## 1 Introduction  
The increasing share of green energy in the total energy balance is evidence of dynamic growth in the use of renewable energy sources at a global level. Wind energy plays a significant role in this growth [1], indicating the necessity for more space to be given over to wind farm projects. Therefore, choosing locations for wind farms and determining the spatial micro-locations for wind turbines [2] is particularly significant.  
There are many different methods and techniques that can be used for the purpose of choosing locations for specific activities [3; 4; 5; 6; 7].  
Manipulating spatial data is one of the key factors in choosing the optimal location for any human activity, with the use of GIS tools and techniques considered to be an essential component of this process [4; 5; 8; 9]. In addition to providing data on the location of certain spatial phenomena and activities, GIS tools offer the possibility of crossing, overlapping and organizing data, as well as carrying out various spatial analyses. Hence, their role in selecting locations for wind farms has become irreplaceable.  
The application of GIS tools, such as multi-criteria analysis (MCDMA), makes various techniques and methods available, which provide a scientific and professional basis for evaluating candidate locations for a particular human activity. The most commonly applied methods of multi-criteria analysis include: Multi-Attribute Utility Theory (MAUT), the Analytic Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DE-MATEL), Elimination and Choice Translating Reality (ELECTRE), the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and the Borda count ranking method [10]. In addition to methods based on multi-criteria analysis, more recent research has increasingly used statistical methods, as well as fuzzy theory and its modifications, when choosing locations [6; 11; 12; 13].  
When it comes to the selection of wind farm locations, previous research can be classified into three categories: GIS, multi-criteria analysis and statistical methods [14]. All of the techniques and methods have been used individually or in combination for selecting wind farm locations, and a significant number of authors have dedicated their scientific work to this field [3; 4; 5; 6; 7]. The aim of the present research was to create a universally applicable and specific approach for determining optimal locations of wind farms based on the principles of integrating the GIS (elimination phase) and PROMETHEE (multi-criteria evaluation phase based on weighting factors and different scenarios) methods [4; 5; 15]. It was important for the approach to be relatively fast, understandable and reliable, based on the principles of preventive protection, which would enable investors and other users of the space to carry out activities with minimal risk (especially important for investors), with no spatial conflicts.  
In addition to integrating the GIS and PROMETHEE methods, this study is novel in its choice of criteria used to evaluate locations for the development of wind energy, which include relevant legislation, empirical data based on specialized software models and specific studies carried out on representative samples, the specificities of the space, and the specifics of each particular wind farm project.  
The methodological procedure was applied to an area of the Republic of Serbia (South-eastern Europe) due to the availability of relevant data required for implementing the procedure, but it is universally applicable.