Lifecycle costing sensitivities for zero energy housing in Melbourne, Australia

Energy and Buildings 79 (2014) 1–11

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Lifecycle costing sensitivities for zero energy housing in Melbourne, Australia Trivess Moore a,∗ , John Morrissey b,1 a b

RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia Cleaner Production Promotion Unit, University College Cork, College Road, Cork, Ireland

a r t i c l e

i n f o

Article history: Received 25 December 2013 Received in revised form 10 March 2014 Accepted 22 April 2014 Available online 5 May 2014 Keywords: Lifecycle costing Sensitivities Assumptions Zero energy housing Sustainable housing

a b s t r a c t Minimum energy efficiency standards for new housing are typically informed by regulatory impact statements, underpinned by lifecycle costing (LCC) analysis. While LCC techniques are empirical and testable, such analysis is informed by considerable assumptions on key parameters. These assumptions are often heavily contested in the literature and by built environment stakeholders, but there is limited exploration of their implications within wider policy developments. This paper addresses this gap by analysing the impact of a number of assumptions and their implications within a LCC analysis of zero energy housing options in Victoria, Australia. The results show that changes to assumptions on key parameters have significant impact on LCC outcomes, with associated policy implications. Analysis shows that there is a requirement for a detailed review and debate of the assumptions applied within LCC analysis which is used to inform the development of minimum energy efficiency standards in Australia and internationally. In particular, as housing is a long-lived infrastructure, the issue regarding the use of assumptions based upon historical data or data based upon future predictions is critical to the development of policy and energy efficiency standards. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Many countries have implemented minimum energy and environmental performance standards and regulations for new housing stock in order to address increasing energy consumption and associated greenhouse gas emissions from the residential sector [1]. Such standards invariably aim to address market failures preventing improved sustainability outcomes from new housing [2,3]. A number of jurisdictions, such as the United Kingdom (UK) and California, have set out policy reforms which mandate zero energy housing (ZEH) performance, or approaching zero energy performance, by the end of the decade [4,5]. Such standards remain elusive in Australian policy development, where the policy agenda remains focused on small incremental performance changes [1]. The development of ZEH standards, as with previous housing performance standards, has been informed by empirical evidence about predicted costs and benefits [5,6]. In this context, lifecycle costing (LCC) techniques have emerged as means of developing

∗ Corresponding author. Tel.: +61 3 9925 9071. E-mail addresses: [email protected], [email protected] (T. Moore), [email protected] (J. Morrissey). 1 Tel.: +353 21 490 3079. http://dx.doi.org/10.1016/j.enbuild.2014.04.050 0378-7788/© 2014 Elsevier B.V. All rights reserved.

objective information on the likely costs and benefits of proposed policy measures and to counter claims of adverse effects on the economics of the residential sector. For example, in the UK, the Code for Sustainable Homes has been developed through application of LCC assessments informing a series of regulatory impact statements [6–9], whereby initial concerns regarding the affordability of improved housing energy performance standards were addressed [10]. Despite the applicability of LCC for policy development, there remains limited empirical research into the LCC implications of increased energy efficiency at the household level, particularly from the point of view of new build houses. Studies to date have tended to focus on state level policy implications [11–13], on the influence of particular envelope components on thermal performance [14–17], or produced findings of limited applicability to the wider housing stock [18]. Furthermore, LCC methods have themselves largely been omitted from the policy debate [19]. Consequently, there has been little discussion on the assumptions currently applied within the limited LCC undertaken; where efforts have been made to test revised assumptions, policy makers have received strong opposition, forcing them to revert to original assumptions [20]. For example, the last regulatory impact statement for proposed improvement to the minimum standards for new housing

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T. Moore, J. Morrissey / Energy and Buildings 79 (2014) 1–11

in Australia [20], attempted to make limited changes to assumptions compared to previous analysis (e.g. discount rates, building costs, future energy prices), arguably applying assumptions which were more in line with international best practice. Strong opposition from key actors to these changes meant that the original assumptions were applied in the final analysis [20]. The change to assumptions had a significant impact on the overall results. The change of discount rate from 5% to 7% meant that the overall benefits of the improvement of minimum standards from 5 star to 6 star (the star ratings are explained in Section 3.1) decreased from +$317 million with a benefit–cost ratio of 1.13 to −$277 million with a benefit–cost ratio of 0.88 (all costs presented in this paper are in US dollars. Conversion from Australian to US dollars where relevant was calculated for 30th June 2011 when US$1 = AUS$1.07 [21]). This example highlights that the selection of the input parameters and associated assumptions is critical in developing robust LCC data as building standards are improved to a ZEH standard, and debate increasingly focuses on the costs and benefits of new housing models. Such parameters are critical not only from the point of view of achieving optimal cost–benefit ratios, but also to establish the scope and targeting of effective policy instruments. This paper firstly explores LCC techniques and discusses a number of central assumptions within LCC, which are typically applied for costing of energy efficiency and renewable energy technologies in new housing. Following this, a method to test a number of identified assumptions in the LCC of ZEH is described. Outcomes of LCC analysis are presented, and the implications of results for policy makers are addressed together with insights to wider debates about sustainable housing and LCC methods. Specifically this paper asks the following research questions: 1. What impact do changes to key assumptions have on the outcome of LCC analysis for ZEH in the temperate climate of Melbourne, Australia? 2. What implications arise from the analysis of research question 1 with regards to selection of appropriate assumptions, LCC analysis and housing standards? For this research ZEH is defined as housing which has the capacity to generate all energy consumed in the dwelling across a calendar year through renewable energy technologies [1].

form, level and period of analysis together with an anticipated level of uncertainty and risks relating to the LCC analysis and reporting should all be explicitly defined [24]. Despite methodological advances, the use of LCC remains contested. Authors such as Pearce [27], have been critical of the concept of placing a monetary value on non-market goods and services, such as on natural resources and ecological services. Another issue raised is that LCC outcomes lead to a determination of the ‘feasibility’ of the options considered in analysis from a costs perspective [28]. However, outcomes of LCC do not determine if the most feasible option from a technical and costs perspective is in fact the most appropriate policy approach [29,30]. Outcomes of LCC must therefore be integrated into wider decision-making processes.

2.1. LCC of housing thermal performance measures – critical parameters The parameters of the LCC analysis depend on the purpose and use of the intended results. The validity and relevance of the analysis can depend on the parameters selected [24]. While the literature raises a number of concerns about the use of LCC, there are steps which can be taken to minimise limitations. The undertaking of sensitivity analysis on results, for example, can help to mitigate limitations of LCC by testing the impact of variations in key assumptions on reported outcomes [31]. However this testing is often limited or for the most part overlooked in the publication of LCC derived analysis. This represents a critical oversight, particularly as the sensitivities in question can result in differences in results of several orders of magnitude as described in Section 1. The International Organisation for Standardisation Standard ISO 15686–5. Buildings and constructed assets – Service-life planning – Part 5: Life-cycle costing describes a number of critical factors to be considered in defining the scope and form of an LCC analysis of buildings. These include lifecycle and time-horizon parameters, operation maintenance and repair cost variables, discount rates, energy and utilities costs and taxes and subsidies [24]. In the case of housing energy and environmental performance LCC analysis, the literature further highlights a number of these parameters. For the purposes of testing the sensitivities of LCC analysis of energy efficiency measures, key assumptions are typically made with regards to the following parameters [8,20]:

2. Lifecycle costing techniques LCC is a type of investment calculus used to rank different investment alternatives [22]. The development of LCC has its origin in normative neoclassical economic theory which states that organisations seek to maximise profits by always operating with full knowledge [23]. The main difference with traditional investment calculus is that the LCC approach has an expanded lifecycle perspective, and thus considers not only investment costs, but also operating costs during the product’s estimated lifetime [22]. LCC analysis should cover a defined list of costs over the physical, technical, economic or functional life of a constructed asset, over a defined period of analysis [24]. LCC thereby seeks to optimise the cost of acquiring, owning and operating physical assets over their useful life by attempting to identify and quantify all of the significant costs involved in that life, using the present value technique. LCC methods enable the quantification of alternative investment scenarios so as to ensure the adoption of the optimum asset configuration, across materials configuration, use and replacement phases [25]. In terms of building thermal performance calculations, LCC analysis should identify the optimum materials investment, operating energy cost, cost saving and pay-back period which minimise the total cost over the building’s lifecycle [26]. The scope,

• • • • • • • • •

Discount rates, Cost of upgrades (materials, construction and design), Future prices of energy, House size, Occupant behaviour and use patterns, Lifespan of building, Occupancy rates, Frequency of the maintenance factor, and Variation of the asset’s utilisation or operating time.

Some assumptions are based on supporting evidence, but this is not always the case. Assumptions which once may have been appropriate can become out-dated as new research, information or trends emerge. For example, average floor area per new house has increased in Australia in recent years while the average number of occupants has decreased in the same period [32,33]. If assumptions are not revised on a regular basis, there is a danger that outcomes of analysis may no longer be representative of current or future conditions and could result in the development of ineffective or misdirected policy approaches. The following presents a summary of current challenges and debates on a number of key assumptions

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applied to minimum energy performance standards analysis in the context of ZEH policy development. 2.1.1. Lifespan of building It is recognised that the lifespan applied in LCC can significantly alter economic and environmental outcomes [34,35]. Typically, the longer the life of the house, the lower the environmental impacts and the greater the economic benefits [36]. However, there is no universal standard for what the lifespan of a building should be. A range of assumed lifespans have been applied within wider housing performance and policy analysis, from 30 years to 100 years (or more) [34,35,37,38]. In Australia the lifespan of a residential building used in typical LCC for policy development is 40 years; a number which has been referred to as an ‘arbitrary measure of accounting’ in a regulatory impact statement by the Australian Building Codes Board [20]. In reality, the lifetime of residential buildings in Australia is often longer than 40 years [39]. Longer lifespans have also been reported in other developed countries such as the USA (average residential building lifetime is 61 years), France (with more than 5 million housing units older than 100 years) and the UK (where 47% of housing was built prior to 1965) [37,40]. 2.1.2. Upfront costs The calculation of upfront costs for proposed building standards has been an ongoing issue for policy makers not only in Australia but internationally. For example, the Master Builders Association of Victoria (in Australia) claimed that the initial introduction of the 5 star building performance standard added $2100–$8600 to the cost of a house and that a move from 5 star to 6 star would add a further $5300–$10,700 per house [41]. However, research has shown that the costs of implementing 5 star were significantly less than predicted. The Building Commission [42] found that the introduction of the 5 star standard for the average sized home (250 m2 ) cost $1600 (for energy improvements), significantly less than the figures predicted by the building industry. Further, the move from 5 star to 6 star has been shown to be achievable for zero additional cost through improved design [43]. 2.1.3. Occupant behaviour and use patterns The assumptions applied within energy modelling software have also been criticised; in particular those assumptions applied for occupant behaviour for heating and cooling energy requirements [44–46]. These assumptions can impact on the overall energy rating of the house. For example, Gill et al. [47] found when all variables were considered, a factor of seven difference in water consumption and a factor of three difference in heat and electricity consumption were observed for 25 low energy households in the UK. Typically, in wider LCC analysis, the behaviour of the occupants for energy consuming activities such as the use of appliances, currently falls outside the scope of minimum building standards. Similarly, the total size of the house, and in turn, the area which is heated and cooled, represents an important determinant of overall energy consumption [48], but which falls outside the minimum energy standards. The issue of behaviour and user practices is therefore largely omitted from LCC analysis, despite having considerable impact on the costs of building use. This is particularly significant in the case of added investment in energy efficiency, or low energy technology, where predicted benefits may not in actuality accrue due to behavioural factors, and LCC analysis may be wholly inaccurate as a result. 2.1.4. Discount rates The selection of an appropriate discount rate is often raised in criticisms of LCC analysis of minimum housing standards, and more

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broadly in environmental cost–benefit analysis. For example Garnaut [49] advocates that a discount rate of 1.65% should be applied for longer term environmental policies (such as the development of ZEH performance standards). In the UK, a discount rate of 3.5%, falling to 3.0% after 30 years, is applied to the LCC of minimum housing standards [50,51]. In Australia the Office of Best Practice mandates that a discount rate of 7.0% is applied to all LCC analysis undertaken in modelling for policy development in Australia [52]. As described earlier, significant economic impacts can be attributed to changes to the discount rate. 2.2. Rational for research The aforementioned are just some of parameters on which assumptions are typically applied in the development of LCC for minimum energy performance standards in the residential sector. The parameters discussed highlight the ongoing debates and challenges of selecting appropriate assumptions for inclusion in analysis. Furthermore, current LCC approaches are reflective of the present policy landscape, and particularly of the current perspective of policy makers. In this regard, LCC is typically applied to assess new policy measures, but from a narrow range of alternative options and using assumptions from previously applied analysis, ingraining the problem of ‘bounded rationality’. Fundamentally, analysis of the full range of cost–benefit potentials from a more-broad ranging series of integrated policies is required, taking account of sensitivities across a range of policy parameters; effectively a paradigm shift, from currently narrowly defined cost–benefit approaches. The first stage of such a paradigm shift is to investigate the assumptions, which significantly affect the business case for the uptake of ZEH. 3. Approach This paper starts from the position that a zero energy standard is current international best practice for new housing [1]. It is not the purpose of this paper to interrogate ZEH standards, in and of themselves, but to investigate the key assumptions applied within typical LCC analysis used to determine policy direction and standards, using the LCC of a series of ZEH scenarios as a case study. The wider context and debates surrounding ZEH and low carbon housing and the wider implications of such housing standards are addressed elsewhere [1,36,53]. The reported approach is based on stated international best practice from the academic literature as well as accepted LCC practice as reported in the ISO Standard ISO 15686–5. Buildings and constructed assets – Service-life planning – Part 5: Life-cycle costing [24]. 3.1. Model development To develop the analytical framework for this analysis, 100 house plans for new detached housing from volume builders in Melbourne, Australia, were modelled in the Nationwide House Energy Rating Scheme (NatHERS) approved software AccuRate using the default occupant behavioural settings [54]. NatHERS outputs are tailored across 69 different climate zones for Australia. Simulations for this study were conducted for NatHERS climate zone 60. The climate is classified as ‘mild temperate’ and is comparable in terms of seasonal and diurnal temperature and humidity to parts of the San Francisco bay and Mediterranean climatic zones in the northern hemisphere [55]. Table 1 provides further details of weather conditions for climate zone 60 [56]. The rating achieved (ranging from 1 (worst) to 10 (best) stars) is based on space heating and cooling demand in MJ/M2 calculated in accredited energy rating software from the sum of the annual

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T. Moore, J. Morrissey / Energy and Buildings 79 (2014) 1–11

Table 1 Average weather data from climate zone 60 (from 1970 to 2014) [56].

Table 2 Triangulated capital costs of selected material additions after [62].

Element ◦

Maximum average annual temperature ( F) Minimum average annual temperature (◦ F) Maximum daily temperature (recorded in 2009) (◦ F) Minimum daily temperature (recorded in 1982) (◦ F) Mean annual rainfall (mm) Mean annual number of days of rain ≥1 mm Mean annual daily sunshine hours

67.6 49.1 116.2 27.5 539.9 87.2 6.5

Fig. 1. Thermal energy load for each star band for climate zone 60.

heating and cooling requirements for the home (set for each different climate zone around Australia) divided by the conditioned floor area, and then adjusted for dwelling size. Fig. 1 presents the thermal energy load for each star rating for climate zone 60. AccuRate is one of three software packages which is certified for use to demonstrate compliance with NatHERS regarding energy efficiency performance of residential building design (the other two being FirstRate 5 and BERS Pro Plus). AccuRate is the most commonly used energy rating software in Australia [57]. AccuRate was developed by the Commonwealth Scientific and Industrial Research Organisation in consultation with the Australian Greenhouse Office and Hearne Scientific Software. The software has been validated through BESTEST [58]. The 100 house plans were initially modelled to meet minimum building and energy standards, which in 2011 was a 6 star standard. An initial analysis of key characteristics, including floor area, wall to floor ratio, external wall area and total area of glazing identified those building parameters of most importance to final thermal performance. Results of this preliminary analysis were applied to ensure that a representative sample of housing was selected, incorporating key characteristic parameters within 95% confidence limits. Eighty house designs were selected for further analysis based on the outcomes of this preliminary research. For each house plan design, a baseline thermal performance model (the 6 star performance) and four improved performance scenarios were developed. This involved systematic adjustments to the baseline 6 star model to develop 7, 8, 9 and 10 star performance iterations respectively. Improved performance scenarios were developed in the AccuRate software through changes to modelled material selections to improve the thermal performance of house plan thermal simulations. This included improvements to ceiling insulation, infiltration control, shading, external wall insulation, window glazing and internal wall insulation. A hierarchy of material changes was developed with reference to materials and building literature, in particular to publications by the Insulation Council of Australia and New Zealand [59] and Wilrath [60] as well as to the Building Code of Australia [61]. No design changes were made, in effect assuming a ‘worst case’ cost outcome, as improvements were achieved through material additions to the existing design rather than through low/no cost design alterations [62]. Data for these additional material and labour costs (Table 2) were obtained through a triangulation approach; including

Cost ($US/m2 )

Material

Specifications

Glasswool insulation ceiling

R1.5 R2.0 R3.0 R4.0

3.8 4.6 6.3 9.1

Glasswool insulation wall

R1.5 R2.0 R2.5

3.7 4.7 5.6

Polystyrene insulation extruded

R1.0 R2.0

19.7 35.2

Shading Windows

Roller shutters Standard single glazing Standard double glazing

Weatherstrip

Doors Windows

234.9 90.7 138.1 18.1 24.4

engagement of a building cost estimator, the use of Rawlingsons Australian Construction Handbook [63] and a review of the costs by an external building expert. Each material change was assigned a corresponding cost through the use of a for purpose building cost database. This allowed additional costs for thermal performance improvements for each house plan to be calculated. The additional costs required to reach specific improved performance standards were then averaged across the 80 house plans to generate an average outcome for each star rating, ranging between 7 and 10 star. Operational energy needs were calculated drawing upon data from the energy rating model outputs from AccuRate software and from Australian Government data [64] (Table 3). AccuRate outputs were adjusted to translate thermal requirement data into energy and gas equivalents, taking account of, for example, COP factors. An initial energy cost of $0.2298/kWh (inclusive of the Goods and Services Tax) was applied, which was the average cost set by energy retailers across Victoria in mid-2011 [1]. Future energy costs were then calculated based upon a low and high energy cost scenarios predicted by Garnaut [49]. Renewable energy technologies (photovoltaics and solar hot water) were then added to the house models to create a series of ZEH scenarios, as per the definition presented in the introduction of this paper. For all developed models, as the thermal envelope of the dwelling improved, there was a reduction of the size of the renewable energy technology required to achieve a net zero energy performance. Costs for renewable energy technologies were obtained by taking an average cost across a number of Australian renewable energy retailers for a range of brands (Table 4). An operation and maintenance cost for all renewable energy technologies of 1% of capital costs/year was added at time of purchase as discussed within the literature [65,66]. Total upfront costs were then calculated for the various scenarios combining additional material, labour and renewable energy technology costs. It was assumed that the renewable energy technologies performance remained constant throughout their lifespan. In reality solar panels may expect a deterioration in performance of 10–20% across their lifespan. By keeping the performance constant, it was in effect offsetting the improved energy efficiency of appliances when replaced within the house which could be reasonably expected. An LCC analysis was then undertaken to determine if ZEH scenarios were more economical over the projected life-time of a house compared to a BAU scenario. The annual energy savings achievable as a result of material additions for improved thermal efficiency, and renewable technology additions to bring the net energy requirement to zero, were quantified and all costs were discounted to 2011 equivalent values. This was to demonstrate

T. Moore, J. Morrissey / Energy and Buildings 79 (2014) 1–11

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Table 3 Energy requirement breakdown for full electric house.

Heating and cooling requirement Water heating requirement Other energy requirement Total energy requirement Cost of energy for 2011 (@ $0.2298/kWh)

6 star (kWh/yr)

7 star (kWh/yr)

8 star (kWh/yr)

9 star (kWh/yr)

10 star (kWh/yr)

1210 3010 4810 9030 $2165

877 3010 4810 8697 $2085

561 3010 4810 8381 $2010

263 3010 4810 8083 $1938

18 3010 4810 7838 $1879

Table 4 Costs of onsite renewable energy options and traditional hot water technology including maintenance costs. Technology

Size

PV – grid connected Inverter small Inverter medium Inverter large SHW – electric boost

Various 0–2 kW 2.01kW–4 kW 4.01 + kW 300 L

Cost ($) (including installation and maintenance) –1

5500 kW 2400/unit 3400/unit 4100/unit 5900/system

Warranty

Assumed replacement frequency

25–30 years 2–10 years 2–10 years 2–10 years 5–12 years

30 years 10 years 10 years 10 years 15 years

tested have been selected based upon feasibility criteria established from available evidence from the wider literature as well as from data emerging from jurisdictions implementing ZEH standards. 4. Results

Fig. 2. LCC approach applied for analysis: schematic after [62].

comparisons at the point of investment, based on the methods reported in Morrissey and Horne [62]. Fig. 2 describes the process. The outcomes of the LCC analysis are reported in terms of additional upfront costs ($), payback periods (years), avoided through-life energy costs ($, against a low and high energy price future) and the calculation of Net Present Value (NPV) ($) of investment. The NPV is calculated according to the following equation: NPV =

 Y 

Results demonstrate that across a 40–60 years time period, for detached houses in Melbourne, Australia, an 8 star building envelope with 4.3 kW of photovoltaics and a solar hot water system achieved the least costs across the life of a house (the full analysis is presented in [1]). This base ZEH scenario was calculated to cost an additional $33,200 upfront when compared to the 6 star BAU scenario. This ZEH scenario forms the focus of the analysis of parameter sensitivities and their impact on ZEH LCC analysis, and is referred to as the base-case scenario. Fig. 3 presents the base ZEH scenario compared to the 6 star BAU scenarios. Payback for the base ZEH scenario was achieved within 13 years for a high energy price future and 17 years for a low energy price future compared to the 6 star BAU scenario. Across a 40 years life of the house, avoided energy costs for the base ZEH scenario were calculated to be $151,100 for the high energy price future and $91,700 for the low energy price future. The above results clearly show that while there is an additional upfront cost, the through-life costs of the base ZEH scenario are significantly lower than the 6 star BAU scenario. However this is based upon typical assumptions. To assist with a more in-depth understanding for policy implications from different assumptions, Table 7 presents a summary of results from each assumption analysed. The table presents the cost difference to the base scenario, payback periods (compared to both a low and high energy price future) and total avoided costs across various time periods.



cft(1 + i)

−y

+ ˛H v − C

(1)

y=0

where NPV is the Net Present Value of energy efficiency investment ($US), cft is the cash flow at time t (positive for earnings, negative for expenditures), i is the discount rate, ˛Hv is the marginal increase in residual house value as a function of the energy efficiency and renewable technology features of the house after Y years and C is the capital cost of investment in energy efficiency technology ($US). This equation is based on the calculation methods applied in Morrissey and Horne [62]. Tables 5 and 6 present the key assumptions which are tested in this analysis. These were selected as they have been shown in previous regulatory impact statements in Australia [20] and other research to be topical, as discussed in Section 2.1. The sensitivities

5. Critical parameter sensitivities 5.1. Upfront costs The analysis shows that there is a reduction on the upfront cost when various assumptions change (Fig. 4). A reduction in the net conditioned floor area resulted in the greatest reduction of upfront costs compared to the BAU scenario. A reduction of 15% and 50% resulted in an initial upfront cost reduction of $17,300 and $48,800 respectively. The reduction of construction and material costs was found to have the least impact to additional upfront costs. Not unexpectedly the economic benefits are enhanced when the scenarios are combined. When scenarios 1–5 are combined, there is a substantial reduction of additional upfront cost of $25,300 (low assumptions scenario) and $56,600 (high assumption scenario).

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Table 5 Developed scenarios. Scenario

Parameter

Sensitivities tested

Rationale

Corresponding changes in the cost–benefit landscape

1

Renewable energy cost

Decrease by 25%

Further development/expansion of the renewable energy marketplace

2

Construction and materials cost

Decrease by 8% and 20%

3

Occupant behaviour total energy consumption

Decrease by 10%

4

House floor size – heating and cooling area

Decrease by 15% and 50%

5

Lifespan of house

40, 60, 80 and 100 years

6

Discount rates

Three different discount rates as per Table 5

7

Combination of scenarios 1–6

Combination of scenarios 1–6

Analysis was conducted in 2011 to determine the ‘average’ price per kW of installed photovoltaics. Analysis of Australian renewable energy retailers found that some retailers offered certain brands for up to 25% less than the average across all brands [1]. Therefore this scenario tests the implications if the cheapest renewable energy systems are used In the UK, costs to build ZEH have reduced by 8% across 4 years, with projections that costs will reduce by 20% as the building industry adjusts to the new building standard [8]. Similarly the Australian RIS [20] applied a scenario whereby construction costs were 20% less. Therefore this scenario investigates the impact that the additional costs required to build a ZEH are reduced by 8% and 20% respectively A brief review of research into the impact of behaviour change programs targeting a reduction in energy consumption in the home found that a reduction of between 5 and 40% has been achieved, with an average of around 10% of reduction in energy consumption [67,68]. Therefore this scenario investigates the impacts from a reduction in occupant behaviour by 10% (note the analysis does not factor in costs of any behaviour change program). In this scenario, changes to occupant behaviour impact on the upfront cost of the dwelling as there is less demand for renewable energy generation, and therefore a smaller renewable energy system can be installed, reducing upfront and through-life costs Research shows that average new house size in the USA and NZ is approximately 15% smaller than in Australia, and 41–67% smaller across a number of European countries [69]. Therefore a reduction in floor area of 15% and an average of 50% have been selected to test the impacts that house size might have In the USA a third of housing was built prior to 1960 [70]. In the UK 47% of housing was built prior to 1965, including 21% which were built prior to 1919 [40]. In Australia policy analysis assumes a lifespan of a house to be 40 years. ABS data [39] show that in 1999, more than 17% of houses in Australia were greater than 50 years old. Therefore this scenario investigates the impact that a longer assumed lifespan of a house has on the analysis. A 60 years lifespan is selected for analysis as it represents two full cycles of renewable energy technology [1], and an 80 years lifespan is also modelled as this is double current lifespan assumptions [20]. A 100 years lifespan is also applied, in line with dwelling age in other countries such as the UK [40] A review on discount rates and environmental costing [19] has shown that discount rates applied in cost–benefit analysis is significantly lower in many developed countries, such as the UK, when compared to the Australian context for addressing longer term environmental challenges such as climate change. This scenario therefore applies three different discount rates to assess the implications for the analysis. Discount scenario 1 is the rate advocated by Garnaut [49] for the Australian context. Discount scenario 2 is the rate applied by the UK Government [50,51]. Discount scenario 3 is the current rate set by the Office of Best Practice (Australian Government) [52] The above scenarios all address the modelling in isolation. This scenario investigates the implications if all the sensitivities were included together

Fig. 3. Base ZEH scenario compared with the BAU low and high energy price future scenarios.

Influence of training Influence of changed policy landscape. Industry learning

Impact of applied behaviour change programs

Impact of lot size, floor space regulations

Policy measures to promote renovation, heritage policies etc.

Application of range of discount rates in RIS

Integration of comprehensive measures for efficiency in the policy tool-kit

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Table 6 Discount scenarios applied. Discount scenario

Real discount rate (%)

1 2 3

Inflation rate (%)

0–30 years

31–60 years

1.65 3.50 7.00

1.15 3.00 6.50

Nominal discount rate (%)

3.00 3.00 3.00

0–30 years

31–60 years

4.70 6.60 10.21

4.18 6.09 9.69

Table 7 Results from assumption analysis. Scenario

Base scenario – 8 star ZEH 1 – reduction in renewable energy technology costs 2 – reduction in additional construction and material costs 3 – occupant behaviour – reduction in total energy consumption 4 – reduction in floor area

Adjusted additional upfront cost compared to base scenario ($)

–

BAU low energy price future

BAU high energy price future

91,700

151,100

–

108,700

168,200

17,000

92,400

151,800

800

Base scenario

By 25%

−6100

By 8%

−800

By 20%

−1700

93,500

152,900

1700

By 10%

−4100

101,600

161,000

9900

By 15%

−17,300

110,100

169,500

18,300

144,100

203,700

52,500

91,700 274,300 590,900 1,297,700

151,200 533,500 1,382,500 3,150,300

−25,300

130,200

189,700

38,600

−56,600

331,600 689,500 1,360,200 162,700

559,200 1,388,300 3,212,800 222,200

420,00 53,100 62,500 71,100

364,100 723,200 1,394,900

591,600 1,326,500 3,029,400

74,500 80,900 90,700

By 50%

−48,800

5 – life of house

40 years 60 years 80 years 100 years

NA

6 – Discount rates 7 – Combination of scenarios 1–5. Reduction

See Section 5.4 By low assumptions

By high assumptions

40 years

60 years 80 years 100 years 40 years 60 years 80 years 100 years

*

Avoided through-life energy costs compared to: ($ across 40 years* )

Except for the life of house scenarios which also have results for 60, 80 and 100.

Fig. 4. Upfront costs of the various scenarios.

NA

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Table 8 Net present value for the different discount rates after 40 years. Discount scenario

Future BAU energy price scenario

1

Low High

40 yrs ($) 4900 26,300

2

Low High

−4700 9800

3

Low High

−14,500 −6600

5.2. Payback periods With a reduction in the additional capital cost resulting from changes to assumptions, this also reduces payback periods (Fig. 5). The lowest payback periods for the individual scenarios were found for the reduction of net conditioned floor area (0 years for a 50% reduction and approximately 8 years for a 25% reduction). Further, the reduction in renewable energy technology costs by 15% reduces the payback period to 13 years for a low energy price future and 11 years for a high energy price future. When scenarios 1–5 are combined, these payback periods are zero years for the high assumption scenarios and 4 years for the low assumption scenarios. 5.3. Avoided through-life energy costs The base ZEH scenario was calculated to achieve avoided through-life energy costs across 40 years compared to the BAU scenario of $91,700 for the low energy price future and $151,100 for the high energy price future (Table 7). The reduction in costs for renewable energy technologies by 25% results in an additional $17,000 being avoided compared to the base scenario. For the reduction of net conditioned floor area, the additional avoided through-life energy costs across 40 years was $38,600 for the 25% reduction scenario and $71,100 for the 50% reduction scenario. The avoided through-life energy costs increase significantly across time. For example, expanding the life of the base ZEH scenario beyond 40 years to a focus on 80 years resulted in avoided through-life energy costs of $590,900 for a low energy price future and $1,382,500 for a high energy price future. 5.4. Discount rates The results from the modelling show that the discount rate selected has a significant impact on the net present value. Applying the discount rate as set by the Australian Government resulted in a negative net present value after 40 years. However discount rate scenario 1 resulted in positive net present values. A positive net present value was also achieved for discount rate scenario 2 for a high energy price future. Overall, the lower the discount rate the greater the net present value (Table 8). The future price of energy also had an impact on the results, with results higher for the high energy price future than for a low energy price future. 6. Discussion The analysis shows that assumptions applied to LCC of energy efficiency standards for ZEH can have a substantial impact on outcomes and the determination of feasibility. The following paragraphs discuss some of the key outcomes from this paper regarding upfront costs, the use of historical data, lifespan of the house and those assumptions with greatest impact on outcomes are identified, together with implications for future policy development. All things being equal, there is an additional capital cost to achieve a ZEH in the range of 0–20%, when applying conservative assumptions. This is broadly in line with other emerging

international research on ZEH [1]. The results from this paper indicate that some LCC parameters have greater economic impacts than others. For example, the cost of renewable energy technologies was found to be a significant factor of reducing upfront and ongoing costs for ZEH. Significant cost reductions of renewable energy technologies have been realised over the past decade globally and costs are projected to continue to fall [71], although this may not be achieved in all countries depending on existing distribution network and economies of scale challenges. In addition there are examples of how individuals and local organisations and governments in Australia are working to further address the costs of renewable energy technologies and other costs and assumptions applied within this analysis. ‘Bulk buying’ schemes whereby groups of owners purchase renewable energy technologies en masse for multiple dwellings can achieve significant cost efficiencies provide one such example. Understanding the nuances of the various cost elements can provide policy makers and actors within the housing and renewable energy industries in Australia and internationally with an impetus for developing appropriate responses to reduce costs. The approach of applying conservative assumptions may help to mitigate protests from the building industry, but at the same time may penalise innovation. In the UK, the Code for Sustainable homes 10 years step policy development plan has arguably driven innovation. Across the first 4 years of the policy, the costs to achieve a ZEH have fallen by 8.2%, and further cost efficiencies are likely as innovation of technologies and building design/practices occur [72]. This is probably both a result of innovation in technologies, materials and building practices as well as consumers (or building industry actors) who shop around for the best value and therefore find ways to reduce any additional costs, as highlighted in the ‘bulk buying’ example above. However, while costs are falling for improved sustainability performance for new housing, a key criticism remains in the policy discussion regarding the additional capital costs which are typically passed on to consumers [41]. However, the issue of additional upfront costs can be negated if policies were adjusted with the specific aim of achieving this, within the context of the drive for higher energy efficiencies. Alternative measures such as a reduction of house size would undoubtedly help in this regard. However, for this to become a topic of policy debate, the parameters being considered need to be broadened. Key, set assumptions in LCC analysis therefore need to be changeable to reflect more accurately the dynamic nature of real world characteristics and trends. The importance of house size in achieving a low carbon future and addressing housing affordability issues is beginning to emerge in research and policy discussions [69,73]. Addressing house size will be a critical requirement in a transition to a low carbon housing future in countries such as Australia. Applying assumptions which are based on current or historical data (which is typically the case) and which does not factor in likely future trends can result in problematic analysis. The average new house size in Australia has increased, from 162.4 m2 of floorspace in 1984 to 241.0 m2 of floorspace in 2012, to the extent that it is amongst the largest in the world [32,69,74]. However if historical yearly data are used, analysis may fail to capture the fact that there is a slight trend towards smaller floor area in new dwellings. The latest figures show that average floorspace has dropped from 248.0 m2 in 2008 [74]. The trend towards smaller dwellings is likely to continue as decreasing average occupant numbers and changes to household composition result in preferences for smaller dwellings. House size has a significant impact on the overall energy requirements of a dwelling and the analysis showed that reducing the net conditioned floor area will reduce or remove additional upfront costs of improved sustainability requirements. A scenario which achieves a ZEH standard

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Fig. 5. Payback period for the various scenarios.

for no additional cost significantly changes the available options for policy makers. The assumed lifespan of the house used in the LCC also emerged as a key parameter, frequently selected on basis of historical precedence. The analysis found that the longer the assumed lifespan of the house, the greater the overall accumulated economic benefits. By extending residential building lifespans from the currently used 40 years to 80 years, economic benefits to the household increase by more than six times – assuming inter-generational sharing of benefits. With a significant portion of Australian housing over the age of 40 years [39], it is timely to question if using a 40 years life of the house is still appropriate, especially in the context of longer housing life spans in many other developed countries [35]. Encouraging longer life of dwellings would also reduce or delay the requirements for resource use for new dwellings. Approaches such as pay as you save retrofit schemes, whereby loans to finance energy efficiency building upgrade are linked to the property, as opposed to the household, may be a first step in acknowledging the long-lived nature of the built environment, and the need for an energy efficiency outlook beyond the lifespan of a single household [73]. Similarly there are assumptions of occupant behaviour, particularly within the energy modelling tools, which may not reflect actual behaviour, or future changes to behaviour. Studies have shown that targeting occupant behaviour can lead to improved energy efficiency outcomes of up to 40% [67,68]. Therefore there is significant potential for this to be addressed in initial analysis and should form part of wider government approaches to reducing energy consumption across the residential sector. It is clear from the analysis presented in this paper that the assumptions included in LCC analysis are critical for policy development. Selecting the ‘right’ value for assumptions is a difficult

but important requirement. It is time that the assumptions applied within the LCC analysis of housing standards in Australia and internationally are reviewed and updated where appropriate. More analysis of the sensitivities of the assumptions applied could help to show a range of outcomes of the analysis and articulate what future developments might look like. This would also help to explore what needs to occur for proposed minimum standards to become more affordable and practical. For example the analysis may find that the efficiencies of the renewable energy generation technologies must reach a certain performance level before they become ‘cost effective’ for a ZEH scenario. A fully considered use of LCC, including a broader range of changeable parameters could also help identify possibilities for alternative policy approaches to achieve cost effective energy and emissions reductions in the built environment. Previous LCC for proposed new minimum energy efficiency standards for dwellings in Australia have often been met with resistance from the building industry. Any update to assumptions will need to occur with the support of detailed information campaigns to ensure that the rationale for assumptions applied are robust and their selection transparent. A comprehensive debate on LCC parameters in the policy-setting context could enable a wider debate on the costs and benefits of housing over expanded time-horizons, serving to shift debate from one based on short-termism typified by the affordability versus sustainability debate on energy efficiency standards. Results of the analysis presented in this paper suggest that LCC analyses offer a much more potent policy tool than is currently realised. Rather than testing the outcomes of narrowly defined policy instruments, LCC can be applied to test assumptions and parameters across a wider range of characteristics of buildings. In this way, the potential of previously unthought-of policy instruments can be identified, through a step-wise and thorough

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consideration of the full range of parameters and assumed variables. New approaches can be identified, and the problems with out-dated assumptions can be evaluated. 7. Conclusions The analysis has shown that variances to the key assumptions applied within LCC analysis can significantly alter the outcome for minimum energy efficiency standards for new housing in Australia. The analysis shows that with some more progressive use of assumptions, a ZEH house can be achieved for no additional upfront cost compared to a BAU scenario. This would be a different policy proposition for policy makers and the wider community. It is time for a detailed review of the assumptions applied within LCC used to inform the development of minimum energy efficiency standards in Australia and internationally. This would ensure that the future development of minimum energy standards towards ZEH standards in Australia would be more accurate, more rigorous and more transparent. Acknowledgements This research is supported under Australian Research Council’s Linkage Projects funding scheme (project LPO776834). The views expressed herein are those of the authors and are not necessarily those of the Australian Research Council. The authors wish to acknowledge the contribution of project partner’s the Building Commission, Land Management Corporation and VicUrban. References [1] T. Moore, Facilitating a Transition to Zero Emission New Housing in Australia: Costs, Benefits and Direction for Policy, in School of Global, Urban and Social Studies, RMIT University, Melbourne, 2012. [2] C. Choguill, The search for policies to support sustainable housing, Habitat International 31 (2007) 143–149. [3] W. Lee, F. Yik, Regulatory and voluntary approaches for enhancing building energy efficiency, Progress in Energy and Combustion Science 30 (2004) 477–499. [4] European Commission, Directive 2010/31/EU on the Energy Performance of Buildings (recast), European Parliament, Luxembourg, 2010. [5] CPUC, California Energy Efficiency Strategic Plan. January 2011 Update, California Public Utilities Commission, San Francisco, 2011. [6] DCLG, Cost Analysis of the Code for Sustainable Homes. Final Report, Department for Communities and Local Government, London, 2008. [7] DCLG, Summary of Responses to the Consultation for The Code for Sustainable Homes (CHS), Department for Communities and Local Government, London, 2006. [8] DCLG, Research to Assess the Costs and Benefits of the Government’s Proposals to Reduce the Carbon Footprint of New Housing Development, Department for Communities and Local Government, London, 2008. [9] DCLG, Housing and Regeneration Bill – Impact Assessment, Department for Communities and Local Government, London, 2008. [10] M. Osmani, A. O’Reilly, Feasibility of zero carbon homes in England by 2016: a house builder’s perspective, Building and Environment 44 (2009) 1917–1924. [11] A. Uihlein, P. Eder, Policy options towards an energy efficient residential building stock in the EU-27, Energy and Buildings 42 (2010) 791–798. [12] H. Tommerup, S. Svendsen, Energy savings in Danish residential building stock, Energy and Buildings 38 (2006) 618–626. [13] B. Tonn, J.H. Peretz, State-level benefits of energy efficiency, Energy Policy 35 (2007) 3665–3674. [14] E. Kossecka, J. Kosny, Influence of insulation configuration on heating and cooling loads in a continuously used building, Energy and Buildings 34 (2002) 321–331. [15] Z. Yilmaz, Evaluation of energy efficient design strategies for different climatic zones: comparison of thermal performance of buildings in temperate-humid and hot-dry climate, Energy and Buildings 39 (2007) 306–316. [16] K. Gregory, et al., Effect of thermal mass on the thermal performance of various Australian residential constructions systems, Energy and Buildings 40 (2008) 459–465. [17] R.M. Pulselli, E. Simoncini, N. Marchettini, Energy and emergy based costbenefit evaluation of building envelopes relative to geographical location and climate, Building and Environment 44 (2009) 920–928. [18] G. Manioglu, Z. Yilmaz, Economic evaluation of the building envelope and operation period of heating system in terms of thermal comfort, Energy and Buildings 38 (2006) 266–272.

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