Optimizing the crude oil trade network: Balancing cost and environmental impact

Abstract

The global crude oil trade network, a cornerstone of energy supply chains, suffers from structural inefficiencies that amplify environmental damage and geopolitical risks. This study aims to (1) develop an optimization model reconciling logistical costs with CO2 emissions, (2) quantify energy savings from route restructuring, and (3) assess how geopolitical disruptions reshape trade patterns. Using network theory—Minimum Spanning Tree and Shortest Path algorithms—we reconfigure the 2018 crude oil network (178 countries, UN Comtrade data) by prioritizing proximity, volume, and emission constraints. Results reveal stark contrasts: the optimized network reduces total energy consumption by 81 % for importers and 85 % for exporters, achieved through streamlined routes and redistributed trade flows. Geopolitical shifts, including post-Ukraine war sanctions and U.S.-China competition, are shown to dynamically reshape trade patterns, favoring regionalized supply chains over traditional hubs. Environmental assessments highlight maritime transport as a critical emission source, underscoring the dual benefit of route optimization in curbing both economic costs and carbon footprints. The study underscores the necessity of integrating environmental metrics into trade policy frameworks. By decoupling logistical optimization from emission analysis, it offers a scalable model for balancing energy security with sustainability. Policymakers are urged to adopt adaptive strategies that mitigate geopolitical risks while advancing decarbonization goals, particularly amid escalating global tensions and climate imperatives. This research bridges structural and behavioral dynamics in oil trade networks, providing a roadmap for resilient, low-carbon energy systems in an era of unprecedented geopolitical and ecological challenges. © 2025 The Authors