Hinterland Kısıtları Altında Türk Limanlarında Liman Kapasitesi Genişlemesi

Öz

Bu çalışma, Türk limanlarında Hinterland Darboğaz Etkisi’ni (Hinterland Bottleneck Effect – HBE), oran temelli bekleme süresi (dwell time) ölçümlerinde ortaya çıkan mekanik endojenlik sorunlarını bertaraf eden revize edilmiş bir tanımlama stratejisiyle incelemektedir. İthal edilen bekleme süresi değişkenleri yerine, bağımlı değişken olarak konteyner elleçleme büyümesi, elleçleme oynaklığı ve teorik durağan denge koşullarından sapmalar gibi doğrudan gözlemlenebilir sistem performans göstergeleri kullanılmaktadır. 2010–2023 döneminde faaliyet gösteren on iki büyük Türk konteyner limanına ait panel veri seti kullanılarak, hinterland kapasite kısıtlarının elleçleme doygunluğu, artan operasyonel oynaklık ve Little Yasası denge öngörülerinden sistematik sapmalarla ilişkili olup olmadığı test edilmektedir (Little, 1961). HBE’nin nedensel tanımlaması, kısa vadeli liman koşullarından ziyade uzun vadeli altyapı planlamasıyla belirlenen demiryolu bağlantılarının devreye alınması üzerinden, yarı-deneysel bir şok olarak ele alınmaktadır. Sabit etkiler tahminleri, hinterland kapasite kısıtlarının elleçleme oynaklığını anlamlı biçimde artırdığını, buna karşın hinterland kapasitesi bağlayıcıyken liman taraflı kapasite artışlarının elleçleme büyümesi üzerinde anlamlı bir etkisi olmadığını göstermektedir. Demiryolu bağlantısının devreye girmesi, oynaklığı yaklaşık %18 oranında azaltmakta ve sistem davranışını teorik durağan dengeye yaklaştırmaktadır. Kantiller regresyon sonuçları, bu etkinin özellikle oynaklık dağılımının üst dilimlerinde yoğunlaştığını ortaya koymaktadır. Bulgular, HBE’nin oran kurgusundan kaynaklanan bir ölçüm yanlılığı değil, gelişmekte olan piyasa limanlarında altyapı sıralamasına ilişkin önemli politika sonuçları olan gerçek bir sistem olgusu olduğunu göstermektedir.

Anahtar Kelimeler

• Liman elleçleme hacmi
• hinterland bağlantısallığı
• kapasite kısıtları
• darboğaz ekonomisi
• Türk limanları

Destekleyen Kurum

Bu araştırma için herhangi bir mali destek alınmamıştır.

Etik Beyan

Bu çalışma insan veya hayvan denekleri içermediğinden etik onay gerekmemiştir.

Port Capacity Expansion Under Hinterland Constraints In Turkish Seaports

Öz

This paper investigates the Hinterland Bottleneck Effect (HBE) in Turkish seaports using a revised identification strategy that avoids mechanical endogeneity inherent in ratio-based dwell time measures. Instead of relying on imputed dwell time, we employ directly observable system-level performance indicators—container throughput growth, throughput volatility, and deviations from theoretical steady-state conditions—as dependent variables. Using panel data from twelve major Turkish container ports over the period 2010–2023, we examine whether hinterland capacity constraints are associated with throughput saturation, heightened operational volatility, and systematic departures from Little’s Law equilibrium predictions (Little, 1961). Causal identification of the HBE is anchored in rail connectivity activation, interpreted as a quasi-experimental shock driven by long-term infrastructure planning rather than short-run port conditions. Fixed-effects estimates indicate that hinterland constraints are significantly associated with higher throughput volatility, while port-side capacity expansion shows no significant relationship with throughput growth when hinterland capacity is binding. Rail activation generates statistically significant structural breaks, reducing volatility by approximately 18 percent and shifting system behavior closer to steady-state benchmarks. Quantile regression results further reveal that these effects are concentrated in the upper tail of the volatility distribution. Overall, the findings confirm that the HBE reflects a genuine system-level phenomenon with important implications for infrastructure sequencing in emerging market port systems.

Anahtar Kelimeler

• Port throughput
• hinterland connectivity
• capacity constraints
• bottleneck economics
• Turkish seaports

Destekleyen Kurum

No financial support was received for this research.

Etik Beyan

Ethical approval was not required as this study did not involve human or animal subjects.

Kaynakça

1. Akgül, E. F., Solak Fışkın, C., Dūzalan, B., Erdoğan, T., & Karataş Çetin, Ç. (2015). Port competitiveness and efficiency: An analysis of Turkish container ports [Conference presentation]. European Conference on Shipping, Intermodalism and Ports (ECONSHIP), Chios, Greece.
2. Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22.
3. Bird, J. (1963). The major seaports of the United Kingdom. Hutchinson.
4. Blonigen, B. A., & Wilson, W. W. (2008). Port efficiency and trade flows. Review of International Economics, 16(1), 21–36.
5. Borjas, G. J. (1980). The relationship between wages and weekly hours of work: The role of division bias. Journal of Human Resources, 15(3), 409–423.
6. Brooks, M. R., Pallis, A. A., & Perkins, S. (2014). Port investment and container shipping markets (International Transport Forum Discussion Paper No. 2014-03). OECD/ITF.
7. Clark, X., Dollar, D., & Micco, A. (2004). Port efficiency, maritime transport costs, and bilateral trade. Journal of Development Economics, 75(2), 417–450.
8. Cullinane, K., Wang, T.-F., Song, D.-W., & Ji, P. (2006). The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis. Transportation Research Part A: Policy and Practice, 40(4), 354–374.

9. De Langen, P. W. (2007). Port competition and selection in contestable hinterlands: The case of Austria. European Journal of Transport and Infrastructure Research, 7(1), 1–14.
10. Ducruet, C., & Notteboom, T. (2012). The worldwide maritime network of container shipping: Spatial structure and regional dynamics. Global Networks, 12(3), 395–423.
11. Economides, N. (1996). The economics of networks. International Journal of Industrial Organization, 14(6), 673– 699.
12. Ellet, C. (1839). An essay on the laws of trade in reference to the works of internal improvement in the United States. P. D. Bernard.
13. Fancello, G., Serra, P., Aramu, V., & Vitiello, D. M. (2021). Evaluating the efficiency of Mediterranean container ports using data envelopment analysis. Competition and Regulation in Network Industries, 22(3–4), 163– 188. https://doi.org/10.1177/17835917211047837
14. Ferrari, C., Parola, F., & Gattorna, E. (2011). Measuring the quality of port hinterland accessibility: The Ligurian case. Transport Policy, 18(2), 382–391.
15. Goldratt, E. M. (1984). The goal: A process of ongoing improvement. North River Press.
16. Haralambides, H. E. (2017). Globalization, public sector reform, and the role of ports in international supply chains. Maritime Economics & Logistics, 19(1), 1–51.
17. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.
18. Koenker, R., & Bassett, G., Jr. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
19. Laffont, J.-J., Rey, P., & Tirole, J. (1998). Network competition: I. Overview and nondiscriminatory pricing. RAND Journal of Economics, 29(1), 1–37.
20. Lagoudis, I. N., Rice, J. B., Jr., & Salminen, J. B. (2014). Port investment strategies under uncertainty: The case of a Southeast Asian multipurpose port. The Asian Journal of Shipping and Logistics, 30(3), 299–319.
21. Leachman, R. C. (2008). Port and modal allocation of waterborne containerized imports from Asia to the United States. Transportation Research Part E: Logistics and Transportation Review, 44(2), 313–331.
22. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558.
23. Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383–387.
24. Luo, M., & Grigalunas, T. A. (2003). A spatial-economic multimodal transportation simulation model for US coastal container ports. Maritime Economics & Logistics, 5(2), 158–178. https://doi.org/10.1057/palgrave.mel.9100067
25. Monios, J., & Wilmsmeier, G. (2012). Giving a direction to port regionalisation. Transportation Research Part A: Policy and Practice, 46(10), 1551–1561.
26. Notteboom, T. E. (2006). The time factor in liner shipping services. Maritime Economics & Logistics, 8(1), 19- 39.
27. Notteboom, T. E., & Rodrigue, J.-P. (2005). Port regionalization: Towards a new phase in port development. Maritime Policy & Management, 32(3), 297–313.
28. Notteboom, T. E., Parola, F., & Satta, G. (2019). The relationship between transhipment incidence and throughput volatility in North European and Mediterranean container ports. Journal of Transport Geography, 74, 371–381. https://doi.org/10.1016/j.jtrangeo.2019.01.002
29. Rodrigue, J.-P., & Notteboom, T. (2012). Dry ports in European and North American intermodal rail systems: Two of a kind? Research in Transportation Business & Management, 5, 4–15.
30. Rodrigues, A. M., Bowersox, D. J., & Calantone, R. J. (2005). Estimation of global and national logistics expenditures: 2002 data update. Journal of Business Logistics, 26(2), 1–16.
31. Roll, Y., & Hayuth, Y. (1993). Port performance comparison applying data envelopment analysis (DEA). Maritime Policy & Management, 20(2), 153–161.
32. Sanchez-Rodrigues, V., Potter, A., & Naim, M. (2010). Evaluating the causes of uncertainty in logistics operations. International Journal of Logistics Management, 21(1), 45–64.
33. Serebrisky, T., Sarriera, J. M., Suárez-Alemán, A., Araya, G., Briceño-Garmendia, C., & Schwartz, J. (2016). Exploring the drivers of port efficiency in Latin America and the Caribbean. Transport Policy, 45, 31-45.
34. Talley, W. K. (2009). Port economics. Routledge.
35. Tongzon, J. (2001). Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transportation Research Part A: Policy and Practice, 35(2), 107–122.
36. UNCTAD. (2024). Review of maritime transport 2024: Navigating maritime chokepoints. United Nations.
37. Van den Berg, R., & De Langen, P. W. (2011). Hinterland strategies of port authorities: A case study of the port of Barcelona. Research in Transportation Economics, 33(1), 6–14. https://doi.org/10.1016/j.retrec.2011.08.002
38. Verschuur, J., Koks, E. E., & Hall, J. W. (2023). Systemic risks from climate-related disruptions at ports. Nature Climate Change, 13(8), 804–806.
39. Wang, K., & Cullinane, K. (2015). A stochastic frontier model of container terminal efficiency. Transportation Research Part E: Logistics and Transportation Review, 73, 87–104.
40. Wang, S., Meng, Q., & Liu, Z. (2013). Containership scheduling with transit-time-sensitive demand. Transportation Research Part B: Methodological, 54, 68–83.
41. World Bank & S&P Global Market Intelligence. (2024). The container port performance index 2023: A comparable assessment of performance based on vessel time in port. World Bank.