More robust offshore wind energy planning through model ensembling
Abstract This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-eco...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | npj Ocean Sustainability |
| Online Access: | https://doi.org/10.1038/s44183-024-00080-8 |
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| author | Daniel Depellegrin Maurizio Ambrosino Sanjoy Roy Javier Sanabria Carolina Martí Llambrich |
| author_facet | Daniel Depellegrin Maurizio Ambrosino Sanjoy Roy Javier Sanabria Carolina Martí Llambrich |
| author_sort | Daniel Depellegrin |
| collection | DOAJ |
| description | Abstract This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-ecological, spatial-efficiency, energy-equity, technical/technological). An ensemble of three multi-criteria decision analysis (MCDA) techniques coupled with a Monte Carlo method based on a large, uniform number of randomly distributed criteria weights is applied for more robust priority rankings of HPA-OWE. The co-existence tier indicates that HPA-OWE should be prioritized in the North Atlantic and in the Levantine–Balearic planning subdivision. The application of machine learning on the MCDA results identified criteria that most influence the rank of each HPA-OWE at planning subdivision. The outcomes highlight the need to include place-based data to better take into account spatial inequalities in coastal regions and re-balance them with socio-economic and energetically privileged coastal territories. |
| format | Article |
| id | doaj-art-d7a9c3d1fe524e618ea5480d8546ed36 |
| institution | OA Journals |
| issn | 2731-426X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Ocean Sustainability |
| spelling | doaj-art-d7a9c3d1fe524e618ea5480d8546ed362025-08-20T02:20:45ZengNature Portfolionpj Ocean Sustainability2731-426X2024-12-013111610.1038/s44183-024-00080-8More robust offshore wind energy planning through model ensemblingDaniel Depellegrin0Maurizio Ambrosino1Sanjoy Roy2Javier Sanabria3Carolina Martí Llambrich4Landscape Analysis and Management Laboratory, Department of Geography, University of GironaDepartment of Science and Technology, University of SannioLandscape Analysis and Management Laboratory, Department of Geography, University of GironaResearch Group on Integrated Coastal Zone Management, Sustainable Social Development Research Institute (INDESS), University of CádizLandscape Analysis and Management Laboratory, Department of Geography, University of GironaAbstract This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-ecological, spatial-efficiency, energy-equity, technical/technological). An ensemble of three multi-criteria decision analysis (MCDA) techniques coupled with a Monte Carlo method based on a large, uniform number of randomly distributed criteria weights is applied for more robust priority rankings of HPA-OWE. The co-existence tier indicates that HPA-OWE should be prioritized in the North Atlantic and in the Levantine–Balearic planning subdivision. The application of machine learning on the MCDA results identified criteria that most influence the rank of each HPA-OWE at planning subdivision. The outcomes highlight the need to include place-based data to better take into account spatial inequalities in coastal regions and re-balance them with socio-economic and energetically privileged coastal territories.https://doi.org/10.1038/s44183-024-00080-8 |
| spellingShingle | Daniel Depellegrin Maurizio Ambrosino Sanjoy Roy Javier Sanabria Carolina Martí Llambrich More robust offshore wind energy planning through model ensembling npj Ocean Sustainability |
| title | More robust offshore wind energy planning through model ensembling |
| title_full | More robust offshore wind energy planning through model ensembling |
| title_fullStr | More robust offshore wind energy planning through model ensembling |
| title_full_unstemmed | More robust offshore wind energy planning through model ensembling |
| title_short | More robust offshore wind energy planning through model ensembling |
| title_sort | more robust offshore wind energy planning through model ensembling |
| url | https://doi.org/10.1038/s44183-024-00080-8 |
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