Search

Given energy and water scarcity, it is necessary to develop an in-depth understanding of the energy-water nexus in China for its sustainable development. Previous studies have focused on nexus accounting, synergy conservation, and system optimization, but its induction mechanism along the supply chains has not been uncovered. This paper proposes a top-down structural path analysis (SPA) and combines it with an environmental input-output model (EIOM) to identify the critical final demand, consumption sectors, and supply chain paths inducing the energy-water nexus. The results show that the largest final demand of water for energy production (WFE) is capital formation, while the largest final demand of energy for water supply (EFW) is urban consumption. The distribution of WFE at different production layers shows an inverted U shape. Most WFE is indirectly consumed by other sectors, such as construction, through three-step supply chain paths. In contrast, the distribution of EFW shows a L shape, and most EFW is directly consumed by the final demand. In addition, some critical supply chain paths inducing more WFE and EFW are identified. Finally, some policies targeting the energy-water nexus management are proposed, which are conducive to resource conservation and the sustainable supply of energy and water.

In recent years, the international steel market has shown increasingly strong cross-regional correlation. To better understand the price trends of various markets, it is necessary to identify their inherent price spillovers. This paper combines a generalized autoregressive conditional heteroskedasticity Baba–Engle–Kraft–Kroner (GARCH-BEKK) model and complex network motifs to explore the price fluctuations among international steel markets. The study selects steel markets in 12 countries and regions and uses daily data on import and export prices from January 2009 to September 2017 to analyze eight steel products. The results show that spillovers are associated with geographical location, market development, product type and status. Spillovers mostly occur between buyer’s markets; additionally, the Asian market, especially the East Asian market, is in most cases the recipient of spillover, whereas the European Union (EU) market is in most cases the sender of spillover effects. Developed markets have clear spillover effects on emerging markets, sheet steel products have clear spillover effects on profile steel products, and the prices of midstream and downstream products in the industrial chain are the most influenced. This paper examines international steel market relationships from the perspective of price transmission, and the results can help manage and prevent large-scale economic risks.

As the largest carbon emitter, China is facing huge pressure and has a heavy task to control emissions. Previous studies have mainly focused on the production- and consumption-based sectors with large emissions, but few draw the whole picture of emission flows in Chinese supply chains in which direct carbon emissions (DCE) become embodied carbon emissions (ECE).This study aims to combine input-output model and structural path analysis to trace carbon emission flows from primary producer sectors to final consumer sectors based on the data of China in 2012. The results show that the total carbon emissions were 9208.33 Mt, and capital formation was the main final demand with the share of 44.22%. Production and Supply of Electricity released the largest DCE, and Construction was responsible for the largest ECE. Nearly 90% of emissions occurred in upstream sectors of the supply chains, Manufacturing and Electricity/Gas/Water are the important transfer sectors. The distribution of DCE and ECE of different sectors have different patterns. Electricity/Gas/Water released most of its DCE for its two-step downstream sectors (22.64%). Construction got more ECE indirectly from its one-step upstream sectors (29%) and two-step upstream sectors (26%). Controlling inputs from upstream or outputs to downstream can help to develop more effective measures to reduce emissions, especially for achieving the reduction target of one certain sector. Furthermore, some supply chain paths with more emissions, such as “Metal Smelting→ Construction→ Capital formation”, should be paid more attention. Reducing intermediate purchases or using cleaner alternatives can stimulate sectors along the supply chains to control emissions together and construct green supply chains.

The indirect carbon emission embodied in the intermediate input is also an important indicator of assessing a producer’s carbon emissions. Structural analysis of indirect carbon emissions is helpful to understand the responsibilities between producers and pay efforts to key areas. The aim of this study is to analyze indirect carbon emissions embodied in intermediate input between sectors and explore the distribution structure of indirect carbon emissions flow network (namely, ICEFN). Based on the modified input-output model and complex network theory, this study constructed four directed and weighted ICEFNs with 28 sectors from 1997 to 2012. The results show that indirect carbon emissions between sectors are significantly higher than direct carbon emissions, accounting for nearly 70% of the total carbon emissions of China. Second, we analyzed the embodied carbon emission intensity (namely, ECI) of each sector. Although the ECI has been decreasing over time, the decrease has increasingly diminished, which indicates that the additional carbon emission reductions are more difficult. Third, we identified the key sectors which play different roles in the ICEFNs. Meanwhile, we studied the key paths which show more closed relationships between some sectors in ICEFNs. Finally, based on the above analysis, we made policy recommendations.

The steel industry in China has been developing slowly, and the prices of this industry’s products have recently fluctuated. To predict the trends of price changes in the future, we must discover the factors that play leading roles in determining prices. In this paper, an adaptive neuro fuzzy inference system (ANFIS) is used to measure the factors influencing the prices of steel products in China's upper, middle and lower reaches from the perspective of the whole industry chain and to identify the most influential variables. The analysis uses daily data pertaining to relevant variables from December 2013 to October 2017 and selects the upper, middle and lower reaches of iron ore, ferrosilicon and rebar as the research objects. The results show that the main factors affecting steel products at different stages are varied. The most significant factors affecting the prices of upstream products, midstream products and downstream products are midstream product prices, market supply and demand, and inventory, respectively. Therefore, from the perspective of the whole industry chain, when the prices of upstream products are high, China should regulate the prices of midstream products. For midstream products, China can consider improving the market structure to improve supply and demand. In response to rising prices of downstream products, China should optimize its inventory structure. This paper provides policy suggestions for the regulation and control of the development of the steel industry.

Market competition and trade activities between different economies’ steel industries induce severe steel product price transmission between different economies. Furthermore, steel products in different phases of the industrial chain present various price transmission features. In this research, we calculate the Granger causality relationship between different steel products in different markets and the midstream industry chain. We then construct the global steel product price transmission network and regional price transmission network. We analyze the network indicators and draw the following conclusions. First, emerging economies play a major role in international steel product price transmission activities. Second, billet and plates of middle thickness transmit the most price in the steel market. Third, China imposes price transmission impacts on the most regions with the widest range. Lastly, steel products at the end of the midstream steel industrial chain are “bridges” of price transmission activities. This research will guide market individuals in the steel industry to adjust their behaviors according to market signals.

As a resource-dependent local economy and heavy industry cluster, the Jing-Jin-Ji region has become China's largest source of air contaminant emissions and experiences frequent pollutant spillovers. integration process. One of the most urgent steps in the integration development process in Jing-Jin-Ji is achieving joint regional air pollution control in the cities comprising the region. Thus, this paper aims to provide a decision-making foundation for the collaborative governance of Jing-Jin-Ji cities with respect to PM2.5. To achieve this goal, spillover networks at multiple timescales were constructed using discrete wavelet transform, the GARCH-BEKK model and complex networks. The results showed that the interaction of the PM2.5 spillover behavior within Jing-Jin-Ji is notably strong and that the spillover relationships are normally active for up to approximately one week. The Shijiazhuang and Baoding cities require more control due to their wide PM2.5 spillover range to other cities, such as Beijing and Chengde. Moreover, the PM2.5 spillover routes differ at different timescales. In the short term, the initial cities of spillover routes, e.g., the “Cangzhou–Tianjin-Handan–Beijing” route, are the critical control cities for the government, and the cities along the routes should take advanced measures to prevent bad air conditions when the former cities have heavy haze days. In addition, the Jing-Jin-Ji cities are divided into two to three clusters, and cities in the same cluster are mostly adjacent to each other. Therefore, the most cost-effective method for achieving joint regional air pollution control in Jing-Jin-Ji cities is to treat the cities in the same clusters as a whole. This study proposes a novel perspective for the regional joint control of air pollution based on complex networks, an approach that can be used to holistically and cost-effectively select areas of collaborative governance.

The coordinated relationship between the environment, resources and economy is necessary for global sustainable development. As the basis of many countries’ economies, the steel industry has received much attention. Considering the three typical stages in the steel life cycle of ore mining, crude steel production and scrap recycling, this study aims to identify the current unbalanced relationship between environmental impacts, resource flows and economic benefits. To that end, life cycle assessment (LCA) is conducted to analyze 18 trade routes involving 8 major steel trading countries, a sustainability evaluation model is constructed containing three indicators at different levels, and the coupling coordinated degree in the steel trade routes under four scenarios is simulated and discussed. The results show that, first, the category of ecotoxicity has the greatest impact on the environment during the whole steel life cycle. Second, inequalities in trade routes are common. The Australia-Russia-US, Australia-Russia-UK, and Brazil-China-Japan routes have obvious disadvantages in terms of the resource-environment, economy-resource, and environment-economy relationships, respectively, for reasons related to yield and production technology. Third, the Australia-Russia-US route has the highest coupling coordinated degree, while the Brazil-Russia-Japan route has the lowest, thereby representing the most significant imbalanced relationship. When the development of the environment-resource relationship is improved, the imbalance of most trade routes improves significantly. When development of the economy-environment relationship increases, the improvement in coordination is mainly related to the trade routes associated with Brazil and China. With the development of the economy-resource relationship, trade routes, including those involving Russia and UK, improve significantly. Based on the above findings, this paper proposes some policy recommendations to address the imbalances of trade routes in terms of environmental impact, evaluation indicators at various levels, and coupling coordination. These suggestions can help countries and related enterprises promote the circular economy and promote the sustainable development of the steel industry chain.

BRICS nations, as a representative of emerging economies, have rapid economic growth and frequent trade activities, which result in a large amount of embodied energy flow (the embodied energy of BRICS accounts for about 42.3% of the global embodied energy) in the BRICS and all over the world. This article uses multilayer network model to study the embodied energy of the BRICS. By using the weighted degree and betweenness centrality indicators, key sectors and key paths are identified. The results show that the embodied energy circulation in BRICS is still dominated by industry and natural resources sectors, such as Petroleum, Chemical and Non-Metallic Mineral Products sector. The analysis of the key paths allows us to understand Russia and South Africa are exporters of embodied energy, China is an intermediate transmission country, and India and Brazil are importers. The roles of the five countries are different, and they need to perform their own duties, cooperate with collaborative governance, to achieve energy-saving, environmentally-friendly, and efficient development of the BRICS.

Current studies have indicated that the type and intensity of the relations in a social network can impact individuals’ creative performance. However, it is more practical to explore how to improve team’s creative performance from the perspective of network and community structure. We conduct a survey of a real scientific team network, in which each team member chooses five members (s)he regards as his (her) friends, five members (s)he always works with, and five members (s)he wants to work with. The members were asked to rank the five members they selected according to their closeness. We derived three networks: a friend network, a real cooperation network and a desired cooperation network. Additionally, we have constructed a directed-weighted network with the team members as nodes, the relations (friend, real cooperation, and cooperation desire) between members as edges, and the closeness of these relations as weights. The main results show the following. (1) This scientific team is a leader network with nodes with weighted degrees significantly larger than those of other nodes. The weighted in-degree sequence and the weighted out-degree sequence have different properties. (2) Creative performance is higher for the leader communities than for the self-organized communities. After removing the leader nodes of the leader communities, creative performance in the communities and in the whole network decreased. (3) Quadratic assignment procedure correlation (QAP) analysis shows that the similarity between the real cooperation network and the desired cooperation network is lowest in self-organized communities, and the creative performance is low if the similarity between the two networks is low; (4) creative performance is lower in a network that only takes into account the desired cooperation of members. This study sheds light on the potential application of complex networks in the issues of group division and team construction in the workplace and in the field of education.

Shareholders of listed companies can be divided into institutional and non-institutional shareholders. Both groups are important to their listed companies. Many scholars have conducted research on institutional shareholders, some research on non-institutional shareholders, and some scholars have researched similarities of the two shareholder types to reveal co-holding relationships at the shareholder level. However, there are seldom studies of the similarity of the co-shareholder relationship at the listed company level. What are the similarities between the co-shareholder relationships from these two perspectives? In this paper, we apply the complex network theory, which is often used in the financial markets to conduct research, and divide the data of ten major circulating shareholders of the listed companies in China into two parts. We then construct two projected networks that are a common institutional shareholder relationship network and a common non-institutional shareholder relationship network based on the bipartite networks using the decreasing-mode method, in which the shareholders are mediate elements. We analyze the evolutionary similarity of the two co-shareholder relationship networks from three angles, including the contribution rate of the common institutional shareholder relationship network and the common non-institutional shareholder relationship network to the whole network, the behavior intensity of the co-shareholder in the two networks, and the similarity of the two networks. The results indicate that (1) The evolution of the contribution rate of the listed companies with the co-shareholder relationship was influenced by different factors in two projected networks. The evolution of the contribution rate of the co-shareholder relationship in the whole network is consistent with that of the common institutional shareholder relationship network. (2) The intensity of co-shareholder behavior in the common institutional shareholder relationship network is stronger than that in the common non-institutional shareholder relationship network, and the trend in the whole network is the same as the former. (3) The similarity of the co-shareholder networks is consistent with the change of the co-shareholder relationship and is influenced by the stock market and the difference in the number of co-shareholder relationships. In this paper, we analyze the evolutionary similarity of the co-shareholder relationship between the common institutional shareholder relationship network and the common non-institutional shareholder relationship network and the influence factor of the two networks. This provides a new perspective for analyzing relationships among the listed companies.

Interinstitutional scientific collaboration plays an important role in knowledge production and scientific development. Together with the increasing scale of scientific collaboration, a few institutions that positively participate in interinstitutional scientific collaboration are important in collaboration networks. However, whether becoming an important institution in collaboration networks could be a contributing factor to research success and how these important institutions collaborate are still indistinct. In this paper, we identified the scientific institutions that possess the highest degree centrality as important institutions of an interinstitutional scientific collaboration network in materials science and examined their collaboration preferences utilizing several network measures. We first visualized the appearance of these important institutions that had the most positive collaborations in the interinstitutional scientific collaboration networks during the period of 2005---2015 and found an obvious scale-free feature in interinstitutional scientific collaboration networks. Then, we measured the advantages of being important in collaboration networks to research performance and found that positive interinstitutional collaborations can always bring both publication advantages and citation advantages. Finally, we identified two collaboration preferences of these important institutions in collaboration networks--one type of important institution represented by the Chinese Academy of Science plays an intermediary role between domestic institutions and foreign institutions with high betweenness centrality and a low clustering coefficient. This type of important institution has better performance in the number of publications. The other type of important institution represented by MIT tends to collaborate with similar institutions that have positive collaborations and possess a larger citation growth rate. Our finding can provide a better understanding of important institutions' collaboration preferences and have significant reference for government policy and institutional collaboration strategies.

With the development of heavy industry, urban soil suffers serious pollution, which threatens the sustainable development of cities. Understanding the spatial distribution characteristics of surface soil pollution aids in pollution prevention and control and promotes sustainable development. We use China’s Baotou as an example. Based on the data of 2820 sampling points in main urban areas and some suburban areas of Baotou, we constructed a relationship network model for sampling points in surface soil by using the complex network method. We combined the network method with spatial geographic information to analyze the spatial agglomeration characteristics of the surface soil pollution in Baotou China. Sampling points at Dalahai Village (including 506D, 538B, and 538D) and Hayenaobao Village (including 509C, 541A, and 541C), Puerhantu Town within the Kundulun District have the most serious pollution problems, and they are all concentrated in the tailings dam. Sampling points 328D and 544A are scattered in the Leng Community, Kunhe Town, Kundulun District and Changhan Village, Haringer Township, Jiuyuan District, but they have a close co-anomaly relationship with the tailings dam. We suggest that these areas should be unified to give priority to pollution control. There is an obvious difference for Al2O3, B, Hg, and U, which are abnormal in the power plant ash storage pools, but normal at the tailings dam. Consequently, pollution control for power plant ash storage pools needs to be different from pollution control at the tailings dam. Sampling points at the Fengying Community (including 580A and 580B), Kunhe Town, Kundulun District, and Gaoyoufang Village (579D and 643B), Rare Earth Road, Qingshan District as well as other sampling points upstream of the Kundulun River have a close co-anomaly relationship with the tailings dam. It is necessary to strengthen the purification treatment of sewage upstream of the Kundulun River to reduce the spread of pollutants. These results provide a theoretical basis for the government to formulate specific cross-regional collaborative governance measures.

In the stock market, there are widespread information connections between economic agents. Listed companies can obtain mutual information about investment decisions from common shareholders, and the extent of sharing information often determines the relationships between listed companies. Because different shareholder compositions and investment shares lead to different formations of the company’s governance mechanisms, we map the investment relationships between shareholders to the multi-attribute dimensional spaces of the listed companies (each shareholder investment in a company is a company dimension). Then, we construct the listed company’s information network based on co-shareholder relationships. The weights for the edges in the information network are measured with the Euclidean distance between the listed companies in the multi-attribute dimension space. We define two indices to analyze the information network’s features. We conduct an empirical study that analyzes Chinese listed companies’ information networks. The results from the analysis show that with the diversification and decentralization of shareholder investments, almost all Chinese listed companies exchanged information through common shareholder relationships, and there is a gradual reduction in information sharing capacity between listed companies that have common shareholders. This network analysis has benefits for risk management and portfolio investments.

The growing global trade disputes have raised concerns about the supply security of key minerals needed for strategic emerging industries. As one of the key minerals for many economically powerful countries, the international trade of graphite is exposed to high supply risks. Each country should not only pay attention to the paths through which trade risks propagate but also increasingly focus on how resource redistribution between countries can reduce the losses due to supply risks. Therefore, based on the construction of global graphite trade network and the principle of maximum entropy, this paper constructs a graphite trade redistribution model. Taking the trade relationship with larger export share and typical trading countries (China, the largest exporter, and Germany, a major intermediary country) as examples and from the global perspective, the resource redistribution among graphite-trading countries under different supply risk scenarios is simulated to analyze the changes in trade relations among countries. The results provide an important reference for national resource recovery after a graphite supply risk and a new way of thinking for those who study international trade.

Nickel, cobalt, and lithium are the most critical raw materials for lithium-ion batteries (LIBs). The increasing demand for these materials has triggered worries about the trade dependency on raw materials. Traditional trade dependency measures make it difficult to identify high-risk countries (regions) from a systematic perspective. This paper proposes a global dependency index to recalculate systemic risks in the trade dependency network. Unlike the traditional trade network analysis, we construct the resource-dependent network by taking the dependency relationship as the edge and the global dependency index as the weight. We use the new network to explore the characteristics of the overall pattern of global trade and the concentration of dependency. Then, considering the heterogeneity of trading agents, country (regional) dependency is analyzed at the local and global levels. The dependency relationship coordinates are constructed to identify high-risk countries (regions). The results show that the trade is highly concentrated. Developed and emerging economies tend to import in quantity. The absolute dependency and global dependency of agents are heterogeneous. The distribution characteristics of agent relationships in the trade dependency coordinates are apparent. According to the results, policymakers and market participants can tailor trade strategies to reduce risks. This accounting method provides ideas for future studies on trade dependency on other resources.

With modern industry's increasing demand for products from all sectors of the copper industry chain, the trade relationships between countries for copper material are becoming increasingly closer. Additionally, different trade characteristics exist in different sectors of the industrial chain. Based on the data for the global copper trade in 2019and from the perspective of the copper industry chain, this paper analyzes the trade characteristics of the global copper industry chain and of important countries by constructing the trade networks for different links. In addition, we establish a two-dimensional evaluation index system to evaluate the comprehensive trade risks of the major importing countries for each link of the industrial chain, including their supply risks and vulnerability to supply constraints. The following conclusions are drawn. Copper processing is the link with the highest density in the global industrial chain supply chain network and the best connectivity and differentiation among trading countries. In the risk assessment of global copper import trade, Germany is a low risk country in the whole industrial chain. The United States and China are countries with relatively low risks in the whole industrial chain, and the trade risks are relatively high only in the selection link. Malaysia and Poland are high risk countries of the whole copper material industry chain, while Japan, the republic of Korea and Spain are the “critical” countries of trade risk, and it is necessary to focus on the control of trade supply risk.

Increasing urbanization day–by–day requires new housing and transportation infrastructures. As a consequence, demand for steel – a basic material for buildings construction as well as for vehicles and railroads – would also increases. This study applies Emergy Accounting (EMA) to assess the Chinas steel industry environmental performance and to identify key application sectors. Subsequently, this study calculates emergy–based indicators capable to assess the present economic performance, environmental sustainability, and land resource appropriate utilization. Building on these indicators, changes of sustainability scenarios in key application sectors are also investigated, with special focus on increased use of recycled steel. The results show that the environmental impacts of steel use in downstream sectors, specially in the Housing and Vehicles Sectors, are significantly higher. Furthermore, the downstream sectors also have a very large requirement for embodied land. Additionally, the Emergy Benefit Ratio (EBR) shows non-negligible advantages to China derived from importing raw iron from abroad at international market prices. Finally, when the recycling rate of scrap steel increases, the performance of downstream sectors improves, with the Vehicle sector showing the most significant changes. Although the benefits of steel-based economy to society are clear, multidimensional sustainability concerns and international competition for primary resources necessitate a transition towards increased recycling and innovative materials within a strictly enforced “circular economy” policy.

With the rising profits of oil companies in the refined oil sector, the optimization of the refined oil supply chain network has received more and more attention. In most supply chains (SCs), transaction relationships between suppliers and customers are commonly considered to be an extrapolation from a linear perspective. However, this traditional linear concept of an SC is egotistic and oversimplified and does not sufficiently reflect the complex and structure of supplier–customer relationships in current economic and industrial situations. But key global knowledge can be obtained from complex network characteristics analysis of the net form sales system. For two-level network like refine oil supply network, this paper proposed an integrated framework to explore its characteristics. Through various analyses of this complex network, including visual, network scale, network agglomeration, network community and geographic information analyses, we could found the characteristics of regular network node relations and regional location characteristics, as well as a strongly correlation between correlation coefficient thresholds and the network interdependence, and also moderated the correlation between SN efficiency and SN resilience. In order to testify this supply network analysis method, we conducted a real-world SN analyses based on a Chinese province diesel supply network and describe an advanced investigation of SN theory. This method enrich the SN theory, which can benefit SN management, community economics and industrial resilience. Also the basic understanding of the diesel sales network system obtained in this paper provides guidance for further research on this network structure, which can also provide a reference for regional sales supervision and resource distribution.

Complex network theory is a widely used tool in the empirical research of financial markets. Two-mode and multi-mode networks are new trends and represent new directions in that they can more accurately simulate relationships between entities. In this paper, we use data for Chinese listed companies holding non-listed financial companies over a ten-year period to construct two networks: a two-mode primitive network in which listed companies and non-listed financial companies are considered actors and events, respectively, and a one-mode network that is constructed based on the decreasing-mode method in which listed companies are considered nodes. We analyze the evolution of the listed company co-holding network from several perspectives, including that of the whole network, of information control ability, of implicit relationships, of community division and of small-world characteristics. The results of the analysis indicate that (1) China’s developing stock market affects the share-holding condition of listed companies holding non-listed financial companies; (2) the information control ability of co-holding networks is focused on a few listed companies and the implicit relationship of investment preference between listed companies is determined by the co-holding behavior; (3) the community division of the co-holding network is increasingly obvious, as determined by the investment preferences among listed companies; and (4) the small-world characteristics of the co-holding network are increasingly obvious, resulting in reduced communication costs. In this paper, we conduct an evolution analysis and develop an understanding of the factors that influence the listed companies co-holding network. This study will help illuminate research on evolution analysis.

A city can obtain most of natural resources from outside of city for its development, but the ecological carrying capacity of it is limited. Urban economic development in some countries or areas is still maintained a high speed, but their ecological environment has deteriorated sharply. That means their economic has grown out of the limit of ecological carrying capacity. In the paper, the urban ecosystem is divided into population subsystem, economy subsystem, and resources and environment subsystem; the relationship among the three subsystems is analyzed based on eco-economic theories; after then, a theoretical framework of urban ecological carrying capacity is established. Due to urban ecosystem is a dynamic system with complex feedbacks, a System Dynamics model including three subsystems for is set up to measure urban ecological carrying capacity. Finally, the relationship between ecological carrying capacity and economic development in Beijing city from 1996 to 2012 were analyzed. Three scenarios in different economic growth pace are designed; the tendencies of deterioration of ecological environment in Beijing under different Scenarios are analyzed to explore the balance between economic development and ecological protection.

As the global warm problem becomes more and more serious, many countries try hard to develop a low carbon economy and build a low carbon society. Along with low carbon development, the carbon market has grown up around the world. Meanwhile, as the key body of the coal industry, coal listed companies suffered a lot due to the low carbon development. As many countries, China also pay lots of effort to cut down the consumption of coal products, so as to realize the restructuring of energy industry. In this paper, we try to find our whether the China's coal stock market, carbon market and coal price co-movement with each other in a certain way. First, we defined the co-movement matrix of the coal stock market, carbon market and coal price (three indices for short) in each trading day which can show the co-movement patterns between any of the two indices. Then, based on complex network method, we took the co-movement matrices as nodes, the time adjacent relations as edges to construct the co-movement matrix transmission network, and the role of each co-movement matrix, the relations between any of the two co-movement matrices, as well as the important group of the co-movement matrix transmission network were analyzed based on different topological features. Finally, based on the results, we analyzed the co-movement relations of carbon-related markets. The method proposed in this manuscript is helpful for the researchers to find out how the low carbon development affects different markets, and it is also helpful for the investors to analyze what the co-movement patterns of different markets in a holistic perspective.

As one of the renewable energy, solar energy is experiencing increased but exploratory development worldwide. The positive or negative influences of regional characteristics, like economy, production capacity and allowance policies, make them have uneven solar energy development. In this paper, we aim at quickly exploring the features of provincial solar energy development, and their concerns about solar energy. We take China as a typical case, and combine text mining and two-actor networks. We find that the classification of levels based on certain nodes and the amount of degree avoids missing meaningful information that may be ignored by global level results. Moreover, eastern provinces are hot focus for the media, western countries are key to bridge the networks and special administrative region has local development features; third, most focus points are more about the application than the improvement of material. The exploration of news provides practical information to adjust researches and development strategies of solar energy. Moreover, the bi-level exploration, which can also be expanded to multi-level, is helpful for governments or researchers to grasp more targeted and precise knowledge.

Due to high population densities and rapid economic development, great number of cities worldwide rely heavily on external resources, and many are experiencing serious environmental pollution. Municipal governments are facing the issue of balancing the relationship between economic growth and environmental preservation. An urban system is an open, complex, dynamic ecological-economic system with different types of materials and resources. This paper combines emergy theory and System Dynamics (SD) and establishes an emergy-flow SD model of an urban eco-economic system that includes economic, population, waste and emergy sub-models. Three scenarios with different economic growth rates and investments in environmental preservation are designed to analyze the sustainable development capacity of Beijing under different scenarios. The results of the analysis show that current economic development in Beijing highly depends on resources consumption, especially the consumption of imported resources. Based on the current growth rate, development in Beijing will heavily depend on external resources that may make the system being more fragile in the future. A lower economic growth rate and a small increase in environmental preservation investment are more suitable for in Beijing than area higher economic growth rate and a large increase in environmental preservation investment.

The logistical issues surrounding the timing and transport of flowback generated by each shale gas well to the next is a big challenge. Due to more and more flowback being stored temporarily near the shale gas well and reused in the shale gas development, both transportation cost and storage cost are the heavy burden for the developers. This research proposed a theoretical cost optimization model to get the optimal flowback distribution solution for regional multi shale gas wells in a holistic perspective. Then, we used some empirical data of Marcellus Shale to do the empirical study. In addition, we compared the optimal flowback distribution solution by considering both the transportation cost and storage cost with the flowback distribution solution which only minimized the transportation cost or only minimized the storage cost.

Listed energy companies play important roles in both energy market and financial market. Relationships between those energy companies also affect both markets. Thus, examining listed energy companies’ relationships is necessary and useful for the further study of the field of energy and finance. To investigate energy companies’ relationships, we applied the Grey Relational Analysis (GRA) and the complex network theory to construct the network. We selected listed energy companies’ ten years’ financial indicators as our input data. We analyzed the network’s evolutionary topological features and explained the implications of these features in detail. We also made panel regression analysis to find out the relationship between network indicators and stock returns. This study provides a new perspective for discovering the relationships of listed energy companies based on financial indicators. It can help government and investors identify the roles of companies in the market so that they can regulate the market in time and make sound investments.

CO2 cost pass-through due to environmental policies will lead to an increase in general prices. Because of factors such as the market structure and technology level, the capability of cost pass-through is significantly different among sectors. This topic is related to the central environmental policies regarding who will bear the CO2 cost burden. To analyze the short-term impacts of CO2 cost pass-through on general prices and the transmission mechanism in China, this study simulates a carbon tax that will be levied in 2020 using a partial input-output model and a complex network theory. Two scenarios are compared: a complete transmission, in which CO2 costs are 100% transferred to downstream industries, and a partial transmission, in which CO2 costs are partially transferred. The results show that the cost pass-through capabilities of sectors vary significantly. Coal mining and service are the source of inflation and have the strongest cost pass-through capability. Second, the impacts of CO2 cost pass-through on general prices are significantly different between a complete transmission scenario and a partial transmission scenario. The average price increase in the partial transmission scenario is 0.24%, which is less than the average price increase of 0.58% in the complete transmission scenario. Finally, this study analyzes the price transmission mechanism between sectors in the process, including key sector analysis, critical paths analysis, and community analysis. Our research is valuable for further studies on the welfare effects of the carbon tax on producers and consumers.

Capturing the interactive shareholding relations among agents of the oil & gas industry chain is an important topic for obtaining a better understanding of the ownership and structure of the global oil & gas market. This paper proposes a multilayer cross-shareholding network method to map the multiplex cross-shareholding relations in the global oil & gas industry chain from the reciprocated, trilateral and largest component perspectives. The results show the following: (1) The cross-shareholding behavior among the listed oil & gas companies is a more popular shareholding pattern for the energy giants (the top 17.49% of companies that make up approximately 60.43% of the total market value). (2) Few (only 9 pairs between different companies) are reciprocated, and most of them belong to Israel, Japan, India, etc. (3) The “co-shareholder type” is the most critical trilateral relation in the global oil & gas industry. (4) The companies from Israel, Japan, and the Marshall Islands formed powerful alliances in different single layers, and the companies from Japan and India formed powerful alliances with cross-layer cross-shareholding relations. This paper is useful for understanding the ownership and alliance structures and the interactions of the oil & gas industry chain, and it can also provide a new perspective to determine the systemic risk paths.

This paper examines the influence factors of Foreign Direct Investment (FDI) and focuses on the two main questions to be answered: (1) How do the foreign direct investment (FDI) network form and (2) what factors affect the formation of foreign direct investment (FDI) Network. First, we identify the basic FDI relationship in the homogeneous complex network of nations from 2001 to 2012. Then, we use the network indicators such as FDI relationships, independence, information control ability, FDI inflows and FDI outflows in FDI relations to construct the panel regression model to analyze the influence factors for Foreign Direct Investment. The results indicate that (1) The number of the nations with FDI relationships is increasing for 2001 to 2012, which highlight the importance of FDI relationship; (2) The nations in the FDI relationship network are getting closer and closer and the information of the network is controlled by few nations; (3) National competitiveness and inflows of FDI are directly related to each other; (4) Depreciation of exchange rate against USD increase FDI. This paper can help policymakers to better understand the features of FDI and thereby provides new insight for attracting FDI to promote economic growth.

The diffusion of breaking news largely relies on propagation behaviors in the media. The tremendous and intricate propagation relationships in the media form a complex network. An improved understanding of breaking news diffusion characteristics can be obtained through the complex network research. Drawing on the news data of Bohai Gulf oil spill event from June 2011 to May 2014, we constructed a weighted and directed complex network in which media are set as nodes, the propagation relationships as edges and the propagation times as the weight of the edges. The primary results show (1) the propagation network presents small world feature, which means relations among media are close and breaking news originating from any node can spread rapidly; (2) traditional media and official websites are the typical sources for news propagation, while business portals are news collectors and spreaders; (3) the propagation network is assortative and the group of core media facilities the spread of breaking news faster; (4) for online media, news originality factor become less important to propagation behaviors. This study offers a new insight to explore information dissemination from the perspective of statistical physics and is beneficial for utilizing the public opinion in a positive way.

Liquefied natural gas (LNG) has been categorized as a global commodity because of the continuous globalization of the LNG trade, and there is very close competition between LNG exporters worldwide. However, the overall competition structure and degree of the global LNG trade system were not well portrayed or quantified in the existing literature. This paper examined the trade competition pattern of the global LNG trade by building LNG trade competition networks (LTCN) from 2005 to 2014. Overall, the competitive relationship growth and competitive globalization features of the LNG trade were analyzed, and the main LNG exporter group and its evolution were studied. Moreover, the market competitiveness of LNG exporters was measured, and accordingly, these exporters were divided into three categories to depict their different development prospects, which would form a more complicated competition pattern of LNG trade in the future.

This study proposed a novel visualization and quantitative method for analyzing the roles and investment relationships of nations as well as the global energy investment structure. We constructed a primitive investment network of two different actors--listed energy companies and their shareholders. Then, based on the two-mode national affiliation relationships, we constructed a derivative investment network of 112 nations. Then, we quantitatively analyzed the national diversity of outward and inward energy investment, the strength of the energy investment relationship between countries (the most powerful group of energy investment) based on the shareholding relationships of global listed energy companies.

Estimating potential trade links is essential for exploring the information implied by international crude oil trade data, which contain obvious trade links among countries. In addition, it is important for governments to assess the evolution trend of international crude oil trade in order to avoid trade risk. This study introduces the link prediction approach to explore potential trade links from the perspective of relations based on the topological attributes of countries. We take the number of common trade partners for each country pair as the potential linking motivation. Based on this, we confirm this as a general feature for most existing trade links and thereby describe the real distribution of trade relations. Furthermore, our study analyzes the practical meanings of explored potential trade links with considerations of countries' crude oil trade roles. We find that the number of common trade partners is indeed one of the structural linking motivations in international crude oil trade. It can not only represent the possibility of trading relations, but also reflect the competition among countries. By using this evaluation index, we then estimate potential trade partners combined with countries’ crude oil trade roles and provide suggestions for governments about future trading strategies.

This paper addresses the problem of relationship analysis of listed companies. It mainly focuses on the analysis of affiliation relationship network among listed companies based on the co-attendance behavior of senior executives. In order to solve this problem we propose a two-stage method. In stage 1, we propose a method of network construction which can change the two-mode affiliation network to one-mode network using model-descending process, and in stage 2, we analyze the networks from stage 1 in four aspects includes analysis of topological characteristic, influence distribution, information control ability distribution and small world phenomenon. We then took a case of Chinese listed companies’ executive-based network as an example of the method and the results of the case study showed that the co-attendance behaviors of executives became more widespread (the number and percentage of related listed companies increased 624% and 322%) and that more companies preferred to relate to others on this level, improving the stability of the massive company structures.

Listed energy companies play an important role in both the energy financial market and energy commodity market. As owners of listed energy companies, shareholders can influence the stability of the energy stock market by increasing or decreasing their holdings. To analyze the evolutionary features and stability of shareholders’ co-holding behavior, we constructed a derivative holding-based structural equivalence network of the shareholders (holding-based network) based on the primitive shareholding network of the listed companies and their shareholders. The main data used in this paper are the semi-annual reports from 2003 to 2012 that contain information about Chinese listed energy companies’ top 10 shareholders. The holding-based network takes the shareholders as nodes, whether holding the same listed energy companies’ stock at the same time as the edges, and the number of listed energy companies co-held as the weights. Due to the unique topological features of the holding-based network, we improved the Heuristic Algorithm to find the associated maximal connected sub-graph of the network, which is the main group of shareholders with shareholding consistency of the listed energy companies. This paper creates a new approach to analyze the relationships in the energy stock market, presenting a new method to analyze shareholder behavior.

Although the steel industry has brought economic benefits to many countries, it also has non-negligible impacts on the environment. To understand how steel production impacts are shared among trading countries, it is necessary to conduct a large scale research from environmental and economic perspectives. This study applies the life cycle assessment method (LCA) to analyze 12 major iron-mining and steel-making countries, calculate a “unit GDP impact intensity” and finally raise an issue of “pollution allocation” among actors in international trade. Results firstly indicate that normalized ecotoxicity impacts over the entire production chain are among the most crucial consequences of iron and steel production, even higher than the non-negligeble impacts from carbon emissions. Secondly, performance indicators indicating the relation between environmental impacts and economic activities can be calculated as Total Normalized Impacts/GDP, an inverse measure of environmental versus economic efficiency. South Africa has the highest unit GDP impact intensity (4.35E-4 TNI/US $) in iron mining, while Canada has the largest intensity in steel manufacturing (5.81E-3 TNI/US $). Thirdly, countries that largely benefit from importing “pollution-free” materials, should share a proportional responsibility for the environmental pollution caused by iron&steel processing activities occurring in their trade partner countries: this is especially true for China and US, which are major iron ore/steel importing economies. Finally, this paper proposes policy implications at local, national and international levels, from a perspective of global sustainable development.

The steel industry is a basic industry of the national economy, and its product structure is complicated. Due to price differences, the prices of products of different varieties, specifications and origins affect each other and transmit each other. In addition, the steel industry covers the stages of product selection, smelting and processing. The transmission patterns between product prices at different stages also vary. At the same time, the steel industry has been confirmed to be a typical cyclical industry. Thus, our work mainly explores the transmission relationship between steel product prices under multiple timescales from the perspective of the industrial chain. First, we use discrete wavelet transform to extract the time series of 553 steel products at different timescales. Second, based on the Granger causality test, the transmission relationship between every two products is measured. Third, by constructing multiscale steel product price transmission networks, important steel products with different roles are identified. Finally, based on motif recognition, the main modes of product price transmission between different reaches of the industrial chain are summarized. The results show that as the timescale increases, the transmission between products becomes increasingly frequent and that the transmission patterns become increasingly clear. In general, there is a high probability of occurrence of important steel products in hot rolled coils, structural steel and medium and heavy plates, especially 4.75 mm and 3 mm hot rolled coils produced in Beijing and 4.75 mm hot rolled coils produced in Beijing and Zhengzhou. In addition, the transmission of steel products at different stages has undergone a process of transmission between two stages to transmission among three stages and then to transmission between two stages. In this regard, under 4–8 days, transmission within a stage and transmission among three stages is the most frequent transmission pattern. Our work will help investors and policy makers gain an in-depth understanding of the steel market and mitigate risks in investment and the policy development process.

The invention of electric vehicle can make great contribution to the transition to low-carbon city, because of its lower pollution feature. Electric vehicle has become a popular development trend in the automotive industry. This industry has a high degree of technological dependence. Electric vehicle technologies involve multiple disciplines, and study of the technology convergence among various disciplines can explain the interdisciplinary development in this field. Previous studies lack detailed estimation of promising and potential technology convergence relations as well as the detailed topics in this field. This paper combined qualitative and quantitative methods (e.g., network analysis, link prediction and text mining) and proposed a novel framework to explore promising and potential technology convergence relationships and topics in the field. The results are:(1) Eight promising relationships were found. For instance, the convergence relationship B60K–B60L is a promising example related to the convergence of “dashboards or the mounting of one or more propulsion units and related devices” and “the propulsion, operation monitoring or electric safety in electric vehicles”. (2) The topics of the five most promising ones are mainly about battery arrangement and protection, control systems, framework design, and charging connectors. Battery arrangement and protection are the most noteworthy promising topics. (3) Five potential convergence relationships are detected. For example, the convergence of “propulsion, operation monitoring or electric safety in electric vehicles” and “image data processing or generation” might occur in future innovation. (4) The potential topics might be electric vehicle condition display, compressor or pump electric control. The results could offer a reference to scientists, policy-makers and investors in the electric vehicle field, and the novel framework proposed in this paper could be applied to other technology fields to estimate the trends of technology convergence.

The Chinese government may promulgate a carbon tax on the emitters outside the national carbon trading market in the future. The emission reduction strategy needs to consider the specific features of the sectors. However, existing literature on the roles of sectors during a carbon tax on the Chinese economy is insufficient. The aim of this study is to analyze the short-term roles of sectors during a carbon tax on China’s economic system via complex network theory. The results show that the small-world nature of intersectoral price change flow network and intersectoral output change flow network is significant, and the key individual sectors play important roles. Second, we investigate the roles of sector groups by network motif analysis. The members of the key sector group are heterogeneous, which mainly exist between the sectors with larger price change or output change and the sectors with lower price change or output change. Although the function of sector groups is significant, the frequency is lower. Finally, we compare the results of the in-process analysis with ex-post analysis and found that there was a significant difference between the two. The biggest difference is that the roles of some low-carbon sectors are ignored in the ex-post analysis. Our study is of valuable reference for policymakers in terms of designing a differentiated sectoral emission reduction strategy.

In stock market investment, the first step is to select stocks. To do so, stock traders have depended on two major tools: fundamental analysis and technical analysis, while ignoring the impact of large shareholder behavior on stocks. The major shareholders in the Chinese stock market show a serious herd effect; they can control or influence the stock price of the listed companies by increasing or reducing their holdings, which may affect the stability of the market. Thus, it is significant to study shareholders’ holding behavior. In this paper, we constructed a shareholder coholding network of listed energy companies in the Chinese stock market based on quarterly shareholder data from 2012 to 2018. We selected stocks by choosing central and peripheral topological indicators in the training period, namely, degree (D), betweenness centrality (BC), closeness centrality (CC) and eigenvector centrality (EC). We also used other traditional trading strategies to select stocks. Then, we used the selected stocks to build the portfolio and measure the performance in the test period. We identified the market conditions in different test periods and evaluated the performance of the portfolios in different market conditions. The results show that the topological indicators of a shareholder coholding network can be used to guide actual investment, and the suggestions for investment in different market environments are presented in the conclusion.

Cross-shareholding is a new type of strategic means for capital operation and is an important component of corporate governance. With the increasing complexity of business motivation, the structure of a cross-shareholding network (CSN) is becoming more intricate, and it exposes various important local patterns with different economic functions. The goal of this paper is to uncover investment mechanisms and economic functions implied in cross-shareholding networks (CSNs) by analyzing the local characteristic patterns of company interactions. In this paper, we construct the CSNs of listed companies and extract the directed triadic motifs to reveal the evolutionary characteristics of local investment patterns at the company and industry levels. On the company level, we find that companies tend to form V-shaped structures with other companies, but bidirectional shareholding patterns and circular relationships in the triads are scarce. On the industry level, we identify the characteristic linking patterns of some industries with a role analysis of the industries. Furthermore, we detect the evolutionary characteristics of industry interrelationships in three implied patterns. Such a motif evolution analysis may provide valuable information for investors and supervisory departments that make decisions about investment portfolios and policy. Meanwhile, this study is also helpful for exploring the implied information in other empirical networks.

Shareholders are the owners of listed companies, and their relationships can directly affect the structure of the stock market. In this paper, we analyze the topological properties and evolution of the cross-shareholding networks of listed companies in the past 5 years in China from 2007 to 2011, an infrequently considered topic, by taking shareholders’ cross-shareholding relationships into account. This analysis arrives at a deeper insight into the inner characteristics of China’s stock market. We find that the cross-shareholding networks of listed companies with shareholders’ cross-shareholding relationships display statistical features that reveal the stock market’s complex relationships more precisely. In particular, the results show that when the shareholders’ cross-shareholding relationships are considered, first, the In-degree and Out-degree of the cross-shareholding networks follow power-law distribution and the R 2 of the linear regression analysis of the cumulative degree distribution is relatively higher; second, the modularity of the communities is larger; finally, both the number of members of top-ranked communities and the number of communities that have a large number of members are larger than those of which only considering the relationships between shareholders and listed companies are taken into account. Such cross-shareholding networks analysis taking shareholders’ cross-shareholding relations into account would be a helpful tool for supervisory departments and for stock market researchers to grasp the inner cross-shareholding relationships of listed companies in China, and it will be also helpful for the further researches about the “agent problems” in the stock markets from a whole point of view.

Listed energy company is the principal part of the energy market, including the energy investment, energy trade, energy operation etc. Stockholders are the owners of the listed energy companies, whose relationships would affect the stability of the energy market directly. In order to analyze the evolution features and stability of the relationships between the stockholders, we constructed a generator holding-based structural equivalence network of the shareholders based on the primitive affiliation network of the listed companies and their shareholders. The main data used in this paper is the semi-annual reports from 2003 to 2012 which contain the information about Chinese listed energy companies’ top 10 shareholders. The holding-based network takes the shareholders as the nodes, whether holding the same listed energy companies’ stock at the same period as the edges, and the number of the listed energy companies holding at the same time as the weights. In this paper, we improved the traditional Heuristic method and then found the maximal connected sub-graphs of the holding-based networks in different time domain by the improved Heuristic method. The main method and theory used in this paper is complex network, we calculated and analyzed the evolution and the stability of the maximal connected sub-graphs. This paper comes up with a new angle to analyze the relationships in the energy stock market, and present a new method to analyze the stockholders’ behaviors.

As a major importer and exporter of steel products, China has trade relations with many markets around the world. However, the development of the Chinese market in recent years has not been stable, and price fluctuations are frequent. To better understand the price trend of China's steel products, based on an adaptive neural fuzzy inference system and cluster analysis, this paper examines the price impacts on the Chinese import and export steel market from the perspective of the international mainstream market and steel products themselves. The study selected steel markets in 12 countries and regions and used daily data on the import and export prices from January 2009 to September 2017 to analyze eight steel products. The results show that in the mainstream market, Asian countries have a greater impact than non-Asian countries on China's import market, and developed countries have a greater impact than developing countries on China's export market. Among steel products, sheet steel products have the most significant impact on China's import and export markets. Based on the above results, this article gives some policy suggestions for monitoring and controlling the development of China's steel industry from an international perspective. When the price of imported products in the Chinese market increases, the government should add or change the import area. In addition, industry regulators should focus on products with large import volumes, such as flat products. When the proportion of imported products is too large, policies should be adjusted appropriately. In addition, the China Steel Industry Association should analyze the developmental trend of the international market in real time and provide timely assistance to related companies, especially when export products encounter issues, such as antidumping investigations in developed countries. Furthermore, steel enterprises should continue to strengthen the export of key products, for instance, by increasing the proportion of high value-added products such as cold rolled coils.

Hazards are a potential source of harm and damage hiding in shadow zones. Without control, they may accumulate and interact with other types of hazards. In the harsh and complicated circumstance, especially, the deep underground space of coal mines, complex and nonlinear interactions among hazards multiply the probabilities that a hazard turns into accidents, more seriously, its effect may trigger more correlated hazards to worsen the accidents and bring huge loss of lives and assets. Therefore, identifying the correlations among hazards and understanding the complexity of interactions among coal mine hazards are significant for ensuring the safety of coal production. From this standpoint, we propose a hybrid method combing text mining and complex network method. First, we abstract the dangerous hazards from a large amount of text data. Then, we establish the coal mine hazard network (CMHN) to capture correlations among hazards. Finally, we analyze and predict the correlations among hazards based on CMHN. Through which, we find the fault-prone hazard and the recurrent hazards, more importantly, we figure out the nonlinear correlations among hazards and reveal the connection preference of hazards. Furthermore, we forecast the unknown correlations among hazards to take precautions of them. This study could be helpful for making prevention strategies for safety management in the coal mine and other highly dangerous industries.

Energy stock investor sentiment reflects the investment desires and expectations of investors and plays a significant role in the investment decision-making process and in asset value. There are many ways to measure investor sentiment; for example, the development of the Internet makes it possible to use the online comments of investors to portray their sentiments. Taking a complex network perspective, this paper selected the PetroChina Company Limited as the research object, obtained 96839 investor online comments and analyzed their emotional tendencies and sentiment transmission process. In addition, on this basis, we examined the relationship between investor sentiment and stock price volatility to analyze the transmission characteristics of investor sentiment. The results show that, overall, PetroChina investor sentiment experienced stable fluctuations over the past five years, and stock returns fluctuated within the range of investors' acceptable volatility. In addition, a weak positive correlation is found between investor sentiment and stock returns. The investor sentiment transmission network has scale-free characteristics and cluster effects. This paper is helpful for understanding the behavior of investors in the stock market, and it provides a new perspective on the relevance of unstructured text and structured data.

The co-fluctuation of two time series has often been studied by analysing the correlation coefficient over a selected period. However, in both domestic and global financial markets, there are more than two active time series that fluctuate constantly as a result of various factors, including geographic locations, information communications and so on. In addition to correlation relationships over longer periods, daily co-fluctuation relationships and their transmission features are also important, since they can present the co-movement patterns of multi-time series in detail. To capture and analyse the features of the daily co-movements of multiple financial time series and their transmission characteristics, we propose a new term—“the co-fluctuation relation matrix”—which can reveal the co-fluctuation relationships of multi-time series directly. Here, based on complex network theory, we construct a multi-time series co-fluctuation relation matrix transmission network for financial markets by taking each matrix as a node and the succeeding time sequence as an edge. To reveal the process more clearly, we utilize daily time series data for four well-known stock indices—the NASDAQ Composite (COMP), the S&P 500 Index, the Dow Jones Industrial Average and the Russell 1000 Index—from 22 January 2003 to 21 January 2015, to examine the concentration of the transmission networks and the roles of each matrix—in addition to the transmission relationships between the matrices—based on a variety of coefficients. We then compare our results with the statistical features of the stock indices and find that there are not many discernible patterns of co-fluctuation matrices over the 12-year period, and few of these play important roles in the transmission network. However, the conductibility of the few dominant nodes is different and reveals certain novel features that cannot be obtained by traditional statistical analysis, such as the “all positive co-fluctuation matrix”, which is the most important node, although one stock index has negative correlation with the other three. This research therefore provides a novel method for analysing the co-movement behaviour of multiple financial time series, which can help researchers obtain the roles and relations of co-fluctuation patterns both over short and long terms. The findings also provide an important basis for further investigations into financial market simulations and the fluctuation of multiple financial time series.