An Index of Regional Sustainability: A GIS-based multiple criteria analysis
decision support system for progressing sustainability
Michelle L.M. Graymore*, Anne M. Wallis, Anneke J. Richards
School of Life and Environmental Sciences, Faculty of Science and Technology, Deakin University, Australia
Rio Summit in 1992, Brundtland’s (1987, p. 43) ‘development that
meets the needs of the present without compromising the ability of
future generations to meet their own needs’ has become the most
widely accepted definition of sustainability. At the same time
sustainability indicators have proliferated with sustainability
assessments becoming more common place. As such, there are a
wide range of approaches taken to sustainability assessment
including indicators or indices, product-related assessment and
integrated assessment tools (Ness et al., 2007). In fact, examples
where indicators are used as a framework for measuring progress
towards sustainability can now be found at the national (e.g.
Wellbeing Assessment: Prescott-Allen, 2001), regional (e.g. Fraser
River Basin, British Columbia: Gustavson et al., 1999) and the local
scale (e.g. Indicators for a Sustainable Community for Newcastle,
Australia: The Australia Institute, 2000). Yet to date no generic
frameworks for assessing sustainability using indicators have
emerged at any of these scales.
One reason no generic frameworks exists is due to the
complexity of interrelated ecological and human systems. Because
these systems comprise of inherent system properties like the
multiplicity of spatial patterns and ecological processes, nonlinear
interactions among components, heterogeneity in space and time
and hierarchical organisation (Wu, 1999; Zurlini et al., 2006), it is
not just a matter of choosing a group of indicators as the generic
framework for sustainability assessment. Sustainability of a
system is characterised by the coevolution of social, economic
and environmental systems and the organisation of these systems,
called the institutional or political system, which includes the
regulation of the economic and social systems and the relations
with the environmental system (O’Connor, 2006). Thus, the
sustainability assessment of a system cannot be understood by
Ecological Complexity 6 (2009) 453–462
A R T I C L EI N F O
Received 23 October 2008
Received in revised form 21 August 2009
Accepted 28 August 2009
Available online 6 October 2009
Regional sustainability assessment
Multiple criteria analysis
Spatial decision support system
A B S T R A C T
GIS (Geographical Information Systems) based decision support tools will be useful in helping guide
regions tosustainability.Thesetools needtobe simple buteffectiveatidentifying,for regional managers,
as a decision support tool for a wide number of applications, as it provides a systematic framework for
evaluating various options. It has the potential to be used as a tool for sustainability assessment, because
it can bring together the sustainability criteria from all pillars, social, economic and environmental, to
give an integrated assessment of sustainability. Furthermore, the use of GIS and MCA together is an
emerging addition to conducting sustainability assessments.
This paper further develops a sustainability assessment framework developed for the Glenelg
Hopkins Catchment Management Authority region of Victoria, Australia by providing a GIS-based
decision support system for regional agencies. This tool uses multiple criteria analysis in a GIS
framework to assess the sustainability of sub-catchments in the Glenelg Hopkins Catchment. The
multiple criteria analysis based on economic, social and environmental indicators developed in previous
stages of this project was used as the basis to build a model in ArcGIS1. The GIS-based multiple criteria
analysis, called An Index of Regional Sustainability Spatial Decision Support System (AIRS SDSS),
produced maps showing sub-catchment sustainability, and environmental, social and economic
condition. As a result, this tool is able to highlight those sub-catchments most in need of assistance with
achieving sustainability. It will also be a valuable tool for evaluation and monitoring of strategies for
sustainability. This paper shows the usefulness of GIS-based multiple criteria analysis to enhance the
monitoring and evaluation of sustainability at the regional to sub-catchment scale.
? 2009 Elsevier B.V. All rights reserved.
* Corresponding author at: School of Life and Environmental Sciences, PO Box 423,
Deakin University, Warrnambool, VIC 3280, Australia. Tel.: +61 3 5563 3211;
fax: +61 3 5563 3462.
E-mail address: firstname.lastname@example.org (Michelle L.M. Graymore).
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/ecocom
1476-945X/$ – see front matter ? 2009 Elsevier B.V. All rights reserved.
examining only one component, either social or natural (Gunder-
son and Holling, 2002; Wu and Hobbs, 2002; Zurlini et al., 2006).
Moreover, there needs to be some understanding of the interac-
tions between and within the systems to really assess the overall
system’s sustainability. This may be different for different systems,
adding to the complexity of developing a generic framework for
In this paper, we focus on sustainability assessment at the
regional scale by building a sustainability index that considers the
interactions occurring between and within the systems. The
regional scale, typically defined as a geographic area that does not
include state or national capital cities, ‘links multiple spatial and
temporal scales of biodiversity with human uses and socio-
economicimperatives’(Brunckhorst,2005, p.6). Thus,it providesa
link between top-down national policy and bottom-up local
policies (Hewett, 2001; Buckingham and Theobald, 2003; Coelho
et al., 2006) making it an appropriate scale for progressing
sustainability. Coelho et al. (2006) also claim it is the scale most
appropriate for natural resource management because it is the
scale where ecological functioning can be reconciled with social
institutions to produce novel solutions to natural resource issues
and sustainability while providing opportunities to re-configure
institutional systems for resource governance (Norton and
Ulanowicz, 1992; Kim and Weaver, 1994; Forman, 1995;
Brunckhorst, 2000). Thus, management at this scale will result
in the integration of the flows and functions of ecological systems
to ensure these ecological systems and their biodiversity are
sustained (Brooke, 1997; Buckingham and Theobald, 2003;
Brunckhorst, 2005). This will help ensure the ecosystem goods
and services that humans rely on are preserved for future
Natural resource management and strategic planning is
increasingly being coordinated at the regional scale throughout
Australia with sustainability as a goal (Conacher and Conacher,
2000). However, to help achieve regional sustainability, this scale
needs sustainability indicators that can together measure sustain-
ability to produce well-informed planning and decision making to
progress regional sustainability. This need was highlighted by the
Standing Committee on Agriculture and Resource Management
(Standing Committee on Agriculture and Resources Management,
1999). Although there are some good examples of sustainability
assessment frameworks at both the national (e.g. Environmental
Sustainability Index: Global Leaders of Tomorrow Environment
Task Force, 2002) and local scales (e.g. Local Agenda 21 and
Sustainable Seattle: Sustainable Seattle, 1998), few attempts have
been made to develop a regional sustainability assessment
framework (e.g. Gustavson et al., 1999 for Fraser River Basin),
particularly in Australia.
must provide up-to-date information about sustainability at
multiple scales, from regional to finer scales, such as sub-
catchments, as it is widely considered that large differences in
sustainability occur across these smaller scales. It needs to be
things important to the sustainability of the region, as it is a
contextual concept with different meanings in different places and
to different people (Wallis et al., 2007). Furthermore, it needs to
take a systems approach to provide essential information about all
important aspects of system viability, performance and sustain-
ability (developed by Bossel, 2001). In this way, it must reduce the
system’s complexity into a framework that is able to capture those
things about the system that are most important to its sustain-
ability. In addition, it must recognize that a system cannot be
assessed in isolation from the systems it is dependent on
(Gustavson et al., 1999; Reed et al., 2005). A sustainability
assessment tool such as this would be able to guide the
implementation of initiatives to achieve sustainability to areas
in the region that need it most.
But to this point, sustainability assessment tools have not been
able to effectively help regional managers progress sustainability
because they are not fully integrated, making an assessment of
sustainability without quantifying the impact on sustainability of
the interactions between the indicators. Many sustainability
assessment frameworks, such as the Pressure-State-Response
framework (OECD, 1993), explicitly acknowledge that there are
interactions occurring between indicators, but they do not attempt
to incorporate these interactions into the assessment tool. Thus,
they do not take into account the interactions between the
indicators or their differing impacts on sustainability (Gustavson
et al., 1999; Bossel, 2001; Buselich, 2002). Gustavson et al. (1999)
have attempted this for Fraser River Basin, British Columbia by
modelling the interactions between indicators. They concluded
that attention should be focused on small sets of indicators and on
systems larger than an ecosystem to increase reliability. However,
most of the commonly used frameworks use a large set of
indicators developed at the national scale, for which data is often
not available at regional and finer scales (Graymore, 2005;
Graymoreet al., 2008). Furthermore,a framework thatis adaptable
and user-friendly has yet to be developed. Therefore, there has not
been a tool available that regional managers can use effectively to
aid decisions about sustainability at the regional scale.
One of the problems with sustainability assessments is how to
bring the indicator information together to determine something
about the overall system sustainability. Ideally, the aggregation
method would objectively take into account the interactions
between the indicators and the differences in influence on system
sustainability. However, this is difficult to quantify and so the
aggregation methods used are usually subjective in the way they
combine indicators, and often look at the indicators impact on
sustainability in isolation from other indicators ignoring any
interactions that may be occurring. Thus, sustainability assess-
decision support system to help develop an objective aggregation
method that more accurately assesses sustainability and that is
more useful for guiding decision making for sustainability.
Decision support systems usually comprise a computer based
system made up of a knowledge based system and a problem
solving system developed to support decision making (Holsappe,
2003). These systems enable decision makers to bring together the
information in the knowledge base using the problem solving
system to help them make well-informed decision about what is
the best option given the criteria considered and the preferences of
the stakeholders (Pietersen, 2006).
In this case, the problem solving system would bring the
indicator information together to rank the sustainability of areas
within the region. Thus, a decision support tool for sustainability
assessment would be able to help regional managers direct the
implementation of strategies to progress sustainability in a way
that will have the greatest impact. But, to be an effective decision
support tool, it has to integrate the best science available into the
decision making process at a level that decision makers under-
stand, preventing the loss of any important information (Giampie-
tro et al., 2006). It also has to be transparent and reliable to ensure
the decision reached is understood by all and given support by
community. Therefore, the aim of this paper is to introduce a
decision support tool that integrates a multiple criteria analysis of
locally selected sustainability indicators into GIS to produce a
spatial decision support system for sustainability assessment in
south west Victoria. This paper will evaluate the robustness and
sensitivity of the tool and demonstrate its usefulness for regional
sustainability monitoring and evaluation by applying it to the sub-
catchments of south west Victoria.
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
2.1. Multiple criteria analysis and spatial decision support systems for
To develop a decision support tool for sustainability assess-
ment, a problem solving system is needed that enables the
aggregation of indicators in an objective and fully integrated
manner. One system that could do this is multiple criteria analysis
(MCA), as this method is well suited to sustainability assessment
due to its ability to consider many criteria at once, even a mixture
of qualitative and quantitative criteria (van Pelt, 1993; Buselich,
2002). It is a practical method for integrating scientific knowledge
into multiple criteria problems in a way that is transparent and
easy to implement (Chiranjeewee and Vacik, 2007). MCA has been
used effectively to develop decision support tools able to rank
decision options considering multiple criteria simultaneously for a
range of planning and natural resource management issues to aid
Pullar, 1999; Joerin et al., 2001; Foxon et al., 2002; Hajkowicz,
2002; Klawitter, 2003; Pietersen, 2006). For example, it has been
used for planning issues such as the location of a new building, the
(Carver, 1991; Rotmans and Van Asselt, 2000; Buselich, 2002). It
has also been used effectively for natural resource management
issues, such as coastal reef management (Fernandes et al., 1999),
forestry management (Mendoza and Prabhu, 2000), water
resources management (Hamalainen et al., 2001; Foxon et al.,
2002; Hyde et al., 2004; Pietersen, 2006) and water pricing
for theevaluationof sustainabilitycriteriafor project appraisaland
it has since been used to develop a framework for assessing the
sustainability of agricultural systems (Lopez-Ridaura et al., 2002)
and the sustainability of policy alternatives (Nijkamp and
Ouwersloot, 1997). Thus, MCA has been shown to be effective
the development of a decision support tool for regional sustain-
The problem with MCA is that it is difficult to develop weights
without value judgments, as the methods used require stake-
holders or the decision maker to subjectively place importance on
each of the criteria. To develop an accurate measure of sustain-
ability for decision support, an objective method for developing
weights based on the current understanding of sustainability is
needed to ensure that the most accurate result is obtained. This
could be done by investigating and quantifying the interactions
between indicators and the impact they have on sustainability
using correlations and pairwise comparisons to help produce an
integrated sustainability assessment.
To enhance MCA as a decision support tool, it can be integrated
with GIS to combine geographical data with multiple criteria
decision models producing maps that show the ranking of options
(Carver, 1991; Crossland et al., 1995; Malczewski, 2006). This
produces a Spatial Decision Support System (SDSS) that is able to
make decision making more transparent, since it produces maps
showing where the decision options are located, the differences in
the levels of the criteria for each option and the ranking of options.
These systems have been developed for a variety of management
issues, such as regional priority setting for natural resource
2006), lake regulation (Mustajoki et al., 2004), water resource
management (Malczewski, 2006), regional and urban planning
(Pettit and Pullar, 1999), land use suitability (Joerin et al., 2001)
and natural hazard management (Barnwell et al., 2005). These
tools are able to enhance stakeholder experience with decision
support tools, decreasing the time taken to make decisions and
enabling them to see the possible implications of the decision
options by producing maps of decision options (Crossland et al.,
1995; Jankowski et al., 2001). Thus, a SDSS for regional
sustainability could be a useful tool for producing well-informed
decision making to help achieve sustainability.
The use of GIS software is increasing among regional managers
for storing data and producing maps. So it would be useful to
develop decision support systems in GIS to decrease data transfer
between software packages, reducing possible errors and loss of
information. However, until recently a MCA able to produce maps
would not be a user-friendly tool as it would require the user to
carry out the MCA in spreadsheet software before transferring the
data to GIS to produce maps, or to write macro programs linking
the GIS functions together to run the MCA, or even manually
running each GIS function separately to carry out the MCA (Carver,
1991; Malczewski, 1999). But improvements in GIS capabilities
mean that a user-friendly MCA tool can now be developed directly
in the GIS software. This was demonstrated by Preda et al. (2006)
who built a GIS-based MCA tool to analyse nutrient export
potential to waterways in ArcGIS1(V. 9.0) using the Model Builder
environment. This tool was able to rank sub-catchments in terms
of their potential to export nutrients to nearby waterways by
considering a number of variables that influence nutrient export
(Preda et al., 2006). This suggests, if a tool similar to this one was
developed for sustainability, it would be able to help regional
managers identify areas within the region that are most in need of
action to progress sustainability.
2.2. Study area: south west Victoria, Australia
The south west region of Victoria extends from the South
Australia border in the west to Camperdown in the east, and from
the Central Highlands in the north to the southern coast (South
WestSustainabilityPartnership,2001). Locatedinthisregion isthe
Glenelg Hopkins catchment management area, which is the focus
of this study (Fig. 1). This catchment management area covers an
area of 2.6 million hectares containing three major river basins –
the Hopkins River basin,the PortlandCoastal basin and the Glenelg
River basin – with a total of 32 sub-catchments (GHCMA (Glenelg
Hopkins Management Authority), 2003).
Since European settlement, the agricultural sector has provided
most of the region’s economic prosperity. In recent years, there
have been significant changes in the types of agriculture, with
increases in cropping, dairying and timber production, and a
reduction in sheep farming. Land use, including agriculture,
industry and urbanization, has had a severe impact on the natural
environment. Some of the major environmental problems facing
the region are salinisation, soil degradation, loss of habitat and
biodiversity and eutrophication of waterways. This is an issue for
the economic sustainability of the region, since it depends on the
health and structural integrity of the natural resource base (South
West Sustainability Partnership, 2001).
The region has a population of 107,000 people mostly located in
the urban centres of Portland, Warrnambool and Hamilton (ABS,
2002). These urban centres are continuing to grow, while some
rural areas in the region are experiencing considerable population
decline. This pattern of decline is creating a number of social and
economic challenges for some of the small rural towns in the
2.3. Catchment to Regional Scale Indicators of Sustainability project
In south west Victoria, Australia, an ongoing project Catchment
a set of indicators applicable for tracking progress toward
sustainable development. The indicator selection framework
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
was based on the South West Sustainability Blueprint (South West
Sustainability Partnership, 2001) using 4 pillars of sustainability:
social (building capacity within the region), economic (creating
prosperity through sustainability), environmental (conserving the
natural resource base) and institutional (monitoring and evalua-
tion). An extensive literature search identified a vast array of
indicators already in use for measuring sustainability at the global,
regional and local scales and these indicators set within the four-
pillar framework provided the starting point for indicator
Using this ‘global’ set of indicators a ‘filtering’ process was
undertaken to refine the indicator set for the south west region. As
depicted in Fig. 2 three ‘filters’ were used to ensure (i) indicators
used were relevant to the region, (ii) both trend and condition (not
just snapshots in time) could be measured for each indicator and
(iii) the relationships between indicators were considered and not
just pillars in isolation. Stakeholder organisations and community
representatives (Table 1) from across the region were involved in
selecting from the ‘global’ indicator set those indicators that were
considered priority indicators for measuring sustainability within
the region (Filter 1). A total of 44 indicators were selected. An
extensive data collection followed (Filter 2), to sort data that
provided good quality, reliable, trend and condition information.
Of the 44 regional indicators selected only 19 across three pillars
were found to have data applicable at the appropriate scale, that
showed both trend and condition for the indicators and could be
mapped using ArcView 3.3 (GIS software) (Wallis et al., 2007). The
process to this stage is not dissimilar to indicator selection
methods used by other authors (e.g. Morse et al., 2001; Becker,
2004). Filter 3 involved identifying the relationships between the
19 indicators so that a sustainability assessment tool using a small
number of highly influential indicators could be developed.
The relationships between the 19 indicators and their impact on
sustainability of the region were determined in a way that was as
objective as possible. Firstly, the relationships between the priority
indicators and their impact on the sustainability of the region were
determined using Spearman’s correlation coefficients to develop
Sustainability Impact Ratings for each of the indicators (Richards
et al., 2007). Using the Analytical Hierarchy Process (AHP) (Saaty,
1980) in Expert ChoiceTM, pairwise comparisons of the indicator’s
Sustainability Impact Ratings were made to determine indicator
weightings. All the indicators were analysed together to form a
contributed little to the overall sustainability assessment of sub-
Fig. 1. The South West region of Victoria, Australia. This shows the location and names of the sub-catchments in the Glenelg Hopkins Catchment area used in this study.
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
catchments, so the indicator set could be reduced without reducing
the accuracy of the sustainability assessment. Indicators that
contributed greater than 5% to the assessment were kept in the
all pillars were assessed. This left 13 priority indicators (Fig. 3) that
form An Index of Regional Sustainability (AIRS) for south west
Victoria. Weightings for these 13 indicators were again determined
using AHP to produce a MCA utilising weighted summation (Fig. 3).
This process means that the 13 remaining indicators not only
provide information about the condition of those particular parts of
the system, they also tell us about the indicators to which they are
linked and thus, about the system’s sustainability. For example, the
region, it incorporates information about the sustainability of land
use practices, vegetation cover and other management practices,
and the impacts of loss of viable land through erosion on waterway
health, soil condition, agricultural production, and economic and
social impacts this is having in the system. Thus, it should be noted
provide the largest amount of information about the region’s
However, AIRS needed further development to enable it to
directly produce visual images showing the variation in sustain-
ability across the region and allow the identification of areas in the
region where more effort is required to progress sustainability.
will make the tool more useful for helping regional managers
target implementation of management actions to areas most in
need (Jones et al., 2007). Furthermore, as Nicholson-Cole (2005)
argue, the use of visual images will enhance the power of the
assessment as visual images can condense a large amount of
information into one image, they can be used to convey strong
messages, are easy to recall and instantaneous. In addition, the
ability of visual images to present messages quickly and power-
fully enough as to change human behaviour has been well
documented (Sheppard, 2005). Thus, this paper reports on the
integration of AIRS with GIS to produce a GIS-based MCA decision
support tool for regional sustainability for south west Victoria,
called An Index of Regional Sustainability Spatial Decision Support
System (AIRS SDSS).
3.1. Integration of AIRS with GIS
To produce an effective and user-friendly decision support tool
for sustainability in south west Victoria, AIRS was integrated with
GIS using ArcGIS1(V. 9.1 ESRI, Redlands, CA) in the ArcMap
environment. To do this, the ArcGIS Model Builder environment
and other data processing tools in ArcMap were used. In Model
Builder, the user can design and use a GIS model to automate data
processing by interactively dragging process tools and data objects
intoa visual diagramofthe model.Thus,ModelBuilderwas chosen
to build the most user-friendly tool possible to make it easy for
regional managers with some experience using GIS to use the tool.
So a number of data processing tools were trialled both inside and
outside Model Builder to build a tool able to carry out the weighted
summation utilised by AIRS.
3.2. Evaluation of SDSS
Once the tool was developed, it was evaluated to ensure it was
reliable and accurate method of sustainability assessment.
Malczewski (1999) argues that a sensitivity analysis of the
criterion weights is most important because they are a part of
the MCA that is most likely to be in dispute. He suggests that an
investigation into the sensitivity of the alternatives to small
changes (around 10%) in the value of the weights be carried out
(Malczewski, 1999). Thus, for this study, the sensitivity analysis
involved changing the indicator’s weights one at a time by ?0.005
to see if this altered the ranking order of sub-catchments in the
results. If no change in the ranking of sub-catchments was observed,
the sensitivity analysis would suggest that the uncertainty in the
weights was insignificant and the tool was robust (Malczewski,
To be useful for sustainability monitoring and evaluation, the
tool developed needs to be easily able to distinguish between sub-
catchments with low relative sustainability from those with high
relative sustainability. Therefore, AIRS SDSS was tested to ensure it
was sensitive enough to show differences in sustainability across
the region. To do this the sustainability ranks for the indicators
were changed to determine if the tool was sensitive enough to
Fig. 2. The ‘filtering’ process used to develop the An Index of Regional Sustainability
Regional stakeholders involved with the prioritisation of sustainability indicators
for south west Victoria.
Statutory and government authoritiesDepartment of Natural
Resources and Environment
Glenelg Hopkins Catchment
Western Coastal Board
Water authorities South West Water
Portland Coast Water
Local government City of Warrnambool
Southern Grampians Shire
Post-secondary education institutionsDeakin University
South West Institute of Technical
and Further Education
Industry Nestle ´
Adapted from Wallis (2006).
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
detect differences in sustainability ranks. Firstly, the sustainability
ranks were changed to a high, medium or low rank for selected
sub-catchments, and secondly the sustainability ranks were set
with a difference in rank of 1. Maps were compared to the
sustainability ranks to ensure the tool was able to reflect the
differences in sustainability ranks.
3.3. Sustainability assessment of south west Victoria
AIRS SDSS was used to produce maps of sub-catchment
sustainability and environmental, economic and social condition.
the potential usefulness of the tool for monitoring sustainability,
evaluating sustainability strategies and guiding the determination
of priority sub-catchments for management action to help achieve
sustainability. To aid in this evaluation a relative scale of
sustainability assessment was established. The sustainability
assessment scores the sub-catchments from 0 to 8, where 8 is
considered most sustainable. Thus, the relative scale of sustain-
ability assessment shows that sub-catchments that receive a low
sustainability score are considered to be sub-catchments with a
low relative sustainability compared to sub-catchments with a
higher score. The scores for the three pillars (social, economic and
environmental) are based on their contribution to overall
sustainability determined by the indicator weights. Thus, the
range of scores the pillars can have is smaller than for
sustainability. The ranges are: 0–1.2 for the economic pillar, 0–
0.8 for the social pillar and 0–6.0 for the environmental pillar. For
each of these, a higher score indicates a better condition.
4.1. AIRS SDSS: the spatial decision support system for regional
The MCA sustainability assessment framework, AIRS, was
integrated into GIS using the simplest approach possible to ensure
the tool developed was easy to use. Although every attempt was
made to develop the tool fully in Model Builder to produce a fully
automated tool, several steps had to occur outside the Model
Builder environment due to the limitations of Model Builder
discussed below. The first step in AIRS SSDS was to make the
indicators comparable by standardising the indicator data using
the sustainability ranking scale (Richards et al., 2007). Then all the
indicator tables were joined and the ‘export data’ tool, located in
the Table of Contents of ArcMap, was used to make a new map
layer containing all the indicator data.
The next step was to apply the MCA GIS model built in Model
Builder to the indicator data. This produced a sustainability score
for each of the sub-catchments, which is the weighted summation
of all the indicators. It also produced a score for the environmental,
social and economic pillars from the weighted summation of their
respective indicators. Each of these scores were then mapped
separately to show the variation in sustainability and environ-
mental, social and economic condition across the region (Fig. 4).
Thus, AIRS SDSS was able to provide information about the
sustainability of the sub-catchment, as well as its economic, social
and environmental condition. This ensures that a sub-catchment
that is performing poorly in one pillar but doing well in the other
pillars, and thus achieving a high sustainability ranking, is still
identified as requiring some attention to progress sustainability.
Thus, the development of AIRS SDSS enabled the MCA of
sustainability to be completely carried out in the GIS environment
enabling the visual representation of the sustainability assessment
in the form of maps and graphs without transferring information
from one software package to another. This enhances the AIRS tool
reducing the steps required to produce maps of sub-catchment
sustainability, pillar and indicator condition. It also increases the
adaptability of the tool, as boundaries can easily be changed to
reflect different administration areas, indicators can be added, and
data can be updated.
4.2. Evaluation of AIRS SDSS
The evaluation of this tool showed the method to be robust and
sensitive. Changing the weight of one indicator by ?0.005 caused
minimal differences in the order of the sub-catchment ranking for
sustainability. For most of the indicators, there was no difference
caused by the change in weight, but three indicators did show a
difference in the order of sub-catchments with two sub-catchments
swapping in their order with a 0.01 difference in sustainability score.
Thisdifferenceisminimal and isnot large enoughtoseparateone sub-
catchment from the other in terms of prioritising them for sustain-
ability initiatives. Thus, it would not impact on any recommendations
made using the results. Therefore, it can be concluded that there was
insignificant uncertainty in the indicator weights used in the tool.
When the sustainability ranks were changed, the changes were
reflected in the ranking of sub-catchments, even when there was
Fig. 3. Indicators of sustainability for South West Victoria with weights in brackets (Richards et al., 2007).
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
only one difference between sustainability ranks of sub-catch-
ments. This demonstrated that the tool was able to pick up small
differences in sub-catchment rank, so a difference in sub-
catchment sustainability, even if it is only small, will be seen in
the assessment results. Therefore, the evaluation demonstrated
that the tool is a robust and reliable method of sustainability
assessment, which is sensitive to differences in sustainability rank
showing small variations in sustainability rank across sub-
catchments, thus making it a valid tool for sustainability
assessment in south west Victoria.
4.3. Sustainability assessment of south west Victoria
AIRS SDSS was used to produce a series of maps showing
sustainability and environmental, social and economic condition
across the sub-catchments in the region. An examination of these
maps (Fig. 4) showed that there was variation in sustainability and
pillar condition across the sub-catchments in the region. This
result suggests the tool would be useful for supporting decision
making to progress sustainability.
Sub-catchments with high relative sustainability are easily
distinguishable from those with relatively low sustainability.
Thus, the map is ableto highlightthe sub-catchments most inneed
of sustainability initiatives to progress regional sustainability,
helping mangers prioritise the sub-catchments in the region for
management. For south west Victoria, the sub-catchments with
the highest sustainability scores were G8, P3, P1, G1 and P2, all in
the south western corner of the region. However, it has to be noted
that noneof the sub-catchmentswere sustainableas there were no
scores of 8. The sub-catchments with the lowest sustainability
scores were in the upper Hopkins catchment, H5, H2, H4 and H12.
But what is causing the sustainability of the sub-catchments in the
south western corner of the region to be higher than for those in
the upper Hopkins River. By looking at the environmental, social
and economic condition maps this question can be addressed.
As would be expected with the large number of environmental
indicators included in the tool, environmental condition displayed
similar variations to the sustainability assessment across the sub-
catchments in the region (Fig. 4b). The sub-catchments with the
highest environmental scores, and thus, best environmental
condition were those in the south western corner of the region
(G8,P3, P1, G1 andP2), while thoseinthe upper Hopkins River(H5,
H12, H4 and H3) had the lowest scores, the same result as for
sustainability demonstrating the link between environmental
condition and overall sustainability. The sub-catchments in the
south western corner all had higher scores for the majority of
environmental indicators, particularly in terms of remnant
vegetation, soil structure decline and water erosion. This is
because a large amount of land in this area is locked up in
national parks. While the upper Hopkins all scored poorly on the
environmental indicators, particularly with land use, dryland
salinity, soilstructuredeclineand erosion, suggesting thatland use
condition and sustainability. These results are backed up by the
recently released State of the Environment Report for Victoria,
which lists the Hopkins River as the most degraded in the state
Fig. 4. AIRS SDSS sub-catchment assessment: (a) overall sustainability; (b) environmental condition; (c) social condition; (d) economic condition.
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
(Commissioner Environmental Sustainability Victoria, 2008). This
suggests that sustainability in south west Victoria’s sub-catch-
ments is intimately linked to environmental quality, and thus,
to improvement the region’s overall sustainability.
The map of social condition (Fig. 4c) shows the variation in the
sub-catchments social condition. However, there is less variation
in social condition than either sustainability or environmental
condition. The region is performing relatively well with most sub-
catchments achieving scores of 0.40 or above. The highest ranked
sub-catchments were H1, H9, H5, P1, P6, H7 and P2. This is in
contrast to sustainability and environmental condition where H5
was the lowest ranked sub-catchment, and other than P1, the rest
were ranked inthe middle, suggestingthatthere may be a negative
relationship between social condition and environmental condi-
tion in the region. G6 was the lowest ranked sub-catchment with
0.21 due toa doublingofpopulationbetween 1996and 2001,a low
percent of people having completed Year 12 and a large change in
age structure having been experienced.
Like the social assessment, there is less variation in sub-
catchment economic condition (Fig. 4d) compared to the sustain-
a relatively good economic performance in most sub-catchments,
with more than two thirds receiving a score of 0.60 or more. The
remaining sub-catchments all scored above 0.50. The highest
ranked sub-catchment is H13, followed by G13, both of which had
high levels of employment diversity. The sub-catchments with the
lowest economic scores were G12, H3, H8 and H9 due mainly to
of employment diversity in economic prosperity suggests that
increasing the number of sectors available to employ people may
be one area that would improve economic condition and
The results of this sustainability assessment point to the
relationships that are occurring between social, economic and
environmental pillars in south west Victoria. For example, with the
upper Hopkins River sub-catchments it appears that there may be
a negative relationship between economic and environmental
condition. This would suggest that the use of the natural system in
this area for economic gains has been to the determent of the
This assessment of south west Victoria’s sub-catchments
demonstrates that AIRS SDSS can highlight the differences in
sustainability across the region, as well as indicating aspects of the
sub-catchments were improvements are needed to progress
sustainability. Thus, it is a valid decision support tool for
monitoring sustainability and evaluation of strategies to progress
sustainability at the regional and finer scales.
This paper has demonstrated that the MCA sustainability
assessment, AIRS, can effectively be integrated with GIS to form a
GIS-based MCA decision support tool for regional sustainability.
The evaluation of AIRS SDSS showed that it was a robust and
sensitive tool valid for the assessment of sustainability in south
west Victoria. It is a holistic method of sustainability assessment,
using the MCA to take into account the interactions between the
indicators (both within and across pillars) and the differences in
impact on sustainability through the aggregation of indicators.
Thus, this study demonstrated that the use of MCA in a
sustainability assessment produces a fully integrated assessment
By directly producing maps of sustainability and pillar
condition the AIRS SDSS is able to highlight the differences in
sustainability and environmental, social and economic condition
across the region. The tool is adaptable, so new data or new
information about regional sustainability can be included in the
tool easily by changing the indicator weights or adding new
indicators. For people who have used GIS, the tool is easy to use,
and it is easy to interpret the results of the assessment. Thus, AIRS
SDSS would be a useful tool for regional mangers for sustainability
monitoring and evaluation, and for the prioritisation of sub-
catchments for sustainability initiatives in south west Victoria.
During the development of AIRS SDSS, the Model Builder
environment, although limited to some extent in what it can
perform, was found to be a real advantage to the integration of the
can be automated and run over a few minutes with little
preparation time once the model is built. Also, the input files
can be easily updated to include new data or indicators, and the
weights can easily be changed if indicator priorities change with
new knowledge. This also makes it easy to run test scenarios with
the tool and helps make the tool adaptable. Another advantage of
the Model Builder is that all output files, including intermediate
ones can be viewed. The run log allows the user to see if there are
any errors during the process enabling them to see what went
wrong and fix it. In addition, the integration of AIRS into GIS
enables the GIS program to be used for data storage, as well as
manipulation for sustainability monitoring. The indicator data can
be stored in the GIS program reducing transfer errors and the
number of steps required to carry out an analysis (Pettit and Pullar,
1999). This is because GIS is a powerful tool for managing spatially
referenced data (Chakhar and Martel, 2003). However, to date it
has been underutilised as an efficient decision support tool. Thus,
the integration of the AIRS into GIS enhances not only the AIRS
assessment but also the GIS’ capabilities, enabling GIS to become a
useful spatial decision support system.
While developing this tool, some of the limitations of the Model
Builder environment were uncovered. Several of the useful data
processing tools in ArcToolbox do not work well in Model Builder.
For instance, the ‘Make Feature Layer’ tool will only make the data
a feature layer and does not change field names, delete fields or
change their order. Furthermore, other data processing tools
available in the ArcMap Table of Contents are not available for use
in Model Builder. Barnwell et al. (2005) have also found faults in
Model Builder. He found the parameters of some tools are removed
each time a model is opened resulting in the user having to reenter
these parameters each time the model is opened. Also, the model
components are not processed in the order they appear in the
model, but in the order they were placed into the model (Barnwell
et al., 2005). This means that if the model is modified, the order in
which the model components are processed changes. But despite
these issues, the Model Builder environment enables the auto-
mation of the MCA in GIS. This opens the way for other decision
support tools to be developed directly in ArcGIS1.
AIRS SDSS provides the integration between MCA and GIS that
has previously not been attempted for sustainability assessment
(Abel et al., 1996; Buselich, 2002). It produces a sustainability
assessment method for the regional scale that assesses sustain-
ability using a fully integrated holistic approach, filling a gap
identified in the available assessment tools. Up until now, the
ability to aggregate interdisciplinary and cross-sectoral informa-
tion objectively to form a useable tool for decision making had
been illusive. But the method employed in this study, enables the
aggregation of information across pillars to provide information
about sustainability based on the current understanding of
In south west Victoria, the analysis of indicator interactions and
impact on sustainability has meant that the environmental pillar
has the greatest number of indicators in the analysis, because the
environment was seen to have the greatest impact on sustain-
M.L.M. Graymore et al./Ecological Complexity 6 (2009) 453–462
ability. This may not be the case in other regions, but in this region
with a history of agriculture and environmental degradation
through landscape change, the condition of the environment has
been found to be the most significant indicator of sub-catchment
sustainability. This study suggests that sustainability in this region
will require more emphasis on the environmental integrity of the
system, than the social and economic condition of the system.
AIRS SDSS has a number of characteristics that make it
potentially useful for regional managers trying to progress
sustainability. These include the ability to show variations in
sustainability across a region at finer scales, as well as allowing
new indicators to be added and indicator weights to be changed.
AIRS SDSS forms a fully integrated assessment of sustainability
providing information about the system’s sustainability as well as
the pillar condition and, it is easy to use and interpret. These
characteristics make the tool useful for the monitoring and
evaluation of regional management strategies, since (if the
assessment is repeated for a number of years) it is able to show
variation in sub-catchment sustainability through time, and it can
regional sustainability. For example, in south west Victoria, the
Glenelg Hopkins Catchment Management Authority could use it as
part of the review process for the Regional Catchment Strategy,
where the overarching goal is to improve regional sustainability,
by using it to monitor sustainability across the region. The Glenelg
Hopkins Catchment Management Authority could also use AIRS
SDSS to guide targeted implementation of sustainability initiatives
across the region to sub-catchments that have lower sustainability
to help them progress regional sustainability. However, the use of
the tool is limited by the managers understanding of the tool,
particularly the fact that it is not just measuring the 13 indicators
and the interactions between them, but also the underlining
interactions occurring within the system that contribute to the
Like other spatial decision support systems AIRS SDSS, will be
able to help reduce the time taken to make well-informed
decisions about sustainability in south west Victoria by providing
up-to-date information about sustainability at the sub-catchment
scale across the region (Crossland et al., 1995). Using the maps
produced will show the regional manager at a glance how sub-
catchments are performing. The tool also provides transparency in
decision making, as it provides information about sustainability,
environmental, social and economic condition that is easily
accessible to various audiences, including the general public. This
transparency is a important key to any good decision support tool
(Mustajoki et al., 2004). Thus, this integrated approach to
sustainability assessment is considered a valuable addition with
the potential to increase decision making efficiency and account-
As new indicator data becomes available for other indicators
not able to be measured at this point in time AIRS SDSS is flexible
enough to be further developed. Since this tool was developed for
the Glenelg Hopkins Catchment Management Authority, it uses
sub-catchment boundaries for the finest scale, however, to make it
more useful for other regional managers, such as Local Councils,
of sub-catchments as the finer scale. Also, it could be developed to
be used in other GIS software, such as in IDRSI (Clark Labs,
Worcester, MA, USA), which also contains a decision support
module, making it available to more GIS users. The other way this
tool could be further advanced, is to develop a user interface using
Visual Basic1or ArcObjects (Barnwell et al., 2005), which would
make the tool more user-friendly and increase its stability. It may
also provide a means for linking the AHP to the GIS MCA to enable
the development of new weights if indicators are added to the tool.
For other regions, the sustainability assessment framework
developed through the Catchment to Regional Scale Indicators
of Sustainability project could be used to develop an AIRS SDSS
applicable to their context.
This paper has demonstrated that the integration of GIS with a
MCA based sustainability assessment, AIRS, produces an effective
decision support tool for sustainability in south west Victoria. AIRS
SDSS is a user-friendly means of monitoring sustainability, as it
uses an easy step by step method that is able to produce maps that
show the variation in sustainability across sub-catchments in the
region, as well as environmental, social and economic condition.
The evaluation of the tool proved that it is a robust and sensitive
methodofsustainabilityassessment.Its abilityto showdifferences
in sustainability and pillar condition across the region could help
managers prioritise sub-catchments in terms of their need for
sustainability initiatives and other management actions, making it
a useful decision support tool for progressing sustainability. If used
for repeated years, it will also be useful for sustainability
monitoring and evaluation of the effectiveness of sustainability
strategies by illustrating any changes in sub-catchment sustain-
ability over time. Thus, this paper has been able to fill the gap in
sustainabilityassessment tools at the regional scale by producing a
GIS-based MCA tool that is a fully integrated sustainability
assessment, which is also user-friendly, robust, transparent and
useful for helping regional managers make decisions to progress
We would like to acknowledge the work carried out by Anne
Wallis, Anneke Richards and others on the Catchment to Regional
Scale Indicators of Sustainability project. We would also like to
thank the anonymous reviewers for their helpful comments. The
funding for this study was from Glenelg Hopkins Catchment
Management Authority (Contract no. 0506-202).
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