Abstract
Energy planning is one of the most important issues affecting the behaviors and policies of different countries at national and international levels. Concerns over the depletion of non-renewable energy sources and the pollution caused by their consumption have led most countries to focus on renewable energy sources. In this regard, governments are trying to develop policies for extensive use of their renewable energy sources. The precise formulation of such policies requires the development of quantitative approaches to assess the potential of the country in this field. Because selecting inappropriate approaches for evaluating the energy resources leads to wrong decisions in planning and policy-making for the whole energy sector and can cause irrecoverable damages to the country. Since energy issues are multi-dimensional in their nature, the development and selection of the appropriate decision-making approach among the available methods have become a critical issue in this field. In order to overcome this problem, this study proposed a hybrid approach by combining data envelopment analysis (DEA) and fuzzy best–worst method (FBWM) for the prioritization of renewable energy sources (RESs) in Iran. For this purpose, it considered five technical, economic, environmental, social, and political sustainability dimensions into account. The obtained results indicated that the solar, hydroelectric, wind, biomass, and geothermal energy sources are respectively the most efficient RESs in Iran. Also, a three-stage sensitivity analysis approach has proved the consistency of DEA-FBWM approach over the other decision-making methods. Consequently, the proposed approach can be used as a reliable method in the energy policy-making process.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abbasi Shureshjani R, Darehmiraki M (2013) A new parametric method for ranking fuzzy numbers. Indag Math 24:518–529. https://doi.org/10.1016/J.INDAG.2013.02.002
Ahmad S, Tahar RM (2014) Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: a case of Malaysia. Renew Energy 63:458–466. https://doi.org/10.1016/J.RENENE.2013.10.001
Ahmed S, Ahmed S, Shumon MRH et al (2016) A comparative decision-making model for sustainable end-of-life vehicle management alternative selection using AHP and extent analysis method on fuzzy AHP. Int J Sustain Dev World Ecol 23:83–97. https://doi.org/10.1080/13504509.2015.1062814
Akash BA, Mamlook R, Mohsen MS (1999) Multi-criteria selection of electric power plants using analytical hierarchy process. Electr Power Syst Res 52:29–35. https://doi.org/10.1016/S0378-7796(99)00004-8
Amigues J-P, Moreaux M (2019) Competing land uses and fossil fuel, and optimal energy conversion rates during the transition toward a green economy under a pollution stock constraint. J Environ Econ Manage. https://doi.org/10.1016/J.JEEM.2019.03.006
Amini A, Nikraz N (2017) A method for constructing non-isosceles triangular fuzzy numbers using frequency histogram and statistical parameters. J Soft Comput Civ Eng 1:65–85. https://doi.org/10.22115/SCCE.2017.48336
Azadeh A, Rahimi-Golkhandan A, Moghaddam M (2014) Location optimization of wind power generation–transmission systems under uncertainty using hierarchical fuzzy DEA: A case study. Renew Sustain Energy Rev 30:877–885. https://doi.org/10.1016/J.RSER.2013.10.020
Azar A, Zarei-MahmoodAbadi M, Anvari-Rostami A (2012) Balanced performance evaluation based on BSC indicators. J Oper Res Its Appl 32:63–79
Badi I, Ballem M (2018) Supplier selection using rough BWM-MAIRCA model: a case study in pharmaceutical supplying in Libya. Decis Mak Appl Manag Eng. https://doi.org/10.31181/dmame1802016b
Badri Ahmadi H, Kusi-Sarpong S, Rezaei J (2017) Assessing the social sustainability of supply chains using Best Worst Method. Resour Conserv Recycl 126:99–106. https://doi.org/10.1016/j.resconrec.2017.07.020
Bahrami M, Abbaszadeh P (2013) An overview of renewable energies in Iran. Renew Sustain Energy Rev 24:198–208. https://doi.org/10.1016/J.RSER.2013.03.043
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30:1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Belyakov N, Belyakov N (2019) Sustainable energy development. Sustain Power Gener. https://doi.org/10.1016/B978-0-12-817012-0.00015-3
Bhowmik C, Bhowmik S, Ray A, Pandey KM (2017) Optimal green energy planning for sustainable development: a review. Renew Sustain Energy Rev 71:796–813. https://doi.org/10.1016/J.RSER.2016.12.105
Bian Y, Song K, Bai J (2019) Market segmentation, resource misallocation and environmental pollution. J Clean Prod 228:376–387. https://doi.org/10.1016/J.JCLEPRO.2019.04.286
Blanco G, Amarilla R, Martinez A et al (2017) Energy transitions and emerging economies: a multi-criteria analysis of policy options for hydropower surplus utilization in Paraguay. Energy Policy 108:312–321. https://doi.org/10.1016/J.ENPOL.2017.06.003
Blokhuis E, Advokaat B, Schaefer W (2012) Assessing the performance of Dutch local energy companies. Energy Policy 45:680–690. https://doi.org/10.1016/J.ENPOL.2012.03.021
Boubaker K (2012) Renewable energy in upper North Africa: Present versus 2025-horizon perspectives optimization using a Data Envelopment Analysis (DEA) framework. Renew Energy 43:364–369. https://doi.org/10.1016/J.RENENE.2011.11.049
Büyüközkan G, Güleryüz S (2016) An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. Int J Prod Econ 182:435–448. https://doi.org/10.1016/J.IJPE.2016.09.015
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chatzimouratidis AI, Pilavachi PA (2008) Sensitivity analysis of the evaluation of power plants impact on the living standard using the analytic hierarchy process. Energy Convers Manag 49:3599–3611. https://doi.org/10.1016/J.ENCONMAN.2008.07.009
Chatzimouratidis AI, Pilavachi PA (2009) Technological, economic and sustainability evaluation of power plants using the Analytic Hierarchy Process. Energy Policy 37:778–787. https://doi.org/10.1016/J.ENPOL.2008.10.009
Chen SH, Wang ST, Chang SM (2006) Some properties of graded mean integration representation of L-R type fuzzy numbers. Tamsui Oxf J Math Sci 22:185
Chen Y, Cook WD, Du J et al (2017) Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China. Ann Oper Res 255:347–366. https://doi.org/10.1007/s10479-015-1827-3
Chou J-S, Ongkowijoyo CS (2014) Risk-based group decision making regarding renewable energy schemes using a stochastic graphical matrix model. Autom Constr 37:98–109. https://doi.org/10.1016/J.AUTCON.2013.10.010
Chou S, Chang Y (2008) A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert Syst Appl 34:2241–2253. https://doi.org/10.1016/j.eswa.2007.03.001
Cook WD (2004) Qualitative data in Dea. Handbook on data envelopment analysis. Kluwer Academic Publishers, Boston, pp 153–175
Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Springer, New York
Demirtas O (2013) Evaluating the best renewable energy technology for sustainable energy planning. Int J Energy Econ Policy 3:23–33
Dombi M, Kuti I, Balogh P (2014) Sustainability assessment of renewable power and heat generation technologies. Energy Policy 67:264–271. https://doi.org/10.1016/J.ENPOL.2013.12.032
Dorini G, Kapelan Z, Azapagic A (2011) Managing uncertainty in multiple-criteria decision making related to sustainability assessment. Clean Technol Environ Policy 13:133–139. https://doi.org/10.1007/s10098-010-0291-7
Evans A, Strezov V, Evans TJ (2009) Assessment of sustainability indicators for renewable energy technologies. Renew Sustain Energy Rev 13:1082–1088. https://doi.org/10.1016/J.RSER.2008.03.008
Garg CP, Sharma A (2018) Sustainable outsourcing partner selection and evaluation using an integrated BWM–VIKOR framework. Environ Dev Sustain. https://doi.org/10.1007/s10668-018-0261-5
Gigović L, Drobnjak S, Pamučar D et al (2019) The application of the hybrid GIS spatial multi-criteria decision analysis best-worst methodology for landslide susceptibility mapping. ISPRS Int J Geo-Inf 8:79. https://doi.org/10.3390/ijgi8020079
Goldrath T, Ayalon O, Shechter M (2015) A combined sustainability index for electricity efficiency measures. Energy Policy 86:574–584. https://doi.org/10.1016/J.ENPOL.2015.08.013
Goumas M, Lygerou V (2000) An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative energy exploitation projects. Eur J Oper Res 123:606–613. https://doi.org/10.1016/S0377-2217(99)00093-4
Grigoroudis E, Petridis K, Arabatzis G (2014) RDEA: a recursive DEA based algorithm for the optimal design of biomass supply chain networks. Renew Energy 71:113–122. https://doi.org/10.1016/j.renene.2014.05.001
Guo S, Zhao H (2017) Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Syst 121:23–31. https://doi.org/10.1016/J.KNOSYS.2017.01.010
Gupta P, Anand S, Gupta H (2017) Developing a roadmap to overcome barriers to energy efficiency in buildings using best-worst multi-criteria decision making methodology. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2017.02.005
Hayat F, Khan AA, Ashraf MA (2019) Energy planning and sustainable development of Pakistan. Int J Energy Sect Manag 13:24–44. https://doi.org/10.1108/IJESM-04-2018-0006
Heinrich G, Basson L, Cohen B et al (2007) Ranking and selection of power expansion alternatives for multiple objectives under uncertainty. Energy 32:2350–2369. https://doi.org/10.1016/J.ENERGY.2007.06.001
Heo E, Kim J, Boo K-J (2010) Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renew Sustain Energy Rev 14:2214–2220. https://doi.org/10.1016/J.RSER.2010.01.020
Herrera-Viedma E, Peis E (2003) Evaluating the informative quality of documents in SGML format from judgements by means of fuzzy linguistic techniques based on computing with words. Inf Process Manag 39:233–249. https://doi.org/10.1016/S0306-4573(02)00049-3
Hua J, Shiu H-G (2018) Sustainable development of renewable energy on Wangan Island. Taiwan. Util Policy 55:200–208. https://doi.org/10.1016/J.JUP.2018.10.003
Iddrisu I, Bhattacharyya SC (2015) Sustainable Energy Development Index: a multi-dimensional indicator for measuring sustainable energy development. Renew Sustain Energy Rev 50:513–530. https://doi.org/10.1016/J.RSER.2015.05.032
Ilbahar E, Cebi S, Kahraman C (2019) A state-of-the-art review on multi-attribute renewable energy decision making. Energy Strateg Rev 25:18–33. https://doi.org/10.1016/J.ESR.2019.04.014
Iribarren D, Martín-Gamboa M, Dufour J (2013) Environmental benchmarking of wind farms according to their operational performance. Energy 61:589–597. https://doi.org/10.1016/J.ENERGY.2013.09.005
Jayaraman R, Colapinto C, La TD, Malik T (2015) Multi-criteria model for sustainable development using goal programming applied to the United Arab Emirates. Energy Policy 87:447–454. https://doi.org/10.1016/J.ENPOL.2015.09.027
Kamran M, Fazal MR, Mudassar M (2019) Towards empowerment of the renewable energy sector in Pakistan for sustainable energy evolution: SWOT analysis. Renew Energy 146:543–558. https://doi.org/10.1016/J.RENENE.2019.06.165
Kan H, Chen R, Tong S (2012) Ambient air pollution, climate change, and population health in China. Environ Int 42:10–19. https://doi.org/10.1016/J.ENVINT.2011.03.003
Kaufmann A, Gupta MM (1991) Introduction to fuzzy arithmetic: theory and applications. Van Nostran Reinho, Co, New York
Kaya İ, Çolak M, Terzi F (2018) Use of MCDM techniques for energy policy and decision-making problems: A review. Int J Energy Res 42:2344–2372. https://doi.org/10.1002/er.4016
Kaya T, Kahraman C (2010) Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy 35:2517–2527. https://doi.org/10.1016/J.ENERGY.2010.02.051
Khatami A, Ghazimatin E (2013) Application of preference ranking organization method for enrichment evaluation method in energy planning—regional level. Iran J Fuzzy Syst 10:67–81
Kim K-T, Lee DJ, Park S-J et al (2015) Measuring the efficiency of the investment for renewable energy in Korea using data envelopment analysis. Renew Sustain Energy Rev 47:694–702. https://doi.org/10.1016/J.RSER.2015.03.034
Koehn PH (2004) Sustainable development frontiers and divides: transnational actors and US/China greenhouse gas emissions. Int J Sustain Dev World Ecol 11:380–396. https://doi.org/10.1080/13504500409469841
Kolagar M (2019) Adherence to urban agriculture in order to reach sustainable cities; a BWM–WASPAS approach. Smart Cities 2:31–45. https://doi.org/10.3390/smartcities2010003
Kowalski K, Stagl S, Madlener R, Omann I (2009) Sustainable energy futures: methodological challenges in combining scenarios and participatory multi-criteria analysis. Eur J Oper Res 197:1063–1074. https://doi.org/10.1016/J.EJOR.2007.12.049
Kumar A, Sah B, Singh AR et al (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev 69:596–609. https://doi.org/10.1016/J.RSER.2016.11.191
Kveselis V, Dzenajavičienė EF, Masaitis S (2017) Analysis of energy development sustainability: the example of the lithuanian district heating sector. Energy Policy 100:227–236. https://doi.org/10.1016/j.enpol.2016.10.019
La Rovere EL, Soares JB, Oliveira LB, Lauria T (2010) Sustainable expansion of electricity sector: sustainability indicators as an instrument to support decision making. Renew Sustain Energy Rev 14:422–429. https://doi.org/10.1016/J.RSER.2009.07.033
Leal Filho W, Azeiteiro U, Alves F et al (2017) Reinvigorating the sustainable development research agenda: the role of the sustainable development goals (SDG). Int J Sustain Dev World Ecol 00:1–12. https://doi.org/10.1080/13504509.2017.1342103
Lee AHI, Kang HY, Lin CY, Shen KC (2015) An integrated decision-making model for the location of a PV solar plant. Sustain 7:13522–13541. https://doi.org/10.3390/su71013522
Lee H-C, Chang C-T (2018) Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renew Sustain Energy Rev 92:883–896. https://doi.org/10.1016/J.RSER.2018.05.007
Lee SK, Mogi G, Hui KS (2013) A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: in the case of energy technologies against high oil prices. Renew Sustain Energy Rev 21:347–355. https://doi.org/10.1016/J.RSER.2012.12.067
Lee Y-S, Tong L-I (2012) Predicting high or low transfer efficiency of photovoltaic systems using a novel hybrid methodology combining rough set theory, data envelopment analysis and genetic programming. Energies 5:545–560. https://doi.org/10.3390/en5030545
Liao M-S, Liang G-S, Chen C-Y (2013) Fuzzy grey relation method for multiple criteria decision-making problems. Qual Quant 47:3065–3077. https://doi.org/10.1007/s11135-012-9704-5
Lin B, Omoju OE, Okonkwo JU (2016) Factors influencing renewable electricity consumption in China. Renew Sustain Energy Rev 55:687–696. https://doi.org/10.1016/J.RSER.2015.11.003
Lin CT, Chen YT (2004) Bid/no-bid decision-making—a fuzzy linguistic approach. Int J Proj Manag 22:585–593. https://doi.org/10.1016/j.ijproman.2004.01.005
Liu G (2014) Development of a general sustainability indicator for renewable energy systems: A review. Renew Sustain Energy Rev 31:611–621. https://doi.org/10.1016/J.RSER.2013.12.038
Liu Y, Ren L, Li Y, Zhao X (2015) The industrial performance of wind power industry in China. Renew Sustain Energy Rev 43:644–655. https://doi.org/10.1016/J.RSER.2014.11.003
Longo L, Colantoni A, Castellucci S et al (2015) DEA (data envelopment analysis)-assisted supporting measures for ground coupled heat pumps implementing in Italy: A case study. Energy 90:1967–1972. https://doi.org/10.1016/J.ENERGY.2015.07.024
Lootsma FA (1980) Saaty’s priority theory and the nomination of a senior professor in operations research. Eur J Oper Res 4:380–388. https://doi.org/10.1016/0377-2217(80)90189-7
Mainali B, Silveira S (2015) Using a sustainability index to assess energy technologies for rural electrification. Renew Sustain Energy Rev 41:1351–1365. https://doi.org/10.1016/J.RSER.2014.09.018
Mardani A, Jusoh A, Zavadskas E et al (2015) Sustainable and renewable energy: an overview of the application of multiple criteria decision making techniques and approaches. Sustainability 7:13947–13984. https://doi.org/10.3390/su71013947
Mardani A, Zavadskas EK, Khalifah Z et al (2017) A review of multi-criteria decision-making applications to solve energy management problems: two decades from 1995 to 2015. Renew Sustain Energy Rev 71:216–256. https://doi.org/10.1016/J.RSER.2016.12.053
Mjachina KV, Baynard CW, Chibilyev AA, Richardson RD (2018) Landscape disturbance caused by non-renewable energy production in a semi-arid region: a case study on the Russian steppe. Int J Sustain Dev World Ecol 25:541–553. https://doi.org/10.1080/13504509.2018.1434569
Moghaddam NB, Nasiri M, Mousavi SM (2011) An appropriate multiple criteria decision making method for solving electricity planning problems, addressing sustainability issue. Int J Environ Sci Technol 8:605–620. https://doi.org/10.1007/BF03326246
Mukhametzyanov I, Pamučar D (2018) A sensitivity analysis in MCDM problems: astatistical approach. Decis Mak Appl Manag Eng. https://doi.org/10.31181/dmame1802050m
Mulliner E, Malys N, Maliene V (2016) Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega 59:146–156. https://doi.org/10.1016/J.OMEGA.2015.05.013
Østergaard PA, Duic N, Noorollahi Y, et al (2019) Sustainable development using renewable energy technology. Renew Energy 146:2430–2437. https://doi.org/10.1016/J.RENENE.2019.08.094
Pamučar DS, Božanić D, Ranđelović A (2017) Multi-criteria decision making: an example of sensitivity analysis. Serbian J Manag 12:1–27. https://doi.org/10.5937/SJM12-9464
Petroleum B (2015) Bp Technology Outlook
Prashar A (2019) Towards sustainable development in industrial small and medium-sized enterprises: an energy sustainability approach. J Clean Prod 235:977–996. https://doi.org/10.1016/J.JCLEPRO.2019.07.045
Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130. https://doi.org/10.1016/J.OMEGA.2015.12.001
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57. https://doi.org/10.1016/J.OMEGA.2014.11.009
Rezaei J, Kothadiya O, Tavasszy L, Kroesen M (2018) Quality assessment of airline baggage handling systems using SERVQUAL and BWM. Tour Manag 66:85–93. https://doi.org/10.1016/J.TOURMAN.2017.11.009
Rezaei J, Nispeling T, Sarkis J, Tavasszy L (2016) A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J Clean Prod 135:577–588. https://doi.org/10.1016/j.jclepro.2016.06.125
Rezaei J, Wang J, Tavasszy L (2015) Linking supplier development to supplier segmentation using Best Worst Method. Expert Syst Appl 42:9152–9164. https://doi.org/10.1016/j.eswa.2015.07.073
Şengül Ü, Eren M, Eslamian Shiraz S et al (2015) Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renew Energy 75:617–625. https://doi.org/10.1016/J.RENENE.2014.10.045
Shafizadeh MA, Amini F, Fattahi LS et al (2017) Energy balance sheet of Iran. Tehran
Shen YC, Lin GTR, Li KP, Yuan BJC (2010) An assessment of exploiting renewable energy sources with concerns of policy and technology. Energy Policy 38:4604–4616. https://doi.org/10.1016/j.enpol.2010.04.016
Stein EW (2013) A comprehensive multi-criteria model to rank electric energy production technologies. Renew Sustain Energy Rev 22:640–654. https://doi.org/10.1016/J.RSER.2013.02.001
Stević Ž, Đalić I, Pamučar D et al (2019) A new hybrid model for quality assessment of scientific conferences based on Rough BWM and SERVQUAL. Scientometrics 119:1–30. https://doi.org/10.1007/s11192-019-03032-z
Sueyoshi T, Goto M (2014) Photovoltaic power stations in Germany and the United States: a comparative study by data envelopment analysis. Energy Econ 42:271–288. https://doi.org/10.1016/J.ENECO.2014.01.004
Suhi SA, Enayet R, Haque T et al (2019) Environmental sustainability assessment in supply chain: an emerging economy context. Environ Impact Assess Rev 79:106306. https://doi.org/10.1016/J.EIAR.2019.106306
Tian Z-P, Zhang H-Y, Wang J-Q, Wang T-L (2018) Green supplier selection using improved TOPSIS and best-worst method under intuitionistic fuzzy environment. INFORMATICA 29:773–800. https://doi.org/10.15388/Informatica.2018.192
Triantaphyllou E, Mann SH (1989) An examination of the effectiveness of multi-dimensional decision-making methods: A decision-making paradox. Decis Support Syst 5:303–312. https://doi.org/10.1016/0167-9236(89)90037-7
Troldborg M, Heslop S, Hough RL (2014) Assessing the sustainability of renewable energy technologies using multi-criteria analysis: suitability of approach for national-scale assessments and associated uncertainties. Renew Sustain Energy Rev 39:1173–1184. https://doi.org/10.1016/J.RSER.2014.07.160
Tsoutsos T, Drandaki M, Frantzeskaki N et al (2009) Sustainable energy planning by using multi-criteria analysis application in the island of Crete. Energy Policy 37:1587–1600. https://doi.org/10.1016/J.ENPOL.2008.12.011
Ueasin N, Wongchai A, Nonthapot S (2015) Performance assessment and optimization of biomass steam turbine power plants by data envelopment analysis. Int J Energy Econ Policy 5:668–672
van de Kaa G, Kamp L, Rezaei J (2017) Selection of biomass thermochemical conversion technology in the Netherlands: a best worst method approach. J Clean Prod 166:32–39. https://doi.org/10.1016/J.JCLEPRO.2017.07.052
van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11:229–241. https://doi.org/10.1016/S0165-0114(83)80082-7
Vera I, Langlois L (2007) Energy indicators for sustainable development. Energy 32:875–882. https://doi.org/10.1016/J.ENERGY.2006.08.006
Vezzoli C, Ceschin F, Osanjo L et al (2018) Energy and sustainable development. Springer, Cham, pp 3–22
Wan Ahmad WNK, Rezaei J, Sadaghiani S, Tavasszy LA (2017) Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. J Clean Prod 153:242–252. https://doi.org/10.1016/J.JCLEPRO.2017.03.166
Wang Y, Hong H, Chen W et al (2018) A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou. China. Remote Sens 11:62. https://doi.org/10.3390/rs11010062
Wojuola RN, Alant BP (2019) Sustainable development and energy education in Nigeria. Renew Energy 139:1366–1374. https://doi.org/10.1016/J.RENENE.2019.03.010
Yokota S, Kumano T (2013) Mega-solar optimal allocation using data envelopment analysis. Electr Eng Japan 183:24–32. https://doi.org/10.1002/eej.22322
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh LA (1975) The concept of a linguistic variable and its applications to approximate reasoning I. Inf Sci (Ny) 8:199–249. https://doi.org/10.1007/978-1-4684-2106-4_1
Zavadskas EK, Govindan K, Antucheviciene J, Turskis Z (2016) Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues. Econ Res Istraživanja 29:857–887. https://doi.org/10.1080/1331677X.2016.1237302
Zhang L, Zhou P, Newton S et al (2015) Evaluating clean energy alternatives for Jiangsu, China: an improved multi-criteria decision making method. Energy 90:953–964. https://doi.org/10.1016/J.ENERGY.2015.07.124
Zhao H, Guo S (2014) Selecting Green Supplier of Thermal Power Equipment by Using a Hybrid MCDM Method for Sustainability. Sustainability 6:217–235. https://doi.org/10.3390/su6010217
Zhao H, Guo S, Zhao H (2018) Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability. Environ Dev Sustain 20:1229–1253. https://doi.org/10.1007/s10668-017-9936-6
Zhao H, Zhao H (2019) Energy Crisis: “Natural Disaster” and “Man-Made Calamity”. Econ Polit China’s Energy Secur Transit. https://doi.org/10.1016/B978-0-12-815152-5.00004-X
Funding
This research received no external funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Appendix
Appendix
See Figs. 10
Alternatives ranking for change in the formulation—scenario 1
, 11
Alternatives ranking for change in the formulation—scenario 2
, 12
Alternatives ranking for change in the formulation—scenario 3
, 13
Alternatives ranking for change in the formulation—scenario 4
, 14
Alternatives ranking for change in the formulation—scenario 5
, 15
Alternatives ranking for change in the formulation—scenario 6
, 16
Alternatives ranking for change in the formulation—scenario 7
.
Rights and permissions
About this article
Cite this article
Kolagar, M., Hosseini, S.M.H., Felegari, R. et al. Policy-making for renewable energy sources in search of sustainable development: a hybrid DEA-FBWM approach. Environ Syst Decis 40, 485–509 (2020). https://doi.org/10.1007/s10669-019-09747-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10669-019-09747-x