Uluslararası Lojistikte Verimlilik: Ticaret, Emisyonlar ve Türkiye Örneği

Öz

Amaç: Bu çalışma, ülkeler arasında ticaret hacmi, liman altyapısı, enerji yoğunluğu ve sera gazı emisyonları arasındaki ilişkileri inceleyerek uluslararası lojistiğin çevresel verimliliğini incelemektedir. Çalışma, lojistik tipolojilerini sınıflandırmayı ve Türkiye'yi odak noktası olarak alarak ticaret odaklı operasyonların çevresel etkilerini değerlendirmeyi amaçlamaktadır. Yöntem: Ülkeler arası ampirik bir çerçeve kullanarak, kişi başına düşen CO₂, metan, toplam sera gazı emisyonları, enerji yoğunluğu ve ticaret hacimleri hakkındaki verileri entegre ediyoruz. Dünya Liman Endeksi (2019), TradeMap (2024) ve Verilerle Dünyamız (2024) verilerini kullanan çalışma, çevresel lojistik tipolojilerini belirlemek için K-ortalama kümeleme yöntemini ve ticaret ve enerji yoğunluğunun CO₂ emisyonları üzerindeki etkilerini incelemek için OLS regresyonunu uyguluyor. Türkiye, karşılaştırmalı bir referans noktası olarak belirlenmiştir. Bulgular: Çalışma, üç lojistik tipolojisini tanımlamaktadır: çevresel olarak verimli, geçişli ve emisyon yoğun. Regresyon sonuçları, enerji yoğunluğunun CO₂ emisyonlarının en güçlü belirleyicisi olduğunu göstermektedir (p < 0,001), ticaret hacmi ve etkileşimi ise anlamlı değildir. Türkiye, önemli ticaret faaliyetlerine rağmen orta düzeyde enerji kullanımı ve nispeten düşük emisyonlarla geçişli küme içerisinde yer almaktadır. Sonuç ve Katkılar: Sonuçlar, sürdürülebilir ticaretin, ticaret hacimlerinin azaltılmasından ziyade enerji verimli lojistik faaliyetlerine dayandığını vurgulamaktadır. Karar vericiler, enerji kullanımını optimize etmeye ve altyapıyı modernize etmeye odaklanmalıdır. Emisyon ölçümlerinin lojistik ve ticaret stratejilerine entegre edilmesi, özellikle gelişmekte olan ekonomilerde daha yeşil dönüşümleri destekleyebilir. Sınırlılıklar: Ülke düzeyindeki verilerin kullanımı, bölgesel eşitsizlikleri ve sektöre özgü emisyonları gizleyebilir. Ayrıca, kümeleme, karmaşık lojistik sistemlerini basitleştirebilir. Gelecekteki araştırmalar, daha fazla hassasiyet için yerel ve lojistik sektörüne özgü verileri içerebilir.

Anahtar Kelimeler

• Çevresel Verimlilik
• Uluslararası Lojistik
• Türkiye Ticaret Analizi

Efficiency in International Logistics: Trade, Emissions and the Case of Türkiye

Öz

Purpose: This study examines the environmental efficiency of international logistics by exploring the relationships among trade volume, port infrastructure, energy intensity, and GHG emissions across countries. It aims to classify logistics typologies and evaluate the environmental impacts of trade-driven operations, with Türkiye as a focal case. Methodology: A cross-country empirical framework integrates data on per capita CO₂, methane, total GHG emissions, energy intensity, and trade volumes. The study uses data from the World Port Index (2019), TradeMap (2024), and Our World in Data (2024), the study applies K-means clustering to identify environmental logistics typologies and OLS regression to examine the effects of trade and energy intensity on CO₂ emissions. Türkiye is used as a comparative reference point. Findings: The study identifies three logistics typologies: environmentally efficient, transitional, and emissions-intensive. Regression results show energy intensity as the strongest predictor of CO₂ emissions (p < 0.001), while trade volume and its interaction are not significant. Türkiye falls within the transitional cluster, characterized by moderate energy consumption and relatively low emissions, even with high trade activity. Implications: The results underscore that sustainable trade hinges on energy-efficient logistics rather than reduced trade volumes. Policymakers should focus on optimizing energy use and modernizing infrastructure. Integrating emissions metrics into logistics and trade strategies can support greener transitions, especially in emerging economies. Limitations: The use of country-level data may obscure regional disparities and sector-specific emissions. Additionally, clustering may oversimplify complex logistics systems. Future research should incorporate subnational and logistics-specific data for greater precision.

Anahtar Kelimeler

• Environmental Efficiency
• International Logistics
• Türkiye Trade Analysis.

Kaynakça

1. Acciaro, M., Hoffmann, P. N., & Eide, M. S. (2013). The energy efficiency gap in maritime transport. Journal of Shipping and Ocean Engineering, 3(1-2), 1.
2. Arya, P., Srivastava, M. K., & Jaiswal, M. P. (2020). Modelling environmental and economic sustainability of logistics. Asia-Pacific Journal of Business Administration, 12(1), 73-94.
3. Chang, Y. T. (2017). Environmental efficiency of ports: a data envelopment analysis approach. In Ports and the Environment (pp. 77-88). Routledge.
4. Cristea, A., Hummels, D., Puzzello, L., & Avetisyan, M. (2013). Trade and the greenhouse gas emissions from international freight transport. Journal of environmental economics and management, 65(1), 153-173.
5. Dharmapriya, N., Gunawardena, V., Methmini, D., Jayathilaka, R., & Rathnayake, N. (2025). Carbon emissions across income groups: exploring the role of trade, energy use, and economic growth. Discover Sustainability, 6(1), 1-19.
6. Dinwoodie, J., Tuck, S., Knowles, H., Benhin, J., & Sansom, M. (2012). Sustainable development of maritime operations in ports. Business Strategy and the environment, 21(2), 111-126.
7. Figueira, J., Greco, S., & Ehrgott, M. (Eds.). (2005). Multiple criteria decision analysis: state of the art surveys.
8. Fulzele, V., & Shankar, R. (2023). Performance measurement of sustainable freight transportation: a consensus model and FERA approach. Annals of Operations Research, 324(1), 501-542.

9. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The quarterly journal of economics, 110(2), 353-377.
10. Gu, G., Zhang, J., & Pan, X. (2025). A Meta-Frontier Approach to Evaluating the Environmental Efficiency of Coastal Ports: Implications for Port Sustainability. Journal of Marine Science and Engineering, 13(7), 1272.
11. Hargrove, A., Hao, F., & Sommer, J. M. (2022). Governing trade: a cross-national study of governance, trade, and CO2 emissions. Journal of Environmental Studies and Sciences, 12(4), 727-738.
12. Heil, M. T., & Selden, T. M. (2001). International trade intensity and carbon emissions: a cross-country econometric analysis. The Journal of Environment & Development, 10(1), 35-49.
13. Herdiana, I., Kamal, M. A., & Estri, M. N. (2025). A More Precise Elbow Method for Optimum K-means Clustering. arXiv preprint arXiv:2502.00851.
14. Humphreys, R. M., & Dumitrescu, A. (2021). Decarbonizing the freight and logistics sector: Discussion paper. The World Bank Group. https://documents1.worldbank.org/curated/en/457501643121421164/pdf/Discussion-Paper.pdf
15. International Energy Agency. (2023). Transport – Energy system. Retrieved on June 15 from IEA. https://www.iea.org/energy-system/transport#tracking
16. Knight, K. W., & Schor, J. B. (2014). Economic growth and climate change: a cross-national analysis of territorial and consumption-based carbon emissions in high-income countries. Sustainability, 6(6), 3722-3731.
17. Lenort, R., Wicher, P., Samolejova, A., Zsifkovits, H., Raith, C., Miklautsch, P., & Pelikanova, J. (2022). Selecting sustainability key performance indicators for smart logistics assessment. Acta logistica, 9(4), 467-478.
18. Li, C., & Wang, F. (2025). Management and control decision of energy intensity in logistics industry under the background of dual carbon strategy in China. Scientific Reports, 15(1), 21732.
19. Lu, M., Xie, R., Chen, P., Zou, Y., & Tang, J. (2019). Green transportation and logistics performance: An improved composite index. Sustainability, 11(10), 2976.
20. McKinnon, A. (2018). Decarbonizing logistics: Distributing goods in a low carbon world. Kogan Page Publishers.
21. Mol, A. P. J., & Sonnenfeld, D. A. (2000). Ecological modernisation around the world: An introduction. Environmental Politics, 9(1), 3–14.
22. OWID (Our World in Data). (2024). CO₂ and greenhouse gas emissions. https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions
23. Pappas, D., Chalvatzis, K. J., Guan, D., & Ioannidis, A. (2018). Energy and carbon intensity: A study on the cross-country industrial shift from China to India and SE Asia. Applied energy, 225, 183-194.
24. Prause, G. (2014). Sustainable development of logistics clusters in green transport corridors. Journal of Security and Sustainability Issues, 4(1), 59-68.
25. Psaraftis, H. N., & Kontovas, C. A. (2014). Ship speed optimization: Concepts, models and combined speed-routing scenarios. Transportation Research Part C: Emerging Technologies, 44, 52-69.
26. Rehan, M., Gungor, S., Qamar, M., & Naz, A. (2023). The effects of trade, renewable energy, and financial development on consumption-based carbon emissions (comparative policy analysis for the G20 and European Union countries). Environmental Science and Pollution Research, 30(33), 81267-81287.
27. Rodrigue, J. P. (2020). The geography of transport systems. Routledge.
28. Roso, V., Woxenius, J., & Lumsden, K. (2009). The dry port concept: connecting container seaports with the hinterland. Journal of Transport Geography, 17(5), 338-345.
29. Shen, Y., Liu, J., & Tian, W. (2022). Interaction between international trade and logistics carbon emissions. Energy Reports, 8, 10334-10345.
30. Tinnes, E., Perez, F., Kandel, M., & Probst, T. (2024, June 19). Decarbonizing logistics: Charting the path ahead. McKinsey & Company. Retrieved on June 14 from https://www.mckinsey.com/capabilities/operations/our-insights/decarbonizing-logistics-charting-the-path-ahead
31. Umargono, E., Suseno, J. E., & Gunawan, S. V. (2020, October). K-means clustering optimization using the elbow method and early centroid determination based on mean and median formula. In The 2nd international seminar on science and technology (ISSTEC 2019) (pp. 121-129). Atlantis Press.
32. United Nations Conference on Trade and Development. (2023). Review of maritime transport 2023: Towards a green and just transition. Retrieved on June 13 from UNCTAD. https://unctad.org/publication/review-maritime-transport-2023
33. Wang, M., Li, H., Chiu, Y. H., Deng, K., & Deng, M. (2023). Research on the carbon emission reduction potential of the ports in the Yangtze River Delta of China. SAGE Open, 13(4), 21582440231206937.
34. Wehner, J. (2018). Energy efficiency in logistics: An interactive approach to capacity utilisation. Sustainability, 10(6), 1727.
35. WPI (World Port Index). (2019). World-wide maritime port information. Retrieved on June 13 from https://msi.nga.mil/Publications/WPI
36. Yazar Okur, İ. G., Doganer Duman, B., Demirci, E., & Yıldırım, B. F. (2025). Evaluating logistics sector sustainability indicators using multi-expert Fermatean fuzzy entropy and WASPAS methodology. Journal of International Logistics and Trade.
37. Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European journal of operational research, 189(1), 1-18.