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Despite existing debates on the maritime cluster's institutional composition, geographical boundary, or evolutionary patterns, few studies have empirically scrutinized the relatedness of industrial sectors inside a maritime cluster. This study aims to develop an industry-relatedness model to quantitatively measure the paired industry relatedness among three maritime sectors: port-related, shipping-related, and maritime business services. Maritime sectors can be labour-intensive or capital-intensive, and thereby we constructed a compound ratio of capital per capita to address the heterogeneity issue in evaluating the industry relatedness. Instead of focusing on a specific maritime cluster, we applied the model to three representative maritime clusters, namely Western Europe, Guangdong-Hong Kong-Macao Greater Bay Area (GBA), and the Yangtze River Delta (YRD), to enhance the generalizability of our findings and implications. Findings suggest that the maritime cluster in Western Europe is characterized by more inter-sector connections. In Western Europe, Ship owning and operating , and Ship management and agency are found to operate with the highest relatedness; GBA has a polarized maritime industry structure with Hong Kong specializing in Ship finance and Shipbroking ; cities in YRD operate with a speciality in Shipbuilding and repairing. This study found that differences in industry relatedness are mainly attributable to the focal region's evolutionary history and governance structure. We also provided a valuable reference for policymakers to plan and improve local and regional maritime cluster development. • Modify the co-occurrence model by considering the heterogeneity of different maritime sectors. • Quantify the industry relatedness between different maritime sectors. • Compare three typical maritime clusters in terms of maritime industry-relatedness. [ABSTRACT FROM AUTHOR]

The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world's 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation. [ABSTRACT FROM AUTHOR]

Seaports play an important role in the global shipping network. Shipping participants often attach great importance to the measurement of container port connectivity, as it reflects countries' access to world markets. As a result, various port connectivity index systems have been proposed by members of the shipping industry and scholars. In recent years, technological developments especially the advancement of high coverage and real-time Automatic Identification System (AIS) data, have provided a chance to improve the scope and frequency of the existing index systems. An improved system is expected to reflect the dynamic changes in a port's connectivity which may be induced by either local disruptions or shocks in the wider economy. This study builds a monthly container port connectivity index system by applying big data mining techniques, graph theory, and principal component analysis (PCA) to AIS data, taking both port factors and shipping network factors into consideration. AIS records from 2020 are used to calculate the connectivity score of 25 major container ports. We also compare our system with the connectivity index commonly used in the shipping industry, the Liner Shipping Connectivity Index (LSCI). Our results show that the measurement of connectivity can be improved over indices that depend primarily on indicators of traffic volume. Ports like Antwerp and Tanjung Pelepas rank high in the proposed system due to their sound performance on their accessibility and strategic position in the local region instead of their traffic volume. The monthly index system is also proven to reflect timely changes in the shipping industry through its accurate portrayal of changes in port connectivity during the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Historical time-series data of container traffic in ports are characterised by strong randomness that causes the time-series prediction performance to vary significantly. In this sense, the prediction accuracy differs as a function of exogenous variables related to port characteristics and the maritime shipping context. Thus, the measuring problem of a priori container traffic predictability (i.e., the ability of methods to predict port traffic variables) arises. Academia suggests that this predictability may be related to entropy as a measure of the information provided. This paper analyses the Sample Entropy (SampEn), as an indicator of the complexity represented by the container traffic time-series data, comparing it with the error metrics provided by a statistical prediction and an artificial intelligent method. The Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) Neural Network are used to obtain the prediction performance of container port traffic in a data set composed of 20 Chinese ports and 5 selected international ports. Unexpectedly, the results indicate that the correlation between the SampEn and the prediction performance of the time series is weak. The complex ecosystem of the container shipping sector and the different port characteristics are suggested as the cause of this poor correspondence. Our findings help to understand the relationship between entropy and the predictability of time-series data in a sector (i.e. port traffic), which has not been explored before. • We use the Sample Entropy as an indicator to measure the complexity of the container traffic time-series data. • We use ARIMA and BP Neural Network to obtain the prediction performance of the container port traffic dataset composed of 20 Chinese ports and 5 international ports. • We find the correlation between the SampEn and the prediction performance of the time series is weak. • We propose the complex ecosystem of the container shipping sector and the different port characteristics are the cause of this poor correspondence. [ABSTRACT FROM AUTHOR]