Döngüsel Ekonomide İlerlemenin Belirleyicileri: AB Üye Ülkeleri ve Türkiye'nin Üretim ve Tüketim Göstergeleri Özelinde Karşılaştırmalı Çok Kriterli Analizi

Öz

Küresel nüfus artmaya devam ettikçe, üretim ve tüketim seviyelerinde ve bu bağlamda atık yaratımında bir artışın beklenmesi mümkündür. Günümüzde sürdürülebilir kalkınmayı ve döngüsel ekonomiye geçişi sağlamak amacıyla, geleneksel doğrusal ekonomi modelinden uzaklaşan ve değerli kaynakların ekonomik sistem içine yeniden entegrasyonunu vurgulayarak atık yönetimi uygulamalarına öncelik veren bir paradigma değişimi yaşanmaktadır. Öte yandan bu geçiş, üretim ve tüketim çerçevelerinde önemli değişiklikler gerektirmekte olup, bu bağlamda Avrupa Birliği, materyal ayak izi, kaynak verimliliği ve kişi başına atık üretimi gibi kriterleri içeren sekiz gösterge bağlamında ülkelerin üretim ve tüketim açısından döngüsel ekonomi performansını değerlendirmektedir. Bu çalışmada AB ülkeleri ve Türkiye'nin mevcut durumu, iki yöntem içeren karşılaştırmalı çok kriterli karar verme analizi ile ele alınmıştır. Elde edilen sonuçlara göre, Hırvatistan her iki analizde çoğunlukla sırlamanın en üstünde yer almış, ardından Letonya ve Slovakya gelmiştir. Ele alınan dönem dahilindeki bazı yıllarda, Çekya, Hollanda ve İspanya gibi ülkelerin dikkate değer performans gösterdikleri görülmüştür. Diğer yandan, Türkiye’nin çoğunlukla 10. sıraya yakın bir pozisyonda yer alarak orta seviyede bir performans sergilediği görülmüştür.

Anahtar Kelimeler

• Döngüsel Ekonomi
• Üretim ve Tüketim Göstergeleri
• Sürdürülebilir Kalkınma
• ÇKKV
• CoCoSo
• WASPAS

DETERMINANTS OF PROGRESS IN CIRCULAR ECONOMY: A COMPARATIVE MULTI-CRITERIA ANALYSIS OF EU MEMBER STATES AND TÜRKİYE WITH A SPECIAL FOCUS ON PRODUCTION AND CONSUMPTION INDICATORS

Öz

As the global population continues to grow, it is reasonable to anticipate a rise in production and consumption levels, leading to increased waste generation. With the aim of achieving sustainable development and transitioning towards a circular economy (CE), nowadays, there has been a paradigm shift away from the traditional linear economic model towards prioritizing waste management practices that emphasize the reintegration of valuable resources into the economic system. In this sense, transitioning to a CE requires substantial changes in production and consumption frameworks, prompting the EU to incorporate eight relevant criteria, including material footprint, resource productivity, and waste generation per capita, to assess countries' progress towards CE from the production and consumption perspective. To ascertain the current status of EU member states and Türkiye, this study conducts a comparative multi-criteria analysis. The results indicate that Croatia consistently ranked at the top in both analyses, followed by Latvia and Slovakia. In certain years, countries like Czechia, the Netherlands, and Spain demonstrated notable performances. On the other hand, Türkiye showed a moderate performance from 2008 to 2020, invariably hovering around the 10th position throughout much of the period.

Anahtar Kelimeler

• Circular economy
• Production and Consumption Indicators
• Sustainable Development
• MCDM
• CoCoSo
• WASPAS

Kaynakça

1. Agovino, M., Cerciello, M., Musella, G., & Garofalo, A. (2024). European waste management regulations and the transition towards circular economy. A shift-and-share analysis . Journal of Environmental Management, 354, 120423. DOI: https://doi.org/10.1016/j.jenvman.2024.120423.
2. Alrasheedi, M., Mardani, A., Mishra, A. R., Rani, P., & Loganathan, N. (2022). An extended framework to evaluate sustainable suppliers in manufacturing companies using a new Pythagorean fuzzy entropy-SWARA-WASPAS decision-making approach. Journal of Enterprise Information Management, 35(2), 333-357. DOI: https://doi.org/10.1108/JEIM-07-2020-0263.
3. Amicarelli, V. & Bux, C. (2021). Food waste measurement toward a fair, healthy and environmental-friendly food system: a critical review . British Food Journal, 123(8), 2907-2935. DOI: https://doi.org/10.1108/BFJ-07-2020-0658.
4. Arion, F. H., Aleksanyan, V., Markosyan, D., & Arion, I. D. (2023). Circular pathways to sustainable development: understanding the links between circular economy indicators, economic growth, social well-being, and environmental performance in EU-27. Sustainability, 15(24), 16883. DOI: https://doi.org/10.3390/su152416883.
5. Bączkiewicz, A., & Wątróbski, J. (2022). A multi-criteria approach to sustainable energy management evaluation focusing on renewable energy sources. Procedia Computer Science, 207, 4640-4650.. DOI: https://doi.org/10.1016/j.procs.2022.09.528.
6. Baç, U. (2020). An integrated SWARA-WASPAS group decision making framework to evaluate smart card systems for public transportation. Mathematics, 8(10), 1723. DOI: https://doi.org/ 10.3390/math8101723.
7. Balezentis, T., Siksnelyte-Butkiene, I., & 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, 73, 103114. DOI: https://doi.org/10.1016/j.scs.2021.103114.
8. Baptista, M. L. (2021). Circular economy in plastic waste-Analysis of resource and energy productivity. https://www.proquest.com/docview/2652593945?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses (Access Date: 16/03/2024).

9. Baušys, R., & Juodagalvienė, B. (2017). Garage location selection for residential house by WASPAS-SVNS method. Journal of civil engineering and management, 23(3), 421-429. DOI: https://doi.org/ 10.3846/13923730.2016.1268645.
10. Bektaş, S. (2022). Türk sigorta sektörünün 2002-2021 dönemi için MEREC, LOPCOW, COCOSO, EDAS ÇKKV yöntemleri ile performansının değerlendrilmesi. BDDK Bankacılık ve Finansal Piyasalar Dergisi, 16(2), 247-283. DOI: https://doi.org/10.46520/bddkdergisi.1178359.
11. Bigdeloo, M., Teymourian, T., Kowsari, E., Ramakrishna, S., & Ehsani, A. (2021). Sustainability and circular economy of food wastes: Waste reduction strategies, higher recycling methods, and improved valorization. Materials Circular Economy, 3(1), 1–9. DOI: https://doi.org/10.1007/s42824-021-00017-3.
12. Bouriama, M. B., Qiu, Y., Stević, Ž., & Simić, V. (2023). Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model. Socio-Economic Planning Sciences, 86, 101475. DOI: https://doi.org/10.1016/j.seps.2022.101475.
13. Can, G. F. (2018). An intutionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies . Human Factors and Ergonomics in Manufacturing & Service Industries, 28(3), 130-147. DOI: https://doi.org/10.1002/hfm.20729.
14. Candan, G., & Cengiz Toklu, M. (2022). A comparative analysis of the circular economy performances for European Union countries. International Journal of Sustainable Development & World Ecology, 29(7), 653-664. DOI: https://doi.org/10.1080/13504509.2022.2084794.
15. Chakraborty, S., & Saha, A. K. (2022). A framework of LR fuzzy AHP and fuzzy WASPAS for health care waste recycling technology. Applied Soft Computing, 127, 109388. DOI: https://doi.org/10.1016/j.asoc.2022.109388.
16. Choudhary, V., & Mishra, A. (2022). Analyzing the critical success enablers of industry 4.0 using hybrid fuzzy AHP–CoCoSo method. Journal of Industrial Integration and Management, 7(04), 493-514. DOI: https://doi.org/10.1142/S2424862221500184.
17. D’Adamo, I., Favari, D., Gastaldi, M., & Kirchherr, J. (2024). Towards circular economy indicators: Evidence from the European Union. Waste Management & Research, 0734242X241237171. DOI: https://doi.org/10.1177/0734242X241237171.
18. Dantas, T. E., de-Souza, E. D., Destro, I. R., Hammes, G., Rodriguez, C. M., & Soares, S. R. (2021). How the combination of Circular Economy and Industry 4.0 can contribute towards achieving the sustainable development goals. Sustainable Production and Consumption, 26, 213-227. DOI: https://doi.org/10.1016/j.spc.2020.10.005.
19. Dehshiri, S. J. H., & Amiri, M. (2023). Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach. Energy, 285, 129493. DOI: https://doi.org/10.1016/j.energy.2023.129493.
20. de Melo, T. A., de Oliveira, M. A., de Sousa, S. R., Vieira, R. K., & Amaral, T. S. (2022). Circular economy public policies: A systematic literature review. Procedia Computer Science, 204, 652-662. DOI: https://doi.org/10.1016/j.procs.2022.08.079.
21. Deveci, M., Pamucar, D., & Gokasar, I. (2021). Fuzzy power heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management. Sustainable Cities and Society, 69, 102846. DOI: https://doi.org/10.1016/j.scs.2021.102846.
22. Dhumras, H., & Bajaj, R. K. (2024). 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, 237, 121550. DOI: https://doi.org/10.1016/j.eswa.2023.121550.
23. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiplecriteria problems: The CRITIC method . Computers & Operations Research, 22(7), 763-770. DOI: https://doi.org/10.1016/0305- 0548(94)00059-H.
24. Ding, Q. K. (2021). Towards sustainable production and consumption: assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations. Sustainable Production and Consumption, 27, 254-268. DOI: https://doi.org/10.1016/j.spc.2020.11.004.
25. dos Santos Gonçalves, P. V., & Campos, L. M. (2022). A systemic review for measuring circular economy with multi-criteria methods. Environmental Science and Pollution Research, 1-15. DOI: https://doi.org/10.1007/s11356-022-18580-w.
26. dos Santos, R. N., & da Silva, P. A. L. (2023). Multicriteria decision support method CRITIC-WASPAS-N in the analysis of transportation companies on the stock exchange in the brazilian market. Revista de Gestão e Secretariado, 14(10), 17560-17578. DOI: https://doi.org/ 10.7769/gesec.v14i10.2958.
27. EC. (2015). Closing the loop-An EU action plan for the Circular Economy. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52015DC0614 (Access Date: 02/03/2024).
28. EC. (2016). European action for sustainability. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2016%3A739%3AFIN (Access Date: 02/03/2024). .
29. Ecer, F., 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, 266, 121981. DOI: https://doi.org/10.1016/j.jclepro.2020.121981.
30. 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. DOI: https://doi.org/10.1016/j.rser.2021.110916.
31. Ecer, F., Küçükönder, H., Kaya, S. K., & Görçün, Ö. F. (2023). Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework. Transportation research part a: policy and practice, 172, 103667. DOI: https://doi.org/10.1016/j.tra.2023.103667.
32. Edina, K. D., Tamás, M., & Katalin, M. B. (2022). The indicator-based measurement of the circular economy in the countries of the European Union. Tér És Társadalom, 36(2), 49-69. DOI: https://doi.org/10.17649/TET.36.2.3374.
33. Eurostat. (2024a). Generation of waste excluding major mineral wastes per GDP unit. https://ec.europa.eu/eurostat/databrowser/view/CEI_PC032/default/table?lang=en (Access Date: 15/03/2024).
34. Eurostat. (2024b). Waste generation per capita. https://ec.europa.eu/eurostat/databrowser/view/CEI_PC034__custom_6528249/default/table?lang=en (Access Date: 15/03/2024).
35. Eurostat. (2024c). Resource productivity. https://ec.europa.eu/eurostat/databrowser/view/CEI_PC030/default/table?lang=en (Access Date: 15/03/2024).
36. Fura, B., Stec, M., & Miś, T. (2020). Statistical evaluation of the level of development of circular economy in European Union member countries. Energies, 13(23), 6401. DOI: https://doi.org/10.3390/en13236401. Garcia-Bernabeu, A., Hilario-Caballero, A., Pla-Santamaria, D., & Salas-Molina, F. (2020). A process oriented MCDM approach to construct a circular economy composite index. . Sustainability, 12(2), 618. DOI: https://doi.org/10.3390/su12020618. Garg, C. P., Görçün, Ö. F., Kundu, P., & 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, 213, 118863. DOI: https://doi.org/10.1016/j.eswa.2022.118863.
37. Garside, A. K., Tyas, R. P. A., & Wardana, R. W. (2023). Intuitionistic Fuzzy AHP and WASPAS to Assess Service Quality in Online Transportation. Jurnal Optimasi Sistem Industri, 22(1), 38-51. DOI: https://doi.org/ 10.25077/josi.v22.n1.p38-51.2023.
38. Gezen, A. (2019). Türkiye’de faaliyet gösteren katılım bankalarının Entropi ve WASPAS yöntemleri ile performans analizi. Muhasebe ve Finansman Dergisi, (84), 213-232. DOI: https://doi.org/10.25095/mufad.625812.
39. Ghorshi Nezhad, M. R., Zolfani, S. H., Moztarzadeh, F., Zavadskas, E. K., & Bahrami, M. (2015). Planning the priority of high tech industries based on SWARA-WASPAS methodology: The case of the nanotechnology industry in Iran. Economic research-Ekonomska istraživanja, 28(1), 1111-1137. DOI: https://doi.org/ 10.1080/1331677X.2015.1102404.
40. Ghosh, S. K. (2020). Circular Economy: Global Perspective. Singapore: Springer. DOI: https://doi.org/10.1007/978-981-15-1052-6.
41. Ghoushchi, S. J., Jalalat, S. M., Bonab, S. R., Ghiaci, A. M., Haseli, G., & Tomaskova, H. (2022). Evaluation of wind turbine failure modes using the developed SWARA-CoCoSo methods based on the spherical fuzzy environment. IEEE Access, 10, 86750-86764.. DOI: https://doi.org/ 10.1109/ACCESS.2022.3199359.
42. Görçün, Ö. F., Pamucar, D., & Küçükönder, H. (2024). Selection of tramcars for sustainable urban transportation by using the modified WASPAS approach based on Heronian operators. Applied Soft Computing, 151, 111127. DOI: https://doi.org/10.1016/j.asoc.2023.111127.
43. Görmüş, A. (2023). Yeşil ve döngüsel ekonomide düzgün olmayan işler: Türkiye’de atık toplama ve geri kazanım faaliyetlerinden bulgular. Journal of Recycling Economy & Sustainability Policy, 2(2), 52-66. Retrieved from: https://respjournal.com/index.php/pub/article/view/28.
44. Gülcemal T., İzci, A. Ç., & Taşcı, M. Z. (2023). BİST 100’de İşlem Gören Sigorta Şirketlerinin CRITIC COCOSO Yöntemiyle Performans Analizi. Muhasebe ve Finansman Dergisi, (97), 63-78. Retrieved from: https://dergipark.org.tr/en/pub/mufad/issue/75216/1168270
45. Güllü, G. (2023). Kentsel dönüşüm atıkları yönetim modeli. İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4(3), 161-170. DOI: https://doi.org/10.47769/izufbed.1177258 .
46. Hartley, K., van Santen, R., & Kirchherr, J. (2020). Policies for transitioning towards a circular economy: Expectations from the European Union (EU). Resources, Conservation and Recycling, 155, 104634. DOI: https://doi.org/10.1016/j.resconrec.2019.104634.
47. Huang, P. H., & Moh, T. T. (2017). A non-linear non-weight method for multi- criteria decision making . Annals of Operations Research, 248, 239-251. DOI: https://doi.org/10.1007/s10479-016-2208-2.
48. Ivanova, V., & Chipeva, S. (2019). Transition to a circular economy model in the european union–state and outlook. IJASOS-International E-journal of Advances in Social Sciences, 5(14), 694-701. DOI: https://doi.org/10.18769/ijasos.591425.
49. Jing, D., Imeni, M., Edalatpanah, S. A., Alburaikan, A., & Khalifa, H. A. E. W. (2023). Optimal selection of stock portfolios using multi-criteria decision-making methods. Mathematics, 11(2), 415. DOI: https://doi.org/ 10.3390/math11020415.
50. Karaca, S. S., Altemur, N., & Çevik, M. (2020). BANKACILIK SEKTÖRÜNDE PERFORMANS ANALİZİ: ENTROPI VE WASPAS YÖNTEMİ UYGULAMASI. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, 1(2), 46-76. Retrieved from: https://dergipark.org.tr/en/pub/mtuiyb/issue/57000/769061.
51. Kaya, S. K., Ayçin, E., & Pamucar, D. (2023). Evaluation of social factors within the circular economy concept for European countries. Central European Journal of Operations Research, 31(1), 73-108. DOI: https://doi.org/10.1007/s10100-022-00800-w.
52. Keshavarz-Ghorabaee, M., Govindan, K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2019). An integrated type-2 fuzzy decision model based on WASPAS and SECA for evaluation of sustainable manufacturing strategies. Journal of Environmental Engineering and Landscape Management, 27(4), 187-200. DOI: https://doi.org/ 10.3846/jeelm.2019.11367.
53. Kieu, P. T., Nguyen, V. T., Nguyen, V. T., & Ho, T. P. (2021). A spherical fuzzy analytic hierarchy process (SF-AHP) and combined compromise solution (CoCoSo) algorithm in distribution center location selection: A case study in agricultural supply chain. Axioms, 10(2), 53. DOI: https://doi.org/ 10.3390/axioms10020053.
54. Kirchherr, J., Piscicelli, L., Bour, R., Kostense-Smit, E., Muller, J., Huibrechtse-Truijens, A., & Hekkert, M. (2018). Barriers to the circular economy: Evidence from the European Union (EU). Ecological Economics, 150, 264-272. DOI: https://doi.org/10.1016/j.ecolecon.2018.04.028.
55. Komal. (2023). Archimedean t-norm and t-conorm based intuitionistic fuzzy WASPAS method to evaluate health-care waste disposal alternatives with unknown weight information. Retrieved from: https://dl.acm.org/doi/abs/10.1016/j.asoc.2023.110751.
56. Koska, A., & Erdem, M. B. (2023). Performance analysis of manufacturing waste using SWARA and VIKOR methods: evaluation of Turkey within the scope of the circular economy . Sustainability, 15(16), 12110. DOI: https://doi.org/10.3390/su151612110.
57. Kozma, D. E., Molnar, T., & Barna, K. M. (2022). The indicator-based measurement of the circular economy in the countries of the European Union. Space & Society/Tér és Társadalom, 36(2). DOI: https://doi.org/10.17649/TET.36.2.3374.
58. Krishnan, A. R., Kasim, M. M., Hamid, R., & Ghazali, M. F. (2021). A modified CRITIC method to estimate the objective weights of decision criteria. Symmetry, 13(6), 973. DOI: https://doi.org/10.3390/sym13060973.
59. Lai, H., Liao, H., Wen, Z., Zavadskas, E. K., & Al-Barakati, A. (2020). An improved CoCoSo method with a maximum variance optimization model for cloud service provider selection. Engineering Economics, 31(4), 411-424. DOI: https://doi.org/10.5755/j01.ee.31.4.24990.
60. Lanzalonga, F., Marseglia, R., Irace, A., & Biancone, P. P. (2024). The application of artificial intelligence in waste management: understanding the potential of data-driven approaches for the circular economy paradigm. Management Decision. DOI: https://doi.org/10.1108/MD-10-2023-1733.
61. Liu, P., Saha, A., Mishra, A. R., Rani, P., Dutta, D., & Baidya, J. (2023). A BCF–CRITIC–WASPAS method for green supplier selection with cross-entropy and Archimedean aggregation operators. Journal of Ambient Intelligence and Humanized Computing, 14(9), 11909-11933. DOI: https://doi.org/ 10.1007/s12652-022-03745-9.
62. Marković, M., Popović, Z., & Marjanović, I. (2023). Towards a circular economy: evaluation of waste management performance in European Union countries. Serbian Journal of Management, 18(1), 45-57. DOI: https://doi.org/10.5937/sjm18-40073.
63. Markowski, Ł., Kotliński, K., & Ostrowska, A. (2023). Sustainable consumption and production in the European Union—An attempt to assess changes and convergence from the perspective of central and eastern European countries. Sustainability, 15(23), 16485. DOI: https://doi.org/10.3390/su152316485.
64. Martinho, V. D., & Mourão, P. R. (2020). Circular economy and economic development in the European Union: A review and bibliometric analysis. Sustainability, 12(18), 7767. DOI: https://doi.org/10.3390/su12187767.
65. Mazur-Wierzbicka, E. (2021a). Circular economy: advancement of European Union countries. Environmental Sciences Europe, 3, 1-15. DOI: https://doi.org/10.1186/s12302-021-00549-0.
66. Mazur-Wierzbicka, E. (2021b). Towards circular economy - A comparative analysis of the countries of the European Union. Resources, 10(5), 49. DOI: https://doi.org/10.3390/resources10050049.
67. Md Saad, R., Ahmad, M. Z., Abu, M. S., & Jusoh, M. S. (2014). Hamming distance method with subjective and objective weights for personnel selection. The Scientific World Journal, 2014. DOI: https://doi.org/10.1155/2014/865495.
68. Memiş, L. (2023). Türkıye’de sıfır atık polıtıkasının aşılması gereken eşikleri. https://usam.arel.edu.tr/wp-content/uploads/2023/07/Kongre-bildiri-Kitapçiği-1.pdf#page=51 (Access Date: 03/03/2024).
69. Mhatre, P., Panchal, R., Singh, A., & Bibyan, S. (2021). A systematic literature review on the circular economy initiatives in the European Union. Sustainable Production and Consumption, 26, 187-202. DOI: https://doi.org/10.1016/j.spc.2020.09.008.
70. Miç, P., & Antmen, Z. F. (2021). A decision-making model based on TOPSIS, WASPAS, and MULTIMOORA methods for university location selection problem. Sage Open, 11(3), 21582440211040115. DOI: https://doi.org/ 10.1177/21582440211040115.
71. Migała-Warchoł, A., Ziółkowski, B., & Babiarz, P. (2023). The circular economy vs the sustainable development approach to production and consumption: the case of the European Union countries. Humanities and Social Sciences, 30(2), 59-74. DOI: https://doi.org/10.7862/rz.2023.hss.15.
72. Mishra, A. R., & Rani, P. (2021). Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex & Intelligent Systems, 7(5), 2469-2484. DOI: https://doi.org/ 10.1007/s40747-021-00407-9.
73. Moraga, G., Huysveld, S., Mathieux, F., Blengini, G.A., Alaerts, L., Van Acker, K., de Meester, S. & Dewulf, J. (2019). Circular economy indicators: what do they measure?. Rsources, Conservation and Recycling, 146, 452-461. DOI: https://doi.org/10.1016/j.resconrec.2019.03.045.
74. Narang, M., Joshi, M. C., Bisht, K., & Pal, A. (2022). Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision Making: Applications in Management and Engineering, 5(1), 90-112.. DOI: https://doi.org/ 10.31181/dmame0310022022n.
75. Nazarko, J., Chodakowska, E., & Nazarko, Ł. (2022). Evaluating the transition of the European Union member states towards a circular economy . Energies, 15(11), 3924. DOI: https://doi.org/10.3390/en15113924.
76. Nguyen, P. H., Dang, T. T., Nguyen, K. A., & Pham, H. A. (2022). Spherical Fuzzy WASPAS-based Entropy Objective Weighting for International Payment Method Selection. Computers, Materials & Continua, 72(1). DOI: https://doi.org/ 10.32604/cmc.2022.025532.
77. Özceylan, A. (2022). An entropy-based COPRAS approach to evaluate the circular economy performance of some European countries. In A. Özpolat, & F. Nakıpoğlu Özsoy (Eds.), Circular Economy in the Framework of Sustainable Development Policy (pp. 35-53). Ankara: Özgür Yayın Dağıtım Ltd. Şti.
78. Parsa Rad, A., Khalilzadeh, M., Banihashemi, S. A., Božanić, D., Milić, A., & Ćirović, G. (2024). Supplier Selection in Downstream Oil and Gas and Petrochemicals with the Fuzzy BWM and Gray COCOSO Methods Considering Sustainability Criteria and Uncertainty Conditions. Sustainability, 16(2), 880. DOI: https://doi.org/10.3390/su16020880.
79. Peng, X., & Huang, H. (2020). Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy, 26(4), 695-724. DOI: http://doi.org/10.3846/tede.2020.11920.
80. Peng, X., Zhang, X., & Luo, Z. (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53(5), 3813-3847. DOI: http://doi.org/ 10.1007/s10462-019-09780-x.
81. Peng, X., Krishankumar, R., & Ravichandran, K. S. (2021). A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Computing, 25, 4213-4241. DOI: http://doi.org/10.1007/s00500-020-05437-y.
82. Pollitt, M. G., & Ajayi, V. (2023). Green growth and net zero policy in the UK: some conceptual and measurement issues. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4644432 (Access Date: 16/03/2024). DOI: http://dx.doi.org/10.2139/ssrn.4644432.
83. Rashidian, F., Eydi, A., & Roghanian, E. (2024). Reliable and green road-rail routing using a hybrid procedure of DANP, COCOSO, and FMEA criticality methods: A case study of cement transportation network in Iran. Journal of Cleaner Production, 141250.. DOI: https://doi.org/10.1016/j.jclepro.2024.141250.
84. Rodriguez-Anton, J. M., Rubio-Andrada, L., Celemín-Pedroche, M. S., & Alonso-Almeida, M. D. (2019). Analysis of the relations between circular economy and sustainable development goals. International . Journal of Sustainable Development & World Ecology, 26(8), 708-720. DOI: https://doi.org/10.1080/13504509.2019.1666754.
85. Romero‐Hernández, O., & Romero, S. (2018). Maximizing the value of waste: From waste management to the circular economy. Thunderbird International Business Review, 60(5), 757-764. DOI: https://doi.org/10.1002/tie.21968.
86. RouhaniRad S., Akhavan Anvari, M. R., & Raissifar, K. (2023). An Integrated Ranking Model of Tehran Stock Exchange Companies Using Bayesian Best-Worst, CoCoSo, and MARCOS Methods (Case Study: Food and Beverage Companies). International Journal of Finance & Managerial Accounting. DOI: https://doi.org/ 10.30495/IJFMA.2023.68893.1895.
87. Saraswat, S. K., Digalwar, A. K., & Yadav, S. S. (2021). Sustainability Assessment of Renewable and Conventional Energy Sources in India Using Fuzzy Integrated AHP-WASPAS Approach. Journal of Multiple-Valued Logic & Soft Computing, 37. Retrieved from: https://openurl.ebsco.com/EPDB%3Agcd%3A5%3A11758805/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A153002433&crl=c.
88. Schroeder, P., Anggraeni, K., & Weber, U. (2019). The relevance of circular economy practices to the sustainable development goals. Journal of Industrial Ecology, 23(1), 77-95. DOI: https://doi.org/10.1111/jiec.12732.
89. Seyhan, N. (2023). AB’de döngüsel ekonomi üretim ve tüketim göstergelerinin değerlendirilmesi: MEREC temelli MARCOS uygulaması. Sosyal Mucit Academic Review, 4(3), 364-391. DOI: https://doi.org/10.54733/smar.1338423.
90. Singh, R. K., & Modgil, S. (2020). Supplier selection using SWARA and WASPAS–a case study of Indian cement industry. Measuring Business Excellence, 24(2), 243-265. DOI: https://doi.org/ 10.1108/MBE-07-2018-0041. Stahel, W. R. (2016). The circular economy. Nature, 531(7595), 435-438. DOI: https://doi.org/10.1038/531435a.
91. Stanković, J. J., Janković-Milić, V., Marjanović, I., & Janjić, J. (2021). An integrated approach of PCA and PROMETHEE in spatial assessment of circular economy indicators. Waste Management,, 128, 154-166. DOI: https://doi.org/10.1016/j.wasman.2021.04.057.
92. Stojić, G., Stević, Ž., Antuchevičienė, J., Pamučar, D., & Vasiljević, M. (2018). A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products. Information, 9(5), 121. DOI: https://doi.org/ 10.3390/info9050121.
93. Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 78, 101052. DOI: https://doi.org/ 10.1016/j.seps.2021.101052.
94. Tseng, M. L., Chiu, A. S., Liu, G., & Jantaralolica, T. (2020). Circular economy enables sustainable consumption and production in multi-level supply chain system. . Resources, Conservation and Recycling, 154, 104601. DOI: https://doi.org/10.1016/j.resconrec.2019.104601.
95. Tumsekcali, E., Ayyildiz, E., & Taskin, A. (2021). Interval valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL Model: P-SERVQUAL 4.0. Expert Systems with Applications, 186, 115757. DOI: https://doi.org/10.1016/j.eswa.2021.115757.
96. Tuş, A., & Aytaç Adalı, E. (2019). The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. Opsearch, , 56, 528-538. DOI: https://doi.org/10.1007/s12597-019-00371-6.
97. Ulutaş, A., Karakuş, C. B., & To pal, A. (2020). Location selection for logistics center with fuzzy SWARA and CoCoSo methods. Journal of Intelligent & Fuzzy Systems, 38(4), 4693-4709. DOI: https://doi.org/10.3233/JIFS-191400.
98. Ulutaş, A., Balo, F., Sua, L., Karabasevic, D., Stanujkic, D., & Popovic, G. (2021a). Selection of insulation materials with PSI-CRITIC based CoCoSo method. Revista de la Construcción, 20(2), 382-392. DOI: https://doi.org/10.7764/rdlc.20.2.382.
99. Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasev ic, D. (2021b). A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technological and Economic Development of Economy, 27(5), 1227-1249. DOI: https://doi.org/10.3846/tede.2021.15058.
100. UN. (2015). Transforming Our World: The 2030 Agenda for Sustainable Development. https://documents-dds-ny.un.org/doc/UNDOC/GEN/N15/291/89/PDF/N1529189.pdf?, (Access Date: 20/03/2024). UN. (2022). World Population Prospects 2022. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf (Access Date: 29/02/2024).
101. UN. (2023). The Sustainable Development Goals Report 2023: Special Edition. https://unstats.un.org/sdgs/report/2023/ (Access Date: 02/03/2024).
102. Voukkali, I., Papamichael, I., Loizia, P., Lekkas, D. F., Rodríguez-Espinosa, T., Navarro-Pedreño, J., & Zorpas, A. A. (2023). Waste metrics in the framework of circular economy. Waste Management & Research, 41(12), 1741-1753. DOI: https://doi.org/10.1177/0734242X231190794.
103. Witjes, S., & Lozano, R. (2016). Towards a more circular economy: Proposing a framework linking sustainable public procurement and sustainable business models. Resources, Conservation and Recycling, 112, 37-44. DOI: https://doi.org/10.1016/j.resconrec.2016.04.015.
104. Yalcin Kavus, B., Ayyildiz, E., Gulum Tas, P., & Taskin, A. (2023). A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem. Environmental Science and Pollution Research, 30(39), 90006-90023. DOI: https://doi.org/ 10.1007/s11356-022-23965-y.
105. Yan, R., Han, Y., Zhang, H., & Wei, C. (2024). Location Selection of Electric Vehicle Charging Stations Through Employing the Spherical Fuzzy CoCoSo and CRITIC Technique. Informatica, 35(1), 203-22. DOI: https://doi.org/10.15388/24-INFOR545.
106. Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019a). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management decision, 57(9), 2501-2519. DOI: https://doi.org/10.1108/MD-05-2017-0458.
107. Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019b). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858-874. DOI: https://doi.org/10.3846/jcem.2019.11309
108. Yazdani, M., Torkayesh, A. E., Stević, Ž., Chatterjee, P., Ahari, S. A., & Hernandez, V. D. (2021). An interval valued neutrosophic decision-making structure for sustainable supplier selection. Expert Systems with Applications, 183, 115354. DOI: https://doi.org/10.1016/j.eswa.2021.115354.
109. Yücenur, G. N., & Ipekçi, A. (2021). SWARA/WASPAS methods for a marine current energy plant location selection problem. Renewable Energy, 163, 1287-1298. DOI: https://doi.org/10.1016/j.renene.2020.08.131.
110. Zavadskas, E. K., Antucheviciene, J., Hajiagha, S. H., & Hashemi, S. S. (2014). Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF). Applied Soft Computing, 24, 1013-1021. DOI: https://doi.org/10.1016/j.asoc.2014.08.031.
111. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. DOI: https://doi.org/10.5755/j01.eee.122.6.1810.
112. Zhang, C., & Tian, J. (2023). An integrated framework for community medical and health services evaluation with fuzzy number intuitionistic fuzzy sets. Journal of Intelligent & Fuzzy Systems, 1-13. DOI: https://doi.org/10.3233/JIFS-231700.
113. Zhang, H., & Wei, G. (2023). Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method. Computational and Applied Mathematics, 42(1), 60. DOI: https://doi.org/10.1007/s40314-022-02183-9.