Abstract: To address the complex structure, low efficiency, high costs, and excessive carbon emissions in provincial coal logistics networks, this study proposes a hybrid hub-and-spoke network optimization method integrating economic and environmental objectives. We develop a bi-objective nonlinear programming model that systematically integrates hub constructioncosts, transportation costs, and carbon emission factors from transfer operations. An enhanced NSGA-II algorithm featuringdynamic crossover probability and hill-climbing-based local search strategies is designed to improve convergence speed andsolution diversity. Empirical analysis of Shanxi Province's network (11 cities and 8 external nodes) demonstrates that whenscale effect coefficient θ=0.55 with 4 hubs, the hybrid network achieves 25.75% cost reduction and 58.75% emission reduction compared with direct transportation. Sensitivity analysis reveals nonlinear coupling between scale effects and hub quantity, confirming optimal configuration at θ=0.55 with 4 hubs for maximal economic-environmental benefits. This research validates that hub-intensive operations in hybrid networks can synergistically optimize scale economies and emission reduction, providing quantifiable decision support for low-carbon transformation of provincial coal logistics.