Data gaps and fields where more research should be carried out

Data gaps indicate to researchers where more investigation should be carried out or where more information should be gathered. As mentioned previously, not all the indicators could be described quantitatively. Among the 70 indicators selected, quantitative data were not available for 16 of them.

Of these lacking indicators, some would require greater means of collection (e.g. past actions taken from the tourism sector in this direction, public participation actions in the region, media and com-munication on snow condition, current water storage capacity), while for some this would be neces-sary but unfeasible (missing statistical data on e.g. guest nights/one-day tourists ratio, job seasonality, average financial health of municipalities, as also on the added value generated by tourism).

Concerning the added value generated by tourism, this would have perhaps been the most needed information in relation to sensitivity⁶³ that was missing. At the moment, however, only general in-formation for Switzerland and some cantons exists (Rütter 1991; Zegg et al. 1993; Rütter et al. 1995;

Rütter et al. 2001; Rütter et al. 2004; Berwert and Mehr 2007; Rütter-Fischbacher and Höchli 2010;

63 This indicator was not included in the analysis, data existing only for some regions. It was replaced by proxies:

e.g. tourism intensity, length of the stay, and skier’s visits.

Höchli and Rütter-Fischbacher 2011). Finally, some indicators were difficult to quantify (like the pos-sibility of autonomous green energy development and landscape beauty). All of them, however, carry significant information in defining vulnerability.

Quantitative data was missing mostly where experts gave the most importance. Considering the weights given to each indicator, quantitative information was lacking for 66% of adaptive capacity and 30% of sensitivity. No data were missing for exposure. Quantitative data gaps in the sensitivity category mainly involved the environment (79%), the economy (41%), and the infrastructure (41%), whereas regarding adaptive capacity, only technological and institutional feasibility seem to be fairly well depicted. The online survey allowed us to gather data in relation in particular to adaptive capaci-ty (e.g. past actions taken from the tourism sector in this direction, average financial health of munic-ipalities, public participation actions in the region, and water availability). The survey and the subse-quent gathering of qualitative data would have allowed us to reduce the data gap from the initial 45%

to only 4% of the total. However, due to the exclusion of inconsistent data, 10% of the information was not covered.

4.5.6 Robustness analysis

In order to check the robustness of the results, we calculated new weights by randomly recombining (N=100) answers given by the different experts in the MCA. We then used these to generate new values of vulnerability for the 85 tourism regions. In Figure 4.12, a boxplot of the results is presented.

The red bars represent the final results obtained with the vulnerability map.

The figure shows that lowland regions around lakes often appear to slightly benefit from the situa-tion. On the other hand, quartiles for regions such as Sierre – Anniviers, the Sion Region - Les 4 vallées and Evolène, Lake Maggiore and Valleys, Brig – Belalp, Aletsch, Bellinzona and Northern Tici-no always show high levels of vulnerability. For these regions our results (red horizontal lines) lie within the whiskers and they perhaps magnify the negative impacts brought by climate change. The variability of the results for the different regions was sometimes high (e.g. Adelboden, Central Grau-bünden - Arosa, Glarnerland, Haslital, and Nidwalden). The results very often encompassed both positive and negative values. This was due mainly to the high variability of experts’ responses con-cerning the relative weights of the seven impacts (see Annex A.10). For instance, three out of five experts gave a high weight to snowpack reduction. The two remaining experts, on the other hand, defined glaciers melting/water scarcity and changes in scenic beauty and climate suitability as more relevant. Therefore, when snowpack reduction was taken as the most relevant impact, regions such as Nidwalden or The Pre-Alps - Gruyères - Moléson were particularly vulnerable whereas Central Graubünden - Arosa or Haslital (which lie in areas which will benefit from this impact) appeared well-off. On the other hand, when impacts like glaciers melting and changes in scenic beauty were given more importance, the latter two regions were particularly vulnerable, whereas the first ones showed almost no vulnerability. In the same way, lowlands like the Zürcher Oberland, the Zürcher Weinland, or the Züri-Unterland showed little variation because these regions generally show little exposure to all of the six negative impacts considered.

On the other hand, answers related to the relative importance of exposure, sensitivity, and adaptive capacity in addition to feasibility and acceptability in adaptive capacity were quite similar among the different experts, which increased the consistency and robustness of the results. In addition, our calculations were near the median and can therefore be considered as robust.

Figure 4.12: Boxplot representing the results of the total vulnerability after randomly recombining the matrices generated during the MCA (N=100). Boxes denote lower and upper quartiles and have notches at the medians. Maximum whisker length is 1.5 times the interquartile range. Outliers are shown with points. In red, the results obtained during the vulnerability mapping. In dark grey the 0 line. Adelb: Adelboden; Alets: Aletsch; Appen: Appenzellerland; Basel: Basel Region; Belli: Bellinzona and Northern Ticino; Bern: Bern; Biel: Biel/Bienne Seeland; Brig: Brig - Belalp; Canto: Canton of Jura; CentA: Central Graubünden - Arosa; CentL: Central Graubünden - Lenzerheide, Savognin, Bergün; Crans: Crans-Montana; Davos: Davos Kloster, Prättigau; Emmen: Emmental; EngaS: Engadin Scuol; EngaM: Engadin St. Moritz; FeriH: Ferienregion Heidiland; FeriL: Ferienregion Lötschberg; Fribo: Fribourg and the Centre; Gantr: Gantrisch Region; Genev: Geneva Region; Glarn: Glarnerland; Goms: Goms; Graub: Graubünden - Italian-speaking area; Grind: Grindelwald; Gstaa: Gstaad-Saanenland; Hasli: Haslital; Inter: Interlaken; Jura: Jura Bernois; LakeA: Lake Gene- va Region - Alps; LakeC: Lake Geneva Region - Countryside; LakeJ: Lake Geneva Region - Jura; LakeT: Lake Geneva Region - Towns and Lakes; LakeL: Lake Lugano; LakeM: Lake Maggiore and Valleys; Laupe: Laupen Region; Lenk: Lenk - Simmental; Leuke: Leukerbad; Luzer: Luzern Region; Marti: Martigny Region; Mendr: Mendrisiotto; MitteI: Mittelland I; MitteII: Mittelland II; Neuch: Neuchâtel; Nidwa: Nidwalden; Obera: Oberaargau; Obere: Obere Surselva; Obwal: Obwalden; Porte: Portes du Soleil - Chablais; Regio: Region of Lake Murten; Rhein: Rheintal; Rhine: Rhine Valley, Bündner Herrschaft, Chur; Saas: Saas-Fee/Saastal; Schaf: SchaffauserLand; Schwy: Schwyz; Sierr: Sierre - Anniviers; SionA: Sion Region - Anzère; SionE: Sion Region - Les 4 vallées et Evolène; Solot: Solothurn and Region; StGal: St. Gallen - Bodensee; TheLa: The Lakes Region; ThePrG: The Pre-Alps - Gruyères - Moléson; ThePrP: The Pre-Alps - Les Paccots; Thune: Thunersee; Thurg: Thurgau - Bodensee; Togge: Toggenburg; Unter: Untere Surselva; Uri: Uri; Verbi: Verbier St-Bernard; Viama: Viamala; Visp: Visp Region; Zerma: Zermatt Matterhorn; ZurS: Zuerichsee; ZurU: Zueri-Unterland; ZugR: Zug Region; ZurchO: Zürcher Oberland; ZurchW: Zürcher Weinland; Zuric: Zurich; ZuriM: Zurich Mittelland; Wenge: Wengen-Mürren-Lauterbrunnental.

4.5.7 Stakeholders’ perceptions of the potential impacts of climate change and the