Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Global Ship Demolition Using Machine Learning Approach

Version 1 : Received: 28 January 2021 / Approved: 1 February 2021 / Online: 1 February 2021 (12:43:52 CET)

How to cite: Rahman, S.M. Predicting Global Ship Demolition Using Machine Learning Approach. Preprints 2021, 2021020027. Rahman, S.M. Predicting Global Ship Demolition Using Machine Learning Approach. Preprints 2021, 2021020027.


Abstract:Global ship demolition is mostly concentrated in south Asian countries, namely Bangladesh, India, Pakistan and China, since 1990’s, having competitive advantage for their high natural tide, and low environmental and social costs. Due to high social and environmental externalities, stakeholders increase monitoring of the externalities and continue to prescribe improvement towards sustainability, which put pressures on profitability and competitiveness. As a consequence, also seen in the past, a leakage effect may emerge, leading to shift of this activity to a region, with relatively less monitored and less stricter on social and environmental impacts. Unfortunately, the leakage effect is never predicted in shipbreaking in order to understand the level of push compatible in the given socio-economic contexts. In this study, we have attempted to predict the future ship demolition landscape, applying machine learning technique to 34,531 in-service vessels worldwide, larger than 500 gross tonnage (GT), which is run against a learning model based on 3500 demolished vessels from 2014. This study shows that redistribution may occur among the top recycling nations: India may emerge out to be a dominant player in shipbreaking, surpassing Bangladesh by a margin of two-fold, while Pakistan and China are in decreasing trend. In addition, the leakage effect is observed, in that Vietnam is predicted to be the fourth largest ship demolition country, while China and Pakistan recede from the third and fourth place to 6th and 8th. Turkey is predicted to advance from fifth position to third position by vessel count but stays same in term of total GT dismantled. Although it is not clear if any leakage is to be observed in the near future, this study may be a model for future predictive analytics and help stakeholders take evidence-based business decisions.


Keywords: Ship Recycling, Predictive Analytics, Big Data, Shipbreaking, Leakage Effect


Computer Science and Mathematics, Other

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