The human well-being and environmental degradation nexus in Africa

Environmental degradation continues to attract interest from academics, policymakers, and other stakeholders. However, empirical studies have been limited in the choice of human well-being indicators. Therefore, this study extends the literature by broadening the nexus between human well-being and environmental degradation in 29 African countries from 1970 to 2019. Preliminary tests adaptable to effects of cross-sectional dependency and heterogeneity in panel dataset were adopted, alongside the cross-sectional auto-regressive distributed lag model. Findings from the study showed that the adopted human well-being indicators such as globalisation, life expectancy, and human capital development were environmentally enhancing both in the short and long terms. In contrast, income growth was environmentally degrading in the short and long terms. At the same time, urbanisation was only environmentally detrimental in the long term with no significant short-term effect. Natural resource rent which served as a control variable was environmentally degrading both in the short and long terms. Consequently, this study confirms the synergy approach between the environment and human well-being and the trade-off hypothesis in African countries. Thus, African countries’ general resource management policy significantly determines the impact their path to human well-being enhancement has on the environment.


Introduction
The African continent has long been known for continued growth in poverty and environmental degradation; hence, governments, policymakers, researchers, and international organisations have continued to seek ways to ameliorate these challenges. For instance, the United Nations Sustainable Development Goals (SDGs) launched in 2015 has poverty eradication as its top priority; while other goals like achieving a sustainable environment and globalisation evolve around it (Asongu and Odhiambo 2019;Sarkodie and Strezov 2019;Dhrifi et al. 2020;Dada and Fanowopo 2020;Aladejare et al. 2020). Similarly, the first ambition of the African Union's 2063 agenda is to guarantee a wealthy Africa based on inclusive growth and sustainable development by wiping out poverty in the continent (African Union Commission 2015).
Human well-being will have to be enhanced if the continent's poverty challenge is to be tackled. However, this might not be without cost to the environment. The reason is that human well-being is naturally associated with ecological factors. Therefore, the effectiveness of the environmental management policy is a determining factor in human interaction with the environment. Although a positive symbiotic nexus between human well-being and environmental quality is highly desirable, the combined quest of these objectives does not demand that the two at all times be mutually reinforcing. Intuitively, trade-offs are bound to occur on many occasions, mainly where there are no well-designed policy actions (UNDP 2011). The reason is that pressures on conventional human development components, including health, wealth, and education, are more likely to grow due to rising human exploitation of environmental resources from urbanisation and globalisation (Kassouri and Altintas 2020). For this purpose, the SDGs were conceived to strike a balance between human well-being enhancement and halt mounting pressure on environmental resources. Nevertheless, achieving these goals still constitutes a fundamental challenge for African countries.
Drawing from the above, a broad human development plan that incorporates issues of urbanisation and globalisation should promote environmental sustainability in Africa. Therefore, understanding the association between human well-being and the environment is particularly compelling for Africa, where various economic and social factors can substantially contribute to resource depletion in the short and long terms. For instance, a significant point from the Rio convention on climate change bordered on enhancing human well-being through poverty alleviation. However, this can only be achieved through the efficient use of environmental resources. A major challenge for African countries has remained the simultaneous tackling of poverty and reduction in environmental degradation. To further bolster this point, Dhrifi et al. (2020) noted that although economic development serves to lower poverty in developing countries, it also exerts pressure on ecological resources and processes.
On the other hand, poverty levels can also be aggravated by environmental degradation when resources for enhancing human well-being are channelled to combat environmental challenges Asongu and Odhiambo 2019). Likewise, when there is a scarcity of resources, individuals could be forced to engage in activities degrading to the environment, which may have long-term consequences on the well-being of the populace. For instance, environmental degradation in a country impacts the investment opportunities available within and outside the country. Consequently, employment and income will fall, and poverty will become prominent. Furthermore, environmental degradation by soil erosion and flooding can result in food insecurity, a rise in unemployment, and low income. Ecological degradation can also diminish human productivity by adversely impacting on the life expectancy of the populace through the rise in communicable and incommunicable diseases and poor social and economic amenities (Rich 2017;Boogaard et al. 2017;Sarkodie 2018;Asongu and Odhiambo 2019).
Although human well-being and environmental degradation have often constituted the core attention of environmentalists and stakeholders, the linkage process between these targets remains uncertain in extant literature. Most studies have often relied on the environmental Kuznets curve (EKC) framework to dissect the impact of human economic activities on environmental quality (Destek et al. 2018;Adzawla et al. 2019;Kong and Khan 2019;Aladejare 2020a). However, studies have also noted the flaw of economic growth when used as an indicator for social welfare in the environmental degradation-economic growth/development nexus (van den Bergh and Botzen 2018; Kassouri and Altintas 2020). The most prominent proxy for economic growth in the environmental literature has been the gross domestic product (GDP), which denotes the aggregate monetary value of all final goods and services produced annually within a country. However, this measure does not intensively encompass human well-being components such as income level, human health, and the educational level of the society associated with the comprehension of environmental issues. Against this backdrop, the human development index (HDI) was proposed as a metric of human welfare. Its computation encompasses life expectancy, income, and educational level. However, the HDI's measure of human well-being can be flawed since it neglects other critical public policy indicators that, alongside its conventional components, impact human well-being. These indicators include globalisation and urbanisation; their impact on human well-being has continued to grow due to the realisation of the potential for collective pooling of resources between and within countries. Consequently, the need to assess the nexus between broader indicators of human well-being and the environment is crucial for related policy enhancement.
Hence, this study proposed using life expectancy, human capital development, income, urbanisation, and globalisation as broader metrics of human well-being. To properly gauged the linkage between the adopted human well-being metrics and environmental resources, the ecological footprint per capita (EF), which evaluates the demand for regenerative capacity, is adopted as a comprehensive measure for ecological degradation. There is growing acceptability of the EF measure in the literature as an adequate representation of the concept of environmental sustainability due to its broad components (Kassouri and Altintas 2020;Nathaniel 2020Nathaniel , 2021Aladejare 2020aAladejare , 2022Erdogan et al. 2021).
Based on empirical evidence, no study has combined the interaction of the above metrics of human well-being with the adopted environmental degradation measure. Hence, this study extends the literature by decomposing the HDI components (life expectancy, income, and education) and broadening the well-being indicators to incorporate urbanisation and globalisation, which have either improved or marred environmental degradation measured by EF between 1970 and 2019 for 29 African countries. Natural resource rent is used as a control variable due to the resource-dependent nature of African economies. African governments rely heavily on receipts from their natural resources to deliver on development projects necessary for enhancing human well-being, but with potentially adverse environmental effects due to poor extractive processes. Thus, establishing trade-offs and synergies between human well-being and environmental degradation can improve decision-making processes towards balancing the human well-being-environmental degradation nexus in Africa. The econometric techniques applied in this study include panel cross-sectional unit root tests, cointegration test, and panel cross-sectional augmented autoregressive distributed lag (CS-ARDL) model.
The focus of this study is on African countries for the following reasons. First, countries in the continent have been witnessing a rapid shift from ecological surplus to ecological deficit due to the region's significant economic growth in the last decade. The continent's economy has consistently grown by 5% annually over the last decade; it is also believed to be as urbanised as China and has as many cities of over a million persons as Europe (Future Agenda 2022). Therefore, feasible policies are needed to comprehend the well-being implications of this ecological deficit to minimise or reverse the current ecological path in African countries. Second, Africa is a wealthy agrarian continent; it also has a prosperous extractive sector that constitutes about 30% of the world's mineral reserves (UNEP 2022). Africa has 40% of the world's gold deposits and is endowed with about 90% of the world's chromium and platinum (Aladejare 2020b;UNEP 2022). Similarly, Africa's rich oil and natural gas reserves make up 12% and 8% of the world's total reserves, respectively (UNEP 2022). Consequently, the use of poor agricultural techniques and over-reliance on primary commodities presents significant environmental challenges for the continent and the world. Therefore, the quest to achieve a balance between improving human well-being and reducing environmental degradation should be a significant plan for policymakers in the continent.
Key findings from this study showed that while an increase in urbanisation leads to environmental degradation in the long term, growth in natural resource rents and income promote environmental degradation in the short and long terms. In contrast, an increase in globalisation, life expectancy, and human capital development significantly condensed environmental degradation in the short and long terms. Consequently, several policy implications of these findings on the continent indicate that African countries will have to significantly embrace the growth of green urban cities, especially in densely populated communities. A change in lifestyle that embraces energy-efficient approaches should also be encouraged. African countries will have to adopt legal and regulatory approaches to deter inflows of environmentally detrimental foreign direct investment (FDI), and propose eco-friendly investment incentives by utilising tax reliefs and joint partnerships. Globalisation serves as the vehicle for transferring eco-friendly technologies between countries. Thus, the African continent needs these environmental policies that will enhance clean business strategies and allow African governments to find a path towards deriving environmental sustainability. Governments in the continent should be committed to cutting reliance on the extraction and utilisation of natural gas, fossil fuel, and minerals for income and energy sources due to their enormous degrading environmental impact. Investment in renewables such as solar, geothermal, wind, tidal, hydropower, and hydrogen should be intensified for sustainable economic growth and eco-friendly development. Also, income growth in Africa will have to be firmly anchored on achieving and sustaining investor-friendly economies, which is a prerequisite for an astronomical development of small, medium, and large enterprises in the continent.
The sequence of this study takes the following order. After the introduction, the second section presents the literature review, the third section is the study's data and methodology, the fourth section contains results and analyses, and the last section concludes the study with policy directives.

Theoretical review
From a theoretical view, two contrasting hypotheses have often been postulated. The first is the synergy approach in which countries can achieve a win-win solution that promotes environmental quality and human well-being. This encouraged the deployment of the HDI as an alternative means for assessing the welfare effect in the literature. In furtherance of this concept, there is an evolving school of thought that environmental sustainability should be integrated into the concept of human development, hence, the introduction of a new concept known as sustainable human development (Kassouri and Altintas 2020). Sustainable human development relates to widening available human opportunities without jeopardising environmental sustainability). By marrying the concepts of human development and ecological sustainability together, this new framework rejuvenated various interests by scholars and stakeholders in evaluating the current compromising path between environmental sustainability and human development enhancement.
Another theoretical perspective is the trade-off approach, whereby the exploration of environmental resources has a negative impact on human well-being. In this approach, the nexus between human well-being and environmental quality is valid when human well-being exacerbates ecological impairment (Kassouri and Altintas 2020). The quest for improved human well-being by a country may exert undue pressures on the natural environment of that country and create an ecosystem sustainability problem even for neighbouring countries (Kassouri and Altintas 2020). It is important to stress that ecological services are essential to the well-being of any society. For instance, human health depends on freshwater supply for agriculture, drinking, and recycling of waste. Rural communities are directly and heavily reliant on good water supplies. Likewise, humans are dependent on productive terrestrial and marine ecological products for a vital supply of medicinal aid, food, and climate regulation, which constitute core inputs in their well-being. Based on those mentioned above, the quest for a better life by humans may adversely impact the ecological balance and create environmental degradation.
From the above contrasting perspectives, we can say that the general resource management policy in African countries will significantly determine the effect their current path to human well-being enhancement will have on their environment.

Empirical review
Interactions between human well-being and environmental degradation have not been exhaustively researched by extant studies, especially in the choice of human wellbeing and environmental indicators. Thus, existing studies have also not been able to achieve a common front on the nature of the relationship between human well-being and environmental degradation, especially in developing countries. A brief review of studies with common submission is provided below.

The synergy studies
Empirical studies such as Steinberger and Roberts (2010) employed the pooled ordinary least square (OLS) in concluding that human development indicators such as life expectancy, income level, literacy rate, and HDI for 156 countries can be moderately enhanced without impacting negatively on the environment. Similarly, Bedir and Yilmaz (2016) assessed the causality association between carbon emissions and human development for 33 Organisation for Economic Cooperation and Development (OECD) countries. Submission deduced from the study affirmed that reducing carbon emissions does not affect human development. Kais and Sami (2016) used the generalised method of moment (GMM) to show that urbanisation and trade openness reduce environmental degradation in North Asia and European countries.
Based on a panel dynamic OLS (PDOLS) and panel fully modified OLS (PFMOLS), Charfeddine and Mrabet (2017) concluded that urbanisation, life expectancy, and fertility rate reduce the EF of 15 Middle East and North African (MENA) countries. In a related study, and by employing the stochastic impacts by regression on population, affluence, and technology (STIRPAT) technique, Abdallh and Abugamos (2017) confirmed that urbanisation reduces carbon emissions for 20 MENA countries. Zaman and Moemen (2017) employed the fixed effect (FE) and GMM procedures and concluded that no carbon emission effect from human development exists in 90 different countries. Using a system-GMM for MENA countries, Tran et al. (2019) showed that human development has no significant effect on carbon emissions in developed countries. However, the same effect was not validated for developing countries. Chen et al. (2019) employed the dynamic seemingly unrelated-cointegration regression (DSUR) to conduct a study on 16 Central and Eastern European countries. Empirical findings from the study showed that human capital reduces EF. By adopting a qualitative technique, Biswas (2020) showed that environmental literacy and attitude are significant predictors of a healthy lifestyle in an emerging economy. Dumor et al. (2021) applied the dynamic ARDL approach for a study on East African countries and found that an increase in HDI has no significant effect on carbon emissions in the short and long terms.

The trade-off studies
In contrast, studies such as Awad and Abugamos (2017) investigated the relationship between income and carbon emissions for 20 MENA countries. The study employed the parametric and semi-parametric FE models for its analysis. Evidence from the study showed an initial trade-off between both variables, which was later replaced with a win-win scenario. Sarkodie (2018) used the panel cointegration, fixed and random effect estimators, and causality technique for 17 African countries. The study divulged that human development components related to food production, birth, and fertility rate significantly degraded the environment by increasing EF and carbon emissions. Nyangena et al. (2019) employed a parametric and non-parametric FE model to determine that urbanisation promotes carbon emissions in East African countries. A different study by Taghizadeh-Hesary et al. (2020) used the GMM approach to conclude that carbon emissions are enormously responsible for undernourishment and death rates in 18 Asian countries.
By using the interactive FE (IFE) and the common correlated effect mean group (CCEMG) for a panel study of 13 MENA countries, Kassouri and Altintas (2020) examined the existing interaction between HDI and environmental quality measured by EF. The study found that there is a substantial trade-off between both variables. Zafar et al. (2020) adopted the PDOLS and PFMOLS for 17 Asian countries and concluded that education, urbanisation, and income worsen environmental quality. Nathaniel (2021) used the augmented mean group (AMG) panel-correlated standard errors (PCSE) and the Driscoll and Kraay (DK) methodologies for a panel of Next-11 countries. Findings from the study showed that while HDI, financial development, and urbanisation degrade the environment, natural resource rent and globalisation promote environmental quality by reducing EF. Hence, the existence of a trade-off effect in the countries was examined. In a study of 39 sub-Saharan African countries, Akinlo and Dada (2021) used the dynamic GMM to show that carbon emissions enhanced poverty, measured by HDI and life expectancy. Aladejare (2022) studied the five richest African economies and employed the feasible generalised least squares (FGLS) technique. Findings from the study showed that globalisation and urbanisation reduce environmental degradation (measured by EF, methane, and carbon emission), while natural resource rents and human capital development significantly promote it.

Literature gap
The above contrasting reviews show that the relationship between human development and environmental degradation is still debatable. A major contending issue is evident in the measurement indicators deployed. For instance, in measuring environmental degradation, many studies have relied on carbon emissions, while only a handful has opted for the broader EF indicator. This study also adopts the EF indicator due to its robust suitability to the resource-based composition of African economies. Furthermore, many studies have employed the HDI as a proxy for human well-being. While a few others have adopted other human well-being indicators such as urbanisation, education, trade openness, fertility, death and birth rates, etc. For a robust measure, this study explored human well-being from the individual perspective of life expectancy, human capital development, income, urbanisation, and globalisation, while controlling for natural wealth with resource rents. The HDI is increasingly proving to be a narrow measure of human well-being since it fails to incorporate recent crucial human factors such as globalisation and urbanisation that can enormously impact well-being. In estimation technique, many studies neglected the cross-sectional nature of the subject matter, which could bias their outcomes. Recent studies that have corrected this flaw are scant. Hence, this study extends the literature based on these identified fronts.

Data sources and variable description
By employing data observation between 1970 and 2019, this study assessed the nexus between human well-being and environmental degradation in 29 African countries. These countries include Algeria, Benin, Botswana, Burkina Faso, Burundi, Cameroun, Congo Republic, Congo democratic, Cote d'Ivoire, Egypt, Gabon, Gambia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Morocco, Niger, Nigeria, Rwanda, Senegal, Sierra-Leone, South Africa, Togo, Tunisia, and Zambia. Aside from the fact that these countries constitute the richest in income and resources among the 54 countries of Africa, their choice is also based on data availability.
As prior noted, the indicator for environmental degradation used in this study is the EF, which is increasingly adopted in recent energy and environmental-related analyses as a robust measure of environmental quality. EF uniquely accounts for the unit of various natural areas necessary for the growth of an economy. These natural areas encompass cropland, forest products, carbon space, built-up land, fishing grounds, and grazing land. Another justification for the EF measure is related to the destructive tendencies extractive activities could have on natural areas, such as loss of biodiversity, pollution of surface water, groundwater, and soil erosion.
The indicators of human well-being as prior establish are life expectancy at birth (LE). Its adoption is based on the intuition that life expectancy is primarily a function of human activities that can either shorten or encourage longevity. Such activities can also promote or reduce ecological well-being. Another indicator is the human capital development (HC) indicator. Its adoption is grounded on the reasoning that the nature of HC policies and strategies existing in a country can either promote or deter the growth of environmental sustainability. Growth in GDP per capita is used to proxy for an increase in income level. As income level rises, the demand by individuals for the better things in life also rises, hence, exerting more pressure on the environment. Urbanisation is another human well-being indicator adopted since it can enhance housing, energy, and transport demands, potentially raising fossil fuel consumption and generating more detrimental environmental effects.
Globalisation is crucial to African countries because natural resource exports constitute the principal source of their income. It involves interaction between people in different countries, sharing ideas and information. Consequently, the concept is multifarious since it covers beyond trade openness and capital flows (Gygli et al. 2019); its impact can be extensive, measuring technology transfer from advanced to developing countries. However, the level of trade and investment in foreign technology can promote or reduce the accumulation of "dirty" technology, resulting in either environmentally unfriendly or enhancing globalisation. The KOF globalisation index, which evaluates globalisation from the economic, social, and political perspectives (Dreher et al. 2008), was adopted as an indicator of globalisation.
The aggregate measure of natural resource rents from minerals, coal, forest, oil, and natural gas served as a control variable. African countries are abundantly endowed with these natural resources, and their income contributes significantly to the provision of economic and social infrastructure required to aid human well-being. However, these resources' extractive and over-exploitative processes can be environmentally degrading. The explained variables shall be marked in Table 1 and arranged in the order in the table.

Cross-sectional dependency test
Of recent, there has been a growing consensus on the adverse impact data cross-sectional dependence (CSD) might have on derivable inferences from panel analysis. Some of these challenges include poor model selection and estimated parameters (Gyamfi et al. 2021;Shen et al. 2021). Thus, ignoring the CSD threat of a panel data analysis creates the opportunity for inconsistency and inefficiency in estimated coefficients. The key factors responsible for CSD in panel data analysis are unobserved components, common shocks, and the possibility of residual interdependency (Su et al. 2020). Thus, when handling panel data analysis related to economic or financial integration, globalisation, trade flows, and common economic policies, CSD is most likely to be an issue that demands attention. Consequently, this study conducted four CSD tests which are Breusch and Pagan's (1980) Lagrange multiplier (LM) test, Pesaran's (2004) scaled LM test, Pesaran's (2004)  where T represent time unit/sample periods, N represents panel cross-sectional size, and ̂ 2 ij denote the pair-wise crosssectional correlation parameters. The Breusch-Pagan LM test is true when the null hypothesis of no CSD for panels with T tending to infinity and N is fixed is accepted. As an advancement, the Pesaran (2004) scaled LM CSD test was developed to handle large panels where T and N tend towards infinity. The test equation is as expressed below. (1) One major challenge with the Pesaran (2004) scaled LM CSD test is its likely bias in exhibiting significant size distortions for large N and small T. Hence, Pesaran (2004) formulated a more adaptable CSD test that can be relied upon when both T and N tends towards infinity as shown below. Baltagi et al. (2012) later developed a biased-scaled LM CSD test based on the assumption of N and T tending to infinity. The test is derived in the context of a fixed effect homogenous panel data model, and its equation is shown below.
Once CSD is confirmed in the dataset, the applicable econometric approaches that treat CSD issue are deployed.

Slope heterogeneity test
Another challenge with panel data analysis is the problem of assuming slope homogeneity, which can bias inferences due to variations in the demographic and economic structure of crosssections being considered. Hence, conducting a slope heterogeneity test is imperative when dealing with panel datasets. Its essence is to determine whether the parameters of interest are genuinely homogenous or differ across cross-sectional units. This study used the Swamy (1970) test and the Pesaran and Yamagata (2008) adjusted or standardised version to determine the presence or absence of heterogeneous slope parameter.
The test statistics for both Swamy (1970) and Pesaran and Yamagata (2008) are represented as

Panel unit root test
In the presence of CSD and heterogeneity in the panel dataset, it becomes obvious that mainstream first-generation unit root techniques such as Levin-Lin and Chu (LLC) and I'm, Pesaran, and Shin (IPS) cannot be relied upon to control for CSD effects in the series. Thus, the need to apply robust panel unit root techniques that can mitigate CSD's impact and account for heterogeneity in a series. An applicable first-generation panel unit root test that corrects for CSD and allows for heterogeneity is the Madalla and Wu (1999) test employed in this study. Also, the second-generation unit root tests are a standard set of unit root tests applicable in treating CSD and heterogeneity issues in panel datasets. In the category are the Pesaran (2003) crosssectional augmented Dickey-Fuller (CADF) and Pesaran (2007) cross-sectional IPS (CIPS), which were both adopted. The equation form of CADF test statistic is expressed as where ∞ it , G it and it represents the intercept, study variables, and error term, respectively; inserting the first lag expression yields the following equation: where G t−j and ΔG i,t−j denote the intercept, mean of lagged and first difference operator, respectively, across the specific cross-sections. The following function is for the CIPS test statistic:

Panel cointegration test
The error correction model (ECM)-based cointegration technique proposed by Westerlund (2007) is applied to ascertain the long-run nexus between the study variables. This test outperforms traditional cointegration tests such as the Kao and Pedroni by producing reliable estimates of heterogeneity and CSD (Kapetanios et al. 2011). Four test statistics are often presented, comprising two group tests (G t and G a ) and two panel statistics (P t and P a ). The Westerlund (2007) equation is expressed as follows: where ∅ , Γ t = (Γ 1i , Γ 2i ) , , and m t = (1, t) , represent the error correction coefficient, vector of the cointegration relationship between X (regressor) and y (regressand), and the deterministic components, respectively. The equation for the four Westerlund test statistics are where ∀ i denotes the OLS estimator and SE ∀ i and ∀ i (1) represents the standard error and semi-parametric kernel estimator of ∀ i respectively.

Panel estimation procedure
The cross-sectional augmented ARDL (CS-ARDL) When handling analysis involving panel datasets with large N and T, the existence of cross-sectional heterogeneity cannot be ignored. Therefore, if CSD and heterogeneity are valid, the application of conventional econometric methodologies such as difference GMM, fixed and random effect becomes invalid (Chudik et al. 2017;Chen 2018). Consequently, to derive the short and long-run estimated results, this study deployed the newly developed technique called the CS-ARDL (Chudik et al. 2013).
The CS-ARDL procedure is well adapted to the issue of CSD, heterogeneity, endogeneity, non-stationarity, and omitted variables in panel data analysis (Chudik et al. 2017;Bindi 2018). Its framework is built on augmenting the conventional ARDL procedure by incorporating cross-section means of covariates, their lags, and the response variable. Unlike firstgeneration techniques (mainstream ARDL method), the CS-ARDL integrates the cross-sectional heterogeneities, which this study sets to tackle. The CS-ARDL procedure controls the structural similarities to produce the regressor coefficient effect on the response variable. Furthermore, the CS-ARDL method is known to outperform the panel ARDL model, particularly when 30 ≤ T < 100 (Chudik et al. 2017) as in this study, hence, its suitability for this study. By transforming Eq. (17), the basic CS-ARDL model is as follows: where Eq. (19) as represented by Z t is the cross-sectional means for the covariates for the response variable ( y t ) and the regressor ( X , t ) . f t represents the unobserved common factor that creates dependency among cross-sectional units. The common factors are expressed through a detrending process of the cross-sectional means and lagged through Eq. (19). Equation (18) is estimated by a pooled mean group (PMG) method, and Eq. (21) is used to derive the long-term parameters.
where Table 2 shows the mean EF for the African countries as 1.47 (gha) approximately. In Table 3, South Africa has the highest mean EF of 3.4 (gha), while Malawi has the lowest mean of 0.81 (gha), thus, indicating that South Africa has the worst case of environmental degradation among the African countries selected. However, the low EF value for Malawi indicates that the country has the best environmental quality. The average NRR for the African countries is 3.0% of their GDP (Table 2). Evidence in Table 3 reveals that Gabon and Burundi have the highest and lowest NRR per GDP with 26.1% and − 0.01%, respectively. The aggregate GI for the countries is 41.24%, suggesting that African countries are increasingly embracing globalisation trends by having favourable policies and terms and growing political, social, and economic integration within and outside the continent.

Descriptive statistic test results
Further evidence in Table 3 shows that Tunisia has the highest GI mean with 57.13%, and Burundi with 27.3% is the lowest. They indicate that Tunisia and Burundi have had more and less international access to trade flows, respectively. However, the mean LE for the African countries is 54.62 years, which falls short of the value for other developing countries such as Latin America and the Caribbean (75.09 years), and Asia (74 years) (WDI 2022). Table 3 further reveals that Tunisia and Sierra Leone have the highest (68.51 years) and lowest (42.47 years) LE, respectively.
The mean HC is 1.55, as shown in Table 2, while South Africa with 2.13 and Burkina Faso with 1.08 are the countries with the highest and lowest HC, respectively (Table 3). Another piece of evidence in Table 2 indicates that the mean rate of YGP for the nations is 7.92%. Further evidence in Table 3 shows Algeria has the highest mean rate of YGP (20.35%), and Congo DR. (− 1.75%) with the lowest mean rate. URB aggregate mean rate is 4.48% for the countries (Table 2), while further result in Table 3 shows Botswana has having the highest URB mean rate (7.32%) and Egypt the lowest mean rate (2.26%). This outcome suggests that urbanisation growth is the fastest and slowest in Botswana and Egypt, respectively. Table 4 reveals a weak correlation between most study regressors, except for the correlation between GI and LE. Consequently, a variance inflation factor (VIF) test was performed, and the estimate is presented in the lower panel of Table 4. The mean VIF value for the study regressors is 1.90, thus, indicating the existence of a low correlation between the study regressors and less severity of multicollinearity issues.

Correlation and CSD test output
Another result, as contained in Table 5, shows that the estimated CSD test offers substantial proof of CSD in the panel dataset. Evidence from the four tests validated the rejection of the null hypothesis of no CSD for the variables in the panel study. Hence, it is imperative to adopt econometric procedures that account for CSD in their estimation process. Table 6 captures the estimated results for slope heterogeneity in the parameters. The null hypothesis of homogenous slope parameters was rejected against the alternative view based on the output. By validating the presence of slope heterogeneity in the parameters, the level of emissions, income growth, human capital development, urbanisation, level of globalisation, and natural resources rents differ among African countries.

Panel unit root and cointegration test
The validation of CSD and slope heterogeneity in the variables necessitated the application of unit root tests incorporating both CSD and heterogeneity into the unit root process. Table 7 summarises the unit root results for both the first-and second-generation unit root techniques. The Maddala and Wu, and Pesaran CIPS unit root tests have the null hypothesis of first-order series integration. This hypothesis was accepted for only EF and HC. Similarly, the Pesaran CADF unit root test's alternative hypothesis of stationary at first difference was also upheld for only EF and HC. Other series were found to attain stationarity at levels in the three tests. Table 8 contains the outcome of the Westerlund CSD cointegration test. Based on the output for the four test statistics, the null hypothesis of no cointegration was rejected with the exception to P t . Nevertheless, we still conclude that long-run associations exist between variables in the model. Table 9 reveal human well-being's short-run and long-run effects on environmental degradation. Globalisation is shown to have short-and long-term cushioning impacts on environmental degradation in the selected African countries. The result suggests that as these African countries continue  to embrace political, social, and economic integration, they are also able to adopt solid environmental policies that act as a guide against degrading the environment. Examples of such policies include the Earth summit of 1992 in Rio de Janeiro, the Kyoto protocol of 1997 in Japan, the Durban Platform for enhanced action of 2011 in South Africa, the Cancun agreement of 2010 in Mexico, and the more recent Paris agreement of 2015 in France. Policies from these environmental summits may lead the way in the growth of green and efficient ecological friendly technologies in Africa, which can enhance environmental quality. This finding is consistent with Nathaniel (2021) and Aladejare (2022). Similarly, life expectancy is adversely linked with environmental degradation in the short and long terms, suggesting that environmental degradation declines as Source: Author's estimated output Hence, reducing these activities will promote longevity and reduce environmental degradation. This outcome aligns with extant studies such as Steinberger and Roberts (2010) and Charfeddine and Mrabet (2017). In contrast, income level is positively related to environmental degradation in the short and long terms. An indication that an increase in income tends to trigger ecological degradation in the same direction. The result shows that the quest for higher income through increased productivity in these countries is followed by higher environmental pollution. It is significant to note that this outcome does not depict African countries as exceeding their threshold point of ecological quality degradation. Instead, it is a product of the less efficient income-yielding assets deployed for productive use, but with a significant adverse impact on the environment. For instance, the use of harmful chemicals for fishing, indiscriminate lumbering and hunting of wildlife, bush burning for hunting and farming, open-air burning of rubber, copper and iron materials for fabrications, etc., are ecologically unsustainable. The result agrees with other literature dedicated to the income-environmental quality nexus, such as Gyamfi et al. (2022).   On the other hand, human capital development has negative short-and long-term impacts on environmental degradation. This implies that human capital development substantially reduces environmental degradation in African countries. This result underscores the role of human capital in Africa's environmental quality enhancement, suggesting that environmental awareness is strongly growing in African countries, possibly through media campaigns, community programmes, and school curriculums. A well-informed population imbibing innovative and efficient environmental friendly measures will promote environmental quality in the continent. This result complements similar findings in extant studies such as Steinberger and Roberts (2010).

Results in
Urbanisation is positively associated with environmental degradation, but only in the long term, hence, indicating that growth in African urbanisation drive has no short-term effect but tends to enhance environmental degradation in the long term. Urbanisation promotes humans' demands on environmental resources. The impact of such demand is known to diminish the biocapacity and worsen EF. An increase in urbanisation can stimulate higher economic activities, which can further increase energy demands, poor energy efficiency, and waste generation. African countries are heavily reliant on non-renewable energy sources (e.g., fossil fuels); therefore, when there is a rise in energy demand, ecological pressure is also likely to rise, and ultimately EF in the long term. Studies such as Nyangena et al. (2019) and Nathaniel (2021) have reported the same effect earlier.
The short-and long-term effects of natural resource rents on environmental degradation are positive, which indicates that increases in resource rents deplete ecological quality in African countries. Hence, the extraction and exploitation of natural resources for revenue and domestic consumption have not been environmentally sustainable. The reason is that the excessive dependence on natural resources creates a depletion in biocapacity since resources are often not allowed to regenerate. This finding further compliments earlier submissions by Nathaniel (2021) and Aladejare (2022).
Drawing from these findings, the long-and short-term condensing effects of globalisation, life expectancy, and human capital development on environmental degradation conform with the synergy hypothesis. On the flip side, the short-and long-term accelerating effects of income and natural resource rents, and the long-term increasing effect of urbanisation on ecological factors conform with the tradeoff hypothesis.
The error-correcting coefficient is rightly signed and significant. Its value of − 0.28 shows that approximately 28% of short-term disequilibrium is corrected annually. Consequently, it would take 42 months to restore the long-term equilibrium path.

Concluding remarks
Environmental degradation continues to attract interest from academics, policymakers, and other stakeholders. However, empirical studies have been limited in the choice of human well-being indicators. Therefore, this study extends the literature by broadening the indicators of human well-being to include life expectancy, income, human capital development, urbanisation, and globalisation, and assessing their association with EF used to proxy for environmental degradation in 29 African countries from 1970 to 2019. Preliminary tests adaptable to the effects of CSD and heterogeneity in the panel dataset alongside the CS-ARDL were adopted. Findings from the study showed that the adopted human well-being indicators have varying effects on environmental degradation in the short and long terms. Specifically, globalisation, life expectancy, and human capital development were found to be environmentally enhancing both in the short and long terms. In contrast, income growth was environmentally degrading in the short and long terms. At the same time, urbanisation was only environmentally detrimental in the long term with no significant short-term effect. Natural resource rent which served as a control variable was environmentally degrading both in the short and long terms. Empirical findings from this study imply a combination of the synergy approach between the environment and human well-being and the trade-off hypothesis in African countries. Thus, African countries' general resource management policy significantly determines the impact their path to human well-being enhancement has on the environment. Therefore, this study highlights essential policy measures that could simultaneously enhance human well-being and environmental quality in African countries. First, since globalisation serves as the vehicle for transferring eco-friendly technologies between countries, environmental policies promoting clean business strategies and allowing African governments to find a path towards deriving environmental sustainability should be adopted. Furthermore, African governments should consider investing robustly in modern environmental-friendly technologies. Investment in efficient green technologies should also be scaled-up. African countries should adopt legal and regulatory approaches to dissuade detrimental environmental FDI inflows, while offering eco-friendly investment incentives by utilising tax relieves and joint partnerships. This move will encourage the growth of efficient environmental-friendly technologies. Determining and administering appropriate sanctions on erring economic agents engaging in degrading environmental activities will help strengthen adherence to environmental laws by all stakeholders. The effect of such measures will critically reduce emissions and assure environmental conservation.
In promoting international trade, there is the need to regulate the exchange of goods and services and enforce only multi-and bilateral trade agreements that potentially lower adverse impact on the environment.
Most African economies are still undergoing structural transformation, which has been sluggish and uneven. This is also reflective of the low life expectancy and income in the continent, hence, its tag as a developing continent. Consequently, the use of cheap but environmentally unsustainable productive techniques is still prevalent and may continue even in the long run. Thus, African governments will have to demonstrate more commitment to tackling issues of poor income in the continent. Poor income is responsible for cheap sourcing of energy from charcoal and firewood, bush burning for farming and hunting, illegal mining, harmful fishing techniques, poor recycling and fabrication techniques, etc. These activities culminate in greenhouse gas emissions, deforestation, pollution of water bodies, and soil erosion, reducing human longevity and eroding environmental sustainability. Hence, it is crucial to stress that the continent's income growth should be anchored on having an investor-friendly economy, which is a prerequisite for the massive development of small, medium, and large enterprises. An economy overwhelmed with volatile macroeconomic policies, insecurity, corruption, political instability, etc., will undoubtedly impact poorly on life expectancy, as individuals would have to embark on the aforementioned crude survival means, which are detrimental to environmental sustainability.
African countries should strive more to develop green urban cities. Smart cities should be replicated importantly in densely populated communities. Similarly, more awareness of the need to embrace energy-saving approaches should be encouraged and the use of energyefficient and environmentally friendly means of transportation and appliances. Consequently, improvement in human capital development is crucial to achieving environmental sustainability. A significant investment in human capital development, especially in developing curriculums that emphasise the two-way relationship between humans and the environment, is required. Such curriculum should, from early learning to advance schooling, robustly cover the beneficial impact of a symbiotic nexus between humans and their environment.
Lastly, policymakers and governments in the continent should also begin to fashion out strategies to lower their reliance on the extraction and utilisation of natural gas, fossil fuel, and minerals for income and energy sources with high adverse environmental impact. Thus, research and investment in renewables such as solar, geothermal, wind, tidal, hydropower, and hydrogen should be intensified. For instance, renewable hydrogen energy is increasingly gaining momentum in the energy world as a suitable replacement for fossil fuels in transport and energy sources for industrial and domestic demands. Hydrogen is also more efficient and ecofriendly than traditional energy sources such as oil and natural gas. Adopting these renewable energy sources can reduce detrimental environmental effects from conventional means, and sustainable economic growth and eco-friendly development can be guaranteed. However, an efficient, conservative approach to resource exploration should be imbibed in the interim. Innovative technologies that have been proven efficient in natural resources exploration should be considered above their purchasing cost, so long as they have a minimal adverse environmental impact.
Author contribution The corresponding author conceived the idea, wrote the introduction, collected and analysed the data, interpreted the results, reviewed the required literature, edited the manuscript, wrote the methodology section, provided the relevant policy directions, and read and approved the final manuscript.

Data availability
The data that support the findings of the study are available from the corresponding author upon reasonable request.

Declarations
Ethics approval This study article does not contain any study with human participants or animals performed by the author.