FBEN - Maritime Economics

Freight rate models are traditionally developed using linear modelling approaches and various exogenous variables. However, freight rates yield to exhibit nonlinear characteristics, making traditional linear models insufficient for capturing complex relationships. To overcome these constraints, this study employs ensemble learning models, which are well-suited to reveal the nonlinear dynamics and identify the key factors influencing freight rates. Notably, while existing literature predominantly focuses on the Baltic Dry Index (BDI), a crucial indicator in the maritime industry, it has been criticized for inadequately reflecting regional shipping conditions. Therefore, this study investigates the interrelationships between regional and international freight indices alongside various influencing factors, thereby providing a more comprehensive understanding of freight rate dynamics. Results indicate that ISTFIX (0.58) is the strongest predictor of BDI, followed by DJIA (0.52), SP (0.47), and EUSSIX. Similarly, ISTFIX emerges as the dominant predictor (0.74), with CUR (0.34) also playing a crucial role. Moreover, ISTFIX is primarily driven by EUSSIX (0.73) and CUR (0.65). These findings will contribute to the development of automated regional freight rate indices and offer deeper insights into the underlying dynamics of freight rate movements.

Fuel consumption prediction for harbour craft is crucial for the maritime industry to enhance efficiency and reduce environmental impact. Traditional application of cubic law in fuel consumption estimation may not provide accurate predictions for harbour crafts due to their different structures and working mechanisms compared to ocean-going vessels. It may also fail in scenarios such as slow steaming. Incorporating ship-specific elasticity in fuel consumption estimation has the potential to improve the results. Therefore, this research aims to conduct empirical analysis in estimating fuel consumption for harbour craft within an econometric framework, with a specific focus on speed and revolution per minute (RPM), exploring their relationship with fuel consumption. The case study will involve a harbour craft operated in the Port of Singapore, where field-trial data will be collected and analysed. The analysis will be conducted under econometric models using piecewise splines, considering the different elasticity values under various conditions. The study provides insights into the fuel consumption estimation for harbour craft and can be used to suggest speed and operating optimisation. The study offers practical recommendations in the port industry to improve fuel and operational efficiency, thus reducing pollutants and greenhouse gas emissions in port areas accordingly.

Maritime transport significantly connects areas and boosts economic development, especially in countries reliant on maritime networks for freight and passenger movement. Remote regions in developing countries face economic and social inequalities due to connectivity issues, including financing gaps and extreme sea conditions. These challenges make shipping economically difficult, yet sustaining maritime services is vital for trade and growth, often requiring government subsidies. Evaluating the impact of maritime transportation on regional development economically, socially, and environmentally is essential. While many studies address the economic impact of maritime transport, they typically focus on international shipping rather than regional operations. Viability studies often emphasize cargo volume, while regional shipping also includes freight and passenger traffic. Additionally, the assessment usually uses a single perspective rather than integrating important aspects of the assessment. This study aims to analyse techniques and variables influencing evaluation criteria for assessing regional maritime transport's contribution to development through a systematic literature review.

Our study focuses on the freight rates of the dry bulk market, which is a crucial market in maritime
trade. More specifically, the Dry Bulk Index (BDI), representing the level of dry bulk rates and the
demand volume in the market, has drawn the attention of numerous researchers. Its relationship with
the rest of maritime markets, as well as with capital markets, has been well established in the
literature. However, the impact of uncertainty in financial markets on BDI has been overlooked.
Hence, we use Granger causality and impulse response functions to assess how the uncertainty in
maritime capital markets and the broader US stock market can increase the volatility of BDI. In
addition, by employing artificial neural networks, we examine whether in periods of high uncertainty
in financial markets, it is less likely to forecast the seaborne activity index accurately.
We provide evidence aligned with the signal theory and highlight uncertainty in the US stock market
as a BDI determinant. Finally, we underline the importance of considering uncertainty in financial
markets when predicting BDI, while a spike in the former renders forecasting models less accurate
and as a result, the maritime transportation networks less efficient and economically sustainable.

This study examines the lead/lag relationships between container freight rates and local macroeconomic indicators in the major economies of the world using 20 years of monthly data and vector autoregressive and cointegration methods. Specific macroeconomic variables include trade volume, industrial production, inflation, and stock prices. Overall we find that industrial organization and trade volume are the macroeconomic variables best predicted by freight rates, and that bulk freight rates have slightly greater predictive power on macroeconomic variables than two indicators of container freight rates. We also find that bulk freight rates are better predicted by macroeconomic variables than container freight rates, a result consistent with prior theories on competition, type of cargo, and contract length in determining the signal present in freight rates.