Artificial intelligence application to analize liquid natural gas use in European short sea shipping: Specific analysis of social and population components

Abstract

The problems of congestion in the main European corridors as a consequence of the globalization of the economy made it necessary to undertake measures and develop policies to avoid the externalities generated by this situation. Thus, the European Union promoted the Short Sea Shipping as an alternative, although it was observed that this measure caused environmental problems in the coastal areas with the environment in the Short Sea Shipping and methodologies are needed to help mitigate these effects. Thus, liquefied natural gas is shown as an alternative in the need to look for more friendly fuels. That is why research has been carried out developing an artificial intelligence tool that allows managers to make decisions to minimize transport congestion in Europe based on a Bayesian network methodology that allows to know the relationships between variables and classify them. The main result obtained establishes that the capacity of liquefied natural gas regasification terminals under construction and the modal distribution of cargo transport by inland water are the two most important variables in the decision to implement liquefied natural gas as fuel. However, the variables of the categories of environment and energy and population and social condition are the most dependent, although the latter are closely related to each other and do not directly affect variables related to liquefied natural gas and Short Sea Shipping, but yes on the gross domestic product of each country.