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

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CLIMATE SCENARIO Tab. 13 CURRENT A1B B1 IPCC climate change scenarios utilised in Auronzo tual model of the climate change impacts and for the analysis of adaptation measures (ScenDPSIR), as depicted in Figure 14, which simulates the ClimAlpTour e-tool interface. The DPSIR approach is adopted by the software interface in order to formalize the problems by means of indicators. These are used to describe the criteria upon which the selection of options is performed. Stakeholders weight them according to their preferences in order to eventually identify the preferred adaptation strategy and explore trade-offs between alternative options based upon their strengths and weaknesses. In preparation of the second workshop the most relevant indicators were short-listed and quantified. 38 DESCRIPTION There are no changes from the recent past despite CO2 emissions Rapid globalisation and economic growth with total exploitation of all Energy resources available (higher variation of climate than B1) More efficient technologies and socioeconomic development oriented towards services 5.1 / Modelling the indicators A list of social, economic, and environmental indicators emerged from both those identified by stakeholders during the first workshop and our judgements developed throughout former project activities and literary reviews. From those, 15 indicators were picked that were more suitable to the evaluation of the strategies (see Table 14). These criteria were selected on the basis of both the preferences emerged during the first workshop and experts’ judgements. For instance, uniqueness and beauty of the territory was often mentioned, hence our stress on those indicators with (either direct or indirect) environmental relevance, such as air quality, erosion, visibility, protected areas affected, garbage disposals, and so forth. In addition, the poor state of public transport remained on the forefront during the whole workshop: hence, during the calculation of air quality, for instance, experts forecasted an improvement of the service by 30%. Lastly, amongst the various inputs in AWS1.0, competitiveness of the neighbouring resorts, another primary concern of our stakeholders, was considered. Then, quantitative data needed for the assessment of the strategies were collected, according to the previously defined criteria. In order to gather data required for the evaluation of the strategies, several tools were utilised. Firstly, the various types of available and relevant information concerning the case study, including economic, demographic and biophysical timeseries, were obtained by secondary sources. Secondly, other social and economic indicators, such as employment opportunities, energy consumption for snow-making, number of tourists, tourists’ peaks, and so forth, were calculated by running AWS1.0. Here, the information available on the tourists’ ethnography aided the creation of the tourist profiles necessary for the ASW1.0 simulation of tourist fluxes, in their turn needed to quantify those indicators mentioned above. This information is for obvious reasons incomplete, because one cannot adequately forecast the decision-making
Tab. 14 Indicators selected to load into the ClimAlpTour e-tool 39 Category Name Description 1 ECON 2 3 4 5 ECON / ENV ECON / SOC ECON / SOC / ENV ECON / ENV Investment costs Energy consumption Tourists' expenditures Total garbage disposal Water consumption for snow-making 6 ENV Air quality 7 ECON SCI affected 8 ECON Erosion 9 10 ECON / SOC / ENV ECON / ENV 11 ECON 12 13 ECON / SOC / ENV ECON / ENV 14 ECON 15 SOC / ENV Arrivals Tourists' peaks Synergies with summer tourism Long-term sustainability Innovativeness Labour tourist sector Visibility of skiing areas Sum of costs per sector per strategy Average cost of energy of a season Average daily expenditures per visitor Garbage disposal per winter season per number of tourist nights Water use for artificial snow CO2 captured by forest - (C02transport + CO2hotels + CO2ski-lifts) Site of Community Importance altered by interventions Delta tons of average soil loss after each strategy's implementation N. of arrivals, Existing data on guest structure Standard deviation of total daily visitors over 40 seasons Sum of contribution of each strategy to summer tourism The strategy seems appropriate from a long-term-sustainability perspective Innovativeness of strategy in terms of green-initiatives, new activities proposed, type of tourism, niche offer etc Approximated with variable cost of running tourist facilities Allows to associate to each cell of the urban landscape the percentage of a given ski area that is visible from there, as a function of terrain topography and land cover Unit of meas. DPSIR Calculated with: € D AWS1.0 € P AWS1.0 €/day I AWS1.0 ton I AWS1.0 (tourist nights), lit review m3/y I AWS1.0 ton/y I AWS1.0 for n. of tourists and their displacement, IDRISI for ha deforested, lit. review for average CO2 emissions ha S GIS/maps Region ton I RUSLE (Simile), GIS (Idrisi), Google Earth N° I AWS1.0 CV% P AWS1.0 0-4 D Experts' judgement 0-4 D Experts' judgement 0-4 D Experts' judgement € I AWS1.0 ha I GIS
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 18 and 19: conceived during the EU FP5 project
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 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).

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

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