Identification of time points for LNG throughput: a complex network approach

Critical nodes play a decisive role in shaping the intrinsic structure of complex networks. They help uncover the interconnections within real-world systems. Recognizing these key nodes is a central topic in complex network research. Liquefied Natural Gas (LNG), as an emerging clean energy source, has become more important in China’s energy supply system. This is especially true under the low-carbon and environmental protection agenda, owing to its clean and efficient properties. The Shenzhen Dapeng Bay LNG hub port, the largest in China and featuring the highest density of receiving terminals, ensures energy provision for the Pearl River Delta as well as Hong Kong and Macao. In the context of rising LNG demand and the substantial disruptions to ship berthing and departure caused by the COVID-19 pandemic, systemic risks in energy supply are progressively spreading. This study transforms LNG throughput data at the Shenzhen Dapeng Bay hub port into a complex network model using a visibility graph approach, focusing on its structural characteristics and key temporal points to analyze LNG throughput dynamics over time. Furthermore, the visual network node contraction algorithm combined with the Entropy Weight - TOPSIS (Entropy Weight - Technique for Order Preference by Similarity to Ideal Solution) method is employed to determine the key nodes in the LNG throughput visualization map, thereby determining pivotal time points along the LNG throughput timeline. By examining the dynamic features of China’s energy market and projecting future supply and demand, this research offers valuable decision-making insights for policymakers, investors, and energy enterprises.