This title appears in the Scientific Report :
2018
Please use the identifier:
http://hdl.handle.net/2128/18803 in citations.
Please use the identifier: http://dx.doi.org/10.3390/en11051246 in citations.
Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe
Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe
The amount and distribution of land which is eligible for renewable energy sources (RES) is fundamental to the role these technologies will play in future energy systems. Unfortunately, land eligibility (LE) investigations in the literature are plagued by many inconsistencies between studies, impedi...
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Personal Name(s): | Ryberg, Severin David (Corresponding author) |
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Robinius, Martin / Stolten, Detlef | |
Contributing Institute: |
Technoökonomische Systemanalyse; IEK-3 |
Published in: | Energies, 11 (2018) 5, S. 1246 - |
Imprint: |
Basel
MDPI
2018
|
DOI: |
10.3390/en11051246 |
Document Type: |
Journal Article |
Research Program: |
Energie System 2050 Electrolysis and Hydrogen |
Link: |
Get full text Get full text OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.3390/en11051246 in citations.
The amount and distribution of land which is eligible for renewable energy sources (RES) is fundamental to the role these technologies will play in future energy systems. Unfortunately, land eligibility (LE) investigations in the literature are plagued by many inconsistencies between studies, impeding the work of researchers and policy makers interested in energy system development planning. As one factor contributing to this, the criteria used to construct land exclusion constraints have not been the focus of scientific investigation on a large scale, and as such their interactions are not well known.Therefore, an open source LE framework was used to perform evaluations in the European context of 36 commonly used constraints. After direct visualization, three measures by which these constraints are valuable to an LE analysis were computed: independence, exclusivity, and overlap. Results show extensive spatial sensitivity to constrain influence. Furthermore, some constraints, such as proximity to agriculture and woodland areas, rank high in all three measures; others, such as distance from airports and camping sites, consistently rank low; and still others, such as elevation, score highly in one measure but not the others. With these results, LE researchers can better understand the contributions of the constraints used in their analyses. |