Assessing Green Transition Dynamics in Europe Through LOPCOW & CODAS Methods

Yıl 2025, Cilt: 10 Sayı: 2, 629 - 659, 30.06.2025

Gülin Zeynep Öztaş

https://doi.org/10.25229/beta.1612931

Öz

The climate crisis is a result of human-caused global issues. Adopting the notion of sustainability is the key for surviving the repercussions of the climate crisis with minimal damage. Sustainability is a holistic approach to address the climate crisis. The green transition is a crucial strategy that can be used to promote sustainable global development. Measuring green transition performance enables countries to track their progress toward sustainability. This study aims to evaluate the green transition performances of 29 European countries through an integrated LOPCOW&CODAS method. The relative importance of performance indicators is calculated objectively by LOPCOW and then the overall performance scores are obtained by CODAS. The findings show that building energy efficiency, environmental impacts, and preserving and managing natural resources are considered the most critical factors in the green transition. Furthermore, this study explores the effects of applying different weight sets to set the robustness of the performances. Norway, the Netherlands, Estonia, and Austria are leading countries across multiple scenarios. By addressing these aspects, the findings provide deeper insights into green transition dynamics across Europe. Investments in the research and development of green transition should be given top priority by policy-makers who also support sustainable practices.

Anahtar Kelimeler

Green transition , Sustainability , Performance analysis , LOPCOW , CODAS

Avrupa’da Yeşil Dönüşüm Dinamiklerinin LOPCOW & CODAS Yöntemleriyle Değerlendirilmesi

Öz

İklim krizi, insan kaynaklı küresel sorunların bir sonucudur. Sürdürülebilirlik kavramının benimsenmesi, iklim krizinin yansımalarından en az zararla kurtulmanın anahtarıdır. Sürdürülebilirlik, iklim krizini ele almak için bütüncül bir yaklaşımdır. Yeşil dönüşüm, sürdürülebilir küresel kalkınmayı teşvik etmek için kullanılabilecek çok önemli bir stratejidir. Yeşil dönüşüm performansının ölçülmesi, ülkelerin sürdürülebilirlik yolunda kaydettikleri ilerlemeyi takip etmelerini sağlar. Bu çalışma, 29 Avrupa ülkesinin yeşil dönüşüm performanslarını entegre bir LOPCOW&CODAS yöntemiyle değerlendirmeyi amaçlamaktadır. Performans göstergelerinin göreli önemi LOPCOW ile objektif olarak hesaplanmakta ve ardından CODAS ile genel performans puanları elde edilmektedir. Bulgular, bina enerji verimliliği, çevresel etkiler ve doğal kaynakların korunması ve yönetiminin yeşil dönüşümde en kritik faktörler olduğunu göstermektedir. Ayrıca bu çalışma, ülkelerin performanslarının sağlamlığını belirlemek için farklı ağırlık setlerinin uygulanmasının etkisini araştırmaktadır. Norveç, Hollanda, Estonya ve Avusturya birden fazla senaryoda önde gelen ülkeler olarak elde edilmiştir. Bu hususların ele alınmasıyla, Avrupa genelinde yeşil dönüşüm dinamiklerine ilişkin daha derin bilgiler sağlanmıştır. Yeşil dönüşümün araştırılması ve geliştirilmesine yönelik yatırımlara, sürdürülebilir uygulamaları da destekleyen politika yapıcılar tarafından öncelik verilmelidir.

Anahtar Kelimeler

Yeşil dönüşüm , Sürdürülebilirlik , Performans analizi , LOPCOW , CODAS

Kaynakça

• Adalı, E. A., & Tuş, A. (2021). Hospital site selection with distance-based multi-criteria decision-making methods. International Journal of Healthcare Management, 14(2), 534-544.
• Afzali, M., Afzali, A., & Pourmohammadi, H. (2022). An interval-valued intuitionistic fuzzy-based CODAS for sustainable supplier selection. Soft Computing, 26(24), 13527-13541.
• Alkan, N. (2024). Evaluation of sustainable development and utilization-oriented renewable energy systems based on CRITIC-SWARA-CODAS method using interval-valued picture fuzzy sets. Sustainable Energy, Grids and Networks, 38, 101263.
• Alkan, N., Otay, I., Gul, A. Y., Kızılkan Demir, Z. B., & Doğan, O. (2024). Continuous intuitionistic fuzzy AHP & CODAS methodology for automation degree selection. Journal of Multiple-Valued Logic & Soft Computing, 43(4-6), 355-393.
• Alsalem, M. A., Alamoodi, A. H., Albahri, O. S., Albahri, A. S., Martínez, L., Yera, R., ... & Sharaf, I. M. (2024). Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach. Expert Systems with Applications, 246, 123066.
• Amusan, O. T., Nwulu, N. I., & Gbadamosi, S. L. (2024). Multi-criteria decision-based hybrid energy selection system using CRITIC weighted CODAS approach. Scientific African, 26, e02372.
• Andukuri, R., & Rao, C. M. (2024). Application of fuzzy CODAS for the optimal selection of condition monitoring equipment in industrial rotating machinery. In Operations Research Forum (Vol. 5, No. 4, p. 88). Cham: Springer International Publishing.
• Bănică, A., Ţigănaşu, R., Nijkamp, P., & Kourtit, K. (2024). Institutional Quality in Green and Digital Transition of EU Regions–A Recovery and Resilience Analysis. Global Challenges, 8(9), 2400031.
• Biswas, S., Bandyopadhyay, G., & Mukhopadhyaya, J. N. (2022). A multi-criteria based analytic framework for exploring the impact of Covid-19 on firm performance in emerging market. Decision Analytics Journal, 5, 100143.
• Biswas, S., Chatterjee, S., & Majumder, S. (2024). A spherical fuzzy framework for sales personnel selection. Journal of Computational and Cognitive Engineering, 3(4), 373-394.
• Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A., et al. (2024). 2024 Environmental Performance Index. Retrieved Sep 5, 2024 from https://epi.yale.edu
• Bolturk, E. (2018). Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm. Journal of Enterprise Information Management, 31(4), 550-564.
• 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.
• Cavalli, F., Moretto, E., & Naimzada, A. (2024). Green transition and environmental quality: an evolutionary approach. Annals of Operations Research, 337(3), 1009-1035.
• Cheba, K., Bąk, I., Szopik-Depczyńska, K., & Ioppolo, G. (2022). Directions of green transformation of the European Union countries. Ecological Indicators, 136, 108601.
• Chen, H., Peng, C., Guo, S., Yang, Z., & Lu, W. (2024). A decision-support framework for industrial green transformation: empirical analysis of the northeast industrial district in China. The Annals of Regional Science, 1-42.
• Cui, H., Liu, X., & Zhao, Q. (2021). Which Factors Can Stimulate China's Green Transformation Process? From Provincial Aspect. Polish Journal of Environmental Studies, 30(1), 47-60.
• Demir, G., Riaz, M., & Almalki, Y. (2023). Multi-criteria decision making in evaluation of open government data indicators: An application in G20 countries. AIMS Mathematics, 8(8), 18408-18434.
• Dhruva, S., Krishankumar, R., Zavadskas, E. K., Ravichandran, K. S., & Gandomi, A. H. (2024). Selection of suitable cloud vendors for health centre: a personalized decision framework with fermatean fuzzy set, LOPCOW, and CoCoSo. Informatica, 35(1), 65-98.
• Do, D. T. (2024). Assessing the Impact of Criterion Weights on the Ranking of the Top Ten Universities in Vietnam. Engineering, Technology & Applied Science Research, 14(4), 14899-14903.
• Dual Citizen LLC, (2024). Global Green Economy Index (GGEI), Retrieved Sep 4, 2024 from https://dualcitizeninc.com/global-green-economy-index/
• Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
• 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.
• Ecer, F., Tanrıverdi, G., Yaşar, M., & Görçün, Ö. F. (2025). Sustainable aviation fuel supplier evaluation for airlines through LOPCOW and MARCOS approaches with interval-valued fuzzy neutrosophic information. Journal of Air Transport Management, 123, 102705.
• European Green Deal, 2019. Retrieved Sep 10, 2024, from https://ec.europa.eu/commission/presscorner/detail/en/ip_19_6691
• Fritz, T., Willendorf, A. M., Zangemeister, L., Neuss, K. (2024). Green Transition Index (GTI), Retrieved Sep 10, 2024 from https://www.oliverwyman.com/our-expertise/insights/2022/jun/green-transition-index.html
• Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antuchevičienė, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19.
• Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (codas) method for multi-criteria decision-making. Economic computation and economic cybernetics studies and research, 50(3), 25-44.
• Ghoushchi, S. J., Garg, H., Bonab, S. R., & Rahimi, A. (2023). An integrated SWARA-CODAS decision-making algorithm with spherical fuzzy information for clean energy barriers evaluation. Expert Systems with Applications, 223, 119884.
• Gonzales, R., Almacen, R. M., Gonzales, G., Costan, F., Suladay, D., Enriquez, L., ... & Ocampo, L. (2022). Priority Roles of Stakeholders for Overcoming the Barriers to Implementing Education 4.0: An Integrated Fermatean Fuzzy Entropy‐Based CRITIC‐CODAS‐SORT Approach. Complexity, 2022(1), 7436256.
• He, W., Li, E., & Cui, Z. (2021). Evaluation and influence factor of green efficiency of China’s agricultural innovation from the perspective of technical transformation. Chinese Geographical Science, 31, 313-328.
• Hezam, I. M., Ali, A. M., Sallam, K., Hameed, I. A., & Abdel-Basset, M. (2024). Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection. Scientific Reports, 14(1), 17718.
• Işık, Ö., Shabir, M., & Moslem, S. (2024). A hybrid MCDM framework for assessing urban competitiveness: A case study of European cities. Socio-Economic Planning Sciences, 96, 102109.
• Jiang, Q., Wang, H., & Tang, L. (2024). Robust Fuzzy Decision Support Framework for comprehensive Evaluating of Food Supply Chain Performance. IEEE Access, 12, 188874-188889.
• Kamber, E., & Baskak, M. (2024). Green logistics park location selection with circular intuitionistic fuzzy CODAS method: The case of Istanbul. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-17.
• Karasan, A., Zavadskas, E. K., Kahraman, C., & Keshavarz-Ghorabaee, M. (2019). Residential construction site selection through interval-valued hesitant fuzzy CODAS method. Informatica, 30(4), 689-710.
• Korucuk, S., Aytekin, A., Ecer, F., Karamaşa, Ç., & Zavadskas, E. K. (2022). Assessing green approaches and digital marketing strategies for twin transition via fermatean fuzzy SWARA-COPRAS. Axioms, 11(12), 709.
• Korucuk, S., Aytekin, A., Görçün, Ö., Simic, V., & Görçün, Ö. F. (2024). Warehouse site selection for humanitarian relief organizations using an interval-valued fermatean fuzzy LOPCOW-RAFSI model. Computers & Industrial Engineering, 192, 110160.
• Laha, S., & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks. Accounting, 5(4), 169-184.
• Leal, J. I., Tofoli, F. L., Melo, F. D. C., & Leão, R. P. S. (2025). Site suitability analysis for green hydrogen production using multi-criteria decision-making methods: A case study in the state of Ceará, Brazil. International Journal of Hydrogen Energy, 97, 406-418.
• Long, R., Bao, S., Wu, M., & Chen, H. (2022). Overall evaluation and regional differences of green transformation: Analysis based on “government-enterprise-resident” three-dimensional participants perspective. Environmental Impact Assessment Review, 96, 106843.
• Mathew, M., & Sahu, S. (2018). Comparison of new multi-criteria decision making methods for material handling equipment selection. Management Science Letters, 8(3), 139-150.
• Muciaccia, S. (2023). Green Transition Efficiency in African Countries: The role of climate aid and climate readiness. [Unpublished Master Thesis]. Delft University of Technology.
• Muscillo, A., Re, S., Gambacorta, S., Ferrara, G., Tagliafierro, N., Borello, E., ... & Facchini, A. (2023). An open data index to assess the green transition-A study on all Italian municipalities. Ecological Economics, 212, 107924.
• Nila, B., & Roy, J. (2023). A new hybrid MCDM framework for third-party logistic provider selection under sustainability perspectives. Expert Systems with Applications, 121009.
• O’Brien, R. (2023). The Green Future Index (GFI), Retrieved Sep 10, 2024 from https://www.technologyreview.com/2023/04/05/1070581/the-green-future-index-2023/
• Ozdemir, S., Demirel, N., Zaralı, F., & Çelik, T. (2024). Multi-criteria assessment framework for evaluation of Green Deal performance. Environmental Science and Pollution Research, 31(3), 4686-4704.
• Pamucar, D., Görçün, Ö. F., & Küçükönder, H. (2023). Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets. Soft Computing, 27(5), 2325-2345.
• Radi, D., & Westerhoff, F. (2024). The green transition of firms: The role of evolutionary competition, adjustment costs, transition risk, and green technology progress. arXiv preprint arXiv:2410.20379, https://doi.org/10.48550/arXiv.2410.20379.
• Remadi, F. D., & Frikha, H. M. (2023). The triangular intuitionistic fuzzy numbers CODAS method for solving green material selection problem. International Journal of Operational Research, 46(3), 398-415.
• Riaz, M., Qamar, F., Tariq, S., & Alsager, K. (2024). AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation. Mathematics, 12(22), 3593.
• Şeker, S., & Aydın, N. (2020). Sustainable public transportation system evaluation: A novel two-stage hybrid method based on IVIF-AHP and CODAS. International Journal of Fuzzy Systems, 22, 257-272.
• Simic, V., Dabic-Miletic, S., Tirkolaee, E. B., Stević, Ž., Ala, A., & Amirteimoori, A. (2023). Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems. Applied Soft Computing, 143, 110400.
• Sirin, S. M. (204) The Green Transition and Tech Firms' Financial Performance: Insights from Patent Data. Retrieved Nov 5, 2024 from https://ssrn.com/abstract=5007828.
• Streimikiene, D. (2023). Sustainability assessment of tourism destinations from the lens of green digital transformations. Journal of tourism and services, 14(27), 283-298.
• Sumrit, D., & Keeratibhubordee, J. (2025). Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW-ARAS Under Trapezoidal Fuzzy Set. Decision Making: Applications in Management and Engineering, 42-81.
• Tadić, S., Krstić, M., Kovač, M., & Brnjac, N. (2022). Evaluation of smart city logistics solutions. Promet-Traffic&Transportation, 34(5), 725-738.
• Ulutaş, A., Balo, F., & Topal, A. (2023). Identifying the most efficient natural fibre for common commercial building insulation materials with an integrated PSI, MEREC, LOPCOW and MCRAT model. Polymers, 15(6), 1500.
• Ulutaş, A., Topal, A., Görçün, Ö. F., & Ecer, F. (2024). Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model. Expert Systems with Applications, 241, 122680.
• Wang, C. N., & Van Thanh, N. (2022). Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain. Computers, Materials & Continua, 73(2), 4003-4015.
• Wang, J., & Cao, H. (2022). Improving competitive strategic decisions of Chinese coal companies toward green transformation: A hybrid multi-criteria decision-making model. Resources Policy, 75, 102483.
• Wątróbski, J., Bączkiewicz, A., Król, R., & Sałabun, W. (2022). Green electricity generation assessment using the CODAS-COMET method. Ecological Indicators, 143, 109391.
• World Commission on Environment and Development (WCED), 1987. Our Common Future. Oxford: Oxford University Press. Retrieved Sep 15, 2024 from https://sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf
• World Meteorological Organization (WMO), 2024. WMO Global Annual to Decadal Climate Update, 2024-2028, Retrieved Sep 8, 2024 from https://library.wmo.int/viewer/68910/download?file=WMO_GADCU_2024-2028_en.pdf&type=pdf&navigator=1
• Wu, C., Li, Y., & Qi, L. (2022). Assessing the impact of green transformation on ecological well-being performance: a case study of 78 cities in Western China. International Journal of Environmental Research and Public Health, 19(18), 11200.
• Xu, X., Huang, Y., Hu, B., Li, C., & Gong, K. (2024). Identifying Key Barriers to Green Transition Development in China's Express Industry Based on the Fuzzy DEMATEL Method. Sustainability (2071-1050), 16(14).
• Xu, Y. (2021). Research on investment environment performance evaluation of blockchain industry with intuitionistic fuzzy CODAS method. Scientific Programming, 2021(1), 1387062.
• Yalçın, N., & Yapıcı Pehlivan, N. (2019). Application of the fuzzy CODAS method based on fuzzy envelopes for hesitant fuzzy linguistic term sets: A case study on a personnel selection problem. Symmetry, 11(4), 493.
• Yin, Q., Wang, Y., Wan, K., & Wang, D. (2020). Evaluation of green transformation efficiency in Chinese mineral resource-based cities based on a three-stage DEA method. Sustainability, 12(22), 9455.
• Yu, Z., Guo, T., Song, X., Zhang, L., Cai, L., Zhang, X., & Zhao, A. (2024). Green Transition Assessment, Spatial Correlation, and Obstacles Identification: Evidence from Urban Governance Data of 288 Cities in China. Land, 13(3), 341.
• Zeng, S., & Yang, C. (2025). Risk evaluation of livestream e-commerce platforms based on expert trust networks and CODAS. Expert Systems with Applications, 260, 125408.
• Zhai, X., & An, Y. (2020). Analyzing influencing factors of green transformation in China’s manufacturing industry under environmental regulation: A structural equation model. Journal of Cleaner Production, 251, 119760.
• Zhai, X., An, Y., Shi, X., & Liu, X. (2022). Measurement of green transition and its driving factors: Evidence from China. Journal of Cleaner Production, 335, 130292.