The tanker freight market is highly volatile and unpredictable, creating opportunities and risks for operators. The observation of historical data reveals that there are times of extreme deviations in freight rates from their mean values. These periods have not been thoroughly analyzed in the relevant maritime literature, and traditional modeling approaches do not account for the statistical properties of extreme values. This study employs Extreme Value Analysis (EVA) to model the stochastic behavior of tanker freight rates at unusually high levels. The Peak Over Threshold (POT) and Block Maxima approaches are tested, with the latter outperforming POT and adapting better to the data set. Following the Block-Maxima approach, the study estimates the probability of extreme values and uses this output to produce short-term forecasts of the Baltic Dirty Tanker Index (BDTI). The findings reveal the limitations of conventional risk models and offer a new statistical framework for modeling extreme freight rates, which can assist operational and investment decisions in the tanker sector.

The European Union’s energy transition framework presents significant challenges for the maritime sector, requiring technical, organizational, and market-based compliance measures. The FuelEU Maritime Regulation (FuelEU) introduces flexible mechanisms such as banking, borrowing, and pooling that broaden compliance options for shipowners. This study examines the role of pooling in European shipping, focusing on its operational models, enabling conditions, and economic implications. Pool formation demands extensive information exchange and effective coordination, creating a need for specialized intermediaries and supporting the emergence of a market in which pooling prices are shaped by supply and demand. The research employs a qualitative methodology combining stakeholder and exploratory market analysis, assessment of regulatory and market drivers, case studies, and comparative analysis, complemented by expert interviews and a targeted survey. The findings highlight pooling as a pivotal compliance instrument, particularly as a fallback option during the reporting period, and show that it currently represents the most cost-efficient means of achieving FuelEU compliance. Intermediary service providers, especially digital platforms, play a central role by facilitating coordination, improving transparency, and reducing compliance costs. The study further identifies key factors influencing shipowners’ costs and revenues in pools, emphasizing the critical importance of alternative fuel availability and prices.

The impetus for our research lies in the multifaceted and pivotal role of China in the dry bulk market, encompassing its positions as a leading global exporter, a dominant shipbuilder, and a major shipowner. However, the effects of uncertainty arising from trade policy and particularly Chinese trade policy (CTPU) on the dynamics of the dry bulk market have remained unexplored by empirical studies. This study addresses this gap by analyzing the impact of CTPU on the Baltic Dry Index (BDI),a key indicator of international trade and the global economy.Performing the Granger causality test and the impulse response function, we demonstrate a decline in sea-transportation demand and cost in the dry bulk market due to a spike in CTPU, yielding results consistent with option theory. This effect is observed across all BDI sub-indices and lasts approximately one year, by the end of which the markets fully recover. By developing artificial neural networks, we effectively avoid in-sample bias and underscore the out-of-sample predictability ofCTPU on BDI, establishing it as a key determinant of the latter. Based on our findings, we propose strategies for various maritime stakeholders and supply chain managers to maximize profitability and enhance operational efficiency in transportation.

Our research focuses on the maritime industry, specifically the dry bulk market, contributing to the emerging research agenda regarding the influence of financial markets on the real economy. Motivated by the documented relationship between the Baltic Dry Index (BDI) and financial markets, we study how different types of investors’ sentiment in the US stock market influence the BDI. Employing Granger causality tests and impulse response functions, we examine how investors’ confidence and ensuing liquidity in both the US maritime and the broader US stock markets impact corporate decisions related to freight rates and secondhand vessel investments. Our findings highlight the critical role of investor sentiment for stakeholders in the dry bulk market and the significance of the BDI in shaping traders’ investment in the US capital market. Additionally, by comparing our results with previous studies, we provide fresh evidence regarding the identity of traders in the maritime capital market. We also employ Long Short-Term Memory (LSTM) neural networks to establish the out-of-sample predictive power of our sentiment proxies, positioning themas novel BDI determinants. The results provided align with signal theory, challenge the efficient market hypothesis, and offer valuable insights for optimizing profitability and fostering economic sustainability in various industries.

In marine container drayage (CD), which refers to the transport of goods over a short distance, empty containers (ECs) are required to be transported long distances between a terminal and a shipper/consignee by conventional transportation. This causes truck traffic congestion in harbor districts that increases CO₂ emissions (CO₂EM). This study addresses the vehicle dispatch problem with respect to the environmental impact of marine CD, considering the tractor dwell time is long at the customer site due to cumbersome container loading/unloading (L/U). We model this problem as an extended version of the vehicle routing problem with precedence constraints to minimize the total CO₂ weighted travel distance. To improve CD operation in long L/U time, we propose a new practice for CD operations. Additionally, we develop a mixed integer programing model (MIP) for the new practice representation, and propose a Simulated Annealing (SA) based heuristic approach to solving the new practice instance. By implementing our proposed new operation, in a long L/U time, the CO₂EM, EC move and number of tractors required can also be reduced by allowing uncoupled tractor move.