• Korea Trade Review
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• v.43 no.5
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• pp.159-177
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• 2018

The container shipping sector is an important international logistics operation that connects open economies. Freight rates rapidly change as the market fluctuates, and staff related to the shipping market are interested in factors that determine freight rates in the container market. This study uses the Vector Error Correction Model(VECM) to estimate the impact of factors affecting container freight rates. This study uses data published by Clarksons. The analysis results show a 4.2% increase in freight rates when world container traffic increases at 1.0%, a 4.0% decrease in freight rates when volume of container carriers increases by 1.0%, a 0.07% increase in freight rates when bunker price increases by 1.0%, and a 0.04% increase in freight rates accompanying 1.0% increase in libor interests rates. In addition, if the current freight rate is 1.0% higher than the long-term equilibrium rate, the freight rate will be reduced by 3.2% in the subsequent term. In addition, if the current freight rate is 1.0% lower than the long-term equilibrium rate, the freight rate will decrease by 0.12% in the following term. However, the adjusting power in a period of recession is not statistically significant which means that the pressure of freight rate increase in this case is neglectable. This research is expected to contribute to the utilization of scientific methods in forecasting container freight rates.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.195-204
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• 2023

In this study, the causes of the boom in the container shipping industry pointed out by the media were analyzed. As a result of the analysis, it was found that on the supply side, shipping companies mooring ships in response to IMO environmental regulations caused a rise in freight rates due to 'ships that could not be operated' even if demand increased. In terms of demand, it was determined that the claim that social distancing promoted purchases and increased demand was somewhat unfounded. The reason is that the relationship between social distancing and rising fares is weak.

• Asian Journal of Atmospheric Environment
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• v.9 no.1
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• pp.39-47
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• 2015

The emissions of ozone precursors, NOx and VOCs from a biogenic source and port-related sources (ship, shipping container truck, and cargo handling equipment) were estimated in Busan during 2013. Total biogenic isoprene emission in Busan during 2013 was estimated to be $4,434ton\;yr^{-1}$ with the highest emission (e.g., $28ton\;day^{-1}$) in summer using a BEIS method. Seasonal ozone production rates by isoprene ranged from 0.15 (winter) to 2.08 (summer) $ppb\;hr^{-1}$, contributing the predominant portion to ambient ozone levels. Total emissions of NOx and VOCs from ship traversing Busan ports were estimated to be 29,537 and $814ton\;yr^{-1}$, respectively, showing the significant contribution to total NOx emission in Busan. The emissions of ozone precursors were significantly different depending on ship tonnage and port location. Compared to the ship emission, the emissions of NOx and VOCs from the shipping container trucks in Busan were insignificant (2.9% for NOx and 3.9% for VOCs). Total NOx and VOCs emissions from the cargo handling equipment were estimated to be 1,440 and $133ton\;yr^{-1}$, respectively with the predominance of yard tractors.

• Journal of Navigation and Port Research
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• v.35 no.7
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• pp.589-596
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• 2011

Terminal market has rapidly crashed and market rates have taken a sharp plunge. The substantial throughput decrease resulted from the world economic downturn has been a finishing blow to the terminal operators in Busan. Every terminal operator is taking cost saving as its first priority and accelerating structural reform and downsizing. Under the desperate situation, the need of effective cost analysis would be highly required to effectively control operation cost and to develop new services to satisfy the different needs of the customers. Furthermore, terminal operators could reduce unnecessary activities and concentrate their resource on the more cost-effective process through the operation cost analysis. In order to suggest a new framework of the cost control of container terminals, this paper seeks to analyze terminal costs based on ABC approach by processing actual data.

• Journal of Korea Port Economic Association
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• v.29 no.4
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• pp.73-95
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• 2013

This paper analyzes a competitive shipping market in East Asia in order to explore how container carriers make decisions on ship size, number of ships, service frequency, and service route. A sequential-move game based on non-cooperative game theory is applied to establish the models for the decision-makings involving the transportation volumes, freight rates, costs, and market shares of the service routes from Shanghai or Hong Kong to the ports in Busan, Gwangyang, and Incheon. According to the sub-game perfect Nash equilibrium solutions proposed by these models, carriers' decisions in such a competitive environment vary depending on sailing distance, transport demand, and freight rates. Therefore, carriers are recommended to reflect the optimal equilibrium solutions and a variety of decision factors when formulating strategies for transportation networks and operating fleets. Furthermore, ports should establish management strategies for these factors to provide optimal equilibrium solutions for carriers' transportation networks.

• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.17-30
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• 2020

In the shipping industry, it is essential to engage in the preemptive prediction of freight rate volatility through market monitoring. Considering that freight rates have already started to fall, the loss of shipping companies will soon be uncontrollable. Therefore, in this study, factors affecting the freight rates of bulk carriers, which have relatively large freight rate volatility as compared to container freight rates, were quantified and analyzed. In doing so, we intended to contribute to future shipping market monitoring. We performed an analysis using a vector error correction model and estimated the influence of six independent variables on the charter rates of bulk carriers by Handy Size, Supramax, Panamax, and Cape Size. The six independent variables included the bulk carrier fleet volume, iron ore traffic volume, ribo interest rate, bunker oil price, and Euro-Dollar exchange rate. The dependent variables were handy size (32,000 DWT) spot charter rates, Supramax 6 T/C average charter rates, Pana Max (75,000 DWT) spot charter, and Cape Size (170,000 DWT) spot charter. The study examined charter rates by size of bulk carriers, which was different from studies on existing specific types of ships or fares in oil tankers and chemical carriers other than bulk carriers. Findings revealed that influencing factors differed for each ship size. The Libo interest rate had a significant effect on all four ship types, and the iron ore traffic volume had a significant effect on three ship types. The Ribo rate showed a negative (-) relationship with Handy Size, Supramax, Panamax, and Cape Size. Iron ore traffic influenced three types of linearity, except for Panamax. The size of shipping companies differed depending on their characteristics. These findings are expected to contribute to the establishment of a management strategy for shipping companies by analyzing the factors influencing changes in the freight rates of charterers, which have a profound effect on the management performance of shipping companies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.137-156
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• 2016

This study analyzes the optimal service levels of exclusive container terminals in terms of the optimal berth occupancy rate and the ships' waiting ratios, based on the number of berths. We develop a simulation model using berth throughput data from pier P, Busan New Port, a representative port in Korea, and apply the simulation results to different numbers of berths. In addition to the above results, we analyze the financial data and costs of delayed ships and delayed cargoes for the past three years from the viewpoints of the terminal operation company (TOC), shipping companies, and shippers to identify the optimal service level for berth occupancy rates that generate the highest net profit. The results show that the optimal levels in the container terminal are a 63.4% berth occupancy rate and 10.6% ship waiting ratio in berth 4,66.0% and 9.6% in berth 5, and 69.0% and 8.5% in berth 6. However, the results of the 2013 study by the Ministry of Maritime Affairs and Fisheries showed significantly different optimal service levels: a 57.1% berth occupancy rate and 7.4% ship waiting ratio in berth 4; 63.4% and 6.6% in berth 5; and 66.6% and 5.6% in berth 6. This suggests that optimal service level could change depending on when the analysis is performed. In other words, factors affecting the optimal service levels include exchange rates, revenue, cost per TEU, inventory cost per TEU, and the oil price. Thus, optimal service levels can never be fixed. Therefore, the optimal service levels for container terminals need to be able to change relatively quickly, depending on factors such as fluctuations in the economy, the oil price, and exchange rates.

• The Journal of shipping and logistics
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• v.34 no.4
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• pp.545-563
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• 2018

This study analyzes the major factors affecting the freight rates of Very Large Crude-Oil Carriers (VLCC) using co-integration and vector error correction models (VECM). Particularly, we estimate the long-term equilibrium function that determines the VLCC freight rate by conducting difference conversion. In the VECM regression analysis, the error term converges toward long-term balance irrespective of whether the previous period's freight rate is bigger or smaller than the long-term equilibrium rate. Thus, even if the current rate is different from the long-term rate, it eventually converges to the long-term balance irrespective of a boom or recession. This study follows Ko and Ahn (2018), which analyzed the factors affecting the chemical carrier freight rate and was published in the Journal of Shipping and Logistics (Vol. 34, No. 2). It is expected that an academic comparison of the results of each study will be possible if further research is conducted on other vessel types, such as container ships and dry cargo vessels.

• Journal of Korea Port Economic Association
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• v.22 no.4
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• pp.81-98
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• 2006

Many studies on port tariff have been done over twenty years using publicly assessed data on tariff. Public data for tariff rates do not reflect, however, the port tariff in a real market, since the cargo handling charge, which is the important fraction of port tariff, is confidentially decided by the negotiations between a shipping company and a container terminal operator. In this paper, we collected the real price data of the port tariff on the world major sixteen container ports from a global shipping company and transformed it into the tariff per TEU(US$/TEU). The comparative analysis of port tariff was performed using the port tariff per TEU, and a panel regression analysis was done to identify the relations between the port tariff and demand variables: throughput, GDP and trade amount.