The global demand for renewable energy is on the rise. Expansion of onshore wind energy is in many parts of the world limited by societal acceptance, and also ecological impacts are a concern. Here, pragmatic methods are developed for the integration of high-dimensional spatial data in offshore wind energy planning. Over 150 spatial data layers are created, which either oppose or support offshore wind energy development, and represent ecological, societal, and economic factors. The method is tested in Finland, where interest in developing offshore wind energy is growing.
Analyses were done using a spatial prioritization approach, originally developed for the prioritization of high-dimensional ecological data, and rarely used in planning offshore wind energy. When all criteria are integrated, it is possible to find a balanced solution where offshore wind farms cause little disturbance to biodiversity and society, while at the same time yielding high profitability for wind energy production. Earlier proposed areas for offshore wind farms were also evaluated. They were generally well suited for wind power, with the exception of a couple of areas with comparatively high environmental impacts.
As an outcome, new areas well suited for large scale wind power deployment were recognized, where construction costs would be moderate and disturbance to biodiversity, marine industries and people limited. A novel tradeoff visualization method was also developed for the conflicts and synergies of offshore energy deployment, which could ease the dialogue between different stakeholders in a spatial planning context.
Overall, this study provides a generic and transparent approach for well-informed analysis of offshore wind energy development potential when conflict resolution between biodiversity, societal factors and economic profits is needed. The proposed approach is replicable elsewhere in the world. It is also structurally suitable for the planning of impact avoidance and conflict resolution in the context of other forms of construction or resource extraction.