalProtectionAgency(EPA)forproviding

data.Seminarparticipantsatthe5thAnnualESRI-UCCSeminaronEnergyPolicyResearchandGlenDimplexprovidedhelpful

comments.Thispaperalsobeneﬁtedgreatlyfromcommentsbytheeditorofthisjournalandtwoanonymousreviewers.

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AppendixA.

A.1. Descriptivestatisticsforallvariablesincludedinestimations

TableA1

Descriptivestatistics.

Variablecategory Variable Obs Mean Std.Dev. Min Max

Gasvariables Gas 9638 0.565 0.328 0 1

Maxlengthyears 9638 14.523 13.134 0 111

log(meandistancetogasnetwork) 9638 3.252 1.074 0.732 6.906

Peatproximity log(distancetobktbog) 9638 8.772 1.167 −3.037 10.638

log(distancetocutbog) 9,638 9.315 0.639 6.147 10.786

Coalbanareas Coalbandummy 9638 0.829 0.377 0 1

Density Householdcount 9638 94.853 22.256 21 252

Areakm 9638 3.691 13.026 0.0163 417.358

Socioeconomic EconWorking 9638 0.516 0.141 0 0.942

EconLookingforﬁrstjob 9638 0.010 0.012 0 0.489

EconUnemployed 9638 0.110 0.064 0 0.440

EconStudent 9638 0.117 0.083 0 0.980

EconHome 9638 0.084 0.036 0 0.297

EconRetired 9638 0.116 0.090 0 0.727

EconDisabled 9638 0.042 0.037 0 0.494

EconOther 9638 0.003 0.016 0 0.595

Age0–14 9638 0.195 0.090 0 0.594

Age15–24 9638 0.353 0.140 0 0.873

Age25–44 9638 0.133 0.077 0 0.987

Age45–64 9638 0.209 0.090 0 0.662

Age65plus 9638 0.110 0.094 0 0.780

EduPrimary 9638 0.128 0.109 0 0.722

EduSecondary 9638 0.344 0.105 0 1

EduTechnical 9638 0.183 0.063 0 0.5

EduDegreeplus 9638 0.296 0.179 0 1

EduRefused 9638 0.049 0.054 0 1

TenOwnmortgage 9638 0.347 0.192 0 0.953

TenOwnnomortgage 9638 0.275 0.196 0 0.808

TenRentland 9638 0.249 0.215 0 0.985

TenRentlocal 9638 0.091 0.165 0 0.987

TenRenvol 9638 0.011 0.043 0 0.688

TenRentfree 9638 0.011 0.020 0 0.890

Dwelling DwellBungalow 9638 0.798 0.292 0 1

DwellFlat 9638 0.176 0.280 0 1

DwellBedsit 9638 0.006 0.025 0 0.746

DwellOther 9638 0.021 0.031 0 0.982

ProportionEFG 9638 0.326 0.292 0 1

AgePre1945 9638 0.138 0.221 0 0.988

Age1945–60 9638 0.088 0.171 0 0.954

Age1960–80 9638 0.214 0.275 0 1

Age1980–2000 9638 0.247 0.268 0 0.990

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A.2. Resultsfromﬁrststagesupplyequation

TableB1

Firststagesupplyequation.

DepVar:maxlengthinyearssincenetworkinplace

Variable Linear Quadratic

Coefﬁcient RobustSE Coefﬁcient RobustSE

Householdcount 0.102*** _(0.028) _6.332*** _(1.790)

Householdcountsq −0.000** _(0.000) ₋_0.022*** _(0.008)

Areakm 0.162*** _(0.016) _2.773** _(1.079)

Areakmsq −0.000*** _(0.000) ₋_0.000** _(0.000)

log(distancetocutbog) 0.972*** _(0.087) _44.660*** _(6.365)

log(distancetobktbog) 0.399*** _(0.140) _8.285 _(10.992)

log(meandistancetogasnetwork) −3.899*** _(0.129) ₋_91.128*** _(9.847)

Coalbandummy 2.319*** _(0.216) _98.124*** _(15.353)

EconWorking [REF] [REF]

EconLookingforﬁrstjob −6.700 (16.052) −181.729 (1166.800)

EconUnemployed −7.628** _(3.704) ₋_542.257* _(301.803)

EconStudent −6.209 (4.497) −423.807 (359.568)

EconHome 9.683* _(5.359) _382.041 _(442.248)

EconRetired −4.277 (6.972) −708.001 (560.839)

EconDisabled 2.635 (5.257) −614.494 (413.031)

EconOther 9.977 (8.273) 241.815 (686.109)

Age25–44 [REF] [REF]

Age0–14 3.929 (3.868) 23.942 (331.530)

Age15–24 16.346*** _(5.366) _706.930 _(430.658)

Age45–64 6.820** _(3.219) ₋_2.660 _(269.857)

Age65plus 14.968** _(7.520) _1291.967** _(639.143)

EduSecondary [REF] [REF]

EduPrimary −2.259 (2.866) −147.458 (237.319)

EduTechnical −5.784* _(3.271) ₋_331.153 _(264.163)

EduDegreeplus 5.392*** _(1.917) _283.807* _(158.999)

EduRefused 9.252** _(3.887) _589.154* _(338.634)

TenOwnmortgage [REF] [REF]

TenOwnnomortgage −0.600 (2.191) −45.547 (184.580)

TenRentland 2.843** _(1.381) _90.053 _(114.931)

TenRentlocal 3.347** _(1.415) _279.572** _(113.928)

TenRenvol 0.160 (2.666) 93.094 (202.774)

TenRentfree 11.902* _(6.710) _321.387 _(517.277)

DwellHouse [REF] [REF]

DwellFlat 0.513 (0.859) 21.788 (72.837)

DwellBedsit 8.401 (10.856) 1315.792 (1011.618)

DwellOther 8.870* _(4.635) _151.917 _(369.691)

ProportionEFG 0.160 (1.163) 66.561 (95.703)

AgePost2000 [REF] [REF]

AgePre1945 9.851*** _(1.721) _652.140*** _(145.094)

Age1945–60 4.592*** _(1.577) _359.113*** _(125.876)

Age1960–80 5.355*** _(0.943) _227.464*** _(76.441)

Age1980–2000 6.254*** _(0.615) _235.929*** _(48.380)

Constant −5.484* _(3.266) ₋_662.089** _(262.435)

N 9638 9638

Weakid(Kleibergen–PaaprkWaldF)a _63.2 _7.72

Weakid(Kleibergen–PaaprkWaldF)b _95.18 _20.73

Underid(Kleibergen–PaaprkLM)a 0.001 0.001

Underid(Kleibergen–PaaprkLM)b 203.642 30.82

* _p_<_0.1. ** _p_<_0.05. ***_p_<_0.01.

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A.3. Robustnessandsensitivityanalysis

TableC1

Resultsofalternativespeciﬁcations.

Variable (1) (2) (3) (4)

Maxlengthyearshat 0.030*** _0.028*** _0.030*** _0.031***

Maxlengthyearssquaredhat −0.000*** ₋_0.000*** ₋_0.001*** ₋_0.001***

log(distancetocutbog) 0.014*** _0.019*** _0.014 _0.018***

log(distancetobktbog) −0.007* ₋_0.010*** ₋_0.013 ₋_0.007

log(meandistancetogasnetwork) −0.115*** ₋_0.132*** ₋_0.119*** ₋_0.120***

Coalbandummy 0.051*** _0.066*** _0.055** _0.062***

EconWorking [REF] [REF] [REF]

EconLookingforﬁrstjob −0.258 −0.312 −0.333

EconUnemployed −0.342*** ₋_0.380* ₋_0.417***

EconStudent −0.344*** ₋_0.359** ₋_0.413***

EconHome −0.334*** ₋_0.282 ₋_0.326*

EconRetired −0.665*** ₋_0.671*** ₋_0.758***

EconDisabled −0.598*** ₋_0.512*** ₋_0.700***

EconOther −0.173 −0.071 −0.147

Age25–44

Age0–14 0.568*** _0.493* _0.585***

Age15–24 0.122 0.176 0.203

Age45–64 −0.269*** ₋_0.283* ₋_0.273**

Age65plus 0.878*** _1.002*** _1.026***

EduSecondary [REF] [REF] [REF]

EduPrimary 0.207*** _0.099 _0.198*

EduTechnical −0.076 −0.130 −0.117

EduDegreeplus 0.224*** _0.216** _0.243***

EduRefused −0.090 −0.020 −0.046

TenOwnmortgage

TenOwnnomortgage −0.350*** ₋_0.304*** ₋_0.347***

TenRentland −0.156*** ₋_0.086 ₋_0.151***

TenRentlocal 0.079** _0.164** _0.110**

TenRenvol −0.038 −0.001 −0.009

TenRentfree −0.263* ₋_0.220 ₋_0.251

DwellBungalow [REF] [REF] [REF] [REF]

DwellFlat −0.223*** ₋_0.275*** ₋_0.277*** ₋_0.219***

DwellBedsit −0.415** ₋_0.550*** ₋_0.290 ₋_0.259

DwellOther −0.322*** ₋_0.443*** ₋_0.426*** ₋_0.327**

ProportionEFG −0.505*** ₋_0.569*** ₋_0.477*** ₋_0.496***

AgePost2006 [REF] [REF] [REF] [REF]

AgePre1945 0.360*** _0.177*** _0.399*** _0.422***

Age1945–60 0.312*** _0.116*** _0.307*** _0.348***

Age1960–80 −0.150*** ₋_0.425*** ₋_0.175*** ₋_0.134***

Age1980–2000 −0.149*** ₋_0.271*** ₋_0.148*** ₋_0.133***

Deprivationindex 0.009**

Averageemploymentcompensation 0.359**

Constant 0.880*** _0.984*** _0.608** _0.858***

N 9638 9642 9638 9638