Jingbo Yin

Bayesian network with quantitative input for maritime risk analysis

This article presents an innovative approach towards integrating logistic regression and Bayesian networks (BNs) into maritime risk assessment. The approach has been developed and applied to a case study in the maritime industry, but has the potential for being adapted to other industries. Various applications of BNs as a modelling tool in maritime risk analysis have been widely seen in relevant literature. However, a common criticism of the Bayesian approach is that it requires too much information in the form of prior probabilities, and that such information is often difficult, if not impossible, to obtain in risk assessment. The traditional and common way to estimate prior probability of an accident is to use expert estimation (inputs) as a measure of uncertainty in risk analysis. In order to address the inherited problems associated with subjective probability (expert estimation), this study develops a binary logistic regression method of providing input for a BN, making use of different maritime accident data resources. Relevant risk assessment results have been achieved by measuring the safety levels of different types of vessels in different situations.

Slow steaming of liner trade: its economic and environmental impacts

From 2000s, there have been three forces provoking slow steaming practice in the liner industry: (1) oversupply of shipping capacity, (2) increase of bunker price and (3) environmental pressure. This paper analyses the background and the recent application of slow steaming in liner shipping. The research looks into the questions of how slow steaming can save bunker consumption and bring benefits to the environment. On the other hand, solutions are also examined to the adverse side of slow steaming practice, i.e., how it affects the container transit time. For which, a cost model is developed to demonstrate the impact of slow steaming on the revenue change, with application to the North Europe—Far East Trade as a case study. The final result shows that the optimal speed for the shipowner is correlated with the designed speed, bunker price and the price of CO2. With the increase of the bunker price and the price of CO2, the optimal speed will also increase, which means that slow steaming practice has a positive impact on the environmental protection.

Dynamics and Interactions between Spot and Forward Freights in the Dry Bulk Shipping Market

ABSTRACT Analyzing the interactions between spot and forward freight agreement (FFA) prices in the dry bulk shipping is important as they play a significant role for shipping companies to secure their profits and avoid potential risks in the volatile market. By applying the vector autoregression (VAR) and the vector error correction model (VECM), this paper identifies the long-run and mutual causal relationship between the spot and FFA prices on the BPI T/C and BCI C7 routes. Along with these cointegrating rates, exogenous factors such as the market demand and supply and some economic indices are also recognized as contributing variables for the dynamic movement of the spot and FFA prices. Importantly, the mean-reverting process is justified on both routes with different mechanisms. When the spot and FFA prices deviate from their equilibrium level in the short run, they will be adjusted to their long-run equilibrium more directly and clearly on the BPI T/C route than those on the BCI C7 route. It also indicates that this adjusting power has direction and size asymmetries on both routes. In addition, the impulse analysis indicates that the spot rate is more volatile than its corresponding FFA prices confronting innovations. The results of this study provide a reference to the participants in the dry bulk shipping market on the causes of fluctuation in spot and FFA prices and their interactions, which can be used to promote the risk management in the market.

Leading factors in job satisfaction of Chinese seafarers

One needs special skills and training to become a seafarer. Seafarers are unique and the most important human resources for the shipping industry. As a major seafarer supply nation, China has to improve quality and increase retention rate to achieve sustainable development for its shipping industry. This paper is to investigate the main factors that contribute to job satisfaction of Chinese seafarer, which factor has a significant bearing on the retention rate in the seafaring profession. The data used in this study was collected through questionnaires, and a structural equation modelling (SEM) method is used to test and estimate causal relations by using a combination of statistical data and qualitative causal assumptions. The results show that the promotion is the most significant factor in job satisfaction, followed by salary and benefits, working environment and feeling of status. This paper further examines relevant measures that can be taken at both national and management levels to increase the retention rate of seafarers. The study provides a scientific basis for policy-making in national maritime education and training, and ship and crew management.

Assessment of investment decision in the dry bulk shipping market based on real options thinking and the shipping cycle perspective

ABSTRACT As the dry bulk shipping market seems to have been stuck in a trough period for a long time, investors need to pay more attention to their investment strategies to survive during this period. This study aimed to find a suitable model to assess dry bulk ship investment decisions in the tough and peak periods based on real options theories. Two options, involving an abandonment option and a deferrable option, were used to define investors’ responses to the uncertainty in investment processes such as stopping or selling vessels. The option valuation was solved by using a binomial valuation model, due to data limitations. In accordance with shipping cycle theories, different volatility parameters for the tough and peak periods were calculated using a generalized autoregressive conditional heteroskedasticity (GARCH) model. The application of the real options model to a case study involving secondhand ship trading indicated its viability. According to the results of the case study, the new model has advantages over the traditional net present value (NPV) method in uncertain investment environments. Thus, the results demonstrate that the real options model is a more suitable method for use in the current dry bulk shipping market.