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Renewable energy sources (RES) strengthen their hold on emerging economies. Record numbers of newly installed RES capacity are being observed in recent years. In 2016, the addition of renewable resources were more than 60% of new capacity investments globally, surpassing fossil fuel-based investments. The majority of these additions take place in developing countries, indicating the vital importance of selecting the best RES technologies for Turkey, an emerging economy. RES is not only becoming less expensive, they also contribute to employment and environmental protection. Selecting the most appropriate RES strategy among alternatives involves many criteria. This chapter introduces a novel RES evaluation model that can guide investors in identifying the most suitable RES strategy from a sustainability perspective. Complex socio-economic decision problems often make it more difficult for Decision Makers to consider different aspects, and to provide exact numerical values. Considering many, usually conflicting sustainability factors that affect this selection process, the chapter proposes a Multi-Criteria Decision-Making (MCDM) model by implementing hesitant fuzzy linguistic term sets (HFLTS) for an effective RES strategy evaluation problem. Group Decision Making (GDM) is also integrated to the method, as it is capable to offset individual DMs’ bias and partiality. HFLTS enables DMs to accurately provide their linguistic expressions. An integrated HFL SAW method (Simple Additive Weighting) and HFL TOPSIS method (Technique for Order Performance by Similarity to Ideal Solution) are employed for this purpose. The criteria priorities are determined with the HFL SAW method and the final RES strategy ranking results are determined with HFL TOPSIS method. The plausibility of the proposed framework is tested in a case study. This combination of MCDM techniques is applied for the first time in the literature for dealing with this problem setting.
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- Strategic Renewable Energy Source Selection for Turkey with Hesitant Fuzzy MCDM Method
- Chapter 11
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