Sustainable urban freight logistics requires routing decisions that jointly account for operating cost, vehicle technology, low-emission-zone (LEZ) access, terrain-sensitive energy use, and battery feasibility. This study develops a policy–terrain-coupled mixed-fleet routing framework integrating LEZ exposure, system-level carbon settlement, terrain-sensitive energy consumption, electric-vehicle (EV) battery feasibility, and route-level EV/internal-combustion-engine vehicle reassignment within a unified daily total operational cost (DTOC) evaluator. An adaptive large-neighborhood search (ALNS) procedure reconstructs feasible routes, while vehicle type is re-evaluated through counterfactual comparison of the complete system objective. The main experiments use 60 enhanced Gehring–Homberger benchmark-derived scenarios and 20 independent seeds, supplemented by ablation, carbon-price, EV-fixed-cost, heuristic-weight, convergence, and customer-scale scalability analyses. The ALNS-based framework achieves the lowest mean DTOC among the tested procedures, albeit with higher runtime. Policy and terrain information alter modeled fleet composition, with topology-dependent cost effects. Lower EV fixed costs consistently increase EV share, whereas carbon-price effects vary across network structures. All runs in the additional 200–1000-customer tests were feasible, although runtime increased with problem size. London- and Madrid-informed cases are treated as archetypes rather than as real-world validation cases. These results provide a basis for scenario screening and comparative planning of policy–terrain interactions before city-specific calibration and deployment.

Conventional truck emission accounting methods based on average activity levels and static emission factors are increasingly inadequate for dynamic regulation and policy comparison at high spatiotemporal resolution. This review synthesizes recent progress in dynamic truck emission estimation from four perspectives: multi-source data support, key feature extraction, physics-constrained emission modeling, and governance-oriented applications. The literature was collected from Web of Science Core Collection and ScienceDirect for the period 2014–2026, supplemented by backward reference checking, and was analyzed through a progressive framework linking data, features, models, and governance tasks. Unlike previous reviews that usually discuss emission inventories, conventional emission models, or data-driven prediction methods separately, this review highlights an integrated governance-oriented chain that connects multi-source data fusion, mechanism-related feature construction, physics-constrained modeling, and environmental management applications. Existing studies suggest that multi-source data, including GPS trajectories, on-board diagnostics (OBDs), on-board monitoring (OBM), portable emissions measurement system (PEMS) measurements, traffic flow monitoring, and road network attributes, provide an important basis for representing real-world operating processes. Meanwhile, key features have expanded from surface-level variables such as vehicle velocity to mechanism-related factors, including payload, road grade, engine operating conditions, vehicle-specific power, and roadway context. Truck emission modeling has also evolved from unconstrained or weakly constrained approaches toward frameworks that place greater emphasis on physical consistency, interpretability, and result credibility. In parallel, application scenarios have extended from emission quantification to high-emission vehicle identification, dynamic inventory development, hotspot detection, policy comparison, and transport optimization. These developments can support policymakers, transportation planners, and environmental agencies in moving from aggregate emission accounting toward targeted and process-based truck emission governance. Current research, however, still faces challenges related to data consistency, model generalizability, uncertainty propagation, and real-time application. Future work should focus on standardized datasets, hybrid AI–physics modeling frameworks, uncertainty-aware validation, real-time deployment in intelligent transportation systems, and improved links between dynamic estimation and practical environmental management.