A Correlational Analysis of Global Climate Risk Indices and Tourism Industry Indicators Using Fractal Statistics

Abstract

In this study, we demonstrate the use of techniques associated to a newly-developed fractal statistics in the analysis of data roughness of climate risk indices across the country as these induce a consequent ruggedness in the tourism industry indicators. Fractal dimensions and roughness correlation are used to show relationship while Pearson r correlation analysis to show association of the variables, climate risk index (as X variable), travel and tourism competitiveness, tourist arrival, tourism income and GDP per country(as Y variable). It is found out that climate risk condition is a cause of the decreases of the tourism activity and income of the country. The result lends proof thatn the weather patterns of a country, specifically climate risk condition, has considerable effects on tourism industry. Changing climate and weather patterns at tourist destinations and tourist generating countries can significantly affect the tourists’ comfort and their travel decisions. Influx in international tourist arrivals in response to climate risk affects all countries across the globe. However, developing countries are more vulnerable as theyhave less resources and mechanisms to mitigate the impacts.