deenapanray and bassi - nrf - system dynamics modeling as an integrated planning tool - feb 2014

The experience of ISLANDS in deploying system dynamicsmodeling as an integrated policy tool

Prakash N K Deenapanray and Andrea M Bassi

Abstract

The ISLANDS project is implemented by the Indian Ocean Commission using the European Development Fund of theEuropean Union to support the implementation of the Mauritius Strategy in beneficiary countries. This paper shares theexperiences of ISLANDS in deploying system dynamics modeling in five countries in the Eastern and Southern African andIndian Ocean Region (Comoros, Madagascar, Mauritius, Seychelles and Zanzibar) for sustainable development planning.Lessons learned regarding the design and conceptualization of the project ISLANDS, including the adoption of systemdynamics modeling as an integrated assessment tool for policy planning, are discussed. Although Madagascar and Zanzibarare not defined as small island developing States (SIDS) in the UN System, the lessons learned from these countries areapplicable to all SIDS. The approach used by ISLANDS for technical assistance consists of nesting learning-by-doing,multi-stakeholder processes, and dedicated capacity-building in system dynamics modeling, as well as advocacy for theintegrated modeling tool with decision-makers.While island developing States are recognized for their inherent vulnerabilitiesto shocks, the adoption of system dynamics modeling helps to achieve policy-induced resilience by exposing the challengesand constraints posed by the lack of reliable statistical data. Institutionalization of the tool is progressing well in the region.

Keywords: System dynamics modeling; integrated policy planning; sustainable development; Mauritius Strategy; SIDS.

1. SIDS: From vulnerability to resilience

Small island developing States (SIDS) were recognized as aspecial case for both environment and development inChapter 17(G) of Agenda 21 (UN, 1992). The groundsfor this special consideration relate to the inherentvulnerabilities to which SIDS are exposed, including theirsmall size, limited and narrow resource bases, geographicdispersion, isolation from markets, susceptibility to climatechange and natural disasters, and exposure to externalshocks from such sources as energy, financial and economiccrises. These vulnerabilities have been reaffirmed in theBarbados Programme of Action (BPOA) (UN, 1994) andthe Mauritius Strategy (MS) for the further implementationof the BPOA (UN, 2005) that have translated Agenda 21 inthe specific contexts of SIDS. The Johannesburg Plan ofImplementation, (JPOI) (UN, 2002) and, more recently, theRio+20 outcome document, The future we want (UN, 2012)have also stressed the unique vulnerabilities of SIDS.

Vulnerability expresses the susceptibility of beingharmed by external forces as a result of exposure. Joinedwith vulnerability are the concepts of resilience andsustainable development: the former describes the ability tocope with the exposure to vulnerabilities through acombination of withstanding damage and/or developing apropensity to recover from any damage (Briguglio et al.,2008), whereas the latter refers to development that meetsthe needs of the present without compromising the abilityof future generations to meet their own needs. While theyare exposed to numerous vulnerabilities, SIDS also exhibitlow levels of coping capacity due mainly to a lack offinancial resources, as well as human and institutionalcapacity limitations that make it even more difficultfor them to achieve sustainable development (UN, 2012).The combination of exposure and low coping capacityplaces SIDS at relatively high risk to the vulnerabilitiesdiscussed earlier. The vulnerabilities faced by SIDS areintrinsic, and by definition therefore, they are also mostlypermanent.This explains the historical shift of focus from the

vulnerability of SIDS towards the practical means toenhance their resilience and foster sustainability. Forinstance, whereas Chapter 17(G) of Agenda 21 that focuses

Prakash Deenapanray is at Ecological Living In Action, La GauletteMauritius, and works with ISLANDS, Ebne, Mauritius. E-mail:[email protected] Bassi is at KnowlEdge Srl, Italy, and works with ISLANDS.E-mail: [email protected]

Natural Resources Forum 38 (2014) 6781 DOI: 10.1111/1477-8947.12037

2014 The Authors. Natural Resources Forum 2014 United Nations

on the sustainable development of SIDS makes reference tothe vulnerabilities of SIDS, it does not make any referenceto their resilience (UN, 1992). Even the BPOA did not makeany reference to building the resilience of SIDS to externalshocks (UN, 1994).Almost a decade later, the MS explicitlyrecognized that particular attention should be given tobuilding resilience in SIDS (UN, 2005). The MS introducedthe vulnerability-resilience nexus as a central theme inaddressing the sustainable development of SIDS. Specificreference is made to the concept that promoting sustainabledevelopment, eradicating poverty and improving thelivelihoods of peoples in SIDS would be achieved throughthe implementation of country-specific strategies that buildresilience and capacity to address their unique andparticular vulnerabilities. It is also important to note thatwhereas the BPOA (UN, 1994) and JPOI (UN, 2002)proposed the development of vulnerability indices andrelated indicators for SIDS, it was in the MS that emphasiswas placed on the development of a resilience index. Astrong emphasis on building resilience through appropriatecontext-based strategies to achieve sustainable developmentand the green economy is very clear in the outcomedocument of Rio+20 (UN, 2012).Since our focus is on SIDS, it is timely here to visit the

five year review of the MS (UN, 2010) that will also serveas a prelude to introducing the regional project ISLANDS.The five year review of the MS (also MSI+5 Review) wascarried out under the office of the Secretary-General togauge progress made by SIDS in achieving sustainabledevelopment through the implementation of the MS. Thereport was presented at the sixty-fifth session of the UNGeneral Assembly (UN, 2010). In the light of the progressmade, the lessons learned and the constraints to theimplementation of the MS, section IV of the MSI+5 Reviewproposed six issues for consideration by SIDS. Theseconsiderations that continue to be relevant to the sustainabledevelopment of SIDS are:

1. strengthen support for national development planningfocused on building resilience to external shocks;

2. undertake vulnerability-resilience profiling of SIDS;3. further focus on key thematic areas. The proposed sub-

areas were: sustainable energy; transport; trade; climatechange mitigation and adaptation; marine and coastalresources; fisheries; tourism; and finance;

4. support partnership initiatives for the furtherimplementation of the BPOA;

5. strengthen access to and provision of financial resourcesfor SIDS; and

6. institutionalize special support for SIDS.

As will be discussed throughout this paper, the focus oflessons learned will be principally on the design andconceptualization of the project ISLANDS, including theadoption of system dynamics modeling as an integratedassessment tool for policy planning. The sharing of lessons

learned is therefore squarely related to addressing the firstissue for consideration discussed above.

2. An overview of ISLANDS

The Rio+20 outcome document noted with concern thatthe outcome of the five-year review of the MauritiusStrategy concluded that small island developing States havemade less progress than most other groupings, or evenregressed, in economic terms, especially in terms of povertyreduction and debt sustainability (UN, 2012). Further,unlike SIDS in the Pacific and the Caribbean, the regionalreview for AIMS (Atlantic, Indian Ocean, Mediterraneanand South China Sea) stated that the ESA-IO (Eastern andSouthern Africa and Indian Ocean) region lacks a sustainedstrategic programme with appropriate specializedinstitutional support and funding (UN, 2010).The MS sets out clearly the strategic objectives,

accompanied by well-defined vehicles for accomplishingchange and well-articulated adaptive mechanisms torespond to each of the thematic issues delineated in its 19thematic chapters, but it has shed less light on the tools andmechanisms for its implementation (chapter 20).The ISLANDS project attempts to bridge these gaps and

address some of the key issues for consideration thatemanated from the MSI+5 Review. The overall objective ofthe project is to contribute to an increased level of social,economic and environmental development and deeperregional integration in the ESA-IO region through thesustainable development of SIDS, and more specifically, toaccelerate the implementation of the MS in the ESA-IOregion. Innovative pillars of the programme are: regionalcooperation and integration, SIDS-SIDS knowledgeexchange, and a learning-by-doing approach to deal withthe large asymmetries between the developmental stages ofthe beneficiary countries.Phase I of ISLANDS is composed of four key stages: (1)

a monitoring and evaluation system for the implementationof the MS is developed and operational at national, regionaland international level; (2) best practices in mitigating thevulnerabilities of SIDS on the four selected themes areestablished (including a high level political strategy fortransforming an island State into a sustainable developmentisland State, where economic and social development andenvironmental sustainability will be optimally integrated, isoperational with Comoros (Union of), Madagascar,Mauritius, Seychelles and Zanzibar (of the United Republicof Tanzania) as beneficiaries; (3) capacity to leveragecommitments for the pursuit of best practices on the fourselected themes and to attract investments forimplementation is developed in the region; and (4)partnership for implementation of the MS at national,regional and global level strengthened.ISLANDS is implemented by the Indian Ocean

Commission (IOC) in collaboration with the United

68 Prakash N K Deenapanray and Andrea M Bassi / Natural Resources Forum 38 (2014) 6781


Nations Department of Economic and Social Affairs, andwith financing from the European Union. The TechnicalAssistance Team and the Secretariat of ISLANDS arelocated at the headquarters of the IOC in Mauritius. Phase Iof the project started in August 2011 and will be completedin June 2014. A Phase II building on lessons learned fromPhase I is being developed and is expected to have a lifetimeof 42 months.

3. Methodological approach

The overall methodology adopted by ISLANDS ispredicated on three building blocks. The first is an ecosystemapproach for integrating complex system theories into theconceptualization and implementation of sustainabledevelopment projects. The second consists of a multi-stakeholder process (MSP), created and reinforced throughthe organization of severalmeetings, to design and implementsustainable development in each country and to establish acommunity of practice (Li et al., 2009). The last buildingblock is the learning-by-doing capacity developmentapproach that seeks to simultaneously address the largeasymmetries between the capacities (human and institutional)in beneficiary countries and increase the likelihood ofsustainable activities beyond the lifetime of the project.

3.1. The ecosystem approach

The ISLANDS project uses an ecosystem-based approach,defined by the Convention of Biological Diversity (CBD) asa strategy for the integrated management of land, water andliving resources that promotes conservation and sustainableuse in an equitable way. The diamond heuristic of theecosystem approach used in the design of ISLANDS isshown in Figure 1. It reflects the cross-disciplinaryevaluation of sustainability that emerges at the intersectionof different conceptualizations of sustainable developmentand analyses based on the science of complex systems.Sustainable development can therefore be seen as anemerging property of the complex eco-social systemwhere visions and preferences of societies interact withecological possibilities. The framework allows scenarios forimplementing sustainable development to be contextualized,in order to reflect specific needs and national developmentpriorities, and guide the elaboration of targeted programmesand action plans. This is very important in theimplementation of ISLANDS since the mix of beneficiarycountries is heterogeneous in terms of geographical scaleand spread, culture and language, and level of socio-economic development. Some of these characteristics arelisted in Table 1.The diamond in Figure 1 is the implementation of theMS

(through ISLANDS) and is the nexus where ecologicalunderstanding and sociocultural preferences meet and wherethey interface with policymakers. At the heart of the process

lies the understanding of the DPSIR framework (OECD,1994), a rigorous approach being implemented at theinternational level to analyse environmental pressures.According to this framework, the Drivers (D) describe thesocial, demographic and economic developments of societiesthat exert pressures on the environment. The primary drivingforces are population growth and developments in the needsand activities of individuals, which result in changing levels ofproduction and consumption. Pressures (P) are emissions ofsubstances, release of physical and biological agents, (e.g.,rate of CO2 emissions) and the use of land and resources byhumans. Pressures on the environment cause the State (S) ofthe environment to alter. State indicators describe the quantityand quality of physical phenomena (e.g., temperature),chemical phenomena (e.g., CO2 levels in the atmosphere) orbiological phenomena (e.g., community structure).Alterationof the environment Impacts (I) the functions of theenvironment. Impacts are changes in the environmental usefunctions (e.g., biodiversity loss, health impacts). Responses(R) are the policy, societal and technological responses bygovernments, groups and individuals to prevent, compensate,ameliorate or adapt to changes, thus fostering sustainabledevelopment.Despite its normative significance, sustainable

development is a contested concept, with theories shaped bypeoples and organizations different worldviews, which inturn influence how issues are formulated and actionsproposed (Giddings et al., 2002; Sderbaum, 2007; Vivien,2008; Christen and Schmidt, 2012). It is clear from all thedebates about sustainable development that there is nocommon philosophy. Neither the BPOA (UN, 1994) nor theMSI (UN, 2005) has provided a functional definition ofsustainable development for SIDS to adopt. United NationsMember States are provided the space to construct theirown definition of sustainable development, first to adherewith the principle of sovereignty, and second, to cater tothe different national contexts and countries varyingdevelopment stages. The ecosystem approach adopted bythe ISLANDS project promotes a functional definition ofsustainable development that prioritizes the conservation ofecosystems and biodiversity, ensuring stable economicgrowth and measurable improvements in social well-being.It aims to support decision-makers in the identification,assessment and implementation of concrete policyinterventions to redress worrying trends (e.g., increasingvulnerability to climate change impacts and progressivedepletion of natural capital). Policy options are assessedwith the help of system dynamics models and analyzedthrough the lens of local culture and values. As a result, inany meaningful dialogue concerning priority issues, systemdynamics analysis requires a MSP that draws on a range ofdisciplinary perspectives, as well as expert and non-expertknowledge (Waltner-Toews and Kay, 2005; Tovey, 2009;Sen, 2013). Given the continuous evolution of sustainabledevelopment challenges, the collaborative process for thesharing of knowledge and the definition of common visions

69Prakash N K Deenapanray and Andrea M Bassi / Natural Resources Forum 38 (2014) 6781


Figure 1. Ecosystem approach used in the design and conceptualization of ISLANDS.Source: Adapted from Waltner-Toews and Kay (2005).

Table 1. Selected socio-economic, cultural and geographic characteristics of beneficiary countries

Characteristics Comoros Madagascar Mauritius Seychelles Zanzibara

Population (2012, millions) 0.8 21.9 1.3 0.1 1.274 (2010)Area (km2) 1,862 587,041 2,040 451 2,654GDP per capita (2011 in 2005PPP US$)

980 853 12,737 23,172 1,334

Languages Comorian/Arabic/French Malagasy/French English/French/Creole English/French/Creole Swahili/EnglishHuman Development Index (2012) 0.429 0.483 0.737 0.806 0.476

Note: a Except for population and area, data are for the United Republic of Tanzania.Source: OCGS (2010) and UNDP (2013).



and preferences are intended as iterative and continuative,aimed at the establishment of an expert community. Ineffect, establishing an MSP involves developing acommunity of practice (Li et al., 2009), which wouldrepeatedly exchange ideas and explore inclusive solutionsbased on cultural values and preferences.In addition to the cultural preferences of individuals,

sustainable development also includes the bio-physicaland monetary flows between the economic, social andenvironmental spheres. The interactions between thesepreferences and bio-physical and monetary flows can betreated as a complex system (Forrester, 1987; Abel, 1998;Sterman, 2000; Waltner-Toews and Kay, 2005). Anunderstanding of these complex interactions requires theuse of appropriate tools and techniques. In ISLANDS, andas proposed by the MSI+5 Review (UN, 2010) to adoptintegrated assessment tools in planning, system dynamicsmodeling (SDM) has been adopted to achieve thisobjective. Scenario analysis using SDM can both create abetter understanding of the behaviour of the complex eco-social system and point to trade-offs that may be madeto achieve the overall vision. Moreover, it has been notedthat SDM generates opportunities for monitoring theperformance of the adopted policies and the possibility ofimproving the effectiveness of the process of waterprovision (Stave, 2003; Winz and Brierley, 2007). A morerecent study has demonstrated the application of simplesystem dynamics models to guide the public policy process,and it also covered references to the use of SDM in variousareas of public affairs, including public health, energy andthe environment, social welfare, sustainable development,and security (Ghaffarzadegan et al., 2011). SDM has beenadopted by UNEP in its green economy endeavour toinvestigate the global socio-economic and environmentalimpacts of green investments (UNEP, 2011). A greeninvestment study has subsequently been carried out usingSDM for Mauritius (Bassi and Deenapanray, 2012).The ecosystem approach provides a framework for

carrying out social-ecological analysis of resilience. It offersthe possibility to frame people-environment transactionsacross varying dimensions, time periods, and scales.Furthermore, in its openness to experiential knowledge andaction research, the eco-social framework coheres well withparticipative-collaborative modes of inquiry, which traverseinstitutional, epistemological, and scale-related boundaries(Stokols et al., 2013). Importantly, the systems approachallows the sustainable development process to becontextualized at the national level, meaning that noprescribed approach (or one size fits all) is imposed onbeneficiary countries. It also enhances inclusive, evidence-based policy decision-making as proposed in the Rio+20outcome document (UN, 2012). In sum, the ecosystemapproach provides the evaluative space to inform policy anddecision-making through scenario analyses that combinesocio-cultural preferences of development and theconcurrent ecological possibilities.

The successful implementation of the ecosystemapproach in several cases remains an elusive goal for avariety of reasons, including: (1) scarcity of sustainedobservations of coastal ecosystems across the land-seainterface (landscape to seascape, which for most SIDSincludes the entire island and its exclusive economic zone,EEZ) that enable rapid detection and timely anticipation ofchanges in ecosystem states and impacts on the provision ofecosystem goods and services; (2) challenges of balancingtrade-offs involved between sustaining goods and servicesand socio-economic development; and (3) lack of adequatesystems of governance. ISLANDS is mainly focused onaddressing the latter problem, the solution of which dependson how well (1) and (2) are addressed.The approach also has a structure consisting of a triad of

activities (monitoring, governance and management) thatallow assessments of performance against the objectives ofthe ecosystem-based approach and adaptive learning at theproject level. A core characteristic of the adaptive learningstructure is the results-oriented management approach thatis shown in the bottom right hand corner of Figure 1. Theconnections between the outcomes and impacts, and theimmediate (specific) and development (overall) objectivesof the project, respectively, are also illustrated. A key aspectof the adaptive learning infrastructure is to capture anddisseminate the outputs and results of the project.1

3.2. Multi-stakeholder process for sustainabledevelopment

The imperative need for multi-stakeholder inclusiveness insustainable development is summarized in the Rio+20outcome document:

We underscore that broad public participation andaccess to information and judicial and administrativeproceedings are essential to the promotion of sustainabledevelopment. Sustainable development requires themeaningful involvement and active participation ofregional, national and subnational legislatures andjudiciaries, and all major groups [. . .]. In this regard,we agree to work more closely with the major groupsand other stakeholders, and encourage their activeparticipation, as appropriate, in processes that contributeto decision-making, planning and implementation ofpolicies and programmes for sustainable development atall levels. (UN, 2012:8)

Similarly, regarding a freedom and capacity-basedapproach for sustainability, Sen (2013) has argued thatfinding solutions to the quandary of unsustainability would

1 For the specific case of Result 2.3 of ISLANDS, all the deliverables interms of reports, statistics and analyses can be accessed at the NationalSustainable Development Platform at: https://coi.sharesrvr.com/islands/nsds, Member NSDS (read-only access), Login for other members ofNSDS: [email protected], Password: Develop2012.



require collaboration and non-divisive commitments. Thisreaffirms the coupling of MSP with the systems perspectiveof the ecosystem approach, supported by the Rio+20outcome document. Individuals have the opportunity andability to participate in setting the agenda for sustainabledevelopment, not only in terms of envisioning the futurethey want but also in deciding the means in terms of thepriorities and process to achieve that end.Within the constraints of time and resources (financial and

human), ISLANDShas attempted to adopt awidely tried andtested MSP for governance and sustainability (Hemmati,2002). The five sequential steps of a generic MSP areillustrated in Figure 2. Each step involves specific actions toensure maximum ownership of the process by thebeneficiary stakeholders so that their visions and preferencesare discussed through dialogue and consequently integratedin the national agenda (Hemmati, 2002). In the framework ofthe ecosystem approach (Figure 1), MSP is used as aniterative and permanent process through which an epistemiccommunity is cultivated (Li et al., 2009). The sequential

steps of MSP are designed in a way to continuously informthe analysis of scenarios and the elaboration of targetedprogrammes and policies for the realization of a sharedvision. MSP was adopted for the conceptualization,customization and validation of system dynamics modelsdeveloped in all the beneficiary countries.

3.3. The learning-by-doing capacity developmentapproach

The vulnerability of SIDS is accentuated by their limitedhuman capacity (UN, 1994; 2005; 2010). During the designphase, it was noted that the level of understanding orframing of sustainable development as a complex issue wasvirtually absent in all the beneficiary countries. Seychelleswas the only country that had already developed itsSeychelles Sustainable Development Strategy that waslargely carried out sectorally (GOS, 2012), and therefore,lacked cross-sectoral interactions or lacked integration. Aparallel process was ongoing in Mauritius to develop the

Figure 2. The five sequential steps of the multi-stakeholder process used in model development.Source: Hemmati (2002).



Maurice Ile Durable (MID) policy, strategy and actionplan.2 This process also lacked cross-sectoral integration orthe integration of economic, social and environmentalissues. In both cases, any possible interactions werecaptured qualitatively using mental models, which from asystems perspective can lead to undesired outcomes (Senge,1990; Sterman, 2000). The lack of a systems perspective inthe public policy arena is well documented (Ghaffarzadeganet al., 2011), and the reasons are also fairly well established.Forrester (2007:4) is of the opinion that the failure ofsystem dynamics to penetrate Governments lies directlywith the system dynamics profession and not with those ingovernment. The low level or lack of knowledge of systemdynamics was also documented earlier (Repenning, 2003);it meant that the institutionalization of SDM for integratedpolicy planning would be gradual and require: (1) capacitydevelopment in systems thinking and SDM; and (2)advocacy and communication of systems thinking andSDM with policymakers. The goal of modelers andpolicymakers should be a relationship of mutual trust, builton a foundation of communication, supported by the twinpillars of policy relevance and technical credibility (Karas,2004).Guided by lessons learned (Karas, 2004; Forrester,

2007), ISLANDS adopted the following principles andstrategies that are continuously being monitored andreviewed.

3.3.1. A two-staged approach

An initial stage of capacity development and advocacy forSDM (February to December 2012), followed by a secondstage (January 2013 to June 2014) of institutionalization ofthe tool for integrated policy planning at the national level.The plan is to reinforce institutional structures forintegrated policy-planning for sustainable development inbeneficiary countries in Phase II of ISLANDS. The initialstage was to build credibility in the process and todemonstrate the usefulness of SDM as a policy planningtool.

3.3.2. Learning-by-doing model

This model (Figure 3) is characterized by a virtuous circleof positive feedback that enhances the acquisition ofknowledge through real-life application of skills. Thelearning-by-doing approach is based on a continuousassessment of policy performance, in turn informing thecapacity development phase. Between February andDecember 2012 an average of three missions were carriedout in each country, with the exception of Mauritius whereonly two missions were carried out and where delaysextended the process to March 2013 (this is furtherdiscussed below). A capacity development component on

systems thinking and/or SDM was included in eachin-country mission to gradually enhance local capacity.Following periodic contact with the working groups, four

persons per country were identified and provided withadvanced training in SDM either at the University ofBergen (Department of Geography) or through a 3-weektraining session in Mauritius in April 2013. Ten people(4-Madagascar; 2-Seychelles; 4-Zanzibar) received trainingat the University of Bergen (UiB), Norway for a period of 4weeks (September-October 2012) by participating in amasters level modeling course on SDM for integratedpolicy planning. In order to prepare the participants for thecourse, they participated in a one-week preparatory courseheld in Mauritius (September 2012). In order to ensure alevel playing field, 3 weeks of fulltime training wasprovided to another 10 persons (4-Comoros; 5-Mauritius;1-Seychelles) in Mauritius (April 2013) based on thesyllabus of UiB. In both sets of training, each country wasallowed to develop models based on priority issues that hadbeen identified through the MSPs (see below). Theinternational consultant worked with the staff of UiB to setthe assignments and final exam for the ISLANDSparticipants. The simple models developed by trainees nowform the basis for further model development based on eachcountrys requirements.Using the learning-by-doing methodology and the MSP

shown in Figure 2, ISLANDS has sought to create anepistemic community (Figure 1), composed of a network ofexperts that have skills in integrated policy planning. Inaddition to gathering the experts during training sessions,regional meetings are carried out periodically to enhancesharing of lessons learned between beneficiary countries. Todate, two regional meetings have taken place, with the firstone (August 2012, Comoros) covering the critical review ofMSPs used by beneficiary countries to develop theirsustainable development strategies, and a training on thedevelopment of economic and environmental vulnerabilityand resilience indicators, including a statistical data gap

2 At the time of writing this paper, the MID policy, strategy and action planwas still in the process of validation, and hence was not yet made public.

Capacity developmenton systems thinking and

SDM

Understanding howsystems work (structure

and behaviour)

Cross-sectoralintegration of issues &

indicators

Policy-inducedresilience

Technical assitanceof ISLANDS

+

+

+

+

virtuouscircle

Policy performanceassessment

+

+

Figure 3. Learning-by-doing approach to capacity development.Source: Authors elaboration.



analysis for each country. The second regional meeting(April 2013, Mauritius) was dedicated to sharingexperiences on the development of systemdynamicsmodels,reviewing training needs and means and ways toinstitutionalize SDM as a sustainable development planningtool at the national level.The outcomes of thesemeetings canbe downloaded using the information provided in footnote 1.The diversity of the countries in terms of their

development stages and constraints not only generatesdifferent problems to solve but also different solutions totest, each at their own pace and adapted to national or localcapabilities, resources and requirements. Moreover thestimulation of local creativity leads to the definition of newneeds over the course of the project, and the learning-by-doing approach has proven to be an adequate means toadapt to and cater to emerging needs.

3.3.3. Valuing local and regional expertise

Either a national or regional (where no national expertisewas identified) expert was seconded to the internationalconsultant in each country. This proved a useful way tofurther enhance national and regional capacity in systemsthinking and SDM. The role of the national or regionalexpert was to support the international expert with a betterunderstanding of the national contexts, carry out follow upactivities with national stakeholders, support the collectionof statistical data, and write reports to capture the outcomesof country missions. The use of national and regionalexperts also favoured continuity of work in each countrybetween the country missions. In countries where nationalexpertise could not be identified previously (e.g., Comoros,Madagascar, and Zanzibar), ISLANDS would henceforthcapitalize on the newly acquired capacity through thetrainings discussed above.

4. Experiences and lessons learned on systemdynamics modeling

4.1. The modeling process

As discussed in the previous section, SDM lies at the heart ofour approach. It has been shownpreviously that downplayingthe modeling to focus on the policy questions plays a criticalrole in the use of the results by decision-makers (Thompsonand Duintjer Tebbens, 2008). Consequently, the entry pointfor policy analysis using SDM was not on mathematicalmodeling but rather on understanding the policy context.Typically, a system dynamics analysis proceeds throughseveral steps (e.g., Richardson and Pugh, 1989; Sterman,2000). The generic steps were designed to establishcredibility for the process used inmodel development, and tobuild trust between stakeholders on the one hand, andbetween these stakeholders and the technical assistance teamof ISLANDS.

4.1.1. Step 1: Identification of key issues andopportunities

The first imperative was to understand the policy context inwhich the model would be developed, while noting a keyaxiom that a model is not built for its own sake but toaddress an issue or a set of issues. In order to achieve this,a working group was established in each country. Thetechnical assistance team provided guidance on the profilefor membership to the multi-stakeholder platform and themodalities for model development in each country. Wherediverse stakeholders are involved in developing the modelfrom the beginning, those stakeholders may come to trust inthe fairness of the process, thus creating an epistemiccommunity that provides diverse knowledge andperspectives in a collaborative environment (Figure 1).Further, if those outside the process see that diversestakeholders were monitoring each others input, they, too,may see the work as more credible (Karas, 2004).In accordance with the defining issues and objectives in

the MS (UN, 2005) and with the cross-sectoral nature of thesimulation methodology utilized for the project, countrieswere advised to ensure the presence of a variety of localactors; these include those from government, academia, civilsociety (e.g., NGOs), the private sector, and any other keystakeholders that the beneficiary country would see fit toparticipate in the visioning andmodel development exercise.Also, three key profiles would be needed throughout theproject: (1) modelers, who are usually younger professionalsand researchers that have an interest in simulation models orsectoral/national planning. Specific knowledge of existingmodels and methodologies was not a requirement, nor anyhigh level proficiency in mathematics (Sterman, 2000); (2)data analysts familiar with national and sectoral datasets andaware of data availability and quality constraints. Theyprovide the information needed to customize and calibratethemodel, working in close collaboration withmodelers andpolicy analysts. In the ISLANDS case, involving the nationalBureau of Statistics from the onset of the project wasinstrumental; and (3) policy analysts, who are experiencedprofessionals and government officials familiar with sectoraland national planning.They are responsible for setting futureperformance, socio-economic and environmental targets,and for identifying existing and possible future policies to beanalyzed. Policy analysts provide inputs on how to shape andwhere to focus the analysis of future scenarios.While the proposed group size was 20-40 persons, the

actual size of working groups varied from around 15 in theSeychelles where human capacity is limited by the smallsize of the population to some 50 participants inComoros. This is a reflection of the different contexts of thebeneficiary countries as highlighted by the indicators inTable 1.Due to time and resources constraints, the MSP was

carried out at the level of the national working groups, andnot broadly at the country level as could transpire from the



discussions on the ecosystem approach. This was not alimitation of the project.To begin the model development, each country identified

four to six priority issues, including identification ofpossible interventions and indicators that could be used tomonitor the impacts of policies. Focus group discussionswere used in each country, and the results synthesized afterplenary discussions. Because of space limitations, theoutcomes of these discussions are shown for Comoros inTable 2, while noting that similar outcomes were obtainedfor each country.

4.1.2. Step 2: Data collection and consistency check

This step is time consuming but important, as the data areused to establish a preliminary but accurate understandingof how the issues identified relate to society, the economyand the environment. This step revealed large data gapsin most countries except for Mauritius, and it reflects thecall for countries to strengthen national data collectionand management systems (UN, 2010). One of the mainconstraints that countries acknowledge as being the weakestlink in policy planning and policy monitoring andevaluation is the lack of reliable and timely statistical data.This certainly imposes limitations to model developmentand validation. Several forms of data gaps have beenobserved and in some instances have been identified. Thereare instances like in the case of Comoros, Seychelles andZanzibar3 where data are not available. The data gapanalysis has therefore sent a signal to policymakers for theneed to collect these data in the future through sustained

monitoring. In the case of Madagascar, data are not collatedcentrally (e.g., by the bureau of statistics or similar) but areavailable from individual institutions, making the datacollection process very tedious and often dependent on agood knowledge of who are the bearers of data. Someinstitutions in Madagascar would only make data availableagainst payment of a fee. There are examples (e.g.,Comoros and Madagascar) where data exists but are notcollected periodically in a way that would provide goodtime series. This is often attributed to a lack of human andfinancial resources. In some cases, consistency checks haverevealed gaps in the quality of data.Lack of reliable and timely statistical data is certainly

acknowledged as a constraint for the modeling process,and for which ISLANDS does not necessarily bringan immediate solution. Nevertheless, this situation hasalso generated opportunities: (a) countries involved inISLANDS have engaged in a regional debate about thecritical importance of data management systems, and thisdialogue is informing national and regional preparations forthe Third International Conference on SIDS that will takeplace in Samoa in 2014. This is a positive step since theneed for reliable data and indicators is not alwaysacknowledged as being necessary in developing countries(Krank et al., 2013); (b) beneficiary countries are providingclear guidance on their need for technical assistance in thestrengthening of national data systems in a potential PhaseII of ISLANDS and/or for the technical assistance forleveraging international financing for achieving thisobjective; (c) in countries where the modeling tool has beeninstitutionalized (e.g., Zanzibar and Rodrigues anautonomous island of the Republic of Mauritius) and wheredata are available in various institutions, the project isenhancing collaboration between institutions; and (d) the

3 Zanzibar forms part of the Republic of United Tanzania, and moststatistical data are collected for mainland Tanzania only.

Table 2. Modeling framework arising from MSP in Comoros, energy sector example

Issues/sectors Problem statement Proposed interventionsIndicators for monitoring and evaluation ofinterventions

Energy Energy supply isconstrained

Develop an energy sector strategy Investment in fuel efficient power generation

with low operation and maintenance costs Development of alternative sources of energy

(solar, wind, hydroelectricity)

Production capacity (MW) Electricity generation (MWh) Cost of electricity generation (local currency/kWh) Potential for generation of power from alternative

energy sources (MW) Generation of renewable electricity (MWh) Cost of generation of renewable electricity (local

currency/kWh)The transmission anddistributionnetworks areconstrained

Enhance the medium voltage network, andinstalling secure cables for the low voltagedistribution network

Distribution network (km) Number of connections Network losses (%) Number of sequential transformers

Commercializationnot optimal

Set up an integrated management system Widespread use of prepaid meters

Number of potential customers Number of prepaid meters installed (per year) Electricity tariff Revenues (local currency) Percentage of sales recovered

Source: Outcome of consultations carried out between 16 and 19 April 2012.



lack of data has forced some countries to develop simplemodels. In situations where familiarity with models is low,it has been shown that using simple models is an efficientway to develop capacity and helps gain acceptance of thetool (Karas, 2004). Small models also yield accessible,insightful lessons for policymaking stemming fromthe endogenous and aggregate perspective of SDM(Ghaffarzadegan et al., 2011). Even in the absence of data,a much better understanding of the complexity surroundingan issue can be obtained through the development of causalloop diagrams (CLDs) as discussed next.

4.1.3. Step 3: Causal mapping and identification offeedback loops

The understanding generated in steps 1 and 2 along with thecross-disciplinary consultations with stakeholders, allowthe drivers and feedback loops present in the systembecome more apparent. The drivers and feedback loops thatdefine the interactions between the variables of the systemand capture the mental models of the participants aremapped out in CLDs (Senge, 1990; Sterman, 2000). TheCLDs highlight the combined effects that may exist whichreinforce or hinder key processes, and enable the setting ofboundaries for the model. Since the CLDs capture thestructural relationships between the elements of the system(Meadows, 2008), they also form the basis for modeldevelopment. CLDs are not used for policy decision-making, and their development is just one of the steps inmodel development. It is the scenario analysis of policiesthat is eventually used. As discussed earlier, and althoughnot explicitly applied in the ISLANDS methodology, theCLDs, by capturing causes and their effects, also capturethe drivers, pressures, states, impacts and responses i.e.DPSIR model (OECD, 1994) of the systems.Figures 4 and 5 show the CLDs emanating from MSPs in

Madagascar and Seychelles, respectively, which reveal acombination of data availability, technical capacity of

participants and use of local language (the CLD shown inFigure 4 was originally developed in French). Figure 4shows the simple CLD for the agriculture sector inMadagascar. In particular, it highlights the competing usesof land for agriculture, urban development, and forests (thatenhance biodiversity), including population pressure.Figure 5 shows the integration of the tourism, fisheries andfinance sectors. The key feedback loops include currentdevelopment patterns, with reliance on tourism and foreigninvestments, as well as emerging issues related to theavailability of natural resources. The parameters shown inorange are policy interventions of the kind identified inTable 2 for the respective countries. A comparison ofFigures 4 and 5 serves to demonstrate how ISLANDS hasbeen able to employ a context specific approach for theintroduction of the integrated assessment tool. Figure 4represents only one sector that is not integrated cross-sectorally, whereas the CDL in Figure 5 reveals a higherlevel of complexity through several feedback loops betweenthe three dominant economic sectors (fisheries, tourism andfinance) in Seychelles. The boundary of the fisheries sectoris highlighted in Figure 5. The comparison between Figures4 and 5 shows the country-specific approach that ISLANDShas taken to deploy SDM as an integrated planning tool.The level of detail contained in a CLD depends on: (1) theissues that are treated; (2) the level of disaggregationrequired to understand the system; (3) the level ofunderstanding of the issues by the epistemic community;and (4) the technical capacity of the community.As previously reported (Karas, 2004), developing CLDs

have proved to have multiple benefits. It provides theopportunity to develop a systemic view of issues throughfeedback loops and allows for integration across sectors.Importantly, it allows a departure from linear thinking thattypically characterizes conventional policymaking usingmental models (Sterman, 2000). Participation in thedevelopment of CLDs has allowed stakeholders to get abetter understanding of the complex relationships betweenthe variables that underlie the issue. The focus is mainly ongetting a better understanding of the problem rather thanon modeling, and hence does not require data collection.It provides reassurances to stakeholders (includingpolicymakers) that their interests and values have been takeninto account, and therefore that they can believe that thesubsequent model is not biased against them. CLDs providea framework for discussion,mutual learning, and negotiationamong stakeholders, which in turn provides an effective wayto further engage and motivate stakeholders in the modelingprocess. They form the basis for model development andmake the process more transparent.

4.1.4. Step 4: Creation of customized mathematicalmodels

SDM should capture and integrate the quantitativedimensions of the sectors (from step 2) and the insightsprovided by step 3 through mathematical models. As

investment jobs

labourproductivity

education

gdp

+

-

+

+

+

+

businessenvironment

political stability

bureaucracy

taxation lawsecurity

+-

+ +

population

urban space

agricultural land

forest land

biodiversityclimate change

agriculturalproductivity

- +

+

-+

--

-

+

+

-

fiscality

investment inclimate adaptation

Figure 4. CLD showing the causal relationships within the agriculturalsector in Madagascar.

Source: Developed by stakeholders during country mission carried inMadagascar, 15-18 May 2012.



explained earlier, the first stage of the model developmentprocess was aimed at capacity development and forgalvanizing credibility of the SDM as a policy planningapproach.With this inmind, and facedwith the constraints oflimited data, small models were developed. Small systemdynamics models have a unique ability to capture importantand often counterintuitive insights and relating behaviourto the feedback structure of the system without sacrificingthe ability for policymakers to easily understand andcommunicate those insights (Karas, 2004). In particular,SDMs bring together the environmental, social andeconomic variables influencing the system analyzed, therebyfacilitating the understanding of the dynamic interplaybetween production and consumption, environmentaldegradation, and vulnerability to climate change. Indicatorsof resilience and vulnerability can be built into the model,and trends may therefore be analyzed to evaluate the impactof policy interventions to the system (e.g., on whether theseinterventions increase or reduce vulnerability and resilience,and support a more coherent socio-economic developmentand environmental preservation). Based on the systemicanalysis of vulnerability, additional relevant policy optionscan be identified and tested, with a view to anticipatepotential unintended consequences, and effectively supportinformed decision-making for sustainable development.

Some selected simple models developed for Zanzibar areshown in Figure 6. Complex models have been developedfor Mauritius to investigate the energy-climate changemitigation nexus and to carry out a green economy scenarioanalysis (Bassi and Deenapanray, 2012). The simple modeldescribing the population dynamics of Zanzibar is shown inFigure 6. Population (stock) can be explained by the flowsof births (in-flow), deaths (out-flow) and net migration(defined as an in-flow). These flows are driven by birth rate,death rate and net migration rate, respectively. Similarmodels were developed for each country.

4.1.5. Step 5: Validation and analysis

For a model to be validated, stakeholders must be satisfiedthat the causal relationships captured within the modelstructure reflect reality; simulation outputs of the base casemust accurately match historical data for a multitude ofsocio-economic and environmental indicators (this mayrequire some calibration to obtain a consistent and reliablebaseline simulation); and stakeholders must feelcomfortable with the overall behaviour of the model, asobserved from simulations for the base case and alternativescenarios.The analysis of the results of the models completed to

date indicates an average error between the simulation and

gdp

tourismindustry

fisheries

++

average price pernight

+

occupancyrate-

+

state of the naturalenvironment +

quality and cost ofhotel infrastructure

quality ofservices

++

cost ofoperation

-

construction+

security

+

climatechange

-

planning andregulation

-

impact ofconstruction onthe environment -

transportinfrastructure

quality andavailability of public

infrastructure+

++

+ touristarrivals

+-

social stability

+

+tourism industry

revenues

+

+

share of revenuessubject to

domestic taxation

+

external economicperformance +

indirectcontribution oftourism to gdp

+

+

availability oflocal manpower

other recurrentcosts

cost ofservices

+

training

availability oflocal quality

training

+

+

+reliance on

foreign manpower

-

+-+

salary of localemployees+

quality of hrmanagement

+

+-

productivity oflocal manpower

+

+

+

+

+

consumptionof inputs

cost ofsupply

++

+

-

technology

consumption ofintermediate

inputs

+

+

-

awarenessraising-

-

-ecotourismandentertainment

+income

consumption

+

+

taxation

-

investment

+-

+

savings

+- -

-

+

-

++

employment

+ +

-

cost and accessto credit

-

+water,

energy,land ...

financialservices

+

+

cost of operation- fisheries

-

fisheryrevenues

+

fishprice

domesticfish catch

-+

turnaroundtime

+ fleet size

workersproductivity

++

+

subsidy (tax removed)+

internationalfish catch

-

licensing

+

+

cost oflandings+

+

byproductsynergies

+

Figure 5. CLD showing the causal relationships between the tourism, fisheries and finance sectors in Seychelles.Source: Developed by stakeholders during country mission carried in Seychelles between 27 and 30 March 2012.



historical data of about 1%, for those variables for whichcausal relations are identified and data are available. Inother cases, the average error is higher, but the trend of thedata and simulation remains consistent. There are someresults that need further model improvement and validation.In general, three shortcomings that have been faced are:(1) implications of the use of calibration in models; (2)commensurability between terminologies and definition ofvariables; and (3) impact of lack of data. Understanding thestrengths and limitations of the approach also forms part ofthe learning-by-doing methodology adopted by ISLANDS.Here the results from Zanzibar will serve as lessons

learned, including their limitations. The simulation of thepopulation model is shown in Figure 7. Historical data isshown in red (-2-) and the simulation of the model is shownin blue (-1-). The basic population model replicates thehistorical increase in population very well and shows that,all else being equal, the total population of Zanzibar willcross the 2 million persons mark in 2026, illustrating thatpopulation growth remains a developmental challenge for

Zanzibar. The error between the simulation and historicaldata is around 1%.Figure 8 shows the results of simulations for two

parameters, namely the stock of hotel rooms and yearlytourist arrivals. Comparison with historical data shows thatthe model is able to explain the causal structure of thetourism sector in Zanzibar. The curve for incoming touristsshows a key attribute of the system dynamics model: themodel is useful for simulating the medium-to-long termtrend in parameters and not year-on-year variations in thenumber of arrivals. For explaining short-term variations,complementary tools and techniques have to be used.A simple model has been developed for energy demand

(not shown), and Figure 9 shows the simulated andhistorical data. The elasticity for energy price and incomeon per capita energy demand has been calibrated so that themodel is able to simulate historical data as shown. However,there is a range of elasticity combinations that may yieldsimilar or better results. Calibration is also used for effectof improved marketing and effect of economic crises on

populationbirths deaths

birth rate death rate

net migration

net migration rate

Figure 6. Stock and flow diagram to describe the population dynamics of Zanzibar.Source: Authors elaboration.

4 M

3 M

2 M

1 M

0

2 2 22 2 2

2 22

1 1 11 1

1 1 11 1

1 11

1 11 1

1990 1994 1998 2002 2006 2010 2014 2018 2022 2026 2030Time (year)

perso

n

population : Baseline Dec 18 1 1 1 1 1 1 1 1 1 1 1population : data 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Figure 7. Simulation of the historical increase in the population ofZanzibar.

Source: Authors elaboration.

15,000 room400,000 person/year

7,500 room200,000 person/year

0 room0 person/year

4

4 4

4 4

33

33

33

3 33 3

22

22

2

2

11

11

1

1

1

11

11

1990 1996 2002 2008 2014 2020 2026Time (year)

moor81ceDenilesaB:smoorletoh 1 1 1 1 1 1 1 1 1mooratad:smoorletoh 2 2 2 2 2 2 2 2 2 2

raey/nosrep81ceDenilesaB:stsiruotgnimocniforebmunylraey 3 3 3 3 3raey/nosrepatad:stsiruotgnimocniforebmunylraey 4 4 4 4 4 4 4

Figure 8. Simulation of number of hotel rooms and annual arrival oftourists.




tourism arrivals to simulate tourist arrivals in Figure 8.Calibration should be used with caution since these datahave to be objectively verified.A powerful feature of systemdynamics modeling is that most parameters are calculatedendogenously, and all calibration should be derivedempirically wherever possible. This has not yet been done inthe current models but will have to be carried out when themodels are further developed and connected by feedbackloops.Figure 10 gives an example of divergence between the

results of simulation of the model and the historical data foraverage adult literacy rate that is generated in the educationsector (see Figure 7). The simulated result shows decliningaverage adult literacy rate because total population isincreasing faster than the stock of adult literate populationover time. In contrast, historical average adult literacy isincreasing.One explanation concerns the consistent definition of

terminology. In the model, average adult literacy rate iscalculated as the quotient of adult literate population andpopulation, and the age for defining adults is calculated byadding the cumulative time delays for students to complete

primary education (7 years) and for the young literates tobecome adults (3 years). In official statistics, adult literacyrate is defined as the fraction of total population of personsabove 15 years who are literate. The variable average adultliteracy rate is calculated using a different methodology inthe model. In order to align the definition of the parameter,the population model shown in Figure 6 will have to beexpanded to calculate the distribution of total population byage cohorts (as per definition used in official statistics) andto disaggregate between male and female populations. Thelatter may be important since there may well be gender-differentiated adult literacy rates.The example shown in Figure 10 allows revisiting of the

first shortcoming, namely, model calibration. It gives moreinsights in the use of calibration to adjust the output of themodel to replicate historical data.Average adult literacy rateis used in the calculation of yearly arrival of tourists (seeFigure 8). Assuming that the historical data for averageadult literacy rate were correct, the results shown inFigure 8 would imply incorrect use of calibrations for theparameters effect of economic crises on tourism arrivalsand/or effect of improved marketing. In other words,parameters (that are constant) can be used to force themodel to replicate historical data without establishing thecorrect causal relationships, that is, structure of causalrelationships between variables in the model. These issueswill be addressed with further model development.As an example, the value added by tourism is not

measured in Zanzibar despite the fact that this has beenidentified as the key sector for economic growth andpoverty reduction. Model development can be used tohighlight the list of key variables for which data would needto be collected in a systematic fashion. It also provides away to review data collection and analysis by the Office ofthe Chief Government Statistician (OCGS) in Zanzibar (andrespective agencies in other countries). This exampleprovides the justification for including OCGS as a keystakeholder in the model development process, and itmotivated the selection of staff at OCGS to undertaketraining on system dynamics modeling at UiB.

5. Conclusions

It is clear that as they stand, the models developed for thefive countries have to be further developed before cross-sectoral policy scenario analysis can be carried out.However, this paper has shown that the ecosystem approachthat combines scenario analysis using system dynamicsmodeling with multi-stakeholder processes and thelearning-by-doing approach is an effective way to developcapacity and generate credibility for the use of SDM inintegrated policy planning at the national level. Inparticular, multi-stakeholder processes have led to thecreation of an epistemic community in each of the countriesaddressed by the ISLANDS project. These communities of

2 M

1.5 M

1 M

500,000

0

2 22

2 2

2 2 2

1 1 11

1 11 1

11

11

11

1

1

1990 1996 2002 2008 2014 2020 2026Time (year)

eq K

tons o

f oil

total energy demand : Baseline Dec 18 1 1 1 1 1 1 1 1 1total energy demand : data 2 2 2 2 2 2 2 2 2 2 2 2

Figure 9. Simulated and historical total energy demand for Zanzibar.Source: Authors elaboration.

100

75

50

25

0

22 2

1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1

1990 1994 1998 2002 2006 2010 2014 2018 2022 2026 2030Time (year)

dmnl

average adult literacy rate : Baseline Dec 18 1 1 1 1 1 1 1 1average adult literacy rate : data 2 2 2 2 2 2 2 2 2 2

Figure 10. Comparison between adult literacy rate from simulation andhistorical data.




experts, which have established iterative and continuativeknowledge-sharing processes, provide invaluable insightsfor the development of system dynamics models, theanalysis of scenarios, and the identification of key policyoptions to increase resilience and reduce vulnerability.The experience with ISLANDS is that the need for

meticulousness in the process cannot be underestimated. Bygrounding the model development process, includingcapacity development within key institutions, theinstitutionalization of the modeling tool is ensured. For this,including key policymakers through MSPs in the modeldevelopment process cannot be bypassed. Also, the resultsof the model development process must be used foradvocacy within the policy arena, while noting both thestrengths and limitations of the approach. The models havebeen used to advocate for the use of integrated planningtools as a way to first capture the complexity of sustainabledevelopment planning and to provide a means for evidence-based policy planning. The model development process hashelped countries carry out data gaps analyses and find waysto bridge these gaps.Applying the ecosystem approach shows that the

institutionalization of the tool for cross-sectoral medium-to-long policy planning which we consider one of thekey objectives of ISLANDS is proceeding well. The toolhas been successfully adopted by the Planning Commissionof Zanzibar that is seeking to formally adopt the SDM fordeveloping MKUZA III by 2015, and the RodriguesRegional Assembly in their efforts to update the integratedsustainable plan for Rodrigues and develop a 10-yearEconomic and Social Transformation Plan. Dialoguesregarding the institutionalization of the tool in Comoros andSeychelles are ongoing. The governance and politicalsituation in Madagascar means that the tool cannot beinstitutionalized at the national level yet. So the emphasis inMadagascar will continue to be on capacity developmentand further advocacy within the political arena. A bookdetailing the development and institutionalization of systemdynamics modeling for sustainable development planningin the five countries will be launched by ISLANDS at theforthcoming Third International Conference on SIDS inSeptember 2014, in Samoa.

Acknowledgements

ISLANDS (FED/2009/021-331) is implemented by theIndian Ocean Commission through the technical assistancefunded under the European Development Fund of theEuropean Union (EuropeAid/129535/D/SER/MULTI). TheUniversity of Bergen (UiB) is duly acknowledged forproviding training on system dynamics modeling for policyplanning between September and October 2012. Ourdeepest gratitude extends to Professor Pl Davidsen and toSantiago Blanco, Department of Geography, UiB.

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states sids

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specific contexts of