Mapping the global liquefied natural gas trade network: A perspective of maritime transportation

Peng, Peng; Lu, Feng; Cheng, Shifen; Yang, Yu

doi:10.1016/j.jclepro.2020.124640

Cited by 41 publications

(18 citation statements)

References 33 publications

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“…Firstly, we build a weighted matrix for each fossil energy trade proportional to node strength and w k max is the maximum of elements in W k . However, existing studies have verified that the distribution of fossil energy trade displayed power-law characteristics [9]. In order to avoid too many small values to reflect variability, we adopt s k ij � w k ij /w k local− max , where w k local− max is the maximum in the local connected networks of node i and j.…”

Section: A Relevance Matrix-based Bpa Methodmentioning

confidence: 99%

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Gao

Tao

et al. 2022

Complexity

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The structure formed by fossil energy trade among countries can be divided into multiple subcommodity networks. However, the difference of coupling mode and transmission mechanism between layers of the multirelationship network will affect the measurement of node importance. In this paper, a framework of multisource information fusion by considering data uncertainty and the classical network centrality measures is build. Then, the evidential centrality (EVC) indicator is proposed, by integrating Dempster–Shafer evidence theory and network theory, to empirically identify influential nodes of fossil energy trade along the Belt and Road Initiative. The initial result of the heterogeneity characteristics of the constructed network drives us to explore the core node issue further. The main detected evidential nodes include Russia, Kazakhstan, Czechia, Slovakia, Egypt, Romania, China, Saudi Arabia, and Singapore, which also have higher impact on network efficiency. In addition, cluster analysis discovered that resource endowment is an essential factor influencing country’s position, followed by geographical distance, economic level, and economic growth potential. Therefore, the above aspects should be considered when ensuring national trade security. At last, the rationality and comprehensiveness of EVC are verified by comparing with some benchmark centralities.

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Section: A Relevance Matrix-based Bpa Methodmentioning

confidence: 99%

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Gao

Tao

et al. 2022

Complexity

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“…Additional semantics can be associated with these links and nodes. For instance, a weighted maritime network can be associated with trade volumes, and graph theory principles have been recently applied to mine the spatio-temporal characteristics of maritime transport network journeys [13,81]. Current research on the development of the spatio-temporal analysis of maritime transport networks generally considers maritime network structural properties through the main transportation routes and/or port connections and flows using graph theory principles.…”

Section: Large Maritime Graphsmentioning

confidence: 99%

“…Close trade relations between different ports and fewer occurrences of transshipments among ports will lead to an increase in the transshipment capacity of the hub ports and promote the formation of a closer trading community around them. A series of works have developed community analysis for the maritime transport networks by combining graph operators and analytics with additional semantic graph properties [12,13,88,89]. As Figure 7 shows, in [12], it appears that the global liquefied natural gas (LNG) trade network has developed several closely connected trading communities since 2013, and ports within individual communities have gradually become more geographically spatially concentrated.…”

Section: Large Maritime Graphsmentioning

confidence: 99%

“…Moreover, graphs offer specific algorithms for browsing through the structural properties and relationships that characterise a given phenomenon (e.g., data inference [10] and knowledge extraction [11]). Last but not least, additional graph visualisation capabilities favour the observation of patterns and outliers from large evolving network structures [12,13]. Figure 1 illustrates the respective representation and computational principles behind spatio-temporal graphs and knowledge graphs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation

Mondo

Peng

Gensel

et al. 2021

IJGI

Self Cite

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This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation.

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“…To date, a few studies on energy trade using the complex network method mainly focus on the traditional energy trade [46][47][48], but the analysis of electricity trade is lacking. For example, the trade networks of coal [49], oil [50], natural gas [51,52], crude oil [53] and virtual water [54] have been studied in detail. The existing articles on electricity trade mainly focus on the analysis of electricity trading market mechanism and environment, rarely focus on the evolution of cross-border electricity trade network, the status of economies in the global electricity trade and the driving force of the formation of electricity trade network from a macro perspective.…”

Section: Introductionmentioning

confidence: 99%

Structure Characteristics and Influencing Factors of Cross-Border Electricity Trade: A Complex Network Perspective

Wang

2021

Sustainability

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Electricity is one of the most widely used forms of energy. However, environmental pollution from electricity generation and the mismatch between electricity supply and demand have long been bothering economies across the world. Under this background, cross-border electricity trade provides a new direction for sustainable development. Based on the complex network approach, this paper aims to explore the structural characteristics and evolution of cross-border electricity trade networks and to figure out the factors influencing the formation of the network by using the more advanced network analysis method—ERGM. The results show that: (1) The scale of the electricity trade network is expanding, but there are still many economies not involved. (2) The centrality of the network shifts from west to east. The level of internal electricity interconnection is high in Europe, and Asian countries’ coordination role in cross-border electricity trade networks is enhanced. (3) Cross-border electricity trade helps to reduce CO2 emissions, achieve renewable energy transformation, and reduce power supply and demand mismatch. Large gaps in GDP, electricity prices, industrial structure, geographical distance and institutional distance between economies are not conducive to form the cross-border trade network, while the common language is on the contrary.

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Mapping the global liquefied natural gas trade network: A perspective of maritime transportation

Cited by 41 publications

References 33 publications

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation

Structure Characteristics and Influencing Factors of Cross-Border Electricity Trade: A Complex Network Perspective

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