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Abstract

This study investigates how increasing economic development affects the green economy in terms of CO₂ emissions, using data from 44 countries in the sub-Saharan Africa for the period 2000–2012. The Generalized Method of Moments is used for the empirical analysis. The following main findings are established. First, relative to CO₂ emissions, enhancing economic growth and population growth engenders a U-shaped pattern whereas increasing inclusive human development shows a Kuznets curve. Second, increasing gross domestic product growth beyond 25% of annual growth is unfavorable for a green economy. Third, a population growth rate of above 3.089% (i.e. annual %) has a positive effect of CO₂ emissions. Fourth, an inequality-adjusted human development index of above 0.4969 is beneficial for a green economy because it is associated with a reduction in CO₂ emissions. The established critical masses have policy relevance because they are situated within the policy ranges of adopted economic development dynamics.

Keywords

economic development Africa

Introduction

What thresholds of economic development are associated with reversals in carbon dioxide (CO₂) emissions in sub-Saharan Africa (SSA)? The positioning of this study on thresholds of economic development for a green economy in SSA is motivated by two main factors in scholarly and policy-making circles, notably the relevance of the green economy in the post-2015 development agenda and gaps in the extant literature.

First, the green economy is a central theme in sustainable development goals because, inter alia, greenhouse gas emissions are a great challenge to the sustainability of the global environment in the post-2015 development agenda (Akinyemi et al., 2015; Akinyemi et al., 2018; Akpan et al., 2015; Asongu et al., 2016a; Collett, 2012; Efobi et al., 2018; Mbah and Nzeadibe, 2016; Rui et al., 2017; Shaw and Bachu, 2012). Moreover, as documented by Asongu (2018a), the consequences of global warming are most detrimental to countries in Africa for numerous reasons, notably: (i) rampant crises in environmental pollution, persistence in energy crisis and (ii) the ramifications of mismanagement of energy across the continent. These fundamental factors are explained in elaborate detail in the following passages.

On the front of mismanagement, consistent with the attendant literature (Anyangwe, 2014; Asongu et al., 2017, 2018; Odhiambo, 2010, 2014a), the energy sector of many countries in SSA is not being managed properly, especially when it pertains to less investment in renewable energy and more allocation of funds to the subsidization of fossil fuels. It is worthwhile to emphasize that economic prosperity in the last two centuries has been fundamentally reliant on energy availability which is indispensable for economic processes, inter alia: production, distribution and consumption activities (Odhiambo, 2009a, 2009b, 2014b). One very glaring case of the inability of policy makers to come up with effective renewable energy policies is the case of Nigeria where instead of promoting sustainable energy sources, fossil fuels are subsidized (Akpan and Akpan, 2012).

Concerning the persistence of CO₂, whereas these emissions makeup about 75% of global greenhouse emissions (Akpan and Akpan, 2012), there are many accounts that are in accordance with the view that the unfavorable ramifications of climate change will be most apparent in SSA (Asongu, 2018b; Kifle, 2008; Shurig, 2015). Accordingly, this change of climate is an immediate consequence of fossil fuels that are consumed unsustainably (Huxster et al., 2015). Moreover, approximately 620 million of inhabitants in SSA (which represents about two-thirds of the population) lack access to electricity (World Energy Outlook 2014 Factsheet). This substantially contrasts with the growing demand for energy in the sub-region which: (i) represents about 4% of the global demand and (ii) increased by approximately 45% during the period 2000–2012 (World Energy Outlook 2014 Factsheet).

Second, the extant literature on linkages between the consumption of energy, CO₂ emissions and development outcomes can be classified in two main categories: while the first emphasizes the relationships between environmental degradation and economic growth, the second strand is concerned with associations between energy consumption and economic development. Two sub-strands are apparent in the second strand, notably: (i) research focusing on linkages between energy consumption and economic development (Ang, 2007; Apergis and Payne, 2009; Begum et al., 2015; Bölük and Mehmet, 2015; Jumbe, 2004; Menyah and Wolde-Rufael, 2010; Odhiambo, 2009a, 2009b; Ozturk and Acaravci, 2010) and enquires into relationships between energy consumption, environmental degradation and economic prosperity (Akinlo, 2008; Esso, 2010; Mehrara, 2007; Olusegun, 2008).

The second strand which is more related to the positioning of this research underlines the investigation of the Environmental Kuznets Curve (EKC)¹ hypothesis (Akbostanci and Turut-Asi Tunc, 2009; Diao et al., 2009; He and Richard, 2010). The attendant literature pertaining to the EKC has largely focused on the nexus between environmental degradation and per capita income. This research departs from the underlying literature on two fronts. On the one hand, we contribute to the EKC literature by investigating the relationship between environmental degradation and economic development using three outcome variables, namely: economic growth, population growth and inclusive human development. This study departs from the engaged contemporary literature which has largely focused on two factors: income and environmental degradation. On the other hand, this study argues that simply assessing the EKC hypothesis is not enough for policy-making initiatives. For instance, rejecting or confirming evidence of an EKC is less relevant to policy than establishing specific policy thresholds that policy makers can act upon to address concerns pertaining to environmental degradation. Accordingly, providing policy makers with a specific critical mass at which more economic growth or population growth is detrimental to the environment is more informative than simply confirming the existence or not, of an EKC. Moreover, in the light of the post-2015 development agenda which is particularly focused on inclusive human development, providing a specific human development critical mass that drives the green economy is more informative and relevant to policy makers. Therefore, the policy relevance of the study is in line with scholarly recommendations for a green revolution (Pingali, 2012).

While the theoretical underpinning of the EKC hypothesis has been substantially documented in the literature (Akbostanci and Turut-Asi Tunc, 2009; Diao et al., 2009; He and Richard, 2010), the theory-building contribution of this study relates to the establishment of specific thresholds at which macroeconomic outcomes can either positively or negatively influence environmental degradation. Hence, while this study builds on an established EKC hypothesis, it also advances knowledge within the perspective that it informs policy makers on thresholds pertaining to the EKC. The applied econometrics framework is consistent with the literature supporting the view that applied econometrics should not be exclusively limited to studies designed to either accept or reject existing theories and established hypotheses (Asongu and Nwachukwu, 2016a; Narayan et al., 2011).

The remainder of the study is organized as follows. The data and methodology are discussed in section 2 while the empirical results are covered in section 3. Section 4 concludes with implications and future research directions.

Data and methodology

Data

The study focuses on 44 nations in the SSA region for the period 2000–2012² with data from three main sources, namely: (i) the World Development Indicators of the World Bank for the dependent variable (i.e. CO₂ emissions), two independent variables of interest (i.e. economic growth and population growth) and a control variable (education quality); (ii) the World Governance Indicators of the World Bank for a control variable (i.e. regulatory quality) and (iii) the United Nations Development Programme (UNDP) for an independent variable of interest (i.e. the inequality-adjusted human development index, IHDI). The geographical and temporal scopes of the study are contingent on data availability constraints at the time of the study.

The adopted CO₂ emissions per capita variable, as a proxy for environmental degradation is in accordance with Asongu (2018a), while the use of the IHDI to proxy for inclusive human development is consistent with recent inclusive human development literature in Africa (Asongu et al., 2015; Asongu and Nwachukwu, 2017a). According to the underlying literature, the human development index (HDI) denotes the average of achievements in three main areas, namely: (i) basic standard of living, (ii) knowledge and (iii) health and long life. Furthermore, the IHDI is the HDI that is adjusted to the equitable distribution of the three main achievements. Hence, the IHDI is the HDI that has been adjusted for inequality.

In the light of the motivation of this study, the three economic development variables are: economic growth in the perspective to gross domestic product (GDP) growth rate; the population growth rate and the IHDI discussed in the preceding paragraph. In order to limit issues pertaining to variable omission bias, two control variables are defined in the conditioning information set, namely: education quality and regulation quality. The control variables which are in line with recent CO₂ emissions literature (Asongu, 2018b) are restricted to two because upon a pilot empirical investigation, it was apparent that focusing on more than two control variables generates concerns of instrument proliferation and over-identification. This procedure of adopting limited control variables in the Generalized Method of Moments (GMM) approach (in order to avoid invalid models that do not pass post-estimation diagnostic tests) is not uncommon in the empirical literature. In essence, there is an abundant supply of GMM literature that has used limited control variables, notably: (i) zero control variable (Asongu and Nwachukwu, 2017b; Osabuohien and Efobi, 2013) and (ii) two control variables (Bruno et al., 2012).

Concerning the anticipated signs, while both variables are expected to significantly influence the outcome variable, the expected effects on the dependent variable may also be contingent on the behavior of the data and other macroeconomic features. For instance, while regulation quality should naturally reduce CO₂ emissions, the effectiveness of such regulation is contingent on the feasible implementation of adopted policies. It is worthwhile to emphasis that the regulation quality variable is negatively and positively skewed. Hence, an overwhelmingly negative skew can weigh unfavorably on the expected sign. The relevance of primary education in countries at initial levels of industrialization is consistent with the attendant literature on the relative importance of this education level (i.e. compared to higher education levels) in development outcomes (Asiedu, 2014; Asongu and Le Roux, 2017; Asongu and Odhiambo, 2019a; Petrakis and Stamatakis, 2002). The Appendix provides the definitions and sources of variables in Table 2, the summary statistics in Table 3 and the correlation matrix in Table 4.

Methodology

GMM: Specification, identification and exclusion restrictions

This study adopts the GMM approach for empirical investigation for four main reasons. First, a baseline requirement is that the number of cross sections should be higher than the corresponding number of periods in each cross section. This is the case in our study which is focusing on 44 countries with data points from 2000 to 2012. Therefore, the N (or 44 > T or 13) primary condition is satisfied. Second, the environmental degradation variable is persistent given that the correlation between the level values and first difference (i.e. 0.9945) is higher than 0.800 which has been established to be the threshold for determining persistence (Tchamyou, 2019a, 2019b). Third, owing to the panel structure of the dataset, it is apparent that cross-country variations are taken into account in the estimations. Fourth, the concern of endogeneity is tackled from two main perspectives. On the one hand, reverse causality, or simultaneity, is addressed with the help of an instrumentation process. On the other hand, the unobserved heterogeneity is accounted for with the help of time-invariant variables.

The GMM strategy is the Roodman (2009a, 2009b) extension of Arellano and Bover (1995) which has been documented in contemporary literature to restrict instrument proliferation with an option that collapses instruments (Asongu and Nwachukwu, 2016b; Boateng et al., 2018; Tchamyou et al., 2019).

The following equations in level (1) and first difference (2) summarize the standard system GMM estimation procedure. $C O_{i, t} = σ_{0} + σ_{1} C O_{i, t - τ} + σ_{2} E D_{i, t} + σ_{3} E DE D_{i, t} + \sum_{h = 1}^{2} δ_{h} W_{h, i, t - τ} + η_{i} + ξ_{t} + ε_{i, t}$ (1) $\begin{matrix} C O_{i, t} - C O_{i, t - τ} = σ_{1} (C O_{i, t - τ} - C O_{i, t - 2 τ}) + σ_{2} (E D_{i, t} - E D_{i, t - τ}) + σ_{3} (EDE D_{i, t} - EDE D_{i, t - τ}) \\ + \sum_{h = 1}^{2} δ_{h} (W_{h, i, t - τ} - W_{h, i, t - 2 τ}) + (ξ_{t} - ξ_{t - τ}) + (ε_{i, t} - ε_{i, t - τ}) \end{matrix}$ (2)where $C O_{i, t}$ is the carbon dioxide emission variable of country $i$ in period $t$ , $σ_{0}$ is a constant, $ED$ entails economic development (GDP growth, population growth and inclusive development), $EDED$ denotes quadratic interactions between economic development indicators (“GDP growth” × “GDP growth,” “population growth” × “population growth” and “inclusive development” × “inclusive development”), $W$ is the vector of control variables (education quality and regulation quality), $τ$ represents the coefficient of auto-regression which is one within the framework of this study because a year lag is enough to capture past information, $ξ_{t}$ is the time-specific constant, $η_{i}$ is the country-specific effect and $ε_{i, t}$ the error term.

Identification and exclusion restrictions

We now devote space to clarifying identification properties and corresponding exclusion restrictions because they are paramount to a robust GMM specification. In accordance with the underlying literature (Asongu and Nwachukwu, 2016c; Boateng et al., 2018; Tchamyou and Asongu, 2017; Tchamyou et al., 2019), the time invariant variables adopted are defined as strictly exogenous whereas all explanatory variables are acknowledged as “suspected endogenous” or predetermined. The intuition underlying this identification strategy is in accordance with Roodman (2009b) who has documented that it is not very probable for the suggested time invariant indicators to be first-differenced endogenous.³

Given the discussed identification strategy, the exclusion restriction assumption is investigated by assessing if the identified strictly exogenous indicator affects CO₂ emissions exclusively via the suggested endogenous explaining variable mechanisms. The criterion used to assess the validity of this exclusion restriction is the Difference in Hansen Test. The null hypothesis of this test is the position that the identified strictly exogenous variable does not affect the CO₂ emission variables beyond the engaged endogenous explaining variables. Hence, we expect the null hypothesis not to be rejected for the exclusion restriction assumption to hold. This expectation is consistent with the standard instrumental variable approach in which the rejection of the null hypothesis of the Sargan Overidentifying Restrictions (OIR) test indicates that the proposed instruments explain the outcome variable beyond the proposed channels or endogenous explaining mechanisms (Asongu and Nwachukwu, 2016d; Beck et al., 2003).

Empirical results

Presentation of results

The empirical results are disclosed in Table 1. There are three main sets of specifications pertaining to each of the economic development variables. Each specific economic development variable is associated with three main specifications. From the left-hand to the right-hand side, the variables in the conditioning information set are increased. Accordingly, the first specification does not include a control variable; the middle specification entails a control variable while the last specification is associated with two control variables. In the light of the explanation in the data section, not involving control variables in the GMM specification is acceptable in the empirical literature. Hence, the step-wise approach of involving control variables can be tacitly considered as a robustness check procedure.

Table 1.

Empirical results.

	Dependent variable: CO₂ emissions per capita
	EG			PG			Inclusive human development (IHDI)
CO₂ emissions (-1)	0.839***	0.917***	0.949***	0.893***	0.892***	0.888***	0.850***	0.974***	0.976***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
EG	−0.0008	−0.005**	0.001	–	0.0007	−0.002 *	–	–	0.0006
	(0.336)	(0.019)	(0.234)		(0.662)	(0.095)			(0.263)
PG	–	−0.096***	0.007	−0.281***	−0.290***	−0.173***	–	0.022	0.027 *
		(0.000)	(0.408)	(0.000)	(0.000)	(0.000)		(0.180)	(0.064)
IHDI	–	–	0.180	–	–	–	−2.429	2.308	2.120 *
			(0.151)				(0.170)	(0.128)	(0.065)
EG ×EG	0.000005	0.0001 *	−0.00005**	–	–	–	–	–	–
	(0.832)	(0.065)	(0.012)
PG ×PG	–	–	–	0.038***	0.038***	0.028***	–	–	–
				(0.000)	(0.000)	(0.000)
IHDI ×IHDI	–	–	–	–	–	–	3.176 *	−1.951	−2.133 *
							(0.077)	(0.238)	(0.084)
Education	–	−0.002	−0.0005	–	–	−0.002**	–	0.001	0.0006
		(0.218)	(0.460)			(0.018)		(0.285)	(0.466)
Regulation quality	–	–	0.048***	–	–	0.219***	–	–	0.053
			(0.003)			(0.000)			(0.167)
Time effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Net effects	na	−0.0040	na	−0.1025	−0.1125	−0.0422	na	na	0.2003
Thresholds	na	25	na	3.6973	3.8157	3.0892	na	na	0.4969
AR(1)	(0.113)	(0.135)	(0.016)	(0.147)	(0.148)	(0.148)	(0.039)	(0.010)	(0.012)
AR(2)	(0.294)	(0.226)	(0.251)	(0.777)	(0.758)	(0.151)	(0.134)	(0.195)	(0.240)
Sargan OIR	(0.046)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Hansen OIR	(0.498)	(0.295)	(0.599)	(0.106)	(0.207)	(0.367)	(0.179)	(0.768)	(0.766)
DHT for instruments
(a) Instruments in levels
H excluding group	–	(0.118)	(0.045)	–	–	(0.106)	–	(0.187)	(0.118)
Dif(null, H=exogenous)	(0.756)	(0.401)	(0.910)	–	(0.473)	(0.493)	(0.333)	(0.851)	(0.925)
(b) IV (years, eq(diff))
H excluding group	–	–	(0.267)	–	–	(0.119)	–	–	(0.741)
Dif(null, H=exogenous)	–	(0.408)	(0.796)	–	–	(0.618)	–	(0.923)	(0.607)
Fisher	307.52***	14270.70***	1.15e+06***	4133.18***	4128.79***	15745.48***	2211.51***	18002.12***	69184.29***
Instruments	22	29	36	21	25	32	22	29	36
Countries	44	44	41	44	44	43	42	41	41
Observations	452	326	244	419	414	296	372	267	244

*, **, ***: Significance levels of 10%, 5% and 1%, respectively; EG: economic growth; PG: population growth; IHDI: inequality-adjusted human development index; DHT: Difference in Hansen Test for exogeneity of instruments’ subsets; Dif: difference; OIR: over-identifying restrictions test. The significance of bold values is twofold. (1) The significance of estimated coefficients, Hausman test and the Fisher statistics. (2) The failure to reject the null hypotheses of (a) no autocorrelation in the AR(1) and AR(2) tests and (b) the validity of the instruments in the Sargan OIR test. na: not applicable because at least one estimated coefficient needed for the computation of net effects is not significant. The mean values of GDP growth, population growth and inclusive development are, respectively, 4.801, 2.335 and 0.450. Constants are included in the regressions.

In the light of the identification strategy and corresponding discussion on exclusion restrictions in the preceding section, four main criteria are used to investigate the post-estimation validity of the GMM findings.⁴ Building on these criteria, it is apparent that all estimated models pass the post-estimation diagnostic tests.

In order to investigate the overall effect of increasing economic development on environmental degradation, net effects are computed in accordance with contemporary literature on the interactive (Agoba et al., 2019; Tchamyou and Asongu, 2017; Tchamyou, 2019b) and quadratic (Asongu and Odhiambo, 2019b) regressions. The corresponding net effects consist of both the unconditional effects and the marginal effects from the associated interactions. For example in the third column of Table 1, the net effect of enhancing economic growth is –0.0040 (2 × [0.0001 × 4.801] + [–0.005]). In this calculation, the average value of economic growth is 4.801, the marginal effect of economic growth on CO₂ emissions is 0.0001 while the unconditional effect of economic growth is –0.005. The leading 2 on the first term is from the differentiation of the quadratic term.

In the light of the same computational analogy, in the last column of the table, the net effect derived from enhancing inclusive human development is 0.2003 (2 × [–2.133 × 0.450] + [2.120]). In this calculation, the mean value of inclusive human development is 0.450, the unconditional effect is 2.120 while the marginal effect is –2.133. Accordingly, the leading 2 on the first term is from the differentiation of the quadratic term.

The following findings can be established from Table 1. The significant control variables have the expected signs. Enhancing economic growth and population growth both have net negative effects on CO₂ emissions while enhancing inclusive human development has an overall net positive effect on the CO₂ emissions. While from the perspective of net effect, the finding on inclusive human development is unanticipated, the corresponding negative marginal effect implies that increasing inclusive human development decreases the positive unconditional effect up to a certain threshold. Hence, the negative net effect can be neutralized at a specific threshold of inclusive human development. The attendant policy thresholds are established in the next section.

Extension with policy thresholds

In the light of the motivation and positioning of this study with respect to the extant literature (which is covered in the introduction), this section engages the policy thresholds and by extension, policy implications of the study. The computations of the thresholds are also substantiated with the attendant literature on thresholds that are relevant for more targeted policy implications.

Given the problem statement motivating this study, it is not enough to stop at establishing overall net effects on CO₂ emissions from improving economic development. Hence, we move a step further by computing thresholds related to the marginal effects. For instance, the unconditional and conditional effects from increasing economic growth and population growth are, respectively, negative and positive whereas the unconditional and conditional impacts from enhancing inclusive human development are, respectively, positive and negative. Therefore, in the light of the attendant positive marginal effects from economic growth and population growth, an extended analysis can be made to assess at what specific thresholds or critical masses the positive marginal or conditional effects completely crowd-out the negative unconditional effects. It follows that at a specific economic development threshold, further increasing economic growth, or population growth, increases CO₂ emissions. This is also translated as a U-shaped pattern. The narrative is the opposite for the relationship between inclusive human development and CO₂ emissions: a Kuznets shape pattern.

It is also relevant to emphasize that the underlying thresholds are critical masses of economic development at which the net effect on CO₂ emissions is completely nullified. However, in order for these thresholds to be economically relevant and make policy sense, they should be situated between the minimum and maximum values disclosed in the summary statistics. Hence, the policy relevance of the thresholds to be computed is contingent on whether policy actions with the established thresholds are feasible. This feasibility exclusively relies on whether the thresholds are consistent with the data underpinning the empirical exercise. This conception and definition of threshold conforms to the extant literature on the subject, notably: thresholds for favorable results that are relevant to policy makers (Asongu and Odhiambo, 2018; Asongu et al., 2019; Batuo, 2015), conditions for Kuznets and U shapes (Ashraf and Galor, 2013) and CO₂ emission thresholds that are detrimental to inclusive development (Asongu, 2018a).

In Table 1, the positive threshold in the second column is 25 (0.005/[2 × 0.0001]). Hence, at 25% of GDP growth rate (i.e. annual %), GDP growth increases CO₂ emissions, or is detrimental to a green economy. In the same vein, a population growth rate of above 3.089% (i.e. annual %) has a positive effect on CO₂ emissions. Moreover, following the same analogy, an IHDI of 0.496 is the critical mass from which inclusive development decreases CO₂ emissions. It follows that sampled countries should target an IHDI of above 0.496 in order to benefit from the relevance of the inclusive development in promoting the green economy.

The above thresholds have economic relevance and can be applied by policy makers because they are within the policy ranges disclosed in the summary statistics, notably: “–32.832 to 33.735,” “–1.081 to 6.576” and “0.219 to 0.768” for, respectively, GDP growth, population growth and inclusive development.

Conclusion and future research directions

This study has investigated how increasing economic development affects the green economy in terms of CO₂ emissions, using data from 44 countries in the SSA region for the period 2000–2012. The GMM is used for the empirical analysis. The following main findings are established. First, enhancing both economic growth and population growth has net negative effects on CO₂ emissions while improving inclusive human development has an overall net positive effect on the CO₂ emissions. Second, there is a U-shaped pattern between two indicators of economic development (i.e. economic growth and population growth) and CO₂ emissions, while there is a Kuznets nexus between inclusive human development and CO₂ emissions.

Third, when the analysis is extended to establish thresholds, the following findings are also established. (i) Increasing GDP growth beyond 25% of annual growth is unfavorable for a green economy; (ii) a population growth rate of above 3.089% (i.e. annual %) has a positive effect on CO₂ emissions and (iii) an IHDI of above 0.496 is beneficial for a green economy because it is associated with a reduction in CO₂ emissions. The established critical masses have policy relevance because they are situated within the policy ranges of economic growth, population growth and inclusive human development.

It will be relevant to investigate whether the established linkages in this study can withstand empirical scrutiny when country-specific studies are involved. These country-specific cases are important because in the modelling of the GMM, country-specific impacts are eliminated by first differencing in order to avoid concerns of endogeneity.

Footnotes

Author note

Simplice A Asongu is now affiliated with Department of Economics,University of South Africa,Pretoria,South Africa.

Acknowledgements

The authors are indebted to the editor and referees for constructive comments.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

Notes

References

Agoba

Abor

Osei

, et al. (2019) Do independent central banks exhibit varied behaviour in election and non-election years: The case of fiscal policy in Africa. Journal of African Business 21(1): 105–125.

Akbostanci

Turut-Asi Tunc

(2009) The relationship between income and environment in Turkey: Is there an environmental Kuznets curve? Energy Policy 37(3): 861–867.

Akinlo

(2008) Energy consumption and economic growth: Evidence from 11 sub-Sahara African countries. Energy Economics 30(5): 2391–2400.

Akinyemi

Alege

Osabuohien

, et al. (2015) Energy Security and the Green Growth Agenda in Africa: Exploring Trade-offs and Synergies. Department of Economics and Development Studies Working Paper, Covenant University, Nigeria.

Akinyemi

Efobi

Asongu

, et al. (2018) Green Growth Strategy and Trade Performance in Sub-Saharan Africa. Department of Economics and Development Studies Working Paper, Covenant University, Nigeria.

Akpan

(2012) Electricity consumption, carbon emissions and economic growth in Nigeria. International Journal of Energy Economics and Policy 2(4): 292–306.

Akpan

Green

Bhattacharyya

, et al. (2015) Effect of technology change on CO₂ emissions in Japan’s industrial sectors in the period 1995-2005: An input-output structural decomposition analysis. Environmental and Resource Economics 61(2): 165–189.

Ang

(2007) CO₂ emissions, energy consumption, and output in France. Energy Policy 35(10): 4772–4778.

Anyangwe

(2014) Without energy could Africa’s growth run out of steam? The Guardian. Available at: www.theguardian.com/global-development-professionals-network/2014/nov/24/energy-infrastructure-clean-cookstoves-africa (accessed 8 September 2015).

10.

Apergis

Payne

(2009) CO₂ emissions, energy usage, and output in Central America. Energy Policy 37(8): 3282–3286.

11.

Arellano

Bover

(1995) Another look at the instrumental variable estimation of error components models. Journal of Econometrics 68(1): 29–52.

12.

Asiedu

(2014) Does foreign aid in education promote economic growth? Evidence from sub-Saharan Africa. Journal of African Development 16(1): 37–59.

13.

Ashraf

Galor

(2013) The out of Africa hypothesis, human genetic diversity, and comparative economic development. The American Economic Review 103(1): 1–46.

14.

Asongu

(2018a) CO₂ emission thresholds for inclusive human development in sub-Saharan Africa. Environmental Science and Pollution Research 25(26): 26005–26019.

15.

Asongu

(2018b) ICT, openness and CO₂ emissions in Africa. Environmental Science and Pollution Research International 25(10): 9351–9359.

16.

Asongu

De Moor

(2017) Financial globalisation dynamic thresholds for financial development: Evidence from Africa. The European Journal of Development Research 29(1): 192–212.

17.

Asongu

Efobi

Beecroft

(2015) Inclusive human development in pre-crisis times of globalisation-driven debts. African Development Review 27(4): 428–442.

18.

Asongu

El Montasser

Toumi

(2016a) Testing the relationships between energy consumption, CO₂ emissions, and economic growth in 24 African countries: A panel ARDL approach. Environmental Science and Pollution Research 23(7): 6563–6573.

19.

Asongu

Le Roux

(2017) Enhancing ICT for inclusive human development in sub-Saharan Africa. Technological Forecasting and Social Change 118: 44–54.

20.

Asongu

Le Roux

Biekpe

(2017) Environmental degradation, ICT and inclusive development in sub-Saharan Africa. Energy Policy 111: 353–361.

21.

Asongu

Le Roux

Biekpe

(2018) Enhancing ICT for environmental sustainability in sub-Saharan Africa. Technological Forecasting and Social Change 127: 209–216.

22.

Asongu

Le Roux

Tchamyou

(2019) Essential information sharing thresholds for reducing market power in financial access: A study of the African banking industry. Journal of Banking Regulation 20(1): 34–50.

23.

Asongu

Nwachukwu

(2016a) Revolution empirics: Predicting the Arab Spring. Empirical Economics 51(2): 439–482.

24.

Asongu SA and Nwachukwu JC (2016) The Mobile Phone in the Diffusion of Knowledge for Institutional Quality in Sub Saharan Africa. World Development 86: 133–147.

25.

Asongu

Nwachukwu

(2016c) The role of governance in mobile phones for inclusive human development in sub-Saharan Africa. Technovation 55–56: 1–13.

26.

Asongu

Nwachukwu

(2016d) Foreign aid and governance in Africa. International Review of Applied Economics 30(1): 69–88.

27.

Asongu

Nwachukwu

(2017a) The comparative inclusive human development of globalisation in Africa. Social Indicators Research 134(3): 1027–1050.

28.

Asongu

Nwachukwu

(2017b) Foreign Aid and inclusive development: Updated evidence from Africa, 2005–2012. Social Science Quarterly 98(1): 282–298.

29.

Asongu

Odhiambo

(2018) Mobile banking usage, quality of growth, inequality and poverty in developing countries. Information Development 35(2): 303–318.

30.

Asongu

Odhiambo

(2019a) Basic formal education quality, information technology and inclusive human development in sub-Saharan Africa. Sustainable Development 27(3): 419–428.

31.

Asongu

Odhiambo

(2019b) How enhancing information and communication technology has affected inequality in Africa for sustainable development: An empirical investigation. Sustainable Development 27(4): 647–656.

32.

Batuo

(2015) The role of telecommunications infrastructure in the regional economic growth of Africa. Journal of Development Areas 49(1): 313–330.

33.

Beck

Demirgüç-Kunt

Levine

(2003) Law and finance: Why does legal origin matter? Journal of Comparative Economics 31(4): 653–675.

34.

Begum

Sohag

Abdullah

SMS

, et al. (2015) CO₂ emissions, energy consumption, economic and population growth in Malaysia. Renewal and Sustainable Energy Reviews 41: 594–601.

35.

Boateng

Asongu

Akamavi

, et al. (2018) Information asymmetry and market power in the African banking industry. Journal of Multinational Financial Management 44: 69–83.

36.

Bölük

Mehmet

(2015) The renewable energy, growth and environmental Kuznets curve in Turkey: An ARDL approach. Renewal and Sustainable Energy Reviews 52: 587–595.

37.

Bruno

De Bonis

Silvestrini

(2012) Do financial systems converge? New evidence from financial assets in OECD countries. Journal of Comparative Economics 40(1): 141–155.

38.

Collett

(2012) Energy resource potential of natural gas hydrates. AAPG Bulletin 86(11): 1971–1992.

39.

Diao

Zeng

Tam

, et al. (2009) EKC Analysis for studying economic growth and environmental quality: A case study in China. Journal of Cleaner Production 17(5): 541–548.

40.

Efobi

Tanankem

Orkoh

, et al. (2018) Environmental pollution policy of small businesses in Nigeria and Ghana: Extent and impact. Environmental Science and Pollution Research 26(3): 2882–2897.

41.

Esso

(2010) Threshold cointegration and causality relationship between energy use and growth in seven African countries. Energy Economics 32(6): 1383–1391.

42.

Richard

(2010) Environmental Kuznets curve for CO₂ in Canada. Ecological Economics 69(5): 1083–1093.

43.

Huxster

Uribe-Zarain

Kempton

(2015) Undergraduate understanding of climate change: The Influences of college major and environmental group membership on survey knowledge scores. The Journal of Environmental Education 46(3): 149–165.

44.

Jumbe

(2004) Cointegration and causality between electricity consumption and GDP: Empirical evidence from Malawi. Energy Economics 26(1): 61–68.

45.

Kifle

(2008) Africa hit hardest by Global warming despite its low greenhouse gas emissions. Institute for World Economics and International Management Working Paper. Available at: www.iwim.uni-bremen.de/publikationen/pdf/b108.pdf (accessed 8 July 2015).

46.

Mbah

Nzeadibe

(2016) Inclusive municipal solid waste management policy in Nigeria: Engaging the informal economy in post-2015 development agenda. Local Environment: The International Journal of Justice and Sustainability 22(2): 203–224.

47.

Mehrara

(2007) Energy consumption and economic growth: The case of oil exporting countries. Energy Policy 35(5): 2939–2945.

48.

Menyah

Wolde-Rufael

(2010) Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics 32(6): 1374–1382.

49.

Narayan PK, Mishra S and Narayan S (2011). Do market capitalization and stocks traded converge? New global evidence. Journal of Banking and Finance 35(10): 2771–2781.

50.

Odhiambo

(2009a) Electricity consumption and economic growth in South Africa: A trivariate causality test. Energy Economics 31(5): 635–640.

51.

Odhiambo

(2009b) Energy consumption and economic growth nexus in Tanzania: An ARDL bounds testing approach. Energy Policy 37(2): 617–622.

52.

Odhiambo

(2010) Energy consumption, prices and economic growth in three SSA countries: A comparative study. Energy Policy 38(5): 2463–2469.

53.

Odhiambo

(2014a) Energy dependence in developing countries: Does the level of income matter. Atlantic Economic Journal 42(1): 65–77.

54.

Odhiambo

(2014b) Electricity consumption, exports and economic growth in the Democratic Republic of Congo: An ARDL-bounds testing approach. Journal of Developing Areas 48(4): 189–207.

55.

Osabuohien

Efobi

(2013) Africa’s money in Africa. South African Journal of Economics 81(2): 292–306.

56.

Olusegun

(2008) Consumption and economic growth in Nigeria: A bounds testing cointegration approach. Journal of Economic Theory 2(4): 118–123.

57.

Ozturk

Acaravci

(2010) CO₂ emissions, energy consumption and economic growth in Turkey. Renewable and Sustainable Energy Reviews 14(9): 3220–3225.

58.

Petrakis

Stamatakis

(2002) Growth and educational levels: A comparative analysis. Economics of Education Review 21(2): 513–521.

59.

Pingali

(2012) Green revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences 109(31): 12302–12308.

60.

Roodman

(2009a) A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics 71(1): 135–158.

61.

Roodman

(2009b) How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal: Promoting Communications on Statistics and Stata 9(1): 86–136.

62.

Rui

Peng

, et al. (2017) Development of industry performance metrics for offshore oil and gas project. Natural Gas Science and Engineering 39(March): 44–53.

63.

Shaw

Bachu

(2012) Screening, evaluation, and ranking of oil reservoirs suitable for CO2-flood EOR and carbon dioxide sequestration. Journal of Canadian Petroleum Technology 41(09): 1–11.

64.

Shurig

(2015) Who will fund the renewable solution to the energy crisis? The Guardian, Available at: www.theguardian.com/global-development-professionalsnetwork/2014/jun/05/renewable-energy-electricty-africa-policy (accessed 8 September 2015).

65.

Tchamyou

(2019a) Education, lifelong learning, inequality and financial access: Evidence from African countries. Contemporary Social Science. Epub ahead of print 05 February 2018. DOI: 10.1080/21582041.2018.1433314

66.

Tchamyou

(2019b) The role of information sharing in modulating the effect of financial access on inequality. Journal of African Business 20(3): 317–338.

67.

Tchamyou

Asongu

(2017) Information sharing and financial sector development in Africa. Journal of African Business 18(7): 24–49.

68.

Tchamyou

Erreygers

Cassimon

(2019) Inequality, ICT and financial access in Africa. Technological Forecasting and Social Change 139: 169–184.

69.

World Energy Outlook (2014) Factsheet, Energy in sub-Saharan Africa today, International Energy Agency, https://www.iea.org/media/news/2014/press/141013_WEO_Africa_Energy_OutlookFactsheet1.pdf (accessed 1 August 2018).