The effects of network topology, climate variability and shocks on the evolution and resilience of a food trade network

Abstract

Future climate change will impose increased variability on food production and food trading networks. However, the effect of climate variability and sudden shocks on resource availability through trade and its subsequent effect on population growth is largely unknown. Here we study the effect of resource variability and network topology on access to resources and population growth, using a model of population growth limited by resource availability in a trading network. Resources are redistributed in the network based on supply and the distance between nodes (i.e. cities or countries). Resources at nodes vary over time with wave parameters that mimic changes in biomass production arising from known climate variability. Random perturbations to resources are applied to study resilience of individual nodes and the system as a whole. The model demonstrates that redistribution of resources increases the maximum population that can be supported (carrying capacity) by the network. Fluctuations in carrying capacity depend on the amplitude and frequency of resource variability: fluctuations become larger for increasing amplitude and decreasing frequency. Our study shows that topology is the key factor determining the carrying capacity of a node. In larger networks the carrying capacity increases and the distribution of resources in the network becomes more equal. The most central nodes achieve a higher carrying capacity than nodes with a lower centrality. Moreover, central nodes are less susceptible to long-term resource variability and shocks. These insights can be used to understand how worldwide equitable access to resources can be maintained under increasing climate variability.

Trade, migration, and the dynamics of spatial interaction

“…Even small initial differences in the number of people the local resource patches can support can increase consumption enough to set off a positive feedback between settlement population and resource extraction. The importance of trade flows here is consistent with related modeling efforts that highlight the role of trade networks in extending local carrying capacity in simple consumer resource networks (Qubbaj et al 2014;Dolfing et al 2019).…”

Most influential countries in the international medical device trade: Network-based analysis

“…Previous studies based on complex network theory have focused mainly on the structural characteristics and evolution patterns of the trade network from many aspects, such as detecting trade communities formed by some countries with tight trade relationships [26] , [27] , [28] , analyzing the structure of the core and periphery [29] , evaluating the centrality of countries [25] , [30] , identifying the relationships between the centrality of countries and other country characteristics [30] , [31] , estimating or predicting trade flows in the trade network [32] , [33] , [34] , revealing the impact factors of trade network formation [27] , [29] , investigating changes in structural characteristics during the evolution [35] , [36] , exploring risk transmission in the trade network [37] , [38] , and identifying the robustness of the trade network [39] , [40] .…”

“…In addition, the centrality of countries in the global trade network is found to be correlated with infections and deaths as a result of the COVID-19 pandemic. The above studies have not only focused on the overall global trade network [27] , [38] but also explored the trade networks of specific commodities, such as energy [29] , [30] , food [35] , [39] , animals [37] , [40] and e-waste [26] .…”

Automatic Recognition Algorithm of Information Architecture Reliability Based on Energy Internet Network Topology

“…can be safely transmitted through unsecured channels such as social networks. In [22], the author applied the network topology to the study of food trade, and the insights obtained can be used to understand how to maintain fair access to resources in the world under climate change. In [23], the author's network topology is used for power network vulnerability analysis and provides the best attack strategy based on network congestion and maximum damage.…”