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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]

In recent years, many regions including China have witnessed fast growth of dry ports. Unlike in the other regions of the world, the amount of cargos handled by most dry ports in China is not in compliance with the significant amount of resources invested, but the reasons behind have not been discussed before. After a brief review on the development pattern of Chinese dry ports, certain dry port functions like customs clearance and rail connection, dry port ownership structure, and inter-competition among dry ports are identified as the unique factors which distinguish Chinese dry ports from others and affect their performance. We investigate the relationship between these factors and the efficiency of Chinese dry ports with a two-stage approach based on a panel data collected from eight dry ports affiliated to the port of Ningbo, China, covering the 2011–2016 period. In the first stage, Data Envelopment Analysis (DEA) is used to measure these dry ports' technical efficiency. In the second stage, Tobit regression analysis is applied to explore the relationship between efficiency and the above mentioned factors. Several insightful findings are observed, further leading to useful managerial insights. • The unique features in development of Chinese dry ports are discussed. • Impacts of these features on dry ports' efficiency are empirically examined. • Provision of customs service implies low efficiency at dry ports. • Seaport's ownership may not be beneficial to dry ports and local economy. [ABSTRACT FROM AUTHOR]

• Propose a standardized method to estimate port turnover rate based on satellite data of ocean ships. • Construct berth polygon areas for terminals based on Greatmaps (GMap) visual technique. • Design two tailor-made algorithms to estimate the berthing time of ship in a berthing event. • Assess port turnover rate with aggregate berthing time at a port and its historical port throughput. • Use calculated turnover rate to estimate the monthly throughput of global ports. Accurate assessment of port turnover rate is essential for port operators and shipping carriers to benchmark and improve their operations. This study proposes a standardized method to estimate the port turnover rate based on satellite data of ocean ships. This method can be generalized to accommodate ports of different geographic and operational characteristics with minimum input and running times. To achieve the research objective, we first construct berth polygon areas for terminals based on Greatmaps (GMap) visual technique. Then, two tailor-made algorithms are proposed to estimate the berthing time of ship in a berthing event. Finally, we assess the port turnover rate with aggregate berthing time at a port and its historical port throughput. Assuming that the turnover rate is unchanged in the short term, we can use the estimated turnover to estimate the monthly throughput of global ports. The findings suggest the average Mean Absolute Percentage Error (MAPE) of our estimation is 3.84%. Standardized and high-frequency port statistics are highly valued by the industry but very costly to access. The proposed method makes high-frequency port turnover rate and throughput available for a wide range of users. The statistics and findings will enhance standardization and transparency of port statistics and promote the sustainable development of port industry. [ABSTRACT FROM AUTHOR]

The port integration problem in a multi-port region (MPR) is studied from the perspective of maximizing the social welfare of the external transport system in a closed region in which the ports are regarded as external transport hubs. First, the economic principle of maximizing the social welfare of the external transport system is analyzed, and a method to calculate the total internal transport cost of the external transport system is proposed. Second, the optimal scale of the port group in an MPR is determined by comparing the total internal costs for different scales of the port group. An integration method with multi-period investment and asset idling is also proposed that takes into account excess port resources in MPRs. Finally, main gateway ports in Northeast China are selected for an empirical study. Based on the findings, suggestions regarding port resource integration and cooperative operation in MPRs are presented. [ABSTRACT FROM AUTHOR]

China's steel output has maintained rapid growth over the past twenty years. Due to this, a large number of iron ore ports/terminals have been built along the Yangtze River, and the Yangtze River bulk port system has experienced a unique development in its structure. This paper aims to understand the evolution of this bulk port system. 1 1 In this paper, bulk cargo mainly refers to iron ore bulk cargo. along the Yangtze River. To achieve this objective, first the development phases of the Yangtze River bulk port system are reviewed, taking the theoretical (container) port evolution model as a benchmark. Then several hypotheses addressing certain features of bulk port system development are proposed, followed by using panel data analysis to test these hypotheses. Based on this discussion and analysis, the major driving forces that are reshaping bulk port development along the Yangtze River are then summarized. It is found that evolution of the Yangtze River bulk port system in general follows the port development models in previous literature. However, the trend toward regionalization and an offshore hub have not appeared. Besides this, iron ore transshipment is moving outward both for sea ports and river ports, and few iron ore transshipment gateway hubs are occurring. Furthermore, the transshipment function of a bulk port plays a significant role in port traffic changes, but this role is affecting sea ports differently to river ports. The container throughput of transshipment sea ports has a significant negative effect on bulk traffic, whereas that of transshipment river ports has a positive effect. Geographical conditions, institutional factors and national policy, industry agglomeration, changes in market supply and demand, and technology updates are major factors driving changes to the port system structure. These factors are observed to function either individually or collectively at different development stages. [ABSTRACT FROM AUTHOR]

Improving transport connectivity has become increasingly significant to international trade research and transport policy agendas. Little has been done so far to investigate the direct association between maritime connectivity and economic growth, let alone the exact effect magnitude. As the connection between ports, local economies, and global logistics networks becomes ever closer, the economic role of port connectivity deserves closer attention. This paper uses the Panama Canal expansion as a quasi-natural experiment, and proposes a dynamic difference-in-differences (DID) method for making causal inferences about the impact of port connectivity on regional economic growth. This study leverages data mining of multiple sources of satellite data to provide innovative measures of ship movement, port area, and economic activity. We construct an index based on the global shipping network data from the Automatic Identification System (AIS) to measure the extent to which the ports are exposed to the canal expansion. This allows careful handling of the endogeneity problem. Change in the port area is used as a proxy for port capacity expansion, and is identified via satellite imaging. Night light data provide a measure of regional economic activity around the ports, and of the effect of port connectivity on regional economic growth. Our findings suggest that improvements in port connectivity have a significant and fairly durable stimulating effect on regional economic growth, and that port infrastructure expansion is one potential channel for realizing this effect. [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]