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To achieve green and low-carbon development in the logistics industry, logistics operators are promoting the electrification of logistics fleets, which imposes requirements for well-developed charging facilities and integrated renewable energy sources. Due to the specific characteristics of logistics activities, which are different from those of normal vehicles, the infrastructure planning for logistics delivery needs to be considered in coordination with the logistics system's operation. The coupling of planning and operation poses challenges for decision-making. This paper presents a planning–operation coupling optimization framework for low-carbon logistics delivery. The planning level optimizes the location and capacity of charging facilities, photovoltaic (PV), and energy storage systems (ESSs) based on the idea of charging demand matching. The operation level uses deep reinforcement learning (DRL) to simulate the logistics fleet's action patterns, optimize routes and charging behaviors, and extract charging demands. Benefiting from the advantages of DRL, planning and operation can interact well through charging demands, mutual coupling, and iterative adjustment. Numerical experiments based on real-world data show that the proposed framework effectively reduces charging costs and carbon emissions and also provides a benchmark for the cost evaluation of carbon reduction in logistics delivery activities. • A joint planning–operation framework suits the properties of logistics delivery. • The infrastructure planning model adopts the idea of charging demand matching. • DRL is used to extract charging demands and interact with the planning model. • Numerical experiments with real-world city data validate the method's effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to incorporate the seasonal variations in gas demand and technical considerations of gas networks into the assessment of environmental sustainability and economic viability of biomethane production and injection into the grid. A consequential life-cycle assessment (LCA) is conducted to determine the environmental impacts of offsetting diesel in heavy goods vehicles (HGVs) and natural gas for residential heating. This is combined with an economic analysis to determine the total cost of carbon abatement. The results find that biomethane use in HGVs or for residential heating shows a significant reduction in emission in the selected impact categories of global warming potential (GWP), fine particulate matter formation (PM), and terrestrial acidification (AP). However, the freshwater eutrophication (EP) impact shows a slight increase when used to offset residential heating. A sensitivity analysis of the methane emissions leakage from the anaerobic digester and upgrader, finds that when the cumulative methane emissions leakage exceeds 5.5%, the production of bioCNG can be more greenhouse gas (GHG) intensive than fossil diesel on a well-to-tank basis. The results of the TCA highlight the importance of considering the technical constraints imposed by gas networks on the grid capacity to accept biomethane when considering the techno-econo-environmental impact of a biomethane plant size and location, with a range of 73 €/tCO 2 –177€/tCO 2 depending on the scenario. The authors conclude that while biomethane has significant potential as a sustainable alternative to reduce the consumption of fossil diesel for transport and natural gas for heating, consideration must be given to technical constraints of the gas network to maximise emission mitigation at minimal cost. [Display omitted] • Incorporates the seasonality of gas demand in an environmental assessment of biomethane. • The cumulative methane emissions leakage has a significant impact on the GHG intensity. • BioCNG offers greater emissions reduction compared to biomethane for residential heating. • The grid capacity to accept biomethane strongly influences the TCA. [ABSTRACT FROM AUTHOR]