Explaining tourists´ support for environmental protection

Any movement towards sustainable tourism is dependent not only upon the industry and other key stakeholders but also the demand side, namely the tourists. Yet, there is a limited literature from the demand point of view. In this area, contributions to an understanding of tourists’ support to sustainable development are necessary. This paper analyzes the main determinants in tourist behavior regarding the environmental considerations when they are making decisions about their holiday plans. General literature on this issue highlights the need to consider socio-economic variables of the individual as well as the attributes related of their style of living. If the econometric model takes into account all these variables simultaneously, then the linkage between contextual changes and tourists´ behaviour is enriched and it may be estimated more accurately. In this sense, a multilevel approach using a random-intercept logistic models is proposed, since tourists belong to a country are affected by the same contextual variables. The analysis comprises a joint dataset composed by microdata belong to the survey Attitudes of Europeans Towards Tourism, which corresponds to Flash Eurobarometer 281, macrodata from Eurostat (GDP in pps and GDP growth) and additional variables profiles from the 2005 Environmental Sustainability Index. Country-specific effects are calculated across the EU-27 countries, which corroborated that attitudes to the sustainable tourism are heterogeneous geo-graphically. The higher the level of GDP, the lower the level of tourists´ support. These results could be explained because tourists of richer countries already have to pay more tax for envi-ronmental protection. Age, gender and educational attainment are relevant. Motivations for travelling, size of the community, type of the destination, and environmental sustainability indi-cators of the place of residence are also important factors.

Cultural Heritage and Mobile Robotics Technologies

This paper reviews current research works at the authors’ Institutions to illustrate how mobile robotics and related technologies can be used to enhance economical fruition, control, protection and social impact of the cultural heritage. Robots allow experiencing on-line, from remote locations, tours at museums, archaeological areas and monuments. These solutions avoid travelling costs, increase beyond actual limits the number of simultaneous visitors, and prevent possible damages that can arise by over-exploitation of fragile environments. The same tools can be used for exploration and monitoring of cultural artifacts located in difficult to reach or dangerous areas. Examples are provided by the use of underwater robots in the exploration of deeply submerged archaeological areas. Besides, technologies commonly employed in robotics can be used to help exploring, monitoring and preserving cultural artifacts. Examples are provided by the development of procedures for data acquisition and mapping and by object recognition and monitoring algorithms.

Robustness in facility location

Facility location concerns the placement of facilities, for various objectives, by use of mathematical models and solution procedures. Almost all facility location models that can be found in literature are based on minimizing costs or maximizing cover, to cover as much demand as possible. These models are quite efficient for finding an optimal location for a new facility for a particular data set, which is considered to be constant and known in advance. In a real world situation, input data like demand and travelling costs are not fixed, nor known in advance. This uncertainty and uncontrollability can lead to unacceptable losses or even bankruptcy. A way of dealing with these factors is robustness modelling. A robust facility location model aims to locate a facility that stays within predefined limits for all expectable circumstances as good as possible. The deviation robustness concept is used as basis to develop a new competitive deviation robustness model. The competition is modelled with a Huff based model, which calculates the market share of the new facility. Robustness in this model is defined as the ability of a facility location to capture a minimum market share, despite variations in demand. A test case is developed by which algorithms can be tested on their ability to solve robust facility location models. Four stochastic optimization algorithms are considered from which Simulated Annealing turned out to be the most appropriate. The test case is slightly modified for a competitive market situation. With the Simulated Annealing algorithm, the developed competitive deviation model is solved, for three considered norms of deviation. At the end, also a grid search is performed to illustrate the landscape of the objective function of the competitive deviation model. The model appears to be multimodal and seems to be challenging for further research.