Innovation in the Measurement of Tourism Competitiveness

The Travel & Tourism Competitiveness Index (TTCI), developed by the World Economic Forum (WEF), is a composite indicator that integrates a total of 90 simple variables organized into 14 pillars or key dimensions of the tourism destination competitiveness. The main problem presented by this index comes from the aggregation of variables expressed in different measures, the duplicity of information, and their non-weighting in the synthetic index. This chapter proposes a new methodology for the construction of the TTCI that solves the previous problems and allows, in addition, identifying which are the pillars or dimensions that determine the differences in tourism competitiveness between the countries. The results have allowed authors to more precisely classify 136 countries according to their level of tourism competitiveness in 2017. To improve the tourism competitiveness of the countries, it is necessary to carry out policies that act on these pillars and others identified in this chapter.

Relations between Agri-Environmental, Economic and Social Dimensions of Farms’ Sustainability

Attempts to measure sustainability of farms are usually based on indicators of a set of sustainability dimensions. According to the literature, analyses should (but quite often do not) cover not only the level, but also the relations between the sustainability dimensions, because we could expect complementarity, synergies or competition between the sustainability goals. The aim of this paper was to measure and assess the interdependencies between dimensions of farms’ sustainability. The research was carried out on 601 farms that participate in the Polish Farm Accountancy Data Network (FADN), with the use of standard FADN data supported by additional information from interviews. Based on many variables, economic, environmental, social, and composite sustainability indices were collected. From the correlation and correspondence analyses it was concluded that the farms reached the balance of all three dimensions simultaneously when the level of sustainability indices was medium, while a high level of sustainability in one dimension made it very difficult to reach a high level in the others. It was also emphasized that assessing farms’ sustainability with the use of a simple aggregation of variables may be not correct since sustainability goals may compete with each other.

A SPECTRAL METHOD FOR AGGREGATING VARIABLES IN LINEAR DYNAMICAL SYSTEMS WITH APPLICATION TO CELLULAR AUTOMATA RENORMALIZATION

We present a method for identifying coarse-grained dynamics through aggregation of variables or states in linear dynamical systems. The condition for aggregation is expressed as a permutation symmetry of a set of dual eigenvectors of the matrix that defines the dynamics. The applicability of the condition is illustrated in examples from three different generic classes of reducible Markov chains: systems consisting of independent subsystems, dynamics with symmetries, and nearly decoupled Markov chains. Furthermore we show how the method can be used to coarse-grain cellular automata.

Applications de méthodes d’agrégation de variables à l’analyse de modèles spatiaux de dynamique des populations

International audience Models in population dynamics can deal with an important number of parameters and variables, which can make them difficult to analyse. Aggregation of variables allow reducing complexity of such models by building simplified models governing fewer variables by use of the existence of different time scales associated to the processes governing the whole system. Those reduced models allows analysing and describing the global dynamics of the system. We present those methods for time discrete models and illustrate their use for the study of spatial host-parasitoids models. Les modèles de dynamique de populations peuvent prendre en compte un nombre important de paramètres et de variables, ce qui les rend difficiles à analyser. Lorsqu’il existe des processus associés à deux échelles de temps différentes, une lente et une rapide, les méthodes d’agrégation de variables permettent de construire un modèle simplifié qui comporte un nombre plus faible de variables. Elles permettent ainsi d’analyser et de décrire un système de manière globale. Nous présentons ces méthodes dans le cas de modèles discrets, puis nous illustrons leur utilisation à l’aide de modèles hôte-parasitoïdes spatialisés.