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A multi-scale life cycle energy and greenhouse gas emissions analysis

model for residential buildings

André Stephana,b,c,1

, Robert H. Crawfordc

aAspirant du F.R.S.- FNRS – Research fellow at the Belgian National Fund for

Scientific Research

bBuilding, Architecture and Town Planning, Université Libre de Bruxelles, 50 Av.F.-D.

Roosevelt, Brussels 1050, Belgium

cFaculty of Architecture, Building and Planning, The University of Melbourne, Victoria

3010, Australia

1Corresponding Author: André Stephan

Tel: +61 452 502 855, +32 485 443 248

Fax: +32 2 650 27 89

e-mail: andre.stephan@ulb.ac.be

Current address:

Faculty of Architecture, Building and Planning

The University of Melbourne

Victoria 3010,

Australia

Permanent address:

bBuilding, Architecture and Town Planning

Université Libre de Bruxelles (ULB)

50 Av.F.-D. Roosevelt,

Brussels 1050

Belgium

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A multi-scale life cycle energy and greenhouse gas emissions analysis

model for residential buildings

Current assessments of residential building energy demand focus mainly on their

operational aspect, notably in terms of space heating and cooling. The embodied

energy of buildings and the transport energy consumption of their occupants are

typically overlooked. Recent studies have shown that these two energy demands

can represent more than half of the life cycle energy of a building over 50 years.

This study presents a holistic model and software tool which take into account

energy requirements at the building scale, i.e. the embodied and operational

energy of the building and its refurbishment, and at the urban scale, i.e. the

embodied energy of nearby infrastructures (roads, power lines, etc.) and the

transport energy (direct and indirect) of its occupants. All associated greenhouse

gas emissions are also quantified.

A case study, located near Melbourne, Australia, confirms that each of the

embodied, operational and transport requirements are nearly equally significant.

Embodied and transport energy consumption represent on average 63% of the life

cycle energy and 60% of the life cycle greenhouse gas emissions. The developed

holistic model provides building designers, planners and decision makers with a

powerful means to reduce the overall energy consumption and associated

greenhouse gas emissions of residential buildings.

Keywords: Life cycle energy analysis, Embodied energy, Operational energy,

Transport energy, Greenhouse gas emissions

1. Introduction

The building sector accounts for around 30% of global final energy consumption (IEA

2011:87). Residential buildings alone represent 26% of final energy consumption in the

EU (Perez-Lombard et al. 2008) and 12% in Australia (DEWHA 2008:20) which makes

them one of the largest single energy-consuming sectors. Hence, it is important to

reduce the energy consumption of residential buildings. However, current building

energy assessments and policies focus solely on the operational energy demand and

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specifically on thermal aspects, e.g. 6-Stars standard (Australian Building Codes Board

2010) and the Energy Performance of Buildings Directive in Europe (European

Parliament and the Council of the European Union 2002). This results in overlooking a

large part of the total energy demand of residential buildings and their users.

Buildings are constituted from multiple materials and assemblies which require large

amounts of energy to manufacture, supply and construct. The energy inputs necessary

for these processes are known as the embodied energy of the building. When calculated

using comprehensive techniques, the embodied energy can represent up to 45% of the

life cycle energy demand (comprising embodied and operational requirements) of a

building over 50 years (Crawford 2011).

At a larger scale, buildings can be seen as the constituting blocks of the built

environment. Therefore, they greatly condition urban patterns and land-use

characteristics. Many studies have found a great correlation between these

characteristics and the transport energy demand of occupants (Newman and Kenworthy

1999; Jenks et al. 2000; Newman and Kenworthy 2000; Newton 2000; van de

Coevering and Schwanen 2006). However, transport energy is rarely taken into

consideration when conducting a life cycle energy analysis of residential buildings.

Fuller and Crawford (2011) and Stephan et al. (2011) have integrated embodied and

transport energy requirements on studies of Melbourne, Australia and Brussels,

Belgium, respectively. They have shown that embodied and transport requirements,

represent on average more than 50% of the life cycle energy demand of residential

buildings.

Current energy saving measures, which are based on a partial assessment, might not

result in net energy savings and could even imply higher overall energy consumption

and greenhouse gas emissions. A comprehensive model, quantifying all energy

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requirements of residential buildings from the material to the city scale is therefore

needed in order to realistically measure their true energy consumption and associated

greenhouse gas emissions.

The aim of this paper is to present a comprehensive method to measure the embodied,

operational and transport energy requirements and related greenhouse gas emissions of

a residential building over its life cycle. By considering the three energy flows in a

single assessment, the method can be used to effectively reduce the overall energy

consumption of residential buildings. The method is presented and tested using a case-

study near Melbourne, Australia.

2. A comprehensive life cycle energy analysis model

The developed model takes into account energy consumption for the raw material

extraction, manufacturing, construction and operation of the building including its

maintenance and the replacement of materials. The embodied energy of nearby

infrastructures is also considered. The transport energy demand resulting from the

building occupants mobility is also taken into consideration. Energy associated with the

end-of-life stage is not considered since it typically represents less than 1% of the life

cycle energy demand of buildings (Winistorfer et al. 2007). The greenhouse gas

emissions associated with each of the life cycle stages are also quantified.

The main equations used to quantify the embodied, operational, transport and total

energy demand are described in this section. The quantification of associated

greenhouse gas emissions is also presented. Aspects related to uncertainty and

variability are briefly presented as well as the software tool associated with the method.

More details about the model will be available in Stephan (In progress).

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2.1. Embodied energy

Various techniques can be used to quantify embodied energy, notably process analysis,

input-output analysis and hybrid analysis. Process analysis is a bottom-up technique

which requires a large amount of data about the specific processes supporting the

production of a certain material. It produces the most accurate embodied energy figures,

regarding these processes. However, it also suffers from a so-called ‘truncation error’

which can omit up to 87% of the energy requirements of a product (Crawford 2008).

Input-output analysis is a top-down technique which establishes a link between

economic transactions and the energy intensity of economic sectors. Using the material

price, input-output analysis can produce embodied energy figures for a specific product

and considers the whole economy as a system. Therefore it does not suffer from the

truncation error. However, since it relies on the whole sector’s energy intensity, it

suffers from the so-called ‘aggregation error’. The energy intensities of specific

processes cannot be accessed since these are aggregated into sectors. Hybrid analysis

combines both techniques. It consists of using process analysis data where available and

filling the remaining gaps in the system with input-output data. Hence, hybrid analysis

provides the most reliable figures for known processes while being systemically

complete. For these reasons, hybrid analysis is used in this study in this work.

The input-output-based hybrid analysis approach, developed by Treloar (1997), is the

most comprehensive hybrid technique for the quantification of embodied energy. To

simplify the assessment of embodied energy using this technique, Treloar and Crawford

(2010) have developed factors known as hybrid energy coefficients. These comprise

process and input-output data for the energy associated with producing individual

materials. These coefficients are multiplied by the quantity of materials in the building

to obtain its initial embodied energy. The figure is then complemented with input-output

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data representing minor goods, services and the energy associated with the construction

of the building.

In order to quantify the life cycle embodied energy demand of a residential building,

both the initial and recurrent embodied energy should be taken into account. The

recurrent embodied energy represents the sum of energy inputs associated with the

production and replacement of materials over the building design life. The number of

material replacements depends on many factors such as climatic conditions,, the

material durability, the building type, etc.. In this research a simplified approach is

adopted regarding the material replacement rates: materials are replaced at the end of

their average useful life. The latter is sourced from the literature (e.g. NAHB and Bank

of America (2007)). The life cycle embodied energy demand (LCEEb) is the sum of the

initial and recurrent embodied energy calculated as per Equations (1) and (2)

respectively. In Equation (2), the ratio (ULb/ULm -1), representing the number of

material replacements over the building useful life, is truncated to its integer part (e.g.

3.7→3).

1 1

M M

b m m n m b

m m

IEE Q EC TER TER P (1)

Where: IEEb = Initial embodied energy of the building in GJ; Qm = Quantity of material

m in the building, ECm = Hybrid energy coefficient of material m; TERn = Total energy

requirements of the building construction-related input-output sector n, in GJ/currency

unit; TERm = Total energy requirements of the input-output pathways representing the

material production processes for which process data is available, in GJ/currency unit

and Pb = Price of the building b in currency units.

1

1M

bb m m n m i m m

mm

ULREE Q EC TER TER TER P

UL (2)

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Where: REEb = Recurrent embodied energy of the building in GJ; ULb = Useful life of

the building; ULm = Useful life of the material m; TERi≠m = Total energy

requirements of all input-output pathways not associated with the installation or

production process of material m, in GJ per currency unit; and Pm = Price of the

material m in currency units. All other variables are the same as in Equation (1).

The life cycle embodied energy of the surrounding infrastructure (LCEEif) is also taken

into account. The infrastructures considered are: roads, electric power lines, water and

gas distribution and sewage. These are attributed to the building by dividing the

infrastructure density (in m/km²) by the population density (in inhabitant/km²) and

multiplying the result by the number of occupants in the building. It is hence assumed

that all the population uses the infrastructures in the same way. The length of

infrastructure is then multiplied by the life cycle embodied energy figure of the

infrastructure (calculated using a hybrid approach) to obtain the associated energy

demand.

2.2. Operational energy

The operational energy demand comprises energy consumption for heating, cooling,

ventilation (if present), domestic hot water, lighting, cooking and appliances. The

integration of all these end-uses within the model differs greatly from most regulations

which focus solely on thermal demands. With the dramatic increase in the energy

demand associated with appliances, in particular, (DEWHA 2008; IBGE 2010), it is

important that this broader scope be considered.

The heating and cooling demands are determined through static heat transfer equations.

The heat loss area of the building (excluding the ground floor slab and party walls) is

multiplied by the average heat transfer coefficient (U-value) and the average

heating/cooling degree hours determined for the local climate. The degree hours should

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take into account solar gains and free internal gains. Ventilation losses/gains are also

taken into account as well the energy demand for mechanical ventilation (if present).

Non-thermal end-uses, such as the energy demand for cooking, appliances and domestic

hot water, are calculated based on the average regional figures per capita. These figures

are multiplied by the number of occupants in the house to obtain the final consumption.

The lighting energy requirements are calculated by multiplying average figures per m²

by the usable floor area of the building.

All operational energy consumption should be expressed in primary energy terms to

account for all the energy input at the source and not only the final consumption. This

will provide a more comprehensive analysis of operational energy consumption based

on the used energy sources (Gustavsson and Joelsson 2010). The life cycle operational

energy requirements is therefore calculated as per Equation (3). If solar systems are

installed, solar fractions are deduced from the final energy consumption of related end-

uses. Solar fractions represent the percentage of the final energy demand (for an end-

use) provided by solar hot water systems or photovoltaic panels.

1

1E

e eb b e

ee

PEF OPELCOPE UL SF (3)

Where: LCOPEb = Life cycle primary operational energy of the building b in GJ; ULb =

Useful life of the building b; SFe = Solar fraction for the end-use e; PEFe = Primary

energy conversion factor of the end-use e based on the energy source; OPEe = Yearly

operational final energy demand of the end-use e in GJ; and ηe = Average efficiency of

the end-use e.

2.3. Transport energy

The transport energy demand takes into account all inputs associated with the mobility

of building occupants. These inputs can be broken down into ‘direct’ and ‘indirect’

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requirements. Direct requirements, which are the focus of most previous studies, are

related to the operation of the transport mode, such as burning fuel in an engine. Indirect

requirements, which are very rarely taken into consideration, represent all other inputs

across the supply chain for the production and maintenance of the transport mode and

all associated infrastructures and services. Lenzen (1999) has demonstrated that these

indirect requirements can represent up to 45% of the total transport requirements for

cars. Both direct and indirect requirements should hence be taken into consideration for

both private and public transport modes.

The annual energy consumption associated with the mobility of building occupants is

determined by multiplying the average annual travel distance per mode and per capita

by the total energy intensity of the travel mode. The life cycle requirements are obtained

by summing annual figures over the useful life of the building as per Equation (4).

1

TM

b b tm tm tm

tm

LCTE UL TD DEI IEI (4)

Where: LCTEb = Life cycle transport energy demand of users in the building b in GJ ;

ULb = Useful life of the building b; TDtm = Total yearly travel distance of all users using

the transport mode tm, in km; DEItm = Direct energy intensity of the travel mode tm in

GJ/km; and IEItm = Indirect energy intensity of the travel mode tm in GJ/km.

2.4. Life cycle energy

The life cycle energy demand of the building and its occupants represents the sum of all

contributions (Equation 5).

b b if b bLCE LCEE LCEE LCOPE LCTE (5)

Where: LCEb = Life cycle energy demand of the building in GJ; LCEEb = Life cycle

embodied energy demand of the building in GJ; LCEEif = Life cycle embodied energy

demand of the infrastructure supporting the building in GJ; LCOPEb = Life cycle

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operational energy demand of the building in GJ; and LCTEb = Life cycle transport

energy demand of the building in GJ.

2.5. Greenhouse gas emissions

Greenhouse gas emissions are calculated by multiplying the primary energy

consumption by an emissions factor. This factor is based on the fuel source used for

operational and direct transport requirements and on an fixed average figure for

embodied energy. The conversion of primary energy to greenhouse gas emissions is

performed as per Equation 6.

1000uS

u u

EFLCGHG LCPE

(6)

Where: LCGHGu = Life cycle greenhouse gas emissions of the use u, in tCO2-e; LCPEu

= Life cycle primary energy consumption associated with the use u, in GJ; EF =

Emissions factor, in kg of CO2-e/GJ; and Su = energy source of the use u, e.g. natural

gas, gasoline, etc..

2.6. Uncertainty and variability

Any model is a representation of the real world and therefore implies a divergence from

the studied phenomenon or process. The method developed in this work follows the

same rule and suffers from uncertainty and variability in the used data. It is hence

crucial to address this issue and integrate it into the method.

Uncertainty relates to the lack of knowledge for a certain parameter while variability is

associated with its possible fluctuations (Heijnungs and Huijbregts 2004). For example,

there is uncertainty regarding the exact number of people in a specific household while

the average number of people in a typical household can present a certain variability.

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Even though uncertainty and variability have different meanings, the techniques to

tackle them are very similar (Heijnungs and Huijbregts 2004).

Interval analysis, which is a simple way of integrating uncertainty and variability, is

used in this work. This technique consists of attributing possible ranges for the

parameters (Moore et al. 2009). Therefore instead of having a single value as a result, a

range is obtained with three important figures: the nominal value (or average) and the

lower and upper boundaries. Interval analysis has been preferred to probabilistic

techniques (such as Monte Carlo simulation) for its simplicity (Alefeld and Mayer

2000) and because the probability distribution functions of the panoply of used

parameters are unavailable. Instead of framing the uncertainty and variability in the

model, assuming the probability functions would result in even greater uncertainty.

2.7. Software tool

In order to bridge theory and practice, an advanced software tool, coded in Python

programming language (Python Software Foundation 2012), has been developed to

fully automate the model. The tool comprises a user interface and relies on multiple

databases which can be updated by the user. It allows the analysis of more than 200

variables and their effect on the total energy demand of the building. A sensitivity

analysis module allows the assessor to test the influence of selected parameters and their

combinations on particular variables and to rank the scenarios accordingly. The tool can

also assess whole districts, composed of multiple residential buildings. A dedicated

plotter module allows the visualisation of the results and the comparison of up to seven

different buildings or districts. All data produced by the tool is exportable in the comma

separated values format and the graphs can be saved as images. Finally, most

calculations can by bypassed in the tool in order to allow the assessor to input his/her

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own data for a specific case. A screenshot of the plotting module of the tool is given in

Figure 1.

Figure 1: Screenshot of the plotter module of the developed software tool

3. A Case study to illustrate the potential of the developed method

3.1. Description

A case study house is used to illustrate the potential of the developed method and tool.

The 240 m² single family detached house is located in the outer suburbs of Melbourne,

Australia and houses four people. It is typical of new energy efficient residential

construction in the area. The characteristics of the building are provided in Table 1.

Table 1: Characteristics of the 7-Star house case study

Characteristic Value Building useful life [years] 50

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Gross floor area [m²] 240

Number of occupants 4

Structure type Timber-framed

Façade Brick veneer wall – 100 mm of fiberglass

insulation - Double glazed aluminium framed

windows

Roof Concrete tiles – 200 mm of fiberglass insulation

Average U-value of the building

[W/(m²K)]

0.58

Natural ventilation duration [hours

per day]

5

Natural ventilation rate [ach-1

] 0.5

Operational energy sources Gas heating (eff. 0.7) and cooking (eff. 0.9);

Electrical cooling (eff. 2.5); Solar domestic hot

water with gas auxiliary system (eff. 0.9)

Primary energy conversion factors Electricity: 3.4 , Gas: 1.4 (based on Treloar

(1998)) Emissions factors [kgCO2-e/GJ] Embodied energy: 60 (based on Treloar (1998)),

Operational energy (electricity): 93.11 (based on

DCCE (2011)), Operational energy (gas): 51.33

(based on DCCE (2011)).

Average car travel distance per

year [km] (no public

transportation)

36 000 (based on BITRE (2011))

Average occupancy rate of cars 1.6 (based on BITRE (2011))

Total energy intensity of cars

[MJ/pkm]

4.41 (based on Lenzen (1999))

Total greenhouse gas emissions

intensitiy of cars [kgCO2-e/pkm]

0.35 (based on Lenzen (1999), BITRE (2009),

and DCCE (2011))

Note: eff. = Efficiency. The calculation of the lighting and appliances energy demand

rely on delivered energy figures.

The building is assumed to be built in a new residential area. Therefore the initial

embodied energy of the infrastructure attributable to the building is taken into account

in the assessment. Also, the uncertainty ranges for embodied energy are set to ±20% for

process data and ±40% for input-output data based on Crawford (2011). The variability

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for operational energy is set to ±20% based on Pettersen (1994) and the same figure is

assumed for transport energy.

3.2. Results

Table 2 shows the life cycle energy demand and associated greenhouse gas emissions,

by use, for the case study house. Results show that each of the embodied, operational

and transport requirements represent comparable shares of the total, at 32.0%, 36.8%

and 31.2%, for energy and 26.3%, 40.0% and 33.6% for greenhouse gas emissions,

respectively.

When combining embodied and transport energy, these represent on average 63.2% of

the life cycle energy demand. These requirements are also responsible for 59.9% of the

life cycle greenhouse gas emissions. When considering the maximum value for

operational energy and the minimal values for embodied and transport requirements, the

life cycle operational energy demand represents at most 50.2% or 53.1% of the

greenhouse gas emissions. Hence, even in this extremely unlikely case, so-called

‘indirect’ requirements still represent almost half of the total energy consumption.

Conversely, these embodied and transport requirements can represent up to 73.7% of

the life cycle energy demand or 70.8% of the life cycle greenhouse gas emissions. This

clearly underlines the importance of considering wider system boundaries and a more

comprehensive approach for the life cycle energy and greenhouse gas emissions

analysis of residential buildings.

Table 2: Life cycle energy demand and associated greenhouse gas emissions of the case

study house, by use (including uncertainty and variability)

Use Lower

boundar

y

Nominal

value

Higher

boundary

Minimal

share

Nominal

share

Maximum

share

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LCEE [GJ] 3 751 6 335 8 919 18.8% 32.0% 45.3%

LCOPE [GJ] 5 835 7 293 8 752 26.3% 36.8% 50.2%

LCTE [GJ] 4 934 6 168 7 401 21.8% 31.2% 43.6%

LCE [GJ] 14 520 19 796 25 072

LCEGHG

[tCO2-e] 227 384 541 15.0% 26.3% 38.6%

LCOPGHG

[tCO2-e] 467 584 700 29.2% 40.0% 53.1%

LCTGHG

[tCO2-e] 392 490 588 24.0% 33.6% 45.9%

LCGHG

[tCO2-e] 1 086 1 458 1 830

Note: Figures may not sum due to rounding. LCEE = Life cycle embodied energy;

LCOPE = Life cycle operational energy, LCTE = Life cycle transport energy,

LCE = Life cycle energy; LCEGHG = Life cycle embodied greenhouse gas emissions;

LCOPGHG = Life cycle operational greenhouse gas emissions; LCTGHG = Life cycle

transport greenhouse gas emissions; LCGHG = Life cycle greenhouse gas emissions

Figure 2 and 3 show the life cycle energy demand and greenhouse gas emissions, by

use, respectively.

The first observation is that no single category dominates the life cycle energy demand.

Indeed, the largest contribution comes from the operation of appliances (17.4% of the

total energy consumption and 22% of the total greenhouse gas emissions over 50 years)

followed by transport requirements and heating. Therefore, the reduction of the total

energy consumption requires reducing the energy requirements of multiple end-uses and

not just the space heating and cooling demand, as is typically the focus.

Secondly, the greenhouse gas emissions associated with embodied energy requirements

are lower than those related to the operation of the house or the mobility of the

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occupants. Indeed, the average greenhouse gas emissions intensities of embodied,

operational and transport energy requirements are 60, 80 and 79 kgCO2-e/GJ. This

explains the lower contribution of embodied greenhouse gas emissions (26.3%)

compared to the embodied energy (32%). The reliance on wet brown coal for the

generation of electricity in Victoria, Australia is responsible for the high emissions

factor of operational energy requirements. Also, the use of fossil fuels for car

transportation and all associated indirect requirements result in a high emissions factor

for transport energy. Based on the contributions of each element, strategies to reduce the

energy consumption and associated greenhouse gas emissions should therefore include

but are not limited to: the use of more efficient appliances, the use of public transport or

electric cars, the installation of LED lighting and the careful selection of building

materials (such as insulation).

Figure 2: Life cycle energy demand of the case study house, by use. Note: figures may

not sum due to rounding; LCEE = Life cycle embodied energy; LCOPE = Life cycle

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operational energy; LCTE = Life cycle transport energy.

Figure 3: Life cycle greenhouse gas emissions of the case study house, by use. Note:

figures may not sum due to rounding; LCGHG = Life cycle embodied greenhouse gas

emissions; LCOPGHG = Life cycle operational greenhouse gas emissions;

LCTGHG = Life cycle transport greenhouse gas emissions.

Thirdly, the primary energy consumption associated with heating and cooling, which

are the focus of most previous building energy assessments, represents only 12.2% of

the total energy demand and 10.3% of the greenhouse gas emissions. Therefore, most

previous studies may overlook up to 87.8% of the total energy demand of a building and

its occupants over 50 years and 89.7% of the associated greenhouse gas emissions.

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4. Discussion

This paper has presented a model for the comprehensive life cycle energy and

greenhouse gas emissions analysis of residential buildings. The method, applied to a

suburban energy-efficient Australian house, confirms that a more holistic perspective to

energy consumption should be adopted for its effective reduction. Indeed, results show

that focusing solely on operational energy overlooks at least 49.8% of the energy

demand over 50 years and 46.9% of the associated greenhouse emissions.

The breakdown of the three main categories of energy demand into elements or sub-

categories enables a greater understanding of the most significant contributors to the

overall life cycle energy demand. This also greatly assists in the efforts to prioritise

strategies for energy reduction.

The developed software tool provides architects and building designers with a powerful

means to effectively reduce the energy consumption of residential buildings over the

life cycle. For instance, the coupling of embodied and thermal requirements helps to

ensure that additional insulation does not result in a higher overall energy consumption

because of the increased embodied energy requirements. The tool can also be used at a

larger scale of the built environment, by planners or decision makers to evaluate the

impact of developing various housing forms. The greenhouse gas emissions associated

with the energy requirements are also taken into consideration. Hence, the software tool

relies on the theoretical developments in embodied, operational and transport energy

and greenhouse gas emissions quantification to offer a practical means to the industry

for reducing the life cycle energy demand of residential buildings and their associated

emissions.

The model relies on a basic operational energy quantification algorithm, using statistical

data and static heat transfer equations. This might result in differences with post-

occupancy measures. Embodied energy figures may vary due to the uncertainty in the

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data but also due to adopted useful lives of materials. Transport energy requirements

can also vary according to user habits and local conditions. Also, embodied greenhouse

gas emissions are calculated using a generic factor for all materials while the emissions

associated with manufacturing can vary considerably between two materials. However,

general results are in concordance with previous studies using the same quantification

techniques (Fuller and Crawford 2011; Stephan et al. 2011). Other studies including

embodied, operational and transport energy requirements, such as Norman et al. (2006)

tend to underestimate the contribution of non-operational energy demand. This is due to

the reliance on less reliable techniques for the quantification of embodied energy, and to

the omission of indirect transport requirements.

5. Conclusion

Current energy assessment methods fail to provide a comprehensive measure of the life

cycle energy demand of residential buildings and their occupants. This paper has

demonstrated how a comprehensive model and an associated software tool can assist in

determining total life cycle energy requirements and associated greenhouse gas

emissions of residential buildings and their users. Results have shown that the typically

overlooked requirements constitute on average more than 50% of the overall energy

consumption and greenhouse gas emissions of a residential building. While these results

comply with previous studies relying on similar techniques, they have to be validated

through comparison with an existing case. This will constitute the next stage of this

research.

In conclusion, the developed method and tool will allow architects, building designers,

planners and decision makers to optimise the environmental performance of residential

buildings by informing their designs with a comprehensive life cycle energy analysis at

both the building and urban scale. This practical tool will ultimately contribute to a

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reduction in the energy consumption of residential buildings and related greenhouse gas

emissions.

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Acknowledgement

This research is funded by the Belgian National Fund for Scientific Research

(F.R.S. - FNRS), by an excellence scholarship from Wallonia-Brussels International

(WBI) and by a BRIC scholarship from the Université Libre de Bruxelles, Belgium.

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