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Ecological Building Design DeterminantsAli Vakili-Ardebili a b c & Abdel Halim Boussabaine ca Faculty of Architecture, Landscape and Design (al&d) ,University of Toronto , 230 College Street, Toronto, Ontario,Canada , M5T 1R2b Department of Architectural Science, Faculty of Engineering andApplied Science , Ryerson University , 325 Church Street, Toronto,Ontario, Canada , M5B 2K3c School of Architecture, The University of Liverpool , Liverpool,L69 3BX, UKPublished online: 06 Jun 2011.

To cite this article: Ali Vakili-Ardebili & Abdel Halim Boussabaine (2010) Ecological Building DesignDeterminants, Architectural Engineering and Design Management, 6:2, 111-131

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ARTICLE

Ecological Building Design DeterminantsAli Vakili-Ardebili1,2,3,* and Abdel Halim Boussabaine3

1Faculty of Architecture, Landscape and Design (al&d), University of Toronto, 230 College Street, Toronto, Ontario, Canada M5T 1R22Department of Architectural Science, Faculty of Engineering and Applied Science, Ryerson University, 325 Church Street, Toronto,Ontario, Canada M5B 2K3

3School of Architecture, The University of Liverpool, Liverpool L69 3BX, UK

Abstract

The sustainable building design process is driven on the basis of a range of design eco-indicators. Consideration

of a multitude of eco-determinants, such as environment, economy, resources, energy consumption and society

values in addition to design characteristics and contexts, makes the process of ecological design even more

complex. A large number of eco-drivers are extracted from the literature and current design practices. To gain

a better insight on eco-design determinants, a survey focusing on the use of eco-design drivers has been

conducted with various architects in the UK. The factor analysis method was used to remove redundant data

from the survey. Through the factor analysis approach, 115 eco-determinants are grouped into six main

clusters. This article presents the process, analysis and findings of this work. The extracted eco-indicators

and their associated clusters can be used to improve the process of ecological building design.

B Keywords – Building design drivers; design ecological indicators; eco-building design indicators; eco-efficiency; eco-indicators

INTRODUCTION

Many researchers such as Giedion (1980) believe that

design is function based. The function itself maps into

space and technology design dimensions. This

definition is a pre-requisite, but it is not inclusive of

all the design parameters. Other dimensions and

contexts such as environmental, socio-economical,

energy and resources are similarly important in the

process of design. Since each variable would carry a

dissimilar level of significance, a different level of

emphasis is placed on each indicator over the

design process. Functional adaptability, relations,

flexibility (Glen, 1994; Slaughter, 2001), durability

(Kibert et al., 2000; NASA, 2001), safety and health

(NASA, 2001; ISO 14000, 2005), human and building

interaction (Du Plessis, 2001), building and

environment interactions (Langston and Ding, 2001;

Roaf et al., 2001; Smith, 2001) and environmental

demands (Fiksel, 1994; Nicholls, 2001) are

characteristics of design that are considered

important in sustainable design. Space-related

attributes are identified as interior spaces (Nicholls,

2001) and exterior spaces (Nicholls, 2001; Roaf

et al., 2001): one focuses on spatial relations in a

building and the other deals with building

interactions with its surrounding spaces. Issues such

as built-ability, flexibility (Slaughter, 2001), durability

and longevity (Kibert et al., 2000; NASA, 2001),

reliability and usability (Markeset and Kumar, 2003)

and disassembling (Macozoma, 2002) are

incorporated in materialization of the building form.

Architectural style, fashion, society and culture (Du

Plessis, 2001) are attributes that are also associated

with spiritual aspects of the form; these aspects are

those characteristics of design having influence on

human (end user’s) emotions and psychosomatic

concerns. Design service life such as longevity

(Kibert et al., 2000), maintainability (Blanchard and

Lowery, 1969; Bhamra et al., 2001; NASA, 2001),

energy efficiency (Langston and Ding, 2001; Roaf

B *Corresponding author: E-mail: [email protected]; [email protected]

ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT B 2010 B VOLUME 6 B 111–131doi:10.3763/aedm.2008.0096 ª2010 Earthscan ISSN: 1745-2007 (print), 1752-7589 (online) www.earthscan.co.uk/journals/aedm

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et al., 2001), embodied energy (Roaf et al., 2001),

eco-efficiency and recycling (Pearce, 2001),

equipment and appliances (Nicholls, 2001) and use

of technology (Langston and Ding, 2001; Roaf et al.,

2001; Smith, 2001) are technological attributes

considered in the form and performance of a building.

Eco-building design deals with green and clean

design. Environmental aspects are addressed in

establishing eco-efficient design whereas ecological

and environmental problems such as greenhouse

effects, ozone-layer depletion, acid rain, air, water

and land pollution, soil deteriorations, toxic wastes,

residues, loss of biodiversity and industrial accidents

are impacts that have been highlighted and

considered by researchers such as Shrivastava

(1995). Boussabaine and Kirkham (2004) classified

environmental impacts into two main groups:

atmospheric and resource-related impacts. Other

environmental issues addressed by Nicholls (2001),

Roaf et al. (2001) and Smith (2001) include energy

and resource characteristics, natural light, passive

heating, natural ventilation, passive cooling,

insulation and air tightness, water-saving devices,

GEO thermal benefits, sewage and landfill gas,

biomass, environmentally adapted technology,

low-energy materials, healthier and safer types of

energy and resources (renewable sources), more

efficient appliances and low-embodied energy

materials. Boussabaine and Kirkham (2004) stated

that socio-economic factors associated with design

include economical aspects of a building concerning

facility management costs, maintenance costs, level

of components replacement costs, pollution

rehabilitation and prevention costs, disposal costs,

risk costs and, more importantly, the trade-off

between capital and running costs. Design

performance (Gibson, 1982) based on customer

expectation, operation and maintenance should be

considered over the long term (Winch et al., 1998).

All the aforementioned concerns are essential for

providing end users with a high quality of life. This

brief review demonstrates that a large number of

attributes are associated directly or indirectly with

sustainable building design. These design

determinants can also interact with each other in a

dynamic and complex manner. To reduce

complexity, this work aims at extracting attributes

that have a high level of significance in ecological

building design. Several research methods exist to

rank, analyse and extract the most significant

attributes from a set of data. This article uses factor

analysis and data reduction techniques to extract

eco-design latent variables. The process, analysis,

findings and investigation are presented in this

article.

RESEARCH METHODOLOGY

Data utilized in this research are derived from a

questionnaire survey carried out among architecture

practices in the UK. To carry out the study, 450

practices out of 829 working on sustainable design

were randomly selected. The questionnaire includes

115 eco-indicators clustered into four groups, as

shown in Figure 1 (Vakili-Ardebili, 2005).

In view of the fact that it is difficult to manage 115

eco-indicators in a design process, this work

challenges to extract the most significant factors of

ecological building design by removing those factors

having less value in achieving sustainability. The

collected data were processed by scale ranking

using the mean value, standard deviation, coefficient

of variation and severity index of factors. Statistical

Package for the Social Science (SPSS) and Microsoft

Excel were used to carry out the ranking process.

Factor analysis and data reduction are the

techniques used to remove redundant data and to

obtain a manageable subset of the indicators that

present the major characteristics of eco-building

design indicators. Factor analysis is often used

in data reduction to identify a small number of

factors that explain most of the variance observed in

a much larger number of manifest variables (SPSS

Inc., 2004).

Factor analysis can be used either in hypothesis

testing or in searching for constructs within a group

of variables (Bartholomew and Knott, 1999). It is a

series of methods for finding clusters of related

variables and hence an ideal technique for reducing

a large number of factors into a more easily

understood framework (Norusis, 2000). It is used to

investigate if there is an underlying relationship

between the different indicators within the

questionnaire. In SPSS, the principal components

method is used to extract the latent components

112 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE

ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT

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FIGURE 1 Eco-indicators questionnaire structure

Ecological Building Design Determinants 113


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and variables. Components are a set of matrices that

present the correlations between different variables.

The process is begun by finding a linear

combination of variables (a component) that accounts

for as much variation in the original variables. It then

finds another component that accounts for as much

of the remaining variation as possible and it is

uncorrelated with the previous component. The

process continues in this way until there are as many

components as original variables. Usually, a few

components will account for most of the variation,

and these components can be used to replace the

original variables (SPSS Inc., 2004). Hence, the

outcome will be a few variables presenting the major

characteristics of eco-building design indicators.

After elimination of redundant data, the 32

remaining indicators are considered as representatives

of the whole initial set of eco-building design

indicators. They are categorized into six pivotal

clusters. These clusters are then subjected to further

statistical analysis.

The process of the analysis is shown in Figure 2.

The figure shows that through the use of data

reduction, the existing 115 components are reduced

to 27 components. The outcome of factor analysis is

the re-organization of the survey data into six new

homogeneous clusters that represent the whole

survey data set. The process, findings and

discussions of the data analysis are presented in the

following sections.

ANALYSIS OF THE FINDINGS

The following stages are needed in order to carry out

factor analysis.

The first stage of factor analysis is to determine

the strength of the relationship among the variables

(Shen and Liu, 2003). In the second stage, a matrix

of correlation coefficients is produced, and then

components carrying eigenvalues – the value of a

variable in an equation (here the equation is

eco-building design) giving a solution that complies

with the conditions that exist at a system’s

boundaries – bigger than 1 are extracted from the

matrix of the correlation coefficient (the most

common extraction method is based on principal

component analysis).

In the third stage, a rotated component matrix is

generated in order to determine which of the

indicators have more effective influence in each

component.

Hence it can be argued that the process begins by

considering factors in the questionnaire (eco-building

design indicators in the questionnaire); then a series

of components is generated based on indicators in

the second stage, and their correlations are

investigated. In the third stage, a set of more

influential indicators is selected and considered as

representatives of the original data set as illustrated

in Figure 2. The results of factor analysis are

presented in Table 1. In Table 1, each component is

set according to series of correlations between

FIGURE 2 Process of data reduction and factor analysis

Source: Vakili-Ardebili (2005)



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TABLE 1 Total variance explained



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different indicators. Thus, it determines how

correlated an indicator could be to other indicators.

The first column of three sections in Table 1 labelled

as initial eigenvalues relates to eigenvalues of the

correlation matrix and indicates which components

of the table remain in analysis. To carry out factor

analysis, only components with eigenvalues greater

than 1 are selected and those with eigenvalues less

than 1 are excluded. In the current context, an

eigenvalue is the amount of the total test variance

that is accounted for by a particular factor, the total

variance for each test being unity (100%).

For example, the eigenvalue of the first factor in

Table 1 is 28.347. Since the total test variance that

could possibly be accounted for by a factor is 115

[i.e. 100% � (number of tests)], the proportion of the

total test variance accounted for by the first factor is

28.347 4 115 ¼ 24.649%, the figure given in the %

of variance column. In this analysis, only 27

components carry eigenvalues greater than 1 and

account for nearly 83.170% of the variance as

shown in the cumulative % column. This means that

the selected components (first 27 factors of analysis

in Table 9.1) present 83.17% of the whole variance,

which statistically includes 95% of all data

(according to data distribution, average weighted

mean and standard deviation).

Therefore, the 27 components are considered as

representative of 115 indicators employed in this

study. The next block of columns (extraction sum of

squared loadings) are the sum of the squared

loadings for the unrotated factor solution and the

last block in the table (rotation sums of squared

loadings) are those for the rotated factor solution.

The scree plot shown in Figure 3 is also used to

graphically determine the optimum number of

clusters. The purpose of a scree plot is to provide a

graphic picture of the eigenvalue for each

component extracted from the original data set. As

shown in Figure 3, the slope of scree is dropping,

while moving towards components with eigenvalues

less than 1. The point of interest is defined between

FIGURE 3 Scree plot of 115 eco-indicators of the study



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components 27 and 28, where the figure curve

connects to the points, starting to become

horizontal. The sudden change of scree determines

that there is an abrupt change in the level of

significance with regard to eco-building design.

Since an eigenvalue describes the value of

components in the eco-building design process as

the proposed equation in this study, it is concluded

that the first 27 components of this study play a

more significant role in achieving the eco-building

design process. Therefore, in a scree plot, the place

where a sharp change in angle occurs can be

considered as the exact point at which eigenvalues

of less than 1 are located. Eigenvalues larger than 1

are located on the sharp slope of the curve, while

eigenvalues smaller than 1 are plotted in the flatter

part of the curve.

From principal component analysis, 27

components having eigenvalues larger than 1 are

selected. The next step is the extraction of a rotated

component matrix. The purpose here is to find which

of the indicators have a high influence on the design

of eco-efficient buildings. Table 2 shows the degree

of influence of each indicator. As shown in Table 2,

each component’s correlation coefficients with all

set eco-determinants are computed. From Table 2

(rotated component matrix), indicators with the

highest rate of influence can be distinguished. For

example, in Table 2, the most important indicators

for component 1 embrace a range of questions

(indicators) from Q76 to Q85, which carry scores

from 0.569 to 0.876, presenting the highest

numerical value based on the significance of each

factor in component 1. Also, the near intervals

(numeric values) among indicators from Q76 to Q85

illustrate a level of affiliation in this set. Indicators

with the highest scores and correlation values are

chosen for each component. The result of this

analysis is presented in Table 3.

Table 3 illustrates the result of factor reduction

based on the information in the rotated component

matrix presented in Table 2.

In Table 3, the most important and influential

eco-indicators of each component are extracted and

shown. The six clusters shown in Table 4 are formed

on the basis of the 27 extracted components and

their most important indicators shown in Table 3.

The new clusters are labelled as eco-building design

indicators clusters for assessing building design

eco-efficiency. The percentages of variance of each

component (extracted from Table 1) are presented

and added up in order to calculate the percentage of

variance of each cluster in eco-building design

indicators.

The percentage of variance of each indicator is

taken from Table 1, and then the cluster percentage

of variance is calculated through summation of each

indicator’s variance (see Table 3). The outcomes of

calculations are presented in Table 4. In Table 4,

each cluster degree of effect in eco-building design

is calculated based on the percentage of variance of

each component derived from Table 1. For example,

the third column in Table 4, which presents site

analysis as one of the six clusters for eco-building

design, is composed of component 9 (%variance of

2.698%), presenting Q19 as the main indicator of its

set; component 17 (%variance of 1.605%),

presenting Q38 and Q37 as the main indicators of its

group; and component 27 (%variance of 0.924%),

presenting Q41 as the main indicator of its set.

Therefore, the percentage of variance for cluster 3

(site analysis) in Table 4 is calculated through

summation of its components’ percentages of

variance. Thus the percentage of variance for cluster

3 is computed as

2:698þ 1:605þ 0:924 ¼ 5:227%

This value of 5.227% is out of 83.170% (4.347% out of

100% of data), which was shown as percentages of

variance for the first 27 components extracted

through principal component analysis presented in

Table 1. The summation of percentages of variance

for the six new clusters is also the same as

83.170%, which means that these clusters can

definitely be appropriate representatives of all

eco-design indicators and explains 83.17% of the

information in the original survey questionnaire. The

use of data reduction techniques in SPSS has helped

to reduce the number of factors (115 indicators) to

32 indicators. These are grouped into six new

clusters, which are highly manageable without losing

a large amount of data and only 100% 2 83.17% ¼

16.83% of existing information are compromised.



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By applying factor analysis and data reduction in

this survey, the 115 indicators in the questionnaire

are reduced to 27 components, and then

categorized into six pivotal clusters which include

only 32 original eco-indicators that have major

relevance to the design of eco-efficient buildings.

The final results of data reduction are presented in

Table 4. The six clusters generated will be subjected

to further statistical analysis.

INTERPRETATION OF THE CLUSTERS

The six new clusters are interpreted as follows.

TABLE 3 Data analysis: elementary factor reduction

Component 1 Component 2 Component 3 Component 4 Component 5

Q81. Water pollution

Q82. Earth pollution

Q106. Social inclusion

Q105. Self-determination

Q9. Added function to main

function

Q27. Energy and

eco-efficiency

Q88. Natural

ventilation

Q80. Air pollution

Q77. Ozone layer

Q103. Personal development Q10. Renovation and

upgrading

Q26. Control of emission Q86. Natural

light

Q84. Landfill

Q85. Solid residues

Q79. Energy consumption

Q83. Ecological

deterioration

Q7. Upgradeability/

extension

Q8. Flexibility in use stage

Q4. Adaptability to new

changes

Q87. Passive

heating

Q89. Passive

cooling

Q76. Greenhouse effect

Q78. More efficient use of

water

Q6. Adaptability to the

environment

Q5. Adaptability to

surroundings


Q17. Effect of function on

human behaviour

Q92. GEO thermal

benefits

Q112. Pollution prevention

costs

Q19. Landscape (blg/

env interactions)

Q2. Functional

zoning

Q13. Physical aspects of

safety and health

Q93. Sewage and landfill gas

benefits

Q111. Pollution

rehabilitation costs

Q35. Landscape design

(exterior spaces)

Q3. Compatibility

Q94. Biomass benefits


Q32. Distribution of

activities

Q115. Saving running costs or

capital costs/running costs

Q60. Maintainability in

design service life

Q59. Longevity in design

service life

Q54. Fashion

Q53. Style


Q73. Innovation in use of

technology

Q38. Climate

Q37. Building orientation

Q42. Form built-ability Q12. Longevity of the

function

Q71. Vibration of

equipments

Q74. Vernacular Q36. Natural physical conditions Q11. Performance Q70. Noise of

equipments


Q24. Government

Q23. Society

Q25. Organizations

Q50. Disassembling

Q51. Reusability and recycling

Q44. Geometry of form

(aesthetic and stability)

Q90. Insulation and air

tightness

Q49. Reliability

and usability

Component 26 Component 27

Q75. Vernacular

technology

Q41. Site restrictions



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TABLE 4 Factor reduction: six new categories (final data reduction and factor analysis)

CLUSTER 1 CLUSTER 2 CLUSTER 3 CLUSTER 4 CLUSTER 5 CLUSTER 6

ENVIRONMENTAL

IMPACTS

DESIGN

ENVIRONMENTAL

STRATEGIES

SITE ANALYSIS SOCIAL ASPECTS ECONOMY DESIGN ASPECTS

Component 1 Component 4 Component 9 Component 2 Component 8 Function


Component 7 Component 27 Component 6

Component 24 Component 10

Component 11

Component 19

Form

Component 15

Component 18

Component 22

Component 23

Component 25

Space technology

Component 13

Component 14

Component 16

Component 20

Component 26

Q81. Water

pollution

Q27. Energy and

eco-efficiency

Q19. Landscape

Q38. Climate

Q106. Quality of

life

Q112. Pollution prevention

and rehabilitation costs

Q7. Flexibility

Q6. Adaptability

Q82. Earth

pollution

Q80. Air pollution

Q26. Control of

emission

Q88. Natural

ventilation

Q86. Natural light

Q87. Passive

heating

Q89. Passive

cooling

Q90. Insulation and

air tightness

Q37. Building

orientation

Q41. Site

restrictions

Q24. Government

Q23. Society

Q25. Organizations

Q115. Saving running costs

or capital costs/running costs

Q17. Mental aspects

Q13. Physical

aspects

Q2. Functional

zoning

Q11. Durability

Q42. Form-built-

ability

Q50. Disassembling

Q51. Reusability and

recycling

Q49. Reliability and

usability

Q60. Maintainability

Q73. Innovation

24.649% 4.241%+ 2.698%+ 7.476%+ 2.771%+ 4.584%+3.815% 1.605% 1.245% 2.045% 3.002%

2.948% 0.924% 2.529%

0.995% 2.305%

Continued



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CLUSTER 1: ENVIRONMENTAL IMPACTS

The extracted indicators for cluster 1 are all related to

environmental impacts resulting from building design

and its following stages. As illustrated in Table 3, there

are 10 indicators in component 1. Careful examination

of the eco-determinants associated with component 1

indicated that these attributes are related to different

types of pollutions caused by building industry

processes. The 10 indicators in component 1 are

linked directly to water pollution, earth pollution, and

air pollution in cluster 1. These eco-determinate are

grouped under the Environmental impacts label.

Cluster 1 embodies a share of 25% of the variance

in the original eco-building design indicators data set

(see Table 4 and calculation of percentage of

variance).

CLUSTER 2: ENVIRONMENTAL PASSIVE

AND ACTIVE DESIGN STRATEGIES

This cluster has a percentage of variance of 12% and

embraces indicators such as energy and

eco-efficiency, control of emission, natural

ventilation, natural light, passive heating, passive

cooling, insulation and air tightness. All these

indicators are related to environmental design

strategies. These indicators, according to the Kano

model, belong to the excitement threshold regarding

a customer’s satisfaction (Vakili-Ardebili and

Boussabaine, 2005). Thus, application of

environmental design strategies in eco-building

results in satisfaction of the end user through

achieving a higher level of comfort and performance,

leading to the improvement of quality of life.

Environmental design strategies must include both

ecological and economical aspects over the whole

life cycle of a building. For example, employment of

renewable sources or passive energies such as solar

energy, passive cooling, natural light and ventilation

not only provides better living conditions but also, in

the long term, saves a huge amount of energy and

financial costs (Langston and Ding, 2001; Roaf et al.,

2001; Smith, 2001).

CLUSTER 3: SITE ANALYSIS

Cluster 3 has a percentage of variance of 5.22% and

consists of indicators such as landscape, climate,

building orientation and site restrictions. The cluster

is labelled as Site analysis cluster. The indicators

presented in this cluster are all concerning a

project’s site analysis and specifications, and include

the policies and strategies that should be followed in

the early design stage of a building to fulfil a higher

quality of design and ultimately to achieve customer

satisfaction. Cluster 3 embraces the context of the

TABLE 4 Continued

CLUSTER 1 CLUSTER 2 CLUSTER 3 CLUSTER 4 CLUSTER 5 CLUSTER 6

ENVIRONMENTAL

IMPACTS

DESIGN

ENVIRONMENTAL

STRATEGIES

SITE ANALYSIS SOCIAL ASPECTS ECONOMY DESIGN ASPECTS

1.361%

1.750%

1.436%

1.152%

1.058%

0.963%

1.931%

1.779%

1.646%

1.304%

0.956%

Total: 24.649% Total: 11.999% Total: 5.227% Total: 8.721% Total: 4.816% Total: 27.756%

Total: 83.168% of original survey data being used



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design. Disregarding design context is not acceptable

in traditional design. However, more importantly in

eco-building design, a specific emphasis is placed

on the relationship between site context and design

functions in the early stages of the design process.

A greater awareness of site characteristics enhances

the quality of design and reduces the risk and

uncertainties that may emerge from the lack of

considering site design layout, orientation and site

massing in relation to site climatology. Design based

on site climate, landscape and an appropriate

orientation for the building will provide the asset

with many advantages such as natural light, natural

ventilation and passive energy applications. The

eco-efficiency gained through the application of

these environmental concerns will lead to cost

efficiency and financial savings over a building’s life

span.

CLUSTER 4: SOCIAL ASPECTS

Cluster 4, with a percentage of variance of 8.72%,

consists of four indicators. This cluster consists of

eco-attributes such as quality of life, government,

society and organizations. Here, end users and

associated stakeholders’ aspirations and expectations

are captured by cluster 4. Users of the building have

the ultimate role in determining the level of success

of a building and its design. Eco-building design as a

design philosophy seeks the fulfilment of a

customer’s expectations through the implementation

of eco-strategies in the design of building assets.

Customer satisfaction and social aspects can be

discussed and explained by the Kano model

(Vakili-Ardebili and Boussabaine, 2005). Eco-building

design as a new design concept attempts to fulfil

needs based on user orientations.

CLUSTER 5: ECONOMY

Cluster 5 involves the financial and monetary aspects

of a building over its life cycle. It embraces two main

indicators: pollution costs and running costs. The

ratio of capital cost in comparison to running cost

should be optimized. Pollution costs are associated

with both pollution avoidance and pollution

rehabilitation. Running costs should be considered

over the life-cycle period of the asset. Recovering

the impacts and pollutions generated by a building

process embraces large amounts of huge budgets.

Hence, building design through the application of

environmental design strategies is capable of

generating solutions that lower the rate of emissions

at both construction and operation of the building

asset. Here, the added value from the use of passive

solar energy and other renewable sources must be

encouraged.

CLUSTER 6: DESIGN ASPECTS AND

STRATEGIES

This cluster consists of 12 main indicators

representing 27.75% of variance in the original

eco-building design indicators data set, more than a

quarter. All the indicators in this cluster are related

to design strategies. The pivotal difference of this

research with others in the eco-design field is that, in

this work, cardinal emphasis is placed on passive

design strategies to produce sustainable buildings,

whereas others base their strategies on the

application of active technological solutions. Passive

design strategies reinforced with environmental

passive strategies will provide solutions that prove

both higher physical and mental quality of life

for end users. There are many passive design

strategies for enhancing the eco-efficiency of

building design. Indicators associated with

design aspects and strategies such as flexibility,

adaptability, mental aspects, physical aspects,

functional zoning, durability, form built-ability,

disassembling, reusability and recyclability, reliability

and usability, maintainability and innovation, if used

properly in the design process, will contribute

immensely to the longevity of the building’s service

life. Eco-indicators in cluster 6 are considered as

functional aspects of design.

CONCLUSIONS

In sustainable building design, environmental

indicators have received the most attention, and

assessment tools have been developed to determine

which indicators should be addressed in the design

of building assets. This work has identified novel

eco-design clusters based on the philosophy that if

design could not provide a proper functionality,

durability or maintainability as anticipated, then

environmental strategies have little value to the



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client. This could also lead to the rapid transition of the

building asset to the obsolescence stage. Based on

this paradigm, design eco-drivers are extracted from

the literature review and interviews. In total, 115

indicators were extracted. Through factor analysis

techniques, this set of eco-determinants was

reduced into 32 indicators (the most significant

eco-indicators) and then grouped into six clusters.

The extracted eco-building design clusters are

1 design aspects and strategies (percentage of

variance 27.75%)

2 environmental impacts (percentage of variance

24.64%)

3 design environmental strategies (percentage of

variance 11.99%)

4 social aspects (percentage of variance 8.72%)

5 site analysis (percentage of variance 5.22%)

6 economy (percentage of variance 4.81%).

Design eco-drivers, included in the design strategies

cluster, such as functional attributes have a pivotal role

in the sustainable design process. Attributes such as

durability, flexibility, longevity and maintainability are

some of those functional aspects that have received

very little attention, if any, in the existing assessment

methods. This research has placed its main emphasis

on the incorporation of passive and active design

strategies to improve the eco-efficiency of building

design and operation. The findings and classification

of eco-design drivers should enable designers to

determine the key eco-design drivers to incorporate in

the design of sustainable buildings.

ACKNOWLEDGEMENTS

This article is part of the research on eco-building

design indicators carried out by the authors at the

University of Liverpool in England, UK (Vakili-Ardebili,

2005).

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APPENDIX

Tables A.1–A.7 including eco-building design

indicators ranking (Vakili-Ardebili and Boussabaine,

2007) are presented for more clarification of the

subject as well as to help readers’ perception of this

study.

TABLE A.1 Building design category – function attributes



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TABLE A.2 Building design category – space attributes

TABLE A.3 Building design category – form attributes



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TABLE A.4 Building design category – technology attributes

TABLE A.5 Environmental profile and eco-efficiency category



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TABLE A.6 Energy and resources categor

TABLE A.7 Socio-economic category



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