Advancing sustainable development goal 8 targets: The role of institutional quality, economic complexity, and state fragility in G20 nations (2000-2023)

Azimi, Mohammad Naim; Rahman, Mohammad Mafizur; Maraseni, Tek Narayan Article Advancing sustainable development goal 8 targets: The role of institutional quality, economic complexity, and state fragility in G20 nations (2000-2023) Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Azimi, Mohammad Naim; Rahman, Mohammad Mafizur; Maraseni, Tek Narayan (2025) : Advancing sustainable development goal 8 targets: The role of institutional quality, economic complexity, and state fragility in G20 nations (2000-2023), Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 10, pp. 1-19, https://doi.org/10.1016/j.resglo.2025.100278 This Version is available at: https://hdl.handle.net/10419/331201 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Advancing sustainable development goal 8 Targets: The role of institutional Quality, economic Complexity, and state fragility in G20 nations (2000–2023) Mohammad Naim Azimi a,* , Mohammad Mafizur Rahman a , Tek Maraseni b a School of Business, University of Southern Queensland, QLD 4350, Australia b Centre for Sustainable Agricultural Systems (CSAS), University of Southern Queensland, QLD 4350, Australia ARTICLE INFO JEL codes: Q01 O40 J20 P48 F50 Keywords: SDG 8 Institutional quality State fragility Economic complexity Economic growth ABSTRACT As the global community nears critical milestones in achieving the Sustainable Development Goals (SDGs), the interplay of dynamic forces continues to reshape progress trajectories. This study explores the advancement of SDG 8 targets (“Decent Work and Economic Growth”) through the lens of three increasingly prominent factors: institutional quality, economic complexity, and state fragility, focusing on the G20 nations from 2000 to 2023. Guided by an extensive literature review, three research questions, and nine hypotheses, this study formulates four empirical models aligned with four SDG 8 targets and employs the cross-sectionally augmented autoregressive distributed lags model, further validated through dynamic common correlated effects mean group estimators. The findings reveal that economic complexity, institutional quality, and renewable energy significantly enhance economic growth and labour productivity, while reducing unemployment, and CO 2 emissions. In stark contrast, state fragility and primary energy use exert detrimental impacts, underscoring the negative influence of macroeconomic instability and the persistent reliance of growth and labour productivity on primary energy sources, which intensify unemployment and CO 2 emissions. Additionally, globalisation, human development, environmental technologies, urbanisation, and foreign direct investment continue to positively influence growth and labour productivity while mitigating unemployment and CO 2 emissions. Under the combined influence of economic complexity, state fragility, and institutional quality, the findings validate the Environmental Kuznets Curve hypothesis, revealing a redefined turning point shaped by these metrics. Beyond this threshold, the environmental consequences of achieving SDG 8 targets are expected to abate, laying a critical foundation for the policy implications outlined in the study. 1. Introduction As the global pursuit of the United Nation’s Sustainable Development Goals (SDGs) accelerates, nations are swiftly approaching critical deadlines to eradicate poverty, reduce unemployment, and ensure decent work for all while safeguarding environmental sustainability (Melethil et al., 2025). By 2024, some progress has been achieved. For example, the global poverty rate has fallen from 10 % in 2015 to 8.6 % in 2023 (UNDP, 2025). Despite this, over 700 million individuals still live in extreme poverty, highlighting persistent challenges. While the global unemployment rate is reported at 4.5 %, regional disparities remain pronounced, with some areas grappling with alarmingly high levels of unemployment. On the environmental front, 15 % of terrestrial ecosystems and 7 % of marine areas have been designated as protected areas (UNSDG, 2024), but significant strides are needed to bolster conservation efforts. Renewable energy’ now accounts for 29 % of global electricity generation, yet the remaining 71 % continues to rely on unsustainable energy resources, underscoring urgent need for transformation in household and industrial energy use. The 2024 United Nations’ SDG Report (UNSDG, 2024) paints a sobering picture, with almost 50 % of the SDGs showing only marginal progress and over a third are either stagnating or regressing. The lingering effects of the COVID-19 pandemic, intensifying geopolitical tensions (Wang et al., 2024), and mounting trade barries between key global players nations (Zuo & Majeed, 2024) have stymied progress. As a result, an additional 23 million people have been thrust into extreme poverty, while more * Corresponding author. E-mail address: [email protected] (M.N. Azimi). Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2025.100278 Received 13 January 2025; Received in revised form 24 February 2025; Accepted 25 February 2025 Research in Globalization 10 (2025) 100278 Available online 4 March 2025 2590-051X/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). than 100 million people now face acute hunger compared to 2019 (Hunger & High, 2025). These setbacks are primarily driven by political instability, economic inequality, and persistent armed conflicts, exacerbating the plight of affected nations. Climate changes further compound the scenario, with 2023 marked as the hottest year on record and global temperature approaching the perilous threshold of 1.5 ◦C (Year, 2024). Regional disparities in SDG achievements remain stark. European countries such as France, Germany, Finland, Sweden, Denmark, and the United Kingdom have recorded impressive scores of over 80 %, reflecting robust achievements (Lafortune et al., 2024). In stark contrast, sub-Saharan African countries, including Chad, South Sudan, and the Central African Republic, struggle with scores below 50 %. In Asia, nations like Japan, China, and South Korea continue to make steady headway, while countries like Afghanistan, Pakistan, India, and Yemen face mounting challenges due to conflict, political unrest, and unchecked population growth. Moreover, Australia, Canada, and the United States achieve scores of 77.5, 79.2 and 74.4, respectively, indicating moderate progress. However, significant challenges persist, particularly in effectively addressing climate change, promoting marine ecosystems, and ensuring responsible production and consumption (UNSDG, 2024; Canada, 2024). Apparently, the G20 nations continue to grapple with significant challenges in meeting their SDG commitments within the stipulated timelines. These setbacks are further compounded by persistent state fragility and trade tensions in several member nations, which disrupt steady progress towards achieving the SDGs. Fragile states often endure reduced gross domestic product (GDP) growth and heightened economic instability, resulting in limited access to market and education, inadequate healthcare services, higher unemployment, and increased genderbased disparities and exclusions. Such fragility poses a particular impediment to the advancement of SDG 8, which focuses on inclusive and sustainable growth and decent work for all. Fig. 1 illustrates the trend of state fragility scores across G20 nations, with a minimum average score of 41.116 and a maximum average score of 41.30, and a projected slight decline to an average score of 40.55 in 2025. However, this score remains significantly high, continuing to disrupt progress towards SDG 8 targets. Furthermore, while real GDP has experienced a slight decline, its recovery trajectory in 2025 appears confined to regaining prior lagged values, offering little evidence of substantial forward momentum. Meanwhile, economic complexity, a critical driver of dynamic market forces, remain relatively unchanged, underscoring the need for targeted interventions to address fragility and foster sustainable economic resilience. Although recent studies by Sianes et al. (2022), Mishra et al. (2024); Alfirevi´ c et al. (2023); Rasool (2023); Gupta and Vegelin (2016); Jia et al. (2021); Yap et al. (2023); Chen et al. (2024); Vera et al. (2022); Martín-Blanco et al. (2022); Elsamadony et al. (2022); Choudhary et al. (2025); Bashir et al. (2024); and Arner et al. (2020) have extensively examined the impacts of numerous traditional factors such as energy consumption, renewable energy, per capita GDP, financial development, FinTech, climate change, financial inclusion, governance, and poverty on SDG outcomes, existing literature significantly lacks a comprehensive focus on the role of emerging dynamic market forces. This gap is particularly evident in the context of SDG 8 targets, which require an ample understanding of how these dynamics reshape inclusive growth, labour productivity, unemployment, and environmental sustainability. Building on this background, this study delves into the intricate influences of state fragility and economic complexity, uncovering their true impacts and magnitudes on the progress of SDG 8 within the G20 nations. By focusing on four core targets—sustainable growth, labour productivity, unemployment, and the environmental repercussions of progressing towards these targets—it primarily aims to provide a nuanced understanding of these challenges. To further enrich this analysis, it evaluates the role of existing institutional quality frameworks in enabling these nations to confront fragility and complexity while chartering a path toward achieving SDG 8 targets with resilience and inclusivity. To guide the research, four pivotal questions are posed: First, to what extent do state fragility, economic complexity, and institutional quality, alongside conventional drivers, impact economic growth? Second, how do these factors shape labour productivity? Third, what role do they play in influencing unemployment rates? Fourth, how do these variables affect environmental degradation, and do they redefine the Environmental Kuznets Curve hypothesis within the G20 nations? Answering these questions will yield valuable insights into the complex dynamics influencing progress towards SDG 8, offering a more contemporary, evidence-based understanding that can effectively guide policymakers in addressing challenges and capitalising on opportunities. Fig. 1. Annual average trends of key dynamic market forces from 2000 to 2023. Notes: Real GDP is in trillion US$, State fragility ranges between 0 and 100, and economic complexity is in Standard International Trade Product Classification (SITP). Sources: The World Bank (WDI National Accounts Data), The Fund for Peace (Fragile State Index), and Organisation for Economic Cooperation and Development (OECD) (OECD, 2023) databases. M.N. Azimi et al. Research in Globalization 10 (2025) 100278 2 This investigation makes a novel contribution to the existing literature, enriching the contemporary body of knowledge from several critical perspectives. First, it offers a focused examination of SDG 8 targets, an area that has largely been underexplored in prior studies. By leveraging the most recent data and trends in progress toward achieving SDGs and applying advanced econometric techniques, this study provides actionable insights that can help policymakers capitalise on existing opportunities and address pressing policy-related challenges. Second, beyond accounting for the effects of traditional socioeconomic and sociodemographic indicators, this study integrates emerging dynamic market forces such as economic complexity and state fragility, which play pivotal role in reshaping existing market strategies aligned with SDG objectives. It does so in the presence of an innovatively constructed institutional quality index, developed through a comprehensive distance-based scoring methodology that captures all dimensions of good governance influencing SDG 8 targeted predictors. This approach underlines the gradual evolution of institutional quality in tandem with SDG 8 predictors, shedding light on specific policy areas that require immediate attention. Third, by validating the EKC hypothesis in the context of new metrics—state fragility, economic complexity, and institutional quality—the study identifies a revised turning point for sustainable and resilient economic growth, emphasising the critical contributions of institutional quality and economic complexity in reducing environmental degradation. This redefined perspective offers policymakers with actionable insights to align growth strategies with SDG 8 targets while fostering environmental sustainability. The structure of the study is as follows: Section 2 reviews the latest empirical literature. Section 3 presents the data and variables. Section 4 outlines the methodologies used for analysis. Section 5 presents the results, followed by a comprehensive discussion in Section 6. Finally, Section 7 concludes the article. 2. Literature review 2.1. Review methodology While the existing body of literature offers extensive empirical investigations into various facets of SDG 8, findings often remain fragmented and mixed across diverse economies. To ensure a comprehensive and systematic understanding of the factors influencing SDG 8 outcomes, this study conducted a structured literature review following a rigorous methodological approach. The search process was executed across several reputable academic databases, including Scopus, Web of Science, JASTOR, Google Scholar, and PubMed. These platforms were selected due to their extensive coverage of peer-reviewed academic publications, particularly those addressing issues relevance to sustainable development, institutional quality, and economic growth. A structured search strategy was performed using targeted keywords and phrases directly aligned with the study’s scope and objectives. These terms included: “SDG 8”, “Decent Work and Economic Growth”, “Institutional Fragility”, “Institutional Quality”, “Good Governance”, Economic Complexity”, “Economic Diversification”, “Political Instability”, “Environmental Kuznets Curve”, “Employment”, Labour Productivity”, “Total Factor Productivity”, “G20”, “Environmental Quality”, “Material Footprint”, “Ecological Footprint”, “Labour Hours”, “Globalisation”, and “Renewable Sources”. To ensure a focused and relevant selection, Boolean operators (AND, OR, NOT) were applied to refine the search results and exclude irrelevant studies. The inclusion criteria for selecting relevant studies were carefully defined to ensure academic rigour and thematic relevance. Only peerreviewed empirical journal articles published between 1990 and 2025 were considered, capturing a comprehensive range of research spanning historical trends and contemporary development. The studies primarily focused on SDG 8 outcomes, such as economic growth, labour productivity, and unemployment. Additionally, research examining the environmental consequences of economic growth was also included to provide a broader understanding of the interconnections between growth and trajectories and sustainability. Studies were excluded if they were non-peer-reviewed (e.g., opinion pieces, conference abstracts) or if they did not address either the direct or spillover impacts of socioeconomic indicators on SDG 8 outcomes. The review highlights a significant gap in the literature: while there is an abundance of research examining various dimensions of SGDs through both qualitative or quantitative lenses, limited attention has been given to emerging phenomena such as state fragility, economic diversification, and the role of institutional setups in shaping progress toward SDG 8. This gap is particularly evident within the context of G20 nations, where varying institutional capacities and governance structures significantly influence economic outcomes. Although studies like Fonseca et al. (2020) have established links between different SDGs, the literature remains underdeveloped in capturing how these emerging phenomena affect progress toward achieving SDG 8. The subsequent subsections delve into the core themes and findings of the reviewed literature, with a particular emphasis on factors shaping SDG 8 progress. 2.2. Growth trajectories Economic growth, a key component of SGD 8, is grounded in the neoclassical growth theory articulated by Solow (1956) and Swan (1956), which emphasises the role of capital accumulation, labour, and technological progress in driving economic growth. However, recent empirical studies have delved deeper into this relationship, incorporating additional drivers that impact economic dynamics. For instance, Chen and Xue (2024); Wang et al. (2024); Acemoglu et al. (2018); Garcia-Lazaro and Pearce (2023); Barr and Roy (2008); Stevy Sama et al. (2024); and Osakede et al. (2023) have analysed the role of labour, often proxied by the human development index, in shaping growth. Using both panel and country-specific data with diverse quantitative techniques, these studies collectively demonstrate the positive and enduring impact of labour on sustaining growth. Additionally, Huo et al. (2024) examined the interplay between financial innovation, technological advancements, and growth across a panel of developing economies (1990–2021) using the CS-ARDL model. Their findings reveal that financial and technological innovations contribute to balancing growth trajectories while advancing SDGs. Similarly, Shahbaz et al. (2022) investigated the asymmetric effects of financial development on growth in a panel of ten most financially developed nations, employing a threshold ARDL model with labour and capital as threshold variables. Their results underscore that financial development, conditional on labour and capital, asymmetrically sustain long-term growth. Moreover, studies by Lukhmanova et al. (2025); Hussein et al. (2025); Alsabhan and Alabdulrazag (2025); and Rahman et al. (2025) provide mixed evidence on the relationship between energy use and economic growth, varying significantly across economic contexts. Further contributions by Ratnawati (2020); Zheng et al. (2024), and Erlando (2020) employed regression and causality techniques to elucidate the link between growth and financial market performance, emphasising the market stabilitygrowth nexus. While existing literature also identifies urbanisation (Huang & Jiang, 2017), institutional quality (Azimi, 2022), and foreign direct investment (Chee & Nair, 2010) as integral drivers of growth, there remains a significant gap understanding the implications of state fragility and economic complexity on sustainable growth within the existing institutional setups. To address this, the present study proposes the following hypotheses: H 1 : State fragility has a negative impact on growth sustainability. H 2 : Economic complexity has a significantly positive impact on economic growth. 2.3. Employment dynamics Labour market theories, such as the Keynes (2018) and structuralist frameworks (Micklewright et al., 1989), underscore the critical role of M.N. Azimi et al. Research in Globalization 10 (2025) 100278 3 macroeconomic stability, human capital, and effective policy interventions in fostering employment generation. This area has garnered considerable scholarly attention, particularly focusing on conventional predictors of unemployment dynamics. For example, Alshyab et al. (2021); Mehry et al. (2021); and Kim et al. (2019) respectively explored the impact of financial inclusion, investment, and liquidity on unemployment rates in emerging economies, employing advanced quantitative techniques. Their results collectively confirmed that financial and investment indicators significantly reduce unemployment. Erdem and Tugcu (2012) took a distinct approach by examining human capital development, proxied by education levels, in relation to unemployment rates in Türkiye through causality analysis. Their analyses reveal a bidirectional causality between human capital and unemployment, highlighting the importance of aligning educational initiatives with market demands. Similarly, Shaaibith et al. (2020) examined the growth-unemployment nexus in Iraq from 1999 to 2017 using a vector autoregressive model, concluding that economic growth substantially reduces unemployment. Banda et al. (2016) extended this analysis to South Asian nations (1994–2012), confirming that robust economic growth effectively mitigates unemployment rates. Siddikee et al. (2022) examined unemployment and growth dynamics across 14 developed and developing Asian nations within the SDG frameworks. Their findings highlight contrasting trends: developed nations exhibit consistent progress in reducing unemployment through growth sustainability, whereas developing nations, led by Türkiye, demonstrate inverse patterns, with growth failing to adequately address unemployment challenges. Another strand of literature, including studies by Rabiu et al. (2019); Sato and Zenou (2015); Chen et al. (2023); and Castells-Quintana and Royuela (2012) delves into the influence of population growth and urbanisation on unemployment. These analyses reveal that while population growth exacerbates unemployment, urbanisation tends to exert a mitigating influence by fostering job creations through industrial and economic clustering. While little is known, merging factors such as state fragility and economic complexity, under existing institutional frameworks, may yield divergent effects on employment dynamics. To explore this dimension, the study proposes the following hypotheses: H 3 : Economic complexity exerts negative impacts on unemployment. H 4 : State fragility negatively influences unemployment. 2.4. Labour productivity Labour productivity, a central pillar of SDG 8, is theoretically anchored in classical production function developed by Solow-Swan (Solow, 1956) and extended by the endogenous growth model (Romer, 1994), which emphasises the pivotal role of innovation and human capital. Despite this importance, this area has received comparatively limited scholarly attention, with existing studies largely focusing on conventional drivers. For instance, Celik et al. (2024) explored the relationship between urbanisation and labour productivity across 6 African countries from 1991 to 2019, employing the CS-ARDL model. Their findings reveal a positive correlation and causal links between urbanisation and labour productivity. Kumar and Kober (2012) expanded the analysis by examining urbanisation, health, and education as determinants of labour productivity in a broader panel of countries. Their results underscore the significantly positive impacts of both health and urbanization on labour productivity. Likewise, C¨ orvers (1997) investigated the role of human capital in labour productivity within manufacturing firms across 7 EU member states. Using cross-sectional analysis, their findings indicate that moderateand highly developed human capital positively influence labour productivity, whereas low levels of human capital development yield inverse effects. Nowak and Kijek (2016) provided further evidence by exploring Poland’s agricultural sector, demonstrating that farms managed by highly educated farmers achieve significantly higher productivity levels, emphasising the role of human capital in labour productivity. Foreign direct investment (FDI) has also been identified as a key driver of labour productivity. Boghean and State (2015) analysed its effects on labour productivity through growth and technological transfers in EU countries between 2000 and 2012. Their regression analysis highlights that increased FDI, through skills and capital transfer, significantly boosts labour productivity. Similarly, Liu et al. (2001) examined FDI’s impact across Chinese industries from 1996 to 1997, revealing that FDI in the form of human capital, capital intensity, and foreign presence in industries substantially enhances labour productivity. Malick (2013) analysed globalisation’s impact on labour productivity in OECD nations from 1990 to 2011 using panel regression models. Their results demonstrate that globalisation, proxied by economic openness, exerts a significantly positive influence on labour productivity, suggesting that greater openness promotes higher productivity. While these studies provide valuable insights, the literature remains sparse regarding the impacts of emerging factors such as state fragility, economic complexity, and institutional quality on labour productivity. Addressing this issue, the study proposes the following three hypotheses: H 5 : State fragility negatively influences labour productivity. H 6 : Economic complexity is positively associated with labour productivity. H 7 : Institutional quality exerts a significantly positive impact on labour productivity. 2.5. Environmental integrity The Environmental Kuznets Curve (EKC) hypothesis, introduced by Grossman and Krueger (1991), provides a theoretical lens to analyse the interplay between growth and environmental sustainability. Building on this foundation, recent empirical studies expanded the EKC narrative by incorporating additional predictors alongside economic output to examine this critical nexus. For instance, Suyanto et al. (2024) investigated the EKC hypothesis within G20 nations (1995–2015), incorporating institutional quality, government expenditures, and CO 2 emissions as key determinants. Their findings validated the EKC hypothesis and underscored the pivotal role of institutional quality in significantly curbing emissions. Conversely, Alakbarov et al. (2024), using panel data spanning 1992 to 2014, confirmed the EKC hypothesis but observed that the inclusion of energy consumption complicated the narrative, revealing that energy use does not invariably exacerbate CO 2 emissions across G20 group. In the same vein, Ozturk et al. (2023) analysed the EKC hypothesis from 1990 to 2020 using trade-adjusted material footprint, environmental governance, and green innovations within G20 nations. Their quantile regression results affirmed the EKC hypothesis, highlighting the significant contribution of environmental technologies in reducing material footprints. In a related study, Mar’I et al. (2023) examined financial and fiscal policy impacts on the EKC framework in G20 panel (1995 to 2019) with CO 2 emissions as the dependent variable. Their findings, derived from ARDL modelling, confirmed the EKC hypothesis and emphasised the need to redirect financial resources toward clean energy initiatives. Likewise, Li et al. (2022) explored the effect of conventional energy consumption on the CO 2 emissions-growth (1995 to 2018), using panel cointegration regression analysis. Their results validated the EKC hypothesis within G20 nations. While these studies collectively provide insights into the EKC hypothesis, they largely rely on conventional predictors and fail to integrate emerging metrics to redefine the EKC framework for G20 nations. This study seeks to fill this gap by incorporating economic complexity and state fragility alongside institutional quality to examine their combined influence on environmental outcomes. Accordingly, we propose the following hypotheses: H 7 : Both state fragility and economic complexity significantly influence CO 2 emissions. H 8 : The EKC hypothesis holds valid under these emerging factors while accounting for institutional frameworks. M.N. Azimi et al. Research in Globalization 10 (2025) 100278 4 3. Data, variables, and sources 3.1. Data This study utilises panel data for the G20 nations—(1) Argentina, (2) Australia, (3) Brazil, (4) Canada, (5) China, (6) European Union (EU), (7) France, (8) Germany, (9) India, (10) Indonesia, (11) Italy, (12) Japan, (13) South Korea, (14) Mexico, (15) Russian Federation, (16) Saudi Arabia, (17) South Africa, (18) Türkiye, (19) the United Kingdom, and (20) the United States—covering the years from 2000 to 2023. Focusing on this group is justified by four primary reasons. First, the G20 collectively accounts for over 80 % of global GDP, approximately threequarters of international trade, and over 60 % of the global population, making it a significant proxy for the global economy (OECD, 2025). Second, these nations include both developed and emerging economies, allowing for insights into varied institutional setups, levels of development, and infrastructures (Azimi & Rahman, 2023). Third, they play a critical role in coordinating international economic and environmental policies, addressing global challenges and rendering solutions particularly insightful for policymakers around the world (Wang et al., 2024). Fourth, the bloc offers consistent and updated datasets for diverse variables used in the study, which makes the analysis robust and reliable. To avoid data duplication and unit overlap, the study excludes individual countries included in EU, such as France, Germany, and Italy, when treating EU as a collective entity. Consequently, the total number of units rounds to 44 (see Fig. 2), encompassing all entities that constitute the G20. This approach ensures the integrity and independence of the data used in the study. 3.2. Variables The variables utilised in this study are carefully selected to align with core objectives of SDG 8, focusing on its four specific targets. Per capita GDP (PCG, constant 2015 US$), labour productivity (LP, ratio)—derived by dividing real GDP in constant 2015 US$ by hours worked per person—unemployment rate (UR, annual %), and CO 2 emissions (CO 2 , metric tonnes per capita) serve as the dependent variables. These variables are also used as explanatory predictors in specific model specifications to capture intricate interrelations. To ensure analytical precision and contextual relevance, the explanatory variables are categorised into distinct dimensions. Macroeconomic variables include foreign direct investment (FDI) as a percentage of GDP, which reflects capital inflows, and trade openness, proxied by the globalisation index (GLO), ranging from 0 and 1, indicating the degree of integration with global trade and investment networks. The economic complexity index (ECI) is also incorporated to measure the diversity and sophistication of export structures. Positive values denote high complexity, negative values reflect low complexity, and zero signifies the absence of diversification. Socioeconomic variables include the stage fragility index (SFI), ranging from 0 (least fragile) to 1 (most fragile), and the human development index (HDI), a composite indicator of health, education, and income levels, also scaled between 0 and 1. The study further introduces a novel institutional quality index (IQI), constructed using a distance-based scoring method, which captures all dimensions of governance and range from 0 (low quality) to 1 (high quality). Energy and environmental variables encompass primary energy consumption (PE), measured in terawatt-hours, capturing the total energy usage, and renewable energy (RE), representing the share of renewable sources in total energy consumption. This analysis also integrates environmentalrelated technologies (ENT), measured as the percentage of green technology patents or innovations, which signify progress towards cleaner production processes and resources efficiency. These metrics are vital for understanding the energy transition, technological innovations, and environmental sustainability within G20 framework. Finally, the sociodemographic variable, urbanisation (URB), is calculated as the percentage of the total population residing in urban areas. It reflects urban development patterns and demographic dynamics, which are critical in shaping economic performance and societal outcomes. 3.3. Construction of IQI To comprehensively capture the influence of all governance aspects on SDG 8 targets, the study constructs the institutional quality index (IQI) using six key metrics from WGI: control of corruption (CC), government effectiveness (GE), political stability (PS), the rule of law (RL), regulatory quality (RQ), and voice and accountability (VA)—each expressed as percentile ranks from 0 to 100. The IQI is developed using the inclusive methodology proposed by Sarma (2012), a robust framework for constructing a multidimensional index that offer several advantages over conventional techniques (Azimi et al., 2025). Unlike methods such as weighted averages and arithmetic means, which tend to oversimplify complex dimensions by assigning equal importance to all indicators, this approach effectively captures disparities in institutional performance both within individual indicators and across the countries (Azimi & Rahman, 2024). Moreover, compared to Principal Component Analysis (PCA) and Factorial Analysis (FA)—which primarily rely on statistical correlations and often reduce the complexity of governance dimensions to a few dominant factors—this distance-based scoring method considers the entire range of institutional performance. This methodology enhances sensitivity by capturing both bestand worstcase scenarios, offering a more nuanced reflection of institutional quality. Therefore, it is particularly effective in monitoring institutional convergence and divergence over time, an aspect that PCA and FA are less equipped to address due to their emphasis on static relationships (Azimi et al., 2023). To construct IQI, we first normalise each dimension into a 0–1 scale, where 0 reflects the worst and 1 represents the best institutional quality, using the following equation: dcj =Xcj 100 (1) where Xcj represents the percentile rank for country c in dimension j—that is, CC, GE, PS, RL, RQ, or VA. Next, we position each country c as a point in a 6-dimensional space (dcj1,dcj2,⋯,dcj6), where dcj ∈ [0,1] denotes country c’s score on dimension j (Park & Mercado, 2015). The ideal point is (1,1,1,1,1,1), which signifies the highest possible institutional performance across all six metrics. Subsequently, we compute the distance from the ideal point for each G20 nation by applying the Euclidean distance (Dcj)between the 6-dimensional space and the ideal point, as follows: Dc= ∑ 6 j=1(1−dcj)2 √ √ √ √(2) When a country score dcj =1 in all six dimensions, Dc=0. Conversely, if a country underperforms in one or several governance dimensions, the distance grows larger. To arrive at the IQI, we invert this distance into a 0–1 scale: IQIc=1−Dc  6 √(3) where  6 √is the maximum distance between the ideal point (1,1,1,1, 1,1)and the worst point (0,0,0,0,0,0), in a 6-dimensional space:  ∑ 6 j=1(1−0)2 √ √ √ √= 6 √(4) Here, IQIc=1 corresponds to (dcj1,dcj2,⋯,dcj6)= (1,1,⋯,1), i.e., high institutional quality in all dimensions, whereas IQIc=0 corresponds to (dcj1,dcj2,⋯,dcj6)= (0,0,⋯,0), i.e., poor institutional quality across all M.N. Azimi et al. Research in Globalization 10 (2025) 100278 5 Fig. 2. Dependent variables’ trends across the G20 nations. Note: Avg. represents average values of the indicators. M.N. Azimi et al. Research in Globalization 10 (2025) 100278 6 dimensions. This comprehensive approach captures the gradual evolution of IQI over time in G20 nations (Azimi et al., 2025). 3.4. Sources of compilation Datasets on nominal GDP, the GDP deflator, per capita GDP, control of corruption, government effectiveness, rule of law, political stability, regulatory quality, voice and accountability, urbanisation, and FDI are obtained from the World Development Indicators (WDI) database of the World Bank Group. CO 2 emissions data are sourced from Our World in Data, curated by Ritchie and Roser (2020). HDI data are sourced from the United Nations Development Programmes (UNDP) sources (UNDP Human Development Index, xxxx). Globalisation index series is drawn from the KOF Swiss Economic Institute, developed by Gygli et al. (xxxx). The state fragility index dataset is retrieved from the Fund for Peace sources (). Finally, hours worked per person, environmental-related technologies, and the economic complexity index data are acquired from OECD resources (). 4. Specification and estimation procedures SDG 8 accentuates “promoting inclusive, sustainable growth, full and productive employment, and decent work for all.” This goal entails multiple interrelated targets: fostering higher levels of productivity via diversification and technological advancement (Target 8.2), achieving full and productive employment (Target 8.5), reducing the proportion of youth not in employment or training (Target 8.6), and improving resource efficiency (Target 8.4). In essence, SDG 8 captures both the quantitative dimension of economic growth and employment, as well as the qualitative aspects of decent work, inclusiveness, and environmental responsibility. Given these multifaceted targets, this study develops four empirical models that comprehensively evaluate these dimensions. 4.1. Model I: Sustainable growth Grounded in the Neoclassical Growth Theory of Solow (1956), this model emphasises labour, technology, and capital accumulation as core determinants of economic output. Building on this foundation, Romer (1990) highlights the transformative role of ENT and ECI spillovers in fostering sustainable growth. Accordingly, Model I integrates these factors to capture the sophistication of production structures and the adoption of cleaner, innovative processes within the growth trajectory. In addition to ENT and ECI, the model incorporates key macroeconomic and sociodemographic variables, including FDI, GLO, HDI, and URB, which collectively contribute to shaping economic performance. IQI and SFI are further included to account for the regulatory, political, and structural environments that sustain market performance, enhance entrepreneurial activities, and attract investment (Salman et al., 2019). Moreover, this model considers the critical role of resource consumption and efficiency, measured by PE and RE. These variables reflect G20 ′ s efforts to reduce ecological impact and transition to cleaner systems, aligning with sustainable development imperatives (Chen et al., 2019; Magazzino, 2024). Given this context, Model-I can be expressed as follows: PCGit =β1+∑ 9 j=1 θjXit +δi+ ε it (5) where PCG represents the dependent variable, βi represents the intercept, θi is the vector of coefficients for Xit which includes ENT, ECI, SFI, IQI, HDI, FDI, GLO, PE and RE. δi captures the unobserved countryspecific effects, and ε is the error term. 4.2. Model II: Labour productivity Labour productivity, a cornerstone of economic performance and a critical target of SDG 8, is rooted in the classical production function (Cobb & Douglas, 1928), which assesses the efficiency with which inputs are transformed into outputs. In this context, IQI and GLO emerge as pivotal factors. Robust IQI and high GLO mitigate rent-seeking behaviour and economic distortion, creating an environment conducive to the optimal allocation of labour resources (Adams-Kane & Lim, 2016). Simultaneously, low SFI ensures political and economic stability, further enhancing productivity dynamics. The role of ENT and HDI as synergetic drivers of productivity gains is well-documented in prior literature (Zilibotti et al., 1999). ENT fosters innovation and efficiency, while HDI equips the workforce with the skills and capacities required for higher productivity. ECI and FDI complement these factors by introducing managerial best practices, enhancing technological transfers, and promoting workforce efficiency. URB also plays a significant role by enabling access to a more skilled and concentrated labour pool, which facilitates knowledge sharing and innovation (Celik et al., 2024). Additionally, the transition from conventional energy sources to renewable energy (RE) directly impacts labour productivity by reducing production costs and fostering sustainable industrial practices. This underscores the growing importance of energy efficiency in shaping productivity outcomes. To encapsulate these dynamics, Model II is specified as follows: LPit =β2+∑ 9 j=1 θjXit +δi+ ε it (6) where LP indicates labour productivity as the dependent variable, β2 is the intercept for Model II, Xit includes IQI, GLO, SFI, ENT, HDI, FDI, ECI, URB, and RE within this framework. All other notations maintain similar meaning as were explained in equation (5). 4.3. Model III: Employment and decent work Rooted in the Keynesian labour market perspective (Keynes, 2018), unemployment is conceptualised as a result of mismatches between labour demand and supply, market frictions, and macroeconomic fluctuations. IQI emerges as a pivotal factor in enhancing labour market efficiency by enforcing contracts, protecting workers, and fostering equitable employment opportunities. Conversely, heightened SFI signals instability, deterring investment and stifling job creations. ECI promotes diversification into high-value industries, contingent upon the availability of a skilled workforce capable of meeting advanced production requirements. Simultaneously, ENT facilitates the creation of green jobs in renewable energy, circular economy, and other sustainable sectors. FDI and GLO further complement these dynamics by driving job creations in export-oriented sectors (Pal & Villanthenkodath, 2024) and fostering cross-border employment opportunities. HDI plays an amplifying role, enhancing the quality of and inclusiveness of employment through improved education and healthcare, aligning labour market demand with supply. However, rapid URB can exacerbate UR in metropolitan areas by straining infrastructure and mismatching labour supply with local opportunities (Pandey et al., 2024). Conversely, the adoption process to renewable sources, RE is expected to create job opportunities within sustainable sectors. To capture these dynamics within the framework of SDG 8, Model III is formulated as follows: URit =β3+∑ 9 j=1 θjXit +δi+ ε it (7) where UR represents unemployment as the dependent variable, β3 is replaced with β2—the intercept, to maintain distinguished notations across models, and Xit includes IQI, SFI, ECI, ENT, FDI, GLO, HDI, URB, and RE within the framework of Model III. All other notations carry same meaning as stated earlier. M.N. Azimi et al. Research in Globalization 10 (2025) 100278 7 4.4. Model IV: Environmental impact Balancing sustainable economic growth with environmental objectives is a cornerstone of the SDGs. The EKC hypothesis, proposed by Grossman and Krueger (1991), offers a theoretical framework for understanding the non-linear relationship between economic growth and environmental degradation. It posits an inverted U-shaped nexus between income and pollution, contingent upon key factors. Robust IQI and lower SFI foster the enforcement of stringent environmental governance, ensuring that growth aligns with ecological sustainability. ECI supports the reorientation of production toward advanced, less resource-intensive sectors (Liu et al., 2019), while ENT facilitates the decoupling of economic growth from emissions (Nkalu et al., 2020). FDI and GLO may exhibit dualistic impacts on emissions. In the absence of robust IQI, these factors may exacerbate pollution due to lax regulations (the “pollution haven” hypothesis) (Pata & Caglar, 2021). Conversely, under strong IQI, they can lead to the diffusion of cleaner technologies, fostering a “pollution halo” effect (Alshubiri & Elheddad, 2020). URB, however, presents mixed implications. While efficient urban planning and infrastructure can reduce emissions through scale efficiency, poorly designed urbanisation may escalate emissions due to overcrowding and inefficient production systems (Dogan & Turkekul, 2016). EP is expected to contribute positively to emissions, reflecting continued reliance on fossil fuels (Nathaniel, 2021), whereas RE plays a counterbalancing role in mitigating emissions (Pata, 2018). To examine the EKC hypothesis in the presence of new metrics, we specify the following equation: CO2,it =β4+λ1PCGit +λ2PCG2 it +∑ 9 j=1 θjXit +δi+ ε it (8) where PCG 2 captures the quadratic term of income to account for the non-linear EKC relationship, β4 is the intercept of Model IV, λ1 and λ2 are the coefficients for PCG and its squared term, θi is the vector of coefficients for other explanatory variables (Xit)such as IQI, SFI, ECI, FDI, GLO, ENT, URB, PE, and RE. The meaning of other vectors remains consistent with those previously defined. Collectively, these four models capture multiple aspects of SDG 8—including growth, productivity, employment, and environmental sustainability—while drawing upon appropriate economic theories. This foundation not only justifies the chosen specifications but also paves the way for empirical testing in the context of G20, where institutional and socioeconomic heterogeneities make these interactions insightful for policymakers. 4.5. Estimation procedures Prior literature highlights several critical empirical challenges in analysing the relationships among predictors of sustainable development. Among the most pervasive issues are cross-sectional dependence (SCD), slope heterogeneity (SLH), and heteroskedasticity in residuals (Udeagha & Ngepah, 2023). Additionally, in certain contexts, coefficients bias arising from sample distortion further complicates empirical analysis. The presence of CSD, as well-documented in prior studies (Guerrero, 2006), is particularly problematic, undermining the reliability of conventional estimation techniques. While Fixed-effects (FE) and random-effects (RE) approaches remain widely used, they are not devoid of significant limitations. The FE model, for instance, simplifies heterogeneity by assuming homogeneity across units and removing time-variant effects, which inadequately addresses dynamics in the presence of SLH (Phillips & Sul, 2007). On the other hand, the RE method relies on stringent assumptions regarding the independence of individual-specific effects, often producing biased estimates under the rejection of homoskedasticity. Although advanced panel estimators such as Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Autoregressive Distributed Lags (ARDL), and the Generalised Method of Moment (GMM) offer alternative approaches, the selection of an appropriate method remain critical. For instance, while panel ARDL becomes inefficient under the rejection of the null hypothesis of no CSD, GMM estimators fails to adequately account for CSD. Given the empirical confirmation of both CSD and SLH within our dataset, standard FE and RE, alongside other conventional estimation techniques, fail to produce consistent and reliable coefficients (Huang et al., 2019). To address these limitations, this study adopts the Cross-Sectionally Augmented ARDL (CS-ARDL) model, developed by Chudik et al. (2016). The CS-ARDL model explicitly accounts for CSD by incorporating CS averages of both the dependent and explanatory variables. This method not only ensures robust coefficient estimation but also enables the simultaneous evaluation of shortand long-run coefficients, providing nuanced insights into the progress of SDG 8 targets. The general form of the CS-ARDL model used in this study is specified as follows: yit =φiyit−1+∑ p j=1 λijXit−j+γiyt∑ K k=0 ζikXtk +ξi+ ε it (9) where yit represents the dependent variables in the respective models (PCG, LP, UR, and CO 2 ), while Xit denotes the vector of explanatory variables (IQI, SFI, ECI, ENT, HDI, FDI, URB, and GLO). The model includes coefficients for the lagged dependent variable (φi) and the explanatory variables (λi), alongside coefficients for the cross-sectional averages of the dependent variable (y)and explanatory variables (X), denoted by γi and ζik, respectively. These cross-sectional averages effectively account for CSD. Country-specific effects (ξik)and idiosyncratic error terms ( ε it)are also incorporated to capture unobserved heterogeneity and stochastic variations. As a robustness check, the study employs the Dynamic Common Correlated Effects Mean Group (DCCEMG) model, an advanced extension of the CCEMG framework proposed by Pesaran and Smith (1995) for MG, Pesaran (2006) for CCE, and Chudik and Pesaran (2015) for DCCE. The DCCEMG model incorporates lagged yit−k, enabling it to capture the dynamic relationships between the dependent and explanatory variables in the presence of CSD and SLH. Although this method does not offer short-run effects, its long run estimators are computed as βlong−run,iβi/(1−φi), where φi denotes the degree of yit persistence. The DCCEMG method effectively accounts for the dynamic nature of the variables, remains robust to CSD through the inclusion of CS averages, and accommodates heterogeneity by allowing coefficients to vary across units. All statistical analyses are conducted using STATA/BE-18 software, while graphical representations are generated using the advanced visualisation tools of Tableau and OriginLab-2024. Table 1 Summary statistics. Variables Mean Standard Deviation Minimum Maximum PCG 29433.940 22906.564 756.704 112417.88 LP 0.823 1.671 0.001 10.384 UR 7.814 4.497 1.805 28.838 CO 2 6.854 4.847 0.058 26.003 IQI 0.878 0.205 0.000 1.000 SFI 43.643 19.132 12.700 89.200 ECI 0.993 0.748 −1.110 2.820 PE 2697.830 6055.95 18.510 47427.56 RE 14.083 14.169 0.000 72.329 ENT 8.321 5.66 0.620 53.850 HDI 0.835 0.109 0.291 0.967 FDI 9.306 40.206 −360.353 452.221 URB 73.008 15.239 21.637 98.153 GLO 76.936 9.666 43.582 91.141 M.N. Azimi et al. Research in Globalization 10 (2025) 100278 8 persistent environmental burden tied to contemporary economic growth, labour productivity, and CO 2 emissions. Our findings validate the Environmental Kuznets Curve (EKC) hypothesis, suggesting a revised turning point influenced by new factors. Only after this inflection point ($55,972), considering these specific factors, can the environmental impacts of sustainable growth begin to decline, setting the stage for the policy implications discussed in the subsequent sub-section. 7.1. Policy implications Our findings provide several critical policy implications for the G20 nations, though we only focus on the implications of the emerging metrics alongside the revised EKC dynamics, which are crucial for immediate attention. Firstly, strengthening institutional quality emerges as vital for fostering inclusive and sustainable economic growth. Policies must prioritise effective governance reforms, emphasising regulatory quality, political stability, comprehensive anti-corruption measures, and an inclusive rule of law, which can enhance economic resilience, labour productivity, and environmental management. Secondly, economic complexity highlights the need for further industrial diversification into knowledge-intensive and high-value sectors. This can be achieved through targeted investments in research and development, trade policies on high-value goods and services, and skill development initiatives to align the workforce with the demands of complex industries. Thirdly, addressing existing state fragility requires stabilising political systems, enhancing social safety nets, and incentivising capital investments in fragile countries to break the feedback loops. Third, the validation of the revised EKC hypothesis suggests that achieving its turning point necessitates strategic interventions, including accelerated adoption of green and eco-friendly technologies, integration of climate goals with existing economic policies, and the promotion of resource-efficient practices in a wider manner. 7.2. Study’s limitations This study like all other empirical research, faces two notable limitations. First, while advanced econometric methods such CS-ARDL and DCCEMG are applied to capture empirical challenges such as crosssectional dependence and heterogeneity of the employed panel, the analysis did not account country-specific progress towards SDG 8 targets. it does not conduct a detailed country-specific analysis due to space and scope limitations. Future research could benefit from focusing on individual G20 nations to uncover country-specific dynamics and provide more targeted policy recommendations. Such an approach would allow for a deeper understanding of regional disparities in SDG 8 progress and enable the formulation of more tailored interventions. Second, although the study incorporated new metrics to examine their impacts on 4 out of 12 SDG 8 targets, it only proposed a direct effect framework. Future studies may enhance this understanding by exploring the indirect and moderated effects of these predictors on these specific SDG 8 targets and include more targets. CRediT authorship contribution statement Mohammad Naim Azimi: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation. Mohammad Mafizur Rahman: Writing – review & editing, Validation, Supervision, Investigation, Conceptualization. Tek Maraseni: Writing – review & editing, Validation, Supervision, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research did not receive any funds from any organisation. Ethical approval This study does not contain any studies with human participation or animals performed by any of the authors. References Abban, O. J., Rajaguru, G., & Acheampong, A. O. (2025). 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