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To reduce carbon emissions, the Chinese government is considering introducing a differentiated industrial carbon tax on enterprises outside the carbon trading market in the future. An efficient carbon tax must consider not only how carbon taxes impact the current economy but also how the size of the tax should be adjusted across time due to external changes. To calculate the optimal industrial carbon tax for China which is subject to certain constraints, this paper investigates the economic and environmental effects of four possible industrial carbon tax rate models under carbon intensity constraints from 2021 to 2030 by a dynamic input-output optimization model. The results show that the dynamic tax rate model leads to larger fluctuations in GDP growth than the other tax models, with a low initial tax rate in the beginning and a high tax rate exceeding ¥180/t in 2030. Second, a large quantity of capital stock is distributed across the energy-intensive industries, which leads the existing capital investment structure to be path-dependent. This offsets the performance of carbon taxes. Third, indirect energy-intensive industries such as construction and transport are insensitive to the industrial carbon tax. Finally, comparing the impacts of the four tax rate models, the optimal industrial carbon tax for China is found to be a fixed differentiated tax rate, in which energy-intensive sectors are taxed ¥75/t and low-carbon sectors are taxed ¥50/t.

To conserve resources and enhance the environmental performance, China has launched the "Zero waste" concept, focused on reutilization of solid waste and recovery of materials, including copper. Although several studies have assessed the copper demand and recycling, there is a lack of understanding on how different waste management options would potentially reduce primary copper demand and associated environmental impacts in China in the context of energy transition. This study addresses this gap in view of a transition to low-carbon energy system and the optimization of copper waste management combining MFA and LCA approaches. Six types of waste streams (C&DW, ELV, WEEE, IEW, MSW, ICW) are investigated in relation to various "Zero waste" strategies including reduction, reuse (repair, remanufacturing or refurbishment), recycling and transition from informal to formal waste management. Under present Chinese policies, reuse and recycling of copper containing products will lead to a somewhat lower dependency on primary copper in 2100 (11187Gg), as well as lower total GHG emissions (64869 Gg CO2-eq.) and cumulative energy demand (1.18x10 boolean AND 12 MJ). Maximizing such "Zero waste" options may lead to a further reduction, resulting in 65% potential reduction of primary copper demand, around 55% potential reduction of total GHG emissions and total cumulative energy demand in 2100. Several policy actions are proposed to provide insights into future waste management in China as well as some of the challenges involved.

The relationship between investor attention and stock prices has been a topic of interest in economics. Previous studies have shown that the correlation relationship between the two changes with time. However, there are few studies to explore the time-varying evolution of the relationship, as well as the transmission characteristics under important cycles. Thus, this paper is dedicated to discover the dynamic transmission characteristics of the correlation between investor attention and stock price. We selected the typical stocks of China’s energy industry, PetroChina and Sinopec, as the research objects, as they occupy a large market share and are representative. And the transaction data and attention data are used to build investor attention indicator. In order to reproduce the dynamic transmission process of correlation at different cycles, sliding time window and complex network are applied. The results show that PetroChina and Sinopec stocks have a weakly negative correlation between investor attention and stock price from 2017 to 2018. However, from the perspective of different cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the five consecutive days of correlation between the two become less, but the transmission intensity between the modes increases and the transition becomes more regular and inclined. In addition, by mining the important transmission modes and main transmission paths under important periods, we find that the series modes of uncorrelated or weakly positive correlation for five consecutive days dominate the transition of modes in the networks. Also, the closed loop formed by these two important modes and related modes is the main transmission path. These findings can reveal the rules of the typical stock market in China’s energy industry and help investors with different investment cycle preferences make sound decisions.

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.

To identify the interaction pattern features and driving forces of intersectoral CO2 emissions, this paper studies the interaction pattern types, interaction linkage strengths and key interaction patterns of 28-sector CO2 emissions flow networks in China from 1997 to 2015 via network motif analysis. Network motif analysis is a useful tool for measuring interactions between agents and their function in networks. This paper extends network motif analysis by investigating the weighted edges of motifs to observe the function of motifs. The results show that in 1997–2002 and 2008–2015, the strongest carbon intensity sectors, including electricity production, metal smelting and gas production, were the greatest contributors to the increase in intersectoral CO2 emissions. From 2003 to 2007, the strongest energy intensity sectors, including electricity production and fuel processing, were the greatest contributors. In contrast, the strongest export intensity sectors, including other electronic equipment, leather manufacturing, instrumentation, and textile, were the largest contributors to the decrease in intersectoral CO2 emissions. Second, the strongest interaction linkages were between construction and nonmetallic products and metal smelting, machinery and construction and metal smelting, construction and nonmetallic products, construction and metal smelting. Third, the key interaction patterns for the increase in intersectoral CO2 emissions were patterns such as infrastructure-export, manufacturing intensive, energy consumption, and energy tracing, while the export-intensive pattern and high export pattern were the key interaction patterns for the decrease in intersectoral CO2 emissions. This paper is beneficial to the establishment of a coordinated emissions reduction mechanism across sectors.

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.

Regional haze pollution has frequently occurred in China over the past several years, and this haze has hindered the development of the economy and harmed the health of people in China. Currently, several studies have analyzed the impact of different influencing factors on haze. However, few studies have comprehensively analyzed the influential factors of haze from different perspectives. In this paper, we utilized global and local regression models to explore the main influential factors on air quality index (AQI) in China from global and local perspectives. The results are as follows: (1) the AQIs of Chinese cities have significant positive spatial correlation, and higher values of AQI were typically found in Beijing-Tianjin-Hebei, Shandong, Henan, Shanxi and Shaanxi Province; (2) from a global perspective, as there is one unit of increase in the average AQI of one city's neighbors, the city's AQI will increase by 0.827 unit. An increase in the industrial structures and the number of civilian vehicles will also lead to an increase in the AQI, but the impact of precipitation is reversed; and (3) from a local perspective, there are spatial differences in the effects of different factors on the AQI. In northern China, an appropriate temperature reduction and an appropriate increase in atmospheric pressure is helpful for reducing haze pollution; however, opposing conditions are found in southern China. Compared with China's coastal cities, the increase in precipitation is more effective at reducing the AQI in inland cities. Compared with other cities, reducing the industrial structure and the number of civilian vehicles was more effective for haze management in Beijing, Tianjin, Shandong, Henan, Shanxi, and Shaanxi provinces. These results of this paper are helpful for government departments to formulate regionally differentiated governance policies regarding haze.

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.

With the application of graphite materials in aerospace, new energy and other emerging industries, the contradiction between the supply and demand of graphite resources strengthens the competition among graphite importers. In this paper, a complex network theory is adopted to analyze the trade competition of the global nature graphite based on trading data from 2007 to 2016. First, the overall structure features and the evolutionary law of the graphite trade competition network are analyzed. The results show that the competition intensity of the graphite trade is increasing and the importers are gradually integrating. Secondly, the role of the countries and the relationships between countries in the graphite trade competition network are analyzed. We find that Japan is always in a dominant position; the competitiveness of both the Republic of Korea and Malaysia is rising; and countries with greater competition intensity are concentrated in Asia. Finally, the influence factors of competition intensity are analyzed by using the extended gravity model. It is found that a country's GDP, technology and graphite consumption can drive the competition intensity between countries and the distance impede the competition between them. According to the above analysis, the graphite importers can understand the current development trend of the graphite trade competition and what cause this competitive situation, therefore, the corresponding trade policies should be formulated to adapt to the graphite trade's current competitive environment.

Because of the high yields and high risks associated with the stock market, investors can hold diversified portfolios with low relativity of stocks to reduce unsystematic risk. The current literature analyzes single factors affecting the relativity of stocks, but in this paper, we analyze the correlations between different factors to provide multiple perspectives of and about investment portfolios. This study analyzes the relationships between the similarities of the main products of listed companies and the varying degrees of correlations of stock price by examining different threshold scenarios of the energy industry between 2012 and 2016 and then constructing stock price correlation threshold networks and co-main product networks to analyze the similarities in their structures. The results indicate that two factors are significantly correlated in 97.5% of the scenarios and that these factors are the most strongly correlated when the threshold is between 0.5 and 0.7. The two networks exhibit a high degree of similarity in degree, weighted degree and community division. Main product similarity, used as supplementary information for stock relativity research, plays a role similar to stock price correlations in certain scenarios. Furthermore, compared with stock price correlations, the similarity of main products is simpler and more intuitive. This paper proposes a new method to study stock relativity based on different threshold scenarios; thus, it could serve as a reference for investors when developing portfolio strategies from multiple perspectives.

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 Jing-Jin-Ji region is one of the most atmospherically polluted regions in the world. In this study, the concepts, calculations, and classifications of the atmospheric environmental overloading index (AEOI) have been further developed. The AEOI has been calculated using the daily monitoring values of the concentrations of six main atmospheric pollutants from a total of 13 cities in the region from 2013.12.2 to 2017.04.18. Based on this index, the spatiotemporal characteristics and trends of the atmospheric environmental carrying capacity (AECC) are evaluated. In the temporal dimension, the regional AECC has gradually improved, and the amplitude of the average annual comprehensive AEOI has decreased, by up to 36.28% and 21.97%, respectively. In the spatial dimension, the AECC in the region decreases gradually from north to south. Regarding the change trend, decreasing comprehensive AEOI values were observed for 78.85% of the cities in the region. A significant reduction of up to 53.85% indicates a significant improvement in the regional AECC. Construction of a fluctuation network for the AEOI further confirmed the overall characteristics of the AEOI change in the atmospheric environment in this region, and the cities that are independent from the overall fluctuation could be identified.

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.

In the rapid process of globalization, merchandise trade has increased year by year, and the circulation and consumption of the energy embodied in the merchandise trade have increased. To study the relevant nature of the global embodied energy trade network (GEETN) from an overall perspective and the control and dependence relationships between various countries and regions from a partial perspective, this paper establishes a GEETN model, through an ecological network analysis (ENA) method and the motif of complex networks for research. From an overall perspective, a GEETN is a system that achieves stability quickly. Some developed countries, such as the United States and the European Union, and some emerging economies, such as China and Russia, play important roles in GEETN. From a partial perspective, in the GEETN model, US is mainly controlled by Asian countries and CA (Canada), EU is mainly controlled by other European countries, both due to their industrial layout in some extent. The scientific value of this article not only includes the improvement of model, but also provides a new perspective and a reference for the formulation of energy and climate change policies.

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.

In recent years, the application potential of graphite materials in new energy and new materials has gradually developed. Thus, graphite has become increasingly regarded as a necessary strategic resource for the development of new energy and has gradually been considered seriously by various countries. However, at present, an imbalance between the increased demand and tighter supply of graphite resources is very likely to lead to a supply crisis for graphite. To understand the transmission process and impact of this supply crisis in various countries and to identify countries with important statuses in the graphite trade, in this paper, we construct the global natural graphite trade network. We set each country as a source of crisis transmission. Then, a computer is used to simulate the spread of the crisis through a cascade failure process, and the influence of these source points is analyzed in the real network. The results show that China, the United States, Germany, and India, have larger impacts. Moreover, the scope of influence of a country is related to its region of the country in addition to its GDP and its export volume. Second, the entire cascade propagation process emerges with the diffusion characteristics with which it first begin to spread in the same region. Most of the directly infected countries are in the same region as the initial outbreak country. In addition, by simulating the changes in the avalanche size when China's graphite export volume declines by varying degrees, we find that China has a strong impact, and a small reduction in its export volume can affect the graphite trade in certain countries. Finally, based on differences in resource endowment, economic strength and the consumption in different countries, relevant policy suggestions are proposed for different countries.

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.