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Article

Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis

by
Xiangyu Huang
1,2,3,
Zhongwei Wang
1,2,3,*,
Yan Pang
1,2,3,
Wujun Tian
4 and
Ming Zhang
2
1
College of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China
2
College of Logistics, Central South University of Forestry and Technology, Changsha 410004, China
3
Hunan Key Laboratory of Intelligent Logistics Technology, Changsha 410004, China
4
College of Computer Science and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(9), 1665; https://doi.org/10.3390/f15091665
Submission received: 24 August 2024 / Revised: 18 September 2024 / Accepted: 19 September 2024 / Published: 21 September 2024
(This article belongs to the Section Wood Science and Forest Products)
Browse Figures
Figure 1
<p>Classification of wood forest products.</p> "> Figure 2
<p>Research framework.</p> "> Figure 3
<p>Evolution of weighted global efficiency from 2002 to 2021.</p> "> Figure 4
<p>Evolution of weighted average clustering coefficient from 2002 to 2021.</p> "> Figure 5
<p>Weighted degree distribution of the trade network for four types of wood forest products in 2002 and 2021.</p> "> Figure 6
<p>Evolution of unweighted assortativity from 2002 to 2021.</p> "> Figure 7
<p>Evolution of weighted assortativity from 2002 to 2021.</p> "> Figure 8
<p>Evolution trend of upstream core nodes resilience.</p> "> Figure 9
<p>Evolution trend of midstream core nodes resilience.</p> "> Figure 10
<p>Evolution trend of downstream core nodes resilience.</p> "> Figure 11
<p>Evolution trend of recycle core nodes resilience.</p> "> Figure A1
<p>Number of nodes representing countries (regions) and edges representing trade relationships in the global wood forest products trade network from 2002 to 2021.</p> "> Figure A2
<p>Temporal changes in the volume and value of global wood forest products trade from 2002 to 2021.</p> "> Figure A3
<p>Topological structure of the trade network for four types of wood forest products in 2021.</p> ">
Versions Notes

Abstract

:
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.

1. Introduction

Wood forest products characterized with economy, ecology, and renewability are widely used in households, papermaking, decoration, and many other fields [1]. As a key component of global economic activities, the trade in global wood forest products plays a crucial role in promoting economic development, meeting the wood demand of various countries and protecting forest resources [2]. The forest resources are distributed unevenly in the world [3], a majority of which are in few countries such as Russia, Brazil, Canada, the US, and China [4]. In such a context, many countries need to develop a wood forest products trade to balance supply and demand, which can boost their economy and satisfy their consumption demands. Therefore, the international trade in wood forest products has witnessed a significant development. According to UN Comtrade data, the global trade volume of wood forest products increased from USD 253.103 billion in 2002 to USD 538.862 billion in 2021, with an average annual growth rate of 5.94%. The trade scale increased from 394.6 million tons in 2002 to 672.8 million tons in 2021, with an average annual growth rate of 3.71%. A complex system has been formed in the global wood forest products trade. In this system, the production process is divided globally and segmented by the global value chain, thus creating an intertwined network structure. This network structure is influenced by domestic and foreign factors. Additionally, the evolution of this network itself carries significance, which is linked with the efficiency and stability of global trade. Furthermore, the evolution of this network proposed higher requirements for the wood forest products trade policies [5]. The global wood forest products trade as a complex issue has played a pivotal role in economic development. Therefore, it is essential to explore its network resilience and master the network’s risk response and adaptation mechanisms to formulate sound policies and pursue sustainable trade development.
Two key analytical methods, social network analysis (SNA) and complex network theory, are frequently used in the study of wood forest products trade networks. SNA is used to analyze the features of trade connection among nodes in the network to identify core participants and peripheral groups. Tian Gang and Jiang Qingqing (2016) utilized SNA to conclude that US was a core country in the roundwood trade, while China became a core country in 2011 [6]. Scholars used SNA to analyze the network density, centrality, and reciprocity to study the complex structure and changes of the wood and nonwood forest products international trade network [7]. They also studied how RCEP countries were connected in the wood forest product trade network [8]. By studying trade data of forest products in 40 countries with SNA, Hou et al. (2022) found that the position of a country in the value chain could be enhanced by the increased network connection and heterogeneity [9]. Gao, Pei, and Tian (2024) used SNA and multi-DID methods to examine the trade network between China and 40 key trading partners in forest products. They pointed out that this network had worsened the uneven distribution of global forest resources [10]. Meanwhile, the structural characteristics of these networks are thoroughly researched in complex network theory, which provides insights into the stability, vulnerability, and risk spread of networks. Pizzol and Scotti (2016) identified the marginal suppliers of wood products through trade network analysis. Based on the historical production data, they revealed the importance of geographical market delineation for life cycle assessment [11]. Long et al. (2019) built both unweighted and weighted networks to show competition in the global forest product market based on the data from 2004 to 2016. They highlighted the central role of the US, China, which had a rapid development, and other Asian countries [2]. Wang et al. (2021) analyzed global forest product trade from 1993 to 2018 and found that the networks had become more complex. They also pointed out that supply and demand security faced various challenges [12]. Wang et al. (2023) studied the global trade of forest products from 2000 to 2020 and found that the trade network for processed forest products had become more closely connected and more efficient, with high-GDP-income countries playing a dominant role [13]. Liu et al. (2024) analyzed the evolution of the global forest product trade network from 1995 to 2020 and identified a consistent growth trend during this period [14]. On this basis, they employed an interruption simulation method to explore changes in network structure, offering a valuable new approach for assessing the resilience of global trade networks [5]. The aforementioned studies mainly used unweighted indicators, with only a few considering weights. The difference between the two lies in the fact that unweighted indicators focus solely on the impact of network topology, while weighted indicators also take the strength of trade connections into account, thereby providing a more accurate network analysis [15]. Consistent research results indicate that the global wood forest product network exhibits significant small-world characteristics. Over time, trade relationships have deepened, trade volumes have expanded, network structures have become more organized, and bidirectional exchanges between trading countries have increased.
In recent years, research on network resilience has witnessed an increase in number. This concept originally stems from mechanics, where it was used to describe the rebound capacity of materials after the shock of external forces [16]. Subsequently, it was innovatively introduced into the ecosystem restoration field and further extended to more complex social–ecological systems [17]. With the continuous development of complex network theory, scholars begin to focus on the study of network resilience, particularly in the fields of urban networks [18], transportation networks [19], economic networks [20], supply chain networks [21], and ecological networks [22]. Network resilience refers to the ability of individuals within a network system through collaboration and complementarity in society, economy, organization, and other fields, with which people can respond and adapt to external acute shocks and chronic stresses, and recover or transform from them [23].
In the economic and social fields, many studies analyze network resilience by starting with the network structure. Caschili et al. (2015) innovatively used the interdependent multilayer model to analyze and simulate the network resilience in the international trade system, providing a new perspective for understanding the resilience of global trade networks [24]. Sharifi (2019) believed that a poorly designed network structure may quickly collapse when facing disturbances and shocks, while a well-designed network structure can rapidly absorb shocks and have a quick recovery [25]. Kharrazi et al. (2020) explored the potential of the network structural characteristics, including redundancy, diversity, and modularity, to enhance the system’s resilience [26]. Karakoc and Konar (2021) used indicators such as average shortest path length, dominant eigenvalue changes, epidemic thresholds, and weighted efficiency to study the efficiency and resilience of global food trade networks. They found that there was a competitive relationship between the two when considering only the network structure, but a cooperative relationship when taking into account the intensity of trade [27]. Yuan et al. (2022) used network connectivity, number of nodes, and average degree as network structure indicators to assess the resilience of the global crude oil trade network [28]. Yu et al. (2022) constructed an international iron ore trade network and comprehensively measured the resilience of the network from both static and dynamic aspects [29]. Shahnazi et al. (2023) not only analyzed the structural characteristics of the global oil trade network but also innovatively introduced the stability index and the effective share index to assess the resilience of the network [15]. Sun et al. (2023) used indicators such as network density, centrality, connectivity, and network size to propose a comprehensive assessment framework, and evaluated the structural resilience of the global oil and gas resource trade network from multiple dimensions [30]. Yu et al. (2023) constructed a directed weighted network and proposed a model to assess the evolution of static structural resilience for the cobalt trade network [31]. Chen Yuran and Chen Minpeng (2023) innovatively combined complex network analysis with information-based ecological network analysis, employing both unweighted and weighted indicators to construct a framework for assessing the resilience of natural resource trade networks [32]. Zuo et al. (2024) innovated the evaluation method of trade network structural resilience by introducing hierarchy and matchability into the assessment of lithium resource trade network structural resilience [33]. Chen et al. (2024) constructed a network resilience measurement framework adapting to the full connectivity characteristics of the Belt and Road trade network through indicators such as node strength, betweenness centrality, diversity coefficient, and global efficiency [34]. Jiao (2024) clearly confirmed the central importance of network structure in assessing and bolstering network resilience [35]. Some scholars, on the basis of evaluating static structural resilience, have further measured the dynamic resilience of different types of trade networks under disruption simulation, such as wheat [36], oil [30], iron ore [29], and Belt and Road trade networks [34]. In sum, existing research has employed one or more network indicators, such as hierarchy, matching, transmission, diversity, and agglomeration, to reflect the resilience of network structures.
The existing research evaluates network resilience from two perspectives: the overall resilience from the perspective of network structure, and the local resilience from the perspective of nodes within the network. Yu et al. (2022) measured the node resilience of the top 20 global economies in the five major iron ore product trade networks from both static and dynamic perspectives, constructing a relatively comprehensive network resilience evaluation system [29]. Shen et al. (2022) constructed a model based on the multilayer complex network theory to assess the resilience of nodes in the nickel ore product supply chain network under different risk scenarios [37].
Research on wood forest product trade networks based on complex network theory has provided valuable insights for this study, but there are still some research gaps. (1) Although there have been some studies of the resilience of global wood forest product networks, there are currently no widely accepted assessment standards. As each network has its unique nature, customized resilience evaluation indicators should be devised for specific networks [38]. (2) Compared to the resilience research in the resource and food trade network industries, there has been some progress in the analysis of the structural features of global wood forest product trade networks. However, the study of network resilience in the wood forest product sector is still in its early stages, which requires a comprehensive analysis of the evolution of wood forest product network resilience from multiple dimensions such as time, space, and supply chain. (3) Although indicators such as trade volume-weighted node degree [12] and network heterogeneity [9] have been used in the analysis of forest product trade networks, many key indicators related to the shortest path have not yet been subjected to weighted analysis. That means that we are not able to conduct a true weighted network resilience analysis for wood forest products.
Therefore, this paper, based on the global trade data of wood forest products from UN Comtrade from 2002 to 2021, constructs directed and weighted trade networks for different stages of the supply chain and provides an empirical research on network static resilience. The specific contributions are as follows: (1) Proposing a calculation method for trade intensity distance weight, specifically designing a weighted network resilience evaluation system for the global forest product trade network from both structural resilience and node resilience perspectives, which fills the gap in the resilience assessment indicator system for the wood forest product trade network. (2) Conducting comprehensive visual analysis of network resilience across multiple dimensions, including time, space, and the supply chain, for the wood forest product trade network highlights the significance of visualization in studying network resilience. This analysis is of great importance for enhancing the security and stability of the wood forest product network supply. (3) This study carefully compares the similarities and differences between the unweighted and weighted assortativity indices in how they evolve. This helps us better understand the resilience of the global wood forest product trade network and also advances research on trade network resilience.
The remainder of this paper is organized as follows. Section 2 introduces the research framework, model construction, and data sources. Section 3 presents the computational results. Finally, the paper concludes with a summary.

2. Research Methodology and Data Sources

2.1. Research Framework

Wood forest products, as direct conversions of forest resources, encompass a wide range of types, from upstream raw materials such as logs, sawn timber, and wood pulp, to midstream processed products like plywood and composites, and downstream consumer goods like paper products, woodwork, and furniture, as well as recycled products like waste paper. Based on the established definitions of wood forest products, we collected data in accordance with HS codes provided in the literature [39,40] (as shown in Table 1). We have further categorized these products into four major groups based on their positions within the supply chain: upstream, midstream, downstream, and recycled wood and forest products (Figure 1).
Network resilience is assessed from multiple dimensions, including macro and micro perspectives: From the macro perspective, the research should focus on the stability and efficiency of the entire network to analyze the structural resilience, which serves as a foundation of the whole analysis. From the micro perspective, the research should mainly examine the ability of each node to resist and recover from shocks, which attaches great importance to the importance of every node [32]. Additionally, resilience assessment can be conducted from two key dimensions, static and dynamic. Static resilience assessment reveals the structural characteristics of a network when undisturbed, marking the starting point of resilience research. In contrast, dynamic resilience assessment evaluates the network’s adaptability and recovery ability in the face of natural disasters, market fluctuations, and other external disruptions, which is crucial for the network’s continuous operation and swift recovery from adversity. This comprehensive approach can help us better understand the wood trade network resilience and enhance the ability of the network to tackle future challenges by developing corresponding strategies.
Our study takes static network resilience as the starting point, marking a preliminary exploration of the resilience analysis of the global wood forest product trade network. At present, our work focuses solely on the static aspects of network resilience, without considering dynamic resilience. We will conduct an in-depth analysis and evaluation of the network from both structural resilience and node resilience. As the global wood forest product trade network is a typical directed structure, its resilience measurement should not only focus on the trade relationship among countries but also the significant impact of trade volume on resilience. Based on existing research findings and the global wood forest product trade, we constructed a weighted indicator system, as shown in Table 2, which covers key factors such as trade volumes and trade intensity distance, thus enabling a more accurate assessment of the resilience level of the global wood forest product trade network.
Based on this indicator system, we further collected data to construct directed weighted trade network models for upstream, midstream, downstream, and recycled wood forest products, and systematically conducted an empirical research on network static resilience. The research framework is illustrated in Figure 2.

2.2. Research Methodology

2.2.1. Construction of the Global Wood Forest Products Trade Network

The global wood forest products trade network consists of nodes, edges, trade, and weights, respectively, representing countries (regions), trade relationships, trade volumes, and trade intensity distance, thereby establishing a complex network model. The following definitions are applied to this global wood forest product trade network.
G m = ( V , E , W 1 , W 2 , T )
Here, m denotes the four distinct categories of wood forest product networks, specifically upstream, midstream, downstream, and recycling networks; V is the set of nodes, each representing a country (regions); E is the set of edges, representing trade relationships among these countries (regions); W 1 is the set of functions defining the trade volumes between pairs of countries (regions), and W 2  is the set of edge weights corresponding to the trade intensity distance between them. Lastly, T represents the year under consideration.
To avoid the uncertainties brought about by inflation and unit price fluctuations, we choose trade volume (in kilograms) instead of trade value (in USD) as one of the edge weights for modeling to reveal the trade relationships and levels of dependency within the supply chain more accurately. This is crucial for analyzing the evolution of network resilience.
In addition, this article adds trade intensity distance as one of the edge weights to model and explores the impact of trade intensity on the length of the shortest path. Generally speaking, the greater the trade intensity between countries and regions, the lower the transaction costs and the smaller the border weights, leading to the corresponding reduction in the length of the shortest path. The formula for calculating the trade intensity distance edge weight W 2 is as follows.
w i j 2 = 1 + ln   M a x ( w i j 1 ) − ln   w i j 1
Here, w i j 2 represents the trade intensity distance between countries (regions) i and j; M a x ( w i j 1 ) denotes the maximum trade volume among all edges within the network; and w i j 1 is the trade volume of wood forest products between countries (regions) i and j.
From the above formula, the edge with the maximum trade volume has a trade intensity distance weight of 1, while other edges have trade intensity distance weights greater than 1 in the global wood forest products trade network.

2.2.2. Related Indicators of Network Structural Resilience

Drawing on existing research and innovatively incorporating trade volume and trade intensity distance weights, this study measures the structural resilience of the weighted network from four aspects: transitivity, clustering, hierarchy, and assortativity.
(1)
Transitivity—Weighted Global Efficiency
Weighted global efficiency refers to the average of the reciprocals of the weighted shortest path lengths between all node pairs in the network, which reflects the speed and capacity of information transmission across the entire network. With reference to existing research [41], the calculation formula is presented below.
E w 2 = 1 N N − 1 ∑ i ≠ j 1 d w 2 i j
Here, E w 2 represents the weighted (trade intensity distance) global efficiency; N is the total number of nodes in the network; and d w 2 i j is the weighted shortest path from node i to node j.
(2)
Clustering—Weighted Average Clustering Coefficient
The weighted average clustering coefficient is the arithmetic mean of the weighted clustering coefficients of all nodes in the network. It reflects the intensity of tight clustering among nodes based on trade volumes. According to existing research findings [42], this coefficient is calculated by taking the ratio of the total actual trade volumes among a country’s (region’s) direct trading neighbors to the theoretical maximum possible trade volumes among those neighbors, and then averaging these ratios across all nodes. The calculation formula is presented below.
C w 1 i = 1 k i ( k i − 1 ) M a x ( w 1 i j ) ∑ s ≠ t s , t   i s   a d j a c e n t   t o   i T w 1 s t
C w 1 = 1 N ∑ i =   1 N C w 1 i
Here, C w 1 i represents the weighted (trade volume) clustering coefficient of node i; T w 1 s t represents the trade volume between node s and node t; k i represents the degree of node i; M a x w 1 i j represents the maximum edge trade volume; C w 1 represents the weighted average clustering coefficient.
(3)
Hierarchy—Weighted Degree Distribution
Hierarchy is measured through the network’s degree distribution index [42]. Weighted degree distribution refers to the probability distribution of node degrees, considering trade volumes as weights. The larger the absolute value of the weighted degree distribution slope, the more significant the weighted degree hierarchy among nodes. In the trade network of wooden products, the power law curve is plotted using weighted nodes (based on trade volume), ranked by their degree, with nodes sorted from largest to smallest. The calculation formula is presented below.
K w 1 i = C ( K w 1 i * ) α
l n   K w 1 i = l n   C + α l n   K w 1 i *
Here, K w 1 i represents the weighted (trade volume) degree of node i in the network; K w 1 i * represents the ranking of node i’s weighted degree among all weighted node degrees; C is a proportional constant; α is the slope of the weighted degree distribution curve, measuring the hierarchical nature of the network.
(4)
Assortativity—Weighted Assortativity Coefficient
The weighted assortativity coefficient refers to the tendency of countries (regions) in the network to connect with countries (regions) that have similar total trade volumes, taking into account the trade volume weighting [43]. If the assortativity coefficient is positive, it indicates an assortative network, where hub countries (regions) are more likely to connect with each other, exhibiting a polarization effect [44]. Conversely, if the assortativity coefficient is negative, it represents a disassortative network, where hubs tend to establish trade relations with countries (regions) with lower total trade volumes. This exhibits a trickledown effect [45]. The calculation formula is presented below.
r w 1 = H − 1 ∑ i w i 1 ( j i k i ) − H − 1 ∑ i 1 2 w i 1 ( j i + k i ) 2 H − 1 ∑ i 1 2 w i 1 ( j i 2 + k i 2 ) − H − 1 ∑ i 1 2 w i 1 ( j i + k i ) 2
Here, r w 1 represents the weighted (trade volume) assortativity, where j i and k i are the degrees of nodes j and k connected by the i-th edge, ω i represents the trade volume weight of the i-th edge, and H = ∑ i w i 1 is the sum of the trade volume weights of all edges.

2.2.3. Indicators Related to Node Resilience in Networks

Node resilience is multidimensional and comprehensive, which covers not only a node’s self-recovery and anti-interference capabilities in the face of failures, attacks, or disruptions but also its role and influence in risk propagation within the network. Based on existing research [29], this study comprehensively assesses node resilience from three major aspects: anti-destruction ability, transit capacity, and recovery capacity, innovatively incorporating weighted factors such as trade volume and trade intensity distance to delineate nodes’ resilience more comprehensively in the network.
(1)
Anti-Destruction Ability—Weighted Degree
The higher the node degree, the more nodes it connects to. This usually means that the node is more important in the network. Therefore, its anti-destruction ability is stronger. Weighted degree represents the total trade volume between a country (region) and all its direct trading partners within a given time period [46]. In the directed global trade network of wood forest products, it is further classified into weighted out-degree and weighted in-degree. The calculation formula is presented below.
W i 1 ,   o u t = ∑ j = 1 N w i j 1  
W i 1 , i n = ∑ j = 1 N   w j i 1
Here, W i 1 ,   o u t represents the weighted out-degree of node i; W i 1 ,   i n represents the weighted in-degree of node i; w i j 1 represents the trade volume from node i to node j; w j i 1 represents the trade volume from node j to node i; N represents the total number of nodes in the network.
(2)
Transit Capability—Weighted Betweenness Centrality
Nodes with high-weighted betweenness centrality play a bridging role in the network. Their failure can have adverse implications on the connectivity of the entire network. That is why they are essential in the network. The resilience and recoverability of these nodes have impact on the network’s ability to respond to and recover from emergencies. Transit capability is characterized with weighted betweenness centrality [29]. It measures the extent to which a country (region) lies on the weighted shortest paths between other trading pairs to evaluate the importance and influence of a country in the network from the perspective of a bridge. The calculation formula is presented below.
B C w i 2 = ∑ s ≠ i ≠ t n w 2 s t i g w 2 s t
Here, B C w i 2 represents the weighted (trade intensity distance) betweenness centrality of node i; g w 2 s t is the number of weighted shortest paths from node s to node t, and n w 2 s t i is the number of weighted shortest paths among the g w 2 s t weighted shortest paths from node s to node t that pass through node i.
(3)
Recovery Capacity—Weighted Closeness Centrality
Recovery capacity is represented by weighted closeness centrality, which is the reciprocal of the sum of weighted shortest path lengths from a node to all other nodes in the network, taking into account trade intensity distance [46]. The extensive connections and multiple path options make nodes have high weighted closeness centrality. Such nodes not only have strong resilience against disruptions but can also rapidly and effectively restore network functionality with strong recovery capabilities. The calculation formula is presented below.
C C w 2 i = 1 d w 2 i = N ∑ j = 1 N d w 2 i j
Here, C C w 2 i represents the weighted (trade intensity distance) closeness centrality of node i; N represents the total number of nodes in the network; d w 2 i represents the weighted shortest path length from node i to node j; ∑ j = 1 N d w 2 i j represents the sum of the weighted shortest paths from node i to all other nodes in the network.

2.2.4. Data Sources and Data Processing

This study is based on the data from 2002 and 2021 from the UN Comtrade Database, covering over 2 million trade records in 231 countries and regions. Considering the data consistency and learning from others’ practices [47], this study adopts import-based data and addresses issues such as data miss, regional consolidation (merging data from Hong Kong and Macao, China, into China, excluding Taiwan, China), and trade relationship thresholds setting (setting a USD 50 threshold, excluding trade relationships below this value). Ultimately, the inter-country trade data are classified into four categories: upstream, midstream, downstream, and recycling. Therefore, the datasets are presented in Appendix A, Figure A1. In the subsequent sections, we will use a static resilience evaluation index system and Python simulation to assess and visualize the global wood forest product trade network’s static resilience.

3. Results Analysis

3.1. Overall Characteristics of the Global Trade Network for Wood Forest Products

3.1.1. Temporal Changes in Trade Scale

This paper analyzes the changes in the trade volume and value of global wood forest products (including upstream, midstream, downstream, and recycled products) from 2002 to 2021 (Appendix A, Figure A2). Upstream products have the largest trade volume but lower unit prices, as they account for more than half of the total trade volume but only a quarter of the trade value; downstream products are the opposite, with trade value accounting for nearly three-fifths while trade volume accounts for one-quarter. This article mainly discusses the global wood forest product trade network based on trade volume.
From 2002 to 2021, the trade volume of upstream, midstream, downstream, and recycled wood forest products grew by 58.0%, 77.3%, 71.2%, and 77.2%, respectively, with notable increases in midstream, downstream, and recycled products. This upward trend was fueled by policies, environmental concerns, market demands, and supply chain optimizations. Due to the improved public awareness of the environment, governments placed restrictions on log exports and boosted domestic processing. Market demand changes made downstream industries prefer midstream products for efficiency and added value. The rise in recycled trade volume indicates enhanced environmental awareness, improved policy support, great technological advancements, and rising market demand, contributing to resource recycling and sustainable development. The increase in recycled trade reflects improved environmental awareness, strong policy backing, solid technological progress, and great market demand, paving the way for resource recycling and sustainable growth.
However, Figure A2 in Appendix A shows a significant drop in data of 2009, 2015, and 2020, primarily due to global economic fluctuations and external shocks. After the 2008 financial crisis, market demand plummeted in 2009, which took a toll on exports. In 2015, the global trade volume was influenced by the slowdown of global economic growth and internal industry issues. The COVID-19 pandemic in 2020 stagnated the global economy, disrupted the supply chains, cut the demand, and slashed exports. These events reveal the inadequate ability of the global wood forest products trade network to address external shocks, highlighting the need to strengthen its resilience and robustness to withstand future risks and challenges.

3.1.2. Network Topology

This paper uses chord diagrams to elucidate the structure of the global wood forest product trade network in 2021, focusing on four network types (Appendix A, Figure A3). Trade patterns varied across supply chain segments and countries (regions). Network density witnessed its peak in the downstream segment, followed by upstream and mid-stream, with recycling as the lowest. Notably, upstream and recycling networks displayed remarkably higher trade scale polarization compared to midstream and downstream networks.
In the upstream network, China, the US, Germany, Canada, and Russia were key players, accounting for nearly one-third of the total trade volume, which further showed the uneven distribution of wood product resources globally. China, a major timber importer, had forged long-term partnerships with forest-rich countries, thereby bolstering its market dominance and node resilience. In the midstream network which was dominated by wood panels, the US, Germany, China, and Canada contributed nearly a quarter of the global trade volume. These countries, as major producers and consumers with advanced manufacturing technology and stable demand, enhanced the midstream’s robustness and resilience to external pressures. In the downstream network, the US, China, and Germany accounted for a quarter of the global trade. With its mature system and extensive government–public involvement, the US took the lead in waste paper exports in the recycling network. Overall, the global wood product trade network demonstrated resilience across segments but still faced challenges, including resource imbalance and heavy market reliance.

3.2. Evolution of Network Structural Resilience

3.2.1. Network Transitivity and Clustering

Figure 3 illustrates the significant differences in the weighted global efficiency evolution trends of the four networks in the global wood forest product trade from 2002 to 2021. While the upstream, midstream, and downstream networks maintained stability, the downstream network stood out with substantially higher efficiency. The high-weighted global efficiency indicates more redundant paths and backup mechanisms to withstand potential failures or shocks. The recycling network, despite its initial low efficiency, showed consistent improvement, underscoring the benefits of circular economic activities. The financial crisis in 2008 and the COVID-19 pandemic in 2020 resulted in noticeable declines in the upstream and midstream networks, exposing their susceptibility to the complex global context.
From 2002 to 2021, the downstream network always had the highest weighted average clustering coefficient, which saw a continuous increase (Figure 4). This indicated the closer trade connections among countries (regions) for downstream consumer goods. In such a context, highly clustered subgroups were formed. This structure enhanced the network’s ability to resist attacks and tolerate faults, thereby improving its resilience. The upstream and midstream networks had moderate average clustering coefficients with an upward trend. Although the recycling network had a relatively low average clustering coefficient, it exhibited a slight increase, indicating enhanced connections among countries (regions).

3.2.2. Network Hierarchy

In the global wood forest product trade network, the weighted degree distribution followed a double logarithmic power law, characterized by a scale-free network (Figure 5). The core nodes played a predominant role in the network functionality. From 2002 to 2021, despite the little change in the fit curve slopes for the four products categories, the absolute values of the slopes in 2021 were generally higher than those in 2002, suggesting a more significant hierarchical structure. This enhancement reflected stronger cohesion and competitiveness among core countries (regions), and improved robustness of the network against random disturbances. However, it also worsened vulnerability to targeted attacks.
Among the four network types, the recycling network showed the steepest curve slope, indicating the most pronounced hierarchy. In this hierarchy, core countries like the US, Germany, and India stood out and exhibited strong network cohesion. The downstream network had the smallest curve slope, suggesting a flatter structure with widespread trade connections, which mitigated the Matthew effect.

3.2.3. Network Matching

To gain a deeper understanding of the matching characteristics of the global wood forest product trade network, this paper assessed and compared the unweighted and weighted assortativity of the four network types. By unweighted assortativity, the author only considered the number of trade connections between countries or regions, while by weighted assortativity, the author also took into account the volume of trade. From 2002 to 2021, the unweighted assortativity of the four wood forest product trade networks was consistently negative (Figure 6), and exhibited disassortative features. That means that hub countries (regions) held a central position in the network as small countries relied on them, which could exert a decisive influence on trade flows and resource allocation.
During the process of feature evolution, weighted and unweighted assortativity exhibited significant differences and even opposite trends (Figure 7). The weighted assortativity of the downstream network was consistently positive. This indicates that although the hub countries showed disassortative tendencies in trade connections, core countries were more inclined to establish closer trade ties, which contributed to the network’s resilience enhancement. The recycling network saw a decline in unweighted assortativity, while an increase in weighted assortativity. This reflects the diversification in trade connections and strengthened cooperation among core countries, thereby improving network stability. In contrast, the upstream and midstream networks witnessed a decrease in weighted assortativity, shifting from assortative to disassortative patterns. This shows that hub countries were expanding their trade partners with the evident trickle-down effect. Though it increased network complexity, this trend was beneficial in the long run for risk diversification and adaptability enhancement, thus strengthening the network’s resilience.

3.3. Evolution of Network Node Resilience

Node resilience offers two perspectives. On one hand, we can assess the shock resistance and recovery capabilities of specific countries or regions by observing changes in node strength, closeness centrality, and betweenness centrality. On the other hand, by analyzing the resilience of core nodes, we can understand the overall resilience of the network.

3.3.1. Evolution Trend of Upstream Core Node Resilience

Figure 8 shows that in the trade of upstream wood forest products, countries such as China, the US, Russia, Canada, and Germany were core players. In particular, China witnessed striking changes. From 2002 to 2021, China became a superpower in importing upstream wood forest products, ranking first in import volume, betweenness centrality, and closeness centrality. This achievement indicates that China was highly dependent on wood imports and had strong resistance and recovery capabilities to deal with shocks. Although China’s dominant position enhanced the resilience of the entire network, it also made China more vulnerable to targeted attacks.
The US increased its influence in the upstream network, shown in the improvement in its out-degree and closeness centrality, indicating better resistance to shocks and recovery. However, the decline in its betweenness centrality means that the US’ role in connecting other countries weakened. This reflected the diversification trend in the global wood forest product trade network, which could improve the overall resilience of the network and reduce vulnerability.
As the major exporters of upstream timber, Russia and Canada kept their status stably, but their performance in connecting with other countries and recovery capabilities was relatively weak. Germany witnessed a significant growth in wood exports. Although Germany continued to play an important role in trade as it had a stable betweenness centrality, it had weakened influence in the network, shown in its decline in closeness centrality.

3.3.2. Evolution Trend of Midstream Core Node Resilience

Figure 9 reveals that in the midstream wood forest products trade, China, the US, Germany, Canada, and Russia continued to occupy the main position. Furthermore, the prevailing lighter shade of the midstream nodes, as depicted in Figure 9, signifies their reduced weighted closeness centrality. This, in turn, highlights the need to enhance the network’s cohesion, risk diffusion, and recovery capabilities. China’s resilience as a node has changed notably. Compared to 2002, China became a major exporter of midstream wood forest products in 2021, taking the lead in both weighted out-degree and weighted betweenness centrality, while its betweenness centrality remained stable. This indicates that China had strong shock resistance. However, due to the high transit role of China, any disruption could have a significant impact on the entire network and increase the network’s vulnerability.
The US ranked first in both weighted in-degree and weighted closeness centrality in 2002 and 2021, but there was a slight decrease in betweenness centrality. This indicates that the United States still had strong capabilities in resisting shocks and recovery, but its role as a trade transit hub had been somewhat diminished.
Germany’s various indicators remained relatively stable, with its export volume consistently ranking second. This reflects its strong resilience and positive influence on maintaining network stability.
Canada, with its abundant forest resources and strong export strength, ranked among the top three in terms of weighted out-degree in 2002 and 2021. However, compared with 2002, the export volume in 2021 significantly declined. Although it had certain resilience, as shown in its increasing weighted closeness centrality, its influence in the network and its ability to resist shocks were weakened, indicated by its continuously low weighted betweenness centrality.
Russia’s weighted out-degree for midstream wood products saw a substantial increase, jumping from the 13th position in 2002 to the 4th in 2021, highlighting its enhanced processing capabilities and export competitiveness. Meanwhile, a slight rise in its weighted betweenness centrality indicates an expanding influence of its nodes within the network. However, a minor decrease in weighted closeness centrality suggests a setback in its resilience and risk distribution capabilities, thereby increasing network vulnerability.

3.3.3. Evolution Trend of Downstream Core Node Resilience

Figure 10 shows that in the trade of midstream wood forest products, China, the US and Germany were still the key players. The downstream network nodes are noticeably darker than those in the other three types of networks, as shown in Figure 10. This indicates that the weighted closeness centrality of downstream nodes is significantly higher than that of other networks. The trade network is featured with a highly interconnected structure, efficient information dissemination and resource flow, and strong network cohesion.
Compared to 2002, China’s export volume surged in 2021, leaping to the top and becoming a key bridge in the network. This move significantly boosted China’s resilience, market influence, and global standing. However, to further strengthen its recovery and risk diversification capabilities, China needed to broaden its connections.
Compared to 2002, the United States experienced a substantial increase in its import and export volumes in 2021. The size and color of the nodes indicate its central position in the network, showcasing its strong resilience, significant influence, and control over the network with a firmly established leadership position.
Compared to 2002, Germany’s export volume in 2021 showed a significant growth, but its betweenness centrality declined slightly. This suggests that while Germany still held an important position in global trade, its role as a trade intermediary or bridge may have relatively weakened. This could be attributed to the intensification of trade activities in other countries or regions and changes in the global trade landscape.

3.3.4. Evolution Trend of Recycling Core Node Resilience

Recycled wood forest products, particularly those derived from waste paper, were a significant component. As shown in Figure 11, the network nodes lighter in color indicate lower weighted closeness centrality recycling network nodes, as well as reduced connectivity and trade volume in the waste paper recycling sector. This phenomenon can be attributed to factors such as low market maturity, high environmental protection technology barriers, and differences in environmental protection policies, tax policies, and technical management levels across countries.
Compared to 2002, there was a notable change in the import core nodes of the recycling network in 2021. In this year, India rose rapidly in this field and replaced China’s position, not only leading in import volumes but also ranking first in terms of betweenness centrality and closeness centrality. This fully demonstrated its strong resilience and extensive influence.
The US has maintained a leading position in the waste paper recycling network for a long time, thanks to its market dominance and well-established industrial chain. These factors together lay a solid foundation for the resilience of its nodes.
Germany consecutively ranked second in import volumes within the recycling network in both 2002 and 2021. This achievement was attributed to its strong domestic demand, abundant resource availability, an efficiently operated recycling system, and significant government support, all of which jointly drove Germany to import large quantities of waste paper. However, it is worth noting that Germany’s weighted betweenness centrality and weighted closeness centrality declined. This indicates that with the continuous development of the recycling trade network and the increasingly competitive market, Germany’s role as a bridging node and its ability to mitigate risks slightly weakened.

4. Discussion

Based on the aforementioned analysis, this study, while ensuring data accuracy, systematic research frameworks, and appropriate indicator selection, truly reflects the evolution of resilience in the global wood forest products trade. This research is featured with innovation, systematicness, and strictness.
By visual comparisons of trade volume trends in data, it is found that our findings align closely with those in references [10,14]. This not only ensures the reliability of our data sources but also proves the reasonableness of our data preprocessing methods. It should be noted that data from 2022 and 2023 are not included in the analysis due to incomplete statistics. Only preliminary estimates were made without delving into detailed discussions (Appendix A, Figure A2).
Regarding the research framework and the breadth of the wood forest products sector, this study innovatively delves into the subject from a supply chain perspective, subdividing the trade network into four subnetworks: upstream, midstream, downstream, and recycling. This not only verifies the effectiveness of our research approach but also significantly enhances the systematicness, strictness, and scientific rationality of the study. Furthermore, the research addresses previous oversights of the different characteristics of various wood forest products network types.
Given the directed nature of wood forest products trade, this study adopts the global efficiency indicator instead of the average path length indicator to provide a more comprehensive assessment of network performance. Additionally, a weighted trade network is constructed based on trade volumes, offering more precise references to formulate and upgrade trade policies. Unlike previous studies, all indicators in this study are weighted, ensuring a comprehensive and accurate analysis of network resilience.

5. Conclusions

This paper delves into the complex directed and weighted networks analysis, and constructs a trade network encompassing the upstream, midstream, downstream, and recycling sectors of the global wood forest products industry. An empirical study is conducted to explore the topological features and static resilience of these four types of networks from 2002 to 2021.
The research findings indicate that during this period, the global wood forest products trade network underwent significant structural evolution and scale expansion, which is consistent with the previous studies [5,10,13,14]. The increasing weighted global efficiency and weighted average clustering coefficient of the four types of trade networks show the enhanced overall network efficiency and resilience. Meanwhile, the increase in weighted hierarchy highlights the dominant position of core countries and the strengthening of network cohesion, but it also reveals a potential rise in network vulnerability.
Furthermore, without regard to trade volume weights, all four types of networks exhibit notable disassortative mixing characteristics, which become more and more evident over time. However, after the introduction of trade volume weights, the diversified trends are displayed in the evolution of weighted assortative mixing in various networks, providing a new perspective for exploring network matching features. This finding significantly enhances our understanding of the complex dynamics of resilience within timber and forest product trade networks, and it provides new perspectives and methodologies for future research.
From 2002 to 2021, China, the United States, and Germany, as core nodes in the network, have both bolstered network resilience and highlighted vulnerabilities to deliberate attacks. Therefore, it is imperative for countries to address the risks associated with disruptions to core nodes by enhancing network diversity and redundancy, reducing excessive dependence on single nodes, and building diversified supply chain systems so as to mitigate the impact of unexpected events on network operations. Core node countries should establish emergency response mechanisms to ensure rapid recovery of network functions in the event of natural disasters, political conflicts, or economic turmoil. At the same time, strengthening node resilience—by improving infrastructure, advancing technology, and refining regulations—is essential for resisting various disturbances and attacks, thereby ensuring the continuous stability of the supply chain.
Within the recycling wood forest products network, trade connections were relatively loose during this period, resulting in weak network resilience. The United States occupied a dominant position, while China saw weakened influence and India grew rapidly. In response to this situation, countries should strengthen policy coordination and cooperation to jointly address global environmental challenges, upgrade waste paper recycling systems, and improve resource utilization efficiency. Additionally, it is imperative to increase diversity and localize the recycling supply chain in order to enhance the resilience of the global recycling network and contribute to the sustainable development of the wood forest products industry.
To comprehensively analyze the resilience of the global wood and forest products trade network, future research is required to refine and improve the construction of indicator systems. In this way, these systems can have the potential to precisely capture the multidimensional characteristics of network resilience. At the same time, we need to conduct more in-depth explorations on the strategies and measures to enhance resilience so that operational and targeted suggestions can be put forward.
Looking forward, building upon the analysis of static network resilience, we will delve deeper into the dynamic resilience evolution patterns exhibited by the wood and forest products trade network under disruption scenarios. In particular, we will focus on the risk transmission mechanisms triggered by core node disruptions and meticulously analyze the specific impacts of these mechanisms on network invulnerability, recoverability, circulation efficiency, and market response speed. Our ultimate goal is to provide solid scientific theoretical support and practical operational guidance for constructing a more systematic and well-rounded global wood forest products trade system.

Author Contributions

Conceptualization, Z.W., X.H. and Y.P.; methodology, Z.W., X.H. and Y.P.; software, X.H. and W.T.; validation, X.H.; formal analysis, X.H.; investigation, Z.W. and X.H.; resources, Z.W. and Y.P.; data curation, X.H. and M.Z.; writing—original draft preparation, X.H.; writing—review and editing, Z.W., X.H. and M.Z.; visualization, X.H. and W.T.; funding acquisition, Z.W. and Y.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by China National Social Science Foundation Project (22BGL114); Hunan Provincial Key R&D Programme Project (2022GK2025); Youth Scientific Research Foundation, Central South University of Forestry and Technology (2018QZ003); Hunan Provincial Key Laboratory of Intelligent Logistics Technology (2019TP1015).

Data Availability Statement

The data presented in this study are available in UN Comtrade Database at https://comtradeplus.un.org, reference number is as shown in Table 1.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

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Figure A1. Number of nodes representing countries (regions) and edges representing trade relationships in the global wood forest products trade network from 2002 to 2021.
Figure A1. Number of nodes representing countries (regions) and edges representing trade relationships in the global wood forest products trade network from 2002 to 2021.
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Figure A2. Temporal changes in the volume and value of global wood forest products trade from 2002 to 2021.
Figure A2. Temporal changes in the volume and value of global wood forest products trade from 2002 to 2021.
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Figure A3. Topological structure of the trade network for four types of wood forest products in 2021.
Figure A3. Topological structure of the trade network for four types of wood forest products in 2021.
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Figure 1. Classification of wood forest products.
Figure 1. Classification of wood forest products.
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. Evolution of weighted global efficiency from 2002 to 2021.
Figure 3. Evolution of weighted global efficiency from 2002 to 2021.
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Figure 4. Evolution of weighted average clustering coefficient from 2002 to 2021.
Figure 4. Evolution of weighted average clustering coefficient from 2002 to 2021.
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Figure 5. Weighted degree distribution of the trade network for four types of wood forest products in 2002 and 2021.
Figure 5. Weighted degree distribution of the trade network for four types of wood forest products in 2002 and 2021.
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Figure 6. Evolution of unweighted assortativity from 2002 to 2021.
Figure 6. Evolution of unweighted assortativity from 2002 to 2021.
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Figure 7. Evolution of weighted assortativity from 2002 to 2021.
Figure 7. Evolution of weighted assortativity from 2002 to 2021.
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Figure 8. Evolution trend of upstream core nodes resilience.
Figure 8. Evolution trend of upstream core nodes resilience.
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Figure 9. Evolution trend of midstream core nodes resilience.
Figure 9. Evolution trend of midstream core nodes resilience.
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Figure 10. Evolution trend of downstream core nodes resilience.
Figure 10. Evolution trend of downstream core nodes resilience.
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Figure 11. Evolution trend of recycle core nodes resilience.
Figure 11. Evolution trend of recycle core nodes resilience.
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Table 1. HS codes of wood forest products.
Table 1. HS codes of wood forest products.
Supply Chain LinksProduct CategoryCommodity CodeDetailed Information Regarding HS Codes
UpstreamLogsHS4403Wood in the rough.
Other Raw
Materials		HS4401, HS4402,
HS4404, HS4405		Various fuel wood, wood chips/sawdust/waste, wood charcoal, hoop wood/poles/stakes, wood wool/flour.
Sawn TimberHS4406, HS4407Wooden sleepers/cross-ties; Sawn/chipped, sliced/peeled wood.
Wood PulpHS4701-HS4706Wood pulp (mechanical, chemical types); combined pulp; recovered paper/cellulosic pulp.
MidstreamWood-based PanelsHS4408-HS4413Veneer, plywood, laminated wood sheets; vood (strips, friezes); particle/OSB/similar boards; fiberboard; densified wood.
DownstreamWood
Products		HS4414-HS4421Wooden frames; packings, cable-drums, pallets; coopers’ products; tools, handles; builders’ woodwork; wood tableware, kitchenware; marquetry, inlaid wood, ornaments; furniture (excl. ch. 94); other wood items.
Paper
Products		HS48, HS49Paper, paperboard, articles thereof; printed products, manuscripts, typescripts, plans.
Wood
Furniture		HS940161, HS940169, HS940330, HS940340,
HS940350, HS940360		Wooden-framed seats (upholstered/not); wooden furniture (office, kitchen, bedroom, other).
RecycledWaste
Paper		HS4707Waste and scrap of paper and paperboard.
Table 2. Weighted indicator system for evaluating the static resilience of the global wood forest products network.
Table 2. Weighted indicator system for evaluating the static resilience of the global wood forest products network.
TypeInfluencing FactorWeighted IndicatorImpact on Network Resilience
Structural ResilienceTransmissibilityWeighted
Global
Efficiency		Measures the speed and capacity of information transmission or material flow within the global wood forest product trade network, considering trade intensity weighting. Higher efficiency indicates smoother transmission and stronger resilience.
ClusteringWeighted
Average
Clustering
Coefficient		Measures the modular characteristics of the global wood forest product trade network, considering trade volume weighting. Higher coefficients indicate tighter local clustering, better network connectivity and transmission efficiency, and stronger resilience.
HierarchyWeighted
Degree
Distribution		Reflects the probability distribution of node-weighted degrees considering trade volumes. Moderate hierarchy and flat structures contribute to balance between robustness and vulnerability, enhancing network resilience.
AssortativityWeighted
Assortativity
Coefficient		Reflects the tendency of countries (regions) to connect with partners of similar total trade volumes. Assortative networks strengthen hub connections, providing stability and rapid recovery, while disassortative networks facilitate information exchange and resource sharing but may lead to over-reliance on hubs, affecting resilience and stability.
Nodal ResilienceAnti-destruction AbilityWeighted
Out-degree &
In-degree		High weighted degrees indicate higher anti-destruction ability but may also make nodes “single points of failure”.
Transit CapacityWeighted Betweenness CentralityHigh values indicate nodes occupying central positions, controlling critical trade flows, and acting as bridges. High centrality reflects both closeness and potential influence in risk transmission.
Recovery CapacityWeighted Closeness
Centrality		Reflects a node’s centrality based on trade intensity distance. High closeness enables nodes to rapidly acquire and disseminate information, mitigating or blocking further risk transmission.
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Huang, X.; Wang, Z.; Pang, Y.; Tian, W.; Zhang, M. Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis. Forests 2024, 15, 1665. https://doi.org/10.3390/f15091665

AMA Style

Huang X, Wang Z, Pang Y, Tian W, Zhang M. Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis. Forests. 2024; 15(9):1665. https://doi.org/10.3390/f15091665

Chicago/Turabian Style

Huang, Xiangyu, Zhongwei Wang, Yan Pang, Wujun Tian, and Ming Zhang. 2024. "Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis" Forests 15, no. 9: 1665. https://doi.org/10.3390/f15091665

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