Document Type : Original Article
Author
Osmaniye Korkut Ata University, Turkey.
Abstract
The article presents an MCDM model based on the Improved Entropy and PIV methods to analyze the development of renewable energy in Nordic-Baltic countries. The analysis was conducted on eight alternatives and ten criteria, and sensitivity analysis was applied to assess the model's suitability. The impact of 34 different variations in criterion weights on the results was examined. The findings demonstrate that Norway emerges as the most appropriate alternative, and the smallest weight change required to alter the current ranking is 18.93%.
Keywords
Main Subjects
[1] Olabi, A. G. and Abdelkareem, M. A. (2022). Renewable energy and climate change. Renewable and Sustainable Energy Reviews, Vol. 158, pp. 1-7.
[2] Owusu, P. A. and Asumadu-Sarkodie, S. (2016). A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Engineering, Vol. 3, No. 1, pp. 1-14.
[3] Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., ... von Stechow, C. (2011). Renewable Energy Sources and Climate Change Mitigation. Cambridge: Cambridge University Press.
[4] Sterpu, M., Soava, G., and Mehedintu, A. (2018). Impact of economic growth and energy consumption on greenhouse gas emissions: Testing environmental curves hypotheses on EU countries. Sustainability, Vol. 10, No. 9, pp. 1-14.
[5] Khan, R. (2020). Agricultural production and CO2 emissions causes in the developing and developed countries: New insights from quantile regression and decomposition analysis. bioRxiv, Vol. 2020, pp. 1-30.
[6] Chien, F., Hsu, C. C., Ozturk, I., Sharif, A., and Sadiq, M. (2022). The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: Evidence from advance panel estimations. Renewable Energy, Vol. 186, pp. 207-216.
[7] Sadik-Zada, E. R. and Gatto, A. (2021). Energy security pathways in South East Europe: Diversification of the natural gas supplies, energy transition, and energy futures. From Economic to Energy Transition: Three Decades of Transitions in Central and Eastern Europe, pp. 491-514.
[8] Hailemariam, A., Ivanovski, K., and Dzhumashev, R. (2022). Does R&D investment in renewable energy technologies reduce greenhouse gas emissions? Applied Energy, Vol. 327, pp. 1-9.
[9] Gökgöz, F. and Yalçın, E. (2021). Analyzing the Renewable Energy Sources of Nordic and Baltic Countries with MCDM Approach. In: Sustainable Engineering for Life Tomorrow, Jacqueline A. Stagner and David S. K. Ting (Ed). Lexington Books, 203-220.
[10] Gernaat, D. E. H. J., de Boer, H. S., Daioglou, V., Yalew, S. G., Müller, C., and van Vuuren, D. P. (2021). Climate change impacts on renewable energy supply. Nature Climate Change, Vol. 11, No. 2, pp. 119–125.
[11] Wang, B., Wang, Q., Wei, Y. M., and Li, Z. P. (2018). Role of renewable energy in China’s energy security and climate change mitigation: An index decomposition analysis. Renewable and sustainable energy reviews, Vol. 90, pp. 187-194.
[12] Ogonowski, P. (2021). Application of VMCM, to assess of renewable energy impact in European Union Countries. Procedia Computer Science, Vol. 192, pp. 4762-4769.
[13] Yadav, G. P. K., Bandhu, D., Reddy, K. J., Reddy, R. M., Srinivasu, C., and Katam, G. B. (2023). Performance Evaluation of a Thermal Barrier-Coated CI Engine using Waste Oil Biodiesel Blends. Renewable Energy Research and Applications.
[14] Murali Mohan, M., Venugopal Goud, E., Deva Kumar, M. L. S., Kumar, V., Kumar, M., & Dinbandhu. (2021). Parametric optimization and evaluation of machining performance for aluminium-based hybrid composite using utility-Taguchi approach. In Recent Advances in Smart Manufacturing and Materials: Select Proceedings of ICEM 2020 (pp. 289-300). Springer Singapore.
[15] Dinbandhu and Abhishek, K. (2020, November). Parametric optimization and evaluation of RMDTM welding performance for ASTM A387 Grade 11 steel plates using TOPSIS-Taguchi approach. In International Conference on Advances in Materials Processing & Manufacturing Applications (pp. 215-227). Singapore: Springer Singapore.
[16] Sadik-Zada, E. R. and Gatto, A. (2023). Civic engagement and energy transition in the Nordic-Baltic Sea Region: Parametric and nonparametric inquiries. Socio-Economic Planning Sciences, Vol. 87, pp. 1-9.
[17] Siksnelyte, I., Zavadskas, E. K., Bausys, R., and Streimikiene, D. (2019). Implementation of EU energy policy priorities in the Baltic Sea Region countries: Sustainability assessment based on neutrosophic MULTIMOORA method. Energy policy, 125, 90-102.
[18] Peng, X., Huang, H. H., and Luo, Z. (2023). Fuzzy dynamic MCDM method based on PRSRV for financial risk evaluation of new energy vehicle industry. Applied Soft Computing, Vol. 136, pp. 1-22.
[19] Radomski, B. and Mróz, T. (2023). Application of the Hybrid MCDM Method for Energy Modernisation of an Existing Public Building—A Case Study. Energies, Vol. 16, No. 8, pp. 1-18.
[20] Brodny, J. and Tutak, M. (2023). Assessing the energy security of European Union countries from two perspectives–A new integrated approach based on MCDM methods. Applied Energy, Vol. 347, pp. 1-26.
[21] Bhowmik, C., Dhar, S., and Ray, A. (2019). Comparative analysis of MCDM methods for the evaluation of optimum green energy sources: A case study. International Journal of Decision Support System Technology (IJDSST), Vol. 11, No. 4, pp. 1-28.
[22] Narayanamoorthy, S., Parthasarathy, T. N., Pragathi, S., Shanmugam, P., Baleanu, D., Ahmadian, A., and Kang, D. (2022). The novel augmented Fermatean MCDM perspectives for identifying the optimal renewable energy power plant location. Sustainable Energy Technologies and Assessments, Vol. 53, pp. 1-11.
[23] Liu, R., Sun, H., Zhang, L., Zhuang, Q., Zhang, L., Zhang, X. and Chen, Y. (2018). Low-carbon energy planning: A hybrid MCDM method combining DANP and VIKOR approach. Energies, Vol. 11, No. 12, pp. 1-18.
[24] Alizadeh, R., Soltanisehat, L., Lund, P. D., and Zamanisabzi, H. (2020). Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy, Vol. 137, pp.1-17.
[25] Supriyasilp, T., Pongput, K., and Boonyasirikul, T. (2009). Hydropower development priority using MCDM method. Energy Policy, Vol. 37, No. 5, pp. 1866-1875.
[26] Kamali Saraji, M., Streimikiene, D., and Ciegis, R. (2022). A novel Pythagorean fuzzy-SWARA-TOPSIS framework for evaluating the EU progress towards sustainable energy development. Environmental monitoring and assessment, Vol. 194, No. 1, pp. 1-19.
[27] Tutak, M. (2021). Using MCDM methods to assess the extent to which the European union countries use renewable energy. Multidisciplinary Aspects of Production Engineering, Vol. 4, No. 1, pp. 190-199.
[28] Suharevska, K. and Blumberga, D. (2019). Progress in renewable energy technologies: innovation potential in Latvia. Environmental and Climate Technologies, Vol. 23, No. 2, pp. 47-63.
[29] Balezentis, T., Siksnelyte-Butkiene, I., and Streimikiene, D. (2021). Stakeholder involvement for sustainable energy development based on uncertain group decision making: Prioritizing the renewable energy heating technologies and the BWM-WASPAS-IN approach. Sustainable Cities and Society, Vol. 73, pp. 1-11.
[30] Piwowarski, M., Borawski, M., & Nermend, K. (2021). The problem of non-typical objects in the multidimensional comparative analysis of the level of renewable energy development. Energies, Vol. 14, No. 18, pp. 1-24.
[31] Bąk, I., Spoz, A., Zioło, M., and Dylewski, M. (2021). Dynamic analysis of the similarity of objects in research on the use of renewable energy resources in European Union countries. Energies, Vol. 14, No. 13, pp. 1-24.
[32] Pak, K. B., Albayrak, Y. E., and Erensal, Y. C. (2014). Renewable Energy Perspective for Turkey Using Sustainability Indicators. International Journal of Computational Intelligence Systems, Vol. 8, No. 1, pp. 187–197.
[33] Hasheminasab, H., Streimikiene, D., and Pishahang, M. (2023). A novel energy poverty evaluation: Study of the European Union countries. Energy, Vol. 264, pp. 1-9.
[34] Siksnelyte-Butkiene, I., Zavadskas, E. K., and Streimikiene, D. (2020). Multi-criteria decision-making (MCDM) for the assessment of renewable energy technologies in a household: A review. Energies, Vol.13, No. 5, pp. 1-22.
[35] Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., and Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, Vol. 69, pp. 596-609.
[36] Lak Kamari, M., Isvand, H., and Alhuyi Nazari, M. (2020). Applications of multi-criteria decision-making (MCDM) methods in renewable energy development: A review. Renewable Energy Research and Applications, Vol. 1, No. 1, pp. 47-54.
[37] Brodny, J. and Tutak, M. (2021a). The comparative assessment of sustainable energy security in the Visegrad countries. A 10-year perspective. Journal of Cleaner Production, 317, 128427.
[38] Ture, H., Dogan, S., and Kocak, D. (2019). Assessing Euro 2020 strategy using multi-criteria decision-making methods: VIKOR and TOPSIS. Social Indicators Research, 142, 645-665.
[39] Brodny, J. and Tutak, M. (2021b). Assessing sustainable energy development in the central and eastern European countries and analyzing its diversity. Science of the Total Environment, 801, 149745.
[40] Chudy-Laskowska, K., Pisula, T., Liana, M., and Vasa, L. (2020). Taxonomic analysis of the diversity in the level of wind energy development in European Union countries. Energies, 13(17), 4371.
[41] Stanujkic, D., Popovic, G., Zavadskas, E. K., Karabasevic, D., and Binkyte-Veliene, A. (2020). Assessment of progress towards achieving Sustainable Development Goals of the “Agenda 2030” by using the CoCoSo and the Shannon Entropy methods: The case of the EU Countries. Sustainability, 12(14), 5717.
[42] Su, W., Zhang, D., Zhang, C., and Streimikiene, D. (2020). Sustainability assessment of energy sector development in China and European Union. Sustainable Development, 28(5), 1063-1076.
[43] Tutak, M. and Brodny, J. (2022). Analysis of the level of energy security in the three seas initiative countries. Applied Energy, 311, 118649.
[44] Wang, C. N., Le, T. Q., Chang, K. H., and Dang, T. T. (2022). Measuring road transport sustainability using MCDM-based entropy objective weighting method. Symmetry, 14(5), 1033.
[45] Wang, T. C. and Lee, H. D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, Vol. 36, pp. 8980-8985.
[46] Zhang, X., Wang, C., Li, E., and Xu, C. (2014). Assessment model of eco-environmental vulnerability based on improved entropy weight method. The Scientific World Journal, Vol. 2014, pp. 1-7.
[47] Goswami, S. S., Mohanty, S. K., and Behera, D. K. (2022). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials today: proceedings, 52, 1153-1160.
[48] Zamiela, C., Hossain, N. U. I., and Jaradat, R. (2022). Enablers of resilience in the healthcare supply chain: A case study of US healthcare industry during COVID-19 pandemic. Research in Transportation Economics, 93, 101174.
[49] Mufazzal, S. and Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, Vol. 119, pp. 427-438.
[50] Eurostat (2020), Available: https://ec.europa.eu/eurostat/web/main/data/database.
[51] Ersoy, N. and Taslak, S. (2023). Comparative Analysis of MCDM Methods for the Assessment of Corporate Sustainability Performance in Energy Sector. Ege Academic Review, 23(3), 341-362.
[52] Bingol, S. (2022). Selection of Semiconductor Packaging Materials by Combined Fuzzy AHP-Entropy and Proximity Index Value Method. Mathematical Problems in Engineering, 2022.
[53] Yahya, S. M., Asjad, M., and Khan, Z. A. (2019). Multi-response optimization of TiO2/EG-water nano-coolant using entropy based preference indexed value (PIV) method. Materials Research Express, 6(8), 0850a1.
[54] Trung, D. D. (2021a). The combination of Taguchi–Entropy–WASPAS–PIV methods for multi-criteria decision making when external cylindrical grinding of 65G steel. Journal of Machine Engineering, 21(4), 90-105.
[55] Trung, D. D. (2021b). Application of TOPSIS and PIV methods for multi-criteria decision making in hard turning process. Journal of Machine Engineering, 21(4), 57-71.
[56] Markatos, D. N., Malefaki, S., and Pantelakis, S. G. (2023). Sensitivity Analysis of a Hybrid MCDM Model for Sustainability Assessment—An Example from the Aviation Industry. Aerospace, Vol. 10, No. 4, pp. 1-17.
[57] Yagmahan, B. and Yılmaz, H. (2023). An integrated ranking approach based on group multi-criteria decision making and sensitivity analysis to evaluate charging stations under sustainability. Environment, Development and Sustainability, vol. 25, no. 1, pp. 96-121.
[58] Zakeri, S., Chatterjee, P., Konstantas, D., and Ecer, F. (2023). A decision analysis model for material selection using simple ranking process. Scientific Reports, Vol. 13, No. 1, p. 8631.
[59] Garg, C. P., Görçün, Ö. F., Kundu, P., and Küçükönder, H. (2023). An integrated fuzzy MCDM approach based on Bonferroni functions for selection and evaluation of industrial robots for the automobile manufacturing industry. Expert Systems with Applications, Vol. 213, pp. 1-22.
[60] Ecer, F. and Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of cleaner production, Vol. 266, pp. 1-18.
[61] Kumar, G. and Parimala, N. (2019). A sensitivity analysis on weight sum method MCDM approach for product recommendation. In Distributed Computing and Internet Technology: 15th International Conference, ICDCIT 2019, Bhubaneswar, India, January 10–13, 2019, Proceedings 15 (pp. 185-193). Springer International Publishing.
)