A comparative approach for the identification and selection of sustainable energy alternatives based on green criteria: Classical and fuzzy WASPAS method

Yıl 2026, Cilt: 11 Sayı: 1, 107 - 141, 17.03.2026

Müslüm Öztürk

https://doi.org/10.58559/ijes.1785823

https://izlik.org/JA47NS37HP

Öz

Increasing energy demand and environmental concerns are guiding decision-makers toward sustainable energy systems. In this study, classical and fuzzy WASPAS methods were jointly employed to evaluate sustainable energy sources and determine the most suitable alternative for Kilis, Turkey. The alternatives considered include solar energy (A1), wind energy (A2), biomass energy (A3), hydroelectric energy (A4), and geothermal energy (A5). The criteria selected are carbon emissions (C1), investment cost (C2), energy efficiency (C3), resource continuity (C4), environmental impact (C5), and local acceptance and social impact (C6). For the classical WASPAS method, criterion weights were assigned as C1=0.15, C2=0.15, C3=0.20, C4=0.20, C5=0.15, and C6=0.15, while for fuzzy WASPAS, the criterion weights were defined using triangular fuzzy numbers. According to the classical WASPAS results, the Q_i scores for the alternatives were A1=1.00000, A2=0.85859, A3=0.63089, A4=0.77590, and A5=0.68140, with solar energy ranking first. In the fuzzy WASPAS analysis, the E(Q) scores were A1=2.188, A2=1.542, A3=1.137, A4=1.278, and A5=1.188, confirming the superiority of solar energy when uncertainties are considered. The results obtained from both classical and fuzzy WASPAS methods were compared, and taking into account the geographical, economic, and social conditions of Kilis, solar energy emerged as the most advantageous alternative. The comparative performance analysis indicates that evaluations conducted in a fuzzy environment provide a more realistic and flexible assessment by incorporating uncertainties. This approach offers decision-makers strategic information based on robust foundations for sustainable energy planning. The study demonstrates the effectiveness of the fuzzy WASPAS method in multi-criteria decision-making and contributes to the development of sustainable energy policies at the local level. The results allow decision-makers to balance environmental, economic, and social factors while ensuring practical guidance for regional sustainable energy strategies. Furthermore, the flexibility provided by fuzzy WASPAS in handling uncertainties enables more reliable and robust evaluations in multi-criteria decision-making processes.

Anahtar Kelimeler

Renewable energy systems , Energy management , Multi-criteria decision-making , Fuzzy logic , Classical and fuzzy WASPAS

Teşekkür

We sincerely thank the members of the expert committee for their valuable insights and suggestions in the preparation of this study. Their guidance and constructive feedback have played a significant role in enhancing the scientific quality of our work.

Kaynakça

• [1] Kahraman C, Ruan D, Doǧan I. Fuzzy group decision-making for facility location selection. Information Sciences 2003; 157: 135-153.
• [2] Kahraman C, Öztayşi B, Sarı İU, Turanoğlu E. Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems 2014; 59: 48-57.
• [3] Bostancı B, Bakır NY, Doğan U, Güngör MK. Bulanık karar verme teknikleri ile CBS destekli konut memnuniyeti araştırması. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 2017; 32(4): 1193-1208.
• [4] Wang CN, Chen YT, Tung CC. Evaluation of wave energy location by using an integrated MCDM approach. Energies 2021; 14(7): 1840.
• [5] Karbassi Yazdi A, Tan Y, Birau R, Frank D, Pamučar D. Sustainable solutions: using MCDM to choose the best location for green energy projects. International Journal of Energy Sector Management 2025; 19(1): 146-180.
• [6] İlbahar E, Cebi S, Kahraman C. Assessment of renewable energy alternatives with pythagorean fuzzy WASPAS method: a case study of Turkey. In: Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019. Springer International Publishing, 2020: 888-895.
• [7] Thanh NV, Lan NTK. Solar energy deployment for the sustainable future of Vietnam: Hybrid SWOC-FAHP-WASPAS analysis. Energies 2022; 15(8): 2798.
• [8] Chattham N, Thanh NV, Jeenanunta C. Renewable energy from solid waste: a spherical fuzzy multi-criteria decision-making model addressing solid waste and energy challenges. Energies 2025; 18(3): 589.
• [9] Masoomi B, Sahebi IG, Fathi M, Yıldırım F, Ghorbani S. Strategic supplier selection for renewable energy supply chain under green capabilities (fuzzy BWM-WASPAS-COPRAS approach). Energy Strategy Reviews 2022; 40: 100815.
• [10] Wang CN, Kao JC, Wang YH, Nguyen VT, Nguyen VT, Husain ST. A multicriteria decision-making model for the selection of suitable renewable energy sources. Mathematics 2021; 9(12): 1318.
• [11] Bozyiğit MC, Ünver M. Solar panel selection using extended WASPAS with disc intuitionistic fuzzy Choquet integral operators: CASPAS methodology. arXiv preprint arXiv:2501.12251, 2025.
• [12] Aydın Ü. Measuring the performance of electric buses developed for the public transportation system using entropy and WASPAS methods. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi 2024; 79: 345-359.
• [13] Erdoğan H, Tutcu B, Talaş H, Terzioğlu M. Performance analysis in renewable energy companies: application of SWARA and WASPAS methods. Journal of Sustainable Finance & Investment 2022; 12(1): 1-22.
• [14] Karaca C, Ulutaş A. Entropi ve Waspas yöntemleri kullanılarak Türkiye için uygun yenilenebilir enerji kaynağının seçimi. Ege Academic Review 2018; 18(3): 483-494.
• [15] Al-Barakati A, Mishra AR, Mardani A, Rani P. An extended interval-valued Pythagorean fuzzy WASPAS method based on new similarity measures to evaluate the renewable energy sources. Applied Soft Computing 2022; 120: 108689.
• [16] Dhumras H, Bajaj RK. On potential strategic framework for green supply chain management in the energy sector using q-rung picture fuzzy AHP & WASPAS decision-making model. Expert Systems with Applications 2024; 237: 121550.
• [17] Pamucar D, Torkayesh AE, Deveci M, Simic V. Recovery center selection for end-of-life automotive lithium-ion batteries using an integrated fuzzy WASPAS approach. Expert Systems with Applications 2022; 206: 117827.
• [18]. Albayrak ÖK. Forecasting of renewable energy generation for Turkey by artificial neural networks and ARIMA Model: 2023 generation targets by renewable energy resources. Verimlilik Dergisi 2022; 57: 121-138.
• [19]. Tasdemir O, Yesilbudak M, Irmak E. Day-ahead photovoltaic power production forecasting Using a hybrid artificial neural network model integrated with metaheuristic algorithms. International Journal of Smart Grid-ijSmartGrid 2025; 9: 210-218.
• [20]. Yaïci W, Entchev E. Adaptive neuro-fuzzy inference system modelling for performance prediction of solar thermal energy system. Renewable Energy 2016; 86: 302-315.
• [21]. Aly HH. A hybrid optimized model of adaptive neuro-fuzzy inference system, recurrent Kalman filter and neuro-wavelet for wind power forecasting driven by DFIG. Energy 2022; 239: 122367.
• [22]. Pohekar SD, Ramachandran M. Application of multi-criteria decision making to sustainable energy planning—A review. Renewable and Sustainable Energy Reviews 2004; 8: 365-381.
• [23]. Mardani A, Nilashi M, Zakuan N, Loganathan N, Soheilirad S, Saman MZM, Ibrahim O. A systematic review and meta-analysis of SWARA and WASPAS methods: theory and applications with recent fuzzy developments. Applied Soft Computing 2017; 57: 265-292.
• [24] Boz E, Çizmecioğlu S, Çalık A. Kaos durumu altında hava kargo şirketi seçimi: bütünleşik Bayesian BWM ve WASPAS çerçevesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2023; 38(3): 1586-1600.
• [25] Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A. Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika 2012; 122(6): 3-6.
• [26] Sorooshian S, Azizan NA, Ale Ebrahim N. Weighted aggregated sum product assessment. Mathematical Modelling of Engineering Problems (MMEP) 2022; 9(4): 873-878.
• [27] Rudnik K, Bocewicz G, Kucińska-Landwójtowicz A, Czabak-Górska ID. Ordered fuzzy WASPAS method for selection of improvement projects. Expert Systems with Applications 2021; 169: 114471.
• [28] Shenify M, Mazarbhuiya FA. The expected value of a fuzzy number. International Journal of Intelligence Science 2014; 5(1): 1-5.
• [29] European Environment Agency. Share of renewable energy in final energy consumption: Türkiye. Europe’s environment 2025. European Environment Agency 2025. Available from: https://www.eea.europa.eu/en/europe-environment-2025/countries/turkiye/renewable-energy-sources. Accessed 17-Jan-2026.
• [30] Günay E, Yildirim S. Yenilenebilir enerji kapasitesi bakımından Türkiye’nin potansiyelinin değerlendirilmesi. Journal of Economics and Research 2024; 5: 61-72.
• [31] Akyazı Ö, Başlık Ş, Khidirzade K, Çavdar B. Türkiye’nin güneş enerjisi potansiyelinin PVSyst ile analizi. Karadeniz Fen Bilimleri Dergisi 2024; 14: 1486-1502.
• [32] Süzek F. Türkiye rüzgar enerjisi potansiyelinin belirlenmesi. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye; 2007.
• [33] AA Haber Terminali. Anadolu Ajansı. Türkiye, rüzgar enerjisinde son 15 yılın en güçlü performansını gerçekleştirdi. Available from: https://www.aa.com.tr/tr/enerjiterminali/ruzgar/turkiye-ruzgar-enerjisinde-son-15-yilin-en-guclu-performansini-gerceklestirdi/53982. Accessed 17-Jan-2026.
• [34] Wikipedia. Geothermal energy in Turkey. Wikipedia, The Free Encyclopedia. Available from: https://en.wikipedia.org/wiki/Geothermal_energy_in_Turkey. Accessed 17-Jan-2026.
• [35] Zavadskas EK, Turskis Z, Kildienė S. State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy 2012; 18(4): 672-695.
• [36] Akusta E, Cergibozan R. Assessment and prioritization of renewable energy alternatives to achieve sustainable development goals in Türkiye: based on fuzzy AHP approach. International Journal of Energy Studies 2024; 9(4): 809 847.
• [37] International Energy Agency. Solar PV. International Energy Agency 2024. Available from: https://www.nrel.gov/analysis/solar-futures. Accessed 17-Sep-2025.
• [38] International Energy Agency. Solar PV. International Energy Agency 2024. Available from: https://www.iea.org/energy-system/renewables/solar-pv. Accessed 17-Sep-2025.
• [39] Mardani A, Zavadskas EK, Khalifah Z, Jusoh A, Nor KM. Application of multiple-criteria decision-making techniques and approaches to evaluating of service quality: a systematic review of the literature. Journal of Business Economics and Management 2015; 16(5): 1034-1068.
• [40] Öztürk M. A hybrid approach for battery selection based on green criteria in electric vehicles: DEMATEL-QFD-interval type-2 fuzzy VIKOR. Sustainability 2025; 17: 6277.
• [41]Öztürk M. Equipment supplier selection for sustainable hydrogen production: a group decision-making supported spherical fuzzy TOPSIS approach. Sustainability, 18(4), 1737.