Please use this identifier to cite or link to this item:
Files in This Item:
There are no files associated with this item.
Title: A modelling approach for offshore wind farm feasibility with respect to ecosystem-based marine spatial planning
Authors: Pinarbasi, Kemal; Galparsoro, Ibon; Depellegrin, Daniel; Bald, Juan; Perez-Moran, German; Borja, Angel
Citation: SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 667, 306-317
Abstract: Demand for renewable energy is increasing steadily and regulated by national and international policies. Offshore wind energy sector has been clearly the fastest in its development among other options, and development of new wind farms requires large ocean space. Therefore, there is a need of efficient spatial planning process, including the site selection constrained by technical (wind resource, coastal distance, seafloor) and environmental (impacts) factors and competence of uses. We present a novel approach, using Bayesian Belief Networks (BBN), for an integrated spatially explicit site feasibility identification for offshore wind farms. Our objectives are to: (i) develop a spatially explicit model that integrates the technical, economic, environmental and social dimensions; (ii) operationalize the BBN model; (iii) implement the model at local (Basque Country) and regional (North East Atlantic and Western Mediterranean), and (iv) develop and analyse future scenarios for wind farm installation in a local case study. Results demonstrated a total of 1\% (23 km(2)) of moderate feasibility areas in local scaled analysis, compared to 4\% of (21,600 km(2)) very high, and 5\% (30,000 km(2)) of high feasibility in larger scale analysis. The main challenges were data availability and discretization when trying to expand the model from local to regional level. The use of BBN models to determine the feasibility of offshore wind farm areas has been demonstrated adequate and possible, both at local and regional scales, allowing managers to take management decisions regarding marine spatial planning when including different activities, environmental problems and technological constraints. (C) 2019 Elsevier B.V. All rights reserved.
Keywords: Bayesian belief network; Renewable energy; Site identification; Trade-off; Decision support tools; BAYESIAN BELIEF NETWORK; MANAGEMENT; ENERGY; FRAMEWORK; IMPACTS; BIODIVERSITY; SERVICES; TOOLS; PERSPECTIVE; INTEGRATION
Issue Date: 2019
Type: Article
DOI: 10.1016/j.scitotenv.2019.02.268
ISSN: 0048-9697
E-ISSN: 1879-1026
Funder: VAPEM project - Fisheries and Aquaculture Directorate of the Basque Government
Appears in Publication types:Artículos científicos

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.