2020
DOI: 10.1016/j.ajsl.2019.08.002
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The effects of consolidation and privatization of ports in proximity: A case study of the Kobe and Osaka ports

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Cited by 25 publications
(6 citation statements)
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“…They suggested that although the efficiency of terminals is significantly affected by privatization, it depends on other factors too. Kawasaki et al (40) explored the effects of ''consolidation'' and ''privatization'' between adjacent ports. They employed a multiagent simulation model applied to a case study for Kobe and Osaka ports, and concluded that consolidation has a larger impact than privatization with respect to cargo volume and total surplus.…”
Section: Research On Port Privatizationmentioning
confidence: 99%
“…They suggested that although the efficiency of terminals is significantly affected by privatization, it depends on other factors too. Kawasaki et al (40) explored the effects of ''consolidation'' and ''privatization'' between adjacent ports. They employed a multiagent simulation model applied to a case study for Kobe and Osaka ports, and concluded that consolidation has a larger impact than privatization with respect to cargo volume and total surplus.…”
Section: Research On Port Privatizationmentioning
confidence: 99%
“…Ōsaka Port subsequently privatised its PMC to the Ōsaka Port Corporation with 100 per cent capital from the City of Ōsaka in 2010 (Kawasaki et al, 2020). As a result, Ōsaka Port has three elements in its container port administration: the national government; the local government of Ōsaka City; and the Ōsaka Port Corporation.…”
Section: Port Administration In the Modern Democratic Eramentioning
confidence: 99%
“…They also recognized that the industrial confidence indicator generated a significant positive impact on container throughput in Antwerp port. Kawasaki et al (2020) used simulation-based analysis to forecast container throughput at Kobe and Osaka ports as a result of the consolidation and privatization of the two ports. Chan et al (2018) compared several timeseries forecasting methods, including machine learning-based methods such as support vector regression to forecast the port's container throughput using historical data.…”
Section: Literature Reviewmentioning
confidence: 99%