OECD Ülkelerinde Veri Tabanlı LPI, Ekolojik Ayak İzi ve CO2 Kriterlerine Dayalı Entropi–TOPSIS Yöntemleri ile Ulusal Yeşil Lojistik Performansının Analizi

Öz

Bu çalışmada, OECD ülkelerinin yeşil lojistik performanslarının belirlenmesi amaçlanmaktadır. Araştırmada, OECD ülkelerinin 2023 yılına ait veri tabanlı lojistik performans endeksi ile ekolojik ayak izi ve kişi başına düşen karbon emisyon verileri kullanılmıştır. Çok kriterli karar verme yöntemlerinin kullanıldığı çalışmada ilk olarak Entropi ile kriter ağırlıkları belirlenmiştir. Elde edilen değerler incelendiğinde, kişi başına düşen karbon emisyon kriteri ile ekolojik ayak izi kriterinin en yüksek öneme sahip olan kriterler olduğu tespit edilmiştir. Bu kriterleri önem düzeyi bağlamında gümrük işlemleri etkinliği ile altyapı kalitesi kriterlerinin takip ettiği ortaya konulmuştur. İzleme ve Takip Kapasitesi kriterinin ise en az öneme sahip olan kriter olduğu gözlemlenmiştir. TOPSIS yöntemiyle ülkelerin yeşil lojistik performanslarına yönelik sıralama gerçekleştirilmiştir. Bu sıralamaya göre; Danimarka, İsviçre, Finlandiya, Kanada ve İsveç’in yeşil lojistik performanslarında en başarılı ülkeler olduğu tespit edilmiştir. Buna karşın Türkiye, Çek Cumhuriyeti, Şili, Kosta Rika, Macaristan, Kolombiya ve Meksika ülkelerine ait yeşil lojistik performanslarının diğer ülkelere göre daha zayıf düzeyde olduğu tespit edilmiştir

Anahtar Kelimeler

• Yeşil Lojistik
• Lojistik
• Sürdürülebilir Çevre
• Ekolojik Ayak İzi
• LPI
• Entropi
• TOPSIS.

Analysis of National Green Logistics Performance in OECD Countries with Data-Based LPI, Ecological Footprint and CO2 Criteria-Based Entropy–TOPSIS Methods

Abstract

This study aims to determine the green logistics performance of OECD countries. The research utilizes the data-based logistics performance index for OECD countries for the year 2023, along with ecological footprint and per capita carbon emission data. In this study, which employs multi-criteria decision-making methods, the criterion weights were first determined using Entropy. Upon examining the obtained values, it was determined that the per capita carbon emissions criterion and the ecological footprint criterion were the most important criteria. It was revealed that these criteria were followed by customs processing efficiency and infrastructure quality criteria in terms of importance. The tracking & tracing score criterion was observed to be the least important criterion. A ranking of countries' green logistics performance was conducted using the TOPSIS method. According to this ranking, Denmark, Switzerland, Finland, Canada, and Sweden were found to be the most successful countries in terms of green logistics performance. In contrast, the green logistics performance of Turkey, the Czech Republic, Chile, Costa Rica, Hungary, Colombia, and Mexico were found to be weaker than that of other countries.

Keywords

• Green Logistics
• Logistics
• Sustainable Environment
• Ecological Footprint
• LPI
• Entropy
• TOPSIS.

References

1. Ayçin, E. (2023). Çok kriterli karar verme: Bilgisayar uygulamalı çözümler. Nobel Akademik Yayıncılık.
2. Abdul Mumin, M., Yakubu, I. N., & Adam, I. O. (2025). The synthesis of logistics performance and technological innovation on environmental quality. Technological Sustainability, 4(1), 114–129.
3. Al-Sheyadi, A., Muyldermans, L., & Kauppi, K. (2019). The complementarity of green supply chain management practices and the impact on environmental performance. Journal of Environmental Management, 2(4), 186–198. https://doi.org/10.1016/j.jenvman.2019.04.078
4. Arıkan Kargı, V. S. (2022). Evaluation of logistics performance of the OECD member countries with integrated ENTROPY and WASPAS method. Yönetim ve Ekonomi Dergisi, 29(4), 801-811. https://doi.org/10.18657/yonveek.1067480
5. Arvis, J. F., Wiederer, C., Ojala, L., Shepherd, B., Saslavsky, D., & Dairabayeva, K. (2023). Connecting to compete 2023: Trade Logistics in An Uncertain Global Economy. Washington, DC: World Bank.
6. Birleşmiş Milletler. (2015). Transforming our World: the 2030 Agenda for Sustainable Development. http://sdgs.un.org/2030agenda adresinden 15.10.2025 tarihinde alınmıştır.
7. Blanco, E. E. & Sheffi, Y. (2024). Green logistics. In Sustainable Supply Chains (Springer Series in Supply Chain Management, 23, Springer.
8. Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel integrated hybrid multi-criteria decision-making approach for logistics performance index. Transportation Research Record, 2677(2), 1392-1400.

9. Ellison, O., Nuertey, D., Poku, E., Agbemude, S., & Owusu, F. (2024). Environmental pressure, green logistics strategy and sustainability performance: the moderating role of competitive intensity. Benchmarking: An International Journal, 32(9), 3631-3658.
10. Thanh Ha, L. (2025). Is it a good idea to select green logistics to enhance environmental sustainability? Insights from global sample. InternThaational Journal of Logistics Research and Applications, 28(5), 558-579.
11. Hwang, C. L. & Yoon, K. (1981). Multiple attribute decision making: Methods and applications: A state-of-the-art survey. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9.
12. Kara, K., Yalçın, G. C., Simic, V., Baysal, Z., & Pamucar, D. (2024). The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries. Socio-Economic Planning Sciences, 92, 101822.
13. Kaya Samut, P. (2023). OECD ülkelerinin yeşil lojistik performansı ile enerji, sağlık ekonomisi ve çevre ilişkisinin analizi. Verimlilik Dergisi, Döngüsel Ekonomi ve Sürdürülebilirlik Özel Sayısı, 67–83.
14. Khan, M. S., Aziz, G., Bakoben, H. B. M., & Saeed, A. (2025). Implications of sustainable logistics on economic, environment, and social dimensions: Pre-and post-implementation of saudi vision 2030. Journal of the Knowledge Economy, 1-30.
15. Khan, S. A. R., Zhang, Y., Anees, M., Golpîra, H., Lahmar, A., & Qianli, D. (2018). Green supply chain management, economic growth and environment: a GMM based evidence. Journal of Cleaner Production, 185, 588–599.
16. Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: evidence from asian countries. Journal of Cleaner Production, 204, 282–291.
17. Lu, Y. & Li, S. (2023). Green transportation model in logistics considering the carbon emissions costs based on improved grey wolf algorithm. Sustainability, 15(14), 11090. https://doi.org/10.3390/su151411090
18. Mao, N., Song, M. & Deng, S. (2016). Application of TOPSIS method in evaluating the effects of supply vane angle of a task/ambient air conditioning system on energy utilization and thermal comfort. Applied Energy, 180, 536–545.
19. Mariano, E. B., Gobbo, J. A., Camioto, F. D. C., & Rebelatto, D. A. D. N. (2017). CO2 emissions and logistics performance: a composite index proposal. Journal of Cleaner Production, 163, 166–178.
20. McKinnon, A. (2010). Green logistics: The carbon agenda. Electronic Scientific Journal of Logistics, 6(3), 1-9.
21. Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. ECONOMICS-Innovative and Economics Research Journal, 10(1), 13-34.
22. Navickas, V., Sujeta, L., & Vojtovich, S. (2011). Logistics systems as a factor of country's competitiveness. Economics & Management, 16, 231-237.
23. Ozmen, M. (2019). Logistics competitiveness of OECD countries using an improved TODIM Method. Sādhanā, 44(5), 108.
24. Özçelik, S. E. ve Töngür, Ü. (2023). Logistics performance and environmental degradation: The case of MENA countries. Ekonomi-Tek, 13(2), 198–229.
25. Özekenci E. K. (2025a). A Hybrid MPSI-extended aroman decision-making model for assessing green logistics performance: The case of Asia-Pacific countries. LogForum, 21(1), 87-105.
26. Özekenci E. K. (2025b). Evaluation of the logistics performance index of OECD countries based on hybrid MCDM methods. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 47(1), 47-76. DOI: 10.14780/ muiibd.1469898
27. Popescu, C. A., Ifrim, A. M., Silvestru, C. I., Dobrescu, T. G., & Petcu, C. (2024). An evaluation of the environmental impact of logistics activities: A case study of a logistics centre. Sustainability, 16(10), 4061.
28. Rezaei, J., van Roekel, W. S. ve Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using best worst method, Transport Policy, 68, 158-169.
29. Roy, S. & Mohanty, R. P. (2024). Green Logistics Operations and its impact on supply chain sustainability: An empirical study. Business Strategy and the Environment, 33(2), 1447–1476.
30. Santosa, W., Nilawati, Y., & Kusuma, R. (2022, January). Analysis of the relationship between logistics performance and carbon emissions in ASEAN. In LePALISSHE 2021: Proceedings of the First Lekantara Annual Conference on Public Administration, Literature, Social Sciences, Humanities, and Education (p. 105). European Alliance for Innovation.
31. Seyranlioglu O. & Han A., 2025. The impact of R&D investments on green logistics on the path to sustainable development: Evidence from Central and Eastern European Countries. LogForum, 21(4), 505-517, https://doi.org/10.17270/J.LOG.001290
32. Silva, Â., Barros, B., & Rodrigues, H. S. (2025). Multidecision criteria models for logistics performance index in the EU countries. Croatian Operational Research Review, 17(1), 1-14.
33. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
34. Stević, Ž., Ersoy, N., Başar, E. E., & Baydaş, M. (2024). Addressing the global logistics performance index rankings with methodological insights and an innovative decision support framework. Applied sciences, 14(22), 10334.
35. Suki, N. M., Suki, N. M., Sharif, A., & Afshan, S. (2021). The role of logistics performance for sustainable development in top ASİAN countries: evidence from advance panel estimations. Sustainable Development, 29(4), 595-606.
36. Tabatabaei, S. (2024).A new model for evaluating the impact of organizational culture variables on the success of knowledge management in organizations using the TOPSIS Multi-Criteria Algorithm: Case Study. Computers in Human Behavior Reports, 14, 100417.
37. Taefi, T. T., Kreutzfeldt, J., Held, T., & Fink, A. (2015). Strategies to increase the profitability of electric vehicles in urban freight transport. In e-mobility in EUROPE: Trends and good practice (pp. 367–388). Cham: Springer International Publishing.
38. Tetteh, F. K., Mensah, J., & Owusu Kwateng, K. (2025). Understanding what, how and when green logistics practices influence carbon-neutral supply chain performance. International Journal of Productivity and Performance Management, 74(6), 2211–2244.
39. Thanh Ha, L. (2025). Is it a good idea to select green logistics to enhance environmental sustainability? Insights From Global Sample. International Journal of Logistics Research and Applications, 28(5), 558–579.
40. Topal, A., & Ulutaş, A. (2024). Evaluating the logistics performance of G8 nations using multi-criteria decision-making models. J. Intell. Manag. Decis, 3, 150-158.
41. Tian, G., Lu, W., Zhang, X., Zhan, M., Dulebenets, M. A., Aleksandrov, A., ... & Ivanov, M. (2023). A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems. Environmental Science and Pollution Research, 30(20), 57279-57301.
42. Ulutaş, A., & Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM Model. Economics and Business Review, 5(4), 49-69. https://doi.org/10.18559/ebr.2019.4.3
43. Vassoney, E., Mammoliti Mochet, A., Desiderio, E., Negro, G., Pilloni, M. G., & Comoglio, C. (2021). Comparing multi-criteria decision-making methods for the assessment of flow release scenarios from small hydropower plants in the alpine area. Frontiers in Environmental Science, 9, 635100.
44. Wang, J., Duan, K., & Zheng, Y. (2025). Green supply chain management, green technology innovation and firms' energy consumption intensity. Energy Economics, 141, 108133.
45. Wang, Y. & Ozturk, I. (2023). Role of green innovation, green internal, and external supply chain management practices: a gateway to environmental sustainability. Economic research-Ekonomska istraživanja, 36(3), 1-19.
46. Wu, R. M., Zhang, Z., Yan, W., Fan, J., Gou, J., Liu, B., ..., & Wang, Y. (2022). A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement. PloS one, 17(1), e0262261.
47. Yang, W., Wang, W., & Ouyang, S. (2019). The influencing factors and spatial spillover effects of CO2 emissions from transportation in China. Science of the Total Environment, 696, 133900.
48. Yildirim, B. F., & Adiguzel Mercangoz, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45.
49. Yu, Y., Zhu, W., & Tian, Y. (2021). Green supply chain management, environmental degradation, and energy: evidence from asian countries. Discrete Dynamics in Nature and Society, 2021(1), 5179964.
50. Yürüyen, A. A., & Topal, B. (2025). G7 ülkelerinin lojistik performanslarının PSI tabanlı COBRA yöntemi ile incelenmesi. Dicle Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 15(29), 39-57. https://doi.org/10.53092/duiibfd.1601852
51. Zhu, Y., Tian, D., & Yan, F. (2020). Effectiveness of entropy weight method in decision‐making. Mathematical problems in Engineering, 2020(1), 3564835.
52. Zhang, M., Sun, M., Bi, D., & Liu, T. (2020). Green logistics development decisionmaking: Factor identification and hierarchical framework construction. IEEE Access, 8, 127897-127912.