Zemin Eitan Liu

Liquefied natural gas (LNG) supply chains rely on extensive field-to-processing-to-liquefaction (FF–PP–LNG) pipeline networks, yet greenhouse gas (GHG) emissions from this midstream segment remain poorly characterised. The 2025 IEA inventory reports 350 MtCO₂-eq across LNG supply stages but excludes midstream emissions, obscuring hotspots, carbon budgets, and mitigation priorities. Here, we present a global bottom-up assessment of FF–PP–LNG pipelines. Covering >90% of 2022 LNG supply across 20 countries (~470,000 km of pipelines, ~2,000 fields, ~400 processing plants, and 45 liquefaction facilities), we identify 2,639 compressor stations emitting 50.6 MtCO₂-eq, corresponding to a volume-weighted carbon intensity of 0.255 gCO₂-eq MJ⁻¹ (FF–PP: 0.085 g; PP–LNG: 0.17 g). Methane leakage accounts for 96.9 MtCO₂-eq (65.7% of total GHGs). Satellite-based data attribute 2.87 MtCH₄ to FF–PP and 0.70 MtCH₄ to PP–LNG, with route intensities of 0.19–50 gCH₄ MJ⁻¹. Optimised transmission and compressor upgrades could abate 30 MtCO₂-eq y⁻¹. Results enable transparent carbon accounting and targeted investment of global LNG supply chains decarbonisation.

Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges significant challenges due to the complexity of interconnectivity and co-ordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL’s application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms for CAVs. Lastly, it examines the current challenges in deploying MARL for CAV control, including safety, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL. The work has been summarized in MARL_in_CAV_Control_Repository.

To reduce the fuel consumption of heavy duty logistic vehicles (HDLVs), P2 parallel hybridization is a promising solution, and deep reinforcement learning (DRL) is a promising method to optimize energy management strategies (EMSs). However, the complicated discrete-continuous hybrid action space lying in the P2 system presents a challenge to achieve real-time optimal control. Thus, this paper proposes a novel DRL algorithm combining auto-tune soft actor-critic (ATSAC) with ordinal regression to optimize the engine torque output and gear shifting simultaneously. ATSAC can adjust the update frequency and learning rate of SAC automatically to improve the generalization and ordinal regression can convert discrete variables into samplings in continuous space to handle the hybrid action. Moreover, a multi-dimensional scenario-oriented driving cycle (SODC) is established through naturalistic driving big data (NDBD) as the training cycle to further improve the EMS generalization. By comprehensive comparison with the widely used twin-delayed deep deterministic policy gradient (TD3) based EMSs, ATSAC achieves significant improvement with 53.70% higher computational efficiency and 12.31% lower negative total reward (NTR) in the training process. Application analysis in unseen real-world driving scenarios shows that only ATSAC based EMS can obtain real-time optimal control in the testing process. Furthermore, the EMS trained through SODC obtains 81.73% lower NTR than the standard China World Transient Vehicle Cycle (CWTVC) which demonstrates that SODC can represent the real-world driving scenarios much more accurately than CWTVC, especially in low-speed high-load conditions which are crucial for HDLVs.

Considering physical constraints in online optimization and training safety is a challenge for implementation of deep reinforcement learning (DRL) algorithm. Especially for the non-linear system, the mapping relationship between output action of the agent and the control signals is difficult to obtain. This paper proposes a novel DRL framework for online optimization in energy management of a power-split hybrid electric vehicle (HEV) which combines a neural-network (NN)-based multi-constraints optimal strategy and a rule-based-restraints system (RBRS). The proposed method named Reward Directed Policy Optimization (RDPO) adopts exterior point method (EPM) and curriculum learning (CL) to direct the agent to recognize and avoid irrational control signals and optimize the fuel economy. The EMS considering fuel consumption minimization and irrational control signals avoidance is optimized by training the agent through WLTC. A competitive fuel economy, 4.495L/100km, is achieved with no irrational control signals. Based on the online adaptability evaluation conducted, the fuel consumption of the vehicle under NEDC and CTUDC has been reduced to 4.113L/100km and 3.221L/100km, respectively, with no irrational control signals. The superiority in optimization, calculation efficiency and safety is verified through the comparisons with various DRL agents.