Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types

Abdullah Açık (Dokuz Eylül Üniversitesi, İzmir, Türkiye)
Gemi ve Deniz Teknolojisi
1 0
Yıl: 2021 Cilt: 0 Sayı: 220 Sayfa Aralığı: 209 - 226 Metin Dili: İngilizce DOI: 10.54926/gdt.1019581 İndeks Tarihi: 29-07-2022

Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types

Öz:
In this study, the average speeds of container, dry bulk and crude oil fleets, which are the main market types in maritime transportation, are estimated and analyzed by using external factors such as freight rate, bunker price, fleet size and interest rate. Freight and interest rates affect the average speeds positively, while bunker price and fleet size negatively affect in accordance with the theoretical justifications. The freight rate mostly affects the average speed of the container fleet, while bunker price, fleet size and interest rate mostly affect the average speed of the crude oil fleet; the average speed of the dry bulk fleet is least affected by the fuel price and fleet size; and the average speed of the container fleet is least affected by the interest rate. In general, when the coefficient sizes between the factors are considered, the most effective factor in average speeds of the three market is their fleet size. Thus, new dimensions have been added to the empirical literature stuck in the framework of freight rate and bunker price.
Anahtar Kelime: Vessel speed freight rate bunker price interest rate

Başlıca Ticari Gemi Tiplerinin Ortalama Filo Hızlarının Modellenmesi ve Analizi

Öz:
Bu çalışmada, deniz taşımacılığında ana piyasa türleri olan konteyner, kuru yük ve ham petrol filolarının ortalama hızlarını navlun oranı, yakıt fiyatı, filo büyüklüğü ve faiz oranı gibi dış faktörleri kullanarak tahmin ve analiz edilmiştir. Teorik çerçeveye uygun olarak, navlun ve faiz oranları ortalama hızları olumlu yönde etkilerken, yakıt fiyatı ve filo büyüklüğü olumsuz yönde etkilemektedir. Navlun oranı en çok konteyner filosunun ortalama hızını etkilerken, yakıt fiyatı, filo büyüklüğü ve faiz oranı en çok ham petrol filosunun ortalama hızını etkilemektedir; kuru yük filosunun ortalama hızı yakıt fiyatından ve filo büyüklüğünden en az etkilenmektedir; ve konteyner filosunun ortalama hızı faiz oranından en az etkilenmektedir. Genel olarak, faktörler arasındaki katsayı büyüklükleri göz önüne alındığında, üç pazarın ortalama hızlarında en etkili faktör filo büyüklükleridir. Böylece navlun ve yakıt fiyatı çerçevesinde sıkışmış olan ampirik literatüre yeni boyutlar eklenmiştir.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Açık, A. and Kayıran, B. (2018). The effect of freight rates on fleet productivity: An empirical research on dry bulk market. Paper Presented at the IV. International Caucasus-Central Asia Foreign Trade and Logistics Congress, Didim, AYDIN, pp. 1080-1089.
  • Açık, A. and Başer, S.Ö. (2018). The reactions of vessel speeds to bunker price changes in dry bulk market. Transport & Logistics: the International Journal, 18(45), 18-25.
  • Adland, R. and Jia, H. (2018). Dynamic speed choice in bulk shipping. Maritime Economics & Logistics, 20(2), 253-266.
  • Adland, R. (2021). Shipping economics and analytics, in Artikis, A. and Zissis, D. (Eds.), Guide to Maritime Informatics (pp. 319-333), Springer, Switzerland.
  • Alderton, P. (2008). “Port Management and Operations”, Informa Law, London.
  • Alizadeh, A. and Nomikos, N. (2009). “Shipping Derivatives and Risk Management”, Palgrave Macmillan, UK.
  • Andersson, H., Fagerholt, K. and Hobbesland, K. (2015). Integrated maritime fleet deployment and speed optimization: case study from RoRo shipping. Computers & Operations Research, 55, 233-240.
  • Asteriou, D. and Hall, S. G. (2011). “Applied Econometrics, 2nd ed.”, Hampshire, Palgrave Macmillan.
  • Aßmann, L. M. (2012). Vessel speeds in response to freight rate and bunker price movements: an analysis of the VLCC tanker market (Master's thesis).
  • Aydin, N., Lee, H. and Mansouri, S. A. (2017). Speed optimization and bunkering in liner shipping in the presence of uncertain service times and time windows at ports. European Journal of Operational Research, 259(1), 143-154.
  • Bendall, H. and Stent, A. (2005). Ship investment under uncertainty: valuing a real option on the maximum of several strategies. Maritime Economics Logistics, 7, 19–35.
  • Bloomberg (2018) Vessel Speeds, Bunker Prices, Freight Indices, https://www.bloomberg.com/professional/solution/content-and-data/ [Online] [Accessed June 20, 2018].
  • Braemar (2021) Fleet Deployment Rates, https://braemarmarkets.com/ [Online] [Accessed August 15, 2021].
  • Cariou, P., Ferrari, C., Parola, F., & Tei, A. (2019). Slow Steaming in The Maritime Industry. In The Routledge Handbook of Maritime Management (Photis M. Panayides ed.) (pp. 140-153), Routledge.
  • Crist, P. (2012) ‘Mitigating greenhouse gas emissions from shipping: potential, cost and strategies’, in Asariotis, R. and Benamara, H. (Eds.), Maritime Transport and the Climate Change Challenge (pp. 193-234), Routledge, New York.
  • Doudnikoff, M. and Lacoste, R. (2014). Effect of a speed reduction of containerships in response to higher energy costs in Sulphur Emission Control Areas. Transportation Research Part D: Transport and Environment, 28, 51-61.
  • Engle, R.F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.
  • Erginer, K., Açık, A., and Yıldız, Ö. (2019). The impact of freight rates on pirate attacks. Turkish Journal of Maritime and Marine Sciences, 5(2), 88-96.
  • Evans, J.J. and Marlow, P.B. (1990). “Quantitative Methods in Maritime Economics, 2nd ed.”, London, Fairplay Publications.
  • Faber, J. F., Huigen, T. and Nelissen, D. (2017). “Regulating speed: a short-term measure to reduce maritime GHG emissions”, CE Delft.
  • FRED (2021). Interest Rate, USA Inflation Rate. https://fred.stlouisfed.org/ [Online] [Accessed August 15, 2021].
  • Gujrati, D.N. (2004). “Basic Econometric, (4th Ed.)”, New York, The McGraw-Hill Companies.
  • Liu, J. (2011). “Supply Chain Management and Transport Logistics”, Routledge, London and New York.
  • Ljung, G. and G. Box. (1979). On a measure of lack of fit in time series models. Biometrika, 66, 265–270.
  • Lorange, P. (2009). “Shipping Strategy: Innovating for Success”, Cambridge University Press, USA.
  • Ma, S. (2020). “Economics of Maritime Business”, Routledge, London and New York.
  • Medina, J. R., Molines, J., González-Escrivá, J. A. and Aguilar, J. (2020). Bunker consumption of containerships considering sailing speed and wind conditions. Transportation Research Part D: Transport and Environment, 87, 102494.
  • Mietzner, A. (2015). The Northern Sea Route: A Comprehensive Analysis. in Keupp, M. M. (Eds.), The Northern Sea Routes as An Alternative Container Shipping Route: A Hypothetical Question or A Future Growth Path? (pp. 107-122), Springer Gabler, Switzerland.
  • Narayan, P. K. and Popp, S. (2010). A new unit root test with two structural breaks in level and slope at unknown time. Journal of Applied Statistics, 37(9), 1425-1438.
  • Newey, W. and West, K. (1987). A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708.
  • Ng, M. (2019). Vessel speed optimisation in container shipping: A new look. Journal of the Operational Research Society, 70(4), 541-547.
  • Norstad, I., Fagerholt, K. and Laporte, G. (2011). Tramp ship routing and scheduling with speed optimization. Transportation Research Part C: Emerging Technologies, 19(5), 853-865.
  • Pagan, A. R. and Hall, A.D. (1983). Diagnostic tests as residual analysis. Econometric Reviews, 2(2), 159-218.
  • Ronen, D. (1982). The effect of oil price on the optimal speed of ships. Journal of the Operational Research Society, 33(11), 1035-1040.
  • Sahin, B., Yilmaz, H., Ust, Y., Guneri, A. F., Gulsun, B. and Turan, E. (2014). An approach for economic analysis of intermodal transportation. The Scientific World Journal, 2014, 1-10.
  • Sheng, X., Lee, L. H. and Chew, E. P. (2014). Dynamic determination of vessel speed and selection of bunkering ports for liner shipping under stochastic environment. OR spectrum, 36(2), 455-480.
  • Song, D. P. (2021). “Container Logistics and Maritime Transport”, Routledge, New York.
  • Stopford, M. (2003). “Maritime Economics 2nd ed.”, Routledge, New York.
  • Sturmey, S.G. (1975). “Shipping Economics: Collected Papers”, Macmillan, UK.
  • von Westarp, A. G. (2020). A new model for the calculation of the bunker fuel speed–consumption relation. Ocean Engineering, 204(2), 1-6.
  • Wang, S., Meng, Q. and Liu, Z. (2013). Bunker consumption optimization methods in shipping: A critical review and extensions. Transportation Research Part E: Logistics and Transportation Review, 53, 49-62.
  • Wang, S., Gao, S., Tan, T. and Yang, W. (2019). Bunker fuel cost and freight revenue optimization for a single liner shipping service. Computers & Operations Research, 111, 67-83.
  • Wen, M., Pacino, D., Kontovas, C. A. and Psaraftis, H. N. (2017). A multiple ship routing and speed optimization problem under time, cost and environmental objectives. Transportation Research Part D: Transport and Environment, 52, 303-321.
  • White, H. (1980). A heteroskedasticity-consistent covariance matrix and a direct test for heteroskedasticity, Econometrica, 48, 817–838.
  • Zivot, E., and Andrews, D.W.K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistic, 10, 251-270.
APA Açık A (2021). Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. , 209 - 226. 10.54926/gdt.1019581
Chicago Açık Abdullah Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. (2021): 209 - 226. 10.54926/gdt.1019581
MLA Açık Abdullah Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. , 2021, ss.209 - 226. 10.54926/gdt.1019581
AMA Açık A Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. . 2021; 209 - 226. 10.54926/gdt.1019581
Vancouver Açık A Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. . 2021; 209 - 226. 10.54926/gdt.1019581
IEEE Açık A "Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types." , ss.209 - 226, 2021. 10.54926/gdt.1019581
ISNAD Açık, Abdullah. "Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types". (2021), 209-226. https://doi.org/10.54926/gdt.1019581
APA Açık A (2021). Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. Gemi ve Deniz Teknolojisi, 0(220), 209 - 226. 10.54926/gdt.1019581
Chicago Açık Abdullah Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. Gemi ve Deniz Teknolojisi 0, no.220 (2021): 209 - 226. 10.54926/gdt.1019581
MLA Açık Abdullah Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. Gemi ve Deniz Teknolojisi, vol.0, no.220, 2021, ss.209 - 226. 10.54926/gdt.1019581
AMA Açık A Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. Gemi ve Deniz Teknolojisi. 2021; 0(220): 209 - 226. 10.54926/gdt.1019581
Vancouver Açık A Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types. Gemi ve Deniz Teknolojisi. 2021; 0(220): 209 - 226. 10.54926/gdt.1019581
IEEE Açık A "Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types." Gemi ve Deniz Teknolojisi, 0, ss.209 - 226, 2021. 10.54926/gdt.1019581
ISNAD Açık, Abdullah. "Modeling and Analyzing the Average Fleet Speed of Major Commercial Ship Types". Gemi ve Deniz Teknolojisi 220 (2021), 209-226. https://doi.org/10.54926/gdt.1019581
Sayfa Oluşturulma Tarihi
17-04-2024 23:48

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