A Bayesian Network model to identify suitable areas for offshore wave energy farms, in the framework of ecosystem approach to marine spatial planning.

Decision support tool Ecological risk MSP Ocean energy Renewable energy Wave energy converter

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
10 Sep 2022
Historique:
received: 26 04 2022
revised: 13 05 2022
accepted: 14 05 2022
pubmed: 23 5 2022
medline: 18 6 2022
entrez: 22 5 2022
Statut: ppublish

Résumé

The production of energy from waves is gaining attention. In its expansion strategy, technical, environmental and socioeconomic aspects should be taken into account to identify suitable areas for development of wave energy projects. In this research we provide a novel approach for suitable site identification for wave energy farms. To achieve this objective, we (i) developed a conceptual framework, considering technical, environmental and conflicts for space aspects that play a role on the development of those projects, and (ii) it was operationalized in a Bayesian Network, by building a spatially explicit model adopting the Spanish and Portuguese Economic Exclusive Zones as case study. The model results indicate that 1723 km

Identifiants

pubmed: 35598669
pii: S0048-9697(22)03134-5
doi: 10.1016/j.scitotenv.2022.156037
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

156037

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Ana D Maldonado (AD)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain; Department of Mathematics, University of Almería, Carretera Sacramento s/n, 04120 La Cañada, Almería, Spain.

Ibon Galparsoro (I)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain. Electronic address: igalparsoro@azti.es.

Gotzon Mandiola (G)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.

Iñaki de Santiago (I)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.

Roland Garnier (R)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.

Sarai Pouso (S)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.

Ángel Borja (Á)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain; King Abdulaziz University, Faculty of Marine Sciences, Jeddah, Saudi Arabia.

Iratxe Menchaca (I)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.

Dorleta Marina (D)

BiMEP, Biscay Marine Energy Platform, C/Atalaia n°2, bajo, 48620, Bizkaia, Spain.

Laura Zubiate (L)

BiMEP, Biscay Marine Energy Platform, C/Atalaia n°2, bajo, 48620, Bizkaia, Spain.

Juan Bald (J)

AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.

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