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Entropi Tabanlı TOPSIS ile Türkiye Elektrikli Araç Pazarında En İdeal Alternatifin Belirlenmesi

Yıl 2025, Cilt: 9 Sayı: 2, 156 - 184, 27.12.2025
https://doi.org/10.47140/kusbder.1777339

Öz

Bu çalışmanın amacı, Türkiye'de %25 ÖTV diliminde yer alan elektrikli araçların seçiminde tüketiciler tarafından dikkate alınan kriterlerin önceliklendirilmesi ve bu önceliklendirme çerçevesinde piyasada mevcut alternatiflerin sıralandırılmasıdır. Sürdürülebilirlik açısından verimli ve çevresel olan elektrikli araçlar birçok ülke tarafından çeşitli teşviklerle desteklenirken ilgili teknolojinin ilerlemesi, elektrikli araç sektörünü çok hızlı bir şekilde büyütmektedir. Gelecek dönemlere ilişkin sektörel büyüme tahminlerinin çok olumlu olması birçok farklı markanın sektöre yatırım yapmasının ve birçok farklı model geliştirmesinin önünü açarken, ürün yelpazesinin geniş olması ve farklı teknik özellikler, tüketicilerin rasyonel tercihler yapmasını zorlaştırmaktadır. Bu çalışmada 11 farklı kriter dikkate alınarak kriterlerin ağırlıklarının belirlenmesinde Entropi yöntemi, %25 ÖTV dilimindeki 40 elektrikli aracın sıralanmasında ise TOPSIS kullanılmıştır. Bütün alternatiflerin birlikte değerlendirildiği durumda “Citroen E-C3 (Max)” en ideal elektrikli araç olarak tespit edilmiştir. “Maksimum Tork” ise en yüksek ağırlığa sahip faktör olarak tespit edilmiştir. Sonuçların tüketicilerin elektrikli araç seçiminde yol gösterici olması ve otomotiv markaları için pazarlama stratejilerine yönelik bilgi sağlaması açısından katkı sağlayacağı düşünülmektedir.

Kaynakça

  • Adams, W. M. (2003). The Future of Sustainability: Re-thinking Environment and Development in the Twenty-first Century, https://cmsdata.iucn.org/downloads/iucn_future_of_sustanability.pdf.
  • Atmaca, M. (2012), İMKB’de İşlem Gören Spor Şirketlerinin TOPSIS Yöntemi İle Finansal Performans Değerlendirmesi, İktisat, İşletme ve Finans Dergisi, 27(320), 91-108.
  • Anderson, R. (2004), Climbing Mount Sustainability, Quality Progress, 37(2), 32-37.
  • Ayçin, E. (2019). Çok kriterli karar verme: Bilgisayar uygulamalı çözümler. Ankara; Nobel Yayıncılık.
  • Aydın, Ü., Ural, M., & Demireli, E. (2024). Comparative Performance Measurement in the Full Electric Vehicle Market. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(2), 612-632.
  • Biswas, T. K., & Das, M. C. (2019). Selection of commercially available electric vehicle using fuzzy AHP-MABAC. Journal of The Institution of Engineers (India), Series C, 100, 531-537.
  • Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V., & Bacanin, N. (2023). An alternative ranking order method accounting for two-step normalization (AROMAN)—A case study of the electric vehicle selection problem. IEEE Access, 11, 39496-39507.
  • Çoşkun, I. T. (2022). Çok kriterli karar verme teknikleri ile elektrikli otomobil seçimi: SDMULTIMOORA yaklaşımı. Third Sector Social Economic Review, 57(1), 68-82.
  • Çoşkun, İ. T. (2022b). Subjektif ve objektif karar verme teknikleri ile elektrikli araç seçiminde etkili olan kriterlerin değerlendirilmesi. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(2), 173-190.
  • Das, M. C., Pandey, A., Mahato, A. K., & Singh, R. K. (2019). Comparative performance of electric vehicles using evaluation of mixed data. Opsearch, 56, 1067-1090.
  • Dumanoğlu, S. ve Ergül, N. (2010). İMKB’de işlem gören teknoloji şirketlerinin mali performans ölçümü. Muhasebe ve Finansman Dergisi, (48), 101-111.
  • Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916.
  • Erol, I. ve Ferrell, W. (2009). Integrated Approach for Reorganizing Purchasing: Theory and A Case Analysis on A Turkish Company. Computers & Industrial Engineering, 56, 1192-1204.
  • Gavcar, E., & Kara, N. (2020). Elektrikli otomobil seçiminde ENTROPI ve TOPSIS yöntemlerinin uygulanması. İş ve İnsan Dergisi, 7(2), 351-359.
  • Golui, S., Mahapatra, B. S., & Mahapatra, G. S. (2024). A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection. Expert Systems with Applications, 237, 121605.
  • Güler, A. (2024). Türkiye’de Satışa Sunulan Elektrikli Araçların BWM ve LOPCOW Yöntemleriyle Ağırlıklandırılması ve Kriterlerin Sıralanması. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 14(1), 106-120.
  • Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to pay for electric vehicles and their attributes. Resource and Energy Economics, 33(3), 686-705.
  • Hoel, M., & Kverndokk, S. (1996). Depletion of fossil fuels and the impacts of global warming. Resource and Energy Economics, 18(2), 115-136.
  • Hosseini, S. E. (2022). Fossil fuel crisis and global warming. In Fundamentals of Low Emission Flameless Combustion and Its Applications (pp. 1-11). Academic Press.
  • Hoque, M. M., Hannan, M. A., Mohamed, A., & Ayob, A. (2017). Battery charge equalization controller in electric vehicle applications: A review. Renewable and Sustainable Energy Reviews, 75, 1363-1385.
  • Hwang, C.L. and Yoon, K. (1981) Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York.
  • Kane, G. (2010) Three Secrets of Green Business: Unlocking Competitive Advantage in A Low Carbon Economy, London: Routledge.
  • Karami, A. ve Johansson, R. (2014). Utilization of Multi Attribute Decision Making Techniques to Integrate Automatic and Manuel Ranking of Options. Journal of Information Science and Engineering, 30, 519-534.
  • Kizilkaya, Y. M. (2024). ENTROPI Destekli COPRAS Yönteminin Elektrikli Otomobil Seçiminde Uygulanması. Third Sector Social Economic Review, 59(3), 1939-1951.
  • Knez, M., Zevnik, G. K., & Obrecht, M. (2019). A review of available chargers for electric vehicles: United States of America, European Union, and Asia. Renewable and Sustainable Energy Reviews, 109, 284-293.
  • Ozaki, R., & Sevastyanova, K. (2011). Going hybrid: An analysis of consumer purchase motivations. Energy Policy, 39(5), 2217-2227.
  • Oztaysi, B., Kahraman, C., & Onar, S. C. (2022, July). Electric vehicle selection by using fuzzy SMART. In International Conference on Intelligent and Fuzzy Systems (pp. 200-207). Cham: Springer International Publishing.
  • Özdağoğlu, A., Yakut, E. & Bahar, S. (2017). Machine Selection in a Dairy Product Company with Entropy and SAW Methods Integration. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), 341-359.
  • Pane Haden, S., Oyler J. D. & Humphreys, J. H. (2009), “Historical, Practical, and Theoretical Perspectives on Green Management: An Exploratory Analysis”, Management Decision, 47(7), 1041-1055.
  • Phyper, J. D. ve MacLean, P. (2009), “Good to Green: Managing Business Risks and Opportunities in The Age of Environmental Awareness”. Ontario: John Wiley & Sons.
  • Pradhan, P., Shabbiruddin, & Pradhan, S. (2022). Selection of electric vehicle using integrated Fuzzy-MCDM approach with analysis on challenges faced in hilly terrain. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(2), 2651-2673.
  • Puška, A., Božanić, D., Mastilo, Z., & Pamučar, D. (2023). Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars. Soft Computing, 27(11), 7097-7113.
  • Puška, A., Stojanović, I., & Štilić, A. (2023). The influence of objective weight determination methods on electric vehicle selection in urban logistics. Journal of Intelligent Management Decision, 2(3), 117-129.
  • Ruiz, V., Pfrang, A., Kriston, A., Omar, N., Van den Bossche, P., & Boon-Brett, L. (2018). A review of international abuse testing standards and regulations for lithium ion batteries in electric and hybrid electric vehicles. Renewable and Sustainable Energy Reviews, 81, 1427-1452.
  • Sonar, H. C., & Kulkarni, S. D. (2021). An integrated AHP-MABAC approach for electric vehicle selection. Research in Transportation Business & Management, 41, 100665.
  • Štilić, A., Puška, A., Đurić, A., & Božanić, D. (2022). Electric vehicles selection based on Brčko District Taxi service demands, a multi-criteria approach. Urban Science, 6(4), 73.
  • Tepe, S. (2021). The interval-valued spherical fuzzy based methodology and its application to electric car selection. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(5), 1970-1983.
  • Tian, Z. P., Liang, H. M., Nie, R. X., Wang, X. K., & Wang, J. Q. (2023). Data-driven multi-criteria decision support method for electric vehicle selection. Computers & Industrial Engineering, 177, 109061.
  • Tran, D. D., Vafaeipour, M., El Baghdadi, M., Barrero, R., Van Mierlo, J., & Hegazy, O. (2020). Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies. Renewable and Sustainable Energy Reviews, 119, 109596.
  • Wątróbski, J., Małecki, K., Kijewska, K., Iwan, S., Karczmarczyk, A., & Thompson, R. G. (2017). Multi-criteria analysis of electric vans for city logistics. Sustainability, 9(8), 1453.
  • Więckowski J, Kizielewicz B, Shekhovtsov A, Sałabun W (2023) RANCOM: a novel approach to identifying criteria relevance based on inaccuracy expert judgments. Eng Appl Artif Intell 122: 106114.
  • Więckowski, J., Wątróbski, J., Shkurina, A., & Sałabun, W. (2024). Adaptive multi-criteria decision making for electric vehicles: A hybrid approach based on RANCOM and ESP-SPOTIS. Artificial Intelligence Review, 57(10), 270.
  • World Commission on Environment and Development (WCED), (1997) Our Common Future, https://sustainabledevelopment.un.org/content/documents/5987ourcommon-future.pdf.
  • Zhang, H., Gu, C., Gu, L. & Zhang, Y. (2011). The Evaluation of Tourism Destination Competitiveness by TOPSIS & Information Entropy – A Case in the Yangtze River Delta of China. Tourism Management, 32, 443-451.
  • Ziemba, P. (2020). Multi-criteria stochastic selection of electric vehicles for the sustainable development of local government and state administration units in Poland. Energies, 13(23), 6299.
  • Zou, Z., Yun, Y. & Sun, J. (2006). Entropy Method for Determination of Weight of Evaluating Indicators in Fuzzy Synthetic Evaluation for Water Quality Assessment. Journal of Environmental Sciences, 18(5), 1020-1023.
  • https://www.statista.com/outlook/mmo/electric-vehicles/worldwide

Determination of the Most Ideal Alternative in the Turkish Electric Vehicle Market with Entropy Based TOPSIS

Yıl 2025, Cilt: 9 Sayı: 2, 156 - 184, 27.12.2025
https://doi.org/10.47140/kusbder.1777339

Öz

The aim of this study is to prioritize the criteria considered by consumers in Türkiye when selecting electric vehicles within the 25% Special Consumption Tax (SCT) bracket and to rank available alternatives within this prioritization framework. Sustainably efficient and environmentally friendly electric vehicles are supported by various incentives in many countries, while the advancement of relevant technology is rapidly expanding the electric vehicle sector. While very positive sectoral growth forecasts for the future have paved the way for many different brands to invest in the sector and develop various models, the wide product range and diverse technical features make it difficult for consumers to make rational choices. In this study, the Entropy method was used to determine the criteria weights by considering 11 different criteria, and TOPSIS was used to rank 40 electric vehicles within the 25% Special Consumption Tax (SCT) bracket. When all alternatives were evaluated together, the Citroen E-C3 (Max) was identified as the ideal electric vehicle. Maximum Torque was determined as the factor with the highest weight. It is thought that the results will contribute to guiding consumers in choosing electric vehicles and providing information on marketing strategies for automotive brands.

Kaynakça

  • Adams, W. M. (2003). The Future of Sustainability: Re-thinking Environment and Development in the Twenty-first Century, https://cmsdata.iucn.org/downloads/iucn_future_of_sustanability.pdf.
  • Atmaca, M. (2012), İMKB’de İşlem Gören Spor Şirketlerinin TOPSIS Yöntemi İle Finansal Performans Değerlendirmesi, İktisat, İşletme ve Finans Dergisi, 27(320), 91-108.
  • Anderson, R. (2004), Climbing Mount Sustainability, Quality Progress, 37(2), 32-37.
  • Ayçin, E. (2019). Çok kriterli karar verme: Bilgisayar uygulamalı çözümler. Ankara; Nobel Yayıncılık.
  • Aydın, Ü., Ural, M., & Demireli, E. (2024). Comparative Performance Measurement in the Full Electric Vehicle Market. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(2), 612-632.
  • Biswas, T. K., & Das, M. C. (2019). Selection of commercially available electric vehicle using fuzzy AHP-MABAC. Journal of The Institution of Engineers (India), Series C, 100, 531-537.
  • Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V., & Bacanin, N. (2023). An alternative ranking order method accounting for two-step normalization (AROMAN)—A case study of the electric vehicle selection problem. IEEE Access, 11, 39496-39507.
  • Çoşkun, I. T. (2022). Çok kriterli karar verme teknikleri ile elektrikli otomobil seçimi: SDMULTIMOORA yaklaşımı. Third Sector Social Economic Review, 57(1), 68-82.
  • Çoşkun, İ. T. (2022b). Subjektif ve objektif karar verme teknikleri ile elektrikli araç seçiminde etkili olan kriterlerin değerlendirilmesi. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(2), 173-190.
  • Das, M. C., Pandey, A., Mahato, A. K., & Singh, R. K. (2019). Comparative performance of electric vehicles using evaluation of mixed data. Opsearch, 56, 1067-1090.
  • Dumanoğlu, S. ve Ergül, N. (2010). İMKB’de işlem gören teknoloji şirketlerinin mali performans ölçümü. Muhasebe ve Finansman Dergisi, (48), 101-111.
  • Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916.
  • Erol, I. ve Ferrell, W. (2009). Integrated Approach for Reorganizing Purchasing: Theory and A Case Analysis on A Turkish Company. Computers & Industrial Engineering, 56, 1192-1204.
  • Gavcar, E., & Kara, N. (2020). Elektrikli otomobil seçiminde ENTROPI ve TOPSIS yöntemlerinin uygulanması. İş ve İnsan Dergisi, 7(2), 351-359.
  • Golui, S., Mahapatra, B. S., & Mahapatra, G. S. (2024). A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection. Expert Systems with Applications, 237, 121605.
  • Güler, A. (2024). Türkiye’de Satışa Sunulan Elektrikli Araçların BWM ve LOPCOW Yöntemleriyle Ağırlıklandırılması ve Kriterlerin Sıralanması. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 14(1), 106-120.
  • Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to pay for electric vehicles and their attributes. Resource and Energy Economics, 33(3), 686-705.
  • Hoel, M., & Kverndokk, S. (1996). Depletion of fossil fuels and the impacts of global warming. Resource and Energy Economics, 18(2), 115-136.
  • Hosseini, S. E. (2022). Fossil fuel crisis and global warming. In Fundamentals of Low Emission Flameless Combustion and Its Applications (pp. 1-11). Academic Press.
  • Hoque, M. M., Hannan, M. A., Mohamed, A., & Ayob, A. (2017). Battery charge equalization controller in electric vehicle applications: A review. Renewable and Sustainable Energy Reviews, 75, 1363-1385.
  • Hwang, C.L. and Yoon, K. (1981) Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York.
  • Kane, G. (2010) Three Secrets of Green Business: Unlocking Competitive Advantage in A Low Carbon Economy, London: Routledge.
  • Karami, A. ve Johansson, R. (2014). Utilization of Multi Attribute Decision Making Techniques to Integrate Automatic and Manuel Ranking of Options. Journal of Information Science and Engineering, 30, 519-534.
  • Kizilkaya, Y. M. (2024). ENTROPI Destekli COPRAS Yönteminin Elektrikli Otomobil Seçiminde Uygulanması. Third Sector Social Economic Review, 59(3), 1939-1951.
  • Knez, M., Zevnik, G. K., & Obrecht, M. (2019). A review of available chargers for electric vehicles: United States of America, European Union, and Asia. Renewable and Sustainable Energy Reviews, 109, 284-293.
  • Ozaki, R., & Sevastyanova, K. (2011). Going hybrid: An analysis of consumer purchase motivations. Energy Policy, 39(5), 2217-2227.
  • Oztaysi, B., Kahraman, C., & Onar, S. C. (2022, July). Electric vehicle selection by using fuzzy SMART. In International Conference on Intelligent and Fuzzy Systems (pp. 200-207). Cham: Springer International Publishing.
  • Özdağoğlu, A., Yakut, E. & Bahar, S. (2017). Machine Selection in a Dairy Product Company with Entropy and SAW Methods Integration. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), 341-359.
  • Pane Haden, S., Oyler J. D. & Humphreys, J. H. (2009), “Historical, Practical, and Theoretical Perspectives on Green Management: An Exploratory Analysis”, Management Decision, 47(7), 1041-1055.
  • Phyper, J. D. ve MacLean, P. (2009), “Good to Green: Managing Business Risks and Opportunities in The Age of Environmental Awareness”. Ontario: John Wiley & Sons.
  • Pradhan, P., Shabbiruddin, & Pradhan, S. (2022). Selection of electric vehicle using integrated Fuzzy-MCDM approach with analysis on challenges faced in hilly terrain. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(2), 2651-2673.
  • Puška, A., Božanić, D., Mastilo, Z., & Pamučar, D. (2023). Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars. Soft Computing, 27(11), 7097-7113.
  • Puška, A., Stojanović, I., & Štilić, A. (2023). The influence of objective weight determination methods on electric vehicle selection in urban logistics. Journal of Intelligent Management Decision, 2(3), 117-129.
  • Ruiz, V., Pfrang, A., Kriston, A., Omar, N., Van den Bossche, P., & Boon-Brett, L. (2018). A review of international abuse testing standards and regulations for lithium ion batteries in electric and hybrid electric vehicles. Renewable and Sustainable Energy Reviews, 81, 1427-1452.
  • Sonar, H. C., & Kulkarni, S. D. (2021). An integrated AHP-MABAC approach for electric vehicle selection. Research in Transportation Business & Management, 41, 100665.
  • Štilić, A., Puška, A., Đurić, A., & Božanić, D. (2022). Electric vehicles selection based on Brčko District Taxi service demands, a multi-criteria approach. Urban Science, 6(4), 73.
  • Tepe, S. (2021). The interval-valued spherical fuzzy based methodology and its application to electric car selection. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(5), 1970-1983.
  • Tian, Z. P., Liang, H. M., Nie, R. X., Wang, X. K., & Wang, J. Q. (2023). Data-driven multi-criteria decision support method for electric vehicle selection. Computers & Industrial Engineering, 177, 109061.
  • Tran, D. D., Vafaeipour, M., El Baghdadi, M., Barrero, R., Van Mierlo, J., & Hegazy, O. (2020). Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies. Renewable and Sustainable Energy Reviews, 119, 109596.
  • Wątróbski, J., Małecki, K., Kijewska, K., Iwan, S., Karczmarczyk, A., & Thompson, R. G. (2017). Multi-criteria analysis of electric vans for city logistics. Sustainability, 9(8), 1453.
  • Więckowski J, Kizielewicz B, Shekhovtsov A, Sałabun W (2023) RANCOM: a novel approach to identifying criteria relevance based on inaccuracy expert judgments. Eng Appl Artif Intell 122: 106114.
  • Więckowski, J., Wątróbski, J., Shkurina, A., & Sałabun, W. (2024). Adaptive multi-criteria decision making for electric vehicles: A hybrid approach based on RANCOM and ESP-SPOTIS. Artificial Intelligence Review, 57(10), 270.
  • World Commission on Environment and Development (WCED), (1997) Our Common Future, https://sustainabledevelopment.un.org/content/documents/5987ourcommon-future.pdf.
  • Zhang, H., Gu, C., Gu, L. & Zhang, Y. (2011). The Evaluation of Tourism Destination Competitiveness by TOPSIS & Information Entropy – A Case in the Yangtze River Delta of China. Tourism Management, 32, 443-451.
  • Ziemba, P. (2020). Multi-criteria stochastic selection of electric vehicles for the sustainable development of local government and state administration units in Poland. Energies, 13(23), 6299.
  • Zou, Z., Yun, Y. & Sun, J. (2006). Entropy Method for Determination of Weight of Evaluating Indicators in Fuzzy Synthetic Evaluation for Water Quality Assessment. Journal of Environmental Sciences, 18(5), 1020-1023.
  • https://www.statista.com/outlook/mmo/electric-vehicles/worldwide
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Çağatay Orçun 0000-0001-7413-6099

Oytun Sezgin 0000-0002-6671-8053

Gönderilme Tarihi 3 Eylül 2025
Kabul Tarihi 3 Kasım 2025
Yayımlanma Tarihi 27 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 2

Kaynak Göster

APA Orçun, Ç., & Sezgin, O. (2025). Entropi Tabanlı TOPSIS ile Türkiye Elektrikli Araç Pazarında En İdeal Alternatifin Belirlenmesi. Kırklareli Üniversitesi Sosyal Bilimler Dergisi, 9(2), 156-184. https://doi.org/10.47140/kusbder.1777339