Blockmodel Analysis Research Articles

Exploring global embodied carbon emissions transfer network—An analysis based on national responsibility

This paper explores the global embodied carbon emissions transfer network. To clarify the carbon emissions reduction responsibilities of each country globally, a complex interdependent network structure based on the 2016 World Multi-Regional Input-Output (MRIO) table and the transfer of production factors between industries is constructed. Using input-output and social network analyses, the structural features of the global carbon emissions flow network is uncovered, and then carbon inequality among countries from a multi-regional perspective is revealed. The research findings show that the scale of global embodied carbon transfer expands continuously. The direction of transfer exhibits asymmetry, regionalism, and complexity. Hungary, Romania, Slovenia, Iran, Ethiopia, and Iran are the main carbon-exporting countries, while the United States, Germany, the United Kingdom, Japan, Canada, Spain are the main carbon-importing countries. Furthermore, the block model analysis reveals that the carbon transfer and transmission effects between global regions tend to be centralized clusters, from “demand-driven” to “internalized supply-demand coexistence”. There is significant uneven exchange between wealth benefits and environmental burdens among blocks.

Cross-market risk spillovers among sovereign CDS, stock, foreign exchange and commodity markets: An interacting network perspective

With the globalisation of the economy and financial markets, cross-market risk spillovers have become increasingly prominent, affecting global financial stability. Using the Diebold-Yilmaz Connectedness Index (DYCI) method, we construct interacting networks to explore these risk spillovers across four multi-country markets: sovereign credit default swap (SCDS), stock, foreign exchange, and commodity markets. We find: First, at the system-level, multiple inter-layer connections between the SCDS and other markets demonstrate the high level of cross-market risk spillovers. They are sensitive to major economic and financial events, such as the COVID-19 pandemic and Russia-Ukraine conflict. SCDS has the closest spillovers with stocks. The block model analysis shows that all relationships between various blocks contain inter-layer connections, and the SCDSs of emerging countries and stocks of developed countries are the sources of risk spillover in the entire system. Second, at the country-level, SCDS' cross-market spillover strength changes by country and time. Five cross-node centrality analysis shows that the SCDSs of emerging countries are the risk spillover engines between the SCDS and other markets. The spillovers of a country's SCDS to gold (oil) are more sensitive to the Fed rate hike in 2016 (the European debt crisis and Russia-Ukraine conflict). Third, to understand the driving factors, we perform time series and panel regressions for cross-market risk spillovers and cross-node centrality, respectively. The influences of economic fundamentals and market sentiment are asymmetric in high- and low-risk spillovers. The greater the total economic linkages, such as trade and capital flows, the greater are the countries' cross-market risk spillovers.

Features of Industrial Green Technology Innovation in the Yangtze River Economic Belt of China Based on Spatial Correlation Network

A generic phrase for technical and managerial innovation geared toward environmental conservation is “green-technology innovation.” It is essential to attain ecologically friendly development that promotes economic progress. Promoting the combined growth of the economy, society, and environment is extremely important. The industrial-green-technology innovation efficiency of 110 cities in the Yangtze River Economic Belt is calculated using the Sup-SBM model from 2011 to 2021 while considering undesirable output. The modified gravity model is then used to convert the attribute data of industrial-green-technology innovation efficiency into relational data. The Yangtze River Economic Belt uses the social-network-analysis (SNA) approach to investigate the geographical correlation-network properties of industrial-green-technology innovation efficiency. The findings demonstrate the following: (1) There is a rising trend in the degree of industrial-green-technology innovation efficiency between different cities in the Yangtze River Economic Belt, and this pattern is known as “three plates.” (2) The examination of network characteristics reveals an indigenous core–edge structure in space, with the network density of the Yangtze River Economic Belt displaying an increasing trend over the research period. (3) Individual characteristic analysis reveals that although the innovation-efficiency network tends to be flat, the degree centrality and closeness centrality of industrial-green-technology innovation efficiency in the Yangtze River Economic Belt indicate an upward trend over the research period. In addition, Chengdu in the upstream region, Wuhan in the center, and Shanghai in the downstream area serve as bridge and intermediary nodes in the spatial correlation network. (4) Block-model analysis reveals a close spatial link between blocks. A more complex and durable spatial link is now possible because of the spatial relationship of green-innovation efficiency in cities, which has shattered the boundaries imposed by traditional geographic space. The Yangtze River Economic Belt will be jointly promoted by several of the policy recommendations in this paper, aligning with that.

Spatial spillover and impacting factors of green development: A study based on China's provincial data.

Green development, an essential part of sustainable development transformation, is spatially correlated intra- and inter-regionally. However, previous research has not fully addressed the spatial characteristics of green development. This study investigates the spatial correlation structures, core-peripheral positions, and factors impacting the spatial network formation of China's green development. Based on the green development evaluation index system modified by the entropy method, this study applies social network analysis, block model analysis, and quadratic assignment procedure analysis to data from 30 provinces in China. The results confirm the spatial spillover effect is overwhelmingly present in China's green development. The findings further distinguish the core roles of provinces including Hunan, Tianjin, Zhejiang, Henan, and Xinjiang, and underline factors of green economic growth, governmental policy support, spatial adjacency, and geographic distance as significantly affecting the spatial network formation of China's green development. Policy recommendations for green development are then put forward.

A Study on the Spatial Correlation Effects of Digital Economy Development in China from a Non-Linear Perspective

Accurate analysis of the spatial correlation effects, spatial aggregation patterns, and critical factors in the development of China’s digital economy is of great significance to the high-quality development of China’s economy. Based on the monthly data of “The Tencent Internet Plus” digital economy index for 31 provinces in China from 2018 to 2020, the non-linear Granger causality test and social network analysis were applied to reveal the spatial correlation effects of China’s digital economy. The quadratic assignment procedure (QAP) was used to empirically examine the factors influencing the formation of non-linear spatial association networks. The results show that the spatially linked relationships of the digital economy in 31 Chinese provinces exhibit a significant non-linear spatially correlated network structure. Block model analysis reveals that the development of the digital economy between the four major sectors is closely linked, and the national linkage effect is significant. The results of the secondary assignment procedure indicate that capital stock, information infrastructure, and geographical proximity have a significant positive impact on the formation of spatial linkages in the digital economy. In contrast, technological innovation has a significant negative impact.

Coronary bioresorbable stents: Non-invasive quantitative evaluation of intra- and juxta-stent plaque composition-A computed tomography longitudinal study.

PurposeCoronary bioresorbable stents (BRS) do not produce blooming artifacts on computed tomography (CT), in contrast to metallic stents, as they are made of a bioresorbable polymer and are radiolucent. They allow to evaluate the coronary plaque beneath. The low-attenuation plaque (LAP) suggests plaque vulnerability and is CT assessable. The aim of our study was to show the possibility of a non-invasive CT evaluation of the volume and the LAP composition of the intra- and juxta-stent plaque.MethodologyIn our prospective longitudinal study, we recruited 27 consecutive patients (35 BRS stents total; mean age 60 +/- 9 years) with bioresorbable stents for a 256-slice ECG-synchronized CT evaluation at 1- and 12-months post stent implantation. Total plaque volume (mm3), absolute and relative (%) LAP volume per block in the pre- intra- and post-stent zones were analyzed; comparison 1- and 12-months post-implantation of BRS. Changes in the previously mentioned variables were assessed by the mixed effects models with and without spline, which also accounted for the correlation between repeated measurements.ResultsOur block or spline model analysis has shown no significant difference in plaque or absolute LAP volumes in pre- intra- and post-stent zones between 1 and 12 months. Interestingly, % LAP volume increases near-significantly in the distal block of the intrastent at 12-mo follow-up (from 23.38 ± 1.80% to 26.90 ± 2.22% (increase of 15%), p = 0.052).ConclusionOur study demonstrates the feasibility of the repeated non-invasive quantitative analysis of the intrastent coronary plaque and of the in-stent lumen by CT scan.

The structural change and influencing factors of carbon transfer network in global value chains.

Based on the gross trade accounting method and WIOD data, this paper quantifies the global carbon transfer from 2000 to 2014. The social network analysis and temporal exponential random graph model (TERGM) are employed to investigate the structural change and influencing factors of global carbon transfer network from a global value chains (GVCs) perspective. Results show that the deepening of the GVC division connection has enhanced the relations in global carbon transfer network, and the low reciprocity and high disassortativity lie in the difference in GVC division positions among economies. Block model analysis manifests that Plate Ⅱ acts as the engine plate in the network which consists of China and the other four economies. The GVC division connection and relative GVC division position significantly facilitate the formation of carbon emission transfer relations. Moreover, economies' attribute effects and endogenous network structure effects are non-negligible important determinants of network formation.

Spatial-Temporal Evolution Analysis of Carbon Emissions Embodied in Inter-Provincial Trade in China.

Under the support of Multi-Regional Input–Output (MRIO) analysis, this study constructs the Embodied Carbon Emission Transfer Network (ECETN) using the input–output tables of 42 sectors in 31 provinces of China in 2012, 2015, and 2017 and applies a series of complex network measurement indicators and analysis methods to describe its evolution features. The results show that the embodied carbon emission transfers between provinces generally narrow over time. With its high clustering coefficient and short average path length, ECETN has small-world characteristics and behaves sensitively, and changes in individual provinces can quickly spread and affect the entire system. In addition, the clustering effect and the spatial spillover structural properties of ECETN are explored based on the block model analysis. Finally, Quadratic Assignment Procedure (QAP) is used to analyze and quantify the contribution of provincial structural roles to ECETN, and it is found that spatial adjacency and differences in strength-in, strength-out, and betweenness centrality have significant positive effects, while differences in eigenvector centrality, clustering coefficient have significant negative effects. The restructuring of domestic trade can help achieve national emission reduction. These findings can provide more insights for the government to formulate future development directions and policies to reduce emissions further.

Analysis of OFDI Industry Linkage Network Based on Grey Incidence: Taking the Jiangsu Manufacturing Industry as an Example

Based on the outward direct investment data of each manufacturing industry segment in Jiangsu Province from 2000 to 2020, this paper establishes a correlation network by constructing a grey incidence model with the average value of absolute grey incidence as the threshold. We further analyze the relationship between each manufacturing industry segment in Jiangsu Province in the process of outward direct investment from two perspectives, namely, point and surface. The study shows that from the perspective of each node, the correlation coefficient between equipment manufacturing and other industries is significantly higher, i.e., the influence of equipment manufacturing on other industries is significantly greater. Chemical raw materials and chemical products manufacturing, general equipment manufacturing, special equipment manufacturing, and transportation equipment manufacturing are the important nodes in the network. From the perspective of the network as a whole, the Jiangsu manufacturing OFDI affiliation network is not concentrated. Still, it has small-world characteristics, which are conducive to disseminating information. In contrast, the close nature of the industry has more commonalities, leading to it being more easily divided into the same module in the network block model analysis.

Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China

This article uses the stock market regional indexes of 31 provinces (include Province-level municipalities and Minority Autonomous Regions) in mainland China as a sample, and constructs an inter-regional volatility spillover network of China’s stock market based on the GARCH-BEKK model. Through network centrality analysis, Diebold and Yilmaz's spillover index method and block model analysis, we comprehensively analyze the risk contagion effect among different regions in China’s stock market. The empirical results show that: (i) The risk contagion intensity (risk reception intensity) in various regions of China’s stock market has a typical “core-periphery” distribution characteristic due to regions’ different levels of economic development. (ii) There are obvious risk spillover effect in China’s stock market, among which the economically developed regions along the southeastern coast of China, such as Beijing, Shanghai, Zhejiang and Jiangsu, are the main risk transmitters, while the economically undeveloped regions in the Midwest of China, such as Xinjiang, Xizang, Gansu, Nei Menggu and Qinghai are the main risk receivers. (iii) Each region is divided into 4 blocks according to their respective roles in the risk spillover process in China’s stock market. Block 1 that is composed of the economically underdeveloped regions in the Midwest is the “main benefit block”, it acts as a “receiver”. Block 2 that is composed of regions with strong economic growth vitality in the Midwest is a “Bilateral spillover block”, it both plays the role of “receiver” and “transmitter”. Block 3 that is composed of developed regions along the southeast coast, it acts as a “transmitter”; Block 4 that is composed of the relatively fast-growing regions in the Southwest is the “brokers block”, it serves as a “bridge”. The results of this article can provide some reference for investors in financial institutions and decision makers in financial regulators.

China’s Spatial Economic Network and Its Influencing Factors

With the deepening of reform and opening-up, China’s economy has been further developed, but there is still a problem of uneven development. It is of great significance to completely construct China’s economic spatial correlation network, to clarify the role and status of each province in the whole network, and to study the influencing factors of the national spatial economic network. In this paper, we employ the network analysis method to analyze China’s economic development in the past 20 years. Based on the modified gravity model, we construct China’s spatial economic network and explore the network structure from three aspects: the whole network structure feature, characteristics of individual provinces in the network, and block model analysis. The results show that (1) China’s spatial economic network has strong internal cohesion, and the hierarchy of the network is becoming less and less obvious. However, the network density is low, and the overall network relationship still needs to be strengthened. (2) The different levels in economic development illustrate the obvious economic unbalance among provinces. (3) The block model analysis results demonstrated that coastal areas are more attractive to other provinces and are playing an important role in driving China’s economy. Finally, we employ Quadratic Assignment Procedure (QAP) regression analysis to analyze the influential factors on spatial economic network. Numerical results show that the geographic proximity and the differences in six factors (industrial structure, level of economic development, degree of opening to the outside world, medical level, size of labor market, and infrastructure) have significant impact on the spatial economic network. Moreover, the influence of these factors on the economic relation among provinces has been gradually strengthened in recent years.

The Social Psychology of Occupational Status Groups: Relationality in the Structure of Deference

Affect Control Theory (ACT) can predict the average deference that occupational identities receive from others. These “deference scores” can capture occupational status better than previous operationalizations of prestige. Combining this new measurement of occupational status with social network methods, this article explores the underlying relational patterns hidden within Freeland and Hoey’s (2018) scores of average deference. I construct a complete network of deference relations across 303 occupational identities using Bayesian ACT simulations. A blockmodel analysis of this network resulted in four positions within the occupational deference structure: everyday specialists, service-to-society occupations, the disagreeably powerful, and the actively revered. These are occupational classes that defer to the same occupational identities and receive deference from the same occupations. Exploring the reduced blockmodel provides a more complete depiction of the occupational status structure as measured by ACT.

Spatial correlation effect of China's outward foreign direct investment in countries along the One Belt and One Road

AbstractPromoted by both the “going out” policy and the “One Belt and One Road” (OBOR) Initiative, China's outward foreign direct investment (OFDI) has grown rapidly in the past two decades. A spatial network has gradually formed that facilitates global production, sales, and innovation. This paper studies the spatial correlation of China's OFDI network along the OBOR and its determinants. The block model analysis suggests that China's OFDI spatial network comprises four gradient sections. In addition, the analyses of quadratic assignment procedure and exponential random graph models demonstrate a tendency of agglomeration of China's OFDI, which may hinder the further development of its spatial correlation networks. Finally, we present some policy suggestions stemming from the study in the hope of helping both the government and firms to optimize their OFDI spatial layout strategy along the OBOR.

King Saud Medical City Intensive Care Unit: A critical and cost-focused appraisal

Intensive care unit (ICU) cost analysis has not been extensively addressed in the Kingdom of Saudi Arabia. We have implemented cost analysis (2015–2016) at the largest polyvalent ICU of the Kingdom (King Saud Medical City). Our block model analysis assimilated both modified Therapeutic Intervention Scoring System (TISS) and Omega scoring points to evaluate the overall cost; while, specific utilization elements were included in such as medication, procedural, laboratory, radiology, physiotherapy, nursing/physician, and overhead/other costs. The overall cost (Saudi Riyals [SAR]/ICU patient/day) averaged for TISS/Omega scores and adjusted for 2015–2016 inflation rates was approximately 23.269 (TISS: 167 points; Omega: 173 points generating predictive costs scores which were approximating the aforementioned score [R2 validated 0.91 and 0.90, respectively, all P

A numerical study on permanent seismic deformation of dry sand slopes

The serviceability of slopes after earthquakes becomes a design concern when footings are founded near the slope crest. The previous studies focused on the sliding block model and numerical analysis of particular slopes or landslides to account for the permanent post-earthquake deformation. In this paper, the post-earthquake deformation of dry sand slopes is calculated by numerical analysis utilizing the finite element method. An artificial earthquake time history is used as the source of the seismic excitation. Different slope heights, surface surcharges, and peak accelerations are investigated. The seismic waves' propagation through the soil mass is also investigated. The results are presented in the form of equations and charts that enable the design engineer to estimate the permanent seismic deformation of dry sand slopes, based on the slope height, the external surcharge, and peak crest acceleration (PCA). In addition, the safe setback distance for footings near slopes is recommended.

Stochastic block model and exploratory analysis in signed networks.

We propose a generalized stochastic block model to explore the mesoscopic structures in signed networks by grouping vertices that exhibit similar positive and negative connection profiles into the same cluster. In this model, the group memberships are viewed as hidden or unobserved quantities, and the connection patterns between groups are explicitly characterized by two block matrices, one for positive links and the other for negative links. By fitting the model to the observed network, we can not only extract various structural patterns existing in the network without prior knowledge, but also recognize what specific structures we obtained. Furthermore, the model parameters provide vital clues about the probabilities that each vertex belongs to different groups and the centrality of each vertex in its corresponding group. This information sheds light on the discovery of the networks' overlapping structures and the identification of two types of important vertices, which serve as the cores of each group and the bridges between different groups, respectively. Experiments on a series of synthetic and real-life networks show the effectiveness as well as the superiority of our model.

Exploring the structure of international technology diffusion

Purpose– This paper aims to provide a macro perspective on diffusion structure research, and to investigate the deep structure of international technology diffusion and structural differences between technology diffusion networks. This work also provides an understanding of the nature of globalization. Globalization has highlighted changes in socioeconomics and is reshaping the world. However, when comparing endogenous factors, exogenous factors are complex and demonstrate themselves as network phenomena. These network phenomena compose themselves as neither sole nor independent units. Countries in the global network act interdependently, and heavily influence one another.Design/methodology/approach– This study utilizes social network analysis to investigate the structural configuration of international technology diffusion. This investigation uses a sample of 42 countries over the period from 1997 to 2008. The data set contains two categories: bilateral trade flow and aggregate R&D expenditure. Meanwhile, this study uses block model analysis to reveal a network structure, which can precisely illustrate a global network configuration.Findings– The findings not only illustrate the pattern change of diffusion from a cascade-like to radial-like structure, but also present the structural configuration of technologically advanced countries and their competitive positions.Practical implications– In the shift to a diffusive structure, time and space are represented in new ways. Therefore, radial-like diffusion structure can provide some technological development approaches for countries interested in exogenous effects for technological growth and managing their international relation.Originality/value– This study is the first to use a multilateral perspective and longitudinal data to examine a cross-country network structure, to provide an understanding of the nature of globalization, its conceptualization and how influence and effects are transmitted through the interconnectedness of international technology diffusion.

Technology exchange patterns in China: an analysis of regional data

There is much interest in technology exchange in the literature, but relatively little research directly provides understanding of technology exchange patterns at a regional level within a country. This study uses a unique dataset of Chinese patent licensing to study technology exchange patterns in thirty Chinese provinces since 2000. Our results indicate that five distinct technology exchange patterns have recently emerged in China: importers, exporters, self-sustainers, active generalists, and isolationists. To illustrate a refined map of these exchange patterns, we used a blockmodel analysis. We found two leading blocks in China that are active in technology exchange within and across blocks. Although many less-developed provinces have begun participating in regional technology exchange networks, the scale of technology exchange in these provinces is lower, and they are more active as net technology importers. Our results provide the basis for firms to form technology strategies and for governors to promote the growth of regional technology exchanges.

A blockmodel study of managerial hierarchies, verbal networks, and organizational culture in four hospitals

This study demonstrates and applies a social network methodology for studying the dynamics of hierarchies in organizations. Social network (blockmodel) analysis of verbal networks in four hospitals contrasted hierarchical and structurally equivalent partitions of the sociomatrices of frequent ties and perceptions of organizational culture. It was found that the verbal networks in these organizations follow a center periphery pattern rather than a hierarchical logic and that perceptions of culture vary more by verbal network than by formal hierarchy. The perceptions of culture of central groups in one organization are much like those of peripheral groups in another. In all four hospitals, structurally equivalent social networks are more important in predicting subcultures than are hierarchical groupings and hierarchy has a limited impact on the development of verbal networks. These findings suggest the value of an amoeba rather than a pyramid metaphor in interpreting the cultures and relational structures of organizations.

Mapping collaborative knowledge production in China using patent co-inventorships

Only a few cases of systematic empirical research have been reported investigating collaborative knowledge production in China and its implications for China’s national and regional innovation system. Using Chinese patent data in the US Patent and Trademark Office (USPTO), this paper examines the geographic variations in intraregional, inter-regional and international knowledge exchanges of China from 1985 to 2007. Degree centrality reveals that intraregional and international collaborations are the main channels of knowledge exchange for the provinces and municipalities of China while inter-regional knowledge exchange is relatively weak. Besides, over the two decades, the knowledge exchange network has been expanding (connecting an increasing number of provinces and countries), becoming more decentralized (increasing number of hubs) and more cohesive (more linkages). A blockmodel analysis further reveals that the inter-regional network of China begins to show characteristics of a core-periphery structure. The most active knowledge exchange occurs between members of the core block composed by the most advanced provinces while the members of the peripheral block from less favored regions have few or no local and extra-local knowledge exchange. Building a strong knowledge transfer network would much improve the innovation capacities in less favored regions and help them break out from their “locked-in” development trajectories.