Introduction

The Association of Southeast Asian Nations (ASEAN) comprises ten member countries: Indonesia, Malaysia, Thailand, Myanmar, Brunei, Singapore, Vietnam, the Philippines, the Lao People’s Democratic Republic, and Cambodia. This regional bloc was established to promote economic, social, and cultural development. Statistical data indicate that the economic growth (EG) rate has averaged ~5.5% over the past three decades (Ke, 2024). With further expansion anticipated (Nawaz and Rahman, 2024), ASEAN has emerged as a significant economic bloc, exhibiting a growth rate of 5.2%. Despite the region’s notable economic progress, its environmental impact cannot be ignored. The degradation of local ecosystems has been escalating. In response to recent catastrophic events, countries have increasingly relied on non-renewable energy sources to sustain economic growth. Reports suggest that until recently, the environmental concerns of these regions have not received adequate attention. However, Singapore stands as an exception, having recently become the third-largest greenhouse gas (GHG) emitter in the world (Ali et al., 2022; Saliba, 2024). Given the critical role of natural resources in the ASEAN region’s economy and lifestyle, the region has traditionally functioned as a resource-based economy. The ASEAN nations are endowed with abundant natural resources, including copper, petroleum, timber, and nickel. They account for 82% of the world’s natural rubber production, as well as 56%, 70%, and 50% of the global supplies of palm oil, coconut, and hardwood products, respectively. Additionally, many countries worldwide possess substantial reserves of fossil fuels, such as petroleum and coal, which contribute to global warming (WBO, 2023).

The globalization of production networks and the significant changes in associated international trade dynamics have led to the emergence of global value chains (GVCs) as the prevailing paradigm of 21st-century economic integration (Thakur and Sharma, 2025). For developing economies, such as the ASEAN countries, integration into GVCs presents substantial opportunities for industrialization and significant challenges to sustainable development (WBO, 2023). The remarkable economic growth in the region over the past 30 years can be largely attributed to its strategic positioning as a node in global production networks, particularly in sectors such as electronics, automotive, and textiles (Mishrif et al., 2024). However, the degree of integration varies considerably in quality and depth across member states owing to differences in economic structure, policy orientation, and institutional capacity (OECD, 2022). Recent global disruptions, including trade wars, the COVID-19 pandemic, and geopolitical realignments, have rendered the GVC landscape increasingly complex (Gopalan and Reddy, 2025), necessitating an urgent reassessment of the conditions for successful value chain participation. This study examines the dynamic interaction of five critical drivers—economic growth, energy mix, quality of institutions, digitalization, and natural resource rents—in determining ASEAN’s role in the GPNSTOT structure (Aly and Zaki, 2025).

Historically, economic growth in ASEAN has served as both a catalyst and consequence of GVC integration, establishing a complex bidirectional relationship that warrants thorough examination (Dou et al., 2025). While the region’s export-led development strategy has resulted in notable GDP growth rates, the sustainability of these advancements necessitates a shift from assembly-based to higher value-added production activities (Liu et al., 2024). The middle-income trap phenomenon presents a significant concern for many ASEAN countries (Reddy and Sasidharan, 2025), as wage increases erode cost advantages without corresponding improvements in productivity or technological capabilities (Swenson, 2024). There is a growing demand for practical and scientific measures to address these impacts, as the ecological costs of rapid industrialization pose challenges (Rahman et al., 2024), and carbon-intensive development patterns are facing increasing scrutiny from both international markets and domestic stakeholders (Blanchard et al., 2025). The COVID-19 crisis exposed vulnerabilities in narrowly focused GVCs, prompting multinational corporations to reassess their regional footprints and resilience (Kumar Rai and Sen, 2025). These developments underscore the importance of a nuanced understanding of how economic growth trajectories interact with other critical factors that influence the long-term competitiveness of GVCs amid global disruption and restructuring (Platitas and Ocampo, 2025).

The energy transition presents a significant challenge for ASEAN’s participation in GVCs as efforts to mitigate climate change are reshaping global production norms and consumer preferences (Solecki et al., 2024). Historically, the region’s manufacturing-led growth trajectories have relied on carbon-intensive energy systems (IEA, 2024), creating a conflict between short-term economic interests and long-term sustainability goals (Wang et al., 2024). International decarbonization pressures, through carbon adjustment mechanisms and environmental product standards (Zhu and Zhu, 2025a), pose a risk to the competitive advantage of the regional export industry if clean energy transitions do not align with their global counterparts (Wei and Liu, 2022). Concurrently, the shift towards renewable energy presents opportunities for industrial advancement and the adoption of green technologies (Yang et al., 2024), with the potential to accelerate some ASEAN economies into more sustainable positions within their value chains (UNESCAP, 2022). The uneven distribution of resources and infrastructure within the region complicates this transition (ADB, 2023), as significant disparities in renewable energy potential and adoption exist between more and less developed member countries (IRENA & ILO, 2025). These energy-related challenges and opportunities must be carefully balanced against other development considerations to ensure that GVC participation remains a viable path for inclusive and sustainable development (WEF, 2023).

Institutional quality remains a critical factor in GVC upgrading, influencing various aspects, such as the investment climate and the quality of innovation ecosystems (Banga, 2022; Yang et al., 2022). The diverse governance structures present across ASEAN significantly affect the countries’ ability to attract high-value chain segments and reclaim domestic value added to GVC participation (Duong et al., 2022). Strong institutions play a pivotal role in the successful transfer of technology, protection of intellectual property, and enforcement of contracts, all of which are essential for advancing the value chain (Jahanger et al., 2022; Tsimisaraka et al., 2023). Conversely, poor governance, characterized by corruption, bureaucratic inefficiency, and policy unpredictability, may constrain economies to subordinate roles within global production networks (Zhou et al., 2023). Furthermore, the institutional dimension is becoming increasingly central in an environment where Information and Communication Technology (ICT) and knowledge activities are gaining prominence in global value chains (Mushtaq and Afzal, 2024). Additionally, institutional quality directly influences a country’s capacity to implement effective industrial policies and manage the complex public-private partnerships necessary for this type of GVC upgrading (Tiwari and Sharif, 2025). This institutional perspective elucidates why some ASEAN economies have successfully moved up the value-added chain, while others remain entrenched in low-skilled manufacturing despite decades of GVC participation (Wen et al., 2025).

The digital revolution is significantly transforming global production networks, presenting both disruptive challenges and opportunities for ASEAN’s integration into the GVC. Digital technologies are lowering the entry barriers for small and medium-sized enterprises (SMEs) while simultaneously raising the competitive threshold owing to the demand for enhanced efficiency, customization, and responsiveness (Zhu and Zhu, 2025b). The COVID-19 pandemic accelerated digitalization across value chains (UNCTAD, 2023), and technological capabilities have become increasingly critical determinants of GVC resilience and competitiveness (WBO, 2023). However, ASEAN faces substantial digital divides, particularly between more developed countries, such as Singapore, which excels in information and communication technology infrastructure and innovation, and less-advantaged countries in the region with infrastructure gaps (OECD, 2022). The extent to which the region can leverage the Fourth Industrial Revolution—encompassing the Internet of Things (IoT), artificial intelligence (AI), blockchain, and advanced robotics—will largely determine its future position in global production networks (Pavlínek, 2015). Digital transformation also interacts in complex ways with other study variables, partly because technological transformation is contingent upon institution building, such as digital governance, which requires energy infrastructure to power data centers and manufacturing automation, and offers new avenues for economic growth through digital services and platforms. These interdependencies necessitate an integrated analytical approach to capture the complexities of ASEAN’s digitally enabled GVC (Farooq et al., 2023).

Natural resource abundance presents both opportunities and challenges for ASEAN in integrating into GVCs, resulting in complex developmental trade-offs that vary among member countries (Amin et al., 2022). Resource wealth has driven export earnings and economic growth in countries such as Indonesia and Malaysia (Arzova and Şahin, 2024). However, excessive reliance on commodities may impede industrial diversification and product upgrading, a phenomenon extensively documented in the resource curse literature (Shi and Yang, 2025). The volatility of global commodity markets further complicates long-term development planning, as economic booms and busts disrupt consistent participation in GVCs (Qamruzzaman and Qamruzzaman, 2023). Concurrently, the responsible management of natural resources can provide essential funding for industry upgrading and human capital development (Jiang et al., 2023), potentially serving as a catalyst for the higher-order integration of value chains (Hausmann et al., 2011). The growing global demand for critical minerals for clean energy technologies offers opportunities for resource-rich ASEAN countries (IEA, 2024), provided that they develop downstream processing capabilities instead of exporting raw materials (Sanni and Erenay, 2023).

The concept of globalization has diminished the boundaries between countries. With the advancement of globalization, the concept of the GVC has progressed. Previous studies (Jahanger et al., 2022; Tsimisaraka et al., 2023) have focused on globalization, with limited investigation into the link between carbon emissions and GVC. However, the relationship between GVC, economic growth, and institutional quality has been overlooked in prior research. This study examines the impact of the energy mix, economic growth, and institutional quality on GVC within the context of ASEAN economies, including Brunei, Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. The selection of this region is based on its robust economic expansion, marked export orientation, and advantageous position resulting from increased regional and global trade integration and active participation in numerous bilateral and intraregional trade agreements. This study is unique in that it employs a combination of factors, such as the energy mix, institutional quality, digital economy, and resource management, to assess their impacts on GVC. Institutional theory is used to evaluate the impact of these factors on a specific location within the GVC. This study employs the Bai and Perron and CS-ARDL models to analyze the current state of ASEAN economies within the global supply chain to achieve sustainable development objectives, such as poverty reduction, gender parity, and the establishment of robust institutions and economic sustainability.

The structure of this paper is as follows: Section “Literature review” presents an empirical study examining the relationship between regional integration, economic growth, and food security. Section “Data and methodology” outlines the model requirements, data collection procedures, and results. Section “Results and discussion” discusses the empirical findings, interpretations, and supporting evidence for policy recommendations. Finally, the section “Conclusion and policy implications” concludes the report.

Literature review

The relationship between economic growth and GVC participation has been extensively examined in development economics research, with scholars emphasizing its reciprocal nature. Awan et al. (2022) initially established a model in which GVC integration enhances productivity through knowledge spillovers and technology transfer, and the resulting growth further promotes value chain integration. In the context of ASEAN economies, Hu et al. (2022) demonstrated that export-oriented industrialization strategies have been particularly successful in manufacturing sectors such as electronics and automotive, albeit with significant variation across member countries. However, Hou and Waqas (2024) caution that participation in GVCs does not inherently ensure economic upgrading, as countries may become entrenched in low-value-added assembly operations without effective industrial policies. The middle-income trap (Islam et al., 2022) remains a pertinent issue for ASEAN countries, as wage increases are not matched by corresponding productivity gains. Recent research underscores that digital transformation (WBO, 2023) and human capital development (OECD, 2022) are increasingly critical determinants of differentiated and sustainable GVC-driven growth. Collectively, these findings underscore the complex nature of the dynamic interaction between economic development and value chain upgrading, which this study explores. Hailiang et al. (2023a) show that GVC membership contributes to GDP development in the BRICS region. Using Input-Output Analysis, studies conducted in China illustrate the importance of GVCs in the country’s export-oriented manufacturing sector and general economic growth. Other BRICS nations have also benefited from GVC integration, which has led to higher GDP growth because it has facilitated the spread of cutting-edge technologies, increased productivity, and opened new international markets.

National energy composition has emerged as a critical factor influencing global value chain (GVC) competitiveness, particularly in light of global initiatives to address climate change. Porter and van der Linde (1995) challenged conventional wisdom by illustrating that effective environmental policies can foster innovation and enhance competitiveness rather than hinder economic performance. In the context of the Association of Southeast Asian Nations (ASEAN), Ameer et al. (2024) highlighted that continued reliance on fossil fuels poses significant risks to carbon-intensive export industries, especially with the implementation of carbon border adjustments by major importing markets. Zhao et al. (2024) demonstrated that the adoption of green energy sources can bolster a firm’s GVC position by enabling compliance with increasingly complex international sustainability requirements. However, data from the IEA (2024) reveal significant disparities within ASEAN regarding clean energy transitions, with Singapore and Thailand making rapid progress, while Indonesia and the Philippines lag behind. Case studies from UNCTAD (2023) on Vietnam’s swift adoption of solar power illustrate how proactive energy policies can attract high-value manufacturing and reduce carbon footprints. These findings underscore the necessity for energy transition policies to be meticulously calibrated to achieve an optimal balance between immediate economic competitiveness and long-term GVC-related sustainability goals.

Enhancing institutional quality is consistently recognized as a critical factor in the successful advancement of GVCs, influencing a wide range of issues such as investment climates and innovation ecosystems. Waqas et al. (2025) foundational institutional theory posited that effective institutions reduce transaction costs and enhance market efficiency, thereby directly improving the institutional context necessary for economic advancement. Zhang et al. (2025) expanded on this theory, demonstrating that robust institutions facilitate technology transfer and attract foreign direct investment, both of which are crucial for deep integration into GVCs. Recent research by Yang et al. (2024) indicate that institutional disparities among ASEAN members significantly affect their potential to progress along the value chain analysis (VCA), with more developed economies benefiting from superior governance. Zhu et al. (2024) illustrate how corruption and bureaucratic obstacles can hinder economies from advancing beyond low-value segments of the GVC, despite long-term participation. Similar conclusions were drawn in Boschma's (2022) cross-country comparison of ASEAN institutions, where nations with stricter rule of law and contract enforcement tend to engage in higher-value activities. These findings underscore the essential role of institutional reforms as a prerequisite for upgrading the GVC, particularly in light of emerging digital technologies that are transforming global production networks and introducing new governance challenges. Yang et al. (2022) analyzed China’s energy mix and GVC involvement. The authors used gravity models to examine China’s GVC participation and energy mix. Tissaoui and Zaghdoudi (2025) studied the impact of GVCs on IQ. This study examines how governance, legislation, and transparency affect GVC participation and performance. It examines institutional quality and GVC company entry, upgrading, and integration. This review synthesizes empirical evidence from many nations and industries to show that solid institutions promote GVC involvement. Obeng et al. (2022) used UK microdata to examine the IQ and productivity of GVCs. Institutional variables, including regulatory quality, the rule of law, and corruption control, affect the GVC firm-level productivity. Institutions create an environment for GVC involvement and performance, because higher institutional quality boosts firm-level output.

The digital revolution is significantly transforming GVCs, presenting both opportunities and challenges for developing countries seeking to advance within these chains. Ando et al. (2024) model elucidates how digital platforms can lower the barriers to entry into GVCs, particularly benefiting small- and medium-sized enterprises in developing economies. Mao et al. (2024) demonstrated that digital technologies simultaneously enhance productivity and foster competitive globalization through automation techniques. In the ASEAN region, the (WBO, 2023) highlights substantial digital divides, with Singapore achieving advanced levels of digitization, while Cambodia and Laos encounter fundamental infrastructure deficiencies. The OECD (2022) observes that Industry 4.0 technologies create new avenues for GVC upgrading by integrating advanced manufacturing and digital services. However, Li et al. (2022) emphasize that successful digital transformation depends on supportive investments in skills and institutional frameworks. Case studies by UNCTAD (2023) suggest that the digital acceleration prompted by the pandemic disproportionately benefited technologically advanced companies, potentially exacerbating inequalities. These findings indicate that the impact of digitalization on GVCs is heavily influenced by underlying ecosystem factors, such as energy infrastructure, human capital development, and the policy environment.

Natural resource abundance presents a paradoxical challenge in the integration of global value chains (GVCs), as extensively discussed in the literature on the resource curse and corroborated by emerging empirical studies. (Nasir et al., 2022) systematically demonstrated that resource abundance may pose paradoxical obstacles to broader industrial and economic diversification. In the ASEAN region, Hailiang et al. (2023b) illustrated that Indonesia and Malaysia’s over-reliance on commodity exports impeded progress towards efficient manufacturing. Rehman et al. (2023) theoretical models elucidate the mechanisms through which the availability of resource rents may displace innovation and productivity growth in non-resource sectors. However, Gereffi et al. (2022) indicated that appropriately managed resource rents can facilitate industrial upgrading, provided that these revenues are coupled with sound economic policies. Qin et al. (2023) specifically examined the ASEAN experience and concluded that developing downstream processing capacity is crucial for countries to overcome the resource curse. The IEA reports highlight emerging opportunities in the clean energy technology chain for critical minerals, which resource-rich ASEAN countries could potentially capitalize on. Overall, this research supports the notion that natural resource abundance need not be antithetical to success in GVCs, but requires targeted strategies to transform natural resource rents into productive capabilities and economic diversification. Uddin et al. (2023) examine natural resource management in GVCs and worldwide production. This study analyzed natural resources, sustainable development, and GVC engagement. It examines GVC natural resource extraction, processing, and use difficulties and potential, emphasizing sustainable methods and responsible resource management. Responsible natural resource management is explained by the OECD. Due Diligence Guidance for Responsible Supply Chains of Minerals from High-Risk Areas. This advice targets conflict minerals and supports value chain-responsible sourcing. Traceability, transparency, and sustainable practices are crucial for managing natural resources to respect human rights, promote environmental sustainability, and maintain GVC integrity (Liu and Lin, 2024).

Nevertheless, the complex and dynamic interplay of the five most influential factors—growth levels, energy mix, quality of institutions, digitalization, and natural resource availability—remains underexplored in the GVC literature. From a policy perspective, however, this interaction is becoming increasingly pertinent. Mushtaq and Afzal (2024) highlighted the necessity for a more integrated analysis of GVC integration elements, noting that much of the existing research examines these elements in isolation (Deka et al., 2023) rather than considering their interrelationships. The widespread digitalization and energy transition represent new convergence points in global production networks that require a holistic approach (Sharma et al., 2025). OECD (2022) has begun to explore these interconnections, particularly focusing on how the expansion of digital infrastructure relies on secure and sustainable energy infrastructure. The recent pandemic and geopolitical shocks have underscored the importance of understanding these interactions (Ali et al., 2024). This study aims to address these research gaps by employing the CS-ARDL (Chudik and Pesaran, 2015a) to examine how these five factors dynamically interact to influence the GVC trajectories of ASEAN. The findings will enhance our theoretical understanding of GVC dynamics and inform practical policy design for sustainable value chain upgrading in developing economies undergoing multiple transitions simultaneously.

The BRICS and MENA studies show consistent results. However, diverse variables and models across nations have failed to capture all essential elements, resulting in conflicting findings. Thus, few studies have explored GVC dynamics in wealthy countries using Bai and Perron and the CS-ARDL technique, creating a research gap. This study uses the CS-ARDL model to examine how GDP, energy mix, institutional quality, digital economy, and resource management affect GVC. New policies that meet the current demands and difficulties are needed to revitalize the ASEAN-based global supply chain.

Data and methodology

Data

This study explores the impact of GDP, EM, IQ, DGT, and NRR on GVC. To attain this objective, panel data consisting of ASEAN countries over the period of 2000 to 2023 has been used. The ASEAN block consists of 10 countries, but in this study, eight countries (Indonesia, Malaysia, Thailand, Singapore, Brunei, Vietnam, the Philippines, and Cambodia) have been selected due to the availability of data. In this study, we investigate the factors that affect a country’s participation in GVCs, using GVC as the dependent variable. The inputs in the GVC are obtained from the UNCTAD-Eora Global Value Chain Database 16, an aggregate integrated database that provides a coherent description of trade in value-added terms. The main explanatory variable, economic growth, is measured in terms of GDP per capita (current US$) from the World Development Indicators (WDI). A positive outcome is expected because an increase in the level of economic development generally expands a country’s production capacity and its linkage to the global value chain. Another crucial energy dimension is the energy mix, for which we use two indicators from the WDI: Energy use per capita (kg of oil equivalent) and the share of renewable energy consumption in the total final energy use. It is also expected that GVC participation will benefit from diversified and efficient sources of energy supply, leading to a reliable energy supply and environmental sustainability.

The analysis also considers the Institutional Quality Index (IQ) based on the Worldwide Governance Indicators (WGI), which comprises factors such as political stability, government effectiveness, regulatory quality, control of corruption, and voice and accountability. Robust institutions are expected to make economies more fungible, more attractive for investment, and produce a consistent environment of deeper GVC engagement. Digitalization serves as a control for technology sophistication and connectivity and is captured by fixed telephone and mobile cellular subscriptions, internet penetration (% of the population), and digital trade. It can be anticipated that these digital enablers will improve communication speed, lower transaction costs, and facilitate cross-border collaboration, which should have a positive effect on GVC participation. Finally, natural resource rents (NRR) as a percentage of GDP, obtained from the WDI, are added to capture the impact of natural endowments on trade phenomena. Resource-abundant countries often participate in resource-based global production chains. However, although a convergence in a positive correlation is anticipated, it could be under the condition of effective and sustainable utilization of these resources. Overall, the theoretical linkages outlined imply that economic performance, institutional quality, infrastructure conditions, energy mix, and natural capital are fundamental factors that influence a country’s degree of integration within the global production and trade networks. Table 1 presents the variables, acronyms, measurement units, and source of data.

Table 1 Variable details.
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Theoretical framework

This study conducts a critical review of various economic theories to examine the factors influencing participation in GVCs within ASEAN economies. The framework is based on (Linke and Zerfass, 2013) governance literature on global value chains, which explores how lead firms coordinate transnational production networks, and incorporates Sen’s (2010) new trade theory, which emphasizes the significance of knowledge spillovers and task fragmentation in modern trade. The model further develops these foundations by integrating Rodrik’s (2018) premature deindustrialization hypothesis to address the unique challenges faced by ASEAN economies as they encounter digital disruption in their pursuit of higher value chains. Utilizing an energy perspective, the environmental regulation hypothesis by Porter and Van Der Linde (1995) incorporates the sustainable GVC framework by Engel et al.’s (2021) to analyze the impact of clean energy transitions on competitive positioning. Institutional quality is conceptualized through North’s (1990) transaction cost perspective and Acemoglu’s (2014) inclusive institution theory, focusing on how governance structures affect GVC upgrading trajectories. Initially, I examine digital transformation through the lens of Kilkki et al. (2018) digital disruption model and Sturgeon’s (2002) modular production network theory to demonstrate how digital platforms alter the cost structures associated with participation.

The framework incorporates natural resource economics, as articulated by the resource curse hypothesis (Manzano and Gutiérrez, 2019), and is further refined by Caulfield et al.'s (2024) argument regarding commodity-embedded value chains. This pluralistic framework recognizes that global value chain (GVC) integration is influenced by contemporary and traditional comparative advantages, institutional path dependencies, technological leapfrogging potential (Wei, 2025), and sustainability constraints (Pezzey, 2017). These intersecting dynamics are (loosely) formalized within the framework of complex adaptive systems (CAS) theory (Onik et al., 2017) because of the diverse economic structures of ASEAN members. By integrating these theories, the framework offers a novel perspective on how economic, technological, institutional, and environmental dimensions influence value chain trajectories in developing economies that are undergoing multiple concurrent transitions. The theoretical framework has been shown in mathematical form:

$${\rm{GVC}}=f({\rm{GDP}}+{\rm{EM}}+{\rm{IQ}},+{\rm{DGT}}+{\rm{NRR}})$$
(1)
$${\rm{LGVC}}=f(L{\rm{GDP}}+{\rm{LEM}}+{\rm{LIQ}},+L{\rm{DGT}}+{\rm{LNRR}})$$
(2)

Equation (2) shows the logarithmic form of Eq. (1). The symphony of panel data econometrics of weighted countries leads to slope heterogeneity (SH). SH test revealed an intriguing tapestry of early slope heterogeneity. The test evaluates a null hypothesis that assumes slope homogeneity and an alternative hypothesis suggesting slope heterogeneity. To examine these hypotheses, two specific statistics, delta \((\widetilde{\Delta })\) and adjusted delta \((\mathop{\Delta }\limits^{ \sim }{\rm{adj}})\), are calculated. These statistics are mathematically represented as follows:

$$\mathop{\Delta }\limits^{ \sim }=\sqrt{N\left(\frac{{N}^{-1}\tilde{S}-k}{\sqrt{2k}}\right)}{\mathop{\Delta }\limits^{ \sim }}_{\mathrm{adj}}=\sqrt{N\left(\frac{{N}^{-1}\tilde{S}-k}{\sqrt{\frac{2k(T-k-1)}{T+1}}}\right)}$$
(3)

In these formulae, N is the cross section size, T is time dimensionality of data, k is number of regressors and \(\widetilde{S}\) the modified (Swamy, 1970) test statistic. The test of Hashem Pesaran and Yamagata (2008) has a strong performance, especially when the time dimension T is greater than the number N of cross-sectional units. Furthermore, the test captures the residual CSD, which underlines its robustness, as pointed out by Bersvendsen and Ditzen (2021).

Presuming cross-sectional independence can lead to distorted, inconsistent, and misleading results (Westerlund and Edgerton, 2007). This study uses a weakly exogenous cross-sectional dependency test in panel data econometrics to determine cross-sectional dependence. This defines cross-section dependency. The test examines the panel unit mean correlation. Pesaran et al. (2004) discovered that panel unit pairwise correlations are standard and normally distributed. Equation (4) summarizes the CSD test equation:

$$CSD=\sqrt{\frac{2T}{n(n-1)n}(\mathop{\sum }\limits_{i=1}^{n-1}\mathop{\sum }\limits_{K=i+1}^{n}{Corr}_{i,t}}$$
(4)

Despite CSD and slope heterogeneity, the CIPS and CADF second-generation unit root tests are utilized to assess variable stationarity in cross-sectional dependence. The formula for CIPS has been shown in Eq. (5).

$$CIPS=\frac{1}{n}\mathop{\sum }\limits_{i=1}^{n}{t}_{i}(n,T)$$
(5)

In the current study, the cross-sectionally augmented Dickey–Fuller (CADF) unit root test Hashem Pesaran (2007), is utilized due to its enhanced capability in addressing key characteristics of ASEAN panel data, specifically cross-sectional dependence resulting from regional economic integrations and common shocks, which first-generation tests are limited in handling (Breitung and Pesaran, 2008). The CADF test is particularly appropriate for ASEAN, which comprises a diverse range of economies compared to homogeneous root tests, as it allows for country-specific autoregressive parameters and demonstrates known robustness in small samples (N = 10), aligning with our sample size (Pesaran, 2015). This choice is consistent with the methodology we employ when estimating the subsequent CS-ARDL (Chudik and Pesaran, 2015a) and aligns with other ASEAN studies where cross-sectional dependence is extensively documented (Baek and Song, 2025; Bai and Yu, 2024). The CADF framework also offers potential flexibility in incorporating structural breaks. The mathematical form has been shown in Eq. (6).

$$\Delta {X}_{it}={\alpha }_{i}+\beta {X}_{i,t-1}+\chi {\bar{X}}_{t-1}+{\mathop{\sum }\limits_{h}^{j}{\delta }_{ij}\Delta \bar{X}}_{i,t-1}+{\eta }_{it}$$
(6)

Where \({\bar{X}}_{t-1}+\Delta {X}_{i,t-1}\) stand for the lagged and first difference of each cross-sectional series, the present study will incorporate the cross-sectionally augmented autoregressive distributed lags model (CS-ARDL).

Cross-sectionally augmented ARDL (CS-ARDL) method

CS-ARDL evaluation was used for long-term and short-term assessments. By another definition, this approach addresses endogeneity, nonstationarity, mixed-order integration, SH, and CSD. However, due to the omission of unobserved standard components, the estimated results are erroneous. The mathematical form of CS-ARDL has been shown in Eq. (7).

$${y}_{{it}}={{\rm{\vartheta }}}_{i}+\mathop{\sum }\limits_{l=1}^{p}{\gamma }_{{il}}{y}_{{it}-1}+\mathop{\sum }\limits_{l=0}^{q}{\theta }_{{il}}^{{\prime} }{X}_{i,t-1}+{\in }_{{it}}$$
(7)

This method is useful when there are issues with heterogeneity, robustness to omitted variables, CSD, nonstationarity, and endogeneity (Paramati and Shahzad, 2022). To the standard ARDL approach, the authors of this paper recommend adding the cross-sectional mean of the variables, their delays, and the explanatory variable. By adjusting the above equation, we may obtain the CS-ARDL basic model, which is as follows:

$${y}_{{it}}={{\rm{\vartheta }}}_{i}+\mathop{\sum }\limits_{l=1}^{p}{\gamma }_{{il}}{y}_{{it}-1}+\mathop{\sum }\limits_{l=0}^{q}{\theta }_{{il}}^{{\prime} }{X}_{i,t-1}+\mathop{\sum }\limits_{l=0}^{q}{\delta }_{{il}}^{{\prime} }{\bar{Z}}_{t-1}+{\in }_{{it}}$$
(8)
$${\bar{Z}}_{t=}({\bar{y}}_{t},{\bar{X}}_{t}^{{\prime} })$$
(9)
$${\epsilon }_{{it}}={\pi }_{i}^{{{\prime} }^{{\prime} }}{f}_{t}+{\mu }_{{it}}$$
(10)

In Eq. (8), Xt and yt are variables that contribute to the mean (cross-sectional mean). In addition, q is the lag for the mean of the cross sections, eit is the disturbance term, and ft is the common factor (unobserved) that generates the dependence of the cross sections. The CS-ARDL method can be used to determine the long-run coefficients (Wei and Liu, 2022). Stata 19 has been used to do this estimation.

Results and discussion

Results

Descriptive analysis refers to the process of presenting and interpreting data meaningfully and comprehensibly. Descriptive analysis entails the examination of central tendency, variability, and distribution. The principal objectives of conducting a descriptive analysis are to provide a concise overview of the data, emphasize significant features and trends, and enhance the comprehensibility and accessibility of the data for the audience. Table 2 displays the characteristics of these variables.

Table 2 Descriptive statistics.
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A higher GVC value in Table 2 indicates significant involvement in global trade and production, whereas a substantial GDP value indicates a high level of economic productivity. However, EM statistics are deficient, indicating a low transformation ratio of fossil fuels to green energy. Ashraf et al. (2022) revealed the importance of IQ for economic prosperity; the IQ of ASEAN countries is nominal and has an increasing trend. The GDT value reflects the adoption of digital technology and digital transformation in ASEAN countries. It measures the sophistication of the technology infrastructure, proficiency of the digital workforce, and prevalence of digital solutions across industries. Next, the NRR describes the management of the available natural resources of ASEAN economies, meaning that these nations manage their available, total natural resources to attain sustainability and prosperity. This error indicates that there was extremely little dispersion from the mean value. The data points were clustered close to the mean. This implies that the observations in the dataset are relatively consistent and similar, with minimal variability, and lie within the thumb rule’s value (±2) distributions evaluated for the degree of skewness. The data were skewed to the left or right of the mean. Additionally, Kurtosis is used to determine the balance of the distribution. This indicates that the data have extreme values and that most of the information is clustered around the mean.

To explore the behavior of study factors among ASEAN economies, this study selects each factor to express the historical trend of each factor individually. Figure 1 describes the GVC of ASEAN economies.

Fig. 1
Fig. 1
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Participation of ASEAN countries in GVC (created by author).

Figure 1 shows how each country’s involvement in global GVC has increased over time. However, the data reveal that, compared to other nations, Malaysia, the Philippines, Singapore, and Thailand are more heavily involved in GVC. Furthermore, the data show that Vietnam’s progress in GVCs has been relatively slow despite a consistently upward trend, which is quite vulnerable.

Figure 2 shows the transition from fossil fuel to renewable energy. The primary energy source in the world is fossil fuels. However, owing to rising temperatures, environmental deterioration, and natural disasters, the globe is switching to green energy. Figure 2 clarifies that each nation in the ASEAN area is working to reduce its dependence on fossil fuels and increase its use of clean energy. According to the graph, Brunei is leading the world in its transition to renewable energy. With the exception of Cambodia, which has significantly replaced fossil fuels with green power since 2000, most nations have seen little movement in this direction.

Fig. 2
Fig. 2
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Profile of energy mix (created by author).

The institution’s quality performance from 2000 to 2023 is depicted in Fig. 3. World governance indicators offer statistical information needed to accurately describe political stability and violence-free governance. Statistics show that institutional quality in Vietnam, the Philippines, Indonesia, and Cambodia is below average, indicating relatively low political stability and government performance in those nations. However, the Philippines and Malaysia constantly improve their institutional quality, whereas Singapore has a robust and effective institutional framework. Figure 4 displays the profile of the digital economy of ASEAN countries.

Fig. 3
Fig. 3
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Institutional quality of ASEAN countries (created by the author).

Fig. 4
Fig. 4
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Profile of the digital economy of ASEAN countries (created by the author).

Statistics show that Indonesia is the top-performing economy when adopting digitalization for sustainability. In contrast, the Philippines, Singapore, and Taiwan imitate Indonesia’s style. Furthermore, Cambodia’s movement towards digitization is modest compared to other ASEAN nations. Figure 5 also illustrates how the ASEAN nations handle their resources.

Fig. 5
Fig. 5
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Natural resource rents of ASEAN economies (created by the author).

Statistics show that Brunei is in a league of its own when it comes to making the most of the tremendous natural resources it possesses and putting these resources to productive use. Brunei is well ahead of other Southeast Asian countries, such as Malaysia, Vietnam, Indonesia, Thailand, and Cambodia, in this particular category.

Next, the correlation measures the degree to which two or more variables vary predictably, and is measured statistically using the correlation coefficient. The degree of correlation does not indicate a causal link between the two variables. Moreover, the variance inflation factor (VIF) assists in measuring multicollinearity in regression analysis. The VIF measures the extent to which the variance of the estimated regression coefficient is inflated due to multicollinearity. The outcomes of correlation and VIF are reported in Table 3.

Table 3 Correlation and variance inflation factor.
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Table 3 reveals that the desired level of correlation prevails among the factors, but IQ is significantly correlated with GDP, which may cause multicollinearity. To clarify doubts about multicollinearity, we employed VIF analysis. The VIF disclosed no multicollinearity problem because the VIF value of all factors was below the thumb-rule value (±10).

Heterogeneity measures the extent to which a dataset contains differences or variances. The outcomes of the slope heterogeneity are presented in Table 4.

Table 4 Testing for slope heterogeneity.
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Table 4 shows that variation (delta) in the dataset prevails, and (p-value) slope is highly significant. The results show that the significant variations in slope throughout the data are not due to random fluctuations.

Structural break analysis (Bai and Perron, 2003) is a statistical method for identifying noteworthy shifts in a dataset. When there is a drastic shift in the data’s underlying connection, trend, or statistical features, it is called a structural break. The results obtained by Bai and Perron tests are presented in Table 5.

Table 5 Bai and Perron analysis.
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Table 5 represents the amount of evidence against the assumption that there is no structural breach. While affirming whether the test statistic is statistically significant, look at the “10%, 5%, and 1% Critical value” columns, which list the exact critical values for each significance level. F(1/0) denotes the lagged value of the dataset. The estimation reveals that the first two lags are statistically significant, and the dataset carries a break in 2007. The economies of several nations, including those in the ASEAN region, were severely impacted by the global financial crisis that began in 2007. The global financial and economic slump has its roots in the United States, where the problem first emerged with the collapse of the subprime mortgage market.

The CD test (Pesaran et al., 2004) is a statistical method for determining whether there are unique effects for each panel member. This aids in determining whether unobserved individual heterogeneities influence the dependent variable. The test hypothesizes that the dependent variables are unrelated to individual effects. The results of the CD tests are presented in Table 6.

Table 6 Pesaran (2004) CD test.
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The result elaborates (Table 6) that there is strong evidence that cross-sectional dependency exists among the variables because the alternative hypothesis against the null is excluded; that is, cross-sections are not independent. It can be concluded from the estimation that cross-sectional dependence prevails in the dataset.

The CIPS extends the conventional IPS unit root test (Westerlund et al., 2016) by considering cross-sectional dependency in panel data. The CIPS unit root test accounts for cross-sectional dependency by including additional lags in the dependent variable. The precision of the unit root test is enhanced by this addition, which helps capture the possible cross-sectional dependence. Using the CIPS, one may learn about the characteristics of the variables of interest and draw valid conclusions regarding their long-term behavior. Furthermore, Common Augmented Dickey–Fuller (CADF) test (M Hashem Pesaran, 2007) has been employed. To address the possibility of cross-sectional dependency in panel data, it expands the standard Dickey-Fuller (DF) test. In cases where the entities in the panel are subject to cross-sectional correlation or spillover effects, CADF appears convenient. The lagged levels of the dependent variable are incorporated as regressors in addition to first-differenced terms in an enhanced Dickey–Fuller regression model, serving as the basis of the test—cross-sectional panel dependency captured via the application of lagged levels. The estimations of CIPS and CADF are presented in Table 7.

Table 7 Unit root test.
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Table 7 reveals that all the study factors have zero mean and constant variance at 1st difference in the case of the CIPS unit root. However, in CADF, IQ and NRR are stationary at a level, whereas all variables have a unit root at 1st difference. Researchers have improved the precision and consistency of their findings by using the CIPS and CADF tests on a panel dataset to control for cross-sectional dependency.

The CS-ARDL findings offer insights into the factors influencing the GVC integration of ASEAN economies in both the long and short terms, corroborating and extending the conclusions of the existing literature (see Table 8). In the long term, digitalization emerges as the most significant factor (β = 1.7549), aligning with Ke (2024), who observed that digital platforms lower the barriers to GVC entry. This pronounced positive effect is consistent with a recent WBO (2023) analysis of ASEAN’s digital transformation; however, our coefficient value is larger, suggesting greater depreciation than previously estimated. The energy mix (β = 0.3362) demonstrates a significant relationship, supporting Jiang et al. (2024) assertion that environmental regulation fosters competition. Institutional quality (β = 0.0411) aligns with Chung and Jin (2025), who emphasized the role of institutions in economic development. The study findings indicate that economic growth (β = 0.0279) exerts a relatively weak influence, which is notably smaller than Jie and Lan's (2024) initial GVC analysis, highlighting the increasing importance of growth quality over mere size in many value chains today.

Table 8 Results from CS-ARDL.
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The short-term dynamics revealed notable deviations from the existing literature. The more substantial immediate effects of natural resource rents (β = 0.0645) and GDP growth (β = 1.4423) may support Yadav et al. (2024) resource curse framework while challenging Xie et al. (2025) pessimistic perspective on the role of commodities in GVCs. The error correction term was highly significant (β = −0.9354), suggesting a faster adjustment than previously reported in studies of ASEAN (Nguyen and Dinh Su, 2021; Xu et al., 2023), which may align with the region’s increasing economic resilience. Additionally, digitalization exhibits unexpectedly significant positive effects (β = 0.0145) in the short term, surpassing Le et al.'s (2024) valuation of digital technologies. This suggests that rapid adaptation to digitalization during the pandemic may have influenced these results (UNCTAD, 2023). These temporal contrasts underscore the potential diminishing durability of traditional drivers, such as resource-rich endowments and GDP growth, compared to digital and institutional drivers as sources of participation in GVCs.

This study offers a significant contribution to the existing literature by enhancing the understanding of the determinants of the GVC in several key areas. First, we validated and quantitatively refined the synergistic interactions among the factors identified by Akeel (2022) and. Second, this study provides empirical support for the conceptualization of digital-industrial convergence along value chains (Wang et al., 2024). Third, our findings indicate that the effects of energy transition are more pronounced than those of social transition, as noted by Śmiech et al. (2025), suggesting that climate policy may increasingly influence the dynamics of global chains (GCs). These comparisons demonstrate that traditional factors, such as resources and growth, remain important, but their impact is being reshaped by digital and sustainability transitions. For policymakers, our findings imply that ASEAN’s GVC strategies must evolve from the export-led models of the 1990s to more sophisticated and integrated approaches. This includes the integration of high-quality digital infrastructure policies, complemented by institutional reforms and clean energy transitions, an implication that extends and challenges the aspects of the region’s historical development theory.

The dynamic common correlated effect (DCCE) model (Chudik and Pesaran, 2015b) is a flexible statistical approach that addresses issues of endogeneity, heterogeneity, and cross-sectional dependency in panel data. The DCCE model incorporates the dynamic effects of individual-specific effects and regressors to capture the dynamic interplay between individual and collective variables over time more accurately. The estimation of the DCCE model typically involves two steps. Initially, the impact of interest on each individual was evaluated using a fixed effects estimator. Subsequently, the residual series was utilized within a dynamic panel data model to evaluate the shared correlated impacts. Table 9 displays the results of DCCE.

Table 9 Dynamic common correlated effect model.
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Table 9 reveals a positive correlation between GVC and EM, IQ, DGT, GDP, and NRR. The findings indicate that a marginal increase of 1% in the EM causes 0.2058% increase in GVC, a marginal increase of 1% in the IQ causes 0.0025% increase in GVC, a marginal increase of 1% in the DGT causes 0.1692% increase in GVC, a marginal increase of 1% in the GDP causes 0.02345% increase in GVC, and a marginal increase of 1% in the NRR causes 0.0329% increase in GVC. This results in a marginal increase in GVC expansion of 0.0329%.

According to Smith and Mcaleer (1994), the application of the newly-West standard error (NWSE) can result in a precise error for regression coefficients, particularly when heteroscedasticity and autocorrelation are considered in the dataset. The outcomes from NWSE estimation have been presented in Table 10.

Table 10 Newey–West standard error test.
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The outcomes from the NWSE test show that economic growth, energy mix, institutional quality, and natural resource rents have a positive influence on GVC. Every one unit increase in GDP, EM, NRR, IQ, and DGT causes an increase in 0.0163%, 0.5964%, 0.009%, 0.013% and 0.0293%, respectively.

The Granger non-causality test, which is proposed by Dumitrescu and Hurlin (2012), is utilized to assess the presence of Granger–causal relationships between variables in panel datasets. The outcomes are given in Table 11.

Table 11 Granger non-causality test.
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Table 11 shows the results from the panel causality test proposed by Dumitrescu and Hurlin (2012), which shows that economic growth, energy mix, digital economy, and use of natural resources have a positive and significant impact on GVC. NRR has the strongest association, followed by EM, DGT, and GDP. IQ also demonstrated a statistically significant correlation, but it was slightly less in magnitude.

Discussion

This study reveals that there exists a significant correlation among the selected variables. However, this correlation is not sufficiently strong, leading to multicollinearity within the dataset. Moreover, the principle of slope homogeneity asserts that the slope of the regression line remains constant across all the variables. Bai and Perron have verified the presence of a structural break in the dataset, which occurred in 2007 as a result of the global financial crisis. Furthermore, the Pesaran CD test confirms the existence of cross-sectional dependency in the given dataset. Consequently, this study utilizes the second-generation unit root methodology, which addresses the issue of cross-sectional dependence with efficiency and effectiveness. Furthermore, the inquiry employs the CS-ARDL approach to account for cross-sectional dependence, which indicates that all variables exhibit significant associations with GVC in both the short and long terms.

GVCs facilitate the production and trade of goods and services across international borders. Nations exhibiting higher levels of GDP tend to allocate greater resources towards the development of technology and infrastructure, with the aim of fostering cross-border trade and manufacturing. As a result, ASEAN nations are expanding their GVC platform. The findings indicate a significant relationship between GDP and GVC in ASEAN. Specifically, a 1% increase in GDP is associated with a 0.0279% increase in GVC. The study results are in line with the previous studies, such as Aly and Zaki (2025), Nawaz and Rahman (2024), and Zhang (2024).

The production and shipping costs of GVC were impacted by EM. Numerous enterprises, particularly those that use electricity or fuel, rely on energy. The competitiveness of GVC nations and enterprises is affected by the EM, encompassing both renewable and non-renewable sources. The degree of GVC participation is positively correlated with the proportion of renewable energy sources in a country’s EM (Li et al., 2022). The utilization of renewable energy sources has been observed to result in a reduction in both manufacturing expenses and carbon emissions, thereby enhancing the competitiveness of GVC firms (Liu et al., 2025). The utilization of renewable energy sources has the potential to draw foreign direct investment and foster sustainable economic development, thereby facilitating the expansion of global value chains. In addition, the study reveals that there is a positive correlation between GVC and IQ. These findings are inline with the previous studies, such as Mushtaq and Afzal (2024), Reddy and Sasidharan (2025) and Zhang (2024).

Conclusion and policy implications

This study is the inaugural examination of the impact of GVC on the energy security systems of both developing and wealthy nations. By analyzing data collected from a sample of 8 ASEAN countries worldwide between 2000 and 2023, we have derived significant findings. This study explores the impact of economic growth, EM, IQ, and DGT on GVC in the ASEAN region. The panel data for ASEAN countries have been collected from WDI and WGI. Additionally, it was found that cross-sectional data were not independent, and the study employed cross-sectional second-generation unit root and cross-sectional ARDL methods. The DCCE and NWSE models have been shown to effectively validate findings and address estimation-related concerns in an efficient manner. The institutional theory posits that the establishment of institutions facilitates the process of engaging in GVC participation. This study underscores the significance of governmental prioritization of the analyzed parameters as a means of attracting commercial enterprises and investors. The proposed measures entail the diversification of energy sources to align with prevailing global patterns, enhance institutional frameworks to establish a stable and conducive milieu, and allocate resources to the digital infrastructure to augment connectivity.

Study implications

Consequently, significant policy implications arise as follows: Initially, nations ought to implement strategies to actively engage in GVCs, including reducing tariffs and non-tariff obstacles, promoting international communication, and fostering mutual understanding. Meanwhile, nations should be vigilant regarding the ripple effects of trading partners experiencing adverse impacts from occurrences such as financial crises, conflicts, or a pandemic. Opting to reduce or disengage from GVCs is not the most effective strategy to address the negative consequences. During the process of integrating into global markets, it is crucial for countries to prioritize the growth and advancement of their home markets. It is imperative to retain a certain level of autonomy in the home economy and avoid excessive dependency on trading partners. The dual-cycle development model pursued by China, which encompasses both internal and foreign aspects, could serve as a valuable reference.

Furthermore, when engaging in GVCs, affluent nations should further enhance their superiorities in technology and capital-intensive manufacturing, while simultaneously reducing technological barriers. This will facilitate the transfer of technology to less affluent nations. Developing nations should enhance their local human capital and infrastructure in order to effectively harness the benefits of technology spillover effects. Furthermore, it is imperative for low-income countries to enhance their domestic production structure by transitioning their competitive advantage from poor environmental standards to the manufacture of high-quality, clean products. Furthermore, it is important for countries to exercise caution and not indiscriminately prioritize participation in GVCs only based on industries that have high value added and low carbon emissions. When countries join in the GVCs of businesses with high carbon emissions, the impact of GVC participation on reducing CO2 emissions becomes more significant. Likewise, nations should recognize that their domestic economy is more susceptible to negative impacts from unfavorable events in the global market when they engage in the GVC of industries with significant value-added components.

Ultimately, decreasing production is not the most effective approach to address environmental issues. Alternatively, enhancing energy efficiency and advocating for energy diversification are more effective resolutions (Jianguo et al., 2022). More precisely, nations ought to augment their investments in research and development as well as education, fostering autonomous advancements in energy-conserving technologies. Additionally, they should actively employ clean and sustainable energy sources, such as solar, wind, and nuclear power. Lastly, efforts should be made to heighten public consciousness regarding low-carbon production and consumption. It is imperative for governments to establish explicit targets for reducing carbon emissions (Zhang et al., 2022). China has put out a proposal stating that CO2 emissions will reach their highest point around the year 2030. Additionally, they aim to increase the share of non-fossil energy in primary energy consumption to approximately 20% by 2030. Furthermore, China plans to reduce CO2 emissions per GDP by 60-65% compared to the levels recorded in 2005 by the year 2030. Furthermore, governments have the ability to oversee and regulate environmental pollution activities by implementing legislation and regulations, such as the implementation of a carbon price. In addition, they have the ability to designate dedicated funds to provide financial incentives for low-carbon actions, such as offering subsidies for the acquisition of electric vehicles.

Advocacy for the extensive adoption of digital technology and the provision of funding for associated infrastructure fall under the purview of policymakers. Furthermore, it is imperative for policymakers to give precedence to establishing methodologies aimed at ensuring the sustainable administration of resources.

Limitations and future study directions

Subsequent studies could address the issue of cross-sectional dependence by creating a matrix that shows spatial correlation and estimating spatial spillovers using the spatial panel VAR model. However, identifying causal relationships across space continues to be a challenging task. Additional enhancements could involve integrating the positional index of a nation’s industries within GVCs. Furthermore, expanding the coverage to encompass a greater number of countries and regions over an extended timeframe would be beneficial.