Analyzing the Impact of Tariff Uncertainty on Civil Aviation: A Bayesian Network Approach

Abstract

The civil aviation industry, a critical enabler of global economic connectivity, is intrinsically linked to the stability of international trade policies. Tariff uncertainty—defined as the unpredictable imposition or threat of import/export duties on aircraft, components, fuel, and related services—poses a profound and systemic risk to its operational and financial resilience. Traditional risk assessment methodologies, often linear and siloed, fail to capture the complex, non-linear interdependencies between geopolitical decision-making, global supply chain dynamics, and airline economics. This study introduces a comprehensive, probabilistic risk analysis framework utilizing a Bayesian Network (BN) to model the cascading impacts of tariff uncertainty on civil aviation systems. The BN synthesizes quantitative and qualitative data across multiple domains, including trade policy volatility, supply chain fragility, operational cost structures, and strategic fleet planning. Through quantitative analysis and scenario testing, the framework identifies critical vulnerability points, assesses the likelihood of severe operational disruption and financial distress, and generates evidence-based mitigation strategies. This approach provides a robust decision-support tool for stakeholders navigating the increasingly volatile landscape of global trade, ultimately contributing to enhanced supply chain resilience and strategic preparedness.