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Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network. [ABSTRACT FROM AUTHOR]

As a project construction organization model, whole-process engineering consulting (WPEC) is currently being promoted in China's engineering consulting industry. However, various factors create numerous obstacles that hinder this model's effective promotion and application. One essential task is to deeply analyze the transmission mechanism of the influencing factors in the promotion and application of WPEC, and explore effective pathways for promoting and applying this model. This paper systematically organizes and summarizes 10 primary influencing factors and 38 secondary influencing factors for promoting WPEC. Using the complex network analysis method, a network of influencing factors for promoting WPEC, a network structure, and motif analyses are conducted. The results show that the influencing factors have a complex transmission mechanism, and complex network analysis can reveal the laws of their construction and their causes. This paper provides a systematic basis and theoretical support for the effective formulation of policies related to the promotion and application of WPEC. [ABSTRACT FROM AUTHOR]

As a special branch of global trade, the trade of agricultural products has an important impact on food security and the environment. In this paper, we studied international trade network of agricultural products from 2000 to 2016 as a whole and in part. We explored the overall characteristics of the network, analyzed the evolution of communities and identified core countries of the communities. The results show that the structure of the trade network became increasingly complex and the trade relations became closer over time. There were four major communities in the network, whose primary core countries were Germany, the United States, Brazil, and China. Since 2007, the community represented by China has disappeared, and the community pattern of the network has been in a three-pillar state and basically stable. We discuss the actual roles of certain trading countries, the formation of communities and the impact of economic events on agricultural products trade. This paper reveals the underlying patterns of the agricultural products trade and provides a way to track its evolution over time. [ABSTRACT FROM AUTHOR]

Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China's coal transportation model. To address this issue, this paper first establishes a static topological structure of China's coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China's coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage. [ABSTRACT FROM AUTHOR]

With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China. [ABSTRACT FROM AUTHOR]

Improving the carbon sequestration capacity (CSC) of an ecosystem by optimizing urban ecological networks is one of the effective ways to achieve the goal of "carbon neutrality" in the world. The contradiction between the irreplaceable ecological function and economic development of Yancheng City is prominent. Therefore, taking Yancheng City as an example, this paper adopted the morphological spatial pattern analysis–minimum cumulative resistance (MSPA-MCR) model to establish the ecological network of Yancheng City in 2020 and combined it with complex network theory to evaluate its ecological base, network quality, and CSC. The results show that the ecological network of Yancheng City has obvious characteristics of coastal cities. There is a significant positive correlation between CSC and the clustering efficiency of ecological sources, and improving the clustering efficiency of vegetation and water ecological nodes is conducive to enhancing the CSC of ecological networks. In terms of functional restoration of ecological networks, four types of 13 ecological stepping stones and 12 ecological corridors have been designed to strengthen the connectivity and balance of the network, and the improvement of network robustness before and after optimization verifies that the optimization scheme is reasonable and effective. This study improved the optimization method of ecological networks in Yancheng City based on enhancing the CSC of ecological nodes, which provided a theoretical framework and practical reference for the realization of global strategic goals of carbon neutrality. [ABSTRACT FROM AUTHOR]

China experiences a serious shortage of soybean supplies and relies heavily on international trade with high vulnerability and large uncertainty, which maybe sows food security risks. It is of great significance to analyze the structural evolution of the global soybean trade network and its implications to China for ensuring food security. This paper constructed a global soybean trade network (GSTN) and analyzed the structural evolutionary characteristics of GSTN from 2000 to 2020 using the complex network analysis method and simulated the impact of targeted destruction on China through scenario analysis. The results showed that GSTN was gradually complex exhibiting a small word and a scale-free network property. The global soybean exporter was dominated by some major soybean-producing countries in America. The US played an important role in maintaining GSTN's robustness. China was the world's largest soybean importer; unfortunately, its soybean imports relied heavily on a few countries, and the anti-interference ability of China's soybean trade tended to decline. Therefore, China's soybean trade was increasingly vulnerable to being tightly controlled by other countries when some uncertain factors occurred, such as trade frictions and changes in policy decisions from importing and exporting countries. The US and Brazil were key countries with significant soybean trade ties to China. To assess the impact of the two countries on China's soybean trade, targeted destruction method was used through destroying them in the network. Targeted destruction scenario analysis indicated the two countries played important roles in the anti-interference ability of China's soybean trade. Brazil played a positive role in China's control of soybean trade flows, while the US did not. Some policies for China's soybean production and international trade were proposed. A balance between the domestic production and import of soybean is needed. Optimizing the soybean trade import system and seeking more trade partners is crucial. Improving soybean self-sufficiency is the fundamental way to reduce the high-import dependence. The study provided some insights for coping with international market fluctuations and improving the sustainability of China's soybean trade. [ABSTRACT FROM AUTHOR]

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China's railway network. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O3 in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O3 pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O3 network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O3 area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O3 between each area revealed that the impact of O3 on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O3 governance. [ABSTRACT FROM AUTHOR]

Based on the complex network of industry space, this paper investigates the industry structure optimization in Jiangxi Province in China. For the prefecture-level cities in Jiangxi Province, the location quotient is applied to obtain the specialized sub-industries. The conditional probability is used to analyze the interdependencies between industries. The complex network of industry space is presented to illustrate the relatedness among industries and analyze the space and structure of the regional overall industry. The index of transitional potential is proposed to measure the potential of a non-specialized industry which will become a specialized one in the future. The transformation difficulty index of industry is obtained to analyze the difficulty level of the transformation of the original specialized industry set to a new specialized one of a city. The results show that more industries have positive than negative interdependencies with one another in Jiangxi Province; rare industries specialize in cities at higher-level GDP, and common industries dominate the cities at lower-level GDP; it is more difficult for rarer non-specialized industries to become specialized ones, more common non-specialized industries can become specialized ones more easily, and the cities with higher GDP can transform industries more easily. By discussing the industries that have positive interdependencies with existing specialized industries, the detailed direction of industry structure optimization is analyzed. Finally, the policy implications are proposed, and these can be applied to other developing countries. • The conditional probability is used to analyze the interdependencies between industries. • The complex network of industry space is presented to illustrate the relatedness among industries. • The index of transitional potential is used to measure the potential of a non-specialized industry becoming specialized. • The transformation difficulty index is the difficulty level of the transformation of the originally specialized industry. • The industry structure can be optimized by developing the industries that have positive interdependencies with existing specialized industries. [ABSTRACT FROM AUTHOR]

The original intention of virtual water trade (VWT) is to help water-scarce areas adjust the crop trade structure to alleviate the water shortage problem. However, China's existing virtual water trade (VWT) must effectively alleviate the problem. This paper's structural features and stability of the VWT network (VWTN) in China's major crop trade between 2000 and 2017 were characterized using complex network theory. The results in terms of time scale showed that the total content of VW in China's major crops increased by ~23.6% for 18 years. Trade relations among most regions remained stable, whereas a few areas changed: Jilin, Henan, and Heilongjiang played essential roles in the output network, and so did Guangdong, Shanghai, and Fujian in the input network. Attributed to policy adjustment, Henan and Jilin became more prominent in the output network with the gradual decline of Jiangsu's position. Regarding spatial scale, the cumulative distribution of degrees consistent with the power–law relationship showed high variability and vulnerability of China's VWTN, especially when nodes were weighted. Since areas with more VWT partners/content provided connectivity to those with fewer partners/content, it is worthwhile to focus on developing protection policies for critical areas. The virtual water trade from North China (water-poor) to South China (water-rich) is contrary to the distribution of water resources. A similar situation showed in a global world that the long-term supply relationship would aggravate the water resources shortage and food security. Furthermore, we suggest combining network theory and VWT to lay the foundation for the invulnerability research of VWTN and the optimal regulation of crops. [ABSTRACT FROM AUTHOR]

• A practical and feasible protection strategy for EC maintaining in highly urbanized areas was proposed. • Key patches for EC maintaining were identified by using stepping stone and complex network theories. • The protection rate for key patches by the new policies were assessed. Maintaining and restoring ecological connectivity (EC) are important measures of mitigating habitat isolation caused by urbanization. Considering the prominent contradiction of land use in highly urbanized areas, the EC protection measures proposed in the previous studies, such as expanding the ecological sources and widening the ecological corridors, are unrealistic. By using the stepping-stone and complex network theories, this paper simulated the ecological network (EN), evaluated the centrality of each ecological source and stepping-stone, and analysed the network robustness under different damage scenarios, then identified the optimal strategy and key patches for EC maintaining. Meanwhile, this paper evaluated whether the key patches are protected by China's new policies, which are, Ecological Protection Red Line (EPRL) and the Three Lines One Permit (TLOP). The results were as follows: (1) The EN is composed of 53 ecological sources and 291 stepping-stones, with 591 links between them. (2) The network stability can be optimally maintained by prioritizing the protection of stepping-stones with high centrality. (3) The 53 ecological sources and 51 stepping-stones with the highest centrality can ensure the stable operation of the EN, that is, they are key patches for EC maintaining. (4) Compared with EPRL, the protection rate for ecological sources and stepping-stones are significantly improved by TLOP; however, stepping-stones with high centrality require stronger protection. This study enriched the research cases of complex network theory applied in landscape ecology, and provided references for the improvement of policies in the future. [ABSTRACT FROM AUTHOR]

Various construction accidents are proven to be caused by multiple unsafe behaviors (e.g., wrong use of PPE), but the risk transmission among different behaviors remains unclear. This paper provides insight into risk transmission through behavioral risk chain that leads to accidents from a system safety perspective. To better understand the coupling mechanism of various unsafe behaviors, integrate different behavioral risk chains and present the risk transmission process, a directed-weighted complex network (DWCN) method was adopted. Historical urban railway construction accidents in China are investigated to extract behavioral risk chain. A DW-BRCNA is applied to integrated behavioral risk chain and the behavioral risk transmission characteristics are explored and clarified by the five network properties, including degree and degree distribution, node strength and node strength distribution, average path length and diameter, weighted clustering coefficient and betweenness centrality. The results show that DW-BRCNA has the characteristics of a small-world, scale-free and hierarchical network, indicating that some unsafe behaviors are of greater importance in the process of risk transmission through behavioral risk chains. In addition, risk transmission in critical behavioral risk chains is more potentially to lead to accidents. This study proposed a new perspective of accident causation analysis from risk transmission among unsafe behaviors. It explains the risk transmission characteristics by a DWCN method based on behavioral risk chains. The findings also provide a practical guidance for developing control strategies on sites to prevent risk transmission and reduce accidents. [ABSTRACT FROM AUTHOR]

In this paper, the complex network of the urban functions in Shenzhen of China under the lockdown of the corona virus disease 2019 (COVID-19) is studied. The location quotient is used to obtain the dominant urban functions of the districts in Shenzhen before and under the lockdown of COVID-19. By using the conditional probability, the interdependencies between the urban functions are proposed to obtain the complex networks of urban functions and their clusters. The relationships between the urban functions, and the overall and cluster characteristics of the urban functions before and under the lockdown of COVID-19 are analyzed based on the complex networks. The mean degree and mean weighted degree of the primary categories of the urban functions are obtained to discuss the classification characteristics of the urban functions before and under the lockdown of COVID-19. Then, the differences and changes of the urban functions before and under the lockdown of COVID-19 are compared, and the corresponding policy implications under the lockdown of COVID-19 are presented. The results show that under the lockdown of COVID-19, the correlation of the urban functions is stronger than that before the lockdown; the common urban functions are more useful and essential, and finance, fine food and medical treatment are important; public service and government departments have the most positive relationship with other urban functions, and finance service has the highest spatial agglomeration distribution trend; and the cluster characteristics of urban functions are more related to people's livelihood, and the urban functions show incomplete and cannot be operated for long term. [ABSTRACT FROM AUTHOR]

In the double carbon background, riding the wind of new energy vehicles and the battery high nickelization, nickel resources rise along with the trend. In recent years, due to the influence of geopolitical conflicts and emergencies, as well as the speculation and control of international capital with its advantages and rules, the world may face price and security supply risks to a certain extent. Therefore, to obtain the most objective trade redistribution strategy, this paper first constructs the nickel material trade network, identifies the core trading countries and the main trade relations of nickel material trade, and finds that the flow of nickel material mainly occurred between a few countries. On this basis, a trade redistribution model is constructed based on the maximum entropy principle. Taking Indonesia, the largest exporter, and the largest trade relationship (Indonesia exports to China) as examples, the nickel material redistribution between countries when different supply risks occur are simulated. The results can provide an important reference for national resource recovery after the risk of the nickel trade. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and society, China's railway transportation system has been dramatically improved in terms of its scale and operational efficiency. To uncover the underlying relationship between urbanization and railway network structure, this paper examines the evolution of China's railway transportation system from 1999 to 2019 by applying complex network theory. The results show that China's railway network (CRN) has become more connected, more "small-world" and more heterogeneous since the beginning of the twenty-first century. Based on the train flow and train travel distance, the evolutionary course of CRN is found to undergo two apparent stages, with a turning point in 2007. By calculating the regional railway connection index (RRCI), it is revealed that the planned core cities in different regions act as bridges connecting the regions to the rest of the whole network. [ABSTRACT FROM AUTHOR]

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major "transfer out" type of land in the Xilingol area, while construction land and water area belong to the major "transfer in" type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol. [ABSTRACT FROM AUTHOR]

The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era. [ABSTRACT FROM AUTHOR]

Forest and grass ecological space is the key component of the ecosystem and plays a vital role in regulating the carbon, water, and energy cycle. The long-term exploitation of forest and grass ecological space and huge population pressure have gradually degraded the function of China's ecosystem. Therefore, forest and grass ecological space plays an important role in maintaining the stability of the ecosystem. The relationship between forest and grass ecospatial network structure and ecosystem service has been the focus of research. In this study, the forest and grass ecospatial network is constructed based on the minimum cumulative resistance (MCR) model. Then, the topological indicators (degree, weight clustering coefficient, node weight, unit weight, weight distribution difference, betweenness, PageRank) of the forest and grass ecospatial network were calculated by combining the complex network theory to analyze the relationship between these topological indicators and the three ecosystems (water retention, soil conservation, carbon storage). Based on the ecological significance of topological indicators, we identified ecologically fragile areas and proposed areas and directions for optimizing the ecospatial structure. Results show that the spatial distribution of the three ecosystem services in the southeast region of China is higher than that in the northwest region of China and shows a gradual decrease from the east to the west. The degree, node weight, unit weight, PageRank, and betweenness were highly significant and positively correlated with the three ecosystem services, among which PageRank had the highest correlation with water retention (p < 0.01, R2 = 0.835). Based on the spatial distribution characteristics of the different topological indicators, the quantitative relationship between the structural characteristics of the forest and grass ecospatial network and ecosystem services is clarified, revealing the intrinsic connection between ecological processes and ecosystem services. Through rational optimization of the forest and grass ecospatial network, ecosystem services can be effectively improved and ecosystem stability can be enhanced. [ABSTRACT FROM AUTHOR]

The lengthening of the global value chain (GVC) and the deepening of the transnational labor division system have led to frequent cross-border flows of metal values worldwide, and the pattern of the international metal trade is facing reconstruction. This paper combines a new trade accounting framework and a complex network model to construct global metal domestic value added (DVA) and foreign value added (FVA) flow networks based on global multi-regional input‒output (MRIO) tables for 2000 and 2007–2022 and then analyzes their structural topological features and spatio-temporal evolution trends at the macro, medium, and micro levels. Then, the main driving factors are investigated using the exponential random graph model (ERGM). The results show that the DVA and FVA networks are expanding and have obvious aggregation effects and small-world characteristics, that the community divisions show a trend toward regional integration and worldwide multipolarity, and that Germany is the most important transportation hub and trade medium in the FVA network, while China and the United States are, respectively, the largest creator and consumer of the world's metal value flow. In addition, the DVA network is more susceptible to node attribute effects such as GDP, trade openness, population size and foreign direct investment, while the FVA network is more susceptible to exogenous network effects such as geographic distance and colonial ties. [ABSTRACT FROM AUTHOR]

Previous studies on the water–energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China was calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the "dual saving" and "bidirectional saving" of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with "dual saving" effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the "bidirectional saving" effect of water and energy in China. The electrical equipment industry (El) in Jiangsu → El in Zhejiang, El in Zhejiang → El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network. [ABSTRACT FROM AUTHOR]

Second emission trading market is not active in China so far, and the reason mainly lies in that enterprises do not have strong initiative for transaction. Thus it's meaningful to study related guiding mechanisms from the perspective of enterprises behavior. In this paper, an evolutionary game model and a small-world network model considering stock heterogeneity are constructed based on four given guiding mechanisms, and a simulation study is conducted through China Zhejiang emission trading market. The result shows that: (1) the optimal guiding mechanism is dynamic and changes from reward-punishment for both enterprises with surplus stock and enterprises with insufficient stock to reward-punishment for enterprises with insufficient stock when trading preferential profit coefficient increases to 1. (2) when penalty coefficient changes, the optimal guiding mechanism is the reward-punishment for both enterprises with surplus stock and enterprises with insufficient stock and keeps unchanged. (3) the enterprises with insufficient stock are more sensitive to penalty coefficient and trading preferential profit coefficient, especially to the latter, so it's better to stimulate the emission rights purchasing side and formulate positive reinforcement methods. The study of this paper can provide decision-making suggestions for Chinese government to stimulate the activity of second emission trading market. [Display omitted] • Four guiding mechanisms are given based on implementation objects. • An evolutionary game model is constructed. • A complex network model is constructed considering stock heterogeneity. • The optimal guiding mechanism is simulated and dynamically analyzed. • The policy recommendations are highlighted. [ABSTRACT FROM AUTHOR]

As the passenger railway network is expanding and improving, the internal connections and interdependence in the network are rising. Once a sudden geological hazard occurs and damages the network structure, the train service is prone to large-scale halt or delay. A geographical railway network is modeled to analyze the spatial distribution characteristics of the railway network as well as its vulnerability under typical geological hazards, such as earthquakes, collapses, landslides and debris flows. First, this paper modeled the geographical railway network in China based on the complex network method and analyzed the spatial distribution characteristics of the railway network. Then, the data of geological hazards along the railway that occurred over the years were crawled through the Internet to construct the hazard database to analyze the time–space distribution characteristics. Finally, based on the data of geological hazards along the railway and results of the susceptibility to geological hazards, the vulnerability of the geographical railway network was evaluated. Among these geological hazards, the greatest impact on railway safety operation came from earthquakes (48%), followed by landslides (28%), debris flows (17%) and collapses (7%). About 30% of the lines of the geographical railway network were exposed in the susceptibility areas. The most vulnerable railway lines included Sichuan–Guizhou Railway, Chengdu–Kunming Railway and Chengdu–Guiyang high-speed Railway in Southwest China, Lanzhou–Urumqi Railway and Southern Xinjiang Railway in Northwest China, and Beijing–Harbin Railway and Harbin–Manzhouli Railway in Northeast China. Therefore, professional railway rescue materials should be arranged at key stations in the above sections, with a view to improving the capability to respond to sudden geological hazards. [ABSTRACT FROM AUTHOR]

After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network. [ABSTRACT FROM AUTHOR]

Based on dynamic monitoring data on China's population, by using complex networks, spatial analysis and mathematical measurement, this study reveals the spatial characteristics and influencing factors of the network of flows of highly educated talents in the Yangtze River Delta region from the national and local perspectives. In the two perspectives, the network has strong isomorphism and certain differences. The in-flow of highly educated talents from cities with high administrative levels and more developed economies to Shanghai constitutes the core of the entire network. From a national perspective, highly educated talents tend to converge to the Yangtze River Delta region. From a local perspective, it was found that these talents cluster towards a limited number of cities in the region. From both perspectives, the flow network has developed into a "core-periphery" progressive hierarchical structure, with Shanghai becoming the sole core city. There is little difference in the influencing factors of talent mobility from both macro and meso perspectives. Highly educated talents would frequently flow between cities with strong economic development levels, and cities with high education level, scientific and technological level, complete infrastructure, and good aesthetics. However, geographical distance still plays a hindering role in the flow of highly educated talents, and factors such as cultural identity, institutional, and social modality differences among regions also have a certain effect on the flow of these talents. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze the topological structure dynamics of the complex network of stocks before and after the outbreak of the COVID-19, so as to provide a basis for preventing financial risks. We calculate Pearson correlation coefficient between enterprises according to logarithmic rate of return and trading volume ratio of enterprises' stocks, and then constructed a complex network of stock market price and volume before and after the outbreak of the COVID-19. First, through thresholding and heat map imaging of the correlation matrix, the change characteristics of the correlation between various industries in 2019 and 2020 are studied. Second, the node degree, average weighted degree, graph density, clustering coefficient, and average clustering coefficient are used to study the topological structure change of the complex network of stock correlation. Third, the principle of node betweenness centrality is used to analyze the characteristics of a complex network after removing the core nodes. The research shows that, first, under the influence of the COVID-19 pandemic, the correlation among industries has the characteristics of industrial clusters, that is, the correlation in a industry is strengthened. In addition to banking, the correlation between industries has weakened, and the correlation between the banking industry and other industries has strengthened. Second, the node difference in betweenness centrality of core nodes in 2020 is higher than that in 2019, indicating that the network stability in 2019 is higher than that in 2020. These two points indicate that under the influence of the COVID-19 epidemic, the complex network topology of China's entire stock market has changed, and companies need to undertake countermeasures in the face of the crisis to effectively prevent and control systemic risks. [ABSTRACT FROM AUTHOR]

Background: The risk of comorbid chronic diseases in elderly people is an important problem affecting their health and quality of life. We analyzed the incidence of chronic diseases for combinations of chronic diseases analyzed. Methods: We used the original data to construct hypothetical cohorts of elderly individuals that evolved with age. The complex network was used to reduce the dimensionality of disease. The multistate transition model is used to calculate the incidence of each chronic disease, exploring comorbidity characteristics and rules. Results: (1) By using complex network, seven chronic diseases were screened out in men, including hypertension, diabetes, heart disease, stroke, chronic lung disease, arthritis and dyslipidemia; six chronic diseases in women showed significant comorbidity except chronic lung disease. (2) Incidence show differences in age and sex; incidence of chronic diseases generally increased with age. (3) The marginal risk increases with the number of basic chronic diseases associated with comorbidities. (4) When hypertension is present as a basic disease, its impact on the risk of other chronic diseases is much less than that of other chronic diseases. (5) When diseases occur as basic chronic diseases, hypertension–heart disease and diabetes–dyslipidemia are combinations that have the greatest impact on each other in men; hypertension–heart disease in women. Conclusions: The incidence of chronic diseases in patients who have chronic diseases and will form comorbidities differs from that in healthy states, and the related effects of different chronic diseases also differ. Among these conditions, hypertension is caused by a special mechanism. [ABSTRACT FROM AUTHOR]

To achieve the 2030 carbon peak target and promote the development of renewable energy, China introduced the renewable portfolio standard (RPS) and tradable green certificate (TGC) trading mechanism in 2017. However, for several years, its TGC market remained depressed. To solve this issue, this paper creatively introduced social influence and complex networks into the evolutionary game model between generators to analyze the evolution of power producers' behaviors, and to study the impacts of social influence, key institutional parameters, and network influence. The main results show that: (1) The positive intervention of public opinion is important to the vulnerable early TGC market, but its efficiency shows a marginal decline. (2) The evolution process can be accelerated through social interactions, and the strategies adopted by central firms in the network significantly influence the evolution result, especially when "TGC alliances" are formed. (3) According to the preference of social influence and profit, participants can be divided into two types: "changeable" (who favors interests), "firm" (who values social approval), and the latter has a greater market pulling effect. This paper provides implications for policymakers from new perspectives and extends the research scope of the RPS scheme and the TGC market. • Both economic and non-economic factors influence power producers' strategies. • Public opinions are important to strategy evolution. • Network topology plays a significant role in strategy evolution. • "Changeable" and "firm" producers have different market pulling effect. [ABSTRACT FROM AUTHOR]

Water transportation has always occupied a large proportion of China's transportation and has become the key to China's economic development. Water transportation is called "green transportation" in the industry due to the advantages of low transportation cost, high safety factor, and large capacity. However, water transportation has caused a great impact on the ecological environment of the waters for a long time, and solving the problem of sewage pollution from ships has also become an unavoidable problem. To control pollution from ships, it is essential to analyze the characteristics of ship domestic sewage discharge. In this study, a ship domestic sewage discharge complex network (SDCN) is established based on the ship voyage data to analyze the discharge characteristics in the Yangtze River. According to the topological analysis, the SDCN is a small-world network with the power-law degree distribution and superliner betweenness-degree correlation as distinguishing characteristics. The top five MDs with the highest in-vertex strength are Chaotianmen MD (S = 165,129,561), Tongling MD (S = 66,426,616), Maanshan MD (S = 62,087,158), Baqu MD (S = 59,964,550), and Shashi MD (S = 55,569,399), which indicates that these MDs receive a large amount of sewage. And the volume of domestic sewage between Chaotianmen MD and Baqu MD is the largest. The results can help us understand the discharge characteristics of ship domestic sewage and how they can be targeted to develop control measures for the countermeasures of "zero discharge" for inland ships. [ABSTRACT FROM AUTHOR]

Logistics security, as the lifeline of the economy connecting production, distribution, and consumption, holds a pivotal position in the modern economic system, where any potential threats like natural disasters or cyber attacks could have far-reaching impacts on the overall economy. With a unique large-scale logistics data set, logistics networks between cities in China are constructed. We thus identify communities of cities that have dense logistics connections in these networks. The cities in the communities are found to exhibit strong connections in the economy, resources, and industry. The detected communities are also aligned with the urban agglomerations mentioned in the guidelines reported by the National Development and Reform Commission of China. We further extend our analysis to assess the resilience of the city logistics networks, especially focusing on the influence of community structures. Random and intentional attacks are considered in our resilience analysis. Our results reveal that the city logistics networks are robust to the random attacks and are vulnerable to the intentional attacks on the nodes with dense links between and within communities. Our results not only deepen our understanding of the community structure and resilience of the city logistics networks but also provide insights on how to improve the efficiency and safety of intercity logistics. [ABSTRACT FROM AUTHOR]

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid. [ABSTRACT FROM AUTHOR]

Abstract Both the direct and indirect need of rare earths (RE) are performing important functions in our daily life with their wide use in a range of products. However, previous studies have focused primarily on the direct use of RE and neglected the indirect effect, especially the indirect RE flow between economies. In this paper, we built an embodied rare earths flow network based on the global multi-regional input-output analysis framework and complex network theory, of which the community structure and small world nature are analyzed using complex network theory. We find that world embodied rare earths link network is clearly divided into 2 communities and the network can reveal the small world nature characteristics. Moreover, China, Germany and USA are the three most important economies based on the comprehensive analysis of the network's degree centrality, strength centrality, betweenness centrality and eigenvector centrality on the basis of network centrality theory. Furthermore, we analyze China's embodied RE outflow paths and the degree of dependence on embodied RE for each economy. Finally, some meaningful policy implications are provided. [ABSTRACT FROM AUTHOR]

Supplier network collaborative efficiency evaluation is important content in the transformation and upgrading of intelligent manufacturing enterprises. Aiming at the shortcomings of existing methods, this paper proposes a new method to evaluate the collaborative efficiency of internal members of a complex supplier network based on complex network theory. Based on the analysis of the characteristics of the complex supplier network, from the perspective of the system, the macro supplier network is divided into multiple multi-level supplier micro subsystems with manufacturing enterprises as the core. In order to reasonably quantify the collaboration relationship of members in the subsystem structure model, the collaboration entropy is introduced as a measurement tool, and combined with the hesitation fuzzy scoring function, and the collaborative evaluation model of the complex supplier network is constructed. By quantifying the collaboration relationship among the members in the subsystem and summarizing it step by step and iteratively, the collaborative efficiency evaluation of the complex supplier network from local to overall is realized. Finally, taking a large battery manufacturing enterprise in China as an example, the proposed method is used to calculate the collaboration entropy, collaborative efficiency, and collaboration ratio of members at different supplier network levels. The results verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Given the national goal of "emission peaking and carbon neutralization", China has become the largest country in the world for offshore wind farm construction. At the same time, navigational safety problems in offshore wind farm waters have become increasingly frequent. Owing to the complexity of offshore wind farm waters and the small number of accident data samples available for reference, the system theory method is more suitable for selection than the traditional method. Based on causal analysis based on system theory (CAST) and a complex network (CN), in this study, a qualitative and quantitative accident analysis model, CAST-CN, is constructed to analyze a complete case of vessel and wind turbine allision in offshore wind farm waters. The results show that, at the micro level, in addition to the master, crew, shipping company, and typhoon Hato, the maritime safety administration and the wind farm operation management department have a certain impact on the development of the accident discussed in this study. At the macro level, internal and external factors leading to the lack of system safety are identified, and measures and suggestions for system safety improvement are proposed based on analysis. This study can fill the research gap in the systematic analysis of traffic accidents in offshore wind farm waters and provide support for the safety assessment and decision-making of government management departments and research institutes. [ABSTRACT FROM AUTHOR]

The rapid development of Internet technology has formed a huge virtual information space. In the information space, information flow has become a link of communication between objects. Information flow is an alternative or supplement to the traditional physical flow for the study of the spatial interaction of geographical entities. The research uses toponym co-occurrence and search index as information flow data, verifies the geographical laws hidden in the information space by spatial autocorrelation analysis and gravity model fitting, and analyzes the spatial interaction patterns of provinces in China in the information space by complex network analysis methods. The results show that: (1) information flow in the information space obeys Tobler's first law of geography and Goodchild's second law of geography. The spatial interaction represented by information flow has a distance decay effect. The best distance decay coefficients for toponym co-occurrence and the search index are 0.189 and 0.186, respectively. (2) The inter-provincial spatial interaction network of China shows a hierarchical pattern of the triangular primary network and diamond secondary network, and the ranking of provinces in the centrality analysis is basically stable, but the network hierarchy is deepening. The gravity center of spatial interaction is located in the east-central region of China. (3) The information flow-based interaction network is of higher asymmetry than the population mobility network, and its spatial structure is also obvious. This research provides a new idea for studying the spatial interaction of geographical entities in the physical world from the perspective of information flow. [ABSTRACT FROM AUTHOR]

Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel travel time, which is influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, and vessel personnel performance. The lack of consideration for these specific aspects limits the accuracy and applicability of current models. We propose a novel context-aware deep learning approach for inland vessel travel time prediction. Firstly, we introduce a complex network that captures vessel–vessel interaction contexts, providing valuable traffic environment information as an input for the deep learning model. Additionally, we employ a convolutional neural network to extract spatial trajectory information, which is then integrated with interaction contexts and indirect context information. In the vessel travel time prediction procedure, we utilize a long short-term memory network to capture the temporal dependence within consecutive channel sections' fused multiple context feature sets. Extensive experiments incorporating historical data from the Wuhan section of the Yangtze River in China demonstrate the superiority of our proposed model over classical models in predicting vessel travel time. Importantly, our model accounts for the specific traffic contexts that had previously been overlooked, leading to improved accuracy and applicability in inland vessel travel time prediction. [ABSTRACT FROM AUTHOR]

Our research contributes a new point of view on China's rare earth dynamic risk spillover measurement; this was performed by combining complex network and multivariate nonlinear Granger causality to construct the time-varying connectedness complex network and analyze the formation mechanism using the impulse response. First, our empirical research found that for the dynamic characteristics of China's rare earth market, due to instability, uncertainty, and geopolitical decisions, disruption can be captured well by the TVP-VAR-SV model. Second, except for praseodymium, oxides are all risk takers and are more affected by the impact of other assets, which means that the composite index and catalysts are main sources of risk spillovers in China's rare earth trading complex network system. Third, from the perspective of macroeconomic variables, there are significant multivariate nonlinear impacts on the total connectedness index of China's rare earth market, and they exhibit asymmetric shock characteristics. These findings indicate that the overall linkage of the risk contagion in China's rare earth trading market is strong. Strengthening the interconnections among the rare earth assets is of important practical significance. Empirical results also provide policy recommendations for establishing trading risk protection measures under macro-prudential supervision. Especially for investors and regulators, rare earth oxides are important assets for risk mitigation. When rare earth systemic trading risk occur, the allocation of oxide rare earth assets can hedge part of the trading risk. [ABSTRACT FROM AUTHOR]

An airport network forms the backbone of any air transportation system. The relationships among the origins and destinations of flights result in a complex network of routes which can be complemented with information associated with the routes themselves, for instance, traffic load and distance. In this paper, we modeled the Australia's civil domestic airport infrastructure as a network and analyzed the resulting network structure and its features using complex network tool. This case study identifies and investigates complex network measures, such as the degree distribution, characteristics path length, clustering coefficient and centrality measure as well as the correlations among them to understand the topology of an airport network. This analysis of the Australian Airport Network (AAN) indicates that it has a cumulative degree distribution described by the power-law function. As it has an average path length of 2.90, it is considered to have small-world properties. It is also found that it has a clustering coefficient of 0.50 which is higher than that of a random network of the same size which indicates that the transitivity and cohesiveness of AAN is different from a random network. In contrast to the World-wide Airport Network (WAN), the AAN is found to have disassortative mixing similar to the airport networks of China and India. [ABSTRACT FROM AUTHOR]

• Build a social network based on the relationship between rural residents. • Propose a rural clean heating acceptance model based on social network. • Simulate the interaction about attitudes towards clean heating between residents. Encouraging rural residents to adopt clean heating is an inevitable demand necessitated by the consolidation and improvement of air pollution control effects. In present-day China, clean heating is mainly promoted by the government via fiscal subsidies, but rarely embraced by rural residents on their own initiatives. As clean heating information propagated by social networks greatly affects the acceptance of clean heating among rural residents, social networks have great potential in promoting clean heating in Chinese rural areas. Relying on the relationships between rural residents in a typical village, a novel model called ACHRA is constructed propagating the acceptance of clean heating in this paper. Further, the propagation paths of clean heating information and the mutual influence between rural residents are simulated. The results indicate that the initial acceptance attitude of rural residents and the weight of influence between them affect the overall developmental trend of the acceptance of clean heating in social networks. For networks with low centralization, it is necessary to widely publicize the benefits of clean heating to fully utilize the group effects. For networks with medium centralization, core nodes in the social network should be particularly guided to maximize their influence on the propagation of clean heating acceptance. On this basis, it is suggested that publicity and guidance about clean heating should be brought into full play in Chinese rural areas. Inspiring village cadres and influential residents to pass on positive clean heating information to other residents should be paid special attention to. [ABSTRACT FROM AUTHOR]

The air traffic control (ATC) network's airspace sector is a crucial component of air traffic management. The increasing demand for air transportation services has made limited airspace a significant challenge to sustainable and efficient air transport operations. To address the issue of traffic congestion and flight delays, improving the operational efficiency of ATC has been identified as a key strategy. A clear understanding of the characteristics of airspace sectors, which are the building blocks of ATC, is essential for optimizing air traffic management. In this research, a novel approach using complex network theory was applied to examine the features and invulnerability of the airspace sector network. We developed a model of the airspace sector network by treating air traffic control sectors as network nodes and the flow of air traffic between these sectors as edges. Network characteristics were analyzed using several metrics including degree, intensity, average path length, betweenness centrality, and clustering coefficient. The static invulnerability of the airspace sector network was evaluated through simulation, and the network efficiency and the size of the connected component were used to assess its invulnerability. A study was conducted in North China based on the ATC sector network. The findings of the study revealed that the sector network did not exhibit the traits of a small-world network model, characterized by short average path lengths and high clustering coefficients. The evaluation of network invulnerability showed that the network's invulnerability varied depending on the attack strategy used. It was discovered that attacking sectors with high betweenness resulted in the most significant harm to network invulnerability, and betweenness centrality was considered to be a useful indicator for identifying critical sectors that require optimization. [ABSTRACT FROM AUTHOR]

Identifying vital nodes in complex networks is of paramount importance in understanding and controlling the spreading dynamics. Currently, this study is facing great challenges in dealing with big data in many real-life applications. With the deepening of the research, scholars began to realize that the analysis on traditional graph model is insufficient because many nodes in a multilayer network share connections among different layers. To address this problem both efficiently and effectively, a novel algorithm for identifying vital nodes in both monolayer and multilayer networks is proposed in this paper. Firstly, a node influence measure is employed to determine the initial leader of a local community. Subsequently, the community structures are revealed via the Maximum Influential Neighbors Expansion (MINE) strategy. Afterward, the communities are regarded as super-nodes for an iteratively folding process till convergence, in order to identify influencers hierarchically. Numerical experiments on 32 real-world datasets are conducted to verify the performance of the proposed algorithm, which shows superiority to the competitors. Furthermore, we apply the proposed algorithm in the graph of adjacencies derived from the maps of China and USA. The comparison and analysis of the identified provinces (or states) suggest that the proposed algorithm is feasible and reasonable on real-life applications. [ABSTRACT FROM AUTHOR]

The stock market is a complex system with unpredictable stock price fluctuations. When the positive feedback in the market amplifies, the systemic risk will increase rapidly. During the last 30 years of development, the mechanism and governance system of China's stock market have been constantly improving, but irrational shocks have still appeared suddenly in the last decade, making investment decisions risky. Therefore, based on the daily return of all a-shares in China, this paper constructs a dynamic complex network of individual stocks, and represents the systemic risk of the market using the average weighting degree, as well as the adjusted structural entropy, of the network. In order to eliminate the influence of disturbance factors, empirical mode decomposition (EMD) and grey relational analysis (GRA) are used to decompose and reconstruct the sequences to obtain the evolution trend and periodic fluctuation of systemic risk. The results show that the systemic risk of China's stock market as a whole shows a downward trend, and the periodic fluctuation of systemic risk has a long-term equilibrium relationship with the abnormal fluctuation of the stock market. Further, each rise of systemic risk corresponds to external factor shocks and internal structural problems. [ABSTRACT FROM AUTHOR]

The investigation of the systemic importance of financial institutions (SIFIs) has become a hot topic in the field of financial risk management. By making full use of 5-min high-frequency data, and with the help of the method of entropy weight technique for order preference by similarities to ideal solution (TOPSIS), this paper builds jump volatility spillover network of China's financial institutions to measure the SIFIs. We find that: (i) state-owned depositories and large insurers display SIFIs according to the score of entropy weight TOPSIS; (ii) total connectedness of financial institution networks reveal that Industrial Bank, Ping An Bank and Pacific Securities play an important role when financial market is under pressure, especially during the subprime crisis, the European sovereign debt crisis and China's stock market disaster; (iii) an interesting finding shows that some small financial institutions are also SIFIs during the financial crisis and cannot be ignored. [ABSTRACT FROM AUTHOR]

Exploring how rapid urbanization affects ecological functional vulnerability is a key issue in regional ecological risk assessment. This paper proposes a complex network approach to explore the response of ecological functional vulnerability to different urbanization scenarios from the perspective of stock and flow for the case study area of the Wuhan City in China. The paper aims to (1) establish a theoretical framework to explain how the urbanization process degrades ecological functions of the land system from a flow and stock perspective, (2) evaluate stock and flow changes of ecosystem service value triggered by land use change, and (3) explore the impact of different urbanization scenarios and different management costs on ecological functional vulnerability by combining two indicators, i.e., ecosystem service value change and ecological load entropy. Our results for Wuhan reveal that (1) non-ecological flows or degraded ecological flows are responsible for the increase in ecological functional vulnerability, (2) the total ecosystem service stock of the land system had slightly increased during 1990–2015, contributing to an enhanced ecological flow, (3) the critical points for maintaining the proper functioning of the land system are 80% or higher for cropland and 40% or higher for lake. These findings can provide critical references for the sustainable management of urban land system in Wuhan and other major cities in China. This study also suggest that the combination of ecosystem service value change and ecological load entropy can be used to comprehensively assess ecological functional vulnerability. [ABSTRACT FROM AUTHOR]

The location and grade setting of a timber logistics center is an important link in the optimization of the timber logistics system, the rationality of which can effectively improve the efficiency of the timber logistics supply chain. There is a long distance between the main forested areas in China, and more than 55% of the timber demand depends on imports. Research and practice of systematically planning timber logistics centers in the whole country have not been well carried out, which reduces the efficiency of timber logistics. In this paper, 47 timber trading markets with a certain scale in China are selected as the basis for logistics center selection. Based on their transportation network relationship and the number of enterprises in the market, combined with the complex network theory and data analysis method, the network characteristics of three different transportation networks are measured. After determining the transportation capacity indicator, the logistics capacity coefficient is measured based on the freight volume of each node. Then, the important nodes are identified, and each node is graded to systematically set up the timber logistics center. [ABSTRACT FROM AUTHOR]

• We investigate the transmission of AQI fluctuation by complex network theory. • AQI networks of various time periods display small-world effect. • It is observed that some extreme short-term AQI fluctuation can be controlled. • Complex network analysis contributes to the prediction of AQI fluctuation. • Policy recommendations for environment management are proposed. Although many works have focused on the issues related to Air Quality Index (AQI) from the perspective of statistics or economics, the dynamic and temporal characteristics of AQI time series remain unclear. The main reason is that AQI time series has both non-linearity and complexity, which cannot be characterized by conventional ways. The inherent transmission laws of AQI volatility are of great importance for environment monitoring and management system. Thus, this paper has endeavored to address the issue from the interdisciplinary perspective of complex network theory. First, we collect the annual and the total AQI data of three cities (Shanghai, Wuhan and Guangzhou) in China from January 1, 2015 to December 31, 2017. Then the coarse graining method is employed in combination with the theory of phase space reconstruction to convert AQI time series into complex network. The coarse graining method is used to transform yearly and total AQI time series into the corresponding symbol sequences. In accordance with the theory of phase space reconstruction, we adopt C C method and false nearest neighbor algorithm to estimate the optimal time delay and embedding dimension of AQI time series, respectively. The optimal time delay is integrated with the embedding dimension to map symbol sequences into AQI complex network. Based on complex network theory, we investigate the transmission of AQI fluctuation by the temporal characteristics and dynamics of yearly AQI networks (YAN) and total AQI networks (TAN), which include node strength, betweenness centrality and community structure. The empirical results show: (1) the AQI networks of various periods display small-world effect; (2) network evolution tends to be more complex and the modality in YAN has less alternative transformation paths than that in TAN; (3) the node strength and betweenness centrality in measuring the importance of nodes achieve a relative consistency; (4) the three cities have the same dominant fluctuation modalities both in YAN and in TAN; (5) the uneven distribution of transmission ability between clusters implies a preferential transmission whether in YAN or TAN. These findings not only help us to interpret the frequent volatility of AQI and control some extreme short-term AQI fluctuation, but also contribute to the reliability assessment of AQI and the prediction of AQI fluctuation. In summary, this paper not only reveals the temporal dynamic properties of AQI time series, but also provides some policy recommendations for environmental monitoring and management. [ABSTRACT FROM AUTHOR]

The street network is considered the skeleton of the city structure; it determines the efficiency and productivity of the city in that it acts like blood vessels transporting people, goods, and information. The relationship between street networks and economic development is an important research topic in urban geography. In recent years, complex network theory has been successfully used for understanding the characteristics of street network structure. However, researchers lack an analytical framework and methods for studying the relationship between the morphological structure of urban streets and the economic development level of cities. Accordingly, this paper proposes a methodological framework for first, quantitatively characterizing the urban morphological structure based on open street network data, and second, exploring the relationship between the morphological structure of the urban street and the urban economic development level. The proposed methodology was applied to 31 provincial capital cities in China. The results indicate that urban morphological structure can be quantitatively described by betweenness and closeness centrality extracted from street networks. Cities with similar structures have similar levels of economic development. Moreover, the results suggest a significant positive correlation between street network betweenness centrality Gini coefficients and cities' economic development levels, indicating that the street network may affect city productivity. This study makes two major contributions to the scholarly literature. Methodologically, the proposed framework provides technical and methodological support for a better understanding of the relationship between cities' economic development and urban street structure. Empirically, the demonstrated case study may guide decision-making involving regional development and the optimization of urban space. [ABSTRACT FROM AUTHOR]

• Exploring the interaction of high slope monitoring points based on complex network. • Improving the quantized evaluation of deformation series by topological parameters. • Proposing a complex network-based risk assessment method for high slope projects. With the continuous expansion of infrastructure coverage, construction is gradually shifting to remote mountainous areas, where high slopes are very prone to safety problems, such as landslides and collapses. Existing projects involving high slopes generally avoid engineering accidents by monitoring the deformation at discrete observation points on the slope. However, this approach tends to ignore the interaction between observation points and fails to assess the risk of excessive slope deformation from a systematic perspective. Existing methods rely mainly on the comparison between observed/predicted values and national standards/design requirements, which may lead to risk assessment bias and wrong decisions due to their subjective nature. In this paper, a method based on the analysis of observed data is proposed, by transforming high slope deformation time series into a complex network, which is then used to calculate and determine its network parameters, aiming to analyze the internal topology of the data and assess the potential risk of excessive slope deformation. China's Menghua Coal Transportation Railway was used as an example to verify the effectiveness of the method. The results showed that the method can reveal the systematic characteristics of the interaction between and influence of different observation points on the slope at the macro level and explore the topological characteristics within the deformation time series at the micro level. The relationship between the macroscopic and microscopic network characteristics and the environmental risk distribution across the high slope is established, which can accurately assess the deformation risk of high-slope construction from a systematic perspective and provide theoretical support for risk assessment and safety management in real projects. [ABSTRACT FROM AUTHOR]

The transfer of embodied exergy from one industry to another can be modeled using a network to help understand industrial characteristics and resource features of a specific area. In this paper, we build a weighted directed network of embodied exergy flows between Chinese industries based on the 135-sector input–output table from 2007. The density, connectedness and degree distribution are used to describe the overall performance of the network. The results show that the network has a low density and a high connectedness, indicating that the network has little influence on specific industries while individual industries have a strong impact on each other. The degree, weighted degree and betweenness centrality are used to determine the main industries in the network. Key industries are characterized by large degrees that play a key role in the network, of which the mining and washing of coal, the production and supply of electric and heat power, construction, and the rolling of steel have stronger influences on the network. Moreover, the production and supply of electric power and heat power, the manufacture of automobiles and farming are the intermediaries of the network. Finally, we use a block-modeling method to decompose the collection of 135 Chinese industries into seven communities. The community of coal-related industries is the core of the network, which transfers embodied exergy to every other community, whereas the community of steel-related industries is also important, forming the base of the rest of the network. [ABSTRACT FROM AUTHOR]

(1) Background: Eco−spatial networks play an important role in enhancing ecosystem services and landscape connectivity. It is necessary to study landscape structure optimization to achieve synergistic gains in network connectivity and ecosystem functionality. (2) Method: Based on remote sensing data, RS and GIS were used to evaluate the spatiotemporal changes in ecosystem services in China. Combined with complex network theory, the spatiotemporal evolution of China's ecological spatial network and its topological structure from 2005 to 2020 is discussed. Network function–structure co−optimization was carried out using the edge augmentation strategy. (3) Result: The "three River resource" has high water conservation and high soil and water conservation in southeastern hilly areas. There is strong windbreak and sand fixation in southeastern Inner Mongolia. In the past 15 years, there have been about 8200 sources and about 14,000 corridors. The network has the characteristics of small−world and heterogeneity. After optimization, 18 sources and 3180 corridors are added, and the network connectivity and robustness are stronger. Finally, five regions are divided according to the network heterogeneity and corresponding protection and management countermeasures are proposed to provide scientific guidance for the country's territorial space planning. [ABSTRACT FROM AUTHOR]