AbstractThis research is an investigation to the establishment of a carbon labelling toolkit for the ski industry. The main research question is therefore: How can ski areas’ GHG emissions be reflected in a carbon label for benchmarking purposes?
Firstly, current labels, standards and toolkits in the tourism industry were researched. Followed by research to the criteria within these labels, standards, and toolkits.
Secondly, a carbon labelling toolkit was established, based on the Wi-EMT from the Smart Altitude project. This was done by combining the Wi-EMT criteria with the labelling criteria from the research in part one of this research.
Thirdly, data of three ski areas in the Trentino/Süd Tirol region are used as input in the established carbon labelling toolkit to investigate the workability of the toolkit and to test if the toolkit is useful for benchmarking the three ski areas.
The method used for the first part is literature research. In the second step the labelling toolkit is established in Excel. Whereas in the third step the labelling toolkit is used for generating the results. This step is expanded with a scenario analysis and sensitivity analysis.
The investigation has demonstrated that it is possible to establish a carbon labelling toolkit for the ski industry for benchmarking purposes. However, for connecting a label to a ski area the norm must be investigated, wherefore from more ski areas data is needed. Therefore, in this research it was only possible to benchmark the three tested ski areas.
The results present the possibility of the establishment of a carbon labelling toolkit for benchmarking ski areas, which could directly be used now by ski areas in the European Alps. Furthermore, the results of benchmarking the three ski areas illustrate that for all the ski areas around 30% of the emissions are from snow production.
The scenario analysis shows that changing fuels to electricity has not a substantial impact since it depends highly on the electricity mix of a country. However, changing to self-generated electricity production is reachable. Furthermore, the scenario analysis presentates that changing fossil fuels to HVO or hydrogen have comparable results, whereas HVO can directly be implemented in a diesel fuel snow groomer. The last four scenario analyses illustrate that changing the number of visitors and ski season length does not have a substantial impact. Furthermore, the sensitivity analyses illustrate that the inputted data is not sensitive and therefore does not have to be very precise.
Further research could be done by using more data as input from different Alpen countries. By doing this the actual label could be established.
|Date of Award||16 Nov 2022|
|Supervisor||Wilfried Ivens (Examiner) & Jetse Stoorvogel (Co-assessor)|
- Master Environmental Sciences