Six star energy efficiency standard review of CBA - Rachel Ollivier-2

Six star minimum efficiency standards for new residential buildings
in Australia – a critical reviewof the cost benefit analysis undertaken
in the regulatory impact statement.
Rachel Ollivier,
This dissertation was awarded a distinction grade after being submitted as partial fulfilment of the
requirements for the degree of MSc Finance (Economic Policy) of the University of London on 25
September 2014.
The author can be contacted on 0457 006 578 or at rachelollivier@gmail.com
Abstract
This dissertation has reviewed and revised the inputs and methodology used in a cost benefit
analysis (CBA) for a regulation impact statement (the RIS) for a 2009 Australian policy proposal to
increase energy efficiency standards in new residential buildings including to six star rating for
thermal efficiency. The revisions reflect recent increases in energy prices and findings of a review of
recent literature. The findings estimate the benefit of this policy will be far greater than previously
thought (the NPV increased to $4 billion at a 7% discount rate and the benefit cost ratio from around
1 to 118). The reason for this is partly because energy prices have increased significantly, but also
because the assumptions about capital costs were not supported by more recent evidence. This
dissertation challenges the assumption that energy efficiency standards result in a change in the
purchase price of new housing. A range of methodological issues associated with CBA for this type
of policy were reviewed including discount rates and use of shadow prices to ensure that cost benefit
analysis achieves its goal of assessing net social value of policy options.

Table of contents
Six star minimum efficiency standards for new residential buildings in Australia – a critical
review of the cost benefit analysis undertaken in the regulatory impact statement...........1
Rachel Ollivier,........................................................................................................................1
Abstract.....................................................................................................................................1
Table of contents......................................................................................................................2
Abstract.....................................................................................................................................3
Chapter I - Introduction............................................................................................................4
Section 1.1 - Energy efficiency, public policy and the economy .................................................4
Section 1.2 - Cost benefit analysis............................................................................................5
Section 1.3 - The research question..........................................................................................5
Chapter II - Literature review...................................................................................................6
Section 2.1 - Similar studies.....................................................................................................6
Section 2.2 – Key similarities and differences with the RIS........................................................8
Section 2.3 - Incidence.............................................................................................................9
Section 2.4 - Discount rate and social cost of carbon .............................................................. 11
Chapter III - Findings – review of inputs................................................................................12
Section 3.1 - Energy prices..................................................................................................... 12
Section 3.2 - Household savings over time.............................................................................. 14
Section 3.3 - Changes to capital outlay................................................................................... 15
Chapter IV – Discussion: implications of revisions................................................................16
Section 4.1 - Revised inputs: household level......................................................................... 16
Section 4.2 - Revised inputs: indirect and social...................................................................... 18
Section 4.3: Revisions to methodology................................................................................... 20
Section 4.4 – Implications: revised net present value.............................................................. 22
Chapter V - Conclusion...........................................................................................................24
Section 5.1 - Limitations and further research ........................................................................ 25
References:.............................................................................................................................26

Abstract
This dissertation has reviewed and revised the inputs and methodology used in a cost benefit
analysis (CBA) for a regulation impact statement (the RIS) for a 2009 Australian policy proposal to
increase energy efficiency standards in new residential buildings including to six star rating for
thermal efficiency. The revisions reflect recent increases in energy prices and findings of a review of
recent literature. The findings estimate the benefit of this policy will be far greater than previously
thought (the NPV increased to $4 billion at a 7% discount rate and the benefit cost ratio from around
1 to 118). The reason for this is partly because energy prices have increased significantly, but also
because the assumptions about capital costs were not supported by more recent evidence. This
dissertation challenges the assumption that energy efficiency standards result in a change in the
purchase price of new housing. A range of methodological issues associated with CBA for this type
of policy were reviewed including discount rates and use of shadow prices to ensure that cost benefit
analysis achieves its goal of assessing net social value of policy options.

Chapter I - Introduction
Section 1.1 - Energy efficiency, public policy and the economy
Policy makers have identified energy efficiency as an option that could make a large contribution1 to
avoiding dangerous climate change2. Incorporating energy efficiency requirements into building
codes has been practiced widely around the world (see summaries by Laustsen, 2008 and Doris et al
2009) and has proven effective, for example in Denmark (Laustsen, 2008, p.15) and California (Doris
et al 2009 p7). Energy efficiency is sometimes used to meet comfort and health needs in extreme
climates or for fuel security (Laustsen 2008, p14).
An important advantage is that energy efficiency has often been found cost effective on a life-cycle
costing basis (for example CEC 2011b), which means that over the lifetime of the investment (usually
the asset lifetime), the present value of the energy savings outweighs the initial investment. Where
energy efficiency options exist and are cost effective, it indicates market inefficiency because
households and firms have failed to do something that would make them financially better off.
In fact, market failures relating to energy use are well known and include3 information asymmetries,
externalities and agency dilemmas. For example purchasing decisions for energy using equipment
(such as a fridge) may be based on sunk costs rather than life-cycle costs. This affects the costs,
profit and competitiveness of households, firms and economies. Agency dilemmas occur between
landlords and tenants where the tenant pays for energy, but the landlord purchases energy-using
items (heaters, coolers) and has no incentive to consider life-cycle costs. Inefficient markets result in
higher energy costs, which also has a disproportionate affect on vulnerable people because it is a
large proportion of their income. In most economies, energy generation also creates pollution in the
form of greenhouse gas emissions, which is an externality with a social cost.
Despite the benefits, building standards have been politically challenging for governments to
introduce because of resistance by the building industry4 and because it seems logical that higher
standards would increase construction cost.
Since 2003 Australia has required five star rating of thermal efficiency for residential buildings as part
of the Building Code of Australia (BCA) (Daniel et al, 2013, p1). The thermal efficiency of a building
indicates how much energy is needed to heat and/or cool to maintain a comfortable temperature.
Generally it indicates the ‘leakiness’ of a building and how much free heating comes from the sun. It
is a function of several factors including insulation, window location, and access to sun in winter and
shading in summer. Star ratings are on a scale of 1 to 10 and applied for each of eight climate zones
meaning the most efficient housing design in temperate Melbourne is different to the most efficient
housing design in tropical Darwin. Assessment is done using a tool called AccuRate, which models
energy demand. Ratings indicate an improvement compared to the energy demand potential for
each climate zone – for example a five star house uses around 70% less energy than a 0 star house
in the same climate (Constructive Concepts 2009 p17). The modelling engine underpinning
AccuRate is called the Chenath engine which was developed by the Commonwealth Scientific and
Industrial Research Organisation (CSIRO) and has been validated through a number of studies (for
example Daniel et al 2013).
1 For example, the International Energy Agency (2008,p450) assessed thatenergy efficiency could reduce
global emissionsoutputby half by 2030.
2 The Intergovernmental Panel on Climate Change assessed whatdangerous intervention with the climate
system is and the reduction in emissions required to avert it in their fourth assessmentreport (IPCC, 2007).
3 This research does not explore barriers,buttakes them as given; there are many studies and papers that
explore barriers in detail,for exampleBond (2011), Abrahamse et al (2005) or the Australian Productivity
Commission (2005).
4
For example HIA (2009) in a letter to the premier of Tasmania on the 9 November 2009 advocated for the six
star requirements to be rejected: “Independent assessments provided to HIA indicatethatthe cost to build a
firsthome buyer dwellingof 160 squaremetres in Hobartwould increaseby nearly $2,960 to justmeet the 6 -
star rating,compared with $530 contained in the Consultation Regulation ImpactStatement.”

Section 1.2 - Cost benefit analysis
Policy makers use cost benefit analysis (CBA) to assess the overall impact of policy (for example the
NZ Treasury (2005) and the Australian Office of Best Practice Regulation [OBPR] (2007)). Distinct
from cost effectiveness studies or financial analysis in corporate finance, CBA includes costs and
benefits across society rather than for a specific stakeholder. CBA can identify which option makes
the community better off and can also help identify winners and losers. Usually analysis assumes
that ‘all other things are equal’ to compare business as usual with a proposed policy. A net present
value (NPV) calculation is used to present the net social impact of a policy, which discounts future
year impacts and allows effects in varying timeframes to be compared. Social costs and benefits
should be quantified in financial terms although these are often difficult to quantify and in practice
may be omitted or highly uncertain.
In its ‘Guide for Best Practice Regulation’ OBPR (2007, p21) says “public policy makers are expected
to make judgments based on what is best for the community as a whole. By measuring 'social', as
opposed to only private, market- based costs and benefits, CBA is a valuable tool when developing
good policy responses to economic and social problems”. OPBR (2007, p22) noted that CBA is
useful for addressing market failures and can incorporate shadow prices and account for spill over
effects.
For energy efficiency, direct costs and benefits to households must be estimated and indirect and
social costs and benefits must also be identified. The more complete a CBAs scope is, the closer it
will be to achieving the public policy goal of informing rational decision making about policy options.
Without reasonably accurate cost benefit analyses, decision makers are constrained in their ability to
make public policy decisions that are economically efficient. This is particularly important in
infrastructure decisions where life-cycle costs and benefits are often used to justify large capital
investment.
Section 1.3 - The research question
In 2009, the Australia Government was considering increasing energy efficiency in new residential
buildings by requiring a 6 star rating for thermal efficiency in the BCA (along with less significant
changes on lighting and water heating efficiency).”). To assess the impact of this policy, a regulatory
impact statement, including CBA was commissioned by the Australian Building Codes Board (ABCB)
and prepared by the Centre for International Economics titled “Final Regulation Impact Statement for
Decision, Proposal to Revise the Energy Efficiency Requirements of the Building Code of Australia
for Residential Buildings – Classes 1,2,4 and 10” (ABCB, 2009)
Although the decision to introduce six star standards has since been made, two reasons have
emerged to think the RIS may not be accurate: since 2009 energy prices have risen significantly and
unexpectedly; and some recent literature contradicts the estimates of capital outlay (sunk cost)
including an ex-post study by CSIRO (2013) that surprisingly found capital outlay decreased rather
than increased.
This dissertation uses applied research to understand the microeconomic effects of increasing
Australian building standards to six star energy efficiency rating and specifically whether the effects
estimated in the RIS are likely to be accurate. The research also seeks to update the cost benefit
assessment to contribute to a better understanding of the likely effect. This is important because the
RIS reported marginal benefit that was sensitive to variation in the discount rate between 5% and 7%
(RIS, 2009 p.19) and there has been significant lobbying against the standards (HIA, 2009). If the
cost benefit assessment understated the value of the policy, the public policy debate could be
redirected to consider higher standards in future.
The purpose of the research is to inform the public policy debate in Australia on building energy
efficiency as a public policy option. This research is also designed to contribute to improvements in
methodology used in similar studies in future.

Chapter II - Literature review
This chapter reviews the literature to identify similar studies and assess the methodology used in The
RIS. It also draws on academic literature and government guidance on CBA where needed to
explore key methodological issues in further depth.
Section 2.1 - Similar studies
Similar policy impact studies were identified from the UK, the United States of America (the state of
California), Canada and Australia. These countries were identified because they are English
speaking with similar public sector cultures and resources to Australia. The methods used were
compared with the RIS (see summary following and in table 2.1). From this, key elements were
identified for detailed consideration: incidence; aggregation of estimates from a small sample to
national level; scope of social and indirect inputs; time frame and discount rate.
In the UK in 2013 the Department for Communities and Local Government (DCLG, 2013) completed
an impact assessment for proposed changes to Part L of the Building Regulations 2013 to increase
energy efficiency of new buildings and appliances (eg, heating) at point of construction. This study
had a reasonably wide scope of inputs: benefits identified included energy savings (which included
both distribution network savings and retail prices) and non-financial benefits including carbon
savings, comfort taking and air quality savings; costs included incremental costs, appliance
replacement costs and transition costs associated with training in the building industry; sensitivity
testing was undertaken on energy prices and other variables; and micro-economic effects on the
building sector were discussed but not quantified.
In the United States of America (California) in 2011 a life-cycle costing was complete as part of
consideration of 2013 California Building Energy Efficiency Standards. California has a legislative
requirement for the building energy efficiency standards to demonstrate that changes are cost
effective “when taken in their entirety” (CEC, 2011a p5) and over their lifetime. This assessment
included detailed consideration of savings related to changes in peak demand for energy and
therefore for transmission and distribution costs. To do this, California produces Time Dependent
Values (TDVs) for energy that internalise transmission and distribution costs (or savings) and also a
social cost of carbon (CEC 2011b, p39). These TDVs are then used as an input for life-cycle cost
analysis. It did not consider health impacts, transition costs or macroeconomic effects.
Three Canadian studies were identified, but all were narrow in scope. PEL (2011) in a study titled
“cost and benefit analysis of proposed changes for energy efficiency in housing and small building in
the National Building Code” sought to identify the “incremental construction costs” and the “energy
savings that these changes would produce” (p 1). The study noted explicitly that no other benefits
were considered “even though these can be substantial” (p4). It presented energy savings benefits
in KWh but did not estimate the financial value of these nor calculate present value of either costs or
benefits. Lio & Associates (2006) for the Ontario Ministry of Municipal Affairs and Housing studied
“Energy Efficiency measures for Part 9 Housing in the Ontario Building Code”. Benefits were
assessed including energy savings and reduction in peak electricity demand (which were both
valued), and greenhouse gas emissions (which was not valued). The overall impact was not
assessed in the form of an NPV calculation, rather specific measures were recommended for
inclusion in the regulation based on lifecycle cost effectiveness of each measure5. In 2012, the City of
Toronto was considering changes that would require buildings in Toronto to “match or exceed those
required by the Ontario Building Code” (SBC, 2012, p1) and commissioned Sustainable Buildings
Canada to prepare a report titled “Cost/Benefit Analysis of Proposed Energy efficiency Requirements
for the Toronto Green Standard”. No social or indirect costs were valued in this analysis.
Greenhouse gas emissions savings were estimated but not valued and incidence of capital costs was
handled in a confused way.
5 This judgement appears to have been made on the basis of only directcosts and benefits and not accounting
for peak electricity savingsor the externality of greenhouse gas emissions.

In Australia in 2002 the Victorian state government was considering introducing five star rating for
building energy efficiency standards. The notable aspect of this study is it identified material macro-
economic benefits including increased employment and gross state product (Building Commission of
Victoria [BCV], 2002, p5), which were detailed in Allen Consulting Group [ACG] (2002). It predicted
that expenditure would move from the energy sector (capital intensive) to the building sector (labour
intensive) and would be brought forward to current years through higher mortgages, which was why a
large increase in state product was identified (ACG 2002, p19). This assumed that capital cost
increases occur and are incident on the buyer (resulting in higher prices and higher mortgages). It
noted but did not value benefit of downward pressure on energy prices and that this “enhances the
competitiveness of Victorian export industries” (BCV 2002, p 22). Beyond this unique aspect of the
study the authors noted that energy savings and capital costs were modelled using conservative
assumptions (BCV 2002, p5) omitted estimates of savings from installation of smaller appliances and
identified greenhouse gas savings but did not value these.
Table 2.1 Comparison of methodology of international studies
Country, year
and study
Discount
rate (%)
Analysis
Timeframe
Incidence Scope of indirect and social
inputs
Appliance
replacement
timeframe
UK (DCLG
2013)
3.5 then
3 after 30
years
60 years Builders/
developers
Distribution costs; transition
costs; social cost of carbon; air
quality
15 years
Canada (SBC
2012)
5.5 and
7.0
25, 50 and 70
years
Buyer Nil Not
apparent, but
not clear
Canada (PEL
2011)
Nil Nil Buyer
(implied)
Nil Nil
Canada (L&A
2006)
3.68 25 years
(based on
mortgage
lifetime not
asset)
Buyer
(implied)
Nil presented in net social
value; distribution network
savings value estimated and
discussed
Nil
USA,
California
(CEC 2011a
and CEC
2011b)
3 30 years Not
applicable
(life cycle
cost study;
not CBA)
Distribution network savings;
social cost of carbon
15 years
Australia,
Victoria (BCV
2002 and
ACG 2002)
3.5 40 years Buyer Macroeconomic effects;
transition and enforcement
costs
Nil
The RIS
(ABCB 2009)
7 and 5 40 years Buyer Industry compliance costs;
administration costs; savings
from smaller appliances;
distribution network savings
Nil

Section 2.2 – Key similarities and differences with the RIS
Common to all studies and to the RIS was a sampling approach where a sample of houses was used
to estimate energy savings and capital outlay required during construction. Results were then
aggregated to a regional or national level. Generally consistent with this, the RIS used a weighted
average approach to account for population and climate variances.
There was wide variation in scope of indirect and social costs or benefits from including none (SBC
2012, PEL 2011) to accounting for distribution network savings, social cost of carbon and air quality
improvements (DCLG, 2013). The most comprehensive accounting for transmission and distribution
network savings was done by (CEC 2011b) and the possibility of significant macroeconomic effects
was identified in Victoria Australia by ACG (2002).
Inclusions had significant impacts on the reported outcome in several cases. In the UK, DCLG
(2013) found that considering only direct costs and benefits the policy impact would have a marginal
net social cost however when the social cost of carbon was included the policy impact was
overwhelmingly positive. ACG (2002) found the lion’s share of benefit was related to macroeconomic
effects (p19), which were not identified in other studies.
All studies except PEL (2011) quantified greenhouse gas reductions, but only two valued the social
cost of carbon (CEC 2011b and DCLG, 2013) in both cases supported by clear government guidance
on how to value it.
Analysis timeframe varied significantly and the RIS assumed a 40-year building shell lifetime
compared to 60 years in the UK (DCLG 2013) and a 30-year timeframe for lifecycle costing in
California (CEC, 2011, p5.) Assumed lifetime makes a fairly small difference to the NPV, but which
can be material if the NPV is marginal: an increase in timeframe to 50 years from 40 years, increases
the present value of future savings by around 3.8% at a 7% discount rate and 11.8% at a 3%
discount rate (author’s own calculations6).
DCLG (2013) was unique in quantifying savings associated with air quality improvements, although
the value of these was small. The RIS investigated this issue in 2009 through a literature review
(prepared by Williamson et al (2009)) that found little local research but concluded that at most
savings would be $9.5 per household per year, which was immaterial to the RIS.
The RIS accounted for three economy wide inputs: industry compliance costs; additional
administration costs and savings from electricity network and transmission infrastructure (ABCB,
2009 p19), which made it more complete that a number of other international studies. The main
omissions were social cost of carbon, macro-economic effects from moving future expenditure from
the energy sector to discretionary expenditure, health effects and discussion of micro-economic
effects in the building industry.
Treatment of incidence, discount rate and social cost of carbon varied significantly and these are
explored in future depth in sections 2.3 and 2.4.
6 Cashflowof $100 per year (real) was projected out for 40 years and 50 years (simil ar to energy savings which
occur each year on an ongoing basis). Presentvaluewas calculated atboth 7% and 3% for each timeframe.
The increaseattributableto increasingto a 50 year timeframe was then calculated as a proportion of the 40
year timeframe ($ 1376.68/$1326.49 =1.0378% (7% discountrate) and $2550.17/$2280.82 = 1.118% (3%
discountrate))

Section 2.3 - Incidence
Incidence is explored in more depth in this section as it was a key area where the DCLG (2013)
treated the methodology differently to other studies and also because the impact of the assumptions
was shown to be highly significant by ACG (2002).
Incidence is a common concept in economic theory, particularly in relation to tax. The incidence of a
measure identifies who bears the burden, as opposed to who technically pays the bill. Goods and
services taxes are a classic example where the tax is paid to the government by businesses, but the
burden is born by consumers: the tax is therefore incident on consumers. Incidence affects who the
‘winners’ and ‘losers’ are and the scale of costs and benefits and how they flow through the
economy.
DCLG (2013) assessed that additional capital cost would be born by business (developers) and
expressed this as a cost to business7. This was different to most similar studies reviewed and
different to the RIS (2009, p56), which assumed that the buyer would bear additional capital cost and
generally also that the price of houses would increase. ACG (2002) identified significant benefits
because of this assumption: because homeowners would pay more for homes current investment in
the building sector would increase through larger mortgages. This resulted in a benefit to the gross
state product equivalent to 60% to 120% of the private benefit8.
This dissertation proposes an alternative hypothesis: that house prices stay fixed (limited by
purchasing power of buyers) and that changes in upfront costs are either incident on builders, sellers
of the land or offset through substitution (dwelling size or features). A study of incidence on stamp
duty in Australia (Davidoff and Leigh, 2013) provides strong support for this hypothesis. Stamp duty
is a tax charged when property is sold and it occurs at the same stage in a property transaction as
requiring higher energy efficiency standards9. Understanding the incidence of stamp duty is
important for policy makers concerned about whether this tax has demand and supply or
distributional effects. Davidoff and Leigh (2013 p19) summarised international literature suggesting
stamp duties are incident on the seller and also tested it in an empirical study of the Australian
market finding that stamp duty falls “entirely on the seller” (underlying house prices are reduced when
tax increases so the total purchase price remains unchanged).
Further support for the hypothesis is provided by the fact that Australians have chosen to buy larger
homes rather than to reduce costs: from 1986 to 2004 the average size of an Australia house
(already some of the largest in the world) increased from around 100m2 to around 130m210. The
cost of building an additional 1.82 m2 per year (the average annual increase11) can be estimated at
around A$280012.
For this dissertation, an attempt was made to test if new house prices are correlated with supply cost
or purchasing power (income, interest rates). If prices were driven by purchasing power, price would
tend to correlate with income side factors. If they instead correlated with supply side factors rather
7 although the impact assessment did not discuss why it concluded this and it added a somewhat
contradictory comment that developers would seek to pass it on to both “landowners (through
reduced land values) and to the eventual owners (through higher property prices).” (page 53)
8 ACG 2002; calculated based on table1 p vii 5 star A and 5 star b Scenarios,2017 figures Real Total Value
added (GSP) as a proportion of Real PrivateConsumption (savings)
9 Although stamp duty is typically an order of magnitude larger than changes to capital outlay estimated by
the RIS.
10 Based on numbers of occupied residential buildings and floor area as reported by Commonwealth of
Australia,2008,p 26
11 The Commonwealth of Australia, (2008 p 27) reported a growth rate of 1.4% per year, about
1.82m2.
12 This is likely to be high as itis based on average figures for buildinga housein its entirety includinghigher
costkitchen and bathroom areas where sizeincreases arelikely to be marginal increases to other areas.
Calculated by takingthe mean costper squaremeter for project houses with low, medium and high finishes
from (BMT 2014) and adding10% for GST.

than income side factors, it may indicate prices were cost driven. Unfortunately prices correlated
strongly with both supply and demand side factors13, which didn’t rule out the hypothesis that
purchasing power drives prices, but it also didn’t confirm it. It is possible that purchasing power
drives prices and then supply costs follow as buyers increase spending on size or quality.
The assumption in the RIS that changes to capital costs are incident on the buyer is not supported by
the evidence identified in this dissertation. However these costs are not necessarily incident on
developers either, as the developer will receive the same total price. Rather, energy efficiency
measures will change how the purchase price is distributed. Homebuyers may adjust spending on
size or quality, or builders may innovate or absorb changes. For the CBA, $0 capital outlay (sunk
costs) should be used at both a private level and an economy wide level, as there is no change in the
total financial transactions to either the developer or the buyer. However, changes in buyer choices
and microeconomic effects on the building sector from product substitution should be discussed (as
per DCLG (2013)). It is possible that underlying land value may be affected, which would make land
sellers the ‘winners’ or ‘losers’, although if this occurs it is more likely where new housing is
predominant.
If the discussion above is correct, it follows that ACG (2002) would have significantly overstated the
benefits from investment in housing being brought forward and The RIS would have significantly
overstated costs. There remains value in assessing the cost-effectiveness of specific energy saving
measures: to help understand the lowest cost measures and to check the magnitude is likely to be
adsorbed through substitution. Capital outlay estimated in The RIS was small relative to house prices
and of around the same value as delaying growth house size by one year14.
Incidence is not expected to change if the buyer is an investor: the budget envelope would still be
constrained by purchasing power. Renters (who often include vulnerable people) would benefit from
reduced energy costs. Note that the incidence of upfront costs fall to builders and sellers as long as
financiers don’t account for energy savings when calculating loan lending. If banks offered buyers
larger loans for more efficient homes this would tend to increase house prices based on the present
value of energy saving rather than capital costs.
13 The correlation function in excel was used to correlate,from September 1997 to May 2012 the Priceindex
for projecthomes (weighted average of 8 capital cities) (ABS series ID A2333591L) with the followingincome
sidefactors:total earnings per person (ABS series ID A2772132V); interest rate (RBA (2014),series ID
FILRHLBVS) multiplied by earnings per person; and with the PriceIndex for Materials Used in House
Building Weighted Average of 6 Capital Cities (ABS series ID A2333592R) and Construction industry total
hourly rates of pay (ABS series ID A2333593T).
14 The sizeof new homes in Australia has been growing by around 1.8m2 per year. One year’s growth is worth
around $2000,which is similar to the estimated energy efficiency costs accordingto the ABCB (2009)

Section 2.4 - Discount rate and social cost of carbon
The central discount rate used in the RIS (7%) was consistent with OBPR (2007) guidance but high
compared to international studies as table 2.1 shows. The RIS CBA was sensitive to discount rates
and the NPV was negative at a 7% discount rate but positive at 5%. The approach Australia uses in
setting its standard discount rate for policy impact statements at 7% is an investment capital
opportunity cost rate. The US (USOMB, 2003) uses a 3% rate for policies that are more likely to
affect consumer spending rather than the allocation of capital. They have assessed that 3% is the
‘social rate of time preference’ as given by the rate of return (real) on long-term government debt.
Low discount rates are also sometimes advocated to account for climate change effects as they are
delayed and long term. In an ‘ideal’ market, resource allocation would be efficient because future
environmental degradation would be priced into current resource prices in anticipation of shortages
and the market would allocate and minimise environmental risks effectively. It is well understood that
the market commonly fails to adequately price these risks and for many years governments have
sought methods to address this. Neither the US (USOMB, 2003) nor Australia (OBPR 2011) nor NZ
(NZ Treasury, 2005a) use lower rates for this purpose, however it remains a challenging question for
economists as Baker et al (2008, pX) highlighted when they said “Rarely do analysts confront cost–
benefit analyses with dimensions so long-term, uncertain and non-marginal”.
An alternative is to use a shadow price, which is an estimate of a hidden or indirect cost that allows
policy makers to quantify social, long term or distributed costs or benefits for inclusion in CBA and
which was used by CEC (2011) and DCLG (2013). The USA EPA (2013 p2) noted that its social cost
of carbon: “is meant to be a comprehensive estimate of climate change damages and includes, but is
not limited to, changes in net agricultural productivity, human health, and property damages from
increased flood risk. Carbon emissions stay in the atmosphere for a long period of time and effects
are cumulative and delayed. To calculate a social cost of carbon, future costs are estimated then
discounted using a NPV method. The social cost of carbon is expressed as a cost per tonne of
carbon emissions. It is counted as a cost15 in the year that the pollution is emitted and then
discounted as part of normal NPV analysis. However, the cost counted in the year of emissions is in
itself the present value of future costs associated with those emissions.
The main benefits of a social cost of carbon approach are: costs can be applied in proportion to the
carbon emissions of a project –a yes or no decision about whether a project qualifies is not required
and therefore avoids the risk of ‘gaming’; it is possible to consider how social costs and cash flows
may change over time; factors such as the carbon intensity of electricity can be accounted for; and
there is no ambiguity about what social cost of carbon represents as it is clearly a financial variable.
The RIS estimated greenhouse gas reductions as a result of the policy change, but didn’t seek to
incorporate this into the CBA either through a reduced discount rate or a shadow price.
15 Noting that this costis in itself the PV of future costs related to the pollution fromthe subjectyear.

Chapter III - Findings – review of inputs
In this chapter, findings of a review of inputs to the CBA are presented. Recent literature was
identified to update the estimates used in the RIS. Sources varied significantly depending on the
variable and are detailed in each section.
Section 3.1 - Energy prices
Energy price estimates are key inputs to business cases for energy efficiency. Price can be affected
by underlying costs, supply and demand or the price elasticity of demand (demand changes in
response to price changes). In Australia in 2009, electricity price growth had been slow for decades.
The RIS based its energy price estimates (ABCB 2009 p217) on Australian Treasury forecasts16 for a
50 year period to 2060.
Although The RIS used a credible source for electricity price estimates, these were significant
underestimates compared to actual prices between 2009 and 2014 (see table 4.1 and figure 4.1)/
Actual prices increased largely because of new investment in distribution infrastructure that is now
being paid off by electricity users. There are significant difficulties and uncertainties with forecasting
energy price for a 50-year period. Given these, the RIS took a reasonable approach: to forecast price
and also to sensitivity test using a Montecarlo analysis.
A literature search on future electricity prices found widespread discussion of future uncertainty, but
not alternative price forecasts accounting for this uncertainty. Price uncertainty stems from four
aspects: climate change policy, demand changes including peak demand and elasticity, technology
change and the policy response to these issues.
Climate change policy remains uncertain. The Australian government repealed its carbon price in
2014 but it is likely that in the 50 year forecasting period another climate focused policy will be
introduced.
Future energy demand is also uncertain and reversed a century long growth trend when demand in
the National Electricity Market dropped by 4.3% in the four years to 201317 (TAI, 2013, p4). If
demand continues to drop it will continue to push prices up because the sunk cost of distribution
infrastructure needs to be covered by ‘sales’ of fewer units of electricity. In the medium term, a drop
in demand should reduce future infrastructure costs, however this is being offset by a growing gap
between peak and off peak power use18 AEMC (2013) p11), which makes infrastructure less efficient.
AEMC (2013 b) identified this as an important price driver.
The price elasticity of demand is also uncertain. TAI, in a detailed report on why energy demand has
dropped (2013, p4) found an “abrupt change in consumer responsiveness to higher prices after
2010”. Pitt and Sherry (2013, p55) found that energy use per capita stopped increasing from around
2004 and that it could not be explained by price or income elasticity alone. Pitt and Sherry (2013)
highlighted the role of technology in elasticity of energy demand – if customers reduce energy use by
installing equipment, this can have a cumulative rather than temporary effect on demand.
Rapid technology advances are also creating uncertainty and may prove to be significant disruptions
in the energy market. For example advances in battery technology may allow households to generate
all their own power and disconnect from electricity networks entirely, further reducing electricity
16 Two forecasts were established:a reference caseand a business as usual case which was also called the
CPRS 5 case. The main difference was that the BAU case incorporated expected policy changes (the
introduction of a carbon price),which was expected to increasethe priceof electricity.
17 Five eastern states, not includingWestern Australia.
18 In the six years to 2011,“peak demand increased ata rate of approximately 1.8%a year, whiletotal
demand grew at 0.5% a year” (AEMC (2013) p11).

demand. This could leave poles and wires as stranded assets and push prices up for remaining
electricity users.
These areas of uncertainty are interrelated: a positive feedback loop could start as increased prices
reduce demand, which in turn increases the per unit price to cover network costs, which further
reduces demand. Several factors could significantly increase costs per unit in the short and medium
term as existing network infrastructure is paid off, however technology developments could also see
prices drop over the long term if distributed technologies become viable avoiding network
infrastructure costs altogether. The costs depend on the policy response, which will dictate their
extent and distribution between private companies, users, non-users or taxpayers.
The estimates of gas prices in the RIS were based on forecast changes in the wholesale market
price, which the retail gas prices were expected to follow (p219). Gas prices are expected to
increase in Australia from around 2015 as the domestic markets connect to the international market,
on which gas trades at a higher price (AER 2013).
A comparison of the RIS estimates for wholesale market price with more recent forecasts
summarised by TAI (2013, p9) found that the RIS estimates were low in the medium term. The
estimated price in the RIS for 2010 and 2011 was higher than actuals, although by 2012, the price
estimate was very similar to actuals. In the medium and long term, gas prices are “uncertain” (TAI,
2013 p13) and international prices are “highly uncertain” (p15) because they are linked significantly to
international markets and also to climate policy response both of which TAI (2013) identified as key
drivers of demand uncertainty for gas.

Section 3.2 - Household savings over time
During a building’s life, a more efficient building shell reduces energy demand and also reduces the
capacity required for heating and cooling appliances. If less heating and cooling is needed, then the
replacement cost of appliances can be reduced.
Energy savings are the source of all benefits from more energy efficient buildings including direct
energy cost savings, indirect distribution network savings and social benefits of reduced greenhouse
gas emissions. The RIS estimated energy savings based on systematic modelling reported in
Constructive Concepts (CC 2009). Rating was done using the same modelling engine required in the
proposed policy changes and by definition, a house built to six star standards uses less energy a five
star house. The AccuRate software engine used is itself very credible. It was developed by CSIRO
and its accuracy has been validated in empirical studies over several decades (recently for example
by Daniel et al 2013) and against international standards using the International Energy Agency
BESTEST protocol (Delsante, 2005). It remains possible that other factors may offset or undermine
improved thermal efficiency for example residents may respond to lower energy costs by increasing
comfort levels (heating to a higher temperature); or oversized appliances may continue to consume
high levels of energy. Daniel et al (2013, p 2714) observed discrepancies between modelled and
measured results in occupied homes, which resolved when assumed behaviour was aligned with
actual behaviour and concluded that the model is robust if the inputs reflect actual use. For rating
purposes, to ensure buildings are comparable, standard inputs must be used. Therefore, it is how
well these standard inputs reflect actual behaviour that should be the focus of research in future, not
the modelling engine itself.
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) completed an ex-post
study in 2013 comparing houses with four stars to those with five stars and above. It was a
reasonable sample size but the energy saving results spanned only a 10 month period which
included an unusually hot summer. It only covered Brisbane, Melbourne and Adelaide, of which
Brisbane and Adelaide have mild climates and were low absolute contributors to energy saving which
made it harder to identify the contribution thermal efficiency made to the results. Noting these
limitations, energy savings were observed in all regions, but areas for further research were
indicated: summer electricity use increased in higher rated homes, possibly due to the hot summer
and air conditioning running at full capacity (CSIRO, p 99); the results suggested that residents of
higher rated homes had offset some savings by increasing the temperature; and there was almost no
modification of window orientation in higher star rating houses so it is possible that inadequate
shading had a disproportionate effect on summer cooling.
As there was little ex-post data available to confirm or challenge the energy saving estimates, there
remains some uncertainty about their magnitude, although the broad proposal that if a building is
more energy efficient, less energy is needed has been well demonstrated at household level (Daniel
et al (2013) and CSIRO (2013) and economy wide (for example in California (Doris et al (2009) p7)).
The case for higher energy efficiency was stronger in extreme climates19.
In regards to savings related to reduced appliance capacity, there is evidence for very little need for
appliances in very efficient homes (Laustsen, 2008, p72), but the case is not as clear cut at
intermediate efficiency because homes often still need active heating and cooling even if used for
less time and on fewer occasions. Perversely, if the standards were higher (say, 8 star), the case for
accounting for savings may be stronger. The CSIRO (2013) found some evidence that in practice
higher rated houses do use smaller appliances: as star ratings increased in Brisbane the capacity of
installed air conditioners decreased; more houses of six star and greater (noting that there were only
11) had no cooling installed; and most six star and greater homes use only one type of appliance for
heating and/or cooling (e.g., a heat pump for heating and cooling or gas only heating).
The RIS accounted for appliance cost savings only upfront. By contrast the UK (DCLG (2013) and
California (CEC 2011a) both assumed equipment life times of 15 years.
19 Constructive Concepts (2009) illustrated the magnitude of these variations: They estimated load
reductions of around 70 MJ.m2.annum in Brisbane, 80 in Mascot (which is similar climate to Sydney),
190 in Melbourne over 200 in Hobart and Canberra and 450 in Darwin.

Section 3.3 - Changes to capital outlay
This section reviews the estimates of sunk costs (changes to capital outlay) in the RIS. It does not
cover incidence of these costs, which is addressed in chapter 2. This dissertation recommends
accounting for sunk costs as neutral for CBA. However, it remains useful to assess the cost
effectiveness of energy savings measures and check the magnitude of substitution effects, which is
why the estimates have been considered in detail. Also, upfront costs have been a key issue in the
public debate and several building industry players linked upfront costs with affordability (for example
HIA, 2009).
The general approach of identifying ‘typical’ houses and then modelling changes in a variety of
climates was similar to international studies in Canada (SBC, 2012), the UK (DCLG 2013,) and
California (CEC, 2011 a). The RIS took an average of two methods (an elemental approach and a
simulation approach) to estimate upfront costs for each climate region (p102, ABCB 2009a) and
assessed three types of houses: house, townhouse and flats.
The elemental approach identified additional elements required to bring an existing house up to six
stars and then estimated costs by referring to industry prices lists or ‘book rates’. Pitt and Sherry
(2012, p40) were critical of this approach (also known as a quantity surveying approach) because it
will “tend to systematically over-estimate incremental costs, both in the short and longer terms”, and
recommended using simulation only for this type of study. Pitt and Sherry (2012, p40) commented
that an elemental approach “ignore[s] industry-standard practices, such as modifying designs,
construction materials, construction methods, and not simply components, which all tend to save
cost.” An elemental approach precludes no-cost measures being used, of which there are many
including changing location and size of glazing or changing the layout of buildings. For some policy
changes an elemental approach is appropriate, for example, changes to lighting requirements in the
BCA were appropriately handled in the RIS using an elemental approach. It was also appropriate for
the CBA for the Toronto Green Standard (SBC 2012), which proposed prescriptive changes with no
opportunity for any no-cost or cost saving alternatives.
CSIRO (2013) comprehensively compared cost of recently constructed five star and above homes
with those below five star based on actual floor plans (p24) and, surprisingly, found significant
savings upfront instead of costs. CSIRO (2013) sampled 414 homes including detached houses, a
few townhouses (around 5%) and no units. 20%20 of the five star and above subset were rated as
six star. CSIRO attributed the observed drop in capital cost to “an observed shift to more rectangular
floor plans” (p17), which lose less heat and have lower materials and labour costs. Two other studies
(by ACIL (2009) and SBE (2010) for the state of Victoria) relied on building industry interviews and
modelling rather than empirical evidence. The CSIRO (2013) methodology was far more robust.
Although CSIRO (2013) compared the effect of moving to five star standards (rather than moving
from five star to six star), their findings suggest that cost reductions would also be expected with a
further increase in efficiency because they indicate significant further no cost and cost saving options
for achieving six stars for houses. CSIRO (2013 p68) noted that although higher rated buildings had
become more rectangular, there had been very little change to the location of windows. This
presents an opportunity for no-cost energy efficiency through passive solar design measures. This is
consistent with a study by Sustainability House (2012) that reviewed existing house designs and
worked with real world constraints (such as street frontage). Sustainability House concluded (p11)
there would be a drop in construction cost of around two per cent at the same time as an increase in
energy efficiency of one star. It was further supported by SBE (2010, p4) which said “there appeared
to be a small drop in the deliberate orientation of living areas facing north and the use of eaves,
indicating that the new legislation has not led to an improved understanding of designing for solar
passive design.”
20 45 of Six Star and 176 of 5 to 5.9 stars. Note that the methodology used by CSIRO (2012,rounded up to the
nearest half star).

Chapter IV – Discussion:implications ofrevisions
Arising from the literature review in chapter two and findings in chapter three, this chapter assesses
the effect of revising the methodology and inputs by modelling the NPV. The input values used in the
RIS were not all available; instead the model was recreated to the extent possible.
Section 4.1 - Revised inputs: household level
Inputs at household level included energy prices, energy savings, savings associated with installation
of smaller capacity appliances and capital costs. Section 3.1 identified evidence that the electricity
prices used in the RIS were likely to be underestimates, but did not identify alternative forecasts. In
the absence of this, forecasts have been revised using actual prices from 2010 to 2012 (AEMC 2011,
2012); price forecasts for 2013 to 2014 (AEMC 2013 adjusted to remove a carbon price
component21); and beyond 2014 by inflating price at the same rate estimated for the RIS reference
case (because this assumed no carbon price policy). This approach did not account for the
uncertainties discussed in chapter three and remains limited.
Table 4.1: Estimated electricity price compared to actual electricity price (real price, c/KWh)
2010 2011 2012 2013 2014
RIS reference case (ABCB, 2009) 14.5 14.5 13 13 13
RIS base case (ABCB, 2009) 15 15.5 16 16 16.5
AEMC actuals and updated
forecasts22 adjusted after 2010 to
show real prices
22.41 25.17 25.78 25.85 (f) 25.97 (f)
f = forecast
Figure 4.1: Actual electricity price compared to estimates (c/KWh real price)
For purpose of this dissertation, retail gas price inflation has been assumed to be equal to wholesale
gas price inflation largely because several price forecasts exist for wholesale gas prices but none
were identified for retail prices. Retail gas prices to 2060 were forecast as follows: wholesale prices
to 2060 were constructed from actual wholesale prices to 2013 (AER, 2013, p100) and averages of
recent price forecasts for the years 2020 and 2030 (TAI, 2013, p9) weighted to account for western
21 This was itemized in AEMC reports and easy to adjustfor through subtraction. Itwas a relatively minor
component of recent pricerises and becauseof this,its effect is not discussed further in this dissertation.
22Based on AEMC: 2013,p12, 2012, p(iv),2011,p6 and adjusted for inflation (ABS 2014) to show real prices.
-
5.00
10.00
15.00
20.00
25.00
30.00
2010 2011 2012 2013 2014
RIS estimate (reference
case; real)
RIS estimate (base case,
(real)
Actuals (real)

markets (13% of new constructions, derived from ABCB, 2009, p214); prices for intermediate years
were estimated using the same price inflation pattern as the RIS, but adjusting the slopes as needed
to smoothly connect the forecast points 2020 and 2030; retail prices to 2060 were derived from the
wholesale price forecast using a ratio of $4.6 / GJ wholesale : 1.8 c / MJ retail which is the 2010 ratio
of wholesale price to retail price in the RIS (p218 table D18 and p219 table D19).
No revision is proposed to the estimates for energy savings as the method was credible and there
was limited literature to assess whether resident behaviour offset savings. There was also limited
literature on the quantum of savings from use of smaller appliances (as noted in section 2.1). More
research is needed because the impact of this on the NPV is potentially very significant. For
example, the Australian Consumer Association indicated the cost of reverse cycle air conditioners in
the order of $2000 (2014a)23 and the cost of flued gas heaters at $1200 to $2200 (2014b) not
including installation costs. At this price just one of these appliances costs about the same as the
average total sunk costs estimated in the RIS. In absence of sufficient data to confirm or challenge
the quantum of savings attributed to reduced appliance costs, the RIS estimates were used but
changed to recur every 15 years in line with the approach of the UK (DCLG (2013)).
23 Of the 45 medium air conditioners reviewed by ACA (2014), 24 were between 1000 and 2000 and
21 were over $2000

Section 4.2 - Revised inputs: indirect and social
Indirect and social inputs include distribution network savings, social cost of carbon and avoided
health costs, cost of industry compliance, administration and macroeconomic effects. Arising from the
literature review in section 2.4, this dissertation included shadow prices for social cost of carbon and
avoided health costs. In absence of a similar estimate specific to Australia, US Government (2013,
p3) estimates of social cost of carbon were used24. The US Government produces four estimates
based on different models and assumptions (shown in figure 4.2) and for this dissertation estimate
(b) was used.
Figure 4.2: Derived from US Government (2013, p3) estimates of social cost of carbon (USD,
real)
The value of distribution network savings used in the RIS was much lower than more recent
estimates by the Australian Energy Market Commission (AEMC 2013 p267, estimate of “conservative
network savings” figure 10.6). AEMC estimates were used to generate a weighted average based on
the forecast location of new dwellings and valued savings at $610/kW/yr25 compared to 130/kW/yr in
the RIS (ABCB (2009) p233). This author’s own modelling suggests that the RIS calculated present
value (PV) of distribution network savings over only 20 years. The effect of these revisions is an
increase in savings of over $90 million per year and the PV to $1.28 billion. This analysis remains
crude but gives an indication that networks savings are likely to be significantly understated. A
detailed investigation of this issue goes beyond the capacity of this dissertation because it would
require detailed modelling of the energy transmission and distribution market in Australia including
scenario analysis and assessment of local market dynamics.
An indication of health savings was also estimated. Since Williamson et al (2009) investigated health
costs for the RIS more recent evidence has emerged from New Zealand using a large sample of over
30,000 households (Telfar Barnard et al 2011) from which estimates have been derived to indicate
savings in health costs. If the health cost savings are apportioned on the assumption that there will
be a proportional benefit by improving from 5 to 6 stars it indicates a savings of $49.09 per
24 Converted to AUD at an exchange rate of 1.07 AUD to 1.0 USD (May 2014)
25 Note this was a weighted average of all states and territories except Western Australia or the
Northern Territory. It accounted for the fact that more new dwellings were located in areas where the
value of savings would be greater, but does not account for variations in peak demand reduction.
0
50
100
150
200
250
300
2010
2013
2016
2019
2022
2025
2028
2031
2034
2037
2040
2043
2046
2049
2052
2055
2058
$USD(real)
Social cost of carbon (a)
Social cost of carbon (b)
Social cost of carbon (c)
Social cost of carbon (d)

household26 in a cold climate per year on average. If 41% of new constructions are assumed to be in
cold climates this is an average of $20.20 AUD per household and at this level it not material ($250
million at 7% discount rate and $500 million at a 3% discount rate). There are methodological
challenges in using Telfar-Barnard et al (2011)’s estimates in an Australian context: the population
studied may not correlate with new home residents in Australia; retrofitting substandard housing is
different to moving from five star to 6 star standard; and the climate is different. However it does
suggest large health benefits, which have not been incorporated in recent CBAs reviewed in this
dissertation.
Chapter two identified that ACG (2002) raised the possibility of significant benefit from
macroeconomic effects. Although the major benefit predicted by ACG is not thought to occur
(increase in current year spending on housing), there would be an effect of moving future year
expenditure from the energy supply sector to less capital intensive sectors, which may stimulate jobs.
Reduced energy demand will also put downward pressure on energy prices which are thought to act
throughout the economy in several ways including by affecting discretionary expenditure directly,
creating certainty (or uncertainty) which affects purchase of consumer durables and affecting
precautionary saving (Kilian L, 2008 p881)). Lower prices may also assist trade-exposed industries
by making them more competitive with overseas firms. Both these effects stimulate business,
employment and reduce risk of bankruptcy27. Revised inputs are not proposed for this dissertation,
however this is potentially a significant benefit of energy efficiency because it acts on all energy
consuming households and businesses in the economy, not just new constructions.
26 Telfar-Barnard et al (2013) reported savings of NZD $818.34 for Community Services Card holders
and NZD $227.42 for non-cardholders. This was converted to AUD at an exchange rate of 1/1.2335
and then weighted on the assumption that the proportion of people living in low economic resources
households in Australia (23% ABS 2013b) was equivalent to the Community Services Card holders in
New Zealand and then the total benefits were divided by 1/6 to indicate the marginal change in
performance from five star to six star.
27 Vivid Economics (2013, p 3) note that “some economists to hypothesise that energy price shocks
are one of the major causes of the contractions that occurred in the United States in the early 1980s
(Killian, 2008) and in 2007-2008 (Hamilton, 2009”

Section 4.3: Revisions to methodology
The three main revisions arising from chapter two relate to incidence, discount rate and aggregation
method from the sample to national level. The method used to aggregate sample estimates to
national level has been revised because the RIS did not sufficiently detail the methodology to
recreate the model exactly. Specifically, it was not clear how results for city locations were
apportioned where these covered several climate zones. To recreate the model, two different
weightings were tested (table 4.1): the first assumed each state was representative of the state and
weighting was based on location of new housing constructions; the second assumed climate zone
samples represented the climate zone and weighting was based on current dwelling location.
Neither method was ideal but there was not a material difference between the two methods (around
$400 million difference in the PV of energy savings). New constructions make up the lion’s share of
residential building work (85 to 90%28) and it is reasonable to focus on new constructions, however
they are forecast to be greater in milder climates (such as Brisbane). If the RIS based its calculation
on current dwelling location rather than new construction location, it may have overstated total
energy savings29. The revised method used was to weight results based on forecast location of new
constructions as this was more logically robust despite the fact that some of these locations may
cross more than one climate zone. The number of new households built was based on estimates
used in the RIS (ABCB, 2009, table 15 p214).
Table 4.1: Proportions used to aggregate private dwelling costs and benefits to national level
as a weighted average.
Table compiled by the author using data from the RIS (p.10 and p.214) combined with data derived
from that data (see footnotes for further detail of derivation method)
BCA Climate
zone
1 2 3 4 5 5 5 6 7 7 8
State
Northern
Territory
Queensland
Queensland
Victoria
South
Australia
Western
Australia
NewSouth
Wales
Victoria
Australian
Capital
Territory
Tasmania
NewSouth
Wales
City
Darwin
Brisbane
Longreach
Mildura
Adelaide
Perth
Sydney
Melbourne
Canberra
Hobart
Cabramurra
Proportion by
climate zone30
2% 13% 8% 8% 2% 8% 18% 34% 5% 0% 0%
Proportion by
new
constructions
by state31
1% 29% 1% 1% 4% 13% 28% 21% 1% 1% 0%
28 Value of alterations and renovations is 10 to 15% of new construction (ABS 2013b)
29 It is interesting to note that the RIS estimate of capital outlay was 10 to 15% higher at $2300 per
household than was achieved in either aggregation method used in this paper. This difference
remains unexplained.
30 Proportions arefrom the RIS (ABCB, 2009) p 210 Figure D11. Where there is more than one state in each
climatezone, the climatezone total has been apportioned between the locations based on the relativeshare
of new constructions expected (TableD15)
31 Proportions arebased on breakdown by state from the RIS, p 214 TableD15. For Victoria and Queensland,
where there were more than two locations within the state, only 1 percent was assumed to come from
Mildura and Longreach respectively, based on the low numbers of existingdwellings in these areas.

Table 4.2: Comparison of results from the RIS (BAU case) and model depending on weighted
average method
RIS reported Recreated model using
RIS inputs weighted
using new
constructions forecast
by state
Recreated model using
RIS inputs weighted
using current dwelling
locations by climate
zone
PV of thermal efficiency
energy savings economy-
wide ($)
$1.28 Billion $1.045 Billion (-18.4%) $1.446 Billion (+13.0%)
Average capital cost per
household ($)
$2300 $1978 (-14%) $2086 (-9.1%)
As discussed in section 2.4 there is a case for using the ‘social rate of time preference’ (currently
around 3%) for this policy because there is not expected to be any change in capital allocation (see
section 2.3) and the primary effect is a switch of future expenditure from energy to discretionary
spending. However, as Australian government guidance requires CBA to use a central 7% rate for
NPV calculations it is recommended that it be calculated at both 7% and 3% (real), which is also
consistent with the US approach. The Office of Best Practice Regulation (2007) also requires
sensitivity testing at a 10% rate, but this author concurs with Pitt and Sherry (2012, p39) that a rate
as high as this is not appropriate for this type of policy.

Section 4.4 – Implications: revised net present value
To understand the implications of revisions, NPV was recalculated based on both RIS inputs and
revised inputs and results were compared. Cash flow was estimated from 2010 to 2059 with costs
and benefits over that time flowing from policy in operation for ten years (from 2010 to 2019), which is
the same approach and timeframe the RIS used. Table 4.1 summarises input values (2010 year) for
the RIS alongside the revised estimates and summarises the effect of changing each of the inputs in
isolation. Table 4.2 summarises the effect on the NPV when all revised inputs are used at both 7%
and 3% real discount rates. Overall, revising the inputs improved the NPV by around $4 billion (7%)
or $8 billion (3%). The benefit cost ratio was 118 in contrast with the RIS estimate of around 1. The
high revised benefit cost ratio highlights that the vast majority of effects of this policy are benefits and
indicates that it is low risk.
Table 4.1 – summary of inputs to the NPV calculation and impact of each change in isolation
RIS estimate Revised
estimate
Comment Effect (7% /
3%discount
rate)
Electricity price
(2010)
15c/kWh 26c/kWh Price rises have
already occurred
+ $715 million /
+ $1.5 billion
Gas price 1.8c per MJ 1.17c per MJ Price forecast to rise
further before 2020
+ $252 million /
+ $605 million
Energy savings
(KWh)
AccuRate
modelled
As RIS No change No change
Upfront costs $2300 Nil Purchase price
remains unchanged
(see section 2.3)
+ $1.97 billion /
+ 2.4 billion
Equipment
replacement
savings
Upfront only 15 years + $49 million /
+136 million
Health benefits to
residents
$0 $20.20/household Cold climate costs
only assumed 41%
households cold
climate
+ $250 million /
+ $500 million
Transmission and
distribution costs
$130/kW/annum $610/kW/annum + $1.09 billion /
+ $2.07 billion
Social cost of
carbon
$0/tonne $32 per tonne Based on US EPA
model
+ $72 million /
+ $152 million
Industry
compliance costs
and additional
administration
$35.25 million No change No change

Table 4.2: comparison of NPV using RIS inputs and revised inputs at 7% and 3% discount
Present value at 7% discount rate Present value at 3% discount rate
RIS inputs Revised inputs Variance RIS inputs Revised inputs Variance
Value of electricity savings (Lighting + HW) $117,050,566 $249,237,982 $132,187,416 $239,290,978 $518,907,942 $279,616,964
Value of electricity savings (thermal) $516,337,718 $1,099,447,657 $583,109,939 $1,055,569,071 $2,289,025,598 $1,233,456,526
Value of gas savings $761,513,143 $1,013,918,699 $252,405,557 $1,591,149,144 $2,196,185,859 $605,036,716
Avoided social cost of carbon $72,567,086 $72,567,086 $152,551,034 $152,551,034
Avoided cost of appliance installation $105,455,759 $154,378,079 $48,922,321 $127,551,963 $263,821,368 $136,269,405
Change in capital outlay -$1,976,362,172 $- $1,976,362,172 -$2,390,470,444 $- $2,390,470,444
Distribution network savings $188,932,972 $1,281,001,894 $1,092,068,921 $251,222,041 $2,325,080,605 $2,073,858,564
Avoided health costs $247,605,145 $247,605,145 $499,843,156 $499,843,156
Industry compliance and admin costs $35,250,000 $35,250,000 $35,250,000 $35,250,000
Total -$251,822,014 $4,153,406,543 $4,405,228,557 $909,562,752 $8,280,665,562 $7,371,102,810
Net present value $4,153,406,543 $8,280,665,562
Figure 4.1: Source of variance in NPV – revised inputs compared to RIS inputs at 7% discount rate
Value of electricity savings (Lighting + HW)
Value of electricity savings (thermal)
Value of gas savings
Avoided social cost of carbon
Avoided cost of appliance installation
Change in capital outlay
Distribution network savings
Avoided health costs
Industry compliance and administration

Chapter V - Conclusion
By revising the inputs and methodology based on recent price changes and literature, this
dissertation found that the increase in energy efficiency standards in the Building Code of Australia
introduced in 2010 will have a significant benefit to Australia, far greater than originally estimated in
the RIS, and that it is a low risk policy with little downside risk. The revised NPV shows net benefit of
around $4 billion (at a 7% discount rate) or $8 billion (at a 3% discount rate) up from a marginally
negative original estimate in the RIS. The benefit cost ratio increased from marginal (around 1) to
118 at a 7% discount rate and 234 at a 3% discount rate.
Since the RIS was published, higher distribution network costs have contributed to significant energy
price increases, so it is no surprise that updating prices and network cost estimates has improved the
estimated NPV significantly. Updating these two inputs improved the NPV by around $2 billion at a
7% discount rate which doubled the benefit estimated compared to the RIS.
The findings relating to changes in construction cost were surprising: in contrast to the assumption
that higher standards increase construction cost a review of recent literature indicated that higher
energy efficiency standards reduce spending on the building shell, mostly due to more rectangular
buildings and reduced glazing. Findings indicated that there are further opportunities to improve
efficiency at reduced cost.
Despite the finding that construction costs reduce, this dissertation does not recommend treating
these as a saving in the NPV because of findings on a related issue: who bears the burden of
changes in construction cost (its economic incidence). Incidence of capital costs was found to have
a critical effect on the final NPV and for understanding winners and losers. The findings challenge
the assumption that costs (or savings) during construction change the price paid for new houses and
instead suggest that house prices stay the same (limited by purchasing power) and substitution
occurs (likely of dwelling size or features). This means that the policy is cost neutral for the purposes
of NPV assessment, which improved NPV by just under $2 billion and increased the benefit cost ratio
from around 1 to 67. If house prices do not change then there will be no impact on investment,
supply or demand although there may be product and service substitution effects within the building
industry.
The findings reinforced the usefulness of CBA for addressing externalities by introducing shadow
prices. Two additional inputs were included in the revised NPV: avoided health costs in cold climates
and avoided social cost of carbon. Estimates of savings from use of smaller appliances were
updated and the impact of extending assumed building lifetime from 40 to 50 years was explored.
None of these revisions alone was material to the NPV at a 7% discount rate, however, taken
together these increased NPV to marginally positive at a 7% discount rate. While it is justifiable to
treat any one of these issues as immaterial, treating them all as immaterial starts to have a material
effect and affects the robustness of the CBA. A better approach would be to include these variables
and account for uncertainty using scenario analysis.
A good case was found for setting the discount rate at the ‘social rate of time preference’ (around
3%) because this policy is not expected to change capital allocation (in line with the findings
regarding incidence) and the primary effect is to switch future expenditure from energy to
discretionary spending. However, discount rates are the subject of much debate and Australian
government guidance requires CBA to be calculated at a central 7% rate so this dissertation has
calculated NPV at 7% and 3% (real), which is also consistent with the US approach. Most similar
studies internationally used a 3% discount rate, which would double the NPV resulting in a greater
increase than any other revision.
The purpose of this dissertation was to inform the public policy debate in Australia by improving
understanding of energy efficiency as a policy option and to contribute to improvements in
methodology. Findings indicate a strong case for further increasing energy efficiency of buildings.
The magnitude of energy use savings per household would be greatest in extreme climates including
Melbourne, Tasmania or Darwin and the greatest value of distribution network savings are expected
in milder climates including Queensland and NSW. In regards to methodology, the findings show
that incidence is a critical issue that warrants in depth consideration during CBA and that the scope

of CBA should generally be broad and include shadow prices to effectively assess net social value.
There is no doubt that choice of discount rate significantly affects the understanding of long term
policy issues like this and this dissertation finds a social rate of time preference more appropriate for
this type of policy.
Section 5.1 - Limitations and further research
No attempt was made to consider if the sample of houses was a reasonable representation of new
dwellings and forecast locations for new dwellings were not reviewed. For several issues limited
sources were available including estimates for health savings and savings from using smaller
appliances. Further research opportunities were suggested by the findings of CSIRO (2013), which
highlighted gaps in understanding of summer energy use and there is value in exploring whether this
relates to limited use of passive solar design elements. As energy use in extreme heat is a driver of
peak energy demand, which in turn drives infrastructure costs and ultimately energy costs,
understanding this better may lead to opportunities to reduce peak demand, and energy and
distribution network costs.
Incidence was handled very inconsistently in similar international studies and due to the impact it can
have in CBA, warrants more attention by policy makers around the world. For energy prices and
distribution network savings, the literature pointed to significant uncertainty about future costs and
long timeframe estimates were not available. These will both have a significant impact on the final
value of this policy, which highlights the important role that price forecasts have on energy use and
energy efficiency decisions. There is an opportunity for governments to improve the quality of
decisions on energy use and energy efficiency by publishing regular energy price forecasts with
several scenarios to account for future uncertainty. This could assist policy makers, private firms and
local governments as all these parties make decisions on energy use and energy efficiency through
the economy. In absence of this type of forecast, forecasting is often based on current year data or
historical trends.

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