Intelligent shipment consolidation in maritime Transport: Efficiency, Competition, and sustainability analysis

Maritime transport is crucial for global trade but contributes significantly to carbon emissions. In practice, shipment consolidation is proven to be an effective way to integrate demand and hence, reduce emissions. Recently, we have observed that Artificial Intelligence (AI)-based consolidation systems have been widely built to automate workflows and optimize cargo planning. However, they actually incur more emissions and energy waste in model training and infrastructure. This study therefore develops a game-theoretic model to analyze how to break such a dilemma. Our analysis reveals an interesting “efficiency improvement dilemma” that although AI-based consolidation system can significantly reduce consolidation waiting time, it lowers the retailer’s incentive to order more, which in turn hurts the shipping company’s profit. We further show that carbon emissions from AI usage may outweigh its environmental benefits. But under moderate competition intensity and manageable AI emissions, AI system can achieve Pareto improvement of profitability and environmental sustainability.