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Chapter 1 Outline of Thesis 1.1 Introduction

The level of energy consumption in modern urban environments is currently subject to

considerable attention. It is apparent that modifications to energy provision and usage are

needed to accommodate future demands and constraints. This requires a thorough

understanding of the different components and patterns of urban energy consumption. A

significant portion of the energy usage of cities and towns arises from houses and other

dwellings in the form of the embodied and operational energy of these buildings and

associated infrastructure. In order that more informed decisions can be made about the

design, construction, operation and redevelopment of residential urban areas, a

comprehensive understanding of energy consumption is required. This thesis identifies a

gap in existing knowledge which renders the current understanding as incomplete. It

proposes the development and application of a general model which spatially depicts the

embodied energy of residential areas and provides the means for a more holistic approach

to urban energy analysis. The model is developed on the Adelaide metropolitan area

although the principles underlying it can be adapted to other urban locations.

1.2 Background

1.2.1 Energy consumption in the urban environment

Australia has become one of the most urbanised societies in the world with some 64% of

its population living in the capital cities and a further 19% in the regional cities and large

towns (ABS, 2005). It is mainly in the cities of Australia that the activities of

manufacturing, commerce, construction and the provision of services occur. In addition,

cities are where the greater proportion of the population are engaged in work and leisure

pursuits as well as where a significant part of the national wealth is created. The

construction, operation and maintenance of these urban areas require resources, materials

and energy.

Currently, the consumption of energy in the urban environments of Australia is mainly

dependent on the supply of fossil fuels. This can present a number of disadvantages and

the solution to this problem is subject to much research (Energy Efficiency and

Greenhouse Working Group, 2003). A dependence on fossil fuels has associated risks in

terms of environmental effects, supply disruption and projected economic costs. Possible

energy supply scenarios in the future, such as a greater proportion of renewable energy

sources and nuclear power also have certain drawbacks, risks and probable higher financial

2

costs (Enkvist et al, 2007). The concept of minimising energy consumption and increasing

energy efficiency is recognised as being a significant contributor to energy management

both now and in the future (McLennan Magasinik, 2004). In order that a suitable emphasis

can be placed on minimising energy usage in the urban environment, a thorough

understanding of current consumption patterns is required.

Achieving long term strategic modifications in energy consumption requires a holistic

approach which takes into account all forms of energy expenditure. According to Troy et

al (2003), the principal components of energy expenditure in the urban environment are:

• embodied energy of the built form (including buildings and infrastructure),

• operational energy consumed by buildings, and

• transport energy used by private and public vehicles.

There is likely to be a degree of inter-relatedness between these three components and

comprehensive analysis offers the possibility of achieving improved outcomes in

minimising energy consumption. In reality, such analyses are rarely applied to urban

developments (Perkins, 2001) except in a broadly qualitative manner. The concept of

comprehensively monitoring energy consumption as a means of achieving the sustainable

operation, maintenance and renewal of cities has been suggested by Troy and Smith

(2000). It was argued that the development of energy ‘profiles’, based on existing datasets

from various government agencies and utility providers, would allow urban planners to

consider different development scenarios for city infrastructure services and the built

environment. The ‘profiles’ would relate data not previously assembled providing a more

holistic understanding of urban energy consumption patterns. A further feature of this

proposal was that urban infrastructure should be re-used and refurbished to minimise

further energy expenditure embodied in new building materials.

1.2.2 Embodied energy of the built form

The embodied energy of a material or component is the energy consumed in its production

including upstream activities such as raw material extraction, conveyance, manufacturing

and assembly. The urban environment is composed of very large quantities of materials

and components as these are used to construct the buildings and infrastructure which create

the built form. Of the three components of energy expenditure in the urban environment,

embodied energy is normally the least considered.

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There has been some attention paid to the embodied energy of buildings as part of life

cycle and environmental impact assessment in building research. Life cycle analysis takes

into account all of the inputs to a product and in the case of life cycle energy, the largest

components are embodied energy and operational energy. Cole (1998) states that life cycle

assessment is the only legitimate method to consider all of the impact over the life cycle of

the component or building concerned. In the life cycle energy analysis of a Melbourne

commercial building, Treloar (1995) indicated that the embodied energy was of a similar

significance to the operational energy when the additional embodied energy of periodic

refurbishments was taken into account. In recognition of this, some designers have begun

to consider the embodied energy of the building materials so that a more holistic approach

can be made to the overall energy consumption of buildings (Maitland, 2005; City of

Melbourne, 2006).

Despite advances in building research, the consideration of embodied energy in a

quantitative manner has not yet become normal practice in the design and assessment of

urban developments. Furthermore, the inclusion of the embodied energy of infrastructure

such as roads and reticulated services is rarely considered. This renders what little

information there is on embodied energy incomplete on the broader scale of urban

planning and development of built areas. Hence, there are significant gaps in the

knowledge available for assessing total energy consumption of the built environment.

It is proposed that the analysis of different existing urban design configurations, which

takes into account embodied energy, could be used to better inform decisions about new

developments. Furthermore, in the case of the redevelopment of existing urban areas,

information on the embodied energy of the built environment would offer the potential for

valuing the existing built form and accounting for saved energy in the re-use or recycling

of construction materials. This would provide a more comprehensive analysis of energy

consumption not currently undertaken and provide a broader understanding of the

significance of embodied energy. Therefore, this research is aimed primarily at the

development of new applications and insights of embodied energy at the urban scale rather

than the methods for deriving embodied energy values for materials.

1.2.3 Focus on residential areas

The built environment is often classified into residential and non-residential forms for the

purposes of regulation, planning, economic and other analyses. This research gives

4

priority to the residential sector. The reason for this is that it can be surmised that the

residential sector is more significant from the life cycle energy perspective and

consequently has a greater potential for the modification of overall energy consumption. A

number of observations support this view.

Firstly, embodied energy is related to the quantities of materials in the built fabric of the

urban environment and, taking total floor areas as a preliminary indicator, dwellings

dominate compared with all other building types (Kellett and Pullen, 2007). Additionally,

from an analysis of energy consumption of various industrial sectors in Australia, Dickson

et al (2003) report that the residential sector consumes more operational energy than the

commercial and services (non-residential) sector which comprises wholesale and retail

trade, communications, finance, government, community services and recreational

services. In the 2005-06 period, the operational energy consumption of the residential

sector in Australia was 447 PJ (petajoules) which was 12% of total final energy use,

whereas the corresponding figure for the commercial and services sector was 258 PJ or 7%

of the total.

Furthermore, any changes to the design and operation of residential areas are likely to offer

the opportunity for the modification of overall energy consumption. Such changes could

arise from the construction of new compact dwelling forms, the densification of older

residential suburbs or the retrofitting of existing dwellings (Bilsborough, 2006). The

ability to analyse both embodied end operational energy consumption would inform urban

planning decisions with regard to total energy consumption.

1.2.4 Spatial dimension

Where relevant information on energy consumption in the built environment is available, it

is normally in the form of data at the urban scale of individual buildings or aggregated

totals for the residential and commercial building sectors at a state or national scale. There

is a paucity of information at the neighbourhood, suburb or urban area scale which might

assist in comparing different built forms. Geographical information systems (GIS) offer

the potential for storing large amounts of information in the form of databases. These

databases can provide a means of aggregating data into larger geographic areas. Where

there are various data types to be considered, GIS can present the information with a

spatial dimension which allows the overlaying and accumulation of these data and a

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greater appreciation of their significance. Hence, this method of data collection is likely to

be suitable for displaying the various components of urban energy consumption.

Supporting this concept is some research work conducted in the UK using GIS to depict

the operational energy consumption of residential areas as well as the associated

greenhouse gas emissions (Jones et al, 2001; Gupta, 2005). However, in terms of a

comprehensive energy analysis, which considers the whole life cycle of residential

buildings, neither research project considered the embodied energy of the dwellings.

A pilot study was carried out in Adelaide at the end of 2001 which considered both

operational energy and embodied energy consumption of buildings in six selected

metropolitan areas (Troy et al, 2003). The addition of transport energy of residents added

a further dimension. An objective of the study was to establish the feasibility of obtaining

the relevant information and of presenting it in a spatial format. The project achieved this

objective within the limitations of the relatively small sample size. It demonstrated that

more comprehensive research was required using a larger proportion of the urban

environment.

1.3 Problem statement

There is a lack of comprehensive information on the life cycle energy consumption of

residential areas particularly with respect to embodied energy. This is because the

derivation of embodied energy of the built environment is very difficult to achieve and its

significance has not been fully explored. Furthermore, there is no convenient way of

linking embodied, operational and transport energy consumption in residential areas. As a

result of this, planning and development decisions related to residential areas are

insufficiently informed to provide outcomes that minimise total energy consumption.

The contribution to knowledge described in this research is the construction of a model that

provides information which has not previously been available on the embodied energy of

residential areas. It is proposed that such a model could be used as a tool to inform life

cycle energy assessment of the built environment in the pursuit of more energy efficient

towns and cities. This problem statement leads to the following hypothesis.

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1.4 Hypothesis

The embodied energy of residential urban areas can be estimated and represented

spatially with sufficient accuracy to usefully contribute to life cycle energy analyses of the

urban environment. Such analyses can better inform urban planning decisions regarding

alternative residential configurations and the redevelopment of existing residential areas.

1.5 Overall aim of the research

The overall aim of the research is to fill a knowledge gap by developing a model which

spatially depicts the embodied energy of residential urban areas as a contribution to the

mapping of total energy consumption in the built environment. This would enable the

model to be used as a tool which can assist in decision making in the urban planning and

development of residential areas. The model will be based on the Adelaide metropolitan

area but the underlying principles will be applicable to other cities.

1.6 Objectives of research

In order to investigate and determine the validity of the hypothesis, the overall aim can be

sub-divided into the following objectives.

Objective 1.

Determine the instrumental value of a model of embodied energy in the built environment

presented in a spatial format as part of a broader analysis of energy consumption.

Objective 2.

Demonstrate a method for determining the embodied energy of buildings and

infrastructure, particularly houses, in the urban environment based on input-output analysis

and other datasets.

Objective 3.

Construct the model of embodied energy based on three knowledge areas of embodied

energy theory, property data of the Adelaide metropolitan area and geographical

information software, in such a way that the model can be applied as a tool for urban

planning and development purposes. The model should offer the possibility of linking

with other components of residential energy expenditure.

Objective 4.

Consider an alternative urban centre to determine which parts of the model are transferable

and to what extent adaptation would be required.

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Objective 5.

Show how the embodied energy maps can be combined with other energy consumption

data to provide a tool which provides more comprehensive and useful information for

decision making in urban development.

1.7 Methodology

The methodology consists of three elements which are aimed at achieving the research

objectives and determining support for the hypothesis:

(a) establishing the instrumental value of the model (Objective 1),

(b) the development of the model (Objectives 2 and 3) and

(c) the application of the model (Objectives 4 and 5).

1.7.1 Establishing the instrumental value of the model

Research work relevant to the proposed model will be reviewed to determine the current

state of knowledge and determine the instrumental value of the model in that context. The

issues to be explored are:

• the case for minimising overall energy consumption with regard to enhanced

greenhouse gas emissions, energy supply disruptions and financial costs.

• the existing knowledge relating to the design of the built form and its possible

influence on energy consumption.

• the significance of embodied energy and its consideration as a ‘sunk’cost.

• the relevance of the existing embodied energy of both newer and older residential

areas for informing decisions about future residential developments.

• the advantages of depicting embodied energy as a baseline for the analysis of other

components of urban energy consumption.

The review will form the basis for constructing the model and establish its value for energy

analysis in urban residential areas.

1.7.2 Development of the model

The method for developing the model will draw upon and synthesise three areas of applied

knowledge which are:

• The emerging body of research on the methods and techniques for estimating the

embodied energy of building materials and components.

• The collection of information on the characteristics of buildings compiled in the

property register of an urban area and additional synthetic data.

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• The techniques developed to depict data in a spatial format by geographical

information systems (GIS).

This represents a cross disciplinary study which includes architectural science, urban

planning and modern surveying. A schematic representation of the development of the

model and its application is shown in Figure 1.1

An ideal model would depend upon the availability of comprehensive information of the

design and materials used for all dwellings and infrastructure in residential areas.

Although property registers may contain a substantial proportion of this information, there

will be a requirement for novel techniques to derive missing data. The property register for

the Adelaide metropolitan area will be used to construct a model of embodied energy

consumption and test the hypothesis that residential buildings and infrastructure can be

estimated and spatially represented. Research based on the study of single exemplars has

Figure 1.1 Development and application of the model for spatially

representing embodied energy

General under-

standing of energy

consumption in the urban environment

Application of model as

a tool for analysing residential

urban development

Depiction of the model in the form of maps and databases using GIS software

Derivation of embodied

energy model

based on a GIS platform

by aggregation of data units

Embodied energy of materials

Embodied energy of dwellings

Property Register synthetic

data

Property register

actual data

Data units Model

development Model

representation Model

application General

understanding

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been rigorously supported by Flyvberg (2004) as a valid method of testing hypotheses.

The model will make provision for the ultimate inclusion of all building types but for this

research will focus on residential areas for reasons previously described.

This part of the methodology will demonstrate the feasibility of depicting embodied energy

in a spatial format by providing examples of maps of suburban locations. It will also show

that the model can provide links with other components of residential energy expenditure.

1.7.3 Application of the model

The application of the model as a tool will be demonstrated using three case studies

relating to different dwelling configurations and the redevelopment of existing residential

areas.

The first case study will be based on an alternative urban centre where the information on

the characteristics of buildings is not as comprehensive as in the Adelaide model. This will

determine the extent to which the model can be adapted and applied elsewhere. This case

study will also provide insights to different residential configurations.

The second case study will use the model as a tool to address the question of urban

consolidation and the preferable form for new dwellings. This will contribute to the

debate about whether low density outer suburbs or higher density inner suburbs are more

environmentally sustainable.

The third case study will relate to older established suburbs and the issues of improving the

life cycle energy efficiency of existing dwellings as well as the redevelopment of older

stock. These processes require various energy inputs such as additional embodied energy

for retrofitting older dwellings and for the construction of new dwellings. At the same

time, the demolition of older buildings liberates materials for re-use and recycling. The

tool will be used to study the energy balance of these alternative strategies.

The question of sufficient accuracy as stated in the hypothesis will also be addressed

during the three case studies described.

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1.8 Structure of thesis

The following provides a brief summary of the thesis structure.

Chapter 2. Literature review.

This chapter establishes the instrumental value of the model proposed. The significance of

energy consumption in the urban environment is described as well as the desirability of

taking a life cycle approach to energy consumption which includes embodied energy. The

relevance of the embodied energy of the existing built form in relation to future

developments is established.

Chapter 3. Embodied Energy.

An overview of the methods of deriving the embodied energy of materials and products

including input-output based hybrid analysis is given in this chapter and comparisons are

made with data from other sources. Embodied energy and carbon dioxide coefficients are

evaluated and presented.

Chapter 4. Estimation of the Embodied Energy of Houses in the Adelaide Metropolitan

Area.

This chapter describes the method for estimating the embodied energy of houses based on

records from the State Property Valuation Register. It details the development of

techniques required to derive information not included in the property register. By

combining derived and property register information, the embodied energy of residential

areas is evaluated.

Chapter 5. Spatial Representation of Results.

The representation of the results in GIS format is shown in this chapter for selected parts of

residential areas in the metropolitan area of Adelaide. This includes the spatial

representation of as-built embodied energy and maintenance embodied energy. Examples

are also provided of greenhouse gas emissions arising from embodied energy and links to

other components of residential energy consumption. The verification of the model and

assessment of potential error of embodied energy estimations are discussed here.

Chapter 6. Case Study 1 - An Alternative Urban Centre.

This chapter summarises a study of operational energy and embodied energy in the Sydney

area which compares inner and outer suburbs. It also represents an application of the

techniques described in this research to a region where the property register does not

provide the type of information available in the Adelaide case study.

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Chapter 7. Case Study 2 – Central Business District Apartments in Adelaide.

An example is provided in this chapter of the application of the model as a planning tool

for new dwellings. This is to guide decision making in the design and planning of different

forms of dwellings with regard to energy consumption.

Chapter 8. Case Study 3 – Redevelopment of Older Established Suburbs.

This chapter provides a further example of the application of the model as a planning tool

and draws on themes of improvement and redevelopment of the existing housing stock. It

uses the model to monitor embodied and operational consumption under different

scenarios.

Chapter 9. Summary and Conclusions.

The research is reviewed in the context of the hypothesis and objectives as stated in

Chapter 1. The significance of the model as a planning tool is summarized as is its

instrumental value as part of decision making processes in urban design. Other potential

uses of the model are described relating to the monitoring of materials resources.

Conclusions and recommendations for further research are given.

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Chapter 2 Review of Literature 2.1 Introduction

The purpose of this chapter is to elaborate on the context for this research and to explore

gaps in the current knowledge on energy consumption in the urban environment. This will

provide the basis for establishing the need for the proposed model which depicts the

embodied energy of residential areas (Objective 1 stated in the outline of this thesis).

Initially, a case is made for the minimisation of overall energy consumption in the urban

environment. The influence of built form on energy consumption is addressed leading to

an appreciation of where a more comprehensive understanding is required. This is

followed by an explanation of the significance of embodied energy. The expected

contribution provided by the proposed model is then defined. Factors relevant to the

model are described from both national and international sources where appropriate, and

more local information relating to South Australia is provided consistent with the initial

focus on the metropolitan area of Adelaide.

2.2 The case for minimizing overall energy consumption

The desirability of minimizing overall energy consumption in the urban environment is

initially canvassed in this section. An ethical context is provided and the possible negative

consequences of energy usage are considered. This provides a context to explore the need

for a more comprehensive understanding of overall energy consumption (including

embodied energy) to assist decision making in urban planning and development.

2.2.1 Energy consumption in Australia

Energy consumption in Australia on a per capita basis is high compared with other nations.

The International Energy Agency (IEA, 2006) reports this to be 5.7 tonnes of oil equivalent

(toe/capita) in 2004. This compares with 7.9 toe/capita for the United States, 4.7 toe/capita

for countries in the Organisation for Economic Co-operation and Development (OECD)

and 1.8 toe/capita for the world. The distribution of this energy consumption in Australia

according to industrial sectors is shown in Figure 2.1 (Cuevas-Cubria and Riwoe, 2006).

NOTE: This figure is included on page 13 in the print copy of the thesis held in the University of Adelaide Library.

Figure 2.1 Energy consumption projections for industrial sectors in

Australia for 2004-05 (source of data: Cuevas-Cubria and Riwoe, 2006)

The combined residential and commercial sectors account for one fifth of the total

energy consumption in the form of direct energy used for the operation of buildings.

In addition, a significant proportion of the energy consumed by the transport sector

can be attributed to the operation of the urban environment as can a proportion of the

manufacturing sector in the fabrication and supply of construction materials (ie

embodied energy of materials manufactured in that period).

Most energy consumed in Australia is from non-renewable sources of fossil fuels ie

coal, oil and gas. Figure 2.2 shows the mix of energy sources and this has tended to

have a

NOTE: This figure is included on page 13 in the print copy of the thesis held in the University of Adelaide Library.

Figure 2.2 Australian primary energy consumption by fuel for 2004-05

(source of data: Cuevas-Cubria and Riwoe, 2006)

13

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greater reliance on coal and gas over the last 20-30 years. Although the diversification of

primary energy sources to more renewable (MRET Review Panel, 2003) and nuclear

(Commonwealth of Australia, 2006) is subject to considerable debate, it is likely that fossil

fuels will remain significant in satisfying total energy demand in Australia in at least the

short to medium term future.

The dependence of modern towns and cities on fossil fuel sources can be placed in an

ethical context which can be used to guide urban design and development. In addition, the

reliance on a constant supply of energy has some disadvantages (Droege, 2004) and these

support the case for minimizing overall energy consumption. These disadvantages include

environmental risks, a necessity for an uninterrupted energy supply and the future burden

of higher energy costs.

2.2.2 The ethical view

An ethical perspective provides the prime reason for minimizing overall energy

consumption in the urban environment. The conflict between individual interests and the

common good in the use of natural resources is exemplified in the parable of the ‘tragedy

of the commons’1 which was popularised by Hardin (1968). At a simple level, the use of

fossil fuel energy in cities is a modern example of this dilemma. Any resolution of this or

related predicaments would benefit from an ethical framework as a reference for

formulating priorities and actions.

In discussing ethics in the context of the built environment, Fox (2006) states that:

…achieving a sustainable way of living is clearly not just a technical issue

(although it is often discussed as if it were) but also (and fundamentally) an

ethical one.

He proposes the ethical theory of responsive cohesion (Fox, 2000) which is the most

valuable form of organization in any area considered by informed judges and this can

include practical applications such as the design and operation of buildings and cities.

Responsive cohesion represents an approach where elements are responsive to each other

1 The tragedy of the commons refers to the use of a common pasture by herders. It is in the interest of each herder to

increase the number of their herd as they will personally gain the proceeds from each extra animal. The loss is the

reduction in available pasture but this is shared by all herders and is outweighed by the personal gain. With all herders

engaged in the same process, the pasture eventually becomes degraded.

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and provides a foundational value which should be used to guide ideas and judgements. It

falls between the extremes of fixed cohesion, where elements are held together in a fixed

and rigid manner, and discohesion, where there is no logic to the combination of the

elements. In responding to conflicts in the organization of elements, Fox argues that

responsive cohesion provides a clear guide to prioritization. The biophysical realm is more

important than the human social realm which, in turn, is more important than the human-

constructed realm. This approach is consistent with that of Lemon (1999) who, in the

context of design and planning decisions, criticized conventional solutions which work

towards a deterministic ‘end point’ in favour of those which are more adaptable to

changing circumstances.

Significant in this debate is the issue of intergenerational equity (Brundtland, 1987) and it

can be argued that there is an increasing acceptance of moral responsibility towards

immediate future generations but a more ambiguous response towards distant future

generations (D’Amato, 1990). This creates a dilemma as the continuation of energy

consumption based on fossil fuels may increase the wellbeing of the current and next

generation but diminish that of more distant future generations. Responsive cohesion

provides an underpinning on which to make decisions and judgements which is cognisant

of this dilemma and which can respond to changing circumstances. This approach can be

used when forming policies dealing with issues such as the diminution of limited

resources, adverse effects of resource extraction and environmental risks associated with

fossil fuel consumption which are a result of activities in the human-constructed realm.

2.2.3 Environmental risks

The greater proportion of energy consumption in the urban environment currently

originates from fossil fuels and an important consideration of this is the contribution to

climate change risks.

Climate change and the link with anthropogenic greenhouse gas emissions from the

combustion of fossil fuels has been investigated by the Intergovernmental Panel on

Climate Change (IPCC) since the late 1980s. A tentative relationship between atmospheric

gases and the absorption of solar radiation through the earth’s atmosphere was first

proposed by Tyndall in 1861 (cited in Perkins, 2001). A more specific link between the

artificial production of carbon dioxide (CO2) and an influence on climate was

controversially made much later by Callender (1938). Over the last twenty years, the rate

16

of increase of atmospheric CO2 concentration has been about 1.5 ppm per year to the

current level of about 380 ppm (IPCC, 2001). As result of these increases, an enhanced

greenhouse effect is believed to be occurring and is thought to have given rise to an

increase in the earth’s surface temperature of 0.6±0.2ºC in the twentieth century (IPCC,

2001).

The IPCC has used models of future increases in greenhouse gases to predict changes in

global climate over the 21st century. The Special Report on Emissions Scenarios (IPCC,

2000) showed a range of scenarios based on assumptions about population growth,

economic development, technological changes and consumption of energy. Modelling of

these scenarios predicts atmospheric CO2 concentrations to rise to between 540 and

970ppm by the end of the century. It is predicted that this would result in a global

warming effect of between 1.4 to 5.8ºC over the period of 1990 to 2100 and a sea level rise

of between 90 and 880mm. Weather patterns would be more variable but with more

precipitation overall in the mid and high latitudes.

The Summary for Policymakers of the Fourth Assessment Report by the Intergovernmental

Panel on Climate Change (IPCC, 2007) has broadly confirmed the findings of the earlier

(third) report but with increased certainty regarding the link between human activities and

global warming. The Summary claims that the rise in globally averaged temperatures over

the last 60 years is now believed to be very likely due to an increase in anthropogenic

greenhouse gas emissions. A warming of about 0.2ºC per decade is predicted for the next

20 years and there is now higher confidence in projected patterns of warming including

wind patterns and precipitation.

Applying similar climate models to the Australian region has enabled the CSIRO (2001) to

predict increases in temperature for the main population centres. Predictions for rainfall in

most locations in summer and autumn vary from -35% to +35% by 2070 or tend towards

an overall increase. In winter and spring, most locations tend towards decreased rainfall

(-35% to +10%). A further CSIRO report (Suppiah et al, 2006) has focused on risk

assessment studies relating to temperature increases, coastal impacts and water resources.

The concept of minimising energy consumption as a contribution to reducing greenhouse

gas emissions has been adopted by the Council for Australian Governments in a national

17

Framework for Energy Efficiency (Energy Efficiency and Greenhouse Working Group,

2003).

2.2.4 Uninterrupted energy supply

The uninterrupted supply of energy is considered to be essential for the operation of

modern towns and cities and disruptions to this situation can result in social and economic

disturbances.

Disruptions to the supply of energy can occur at a various levels in the complex

international and national supply lines. The most notable international disruptions to

energy supply took place in the decade of the 1970s and arose from political and economic

conflicts (Mather and Chapman, 1995) as a result of the Arab Israeli, Yom Kippur war in

1973 and the revolution in Iran later in the decade.

There are also risks associated with the supply of energy when the sources are within

national boundaries from accidents, unusual weather conditions or major breakdowns.

Among these are the severe ice storms in Montreal in January 1998 when 150,000 people

were still not reconnected to the electricity supply after four weeks (BBC, 1998) and the

five weeks of power outages in Auckland commencing on 20th February 1998 causing

direct costs to business of $60 million a week (CNN, 1998). At the end of August 2005,

Hurricane Katrina wreaked such havoc that a month later, 400,000 people in Texas and

Louisiana were without power and 90% of crude oil production and 70% of natural gas

production in the region were shut down (Congressional Budget Office, 2005).

Another type of supply disruption that is less serious but which may well become more

common is when supply can not meet high demand such as that caused by air conditioning

use during hot summer conditions. In Australia, peak electricity demands are substantially

above normal base loads (up to double) and have been rising steadily over recent years.

The gap between base load and peak load is a key driver of internal policy within the

electricity supply industry (ESIPC, 2006) and demand side management is seen to play a

part in the amelioration of the risks of supply disruptions (CSIRO, 2002; Pareto Assoc.

P/L, 2004). The relationship between summer peak load and the design of dwellings is

currently subject to research in South Australia where some of the highest relative peaks

are experienced (Denlay, 2007).

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The minimisation of energy demand and diversification of energy supplies are two

methods of reducing the susceptibility of towns and cities to energy supply disruptions.

Furthermore, analysis of the interactions between energy components (for example, the

embodied energy of energy efficient houses and their operational energy) may offer

potential for energy reduction. A more comprehensive understanding of the energy inputs

to urban areas would assist in formulating strategies to minimize energy usage.

2.2.5 Higher energy costs

Although Australia has a high per capita energy consumption, it benefits from low energy

costs compared with other developed countries (Australian Government, 2005). In the

urban environment, costs to the energy customer can be either direct, such as the charges

for electricity and gas consumed to operate buildings, or indirect as reflected in the price of

purchased goods including materials for the construction and maintenance of buildings.

Increases in energy costs have disadvantages both economically and socially and there

have been changes to the structure of the energy supply industry to mitigate these

(Anderson, 2003; Willett, 2006). There has also been a focus on improving energy

efficiency in various sectors of the economy including the operation of buildings (Energy

Efficiency and Greenhouse Working Group, 2003; ABCB, 2006).

Of the many factors that may influence future energy costs, global reserves of crude oil and

possible measures to curb greenhouse gas emissions are significant. The debate about oil

reserves carries on between the ‘depletionist’ and ‘anti-depletionist’ advocates (Bureau of

Transport and Regional Economics, 2005) and attention is now focussed on predicting if

and when the loss of a cheap and abundant supply will occur.

Measures to reduce greenhouse gas emissions have been economically modeled

(Ahammad et al, 2006) in Australia through to the year 2050 by the Australian Bureau of

Agricultural and Resource Economics (ABARE). The modeling process was aimed at

achieving a target for a stabilized atmospheric carbon dioxide concentration of 575 ppm by

2100 based on the ABARE’s global trade and environment model (GTEM). A range of

scenarios were adopted including the availability of carbon storage technology and the use

of nuclear power. Depending on the level of international cooperation proposed in the

scenarios, carbon taxes of A$77 to A$157/tonne of CO2 equivalent gases were used as well

as more extreme tax scenarios. It was recognised that, in reality, these tax levels would

require frequent adjustment. At A$100/tonne and at an approximate emissions rate of

19

1kg/kWh for electricity generation, this would amount to a tax of 10c/kWh which is a

substantial increase on current tariffs. It was considered that the economic outcomes

would be similar with either a taxation or trading scheme instrument. Economic modeling

of the substantial increases in costs associated with severe cuts to greenhouse gas

emissions have also been carried out by Allen Consulting (2006).

In contrast to the methodology adopted by Ahammad et al (2006) using stringent emissions

targets, the Prime Ministerial Task Group on Emissions Trading (Shergold, 2007)

proposed a ‘pledge and review’ approach to limiting greenhouse gas emissions. This

suggested aspirational targets and favoured the emissions trading approach following the

setting of emissions caps on industrial sectors. Emissions trading was preferred as it was

seen to be more focused on the environmental objective of reducing greenhouse gas

emissions in the most efficient manner. Furthermore, this instrument allows the market to

select the best low emissions technologies, encourages the use of carbon ‘offsets’ (eg tree

planting) and is compatible with emerging schemes around the world. Some economic

modeling was undertaken at lower prices of around A$45/tonne of CO2 equivalent gases.

However, the report recognised that both households and business will pay more for their

energy.

The effects of both supply factors and demand side economic instruments on energy

consumption and greenhouse gas emissions will be related to the price elasticity (Case &

Fair, 2007) of the various forms of energy1. In the short term, energy price elasticity is

normally low (ie inelastic) as consumers have few alternatives or are unwilling to modify

consumption patterns whereas, in the longer term, greater price elasticity occurs as

alternative consumption strategies are adopted (Oxera, 2006).

The minimization of overall energy consumption offers the possibility of moderating the

impacts arising from increases in energy costs due to supply restrictions or greenhouse gas

mitigation instruments. These costs will be incurred directly in the form of operational

energy, and indirectly in the form of embodied energy of materials used for maintenance,

and for new buildings and infrastructure. Since energy use in the urban environment is

1 The price elasticity of demand (PED) for goods is the ratio of the change in demand to the change in price. Where the

demand is highly responsive to price, the PED is greater than 1 and the goods are price elastic. Conversely, where

demand is not greatly affected by price, the PED is less than 1 and the goods are said to be price inelastic.

20

significant, its analysis over a broad front would contribute to this effort. This requires a

holistic approach which considers all energy inputs to determine lower energy outcomes.

2.3 The built form and energy consumption

2.3.1 Possible relationship

The possible relationship between energy consumption and the built form in the urban

environment has, and will continue to be, subject to research (Owens, 1986; Perkins, 2001;

Steemers, 2003). Analysis of these relationships can occur at the building scale, such as

comparing different types of dwelling, through to the examination of whole

neighbourhoods or regions of towns and cities.

At the dwelling scale, a significant factor over the last two decades in Australia has been

the substantial increase in average floor areas of new houses from approximately 160m2 to

230m2 (RMIT, 2006). This trend has contributed to greater energy consumption both in

terms of the embodied energy of building materials and the operational energy required to

provide air conditioned spaces.

To a degree, this trend has run counter to the newer planning concepts for residential areas

that are denser in terms of numbers of dwellings per hectare and which have some mixed

use that provides services and employment to minimize travel. Newman and Kenworthy

(1989) expressed a prescription for denser, more compact urban designs following a study

of Australian and world cities in the 1980s. The purpose of this was to avoid highly

dispersed cities which involved high travel energy usage, pollution and social costs.

Sustainable residential development in Australia which minimises the use of resources and

focuses on environmental performance was described in detail in the AMCORD Practice

Notes (Department of Housing and Regional Planning, 1995). More recently, the House of

Representatives Standing Committee on Environment and Heritage conducted an inquiry

into Sustainable Cities in Australia (Washer, 2005) which revisited urban transport and

densification issues and called for more leadership from the Federal Government in this

area. The Committee’s first recommendation was for the establishment of an Australian

Sustainability Charter that sets national targets across a number of areas including energy,

building design and planning.

There are few studies comparing the operational energy consumption of different forms

and densities of urban dwellings and even fewer comprehensive analyses involving other

energy inputs such as embodied and transport energy. A study of energy consumption

in housing forms of various density in Sydney was commenced in 2003 (Myors et al,

2005) by Energy Australia and the New South Wales Department of Planning. The

types of dwellings considered were high-rise apartments (9 or more storeys), mid-rise

apartments (4-8 storeys), low rise apartments (up to 3 storeys), townhouses and

detached houses. The study reported on total annual greenhouse gas emissions arising

from operational energy consumption as determined from electricity and gas accounts

of residents. The results of the study are shown in Table 2.1 and the relevant points

arising are as follows:

• significantly increasing emissions with increase in number of storeys (from

5.1 to 10.4 tonnes CO2/dwelling/year) for multi-unit dwellings.

• large emissions from detached dwellings (9.0 tonnes CO2/dwelling/year).

• a substantial moderation of the emissions of detached dwellings compared

with other types of dwelling when the number of occupants per dwelling is

considered. Indeed, on this basis of comparison, only townhouse and villa

dwelling forms produce lower emissions.

• the emissions per person in high-rise, mid-rise and low-rise dwellings are all

above the benchmark of 3.39 tonnes CO2/dwelling/year which has been

adopted by BASIX (Building Sustainability Index) as the average residential

greenhouse gas emissions level for New South Wales. The BASIX target

requires a 25% reduction on this benchmark.

• Sites equipped with central air conditioning systems created an average peak

demand for electricity in hot weather that was 111% higher than those sites

without.

Table 2.1 Results of a study by Energy Australia and NSW Department of Planning

into the total annual greenhouse gas emissions of different dwelling types (source:

Myors et al, 2005).

NOTE: This table is included on page 21 of the print copy of the thesis held in the University of Adelaide Library.

21

22

Overall, the study suggested that occupants of apartments caused greater greenhouse gas

emissions than those in detached houses. Of the various dwelling forms, only townhouses

and villas appeared to offer advantages in terms of reducing emissions on a per occupant

basis.

The complexities of designing urban areas within cities to minimize energy consumption

has been discussed by Steemers (2003) who showed that high density cities such as Hong

Kong consumed far less energy on transport than lower density cities in the USA or

Australia. However, Steemers questioned whether increasing urban density would reduce

transport energy in the short term. Furthermore, focussing on housing in the UK, the

arguments for and against densification were finely balanced when operational and

transport energy consumption were considered.

Despite the adoption of compact city concepts by many in the urban planning profession,

there remain opponents to urban densification. Whilst referring to the proponents of the

compact city, Troy (1996, p167) states:

Solutions to pollution problems have been proffered and adopted with scant

regard for scientific evidence either about the extent of the problems and their

sources or any understanding of the history of cities and why they take their

form. Moreover, there is little scientific evidence that the solutions proposed to

cope with environmental stress can or will have the beneficial effects claimed.

As part of the research project at the Warren Centre for Advanced Engineering at Sydney

University called ‘Sustainable Transport for Sustainable Cities, Troy and Smith (2000)

outlined a number of objectives in the quest for a transition to sustainability for the city of

Sydney which included reduced consumption of energy from non-renewable sources and

increased energy efficiency in the operation of buildings and structures.

The need for a better understanding of the relationship between the built form and energy

consumption is echoed by Droege (2004). In the context of changing cities to match

energy supply constraints, he comments:

Another challenge is the energy-blind nature of contemporary physical

planning, typically very short planning horizons and political uncertainties that

prevail on the local level.

23

In the existing built form, an example of improving energy consumption and greenhouse

gas emissions has been undertaken by the City of Newcastle using the measuring tool

known as ClimateCam (City of Newcastle, 2002) and this has resulted in a reduction in

energy consumption of 40%. ClimateCam is an initiative under the umbrella of the Cities

for Climate Protection (CCPTM) campaign which was initiated by the International Council

for Local Environmental Initiatives in the early 1990s (Brugmann and Jessup, 1993). The

City of Newcastle is one of the founding members of the CCPTM campaign and in the

subsequent 10 years over 200 local councils in Australia have contributed to greenhouse

gas reductions of 8.8 million tonnes (Turnbull, 2007). This tool can create a greater

awareness of operational energy consumption and greenhouse gas emissions within the

community although it is not intended as a planning tool for guiding the design of new

developments.

2.3.2 Future issues

A challenge for urban development in many cities in the future is to reconcile increases in

the population of residents with commitments to what is broadly termed ‘sustainability’ in

the local and greater environment. An example of this is the city of Adelaide where the

Strategic Plan of the South Australian Government has set an increased population target

whilst committing to limits on greenhouse gas emissions consistent with the Kyoto

Protocol (Department of the Premier and Cabinet, 2004). The population target for the

State of 2 million by 2050 is ambitious compared with the relatively low growth rate in the

past compared with other Australian cities (Nicolson et al, 2003; ABS, 2003). Recent

trends indicate that much of this growth could occur in the Adelaide metropolitan area

(ABS, 2006) as the State Government aims to prevent urban sprawl. Urban consolidation,

energy efficient buildings and the retro-fitting of existing dwellings (Bilsborough, 2006)

are possible means of mitigating increased energy consumption. This is compounded by

any requirements to adapt the performance of buildings to probable climate change

(Shimoda, 2003). The comprehensive analysis of different mitigation and adaptation

strategies is required to determine optimum outcomes for overall energy consumption.

A further target nominated by the South Australia Strategic Plan with regard to

sustainability is to limit the amount of waste going to landfill by 25% within 10 years.

More than 1000 dwellings are demolished each year in the Adelaide metropolitan area

(Burrows & McQueen, 2002) and this is a substantial proportion of all building and

24

demolition waste (EPA, 2001). The re-use and recycling of demolition waste as substitutes

for new materials represents a potential saving of embodied energy consumption.

With regard to the risks associated with climate change the Government of South Australia

has embarked on a program to reduce the State’s greenhouse gas emissions by 60 per cent

of 1990 levels by 2050. It has been the first State in Australia to introduce a Climate

Change and Greenhouse Reduction Bill (SA Govt, 2006b) which sets targets to achieve

this reduction in emissions. The strategy for achieving these targets includes goals for

buildings, transport and planning in the urban environment. Since climate change is a

global phenomenon and South Australian greenhouse gas emissions are only a very small

proportion of the world’s output, this program by itself will obviously have a negligible

effect on climate change. (Australia contributes less than 2% to global greenhouse gas

emissions (Campbell, 2006) of which a fraction is generated in South Australia).

However, it sets an example for other communities, enables South Australia to participate

in international mitigation efforts and positions the State to exploit possible opportunities

arising from climate change.

In summary, the investigation of the relationship between urban energy consumption and

the built form has been mainly at the scale of whole cities or city areas, but this has been

confined to the analysis of operational energy and transport energy. There has been little

analysis of this relationship at the smaller scale of neighbourhoods or groups of buildings,

and this has been compounded by the lack of attention given to embodied energy. A

consequence of this is that possible interactions between embodied energy and other

energy components have not been considered. For future changes in the urban

environment, the inclusion of embodied energy would provide a more comprehensive

understanding of energy consumption which could be used for informing planning and

development decisions.

2.4 The significance of embodied energy

The significance of embodied energy as part of overall energy consumption can be

assessed at the large scale of the national economy and at the smaller scale of urban areas

or individual buildings. Embodied energy is also of importance when considering the re-

use of the existing built fabric which is an area of research not previously addressed in a

quantitative manner.

25

2.4.1 Energy flows in the national economy

Focusing on the residential sector of the national economy, it is possible to compare the

total annual energy expended in the construction of new dwellings with the energy used to

operate all existing dwellings. The primary energy flows into the residential construction

sector have been calculated by Foran et al (2005) in the Balancing Act report based on

economic statistics for the national economy for 1994-95 and amount to 149PJ. A major

contributor is the energy consumed in the manufacture and supply of building materials ie

embodied energy. This compares with the primary energy consumed in operating the

existing residential dwelling stock for the same annual period of 360PJ estimated by the

Australian Bureau of Agricultural and Research Economics (Bush et al, 1997). The

primary energy flows into the non-residential construction sector (Foran et al, 2005) for

1994-95 are similar to that of the residential construction sector at 147PJ. These data

provide an indication of the significance of embodied energy. They also suggest the extent

to which the energy consumed by the built environment, as represented by the Residential

and Commercial sectors of the national economy in Figure 2.1, can be increased to include

indirect (embodied) energy inputs. Earlier research indicated that direct and indirect input

to the whole Australian construction sector in terms of embodied energy was 19.5% of

national energy consumption (Tucker et al, 1993).

2.4.2 Buildings and urban areas

At the smaller scale of individual buildings or urban areas, embodied energy can be

usefully compared with operational energy using a life cycle approach. Life cycle

assessment in a broad sense requires a detailed inventory to be made of a wide range of

impacts arising from the manufacture and supply of a product including effects from all of

the upstream processes in handling raw materials and other pre-manufactured parts (Fava

et al, 1993; Todd & Curran, 1999; Guinee, 2002; ISO 14040, 2006). This form of

assessment has been recommended to study the environmental impacts of building

materials so that the various aspects of manufacture, usage, re-use, recycling and disposal

are all considered (RMIT, 2006).

Life cycle energy analysis is a form of life cycle assessment which focuses specifically on

energy consumption, often in conjunction with an analysis of the associated greenhouse

gas emissions (Treloar et al, 2000). If applied to a building, this type of analysis adopts a

cradle-to-grave approach and considers the total energy inputs over the life cycle including

the embodied energy of construction materials and process, the operational energy over the

26

lifetime of the building and, sometimes, the demolition energy. It provides a greater

understanding of the energy inputs to the urban environment and more scope for modifying

energy consumption.

In identifying the critical factors influencing the total energy consumption of low and

medium density dwellings in Melbourne, Fay (1999) took a life cycle approach and

considered both operational and embodied energy. In the study of a small house also in

Melbourne, Treloar et al (2000) found that the embodied energy was approximately half of

the operational energy over a 30 year life cycle. This proportion increased to nearly two

thirds when the additional embodied energy arising from maintenance and repair was taken

into account. The time required for the operational energy to equal the initial embodied

energy was 14 years.

At the urban scale of neighbourhoods, Perkins (2001) compared two different styles of

housing development in Adelaide by studying samples of houses in an outer suburb at

Seaford about 40km south of the city centre and in an inner suburb at Norwood,

approximately 4km east of the city centre. In considering just the built form, it was found

that in Norwood, the split between embodied energy and operational energy was 37% and

63%, respectively. The corresponding figures for Seaford were 33% and 67% with the

higher figure for operational energy arising from higher lighting, heating and cooling

loads. Taking the two sets of results together, it can be seen that embodied energy is more

than half of the operational of the houses studied over the assumed lifetime of 50 years.

A further study in Adelaide (Troy et al, 2003) studied six small urban areas for embodied

and total operational energy consumption where the latter included estimates for the

transport energy expended by residents. The size of the embodied energy components

varied from 22.5% to 45% of the total operational energy.

The role of embodied energy in energy modelling of the built environment has been

referred to in a report by the Australian Greenhouse Office (AGO, 1999a) on greenhouse

gas emissions arising from the residential sector. Although embodied energy was not

considered in the modelling, it was mentioned as being significant being the equivalent of

as much as 20 years of operational energy. For a building with a relatively long life of 100

years, it was stated that the embodied energy for refurbishments and renovations could

amount to the equivalent of a further 20 years of operational energy ie total embodied

27

energy is 40% of operational energy. Furthermore, the report recommended a baseline

study for the embodied energy of residential buildings.

2.4.3 Embodied energy as a sunk cost

(a) The concept of embodied energy as a sunk cost

The concept of treating embodied energy as a sunk cost has been referred to by Stein

(1979) in discussing the energy value of existing stocks of buildings. He suggested that the

built environment represented an enormous investment in both dollars and energy. Stein

proposed that these sunk costs should be evaluated on the basis that urban settlements

allow the full range of human activities to take place.

In the context of cultural heritage, the World Bank (2001) relates sunk costs to cultural

patrimony:

…the key economic reason for the cultural patrimony case is the vast body of

assets, for which sunk costs have already been paid by prior generation, is

available. It is a waste to overlook such assets …

Rypkema (2005) has directly linked the argument by the World Bank to the concept of

embodied energy in the valuing of buildings as part of the stewardship and preservation of

the built environment.

These observations reflect the capital theory approach to sustainable development which

describes the interrelation of man-made, human, social and environmental capital and the

potential for substitution of one type by another (Kohler 2006, Kohler and Wang, 2007).

Pearce (2003) has commented on the large, but as yet largely unquantified, benefits of a

well designed and maintained built environment to human wellbeing and cultural identity.

(b) Comparison with sunk costs in economic models

Reference to the use of the term sunk costs in economics theory provides some background

to a comparison with embodied energy. The significance of sunk costs in economic

models is subject to some interpretation. In microeconomics theory, sunk costs are

conventionally defined as costs that have been irrevocably committed and cannot be

recovered (Wang and Yang, 2001). Hölzl (2005) distinguishes between tangible sunk

costs, that is physical capital such as buildings and machinery, and intangible sunk costs,

such as advertising and technical knowledge. In macroeconomics theory, the conventional

definitions of sunk costs are strongly disputed by Owen (2007) who claims that they are

28

inadequate and dismissive of the significance of these costs. His main criticism is that the

definitions do not consider how sunk cost evaluations might evolve over time due to

unforeseen events and contingencies.

These observations are consistent with the treatment of the embodied energy of the

existing built environment as a sunk cost. Hence, sunk embodied energy includes tangible

assets such as buildings and infrastructure as well as the associated aesthetic, cultural and

social benefits. In recognition of the possible future recovery of some construction

materials from demolished buildings, a portion of sunk embodied energy may eventually

be liberated in the form of construction materials for re-use or recycling. The

transformation of this portion of sunk embodied energy to recoverable embodied energy at

the end of the life cycle of a building is consistent with the views of Owen (2007) and the

evolutionary changes of sunk costs with time. Future costs of construction materials

(influenced by higher energy costs) and probable higher landfill disposal costs are likely to

create the conditions favourable for increased recoverable embodied energy.

(c) Valuing the existing urban environment

The use of sunk embodied energy as a proxy for the buildings, infrastructure and

associated cultural benefits offers a potentially useful method in valuing the existing urban

environment. Since these combined benefits may be difficult to define in terms of

conventional capital accounting, the sunk embodied energy would have a floating value

beyond a quantifiable minimum, where the minimum value was the future recoverable

embodied energy of construction materials destined for reuse and recycling. The

associated cultural benefits within the sunk embodied energy proxy would be represented

by the difference between the embodied energy value for buildings and infrastructure

minimum and the floating value as shown in Figure 2.3.

29

Figure 2.3 Representation of the sunk embodied energy concept

This concept is relevant to the use of embodied energy knowledge of the existing urban

environment in the selection of alternative urban development proposals. Three examples

are discussed which explain this in the context of life cycle energy consumption.

Example 1. Green field site.

With the example of a green field site development, there is no direct input of embodied

energy from the existing urban environment. Total life cycle energy consumption consists

of the operational and embodied energy components as shown in Figure 2.4. However, the

study of energy consumption in the various types of existing residential urban form can

assist in the selection and design of green field developments eg high or low density, low

rise or high rise dwelling forms.

Increasing Embodied Energy

Sunk embodied energy

Associated cultural benefits

Embodied energy of buildings and

infrastructure

Future recoverable embodied energy of building materials

Minimum value

Floating value

30

Figure 2.4 Comparison of the energy consumption of three development proposals

Example 2. Redeveloped site with recycled materials.

This example consists of an older urban area which is redeveloped as a new residential

suburb. Some of the materials generated from demolition activities are re-used or recycled

in the new buildings and infrastructure. This is shown as a lowering of embodied energy

in the form of an energy credit and a subsequent reduction in total life cycle energy

consumption. A knowledge of the embodied energy of materials in older buildings and

infrastructure is required for this analysis.

Example 3. Redeveloped site with the use of infrastructure and recycled materials.

This development is similar to example 2 except that it retains some of the existing

buildings and infrastructure and uses some infill construction. This increases the energy

credit for reused materials and infrastructure. Furthermore, it provides an additional

dimension of the cultural value of utilising existing built form and this is indicated as a

further benefit. For convenience, this benefit is shown as the downward dotted arrow as it

contributes to making the particular redevelopment a more attractive proposition from both

energy consumption and cultural benefit perspectives. A knowledge of the embodied

energy of materials in older buildings and infrastructure is also required for this analysis.

Life

cyc

le e

nerg

y co

nsum

ptio

n of

dev

elop

men

t

Redeveloped site with recycled

materials

Embo

died

ene

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Ope

ratio

nal e

nerg

y

Green field site

Energy credit due to recycled materials

Tota

l ene

rgy

Ope

ratio

nal e

nerg

y

Tota

l ene

rgy

Ope

ratio

nal e

nerg

y

Redeveloped site with use of infrastructure

and recycled materials

Tota

l ene

rgy

Energy credit due to recycled materials and use of infrastructure

Cultural benefits

31

The use of embodied energy as a proxy for sunk costs and associated cultural benefits has

not previously been considered in quantifying the merits of urban development proposals

and this research makes an advance in this concept.

2.4.4 Embodied energy in re-used and recycled materials

The re-use and recycling of construction materials from the demolition of buildings and

infrastructure represents an energy flow in the built environment. By using these materials

as substitutes for new materials, a significant reduction in the embodied energy input to

new urban developments in Australia is possible (Tucker et al, 1993). As part of a

scoping study for the Commonwealth Government into improving the environmental

sustainability of building materials, the importance of re-use and recycling activities has

been emphasised (RMIT, 2006). The cradle-to-cradle concept (Ness & Field, 2003) based

on cyclical flows of materials and products is considered to be a more sustainable model

for manufacturing and construction.

Research into mass and energy flows in the building stock has been suggested by Kohler et

al (1997) in a German context. The value of such an analysis is to predict the

environmental impact of new and recycled building materials. This type of research is

particularly relevant for rapidly developing economies where environmental degradation is

an unfortunate consequence of intense construction activity (Yang and Kohler, 2005).

It has been estimated that between 150 and 250 tonnes of waste materials are generated

during the demolition of a typical house in Australia and part of this contributes to all

waste from building and demolition activities which creates more than half of landfill

waste (EPA, 2001). Tucker et al (1993) found that the ratio of houses demolished to

houses constructed in Australia over the 1954-1991 period was 1:8 and that an average

recovery rate of building materials of up to 52% was indicated. For all demolished

buildings, the rate of recovery was found to be higher for metals although substantial

proportions of lower value materials such as concrete are ‘down-cycled’ into uses such as

roadbase (RMIT, 2006).

Information on the embodied energy of the built form would provide a means of

accounting for energy flows from re-used and recycled materials as part of overall energy

analysis in the urban environment. In addition, the process of deriving the embodied

energy would require a knowledge of the materials quantities in existing buildings and

32

infrastructure, and this could be used to form an inventory of materials stocks to predict

their future availability for re-use, recycling and landfill.

2.5 Knowledge gaps

There are identifiable gaps in the knowledge required to comprehensively analyse energy

consumption in the urban environment and this is an impediment to efforts aimed at

minimising overall energy expenditure. Previous research on energy consumption in the

urban environment has mainly focussed on operational energy consumption of buildings

and the improvement of their energy efficiency as well as transport energy. In recent

years, studies have been carried out on embodied energy as part of life cycle energy

consumption but this has mainly related to individual buildings. There is currently no

convenient way to represent the embodied energy of residential areas (including

infrastructure) in the urban environment which demonstrates the significance of this

component of energy consumption in relation to other energy inputs.

A model which represents the embodied energy of residential areas in an urban

environment would contribute to knowledge in the following ways.

(a) Provide a comparison of modern dwelling configurations.

This applies to newer residential developments and uses the existing urban environment to

inform future development decisions. Different examples of dwelling configurations eg

apartments, town houses, detached house would be compared on the basis of life cycle

energy consumption. The embodied energy of the developments would form a baseline

upon which other components of life cycle energy consumption would be added. This

would enable a comparison of the energy consumption of existing residential areas with

different built configurations to be made. Such comparisons would offer useful guidance

for the design of future residential developments with lower energy consumption.

(b) Offer guidance on sunk costs

The evaluation of sunk costs in terms of embodied energy would provide a means of

valuing the existing built form. This information would be of use when comparing

alternative new urban developments where at least one proposal was based on existing

infrastructure. Urban development proposals utilising existing built infrastructure would

attract an energy credit based on the proposed embodied energy model.

(c) Assist the estimation of additional embodied energy

The embodied energy used to initially construct the built form can be used as a guide to

estimate future additional embodied energy for maintenance, repair and refurbishment.

33

This would be of interest in comparing future scenarios for the redevelopment of existing

suburbs. In the ‘business as usual’ scenario (ie no redevelopment), future energy

consumption would include additional embodied energy which would need to be included

in any comparison with alternative development scenarios.

(d) Quantify the potential for re-use and recycling of materials

A knowledge of the embodied energy of the existing urban environment would provide

information on the quantity of energy to be credited for the re-use and recycling of

materials in new development projects.

(e) Provide a means of compiling a materials inventory

It is necessary to quantify materials in buildings and infrastructure in order to evaluate their

embodied energy. Hence, a model of the embodied energy of the existing urban

environment would provide a means of compiling an inventory of materials stocks for

future re-use.

2.6 Representation of energy consumption in the urban environment

The method to comprehensively represent energy consumption in the urban environment

must have the ability to store and manipulate datasets corresponding to large numbers of

buildings. It should also be able to combine the different energy components consisting of

the embodied energy of buildings and infrastructure, operational energy and transport

energy.

The derivation of energy consumption data for these three main components is problematic

and a basic unit of scale must be selected on which the datasets can be assembled.

Research on embodied and operational energy suggests that some data, albeit very limited,

may be available at the scale of individual buildings. Hence, this research is based on the

assembly of data at that scale. Furthermore, as embodied energy is the first energy

component to be expended in the life cycle of the built form, the research is aimed at

providing a baseline of this energy expenditure upon which other components can be

added.

By themselves, datasets are inconvenient in presenting information in a format required for

a comprehensive analysis of energy consumption in the urban environment. They do not

easily allow for the assembly of the different components of energy consumption data

within urban areas such as neighbourhoods or suburbs. For instance, an estimate of

transport energy used by suburban residents may be achieved by analysing ‘journey to

work’ data from surveys by the Australian Bureau of Statistics. This information is

34

available at the scale of census collectors districts not individual dwellings. Furthermore,

the inclusion of the embodied energy of urban infrastructure such as roads requires an

urban area focus. In addition, datasets do not conveniently allow for the delineation,

comparison and observation of variability of energy consumption in different urban areas.

The provision of a spatial dimension to datasets overcomes these disadvantages and

provides additional benefits. Data maps are a powerful method for displaying statistical

information which direct attention to the substantive content of the data and allow for the

consideration of overall patterns as well as the detection of fine detail (Tufte, 2001). They

enable a layering and separation of information using colour and other devices which

conveys a large volume of data in a small space (Tufte, 2003). The production of data

maps can be accomplished using geographical information systems (GIS) that link

information to geography and allows the visualization and analysis of data. The potential

uses of spatially based data for recording and analyzing issues in urban planning was

described in the early 1990s (Huxhold, 1991) and it has taken some time for its more

widespread use. More recently, Okpala (2001) was advocating the greater use of GIS for

the effective planning of development of cities to achieve sustainability. He confirmed that

GIS was a powerful tool in decision making processes which integrated environmental,

social and economic factors in urban management.

The advantages of using GIS to represent energy consumption has been recognized by

Jones et al (2000; 2001) who developed a tool which assists in predicting the effect of

planning decisions on energy usage at the level of a whole city. The tool was developed

for the city of Cardiff in Wales and is known as the Energy and Environmental Prediction

model. It depicts building energy consumption down to the level of individual post-codes

which, typically, contain 10-15 houses. Domestic energy use was estimated using a

method whereby each house was categorised according to one of 20 types based on

dimensions, age and built form. The method assumes that the energy consumption of

houses in any given category is broadly the same. Hence, having ascribed each dwelling to

a category, the total domestic energy use and greenhouse gas emissions could be estimated.

One use of this model is to estimate the effects on energy consumption of retrofitting the

existing housing stock. Alternative retrofitting measures include double glazing, draught

proofing and improvements in insulation.

35

A more recent development is the use of GIS for depicting greenhouse gas emissions for

residences in Oxford, UK (Gupta, 2005). This research is able to display hot spots of

energy use and CO2 emissions on an individual dwelling level and then aggregate these to

an urban scale – street, district or city level. The next step in the use of this technology is

the analysis of the various options for greenhouse gas reductions and the development of

strategies to bring about these improvements. In terms of a comprehensive energy analysis

which considers the whole life cycle of residential buildings, a shortcoming of both the

Cardiff and Oxford research is that the embodied energy of the houses is not considered

and not depicted in the form of GIS maps.

Research aimed at mapping energy consumption including embodied energy has been

attempted in the city of Toyohashi in Japan (Matsumoto et al, 2006). The authors initially

report on embodied energy intensities (per square metre of floor area) of certain non-

residential buildings including retail stores, education institutional buildings and a hospital.

In 1995, department stores showed the highest embodied energy at 3.91 GJ/m2 followed by

convenience stores. However, the high ranking of the convenience stores was linked with

24 hour opening and illumination which is confusing as this would relate to operational

energy not embodied energy. The embodied energy intensities are also shown to increase

significantly between 1995 and 2000 which is not explained. A model of energy

consumption for the city is described comparing 2003 with 2053 and based on population

changes, variations to the energy supply system and new house construction in both the

central and suburban areas. The city was divided into cells and the energy consumption in

these cells was estimated using the model. Examples of the mapping of total energy

consumption suggest a reduction in the central area with little change in the suburb over

the 50 year period. There was no differentiation in the mapping between operational

energy and embodied energy or the significance of the latter. A possible difference

between future projections for Japanese cities compared with Australian cities is the effect

of substantial migration to the latter which has been a historical feature in urban

development in Australia.

2.7 Summary

The review of literature suggests that embodied energy is a significant part of total energy

consumption in the built environment. Although research has been carried out on the

embodied energy of individual buildings, this has not generally been applied to urban areas

36

such as residential suburbs, nor has it been integrated with other components of urban

energy consumption. An understanding of the embodied energy of the existing built form

can inform decision making on the planning and development of residential areas in the

future. A comprehensive analysis of energy consumption would be greatly assisted by a

model which spatially depicts embodied energy and forms a baseline upon which other

urban energy components can be added.

37

Chapter 3 Embodied Energy 3.1 Introduction

It has been argued in Chapter 2 that an understanding of the embodied energy of the

existing built form can inform decision making on the planning and development of

residential areas. This chapter focuses on the methods used to derive the embodied energy

of construction materials used in dwellings based on input-output analysis and other

datasets (Objective 2 stated in the outline of this thesis). Values for the embodied energy

of materials can then be used to estimate the embodied energy of dwellings and related

infrastructure. This is the first step in the development of a model for the spatial

representation of the embodied energy of residential areas in the urban environment.

3.2 Background

Post Second World War research into the thermal performance of buildings in Australia

was driven initially by thermal comfort considerations (Williamson, 1997). The

subsequent development of thermal simulation models enabled quantitative links with

energy consumption to be made. In the 1970s, attention became focused on energy

consumption as supplies of crude oil became disrupted arising from the Arab oil embargo

and Middle East war. Although factors affecting the operational energy of buildings

became an obvious area of research, the embodied energy of the built environment did not

go unnoticed. Researchers at the Division of Building Research at the CSIRO undertook

some calculations on the embodied energy of a typical house (at the time the term ‘capital

energy’ was more usual to describe embodied energy). Hill (1978) estimated that over the

lifetime of a house, the operational energy consumed in the house would exceed the

embodied energy by over 8 times. They concluded that efforts to minimize energy

consumption in the built environment would be better spent reducing operational energy

rather than embodied energy.

Research into embodied energy in construction was also being carried out in other parts of

the world with a significant contribution from Stein et al (1976) who surveyed energy used

in the U.S. construction industry using input-output analysis. Input-output analysis is

described in the next section. They found that 11.1% of national energy consumption

could be attributed to the construction industry. In the United Kingdom, Boustead and

Hancock (1979) published the Handbook of Industrial Energy Analysis which provided

information on the amount of energy required to manufacture a wide range of industrial

38

materials. In New Zealand, Baird and Chan (1983) found that the construction industry

consumed about 10% of national energy.

In Australia, research in the embodied energy of construction and buildings resumed in the

early 1990s as a result of the possible effect of greenhouse gas emissions from the

manufacture of construction materials on the environment (Tucker et al, 1993). Using

input-output analysis, Tucker and Treloar (1994) estimated that the proportion of national

energy consumed by the construction industry was considerably higher at about 20%. One

of the difficulties for researchers working in this area was the lack of reliable data on

embodied energy. To assist with this problem, Lawson published embodied energy data

for certain types of floor, wall and roof construction (Lawson, 1996) based on typical

values for the embodied energy of the component materials.

3.3 Methods for deriving embodied energy coefficients

The embodied energy of a material or component is the energy consumed in its production

including upstream activities such as raw material extraction, transport, manufacturing and

assembly. The embodied energy coefficient of a construction material is the quantity of

energy used in its production per unit and can be expressed in a variety of ways such as

MJ/kg, GJ/tonne, GJ/m3, etc. When expressed in the same way, comparisons can be made

between materials to select lower energy alternatives.

A classification of energy analysis was defined by the International Federation of Institutes

of Advanced Study (IFIAS, 1974) consisting of 4 levels which depended upon the energy

boundary stipulated and this can be used as a basis for considering embodied energy. A

Level 1 analysis considers only the direct energy consumed in a manufacturing process.

Level 2 encompasses the energy consumed in the preparation and transportation of raw

materials and other pre-manufactured inputs to the process. Higher levels (up to the

maximum of Level 4) consider other indirect energy inputs to a process such as the

primary energy required to supply delivered energy inputs and energy required to fabricate

plant and other process infrastructure.

3.3.1 Process analysis

The direct energy consumed in a manufacturing process (Level 1) can be determined by

process analysis which consists of an energy audit at the manufacturing location of a

product. Hence, for a Level 1 process analysis, the energy boundary will be the factory

39

fence and it is the energy consumed within this boundary which is calculated. For some

manufacturing processes such as fired clay products eg hard burnt bricks and terra cotta

roof tiles, the direct energy (Level 1) can amount to more than 50% of the total energy

consumed (sum of Levels 1, 2, 3 and 4). With other products such as window frames or

ready-mixed concrete, the direct energy is a much lower proportion due to large energy

inputs in upstream raw material manufacture. Hence, a Level 1 process analysis for a

product is substantially incomplete and requires further process analysis of upstream inputs

which becomes progressively more complex beyond Level 2. This results in a significant

disadvantage to the use of embodied energy coefficients derived from process analysis as

data from different sources may have different and uncertain energy boundaries.

Conversely, the advantage of process analysis is that it can be reasonably accurate and

specific to a particular product studied.

3.3.2 Input-output analysis

Input-output analysis is an alternative method for estimating the embodied energy of a

product and depends upon the use of national economic data representing all of the

economic transactions between the industrial sectors and includes imports. This approach

to the analysis of economic interrelationships is based on the original work by Leontief

(1941). Some time later, Bullard and Herendeen (1975) used input-output energy analysis

as a tool for determining the potential for energy savings in the US economy by means of

the substitution of products and services. In Australia, James (1980) developed a model of

the economy using input-output data to study the effects of energy supply variations on

particular industries and related environmental issues. Lenzen (1998) has used input-

output analysis to investigate energy and greenhouse gas flows in the Australian economy

and has developed this further (Lenzen, 2003) to assist in environmental policy design and

analysis. More recently, the application of input-output data has been extended to include

the capital input to products (Lenzen and Treloar, 2004) to provide a more complete

analysis.

Input-output tables for the Australian economy are produced every few years by the

Australian Bureau of Statistics in the form of a matrix of industrial sectors in rows and

columns normally just exceeding 100 x 100 sectors. The input-output tables based on

1996/97 data consisted of a 106 x 106 matrix and were available in the form of Direct

Requirement Coefficients (ABS, 2001a). The direct fiscal inputs of each of the sectors

(rows) are given for a unit output of each of the 106 sectors (columns). Four of the sectors

40

represent the energy supply to the economy, namely sectors 1100 Coal, oil and gas; 2501

Petroleum and coal products; 3601 Electricity supply and 3602 Gas supply. The fiscal

inputs of the four energy sectors to the 106 industrial sectors can be converted to energy

inputs by means of average energy costs making it possible to convert the direct input-

output matrix to a direct energy matrix. The mathematical inversion of direct input-output

matrices to provide total input-output matrices is well established (Leontif, 1966). This

technique enables all of the indirect inputs as well as the direct inputs to be included.

Hence, this approach can provide total energy requirements for particular industrial sectors

which is the equivalent to the sum of Levels 1, 2, 3 and 4 as classified by the International

Federation of Institutes of Advanced Study. These energy requirements are known as

energy intensities (MJ/$) and indicate the amount of energy used for every dollar output of

each industrial sector. Since, certain industrial sectors manufacture particular types of

construction materials (for example sector 2602 Ceramic products is dominated by the clay

brick manufacturing industry), the energy intensity for that sector can be used to evaluate

an embodied energy coefficient for hard burnt clay bricks using an average price for the

product.

An alternative technique for transforming a direct energy matrix to a total energy matrix is

to use the expansion of powers method (O’Connor and Henry, 1975). This is an iterative

process that calculates successive orders of indirect energy inputs. The sum of these

indirect energy inputs in combination with the direct energy inputs is similar to the results

of the matrix inversion providing sufficient rounds of calculations are undertaken,

normally about 8 stages (Miller and Blair, 1985). An advantage of the expansion of

powers method is that data can be scrutinized at each iteration. The relationship of the

expansion of powers and matrix inversion techniques is shown in Appendix 1a.

A range of embodied energy coefficients can be derived using input-output data which has

the advantage of including all levels of energy input resulting in a more ‘complete’ system

compared with process analysis. Furthermore, the coefficients represent national averages

for embodied energy for any given material which avoids the possibility of using

unrepresentative process analysis data from particular manufacturing plants or spurious

process analysis data where the definition of the energy boundary is uncertain.

41

3.3.3 Disadvantages of input-output analysis

There are a number of potential sources of error arising from input-output analysis

originating from the nature and manipulation of the source input-output data, energy costs

and materials prices. A previous study of these errors has estimated that embodied energy

coefficients derived from input-output analysis could be erroneous by as much as ± 20%

(Pullen, 1996).

Energy costs

The method described for deriving embodied energy coefficients uses national average

costs or tariffs for energy. In reality, building materials producers may pay different, and

possibly, lower energy prices which would have the effect of raising embodied energy

coefficients.

Materials prices

The most likely sources of error in building materials prices are associated with those

which were unavoidably derived from current prices (as opposed to those obtained from

1996/97 publications) and converted to 1996/97 values (which is the period when the

input-output data was compared) using building materials price indices.

Input-output data

Common potential sources of error when using the input-output data arise from the

phenomenon of double counting, homogeneity and proportionality (ABS, 2000). Double

counting refers to the inclusion of inter-energy transactions when energy sector

contributions to the relevant building materials sectors are calculated. This particular

source of error is dealt with further in the next section of this chapter and its minimisation

described.

Homogeneity in input-output analysis refers to the extent that the industrial sectors of the

national economy produce a single output and have a single input structure. In the

calculation of embodied energy coefficients, potential errors can arise from the assumption

of homogeneity ie that the energy intensity derived for any industrial sector is

representative of all of the sub-groups within that sector. Proportionality relates to whether

the change in output of an industry causes proportional changes in the amounts of

intermediate and primary inputs.

42

An assessment of potential error in estimating embodied energy in this research is provided

in the context of three case studies presented later in Chapters 5, 6 and 7.

3.3.4. Variations in input-output data

Variations in input-output data can arise from the methods used to harvest data from the

various industrial sectors. Lenzen (2001) reports on a personal communication with the

Australian Bureau of Statistics in 1999 where the standard error of source data items was

reported to be mostly in the 15% to 30% range, with the lowest at just over 1% and the

highest at 58%. The Australian Bureau of Statistics publishes input-output tables at

various cycles ranging from annual to triennial where the data collected refers to an annual

period of economic activity about 3½ years prior to the publication date. A longitudinal

comparison of energy intensities for particular industrial sectors derived using input-output

analysis over 4 rounds of publication serves to highlight possible variations in the data.

Table 3.1 Particular industrial sectors and their energy intensities

Table 3.1 lists 15 industrial sectors which are associated with the manufacture and supply

of construction materials and products. The energy intensities have been evaluated using

input-output analysis based on Table 9. Direct Requirement Coefficients for 1986/87,

1992/93, 1993/4 and 1996/97 (ABS, 1990, 1996, 1997 and 2001a) using the same method

and sources of supplementary data. Energy prices were derived by combining energy

supply data from the Australian Bureau of Agricultural and Resource Economics (Bush et

al, 1999) with the fiscal value of the various energy sectors given by the relevant Product

Code Industrial sector Energy Intensity (MJ/$) 86/87 92/93 93/94 96/97

1400 Other mining 24.8 10.4 14.7 11.6 2202 Textile products 36.0 27.4 24.9 19.0 2301 Sawmill products 35.0 23.7 19.8 21.6 2302 Other wood products 54.0 35.7 40.9 41.1 2503 Paints 67.6 46.3 62.0 37.0 2509 Plastic products 52.4 38.6 70.4 49.0 2601 Glass and glass products 38.6 25.6 17.7 15.6 2602 Ceramic products 45.1 29.6 25.6 23.2 2603 Cement, lime and concrete slurry 65.7 33.0 32.8 41.0 2604 Plaster and other concrete products 115.0 52.1 58.6 39.8 2605 Other non-metallic mineral products 40.3 21.5 15.2 18.7 2701 Iron and steel 24.7 12.8 12.4 18.2 2702 Basic non-ferrous metal and products 24.8 10.4 14.7 11.6 2704 Sheet metal products 36.0 27.4 24.9 19.0 2807 Household appliances 35.0 23.7 19.8 21.6

43

Details data from input-output tables (ABS, 2001b). The method used for combining the

data was the same for each period of input-output tables. The comparison of energy

intensities of relevant industrial sectors rather than embodied energy coefficients for

particular construction materials eliminates the use of materials prices and any associated

errors. The detailed explanation of the derivation of energy intensities and embodied

energy coefficients is given in the next section of this chapter.

Figure 3.1 shows the energy intensities of the selected industrial groups in a graphical form

indicating that some materials are susceptible to change with different datasets. There is

no increasing or decreasing trend of energy intensity with the passage of time over the ten

year period from 1986/87 to 1996/97. It may be inferred that the energy intensities based

on 1986/87 input-output data were exceptional with subsequent sets of data showing much

less variation. Although these differences may be significant for the comparison of

embodied energy coefficients of particular materials over time, their effect is minimised

when considering the embodied energy of whole buildings where there is a substantial mix

of different materials.

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

1400

2202

2301

2302

2503

2509

2601

2602

2603

2604

2605

2701

2702

2704

2807

Industrial Sector Code

Ener

gy In

tens

ity (M

J/$)

86/8792/9393/9496/97

Figure 3.1. Energy intensities for selected construction materials

This raises a further issue that is relevant to the estimation of the embodied energy in the

urban environment where existing buildings are composed of construction materials and

44

components which were supplied and manufactured over a period of time. In the case of

Australian cities this period spans up to over 200 years. This research derives embodied

energy coefficients based on input-output data collected in 1996/97 and applies them to the

existing built form. No attempt has been made to source data relevant to the manufacture

of materials over this period simply because such information required to develop historic

embodied energy coefficients is largely unobtainable. It has been necessary to assume that

the energy required to manufacture a given product in the past is similar to that required in

the present.

There are contradictory historical trends in the manufacture of products including building

materials. On the one hand, manufacturing processes have become more automated and

energy intensive whilst on the other hand, those processes have been subject to continuous

improvement with gains in energy efficiency. The energy intensity (amount of energy

required to produce one unit of output eg MJ/$) of goods and services in the Australian

economy has been studied over a 25 year period until 2000-01 (Tedesco and Thorpe,

2003). After eliminating the effects of structural change (the trend from manufacturing to

services industries) and fuel mix (the change from solid and liquid fuels to electricity and

gas) in the national economy, it was found that any technical improvements in energy

intensity due to technological advances or operational changes had been small in many

industrial sectors over the period.

The approach taken in this research is to estimate the energy embodied in the built

environment at contemporary levels. Put simply, the embodied energy estimates carried

out are the equivalent of the energy cost of replacement. This avoids the difficulty of

estimating historic embodied energy coefficients for materials manufactured in the past.

Replacement energy cost allows for the comparison of different existing built forms to

inform decisions about new developments. It also provides an accounting basis for the use

of existing built infrastructure and recycled construction materials where the embodied

energy of new materials would be avoided.

The use of replacement energy cost has direct parallels with the financial valuing of

buildings and infrastructure where the latter is often owned by governments entities.

Under the ‘deprival value’ approach, assets are valued according to the loss that might be

expected in the case of a government entity being deprived of the service potential of the

assets.

45

Thus the value to the entity in most cases will be measured by the replacement

cost of the services or benefits currently embodied in the asset, given that the

deprival value will normally represent the cost avoided as a result of

controlling the asset and that the replacement cost represents the amount of

cash necessary to obtain an equivalent or identical asset.

(Commonwealth of Australia, 1994).

3.3.5 Hybrid analysis

The combination of process analysis data with that derived from input-output analysis

overcomes many of the disadvantages of both types of analysis and was proposed by

Bullard et al (1978) and is the subject of more recent research in Australia (Treloar, 1997;

Lenzen & Dey, 2000). It offers a greater accuracy in evaluating embodied energy

coefficients as a result of the process energy component as well as the ‘completeness’ of

the analysis afforded by the input-output component.

Two types of hybrid analysis have been defined by Treloar et al (2001a) and later by

Crawford (2005) which are process based and input-output based. Process based hybrid

analysis uses a knowledge of the material inputs to a product as the framework for the

estimating its total embodied energy. The embodied energy of the individual material

inputs are calculated from process analysis for the direct energy (where known) and from

input-output tables for the indirect energy. These are summed in proportion to the material

quantities in the product. The quality of this process data is crucial and should include all

Level 1 energy inputs but no other higher level inputs. The energy of manufacture (direct

energy into the product) can also be estimated from input-output analysis and added to the

total. Clearly, this type of analysis is likely to be more complete for less complex

products.

Input-output hybrid analysis uses the energy intensity (from input-output analysis) of the

industrial sector relevant to the product subject to analysis as the framework for estimating

the total embodied energy. For instance, the energy intensity for the Residential

Construction Sector (ANZSIC group 4101) is used when estimating the embodied energy

of a house. The inputs to this sector of the economy are extracted from the input-output

model using a technique developed by Treloar (1998) and, once identified, the indirect

energy paths are replaced with data derived from process based input-output analysis. This

46

results in a more complete analysis where the total embodied energy can be as much as

twice that derived from process analysis or input-output analysis (Crawford, 2005).

3.3.6 Other approaches

A criticism of the methods described so far is that they are deterministic and present mean

values for the embodied energy of building materials. It is known that different plants

producing the same products can consume different amounts of energy for the same output

depending on a number of factors including the efficiency and age of the manufacturing

plant used and the extent of waste production and its re-use. For example, in a study of

two softwood timber mills at the same South Australian location, one plant consumed

twice as much non-renewable energy for the same output as the other (Pullen, 2000). This

was due to different steam raising and drying practices, materials handling techniques and

amounts of waste timber that are recycled. With construction products which are

manufactured in quite a number of locations, the deterministic approach does not give any

information about the uncertainty or distribution of individual embodied energy

coefficients.

Shipworth (2002) has proposed the use of a stochastic modelling framework using

statistical techniques based on probability distributions. The advantage of this approach is

that it is more suitable to the monitoring of improvements in fossil fuel consumption and

greenhouse gas emissions which is important for those countries who have agreed to

comply with the requirements of the Kyoto protocol. The establishment of baselines for

greenhouse gas emissions and the monitoring of reductions in emissions are an important

part of the protocol compliance. Stochastic data on greenhouse gas emissions resulting

from product manufacture (ie embodied energy) can indicate which industries have a larger

variability in emissions and would benefit from government policies encouraging better

production technologies.

Other researchers have used various statistical methods to estimate embodied energy

coefficients for particular construction materials. In some cases this has amounted to

collecting coefficients from a wide variety of sources and using average values for

particular materials (Lawson, 1996). The importance of ensuring that data is comparable is

emphasised by Hammond and Jones (2006) who have assembled a large database of 1400

records of embodied energy and carbon emissions values for materials in the UK. They

argue the preference for ‘cradle to site’ rather than ‘cradle to factory’ data and provide

47

minimum and maximum values for the embodied energy coefficient of each material as

well as an average value.

3.4 Derivation of embodied energy coefficients

The embodied energy coefficients for construction materials used in this research were

derived using a relatively simple form of hybrid analysis. This commenced with the

evaluation of the indirect embodied energy used to manufacture the product which was

estimated from the difference between embodied energy intensities from the total and

direct input-output data. The second step was to determine reliable direct embodied energy

from process analysis. The quality of this data is crucial and should include all Level 1

energy inputs but no other higher level inputs. The hybrid embodied energy coefficient

can then be calculated for a particular material by combining both the direct and indirect

components.

The input-output analysis was based on the most recently published data at the

commencement of the research, namely input-output tables for the year 1996/1997 (ABS,

2001a). This provided the indirect energy component of the embodied energy coefficients.

The direct components of the embodied energy coefficients of the most common

construction materials were obtained from process analysis. The following subsections

describe the development of the input-output and process analysis methods and the final

derivation of embodied energy coefficients used in this research.

3.4.1 Input-output analysis for indirect energy component

The starting point for deriving the indirect energy components of embodied energy

coefficients based on input-output analysis in this research is the Australian National

Accounts. Input-Output Tables (ABS, 2001a), specifically Table 9. Direct Requirements

Coefficients 1996-97 (see Appendix 1b for further information). The principles of this

method using previous input-output tables have been described earlier by Treloar (1994)

and Pullen (1995). Firstly, the direct energy intensities for the four energy sectors, 1100

Coal, oil and gas; 2501 Petroleum and coal products; 3601 Electricity supply and 3602 Gas

supply are calculated across the 106 output columns which represent the various sectors of

the Australian economy. Each direct requirement coefficient representing an energy input

is divided by the average energy cost and converted to a primary direct energy intensity by

multiplying by a primary energy factor. These are partial primary direct energy intensities

48

arising from each energy sector. The total primary direct energy intensity for each sector

output is then found by summing the four partial energy intensities.

DEI = (DRC x PEF1100) + (DRC x PEF2501) + (DRC x PEF3601) + (DRC x PEF3602) P1100 P2501 P3601 P3602

Where DEI is the total primary direct energy intensity for a

particular sector output (in MJ/$).

DRC is the direct requirements coefficient for that particular

sector output (in $/$100).

PEF is the relevant primary energy factor.

P is the relevant average energy cost (in c/MJ).

Average energy costs for the period 1996/1997 were derived by combining energy supply

data from Table D1 of Australian Energy: Market Development & Projections to 2014-15

produced by the Australian Bureau of Agricultural and Resource Economics (Bush et al,

1999) with the fiscal value data of the various energy sectors given by the relevant Product

Details data from 1996/97 input-output tables (ABS, 2001b). In the case of the energy

sector 1100 Coal, oil and gas, there is a slight mismatch between the energy products

described in the two data sources and selected figures must be used to ensure consistency.

The average energy costs are shown in Table 3.2. The primary energy factors indicate the

amount of primary energy used to manufacture, distribute and supply a unit of delivered

energy to the consumer. To be compatible with the input-output data, they must be

representative of the national economy as a whole and Table 3.2 summarises the primary

energy factors used which were developed by Treloar (1998). These factors are used in

combination with the elimination of inter-energy sector transactions to avoid the ‘double

counting’ of energy inputs. Due to the classification of energy supply sectors in the input-

output tables, there is potential for the double counting of energy inputs. This is avoided

by setting all coefficients in the direct requirements matrix which relate to purchases by the

energy supply sectors to zero.

49

Table 3.2 Average energy costs and primary energy factors

Energy sector Energy value

($million)

Energy supply

(PJ)

Average energy price

(c/MJ)

Primary energy factor

1100 Coal, oil and gas 12478.3 4316.6 0.29 1.2 2501 Petroleum and coal products 10650.6 1686.8 0.63 1.4 3601 Electricity supply 14808.9 718.1 2.06 3.4 3602 Gas supply 1552.9 823.3 0.19 1.4

A 106 by 106 energy matrix is created by multiplying each row of the direct requirements

coefficients by the relevant total primary direct energy intensity (DEI). The sum of the

columns of the energy matrix gives the first indirect energy intensity for each industrial

sector. A second energy matrix is then created by multiplying each row of the indirect

requirements coefficients by the relevant first indirect energy intensity. The sum of the

columns of the indirect energy matrix gives the second indirect energy intensity for each

industrial sector. This iterative process calculates successive orders of indirect energy

inputs and is known as the expansion of powers method. To ensure that the method

converged with that of matrix inversion, 12 rounds of calculations were undertaken. The

total energy intensity (TEI) for each economic sector was determined from the summation

of the direct energy intensity and the 12 indirect energy intensities. Due to the size of the

matrices involved, it is necessary to manipulate data using a computer spreadsheet

software such as Microsoft Excel. A summary of this process is shown in Appendix 1c.

With input-output analysis, the next stage in deriving embodied energy coefficients (in

MJ/kg or GJ/tonne) is to multiply the total energy intensity (in MJ/$) of a particular sector

with a cost (in $/tonne) of the construction material. For instance, the total energy

intensity for the sector ‘2602 Ceramic products’ is multiplied by a national average price

for bricks in 1996/97 corresponding to the year of formation of the input-output tables.

The prices for materials were taken from either Rawlinson’s Australian Construction

Handbook (Rawlinson, 1997) or Cordell’s Building Cost Guide (Cordell, 1997). For a

minority of materials which were not included by either of these two price guides, a local

merchants price was taken and converted to a 1996/97 price by means of Rawlinson’s

historical cost factors. Hence, a series of embodied energy coefficients can be derived

based solely on input-output analysis. However, the method used in this research uses

hybrid embodied energy coefficients where the direct energy is derived from process

analysis and the indirect energy from input-output analysis. For this purpose the difference

between the total energy intensity and the direct energy intensity (ie the indirect energy

50

intensity) is multiplied by the material cost. This indirect embodied energy is then

combined with the direct embodied energy (from process analysis) to form a total

embodied energy coefficient.

EEM = EED + [(TEI – DEI) x CM]

where EEM is the embodied energy coefficient of a material.

EED is the direct embodied energy of a material from process

analysis.

TEI is the total energy intensity of the material from input-

output analysis.

DEI is the direct energy intensity of the material from input-

output analysis.

CM is the cost of the material.

3.4.2 Process analysis for direct energy component

Since the research model is based on the Adelaide built environment, information on the

manufacture of construction materials in South Australia has been sought. This is possible

for materials where there is a significant local manufacturing base such as with clay brick

manufacture and ready mix concrete production. Other common materials such as ceramic

tiles are imported to the state and information has been obtained from likely suppliers.

Details of the direct energy used in the manufacture of certain construction materials from

process analysis is shown in Appendix 2 along with the source of this information and the

final derivation of the input-output hybrid embodied energy coefficient. In most cases,

information on process analysis was used which was relevant to the period of the input-

output data. For instance, data for timber was based on a process analysis carried out in

South Australia in 1997 (Pullen and Varley, 1997) and data for some non-locally

manufactured materials was drawn from the Life Cycle Inventory developed by the Centre

for Design at the Royal Melbourne Institute of Technology (RMIT, 1998).

The data in Appendix 2 is summarized for convenience in Table 3.3 which shows the

embodied energy coefficients of common construction materials derived from the hybrid

analysis. The direct component of the embodied energy coefficient is taken from process

analyses and the indirect component from the product of the price and indirect embodied

energy intensity. A number of embodied energy coefficients were evaluated for concrete

51

corresponding to 20, 30, 40 and 50MPa compressive strength and details of these are given

in Appendix 2.

Table 3.3 Summary of hybrid embodied energy coefficients for common materials

Material/product Embodied energy intensity (MJ/$)

Price ($/kg)

Embodied energy coefficient (MJ/kg)

Direct Indirect Direct Indirect Total Clay brick 33.7 7.8 0.09 4.8 0.7 5.4 Concrete (20MPa) See Appendix 2 for details 0.1 2.3 2.4 Steel 27.2 15.6 1.38 33.3 22.2 55.5 Timber 4.2 7.1 1.93 8.8 13.8 22.6 Concrete products 3.0 13.8 0.27 0.8 3.7 4.5 Carpet 11.1 8.8 17.00 62.8 149.5 212.3 Appliances 2.0 17.1 17.08 9.0 292.1 301.1 Plasterboard 3.0 13.8 0.63 4.6 8.7 13.3 Aluminium 23.3 18.9 10.55 178.9 199.6 378.5 Insulation 13.3 11.7 4.59 53.9 53.6 107.6 Glass 31.7 14.0 3.78 30.8 52.8 83.6 Timber products 3.7 8.5 2.51 7.1 21.2 28.3 Ceramic tiles 33.7 7.8 2.90 9.8 22.2 32.0

3.4.3 Spreadsheet for calculating the embodied energy of residential construction

The use of embodied energy coefficients to estimate the embodied energy of buildings or

urban infrastructure requires a knowledge of the quantities of materials used in the

structure. In the case of non-residential buildings, this information can often be taken from

Bills of Quantities drawn up prior to construction. In other cases, documentation from pre-

tender estimates can be used. Where a large number of buildings have broadly similar

features such as residential buildings, a more methodical approach to estimating the

embodied energy can be employed. A specially designed spreadsheet referred to as the

spreadsheet for the embodied energy of dwellings (SEED), has been used to estimate the

embodied energy by entering data on the principal dimensions and type of building

materials of the dwelling (Pullen, 2000a). The spreadsheet consists of a list of building

elements categorised from the footings through to the finishes. These are divided into sub-

elements and then into individual materials amounting to approximately 80 items. Each

building element category offers a choice of sub-elements e.g. brick veneer or solid brick

for walls. The list includes finishes, fitments, services and external works. The principal

dimensions of the house consisting of floor, window and external and internal wall areas

are entered alongside the appropriate sub-elements thereby making materials selections.

Items such as baths, WC's, water heaters, etc are entered as numbers of items. An example

is shown in Appendix 3 for two houses from different periods in the last 60 years.

52

The principal dimensions are converted to masses of individual materials by means of the

materials intensities. For instance, a floor area of 170m2 converts to 1.46 tonnes of steel

reinforcement/mesh and 89.8 tonnes of concrete for a concrete slab on ground on a flat site

with medium reactive soil based on materials intensities of 8.6 kg/m2 and 528 kg/m2,

respectively (Appendix 3b). The material intensities have been evaluated by the study of a

number of similar components on houses of various sizes to find relationships between

quantity and area. If the quantity of a material is multiplied by its embodied energy

coefficient, then the energy used to produce that material in the house can be calculated.

The total embodied energy for all the materials can then be found by summing the

individual values. The spreadsheet can accept values for the embodied energy coefficients

of building materials from any source. Hence, the model for spatially representing

embodied energy described later in this research (Chapters 4 and 5) can also be adjusted,

making it adaptable for embodied energy data derived using different methods and system

boundaries and from alternative locations.

3.4.4 Approach taken for embodied energy estimation of residential buildings

As an initial estimate of the proportion of existing single storey detached houses that were

constructed after the Second World War, a sample of property records from the State

Property Valuation Register consisting of six local government areas in the Adelaide

metropolitan region were analysed for the age distribution. More detailed information on

the State Property Valuation Register and the sample of records is given in the next chapter

and a comprehensive analysis is given in ‘Chapter 5 Spatial Representation of Results’.

Table 3.4 Distribution of single storey houses according to construction date in six

local government areas as at December 2003 Date of construction Number of houses Proportion (%)

1836 – 1900 7403 3.4 1901 – 1945 19376 9.3 1946 – 1978 98336 47.3 1979 – 2003 82851 39.8

Table 3.4 indicates that the majority (87%) of the sample of single storey detached houses

were constructed after 1945 which is a substantial proportion.

53

0.02.04.0

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Figure 3.2 Embodied energy of various material groups in the two post war houses

The embodied energy of two ‘typical’ houses of the post second world war era, one built in

the period 1946 to 1978 and one built in the period 1979 to 2003, has been estimated

calculated based on embodied energy coefficients derived purely from input-output

analysis. (An explanation of ‘typical’ houses is given in the next chapter). The

spreadsheets for these calculations are shown in Appendix 3a and 3b. The approximate

embodied energy of the various materials groups has been calculated and from these

spreadsheets and this is shown in decreasing order in Figure 3.2. The first dozen materials

account for 94.2% of the total embodied energy as shown in Table 3.5.

Table 3.5 Twelve materials with the most embodied energy in typical houses

Material % of total Brickwork 17.9 Concrete 16.4 Steel 15.1 Timber 12.7 Concrete products 7.2 Carpet 5.4 Appliances 5.2 Plasterboard 4.7 Aluminium 3.4 Insulation 2.5 Glass 2.0 Timber Products 1.7 Total 94.2

54

Hence, the approach taken to evaluating embodied energy coefficients for the main part of

this research ensures that the twelve materials which account for the majority of embodied

energy are based on a simple input-output hybrid analysis using both input-output and

process analysis data.

The advantages of using embodied energy coefficients based on process analysis have

previously been outlined. Of interest, therefore, is the proportion of the embodied energy

of the ‘top twelve’ materials in typical post second world war houses that is derived from

process analysis as opposed to input-output analysis. Table 3.6 shows the proportion of the

embodied energy coefficients for the ‘top twelve’ materials derived from process analysis.

When combined using the same spreadsheets (Appendix 3a and 3b), the proportion of

embodied energy of the twelve materials used in typical houses derived from process

analysis is 49.2%. This reduces the potential for error of embodied energy estimations that

would arise if solely input-output analysis was used.

Table 3.6 Proportion of hybrid embodied energy coefficients derived from process analysis

Material % Process Brickwork 89.9 Concrete 30.2 Steel 60.1 Timber 38.9 Concrete products 17.5 Carpet 29.6 Appliances 3.0 Plasterboard 34.6 Aluminium 47.3 Insulation 36.5 Glass 36.8 Timber products 18.8

3.4.5 Carbon Dioxide coefficients

The carbon dioxide equivalent emissions (CO2-e) associated with embodied energy

consumption (also known as embodied emissions) are estimated using an extension of the

spreadsheet technique. For this purpose, carbon dioxide equivalent coefficients for the

materials were evaluated using a similar technique based on 1996-97 input-output tables.

The energy inputs to the industrial sectors of Table 9. Direct Requirements Coefficients

were converted to carbon dioxide equivalent emissions using factors derived by the

Australian Greenhouse Office (AGO, 2001). These were 83.5, 294.0 and 64.5 kgCO2-

e/GJ for oil (sector 2501), electricity (sector 3601) and gas (sector 3602), respectively. For

55

the Coal, Oil & Gas energy sector (1100), a composite factor was evaluated based on the

product of the sizes of the sub sectors (from Bush et al, 1997) and the factors for black

coal, brown coal and crude oil of 93.4, 75.8 and 83.5 kgCO2-e/GJ, respectively. As with

the evaluation of embodied energy coefficients, the input-output matrix was inverted 12

times for convergence resulting in CO2-e intensities kgCO2-e/$ for all of the industrial

sectors in the input-output table. The product of these intensities for particular industrial

groups associated with construction materials and typical materials prices resulted in

CO2-e coefficients in units of kgCO2-e/kg. Hybrid CO2-e coefficients were then evaluated

by replacing the part of the coefficient representing direct carbon dioxide equivalent

emissions with a value determined from the energy consumed in process analysis.

CO2-eEM = CO2-eED + [(TCO2-eI – DCO2-eI) x CM]

where CO2-eEM is the carbon dioxide equivalent emissions

coefficient of a material.

CO2-eED is the direct carbon dioxide equivalent

emissions from process analysis.

TCO2-eI is the total carbon dioxide equivalent

intensity of the material from input-output analysis.

DCO2-eI is the direct carbon dioxide equivalent

intensity of the material from input-output analysis.

CM is the cost of the material.

A list of embodied energy and greenhouse gas equivalent coefficients evaluated using the

methods described above is provided in Table 3.7. This includes the twelve materials

providing the large proportion of embodied energy in typical houses evaluated using a

simple hybrid analysis.

56

Table 3.7 List of embodied energy and greenhouse gas equivalent coefficients

Material EE Coefficient CO2-e Coefficient(MJ/kg) (kgCO2-e/kg)

AAC Blocks 6.8 0.52Aluminium 378.5 31.45Appliances 301.1 24.18Blinding 1.7 0.13Brick (modular) 5.4 0.38Brick (standard) 5.4 0.38Carpet 212.3 16.78Ceramic tiles/ware 31.7 2.38Clay roof tile 17.3 1.20Concrete 2.4 0.18Concrete pavers 3.2 0.25Concrete tile 4.5 0.35Copper tubing 384.6 30.91Door (solid) 74.3 6.12Door (hollow) 48.3 3.98DPC 59.2 4.85Electrical wire 136.0 11.07Fibre cement board 19.3 1.48Glass 83.6 6.23Insulation (glass wool) 107.6 8.42Insulation (reflective) 303.0 24.35Mortar 2.6 0.18Paint 194.3 15.47Plaster 8.9 0.68Plaster board 13.3 1.00Plastics (moulded) 64.2 5.26Polyethylene membrane 65.2 5.34PVC pipe 121.5 9.95Steel (stainless) 216.2 17.49Steel (basic structural) 55.5 4.34Steel sheeting (colorbond) 192.1 15.55Timber 22.6 1.81Veneered particle board 28.3 2.34

3.5 Comparison with other researchers.

There are many factors which can contribute to differences in the embodied energy

coefficients of materials and some of these have been addressed in this chapter. In the

analysis of the life cycle energy consumption of the built environment where comparative

estimates of energy consumption are required (ie determining the lower energy option), it

could be argued that broad estimates of energy components are sufficient. However, a

57

comparison of derived embodied energy values between researchers has some benefit in

establishing the range of values.

3.5.1 Basis of comparison

To introduce a broader international dimension to this comparison, recently updated

information from two databases outside of Australia have been used. The first is the

database assembled by Hammond and Jones (2006) in the UK consisting of average values

for embodied energy coefficients based on collected published data which have preferably

been evaluated on a ‘cradle to site’ basis (DB1). The second is the database assembled by

Alcorn (2006) in New Zealand consisting of a comprehensive list of construction and

related materials where the coefficients have been derived using process based hybrid

analysis (DB2).

For the comparison of data derived in Australia two sources have also been used. The first

is the Balancing Act publication (Foran et al, 2005) which analyses the Australian

economy using input-output analysis to provide triple bottom line accounting for the

various industrial sectors (DB4). This study is based on 1994/95 input-output data from

the Australian Bureau of Statistics (ABS, 1999). Of the financial, environmental and

social indicators used in this study, a comparison is made here with the total primary

energy derived for the Residential Building industrial sector (code 4101). The second

comparison is made with the data derived by Treloar (2006) using process based hybrid

analysis (DB3) This is partly based on 1996/97 input-output data from the Australian

Bureau of Statistics.

Since there are numerous materials used in the construction of buildings in various

proportions, a comparison of the embodied energy coefficients of individual materials

becomes confusing and is not very helpful in achieving an overview. Hence, a ‘basket of

materials’ is used as the basis of the comparison. The materials chosen are those

commonly used in the construction of a brick veneer single storey house which is typical

of Australian residential construction over the last 30 years. The proportions of materials

required are for a modest dwelling of 170m2 enabling a total embodied energy value to be

estimated in both GJ and GJ/m2 of floor area for the various databases. The SEED

spreadsheet technique was used for this purpose (See Appendix 3b).

58

3.5.2 Results of comparison

The ‘basket of materials’ required for use in the SEED spreadsheet are shown in Table 3.7.

The databases DB1, DB2 and DB3 possessed coefficients for most of these materials.

Where there were the occasional omissions, the author’s coefficient was substituted and

these are shown in italics in Table 3.8. This substitution caused only a moderate distortion,

even in the case of DB1 where coefficients for appliances, electrical wire, steel sheeting

and stainless steel were used, as these components comprise about 7% of the total

embodied energy for the typical dwelling. The coefficients in DB1 and DB2 were all in

units of MJ/kg and could easily be transferred. Conversely, the units for coefficients in

DB3 were mixed (eg GJ/tonne, GJ/m3 and GJ/m2) and these required conversion using

density or mass/unit area data. There are no data from DB4 shown in this particular table

as this database provided information at macro level which is referred to later. Table 3.8

shows some large differences with particular materials with a tendency for the DB1 and

DB2 data to be lower or substantially lower in some cases.

Table 3.8 Embodied energy coefficients from various databases (MJ/kg)

Material DB1 DB2 DB3 Author AAC Blocks 200mm thick 3.5 6.8 4.0 6.8 Aluminium 154.3 219.2 252.6 378.5 Appliances 301.1 301.1 250.8 301.1 Blinding 0.2 0.04 0.4 1.7 Brick (modular) 8.2 2.7 3.3 5.4 Brick (standard) 8.2 2.7 3.3 5.4 Carpet 74.4 72.4 288.4 212.3 Ceramic tiles/ware 9.0 2.5 22.5 32.0 Clay roof tile 6.5 2.5 20.5 17.3 Concrete 1.1 1.2 1.8 2.4 Concrete pavers 2.0 1.0 3.2 3.2 Concrete tile 2.0 0.8 4.8 4.5 Copper tubing 55.0 70.6 378.9 384.6 Door (solid) 23.0 24.2 74.3 74.3 Door (hollow) 23.0 24.2 48.3 48.3 DPC 140.0 51.0 163.4 59.2 Electrical wire 136.0 136.0 378.9 136.0 Fibre cement board 10.9 9.4 30.0 19.3 Glass 13.5 15.9 168.8 83.6 Insulation (glass wool) 28.0 32.1 172.2 107.6 Insulation (reflective) 154.3 219.2 370.3 303.0 Mortar 1.3 2.1 1.8 2.6 Paint 80.0 90.4 284.0 194.3 Plaster 1.8 3.6 27.2 8.9 Plaster board 2.7 7.4 27.2 13.3 Plastics (moulded) 87.0 64.2 163.4 64.2

59

Table 3.8 (continued). Embodied energy coefficients from various databases (MJ/kg)

Material DB1

DB2 DB3 Author

Polyethylene membrane 83.4 51.0 163.4 65.2 PVC pipe 63.1 64.0 156.5 121.5 Steel (stainless) 216.2 75.6 445.2 216.2 Steel (basic structural) 24.0 31.6 85.3 55.5 Steel sheeting (colorbond) 192.1 59.3 158.8 192.1 Timber 9.0 6.3 21.9 22.6 Veneered particle board 23.0 30.6 37.7 28.3

The results of placing these data into the spreadsheet is given in Table 3.9 showing the

total embodied energy in GJ and GJ/m2. The figures from DB4 are calculated from

Volume 1 of Balancing Act (Foran et al, 2001) where the absolute primary embodied flow

for the Residential Building (construction activity) sector is 149 PJ. This is combined with

statistical data from volume 4 from the same publication where construction rates of

approximately 120,000 dwellings or 25 million square metres of floor space per year are

stated.

Table 3.9 Embodied energy of the materials in a typical brick veneer house

using various databases

EE Total

(GJ) EE Intensity

(GJ/m2) % Difference from author

DB1 623.1 3.69 -42.4 DB2 478.5 2.84 -55.7 DB3 1097.4 6.46 0.8 DB4 1241.7 5.96 -7.0 Author 1090.3 6.41

3.5.3 Discussion of results

The comparison with DB1 and DB2 from the UK and New Zealand indicate that some

estimates of embodied energy coefficients are substantially lower than this study resulting

in embodied energy intensities for houses in the region of 3 – 4 GJ/m2. There are

numerous factors which can contribute to the large differences including the method of

deriving the embodied energy coefficients which was based on statistical methods for DB1

and process based hybrid analysis for DB2. Of significance is the fact that the Australia is

a major producer of primary energy with significant differences in the structure of its

economy compared with the UK and New Zealand where the latter country generates a

large proportion of electricity from hydroelectric sources. Hence, the information obtained

from the overseas databases provides an interesting perspective, but is unlikely to be

60

applicable to the Australian context. The comparison with DB3 and DB4 is more

meaningful and in these cases, the results are reasonably close with embodied energy

intensities for houses in the region of 6 – 7 GJ/m2.

A further consideration is that Treloar outlines a more complete method for estimating the

embodied energy of a dwelling (Treloar et al, 2001a). This has further steps beyond the

‘bottom-up’ approach of evaluating the embodied energy of the materials in the dwelling

described in this chapter. It uses a disaggregation of the inputs to the Residential Building

sector data from input-output analysis using an extractor tool (Treloar, 1998). This

provides a framework whereby inputs for specific materials from the input-output analysis

can be substituted with those derived from process based hybrid analysis. The effect of

this is to substantially increase the embodied energy of a typical dwelling to approximately

1760 GJ or an intensity of 14.3 GJ/m2 (Treloar et al, 2001a). The difference between the

‘bottom-up’ estimation of the embodied energy of a dwelling from its basic materials and

the ‘top-down’ approach which feeds data into Residential Building sector data amounts to

a multiplier factor of approximately 1.9. Within this factor are other inputs such as on-site

construction, infrastructure services (water, sewer, etc), financial, capital and other

services. Using this ‘complete’ estimate for the embodied energy intensity of 14.3 GJ/m2

and an approximate floor area of 25 million square metres results in a total primary energy

consumption for the Residential Building sector of 360 PJ which is more than double that

from the Balancing Act (Foran et al, 2005) study. If similar analyses for ‘complete’ energy

consumption are carried out for all of industrial sectors in the Australian economy, then the

sum of all of the totals will result in an annual primary energy consumption for the

Australian economy in excess of that of around 4000PJ for the mid 1990s indicated by

ABARE (Bush et al, 1997) as used in the Balancing Act study. This reflects the issue of

definition of system boundaries for estimating the energy consumption of products and this

principle can be represented in a simple two dimensional way by the diagram in Figure 3.3.

This shows the energy consumption of the Australian economy in the form of a pie chart

with the various ‘slices’ representing the different industrial sectors. Comprehensive

analyses of the energy consumption of individual sectors causes the boundary to overlap

into adjacent sectors. In reality, the system for the whole economy is much more complex

and the overlapping of the sectors is multidimensional.

61

3.5.4 Reconciliation of ‘bottom up’ and ‘top-down’approaches

Within the ‘top-down’ energy intensity of 14.3 GJ/m2 of a typical dwelling are other

energy inputs such as on-site construction, roads, infrastructure services (water, sewer,

etc), financial, capital and other services. Later research in this thesis (Chapter 6) will

indicate that the additional energy intensity arising from on-site construction energy,

embodied energy in associated road pavement and the combined embodied energy of water

supply, sewer, stormwater and other services for a dwelling are approximately 0.7, 1.7 and

0.6 GJ/m2 amounting to 3.2 GJ/m2. A measure of the additional energy input arising from

capital provided for residential building can be obtained from analysis of the industrial

sector ‘7701 Ownership of Dwellings’. The primary energy intensity from input-output

analysis based on 1996/97 tables (ABS, 2001a) is 2.07 MJ/$ and the corresponding value

of the residential building sector (ABS, 2001b) is $56 million resulting in an energy input

of 116 PJ. Based on an approximate floor area for residential building in the mid 1990s of

25 x 106 m2 (Foran et al, 2005), the energy intensity for capital is 4.6 GJ/m2. Pursuing the

‘bottom-up’ approach combines the energy intensity for a typical dwelling of 6.4 GJ/m2

(from Table 3.9) with that of 3.2 GJ/m2 (from on-site construction and infrastructure) and

4.6 GJ/m2 (from capital) which amount to 14.2 GJ/m2. This is similar to the reported

embodied energy intensity obtained using the bottom-down approach of 14.3 GJ/m2.

Sector A

Sector B

Sector A

Sector B

Figure 3.3 Simple two dimensional representation of

overlap of energy consumption between industrial sectors

62

3.6 Summary

The complexity of modelling embodied energy and greenhouse gas emissions with some

degree of precision by either deterministic or stochastic methods has been described in this

chapter. Conversely, the widespread usage of life cycle energy analysis of buildings would

be encouraged by methods and data that are relatively simple and easily accessible to

practitioners in the field such as designers and quantity surveyors.

The need for absolute values for embodied energy data is less important when a

comparison is being made between similar buildings with different materials or between

residential developments with different urban form as it is the design solution with the

lower embodied energy that would normally be sought. When life cycle energy analysis is

considered, then the magnitude of embodied energy becomes more important, but this too

must be weighed against the level of sophistication in determining operational energy and

its production with regard to indirect energy inputs.

Overall, the principles of relativity (for comparing building types), comparability (for

undertaking life cycle analyses involving operational energy) and simplicity (for making

data accessible to practitioners) should be considered when modelling the embodied

energy and greenhouse gas emissions in the built environment. For these reasons, the

research presented in this work is based on ‘bottom-up’ estimations of embodied energy

using hybrid embodied energy coefficients evaluated for basic construction materials and

components. A major justification for this approach is that embodied energy estimates of

dwellings produced in this way are compatible with the energy consumed by the residential

building industry as derived by input-output analysis of the national economy and the total

energy consumed on an annual basis in Australia. Futhermore, this approach provides a

method which is relatively simple and conducive to a more widespread adoption in the life

cycle analysis of the built environment with the aim of providing more environmentally

sustainable buildings.

A particular method used for estimating the embodied energy of construction materials in

the built environment with particular reference to dwellings has also been described in this

chapter. This is Objective 2 of this research and part of the methodology for developing a

model for spatially representing embodied energy of residential areas in the urban

environment. Embodied energy theory is one of the three components required for this

purpose in addition to property register data and GIS software.

63

Chapter 4 Estimation of the Embodied Energy of Residential Areas in the Adelaide Metropolitan Area

4.1 Introduction

This chapter describes the method for determining the embodied energy of houses in the

Adelaide urban environment as a precursor to the spatial representation of the embodied

energy of residential areas. The basis of the method depends on the records kept by the

South Australian Government of ownership and valuation of land parcels. These records

comprise the State Property Valuation Register. This register also provides information on

buildings constructed on the land parcels and this enables a method to be developed

whereby the embodied energy of the buildings can be estimated. To provide a spatial

dimension to this information, the estimates of the embodied energy of buildings may then

be depicted using geographical information system (GIS) software. The embodied energy

methods described in the previous chapter in combination with the two components of the

property register data and GIS software form the basis of the proposed model.

4.2 State Property Valuation Register

The South Australian Property Valuation Register provides information on the ownership

of land parcels or allotments and is used by the Office of the Valuer-General to determine

site and property values according to the Valuation of Land Act 1971.

The Property Register as of 2001 contained 764,751 records of land parcels and allotments

and approximately one third of these refer to country, rural and outback areas of South

Australia. The Adelaide metropolitan area is administered by the 19 local government

areas of the Town of Gawler, City of Playford, City of Salisbury, City of Tea Tree Gully,

Adelaide Hills Council, City of Port Adelaide Enfield, Campbelltown City Council, City of

Charles Sturt, City of Prospect, Corporation of the Town of Walkerville, City of West

Torrens, Adelaide City Council, City of Norwood, Payneham & St.Peters, City of

Burnside, City of Holdfast Bay, City of Unley, City of Marion, City of Mitcham and the

City of Onkaparinga (Local Government Association of South Australia, 2006).

Collectively, these metropolitan local government areas consisted of 503,065 land parcels

and allotments recorded in the Property Register in 2001.

The Adelaide Statistical Division (ASD) covers a similar area as the metropolitan local

government councils except for outer regions of the Adelaide Hills Council area which are

excluded resulting in a total of 496,341 records of the Property Register relevant to

the Division (see Figure 4.1).

NOTE: This figure is included on page 64 of the print copy of the thesis held in the University of Adelaide Library.

Figure 4.1 The Adelaide Statistical Division in relation to the metropolitan Local

Government Councils (source: Atlas of South Australia (http://www.atlas.sa.gov.au)

2007) © Copyright Government of South Australia

64

65

The derivation of the method used to determine embodied energy employed a sample of

records from the State Property Valuation Register with a geographical spread representing

northern, central and southern areas in the linear configuration of metropolitan Adelaide.

This selection comprised approximately half of the records in the ASD and contained a

range of building age from the older central areas to the newer post second world war

developments in the outer suburbs. The sample of records represented 254,165 land

parcels which formed the local government authorities of City of Salisbury (northern

suburb), City of Charles Sturt, Adelaide City Council, City of Norwood, Payneham &

St.Peters (city and inner central suburbs), City of Mitcham and the City of Onkaparinga

(southern suburbs). All of these council areas are part of the Adelaide Statistical Division

and make up approximately one third of the 19 local government authorities in the

Adelaide metropolitan area. The 254,165 records analysed were the current assessment of

property valuation as of 22 December 2003.

The records were supplied by the Land Services Group of the South Australian Department

of Administrative and Information Services in the form of a 42Mb tab delimited text file.

They were intended for research purposes only as described in Appendix 4.

Information on each record was provided in 29 fields as shown in Table 4.1. These fields

include identification numbers, land use code, address, year built, built area, construction

materials and building style. Particular fields or descriptors relating to built area, parcel

area, land use code, year built and construction materials represent important information

for estimating the embodied energy of the buildings on each parcel. The records are

updated on a regular basis as information is received about building approvals from local

government authorities (Shalders, 2007). This includes additions and extension to

buildings which affects the ‘built area’ field.

4.3 Derivation of embodied energy

Since land parcels with dwellings form the majority of the Property Valuation Register, the

embodied energy of houses on each parcel of land was estimated in conjunction with the

data contained in certain fields in Table 4.1. The origins of this method using typical

houses from particular historical periods were devised by the author of this thesis during a

pilot project for evaluating both operational energy and embodied energy consumption of

six selected metropolitan areas in Adelaide (Troy et al, 2003 and Pullen et al, 2002) and

referred to in Chapter 1. The following sections describe the development of this method

66

and its integration with the Property Valuation Register to estimate embodied energy and

depict this information spatially.

Table 4.1. List of fields used to describe land parcels from the Property Valuation Register

Descriptor Details Valnno Valuation number – the number allocated by the Valuer-General

to the ‘assessment” parcel Rectype Valuation type – 1 indicates a combined water and land

assessment, 2 indicates a land assessment only and 3 indicates a water assessment

Luc_code Land use code – a 4 digit code from a comprehensive list of 1017 uses (see Appendix 5a)

Lot_number Lot number Unit_no Unit number House_no House number Street_nam Street name Street_typ Street type Suburb_nam Suburb name Norooms Number of living rooms excluding bathrooms, toilets, etc Year_built Year building constructed Btype Text description for non-residential buildings Barea Built area (see Appendix 5b for definition) Wall_mater Wall material – major material using in external wall construction

up to 34 categories (see Appendix 5c) Roof_mater Roof material – major material used in roof cladding up to 14

categories (see Appendix 5d) Bldstyle Building style (see Appendix 5e for codes) Ensuite Presence of ensuite bathroom in houses Mlystys Number of storeys Buildcond Building condition (Good 7 – 9, Average 4 – 6, Poor 1 – 3) Bedrooms Number of bedrooms Plidtype Plan type (see Appendix 5f) Plnumber Plan number – the reference number of the plan generating the

associated parcel Parctype Parcel type (see Appendix 5f) Parcelno Parcel number – the identifier shown on plan or map which forms

part of the legal land description of title Titlpref Land title prefix Titlvol Land title volume Titlfol Land title folio Area Area of land parcel (in hectares) Postcode Postcode

4.4. Rules for estimating embodied energy

For houses and other residential buildings, a series of rules were devised so that the

descriptors of the building on each land parcel could be used to estimate embodied energy.

The age of the buildings was used as a surrogate identification of certain construction

67

features. Although the Property Valuation Register provided information on wall and roof

materials of existing residential buildings, no information was available from this source

on ceiling and wall heights, type of flooring construction or material used for window

frames. These three factors are important in determining the embodied energy of a house.

The ceiling height can indicate wall height and this parameter affects the area of external

and internal walls. The size of window openings can also affect these areas. The type of

flooring construction determines whether concrete-slab-on-ground/raft footing or

suspended timber floor with strip footings have been used. Window frames are commonly

manufactured in timber or aluminium which have widely different embodied energy.

Furthermore, the information on wall and roof materials did not distinguish entirely

between particular materials with some similarity. Hence, certain assumptions were made

about the construction features and materials depending on the age of the houses. The

following section outlines the rationale for these assumptions.

(a) Floor and footing types

The Property Valuation Register does not provide any information regarding the floor

and footing type used in the construction of houses. The predominant type used in

modern houses is the concrete-slab-on-ground/raft footing. The introduction of this

type of footing/floor system occurred over a period of time as designs and techniques

changed. In discussing the design of footings prior to the introduction of modern

footings, Persse and Rose (1994, p148) state:

There was no significant progress until the early 1960’s when Consulting Engineers

began recommending footings for different problems, e.g. grillage raft and pier and

beam. Raft footings/floor systems prevail today.

In a history of the development of footing types in Adelaide, Fargher (1979) reported

that grillage raft footings were first adopted in 1965. The common shallow stiffened

slab was becoming common by the early 1970s assisted by the development and

improvement of design methods (Walsh, 1975). By the early 1980s, the transition to

the reinforced concrete raft footing in South Australia was complete with more

rigorous design approaches (Mitchell, 1984).

Prior to the concrete-slab-on-ground, the normal footing and floor system consisted of

steel reinforced concrete strip footings with a suspended timber floor. In a review of

68

cracking in houses, Khor (2003) describes the introduction of pier-and-beam footing

for reactive soils in the 1950s by the Department of Mines and Energy. Strip footings

in various forms with timber floors have been in use since the early 1900s. At the turn

of the century, the use of shale quarry overburden with lime mortar was common as a

footing although concrete footings were employed after 1905. Initially, little

reinforcement was used other than a light expanded metal in the top and bottom of the

strip footing (Fargher, 2006). From 1923, when the South Australian Building Act and

Regulation came into effect, mild steel rods were used to reinforce the concrete except

for footings less than 9 inches (225mm) deep. Although not specified, the depth of

reinforced footings was up to 24 inches (600mm) whereas the width was prescribed at

1½ times the aggregate width of the wall which for footings for external walls

amounted to approximately 15 inches (400mm). In the 19th century, limestone rubble

and mortar was the common footing material with sizes varying according to

conditions. These were laid in shallow trenches with common dimensions of 12 – 16

inches (300 – 400mm) deep and 24 inches (600mm) wide. Some further information

on strip footings are provided in Appendix 6 and this has been used in the estimation of

the embodied energy of older houses.

In modern times, by far the most significant change in floor and footing design was the

transition referred to earlier from timber suspended floors to reinforced concrete slab-

on-ground. A further indication of when this occurred can be found in a study of the

thermal performance of houses in three locations in Australia which included the inner

suburb of Norwood in Adelaide (Williamson et al, 1989). As part of this research, the

materials used in the construction of the sample of single storey dwellings in Norwood

were determined by means of a survey of over 800 households. An analysis of these

results shows that the transition from timber suspended floors (with reinforced concrete

strip footings) to reinforced concrete slab-on-ground commenced in the mid 1960s with

the latter dominating house construction by the early 1980s. It should be highlighted

that this study focused on an established inner suburban area which commenced

development over 60 years ago and for this reason the proportion of data relating to the

period in question was relatively small (approximately 60 houses out of the total). In

addition, it is possible that the methods used to construct new houses in older suburbs

may have been conservative compared with housing developments on green-field sites.

The data are presented in Figure 4.2 by taking a chronological midpoint for each of the

periods chosen in the study which were 1983 – 1988, 1968 – 1982, 1938 – 1967 and

older than 1938. This shows that concrete slab-on ground became the dominant

flooring material by the late 1970s.

NOTE: This figure is included on page 69 of the print copy of the thesis held in the University of Adelaide Library.

Figure 4.2 Proportion of houses with either timber of concrete floors (based on data derived by Williamson et al, 1989)

The period of this transition is broadly confirmed by the Timber Development

Association (Lewellyn, 2006) who observed a rapid decline in the use of timber for

floors between 1970 and early 1980s whilst seeing a coincident increase in the use of

timber for brick veneer walls in residential construction.

Based on the survey data and anecdotal information, the end of 1978 was nominated

as the mid point in time for the transition period. For the purposes of evaluating

embodied energy, houses built before this date (up to and including 1978) as shown in

the property register would be treated as having suspended timber floors. Similarly,

houses built after this date would be treated as having concrete slab-on-ground floors.

The potential for error arising for houses within the transition period being assigned

floor and footing types which were different from reality is discussed at the end of the

next chapter.

(b) Ceiling and wall heights

No information on ceiling or wall height is included in the Property Valuation

Register and assumptions must be made for estimating the embodied energy of

residential buildings.

69

In the publication ‘House Styles in Adelaide – A Pictorial History’, Persse and Rose

(1994, p148) summarise walling heights as follows:

Walling heights of 4.27metres (14 feet) or more were not uncommon in the better

class of construction. These were reduced to about 3.66 metres (12 feet) at the turn of

the century and to 3.05 metres (10 feet) after World War 1. After the Second World

War, a further reduction to 2.74 metres (9 feet), due to a shortage of materials,

occurred and in the early 1970’s to 2.44 metres (8 feet).

As suggested, the ‘class of construction’ would have an effect on walling height

particularly prior to the Second World War when building practices were more varied.

Pikusa (1986) provides more detailed information on ceiling heights of particular

nineteenth century houses in Adelaide and these have been summarised in Table 4.2.

Table 4.2 Ceiling heights of particular houses (extracted from Pikusa, 1986).

NOTE: This table is included on page 70 of the print copy of the thesis held in the University of Adelaide Library.

Of course, ceiling and wall heights of a building are not necessarily the same but there

is normally a correlation between in dwellings constructed prior to the Second World

War. After this time, it became common to set the eaves height lower than the ceiling

height such that the height of the outer leaf of a cavity external wall was lower than

the inner leaf.

70

71

Based on this information, the ceiling heights assumed for the evaluation of

embodied energy are 3.6m up until 1900, 3.3m between 1901 and 1945, 2.7m

between 1946 and 1978, and 2.4 m from 1979 to 2003.

(c) Window frame materials

Aluminium window frames were introduced into the South Australian market in the

mid 1960s in a basic mill finish but it was not until the early 1970s that coloured

anodized finishes became available and these frames became a major competitor to

timber (Judd, 2004). Based on this information, it has been assumed that aluminium

was the main window framing material from 1979 onwards.

(d) Wall types

The wall material codes used in the Property Valuation Register include Brick, Iron,

Rendered, Asbestos, Weatherboard, Log, various types of natural building Stones and

Block and the full list can be seen in Appendix 5c. Other composite types are also

specified which include concrete and walls including framing but these codes are only

used to describe the walls of non-residential buildings. For houses, the codes used in

the Property Valuation Register do not distinguish between double brick (either solid or

cavity) and brick veneer construction, the latter possessing considerably lower

embodied energy.

Brick veneer construction was gradually adopted in South Australia during the mid

1960s and became commonplace by the late 1970s. The transition from double brick

to brick veneer coincided with the introduction of articulation in masonry walls to

minimize cracking arising from reactive soils.

The South Australian Housing Trust was an early innovator with brick veneer

construction (SAHT, 1972) for State owned housing. The 1972 Annual Report states:

There has, over the last few years, been a gradual change in the method of house

construction in South Australia. The traditional method of construction is to build a

house of double brick walls supported on a concrete foundation which is an excellent

method for building on good quality, low clay content soils and provides a house of

strength and endurance. However, on poorer soils such a house needs to have special

and costly foundations to prevent walls cracking and often, even then, requires

expensive maintenance. In some localities even the most costly foundations appear to

give no assurance that, with the inevitable soil movement, cracking will not occur.

As Adelaide has grown larger, almost all the stable good building quality soils have

been used. Much of the land now available for development is of doubtful building

character and the Trust is satisfied that in many parts of the State the only sensible

way to build is to use brick veneer construction.

In the private sector, the transition to brick veneer appears to have been slower. The

study by Williamson et al (1989) on the thermal performance of houses in established

suburbs provides some data on this as shown in Figure 4.3 indicating that the

transition occurred by the early 1980s. The Timber Development Association

(Lewellyn, 2006) observed that the majority of new houses on green-field sites were

constructed with brick veneer external walls by the late 1970s.

NOTE: This figure is included on page 72 of the print copy of the thesis held in the University of Adelaide Library.

Figure 4.3 Proportion of houses with either double brick or brick veneer

external walls (based on data derived by Williamson et al, 1989)

Based on this background, brick veneer rather than double brick has been assumed for

houses built in and since 1979 which have been ascribed wall type ‘code 1 – Brick’

for the purposes of estimating embodied energy. Prior to this date, houses with the

wall type ‘code 1 – Brick’ have assumed to be constructed from double brick.

72

73

(e) Roof tile types

The Property Valuation Register includes a field which describes roof type and this

includes 14 variations including Tiled, Galvanised iron, Corrugated asbestos, Steel

decking, Slate and asbestos Shingles. The complete list is shown in Appendix 5d. The

roof type ‘code 1 – Tiled’ includes both concrete and terra cotta (clay) type of roof tiles

and these construction materials have quite different embodied energy. Persse and

Rose (1994) indicate that corrugated steel ie galvanised iron roof cladding was a

common roofing material from early settlement of the South Australian colony through

to the post Second World War period and this material is taken as the default for this

period (1836 – 1945). From that time, concrete roof tiles became the common roofing

material. Terra cotta tiles were used from 1911 with a period of popularity between the

World Wars coinciding with the availability of Wunderlich clay tiles from 1921.

Hence, the specification of roof tile type ‘code 1 – Tiled’ in the property Valuation

Register was assumed to mean terra cotta tiles between 1900 and 1945 and concrete

tiles in the post Second World War period.

(f) Floor to window area ratio

A further change in the design and construction of houses that has occurred in South

Australia since the proclamation of the colony in 1836 and which affects the estimation

of materials used is the size of the external openings, particularly the fenestration. For

the purposes of estimating the quantities of materials in houses from particular periods,

the approximate floor to window area ratios have been examined as summarized in

Appendix 7. This indicates that for existing houses constructed prior to the Second

World War, window sizes were proportionally smaller with the floor to window ratio at

approximately 9 to 10. During the last 60 years, houses were constructed with larger

expanses of glazed area to enhance interior daylighting of homes and the ratio reduced

to approximately 5½ to 6.

4.5 Default combinations of construction features

The most representative combinations of construction features for houses at different

periods of time are summarised in Table 4.3. These combinations were used as a default

when estimating the embodied energy of houses. Essentially, a number of embodied

energy intensities (embodied energy per square metre of floor area) were calculated for

typical (default) houses constructed in the historical periods shown in Table 4.3.

74

Table 4.3 Summary of default combinations of materials

Period Ceiling height

(m)

Footing/floor type Wall construction when ‘Type 1 – Brick’ specified

Window frames

Roof material

1836 – 1900 3.6 Rubble and mortar

strip/timber

Double brick Timber Galvanised iron

1901 – 1945 3.3 Lightly reinforced concrete

strip/timber

Double brick Timber Galvanised iron (terra cotta tiles when ‘Type 1 – Tiled’ specified

1946 – 1978 2.7 Reinforced concrete

strip/timber

Double brick Timber Concrete (when ‘Type 1 – Tiled’

specified) 1979 – 2003 2.4 Reinforced

concrete raft Brick veneer Alumin-

ium Concrete (when ‘Type 1 – Tiled’

specified)

In addition to these combinations of main materials and dimensions, further refinements of

the features were used relating to wall and ceiling insulation, window/floor area ratio and

roof pitch as follows:

• Insulation. For the 1979 to 2003 period, R2.5 ceiling with R1.5 wall insulation was

included. For the 1946 to 1978 period, only R1.5 ceiling insulation was included.

No insulation was specified for houses from the earlier two periods.

• Window/floor area ratio. For the two periods since the Second World War, a ratio

of between 9 and 10 was used whereas a ratio of between 5½ and 6 was used for

earlier two periods.

• Roof pitch. For the two periods since the Second World War, a pitch of 20º was

used whereas a pitch of 30º was used for earlier two periods.

The embodied energy intensities were derived using a spreadsheet technique (Pullen,

2000a) which estimates embodied energy from basic dimensions and material

specifications of houses. The spreadsheet can accept embodied energy coefficients for

building materials from a variety of sources to accommodate advances in the knowledge of

energy consumption for the manufacture of building materials. For this exercise, the

hybrid embodied energy coefficients were used as described in Chapter 3 based on process

analysis and input-output tables based on statistics from the period 1996/97 (ABS, 2001a).

Figure 4.4 shows a general representation of the process for estimating embodied energy of

houses.

75

Figure 4.4 General representation of process for estimating embodied energy of houses

(source: Pullen et al, 2002).

Basic data about the dimensions of each typical house is entered into the spreadsheet

including total floor area, wet floor area, external wall area, internal wall area, window

area, insulated roof area, external paved or concreted areas, shed area, pergola or covered

area and fence length. For each building element, the default combination of materials

for the period of the typical house were selected. The materials intensities are factors that

convert areas of building elements to weights of the materials comprising those elements.

For instance, 10m2 of floor area for a post 1979 house will convert to approximately 85 kg

of steel reinforcement, 5 tonnes of concrete, 800 kg of dolomite blinding and 3kg of

polyethylene membrane and 11kg of PVC waste pipe. The material intensities are

embedded in the spreadsheet. Similarly, the embodied energy coefficients which convert

the weights of materials to embodied energy are embedded in the spreadsheet. The

embodied energy of each typical house was expressed as an embodied energy intensity

(MJ/m2) ie per square metre of floor area.

Four spreadsheets corresponding to houses typical of the four periods are shown in

Appendix 8. Initially, the spreadsheet for the period 1979 to 2003 (Appendix 8a) was

derived corresponding to the default combination of dimensions and materials as shown in

Table 4.3 and with the feature refinements. This spreadsheet was modified to form the

default spreadsheet for the period 1946 to 1978 (Appendix 8b) by changing material

Databases and Spreadsheets Property Register calculations

Elem e nt Su b - De ta il A re a o r M a te ria l M a te ria l Ene rg y Em b o d ied Ele m e nt n um b e r Inte nsity C o e ff. Ene rg y

m 2 or no . (kg / m 2) (M J/ kg ) (M J) Pro p . o f(e x ce p t it e m s) Tot a l(%)

01 Fo o tin g s/ Flo o r C o nc ret e sla b o n g ro un d 170 Ste e l 8.6C o nc ret e 528.0Blin d ing 80.0M e m b ra ne 0.3

Susp e n d e d t im b e r Ste e l 5.2C o nc ret e 348.0Bric kw o rk 29.5Tim b e r 18.0

Susp e n d e d t im b e r Ste e l 2.2(AS2870.1 d e sig n - V ic t o ria ) C o nc ret e 165.8

Bric kw o rk 56.0Tim b e r 26.9Dra in s 0.3

05 Ro o f Fra m in g Tim b er 170 Tim b e r 16.8St ee l Ste e l 16.3

C la d d in g C o n c re te Til e 170 C o nc ret e Tile 52.6C la y Tile C la y Ti le 48.1St ee l She e t Ste e l Sh e e t 4.3

Ea v e s so ff it 1.6In su la t io n(R2) 144 In su la t io n 1.0Re fle c . In su l. A lum in ium Fo il 0.4C e ilin g Pla st e rb o a rd 7.6G ut te rin g Ste e l 0.5

06 Exte rn a l W a l ls Do u b le Bric k Bric k(St a n d a rd ) 352.0DPC 0.1M o rt a r 48.6Pla st e r 14.0

Bric k V en e e r St a nd a rd b r ic k 80 Bric k(St a n d a rd ) 176.0M o rt a r 23.4

M o d ula r b ric k Bric k(M o d ula r) 143.0M o rt a r 16.2

Tim b er f ra m in g 124 Tim b e r Fra m ing 7.1St ee l f ra m in g Ste e l Fra m ing 6.2In su la t io n (R1.5) 124 In su la t io n (R1.5) 1.0

DPC 0.1Pla st e r Bo a rd 7.6

AA C Blo c k 200m m t h ic k AA C Blo c k 100.04m m Ren d e r 8.0C o a t ing 0.1Pla st e r Bo a rd 7.6

Tim b e r c la d C la d d in g C la d d in g 10.0Pa in t Pa in t 0.2Tim b er f ra m in g Tim b e r f ra m in g 7.1In su la t io n (R1.5) In su la t io n(R1.5) 1.0

DPC 0.1Pla st e r Bo a rd 7.6

07 W in d o w s Fra m e s Tim b er Tim b e r 16.3A lum in ium 28 Alum in ium 6.0

G la ss 7.5

EM BO DIED ENERG Y SPREA DSHEET

Elem e nt Su b - D eta il Are a or M a te ria l M a teria l En erg y Emb o d ied Ele m en t num b e r Inte n sity C oe ff. Ene rg y

m 2 o r n o. (k g / m 2) (M J / kg ) (M J) Pro p . o f(exc e p t ite m s) Tot a l(%)

01 Fo o tin g s/ Flo o r C o nc re t e sla b o n g rou nd 170 St e e l 8 .6C on c re te 528.0Blind in g 80.0M e m b ra n e 0.3

Susp e n d e d tim b e r St e e l 5 .2C on c re te 348.0Bric k w o rk 29.5Tim b e r 18.0

Susp e n d e d tim b e r St e e l 2 .2(A S2870.1 d esig n - V ic t or ia ) C on c re te 165.8

Bric k w o rk 56.0Tim b e r 26.9D ra ins 0.3

05 Ro o f Fra m ing Tim b e r 170 Tim b e r 16.8Ste e l St e e l 16.3

C la d d ing C o nc re t e Tile 170 C on c re te Tile 52.6C la y Til e C la y Tile 48.1Ste e l Sh e et St e e l She e t 4 .3

Ea v e s so ffit 1 .6Insu la t io n(R2) 144 Insu la tio n 1.0Re fle c . In su l. A lum in ium Fo il 0 .4C e ilin g Pla ste rb oa rd 7.6G utt e rin g St e e l 0 .5

06 Ex te rn a l W a lls D o u b le Bric k Bric k (Sta nd a rd ) 352.0D PC 0.1M o rta r 48.6Pla ste r 14.0

Bric k Ve n e er Sta n d a rd b ric k 80 Bric k (Sta nd a rd ) 176.0M o rta r 23.4

M o d u la r b ric k Bric k (M o d u la r) 143.0M o rta r 16.2

Tim b e r fra m ing 124 Tim b e r Fra m in g 7.1Ste e l fra m ing St e e l Fra m in g 6.2Insu la t io n(R1.5) 124 Insu la tio n (R1.5) 1 .0

D PC 0.1Pla ste r Bo a rd 7.6

A A C Bloc k 200m m th ic k A A C Blo c k 100.04m m Re nd er 8.0C oa tin g 0.1Pla ste r Bo a rd 7.6

Tim b e r c la d C la d d ing C la d d ing 10.0Pa in t Pa in t 0 .2Tim b e r fra m ing Tim b e r fra m ing 7.1Insu la t io n(R1.5) Insu la tio n (R1.5) 1 .0

D PC 0.1Pla ste r Bo a rd 7.6

07 W in d o w s Fra m e s Tim b e r Tim b e r 16.3A lu m in iu m 28 A lum in ium 6.0

G la ss 7.5

EM BO DIED ENERG Y SPREA DSHEET

Elem e nt Su b - D eta il Are a or M a te ria l M a teria l En erg y Emb o d ied Ele m en t num b e r Inte n sity C oe ff. Ene rg y

m 2 o r n o. (kg / m 2) (M J / kg ) (M J) Pro p . o f( ex c e p t i te m s) Tot a l(%)

01 Fo o tin g s/ Flo o r C o nc re t e sla b o n g rou nd 170 St e e l 8 .6C on c re te 528.0Bli nd in g 80.0M e m b ra n e 0.3

Susp e n d e d tim b e r St e e l 5 .2C on c re te 348.0Bri c k w o rk 29.5Tim b e r 18.0

Susp e n d e d tim b e r St e e l 2 .2(A S2870.1 d esig n - V ic t oria ) C on c re te 165.8

Bri c k w o rk 56.0Tim b e r 26.9D ra ins 0.3

05 Ro o f Fra m ing Tim b e r 170 Tim b e r 16.8Ste e l St e e l 16.3

C la d d ing C o nc re t e Tile 170 C on c re te Tile 52.6C la y Tile C la y Tile 48.1Ste e l Sh e et St e e l She e t 4 .3

Ea v e s so ffit 1 .6Insu la t io n(R2) 144 Insu la tio n 1.0Re fle c . In su l. A lum in ium Fo il 0 .4C e ilin g Pla ste rb oa rd 7.6G utt e rin g St e e l 0 .5

06 Ex te rn a l W a ll s D o u b le Bric k Bri c k (Sta nd a rd ) 352.0D PC 0.1M o rta r 48.6Pla ste r 14.0

Bric k Ve n e er Sta n d a rd b ric k 80 Bri c k (Sta nd a rd ) 176.0M o rta r 23.4

M o d u la r b ri ck Bri c k (M o d u la r) 143.0M o rta r 16.2

Tim b e r fra m ing 124 Tim b e r Fra m in g 7.1Ste e l fra m ing St e e l Fra m in g 6.2Insu la t io n(R1.5) 124 Insu la tio n (R1.5) 1 .0

D PC 0.1Pla ste r Bo a rd 7.6

A A C Bloc k 200m m th ic k A A C Blo c k 100.04m m Re nd er 8.0C oa tin g 0.1Pla ste r Bo a rd 7.6

Tim b e r c la d C la d d ing C la d d ing 10.0Pa in t Pa in t 0 .2Tim b e r fra m ing Tim b e r fra m ing 7.1Insu la t io n(R1.5) Insu la tio n (R1.5) 1 .0

D PC 0.1Pla ste r Bo a rd 7.6

07 W in d o w s Fra m e s Tim b e r Tim b e r 16.3A lu m in iu m 28 A lum in ium 6.0

G la ss 7.5

EM BO DIED ENERG Y SPREA DSHEET

Elem e nt Su b - D eta il Are a or M a te ria l M a teria l En erg y Emb o d ied Ele m en t num b e r Inte n sity C oe ff. Ene rg y

m 2 o r n o. (k g / m 2) (M J / kg ) (M J) Pro p . o f( ex c e p t ite m s) Tot a l(%)

01 Fo o tin g s/ Flo o r C o nc re t e sla b o n g rou nd 170 St e e l 8 .6C on c re te 528.0Bli nd in g 80.0M e m b ra n e 0.3

Susp e n d e d tim b e r St e e l 5 .2C on c re te 348.0Bric k w o rk 29.5Tim b e r 18.0

Susp e n d e d tim b e r St e e l 2 .2(A S2870.1 d esig n - V ic t or ia ) C on c re te 165.8

Bric k w o rk 56.0Tim b e r 26.9D ra ins 0.3

05 Ro o f Fra m ing Tim b e r 170 Tim b e r 16.8Ste e l St e e l 16.3

C la d d ing C o nc re t e Tile 170 C on c re te Tile 52.6C la y Tile C la y Tile 48.1Ste e l Sh e et St e e l She e t 4 .3

Ea v e s so ffit 1 .6Insu la t io n(R2) 144 Insu la tio n 1.0Re fle c . In su l. A lum in ium Fo il 0 .4C e ilin g Pla ste rb oa rd 7.6G utt e rin g St e e l 0 .5

06 Ex te rn a l W a ll s D o u b le Bric k Bric k (Sta nd a rd ) 352.0D PC 0.1M o rta r 48.6Pla ste r 14.0

Bric k Ve n e er Sta n d a rd b ric k 80 Bric k (Sta nd a rd ) 176.0M o rta r 23.4

M o d u la r b ri c k Bric k (M o d u la r) 143.0M o rta r 16.2

Tim b e r fra m ing 124 Tim b e r Fra m in g 7.1Ste e l fra m ing St e e l Fra m in g 6.2Insu la t io n(R1.5) 124 Insu la tio n (R1.5) 1 .0

D PC 0.1Pla ste r Bo a rd 7.6

A A C Bloc k 200m m th ic k A A C Blo c k 100.04m m Re nd er 8.0C oa tin g 0.1Pla ste r Bo a rd 7.6

Tim b e r c la d C la d d ing C la d d ing 10.0Pa in t Pa int 0 .2Tim b e r fra m ing Tim b e r fra m ing 7.1Insu la t io n(R1.5) Insu la tio n (R1.5) 1 .0

D PC 0.1Pla ste r Bo a rd 7.6

07 W in d o w s Fra m e s Tim b e r Tim b e r 16.3A lu m in iu m 28 A lum in ium 6.0

G la ss 7.5

EM BO DIED ENERG Y SPREA DSHEETAddress Age Built Area W all type Roof type

Embodied energy of each building

Principal Dimensions

Materials Intensities

Materials & Energy Prices

Input-output Tables

Embodied Energy

Coefficients

76

selections and dimensions. Both of these spreadsheets incorporated a 300mm difference in

inner and outer leaf height of external walls reflecting the construction feature of most post

Word War Two houses where the roof eaves height is lower than the ceiling height. The

1901 – 1945 spreadsheet (Appendix 8c) and the 1836 - 1900 (Appendix 8d) spreadsheet

were then derived with appropriate default material selections and dimensions. These two

spreadsheets were adjusted corresponding to the higher floor to window area ratio, similar

external wall inner and outer leaf heights and higher roof pitch as well as their individual

differences in materials and dimensions.

Each of the four spreadsheets was then adjusted for alternative external walling materials

as defined in the Property Register and the variations in embodied energy intensity were

noted in each case. Depending on the external wall material, appropriate assumptions were

made with regard to internal wall construction. For example, where asbestos cement or

weatherboard external walls were listed (as opposed to the default of brick), it was

assumed that the internal walls consisted of plasterboard lining on a timber frame rather

than brick. After returning to the default combinations of materials and dimensions, a

similar exercise was carried out for the alternative roofing materials and the variations in

embodied energy coefficient was noted. The footings/floor section of the original SEED

spreadsheet (Appendix 8a) was also modified to incorporate materials intensities for strip

footings and timber floors of older houses as detailed in Appendix 6. These are shown in

parts b, c and d of Appendix 8.

Table 4.4 summarises the embodied energy intensities (in GJ/m2) for the default

combinations for houses in each of the four historical periods. In addition, variations from

these intensities are shown corresponding to possible alternative materials which might be

recorded for houses on particular titles in the Property Valuation Register.

77

Table 4.4 Embodied energy intensities and variations for alternative materials (GJ/m2)

Period Default embodied

energy intensity

Wall Type

Embodied energy

intensity variation

Roof Type Embodied energy

intensity variation

1836 - 8.9 Brick 0.00 Tiled -0.82 1900 Iron -0.37 Galvanised iron 0.00 Rendered 0.02 Corr. asbestos -0.79 Weatherboard -2.54 Steel decking 0.00 Stone, Freestone 0.30 Imitation tile 0.46 Bluestone -0.29 Slate -1.07 Basket Range -0.08 Shingles (asbestos) -0.48 Block -0.08 1901 - 8.5 Brick 0.00 Tiled 0.00 1945 Iron -0.76 Galvanised iron 0.00 Rendered 0.01 Corr. asbestos -0.79 Weatherboard -1.13 Steel decking 0.00 Stone, Freestone 0.28 Imitation tile 0.46 Bluestone -0.26 Slate -1.08 Basket Range -0.07 Shingles (asbestos) -0.48 Block -0.07 1946 - 7.2 Brick 0.00 Tiled 0.00 1978 Iron 0.03 Galvanised iron 0.71 Rendered 0.02 Corr. asbestos 0.03 Weatherboard -1.71 Steel decking 0.71 Stone, Freestone 0.19 Imitation tile 1.18 Bluestone -0.17 Slate -0.23 Basket Range 0.05 Shingles (asbestos) 0.32 Block 0.05 1978 - 6.4 Brick 0.00 Tiled 0.00 2003 Iron -0.09 Galvanised iron 0.75 Rendered 0.01 Corr. asbestos 0.02 Weatherboard -0.28 Steel decking 0.75 Stone, Freestone -0.06 Imitation tile 1.19 Bluestone -0.17 Slate -0.24 Basket Range -0.13 Shingles (asbestos) 0.31 Block 0.02

4.6 Disaggregation of Property Valuation Register files into building type.

To conveniently carry out the estimation of embodied energy for houses on each land title,

the original State Property Valuation Register file was divided into smaller files according

to similar land use codes. These files were imported into Microsoft Excel to enable these

estimations to be carried out. After carrying out the calculations, the smaller Excel files

were reassembled into the large State property valuation register file using Microsoft

Access software (Adamski and Finnegan, 2002).

Table 4.5 lists the building groups into which the records from the State Property

Valuation Register (ie the initial 254,165 records from the Adelaide metropolitan region)

were sub-divided. These 12 groups were devised on the basis of different building form

and use. Single, double and multi-storey buildings make obvious categories for building

78

groups. A further consideration, particularly for dwellings, is that of detached or non-

detached buildings. In addition, other groups are required to include more specialized

buildings such warehouses or shopping centres. A graphical summary of these forms is

presented in Appendix 9.

There are 1017 land use codes (Office of the Valuer General, 2003) and the index for these

codes is given in Appendix 5a. Many of these land use codes refer to activities which are

carried out in buildings of similar form. Examples of this (with land use codes) are

footwear (2125), delicatessen (2141) and chemist (2161) retail trades which are undertaken

in shop-like premises. Other examples are employment agencies (2540), engineering

services (2710) and legal services (2760) which are carried out in office-like premises.

Other land use codes refer to land parcels where there are few, if any, buildings such as

agriculture (9100), horticulture (9300) and forestry (9400). In the latter case, the

corresponding records in the State Property Valuation Register would show very low or

zero entries in the ‘Built Area’ field.

By examining all of the land use codes, it is feasible to assign them to the 12 building

groups based on built form as shown in Table 4.5. For dwellings, single storey detached

and two storey detached houses form Groups 1 and 2, respectively. Single storey home

units (land use code 1310) are assigned to Group 3 where a party wall between dwellings is

a similar construction feature. Other building groups describing dwellings (4 to 7) also

have particular construction features such as terraced design, multi-storeys or a large

number of private rooms. Retail premises, commercial offices, institutional buildings and

factories or warehouses form other building groups. Further details of the grouping of

these codes can be obtained from the land use codes in Appendix 5a.

79

Table 4.5. List of building groups with similar built forms

Group number

Building Group Land use codes (Luc_code)

1 Single storey detached dwelling 1100 to 1119 (if Mlystys = 0 or 1) 1900 to 1999, 1315

2 Two storey detached dwelling 1100 to 1119 (if Mlystys = 2) 1330 to 1335

3 Maisonette, semi-detached house – single storey

1220, 1300, 1310, 1412

4 Row house – single storey 1200, 1230, 1410, 1411, 1413 5 Town house, two storey

maisonette 1420 to 1433

6 Apartments, flats 1319 to 1329, 1400 7 Hotels, hostels 1500 to 1899 8 Retail premises 2100 to 2499 9 Commercial offices 2500 to 2999 10 Institutional 5000 to 5999

7590 to 7690 11 Manufacturing, warehouses 2000 to 2099

3000 to 3909 12 Utilities, miscellaneous 0000, 0100, 4100 to 4999

6100 to 7584 7700 to 9999

Note: Mlystys is the abbreviation used as the heading for the 18th field of the Property Valuation Register

indicating number of storeys of the building on the land parcel. Mlystys = 0 or 1 indicates single storey,

Mlystys = 2 indicates two storeys.

4.7 Estimation of the embodied energy of single storey detached houses

The embodied energy of each single storey detached house listed on the Property

Valuation Register was estimated from the product of the ‘built area’, the relevant default

embodied energy intensity and any variations to the embodied energy intensity as given in

Table 4.4. The relevant default embodied energy intensity was selected by means of the

‘year built’ field for each record enabling one of the four to be used. A ‘LOOKUP’

function in Excel software was then used to vary the embodied energy intensity for wall

and roof materials that differed from the default selections.

Finally, the embodied energy of each house was determined from the product of the

embodied energy intensity and the ‘built area’ field. Due to the limit in Excel software of

60,000 records, three Excel files were necessary to list all of the records of titles with land

use codes corresponding to single storey detached houses (147,032 in total) in the six local

government areas selected for this study. Once the embodied energy of each title had been

estimated, the three Excel files were joined in Microsoft Access and exported as a

Database IV file for use in ArcView 3.3 software.

80

4.8 Presentation of embodied energy in ArcView GIS 3.3

In order that the data provided and its subsequent analysis could be depicted spatially,

further information on the location of each land parcel was obtained from the Department

of Environment & Heritage (DEH) by providing them with a list of Valuation Numbers

only. No other fields were provided to ensure security of data. The DEH data related

Valuation Number to the digital cadastral data base for South Australia (Dept. of

Environment & Heritage, 2003 ). A point coverage was obtained consisting of two

dimensional x and y coordinates of the centroid of each parcel. This information was also

provided in a spatial format by means of the corresponding set of shapefiles enabling each

parcel to be viewed in ArcView GIS 3.3 software. The number of records received from

DEH numbered 235,362 which was 18,803 fewer than in the original State Property

Valuation Register file. This anomaly was explained by inspection and comparison of the

files which showed that there were duplicate records in the original file which were not

present in the DEH file.

Following the derivation of embodied energy previously described in section 4.5 and 4.7,

the reassembled State Property Valuation Register file (source file) was joined to the DEH

shape file (destination file) in ArcView GIS 3.3 software. This enabled the embodied

energy estimates for houses on each parcel of land, which was an attribute in the joined

file, to be presented as a theme. A graduated colour format was selected from the Legend

Editor and the embodied energy chosen as the Classification Field. An inventory of spatial

and data files used for this and subsequent analysis is listed in Appendix 10.

The whole process of deriving the embodied energy for property titles with a single storey

detached houses and presenting in the form of maps is shown in Figure 4.5.

81

Figure 4.5 Representation of method for deriving embodied energy maps

Property Register files (delimited text file)

1a 1b 1c 2 3 12

Excel files containing records for each building type

Spread-sheet 1836 - 1900

Spread-sheet 1901 - 1945

Spread-sheet 1946 - 1978

Spread-sheet 1979 - 2003

DefaultWall Roof

DefaultWall Roof

DefaultWall Roof

DefaultWall Roof

1a ee

1b ee

1c ee

SEED Variations from default (LOOKUP)

4

Database file with embodied energy for each single storey detached house

ArcView

Property Register files

XY cords shape files

Database file with ee

Maps of embodied energy

join join

Building types other than single storey houses for future derivation of embodied energy and mapping

82

4.9 Associated embodied emissions

The greenhouse gas emissions associated with embodied energy consumption are often

referred to as embodied emissions. This section explores the link between embodied

energy and embodied emissions with respect to the development of the proposed model.

The significance of embodied energy to the analysis of urban environment has been

described in Chapter 2 in terms of its contribution to life cycle energy consumption,

valuing of sunk costs, consideration of the re-use of infrastructure and recycling of

building materials. If an analysis was confined only to the environmental risks linked to

climate change, then the narrower indicator of life cycle greenhouse gas emissions (of

which embodied emissions are a part) might be considered in the comparison of alternative

building developments.

The estimation of embodied energy in this research has been carried out in terms of

primary energy as opposed to delivered energy. To an extent, this reflects embodied

emissions as estimations of embodied energy relate to the upstream primary energy

sources. In addition, the emissions factors for fossil fuels used as primary energy in

Australia are of the same order ie very approximately 95, 90, 80 and 70 kg CO2-e/GJ of

brown coal, black coal, liquid fuels and natural gas, respectively for the full fuel cycle

(AGO, 2005). Consideration of all of the upstream energy inputs into materials, which is

implicit in evaluating hybrid embodied energy coefficients partly based on input-output

analysis, draws on a mix of fossil fuel energy inputs to construction materials thereby

reducing the dominance of any particular emissions factor. Furthermore, consideration of

whole buildings consisting of a large number of materials and components, each with their

own mix of fossil fuel energy inputs, further reduces the dominance of any particular

emissions factor. Hence, embodied energy expressed in primary energy terms is an

approximate surrogate for embodied emissions when whole buildings are being compared.

This can be shown by examining the proportions of embodied emissions from the ‘top

twelve’ materials used in typical houses (referred to in Chapter 3) as given in Table 4.6.

Comparing this with Table 3.5 shows an identical order of materials and with broadly

similar proportions for embodied energy and embodied emissions. This can be further

confirmed by examining the proportions of embodied energy and emissions in the

spreadsheets for typical houses in Appendix 8.

83

Table 4.6 Twelve materials with the most embodied emissions in typical houses

Material % of total Brickwork 18.1 Concrete 15.6 Steel 14.9 Timber 14.2 Concrete products 7.2 Carpet 5.6 Appliances 3.3 Plasterboard 2.8 Aluminium 3.2 Insulation 2.0 Glass 1.6 Timber Products 1.6 Total 89.9

However, the method described in this research which estimates the embodied energy of

houses can also be used for estimating embodied emissions. The following section

provides some detail on this facility although the focus in subsequent analysis is on the

broader indicator of embodied energy.

4.10 Estimation of embodied emissions of houses

The estimation of embodied emissions, expressed in terms of carbon dioxide equivalent

(CO2-e) emissions, follows a similar method to that used to derive the embodied energy of

houses. The derivation of carbon dioxide equivalent (CO2-e) coefficients from input-

output tables has been described in Chapter 3. These coefficients are used in an annex to

the SEED spreadsheet to estimate the CO2-e emissions intensities (in kgCO2-e/m2) for a

house. The annexes for typical houses from the four different periods with the default

materials and relevant dimensions are given in Appendix 8a to 8d. Possible variations to

the embodied emissions intensities of the typical houses arising from alternative wall and

roof materials have been evaluated in a similar way to that used in the estimation of

embodied energy and these are given in Table 4.7.

84

Table 4.7 Embodied emissions intensities and variations for

alternative materials (kgCO2-e/m2)

Period Default embodied emissions intensity

Wall Type

Embodied emissions intensity variation

Roof Type Embodied emissions intensity variation

1836 - 675.6 Brick 0.0 Tiled -67.6 1900 Iron 30.5 Galvanised iron 0.0 Rendered 1.4 Corr. asbestos -87.8 Weatherboard -112.0 Steel decking 0.0 Stone, Freestone 80.2 Imitation tile 37.2 Bluestone -39.1 Slate -87.0 Basket Range 51.1 Shingles (asbestos) -41.6 Block 51.1 1901 - 647.5 Brick 0.0 Tiled -13.5 1945 Iron -41.4 Galvanised iron 0.0 Rendered 1.3 Corr. asbestos -71.0 Weatherboard -37.2 Steel decking -13.5 Stone, Freestone 73.0 Imitation tile 54.1 Bluestone 52.6 Slate -70.1 Basket Range 46.5 Shingles (asbestos) -24.7 Block 46.5 1946 - 526.6 Brick 0.0 Tiled 0.0 1978 Iron 23.0 Galvanised iron 62.3 Rendered 0.8 Corr. asbestos -18.7 Weatherboard -60.4 Steel decking 62.3 Stone, Freestone 49.5 Imitation tile 96.6 Bluestone 24.2 Slate -17.9 Basket Range 31.5 Shingles (asbestos) 24.0 Block 31.5 1978 - 494.3 Brick 0.0 Tiled 0.0 2003 Iron 9.7 Galvanised iron 62.4 Rendered 0.7 Corr. asbestos -18.6 Weatherboard -7.4 Steel decking 62.4 Stone, Freestone 9.2 Imitation tile 96.6 Bluestone 1.8 Slate -17.8 Basket Range 4.0 Shingles (asbestos) 24.0 Block 7.6

The embodied emissions of each single storey detached house listed on the Property

Valuation Register was estimated from the product of the ‘built area’, the relevant default

embodied emissions intensity and any variations to the embodied emissions intensity as

given in Table 4.7. Similar ‘LOOKUP’ functions selected the appropriate data for each

record of a single storey house and the embodied emissions was then evaluated from the

product of the final intensity and the ‘built area’ in each case. After similar manipulation

of files including the joining with shapefiles, the embodied emissions of single storey

houses can be depicted spatially in GIS Software as with embodied energy.

85

4.11 Considerations of the embodied energy of infrastructure

The infrastructure associated with houses is a further consideration when estimating

embodied energy. This has been recognized by various researchers and some work has

been carried out on roads (Treloar et al, 2004) and piping systems for water supply,

sewerage systems and stormwater disposal (Ambrose et al, 2002; Pullen et al, 1998). An

issue that must be addressed when considering infrastructure is that of defining the parts of

reticulated systems which are attributable to a particular building ie defining the boundary.

The largest boundary takes into account the whole of the infrastructure system which in the

case of, say, water supply for a residential area would include dams, trunk pipelines and,

possibly, pumping stations. If such a condition is adopted, then there are two methods by

which the embodied energy of the water supply system can be estimated for the particular

dwelling (Pullen et al, 1998). The first method considers all of the water system

infrastructure within a certain area of urban development ie within a boundary. After

evaluating the materials quantities and embodied energy of the infrastructure within this

boundary, the amount of energy can be equally apportioned with a knowledge of the

number of dwellings within the boundary. The second method considers particular

sections or components of the water system between the source and destination. These

sections are typically lengths of different diameter pipes manufactured from various

materials. The amount of embodied energy for each section that is apportioned to the

particular dwelling under study is determined by the number of properties serviced. By

inspecting all of the sections between source and destination and observing the number of

dwellings serviced by each, the embodied energy for the system that is apportioned to a

particular dwelling can be calculated. The advantage of this method compared with the

‘boundary’ method is that the amount of information required is far less. On the other

hand, the energy calculated using the second method is only relevant to the

source/destination system analysed and can not necessarily be applied to adjacent systems.

The smallest boundary considers infrastructure directly related to the cadastral boundary of

the land parcel where the dwelling is situated. In the case of roads, this would be the

length of road running along any part of the cadastral boundary. Where there are houses

on both sides of the street, a factor of 0.5 would apply as the road would be shared.

A disadvantage of taking the largest boundary approach is that its logical extension is to

include all other infrastructure such as airports, docks, harbours, etc and apportion the

86

embodied energy of these facilities to all of the dwellings that they directly or indirectly

service. This becomes both impractical and unrealistic. On the other hand, the smallest

boundary approach would not, in the case of roads, take into account collector roads which

are an essential part of the road network without which residential roads would be isolated.

Clearly, the approach adopted will depend upon the purpose of the analysis. Since one of

the aims of this research is to compare energy consumption of different built forms, the

embodied energy of infrastructure is considered within the boundary of the areas selected

for study.

The embodied energy estimates for roads are based on residential and collector roads

consisting of standard construction (Perkins, 2001) with a base of granular dolomite and a

top of asphalt (with 5% bitumen) details of which are given in Table 4.8. Information on

the road system in South Australia is available in GIS format with data relating to sections

of road of typically 20m to 200m in length. For each of these sections a width is also

provided which can be used to determine the road area and embodied energy. The width

relates to the road reserve which is wider than the paved width of the road and a factor of

0.75 has been used. This is to allow for a pedestrian strip on each side of the road reserve

in metropolitan areas. As well as the embodied energy of the materials in the paved road

areas, a nominal allowance for road construction activities of 10% has been applied in

addition to a maintenance allowance of 15% which corresponds to one resurfacing

operation during the life cycle of roads. Embodied emissions can also be similarly

estimated if desired. When the embodied energy of the road system is required for an area

of housing, then the various road sections within the study area are selected in the ArcView

software. The sum of the embodied energy of the road sections can then be obtained and

apportioned to the number of houses in the study area.

Table 4.8 Embodied energy intensity of roads

Road type Material Material intensity (kg/m2)

Embodied energy

coefficient (MJ/kg)

Embodied energy

intensity (GJ/m2)

Embodied emissions intensity

(kgCO2-e/m2)

Residential Dolomite base 625 1.7 Asphalt top 72 2.6 1.6 125 Collector Dolomite base 736 1.70 Asphalt top 96 2.6 1.9 150

Examples of the depiction of embodied energy and emissions of houses in selected parts of

the Adelaide metropolitan area are shown in the following chapter.

87

The estimation of other urban infrastructure within the boundaries of suburban areas

selected for study is developed further in Chapter 6. This infrastructure includes water

supply, sewer pipes, storm water disposal, gas and electricity supply, street lighting and

telephone service.

4.12 Summary

This chapter has described the principles underlying the development and construction of a

model which spatially depicts the embodied energy of houses in the Adelaide metropolitan

area. The model combines the three components of embodied energy theory, property

register data and geographical information systems software.

The South Australian State Property Valuation Register contains important information on

the buildings constructed on the individual land parcels for the purposes of estimating

embodied energy. This includes built area, wall materials and roof materials. Other

building features for estimating the embodied energy of houses is not recorded, such as

type of floor and ceiling heights. The missing data has been derived using techniques

based on the development of house design and construction over the period of time since

the South Australian colony was founded. Since property register data is one of the three

components of the model, its comprehensiveness is a key factor of the model . This aspect

will become more significant when the principles used in this chapter are applied to an

alternative case study in Chapter 6.

88

Chapter 5 Spatial Representation of Results 5.1 Introduction

This chapter presents aspects of the model for the spatial depiction of the embodied energy

of residential areas in the urban environment. The model is based on the integration of

embodied energy theory and property data, previously described in Chapters 3 and 4, with

GIS software. This provides a database from which statistics and GIS maps can be sourced

relating to the embodied energy of houses and infrastructure in the Adelaide metropolitan

area. The feasibility of depicting embodied energy in a spatial format is shown, as well as

the provision of links with other components of residential energy expenditure and these

comply with the requirements of Objective 3 described in the outline of this thesis. The

verification of the model is addressed in terms of an estimation of potential error in

depicting embodied energy.

5.2 Predominance of houses in the built form of metropolitan Adelaide

Table 5.1 shows the proportions of property titles with different types of land use in the

metropolitan area of Adelaide corresponding to the Adelaide Statistical Division. These

different types of land use have been aggregated according to 12 groups based on building

type as described in Chapter 4. The 12 groups commence with single storey detached

Table 5.1 Proportions of property titles according to aggregated building types in the

metropolitan area of Adelaide

Group Land use Number Proportion of: of titles All

dwellings (%)

All buildings

(%)

All titles (%)

1 Single storey detached houses 322688 75.1 71.4 65.2 2 Two storey detached houses 21695 5.0 4.8 4.4 3 Maisonettes, semi-detached houses 60105 14.0 13.3 12.1 4 Row houses 5803 1.4 1.3 1.2 5 Two storey flats and maisonettes 699 0.2 0.2 0.1 6 Higher rise apartments and flats 12372 2.9 2.7 2.5 7 Hotels, hostels, student accomm. 6287 1.5 1.4 1.3 8 Retail premises, personal services 7428 1.6 1.5 9 Commercial offices, showrooms 8401 1.9 1.7

10 Institutional, government 3463 0.8 0.7 11 Factories, warehouses 3247 0.7 0.7 12 Vacant land, utilities, primary inds. 42851 8.7

89

houses (group 1) and conclude with a miscellaneous group which includes vacant land,

utilities and primary industries (group 12). It can be seen that dwellings (groups 1 to 7)

dominate the built form in terms of numbers of property titles. Collectively these seven

groups constitute 86.8% of all property titles or 95.1% of all titles with buildings (groups 1

to 11). Within the seven groups describing dwellings, single storey houses are the

dominant type of residence comprising 75.1% of property titles.

The predominance of dwellings, particularly single storey detached houses, compared with

other types of buildings is very clear as far as numbers of property titles is concerned.

However, this may not necessarily be the case if the total floor area of the different groups

is considered as the floor area of houses is often much less than the floor area of factories,

warehouses, commercial offices or retail premises. Furthermore, a comparison of floor

area would provide a more relevant perspective as it is one factor that is related to the

quantities of construction materials used in a building and their combined embodied

energy. For this reason an analysis of floor area of the property register for the

metropolitan area of Adelaide was undertaken. ‘Built area’ is the relevant field in the

Property Register from which data can be obtained although this is not complete for some

titles.

Table 5.2 shows the number of titles in each group where records of building floor area

were complete. Generally, the records for dwellings (Groups 1 to 7) were more complete

than other groups. Based on the complete records that were available, an average floor

area for each group was calculated and is given in the fourth column of this table. It

should be noted that complete records where the floor area was stated at less than 40m2

were not considered. This represented an attempt to eliminate titles where small buildings

or outhouses had been constructed without a main building. For instance, some titles in

Group 1 ‘Single storey detached houses’ indicated the construction of a shed but no

dwelling. A larger threshold floor area (> 40m2) was not considered suitable as this would

have eliminated a number of complete titles in Group 3 ‘Maisonettes, semi-detached

houses’ arising from a large number of small semi-detached home units.

90

Not surprisingly, this analysis shows significantly larger floor areas for non-dwelling type

buildings. Higher rise apartments and flats (group 6) have the lowest average floor area of

75m2 and there is a significant difference between the average floor area of single storey

detached houses (142m2) and two storey detached houses (201m2).

Table 5.2 Notional total floor areas for the different groups of property titles corresponding to building types

Group Land use Number of title

Average floor area

Notional total floor

Notional proportion

records complete

for each title (m2)

area (hectares)

of total floor area

(%) 1 Single storey detached houses 352569 142 4582 61.0 2 Two storey detached houses 21277 201 436 5.8 3 Maisonettes, semi-detached houses 26294 95 571 7.6 4 Row houses 3316 134 78 1.0 5 Two storey flats and maisonettes 186 251 18 0.2 6 Higher rise apartments and flats 11862 75 93 1.2 7 Hotels, hostels, student accomm. 3518 115 72 1.0 8 Retail premises, personal services 1268 584 434 5.8 9 Commercial offices, showrooms 2094 495 416 5.5

10 Institutional, government 472 701 243 3.2 11 Factories, warehouses 599 1761 572 7.6

A profile of floor areas of different types of buildings within the Adelaide metropolitan

area was obtained from the products of the total number of titles in each group (including

both complete and incomplete records) and the average floor area for each group. This

provided a notional total floor area (in hectares) for each group as shown in the fifth

column of Table 5.2. Given the limitations of this analysis with regard to numbers of

complete records, there is a suggestion that the floor area of all dwellings (Groups 1 to 7) is

very large (77.9%) compared with other non-dwelling groups of buildings. Single storey

detached houses (Group 1) constitute 78.3% of the floor area of all dwellings and 61% of

the estimated floor area of all buildings in the Adelaide metropolitan area and this is shown

graphically in Figure 5.1. Although the embodied energy intensity (ie the energy per

square metre of floor area) will differ between different building groups, this analysis does

suggest that single storey houses are the dominant type of building as far as embodied

energy in the Adelaide built environment is concerned. Later results will substantiate this.

91

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Gro

up 1

Gro

up 2

Gro

up 3

Gro

up 4

Gro

up 5

Gro

up 6

Gro

up 7

Gro

up 8

Gro

up 9

Gro

up 1

0

Gro

up 1

1

Not

iona

l pro

porti

on o

f tot

al fl

oor a

rea

(%)

Figure 5.1. Notional proportion of total floor area for building groups

The average floor area for single storey houses of 142m2 appears low by modern standards

and is a result of the influence of a number of factors including:

a) smaller floor areas for existing smaller houses constructed in the post second world war

period

b) the fact that data available for analysis for the Adelaide Statistical Division did not

include houses constructed after 2001 when floor areas have increased

c) the estimation of average floor areas included a number of titles with large sheds and

outhouses over 40m2 but no main dwelling.

5.3 Age distribution of single storey houses

The age distribution of single storey detached houses is shown in Figure 5.2. Only 16.6%

of the existing single storey detached housing stock were constructed before the Second

World War. This proportion does not represent the actual number of houses constructed as

some dwellings from this period would have been demolished to make way for new

construction.

A large proportion of houses were constructed after the Second World War, particularly in

the 1960s and 1970s when there was significant migration to South Australia. Construction

of single storey detached houses in the two decades up to the turn of the turn of the

92

twentieth century was at a more modest pace of approximately 5000 per year in the

Adelaide Statistical Division area.

In terms of the periods selected for this research which define particular house types, the

proportions are given in Table 5.2. (Note that these proportions differ slightly from those

given in Table 3.4 as the Chapter 3 data was exploratory in nature and was based on the

sample of six local government areas from the Adelaide Statistical Division). These data

indicate that the greatest contributions of the single storey detached housing stock to the

embodied energy of the metropolitan built environment are from house types 3 and 4 ie

houses with double brick walls/suspended timber floors and brick veneer walls/concrete

slab on ground and constructed in the periods between 1946 – 1978 and 1979 - 2001,

respectively. This table also shows the average floor area for houses in the four periods

corresponding to the different house types.

0

5

10

15

20

25

Period of construction

Pro

porti

on o

f sin

gle

stor

ey h

ouse

s

Figure 5.2 Age distribution of single storey houses in metropolitan area of Adelaide

The larger floor areas of nineteenth century houses is likely to be due to the fact that

grander dwellings using more durable materials and higher construction standards have a

greater survival rate.

93

Table 5.2 Proportion of defined types of single storey houses

House type

Period Proportion(%) Average floor area (m2)

1 1836 – 1900 3.5 157 2 1901 – 1945 13.6 149 3 1946 – 1978 53.1 129 4 1979 – 2001 29.7 161

5.4 Embodied energy of single storey detached houses

Analysis of the sample of 254,165 records from six metropolitan local government

councils resulted in a distribution of embodied energy of single storey detached houses as

shown in Figure 5.3. This depicts the as-built embodied energy which is the embodied

energy of the materials and components used to construct the houses. An estimate for the

on-site energy for construction activities has also been included. For houses, there is some

research indicating that construction energy could be in the region of 6 – 10% of the

embodied energy of the construction materials (See, 1998) and possibly higher for multi-

storey commercial and residential buildings. In this research, a factor of 7% has been used

for the on-site construction energy. Further increases in embodied energy over the life

cycle of the houses due to maintenance and refurbishments have not been considered at

this stage. Approximately 71% of houses have an as-built embodied energy of between

601 and 1200 GJ.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Up

to 6

00

601

- 800

801

- 100

0

1001

- 12

00

1201

- 14

00

1401

- 16

00

1601

- 18

00

1801

- 20

00

Mor

e th

an20

00

As-built embodied energy of houses (GJ)

Prp

ortio

n of

hou

ses

(%)

Figure 5.3 Proportions of as-built embodied energy of houses in the metropolitan area

94

Examination of the as-built embodied energy of single storey detached houses over the

period since 1841 shows an overall decreasing trend. However, there is some indication

that this trend is reversed over the last 60 years as can be seen in Figure 5.4 which is

consistent with increasing floor area. Of course, the majority (over 80%) of existing single

storey houses were constructed in the period since the Second World War so that data for

the period before this are not as numerous. Pre-Second World War houses which are still

surviving are likely to be constructed more substantially with higher ceiling heights and

materials of greater durability which generally have a higher embodied energy based on

contemporary energy analysis.

0

200

400

600

800

1000

1200

1400

1600

1800

1841

- 18

60

1861

- 18

80

1881

- 19

00

1901

- 19

20

1921

- 19

40

1941

- 19

60

1961

- 19

80

1981

- 20

00

Period of construction

Ave

rage

as-

built

embo

died

ene

rgy

(GJ)

Figure 5.4 Average as-built embodied energy for houses from certain periods

5.5 Spatial depiction of the embodied energy of single storey houses

To show examples of the spatial depiction of embodied energy of single storey detached

houses, six suburbs have been selected based on the factors of age of buildings, geographic

location within the north/south corridor of metropolitan Adelaide, and type of construction

and materials (See Figure 5.5). Brahma Lodge is a suburb approximately 15 kilometres to

the north east of the Adelaide central business district. Single storey houses with relatively

small floor areas dominate the suburb and were constructed mainly in the mid to late 1960s

with timber suspended floors and double brick external walls.

Norwood is an older inner suburb located approximately 3½ kilometers to the east of

the Adelaide CBD and comprises a mix of residential and retail uses constructed over

a longer period since the end of the nineteenth century. Consequently, the dwellings

consist of a mix of types and of varying age with a considerable amount of recent

reconstruction of dwellings of modest floor area. The result is a suburb of higher built

density than middle or outer suburbs.

The Adelaide City central area is comprised of approximately 11,700 land titles and a

relative few of these (just over 600) are recorded according to their land use codes as

single storey detached houses as defined by the Group 1 classification of this research.

These dwellings are usually of older construction with relatively small floor area and

located in the south east and south west corners of the city area. Most other dwelling

forms are also represented as part of the residential building stock of this area

including row houses, two storey houses and multi-storey dwellings and apartments.

NOTE: This figure is included on page 95 of the print copy of the thesis held in the University of Adelaide Library.

Figure 5.5 Map showing six locations chosen as examples for the depiction of

embodied energy (source: Atlas of South Australia (http://www.atlas.sa.gov.au) 2007)

95

96

A range of ages of dwellings is evident of the suburb of Hawthorn as this area developed

over a period of time. Single storey detached houses of larger proportions dominate the

suburb which is 5 kilometres from the CBD although some titles have been used for the

construction of units reflecting the popularity of this location. Of the 580 titles, 380 were

constructed from 1901 to 1945 and 47 before 1900. Consequently, this case study area has

significant diversity in the age, style, size and material of construction of its housing

Two relatively new residential areas to the south of Adelaide have also been selected as

examples of developments in the outer southern suburban suburbs. These were

constructed in the 1990s and predominantly consist of single storey detached houses in a

low density configuration . They are located at the Woodcroft and Seaford suburbs being

21 and 30 kilometres from the Adelaide CBD, respectively.

5.6 As-built embodied energy maps

The following maps depict the as-built embodied energy of single storey detached houses

in the selected areas previously described. This amounts to the embodied energy of the

materials and components and the on-site construction energy. This does not include any

estimation of the materials used for maintenance or periodic refurbishment.

97

Census collectors districts

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Brahma Lodge suburb

0.2 0 0.2 Kilometers

Figure 5.6 Embodied energy map of houses in the Brahma Lodge suburb

Figure 5.6 shows the Brahma Lodge suburb to the north of Adelaide revealing a

consistency in embodied energy as would be expected with most of the houses of similar

construction and size. Although some land lots indicate houses of between 1000 and

1500GJ embodied energy, the majority are in the range 500 – 1000GJ with just a few

houses over 1500GJ. The map is also shown with the boundaries of census collector’s

districts, each containing approximately 200 – 300 lots, indicating the potential for linking

embodied energy with information data for houses collected by the Australian Bureau of

Statistics. Such information includes data on vehicle ownership and journey to work

98

distances which can be interpreted to give a measure of transport energy as part of an

extended energy analysis.

Census collectors districts

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Norwood suburb

0.2 0 0.2 Kilometers

Figure 5.7 Embodied energy of single storey detached houses in the Norwood suburb

By comparison, the embodied energy of single storey houses in the Norwood suburb is

more variable with a greater proportion in the higher categories and some greater than

2000GJ. This is consistent with a housing stock constructed over a significant period of

time using different designs, construction methods and materials. Figure 5.7 shows this

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variability as well as a large proportion of land lots with other types of land use codes such

as retail premises, double storey houses, home units and flats consistent with the built form

in this denser inner suburb.

Census collectors districts

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Adelaide city centre - south east corner

0.2 0 0.2 Kilometers

Figure 5.8 Embodied energy of single storey detached houses in the

south east part of the Adelaide City centre.

The map shown in Figure 5.8 depicts the embodied energy of single storey detached

houses in the south east part of Adelaide City centre. It reveals a similar range of

embodied energy for single storey houses as the inner suburb of Norwood but with a

100

greater proportion of land lots with other land use codes in keeping with the increased

range of activities in this part of the urban environment.

Census collectors districts

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Hawthorn suburb

0.2 0 0.2 Kilometers

Figure 5.9 Embodied energy of single storey detached houses in the Hawthorn suburb

The map of the Hawthorn suburb shown in Figure 5.9 shows the dominance of single

storey houses but with a variable and generally higher level of embodied energy. This is

consistent with a significant proportion of larger and older houses on land lots of generous

proportions in a relatively low density configuration.

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Census collectors districts

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Woodcroft suburb

0.2 0 0.2 Kilometers

Figure 5.10 Embodied energy of single storey houses in the Woodcroft suburb

The embodied energy map for the Woodcroft suburb shows a range of values with a small

proportion greater than 2000GJ. Compared with the outer northern suburb of Brahma

Lodge, a greater proportion of higher embodied energy values is shown in Figure 5.10

which is consistent with more modern houses with larger floor areas.

102

Census collectors districts

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Seaford suburb

0.2 0 0.2 Kilometers

Figure 5.11 Embodied energy of single storey houses in the Seaford suburb

Broadly similar features can be seen in the map of the Seaford suburb in Figure 5.11

although there is a lower proportion of houses with higher embodied energy values which

is consistent with a development where the uniformly smaller land lot size influences

house floor area.

5.7 As-built embodied emissions map

Figure 5.12 shows the greenhouse gas emissions in terms of tonnes of carbon dioxide

equivalents associated with the embodied energy of single storey houses in the Hawthorn

103

suburb. Although the main thrust of this research is concerned with embodied energy, the

embodied emissions are depicted to demonstrate the capability of the model and the

As-built embodied emissions< 5 0 (t)50 - 100 (t)100 - 150 (t)150 - 200 (t)> 200 (t)

Lot boundaries

Hawthorn suburb

0.2 0 0.2 Kilometers

Figure 5.12 Embodied emissions (carbon dioxide equivalents) of single storey detached

houses in the Hawthorn suburb

possibility for a more comprehensive analysis which compared total embodied emissions

of different residential areas. Examination of Figure 5.12 in conjunction with Figure 5.9

for the same suburb indicates similar high and low levels for houses on particular land lots

showing that embodied energy estimations based on primary energy are a reasonable

surrogate for embodied emissions.

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5.8 Embodied energy with maintenance map

The embodied energy of buildings can be estimated at particular points in their life cycle

other than in the as-built condition. Further embodied energy is cumulated as a result of

maintenance (such as repainting), repair (such as renewal of deteriorated timber) and

Brahma Lodge suburb

0.2 0 0.2 Kilometers

Embodied energy with maintenance< 500 (GJ)500 - 1000 (GJ)1000 - 1500 (GJ)1500 - 2000(GJ)> 2000 (GJ)

Lot boundaries

Figure 5.13 Embodied energy with maintenance for houses

in the Brahma Lodge suburb

periodic renewal of components and materials (such as carpets, kitchen and bathroom

fixtures and fittings). Research suggests that this is of the order of 10% per decade of the

initial as-built embodied energy (Treloar et al, 2000; Pullen, 2000a). More recent analysis

105

of dwelling maintenance by Tweedie (2005) canvassed the views of a number of

householders, designers, builders, tradesmen and demolition contractors on the frequency

of maintenance, repair and renewal of materials and components in houses in South

Australia. Converting the overall findings to embodied energy confirmed the earlier

estimates of additional energy input required over the lifetime of dwellings of 1% per

annum.

Embodied energy with maintenance can be estimated at any point during the lifetime of a

building up to the full life cycle. As an example of the possibilities for the spatial

depiction of embodied energy with maintenance, Figure 5.13 shows houses in the Brahma

Lodge suburb after approximately 40 years of assumed maintenance. The embodied

energy with maintenance can be evaluated for each dwelling by reference to the ‘Year

built’ field in the Property Register. Comparison with Figure 5.6 shows that as-built

embodied energy for the majority of houses is in the range of 500 to 1000 GJ but this

changes to 1000 to 1500 GJ when the 40 years of maintenance embodied energy is

included.

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As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundariesRoads

Adelaide city centre - south east corner

0.2 0 0.2 Kilometers

Figure 5.14 Highlighted roads in the Adelaide city centre area

5.9 Embodied energy of roads

Figure 5.14 shows the lot boundaries, as-built embodied energy of single storey detached

houses and the location of roads in a part of the Adelaide city centre. A small area of roads

is shown highlighted in yellow to demonstrate the technique of estimating the embodied

energy of this component of infrastructure. By interrogating the ArcView software, the

embodied energy of the roads can be determined and apportioned to the houses in this area.

107

5.10 Life cycle energy of houses

The purpose of depicting embodied energy in the urban environment is to contribute to a

better understanding of energy consumption over the whole life cycle of buildings. Hence,

the embodied energy maps can form a baseline of energy expenditure upon which other

energy inputs can be superimposed to provide a more comprehensive analysis.

This section provides an example of life cycle energy of dwellings based on small samples

of 30 houses in the outer northern suburb of Brahma Lodge and the inner southern suburb

of Hawthorn. The operational energy of the houses has been calculated in terms of annual

primary energy consumption based on both electricity and gas usage over two consecutive

years in the period 1999 – 2001. The electricity and gas data was originally obtained for

the pilot study project of energy consumption in Adelaide (Troy et al, 2002). This project

aimed to provide energy ‘profiles’ of six areas in Adelaide, most of which were residential

in nature, where the profiles combined embodied energy, operational and transport energy.

The maps shown in Figures 5.15 and 5.16 represent the first time that operational energy

has been combined with embodied energy to depict life cycle energy consumption of

houses in Adelaide in a spatial format.

The embodied energy component (in primary energy terms) has been estimated based on a

nominal life cycle of 70 years for the houses and consists of:

• As-built embodied energy of the houses which includes the energy of the materials

at the completion stage and the on-site energy usage for the construction activities.

• Embodied energy for maintenance and periodic renewal of materials and

components in the houses but with minimal maintenance activity during the first

and last five year periods of the 70 year life.

• The embodied energy of residential roads and any collector roads in the areas of the

house samples. Within these areas, the embodied energy of roads has been equally

apportioned on a per house basis. This amounted to 303 GJ, 355 GJ and 290 GJ for

houses in the Brahma Lodge, Hawthorn and Woodcroft suburbs, respectively.

The operational energy has been converted to primary energy using factors of 3.17 and

1.25 for electricity and gas, respectively (SA Govt, 1998) and this enabled the total

primary operational energy to be calculated. A proportion of houses in the three suburbs

consumed both normal tariff and off peak electricity and the quantities of these were

summed to determine the total annual electricity consumption. For the samples of houses

108

in the Brahma Lodge, Hawthorn and Woodcroft suburbs, the average annual operational

energy (in primary energy terms) was 110 GJ, 124 GJ and 87 GJ, respectively. (The

standard deviations were 41.6, 45.7 and 37.0 GJ). The life cycle energy is the combination

of the 70 year embodied energy components and the annual operational energy multiplied

by 70 and this assumes an unchanging consumption of electricity and gas usage from year

to year.

Figure 5.15 shows the life cycle energy of the sample of houses in the Brahma Lodge

suburb in the form of graduated monochromatic blue. This has been superimposed on the

baseline embodied energy for the whole area (in red). Note that the divisions in the

embodied energy legend have been increased compared with earlier maps (Figures 5.5 to

5.10) to accommodate the increases due to additional embodied energy during the life

cycle of houses.

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Lot boundaries

Embodied energy< 800 GJ800 - 1600 GJ1600 - 2400 GJ2400 - 3200 GJ> 3200 GJ

Life cycle energy< 4000 GJ4000 - 8000 GJ8000 - 12000 GJ12000 - 16000 GJ> 16000 GJ

Area within Brahma Lodge suburb

0.2 0 0.2 Kilometers

Figure 5.15 Life cycle energy of houses in Braham Lodge suburb

It can be seen that the embodied energy of most of the houses in the area over a 70 year

period (in red) is in the region of 800 to 1600 GJ but with a sizeable minority in the 1600

to 2400 GJ band or higher. When the operational energy is superimposed on the embodied

energy, then life cycle energy consumption (in blue) over 70 years is spread over the two

bands of 400 – 8000 GJ and 8000 to 12000 GJ.

A similar map is provided for the Hawthorn suburb in Figure 5.16. Here, the embodied

energy over 70 years is represented in the bands 1600 to 2400 GJ, 2400 to 3200GJ and

greater than 3200 GJ which is higher than that of the Brahma Lodge area.

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Lot boundaries

Embodied energy< 800 GJ800 - 1600 GJ1600 - 2400 GJ2400 - 3200 GJ> 3200 GJ

Life cycle energy< 4000 GJ4000 - 8000 GJ8000 - 12000 GJ12000 - 16000 GJ> 16000 GJ

Area within Hawthorn suburb

0.2 0 0.2 Kilometers

Figure 5.16 Life cycle energy of houses in Hawthorn suburb

The life cycle energy of the sample of houses is mainly in the 8000 to 12000 GJ band

although there are some houses in the bands below and above this. Overall, the life cycle

energy of the Hawthorn sample of houses is greater than that of the Brahma Lodge sample.

This is consistent with both a greater embodied energy and operational energy for the

Hawthorn suburb.

Figure 5.17 shows a similar comparison for an area within the outer southern Woodcroft

suburb. Generally, the life cycle embodied energy is less than for Hawthorn but greater

than Brahma Lodge. This is consistent with modern brick veneer houses compared with

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large older dwellings (Hawthorn) and older smaller dwellings (Brahma Lodge). The

average annual operational energy for Woodcroft of 87 GJ is less than that of both Brahma

Lodge and Hawthorn. This may be as a result of better insulation in the more recently

constructed Woodcroft houses although this is speculative bearing in mind the limited

sample size. The combination of 70 year operational energy and life cycle embodied

energy results in a similar distribution of total life cycle energy to that of Brahma Lodge.

As-built embodied energy< 500 GJ500 -1000 GJ1000 -1500 GJ1500 -2000 GJ> 2000 GJ

Lot boundaries

Life cycle energy<4000 GJ4000 - 8000 GJ8000 - 12000 GJ12000 - 16000 GJ>16000 GJ

Area within Woodcroft suburb

0.2 0 0.2 Kilometers

Figure 5.17 Life cycle energy of houses in Woodcroft suburb

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5.11 Verification and Error Assessment

5.11.1 Background

Having constructed and demonstrated a spatial model of the embodied energy of

residential areas in an urban environment, the questions are raised of how representative is

the model, how can it be verified, what is the potential for error and does it provide

sufficient accuracy for use as an analytical tool? These questions arise within the

limitations already stated in Chapter 3 relating to:

(a) the system boundaries set for estimating embodied energy coefficients

(b) possible errors in the source data for particular editions of input-output tables

(c) the fact that it is the replacement embodied energy that is depicted not the actual

embodied energy consumed during the period of house construction

The method used here for verification within the limitations described, consists of

identifying possible locations in the derivation of the model where there is likely to be

substantial potential for error. High and low values of variables are then applied in the

form of a sensitivity analysis. The locations in the model where there is likely to be

potential for error can be divided into those that arise from:

• the SEED spreadsheet which is used to estimate embodied energy of houses where

there is an inherent potential for error principally arising from input–output analysis

and calculations of materials intensities.

• assumptions about certain materials and dimensions specified in the four typical

house types corresponding to the relevant historical periods which may result in a

potential for error.

5.11.2 Potential for error in the SEED spreadsheet

The sources of error in the SEED spreadsheet arise from the various databases on which it

depends. These databases are Input-Output Data, Energy Prices, Materials Prices and

Materials Intensities. An analysis of these errors has previously been carried out (Pullen,

1996) and the details are provided in Appendix 11 in the form of a conference paper. In

summary, the analysis resulted in the following estimates of potential error.

(a) Input-Output Data

The main sources of potential error in manipulating input-output data are Double Counting

and Homogeneity. Double Counting has been eliminated from the input-output analysis.

Homogeneity refers to the extent that the industrial sectors of the national economy

produce a single output and have a single input structure and this varies according to each

particular industrial sector. For instance, sector 2601 Glass and glass products is not

113

particularly homogeneous as it includes glass containers and scientific glassware as well as

float glass for windows, all of which have different process characteristics. A method has

been developed to estimate the potential error arising from this factor for the embodied

energy of the materials in a typical house and this estimate is ±18.2% (See Appendix 11).

(b) Energy Prices

Input-output tables include two composite energy sectors known as1100 Coal, oil and gas

and 2501 Petroleum and coal products. No information is available for specific prices that

are paid by building materials producers for these composite sectors and, for this reason,

the potential error for possible variations in prices has been assessed based on certain

assumptions. This introduces an under-estimate of up to 3.6% in embodied energy for the

materials in an average house (See Appendix 11).

(c) Materials Prices

The most likely sources of error in building materials prices are associated with those

which were unavoidably derived from current prices and converted to 1996/97 values

using building materials price indices. An assessment of this error amounts to ± 2% on the

embodied energy for the materials in an average house (See Appendix 11).

These three databases are used in deriving the embodied energy coefficients based on

input-output analysis. However, this research uses hybrid input-output analysis where a

substantial portion of each coefficient is substituted with process analysis data. It is

reasonable to assume that the process analysis data does not have any significant potential

for error as it is taken from actual material production and process energy figures. Hence,

the estimates for error from Input-Output Data, Energy Prices and Materials Prices

databases can be moderated by a factor of 50.8% (or 0.51) since the proportion of

embodied energy of typical post world war two houses defined by the process component

is 49.2% (see Chapter 3).

(d) Materials Intensities

Potential errors in this database arise from possible variations in the densities of materials

and derived area/quantity relationships. Taken together, the potential error for a typical

house results in a variation of ± 6.6% on the embodied energy (see Appendix 11).

5.11.3 Potential for error in the selection of materials and dimensions

The potential for error in this category will vary according to which historical period is

considered. For post second world war houses, the greatest period of uncertainty is from

about 1970 through to the middle 1980s when the transition from timber to concrete floors

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and from double brick to brick veneer walls was occurring. Other than this period, the

variations that may have occurred from the ‘typical’ 1979 – 2003 house or from the

‘typical’ 1946 – 1978 house, which were not itemised in the Property Valuation Register,

would not have been as significant but would have included ceiling height, window frame

material and extent of paving.

It is not feasible to survey every house in a large urban area to determine these variables

and sampling is unlikely to provide sufficient certainty unless a very large sample size is

used. The approach taken for the verification of the model in this respect is to compare

two conditions, one being the base condition and the other representing a set of possible

variations. This is undertaken for post Second World War single storey houses which

constitute approximately 83% of this type of dwelling. Hence the comparisons are made

on the ‘typical’ (1946 – 1978) house and on the ‘typical’ (1979 – 2003) house. In addition

a further comparison is made on houses in the transition period 1970 to the mid 1980s

limiting the analysis to floor and wall variations.

(a) Typical 1946 – 1978 house

Using the SEED spreadsheet, the embodied energy of two solid brick houses with timber

floors were compared. The default version featured a 2.7m ceiling height, and typical

paved area. The higher energy version featured a 3.0m ceiling height and paved area

enlarged by a factor of 1.25. The embodied energy of the higher energy version was

+4.4% compared with the default version. The SEED spreadsheets for this comparison are

given in Appendix 12 (parts a and b).

(b) Typical 1979 – 2003 house

Using the SEED spreadsheet, the embodied energy of two contemporary brick veneer

houses with concrete floors were compared. A lower energy version featured a 2.4m

ceiling height, timber window frames and typical paving area. The higher energy version

featured a 2.7m ceiling height, aluminium window frames and paving area enlarged by a

factor of 1.25. Compared with the embodied energy of the default house for that period

(2.4m ceiling height, aluminium window frames and typical paved area), the differences

were +2.8% for the higher energy version and -4.9% for the lower energy version. The

SEED spreadsheets for this comparison are given in Appendix 12 (parts c and d).

(c) House in the early 1970s to mid 1980s transition period

This comparison considered only the two conditions in the transition period ie timber floor

with double brick walls and concrete floor with brick veneer walls. The spreadsheets for

these conditions are provided in Appendix 12 (parts e and f). This analysis indicates that

115

for the proportion of houses constructed in 1978 or before, but which were built with

concrete floors and brick veneer walls, the model over-estimates the embodied energy

8.8%. Conversely, for the proportion of houses constructed after 1978, but which were

built with timber floors and double brick walls, the model under-estimates the embodied

energy by 9.7%. Although the change in construction technology was significant, the

difference in embodied energy is moderated by the fact that the higher embodied energy of

a concrete slab-on-ground floor is compensated by the lower embodied energy of brick

veneer external walls (in conjunction with timber framed internal walls). Since the

maximum proportion of houses in the transition period between the early 1970s and mid

1980s that can be constructed with the alternative materials is 50%, a factor 0.5 has been

used for evaluating the modified potential error as shown in Table 5.3.

5.11.4 Combined potential for error in the model

Table 5.3 summarises the various sources of potential error in terms of both possible

underestimates and overestimates. Since the sources are independent, a likely percentage

combined error is expressed as the square root of the sum of the squares of the individual

percentage errors.

Table 5.3 Potential error in spatially representing embodied energy of houses

Source Underestimate (%) Overestimate (%)

Potential Error

Factor

Modified error

Combined

Potential error

Factor

Modified error

Combined

Input-output 18.2 0.51 9.3 18.2 0.51 9.3 Energy prices 3.6 0.51 1.8 0 - 0 Materials prices 2.0 0.51 1.0 2.0 0.51 1.0 Materials intensities 6.6 - 6.6 6.6 - 6.6 Selection 1946-1978

4.4 - 4.4 12.4 0 - 0 11.5

Selection 1978-2003

2.8 - 2.8 11.9 4.9 - 4.9 12.5

Selection, transition 1970 to mid 1980s

9.7 0.5 4.9 12.6 8.8 0.5 4.4 12.4

Therefore it can be said that as a broad rule-of-thumb, the combined potential for error in

the model in representing embodied energy is approximately ±12%. The transition period

from the early 1970s to the mid 1980s increases this slightly.

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5.12 Summary

This chapter has presented information taken from the model for the spatial depiction of

the embodied energy of residential areas in the urban environment. The predominance of

single storey houses in the built form in the Adelaide metropolitan area in terms of both

numbers of buildings and floor area has been shown. The model has also provided maps

depicting the embodied energy of residential areas and these have formed a baseline for the

more comprehensive analysis of energy consumption in the built environment. This

includes the embodied energy in road infrastructure and the additional embodied energy

consumed in the maintenance of dwellings. The superimposition of the operational energy

for a sample of houses has enabled an estimate of life cycle energy consumption to be

made and depicted in a spatial format. In addition, the model has provided a means of

linking with census collectors districts from which information on the transport energy

consumed by residents may be obtained. The potential for error in the process of

estimating embodied energy of houses in the Adelaide urban environment has been

assessed and amounts to approximately ±12%.

Overall, this chapter has demonstrated that the embodied energy of residential areas can be

estimated and represented spatially as a contribution to the mapping of total energy

consumption in the built environment. This fills a gap in the current knowledge of urban

energy consumption and offers the possibility of more comprehensive analyses.