Journals / İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi / 2026 / Cilt: 12 Sayı: 4 Özel Sayı

Evaluation of Logistics, Sustainability and Economic Performances of OECD Countries Through the Integrated CRITIC-MAIRCA Methods

OECD Ülkelerinin Lojistik, Sürdürülebilirlik ve Ekonomik Performanslarının CRITIC-MAIRCA Yöntemleriyle Analizi

Tuğrul Bayat(AFYON KOCATEPE UNIVERSITY, BOLVADİN ACADEMY OF APPLIED SCIENCES)0000-0003-1491-6178
Muhammed Karataş(AFYON KOCATEPE UNIVERSITY)0000-0002-3738-7486
Pages
1742–1773
Publication Date
2026-05-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this context, this study aims to determine the relative importance of logistics, sustainability, and economic performance indicators in OECD member countries and to rank these countries based on their overall performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: The increasing complexity of global supply chains and the growing emphasis on sustainability have highlighted the need for integrated performance evaluation frameworks that jointly consider logistics, economic, and environmental dimensions.
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Key Findings & Data:In this context, this study aims to determine the relative importance of logistics, sustainability, and economic performance indicators in OECD member countries and to rank these countries based on their overall performance.
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Conclusion & Impact:Overall, the study highlights the necessity of coordinated policy frameworks that effectively balance economic performance, sustainability goals, and logistics efficiency.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Lojistik Performans · Lojistik Performans Endeks · Sürdürülebilirlik

Abstract

Aim: The increasing complexity of global supply chains and the growing emphasis on sustainability have highlighted the need for integrated performance evaluation frameworks that jointly consider logistics, economic, and environmental dimensions. In this context, this study aims to determine the relative importance of logistics, sustainability, and economic performance indicators in OECD member countries and to rank these countries based on their overall performance. The analysis incorporates six widely used macro-level indicators: the Logistics Performance Index (LPI), Sustainable Development Goals (SDGs) Index, Foreign Direct Investment (FDI) inflows, Gross Domestic Product (GDP), carbon dioxide (CO₂) emissions, and trade openness (TO). Method: The study employs an integrated Multi-Criteria Decision-Making (MCDM) framework. The CRITIC method is used to objectively determine the weights of the evaluation criteria, while the MAIRCA method is applied to rank OECD countries according to their relative performance. To ensure the robustness of the findings, sensitivity analyses are conducted using alternative weighting (ENTROPY, CILOS, and Standard Deviation (SD)) and ranking (CoCoSo, MABAC and VIKOR) methods. Results: The CRITIC methodology results reveal that trade openness is the most dominant criterion in determining overall performance in OECD member countries, with a weighting of 0.284. This is followed by the Sustainable Development Goals Index (0.203) and the Logistics Performance Index (0.189), while carbon emissions (0.109), gross domestic product (0.108), and foreign direct investment inflows (0.106) have relatively lower weightings. These findings indicate that performance assessment in OECD countries is being reshaped around global trade integration, sustainability, and logistics efficiency, rather than traditional macroeconomic indicators. The ranking results obtained using the MAIRCA methodology show that Belgium has the highest performance, followed by the Netherlands and Denmark; while Colombia, Mexico, and Costa Rica were identified as the lowest performing countries in the analysis. Overall, the results confirm that strong trade integration, advanced logistics infrastructure, and high sustainability performance play a decisive role in the overall performance of countries. Conclusion: The findings suggest that policymakers seeking to enhance national performance should prioritize trade openness, sustainability-oriented indicators, and logistics performance rather than relying predominantly on traditional macroeconomic measures such as GDP and foreign direct investment inflows. The results demonstrate that countries with stronger integration into global trade networks and higher sustainability performance tend to achieve superior logistics outcomes, underscoring the strategic interdependence among these dimensions. Accordingly, policymakers’ effort should be designed in an integrated manner, whereby improvements in lower-weighted criteria are supported through policies that reinforce higher-weighted dimensions. For instance, fiscal incentives aimed at attracting foreign direct investment into sustainable projects can simultaneously strengthen sustainability objectives and trade-related performance. Despite the growing global emphasis on sustainability, the findings also indicate that commercial considerations continue to play a significant role in OECD member countries. Overall, the study highlights the necessity of coordinated policy frameworks that effectively balance economic performance, sustainability goals, and logistics efficiency.

Özet

Amaç: Küresel tedarik zincirlerinin artan karmaşıklığı ve sürdürülebilirliğe verilen önemin artması, lojistik, ekonomik ve çevresel boyutları birlikte ele alan entegre performans değerlendirme çerçevelerine duyulan ihtiyacı ortaya koymuştur. Bu bağlamda, bu çalışma OECD üye ülkelerinde lojistik, sürdürülebilirlik ve ekonomik performans göstergelerinin göreceli önemini belirlemeyi ve bu ülkeleri genel performanslarına göre sıralamayı amaçlamaktadır. Analiz, yaygın olarak kullanılan altı makro düzey göstergeyi içermektedir: Lojistik Performans Endeksi (LPE), Sürdürülebilir Kalkınma Hedefleri (SKH) Endeksi, Doğrudan Yabancı Yatırım (DYY) girişleri, Gayri Safi Yurtiçi Ürün (GSYİH), karbondioksit (CO₂) emisyonları ve ticaret açıklığı (TA). Yöntem: Bu çalışma, entegre bir Çok Kriterli Karar Verme (MCDM) çerçevesi kullanmaktadır. Değerlendirme kriterlerinin ağırlıklarını objektif olarak belirlemek için CRITIC yöntemi kullanılırken, OECD ülkelerini göreceli performanslarına göre sıralamak için MAIRCA yöntemi uygulanmaktadır. Bulguların sağlamlığını sağlamak için, alternatif ağırlıklandırma ve sıralama yöntemleri kullanılarak duyarlılık analizleri yapılmaktadır. Bulgular: CRITIC yöntem sonuçları, OECD üyesi ülkelerde genel performansın belirlenmesinde ticaret açıklığının 0,284 ağırlık değeriyle en baskın kriter olduğunu ortaya koymaktadır. Bunu, sürdürülebilir kalkınma hedefleri endeksi (0,203) ve lojistik performans endeksi (0,189) izlerken, karbon emisyonları (0,109), gayri safi yurtiçi hasıla (0,108) ve doğrudan yabancı yatırım girişleri (0,106) görece daha düşük ağırlıklara sahiptir. Bu bulgular, OECD ülkelerinde performans değerlendirmesinin geleneksel makroekonomik göstergelerden ziyade, küresel ticaret entegrasyonu, sürdürülebilirlik ve lojistik etkinlik ekseninde yeniden şekillendiğini göstermektedir. MAIRCA yöntemiyle elde edilen sıralama sonuçları, Belçika’nın en yüksek performansa sahip ülke olduğunu, onu Hollanda ve Danimarka’nın takip ettiğini ortaya koyarken; Kolombiya, Meksika ve Kosta Rika analiz kapsamında en düşük performansı sergileyen ülkeler olarak belirlenmiştir. Genel olarak sonuçlar, güçlü ticaret entegrasyonu, gelişmiş lojistik altyapı ve yüksek sürdürülebilirlik performansının ülkelerin toplam performansında belirleyici bir rol oynadığını teyit etmektedir. Sonuçlar: Araştırma bulguları, ulusal performansı artırmak isteyen politika yapıcıların, GSYİH ve doğrudan yabancı yatırım girişleri gibi geleneksel makroekonomik ölçütlere ağırlıklı olarak güvenmek yerine, ticaret açıklığına, sürdürülebilirlik odaklı göstergelere ve lojistik performansına öncelik vermeleri gerektiğini göstermektedir. Sonuçlar, küresel ticaret ağlarına daha güçlü entegrasyona ve daha yüksek sürdürülebilirlik performansına sahip ülkelerin, bu boyutlar arasındaki stratejik karşılıklı bağımlılığı vurgulayarak, üstün lojistik sonuçları elde etme eğiliminde olduğunu göstermektedir. Buna göre, politika yapıcıların çabaları, daha düşük ağırlıklı kriterlerdeki iyileştirmelerin, daha yüksek ağırlıklı boyutları güçlendiren politikalarla desteklendiği entegre bir şekilde tasarlanmalıdır. Örneğin, sürdürülebilir projelere doğrudan yabancı yatırım çekmeyi amaçlayan mali teşvikler, sürdürülebilirlik hedeflerini ve ticaretle ilgili performansı aynı anda güçlendirebilir. Sürdürülebilirliğe yönelik artan küresel vurguya rağmen, bulgular ayrıca ticari hususların OECD üyesi ülkelerde önemli bir rol oynamaya devam ettiğini de göstermektedir. Genel olarak, çalışma, ekonomik performans, sürdürülebilirlik hedefleri ve lojistik verimliliğini etkili bir şekilde dengeleyen koordineli politika çerçevelerinin gerekliliğini vurgulamaktadır.

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Cite This Article

Bayat, T., Karataş, M. (2026). Evaluation of Logistics, Sustainability and Economic Performances of OECD Countries Through the Integrated CRITIC-MAIRCA Methods. İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi, Cilt: 12 Sayı: 4 Özel Sayı, 1742–1773. https://doi.org/10.17336/igusbd.1719581

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References (113)

  1. AHMAD, A. U., JEEVAN, J., & RUSLAN, S. M. M. (2025). Effect of green logistics performance on sustainability in african countries. WMU Journal of Maritime Affairs, 1-44. https://doi.org/10.1007/s13437-025-00373-w
  2. AKDAG, N. O., & KARAATLI, M. (2022). An EDAS Method-Based Clustering Study to Assess the Logistics Performances of Selected Countries. European Journal of Digital Economy Research, 3(2), 59-73.
  3. AKOGLU, H. (2018). User's guide to correlation coefficients. Turkish journal of emergency medicine, 18(3), 91-93.
  4. ALFARO, L. (2017). Gains from foreign direct investment: Macro and micro approaches. World Bank Economic Review, 30(S1), S2-S15. https://doi.org/10.1093/wber/lhw007
  5. ALMULHIM, A. A., QAMRUZZAMAN, M., & ALJUGHAIMAN, A. A. (2025). The influence of green trade openness, natural resources rent, institutional quality, and R&D investment on environmental sustainability in the OECD: testing the EKC and LCC hypotheses. Frontiers in Environmental Science, 13, 1572439.
  6. ALTINTAŞ, F. F. (2025). A Novel Technique for Criteria Weighting in Multi-Criteria Decision-Making: Tanimoto Contrast Approach (TCA). Gazi University Journal of Science Part A: Engineering and Innovation, 12(2), 432-466.
  7. ARIKAN KARGI, V. S. (2022). Evaluation of Logistics Performance of The OECD Member Countries with Integrated Entropy and Waspas Method. Yönetim ve Ekonomi Dergisi, 29 (4).
  8. ARIYANTI, Y. D. P., & FU'ADI, D. K. (2025). A comparative analysis of the CRITIC and entropy methods for objective weighting of priority criteria. Jurnal Masyarakat Informatika, 16(2), 148-161.
  9. ARSHAD, M. W., SETIAWANSYAH, S., SURYONO, R. R., & RAHMANTO, Y. (2024). Combination of CRITIC Weighting Method and Multi-Atributive Ideal-Real Comparative Analysis in Staff Admissions. Explorer, 4(2), 77-86.
  10. ARSU, T., & AYAÇİN, E. (2021). Evaluation of OECD countries with multicriteria decision-making methods in terms of economic, social and environmental aspects. Operational Research in Engineering Sciences: Theory and Applications, 4(2).
  11. ARVIS, J.-F., OJALA, L., WIEDERER, C., SHEPHERD, B., RAJ, A., DAIRABAYEVA, K. & KIISKI, T. (2023). Connecting to Compete: Trade Logistics in an Uncertain Global Economy, World Bank, Washington, DC, available at: https://documents.worldbank.org/
  12. ARVIS, J.-F., OJALA, L., WIEDERER, C., SHEPHERD, B., RAJ, A., DAIRABAYEVA, K. & KIISKI, T. (2018). Connecting to Compete: Trade Logistics in the Global Economy, World Bank, Washington, DC, available at: https://documents.worldbank.org/. DOI: https://doi.org/10.1596/29971
  13. ASUERO, A. G., SAYAGO, A., & GONZÁLEZ, A. G. (2006). The correlation coefficient: An overview. Critical reviews in analytical chemistry, 36(1), 41-59.
  14. ATALAN, A. (2020). Logistic Performance Index of OECD Members. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), 12(23), 598-608.
  15. AYCIN, E. (2020). Personel seçim sürecinde CRITIC ve MAIRCA yöntemlerinin kullanılması. İşletme, 1(1), 1-12.
  16. BARAKAT, M. & GERGED, A.M. (2025). Does logistics performance foster sustainable development? A moderated mediation effect, International Journal of Social Economics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJSE-05-2024-0392
  17. BARAKAT, M., MADKOUR, T., & MOUSSA, A. M. (2023). The role of logistics performance index on trade openness in Europe. International Journal of Economics and Business Research, 25(3), 379-394
  18. BEHERA, P., BEHERA, B., SETHİ, N., & HANDOYO, R. D. (2024). What drives environmental sustainability? The role of renewable energy, green innovation, and political stability in OECD economies. International Journal of Sustainable Development & World Ecology, 31(7), 761-775.
  19. BELKE, M. (2020). Evaluation of macroeconomic performance of G7 countries with CRITIC and MAIRCA methods. Istanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 19, 120-139.
  20. BORAL, S., HOWARD, I., CHATURVERDI, S. K., MCKEE, K., & NAIKAN, V. N. A. (2020). An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA. Engineering Failure Analysis, 108, 104195.
  21. BORENSZTEIN, E., DE GREGORIO, J., & LEE, J.-W. (1998). How does foreign direct investment affect economic growth? Journal of International Economics, 45(1), 115-135. https://doi.org/10.1016/S0022-1996(97)00033-0
  22. BOŽANIĆ, D., MILIĆ, A., TEŠIĆ, D., SALABUN, W., & PAMUČAR, D. (2021). D numbers–FUCOM–fuzzy RAFSI model for selecting the group of construction machines for enabling mobility. Facta Universitatis, Series: Mechanical Engineering, 19(3), 447-471.
  23. BROOKS, D. H., & MENON, J. (Eds.). (2008). Infrastructure and trade in Asia. Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781848442733
  24. BUGARCIC, F. Ž., SKVARCIANY, V., & STANISIC, N. (2020). Logistics performance index in international trade: case of Central and Eastern European and Western Balkans countries, Vilnius Gediminas Technical University.
  25. CAN, M., AHMED, Z., MERCAN, M., & KALUGINA, O. A. (2021). The role of trading environment-friendly goods in environmental sustainability: Does green openness matter for OECD countries?. Journal of environmental management, 295, 113038.
  26. CARTER, C. R., & ROGERS, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International journal of physical distribution & logistics management, 38(5), 360-387.
  27. CELEBI, U. (2021). The impact of logistics performance index upon Gross Domestic Product: mediating roles of foreign direct investment and patents. Journal of Global Strategic Management. 29-46 DOI: 10.20460/JGSM.2021.297
  28. CHRISTOPHER, M. (2022). Logistics and supply chain management. Pearson UK.
  29. COTO-MILLAN, P., & PESQUERA, M. A. (2024, November). The Impact of Logistics Performance on the Convergence of Economic Growth in the Countries of the World. In International Conference on Innovative Intelligent Industrial Production and Logistics (pp. 156-165). Cham: Springer Nature Switzerland.
  30. ÇAKIR, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24(3-4), 177-186.
  31. D’ALEO, V., & SERGI, B. S. (2017). Does logistics influence economic growth? The European experience. Management Decision, 55(8), 1613-1628.
  32. DEKKER, R., BLOEMHOF, J., & MALLIDIS, I. (2012). Operations Research for green logistics–An overview of aspects, issues, contributions and challenges. European journal of operational research, 219(3), 671-679. doi: https://doi.org/10.1016/j.ejor.2011.11.010
  33. DEL RIO-CHANONA, R.M., MEALY, P., PICHLER, A., LAFOND, F. & FARMER, D. (2020). “Supply and demand shocks in the Covid-19 pandemic: an industry and occupation perspective”, Covid Economics: Vetted and Real-Time Papers, Vol. 6 No. 6, pp. 65-103.
  34. DEMIRBAS, M., URE, S., DEMIR, O., KARAKOY, Ç., & ULUTAS, A. (2024). The Relationship Between Financial Development and Logistics Performance Index: A Panel ARDL Approach in Emerging Markets. Spectrum of Engineering and Management Sciences, 2(1), 202-213.
  35. DIAKOULAKI, D., MAVROTAS, G., & PAPAYANNAKIS, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H
  36. DJANKOV, S., FREUND, C., & PHAM, C. S. (2010). Trading on time. The Review of Economics and Statistics, 92(1), 166-173. https://doi.org/10.1162/rest.2009.11498
  37. DOLLAR, D., & KRAAY, A. (2004). Trade, growth, and poverty. The Economic Journal, 114(493), F22-F49. https://doi.org/10.1111/j.0013-0133.2004.00186.x
  38. ERDOĞAN, B. (2023). Sigorta Şirketlerinin Performansının CRITIC-MAIRCA Modeliyle Değerlendirilmesi: Türk Sigortacılık Sektörü Üzerine Bir Araştırma. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 25(4), 1438-1455.
  39. EUROPEAN ENVIRONMENT AGENCY. (2021). Transport and environment report 2021. https://www.eea.europa.eu/publications/transport-and-environment-report-2021
  40. EVANGELISTA, P., SANTORO, L., & THOMAS, A. (2022). Environmental sustainability in third-party logistics service providers: A systematic literature review and research agenda. International Journal of Production Economics, 247, 108457. https://doi.org/10.1016/j.ijpe.2022.108457
  41. FLEURBAEY, M., & BLANCHET, D. (2013). Beyond GDP: Measuring welfare and assessing sustainability. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199767199.001.0001
  42. FRANKEL, J. A., & ROMER, D. (1999). Does trade cause growth?. American Economic Review, 89(3), 379-399. https://doi.org/10.1257/aer.89.3.379
  43. FREUND, C., & WEINHOLD, D. (2004). The effect of the Internet on international trade. Journal of International Economics, 62(1), 171-189. https://doi.org/10.1016/S0022-1996(03)00059-X
  44. GHARIZADEH BEIRAGH, R., ALIZADEH, R., SHAFIEI KALEIBARI, S., CAVALLARO, F., ZOLFANI, S. H., BAUSYS, R., & MARDANI, A. (2020). An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies. Sustainability, 12(3), 789. https://doi.org/10.3390/su12030789
  45. GIGOVIC, L., PAMUČAR, D., BAJIC, Z., & MILICEVIC, M. (2016). The combination of expert judgment and GISMAIRCA analysis for the selection of sites for ammunition depots. Sustainability, 8(4), 372.
  46. GUNER, S., & COSKUN, E. (2012). Comparison of impacts of economic and social factors on countries' logistics performances: a study with 26 OECD countries. Research in logistics & production, 2(4), 330-343.
  47. HAUSMAN, W. H., LEE, H. L., & SUBRAMANIAN, U. (2013). The impact of logistics performance on trade. Production and Operations Management, 22(2), 236-252. https://doi.org/10.1111/j.1937-5956.2011.01312.x
  48. HENDRICKS, K. B., SINGHAL, V. R., & ZHANG, R. (2015). The effect of operational slack, diversification, and vertical relatedness on stock market reaction to supply chain disruptions. Journal of Operations Management, 36, 1-12. https://doi.org/10.1016/j.jom.2015.02.003
  49. HUMMELS, D., & SCHAUR, G. (2013). Time as a trade barrier. American Economic Review, 103(7), 2935-2959. https://doi.org/10.1257/aer.103.7.2935
  50. IKRAM, B., & MARIAME, A. (2024). Green Growth or Economic Gain? Assessing Environmental Efficiency Using Data Envelopment Analysis: Case of Africa. International Journal of Energy Economics and Policy, 14(4), 426-433.
  51. JAHAN, A., MUSTAPHA, F., SAPUAN, S. M., ISMAIL, M. Y., & BAHRAMINASAB, M. (2012). A framework for weighting of criteria in ranking stage of material selection process. The International Journal of Advanced Manufacturing Technology, 58(1), 411-420.
  52. JAYATHILAKA, R., JAYAWARDHANA, C., EMBOGAMA, N., JAYASOORIYA, S., KARUNARATHNA, N., GAMAGE, T., & KURUPPU, N. (2022). Gross domestic product and logistics performance index drive the world trade: A study based on all continents. PloS one, 17(3), e0264474.
  53. KARABULUT, M., SAYIN, C., & ERDOĞAN, S. (2025). Investigation of the optimum biodiesel fuel blend and engine operating conditions in an engine with a waste heat recovery system using CRITIC and MAIRCA. Arabian Journal for Science and Engineering, 1-28.
  54. KAYA, S. K. (2020). Evaluation of the Effect of COVID-19 on Countries’ Sustainable Development Level: A comparative MCDM framework. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 101-122.
  55. KETCHEN Jr, D. J., & HULT, G. T. M. (2007). Bridging organization theory and supply chain management: The case of best value supply chains. Journal of operations management, 25(2), 573-580.
  56. KHAN, S. A. R., & QIANLI, D. (2017). Does national scale economic and environmental indicators spur logistics performance? Evidence from UK. Environmental Science and Pollution Research, 24, 26692-26705.
  57. KHAN, S. A. R., QIANLI, D., SONGBO, W., ZAMAN, K., & ZHANG, Y. (2017). Environmental logistics performance indicators affecting per capita income and sectoral growth: evidence from a panel of selected global ranked logistics countries. Environmental science and pollution research, 24, 1518-1531. https://doi.org/10.1007/s11356-016-7916-2
  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.
  59. KUMARI, M., & BHARTI, N. (2021). Trade and logistics performance: does country size matter?. Maritime Economics & Logistics, 23, 401-423.
  60. LARSON, P. D. (2021). Relationships between logistics performance and aspects of sustainability: A cross-country analysis. Sustainability, 13(2), 623.
  61. LE, M. H. (2024). Sustainable Development Goals and Logistics Performance: Insights from ASEAN countries. International Journal of e-Navigation and Maritime Economy, 23, 36-40.
  62. LEGG, S. (2021). IPCC, 2021: Climate change 2021-the physical science basis. Interaction, 49(4), 44-45. https://www.ipcc.ch/report/ar6/wg1/
  63. LI, J., WANG, H., & ZHANG, Y. (2023). Green logistics and sustainability: a review of recent empirical evidence. Sustainability 15(3):1234–1245
  64. MADIC, M., & RADOVANOVIC, M. (2015). Ranking of some most commonly used nontraditional machining processes using ROV and CRITIC methods. UPB Scientific Bulletin, Series D: Mechanical Engineering, 77(2), 193-204.
  65. MARTI, L., PUERTAS, R., & GARCIA, L. (2014). The importance of the Logistics Performance Index in international trade. Applied economics, 46(24), 2982-2992.
  66. MARUF, M., & ÖZDEMİR, K. (2023). Ranking of tourism web sites according to service performance criteria with CRITIC and MAIRCA methods: The case of Turkey. Uluslararası Yönetim Akademisi Dergisi, 6(4), 1108-1117.
  67. MCKINNON, A. (2018). Decarbonizing logistics: Distributing goods in a low-carbon world. London: Kogan Page.
  68. MISHRA, A. R., RANI, P., CAVALLARO, F., & ALRASHEEDI, A. F. (2023). Assessment of sustainable wastewater treatment technologies using interval-valued intuitionistic fuzzy distance measure-based MAIRCA method. Facta Universitatis, Series: Mechanical Engineering, 21(3), 359-386.
  69. MO, F., & WANG, D. (2019). Environmental sustainability of road transport in OECD countries. Energies, 12(18), 3525.
  70. MUHAMMAD, I., OZCAN, R., JAIN, V., SHARMA, P., & SHABBIR, M. S. (2022). Does environmental sustainability affect the renewable energy consumption? Nexus among trade openness, CO2 emissions, income inequality, renewable energy, and economic growth in OECD countries. Environmental Science and Pollution Research, 29(60), 90147-90157.
  71. MUHURI, B. R., MONDAL, M. K., SMARANDACHE, F., MAHAPATRA, G. S., & MAHAPATRA, B. S. (2024). Strategy selection for natural resource conservation capturing vagueness through integrated neutrosophic CRITIC-MAIRCA approach. Neutrosophic Sets and Systems 73, 1.
  72. MUMIN, M. A., & YAKUBU, I. N. (2025). The Impact of Trade Openness on Logistics Performance in Africa. Cureus Journal of Business and Economics, 2(1).
  73. MUNIM, Z. H., & SCHRAMM, H.-J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1-19. https://doi.org/10.1186/s41072-018-0027-0
  74. NEGI, A., RANAKOTI, L., SINGH, V., BHANDARI, P., WANG, M., FEKETE, G., & SINGH, T. (2025). A hybrid CRITIC-MAIRCA framework for optimal phase change material selection in solar distillation systems. International Journal of Thermofluids, 27, 101167.
  75. ORGANIZATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT (OECD). (2022). FDI in figures. https://www.oecd.org/investment/FDI-in-Figures-April-2022.pdf
  76. OZGUNER, Z., & KOSE, Z. (2025). Relationship Between Logistics Performance, Trade Volume and Economic Growth. Iğdır Üniversitesi Sosyal Bilimler Dergisi, (38), 230-252.
  77. OZMEN, M. (2019). Logistics competitiveness of OECD countries using an improved TODIM method. Sādhanā, 44(5), 108.
  78. PAGELL, M., & WU, Z. (2009). Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. Journal of supply chain management, 45(2), 37-56.
  79. PAMUČAR, D. S., PEJCIC TARLE, S., & PAREZANOVIC, T. (2018). New hybrid multi-criteria decision-making DEMATELMAIRCA model: sustainable selection of a location for the development of multimodal logistics centre. Economic research-Ekonomska istraživanja, 31(1), 1641-1665.
  80. PAMUČAR, D. VASIN, L., & LUKOVAC, L. (2014). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. In XVI International Scientific-expert Conference on Railways, Railcon, Niš, Serbia, 9-10 October 2014. (Vol. 10, pp. 89-92).
  81. PAMUČAR, D., ĆIROVIĆ, G., & BOŽANIĆ, D. (2019). Application of interval valued fuzzy-rough numbers in multi-criteria decision making: The IVFRN-MAIRCA model. Yugoslav journal of operations research, 29(2), 221-247.
  82. PAMUCAR, D., MACURA, D., TAVANA, M., BOŽANIĆ, D., & KNEŽEVIĆ, N. (2022). An integrated rough group multicriteria decision-making model for the ex-ante prioritization of infrastructure projects: The Serbian Railways case. Socio-Economic Planning Sciences, 79, 101098.
  83. PIECYK, M. I., & MCKINNON, A. C. (2010). Forecasting the carbon footprint of road freight transport in 2020. International Journal of Production Economics, 128(1), 31-42. https://doi.org/10.1016/j.ijpe.2009.08.027
  84. PORTER, M. E. (2001). The competitive advantage of nations. Harvard business review, 68(2), 73-93.
  85. PORTER, M. E., & VAN DER LINDE, C. (1995). Toward a new conception of the environment-competitiveness relationship. Journal of Economic Perspectives, 9(4), 97-118. https://doi.org/10.1257/jep.9.4.97
  86. RASHIDI, K., & CULLINANE, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46.
  87. RODRIGUE, J.-P. (2020). The geography of transport systems (5th ed.). Routledge. https://doi.org/10.4324/9780429346323
  88. SACHS, J. D., LAFORTUNE, G., KROLL, C., FULLER, G., & WOELM, F. (2022). From Crisis to Sustainable Development: the SDGs as Roadmap to 2030 and Beyond. Sustainable Development Report 2022. Cambridge: Cambridge University Press.
  89. SACHS, J. D., SCHMIDT-TRAUB, G., KROLL, C., LAFORTUNE, G., & FULLER, G. (2019). Sustainable Development Report 2019. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN). https://doi.org/10.1017/9781108765015
  90. SEROKA-STOLKA, O., & OCIEPA-KUBICKA, A. (2019). Green logistics and circular economy. Transp Res Procedia 39:471–479. https://doi.org/10.1016/j.trpro.2019.06.048
  91. SEURING, S., & MÜLLER, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of cleaner production, 16(15), 1699-1710.
  92. SHARMA, M., LUTHRA, S., JOSHI, S., KUMAR, A., & JAIN, A. (2023) Green logistics driven circular practices adoption in industry 4.0 Era: a moderating effect of institution pressure and supply chain flexibility. Journal of Cleaner Production, 383:135284.
  93. SHI, X., & LI, F. (2025). Intelligent data driven assessment of physical education programs using circular intuitionistic fuzzy MAIRCA CRITIC model. Scientific Reports, 15(1), 35428.
  94. SIKDER, M., WANG, C., YEBOAH, F. K., & WOOD, J. (2024). Driving factors of CO2 emission reduction in the logistics industry: an assessment of the RCEP and SAARC economies. Environment, Development and Sustainability, 26(1), 2557-2587. https://doi.org/10.1007/s10668-022-02840-3
  95. SIKSNELYTE-BUTKIENE, I., ZAVADSKAS, E. K., & STREIMIKIENE, D. (2020). Multi-criteria decision-making (MCDM) for the assessment of renewable energy technologies in a household: A review. Energies, 13(5), 1164.
  96. SLACK, N., BRANDON-JONES, A., & BURGESS, N. (2022). Operations management 10th edition. Pearson.
  97. SONG, M., ROH, S., & LEE, H. (2024). Exploring the implications of logistics efficiency and renewable energy for sustainable development. Environment, Development and Sustainability, 1-22.
  98. STANK, T. P., KELLER, S. B., & DAUGHERTY, P. J. (2001). Supply chain collaboration and logistical service performance. Journal of Business logistics, 22(1), 29-48.
  99. TURANOĞLU ŞİRİN, B. (2023). A hybrid approach based on fuzzy BWM and MAIRCA for selection of engine in rotary wing unmanned aerial vehicle design. Journal of Intelligent & Fuzzy Systems, 45(3), 3767-3778.
  100. TUŞ, A., & ADALI, E, A. (2019). The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem. Opsearch, 56(2), 528-538.
  101. UCA, N., INCE, H. & SUMEN, H. (2016). “The mediator effect of logistics performance index on the relation between corruption perception index and foreign trade volume”, European Scientific Journal, Vol. 12 No. 25, pp. 1857-7881, https://doi.org/10.19044/esj.2016.v12n25p37
  102. UNITED NATIONS (UN). (2015). Transforming our world: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda
  103. UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT (UNCTAD). (2020a). Review of maritime transport 2020. United Nations. https://unctad.org/system/files/official-document/rmt2020_en.pdf
  104. UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT (UNCTAD). (2020b). Maritime transport and the Sustainable Development Goals. United Nations. https://unctad.org/system/files/official-document/dtltlb2019d2_en.pdf
  105. VIENAZINDIENE, M., TAMULIENE, V., & ZALECKIENE, J. (2021). Green logistics practices seeking development of sustainability: evidence from Lithuanian transportation and logistics companies. Energies 14(22): 7500
  106. WANG, C. N., TRAN THI, D. O., NHIEU, N. L., & HSUEH, M. H. (2025). An Integrated Entropy–MAIRCA Approach for Multi-Dimensional Strategic Classification of Agricultural Development in East Africa. Mathematics, 13(15), 2465.
  107. WANNISINGHE, P., JAYAKODY, S., RATHNAYAKE, S., WIJAYASINGHE, D., JAYATHILAKA, R., & MADHAVIKA, N. (2023). Determining the influence of LPI, GCI and IR on FDI: A study on the Asia and Pacific Region. PloS one, 18(2), e0281246.
  108. WONG, C.Y., WONG, C.W.Y. & BOON-ITT, S. (2020), “Effects of green supply chain integration and green innovation on environmental and cost performance”, International Journal of Production Research, Vol. 58 No. 15, pp. 4589-4609, https://doi.org/10.1080/00207543.2020.1756510
  109. WORLD BANK. (2020). World development report 2020: Trading for development in the age of global value chains. https://doi.org/10.1596/978-1-4648-1457-0
  110. WORLD BANK. (2023). World Development Indicators. https://databank.worldbank.org/reports.aspx?source=world-development-indicators.
  111. YILDIRIM, B. F., & ADİGUZEL MERCANGOZ, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45.
  112. ZHANG, X. (2025). Pythagorean fuzzy MAIRCA CRITIC for energy price and demand forecasting involving eco economic factors for sustainable economy. Scientific Reports, 15(1), 34340.
  113. ZOLFANI, S. H., ECER, F., PAMUČAR, D., & RASLANAS, S. (2020). Neighborhood selection for a newcomer via a novel BWM-based revised MAIRCA integrated model: a case from the Coquimbo-La Serena conurbation, Chile. International Journal of Strategic Property Management, 24(2), 102-118.