Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland

Data Brief. 2020 May 15:31:105687. doi: 10.1016/j.dib.2020.105687. eCollection 2020 Aug.

Abstract

This dataset corresponds with the manuscript "The impact of geothermal resources on the competitiveness of municipalities: evidence from Poland" [1]. In the paper, the geothermal resources are assumed as a local competitive advantage for the municipalities that exploit them. In order to examine the relation between the exploitation of the geothermal resources and local competitiveness we determine a model of municipality competitiveness in Poland. Concept of the local competitiveness is referred to place-based measures (Lovering [2], Mytelka and Farinelli [3], Plummer and Taylor [4], Kitson et al.[5]) and it is related to the management of local resources (Malecki [6], Turok [7]). Literature review suggests that the local competitiveness is best reflected in the indicators of economic welfare and sustainability (Meyer-Stamer [8], Audretsch et al.[9]). Therefore, we use an expert method to build the model of a municipality competitiveness indicators on the example of Poland. Throughout the Analytical Hierarchy Process (AHP) method engaged experts select the 24 indicators of local competitiveness. This method serves in situations of a problem complexity (Kamenetzky [10], Saaty [11]) and as a multicriteria method in the regional studies (Dinc et al. [12]). Aggregation of the AHP selected indicators yields a synthetic competitiveness index for each of the municipalities that we examine. This index constitutes the model dependent variable in the related research article. This procedure of building municipality competitiveness model sets an example of approaching a complex phenomenon such as the local competitiveness definition. The versatility of this method enables its application into related research cases.

Keywords: Analytical Hierarchy Process; Geothermal energy; Local competitiveness; Multicriteria decision analysis; Socioeconomic indicators.