Mutual information analysis of the factors influencing port throughput. Issue 2 (30th December 2020)

• Record Type: Journal Article
• Authors: Eskafi, Majid; Kowsari, Milad; Dastgheib, Ali; Ulfarsson, Gudmundur F.; Taneja, Poonam; Thorarinsdottir, Ragnheidur I.
• Is Part Of: Maritime business review. Volume 6:Issue 2(2021)
• Page Start: 129
• Page End: 146
• Publication Date: 2020-12-30
• Subjects: Iceland -- Macroeconomics -- Mutual information -- Port throughput; Shipping -- Periodicals; 387.5
• DOI: 10.1108/MABR-05-2020-0030
• Languages: English
• ISSNs: 2397-3757

Abstract

Abstract : Purpose: Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables. Design/methodology/approach: Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable. Findings: This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput. Originality/value: Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.