Sustainability Indicators for TourismDestinations: A Complex AdaptiveSystems Approach Using SystemicIndicator Systems

Karin Schianetz and Lydia KavanaghSchool of Engineering, University of Queensland, Brisbane, Australia

This article discusses the necessity for complementing linear sustainability assess-ment tools, which disregard the complex and dynamic nature of tourism, with complexadaptive systems (CASs) approaches. A methodological framework for the selectionand evaluation of sustainability indicators for tourism destinations, the systemic in-dicator system (SIS), is proposed; this framework takes the interrelatedness of socio-cultural, economic and environmental issues into account. The SIS methodology istested using a case study of a holiday eco-village project near Lamington NationalPark in Queensland, Australia. The results show that tourism destinations need tobe viewed and studied as CASs, and that sustainability indicator systems need tobe applied in the context of an adaptive management approach. Special attention isgiven to the capability of the SIS methodology as a decision aid for resort developersand planners to improve the effectiveness of measures for pollution prevention andmitigation.

doi: 10.2167/jost766.0

Keywords: adaptive management, complex adaptive systems, sustainabilityindicators, social–ecological system, tourism destinations

IntroductionThis paper introduces and discusses a new methodology for the assessment of

the sustainability of tourism destinations through the use of a systemic indicatorsystem (SIS). The SIS uses a complex adaptive systems (CASs) approach, basedon the understanding that all natural and social systems are interdependent,and nonlinear with feedback at many different levels that allow these systems toself-organise, adapt continually and change in an unpredictable manner (Folke,2006; Gell-Mann, 1994; Gunderson & Holling, 2002; Holland, 1995; Levin, 1998).Researchers from a wide range of disciplines have studied CASs, and discussedtheir existence and behaviour in ecosystems, the biosphere, economies, organ-isms and in brains (Center for the Study of Complex Systems, 2007; ResilienceAlliance, 2007; Santa Fe Institute, 2007). While there does not seem to exista formal definition for a CAS (Levin, 1998), most researchers emphasise thenonlinearity and interconnectedness of CASs (Holling, 1978, 2001; Walker &Salt, 2006), and the sustained diversity and individuality of their components(Gell-Mann, 1994), whose maintenance is fundamental to adaptive evolution(Levin, 1998). Understanding the structures and patterns of interactions within0966-9582/08/06 601-28 $20.00/0 C© 2008 Taylor & FrancisJOURNAL OF SUSTAINABLE TOURISM Vol. 16, No. 6, 2008

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and between these systems is necessary to explore and ultimately increasetheir resilience and adaptive capacity (Folke et al., 2002; Gunderson & Holling,2002; Walker et al., 2004) or their robustness to induced or unexpected changes(Anderies et al., 2004).

Farrell and Twining-Ward (2004) emphasise the need to acknowledge the ex-istence of ‘complex adaptive tourism systems’, and their similarity with otherecosystems; to account for this affinity, the authors argue that tourism studieshave to incorporate new knowledge from ecosystem research and management(Gunderson et al., 1995; Gunderson & Holling, 2002; Holling, 1978) and sus-tainability science, which seeks to facilitate a ‘transition towards sustainability’(Clark, 2007; Kates et al., 2001).

Research findings in these fields point out that all natural and social sys-tems, which are frequently termed social–ecological systems (SESs), and aredefined as systems that include ‘societal (human) and ecological (biophysical)subsystems in mutual interactions’ (Gallopin, 2006: 294), are CASs (Gunderson& Holling, 2002; Levin, 1998; NRC, 1999; Walker & Salt, 2006). Therefore, Farrelland Twining-Ward (2005) postulate that nature and tourism activity should beviewed and studied, not separately but as integrated SESs. This is particularlyimportant for the study of tourism destinations that are located in or close toecologically fragile areas that are highly dependent on their natural resourcesand attractions (Honey, 1999).

While the dynamic nature and unpredictability of tourism systems have beenobserved and discussed (Butler, 1980; McKercher, 1999), little research has beendone in order to adopt tourism assessment and management tools that accountfor uncertainty, non-linearity and unexpected changes, such as resilience anal-ysis, adaptive management and system dynamics modelling (SDM) (Farrell &Twining-Ward, 2004, 2005).

Resilience analysis has evolved from the observation that shocks and distur-bances to SESs (e.g. fires, floods, epidemics, wars, market changes) can drivethem across a threshold into a qualitatively different and often unwelcome state(Holling, 1973; Walker & Salt, 2006). The resilience of SESs has three definingcharacteristics ‘(a) the amount of change the system can undergo and still remainwithin the same domain of attraction; (b) the degree to which the system is capable ofself-organisation; and (c) the degree to which the system can build the capacity to learnand adapt’ (Carpenter et al., 2001). The concepts of resilience have been around fordecades. However, it is only recently that the significance of resilience analysisfor governing and managing a transition towards sustainability has been anal-ysed (Folke et al., 2002; Folke, 2006). Walker and Salt (2006) postulate that the keyto sustainability lies in enhancing the resilience of SESs, instead of optimisingisolated components of the system.

Adaptive management (Holling, 1978; Walters, 1986) formulates managementpolicies as experiments that probe the responses of SESs to introduced changes.The principal aim of these experiments is to improve knowledge about thebehaviour and structures of SESs, and to design better policies and contrivebetter experiments (Lee, 1999). Hence, adaptive management is an importanttool to enhance the resilience of an SES by increasing the collective capacityof the human actors to learn and adapt (Walker et al., 2004). The principleshave begun to be recognised by tourism researchers (Miller & Twining-Ward,

Sustainability Indicators: Systemic Indicator Systems 603

2005; Reed, 2000; Schianetz et al., 2007), but there are few documented studiesof where the tool has been applied for tourism management and assessment(Miller & Twining-Ward, 2005; Holling, 1978; Walker et al., 1999, 2005), andthese are primarily in connection with the SDM.1

The SDM has been used by CASs researchers of various disciplines, predom-inantly ecologists and economists, for regional planning (van den Berg, 1991;Wiranatha, 2001), scenario testing (Walker et al., 1999, 2004) and organisationallearning (Senge, 1990; van den Belt, 2004). Since 1970s, there have been iso-lated SDM projects for the sustainability assessment of tourism destinations(Schianetz et al., 2007). However, the development of more complex models,which are necessary for this purpose, is data intensive, time-consuming and re-lies on skilled modelling experts. Although this has undoubtedly been a factorcontributing to the sporadic use of the tool, the potential of the SDM to assessCASs and to foster learning and collaboration in a tourism destination, and thuspromote sustainability, remains.

The SIS methodology proposed in this paper brings together the basic con-cepts of resilience thinking, adaptive management and the SDM in order to fostersustainability in tourism destinations through a CASs approach that defines thetourism destination as an integrated SES and aims to assist in the following:

(1) Definition of goals and priorities that lead to long-lasting improvement ormaintenance of the system functions.

(2) Improvement of the understanding of the economic, social and ecologicalsystem: its interconnections, behaviour and thresholds.

(3) Building resilience to change and shock in tourism destinations, and thesystem’s capacity for learning and adaptation.

(4) Prediction and prevention of negative side effects of interferences and miti-gation measures.

To aid the uptake and the use of the SIS methodology, the need for highlevels of modelling skills and quantitative data are minimised. Validation of theproposed SIS methodology is made through a case study on an eco-resort nearLamington National Park in Queensland, Australia.

Current Practices and Frameworks for Deriving SustainabilityIndicators for Tourism Destinations

In recent years, the use of indicators for monitoring progress towards sustain-ability has been advocated and discussed by tourism researchers and tourismorganisations (Allin et al., 2001; Ceron & Dubois, 2003; Choi & Sirakaya, 2006;Hyde et al., 2007; Miller, 2001; Miller & Twining-Ward, 2005; WTO, 2004). Since1993, the WTO has pioneered the application of sustainability indicators as a‘fundamental part of overall destination planning and management, and an in-tegral element in efforts to promote sustainable development for the tourismsectors at all scales’ (WTO, 2004: 8). Meadows (1998) argues that sustainabilityindicators are often poorly chosen, and that this can cause serious misinterpreta-tion of assessment results. This problem was also perceived by Manning (1999:179), who reports that the task force commissioned by the WTO to develop sus-tainability indicators for tourism development ‘was immediately faced with the

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tension between different perceptions of what a “good” set of indicators reallywas’. Various researchers have listed the criteria for ‘good indicators’ such as:resonance to target audience, robustness, credibility, sensitivity, availability ofdata, regularity, cost-effectiveness, lack of ambiguity and comparability (Allinet al., 2001; Ceron & Dubois, 2003; Moldan et al., 1997). In reality, it is difficultto find sustainability indicators for the assessment of tourism destinations thatactually meet these ideal characteristics (Hughes, 2002).

Despite this impediment, Butler (1999: 16) suggests that without measuresor indicators for tourism development ‘the use of the term “sustainable” ismeaningless’ and ‘becomes hyperbole and advertising jargon’. In addition, sus-tainability indicators are not only useful for measuring progress, but they canalso stimulate a learning process to enhance the overall understanding of envi-ronmental and social problems, facilitate community capacity building and helpidentifying sustainable development goals and suitable management strategies(Miller & Twining-Ward, 2005; Reed et al., 2006). However, chosen indicators re-flect stakeholder values (Meadows, 1998), and therefore, the selection process isnecessarily subjective (Gallopin, 1997). In order to reduce this bias, methodolog-ical frameworks for selecting, organising and evaluating sustainability have de-veloped that are either expert-led (top-down) approaches or based on a bottom-up participatory philosophy (Bell & Morse, 2001). Top-down approaches useexplicitly quantitative indicators and acknowledge the complexity of SESs, butdo not necessarily emphasise the complex variety of stakeholder perspectives,and often fail to engage local communities (Reed et al., 2006). Bottom-up ap-proaches enhance the ongoing collective learning processes in the community ortourism destination by defining sustainability goals and priorities within the lo-cal context, but might not necessarily cover all important sustainability aspects.Hence, both approaches have their strengths and weaknesses, and there is anincreasing awareness of the need to develop innovative hybrid methodologiesin order to develop locally relevant, objective and easy-to-collect sustainabilityindicators capable of informing management decisions (Miller & Twining-Ward,2005; Reed et al., 2006).

The WTO core indicators of sustainable development (Manning et al., 1996)are an example of a top-down approach, which Twining-Ward and Butler (2002:366) perceive as a ‘useful starting point’ but too narrow in focus, lacking cleartourism stakeholder participation in the indicator development process andomitting site or area-specific conditions. These stakeholders may include mem-bers of the local community, industry representatives, policymakers, adminis-trators and ‘experts with specific skills relevant to the nature of the project’(Miller & Twining-Ward, 2005: 135). The more heterogeneous the group how-ever, the more differences in values, worldviews and paradigms will be ap-parent. These differences often result in ‘a collection of long “laundry lists” ofvariables’ (Gustavson et al., 1999) being developed as sustainability indicatorswith the knock-on effect of indicator measurements becoming too costly andtime-consuming (Meadows, 1998). Reducing the indicator set is necessary toallow practical implementation, but guidelines and systematic methodologiesthat facilitate this process are currently lacking (Bossel, 2001). Systemic or sys-tems thinking approaches through the identification of interconnections havebeen shown in practical applications in technology, science and management

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to significantly reduce the amount of data necessary to characterise, and thusmonitor, a system (Lootsma, 1997; Zadeh & Kacprzyk, 1992).

Any methodology developed needs to recognise ‘that there is an inter-relationbetween indicators’ and that indicators should not be considered separately asdiscrete variables (Miller & Twining-Ward, 2005: 116). Thematic indicator devel-opment methodologies, which are more commonly used in sustainable tourismmonitoring because of their flexibility, organise indicators in self-contained cat-egories, such as economic, social, cultural, technical, political or institutional(Allin et al., 2001; Choi & Sirakaya, 2006; Miller & Twining-Ward, 2005; Twining-Ward & Butler, 2002; WTO, 2004). Thematic methodologies can be easily usedfor a bottom-up approach, but they disregard interrelations and causal chainsbetween systems, and therefore need to be complemented with systems ap-proaches (Meadows, 1998). Some tourism researchers (Ceron & Dubois, 2003;Li, 2004; Miller & Twining-Ward, 2005) recommend linking thematic indicatorcriteria with the Pressure–State–Response (PSR) model (OECD, 2003), or therelated and further developed Driving force–Pressure–State–Impact-Response(DPSIR) matrix (EEA, 1999). These frameworks consider multiple parallel causalchains and therefore assist in structuring our thinking about indicators in termsof causality chains (Neijmeijer & de Groot, 2007). They do not include the in-terrelations between the various causal chains, and Miller and Twining-Ward(2005: 117) argue that they ‘are best suited to expert developed systems’. There-fore, the SIS methodology proposed in this paper integrates a comprehensiveCASs approach with a more flexible bottom-up approach that can be applied bynon-expert groups.

Linking Sustainability Indicators with Systems ApproachesReed et al. (2005) argue that Bossel’s (1996, 1999, 2001) system-based approach

for deriving sustainability indicator sets provides one of the most holistic andrationalised frameworks, as it ensures that indicators cover all important as-pects of system viability, performance and sustainability, and recognises thatSESs cannot be assessed in isolation from the systems upon which they depend.This framework therefore has an advantage over the PSR or DPSIR in the contextof tourism. Bossel (1999, 2001) applied orientation theory to derive the frame-work and defined a set of seven basic system immanent ‘orientors’, which aresummarised as follows:2

• Existence: The system must be compatible with and able to exist in the normalenvironmental state. The information, energy and material inputs needed tosustain the system must be available.

• Effectiveness: The system should, on balance over the long term, be effective(not necessarily efficient) in its efforts to secure required scarce resources(information, matter, energy) and to exert influence on its environment whennecessary.

• Freedom of action: The system must have the ability to cope in various wayswith the challenges posed by environmental variety.

• Security: The system must be able to protect itself from the detrimental ef-fects of environmental variability, i.e. variable, fluctuating and unpredictableconditions outside the normal environmental state.

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• Adaptability: The system should be able to learn, adapt and self-organise togenerate more appropriate responses to the challenges posed by environmen-tal change.

• Coexistence: The system must be able to modify its behaviour to respondappropriately to the behaviour of the other systems in its environment.

• Psychological needs: These constitute an additional orientor for sentient be-ings.

Indicators are then defined for all orientors within three functional subsys-tems: the human system (comprising social aspects), the support system (physi-cal and financial aspects) and the natural system (environmental and ecologicalaspects) (Bossel, 1999). In order to determine the sustainability of a systemsuch as a tourism destination, Bossel argues that indicators are needed for thetourism destination and for the destination’s contribution to a bigger system,which means that a sustainability indicator set will need to contain at least 42indicators (7 orientors × 3 subsystem × 2 internal + external). This number isprobably above the upper limit for practical application (Meadows, 1998).

The advantage of this approach is that it provides a systematic procedure andthus avoids much of the arbitrariness implicit in current sustainability indicatorsets (Bossel, 2001; Reed et al., 2005). Bossel (1999) acknowledges the importancefor community participation (the bottom-up approach), but it takes place withinthe limits of the framework. The apparent rigidity of the framework might be oneof the reasons why there appears to have been very few practical applicationsof this expert-led approach; another is that defining appropriate indicators forthe different orientors in the context of tourism destinations is difficult.

Other system-based approaches that could be used for deriving sustainabil-ity indicator sets, such as adaptive management in connection with the SDM(Holling, 1978) and resilience analysis (Folke et al., 2002), are also top-downapproaches (Reed et al., 2006) as they are heavily dependent on ecosystems andSDM experts. One exception that should be mentioned is Checkland’s (1981)Soft Systems Methodology (SSM), which builds on systems thinking and exper-imental learning. It is an approach ‘to inquiry into problem situations perceivedto exist in the real world’ (Checkland & Scholes, 1990: 18) and could be usedto develop sustainability indicators as part of a participatory learning process(Reed et al., 2006). However, this bottom-up method has its origin in businessmanagement and it is argued that it is lacking the methodological rigour neces-sary to tackle the multifaceted ‘problem situations’ that occur in the context ofsustainability assessment for tourism destinations.

In summary, it is reasoned that the development of indicators for monitor-ing sustainability in tourism destinations needs to be based on a comprehen-sive systemic approach, which recognises interrelations between indicators andconcentrates on enhancing systems and resilience thinking rather than on theinterpretation of individual system variables. Due to the complex and dynamicnature of tourism development, sustainability indicators need to allow for adap-tive learning processes within the local community or tourism destination. Sus-tainability assessment therefore needs to apply participatory bottom-up as wellas adaptive management approaches. This necessitates a hybrid methodology,which can be used by non-expert groups that assists in reducing indicator lists

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systemically, so that all essential indicators from a systems perspective are in-cluded. The methodology should increase the understanding of the behaviourof the system, and allow the analysis of the best options for system monitoringand control. Therefore, the authors have adapted a number of different tools,previously used in the context of complex systems analysis, and tested their usefor sustainability assessment of tourism destinations through indicators.

It should be noted that the SIS methodology does not replace simulationmodelling, but rather assists the transition between the use of linear indicatorsystems and sophisticated quantitative system dynamics models.

Methodology for the Establishment of an SISThe proposed methodology has its origins with Frederic Vester’s Sensitivity

Model (SM) (Vester, 2002). The SM is a semi-quantitative modelling tool basedon systems thinking and fuzzy logic, which was developed in 1975 duringwork on the UNESCO programme, Man and the Biosphere (MAB II) (Vester &Hesler, 1982). The SM has been applied to different fields of research, regionaland environmental planning and risk management; it has been used by majorGerman corporations such as IBM, Siemens, Daimler-Benz, Hoechst, as wellas by governmental agencies and university institutes (Chan & Huang, 2004;Ulrich, 2005). In 2002, the Club of Rome, an international group of expertsconcerned with global development issues recognised the value of the SM bypublishing Vester’s introduction of the methodology as a ‘Report to the Club ofRome’ (Vester, 2002).

The authors propose that the conceptual tools used by Vester to select andevaluate variables for running simulation models can be directly applied toderive sustainability indicator sets for tourism destinations and to promotecollective learning processes amongst tourism stakeholders.

In the following discussion, the term ‘indicators’ is used not in the senseof quantitative indicator measures (e.g. percentage drop in visitor numbersor amount of water used per guest night) but as indicator themes (e.g. waterconsumption, biodiversity, tourism attractiveness).

The SIS combines three different tools that were developed and applied byVester to select and assess variables of CAS; these three tools were adapted bythe authors for the systemic selection and evaluation of indicators.

(1) A Criteria Matrix to verify that the selected indicators represent the systemsufficiently.

(2) A Correlation Matrix for pair-wise comparison of indicators in order to eval-uate their direct influence on each other, and their degree of interrelatedness.

(3) An Effect Analysis Diagram for the assessment of the systemic role of theindicators.

These three tools are discussed in detail in the following sections.

Criteria Matrix for indicator selectionThe Criteria Matrix establishes the systemic selection criteria for the sustain-

ability indicator set; it ensures that the indicators address all aspects of thesystem, and thus assists in the definition of a set of essential indicators for

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Table 1 Criteria for indicator selection

No. Criteria Definition Examples for a tourismdestinations

Sectors of life category

1 Stakeholders Who is involved andaffected?

Tourists, residents/natives,employees, developers,planners, businesses,ecologists

2 Stakeholderactivities

What are they doing? Tourism activities,employment, turnover,services, sales, production,investment

3 Area Where does it happen? Land use, density, size,location and use of buildings,distances

4 Stakeholdersfeelings

How do they feel? Motivation, competition,creativity, quality of life,security, education, health

5 Interaction withnature

How do thestakeholders affect thenatural resources?

Ecosystem, natural balance,resource use, wastegeneration, environmentalimpacts

6 Inter-connections How do thestakeholders connectand communicate?

Transport, infrastructure,accessibility, communication,information, supply

7 Organisation How is the resortorganised?

Community, legislation,management, taxes,procedures, culturalbehaviour codes

Physical category

8 Material/Matter Predominantlyconstituted of materialor used to transport ortransform matter

Accommodation, businesses,means of transport, rawmaterial, waste, people, flora,fauna, funds, infrastructure,traffic

9 Energy Energy resources orgenerators thattransform or consumeenergy

Energy consumption, energyresources, finances,employment, funds,infrastructure

10 Information Responsible for theflow of information andfor communication

Media, decisions, informationcentres, procedures,requirements, attractiveness,education, finances,recreation facilities

Dynamic category

11 Flow determinant Flow of matter, energy,and information withina system

Traffic, energy/waterconsumption, raw materialuse, waste generation,finances, visitors

(Continued)

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Table 1 (Continued)

No. Criteria Definition Examples for a tourismdestinations

12 Structuraldeterminant

Structure of the system Infrastructure, recreationfacilities, accommodation,population, local businesses

13 Temporaldynamics

Location-specific itemsthat change with time

Tourist seasons, climate,employment, traffic, finances,recreational quality, quality oflife

14 Spatial dynamics Instantaneous itemsthat change withlocation

Wastewater, traffic,disturbances, land use,conservation zone,infrastructure

System relations category

15 System input Variables that open thesystem to input

Access routes, tourists, publictransport, water/energysupply

16 System output Variables that open thesystem to output

Quality of life, attractiveness,ecological value, recreationalfacilities

17 Endogenous Variables that can beinfluenced or controlledby internal processes oractions

Cultural activities,recreational quality, waterpollution, energy/waterconsumption, wastegeneration, tourisminfrastructure, local security

18 Exogenous Variables that areinfluenced or controlledby external processes oractions

Competition, tourists,accessibility, energy/watersupply, tourism attractivenessof region

Source: Vester (2002).

comprehensive sustainability assessment of tourism destinations. The matrix isbased on Vester’s (2002) criteria for variable selection, and contains 18 criteriain four categories: Sectors of Life, Physical, Dynamic and Systems Relations.Table 1 gives a brief definition of the 18 criteria, adapted from Vester (2002).Possible examples for the assessment of tourism destination assessment wereadded by the authors. The full matrix is presented and explained in the contextof the case study.

Based on current knowledge in complex systems theory, a sustainability indi-cator set that covers all the criteria sufficiently represents a system (Vester, 2002).As shown later in the case study, each indicator is usually applicable to morethan one of the 18 criteria due to the complex interrelations between variables.Vester does not define the number of variables needed for each of the criterionas this depends on various factors, such as the complexity of the system itself, itsinterdependence with other systems, and also on practical aspects such as dataavailability (Meadows, 1998). A balanced indicator set however is reflected in aneven distribution of indicators responding to the different criteria. Those areas

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that are insufficiently covered or over-represented can be identified through theCriteria Matrix and the situation rectified so that the indicator set represents theessential indicators from a systems perspective.

In order to verify the completeness of Vester’s criteria for tourism destinationassessment, the sectors were compared with Bossel’s basic orientors for indica-tor selection (Bossel, 1999, 2001). This comparison gives additional credibilityto the use of Vester’s selection criteria, which are mainly developed throughempirical research and practical experience with the assessment of CASs andSDM (Vester, 2002), as Bossel presented supporting theoretical and empirical ev-idence of the emergence of system-determined basic orientors from such fieldsas thermodynamics, ecology, psychology, sociology and the study of artificiallife (Bossel, 1999, 2001).

The comparison is shown in Table 2; an ‘X’ indicates a correlation betweenVester’s criteria for variable selection and Bossel’s basic orientors. For example,the orientor Existence, referring to indicators that monitor scarce resources of thesystem (e.g. material, energy and information), corresponds directly to Vester’sthree Physical Criteria.

The comparison shows that all of Bossel’s basic orientors and functional sub-systems are represented by one or more of Vester’s criteria. In a tourism destina-tion, these subsystems are highly interconnected, and therefore, it is argued herethat it is not essential to define one indicator per system as suggested by Bossel(2001). As previously mentioned, Bossel’s methodology requires the identifica-tion of indicators that measure the systems contribution to a bigger system, andthis aspect is covered in Vester’s methodology by the identification of whether avariable is influenced or controlled by internal (endogenous) or external (exoge-nous) processes. This ensures that the indicator set ‘opens the system’ to inputfrom, and output to, other systems: the System Relations Criteria.

In summary, it is argued that Vester’s criteria for the selection of system vari-ables cover the principal aspects of Bossel’s framework for systemic indicatorselection, and can be more easily applied for the assessment of tourism des-tinations without relying on comprehensive support and guidance of systemexperts.

Correlation Matrix for pair-wise indicator comparisonAs mentioned earlier, indicators for a CAS should not be defined and eval-

uated in isolation but rather in the context of their interconnections with otherindicators and the system itself (Miller & Twining-Ward, 2005). This allows thetourism destination to be assessed from a systems perspective and acknowl-edges the complexity and interrelatedness of sociocultural, economic and envi-ronmental issues.

The Correlation Matrix is a simple tool, based on pair-wise comparison, whichis used to assess the strength of an indicator’s effect on another indicator byassigning the effect an integral value between 0 and 3 depending on the scaleof the effect (Table 3). It has previously been applied to reduce the number ofvariables a modeller or a decision-maker has to deal with (Rutherford, 1997;Vester, 2002). The Correlation Matrix, which is shown in the context of the casestudy in Table 7, has an additional benefit, in that it will encourage systems

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Table 2 Comparison of Vester’s and Bossel’s Selection CriteriaVester’s criteria

Sector of life Physical criteria Dynamic criteria System relations

Bossel’s criteria Stak

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Basic orientors

1 Existence X X X X X X X

2 Effectiveness X X X X X

3 Freedom of Action X X X X X X

4 Security X X X

5 Adaptability X X X X X

6 Coexistence X X X X X X X X X

7 Psychological Needs X X X X

Functional subsystems

1 Human System X X

2 Support System X X X

3 Natural System X X X

Contribution to sustainability

1 Assessed Development X X

2 Bigger System X X

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Table 3 Evaluation scale for correlation matrix

Score Relation Definition

3 Strong, disproportionately high Small change of indicator A causesstrong change of B

2 Medium, proportionate Change of indicator A results in similarchange of B

1 Weak Strong change of indicator A causessmall change of B

0 No relation Change of indicator A causes no/veryweak change of B or change aftersignificant time delay

thinking by increasing the understanding of cause–effect relationships betweenvariables.

Vester (2002) used the results of pair-wise comparison to calculate four ImpactIndices. These indices evaluate the systemic roles of variables as follows:

(1) The Active Sum (AS), found by summation of all scores given to a variablefor its influence on other variables, is a measure of how strongly a variableis affecting the system.

(2) The Passive Sum (PS), found by summation of all scores received by a vari-able through being affected by other variables, is a measure of the sensitivityof a variable to changes within the system.

(3) The Product (P), calculated as the product of AS and PS, identifies a variableas either buffering (low values) or a critical (high values).

(4) The Quotient (Q), calculated as AS/PS × 100, identifies a variable as reactive(low values) or active (high values).

The numerical values of the Impact Indices will depend on the system: thedegree of interrelatedness of the variables and the amount of variables includedin the assessment. Thus, the classification of a value as high or low can only bemade in relation to the highest score achieved for the system.

Effect Analysis DiagramThe Effect Analysis Diagram graphically represents the indices calculated by

the Correlation Matrix. It is constructed by positioning the indicators withina two-dimensional diagram with the PS as the horizontal axis and the AS asthe vertical axis. Critical indicators with high P values are located in the up-per right-hand corner of the diagram, while buffering indicators (low P values)are situated close to the origin. Similarly, reactive variables (Q values less than1) are to the right of the diagonal, while passive variables (Q values greaterthan 1) are to the left. Figure 1 is a template for an Effect Analysis Diagram;the grid placed on the diagram shows nine sectors that are discussed in Table 4.These sectors further define the indicators with respect to their use for sys-tem control and thus enhance both effectiveness and prevention of negative

Sustainability Indicators: Systemic Indicator Systems 613

Figure 1 Indicator classification diagram (adapted from Vester, 2002).

Table 4 The system roles of the indicators

Sector Indicator feature AS PS Use for system control

1 Active High Low Potential leverage points forsystem influence; change inthese areas will stabilise thesystem, and increase systemresilience

2 Active-Critical High Medium High leverage, but outcomesare less stable, more difficult tocontrol than Sector 1 indicators

3 Critical High High Catalysts that are suitable aschange starters, but outcomesare very difficult to control, andcan put the systems resilienceat risk

4 Buffering-Active Medium Low Medium leverage points withminimal side effects

5 Neutral Medium Medium Areas are difficult to externallycontrol, but useful forself-regulation of system

6 Critical-Reactive Medium High Changes in this area do notachieve expected results

7 Buffering Low Low Low leverage for systemcontrol

8 Buffering-Reactive Low Medium Sluggish system reaction withindicator change, but may besuitable for systemexperimentation

9 Reactive Low High No lasting system changecaused by change in these areas– indicative of ‘Fixes that fail’

Source: Vester (2002).

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side effects of possible mitigation measures. There are two points to notewith respect to the template: the boundaries of the nine sectors are approxi-mate and the value ‘Max’ is calculated as the absolute maximum of PS andAS.

Testing an SIS – Case Study ResultsIn order to test the SIS methodology, a development of a medium size tourism

resort in project or design stage was sought as a case study. The requirementsfor the case study were that:

• the development be located in an ecologically and/or culturally fragile area;• the principal goal of the planning/development team was to achieve best

possible practice with respect to the sustainability of the development;• the developers have clearly formulated sustainability goals and aims for the

development; and• the developers want to establish a sustainability indicator system to measure

and improve performance.

The main objective of the case study was to evaluate if the proposed SISmethodology improved the effectiveness of a sustainability indicator set fortourism destinations by providing a better understanding of the complexity,interconnectedness and behaviour of the system and its economic, social andecological subsystems.

The case study chosen was an eco-resort development with 66 propertiesand support buildings in Queensland, Australia, called ‘The Ridge on BinnaBurra’ (Ethos, 2006). It has been planned, developed and managed by The EthosFoundation3 (Ethos, 2007), a non-profit education and land care organisationbased in Beechmont in southeast Queensland. The Ridge on Binna Burra isintended to function as an educational eco-village and restorative resort andis located in the midst of 96 hectares of rainforest nature reserve borderingthe World Heritage listed Lamington National Park. The mission of Ethos isto ‘cultivate effective new thinking and action for positive, ecologically sus-tainable futures’ (Ethos, 2006: 28), and hence, the sustainable design and man-agement of the eco-village is the top priority for the developers. The Ethoseco-village therefore fulfilled the main requirements for the case study listedabove.

Two workshops were conducted in the design phase of the eco-village inNovember and December 2005 with participants from a multi-disciplinary de-sign and development team, consisting of engineers, architects, permacultureexperts, environmental consultants, sustainability researchers, economists, ecol-ogists, and a social scientist, and community representatives. The principal goalof these workshops was to discuss sustainability design and management goalsand to provide a common framework for the establishment of a suitable sustain-ability indicator system for the eco-village. Indicators (Table 5) were extractedfrom the results and findings of these two workshops, in combination with otherproject information, such as impact assessment reports, the master plan and thecommunity consultation plan.

Sustainability Indicators: Systemic Indicator Systems 615

Table 5 Systemic indicators of the Ethos eco-village

No. Indicatortheme

Indicator measures Why important for the Ethoseco-village

1 Biodiversity Amount ofnative/threatened species

Located in ecologicallyfragile and valuable area

2 Resource use Use of energy, water, andmaterial

Ethos wants to act as showcase

3 Pollution Contamination of air, water,soil

Area has low resilience topollution

4 Land use Changes in land use (e.g.reduction of forest area,wetlands, protected areas)

Property is part pristine rainforest, and part old grazingcountry that will berevegetated

5 Tourismattractiveness

Visitor satisfaction,returning visitors

Necessary for economicsurvival of centre

6 Environmentalawareness

Environmental awareness ofvisitors, staff andneighbouring community

Promotion of environmentalawareness is an importantgoal of the centre

7 Recreationalquality

Amount and quality ofrecreational facilities, andpossible activities

Important for visitor’ssatisfaction and promotion ofcentre

8 Healthy living Includes factors such assecurity, health, education,income, recreation

Principal objective of Ethos

9 Culturalheritage

Potential for indigenouspeople to retain/exercisetheir traditions

Ethos wants to foster thedialogue between Aboriginaland non-Aboriginal people

10 Educationalopportunities

Formation/loss ofapprenticeship trainingpositions

Ethos is a not for profiteducation organisation;centre to be educationaleco-village

11 Localbusinesses

Amount of local businesses;turnover of local industry

Ethos aims to support localbusinesses

12 Green design Implementation/application/use of Green Designtechnology

Important for impactminimisation and reductionof water and energy demand

13 Permaculturegardens

Implementation and use ofpermaculture principles

Expected to play a major rolein educational programs andprovide fresh food for thecommunity

14 Waste(water)Management

Amount of treated, recycledand reused waste andwastewater

Wastewater andcompostables will be treatedon site and reused, as part ofenvironmental sustainability

15 Water supply Long-term water availability Rainwater is only watersupply for the eco-village

(Continued)

616 Journal of Sustainable Tourism

Table 5 (Continued)

No. Indicatortheme

Indicator measures Why important for the Ethoseco-village

16 Ecologicalvalue of area

Special features of area:rare species, geology;potential for research

The main tourist attractionand income resource

17 Communitylearning

Community activities thatfoster collective learning

Ethos is aiming to fostercommunity learning

18 Affordability Percentage of facilitiesprovided for low-incomegroups

Affordability has beenemphasised as importantgoal; price strategies will bedeveloped to supportlow-income groups

19 Traffic Changes in volume oftraffic

Remote location; small accessroad through National Park

20 Natural capital Income from ecosystemservices such as fishing,harvesting and recreation

Important ecosystem servicesare gardening, water supply,education and recreation

21 Localemployment

Employment of localpopulation

The centre is expected toimprove local employment

22 Propertyvalues

Costs for land, houses, rent Has effect on localcommunity and affects theattractiveness ofaccommodations for investors

23 Social cohesion Amount of communalactivities, relief operations,etc.

Ethos is actively involved incommunal activities

24 Alternativeenergy

Percentage of energyobtained from alternativeenergy sources

Environmental sustainabilityis principal objective of Ethos

25 Revegetation Revegetated areas Site has been formerly used asfarmland; Ethos wants toreintroduce native plants

26 Conservationzone

Area allocated forconservation and wildlifeprotection

80% of the site is rain forest,and its conservation is ofecological importance

Using the Criteria MatrixThe 26 indicators were evaluated systematically using the Criteria Matrix

and reduced to 18 in number. Table 6, which shows this evaluation, lists theeliminated indicators in brackets. In the matrix, those indicators that correspondto a particular criterion are marked with • and given a value of 1 if fullyapplicable, and ◦ with a value of 0.5 if partly applicable. The vertical sumsof the original and the reduced indicator set are shown in the bottom rowsof the matrix. These scores show that the original indicator set covered allcriteria and that no further indicators are necessary from a systems perspective.Some criteria, such as Matter, Stakeholders’ Feelings, Temporal Dynamics and

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Table 6 Criteria matrix of Ethos eco-village

Criteria

Sector of life Physical criteria Dynamic criteria System relations

Indicators Tour

ism

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1 Biodiversity • – ◦ ◦ • – – • ◦ ◦ – • • • ◦ • • ◦2 Resource use – • ◦ ◦ • ◦ – • • – • ◦ • ◦ • – • –

3 Pollution – • ◦ • • – ◦ • – – • – • • ◦ • • ◦4 Land use ◦ • • ◦ • ◦ ◦ • – – – • ◦ • ◦ ◦ • –

5 Tourism attractiveness ◦ – – • ◦ ◦ – – – • • – • – ◦ • • ◦6 Environmental awareness – ◦ – – ◦ ◦ ◦ – – • • – • – • • • •7 Recreational quality – ◦ – • ◦ – – • ◦ • – • • ◦ ◦ • • ◦8 Healthy living – – – • – – – ◦ – • ◦ ◦ ◦ ◦ ◦ • • •9 Cultural heritage • ◦ ◦ ◦ – ◦ ◦ ◦ – • ◦ • ◦ ◦ – – • ◦10 Educational opportunities – • – • – • • ◦ ◦ • • • • – – • • ◦

(Continued)

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Table 6 (Continued)

Criteria

Sector of life Physical criteria Dynamic criteria System relations

Indicators Stak

ehol

ders

Stak

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ders

acti

viti

es

Are

a

Stak

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ders

feel

ings

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11 Local businesses • • ◦ – – • – • – ◦ – • ◦ • ◦ – • •12 Green design – – • ◦ ◦ – – • • – – • – • – – • –

13 Permaculture gardens ◦ • • ◦ • ◦ – • • ◦ – • ◦ • – • • –

14 Waste(water) management – ◦ • ◦ • – • • ◦ • • • • ◦ – • • ◦15 Water supply – – ◦ • ◦ – • • – – • – • – • – • •16 Ecological value of area ◦ – ◦ ◦ ◦ – – ◦ – • • ◦ • • ◦ • • •17 Community learning ◦ • – • – • ◦ – – • • – • – ◦ • • •18 Affordability ◦ – – • – – ◦ – • – ◦ ◦ • ◦ • – • ◦(19) Traffic ◦ • ◦ • • • ◦ • • ◦ • – • • • – • •(20) Natural capital • • ◦ ◦ • ◦ – • • – – • • • ◦ • • ◦(21) Local employment ◦ • – ◦ – • ◦ • ◦ – ◦ ◦ • • ◦ – • ◦

(Continued)

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Table 6 (Continued)

Criteria

Sector of life Physical criteria Dynamic criteria System relations

Indicators Stak

ehol

ders

Stak

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ders

acti

viti

es

Are

a

Stak

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feel

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(22) Property values – – ◦ ◦ – – ◦ • • – – • • • ◦ • – •(23) Social cohesion ◦ ◦ – • – • ◦ ◦ – • • ◦ • – ◦ • • ◦(24) Alternative energy – – ◦ – ◦ – • ◦ • – • ◦ • – • – – •

supply

(25) Revegetation – – • ◦ • – ◦ • – ◦ – • – • – – • –

(26) Conservation zone – – • ◦ • – ◦ • – ◦ – • – • ◦ ◦ • –

Total (original indicator set) 8.5 12.5 11.5 16.5 13.5 9.5 10 29 10.5 13 14 16.5 20.5 16 13 15 24 14.5

Total (reduced indicator set) 6 9 7.5 12 9 6 6 12 6 10.5 10.5 11 14.5 10 8.5 11.5 18 10

Note: x: not applicable (Score = 0); ◦: partly applicable (score = y 0.5); •: fully applicable (score = 1).

620 Journal of Sustainable Tourism

Endogenous, were over-represented by the initial indicator set, allowing for thereduction of the set. In this case study, the reduction was achieved by identifyingindicator themes that duplicated others and that were not highly interconnected.Eight indicator themes were found to match this criteria and the remaining setwas found, by virtue of the vertical sum, to sufficiently represent the systemfrom a systems thinking point of view. The reduction could be achieved by astakeholder workshop to re-evaluate sustainability goals and defining priorities.

The matrix evaluation also showed, through the high scores attributed to En-dogenous and Temporal Dynamics, that the system is relatively self-sufficient,and that it is strongly influenced by temporal dynamics, such as seasonalchanges. The former finding reflects the aim of the developers of the Ethoseco-village to create a self-contained community with its own waste treatmentfacilities and food production. The latter finding is not surprising as tourismdestinations in the early stage of their life cycle are usually strongly influencedby temporal dynamics as Butler’s (1980) life cycle model suggests.

Using the Correlation MatrixTable 7 shows the Correlation Matrix for the Ethos eco-village case study. The

values of the pair-wise comparison between indicators have been generatedboth by analysing the discussions at the project workshop and through personalinterviews with stakeholders.

The scoring for the Correlation Matrix will depend on the evaluators’ un-derstanding of the system and its interconnections. In order to capture theperceptions of different stakeholder groups, it is recommended that the SISmethodology is used in conjunction with various stakeholder teams. This ex-ercise should be followed by the comparison and discussion of results in aworkshop that includes experts from various disciplines as this will lead bothto a better understanding of the system and to a collective learning process thatshould reduce the danger of inappropriate insights and wrong inferences.

Using the Effect Analysis DiagramThe indices AS and PS, calculated for each variable from the Correlation

Matrix (Table 7), are illustrated by the Effect Analysis Diagram (Figure 2).Figure 2 shows that the Active-Critical aspects (Sector 2) of this system are

Land Use (4), Resource Use (2), Pollution (3) and Waste(water) Management(14). This is understandable as the Ethos eco-village is located in a pristine andecologically fragile area with low resilience to human impact. Change in theseareas, although highly levered, is critical and should be approached with care,as impacts and outcomes are difficult to predict. The best strategies to achievesustainability are pollution prevention and minimisation measures. This is alsoreflected by the position of Green Design (12) and Permaculture Gardens (13)in Sector 4 (Buffering-Active), which indicates that Cleaner Technology willimprove the system with minimal side effects. Similarly, Ecological Value ofArea (16) is located in Sector 4 indicating that improvements in this area willinfluence the system effectively.

Areas with lower leverage for system control and influence are Water Supply(15) and Cultural Heritage (9), and this is because these indicators are relativelyindependent from the others. For example, the water supply for the eco-village

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Table 7 Correlation matrix of Ethos eco-village

Effects of �on � 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 AS (Total) P (ASxPS)

1 Biodiversity � 0 0 1 2 2 2 1 1 2 0 0 0 0 0 3 1 0 15 1652 Resource use 1 � 3 1 1 1 2 2 1 2 1 2 1 3 2 0 0 2 25 4253 Pollution 3 1 � 1 2 2 2 2 1 2 0 0 1 2 2 2 0 0 23 4144 Land use 3 1 1 � 2 1 3 2 3 2 2 0 1 2 1 2 0 2 28 4205 Tourism attractiveness 0 1 0 1 � 0 0 0 1 2 3 0 0 0 0 0 0 2 10 2406 Environmental awareness 0 2 2 2 1 � 1 1 1 1 1 1 1 2 0 0 1 0 17 2557 Recreational quality 0 0 1 0 3 0 � 2 0 0 1 0 0 0 0 0 0 0 7 1408 Healthy living 0 1 1 1 1 1 0 � 0 1 1 0 0 2 0 0 1 0 10 1809 Cultural heritage 0 0 0 1 1 1 1 0 � 2 1 0 0 0 0 0 1 0 8 7210 Educational opportunities 0 0 0 0 1 0 0 1 0 � 1 0 0 0 0 0 2 0 5 12511 Local businesses 0 2 2 2 1 0 1 0 0 1 � 0 0 1 0 0 1 0 11 14312 Green design 1 3 2 1 1 1 1 2 0 2 0 � 0 2 0 0 0 2 18 10813 Permaculture gardens 1 2 1 1 2 1 2 2 0 2 0 0 � 1 0 0 1 0 16 12814 Waste(water) Management 2 3 3 1 1 1 1 2 0 2 0 0 1 � 2 0 1 1 21 37815 Water supply 0 0 1 0 0 1 0 0 0 1 0 1 3 0 � 0 0 1 8 5616 Ecological value 0 0 0 1 2 1 2 0 0 2 1 1 0 2 0 � 1 0 13 9117 Community learning 0 1 1 1 1 2 1 1 1 1 1 1 0 1 0 0 � 0 13 13018 Affordability 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 � 3 30

PS (Total) 11 17 18 15 24 15 20 18 9 25 13 6 8 18 7 7 10 10Q (AS/PS) 1.4 1.5 1.3 1.9 0.4 1.1 0.4 0.6 0.9 0.2 0.8 3.0 2.0 1.2 1.1 1.9 1.3 0.3

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Figure 2 Effect analysis diagram of Ethos eco-village.

is secure as the development is situated in a high rainfall area and the demandis expected to be reliably satisfied through the installation of water tanks.

The relatively high number of indicators in Sector 5 (Neutral) suggests thatthere are many system aspects (i.e. Biodiversity, Environmental Awareness,Local Businesses, Healthy Living) that are difficult to control directly. Thesefactors however will be influenced by the system and are important for itsself-regulation. Of more interest are those indicators that are in or close toSector 9 (Reactive) such as Tourism Attractiveness (5), Recreational Quality (7)and Educational Opportunities (10). The placement of these indicators in theEffect Analysis Diagram indicates that a change in these areas would not leadto a lasting change in the system. This can be explained by the fact that thereis a strong emphasis on environmental factors inherent in the indicator set andenvironmental factors are not directly influenced by these indicators.

DiscussionThe proposed SIS methodology based on the Criteria Matrix, the Correlation

Matrix and the Effect Analysis Diagram is a semi-quantitative methodology. Itcombines qualitative system dynamics with a more rigorous systematic systemanalysis. It can be used for qualitative and quantitative indicator measures aswell as indicator themes. The conceptual tools used allow the selection of in-dicators and the evaluation of their system roles without the necessity to firstcollect data. As shown by the case study, it can be applied in the early designand construct phases of a tourism destination, when data availability is still

Sustainability Indicators: Systemic Indicator Systems 623

limited. Indeed, the SIS methodology reduces the requirement for quantitativedata; indicators identified as system balancing and resilient to change will notneed to be monitored as frequently as indicators with high leverage and uncon-trollable effects on the system. This constitutes an advantage over the SDM, asthe informative value of simulation models depends on time series data, whichare often not available for tourism resorts. Furthermore, the SIS methodologydoes not require modelling skills, essential for most system-based approachesincluding the SSM, which ‘requires people to have undertaken much quantita-tive modelling’ (Wolstenholme, 1999: 424).

The importance of defining ‘a “good” set of indicators’ (Manning, 1999: 179)and the right number of indicators has been frequently discussed (Bossel, 1999;Meadows, 1998; WTO, 2004). Currently, the selection of indicators in the contextof tourism assessment is directly related to the ‘identification of the most impor-tant issues from the perspective of all stakeholders’ (WTO, 2004: 35) or is basedon the ‘intuitive assessment of experts’ (Bossel, 2001: 2), and does not take cur-rent knowledge about CASs into account. Consideration of CASs is necessaryas systems behave ‘counter-intuitive[ly]’ (Forrester, 1961; Senge, 1990; Sterman,2000) and intuitive assessments usually fail as people tend to think in singlestrand causal series and therefore have difficulties in considering systems withside effects, multiple causal pathways and feedback loops (Dorner, 1980, 1996).

The authors do not propose that the SIS is a replacement for thematic in-dicator development frameworks that identify indicators in different sectorsor dimensions, and that are based on linear assessment approaches (Miller &Twining-Ward, 2005; Choi & Sirakaya, 2006; Reed et al., 2006). Instead, the SISis recommended as a complementary tool, which promotes a systems view ofsustainability. The SIS methodology becomes a learning tool, if applied in thecontext of adaptive management to improve the understanding of the behaviour,and threshold levels of the SESs on which the tourism economy largely depends.As the SIS focuses on promoting the maintenance of the system functions andsystem resilience to induced and unexpected changes, it is in line with new ap-proaches to sustainability science that view sustainability as a transition ratherthan an end goal (Farrell & Twining-Ward, 2004; NRC, 1999; Walker & Salt, 2006).

The SIS incorporates both the expert knowledge that arises from a top-downapproach and the community engagement and learning that results from abottom-up approach. It does this by providing three conceptual tools that com-prise a structured framework based on complex systems theory, and allowingfor the definition of sustainability goals and selection of indicators by the stake-holders. This distinguishes the SIS from other system-based frameworks forindicator selection and evaluation, which are mainly top-down approaches.

The process of assessing the indicators in the Criteria and Correlation ma-trices is valuable for the definition and refinement of goals and priorities thathave been shown to lead long term tourism sustainability. The interpretationof the sectors in the Effect Analysis Diagram can be used to design and imple-ment impact minimisation and mitigation measures that will have the desiredeffect on the system. In the case of the Ethos eco-village, the results were notsurprising, but they raised important questions that stimulated discussion andfurther enhanced the learning process, such as possible implementation strate-gies for Cleaner Technology, which at the same time can be used as a mediumfor community learning and within education schemes.

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While the SIS methodology can assist decision-making processes by improv-ing the understanding of SESs, it should not be seen as a substitute for envi-ronmental or social impact assessments. Several detailed impact studies of theEthos eco-village had been undertaken prior to the SIS methodology case study,and the results and insights gained in these assessment processes proved tobe essential for the indicator selection and definition process. In addition, theparticipants of the workshops were highly qualified in sustainability issues, andexperienced in, or open to, collective learning processes. Thus, the applicationof the SIS methodology may be more difficult in the context of ‘normal’ tourismdevelopments, and might require additional consultancy work to effect.

Further research is indicated to test the applicability and effectiveness of theSIS methodology for the assessment of whole tourism destinations.

ConclusionsThis paper proposes a practical methodology for complementing sustainabil-

ity indicators with a CAS approach in order to improve the effectiveness ofindicator systems for sustainability assessment of tourism destinations. Vester’ssystems thinking tools for variable testing were incorporated into the processof indicator selection and evaluation in the framework of an SIS and their com-prehensive nature validated by comparison with Bossel’s basic orientors. TheSIS methodology was applied in the design and construct phase of a large scaletourism development with the results substantiating the assumption that the SISmethodology is capable of (a) assisting in the identification and selection of anindicator set, (b) allowing a systematic analysis of the interconnections betweenthe economic, social and ecological systems, and (c) enhancing the understand-ing of both system behaviour and threshold levels. Applied within the contextof collective learning and adaptive management processes, the SIS methodol-ogy has the potential to improve the prediction and prevention of negative sideeffects of interferences and mitigation measures.

It is thought that the practical value of the proposed framework is high, asthe methodology is applicable in the early design and construct phase and doesnot rely on the availability of accurate quantitative data. While the method-ology is based on a systems thinking approach, it does not require modellingskills and as such can be adopted more easily by those involved with tourismdestination management. The SIS methodology is a means of beginning thecollaborative learning process of tourism stakeholders within the context of anadaptive management approach.

AcknowledgementsThis project was financed by the Sustainable Tourism Cooperative Research

Centre, established by the Australian Commonwealth Government. We thankseveral anonymous reviewers and the editor Bernard Lane for constructivecomments, which substantially improved this paper.

CorrespondenceAny correspondence should be directed to Karin Schianetz, School of En-

gineering, University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia(karin.schianetz@uq.edu.au).

Sustainability Indicators: Systemic Indicator Systems 625

Notes1. The term SDM is used in the context of quantitative system dynamics based on

computer simulation modelling; qualitative system dynamics is termed as systemsthinking (Richmond, 1994; Wolstenholme, 1999).

2. For a full study, the reader is referred to the original literature, in particular Bossel(1999, 2001).

3. The Ethos Foundation is a not for profit sustainability learning and earthcare organ-isation in Queensland, Australia. It is not linked to the Swiss Ethos Foundation forSustainable Development. The Ethos Foundation was established with seed fundingwhen the Ridge on Binna Burra was sold in November/December 2007 because offinancial stresses. The vendee has the intention to continue with the development inways that adhere to the original sustainability goals of the centre. The Ethos founda-tion continues to operate independently from the ecovillage and a core componentof its activities support local and share-wide business and economic revitalisationwithin the region.

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