1 Introduction

Sustainability mandates that products and services be produced to satisfy current needs without compromising the ability to satisfy those of future generations [35]. Economic development and climate change are cyclically related; development activities shape greenhouse gas (GHG) emissions and adaptation capacity, while climate change consequences impact development results [13]. Climate change impacts the natural environment, slows economic growth, and threatens societal well-being [34]. Therefore, analyzing the environmental implications of economic growth is crucial for designing effective environmental management policies in developing regions [38].

Africa has made notable strides toward achieving the Sustainable Development Goals (SDGs), yet environmental degradation remains a persistent issue. Despite averaging 5% economic growth over the past decade, the continent continues to suffer from poor urban air quality and ecosystem degradation [56]. Natural resource extraction is a key driver of environmental damage, as Africa generates substantial revenue from resource exports [27]. According to UNEP, over 50% of the continent’s eco-regions have experienced land degradation, largely due to agriculture and urbanization. While these challenges are evident across Africa, they are particularly acute in Sub-Saharan Africa (SSA), where rapid population growth, limited infrastructure, and lower adaptive capacity amplify the effects of environmental degradation [19]. As such, narrowing the analytical lens to SSA is both timely and essential, given the region’s unique socio-economic dynamics and pressing sustainability concerns.

Research on the environmental effect of economic growth frequently relies on carbon dioxide (CO₂) emissions as an indicator for environmental quality [57]. However, this approach may offer an incomplete picture, as it neglects other forms of environmental harm, such as water and soil degradation. Pollutants have diversified, moving from nitrogen oxides and sulfur to solid waste and CO₂ [51]. The ecological footprint (EFP) offers a more holistic metric, encompassing multiple environmental dimensions. It evaluates the demand placed on ecosystems by human activity and is widely recognized for its comprehensiveness and reliability Danish et al. [21]; Charfeddine and Mrabet [17].

The integration of human capital into the Environmental Kuznets Curve (EKC) framework significantly enhances its explanatory power, particularly in the context of SSA. Education and skill development empower individuals to engage in environmentally responsible behavior, adopt clean technologies, and support sustainability policies. A well-educated population is more aware of the health and environmentalimpacts of pollution, more inclined to adopt cleaner energy solutions, and better equipped to use resources efficiently [36]. In this way, human capital fosters innovation, enhances conservation efforts, and underwrites to the effective application of environmental regulations [15]; Wang et al. [54]). These factors collectively help shift the EKC downward or forward, promoting earlier and more sustainable development outcomes.

Urbanization, while often associated with increased energy consumption and emissions, can also drive environmental sustainability if managed properly. Rapid urban growth, when supported by adequate infrastructure and planning, can facilitate access to cleaner technologies, efficient public transportation, and improved waste management systems. Urban economies of scale further contribute to reducing per capita emissions. However, the extent to which urbanization yields environmental benefits depends largely on the quality of governance and the educational attainment of the population [22, 31]. In this regard, human capital plays a complementary role by enhancing the capacity of urban centers to adopt and manage sustainable practices.

Another key element is renewable energy consumption, which provides a pathway for reducing environmental stress while maintaining economic growth. By switching from fossil fuels to renewable energy, nations can separate environmental degradation from economic growth. As income levels rise and access to clean technologies improves, the environmental impact of growth may peak earlier and at lower levels. Here again, human capital is essential as it equips the workforce with the skills and knowledge needed to adopt, manage, and innovate within renewable energy systems [48]; Al-Mulali et al. 2015. Together, these interconnected factors of human capital, urbanization, and renewable energy provide a more thorough comprehension of SSA’s environmental sustainability. By integrating them into the EKC framework, this study captures the dynamic interplay between social development and environmental outcomes, offering more actionable insights for policy and planning. Biocapacity plays a critical role in this extended model as it represents the ecological capacity of a region to regenerate resources and absorb wastes, including carbon emissions. It acts as a form of environmental resilience, mitigating the adverse effects of economic activities. Higher levels of biocapacity can absorb more environmental stress, which may reduce the intensity of degradation at any given level of income. This buffering effect may lead to a flatter EKC curve or lower the income threshold at which environmental quality begins to improve [14, 53]. To better understand the joint influence of social and structural factors, this study introduces an interplay between urbanization and human capital. This allows for an examination of whether better-educated urban populations are more effective at adopting sustainable practices. A positive interaction would suggest that human capital strengthens the environmental benefits of urbanization, accelerating the shift toward cleaner growth paths [28].

The expanded EKC model proposed here incorporates critical contextual factors like renewable energy, human capital, biocapacity, and urbanization that offer a more accurate and policy-relevant understanding of environmental sustainability in SSA. By exploring these interdependencies, the model provides deeper insights into how economic development can be harmonized with long-term ecological well-being. Despite extensive research on the EKC theory, the findings are still equivocal and contingent on methodological design and variable selection [50]. Furthermore, few studies have used the EFP metric to analyze the combined effects of urbanization, biocapacity, human capital, and renewable energy on ecological sustainability, particularly in SSA. To close this gap, this study aimed to examine the effects of human capital, renewable energy, urbanization, biocapacity, and foreign direct investment on environmental sustainability using the robust Driscoll and Kraay standard errors static panel data estimator. The study’s remaining sections are structured as follows: The pertinent literature is reviewed in Sect. 2, the methodology is described in Sect. 3, the empirical results are presented in Sect. 4, and important policy recommendations are made in Sect. 5.

2 Review of literature

2.1 Theoretical review

The existing literature on environmental quality as determined by ecological footprint and its determinants is reviewed in this section. Numerous studies have examined the connection between the energy, environment, and growth nexus in recent years, which has sparked a contentious debate among environmental economists and policymakers. This nexus is typically modelled using the Environmental Kuznets Curve (EKC). In order to examine the negative relationship between per capita income and income inequality, Kuznets [37] first suggested the EKC. After then, it was brought back to examine the relationship between economic growth and environmental quality [29]. According to the EKC theory, environmental degradation rises in line with increases in national income throughout the early stages of economic development. Following that, a nation reaches a point in its growth where an increase in revenue does not coincide with a rise in environmental contamination. This indicates that, in proportion to per capita income, environmental degradation (emissions) follows an inverted U-shaped pattern. However, during the past three decades, numerous studies on the EKC have been conducted in various countries employing time series and panel data analysis [21, 41, 43, 47]. The ecological footprint is still regarded as a more complete indicator of environmental quality than CO2 emissions, which are commonly used in these studies as an indicator of environmental pollution, even though it has recently emerged as a new indicator of environmental degradation [1, 10, 32, 46, 49]. Chen et al. [18]; Danish et al. [20]; Wasif et al. [4, 8, 38, 55]. Numerous research have confirmed the EKC theory regarding the relationship between income and the ecological footprint in countries with varying income levels [9, 12]. ; Charfeddine [16]; Destek and Sarkodie [6, 23], Nonetheless, some researchers have contested the existence of the EKC using the ecological footprint as a gauge of environmental sustainability. Aydin et al. [11, 26] ; Hussain et al. [33].

2.2 Empirical review

Hassan et al. [30] examined the connection between biocapacity, ecological footprint, economic growth, and human capital using the autoregressive distributive lag (ARDL) econometric approach with a structural break extending from 1971 to 2014. The authors discovered that whereas economic expansion positively corresponds with ecological footprint, human capital has no discernible effect on it, which leads to environmental degradation. Ahmed [4], using a similar study framework, looked at how human capital affected India’s ecological footprint between 1971 and 2014 and discovered that it had a negative influence on environmental quality. Regarding causation, the author pointed out that Granger’s human capital creates the ecological footprint without any kind of feedback. Their research also shows that developing human capital can help lessen the ecological footprint. In a related study, Nathaniel and Solomon [40] used yearly data from 1970 to 2016 to examine the relationship between ecological footprint (EF), energy use, and human capital in South Africa. Using a similar methodology, these authors discovered that human capital reduces environmental degradation, whereas energy usage increases the EF. Within the same research framework, Human capital is also useful in reducing ecological footprint, according to Wasif et al. [55]. Similarly, Ahmed et al. [5] found that urbanization increases the ecological footprint while human capital decreases it using a more sophisticated panel data estimator for G7 nations from 1971 to 2014. Whileadjusting for economic development, Ahmed et al. [5] investigated the effects of urbanization, human capital, and the abundance of natural resources on China’s ecological footprint in a different research paradigm. The study examined the cointegration and causal links between the variables using the bootstrap causality technique and the well-known Bayer and Hack cointegration test. Their results showed that the factors have a long-term link and that human capital contributes to reducing environmental degradation. A positive trend may be indicated by the observed inverse link between ecological footprint and human capital, indicating that policymakers should continue to support the development of human capital. According to a recent study, Chen et al. [18] investigated the relationships among ecological footprint, urbanization, and human capital using cross-country data from 110 economies between 1990 and 2016. The ecological footprint is first increased by human capital and then subsequently decreased, according to the authors’ findings. According to their findings, human capital tends to reduce the ecological footprint in high-income countries while increasing it in low-income and densely populated ones. Zahid et al. [58] examined the connections among economic complexity, human capital, commerce, ecological footprint, export quality, urbanization, economic growth, and the generation of renewable energy in the top ten economically complex countries. Using data from 1980 to 2017, the researchers employed system-GMM long-run estimators, dynamic ordinary least squares (DOLS), and completely modified ordinary least squares (FMOLS). According to their long-term projections, urbanization, trade, export quality, economic complexity, and economic growth all raise the ecological footprint, whilst human capital and the production of renewable energy help to lessen it. The authors came to the conclusion that both industrialized and emerging nations’ economic complexity, export quality, and environmental quality would all be improved by investing in renewable energy and improving the effective use of human capital. For the case of SSA, this negative association may arise because improved human capital, achieved through better education, health, and skills, empowers individuals to adopt environmentally responsible behaviors. It also promotes the use of clean technologies and supports sustainable practices. A well-educated population is typically more aware of environmental issues and more likely to engage in actions that reduce ecological degradation (Omri et al.[44]), .

Nathaniel et al. [39] examined the connections between renewable energy, urbanization, economic growth, trade openness, and the ecological footprint in six CIVETS countries using the augmented mean group estimator, panel cointegration, and causality tests. According to their findings, commerce has no appreciable negative environmental effects, but renewable energy improves environmental quality. Furthermore, according to the causation test, urbanization exacerbates environmental deterioration. In a related study, Nathaniel and Khan [38] examined how economic expansion, urbanization, and the use of both renewable and non-renewable energy affected the ecological footprint, a more accurate environmental indicator between 1990 and 2016. The results demonstrate that nonrenewable energy, commerce, and economic expansion all have a major impact on environmental deterioration in ASEAN nations. Renewable energy (REN) does, nevertheless, lessen environmental degradation, albeit not statistically significantly. Danish, Ulucak, and Khan [21] assessed the ecological footprint, natural resource rent, urbanization, renewable energy, and real income of the BRICS economies. To obtain precise estimates for the years 1992–2016, they used panel data estimators, such as the fully modified ordinary least squares (FMOLS) and the dynamic ordinary least squares (DOLS) long-run estimators. Their empirical findings show that natural resource rent, urbanization, and renewable energy all reduce ecological footprints and enhance environmental quality. Wang and Dong [54] investigated how urbanization, economic expansion, and the use of renewable and non-renewable energy contributed to environmental degradation in 14 SSA between 1990 and 2014. The results demonstrate that non-renewable energy use, urbanization, and economic expansion have positive effects on the environment, while the use of renewable energy has a negative one. Furthermore, urbanization is a major factor in environmental deterioration and calls for prompt governmental action in SSA nations. Quito et al. [47] investigated how urbanization and economic expansion affected environmental quality between 1995 and 2017. The authors used quantile regressions and Westerlund cointegration to find that, globally, economic development increases environmental degradation in all quantiles whereas urbanization and renewable energy decrease it in all quantiles. The growth of cleaner and more effective renewable energy sources, such solar and wind, may be the cause of the negative correlation between SSA’s ecological footprint and renewable energy, as previously noted. According to Alam et al. [7], this change results in a smaller ecological footprint as energy systems become less carbon-intensive and more sustainable.

Despite the well-known fact that environmental distortions are frequently caused by humans, the review found that human capital is rarely taken into account in the energy-environment growth nexus. Although urbanization has the ability to either increase or decrease the Ecological Footprint (EFP), its effects on the environment are still unknown. Most significantly, variations in technique, the areas and time periods examined, and the variables chosen for the models are the main causes of disparities in results and findings. To the best of our knowledge, no known research has examined the combined impacts of biocapacity, urbanization, renewable energy, human capital, and economic growth on the ecological footprint of SSA (SSA). Hence, this paper presents the following first, second, and third hypotheses: Hypothesis 1: Human capital will lead to a significant decrease in the ecological footprint in the long run. Hypothesis 2: Renewable energy will lessen environmental impact in the long run. Hypothesis 3: Urbanization contributes to an increasing ecological footprint in the long run.

3 Methodology

3.1 Variable description and model specification

To examine how urbanization and human capital interact in the energy-environment-growth nexus between 2000 and 2018, this study considers nations in SSA. The availability and completeness of pertinent data for the major variables were the primary factors in the selection of the SSA countries sample. Thus, 26 countries are included by this study, for a total of (26 * 18 =) 468. The typical panel data approach will be employed in this investigation due to the fact that the individual dimension (N = 26) is larger than the time dimension (T = 18). Human capital, biocapacity, renewable energy, and the ecological footprint, and urbanization are among the elements under investigation. The study makes use of the most complete human capital index, which is taken from the Penn World Table (PWT 100) and contains data on years of education, returns to education, and the labor market. A variety of elements of environmental deterioration are included in the Ecological Footprint (EFP), including farmland, carbon footprint, ocean, forest land, built-up land, and grazing land. In the African context, many studies have typically overlooked the use of the Ecological Footprint (EFP) as a measure of environmental sustainability, which may lead to biased findings [8].

The term “biocapacity” describes an area’s biological production, which includes its capacity to absorb wastes, especially carbon emissions, and regenerate renewable resources. It represents the ecological resources available to satisfy human needs and is commonly expressed in global hectares per person. In the context of our ecological footprint model, biocapacity is included to capture the natural environmental capacity that mitigates the impact of human activities. Countries with higher biocapacity are better positioned to absorb environmental stress and maintain sustainability, whereas those with lower biocapacity are more vulnerable to ecological overshoot. By incorporating biocapacity, we aim to account for the heterogeneity in SSA natural endowments and ecological resilience, which is essential for a more accurate and meaningful interpretation of the drivers of ecological degradation. This inclusion is consistent with prior empirical studies that emphasize the role of natural capital in shaping environmental outcomes. In this study FDI is used as a control variable. The rationale for its inclusion is grounded in both theoretical and empirical literature, which highlights their significant influence on environmental outcomes and economic development. Specifically, FDI is widely recognized as a key driver of industrial activity, technology transfer, and capital inflows, all of which may have both positive and negative environmental implications depending on the host country’s regulatory framework and absorptive capacity. Including FDI as a control variable allows us to isolate its potential effect on environmental degradation or improvement, independent of the coreexplanatory variables of interest. The Table 1 provides further details on the variables included in this study.

Table 1 Description of variables.
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Consistent with the studies of Hassan et al. [30], this study models ecological footprint (EFP) as a function of GDP per capita (GR), its square (GR²), human capital (HC), biocapacity (BC), urbanization (UB), foreign direct investment (FDI), and the interaction between human capital and urbanization (HC*UB) to assess their combined impact on environmental degradation.

The baseline model is given by;

$$\:{\text{E}\text{F}\text{P}}_{\text{i}\text{t}}={{\upalpha\:}}_{1}{\text{R}\text{E}}_{\text{i}\text{t}}+{{\upalpha\:}}_{2}{\text{H}\text{C}}_{\text{i}\text{t}}+{{\upalpha\:}}_{3}{\text{G}\text{R}}_{\text{i}\text{t}}+{{\upalpha\:}}_{4}{\text{U}\text{B}}_{\text{i}\text{t}}+{{\upalpha\:}}_{5}{\text{B}\text{C}}_{\text{i}\text{t}}+{{\upalpha\:}}_{6}{\text{H}\text{C}\text{*}\text{U}\text{B}}_{\text{i}\text{t}}+{{\upepsilon\:}}_{\text{i}\text{t}}$$
(1)

While the extended equation of the EKC is specified as (Fig. 1):

$$\:{\text{E}\text{F}\text{P}}_{\text{i}\text{t}}={{\upbeta\:}}_{1}{\text{R}\text{E}}_{\text{i}\text{t}}+{{\upbeta\:}}_{2}{\text{H}\text{C}}_{\text{i}\text{t}}+{{\upbeta\:}}_{3}{\text{G}\text{R}}_{\text{i}\text{t}}+{{{\upbeta\:}}_{4}{\text{U}\text{B}}_{\text{i}\text{t}}+{{\upalpha\:}}_{5}{\text{B}\text{C}}_{\text{i}\text{t}}+{{\upbeta\:}}_{6}{\text{H}\text{C}\text{*}\text{U}\text{B}}_{\text{i}\text{t}}+{\upbeta\:}}_{7}{{GR}^{2}}_{\text{i}\text{t}}+{{\upbeta\:}}_{8}{\text{F}\text{D}\text{I}}_{\text{i}\text{t}}+{{\upmu\:}}_{\text{i}\text{t}}$$
(2)

While I = 1,2, 3…, N represents individual countries, and t = 1,2, 3…, T represents the time dimension. The coefficients\(\:\:{\alpha\:}_{1}\),\(\:{\beta\:}_{1}\), \(\:{\alpha\:}_{2}\),\(\:{\:\beta\:}_{2},{{\alpha\:}_{5},\beta\:}_{5},{{\upalpha\:}}_{6,}\) \(\:{{\upbeta\:}}_{6}\) and \(\:{{\upbeta\:}}_{7}\) are expected to be negative while \(\:{\alpha\:}_{3},{\:\beta\:}_{3},\:{\alpha\:}_{4},{\:\beta\:}_{4}\:\:and\:{{\upbeta\:}}_{8}\) positive. As depicted in Figs. 2 and 3 as shown in the appendix, South Africa dominates the other African countries in terms of the ecological footprint. Similarly, South Africa, Zimbabwe, Zambia, and Ghana top the list in human capital development while Togo, the Republic of Congo, and Rwanda are the most urbanized countries in SSA. The EKC theory would be supported by the positive or negative coefficients of β3 and β4, which show that environmental degradation is initially caused by economic expansion but eventually. The positive or negative coefficients of β3 and β4 would suggest that economic growth first causes environmental degradation before eventually contributing to environmental improvement in later stages, which would support the existence of the EKC hypothesis.

Fig. 1
figure 1

Ecological Footprint in Different Parts of the World (1961–2018)

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3.2 Estimation technique

There are three basic types of traditional panel data models. Pooled Ordinary Least Squares (POLS) is the first method. The panel data is used as a sample and is shown as follows because the POLS has a tendency to overlook the impacts of time and individual dimensions.:

$$\:{\text{Y}}_{\text{i}\text{t}}={\upalpha\:}+{\upbeta\:}{\text{X}}_{\text{i}\text{t}}+{{\upepsilon\:}}_{\text{i}\text{t}}$$
(4)

in which β is the slope parameters of the common effects, α is the constant term of the common effects, and\(\:{{\upepsilon\:}}_{\text{i}\text{t}}\sim\text{N}(0,{{\upsigma\:}}_{{\upepsilon\:}}^{2})\) as a mean of 0 and is normally distributed. Generally speaking, it is difficult to assume that a model using panel data has no individual effects.Consequently, two methods are used to examine the individual influence. The first is the Fixed Effects Model (FEM). Individual effects are supposed to correlate with the independent variables in the panel data model according to the Fixed Effects Model (FEM). Consequently, the FEM looks like this:

$$\:{\text{Y}}_{\text{i}\text{t}}={{\upalpha\:}}_{\text{i}}+{\upbeta\:}{\text{X}}_{\text{i}\text{t}}+{{\upepsilon\:}}_{\text{i}\text{t}}$$
(5)

Unlike in the pooled model, where \(\:{{\upalpha\:}}_{\text{i}}\) is a constant term, dummy variables are employed in the FEM to demonstrate the individual impacts. As a result, the Fixed Effects Model (FEM) estimate is carried out utilizing the method known as Least Squares Dummy Variables (LSDV). Using a lot of dummy variables in the model might lead to a lot of problems, especially with degrees of freedom. When there are uncorrelated effects between people and independent causes, the Random Effects Model (REM) is applied. This is illustrated below:

$$\:{\text{Y}}_{\text{i}\text{t}}={{\upalpha\:}}_{\text{i}}+{\upbeta\:}{\text{X}}_{\text{i}\text{t}}+{{\upepsilon\:}}_{\text{i}\text{t}}$$
(6)

Where \(\:{{\upalpha\:}}_{\text{i}}={\upalpha\:}+{\text{u}}_{\text{i}}\) are regarded as a component of the random term. The REM can then be rewritten as follows:

$$\:{\text{Y}}_{\text{i}\text{t}}={\upalpha\:}+{\upbeta\:}{\text{X}}_{\text{i}\text{t}}+{\text{w}}_{\text{i}\text{t}}$$
(7)
$$\:\text{W}\text{h}\text{e}\text{r}\text{e}{\:\text{w}}_{\text{i}\text{t}}={\text{u}}_{\text{i}}+{{\upepsilon\:}}_{\text{i}\text{t}},\:\text{a}\text{n}\text{d}{\:{\upepsilon\:}}_{\text{i}\text{t}}\sim\text{N}(0,{{\upsigma\:}}_{{\upepsilon\:}}^{2}),\:{\text{u}}_{\text{i}}\sim\text{N}(0,{{\upsigma\:}}_{\text{u}}^{2})\:\text{a}\text{n}\text{d}\:{\text{w}}_{\text{i}\text{t}}\sim\text{N}(0,{{\upsigma\:}}_{{\upepsilon\:}}^{2}+{{\upsigma\:}}_{\text{u}}^{2})$$

.

The POL, FEM, and REM models are the most critical stages in determining the proper model. Despite the use of non-formal procedures for this goal, revealing the three-stage process would be more accurate. The following steps are performed to find the right model structure: In order to distinguish between Fixed Effects Models (FEM) and Pooled OLS (PLOS), we first use the Chow F-homogeneity test. It uses the Fixed Effects Model (FEM) if the null hypothesis is rejected. However, if the null hypothesis is not disproved, the Pooled Ordinary Least Squares (POL) model ought to be applied. In the second step, we employ LM-type tests (which are comparable to the Honda LM test employed in another work) to select between the Random Effects Model (REM) and the Pooled Model (POL). When the null hypothesis is disproved, Random Effects Model (REM) is thought to be appropriate. If the null hypothesis is not rejected, the Pooled Ordinary Least Squares (POLS) model ought to be used. Lastly, the relative appropriateness of the Fixed Effects Model (FEM) and the Random Effects Model (REM) is evaluated using the Hausman test. In case the null hypothesis is refuted, the Fixed Effects Model (FEM) is considered accurate. However, if the null hypothesis is not refuted, the Random Effects Model (REM) will be the optimal choice.

4 Results and discussion

This section is opened with the descriptive statistics of the variables shown in Table 2. Except for the interaction term (UB*HC), renewable energy has the highest average. This indicates that the energy policy in SSA is still in place. Human capital is the least variable, followed by biocapacity. With the exception of human capital and biocapacity, it is equally noted that every variable has a negative relationship with the EFP.

Table 2 Descriptive statistics.
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Specification tests are required to ascertain how urbanization, economic expansion, human capital, and renewable energy affect the ecological footprint. The results of the specification tests for SSA nations are shown in Table 3. At the 1% level, the Chow F test in Table 1 is statistically significant. This suggests that Pooled Ordinary Least Squares (POLS) is not as good as the Fixed Effects Model (FEM). The Hausman Chi-Square test is used in the last testing stage to compare the Fixed Effects Model (FEM) and the Random Effects Model (REM). At the 1% level, the computed chi-square value is statistically significant. The rejection of the null hypothesis indicates that the FEM is the most suitable. Stated differently, the Hausman’s test suggests that the FEM is superior to the REM.

Table 3 Specification tests results
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Source: Authors’ calculation.

After determining the appropriate model structure, we conducted cross-sectional dependence (CSD) tests, as presented in Table 3. The findings offer enough proof that CSD exists among SSA nations, most likely as a result of their similar environmental policy philosophies and intertwined economic activity. Given this interdependence, it would be inappropriate to ignore the potential presence of CSD in our panel dataset. While the standard Fixed Effects Model (FEM) provides consistent coefficient estimates, its standard errors may be biased and inefficient in the presence of econometric issues such as heteroskedasticity, autocorrelation, and cross-sectional dependence. To address these concerns and enhance the reliability of our inference, we re-estimated the model using Driscoll-Kraay standard errors. Driscoll-Kraay standard errors are particularly suitable for macro-panel data, as they provide robust estimates that are consistent even when the error structure exhibits heteroskedasticity (non-constant variance), autocorrelation (serial correlation within panels), and cross-sectional dependence (contemporaneous correlation across panels). This technique adjusts the estimated standard errors to account for these issues without altering the coefficient estimates themselves. By applying this method, the results presented in Table 4 offer more robust and reliable inferences, ensuring that our statistical conclusions remain valid even under less-than-ideal econometric conditions. This reinforces the credibility of our findings regarding the key determinants of environmental performance in SSA countries.

Table 4 Panel Estimation results with robust standard errors (Model 1).
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Table 5 estimated results demonstrate that the indications of the estimated parameters align with the econometric literature apart from human capital. Additionally, the sum of squared residuals and regression standard errors are both quite low. The estimated model is globally significant, according to the computed F-statistics, indicating that it fits the data quite well. The results showed that human capital, economic growth, and urbanization are positively correlated with ecological footprint while renewable energy, biocapacity and the interactive term (UB*HC) relate negatively with the ecological footprint. This signifies that human capital, economic growth, and urbanization raise environmental degradation whereas renewable energy, biocapacity and the interactive term (UB*HC) tend to reduce it.

Table 5 Panel Estimation results with robust standard errors (Model 2)
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Several past study have validated the Environmental Kuznets Curve hypothesis between the ecological footprint and economic growth [6, 9, 12]; Charfeddine [16], ; Destek and Sarkodie [23], . By looking into each variable in detail as shown in Eqs. 1 and 2, the following conclusions were reached. The coefficient of both GR and \(\:{GR}^{2}\) (measured by GDP per capita) is found to be positively related to ecological footprint, indicating that economic growth emerges with environmental degradation. This study indicates that the inverted EKC theory does not exist in SSA nations. This result is not unique to Africa, as numerous investigations, including those by Aydin et al. [11, 26, 33], who have failed to show empirical evidence for the EKC. Destek and Sinha [24] also noted that the inverted U-shaped Environmental Kuznets Curve hypothesis is not applicable to OECD countries. This may be due to the fact that these nations are still in their infancy and the government is more concerned with reducing poverty, the EKC might be deemed invalid. The form of the EKC may also be influenced by a lack of environmentalawareness. Early economic growth is characterized by high rates of poverty, ineffective tax collection, and environmental consciousness (which is income elastic). relatively low, according to Panayotou [45]. The government spends less money on environmental conservation in favor of reducing poverty. This clarifies the reasons behind the low demand for a high-quality environment in third-world nations. Additionally, Dinda [25] found that only local air contaminants such as sulphur, standard particle matter, mono-nitrogen oxide, and carbon monoxide cause substantial EKCs. Meanwhile, as wealth increases, so do global environmental indicators like CO2, municipal waste, and ecological footprint.

Contrary to our initial expectation in the methodology section, this research shows that, in SSA (SSA), human capital and ecological footprint have a positive and statistically significant association), suggesting that, in the current context, human capital is contributing to increased environmental degradation rather than mitigating it. This unexpected result can be understood by examining the structural and developmental context of SSA countries. Unlike more advanced economies where human capital is often associated with environmental awareness, green innovation, and the adoption of renewable energy (as noted in studies such as Chen et al. [18], in SSA, the accumulation of human capital appears to be channeled toward economic activities that are resource- and energy-intensive. This includes expansion in sectors such as construction, transport, and informal manufacturing, which heavily rely on fossil fuels and are often less regulated in terms of environmental standards. Furthermore, as highlighted by Zia et al. [59], the development of human capital without parallel investment in green infrastructure or environmental governance can exacerbate environmental degradation, especially in economies dependent on non-renewable resources. In SSA, the mismatch between rising human capital and the limited availability of green technologies, environmental policy enforcement, and clean energy alternatives likely contributes to this unintended effect. Thus, while previous studies in more developed or environmentally progressive economies have found human capital to be negatively associated with environmental degradation (e.g., through increased awareness, adoption of sustainable practices, and innovation), our findings suggest that in the context of SSA, human capital currently amplifies environmental pressures, largely due to structural limitations and developmental priorities.

The finding also shows that urbanization correlates positively to ecological footprint which implies that this construct raises environmental degradation. Put another way, urbanization has a significant role in environmental deterioration in SSA, much like economic growth does. Given that most African nations are seeing an increase in their urban populations, the long-term environmental repercussions of urbanization are consistently alarming. As a result, policies relating to urban planning that might assist in reducing urban oddity and boost renewable energy consumption should be prioritized if environmental sustainability in SSA is to be realized. This outcome is supported by the study of Wang and Dong [54]. Interestingly, interacting human capital with urbanization reduces the level of ecological footprint, pinpointing that countries with a bigger ecological footprint must give human capital development top priority in order to lessen the adverse environmental effects of urbanisation. This finding’s economic implication is that human capital is crucial to urban sustainability as its interaction with urbanization (UB*HC) help to reduce EFP in SSA. This finding is similar to research conducted by Nathaniel [42], . On the other hand, there is a negative relationship between renewable energy and ecological footprint. especially woody biomass. This demonstrates that using renewable energy reduces environmental damage. This result supports the findings of Nathaniel et al. [39, 52, 58], who found that renewable energy plays a significant role in mitigating environmental degradation. Given the large proportion of woody biomass, this finding probably indicates poverty in SSA. This research implies that if these nations make investments in and promote the use of renewable energy, environmental quality may improve. This outcome, however, contradicts a prior study by Mlaskawam (2022) that examined the relationship between poverty and renewable energy in the context of sustainable development in the European Union and discovered that poverty rates are increased by renewable energy.

Biocapacity was included to capture the ecological regenerative capacity of each country to absorb environmental degradation and regenerate renewable resources. The results revealed that biocapacity is not statistically significant in impacting the environment as supported by the recent work of Nathaniel and Adeleye [42]. Nevertheless, the statistical insignificance of biocapacity in our results can be attributed to several contextual and structural factors specific to the SSA region. Although many SSA countries possess considerable ecological resources, these are often underutilized or poorly managed due to weak institutional frameworks, limited technological capacity, and inadequate environmental governance. Moreover, ecological degradation in the region is more immediately driven by economic and demographic pressures such as rapid population growth, urbanization, and foreign investment which tend to exert stronger short-term environmental impacts than the mitigating influence of natural endowments. While biocapacity serves as a crucial long-term buffer against environmental stress, its effects may not be statistically evident in short- to medium-term panel analyses, particularly when overshadowed by more dominant anthropogenic factors.

The empirical results show that although the coefficient for foreign direct investment (FDI) is positive, it is statistically insignificant, despite the study’s initial hypothesis that FDI would significantly improve environmental degradation, which is consistent with the pollution haven hypothesis. This implies that foreign direct investment (FDI) has no discernible impact on the ecological footprint in SSA (SSA). One possible explanation for this outcome lies in the composition and nature of FDI inflows into SSA. Much of the region’s FDI is directed toward sectors such as extractive industries, infrastructure, and primary agriculture. These sectors may contribute to localized environmental degradation, but their effects may not be broad or systematic enough to significantly impact national ecological footprint metrics. Moreover, the weak regulatory frameworks and limited enforcement capacity in many SSA countries may prevent FDI from having a pronounced or consistent environmental impact. This aligns with findings by Acheampong [2], who reported heterogeneous environmental effects of FDI in African countries depending on sectoral allocation and institutional quality. Similarly, Nathaniel and Adeleye [42] found that the environmental consequences of FDI in low-income African economies are often minimal or statistically insignificant, largely due to underdeveloped industrial bases and limited absorptive capacity. Adu and Denkyirah [3] also emphasized that when governance considerations are taken into account, the association between FDI and environmental deterioration in SSA is not strong. Therefore, while the direction of the relationship aligns with theoretical expectations, the lack of statistical significance suggests that FDI in SSA has not yet reached a scale or form that significantly affects environmental outcomes at the regional level. This reinforces the need for targeted policies that strengthen environmental governance and monitor sector-specific FDI activities to ensure their alignment with sustainable development objectives.

5 Policy implications

This study investigated how the ecological footprint is impacted by biocapacity, renewable energy, urbanization, and human capital of 26 Sub Saharan African countries in presence of interaction term (UB*HC). The static panel data estimator, an advanced econometric technique which tackles the heterogeneity and cross-sectional dependence (CD) between countries, was applied. The FEM model’s findings demonstrated that, with the exception of biocapacity and foreign direct investment, every variable was statistically significant in explaining the ecological footprint of SSA. In particular, it was discovered that the ecological footprint was positively correlated with both urbanization and human capital. This indicates that as urbanization raises the demand for primarily non-renewable energy, human capital and urbanization may both contribute to environmental degradation. Stated differently, urbanization and human capital may contribute to environmental degradation, while their interaction term (UB*HC) helps to improve environmental quality. This suggests that human capital plays a moderating effect in reducing environmental degradation.

The results of this investigation demonstrate the vital significance of promoting renewable energy, managing foreign direct investment (FDI) responsibly, and developing human capital to improve environmental quality in SSA (SSA). Given the provenpositive effect of renewable energy on reducing ecological degradation, governments should adopt a multi-pronged policy approach. This includes implementing carbon taxes to discourage fossil fuel use, providing grants and subsidies for solar and wind energy deployment, especially in rural and underserved areas and incentivizing private sector investment in clean technologies. Tax breaks, import duty exemptions, and accelerated depreciation on green capital assets should be extended to both multinational companies and local enterprises that adopt sustainable practices. Public awareness campaigns must also be prioritized to educate citizens on the consequences of environmental degradation for human health, wildlife, and ecosystems. As signatories to the Paris Agreement, SSA governments should revise their national energy strategies to prioritize renewable resources, taking advantage of the region’s abundant solar, wind, and biomass potential to mitigate climate change and reduce energy poverty through both centralized and decentralized interventions.

In parallel, the development of human capital must be central to sustainability efforts. Investment in education systems should focus on integrating environmental content across all levels of schooling, while technical and vocational education should be expanded to include training in green technologies and sustainable resource management. Enhancing healthcare systems and access to information and communication technologies (ICTs) can also empower communities to engage in more environmentally conscious behaviors. The interaction effect observed between urbanization and human capital in this study underscores the importance of leveraging human capital to counterbalance the environmental pressures of rapid urban growth. Considering the detrimental effects of urbanization on environmental sustainability, it is recommended that smart urban planning that is supported by skilled professionals in environmental engineering, urban design, and public administration will be critical for building resilient, low-impact cities.

Moreover, the study’s findings indicate that while FDI is positively associated with the ecological footprint, this relationship is often driven by environmentally harmful investments in resource extraction and high-emission industries. To address this, governments must strengthen environmental governance through pollution taxes, strict enforcement of environmental regulations, and mandatory impact assessments for foreign projects. Promoting “clean FDI” will require rigorous environmental management practices, greater transparency, and monitoring systems that ensure compliance. Financial institutions also have a key role to play by offering green bonds, sustainability-linked loans, and climate finance instruments to support eco-innovation and the expansion of the green high-tech sector. By integrating policies across the areas of energy, education, investment, and finance, SSA can pursue a development trajectory that safeguards both economic growth and environmental sustainability. The study’s shortcomings, however, include the removal of important ecological footprint determinants and the lack of data in some SSA nations, which restricted the analysis to 26 nations. By using a larger dataset over a longer time span, newly developed estimating methodologies, more control variables, and more thorough measures of environmental degradation, future research could overcome these constraints. Considering the potential differences in institutional frameworks, colonial legacies, governance structures, or language-based policy approaches we believe that a comparative study between the French-speaking and English-speaking African countries may also yield interesting results.