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When port authorities or terminal operators set the free time or increase storage density, the decision is often made without a clear understanding of their effects on throughput and rehandling productivity. This is partly because practical methods that deal specifically with the effect of dwell time on throughput and productivity are limited in the literature; hence the motivation for this work. This paper introduces simple methods to evaluate the effect of container dwell time and storage policies on import throughput, storage density, and rehandling productivity. The analysis considers two import storage strategies 1) non-mixed - no stacking of new import containers on top of old ones, and 2) mixed - stacking of new import containers on top of old ones. The results highlight the effect dwell time has on throughput and rehandling productivity. For the non-mixed storage policy, the increasing container dwell time lowers throughput and average stack height - resulting in an increase in rehandling productivity. On the other hand, for the mixed storage policy, the increasing container dwell time raises throughput and average stack height - resulting in a decrease in rehandling productivity. Using the presented methods, port authorities and terminal operators are able to assess and quantify the benefits of their decisions regarding container free time and subsequently make an informed decision.

This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index.

This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM).

The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2.

The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.

Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.

In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.

The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.

The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

This paper aims to forecast inbound and outbound container throughput for Bangkok Port to 2041 and uses the results to inform the future planning and management of the port’s container terminal.

The data used cover a period of 16 years (192 months of observations). Data sources include the Bank of Thailand and the Energy Policy and Planning Office. Cause-and-effect forecasting is adopted for predicting future container throughput by using a vector error correction model (VECM).

Forecasting future container throughput in Bangkok Port will benefit port planning. Various economic factors affect the volume of both inbound and outbound containers through the port. Three cases (scenarios) of container terminal expansion are analyzed and assessed, on the basis of which an optimal scenario is identified.

The economic characteristics of Thailand differ from those of other countries/jurisdictions, such as the USA, the EU, Japan, China, Malaysia and Indonesia, and optimal terminal expansion scenarios may therefore differ from that identified in this study. In addition, six particular countries/jurisdictions are the dominant trading partners of Thailand, but these main trading partners may change in the future.

There are only two major projects that have forecast container throughput volumes for Bangkok Port. The first project, by the Japan International Cooperation Agency, applied both the trend of cargo volumes and the relationship of volumes with economic indices such as population and gross domestic product. The second project, by the Port Authority of Thailand, applied a moving average method to forecast the number of containers. Other authors have used time-series forecasting. Here, the authors apply a VECM to forecast the future container throughput of Bangkok Port.

Seaports are a signifier for the world economy and international trade. Notwithstanding the considerable role of Chinese ports in global trade, only few studies have explored the efficiency of Chinese container terminals. Furthermore, studies on Chinese port efficiency has typically centered on port-level analysis, not terminal level. Therefore, this study aims to examine the operation efficiency of Chinese container terminals.

This study uses super-efficiency data envelopment analysis (SE-DEA) approach. SE-DEA is superior than basic DEA model because it is feasible for categorizing and ranking the efficiency of container terminals more accurately and comprehensively. In the basic model, if the several decision-making units (DMUs) are efficient, the efficiency value of them is “1.” However, in the SE-DEA model, the most efficient DMU is over “1.” Based on the level of container throughput in 2018, the top 20 Chinese container terminal companies were selected. Various production quotas were selected as inputs, while the container throughput was considered output.

The findings show that Terminal Shanghai Mingdong Container Terminal Co., Ltd. was ranked 1, followed by Shanghai Shengdong International Container Terminal Co., Ltd., Shanghai International Port (Group) Co., Ltd. and Yidong Container Terminal Branch.

This study contributes to providing some insights into Chinese container terminal industry to augment the efficiency. This study also provides practical and policy implications (e.g. better terminal operations) for container terminals.

Developing waterway-waterway transfer is an important path for Shanghai's container logistics to innovate service models. Taicang Express Line, a typical case of service model innovation, plays an important role in elevating the standing of Shanghai Port as a container hub port and in developing China (Shanghai) Pilot Free Trade Zone. From the three dominant transfer service models, the waterway-waterway transfer for container logistics of Taicang Express Line has the traits and experience in streamlining logistics processes, innovating logistics clearance models, saving logistics operating costs, offering port logistics cooperation experience for replications and promoting integration of regional port logistics resources. However, it also harbors issues in infrastructure construction, staffing, container resources allocation and transportation, transportation efficiency and policy innovation. In the future, efforts should be invested to strengthening the construction and staffing of port logistics infrastructure, optimizing the container resources allocation and transport of port logistics systems, improving the logistics transportation efficiency of Taicang Express Line, and pushing forward innovation of the synergistic policy mechanism for regional port logistics.

Developing waterway-waterway transfer is an important path for Shanghai's container logistics to innovate service models. Taicang Express Line, a typical case of service model innovation, plays an important role in elevating the standing of Shanghai Port as a container hub port and in developing China (Shanghai) Pilot Free Trade Zone. From the three dominant transfer service models, the waterway-waterway transfer for container logistics of Taicang Express Line has the traits and experience in streamlining logistics processes, innovating logistics clearance models, saving logistics operating costs, offering port logistics cooperation experience for replications and promoting integration of regional port logistics resources. However, it also harbors issues in infrastructure construction, staffing, container resources allocation and transportation, transportation efficiency and policy innovation. In the future, efforts should be invested to strengthening the construction and staffing of port logistics infrastructure, optimizing the container resources allocation and transport of port logistics systems, improving the logistics transportation efficiency of Taicang Express Line, and pushing forward innovation of the synergistic policy mechanism for regional port logistics.

This paper introduces a macro traffic flow model of carbon dioxide emissions from container ports. As long as both the throughput and the transshipment rate of the port are available, any port in the world can use it to estimate emissions. Initially, two Japanese container ports are used as reference points to derive the equivalent units of carbon dioxide per TEU for application to other ports. Then macro traffic flows within a container port are defined. Finally, carbon dioxide emissions from different container ports are estimated using the macro estimation procedure introduced in this paper. The results of trial estimations for selected ports among different countries highlight that the impacts of container ports on global warming are serious. This issue will be intensified if competition is increased by the larger container ports aspiring to be international mega hubs.

Since the 1980s the economic development of countries in East Asia has had a marked impact on the world port community, particularly in container transport. This paper analyses changes in the competitive environment of the world container port sector using some standard tools of market concentration. Initially, this paper reviews the competitive position of world container port system and then examines the East Asian economic environment. Both ordinal and cardinal measures of port system inequality are used to demonstrate both the rankings and levels of container throughput have been diverging in the world's major economic blocs. Conversely, East Asian countries during the 1990s have shown a trend towards convergence. Measures of dispersion suggest that ports in East Asian countries have become more competitive in their levels of container throughput.

This paper aims to explore the spatio-temporal patterns of Chinese foreign direct investment (FDI) since the inception of the Belt and Road Initiative (BRI) in 2013 as an extended version of geographically weighted regression.

The panel data are used to examine spatial and temporal dynamics of the magnitude and the direction of China's outward FDI stock and its flow from 2011 to 2015 at a country level. Using the geographically and temporally weighted regression (GTWR), spatio-temporal distribution of FDI is explained through Logistic Performance Index, the size of gross domestic product (GDP), Shipping Linear Connectivity Index and Container Port Throughput.

A comparative analysis between participating and non-participating countries in the BRI shows that the size of GDP and Container Port Throughput of the participating countries have a positive effect on the increases of China's outward FDI Stock to Asia especially after 2013, while non-participating countries, such as North America, Western Europe and Western Africa, have no significant effect on it before and after the implementation of the BRI.

The findings, however, will not necessarily provide insight into the needs of China's outward FDI in certain countries to develop their economy. The findings provide the evidence to inform policy making to help identify the winners and losers of the investment, scale and direction of investment and the key drivers that shape the distributive investment patterns globally.

The study provides the empirical evidence to inform investment policy and strategic realignment by quantifying scale, direction and drivers that shape the spatio-temporal shifts of China's FDI.

The analysis also guides the Chinese government improve bilateral trade, build infrastructure and business partnerships with preferential countries participating in the BRI.

There is an urgent need to adopt a new perspective to unfold the spatial temporal complexity of FDI that incorporates space and time dependencies, and the drivers of the situated context to model their effects on FDI. The model is based on GTWR and an extended geographically weighted regression (GWR) allowing the simultaneous analysis of spatial and temporal decencies of exploratory variables.