Search Results (42)

Intra-urban commuting plays a fundamental role in shaping urban spatial structure and daily mobility patterns. Existing studies have largely explained commuting flows using attribute-based or distance-centred approaches. Such approaches overlook the interdependent and relational nature of commuting within complex urban systems. This study constructs a subdistrict-level commuting network using anonymised mobile phone signalling data from Hangzhou, China, and a valued exponential random graph model (valued ERGM) to examine how commuting flows are generated through the interaction of network self-organization, local job-housing conditions, and multi-dimensional proximity. The results reveal strong endogenous dependence exemplified by reciprocal commuting ties. Employment agglomeration and public rental housing provision are associated with stronger integration of subdistricts within the commuting network, while high housing prices and certain residential amenities are associated with reduced inter-subdistrict commuting. Beyond geographic distance, metro connectivity, administrative affiliation, and social interaction are significantly associated with commuting flows. This study advances a relational explanation of intra-urban commuting and demonstrates the methodological value of valued ERGMs for analysing weighted urban flow networks. The findings have implications for integrated transport, housing, and governance strategies, particularly transit-oriented development, cross-jurisdictional coordination, and the strategic siting of affordable housing, aimed at promoting more locally embedded and sustainable urban mobility. Full article

The aim of this study is to identify the factors that shape the ability of producers in short food supply chains in Slovakia to utilize different types of distribution channels and to penetrate higher-demand markets. The analysis was based on a database compiled from a public SFSC platform, comprising 986 agri-food producers, 1434 points of sale, and 1908 producer–point of sale ties. The data were analyzed as a two-mode network using ERGM models. The results show that most producers remain tied to local direct sales, while access to more demanding channels and distant markets is concentrated among a small group of actors. The study shows that the functioning of SFSCs in Slovakia is strongly shaped by producer size, value added, and the form of production organization. Organic certification emerges as a key tool of product differentiation that enhances ability to access distant and urban markets, although its importance in a post-socialist context is highly dependent on market characteristics. Family farms are selectively able to supply distant markets, while cooperatives, despite their expected association with commodity-oriented production, are able to overcome capacity and logistical barriers within SFSCs, indicating the emergence of new collaborative structures and business models. Full article

This study investigates the evolution of the structures of China’s domestic intercity tourism information flow networks, an increasingly important issue in an information-driven society. Moving beyond prior research that primarily emphasizes urban node attributes and multidimensional distances, this study applies social network analysis to develop an integrated analytical framework that incorporates endogenous structural effects, exogenous network effects, node attributes, and similarity effects. Using tourism information flows in China as an empirical proxy, the study examines the mechanisms underlying the formation and persistence of intercity relationships within the country. The results indicate that the self-organization of microscopic network structures plays a significant role in both tie formation and persistence, particularly through reciprocity, cyclicity, and convergence. Notably, the effect of cyclicity reversed during the COVID-19 pandemic and changed direction from relationship formation to persistence. In addition, cultural distance (proxied by dialect distance), geographical distance, and institutional distance significantly inhibit both the formation and persistence of intercity tourism information flows. Changes in urban node scale and node similarity also exert significant influences on network evolution. This study deepens the understanding of the spatial structural dynamics of China’s domestic intercity tourism information flows and provides a conceptual basis for future research on the evolutionary mechanisms of tourism network structures within a domestic context. Its direct significance lies in promoting sustainable urban tourism development, network resilience, and adaptive governance of urban systems. Full article

The interdependence inherent in interbank networks amplifies vulnerability to systemic risk, particularly through correlated asset exposures during exogenous negative shocks. This study employs exponential random graph models (ERGMs) to reconstruct a bipartite network of asset-holding correlations based on the balance sheets of Chinese commercial banks from 2016 to 2022. The reconstructed network closely approximates the topological features of the actual banking system. We then introduce a novel framework for measuring aggregate network vulnerability, which incorporates bank size, initial shocks, interconnectedness, leverage, and asset fire sales to capture key channels of financial contagion. Our results indicate that the reconstructed network aligns closely with empirical data in both link structure and weight distribution. Furthermore, cumulative systemic vulnerability increases non-linearly with the severity of the initial shock and the discount depth of fire sales. For individual banks, indirect vulnerability driven by contagion via deleveraging and fire sales significantly exceeds direct losses from initial shocks. Systemic risk contributions are concentrated in large state-owned banks and nationwide joint-stock commercial banks, whereas the institutions most susceptible to risk shocks are predominantly small and medium-sized rural and urban commercial banks. Full article

Against the backdrop of China’s commitment to achieving carbon peaking by 2030 and carbon neutrality by 2060, inter-provincial carbon emissions form a complex interconnected spatial network—clarifying its operational mechanisms is crucial for optimizing regional carbon reduction strategies. Based on 2006–2021 data from 30 Chinese provinces, this study constructs the China Provincial Carbon Emission Spatial Correlation Network (CPCESCN) using a modified gravity model. Social Network Analysis (SNA) explores its structural characteristics, while motif and QAP correlation analyses identify endogenous structural and attribute variables. Innovatively integrating Exponential Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM), it investigates the network’s static formation mechanisms and dynamic evolution drivers. Results show CPCESCN has a stable multi-threaded structure without isolated nodes, with Jiangsu, Guangdong, Shandong, Zhejiang, Henan, and Sichuan as high-centrality core nodes with high centrality. GDP, green technology innovation, urbanization rate, industrialization rate, energy consumption intensity, and environmental regulations significantly influence network dynamics, with reciprocal relationships as key endogenous drivers. While geographic proximity still facilitates network formation, its impact has weakened notably, and functional complementarity has become the dominant evolutionary driver—based on the findings, policy suggestions are proposed, including deepening inter-provincial functional cooperation, implementing differentiated carbon reduction policies, and optimizing multi-dimensional low-carbon transformation systems. Full article

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

Grassroots and community-led initiatives are increasingly recognized as important actors of local development, yet their role of “local networkers” capable of co-designing and co-creating solutions remains insufficiently explored, particularly in post-socialist contexts. The aim of this empirical study is to evaluate the depth of participation and the patterns of co-design in the process of strategic planning in grassroots initiatives. The research draws on primary data from 106 grassroots initiatives. To examine stakeholder involvement, we construct six bipartite networks representing actor participation across distinct phases of strategic planning. These networks are analyzed using social network analysis to identify structural patterns, followed by exponential random graph models (ERGMs) to test hypotheses concerning actor-level characteristics such as income, commercial activities, community size, and experience with social innovation. The findings show that the core co-designers in all planning phases are the initiatives’ own communities and volunteers, who consistently dominate the planning, decision-making, and implementation processes. External actors—local governments, NGOs, activists, firms, and universities—participate selectively, mainly during initial information gathering, consultations, and project preparation. Overall, the study demonstrates that grassroots initiatives operate primarily as community-anchored civic networks, with external actors engaged pragmatically around specific collaborative tasks rather than across the full planning cycle. Full article

Against the backdrop of China’s “dual carbon” strategy and regional integration, enhancing green innovation efficiency (GIE) has become a core issue for the Yangtze River Delta Urban Agglomeration (YRDUA) in achieving sustainable and high-quality development. This study employs the Super EBM model to measure the GIE of 41 cities in the YRDUA from 2012 to 2022 and further integrates a modified gravity model with social network analysis to uncover the structural complexity and spatial directionality of its spatial association network. In addition, the Exponential Random Graph Model (ERGM) is applied to explore the formation mechanisms of the green innovation efficiency network. Results show the following: (1) GIE presents a fluctuating upward trend, with the mean rising from 0.747 in 2012 to 0.906 in 2022 and disparities gradually narrowing, but provincial gradients persist, implying potential “Matthew effect” risks. (2) Network density continues to increase, with S-density rising from 0.0061 in 2012 to 0.0335 in 2022; supporting and basic connections serve as key drivers of network complexity, whereas the significant decline of edge connections may weaken the network’s extensibility. (3) Node connections display preference and attachment, causing polarization; transitivity and triadic cooperation rise markedly, increasing by 41.89% and 40.86%, respectively, reflecting strong self-organization. (4) Reciprocity and agglomeration drive network formation, and economic and technological differences promote it, while disparities in innovation input and government roles vary across periods. Geographic distance hinders formation, though its effect is weakening. These findings enhance the methodological approaches to sustainability research and provide insights for optimizing regional cooperation and advancing green integration in the YRDUA. Full article

In recent decades, with the urgent need to move toward more low-carbon and sustainable development, green infrastructure (GI) has gained increased attention. Due to the complexity and uniqueness of GI, and involvement of numerous organizations, it is imperative to address it via inter-organization collaborative innovation effectively. However, few studies have explored the dynamic evolution of collaborative innovation in GI and how proximity mechanisms drive collaborative innovation (CI). To explore CI in GI, this study is based on 610 patents in the field of GI in China from 2010 to 2024 and uses social network analysis (SNA) to construct CI networks, applying the Exponential Random Graph Model (ERGM) to explore the driving factors of CI in GI, with a specific focus on the driving effect of multidimensional proximity on CI. The results show that the network density decreases from 0.153 to 0.033, and the average path length remains below 3.4, presenting typical small-world characteristics. Furthermore, our results demonstrate that multidimensional proximity drives the evolution of CI in GI. Among them, the positive driving effect of social proximity is the most significant, while the coefficient of geographical proximity decreased from 2.83 in the budding stage to 0.003 in the mature stage. Similarly, the coefficient of technical proximity decreased from 2.528 to 1.735, indicating that its driving effect gradually weakened. These results contribute to the theory implications regarding dynamic perspectives for complex CI relationships in GI, and provide useful guidance for effectively allocating resources and talent training. Full article

The digital economy (DE) serves as a crucial engine for breaking through technological stagnation at the low end and achieving carbon neutrality. However, existing studies predominantly explore the impact of the DE on local carbon reduction based on “attribute data”, with less focus on regional carbon collaborative reduction. This study employs a directed-weighted complex network analysis, using provincial panel data from China spanning 2012 to 2022, to characterize the evolutionary features of China’s Inter-regional Collaborative Carbon Reduction Governance Network (ICCGN). Using the Exponential Random Graph Model (ERGM) as an empirical test, the study explores how the DE facilitates collaborative carbon reduction. The results indicate the following: (1) The ICCGN demonstrates transitive triadic linkages, accompanied by increasingly blurred governance boundaries. The Eastern coastal areas have the highest network centrality, and the network core areas, including Guangdong, Chongqing, Gansu, and Qinghai, are gradually expanding, leading to further weakening of governance boundaries. The network’s spatial clustering structure presents four distinct blocks, with network spillover relationships concentrated in the first, third, and fourth blocks. The Eastern coastal areas play a “hub” role in undertaking carbon collaborative reduction, radiating and driving the central and western provinces. (2) From the perspective of the induced effect, the DE enables carbon collaborative reduction, exhibiting isotropic characteristics. (3) Heterogeneity tests show that regions with well-developed digital infrastructure and those with free trade zone constructions promote better effects, with a positive feedback effect in network status: betweenness centrality > degree centrality > closeness centrality. (4) Regarding the enabling mechanism, the DE drives carbon collaborative governance by enhancing technological innovation, promoting industrial structure upgrades, nurturing scientific talents, and reducing educational disparities. Full article

Despite the underdeveloped formal institutional system in China’s capital market, the venture capital (VC) industry has continued to grow rapidly, exhibiting a clear trend of network formation. To better understand the formation of VC networks, this study systematically analyzes factors from three dimensions: endogenous network structures, multidimensional relational networks among VC firms, and informal networks of venture capitalists. Using data from the Wind database and other sources, networks are constructed based on 1317 investment events involving 157 VC firms. An exponential random graph model is applied to assess the effects of multiple network embeddings on VC network formation. The results reveal that, among endogenous structural factors, triad closure structures are more likely to be embedded in VC networks than two-path structures with brokerage functions. In terms of exogenous factors, the geographic distance network among VC firms exerts a negative effect on VC network formation, while knowledge proximity networks—i.e., those based on industry, investment stage, and region—positively influence VC networks formation. Informal networks of venture capitalists increase the probability of VC network formation. Compared with previous studies, this research is based on self-organization, market-oriented, and relational logics, integrating multiple factors—including endogenous network structures, venture capital firm characteristics, and venture capitalists—and introduces a cross-network perspective to build a novel multilevel network embedding ERGM framework to examine VC network formation. Furthermore, the study reveals how informal ties substitute for formal institutions in China’s VC network formation. Full article

In an era of intensifying global technological competition and systemic disruptions, the resilience of metropolitan innovation networks has emerged as a cornerstone of sustainable regional development. Based on joint invention patents, this study employs a multi-method analytical framework integrating social network analysis, network motif analysis, a random walk algorithm, and the Exponential Random Graph Model (ERGM) to trace the evolution of resilience across node, structural, and community levels in the Shanghai Metropolitan Area (2011–2020). Our findings reveal a significant trajectory of strengthening resilience, marked not only by a shift from a monocentric to a polycentric structure at the node level but also by a qualitative change in collaborative patterns at the structural level, and enhanced integration at the community level. ERGM analysis identifies policy coordination and industrial upgrading as the most potent drivers of this evolution, with a pivotal finding being that digital connectivity, measured by information proximity, has superseded geographic proximity in facilitating collaboration. This study develops and applies a multi-scale resilience framework, while also extending proximity theory by highlighting the growing importance of policy and information dimensions over geographic distance. It offers actionable insights for building resilient innovation ecosystems in policy-driven metropolitan regions. Full article

Against the backdrop of green and sustainable development, green innovation has become a central issue of concern for both society and academia. Based on regional innovation system and network theories, this study conceptualizes the urban knowledge base as a network structure rather than a simple collection of isolated knowledge elements. Using green patent licensing data, a multi-layer network is constructed, and the Exponential Random Graph Model (ERGM) is employed to examine the impact of urban knowledge network structures on city-level innovation diffusion. The study finds that in the green ICT field, cities’ deep embedding in knowledge networks weakens their ability to absorb external innovations, while broad embedding facilitates the introduction of external innovations. In the green transportation field, deep embedding in knowledge networks enhances the absorption of external innovations, whereas broad embedding has no significant effect. In both fields, knowledge combination potential and knowledge uniqueness promote the outward diffusion of local innovations but weaken the inflow of external innovations. This study not only offers theoretical insights into innovation diffusion at the city level but also provides guidance for policymakers in developing targeted urban sustainable development strategies. Full article

As a crucial vehicle for green technological innovation, cooperative networks significantly promote resource integration and knowledge sharing. Yet, their dynamic evolution and micro-mechanism remain underexplored. Drawing on data from the joint applications of green invention patents between 2006 and 2021, this study constructed a multi-agent GTCIN involving multiple stakeholders, such as enterprises, universities, and research institutions, and analyzed the topological structure and evolutionary characteristics of this network; an exponential random graph model (ERGM) was introduced to elucidate its endogenous and exogenous driving mechanisms. The results indicate that while innovation connections increased significantly, the connection density decreased. The network evolved from a “loose homogeneity” to “core aggregation” and then to “outward diffusion”. State-owned enterprises in the power industry and well-known universities are located at the core of the network. Preferential attachment and transitive closure as endogenous mechanisms exert strong and continuous positive effects by reinforcing local clustering and cumulative growth. The effects of exogenous forces exhibit stage-specific characteristics. State ownership and regional location become significant positive drivers only in the mid-to-late stages. The impact of green innovation capability is nonlinear, initially promoting but later exhibiting a significant inhibitory effect. In contrast, green knowledge diversity exerts an opposite pattern, having a negative effect in the early stage due to integration difficulties that turns positive as technical standards mature. Geographical, technological, social, and institutional proximity all have a positive promoting effect on network evolution, with technological proximity being the most influential. However, organizational proximity exerts a significant inhibitory effect in the later stages of GTCIN evolution. This study reveals the shifting influence of endogenous and exogenous mechanisms across different evolutionary phases, providing theoretical and empirical insights into the formation and development of green innovation networks. Full article

This study investigates the mechanisms underlying the diffusion of risk information about genetically modified organisms (GMOs) on the Chinese social media platform Weibo. Drawing upon social contagion theory, we examine how endogenous and exogenous mechanisms shape users’ information-sharing behaviors. An analysis of 388,722 reposts from 2444 original GMO risk-related texts enabled the construction of a comprehensive sharing network, with computational text-mining techniques employed to detect users’ attitudes toward GMOs. To bridge the gap between descriptive and inferential network analysis, we employ a Shannon entropy-based approach to quantify the uncertainty and concentration of attitudinal differences and similarities among sharing and non-sharing dyads, providing an information-theoretic foundation for understanding positional and differential homophily. The entropy-based analysis reveals that information-sharing ties are characterized by lower entropy in attitude differences, indicating greater attitudinal alignment among sharing users, especially among GMO opponents. Building on these findings, the Exponential Random Graph Model (ERGM) further demonstrates that both endogenous network mechanisms (reciprocity, preferential attachment, and triadic closure) and positional homophily influence GMO risk information sharing and dissemination. A key finding is the presence of a differential homophily effect, where GMO opponents exhibit stronger homophilic tendencies than non-opponents. Despite the prevalence of homophily, this paper uncovers substantial cross-attitude interactions, challenging simplistic notions of echo chambers in GMO risk communication. By integrating entropy and ERGM analyses, this study advances a more nuanced, information-theoretic understanding of how digital platforms mediate public perceptions and debates surrounding controversial socio-scientific issues, offering valuable implications for developing effective risk communication strategies in increasingly polarized online spaces. Full article