Pollution adjacent to the continent's shores has increased in the last decades, so it has been necessary to establish an energy policy to improve environmental conditions. One of the proposed solution was the search of alternative fuels to the commonly used in Short Sea Shipping to reduce pollution levels in Europe. Studies and researches show that liquefied natural gas could meet the European Union environmental requirements. Even environmental benefits are important; currently there is not significant number of vessels using it as fuel. Moreover, main target of this article is exposing result of a research in which a methodology to establish the most relevant variables in the decision to implement liquefied natural gas in Short Sea Shipping has been development using data mining. A Bayesian network was constructed because this kind of network allows to get graphically the relationships between variables and to determine posteriori values that quantify their contributions to decision-making. Bayesian model has been done using data from some European countries (European Union, Norway and Iceland) and database was generated by 35 variables classified in 5 categories. Main obtained conclusion in this analysis is that variables of transport and international trade and economy and finance are the most relevant in the decision-making process when implementing liquefied natural gas. Even more, it can be stablish that capacity of liquefied natural gas regasification terminals under construction and modal distribution of water cargo transportation continental as the most decisive variables because they are the root nodes in the obtained network.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Acedo A. Almazán J. Pasado Presente y futuro de las Autopistas del Mar en Europa Revista de Obras Públicas No. 3565 pp. 31-38 2015.
 Acid S. De Campos L. M. Approximations of causal networks by polytrees: An empirical study Advances in Intelligent Computing IPMU’94 pp. 149-158 1995.
 Almazán J. L. Palomino M. González N. Soler F. y Iribarren E. Estimation of Spanish bunkering at EU level of secas Research Conference In Technical Disciplines Section Transport and Logistic pp. 136-141 2013.
 Almazán-Gárate J. L. Palomino-Monzón M. C. González-Cancelas N. Soler-Flores F. Relationship between air pollution and natural gas with respect to maritime transport Methodology based on Bayesian Networks. Global Virtual Conference Transport and Logistics Section 7-11 April 2014.
 Bengtsson S. K. Fridell E. Andersson K. Fuels for short sea shipping: A comparative assessment with focus on environmental impact Journal of Engineering for the Maritime Environment Vol. 228 pp. 44-54 2014.
 Brynolf S. Andersson K. E. Fridell E. A comparative life cycle assessment of marine fuels Journal of Engineering for Maritime Environment Institution of mechanical engineers 2011.
 Cánovas B. Short Sea Shipping una estrategia Europea Documento de Opinión No. 70. Instituto Español de Estudios Estratégicos 2015.
 Comisión Europea Libro Blanco La política europea de transportes de cara al 2010: la hora de la verdad (pp. 11-53) COM (2001) 370 final Bruselas: Commission of the European Communities.
 Comisión Europea Libro Verde Estrategia europea para una energía sostenible competitiva y segura (pp. 3-19) COM (2006) 105 final. Bruselas: Commission of the European Communities.
 Cooper G. F. Herskovitz E. A Bayesian method for the induction of probabilistic networks from data Machine Learning 9(4):309-348 1992.
 Friedman N. Goldszmidt M. Building classifiers using Bayesian networks Proceedings of the National Conference on Artificial Intelligence Menlo Park Ca: AAAI Press 1996.
 Gritsenko D. Yliskylä-Peuralahti J. Governing shipping externalities: Baltic ports in the process of SOx emission reduction Maritime Studies Vol. 12 No. 10 2013.
 Heckerman D. A tutorial on learning with Bayesian networks. Innovations in Bayesian networks (pp. 33-82) Springer Berlin Heidelberg 2008.
 Kegalj I. Traven L. Influence of Cargo flows on Sustainable Development of East Mediterranean “Motorways of the sea” Journal of Maritime & Transportation Sciences Vol. 53 No. 1 pp. 19-33 2017.
 Laskey K. B. Sensitivity analysis for probability assessments in Bayesian networks In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence 2014.
 Lloyd M. Regional action for logistical integration of shipping across Europe (realise) Work package 4 Task 4.1 multi-modal pricing and costing analyses inception report 2003. Available in: http://www.realise-sss.org/.
 Niedermayer D. An introduction to Bayesian networks and their contemporary applications In Innovations in Bayesian Networks Springer Berlin Heidelberg pp. 117-130 2008.
 Nižić F. Frančić V. Orović J. Ships’ Solutions for meeting the International requirements regarding the reduction of Air Pollution Journal of Maritime & Transportation Sciences Vol. 53 No. 1 pp. 53-65 2017.
 Puga J. L. García J. L. De la Fuente Sánchez L. de la Fuente Solana E. I. Las redes bayesianas como herramientas de modelado en psicología Anales de Psicología 23(2) 307-316 2007.
 Psaraftis H. N. Kontovas C. A. CO2 Emission Statistics for the World Commercial Fleet WMU Journal of Maritime Affairs Vol. 8 No. 1 pp. 1-25 2009.
 Psaraftis H. N. Kontovas C. A. Balancing the Economic and Environmental Performance of Maritime Transportation Transportation Research Part D: Transport and Environment Vol. 15 No. 8 pp. 458-462 2010.
 REALISE Regional Action for Logistical Integration of Shipping across Europe The Alliance of Maritime Regional Interests in Europe (AMRIE) GTC2-2000-33032. 2002. Available in: http://www.realise-sss.org.
 REALISE The Alliance of Maritime Regional Interests in Europe (AMRIE) Final Report No. GTC2-2000-33032 Available in: http://www.realise-sss.org 2005.
 RECORDIT Actions to Promote Intermodal Transport: Final Report No. WP9 2003 Available in: http://www.recordit.org/.
 Rodríguez D. Dolado J. Redes Bayesianas en la Ingeniería del Software 2007.