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FINAL REPORT: QUANTITATIVEASSESSMENT OF ENERGY SAVINGSFROM BUILDING ENERGY EFFICIENCYMEASURES
Prepared for: Department of Climate Change & Energy Efficiency
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Date: 11 March 2013
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Table of Contents
1. Executive Summary ...................................................................................... 62. Introduction ............................................................................................... 93. Methodology ............................................................................................. 10
3.1 Overview ........................................................................................ 103.2 Autonomous Energy Efficiency Improvement ............................................. 123.3 Stock Modelling ................................................................................ 123.4 Baseline Energy Projection .................................................................. 14
4. The Impact of Nominated Measures ................................................................ 224.1 Residential Buildings .......................................................................... 224.2 Commercial Buildings ......................................................................... 28
5. Network Savings ........................................................................................ 375.1 Methodology .................................................................................... 375.2 Results ........................................................................................... 38
6. RIS Limitations and Recommendations ............................................................. 396.1 Methodological Inconsistencies ............................................................. 396.2 Insufficient Transparency .................................................................... 436.3 Data Limitations/Research Gaps ............................................................ 436.4 Emerging Issues ................................................................................ 44
7. Bibliography ............................................................................................. 45D1. Residential Measures ................................................................................ 61D2. Commercial Measures – Energy Savings .......................................................... 65
Appendices
Appendix A: Statement of Requirement…………………………………………………………………………….……47Appendix B: Autonomous Energy Efficiency Improvement………………………………………………….….49Appendix C: Residential Baseline – Additional Analysis…………………………………………………….…….54Appendix D: Energy Savings by State/Territory and Climate Zone…………………………………………61
Index of Figures
1. Methodology Overview 122. Residential consumption of electricity, FY2001 to FY2011 153. Residential consumption of gas (and other fuels), FY2001 to FY2011 154. Commercial Building Baseline Energy Consumption, FY 2002-FY2050 205. Total past and project residential electricity consumption, and savings from residential
building measures, Australia 266. Total past and project residential gas consumption, and savings from residential
building measures, Australia 267. Per dwelling past and projected residential electricity consumption, and savings from
residential building measures, Australia 278. Per dwelling past and projected residential gas consumption, and savings from
residential building measures, Australia 279. Total energy consumption and expected savings, commercial buildings, Australia,
FY2002 – FY2050 3510. Projected energy intensity – baseline and ‘with measures’, commercial buildings,
Australia, FY2002 – FY2050 3611. Decomposition of Change in Energy Consumption 5012. Residential electricity consumption baseline, with measures and baseline+autonomous
energy efficiency improvement, FY2001 – FY 2050 5213. Residential gas consumption baseline, with measures and baseline+autonomous energy
efficiency improvement, FY2001 – FY 2050 52
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14. Commercial building energy consumption ‘without measures’, ‘with measures’ andwithout measures + AEEI, FY2002 – FY 2050, Australia (PJ) 53
15. Per dwelling consumption of electricity, showing actual consumption and the modelledeffect of ‘baseline’ policy measures, 2001 - 2011 54
16. Per dwelling consumption of gas etc., showing actual consumption and the modelledeffect of ‘baseline’ policy measures, 2001 - 2011 55
17. Residential electricity consumption per capita, based on AES data 5618. Residential electricity consumption per capita, based on ESAA data 5619. ‘Houses continue to grow’ 5820. Australian Average Temperature Anomalies, 1999 – 2011 59
Index of Tables
1. Residential energy savings by measure and fuel, 2003 – 2050, Australia (PJ) 252. Commercial building energy savings by measure and fuel, 2002 – 2050,
Australia 353. Estimated network cost savings consequent upon selected building energy efficiency
measures, Australia 384. 4 star energy savings by state/territory 615. 5 star energy savings by state/territory 626. 6 star energy savings by state/territory 637. Residential mandatory disclosure energy savings by state/territory 648. BCA2006 energy savings by state/territory 659. BCA2010 energy savings by state/territory 6510. Commercial building disclosure energy savings by state/territory 66
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1. Executive SummaryThis study estimates the energy savings realised by a range of Commonwealth buildingenergy efficiency measures over the period FY2002 to FY2050, and also the costsassociated with these measures. The measures include:
All building energy efficiency regulations that have been included in the BuildingCode of Australia (BCA), for residential and commercial buildings;Commercial Building Disclosure (CBD) program;Energy Efficiency in Government Operations (EEGO) policy;Residential Mandatory Disclosure (assuming the program commences in FY2014)(RMD).
The study takes into account current data with respect to past and expected growth inthe building stock, and also current energy prices (including carbon pricing). Further,it takes into account the effect of a range of other measures, not specifically understudy here, including relevant state energy efficiency measures and some otherCommonwealth policy measures that may affect the savings attributable to themeasures under study. We estimate the energy savings attributable to the measures,and also the flow on effect of the electrical energy savings for reduced expenditure onelectricity networks. In Appendix C, we also discuss how the results compare withestimates of autonomous energy efficiency improvement. Energy savings arepresented by fuel and by state and territory, and also by BCA climate zone wherepossible (Appendix D).
We have utilised recent estimates of the actual effect of measures where available(eg, data to November 2012 for CBD). For the energy performance requirements in theBCA, we have relied upon the original estimates of energy savings (and unit costs) fromthe relevant regulatory impact analyses, although these estimates have been appliedto our updated stock model. For RMD, we also rely on the RIS, as this measure is yet totake effect. Generally our estimates of energy savings attributable to measures arehigher than in the relevant regulatory impact assessments, however the main reasonfor this is that we assume that all policy measures continue to apply to 2050, whereasthe majority of RIS’ assume that measures cease after 10 years.
The study is accompanied by three spreadsheet models covering the residential,commercial and network savings results.
In total, the measures listed above are estimated to have saved some 16.5 PJ inFY2012, and those savings are projected to reach 54 PJ by 2020 and 222 PJ by 2050(see Table ES1 below).
Table ES1: Summary of Energy Savings, All Measures, by Sector, 2012 – 2050 (PJ)2012 2020 2030 2040 2050
Residential 11.3 31.7 63.7 94.4 121.8Commercial 5.2 22.3 44.6 70.3 100.5Total 16.5 54.0 108.3 164.7 222.3
Source: pitt&sherry
The residential measures accounted for just over 11 PJ in energy savings in 2012(noting that these measures applied in earlier years than the commercial measures),and are expected to reach nearly 32 PJ by 2020, and nearly 122 PJ by 2050. Thecommercial measures accounted for some 5.2 PJ in energy savings in 2012, and areexpected to exceed 22 PJ by 2020 and just over 100 PJ by 2050.
Overall, the energy savings are weighted towards electricity, which accounts for nearly136 PJ out of the 2050 total savings, while gas accounts for just under 87 PJ (see Table
7
ES2 below). However, the fuel savings differ markedly by sector, with electricityaccounting for over 93 PJ out of the 100 PJ savings in 2050, while for residential, gasaccounts for over 79 PJ out of the almost 122 PJ total.
Table ES2: Summary of Energy Savings, All Measures, by Fuel, 2012 – 2050 (PJ)2012 2020 2030 2040 2050
Electricity 6.2 29.3 62.4 97.8 135.7Gas 10.3 24.8 45.9 66.9 86.6Total 16.5 54.0 108.3 164.7 222.3
Source: pitt&sherry
Table ES3 summarises the savings attributable to the residential energy efficiencymeasures by fuel in selected years to 2050. Table ES4 shows the same data but forcommercial building energy efficiency measures.
Table ES3: Summary of Energy Savings, Residential Energy Efficiency Measures, byfuel, 2012 – 2050, Australia (PJ)
2012 2020 2030 2040 2050MandatoryDisclosure
Electricity 2.9 8.8 14 17.4
Gas 2.2 6.6 10.5 12.9BCA WaterHeating
Electricity 0.3 0.7 1.1 1.5
Gas -0.1 -0.2 -0.2 -0.3BCALighting
Electricity 0.3 0.8 1.2 1.6
GasBCA 6 star Electricity 0.3 2.7 6.1 9.4 12.8
Gas 0.3 2.7 6.1 9.5 12.9BCA 5 star Electricity 0.5 1.4 2.7 4 5.3
Gas 0.9 2.4 4.3 6.3 8.2BCA 4-star Electricity 0.9 1.3 2.2 3 3.9
Gas 8.4 15.6 25.6 35.6 45.6Subtotals Electricity 1.7 8.9 21.3 32.7 42.5
Gas 9.6 22.8 42.4 61.7 79.3Total 11.3 31.7 63.7 94.4 121.8
Source: pitt&sherry
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Table ES4: Summary of Energy Savings, Commercial Energy Efficiency Measures, byfuel, 2012 – 2050, Australia (PJ)
2002 2008 2012 2020 2030 2040 2050BCA 2006 Electricity 0.5 2.5 6.7 12.6 19.6 27.9
Gas 0.1 0.6 1.6 3.0 4.6 6.6BCA 2010 Electricity 1.1 10.3 23.6 39.2 57.5
Gas 0.0 0.0 -0.1 -0.1 -0.2CBD Electricity 0.4 2.8 4.2 5.5 7.0
Gas 0.0 0.3 0.5 0.7 0.9EEGO Electricity 0.0 0.4 0.5 0.6 0.7 0.8 0.8
Gas 0.0 0.0 0.1 0.1 0.1 0.1 0.1Subtotals Electricity 0.0 0.9 4.5 20.4 41.1 65.1 93.2
Gas 0.0 0.2 0.7 2.0 3.5 5.2 7.3Total 0.0 1.1 5.2 22.3 44.6 70.3 100.5Source: pitt&sherry
We are not able to succinctly summarise the total costs associated with thesemeasures, due to methodological inconsistencies between the relevant RISs. Notably,the issue of ‘learning’, or the rate of change in compliance costs through time, differswidely between sources. Given that this study encompasses a time period of nearly 50years, these differences could lead to significant errors in aggregated cost estimates.This issue is discussed further in Chapter 6, while estimates of the costs associatedwith individual measures, and issues relating to these estimates, are provided inChapter 4.
The electricity savings generated by these policy measures are estimated to reduce thecost of network infrastructure by some $590 million in 2020 and by over $2.6 billion by2050. The present value of these savings is estimated at some $4.7 billion at a 7% realdiscount rate. Please refer to Chapter 5 for details.
As requested, in Chapter 6 we make a range of observations and recommendations withrespect to the quality and consistency of regulatory impact assessments of buildingsmeasures, the adequacy of data to undertake analyses of this type, and relatedresearch requirements.
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2. IntroductionThe Department of Climate Change and Energy Efficiency’s Building Energy EfficiencyBranch (BEEB) commissioned pitt&sherry in November 2012 to quantitatively analyseand report on the energy savings, and associated costs, attributed to the followingpolicy measures:
All building energy efficiency regulations that have been included in the BuildingCode of Australia (BCA);Commercial Building Disclosure (CBD) program;Energy Efficiency in Government Operations (EEGO) policy;Heating, Ventilation and Air Conditioning High Efficiency Systems Strategy (HVACHESS);Residential Mandatory Disclosure (assuming the program commences in FY2014)(RMD).
The Statement of Requirement, setting out the terms of reference for and scope of thestudy, may be found at Appendix A. We note that the timeline, scope and budget ofthe study were both restricted, necessitating certain simplifying assumptions to bemade. These are noted where relevant. Also the scope excludes consideration of thedirect benefits of these policy measures – such as the value of avoided energy costs –although indirect benefits such as avoided network costs are included. This is becausethe study will feed into annual Australian emissions projections undertaken by DCCEE.The Report is accompanied by three spreadsheet models relating the analysis of theresidential energy consumption baseline and measures, commercial energyconsumption and measures, and network infrastructure savings.
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3. Methodology3.1 Overview
The energy savings attributable to the measures under study are analysed againstcounterfactual baselines (one each for residential and commercial) that attempt toaccount for known historical conditions (eg, energy prices, stocks) and extant policies –to the extent permitted by available data. As discussed further below, this is currentlymore feasible in the residential sector than in the commercial sector, due to datalimitations with respect to the detailed structure of commercial building energy use1.
The methodology employed is a ‘bottom up’ analytical approach that begins byprojecting the relevant, energy-using stock growth and turnover independently fromthe measures under study. Rather, the stock growth reflects the fundamentals ofeconomic and population growth, and expected structural and demographic changes.The approach assumes that the policy measures under study are not large enough toinfluence these stock outcomes materially. The stock turnover is projected toFY20502. In stock modelling, account is taken of the economic lives of investments.These vary depending upon the nature of product/investment made. For example,residential buildings are generally assumed to have an economic life of 50 years andcommercial buildings 40 years: both periods exceed the projection timeline of thisstudy.
A ‘frozen efficiency’ projection is then made by applying the energy intensities fromthe beginning of the study period (FY2002 in this study) to the projected stock growth.We note that the term ‘frozen efficiency’ could be misleading, as changes in buildingenergy consumption may be caused, for example, by occupants choosing a higher (orlower) level of thermal comfort (a change in energy service levels) or by structuralchanges (eg, larger house sizes). The sole purpose of making a frozen efficiencyprojection is to provide a reference scenario to which policy measures, price and otherknown effects (eg, structural changes, income elasticities) may then be applied. If allrelevant structural, policy and elasticity-related factors are taken into account, thenthe resulting projection will replicate historical energy consumption in the relevantsector, and thus allow for projections to be made forward on a ‘calibrated’ basis.
In practice, model calibration is reasonably achievable in the residential sector –although we conclude that over at least part of the historical period, structural and/orenergy service level changes are influencing observed energy consumption in ways thatare not fully transparent. In the commercial sector, it is not practically feasible toclosely reconcile modelled building related energy consumption with published (oreven unpublished) energy statistics, as the Australian Energy Statistics3 are reportedfor the ‘commercial and services’ sector, based on ANZSIC classifications, and includesenergy end uses that are unrelated to buildings. pitt&sherry has previously attemptedsuch a reconciliation for the Department (in the ‘commercial building baseline study’ –unpublished), but it is not possible to be precise given the data structure. Further, thepicture of energy end-use and structural changes in the commercial sector is less welldocumented than in residential. However, even in the residential sector there is anincomplete statistical picture of all of the factors affecting historical energy end-use,notably changes in structure and/or energy service levels. The significance of thesefactors is discussed further below.
1 pitt&sherry (2012b) notwithstanding. We have also previously made detailed recommendations forimproving energy efficiency data and statistics in Australia, including the residential and commercialsectors in pitt&sherry (2012a).2 Throughout this report, we adopt the convention that FY2050 means the financial year 2049-50.3 BREE (2012a)
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The impact of the policy measures under study in this project is then assessed againstthe counterfactual baseline described above. It should be noted that many of themeasures apply only to new building work, or impact on only a part of the totalbuilding stock (eg, CBD currently impacts on office spaces greater than 2,000 sqm).Indeed, the energy savings attributed to each measure is affected by:
the stringency of the measure (how much energy savings are generated foreach building or transaction);the scope of its application (what portion of the building stock does it affect);andthe rate of uptake (or incidence) of the measure (of the population affectedby the measure, how many respond by taking actions that save energy - this isparticularly relevant for voluntary measures).
Generally, therefore, the energy savings attributable to measures are modest initiallybut grow through time.
It was agreed with the Department that measures would be analysed using anassumption that current policy measures stay in place, with their current designs,through the projection period (to FY2050) – which we refer to as a ‘frozen policy’assumption. While it is in fact likely that measures will change before FY2050, thisassumption obviates the need to predict future policy decisions (such as the date atwhich certain measures expire, or the timing and extent of future increases instringency). Also, it is not likely that policy settings will regress in future.
Further, the practical consequences of this assumption (compared to the principalalternative, which is to assume an arbitrary end point for measures, eg, after 10years), are limited. This is because the efficiency levels represented in frozen policysettings may be overtaken by general market trends, or autonomous energy efficiencyimprovement (AEEI)4, over the nearly five decade timeframe covered in this study. Inthis case, the energy savings attributable to the measures may no longer be considered‘additional’ to those that may have been expected to occur without the policymeasures. Also, with respect to the costs of measures, it is generally the case thatincremental investment and compliance costs associated with measures tend to declinethrough time, including to zero. For example, under a frozen policy scenario, it wouldbe difficult to ascribe any incremental building costs in FY2050 to BCA2004: anyincremental costs that arose in FY2004 will have long since been incorporated withinchanged industry practices and technologies (a process referred to as ‘learning’).Some administrative costs could be considered to remain, however, at least wheremeasures would require ongoing management by government.
Note that where relevant – and without departing from the frozen policy assumption -we model effects that tend to lessen the energy savings of some measures over time.These effects include saturation, diminishing returns or equipment life effects. Forexample, chillers built to 2008 minimum energy performance standards (MEPS) areassumed to have an average economic life of 15 years. This assumption is derived fromthe 2008 RIS and represents the a value for air-cooled chillers, whose market share isincreasing relative to water-cooled chillers, which have a longer life of between 20 – 30years.5 Therefore, from year 16 of this measure onwards, incremental energy savingsonly accrue to the net annual increase in the post-MEPS chiller stock (ie, those addedin year 16 minus those added in year 1).
Figure 1 below provides an overview of the methodology.
4 AEEI is discussed in more detail in Appendix C.5 MCE (2008), p. 10.
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Figure 1: Methodology Overview
Source: pitt&sherry
3.2 Autonomous Energy Efficiency ImprovementAs noted above, our methodology for this study includes examining an effect known as‘autonomous energy efficiency improvement’ (AEEI). We provide estimates of thiseffect for each of the residential and commercial buildings in the respective resultssections below. However we do not deduct this effect from the counterfactual‘without measures’ baseline, as we believe it is unclear that quantitative estimates ofAEEI are truly ‘autonomous’; that is, that they exclude the effect of energy efficiencypolicies, price elasticity effects and other factors that are under study in this project.In this circumstance, deducting an allowance for AEEI from the baseline would risk‘assuming away’ the measures and effects under study in this Report. Please refer toAppendix B for further analysis of this issue.
3.3 Stock Modelling
3.3.1 ResidentialFor measures affecting residential energy efficiency, our analysis required an estimateof the stock of occupied Class 1 (separate houses, semi-detached and row houses) andClass 2 (flats) dwellings in each year. The total number of occupied dwellings inFY2001, FY2006 and FY2011 was obtained from the census data for those years.Estimates of the total stock in the intervening years were calculated using the averageannual growth rate of stock between the census periods. Unoccupied dwellings werenot included, as the portion of the total stock that is unoccupied – representingtemporary vacancies in letted property, unoccupied houses for sale, unoccupied
Construct a stockturnover model
for relevantbuildings from
2001 – 2050 (bystate/territory
and climate zone)
Use historicalenergy
consumptiondata (by fuel) tocharacterize the
energyconsumption,
and hence energyintensity, of thestock in the base
year.
Project theenergy
consumption ofthe stock
annually to 2050based (initially)
on a ‘frozen 2001efficiency’
projection andthe stock
evolution asabove.
Subtract from this projection (year byyear to 2050, or ‘life of measure’, asappropriate):
a) An additional allowance for energysavings due to price elasticity effectsfrom the spike in real market prices(temporary effect?) and carbon prices;
b) An allowance for ‘other’ measures(state, plus federal measures not understudy, insofar as they impact on energyconsumption in buildings)
Calculate the incrementalenergy savings
attributable to each of themeasures under studyagainst that baseline.
Calibrate the projectedenergy consumption withall of the above includedagainst historical data –
2001 to 2012 – and go backand adjust as necessary.
Calculate incrementalcosts (private sector &
government) associatedwith for each measure
Calculate autonomous EEimprovement for each
sector and compare withimpact of savings
measures
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holiday houses and apartments, etc, and estimated at around 10% of the stock –appears reasonably steady though time. Therefore these dwellings are not likely toimpact materially on changes in the demand for energy.
Annual incremental increases in the housing stock were based on BIS Shrapnel’s report,Building in Australia 2012-2027.6 The report contains historical and forecast five-yearperiod averages of new dwelling completions for each state/territory, from FY2001 toFY2027. The forecasts are based on an assessment of the long-term underlying demandfor each dwelling type in light of long-term economic and demographic trends. FromFY2027 to FY2050 it was assumed that annual completions of new dwellings remainconstant.
BIS splits its residential stock forecasts into houses and medium and high densitydwellings. They classify semi-detached, terrace housing and flats in buildings of 3-storeys or less as medium density dwellings, whereas flats in buildings greater than 3-storeys are classified as high-density dwellings. For the purposes of dividing the stockinto Class 1 and Class 2 dwellings we assumed that 50% of medium-density dwellingsare flats (Class 2) and 50% are semis, row houses etc (Class 1), an assumption that BISShrapnel considers reasonable. This 50/50 share was assumed for each state/territory.It is forecast that in most states/territories, the proportion of houses that make upnew dwelling completions falls over time, with the proportion of flats/semis increasingaccordingly. Australia wide, the share of flats in new building work rises from 20% inFY2012 to nearly 24% by FY2050. Over the same period, the share of semi-detachedhouses increases from around 10.5% to nearly 15%, while the share of stand-alonehouses falls from around 69% to just over 61%.
In addition to state/territory stock numbers, the residential building stock was alsosplit into BCA climate zones. Shares by climate zone were based on previous workundertaken by the Centre for International Economics for the Australian Building CodesBoard (ABCB)7. Validating the CIE estimates for each climate zone would have involvedestimating the stock for each local government area within each climate zone usingABS data; a very time-consuming process. However, as a “sense-check” we didundertake the process for Climate Zone 7 which yielded a total stock estimate that waswithin 3% of the result obtained using the CIE stock share assumption. The distributionof dwelling types by climate zone is assumed to remain constant through time. This isa simplifying assumption that could be relaxed with additional analysis.
3.3.2 CommercialAn estimate of the commercial building stock in FY2012, as well as the shares of totalstock by building type, was based on pitt&sherry’s ‘commercial building baselinestudy’8, supplemented by additional information provided by the Department from BISShrapnel reports. Estimates of the commercial stock for financial years FY2001-FY2012and FY2012- FY2050 were prepared using the BIS Shrapnel’s estimates of annual growthof stock between those two periods. The annual growth of stock between FY2001 andFY2012 was estimated to be around 1.94%, while for future years it is forecast to growat a slower rate, around 1.6%.
For comparison, we note that for the 2006 Commercial Building Regulatory ImpactStatement (RIS)9, Access Economics was commissioned to examine past stock growthtrends and to make projections to 2020. The average growth rate in constructionactivity in that study was set at 2% per annum, based on analysis of Australia Bureau ofStatistics (ABS) construction activity data. However, it should be noted that the ABSdata for construction activity is value, rather than area, based, and also that it fails to
6 BIS Shrapnel (2012)7 ABCB (2009a).8 pitt&sherry, 2012b.9 ABCB (2006), p. 82.
14
distinguish between demolitions, refurbishment and new builds. For the 2009 RIS10,stock growth is reported in a highly summarised form, but appears to be based on asomewhat slower growth trend of around 15% in total over the 2010 – 2020 period. Aswith the residential stock, commercial stock shares are split between states/territoriesas well BCA climate zones the latter using a methodology described in a ‘model RIS’tool previously provided to pitt&sherry by DCCEE (unpublished). Such estimates couldbe improved but, as noted in section 3.3.1, appear to be reasonably accurate.
3.4 Baseline Energy Projection
3.4.1 Residential buildingsThe starting point for modelling energy consumption in residential buildings was actualresidential sector energy consumption as reported in Australian Energy Statistics (AES)(BREE, 2012). Initial analysis was based on the period from FY2001 and FY2011). Allenergy use by the residential sector was classified as either electricity or gas etc.; thelatter includes natural gas (pipeline gas), LPG and distillate. Use of fuel wood wasignored.
Since this study is primarily concerned with that part of residential energy consumptionwhich is related to the building fabric and covered by the BCA11, it was necessary firstto subtract energy uses outside this scope. These are energy use by electric appliances(plug loads) and energy used for cooking. Estimates of average quantities of energyused per dwelling for these purposes in each year, disaggregated by State/Territory,were sourced from EES (2008), noting that this important reference used historicaldata current up to around FY2006, and therefore is now dated. pitt&sherry haspreviously recommended that this reference be updated at intervals not exceedingthree years.12 The resultant average per dwelling estimates of energy consumption foractivities covered by the BCA were aggregated to State/Territory total energyconsumption, using stock model data.
A further adjustment to these totals was made to allow for energy used in apartmentbuilding central services and accounted in AES under the Services and Commercial,rather than the Residential, sector. pitt&sherry has previously estimated thisadjustment factor in pitt&sherry (2012b). The national totals for each year were pro-rated to each State/Territory in proportion to the respective shares of Class 2dwellings. The effect of this adjustment is to increase the estimate of energyconsumption in residential buildings.
Total national energy consumption affected by the BCA for residential (Class 1 andClass 2) buildings - that is, new building work including new buildings, rebuilds afterdemolition and major refurbishments/extensions - was calculated by summing theindividual State/Territory totals. Figures 2 and 3 plot these figures both in total andper dwelling, for electricity and gas (and other fuels), respectively. It can be seenthat, on a per dwelling basis, consumption of electricity increased to around FY2004,and then levelled off, whereas consumption of gas etc. was level throughout the periodfrom FY200113. The rise in total gas consumption shown in Figure 3, while per-dwellingconsumption remains flat, is broadly attributable to the increase in total dwellings overthe period. However, we note that there is no data source in Australia that documentsthe number of houses with/without gas connections. As the consumption patterns andresponse options available to these households differ considerably, pitt&sherry
10 ABCB (2009), p. 130.11 Noting that Residential Mandatory Disclosure may have a wider end-use scope than this.12 pitt&sherry (2012a).13 Note that this change in consumption trends around 2004 is also replicated in ESAA data. Note thatseveral years ago, AES misallocated some commercial energy consumption to the residential sector, butthis error has since been corrected.
15
considers this to be an important gap in energy statistics in Australia. We havepreviously made recommendations as to how to redress this gap.14
Figure 2: Residential consumption of electricity (excl. plug loads & cooking),FY2001 to FY2011
Source: pitt&sherry, from Australian Energy Statistics
Figure 3: Residential consumption of gas (and other fuels), excl. cooking, FY2001 toFY2011
Source: pitt&sherry, from Australian Energy Statistics
The next step in the estimation of a residential energy consumption baseline was tomodel the effects of the various policies and measures which came into force duringthe historical period of this study. These include the energy savings that resulted from
14 ibid.
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0
50
100
150
200
250
300
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Per d
wel
ling
enre
gy (G
J)
Tota
l ene
rgy
(PJ)
Total Per dwelling
0.0
5.0
10.0
15.0
20.0
25.0
0
30
60
90
120
150
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Per d
wel
ling
enre
gy (G
J)
Tota
l ene
rgy
(PJ)
Total Per dwelling
16
the introduction of 4-star and 5-star minimum performance standards into the BCA.Note that these are measures under study, but they are modelled with the baseline inorder to facilitate reconciliation of actual and modelled energy consumption. Thesavings attributable to these measures are reported below.
4 star energy efficiency standards for new housingFor residential buildings, the specific requirement was that all new Class 1 dwellingsachieve a NatHERS energy performance of at least 4 stars. This requirement came intoforce in May 2001 and in the modelling was assumed to affect new houses occupiedfrom July 2002 onward, i.e. from the beginning of the FY2003 (noting that jurisdictionshad then, and retain now, discretion regarding the timing of the introduction of energyperformance requirements, and also to make state variations: these have not beenmodelled).15
The published Regulatory Impact Statement (RIS) for this measure provides onlynational average per dwelling estimates of expected savings in total energyconsumption and greenhouse gas emissions. Using approximate weighted averagenational emission factors for electricity and gas, these numbers were reverseengineered to give national average savings of electricity and gas. The resultantnational total energy savings in new houses were then allocated to eachState/Territory in proportion to the respective shares of total national consumption ofelectricity and gas etc. in Class 1 dwellings in each year. This approach implicitlyassumes that the rate of growth of the stock of Class 1 dwellings is the same in eachState/Territory. While this is in fact not the case, allowing for different growth rateswould make only a very small difference to the distribution of savings betweenStates/Territories.
Applying the energy intensity changes in the RIS to our residential stock model, weestimate the annual national energy savings attributable to this measure as around20PJ in 2020, rising to nearly 50 in 2050 (please refer to Table 1, Section 3, for detailsand to Appendix E for a break-down of savings by fuel and state/territory).
The final Regulation Impact Statement16 for this measure considered dwellingsconstructed in the period 2003 to 2010. The additional capital expenditure wasestimated at a present value cost of $781 million (at a 5% discount rate). Governmentcosts were considered too small to have an appreciable impact on the cost-benefitanalysis. Total compliance costs were not separately calculated, but were included intotal capital cost at $25 per dwelling. The net additional cost per house was estimatedat $977 per dwelling. Applying these cost parameters to pitt&sherry’s estimates ofstock provides an estimate of undiscounted annual cost in 2013 of $98 million.
However, these cost estimates take no account of learning effects (the reduction incompliance costs that arise through changed behaviours, technologies, designs andpractices over time). In our view, it would not be reasonable to assume anyincremental costs are still being incurred with respect to 4 star, nor would anyadditional costs be incurred through to 2050 under a frozen policy assumption.
5-star energy efficiency standards for new housesIn 2006 the BCA was amended to require all new Class 1 dwellings to achieve aminimum energy rating of 5 stars. This measure is assumed to generate energy savingsfrom FY2008 onwards, relative to dwellings built to the previous 4 star standard. TheRIS for this measure provides estimates of total savings of both electricity and gas, and
15 Note that small variations in the starting point from state to state would not make any materialdifference to the estimates presented in this study; however, substantive state policy variations (such asBASIX in NSW and ‘outdoor living area’ allowances in QLD) may have a larger impact. This would requireseparate analysis to quantify, however.16 ABCB (2002)
17
numbers of new dwellings in each BCA climate zone. These were used to calculatesavings per new dwelling in each climate zone, from which pitt&sherry’s estimate oftotal savings in each climate zone was made using the housing stock data constructedfor this study. National savings were then calculated as the sum of the savings in eachclimate zone. A further calculation was made to allocate savings to each State andTerritory, using, in reverse, as it were, the shares of existing and new housing stock ineach State and Territory assumed to be in each climate zone occurring in that State orTerritory. We note that the Department has commissioned work from CSIRO tovalidate actual savings achieved by 5 star houses – this data source will be valuable toupdate RIS estimates when it becomes available.
We estimate the annual national energy savings attributable as around 3.3PJ in 2020,rising to 11.6PJ in 2050 (please refer to Table 1 in Section 3 for details, and Appendix Efor disaggregation by fuel and state/territory). With respect to costs, the relevantRegulation Impact Statement17 reported a present value cost of $429 million for themeasure over a ten year period (at a 6% discount rate). This additional capital costincludes a 5% increase over base construction estimates to account for compliancecosts and, again, no allowance was made for learning effects. Applying our updatedstock numbers to the per house estimates provides an estimate of annual cost inFY2011 of $31 million. As with the 4 star measure above, we expect that the majorityof incremental construction costs associated with 5-star will already, by 2013, havebeen accommodated by changes in designs, technologies and practices. However, asdiscussed in Section 5 below, there is a lack of quality research to substantiate suchconclusions. We note that AECOM (2012) found no quantitative evidence of learningeffects; however, it also noted that traditional approaches to cost estimation (eg,quantity surveying) tend to systematically over-estimate costs. This is identified as anongoing research issue in section 5.3 below.
State measuresOther measures which came into effect during the historical period of this study arethe three state retailer mandate schemes: the Victorian Energy Efficiency Target(VEET), the NSW Energy Saving Scheme (ESS) and the SA Residential Energy EfficiencyScheme (REES). For NSW, the most recent scheme report (IPART, 2012) provides verydetailed year by year estimates of the electricity savings generated by the scheme,separated into residential and commercial sector savings. Gas is not covered by thescheme. For SA and Victoria, savings are defined in terms of emissions, not energy.For SA, the published consultant reports, which were used by the scheme’s designersto define deemed emissions savings values and the deeming life for the variousapproved actions, provided the data needed to “reverse engineer” emissions savings tosavings of electricity and gas. These very complex calculations were undertaken onlyfor the three largest and longest lived classes of actions, which are, to date, low flowshowerheads, water heating fuel switching and ceiling insulation. Similar calculationsfor VEET, under which the number of approved actions undertaken is much broader,have not yet been undertaken. However, estimates of total (residential) energysavings under both ESS and REES are small (together around 0.3 PJ in 2012).
Please refer to Appendix C for further analysis of issues relating to the residentialbaseline.
Energy (including carbon) prices and elasticity effectsProjections of residential electricity prices in each state and territory, prepared forpitt&sherry (2012c), were compiled for the purpose of examining price elasticityeffects. These projections were based on the generation (wholesale) cost resultscontained in the Treasury modelling for the Clean Energy Future package, Treasury(2011), including the carbon price assumptions of that modelling. That is, wholesaleelectricity prices are assumed to carry the default carbon price trajectory from the
17 ABCB (2006b)
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Treasury modelling, from 2012 onwards. We note that international linking of the CPM,together with low international carbon prices, may mean these estimates may be toohigh in the short-medium term.
With respect to future wholesale pool costs, it has been suggested that retirements ofageing coal generation plant – that was already occurring prior to the introduction ofthe CPM, but which has accelerated since18 – could lead to higher pool prices in future.Our view is that, while pool prices will reflect many factors other than this one, theretirement of such plant is more likely to lead to reductions in pool prices. This isbecause the plant being retired typically has the highest short run marginal cost in theNEM, while the plant that replaces it will have lower SRMC (eg, combined cycle gas) orclose to zero SRMC (wind, solar), induced by the national Renewable Energy Target.Indeed, this effect is already visible in the NEM, as documented by the Australia EnergyMarket Operator.19
They also assumed modest but steady increase in the network (transmission anddistribution) cost component in each State and Territory for some years into thefuture. These and other components, such as retail costs and margins, were notmodelled by Treasury. For this study it was decided to amend the network costcomponent so that it remains constant, in real terms, from FY2014 onward. Thisdecision was made on the basis of two main considerations: firstly, the proposedchanges to regulatory arrangements affecting network costs, announced by the PrimeMinister late last year, and secondly, the greatly reduced expectations for growth inelectricity demand, which are already evident and are expected, by AEMO, tocontinue, affecting both total annual electricity consumption and the annualinstantaneous peak demand. It is growth in annual peaks which, in the past, has beena major driver of network capacity augmentation, with consequent large increases inapproved capital expenditure, flowing through to retail electricity prices.
Own price elasticity of demand for electricity and gas was assumed to be -0.25 out toFY2020 and -0.1 thereafter, while income elasticity was set at 0.25 to FY2020 and 0.1thereafter. The initial values were selected as those that provided the ‘best fit’relative to historical data from within the range cited in Australian literature.However, the subsequent reduction in both elasticity values to -0.1 represents apragmatic assumption that demands some explanation.
We note that the rate of growth in household income is expected to be faster than therate of growth of energy prices assumed in this study, over the period to 2050. Thiscould lead to per-dwelling energy consumption approximately doubling by 2050 due tothe net effect of these elasticities alone. This result would appear inconsistent withthe historical trend of flattening (or recently, declining) per-dwelling consumption. Itwould also appear inconsistent with data suggesting a growing saturation of at leastsome (major) residential end uses (eg, refrigeration, TVs, hot water, spaceconditioning) and declining dwelling sizes. While a full investigation of these issues isbeyond the scope of this study, there is some further analysis presented in Appendix D.Our assumption is perhaps best interpreted as implying a change in the consumptionpreferences of households through time driven by saturation of energy servicedemands. This thesis would merit testing through targeted research.
Trends in consumption of gas per household are very much different from electricity.Figure 16 (in Appendix D) indicates that gas consumption per household has beenconstant or declining throughout the period. Some year to year variation is to beexpected in response to different severity of the winter in Victoria, which accounts forover 70% of total national residential gas consumption. This study has not examinedthe possible effect on gas consumption of year to year variations in the number ofheating degree days (although refer to Appendix C and Figure 20).
18 Refer to www.pittsh.com.au/CEDEX, for example.19 AEMO (2012), p. 2-9.
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However, it is important to note that real gas prices have been rising steadilythroughout Australia for almost the entire period being examined; in particular, theincrease in real prices started in 2003 in Victoria. Application of the simple economicmodel tracks actual consumption quite closely, with rising prices being offset by risinghousehold incomes. Accordingly, gas consumption was projected forward to FY2050 onthe same basis. Gas price projections used are those from the earlier pitt&sherrystudy, previously mentioned. In the case of gas, unlike electricity, they were notamended for this study, and are expected to increase significantly on a weightednational average basis, as consumption in NSW, Victoria and SA is increasingly sourcedfrom Queensland CSG fields and domestic prices increase steadily towards exportparity levels. We have assumed that these levels relate to the initial contract pricesachieved by the various LNG projects currently under construction in Queensland. Inthe longer run it is possible that the increased availability of shale gas from the USAwill push international prices down, but no allowance was made for this possibleeffect. The overall outcome is that gas consumption per household is projected todecrease slightly until about 2030, and grow slightly thereafter.
3.4.2 Commercial buildingsThe baseline projection of energy consumption in commercial buildings is conceptuallysimilar to that in residential. That is, historical energy intensity levels for the keybuilding classes (limited to those covered by Section J of the Building Code ofAustralia, eg, excluding those that are largely or wholly unconditioned spaces) wereestimated and applied to the stock model (described above), to firstly generate a‘frozen efficiency’ projection. The intensity values are based on whole buildings butexclude ‘plug load’, or appliances and equipment, as these are not affected by thepolicy measures in question. This means that certain trends, such as the‘intensification’ of appliance and equipment use in commercial offices in particular(but also other building classes such as hospitals), are not directly represented in thedata. At the same time, plug loads and the intensity of building occupation do impactindirectly on space conditioning loads. Therefore this effect may be assumed to bepresent in the whole building intensity values modelled.
The intensity values used are national averages. This reflects the fact that while thereis in fact variation from state-to-state in average building energy intensities, thesevariations are yet to be documented in a statistically significant manner. We note thatthe Energy Efficiency in Government Operations (EEGO) measure includes tenant lightand power. Lighting power consumption is included in the data set, as a ‘fixedappliance’, however office equipment is not. However, this measure is analysed insuch a way that this limitation in the baseline is not material – as described in Section3.3.4 below.
The next key step was to adjust the frozen efficiency projection for significant extantpolicies. There are a smaller set of these for commercial, as compared to residential,buildings, and in practice we estimated the impact of chiller minimum energyperformance standards (MEPS), the NSW Energy Savings Scheme (commercial elementonly) and NABERS.
NABERS energy savings were modelled from FY2002 on the basis of the reportedresponse to this measure on a voluntary basis; that is, a reported take up rate of 66% ofthe national office stock by FY2012 and a reported average of 9% energy savings for atleast those buildings rated more than once. We assume that the take-up rate ofNABERS reaches a plateau of 90% by FY2020 and then remains at that level through toFY2050.
In line with our analysis of CBD, we make an assumption that the savings rate achievedby NABERS will decline through time. This reflects an expectation that the most cost-effective savings opportunities motivated by NABERS (and CBD) will be implementedfirst. As the same building is rated multiple times (over decades), it is more likely thannot that fewer cost-effective savings options will be available through time –
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notwithstanding the effect of ‘autonomous energy efficiency improvements’ discussedelsewhere. The normal cycle of building demolition and major refurbishment will, overthe longer term, also lead to an increasing share of the office/commercial buildingstock coming within the purview of the Building Code of Australia, reducing to someextent the scope for additional energy savings attributable to NABERS.
In practice we assume the savings rate falls to 4.5% by FY2018, and then halves again afurther six years later, before plateauing out. For consistency, we make the sameassumptions with respect to CBD – and we stress they are assumptions only. As aresult, savings attributed to NABERS are assumed to peak in FY2017 at around 4.6 PJ,then fall back to around 2.2 PJ by FY2050. The energy savings rate attributed toNABERS is assumed to apply equally to electricity and gas, as the actual fuel split is notpublished. Note that we have not attempted to model the impact of NABERS onbuilding types other than offices due to limited roll-out for other buildings types andno public reporting of induced savings in these other building types.
The effect of the baseline measures is shown in Figure 4 below. As may be noted, thescale of the measures tending to reduce energy consumption, relative to frozenefficiency and prior to the application of the measures under study, is modest relativeto the significant scale of total commercial building energy consumption. This reflectsthe fact that there are relatively few measures affecting commercial energyconsumption, both in total and by comparison with the residential sector. As notedabove, however, this projection does not take into account structural, energy intensityand other factors that may well be changing at the level of individual building classes,in response to market and technology factors.
Figure 4: Commercial Building Baseline Energy Consumption, FY2002-FY2050, PJ
Source: pitt&sherry
Unlike for the residential sector, we have made no attempt to normalise the baselineenergy consumption projection for commercial buildings for price or incomeelasticities, as these effects are generally associated with end consumption sectors. Bycontrast with residential buildings, energy demand in commercial buildings is largely a‘derived’ demand for the commercial services that are delivered in the building (eg,health services for hospitals; accommodation services for hotels). This should not betaken to mean that commercial building energy consumption is completely insensitiveto changes in energy prices. However, there appears to be general agreement in theliterature that this is a sector where price impacts face significant barriers, dueprimarily to principal-agent market failures, the nature of contracting/leasing in thesector, and also to the relatively small share of energy costs in the operating costs of
180.0200.0220.0240.0260.0280.0300.0320.0340.0360.0380.0400.0420.0
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many commercial buildings.20,21 Also, we are not aware of any research that correlatesthe energy consumption of commercial buildings with real energy prices.
Finally, and as noted above, it is not feasible at present to reconcile modelledcommercial building energy consumption with historical energy consumption data.That is primarily because the Australian Energy Statistics captures and reports energyuse in the ‘commercial and services’ sector on a highly aggregated basis, based onANZSIC codes, and does not distinguish buildings-related from other energy end-use.
20 See for example ABCB (2009b), p. 31.21 ASBEC (2009), pp 16 – 19.
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4. The Impact of Nominated Measures4.1 Residential Buildings
4.1.1 IntroductionChanges to the Building Code made from 2010 onward are additional to changes madeprior to that year. In modelling terms, therefore, these measures continue to affectthe energy performance of new dwellings throughout the projection period. Indeed,their impact continues to increase, as dwellings built since 2005 constitute a steadilygrowing proportion of the total housing stock. Changes to the Code made from 2010onward have the effect of reducing energy consumption below the levels which wouldhave prevailed with only the earlier measures in place.
This is not the case for the State measures which have been modelled. Neither theNSW nor the SA programs have any certainty of extension beyond their currentlyannounced lifetimes. This means that the impact of these programs is not only verysmall, but also has no potential for significant growth. In the modelling, the impact ofthese measures has been further curtailed by assuming that it lasts only for thedeeming life of the various categories of energy saving action. It could well be arguedthat, after the end of the deeming period, most householders would be most unlikelyto allow their dwellings to revert to the less efficient condition they were in prior tothe upgrades received through the program. That may well be valid, but in practicalterms the impact of these programs on residential energy consumption is so small thatthe limited life assumption has no material effect on the overall conclusions of thisstudy.
4.1.2 BCA 2010 (6 star energy efficiency requirements forhousing)
The recent measures modelled for new residential buildings were the 2010 change tothe BCA, noting that energy savings from earlier iterations of the Code were quantifiedas described in Section 2 above, and these are summarised in Table 1 below. BCA2010required all new residential buildings (both Class 1 and Class 2) to achieve a NatHERSenergy rating of 6 stars, and the concurrent changes affecting maximum energyconsumption for lighting and excluding large electric resistance water heaters.Although several States and Territories are yet to implement one or more of thesemeasures, it was assumed, for modelling simplicity that savings started to flow fromnew dwellings completed throughout Australia from FY2012 onward.
For the 6 star measure, estimates of average electricity and gas etc. savings per newdwelling of each type (Class 1a, Class 1b, Class 2) in each climate zone were takenfrom the RIS for this measure. It should be noted that the incremental savings in mostclimate zones are quite small, with the main exceptions being electricity in climatezone 1, relating to air conditioning, and gas in climate zones 6 and 7 (and electricity inthat part of climate zone 7 covering Tasmania), relating to space heating. It isimportant to note that the savings calculated in the RIS are increments to the savingsalready achieved by the previous 4 and 5 star changes to the BCA (meaning that thesavings can be summed without double counting). In projecting forward, these savingscontinue to arise in new dwellings, relative to the pre-2003 levels of dwellingefficiency. The distribution of energy savings for BCA 2010 across climate zones wasallocated to States and Territories using the same procedure employed for the BCA 5star savings.
Treatment of the other measures was much simpler. For the lighting measure, the RISestimates a single average per dwelling figure for each of the three residential buildingclasses, applicable in every climate zone. For water heating, the RIS assesses that themeasure makes no significant change to pre-existing State measures everywhere
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except Victoria, Tasmania and the NT. Detailed data are contained in the RIS for theprior shares of the different water heater types in new dwellings in each of these threeStates/Territories, the shares with the new measure, and the average consumption ofpurchased energy by each water heater type. These data were used to calculateaverage per dwelling changes in consumption of electricity and gas in eachState/Territory as a result of the measure. In Victoria and Tasmania consumption ofgas, unsurprisingly, increases. Overall the annual national energy savings, attributableto the measure, rise from 5.4PJ in 2020 to 25.7PJ in 2050.
The final Regulation Impact Statement reported a present value cost of $2.2 billion forthe measure over a ten year period (at a 7% discount rate). The costs attributed to themove to 6 stars are: an administration cost of $250,000 in the implementation year,total compliance costs of $35 million, with close to $2.2 billion in additional capitalcosts. The RIS provides estimates of additional construction costs disaggregated tothree dwelling types - flats, semi-detached houses and detached houses across 7climate zones.22 Applying pitt&sherry’s estimates of total stock yields an estimate ofundiscounted annual cost in 2013 of $303 million.
Note that the RIS does not include any reduction in the additional costs resulting fromthe measure over the 10 year period. That is, the additional capital costs per dwellingare the same in year 10 as they were in year 1. This is a very conservative assumption.The effect of the measure is to introduce a new set of standard practices andmaterials. Given the competitive and dynamic nature of the construction industry it isreasonable to expect that the additional costs initially faced would be greatly reducedafter 10 years. The suppliers of materials would shift to ‘6 star’ inputs, andconstruction firms would have mastered any necessary change in constructiontechnique.
The 6 star measure achieves quite substantial savings, amounting to about 4 per centof average per dwelling consumption of both electricity and gas by around 2030.Savings from the lighting measure are much less and, of course, of electricity only.Savings from the water heating measure are also very small, but in this case because,as discussed above, for most states it is considered that the potential savings are beingachieved by state measures already in place.
4.1.3 Residential mandatory disclosureThe Residential Mandatory Disclosure (RMD) measure was announced as part of theNational Framework on Energy Efficiency in July 2009. As a measure still underdevelopment, the projected energy savings and associated costs for the measure aredrawn from the Consultation RIS23. We do not attempt to independently verify theenergy savings (or cost) estimates in this study, although we note that the savingsestimates appear plausible. Our savings and cost estimates nevertheless differ fromthose in the RIS due to the requirement that we model measures on a frozen policyassumption, whereas the RIS assumes that the measure ceases after 10 years, with a‘tail’ of energy savings after that as a function of the economic life of the investmentsmade during the 10 year period. Also, we apply the savings assumptions to ourupdated building stock model.
Specifically, we assume that Option 2 in that study (simplified thermal assessment) willapply from FY2016. Option 2 assumes that RMD:
is mandatory;requires a simplified thermal assessment of building energy performanceproviding ‘mid-level’ accuracy and ratings for various aspects of energyperformance;
22 ABCB (2009c).23 ACG (2011).
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is triggered by every sale or lease of a residential building in Australia.
For the energy savings analysis, RIS results were first replicated on the basis modelled;that is, on the assumption that the measure operates for 10 years. Key drivers includean assumption of an average of just over 1 million transactions/year (residential saleand leases). This value was expressed as a share of the mid-point stock over the tenyear period, to create a value that correlates growth in transactions through time withgrowth in the stock. On this basis, the annual transactions represent some 12.3% ofthe stock each year. Other key values from the RIS (Table 6.9) include the expectationthat there will be some 1.75 million investments induced by this measure over 10years, at a cost of some $541 million, realising energy savings over the life of theinvestments of some 152 PJ. Also, the RIS expects around 18% (on average) of thetransactions to induce some investment action. Savings were then calculated by Stateand Territory.
In a second step, the measure was then analysed instead assuming a frozen policyscenario; that is, investments are assumed to continue to be made (and costs incurred)right through to FY2050. Under this assumption, two additional considerations comeinto play. First, is the same rate of energy savings maintained over decades, or isthere a saturation effect? Given that the measure will affect over 1 milliontransactions per year, over a long period it is likely that the same property will beassessed many times. Other things being equal, it is likely that the incremental savingsinduced each time will fall, as owners are likely to choose the most cost-effectiveoptions first. We therefore apply a modest savings ‘penalty’ from year 11 onwards,that gradually reduces the induced savings by more than 50% by FY2050. In the model,this assumption may be varied. Second, we need to assume whether the costsassociated with the measure remain constant in real terms or change through time. Tomaintain parallelism with the energy savings saturation effect, we assume thatinvestment effort (cost) is maintained, while the productivity of that effort (savings)falls. Therefore we assume no change in real investment costs. Costs associated withassessment and program administration are labour-intensive and therefore alsoassumed to remain constant in real terms.24
On the frozen policy assumption, we estimate that energy savings attributable to themeasure peak increase each year, rising to around 30.3 PJ in FY2050. Note that the RISdoes not split energy savings by fuel; however, these have been estimated at a nationallevel as a function of average fuel shares in the residential stock. State/territorysavings estimates are provided in Appendix D.
Costs for this measure are higher than the RIS, due primarily to the longer timeframefor analysis in this study, as explained above. Investment costs over the period toFY2050 have a present value (at 7% real discount rate) of nearly $800 million, whileassessment costs are much higher at some $3,400 million on the same basis. Togetherwith industry insurance and training costs, total private sector costs amount to some$4,200 million in present value terms. Government costs amount to some $132 millionon the same basis.
4.1.4 Summary – energy savings from nominated residentialenergy efficiency measures
Energy savings from the nominated residential energy efficiency measures aresummarised below at a national level, showing savings of electricity and gas separatelywhere relevant, in Table 1 below. In preparing the projections shown in Figures 5 to 8,account was taken of savings achieved, relative to the pre-2003 level of building
24 Note that, from a methodological point of view, we are not persuaded that the assessment industry’straining and insurance costs should be added to the direct costs of assessment, as the RIS does, as thesecosts would already be expected to be reflected in assessment pricing, meaning they could be double-counted. This is a relatively small cost element, however.
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efficiency, in all dwellings constructed from FY2012 onward. For gas etc., inparticular, these earlier measures (notably, 4 star) are responsible for the larger partof the ongoing savings.
These savings represent the reduction in total energy consumption, in the yearsnamed, attributable to these measures, compared to a counterfactual baseline thatincludes the state measures and price and income elasticity effects. As noted inSection 2 above, the energy savings attributable to each of the measures under analysisin this study are calculated taking into account both a) the starting year for thatmeasure and b) the portion of the building stock and its energy use that is affected bythat measure. Further, energy savings for each measure are the incremental savingsonly, meaning that they can be safely added up without double-counting.
It may be seen in Table 1 that total savings are estimated at around 11 PJ in FY2012,and are expected to rise to around 32 PJ by FY2020 and almost 122 PJ by FY2050. Wealso note that the largest single source of energy savings is the gas consumptionavoided due to the introduction of 4 star. This is because the key (modelled) effect of4 star was to save space conditioning – and particularly space heating – energy forwhich gas was the dominant fuel.25
Table 1: Residential Energy Savings by Measure and Fuel, FY2003 – FY2050,Australia (PJ)
2003 2008 2012 2020 2030 2040 2050BCA 4-star Electricity 0.1 0.4 0.7 1.3 2.2 3.0 3.9
Gas 0.9 5.1 8.4 15.6 25.6 35.6 45.6BCA 5-star Electricity 0.1 0.5 1.4 2.7 4.0 5.3
Gas 0.2 0.9 2.4 4.5 6.3 8.2BCA 6-star Electricity 0.3 2.7 6.1 9.4 12.8
Gas 0.3 2.7 6.1 9.5 12.9BCALighting
Electricity 0.0 0.3 0.8 1.2 1.6
BCA Waterheating
Electricity 0.0 0.3 0.7 1.1 1.5Gas 0.0 -0.1 -0.2 -0.2 -0.3
MandatoryDisclosure
Electricity 2.9 8.8 14.0 17.4Gas 2.2 6.6 10.5 12.9
Sub-totals: Electricity 0.1 0.5 1.5 8.9 21.3 32.7 42.5Gas 0.9 5.3 9.5 22.8 42.4 61.7 79.3
Total: 0.9 5.8 11.1 31.7 63.7 94.4 121.8Source: pitt&sherry
Savings estimates have also been prepared by state and climate zone (for the BCA),and these are provided in Appendix D.
Figures 5 and 6 below summarise the data from Table 1 in graphical form, forelectricity and gas respectively. For both fuels, continuing strong growth in dwellingnumbers means that total residential sector energy consumption continues to increaseover the whole period to 2050, notwithstanding the reductions achieved by theefficiency measures. Further, it may be noted that the relative impact on consumptionof natural gas (Figure 6) is larger than the impact on consumption of electricity (Figure
25 As discussed further in Section 5, however, this modelled result is not well substantiated in the relevantRIS and would ideally be validated through primary research techniques.
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5), mainly because of the larger reductions in gas consumption achieved by the initialmove to 4 star minimum energy performance of new houses.
Figure 5: Total past and projected residential electricity consumption, excl. plugloads and cooking, and savings from residential building measures,Australia
Source: pitt&sherry
Figure 6: Total past and projected residential consumption of gas, excl. cooking,and gas savings from residential building measures, Australia
Source: pitt&sherry
Figures 7 and 8 below express this data per-dwelling, rather than in total, and the y-axis has been selected to accentuate the impact of the measures. The instability inthe historical baseline, described in Section 2 and in more detail in Appendix D, is alsoaccentuated. The baseline energy consumption per dwelling rises only slightly throughtime, as a direct result of rising household incomes, and the assumption that this will,on average, lead to higher energy consumption. Overall however, and taking intoaccount the effect of policy measures, these figures indicate that the per-dwellingconsumption of electricity (net of plug and cooking loads) is projected to fall to levelsachieved in the early 2000s by around 2040, without some modest growth thereafter
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(again due to rising incomes). For gas, the effect of measures is to achieve continuingand significant reductions in per dwelling energy consumption, against a projectedbaseline of almost flat consumption. This is quite a strong result, particularly when itis recalled that no strengthening of policy through time is assumed. It also indicatesthat the rising total energy consumption in the residential sector in Australia isprimarily being driven by rising dwelling numbers, in turn reflecting significantprojected population growth over the period to 2050 (although it should be noted thatthis analysis excludes the effect of ‘plug loads’, which may show a different trend.DCCEE is commissioning a separate study in this area).
Figure 7: Per dwelling past and projected residential electricity consumption, excl.plug load and cooking, and savings from residential building measures, Australia,GJ/annum
Source: pitt&sherry
Figure 8 below indicates that the impact of the measures – together with thoseconsidered in the baseline – is sufficient to see a significant reduction in per-capita gasconsumption in the residential sector. As noted in Section 5, however, the RISprojecting a large gas saving as a result of 4-star is poorly documented, and this resultwould ideally be verified through retrospective evaluation techniques. Unlike forelectricity, baseline gas consumption is flat, on average, over the period, and thisdespite rising household incomes. This reflects a number of factors including changesin the fuel mix of residential space conditioning and hot water technologies, andlimited growth in new gas demand applications in the residential sector.
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Figure 8: Per dwelling past and projected residential consumption of gas, excl.cooking, and gas savings from residential building measures, Australia, GJ/annum
Source: pitt&sherry
4.2 Commercial BuildingsThe key commercial measures analysed in this study are the 2006 and 2010 BuildingCode of Australia Section J requirements, Commercial Building Disclosure (CBD) andthe Commonwealth’s Energy Efficiency in Government Operations (EEGO) policy – withthe latter limited to the buildings-related targets and the Heating, Ventilation and AirConditioning High Efficiency Systems Strategy (HVAC HESS). As noted below, we havenot been able to identify quantifiable energy savings attributable to this measure,although such analysis could be attempted through primary data collection techniques.
4.2.1 BCA 2006The Building Code measures have been quantified, against the baseline as describedabove, using specific energy savings values (MJ/m2.a) derived from savings reported inthe relevant RIS, applied to the updated stock model. We are not aware of anyresearch that retrospectively validates these RIS projections for commercial buildings.Energy savings continue to grow through time as each change to the Code (2006 and2010) is treated incrementally and assumed to continue to FY2050 under the ‘frozenpolicy’ approach requested by the Department. Savings have been calculated by stateand climate zone (refer to Appendix D), and fuel splits have been estimated only at thenational level based on the stock-weighted average of the building types covered bySection J). The energy savings amount to a little over 3 PJ by FY 2012, but continue toincrease through time, reaching nearly 35 PJ by FY 2050 (see Table 2 below).
The 2006 RIS26 provided estimates of total energy savings by state and by climate zone,with information on the savings and costs expected to be incurred by different buildingforms (such as small offices, large offices) but not broken down by fuel. Costs arepresented as present values over 10 years. While the cost trajectory through time isnot clear, some small cost reduction (learning) is implied, in that first year costs arestated at $78 million, while the average cost over 4 years is around $74 million, andthe average cost over 10 years, around $86 million.
26 ABCB (2006a).
12
13
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17
18
19
20
21
22
2001 2011 2021 2031 2041
GJ
With MandatoryDisclosure
With BCA Water heating
With BCA Lighting
With BCA 6 star
With BCA 5 star
With BCA 4 star
With State measures(actual)
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However, it should be noted that these costs relate only to the incremental costs ofthe building shell, while the RIS notes that, in most cases, savings in HVAC plant(consequent upon a more thermally efficient shell) completely offset these costs,leading to net cost construction reductions for most building forms and climate zones.27
Further, the implied rate of ‘learning’ is very low, and most probably substantiallyunderstates the rate at which the implied investments were accommodated withinbusiness-as-usual practices in the construction industry. Gross construction costincreases were based on an assumption of ‘deemed to satisfy’ (DTS) methodologiesbeing followed, which understate opportunities for learning and innovation andoverstate costs. Finally, we note that the 2009 RIS – draft just some three years later –retrospectively describes the compliance costs attributable to BCA2006 as “minimal”.28
Therefore it far from clear that any net investment cost should be ascribed to thismeasure. While the RIS does not report on administrative or compliance costs, thesewill be small compared to the scale of the net savings generated by the inducedefficiency improvements, and are not likely to represent a net cost.
4.2.2 BCA 2010A similar analytical approach was adopted for BCA2010. RIS 2009-04 (referencedabove) provides more complete information than the one prepared in 2006, includingsavings estimates for a wider range of building forms (but still excluding certain typesincluding hotels, while ‘education’ buildings are represented by schools, which havemuch lower energy intensity than tertiary education buildings) and for electricity andgas. Savings were therefore able to be calculated by state and fuel. In consultationwith the Department, we added hotels (Class 6) to our analysis, using the assumptionthat the realisable energy savings for this class were similar to retail buildings, notingthe similarity (on average) in energy intensity of the two (both vary widely frombuilding to building however). In total, the energy savings from this measure (note,incremental to BCA 2006) amount to around 1 PJ in FY2012, rising over 10 PJ by FY2020and over 57 PJ in FY2050.
It may be noted in Table 2 below that the savings attributable to BCA2010 forcommercial buildings are larger than those attributable to BCA2006, whereas forresidential buildings, the initial step to 4-star provides the largest energy savingsthrough time. This reflects the relative sizes of the incremental changes in efficiencyimplied by each regulatory step. In the case of commercial buildings, the initialapplication of Section J required only very modest energy savings compared withbusiness as usual (reported benefit cost ratio of 4.9), whereas BCA 2010 is somewhatlarger (although still modest in stringency, with a reported benefit cost ratio of over2). By contrast, in residential buildings, each additional star represents a smallerincremental energy saving.29
Costs attributable to the BCA2010 measure are reported as around $2 billion (presentvalue at 5% real discount rate, and assuming the measure is applied for 10 years),comprising around $1.9 billion in additional capital outlays, $15 million in compliancecosts and $0.25 million in additional administrative costs. These are compared with anestimated $3.6 billion in energy savings and around $400 million in avoided electricitysystem costs. These cost estimates appear to exclude hotels. As with the BCA2006RIS, incremental costs are based on DTS solutions. Avoided HVAC costs are representedin the estimates of incremental capital costs.
As with BCA2006 and other measures, it is difficult to assess the extent to whichincremental costs should be attributed to BCA 2010 under a frozen policy assumption toFY2050. This RIS was criticised at the time of its release for failing to make
27 ABCB (2006a), p. 21.28 ABCB (2009a), p. 61.29 See pitt&sherry (2012c), pp 54 and 9.
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assumptions about the rate of ‘learning’ by industry which would lead to a reduction inincremental costs attributable to the measure over time. Subsequent analysis (bypitt&sherry) suggests that, after implementation of this measure, very substantial costeffective opportunities for energy savings remained of around 68% on average byFY2020, although this result masks considerable variation by building type and climatezone.30 Savings of around 40% on average were found to be cost-effective even withouta carbon price and without assuming any learning31. These results indicate that thestringency of BCA2010 is modest, meaning that any incremental costs are likely to havebeen rapidly offset by changes in industry practices.
4.2.3 Commercial Building DisclosureThe Commercial Building Disclosure measure requires that a Building Energy EfficiencyCertificate, or BEEC, is obtained and disclosed to prospective purchasers/tenants,where office space above 2000 sqm is offered for sale or lease. The BEEC comprisesthree elements: a NABERS energy rating (which may relate to either the base buildingor the whole building); a tenancy lighting assessment; and general energy efficiencyguidance.
Our analysis of expected energy savings from the scheme has been based primarily onreported data from the first twelve months of the scheme’s full operation (the CBDStatistical Overview as at 30 November 2012), although these results have beencompared and contrasted with those expected in the relevant RIS (ACG 2011).Therefore it is possible that short term effects – such as economic conditions that mayaffect the rate of turnover of office space – could impact upon the results. The datawe rely upon is reported energy savings across all three elements of the BEEC/programas described above.
It has been reported that some 10.5 million sqm (net lettable area) was rated in thefirst 12 months of the scheme’s operation, representing 874 unique buildings. Thistake-up rate represents some 17% of the total floor area of commercial offices > 2000sqm in that year. Of these 874, some 290 buildings were rated more than once duringthe 12 month period. For this subset of buildings, the reported energy savingsamounted to some 38 MJ/m2.a, or something over 6.4% of the energy used at the timeof the first rating. This is a significant result, particularly when it is recalled thatbuilding owners are not required to make energy savings under this scheme, but only toassess and disclose energy efficiency. It appears to vindicate – admittedly only afterone year of operation – the validity of the mandatory efficiency disclosure policymodel.
To estimate national energy savings, we assume that the initial take-up rate (17%) ismaintained through time (that is, the area of office space assessed annually is assumedto grow in line with growth in the underlying stock of office space > 2000sqm). This isa little higher than the estimate contained in the RIS of 14% per year. Colloquially weunderstand that many major property portfolio owners are choosing to rate theentirety of their portfolios annually, even if not required under CBD, for internalpurposes such benchmarking, portfolio valuation and marketing advantage. Thismotivation is unlikely to diminish through time, suggesting that the higher rate of take-up seen to date is likely to be sustained.
In terms of the savings rate, we assume that the value of 38 MJ/m2.a is maintained fora period of six years, during which time, on average, the entire eligible office stockwill have been rated at least once (in practice, some spaces will be rated many times,and some not at all). Overall the longer term, however, it is reasonable to assumethat, as the same office space is rated multiple times, there will be diminishing returnsin terms of the additional energy efficiency investments induced by each disclosure
30 pitt&sherry (2012c), pp 12 – 13.31 ibid, p. 56.
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event. It is likely that rational building owners will implement the most cost effectiveenergy savings measures first (noting that ‘most cost effective’ and ‘largest’ are notthe same thing). Therefore we make an arbitrary assumption that the per-square-metre energy savings rate is halved after six years and halved again six years later. Wenote that the RIS was silent on this aspect of the policy design, possibly because theanalysis was confined to a 10-year policy duration, and that annual program monitoringwill inform the validity of this assumption through time.
On this basis, energy savings attributable to the scheme rise from around 1.6 PJ inFY2015 to nearly 8 PJ by FY2050. As above, these estimates represent ‘gross’ savings,not discounted by any assumptions with respect to autonomous energy efficiencyimprovement.
We note that some (negative) interactions between Section J performancerequirements and CBD are likely. Given the timing of the relevant RIS’, in principle the2006 Code changes should already have been factored into the CBD RIS. However, theBCA2010 changes may not have been so factored, as the two measures were underdevelopment at around the same time. We note, however, that scope of the twomeasures overlap only in one area, and that is offices > 2000 sqm constructed afterFY2010. We estimate that the portion of the stock of offices > 2000 sqm built afterFY2010 will represent only some 16% of the total stock in 2020, so the scope fornegative interaction is modest in the short term. However, we estimate that the shareof post-2010 offices > 2000sqm will have risen to over 50% by FY2050. This effect (therising efficiency of the new office stock induced by BCA 2010) also supports theassumption of diminishing returns from CBD through time.
In terms of costs for this measure, it should be noted that the design of the measure asimplemented differs from the options costed in the RIS.32 We base our estimates,however, on RIS Option 1a). Second, as noted above, it is clear that the take-up of thismeasure has exceeded the rate assumed in the RIS, and there are grounds for assumingthis will continue. As noted, this additional ‘compliance’ appears to be entirelyvoluntary, suggesting that building owners are deriving significant value from themeasure. On this basis, it would not be appropriate to attribute additional costs to themeasure, even though additional ratings costs are clearly being incurred. Finally, theRIS does not model the investment costs to building owners that respond to CBD byupgrading their properties, as such upgrades are not mandated by the measure and areassumed to be cost-effective in any case.
Over a 10-year period, total costs of around $135 million were attributed to Option 1(present value at 5% real discount rate). This is based on a total private cost estimateof $5,919 per assessment in FY2009, falling to $4,600 from FY2014 onwards due tolearning effects including scale economies.
If we recalculate costs based on performance to date, we find a lower value for totalcosts (than reported above in the RIS) of some $110 million (PV at 7% real discount rateover the period to FY2050, or around $144 million at a 5% real discount rate). Thetotal cost estimate breaks down into some $101 million (PV @ 7%) for the private sectorand nearly $16 million (PV @ 7%) in government costs, the latter assuming reported2011 administration costs of $2.1 million are halved from 2012 onwards, once theestablishment phase for the measure in passed.33 Unit costs of assessments are drawnfrom the RIS as reported above. The lower overall cost for the measure than expectedin the RIS, despite the larger number of transactions, appear to be driven by the higherpercentage of (lower cost) tenancy lighting assessments within the total assessmentmix. The RIS assumes that both ratings are required in all instances. We note that thecosts attributable to the measure could be considered lower if costs associated with
32 ACG (2009).33 Note that the component values don’t add exactly (to $110m) in present value terms, as the public andprivate costs occur over slightly different time periods).
32
voluntary ‘over compliance’ are discounted. However, we have included these costs inour analysis, to parallel the fact that we have counted the additional energy savingsresulting from this additional uptake.
4.2.4 Energy Efficiency in Government OperationsThe Energy Efficiency in Government Operations (EEGO) scheme embraces more thanjust building energy use. Indeed, the largest share of Commonwealth Governmentenergy use is accounted for by Defence operations – mainly transport fuels. Theprogram identifies, monitors and reports on a range of metrics, but imposes two maintargets which relate to buildings. The first is a target of 7500 MJ/person for tenantlight and power in offices occupied by Commonwealth agencies. This target was to beachieved by 2011, however we note that annual reports are currently available only to2010-11. Under the frozen policy assumption, this target is assumed to continue toapply through to FY2050. The second target is that base building energy consumption(in offices occupied by Commonwealth agencies) should not exceed 400 MJ/m2.a by2011.
The primary data sources used for this analysis are the detailed annual reportspublished by the Commonwealth Government. We note that there are minorinconsistencies in certain data series over the 10 years for which data (judgedstatistically significant by the report authors) is reported, which we assume representrevisions – these do not materially affect the results reported here.
To analyse the (gross) energy savings attributable to this measure in the historicalperiod, we first calculate energy intensities for light and power and base buildingsachieved in FY2001 (the first year for which reliable data under EEGO is available) andthen compare the actual results in succeeding years with FY2001 frozen efficiency(using both energy/person and energy/sqm for tenant light and power). This approachaccounts for the fact that both persons occupying Commonwealth offices, and the totalarea occupied, both vary from year to year. As with other measures, it does notaccount for any changes that may have been expected in the absence of this measure.We note that the two approaches (per person and per sqm) for tenant light and poweryield slightly different results and that the larger of the two (per person) is reportedhere.
For tenant light and power, reported average intensity across all agencies falls from10,848 MJ/person in FY2001 to 7,636 MJ/person by FY2010; ie, very close to theFY2011 target. We therefore assume – for the purposes of projections – that theFY2011 target of 7500 MJ/person is in fact met in that year and then sustained throughto FY2050 under the frozen policy assumption. We also assume that the number ofpersons occupying Commonwealth offices grows at a modest 1% per year through toFY2050. A final (necessary) assumption is area/person. We calculate that this valuefell slightly from 21.6 m2/person in FY2001 to 20.9 in FY2010, although was as low as19.5 m2/person in FY2005. We make the assumption for the projection period thatarea/person stabilises at 20 m2/person from FY2019 onwards. On this basis, the grossenergy savings attributable to this element of the program rise from around 0.5 PJ inFY2010 to around 0.8 PJ in FY2050. Since the savings represent expected annualperformance relative to frozen efficiency counterfactual scenario, the savings do notaccumulate through time.
For the base building target, a similar approach is taken comparing actual intensityresults with FY2001 frozen efficiency. However, unlike for the tenant light and powertarget, the results achieved by base buildings since 2001 show no statisticallysignificant trend and are actually higher in 2010 than in 2001. We note that basebuilding area per person fell from 9.4 sqm/person (on average) in 2001 to 7.7sqm/person by 2010, indicating an intensification of the use of office space, which isconsistent with wider trends in the general office market. As noted earlier, this effectfirstly increases the ‘plug load’ of buildings (due to more computers and other office
33
equipment per sqm), but secondarily imposes higher cooling loads on air conditioningand ventilation systems, leading to higher energy intensities for base buildings.Strictly, this should be accounted for as a structural change rather than a change inenergy efficiency.
For the projection period, we make the assumption that the target is actually met byFY2020 and that this performance level is then sustained through to FY2050. Basebuilding area is projected from the implied number of persons from the tenant lightand power analysis above multiplied by a base building area per person assumption,which stabilises at 7.5 sqm/person through to FY2050. Projected base building energyconsumption is then able to be calculated. This analysis generates only very modestenergy savings through time, peaking at around 100 TJ.
When the TLP and BB elements are added together, total savings rise from around 0.5PJ in FY2010 to around 1 PJ in FY2050. We note these estimates do not take intoaccount the Green Leases Schedule, including any positive spillover from this policyinto the private buildings market, as this measure fell outside the Statement ofRequirements.
In terms of cost, the only reported costs are an average of $1.2 million per year inadministration costs over the 2006 – 2012 period. While it is possible that someincremental costs are incurred by agencies in meeting the targets, these are notreported. The scale of the changes is modest, however, and it is possible that acombination of behavioural changes and business-as-usual efficiency upgrades asbuildings, plant and equipment is turned over, account for the reported and projectedenergy savings, particularly noting the frozen policy assumption which implies noincrease in the targets through to FY2050. We assume that administrative effort,including reporting, is streamlined through time once the two targets are met, leadingto a halving of costs from FY2020 onwards. On this basis, the present value of costsassociated with the measure (at 7% real discount rate) is just under $14 million.
4.2.5 HVAC HESSThe Heating, Ventilation and Air-Conditioning High Efficiency Systems Strategy (HVACHESS) is a ten-year initiative under the National Strategy on Energy Efficiency (NSEE)that commenced in 2007. It aims to drive long term improvements in the energyefficiency of HVAC by addressing the design, manufacture, installation, operation andmaintenance stages of the HVAC lifecycle. The Strategy recognises that largeefficiency gains can be achieved through the maintenance and operation of existingsystems in existing building stock, and seeks to establish national system standards ofdocumentation for design, installation, operation and maintenance of HVACequipment/systems.
Pitt&sherry has previously analysed the potential savings that could be generated bythis measure (or more specifically, the “Framework Cool Efficiency Program” underHVAC HESS).34 The measure was announced as comprising a range of non-regulatoryprograms that are being developed and implemented in close co-operation with theHVAC industry, including:
A code of best practice for HVAC operation and maintenance;A building services log book (best practice documentation in support of theabove);Voluntary maintenance standards;An online, interactive tool for HVAC systems advice, “Calculating Cool”.
With the (apparent) exception of the on-line tool, it appears that these initiatives havebeen developed and put in place, largely via AIRAH, the Australian Institute of
34 pitt&sherry (2010), p. 40.
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Refrigeration, Airconditioning and Heating. However, it appears that no quantitativeanalyses have been undertaken to this point to try and quantify the extent of actual, asdistinct from potential, energy savings attributable to these initiatives.
We note that, as voluntary, information-based measures, it is inherently challenging toquantify the impact of the HVAC HESS initiatives. The only robust methodology fordoing so that we are aware of would be to directly survey AIRAH members (and otherHVAC HESS product users), to seek to establish the take-up rate (or use) of theseinformation products, and also the savings that they would attribute to the specificinitiatives, validated by quantitative analysis of actual changes in HVAC energyconsumption over the same period.
As a proxy for such work, we contacted the Australian Institute of Refrigeration Air-Conditioning and Heating Institute (AIRAH), who offered us the view that it would bevery difficult to quantify any savings attributable to HVAC HESS alone35. Further,AIRAH was willing to share with us – on a confidential basis – a very recent draftdiscussion paper, which has been compiled for the purpose, when finalised, ofcapturing member views on a range of issues, including government energy efficiencymeasures, and specifically including HVAC HESS. Without breaching confidences, wecan say that the draft paper raises questions as to the effectiveness of HVAC HESS andseeks the views of members with respect to a range of alternative approaches.
We conclude that it is not possible to attribute any ‘firm’ energy savings to HVAC HESSat this point in time. We recommend that the Department commissions a formal,survey-based quantitative assessment, as briefly described above, for this purpose.
We note that we have not been able to find a definitive statement of the costsassociated with HVAC HESS, including in consultation with the Department. The 2012Budget papers show $0.8m for forward costs for the measure over the period FY2013 toFY2016.
4.2.6 Summary – Commercial Buildings MeasuresTable 2 below summarises the energy savings, by fuel, for the commercial buildingsmeasures. These total over 5 PJ in FY2012, rising to around 22 PJ by FY2020 and 100PJ by FY2050. The majority of these savings are attributable to BCA 2010 (which, asnoted above, has modest ‘stringency’, suggesting that significant additional cost-effective savings are available). Unlike for the residential measures, electricity is themain energy source conserved by these policy measures, which is important whenconsidering both the economic value, and the implications for greenhouse gasemissions, of the energy savings realised.
35 Personal communication, December 2012.
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Table 2: Commercial Building Energy Savings by Measure and Fuel, FY2002 –FY2050, Australia (PJ)
PJ 2002 2008 2012 2020 2030 2040 2050BCA2006
Electricity 0.5 2.5 6.7 12.6 19.6 27.9Gas 0.1 0.6 1.6 3.0 4.6 6.6
BCA2010
Electricity 1.1 10.3 23.6 39.2 57.5Gas 0.0 0.0 -0.1 -0.1 -0.2
CBD Electricity 0.4 2.8 4.2 5.5 7.0Gas 0.0 0.3 0.5 0.7 0.9
EEGO Electricity 0.0 0.4 0.5 0.6 0.7 0.8 0.8Gas 0.0 0.0 0.1 0.1 0.1 0.1 0.1
Subtotals Electricity 0.0 0.9 4.5 20.4 41.1 65.1 93.2Gas 0.0 0.2 0.7 2.0 3.5 5.2 7.3
Total 0.0 1.1 5.2 22.3 44.6 70.3 100.5Source: pitt&sherry
Figure 11 below summarises the total energy consumption in commercial buildings toFY2050 in the baseline scenario, and then shows the savings wedges that areattributable to the measures under study. Note that the y-axis is not set to zero.EEGO savings are difficult to distinguish from the baseline due to their relatively smallsize.
Figure 9: Total energy consumption and expected savings, commercial buildings,Australia, FY2002 – FY2050 (PJ).
.Source: pitt&sherry
Figure 12 expresses the same data, but with expected energy consumption appliedacross the total projected area of commercial buildings in Australia to FY2050. Thisprovides a broad indication of the impact of the measures in reducing the energyintensity of buildings in Australia. Again note that the y-axis is not set to zero and thatthe savings attributed to EEGO are difficult to resolve from the baseline.
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Figure 10: Projected Energy Intensity – Baseline and With Measures, CommercialBuildings, Australia, FY2002 – FY2050, MJ/m2.a
.pitt&sherry
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5. Network SavingsThis section examines the savings in peak demand consequent upon the measuresunder study, and the flow on effect of these savings in avoiding electricity networkexpenditure.
We note that no similar analysis can readily be performed with respect to natural gas,as the nature of natural gas infrastructure is such that, for long periods, it exhibitssignificant spare capacity. Under such condition, marginal costs associated withmarginal increases in peak load may be very small indeed. However, wheninfrastructure constraints do arise (as, for example, has been occurring in WA in recentyears), the marginal costs of accommodating these peaks risk to be very high indeed.This is a matter for further research.
A second general point is that the methodology deployed here for electricity networksrests on historical observations of a Conservation Load Factor (CLF, as explainedbelow). However, it is noteworthy that both peak and average demand for electricityin Australia is currently falling – something that is unique in Australia’s modern history.This implies that the demand for network capacity enhancements, in at least the nearfuture, may be very small. Thus, historical CLF values may overestimate avoidednetwork costs in the near term at least. The future path of demand for electricity inAustralia, including attribution of the differing economic, policy and structural effects,is also a matter for further research.
5.1 MethodologyThe connection between energy efficiency and the economic benefits of peak loadreduction involves two steps: firstly, to link energy efficiency improvements toreductions in consumer demand; and secondly, to link reductions in consumer demandto reduced network costs. Recent studies in Australia (UTS 2010) and (EES 2011) haveaddressed these issues to develop estimates of the economic benefits of peak loadreduction as a consequence of energy efficiency. Both studies drew on the concept ofthe Conservation Load Factor (CLF) (Koomey 1990) which is a method of estimating thelikely energy savings in peak load due to the application of an energy saving measure.The EES study on the peak load benefits of the national Household Insulation Projectwas also able to draw on direct energy modelling, and provide estimates of CLF factorsby State in Australia.
The CLF concept was developed in order to provide a simple basis for estimating thepeak load savings and consequential financial benefit from a reduction in peak load.The CLF is defined as the average annual load savings divided by the peak load savings,where both are based on measured data or the output of an hourly simulation model.
CLF = [Annual Energy Savings (kWh)/8760]/Peak Load Savings (kW)
The concept is analogous to a demand side capacity factor, or a measure of thepeakiness of end use. For end-uses like refrigeration with a relatively flat based loadthroughout the year, values of 0.7 are typical. For end-uses such as residential airconditioning with a relatively peaky performance throughout the year, the CLF value ismuch lower, typically between 0.01 and 0.1. High air conditioning demand is weatherrelated, so that air conditioning use is peak coincident with large peak demand relativeto total annual energy used. In the US, CFL factors have been determined for anumber of locations, and as expected for air conditioning, the smallest CFL occurs forthe mildest climate where air conditioning use is rarer, while the largest value occursfor the Florida climate where air conditioning is a more regular feature of summerliving. (LBNL 2002, UTS 2010, EES 2011b) The CLF approach also provides a simplemethodology to use the annual equivalent cost for new generating plant to valueenergy savings (Koomey 1990).
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5.2 ResultsFor the residential sector, a weighted national average CLF factor (0.045) wascalculated based on state/territory factors used in the EES study. Those factors are forsummer electrical peak and apply to space cooling loads only. Accordingly, theweighted average CLF factor was applied to an estimate of the savings in space-conditioning energy used for cooling. For the commercial sector, a moderate CLFfactor of 0.4 was used. This was based on CLF factors reported in the UTS study forthe commercial sector, noting however that the CLF factor varies by end-usetechnology.
Infrastructure cost savings were based on the UTS report, which contains annualizedelectricity infrastructure cost savings by jurisdiction for MW saved. A weightednational average was calculated to estimate total national electricity infrastructurecost savings, for the cited years, consequent upon all of the residential and commercialbuildings measures only (ie, excluding baseline measures). This is shown in Table 3below. The values represent the avoided network infrastructure cost in each year dueto the measures under study, compared with the baseline (without measures).
Table 3: Estimated Network Cost Savings Consequent Upon Selected BuildingEnergy Efficiency Measures, Australia, 2020 – 2050, $million (real 2013)
2020 2030 2040 2050
$588 $1185 $1861 $2639
Source: pitt&sherry
In present value terms at a 7% real discount rate, the value of electricity network(transmission and distribution) infrastructure savings due to the building measures inthis study is estimated at some $4.7 billion.
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6. RIS Limitations and RecommendationsThe Statement of Requirement for this study asks us to identify limitations in past RIS’,particularly from the perspective of this kind of study, and also to makerecommendations that could be considered for future RIS’ and another analyses.
As a general statement, the quality and comprehensiveness of the RIS’ reviewed forthis study has improved considerably over time. That said, we have ‘found fault’ withmost RIS’ reviewed, including the more recent ones, as noted in the relevant sectionsabove.
We believe that there are three key issues that could be addressed to further improvethe quality and consistency of building-related RIS’:
1. Methodological inconsistencies;2. Insufficient transparency in reporting;3. Data limitations/research gaps (including ex-post evaluations).
These are considered further below, along with a fourth category – emerging issues.We note that there are often interactions between these three issues, with datalimitations often but not always at the root of the first two.
6.1 Methodological InconsistenciesThe RIS’ reviewed, when considered as a set, contain numerous inconsistencies inapproach and methodology. Clearly we must make allowances for their differentvintages and authorship. However, from the perspective of consistency in public policyanalysis, it would seem to be advantageous if these inconsistencies were able to beminimised, for example through issuing (revised) RIS guidelines.
Some key examples of methodological inconsistencies are discussed below.
Benefit cost analysis or cost effectiveness analysis?While the majority of RIS’ adopt a social benefit cost analysis framework, some adopt asimpler, cost-effectiveness framework. In the latter, no attempt is made to quantifythe expected benefits. Rather, costs are quantified and an analysis made of the rateof take-up, or response rate, that would be needed, on this basis, for the measure tobreak-even, or for benefits to exceed costs.
We recommend that social benefit cost analysis should be the preferred approach,including an explicit assessment of the expected take-up rate of the measure, based onthe fundamentals of the size/stringency of the policy intervention, the segment of theeconomy (or building stock) that it acts upon, and the nature of energy end-use in thatsegment, including an understanding of the stock characteristics.
Stock dataAn important cause of quantitative (and qualitative!) differences between RIS’ aredifferent assumptions regarding building stock characteristics. This reflects the factthat there is currently no authoritative data of this kind in the public domain (eg, fromthe ABS). Many RIS’ use highly simplified stock models, for example with limitedresolution of different building types, limited disaggregation by State, etc. As noted inpitt&sherry (2012a), this issue is aggravated by the limited data available from the ABSin its building survey series, in particular because the data is value based and does notdistinguish between expenditure on demolitions, new builds or major refurbishments.Secondly, it provides no information on physical quantities (sqm) demolished,constructed or refurbished in any year. Therefore, a key limitation in many stockmodels is the need to treat retirements and refurbishments in an approximate manner(as indeed was the case for this study).
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While private companies such as BIS Shrapnel provide excellent stock data for a fee,their work – along with that of the research community generally - would be greatlyfacilitated by greater precision in the ABS building survey series. Please refer topitt&sherry (2012a) for detailed recommendations.
Discount ratesThe Office of Best Practice Regulation recommends 7% real discount rates be used as adefault, with sensitivity analysis at 3% real and 10% real. While we accept that this is areasonable approach, we note that it is difficult to conceive of an application where a10% real discount rate may be appropriate. Our recommendation is that both 3% and7% real always be presented, with other values optional.
Fuel coverageRIS’ should always be explicit about the savings of different fuels consequent upon apolicy intervention. The reasons for this include:
the absolute magnitude of energy savings may depend upon the fuels displaced(given differences in the conversion efficiencies of different fuels in the sameend-use);the greenhouse consequences of different fuel savings vary widely;the direction and magnitude of fuel savings may differ widely in response to asingle policy measure (for example, where additional gas use for hot waterheating is induced in response to tighter building and fixed appliance energyperformance standards directed towards reducing emissions);the energy efficiency (or greenhouse abatement) options available to differentbuilding owners may differ widely depending upon the availability to them ofdifferent fuels (particularly networked gas), generating potentially widelydiffering cost implications.
We recommend the electricity and natural gas use is always explicitly covered, withother lesser fuels (from a building perspective) - including LPG, firewood, heating oil –to be included where relevant. At a minimum, individual fuel shares in energy savingsshould be explicitly specified and, if one fuel is particularly high, the reasons for thisshould be explained. For context, we note that the 4 star RIS presents aggregateenergy and emission savings which imply a gas share in total energy savings thatappears implausibly high.
A major difficulty in implementing this recommendation will be lack of up to date,quality data on the proportions of each fuel type used for space heating and coolingand, more generally, the overall structure of energy use in buildings (please refer tosection 5.3 below). The lack of data is particularly severe in the case of commercialbuildings. In the absence of such data, it appears that different RIS’ (with differentauthors) use different and mutually inconsistent sets of assumptions about these fuelshares, and in particular how they vary between states.
Spatial ResolutionGenerally most (but not all) RIS’ provide break-downs of energy savings by State andTerritory, and some (generally those associated with the BCA) by Climate Zone. Theformer is likely to be relevant for most policy measures and should be reported as adefault.
We note that the data issues mentioned above are highly relevant in this context. Forresidential buildings, for which energy consumption is very sensitive to climate,inconsistent and inaccurate assumptions about how energy use breakdown varies bystate and/or Climate Zone means that results at the state level are subject to aparticularly high level of uncertainty. For commercial buildings, energy consumption ismuch less sensitive to climate, but the lack of quality energy use breakdown data at
41
the national level means that the overall results are subject to high uncertainty. See5.3 below.
Cost Estimation TechniquesWe support AECOM (2012) in noting that traditional quantity surveying techniques, thatare invariably used to estimate incremental construction costs associated with BCAchanges in particular, tend to systematically over-estimate incremental costs, both inthe short and longer terms.
First, ‘book rates’ (such as Rawlinsons, etc) tend to be conservative industry averages.Most construction firms would be able to access materials at lower prices, potentiallysignificantly lower prices. Second, unless the RIS/BCA process includes optimisedwhole building modelling, based on achieving the proposed new energy performancerequirements at least cost, the short term costs will be over-stated as they wouldignore industry-standard practices, such as modifying designs, construction materials,construction methods, and not simply components, which all tend to save cost. Thisapproach was taken in pitt&sherry (2012c), but this is rarely the case in other studieswe have reviewed, no doubt because it is very expensive and time consuming.Incremental construction and compliance costs over time also tend to be systematicallyoverstated, as learning effects are generally not taken into account. This issue isdiscussed further below.
We recommend that the Department at least (if not OBPR) specifies thermal simulationmodelling, cost optimisation and consideration of learning effects in all BCA/RIS workit commissions regarding building energy performance.
Relatedly, cost estimations should not be commissioned (or allowed) on a deemed-to-satisfy (DTS) basis. Such solutions risk to be a) excessively costly, b) non-compliantwith the actual performance requirement, or c) both of the above.
Learning Rates and Autonomous Energy Efficiency Improvement (AEEI), PriceElasticitiesMany RIS’ vary in the extent to which these factors are taken into account in theiranalysis. The underlying reasons for this include:
An absence of clear guidance on these issues, eg, from the Office of BestPractice Regulation (OBPR);An absence of clear, conceptually-robust and agreed (ie, peer-reviewed)definitions of these terms; andA lack of recent research and data to support a conceptually soundmethodology.
We have noted in this study, for example, that commonly-used values for income andprice elasticities of demand for energy appear to fit poorly with observed consumptiondata.
We recommend that:OPBR consults with the research community on these issues (for example, byissuing (or commissioning) a Discussion Paper for consultation purposes);DCCEE and other relevant Federal agencies design and implement an ongoingresearch program to address key research and data gaps (see 5.3 below).
Timeframe for AnalysisMany RIS’ differ on the timeframe for analysis, both in respect to how long a policymeasure is assumed to remain in place, and also over what period costs and benefitsare counted. While policy-specific considerations may always require some divergencefrom a norm, we recommend that a norm be created (for example through OBPRguidelines) which could include the following features:
42
Policy measures are assumed to have a defined life, defaulting to 10 years. Inour view, the balance of probabilities rests with most policies beingsubstantially amended (either strengthened or abandoned) within a 10 yearperiod. Further technology choices and societal preferences are also likely tohave evolved significantly over this time period;The period over which benefits are counted should be the life of theinvestments, or other impacts, induced by the policy intervention. Byimplication, the analysis must include an explicit analysis of these economiclife considerations. Given discounting effects and other uncertainties,extending the analysis beyond 20 or 30 years (after the cessation of themeasure) rarely adds material insights;The period over which costs should be counted is the life of the measure(defaulting to 10 years). However, where the timeframe for analysis exceedsthe economic life of the investments induced by the policy measure – forexample, because the measure has a defined life greater than 10 years, thenre-investment costs should be transparently modelled. Note that transparenttreatment of learning effects and AEEI will be important in such cases, as costsmay vary significantly over such time periods.
Scope of Costs and BenefitsWe noted in the set of RIS’ reviewed that there were material differences in the scopeof costs and benefits included. These included:
Avoided network costs (for all fuels, or only electricity?);Similarly, estimates of changes in peak, as distinct from average annual,energy demand;‘Industry costs’, such as training and insurance costs;Compliance and transactions costs.
In pitt&sherry (2012c) we also found that the inclusion or non-inclusion of distributedgeneration, including distributed renewable energy systems such as photovoltaic panelsand systems, can make profound differences to the net benefits associated withbuilding energy performance requirement. This raises complex issues, however, theseare conceptually no more complex than the inclusion with the current BCA of hot waterenergy performance requirements expressed in greenhouse metrics. With thecontinuing reduction in cost of PV panels, along with innovations such as building-integrated PV (solar facades, windows, tiles, etc), it will be increasingly difficult todistinguish active and passive energy management strategies in buildings. In principal,all such strategies should be included within the scope of RIS’, since the underlyinggoal must be to enable any given performance requirement to be met at least cost,regardless of the strategy used. Similarly, regulatory solutions should not constrain(nor specify) particular technical solutions.
Subject to a materiality test, we believe that changes in peak demands and avoidednetwork costs are important considerations to take into account in social benefit costanalysis. Practical considerations include:
a lack of transparency in load profiles for combinations of sectors, end-usesand geographies;similarly, limited transparency in future network (marginal) costs (particularlyin gas, which is susceptible to binary costs – none or extremely high,depending upon the extent of spare capacity in a particular gas network, eg,in WA).
With respect to industry costs, and classes of ‘hidden’ costs (such as transactions costs,or costs that may be ascribed to a change in a utility function, such as a diminution ofchoice), there are both conceptual and data limitations. Generally, no class of costs(or benefits) should be included in the absence of robust, peer-reviewed data sources.Assumptions about such values may overwhelm other factors and yet lack transparencyand credibility.
43
Treatment of greenhouse gas abatement, particularly in electricity supplyWhere estimates of avoided greenhouse gas emissions are called for, key issuesinclude:
the attribution of savings by State, particularly projections into the distantfuture;calculation of annual or cumulative emissions savings.
Our considered view is that increasing trading of electricity between states, and aconsistent national carbon price that provides a single opportunity cost for emissionsregardless of the State in which the change in emissions occurs, mean that a singlenational average value for the emissions intensity of electricity supply should be usedin all cases, including for all states. The alternative assumption (essentially, closedState borders and the absence of a common atmosphere) often leads to erroneousassumptions being made about the value, in greenhouse terms, of energy savings inparticular States.
6.2 Insufficient TransparencyAs a general statement, most RIS’ lack transparency with respect to their keyassumptions and the drivers of their quantitative conclusions. We believe that RIS’should aim for a degree of transparency that readily facilitates third party validation ofthe key results.
The tendency towards a lack of transparency might be improved by a consistently-applied requirement for a summary table of the key quantitative assumptions thatdrive the analysis. Most benefit cost analyses that underpin RIS analysis are capable ofbeing expressed in one or two pages of key assumptions and drivers. OBPR coulddevelop a template for these purposes, using the Discussion Paper approach notedabove. While no template will adequately capture all relevant factors, or be immuneto misinterpretation (or obfuscation), this approach would at least minimise the scopefor such outcomes and, as a side benefit, aid in the rapid interpretation/critique of RISanalyses.
6.3 Data Limitations/Research GapsWhile the data and research needs of all buildings-related analyses cannot bepredicted, there are many common elements that are in need of redress. As notedearlier, pitt&sherry has made detailed recommendations in a report on an EnergyEfficiency Data Framework which remain relevant in this context.
A key requirement is a detailed and statistically-significant picture of the structure ofenergy use in the residential and commercial sectors, resolved to the scale demandedfor policy analysis. This includes a statistical understanding specific end-usetechnologies (eg, for MEPS and labelling purposes) including stock vintage andturnover.
Practically these factors could be addressed by:Updating immediately, and then at no more than three-year intervals, theResidential Baseline Study (DEWHA/ESS 2008);Completing the Commercial Building Baseline Study (pitt&sherry 2012b) to asimilar standard as the residential study, publishing it, and then updating it atno more than three-yearly intervals;Making a range of practical, low-cost and low-intervention changes tostatistical collection processes by key data owners including the ABS, NGER,ABARE/BREE, the EEO program – to improve consistency and completeness inthese data sets, avoid duplication of data collection processes and to maximiseaccess to the data in the public interest (in confidentialised form where
44
necessary). As noted, a set of such recommended changes has been detailed inthe Energy Efficiency Data Framework (pitt&sherry 2012a);
Key research, as distinct from data, requirements include quantitative historicalanalyses of:
learning rates in key sectors;AEEI (distinguishing technology change and uptake from other factors);income and price elasticity responses in key end-use sectors and time periods.
Practically this research would also require access to the kind of technology-rich datacalled for above. Decomposition analyses have a role to play but will not substitute forbottom-up, technology-rich, vintage-dated stock turnover models if we are tounderstand the composition of energy use in Australia, including the real potential forenergy and greenhouse gas savings. While it may be considered ‘out of scope’, we doalso note that there appears to be an underlying institutional gap in this area. There isno institution that we are aware of with an ongoing mandate to undertake, or lead,research and data compilation of the kind described above. Policy agencies are notbest placed to undertake this role. Traditionally the role fell to appropriatelyspecialised statutory authorities, and indeed this is the model that most OECDcountries pursue.
6.4 Emerging IssuesA key emerging issue in buildings analysis is the extent to which a changing climateshould be factored into the analysis of buildings policy measures. Traditionally a staticclimate was either implicitly or sometime explicitly assumed. The latter includes thehistorical climate files utilised by building simulation models. Increasingly, however,this assumption is inconsistent with both past trends and future expectations. Policysettings (particularly for thermal integrity and related issues such as air-tightness) maybe systematically biased towards inadequate stringency if a changing climate is nottaken into account.
45
7. BibliographyABCB(2002)
Australian Building Codes Board. Energy Efficiency Measures BCA Volume2 (Housing Provisions: Regulatory Assessment), December 2002.
ABCB(2004)
Australian Building Codes Board. Australian Building Codes Board.Regulation Impact Statement: proposal to amend the building code ofAustralia to include energy efficiency requirements for residentialbuildings other than houses (class 2, 3 & 4 buildings) (RD 2003-1), August2004.
ABCB(2006a)
Australian Building Codes Board. Regulation Impact Statement (RIS 2006-02), Commonwealth of Australia, 2006.
ABCB(2006b)
Australian Building Codes Board. Regulation Impact Statement (RIS 2006-01): proposal to amend the building code of Australia to increase theenergy efficiency requirements for houses, Commonwealth of Australia,March, 2006.
ABCB(2009a)
Australian Building Codes Board. Consultation Regulation ImpactStatement (Consultation RIS 2009-04): proposal to revise the energyefficiency requirements in the building code of australia for commercialbuildings, September 2009, prepared by the Centre for InternationalEconomics.
ABCB(2009b)
Australian Building Codes Board. Final Regulation Impact Statement forDecision (RIS 2009-07): proposal to revise the energy efficiencyrequirements in the building code of Australia for commercial buildings,December 2009, prepared by the Centre for International Economics.
ABCB(2009c)
Australian Building Codes Board. Final Regulation Impact Statement forDecision (Final RIS 2009-06): proposal to revise the energy efficiencyrequirement of the building code of Australia for residential buildings,December 2009, prepared by the Centre for International Economics.
ABCB(2009d)
Australian Building Codes Board. Consultation Regulation ImpactStatement (Consultation RIS 2009-03): proposal to revise the energyefficiency requirement of the building code of Australia for residentialbuildings – classes 1, 2,4 and 10, September 2009, prepared by theCentre for International Economics.
ACG (2009) Allen Consulting Group. Mandatory Disclosure of Commercial OfficeBuilding Energy Efficiency: regulation impact statement, November2009.
ACG (2011) Allen Consulting Group. Mandatory disclosure of residential buildingenergy, greenhouse and water performance: consultation regulationimpact statement, July 2011.
AECOM(2012)
AECOM. Understanding how the Building Industry Responds to EnergyEfficiency Standards: final report, June 2012.
AEMO(2012)
Australian Energy Market Operator. South Australian Wind Study Report:2012.
ASBEC(2009)
Australian Sustainable Built Environment Council. The Second Plank –Building a Low Carbon Economy with Energy Efficient Buildings, 2009.
BISShrapnel(2012)
BIS Shrapnel. Building in Australia 2012-2027, July 2012.
BREE(2012a)
Bureau of Resource & Energy Economics. Australian Energy Statistics,Commonwealth of Australia, 2012.
BREE(2012b)
Bureau of Resource & Energy Economics. Economic Analysis of End-useEnergy Intensity in Australia, May 2012.
Che &Pham(2012)
Che, N. and Pham, P. Economic Analysis of End-use Energy Intensity inAustralia, BREE, Canberra, May 2012.
COAG Council of Australian Governments. Guide to Best Practice Maintenance
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(2012) & Operation of HVAC Systems for Energy Efficiency, January 2012.CommSec(2009)
CommSec. Economic Insights: Australian homes are biggest in theworld, 30 November 2009. Accessed on 15/2/2013 fromhttp://images.comsec.com.au/ipo/UploadedImages/craigjames3f6189175551497fada1a4769f74d09c.pdf
Cuevas-Cubria andRiwoe(2006)
C. Cuevas-Cubria and D. Riwoe, Australian energy national and stateprojections to 2029-30, abare research report 06.26, December 2006.
DEWHA(2008)
Department of the Environment, Water, Heritage & the Arts. Energy Usein the Australian Residential Sector, 1986 – 2020, Commonwealth ofAustralia, 2008, prepared by Energy Efficient Strategies.
DCCEE(2012)
Department of Climate Change and Energy Efficiency. Energy Use in theAustralian Government’s Operations, 2009-10, Commonwealth ofAustralia, 2012.
EES (2011) Energy Efficient Strategies. The Value of Ceiling Insulation: impacts ofretrofitting ceiling insulation to residential dwellings in Australia,September 2011.
Graus et al(2009)
Graus et al, 2009, Global technical potentials for energy efficiencyimprovement, presented at IAEE Conference September 2009.
IPART(2012)
Independent Pricing and Regulatory Tribunal, 2012. Compliance andoperation of the NSW Energy Savings Scheme during 2011, Government ofNSW, 2012.
Koomey(1990)
Koomey et al (1990) Conservation Screening Curves to CompareEfficiency Investments to Power Plants, Applications to CommercialSector Conservation Programs, Proceedings 1990 ACEEE Summer Study onEnergy Efficiency in Buildings.
MCE (2008) Ministerial Council on Energy. Decision Regulatory Impact Statement:minimum energy performance standards and alternative strategies forchillers, Equipment Energy Efficiency (E3) Committee, July 2008.
NHSC(2011)
National Housing Supply Council. Key findings of the 2011 State ofSupply Report, Commonwealth of Australia, 2011.
pitt&sherry(2010)
pitt&sherry. National Strategy on Energy Efficiency: Energy,Greenhouse & Financial Savings Assessment, 2010, prepared for theDepartment of Climate Change & Energy Efficiency (unpublished).
pitt&sherry(2012a)
pitt&sherry. Energy Efficiency Data Framework, prepared for theDepartment of Resources, Energy & Tourism, 2012 (unpublished)
pitt&sherry(2012b)
pitt&sherry. Baseline Energy Consumption and Greenhouse Gas Emissionsin Non-Residential, Non-Industrial Buildings in Australia, prepared forthe Commonwealth Department of Climate Change & Energy Efficiency,2012 (unpublished).
pitt&sherry(2012c)
pitt&sherry. Pathway to 2020 for Increased Stringency in New BuildingEnergy Efficiency Standards: Benefit Cost Analysis, published by theDepartment of Climate Change & Energy Efficiency, 2012.
Robertson& Flores(2012)
F. Robertson and Cl. Flores, A Report to show energy consumption trendsin NABERS rated buildings 2004 – present (Base Building), June 2016(unpublished).
Tedesco &Thorpe(2003)
L. Tedesco & S. Thorpe, Trends in Australian energy intensity, 1973-74 to2000-01, ABARE eReport 03.9, June 2003.
Treasury(2011a)
Commonwealth Treasury. Strong Growth, Low Pollution: modelling acarbon price: update, Commonwealth of Australia, 2011.
Treasury(2011b)
Commonwealth Treasury. Australia’s Low Pollution Future,Commonwealth of Australia, 2011.
UTS (2010) UTS (2010) Building Our Savings : reduced Infrastructure Costs fromImproving Building Energy Efficiency, Institute for Sustainable Futures,University of Technology Sydney and Energetics, 2011 for Department ofClimate Change and Energy Efficiency.
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Appendix AStatement of Requirement
A1 Background
The Building Energy Efficiency Branch (BEEB) develops policy and delivers programsthat improve the energy efficiency performance of buildings in Australia.
In 2009/10 a report was completed for the BEEB that quantified the energy savings andgreenhouse gas abatement projected to arise from the National Strategy on EnergyEfficiency (NSEE) measures. This report adopted a baseline from the commencement ofthe NSEE (2009).
Apart from this report, there is no assessment that has brought together the latestavailable data to quantify the energy savings projected to arise from buildingmeasures.
The Department requires updated energy savings figures and associated private sectorand state/territory government costs to underpin its internal calculations ofgreenhouse gas abatement and cost of abatement for these measures.
Existing reports that can inform this work include, but should not be limited to:
National Strategy on Energy Efficiency: Energy, Greenhouse & Financial Savings –March 2010Renewables and Energy Efficiency (REED) Measures: Energy, Greenhouse &Financial Savings – March 2010Proposal to Amend the Building Code of Australia to include Energy EfficiencyRequirements for Residential Buildings other than Houses (Class 2, 3 & 4 Buildings)– August 2004Proposal to Amend the Building Code of Australia to increase the Energy EfficiencyRequirements for Houses – March 2006Proposal to Amend the Building Code of Australia to include Energy EfficiencyRequirements for Class 5 to 9 Buildings – March 2006Proposal to Revise the Energy Efficiency Requirements of the Building Code ofAustralia for Residential Buildings — Classes 1, 2, 4 and 10 – December 2009Proposal to Revise the Energy Efficiency Requirements in the Building Code ofAustralia for Commercial Buildings — Classes 3 and 5 to 9 – December 2009Mandatory Disclosure of Commercial Office Building Energy Efficiency RegulationImpact Statement – 19 June 2009Mandatory disclosure of residential building energy, greenhouse and waterperformance – Consultation Regulation Impact Statement – July 2011Commercial Building Baseline Study – 2012 (available from the Department)
A2 Contract Services/Outcomes Required
The building measures to be examined are:
All building energy efficiency regulations that have been included in the BuildingCode of Australia;Commercial Building Disclosure program;Energy Efficiency in Government Operations policy;Heating, Ventilation and Air Conditioning High Efficiency Systems Strategy (HVACHESS);Residential Building Disclosure (assuming the program commences in 2014).
48
The requirement of the services is to deliver an Excel spreadsheet model andaccompanying report, including assumptions about ‘per unit’ energy savings and costs,that consolidates and updates data used in previous reports and provides:
A quantitative estimate of the energy savings (in megawatt hours for electricityand megajoules for gas) arising from each measure, for each year from the start ofthe measure to 2050, taking into account the latest building stock projections andother relevant factors. Estimates should be broken down by State/Territory, andby Building Code of Australia climate zones where possible. Estimates should onlybe for additional energy savings attributable to each measure and should identify:o Energy savings that would occur in the absence of the measure under “business
as usual”, including:Energy savings stimulated by rising electricity and gas prices, driven bythe carbon price and other factors; andEnergy savings stimulated by technological improvements and industrylearning over time.
o Overlap between these and other measures (for example, between buildingstandards and Mandatory Energy Performance Standards for air-conditioners),including overlap between the buildings measures being evaluated. Whereoverlap occurs, the service provider is to clearly document assumptions andevidence for attributing energy savings to a particular measure.
An assessment of the annual costs of each building measure, for the same period,where possible on a per unit basis. Costs are to include:o All capital costs associated with the energy savings actions stimulated by the
measure; ando Any compliance costs associated with the measure;o The annual costs should not include dollar savings from reduced electricity and
gas consumption.An assessment of the annual cost savings (if any) of the building measures fromdeferred expenditure on electricity and/or gas networks, for the same years.A description of the methodology and assumptions underpinning all calculations,including assumptions underpinning estimations of the uptake of energy efficiencyactions under “business as usual”.An identification of assumptions used in past Regulatory Impact Statements thatmay have limitations for this type of analysis and recommendations that could beconsidered for future Regulatory Impact Statement assumptions and analysis.
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Appendix B
Autonomous Energy Efficiency Improvement
Introduction
Broadly, autonomous energy efficiency improvement (AEEI) refers to the rate ofimprovement in energy efficiency through time, generally across the economy as awhole, with the qualifier ‘autonomous’ implying ‘in the absence of policy measures’.However in practice, there are many definitions of AEEI in use.36,37 ,38, 39 The majorityof these (excepting Graus et al 2009) fail to distinguish between the various drivers ofenergy efficiency change through time, which include at least:
Technological change unattributable to any domestic policy measure (notingthat the efficiency of imported products may be affected by efficiency policiesin other countries);Policy-induced effects;Fuel mix effects;Price-induced effects;Income-induced effects; andStructural changes in the economy (particularly at the sub-sectoral level).
Further, many of the observations of (or assumptions about) the value of AEEI arebased on measures (or concepts) of energy intensity, and not energy efficiency40.
Despite conceptual disagreements, estimates as to the value of AEEI in the literature(which is relatively limited) show a remarkable degree of uniformity. Cuevas-Cubria etal (2006) assumes that the demand for each fuel per unit of output [note: a measureof energy intensity] reduces by 0.5% per year. This reference does note that the rate islikely to be different in regions or sectors where government greenhouse gasabatement policies are in place. For example, the New South Wales Government’sgreenhouse gas benchmarks scheme is expected to accelerate the rate of efficiencyimprovements in the use of electricity in New South Wales. Clearly, then, this measureof efficiency change is not ‘autonomous’ at all, but rather includes policy effects.
Tedesco & Thorpe (2003) finds that the negative real intensity effect in the residentialsector of –2.5 per cent, comprised of fuel mix effects (–1.3 per cent) and technicaleffects (–1.2 per cent). While the latter is likely to better represent underlying energyefficiency improvement, it is invariably measured as a residual after other effects areaccounted for, and therefore is likely to include sub-sectoral structural and qualitychanges as well as changes in technical efficiency. The study concluded thathouseholds are adopting new technologies, ‘housekeeping’ practices and different fuelsto improve energy efficiency when these are considered to be economic investments.
36 Cuevas-Cubria & Riwoe (2006).37 Tedesco & Thorpe (2003).38 Che & Pham (2012).39 Graus et al (2009).40 In practice many of the quantitative observations of (or assumptions about) AEEI are based onmeasurements or concepts of energy intensity – that is, energy consumption per unit output. Particularlywhere output is measured in financial units (such as value added), the correlation between this andenergy efficiency may be poor. Energy efficiency is defined as output (work done) per unit energyconsumed. At a narrow level of observation, the two concepts are inverses. However, energy intensityoften expresses output in monetary, not physical terms, meaning that changes in energy intensity may bedue to factors unrelated to changes in energy efficiency.
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Che, N. and Pham, P. 2012 analyses end use energy intensity in key sectors of theAustralian economy – including the services sector, using a decomposition method –summarized in the figure below:
Figure 11: Decomposition of Change in Energy Consumption
Source: BREE (2012)
The authors found that the efficiency effect resulted in an average annual reduction inenergy use in the services sector of 1.0% (the services sector comprising the sub-sectors of water supply; wholesale & retail trade; communications; finance;government & defence; education; and accommodation) .
The authors state that key factors that may impact on energy intensity in the servicessector include technological advancement and government policies. However theenergy efficiency effect is not further decomposed to enable individual quantificationof those factors. As noted above, the ‘efficiency effect’ in this and similarmethodologies is in fact a residual. Also, this effect explicitly includes policy effects.
Finally, Graus et al, 2009 notes that energy intensity tends to decrease through time asa function of:
Autonomous energy efficiency improvement. These energy efficiencyimprovements occur because due to technological developments each newgeneration of capital goods is likely to be more energy efficient than the onebefore;Policy-induced energy efficiency improvement as a result of which economicactors change their behaviour and invest in more energy efficient technologies;andDecoupling of economic growth and energy use.
This paper correctly notes that a decline of the ratio of energy over GDP is not causedonly by energy efficiency improvement (that includes technical changes andoperational improvements). In addition, structural changes can have a downwardeffect on the energy over GDP ratio, for example, a shift in the economy away fromenergy-intensive industrial activities to services related activities. Also there may bedemand saturation, for example, energy use for heating tends to grow in proportion tofloor surface rather than in proportion to economic growth. For their particularpurpose, the authors assumed an autonomous energy efficiency improvement of 1%annually.
Finally, Treasury (2011) notes that energy efficiency increases when the same amountof output is produced using less energy. This can occur when:
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energy prices rise relative to other inputs;existing technology is used more efficiently;upgrading existing technology; orwhen new technology is developed through research and development andlearning by doing.
The models used in this reference (GTEM and MMRF) have different treatments ofenergy efficiency due to the different structures of the models. In the baselinescenario, GTEM assumes a rate of improvement in energy efficiency of 0.5 per cent peryear, except for specific sectors such as transport, iron and steel, non-metallicminerals, non-ferrous metals, chemicals, rubber and plastics.41 It is noted thatadditional technology and behavioural changes in sectors not modelled in detail arerepresented through an autonomous energy-efficiency improvement (AEEI) parameter.
Arriving at estimates of future energy efficiency is difficult given uncertainty abouthow energy efficiency will evolve over long timeframes. In the Treasury analysis, asensitivity scenario explored a higher energy-efficiency assumption in the referencescenario, with an additional 1 per cent per year from 2013 to 2030, an extra 0.5 percent per year from 2031 to 2040, and no extra improvement thereafter assumed.
In summary, there are both conceptual and quantitative uncertainties with respect tomeasures of AEEI. Most observations fail to distinguish between price, policy andunderlying (or technical) efficiency changes – and indeed other factors. Since themethodology adopted in this study requires separate identification of these effects, tothe extent possible, it would assume away the answer if we were to deduct AEEI fromthe counterfactual baseline. Conceptually, it would be appropriate to deduct awayjust that portion of AEEI which is solely attributable to non-policy-inducedtechnological change; however, no observations of this rate are available in theliterature.
Despite this, and as requested by the Department, we do show in the Figures 9 – 11below the impact of an assumed rate of AEEI (which we default to 0.5% per annum),for comparison with other effects reported. For the reasons above, we do not attemptto draw any conclusions from this analysis. In the underlying model supplied to theDepartment, this default rate may be varied.
Residential Sector Results
Figure 9 shows the residential baseline (without measures) projection for electricity,the with-measures projection, and then an AEEI assumption of 0.5% per year againstthe without-measures baseline. Figure 10 shows the same data but for gas. It may benoted that the AEEI assumption implies a greater level of electricity savings than eitherthe historical record to 2011 or the projection to 2050 with measures, while for gas,AEEI is shown to be smaller than the impact of measures. Without wishing to draw anystrong conclusions from this, we conclude that this data is supportive of our decisionnot to deduct AEEI from the without-measures baseline, as it is clear that such abaseline, minus the impact of residential measures, would dramatically overstate theactual amount of electricity savings achieved in this sector historically, and may alsooverstate this amount in the projection period as well. Our view is that additionalresearch would be required to disaggregate the components of AEEI to ensure thatthere is no double counting of policy and price effects, while also completing theadditional normalisation steps suggested in Appendix D with respect to the baseline,before stronger conclusions are drawn.
41 Treasury (2011b)
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Figure 12: Residential electricity consumption baseline, with measures, andbaseline + autonomous energy efficiency improvement, FY2001 – FY 2050
Source: pitt&sherry
Figure 10: Residential Gas Consumption Baseline, With Measures, and Baseline +Autonomous Energy Efficiency Improvement Projections, FY2001 – FY 2050
Source: pitt&sherry
Commercial Sector Results
Figure 14 below compares the baseline and ‘with measures’ projections with anestimate of autonomous energy efficiency improvement (at 0.5% per year) relative tothe baseline.
150
200
250
300
350
400
2001 2011 2021 2031 2041
PJ
With all measures
With autonomous energyefficiency improvement
Baseline (withoutmeasures)
100
150
200
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300
350
2001 2011 2021 2031 2041
PJ
With all measures
With autonomousenergy efficiencyimprovement
Baseline (withoutmeasures)
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Figure 14: Commercial Building Energy Consumption Without Measures, WithMeasures and Without Measures + Autonomous Energy Efficiency Improvement,FY2002 – FY2050, Australia (PJ)
Source: pitt&sherry
As may be seen, the ‘with measures’ and AEEI projections are very similar. We do notdraw any strong conclusions from this analysis, given conceptual and data limitations inthe valuation of AEEI.
150
200
250
300
350
400
450
2002
2005
2008
2011
2014
2017
2020
2023
2026
2029
2032
2035
2038
2041
2044
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Autonomous EE
With measures
Baseline withoutmeasures
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Appendix C
Residential Baseline – Additional AnalysisThe residential baseline results presented in Section 2.4 create challenges in seeking todefine a future baseline for residential energy consumption, against which to assessthe impact of later efficiency measures. This Appendix analyses the results in moredetail and offers some tentative analysis. We note that a full examination of theseissues is beyond the scope of this study.
Figures 15 and 16 below show, for electricity and gas and other fuels respectively,actual residential energy consumption (per dwelling), a 2001 ‘frozen efficiency’projection, and the energy savings effects of those measures examined as part of theresidential baseline.
Figure 15: Per dwelling consumption of electricity, showing actual consumptionand the modelled effect of ‘baseline’ policy measures, 2001 to 2011, GJ/annum
Source: pitt&sherry
23.0
23.5
24.0
24.5
25.0
25.5
26.0
26.5
27.0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
GJ
With State measures (actual) With BCA 5 star
With BCA 4 star Without measures
55
Figure 16: Per dwelling consumption of gas etc., showing actual consumption andthe modelled effect of measures, 2001 to 2011, GJ/annum
Source: pitt&sherry
The challenge is particularly difficult in the case of electricity. At first glance, itappears, as previously noted, that the year 2004 marked an important turning point inthe trend towards greater energy intensity in residential buildings. It should be noted,however, that this apparent change is less clear cut when examined at the individualState level (not shown here). In order to determine whether 2004 was indeed a turningpoint, as distinct from a data anomaly, for example, residential electricityconsumption per capita has been plotted from 1990 onward, using both AES data anddata on electricity sales to residential consumers reported by the ESAA. The resultsare shown in Figure 17, for the AES data and Figure 18 for the ESAA data. Two pointsshould be noted. Firstly, in both cases total residential electricity consumption isanalysed, not consumption net of plug loads and cooking energy. Secondly, all dataare expressed per capita, rather than per household, because we have concerns aboutthe quality of available housing stock data prior to the 2001 Census.
15.0
16.0
17.0
18.0
19.0
20.0
21.0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
GJ
With State measures (actual) With BCA 5 star
With BCA 4 star Without measures
56
Figure 17: Residential electricity consumption per capita, based on AES data
Source: pitt&sherry
Figure 18: Residential electricity consumption per capita, based on ESAA data
Source: pitt&sherry
In each graph, lines of best fit are shown separately for the periods 1991 to 2004 and2004 to 2011. It can be seen that this analysis is consistent with a marked change inconsumption occurring around 2004. The relationship between this change inelectricity consumption to the increases in real price of electricity was examined, usingthe electricity cost component of the Consumer Price Index, deflated by the totalIndex for each capital city to obtain a real price series. It was found that realhousehold electricity prices started to increase in 2004 in SA and the ACT, in 2005 inNSW and Queensland, in 2008 in Victoria and Tasmania and not until 2011 in WA (wherethe previous state government held regulated prices constant in nominal terms formany years). Throughout this period, household disposable income grew steadily ineach State and Territory. It does not appear that price or income elasticities fullyexplain the data.
Separate analysis for each individual State and Territory was undertaken, using a rangeof plausible values from the economic literature for the own price elasticity and the
y = 0.0373x + 2.1711
y = 0.0093x + 2.5742
2.0
2.2
2.4
2.6
2.8
3.0
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
MW
h/ca
pita
y = 0.0392x + 2.1714
y = -0.0028x + 2.7805
2.0
2.2
2.4
2.6
2.8
3.0
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
MW
h/ca
pita
57
income elasticity of demand for electricity. In no case does the modelledconsumption, starting from 2001, come close to the pattern of actual energyconsumption seen in Figures 2 and 3 (Chapter 3). Up to 2004, the rate of growth of perdwelling consumption requires implausibly high values of income elasticity,notwithstanding the constant or slightly declining real prices. We therefore concludethat the consumption growth up to 2004, and the change after that year, is most likelyattributed to structural/energy service level changes. Possible contributory factors arelikely to have included:
an increase in the conditioned area in existing dwellings, for both heating andcooling, notably an increase in ducted heating and cooling systems fitted toexisting houses, andan increase in the use of air conditioning.
Changes after 2004 – that is, the flattening of per-capita and per-household energyconsumption – may have been caused by:
saturation in the use of heating and cooling (comfort levels);a marked increase in the efficiency of new air conditioning units (noting thatincreasing minimum energy performance standards took effect in Australia in2001, 2004 and 2011);increased replacement of electric resistance water heaters with solar, heatpump and gas systems;changes in consumers’ energy using behaviour in response to the increasedavailability of information about energy efficiency options and opportunities,provided by governments, businesses and NGOs; andin the last couple of years, the effect of increasing the proportion of existingdwellings with adequate levels of ceiling insulation.
Other factors that may have impacted on historical energy consumption in theresidential sector include changes in average size of new dwellings (although, sinceturnover of the housing stock is only around 1% per year, this effect is unlikely to havea material effect on total residential consumption in the short term), changes inclimate conditions and, possibly, ‘rebound’ effects in response to policy changes.While a careful analysis of these factors is beyond the scope of this study, somecomment is nevertheless offered.
With respect to house size, Commsec produces an Economic Insights series42 which in2009 reported that the average floor area of new homes (houses and apartments) inAustralia rose from 150 sqm in 1985 to around 214 sqm in 2009 (see Figure 19 below –low resolution image). The report also notes that new stand-alone dwellings increasedin size, on average, from around 160 sqm to around 245 sqm over the same period – thelargest in the world. A further report appears to have been released in 2011 but is notreadily available online. We note that this data is likely to include areas devoted togarages, verandahs and porches, which may distort its comparability with data fromother sources.
42 CommSec (2009)
58
Figure 19: ‘Houses Continue to Grow’
Source: CommSec
However, from the perspective of building shell and fixed appliance energyconsumption (which is the portion of total residential energy consumption that isregulated by the BCA), an increase in average house area does not necessarily imply anincrease in total energy consumption (in the new housing stock) for these purposes,particularly when the increasing efficiency of lighting, hot water and spaceconditioning appliances is taken into account. The analysis would need to examine thechange in ‘space conditioned area’, rather than total area; the change in thecomposition and efficiency of the fixed appliance stocks over time; and any changes inoccupant behaviours (such as hours of use, thermostat set points, etc). Further, in anyyear, or indeed any succession of several years, new houses are only a small proportionof the total housing stock, so it is the older, and on average less efficient, stock whichwill dominate energy consumption.
With respect to temperature, Figure 9 below was sourced from the Bureau ofMeteorology43. It shows the temperature anomaly, relative to the 1961 – 1990 meanannual temperature, over the historical period of this study. While this is a relativelycrude representation of all temperature effects that are relevant to residential energyconsumption – ideally, separate analyses would be undertaken of heating and coolingdegree days, or else winter and summer temperature anomalies, by state, correlatedwith summer and winter peak loads as well as annual averages – nevertheless, thereappears to be a broad correlation between this trace and the ‘actual’ line in Figure 4above. Unlike trends with new house sizes, however, this temperature effect impactson the whole of the housing stock and would have a material impact on energyconsumption patterns. However, it would not be appropriate to draw conclusionsabout causation without a serious study of this correlation.
43 Sourced from the Bureau of Meteorology website, accessed on 15/2/2013, athttp://www.bom.gov.au/cgi-bin/climate/change/timeseries.cgi?graph=tmean&area=aus&season=0112&ave_yr=2
59
Figure 20: Australian Average Temperature Anomalies, 1999 - 2011
Source: Bureau of Meteorology
Turning to possible rebound effects, we note that there are a range of different effectsthat could be analysed (income, price, substitution effects). All effects, however, turnon assumptions about changes in household disposable income as a consequence ofpolicy changes, and also (often unstated) assumptions about energy service demands.For example, more stringent housing codes may be considered to reduce the short termmarginal cost to the householder of achieving a given comfort outcome, as less energyis required to space condition such a house. A rebound effect could occur if thereduction in expenditure on space conditioning facilitates increased expenditure whichis equally or more energy intensive (eg, the householder now can afford to heat thehouse more, or instead can afford a third television – noting these are two differentrebound effects, with the first often referred to as ‘comfort creep’).
However, such analyses typically overlook at least two key factors. First, the reductionin short term marginal cost in the example above is a consequence of previousadditional capital expenditure (ie, the higher incremental cost of a more energyefficient house), and this capital has a cost (the cost of the incremental capital) thatoffsets to some extent the policy-induced reduction in short term marginal cost.Second, changes in household disposable income are affected by other (non-correlated)factors including increases in real energy prices. As there have been significant realprice increases over the historical period of this study, these will have tended to offsetthe increases in household disposable income attributable to the policy measures understudy. Further, it should be noted that the scale of the policy changes underconsideration here are small relative to total household income, and ‘householdbudget’ theory suggests that small changes in disposable income do not typically leadto material changes in consumption patterns (whereas larger changes may lead todisproportionately large responses once a (hidden) ‘pain threshold’ is passed).
Finally, we note that the potential for rebounds must take into account the extent towhich latent demands for specific energy services are already saturated. For example,a large rebound may be expected when an uninsulated house is first insulated, as itmay enable a latent demand for energy services (comfort) to be met more affordablythan before (noting the proviso with respect to the financing of the necessaryinvestment). However, if further insulation is added to an already comfortable house(ie, where the latent demand for energy services is already being met, for example viaspace conditioning equipment), then no rebound is likely. Indeed, it is likely that thedemand for energy consumption to maintain comfort levels will fall.
60
In short, little can be said in the abstract about rebound effects that is meaningful.Instead, detailed and case-by-case analysis is required to understand the potential forrebounds in response to specific policy changes.
From 2004 onward, per dwelling consumption can be replicated by simple economicequations using plausible price and demand elasticities. In broad terms, the effect ofcontinuing income growth is offset by the effect of higher real prices. It was thereforedecided to project baseline electricity consumption per dwelling forward in time, usingthis relationship. Real household income growth of 2% p.a. was assumed.
61
Appendix D
Energy Savings of Measures by State/Territory, Climate Zoneand FuelThis Appendix presents energy savings attributable to measures by State/Territory, by fuel andby BCA Climate Zone where this data is available and relevant.
D1. Residential MeasuresTable 4: 4 Star Energy Savings by State/Territory
PJ 2012 2020 2030 2040 2050NSW +
ACT Electricity 0.3 0.5 0.9 1.2 1.6
Gas 1.4 2.6 4.2 5.8 7.4VIC Electricity 0.1 0.2 0.4 0.6 0.7
Gas 5.6 10.4 17.1 25.8 30.5QLD Electricity 0.1 0.3 0.4 0.6 0.8
Gas 0.2 0.5 0.8 1.0 1.3WA Electricity 0.1 0.1 0.2 0.3 0.3
Gas 0.5 1.0 1.7 2.3 2.9SA Electricity 0.1 0.1 0.2 0.2 0.3
Gas 0.6 1.1 1.8 2.5 3.2NT Electricity 0.0 0.0 0.0 0.0 0.0
Gas 0.0 0.0 0.0 0.0 0.0TAS Electricity 0.0 0.1 0.1 0.1 0.1
Gas 0.0 0.0 0.1 0.1 0.1Totals Electricity 0.7 1.3 2.2 3.0 3.9
Gas 8.4 15.6 25.6 35.6 45.6
62
Table 5: 5 Star Energy Savings by State/TerritoryPJ 2012 2020 2030 2040 2050
NSW +ACT Electricity 0.0 0.1 0.2 0.3 0.3
Gas 0.2 0.7 1.3 1.9 2.5VIC Electricity 0.1 0.2 0.4 0.5 0.7
Gas 0.5 1.1 2.1 3.0 3.9QLD Electricity 0.1 0.3 0.6 1.0 1.2
Gas 0.0 0.0 0.0 0.0 0.0WA Electricity 0.1 0.2 0.4 0.5 0.7
Gas 0.1 0.3 0.5 0.7 1.0SA Electricity 0.0 0.1 0.1 0.2 0.2
Gas 0.0 0.1 0.2 0.3 0.3NT Electricity 0.0 0.0 0.0 0.1 0.1
Gas 0.0 0.0 0.0 0.0 0.0TAS Electricity 0.0 0.0 0.0 0.1 0.1
Gas 0.0 0.0 0.0 0.1 0.1Totals Electricity 0.5 1.4 2.7 4.0 5.3
Gas 0.9 2.4 4.3 6.3 8.2ClimateZone 1 Electricity 0.0 0.1 0.1 0.2 0.3
Gas 0.0 0.0 0.0 0.0 0.0ClimateZone 2 Electricity 0.1 0.3 0.5 0.8 1.0
Gas 0.0 0.0 0.0 0.0 0.0ClimateZone 3 Electricity 0.2 0.5 1.0 1.5 2.0
Gas 0.0 0.0 0.0 0.0 0.0ClimateZone 4 Electricity 0.1 0.2 0.3 0.5 0.7
Gas 0.1 0.3 0.5 0.7 0.9ClimateZone 5 Electricity 0.1 0.3 0.5 0.8 1.0
Gas 0.1 0.2 0.4 0.6 0.8ClimateZone 6 Electricity 0.0 0.1 0.2 0.2 0.3
Gas 0.5 1.4 2.6 3.9 5.1ClimateZone 7 Electricity 0.0 0.0 0.0 0.0 0.0
Gas 0.2 1.5 0.8 1.1 1.5
63
Table 6: 6 Star Energy Savings by State/TerritoryPJ 2020 2030 2040 2050
NSW + ACT Electricity 0.7 1.6 2.5 3.2Gas 0.8 1.9 3.0 4.0
VIC Electricity 0.5 1.1 1.7 2.3Gas 1.2 2.7 4.3 5.8
QLD Electricity 0.6 1.4 2.3 3.1Gas 0.1 0.2 0.2 0.3
WA Electricity 0.6 1.3 2.0 2.7Gas 0.3 0.8 1.1 1.4
SA Electricity 0.2 0.4 0.6 0.8Gas 0.2 0.3 0.5 0.7
NT Electricity 0.1 0.2 0.3 0.4Gas 0.0 0.0 0.0 0.0
TAS Electricity 0.0 0.0 0.1 0.1Gas 0.0 0.0 0.1 0.1
Totals Electricity 2.7 6.1 9.4 12.8Gas 2.7 6.1 9.5 12.9
Climate Zone 1 Electricity 0.2 0.6 0.9 1.2Gas 0.0 0.0 0.0 0.0
Climate Zone 2 Electricity 0.2 0.4 0.6 0.8Gas 0.0 0.0 0.1 0.1
Climate Zone 3 Electricity 0.4 0.9 1.4 1.9Gas 0.0 0.1 0.2 0.2
Climate Zone 4 Electricity 0.5 1.1 1.7 2.3Gas 0.1 0.3 0.5 0.6
Climate Zone 5 Electricity 0.8 1.7 2.6 3.5Gas 0.5 1.2 1.9 2.5
Climate Zone 6 Electricity 0.6 1.3 2.0 2.7Gas 1.6 3.7 5.8 7.9
Climate Zone 7 Electricity 0.1 0.2 0.3 0.4Gas 0.4 0.7 1.1 1.5
64
Table 7: Residential Mandatory Disclosure Energy Savings by State/TerritoryPJ 2020 2030 2040 2050
NSW +ACT 1.4 4.5 7.0 8.5
VIC 2.0 6.0 9.5 11.8QLD 0.7 2.1 3.4 4.3WA 0.4 1.3 2.2 2.8SA 0.4 1.1 1.8 2.2NT 0.0 0.1 0.2 0.2
TAS 0.1 0.3 0.5 0.6Totals 5.0 15.4 24.6 30.3
65
D2. Commercial Measures – Energy SavingsTable 8: BCA 2006 Energy Savings by State/Territory
PJ 2012 2020 2030 2040 2050NSW +
ACT 1.0 2.8 5.2 8.2 11.6
VIC 0.9 2.4 4.5 7.1 10.0QLD 0.6 1.7 3.2 5.0 7.0WA 0.2 0.7 1.2 1.9 2.7SA 0.2 0.5 0.9 1.4 2.0NT 0.0 0.1 0.3 0.4 0.6
TAS 0.0 0.1 0.2 0.3 0.5Totals 3.1 8.2 15.6 24.3 34.4
ClimateZone 1 0.2 0.5 0.9 1.3 1.9
ClimateZone 2 0.5 1.4 2.7 4.1 5.8
ClimateZone 3 0.0 0.1 0.1 0.2 0.3
ClimateZone 4 0.1 0.3 0.6 1.0 1.4
ClimateZone 5 1.0 2.5 4.8 7.5 10.6
ClimateZone 6 1.1 3.0 5.7 8.9 12.6
ClimateZone 7 0.2 0.4 0.8 1.2 1.7
Source: pitt&sherryNotes: NSW and ACT are not separately reported
Table 9: BCA 2010 Energy Savings by State/TerritoryPJ 2012 2020 2030 2040 2050NSW 0.3 2.7 6.2 10.3 15.2VIC 0.2 2.3 5.3 8.8 12.9QLD 0.2 2.3 5.2 8.7 12.7WA 0.1 1.4 3.2 5.4 7.9SA 0.1 0.8 1.8 3.1 4.5NT 0.0 0.3 0.7 1.2 1.7TAS 0.0 0.2 0.5 0.8 1.2ACT 0.0 0.2 0.5 0.8 1.2Total 1.1 10.3 23.5 39.0 57.3
Source: pitt&sherryNotes: Results not available by BCA climate zone
66
Table 10: Commercial Building Disclosure Energy Savings by State/TerritoryPJ 2012 2020 2030 2040 2050NSW 0.1 0.8 1.3 1.7 2.1VIC 0.1 0.8 1.1 1.5 1.9QLD 0.1 0.6 0.9 1.2 1.6WA 0.1 0.4 0.7 0.9 1.1SA 0.0 0.3 0.4 0.5 0.6NT 0.0 0.1 0.1 0.1 0.2TAS 0.0 0.1 0.1 0.2 0.2ACT 0.0 0.1 0.1 0.2 0.2Total 0.4 3.1 4.7 6.2 7.9Source: pitt&sherryNotes: Distribution of savings by State/Territory is based on the annualdistribution of the stock of offices >2000 sqm
Energy Efficiency in Government Operations
The energy savings resulting from the Energy Efficiency in Government Operations(EEGO) measure are reported as a national aggregate and therefore cannot (reliably)be broken down by State/Territory or Climate Zone.
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