The Impact of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership and Regional Comprehensive Economic Partnership on the Global Value Chain of Manufacturing

Abstract

Manufacturing global value chains (GVCs) play a central role in shaping countries’ export competitiveness. However, existing studies have given limited attention to the impact of regional trade agreements (RTAs) on manufacturing GVCs. This study examines the effects of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the Regional Comprehensive Economic Partnership (RCEP) on manufacturing GVCs. Using the Global Trade Analysis Project (GTAP) model, based on the GTAP 10 database with dynamic recursion to 2025, this study simulates various scenarios of tariff and non-tariff barrier (NTB) reductions. This model is linked to a GVC accounting framework to evaluate member countries’ trade performance in manufacturing value added, as well as their participation and position in GVCs. The results show that the CPTPP and RCEP, when implemented separately, significantly boost bilateral value-added trade within their regions, with increases of 99.4% and 65.7%, respectively. Their combined effect further strengthens global value-added trade, raising it by 5.1%. Both agreements also promote greater GVC participation in most manufacturing sectors across member economies, although their influence on sectoral positioning differs across countries. Overall, the findings demonstrate that the CPTPP and RCEP are reshaping regional production networks and affecting manufacturing development in member states. They highlight the growing importance of RTAs in shaping value chains and underscore the need to revitalize global partnerships for sustainable development. For policymakers, the results provide timely evidence on how RTAs can be leveraged to support sustainable growth in manufacturing.

1. Introduction

Manufacturing is widely regarded as a key driver of national economic development [1]. According to the 2024 International Industrial Statistics Yearbook, manufacturing value added, measured at 2015 constant prices, reached USD 15.5 trillion in 2023, accounting for 78.6% of total industrial output. Participation in global value chains (GVCs) significantly shapes the export of manufacturing value added and enhances international competitiveness [2,3,4]. Integration into GVCs can improve total factor productivity [5], promote industrial upgrading [6], and foster green growth [7]. As the international division of labor becomes increasingly fragmented, trade agreements play a critical role in facilitating manufacturing sector participation in GVCs [8] and improving the position of this sector within them [9]. Moreover, different types of trade agreements have distinct effects on GVCs. Shallow agreements show limited ability to foster integration, while deep agreements are more capable of addressing the complex demands of GVCs [10]. Despite growing interest, limited research has examined the impact of multiple overlapping trade agreements on the manufacturing sector’s GVC participation. To address this gap, this study focuses on two major regional trade agreements (RTAs): the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the Regional Comprehensive Economic Partnership (RCEP). The CPTPP is widely regarded as the most comprehensive trade agreement in the Asia-Pacific region, encompassing tariff reductions, non-tariff measures, intellectual property, labor, and environmental standards, and is often described as a “deep trade agreement.” In contrast, the RCEP brings together nearly one-third of global GDP and population, making it the largest RTA in existence.

The GVC of manufacturing is influenced by a wide range of factors. Previous studies have examined the impact of green transformation [11], rising housing prices [12], digitization [13], digital trade regulation [14], servitization [15], participation in regional value chains [9,16,17], and the adoption of industrial robotics [18] on the development of manufacturing GVCs. Moreover, given that manufacturing spans multiple sectors, scholars have also investigated the GVCs of specific manufacturing subsectors. Examples include the automotive industry [19], solar photovoltaic [20], apparel [21], medical gloves [22], semiconductors [23], and the electronic information industry [24].

As international trade increasingly shifts from global to regional structures, the impact of RTAs on GVCs has attracted growing attention [25]. Previous studies have explored the influence of RTAs on African GVCs [26], Thai GVCs [27], and broader value chain restructuring [28]. Among RTAs, the CPTPP and RCEP are especially influential, with significant implications for GVCs since their implementation [29,30]. Existing research has analyzed RCEP’s effects on GVCs [31], regional value chains [32], agricultural value chain participation [33], and the broader restructuring of GVCs [30], as well as the “spaghetti bowl” effect of RCEP and CPTPP on GVCs [34]. Despite these contributions, most studies focus on individual agreements, with limited systematic analysis of the manufacturing GVC under the overlapping influence of the CPTPP and RCEP. According to the International Yearbook of Industrial Statistics 2024, Asia and Oceania account for more than half of the global manufacturing value added, making them key regions in the global manufacturing landscape. Notably, both CPTPP and RCEP comprise countries primarily located in these regions. This overlap highlights the significance of these two agreements, as their implementation directly affects the areas where global manufacturing is most concentrated. Accordingly, this study aims to assess the specific impact of the CPTPP and RCEP on the GVC in manufacturing. It contributes to the existing literature by providing a new perspective on how RTAs influence GVCs and offers valuable insights into the restructuring of the manufacturing GVC.

To this end, this study employs the Global Trade Analysis Project (GTAP) model to simulate various policy scenarios. The simulation results are transformed into multi-regional input-output (MRIO) data, which are then integrated with the GVC accounting framework to construct the GTAP-GVC model. Using this model, this study evaluates the impacts of tariff and non-tariff measure reductions under the CPTPP and RCEP on member countries’ manufacturing value-added trade, GVC participation, and position [35,36]. Additionally, the study incorporates theories of economic integration to analyze the mechanisms through which CPTPP and RCEP influence manufacturing GVCs. These analyses offer deeper insight into the challenges and opportunities facing member countries amid shifts in manufacturing GVCs driven by RTAs and underscore the importance of revitalizing the Global Partnership for Sustainable Development (SDG 17).

The potential contributions of this study are threefold: (1) It constructs a GTAP-GVC model that not only quantifies the impacts of CPTPP and RCEP on manufacturing GVC participation and status but also incorporates value-added exports into the analysis. This provides a more comprehensive assessment of RTA effects. (2) It applies the concepts of trade creation and diversion from integration theory to explore how these agreements drive the regionalization of manufacturing GVCs. (3) It investigates the combined effects of CPTPP and RCEP, highlighting their joint influence on manufacturing value chains. This perspective remains underexplored and offers valuable implications for policymakers.

2. Literature Review

2.1. Economic Impacts of RCEP and CPTPP

Trade liberalization is a key driver of long-term regional economic growth [37], and trade agreements are one of the main instruments for advancing it [38]. Existing literature has widely examined how such agreements generate welfare gains [39], stimulate trade and foreign direct investment [40], enhance productivity [41], and increase corporate value [42]. However, much of this research remains ex post, while studies that conduct ex ante simulations often fail to provide a comprehensive analysis of manufacturing subsectors within GVCs. The CPTPP and RCEP are the two major RTAs in the Asia-Pacific, and they exert a significant influence on trade liberalization in this region [43]. This study employs a pre-simulation and agreement-combination approach to assess the long-term effects of the CPTPP and RCEP and to explore how trade liberalization reshapes GVCs.

RTAs (RTAs) are generally defined as arrangements between two or more countries or regions that aim to reduce trade barriers and facilitate the free flow of goods and services among members [44]. According to the World Trade Organization, RTAs can take the form of free trade areas, customs unions, or broader economic integration agreements. Jacob Viner’s classic theory of customs unions [45] introduced the concepts of “trade creation” and “trade diversion,” which remain central to understanding the effects of RTAs [46].

According to Grossman and Helpman’s theory [47], trade liberalization fosters economic growth by improving industrial efficiency and productivity. When trade barriers are reduced among member states, regional trade flows expand, which, in turn, stimulates growth. This expansion of intra-regional trade is known as the trade creation effect, while the reduction in trade with non-member countries is described as the trade diversion effect [48]. In practice, the CPTPP lowers trade costs and facilitates imports and exports among its members, whereas many non-members experience trade losses as a result [49]. Similarly, tariff reductions under the RCEP have gradually triggered trade creation, significantly strengthening ties between its member states [50]. At the same time, RCEP’s trade diversion effect has led to declining trade with non-member regions [32].

The influence of the CPTPP and RCEP extends beyond trade flows. Both agreements generate positive spillovers for regional GDP and welfare by reducing tariffs and technical barriers. For instance, RCEP has contributed to GDP growth among its members, although it has imposed costs on non-member economies [51]. The CPTPP demonstrates similar effects, though generally less pronounced than those of RCEP [52]. Beyond GDP, RCEP has also had notable effects on areas such as social welfare [53] and agriculture [54].

2.2. Impact of CPTPP and RCEP on Manufacturing GVCs

RTAs influence not only macroeconomic outcomes such as trade volumes and social welfare [55], but also carry significant implications for GVCs [56]. GVCs refer to the fragmentation of production processes across multiple countries or regions, where each stage of production takes place in the location best suited to that task [57]. Manufacturing GVCs are a critical component of this system, spanning industries such as textiles and apparel, machinery and equipment, and electronics.

RTAs and GVCs interact through unique mechanisms of mutual influence. The implementation of RTAs generates trade creation effects by lowering transportation and transaction costs and reducing trade barriers. This promotes the cross-border flow of intermediate and final goods and strengthens member countries’ participation in GVCs [9]. Deeper provisions in RTAs also enhance the movement of intermediate goods by improving policy predictability, thereby deepening members’ integration into value chains [58]. Conversely, non-member countries may face weaker trade links due to trade diversion, as the removal of intra-regional barriers encourages members to replace higher-cost imports from outside the region with lower-cost imports from within it [59].

The effects of RTAs on GVC status, however, are heterogeneous [60]. Because member countries differ in technological capabilities and resource endowments, developed economies often strengthen forward participation through technological advantages, while developing economies tend to experience greater gains in backward participation [61]. Such differences arise because RTAs magnify comparative advantage gaps, which can undermine the GVC status of industries in developing countries that lack these advantages.

In multi-stage production systems, these dynamics are further amplified. Trade costs incurred at each stage accumulate along the chain and are passed on to the next stage through higher offshore prices [62]. This “amplification effect” raises the costs of cooperation between RTA and non-RTA members [63], making it more likely that RTA members will deepen value chain linkages among themselves. For example, tariff and non-tariff barrier (NTB) reductions under RCEP have promoted regional value chain (RVC) integration, leading to greater product regionalization [32]. Lower intra-regional trade costs have also boosted participation for most RCEP members, improving agricultural GVC positions for China, South Korea, and Australia, while reducing competitiveness for others [33]. These outcomes reflect underlying differences in comparative advantage [31].

It is also important to note that both the CPTPP and RCEP are classified as “deep” RTAs, characterized by commitments that go well beyond tariff reductions, including regulatory convergence and investment facilitation [55]. The impacts of such deep RTAs on non-members differ from those of shallow agreements. Exporters of regulation-intensive goods may even benefit indirectly through spillovers from regulatory harmonization and higher standards [64]. When the CPTPP and RCEP coexist, their overlap enlarges the regional market size and generates scale effects, thereby reinforcing members’ positions in GVCs [9]. Yet the benefits remain uneven: countries such as Japan, Australia, and New Zealand are more likely to climb within GVCs in industries where they already hold comparative advantages [31].

2.3. Hypothesis

Based on this context, the study hypothesizes that both the CPTPP and RCEP have a positive impact on manufacturing GVC participation, primarily through the expansion of bilateral value-added trade within their regions. At the same time, the effects of these RTAs on the GVC positions of manufacturing subsectors are expected to be heterogeneous across member countries. In particular, sectors where members hold comparative advantages are likely to move upward in the value chain, whereas industries without such advantages may face relative declines in their positions.

3. Materials and Methods

3.1. The GTAP-GVC Model

Given the relatively short time since the formal implementation of the CPTPP and RCEP, most existing literature focuses on policy evaluation or ex ante simulations. In assessing the economic impacts of these agreements, computable general equilibrium (CGE) models are commonly employed. For instance, CGE models have been used to analyze the impact of RCEP on regional value chains [32], global carbon emission reductions [50], labor markets [65], the “spaghetti bowl” effect between RCEP and CPTPP [34], and their implications for economies such as Bangladesh [43]. These examples demonstrate that CGE models possess strong adaptability and explanatory power for analyzing RTAs. They are not only widely used to evaluate impacts on GDP and welfare but are also capable of addressing more complex issues such as carbon emissions reduction and labor market dynamics.

The GTAP model is a multi-country, multi-sector computable general equilibrium (CGE) model developed by the GTAP team at Purdue University, grounded in neoclassical economic theory. It is primarily used for quantitative analysis of trade policy changes and is one of the most widely adopted CGE models in current use. The GTAP model enables the simulation of policy shifts to predict their effects on the economies and trade flows of different countries.

In the standard static GTAP framework, several assumptions are made: markets are perfectly competitive, returns to scale are constant, labor is fully mobile within each country, and land is immobile across sectors. Based on these assumptions, the output of an economy is categorized into two types: domestically consumed goods and exports. This reflects the interaction between the domestic market and international markets. Under the constant elasticity of substitution (CES) assumption, firms operate under a profit maximization constraint, while consumers make utility-maximizing choices based on the constant difference elasticity (CDE) function. The model treats households, governments, and firms as the main economic agents, each capable of choosing between domestic and foreign goods. Incorporating the Armington assumption, which posits that domestic and imported goods are imperfect substitutes, the model uses a system of equations solved numerically to determine general equilibrium outcomes. By simulating trade changes under different policy scenarios, the GTAP model can predict the impact of tariffs, NTBs, or other trade policies on national economies and trade flows. The GTAP model provides a comprehensive numerical simulation and analysis framework, enabling us to understand how different countries interact in the global market. The detailed structure of this framework can be found in the Supplementary Information.

The GTAP model comprises two main components: the main program (RunGTAP) and the database (GTAPAgg). The database includes a wide range of real-world economic data from various regions, such as trade flows, economic indicators, and industrial output. Most data are derived from countries’ input-output (I-O) tables. For simulating changes in trade barriers, the model uses two primary variables: tms (for tariff rate shocks) and ams (for NTB shocks). In this study, tms is used to model tariff reductions, while ams is used to simulate reductions in NTBs.

In this study, a GTAP-GVC model is constructed based on the standard GTAP framework. Building on the simulation results of the standard GTAP model, this study converted the GTAP data into a multi-region input-output table using the method outlined by Peters [66]. Due to the length constraints of the paper, the conversion method can be found in the Supplementary Information. Building on this, the export decomposition framework by Koopman et al. [67] is applied to separate gross exports into domestic and foreign value-added components. Additionally, the international division of labor positions are further measured using the accounting method proposed by Wang et al. [68]. Equations (1)–(4) provide the detailed methodology.

Table 1. Basic types of multi-regional input–output tables.

Output Input		Intermediate Demand				Final Demand				Total Output
		S	R	…	T	S	R	…	T
Intermediate input	S	$Z^{SS}$	$Z^{SR}$	…	$Z^{ST}$	$Y^{SS}$	$Y^{SR}$	…	$Y^{ST}$	$X^S$
	R	$Z^{RS}$	$Z^{RR}$	…	$Z^{RT}$	$Y^{RS}$	$Y^{RR}$	…	$Y^{ST}$	$X^R$
	…	…	…	…	…	…	…	…	…	…
	T	$Z^{TS}$	$Z^{TR}$	…	$Z^{TT}$	$Y^{TS}$	$Y^{TR}$	…	$Y^{TT}$	$X^T$
Value-added		${VA}^S$	${VA}^R$	…	${VA}^T$			…
Total inout		${\left(X^S\right)}^T$	${\left(X^R\right)}^T$	…	${\left(X^T\right)}^T$			…

Note: ${\left(X^S\right)}^T$ denotes the transpose of the gross output vector of country S, aligning sectoral outputs in a row format to facilitate cross-regional comparisons.

presents the structure of a basic MRIO table. In this table, Z represents the use of intermediate goods, Y denotes final goods, X is the gross output, and VA represents value added. For example, $Z^{SR}$ indicates the volume of intermediate goods produced in country S and used in country R, $Y^{SR}$ refers to final goods produced in S and consumed in R, $X^S$ is the gross output of country S, and ${VA}^S$ denotes the value added generated in country S.

$$\begin{array}{c}{E}_{s^{\mathrm{*}}}=\left\{V_{\mathrm{s}}{\displaystyle \sum _{r\ne s}^G}{B}_{\mathrm{s}\mathrm{s}}{Y}_{sr}+V_{\mathrm{s}}{\displaystyle \sum _{r\ne s}^G}{B}_{\mathrm{s}\mathrm{r}}{Y}_{rr}+V_{\mathrm{s}}{\displaystyle \sum _{r\ne s}^G}{\displaystyle \sum _{t\ne s,r}^G}{B}_{\mathrm{s}\mathrm{r}}{Y}_{rt}\right\}\\ +\left\{V_{\mathrm{s}}{\displaystyle \sum _{r\ne s}^G}{B}_{\mathrm{s}\mathrm{r}}{Y}_{rs}+V_{\mathrm{s}}{\displaystyle \sum _{r\ne s}^G}{B}_{\mathrm{s}\mathrm{r}}{A}_{rs}{\left(I-A_{ss}\right)}^{-1}{Y}_{ss}\right\}\\ +V_{\mathrm{s}}{\displaystyle \sum _{r\ne s}^G}{B}_{\mathrm{s}\mathrm{r}}{A}_{rs}{\left(I-A_{ss}\right)}^{-1}{E}_{s^{\mathrm{*}}}\\ +\left\{{\displaystyle \sum _{t\ne s}^G}{\displaystyle \sum _{r\ne s}^G}{V}_{\mathrm{t}}{B}_{\mathrm{t}\mathrm{s}}{Y}_{sr}+{\displaystyle \sum _{t\ne s}^G}{\displaystyle \sum _{r\ne s}^G}{V}_{\mathrm{t}}{B}_{\mathrm{t}\mathrm{s}}{A}_{sr}{\left(I-A_{rr}\right)}^{-1}{Y}_{rr}\right\}\\ +{\displaystyle \sum _{t\ne s}^G}{V}_{\mathrm{t}}{B}_{\mathrm{t}\mathrm{s}}{A}_{sr}{\displaystyle \sum _{r\ne s}^G}{\left(I-A_{rr}\right)}^{-1}{E}_{r^{\mathrm{*}}}\end{array}$$

(1)

In Equation (1), $E_{s^{\mathrm{*}}}$ represents the total exports of country S, which are decomposed into five components comprising nine specific items. The three items in Part 1 represent value-added exports. Item 1 refers to domestic value added that is directly absorbed by the importing country ($\mathit{DVA\_FIN}$); item 2 captures domestic value added that is absorbed by the importing country through intermediate exports ($\mathit{DVA\_INT}$); and item 3 accounts for domestic value added that is used by the importing country and subsequently re-exported to a third country ($\mathit{DVA\_INTrex}$). In part 2, labelled $RDV$, represents value added that returns to the exporting country through exports; part 3 is the domestic double-counting component ($DDC$), reflecting the overstatement of domestic value added in gross exports; and part 4 includes two items that represent foreign value added contained in the gross output of the source country. The first item is foreign value added and directly absorbed by the importing country ($\mathit{FVA\_lmp}$). The second is foreign value added, passed on to, and absorbed by a third country ($\mathit{FVA\_oth}$). Part 5 captures the foreign double-counting component ($FDC$), which reflects the overstated foreign content in exports.

$$VAX=DVA+RDV=\mathit{DVA\_FIN}+\mathit{DVA\_INT}+\mathit{DVA\_INTrex}+RDV$$

(2)

In Equation (2), VAX denotes total value-added exports, i.e., the domestic value-added portion of gross exports. DVA refers to domestic value added absorbed abroad, and RDV refers to domestic value added that returns and is absorbed by the home country.

$${GVCparticipation}_{ir}={\displaystyle \frac{{IV}_{ir}}{E_{ir}}}+{\displaystyle \frac{{FV}_{ir}}{E_{ir}}}={\displaystyle \frac{{\mathit{DVA\_INT}}_{rex}}{E_{ir}}}+{\displaystyle \frac{\mathit{FVA\_lmp}+\mathit{FVA\_oth}}{E_{ir}}}$$

(3)

$$\begin{array}{c}{GVCposition}_{ir}=ln\left(1+{\displaystyle \frac{{IV}_{ir}}{E_{ir}}}\right)-ln\left(1+{\displaystyle \frac{{FV}_{ir}}{E_{ir}}}\right)\\ =ln\left(1+{\displaystyle \frac{{\mathit{DVA\_INT}}_{rex}}{E_{ir}}}\right)-ln\left(1+{\displaystyle \frac{\mathit{FVA\_lmp}\text{+}\mathit{FVA\_oth}}{E_{ir}}}\right)\#\end{array}$$

(4)

Let ${IV}_{ir}$ represent the indirect value-added exports from sector $i$ in country $r$ and ${FV}_{ir}$ represent the foreign value added included in exports from the same sector and country. Let $E_{ir}$ denote the total value-added exports from sector $i$ in country $r$. The forward GVC participation index is given by ${IV}_{ir}/E_{ir}$, and the backward GVC participation index is given by ${FV}_{ir}/E_{ir}$. Equation (3) measures the extent of a country’s participation in GVCs, while Equation (4) reflects its position within the GVC.

3.2. GTAP Analogue Area Settings

Both the RCEP and CPTPP agreements are now fully in effect for their respective signatories. RCEP includes China, Japan, South Korea, Australia, New Zealand, and the ten ASEAN economies: Singapore, Malaysia, Indonesia, Thailand, Vietnam, Laos, Myanmar, Cambodia, Brunei, and the Philippines. CPTPP comprises Japan, Australia, New Zealand, Brunei, Malaysia, Singapore, Vietnam, Canada, Chile, Mexico, Peru, and the United Kingdom.

For the purposes of this study, China, Japan, South Korea, Australia, and New Zealand are treated as individual economies. Brunei, Vietnam, Malaysia, and Singapore are grouped as “CPTPP countries in ASEAN,” while Indonesia, the Philippines, Thailand, Laos, and Cambodia are classified as “non-CPTPP countries in ASEAN.” Canada, Mexico, Chile, Peru, and the United Kingdom are categorized as “other CPTPP countries.” All other countries or regions are aggregated into the category of “rest of the world.”

Based on the composition of RCEP and CPTPP, and consistent with the needs of this study, the 141 countries or regions in the GTAP database (GTAPAgg2) are thus consolidated into nine categories: China, Japan, South Korea, Australia, New Zealand, CPTPP countries in ASEAN, non-CPTPP countries in ASEAN, other CPTPP countries, and the rest of the world.

It should be noted that Myanmar is not listed separately in the GTAP database and is instead included in the “Rest of Southeast Asia” region. Given the relatively small size of Myanmar’s economy and its limited influence on aggregate results, Myanmar is not included in the ASEAN regional classification in this study. The specific country groupings used are presented in the

Table 2. Division of regional groups.

Code	Corresponding Economies/Regions in the GTAP10 Database
CHN	China (excluding Hong Kong, Taiwan and Macao)
JPN	Japan
KOR	Korea
AUS	Australia
NZL	New Zealand
ASEAN-1	Singapore, Malaysia, Vietnam, Brunei
ASEAN-2	Indonesia, Philippines, Thailand, Laos, Cambodia
OthCPTPP	United Kingdom, Canada, Mexico, Chile, Peru
RestofWorld	Countries and territories in GTAPAgg2 other than those set above

.

In this study, the 65 industries in the GTAP 10.0 database are reclassified into eight broad categories: food processing, textiles and apparel, light industry, chemicals, metals and metal products, electronic information, machinery and equipment, and other industries. The specific classification is detailed in the

Table 3. Industry setting.

Code	Corresponding Sectors in the GTAP 10 Database
Food	Vegetable oils, Dairy products, Sugar, Beverages and tobacco, Beef products, Other meat products, Other agricultural products, etc.
Text	Textiles, Synthetic fibers, Dress clothing
Ligh	Fur, Leather, Bags and handbags, Shoes, Wood, Wood products, Straw preparation material products, Paper products, Printing
Chem	Coking products, Petroleum, Refined oil products, etc., Basic chemicals, Other chemicals, Rubber and plastic products, Cement, Glass, Concrete, etc.
Meta	Basic production and forging, Sheet metal products, Non-machinery and equipment, Production and forging of copper, Aluminum, Zinc and lead, etc.
Ele	Calculators, etc., Radios, Precision optical instruments, Medical, TV and communication equipment, etc.
Mach	Electrical machinery and equipment, Clocks and watches, etc., Transportation equipment other than motor vehicles
Other	Industries other than those listed above in GTAPAgg2

.

3.3. Scenario

This study uses the GTAP 10.0 database, which is based on 2014 data and includes 141 countries or regions and 65 industrial sectors. The database provides comprehensive bilateral trade information, including both exports and imports of goods and services. The predictive capability of this database for assessing the impact of exogenous shocks has been widely validated in various studies. Previous research has utilized this database to evaluate the effects of trade sanctions on the environment and health [69], the impact of Sino-US trade tensions on sustainable development goals [70], and the influence of the RCEP on the agricultural value chain [71]. Since 2014 is the base year, using the data directly for simulations may introduce bias. To address this, the study follows the approach of Ahmed et al. [72], dynamically updating the 2014 data to 2025 through recursive simulations. This process uses real GDP, population, capital stock, and labor force data from the Econmap database of the French Centre for Research on the World Economy.

This study is based on the tariff commitment schedules of RCEP member countries. It uses bilateral trade values between RCEP members at the HS6 code level as weights to calculate the weighted average tariff levels in each member’s manufacturing sectors. These averages are then compared with base-period tariffs to determine the extent of reductions [24]. The CPTPP entered into force in December 2018 and follows the “three zeros” framework: zero tariffs, zero barriers, and zero subsidies. Under this framework, “zero tariffs” means that the average tariff rate on goods traded among member countries ultimately falls to 1%, implying a 99% reduction. Accordingly, in this study, when analyzing tariff impacts among CPTPP members, a 1% residual rate is retained to reflect this assumption [73,74,75].

Both the CPTPP and RCEP are now in effect. This study sets up scenarios based on the tariff reduction commitments made by RCEP and CPTPP member countries. Due to the highly heterogeneous nature of NTBs, which include factors like customs clearance times, technical certification procedures, health standards, and licensing requirements, there are significant cross-country variations that make it difficult to represent them through a single tariff equivalence coefficient. This creates considerable challenges for the standardized collection of NTB data [76]. Previous studies have commonly applied direct parameter specifications to assign explicit proportional targets to provisions in RTAs (e.g., RCEP). This approach not only facilitates monitoring by member states but also simplifies the incorporation of exogenous shocks into the GTAP model. Therefore, this study refers to the work of Sun et al. [32], Liu et al. [65], Xu et al. [71], and Zhou et al. [77] to set NTBs for the CPTPP and RCEP at 20%. Given the simulation time span, this setting reasonably reflects the progress made by member countries in advancing free trade within the agreements’ frameworks, aligning with the long-term implementation logic of these agreements.

Based on the above considerations, this paper outlines three hypothetical scenarios to simulate the impact of the CPTPP and RCEP on global manufacturing value chains, as well as the combined impact of both agreements. Specifically:

Scenario 1 (S1): A 99% reduction in tariffs and a 20% reduction in NTBs among CPTPP member countries.

Scenario 2 (S2): Tariff reductions in line with RCEP’s 2040 commitments and a 20% reduction in NTBs among RCEP members.

Scenario 3 (S3): A combination of Scenarios 1 and 2, simulating the concurrent implementation of both agreements.

4. Results

4.1. Impact of RCEP and CPTPP on Domestic Value Added in Exports

Figure 1 illustrates the changes in bilateral manufacturing value-added exports (VAX), by economy and region, following the entry into force of the CPTPP compared with the baseline. The reduction in CPTPP tariffs and NTBs has substantially strengthened trade linkages in manufacturing among member states, with value-added exports generally showing an upward trend. At the regional level, the CPTPP increased intra-regional manufacturing value-added exports by 99.4%. However, due to trade diversion effects, manufacturing VAX from CPTPP members to non-members declined by 3.3%, while imports from non-members fell by 3.5%. Trade among non-CPTPP countries increased only marginally, by 0.1%. By contrast, the CPTPP had a greater effect on global exports than on imports. For instance, manufacturing VAX from CPTPP members increased by 6.7%, exceeding the import growth rate by 3.3%. Meanwhile, manufacturing VAX from non-CPTPP countries decreased by 1.2%, and their imports fell by 0.8%. This suggests that the CPTPP contributed to the contraction of manufacturing in non-member regions. Overall, global manufacturing value-added increased by 0.4%.

From a country- and region-specific perspective, the benefits varied across members. CPTPP members experienced declines in manufacturing VAX to non-members. New Zealand recorded the largest decline in manufacturing VAX to non-CPTPP members. Specifically, its exports to China, Korea, ASEAN-2, and the Rest of the World decreased by 12.7%, 12.0%, 14.1%, and 15.3%, respectively. Japan registered the highest growth rate in manufacturing VAX within the CPTPP region. Its exports to Australia, New Zealand, and ASEAN-1 increased by 119.4%, 112.8%, and 104.4%, respectively. Clearly, the removal of tariffs and the reduction in NTBs under the CPTPP have promoted production fragmentation and value chain restructuring among members, allowing them to gain greater benefits from export trade. The regionalization of manufacturing VAX has been reinforced. At the same time, most non-members (e.g., China) recorded negative growth rates in manufacturing VAX to the CPTPP region, indicating that they were adversely affected by the agreement’s trade barrier reductions.

Figure 2 illustrates the changes in bilateral manufacturing value-added exports (VAX), by economy and region, following the reduction in RCEP tariffs and NTBs compared with the baseline. The results show that RCEP strengthened manufacturing VAX linkages among its member countries. Intra-regional manufacturing VAX within RCEP increased by 65.7%. However, most RCEP members experienced varying degrees of decline in manufacturing VAX to non-members. Exports, from RCEP members to non-members, fell by 6.4% in total. Non-RCEP members as a whole also recorded declines in manufacturing VAX. This suggests that RCEP partly constrained trade between members and non-members and negatively affected manufacturing in non-member economies. For example, manufacturing VAX from “Other CPTPP” economies declined by 29.5% to RCEP members, 1.4% to non-RCEP members, and 6% globally. This indicates that RCEP not only weakened trade linkages between “Other CPTPP” economies and their partners but also reduced their ability to benefit from manufacturing in GVCs. Overall, the implementation of RCEP increased global manufacturing VAX by 4.9%.

Compared with the CPTPP, RCEP generated larger gains in manufacturing VAX for both its members and the world, while imposing more severe negative effects on non-members. Its broader coverage enabled more countries to benefit. For most CPTPP members, the increase in manufacturing VAX from participating in RCEP exceeded the gains obtained from the CPTPP.

Figure 3 presents the changes in bilateral manufacturing value-added exports (VAX), by economy and region, under the simultaneous implementation of the CPTPP and RCEP compared with the baseline. At the regional level, manufacturing VAX within the CPTPP and RCEP areas increased by 49.7%. However, exports from RCEP and CPTPP regions to non-members declined by 6.3%, while imports from non-members fell by 22.7%. Specifically, intra-regional manufacturing VAX rose by 72.2% within the CPTPP and by 65.0% within the RCEP. Compared with Scenarios 1 and 2, intra-regional manufacturing VAX in Scenario 3 increased by 27.2% less within the CPTPP and by 0.7% less within the RCEP. From a country- and region-specific perspective, economies participating in both agreements benefited more. For example, Japan’s manufacturing VAX increased by 35.5% in Scenario 3, compared with 9.9% in Scenario 1 and 32.0% in Scenario 2. By contrast, economies participating in only one RTA gained relatively less. For instance, China’s manufacturing VAX rose by 8.6% in Scenario 3, lower than the 8.8% recorded in Scenario 2 but higher than the −0.8% observed in Scenario 1. This outcome can be attributed to the combined negative and positive effects of the CPTPP and RCEP on China’s manufacturing VAX.

4.2. Impact of RCEP on Manufacturing GVC Participation

Table 4. GVC participation index for manufacturing industries in classified countries and regions.

Region	Scenario	Food	Text	Ligh	Chem	Meta	Ele	Mach
CHN	Base	0.0807	0.0674	0.0626	0.1001	0.0676	0.1771	0.1567
	S1	0.0818	0.0709	0.0639	0.1016	0.0690	0.1789	0.1574
	S2	0.0873	0.0805	0.0665	0.1095	0.0731	0.1980	0.1671
	S3	0.0880	0.0832	0.0673	0.1105	0.0741	0.1989	0.1677
JPN	Base	0.1912	0.2602	0.1129	0.1752	0.1222	0.2476	0.1838
	S1	0.1910	0.2625	0.1149	0.1760	0.1255	0.2497	0.1828
	S2	0.2356	0.2747	0.1315	0.1885	0.1436	0.2701	0.2070
	S3	0.2349	0.2741	0.1311	0.1884	0.1438	0.2693	0.2071
KOR	Base	0.3033	0.2626	0.1345	0.2618	0.1374	0.2842	0.2497
	S1	0.3067	0.2673	0.1365	0.2632	0.1385	0.2851	0.2504
	S2	0.3427	0.2963	0.1548	0.2717	0.1659	0.3016	0.2709
	S3	0.3444	0.2990	0.1558	0.2722	0.1667	0.3020	0.2713
AUS	Base	0.1035	0.1727	0.0864	0.2039	0.1432	0.1625	0.2476
	S1	0.0989	0.1638	0.0858	0.2038	0.1567	0.1644	0.2396
	S2	0.1127	0.2726	0.1006	0.2275	0.1720	0.2438	0.3085
	S3	0.1129	0.2651	0.1008	0.2278	0.1736	0.2301	0.3030
NZL	Base	0.1175	0.1014	0.0841	0.1727	0.1048	0.1380	0.1826
	S1	0.1202	0.1013	0.0867	0.1763	0.1177	0.1445	0.1776
	S2	0.1288	0.1413	0.0982	0.1929	0.1280	0.1891	0.2008
	S3	0.1291	0.1405	0.0984	0.1934	0.1303	0.1859	0.2031
ASEAN_1	Base	0.2884	0.4372	0.1531	0.3373	0.1874	0.3592	0.3599
	S1	0.2835	0.4304	0.1495	0.3311	0.1872	0.3607	0.3543
	S2	0.3013	0.4239	0.1567	0.3434	0.2082	0.3647	0.3622
	S3	0.3001	0.4246	0.1568	0.3422	0.2080	0.3640	0.3608
ASEAN_2	Base	0.1236	0.1580	0.1040	0.1997	0.1349	0.3266	0.2753
	S1	0.1240	0.1590	0.1064	0.2020	0.1377	0.3281	0.2772
	S2	0.1391	0.1882	0.1172	0.2199	0.1605	0.3407	0.2890
	S3	0.1396	0.1889	0.1185	0.2211	0.1622	0.3413	0.2897
OthCPTPP	Base	0.1113	0.1148	0.0826	0.2093	0.1101	0.1906	0.2704
	S1	0.1113	0.1164	0.0835	0.2096	0.1117	0.1954	0.2709
	S2	0.1097	0.1104	0.0798	0.2072	0.1040	0.1723	0.2689
	S3	0.1097	0.1121	0.0809	0.2077	0.1063	0.1800	0.2698

Note: Calculated from the dataset obtained from the GTAP shock. GVC participation is the sum of forward and backward participation, and it is expressed as a dimensionless ratio.

reports the GVC participation indices of different manufacturing industries across countries and regions. Across scenarios, the implementation of the RCEP and CPTPP positively affected the GVC participation of national manufacturing sectors. Relative to the baseline, all three simulated scenarios increased the extent of manufacturing GVC participation for most member countries and regions, suggesting that both agreements facilitate deeper integration into GVC production networks. Compared with the CPTPP, the RCEP generated stronger positive effects on the GVC participation of its members, thereby better promoting their integration into global production networks. These findings highlight that strengthening and expanding regional cooperation helps countries integrate more effectively into GVCs.

In Scenario 1 (S1), the CPTPP not only increased GVC participation in member countries but also enhanced participation in non-members such as China, Korea, and ASEAN-2. This is because China, Korea, and ASEAN-2 are major Asia-Pacific economies that play key roles in global manufacturing value chains, making them more likely to benefit from the CPTPP’s “spillover effects.” In Scenario 3 (S3), the growth in manufacturing GVC participation leveled off, largely due to the combined effects of the CPTPP and RCEP.

Table 5. Forward Participation in Manufacturing GVCs Across Different Countries and Regions.

Region	Scenario	Food	Text	Ligh	Chem	Meta	Ele	Mach
CHN	Base	0.0807	0.0674	0.0626	0.1001	0.0676	0.1771	0.1567
	S1	0.0818	0.0709	0.0639	0.1016	0.0690	0.1789	0.1574
	S2	0.0873	0.0805	0.0665	0.1095	0.0731	0.1980	0.1671
	S3	0.0880	0.0832	0.0673	0.1105	0.0741	0.1989	0.1677
JPN	Base	0.1912	0.2602	0.1129	0.1752	0.1222	0.2476	0.1838
	S1	0.1910	0.2625	0.1149	0.1760	0.1255	0.2497	0.1828
	S2	0.2356	0.2747	0.1315	0.1885	0.1436	0.2701	0.2070
	S3	0.2349	0.2741	0.1311	0.1884	0.1438	0.2693	0.2071
KOR	Base	0.3033	0.2626	0.1345	0.2618	0.1374	0.2842	0.2497
	S1	0.3067	0.2673	0.1365	0.2632	0.1385	0.2851	0.2504
	S2	0.3427	0.2963	0.1548	0.2717	0.1659	0.3016	0.2709
	S3	0.3444	0.2990	0.1558	0.2722	0.1667	0.3020	0.2713
AUS	Base	0.1035	0.1727	0.0864	0.2039	0.1432	0.1625	0.2476
	S1	0.0989	0.1638	0.0858	0.2038	0.1567	0.1644	0.2396
	S2	0.1127	0.2726	0.1006	0.2275	0.1720	0.2438	0.3085
	S3	0.1129	0.2651	0.1008	0.2278	0.1736	0.2301	0.3030
NZL	Base	0.1175	0.1014	0.0841	0.1727	0.1048	0.1380	0.1826
	S1	0.1202	0.1013	0.0867	0.1763	0.1177	0.1445	0.1776
	S2	0.1288	0.1413	0.0982	0.1929	0.1280	0.1891	0.2008
	S3	0.1291	0.1405	0.0984	0.1934	0.1303	0.1859	0.2031
ASEAN_1	Base	0.2884	0.4372	0.1531	0.3373	0.1874	0.3592	0.3599
	S1	0.2835	0.4304	0.1495	0.3311	0.1872	0.3607	0.3543
	S2	0.3013	0.4239	0.1567	0.3434	0.2082	0.3647	0.3622
	S3	0.3001	0.4246	0.1568	0.3422	0.2080	0.3640	0.3608
ASEAN_2	Base	0.1236	0.1580	0.1040	0.1997	0.1349	0.3266	0.2753
	S1	0.1240	0.1590	0.1064	0.2020	0.1377	0.3281	0.2772
	S2	0.1391	0.1882	0.1172	0.2199	0.1605	0.3407	0.2890
	S3	0.1396	0.1889	0.1185	0.2211	0.1622	0.3413	0.2897
OthCPTPP	Base	0.1113	0.1148	0.0826	0.2093	0.1101	0.1906	0.2704
	S1	0.1113	0.1164	0.0835	0.2096	0.1117	0.1954	0.2709
	S2	0.1097	0.1104	0.0798	0.2072	0.1040	0.1723	0.2689
	S3	0.1097	0.1121	0.0809	0.2077	0.1063	0.1800	0.2698

Note: Calculated from the dataset obtained from the GTAP shock. Forward participation in the GVC is expressed as a dimensionless ratio.

and

Table 6. Backward Participation in Manufacturing GVCs Across Different Countries and Regions.

Region	Scenario	Food	Text	Ligh	Chem	Meta	Ele	Mach
CHN	Base	0.0465	0.0259	0.0294	0.0574	0.0121	0.1314	0.1190
	S1	0.0462	0.0258	0.0293	0.0576	0.0122	0.1313	0.1189
	S2	0.0501	0.0295	0.0293	0.0621	0.0121	0.1495	0.1260
	S3	0.0499	0.0294	0.0293	0.0622	0.0121	0.1496	0.1260
JPN	Base	0.1404	0.1095	0.0167	0.0902	0.0145	0.0684	0.1318
	S1	0.1386	0.1082	0.0169	0.0899	0.0145	0.0692	0.1299
	S2	0.1748	0.0994	0.0173	0.0885	0.0152	0.0638	0.1452
	S3	0.1748	0.0998	0.0174	0.0890	0.0153	0.0645	0.1439
KOR	Base	0.2595	0.1358	0.0522	0.1875	0.0400	0.1059	0.2012
	S1	0.2613	0.1348	0.0521	0.1879	0.0401	0.1056	0.2014
	S2	0.2912	0.1362	0.0501	0.1830	0.0408	0.0917	0.2100
	S3	0.2922	0.1355	0.0500	0.1831	0.0408	0.0917	0.2102
AUS	Base	0.0683	0.0935	0.0130	0.1350	0.0331	0.0552	0.2029
	S1	0.0655	0.0854	0.0136	0.1320	0.0330	0.0553	0.1926
	S2	0.0747	0.1624	0.0131	0.1462	0.0328	0.0715	0.2569
	S3	0.0746	0.1584	0.0132	0.1464	0.0334	0.0711	0.2494
NZL	Base	0.0886	0.0611	0.0235	0.1178	0.0257	0.0647	0.1459
	S1	0.0901	0.0613	0.0252	0.1181	0.0283	0.0677	0.1380
	S2	0.0948	0.0919	0.0241	0.1227	0.0316	0.0748	0.1600
	S3	0.0953	0.0928	0.0241	0.1232	0.0315	0.0748	0.1575
ASEAN_1	Base	0.2662	0.4069	0.1100	0.2882	0.1011	0.2539	0.3175
	S1	0.2615	0.4027	0.1077	0.2825	0.0998	0.2625	0.3090
	S2	0.2754	0.3844	0.1034	0.2850	0.1013	0.2309	0.3106
	S3	0.2743	0.3873	0.1043	0.2840	0.1006	0.2325	0.3082
ASEAN_2	Base	0.0991	0.1221	0.0462	0.1327	0.0497	0.2226	0.2332
	S1	0.0988	0.1218	0.0459	0.1333	0.0498	0.2218	0.2332
	S2	0.1082	0.1405	0.0425	0.1385	0.0546	0.2097	0.2410
	S3	0.1081	0.1403	0.0424	0.1387	0.0546	0.2097	0.2410
OthCPTPP	Base	0.0990	0.0997	0.0504	0.1867	0.0651	0.1467	0.2539
	S1	0.0977	0.1000	0.0499	0.1848	0.0646	0.1450	0.2531
	S2	0.0984	0.1006	0.0519	0.1887	0.0678	0.1599	0.2565
	S3	0.0969	0.1008	0.0512	0.1864	0.0673	0.1566	0.2557

Note: Calculated from the dataset obtained from the GTAP shock. Backward participation in the GVC is expressed as a dimensionless ratio.

present the forward and backward GVC participation of manufacturing industries across countries and regions under different scenarios. The effects of the CPTPP and RCEP on forward and backward GVC participation varied across countries and industries. Specifically, China’s chemical and machinery sectors showed higher forward participation, gradually moving upstream in GVCs. However, in Scenarios 2 and 3, the electronics sector displayed more pronounced increases in backward participation, suggesting that China’s electronics industry may be locked into “low-end” positions.

Japan’s manufacturing already exhibited high forward participation, which further increased under the CPTPP and RCEP, while backward participation in some industries declined. This indicates that Japan’s high-value-added industries remain firmly upstream in GVCs, exporting large volumes of intermediate and core technology products. Korea’s manufacturing also showed an upward trend in forward GVC participation. However, the implementation of the RCEP reduced backward participation in Korea’s electronics and chemical industries. This suggests that, under the RCEP, Korea’s electronics and chemical industries are increasingly embedded in GVCs through forward participation.

In Australia and New Zealand, backward participation rose more in low-value-added industries, while forward participation increased more in high-value-added industries. ASEAN manufacturing benefited from greater forward participation under the RCEP, while backward participation in some sectors declined. This indicates that ASEAN economies are climbing upstream in GVCs by leveraging the RCEP. Manufacturing in “Other CPTPP” economies also moved upstream within GVCs under the CPTPP framework. However, in Scenarios 2 and 3, shifts in trade patterns following the RCEP exposed “Other CPTPP” economies to the risk of sliding downstream in GVCs.

4.3. Impact of RCEP on the Position of Manufacturing GVCs

Table 7. GVC position index for manufacturing industries in classified countries and regions.

Region	Scenario	Food	Text	Ligh	Chem	Meta	Ele	Mach
CHN	Base	−0.0119	0.0150	0.0037	−0.0140	0.0419	−0.0787	−0.0755
	S1	−0.0103	0.0186	0.0052	−0.0129	0.0431	−0.0768	−0.0745
	S2	−0.0123	0.0207	0.0075	−0.0139	0.0472	−0.0920	−0.0784
	S3	−0.0114	0.0235	0.0085	−0.0131	0.0481	−0.0913	−0.0779
JPN	Base	−0.0818	0.0364	0.0754	−0.0047	0.0879	0.0986	−0.0731
	S1	−0.0787	0.0407	0.0768	−0.0035	0.0908	0.0990	−0.0705
	S2	−0.1021	0.0667	0.0911	0.0106	0.1056	0.1258	−0.0756
	S3	−0.1028	0.0655	0.0904	0.0096	0.1057	0.1238	−0.0731
KOR	Base	−0.1878	−0.0079	0.0282	−0.1002	0.0538	0.0634	−0.1359
	S1	−0.1878	−0.0020	0.0302	−0.0996	0.0546	0.0647	−0.1357
	S2	−0.2053	0.0208	0.0507	−0.0831	0.0780	0.1028	−0.1314
	S3	−0.2054	0.0245	0.0518	−0.0827	0.0786	0.1031	−0.1314
AUS	Base	−0.0315	−0.0132	0.0580	−0.0600	0.0720	0.0483	−0.1410
	S1	−0.0305	−0.0064	0.0562	−0.0545	0.0841	0.0497	−0.1302
	S2	−0.0348	−0.0460	0.0709	−0.0583	0.0979	0.0899	−0.1783
	S3	−0.0344	−0.0457	0.0709	−0.0585	0.0983	0.0788	−0.1704
NZL	Base	−0.0564	−0.0198	0.0355	−0.0580	0.0508	0.0080	−0.1001
	S1	−0.0565	−0.0203	0.0347	−0.0551	0.0576	0.0085	−0.0905
	S2	−0.0572	−0.0397	0.0476	−0.0479	0.0609	0.0361	−0.1085
	S3	−0.0578	−0.0421	0.0479	−0.0483	0.0632	0.0332	−0.1017
ASEAN_1	Base	−0.2141	−0.3116	−0.0621	−0.2053	−0.0136	−0.1261	−0.2342
	S1	−0.2105	−0.3110	−0.0613	−0.2013	−0.0113	−0.1395	−0.2250
	S2	−0.2177	−0.2866	−0.0464	−0.1941	0.0051	−0.0822	−0.2202
	S3	−0.2170	−0.2907	−0.0480	−0.1934	0.0062	−0.0856	−0.2174
ASEAN_2	Base	−0.0702	−0.0799	0.0111	−0.0598	0.0333	−0.1020	−0.1684
	S1	−0.0692	−0.0785	0.0139	−0.0587	0.0356	−0.0993	−0.1665
	S2	−0.0722	−0.0848	0.0304	−0.0514	0.0476	−0.0673	−0.1691
	S3	−0.0717	−0.0837	0.0318	−0.0508	0.0490	−0.0668	−0.1683
OthCPTPP	Base	−0.0821	−0.0801	−0.0175	−0.1489	−0.0190	−0.0940	−0.2099
	S1	−0.0796	−0.0790	−0.0157	−0.1450	−0.0166	−0.0863	−0.2081
	S2	−0.0825	−0.0862	−0.0231	−0.1545	−0.0300	−0.1360	−0.2159
	S3	−0.0797	−0.0848	−0.0207	−0.1498	−0.0267	−0.1223	−0.2137

Note: Calculated from the dataset obtained from the GTAP shock. The GVC Position Index is expressed as a dimensionless ratio.

presents the changes in the GVC positions of manufacturing industries across CPTPP and RCEP members under different scenarios. From a scenario perspective, both the CPTPP and RCEP improved the GVC positions of most manufacturing industries in member countries and regions under Scenarios 1 and 2. Moreover, a comparison of Scenarios 1 and 2 indicates that for economies participating in both agreements, the RCEP exerted stronger positive effects on manufacturing GVC positions than the CPTPP. This suggests that larger-scale regional economic cooperation provides greater benefits to members. It also implies that once RCEP reaches its 2040 tariff reduction commitments and higher-level rule alignment, it will play an even greater role in fostering manufacturing development among its members.

Comparing Scenarios 2 and 3 shows that the magnitude of changes in manufacturing GVC positions was slightly lower in Scenario 3. This can be attributed to the offsetting effects of overlapping trade agreements. While the RCEP strengthened regional specialization and cooperation, the CPTPP intensified cross-regional competition, which moderated overall changes.

At the national level, developed economies such as Japan and Australia performed particularly well under the CPTPP, with high-value-added manufacturing industries (e.g., chemicals, machinery, electronics) steadily rising across all scenarios. Korea benefited most from the RCEP (Scenario 2), as all manufacturing industries, except food processing, showed upward trends in their GVC positions. China also gained from the RCEP, with notable improvements in textiles and apparel, light industries, and metals. However, in Scenario 3, China’s gains leveled off, mainly due to competitive pressures from the coexistence of both agreements. ASEAN economies displayed notable divergence, with ASEAN-1 (e.g., Vietnam, Singapore) consolidating their advantages in low-value-added industries under Scenarios 1 and 2, while ASEAN-2 (e.g., the Philippines, Thailand) achieved significant gains in mid- to low-value-added industries under the RCEP. “Other CPTPP” economies improved their GVC positions under the CPTPP framework but were negatively affected by the RCEP in Scenarios 2 and 3.

At the industry level, the CPTPP enhanced the GVC positions of low-value-added sectors (e.g., food processing, textiles, light industries) in member states and also benefited similar industries in non-members. For example, China’s GVC positions in food processing, textiles, and light industries rose under Scenario 1. This can be attributed to the “spillover effects” of trade agreements, which may benefit certain industries in non-member economies. The RCEP produced heterogeneous effects on low-value-added industries among its members. For instance, textiles and light industries improved in China, Japan, Korea, and ASEAN-1 economies, whereas food processing declined. In Scenario 2, food processing GVC positions declined in all RCEP members, while they improved in the Rest of the World. This suggests that industries such as food processing, which are highly sensitive to regulatory standards, are more prone to the spillover effects of trade agreements. In mid-value-added industries (e.g., chemicals, metals), developed economies such as Japan and Australia displayed strong competitiveness under the CPTPP (Scenario 1), whereas China and Korea consolidated their positions under the RCEP. In high-value-added industries (e.g., electronics, machinery), competition was most intense in Scenario 3. Japan maintained its lead, but the gains of China, Korea, and ASEAN economies leveled off in these sectors. These outcomes reflect the coexistence of cooperation and competition between the RCEP and CPTPP regions under the joint influence of both agreements.

5. Discussion

5.1. Theoretical Implications

This study constructs a GTAP-GVC model that incorporates the KWW export decomposition method to calculate manufacturing value-added exports, GVC participation, and the GVC position of different countries and regions. The model is applied to assess the effects of tariff and NTB reductions under the CPTPP and RCEP on global manufacturing value chains. The main contributions of this study are as follows:

First, both the CPTPP and RCEP facilitated the expansion of bilateral value-added trade in manufacturing within member countries and promoted growth in global value-added trade, consistent with previous findings. The reduction in trade costs under the RCEP released trade-creation effects, not only boosting intra-regional trade [50] but also expanding bilateral value-added flows within the region [32], in line with Wei et al. [78] and Ken and Hiro [79]. Similarly, the CPTPP generated comparable trade-enhancing effects [52], and countries that joined both agreements reaped greater benefits, as confirmed by Park et al. [51].

Second, tariff and non-tariff reductions under the CPTPP and RCEP positively influenced the GVC participation of member states. This finding is consistent with earlier studies, which suggest that RTAs reduce dependence on distant partners and foster the development of regional value chains [58], as also confirmed by Nida et al. [80] and Cheng et al. [61]. However, such effects are not uniform [81]. For instance, under the RCEP, participation in agricultural value chains increased in most countries, but the extent of this increase varied across members [33], consistent with Xu et al. [71].

Finally, the impacts of the CPTPP and RCEP on GVC positions varied across industries, with sectors holding comparative advantages benefiting the most. This result is consistent with Han and Zhu [60] and Cheng et al. [61]. In particular, the RCEP strengthened the GVC positions of its signatories through tariff reductions, allowing members to gain more in sectors with comparative advantages [31]. By contrast, the RCEP had uneven effects on exports across industries and exerted a dampening influence on technology-intensive sectors in economies with lower GVC positions [82], a finding aligned with Hummels et al. [83].

5.2. Practical Implications

The practical implications derived from this study are as follows:

First, this study finds that both the CPTPP and RCEP promote the expansion of manufacturing value-added trade among member countries, while limiting such growth in non-member economies. Through trade diversion, the two agreements partly suppress value-added trade with non-members by redirecting flows from external partners toward member states. Because the positive effects for members are much greater than those for non-members, and since the agreements overlap while also competing with each other, this study recommends that members actively consider joining the other pact. Dual membership would allow economies to capture the gains of both agreements while at the same time lessening the competitive pressures on non-members. Moreover, it could limit trade diversion and thereby lessen its adverse spillover effects on external partners.

Second, the study finds that both the CPTPP and RCEP have contributed to the overall growth of global manufacturing value-added trade. Yet the RCEP seems to have a stronger impact than the CPTPP for two reasons. First, it is the first trade agreement jointly signed by China, Japan, and South Korea, three economies that play central roles in global manufacturing, which amplifies its influence worldwide. Second, the average level of trade protection among RCEP members has been higher than that of CPTPP members, so reductions in barriers have produced more significant effects. Together, the coexistence of the two agreements has driven a larger increase in global manufacturing value-added trade, with dual members gaining the most. At the same time, however, non-members are likely to face greater losses in their manufacturing trade. Based on these findings, the study suggests two directions for RCEP’s development. First, it should expand regionally by encouraging wider participation from developing countries in the Asia-Pacific. Broader membership would help lower trade barriers and promote integration, thereby supporting manufacturing trade growth in developing economies. Second, RCEP should gradually align its non-tariff trade rules with those of the CPTPP. This would allow it to preserve the traditional trade-creation effects of integration while more rapidly capturing the benefits of high-standard non-tariff provisions.

Finally, the study highlights how the CPTPP and RCEP affect manufacturing participation and positioning within GVCs. Both agreements have strengthened the participation of most members’ manufacturing sub-sectors in GVCs. Their influence on relative positions, however, differs across countries, reflecting variations in comparative advantages across specific manufacturing domains. Some non-members have also gained indirectly from the spillover effects of these “deep” RTAs, which have helped improve the standing of their certain manufacturing sectors in GVCs. Based on these findings, the study recommends that member states promote industrial restructuring and enhance their ability to adapt to international trade rules through technological innovation and institutional openness. By making full use of the institutional advantages offered by the CPTPP and RCEP, particularly with regard to non-tariff measures, members can foster transnational cooperation and attract cross-border investment in core manufacturing technologies and critical equipment sectors. These efforts would help prevent certain industries from falling into “low-end lock-in.” For non-members, the study suggests strengthening trade ties with members through bilateral or subregional free trade agreements to build greater capacity for adjusting to high-standard trade rules. In addition, non-members are encouraged to actively pursue accession to either the CPTPP or the RCEP in order to share in the wider benefits of regional economic integration.

In addition, the GTAP-GVC model developed in this study is not only applicable to assessing the impact of the CPTPP and RCEP on the participation and position of manufacturing in GVCs. More broadly, it can also be used to forecast value chain dynamics in agriculture and services, to evaluate the effects of different trade policies on various segments of GVCs, and to analyze shifts in the competitiveness of countries and regions within the global production network.

5.3. Limitations and Future Research Directions

First, while this study sheds light on the impact of the CPTPP and RCEP on the GVC of manufacturing, it does not examine this relationship in depth. Future research could explore these effects more comprehensively by combining firm-level data with dynamic modelling approaches, which would better capture the evolving structure and complexity of GVCs. Additionally, as this study primarily focuses on the manufacturing sector, it may overlook spillover effects on services and other industries that are increasingly embedded within GVCs. Subsequent work should therefore consider the wider economic and social impacts of RTAs, including their implications for enterprises, labor markets, and inter-industry linkages. This study also emphasizes long-term effects in order to provide policymakers with a sustainable perspective. However, short-term impacts involve factors such as firm adaptability and domestic market responses. These are highly uncertain and vary across regions, making them difficult to quantify within the scope of the present model. As a result, short-term challenges were not addressed here. Future research could refine the modeling framework to incorporate these dynamics and offer a more complete view of trade liberalization’s impacts.

Second, although this study provides empirical evidence of the heterogeneous effects of the CPTPP and RCEP on GVC participation and positioning, it is not without limitations. Due to constraints in data and scope, the analysis did not include further statistical verification or sensitivity testing, which would have enhanced the robustness of the findings. Expanding the dataset in future studies would allow for more extensive robustness checks and supplementary analyses.

Finally, this study applies the GTAP-GVC model to estimate GVC participation within the manufacturing sector. While the GTAP model is a powerful tool for simulating trade policy effects, its assumptions may not fully reflect real-world complexities. To improve the credibility and validity of model outcomes, future research could incorporate empirical case studies, conduct robustness checks and sensitivity analyses, or integrate methodologies such as network analysis. Another limitation is that the GTAP framework makes it difficult to reassess recent structural shocks such as the COVID-19 pandemic or geopolitical crises. Moreover, the simulation of tariff and NTB reductions relies on two variables, tms and ams, restricting the precision of NTB analysis. Future studies should therefore focus on extending the GTAP model to strengthen its treatment of non-tariff measures, thereby improving both the timeliness and relevance of findings. Given model and resource constraints, additional control variables such as rules of origin were not incorporated here. Expanding the model and disaggregating manufacturing industries in future work would allow researchers to capture intra-industry differences and enhance model precision.