Has the proliferation of multilateral trade agreements generated greater risk exposure to the global production network?

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Chuke Jiang, Lizhi Xing, Yuanpeng Ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7122094/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In the context of global value chain restructuring, identifying and quantifying industry chain risks is essential for the study of global economic security. This paper treats Multilateral Trade Agreements (MTAs) as sources of structural risk, integrating network science, input–output analysis, and counterfactual methods. Using ADB-MRIO data, we construct a Global Industrial Value Chain Network model and apply XIFA algorithm to reduce dimensionality, extracting both the actual post-MTA network and a counterfactual network without MTAs. Through Hypothetical Elimination Method, we identify MTA-induced risk exposures, thereby establishing a multi-perspective risk-measurement framework. Our findings are: (1) The risk exposure of the global industrial chain presents a “core-periphery” structure, core countries face the most exposures, a lower number of risk exposures in the peripheral regions, but under the influence of global economic uncertainty, and semi-core areas remain relatively stable. (2) High-tech manufacturing and business services have a high number of risk exposures and a fast growth rate due to strong upstream and downstream linkages and strong reliance on technological capital; primary industries with simple production chains and low technological reliance have relatively stable risk exposures. (3) There are differences in the risk patterns of the industrial chain between China and the United States, with China being more sensitive to the risks of resources and wholesale trade, and the United States relying more strongly on the risks of high-end manufacturing. (4) From the point of view of the node position of the two countries in the global production network, as an upstream node, China is exposed to higher risks in mining and wholesale trade, while the U.S. is more vulnerable to technology-intensive industries, such as machinery, electrical and transportation equipment; as a downstream node, the construction industry is a common risk plateau between China and the U.S., but China is affected by the impact of residential leverage and the slowdown of infrastructure, while the U.S. is pressured by the vacancy of commercial real estate and fluctuations in energy costs. The U.S. is under pressure from commercial real estate vacancies and energy cost volatility. Physical sciences/Engineering Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing Risk analysis Intermediate Goods Trade Counterfactual Analysis Multi-Regional Input-Output Table Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction In the process of globalization, the industrial and supply chains of various countries have formed a complex division of labor that intertwines upstream and downstream activities, intertwines supply and demand, and blends industry, academia, and research, significantly enhancing the specialization and production efficiency of the global production network (GPN) . Consequently, issues in specific industrial chains or supply chains are now more likely to evolve into global issues. The depth and breadth of the impact of global systemic shocks on the production networks of various countries and regions have also greatly increased. In addition, due to profound influences from environmental changes, policy adjustments, technological innovations, and other factors, various uncertainties—such as natural disasters, accidents, and sudden public incidents—are more frequently sweeping across and threatening social stability in countries around the world. These uncertainties exert a bidirectional pressure on both supply and demand, spreading risks across all interconnected regions and industrial sectors, causing sustained negative impacts on national, regional, and even global economies (Yang et al., 2020b). Under these shocks, economies of different countries exhibit varying paths of recession or recovery, depending on their economic size, structural scale, and governance effectiveness (Eraydin, 2016). Against this backdrop, the security of the global economic system has become an important research branch in political economy. Identifying and measuring the risk exposures on the industry chain of various sectors in different countries and regions is key to exploring this issue. In recent years, we have seen an emergence of research on the safety resilience of regional economic systems, which includes analysis frameworks encompassing various elements (Briguglio et al., 2006 ), adaptive cycle models established from an evolutionary perspective (Simmie and Martin, 2010), and econometric analysis models using employment numbers or GDP as core variables (Davies, 2011, Brakman et al., 2015 , Tang and Li, 2022 ), among others. Network science, an effective means for depicting the non-linearity, uncertainty, self-organization, and emergent properties of various complex systems, can not only explain the synchronization, propagation, and game rules behind various economic phenomena but also combine dissipative structure theory, synergy theory, and mutation theory to study the impact mechanisms of economic and non-economic factors on the safety resilience of industrial economic systems (Tran et al., 2019, König et al., 2022 ). Therefore, proactively identifying production-chain risk exposures and assessing the potential impacts of exogenous shocks on these chains are vital for effective production-chain risk management. From the perspective of government macro-level authorities, it is necessary to monitor these risks at the meso-industrial and even higher macro levels in order to better control production-chain risks and enhance their resilience. Literature Review Currently, the global economy is characterized by regionalization, nearshoring, and digitalization (Yi et al., 2023). Taking the Sino-U.S. trade conflict as the main thread, and COVID-19 shocks, digitalization, and decarbonization as three auxiliary lines, it forms the developmental context of the Global Value Chain (GVC) restructuring period (Xu, 2021), driving the shift in GVC development from an efficiency-first to a security-and-resilience-first orientation. Related research has also turned to value-chain restructuring, resilience, and stability(Antràs, 2020 , Baldwin and Freeman, 2022 , Baldwin et al., 2022, Yang et al., 2023, Ni et al., 2024, Su and Wang, 2024, Yin et al., 2023 , Zhu and Huang, 2021, Yi et al., 2023). As a key institutional tool for addressing Trade Policy Uncertainty, Multilateral Trade Agreements (MTAs) not only play a critical role in stabilizing the global economic and trade system through rule-based mechanisms, but also profoundly influence the structural evolution of GPN through their exclusionary effects on non-member countries, becoming an important force driving the regionalization of the global economy. Therefore, the structural risks triggered by MTAs urgently require systematic identification and assessment. The literature review section of this paper unfolds along three dimensions: the definition and sources of risk, risk measurement, and the impact of trade agreements on value chains. Definition and Sources of Risk The various levels and categories of agents within an economic system, along with their interconnections, form a complex network topology. According to Jüttner's classification framework, the risks faced by firms can be divided into internal risks, external risks, and network-related risks (Jüttner, 2005 ). In other words, during its operation, an economic system is subject not only to various uncertainties in the external environment, but also to risks that arise due to the heterogeneity of agents in terms of their countries or regions, industrial sectors, positions within the industrial and supply chains, corporate strategies, and distribution channels. Consequently, these risks are typically difficult to measure, control, and manage (Cattaneo et al., 2010). From a governmental perspective, to mitigate industrial chain disruptions and their adverse impact on national or regional economic development, the core of risk management lies in identifying risk exposures and quantifying their ripple effects. Based on an extensive review of the literature, this paper categorizes the sources of risk into two types: Endogenous Risks and Exogenous Risks . On one hand, endogenous (structural) risks of the industrial chain stem from its operational model and reflect the inherent fragility of GPN, which exhibits the characteristics of “scale-free” and “small-world” structures (Liu et al., 2021), meaning that a few hub nodes—representing key industrial sectors linking upstream and downstream segments on a global scale—are highly interconnected, while most nodes are scattered at the periphery of the network (Yin et al., 2023 ). This “core-periphery” topology implies that global industry supply chains are simultaneously robust and vulnerable. If hub nodes or the links connecting them (intermediate goods trade links) change during the process of network restructuring, the number of industrial and supply chains functioning as shortcuts will decline significantly, thereby impairing the operational efficiency of GPN. On the other hand, exogenous risks in the industrial chains refer to the risks that firms are exposed to when facing external shocks. As Miroudot notes, these shocks may be supply-side—such as tariff increases, natural disasters, labor strikes and supplier insolvencies—or demand-side, including customer bankruptcies, the entry of new competitors and macroeconomic downturns (Miroudot, 2020). Transport disruptions can be classified separately, as they occur very frequently and are not entirely related to either supply or demand (Heiland and Ulltveit-Moe, 2020). Regardless of the type of exogenous risk, the resulting economic shocks can rapidly propagate throughout the network via shortcuts between industrial sectors, thereby triggering systemic risk (Buckley, 2021). Within the foregoing framework, MTAs can be regarded as a quintessential source of structural risk in the GVC. By reducing tariff and non-tariff barriers through preferential measures, MTAs reshape the network structure of GVC. On the one hand, MTAs promote regional clustering of value chains, tightening trade and investment linkages among member countries and concentrating production networks within the agreement. On the other hand, MTAs are exclusionary toward non-members: the preferential trade policies established within the agreement lower transaction costs among members but relatively weaken connections with peripheral countries. This implies that the GVC is transitioning from an open network structure to one composed of multiple Regional Value Chain (RVC) . Under such a configuration, intra-regional coupling is high while inter-regional linkages remain relatively weak, and MTAs alter the transmission paths and scope of risks on a global scale. Should a major agreement region experience a policy change or shock event, its impact can no longer be absorbed and dispersed globally as before; instead, it will be amplified within the region and propagate outward through limited cross-regional links, thereby triggering systemic risk. Hence, this paper considers the network restructuring induced by MTAs as a source of structural risk in the GVC. Risk Measurement Existing methods for measuring industrial chain risks from a proactive perspective primarily include Trade Network Analysis Tools and Production Network Analysis Tools . The former generally builds network models based on the UN Comtrade Database to measure the risk exposure or vulnerability of industrial chains. The approach first identifies those commodities most susceptible to disruption in international markets and then traces their principal exporters and importers, thereby pinpointing the critical goods that may render a country vulnerable to external supply shocks (Fagiolo et al., 2010, De Benedictis et al., 2014, Bode et al., 2011 , Brandon-Jones et al., 2014, Korniyenko et al., 2017, Cui et al., 2022 , Huang et al., 2022). By contrast, this approach exploits the intermediate use section of the Input Output table to map the network topology of an economy's production system. Building on this representation, several studies have employed Input-Output Analysis to quantify risk exposure along industrial chains (Antràs, 2020 , Borin et al., 2021, Baldwin and Freeman, 2022 , Baldwin et al., 2022). The risk measurement indicators advanced in these works remain confined to metrics of value chain participation. They chiefly employ trade decomposition techniques—underpinned by the value-flow frameworks—to operationalize both participation and exposure to external risks (Wang et al., 2013, Koopman et al., 2014). With the rapid expansion of intermediate goods trade and deepening international vertical specialization, scholars introduced the concept of GVC (Gereffi et al., 2005), and developed corresponding accounting frameworks to examine value creation and distribution processes worldwide. Since the early 2000s, the compilation and public release of Multi-Regional Input-Output (MRIO) tables have opened new paradigms for GVC accounting research. Specifically, the intermediate use and final demand sections of MRIO tables capture the complex topologies of global production and trade networks, respectively. These matrices serve as primary data sources for assessing distributional efficiency—tracing flows of intermediate and final goods through industrial chains—and for evaluating production efficiency, with a focus on intermediate goods transactions. Building on these data, mainstream economists have established mature GVC accounting methodologies (Hummels et al., 2001 , Koopman et al., 2010 , Timmer et al., 2012 , Wang et al., 2017 , Mi et al., 2018 , Yang et al., 2020a ) and employed reductionist approaches to analyze sectoral trade structures and developmental trends. Impact of MTAs on GVC The role of MTAs in shaping industrial and value chains has become a research hotspot in recent years. A large body of literature, focusing on specific regional accords, has examined the impact of joining MTAs such as the CPTPP (Zhang and Ling, 2023), RCEP (Li and Li, 2022 , Gao and Wei, 2023, Lv and Wang, 2024), and BRI (Ma et al., 2021) on RVC. In addition, numerous studies have explored how MTAs alter trade and investment patterns, economic growth, welfare levels, industrial division of labor, and positions within the value chain. Cheng and Fan analyze the reshaping of power structures in RVC, arguing that the new generation of large-scale regional agreements consolidates the core position of leading countries and creates a “core–periphery” division of labor within the region (Cheng and Fan, 2025). Bondi et al. focus on how GVC participation influences the design of trade agreements (Bondi et al., 2025b ). They contend that greater GVC-type trade fosters the formation of deeper accords covering broad issues, indicating that the evolution of GVC, in turn, shapes the form and content of MTAs. Bondi et al. further find that the regionalization of GVC disaggregation and the surge in regional agreements mutually reinforce one another: economies with a higher degree of value-chain regionalization tend to conclude regional accords to consolidate their regional supply chains (Bondi et al., 2025a ). Zhao et al. show that MTAs significantly promote member countries’ economic growth and industrial upgrading by expanding trade and investment, thereby enhancing overall regional welfare (Zhao et al., 2025). Zhang et al. argue that the signing and deepening of RTAs improve the export stability of trading partners (Zhang et al., 2024). Xue’s study of the cumulation rule of origin—a specific agreement element—reveals that it enhances value-chain positioning by increasing intra-regional trade in intermediate goods (Xue, 2024). Fan et al. demonstrate that the deepening of factor provisions in MTAs produces significant asymmetric effects on member participation in GVC (Fan et al., 2023). Hu et al. find that MTAs markedly facilitate economies’ participation in GVC (Hu et al., 2024). Collectively, these studies highlight the significant role of MTAs in enhancing intra-member economic performance, value-chain embedment, and trade stability. As the GVC enters its restructuring phase, scholars have shifted their focus to how MTAs affect the stability of domestic value chains. For example, Song and Chen examine the depth of regional services trade agreements and find that the deeper the accord, the stronger a firm’s GVC resilience: under external shocks, firms recover more quickly and maintain their position within the value chain (Song and Chen, 2024). Song et al. further investigate how the depth of services trade agreements drives GVC reconfiguration, revealing that integration of service factors prompts manufacturing firms to reconfigure their value chains regionally, thereby enhancing the coherence and alignment of regional industrial division of labor (Song et al., 2024). Shen and Shen explore the impact of high-standard trade agreements on global industry chain resilience, arguing that the institutional environment is a key determinant of industry chain robustness and that deeper trade accords reduce the risk of global industry chain disruptions (Shen and Shen, 2024 ). Yu et al. study the effect of deepened regional trade agreements (RTAs) on global industry chain resilience from the perspective of intermediate-goods trade (Yu et al., 2024). Zhang et al. measure the reconfiguration effects on GVC participation depth and breadth resulting from RCEP tariff reductions (Zhang et al.). Overall, existing studies primarily focus on member countries, use GVC participation metrics as core indicators, and employ empirical methods combining matched data with econometric identification. However, systematic research is still lacking on the risk-exposure effects and industry-relocation mechanisms that MTAs may impose on non-member countries, particularly the exclusionary and spillover risks they generate within the global value-chain structure. Research Gap The above studies offer various approaches for the formation and quantitative analysis of value-chain risk exposure but exhibit several shortcomings. (1) Single-perspective research and lack of a comprehensive analytical framework. Existing work typically measures value-chain risk using only one method—focusing on network structure, trade relationships, or macro-level indicators. Such single perspectives cannot fully capture the complexity of the global industrial system and lack a systematic framework. (2) Absence of identification and quantitative assessment of MTA-induced network structural risks. While current studies have examined the positive effects of MTAs on regional value-chain stability and economic performance, they neglect the role of MTAs as sources of risk arising from shifts in the global value-chain network structure, overlooking vulnerabilities and risk exposures triggered by regional integration. (3) Lack of a global systemic perspective and analysis of overlapping agreements. Most literature focuses on a single MTA and its intra-regional effects, pays little attention to spillovers onto non-member regions, and does not assess the combined impact of multiple concurrent agreements on the global value-chain network. To address these gaps, this paper makes the following contributions: (1) Development of a multi-perspective risk-exposure identification framework. We innovatively integrate input–output analysis, trade-network analysis, and counterfactual analysis into a unified framework, thereby enhancing the precision of risk quantification. By comparing a counterfactual model with a baseline “zero” model, we can accurately identify the risk exposures induced by MTAs within the global value-chain network. (2) Incorporation of MTAs as structural risk sources. For the first time, we treat MTAs as risk variables that restructure the global value-chain architecture, systematically identifying their mechanisms of impact on value-chain reconfiguration and risk exposure. This expands understanding of the dual (positive and negative) impact mechanisms of MTAs and fills a gap in analyses of their adverse effects. (3) Systematic synthesis of network-structure changes under multiple MTAs. We map the temporal evolution and regional coverage of major global MTAs, such as the CPTPP, RCEP, and USMCA, considering not only the strengthening of intra-agreement chains but also the risk-exposure effects on regions outside the agreements. In summary, the integrated methodological framework proposed here can comprehensively identify global value-chain risk exposures, providing a powerful analytical tool and decision-support basis for risk early warning and policy formulation. Methodology Mechanisms of Risk Exposure Formation: Multilateral Trade Agreements The reshaping of the GVC structure by MTAs is primarily reflected in two key mechanisms: the Trade Creation Effect (TCE) and the Trade Diversion Effect (TDE) , with the latter forming the theoretical basis for regarding MTAs as sources of structural risk in the value chain. The TCE refers to an agreement’s reduction of intra-regional trade barriers and transaction costs, thereby promoting improvements in production efficiency and industrial agglomeration, and tightening production and trade linkages among member countries. This creation effect drives the concentration of industry chain and supply chain activities within the region, strengthening the connectivity of the regional value-chain network. In contrast, the TDE reveals the structural distortions and risk spillovers induced by MTAs. Thanks to preferential treatment among member countries, certain segments of the value chain shift from non-member to member economies. This not only weakens the position of non-members in the GVC but also increases their risk exposure, particularly when non-member economies are highly dependent on member markets or industry chain nodes, amplifying potential risks. According to classic customs union theory, when member states eliminate tariffs among themselves while maintaining barriers for non-members, firms will import higher-cost goods from within the union instead of lower-cost non-member products, solely due to tariff differentials—even if the latter have a cost advantage. Such distortion of trade flows contravenes the principle of comparative advantage and leads to a decline in global resource-allocation efficiency. MTAs can both promote global trade liberalization and economic integration and accelerate trends toward regional industrial clustering, nearshoring, and localization. Empirical research supports this view. For example, Flaaen et al. investigated various combinations of RTAs among suppliers, the United States, and destination countries within GVC. They found that when the United States has a bilateral RTA with either an input-source country or an export-destination country, GVC trade flows increase by an average of 22%, and when a trilateral RTA is in place among three countries, GVC trade flows rise by an average of 55% (Flaaen et al., 2024 ). Conversely, countries that are excluded from such agreements typically face higher market-access barriers and more intense competitive pressure. Although signing an MTA does not guarantee close economic cooperation among the participating economies, the agreement exerts adverse effects on those left out, potentially altering their domestic industrial structures. This is because reduced trade costs among member states erode the relative competitiveness of non-member goods and services. Therefore, for many economies, MTAs that do not include them can be seen as manifestations of trade protectionism and GVC reconfiguration, and their negotiation and implementation may become endogenous sources of risk. Figure 1 shows the effective dates and covered economies of the major global MTAs. Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is a flagship MTA covering multiple Asia–Pacific economies. It evolved from the original Trans-Pacific Partnership (TPP), launched in 2005 by Singapore, New Zealand, Chile, and Brunei and later expanded to 12 members including the United States. After the U.S. withdrew in 2017, the remaining 11 countries signed the CPTPP in 2018, and the UK joined in 2023. The CPTPP establishes high-standard rules on intellectual property, labor, and environmental protection and serves as an alternative economic framework to counterbalance China’s growing trade influence. United States–Mexico–Canada Agreement (USMCA) is the trilateral FTA replacing NAFTA (in force since 1994). Renegotiated between the three parties, it was signed on November 30, 2018, and entered into force on July 1, 2020. The USMCA modernizes the pact by including chapters on digital trade, labor rights, and environmental protection, reflecting a broader strategy to promote balanced and resilient trade. EU–Japan Economic Partnership Agreement (EUJEPA) is a landmark FTA between the EU—now comprising 27 member states after decades of integration (1951 ECSC→1957 EEC→1973 enlargement→1986 Single European Act→1992 Maastricht→2004 Eastern enlargement→2007 Lisbon→2020 Brexit)—and Japan. Negotiations began in 2013, the agreement was signed in July 2018, and it entered into force in February 2019. It eliminates tariffs, reduces trade barriers, and deepens economic ties. Regional Comprehensive Economic Partnership (RCEP) , initiated by ASEAN in 2012, integrates regional trade rules. Its 15 members—10 ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, the Philippines, Vietnam, Laos, Myanmar, Cambodia, Brunei) plus China, Japan, South Korea, Australia, and New Zealand—account for nearly one-third of global population and GDP. RCEP’s rules of origin allow tariff-free treatment for over 90% of goods, and its liberalization of services and investment far exceeds that of earlier “10 + 1” FTAs. ASEAN Free Trade Area (AFTA) , launched at the 1992 ASEAN Summit in Singapore, is another key example of regional integration. By reducing or eliminating tariffs and non-tariff barriers, AFTA promotes the free flow of goods, services, and investment, thereby strengthening economic cooperation and competitiveness within ASEAN. Currently, Asian economies have gradually evolved into strategic partners actively sought after by major powers. As the trend of anti-globalization becomes increasingly pronounced, MTAs and economic organizations serving similar functions have successively emerged as important vehicles for advancing regional economic collaboration. It is noteworthy that, although China wields considerable influence as a key member of RCEP, many member states remain committed to reducing their reliance on its manufacturing and markets through alternative agreements. Their motivation for doing so lies not only in strengthening their own autonomous development capacities, but also in response to the intervention from the U.S. in their international trade through measures such as reciprocal tariffs. MRIO Table In order to conduct empirical studies on the endogenous and exogenous risks from the perspectives of the GVC restructuring and global emergencies, the MRIO table used for network modeling must encompass a significant number of emerging economies and cover a longer time span. Considering these two points, we finally adopted the Asian Development Bank Multi-Regional Input-Output Tables (ADB-MRIO), as the source of global intermediate goods trade data, which provides MRIO tables building on the World Input-Output Database (WIOD) to cover 29 Asia and the Pacific economies. It has facilitated the production and analysis of GVC related statistics for Asian economies. Economies explicitly identified in the ADB-MRIO account for at least 93% of the world GDP. The data structure of the MRIO table is shown in Table 1 . Table 1 Comparison of most relevant papers. Output Intermediate Use Final Demand Total Output Input Country A B \(\:\cdots\:\) R A B \(\:\cdots\:\) R Sector \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:\cdots\:\) \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:\cdots\:\) \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) Intermediate Use A \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:{\varvec{Z}}^{\varvec{A}\varvec{A}}\) \(\:{\varvec{Z}}^{\varvec{A}\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{Z}}^{\varvec{A}\varvec{R}}\) \(\:{\varvec{Y}}^{\varvec{A}\varvec{A}}\) \(\:{\varvec{Y}}^{\varvec{A}\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{Y}}^{\varvec{A}\varvec{R}}\) \(\:{\varvec{X}}^{\varvec{A}}\) B \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:{\varvec{Z}}^{\varvec{B}\varvec{A}}\) \(\:{\varvec{Z}}^{\varvec{B}\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{Z}}^{\varvec{B}\varvec{R}}\) \(\:{\varvec{Y}}^{\varvec{B}\varvec{A}}\) \(\:{\varvec{Y}}^{\varvec{B}\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{Y}}^{\varvec{B}\varvec{R}}\) \(\:{\varvec{X}}^{\varvec{B}}\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) \(\:⋮\) R \(\:{\varvec{S}}_{1},\cdots\:,{\varvec{S}}_{\varvec{n}}\) \(\:{\varvec{Z}}^{\varvec{R}\varvec{A}}\) \(\:{\varvec{Z}}^{\varvec{R}\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{Z}}^{\varvec{R}\varvec{R}}\) \(\:{\varvec{Y}}^{\varvec{R}\varvec{A}}\) \(\:{\varvec{Y}}^{\varvec{R}\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{Y}}^{\varvec{R}\varvec{R}}\) \(\:{\varvec{X}}^{\varvec{R}}\) Value-Added \(\:{\varvec{V}\varvec{A}}^{\varvec{A}}\) \(\:{\varvec{V}\varvec{A}}^{\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{V}\varvec{A}}^{\varvec{R}}\) Total Input \(\:{\varvec{X}}^{\varvec{A}}\) \(\:{\varvec{X}}^{\varvec{B}}\) \(\:\cdots\:\) \(\:{\varvec{X}}^{\varvec{R}}\) Assume the MRIO table includes \(\:m\) countries and \(\:n\) industrial sectors ( \(\:u,v=\text{1,2},\dots\:,m\) and \(\:s,t=\text{1,2},\dots\:,n\) ), the fundamental linear equation within it is: \(\:X={\left(I-A\right)}^{-1}Y\) , where: \(\:X=\left({x}_{s}^{v}\right)\) is the total output vector of sector \(\:s\) in country \(\:v\) ; \(\:I\) is the identity matrix; \(\:{\left(I-A\right)}^{-1}\) is the Leontief inverse matrix; the technical coefficient matrix \(\:{A}^{uv}=\left({a}_{st}^{uv}\right)\) , where \(\:{a}_{st}^{uv}={z}_{st}^{uv}/{x}_{t}^{v}\) , represents the intersectoral monetary flow from sector \(\:s\) in country \(\:u\) to sector \(\:t\) in country \(\:v\) ; \(\:{x}_{t}^{v}\) represents the total output vector of sector \(\:t\) in country \(\:v\) ; \(\:Y=\left({y}_{s}^{uv}\right)\) is the final demand vector of goods from sector \(\:s\) in country \(\:v\) by country \(\:u\) . The countries/regions and industrial sectors covered by ADB-MRIO are shown are shown in Fig. 2 and Fig. 3 . In the chord diagrams on the left side of both figures, the differences in intermediate goods trade volumes between sectors and between economies are represented by the width of the chords. Null Model and Counterfactual Model The typical MRIO table consists of three parts (see Fig. 4 a) and can be transformed into a triple-layer network structure (see Fig. 4 b). From value-added to intermediate use and final demand, the MRIO framework captures the flow of value along the GVC. Generally, economists are most concerned with how economies and their production units (i.e., industrial sectors) jointly create greater value. This collaborative relationship forms the foundation of GPN. Recognizing that the value flow also entails the risk flow, we construct the Global Industrial Value Chain Network (GIVCN) model based on the input-output data from the intermediate use part (see Fig. 4 c). We view the industrial sectors of various countries or regions as nodes, and their input-output relationships via industrial chains as edges, with the trade volume of intermediate goods reflecting the strength of these relationships as weights. When the total number of nodes in the network is \(\:N\) ( \(\:N=m\times\:n\) , \(\:i,j=\text{1,2},\dots\:,N\) ), the node set is represented as \(\:V=\left\{{v}_{1},{v}_{2},\cdots\:{,v}_{N}\right\}\) , the edge set as \(\:E=\left\{{e}_{11},{e}_{12},\cdots\:,{e}_{ij}{,\cdots\:{,e}_{N\left(N-1\right)},e}_{NN}\right\}\) , and the weight set as \(\:W=\left\{{w}_{11},{w}_{12},\cdots\:,{w}_{ij}{,\cdots\:{,w}_{N\left(N-1\right)},w}_{NN}\right\}\) . While the GIVCN model applies the weight set \(\:W\) to display the adjacency matrix, each row refers to the distribution of intermediate goods output from an upstream sector to several downstream sectors, and each column the intermediate goods input obtained by a downstream sector from several upstream sectors. The MDS map of the GIVCN–ADB model is presented in Fig. 5 . This map encompasses 63 countries (regions) and 35 industry sectors. The migration trend of China’s industrial sectors toward the global industrial core is depicted in Fig. 5 , illustrating the emergence of a “core–periphery” structure in GPN. In the early 1970s, American scholar Immanuel Wallerstein introduced world-systems theory, emphasizing the unequal distribution of global capital, technology, and wealth. According to Wallerstein, North America and Europe occupy the “core” of the world economy, while much of Asia, Africa, and Latin America lie in the “periphery.” Core countries set the strategic and economic direction worldwide. However, the core–periphery structure is not static: peripheral countries can, through strategic development, ascend to the semi-periphery or even the core, while core countries may decline to semi-periphery or periphery status. Semi-peripheral countries—positioned between core and periphery—often succeed in dependent development. From a dynamic-development perspective, peripheral countries are not invariably trapped in exploitation. Indeed, the relocation of industries by core countries creates opportunities for peripheral economies: by leveraging capital and technology from developed nations, these countries advance their own industrial development and technological innovation, achieving industrial upgrading and even “leapfrog” growth. As GVC deepen and expand, many manufacturing processes have shifted to developing countries—most notably China—while developed economies have transitioned from production-based societies to consumption-oriented ones. This shift has led to industrial hollowing-out in certain developed nations and has heightened their dependency on China as the “world’s factory.” Obviously, the GIVCN model is a highly dense (nearly fully connected) weighted directed network, and therefore requires dimensionality reduction to visualize changes in its network topology. Given the substantial heterogeneity in intermediate-goods trade between upstream and downstream industries, this paper employs the X-Index Filtering Algorithm to extract a subnetwork from the GIVCN model (Xing et al., 2024 ). This subnetwork is termed the Global Industrial Value Chain Backbone Network (GIVCBN) and is denoted as \(\:\stackrel{´}{G}=\left(V,\stackrel{´}{E},\stackrel{´}{W}\right)\) . The GIVCBN model removes over 90% of edges while losing less than approximately 1% of the intermediate-goods trade volume present in the original GIVCN. This finding illustrates a pronounced “Matthew effect” in the economic linkages within the GVC: a small number of upstream sectors supply most intermediate inputs to a given sector, and a small number of downstream sectors consume the majority of its intermediate outputs. In addition, to reflect the potential impact of specific MTAs on the industry chain, we need to further extend the GIVCBN model into two categories: the null model (GIVCBN-N) and the counterfactual model (GIVCBN-C). The extraction idea is shown in Fig. 6 : GIVCBN-N Model : First, apply the XIFA algorithm to prune the GIVCN model. Second, aggregate the industry sectors of those countries/regions related to MTA and treat them as a loosely integrated entity. GIVCBN-C Model : First, merge the industry sectors of MTA-related countries/regions within the GIVCN model to form a tightly integrated entity. Second, apply the XIFA algorithm to prune this network. As shown in Fig. 6 (b) , the GIVCBN-C model treats the internal industry sectors of MTA-related countries/regions as a tightly integrated whole, enabling them to further compete for additional intermediate-goods resources. The result is the elimination of relatively inefficient production chains in other economies. Compared with the GIVCBN-N model, the GIVCBN-C model more fully captures the network agglomeration effects of MTA-related countries/regions and the network deconstruction of the remaining economies. Therefore, by comparing the adjacency matrices of the GIVCBN-N and GIVCBN-C models, we can identify the segments of economic production chains that are impacted by the potential effects of a specific MTA, manifesting as risk exposures in the production network (Xing et al., 2024 ). Based on the above modeling approach, this study constructs a 17-year GIVCBN model, covering the effective periods and scopes of major MTAs worldwide from 2007 to 2023. Each year comprises one GIVCBN-N model and three to five GIVCBN-C models reconstructed for specific MTAs. The annual risk exposures triggered by MTAs are then aggregated—specifically, the production-chain links in GPN that may face disruptions. Results Risk Exposure at National Level The countries/regions within a specific MTA will jointly endeavor to reduce trade barriers, strengthen regional economic integration, enhance economic connectivity among themselves, and bolster their links with other global economies. At the same time, the domestic production networks of other economies (i.e., the assemblage of their internal industry and supply chains) will be exposed to exogenous risks, thereby revealing their risk exposures. This subsection counts the number of exposures between and within each of the world's economies, as shown in Fig. 7 . From the number of risk exposures between economies, cross-border industry chain uncertainty has risen sharply. Between 2017 and 2023, major global events—US–China trade frictions, the COVID-19 pandemic, and the Russia–Ukraine conflict—triggered industry chain disruptions, production delays, and cost increases. To safeguard their industrial chains, countries have increasingly relied on MTAs with other economies, hoping regional chain integration would curb external risks. Yet this strategy has paradoxically reinforced interregional risk transmission and further accelerated GVC restructuring. In terms of regional characteristics, different areas exhibit significant differences in industry chain risk exposure. Within North America, the United States, Canada, and Mexico have consistently maintained a relatively high number of industry chain risk exposures. Although NAFTA and its successor USMCA were designed to promote trade liberalization among the three nations and strengthen regional integration, the U.S. “de-risking” strategy has led to a decoupling from GVC, producing counterproductive effects on economic development. The economic “prisoner’s dilemma” behind this phenomenon is complex and multifaceted, primarily because emerging economies—exemplified by China—have continuously enhanced their competitiveness in labor costs, technological innovation, high-end manufacturing, market scale, and policy environments during globalization, thereby weakening the industrial linkages between the U.S. and its neighbors. Consequently, the U.S. is pursuing nearshoring and friendshoring to bolster the resilience of its transnational industrial and supply chain and reduce its number of risk exposures. From the perspective of internal risk exposures within economies, the uncertainty of domestic industrial-chain supply networks exhibits varied trends. Since the United Kingdom initiated the Brexit process in 2016, the number of domestic industry chain risk exposures has climbed markedly, indicating that Brexit has profoundly disrupted the previously highly integrated industrial and industry chain system with EU member states, resulting in structural imbalances within the domestic economy. In contrast, the number of domestic risk exposures in the United States has decreased, likely because its manufacturing reshoring policies have enhanced the robustness of its production network. It is worth noting that China’s domestic risk exposures have remained relatively stable, suggesting that the China-led RCEP has effectively hedged against the negative impacts of other MTAs on its production network. As a collective of smaller-scale economies, the Rest of the World (ROW) experienced a rise in risk exposures from 2007 to 2012, followed by a decline from 2012 to 2023. We attribute the initial increase to the 2008 subprime crisis, which severely impacted economies with high trade dependence and close financial ties to the U.S., while the subsequent decrease reflects emerging economies’ strengthened positions in global trade patterns and their improved risk resilience through deeper vertical specialization. Risk Exposure at Industry Level This subsection compiles all risk exposures associated with each of the 35 sectors and utilizes the heatmap and boxplot presented in Fig. 8 to respectively illustrate the temporal trends and dispersion of their risk-exposure counts. Between 2007 and 2023, risk exposures in most sectors of the GVC exhibited a sustained and significant upward trend. This acceleration was particularly pronounced after 2017 under the cumulative influence of multiple external shocks—namely, Sino–US trade frictions, the COVID-19 pandemic, and heightened geopolitical tensions—which sharply magnified industry-wide risk exposures. Nearly one-third of sectors reached historically unprecedented abnormal peaks in 2023, underscoring the systemic impact of GVC restructuring on sectoral security. While the era of global economic integration enhanced vertical specialization and the division of labor across sectors, it also increased their uncertainties with upstream and downstream actors; in recent years, the accelerated restructuring of GVC has converted these latent interdependencies into overt industry chain risks. The Primary sectors display low dispersion in their risk-exposure counts, indicating that their production and trade structures remain relatively stable. Most Low-tech sectors also show low dispersion; however, “Construction (S18)” has experienced a marked increase in exposures and consecutive outliers over the past two years, reflecting its vulnerability to raw-material price volatility, labor shortages, and a deteriorating financing environment. Within the High- and Medium-tech sectors, “Machinery, Nec (S13)” shows a larger increase in risk exposures compared to its peers. We attribute this to technological advances in automation and robotics, the Internet of Things, and energy-saving and emission-reduction technologies, which impose higher development requirements and thus accentuate this sector’s weaknesses in less developed economies. In the Business Services category, sectors such as retail, hospitality, transportation, telecommunications, finance, and leasing exhibit very significant increases in risk exposures. These industries are highly sensitive to geopolitical risks, causing their industry chain risks to amplify as GVC restructuring deepens. The Public and Welfare Services sectors are non-traded; their industry chain risks depend primarily on domestic market stability. Clearly, the widespread increase in uncertainties within domestic production and supply networks has adversely affected the security and stability of these sectors. The causes of industry heterogeneity are manifold. First, GVC restructuring has shifted firms from a sole focus on cost efficiency to an emphasis on industry chain security and resilience, making sectors with deep integration and strong upstream–downstream linkages more prone to risk exposure under external shocks. Second, different industries exhibit variations in technological dependence, capital intensity, and market sensitivity, which affect their inherent capacity to withstand disruptions. Moreover, while emerging technologies and MTAs enhance resilience in certain regions, they also render the value-chain network structurally more vulnerable during geopolitical and public health events. China–US Risk Exposure Comparison This subsection compares the topology of the 2023 GIVCBN-N model with those of five GIVCBN-C models and provides an overview of the cross-border risk exposures for China and the United States. Furthermore, to emphasize the significance of these risk exposures, the flow magnitudes in the Sankey diagrams are scaled in proportion to the volume of intermediate-goods trade, as illustrated in Fig. 9 . From the perspective of cross-national value chains, the sources and magnitudes of risk exposures in China and the United States exhibit both commonalities and significant differences. Overall, the upstream risk exposures in both countries are concentrated in broad basic-industry sectors—namely, Coke, Refined Petroleum and Nuclear Fuel (S08), Chemicals and Chemical Products (S09), Basic Metals and Fabricated Metal (S12), and Renting of Machinery and Equipment and Other Business Activities (S30). This finding indicates that both nations possess large-scale production capacities and are highly dependent on importing critical materials, products, and services from abroad. Building upon these commonalities, China’s upstream risk exposures are further concentrated in the Mining and Quarrying sector (S02) and the Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles sector (S20). As the “world’s factory,” China consumes vast amounts of minerals and bulk commodities from around the globe and thus bears significant risks stemming from shifts in international policy regimes—such as trade barriers, environmental regulations, and carbon tariffs. In contrast, the United States’ upstream industry chain risks are focused in long-process manufacturing sectors—Machinery, Nec (S13); Electrical and Optical Equipment (S14); and Transport Equipment (S15)—whose production processes involve complex technologies and equipment, typically require extended production cycles and substantial capital investments. The US reliance on imports in these sectors precisely highlights the “hollowing out” of its manufacturing base. In terms of downstream risk exposures, both countries exhibit significant exposures in the Construction (S18) sector, however, the composition of these risks differs. In China, downstream risk exposures primarily originate from a highly leveraged residential market and a slowdown in infrastructure investment, whereas in the United States they reflect the dual effects of rising vacancy rates in commercial real estate and volatility in energy costs. In any event, risks from numerous upstream sectors converge upon this sector through the value and industry chain, thereby magnifying or even triggering its inherent vulnerabilities. Figure 10 reports the number of risk exposures for each industrial sector in China and the United States when serving as upstream and downstream nodes within the GVC in 2023. China’s high-risk exposures as upstream sectors are mainly concentrated in Machinery and Products (S13), Other Business Activities (S30), and Chemicals and Chemical Products (S09), reflecting its deep reliance as the “world factory” on critical intermediate inputs; as downstream sectors, China’s high-risk exposures focus on Basic Metals (S12), Transport Equipment (S15), and Construction (S18), indicating that upstream shocks are transmitted intensely through heavy industry and infrastructure. In contrast, in the United States upstream risk exposures are more pronounced in service- and technology-intensive industries such as Financial Intermediation (S28), Inland Transport (S23), and Machinery and Products (S13), while downstream risk exposures are mainly distributed across consumer and service sectors—Wholesale Trade (S20), Retail Trade (S21), and Hotels and Catering (S22)—highlighting an economic structure reliant on domestic demand and high value-added services. The sector-level differences between the two countries arise from their distinct industrial development paths, division of labor within GVC, macroeconomic policies, and regional cooperation frameworks. First, China relies on export-oriented manufacturing and resource processing, whereas the United States is driven by services and technological innovation; thus, their value-chain vulnerabilities and risk transmission mechanisms under global shocks differ markedly. Second, China’s historical GVC positioning emphasized high-volume manufacturing and resource processing under a low-cost advantage, while the United States focused on high-tech, capital-intensive, high value-added segments. Moreover, differences in macroeconomic and policy environments cause the same sectors to exhibit varying susceptibilities to global shocks. Finally, their strategies for industrial upgrading and regional cooperation are reshaping risk patterns: China disperses external risks through multilateral agreements such as RCEP, whereas U.S. nearshoring and friendshoring strategies have partially mitigated dependence on a single supply source. Conclusion and Discussion This paper treats MTAs as structural sources of risk within GVC. We construct a GIVCN model based on ADB-MRIO data, apply the XIFA algorithm for dimensionality reduction to extract both the factual network and its counterfactual counterpart, and then employ a hypothetical elimination method to identify and quantify the risk exposures induced by these agreements. This results in a multi-perspective risk measurement framework that integrates network science, IO analysis, and counterfactual evaluation. The main findings are: (1) Global industry chain risk exposures exhibit a pronounced “core–periphery” structure. Developed countries occupying the “core” of GPN face the highest and most sensitive exposures; “periphery” regions, while exhibiting lower overall exposure, are more vulnerable due to their less diversified economies; and “semi-periphery” regions achieve relatively stable exposures by signing multilateral trade agreements and optimizing regional network connections to bolster chain resilience. This structural pattern arises from the core countries’ deep reliance on international industry chain, the limited buffering capacity of peripheral regions owing to low participation, and the enhanced industry chain security in semi-core regions through institutional cooperation and network optimization. (2) At the sectoral level, risk exposures diverge markedly: high-tech manufacturing and commercial services—characterized by strong upstream–downstream linkages and high dependence on technology and capital—are most sensitive to trade frictions, pandemics, and geopolitical conflicts, exhibiting both high exposure counts and rapid growth; by contrast, primary industries, with simpler production chains and lower technological dependence, display more stable exposure profiles. This pattern reflects the shift from efficiency-driven to resilience-oriented firm strategies, heterogeneous endogenous buffering capacities across industries, and the dual shaping effects of emerging technologies and multilateral agreements on value-chain structures. (3) The distinct GVC positional roles and industrial structures of China and the United States produce markedly different risk landscapes at the sectoral level. Common upstream risks cluster in basic industries, but China additionally faces high exposures in mining and wholesale trade, whereas the United States shows greater vulnerability in technology-intensive sectors such as machinery, electrical, and transport equipment. Downstream, construction is the principal risk-concentration sector in both countries, yet China’s pressures stem primarily from high residential leverage and slowed infrastructure investment, while the United States is challenged by rising commercial real-estate vacancies and energy-cost volatility. These differences originate from China’s export-oriented manufacturing and resource-processing focus versus the United States’ emphasis on high-value services and advanced manufacturing, as well as each country’s divergent macroeconomic policies and regional cooperation strategies—providing empirical guidance for targeted policies to enhance value-chain resilience and risk management. Based on the above findings, this paper proposes the following policy recommendations: (1) Deepen regional coordination and diversification. Continuously advance alignment with regional economic and trade agreements such as RCEP and CPTPP; actively broaden cooperation channels under multilateral frameworks like China–Japan–Republic of Korea and China–Europe; and build multi-layered, diversified market networks to disperse reliance on single markets and raw-material sources, thereby enhancing the overall resilience of RVC. (2) Strengthen upstream industrial resilience. Support key upstream sectors—mining, chemicals, and machinery equipment—to accelerate greening and decarbonization; encourage firms to pursue local substitution and circular-economy models; and establish robust strategic reserves for mineral resources to effectively withstand shocks from international raw-material price volatility and geopolitical conflicts. (3) Enhance capabilities in service and trade-intermediation links. In segments such as wholesale and commission trade, accelerate digital transformation and innovate regulatory frameworks; optimize cross-border logistics networks and customs-clearance procedures; reduce intermediaries’ risk exposures; and build an efficient, secure trade-intermediation system. (4) Optimize downstream investment structure and resource supply chain security. Guide real estate and infrastructure investment prudently, employing differentiated credit policies and macroprudential tools to guard against financial risks from high residential leverage and overreliance on shantytown redevelopment; concurrently, diversify import channels, strengthen strategic resource reserves, and promote R&D of critical mineral-recovery technologies to reduce dependence on single sources and ensure stable, controllable supply of essential resources. (5) Promote coordinated upgrading of high-tech manufacturing and services. Proactively localize supply chains and tackle key technologies for high-end manufacturing—such as semiconductors and precision machinery—to reduce passive dependence on highly volatile downstream sectors; and leverage the Belt and Road Initiative to deepen value-chain collaboration with semi-periphery countries, thereby dispersing transmission risks from market fluctuations in core nations. Declarations Competing Interests The authors declare no competing interests. Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies with human participants performed by any of the authors. Additional Information Correspondence and requests for materials should be addressed to LZX. Funding Statement We acknowledge financial support from the National Natural Science Foundation of China (72471007), and the Beijing Municipal Social Science Foundation, China (23ZGB005). Author Contribution CKJ: Conceptualization, Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization; LZX: Conceptualization, Methodology, Data Curation, Writing - Review & Editing, Project administration, Funding acquisition; YPJ: Data Curation, Software. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7122094","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504214959,"identity":"7f388b6d-646c-4fea-9923-9d00f4f17026","order_by":0,"name":"Chuke Jiang","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chuke","middleName":"","lastName":"Jiang","suffix":""},{"id":504214960,"identity":"086845dd-abee-409a-af7d-2c13a4df7346","order_by":1,"name":"Lizhi Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACCQY2IFlxgBnM4yFeyxmgFjaStDC2HWAgXotke1vi48J5d9gN7jcwPnjbxiBvTkiLNM+xw8Yztz1jNjjGwGw4t43BcGcDAS1yEult0rzbDoO0sEnztjEkGBwgrKX9N+8csBb230RpkZZIO8bM2wCxhZkoLZI9x5KB/nnGLHkssVlyzjkJww2EtEgcbzP8zFNzJ5nv8OGDH96U2cgTtAUGkoGx0wAygkj1QGBHvNJRMApGwSgYcQAAkHY744xrU60AAAAASUVORK5CYII=","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Lizhi","middleName":"","lastName":"Xing","suffix":""},{"id":504214961,"identity":"9cd4b5a8-b55c-4667-9bf2-e9f1fb1b4ba2","order_by":2,"name":"Yuanpeng Ji","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuanpeng","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2025-07-14 14:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7122094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7122094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90337241,"identity":"1e6949e4-cf07-42d8-b8a1-bc08a2562bf2","added_by":"auto","created_at":"2025-09-01 14:30:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1778515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effective dates and contained economies of main MTAs in the world. \u003c/strong\u003ePanel \u003cstrong\u003e(a)\u003c/strong\u003e shows the timeline of key MTAs entering into effect from 2007 to 2023. Panel \u003cstrong\u003e(b)\u003c/strong\u003ecompares their respective shares in global GDP. Panel \u003cstrong\u003e(c)\u003c/strong\u003e lists the member countries of each MTA, grouped by agreement and year of accession. The figure highlights the evolving landscape of regional trade integration and the economic scale of major MTAs including RCEP, CPTPP, EUJ, AFTA, and USMCA. Color depth changes indicate new members joining or leaving; The USMCA, also known as NAFTA 2.0, replaced NAFTA in 2020. Given the longstanding cooperation among member countries since NAFTA began in 1994, this study includes NAFTA, covering 2007-2023; EUJ summarizes changes in EU members; Brexit: The UK's withdrawal from the EU.\u003c/p\u003e","description":"","filename":"Figure1TheEffectiveDatesandEconomiesofMTAs.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/a7988812738e95f0f80c7872.jpg"},{"id":90337242,"identity":"9ed13573-65f3-405b-b2da-5560e0b3493e","added_by":"auto","created_at":"2025-09-01 14:30:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2147991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCountries or regions’ names and abbreviations in ADB.\u003c/strong\u003e This figure lists the standardized abbreviations of countries (regions) adopted in ADB-MRIO dataset. The chord diagram on the left visualizes the volume of intermediate goods flows between economies in 2023, highlighting key trade linkages in the global production network.\u003c/p\u003e","description":"","filename":"Figure2CountriesorRegionsNamesandAbbreviationsinADB.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/473ceb65e407051666d40df8.jpg"},{"id":90337249,"identity":"fd1ce53f-4beb-4a99-9680-c3c9d0596ca4","added_by":"auto","created_at":"2025-09-01 14:30:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2321764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndustrial sectors’ names and abbreviations in ADB.\u003c/strong\u003e This figure lists the standardized abbreviations of the 35 industrial sectors defined in the ADB-MRIO dataset. The chord diagram on the left illustrates the volume of intermediate goods flows across sectors in 2023.\u003c/p\u003e","description":"","filename":"Figure3IndustrialSectorsNamesandAbbreviationsinADB.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/7c34942217d7c8b0709aa007.jpg"},{"id":90337622,"identity":"c8ef010c-dbad-472d-a9e5-67cbe2666aa0","added_by":"auto","created_at":"2025-09-01 14:38:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1623109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between MRIO table and network.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e An example of the MRIO table including two countries with two sectors. The typical MRIO table includes three areas: value-added, intermediate use, and final demand. \u003cstrong\u003e(b) \u003c/strong\u003eTripartite valued graph. The whole global economic system can be extracted into a multi-layer network, which includes three layers: the value-added layer, the intermediate use layer, and the final demand layer. \u003cstrong\u003e(c) \u003c/strong\u003eRegional production network topology based on intermediate use. The intermediate use layer depicts the topological structure of GPN, in which the nodes are industrial sectors, and the edges are the intermediate goods from the upstream sectors to the downstream ones.\u003c/p\u003e","description":"","filename":"Figure4RelationshipbetweenMRIOtableandnetwork.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/06bfa209ea4d30549d3f5faa.jpg"},{"id":90337244,"identity":"3b81a8ec-3109-4986-96ff-2c39de3cc58b","added_by":"auto","created_at":"2025-09-01 14:30:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1066369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGIVCN-ADB-2023 model topology.\u003c/strong\u003e This figure illustrates the topology of GIVCN model constructed from ADB data for 2023. Each node represents an industry sector in a specific economy, and each edge indicates a trade relationship between sectors.\u003c/p\u003e","description":"","filename":"Figure5GIVCNADB2023ModelTopology.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/63d728b781b7eb35f879688f.jpg"},{"id":90338695,"identity":"f06cd481-f3c2-4969-9388-533e159c05d5","added_by":"auto","created_at":"2025-09-01 14:46:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":418408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtraction steps for GIVCBN-N and GIVCBN-C model.\u003c/strong\u003e The XIFA algorithm is used to prune individual networks in order to extract their backbone network. Suppose that the key nodes outlined in red (with weights of 10, 5, 5, 5, and 5, respectively) can be merged into a single super node. In \u003cstrong\u003e(a)\u003c/strong\u003e, during pruning all nodes except the one with weight 10 disappear, so the resultant super node has a weight of only 10. If, as shown in \u003cstrong\u003e(b)\u003c/strong\u003e, the nodes are first merged—yielding a super node of weight 30—and then pruning is applied, the final backbone network obtained will be completely different. Source: (Xing et al., 2024).\u003c/p\u003e","description":"","filename":"Figure6ExtractionstepsforGIVCBNNandGIVCBNCmodel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/563d3b21a614537a31a240b6.jpg"},{"id":90339670,"identity":"dace38b9-4cdd-4a90-b722-a59306727cc7","added_by":"auto","created_at":"2025-09-01 14:54:03","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1359416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of exposures between and within economies.\u003c/strong\u003e The thickness of the edges of the network diagram depicts the number of chain exposures between each pair of economies, with thicker edges implying a higher number of exposures. The color of the geographic heat map depicts the number of chain exposures within each economy, with a greater tendency towards warmer shades of red implying a greater number of exposures, and cooler shades implying a lesser number of exposures. Panels \u003cstrong\u003e(a)\u003c/strong\u003e, \u003cstrong\u003e(b)\u003c/strong\u003e, \u003cstrong\u003e(c)\u003c/strong\u003e, and \u003cstrong\u003e(d)\u003c/strong\u003e show the statistics for 2007, 2012, 2017, and 2023, respectively.\u003c/p\u003e","description":"","filename":"Figure7aRiskExposuresin2007.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/1ed1f530176fb24dab5cd0b5.jpg"},{"id":90339684,"identity":"62164f0e-9e4c-49b7-8d6e-5bc44997a26a","added_by":"auto","created_at":"2025-09-01 14:54:03","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":593768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of risk exposures by industrial sector. \u003c/strong\u003eThe upper panel shows the temporal evolution of risk exposure counts across 35 industrial sectors from 2007 to 2023. The lower panel presents sector-level boxplots, grouped by ERDI classification. Each box represents the distribution of risk exposure counts across years, highlighting heterogeneity in risk concentration among sectors and sector types.\u003c/p\u003e","description":"","filename":"Figure8Numberofriskexposuresbyindustrialsector..jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/148a464a3ceb3cf4f0fecb2e.jpg"},{"id":90337619,"identity":"e00dfe2c-76d6-4962-a350-733ae6776c1c","added_by":"auto","created_at":"2025-09-01 14:38:03","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":457940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSources and scales of risk exposures in 2023\u003c/strong\u003e. The left side represents upstream sectors (risk sources), and the right side shows downstream sectors (risk recipients). The width of each flow indicates the relative scale of risk exposure between sectors, revealing the sectoral structure and intensity of bilateral industry chain vulnerabilities.\u003c/p\u003e","description":"","filename":"Figure9SourcesandScalesofRiskExposuresin2023.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/098356b9a3265b9e3d02fb0b.jpg"},{"id":90337627,"identity":"824b8512-3e1b-4c55-901a-2cc0ad15bc4e","added_by":"auto","created_at":"2025-09-01 14:38:03","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":954288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChina–US upstream and downstream industry risk exposures.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e and \u003cstrong\u003e(b)\u003c/strong\u003e respectively present the risk exposures faced by 35 industrial sectors when China and the United States act as upstream and downstream nodes in GPN. Red bars represent China, and blue bars represent the United States, highlighting differences in sectoral vulnerability under different network positions.\u003c/p\u003e","description":"","filename":"Figure10aChinaUSUpstreamIndustryRiskExposures.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/7f760bd6f2396240faa8396b.jpg"},{"id":90340011,"identity":"4e0cad3b-f97d-4901-be06-75fbde10e0e0","added_by":"auto","created_at":"2025-09-01 15:02:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13897867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7122094/v1/0b94c06d-fe54-438c-928f-a3c57cf2a5c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Has the proliferation of multilateral trade agreements generated greater risk exposure to the global production network?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the process of globalization, the industrial and supply chains of various countries have formed a complex division of labor that intertwines upstream and downstream activities, intertwines supply and demand, and blends industry, academia, and research, significantly enhancing the specialization and production efficiency of the \u003cb\u003eglobal production network (GPN)\u003c/b\u003e. Consequently, issues in specific industrial chains or supply chains are now more likely to evolve into global issues. The depth and breadth of the impact of global systemic shocks on the production networks of various countries and regions have also greatly increased. In addition, due to profound influences from environmental changes, policy adjustments, technological innovations, and other factors, various uncertainties\u0026mdash;such as natural disasters, accidents, and sudden public incidents\u0026mdash;are more frequently sweeping across and threatening social stability in countries around the world. These uncertainties exert a bidirectional pressure on both supply and demand, spreading risks across all interconnected regions and industrial sectors, causing sustained negative impacts on national, regional, and even global economies (Yang et al., 2020b). Under these shocks, economies of different countries exhibit varying paths of recession or recovery, depending on their economic size, structural scale, and governance effectiveness (Eraydin, 2016).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, the security of the global economic system has become an important research branch in political economy. Identifying and measuring the risk exposures on the industry chain of various sectors in different countries and regions is key to exploring this issue. In recent years, we have seen an emergence of research on the safety resilience of regional economic systems, which includes analysis frameworks encompassing various elements (Briguglio et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), adaptive cycle models established from an evolutionary perspective (Simmie and Martin, 2010), and econometric analysis models using employment numbers or GDP as core variables (Davies, 2011, Brakman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Tang and Li, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), among others. Network science, an effective means for depicting the non-linearity, uncertainty, self-organization, and emergent properties of various complex systems, can not only explain the synchronization, propagation, and game rules behind various economic phenomena but also combine dissipative structure theory, synergy theory, and mutation theory to study the impact mechanisms of economic and non-economic factors on the safety resilience of industrial economic systems (Tran et al., 2019, K\u0026ouml;nig et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, proactively identifying production-chain risk exposures and assessing the potential impacts of exogenous shocks on these chains are vital for effective production-chain risk management. From the perspective of government macro-level authorities, it is necessary to monitor these risks at the meso-industrial and even higher macro levels in order to better control production-chain risks and enhance their resilience.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eCurrently, the global economy is characterized by regionalization, nearshoring, and digitalization (Yi et al., 2023). Taking the Sino-U.S. trade conflict as the main thread, and COVID-19 shocks, digitalization, and decarbonization as three auxiliary lines, it forms the developmental context of the \u003cb\u003eGlobal Value Chain (GVC)\u003c/b\u003e restructuring period (Xu, 2021), driving the shift in GVC development from an efficiency-first to a security-and-resilience-first orientation. Related research has also turned to value-chain restructuring, resilience, and stability(Antr\u0026agrave;s, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Baldwin and Freeman, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Baldwin et al., 2022, Yang et al., 2023, Ni et al., 2024, Su and Wang, 2024, Yin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Zhu and Huang, 2021, Yi et al., 2023). As a key institutional tool for addressing Trade Policy Uncertainty, \u003cb\u003eMultilateral Trade Agreements (MTAs)\u003c/b\u003e not only play a critical role in stabilizing the global economic and trade system through rule-based mechanisms, but also profoundly influence the structural evolution of GPN through their exclusionary effects on non-member countries, becoming an important force driving the regionalization of the global economy. Therefore, the structural risks triggered by MTAs urgently require systematic identification and assessment. The literature review section of this paper unfolds along three dimensions: the definition and sources of risk, risk measurement, and the impact of trade agreements on value chains.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDefinition and Sources of Risk\u003c/h2\u003e\u003cp\u003eThe various levels and categories of agents within an economic system, along with their interconnections, form a complex network topology. According to J\u0026uuml;ttner's classification framework, the risks faced by firms can be divided into internal risks, external risks, and network-related risks (J\u0026uuml;ttner, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In other words, during its operation, an economic system is subject not only to various uncertainties in the external environment, but also to risks that arise due to the heterogeneity of agents in terms of their countries or regions, industrial sectors, positions within the industrial and supply chains, corporate strategies, and distribution channels. Consequently, these risks are typically difficult to measure, control, and manage (Cattaneo et al., 2010). From a governmental perspective, to mitigate industrial chain disruptions and their adverse impact on national or regional economic development, the core of risk management lies in identifying risk exposures and quantifying their ripple effects. Based on an extensive review of the literature, this paper categorizes the sources of risk into two types: \u003cb\u003eEndogenous Risks\u003c/b\u003e and \u003cb\u003eExogenous Risks\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eOn one hand, endogenous (structural) risks of the industrial chain stem from its operational model and reflect the inherent fragility of GPN, which exhibits the characteristics of \u0026ldquo;scale-free\u0026rdquo; and \u0026ldquo;small-world\u0026rdquo; structures (Liu et al., 2021), meaning that a few hub nodes\u0026mdash;representing key industrial sectors linking upstream and downstream segments on a global scale\u0026mdash;are highly interconnected, while most nodes are scattered at the periphery of the network (Yin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This \u0026ldquo;core-periphery\u0026rdquo; topology implies that global industry supply chains are simultaneously robust and vulnerable. If hub nodes or the links connecting them (intermediate goods trade links) change during the process of network restructuring, the number of industrial and supply chains functioning as shortcuts will decline significantly, thereby impairing the operational efficiency of GPN.\u003c/p\u003e\u003cp\u003eOn the other hand, exogenous risks in the industrial chains refer to the risks that firms are exposed to when facing external shocks. As Miroudot notes, these shocks may be supply-side\u0026mdash;such as tariff increases, natural disasters, labor strikes and supplier insolvencies\u0026mdash;or demand-side, including customer bankruptcies, the entry of new competitors and macroeconomic downturns (Miroudot, 2020). Transport disruptions can be classified separately, as they occur very frequently and are not entirely related to either supply or demand (Heiland and Ulltveit-Moe, 2020). Regardless of the type of exogenous risk, the resulting economic shocks can rapidly propagate throughout the network via shortcuts between industrial sectors, thereby triggering systemic risk (Buckley, 2021).\u003c/p\u003e\u003cp\u003eWithin the foregoing framework, MTAs can be regarded as a quintessential source of structural risk in the GVC. By reducing tariff and non-tariff barriers through preferential measures, MTAs reshape the network structure of GVC. On the one hand, MTAs promote regional clustering of value chains, tightening trade and investment linkages among member countries and concentrating production networks within the agreement. On the other hand, MTAs are exclusionary toward non-members: the preferential trade policies established within the agreement lower transaction costs among members but relatively weaken connections with peripheral countries. This implies that the GVC is transitioning from an open network structure to one composed of multiple \u003cb\u003eRegional Value Chain (RVC)\u003c/b\u003e. Under such a configuration, intra-regional coupling is high while inter-regional linkages remain relatively weak, and MTAs alter the transmission paths and scope of risks on a global scale. Should a major agreement region experience a policy change or shock event, its impact can no longer be absorbed and dispersed globally as before; instead, it will be amplified within the region and propagate outward through limited cross-regional links, thereby triggering systemic risk. Hence, this paper considers the network restructuring induced by MTAs as a source of structural risk in the GVC.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRisk Measurement\u003c/h3\u003e\n\u003cp\u003eExisting methods for measuring industrial chain risks from a proactive perspective primarily include \u003cb\u003eTrade Network Analysis Tools\u003c/b\u003e and \u003cb\u003eProduction Network Analysis Tools\u003c/b\u003e. The former generally builds network models based on the UN Comtrade Database to measure the risk exposure or vulnerability of industrial chains. The approach first identifies those commodities most susceptible to disruption in international markets and then traces their principal exporters and importers, thereby pinpointing the critical goods that may render a country vulnerable to external supply shocks (Fagiolo et al., 2010, De Benedictis et al., 2014, Bode et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Brandon-Jones et al., 2014, Korniyenko et al., 2017, Cui et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Huang et al., 2022). By contrast, this approach exploits the intermediate use section of the Input Output table to map the network topology of an economy's production system. Building on this representation, several studies have employed Input-Output Analysis to quantify risk exposure along industrial chains (Antr\u0026agrave;s, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Borin et al., 2021, Baldwin and Freeman, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Baldwin et al., 2022). The risk measurement indicators advanced in these works remain confined to metrics of value chain participation. They chiefly employ trade decomposition techniques\u0026mdash;underpinned by the value-flow frameworks\u0026mdash;to operationalize both participation and exposure to external risks (Wang et al., 2013, Koopman et al., 2014).\u003c/p\u003e\u003cp\u003eWith the rapid expansion of intermediate goods trade and deepening international vertical specialization, scholars introduced the concept of GVC (Gereffi et al., 2005), and developed corresponding accounting frameworks to examine value creation and distribution processes worldwide. Since the early 2000s, the compilation and public release of \u003cb\u003eMulti-Regional Input-Output (MRIO)\u003c/b\u003e tables have opened new paradigms for GVC accounting research. Specifically, the intermediate use and final demand sections of MRIO tables capture the complex topologies of global production and trade networks, respectively. These matrices serve as primary data sources for assessing distributional efficiency\u0026mdash;tracing flows of intermediate and final goods through industrial chains\u0026mdash;and for evaluating production efficiency, with a focus on intermediate goods transactions. Building on these data, mainstream economists have established mature GVC accounting methodologies (Hummels et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Koopman et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Timmer et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Mi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Yang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) and employed reductionist approaches to analyze sectoral trade structures and developmental trends.\u003c/p\u003e\n\u003ch3\u003eImpact of MTAs on GVC\u003c/h3\u003e\n\u003cp\u003eThe role of MTAs in shaping industrial and value chains has become a research hotspot in recent years. A large body of literature, focusing on specific regional accords, has examined the impact of joining MTAs such as the CPTPP (Zhang and Ling, 2023), RCEP (Li and Li, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Gao and Wei, 2023, Lv and Wang, 2024), and BRI (Ma et al., 2021) on RVC. In addition, numerous studies have explored how MTAs alter trade and investment patterns, economic growth, welfare levels, industrial division of labor, and positions within the value chain. Cheng and Fan analyze the reshaping of power structures in RVC, arguing that the new generation of large-scale regional agreements consolidates the core position of leading countries and creates a \u0026ldquo;core\u0026ndash;periphery\u0026rdquo; division of labor within the region (Cheng and Fan, 2025). Bondi et al. focus on how GVC participation influences the design of trade agreements (Bondi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). They contend that greater GVC-type trade fosters the formation of deeper accords covering broad issues, indicating that the evolution of GVC, in turn, shapes the form and content of MTAs. Bondi et al. further find that the regionalization of GVC disaggregation and the surge in regional agreements mutually reinforce one another: economies with a higher degree of value-chain regionalization tend to conclude regional accords to consolidate their regional supply chains (Bondi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Zhao et al. show that MTAs significantly promote member countries\u0026rsquo; economic growth and industrial upgrading by expanding trade and investment, thereby enhancing overall regional welfare (Zhao et al., 2025). Zhang et al. argue that the signing and deepening of RTAs improve the export stability of trading partners (Zhang et al., 2024). Xue\u0026rsquo;s study of the cumulation rule of origin\u0026mdash;a specific agreement element\u0026mdash;reveals that it enhances value-chain positioning by increasing intra-regional trade in intermediate goods (Xue, 2024). Fan et al. demonstrate that the deepening of factor provisions in MTAs produces significant asymmetric effects on member participation in GVC (Fan et al., 2023). Hu et al. find that MTAs markedly facilitate economies\u0026rsquo; participation in GVC (Hu et al., 2024). Collectively, these studies highlight the significant role of MTAs in enhancing intra-member economic performance, value-chain embedment, and trade stability.\u003c/p\u003e\u003cp\u003eAs the GVC enters its restructuring phase, scholars have shifted their focus to how MTAs affect the stability of domestic value chains. For example, Song and Chen examine the depth of regional services trade agreements and find that the deeper the accord, the stronger a firm\u0026rsquo;s GVC resilience: under external shocks, firms recover more quickly and maintain their position within the value chain (Song and Chen, 2024). Song et al. further investigate how the depth of services trade agreements drives GVC reconfiguration, revealing that integration of service factors prompts manufacturing firms to reconfigure their value chains regionally, thereby enhancing the coherence and alignment of regional industrial division of labor (Song et al., 2024). Shen and Shen explore the impact of high-standard trade agreements on global industry chain resilience, arguing that the institutional environment is a key determinant of industry chain robustness and that deeper trade accords reduce the risk of global industry chain disruptions (Shen and Shen, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yu et al. study the effect of deepened \u003cb\u003eregional trade agreements (RTAs)\u003c/b\u003e on global industry chain resilience from the perspective of intermediate-goods trade (Yu et al., 2024). Zhang et al. measure the reconfiguration effects on GVC participation depth and breadth resulting from RCEP tariff reductions (Zhang et al.). Overall, existing studies primarily focus on member countries, use GVC participation metrics as core indicators, and employ empirical methods combining matched data with econometric identification. However, systematic research is still lacking on the risk-exposure effects and industry-relocation mechanisms that MTAs may impose on non-member countries, particularly the exclusionary and spillover risks they generate within the global value-chain structure.\u003c/p\u003e\n\u003ch3\u003eResearch Gap\u003c/h3\u003e\n\u003cp\u003eThe above studies offer various approaches for the formation and quantitative analysis of value-chain risk exposure but exhibit several shortcomings. (1) Single-perspective research and lack of a comprehensive analytical framework. Existing work typically measures value-chain risk using only one method\u0026mdash;focusing on network structure, trade relationships, or macro-level indicators. Such single perspectives cannot fully capture the complexity of the global industrial system and lack a systematic framework. (2) Absence of identification and quantitative assessment of MTA-induced network structural risks. While current studies have examined the positive effects of MTAs on regional value-chain stability and economic performance, they neglect the role of MTAs as sources of risk arising from shifts in the global value-chain network structure, overlooking vulnerabilities and risk exposures triggered by regional integration. (3) Lack of a global systemic perspective and analysis of overlapping agreements. Most literature focuses on a single MTA and its intra-regional effects, pays little attention to spillovers onto non-member regions, and does not assess the combined impact of multiple concurrent agreements on the global value-chain network.\u003c/p\u003e\u003cp\u003eTo address these gaps, this paper makes the following contributions: (1) Development of a multi-perspective risk-exposure identification framework. We innovatively integrate input\u0026ndash;output analysis, trade-network analysis, and counterfactual analysis into a unified framework, thereby enhancing the precision of risk quantification. By comparing a counterfactual model with a baseline \u0026ldquo;zero\u0026rdquo; model, we can accurately identify the risk exposures induced by MTAs within the global value-chain network. (2) Incorporation of MTAs as structural risk sources. For the first time, we treat MTAs as risk variables that restructure the global value-chain architecture, systematically identifying their mechanisms of impact on value-chain reconfiguration and risk exposure. This expands understanding of the dual (positive and negative) impact mechanisms of MTAs and fills a gap in analyses of their adverse effects. (3) Systematic synthesis of network-structure changes under multiple MTAs. We map the temporal evolution and regional coverage of major global MTAs, such as the CPTPP, RCEP, and USMCA, considering not only the strengthening of intra-agreement chains but also the risk-exposure effects on regions outside the agreements. In summary, the integrated methodological framework proposed here can comprehensively identify global value-chain risk exposures, providing a powerful analytical tool and decision-support basis for risk early warning and policy formulation.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMechanisms of Risk Exposure Formation: Multilateral Trade Agreements\u003c/h2\u003e\u003cp\u003eThe reshaping of the GVC structure by MTAs is primarily reflected in two key mechanisms: the \u003cb\u003eTrade Creation Effect (TCE)\u003c/b\u003e and the \u003cb\u003eTrade Diversion Effect (TDE)\u003c/b\u003e, with the latter forming the theoretical basis for regarding MTAs as sources of structural risk in the value chain.\u003c/p\u003e\u003cp\u003eThe TCE refers to an agreement\u0026rsquo;s reduction of intra-regional trade barriers and transaction costs, thereby promoting improvements in production efficiency and industrial agglomeration, and tightening production and trade linkages among member countries. This creation effect drives the concentration of industry chain and supply chain activities within the region, strengthening the connectivity of the regional value-chain network. In contrast, the TDE reveals the structural distortions and risk spillovers induced by MTAs. Thanks to preferential treatment among member countries, certain segments of the value chain shift from non-member to member economies. This not only weakens the position of non-members in the GVC but also increases their risk exposure, particularly when non-member economies are highly dependent on member markets or industry chain nodes, amplifying potential risks. According to classic customs union theory, when member states eliminate tariffs among themselves while maintaining barriers for non-members, firms will import higher-cost goods from within the union instead of lower-cost non-member products, solely due to tariff differentials\u0026mdash;even if the latter have a cost advantage. Such distortion of trade flows contravenes the principle of comparative advantage and leads to a decline in global resource-allocation efficiency.\u003c/p\u003e\u003cp\u003eMTAs can both promote global trade liberalization and economic integration and accelerate trends toward regional industrial clustering, nearshoring, and localization. Empirical research supports this view. For example, Flaaen et al. investigated various combinations of RTAs among suppliers, the United States, and destination countries within GVC. They found that when the United States has a bilateral RTA with either an input-source country or an export-destination country, GVC trade flows increase by an average of 22%, and when a trilateral RTA is in place among three countries, GVC trade flows rise by an average of 55% (Flaaen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, countries that are excluded from such agreements typically face higher market-access barriers and more intense competitive pressure. Although signing an MTA does not guarantee close economic cooperation among the participating economies, the agreement exerts adverse effects on those left out, potentially altering their domestic industrial structures. This is because reduced trade costs among member states erode the relative competitiveness of non-member goods and services. Therefore, for many economies, MTAs that do not include them can be seen as manifestations of trade protectionism and GVC reconfiguration, and their negotiation and implementation may become endogenous sources of risk. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the effective dates and covered economies of the major global MTAs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP)\u003c/b\u003e is a flagship MTA covering multiple Asia\u0026ndash;Pacific economies. It evolved from the original Trans-Pacific Partnership (TPP), launched in 2005 by Singapore, New Zealand, Chile, and Brunei and later expanded to 12 members including the United States. After the U.S. withdrew in 2017, the remaining 11 countries signed the CPTPP in 2018, and the UK joined in 2023. The CPTPP establishes high-standard rules on intellectual property, labor, and environmental protection and serves as an alternative economic framework to counterbalance China\u0026rsquo;s growing trade influence.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnited States\u0026ndash;Mexico\u0026ndash;Canada Agreement (USMCA)\u003c/b\u003e is the trilateral FTA replacing NAFTA (in force since 1994). Renegotiated between the three parties, it was signed on November 30, 2018, and entered into force on July 1, 2020. The USMCA modernizes the pact by including chapters on digital trade, labor rights, and environmental protection, reflecting a broader strategy to promote balanced and resilient trade.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEU\u0026ndash;Japan Economic Partnership Agreement (EUJEPA)\u003c/b\u003e is a landmark FTA between the EU\u0026mdash;now comprising 27 member states after decades of integration (1951 ECSC\u0026rarr;1957 EEC\u0026rarr;1973 enlargement\u0026rarr;1986 Single European Act\u0026rarr;1992 Maastricht\u0026rarr;2004 Eastern enlargement\u0026rarr;2007 Lisbon\u0026rarr;2020 Brexit)\u0026mdash;and Japan. Negotiations began in 2013, the agreement was signed in July 2018, and it entered into force in February 2019. It eliminates tariffs, reduces trade barriers, and deepens economic ties.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegional Comprehensive Economic Partnership (RCEP)\u003c/b\u003e, initiated by ASEAN in 2012, integrates regional trade rules. Its 15 members\u0026mdash;10 ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, the Philippines, Vietnam, Laos, Myanmar, Cambodia, Brunei) plus China, Japan, South Korea, Australia, and New Zealand\u0026mdash;account for nearly one-third of global population and GDP. RCEP\u0026rsquo;s rules of origin allow tariff-free treatment for over 90% of goods, and its liberalization of services and investment far exceeds that of earlier \u0026ldquo;10\u0026thinsp;+\u0026thinsp;1\u0026rdquo; FTAs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eASEAN Free Trade Area (AFTA)\u003c/b\u003e, launched at the 1992 ASEAN Summit in Singapore, is another key example of regional integration. By reducing or eliminating tariffs and non-tariff barriers, AFTA promotes the free flow of goods, services, and investment, thereby strengthening economic cooperation and competitiveness within ASEAN.\u003c/p\u003e\u003cp\u003eCurrently, Asian economies have gradually evolved into strategic partners actively sought after by major powers. As the trend of anti-globalization becomes increasingly pronounced, MTAs and economic organizations serving similar functions have successively emerged as important vehicles for advancing regional economic collaboration. It is noteworthy that, although China wields considerable influence as a key member of RCEP, many member states remain committed to reducing their reliance on its manufacturing and markets through alternative agreements. Their motivation for doing so lies not only in strengthening their own autonomous development capacities, but also in response to the intervention from the U.S. in their international trade through measures such as reciprocal tariffs.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMRIO Table\u003c/h3\u003e\n\u003cp\u003eIn order to conduct empirical studies on the endogenous and exogenous risks from the perspectives of the GVC restructuring and global emergencies, the MRIO table used for network modeling must encompass a significant number of emerging economies and cover a longer time span. Considering these two points, we finally adopted the Asian Development Bank Multi-Regional Input-Output Tables (ADB-MRIO), as the source of global intermediate goods trade data, which provides MRIO tables building on the World Input-Output Database (WIOD) to cover 29 Asia and the Pacific economies. It has facilitated the production and analysis of GVC related statistics for Asian economies. Economies explicitly identified in the ADB-MRIO account for at least 93% of the world GDP. The data structure of the MRIO table is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of most relevant papers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOutput\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eIntermediate Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003eFinal Demand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTotal Output\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eInput\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSector\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eIntermediate Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{A}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{A}\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{A}\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{A}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{A}\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{A}\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{B}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{B}\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{B}\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{B}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{B}\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{B}\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:⋮\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{1},\\cdots\\:,{\\varvec{S}}_{\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{R}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{R}\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Z}}^{\\varvec{R}\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{R}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{R}\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}^{\\varvec{R}\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eValue-Added\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}\\varvec{A}}^{\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}\\varvec{A}}^{\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}\\varvec{A}}^{\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" morerows=\"1\" nameend=\"c12\" namest=\"c8\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Input\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{B}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\cdots\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{R}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAssume the MRIO table includes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m\\)\u003c/span\u003e\u003c/span\u003e countries and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e industrial sectors (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:u,v=\\text{1,2},\\dots\\:,m\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s,t=\\text{1,2},\\dots\\:,n\\)\u003c/span\u003e\u003c/span\u003e), the fundamental linear equation within it is: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X={\\left(I-A\\right)}^{-1}Y\\)\u003c/span\u003e\u003c/span\u003e, where: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X=\\left({x}_{s}^{v}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the total output vector of sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\)\u003c/span\u003e\u003c/span\u003e in country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:v\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e is the identity matrix; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(I-A\\right)}^{-1}\\)\u003c/span\u003e\u003c/span\u003e is the Leontief inverse matrix; the technical coefficient matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}^{uv}=\\left({a}_{st}^{uv}\\right)\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{st}^{uv}={z}_{st}^{uv}/{x}_{t}^{v}\\)\u003c/span\u003e\u003c/span\u003e, represents the intersectoral monetary flow from sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\)\u003c/span\u003e\u003c/span\u003e in country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:u\\)\u003c/span\u003e\u003c/span\u003e to sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e in country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:v\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{t}^{v}\\)\u003c/span\u003e\u003c/span\u003e represents the total output vector of sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e in country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:v\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y=\\left({y}_{s}^{uv}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the final demand vector of goods from sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\)\u003c/span\u003e\u003c/span\u003e in country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:v\\)\u003c/span\u003e\u003c/span\u003e by country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:u\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe countries/regions and industrial sectors covered by ADB-MRIO are shown are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the chord diagrams on the left side of both figures, the differences in intermediate goods trade volumes between sectors and between economies are represented by the width of the chords.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eNull Model and Counterfactual Model\u003c/h3\u003e\n\u003cp\u003eThe typical MRIO table consists of three parts (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and can be transformed into a triple-layer network structure (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). From value-added to intermediate use and final demand, the MRIO framework captures the flow of value along the GVC. Generally, economists are most concerned with how economies and their production units (i.e., industrial sectors) jointly create greater value. This collaborative relationship forms the foundation of GPN. Recognizing that the value flow also entails the risk flow, we construct the \u003cb\u003eGlobal Industrial Value Chain Network (GIVCN)\u003c/b\u003e model based on the input-output data from the intermediate use part (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe view the industrial sectors of various countries or regions as nodes, and their input-output relationships via industrial chains as edges, with the trade volume of intermediate goods reflecting the strength of these relationships as weights. When the total number of nodes in the network is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N=m\\times\\:n\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i,j=\\text{1,2},\\dots\\:,N\\)\u003c/span\u003e\u003c/span\u003e), the node set is represented as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=\\left\\{{v}_{1},{v}_{2},\\cdots\\:{,v}_{N}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e, the edge set as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E=\\left\\{{e}_{11},{e}_{12},\\cdots\\:,{e}_{ij}{,\\cdots\\:{,e}_{N\\left(N-1\\right)},e}_{NN}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e, and the weight set as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W=\\left\\{{w}_{11},{w}_{12},\\cdots\\:,{w}_{ij}{,\\cdots\\:{,w}_{N\\left(N-1\\right)},w}_{NN}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e. While the GIVCN model applies the weight set \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W\\)\u003c/span\u003e\u003c/span\u003e to display the adjacency matrix, each row refers to the distribution of intermediate goods output from an upstream sector to several downstream sectors, and each column the intermediate goods input obtained by a downstream sector from several upstream sectors. The MDS map of the GIVCN\u0026ndash;ADB model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This map encompasses 63 countries (regions) and 35 industry sectors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe migration trend of China\u0026rsquo;s industrial sectors toward the global industrial core is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, illustrating the emergence of a \u0026ldquo;core\u0026ndash;periphery\u0026rdquo; structure in GPN. In the early 1970s, American scholar Immanuel Wallerstein introduced world-systems theory, emphasizing the unequal distribution of global capital, technology, and wealth. According to Wallerstein, North America and Europe occupy the \u0026ldquo;core\u0026rdquo; of the world economy, while much of Asia, Africa, and Latin America lie in the \u0026ldquo;periphery.\u0026rdquo; Core countries set the strategic and economic direction worldwide. However, the core\u0026ndash;periphery structure is not static: peripheral countries can, through strategic development, ascend to the semi-periphery or even the core, while core countries may decline to semi-periphery or periphery status. Semi-peripheral countries\u0026mdash;positioned between core and periphery\u0026mdash;often succeed in dependent development.\u003c/p\u003e\u003cp\u003eFrom a dynamic-development perspective, peripheral countries are not invariably trapped in exploitation. Indeed, the relocation of industries by core countries creates opportunities for peripheral economies: by leveraging capital and technology from developed nations, these countries advance their own industrial development and technological innovation, achieving industrial upgrading and even \u0026ldquo;leapfrog\u0026rdquo; growth. As GVC deepen and expand, many manufacturing processes have shifted to developing countries\u0026mdash;most notably China\u0026mdash;while developed economies have transitioned from production-based societies to consumption-oriented ones. This shift has led to industrial hollowing-out in certain developed nations and has heightened their dependency on China as the \u0026ldquo;world\u0026rsquo;s factory.\u0026rdquo;\u003c/p\u003e\u003cp\u003eObviously, the GIVCN model is a highly dense (nearly fully connected) weighted directed network, and therefore requires dimensionality reduction to visualize changes in its network topology. Given the substantial heterogeneity in intermediate-goods trade between upstream and downstream industries, this paper employs the X-Index Filtering Algorithm to extract a subnetwork from the GIVCN model (Xing et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This subnetwork is termed the \u003cb\u003eGlobal Industrial Value Chain Backbone Network (GIVCBN)\u003c/b\u003e and is denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\u0026acute;}{G}=\\left(V,\\stackrel{\u0026acute;}{E},\\stackrel{\u0026acute;}{W}\\right)\\)\u003c/span\u003e\u003c/span\u003e. The GIVCBN model removes over 90% of edges while losing less than approximately 1% of the intermediate-goods trade volume present in the original GIVCN. This finding illustrates a pronounced \u0026ldquo;Matthew effect\u0026rdquo; in the economic linkages within the GVC: a small number of upstream sectors supply most intermediate inputs to a given sector, and a small number of downstream sectors consume the majority of its intermediate outputs.\u003c/p\u003e\u003cp\u003eIn addition, to reflect the potential impact of specific MTAs on the industry chain, we need to further extend the GIVCBN model into two categories: the null model (GIVCBN-N) and the counterfactual model (GIVCBN-C). The extraction idea is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGIVCBN-N Model\u003c/b\u003e: First, apply the XIFA algorithm to prune the GIVCN model. Second, aggregate the industry sectors of those countries/regions related to MTA and treat them as a loosely integrated entity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGIVCBN-C Model\u003c/b\u003e: First, merge the industry sectors of MTA-related countries/regions within the GIVCN model to form a tightly integrated entity. Second, apply the XIFA algorithm to prune this network.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(b)\u003c/b\u003e, the GIVCBN-C model treats the internal industry sectors of MTA-related countries/regions as a tightly integrated whole, enabling them to further compete for additional intermediate-goods resources. The result is the elimination of relatively inefficient production chains in other economies. Compared with the GIVCBN-N model, the GIVCBN-C model more fully captures the network agglomeration effects of MTA-related countries/regions and the network deconstruction of the remaining economies. Therefore, by comparing the adjacency matrices of the GIVCBN-N and GIVCBN-C models, we can identify the segments of economic production chains that are impacted by the potential effects of a specific MTA, manifesting as risk exposures in the production network (Xing et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on the above modeling approach, this study constructs a 17-year GIVCBN model, covering the effective periods and scopes of major MTAs worldwide from 2007 to 2023. Each year comprises one GIVCBN-N model and three to five GIVCBN-C models reconstructed for specific MTAs. The annual risk exposures triggered by MTAs are then aggregated\u0026mdash;specifically, the production-chain links in GPN that may face disruptions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRisk Exposure at National Level\u003c/h2\u003e\u003cp\u003eThe countries/regions within a specific MTA will jointly endeavor to reduce trade barriers, strengthen regional economic integration, enhance economic connectivity among themselves, and bolster their links with other global economies. At the same time, the domestic production networks of other economies (i.e., the assemblage of their internal industry and supply chains) will be exposed to exogenous risks, thereby revealing their risk exposures. This subsection counts the number of exposures between and within each of the world's economies, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom the number of risk exposures between economies, cross-border industry chain uncertainty has risen sharply. Between 2017 and 2023, major global events\u0026mdash;US\u0026ndash;China trade frictions, the COVID-19 pandemic, and the Russia\u0026ndash;Ukraine conflict\u0026mdash;triggered industry chain disruptions, production delays, and cost increases. To safeguard their industrial chains, countries have increasingly relied on MTAs with other economies, hoping regional chain integration would curb external risks. Yet this strategy has paradoxically reinforced interregional risk transmission and further accelerated GVC restructuring.\u003c/p\u003e\u003cp\u003eIn terms of regional characteristics, different areas exhibit significant differences in industry chain risk exposure. Within North America, the United States, Canada, and Mexico have consistently maintained a relatively high number of industry chain risk exposures. Although NAFTA and its successor USMCA were designed to promote trade liberalization among the three nations and strengthen regional integration, the U.S. \u0026ldquo;de-risking\u0026rdquo; strategy has led to a decoupling from GVC, producing counterproductive effects on economic development. The economic \u0026ldquo;prisoner\u0026rsquo;s dilemma\u0026rdquo; behind this phenomenon is complex and multifaceted, primarily because emerging economies\u0026mdash;exemplified by China\u0026mdash;have continuously enhanced their competitiveness in labor costs, technological innovation, high-end manufacturing, market scale, and policy environments during globalization, thereby weakening the industrial linkages between the U.S. and its neighbors. Consequently, the U.S. is pursuing nearshoring and friendshoring to bolster the resilience of its transnational industrial and supply chain and reduce its number of risk exposures.\u003c/p\u003e\u003cp\u003eFrom the perspective of internal risk exposures within economies, the uncertainty of domestic industrial-chain supply networks exhibits varied trends. Since the United Kingdom initiated the Brexit process in 2016, the number of domestic industry chain risk exposures has climbed markedly, indicating that Brexit has profoundly disrupted the previously highly integrated industrial and industry chain system with EU member states, resulting in structural imbalances within the domestic economy. In contrast, the number of domestic risk exposures in the United States has decreased, likely because its manufacturing reshoring policies have enhanced the robustness of its production network. It is worth noting that China\u0026rsquo;s domestic risk exposures have remained relatively stable, suggesting that the China-led RCEP has effectively hedged against the negative impacts of other MTAs on its production network. As a collective of smaller-scale economies, the Rest of the World (ROW) experienced a rise in risk exposures from 2007 to 2012, followed by a decline from 2012 to 2023. We attribute the initial increase to the 2008 subprime crisis, which severely impacted economies with high trade dependence and close financial ties to the U.S., while the subsequent decrease reflects emerging economies\u0026rsquo; strengthened positions in global trade patterns and their improved risk resilience through deeper vertical specialization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRisk Exposure at Industry Level\u003c/h2\u003e\u003cp\u003eThis subsection compiles all risk exposures associated with each of the 35 sectors and utilizes the heatmap and boxplot presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e to respectively illustrate the temporal trends and dispersion of their risk-exposure counts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBetween 2007 and 2023, risk exposures in most sectors of the GVC exhibited a sustained and significant upward trend. This acceleration was particularly pronounced after 2017 under the cumulative influence of multiple external shocks\u0026mdash;namely, Sino\u0026ndash;US trade frictions, the COVID-19 pandemic, and heightened geopolitical tensions\u0026mdash;which sharply magnified industry-wide risk exposures. Nearly one-third of sectors reached historically unprecedented abnormal peaks in 2023, underscoring the systemic impact of GVC restructuring on sectoral security. While the era of global economic integration enhanced vertical specialization and the division of labor across sectors, it also increased their uncertainties with upstream and downstream actors; in recent years, the accelerated restructuring of GVC has converted these latent interdependencies into overt industry chain risks.\u003c/p\u003e\u003cp\u003eThe Primary sectors display low dispersion in their risk-exposure counts, indicating that their production and trade structures remain relatively stable. Most Low-tech sectors also show low dispersion; however, \u0026ldquo;Construction (S18)\u0026rdquo; has experienced a marked increase in exposures and consecutive outliers over the past two years, reflecting its vulnerability to raw-material price volatility, labor shortages, and a deteriorating financing environment. Within the High- and Medium-tech sectors, \u0026ldquo;Machinery, Nec (S13)\u0026rdquo; shows a larger increase in risk exposures compared to its peers. We attribute this to technological advances in automation and robotics, the Internet of Things, and energy-saving and emission-reduction technologies, which impose higher development requirements and thus accentuate this sector\u0026rsquo;s weaknesses in less developed economies. In the Business Services category, sectors such as retail, hospitality, transportation, telecommunications, finance, and leasing exhibit very significant increases in risk exposures. These industries are highly sensitive to geopolitical risks, causing their industry chain risks to amplify as GVC restructuring deepens. The Public and Welfare Services sectors are non-traded; their industry chain risks depend primarily on domestic market stability. Clearly, the widespread increase in uncertainties within domestic production and supply networks has adversely affected the security and stability of these sectors.\u003c/p\u003e\u003cp\u003eThe causes of industry heterogeneity are manifold. First, GVC restructuring has shifted firms from a sole focus on cost efficiency to an emphasis on industry chain security and resilience, making sectors with deep integration and strong upstream\u0026ndash;downstream linkages more prone to risk exposure under external shocks. Second, different industries exhibit variations in technological dependence, capital intensity, and market sensitivity, which affect their inherent capacity to withstand disruptions. Moreover, while emerging technologies and MTAs enhance resilience in certain regions, they also render the value-chain network structurally more vulnerable during geopolitical and public health events.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eChina\u0026ndash;US Risk Exposure Comparison\u003c/h2\u003e\u003cp\u003eThis subsection compares the topology of the 2023 GIVCBN-N model with those of five GIVCBN-C models and provides an overview of the cross-border risk exposures for China and the United States. Furthermore, to emphasize the significance of these risk exposures, the flow magnitudes in the Sankey diagrams are scaled in proportion to the volume of intermediate-goods trade, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom the perspective of cross-national value chains, the sources and magnitudes of risk exposures in China and the United States exhibit both commonalities and significant differences. Overall, the upstream risk exposures in both countries are concentrated in broad basic-industry sectors\u0026mdash;namely, Coke, Refined Petroleum and Nuclear Fuel (S08), Chemicals and Chemical Products (S09), Basic Metals and Fabricated Metal (S12), and Renting of Machinery and Equipment and Other Business Activities (S30). This finding indicates that both nations possess large-scale production capacities and are highly dependent on importing critical materials, products, and services from abroad.\u003c/p\u003e\u003cp\u003eBuilding upon these commonalities, China\u0026rsquo;s upstream risk exposures are further concentrated in the Mining and Quarrying sector (S02) and the Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles sector (S20). As the \u0026ldquo;world\u0026rsquo;s factory,\u0026rdquo; China consumes vast amounts of minerals and bulk commodities from around the globe and thus bears significant risks stemming from shifts in international policy regimes\u0026mdash;such as trade barriers, environmental regulations, and carbon tariffs.\u003c/p\u003e\u003cp\u003eIn contrast, the United States\u0026rsquo; upstream industry chain risks are focused in long-process manufacturing sectors\u0026mdash;Machinery, Nec (S13); Electrical and Optical Equipment (S14); and Transport Equipment (S15)\u0026mdash;whose production processes involve complex technologies and equipment, typically require extended production cycles and substantial capital investments. The US reliance on imports in these sectors precisely highlights the \u0026ldquo;hollowing out\u0026rdquo; of its manufacturing base.\u003c/p\u003e\u003cp\u003eIn terms of downstream risk exposures, both countries exhibit significant exposures in the Construction (S18) sector, however, the composition of these risks differs. In China, downstream risk exposures primarily originate from a highly leveraged residential market and a slowdown in infrastructure investment, whereas in the United States they reflect the dual effects of rising vacancy rates in commercial real estate and volatility in energy costs. In any event, risks from numerous upstream sectors converge upon this sector through the value and industry chain, thereby magnifying or even triggering its inherent vulnerabilities.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e reports the number of risk exposures for each industrial sector in China and the United States when serving as upstream and downstream nodes within the GVC in 2023. China\u0026rsquo;s high-risk exposures as upstream sectors are mainly concentrated in Machinery and Products (S13), Other Business Activities (S30), and Chemicals and Chemical Products (S09), reflecting its deep reliance as the \u0026ldquo;world factory\u0026rdquo; on critical intermediate inputs; as downstream sectors, China\u0026rsquo;s high-risk exposures focus on Basic Metals (S12), Transport Equipment (S15), and Construction (S18), indicating that upstream shocks are transmitted intensely through heavy industry and infrastructure. In contrast, in the United States upstream risk exposures are more pronounced in service- and technology-intensive industries such as Financial Intermediation (S28), Inland Transport (S23), and Machinery and Products (S13), while downstream risk exposures are mainly distributed across consumer and service sectors\u0026mdash;Wholesale Trade (S20), Retail Trade (S21), and Hotels and Catering (S22)\u0026mdash;highlighting an economic structure reliant on domestic demand and high value-added services.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sector-level differences between the two countries arise from their distinct industrial development paths, division of labor within GVC, macroeconomic policies, and regional cooperation frameworks. First, China relies on export-oriented manufacturing and resource processing, whereas the United States is driven by services and technological innovation; thus, their value-chain vulnerabilities and risk transmission mechanisms under global shocks differ markedly. Second, China\u0026rsquo;s historical GVC positioning emphasized high-volume manufacturing and resource processing under a low-cost advantage, while the United States focused on high-tech, capital-intensive, high value-added segments. Moreover, differences in macroeconomic and policy environments cause the same sectors to exhibit varying susceptibilities to global shocks. Finally, their strategies for industrial upgrading and regional cooperation are reshaping risk patterns: China disperses external risks through multilateral agreements such as RCEP, whereas U.S. nearshoring and friendshoring strategies have partially mitigated dependence on a single supply source.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eConclusion and Discussion\u003c/h2\u003e\u003cp\u003eThis paper treats MTAs as structural sources of risk within GVC. We construct a GIVCN model based on ADB-MRIO data, apply the XIFA algorithm for dimensionality reduction to extract both the factual network and its counterfactual counterpart, and then employ a hypothetical elimination method to identify and quantify the risk exposures induced by these agreements. This results in a multi-perspective risk measurement framework that integrates network science, IO analysis, and counterfactual evaluation. The main findings are: (1) Global industry chain risk exposures exhibit a pronounced \u0026ldquo;core\u0026ndash;periphery\u0026rdquo; structure. Developed countries occupying the \u0026ldquo;core\u0026rdquo; of GPN face the highest and most sensitive exposures; \u0026ldquo;periphery\u0026rdquo; regions, while exhibiting lower overall exposure, are more vulnerable due to their less diversified economies; and \u0026ldquo;semi-periphery\u0026rdquo; regions achieve relatively stable exposures by signing multilateral trade agreements and optimizing regional network connections to bolster chain resilience. This structural pattern arises from the core countries\u0026rsquo; deep reliance on international industry chain, the limited buffering capacity of peripheral regions owing to low participation, and the enhanced industry chain security in semi-core regions through institutional cooperation and network optimization. (2) At the sectoral level, risk exposures diverge markedly: high-tech manufacturing and commercial services\u0026mdash;characterized by strong upstream\u0026ndash;downstream linkages and high dependence on technology and capital\u0026mdash;are most sensitive to trade frictions, pandemics, and geopolitical conflicts, exhibiting both high exposure counts and rapid growth; by contrast, primary industries, with simpler production chains and lower technological dependence, display more stable exposure profiles. This pattern reflects the shift from efficiency-driven to resilience-oriented firm strategies, heterogeneous endogenous buffering capacities across industries, and the dual shaping effects of emerging technologies and multilateral agreements on value-chain structures. (3) The distinct GVC positional roles and industrial structures of China and the United States produce markedly different risk landscapes at the sectoral level. Common upstream risks cluster in basic industries, but China additionally faces high exposures in mining and wholesale trade, whereas the United States shows greater vulnerability in technology-intensive sectors such as machinery, electrical, and transport equipment. Downstream, construction is the principal risk-concentration sector in both countries, yet China\u0026rsquo;s pressures stem primarily from high residential leverage and slowed infrastructure investment, while the United States is challenged by rising commercial real-estate vacancies and energy-cost volatility. These differences originate from China\u0026rsquo;s export-oriented manufacturing and resource-processing focus versus the United States\u0026rsquo; emphasis on high-value services and advanced manufacturing, as well as each country\u0026rsquo;s divergent macroeconomic policies and regional cooperation strategies\u0026mdash;providing empirical guidance for targeted policies to enhance value-chain resilience and risk management.\u003c/p\u003e\u003cp\u003eBased on the above findings, this paper proposes the following policy recommendations: (1) Deepen regional coordination and diversification. Continuously advance alignment with regional economic and trade agreements such as RCEP and CPTPP; actively broaden cooperation channels under multilateral frameworks like China\u0026ndash;Japan\u0026ndash;Republic of Korea and China\u0026ndash;Europe; and build multi-layered, diversified market networks to disperse reliance on single markets and raw-material sources, thereby enhancing the overall resilience of RVC. (2) Strengthen upstream industrial resilience. Support key upstream sectors\u0026mdash;mining, chemicals, and machinery equipment\u0026mdash;to accelerate greening and decarbonization; encourage firms to pursue local substitution and circular-economy models; and establish robust strategic reserves for mineral resources to effectively withstand shocks from international raw-material price volatility and geopolitical conflicts. (3) Enhance capabilities in service and trade-intermediation links. In segments such as wholesale and commission trade, accelerate digital transformation and innovate regulatory frameworks; optimize cross-border logistics networks and customs-clearance procedures; reduce intermediaries\u0026rsquo; risk exposures; and build an efficient, secure trade-intermediation system. (4) Optimize downstream investment structure and resource supply chain security. Guide real estate and infrastructure investment prudently, employing differentiated credit policies and macroprudential tools to guard against financial risks from high residential leverage and overreliance on shantytown redevelopment; concurrently, diversify import channels, strengthen strategic resource reserves, and promote R\u0026amp;D of critical mineral-recovery technologies to reduce dependence on single sources and ensure stable, controllable supply of essential resources. (5) Promote coordinated upgrading of high-tech manufacturing and services. Proactively localize supply chains and tackle key technologies for high-end manufacturing\u0026mdash;such as semiconductors and precision machinery\u0026mdash;to reduce passive dependence on highly volatile downstream sectors; and leverage the Belt and Road Initiative to deepen value-chain collaboration with semi-periphery countries, thereby dispersing transmission risks from market fluctuations in core nations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAdditional Information\u003c/h2\u003e\u003cp\u003eCorrespondence and requests for materials should be addressed to LZX.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eWe acknowledge financial support from the National Natural Science Foundation of China (72471007), and the Beijing Municipal Social Science Foundation, China (23ZGB005).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCKJ: Conceptualization, Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization; LZX: Conceptualization, Methodology, Data Curation, Writing - Review \u0026amp; Editing, Project administration, Funding acquisition; YPJ: Data Curation, Software.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used in this article are available at the website of ADB Key Indicators Database (KIDB), which is https://kidb.adb.org/globalization, and the results of the process can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eANTR\u0026agrave;S P 2020. 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Reform [J]: 60-67.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Risk analysis, Intermediate Goods Trade, Counterfactual Analysis, Multi-Regional Input-Output Table","lastPublishedDoi":"10.21203/rs.3.rs-7122094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7122094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of global value chain restructuring, identifying and quantifying industry chain risks is essential for the study of global economic security. This paper treats Multilateral Trade Agreements (MTAs) as sources of structural risk, integrating network science, input\u0026ndash;output analysis, and counterfactual methods. Using ADB-MRIO data, we construct a Global Industrial Value Chain Network model and apply XIFA algorithm to reduce dimensionality, extracting both the actual post-MTA network and a counterfactual network without MTAs. Through Hypothetical Elimination Method, we identify MTA-induced risk exposures, thereby establishing a multi-perspective risk-measurement framework. Our findings are: (1) The risk exposure of the global industrial chain presents a \u0026ldquo;core-periphery\u0026rdquo; structure, core countries face the most exposures, a lower number of risk exposures in the peripheral regions, but under the influence of global economic uncertainty, and semi-core areas remain relatively stable. (2) High-tech manufacturing and business services have a high number of risk exposures and a fast growth rate due to strong upstream and downstream linkages and strong reliance on technological capital; primary industries with simple production chains and low technological reliance have relatively stable risk exposures. (3) There are differences in the risk patterns of the industrial chain between China and the United States, with China being more sensitive to the risks of resources and wholesale trade, and the United States relying more strongly on the risks of high-end manufacturing. (4) From the point of view of the node position of the two countries in the global production network, as an upstream node, China is exposed to higher risks in mining and wholesale trade, while the U.S. is more vulnerable to technology-intensive industries, such as machinery, electrical and transportation equipment; as a downstream node, the construction industry is a common risk plateau between China and the U.S., but China is affected by the impact of residential leverage and the slowdown of infrastructure, while the U.S. is pressured by the vacancy of commercial real estate and fluctuations in energy costs. The U.S. is under pressure from commercial real estate vacancies and energy cost volatility.\u003c/p\u003e","manuscriptTitle":"Has the proliferation of multilateral trade agreements generated greater risk exposure to the global production network?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 14:29:58","doi":"10.21203/rs.3.rs-7122094/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T06:42:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T09:07:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210005557295068693036398759660384445638","date":"2026-01-13T16:01:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T04:48:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115697141736805345262900964665199117893","date":"2025-08-22T12:29:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-22T08:48:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-22T08:40:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-20T13:14:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-10T16:01:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-08-10T15:57:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"670a54ca-e246-4f4f-a10d-e1c6c6a734d6","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53572407,"name":"Physical sciences/Engineering"},{"id":53572408,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":53572409,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-10T14:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 14:29:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7122094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7122094","identity":"rs-7122094","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}