Green economy planning in tourism destinations: An integrated, multi-method decision support aid

Abstract

Currently, tourism is estimated to contribute around 5% to the world’s total anthropogenic GHG emissions and estimates show that tourism’s GHG emissions could grow by 161% by 2035 in a ‘business as usual’ scenario. The term ‘green economy’ refers to the realization of material wealth without major, consequent environmental and social problems. Many tourism destinations are currently pursuing green economy strategies but development of appropriate policies is complex and, consequently, decision support technologies can be used to advantage here. The design of one such decision support system is described in this paper. The research approach is based on the notion that the development (and use) of an information system can be considered a legitimate research activity in its own right and, in particular, a parallel is drawn with case study research: specifically, that systems may evolve through a series of prototypes with results of each stage informing requirements for the next and subsequent iterations. Innovative features of the system are that its design is underpinned both by a need to effectively manage the inherent complexity of the analysis domain and to allow iterative development with minimum impact on previous versions (i.e. to minimize ongoing maintenance costs). An additional important feature is that, while various subsystems may be developed using whatever software platform is deemed most appropriate, an abstracted conceptual schema facilitates effective integration of all components. To date, the DSS has been used in the field at two locations, Sharm El Sheik in Egypt and Bali, Indonesia. The Bali experience is overviewed and a specific information-sharing example is presented. The example is concerned with visitor goodwill and this concept’s link with the strategy objective of improving the environment through the establishment of more open space (especially forest land). Part of a system dynamics (SD) stock-flow application used to support scenario generation and testing, related to this objective, is presented and the importance of sharing data beyond this individual application was highlighted: specifically, expert system rules (from another application) utilizing this data are specified. Potentially, outputs from the expert system analysis could usefully be fed back into the original SD application.