With the intensification of global resource competition, the issue of timber supply has escalated from an economic concern to a significant strategic challenge. This study focuses on the evolution of disruption resilience in the global trade network for wood forest products, aiming to reveal the patterns of resilience dynamics under disruption risks by simulating underload cascading failure phenomena. The study provides theoretical support for enhancing the security and stability of the global wood forest product supply chain. Utilizing global trade data from the UN Comtrade Database 2023, a directed weighted complex network model was constructed, spanning upstream, midstream, and downstream sectors, with trade intensity distances serving as edge weights. By developing an underload cascading failure model, the evolution of disruption resilience was simulated under various disruption scenarios from 2002 to 2023, and the long-term impacts of critical node failures on network performance were analyzed. The results demonstrate significant spatiotemporal heterogeneity in the disruption resilience of the global wood forest product trade network. The upstream network exhibits improved resilience in total node strength but reduced global efficiency. The midstream network shows marked volatility in resilience due to external shocks, such as the global financial crisis, while the downstream network remains relatively stable. Simulations reveal that failures in core nodes (e.g., China, the United States, and Germany) disproportionately degrade global efficiency and node strength, with node centrality metrics positively correlated with network performance loss. This study elucidates the evolutionary mechanisms of disruption resilience in the wood forest product trade network under risk propagation, offering actionable insights for optimizing network robustness and supply chain stability. It is recommended that policymakers promote green supply chain initiatives, accelerate afforestation projects, and enhance domestic timber self-sufficiency to reduce reliance on imported timber, thereby strengthening node resilience and fostering sustainable forest resource utilization for economic and environmental benefits.

This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, they also introduce certain vulnerabilities. This study finds notable disassortative mixing without trade volume weights and diversified trends with weights, offering new insights into network dynamics. Core nodes must address disruption risks, enhance diversity, and establish emergency response mechanisms. In the recycling sector, this paper highlights weak trade connections and low resilience, with the US maintaining dominance, China’s influence waning, and India’s rapid ascent. This paper concludes by emphasizing the need for refined indicator systems and deeper explorations into resilience enhancement strategies for operational and targeted suggestions.

This study aims to reveal the spatiotemporal evolution patterns of the global frozen meat trade network from 2003 to 2022 and assess its static and dynamic structural resilience. Based on UN commodity trade data, directed weighted networks for beef, pork, mutton, and poultry are constructed. By combining complex network theory and simulation and emulation models, the static resilience is analyzed from four dimensions: transmissibility, clustering, hierarchy, and assortativity. The impact of single node failures on network performance is simulated to assess dynamic resilience. The results indicate that the global frozen meat trade network shows a trend of polarization, with China emerging as the largest import hub and Brazil becoming the dominant exporter of beef and poultry, reflecting the coexistence of regionalization and globalization. The static resilience analysis reveals that the weighted network exhibits prominent hierarchy and increased assortativity. The poultry network has the highest transmissibility, while the beef network demonstrates the strongest resilience. Interruption simulation results show that the failure of core nodes causes more significant damage to the weighted network, but the network’s invulnerability to disruption increases over time. The conclusion emphasizes that the resilience of the frozen meat trade network is driven by both weights and topological structure. Reducing dependence on key nodes through diversified trade partners and optimizing regional cooperation is crucial for enhancing trade stability. The weighted directed network model and dynamic resilience evaluation framework proposed in this study provide new methods for global food trade risk management and supply chain optimization.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

This study constructs a global timber trade network (2004-2023) to model underload cascading failures, assessing node-level disruptiveness and resilience. The network has grown structurally integrated, with density rising and path length shortening. Export-oriented economies (e.g., Canada, Vietnam) prove more susceptible to cascading failures than import-oriented ones (e.g., China, Japan), confirming upstream-to-downstream failure propagation. Node disruptiveness is heterogeneous: major exporters (Canada, Germany) cause the largest efficiency losses, while intermediaries (South Africa) impair connectivity. Out-strength and betweenness centrality are key determinants of disruptiveness. Resilience in 2023 varies widely; China's diversified sourcing enhances its resilience, whereas the U.S., Japan, and South Korea face higher vulnerability due to supply concentration. Overall, declining resilience among major importers underscores growing systemic risks. Although strategic diversification improves individual positions, large-scale disruptions remain a considerable threat, highlighting the imperative for optimized sourcing and robust contingency planning to stabilize global timber flows.