Driven by concerns over market concentration and tacit collusion, container shipping has come under increased regulatory pressure. Utilizing the New Empirical Industrial Organization method, this study evaluates the industry’s competitive evolution within the Trans-Pacific container shipping market across four distinct periods over an 11-year span covering the COVID-19 pandemic. Through the estimation of competition parameters and the Lerner Index, this study finds that, pre-pandemic, carrier behavior mirrored a Cournot oligopoly. Despite the unprecedented surge in freight rates during the pandemic, the analysis reveals an unexpected shift toward near-perfect competition, implying that record-high prices resulted from supply-side shocks and demand inelasticity rather than the deliberate exercise of market power. As market conditions normalized post-pandemic, the oligopolistic structure returned. Overall, empirical findings demonstrate that competition parameters remain consistently below unity, indicating that container shipping is not structurally prone to collusion. These results suggest a robust competitive landscape and provide empirical justification for the continued provision of antitrust immunity to shipping alliances.
While weather conditions are recognized as critical factors influencing global transport efficiency, empirical research examining their micro-level impact on port operations remains scarce. We leverage a unique and high-fidelity dataset encompassing 27,866 vessel calls at Tangshan Port, the world’s third-largest. Distinct from prior studies, we analyse exclusive, non-public internal operational logs precisely matched with proprietary weather observations recorded by on-site maritime stations. Crucially, both datasets are at an hourly-level granularity, to investigate how regular, high-frequency weather fluctuations affect port operational efficiency. Grounded in the Socio-Technical Systems theory, our multi-dimensional analyses reveal that meteorological volatility significantly affects vessel stay duration. Specifically, wind speed, wave height, and precipitation consistently prolong stay duration by inducing mechanical instability and hazardous operational conditions. In contrast, visibility shows a negligible effect, suggesting that advanced navigation technologies effectively buffer visual constraints. Furthermore, once seasonality is filtered out, heat stress significantly impairs operational productivity. These findings demonstrate the substantial value of integrating high-frequency environmental data into refined maritime management. By providing a detailed understanding of weather-driven disruptions, this research offers actionable insights for port scheduling optimisation and proactive climate adaptation strategies for global maritime hubs.
Against China’s drive to build world-class ports, and addressing the issue of the separation of competitiveness and network attributes in existing research, this study targets major Chinese ports to construct an integrated evaluation framework. To that effect, port connectivity is measured using four standard network centrality indicators: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. Connectivity and competitiveness are evaluated, with competitiveness covering infrastructure, productivity and the economic environment, to identify performance gaps and provide differentiated improvement paths. The results show Shanghai, Ningbo, Shenzhen, and Guangzhou being in the first tier, with connectivity and competitiveness as the core factors influencing port performance. The findings suggest that hub ports should prioritize the growth of international direct routes, while other major ports should strengthen weak infrastructure and pursue differentiated quality upgrading. Local port and maritime authorities are advised to allocate resources based on tiered differentiation, in order to avoid homogeneous competition and the waste of scarce resources. By integrating connectivity and competitiveness into a unified framework, this study offers a more holistic approach to port performance evaluation.
Ports play a central role in regional economic systems, facilitating trade, logistics, and industrial activity. Yet, empirical evidence on the economic impact of medium-sized ports remains relatively scarce. This paper estimates the economic impact of a medium-sized Mediterranean port system located in a peripheral region of Southern Europe using an Input–Output framework. The port authority is considered as one institutional component within the broader port of Almería system, which also includes private firms, logistics operators, transport services, and other port-dependent activities. Based on firm-level data and the regional Input–Output Table of Andalusia (2021), the analysis distinguishes between a core port sector, directly related to port operations, and an extended port-related sector, comprising firms economically dependent on port functions. Direct, indirect, and induced effects are estimated in terms of output, value added, and employment, and sectoral multipliers are used to assess intersectoral linkages. The results show that port-related activities generate a substantial economic impact on the regional economy, with total effects significantly exceeding direct contributions due to strong backward linkages with transport, logistics, and industrial services. When expressed as a share of provincial GDP and employment, the port of Almería system represents a strategic component of the local economy, in broader terms highlighting the relevance of medium-sized ports in peripheral regions. From a policy perspective, the findings support the integration of port investments into broader regional development strategies.
Under the dual constraints of the International Maritime Organization’s decarbonization efforts and the European Union’s Emissions Trading System, this study develops an evaluation framework, integrating large language models and knowledge graphs to systematically assess the potential of alternative fuels. An ontological model with fifteen indicators across four dimensions evaluates the transition potential of LNG, methanol, hydrogen, ammonia, and biofuels. A TVP-VAR model further examines the dynamic effects of alternative fuel prices on the shipping transition. The results show that LNG remains the most practical and less risky transitional option, methanol serves as a key mid-term solution, while hydrogen and ammonia offer zero-carbon potential but face cost and infrastructure constraints. The effects of alternative fuel prices on shipping transition are highly time-varying, and major policy events significantly amplify the influence of price signals on transition investment decisions. The paper provides new methodological and empirical evidence to support the advancement of the transition to green shipping.
This study develops an integrated framework to evaluate the competitiveness of 20 major container ports in Northeast Asia, one of the most strategically significant and competitive maritime regions worldwide. Network connectivity is measured through four centrality dimensions, degree, closeness, hub, and authority, derived using Social Network Analysis, while throughput and infrastructure performance are captured through operational indicators. Objective criterion weights are determined via entropy, and a two-stage VIKOR procedure aggregates the weighted indicators into a composite competitiveness ranking. The results reveal a structurally hierarchical and polarised port system in which a small group of mega-hubs consistently outperform others due to strong centrality, sustained traffic expansion, and substantial infrastructural capacity. Sensitivity tests across alternative VIKOR parameters and comparison with TOPSIS confirm the robustness of the competitiveness hierarchy. Overall, the findings highlight the cumulative and mutually reinforcing mechanisms underlying port development and emphasise the need for balanced strategies that integrate network embeddedness, infrastructure depth, and operational performance.
Recent geopolitical events in the Red Sea have disrupted the passage of commercial vessels transiting through the Suez Canal. In response, shipping lines have rerouted services via the Cape of Good Hope to secure trade between Asia, the Mediterranean and Northern Europe. This paper offers a strategic analysis of the reconfiguration of containerised flows in the Mediterranean, based on Automatic Identification System (AIS) data collected over a 240-day period centred on the onset of the crisis. Rather than seeking to identify the internal decision-making processes of shipping companies, the study examines how network reconfigurations materialised at port level, using a multi-scale analytical approach focused on observable changes in port positioning within the Mediterranean port system. By combining port typology (hubs and gateways), vessel size categories and the reallocation of deployed capacity, this paper analyses relative changes in attractiveness and resilience of the main Mediterranean container ports. The results highlight a pronounced shift of capacity, particularly from the largest vessels, towards ports in the western Mediterranean, contrasting with the sharp contraction observed in several Eastern Mediterranean hubs. Within this broader East–West reconfiguration, ports such as Tanger Med illustrate how certain Western Mediterranean hubs were able to limit capacity losses, consistent with their geographical positioning and integration within carrier service networks. Overall, the study demonstrates the value of AIS data, as a strategic-monitoring tool, in documenting port-level manifestations of sudden geopolitical disruption, and supporting situational awareness for port authorities and operators, while acknowledging the limits of AIS-based inference regarding underlying strategic intentions.
This paper quantifies how shocks at strategic maritime chokepoints propagate into vessel traffic, capacity, and route choice, and when apparently temporary disruptions become persistent through port‑congestion hysteresis. Using high-frequency PortWatch IMF data, this study analyzes 70,728 observations across eight major chokepoints spanning 2019–2025, covering three structurally distinct disruptions: the COVID-19 demand shock, the Ever Given–induced Suez blockage, and the Red Sea security crisis. Our empirical strategy combines difference-in-differences (with event-study dynamics), interrupted time-series segmented regression for recovery trajectories, and state-dependent interaction models that allow treatment effects to vary with pre-shock capacity utilisation and vessel composition. Results indicate sharp heterogeneity by shock type and cargo: COVID-19 reduced daily chokepoint-level traffic by 16.5
To reduce pollution from the maritime industry, the International Maritime Organization (IMO) has introduced the Carbon Intensity Indicator (CII) and related regulatory requirements. The implementation of the CII is expected to influence the structure of the chartering market and the decision-making processes of both shipowners and cargo owners. This will lead to a new market equilibrium and necessitate new approaches for analyzing and forecasting market behavior. This study examines the strategic interactions between shipowners and charterers under the CII regulations. It aims to identify the potential stable states of future markets, analyze the effects of key parameter changes, and offer relevant recommendations. To achieve this, a static mixed-strategy game model and a dynamic evolutionary game model are developed. Based on the results of model calculations and simulations, the following conclusions are drawn: 1) After long-term evolution, strategy combinations tend to cluster around the positions defined by the mixed-strategy Nash equilibrium. 2) Compared with other vessel types, shipowners and charterers in the Handysize bulk carrier market are more likely to disregard CII regulations in the steady state. 3) Excessive concessions or overly low penalties set by shipowners generally lead charterers to ignore speed restriction strategies.
The container shipping industry, characterized by high volatility and uncertainty in freight markets, requires shipping companies to adopt flexible and responsive operational fleet strategies. Vessel idling—temporarily withdrawing ships from active operation while keeping them technically available—is a key practice, yet the determinants underlying its application in today’s volatile environment are not yet fully understood. This study investigates the determinants of vessel idle duration, measured as monthly idle days per vessel. Using a panel dataset of over 19,544 vessel-month observations from January 2022 to December 2024, a Zero-Truncated Negative Binomial (ZTNB) model examines how vessel-specific characteristics, company-level factors, and market conditions influence idle decisions. Results show older vessels endure longer idling, while larger vessels and those in bigger fleets are idled for shorter durations, reflecting a more strategic use of this practice. Market factors such as freight rates and supply-demand balance significantly affect idle duration. These findings offer a data-driven perspective on short-term capacity management and practical insights for improving fleet deployment strategies, such as optimizing vessel reactivation timing and fleet composition during market downturns.
Digital Twin (DT) technology is emerging as a transformative tool in inland waterway transport, enhancing operational reliability and efficiency. This paper explores the application of DTs in predicting water levels and optimizing transport infrastructure, based on the experience of the European Union’s CRISTAL project. The study highlights challenges such as data availability constraints and the need for continuous monitoring. By leveraging real-time data, historical records, and machine learning, DTs provide predictive analytics that improve navigability, reduce maintenance costs, and enhance decision-making for port operations. Key functionalities of DTs in Inland Waterway Transport (IWT) include real-time condition monitoring, advanced forecasting capabilities, and early warning systems. The findings demonstrate that DT implementation leads to increased resilience, sustainability, and efficiency in inland navigation. The study concludes that while DTs offer significant advantages, further improvements in data granularity and interoperability are necessary to maximize their potential in the maritime transport sector.
This study investigates how ESG-linked debt structures and green finance developments are reflected in maritime debt financings. Using a manually collected dataset of 1,470 debt transactions by 564 shipping companies between January 1998 and August 2024, we compare ESG-linked and conventional instruments. Qualitative content analysis of transaction descriptions shows an emphasis on sustainability-related terminology, particularly references to sustainability, ESG, green, and environmental factors, concentrated in ESG-labelled deals. Quantitative analysis indicates no statistically significant differences in loan amounts or maturities, suggesting that ESG-labelled instruments largely retain the structural features of traditional debt products. Nonetheless, ESG-linked financings are associated with lower interest or coupon rates, consistent with lenders rewarding credible sustainability commitments. Despite their growing visibility, ESG-linked instruments remain a minority of maritime financings, while conventional structures dominate. These findings have important managerial and academic implications, which are discussed herein.
Freight rates have long been of great interest to maritime economists and practitioners. In recent years, however, they have become increasingly volatile, with widespread impacts across the maritime value chain. As traditional linear and time series decomposition approaches often struggle with forecasting highly fluctuating freight rates, machine learning (ML) applications are becoming increasingly relevant. Nonetheless, there is no comprehensive study that consolidates both proven ML models and the influential factors used as inputs in these models. Therefore, this study systematically reviews 28 articles published between 2012 and 2024, identifying 17 output (target or dependent) and 59 input (explanatory or independent) variables used in building ML models. Neural Network (NN) architectural framework types of ML models are most commonly employed, although hybrid and specialized models often outperform such standalone approaches. The existing literature has a strong focus on the dry bulk market (61
This study investigates the evolution of the global container shipping network through a multi-index link prediction framework. Simulations of adding high-probability predicted links reveal improvements in network connectivity, clustering and accessibility. Regional analysis reveals spatial heterogeneity in the distribution of potential links. These potential links are primarily concentrated within intraregional connections in Europe and East Asia, reflecting the demand for regional economic integration and the deepening of short-sea shipping networks. Interregional links are also prominent, especially between East Asia and Southeast Asia, Europe and West Asia, Europe and Africa, North America and Central America, East Asia and North America, and East Asia and Oceania. These patterns align with broader macro-trends, including global supply chain restructuring, nearshoring, and the ongoing importance of major trans-oceanic trade routes. The findings offer valuable insights into the evolution of global container shipping network and provide practical guidance for shipping companies and port authorities in route planning, service deployment, and long-term infrastructure strategy.
Shipping markets are volatile in nature and surrounded by many uncertainties. Understanding such volatility helps stakeholders mitigate risks. This research aims to study the volatility in the shipping industry based on the patterns of changes in the three major cargo shipping sectors—container, dry bulk, and tanker. Variance analysis, time series models as well as long short-term memory (LSTM) deep learning models are applied to investigate the long-term trend of volatility over the 30 years from 1995 to 2024. The Port of Singapore is our theatre and volatility is investigated by means of cargo volumes, vessel arrival statistics and freight and charter rates. The results quantitatively reveal the volatile patterns of shipping markets and give a reference ranking in terms of the degree of volatility. The dry bulk shipping sector is the most volatile one, largely because of the highly volatile commodity and energy markets, followed by the tanker and container shipping sectors. Methodology-wise, the LSTM model is more efficient in predicting market change, compared to time series models, with lower error metrics value and higher adjusted R-squared. The implications of the research offer applicable solutions when facing risks and uncertainties, and address the necessity of developing automation and digitalisation in the industry.
The paper argues that Donald Trump’s return to the White House has elevated competition over overseas ports into one of the core components of U.S.–China rivalry. As a revisionist of the liberal globalization era—an era that enabled China to build out a worldwide port network and exposed acute strategic vulnerabilities for the U.S.—Trump seeks to reconfigure the established infrastructure paradigm in America’s favor: narrowing China’s foothold, regaining leverage in critical nodes, and expanding into new arenas. As a result, port infrastructure is becoming the terrain of a protracted, multi-round contest for geoeconomic and geopolitical advantage. To show how this approach operates in practice, the article examines two illustrative cases. Panama demonstrates a mechanism of regaining leverage: diplomatic and legal pressure shifts a port concession from the realm of commercial management to that of strategic vulnerability, labeling the Hong Kong operator as China-linked infrastructure and opening the way for a broader reconfiguration of port assets in favor of a Western infrastructure–finance coalition. Ukraine, by contrast, embodies a seize logic by embedding Western capital and management in reconstruction and linking ports to critical minerals and secure supply chains. The analysis concludes that the current systemic transition of the global order offers a window for assertive U.S. action, but durable success requires coalition-wide convergence around shared threat definitions, sustained domestic support, and the delivery of credible alternatives—supported by discreet diplomacy and disciplined messaging to maintain legitimacy and shape the narrative.
The rapid growth of cargo volumes has highlighted the increasing importance of marine terminals. To enhance efficiency and productivity, the port technology sector has been actively adopting digital technologies. However, despite the growing need for continuous innovation, there is a lack of studies analyzing quantitative data on digital technologies in ports. To address this gap, through a port patent analysis, this study aims to identify promising technological areas, as well as promising new technologies that require further research and development. Using the Latent Dirichlet Allocation (LDA) algorithm for topic modeling, we identify four key technological areas in port digitalization: (1) Intelligent vessel condition monitoring, (2) Shipping wireless communication network, (3) Port operations optimization, and (4) Container inspection and monitoring. Moreover, we apply Generative Topographic Mapping (GTM) to visualize the technological landscape. This revealed technological vacuums in intelligent vessel condition monitoring and port operations optimization. By providing a quantitative analysis of port digital technologies, based on 989 relevant patents extracted from 35,410 records, this study offers insights into technological trends and uncovers areas for future innovation. The findings are expected to guide innovation in port-related technologies, contributing to more efficient port operations and advancements in the maritime sector.
Spot freight rates of liquefied natural gas (LNG) carriers have become increasingly volatile due to rising demand for cargo transportation and dynamic market conditions. This study aims to enhance the accuracy of freight rate volatility forecasting using artificial intelligence, evaluating the performance of the gated recurrent unit (GRU) model for long-term monthly predictions. Key predictive variables, including LNG prices, LNG inventory levels, sailing speeds of LNG carriers, port call indices, and charter rates, were selected for analysis. The study compares the performance of the long short-term memory (LSTM) model and the GRU model by incrementally extending the prediction period up to 16 weeks. The results demonstrate that the GRU model achieved approximately 70
The Gulf of Guinea (GoG) is a region that is used as a hub for piracy. In 2020, more than 95
While the literature has documented the effects of shipping connectivity on international trade flows, evidence for low and middle income regions remains limited. This paper examines the relationship between container shipping connectivity and bilateral trade between Latin America and the Caribbean and other regions (Europe, North America, East Asia, South Asia, West Asia, Africa, and Oceania) from 2006 to 2022. Using a higher-order fixed effects gravity model of trade, the study disentangles the impact of shipping connectivity on export trade value. The findings indicate an average trade elasticity of 0.34
