Technological Forecasting & Social Change 78 (2011) 621–634

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Technological Forecasting & Social Change

Modelling intervention options to reduce GHG emissions in housingstock — A diffusion approach

Andrew Higgins a,⁎, Greg Foliente b,1, Cheryl McNamara b,2

a CSIRO Sustainable Ecosystems, 306 Carmody Road, St. Lucia, Queensland, 4067, Australiab CSIRO Sustainable Ecosystems, PO Box 56 (Graham Rd), Highett, Vic, 3190, Australia

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +61 7 3833 5738.E-mail addresses: Andrew.higgins@csiro.au (A. Hig

1 Tel.: +61 3 9252 6038.2 Tel.: +61 3 9252 6118.

0040-1625/$ – see front matter © 2010 Elsevier Inc.doi:10.1016/j.techfore.2010.12.003

a b s t r a c t

Article history:Received 4 February 2010Received in revised form 16 November 2010Accepted 15 December 2010Available online 22 January 2011

The building sector is regarded as having one of the highest benefit–cost ratios from greenhousegas (GHG) emission reduction strategies. However, because of uncertainties around householdbehaviour patterns, it is very difficult to assess and compare the GHG reduction impacts ofdifferent intervention schemes for whole housing stock. Intervention schemes include policyinstruments such as incentives or rebates for energy efficient appliances or renewable energy,and regulatory building code requirements for energy efficiency. This paper presents a decisionsupport tool based on mathematical diffusion that evaluates the adoption levels of differentschemes or pathways towards reducing GHG emissions in housing stock. It is an extension of theBass diffusion model that accommodates financial and non-financial benefits, ceilings ofadoption and interactions between intervention options. The model capability was tested usinga case study of seven suburbs in Brisbane, Australia, comprising of 25,000 houses and units.Estimates of GHG emission reductions to 2019 of a household rebate scheme for solar panels anda rebate scheme for solar hot water compared to a base case of no rebates were presented andanalysed. Modelling also allowed identification of important characteristics of adoption trendsthat could assist policy makers and industry to substantially improve the design of effectiveintervention options.

© 2010 Elsevier Inc. All rights reserved.

Keywords:Bass diffusionAdoption forecasting

1. Introduction

Mitigating green house gas (GHG) emissions is recognised as one of the greatest challenges facing the world today. The 2007Intergovernmental Panel on Climate Change (IPCC) Mitigation Report has identified the building sector as having one of thehighest benefit–cost ratios of many possible GHG mitigation measures across different sectors [1]. A global cost curve for GHGabatement, [2], shows that improving retrofit to buildings (e.g. insulation, lighting, water heating, and air conditioning) wereamongst the lowest cost options. In Queensland and Victoria, Australia, about 60% of the total GHG emissions were produced fromelectricity in 2006 [3]. In the UK, this figure is higher with housing accounting for 28% of the national GHG emissions in 1996 [4].

In theGreen Paper CarbonPollutionReduction Scheme: Australia's LowPollution Future [5], theAustralianGovernment set a long-termgoal of reducing Australia's GHG emissions by 60% of 2000 levels by 2050 and amedium-term reduction target of 5% of 2000 levels by2020. As governmentand industry plan to reduceGHGemissions inhousing through regulations, policyor incentives, there is aneed toestimate likely GHG benefits of any combination of these “interventions” across housing stock over time.

CSIRO's Australian Zero Emission House (AusZEH) project aims to significantly contribute to GHG emissions reduction fromresidential buildings, and to influence a large industry sector and most Australian households to become more responsible

gins), Greg.Foliente@csiro.au (G. Foliente), Cheryl.Mcnamara@csiro.au (C. McNamara).

All rights reserved.

622 A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

towards energy use and GHG emissions. To achieve this, there is a need for tools to benchmark different intervention schemes(e.g. regulations, policy, incentives and/or technologies) in terms of GHG benefits versus cost as part of setting and meetingnational or jurisdictional objectives. Estimating the likely GHG emission reductions at housing stock level over time is currentlyvery difficult. There is particularly a need for tools to evaluate the GHG reductions from intervention schemes thataccommodate:

• The complex diffusion process of adoption over different time steps by spatial scales by demographics;• Likely consumer adoption profiles by demographics such as location, family size, income, debt levels;• Likely ceiling levels of adoption;• The dynamics of introducing different GHG reduction strategies at different time steps to meet GHG targets, and interactionsbetween strategies;

• Evolution of housing stock and demographics in different regions or precincts;• Changes in climate and supporting infrastructure (e.g. transport) and the consumer response; and• Uncertainty in any of the above.

The desired capability of this important decision support tool is illustrated in Fig. 1, say for a government jurisdiction planningor developing a roadmap of big picture GHG reduction options over a given planning horizon, as illustrated in Fig. 1. Each roadmapoption contains a set of reduction strategies and policies introduced at different dates from the present to 2050, with the aim ofreaching a desired level of GHG reduction by 2050. Here, Option 3 aims to introduce ambitious strategies early in an attempt toachieve most of the savings by 2030. Option 2 introduces a larger number of less ambitious strategies early, achieves a smallamount of GHG reductions by 2030, and emphasises trying to achieve large GHG reductions further in the future. A differencebetween Options 2 and 3 is that Option 3 may involve a much larger Government expenditure earlier on in rebates, newtechnologies and strict house star rating policies.

This paper presents the development of a model based on mathematical diffusion that can evaluate the effectiveness ofdifferent intervention schemes or roadmap options to reduce GHG emissions or reaching GHG targets for housing over time. Thisnew model is tested, in collaboration with the Queensland government and Brisbane City Council, using a case study of sevensuburbs in Brisbane, Australia, comprising of 25,000 houses/units with a mixture of demographics, new and old suburbs. Threeoptions are tested as part of model validation: (a) base case (or business as usual, BAU); (b) AU$8000 rebate for solar panels; and(c) AU$2000 rebate for solar hot water.

The paper is organised as follows: Section 2 sets the scene for the novel contribution of the diffusion model in this paper;Section 3 describes the diffusion model for voluntary and mandatory adoption of policies; Section 4 outlines the application of thediffusion to eight case study suburbs in Brisbane, with particular attention to data preparation and calibration of modelparameters; Section 5 discusses the results from the case studies. This is followed by conclusions and discussion of furtherresearch.

2. Literature review

Review of literature begins at the single house scale and then proceeds to housing stock and housing stock over time scales(intervention options). By doing this, linkages between methods and applications are articulated, and the gaps in the literature atthe policy intervention scale are more clearly identified.

2.1. Estimating GHG emissions from energy use in a house

Prior to development of software tools, analysis of GHG reduction strategies at house scale was conducted using experimentationand monitoring of different schemes. Ref. [4] applied this approach in the mid-1990s by testing technologies for modernising Britishlow rise housing. The use of simulation software to estimate energy consumption and GHGemission of individual residential buildings

Desired Level

Current Level

GHG Reduction potential

Year2008 2050

Option 1

Option 2

Option 3

Fig. 1. Comparison of three long term GHG reduction options.

623A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

in Australia began in the early 1990s [6] with the introduction of the Nationwide House Energy Rating Scheme (NatHERS) — a jointFederal, State and Territory Government initiative. To meet this challenge, CSIRO developed the software tool AccuRate [7,8] thatuses a ground up approach to calculate annual totals of hourly heating and cooling energy requirements for a residential building ofa pre-specified envelope. Tools to estimate energy at the house scale often employ a ground up approach of linking performancesub-models of building performance [9]. AccuRate integrates models for different energy components of housing, for exampleventilation [10] and roof spaces. AccuRate is a second generation of residential energy rating software for Australia consideringadditional climatic zones, user-defined constructions and better modelling of natural ventilation.

2.2. Estimating GHG emissions across housing stock

Modelling GHG emissions at a housing stock scale could follow either a bottoms-up or a top-down approach [11]. The bottoms-up approach typically involves aggregating scenarios from the individual scale up through the distribution of houses in the regionof interest. Past analyses have usually accommodated differences in the types of housing such as type (unit, semi-detached house),size, location and age. Modelling at the house scale is usually performed using much more simplified methods than AccuRate. Forexample, Ref. [12] used three statistical equations for modelling energy consumption at house scale base on heating, tapwater andgeneral appliances. The authors applied these equations across the Belgian housing stock to assess the GHG emissions given theevolution of average housing stock and sources of electricity.

The paper by Ref. [13] compares three methods for estimating energy consumption at a housing stock scale — aggregation,conditional demand analysis, and neural networks. The conditional demand method is an extension to their aggregation methodby considering energy usage across different types of appliances and demographics. The neural network model was applied in theabsence of complete data sets for housing stock, demographics or energy use within different types of houses.

The study by Ref. [14] estimates the GHG emissions across housing stock in Queensland by integrating and extrapolatingsummary information about housing stock, demographics and energy use across appliances etc. It was an extensive analysis thatutilised the following summary information: distribution of people across housing types in Queensland, household sizes, tenure,energy sources, appliance profiles across demographics, peak energy demand versus average energy demand, hot water systems,and utilisation of different appliances. Most of these data were available in summary form through surveys or Australian Bureau ofStatistics. Through statistical extrapolation across housing stock, a GHG emission profile was produced.

The above models estimated GHG emissions for the business as usual scenario at the present time. Aggregation methods canalso estimate future GHG emissions by building in assumptions of future changes in the building envelope, appliance/insulationtechnologies and demographics. The papers by Refs. [15,16] consider the impact of GHG emissions of housing stock over timethrough comparing three known scenario pathways of 60% reduction in emissions by 2050. The scenarios are based on technicalchanges in sources of energy and building envelope, as modelled by their DECarb software. Instead of incorporating diffusionmodels for uptake of technologies, the authors used target time points of percentage of dwellings adopting different technologies.Ref. [17] also explores the feasibility of achieving a 60% reduction in GHG emissions in housing stock of the UK but also includes ascenario with energy supply. The paper focuses on the projected evolution of the typical housing envelope rather than thedistribution of different housing types. An unpublished analysis in CSIRO's AusZEH project (www.auszeh.org.au) estimated theGHG impacts improved star ratings in new housing stock in Victoria through to 2030, with a comparison with business as usual.The AccuRatemodel was used to simulate the improved star rating of the individual houses, which were then aggregated up to theprojected housing stock through to 2030.

Some housing stock aggregation methods have been used to simulate future scenarios for reducing GHG emissions, ashighlighted above. They have been based on assumptions of projected levels of uptake, changes in technology and housing stockgrowth. This makes them useful for evaluating mandatory policy options (e.g. minimum star rating for new houses) or estimatingGHG impacts from general future trends (e.g. projected changes in household size and demographics). However, housing stockaggregation methods cannot analyse the impact of voluntary intervention or technology options. The likely rate of adoption isusually not known but can be estimated using a diffusion model as a complex function of household demographic, buildingenvelope and financial variables.

2.3. Diffusion models for policy options

Diffusion models for estimating the uptake of technologies/appliances/services over time amongst consumers have beenextensively considered in the literature. However, their application to energy reduction interventions (e.g. policies/technologies)in housing stock is novel. The wide range of intervention options in the building or housing sector and their varying cost-effectiveness [18] makes such a model very valuable in assessing the potential effectiveness of any scheme or set of schemes in aparticular socio-geographic context before costly implementation, amongst a number of applications. In this sub-section wediscuss some of the diffusion models developed and their suitability for the applications described in this paper.

The mainstream mathematical modelling of diffusion commenced in the 1960s particularly with the introduction of the wellknown Bass model [19] and the logistics model [20]. The simple Bass model calculates the rate of change in adoption, as a functionof a coefficient of innovation amongst individuals and a coefficient of imitation between individuals. It basically shows that thelevel of adoption forms a sigmoidal function over time, where adoption is slow at first, then accelerates as the level of adoptionreaches 50% of the population, and then slows as adoption reaches the population size. It had very minimal application to

624 A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

reduction of GHG emissions, with Ref. [21] applying the basic logistic diffusion model to estimate the penetration rates of newenergy technologies such as compact fluorescent lamps.

Since the early 1970s, there have been numerous extensions to the early diffusion models, but not to the types of applicationsconsidered in this paper. Extensions of interest include accommodating marketing time-dependent factors such as price oradvertising [22,23], and supply constraints [24]. Ref. [25] extends the modelling to accommodate willingness to pay for a productas a function of the perceived reduced risk and uncertainty as the level of adoption increases. Choice behaviours of consumers havebeen accommodated by Refs. [26,27]. This is particularly in the case where consumers are faced with the decision to wait forimproved next-generation technologies, thus creating dynamic diffusionmodels. An important consideration for a diffusionmodelin our paper is the non-financial benefits. For example, adoption of solar panels provides financial savings in electricity but alsoprovide greater power security and the recognition of being a green household. Some consumers will pay more for green energy ifthey feel it will reduce carbon emissions. The paper by Ref. [28] accommodates the non-financial benefits in their diffusion model(for general household appliances) through the creation of an amusement factor within their model.

An important consideration for our paper is the evolution of the demographics or market size. Ref. [29] and later Ref. [30]develop a binomial diffusion model for a variable market size of cellular phones [30] by modelling the demographic processchanges in each market compartment. This will be an important consideration in our paper due to changes in household size andhousing types over time. Refs. [31,32] consider an extension of the Bass model with dynamic market sizes from competingmarkets. Ref. [33] introduces a cross-population adaptive diffusion model which incorporates interpersonal communicationbetween adopters of different populations. This could be a useful extension to the model of our paper in the case of a policy optionintroduced into a state/province some time after it was introduced in other states/provinces.

The version of the diffusion model developed in our paper captures inter-dependencies between non-competing policy and/ortechnological options. This appears new to the literature, and the most closely relevant research is by Ref. [34] who developed adiffusion model for appliance hardware (e.g. DVD players) that accounts for the availability of suitable software.

In summary, the main modelling contributions of our paper are: (a) application of diffusion models to policies for GHGreductions in housing stock; (b) accommodating many of the types of Bass model extensions discussed above; (c) a new diffusionmodel that accommodates interdependencies between policy and technology options; and (d) a scaling up capacity for the widelyused AccuRate energy rating tool for individual houses. As a package, it provides a novel capability for assessing the impact overtime from large scale policy options for reducing GHG emissions from the housing stock.

2.4. Linkages between scales

We conclude this section by providing a summary comparison between analysis capabilities from themodels at the three scalesdescribed in this review in Table 1. The first two approaches, “House rating or design tool incorporating AccuRate” and “Housingstock aggregation”, represent tools and techniques already available. The last column represents the new capability contribution ofthe model developed in this paper, e.g. evaluating very large government interventions (and budgets) aimed at reducing GHGemissions through policy, education and/or incentive means.

3. Diffusion model

3.1. Voluntary adoption

Voluntary adoption is the case where the household owner (single unit dwelling, unit, and semi-detached house) makes adecision to implement a reduction technology outside of a minimummandatory regulatory requirement. This includes incentives

Table 1Comparison of decision support tools and application compatibility.

Application/scope House rating or designtool incorporating AccuRate

Housing stockaggregation

Diffusion modelof this paper

HouseSelection of reduction technologies XOptimisation of reduction technologies XEnvironmental impacts of different system designs X

Housing stockResidential buildings — snapshot of GHG emissions X X XImproved star rating buildings X X XMixed building scenarios X X X

Housing stock over timeUrban development or redevelopment options X X XIntroducing policy and regulatory options XOptimal intervention options to meet GHG targets at key dates XCommunity awareness and social changes XIncentive schemes introduced at different dates XShock events such as rapid energy price or financial crisis X

625A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

(e.g. rebates) and no additional incentive (e.g. marketing of new technologies, personal desire to be greener, and general pricereduction of new technologies).

In developing a diffusion model to represent voluntary adoption, we firstly present the basic Bass model [19]:

where

where

A′ðtÞ = p +

qMðtÞ ⋅AðtÞ

!⋅ðMðtÞ−AðtÞÞ ð1Þ

M(t) population (dwelling or household) at time tp coefficient of innovationq coefficient of imitationA′(t) change in adoption at time tA(t) level of adoption (number of households) at time t.

For modelling the diffusion of reduction strategies in housing stock; the basic Bass diffusion model needed to be extended toaccommodate:

• Differences across housing type and location, where the location parameter accommodates differences in income, propertyvalues, etc.

• Impacts of changes in cost on adoption rate• Impacts of cost on the ceiling level of adoption• Impacts of benefits on diffusion• Changes in population at different locations• Multiple options o∈O introduced at different dates, which have confounding effects amongst one another.

By partitioning the Bass model into different locations c and housing types d, the original model becomes:

A′c;do ðtÞ =

po +

qo

Moc;dðtÞ

⋅Aoc;dðtÞ

!⋅ðMo

c;dðtÞ−Aoc;dðtÞÞ ð2Þ

tion o∈O, where c can represent different suburbs or communities and d represents stand alone house versus semi detached

for ophouse versus unit.

The original Bass model assumes the entire population Mc,do (t) are potential adopters, and that the adoption population of

households Ac,do (t) will converge to this figure. In many circumstances, there will never be a full adoption amongst the population

Mc,do (t), due to many circumstances (prohibitive costs, low benefits, more attractive alternative options, etc.). Therefore we

redefine Mc,do (t) as the ceiling of adoption of location c and housing type d, at time t, for option o∈O where:

Moc;dðtÞ = Soc;dðtÞ⋅Lc;dðtÞ: ð3Þ

Lc,d(t) is the population size, independent of the options being considered and Sc,do (t) is the proportion of the population size

(number of households) that are potential adopters for option o∈O at time t.In formulating Sc,d

o (t), Ref. [28] developed a model for the proportion of the population that will purchase, as a function ofwage, amusement factor (or non financial benefits), price and savings. By using the model of Ref. [28] for a single option, we canwrite Sc,d

o (t) as:

Soc;dðtÞ =1

1 + e−ð wc;d + Koe + boc;d−δko;t

c;dÞ=λ

ð4Þ

wc;d is the average income for occupants in housing type d in location cKeo is the non-financial benefit (or amusement factor) for option o∈O in demographic e

kc,do, t is that cost of implementing the reduction strategy for option o∈O in housing type d in location c at time t. This is the net

cost including the rebate/subsidy at time t.bc,do is the financial benefit of the reduction strategy

e is the demographic which includes the variables that impact attitude towards energy use. We use e(t) as thedemographic at time t, accommodating the change in household size at time t.

λ is a scaling factorδ is the weighting parameter for cost of implementing the technology.

The non financial benefit factor Keo captures the household attitude to adopting energy reduction strategies (e.g. personal views

for reducing energy consumption) or other benefits such as reduced risk or increased energy security. The higher Keo is, the more

likely the household will adopt the reduction strategy for non-financial benefits and thus the higher the ceiling of adoption. With

626 A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

available data, Keo can be calibrated using parameters (e.g. size of household, career, income, and gender) that define the likelihood

of adopting the reduction strategy. These parameters can be extracted through consumer attitude survey work by Ref. [35].The model represented by Eqs. (2) to (4) assumes the adoption rate of option o∈O is independent of all other options being

offered. In reality, the likelihood of adopting option o∈O may depend on other options. This co-dependency can be capturedthrough the coefficient of innovation/imitation or via the ceiling of adoption, Sc,do (t). In our model, we captured co-dependency inthe latter, since Sc,do (t) can accommodate co-dependency within the non-financial benefit, financial benefit and cost. To do this, weintroduce the following parameter weights:

αo1,o is the weighting of non-financial benefit Keo if option o1∈Owas adopted prior to o∈O. For o=o1, αo1, o is the weighting

of Keo independent of other options.

βo1,o is the weighting of financial benefit bc,do if option o1∈O was adopted prior to o∈O.δo1,o is the weighting of cost for option o∈O if option o1∈O was adopted prior. This value is decreased if adoption of option

o1∈O first lowers the cost of o∈O.

These parameters capture the relative weightings (like that used in Multi-Criteria Analysis) between income, cost oftechnology, financial and non-financial benefits. For example, household income and cost of technology cannot be purely additivein a function that describes the ceiling of adoption. Their values have a relative importance between one another as described bythe weights αo1,o, βo1,o, δo1, o, which are calibrated using real data.

If there are only two options considered o,o1∈O, Eq. (4), becomes rewritten as:

S

Soc;dðtÞ =Ao1c;dðtÞ

Lc;dðtÞ⋅ð1 + e−ð wc;d + αo1;oKoe + βo1;ob

oc;d−δo1;ok

o;tc;dÞ=λÞ

þ Lc;dðtÞ−Ao1c;dðtÞ

Lc;dðtÞ⋅ð1 + e−ð wc;d + αo;oKoe + βo;ob

oc;d−δo;ok

o;tc;dÞ=λÞ

ð5Þ

the Ac,do1 (t)/Lc,d(t) represents the proportion of the households that has adopted option o1∈O prior to time t. The first term

where

of Eq. (5) captures the case for when o1∈O was adopted prior to o∈O, whilst the second term is when o1∈O has not beenadopted yet. In the case of more than two options, Eq. (5) becomes:

oc;dðtÞ =

1NO−1 ∑

o1∈O

o1≠o

Ao1c;dðtÞ

Lc;dðtÞ⋅ð1 + e−ð wc;d + αo1;oKoe + βo1;ob

oc;d−δo1;ok

o;tc;dÞ=λÞ

+Lc;dðtÞ−Ao1

c;dðtÞLc;dðtÞ⋅ð1 + e−ð wc;d + αo;oK

oe + βo;ob

oc;d−δo;ok

o;tc;dÞ=λÞ

ð6Þ

NO is the number of options in O.

whereThe model represented by Eqs. (2) to (6) can be used to assess the adoption levels across time of various voluntary reduction

(or energy generation) strategies. In particular the model can handle various types of incentives such as rebates or discounts, andcan accommodate the changes in price of the strategies. It can also accommodate a changing housing stock or demographics,which is particularly important for modelling long term GHG reduction goals.

Let Vc,de(t),o and V1c,de(t) be the expected GHG of a house at location c of housing type d having demographic e(t) after and before

option o∈O is adopted. Vc,de(t),o and V1c,de(t) are estimated using the AccuRate software. Therefore the GHG change from the combined

options by time t =

∑o∈O

∑e∑

c∑

d

Aoc;dðtÞ⋅ðVeðtÞ;o

c;d −V1eðtÞc;d Þ: ð7Þ

It is not desirable to use AccuRate to calculate Vc,de(t),o at every time step. Suppose that Vc,d

e(0),o is calculated using AccuRate at time0. If the change in Vc,d

e(t),o at time t is characterised by the changes in average household size, Vc,de(t),o can be estimated as follows:

Vc,de(t),o Vc,d

e(0),o⁎(1+(1−GHGHl−1/GHGHl)⁎(ALc,dt −ALc,d0 ))

ALc,dt average household size at time t for housing type d in location c

GHGHl GHG emissions for a household of size l.

3.2. Mandatory adoption

Mandatory adoption is where there is a regulatory or legal requirement for compliance (i.e. to achieve a minimum standard orperformance). Mandatory adoption takes place in either the construction of a new house, renovations or the sale of an existinghouse. The adoption model will be different for the new versus existing house, particularly in terms of spatial and temporalinteractions. We consider the two cases separately here.

627A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

3.2.1. New housing stockThe model for mandatory adoption is just a refinement of the voluntary model. As before we define Mc,d

o (t) as the number ofhouses (including single unit dwelling, unit, duplex, townhouse) at location c and housing type d (e.g. different designs or ages, ordifferent combinations of reduction strategies), at time t. In Eq. (2), we set p=1 and q=1 and Sc,d

o (t), which means there is fullimplementation when new housing becomes available. We assume that Mc,d

o (t) will be available with a reasonable level ofaccuracy from:

• Long term council and state government urban development plans of up to 5 years ahead, which highlights the quantities ofdifferent types of housing across the prospective locations

• Developer plans of up to 2 years ahead.

Beyond about a 5 year planning horizon, there may be a need for additional modelling or forecasting to estimate the number ofnew houses at different locations over time.

3.2.2. Existing housing stockWe expect that mandatory adoption will be linked to a sale of a house. Here we assume the owner of the house will implement

the mandatory energy reduction strategy (required for a house sale) immediately prior to the sale of the property. Anyimplementation of reduction strategies beyond the mandatory regulatory requirement for a house sale is considered as avoluntary adoption.

The key to predicting GHG emission reductions in existing housing stock from mandatory adoption is to forecast housingsales. This is a difficult task using a modelling approach since decisions on whether to sell a house depend on depend on alarge amount of confounding social and financial trends at a local and global scale. At a housing stock level, the proportionof existing housing stock sold in any given year can be assumed to follow a long term trend. The probability of a housebeing sold is likely to depend on the location of the house and the type of house. We let: OSc, d(t)=estimated number ofhouse sales at location c of housing type d at time t. Here we assume t to be a discrete time interval such as a month or year.OSc, d(t) can be estimated using trends derived from historical RPdata (www.rpdata.com.au) along with long term housingvalue forecasts.

Let Vc,de, o be the expected GHG of a house at location c of housing type d having demographic e after and before the mandatory

options o∈O are incorporated. Both Vc,de, o and V1c,de are estimated using AccuRate. The GHG reduction in time t from the mandatory

strategies =

Table 2Numbe

Subur

CampChermCoorpFitzgiIndooNundUppe

∑o∈O

∑e∑

c∑

d

OSc;dðtÞ⋅ðVe;oc;d−V1e

c;dÞ: ð8Þ

4. Case study setup

Brisbane is the fastest growing city in Australia with a population of 1.9 million in 2006 and a projected growth to2.8 million in 2031 [36]. This will require an estimated 400,000 new houses/units to be built to accommodate the expandingpopulation. In 2007, 84% of Queensland households were separate house, with 11% unit and 4% semi-detached [36]. Table 2contains the number of dwellings for each case study suburb by suburb type. In most of these suburbs, the number of units, asa proportion of total housing stock for the suburb, is much greater than the Queensland average of 10%. The main reason isthat most of the suburbs are within 10 km of the city centre (Fig. 2), which attracts a much higher density of housing. Beyond20 km of the city centre, most urban expansion is green-field consisting of separate houses. Table 3 contains a profile for eachsuburb in the case study. Table 4 contains the distributions of household income, and there is a general trend towards higherincome earners living in suburbs closer to the CBD.

Average household size projections to 2026 were obtained from Queensland Government [34] which provided averageprojections by council region, and was used to calibrate ALc,d

t for the average household sizes in Table 2.

r of dwellings by suburbs and average household size.

b Number of dwellings Av householdsize (persons)

Separate house Semi-detached Units/apartments

Hill 3198 213 323 2.5side 1319 505 1066 1.9aroo 2956 342 2777 2.1bbon 889 157 30 2.2roopilly 2278 416 1457 2.5ah 1474 508 2007 1.9r Mount Gravatt 2422 240 337 2.3

Fig. 2. Location of case study suburbs in relation to Brisbane central business district.

Table 3Suburb profile.

Camp Hill Timber housing built between 1935 and 1955. Units are mostly brick complexes built between 1970 and 1985?Chermside Mostly low set timber housing built between 1945 and 1965. Units/apartments are mostly multi-story (6–10 levels) built post 2001.Coorparoo Timber housing (high and low set) built between 1925 and 1945. Units are mostly brick complexes built between 1970 and 1985?Fitzgibbon Lowset brick housing built from 1990 to 2008. Units are brick complexes built in the same eraIndooroopilly Timber housing (high and low set) built between 1935 and 1955. Units are mostly brick/concrete complexes built between 1980 and 2008?Nundah Mostly timber housing built between 1945 and 1965. Units/apartments are mostly two level brick built 1980 to 1995.Upper MountGravatt

Mixture of low set timber housing 1960s and high set brick (1970s). Units are 2–3 story, 1980–1995.

628 A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

We consider three options in this paper:

▪ Option 1a: business as usual (BAU) case for adoption of solar hot water▪ Option 1b: BAU for adoption of solar panels▪ Option 2: AU$1500 rebate for solar hot water▪ Option 3: AU$8000 rebate for solar panels (1.5KW) up to a household income of AU$75,000/year.

Intervention Options 2 and 3 will be compared to a business as usual (BAU) case (Option 1), where BAU represents adoption ofthe technology without the rebate. A further baseline of no further adoption can be used to compare the impact of the rebateversus BAU versus no adoption.

4.1. Pre-processing of data

The pre-processing phase goes beyond just formatting raw data for the main model. From a data perspective, pre-processing isrequired to produce a complete data set for the scale of the model application (i.e. seven Brisbane suburbs) in the presence ofincomplete data. Whilst some data can be obtained at housing stock scale (e.g. house type, location, land and size), some data willonly be available at a coarse summary scale.

Table 4Income profiles.

Suburb bAU$25k AU$25k–$50k AU$50k–$75k AU$75k–$100k NAU$100k

Camp Hill 14% 21% 26% 19% 20%Chermside 28% 32% 26% 10% 4%Coorparoo 17% 25% 28% 15% 15%Fitzgibbon 25% 29% 30% 14% 3%Indooroopilly 15% 22% 26% 15% 23%Nundah 22% 32% 27% 12% 8%Upper Mount Gravatt 23% 26% 28% 15% 8%

Fig. 3. Input structure and parameters for the diffusion model.

629A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

A summary of inputs for the model is illustrated in Fig. 3. Inputs in italic will be incorporated in future versions of themodel. Some of the data will be specific to the case study under consideration, whilst other data is information about thepopulation and housing stock. Data has gathered from several sources. Housing stock and building information wasobtained from the Brisbane City council (Natural Environment and Sustainability department) in the form of MapInfo.Demographic and household size information was obtained from the Australian Bureau of Statistics (www.abs.gov.au)Census Data using their TableBuilder online tool. Data from Australian Bureau of Statistics was summarised at a collectiondistrict scale. Information on appliance and hot water profile was obtained in summary from a GHG study conducted fromthe Queensland Government [14].

Information from the Australian Bureau of Statistics census data was available through census collection district (CCD) whereeach CCD represents about 200 households. CCDs were aggregated and linked to the respective suburb in the Brisbane case study.The priority ABS fields extrapolated to housing stock summary scale are household income, household size, and type of dwelling.This provided the mode with information such as:

– number of households having income at each salary scale– number of households having 1,2,3,4 or 5+ occupants– number of households of each type (detached house, semi-detached house, and unit).

630 A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

Information on appliance profiles, heating/cooling and water heating is only available as a summary at the region or city scaleand by building type. This will be sufficient for the purpose of policy analysis in the case study suburbs.

The next step was to estimate the non-financial benefits Keo from the demographic data. This was done using a “attitude to

energy reduction” survey conducted by Refs. [35,37]. It required a 2-stage method. The first stage was to partition the housingstock and demographic information into housing type by suburb by household income by household size. Next, categories ofattitude (most likely, likely and least likely) to energy reduction need to be defined from the survey. We did this by applying aChi-squared test to select the most significant variables for the attitude categories. The most significant variables in the surveywere household size and household income, where the latter represented wc;d. We then linked these attributes with housingstock by demographic data to provide a category of either most likely, likely or least likely. Unfortunately, household size wasthe only non-financial variable that could be used in the case study, given the data available.

4.2. Coding of model and calibration of parameters

The model was coded using MS Excel 2003, mainly for the ease of transparency to government and industry stakeholders.Individual worksheets were used to store model input data (housing stock summaries, demographics, trends, model parameters)as well as the model Eqs. (2)–(8). Whilst the diffusionmodel of Eqs. (2)–(4) is continuous with respect to time t, we implementedthe model with 3-monthly time intervals from July 2008 through to June 2019, with the start month aligning with the commenceof the rebates in the calibration data. The GHG emissions parameter, Vc,d

e, o, is calculated in batch using AccuRate, with the valuesmanually inserted in the Excel model.

The goal of calibration is to determine the best-fit values of the model parameters αo1, o, βo1, o, δo1,o, po, qo so that the diffusionmodel forecasts diffusion according to reality. To do thiswe optimise themodel parameters so the forecasted number of adopters isas close as possible to actual data on adoption over time. To reduce the complexity of the calibration for this paper, and in theabsence of information, we don't calibrate the co-dependency between options αo1,o, βo1, o, δo1,o when o1≠o. These are setmanually to arbitrary values. Let A

o

c;dðtÞ be the actual adoption of option o∈O at time t as per the historical data. Themathematicalobjective is to obtain aweighted least squaresfit between Ac,d

o (t) and Ao

c;dðtÞ by optimising over the variable parametersαo1, o,βo1,o,δo1,o, po, qo. The mathematical form is a non-linear program:

Table 5Diffusio

o1=o1=

a Par

Min Z =∑c∈C

∑d∈D

∑t∈T

OCd⋅ ð Aoc;dðtÞ−Ao

c;dðtÞÞ2 ð9Þ

t to Eqs. (2) to (6), where OCd is the number of occurrences in the historical data for the combination d∈D. Values of the

subjecparameters αo1,o, βo1,o, δo1,o were constrained to between 0 and 10. Calibration was conducted for each intervention optionseparately.

Data gathering of historical uptake of solar hot water and solar panel systems was difficult, due to confidentiality at the idealscale needed for the calibration. Ideally, the historical data used for calibration would be the number of units (solar hot water orpanels) adopted at each location by income bracket by household size by date (monthly scale from 2000 to 2009). Data wouldideally include price paid and rebate size for each category. Themain confidentiality is the uptake by location due to the perceptionof identifying individual households. Breakdown by income by household size is not available unless a survey was conducted.Information was gathered from the following sources:

• Australian Government Department of Environment, Water, Heritage and the Arts— Number of solar panel systems installed bymonth from 2000 to 2009 [38]

• Queensland Government Department of Environment and Resource Management — Number of solar hot water systems bymonth from 1995 to 2009 [14].

• Australian Government Energy Efficient Homes Package — Cumulative number of adopters of solar panel and hot water as atNovember 2009 for each post code (location), unpublished data.

From these data sets, we estimate the actual uptake for location for each 3-month interval from 2000 to 2009. Otherinformation gathered was the start and end dates of the rebates for solar hot water and solar panels. The federal government solarhot water rebate (Option 2) commenced in early 2009, whilst the solar panel rebate (Option 3) commenced in mid 2008. Forcalibration, we have 18 months of data for uptake after rebate introduction, as rebates were not available prior to June 2008.

n model parameters use for case study.

αo1,o βo1, o δo1,o

o=2 o=3 o=2 o=3 o=2 o=3

2 0 0 a 10 1.1 a 10 8 a

3 0 a 0 1.1 a 10 8 a 10

ameters set manually rather than optimised in the calibration.

0

5000

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25000

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Fig. 4. Overall comparison of Options 2 and 3 and their BAU (Option 1a and 1b respectively).

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AU$112,500

Mean household income

Fig. 5. GHG reductions for Option 3 versus BAU (Option 1b), for separate houses.

631A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

Further years of data for post rebate introduction would substantially improve the diffusion calibration and sensitivity to the sizeof the rebate.

Eq. (9) can be solved using a constrained non-linear programming method or a heuristic method such as genetic algorithms.For the small case study of this paper, we used Excel Solver. Table 5 contains the values of calibrated parameters αo1,o, βo1,o, δo1,o.The model parameter λwas not calibrated but rather set to 30,000. The annual financial benefits and cost had the highest weightand they are strong drivers of uptake amongst the criteria. The weight for the non-financial benefit was 0 since household size(only non-financial variable used) had no significant influence on adoption. The optimal calibration for the other parameters are:po=0.00010562 and 0.00000303 for Options 2 and 3 respectively; qo=0.0878 and 0.325 for Options 2 and 3 respectively.

GHG emissions Vc,de(t),o on a house basis was calculated for each building envelope used for the case studies. GHG (tonnes

per house per year) ranged from 3.39 t to 6.75 t based on single occupancy and separate house, ranged from 4.3 t to 5.3 t for asemi-detached house, and 3.9 t to 4.8 t for a unit. For a 3 bedroom house (150 m2) with electric hot water, a solar hot waterunit reduced the GHG emissions from 5.45 t/yr to 4.23 t/yr, whilst a 1.5 kW solar panel reduced the GHG emissions to 3.37 t/yr,based on a single occupancy.

During the timeframe of application, June 2008 to June 2019, the cost of solar hot water and solar panels, kc,do, t , was assumed todecrease in price due to the introduction of less expensive technologies. In the absence of information on likely future prices, weassumed a 3% price drop per year, with an overall reduction of about 37% between 2008 and 2019. Sensitivity of likely price dropscan be tested.

5. Case study results

In this sectionwe illustrate the capability of themodel for estimating the likely impact from intervention options using the casestudy suburbs. The first analysis is a summary of Options 2 and 3 versus BAU, where the BAU represents adoption of solar hot waterand solar panels without the Options 2 and 3 rebates. Fig. 4 shows the results which is an aggregation across suburbs, housingtypes etc. A zero reduction in GHG emissions would represent the case of no adoption of solar hot water or solar panels between2008 and 2019. Fig. 4 shows that the Option 2 rebate has less impact on GHG emission reductions compared to that of Option 3.Whilst the cost of solar hot water was less than solar panels, the impact of the intervention Option 2 was less than Option 3. Thelikely reasons were the attractiveness of large rebate for 1.5 KW solar panels, which reduced the overall cost by up to 60%.

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Separate house

Unit

Fig. 6. GHG reductions for each housing type for Option 2 versus BAU.

0

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HG

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issi

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s (t

/yr)

Year

Camp HillChermsideCoorparooFitzgibbonIndooroopillyNundahUpper Mount Gravatt

Fig. 7. GHG reductions (per 100 houses) of Option 2 in separate houses by suburb.

632 A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

The model can also be used to assess the impacts of intervention options across different types of demographics and buildingenvelopes. Fig. 5 shows an example (Option 3) for different mean household incomes for separate houses. Unlike Fig. 4, Fig. 5results are shown in terms of the difference between the Option 3 and BAU (Option 1b), whichmeans a zero GHG reductionmeansthe option has no further GHG reduction compared to the BAU. If the impact of adopting solar panels was compared to noadoption, we found that adoption of solar panels increased with increased household income. This is not surprising sincehousehold income is the major driver for the purchase of an expensive retrofit such as a solar panel. However, when assessing theimpact of the rebate in Option 3 (i.e. compared to BAU), results were significantly different. Fig. 5 shows that the highest andlowest household incomes achieved the least reduction in GHG from the rebate of Option 3, whilst the middle-income householdshas the highest GHG emission reductions. This is because high income households can more readily afford solar hot water and theOption 3 rebate makes little difference to their decision to adopt. For middle-income households the Option 3 rebate made thedecision to adopt much more attractive, particularly given the non-financial benefit. Low income households are the least likelygroup to take up Option 3, since even with the rebate, the price is still too high. However, one interesting observation is that as theprice of solar panels drop from 2009 to 2019, the lower income households achieve further GHG reductions from further adoptionof Option 3. This is because increased affordability makes the rebate evenmore attractive. The slight decline in GHG reductions forthe higher income brackets is due to long term trend of slight reduction in average household size.

Fig. 6 shows the GHG reductions for Option 2 by housing type. Separate house achieves greater GHG reductions due to a muchlarger number of dwellings compared to Semi-detached and Units. However, the GHG benefits from the rebate decline after 2012,mainly due to the ceiling of adoption being reached. For Units the GHG reductions continue to rise through to 2019. The likelyreason is that units comprise of a greater number of middle-income households, thus leading to a later peak compared todemographics in separate houses. Fig. 7 shows the GHG reductions (per 100 houses) for Option 2 for separate houses by suburb.The low GHG reductions for Fitzgibbon and Coorparoo (lines are overlapping) are due to the large proportion of houses in thesesuburbs having gas hot water, which means solar hot water would have substantially less GHG benefits compared to electric hotwater. There is very little differentiation between the other suburbs. Fig. 8 shows a summary of Options 2 and 3 versus BAU, exceptwhen the rebates are terminated on 30 June 2011. When terminated on June 2012, GHG benefits of Option 2 converge to the BAUby 2017, and nearly all (N90%) of the cumulative GHG savings (area under curve compared to Fig. 4) are lost, despite theintervention options being available for about 30% of the planning horizon between 2008 and 2019. This example shows thecapability of the model for assessing the impact of start and finish dates of proposed intervention options.

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Fig. 8. Overall comparison of Options 2 and 3 versus BAU, except with rebates terminated on June 2012.

633A. Higgins et al. / Technological Forecasting & Social Change 78 (2011) 621–634

6. Conclusions and further research

A diffusionmodel was developed to evaluate the effectiveness of different intervention schemes to reduce GHG emissions fromresidential buildings in Australia over time. Its validity was assessed using a case study of seven suburbs in Brisbane, Australia,comprising of 25,000 houses/units with a mixture of household demographics, new and old suburbs, including suburbsundergoing redevelopment. The GHG emission potentials of three cases in these suburbs to year 2019 were considered: (a) basecase or BAU; (b) AU$8000 rebate for solar panels; and (c) AU$2000 rebate for solar hot water.

We demonstrated the capability of the model to quantify and analyse the GHG reduction potential of the two interventionoptions, relative to the BAU, to 2019. In addition, the results allowed identification of important trends that could assist policymakers to better target intervention options. For example, whilst the adoption rate of solar panels in the case study increased withincreased household income, due to the high cost of the retrofit, the rebate of Option 3 had amuch bigger impact inmiddle incomehouseholds. Another important trend was the impact of terminating intervention options before they achieve their potentialoutcome. By terminating the rebates of Options 2 and 3, 30% into the planning horizon, more than 90% of the potential cumulativeGHG savings were lost.

This application of the diffusion model helps contribute to identifying and understanding the more cost-effective ways oflarge-scale and significant GHG emission reduction from the housing sector, relevant to a given socio-geographic context. Themodel has been formulated to accommodate non-financial and financial benefits and the interdependencies between differentintervention options, and to interface with bottom-up or aggregation approaches.

Further research is underway to improve the calibration of the diffusion model parameters and weights, particularly in theabsence of past data for new technology (e.g. electric vehicles). Inclusion of non-financial parameters has been limited to date. Themodel will be extended to accommodate the large range of non-financial reasons leading to adoption of rebates/incentives byhouseholds. This includes reactions towards adopting a rebate when there is knowledge that it was to terminate in the near future.One would expect a large rush of households adopting the rebate in the weeks leading up to its termination, which was notconsidered in the example of this paper. Performance of calibration could be improved by considering non-linearity of weights orinteractions between them. For example large non-financial benefits that lead to a rush for the technology may influence the cost,due to increased demand.

The case study suburbs of this paper were the first step in developing and testing the model. Ultimately, the model will beapplied to evaluate intervention options being considered at an entire Queensland or state scale with a housing stock of almost2 million. Currently, the model provides a forward analysis in that it estimates the GHG impact from specified interventionoptions. Future developments could also include a backward analysis capability that optimises the set of intervention options tomeet national or jurisdictional GHG targets within a specified timeframe.

Acknowledgements

The authors thank our CSIRO colleagues Dr Zhengen Ren and Dr Dong Chen of CSIRO for producing the GHG emissions fordifferent building envelopes using AccuRate, and Dr Xiaoming Wang and Dr Phillip Paevere for helpful technical discussions. Theauthors also thank the anonymous reviewers for their valuable feedback to the earlier version of the paper.

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Dr Andrew Higgins is a Principal Research Scientist at CSIRO, and specialises in mathematical forecasting adoption of technologies, supply chain management,logistics, and mathematical optimisation. Andrew has previously developed methods to increase profitability for agriculture production and processors inAustralia. He is currently the vice-president of the Australian Society for Operations Research. Andrew received his PhD from Queensland University of Technologyin 1996.

Dr Greg Foliente is a Senior Science Leader at CSIRO, Australia's national science agency. He received his post-graduate degrees, including a PhD, at VirginiaPolytechnic Institute and State University in the USA. Hewas co-chair of the 2008World Sustainable Building Conference (SB08Melbourne), chair of the GoverningCommittee for the Australian Life Cycle Inventory (AusLCI) Database Initiative, and Program Director of the Performance Based Building networks in Australia andthe Asia-Pacific region, amongst others. He leads and manages large research projects related to climate adaptation and mitigation, sustainable built environmentand transitions to urban sustainability.

Cheryl McNamara currently works as a software developer for CSIRO Sustainable Ecosystems. She also has expertise in database management, GeographicalInformation Systems and statistical analysis. She has preformed the role of project manager on numerous social science surveys and has provided support to theresearch activities and consultancy work of numerous and varied projects over many years.