Reconciling big data on trade statistics and ship traffic: a case study
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Globalisation and its trend to increase the interaction between nations on a worldwide scale has booster the commercialisation of raw materials to unprecedented levels. From the large proportion that trade activities share in the global impacts; shipping stands as one of the most important. By itself contributes with 2.2 per cent of the global anthropogenic CO2 emissions and is responsible for more than the 80 per cent of the globally traded tonnage. Thus, it is vital to comprehend how international trade drives maritime traffic and the subsequent environmental impacts embodied in such activity. The objective of this thesis is to reconcile big data on trade statistics with big data on ship traffic using as a case study the bilateral trade between Australia and China for seven major dry-bulk commodities. To achieve the thesis goal two approaches are proposed. The first approach estimates the maritime traffic using flow trades from the United Nations Comtrade and the second approach builds a model able to evaluate trade flows using naval traffic information provided by the Norwegian Coastal Administration. Although the results show discrepancies when the two approaches are collated it is concluded that the United Nations Comtrade database has the potential to estimate maritime traffic if trade flows are combined with the right information. The same thought applies to the Norwegian Coastal Administration Satellite AIS database when is used to determine international trade flows. At the end of the thesis is proposed as a way to increase the reliability of the results and the benefits of the harmonisation of both databases are presented.