Spatial analysis of indicators affecting the exploitation of shallow geothermal energy at European scale
[EN] Shallow Geothermal Energy (SGE) exploited by vertical close loop Ground Source Heat Pumps (GSHP) is a proven, reliable, and widespread renewable heating and cooling technology. However, in many regions there is still a lack of awareness among policy makers and end users, constituting a major co...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/183621 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/183621 |
| Access Level: | acceso abierto |
| Palabra clave: | Renewable heating and cooling GIS Multi-criteria decision-making Continental scale FISICA APLICADA 07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos |
| Sumario: | [EN] Shallow Geothermal Energy (SGE) exploited by vertical close loop Ground Source Heat Pumps (GSHP) is a proven, reliable, and widespread renewable heating and cooling technology. However, in many regions there is still a lack of awareness among policy makers and end users, constituting a major constraint to wider deployment of SGE. In order to contribute to its market consolidation, this work focuses on bringing to light relevant spatial information affecting the suitability of SGE exploitation. This information is the result of the systematization of geological, climatic, and environmental open and available data translated into performance indicators. A set of thematic maps was created using Geographic Information Systems (GIS) comprising the European Member States and other European countries. The relative area and the amount of population affected per indicator was spatially analyzed to determine the most common values found and the affected population. The relationship between area percentage and population affected percentage per indicator was also analyzed and allowed to identify the most common indicators values in areas where high energy demands are expected. Additionally, an example of how this data can be used into a Multi-Criteria Decision-Making (MCDM) framework is shown. (c) 2020 Elsevier Ltd. All rights reserved. |
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