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As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0–1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency. [ABSTRACT FROM AUTHOR]

China's carbon peaking and carbon neutrality targets have created huge challenges for all the economic sectors in China, including the shipping industry. Various emission reduction measures, such as Waste Heat Recovery Systems (WHRSs), Drag Reduction Coatings (DRCs), and Slow Steaming (SS), are the main options for container ship companies to select in advance. This paper aims to find the optimal combination of measures for container ship companies to reach the carbon reduction targets, which are mainly set according to the carbon reduction requirements of the International Maritime Organization (IMO). A 0–1 integer programming model is proposed under the Maritime Emissions Trading Scheme (METS) to help container ship companies select the optimal combination of measures in the context of carbon peaking and carbon neutrality. Our results show that combination 6 (using a WHRS alone and a 5.0% reduction in the original speed) is the most suitable solution with the selected values of parameters. Sensitivity analyses of the parameters are performed, such as bunker price, the auction and purchase prices of carbon and incentive levels. From the sensitivity analysis, it is found that using a WHRS is the optimal combination of abatement measures within the fluctuation range of the parameters. At the same time, according to the results, container ship companies could choose the most appropriate and profitable strategy in the dual-carbon context. Therefore, container ship companies and policymakers have access to relevant carbon reduction suggestions to encourage the implementation of carbon reduction initiatives. [ABSTRACT FROM AUTHOR]

To improve the market competitiveness of railway transportation in a multimodal transport system, China's local governments have adopted their own freight subsidy policies. This has resulted in a disorderly competition for the China Railway (CR) Express. To address this problem, this study aims to determine a suitable freight subsidy mechanism that considers various influencing factors, such as operating costs, external costs, and transshipment costs of various modes of transportation. A bi-level programming model was developed. The upper-level model aimed to maximize the freight carried by the CR Express through unit subsidy. In contrast, the objective of the lower-level model was to minimize the OD transport cost. The solution to the bi-level model was based on Dijkstra's algorithm and the pattern search algorithm. Taking the freight transport in the Yangtze River Delta and its surrounding areas as an example, we found that when the unit subsidy was increased from 0% to 30%, the proportion of the freight carried by the CR Express increased from 0% to 20% (in terms of the total cargo). When the subsidy was 14.28%, the cargo volume's rate of increase was at its highest, with the proportion of the CR Express being 13.33%. We infer that goods of different time value should have different subsidy values. Furthermore, we also conclude that with an increase in the loading coefficient, the subsidy could be eliminated in a phased manner. [ABSTRACT FROM AUTHOR]