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

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...

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Autores principales: Kurek, Katarzyna A., Heijman, Wim, van Ophem, Johan, Gędek, Stanisław, Strojny, Jacek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287252/
https://www.ncbi.nlm.nih.gov/pubmed/32551341
http://dx.doi.org/10.1016/j.dib.2020.105687
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author Kurek, Katarzyna A.
Heijman, Wim
van Ophem, Johan
Gędek, Stanisław
Strojny, Jacek
author_facet Kurek, Katarzyna A.
Heijman, Wim
van Ophem, Johan
Gędek, Stanisław
Strojny, Jacek
author_sort Kurek, Katarzyna A.
collection PubMed
description 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.
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spelling pubmed-72872522020-06-17 Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland Kurek, Katarzyna A. Heijman, Wim van Ophem, Johan Gędek, Stanisław Strojny, Jacek Data Brief Energy 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. Elsevier 2020-05-15 /pmc/articles/PMC7287252/ /pubmed/32551341 http://dx.doi.org/10.1016/j.dib.2020.105687 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Energy
Kurek, Katarzyna A.
Heijman, Wim
van Ophem, Johan
Gędek, Stanisław
Strojny, Jacek
Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland
title Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland
title_full Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland
title_fullStr Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland
title_full_unstemmed Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland
title_short Dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in Poland
title_sort dataset for the model of a municipality competitiveness in relation to the geothermal resources exploitation in poland
topic Energy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287252/
https://www.ncbi.nlm.nih.gov/pubmed/32551341
http://dx.doi.org/10.1016/j.dib.2020.105687
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