The challenges arising from the management of municipal solid waste (MSW) have a profound impact on the sustainable development of urban areas. As a sustainable solution, the transportation of MSW underground offers the potential to alleviate traffic congestion and reduce environmental pollution. In this study, we propose the implementation of a large-scale underground waste collection system (UWCS). To begin, a comprehensive operational process for the UWCS is designed based on an intelligent technology system, including facility operation, processing workflow, and technical parameters. Additionally, network planning methods for the UWCS are presented. A mixed-integer linear programming model is formulated with the objective of minimizing total cost. This model determines the optimal location and allocation of nodes within the network, as well as the pipeline layout and flow direction. Given the computational complexity, a hybrid optimization method, namely the genetic greedy algorithms and genetic variable neighborhood search algorithms (GGA-GVNS), is devised to obtain high-quality solutions for the model. Finally, to validate the efficacy of the proposed method, a simulation is conducted in the central city of Nanjing, China. The results demonstrate that the implementation of the UWCS network in Nanjing’s city center can yield an annual benefit of USD 5.99 million. Moreover, a sensitivity analysis reveals further MSW management-related insights and long-term planning strategies.

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems.

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms.

Urban underground space is increasingly being developed to alleviate surface land constraints and support low-carbon urban development. However, carbon emission reduction (CER) benefits remain inadequately quantified and are not comparable across underground infrastructure types, largely due to the absence of a unified assessment framework, inconsistent system boundaries, and the omission of multi-pathway mitigation mechanisms such as carbon capture and storage and biological sequestration. This study proposes a CER benefit assessment framework for urban underground space that integrates mitigation mechanism identification, pathway analysis, and benefit accounting, explicitly incorporating biological carbon sequestration, carbon substitution, and carbon capture and storage within a unified accounting structure. Accounting models are then established for three representative underground infrastructure systems: transportation, public and commercial services, and municipal utilities. Using Nanjing as a case city to operationalize and validate the proposed assessment framework, we estimate CER across multiple pathways and compare regional differences. The results indicate that underground transportation infrastructure provides the largest benefit (8.74 × 10⁵ tCO₂e per year), mainly driven by travel substitution and energy savings in station buildings. Underground public and commercial facilities achieve 6.64 × 10⁵ tCO₂e per year, dominated by green-building energy savings and geothermal integration. Municipal utilities contribute a smaller but strategically important reduction, as they provide a long-term carrier for carbon capture and storage and are structurally integrated within underground utility corridors, totaling 0.98 × 10⁵ tCO₂e per year citywide. Overall, the findings reveal differentiated mitigation mechanisms and spatial heterogeneity across underground infrastructure systems, providing a theoretical basis for optimizing urban spatial planning and informing low-carbon transition policies.