Moderating Effect of Institutional Quality on the Population Growth-Environmental Sustainability Nexus in Sub-Saharan Africa

Moderating Effect of Institutional Quality on the Population Growth-Environmental Sustainability Nexus in Sub-Saharan Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Moderating Effect of Institutional Quality on the Population Growth-Environmental Sustainability Nexus in Sub-Saharan Africa Christian Agu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3446276/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the moderating effect of institutional quality on the population growth-environmental sustainability nexus in Sub-Saharan Africa (SSA) over the period 2000–2020. Applying the Generalized Method of Moments (GMM) estimation technique and a Granger causality test to check if there exists any causality between population growth and emission levels, the findings indicate that population growth positively impacts on emission level in Sub-Saharan Africa, thus, affecting the environment negatively. However, its observed effect was statistically insignificant due to the interaction of institutions with population growth which proved significant. The results further indicate that other macroeconomic variables impacting positively and significantly on emission level in SSA are economic complexity index and per capita GDP. The study also establishes that there is no causal relationship between population growth and emission level in SSA. Lastly, the study finds that institutions play a vital role in reducing emission levels in the zone. It is therefore recommended that the government should vigorously pursue population and environmental policies directed at promoting environmental sustainability by controlling population, and promoting sustainable environmental practices. JEL Classification : O43; 044; J130; J180 Institutions Population Environmental sustainability Difference GMM SSA Figures Figure 1 1. Background to the study Population is a minefield in the global environmental movement. Many environmental groups and leaders have stopped trying to cross this minefield, because negotiating it is risky. However, concealing the reality of the situation is focusing on other important environmental issues such as global warming, sprawl, water and air pollution, the loss of agricultural land, biodiversity, and animal habitat while neglecting the main issue of population. The struggle for survival, to take use of nature's plentiful resources, and to care for the expanding human population has always been crucial to human evolution. However, using environmental resources for human use is nothing new (Wolman, 1993 ). The human species has faced environmental issues in one way or another throughout the majority of its tenure on this planet. The initial issue was the lack of local resources including food, housing, and other necessities that were at least partially given by nature (Fisher & Peterson, 1976 ). Hunter ( 2000 ), however, asserts that what is novel is the degree of resource consumption required by a "larger-than-ever" global population, which is currently growing by roughly 80 million people each year (World Bank, 2022 ). Life does not exist in a vacuum, it follows that human existence will have some relationship with the system supporting it (i.e. the environment). Where the human struggle for survival involves burning energies to harness resources from such system, it follows again that human economic processes generate wastes that are ejected into the environment which serves also as resource base, and a life support system (through provision of air and water). The apparently obvious implications are that more people on earth would require greater productive and consumption processes. On the one hand, it is anticipated that as the human population grows, more resources will need to be exploited for survival, and the objects of labor—primarily represented by the natural environment—will be subjected to additional stress and pressure. On the other hand, two significant factors raise and exacerbate a sustainability dilemma. First, the fact that so many of the resources currently being used are finite and unreplaceable shows that we are getting close to the point where our planet's natural resources can no longer be used indefinitely. Second, the generation of wastes by these human economic processes as well as increased use of facilities and services of the environment, risk the danger of deteriorating the quality of the environment; human existence is threatened by a process that in many ways pollutes its life support system. From the viewpoint of one person, the size of the Earth seems immense. The natural resources of the world have no apparent boundaries. But the world does not consist of just one person; there are currently over eight billion of us, and we are growing by a million people every 4.8 days on average (US Census Bureau, 2011 ). In contrast to 1950, when the world's population doubled, today some people have seen the population triple during their lifetimes (Cohen, 2003 ). The Anthropocene Epoch was created by scientists to define our era because of how many people currently live on the earth and how much impact they are having (Crutzan, 2002 ). Unfortunately, this is not without implications, as it adds to the uncertainties about human survival in the future, especially in an era where global human population needed only the second half of 20th century and early 21st century to more than triple what it had been for the whole of preceding years of human evolution. According to Hotelling ( 1931 ), concerns over the world's diminishing mineral resources, forested areas, and other exhaustible resources have prompted calls for restrictions on their utilisation. The conservation movement has its roots in the belief that these products are currently too inexpensive for the benefit of future generations, that they are being exploited selfishly at an excessive rate, and that as a result of their excessive affordability, they are being produced and consumed wastefully. The environment was being harmed by modern industrialization and population increase as early as the middle of the nineteenth century, according to writers. Unchecked population growth would exceed the capacity of the planet to provide enough food, according to Robert Malthus, whose "Essay on the Principle of Population" has either been extensively praised or ridiculed. Malthus started a discussion on carrying capacity, and his influence can still be felt today. However, this perspective has come under fire for its too simplistic focus on population size as the only factor influencing resource change. To some other researchers and theorists, however, the Malthusian ideation about the impact of human population and activities on the earth is rather defective and implausible. In fact, Thomas Malthus is cited by Zhou ( 2009 ) as an example of a scholar who had negative views on how population expansion will affect industrialisation and ultimately the environment. In addition, a Neo-Malthusian approach has recently emerged in discussions of the interaction between population and the environment, concentrating on the contribution of population size and increase to environmental decline (Jolly, 1994 ). The sustainability of the planet's life support systems and human well-being are both impacted by socioeconomic and environmental effects of population growth (Engelman, 1997 ). Farmland can get degraded, which can lower or even completely eliminate its productivity. According to Coffin (1993), this is due to the fact that satisfying the resource needs of a growing population ultimately necessitates some type of land-use change, whether to increase food production by clearing forests, to intensify production on already-cultivated land, or to build the infrastructure required to support increased population. Clarke ( 1996 ) stated that fast population expansion and global environmental change are two themes that have attracted major public attention during the past several decades. Population rise became a global public policy challenge during the mid-twentieth century as mortality drops in many developing nations were not matched by reductions in fertility, resulting in record growth rates. The public's awareness of environmental change has increased significantly since 1970, when observable levels of environmental degradation and the development of satellite photography to support environmental study fuel popular concern. The discussion of "sustainable development," which aims to fulfil the needs and aspirations of the current population without sacrificing the welfare of future generations, frequently includes both population and environmental concerns today (WCED, 1988 ). Without environmental sustainability, there can be no sustainable development. The fact that "safeguarding the environment" through a set of specified practises is one of the main objectives of both the current Sustainable Development Goals (SDGs) and the previous Millennium Development Goals (MDGs) of the United Nations comes as no surprise. The concept of sustainability is inextricably linked to the prudent management of the planet's natural resources. Population stabilisation may minimise the influence of people on the environment, with the idea being that less population means less strain on the land, air, and water environments (Hunter, 2000 ). The precise interaction between population dynamics and the environment is therefore complicated and poorly understood. The multiplicity of "mediating" factors that eventually shape this linkage make things more difficult. These include technological aspects (such as ways of producing energy), institutional and political aspects (such as environmental control), and cultural aspects. These complexities, notwithstanding, it is becoming increasingly clear that population growth has a powerful effect on the natural environment. Consequently, investigations have continued at local, regional and global levels. With the sustainability problem being most dreadful alongside its incriminating consequences, it is imperative to carry out this investigation, to examine the population-environment interactions in the most populous region in Africa- Sub-Saharan Africa. As much as the increase in population undoubtedly has its unavoidable impact on the environmental sustainability problem, it is imperative to consider the role of institutions in minimizing or controlling to some extent, the impacts of the increasing population on the environment via sound and consistent policies. Therefore, this study is conducted to investigate what is known about the association between population dynamics and the natural environment, and the role of institutions in this association, to determine the impact of population changes (growth) on the environment and examine the moderating effect of institutional quality on the population growth- environmental sustainability nexus in Sub-Saharan Africa, drawing from existing demographic and environmental literature. 2. Statement of the problem Man's activities are driven mainly by his needs, desires and interest (Smith, 1776 ). His primary needs are food, shelter and clothing which are all gotten from the environment’s resources. In the end, the health and integrity of the entire ecosystem in which we live determines human health, well-being, and even survival. Today, a variety of human activities and the sheer weight of human population are waging unprecedented war on the natural environment that we share with all life forms on this planet (Engelman, 1997 ; Engelman, Cincotta, Dye, Gardner & Wisenwski, 2000). Our demands on the world are beyond its natural boundaries due to the extraordinary increase in population, increased individual consumption, water pollution, eutrophication, and global warming. While a lot of positive efforts are being made to ensure the sustainability of humans on earth, the problem of having too many people has made the lasting remedies hard to discover. In 2020, the changes in population status of the Sub-Saharan Africa regions were recorded as shown in Table 1 : Table 1 Changes in population status of the Sub-Saharan Africa regions Region Population (2019) Population (2020) Yearly Change (%) Net Change Fertility Rate (%) Eastern Africa 433,904,943 445,405,606 2.65 11,500,663 4.434965689 Western Africa 391,440,157 401,861,254 2.66 10,421,097 5.183416088 Middle Africa 174,308,432 179,595,134 3.03 5,286,702 5.531989159 Southern Africa 66,629,895 67,503,635 1.31 873,740 2.499612524 Source : Statista, 2020 The Sub-Saharan African population has more than doubled since its discovery in the fifteenth century. With the data obtained from past censuses, the Sub-Saharan African population increased steadily from 904.28 million in 2011 to 1,181.16 billion in 2021. The region has one of the fastest growing populations in the world with present value estimated growth rate of about 2.6%. The trend of population growth in Sub-Saharan Africa from 2011 till 2021 is shown in the Fig. 1. Figure 1: Trend of population growth in Sub-Saharan Africa from 2011–2021 Source : Authors’ Excel Computation Using Data from World Bank By the middle of the century, Sub-Saharan Africa's population is expected to nearly quadruple to more than two billion people if current trends continue. The region's population is expanding three times faster than the worldwide average, and by 2070, it will surpass Asia as the world's most populous region, based on UN projections. Of course, the issue is not that the population is increasing; rather, it is that throughout time, this increase has been accompanied by some environmental problems, the majority of which are getting worse. The majority of the time, the evidence indicates that anthropogenic activities including trade, agriculture, forestry, deforestation, the use of fossil fuels, and other activities linked to economic growth are to blame for the production of greenhouse gases, such as CO2. However, anthropogenic activities increase as the human population grows, which results in an increase in CO2 emissions. For instance, Liddle ( 2015 ) argues that rising population over the coming decades may result in higher energy demand and higher CO2 emissions. The world ranks of some Sub-Saharan African countries by emission levels in 2016 is given in the Table 2 . Table 2 World rank of Sub-Saharan African countries by emission levels in 2016 Country Region Rank CO2 Emission (Tons) Population Change (%) Nigeria Western Africa 43rd 82,634,214 0.70 Angola Middle Africa 75th 30,566,933 3.13 Kenya Eastern Africa 91st 16,334,919 3.60 Ghana Western Africa 94th 14,469,986 3.54 Sudan Eastern Africa 96th 13,294,106 4.18 Ethiopia Eastern Africa 98th 10,438,855 4.03 Zimbabwe Southern Africa 100th 10,062,628 -4.17 Cote d’Ivoire Western Africa 101st 10,056,492 1.16 Tanzania Eastern Africa 103rd 9,731,560 2.50 Cameroon Western Africa 104th 9,454,331 2.21 Benin Western Africa 120th 6,563,709 4.13 Source : Worldometer, 2016 Concern over the rate at which CO2 emissions are increasing along with the zone's population growth develops at the same time. As a result, it is expected that the rapid population expansion will cause the zone's per capita emissions to increase. Expectedly, this will lead to a large increase in overall CO2 emissions. The National Aeronautics and Space Administration (NASA) recently assessed that methane, a chemically reactive GHG, has far greater impacts than previously thought. The acid rain that sulphur dioxide causes has a negative impact on both terrestrial and aquatic ecosystems (National Pollutant Inventory, 2006). Evidence from the literature has demonstrated that human activities are the primary cause of the production of greenhouse gases (GHG) such CO2 (carbon dioxide) and SO2 (sulphur dioxide), which are the two main causes of climate change. These consequences are growing daily. Although the African continent contributes a very small amount of GHGs to global emissions (Intergovernmental Panel on Climate Change, 2001), the primary reasons of GHG emission in Africa have not yet been thoroughly identified in the literature. Researchers have found that the agricultural industry has recently faced environmental pollution issues that can be attributed to new production practises and expanded production structures imbibed to meet the growing population and the demand for new energy globally (Narayan et al., 2008 ; Agbonlahor & Phillip, 2015 ; Siyan & Adegoriola, 2017 ). The Environmental Health Committee ( 2004 ) and Wood ( 2004 ) reports state that these gaseous contaminants have reached alarming levels. The health of the population is negatively impacted by exhaust from all combustion engines that contain these pollutants, which in turn has a negative impact on their productivity. When applied to the larger environment, engine combustion causes the buildup of carbon dioxide in the atmosphere and is to blame for climatic shifts (Gislason, 2006 ). Due to their ability to trap heat without releasing it as infrared or thermal radiation, GHGs like carbon dioxide and methane are among the air pollutants that contribute significantly to global warming (Oguntoke & Adeyemi, 2017 ). From the foregoing, it suffices to say the obvious that an investigation into the sustainability problem is both imperative and urgently called for. The significance of population variables to the status of the environment is demonstrated by the size, extent, and irreversibility of modern environmental change as well as the contribution of the population to that change. However, few studies have been done on Sub-Saharan Africa, despite the fact that many have been done on the effects of population growth on the environment. It is on these premises that this study is consummated to empirically address the following research questions, employing an econometric methodology to illuminate the process: (i) what is the impact of population growth on environmental sustainability in Sub-Saharan Africa?; (ii) is there any causal relationship between population growth and emission level in Sub-Saharan Africa?; and (iii) what is the moderating role of institutional quality on the population growth-environmental sustainability nexus in Sub-Saharan Africa? 3. Review of related literature Some theorists contend that rapid population expansion is a direct source of environmental deterioration, meaning that other factors influence the ecosystem indirectly through population growth. Rapid population expansion serves to amplify the environmental effects of the root causes, as opposed to eventually generating environmental degradation. These factors, which differ from place to region, include resource demand from wealthy countries, poverty, conflict, polluting technologies, and distorting policies. Shaw ( 1989 ) contends that it is possible to reconcile the two competing ideas, which both exonerate population expansion from any environmental harm and the other blames it for it. Population serves to amplify the effects of the root causes, but because it is a secondary factor, it does not directly contribute to environmental deterioration. By affecting the root causes, population growth might make things worse. Population growth would not matter much if the underlying factors were not active. Consider how the environment would not be impacted by the number of users if all polluting technologies were made pure. However, population expansion makes the issue worse because the root causes have not been addressed (Shaw, 1989 ). If a polluting technology is utilised by many individuals, the deterioration it causes will be greater than if it is only used by a few. Numerous research investigations have been carried out in this field as a result of the population-environmental sustainability nexus having sparked discussions throughout the years. The first study to look into the connection between energy use and income was conducted by Kraft and Kraft in 1978. Utilising data from the United States from 1947 to 1974, the study was carried out. Since the publication of this study, a number of empirical studies have been conducted using various methodologies and sample sizes to investigate the causality and/or relationship between energy use, trade openness, economic growth, population density, and CO2 emissions in various nations and regions of the world. In California, Cramer ( 1998 ) identified county-level correlations between emissions, population density, regulatory initiatives, and other relevant variables. Though population expansion affects different types of pollutants differently, results point to a 7.5–8% rise in emissions for every 10% increase in population. This is also consistent with information from Khan et al. ( 2013 ), who reported that Pakistan's energy use from 1975 to 2011 dramatically increased CO2 emissions. Begum et al. ( 2015 ) looked at how population growth, energy use, and GDP growth affected CO2 emissions. The ARDL bound testing method is used in the study for the 1970–2009 time frame. The findings showed that while population growth rate did not significantly affect per capita CO2 emissions, per capita energy consumption and GDP did have a long-term favourable impact. Ohlan ( 2015 ) examined the effects of India's population density, energy use, trade openness, and economic growth on CO2 emissions between 1970 and 2013. The study found that, in both the long- and short-term, population density, economic growth, and energy consumption have a considerable beneficial impact on CO2 emissions. However, it has been shown that India's population is the biggest cause of CO2 emissions. Mamudul et al. ( 2016 ) used time series data for the years 1970–2012 for China, Brazil, India, and Indonesia to assess the effects of energy use and population growth on CO2 emissions. For related time series data for the top emerging CO2 emitter countries in both the short run and the long run, the study applied the ARDL bound test approach while taking into account both the linear and non-linear assumptions. The findings showed that as wealth and energy consumption rose in the four countries, CO2 emissions also rose dramatically. In Brazil and India, the link between CO2 emissions and population increase was shown to be considerable, whereas in China and Indonesia, it was both short- and long-term inconsequential. The impact of population growth on carbon emissions in Nigeria is estimated by Casey and Galor ( 2016 ) using the STIRPAT model on an unbalanced annual panel of cross-country data from 1950 to 2010. Before taking into account the feedback from environmental damages to economic damages, the results demonstrate that population policies have a favourable impact on economic outcomes. Population measures would undoubtedly continue to produce these feedback benefits, but they are not required to produce favourable economic results. Similar to this, Goodness and Prosper ( 2017 ) used the dynamic panel threshold approach to examine how population growth and economic growth affect CO2 emissions. Data from a panel of 31 developing nations form the basis of the study. According to the findings, economic expansion has a negative impact on CO2 emissions under low growth conditions, but a positive impact under high growth conditions, with the marginal impact being greater under high growth conditions. The result establishes a U-shaped association rather than supporting the EKC theory. Population increase and energy use were both found to have a positive and considerable impact on CO2 emissions. Azam ( 2018 ) also looked at the relationship between population growth and the environment for Iran and other MENA nations from 2014 to 2020. According to the study's findings, air pollution and population growth rates are positively and significantly correlated in the MENA region. Additionally, it supports the existence of the kuznets environmental curves for the MENA region's nations. From 2013 to 2017, Yuan, Hongyuan, and Zeng ( 2022 ) looked at how green innovation affected CO2 emissions in China and how institutional quality had a moderating effect. Green innovation dramatically decreased CO2 emissions, according to the findings. The association between CO2 emissions and green innovation is negatively moderated by institutional quality, which results in a higher CO2 emission decrease when institutional quality is high. In 39 developing countries (mostly in the Sub-Saharan African region), Haldar and Sethi ( 2022 ) looked at the role of institutions in reducing the effect of energy consumption on CO2 emissions while controlling for other factors like trade, capital formation, FDI, financial development, and population from 1995 to 2017. The panel grouped-mean and panel quantile regression, mean group (MG), augmented mean group (AMG), common correlated effect mean group (CCEMG) estimator, dynamic system GMM, and were used for the empirical results. The results demonstrated that institutional quality controls energy use and improves its efficiency in reducing CO2 emissions. Energy use by industry and institutional quality have a large and detrimental combined effect on emissions. Long-term CO2 emissions are found to be greatly reduced by using renewable energy. The ARDL approach was used by Edmund and Tosun ( 2022 ) to look at potential international best practises for achieving sustainable environmental development in China. For this analysis, they used data from China from 1996Q1 to 2018Q4 and appropriate tools including institutions, technical innovation, and renewable energy. The environmental kuznet curve's inverted U-shaped idea is shown to be invalid for the instance of China by the findings. It also said that there was a proven inverse association between the chosen factors and the CO2. 2.4 Limitations of Previous Studies and Value Added There is no agreement on how to examine people and the environment, as is seen from the examination of the main theoretical frameworks. The discussion has mostly focused on the two opposing ideologies of the classical and neoclassical schools of economics. It has been challenging to obtain consensus since it is difficult to generalise policy from the varied experiences of different locations. Further research is required because the connection is not evident. It is clear from the foregoing studies that most studies that have been done in this area have focused on the direct impact of population on the environment. No study, to the best of our knowledge, has examined the moderating role of institutions in this relationship. The role of institutions is lacking, yet necessary for any meaningful debate on population and the environment. To fill this gap, this study adopts institutional quality as a moderating variable and examine its role in moderating the population-environmental sustainability nexus in Sub-Saharan Africa. 4. Methodology 4.1 Theoretical Framework To examine the interaction effect of institutions on the population-environment relationship in Sub-Saharan Africa, this study will draw from the STIRPAT model. The basic STIRPAT model as offered by Ehrlich and Holdren ( 1971 ), specified that environmental impact is a function of population, affluence, and technology, that is: I = P × A × T (1) Where; I = Environmental Impact P = Population A = Affluence T = Technology I = PCT, where C stands for consumption, was the original formula, but IPAT is a better acronym than IPCT since income per capita, not consumption, is what matters and is the easiest to assess. According to Perman et al. ( 2003 ), the identity reflects the amount of technical advancement required to maintain the same impact for any given change in population or wealth. The model is critiqued, nonetheless, due to some alleged flaws. It is a deterministic model, for instance, in that it emphasises population increase, prosperity, and technology as the only factors that can precisely account for environmental changes. In reality, events in the actual world don't always happen precisely; instead, they sometimes involve unpredictability and other complications that are not represented in deterministic models like the IPAT identity. As a result, there has been discussion among environmentalists over the validity of presenting environmental impact as a simple product of independent elements as well as the factors that should be included and their relative importance. Some have highlighted potential interactions between the three factors in particular, while others have wished to draw attention to other elements that were left out of the equation, such as societal and political structures and the potential for both advantageous and detrimental environmental actions (Alcott, 2010 ). Dietz and Rosa ( 1994 ) created the Stochastic Impact by Regression on Population, Affluence, and Technology (STIRPAT) model in an effort to address the shortcomings of the IPAT model. By including additional variables that can have an impact on the environment and utilising the stochastic term, ɛ, the STIRPAT model represents the randomness and complexity in the real world. Again, the model allows for great flexibility by taking into consideration the non-linearity in environmental impact from one region to another. The non-linear form of the STIRPAT model can be mathematically specified as follows: I = αPβ Aγ Tϕ ɛ (2) Where; I = Environmental impact variable P = Population A = Affluence T = Technology ɛ = Stochastic term A natural logarithmic transformation will yield a linearized form of the model as follows: lnI = lnα + βlnP + γlnA + ϕlnT + ɛ (3) Where the prefix ‘ln’ represents the natural logarithm of the variables in the STIRPAT model, and α, β, γ, and ϕ represent coefficient parameters 4.2 Model Specification The model for this investigation will be estimated using the system Generalized Method of Moment (GMM) technique, as described by Arellano and Bover ( 1995 ); and Blundel and Bond (1998). Functional Form : EML = f (POPG, RQ, POPG*RQ, ECI, EMP, PCGDP, GFCF) (4) Specifying Eq. (3.4) in a panel data and econometric form, gives: lnEML it = α i + λ i1 lnEML it−1 + β i1 lnPOPG it + β i2 RQ it + β i3 (lnPOPG it *RQ it ) + β i4 ECI it + β i5 lnEMP it + β i6 lnPCGDP it + β i7 GFCF it + ɤ it + ϵ it (5) The above model will be used to answer the objectives of the study. That is, to examine the impact of population growth on emission levels in Sub-Saharan Africa; to determine if there exists any causal relationship between population growth and emissions levels in Sub-Saharan Africa; and examining the moderating effect of institutional quality on the population growth- environmental sustainability nexus in the zone. Where; EML = Emission Levels POPG = Population Growth (annual %) RQ = Regulatory Quality ECI = Economic Complexity Index UNEMP = Unemployment (% of total labour force) PCGDP = Per Capita Gross Domestic Product GFCF = Gross Fixed Capital Formation α = Constant term ɤ = Unobserved country-specific effect ϵ = Stochastic error term EML it−1 = Lagged level of EML λ i1, β i1, β i2,… β i8, are the parameters to be estimated i = Cross-sectional index (i = 1, 2, … 26) t = The time period (t = 2000, 2001, … 2020) All variables except economic complexity index and regulatory quality will be transformed into their natural logarithms. The interaction term (lnPOPG it *RQ it ) is included to ascertain the moderating role of institutional quality on the relationship between population growth and environmental sustainability in Sub-Saharan African countries. 4.3 Method of Estimation This study employs the Generalized Method of Moments (GMM) estimation technique. In a dynamic panel model, the GMM is a dynamic panel data estimator that is especially designed to compensate for endogeneity of the lagged dependent variable. When the explanatory variable and the error term have a correlation, this is known as endogeneity. Hansen ( 1982 ) was the one who first introduced GMM. It employs orthogonality criteria to allow for effective estimate in the presence of unknown form heteroscedasticity. The general form of the GMM estimation is stated as follows: InY it = ɸInY it−1 + βX' it + αZ' it + (ɤ i + ϵ it ) (6) Incorporating our model into this, we have that: InY it = lnEML it - represents (N x 1) vector of regressand ɸInY it−1 = λ i1 lnEML it−1 – represents the lagged value of the regressand X' it = represents (2 x K) vector of regressors (i.e lnPOPG it RQ it ) Z' it = is a 4 x K vector of control variables i.e, ECI it , lnEMP it , InPCGDP it , lnGFCF it β = is a K x 1 vector of parameters to be estimated ɤ i = Unobserved country-specific effect ϵ it = Stochastic error term Following this, our STIRPAT model can be specified as follows: I it = f (X' it , Z' it ) (7) From Eq. (3.6), we take the natural logarithm, and introduce unobserved country-specific effects ɤ i , and the interactive terms lnPOPG it *RQ it . Measurement error is represented by ϵ it . Therefore, we have: InY it = ɸInY it−1 + βInX it + αInZ it + πlnPOPG it *RQ it + (ɤ i + ϵ it ) (8) The main goal is getting consistent estimate of β when N < T, holding that cov(X it , ɤ i , ϵ it ) ≠ 0, implying that all the regressors are respectively correlated with country-specific effects and measurement errors. To remove the country-specific effects in Eq. (3.8), first difference is applied thus: InY it - InY it−1 = ɸInY it−1 - ɸInY it−2 + βInX it - βInX it−1 + αInZ it - αInZ it−1 + π lnPOPG it *RQ it−1 + ɤ i - ɤ i + ϵ it - ϵ it−1 (9) So that Cov(X' it, ϵ it ) = 0 However, notwithstanding the first differencing, the problem of endogeneity remains because Cov(Y it−1, ϵ it−1 ) ≠ 0, that is, Y it is correlated with the past errors in Δ ϵ it and probably present Cov(Y it , ϵ it ) ≠ 0. Additionally, E(ɤ i ) = E(ϵ it ) = E(ɤ i, ϵ it ) = 0; E(ϵ it , ϵ js ) = 0. Arellano and Bond ( 1991 ) propose a Generalized Method of Moments estimator that uses all available delays in levels to instrument differenced variables that are not strictly exogenous. They also devised a test for autocorrelation, which can render some lags useless as instruments if it exists. Lagged levels are poor instruments for first differences if the variables are close to a random walk, which is a problem with the original Arellano-Bond estimator. In view of the foregoing, forward orthogonal deviations transformation will be applied instead of first differencing. The orthogonal deviations transform, proposed by Arellano and Bover ( 1995 ), subtracts the average of all possible future data rather than the prior observation. It is computable for all observations except the last for each individual, regardless of how many gaps there are, minimizing data loss. Therefore, Eq. (3.5) is specified thus: Δ lnEML it = λ i1 Δ lnEML it−1 + β i1 Δ lnPOPG it + β i2 Δ RQ it + β i3 (lnPOPG it *RQ it ) + β i4 Δ ECI it + β i5 Δ lnEMP it + β i6 Δ lnPCGDP it + β i7 Δ lnGFCF it + Δ ϵ it (10) Where; lnEML it−1 is the lag of the dependent variable emission levels lnPOPG it *RQ it is the interactive term ϵ it is the error term Levels Assuming that E[ Δ X it , ɤ i ] = E[ Δ Z it , ɤ i ] = 0 and that E[ Δ Y i2 , ɤ i ] = 0 satisfies initial conditions, then the additional moment conditions can be obtained as follows: E[ Δ X it−s ,( ɤ i + Z it )] = 0, since s = 1 when Z it ̴ MA(0), and for s = 2 when Z it ̴ MA(0) (Arellano and Bover, 1995 ). This allows for the use of appropriately lagged initial differences of variables as instruments for level equations. In a system with both first-differences and level equations, both sets of moment conditions can be used as a linear GMM estimator. A system GMM estimator is created by combining both sets of moment conditions. Moreover, because the autoregressive parameters will be too large given the nature of the data (relatively large panels over a short period), the Arellano and Bond estimator is likely to perform poorly. Therefore, building on further development of Arellano and Bover’s ( 1995 ) work by Blundell and Bond ( 1998 ), the system GMM is introduced which uses additional moment conditions. And as Roodman ( 2009 ) clearly stated it, the two-step system GMM is more efficient and robust to treat heteroscedasticity and autocorrelation. Using the system GMM, there are some basic diagnostic tests to be carried out in order to check for the suitability of the instrument sets used. These include the test for instrument validity developed by Hansen ( 1982 ) and Sargan ( 1985 ) known as the J-test. Secondly, test for autocorrelation/serial correlation of the second order (AR(2)) of the error term by Arellano and Bond ( 1991 ). These tests will be carried out accordingly. 4.4 Data source and software This study sampled 26 Sub-Saharan African countries between 2000 and 2020. The countries sampled were based on availability of data. The data was sourced from the World Development Indicators (WDI), Climate Change Data, and Economic Complexity Index (ECI). The definition and measurement of the variables used in the analysis are presented in Table 3.1 below. For the a priori expectations from both theoretical and empirical point of view, population growth has been found to have negative impact on environmental sustainability. Therefore, in this study, it will be expected that an increase in population growth will increase the emission levels. Stata 17 econometric software shall be employed in the analyses. Table 3 Description of variables, measurement and sources Variable Unit of measurement A priori sign Source EML Environment conditions (total greenhouse gases emissions) Climate Change Data POPG Population growth (annual %). An increase in population will increase emission levels, ceteris paribus Positive World Development Indicator RQ Regulatory quality estimate. An increase in the quality of regulations will decrease emission levels, ceteris paribus Negative World Development Indicator ECI Economic complexity index. An increase in ECI will increase emission levels, ceteris paribus Positive Economic Complexity Index EMP Employment rate (% of total labour force). An increase in EMP will decrease emission levels, ceteris paribus Negative World Development Indicator PCGDP GDP per capita. An increase in PCGDP will increase emission levels, ceteris paribus Positive World Development Indicator GFCF Gross Fixed Capital Formation. An increase in GFCF will decrease emission levels, ceteris paribus Negative World Development Indicator Source : Authors 4. Presentation and analysis of results 4.1. Summary statistics Table 4 shows the summary statistics of the data used in the analysis. It shows the mean, minimum, maximum, standard deviation, skewness and kurtosis. Variables whose skewness values are higher than 0 are long right tailed and skewed to the right, while those less than 0 are long left tailed and skewed to the left. Variables whose kurtosis values fall within 3 are mesokurtic, those which are less than 3 are platykurtic and those which are greater than 3 are leptokurtic. Table 4 Summary statistics Variable Mean Minimum Maximum Std. Dev Skewness Kurtosis LOGEML68980.882568.665555409.112482.42.7106629.951907 LOGPOPG2.393010.0022907 4.1558970.8169877 -0.99156663.580655 RQ-0.4859042 -2.201544 1.196970.5879908 0.25650383.550034 ECI-0.8352068 -2.5056750.8949380.5780881 -0.03705762.84022 LOGEMP59.259 36.071 85.866 13.99581 0.15717791.898115 LOGPCGDP643249.4 2699.761 5359616990368.3 2.2318937.953559 LOGGFCF22.98724 2.000441 81.021028.730262 1.71322910.30447 Source : Authors 4.2: Multicollinearity Test The multicollinearity test is conducted to ascertain if any correlations exist among the independent variables. A high degree of correlation among the explanatory variables, whether positive or negative, depicts a problem of multicollinearity in the model. A correlation is regarded as high and hence, problematic, if its coefficient exceeds 0.8. It is not desirable because it makes it difficult to determine the individual impact of such correlated regressors on the dependent variables. This is achieved by generating a correlation matrix like the one in Table 5 . Table 5 Correlation Matrix EML POPG RQ POPG*RQ ECI EMP PCGDP GFCF EML 1.0000 POPG 0.2660 1.0000 RQ -0.1382 -0.4256 1.0000 POPG*RQ -0.0591 0.1580 0.3159 1.0000 ECI -0.1273 -0.5097 0.5150 0.4066 1.0000 EMP 0.2453 0.2987 -0.2654 -0.2372 0.2654 1.0000 PCGDP -0.0898 0.1118 -0.0216 -0.1735 -0.2314 0.0541 1.0000 GFCF -0.0289 0.2931 0.1025 -0.2113 -0.2617 0.0005 0.1253 1.0000 Source : Authors Table 5 reports the degree of correlation among the variables of interest. It is observed that the correlation coefficients among these variables are healthy as none of these coefficients exceed 0.8, which could pose a problem of multicollinearity. Hence, there exist no problem of multicollinearity among the independent variables. 4.3: Bond Test It is essential to do the bond test in order to decide whether to use the system GMM style or the difference GMM style. In his second piece of advice, Bond (2001) advises that the autoregressive model be initially estimated using a pooled OLS and fixed effects technique. The corresponding fixed effects estimate should be regarded as a lower bound estimate, whereas the pooled OLS estimate should be regarded as an upper bound estimate. The system GMM estimator should be favoured over the difference GMM estimator if the obtained difference GMM estimate is near to or lower than the fixed effect estimate, indicating that the difference GMM is downward due to inadequate instrumentation. Table 6 Bond test result Test Coefficient of Lagged Dependent Variable Pooled OLS 0.9960364 Fixed Effect 0.6874917 Difference GMM 0.8811381 Decision Use Difference GMM Source : Author Table 6 shows the results of the pooled OLS, fixed effect and two step difference GMM. The result shows the coefficients of the lagged dependent variable (logeml). L1.logeml has a coefficient of 0.8811381 in the difference GMM estimation, which is no way close to or less than the fixed effect estimate (0.6874917). This then suggests that the difference GMM style will be the best estimator for this analysis instead of the system GMM. 4.4. System GMM results Table 7 Two step difference GMM results VARIABLES DIFFERENCE GMM L.LOGEML 0.8811381*** (0.3415) LOGPOPG 0.1295233 (0.1475) RQ -0.057007 (0.0998) POPG*RQ -0.2255209** (0.1285) ECI 0.1551789** (0.0740) LOGEMP -0.7454399 (0.5394) LOGPCGDP 0.8777867* (0.2797) LOGGFCF -0.0366819 (0.0668) OBSERVATIONS 494 INSTRUMENTS 12 GROUPS 26 AR (2) 0.412 HANSEN STATISTICS 0.313 Source : Authors. Note : *, ** and *** represent 10%, 5% and 1% levels of significance respectively. The corrected standard errors are in parentheses. From the results presented in Table 7 , it can be observed that the coefficient of population growth is 0.1295, suggesting that population growth has a positive relationship with emission levels. Therefore, holding other variables constant, a 1 per cent increase in the population growth leads to about 12.95% increase in emission level, and reduces environmental sustainability by the same amount. This result conforms to a priori expectation, and in fact supports the argument of the Classical theory. Although this conforms (at face value) to a prior expectation, the observed effect of population growth is not statistically significant. This research output equally agrees with the works of some researchers in the likes of Goodness and Prosper ( 2017 ) who found that population growth has a positive relationship with emission levels, such that the higher the population growth, the higher the emission levels. The coefficient of regulatory quality is -0.0570, implying a negative relationship between regulatory quality and emission levels. So that on the average, and holding other variables constant, a percentage increase in regulatory quality leads to about 5.70% decrease in emission level. This result conforms to a priori expectation, but the observed effect of regulatory quality is not statistically significant. This output agrees with Casey and Galor ( 2016 ), who found out that population policies have a positive effect on environmental performance, such that higher implementation of policies will lead to better environmental performance. The interaction of population growth and regulatory quality resulted in a negative coefficient − 0.2255. This explains that institution indeed moderates the population growth-environmental sustainability nexus in Sub-Saharan Africa. A percentage increase in institutional quality then would lead to a decrease in population growth by 22.55%, which will in turn lead to a decrease in emission levels by the same amount, thus sustaining the environment. The observed effect of the interaction between population growth and institutions is statistically significant at 5% level of significance. Regarding the control variables employed in the study, the coefficient of economic complexity is 0.1551, which indicates a positive relationship between the economic complexity index (ECI) and emission levels in SSA. The observed effect of ECI is statistically significant at 5% level of significance. So that holding other variables constant, a percentage increase in ECI leads to 15.51% increase in emission level. Every increase in emission level is certainly a decrease in environmental quality. This relationship conforms to a prior expectation because increasing economic complexity index is a result of increased economic activities, and hence, the generation of wastes, including gases. This output also agrees with Ogbuabor et al. ( 2023 ) who both found out that economic complexity index has a positive relationship with emission levels. The coefficient of employment is - 0.7454, which indicates a negative relationship between the level of employment and emission levels. The observed effect of employment is statistically insignificant at 5% level of significance. Holding other variables constant, a percentage increase in employment leads to 74.54% decrease in emission level. This relationship conforms to a prior expectation because increasing employment is expected to decrease the tendency of moving towards a linear economy, and hence, sustaining the environment. Furthermore, the coefficient of per capita GDP is 0.8777, implying a positive relationship between per capita GDP and emission levels. So that on the average, and holding other variables constant, a percentage increase in per capita GDP leads to about 87.77% increase in emission level. The observed effect of per capita GDP is statistically significant at 5% level of significance and conforms to the a priori expectation. This finding agrees with the finding of Alam and Kabir ( 2013 ). They found that GDP per capita has a significant positive impact on emission levels. Again, though it is somewhat in line with the argument of the environmental Kuznets curve (EKC), that as countries develop economically, moving from lower to higher levels of per capita income, overall levels of environmental degradation will eventually decrease. However, this cannot necessarily imply a proof of Kuznets’ ideation since Sub-Saharan Africa has not yet acquired the level of technology that can be used to control the level of emission, so that it can fall with every increase in per capita income. Finally, the coefficient of gross fixed capital formation (GFCF) is -0.0366, implying a negative relationship between GFCF and emission levels. So that on the average, and holding other variables constant, a percentage increase in GFCF leads to about 3.66% decrease in emission level. The observed effect of GFCF is statistically insignificant at 5% level of significance but conforms to the a priori expectation. 4.5. Long Run Coefficients From the above results, the interaction term, ECI and PCGDP were the only variables whose observed effects proved to be statistically significant. It is important to note that GMM estimates here apply only to the short run. To this effect, long run coefficients were generated to determine if these 3 variables also have statistical significance in the long run. Interaction Term Table 8 Long Run Coefficient of Interaction Term Logeml Coefficient Std. err . z P>|z| [95% conf. interval] _nl_1 − .1869271 .1142464 -1.64 0.102 − .4108459 .0369918 Source : Authors The long run coefficient explains that this interactive term will not be significant in the long run. Economic Complexity Table 9 Long Run Coefficient of Economic Complexity Index Logeml Coefficient Std. err . z P>|z| [95% conf. interval] _nl_1 .1286229 .0564963 2.28 0.023 .0178922 .2393536 Source : Authors The long run coefficient explains that economic complexity index will be significant in the long run. Per Capita GDP Table 10 Long Run Coefficient of Per Capita GDP Logeml Coefficient Std. err . z P>|z| [95% conf. interval] _nl_1 .7275694 .1002442 7.26 0.000 .5310943 .9240444 Source : Authors The long run coefficient explains that per capita GDP will be significant in the long run. 4.7. Granger Causality Test The Granger test explains the nature of causal relationship between population growth and emission levels in Sub-Saharan Africa as stated in objective 2. The result of this causality test as well as the computed f-Statistics, and their respective probabilities, with specific lag period is presented in Table 4.6 .1 below. To assess whether the null hypothesis would be accepted or rejected, a significance level of 5 percent was chosen. Table 4.6 Granger Causality Test Null Hypothesis: F-Statistic Prob. LOG_POPG_ does not Granger Cause LOG_EML_ 0.55762 0.7325 LOG_EML_ does not Granger Cause LOG_POPG_ 0.60314 0.6976 Note : The lag length of this test is 5. Source : Authors If the probability value of the F-statistic is less than 0.05 threshold of significance, the null hypothesis must be rejected in the Granger causality test. Table 4.6 ’s results show that none of the F-statistics' probability values are less than the 0.05 level of significance, thus we cannot rule out the null hypothesis and draw the conclusion that there is no connection between population increase and emission levels. This indicates that adjustments in the population growth five-year lag value have no effect on changes in emission levels. In a similar vein, the five-year lag value of emission levels has little bearing on changes in population growth. Thus, emission levels and population growth do not granger cause each other, implying that there is no causal relationship between them. 6. Conclusion and policy recommendations This study has fully lent itself to an empirical investigation of the relationship between population growth and environmental sustainability (measured in terms of emission levels) in Sub-Saharan Africa, from the period 2000-2020. Following formal econometric methodology, some findings about the research objectives and their corresponding hypothesis have been made, using time series data from the World Bank’s World Development Indicators, Economic Complexity Index and Climate Change Data. These findings, inter alia, principally include that population growth has an insignificant and positive impact on emission levels in Sub-Saharan Africa, and that no causal relationship exists between them. The implication of the former is that by positively impacting emission levels, population growth increases environmental pollution, thereby reducing environmental sustainability. Perhaps the absence of causal relationship between population growth and emission levels suggest that it is not necessarily population size that leads to generation of emissions, but the level of economic activities, for instance. However, the interaction between population growth and institutions shows a significant negative impact on emission level, indicating that strong institution is capable of moderating the impact of increasing population on the environment. On the basis of these empirical findings, therefore, that the following policy recommendations are advanced. This study's key policy conclusion is that population screening policies may be an efficient way to cut CO2 and other petrol emissions. Therefore, Sub-Saharan Africa can reduce CO2 emissions by implementing a cautious population stabilisation programme. Fertility control techniques, for example, can be crucial to halting environmental deterioration and raising living standards. To keep the human population within the boundaries of the earth's carrying capacity and to keep energy demand at a sustainable and environmentally acceptable level, fertility must be reduced by setting a restriction on the number of children a household can have in a certain period of time. The government must also make family planning available everywhere. By spacing out births, family planning will lower infant mortality, drop birth rates, increase demand for its services (through raising awareness), and maybe lower fertility. However, family planning is insufficient to decrease fertility as soon as is required. Other fertility-reduction strategies that the government should implement include maternity benefit restrictions, financial incentives, and educational programmes. These demographic initiatives need to be prioritised right away because they take a while to show benefits and can be difficult politically to implement. Sub-Saharan African governments and businesses should actively encourage and carry out green innovation ideas. The first step is for the federal, state, and local governments to actively foster an atmosphere that promotes green innovation. In order to promote green innovation, it is essential to strictly execute intellectual property protection mechanisms, concentrate on the pressing issues of CO2 emission reduction and environmental pollution control, and offer targeted fiscal and taxation policy support. Second, create an effective framework for conducting research on green innovation, boost funding for fundamental studies on the development of green technologies, and hasten the creation of scientific research partnerships between businesses, academic institutions, and governments. Accountability mechanisms, political stability, government effectiveness, regulatory quality, the rule of law, and corruption prevention should be the main concerns of the various governments in Sub-Saharan Africa. To ensure that the obligation to reduce CO2 emissions is placed on certain party and government leaders, it is necessary to improve and implement the accountability system in the field of ecological and environmental protection. Make an effort to create a sound and stable political environment that fosters green innovation and economic growth. References Agbonlahor MU, Phillip DOA (2015) Deciding to settle: Rural-rural migration and agricultural labour supply in Southwest Nigeria. J Developing Areas, 267–284 Alam S, Kabir N (2013) Economic growth and environmental sustainability: Empirical evidence from East and South-East Asia. Int J Econ Finance, 5 (2) Alcott B (2010) Impact caps: Why population, affluence and technology strategies should be abandoned. 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Econ Inq 47(2):249–265 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3446276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266011411,"identity":"84c1cbd8-6216-4077-a97f-04d8f893d8f3","order_by":0,"name":"Christian Agu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACCQaGA2AGewOYYmwgXgvPARK0QBkJRGrhn9378OAPBju7DTffmD0uYLCR3XCA9/ALvJbcOW5wmIchOXnD7Rxz4xkMacYbDvClWeDTYiCRxnCYgYE52eB2jpk0D8PhxA0HeMwMCGkBOqw+2eDmGZCW/8RpOQA03M7gBg9IywGQFuMHeP1yA+gwHoPjCZJn0sqkeQySjWce5jHDp4OBf0Ya88cfFdX2fMcPb5PmqbCT7TveY/wBrx6I8xgSG6AMYFAwsEngVQ0F9sgcZiJsGQWjYBSMghEEAHeTRe41Fd6JAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3289-1595","institution":"University of Nigeria","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Agu","suffix":""}],"badges":[],"createdAt":"2023-10-14 16:05:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3446276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3446276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49401996,"identity":"ffc35540-87bd-4636-ad2a-df1b246f3c4e","added_by":"auto","created_at":"2024-01-10 06:47:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend of population growth in Sub-Saharan Africa from 2011-2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Authors’ Excel Computation Using Data from World Bank\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3446276/v1/db162d42771371c344f80b13.png"},{"id":52962482,"identity":"ad86a5ce-0bb3-4ef2-978a-8e8c1dbb56c9","added_by":"auto","created_at":"2024-03-19 06:32:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":544996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3446276/v1/ee64fb1b-15bf-4568-8974-997aba951cb5.pdf"}],"financialInterests":"","formattedTitle":"Moderating Effect of Institutional Quality on the Population Growth-Environmental Sustainability Nexus in Sub-Saharan Africa","fulltext":[{"header":"1. Background to the study","content":"\u003cp\u003ePopulation is a minefield in the global environmental movement. Many environmental groups and leaders have stopped trying to cross this minefield, because negotiating it is risky. However, concealing the reality of the situation is focusing on other important environmental issues such as global warming, sprawl, water and air pollution, the loss of agricultural land, biodiversity, and animal habitat while neglecting the main issue of population. The struggle for survival, to take use of nature's plentiful resources, and to care for the expanding human population has always been crucial to human evolution. However, using environmental resources for human use is nothing new (Wolman, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The human species has faced environmental issues in one way or another throughout the majority of its tenure on this planet. The initial issue was the lack of local resources including food, housing, and other necessities that were at least partially given by nature (Fisher \u0026amp; Peterson, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Hunter (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), however, asserts that what is novel is the degree of resource consumption required by a \"larger-than-ever\" global population, which is currently growing by roughly 80\u0026nbsp;million people each year (World Bank, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLife does not exist in a vacuum, it follows that human existence will have some relationship with the system supporting it (i.e. the environment). Where the human struggle for survival involves burning energies to harness resources from such system, it follows again that human economic processes generate wastes that are ejected into the environment which serves also as resource base, and a life support system (through provision of air and water). The apparently obvious implications are that more people on earth would require greater productive and consumption processes. On the one hand, it is anticipated that as the human population grows, more resources will need to be exploited for survival, and the objects of labor\u0026mdash;primarily represented by the natural environment\u0026mdash;will be subjected to additional stress and pressure. On the other hand, two significant factors raise and exacerbate a sustainability dilemma. First, the fact that so many of the resources currently being used are finite and unreplaceable shows that we are getting close to the point where our planet's natural resources can no longer be used indefinitely. Second, the generation of wastes by these human economic processes as well as increased use of facilities and services of the environment, risk the danger of deteriorating the quality of the environment; human existence is threatened by a process that in many ways pollutes its life support system.\u003c/p\u003e \u003cp\u003eFrom the viewpoint of one person, the size of the Earth seems immense. The natural resources of the world have no apparent boundaries. But the world does not consist of just one person; there are currently over eight billion of us, and we are growing by a million people every 4.8 days on average (US Census Bureau, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast to 1950, when the world's population doubled, today some people have seen the population triple during their lifetimes (Cohen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The Anthropocene Epoch was created by scientists to define our era because of how many people currently live on the earth and how much impact they are having (Crutzan, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnfortunately, this is not without implications, as it adds to the uncertainties about human survival in the future, especially in an era where global human population needed only the second half of 20th century and early 21st century to more than triple what it had been for the whole of preceding years of human evolution. According to Hotelling (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1931\u003c/span\u003e), concerns over the world's diminishing mineral resources, forested areas, and other exhaustible resources have prompted calls for restrictions on their utilisation. The conservation movement has its roots in the belief that these products are currently too inexpensive for the benefit of future generations, that they are being exploited selfishly at an excessive rate, and that as a result of their excessive affordability, they are being produced and consumed wastefully.\u003c/p\u003e \u003cp\u003eThe environment was being harmed by modern industrialization and population increase as early as the middle of the nineteenth century, according to writers. Unchecked population growth would exceed the capacity of the planet to provide enough food, according to Robert Malthus, whose \"Essay on the Principle of Population\" has either been extensively praised or ridiculed. Malthus started a discussion on carrying capacity, and his influence can still be felt today. However, this perspective has come under fire for its too simplistic focus on population size as the only factor influencing resource change. To some other researchers and theorists, however, the Malthusian ideation about the impact of human population and activities on the earth is rather defective and implausible. In fact, Thomas Malthus is cited by Zhou (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) as an example of a scholar who had negative views on how population expansion will affect industrialisation and ultimately the environment. In addition, a Neo-Malthusian approach has recently emerged in discussions of the interaction between population and the environment, concentrating on the contribution of population size and increase to environmental decline (Jolly, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sustainability of the planet's life support systems and human well-being are both impacted by socioeconomic and environmental effects of population growth (Engelman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Farmland can get degraded, which can lower or even completely eliminate its productivity. According to Coffin (1993), this is due to the fact that satisfying the resource needs of a growing population ultimately necessitates some type of land-use change, whether to increase food production by clearing forests, to intensify production on already-cultivated land, or to build the infrastructure required to support increased population. Clarke (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) stated that fast population expansion and global environmental change are two themes that have attracted major public attention during the past several decades. Population rise became a global public policy challenge during the mid-twentieth century as mortality drops in many developing nations were not matched by reductions in fertility, resulting in record growth rates. The public's awareness of environmental change has increased significantly since 1970, when observable levels of environmental degradation and the development of satellite photography to support environmental study fuel popular concern.\u003c/p\u003e \u003cp\u003eThe discussion of \"sustainable development,\" which aims to fulfil the needs and aspirations of the current population without sacrificing the welfare of future generations, frequently includes both population and environmental concerns today (WCED, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Without environmental sustainability, there can be no sustainable development. The fact that \"safeguarding the environment\" through a set of specified practises is one of the main objectives of both the current Sustainable Development Goals (SDGs) and the previous Millennium Development Goals (MDGs) of the United Nations comes as no surprise. The concept of sustainability is inextricably linked to the prudent management of the planet's natural resources. Population stabilisation may minimise the influence of people on the environment, with the idea being that less population means less strain on the land, air, and water environments (Hunter, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe precise interaction between population dynamics and the environment is therefore complicated and poorly understood. The multiplicity of \"mediating\" factors that eventually shape this linkage make things more difficult. These include technological aspects (such as ways of producing energy), institutional and political aspects (such as environmental control), and cultural aspects. These complexities, notwithstanding, it is becoming increasingly clear that population growth has a powerful effect on the natural environment. Consequently, investigations have continued at local, regional and global levels. With the sustainability problem being most dreadful alongside its incriminating consequences, it is imperative to carry out this investigation, to examine the population-environment interactions in the most populous region in Africa- Sub-Saharan Africa. As much as the increase in population undoubtedly has its unavoidable impact on the environmental sustainability problem, it is imperative to consider the role of institutions in minimizing or controlling to some extent, the impacts of the increasing population on the environment via sound and consistent policies. Therefore, this study is conducted to investigate what is known about the association between population dynamics and the natural environment, and the role of institutions in this association, to determine the impact of population changes (growth) on the environment and examine the moderating effect of institutional quality on the population growth- environmental sustainability nexus in Sub-Saharan Africa, drawing from existing demographic and environmental literature.\u003c/p\u003e"},{"header":"2. Statement of the problem","content":"\u003cp\u003eMan's activities are driven mainly by his needs, desires and interest (Smith, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1776\u003c/span\u003e). His primary needs are food, shelter and clothing which are all gotten from the environment\u0026rsquo;s resources. In the end, the health and integrity of the entire ecosystem in which we live determines human health, well-being, and even survival. Today, a variety of human activities and the sheer weight of human population are waging unprecedented war on the natural environment that we share with all life forms on this planet (Engelman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Engelman, Cincotta, Dye, Gardner \u0026amp; Wisenwski, 2000). Our demands on the world are beyond its natural boundaries due to the extraordinary increase in population, increased individual consumption, water pollution, eutrophication, and global warming. While a lot of positive efforts are being made to ensure the sustainability of humans on earth, the problem of having too many people has made the lasting remedies hard to discover. In 2020, the changes in population status of the Sub-Saharan Africa regions were recorded as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in population status of the Sub-Saharan Africa regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003e(2019)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003e(2020)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYearly\u003c/p\u003e \u003cp\u003eChange (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFertility\u003c/p\u003e \u003cp\u003eRate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e433,904,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e445,405,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,500,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.434965689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391,440,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e401,861,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,421,097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.183416088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174,308,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179,595,134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,286,702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.531989159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66,629,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67,503,635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e873,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.499612524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Statista, 2020\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Sub-Saharan African population has more than doubled since its discovery in the fifteenth century. With the data obtained from past censuses, the Sub-Saharan African population increased steadily from 904.28\u0026nbsp;million in 2011 to 1,181.16\u0026nbsp;billion in 2021. The region has one of the fastest growing populations in the world with present value estimated growth rate of about 2.6%. The trend of population growth in Sub-Saharan Africa from 2011 till 2021 is shown in the Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1: Trend of population growth in Sub-Saharan Africa from 2011\u0026ndash;2021\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource\u003c/b\u003e: Authors\u0026rsquo; Excel Computation Using Data from World Bank\u003c/p\u003e \u003cp\u003eBy the middle of the century, Sub-Saharan Africa's population is expected to nearly quadruple to more than two billion people if current trends continue. The region's population is expanding three times faster than the worldwide average, and by 2070, it will surpass Asia as the world's most populous region, based on UN projections. Of course, the issue is not that the population is increasing; rather, it is that throughout time, this increase has been accompanied by some environmental problems, the majority of which are getting worse. The majority of the time, the evidence indicates that anthropogenic activities including trade, agriculture, forestry, deforestation, the use of fossil fuels, and other activities linked to economic growth are to blame for the production of greenhouse gases, such as CO2. However, anthropogenic activities increase as the human population grows, which results in an increase in CO2 emissions. For instance, Liddle (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) argues that rising population over the coming decades may result in higher energy demand and higher CO2 emissions. The world ranks of some Sub-Saharan African countries by emission levels in 2016 is given in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWorld rank of Sub-Saharan African countries by emission levels in 2016\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO2 Emission\u003c/p\u003e \u003cp\u003e(Tons)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation Change\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82,634,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30,566,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,334,919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,469,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSudan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,294,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,438,855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,062,628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCote d\u0026rsquo;Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,056,492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,731,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCameroon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,454,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,563,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Worldometer, 2016\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcern over the rate at which CO2 emissions are increasing along with the zone's population growth develops at the same time. As a result, it is expected that the rapid population expansion will cause the zone's per capita emissions to increase. Expectedly, this will lead to a large increase in overall CO2 emissions. The National Aeronautics and Space Administration (NASA) recently assessed that methane, a chemically reactive GHG, has far greater impacts than previously thought. The acid rain that sulphur dioxide causes has a negative impact on both terrestrial and aquatic ecosystems (National Pollutant Inventory, 2006). Evidence from the literature has demonstrated that human activities are the primary cause of the production of greenhouse gases (GHG) such CO2 (carbon dioxide) and SO2 (sulphur dioxide), which are the two main causes of climate change. These consequences are growing daily.\u003c/p\u003e \u003cp\u003eAlthough the African continent contributes a very small amount of GHGs to global emissions (Intergovernmental Panel on Climate Change, 2001), the primary reasons of GHG emission in Africa have not yet been thoroughly identified in the literature. Researchers have found that the agricultural industry has recently faced environmental pollution issues that can be attributed to new production practises and expanded production structures imbibed to meet the growing population and the demand for new energy globally (Narayan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Agbonlahor \u0026amp; Phillip, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Siyan \u0026amp; Adegoriola, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Environmental Health Committee (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and Wood (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) reports state that these gaseous contaminants have reached alarming levels. The health of the population is negatively impacted by exhaust from all combustion engines that contain these pollutants, which in turn has a negative impact on their productivity. When applied to the larger environment, engine combustion causes the buildup of carbon dioxide in the atmosphere and is to blame for climatic shifts (Gislason, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Due to their ability to trap heat without releasing it as infrared or thermal radiation, GHGs like carbon dioxide and methane are among the air pollutants that contribute significantly to global warming (Oguntoke \u0026amp; Adeyemi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the foregoing, it suffices to say the obvious that an investigation into the sustainability problem is both imperative and urgently called for. The significance of population variables to the status of the environment is demonstrated by the size, extent, and irreversibility of modern environmental change as well as the contribution of the population to that change. However, few studies have been done on Sub-Saharan Africa, despite the fact that many have been done on the effects of population growth on the environment. It is on these premises that this study is consummated to empirically address the following research questions, employing an econometric methodology to illuminate the process: (i) what is the impact of population growth on environmental sustainability in Sub-Saharan Africa?; (ii) is there any causal relationship between population growth and emission level in Sub-Saharan Africa?; and (iii) what is the moderating role of institutional quality on the population growth-environmental sustainability nexus in Sub-Saharan Africa?\u003c/p\u003e"},{"header":"3. Review of related literature","content":"\u003cp\u003eSome theorists contend that rapid population expansion is a direct source of environmental deterioration, meaning that other factors influence the ecosystem indirectly through population growth. Rapid population expansion serves to amplify the environmental effects of the root causes, as opposed to eventually generating environmental degradation. These factors, which differ from place to region, include resource demand from wealthy countries, poverty, conflict, polluting technologies, and distorting policies. Shaw (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) contends that it is possible to reconcile the two competing ideas, which both exonerate population expansion from any environmental harm and the other blames it for it. Population serves to amplify the effects of the root causes, but because it is a secondary factor, it does not directly contribute to environmental deterioration. By affecting the root causes, population growth might make things worse. Population growth would not matter much if the underlying factors were not active. Consider how the environment would not be impacted by the number of users if all polluting technologies were made pure. However, population expansion makes the issue worse because the root causes have not been addressed (Shaw, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). If a polluting technology is utilised by many individuals, the deterioration it causes will be greater than if it is only used by a few.\u003c/p\u003e \u003cp\u003eNumerous research investigations have been carried out in this field as a result of the population-environmental sustainability nexus having sparked discussions throughout the years. The first study to look into the connection between energy use and income was conducted by Kraft and Kraft in 1978. Utilising data from the United States from 1947 to 1974, the study was carried out. Since the publication of this study, a number of empirical studies have been conducted using various methodologies and sample sizes to investigate the causality and/or relationship between energy use, trade openness, economic growth, population density, and CO2 emissions in various nations and regions of the world. In California, Cramer (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) identified county-level correlations between emissions, population density, regulatory initiatives, and other relevant variables. Though population expansion affects different types of pollutants differently, results point to a 7.5\u0026ndash;8% rise in emissions for every 10% increase in population. This is also consistent with information from Khan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who reported that Pakistan's energy use from 1975 to 2011 dramatically increased CO2 emissions.\u003c/p\u003e \u003cp\u003eBegum et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) looked at how population growth, energy use, and GDP growth affected CO2 emissions. The ARDL bound testing method is used in the study for the 1970\u0026ndash;2009 time frame. The findings showed that while population growth rate did not significantly affect per capita CO2 emissions, per capita energy consumption and GDP did have a long-term favourable impact. Ohlan (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) examined the effects of India's population density, energy use, trade openness, and economic growth on CO2 emissions between 1970 and 2013. The study found that, in both the long- and short-term, population density, economic growth, and energy consumption have a considerable beneficial impact on CO2 emissions. However, it has been shown that India's population is the biggest cause of CO2 emissions. Mamudul et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) used time series data for the years 1970\u0026ndash;2012 for China, Brazil, India, and Indonesia to assess the effects of energy use and population growth on CO2 emissions. For related time series data for the top emerging CO2 emitter countries in both the short run and the long run, the study applied the ARDL bound test approach while taking into account both the linear and non-linear assumptions. The findings showed that as wealth and energy consumption rose in the four countries, CO2 emissions also rose dramatically. In Brazil and India, the link between CO2 emissions and population increase was shown to be considerable, whereas in China and Indonesia, it was both short- and long-term inconsequential.\u003c/p\u003e \u003cp\u003eThe impact of population growth on carbon emissions in Nigeria is estimated by Casey and Galor (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) using the STIRPAT model on an unbalanced annual panel of cross-country data from 1950 to 2010. Before taking into account the feedback from environmental damages to economic damages, the results demonstrate that population policies have a favourable impact on economic outcomes. Population measures would undoubtedly continue to produce these feedback benefits, but they are not required to produce favourable economic results. Similar to this, Goodness and Prosper (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used the dynamic panel threshold approach to examine how population growth and economic growth affect CO2 emissions. Data from a panel of 31 developing nations form the basis of the study. According to the findings, economic expansion has a negative impact on CO2 emissions under low growth conditions, but a positive impact under high growth conditions, with the marginal impact being greater under high growth conditions. The result establishes a U-shaped association rather than supporting the EKC theory. Population increase and energy use were both found to have a positive and considerable impact on CO2 emissions.\u003c/p\u003e \u003cp\u003eAzam (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also looked at the relationship between population growth and the environment for Iran and other MENA nations from 2014 to 2020. According to the study's findings, air pollution and population growth rates are positively and significantly correlated in the MENA region. Additionally, it supports the existence of the kuznets environmental curves for the MENA region's nations. From 2013 to 2017, Yuan, Hongyuan, and Zeng (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) looked at how green innovation affected CO2 emissions in China and how institutional quality had a moderating effect. Green innovation dramatically decreased CO2 emissions, according to the findings. The association between CO2 emissions and green innovation is negatively moderated by institutional quality, which results in a higher CO2 emission decrease when institutional quality is high.\u003c/p\u003e \u003cp\u003eIn 39 developing countries (mostly in the Sub-Saharan African region), Haldar and Sethi (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) looked at the role of institutions in reducing the effect of energy consumption on CO2 emissions while controlling for other factors like trade, capital formation, FDI, financial development, and population from 1995 to 2017. The panel grouped-mean and panel quantile regression, mean group (MG), augmented mean group (AMG), common correlated effect mean group (CCEMG) estimator, dynamic system GMM, and were used for the empirical results. The results demonstrated that institutional quality controls energy use and improves its efficiency in reducing CO2 emissions. Energy use by industry and institutional quality have a large and detrimental combined effect on emissions. Long-term CO2 emissions are found to be greatly reduced by using renewable energy. The ARDL approach was used by Edmund and Tosun (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to look at potential international best practises for achieving sustainable environmental development in China. For this analysis, they used data from China from 1996Q1 to 2018Q4 and appropriate tools including institutions, technical innovation, and renewable energy. The environmental kuznet curve's inverted U-shaped idea is shown to be invalid for the instance of China by the findings. It also said that there was a proven inverse association between the chosen factors and the CO2.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Limitations of Previous Studies and Value Added\u003c/h2\u003e \u003cp\u003eThere is no agreement on how to examine people and the environment, as is seen from the examination of the main theoretical frameworks. The discussion has mostly focused on the two opposing ideologies of the classical and neoclassical schools of economics. It has been challenging to obtain consensus since it is difficult to generalise policy from the varied experiences of different locations. Further research is required because the connection is not evident.\u003c/p\u003e \u003cp\u003eIt is clear from the foregoing studies that most studies that have been done in this area have focused on the direct impact of population on the environment. No study, to the best of our knowledge, has examined the moderating role of institutions in this relationship. The role of institutions is lacking, yet necessary for any meaningful debate on population and the environment. To fill this gap, this study adopts institutional quality as a moderating variable and examine its role in moderating the population-environmental sustainability nexus in Sub-Saharan Africa.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":" \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Theoretical Framework\u003c/h2\u003e \u003cp\u003eTo examine the interaction effect of institutions on the population-environment relationship in Sub-Saharan Africa, this study will draw from the STIRPAT model. The basic STIRPAT model as offered by Ehrlich and Holdren (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), specified that environmental impact is a function of population, affluence, and technology, that is:\u003c/p\u003e \u003cp\u003eI\u0026thinsp;=\u0026thinsp;P \u0026times; A \u0026times; T (1)\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003eI\u0026thinsp;=\u0026thinsp;Environmental Impact\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;Population\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;Affluence\u003c/p\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;Technology\u003c/p\u003e \u003cp\u003eI\u0026thinsp;=\u0026thinsp;PCT, where C stands for consumption, was the original formula, but IPAT is a better acronym than IPCT since income per capita, not consumption, is what matters and is the easiest to assess. According to Perman et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), the identity reflects the amount of technical advancement required to maintain the same impact for any given change in population or wealth. The model is critiqued, nonetheless, due to some alleged flaws. It is a deterministic model, for instance, in that it emphasises population increase, prosperity, and technology as the only factors that can precisely account for environmental changes. In reality, events in the actual world don't always happen precisely; instead, they sometimes involve unpredictability and other complications that are not represented in deterministic models like the IPAT identity.\u003c/p\u003e \u003cp\u003eAs a result, there has been discussion among environmentalists over the validity of presenting environmental impact as a simple product of independent elements as well as the factors that should be included and their relative importance. Some have highlighted potential interactions between the three factors in particular, while others have wished to draw attention to other elements that were left out of the equation, such as societal and political structures and the potential for both advantageous and detrimental environmental actions (Alcott, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDietz and Rosa (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) created the Stochastic Impact by Regression on Population, Affluence, and Technology (STIRPAT) model in an effort to address the shortcomings of the IPAT model. By including additional variables that can have an impact on the environment and utilising the stochastic term, ɛ, the STIRPAT model represents the randomness and complexity in the real world. Again, the model allows for great flexibility by taking into consideration the non-linearity in environmental impact from one region to another. The non-linear form of the STIRPAT model can be mathematically specified as follows:\u003c/p\u003e \u003cp\u003eI\u0026thinsp;=\u0026thinsp;αPβ Aγ Tϕ ɛ (2)\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003eI\u0026thinsp;=\u0026thinsp;Environmental impact variable\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;Population\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;Affluence\u003c/p\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;Technology\u003c/p\u003e \u003cp\u003eɛ = Stochastic term\u003c/p\u003e \u003cp\u003eA natural logarithmic transformation will yield a linearized form of the model as follows:\u003c/p\u003e \u003cp\u003elnI\u0026thinsp;=\u0026thinsp;lnα\u0026thinsp;+\u0026thinsp;βlnP\u0026thinsp;+\u0026thinsp;γlnA\u0026thinsp;+\u0026thinsp;ϕlnT + ɛ (3)\u003c/p\u003e \u003cp\u003eWhere the prefix \u0026lsquo;ln\u0026rsquo; represents the natural logarithm of the variables in the STIRPAT model, and α, β, γ, and ϕ represent coefficient parameters\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Model Specification\u003c/h2\u003e \u003cp\u003eThe model for this investigation will be estimated using the system Generalized Method of Moment (GMM) technique, as described by Arellano and Bover (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); and Blundel and Bond (1998).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional Form\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eEML\u0026thinsp;=\u0026thinsp;f (POPG, RQ, POPG*RQ, ECI, EMP, PCGDP, GFCF) (4)\u003c/p\u003e \u003cp\u003eSpecifying Eq.\u0026nbsp;(3.4) in a panel data and econometric form, gives:\u003c/p\u003e \u003cp\u003elnEML\u003csub\u003eit =\u003c/sub\u003e α\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;λ\u003csub\u003ei1\u003c/sub\u003elnEML\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei1\u003c/sub\u003elnPOPG\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei2\u003c/sub\u003eRQ\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei3\u003c/sub\u003e(lnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u003c/sub\u003e) + β\u003csub\u003ei4\u003c/sub\u003e ECI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei5\u003c/sub\u003elnEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei6\u003c/sub\u003elnPCGDP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei7\u003c/sub\u003eGFCF\u003csub\u003eit\u003c/sub\u003e + ɤ\u003csub\u003eit\u003c/sub\u003e + ϵ\u003csub\u003eit\u003c/sub\u003e (5)\u003c/p\u003e \u003cp\u003eThe above model will be used to answer the objectives of the study. That is, to examine the impact of population growth on emission levels in Sub-Saharan Africa; to determine if there exists any causal relationship between population growth and emissions levels in Sub-Saharan Africa; and examining the moderating effect of institutional quality on the population growth- environmental sustainability nexus in the zone.\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003eEML\u0026thinsp;=\u0026thinsp;Emission Levels\u003c/p\u003e \u003cp\u003ePOPG\u0026thinsp;=\u0026thinsp;Population Growth (annual %)\u003c/p\u003e \u003cp\u003eRQ\u0026thinsp;=\u0026thinsp;Regulatory Quality\u003c/p\u003e \u003cp\u003eECI\u0026thinsp;=\u0026thinsp;Economic Complexity Index\u003c/p\u003e \u003cp\u003eUNEMP\u0026thinsp;=\u0026thinsp;Unemployment (% of total labour force)\u003c/p\u003e \u003cp\u003ePCGDP\u0026thinsp;=\u0026thinsp;Per Capita Gross Domestic Product\u003c/p\u003e \u003cp\u003eGFCF\u0026thinsp;=\u0026thinsp;Gross Fixed Capital Formation\u003c/p\u003e \u003cp\u003eα\u0026thinsp;=\u0026thinsp;Constant term\u003c/p\u003e \u003cp\u003eɤ = Unobserved country-specific effect\u003c/p\u003e \u003cp\u003eϵ = Stochastic error term\u003c/p\u003e \u003cp\u003eEML\u003csub\u003eit\u0026minus;1\u003c/sub\u003e = Lagged level of EML\u003c/p\u003e \u003cp\u003eλ\u003csub\u003ei1,\u003c/sub\u003e β\u003csub\u003ei1,\u003c/sub\u003e β\u003csub\u003ei2,\u0026hellip;\u003c/sub\u003eβ\u003csub\u003ei8,\u003c/sub\u003e are the parameters to be estimated\u003c/p\u003e \u003cp\u003ei\u0026thinsp;=\u0026thinsp;Cross-sectional index (i\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip; 26)\u003c/p\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;The time period (t\u0026thinsp;=\u0026thinsp;2000, 2001, \u0026hellip; 2020)\u003c/p\u003e \u003cp\u003eAll variables except economic complexity index and regulatory quality will be transformed into their natural logarithms.\u003c/p\u003e \u003cp\u003eThe interaction term (lnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u003c/sub\u003e) is included to ascertain the moderating role of institutional quality on the relationship between population growth and environmental sustainability in Sub-Saharan African countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Method of Estimation\u003c/h2\u003e \u003cp\u003eThis study employs the Generalized Method of Moments (GMM) estimation technique. In a dynamic panel model, the GMM is a dynamic panel data estimator that is especially designed to compensate for endogeneity of the lagged dependent variable. When the explanatory variable and the error term have a correlation, this is known as endogeneity. Hansen (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) was the one who first introduced GMM. It employs orthogonality criteria to allow for effective estimate in the presence of unknown form heteroscedasticity.\u003c/p\u003e \u003cp\u003eThe general form of the GMM estimation is stated as follows:\u003c/p\u003e \u003cp\u003eInY\u003csub\u003eit\u003c/sub\u003e = ɸInY\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;βX'\u003csub\u003eit\u003c/sub\u003e + αZ'\u003csub\u003eit\u003c/sub\u003e+ (ɤ\u003csub\u003ei\u003c/sub\u003e + ϵ\u003csub\u003eit\u003c/sub\u003e) (6)\u003c/p\u003e \u003cp\u003eIncorporating our model into this, we have that:\u003c/p\u003e \u003cp\u003eInY\u003csub\u003eit\u003c/sub\u003e = lnEML\u003csub\u003eit\u003c/sub\u003e - represents (N x 1) vector of regressand\u003c/p\u003e \u003cp\u003eɸInY\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;λ\u003csub\u003ei1\u003c/sub\u003elnEML\u003csub\u003eit\u0026minus;1\u003c/sub\u003e \u0026ndash; represents the lagged value of the regressand\u003c/p\u003e \u003cp\u003eX'\u003csub\u003eit\u003c/sub\u003e = represents (2 x K) vector of regressors (i.e lnPOPG\u003csub\u003eit\u003c/sub\u003e RQ\u003csub\u003eit\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eZ'\u003csub\u003eit\u003c/sub\u003e = is a 4 x K vector of control variables i.e, ECI\u003csub\u003eit\u003c/sub\u003e, lnEMP\u003csub\u003eit\u003c/sub\u003e, InPCGDP\u003csub\u003eit\u003c/sub\u003e, lnGFCF\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;is a K x 1 vector of parameters to be estimated\u003c/p\u003e \u003cp\u003eɤ\u003csub\u003ei\u003c/sub\u003e \u003cb\u003e=\u003c/b\u003e Unobserved country-specific effect\u003c/p\u003e \u003cp\u003eϵ\u003csub\u003eit\u003c/sub\u003e = Stochastic error term\u003c/p\u003e \u003cp\u003eFollowing this, our STIRPAT model can be specified as follows:\u003c/p\u003e \u003cp\u003eI\u003csub\u003eit\u003c/sub\u003e = f (X'\u003csub\u003eit\u003c/sub\u003e, Z'\u003csub\u003eit\u003c/sub\u003e) (7)\u003c/p\u003e \u003cp\u003eFrom Eq.\u0026nbsp;(3.6), we take the natural logarithm, and introduce unobserved country-specific effects ɤ\u003csub\u003ei\u003c/sub\u003e, and the interactive terms lnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u003c/sub\u003e. Measurement error is represented by ϵ\u003csub\u003eit\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eTherefore, we have:\u003c/p\u003e \u003cp\u003eInY\u003csub\u003eit\u003c/sub\u003e = ɸInY\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;βInX\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;αInZ\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;πlnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u003c/sub\u003e + (ɤ\u003csub\u003ei\u003c/sub\u003e + ϵ\u003csub\u003eit\u003c/sub\u003e) (8)\u003c/p\u003e \u003cp\u003eThe main goal is getting consistent estimate of β when N\u0026thinsp;\u0026lt;\u0026thinsp;T, holding that cov(X\u003csub\u003eit\u003c/sub\u003e, ɤ\u003csub\u003ei\u003c/sub\u003e, ϵ\u003csub\u003eit\u003c/sub\u003e)\u0026thinsp;\u0026ne;\u0026thinsp;0, implying that all the regressors are respectively correlated with country-specific effects and measurement errors.\u003c/p\u003e \u003cp\u003eTo remove the country-specific effects in Eq.\u0026nbsp;(3.8), first difference is applied thus:\u003c/p\u003e \u003cp\u003eInY\u003csub\u003eit\u003c/sub\u003e - InY\u003csub\u003eit\u0026minus;1\u003c/sub\u003e = ɸInY\u003csub\u003eit\u0026minus;1\u003c/sub\u003e - ɸInY\u003csub\u003eit\u0026minus;2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;βInX\u003csub\u003eit\u003c/sub\u003e - βInX\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;αInZ\u003csub\u003eit\u003c/sub\u003e - αInZ\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;π lnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u0026minus;1\u003c/sub\u003e + ɤ\u003csub\u003ei\u003c/sub\u003e - ɤ\u003csub\u003ei\u003c/sub\u003e + ϵ\u003csub\u003eit\u003c/sub\u003e - ϵ\u003csub\u003eit\u0026minus;1\u003c/sub\u003e (9)\u003c/p\u003e \u003cp\u003eSo that Cov(X'\u003csub\u003eit,\u003c/sub\u003e ϵ\u003csub\u003eit\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003cp\u003eHowever, notwithstanding the first differencing, the problem of endogeneity remains because Cov(Y\u003csub\u003eit\u0026minus;1,\u003c/sub\u003e ϵ\u003csub\u003eit\u0026minus;1\u003c/sub\u003e)\u0026thinsp;\u0026ne;\u0026thinsp;0, that is, Y\u003csub\u003eit\u003c/sub\u003e is correlated with the past errors in Δ ϵ\u003csub\u003eit\u003c/sub\u003e and probably present Cov(Y\u003csub\u003eit\u003c/sub\u003e, ϵ\u003csub\u003eit\u003c/sub\u003e)\u0026thinsp;\u0026ne;\u0026thinsp;0. Additionally, E(ɤ\u003csub\u003ei\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;E(ϵ\u003csub\u003eit\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;E(ɤ\u003csub\u003ei,\u003c/sub\u003e ϵ\u003csub\u003eit\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;0; E(ϵ\u003csub\u003eit\u003c/sub\u003e, ϵ\u003csub\u003ejs\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;0.\u003c/p\u003e \u003cp\u003eArellano and Bond (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) propose a Generalized Method of Moments estimator that uses all available delays in levels to instrument differenced variables that are not strictly exogenous. They also devised a test for autocorrelation, which can render some lags useless as instruments if it exists. Lagged levels are poor instruments for first differences if the variables are close to a random walk, which is a problem with the original Arellano-Bond estimator.\u003c/p\u003e \u003cp\u003eIn view of the foregoing, forward orthogonal deviations transformation will be applied instead of first differencing. The orthogonal deviations transform, proposed by Arellano and Bover (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), subtracts the average of all possible future data rather than the prior observation. It is computable for all observations except the last for each individual, regardless of how many gaps there are, minimizing data loss.\u003c/p\u003e \u003cp\u003eTherefore, Eq.\u0026nbsp;(3.5) is specified thus:\u003c/p\u003e \u003cp\u003e \u003cb\u003eΔ\u003c/b\u003elnEML\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;λ\u003csub\u003ei1\u003c/sub\u003e\u003cb\u003eΔ\u003c/b\u003elnEML\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei1\u003c/sub\u003e\u003cb\u003eΔ\u003c/b\u003elnPOPG\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei2\u003c/sub\u003e\u003cb\u003eΔ\u003c/b\u003eRQ\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei3\u003c/sub\u003e (lnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u003c/sub\u003e) + β\u003csub\u003ei4\u003c/sub\u003e \u003cb\u003eΔ\u003c/b\u003eECI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei5\u003c/sub\u003e\u003cb\u003eΔ\u003c/b\u003elnEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei6\u003c/sub\u003e\u003cb\u003eΔ\u003c/b\u003elnPCGDP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei7\u003c/sub\u003e\u003cb\u003eΔ\u003c/b\u003elnGFCF\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cb\u003eΔ\u003c/b\u003eϵ\u003csub\u003eit\u003c/sub\u003e (10)\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003elnEML\u003csub\u003eit\u0026minus;1\u003c/sub\u003e is the lag of the dependent variable emission levels\u003c/p\u003e \u003cp\u003elnPOPG\u003csub\u003eit\u003c/sub\u003e*RQ\u003csub\u003eit\u003c/sub\u003e is the interactive term\u003c/p\u003e \u003cp\u003eϵ\u003csub\u003eit\u003c/sub\u003e is the error term\u003c/p\u003e \u003cp\u003e \u003cb\u003eLevels\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAssuming that E[\u003cb\u003eΔ\u003c/b\u003e X\u003csub\u003eit\u003c/sub\u003e, ɤ\u003csub\u003ei\u003c/sub\u003e]\u0026thinsp;=\u0026thinsp;E[\u003cb\u003eΔ\u003c/b\u003e Z\u003csub\u003eit\u003c/sub\u003e, ɤ\u003csub\u003ei\u003c/sub\u003e]\u0026thinsp;=\u0026thinsp;0 and that E[\u003cb\u003eΔ\u003c/b\u003eY\u003csub\u003ei2\u003c/sub\u003e, ɤ\u003csub\u003ei\u003c/sub\u003e]\u0026thinsp;=\u0026thinsp;0 satisfies initial conditions, then the additional moment conditions can be obtained as follows:\u003c/p\u003e \u003cp\u003eE[\u003cb\u003eΔ\u003c/b\u003e X\u003csub\u003eit\u0026minus;s\u003c/sub\u003e,( ɤ\u003csub\u003ei\u003c/sub\u003e + Z\u003csub\u003eit\u003c/sub\u003e)]\u0026thinsp;=\u0026thinsp;0, since s\u0026thinsp;=\u0026thinsp;1 when Z\u003csub\u003eit\u003c/sub\u003e ̴ MA(0), and for s\u0026thinsp;=\u0026thinsp;2 when Z\u003csub\u003eit\u003c/sub\u003e ̴ MA(0) (Arellano and Bover, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). This allows for the use of appropriately lagged initial differences of variables as instruments for level equations. In a system with both first-differences and level equations, both sets of moment conditions can be used as a linear GMM estimator. A system GMM estimator is created by combining both sets of moment conditions.\u003c/p\u003e \u003cp\u003eMoreover, because the autoregressive parameters will be too large given the nature of the data (relatively large panels over a short period), the Arellano and Bond estimator is likely to perform poorly. Therefore, building on further development of Arellano and Bover\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) work by Blundell and Bond (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), the system GMM is introduced which uses additional moment conditions. And as Roodman (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) clearly stated it, the two-step system GMM is more efficient and robust to treat heteroscedasticity and autocorrelation.\u003c/p\u003e \u003cp\u003eUsing the system GMM, there are some basic diagnostic tests to be carried out in order to check for the suitability of the instrument sets used. These include the test for instrument validity developed by Hansen (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and Sargan (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) known as the J-test. Secondly, test for autocorrelation/serial correlation of the second order (AR(2)) of the error term by Arellano and Bond (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). These tests will be carried out accordingly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data source and software\u003c/h2\u003e \u003cp\u003eThis study sampled 26 Sub-Saharan African countries between 2000 and 2020. The countries sampled were based on availability of data. The data was sourced from the World Development Indicators (WDI), Climate Change Data, and Economic Complexity Index (ECI). The definition and measurement of the variables used in the analysis are presented in Table\u0026nbsp;3.1 below. For the a priori expectations from both theoretical and empirical point of view, population growth has been found to have negative impact on environmental sustainability. Therefore, in this study, it will be expected that an increase in population growth will increase the emission levels. Stata 17 econometric software shall be employed in the analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of variables, measurement and sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit of measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA priori sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironment conditions (total greenhouse gases emissions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate Change Data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation growth (annual %). An increase in population will increase emission levels, ceteris paribus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory quality estimate. An increase in the quality of regulations will decrease emission levels, ceteris paribus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic complexity index. An increase in ECI will increase emission levels, ceteris paribus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEconomic Complexity Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployment rate (% of total labour force). An increase in EMP will decrease emission levels, ceteris paribus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP per capita. An increase in PCGDP will increase emission levels, ceteris paribus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross Fixed Capital Formation. An increase in GFCF will decrease emission levels, ceteris paribus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Presentation and analysis of results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Summary statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the summary statistics of the data used in the analysis. It shows the mean, minimum, maximum, standard deviation, skewness and kurtosis. Variables whose skewness values are higher than 0 are long right tailed and skewed to the right, while those less than 0 are long left tailed and skewed to the left. Variables whose kurtosis values fall within 3 are mesokurtic, those which are less than 3 are platykurtic and those which are greater than 3 are leptokurtic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Mean Minimum Maximum Std. Dev Skewness Kurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGEML68980.882568.665555409.112482.42.7106629.951907\u003c/p\u003e \u003cp\u003eLOGPOPG2.393010.0022907 4.1558970.8169877 -0.99156663.580655\u003c/p\u003e \u003cp\u003eRQ-0.4859042 -2.201544 1.196970.5879908 0.25650383.550034\u003c/p\u003e \u003cp\u003eECI-0.8352068 -2.5056750.8949380.5780881 -0.03705762.84022\u003c/p\u003e \u003cp\u003eLOGEMP59.259 36.071 85.866 13.99581 0.15717791.898115\u003c/p\u003e \u003cp\u003eLOGPCGDP643249.4 2699.761 5359616990368.3 2.2318937.953559\u003c/p\u003e \u003cp\u003eLOGGFCF22.98724 2.000441 81.021028.730262 1.71322910.30447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"1\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2: Multicollinearity Test\u003c/h2\u003e \u003cp\u003eThe multicollinearity test is conducted to ascertain if any correlations exist among the independent variables. A high degree of correlation among the explanatory variables, whether positive or negative, depicts a problem of multicollinearity in the model. A correlation is regarded as high and hence, problematic, if its coefficient exceeds 0.8. It is not desirable because it makes it difficult to determine the individual impact of such correlated regressors on the dependent variables. This is achieved by generating a correlation matrix like the one in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelation Matrix\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEML\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOPG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOPG*RQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eECI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePCGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGFCF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEML\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePOPG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePOPG*RQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eECI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEMP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCGDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFCF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the degree of correlation among the variables of interest. It is observed that the correlation coefficients among these variables are healthy as none of these coefficients exceed 0.8, which could pose a problem of multicollinearity. Hence, there exist no problem of multicollinearity among the independent variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3: Bond Test\u003c/h2\u003e \u003cp\u003eIt is essential to do the bond test in order to decide whether to use the system GMM style or the difference GMM style. In his second piece of advice, Bond (2001) advises that the autoregressive model be initially estimated using a pooled OLS and fixed effects technique. The corresponding fixed effects estimate should be regarded as a lower bound estimate, whereas the pooled OLS estimate should be regarded as an upper bound estimate. The system GMM estimator should be favoured over the difference GMM estimator if the obtained difference GMM estimate is near to or lower than the fixed effect estimate, indicating that the difference GMM is downward due to inadequate instrumentation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBond test result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient of Lagged Dependent Variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePooled OLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9960364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6874917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference GMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8811381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDecision\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUse Difference GMM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results of the pooled OLS, fixed effect and two step difference GMM. The result shows the coefficients of the lagged dependent variable (logeml). L1.logeml has a coefficient of 0.8811381 in the difference GMM estimation, which is no way close to or less than the fixed effect estimate (0.6874917). This then suggests that the difference GMM style will be the best estimator for this analysis instead of the system GMM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4. System GMM results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo step difference GMM results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDIFFERENCE GMM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.LOGEML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8811381*** (0.3415)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGPOPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1295233 (0.1475)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.057007 (0.0998)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOPG*RQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2255209** (0.1285)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1551789** (0.0740)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.7454399 (0.5394)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGPCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8777867* (0.2797)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGGFCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0366819 (0.0668)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBSERVATIONS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINSTRUMENTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGROUPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHANSEN STATISTICS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource\u003c/b\u003e: Authors. \u003cb\u003eNote\u003c/b\u003e: *, ** and *** represent 10%, 5% and 1% levels of significance respectively. The corrected standard errors are in parentheses.\u003c/p\u003e \u003cp\u003eFrom the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, it can be observed that the coefficient of population growth is 0.1295, suggesting that population growth has a positive relationship with emission levels. Therefore, holding other variables constant, a 1 per cent increase in the population growth leads to about 12.95% increase in emission level, and reduces environmental sustainability by the same amount. This result conforms to a priori expectation, and in fact supports the argument of the Classical theory. Although this conforms (at face value) to a prior expectation, the observed effect of population growth is not statistically significant. This research output equally agrees with the works of some researchers in the likes of Goodness and Prosper (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) who found that population growth has a positive relationship with emission levels, such that the higher the population growth, the higher the emission levels.\u003c/p\u003e \u003cp\u003eThe coefficient of regulatory quality is -0.0570, implying a negative relationship between regulatory quality and emission levels. So that on the average, and holding other variables constant, a percentage increase in regulatory quality leads to about 5.70% decrease in emission level. This result conforms to a priori expectation, but the observed effect of regulatory quality is not statistically significant. This output agrees with Casey and Galor (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who found out that population policies have a positive effect on environmental performance, such that higher implementation of policies will lead to better environmental performance.\u003c/p\u003e \u003cp\u003eThe interaction of population growth and regulatory quality resulted in a negative coefficient \u003cb\u003e\u0026minus;\u003c/b\u003e\u0026thinsp;0.2255. This explains that institution indeed moderates the population growth-environmental sustainability nexus in Sub-Saharan Africa. A percentage increase in institutional quality then would lead to a decrease in population growth by 22.55%, which will in turn lead to a decrease in emission levels by the same amount, thus sustaining the environment. The observed effect of the interaction between population growth and institutions is statistically significant at 5% level of significance.\u003c/p\u003e \u003cp\u003eRegarding the control variables employed in the study, the coefficient of economic complexity is 0.1551, which indicates a positive relationship between the economic complexity index (ECI) and emission levels in SSA. The observed effect of ECI is statistically significant at 5% level of significance. So that holding other variables constant, a percentage increase in ECI leads to 15.51% increase in emission level. Every increase in emission level is certainly a decrease in environmental quality. This relationship conforms to a prior expectation because increasing economic complexity index is a result of increased economic activities, and hence, the generation of wastes, including gases. This output also agrees with Ogbuabor et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who both found out that economic complexity index has a positive relationship with emission levels. The coefficient of employment is \u003cb\u003e-\u003c/b\u003e0.7454, which indicates a negative relationship between the level of employment and emission levels. The observed effect of employment is statistically insignificant at 5% level of significance. Holding other variables constant, a percentage increase in employment leads to 74.54% decrease in emission level. This relationship conforms to a prior expectation because increasing employment is expected to decrease the tendency of moving towards a linear economy, and hence, sustaining the environment.\u003c/p\u003e \u003cp\u003eFurthermore, the coefficient of per capita GDP is 0.8777, implying a positive relationship between per capita GDP and emission levels. So that on the average, and holding other variables constant, a percentage increase in per capita GDP leads to about 87.77% increase in emission level. The observed effect of per capita GDP is statistically significant at 5% level of significance and conforms to the a priori expectation. This finding agrees with the finding of Alam and Kabir (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). They found that GDP per capita has a significant positive impact on emission levels. Again, though it is somewhat in line with the argument of the environmental Kuznets curve (EKC), that as countries develop economically, moving from lower to higher levels of per capita income, overall levels of environmental degradation will eventually decrease. However, this cannot necessarily imply a proof of Kuznets\u0026rsquo; ideation since Sub-Saharan Africa has not yet acquired the level of technology that can be used to control the level of emission, so that it can fall with every increase in per capita income. Finally, the coefficient of gross fixed capital formation (GFCF) is -0.0366, implying a negative relationship between GFCF and emission levels. So that on the average, and holding other variables constant, a percentage increase in GFCF leads to about 3.66% decrease in emission level. The observed effect of GFCF is statistically insignificant at 5% level of significance but conforms to the a priori expectation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Long Run Coefficients\u003c/h2\u003e \u003cp\u003eFrom the above results, the interaction term, ECI and PCGDP were the only variables whose observed effects proved to be statistically significant. It is important to note that GMM estimates here apply only to the short run. To this effect, long run coefficients were generated to determine if these 3 variables also have statistical significance in the long run.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInteraction Term\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong Run Coefficient of Interaction Term\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogeml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e. z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e[95% conf. interval]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_nl_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1869271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1142464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.4108459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0369918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe long run coefficient explains that this interactive term will not be significant in the long run.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEconomic Complexity\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong Run Coefficient of Economic Complexity Index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogeml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e. z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e[95% conf. interval]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_nl_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.1286229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0564963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.0178922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.2393536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe long run coefficient explains that economic complexity index will be significant in the long run.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePer Capita GDP\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong Run Coefficient of Per Capita GDP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogeml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e. z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e[95% conf. interval]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_nl_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.7275694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1002442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.5310943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.9240444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe long run coefficient explains that per capita GDP will be significant in the long run.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Granger Causality Test\u003c/h2\u003e \u003cp\u003eThe Granger test explains the nature of causal relationship between population growth and emission levels in Sub-Saharan Africa as stated in objective 2. The result of this causality test as well as the computed f-Statistics, and their respective probabilities, with specific lag period is presented in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e4.6\u003c/span\u003e.1 below. To assess whether the null hypothesis would be accepted or rejected, a significance level of 5 percent was chosen.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4.6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger Causality Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOG_POPG_ does not Granger Cause LOG_EML_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOG_EML_ does not Granger Cause LOG_POPG_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote\u003c/b\u003e: The lag length of this test is 5. \u003cb\u003eSource\u003c/b\u003e: Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIf the probability value of the F-statistic is less than 0.05 threshold of significance, the null hypothesis must be rejected in the Granger causality test. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e4.6\u003c/span\u003e\u0026rsquo;s results show that none of the F-statistics' probability values are less than the 0.05 level of significance, thus we cannot rule out the null hypothesis and draw the conclusion that there is no connection between population increase and emission levels. This indicates that adjustments in the population growth five-year lag value have no effect on changes in emission levels. In a similar vein, the five-year lag value of emission levels has little bearing on changes in population growth. Thus, emission levels and population growth do not granger cause each other, implying that there is no causal relationship between them.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and policy recommendations","content":"\u003cp\u003eThis study has fully lent itself to an empirical investigation of the relationship between population growth and environmental sustainability (measured in terms of emission levels) in Sub-Saharan Africa, from the period 2000-2020. Following formal econometric methodology, some findings about the research objectives and their corresponding hypothesis have been made, using time series data from the World Bank\u0026rsquo;s World Development Indicators, Economic Complexity Index and Climate Change Data. These findings, inter alia, principally include that population growth has an insignificant and positive impact on emission levels in Sub-Saharan Africa, and that no causal relationship exists between them. The implication of the former is that by positively impacting emission levels, population growth increases environmental pollution, thereby reducing environmental sustainability. Perhaps the absence of causal relationship between population growth and emission levels suggest that it is not necessarily population size that leads to generation of emissions, but the level of economic activities, for instance. However, the interaction between population growth and institutions shows a significant negative impact on emission level, indicating that strong institution is capable of moderating the impact of increasing population on the environment.\u003c/p\u003e\n\u003cp\u003eOn the basis of these empirical findings, therefore, that the following policy recommendations are advanced.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThis study\u0026apos;s key policy conclusion is that population screening policies may be an efficient way to cut CO2 and other petrol emissions. Therefore, Sub-Saharan Africa can reduce CO2 emissions by implementing a cautious population stabilisation programme. Fertility control techniques, for example, can be crucial to halting environmental deterioration and raising living standards. To keep the human population within the boundaries of the earth\u0026apos;s carrying capacity and to keep energy demand at a sustainable and environmentally acceptable level, fertility must be reduced by setting a restriction on the number of children a household can have in a certain period of time.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe government must also make family planning available everywhere. By spacing out births, family planning will lower infant mortality, drop birth rates, increase demand for its services (through raising awareness), and maybe lower fertility. However, family planning is insufficient to decrease fertility as soon as is required. Other fertility-reduction strategies that the government should implement include maternity benefit restrictions, financial incentives, and educational programmes. These demographic initiatives need to be prioritised right away because they take a while to show benefits and can be difficult politically to implement.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSub-Saharan African governments and businesses should actively encourage and carry out green innovation ideas. The first step is for the federal, state, and local governments to actively foster an atmosphere that promotes green innovation. In order to promote green innovation, it is essential to strictly execute intellectual property protection mechanisms, concentrate on the pressing issues of CO2 emission reduction and environmental pollution control, and offer targeted fiscal and taxation policy support. Second, create an effective framework for conducting research on green innovation, boost funding for fundamental studies on the development of green technologies, and hasten the creation of scientific research partnerships between businesses, academic institutions, and governments.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAccountability mechanisms, political stability, government effectiveness, regulatory quality, the rule of law, and corruption prevention should be the main concerns of the various governments in Sub-Saharan Africa. To ensure that the obligation to reduce CO2 emissions is placed on certain party and government leaders, it is necessary to improve and implement the accountability system in the field of ecological and environmental protection. Make an effort to create a sound and stable political environment that fosters green innovation and economic growth.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgbonlahor MU, Phillip DOA (2015) Deciding to settle: Rural-rural migration and agricultural labour supply in Southwest Nigeria. J Developing Areas, 267\u0026ndash;284\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam S, Kabir N (2013) Economic growth and environmental sustainability: Empirical evidence from East and South-East Asia. Int J Econ Finance, \u003cem\u003e5\u003c/em\u003e(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlcott B (2010) Impact caps: Why population, affluence and technology strategies should be abandoned. 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Econ Inq 47(2):249\u0026ndash;265\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Institutions, Population, Environmental sustainability, Difference GMM, SSA","lastPublishedDoi":"10.21203/rs.3.rs-3446276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3446276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the moderating effect of institutional quality on the population growth-environmental sustainability nexus in Sub-Saharan Africa (SSA) over the period 2000–2020. Applying the Generalized Method of Moments (GMM) estimation technique and a Granger causality test to check if there exists any causality between population growth and emission levels, the findings indicate that population growth positively impacts on emission level in Sub-Saharan Africa, thus, affecting the environment negatively. However, its observed effect was statistically insignificant due to the interaction of institutions with population growth which proved significant. The results further indicate that other macroeconomic variables impacting positively and significantly on emission level in SSA are economic complexity index and per capita GDP. The study also establishes that there is no causal relationship between population growth and emission level in SSA. Lastly, the study finds that institutions play a vital role in reducing emission levels in the zone. It is therefore recommended that the government should vigorously pursue population and environmental policies directed at promoting environmental sustainability by controlling population, and promoting sustainable environmental practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e: O43; 044; J130; J180\u003c/p\u003e","manuscriptTitle":"Moderating Effect of Institutional Quality on the Population Growth-Environmental Sustainability Nexus in Sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 06:47:07","doi":"10.21203/rs.3.rs-3446276/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"45019e7d-b586-4011-ae39-dbd363f148cc","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-19T06:24:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-10 06:47:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3446276","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3446276","identity":"rs-3446276","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}