ecological building design determinants
TRANSCRIPT
This article was downloaded by: [UQ Library]On: 01 June 2014, At: 20:57Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Architectural Engineering and DesignManagementPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/taem20
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
To link to this article: http://dx.doi.org/10.3763/aedm.2008.0096
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
FIGURE 1 Eco-indicators questionnaire structure
Ecological Building Design Determinants 113
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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)
114 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
TABLE 1 Total variance explained
Ecological Building Design Determinants 115
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
116 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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.
Ecological Building Design Determinants 117
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
TA
BL
E2
Rot
ated
com
pone
ntm
atrix
a118 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
Ecological Building Design Determinants 119
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
TA
BL
E2
Con
tinue
d120 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
Ecological Building Design Determinants 121
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
Component 6 Component 7 Component 8 Component 9 Component 10
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
Component 11 Component 12 Component 13 Component 14 Component 15
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
Component 16 Component 17 Component 18 Component 19 Component 20
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
Component 21 Component 22 Component 23 Component 24 Component 25
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
122 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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 5 Component 17 Component 21 Component 12 Component 3
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
Ecological Building Design Determinants 123
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
124 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
Ecological Building Design Determinants 125
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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).
REFERENCES
Bartholomew, D.J. and Knott, M., 1999, Latent Variables Models and Factor
Analysis, London, Oxford University Press.
Bhamra, T., Evans, S., van der Zwan, F. and Cook, M., 2001, ‘Moving from
eco-products to eco-service’, Journal of Design Research, 1(1), Cranfield
University. Available at: http://jdr.tudelft.nl/articles/issue2001.02/
article3.html [accessed 7 September 2005].
Blanchard, B.S. and Lowery, E.E., 1969, Maintainability: Principles and
Practices, New York, McGraw-Hill.
Boussabaine, A.H. and Kirkham, R., 2004, Whole Life-Cycle Costing: Risk and
Risk Responses, Oxford, Blackwell Publishing.
Du Plessis, C., 2001, ‘Sustainability and sustainable construction: the African
context’, Building Research and Information, 29(5), 374–380.
Fiksel, J., 1994, Design for Environment: Creating Eco-Efficient Products and
Processes, New York, McGraw-Hill.
Gibson, E.J., 1982, Working with the Performance Approach in Building,
Report 64, Rotterdam: CIB.
Giedion, S., 1980, Space, time and architecture: the growth of a new
tradition, Massachusetts, Harvard University Express.
Glen, W., 1994, ‘Use value of historical space structures in relation to
adaptability for housing’, International Journal for Housing Science and Its
Applications, 18(1), 63–68.
ISO 14000, 2005, [online Source] Available at: www.iso14000.com/
[accessed 3 September 2005].
Kibert, C.J., Sendzimir, J. and Guy, B., 2000, ‘Construction ecology and
metabolism: Natural system analogues for a sustainable built environment’,
Construction Management and Economics, 18(8), 903–916.
Langston, C.A. and Ding, K.C.G., 2001, Sustainable Practices in the Built
Environment, 2nd edn, Oxford, Butterworth-Heinemann.
Macozoma, D.S., 2002, ‘Understanding the concept of flexibility in design for
deconstruction, design for deconstruction and materials reuse CIB
publication 272’, in A.R. Chini and F. Schultmann (eds), Proceedings of the
CIB Task Group 39 – Deconstruction Meeting, 9 April 2002, 118–127.
Markeset, T. and Kumar, U., 2003, ‘Integration of RAMS and risk analysis in
product design and development work processes: a case study’, Journal
of Quality in Maintenance Engineering, 9(4), 393–410.
NASA, 2001, Report on Sustainable Design, Design for Maintainability and
Total Building Commissioning, for National Aeronautics and Space
Administration Facilities Engineering Division (NASA), March 7, 2001.
Available at: www.wbdg.org/pdfs/nasacommissioning.pdf#search=
’building%20maintainability [accessed 22 August 2005].
Nicholls, R., 2001, Heating, Ventilation and Air Conditioning, 3rd edn,
Oldham, England, Interface Publishing.
Norusis, M.J., 2000, SPSS 10.0 Guide to Data Analysis, Englewood Cliffs,
NJ, Prentice-Hall.
Pearce, D.W., 2001, ‘Measuring resource productivity’, Paper to DTI/Green
Alliance Conference February 2001.
Roaf, S., Fuentes, M. and Thomas, S., 2001, Eco House: A Design Guide,
Oxford, Architectural Press.
Shen, Q. and Liu, G., 2003, ‘Critical success factors for value management
studies in construction’, Journal of Construction Engineering and
Management, 129(5) (September/October 2003), 485–491.
Shrivastava, P., 1995, ‘Environmental technologies and competitive
advantages’, Strategic Management Journal, 16, 183–200.
126 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
Slaughter, E.S., 2001, ‘Design strategies to increase building flexibility’,
Building Research and Information, 29(3), 208–217.
Smith, P.F., 2001, Architecture in a Climate of Change: A Guide
to Sustainable Design, Oxford, Architectural Press.
SPSS Inc. 2004. SPSS 12.0.1 for Windows, Apache Software foundation,
The University of Liverpool, Computer Service Department; July 2004.
Vakili-Ardebili, A., 2005, ‘Development of an assessment framework for
eco-building design indicators’, PhD thesis, Liverpool, The University of
Liverpool.
Vakili-Ardebili, A. and Boussabaine, A.H., 2005, ‘The intricacy of eco-building
design’, in Proceedings 4th International Symposium on Environmentally
Conscious Design and Inverse Manufacturing Tokyo, Japan, Eco-Design
2005, 649–654
Vakili-Ardebili, A. and Boussabaine, A.H., 2007, ‘Design EcoDrivers’,
The Journal of Architecture, 12(3), 315–332.
Winch, G., Usmani, A. and Edkins, A., 1998, ‘Towards total project quality: A
gap analysis approach’, Construction Management and Economics, 16(2),
193–207.
Ecological Building Design Determinants 127
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
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
128 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
TABLE A.2 Building design category – space attributes
TABLE A.3 Building design category – form attributes
Ecological Building Design Determinants 129
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
TABLE A.4 Building design category – technology attributes
TABLE A.5 Environmental profile and eco-efficiency category
130 A. VAKILI-ARDEBILI AND A. H. BOUSSABAINE
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014
TABLE A.6 Energy and resources categor
TABLE A.7 Socio-economic category
Ecological Building Design Determinants 131
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT
Dow
nloa
ded
by [
UQ
Lib
rary
] at
20:
57 0
1 Ju
ne 2
014