The pilot area of Auronzo di Cadore (Belluno) - Università Ca

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conceived during the EU FP5 project MULINO “MULtisectoral, INtegrated and Operational Decision Support System for sustainable use of water resource at the catchment scale” (2) 16 ous step, Actor analysis) hold different perceptions and beliefs about what are the causes of the problem or how it should be tackled. Different techniques have been developed to surface tacit knowledge and deeply held beliefs, including conflict assessment, problem-structuring methods, discourse analysis. The individual perspectives are further elaborated in the next step (Creative system modelling) to facilitate collective learning and shared (agreed) boundaries of the problem. The main outputs of this phase are: (i) a list of most relevant drivers governing the perception of the problem at hand; (ii) a preliminary list of possible solutions candidate to be assessed; (iii) A set of scenarios regarding the future development of the main drivers and cause-effect relations; (iv) an extensive list of indicators against which the performance of the possible solutions (alternative options) should be measured. 3. Creative system modelling / (CSM), to represent in a correct manner the knowledge, opinions, and the preferences of the stakeholders involved. The key actors gather in a meeting during which the problem is conceptualised through the construction of cognitive maps - individual and/or collective. Such exercises, elicited according to the context, allow the representation of the various understanding of the system under analysis. During this phase one can also proceed to the shared construction of the cause-effect chains, through either the conceptual model DPSIR (Determinants, Pressures, State, Impacts, and Response) or the elaboration of future scenarios that stimulate the identification of potential solutions/ innovative approaches to the problem under scrutiny. 4. DSS Design / In this phase, all the information gathered during the previous phases of the NetSyMoD methodology are assembled through the Decision-Support-System ClimAlpTour e-tool, an updated version of mDSS, a software (2) , capable of managing the data required for providing informed and robust decision in the following phase. The latest version of the ClimAlpTour e-tool adds a multicriteria spatial analysis to the previous mDSS versions. This is necessary to manage and communicating the information flow between various process phases, including exchange, transformation, integration, validation and documentation of gathered knowledge. Many of the previous analyses employ computer-based tools such as databases (and data management systems), visualisation components, and simulation models. Different tools are frequently assembled into a comprehensive Decision Support Systems, normally employing various interconnected and adapted components, controlled by an user interface. This phase address all activities related to the development of interoperable and useable software components; and collection of well-documented and easily exchangeable data sets (including spatial data and time series). In the end, one disposes of (i) seamless data flow between various tools and software component; (ii) user interface which guides
17 user though various stages of the NetSyMoD process; (iii) quality assurance regarding the integration of different components, and (iv) documentation and report facilities which explain the process and facilitate the interpretation of results. 5. Analysis of options / The analysis of options consist of evaluating and choosing one (or more) solution to the problem (e.g. a policy measure, plan or project) from a set of alternatives, or producing their complete ranking. Numerous methods and techniques have been developed in decision theory to make explicit (transparent) value judgements and assess the extent to which different options may contribute to achieve the pursued goals and objectives. Decision models result from the systematic exploration of a ‘problem’, including its existence, boundaries and structure. They comprise alternative courses of actions; decision goals - translated into more tangible evaluation criteria - against which the policies are weighed; and preferences, which describe how well the various options satisfy the objectives. Decision methods help to avoid inconsistencies underlying judgement and choice, and make decisions more compatible with normative axioms of rationality. Furthermore, if combined with deliberative techniques, decision methods render policy processes transparent and informed the perspectives or viewpoints of all actors. This is translated into a higher acceptance of the policies. The ClimAlpTour e-tool allows the ordering of the various options under examination and thus it facilitates the decision-makers’ final choice. Given that the indicators selected then truly represents the various interests and opinions of the actors, a multicriteria analysis is carried out - both individually and collectively. 2.4 / NetSyMoD in Auronzo di Cadore The work carried out in Auronzo di Cadore consists primarily of two workshops, for whose organisation other parallel activities took place. Overall, it aimed at raising awareness of climate change and its future impacts on the tourist sector (WP7 of ClimAlpTour project). The process was structured around the NetSyMoD phases, as visualised in Figure 5. Actors’ Analysis (1) aimed at the selection of workshop’s participants. Field data was processed with two softwares for SNA, namely AGNA (analysis) and Pajek (visualisation). Problem Analysis (2) and Creative System Modelling (3) led to consolidated strategies, visualised in IHMC Cmap, ranked by stakeholders and assessed in terms of a Strengths/Weaknesses/Opportunities/Threats framework (SWOT) frame. In the DSS design (4-5) phase, the performance of each selected strategy under future scenarios was calculated, utilising several modelling tools for quantifying various families of (social, economic, and environmental) indicators. For instance, an agent based model - AuronzoWinSim 1.0 (AWS1.0) - simulated the behaviour of tourism demand and derived socio-economic indicators, whilst system dynamics (Simile) and statistical models (SkiSim 2.0) were applied for cal-
Page 1: Climate change and its impacts on t
Page 4 and 5: work group Balbi S. 1,2 , Bonzanigo
Page 6 and 7: 5.1.4 5.2 6 6.1 6.1.1 6.2 6.3 7 Fig
Page 8 and 9: Preface The mountain area of the Ve
Page 10 and 11: 1 / Introduction: the ClimAlpTour p
Page 12 and 13: Fig. 1 Project’s structure for st
Page 14 and 15: Box 1 The report in brief 2 / Venet
Page 16 and 17: Fig. 3 The Three Peaks of Lavaredo
Page 20 and 21: culating environmental indicators,
Page 22 and 23: Box 2 SNA in brief 3 / Phase 1 / Ac
Page 24 and 25: (3) As it appears, tourists were ex
Page 26 and 27: Tab. 4 Characteristics of Auronzo
Page 28 and 29: Tab. 6 List of actors to invite to
Page 30 and 31: Box 3 Participatory development of
Page 32 and 33: Fig. 8 GIS map of areas of interest
Page 34 and 35: Fig. 10 Strategy SKINT after brains
Page 36 and 37: Tab. 10 Factors characterising wint
Page 38 and 39: Box 4 DSS design in brief 5 / Phase
Page 40 and 41: CLIMATE SCENARIO Tab. 13 CURRENT A1
Page 42 and 43: Fig. 15 Example of Google Earth bas
Page 44 and 45: 5.1.3 / Agent-Based Model (AWS1.0 )
Page 46 and 47: Fig. 17 ClimAlpTour e-tool’s cata
Page 48 and 49: Fig. 20 Indicators’ clustering 46
Page 50 and 51: Fig. 21 Weight elicitation exercise
Page 52 and 53: Fig. 23 Strategies ranking after we
Page 54 and 55: the need of defining a commonly agr
Page 56 and 57: ponentially increased the number of
Page 58: References 56 Balbi et al. (2011).

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