Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction
Abstract
:1. Introduction
2. Literature Review
2.1. Studies on Global Natural Gas Trade
2.2. Studies on International Trade Prediction and Its Methods
3. Methods
3.1. Link Prediction Model
3.2. Common Link Prediction Indices
3.2.1. Indices Based on Local Information Proximity
- Common neighbors index (CN)
- 2.
- Adamic–Adar index (AA)
- 3.
- Resource allocation index (RA)
- 4.
- Preferential attachment index (PA)
3.2.2. Indices Based on Path Proximity
- Local path index (LP)
3.2.3. Indices Based on Random Walk
- Average commute time index (ACT)
3.3. Indices of Added Centrality
3.3.1. Definition of Centrality Index
- Degree centrality
- 2.
- Betweenness centrality
- 3.
- Closeness centrality
3.3.2. Indices of Added Centrality
3.4. Node Attraction Index
Calculation of Mutual Attraction between Nodes
3.5. Coupling Proximity Index
4. Experiment and Evaluation
4.1. Accuracy Analysis of Each Link Prediction Index
4.2. Accuracy Analysis of Coupling Index
4.3. Comparison between Potential Trade Links and Actual Situation
5. Discussion
5.1. Analysis of Global Potential Trade
5.2. Analysis of Potential Trade of Countries or Regions with High Dependence on Foreign Countries
6. Conclusions
- (1)
- For the global natural gas trade network, among the single forecasting indices, the LP index based on path proximity had the highest forecasting accuracy; for the indices based on local information proximity, the prediction accuracy of the index could be improved by replacing the traditional node value with the centrality value. Economic and political factors also had a certain influence on the prediction results, and the prediction accuracy of multi-factor coupling indices was obviously better than that of single indices.
- (2)
- The correct rate of link prediction cannot reach 100% because changes in political relations, newly promulgated policies of the state, and sudden epidemics all have certain influences on trade relations. Therefore, it is a normal phenomenon for some predicted links to fail. For example, the shale revolution of the United States led to the country becoming a big exporter of LNG, instead of a net importer whose natural gas production could not keep up with the demand growth as originally predicted by the International Gas Union. For LNG trade, the price difference between river basins, the change in domestic output, the competition with alternative energy, the geopolitical situation, the change in natural environment (temperature, climate, etc.), and the relevant restrictions of COVID-19 all have certain influences. At the same time, the main influencing factors are also different for different countries. For example, they are different for France, Belgium, and other countries engaging in re-export trade, where the price difference between river basins is the main factor affecting LNG trade relations.
- (3)
- For those successful predicted trade relationships, in terms of prediction timeliness, it generally took 3 years for a potential global LNG trade relationship to change from the first prediction to an actual trade relationship. For countries or regions such as China, India, Japan, and South Korea with high dependence on foreign countries, this timeframe was generally 2 years. At the same time, previous trade cooperation relationships led to countries re-establishing trade relations, whereby most countries tended to establish trade relations with those countries they are familiar with.
- (4)
- Trinidad, Russia, Algeria, Nigeria, Angola, and Equatorial Guinea are more likely to establish new LNG trade relations with other countries. Trinidad and Portugal, Trinidad and Dubai, Trinidad and Malaysia, Russia and Turkey, Russia and Dubai, Algeria and Egypt, and Nigeria and Thailand are more likely to establish trade relations in the next five years. The shortage of natural gas supply in European countries caused by the Russia–Ukraine conflict may temporarily restrict their export and re-export trade. The forecast of the IEA (International Energy Agency) also shows that African countries will be the biggest driving force of global natural gas production growth in the next 5 years, which proves the accuracy of the link forecast results to some extent.
- (5)
- At present, about 90% of the LNG imported by China, India, Japan, and South Korea comes from Australia, Qatar, Malaysia, and Indonesia. Considering the security of energy supply, Algeria, Angola, Equatorial Guinea, Trinidad, the United States, Peru, and Norway may become future partners. China, India, S. Korea, and Taiwan Province are more likely to import LNG from Algeria in the next 2 years. In addition, Angola and Taiwan Province, Eq. Guinea and Taiwan Province, Trinidad and S. Korea, Peru and Japan, Peru and S. Korea, and America and Taiwan Province are more likely to establish trade relations in the next 2 years.
- (1)
- In this algorithm, only the key factors affecting the LNG trade precipitated from the existing literature were quantitatively considered, such as the price of LNG, the competition of alternative energy, and the change in technology, but not quantified. In the future, the potential factors affecting the global LNG trade can be comprehensively studied through methods such as the trade gravity model [9] and incorporated into the link prediction algorithm to make the algorithm more realistic.
- (2)
- The data used in the link prediction algorithm in this paper were national statistical data with a unit of 1 year, but the temporal resolution of the data is still insufficient. Therefore, the response to unexpected events (e.g., Russia–Ukraine conflict) and the characteristics of real-time LNG trade cannot be well reflected. In the future, the scale and research timescale of the research object can be further refined by obtaining ship history and real-time data [13].
- (3)
- In the future, countries can be further classified according to the main factors that affect the LNG trade to analyze the international trade relations; then, then combined with the factors such as trade volume and trade direction, the potential trade relations can be predicted more accurately and evaluated more deeply.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Lv, L.Y.; Zhou, T. Link rediction in Complex Networks: A Survey. Phys. A Stat. Mech. Its Appl. 2011, 390, 1150–1170. [Google Scholar]
- Lin, N.; Brooks, R.E. Global liquified natural gas trade under energy transition. Energies 2021, 14, 6617. [Google Scholar] [CrossRef]
- Egging, R.; Holz, F. Risks in global natural gas markets: Investment, hedging and trade. Energy Policy 2016, 94, 468–479. [Google Scholar] [CrossRef]
- Guo, Y.J.; Hawkes, A. The impact of demand uncertainties and China-US natural gas tariff on global gas trade. Energy 2019, 175, 205–217. [Google Scholar] [CrossRef]
- Kan, S.Y.; Chen, B.; Meng, J.; Chen, G.Q. An extended overview of natural gas use embodied in world economy and supply chains: Policy implications from a time series analysis. Energy Policy 2020, 137, 111068. [Google Scholar] [CrossRef]
- Chen, J.D.; Yu, J.; Ai, B.; Song, M.L.; Hou, W.X. Determinants of global natural gas consumption and import–export flows. Energy Econ. 2019, 83, 588–602. [Google Scholar] [CrossRef]
- Farag, M.; Zaki, C. On the Determinants of Trade in Natural Gas: A Political Economy Approach; EWI Working Papers; Institute of Energy Economics at the University of Cologne (EWI): Cologne, Germany, 2021; Volume 8. [Google Scholar]
- Rasoulinezhad, E.; Sung, J.; Talipova, A.; Hesary, F.T. Analyzing energy trade policy in Central Asia using the intercountry trade force approach. Econ. Anal. Policy 2022, 73, 441–454. [Google Scholar] [CrossRef]
- Zhang, H.Y.; Xi, W.W.; Ji, Q.; Zhang, Q. Exploring the driving factors of global LNG trade flows using gravity modelling. J. Clean. Prod. 2018, 172, 508–515. [Google Scholar] [CrossRef]
- Magnier, H.J.; Jrad, A. A minimal simplified model for assessing and devising global LNG equilibrium trade portfolios while maximizing energy security. Energy 2019, 173, 1221–1233. [Google Scholar] [CrossRef]
- Meza, A.; Ari, I.; Al-Sada, M.S.; Koc, M. Future LNG competition and trade using an agent-based predictive model. Energy Strategy Rev. 2021, 38, 100734. [Google Scholar] [CrossRef]
- Li, J.M.; Dong, X.C.; Jiang, Q.Z.; Dong, K.Y.; Liu, G.X. Natural gas trade network of countries and regions along the belt and road: Where to go in the future? Resour. Policy 2021, 71, 101981. [Google Scholar] [CrossRef]
- Peng, P.; Lu, F.; Cheng, S.F.; Yang, Y. Mapping the global liquefied natural gas trade network: A perspective of maritime transportation. J. Clean. Prod. 2020, 283, 124640. [Google Scholar] [CrossRef]
- Barnes, R.; Bosworth, R. LNG is linking regional natural gas markets: Evidence from the gravity model. Energy Econ. 2015, 47, 11–17. [Google Scholar] [CrossRef]
- Emikonel, M. The Impact of International Organizations on Chinese Trade as the Determiner of Trade: The Gravity Model Approach. Chin. Econ. 2022, 1, 26–40. [Google Scholar] [CrossRef]
- Bakouan, M.; Ouedraogo, I.M. Intra-African Trade and Spatial Dependence: Revisiting Africa Intra-Trade Determinants with a Spatial Structural Gravity Model. Theor. Econ. Lett. 2022, 12, 149–171. [Google Scholar] [CrossRef]
- Chen, J.D.; Xie, Q.L.; Shahbaz, M.; Song, M.L. Impact of bilateral trade on fossil energy consumption in BRICS: An extended decomposition analysis. Econ. Model. 2022, 106, 105698. [Google Scholar] [CrossRef]
- Shumilov, A. Estimating gravity models of international trade: A survey of methods. HSE Econ. J. 2017, 21, 224–260. [Google Scholar]
- Du, R.J.; Wang, Y.; Dong, G.G.; Tian, L.X.; Liu, Y.X.; Wang, M.G.; Fang, G.H. A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013. Appl. Energy 2017, 196, 142–151. [Google Scholar] [CrossRef]
- Wang, W.Y.; Fan, L.W.; Zhou, P. Evolution of global fossil fuel trade dependencies. Energy Part C 2022, 238, 121924. [Google Scholar] [CrossRef]
- Chen, B.; Li, J.S.; Wu, X.F.; Han, M.Y.; Zeng, L.; Li, Z.; Chen, G.Q. Global energy flows embodied in international trade: A combination of environmentally extended input–output analysis and complex network analysis. Appl. Energy 2018, 210, 98–107. [Google Scholar] [CrossRef]
- Chen, X.C.; Tan, Z.L.; Li, S.W. Study on the characteristics of international coal trade on complex network. J. Bus. Econ. Manag. 2022, 23, 15670. [Google Scholar] [CrossRef]
- Zhou, T. Progresses and Challenges in Link Prediction. iScience 2021, 24, 103217. [Google Scholar] [CrossRef] [PubMed]
- Guan, Q.; An, H.Z.; Gao, X.Y.; Huang, S.P.; Li, H.J. Estimating potential trade links in the international crude oil trade: A link prediction approach. Energy 2016, 102, 406–415. [Google Scholar] [CrossRef]
- Zhou, X.R.; Zhang, H.; Zheng, S.X.; Xing, W.L.; Zhao, P.; Li, H.Y. The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links. Energies 2022, 15, 2395. [Google Scholar] [CrossRef]
- Zhang, Y.C.; Dong, Z.L.; Liu, S.; Jiang, P.X.; Zhang, C.Z.; Chao, D. Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries. Sustainability 2021, 13, 1251. [Google Scholar] [CrossRef]
- Liu, S.; Dong, Z.L. Who will trade bauxite with whom? Finding potential links through link prediction. Resour. Policy 2019, 63, 101417. [Google Scholar] [CrossRef]
- Liu, S.; Dong, Z.L.; Ding, C.; Wang, T.; Zhang, Y.C. Do you need cobalt ore? Estimating potential trade relations through link prediction. Resour. Policy 2020, 66, 101632. [Google Scholar] [CrossRef]
- Feng, S.; Li, H.J.; Qi, Y.B.; Guan, Q.; Wen, S.B. Who will build new trade relations? Finding potential relations in international liquefied natural gas trade. Energy 2017, 141, 1226–1238. [Google Scholar] [CrossRef]
- Filimonova, I.V.; Komarova, A.V.; Sharma, R.; Novikov, A.Y. Transformation of international liquefied natural gas markets: New trade routes. Energy Rep. 2022, 8, 675–682. [Google Scholar] [CrossRef]
- Ibrahim, N.M.A.; Chen, L. Link prediction in dynamic social networks by integrating different types of information. Appl. Intell. 2015, 42, 738–750. [Google Scholar] [CrossRef]
- Lv, L.S.; Bardou, D.; Hu, P.; Liu, Y.Q.; Yu, G.H. Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality. Chaos Solitons Fractals 2022, 159, 112107. [Google Scholar] [CrossRef]
- Zhou, M.Q.; Jin, H.J.; Wu, Q.W.; Xie, H.; Han, Q.Z. Betweenness centrality-based community adaptive network representation for link prediction. Appl. Intell. 2022, 52, 3545–3558. [Google Scholar] [CrossRef]
- Hajarathaiah, K.; Enduri, M.K.; Anamalamudi, S.; Reddy, T.S.; Tokala, S. Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks. Entropy 2022, 24, 704. [Google Scholar] [CrossRef]
- Gao, C.; Lan, X.; Zhang, X.G.; Deng, Y. A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks. PLoS ONE 2013, 8, e66732. [Google Scholar] [CrossRef]
- Li, L.J.; Wen, Y.H.; Bai, S.S.; Liu, P.F. Link prediction in weighted networks via motif predictor. Knowl.-Based Syst. 2022, 242, 108402. [Google Scholar] [CrossRef]
- Yu, J.T.; Wu, L.Y. Multiple Order Local Information model for link prediction in complex networks. Phys. A Stat. Mech. Its Appl. 2022, 600, 127522. [Google Scholar] [CrossRef]
- Anand, S.; Rahul; Mallik, A.; Kumar, S. Integrating node centralities, similarity measures, and machine learning classifiers for link prediction. Multimed. Tools Appl. 2022, 1–29. [Google Scholar] [CrossRef]
- Zhu, Y.H.; Liu, S.X.; Li, Y.L.; Li, H.T. TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion. Entropy 2022, 24, 296. [Google Scholar] [CrossRef]
- Zhao, Z.L.; Gou, Z.Y.; Du, Y.H.; Ma, J.; Li, T.F.; Zhang, R.S. A novel link prediction algorithm based on inductive matrix completion. Expert Syst. Appl. 2022, 188, 116033. [Google Scholar] [CrossRef]
- Gong, Z.Q.; Talwalkar, A.; Mackey, L.; Huang, L.; Shin, E.C.R.; Stefanov, E.; Shi, E.; Song, D. Joint Link Prediction and Attribute Inference Using a Social-Attribute Network. ACM Trans. Intell. Syst. Technol. 2014, 5, 1–20. [Google Scholar] [CrossRef]
- Fan, X.H.; Xu, R.Y.D.; Cao, L.B.; Song, Y. Learning Nonparametric Relational Models by Conjugately Incorporating Node Information in a Network. IEEE Trans. Cybern. 2017, 47, 589–599. [Google Scholar] [CrossRef] [PubMed]
- Wang, B.; Guan, W.H.; Sheng, Y.X.; Sheng, J.F.; Dai, J.Y.; Zhang, J.K.; Li, Q.; Dong, Q.Q.; Chen, L. A novel measure for influence nodes across complex networks based on node attraction. Int. J. Mod. Phys. C 2021, 32, 2150012. [Google Scholar] [CrossRef]
- Sheng, J.F.; Liu, C.; Chen, L.; Wang, B.; Zhang, J.K. Research on Community Detection in Complex Networks Based on Internode Attraction. Entropy 2020, 22, 1383. [Google Scholar] [CrossRef]
- Li, F.; Yang, C.Y.; Li, Z.H.; Failler, P. Does Geopolitics Have an Impact on Energy Trade? Empirical Research on Emerging Countries. Sustainability 2021, 139, 5199. [Google Scholar] [CrossRef]
- Taghizadeh-Hesary, F.; Rasoulinezhad, E.; Yoshino, N.; Sarker, T.; Mirza, N. Determinants of the Russia and Asia–Pacific energy trade. Energy Strategy Rev. 2021, 38, 100681. [Google Scholar] [CrossRef]
- Chi, K.; Yin, G.S.; Dong, Y.X.; Dong, H.B. Link prediction in dynamic networks based on the attraction force between nodes. Knowl.-Based Syst. 2019, 181, 104792. [Google Scholar] [CrossRef]
- Wang, W.J.; Tang, M.H.; Jiao, P.F. A unified framework for link prediction based on non-negative matrix factorization with coupling multivariate information. PLoS ONE 2018, 13, e0208185. [Google Scholar] [CrossRef] [PubMed]
- Umar, M.; Riaz, Y.; Yousaf, I. Impact of Russian-Ukraine war on clean energy, conventional energy, and metal markets: Evidence from event study approach. Resour. Policy 2022, 79, 102966. [Google Scholar] [CrossRef]
Variable | Unit | Data Source |
---|---|---|
GDP | US dollars | World Bank |
Natural gas consumption | Billion cubic meters | BP |
Natural gas production | Billion cubic meters | BP |
Political Stability index | - | WGI |
Distance | Kilometers | CEPII |
CN | AA | RA | PA | LP | ACT | CAA | CRA | CPA | |
---|---|---|---|---|---|---|---|---|---|
2010 | 0.669 | 0.681 | 0.681 | 0.928 | 0.957 | 0.516 | 0.685 | 0.691 | 0.954 |
2011 | 0.659 | 0.675 | 0.679 | 0.927 | 0.964 | 0.537 | 0.677 | 0.687 | 0.955 |
2012 | 0.705 | 0.719 | 0.728 | 0.920 | 0.950 | 0.531 | 0.721 | 0.733 | 0.940 |
2013 | 0.798 | 0.805 | 0.806 | 0.932 | 0.953 | 0.638 | 0.817 | 0.826 | 0.946 |
2014 | 0.813 | 0.822 | 0.827 | 0.916 | 0.943 | 0.634 | 0.831 | 0.845 | 0.929 |
2015 | 0.772 | 0.786 | 0.792 | 0.914 | 0.932 | 0.653 | 0.791 | 0.802 | 0.939 |
2016 | 0.720 | 0.733 | 0.737 | 0.869 | 0.912 | 0.665 | 0.733 | 0.742 | 0.901 |
2017 | 0.718 | 0.734 | 0.746 | 0.886 | 0.929 | 0.678 | 0.738 | 0.757 | 0.936 |
2018 | 0.689 | 0.695 | 0.698 | 0.865 | 0.909 | 0.745 | 0.697 | 0.711 | 0.918 |
2019 | 0.604 | 0.628 | 0.651 | 0.853 | 0.915 | 0.729 | 0.629 | 0.666 | 0.913 |
2020 | 0.593 | 0.605 | 0.616 | 0.829 | 0.907 | 0.732 | 0.606 | 0.626 | 0.903 |
Average | 0.704 | 0.717 | 0.724 | 0.894 | 0.934 | 0.642 | 0.721 | 0.735 | 0.930 |
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Egypt | Brazil | |||||||||||
Nigeria | Chile | |||||||||||
Yemen | Brazil | |||||||||||
Peru | Kuwait | |||||||||||
Yemen | Argentina | |||||||||||
Portugal | Japan | |||||||||||
Algeria | Dubai | |||||||||||
Algeria | Kuwait | |||||||||||
Peru | Egypt | |||||||||||
Peru | Pakistan | |||||||||||
Peru | Dubai | |||||||||||
America | Sweden | |||||||||||
Trinidad | Sweden | |||||||||||
Yemen | Spain | + | ||||||||||
Peru | Brazil | + | ||||||||||
Peru | India | + | + | |||||||||
Trinidad | Belgium | + | + | |||||||||
Norway | Dubai | + | + | |||||||||
Qatar | Dubai | + | + | + | + | + | + | + | + | |||
Trinidad | Japan | + | + | + | + | + | + | + | + | + | ||
Angola | Japan | + | + | + | + | |||||||
Australia | Pakistan | + | + | + | ||||||||
Trinidad | Pakistan | + | + | + | ||||||||
Trinidad | Lithuania | + | ||||||||||
America | Finland |
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Norway | Japan | |||||||||||
Norway | Malaysia | + | ||||||||||
Norway | Jordan | |||||||||||
Norway | Japan | |||||||||||
Norway | Taiwan | + | + | + | ||||||||
Norway | America | + | + | + | + | + | + | + | ||||
Norway | Kuwait | |||||||||||
Norway | China | |||||||||||
Norway | S. Korea | + | + | + | + | |||||||
Norway | Jamaica | + | ||||||||||
Trinidad | S. Korea | + | + | + | + | + | + | |||||
Trinidad | Portugal | + | + | + | + | + | ||||||
Trinidad | Dubai | + | + | + | + | + | ||||||
Trinidad | Malaysia | |||||||||||
France | S. Korea | |||||||||||
France | China | |||||||||||
France | Taiwan | + | ||||||||||
Belgium | S. Korea | + | + | + | + | + | + | |||||
Russia | Turkey | |||||||||||
Russia | Dubai | |||||||||||
Egypt | S. Korea | + | + | + | + | + | ||||||
Egypt | Taiwan | + | + | + | + | |||||||
Algeria | China | + | + | + | + | |||||||
Algeria | Egypt | + | + | |||||||||
Algeria | S. Korea | + | ||||||||||
Nigeria | Thailand | + | + | + | + | + | + | + | + | |||
Angola | Taiwan | |||||||||||
Eq. Guinea | Taiwan | + | + | + | + | + | + | + |
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Norway | Japan | |||||||||||
Peru | Japan | |||||||||||
America | Taiwan | |||||||||||
Portugal | Japan | |||||||||||
Algeria | Taiwan | + | ||||||||||
France | S. Korea | |||||||||||
Belgium | Japan | + | + | + | ||||||||
Norway | Taiwan | + | + | + | ||||||||
Norway | China | |||||||||||
Norway | India | + | + | + | ||||||||
France | China | |||||||||||
Belgium | China | |||||||||||
Peru | S. Korea | + | + | + | ||||||||
Norway | S. Korea | + | + | + | + | |||||||
Algeria | India | + | + | + | + | |||||||
Trinidad | S. Korea | + | + | + | + | + | + | |||||
Algeria | China | + | + | + | + | |||||||
Belgium | S. Korea | + | + | + | + | + | + | |||||
Peru | India | + | + | |||||||||
Eq. Guinea | Taiwan | + | + | + | + | + | + | + | ||||
Angola | Taiwan | |||||||||||
Egypt | S. Korea | + | + | + | + | + | ||||||
Egypt | Taiwan | + | + | + | + | |||||||
Algeria | S. Korea | + | ||||||||||
France | Taiwan | + | ||||||||||
Trinidad | Japan | + | + | + | + | + | + | + | + | + | ||
Angola | Japan | + | + | + | + |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jin, Y.; Yang, Y.; Liu, W. Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction. Sustainability 2022, 14, 12403. https://doi.org/10.3390/su141912403
Jin Y, Yang Y, Liu W. Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction. Sustainability. 2022; 14(19):12403. https://doi.org/10.3390/su141912403
Chicago/Turabian StyleJin, Yuping, Yanbin Yang, and Wei Liu. 2022. "Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction" Sustainability 14, no. 19: 12403. https://doi.org/10.3390/su141912403