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An advantage of the geothermal over other renewables is its non-intermittent character (Richard Wolfson, 2011). Unlike the wind, hydro or solar power the geothermal energy is continuously produced uninterrupted by any weather conditions, which means it can be used at any time of the day, any day of the year. This feature allows for a reliable baseload power provider, especially for the electric grid. The total global geothermal energy amounts to about 42 million megawatts, however only a fraction of it can be utilized (Meyers, 2012). Exploitation of this renewable is limited to areas where geological conditions permit a carrier i.e. water or steam to conduct the heat from the earth core nearer the earth surface. The Earth becomes hotter by about 25°C per kilometer of depth by average. In the geothermal hot spots, the temperature increases 60°C per kilometer of depth, creating optimal conditions for harvesting

This article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination.

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