Network effects govern the evolution of maritime trade
Maritime transport accounts for over 80% of the world trade volume and is the backbone of the global economy. Global supply chains create a complex network of trade flows. The structure of this network impacts not only the socioeconomic development of the concerned regions but also their ecosystems....
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ftpubmed:oai:pubmedcentral.nih.gov:7293592 2023-05-15T15:09:31+02:00 Network effects govern the evolution of maritime trade Kosowska-Stamirowska, Zuzanna 2020-06-09 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293592/ http://www.ncbi.nlm.nih.gov/pubmed/32457136 https://doi.org/10.1073/pnas.1906670117 en eng National Academy of Sciences http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293592/ http://www.ncbi.nlm.nih.gov/pubmed/32457136 http://dx.doi.org/10.1073/pnas.1906670117 https://www.pnas.org/site/aboutpnas/licenses.xhtmlPublished under the PNAS license (https://www.pnas.org/site/aboutpnas/licenses.xhtml) . Proc Natl Acad Sci U S A PNAS Plus Text 2020 ftpubmed https://doi.org/10.1073/pnas.1906670117 2020-11-29T01:17:22Z Maritime transport accounts for over 80% of the world trade volume and is the backbone of the global economy. Global supply chains create a complex network of trade flows. The structure of this network impacts not only the socioeconomic development of the concerned regions but also their ecosystems. The movements of ships are a considerable source of CO(2) emissions and contribute to climate change. In the wake of the announced development of Arctic shipping, the need to understand the behavior of the maritime trade network and to predict future trade flows becomes pressing. We use a unique database of daily movements of the world fleet over the period 1977–2008 and apply machine learning techniques on network data to develop models for predicting the opening of new shipping lines and for forecasting trade volume on links. We find that the evolution of this system is governed by a simple rule from network science, relying on the number of common neighbors between pairs of ports. This finding is consistent over all three decades of temporal data. We further confirm it with a natural experiment, involving traffic redirection from the port of Kobe after the 1995 earthquake. Our forecasting method enables researchers and industry to easily model effects of potential future scenarios at the level of ports, regions, and the world. Our results also indicate that maritime trade flows follow a form of random walk on the underlying network structure of sea connections, highlighting its pivotal role in the development of maritime trade. Text Arctic Climate change PubMed Central (PMC) Arctic Proceedings of the National Academy of Sciences 117 23 12719 12728 |
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PNAS Plus Kosowska-Stamirowska, Zuzanna Network effects govern the evolution of maritime trade |
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PNAS Plus |
description |
Maritime transport accounts for over 80% of the world trade volume and is the backbone of the global economy. Global supply chains create a complex network of trade flows. The structure of this network impacts not only the socioeconomic development of the concerned regions but also their ecosystems. The movements of ships are a considerable source of CO(2) emissions and contribute to climate change. In the wake of the announced development of Arctic shipping, the need to understand the behavior of the maritime trade network and to predict future trade flows becomes pressing. We use a unique database of daily movements of the world fleet over the period 1977–2008 and apply machine learning techniques on network data to develop models for predicting the opening of new shipping lines and for forecasting trade volume on links. We find that the evolution of this system is governed by a simple rule from network science, relying on the number of common neighbors between pairs of ports. This finding is consistent over all three decades of temporal data. We further confirm it with a natural experiment, involving traffic redirection from the port of Kobe after the 1995 earthquake. Our forecasting method enables researchers and industry to easily model effects of potential future scenarios at the level of ports, regions, and the world. Our results also indicate that maritime trade flows follow a form of random walk on the underlying network structure of sea connections, highlighting its pivotal role in the development of maritime trade. |
format |
Text |
author |
Kosowska-Stamirowska, Zuzanna |
author_facet |
Kosowska-Stamirowska, Zuzanna |
author_sort |
Kosowska-Stamirowska, Zuzanna |
title |
Network effects govern the evolution of maritime trade |
title_short |
Network effects govern the evolution of maritime trade |
title_full |
Network effects govern the evolution of maritime trade |
title_fullStr |
Network effects govern the evolution of maritime trade |
title_full_unstemmed |
Network effects govern the evolution of maritime trade |
title_sort |
network effects govern the evolution of maritime trade |
publisher |
National Academy of Sciences |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293592/ http://www.ncbi.nlm.nih.gov/pubmed/32457136 https://doi.org/10.1073/pnas.1906670117 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
Proc Natl Acad Sci U S A |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293592/ http://www.ncbi.nlm.nih.gov/pubmed/32457136 http://dx.doi.org/10.1073/pnas.1906670117 |
op_rights |
https://www.pnas.org/site/aboutpnas/licenses.xhtmlPublished under the PNAS license (https://www.pnas.org/site/aboutpnas/licenses.xhtml) . |
op_doi |
https://doi.org/10.1073/pnas.1906670117 |
container_title |
Proceedings of the National Academy of Sciences |
container_volume |
117 |
container_issue |
23 |
container_start_page |
12719 |
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12728 |
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1766340693954920448 |