The current paper contributes to the literature in several ways. We follow a two-stage methodology in the selected region: elimination and evaluation. We integrate two approaches (GIS and MCDA) in order to propose a structural procedure for the site selection problem for wind turbines. GIS serves as an elimination and data generation tool and feeds the evaluation stage. In order to be robust about the elimination, we create layers of data and keep it AZD-4547 as rich as possible so that the remaining land areas are totally feasible to consider. Here, we contribute to the site selection problem with a wide literature research to identify the elimination constraints. After a tight elimination, we have several land alternatives to evaluate, and their data are generated in the GIS. In the evaluation stage (which is a multiple criteria decision problem), we handle the alternative areas with a novel gridding approach, where each grid is technically and legally large enough to construct one turbine. We provide three different perspectives via application of three contemporary MCDA techniques for ranking and sorting purposes. Our evaluations are both at micro and macro levels, which means we have results for both for the grids and collection of grids (fields). At the micro level, we work with deterministic data for each grid. We rank and sort alternative grids using Elimination and Choice Translating Reality (ELECTRE) methods (ELECTRE III for ranking, ELECTRE-TRI for sorting). At the macro level, we work with collections of grids (fields) as alternatives. The idea of fields is important because motor units enables us to evaluate neighbouring land areas for multiple turbines. The motivation behind this is that no investor would search for an area to install only one turbine. Therefore, a selected site\'s performance is not only underlies its individual performance but also its neighbour grid\'s performance. The data for a field are the collection of deterministic data forming the given field, so the data becomes bounded. Therefore, in order to perform analysis at the field level, we also have to deal with uncertain data. The Stochastic Multiobjective Acceptability Analysis (SMAA) method is used for analysing the field level data. The results are presented at the field level, and they reveal a consistency between different methods applied and highlight a number of fields as highly ranked or of a top category.