Impact of changes in crude oil trade network patterns on national economy

Xi, Xian; Zhang, Jinsheng; Gao, Xiangyun; Liu, Donghui; Zheng, Huiling; Sun, Qingru

doi:10.1016/j.eneco.2019.104490

Cited by 60 publications

(28 citation statements)

References 55 publications

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“…Complex network method can analyze the world-wide trade system entirely and partly so as to reveal many new features of the crude oil trade topologically and dynamically. Some studies are related to the characteristics and evolution of international crude oil trade relations [9][10][11]. Yang et al [9] examined the geography of global crude oil flows and its evolution based on complex network.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al [9] examined the geography of global crude oil flows and its evolution based on complex network. Xi et al [10] studied the impact of crude oil trade pattern changes of the Belt and Road countries on each country's GDP based on the complex network and econometric theory. Zhang et al [11] applied social network to investigate structure characteristics and evolution pattern with petroleum trade data of countries along the Belt and Road Initiative.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Crude Oil Trade Structure: A Complex Network Analysis

Dong

Qing

et al. 2021

Complexity

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With the contradiction between supply and demand being intensified, the unreasonable crude oil trade structure has become increasingly prominent. Therefore, optimizing the supply and demand structure of the global crude oil trade is an urgent problem, which has become one of the crucial factors affecting the energy strategy and economic development of each country. This paper builds an optimization model that aims at minimizing crude oil trade costs based on the complex network theory. Meanwhile, an optimal solution generation approach is proposed and developed in this paper. Compared with the preoptimized trade network, the proposed model can effectively reduce the trade cost. By topological analysis of the trade network and its optimal configuration, we obtain that both the preoptimized and optimized crude oil trade networks follow power-law distribution. By using the minimum spanning tree, we find that the major crude oil net exporting countries have the most significant influence and are at the core in the optimized trade structure. This work focuses on the sustainable development of the global crude oil trade and provides a fresh perspective for the optimal crude oil trade system. Moreover, the methodology and model may be applied in the investigation of optimization for other energy system structures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Crude Oil Trade Structure: A Complex Network Analysis

Dong

Qing

et al. 2021

Complexity

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“…Countries' energy trade relationships are significant in analyzing trade network patterns (Zhao and Chen, 2014;Kremer et al, 2019;Mealy et al, 2019). A national energy trade network would affect a country's GDP and its centrality on international trade significantly (Xi et al, 2019). The identification and assessment of energy security risks are also important for the behavior of exporters and importers that changed the energy industry.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of the World Energy Trade Structure by Multiscale Embedding

Xue

Chen

et al. 2021

Front. Energy Res.

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This study investigates the dynamic trading network structure of the international crude oil and gas market from year 2012 to 2017. We employed the dynamical similarity analysis at different time scales by inducing a multiscale embedding for dimensionality reduction. This analysis quantifies the effect of a global event on the dependencies and correlation stability at both the country and world level, which covers the top 53 countries. The response of China’s trading structure toward events after the unexpected 2014 price drop is compared with other major traders. China, as the world’s largest importing country, lacks strong stability under global events and could be greatly affected by a supply shortage, especially in the gas market. The trend of multi-polarization on the market share gives a chance for China to construct closer relationships with more stable exporters and join in the trade loop of major countries to improve its position in the energy trading networks. The hidden features of trade correlation may provide a deeper understanding of the robustness of relationship and risk resistance.

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“…Oil prices have a profound influence on the economy. From a macro perspective, it is an important factor, which could affect the GDP of many countries (Xi et al, 2019;Nonejad, 2020). It is also an important pillar of the national economy for petroleum-producing countries.…”

Section: Introductionmentioning

confidence: 99%

Crude oil price prediction based on LSTM network and GM (1,1) model

Yao

Wang

2020

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PurposeAccording to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.Design/methodology/approachFirst, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.FindingsThe model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.Originality/valueThis paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

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Impact of changes in crude oil trade network patterns on national economy

Cited by 60 publications

References 55 publications

Optimization of Crude Oil Trade Structure: A Complex Network Analysis

Optimization of Crude Oil Trade Structure: A Complex Network Analysis

Stability Analysis of the World Energy Trade Structure by Multiscale Embedding

Crude oil price prediction based on LSTM network and GM (1,1) model

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