Assessment and prioritization of renewable energy alternatives to achieve sustainable development goals in Türkiye: Based on fuzzy AHP approach

Yıl 2024, Cilt: 9 Sayı: 4, 809 - 847, 25.12.2024

Emre Akusta , Raif Cergibozan

https://doi.org/10.58559/ijes.1494256

Öz

The aim of this study is to prioritize renewable energy sources to achieve sustainable development in Türkiye by using fuzzy AHP method. In our study, we used 30 criteria that affect the investment in renewable energy sources. We also calculated the weights of these criteria in investment decisions. In addition, we analyzed the advantageous renewable energy sources according to each criterion. Thus, it was determined which renewable energy source is advantageous according to which criteria. The results show that the most important main criteria for renewable energy investments in Türkiye are economic, political, technical, environmental and social criteria, respectively. The most appropriate renewable energy sources according to economic, political, technical and social criteria are solar, wind, hydroelectric,
biomass and geothermal respectively.

Anahtar Kelimeler

Renewable energy, Sustainable development, Türkiye, Multi-criteria decision, Fuzzy AHP

Kaynakça

• [1] Kızıldere C. Türkiye’de cari açık sorununun enerji tüketimi ve ekonomik büyüme açısından değerlendirilmesi: Ampirik bir analiz. Business & Management Studies: An International Journal 2020; 8(2): 2121-2139.
• [2] Outlook AE. Annual Energy Outlook 2019: with Projections to 2050. US Energy Information Administration 2019.
• [3] Cherp A, Jewell J. The concept of energy security: Beyond the four As. Energy Policy 2014; 75: 415-421.
• [4] Rosen MA. Issues, concepts and applications for sustainability. Glocalism: Journal of Culture, Politics and Innovation 2018; 3(1): 5-22.
• [5] Tomislav K. The concept of sustainable development: From its beginning to the contemporary issues. Zagreb International Review of Economics & Business 2018; 21(1): 67-94.
• [6] Gürlük S. Sürdürülebilir kalkınma gelişmekte olan ülkelerde uygulanabilir mi? Eskişehir Osmangazi Üniversitesi İİBF Dergisi 2010; 5(2): 85-99.
• [7] Ibimilua FO. Linkages between poverty and environmental degradation. African Research Review 2011; 5(1): 102-121.
• [8] Neumayer E. The human development index and sustainability-a constructive proposal. Ecological Economics 2001; 39(1): 101-114.
• [9] Ediger VŞ, Huvaz O. Examining the sectoral energy use in Turkish economy (1980–2000) with the help of decomposition analysis. Energy Conversion and Management 2006; 47(6): 732 745.
• [10] Ediger VŞ. Türkiye’nin Sürdürülebilir Enerji Gelişimi. TÜBA, Günce 2009; 39(1): 18-25.
• [11] Batı O. Türkiye’de yenilenebilir enerji kaynaklarının sürdürülebilir kalkınmaya etkisi konusunda bir alan araştırması. Trakya Üniversitesi Sosyal Bilimler Dergisi 2014; 16(2): 27-38.
• [12] Fotis P, Polemis M. Sustainable development, environmental policy and renewable energy use: A dynamic panel data approach. Sustainable Development 2018; 26(6): 726-740.
• [13] Güney T. Renewable energy, non-renewable energy and sustainable development. International Journal of Sustainable Development & World Ecology 2019; 26(5): 389-397.
• [14] Dinçer H, Karakuş H. Yenilenebilir enerjinin sürdürülebilir ekonomik kalkınma üzerindeki etkisi: BRICS ve MINT ülkeleri üzerine karşılaştırmalı bir analiz. ESAM Dergisi 2020; 1(1): 75 99.
• [15] Öymen G. Yenilenebilir enerjinin sürdürülebilirlik üzerindeki rolü. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi 2020; 19(39): 1069-1087.
• [16] Tiba S, Belaid F. Modeling the nexus between sustainable development and renewable energy: The African perspectives. Journal of Economic Surveys 2021; 35(1): 307-329.
• [17] Kahraman C, Kaya İ, Cebi S. A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy 2009; 34(10): 1603-1616.
• [18] Kahraman C, Kaya I. A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications 2010; 37(9): 6270-6281.
• [19] Atmaca E, Basar HB. Evaluation of power plants in Turkey using Analytic Network Process (ANP). Energy 2012; 44(1): 555-563.
• [20] Demirtas O. Evaluating the best renewable energy technology for sustainable energy planning. International Journal of Energy Economics and Policy 2013; 3(4): 23-33.
• [21] Yakıcı Ayan T, Pabuçcu H. Yenilenebilir enerji kaynakları yatırım projelerinin analitik hiyerarşi süreci yöntemi ile değerlendirilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2013; 18(3), 89-110.
• [22] Kabak M, Dağdeviren M. Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Conversion and Management 2014; 79(1): 25-33.
• [23] Şengül Ü, Eren M, Shiraz SE, Gezder V, Şengül AB. Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy 2015; 75(1): 617-625.
• [24] Çelikbilek Y, Tüysüz F. An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources. Energy 2016; 115(3): 1246-1258.
• [25] Sağır H, Doğanalp B. Bulanık çok kriterli karar verme perspektifinden Türkiye için enerji kaynakları değerlendirmesi. Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2016; 11(1): 233-256.
• [26] Balin A, Baraçli H. A fuzzy multi-criteria decision making methodology based upon the interval type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey. Technological and Economic Development of Economy 2015; 23(5): 742-763.
• [27] Büyüközkan G, Güleryüz S. Evaluation of Renewable Energy Resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations. Energy 2017; 123: 149-163.
• [28] Çolak M, Kaya İ. Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey. Renewable and Sustainable Energy Reviews 2017; 80: 840-853.
• [29] Özcan EC, Ünlüsoy S, Eren T. ANP ve TOPSIS yöntemleriyle türkiye’de yenilenebilir enerji yatirim alternatiflerinin değerlendirilmesi. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi 2017; 5(2): 204-219.
• [30] Özkale C, Celik C, Turkmen AC, Cakmaz ES. Decision analysis application intended for selection of a power plant running on renewable energy sources. Renewable and Sustainable Energy Reviews 2017; 70: 1011-1021.
• [31] Boran FE. A new approach for evaluation of renewable energy resources: A case of Turkey. Energy Sources, Part B: Economics, Planning, and Policy 2018; 13(3): 196-204.
• [32] Büyüközkan G, Karabulut Y, Güler M. Strategic Renewable Energy Source Selection for Turkey with Hesitant Fuzzy MCDM Method. In: Kahraman C, Kayakutlu G, editors. Energy Management—Collective and Computational Intelligence with Theory and Applications, vol. 149. Cham: Springer International Publishing; 2018. p. 229-250.
• [33] Karaca C, Ulutaş A. Entropi ve Waspas yöntemleri kullanılarak Türkiye için uygun yenilenebilir enerji kaynağının seçimi. Ege Academic Review 2018; 18(3): 483-494.
• [34] Engin O, Sarucan A, Baysal ME. Türkiye için çok kriterli karar verme yöntemleri ile yenilenebilir enerji alternatiflerinin analizi. International Journal of Social and Humanities Sciences Research (JSHSR) 2018; 5(23): 1223-1231.
• [35] Toklu MC, Taşkın H. A fuzzy hybrid decision model for renewable energy sources selection. International Journal of Computational and Experimental Science and Engineering 2018; 4(1): 6 10.
• [36] Karakaş E, Yıldıran OV. Evaluation of renewable energy alternatives for Turkey via modified fuzzy AHP. International Journal of Energy Economics and Policy 2019; 9(2): 31-39.
• [37] Derse O, Yontar E. SWARA-TOPSIS yöntemi ile en uygun yenilenebilir enerji kaynağinin belirlenmesi. Endüstri Mühendisliği 2020; 31(3): 389-419.
• [38] Yilan G, Kadirgan MN, Çiftçioğlu GA. Analysis of electricity generation options for sustainable energy decision making: The case of Turkey. Renewable Energy 2020; 146: 519-529.
• [39] Solangi YA, Tan Q, Mirjat NH, Valasai GD, Khan MWA, Ikram M. Analyzing renewable energy sources of a developing country for sustainable development: An integrated fuzzy based decision methodology. Processes 2020; 8(7): 825.
• [40] Deveci K, Cin R, Kağızman A. A modified interval valued intuitionistic fuzzy CODAS method and its application to multi-criteria selection among renewable energy alternatives in Turkey. Applied Soft Computing 2020; 96: 106660.
• [41] Karatop B, Taşkan B, Adar E, Kubat C. Decision analysis related to the renewable energy investments in Turkey based on a Fuzzy AHP-EDAS-Fuzzy FMEA approach. Computers & Industrial Engineering 2021; 151: 106958.
• [42] Bilgili F, Zarali F, Ilgün MF, Dumrul C, Dumrul Y. The evaluation of renewable energy alternatives for sustainable development in Turkey using intuitionistic fuzzy-TOPSIS method. Renewable Energy 2022; 189: 1443-1458.
• [43] San Cristóbal JR. Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method. Renewable Energy 2011; 36(2): 498-502.
• [44] Yi SK, Sin HY, Heo E. Selecting sustainable renewable energy source for energy assistance to North Korea. Renewable and Sustainable Energy Reviews 2011; 15(1): 554-563.
• [45] Sadeghi A, Larimian T, Molabashi A. Evaluation of renewable energy sources for generating electricity in province of Yazd: a fuzzy MCDM approach. Procedia-Social and Behavioral Sciences 2012; 62: 1095-1099.
• [46] Mourmouris JC, Potolias C. A multi-criteria methodology for energy planning and developing renewable energy sources at a regional level: A case study Thassos, Greece. Energy Policy 2013; 52: 522-530.
• [47] Stojanović M. Multi-criteria decision-making for selection of renewable energy systems. Safety Engineering 2013; 3(2): 115-120.
• [48] Ahmad S, Tahar RM. Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia. Renewable Energy 2014; 63: 458-466.
• [49] Troldborg M, Heslop S, Hough RL. Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties. Renewable and Sustainable Energy Reviews 2014; 39: 1173-1184.
• [50] Tasri A, Susilawati A. Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments 2014; 7: 34-44.
• [51] Al Garni H, Kassem A, Awasthi A, Komljenovic D, Al-Haddad K. A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable Energy Technologies and Assessments 2016; 16: 137-150.
• [52] Afsordegan A, Sánchez M, Agell N, Zahedi S, Cremades LV. Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives. International Journal of Environmental Science and Technology 2016; 13: 1419-1432.
• [53] Algarín CR, Llanos AP, Castro AO. An analytic hierarchy process based approach for evaluating renewable energy sources. International Journal of Energy Economics and Policy 2017; 7(4): 38-47.
• [54] Ishfaq S, Ali S, Ali Y. Selection of optimum renewable energy source for energy sector in Pakistan by using MCDM approach. Process Integration and Optimization for Sustainability 2018; 2: 61-71.
• [55] Yuan J, Li C, Li W, Liu D, Li X. Linguistic hesitant fuzzy multi-criterion decision-making for renewable energy: A case study in Jilin. Journal of Cleaner Production 2018; 172: 3201-3214.
• [56] Rani P, Mishra AR, Pardasani KR, Mardani A, Liao H, Streimikiene D. A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production 2019; 238: 117936.
• [57] Solangi YA, Tan Q, Mirjat NH, Valasai GD, Khan MWA, Ikram M. An integrated Delphi AHP and fuzzy TOPSIS approach toward ranking and selection of renewable energy resources in Pakistan. Processes 2019; 7(2): 118.
• [58] Rani P, Mishra AR, Mardani A, Cavallaro F, Alrasheedi M, Alrashidi A. A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection. Journal of Cleaner Production 2020; 257: 120352.
• [59] Niu D, Zhen H, Yu M, Wang K, Sun L, Xu X. Prioritization of renewable energy alternatives for China by using a hybrid FMCDM methodology with uncertain information. Sustainability 2020; 12(11): 4649.
• [60] Li X, Zhu S, Yüksel S, Dinçer H, Ubay GG. Kano-based mapping of innovation strategies for renewable energy alternatives using hybrid interval type-2 fuzzy decision-making approach. Energy 2020; 211: 118679.
• [61] Chen T, Wang Y, Wang J, Li L, Cheng PF. Multistage decision framework for the selection of renewable energy sources based on prospect theory and PROMETHEE. International Journal of Fuzzy Systems 2020; 22: 1535-1551.
• [62] Wang Y, Xu L, Solangi YA. Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustainable Cities and Society 2020; 52: 101861.
• [63] Mohammed HJ, Naiyf AT, Thaer AJ, Khbalah SK. Assessment of sustainable renewable energy technologies using analytic hierarchy process. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing 2021; 779(1): 12-38.
• [64] Abdul D, Wenqi J, Tanveer A. Prioritization of renewable energy source for electricity generation through AHP-VIKOR integrated methodology. Renewable Energy 2022; 184: 1018 1032.
• [65] Assadi MR, Ataebi M, Sadat Ataebi E, Hasani A. Prioritization of renewable energy resources based on sustainable management approach using simultaneous evaluation of criteria and alternatives: A case study on Iran’s electricity industry. Renewable Energy 2022; 181: 820-832.
• [66] Goswami SS, Mohanty SK, Behera DK. Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials Today: Proceedings 2022; 52: 1153-1160.
• [67] Li P, Xu Z, Wei C, Bai Q, Liu J. A novel PROMETHEE method based on GRA-DEMA℡ for PLTSs and its application in selecting renewable energies. Information Sciences 2022; 589: 142-161.
• [68] Saaty TL. The analytic hierarchy process (AHP). The Journal of the Operational Research Society 1980; 41(11): 1073-1076.
• [69] Saaty TL. Decision making with the analytic hierarchy process. IJSSCI 2008; 1(1): 83.
• [70] Dhami I, Deng J, Strager M, Conley J. Suitability-sensitivity analysis of nature-based tourism using geographic information systems and analytic hierarchy process. Journal of Ecotourism 2017; 16(1): 41-68.
• [71] Duke JM, Aull-Hyde RA. Identifying public preferences for land preservation using the analytic hierarchy process. Ecological Economics 2002; 42(1-2): 131-145.
• [72] Saaty TL. Theory and applications of the analytic network process: decision making with benefits, opportunities, costs, and risks. RWS Publications 2005.
• [73] Kuru A, Akın B. Entegre yönetim sistemlerinde çok kriterli karar verme tekniklerinin kullanimina yönelik yaklaşımlar ve uygulamaları. Öneri Dergisi 2012; 10(38): 129-144.
• [74] Mastrocinque E, Ramírez FJ, Honrubia-Escribano A, Pham DT. An AHP-based multi-criteria model for sustainable supply chain development in the renewable energy sector. Expert Systems with Applications 2020; 150: 113321.
• [75] Saaty TL, Tran LT. On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process. Mathematical and Computer Modelling 2007; 46(7-8): 962-975.
• [76] Damgacı E, Boran K, Boran FE. Sezgisel bulanık TOPSIS yöntemi kullanarak Türkiye’nin yenilenebilir enerji kaynaklarının değerlendirilmesi. Politeknik Dergisi 2017; 20(3): 629-637.
• [77] Karakul AK. Bulanık AHP yöntemi ile yenilenebilir enerji kaynağı seçimi. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi (BUSBED) 2020; 10(19): 127-150.
• [78] Karaca C, Ulutaş A, Eşgünoğlu M. Türkiye’de optimal yenilenebilir enerji kaynağının COPRAS yöntemiyle tespiti ve yenilenebilir enerji yatırımlarının istihdam artırıcı etkisi. Maliye Dergisi 2017; 172: 111-132.
• [79] Doğan H, Uludağ AS. Yenilenebilir enerji alternatiflerinin değerlendirilmesi ve uygun tesis yeri seçimi: Türkiye’de bir uygulama. Ekonomik ve Sosyal Araştırmalar Dergisi 2018; 14(2): 157 180.
• [80] Karaaslan A, Aydın S. Yenilenebilir enerji kaynaklarının çok kriterli karar verme teknikleri ile değerlendirilmesi: Türkiye örneği. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi 2020; 34(4): 1351-1375.
• [81] Albayrak ÖK. Yenilenebilir enerji kaynaklarının değerlendirilmesinde kullanılan çok kriterli karar verme teknikleri ve değerlendirme kriterlerinin incelenmesi: 2017-2020. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi 2020; 34(4): 1287-1310.
• [82] Alkan Ö, Albayrak ÖK. Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy 2020; 162: 712 726.
• [83] Ulutaş BH. Determination of the appropriate energy policy for Turkey. Energy 2005; 30(7): 1146-1161.
• [84] Kaya T, Kahraman C. Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications 2011; 38(6): 6577-6585.
• [85] Erol Ö, Kılkış B. An energy source policy assessment using analytical hierarchy process. Energy Conversion and Management 2012; 63: 245-252.
• [86] Yücenur GN, Çaylak Ş, Gönül G, Postalcıoğlu M. An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy 2020; 145: 2587-2597.
• [87] Kayakutlu G, Ercan S. Regional Energy Portfolio Construction: Case Studies in Turkey. In: Cucchiella F, Koh L, editors. Sustainable Future Energy Technology and Supply Chains. Cham: Springer International Publishing; 2015. p. 107-126.
• [88] Topcu YI, Ulengin F. Energy for the future: An integrated decision aid for the case of Turkey. Energy 2004; 29(1): 137-154.