Modelling building stockenergy use and carbon

emission scenariosGreg Foliente and Seongwon Seo

Ecosystem Sciences, CSIRO, Highett, Australia

Abstract

Purpose – A systematic approach is needed to engage a broad range of stakeholders to reducegreenhouse gas (GHG) emissions from energy use in the building sector. The purpose of this paper isto develop a systems-based bottom-up approach for this purpose, and to demonstrate its application ina case study of office building stock in the State of New South Wales (NSW) in Australia.Design/methodology/approach – A conceptual framework for the general method is developedbased on a cross-typology matrix of energy consumption and supply on the one hand, and interventionschemes or policy instruments on the other. This is then tested and demonstrated using a case study ofcommercial office building stock, with building energy demand calculated using a validated computerenergy simulation tool for a representative set of office buildings within a local government area(LGA). The energy consumption and associated GHG emissions are then aggregated up from LGA tothe whole State. The impact projections of different intervention schemes are compared and mappedspatially across the State.Findings – Results have demonstrated the significance and capability of the proposed approach, inallowing quantitative comparisons of the relative effectiveness of a specific set of regulatory, technicaland behavioral scenario settings on the spatial distribution and trends in energy consumption andGHG emissions of the NSW office building stock to 2020.Research limitations/implications – Further case studies involving mixed building types andspecific building types with greater granularity of modelling details, energy use and supply options,and spatial resolution are needed.Originality/value – The structured cross-typology approach is a novel contribution to bottom-upmodelling approaches to designing and/or assessing the effectiveness of specific policy instruments,alone or in combination. It will enable policy makers, property portfolio owners, utility companies andbuilding tenants to assess the broader impacts of their specific actions towards a common goal.

Keywords Efficiency, Greenhouse gas, Carbon dioxide, Spatial analysis, Bottom-up, Australia,Buildings

Paper type Research paper

1. IntroductionThe high carbon abatement potential of the building sector, which accounts for abouta third of the global energy-related greenhouse gas (GHG) emissions, is widelyrecognized (urge-Vorsatz et al., 2007a, b, 2009; United Nations EnvironmentProgramme (UNEP), 2009; ClimateWorks Australia, 2010). However, progresstowards fully realizing this potential seems slow, even with stretch goals and theimplementation of various policy initiatives in many countries (European Parliament

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The authors would like to thank their CSIRO colleagues and members of the AusZEH projectteam, in particular Dr Zhengen Ren, Dr Andrew Higgins and Dr Phillip Paevere, for helpfultechnical discussions and for undertaking specific tasks in other parts of the AusZEH project(cited herein) that supported some of the concepts proposed in this paper.

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and Council of the European Union, 2003; Committee on Climate Change, 2008;Department of Environment, Climate Change & Water (DECCW), 2009).

The key barriers, and the range of policy instruments and actions to address them,have been analysed from various viewpoints (urge-Vorsatz et al., 2007a, 2009).Technological and innovative system solutions are within the broad scope of what canbe covered or addressed directly by policy instruments. But the latter are commonlypart of a government’s fundamental responsibility, thus the roles of other stakeholdersin reducing energy consumption and GHG emissions seem to take a back seat(or passive mode) in discussions about “policy instruments”. This is not the intentnor desired effect, of course, but to emphasize the important roles and potentialcontributions of other stakeholders such as building professionals and contractors,property owners and investors, energy and utility decision makers, technologyand services firms, education and training institutions, and non-governmentalorganizations (NGOs), here we use the term “intervention schemes” to include policyinstruments and other stakeholders’ direct actions. We present a framework that willallow the different stakeholders to explore the broad impacts of their own decisions andactions. This definition is needed for this purpose, but does not change the range ofresponses and actions available to reduce GHG emissions in the building sector; thesecan be broadly categorized as “sticks” (regulations and legal mandatory requirements),“carrots” (rebates, incentives and various benefits-based schemes) and “tambourines”(awareness and education) (UNEP, 2007). There are alternative ways of classifyingintervention options (urge-Vorsatz et al., 2007a, b). Table I presents an overview of theintervention typologies adopted herein. Representative examples of interventionschemes and instruments are listed under each type to demonstrate how otherclassifications might fit in, and the primary responsible stakeholders (or “actors”) areidentified. According to the definition adopted herein, all voluntary schemes will fallunder carrots and/or tambourines (see also Table I footnotes).

Assessment of the effectiveness of a particular scheme or a combined set ofinterventions has been undertaken at a high level and mostly qualitatively (urge-Vorsatzet al., 2007a, b; UNEP, 2007), and results are difficult to apply or extrapolate to specificlocal context, and to future scenarios. Methods used in quantitative assessments andprojections of future energy demand and GHG emissions in building stock areparticularly useful in assessing the potential impact of specific types of interventions.These can be by either top-down or bottom-up modelling approaches. The key types andfeatures, strengths and limitations of these approaches have been extensively reviewedand analysed (Swan and Ugursal, 2009; Kavgic et al., 2010; Higgins et al., 2011).

The top-down modelling approach works at an aggregated level, typically national,and is primarily focused on broad econometric or technological impacts. But these areneither designed nor useful for investigating the impacts and effectiveness of a specificaction or policy. The bottom-up modelling approach works at a disaggregated level,providing flexibility and the powerful capability to investigate the impact of a specificaction, measure or intervention at higher levels of aggregation (i.e. building stockat the state or national level). However, this approach is highly dependent on theavailability, reliability and quality of data at the starting point of analysis (or level ofdisaggregation) and the nature and capability of the model used at the lowest levelof analysis. The modelling technique may be based on building physics, statisticalmodels or a hybrid of the two (Kavgic et al., 2010).

This paper aims to develop a systems-based bottom-up modelling approach toassess the effectiveness of specific actions and decisions of different stakeholders in the

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building sector. The focus is to take into account specific stakeholder action, whether itis setting regulations, designing policy or an incentive scheme, investing in technologydevelopment or adopting new technologies and systems, and/or encouragingbehavioural change – alone or in combination. Thus, for this purpose, despite thedata and modelling challenges, a more rigorous physics-based modelling approach isrequired. Relevant recent developments in building stock modelling via a bottom-upapproach are discussed. Then a cross-typology framework is introduced, anddemonstrated in a case study of office building stock in the state of New South Wales(NSW) in Australia.

2. Building stock bottom-up modellingThe development and application of bottom-up modelling of building stock started inthe mid-1990s, and was first applied to the residential building stock in the UK(Shorrock and Dunster, 1997) and Canada (Swan and Ugursal, 2009), then to a mix ofbuilding stock in Finland (Snakin, 2000) and the USA (Huang and Broderick, 2000).Further developments and detailed comparisons and analyses of the capabilities andlimitations of various methods have been comprehensively discussed, and futureresearch opportunities have been identified (Swan and Ugursal, 2009; Kavgic et al.,2010). While the pure statistical approach in bottom-up modelling based on fittinglarge datasets has much appeal, it lacks the capability to capture the impact of specific

Simplifiedtype Sticks Carrots Tambourines

Briefdescription

Mandatory, legal orregulatory schemes,including mandatoryfinancial schemesa

Benefits-based schemes,whether financial ornon-financial (notmandatory)b

Advocacy, awareness andeducation, including anyprogrammes that influencebehaviours (not mandatory)b

Examples Building codesPlanning regulationsProcurement regulationsAppliance and equipmentstandardsMandatory certificationand disclosureprogrammesTaxation and leviesBanned product(s)

Rebates and incentivesTax exemptions orreductionsCapital subsidies andgrants, loan subsidiesEnergy efficiencycertificate schemes

Energy efficiency informationcampaignsVoluntary certification andlabellingVoluntary disclosureprogrammesProfessional and tradeseducation and trainingprogrammes

Stakeholders(primaryactors)

Government GovernmentFinancial institutionsUtility industryBuilding and propertyindustryInvestors

GovernmentIndustryIndividual firmsEducation and traininginstitutionsNGOsConsumers and communityorganizations

Notes: aMandatory financial schemes are included in this type because failure to comply is subject tospecific legal or other formal consequences from relevant government authorities; bin non-mandatoryor voluntary schemes, failure to follow or adopt has no legal consequencesSource: Adapted from UNEP (2007), but with concept redefinition

Table I.A typology of policyinstruments andintervention schemes forGHG emission reductionsin the building sector

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changes in subsets of the stock being modelled, and to take into account the impactof new technologies that have no historical performance data (e.g. new materials,advanced energy management technologies). Hybrid statistical and physics-basedapproaches such as that utilized by Aydinalp et al. (2004) have been shown to addressthis innate limitation of the statistical modelling approach for certain technologies.However, it is clear that accounting for specific changes in any critical elementaffecting the energy performance and GHG emission of a building, and therefore thebuilding stock, will require the use of physics-based models (Huang and Broderick,2000; Kavgic et al., 2010).

In recent decades, there has been much progress in the development, calibration andvalidation of whole-building energy performance simulation tools (Crawley et al., 2008;see also the US Department of Energy Building Energy Software Tools Directory athttp://apps1.eere.energy.gov/buildings/tools_directory/). These take into account theinfluence of local climate, building location and orientation, and the effects of changesin a building’s components (walls, roof, windows, etc.), space conditioning systems andoperating conditions (including building management systems, occupancy hours).Thus, it is straightforward to study the potential impact of new technologies thatcould improve energy efficiency in individual buildings. Some of the tools also takeinto account the use of onsite renewable energy systems, and a few include a basiceconomic evaluation capability. Scaling the potential impacts in one building to abuilding stock in a given area (city, region/state or whole country) is typicallyundertaken using building prototypes (i.e. a building typology approach) (Huang andBroderick, 2000; Shimoda et al., 2004). As a basis for bottom-up building stockmodelling, we note that:

. The meaning, application and limitations of building stock modelling results areconstrained by the capabilities and limitations of the building simulation toolselected for building modelling at the disaggregated level. As a positiveexample, a tool with an hourly energy demand resolution at the building scalecan be used to calculate an aggregated hourly demand profile for the buildingstock being considered in a specified area. However, if the selected tool is limitedto calculating energy from heating and cooling loads only (without appliances,equipment and other end-use loads), this should be specially noted at the highestlevel of aggregation and matched by appropriate data for validation (see lastpoint also).

. The whole building stock representation in a given area by building prototypescan range from being very extensive (i.e. many hundreds to thousands ofbuilding types) to highly simplified (several dozens to a few hundreds).

. The model validation and calibration process is designed to match availabledata, thus, this can be undertaken at one level (e.g. building type comparisonsonly, or at next level aggregation such as local government area (LGA) only, orbased on national statistics only) or at multiple scales/levels.

In Australia, energy performance modelling of individual commercial buildings islargely undertaken using the most common tools reviewed by Crawley et al. (2008).For residential buildings on the other hand, the Nationwide House Energy RatingScheme (NatHERS) (2011) can be used to analyse the energy performance of a residentialbuilding envelope and calculate the heating and cooling energy load on the house. Thecapability of the reference tool in NatHERS, the AccuRate software (Ren and Chen, 2010),

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has recently been extended with the development of the AusZEH design tool (Ren et al.,2011), which considers different occupancy profiles with energy end-use modules forspace heating, space cooling, water heating, lighting and all appliances.

Residential building stock analysis has been undertaken somewhat informally forthe state of Queensland, without using a simulation model or a data-fitting model(Mills, 2010). Mills applied his knowledge and experience in qualitatively integratingsummary information gathered by government departments (local, state and federal)about housing stock (age, type and energy efficiency rating), demographics(distribution of people across housing types, household sizes, tenure), and energytypes and use across appliances (appliance profiles across demographics, peak energydemand vs average energy demand, hot water systems and utilization of differentappliances). His method provided a simple approach to identify opportunities to reduceGHG emissions from home energy use, but lacked both the efficiency and capability torun and readily compare different scenarios.

Newton and Tucker (2011) used AccuRate and data from a range of sources(like Mills) in investigating the cost-effectiveness of a number of strategies fordetached housing in metropolitan Melbourne (Victoria) to reduce its carbon footprint.They explored a set of technical and policy options considering specific decisions onhot water heating, space heating and cooling, appliances and energy supply systems.

Higgins et al. (2011) established a baseline case of the heating and cooling energyconsumption of the housing stock in metropolitan Brisbane (Queensland) usingAccuRate, and then explored future scenarios based on business-as-usual (BAU) andalternative policy settings. They significantly extended the capability of bottom-upmodelling approaches by explicitly modelling voluntary consumer adoption ofspecific technologies, available through government rebates, based on consumers’socio-demographic profiles. The diffusion model has been formulated to interface withbottom-up approaches and to accommodate the interdependencies between differentintervention options. The model has also been recently integrated with multi-criteriaanalysis to enable explicit consideration and balancing of the impacts of technologycosts, financial benefits, demographic suitability and household income on thelikelihood of adoption (Higgins and Foliente, 2012). They demonstrated an applicationof the model to optimize the size and timing of government rebates for solar hot watersystems and solar photovoltaic panels to maximize adoption rate. Adoption patternsand impacts related to energy use and GHG emission reduction can also be analysed inthe socio-geographic context.

The natural progression is to integrate improved physics-based energyperformance simulation tools for individual buildings with developments in spatialdiffusion modelling and consumer adoption rates and patterns, and with improvingquality and variety of data (e.g. from smart metres). This opens up new possibilities fora more systematic way of incorporating specific stakeholder actions in modelling andprojecting building stock energy demand and GHG emissions into the future.

3. A cross-typology-based systems approach3.1 OverviewWith recent developments, there is now a need for a general scenarios analysisframework that meets the following criteria:

. enables stakeholders (i.e. those listed in Table I) to identify what they cando in particular, and assess their potential effectiveness before and afterimplementation;

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. is scalable across geographical scales – from a block to an LGA, to a state andthe whole country – to match available datasets, especially for validation (i.e.different levels of spatial and data granularity); and

. can take into account specific local conditions and industry/marketenvironments, allows the use of different bottom-up modelling tools andplatforms, and is amenable to different levels of modelling granularity.

The next sections present a cross-typology framework that satisfies theserequirements.

3.2 A new scenarios analysis frameworkTaking advantage of the improving capabilities of building energy performancesimulation tools, the typology approach to building stock modelling can be systematicallyorganized and refined by considering sub-typology classifications. First is thesub-classification into energy demand/consumption and energy supply systems.Since there are more clearly identifiable factors that can be changed and investigated inthe former, we can introduce further partitions or sub-classifications of physicalbuilding factors, energy loads and occupancy or use profile. This sub-classificationsystem is presented in Table II, together with examples of what may be included undereach one. Some of the building energy demand factors may be associated with morethan one sub-typology and, depending on the input requirements and calculationprocedure of the selected building simulation tool, could be modelled under one sub-typology or another.

Energy demandTypologyclasses Physical building Energy “load”

Occupancy oruse profile Energy supply

Description Location and climatezoneAge or year built(and year of lastmajor renovation orrefurbishment)Physicalcharacteristics andproperties of building(main constructiontype, features ofbuilding envelope,etc.)Energy efficiencyrating

Heating, ventilationand air-conditioning(HVAC) systemHot water systemLightingAll “built-in” and“plug-in” or“pluggable”appliances andequipment; energyratingOther equipment(e.g. lifts)

Type of useor occupancyOccupancyand operatingpattern (e.g.hours ofoperation)Number ofoccupantsSocio-demographicprofile ofoccupants

Energy supply mix bytype (electricity, gas,diesel, renewables, etc.)and source (grid,district or “near-site” oronsite, etc.)Nature/type, sizeand age of low- orzero-emission energysource(s)

Stakeholders(primaryactors)

GovernmentIndustryProfessionalsClients/ownersInvestors

GovernmentUtility firmsBuilding andproperty industryClients or ownersFacility managersTenants

OwnersFacilitymanagersTenants

Clients or ownersInvestorsUtility firms

Table II.A typology of energy

demand/consumption andsupply systems that

determine associated GHGemissions in a building

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In order to satisfy the first criteria in the previous section, we can extract the“stakeholders” from Tables I and II by creating a three-dimensional matrix andcrossing the typologies for intervention schemes and policy instruments (Table I) withthe typologies for building energy demand and supply (Table II). From a (or any)stakeholder’s viewpoint then, the resulting two-dimensional matrix for stakeholderdecision and action will be as shown in Figure 1.

This cross-typology framework facilitates the systematic investigation andcomparison of the relative impacts on carbon emissions of different types ofintervention schemes. For example, in the conceptual example Option 1 (Figure 1),a government body can impose a new minimum energy performance requirement ina building code. This can be assessed and implemented on its own, or with othermandatory measures such as a carbon tax. As an example of Option 2, a governmentbody can provide tax exemptions or rebates to building owners for energy performancesbetter than the minimum building code requirement, mandate a minimum energyefficiency rating for a class of appliances and introduce an energy efficiency campaign tochange tenant behaviour. In Option 3, government can work with the appliancesindustry to undertake an awareness and training campaign on the benefits of energy-efficient appliances and energy management systems, and government can mandate theutility industry to meet GHG emission targets through the provision of renewable energysupply systems. These are just a few examples; the combination possibilities for specificactions, alone or in combination, by specific stakeholders, on their own or with otherstakeholders, is vast (e.g. Option n). The framework helps set-up meaningful scenariooptions in a more systematic fashion, and could potentially also provide useful insightsduring the analysis of scenario modelling results.

The discussions so far have only considered point-in-time actions and scenarios.In practice, stakeholders are really interested in planning horizons, and the series ofdecisions and implementations required over a period of time to meet a future goal.Thus, Figure 2 illustrates an application of the cross-typology framework in Figure 1,showing different sets of interventions introduced at different points in time to meet thesame goal. For example, government may consider a set of interventions that are fewerbut relatively more aggressive (Pathway i ), and an alternative set based on multiplebut less-aggressive measures (Pathway j ). These can be explicitly considered inscenarios analysis following the framework proposed herein. Analysis of costs and

Interventionschemes

Sticks

Physical building

Energy demand Energy‘‘supply’’system

1

1

n n

n

n n

n

2

2

2

23

Example intervention scenarios (alone or in combination)

3

3

3

Energy ‘‘Loads’’ Occupancy

Carrots

Tambourines

Option Option Option Option

Notes: The framework facilitates a systematic way of designing scenarios with specificactions (or intervention schemes, alone or in combination) by specific stakeholders,as illustrated with Scenario Options 1 to n

Figure 1.A cross-typologyframework to scenariosmodelling and analysisof energy consumption/demand and GHGemissions in thebuilding stock

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burden distribution to different stakeholders can be undertaken separately, thencombined with the scenario results to assess the cost-effectiveness of different pathways.

In summary, Tables I and II provide some examples of the many options thatcan be considered in the new cross-typology scenarios analysis framework illustratedin Figures 1 and 2. This specifically satisfies the first criteria of assisting keystakeholders in decision making as follows:

. government – policy instrument planning/design and assessment;

. property/asset portfolio owners and investors – new construction and retrofitinvestment decisions;

. utility industry and firms – improved granular demand projections to plancurrent and future supply infrastructure planning and investments (especially toaccount for peak demands);

. technology and services industry (including renewable energy system suppliers)– opportunities to develop new technologies and/or cost-effective solutions forboth clients and public/environment; and

. NGOs and educational/training institutions – advocacy and developing trainingcurriculum and awareness programmes.

Although the cross-typology framework’s full capabilities and advantages are clear forbottom-up building stock modelling, this can also potentially guide top-down analysisapproaches.

3.3 General methodology overviewThe general steps, although similar to previous approaches (Kavgic et al., 2010), arebriefly described herein specifically to demonstrate the cross-typology frameworkapplication, and for previewing the later case study.

Figure 3 illustrates the general approach. First, the geographical boundary forbuilding stock analysis needs to be selected. Depending on the purpose of analysis and

GH

G e

mis

sion

s re

duct

ion

Desiredlevel

(goal)

Currentlevel

Pathway option iPathway option j

Year

2010 2020 2030 2040 2050

i

i

i

i

i

j

j

j

jj

jjj

Note: A circle signifies an intervention, one or a combination,at that specific point in time

Figure 2.Comparison of two long-

term GHG reductionpathways to meet a

future goal

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the availability of validation data, there may be two considerations: the immediateaggregation level (e.g. block or district) and the ultimate aggregation level (e.g. state ornational). For some studies, this may be one and the same. In any case, the climatezones within the study area need to be clearly identified.Second, the building stock inventory within the area of study needs to be analysed andbroken down to meaningful typologies, assigned to different climate zones. Dependingon available information, the typology classes and sub-classes are broken downaccording to the columns in Table II. This can be highly simplified; for example, underenergy loads the options can simply be low, medium or high. Or it can be highlydetailed; for example, energy loads can have separate sub-classes for HVAC systems,hot water systems, lighting, appliances, etc. each having further options with differentconsumption/efficiency performance levels.

Third, a whole building energy performance modelling tool is selected and used tomodel the building typologies (reflecting the different level settings for the differentsub-types). Ideally, the selected tool for this purpose is one that has undergoneextensive verification and general validation through the International EnergyAgency’s BESTEST protocol ( Judkoff and Neymark, 1995). The model calibrationprocess for individual building types is required; the reference or baseline performancefor the building type being analysed needs to be established and used.

Fourth, the inventory of energy consumption and associated GHG emissions isestablished and merged with the inventory of buildings, both existing and new.The building stock’s baseline energy consumption (an aggregation of results from the

1

2

3

4

5

Selection of prototypecommercial buildings

Condition (type, climate,appliance, HVAC, etc)

Modelling

Energy and GHG Building stock

Selection ofgeographical boundary

Commercial building stock

Existing buildings

New buildings

Energy and GHG forexisting buildings

Energy and GHG fornew buildings

Output (total energy andGHG emissions)

Calibration

Energy and GHGinventory database

Scenariochanges

Figure 3.Overview of generalprocedure for calculatingthe energy consumptionand associated GHGemissions from buildingstock in a specified area

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building typologies considered) is established for the geographic area being consideredand ideally validated with available data at this higher level of aggregation. The BAUfuture scenario can then be made based on trends in key factors known at that time(e.g. official population forecast).

Then, alternative future scenarios with different intervention settings and timingsare identified based on the concept illustrated in Figures 1 and 2 and detailed inTables I and II. The models are modified to reflect these scenario settings, and rerun todetermine the energy consumption and associated GHG emissions. Results are plottedand compared/analysed with the BAU scenario. Complementary analyses such ascost-benefit studies can also be undertaken.

The methodology described above meets the last two criteria for a new systemsapproach listed in Section 3.1. The procedure is scalable across geographical scales; theanalyst in Step 1 selects this. The same holds for decisions related to the desired level ofmodelling granularity and the choice of a physics-based whole building modelling toolin Steps 2 and 3.

3.4 Developments and applicationsThis cross-typology approach to bottom-up modelling of building stock energyconsumption and GHG emissions was first explored in an unpublished preliminaryanalysis to understand the BAU emissions scenario for the residential housing stockin the state of Victoria to 2030. Part of CSIRO’s larger Australian Zero EmissionHouse (AusZEH) project (www.auszeh.org.au/), the main aim was to investigate thepotential impact of specific intervention schemes that can be implemented by differentstakeholders to meet the government’s stated emission targets. Thus, it was necessary topartition the different intervention options by different stakeholders for differentelements of the individual building and the building stock – i.e. formulation of Tables Iand II, respectively. In that study, the physics-based AccuRate tool was used to simulatethe changes in the heating and cooling loads due to the improved energy efficiency ratingof individual houses (new and existing), which were then aggregated up to the projectedhousing stock through to 2030. The energy consumption for hot water systems, lightingand appliances, the influence of simplified settings of occupant behaviour and theemission profiles of different energy supply systems (onsite and from the grid) wereroughly estimated and compared. The multitude of leverage points that can be changedand tested to assess different decarbonization pathways were a revelation.

So the AusZEH research team embarked on developing a more rigorous andvalidated method of estimating the total energy consumption (not just from heatingand cooling loads) with hourly resolution of residential housing (Ren et al., 2011). Andto improve the treatment of voluntary schemes (Table I), which have previously beenbased on fixed or crude assumptions of uptake levels and changes in technology, theyapplied diffusion models to estimate the likely rate of adoption as a dynamic functionof household demographic, building envelope and financial variables, as reviewedearlier (Higgins et al., 2011; Higgins and Foliente, 2012).

Related research is underway to further enhance the methodologies and toolsneeded to fully demonstrate and exploit the capabilities of the cross-typology systemsframework proposed herein. Various specific applications in residential housingand uptake of low-emission vehicles by specific stakeholders such as government andutility companies are underway.

For the purpose of this paper, we next demonstrate the application of the frameworkin a simple case study of office building stock in the state of NSW.

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4. Case study – NSW office building stock4.1 Geography and aggregation levelIn June 2010, it was estimated that NSW had a population of more than seven million –or 32 per cent of the national total – making it Australia’s most populous state. Itscapital, Sydney, is also the nation’s largest city (Figure 4). The NSW economy is diverseand the largest nationally, contributing about one-third of the nation’s gross domesticproduct (GDP). The state economy is higher than for many national economies in theAsia Pacific (refer www.business.nsw.gov.au/invest-in-nsw/about-nsw).

While NSW generally has a temperate climate with mild to warm summers and coolwinters, the Building Code of Australia (ABCB, 2009) assigns it six of the eight nationalclimate zones (Zones 2, 4, 5, 6, 7 and 8, as shown in Figure 4). This range of climatezones directly influences building heating and cooling needs in NSW.

In the present case study, we focus the data gathering and reporting of results on theintermediate aggregation level identified in Australian census surveys as LGAs. Since2005, there have been 152 LGAs in NSW (Figure 4), each lying within a single climatezone.

4.2 Office building stockThe Building Code of Australia has only one classification for office buildings (Class 5 –Office Building) (ABCB, 1996), but Cordell Costing Solutions (2003) have classifiedcommercial office buildings into three types depending on the number of storeys, namely:

. low-rise (LR): one to three storeys;

. medium-rise (MR): four to seven storeys, with lifts; and

. high-rise (HR): more than eight storeys, with lifts.

Statistics on the existing office building stock in NSW under these classifications areshown in Table III. The floor area for LR buildings is dominant with 53.4 per cent of thetotal, followed by MR buildings (32 per cent) and HR buildings (14.6 per cent). Steel- andconcrete-framed buildings dominate, accounting for more than 99 per cent of allbuildings. Concrete-framed buildings are particularly dominant in MR and HR buildings.

New south wales

Sydneyarea

Climate zones12345678

Source: ABCB (2009)

Figure 4.LGAs and climate zoneclassifications in NSW

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Recently, the Australian Construction Industry Forum (ACIF) (2012) forecastnon-residential building stock considering the GDP, the economic position and otherfactors across Australia. Figure 5 shows the historical value (in Australian dollars,AUD) of the commercial office building stock in NSW to 2010, and the forecastvalues for 2011-2020. The value of new office buildings increased continuously until2006-2007, but then declined until 2009-2010 due to the global economic crisis. From2011, it is predicted that values will again continuously increase. New construction isshown to occur predominantly in the Sydney metropolitan area, with more than 90 percent of the total for NSW.

Using current market values (Rawlinsons, 2011) for different types of officebuildings in NSW, new building stock floor area (m2) is obtained by dividing the totaloffice building stock value (AUD) by the current market value of different types ofoffice buildings in different areas in NSW. As an example, Figure 6 shows the officebuilding stock as floor area for the Sydney City LGA, and it can be seen that the MR

LR MR HRNo. % No. % No. %

Building informationBuilding number 34,402 65.6 12,875 24.5 5,204 9.9Floor area (m2) 51.53 53.4 30.89 32.0 14.05 14.6Building numberConcrete framed 19,581 56.9 11,776 91.5 4,399 84.5Masonry 248 0.7 3 0.0 – –Steel framed 14,573 42.4 1,096 8.5 805 15.5Total building number 34,402 100 12,875 100 5,204 100Floor area (m2)Concrete framed 24.73 48.0 28.06 90.8 10.60 75.4Masonry 4.54 8.8 31,987 0.1 – –Steel framed 22.26 43.2 2.80 9.1 3.45 24.6Total floor area 51.53 100 4,235,512 100 14.05 100

Source: Geoscience Australia (2010)

Table III.Commercial office

building stock in NSW

Sydney (metropolitan)Rest of NSW

Forecast (F)

AU

D (

mill

ion)

Year

3,000

2,500

2,000

1,500

1,000

500

0

2002

-200

3

2003

-200

4

2005

-200

6

2006

-200

7

2007

-200

8

2008

-200

9

2009

-201

0

2011

-201

2

2012

-201

3

2013

-201

4

2014

-201

5

2015

-201

6

2016

-201

7

2017

-201

8

2018

-201

9

2019

-202

0

2010

-201

1 (F

)

2004

-200

5

Note: Data from 2011 onwards is based on forecasts

Figure 5.Value (in AUD) of

new office building stockin NSW

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building stock accounts for more than 57 per cent of the total. We have extended thisapproach to estimate the office building stock in NSW by LGA.

4.3 Building stock modelling4.3.1 Office building prototypes. For modelling, the selected office building prototypesfollow the LR, MR and HR classifications of Cordell (2003), and the availability of officebuilding stock statistical data such as that shown in Table III and Figures 5 and 6.For streamlined modelling, the structural frame – whether concrete or steel – isassumed not to affect energy performance, and two-storey, six-storey and 20-storeybuilding models are assumed sufficient to represent LR, MR and HR buildings,respectively. Thus, the baseline specifications of the office building prototypes areshown in Table IV.

Ideally, a different set of prototypes would be defined for each size of building,stratified by age of the building shell and age of the HVAC system (the latter issignificant due to improvements in the efficiency of such systems). GeoscienceAustralia (2010) has two classifications for building age: pre-1980 and post-1980.Considering new constructions, we extend this to include a third classification: pre-1980, post-1980 and less than five years old.

4.3.2 Office building stock model structure and calculations. The main buildingperformance model has a “nested” structure, applicable to each prototype. Table Vshows the breakdown and total number of building models (108 unique combinations)considered for each LGA. Compared to the range of variables and options that can beexplicitly changed in Table II, it is clear that this case study is a highly simplifiedexample of how the cross-typology framework can be applied.

The specific procedure follows the concept shown as Steps 2, 3 and 4 in Figure 3.Each prototype building is modelled using eQUEST 3.64 (http://doe2.com/equest/) foreach LGA (this tool runs DOE-2.2 as its simulation engine and performs an hourlysimulation of a building for a year (Zhu, 2006)). That is, for each prototype t in a givenLGA s, the model parameters are established for an average building of type p in LGAs; the building model is then run; and the results from this run (GHG and energy usage)are scaled up by a factor of A/a, where A is the total floor space of buildings of type p ins, and a is the average floor space of each such building.

High-rise (HR)Medium-rise (MR)Low-rise (LR)

Flo

or a

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(× 1

,000

m2 )

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Figure 6.Floor area (m2) of officebuilding stock in Sydney(2006-2007 to 2011-2020)

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The individual building’s energy performance is expressed in terms of energyintensity, and the associated GHG emissions are expressed in terms of carbonintensity.

4.3.3 Validation. The energy intensities of the LR, MR and HR prototype buildingsconsidered are calculated taking account of two different types of energy usage, asshown in Table V. As an example, Figure 7 shows the calculations for the Sydney LGA,which has the highest concentration of office buildings in NSW. It can be seen thatenergy intensity increases from LR to HR buildings. For example, electricity usage forthe LR building is 824 MJ/m2/year, while that of the MR building is 27 per cent moreand that of the HR building is 43 per cent more. The mixed energy usage buildings(electricity and gas heating and cooking) produced similar but slightly higher resultsthan their individual electricity-only counterparts.

On average, the existing Australian commercial building stock is rated three starson a five-star energy efficiency rating (Allen Consulting Group (ACG), 2009).

Energy demandTypologyclasses Physical building Energy “load”

Occupancyor useprofile Energy supply

Numberof settings

Building type (3): LR,MR and HRVintage (age and energyefficiency rating (3): pre-1980,post-1980 and last five years

Total energy loadprofile (3): low,medium and high

Occupanytype (1)

Energy type (2):electricity and gasRenewables (2):on- and off-site/grid electricity

Total 3� 3� 3� 1� 4¼ 108 building energy demand – supply combination settings for eachLGA

Table V.Case study whole-building

model partition settingsfor the NSW office

building stock for eachclimate zone and LGA

Building typeLR MR HR

Total floor area (m2) 2,450 9,600 32,000Floors (storeys) 2 (o3 storeys) 6 (4-7 storeys) 20 (48 storeys)Wall Brick 150 mm LW concrete Metal framed glass

spandrelFloor 150 mm concreteGlazing 20% single-colour tinted single glazing

(colour tinted double glazing for newbuilding stock)

Double glazing

Building operation 8:00 a.m.-6:00 p.m.HVAC system DX split-system air¼ conditioning VAV with reheat

(chilled water)Temperature cooling (1C) 23Temperature heating (1C) 22Lift n/a One of 7 kW of

electrical power loadThree of 7 kW of electricalpower load

Occupancy(m2/person)a 15

Equipment load(W/m2)a 11

Lighting load(W/m2)a 12

Source: aDECCW (2009)

Table IV.Specification of NSW

office building prototypesfor modelling whole-

building energyperformance

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To compare energy intensity results obtained from modelling with actual data, weobtained the reported energy consumption data for 300 commercial buildings in NSWfrom the Commercial Building Disclosure Scheme (Commercial Building Disclosure(CBD), 2011), which began on 1 November 2011 to improve the energy efficiency ofAustralia’s commercial buildings. A typical commercial building with a three-starenergy rating in NSW has an energy consumption of 1,085 MJ/m2/year (220 MJ/m2/yearof standard deviation). This is similar to Bannister’s (2004) survey, which investigatedAustralian office building energy consumption (923 MJ/m2/year in Sydney with 243 MJ/m2/year of standard deviation). When compared with our modelling results, we obtain91-261 MJ/m2/year of mean differences for electricity energy use only and 160-204 MJ/m2/year for combined electricity and gas usage.

4.4 Results and analysisThe total energy consumption and corresponding GHG emissions of office buildings inNSW were calculated for each LGA and aggregated up to the state level from 2006through to 2020. The projected calculations were based on the new office buildingforecasts shown in Figures 5 and 6.

Figure 8 shows that 15,226 TJ of energy was consumed through operating officebuildings in NSW in 2006-2007, and that this energy consumption level will have increasedby 50 per cent in 2015-2016 and by 85 per cent in 2019-2020. Of the total energyconsumption in each year, office equipment uses the most energy (30-32 per cent), followedby lighting (23-25 per cent) and then cooling energy consumption (20-21 per cent).

Total carbon emissions from energy use are shown as a trend line in Figure 8 (withvalues corresponding to the ordinate axis label on the right of the chart). The totalcarbon emission in 2006-2007 was 3.98 million tons, which will have increased by50 and 85 per cent in 2015-2016 and 2019-20, respectively. The relative distribution ofemissions by energy end-use category is exactly the same annually as that for theenergy bar charts.

Since we earlier selected an intermediate aggregation level at the LGA, we can thusplot the spatial distribution of NSW office building energy consumption and carbonemissions by LGA from 2006 to 2020; the plot for the former is shown in Figure 9.It is evident that breaking down building stock energy use and carbon emissionresults by types of building, end-use categories and spatial distribution, will enablestakeholders and decision makers to better analyse “hot spots” that can be targeted by

824

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ExternalEquipmentLiftFanHotwaterLightingCoolingHeating

Electricity use only Electricity and gas usage

Medium HighLow

Figure 7.Energy consumptionof office buildings inSydney LGA

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specific actions or solutions. Such hot spots may be a building type or vintage, anend-use category for specific building types (e.g. HVAC for older buildings) or a specificfuture growth area. The latter will be of interest to utility companies, for example,when deciding infrastructure maintenance and/or expansion investments to meetfuture market needs. Government agencies at national, state and local levels can thenplan and initiate tailored intervention schemes that address these hot spots.

2006/2007

2015/2016

< 215 (1,000 TJ)

215-969 (1,000 TJ)

969-1406 (1,000 TJ)

1406-3078 (1,000 TJ)

> 3,078(1,000 TJ)

2019/2020

2010/2011

Figure 9.Spatial distribution ofoffice building energy

usage in NSW (2006-2020)

Ene

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(TJ)

35,000

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ExternalEquipmentLiftFanHotwaterLightingCoolingHeatingGHG emissionC

arbo

n em

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on (

100

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CO

2)

4,000

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energy consumption andcarbon emissions in NSW

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4.5 Future scenarios analysesIn order to compare the relative effectiveness of various intervention options on theoverall carbon emissions of the office building stock in NSW to 2020, here weinvestigate seven future scenarios relative to the BAU projections given in the previoussection and plotted in Figures 8 and 9. The simplified scenario settings are as follows:

. Scenario 1: introduce a five-star minimum energy performance requirement(i.e. o223 MJ/m2/year for “base” building, o63 kg/m2/year for tenancy) for newconstruction from 2010-2011 onwards;

. Scenario 2: improve the energy efficiency of HVAC systems by 30 per cent;

. Scenario 3: change tenant behaviour to reduce energy use by 20 per cent;

. Scenario 4: improve the energy efficiency of HVAC systems by 30 per cent andreduce tenant energy use by 20 per cent (i.e. combined Scenarios 2 and 3);

. Scenario 5: introduce five-star minimum energy performance for new buildingsand uptake of 20 per cent renewable energy in existing buildings;

. Scenario 6: introduce five-star minimum energy performance for new buildingsand uptake of 50 per cent renewable energy in existing buildings; and

. Scenario 7: introduce five-star minimum energy performance for new buildings,improve the energy efficiency of HVAC systems by 30 per cent, reduce tenantenergy use by 20 per cent and uptake of 20 per cent renewable energy in existingbuildings (i.e. combined Scenarios 4 and 5).

Figure 10 shows these case study scenario settings mapped in the proposed cross-typology framework. In this way, it becomes clear what kind of specific interventioncan be undertaken by specific stakeholders to address the critical elements of aparticular scenario. The other potentially more beneficial application of the frameworkis to use it at the very start of planning the different scenarios of interest to the decisionmaker.

Simulation calculations for these scenarios were undertaken following the samegeneral procedure in Figure 3 and Section 3.3, and the specific methodology describedin Section 4.3.

Figure 11 shows the relative impacts of the seven different scenarios compared tothe BAU case to 2020. Scenario 1 would reduce emissions by more than 1.014 milliontons of carbon by 2020, which is 14 per cent less than the BAU case. Scenario 2 wouldreduce carbon emissions by 1.49 million tons by 2020, which is similar to the results for

Interventionschemes

Energy demand

1

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6 7

Energy‘‘supply’’system

Sticks

Carrots

Tambourines

NSW office building stock case study

Scenario Scenario Scenario Scenario Scenario Scenario Scenario

Physical building Energy ‘‘loads’’ Occupancy

Figure 10.Mapping of the scenariosettings for the NSW officebuilding stock case study(2006-2020) based on thecross-typology framework

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Scenario 3. The combined Scenario 4 would result in a reduction in carbon emissions of1.98 million tons, which is 27 per cent less than the BAU case.

Introducing the uptake of renewable energy sources in existing buildings, as set inScenarios 5 and 6, results in 22 and 36 per cent less carbon emissions than the BAUcase, respectively. While the practical feasibility of Scenario 6 (replacing 50 per cent ofthe energy supply with renewable energy sources for existing buildings) seemsoverly ambitious, it is seen that Scenario 7 would have a similar effect of a 36 per centreduction in carbon emissions by 2020. Implementing Scenario 7 may be achievable,but it would require the actions and cooperation of a number of stakeholders. We knowthere are different pathways to achieving a carbon reduction goal, but knowing thespecific schemes in each option allows the assessment of other factors that may beimportant in final selection.

In the Carbon Pollution Reduction Scheme Green Paper, the Australian Government(2008) sets a long-term goal of reducing Australia’s GHG emissions by 60 per cent of2,000 levels by 2050, and a medium-term reduction target of 5 per cent of 2,000 levelsby 2020. If we assume that the same target levels are distributed proportionally bystate and industry sector, the 2020 GHG emissions target for the commercial officebuilding stock in NSW will be a 45 per cent reduction from the 2006-2007 baseline level(as marked in Figure 11). While none of the scenarios explored in this case study meetsthis reduction level, the cross-typology framework and bottom-up modelling approachpresented herein will enable different stakeholder groups, and especially government,to systematically design and assess different intervention schemes that have thepotential to meet this target before actual implementation.

5. ConclusionsWe have presented a systems-based bottom-up modelling approach to systematicallyassess the effectiveness of specific actions and decisions by different stakeholders toreduce GHG emissions from energy use in the building sector. The cross-typology

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Figure 11.Carbon reduction

scenarios forthe commercial office

building stock in NSW

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framework takes into account the potential impacts of broad policy instruments andspecific stakeholder action, whether it is setting regulations and formulating policy(by government), developing incentive schemes (government and/or industry), investingin technology development (industry) or adopting new technologies and systems(industry and consumers), and/or encouraging behavioral change (government, NGOsand industry) – alone or in combination. The application and benefits of this approachhave been demonstrated in a simple case study of office building stock in the state ofNSW, Australia, first at the LGA level and then aggregated up to the whole state. Withthe building stock energy use and GHG emission results broken down by type ofbuilding, end-use categories and spatial distribution, stakeholders and decision makersare able to better analyse the hot spots that can be targeted by specific actions orsolutions. The structured cross-typology framework is a novel contribution to bottom-upmodelling approaches to building stock energy use and GHG emission analysis.

The application and advantages of the proposed framework need to bedemonstrated further for mixed building types (e.g. Howard et al., 2012) and specificbuilding types with greater granularity of building modelling details, energy use andsupply options, and spatial resolution. In particular, the diffusion models developed forthe residential sector (Higgins et al., 2011; Higgins and Foliente, 2012) need to be closelyintegrated with physics-based modelling, and explicitly incorporated in futurebuilding stock scenarios analysis to improve the treatment of the uptake of voluntaryschemes and new technologies in energy and GHG emission projections.

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About the authors

Greg Foliente is a Senior Principal Research Scientist and Group Leader at CSIRO. He leads andmanages R&D initiatives and projects related to climate mitigation and adaptation andsustainable built environment. A recipient of a number of prestigious international awards –including the 2003 James Croes Medal from the American Society of Civil Engineers (ASCE) – hewas co-chair of the 2008 World Sustainable Building Conference (SB08 Melbourne). He serves onthe editorial board of three international journals, and is an expert consultant to industry,government and international organisations. He received his PhD and other post-graduatedegrees at Virginia Polytechnic Institute and State University (Virginia Tech) in the USA.Greg Foliente is the corresponding author and can be contacted at: greg.foliente@csiro.au

Seongwon Seo is a Senior Research Scientist at CSIRO. He specialises in life cycle assessmentand modelling, and undertakes research to minimise the environmental impact of urbaninfrastructure and improve their overall performance and efficiency. He received his PhD andMaster’s degree from Chung Ang University (CAU) in South Korea. Prior to joining CSIRO, heworked at the University of Tokyo in Japan as a Post-Doctoral Fellow. He was a recipient of theCenter of Excellence Research Fellowship at the Research Center for Advanced Science andTechnology (RCAST) from the Japanese Government.

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