Abstract: To address the issues of complexity, low efficiency, high costs, and excessive carbon emissions in provincial coal logistics networks, this study proposes a hybrid hub-and-spoke network optimization method considering economic and environmental objectives. We develop a bi-objective nonlinear programming model that systematically integrates hub construction costs, transportation costs, and carbon emission factors in transfer operations. An enhanced NSGA-II algorithm incorporating dynamic crossover probability and hill-climbing-based local search strategies is designed to improve convergence speed and solution diversity. A case study is conducted using the network of Shanxi Province with 11 cities and 8 external nodes. Results demonstrate that when scale effect coefficient a=0.55 with the number of hubs p=4, the hybrid hub-and-spoke network achieves 25.75% cost reduction and 58.75% carbon emission reduction compared with the traditional direct transportation. Sensitivity analysis reveals nonlinear coupling between scale effects and hub quantities. The optimal configuration is a=0.55 with 4 hubs for the maximal economic-environmental benefit. This study validates that hub-intensive operations in hybrid networks can achieve coordinated optimization of scale economies and emission reduction, providing quantitative decision support for the low-carbon transition of provincial coal logistics.