Assignment Procedure Research Articles

Study on spatial correlation and driving factors of industrial carbon emission in the Hefei metropolitan area based on SNA-QAP method

In this paper, the Hefei metropolitan area is selected as the research object to measure industrial carbon emissions in this area during 2010–2022. The main contribution is to deeply analyze the characteristics of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area with the modified gravity model and social network analysis(SNA), and to explore the driving factors of its formation with quadratic assignment procedure(QAP). It establishes the foundation for the Hefei metropolitan area to differentiated green city development policies. The results show that (1) The industrial carbon emission in the Hefei metropolitan area showed a fluctuating and rising trend, forming a strongly connected carbon emission network spatial patterna; (2) The overall structure of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area is stable, but the tightness of the spatial correlation relationship among cities is not enough and the hierarchy is evident; (3) Hefei and Huainan are the centers of the carbon emission spatial correlation network of the Hefei metropolitan area, the position of each city in the carbon emission spatial correlation network is obviously unbalanced; (4) Urbanization level, economic development level, technological innovation, level of opening up and industrial structure exert considerable influence on the establishment and evolution of the spatial correlation network pertaining to industrial carbon emissions within the Hefei metropolitan area.

A High-Speed Rail-Facilitated Evolution of the City-Network Structure in Less-Developed Regions: Evidence from Jiangxi Province, China

The role of high-speed rail (HSR) in facilitating the spatial restructuring of regional and urban networks has long interested scholars and policymakers. This study explored HSR-facilitated city-network evolution in less-developed regions through the example of Jiangxi Province, China. Applying social network analysis methods, the study calculated indicators of network association strength, density, centrality, and cohesive subgroups using HSR passenger flow data from Jiangxi’s counties and cities for the years of 2009, 2014, 2019, and 2024. The quadratic assignment procedure (QAP) method was utilized to measure the factors that influence of HSR passenger flow network in Jiangxi Province. Research on the spatial network structure of HSR passenger flow and its influencing factors to promote the sustainable development of less-developed regions via HSR construction was explored. The main findings are as follows: Jiangxi’s city-network structure experienced a substantial increase from 2009 to 2024 in network properties, including the degree of association, network density, and the centrality of cities. The structure of the subgroups remained relatively stable, with a few exceptions. Cities in different subgroups form close and strong connections with each other resulting from HSR. HSR has effectively shortened the time and space distances and enhanced the spatial links between cities. Five factors, including population size, geographic distance, spatial proximity, economic development, and administrative hierarchy, demonstrated significant impacts on the HSR passenger flow network in Jiangxi Province. As HSR services continue to expand throughout the less-developed regions, optimizing HSR’s network effects should focus on strengthening the driving role of central HSR cities, promoting cross-regional coordination, and integrating HSR fully with other transportation modes.

Active guidance systems in maritime evacuations: optimizing the lifeboat allocation for cruise ships

Abstract We study the evacuation of large cruise vessels under hazardous conditions. Based on a new sensor mesh technology, allowing wireless two-way communication also when electricity is lost, we propose an optimization-based procedure that provides real-time guidance to passengers. As a first step, we explore a lifeboat assignment procedure to illustrate some of the aspects linked to pedestrian evacuations on vessels and open the discussion for future work using operations research in a context where human behavior is part of the problem.

Traditional Chinese medicine trade among RCEP countries: structural characteristics and determinants.

With the increasing global focus on health and the growing popularity of natural therapies, Traditional Chinese Medicine (TCM) products, including extracts, crude drugs, and herbal preparations, are widely utilized as both primary and complementary medicines worldwide. The Regional Comprehensive Economic Partnership (RCEP), spanning 15 countries across East Asia, Southeast Asia, and Oceania, offers a vast market for TCM. However, limited research has been conducted on the complex trade relations among RCEP members. The structural features and nodes attributes are analyzed using Social Network Analysis (SNA). Influencing factors are studied through the Quadratic Assignment Procedure (QAP) model. We also compiled a list of medicinal plants among RCEP member countries and the main TCM export products. The scale of TCM trade among RCEP members has fluctuated over time, with a temporary spike during the COVID-19 pandemic, followed by a gradual normalization. The trade network does not exhibit small-world properties, indicating a relatively balanced trade relationship. Due to its resource advantages, China occupies a central role acting as a dominant producer and leading exporter. Vietnam's export performance has been excellent in recent years, with the highest annual growth rate. Emerging markets, such as Myanmar, warrant closer attention. Economic size and population significantly positively affect trade value, while geographic distance and land adjacency have no significant impact. Trade activity is positively influenced by cultural and linguistic similarities, and countries with higher levels of economic freedom tend to engage in more trade. Tonifying TCM products with antioxidant and immune-boosting properties are more widely recognized in international markets. RCEP has established an excellent trade platform for the export of TCM. The factors influencing TCM trade are predominantly long-term and structural, rather than being driven by the occurrence of any single, isolated event. Member states should strengthen collaboration in standardization, technology coordination, and knowledge sharing to establish a mutually beneficial trade ecosystem for TCM.

Response-Adaptive Randomization Procedure in Clinical Trials with Surrogate Endpoints.

In clinical trials, subjects are usually recruited sequentially. According to the outcomes amassed thus far in a trial, the response-adaptive randomization (RAR) design has been shown to be an advantageous treatment assignment procedure that skews the treatment allocation proportion to pre-specified objectives, such as sending more patients to a more promising treatment. Unfortunately, there are circumstances under which very few data of the primary endpoints are collected in the recruitment period, such as circumstances relating to public health emergencies and chronic diseases, and RAR is thus difficult to apply in allocating treatments using available outcomes. To overcome this problem, if an informative surrogate endpoint can be acquired much earlier than the primary endpoint, the surrogate endpoint can be used as a substitute for the primary endpoint in the RAR procedure. In this paper, we propose an RAR procedure that relies only on surrogate endpoints. The validity of the statistical inference on the primary endpoint and the patient benefit of this approach are justified by both theory and simulation. Furthermore, different types of surrogate endpoint and primary endpoint are considered. The results reassure that RAR with surrogate endpoints can be a viable option in some cases for clinical trials when primary endpoints are unavailable for adaptation.

Deconstruction and analysis of global biodiversity loss transfer network based on the social network analysis method.

Biodiversity is crucial for maintaining ecosystem stability and achieving sustainable development. However, global biodiversity loss is a common challenge faced by most countries. Therefore, based on the data from the International Union for Conservation of Nature (IUCN) Red List of Threatened Species and the Eora database, we used the multi-regional input-output (MRIO) model to calculate biodiversity loss in 188 countries. We constructed a global biodiversity loss transfer network from the binary and weighted perspectives and deconstructed the evolution characteristics and the factors influencing the network from the "relationship" perspective using social network analysis (SNA) and quadratic assignment procedure (QAP) method. The global biodiversity loss transfer network had a typical network structure with dense connections, demonstrating spatial correlation characteristics. The countries with top in- and out-degree centrality rankings were developed and large-scale emerging economies and developing countries in Africa, respectively, implying that the former are responsible for "importing" large amounts of biodiversity and transferring biodiversity loss to the latter. The block model analysis indicated that the transfer network was divided into different functional blocks, with biodiversity spillover effects. The QAP analysis revealed that the differences in geographical adjacency, per capita GDP, urbanization rate, environmental regulation, and agricultural land proportion explained 3.627% of the changes in the global biodiversity loss transfer network. Our results suggested that the relationships of biodiversity loss transfer among countries should be considered by policymakers to address biodiversity challenges. Therefore, governments should recognize the remote responsibility, reduce unsustainable consumption and production, develop sustainable trade, and make trade policies considering the transfer of biodiversity impacts.

Spatial Correlation Network Characteristics and Influencing Factors of Industrial Carbon Emission Efficiency of Cities in Pearl River Basin

China has experienced rapid development of urbanization and industrialization, and urban industrial carbon reduction has become the key to green and sustainable development. Exploring urban industrial carbon emissions and its spatial correlation characteristics is of great importance for promoting collaborative cooperation between cities and achieving the "dual carbon" goal. Considering 49 cities in the Pearl River Basin as research objects, the super slacks-based measure model was used to measure the urban industrial carbon emission efficiency from 2010 to 2020, and exploratory spatiotemporal data analysis was used to explore its spatial pattern. On this basis, we used social network analysis and quadratic assignment procedure （QAP） methods to explore the spatial correlation network characteristics and influencing factors of urban industrial carbon emission efficiency. The results indicated that the industrial carbon emission efficiency of the Pearl River Basin showed a downward trend. Additionally, a strong spatial imbalance and heterogeneity was observed. The Pearl River Delta Region always dominated the spatial correlation network of carbon emission efficiency, forming a multi-level composite network system with the core network as the primary component and the conventional network as the auxiliary component. Economically developed cities, such as Guangzhou, Shenzhen, and Dongguan have long been in the center of the network and have extensive connections with other cities, while playing an intermediary role. The level of urbanization, science and technology, and industrialization have an increasingly significant positive impact on the strength of the connection between urban industrial carbon emission networks. The Pearl River Basin industrial carbon emission efficiency network has significant individual differences and hierarchical characteristics. According to the individual centrality, network role, and clustering characteristics, combined with the corresponding influencing factors, the Pearl River Basin urban collaborative carbon emission reduction strategy was formulated. This study can provide reference for the formulation and implementation of regional low-carbon collaborative development strategies.

The correlation evolution and formation mechanism of energy ecological efficiency in China: A spatial network approach

Enhancing energy ecological efficiency is critical for addressing climate change and advancing green, low-carbon economic development. This study utilizes an innovative Super Epsilon-based Measure (EBM) model alongside a gravity model to evaluate the energy ecological efficiency across 30 provincial-level regions in China from 2011 to 2021. This research fills a critical gap in the literature by combining efficiency assessments with complex network analysis, offering a novel perspective on the spatiotemporal evolution and distribution of energy efficiency. The findings reveal that: (1) China's energy ecological efficiency exhibited a fluctuating growth trend over the study period, with significant regional disparities characterized by the pattern “eastern region > central region > western region.” (2) Overall network density, network correlation, and the number of network relationships maintaining relatively stable growth, with a growth rate of 28.93 %, 27.51 % and 28.89 %, respectively. However, the efficiency network's overall connectivity remains low, with fewer correlation lines in the western and central regions and higher network density in the eastern coastal areas. (3) Quantitative analysis using the Quadratic Assignment Procedure (QAP) method identifies key factors influencing network formation, such as urban openness, industrialization level, environmental regulation, economic development, geographical proximity, population quality, and resource endowment. Notably, every 1 % increase in the industrial agglomeration level will have a negative impact of −0.248 % on the energy ecological efficiency network. This research fills a gap in understanding the structural and dynamic aspects of China's energy ecological efficiency, offering novel insights that can guide policymakers in formulating strategies to enhance sustainable development.

Coupling coordination and spatial network characteristics of carbon emission efficiency and urban green innovation in the Yellow River Basin, China

Carbon emission and sustainable development have attracted global attention. Promoting urban green innovation (UGI) in the Yellow River Basin (YRB) will help in lowering the intensity of carbon emissions and improve the safety and sustainability. A SBM-DEA model was constructed to measure carbon emission efficiency (CEE) and the degree of coupling and coordination with UGI was calculated in 73 prefecture-level cities in the YRB. The spatial association network of CEE coupled with UGI is constructed by using a modified gravity model, social network analysis and the quadratic assignment procedure (QAP), to analyze spatial potential energy, network characteristics and clustering characteristics. The study found that: (1) The coupling coordination degree of CEE and UGI in the YRB shows fluctuating growth, mutual promotion and continuous coordinated development. (2) The spatial linkage between CEE and UGI is gradually close, and the potential energy of the spatial linkage increases year by year, with obvious spatial spillover effect, indicating that the radiation and influence between cities are gradually increasing. In contrast to the middle stream, the upstream and downstream regions show a higher percentage of spatial potential energy in the entire network, and their network structure is more intricate and robust. (3) The clustering patterns of the three major urban clusters are examined using the block model, exploring the positioning and functions of various cities in these urban conglomerations, which includes the net spillover, net benefit, two-way spillover and broker plate, so as to strengthen the connection and coordinated development between cities. (4) Factors such as spatial adjacency, industrial structure, population density, digital economy and urbanization level, and energy intensity significantly impact the spatial association network, along with temporal and regional heterogeneity. Therefore, tailored policies are needed in the YRB to strengthen collaboration between CEE and UGI, fostering the development of a circular economy and promoting sustainable development.

The impact of multiplex relationships on households’ informal farmland transfer in rural China: A network perspective

Over the last three decades, farmland transfers in rural China have significantly increased. Despite this rise, informal farmland transfers within social networks continue to be common in many villages, underscoring the need for a more nuanced understanding of their determinants. Existing literature extensively explores the influence of social networks on farmland transfers, it often overlooks the distinction between formal and informal transactions and primarily focuses on binary relationships. Limited data availability and constraints in econometric methodology have impeded quantitative estimation of the impact of farmers’ interactions on informal farmland transfers at the local levels. To address this issue, we developed a framework that integrates embeddedness theory and social interaction theory to uncover the mechanism between multiplex relationship and informal farmland transfers from a network perspective. Using survey data collected from all 341 households in a village in central China, we constructed both relationship networks and informal farmland transfer network among peasant households. We then employed the logistic regression quadratic assignment procedure (LRQAP) to quantify the impact of multiplex relationships and network structures on informal farmland transfers. The results reveal a significant positive correlation between multiplex relationships and informal farmland transfer behaviors among peasant households. Specifically, households are more likely to engage in informal farmland transfer through relationships characterized by emotional connections and reciprocal exchanges, rather than through relationships based on cooperative support. Furthermore, our findings indicate that multiplex relationships influence informal farmland transfer behaviors through network structure effects, such as reciprocity and preferential attachment, rather than through transitivity. These results highlight the importance of leveraging multiplex relationships to facilitate and regulate informal farmland transfers amid rural decline and land use change.

External comparator studies and the joint application of the estimand and target trial emulation frameworks

The estimand framework (EF) and the target trial emulation framework (TTEF) are two important frameworks that can inform the design and analysis of external comparator (EC) studies. The EF helps clarifying the quantity to be estimated (the “estimand”), especially regarding the handling of post-baseline intercurrent events which interfere with the existence or interpretation of endpoints. Similarly, the TTEF is instrumental for specifying key design components of a hypothetical randomized trial and identifying which of these an EC study can and cannot emulate. We offer considerations about the joint application of both frameworks by combining the five EF attributes— treatment, population, endpoint, intercurrent events, and population-level summary—with the seven TTEF components— eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcomes, causal contrasts, and data analysis plan. Any overlap is identified, as well as omissions and unique contributions from both frameworks. Furthermore, we highlight specific considerations when applying these joint elements to EC studies.

Work together, walk forward: Unveiling collaborative innovation networks and mapping paths to breakthrough innovation in electric vehicles

The electric vehicle (EV) industry has made significant progress in practice, yet persistent technology bottlenecks impede its continuous progress. Collaborative innovation has emerged as a promising strategy to tackle these bottlenecks. To explore collaborative innovation within the EV sector, this study constructed EV collaborative innovation networks using patent data from 2009 to 2023 and applied the quadratic assignment procedure (QAP) to explore the internal mechanism of EV collaborative innovation networks. Furthermore, fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate the configurations for the realization of breakthrough innovation. The results show that (1) state-owned enterprises, along with universities and research institutions are crucial within the EV collaborative innovation network. Among them, state-owned enterprises play the most critical role. (2) Geographical proximity and institutional proximity positively impact the formation of EV collaborative innovation networks, while the effects of organizational proximity are insignificant. (3) Two pathways are identified for the realization of breakthrough innovation of innovation actors, with all proximity variables acting as core conditions in these pathways. This research engages in a dialogue between collaborative innovation networks and breakthrough innovation and enriches relevant theories and literature on this topic. Besides, this study provides practical implications for innovation actors to select collaborative partners and take tailored pathways for the realization of breakthrough innovations in the EV industry.

Structure matters: Assessing the statistical significance of network topologies.

Network analysis has found widespread utility in many research areas. However, assessing the statistical significance of observed relationships within networks remains a complex challenge. Traditional node permutation tests are often insufficient in capturing the effect of changing network topology by creating reliable null distributions. We propose two randomization alternatives to address this gap: random rewiring and controlled rewiring. These methods incorporate changes in the network topology through edge swaps. However, controlled rewiring allows for more nuanced alterations of the original network than random rewiring. In this sense, this paper introduces a novel evaluation tool, the Expanded Quadratic Assignment Procedure (EQAP), designed to calculate a specific p-value and interpret statistical tests with enhanced precision. The combination of EQAP and controlled rewiring provides a robust network comparison and statistical analysis framework. The methodology is exemplified through two real-world examples: the analysis of an organizational network structure, illustrated by the Enron-Email dataset, and a social network case, represented by the UK Faculty friendship network. The utility of these statistical tests is underscored by their capacity to safeguard researchers against Type I errors when exploring network metrics dependent on intricate topologies.

Development and validation of a predictive model for end-stage renal disease in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) affects many populations. This study aims to develop a predictive model and create a nomogram for assessing the risk of end-stage renal disease (ESRD) in patients diagnosed with SLE. Data from electronic health records of SLE patients treated at the Affiliated Hospital of North Sichuan Medical College between 2013 and 2023 were collected. The dataset underwent thorough cleaning and variable assignment procedures. Subsequently, variables were selected using one-way logistic regression and lasso logistic regression methods, followed by multifactorial logistic regression to construct nomograms. The model's performance was assessed using calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Statistical significance was set at P < 0.05. The predictive variables for ESRD development in SLE patients included anti-GP210 antibody presence, urinary occult blood, proteinuria, white blood cell count, complement 4 levels, uric acid, creatinine, total protein, globulin, glomerular filtration rate, pH, specific gravity, very low-density lipoprotein, homocysteine, apolipoprotein B, and absolute counts of cytotoxic T cells. The nomogram exhibited a broad predictive range. The ROC area under the curve (AUC) was 0.886 (0.858-0.913) for the training set and 0.840 (0.783-0.897) for the testing set, indicating good model performance. The model demonstrated both applicability and significant clinical benefits. The developed model presents strong predictive capabilities and considerable clinical utility in estimating the risk of ESRD in patients with SLE.

A COMPARATIVE STUDY OF HEADWAY-BASED AND TRANSPORT SYSTEM-BASED ASSIGNMENTS OF PUBLIC TRANSPORT IN VISUM: THE CITY OF KRYVYI RIH CASE

Many researchers have explored public transport assignment methodologies employing transport modelling software. Nevertheless, there remains a gap in evaluating real-world public transit networks utilizing diverse assignment procedures within Visum software. This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. The three scenarios of the model were developed depending on the public transport assignment procedure: headway-based, transport system-based utilized to all links, turns, and major turns in the network graph, and transport system-based applied only to the links, turns, and major turns traversed by the active public transport lines. The model of the network comprises 13 transport systems, 7 transport modes, 27598 links, 10097 nodes, 83270 turns, 238 zones, 1748 connections for private transport, 3013 connections for public transport, 534 stops, 1165 stop areas, 1190 stop points, 130 lines and 218 line routes. The transport demand model encompassed 14 demand segments. Compared to the outputs of the model calculation using the headway-based procedure, in the scenarios with transport system-based assignment, passenger flows on rail tram lines significantly decreased. Also, the results of scenarios with transport system-based assignment showed that the passengers extremely use parallel streets for travel from an origin to a destination. The modelling outputs for an actual urban network illustrate that the choice of the shortest route in the transport system-based assignment is closely linked to the main urban roads. This circumstance could potentially result in a rise in the number of transfers needed to sustain accessibility to districts residing far from the central highway.

IMPACT OF THE OBJECTIVE ATTRIBUTE WEIGHTING ON FIVE POPULAR MULTI-CRITERIA DECISION-MAKING METHODS: AN EMPIRICAL STUDY

In a Multi Criteria Decision Making (MCDM) problem, it is rarely possible to optimize all objectives simultaneously, since they can be contradictory, ambiguous or may involve other types of inconsistencies or uncertainties. Therefore, when trying to choose from a number of available alternatives, a decision maker is expected to assign weights to attributes whose values are utilized to evaluate the alternative under consideration for ranking. Attributes can be qualitative or quantitative, and their weights can be assigned by the decision maker in a somewhat subjective manner or algorithmically. In this paper, the impact of attribute weighting approaches on the ranking results across a number of widely used MCDM methods are discussed. That is, it examines how different weighting methods affect the results on the same multi-criteria decision-making methods when making a rating. In doing so, consider five MCDM methods, namely, Evamix, Aras, Topsis, Vikor, Waspas, under three different objective attribute weight assignment procedures, namely, Critic, Entropy, and Standart Deviation (SD). Results indicate that, in some cases, the employed attribute weight-assignment mechanism influences the rating results more heavily than the MCDM method itself. In other words, different MCDM methods tend to yield similar results under the same weight assignment method whereas, the same method produces more distinguishable results under different weighting schemes.

A dynamic evaluation of an underground transportation system using image processing and centrality index computation

ABSTRACT The study of transportation networks covers a wide range of topics related to managing services and maintaining structures. In order to represent the topological structure of such a network, we associated a graph to a network. Then some different properties or attributes of the network can be managed as weights to be applied to the graph itself. In addition, different centrality indices can be computed in order to identify nodes or edges that are more important or more exposed to capacity changes (such as, for example, disruptive events). The purpose of this research is to provide a methodology for dynamically evaluating the significance of stations in a transportation network. This is accomplished through two distinct phases of activity. Firstly, we propose a dynamic analysis of the underground transportation system of the city of Milan, Italy. This transportation system is the mobility backbone of the city and counts four lines with 110 links and 107 stations, including 21 junctions (the transfer stations that connect the lines). Two data sets about passenger flows (both entering and leaving stations) are used to calculate the flows on links with a resolution of 1 min. The data sets refer to a week in 2018, without the changes in demand due to the pandemic scenario. Data are processed through an ad hoc written assignment procedure. Secondly, a centrality index is calculated by using passenger flows (entering, exiting, and on-segments) as weights of the underground graph. After maps reporting the outcomes of those calculations are drawn, an image comparison is carried out by using image processing tools and different aggregation intervals, in order to investigate how the importance or exposure of each station changes over time. The findings demonstrate that over time, indices vary by station, with junction stations naturally having the highest values. By increasing the observation interval from 1 min up to 30 min, index changes become progressively smoother, like an application of a low pass filter, suggesting that for certain applications (e.g. those concerning security), aggregating data could lead to misleading conclusions.

Value assignment and uncertainty evaluation for certified reference gas mixtures

The procedures used to assign values to certified reference gas mixtures and to evaluate their associated uncertainties, which are described in ISO 6143, and that were variously improved by Guenther and Possolo (Anal Bioanal Chem 399:489–500, 2011. 10.1007/s00216-010-4379-z), are further enhanced by the following developments: (i) evaluating and propagating uncertainty contributions derived from comparisons with historical reference gas mixtures of similar nominal composition; (ii) recognizing and quantifying mutual inconsistency (dark uncertainty) between primary standard gas mixtures used for calibration; (iii) employing Bayesian procedures for calibration, value assignment, and uncertainty evaluations; and (iv) employing state-of-the-art methods of meta-analysis to combine cylinder-specific measurement results. These developments are illustrated in examples of certification of two gas mixture Standard Reference Materials developed by the National Institute of Standards and Technology (NIST, USA). These examples serve only to demonstrate the methods described in this contribution and do not replace any official measurement results delivered in the certificates of any reference materials developed by NIST.

Patterns and determinants of patient sharing in end-of-life health services: Empirical evidence from the Italian National Health Service

ABSTRACT End-of-life (EoL) care is critical for cancer patients, who tend to have high service needs that are dispersed across organizations and different levels of care. Although EOL has been widely studied, little is known about the patterns of coordination among EoL health care providers and how they contribute to the care of cancer patients. This study adopts a network perspective to examine the complex patterns of patient sharing among health care providers involved in EoL care, using data on the use of EoL health care services by 266 cancer patients in a large Local Health Authority in Italy. We conducted a social network analysis of the structural properties of the emerging network and used logistic regression-quadratic assignment procedures (LR-QAP) to explore how characteristics of health care providers, their collaborative network, and their distances predict the likelihood of observing patient sharing relationships. Our results show that complementarities in terms of medical specialization and co-location of services positively predict the likelihood of cancer patient sharing. This probability is also positively related to the difference in terms of eigenvector centrality as well as the degree of network transitivity. We discuss the policy implications of our findings.

China’s agricultural trade networks and its influencing factors

ABSTRACT This study employs the Standard International Trade Classification (SITC rev.3) to categorize agricultural products and examines the overall and each type of agricultural trade networks in China. It utilizes Social Network Analysis and the Quadratic Assignment Procedure to explore pattern changes and identify influencing factors. The findings indicate that between 2000 and 2019, China’s agricultural trade networks experienced expansion in scale and density, with China positioned at the network’s centre. However, challenges exist, such as insufficient control and over-reliance on specific trading partners. Key factors influencing the networks include the business environment (BUE), regional trade agreements (RTA), and government governance capacity (GOC). Differences in BUE and GOC are negatively associated with the development of trade networks, while RTAs are positively associated with trade network connectivity. Economic and demographic differences are also positively associated with the closeness of trade networks. Based on these findings, policy recommendations are proposed to address the identified issues.