Addressing offshore wind farms compatibilities and conflicts with marine conservation through the application of modelled benchmarking scenarios

• Published in: Renewable & sustainable energy reviews 2025-01, Vol.207, p.114894, Article 114894
• Main Authors: Couce Montero, L., Abramic, A., Guerra Marrero, A., Espino Ruano, A., Jiménez Alvarado, D., Castro Hernández, José J.
• Format: Article
• Language: English
• Subjects: Canary islands; Ecospace; Ecosystem modelling; Marine protected areas; Marine spatial planning; Offshore wind farms
• ISSN: 1364-0321
• DOI: 10.1016/j.rser.2024.114894

Offshore wind power generation structures are scheduled for development in the Canary Islands, potentially resulting in spatial overlap with various maritime activities, particularly fishing. It is crucial to point out that some areas that are considered suitable for installing these structures are in protected zones that are part of the Natura 2000 network, and do not have any prior environmental impact assessments. The research delved into the efficacy of utilizing Ecopath with Ecosim software to examine the consequences of implementing this technology in the study area, employing an ecosystem-based approach. To address this question, simulations were executed by assessing three distinct scenarios. The results suggest that there would be changes in the distribution of keystone species such as top predators, alongside a conspicuous decline in the abundance and catches of target species of the fishery. The Ecospace model holds the potential to forecast the impacts of offshore wind installations; however, crucial factors must be carefully considered, such as the lack of information. Notwithstanding the constraints, research like this demonstrates the efficacy of spatial ecosystem modelling in exploring this issue. •Ecosystem model could be a useful tool to investigate impacts of offshore wind energy.•Potential conflicts between offshore wind farm and fishing were evaluated.•The development of offshore wind energy could affect keystone species.•Addressing data gaps will reduce model uncertainty and allow accurate estimates.