Exasperating environmental degradation has necessitated an increased focus on strategies and policies that emphasise green growth.
Research purpose
This study explores the effect of green growth on human capital development in South Africa.
Motivation for the study
Despite the alarming environmental degradation and the growing demand for green growth, the effect of green growth on human capital development remains largely unexplored.
Research approach/design and method
The study employs an Autoregressive Distributed Lag (ARDL) framework to examine the relationship between green growth and human capital development in South Africa. The model used secondary data from 1990 to 2021.
Main findings
The results confirm the existence of a long-run, positive, significant relationship between green growth and human capital development. Educational expenditure and financial development also showed a positive and significant effect on human capital development. In contrast, trade openness was found to have a negative and insignificant effect on the dependent variable.
Practical/managerial implications
The implications of these results suggest that South Africa should prioritise green growth strategies, not only as a means of promoting environmental sustainability but also as a catalyst for human capital development.
Contribution/vale-add
This study provides South Africa–specific evidence on the long-run relationship between green growth and human capital using an ARDL (bounds testing) approach, highlighting the roles of education expenditure and financial development for policy.
green growthhuman capital developmentARDL bounds testingeconomic growthSouth Africafinancial developmenttrade opennesseducation expenditureFunding information This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.Introduction
Economic growth and environmental preservation have become two of the most critical challenges facing economies across different regions and levels of development. While economic growth is vital for improving living standards, creating jobs, and reducing poverty, it has often come at the cost of environmental damage (Boqiang 2003). The careless use of natural resources and increasing carbon emissions have caused serious problems, including climate change, loss of biodiversity, and declining human well-being. Because these issues are urgent, the concept of green growth has gained importance as a sustainable approach to economic development. Unlike traditional growth models, which often overlook environmental issues, green growth focuses on economic progress that supports ecological sustainability. The Organisation for Economic Co-operation and Development (OECD 2011) describes green growth as promoting economic expansion while making sure that natural resources continue to supply the resources and ecosystem services needed for long-term health. This understanding has led to the development of policies that promote renewable energy, resource efficiency, and sustainable industrial practices, aiming to strike a balance between economic prosperity and environmental responsibility (Ackah & Kizys 2015).
Within the African and Sub-Saharan African context, discussions on green growth have sparked significant debate about its feasibility and implementation. While some scholars question the practicality of green economic strategies in developing regions facing urgent social and economic challenges (Resnick, Tarp & Thurlow 2012), others argue that green growth is essential for sustainable and inclusive development (Klein & Reiher 2016). Africa’s reliance on natural resource exploitation makes environmental sustainability a critical concern (Klein et al. 2015). The depletion of non-renewable resources and the region’s vulnerability to climate change demand urgent policy measures that incorporate green economic principles. The African Development Bank (AfDB 2013) has highlighted the importance of sustainable infrastructure, efficient resource management, and climate resilience as core components of Africa’s green growth strategy. In South Africa, where energy security and economic growth remain key policy priorities, green growth strategies are increasingly being integrated into national frameworks (Ilesanmi & Tewari 2017). The country’s electricity infrastructure, already strained by rising energy demand, underscores the need to shift towards more sustainable and resilient energy systems (Republic of South Africa 2014). However, balancing economic growth with environmental sustainability requires significant investments in human capital to develop the skills necessary for a growing green economy.
In recent times, several countries in Africa have embraced efforts to adopt green growth strategies. Examples include Sierra Leone’s Rural Renewable Energy Project and Kenya’s Off-Grid Solar Access Project for Underserved Counties. Despite these increasing efforts, developing human capital remains a persistent challenge (Shuaibu 2016). Human capital, encompassing the skills, development, and training of the population, is crucial for economic growth. A country’s economic potential has traditionally been seen as a combination of its physical and human capital (Ahamefule Amaghionyeodiwe 2009; Akpolat 2014; Chuadry & Rahman 2009; Holden & Biddle 2017; Khembo & Tchereni 2013; Mehrara & Musai 2013; Rahman 2011; Sharma & Sahni 2015). Although some progress has been made in education and workforce skills across various African economies (Lee & De Gregorio Rebeco 2003; Macham 2015), the overall human capital stock remains underdeveloped. South Africa, as one of the continent’s leading economies, faces significant challenges in building a skilled workforce, particularly with its rapidly growing population. If not adequately managed, rapid population growth can exacerbate existing socioeconomic issues, including unemployment, poverty, and inequality. Without targeted policies to enhance education, vocational training, and workforce development, a large segment of the population may become economically marginalised, ultimately hindering national progress. As green growth policies gain momentum, it is crucial to consider their potential trade-offs, particularly in terms of resource allocation between sustainability initiatives and investments in human capital. An overemphasis on green growth without simultaneous efforts in human capital development could inadvertently increase socio-economic inequalities.
Although literature extensively examines the role of human capital in supporting green growth, much less attention has been given to the reverse relationship – how green growth impacts the development of human capital. While green growth is expected to boost human capital development as the green industry expands, driving the acquisition of green skills and knowledge (green skills and green knowledge refer to specific skills and knowledge required by those who work in the renewable energy sector), there is little empirical evidence to verify this. Most empirical studies examine how investments in education and technology promote environmental sustainability, but few investigate whether green growth itself enhances or hinders human capital formation (Khan 2023). Khan (2023) further explains that achieving green growth is unlikely without technological progress and a strong human capital base, emphasising the interconnectedness of these factors. Additionally, the few studies that have explored this relationship mainly analyse how green growth policies and regulations affect human capital, without using comprehensive measures of green economic performance, such as an aggregated green growth index. This creates a significant research gap, particularly in South Africa, where empirical studies on the relationship between green growth and human capital development are scarce.
Considering South Africa’s ongoing challenges in developing human capital, it is important to assess whether a greater emphasis on green growth strategies diverts attention and resources away from education, workforce training, and skills development. To address the aforesaid, this study aims to empirically examine the effect of green growth on human capital development in South Africa, offering policymakers valuable insights on how to balance sustainability efforts with workforce development strategies. This study contributes to the green growth–human capital relationship in three ways: (1) it adds to the literature by proposing a new method for calculating green growth that is tailored to the South African context; (2) it enriches the existing literature on the green growth–human capital nexus by analysing this relationship within South Africa and (3) as the first study to explore how green growth influences human capital (rather than the other way around), it seeks to expand the boundaries of current knowledge.
The remainder of this article is organised as follows: the Literature review section reviews the literature, while the Research design section outlines the methodology. The Results and discussion section presents the findings, and finally, the Conclusion section offers concluding remarks and discusses policy implications.
Literature review
The concept of human capital was historically neglected in economic growth models, despite its implicit presence in classical economic theories. Early economists mainly linked growth to land, labour, and physical capital, viewing labour as a uniform input rather than recognising the different contributions of individuals based on education, skills, and expertise (Keeley 2007). However, Smith (1776) introduced a basic idea of human capital by arguing that workers with specialised knowledge and abilities contribute more effectively to economic productivity. He suggested that acquired skills, although costly to obtain, become ‘a capital, fixed and realised’ within individuals. Despite these early ideas, classical economic models, including those of Ricardo and Malthus, continued to see labour as a non-differentiated factor of production. It was only in the 20th century that economists began to explicitly incorporate human capital into growth models. Solow and Swan (1956) included technological change as a key factor of growth but treated it as an external element. Nelson and Phelps (1966) further highlighted education as a mechanism for technological adoption, marking the first formal recognition of the role of human capital in development.
The formalisation of human capital theory emerged through the works of Becker (1918), Mincer (1958), and Schultz (1961), who argue that investments in education, training, and health enhance worker productivity and, consequently, economic output. Becker (1918) compared human capital accumulation to investments in physical capital, where education and skill acquisition provide long-term economic returns. This perspective gained further support when Lucas (1988) and Mankiw, Romer and Weil (1992) extended Solow’s (1956) model by integrating human capital as an endogenous growth factor. These scholars showed that economies with higher levels of education and workforce skill development experience sustained productivity improvements. Marshall (1890) had previously proposed that education is ‘the most important of all capital investments’, a statement that later received empirical support. Although early economic theories emphasised labour as a physical input, modern studies recognise that knowledge-based economies depend more on human capital than on traditional production factors. Marimuthu, Arokiasamy and Ismail (2009) distinguished between general human capital, which encompasses broad-based education, and specific human capital, which relates to job-specific skills and knowledge. Thus, it highlights the complex role that human capital plays in the labour market and the broader economy.
The impact of human capital on economic growth is evident in the gap between developed and developing countries. Khan (2023) notes that advanced economies benefit from a highly educated workforce, which promotes innovation, efficiency, and structural stability. For example, Osiobe (2019) notes a positive correlation between human capital and economic growth, with countries such as Estonia, Switzerland, and the United States leading in innovation. In contrast, underdeveloped nations face challenges because of limited investment in human capital, resulting in lower productivity and slower economic growth. Dinda (2016) supports this view by emphasising that economic growth depends on the total capital stock, which includes physical, human, and social capital. Therefore, Oketch (2000) noticed the importance of human resource development as a key factor for achieving sustainable economic growth in Africa (McMahon 2000; Oketch 2000, 2002). Hjerppe (2000) estimates that human capital accounts for between 15% and 30% of total capital, highlighting its significant role in building national wealth. Abel, Mhaka and Le Roux (2019) further argue that human capital, like physical capital, requires ongoing investment in education, health, and vocational training to unlock its full economic potential. While criticising the overemphasis on formal education in traditional human capital theory, some studies suggest that informal learning, non-cognitive skills, and socio-emotional development are equally vital in human capital formation (Heckman & Kautz 2012). In addition, recent research indicates that digital skills, creativity, flexibility, and adaptability also play a crucial role in shaping the impact of human capital on economic progress.
Modern human capital theory continues to evolve, particularly in response to technological advancements and shifting labour market demands. For example, Becker (1918) limited the scope of human capital formation to investment in physical capital; in contrast, recent research has emphasised the equally important roles of non-cognitive skills and early childhood development. Autor (2019) explains that lifelong learning and reskilling are vital for developing human capital in the digital economy. Mayilyan and Yedigaryan (2022) contend that in the post-industrial era, human capital has become the primary driver of economic progress, surpassing physical capital in importance. The World Economic Forum (2020) emphasises that the rise of automation and artificial intelligence necessitates ongoing investment in human capital to maintain economic competitiveness. Recent studies also underline the significance of health and well-being in labour productivity, supporting Schultz’s (1961) view that human capital encompasses both intellectual and physical development. There is a broad consensus that educational attainment remains a key indicator of a nation’s economic performance, with research showing that differences in human capital accumulation explain variations in GDP growth across countries (Hanushek & Woessmann 2020). As economies undergo rapid transformation, investing in human capital through education, healthcare, and workforce development remains essential for long-term growth and stability. This underscores the importance of human capital for the economic and socio-economic development of a society.
The bidirectional relationship between economic growth and human capital development
The relationship between economic growth and human capital development is not unidirectional but rather bidirectional, with extensive literature providing empirical evidence that economic growth enhances human capital development while, in turn, human capital development contributes to sustained economic growth. According to Barro (2000), Lall (1990) and Wolf (2002), human capital must be developed for economic growth to occur, as an educated and skilled workforce is essential for increasing productivity and stimulating economic activity. However, they also emphasise that for human capital to develop and be effectively utilised, productive sectors must grow and diversify, creating more opportunities for employment and skill application. This dynamic interplay between economic growth and human capital formation underscores the importance of policies that simultaneously foster economic expansion and invest in workforce development.
Empirical studies further support this bidirectional relationship. Rastogi and Gaikwad (2018) examine the impact of economic growth, measured by Gross domestic product (GDP) and Foreign direct investment (FDI), on human capital development in Brazil, Russia, India, China and South Africa (BRICS) nations – including South Africa. Using panel data from 2005 to 2015 and employing a fixed effects model, the study finds that both GDP growth and FDI inflows have a positive influence on human capital development in these emerging economies. Similarly, Oketch (2006) investigates the two-way relationship between human capital development (measured by formal education) and economic growth, as well as the interaction between physical capital and economic growth. Using the two-stage least squares method to estimate a three-equation structural system, the study confirms that economic growth enhances human capital development. Conversely, human capital development strengthens physical capital accumulation, which in turn contributes to subsequent economic growth. Furthermore, a trend analysis of African economies from 1965 to 1998 reveals that many countries experienced low or even negative economic growth alongside persistently weak human capital development, highlighting the importance of sustained investment in education and skills training for long-term economic resilience.
Recently, Tsaurai (2020) used various econometric methods – fixed effects, random effects, pooled ordinary least squares (OLS), and the dynamic generalised method of moments (GMM) – to analyse the effects of FDI and economic growth on human capital development in Africa from 2001 to 2015. The results support earlier findings that FDI and economic growth have a positive impact on human capital development. However, the effect may differ across countries depending on institutional quality, labour market structures, and policy environments. Besides economic growth and FDI, other important factors for human capital development identified in the literature include trade openness, information and communication technology (ICT) adoption, and infrastructure development (Tsaurai 2020). These factors collectively influence how economies can improve human capital, indicating that a comprehensive approach is essential for promoting sustainable human development alongside economic progress.
Human capital development and the green growth nexus
Beyond its importance in driving economic growth, human capital is crucial for promoting sustainable development and fostering environmentally friendly growth. The concept of green growth, which combines economic progress with environmental sustainability, has gained increasing prominence in recent years. As policymakers and scholars increasingly emphasise the shift towards a low-carbon economy, research exploring the link between human capital and green growth has grown. Khan et al. (2023) argue that developing human capital is essential for environmental sustainability, as it enhances the ability to adopt and implement green technologies, fosters environmental innovation, and supports the structural transition towards a resource-efficient economy. While traditional economic models often neglected the role of human capital in environmental sustainability, modern studies have started to explore this connection more thoroughly. Campbell (2019) emphasises that investing in human capital – primarily through education, skill development, and technological training – is crucial for building a productive workforce that supports green development. His analysis of public investment in human capital via capacity-building programs suggests that such investments boost competitiveness while promoting inclusive and sustainable economic growth.
The World Bank (2023) considers human capital essential for the green industries of the future, which depend on skilled and healthy individuals. Increasing empirical evidence supports the idea that developing human capital promotes green growth. Hao et al. (2021) employed a Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model to demonstrate that environmental tax policies, renewable energy adoption, and investments in human capital have a significant impact on reducing carbon dioxide (CO2) emissions. Similarly, Chen et al. (2023) confirmed that environmental innovations, patents, and human capital have a positive influence on green growth in China and India. These findings provide empirical proof of the benefits of developed human resources for advancing environmentally sustainable technology in knowledge-based economies. Other scholars, such as Aqib and Zaman (2023), who employed robust least squares regression and Granger causality analysis, found that investments in education, healthcare, and improvements in employment markets support green development strategies. Liu et al. (2023) applied ARDL models to analyse long-term data (1991–2019), discovering a positive link between various levels of education and China’s green growth trajectory. These results suggest that human capital not only directly promotes green growth but also influences it through various channels, including green innovation, environmental governance, and policy effectiveness. Tufail, Song and Khan (2024) reinforce this view by employing quantile regression methods to demonstrate that green finance accelerates green growth, with human capital serving as a key driver. However, their study also indicates that globalisation and GDP growth – especially when reliant on traditional high-emission industries – may hinder the transition to a greener economy. Hwang and Diez (2024) employ a panel mediation analysis to demonstrate that human capital serves as an enabling mechanism through which the shift to renewable energy promotes green economic expansion.
Despite the growing body of research linking human capital to green growth, this area remains relatively underdeveloped compared to traditional studies on economic growth (Shuaibu & Timothy 2016; Tsuarai 2020). Existing research is heavily focused on certain economies, particularly China, which has taken the lead in integrating human capital development into its green growth strategies. Wang et al. (2023) provide a comprehensive assessment of how human capital stock and structure contribute to China’s green economy, utilising the Super-Efficiency Slack-Based Measure (SBM) model to evaluate the efficiency of human capital investments in ecological sustainability. This study notably differs from traditional human capital literature by adopting the J-F Income Approach to capture a broader range of human capital attributes, including skills, experience, and workforce adaptability. Their findings reinforce earlier conclusions from Wang, Xu and Ma (2021), which suggest that green innovation and industrial upgrading serve as transmission channels through which human capital influences green growth. However, Liu et al. (2022) argue that the relationship between human capital and green growth is nonlinear, meaning that human capital’s contribution depends on reaching a critical threshold. In the early stages, human capital accumulation may hinder green growth because of the dominance of high-emission industries; however, once a sufficient level of skill development and technological expertise is achieved, the relationship shifts to a positive one.
While research on human capital and green growth is developing globally, empirical studies within Africa, especially South Africa, are limited. Jochuad (2016) emphasises that developing human capital is essential for achieving green growth, highlighting key measures such as monitoring and evaluating green interventions, expanding green skills training programmes, involving the private sector, encouraging green entrepreneurship, and building inclusive green value chains. These strategies are particularly relevant in Africa, where the transition to sustainable economic models is hindered by limited access to education, infrastructure, and financial resources. More recent literature discussions have also begun to examine the opposite relationship, that is, the influence of green growth on human capital development. The International Labour Organisation (ILO) (2021) notes that transitioning to renewable energy is likely to increase demand for Science, Technology, Engineering, and Mathematics (STEM)-based and technical jobs, necessitating the retraining and upskilling of the workforce. Meanwhile, the OECD stresses that a strong regulatory framework supporting the green economy will help establish structured green job training programmes, ensuring that labour markets align with sustainability goals. However, environmental issues such as climate change and extreme weather events negatively affect human capital accumulation by disrupting education, reducing worker productivity, and elevating health risks. These dual effects suggest that the relationship between human capital and green growth is complex, dynamic, and influenced by both economic policies and environmental factors.
Research design
We study the impact of green growth on human capital development in South Africa. We measure green growth with the logarithm of GreenGrowth2 (ln_GreenGrowth2), an outcome index rather than a set of policy dummies. At the same time, we proxy human capital development using the Education Index (EI, hereafter). To achieve this, we estimate an ARDL model and apply the Bounds approach to co-integration. We choose ARDL because it accommodates a mix of I(0) and I(1) variables while excluding I(2), and because it performs well in small samples (Djamal, Fairou & Oulad Brahim 2023). We select lag orders by the Akaike Information Criterion (AIC). We tailor the deterministic components to Case 3 (unrestricted constant, no trend).
Our estimation sequence is as follows. We firstly run Augmented Dickey-Fuller (ADF) unit-root tests with a constant to confirm that no series is I(2). We then use centred variance-inflation factors (VIFs) to ensure the absence of multicollinearity. The results show a mix of I(0) and I(1) processes, which satisfy the ARDL assumptions. We then apply the Bounds procedure: the F-bounds test evaluates whether a level relationship (co-integration) exists, and the t-bounds assess the significance of the error-correction term. Conditional on co-integration, we recover long-run elasticities from the levels equation and estimate the error correction model (ECM) to summarise short-run dynamics and the speed of adjustment. We report coefficients, standard errors, t-statistics, and p-values and use conventional thresholds for statistical significance (***p < 0.01, **p < 0.05, *p < 0.1). Finally, we review compact diagnostics – Breusch-Godfrey serial correlation LM, Breusch-Pagan-Godfrey heteroskedasticity, Jarque-Bera (normality), Ljung-Box residual correlogram Q-statistics, RESET (functional form), and the CUSOM stability plot that supports well-behaved residuals and stable parameters – to verify the reliability of the inference.
Data and variables
The dataset comprises annual South African observations from 1990 to 2021, encompassing significant years when green growth initiatives, such as the Green Economy Accord (2011), were introduced. This period also includes carbon emissions data, a key element in calculating the Green Growth measure. The appropriate data transformation was employed and the analysis utilises 29 observations after adjustments.
Variable selection and justification
The empirical literature links human capital development to a broad set of macroeconomic factors – including unemployment (Magida et al. 2025), foreign direct investment (Rastogi & Gaikwad 2018), trade openness, financial development, infrastructure (Shuaibu & Popoola 2016), ICT (Voto & Ngepah 2024), government expenditure, and economic growth (Praise & George-Anokwuru 2018; Tsaurai 2020; Zulkifi et al. 2017). For this study, we restrict attention to five variables. The selection is driven by (1) consistent data availability for South Africa over the sample, (2) the time-series properties of the series after transformation, and (3) the need to avoid high multicollinearity in a small-sample ARDL setting (we screened candidates using VIFs). The five variables we discuss capture the main channels emphasised in the literature while yielding a parsimonious and stable specification for estimating the long-run effect of green growth on human capital.
Education index: The human capital development literature lacks a universally accepted proxy for measuring human capital development. The unobserved construct of human capital makes it a complex concept to measure (Friderichs & Correa 2022). Several studies investigating the impact of human capital have used different proxies, with most favouring the education variable as a measure of human capital (Awad, Halid & Yussof 2013; Barro & Lee 1993; Ngepah, Saba & Mabindisa et al. 2021). Friderichs and Correa (2022) observe that an education-based approach is the most used in the literature to measure human capital, given that it is a fundamental dimension of human capital. Education greatly affects people’s productive skills and capacity. ‘As a primary source of human capital, education makes the labour force more productive, improves welfare and fosters growth’ (Dissou, Didic & Yakautsava 2012, 2016). Some education-based human capital indices include the Penn World Table’s human capital index (hc), which is developed from the mean schooling years and assumes the Mincer returns (piecewise) and Borro-Lee’s mean schooling years at 5-year periods.
In this study, we use the United Nations Development Programme Education Index as a proxy for human capital development. We chose this EI because it is transparent to replicate and provides an annual series for the years under consideration. Specifically, in line with United Nations Development Programme’s (UNDP) methodology since 2010, we compute the arithmetic mean after converting both mean years of schooling (MYS) and expected years of schooling (EYS) to a [0,1] sub-index using fixed goalposts (Equation 1).
MYSIt=MYSt−015−0=min(MYSt15,1),EYSIt=EYSt−018−0=min(EYSt18,1)EducationIndex(EI)t=MYSIt+EYSIt2
After obtaining South Africa’s data on MYS and EYS, we apply UNDP’s caps (exactly as specified in the HDI technical notes (United Nations Development Programme [UNDP], 2022; 2024) (MYS ≤ 15; EYS ≤ 18) and then compute the EI values for South Africa from 1990 to 2021. In line with standard practice when estimating the long-run model, we apply a monotone log transformation. This transformation ensures EI is on a real-valued scale, stabilises variance and yields elasticities.
Green growth model 2: The major independent variable in our model is the green growth index. In carrying out this study, we obtain our green growth measure from Sarkodie, Owusu and Taden’s (2023) peer-reviewed Comprehensive Green Growth Indicators dataset, which covers 203 economies and periods from 1990 to 2021. This comprehensive dataset is advantageous for its aggregation of aggregation of 152 underlying (raw) green-growth indicators/variables into 17 categories across five green growth dimensions: the four OECD’s Green Growth measurement framework of environmental productivity (emissions, energy, non-energy materials, and multifactor productivity), natural asset base (water, land, forest, wildlife, temperature), quality of life (environmental risks, access), and policy responses (patents, research and development, Official development assistance (ODA), taxes, regulations), and an additional socio-economics (economics and social outcomes) dimension. The values were constructed using a generalized least squares (GLS)-based standardised weighted index (swindex), which relatively handles multicollinearity and missing values, and are normalised on [0,1]. This dataset provides 10 green growth indices. Specifically, we extract the Green Growth Model 2 for South Africa because the authors note that it is the optimal configuration with environmental quality making up 19.74%, natural asset base 18.87%, environmental productivity 16.18%, policy response 16.25% and the extended socio-economics dimension 28.95%. We apply a monotone log transformation to this data when estimating the long-run model.
Education expenditure: Government expenditure on education is a direct input that is expected to substantially increase school enrolment rates and education attainment levels. Lucas (1988) acknowledges that government investment in education boosts human capital development (Voto & Ngepah 2024). This is because such investment is expected to increase the quality of the education system in line with changing societal needs. Hajebi, Billing and Hajebi (2023) highlighted the role of government expenditure on education in enhancing knowledge acquisition in society, which consequently affects human capital development outcomes. Also, government expenditure on education determines the education landscape and influences access to quality education (Khatri, Bhatia & Maheshwari 2024). Particularly within South Africa and using gross enrolment ratio for secondary school as a proxy for human capital development, Luthuli (2017) revealed that government expenditure has a significant long-run effect on human capital in South Africa.
We obtain these data from World Development Indicators (WDI) and use its log transformation.
Financial development: The literature identifies financial development as a driver of human capital development among economies in the Global South (Vo, Tran & Nguyen 2021; Sehrawat & Giri 2017). Noting expenses relating to education and healthcare as some of the biggest household expenses after food, Calice (2019) specifies that financial development could enhance human capital development through both the demand and supply dimensions. A well-developed financial system comprising access to credit, a proper savings system and available insurance services could not only help people plan and manage healthcare and education expenses but could also enhance their ability to mitigate against unplanned shocks, hence, sustaining the demand for these human capital-enriching components. On the supply side, a well-developed financial system enhances both public and private providers of healthcare and education to ensure a consistent supply. This study utilises the World Bank’s financial development data for South Africa.
Trade openness: Existing literature suggests that trade openness is a crucial factor in human capital development, often outweighing other elements such as physical capital, government consumption, and economic growth in economic progress (Binder & Geordiadis 2011). Wirajing et al. (2023) explained that trade openness enhances human capital accumulation through exchanges between countries with labour-intensive and capital-intensive sectors, which is less common in closed economies. Trade openness is expected to raise skills and spread knowledge. Tsaurai (2020) also emphasised the significance of trade openness in influencing human capital development in Africa. Nonetheless, there is mixed evidence concerning its impact on human capital development. For estimation, we obtain data on trade as a percentage of GDP from the WDI and convert it to its log (trade% of GDP), lag 1 year. The unit-root test results are reported in Table 1.
Unit-root tests (augmented Dickey-Fuller).
Variable
Deterministic
ADF t-stat
p-value
Decision @5%
Order
EducationIndex_LOG
Constant
−4.1919
0.0027
Stationary in first difference
I (1)
GreenGrowth2_LOG
Constant
−4.2182
0.0025
Stationary in levels
I (0)
Education_Expenditure_LOG
Constant
−5.5088
0.0001
Stationary in first difference
I (1)
Trade_pct_of_GDP_LOGPROP
Constant
−6.1353
< 0.0000
Stationary in first difference
I (1)
FIN_DEV_LOG
Constant
−3.1739
0.0310
Stationary in levels
I (0)
Note: MacKinnon’s one-sided p-values are reported. The decision rule is based on a 5% significance level.
ADF, augmented Dickey-Fuller.
Results and discussionUnit-root pre-estimation tests (augmented Dickey-Fuller)
To verify the ARDL assumption that no regressor is integrated of order two, we applied the ADF test to the series used in the model. Tests were run with a constant and Schwarz information criterion (SIC)-selected lag length. The result shows a combination of level and first difference orders of integration. Green growth and financial development are stationary in levels, while the other three variables (EI, education expenditure and trade openness) are non-stationary in levels but become stationary after first differencing I [1]). This integration mix is precisely what the autoregressive distributed lag (ARDL) bounds-testing framework/approach designed for, and it justifies proceeding to co-integration testing and ECM estimation.
Co-integration tests
The results, presented in Table 2, confirm the presence of co-integration between green growth and human capital development. Notably, the F-statistics exceed the upper bound critical value at 5%, suggesting that the variables in the model (green growth, financial development, trade openness and education expenditure) have a long-term effect on human capital.
Bounds test.
Item
Green growth → HCI
Selected ARDL (AIC)
ARDL (3, 0, 0, 2, 3)
Case (deterministic)
Case 3: constant, no trend
Included observations
29
Number of levels regressors (k)
4
F-bounds statistics (H₀: no levels relationship)
4.787982
Upper critical values, I(1)
10% = 3.994; 5% = 4.774; 1% = 6.670
Decision (F-test)
Reject H₀ at 5%
t-bounds statistic (on CointEq [-1])
−5.470364
t-bounds criticals (Case 3)
1%: I(0) = -3.43; I(1) = -4.60
t-stat on ECM term (for reference)
−5.4704 (p ≈ 0.0001)
Overall Decision
Co-integration confirmed (t-bounds significant at 1% and F-bounds significant at 5%)
Note: The decision rule is based on a 5% significance level.
AIC, Akaike Information Criterion; ECM, error correction model; HCI, human capital index; ARDL, autoregressive distributed lag.
Long-run elasticities
The long-run coefficients reported in Table 3 show that green growth, financial development and education expenditure all have positive and significant long-run effects on human capital development. Only trade openness has an insignificant effect on human capital development in the long run. Education expenditure has the most effect in the long run, which is followed by financial development in magnitude. Green growth has the least effect on human capital development; it is nonetheless statistically significant. Given this study’s focus, we centre our discussion around the effect green growth has on human capital.
Long-run elasticities.
Variable (levels)
Model (coef/t/p)
ΔHCI for +10% in X
ln education expenditure
0.737735/9.6519/0.0000
0.0738
In green growth
0.088680/3.1435/0.0063
0.0089
ln trade openness
−0.123590/-0.7534/0.4622
Insignificant
ln financial development
0.736636/3.5781/0.0025
0.0737
Note: The decision rule is based on a 5% significance level.
HCI, human capital index.
Specifically, in the long run, a 10% increase in the green growth index is proportionally associated with about 0.0085% increase in the EI, implying that green growth boosts human capital development. This finding is theoretically expected as improved economic and environmental structures improve learning processes. Hence, in providing a coherent explanation for this observed phenomenon, we interpret five channels through which green growth corroborates and plausibly enhances human capital.
Firstly, in the long run, green growth reduces air pollution, improves health outcomes and cognitive abilities, thereby enhancing learning abilities. Specifically in relation to the EI, the improved health outcomes because of environmentally inclusive growth can boost school enrolment, attendance and completion of educational programmes. Empirically, Roth (2017) demonstrates that reductions in air pollution improve educational performance, while Chandra et al. (2022) find a link between lower emissions and reduced cognitive impairment. Both outcomes contribute directly to healthier, better-skilled populations.
Secondly, green growth implies budget cuts in fossil imports, freeing up resources for increased social investments, including education. This phenomenon is explained by public finance models where increased fiscal space raises expenditure on high-return public goods such as education and health. Higher government expenditure on education is a direct input on human capital outcome levels. Empirical evidence, as well as the results presented above, reveal the high return of education expenditure (Duflo 2001; Oreopoulos 2006; Psacharopoulos & Patrinos 2018). In line with this, external evidence from Pradiptyo et al. (2016) show reallocation towards social programmes, including education, from recovered subsidies reforms. Similarly, Cooke, Hague and Tiberti (2016) demonstrate evidence of recycling savings from fuel outlays towards social protection.
Thirdly, we interpret findings in terms of risk reduction. Extreme climate events persistently disrupt educational programmes in South Africa. For instance, the 2022 KZN floods necessitated the temporary closure of over 600 schools and affected over 200 000 learners within the province (KwaZulu-Natal Department of Education 2022). Also, the Western Cape storms in 2023–2024 led to precautionary closures across entire districts (Western Cape Education Department 2023). By emphasising the reduction of emissions, green growth reduces global warming and consequently, the likelihood and intensity of extreme climate events (Intergovernmental Panel on Climate Change [IPCC] 2021, 2023; Vousdoukas et al. 2018; Zhang et al. 2018). This is important to prevent the disruption of learning processes and negative effects on educational outcomes. Green growth curbs the persistence of these extreme climate events that reduce the EI.
Fourthly, we explain green growth’s positive effect on human capital by the higher remuneration feedback mechanism. Expanding green sectors require intensive skills that attract high financial compensation. The prospects of increased financial remuneration act as an incentive for increased investment in education. Romer’s quality-ladder framework denotes expansion in R&D, skill-biased demand, and learning by doing because of shifting investment. These raise returns to education investment, which feeds back into school enrolment and attendance. Fifthly, greener growth is often associated with the expansion of energy and transport infrastructure. Such expansions create a better environment for easier school commute and attendance. Greener grids increase access to digital learning, hence fostering human capital development.
Short-run dynamics
The short-run ECM results are presented in Table 4. In the short run, only trade openness and financial development have a significant effect on human capital development. The effects of green growth and government expenditure on capital are only captured in the long run. Both trade openness and financial development exert significant negative contemporaneous effects on human capital. The negative coefficients indicate the presence of short-run correction mechanisms.
Error correction model regression results.
Short-run variable
Green growth model (coef, p)
Δ EDUCATIONINDEX_LOG (−1)
0.682692, p = 0.0036
Δ EDUCATIONINDEX_LOG (−2)
−0.575118, p = 0.0050
Δ TRADE_PCT_OF_GDP_LOGPROP
−0.079860, p = 0.01563
Δ TRADE_PCT_OF_GDP_LOGPROP(−1)
−0.128140, p = 0.0293
Δ FIN_DEV_LOG
−0.019678, p = 0.6775
Δ FIN_DEV_LOG(−1)
−0.176677, p = 0.0244
Δ FIN_DEV_LOG(−2)
−0.108310, p = 0.1209
Note: The decision rule is based on a 5% significance level.
The adjustment dynamics, reported in Table 5, indicate that the system corrects around 69% disequilibrium per period, with half-life estimates of about 7 months.
Speed of adjustments.
Adjustment measure
Green growth model
ECM coefficient (φ)
−0.685667 (p = 0.0001)
Persistence (1+φ)
0.3143
Half-life (periods)
0.60
Note: The decision rule is based on a 5% significance level.
ECM, error correction model.
Post-estimation tests
In the specified model, residual diagnostics (Table 6) are satisfactory, confirming robustness. Residuals are free from serial correlation (Breusch–Godfrey LM test), exhibit homoskedasticity (Breusch-Pagan-Godfrey), and approximate normality in the model. The Durbin–Watson statistics (approximately 2.3) indicate little autocorrelation. At the same time, Ramsey RESET tests suggest no evidence of functional form misspecification. Cumulative sum (CUSUM) plot corroborates structural stability, while centred VIFs indicate no severe multicollinearity (maximum is the lag of the dependent variable ≈ 7.70, mean ≈ 4.81). Overall, these diagnostics validate the reliability of the estimated relationships and reinforce confidence in the robustness of the empirical findings.
Diagnostics summary for both models.
Test/Metric
Green growth index → HCI (stat; p)
Conclusion
R2 (ECM)/Adj. R2
R2 = 0.6592; Adj R2 = 0.5229
Moderate fit for annual macro data
Model F-statistic (ECM)
F = 4.8353; p < 0.0020
Regressors jointly significant
Durbin–Watson
2.3093
≈ 2 → little AR(1) evidence
Breusch–Godfrey LM (F)
F (3,13) = 1.2937; p = 0.3182
Fail to reject no serial correlation
Breusch–Godfrey LM (Obs*R2)
χ2(3) = 6.6674; p = 0.0833
Consistent with no autocorrelation at 5%
Correlogram/Ljung-Box (to lag 12)
Q (12) = 3.885; p = 0.985 (all p’s > 0.44)
No residual autocorrelation across lags
Breusch–Pagan–Godfrey (F)
F = 0.7392; p = 0.6980
Accept → homoskedasticity
Breusch–Pagan–Godfrey (Obs*R2)
χ2(12) = 10.3435; p = 0.5859
Consistent with homoscedastic errors
BPG scaled explained
χ2(12) = 2.8523; p = 0.9965
Consistent with homoscedastic errors
Jarque–Bera (normality)
JB = 0.2677; p = 0.8747
Residuals approximately normal
Ramsey RESET (squares of fitted)
t = 0.0457; F (1, 15) = 0.0021; p = 0.9642; LR = 0.0040; p = 0.9493
No functional form misspecification
CUSUM
Within 5% bands
Parameters stable
Multicollinearity (centred VIFs)
Max VIF = 7.70; Mean VIF = 4.81
Moderate collinearity (none > 8)
Note: These validate the robustness of the estimated results.
VIFs, variance-inflation factors; ECM, error correction model; HCI, human capital index; BPG, Breusch–Pagan–Godfrey; JB, Jarque–Bera; LM, Lagrange multiplier; CUSUM, cumulative sum test.
p-values > 0.05 indicate diagnostics are satisfactory for classical inference.
Conclusion
The dual and bidirectional relationship between human capital development and economic growth is well established in economic literature. However, the link between green growth and human capital development remains underexplored. Existing studies have tended to emphasise how human capital influences environmental performance while overlooking the reverse channel. This study contributes to closing this gap by employing the Green Growth Index as a direct measure of environmentally sustainable growth and examining its long-run relationship with human capital development proxied by the EI in South Africa.
Our results provide evidence that green growth exerts a positive long-run effect on human capital development. We explain this empirical finding through five channels. Firstly, green growth reduces carbon emissions that cause the propagation of certain diseases. Hence, enabling a healthier populace to acquire better skills. Secondly, green growth reduces fossil dependence and costs, freeing up budget space for investment in education. Thirdly, it reduces exposure to climate risks that disrupt learning processes. Fourthly, it encourages the acquisition of certain skills for better remuneration. Fifthly, the infrastructure investment associated with green growth fosters a more enabling environment for human capital development.
Our study further reveals that, as expected, education expenditure and financial development both have positive and significant effects on human capital development in South Africa. These findings agree with Baldacci et al. (2004) and Kiliç and Özcan (2018). Contrary to expectations, but in line with Tsaurai (2020) in Africa and Stylianou et al. (2025) in another Global South context, our study further reveals that trade openness has no significant effect on human capital development in South Africa. South Africa’s export market is dominated by capital-intensive industries and expands on resource-based exports, which could limit broad skills spillovers.
Taken together, these findings advance the literature by extending the growth–human capital nexus into the realm of sustainable growth. Results highlight the dual dividend of green growth – it sustains economic momentum while simultaneously strengthening the human capital base. The implication is clear: embedding sustainability into growth strategies is not only environmentally desirable but also socio-economically beneficial, reinforcing the case for sustainable development models. With coordinated enforcements by relevant stakeholders, South Africa can translate green growth into a tangible engine of human capital development.
Policy recommendations
In line with the aforementioned findings, stakeholders in the country must increase focus on green growth strategies not only as a means of environmental sustainability but also as a catalyst for human capital development. In doing this, the following recommendations are proposed to enhance human capital development through green growth:
Accelerate the transition to renewable energy prioritising schools and hospitals: The government, in collaboration with private sector stakeholders, should intensify efforts to shift towards renewable energy sources to reduce carbon emissions and boost the resilience of learning infrastructure. The load shedding hours that disrupt teaching and learning time are a threat to the development of South Africa’s future human capital. Stakeholders should increase their efforts to rapidly build more clean power and storage, prioritising educational institutions. Similar efforts in Africa include the Energising Education Programme Phase II in Nigeria, which recently witnessed the installation of a 2.5 MW captive solar hybrid power at the National Defence Academy (Rural Electrification Agency 2025) and the Kenya Off-Grid Solar Access Project for Underserved Counties (World Bank 2024).
In South Africa, beyond the Western Cape – where a Stellenbosch University pilot replaced conventional bulbs with energy-saving light-emitting diodes (LEDs) and several schools have applied to install solar power – stakeholders in other provinces can partner with relevant organisations to expand green power in local schools. For example, schools in KwaZulu-Natal could collaborate with SOLA Group; in Gauteng with SolarAfrica or Solareff; and in the Eastern Cape with Genergy. Also, priority must be given to educational facilities in rural settings. UNOPS and IGC (2022) reveal that significantly more students attended the national primary school exam in communities where Sierra Leone’s Rural Renewable Energy Project was undertaken. Concurrent locally led nationwide efforts would accelerate the transition to clean energy infrastructure, thereby protecting learning hours, reducing carbon emissions and ensuring green growth boosts human capital development while protecting the environment.
Gradually phase out fossil dependence and earmark a portion for education from savings: South Africa currently depends heavily on fossil fuels. Besides its capital investment, the country often incurs recurrent costs. According to the International Institute for Sustainable Development (2024), the country spent about 118 billion dollars in fossil subsidies during FY2022/2023, some of which could have been redirected to education. Considering the long-term nature and high initial investment costs of the renewable energy transition, the South African government should employ a gradual approach in phasing out fossil fuel dependence. Upfront investments in renewables could be financed through green bonds and concessional climate loans. An example of the success of this approach is the four-times oversubscription of green bonds in Cape Town (City of Cape Town 2017). Such renewable energy investment causes a gradual reduction in fossil dependence, freeing up budget space. The government must ensure a portion of such savings is earmarked for investment in education. Such an approach increases the transition to renewable energy and expands education expenditure, ensuring that human capital is not compromised.
Boost financial access to education prioritising the acquisition of skills and knowledge for the clean energy sector: Like South Africa’s ongoing initiative to enable access to finance for higher education (National Student Financial Aid Scheme [NSFAS]), the government could create an initiative solely to finance those willing to acquire green skills and green knowledge. In addition, a financial structure that prioritises access to loans by educational institutions aligning with green practices should be encouraged.
Directions for future research
Beyond investigating relationships and associations, future research should consider causation identification on the green growth–human capital nexus. These designs should include panel difference-in-differences designs to evaluate the effects of policy shocks. Also, further investigation should assess the mediation of the fiscal space channel between green growth and educational outcomes to specify the payoff. Results from these would provide policymakers with valuable insights for aligning sustainability goals with human capital advancement.
AcknowledgementsCompeting interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Mary T. Adebayo: Conceptualisation, Methodology, Formal analysis, Investigation. Kehinde D. Ilesanmi: Conceptualisation, Writing – original draft, Writing – review & editing.
Ethical considerations
This article followed all ethical standards for research without direct contact with human or animal subjects.
Data availability
The authors confirm that the data supporting the findings of this study are available within the article.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.
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How to cite this article: Adebayo, M.T. & Ilesanmi, K.D., 2026, ‘The human capital – Green growth nexus in South Africa’, Journal of Economic and Financial Sciences 19(1), a1061. https://doi.org/10.4102/jef.v19i1.1061