Finding the best compromises for wind power locations in Finland via spatial prioritization modeling

Abstract

Anthropogenic climate warming is a great threat to the future wellbeing of nature and people alike. Therefore decreasing greenhouse gas emissions as quickly as possible is of utmost importance. Wind power is one of the most applied “carbon-free” alternatives for producing electricity, and its popularity is still growing. Numerous studies have shown that wind turbines can kill, disturb or displace animals, especially certain bird and bat species. Building a wind turbine also causes local habitat changes to the area immediately beneath the turbine, and the noise can affect nearby people and other animals. Studies have shown large location-dependent variation in the strength of negative effects of individual wind turbines and wind farms. The environmental factors contributing to this variation are fairly well known, as well as the most vulnerable species. Therefore, careful planning of the locations of the wind turbine construction is generally seen as the most efficient method for minimizing the negative effects of wind turbines. The potential population-level effects of wind power are caused by the combined effects of wind turbines over large areas, especially in migratory species covering hundreds to thousands of kilometers annually. Hence, the planning of the wind power should be done in as large scale, but currently planning as well as assessment and monitoring of environmental effects is mostly done at a level of individual wind farms or counties.

In my study I model the relative suitability of land areas for building wind power over the whole Finland (ca 340 000 km2). Suitability is defined as the best trade-offs between the wellbeing of nature and people, wind power building and maintenance costs, and potential for energy production (average wind speed, based on Finnish wind atlas), but without compromising the most vulnerable species and habitats. In the spatial models I combine species data (occurrences), environmental data (e.g. habitat types, forest ages, wind speed data, populated areas) and wind power building and maintenance cost data. Experts from nature conservation, wind power companies and state officials are involved in order to provide the most reliable and relevant measures. The modeling is done with Zonation, which is a software developed for spatially prioritizing areas based on their features. From the model I will produce a map showing the small-scale suitabilities for wind power across Finland. I will also present comparisons in the modeled suitabilities between different environmental factors and show which combinations of several environmental factors were found especially suitable or non-suitable. I will also compare the modeled suitabilities with the areas already designated to wind power construction in Finland. If discrepancies between these previously designated areas and the most suitable areas in this study are found, the reasons behind the observed differences are scrutinized and discussed.