Energy Policy 50 (2012) 723–735

Contents lists available at SciVerse ScienceDirect

Energy Policy

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journal homepage: www.elsevier.com/locate/enpol

The structure of energy efficiency investment in the UK householdsand its average monetary and environmental savings

Miguel A. Tovar n

Department of Economics, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, United Kingdom

H I G H L I G H T S

c Analysis of socioeconomic and behavioural factors that can affect the Green Deal uptake.c The Energy Company Obligation (ECO) needs to follow a tailored strategy.c Average adoption benefits of cavity and loft insulation and upgrades to the boiler are estimated.c There is an important energy efficiency gap in the UK domestic sector.c A poor state of dwelling repair could bring financial pressure to the ECO program.

a r t i c l e i n f o

Article history:

Received 4 November 2011

Accepted 7 August 2012Available online 7 September 2012

Keywords:

Energy efficiency adoption

Household space heating

Government policy

15/$ - see front matter & 2012 Elsevier Ltd. A

x.doi.org/10.1016/j.enpol.2012.08.019

esponding author. Tel.: þ441206874373; fax

ail address: matova@essex.ac.uk

e Green Deal and Energy Company Obligatio

ilable at http://www.decc.gov.uk/assets/decc

een-deal-eco-ia.pdf.

e Meier and Rehdanz (2010).

a b s t r a c t

Socioeconomic and behavioural variables that influence the household’s adoption of energy efficiency

measures such as cavity and loft insulation and upgrades to the boiler are identified, contrary to

previous literature. By extending Brechling and Smith’s (1994) and Hassett and Metcalf’s (1995)

models, it is shown that the application of the Energy Act 2011, which contains provisions on the Green

Deal, the new Energy Company Obligation (ECO) and the private rented sector, needs to follow a

tailored strategy to reach the low adoption households identified by my model. Moreover, for the

current adopters of the analysed measures, average monetary and environmental adoption benefits are

estimated based on Parti and Parti’s (1980) demand model. These estimates are smaller than their

expected values showing an important energy efficiency gap in the sector. Particularly low cost

measures can bring important savings that can help to meet the ’’pay as you save’’ rule (i.e., the Golden

rule) of the new regulation. My model also shows that a poor state of dwelling repair can reduce the

adoption benefits increasing the need of subsidies that will be financed through consumer’s energy

bills. However, this can increase the number of households in fuel poverty.

& 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Maintaining the security of the UK energy supply, reducinggreen house emissions and addressing the drivers of fuel povertyare the current objectives of the UK energy efficiency policy in theresidential sector1. This sector contributes 30% of the totalconsumption of which 58% is used for heating purposes2. How-ever, the uptake of energy efficiency measures by British house-holds has had a slow growth. Unlike other sectors, increasingenergy efficiency has also a social objective because having a

ll rights reserved.

: þ44 1206 87 2724.

n Impact Assessment (GDE-

/11/consultation/green-deal/

warm house is considered a basic need. It is argued that groupssuch as benefit recipients, lone parents with dependent childrenand elderly households face fuel poverty (see Jamasb and Meier(2010), Roberts (2008) and Bradshaw and Hutton (1983)). House-holds in this condition spend more than 10% of their income onfuel. To the best of my knowledge however, there is no researchthat analyses the degree of adoption of the most cost-effectivemeasures such as cavity and loft insulation and upgrades to theboiler of these groups3.

Despite the importance of the adoption of these measures,research about it is extremely limited. Most of the research thatcan be found in the literature focuses on fuel substitution and itseffect on energy consumption (see Vaage (2000), Jeong et al.(2011) and Braun (2010)); however, according to the English

3 See Energy Saving Trust at http://www.energysavingtrust.org.uk/.

M.A. Tovar / Energy Policy 50 (2012) 723–735724

Household Condition Survey (EHCS) in Great Britain 94% ofdwellings that can adopt these measures have gas fired systemsand therefore, the scope for fuel substitution is narrow4. Brechlingand Smith (1994) model the adoption of cavity and loft insulationand double glazing using only one year data of the EHCS andindividual models for each measure. However, they did not findempirical evidence of the effect of other socioeconomic variablesapart from tenure and income. Scott (1997) analysed the Irishcase using a similar approach to the one used by Brechling andSmith (1994). He found that the main reasons why households donot undertake measures of energy efficiency are the following: (1)lack of information about energy efficiency measures, (2) whenbenefits cannot be accrued to the investor (i.e., this is the case ofhouseholders that have plans to leave their dwelling and the casetenants-landlords), (3) lack of access to the credit market (i.e., lowincome households with limited collateral may not have access tocredits) and (4) uncertainty about the gains from investment. Thenew UK legislation has considered some of these issues in theEnergy Act 2011 which contains provisions on three main topics:(a) the Green Deal, (b) private rented sector and (c) the EnergyCompany Obligation (ECO). The Green Deal, is a financial frame-work that allows private companies to offer upfront energyefficiency investments where its recoup will come from thesavings obtained from the uptake. This framework is based onthe Golden rule that specifies that any charge attached to theenergy bills must be less than the expected savings from theretrofit. This new legislation also puts the onus on tenants torequest energy efficiency measures. Finally, the ECO scheme hastwo main targets, the Carbon and the Affordable Warm Targets. Theformer one will help any household that faces high cost measuresto meet the Golden rule, while the latter one will be in charge ofimproving the energy efficiency levels of low income house-holds5. Brechling and Smith (1994) argue that previous policieshave succeeded in England reducing income differences thatrefrain households from investing in energy efficiency, as incomein their estimation has a very small effect on the adoptiondecision. In this regard several programs have been put in placeto encourage the uptake of low income households. However, theeffectiveness of these policies to reach the poorest households hasbeen questioned6. It is argued that these policies target only lowincome households, forgetting about groups that can experiencefuel poverty but not income poverty7. Moreover, there is aconcern about the narrowness of the grant eligibility criteriaunder the new ECO scheme to tackle fuel poverty8 . Hence, themain goal of this paper is to bring new insights, by identifying thesocioeconomic and behavioural factors that drive the household’sadoption of the analysed measures. Furthermore, identifyinghousehold types that may be less likely to act upon the newgovernment initiatives will lead to the design of a more cost-effective energy efficiency policy. The average monetary andenvironmental savings of the households that have alreadyundertaken cavity and loft insulation and upgrades to the boilerare also estimated to quantify the average adoption benefits. Inorder to investigate these issues, five years of the EHCS are usedexpanding the models proposed by Hassett and Metcalf (1995);Brechling and Smith (1994) to allow households to choosedifferent investment levels. Brechling and Smith (1994) and

4 The survey used is the continuous survey that comprises five years (from

2003 to 2006).5 See the GDECOI.6 See Warm Front: Helping to Tackle Fuel Poverty, National Audit Office, June

2003 available at www.publications.parliament.uk/.7 See Palmer et al. (2008).8 See The Green Deal and ECO consultation, British Gas Response available at

http://www.centrica.com/index.asp? pageid¼1044&asset¼response.

Scott (1997) use a logistic regression of each measure. However,I use different investment levels that allow me to model theadoption of different combinations of the analysed measuressimultaneously using discrete choice models. In contrast toBrechling and Smith (1994), double glazing is not considered inthe estimation because in the analysed time period, it is notregarded as a measure that can contribute significantly toimproving the energy efficiency in the sector9. In the same line,according to Brechling and Smith (1994), there is some evidencethat this kind of investment cannot be justified in terms of theratio gains-cost. In this estimation, controls for external tempera-ture and other socioeconomic and behavioural factors apart fromincome and tenure are also included. Additionally, an estimate forthe average adoption benefits based on Parti and Parti’s (1980)Conditional Demand Model (CDM) is proposed.

Contrary to Brechling and Smith (1994), it is found that manysocioeconomic and behavioural factors can explain the adoptiondecision as well as tenure and income. Therefore, the applicationof the initiatives contained in the Energy Act 2011 needs to followa tailored strategy to reach the low adoption households identi-fied by my model.

Concerning tenure and behavioural variables, the model showsthat owners that are trying to move from their current dwellingand those who do not spend most of the winter at home face alsolow adoption levels. Hence under an efficient consumer protec-tion mechanism, not attaching the Green Deal charge of theupfront cost to the householder but to the dwelling can increasethe uptake of the analysed measures. For tenants renting in theprivate sector, the estimation shows that residence length, dwell-ing type and location are important drivers in the adoption of thisgroup. With reference to the estimated average monetary andenvironmental adoption benefits, they are found to be below theirpotential values. This shows a possible gap in the British dwellingenergy efficiency. Particularly, low cost measures such as cavityand loft insulation can bring important savings to meet the Golden

rule. However, given that the investment cost of upgrades to theboiler is high, it is likely that the ECO program will be required tocover its adoption. My model also shows that a poor state ofdwelling repair can reduce the adoption savings increasing thepressure over the ECO budget. It is important to consider thatunder the new UK regulations, this budget will be financedthrough consumer energy bills and increases in energy pricescan also increase the number of households in fuel poverty.Hence, in order to minimise the ECO’s social cost, it is essentialto prioritise adults living alone or in cities, lone parents andtenants in the private sector as they are found to have lowadoption levels. It is also important to broaden the grant elig-ibility criteria to focus on those households where energy bills area financial burden as they can be more conscious of energy costs.

Regarding the structure of this paper, there are four sections.In the following section, the methods used to model the adoptiondecision and energy savings are depicted. Section 3 describes thedataset and the results from the empirical analysis. The last twosections comprise policy implications and conclusions.

2. The model and methodology

2.1. Investment decision

Following Hassett and Metcalf (1995), households minimisethe life time of cost of energy expenditures and capital costof certain level of energy efficiency. Households choose an

9 See EHCS’s annual report 2007 available at www.communities.gov.uk/ehcs.

M.A. Tovar / Energy Policy 50 (2012) 723–735 725

investment level that provides a percentage d of energy savings.Therefore they have to choose an optimal time T to make theinvestment

V ¼

Z T

0Pte�gtdtþ

Z 1Tð1�dZÞPte

�gtdtþKT e�gT , ð1Þ

where Pt and KT are energy and investment cost. The parametersg and Z are the discount factor and a variable that determineswhether the energy saving are accrue to the investor or not. Thefirst order condition is given by the next expression10

dZPT�gKT Z0 ð2Þ

It can be seen that the investment will be made only if thegains from the investment are greater or equal than the cost. Asnoticed by Hassett and Metcalf (1995) the estimation of thisexpression is not possible as we do not know the measures Z, g, d:Additionally, these measures are likely to be a function of thehousehold underlying characteristics. However, expression(2) gives us a guidance of the variables that should be analysed.Therefore higher prices should encourage households to invest inenergy efficiency while higher discount rates should preventthem from doing so. In this vein, Hausman (1979) found thathouseholds with higher income have a smaller discount rate andconsequently they will have higher investment levels11 . Henceincome should also be included in expression (2). According toHassett and Metcalf (1995) it is expected that high levels ofincome will increase the probability of investment. Moreover, themeasure d requires including controls for tenure in the estima-tion. Hence the estimated equation is

a1PT�a2KTþX0TbZ0, ð3Þ

where XT is the vector that comprises variables such as incomeand tenure and other socioeconomic characteristics of the house-hold. There is however, an extra problem in my estimation. Thesurvey does not report the investment cost KT . In this regard,Roberts (2008) provides an estimate of the cost of differentmeasures that could be imputed to each household according tothe adopted measure. Nevertheless, this imputation will beextremely arbitrary as households face different KT even for thesame measure. This is due to the fact that some households areeligible for the grants that could cover up to the total cost ofthe adopted measure in the dwelling. Moreover, the rest of thehouseholds pay a KT according to their tenure, ability to finddiscounts with installers and knowledge about energy efficiencymeasures.

The Warm Front is a tax-funded program that has currentlybeen the main vehicle for funding energy efficiency measures forvulnerable households12. The general criteria to be eligible for thegrants are based on household age, income level, whether thehousehold is eligible for state benefits and dependent children13.Hence KT can be estimated using the following expression

KT ¼ X01To1þo0þoc , ð4Þ

where X01T is the sub vector with elements from XT of expression(3), o0 is the average cost per square metre (i.e., m2) and oc

captures heterogeneity among households coming from ability tofind discounts with installers and knowledge of energy efficiencymeasures. Hence substituting expression (4) into (3) gives us the

10 This expression is obtained by applying Leibniz’s rule and assuming that Pt

increases exponentially at rate r: Therefore dpdt ¼ rPt(see Hassett and Metcalf

(1995)).11 Long (1993) and Scott (1997) also support this idea.12 The Affordable Warm group from the ECO scheme will replace the Warm

Front program by 2012/2013.13 See Energy Saving Trust.

following equation

a1PTþX01T ðb1�a2o1ÞþXn0T b

n�a0þtc Z0, ð5Þ

where tc ¼ a2oc ,a0 ¼ a2o0 and Xn

T comprises the rest of vari-ables that are uncommon from expression (4) and (3). Moreover,Hassett and Metcalf’s (1995) model is extended by allowinghouseholds to have different investment levels and in order todo so, expression (6) is estimated using an ordered model for theprobability of investing in level l as follows

Prð09xÞ ¼ Prðprk0Þ ¼Fðk0�pÞPrð19xÞ ¼ Prðk0oprk1Þ ¼Fðk1�pÞ�Fðk0�pÞ:::::

Prðl9xÞ ¼ Prðkl�1oprklÞ ¼Fðkl�pÞ�Fðkl�1�pÞ,

ð6Þ

where p¼ a1PTþX01T ðb1�a2o1ÞþXn0T bþtc , kl comprises the esti-

mates a0l and other unincluded factors in each level of investmentand F is the cumulative distribution function for tc . Therefore, itcan be seen that a household will choose their investments levelscomparing the benefits from the investment p with the thresholdparameters kl for different investment levels. Notice also that theestimation of the expression (6) is a case of the so called Neglected

Heterogeneity (see Wooldridge (2002)) which is a case of theomission of a variable where the direct consequence is that theestimates will be downwards biased and they will produceunreliable predicted probabilities. However, as noticed byWooldridge (2002) the fact that the estimates are biased doesnot prevent us from learning about the direction of the effect ofthe explanatory variables on the dependent one. This assumesthat tc does not depend on the explanatory variables. Expression(6) is estimated using an ordered probit which are the mostcommonly used specifications in the literature for ordered choices(see Cramer (2003)). This implies that F is a normal cumulativedistribution14. Notice that the choice of investment level ismodelled here by using a discrete variable (i.e., numbers from0 to 5 are used to depict different investment levels) while energyconsumption is modelled using a continuous one. Most of thepapers that analyse discrete-continuous datasets apply the meth-ods proposed by Hanemann (1984) and Durbin and McFadden(1984). These procedures link the discrete and continuous esti-mations through the predicted probabilities obtained by thediscrete choice model (i.e., investment level choice). If theseprocedures were used in my estimation the bias coming fromthe estimated probabilities in the discrete model given theproblem of Neglected Heterogeneity would be transmitted to thecontinuous model. As a consequence both discrete and contin-uous models would be biased. To avoid these problems I use Jeonget al.’s (2011) approach, which estimates independently thediscrete model and the continuous one. The way that thecontinuous model will be estimated is explained in the nextsubsection.

Gains and cost from the investment will depend from externaltemperature, usage, dwelling size age and type, region where thehousehold live, time that householders have lived in the dwellingand their intention to move from their current dwelling; thereforecontrols for these variables are also used to estimate expression(6). This estimation will help us to learn about the drivers of theadoption decision of energy efficiency levels. However, in order toanalyse the drivers of each investment level k, a discrete choice

14 The two most common cumulative distribution functions used to model

residual distributions (i.e.tl) are normal and logistic. When the former one is used

the model is called probit while a use of the latter one gives rise to the logit model.

The main outcomes of these models are coefficients that relate the dependent and

independent variables and estimated probabilities (see Train (2003)).

M.A. Tovar / Energy Policy 50 (2012) 723–735726

model is estimated as follows

PrðpkÞ ¼ probðpk4poÞ ¼expðpkÞPm

l ¼ 1 expðplÞ, ð7Þ

where PrðpkÞ denotes the probability that a household invests at levelk. Unlike expression (6) that compares gains with different invest-ment thresholds, the estimation of PrðpkÞ implies that householdscompare gains from different investment levels. Moreover, p isdefined as before, however, the residuals tl are assumed to beindependently, identically distributed extreme value. That is, a logisticcumulative distribution is used to model the distribution of tl.

2.2. Energy expenditure and CO2 emissions

In order to estimate the economic saving from the analysedenergy efficiency measures, household’s expenditure is dividedinto two components. One is the Standard Cost (SC) that house-holds have to pay assuming that they have not adopted any of theanalysed measures. The second component is the savings (S) thathouseholds can get by investing in energy efficiency. FollowingMeier and Rehdanz (2010), SC for heating cost of a square metre isestimated using a lineal function. Additionally explanatory vari-ables for the SC such as Income, Household type, age (i.e., HRP age),Tenure, Dwelling Size, whether the householders live in a City andHeating Degree Days (HDD) are also included. The savings thathouseholds can get by these measures are estimated followingLarsen and Nesbakken (2004) who use the Conditional Model(CDM) proposed originally by Parti and Parti (1980). The latterauthors estimated the demand of electricity conditional on appli-ance specific characteristics of the analysed households. They useddummy variables which take value of 1 if a household has adoptedcertain appliance and zero otherwise. These dummy variablesinteract with the household socioeconomic characteristics toidentify appliance adoption effects on energy consumption acrossdifferent household types. Let us assume that we can observeindividual savings associated with several efficiency measures andconsequently the total savings can be expressed as15 .

S¼XN

i ¼ 1

XMj ¼ 0

bijðVjDiÞ, ð8Þ

where bij are the parameters to be estimated for the energyefficiency measure i and the explanatory variable j, Vj is theexplanatory variable j and Di is the dummy variable for measure i.According to Parti and Parti (1980) a lineal form for the relation-ship between the amount saved and the explanatory variables isassumed16. Moreover the average savings from measure i can beestimated as follows

Si ¼ bn

i0þXJ

j ¼ 1

bijðVij Þ, ð9Þ

where Vij is the average of the interaction of the vector ofexplanatory variables with the dummy variable of measure i.Furthermore, adding and subtracting the sum across measures i ofexpression (9) from expression (8) give us the following expression

S¼XN

i ¼ 1

bn

i0DiþXN

i ¼ 1

XMj ¼ 1

bijVij DiþXN

i ¼ 1

XMj ¼ 1

bijðVj�Vij ÞDi, ð10Þ

notice that the sum of the first two terms of this expression is thesum of expression (9) across measures i. Following Parti and

15 Notice that in Parti and Parti’s (1980) application, they used total energy

consumption as dependent variable; however, I am using expenditure on heating

because of data availability.16 It is assumed that V0 ¼ 1 (see Parti and Parti (1980)).

Parti’s (1980) notation this sum is expressed asPN

i ¼ 1

Si ½ðDiÞ� whereSi ½ðDiÞ� indicates that the mean savings are the estimates relatedonly to the adoption dummy variables when the other explana-tory variables are equal to one. Consequently, expression (10) canbe written as

S¼XN

i ¼ 1

Si ½ðDiÞ�þXN

i ¼ 1

XMj ¼ 1

bijðVj�Vij ÞDi: ð11Þ

As in Parti and Parti’s (1980) model, it is not possible toidentify the estimates bi0. Nevertheless, we can only have anestimate for the total mean savings Si ½ðDiÞ�. We could estimateexpression (11) by using Ordinary Least Squares (OLS). However, Ido not have information about the savings attributed to thedifferent measures. Consequently, instead of estimating expres-sion (11) by OLS, one needs to estimate the Total Cost (TC) that isthe sum of SC and S. Therefore, the TC for household n is estimatedby the following expression

TCn ¼w0nlþXN

i ¼ 1

Si ½ðDiÞ�þXN

i ¼ 1

XMj ¼ 1

bijðVjn�Vijn ÞDinþl0þmn, ð12Þ

where w0lþl0 is the Standard Cost (SC) and mn is the error term.Therefore, this expression can be estimated by using OLS wherethe dependent variable is the heating cost per square metre (i.e.,TCn) and the coefficients to be estimates are: the estimates foradoption dummy variables in

PNi ¼ 1

Si ½ðDiÞ� represented by b̂kS

.The estimates for the adoption dummies and their interactionwith the mean deviation of other explanatory variables (i.e.,ðVjn�Vijn ÞDin) are represented by b̂kj. Finally, the estimates forthe controls of the standard cost (i.e.,w) are represented by l̂.Following Larsen and Nesbakken (2004), the average savingrelated to specific household characteristics for the measure k

are computed as follows

Sk

L¼ b

kSL Dkþ

XMj ¼ 1

bkj

LðVj�Vjk ÞDk , ð13Þ

where Dk and ðVj�Vjk Þ are the average of Dk and ðVj�Vjk Þ: In orderto quantify the adoption benefits, I compute the ratio of theaverage savings of a household type g that has adopted themeasure k and the average heating expenditure of a similarhousehold that has not adopted any of the analysed measuresas follows

^Sgk

^SCg

, ð14Þ

where ^SCg ¼wg0 l̂þ l̂0. Notice that ^SCg is estimated by substitut-

ing the average of w for a household type g that has not adoptedany of the analysed energy efficiency measures17.

3. Dataset and empirical results

3.1. The dataset

The dataset used in the estimation is the English HouseCondition Survey (EHCS) and it comprises information at indivi-dual and household level. The data is generated by an annualsurvey carried out continuously in England since 2002 andreported the first time in 2003; however, in 2008 the surveywas merged with the English Housing to form the EnglishHousing Survey (EHS). The time period considered in this estima-tion extends from 2003 to 2007, since these five years are the only

17 Standard errors are estimated using the delta method (see Greene (2008)).

Table 1Description of the investment level (Dependent variable used in the estimation).

Investment level description Period1 Period2[%] Period3

0 None of the measures 24 20 17

1 Either cavity or loft 13 12 13

2 Both cavity and loft insulation 2 2 3

3 Only boiler in standard age 32 33 28

4 Both level 1 and 3 23 25 27

5 The three measures 6 7 11

100 100 100

Period1 comprises years from 2003 to 2004, Period2 includes year 2005 and

Period3 goes from 2006 to 2007.

Table 2investment level distribution per income levels.

Investment level Low Medium[%] High

Period1

0 24 25 23

1 14 12 13

2 2 2 1

3 29 34 35

4 24 22 23

5 7 6 5

100 100 100

Period2

0 19 22 20

1 14 12 11

2 4 2 1

3 29 31 36

4 26 26 24

5 8 6 7

100 100 100

Period3

0 16 19 17

1 14 14 12

2 6 3 2

3 25 28 31

4 27 28 28

5 13 9 10

100 100 100

The lowest income level comprises the two low quantiles. Medium

income comprises the third quantile and the high income level

comprises the two highest quantiles. Period1 comprises years from

2003 to 2004, Period2 includes year 2005 and Period3 goes from

2006 to 2007. See footnote in Table A1 for notation used in this table.

M.A. Tovar / Energy Policy 50 (2012) 723–735 727

available data at the moment of the estimation18 . The datasetprovides information about the socioeconomic and physicalcharacteristics of households and dwellings. Information relativeto energy prices is taken from the International Energy Agency(IEA) while Heating Degree Days (HDD) is taken from the Eurostatdatabase. The analysed measures are cavity and loft insulationand upgrades to the boiler. The EHCS does not report insulationlevels for dwellings with solid walls and consequently, dwellingswith cavity walls, loft and central heating boiler systems are onlyconsidered19. In order to model different combinations of invest-ment levels, I construct an index variable to indicate the level ofenergy efficiency adoption. This index takes values from 0 to5 where 0 means that the household does not have any of theanalysed measures in the dwelling. The description of this indexis displayed in Table 1.

Cavity and loft insulation are considered low cost measures,while upgrades to the boiler is considered a high cost measure20.It can be seen that the number of dwellings that have adoptedthe three analysed measures is very small. According toHausman (1979) and Long (1993) high income householdsare more likely to adopt higher energy efficiency levels,however, Table 2 displays the investment level distribution perdifferent income levels and as it can be seen the uptake levelsbetween low and high income levels are very similar acrossinvestment levels. Therefore in this case, Table 2 does not supportthis argument.

Notice also that at the highest investment level, both low andhigh income levels show a very similar level of adoption. This factwas noticed originally by Brechling and Smith (1994) who arguedthat government policies have made it possible for low incomehouseholds to access high efficiency levels. Moreover, low adop-tion of high income households is linked to the household’suncertainty about the gains from the investment. Therefore,expectations about behaviour of energy prices, technical changesand possible government grants reduce the possibility to under-take the investment (see Hassett and Metcalf (1993)). In the sameline, McDonald and Siegel (1986) argue that when the investorfaces uncertainty, a rational reaction is to postpone the invest-ment. Additionally, Scott (1997) argues that when households areexpecting to move they cannot appropriate the gains from theinvestment and therefore, the adoption will not take place.Consequently, the variable Trying to move is included to accountfor this.

A dwelling has operation and maintenance costs where energyis the one of the operation costs. However, my dataset does notprovide information about any maintenance cost that can affectthe adoption decision and the gains from the investment. There-fore, given that adoption decision and gains are relative to thegeneral physical state of the building the variable State of repair isused as a proxy to control for it. This variable is an index for theself rating of state repair of home. It takes values from 1 to 5, inthis range the highest level is 1 for ’’excellent: Nothing needs tobe done’’ and 5 is the lowest level for ’’very poor: a lot of majorproblems’’. Notice that in expression (2) energy prices are themain source of investment gains, however, gains are also relative

18 The EHCS is available for previous years to 2003, however, there are

significant differences in the way that cavity and loft insulation and income were

measured in those years (see EHCS’ Technical Report 2007).19 The EHCS’ annual report suggests specifically upgrading boilers to A class

condensing boilers. However, only the last survey contains information about the

type of boiler. Hence I consider that households need to make the investment in

upgrading their boiler when the boiler age is more than 12 years, as according to

the Energy Saving Trust, after this age the boiler has to be replaced.20 Regarding, loft insulation, the Energy Trust Saving recommends a depth of

more than 200 mm of loft insulation. Therefore households below this threshold

are considered to lack this measure.

to the general physical state of the building. Hence, energy pricesare divided by the variable State of repair in the discrete choicemodel21 . Additionally following Hassett and Metcalf (1995),energy prices are also divided by an index of the cost of energyefficiency to make the gains relative to their cost22. Table 3 alsoprovides a summary of the variables used in the estimation.

It can be seen that the average investment level is around 3 inthe last period which is a low efficiency level. Moreover, increas-ing energy expenditure across periods can be due to almost nochanges in high efficiency levels and growing energy prices. It isimportant to mention that the heating cost provided by the EHCSis an estimate and is not reported data by the householders. Theestimation of this cost was carried out by considering dwellingcharacteristics and information from the survey about house-hold’s behaviour that is relevant for energy consumed for heating

21 This follows Hausman’s (1979) idea that households face different energy

prices according to their energy efficiency levels. However, the EHCS does not

provide a technical efficiency expressed in terms of physical units.22 I used the index for products for regular repair of dwelling provided by the

Economic and Social Data Service available at www.esds.ac.uk/.

Table 3Summary of the main variables used in the estimation.

Variable Period1 Period2 Period3

Source Min max Sample size Mean SD Sample size Mean SD Sample size Mean SD

Investment level EHCS 0 5 22,815,379 2.37 1.65 11,652,738 2.53 1.63 24,289,976 2.69 1.64

Energy Expenditure EHCS 52 3472 22,815,379 342 160 11,652,738 430 228 24,289,976 581 279

CO2 emissionsa EHCS 0 13 0 0.00 0.00 0 0.00 0.00 24,289,976 6.27 3.15

Energy priceb IEA 0 0.09 22,815,379 0.02 0.00 11,652,738 0.02 0.00 24,289,976 0.03 0.00

HDD Eurostat 2422 3129 22,815,379 2,692 154 11,652,738 2,692 174 24,289,976 2,578 208

Income EHCS 2263 433501 22,815,379 22,842 19131 11,652,738 23,931 18,471 24,289,976 25,330 19,375

Benefits EHCS 0 1 22,815,379 0.19 0.39 11,652,738 0.20 0.40 24,289,976 0.19 0.39

HRP age EHCS 1 6 22,815,379 4.13 1.44 11,652,738 4.17 1.44 24,289,976 4.19 1.43

No dep children EHCS 0 8 22,815,379 0.63 1.01 11,652,738 0.65 1.03 24,289,976 0.62 0.99

Household size EHCS 1 10 22,815,379 2.56 1.29 11,652,738 2.58 1.31 24,289,976 2.55 1.27

Household type EHCS 1 6 22,815,379 2.55 1.77 11,652,738 2.52 1.75 24,289,976 2.55 1.78

Tenuare EHCS 1 5 22,815,379 1.96 1.21 11,652,738 2.01 1.23 24,289,976 2.02 1.23

Time at home EHCS 0 1 22,815,379 0.50 0.50 11,652,738 0.50 0.50 24,289,976 0.48 0.50

Trying to move EHCS 0 1 22,815,379 0.07 0.26 11,652,738 0.08 0.27 24,289,976 0.08 0.27

State of repair EHCS 1 5 22,793,853 2.19 0.81 11,639,927 2.18 0.81 24,272,209 2.18 0.82

Length of residence EHCS 1 8 22,815,379 5.21 1.99 11,652,738 5.31 1.93 24,289,976 5.33 1.97

Dwelling size (m2) EHCS 15 856 22,815,379 89.90 41.35 11,652,738 89.92 40.70 24,289,976 96.49 45.45

Dwelling type EHCS 1 2 22,815,379 1.04 0.21 11,652,738 1.04 0.21 24,289,976 1.04 0.21

Dwelling age EHCS 1 4 22,815,379 1.69 1.10 11,652,738 1.73 1.12 24,289,976 1.75 1.14

City EHCS 0 1 22,815,379 0.13 0.34 11,652,738 0.12 0.33 24,289,976 0.12 0.32

SD stands for standard deviation. See footnote in Table 1 for notation used in this Table. Monetary values are annual values in Great Britain Pound (GBP), a Tonnes per year,

c GBP per kW h. Additionally, notice that min and max values for the six first variables are averages across time.

24 The variable related to education in the EHCS has considerable missing

values and therefore, its inclusion in the -model would complicate the estimation.

M.A. Tovar / Energy Policy 50 (2012) 723–735728

purposes. Hence any attempt to infer about the sensitivity of thehousehold’s energy demand to energy prices based on theestimated heating costs will fail, as these estimates are unableto capture the real heating patters of each household. However, itis a good measure of the relationship of energy expenditure anddwelling characteristics because this allows us to isolate effects ofchanges in heating patterns such as household’s adjustments oftheir thermostat when they face increases in energy prices. Noticealso that CO2 emissions are only available for the last two years ofthe EHCS. To find groups of the population that can facelow efficiency level, I use the variable Household RepresentPerson age (i.e., HRP age) displayed in Table 3 to create thedummy variable Over 60 that takes values of 1 if the HRP is over60 years23. Regarding Household type, it takes values from 1 to6 where 1 and 2 are couples without and with dependentchildren. Numbers 3 and 4 are for lone parents with dependentchildren and other multiperson households. Numbers 5 and 6are for person living alone in different age categories. Jamasband Meier (2010) and Palmer et al. (2008) argued that Lone

parents and Adults living alone can face fuel poverty. Hence adummy variable is used for each of these categories. Time at home

is also a dummy variable that takes value of one if the HRP

spends most of the time in winter at home. Additionally, I includethe binary variable Trying to move if the householders arelooking for another dwelling. Length of residence is a variable thattakes values from 1 to 8. The numbers from 1 to 7 are number ofyears that householders have been living in the dwelling,while the number 8 denotes that they have been living in theirdwelling for more than 30 years. Dwelling type takes values from1 to 2, where 1 is for households living in a house, and 2 is forhouseholds living in flats. Dwelling age takes values from 1 to 4,where 1 is for dwellings built before 1975, 2 for the onesbuilt from 1975 to 1980, 3 for the ones built between 1981 and1990 and number 4 for post 1990. Therefore, as Table 3shows most of dwellings are houses owned and built around1975. Brechling and Smith (1994) noticed that employment

23 Liao and Chang (2002) defined elderly households as those with house-

holders over 60 years old.

levels are not able to explain the investment decision as theemployment status may be different at the time that thesurvey was carried out from the time that the investment wasdone. Variables such as income and Benefits which takes valueof 1 if the household is eligible for state benefits, can alsocapture employment and education levels and therefore,neither employment nor education levels were included in theestimation24. To analyse the tenure dimension of the uptake ofenergy efficiency measures, the variable Tenure is used. In thedataset this variable is a number from 1 to 5 where 1 and 2 are forowners with and without mortgage and the rest are for renters inprivate and for households that rent with local authorities orregistered social landlords. Dummy variables for Private renting

and Other renting for households renting in public schemes arealso used. Table 3 shows, the most common form of tenure isownership.

3.2. Analysis of the uptake drivers by different tenure and income

levels

Table 4 displays the results from the estimation of expression(6) for different income and tenure levels25. Contrary to Brechlingand Smith (1994), they show that socioeconomic and behaviouralvariables play an important role in the adoption decision. There-fore, it is possible to identify which type of household may be lesslikely to act upon government initiatives to improve the energyefficiency of their dwellings.

Regarding age, Liao and Chang (2002) found that elderlyhouseholds spend on average more money on heating. However,the estimates from the variable Over 60 which takes value of 1 ifthe Household Represent Person (HRP) is over 60 years old, doesnot provide strong evidence across tenure and income groups that

25 To rule out any possible influence of the normality assumption of tc over

the estimation, I also estimate expression (6) by a lineal model which gives me

similar results to the ordered model. Therefore, the assumption of normality does

not drive the main conclusions of this estimation. These results are available upon

request from the author.

Table 4Estimation of expression (6) by an Ordered probit (Oprobit). Dependent variable:

different investment levels from 0 to 5.

All sample Owners Tenants

Estimates for Energy pricea and

Low income households (LowI) 12.575nnn 12.905nnn 11.479nnn

(1.276) (1.742) (1.623)

Medium income households (MediumI) 11.741nnn 12.377nnn 10.048nnn

(1.471) (1.690) (2.943)

High income housholds (HighI) 13.407nnn 14.581nnn 9.282nnn

(1.259) (1.406) (3.406)

Estimates for Over 60 and

LowI 0.083nnn 0.097nnn 0.037

(0.022) (0.031) (0.027)

MediumI 0.007 0.024 0.043

(0.033) (0.036) (0.088)

HighI 0.019 0.048 �0.081

(0.031) (0.033) (0.114)

Time at home 0.037nnn 0.045nnn 0.025

(0.013) (0.016) (0.020)

Rent privately

LowI �0.146nnn�0.371nnn

(0.033) (0.033)

MediumI �0.305nnn�0.518nnn

(0.045) (0.049)

HighI �0.259nnn�0.431nnn

(0.037) (0.047)

Other rent 0.320nnn 4.288nnn

(0.018) (1.046)

Estimates for No dep children and

LowI 0.002 0.082nnn�0.034nn

(0.014) (0.029) (0.014)

MediumI 0.010 0.015 0.001

(0.013) (0.018) (0.017)

HighI 0.029nnn 0.029nnn 0.002

(0.009) (0.010) (0.018)

Estimates for Lone parent and

LowI �0.071nn�0.038 �0.021

(0.032) (0.070) (0.032)

MediumI �0.072 �0.098 �0.030

(0.050) (0.072) (0.059)

HighI 0.009 0.058 �0.218nn

(0.066) (0.077) (0.103)

Estimates for Adult living alone and

LowI �0.082nnn�0.082nnn

�0.079nnn

(0.022) (0.030) (0.027)

MediumI �0.019 �0.035 0.046

(0.037) (0.041) (0.084)

HighI �0.120nnn�0.126nnn

�0.177n

(0.036) (0.039) (0.099)

Estimates for Benefits and

LowI 0.075nnn 0.053 0.044

(0.023) (0.035) (0.029)

MediumI 0.092nn 0.251nnn�0.015

(0.045) (0.076) (0.053)

HighI �0.085 �0.136 0.045

(0.073) (0.121) (0.088)

log(Income) 0.009 0.001 0.018

(0.017) (0.021) (0.027)

log(m2) 0.053nnn 0.053nn 0.057

(0.018) (0.021) (0.035)

Length of residence (LR) �0.031nnn�0.053nnn 0.030nnn

(0.003) (0.004) (0.005)

Trying to move �0.062nnn�0.056n

�0.040

(0.022) (0.030) (0.027)

Dwelling type �0.057nn 0.005 �0.075nnn

(0.025) (0.054) (0.027)

Dwelling age 0.197nnn 0.196nnn 0.188nnn

(0.005) (0.006) (0.008)

log(HDD) 0.939nnn 1.092nnn 0.530nnn

(0.084) (0.105) (0.123)Estimates for City and

LowI �0.056nn�0.048 �0.050nn

(0.024) (0.044) (0.024)

MediumI 0.023 0.053 �0.065

(0.035) (0.044) (0.050)

Table 4 (continued )

All sample Owners Tenants

HighI �0.058n�0.075nn 0.012

(0.030) (0.033) (0.063)

Year 0.061nnn 0.060nnn 0.062nnn

(0.005) (0.006) (0.007)

k0 7.501nnn 8.590nnn 4.288nnn

(0.713) (0.890) (1.046)

k1 7.914nnn 9.000nnn 4.721nnn

(0.713) (0.890) (1.046)

k2 7.986nnn 9.060nnn 4.838nnn

(0.713) (0.890) (1.045)

k3 8.813nnn 9.916nnn 5.571nnn

(0.713) (0.890) (1.045)

k4 9.845nnn 10.974nnn 6.542nnn

(0.713) (0.890) (1.045)

Standard errors are given in parenthesis. ‘‘*n’’: significant at 10 percent level.

‘‘nn’’: significant at 5 percent level. ‘‘nnn’’: significant at 1 percent level.a In all the following tables Energy Price is divided by the product of State of

repair and an index of dwelling repair costs. In this estimation Low, Medium and

High are dummy variables used to interact with other variables as this table

shows. Income levels are described in the footnote of Table 2.

M.A. Tovar / Energy Policy 50 (2012) 723–735 729

low efficiency levels are driven just by being in this age category.Moreover, if elderly householders face higher energy expenditureit can be due to not only a problem of low energy efficiency butalso to a problem of under occupancy as argued by Roberts(2008). This argument is supported by the estimates of thevariable Adult living alone where being in this category can triggerlow efficiency levels across income and tenure levels. Palmer et al.(2008) argue that the situation of adults living alone in any agecategory can be one of the most serious cases of fuel poverty astheir energy cost tends to be larger than their income comparedto other household types. In my sample, around 23% of house-holds are adults living alone and 59% of them are adults over 60years old. Another type of household with high energy expendi-tures are, as noticed by Meier and Rehdanz (2010), householdswith dependent children. The model shows that this type ofhouseholder renting in the private sector and in low incomecategories faces low adoption levels. Having a dependent child inthe household can increase the eligibility for grants and thereforeit can explain the fact that other household types with dependentchildren have a higher probability of having better energyefficiency levels. On the other hand, Lone parents with dependentchildren face low efficiency levels as shown by its estimates forlow income levels and tenants with high income levels (see Table4). Regarding behavioural variables, the estimate for Time at home

is statistically significant and positive for owners. This variabletakes value of 1 if the HRP spends most of the winter time athome and 0 otherwise. These types of households could includeelderly and disabled householders and households with veryyoung dependent children as they may face limitations to leavetheir houses. However mobility and health statues are notconsidered specifically in the eligibility criteria of the Affordable

warm target of the new ECO26. Additionally, the variable Trying to

move that takes a value of 1 if householders are looking foranother dwelling is statistically significant and negative in mostof the estimations. This also supports Scott’s (1997) argumentthat households with the intention to move will face low adop-tion levels. Therefore, attaching the upfront cost of the invest-ment to the property and not to a householder is one of thestrategies of the Energy Act 2011 to encourage the uptake ofhouseholders with intention to move. However, providing newoccupiers with the ability to renegotiate their inherited

26 See The Green Deal, House of Commons Library.

M.A. Tovar / Energy Policy 50 (2012) 723–735730

agreements and evaluation of their grants eligibility, are some ofthe issues that need to be considered in the Energy Act 2011

application27. Notice that the estimates related to Energy Price

show that low and medium income households are generally lesssensitive to changes in energy prices28. Therefore, as noticed byBradshaw and Hutton (1983), a policy based on increasing energyprices to induce households to undertake the analysed measurescan increase the number of individuals in fuel poverty. This is alsoconsistent with Guertler (2011) who found that given thatsubsidies under the ECO scheme are financed through energybills, the effect of increases in grants are offset by increases inenergy bills. Moreover, in order to help low income households toincrease their adoption levels in England, the Warm Front pro-gram provides grants where eligibility increases if the householdreceives state benefits. In this regard, the estimates for thevariable Benefits show that recipients can improve their energyefficiency situation. However, the size of the estimate for mediumincome levels is slightly bigger than for low income ones. Noticealso that there is no statistical significance of this variable for thisincome group across tenure showing a possible failure of policiesto reach the poorest ones as noticed by Hong et al. (2006). In thisregard, the Affordable Warm target of the new ECO scheme willsubstitute the Warm front program, however, it follows similareligibility criteria than its predecessor; and therefore to increaseits effectiveness, these criteria need to be revised. Regarding theestimates for the logarithm of the variable income, I did not findany relevant effect on the adoption decision. This is in line withBrechling and Smith (1994) who found a very small effect ofincome on the decision of investment. According to the authorsthis can be due the effectiveness of the government policies toeliminate income-related differences that affects the investmentbehaviour. Moreover, they also noticed that the survey wascarried out some time after the investment has taken place andtherefore income fluctuations cannot explain household beha-viour of energy efficiency adoption.

Another issue considered in the Energy Act 2011 is the privaterented sector where landlords do not always receive the financialbenefits from the retrofits. Given this lack of incentive, propertiesrented in the private sector have low energy efficiency levels. Thiscan be seen by the estimates of the variable Rent privately

displayed in Table 4 where tenants renting in this scheme facelow efficiency levels across income levels. For this reason, the newUK legislation will encourage landlords to improve the energyefficiency in their dwellings by banning letting of properties thatdo not have a minimum of energy efficiency by the year 201829.Notice that the estimates related to Length of residence positivelyaffect the probability of having better energy efficiency levels fortenants. This can be due to the fact that this household type hasmore flexibility to move and choose dwellings with better energyefficiency levels in the short run. In addition, they stay for longerperiods of time where energy efficiency levels are higher. This istherefore another incentive for landlords to improve energyefficiency levels, as they can keep their tenants for longer periodsavoiding the costing process of finding new ones. On the otherhand, for owners, Length of residence has an opposite direction;this can be attributed to the fact that this variable can alsocapture dwelling age. The estimates for the variable Dwelling age

show that the newer the house, the better energy efficiencyinvestment levels that can be reached. The estimates related to

27 See A Future Obligation on Energy Companies available at http://www.u-

kace.org/publications/.28 In my estimation low income tenants are more sensitive to energy prices

given that they have more flexibility to move and find more efficient dwellings

when they face increasing energy prices (see Table 4).29 See The Green Deal, House of Commons Library.

the variable Dwelling type are negative and statistically signifi-cant. This shows that flats have lower energy efficiency levelsthan houses and this is due to the fact that most of the flats arerented30. The Energy Act 2011 considers provisions to ensure thatlandlords will be unable to refuse a tenant’s request for energyefficiency measures. However, flats can be side-by-side apart-ments that share common walls and therefore, lack of coordina-tion of the occupiers in the whole building can complicate theuptake of energy efficiency measures in this type of dwelling.Notice that the property size is also important for owners asshown by the estimates related to log(m2) where the larger theproperty is, the higher energy efficiency investments that can befound. Moreover, living in a City has a negative effect on theuptake of the analysed measures31. Regarding HDD, Table 4 showsthat increases in the number of days with low temperature willincrease the efficiency levels across tenure. The previous estima-tion is useful to show the direction of estimates across differenthousehold characteristics, however, it does not allow analyses ofthe within investment group characteristics. To investigate theseissues, Table 5 provides the estimates from expression (7) fordifferent investment levels.

The estimates for the variable Over 60 show that it is possibleto find households in this category of age, at any level ofinvestment, including zero. Therefore, it is important to continuesupporting this group through grants especially if they live alone.Notice that households eligible for state benefits can be at anyinvestment group, including high levels. The estimates of thevariables Lone parent and Adult living alone show that these typesof households tend to have low energy investment levels (i.e.,between zero and level two of investment). Moreover, as shownin Table 4, these types of households in low income levels can beat the bottom of this range of investment. Regarding the esti-mates related to the variable log(income), most of the estimatesare small and negative across the investment groups. This showsthat income differences among households have a weak influencein the adoption of low efficiency levels and none for the highestones. Regarding tenure, as in the previous cases renting in theprivate sector increases the probability of having zero level ofinvestment and decreases the probability of higher levels. Theestimates related to the variable Time at home have the biggestsize for the highest level of investment and therefore house-holders that spend most of winter time at home increase theprobability of having cavity and loft insulation and upgrades tothe boiler. Moreover, the estimates related to the variable Trying

to move show that households in this situation will not invest inany measure.

3.3. Average monetary and CO2 emissions benefits

The Green Deal and the ECO scheme require measures of theaverage benefits from the households that have already adoptedthe analysed measures. Therefore, it can be possible to identifysaving sizes, gaps and opportunities to improve energy efficiencylevels in this sector. The estimates for the heating cost and CO2

emissions functions are displayed in Table 6.As noticed by Meier and Rehdanz (2010) flats can absorb heat

from their neighbours and consequently the benefits from theadoption will be bigger than in houses. Hence, the estimates b

kS inexpression (12) are allowed to be different for houses and flats.These estimates are interpreted as the mean savings regardless ofthe household characteristics. They show that cavity and

30 In the analysed sample 85% of flats are rented.31 Palmer et al. (2008) argues that it is important to revise the concept of rural

areas used by the British Government as households in remote rural areas are

more likely to face fuel poverty.

Table 5Multinomial estimation of expression (7). Dependent variable: different investment levels from 0 to 5.

Variables Investment levels

0 1 2 4 5

Energy price �17.656** 4.282 7.171 12.929*** 29.686***

(3.029) (3.131) (5.157) (2.533) (3.320)

Over 60 0.120*** 0.221*** 0.626*** 0.291*** 0.335***

(0.046) (0.051) (0.091) (0.044) (0.062)

Time at home 0.012 0.145*** 0.148* 0.069** 0.231***

(0.036) (0.041) (0.083) (0.035) (0.051)

Rent privately 0.402*** �0.071 0.017 �0.256*** �0.300***

(0.058) (0.075) (0.150) (0.063) (0.102)

Other rent �0.138*** 0.422*** 0.910*** 0.609*** 1.123***

(0.049) (0.054) (0.097) (0.046) (0.067)

No dep children �0.046** �0.063*** 0.035 0.018 �0.009

(0.019) (0.022) (0.042) (0.017) (0.026)

Lone parent 0.030 �0.012 0.257** �0.139** �0.250***

(0.067) (0.078) (0.131) (0.062) (0.093)

Adult living alone 0.077* 0.049 0.181* �0.121*** �0.047

(0.046) (0.052) (0.095) (0.046) (0.065)

Benefits �0.041 0.144** 0.318*** 0.135*** 0.289***

(0.055) (0.062) (0.109) (0.051) (0.074)

log(Income) �0.076** �0.068 �0.200** �0.092*** �0.026

(0.036) (0.041) (0.083) (0.035) (0.051)

log(m2) 0.058 0.135** 0.239** 0.179*** 0.296***

(0.052) (0.058) (0.119) (0.048) (0.068)

Length of residence 0.087*** 0.108*** 0.087*** 0.036*** �0.009

(0.009) (0.011) (0.021) (0.009) (0.013)

Trying to move 0.117** �0.097 �0.179 �0.066 �0.161*

(0.056) (0.070) (0.141) (0.056) (0.089)

Dwelling type �0.252*** �0.397*** �0.688*** �0.403*** �0.629***

(0.073) (0.087) (0.155) (0.071) (0.109)

Dwelling age �0.096*** 0.119*** �0.003 0.416*** 0.495***

(0.017) (0.018) (0.037) (0.016) (0.021)

log(HDD) �1.003*** �0.064 3.456*** 0.839*** 3.044***

(0.243) (0.275) (0.436) (0.228) (0.307)

City �0.123*** �0.524*** �0.569*** �0.309*** �0.418***

(0.045) (0.058) (0.104) (0.045) (0.068)

Year �0.047*** 0.031** 0.325*** 0.082*** 0.228***

(0.013) (0.015) (0.028) (0.013) (0.019)

Constant 8.354*** �0.965 �30.907*** �7.926*** �28.555***

(2.027) (2.286) (3.630) (1.884) (2.569)

See footnote in Table 4 for notation used in this Table. The base category is level three which corresponds to investment in upgrades to the boiler.

Additionally, the investment levels are defined in Table 1. Note: By default Stata 10 takes as the base category the outcome with more observations.

M.A. Tovar / Energy Policy 50 (2012) 723–735 731

upgrades to the boiler have the strongest saving effects. The lowmean saving for loft insulation can be attributed to its lowadoption. Following Parti and Parti’s (1980) philosophy of inter-acting explanatory variables, I interact the dummy variables withthe variables m2 (i.e., the extension of the dwelling in squaremetres) and HRP age. This is because gains are relative to usageand dwelling dimension32. Notice most of the estimates for thesaving functions are statistically significant. Additionally, thesigns of these estimates related to the interaction terms indicatethat savings increase with deviations from the mean of HRP age

and dwelling size as shown by expression (12)33. Regarding theestimates for the Standard Cost, the sign of the estimates relatedto HRP age, Energy Price, log(HDD) are positive as found by Meierand Rehdanz (2010). Moreover, the variable Space whichexpresses the space in m2 per householder shows that increasesin this variable will decrease the heating cost per m2. Theestimate related to Household type shows that as the household

32 The estimated heating cost provided by the EHCS also takes into considera-

tion dwelling size and different heating patters for the elderly. See Fuel Poverty

Methodology at http://www.decc.gov.uk.33 Unlike the rest of interaction terms, the positive sign of the interaction term

of energy prices and HRP age for loft insulation indicates that its adoption gains

increase when the householder’s age is below its group mean. However, this result

can be driven by its low adoption in the sample size.

became smaller the cost per m2 increases. In order to control forthe general physical state of the building, I used the variable Poor

state that takes value of one if the variable State of repair showsthat householder considers that the building is in either fairly orvery poor conditions and zero otherwise. Notice that house-holders that consider that their dwellings need a significantdegree of repair have higher energy expenditures as the estimateof this variable shows. Finally, the variable City shows that livingin a city reduces the expenses in heating cost. This can be due tothe fact that households living in cities have access to cheaperfuels such as gas. Moreover, flats tend to consume less energy andthe proportion of flats in cities is higher than in the rest of areas34.Notice that income has a negative sign; this can be due to the factthat high income households can have bigger dwellings andconsequently the heating cost per m2 decreases. Notice thatVaage (2000) also found a negative income effect in the contin-uous model for energy consumption. In order to identify theaverage savings per household type, these ones are estimated aspercentage of the total expenditure of not having any measureand they are displayed in Table 7.

34 In the sample 14% of the dwellings are flats in cities while only 5% are this

kind of dwellings in other areas.

Table 6Heating and CO2 emissions functions. Estimated for the CDM using expression

(12). Dependent variables: annual heating cost and CO2 emissions per m2.

Cost CO2

Saving function estimates

Mean saving for flats

Cavity Insulation (CI) �1.215*** �0.012***

(0.073) (0.001)

Loft Insulation (LI) �0.783*** �0.008***

(0.096) (0.001)

Upgrades to the boiler (UPB) �1.124*** �0.015***

(0.061) (0.001)

Mean savings for houses

CI �1.074*** �0.010***

(0.023) (0.001)

LI �0.148*** �0.004***

(0.027) (0.001)

UPB �0.221*** �0.007***

(0.022) (0.001)

Interaction terms: energy price with

CI and m2 �0.123*** �0.001**

(0.022) (0.000)

CI and HRP age �0.185*** �0.002**

(0.068) (0.001)

LI and m2 �0.092*** 0.000

(0.025) (0.000)

LI and HRP age 0.126* 0.001

(0.068) (0.001)

UPB and m2 �0.329*** �0.001***

(0.018) (0.000)

UPB and HRP age 0.044 �0.000

(0.074) (0.001)

Standard cost estimates

Income �0.000*** �0.000

(0.000) (0.000)

HRP age 0.016*** 0.000***

(0.001) (0.000)

Dwelling age �0.702*** �0.007***

(0.008) (0.000)

Energy price 124.652*** �0.004

(21.127) (0.206)

Space �0.009*** �0.000***

(0.001) (0.000)

Household type 0.123*** 0.001***

(0.008) (0.000)

log(HDD) 4.814*** 0.008***

(0.154) (0.002)

Tenure �0.002 �0.001***

(0.008) (0.000)

Poor state 0.146*** 0.001*

(0.044) (0.001)

City �0.233*** �0.003***

(0.027) (0.000)

Period1 �35.404***

(1.279)

Period2 �34.736***

(1.304)

Period3 �33.748*** 0.026

(1.362) (0.017)

R2 0.901 0.934

aInteraction variables represent deviation from the mean values of households

that have adopted the analysed energy efficiency measures. See footnote in Table

4 for notation used in this table.

35 In the sample only 19% of dwellings have a standard level of loft insulation.36 See Energy Saving Trust.37 See In the Energy Act 2011, Green Deal Impact Assessment.38 Hong et al. (2006) did not find a significant contribution of upgrades to the

boiler; however, they also mention lack of appropriate data to evaluate the

benefits from the adoption of this measure.

M.A. Tovar / Energy Policy 50 (2012) 723–735732

To the best of my knowledge this is the first attempt to measureenergy efficiency savings for different English household types thathave adopted the analysed measures. As previously mentioned, it isdifficult to find a benchmark to compare my estimates as the relatedresearch is limited; however, Hong et al. (2006) used an engineeringapproach to estimate the total saving for only low income house-holds. They found an estimate of 11% while my model estimates anaverage of 10% across years for this group. Moreover, Hong et al.(2006) estimate that the potential savings coming from cavity andloft insulation should be around 45% while in Period 3 the highest

total average savings for reference household is 15.3%. According tothe authors, there can be a difference between the potential and thereal savings given that policies could not reach the poorest house-holds and therefore households that receive grants increase theirenergy consumption reducing the benefits of the measures. How-ever, the estimated heating cost used in this estimation does notassume changes in heating patterns coming from improvements inenergy efficiency. Notice also that the estimates are average benefitsand therefore, an increase in the number of adopters in eachhousehold category can increase their values. Hence, my estimatesshow that there is still a considerable scope for energy efficiencyimprovements in this sector. Moreover, Poor state of repair of thedwelling can also explain the difference between potential and realsavings found by Hong et al. (2006). Given the lack of information inthe dataset about maintenance cost, it is difficult to know whatwould be its impact on the net monetary benefits of householdersthat consider that their dwellings need a high degree of repair.However, they are likely to face higher investment costs and smalleraverage savings than the household reference (see Table 7). In theanalysed sample around 8% of households consider that theirdwellings are in a poor state of repair. This is an important elementto be considered as increasing the average savings of this group mayrequire improvements to be made not only in their energy efficiencylevels, but also in the general physical state of their dwelling. Theseextra costs can also increase the cost of the ECO program.

Notice that the second lowest savings are surprisingly comingfrom loft insulation which is considered a low cost measure;however, it has the lowest rate of adoption35. According to theEnergy Saving Trust, the potential savings coming from low costmeasures such as cavity and loft insulation shows that themaximum recuperation period for the investment would be threeyears36. Therefore, it is highly probable that the uptake of thesemeasures can be justified under the Golden rule. On the other hand,upgrades to the boiler have the highest potential savings among thethree analysed measures. However, its low uptake can primarily bedue to its high cost. In addition, unlike the adoption of cavity andloft insulation, the repayment period for the adoption of upgrades tothe boiler can exceed its expected life period under the Green Deal.Hence, it is more likely that its adoption will require funding fromthe Carbon Target element of the ECO scheme37. There is somefinancial evidence that shows that the Net Present Value (NPV) forthis investment is smaller than its cost, however, the equityweighted NPV that considers factors such as environmental impactand thermal comfort is bigger than its cost. Hence, there will besocial benefits that justify the adoption of this measure. Notice thatsavings will be bigger in flats than in houses as Table 7 shows. Inaddition, the research about the maximum measured benefits of theadoption of the analysed measures is limited and therefore furtherresearch is needed 38 .

Table 7 shows that the increase in the savings across years isvery small. Additionally, the savings coming from low incomehouseholds are behind the household reference in cavity insula-tion and upgrades to the boiler. Regarding households living incities, they have lower savings than low income ones in the threeanalysed measures. Notice that in period 3 there is a bigdifference in the saving coming from upgrades to the boilerbetween households living in flats and tenants renting in theprivate sector. This comparison of savings is interesting becauseaccording to the sample, 85% of flats are rented and 16% of them

Table 7Mean monetary savings for the British households, estimated by using expression (13).

Measure Reference householda Low incomeb City Privite renting Flat Lone parent Adult living alone Poor state

Period1Cavity �8.081nnn

�6.272nnn�4.818nnn

�6.989nnn�2.943nnn

�6.843nnn�7.646nnn

�6.694nnn

(0.309) (0.269) (0.191) (0.326) (0.205) (0.283) (0.203) (0.263)

Loft �0.788nnn�0.958nnn

�0.546nnn�0.607nnn

�0.442nnn�0.442nnn

�0.286nnn�0.468nnn

(0.144) (0.253) (0.102) (0.127) (0.056) (0.116) (0.084) (0.103)

Boil �3.023nnn�1.161nn

�2.248nnn�1.654nnn

�12.402nnn�1.119nn

�0.216 �2.220nnn

(0.600) (0.537) (0.618) (0.595) (0.898) (0.535) (0.306) (0.633)

Total savings �11.892 �8.391 �7.611 �9.249 �15.786 �8.403 �8.147 �9.381

Period3Cavity �7.730nnn

�6.827nnn�5.185nnn

�5.216nnn�7.898nnn

�7.460nnn�7.769nnn

�5.770nnn

(0.223) (0.135) (0.117) (0.116) (0.472) (0.153) (0.367) (0.128)

Loft �0.886nnn�0.558nnn

�0.790nnn�0.833nnn

�1.094nnn�0.631nnn

�0.300n �0.646nnn

(0.127) (0.073) (0.102) (0.103) (0.140) (0.079) (0.172) (0.081)

Boil �6.709nnn�4.888nnn

�5.918nnn�4.173nnn

�14.445nnn�3.435nnn

�2.820nnn�3.131nnn

(0.310) (0.255) (0.290) (0.207) (0.803) (0.191) (0.427) (0.177)

Total savings �15.325 �12.273 �11.892 �10.221 �23.437 �11.525 �10.888 �9.546

See footnote in Table 4 for notation used in this Table. aReference household has average: Income, HRP age, Space, Dwelling age, HDD, Energy price, is an owner, does not

live in a city, lives in a house, it is a couple household with dependent children and they do not consider that their dwelling is in a poor state of repair. All the rest of the

columns represent the same as the reference household but with the variation that the title of columns shows. bThe lowest income level comprises the two lowest

quantiles.

Table 8Mean CO2 savings for the British households, estimated by using expression (13).

Measure Reference householda Low incomeb City Privite renting Flat Lone parent Adult living alone Poor state

Period3Cavity �6.087nnn

�5.754nnn�4.278nnn

�4.309nnn�6.816nnn

�6.273nnn�6.646nnn

�4.745nnn

(0.233) (0.277) (0.196) (0.233) (0.577) (0.310) (0.274) (0.242)

Loft �0.863nnn�0.934nnn

�1.010nnn�1.101nnn

�1.035nnn�1.028nnn

�0.621nnn�0.834nnn

(0.119) (0.145) (0.142) (0.184) (0.178) (0.166) (0.147) (0.131)

Boil �7.968nnn�7.473nnn

�8.506nnn�6.067nnn

�16.345nnn�6.046nnn

�5.626nnn�5.540nnn

(0.422) (0.427) (0.513) (0.418) (0.826) (0.403) (0.302) (0.420)

Total savings �14.917 �14.161 �13.795 �11.478 �24.196 �13.348 �12.894 �11.119

See footnote in Table 4 and 7 for notation used in this table.

39 Long (1993) argues that income taxes credits can stimulate households to

undertake the investment, while Walsh (1989) found empirical evidence that tax

credit did not lead to more adoption of the investment.

M.A. Tovar / Energy Policy 50 (2012) 723–735 733

are rented in the private sector. These differences can be attrib-uted to differences across tenure in the explanatory variablesused in the estimation. However, this could also show thathouseholds that own a flat have additional energy efficiencymeasures that increase their savings. Regarding Lone parents

and Adults living alone, they have the lowest savings among theexamples displayed in Table 7. This confirms the finding of lowuptake of these household types in the previous sections. I alsoestimate the saving in CO2 emissions per m2 using a similarprocedure to the one used for the heating cost. The estimatesrelated to the CDM and the adoption benefits are displayed inTable 6 and 8, respectively.

Notice that the estimates have a sign that is consistent to theones related to the heating cost function (see Table 6). Addition-ally, Table 8 shows as noticed by Hargreaves et al. (1998), thatenvironmental benefits tend to be larger than monetary ones.Consequently this could give a margin to households that haveadopted the analysed measures to increase their dwelling warmthand still have environmental benefits. Regarding the adoption ofhigh cost measures such as upgrades to the boiler, they can bringimportant environmental savings that justifies its adoption.Nevertheless, it is important to define clearly and transparentlyhow savings will be computed, as in the context of the Green Deal

it will define the extension of the payback period and the need ofsubsidies. In the same vein, it is also important to guarantee animpartial assessment of the measure that suits the consumerbetter.

4. Summary and policy implications

Table 9 display the main results of the previous sections. It isfound that contrary to Brechling and Smith (1994) socioeconomicand behavioural characteristics can identify household types thatmay not uptake energy efficiency measures in their dwellingsunder the new government initiatives. Moreover, the estimatedaverage adoption benefits show that there is a gap in energyefficiency in the English dwellings and therefore cavity and loftinsulation and upgrades to the boiler can still bring considerablebenefits to the English households.

This shows that a tailored strategy to reach different house-hold types will lead to more cost-effective results. It is unclearhowever, which is the most effective mechanism to encouragehouseholds to undertake the analysed measures. The effect of afiscal induction mechanism has been questioned39 . Nevertheless,the government role in supporting strong publicity and informa-tion campaigns directed to the low adoption groups identified bymy model can be very important. It is argued that this canincrease the uptake and create awareness about the adoptionbenefits and energy use, to avoid waste of energy (see Bradshawand Hutton (1983)). The size of the ECO and its distribution

Table 9Summary of results.

Estimation level Main results

Analysis of the uptake drivers � Unlike previous research, my new model shows that socioeconomic and behavioural drivers of the uptake can be identified.

� Adults living alone or in cities, Lone parents and tenants in the private sector have between 0 and level 2 of investment. However,

these types of households with low income levels can be at the bottom of this range.

� Increases of energy prices can lead to an increase of households experiencing fuel poverty.

� Behavioural variables such as Length of residence, Time at home and Trying to move are important drivers of the uptake

Monetary and environmental

benefits

� There is still scope for further improvements as in general the average is low compared to their potential savings.

� The adoption of low cost measures such as cavity and loft insulation can bring important savings that can justify the uptake under

the Golden rule. Upgrades to the boiler can bring more savings to flats than to houses.

� Low average savings are found for Lone parents, Adult living alone and low income households.

� Households that consider themselves having a poor state of repair in their dwellings face lower average savings than the household

reference.

� Environmental adoption benefits are higher than their monetary ones.

M.A. Tovar / Energy Policy 50 (2012) 723–735734

between helping to meet the Golden rule and tackling fuel povertyis another issue that has to be carefully analysed. At the momentthe proposal is to devote only 25% of the ECO budget to target lowincome households40. Given that the ECO program will take overfrom the Warm front scheme compromises, this budget could notbe enough. It is also significant to consider that other costs relatedto potential dwelling repairs as shown before can bring evenmore pressure to the ECO program. The success and achievementsin terms of reductions in fuel poverty, energy consumption andCO2 emissions of the ECO scheme will be determined by the sizeof the energy suppliers’ obligations and the ECO budget. Thereforeit is argued that this program is a ’’self-limiting scheme’’41 .Moreover, given that the ECO program will be financed throughconsumers’ energy bills, it is essential to minimise its cost, aspreviously shown, increasing energy prices can also increase thenumber of households in fuel poverty. Hence, the budget devotedto helping vulnerable households of the ECO scheme has toprioritise the low adoption groups identified by my modelfocusing on those households where energy bills are a financialburden. This can help to distinguish those households that aremore conscious of energy cost and therefore, the adoptionbenefits can be greater (see Hong et al. (2006)). This can alsoincrease the probabilities of targeting the poorest households. Ithas also been suggested that repayments to the Green Deal for theenergy efficiency adoption by vulnerable households can be madeby the ECO budget. As a result making repayments on the capitalinstead of providing the capital funds can reduce the financialpressure over the ECO42. Concerning the adoption of householdsrenting in the private sector, my model shows that Length of

residence, Dwelling type and location play an important role in theadoption of this group, however, to increase its energy efficiencylevels is not only enough to set minimum energy efficiency levelsfor let properties but also it is needed to enforce its application. Inthe case of owners, it is found that households that are trying tomove from their current dwelling and householders that do notspend most of the winter at home face also low adoption levels.Hence under an efficient consumer protection mechanism attach-ing the Green Deal charge of the upfront cost to the dwelling andnot to householder can increase the uptake of the analysedmeasures.

The Green Deal can overcome some of the barriers to theuptake pointed out by Scott (1997), however, its application

40 The rest of the budget is devoted to the carbon target that will be used to

help any household that faces high energy efficiency measures to meet the Golden

rule. See the Green deal and Energy Company Obligation, Consultation Document.41 See by James (2012).42 See the report A Future Obligation on Energy Companies.

needs active government participation to bring flexibility to thisprogram, to adapt to and drive market changes. Therefore it canoffer incentives for developing of cheaper and more effectivetechnology and cheaper finance (see James (2012)). This can alsoreduce the financial pressure over the ECO scheme. The success ofthe application of the Energy Act 2011 needs to be based onengaging market actors in a game where their business strategieshave to change. As noticed by Bertoldi et al. (2010) energysuppliers need to change from being energy sellers to energyservice sellers. Moreover, the application of the Green Deal needsto consider that across different tenure and income levels, theadoption decision is driven by different socioeconomic andbehavioural factors. It is also important to coordinate and defineclearly the ECO and Green Deal objectives to allow transitinggradually from a subsidised market to a more competitive one.

5. Conclusions

In this paper Brechling and Smith (1994) and Hassett andMetcalf’s (1995) models are extended by allowing households tochoose different investment levels and using a five years samplefrom the EHCS. Contrary to Brechling and Smith (1994), householdssuch as adults living alone or in cities, lone parents and tenants inthe private sector are identified with low adoption levels of cavityand loft insulation and upgrades to the boiler. Furthermore, esti-mates for the adoption monetary and environmental benefits basedon the Parti and Parti’s (1980) model for the actual adoptionhouseholders are proposed. These estimates are below their poten-tial values and therefore they show a possible gap in the Britishdwelling energy efficiency. In particular, the adoption of low costmeasures such as cavity and loft insulation can bring savings thatwould allow households to pay as they save. However, as previouslyshown, a poor state of dwelling repair can bring more pressure tothe ECO budget. Therefore, given that the ECO will be financedthrough consumer energy bills, it can increase the number ofhouseholds in fuel poverty. These facts leads to the following finalremarks: (a) In order to minimise the social cost of the ECO scheme,its budget devoted to tackling fuel poverty has to prioritise thegroups identified by my model. It is important to focus on thosehouseholds where energy bills are a financial burden. Therefore thedesign of publicity and promotional campaigns and grant schemeshas to follow a tailored strategy to increase the cost-effectiveness ofthe new initiatives. (b) Active government participation is needed tobring flexibility to this program, to adapt to and drive marketchanges to reduce the adoption cost and its financial pressure overthe ECO budget.

M.A. Tovar / Energy Policy 50 (2012) 723–735 735

Acknowledgment

I am grateful to two anonymous referees, Dr. Emma Iglesiasand Professor Jo~ao Santos Silva for their helpful comments. All theremaining mistakes are my own.

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