The role of maritime, land, and air transportation in economic growth: Panel evidence from OECD and non-OECD countries

Park, Jin Suk; Seo, Young-Joon; Ha, Min-Ho

doi:10.1016/j.retrec.2019.100765

Cited by 60 publications

(31 citation statements)

References 62 publications

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“…An increase in the share of maritime transportation has created a dynamic interface for hinterland transportation, as it has helped to evolve world trade [7] through so-called economies of scales, the impact of container ship sizes on maritime transportation, and the reduction of transportation costs [8]. Having considered all this progress in global trade, maritime transportation improvement depends on setting up an interorganizational information exchange connection among different organizational users, including manufacturers, customers, shipping lines, and customs and port authorities from different countries [9].…”

Section: Of 17mentioning

confidence: 99%

Improvement of Container Terminal Productivity with Knowledge about Future Transport Modes: A Theoretical Agent-Based Modelling Approach

Mazloumi

Hassel

2021

Sustainability

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Despite all the achievements in improving container terminal performance in terms of equipment and container stacking systems (CSS), terminal operators are still facing several challenges. One of these challenges is the lack of information about further transportation modes of the container, which leads to extra movements of the container inside the stacking area. Hence, we aimed to examine factors that affect container handling processes and to evaluate a container terminal’s overall equipment effectiveness. This study used data from a container terminal at the Port of Antwerp, Belgium. An agent-based model was developed based on a block-stacking strategy to investigate two scenarios: (1) having information about further transportation modes and (2) a base scenario. The Overall Equipment Effectiveness Index (OEE) was also adopted to evaluate the container terminal’s effectiveness in both scenarios. Results showed that having information on further transportation mode significantly increased the container outflow, and the OEE index improved compared to the base scenario’s results. Therefore, we recommend an integrated data-sharing system where all the stakeholders can share their information with no fear of losing their market share.

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Section: Of 17mentioning

confidence: 99%

Improvement of Container Terminal Productivity with Knowledge about Future Transport Modes: A Theoretical Agent-Based Modelling Approach

Mazloumi

Hassel

2021

Sustainability

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“…Maritime transportation is a stronger necessity for economic growth than air and land transportation (Park et al, 2019;Seo and Park, 2016). Recently, General Secretary Xi Jinping proposed at the 19th CPC National Congress to build transportation power.…”

Section: Literature Reviewmentioning

confidence: 99%

The efficiency of major container terminals in China: super-efficiency data envelopment analysis approach

Seo

2021

MABR

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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“…Hu researched the impact of port logistics on the development of the economic hinterland and a method for automatic prediction was introduced, so called SVM (Support Vector Machine) model for forecasting the scale of the logistic needs according to its characteristics [23]. Thus, Park et al in their research use a regression method together with the PCA method to determine the so-called Principal Component with the largest variance and related other orthogonal directions to analyze the impact of all branches of transport on the economic growth of different countries, so it is concluded that maritime transport has a stronger impact on economic development than other branches of transport [24]. Lou et al estimated the movement of container shipping on the basis of data from 1980-2008 by using the LSM method [25].…”

Section: Overview Of the Present Researchesmentioning

confidence: 99%

Model of short-term forecasting liner maritime transport in the port system

et al. 2020

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One of the preconditions for good quality management of seaports is forecasting the traffic according to the number of passengers and the number of vehicles; in this way it is possible to plan and prepare activities for the smooth operation of the ports. This paper researches the port system as part of the coastal liner maritime transport. The set hypothesis is that the model of forecasting the traffic could be presented as a function of two variables. The Principal Component Analysis (PCA) method is used to select the forecasting parameters. Based on the choice of parameters, using the Least Squares Method (LSM), the trend analysis is performed to choose the forecasting functions for maritime liner transport on the example of the Split City port. The statistical analysis on the choosed forecasting model using the coefficient of determination r2 and adjusted r2 model is performed to confirm the choice.

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The role of maritime, land, and air transportation in economic growth: Panel evidence from OECD and non-OECD countries

Cited by 60 publications

References 62 publications

Improvement of Container Terminal Productivity with Knowledge about Future Transport Modes: A Theoretical Agent-Based Modelling Approach

Improvement of Container Terminal Productivity with Knowledge about Future Transport Modes: A Theoretical Agent-Based Modelling Approach

The efficiency of major container terminals in China: super-efficiency data envelopment analysis approach

Model of short-term forecasting liner maritime transport in the port system

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