The Dynamic Evolution of Agricultural Trade Network Structures and Its Influencing Factors: Evidence from Global Soybean Trade DOI Creative Commons
Yue Liu, Liang Zhang, Pierre Failler

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(4), P. 279 - 279

Published: April 10, 2025

Under the rapid advancements in information technology, complex network characteristics of agricultural product trade relationships among global economies have exhibited increasing prominence. This study takes soybean market as an empirical case, employing a combination social analysis to investigate dynamic evolution structures; then, Temporal Exponential Random Graph Model (TERGM) is adopted analyse factors influencing network. Based on comprehensive data encompassing 126 from 2000 2022, this research demonstrates several key findings: Firstly, characterised by pronounced agglomeration effects and “small-world” properties, accompanied heightened substitutability. Secondly, network’s structural configuration has undergone distinct transformation, shifting traditional single-core–periphery structure more multi-core–periphery architecture. Thirdly, response external shocks impacting topology, core exhibits greater resilience stability, whereas periphery displays heterogeneous responses. Finally, relations governed dual mechanism involving both endogenous dynamics exogenous influences.

Language: Английский

The Dynamic Evolution of Agricultural Trade Network Structures and Its Influencing Factors: Evidence from Global Soybean Trade DOI Creative Commons
Yue Liu, Liang Zhang, Pierre Failler

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(4), P. 279 - 279

Published: April 10, 2025

Under the rapid advancements in information technology, complex network characteristics of agricultural product trade relationships among global economies have exhibited increasing prominence. This study takes soybean market as an empirical case, employing a combination social analysis to investigate dynamic evolution structures; then, Temporal Exponential Random Graph Model (TERGM) is adopted analyse factors influencing network. Based on comprehensive data encompassing 126 from 2000 2022, this research demonstrates several key findings: Firstly, characterised by pronounced agglomeration effects and “small-world” properties, accompanied heightened substitutability. Secondly, network’s structural configuration has undergone distinct transformation, shifting traditional single-core–periphery structure more multi-core–periphery architecture. Thirdly, response external shocks impacting topology, core exhibits greater resilience stability, whereas periphery displays heterogeneous responses. Finally, relations governed dual mechanism involving both endogenous dynamics exogenous influences.

Language: Английский

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