ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Climate-Smart Food Systems
Volume 9 - 2025 | doi: 10.3389/fsufs.2025.1637564
This article is part of the Research TopicBuilding Resilience Through Sustainability: Innovative Strategies In Agricultural SystemsView all 8 articles
Modeling Interprovincial Migration in China Using Dual Mechanism ERGM: Toward Resilient and Sustainable Mobility
Provisionally accepted
- 1Nanjing Agricultural University, Nanjing, China
- 2Shanxi Normal University, Taiyuan, China
- 3Xi'an Jiaotong University, Xi'an, China
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Understanding the dynamics of internal migration is essential for promoting regional resilience and sustainable development. This study applies a weighted Exponential Random Graph Model (ERGM) to investigate the structural and functional evolution of China's interprovincial migration networks in 2000, 2010, and 2020. By integrating embeddedness, information cascades, resource endowments, proximity, and emergence theory, the analysis reveals the dual influence of endogenous and exogenous mechanisms.Results highlight that information cascades are a significant endogenous mechanism influencing migration outcomes. Specifically, mutual and popularity cascades are positively correlate with migration network formation, while transitive cascades exhibit an inhibitory effect. Exogenous drivers, including favorable economic, educational, healthcare, ecological attributes, cultural similarity, and geographic proximity, consistently facilitate migration flows. The interprovincial migration networks have shifted from a "coreperiphery clusters" to a pattern of "key agglomeration and regional balance". The dominance of endogenous mechanisms underscores the self-reinforcing nature of migration systems. These findings provide critical insights for designing evidence-based policies that address regional inequality, enhance human mobility infrastructure, and foster sustainable and inclusive internal migration systems.
Keywords: Migration networks, resilience, Ecological attributes, Exponential random graph model, Endogenous and Exogenous Drivers
Received: 29 May 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Wang, Ren and Du. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yike Ren, Shanxi Normal University, Taiyuan, China
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