1
Energyconsumptionandurbansprawl:evidencefortheSpanishcase
Abstract
Electricity accounted for 41,2% of the total final energy consumption ofSpanishhouseholdsin2014.InSpain,electricityisstillgeneratedfromfossilfuels,whichaffectsthelevelofgreenhousegasemissionsandSpanishenergydependency. Therefore, policies oriented towards reducing householdelectricityconsumptioncouldhelptoachieveamoreenergy‐sustainableandlow‐carbon economy. Some of those policies seek energy building savingsfrom energy rehabilitation of the thermal envelope, the renewal ofappliances,ortheconstructionofnearly‐zeroenergybuildings.However,therelevance of urban structure andurbanphenomena, such as urban sprawl,hasnotnormallybeenconsideredforhouseholdelectricitysavings.
This paper focuses on the impact of urban design and urban sprawl onhouseholdelectricityconsumption.Thus,anelectricityconsumptionmodelisestimated by using urban characteristics as its determinants, such us thelevelofurbanagglomerationor if the families live inadetached, sprawlinghouse,andalsohouseholdsocioeconomicvariables.Themodel isestimatedby using 2014 Household Budget Survey (HBS) microdata and applyingOrdinary Least Squares as well as quantile regressions as econometricprocedures.
Results confirm that living in a detached house significantly increases theelectricityconsumptionwhileurbanagglomerationshavetheoppositeeffect.SprawlisoccurringrapidlyinSpanishcitiesand,accordingtoourresults,itcould constitute a main source of increase in electricity demand in thefollowingyears.Thispapercommentsontherelevanceofurbanpoliciesandurbanplanningfromtheperspectiveofhouseholdenergyefficiency.
Keywords: household energy consumption, house characteristics, urbansprawlandurbanenergyefficiency.
2
1.Introduction
Currently,55%oftheworld’spopulationlivesinurbanareas,aproportionthatis
expected to increase to66%by2050and85%by2100. In a rapidlyurbanizing
world, the way in which cities are planned, built, operated and redefined has a
hugesocialandeconomicimpact.Urbanpoverty,inequalities,affordablehousing,
mobilityandcongestionareattheheartoftraditionalurbanchallenges.However,
the environmental problems of pollution, increased global energy consumption
and climate change as well as the energy dependency of many countries draw
attention to sustainable urban growth and, particularly, to the understanding of
energy consumption patterns in cities, and drive innovation in urban energy
savingsandgreenbuilding.
Urban energy efficiency engages a complex and nuanced spectrum of issues. In
recent years, one of themain focuses of innovation in cities was placed on the
capacity of new technologies to develop “smart cities”, those that aremonitored
withnetworksofdevices that giveprecise informationaboutpollutionand local
mobility,allowingabetterunderstandingofcitylifeandproblems.Manyscientists
are also investigating the use of new materials and building technologies to
improve housing energy performance. By contrast, the relevance of energy
consumption of urban phenomena, such as urban sprawl, is still not widely
studied.
Thefirstcitiestobegintoexperienceurbansprawlweretheemergentcitiesofthe
centralandwesternUnitedStates.Thismodelofasprawlingcityrapidlyextended
firsttoLatinAmerica(seeGilbert1996andPolèseandChampain2003)andlater
to Asian cities (see Bunnel et al., 2002), finally becoming a global phenomenon.
Traditionally, most old European cities grew differently from the new cities of
AmericaorAsia:citiesontheoldcontinentwerestronglyconcentratedarounda
denselypackedhistoricalcentreand itscommercialandbusinessextensionsand
usually adhered to a monocentric growth model with a strong centre and
hierarchicalstructureofsub‐centres.However,urbansprawlinEuropehasgrown
inmanycasesoverthelastfourdecades(seeCouchetal.,2007).Accordingtothe
EuropeanCommission (2006), countries in the east and southofEuropeare the
onesmostatriskofanexplosiveprocessofurbansprawl.
3
The caseof Spain is oneof themost interesting inEurope. In someareasof the
Iberian Peninsula there is very high building pressure due to tourism and the
demandforasecondresidence.TheSpanisheconomyhasbeendrasticallyaffected
bytheconstructionsectorsufferingoneofthebiggestrealestatebubblesofallof
Europe(Romero,2012).Spainhadveryfasteconomicgrowthduringthelastfour
decades of the past century, presenting a very strong, concentrated process of
urbanization.CitiessuchasMadridandBarcelonadoubledtheirpopulationinless
thantwentyyears.Otherbigmetropolitanareasof thecountryexperiencedsuch
growththatdifferentcitiesor townsgrewintoone.Ruralareasalso lostmostof
their population in just two decades. The strong changes in income per capita,
social customs and land use pressures make Spain a victim of sprawled urban
growth(Rubieraetal.,2016).
Inthispaper,weareparticularlyinterestedinmeasuringandevaluatingtheeffect
of increasing urban sprawl in Spanish cities on residential energy consumption.
Residentialenergyconsumptionconstitutesoneof the largestsourcesofSpanish
finalenergydemand,19%in2014accordingtotheEuropeanCommission(2016).
Thus,wearegoingtofocusonelectricityconsumptionasitisthelargestsourceof
householdenergydemand.Electricityaccountedfor41,2%ofthetotalfinalenergy
consumptionin2014withanincreasingtrend(householdelectricityconsumption
increased21,8%from2004to2014,seeEuropeanCommission,2016). Itshould
benotedthatnearly72%ofSpanishelectricity isstillgeneratedfromfossil fuels
(Spanish Government, 2016), which affects the level of greenhouse gas (GHG)
emissions and Spanish energy dependency (European Commission, 2016).
Therefore, household electricity saving achieves amore energy‐sustainable, low‐
carbonandclimatefriendlyeconomy.Toreducehouseholdenergyconsumption,it
isimportanttoanalyseitsdeterminants.Mostoftheexistingempiricalworkshave
used localclimate,homeappliances,householdsizeorhouseholdcharacteristics,
amongother aspects, as determinants of energy consumption forhouseholds. In
contrast,thispaperfocusesthispaperfocusesontheimpactofurbandesignand
urbansprawlonhouseholdelectricityconsumption.
The phenomenon of urban sprawl has been studied within different disciplines
(geography, urban planning, the environment, economics, sociology and even
4
public health) and from very different standpoints, which has led to numerous
definitions. Glaeser and Kahn (2004) and, more recently, Jaeger and Schwick
(2014) have compiled one of the most complete reviews of leading papers on
urbansprawlanditsconsequences.Althoughallthestudiesconsiderurbansprawl
asacomplexphenomenonhavingmanydimensions, theyallcoincide inthe idea
that sprawled cities alwaysdenote the extent of the area that is built up and its
dispersioninthelandscape,themoreareabuiltoverandthemoredispersedthe
buildings,thehigherthedegreeofurbansprawl.Thus,fromtheindividualpointof
view(household)sprawlmeansthepredominanceofdetachedhouses insteadof
the building of apartments and low building density. Thus, from the energy
consumptionperspective, theeffectof sprawlcouldbe identifiedby theeffectof
livingindetachedhousesinlessintenselyagglomeratedareas.
According to statistical data from the Household Budget Survey of the National
StatisticalInstitute,in2014,approximately35%ofthepopulationofSpainlivedin
houses,with11% living indetachedhousesand24.2% in semi‐detachedhouses
(INE, 2014). The remaining percentage of the population is distributed among
other types of houses, such as flats.What we propose in this paper is to study
energy consumption factors of households, including in the analysis the urban
environment (urbansizeandurbanversus ruralareas)and the typeofhouse to
identify how they affect energy consumption. The obtained results could orient
state and local governments in energy efficiency strategies of urban planning. A
more electricity efficient urban design can not only decrease the energy
dependencyoftheSpanisheconomyandachieveenergysavingsbutalsoimprove
theenvironmentbyreducingGHGemissionsandhelpto fulfil theEU's2020and
2030climateandenergygoals(CounciloftheEuropeanUnion,2010,2014).
The paper is structured as follows. In the next section,we review the empirical
modelsofenergyconsumptionand theirpreviousconclusions, focusingon those
papers thatparticularly include in someway theurban/rural effect andhousing
characteristics. From this literature review we obtain the household electricity
model specification to be estimated. Section three revises the dataset, the
HouseholdBudget Survey (INE,2014) and specific characteristicsof theSpanish
case.Theestimationstrategyandmainresultsarepresentedinsectionfour.The
5
main conclusions are summarized in the final section, along with a number of
recommendationsregardingpolicy.
2. Household energy consumption in urban areas: literature review and
empiricalmodelproposal
Theeffectofseveralvariablesonhouseholdenergyconsumptionhasbeenwidely
studied.Previousstudieshavefoundthathouseholdenergyconsumptioncouldbe
explained by various factors, including household economic characteristics
(Pachauri 2004, Druckman and Jackson 2008, Cayla et al. 2011, Brounen et al.
2012, O'Neill and Chen 2002, Santin 2011, Rahut et al. 2016), household
sociodemographicfactors(Kaza2010,Miahetal.2011,Rahutetal.2014,Joneset
al.2015,Kelly2011,Brounenetal.2012,Kaza2010,O'NeillandChen2002,Özcan
etal.2013),physicalcharacteristicsofthehouseorconstructionquality(Daioglou
etal.2012,Kavousianetal.2013,Valenzuelaetal.2014),homeappliances(Stiri
2014,Huang2015),residential location(Fengetal.2011,DruckmanandJackson
2008,Daioglouetal.2012),localclimatefactors(Kavousianetal.2013,Valenzuela
etal.2014),andenergycosts(LarsenandNesbakken2004,Kasparian2009,Niuet
al.2016).However, thenumberofstudiesconsideringhousing typeand/or local
degreeofurbanization(rural‐urban)asadditionaldriversofenergyconsumption
has not been extensive (Wiesmann et al. 2011,Heinonen and Junnila 2014, Stiri
2014,Huang2015).
For example, Wiesmann et al (2011) estimated electricity consumption at the
municipality level for 2001 and at household level by using the Portuguese
consumer expenditure survey that was collected in 2005 and 2006. They used
socioeconomic and demographic household characteristics, but also housing
characteristicsasexplanatoriesvariables.Regarding thesecondsones, theyused
dwellingtype(detachedhouse,semidetachedhouse,smallapartmentbuildingand
large apartment building) and the level of urbanization (rural, half‐urban and
urban).TheOrdinaryLeastSquaresestimationsatbothlevelswereconsistentand
indicatethatanapartment inabuildingconsumed less thandetachedhouse,but
alsourbanhouseholdsconsumesmoreelectricitythanruralhouseholds.
6
Heinonen and Junnila (2014) studied Finland residential energy consumption
patternsindifferenthousingtypes(apartmentbuildings,row‐terracedhousesand
detachedhouses)anddegreesofurbanization(cities,semi‐urbanareasandrural
areas). They found significant behavioural differencesbetweendifferenthousing
types and rural‐urbanmodes. In fact, they found lowerenergyuse in apartment
buildingscomparedtodetachedhousesandlessintensiveenergyconsumptionfor
all types of houses in rural areas. In this study, Heinonen and Junnila (2014)
holisticallyanalysedtheresidentialenergyconsumptionindifferentbuildingtypes
byusingtheHouseholdBudgetSurveyfor2006.Thus,theyexplicitlyobservedthe
residential energy consumption patterns of 3984 households and compared
explicitlybybuilding type,butnoeconometricmodelwasestimated, thus itwas
notpossibletogeneralizetheresults.
Stiri(2014)analysedtheimpactofhouseholdcharacteristicsandbuildingphysical
attributes(housingtypeandsize)onresidentialenergyconsumption intheUSA.
He used information provided by the Residential Energy Consumption Survey
(RECS)for11,590householdsin2009.Thecategoriesofhousingtypeconsidered
weresinglefamilydetached,singlefamilyattachedapartmentswith2–4unitsand
apartments with more than 5 units. By using a structural equation model he
demonstrated that the impact of housing type on energy consumption is higher
thanthecorrespondingdirectimpactfromhouseholdcharacteristics.
Huang (2015) employed a quantile regression to analyse the determinants of
household electricity consumption in Taiwan over the period 1981‐2011. The
information was provided by Taiwan's Family Income and Expenditure Survey,
where 15,000 households were sampled each year. In regard to housing type,
Huang(2015)foundthatlargerhousingareasandmulti‐floorhousescontributed
to higher household electricity consumption and that urban households showed
higherelectricityusethandidruralhouseholds.
In general, existing empirical studies seem to agree that energy consumption is
lower for apartment buildings than for detached houses; however, the results
relatedtoruralversusurbanareambiguous:someof themconcludethatenergy
use seems to be lower in rural areas than in urban areas (Poumanyvong and
Kaneko2010,Niuetal.2012,HeinonenandJunnila2014,Huang2015)andothers
7
find theopposite (Normanet al.2006,Rickwoodet al. 2008).However,manyof
theconcretefindingsarecountryspecific,andtheresultsalsorelyonthemodel,
thestudiedenergytype,thestudiedperiod,theconsideredexplainedvariablesor
theusedeconometrictool.
Thispaperanalyseshouseholdelectricityconsumptionfor2014inSpain.Forthe
Spanishcase,toourbestknowledge,onlyLabandeiraetal.(2006,2012),Blázquez
et al. (2013) and Romero‐Jordán et al. (2014, 2016) have analysed household
electricitydemandbyusingasitsdeterminantsnotonlyhouseholdsocioeconomic
anddemographiccharacteristicsandclimatefactorsbutalsosomevariablerelated
tothelocationofthehouse.
Labandeiraetal.(2006)estimated,forthefirsttimeinSpain,aresidentialenergy
demand system using 51,691 household annualmicrodata for the period 1975–
1995, by applying an extension of the Almost Ideal DemandModel (Deaton and
Muellbauer, 1980). They also estimated the model by considering types of
municipalities(rural,village,city).Labandeiraetal.(2012)estimatedthedemand
model using monthly data for the period 2005‐2007 with information from
422,696households, by applyingapanel randomeffectsmodel.Theyperformed
estimations for different regions of the country. Blázquez etal. (2012), by using
aggregatepanel data at theprovince level for47 Spanishprovinces, estimated a
log–log demand equation for electricity consumption using a dynamic partial
adjustment for the period from 2000 to 2008. Romero‐Jordán et al. (2014)
analysed the determinants of household electricity demand with a panel data
partial adjustment model of the Spanish regions between 1998 and 2009.
Furthermore, Romero‐Jordán et al. (2016) estimated these determinants by
applyingaquantileregressionmethodusingdatafrom2006–2012.Asexplanatory
variables related to housing location, they used the region and the type of
municipality(ruralorurban)wherehousingissited.
Asshown,theabovementionedliteraturehasconsideredinsomewaythelocation
of households in Spain as electricity consumption determinants, but the urban
sprawldeterminanthasnotyetbeenspecificallyconsidered.Thispaperfocuseson
theimpactofurbansprawlonSpanishhouseholdelectricityconsumptionin2014.
Apart from the impact of household socioeconomic characteristics and physical
8
characteristics of the dwelling, we also studied the urban environment and
housingtype(detachedindependenthouseorpartofabuilding).Finally,asprior
studies (Kaza 2010, Valenzuela et al. 2014, Huang 2015, Niu et al. 2016, and
Romero‐Jordán et al. 2016 for the Spanish case) suggested that explanatory
variables may exhibit variation in magnitude and sign depending on household
energy use quantile, the empirical study is extended by estimating a quantile
regression model. The obtained information could be very useful for designing
specificenergyefficiencymeasuresongroupswithhigherelectricityconsumption.
3.TheSpanishcase:datasetandselectedvariables
The data used in this analysis are obtained from the Household Budget Survey
(HBS)oftheNationalStatisticalInstitute(INE),asurveythatprovidesinformation
about the patterns of consumption, income and other socioeconomic and
demographic characteristics of Spanish households. The dataset is formed from
21,790observations that aredisaggregatedacross the17 regionsat theNUTS‐II
level.This survey is obtainedyearly since2006. In thispaper, themost recently
availableinformationof2014isused.
The HBS is composed of three data files: (i) the Household file, which provides
informationabouttheAutonomousCommunitythatthehouseholdbelongsto,the
citysize,thepopulationdensity,theincomelevel,numberofmembers,numberof
employees, some characteristics about the household head such as age, sex,
education level, marital status, among others, and some information about the
characteristicsofthehouse,suchasthesizeinm2,theconstructionyearor,among
others, if it is a detached house, a semidetached house or an apartment in a
residentialbuilding;(ii)theExpenditurefile,whichrepresentsallhouseholdswith
any expenditure; and, (iii) the Household members file, which contains all the
informationabouteachmemberinthehousehold.
As an explicative variable of our analysis,we use the electricity consumption in
terms of kW (LELECTRICITYQ, in logarithm). In accordance with the literature
quoted in theprevioussection,weuseastandardsetofvariables toexplain this
electricity consumption assuming the limitations of the database. The set of
selectedvariablesissummarizedinTable1.
9
A particularly relevant variable in any consumption analysis is the price of the
product.TheHBSgiveusanespeciallyprecisewayofobtainingtherealfinalprice
thatthefamiliesarefacingfortheirelectricityconsumption.Householdsnotonly
reporttheirexpendituresforeachgoodbutalsothephysicalamounttheybought.
Therefore, individual prices atwhich households purchase the electricity can be
recovered by dividing monetary expenditures on it by physical quantities (kW)
consumed. In accordancewith thedemand system literature (Deaton, 1988),we
refertothese“prices”asunitvalues.Theempiricalexperiencedemonstratedthat
unitvaluesareveryusefulasanindicatoroftimeandspatialpricevariations.The
LELECTRICITYP variable is the logarithm of this unit value obtained for each
household.
Although all households in Spain need to use electricity for certain purposes, in
somebuildings,itispossibletouseotherenergysourcessuchasgas,petrolorcoal
forheatingorhotwater.Thedatabasegivesusinformationtoproposeproxiesthat
inform us about the consumption of these other energy sources. Using this
information,wedefinethedummiesHEATINGandHOTWATERtotakeavalue1if
thehouseonlyusedelectricity forall theenergyconsumptionand0 if thehouse
used other energy sources for heating, in the first case, and hot water, in the
secondone.
To complete this basic information with socioeconomic or housing observable
characteristicsweadd to themodel variables such asHOUSEAGE, a dummy that
takes a value 1 if the house is older than 25 years and 0 otherwise, INCOME, a
categoricalvariablethatgivesusinformationabouttheincomeleveloftheheadof
the family, FAMILYMEMBERS, a variable that informs us about the number of
membersof the family,andHOUSE,adummyvariable, that takesavalue1 if the
family lives inadetachedisolatedorsemidetachedhouseand0 if they live inan
apartmentorflatinaresidentialbuildingorsimilarconstruction.
For thepropose of this researchwe focus special attention on this last variable,
HOUSE,because it informsusabouttherelevanceof living inadetachedisolated
house typical in sprawled cities in comparisonwith the usual type of houses in
compacted cities, flats in buildings. We complete this variable with others that
provide complementary information about the urban environment. URBAN is
10
anotherdummythattakesvalue1whenthehouseislocatedinamunicipalitywith
more than 100,000 inhabitants and 0 otherwise. Although the limit of 100,000
inhabitants is not themost proper to delimit an agglomeration in the particular
caseoftheSpanishurbansystem,thisboundaryisabletodelimitquiteprecisely
thelargermunicipalitiesandmaincitiesofthecountry(seePolèseetal.,2009).We
alsoincludespatialvariablesthatallowustomeasuretheregionalandlocaleffect.
In particular, we add a dummy variable of the regional zone (NUTS2 level of
desegregation)inwhichthehouseislocated(variableREGION).
>>>INSERTARROUNDHERETABLE1<<<
4. Estimation strategy: a two‐stage estimation method and quantile
regression
Basedontheaboveinformation,thebasicempiricalmodelthatweproposecanbe
writtenas
[1]
where LELECTRICITYQ is our dependent variable, HOUSE and URBAN are the
variablesthatwefocusouranalysisontoobservetherelevanceofurbansizeand
typeofhouse,andXisavectorofallcontrolvariablesnormallyconsideredinthe
literature: LELECTRICITYP, HOTWATER, HEATING, INCOME, OWNERSHIP,
HOUSEAGEandREGION.Finallyuistherandomerrortermoftheestimation.
Itshouldbenotedthathousingsocioeconomiccharacteristicscouldinfluencenot
onlyhouseholdelectricityconsumption(Rahutetal.2016)butalsohousingtype
choice.Forexample,havingalargefamilysizeorahighincomecouldincreasethe
likelihoodof living inadetachedhouse(Beguin,1982;Boehm,1982).Therefore,
anendogeneityproblemcanemergeifthedecisiontoliveinadetachedhouseis
partiallyaffectedby theanticipatedenergyconsumption,aswell asby theother
repressors included in the model, making the OLS estimator inconsistent. To
address this problem, the dummy of detached is instrumented by defining a
variable,observableintheHBS,associatedwithdetachedbutnotcorrelatedwith
theenergyconsumption.
11
Using this instrumental variable, we propose a two‐stage estimation method
(2SLS).Thedecision to live inadetachedhouseornot is instrumentedbyusing
twoadditionalexogenousvariables:
‐ RURAL:whichindicatesifthehouseissituatedinaruralarea.Itshouldbe
remarked that this “rural”classification is related to theenvironment,not
the population. The HBS classifies the households situated in industrial
rural settings, agricultural rural settings and fishing rural settings;
conversely,therearehouseholdslocatedindifferenturbansettingssuchas
luxury urban, semi‐luxury and poor urban settings. With all this
information adummyvariable is constructed and takes the value1 if the
householdsarelocatedinaruralsettingand0otherwise.
‐ LFUELEXP: a continuous variable defined as the logarithm of the
expenditureinEurosoffuelusedfortransportmotorssuchascars.Thisis
clearly related to the type of house, a detached isolated house normally
implies higher expenditure in fuel but is independent of electricity
consumption.
Aftercontrollingtheendogeneityproblembythisprocedureanadditionalissueis
thatweareinterestedinobservingiftheconsumptionpatternschangedepending
onthedistributionofconsumption.Thisispossibleusingthequantileregressions
(QR)approach(KoenkerandBasset,1978),whichfitsquantilesofconsumptionof
electricitytoalinearfunctionofcovariates.Initssimplestform,theleastabsolute
deviationestimatorfitsmedianstoalinearfunctionofcovariates.Themethodof
quantile regression is more attractive because medians and quantiles are less
sensitive to outliers than means, and therefore ordinary least squares (OLS).
Indeed, the likelihood estimator is more efficient than the OLS one. Quantile
regressionsallowthatdifferentsolutionsatdifferentquantilesmaybeinterpreted
as differences in the response of the dependent variable to changes in the
regressors; thus,quantileregressionsdetectasymmetries in thedatathatcannot
be detected by OLS. However, the most important feature is that quantile
regression analyses the similarity or dissimilarity of regression coefficients at
different points of the dependent variable, which in this case is the electricity
12
consumption; it allows one to consider the possible heterogeneity across the
intensityinelectricitydemand.
Thefollowingexpressionpresentstheadaptationofequation[1]intermsofQR:
[2]
wherecoefficients representthereturnstocovariatesattheτthquantileofthe
logarithmofelectricityconsumption.
QRestimatescanbeaffectedbythesameendogeneityproblemsdiscussedforthe
caseofOLS.Thisissueisaddressedbyapplyingtheinstrumentalvariablequantile
regression (IVQR) estimator developed by Chernozhukov and Hansen (2005,
2006).TheIVQRestimationisbasedonthesameinstrument,thevariableRURAL,
asthe2SLSestimation.
Themodel isestimatedbyusingthe least‐absolutevalueminimizationtechnique
andbootstrapestimatesoftheasymptoticvariancesofthequantilecoefficientsare
calculatedwith20repetitions.
5.Mainresults
ThefinalresultsofalltheseestimationstrategiesaresummarizedinTable2.The
firstcolumnofthetablepresentstheresultsofthe2SLSprocedure.Thefollowing
columnsshowthecoefficientsandtheirsignificanceunderthedifferentquantiles
10,25,50,75and90,usingIVQR.Basictests,R2andχ scoreforoveridentifying
restrictionsarepresentedattheendofthetable.
First, we are going to discuss briefly the control variable results to focus our
attention.Second,wewillfocusontheurbancontextconclusions.
>>>INSERTARROUNDHERETABLE2<<<
5.1.Socioeconomiccharacteristics
In general, all the socioeconomic and household and housing characteristics are
significantandsimilartowhatwecanexpectaccordingtopreviousliterature.
Thepriceofelectricityhastheexpectednegativeandsignificanteffect.Thevalue
of the coefficient is close to1 and is stable across thequantiledistribution.This
13
indicatesthattheelectricitydemandinSpain,accordingtoourresults,isunitary.
This result is slightly higher than that obtained by Labandeira et al. (2006) and
Labandeiraet al. (2012)andclearlyhigher than thatobtainedbyBlazquezetal.
(2013).Nevertheless,theresultisinlinewithwhatwasobtainedininternational
studies of electricity demand (see the summary of international resultsmade in
Labandeiraetal.2012).
Asexpected,thehousesthatcombinegas,petrolorcoalforheatingandhotwater,
with electricity for the remainder, reduce in a very significant way the final
consumptionofelectricity.Thiseffectishigherforthehigherquantiles,indicating
thatthehigherthedemand,thelargertheeffectofusingotherenergysources.Our
result is consistent with most of the previous literature for the Spanish case.
Blázquezetal.(2013)usedagaspenetrationrate(percentageofhouseholdsthat
haveaccesstogas)asanexplanatoryvariableandfoundasignificantandnegative
sign;Moreover,Romero‐Jordánetal(2014,2016)foundthatagreaterpenetration
of electric heating and electric water heating had a clear positive impact on
Spanish electricity consumption. Those studies indicated a clear competition
betweenelectricityandgas in theprovisionofheatingandhotwater systems in
Spanishhouseholds.Moreover,theimpactofthisvariableincreaseswhenquantile
increasesasRomero‐Jordánetal(2016)alsofound.
Regardingtheageofthehouse,wefinditissignificantandpositive,indicatingthat
olderhouses consumemoreelectricity.However, this effect isnot significant for
thehigherquantilesofthedistribution.
Thenumberofmembersinthefamilyisalsoclearlysignificantandpositivewith
the expected result that the larger the family is the higher the electricity
consumption. This resultwas also found byBlázquez et al. (2013) andRomero‐
Jordán et al (2016) in the quantile regression‐ they also found this result was
stableamongthequantilesaswefind.ContraryRomero‐Jordánetal(2014)found
householdsizenotsignificant.
Our results indicate that to be an owner of the house significantly increases
electricityconsumption,andsimilarresultswerefoundbyLabandeiraetal.(2006)
forSpain.
14
The demand for electricity is responsive to the level of income with elasticity
clearly below 1. This result is stable for the different income levels and for the
different quantiles in the quantile estimation. This means that income growth
apparently results in a less than proportional increase in electricity demand,
implyingthatapossibleconvergenceinthepercapitaincomeofSpainwiththatof
the most advanced countries in the EU‐15 will not translate into proportional
increases in electrical equipment, and in turn into proportional increases in
electricityconsumption.
Regionaldummiesarenotstableacrossthedistributionandshowdifferenteffects.
TheclearesteffectisfortheCanaryIslandsdummy,whichreflectsthehigherfinal
realpricesoftheelectricityduetotheisolatedsituationofthisarchipelago.Inthe
remaining cases, the effects change between quantiles, preventing clear
conclusions. Previous papers obtained similar results indicating that households
located in areas with Mediterranean climate (Regions South and East) tend to
consumemoreelectricityonaveragethantheircounterpartsinotherpartsofthe
country(Jordánetal2016).
5.2.Urbancontext:sprawlandagglomerationeffects
Ourinterestismainlyfocusedonanalysisofthetypeofhouseandthesizeofthe
urbanarea,whatwecalltheurbancontext.
Theestimations indicate thatahousehold living inanurbanarea(municipalities
with more than 100,000 inhabitants) significantly decreases electricity
consumptioncomparedtothoselivinginasmallcityorinaruralarea.Ourresults
for thequantileregressionare thesame forallquantilesof thedistributionwith
theexceptionofthehigherelectricityconsumers,thosesitedinquantile90,asfor
themthevariableisnotsignificant
Previous literature about variables similar to this one is inconclusive. Romero‐
Jordánetal. (2016) foundthathouseholds in lowerquantiles(1‐30quantiles)of
the electricity consumption distribution consumedmore in urban areas than in
ruralareas.However,fortheremaininghouseholds,theconsumptionincreasesed
iftheyweresituatedinruralareas.Incontrast,Labandeiraetal.(2006)concluded
that rural and village households (those living inmunicipalitieswith fewer than
10,001inhabitantsandwithmorethan10,000inhabitantsbutfewerthan50,001,
15
respectively) spent more on electricity that households living in an urban
municipality (municipalities with more than 50,000 inhabitants) for the period
1973 to1995.However,once theyaccounted forbothdirectand indirecteffects
through an interaction term between total household expenditure and type of
municipality,theyfoundthatruralhouseholdsspentmoreonelectricity.
There are several possible explanations for our result. First, it could be due to
building construction practices in high density urban areas resulting in a larger
proportion of apartment buildings with comparatively less heating and cooling
requirements than detached or semidetached houses (which consume more
electricity as we have shown). Second, urban environments change the life
patternssuchthatmoretime isspentawayfromhome,moreoftenhaving lunch
anddinneroutside,consumingmore intensivelyexternalservices for takingcare
of children or dependentmembers of the family, andmore frequently pursuing
leisureactivities.
Finally, the variable in which we focus special attention is the type of house.
Basically,thisvariableinformsusabouttheimpactof livinginanisolatedhouse,
insteadofaflatsbuilding,ontheenergyconsumption.Whatwecanobserveisthat
detachedorsemidetachedhousesconsumesignificantlymoreelectricitythanflats,
apartmentsandotherboundingsolutions.Theeffectissignificantlyrelevant,even
after controlling for all the socioeconomic variables and after solving a possible
endogeneityproblem.Ourresultsaresimilartothosestudiesanalysingtheimpact
of the typeofhousingonhouseholdelectricityconsumptionas itwasshowed in
the above literature review‐section 2 (Heinonen and Junnila 2014, Stiri 2014,
Huang2015,Wiesmannetal2011).
It isalsoveryinterestingobservingwhatoccursacrossthequantilesdistribution
because thiseffect isnotsignificant in thosehouseswith lowerenergy‐intensity,
butitishigherforthehigherquantilesofelectricityconsumption.Supposedlythe
householdswith lower incomeand less energypurchase capacity are in the low
partofthedistributionandthosethatcanaffordhigherdemandareinthehigher
part of the distribution. Thus, this result implies that the effect of sprawl is
especiallyrelevantforhigh‐incomefamilies.
16
5.Conclusionsandpolicyimplications
ThispaperestimatedtheimpactofurbansprawlandmunicipalitytypeonSpanish
household electricity consumption. Moreover, household socioeconomic and
demographic characteristics are also analysed as determinants of electricity
consumption. By using 2014 Household Budged Survey microdata with OLS, as
wellasaquantileregressioneconometricapproach,ourresultsconfirmbehaviour
ofSpanishfamiliessimilartothestandardsinotheradvancedeconomiesanalysed
inpreviousliterature.
Oneofthefactorsthatcontributemoretohigherelectricitydemandisthenumber
ofmembersinthefamily,butthesizeofSpanishfamiliesissupposedtobestable
in the future. A unitary electricity‐price indicates that households seem to be
reactive to energy prices; thus, we can be optimistic about the effectiveness of
pricing policies to reduce electricity consumption in Spanish households. At the
sametime,thelowincome‐elasticityobservedindicatethatwecannotexpectlarge
increases inelectricitydemandeven in thepossiblecontextof incomegrowth in
thenearfuture.
Ontheotherhand,olderhousesconsumemoreelectricity.Newconstructionsand
materialsaremuchmoreefficient intermsofenergyconsumption.Theyarealso
alwayspreparedtousesolarenergy.Theuseofotherenergysourcesforheating
andhotwater, such as gas, can clearly help in reducing electricity consumption.
Theproblemisthattheaccessibilityandpenetrationofthegassupplyisrestricted
to some areas connectedwith the national gas network. These areas aremainly
cities with higher density, so the accessibility to gas is going to be reduced for
sprawledhouseslocatedfarfromthecentreofthecity.
We include in our electricity consumption model two variables to evaluate the
effectofurbanagglomerationsandsprawlbehaviour.Weidentifythatlivinginan
urban areawithmore than100,000 inhabitants,which corresponds to themain
cities in Spain, has a significant effect on reducing the electricity demand.
Simultaneously, we found that detached and semidetached houses, typical in
sprawledlandscapes,clearlyconsumemoreelectricity.
The new trends and housing demands of Spanish families show an increase in
sprawl.Fordifferentreasons,thenumberofcitiesthatsufferhighlevelsofsprawl
17
isgrowingandtheproportionoffamiliesthatchoosetoliveindetachedhousesis
increasing significantly.Detachedand sprawledhousesmeanmore consumption
of petrol for mobility and reduce the possibility of gas penetration because the
infrastructure in a low‐density context ismuchmoreexpensive.Whatwewould
like to test in thispaper is if italso increases theelectricitydemand.We include
severalurban/ruralvariables,aswellasaspecificdummyvariable,totestif,after
controllingforallthesocioeconomicandhousingcharacteristics,wecanobservea
significantpositiveeffectoflivinginadetachedhouse.Ourresultsclearlyconfirm
thishypothesis.Thus,increasingenergyefficiencyindetachedhousesisessential
formakingprogresstowardsasustainablecity.
In that sense, the Spanish Energy Saving and Efficiency Action Plan 2011‐2020
elaboratedby theMinistryof Industry,TourismandCommerce,and Institute for
Energy Diversification and Saving‐ IDEA (Spanish Government 2011), includes
recommendationsonenergysavingmeasuresintheBuildingsector,inaccordance
withthecontentsofDirective2010/31/EUrelatedtoenergyefficiencyinbuildings
(European Commission, 2010) and the Resource Efficient Europe strategy
(European Commission, 2011). Themeasures included in this Action Plan 2011‐
2020willinvolvesavingsoffinalenergyfor2020worth2,867ktoeintheBuilding
sector. In the residential building sector, savings are derived from energy
rehabilitationofthethermalenvelopeinbuildingstock,renewalofboilersandair
conditioning equipment, improvements in the energy efficiency of thermal
installations and indoor lighting installations in existingbuildings, andalso from
constructionofnearly‐zeroenergybuildingsandtherehabilitationofexistingones
with high energy qualification (of 8.2 million m2/year). A summary of other
policiesadoptedbydifferentcountriestoreduceenergyconsumptioninbuildings
canbefoundinAllouhietal.(2015).
Another strategy to achieve amore sustainable city is the introduction of small
installations of renewable thermal energy and cogeneration in household
electricity consumption. In that sense, theSpanishGovernment (2014)approved
theLaw413/2014,whichestablishes,forthefirsttimeinSpain,theconditionsof
auto‐consumptionofelectricalenergypowerfromrenewableenergy.
18
As shown, the quantile regression indicated that electricity consumption
determinantsvarieddependingonthehouseholdenergyusequantile.Thus,smart
meteringandvariousconsumption‐feedbacksystems(Podgorniketal.2016,Salo
et al. 2016), in combination with personal consultations (Stieß and Dunkleberg
2013, Laakso and Lettenmeier, 2016) are proposed instruments for obtaining
more efficient and sustainable household energy consumption according to
specificcharacteristicsofeachhousehold.
Inlinewithcurrentresults,furtherresearchonthistopicwouldbedesirable.For
example, toexamine the impactof thedegreeofurbanizationand/or the typeof
energy used by the household on household carbon dioxide emissions could be
veryinterestinginfutureresearch,asotherauthorshavenoted(Hanetal.,2015,
Liuetal.,2011,WangandYang,2014,Lietal.,2015,Yeetal.,2017).
19
References
BeguinH(1982):Theeffectofurbanspatialstructureonresidential‐mobility.TheAnnalsofRegionalScience16,16–35.
BlázquezL,BoogenN,FilippiniM(2013):ResidentialelectricitydemandinSpain:Newempiricalevidenceusingaggregatedata.EnergyEconomics36,648–57.
BoehmTP(1982):Ahierarchicalmodelofhousingchoice.UrbanStudies19,17–31.
BrounenD, KokN, Quigley JM (2012): Residential energy use and conservation:economicsanddemographics.EuropeanEconomicReview36,931–45.
Bunnel, T., Drummond, L.B.W., Ho, K.C. (2002): Critical reflections on cities inSouthernAsia.Singapore:BrillAcademicPress.
CaylaJM,MaiziN,MarchandC(2011):Theroleofincomeinenergyconsumptionbehavior:evidencefromFrenchhouseholdsdata.EnergyPolicy39,7874‐83.
ChernozhukovV,HansenC(2005):AnIVmodelofquantiletreatmenteffects.Econometrica73,245–261.
ChernozhukovV,HansenC(2006):Instrumentalquantileregressioninferenceforstructuralandtreatmenteffectmodels.JournalofEconometrics132,491‐525.
Couch, C., Ldontidou, L., Petschel‐Held, G. (2007): Urban Sprawl in Europe.Landscapes,Land‐UseChange&Policy.Oxford:BlackwellPublishing.
Council of the European Union (2010: Europe 2020 — A strategy for smart,sustainableandinclusivegrowth.COM(2010)2020final,Brussels.
Councilof theEuropeanUnion(2014):2030FrameworkforClimateandEnergy.Availableatwww.ec.europa.eu/energy/2030.
Daioglou V, Van Ruijven BJ, Van Vuuren DP (2012). Model projections forhouseholdenergyuseindevelopingcountries.Energy37,601‐15.
Deaton AS, Muellbauer JN (1980): An Almost Ideal Demand System. AmericanEconomicReview83,570‐97.
DeatonA(1988):Quality,quantityandspatialvariationofprice.AmericanEconomicReview70,418‐430.
DruckmanA,JacksonT(2008):HouseholdenergyconsumptionintheUK:ahighlygeographically and social‐economically disaggregatedmodel. Energy Policy36,3177‐92.
EuropeanCommission(2010):Directive2010/31/UEoftheEuropeanParliamentandoftheCouncilof19May2010ontheenergyperformanceofbuilding.EuropeanCommission,Brussels
European Commission (2011). Energy Roadmap 2050. European Commission,Brussels.
European Commission (2016): Eurostat database. Retrieved 26 September 2016http://ec.europa.eu/eurostat/data/database.
20
Feng Z‐H, Zou L‐L, Wei Y‐M (2011): The impact of household consumption onenergyuseandCO2emissionsinChina.Energy36,656‐70
Gilbert, A. (1996): The Mega‐city in Latin America. Tokyo: United NationsUniversityPress.
Glaeser, E., Kahn, M. (2004): Sprawl and urban growth. In Henderson, V. andThisse, J.F. (eds.) Handbook of Regional and Urban Economics, vol. 4.Amsterdam:North‐Holland,2481‐2527.
Heinonen J, Junnila S (2014): Residential energy consumption patterns and theoverall housing energy requirements of urban and rural households inFinland.EnergyandBuildings76,295–303.
Huang W‐H (2015): The determinants of household electricity consumption inTaiwan:Evidencefromquantileregression.Energy87,120‐133
Jaeger,J.A.G., Schwick, C. (2014):Improving the measurement of urban sprawl:WeightedUrbanProliferation (WUP) and its application to Switzerland.EcologicalIndicators,38,294‐308.
JonesRV,FuertesA,LomasKJ(2015):Thesocio‐economic,dwellingandappliancerelated factors affecting electricity consumption in domestic buildings.Renewable&SustainableEnergyReviews43,901‐17.
Kasparian J (2009): Contribution of crude oil price to households' budget: theweightofindirectenergyuse.EnergyPolicy37,111‐4.
KavousianA,RajagopalR,FischerM(2013):Determinantsofresidentialelectricityconsumption: using smart meter data to examine the effect of climate,buildingcharacteristics,appliancestock,andoccupants'behavior.Energy55,184‐94.
KazaN(2010):Understandingthespectrumofresidentialenergyconsumption:aquantileregressionapproach.EnergyPolicy38,6574‐85.
KellyS (2011): Dohomes thataremoreenergyefficientconsume lessenergy?AstructuralequationmodeloftheEnglishresidentialsector.Energy36,5610–20.
KoenkerR,&BassetJrG(1978).Regressionquantiles.Econometrica,46(1),33‐50.
KyröR,HeinonenJ,SäynäjokiA, JunnilaS(2011):Occupantshavelittle influenceon theoverall energyconsumption indistrictheatedapartmentbuildings.EnergyandBuildings43,3484–90.
LabandeiraX,LabeagaJM,López‐OteroX(2012):Estimationofelasticitypriceofelectricitywithincompleteinformation,EnergyEconomics34,627–33
Labandeira X, Labeaga JM, Rodríguez M (2006): A residential energy demandsystemforSpain.EnergyJournal27,87–112.
Larsen BM, Nesbakken R (2004): Household electricity end‐use consumption:results from econometric and engineeringmodels, Energy Economics 26,179–200.
21
MiahMD,FoysalMA,KoikeM,KobayashiH(2011):Domesticenergy‐usepatternby the households: a comparison between rural and semi‐urban areas ofNoakhaliinBangladesh.EnergyPolicy39,3757‐65.
Nesbakken R (1999): Price sensitivity of residential energy consumption inNorway.EnergyEconomics21,493‐515.
Niu S, Jia Y, Ye L, Dai R, Li N (2016): Does electricity consumption improveresidential living status in less developed regions? An empirical analysisusingthequantileregressionapproach.Energy95,550‐60.
Niu S, Zhang X, Zhao C, Niu Y (2012): Variations in energy consumption andsurvival status between rural and urban households: a case study of theWesternLoessPlateau,China.EnergyPolicy49,515‐27.
O'NeillBC,ChenBS(2002):DemographicdeterminantsofhouseholdenergyuseintheUnitedStatesPopulationanddevelopmentreview28,53–88.
ÖzcanKV,GülayE,Üçdoğru(2013):EconomicanddemographicdeterminantsofhouseholdenergyuseinTurkey.EnergyPolicy60,550‐57.
Pachauri S (2004): An analysis of cross‐sectional variations in total householdenergy requirements in India using micro survey data. Energy Policy 32,1723‐35.
Polèse,M.,Champain,C.(2003):Laevolucióndeloscentrosurbanos:LaexperienciadeAméricadelNorte.Washington:WorldBank.
PoumanyvongP,KanekoS(2010):Doesurbanizationleadtolessenergyuseandlower CO2 emissions? Across‐country analysis. Ecological Economics 70,434‐44.
RahutDB,DasS,DeGrooteH,BeheraB(2014):DeterminantsofhouseholdenergyuseinBhutan.Energy69,661‐72.
Rahut DH, Behera B, Ali A (2016): Household energy choice and consumptionintensity: Empirical evidence from Bhutan. Renewable and SustainableEnergyReviews53,993–1009.
Romero,J.(2012):ConstrucciónresidencialygobiernodelterritoriodeEspaña.Dela burbuja especulativa a la recesión, causas y consecuencias. CuadernosGeográficos,47,17‐46.
Romero‐Jordán D, del Río P, Peñasco C (2014): Analysing the determinants ofhouseholdelectricitydemandinSpain.Aneconometricstudy.InternationalJournalofElectricityandPowerEnergySystem63,950–61.
Romero‐Jordán D, del Río P, Peñasco C (2016): An analysis of the welfare anddistributive implications of factors influencing household electricityconsumption.EnergyPolicy88,361‐70.
Rubiera, F., González. V., Rivero, J.L. (2016): Urban sprawl in Spain: differencesamongcitiesandcauses.EuropeanPlanningStudies,24(1),204‐226.
Santin OG (2011): Behavioural patterns and user profiles related to energyconsumptionforheating.EnergyBuilding43,2662–72.
22
SpanishGoverment(2016):LibrodelaEnergíaenEspaña2014,SpanishMinistryofIndustry,EnergyandTourism:Madrid(Spain).
Spanish Government (2011): Spanish Energy Saving and Efficiency Action Plan2011‐2020‐ Ministry of Industry, Tourism and Commerce and IDEA.Madrid.
SpanishGovernment(2014):RealDecreto413/2014,de6de junioporelqueseregulalaactividaddeproduccióndeenergíaeléctricaapartirdefuentesdeenergíarenovables,cogeneraciónyresiduos‐BOE‐A‐2014‐6123,Madrid.
StiriH (2014):Building andhouseholdX‐factors and energy consumption at theresidentialsector.Astructuralequationanalysisoftheeffectsofhouseholdand building characteristics on the annual energy consumption of USresidentialbuildings.EnergyEconomics43,178–184.
ValenzuelaC,ValenciaA,WhiteS, Jordan JA,CanoS,Keating J, et al. (2014): Ananalysis of monthly household energy consumption among single‐familyresidencesinTexas2010.EnergyPolicy69,263‐72.
PodgornikA,SucicB,BlazicB(2016):Effectsofcustomizedconsumptionfeedbackonenergyefficientbehaviour in low‐incomehouseholds. JournalofCleanerProduction130,25‐34
SaloM,NissinenA,LiljaR,OlkanenE,O'NeillM,UotinenM(2016):Tailoredadviceand services to enhance sustainable household consumption in Finland.JournalofCleanerProduction121,200‐7.
AllouhiA, El FouihY,KousksouT, JamilA, Zeraouli Y,MouradY (2015): Energyconsumption and efficiency in buildings: current status and future trends.JournalofCleanerProduction109,118‐130.
Stieß I, Dunkleberg E (2013): Objectives, barriers and occasions for energyefficient refurbishment by private homeowners. Journal of CleanerProduction48,250–259.
LaaksoS,LettenmeierM(2016):Household‐leveltransitionmethodologytowardssustainablematerialfootprints.JournalofCleanerProduction132,184–191.
Ye H, Ren Q, Hu X, Lin T, Xu L, Li X, Zhang G, Shi L, Pan B (2017): Low‐carbonbehavior approaches for reducing direct carbon emissions: Householdenergyuseinacoastalcity.JournalofCleanerProduction141,128‐136.
Han L, Xu X, Han L (2015): Applying quantile regression and Shapleydecompositiontoanalyzingthedeterminantsofhouseholdembeddedcarbonemissions: evidence from urban China. Journal of Cleaner Production 103,219‐230.
LiuLC,WuG,WangJN,WeiYM(2011):China'scarbonemissionsfromurbanandruralhouseholdsduring1992–2007.JournalofCleanerProduction19,1754–1762.
Wang Z, Yang L (2014): Indirect carbon emissions in household consumption:evidence from the urban and rural area in China. Journal of CleanerProduction78,94–103.
23
Li Y, Zhao R, Liu T, Zhao J (2015): Does urbanization lead to more direct andindirect household carbon dioxide emissions? Evidence from China during1996–2012.JournalofCleanerProduction102,103‐114.
Norman J,MacLeanHL,KennedyCA(2006):Comparinghighand lowresidentialdensity: life‐cycle analysis of energy use and greenhouse gas emissions.JournalofUrbanPlanningDevelopment132,10‐21.
RickwoodP,GlazebrookG,SearleG(2008):Urbanstructureandenergy–areview.UrbanPolicyandResearch26,57–81.
Wiesmann D, Lima Azevedo I, Ferrão P, Fernández JE (2011): Residentialelectricityconsumption inPortugal: findingsfromtop‐downandbottom‐upmodelsEnergyPolicy39,2772–9.