Bayesian network and structural equation modeling of dependencies between country-level sustainability risks and logistics performance

The 17 Sustainable Development Goals (SDGs), introduced by the United Nations in 2015, provide a framework for individual countries to track their performance on social, economic, and environmental dimensions of sustainable development. Logistics is considered the backbone of an economy that can influence the development and growth of a country. Using a dataset of 157 countries, this study aims to explore dependencies between the 17 SDG-related risks and logistics performance in a probabilistic network setting. A hybrid methodology, combining Bayesian Belief Networks and Structural Equation Modeling, is used to develop and quantify a network structure. Logistics performance is significantly influenced by four SDGs, namely ‘ industry, innovation and infrastructure ’, ‘ climate action ’, ‘ partnerships for the goals ’, and ‘ gender equality ’. On the other hand, another cluster of SDGs is observed surrounding the ‘ no poverty ’ goal, including ‘ industry, innovation and infrastructure ’, ‘ decent work and economic growth ’, ‘ reduced inequality ’, ‘ zero hunger ’, ‘ sustainable cities and communities ’, ‘ affordable and clean energy ’, and ‘ clean water and sanitation ’. This paper proposes and operationalizes a new hybrid methodology to explore dependencies between the SDGs and logistics performance, thereby providing valuable insights to policymakers and researchers about the association between sustainable development and logistics performance.