The Impact of Tariffs and Trade Tensions on Global Aviation Logistics: A Bayesian Network Risk Analysis - Wang, Lu (International Journal of Advanced AI Applications, 2025)
The global aviation logistics sector, a critical enabler of international trade and just-in-time supply chains, is acutely vulnerable to geopolitical and economic disruptions. This study investigates the multifaceted impact of Sino-American trade tensions and reciprocal tariff impositions on the resilience and operational efficiency of aviation logistics networks. We develop a comprehensive Bayesian Network (BN) model to quantify the complex, probabilistic interdependencies among key risk variables, including tariff levels, trade policy uncertainty, fuel price volatility, cargo demand fluctuations, and regulatory constraints. The model is parameterized using a combination of empirical trade data, industry reports, and expert elicitation. A focused case study on the US-China trade war (2018-2020) validates the model's utility, demonstrating significant cascading effects on transpacific air cargo routes. Our analysis reveals that high tariff scenarios increase the probability of severe logistics disruptions by over 65%. The results provide critical, actionable insights for stakeholders, highlighting the necessity of strategic diversification, dynamic pricing models, and policy engagement. This research contributes a novel, adaptable analytical framework for enhancing the resilience of aviation-dependent supply chains in an era of escalating protectionism and economic uncertainty.