Purpose This study aims to propose a multi-objective optimization framework for dry port location selection, integrating transport costs, intermodal transport ratios and carbon emissions. Through empirical validation with real-world data from Northeast China, it demonstrates the feasibility of constructing a low-carbon logistics network, offering actionable strategies for sustainable dry port planning. Design/methodology/approach A multi-objective mixed-integer model integrates NSGA-II and α-conditional lexicographic algorithms. Multi-scenario experiments validate the model, supported by empirical datas from 33 freight nodes. Findings The results demonstrate that the optimized dry port location plan significantly reduces total costs and increases the proportion of intermodal transport. Rail electrification and cost reduction show notable effects on emission reduction and efficiency improvement, while solely enhancing dry port capacity has limited short-term impacts on logistics networks. This study also highlights the advantages of combining algorithms to balance economic and environmental objectives. Originality/value First, this study combines NSGA-II and α-conditional lexicographic methods to resolve multi-objective conflicts. Second, it innovatively integrates cost, emissions and intermodal targets with dynamic thresholds. The Northeast China case pioneers a dynamic decision framework for dry port locations under environmental constraints, offering a paradigm for regional low-carbon logistics planning.
Bei et al. (Fri,) studied this question.