Income Heterogeneity and the Environmental Kuznets Curve Turning Points: Evidence from Africa

sustainability

Article Income Heterogeneity and the Environmental Kuznets Curve Turning Points: Evidence from Africa

Mark Awe Tachega 1,* , Xilong Yao 1,*, Yang Liu 2 , Dulal Ahmed 1, Wilhermina Ackaah 1, Mohamed Gabir 1 and Justice Gyimah 1

1 College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China; [email protected] (D.A.); [email protected] (W.A.); [email protected] (M.G.); [email protected] (J.G.) 2 African Development Bank, Abidjan 01, Côte d’Ivoire; [email protected] * Correspondence: [email protected] (M.A.T.); [email protected] (X.Y.)

Abstract: The concept of environmental sustainability aims to achieve economic development while achieving a sustainable environment. The inverted U-shape relationship between economic growth and environmental quality, also called Environmental Kuznets Curve (EKC), describes the correlation between economic growth and carbon emissions. This study assesses the role of agriculture and energy-related variables while evaluating the EKC threshold in 54 African economies, and income groups, according to World Bank categorization, including low income, lower-middle, upper-middle, and high-income in Africa. With 1990–2015 panel data, the results are estimated using panel cointegration, Fully Modiﬁed Ordinary Least Square (FMOLS), and granger causality tests. The results are: (1) The study validated the EKC hypothesis in the low-income, lower-, and upper-middle-income economies. However, there is no evidence of EKC in the full African and high-income panels. Furthermore, the turning points of EKC in the income group are meagerly low, Citation: Tachega, M.A.; Yao, X.; Liu, Y.; Ahmed, D.; Ackaah, W.; showing that Africa could be turning on EKC at lower income levels. (2) The correlation between Gabir, M.; Gyimah, J. Income agriculture with CO2 is found positive in the high-income economy. However, agriculture has a Heterogeneity and the Environmental mitigation effect on emissions in the lower-middle-income and low-income economies, and the full Kuznets Curve Turning Points: sample. Also, renewable energy is negatively correlated with emissions in Africa and the high-income Evidence from Africa. Sustainability economy. In contrast, non-renewable energy exerts a positive effect on emissions in all income groups 2021, 13, 5634. https://doi.org/ except the low-income economies. 10.3390/su13105634

Keywords: CO2 emissions; agriculture; renewable energy consumption; non-renewable energy Academic Editor: Roberto Mancinelli consumption; environmental kuznets curve

Received: 4 February 2021 Accepted: 27 February 2021 Published: 18 May 2021 1. Introduction

Publisher’s Note: MDPI stays neutral In the last few years, Africa has become home to several of the world’s strongest with regard to jurisdictional claims in growing economies, with an annual growth rate of 5% [1]. Simultaneously, Africa’s published maps and institutional affil- energy consumption and carbon dioxide (CO2) emissions have increased due to the critical iations. synergy between rising incomes, energy demand, and CO2 emissions [2]. The surge in energy consumption is linked to environmental pressures such as greenhouse gas (GHGs) emissions, particularly CO2 emissions [3,4]. Extant studies assumed an inverted U-shaped curve linkage between CO2 emissions and Gross Domestic Product (GDP). The U-shaped curve assumes that environmental quality correlates directly with economic Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. growth until a defining moment where rising economic growth induces environmental This article is an open access article pollution decline, culminating in an inverted curve [5–8]. The Environmental Kuznets distributed under the terms and curve (EKC) describes the hypothesized inverted curve. In Africa, nations are moving from conditions of the Creative Commons agrarian to industrialized economies, raising concerns about Africa’s contribution to the Attribution (CC BY) license (https:// global green effect [9]. Several research studies have tried to validate the EKC theory in creativecommons.org/licenses/by/ the region and have documented contradictory findings. For example, while Mehdi [10] 4.0/). and Osabuohien’s [11] empirical studies lent credence to the EKC theory, Adu [1] and

Sustainability 2021, 13, 5634. https://doi.org/10.3390/su13105634 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 5634 2 of 22

Ogundipe [12] could not validate the same theory. According to Ogundipe [12], the income difference across the region might account for the inconclusive results. Perman [13] posited that it is imperative to disaggregate economies based on their income when investigating the EKC hypothesis because it is possible to have the investigated variables cointegrated but their relationship not being concave. It is also the case that studies that investigated the EKC hypothesis across the different income groups recorded uneven results [14–19], making a case for disaggregated studies. Unfortunately, the few studies in Africa conducted on this phenomenon adopted an aggregate level approach, where all African nations are lumped together irrespective of their income levels. This paper addresses this lacuna by employing an integrative framework approach to examine the linkage between economic growth, agriculture added value, energy consumption (both renewable and non-renewable), and carbon emissions within the context of the EKC theory for Africa, considering the different income groups classiﬁed by the World Bank [20]. Under the World Bank income categorization, African countries can be categorised into 24 low-income economies (LICs, with GNI (gross national income) per capita between $1025 or less), 21 lower-middle- income economies (LMICs, with GNI per capita between $1026 to $3995), 8 upper-middle- income economies (UMICs, with GNI per capita between $3996 to $12,375), and one high-income economy (HICs, with GNI per capita $12,376 or more), herein preferred to be known as ALICs, ALMICs, AUMICs, and AHIC, respectively. Our model seeks to validate the EKC hypothesis and estimate the turning points in each of these income groups, as well as the entire African sample, a clear departure from what previous studies examined in the region.

1.1. Economic Growth and CO2 Emissions No nation achieves global competitiveness and prosperity by adopting a micro-scale or household-facing energy option [21]. Countries, regardless of their income status, heavily invest in large-scale energy approaches to propel fast-growing economies. High-income economies employ high-energy strategies to ensure a consistent, abundant, and reliable supply of power at scale to power growth. Therefore, energy consumption is an essential driver of industrialization and economic strength in any economy regardless of income status. Rising capita incomes correlate with energy consumption, which also correlates with CO2 emissions. Therefore, economic development and environmental preservation have become the front-burner challenges in this century, confronting the world [22]. The necessity of examining the economic growth–environmental quality nexus hinges on the need to understand rising incomes and their ramifications on the environment, since sustainable economic development is linked to sustainable environmental development in any economy [23]. African economies broadly fall under low-income and middle-income categories, which means vast untapped economic space and potential for expanded economic growth and activity. With the continent fashioning an entirely new growth path and harnessing its resources’ potential, its current economic growth rate will increase with a corresponding increase in energy consumption, which will have predictable telling-effects on the CO2 emissions levels. Thus far, though minimal, African economies influence the global economy and the greenhouse effect. Explicitly, based on the income classification grouping in Table1, AHIC, AUMICs, ALMICs, and ALICs hold approximately 66%, 43.4%, 11.4%, and 3% mean share of economic growth in 2014 relative to the total global economic growth figures respectively, with the share of CO2 emissions being 86.3%, 74.8%, 13.9%, and 2.7% respectively, over the same year. Again, the mean share of Africa’s total contribution to the global economy rose from 11.6% in 1990 to 13.9% in 2014, while the mean share of CO2 emissions increased from 15.3% to 19.6%. Figure1 indicates the linkage between GDP growth and emissions in Africa. The figure depicts an increasing trend of GDP as well as CO2, except in 2008–2013, where there is fluctuation in GDP figures, explained by the global recession during that period. Thus, there is an affirmed positive correlation between growth and CO2 emissions. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 23

global recession during that period. Thus, there is an affirmed positive correlation between growth and CO2 emissions.

SustainabilityTable2021 1. The, 13, 5634nexus between CO2 emissions, agriculture (AGR), GDP, renewable (RE) and non-renewable energy (NRE)3 of 22 use.

Variables GDP CO2 AGR NRE RE

Table 1. The nexus between CO2 emissions, agriculture (AGR),1990 GDP, renewable (RE) and non-renewable energy (NRE) use. World Total 14,195.14 5.621 323.662 0.054 0.009 Variables GDP CO AGR NRE RE African High-Income Countries 7542.559 2 2.163 414.204 0.018 0.001 Share 53.1%1990 38.5% 128% 33.7% 9% AfricanWorld Upper Total-Middle Countries 14,195.144677.346 5.621 3.525 323.662 303.086 0.0540.019 0.0090.011 African High-IncomeShare Countries 7542.55933% 2.16362.7% 414.204 93.6% 0.01836.2% 0.001129.5% Share 53.1% 38.5% 128% 33.7% 9% AfricanAfrican Upper-Middle Lower-Middle Countries Countries 4677.3461484.587 3.525 0.57 303.086 243.601 0.0190.005 0.0110.009 ShareShare 33%10.5% 62.7% 10.1% 93.6% 75.3% 36.2%10.1% 129.5%100.3% AfricanAfrican Lower-Middle Low-Income Countries Countries 1484.587444.066 0.57 0.104 243.601 137.277 0.0050.009 0.0090.01 ShareShare 10.5%3.1% 10.1% 1.8% 75.3% 42.4% 10.1%17% 100.3%113.9% African Low-Income Countries 444.066 0.104 137.277 0.009 0.01 Full Africa 1646.222 0.858 211.149 0.022 0.064 Share 3.1% 1.8% 42.4% 17% 113.9% Full AfricaShare 1646.22211.6% 0.858 15.3% 211.149 65.2% 0.02241.6% 0.064744.5% Share 11.6% 15.3%2014 65.2% 41.6% 744.5% World Total 19,474.912014 6.278 625.506 0.053 0.011 AfricanWorld High Total-Income Countries 19,474.9112,850.49 6.278 5.419 625.506 288.164 0.0530.046 0.0110.001 African High-IncomeShare Countries 12,850.4966% 5.41986.3% 288.164 46.1% 0.04686.6% 0.0015.2% African UpperShare-Middle Countries 66%8449.037 86.3% 4.696 46.1% 299 86.6%0.035 5.2%0.015 African Upper-MiddleShare Countries 8449.03743.4% 4.696 74.8% 299 47.8% 0.03566.9% 0.015131.6% Share 43.4% 74.8% 47.8% 66.9% 131.6% African Lower-Middle Countries 2222.563 0.874 288.508 0.008 0.009 African Lower-Middle Countries 2222.563 0.874 288.508 0.008 0.009 ShareShare 11.4%11.4% 13.9% 13.9% 46.1% 46.1% 14.8%14.8% 77.7%77.7% AfricanAfrican Low-Income Low-Income Countries Countries 592.939592.939 0.169 0.169 166.8 166.8 0.0080.008 0.0080.008 ShareShare 3%3% 2.7%2.7% 26.7% 26.7% 14.7%14.7% 68%68% Full Africa 2704.68 1.23 236.419 0.035 0.066 Full Africa 2704.68 1.23 236.419 0.035 0.066 Share 13.9% 19.6% 37.8% 67.1% 571.3% Share 13.9% 19.6% 37.8% 67.1% 571.3% Note: The mean share of the variables in each income category relative to global totals is calculated. Note: The mean share of the variables in each income category relative to global totals is calculated.

160,000 70 140,000 60 120,000 50 100,000 40

80,000 CO2 GDP 30 60,000 40,000 20 20,000 10

- -

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year

(GDP) (CO2)

Figure 1. GDP and carbon emissions correlation in Africa. Figure 1. GDP and carbon emissions correlation in Africa.

1.2.Agriculture and CO2 Relationship As energy continues to be a necessity on the road to prosperity, energy at scale, especially fossil energy, is required and heavily relied on by economies in pursuing agriculture with the resultant consequence of CO2 emissions within intensive agricultural places worldwide [10]. Chen [24] underscores the increasing growth in anthropogenic greenhouse gas (GHG) emissions culminating in climate change, basically because of the over-reliance on fossil fuels and land-use changes. Indeed, the United Nations’ Food and Agriculture [25] ranks agriculture as the second highest GHGs emitter globally, responsible for about 21% Sustainability 2021, 13, x FOR PEER REVIEW 4 of 23

1.2. Agriculture and CO2 Relationship As energy continues to be a necessity on the road to prosperity, energy at scale, especially fossil energy, is required and heavily relied on by economies in pursuing agriculture with the resultant consequence of CO2 emissions within intensive agricultural places worldwide [10]. Chen [24] underscores the increasing growth in anthropogenic green- Sustainability 2021, 13, 5634 4 of 22 house gas (GHG) emissions culminating in climate change, basically because of the over- reliance on fossil fuels and land-use changes. Indeed, the United Nations’ Food and Agri- culture [25] ranks agriculture as the second highest GHGs emitter globally, responsible offor the about overall 21% global of theGHG overall emissions. global GHG African emissions. economy’s African mean econom sharey’s of mean the world’s share of total the agricultureworld’s total was agriculture 37.8% in 2014was (see37.8% Table in 20141). (see Table 1). Again,Again, agriculturalagricultural activities,activities, includingincluding thethe usageusage ofof fossilfossil fuel-poweredfuel-powered ﬁeldfield equip-equip- mentment in in mechanizationmechanization and and irrigation, irrigation, nitrogen-rich nitrogen-rich fertilizers fertilizers in in fertilization, fertilization, and and burning burning ofof biomass,biomass, contribute contribute directly directly to to greenhouse greenhouse gas gas emissions emissions [ 10[10,26],26].. However, However, agricultural agricultural landslands also also extract extract atmospheric atmospheric CO CO22through through sustainablesustainable agriculture,agriculture, soil soil conservation, conservation, and and otherother activities.activities. Indeed,Indeed, thethe agriculturalagricultural sectorsector has has a a CO CO22emission emission reductionreduction potential potential of of 80–88%80–88% [ 27[27]],, achieved achieved by by storing storing biomass biomass products products and and soil soil organic organic matter matter [ 28[28]].. Grassland Grassland carboncarbon sequestrationsequestration was estimate estimatedd to to contribute contribute to to the the global global CO CO2 mitigation2 mitigation effort effort of of0.6 0.6gigatons gigatons of carbonof carbon dioxide dioxide equivalent equivalent (GT (GT CO CO2-eq)2-eq) in 2018 in 2018[29] [.29 Figure]. Figure 2 illustrates2 illustrates the theCO2 COand2 andagriculture agriculture relationship relationship.. Agriculture Agriculture experiences experiences a fluctuating a ﬂuctuating trend, trend, experiencing experiencing the the highest score in 2004. On the contrary, the CO emissions have been increasing over highest score in 2004. On the contrary, the CO2 emissions2 have been increasing over the theperiod, period, with with 1995 1995 being being the the cleanest cleanest year. year.

70 14,000 60 12,000 50 10,000 40 8,000

CO2 30 6,000

20 4,000 Agriculture 10 2,000

- -

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year

CO2 AGR

Figure 2. Agriculture and carbon emissions correlation in Africa. Figure 2. Agriculture and carbon emissions correlation in Africa.

1.3.1.3. RenewableRenewable andand Non-RenewableNon-Renewable EnergyEnergy Use Use and and CO CO22Emissions Emissions Non-renewableNon-renewable energy energy sources, sources, including including fossil fossil fuel: fuel: petroleum, petroleum, coal, coal, and naturaland natural gas, aregas, the are primary the primary sources sources of global of global CO2 emissionsCO2 emissions [30–32 [30].– However,32]. However, renewable renewable energy energy has thehas least the increasingleast increasing impact impact on CO on2 emission, CO2 emission, making making it an alternative it an alternative energy optionenergy to option non- renewableto non-renewable energy [energy30–33]. [30 This–33 consideration]. This consideration showsthe shows necessity the necessity for the diversiﬁcationfor the diversi- offication energy of options energy tooptions include to usinginclude renewable using renewable energy sourcesenergy sources in agricultural in agricultural services ser- to powervices to economic power economic growth [ 34growth]. High-income [34]. High economies-income economies have seen have tremendous seen tremendous investment in- investment the potential in the ofpotential energy consumption,of energy consumption, but the low-income but the low countries-income havecountries a different have a storydifferent of underutilization story of underutilization of renewable of energy.renewable In 2018, energy. BP (formerlyIn 2018, BP British (formerly Petroleum) British [35 Pe-] reportedtroleum) that [35] renewable reported that energy renewable consumption energy rose consumption to 561.3 million rose metricto 561.3 tons million of oil. metric Even withtons theof oil. rise Even in consumption with the rise levels, in consumption renewable levels, energy renewable capacity isenergy still lower capacity than is thestill primarylower than energy the primary sources [energy35]. For sources instance, [35] in. For Africa, instance, renewables in Africa, grew renewables by 18.5%, grew driven by by18. solar5%, driven (37%) by and solar wind (37%) (15%), and but wind had (15%) a minimal, but ha shared a minimal of only 1.6%share in of the only total 1.6% energy in the mixtotal in energy 2018 [35 mix]. Again, in 2018 the [35] combined. Again, contributionthe combined of contribution African economies of African to the economies global non- to renewable energy mix is increasing. Speciﬁcally, the mean share of Africa’s contribution to total global non-renewable energy rose from 41.6% in 1990 to 67.1% in 2014 (Table1). Finally, another detectible fact is the substantial variations in the considered variables across the distinct income categories. For example, the mean share of GDP growth increased in all the income groups from 1990 to 2014. However, in the low-income group, GDP growth decreased from 3.1% in 1990 to 3.0% in 2014. Again, the mean share of renewable energy had a downward trend in all the income categories during the period but achieved an upward trend from 129.5% in 1990 to 131.6% in 2014 in the upper-middle-income countries. Sustainability 2021, 13, 5634 5 of 22

Many studies mostly ignore these differences as they adopted aggregated studies in the region, creating a knowledge gap [36–38]. The above context makes it imperative to study the nexus between agriculture, GDP, renewable and non-renewable energy consumption, and CO2 emissions in each of the income categories in Africa. The current study motivation is drawn from the gaps in the emerging EKC literature in the case of Africa. (1) Some related EKC studies in the region focused on aggregate African studies without regard to the region’s different income groups. However, the validation of the EKC hypothesis may be determined differently by specific income groups. The research works that conducted disaggregated studies also consider middle-income countries at an aggregate level. Therefore, our study considers the various income groups and the whole African sample datasets for analysis. We further disaggregate the middle-income countries into lower- and upper-middle-income groups. (2) Again, most related literature in the region only examined the validation of the EKC hypothesis without evaluating the threshold of EKC. Our study estimates the EKC turning point of GDP per capita for each income group in the region. (3) Many studies focused on aggregate energy consumption. Nevertheless, the impact of energy consumption on GDP or emissions may be motivated by using individual energy types (renewable or non-renewable energy consumption). The problem with the aggregate energy consumption usage is that it can confound each specific energy type’s effect, necessary for policy recommendations. Meanwhile, studies that consider the individual energy types either examined renewable or non-renewable energy consumption impact on either GDP or emissions. Therefore, in this study, we examine the EKC theory using specific energy types. (4) Given that Africa is generally agrarian, agriculture is central to the continent’s economic development. Yet very few studies incorporated agriculture in the EKC model. Our study assesses the EKC theory by adding agriculture to the current EKC model to look at agriculture’s role in emissions, thereby enriching the EKC model. The rest of the paper is organized as follows: Section2 presents the methodology and data. The empirical findings are presented and discussed in Sections3 and4, respectively. Finally, Section5 outlines the general conclusions and policy suggestions.

2. Literature Review Many researchers have explored the validity of the EKC effect. Generally, there are two strands of literature regarding the EKC hypothesis: (1) the first category studies the connection between economic growth and environmental pollution for individual economies [8,39,40], and (2) the second strand studies the correlation economic development has with the environmental pollution in a panel of economies [12,19,41]. The findings from these strands of literature generated divergent results based on (a) the application of different methodologies, countries, datasets, and timeframes by researchers, (b) different dependent variable of environmental pollution used, i.e., CO2, arsenic, cadmium, nitrate, lead, coliform, phosphorus [42], (c) different explanatory variables used, i.e., GDP (income), financial development, non-renewable or renewable energy consumption, and others, and (d) models used, i.e., linear, quadratic, or cubic or N-shaped [43]. For instance, Jebli [10], based on 1980–2011 data, analyzed the nexus between renewable energy consumption, agriculture, and CO2 emissions in North African economies and validated the EKC effect. Also, Liu [44] examined the U-shape effect for four Association of Southeast Asian Nations (ASEAN) using agriculture, renewable energy consumption, and CO2 emissions and affirmed the relationship. Recently, further study was carried out on the influence of agriculture, economic growth, and renewable energy on greenhouse emissions from 1990 to 2014 in Group of Twenty (G20) countries, employing panel cointegration FMOLS model techniques, and mixed evidence of EKC for CO2 for developing and developed countries were found [18]. On the contrary, Olusegun [45] used annual CO2 and GDP data from 1970 to 2005 to analyze EKC for Nigeria and established no EKC effect veracity. Also, Omojolaibi [46] investigated the EKC hypothesis and invalidated the theory in West Africa. Sustainability 2021, 13, x FOR PEER REVIEW 6 of 23

EKC effect. Also, Liu [44] examined the U-shape effect for four Association of Southeast Asian Nations (ASEAN) using agriculture, renewable energy consumption, and CO2 emissions and affirmed the relationship. Recently, further study was carried out on the influence of agriculture, economic growth, and renewable energy on greenhouse emissions from 1990 to 2014 in Group of Twenty (G20) countries, employing panel cointegration FMOLS model techniques, and mixed evidence of EKC for CO2 for developing and developed countries were found [18]. On the contrary, Olusegun [45] used annual CO2 and GDP data from 1970 to 2005 to analyze EKC for Nigeria and established no EKC effect veracity. Also, Omojolaibi [46] investigated the EKC hypothesis and invalidated the theory in West Africa. In the context of Africa, the available empirical results are also inconclusive. As a Sustainability 2021, 13, x FOR PEER REVIEW result,6 of 23 new studies employed new datasets and improved econometric methods to im-

prove on earlier works. Table 2 details some studies and their conclusions that studied the EKC hypothesis in Africa. The research that confirmed the EKC hypothesis is illustrated ✓ EKC effect. Also, Liu [44] examined the U-shape effect for four Association of Southeastby , and the other considerable works that failed to validate the EKC model are represented by ˟. Indeed, the foregoing review (see Table 2) revealed that no study examined Asian Nations (ASEAN) using agriculture, renewable energy consumption, and CO2 emissions and affirmed the relationship. Recently, further study was carried out onEKC the in Africa considering the four different income levels in the region, except for Ogun- influence of agriculture, economic growth, and renewable energy on greenhouse dipeemis- [14], who employed different variables and empirical methods. Therefore, it is better sions from 1990 to 2014 in Group of Twenty (G20) countries, employing panel cointegra-to consider the four different income groups in the region in the EKC’s theory estimation. Additionally, while related variables such as CO2, economic growth, agriculture, non-re- tion FMOLS model techniques, and mixed evidence of EKC for CO2 for developing and newable, and renewable energy consumption are used in the literature, they are rarely developed countries were found [18]. On the contrary, Olusegun [45] used annual CO2 and GDP data from 1970 to 2005 to analyze EKC for Nigeria and established no EKC adoptedeffect jointly in the EKC framework. Furthermore, the research on African countries veracity. Also, Omojolaibi [46] investigated the EKC hypothesis and invalidated theis the- very inadequate, and several of the available studies analyzed only sub-regional ory in West Africa. datasets. In the context of Africa, the available empirical results are also inconclusive. As a result, new studies employed new datasets and improved econometricTable 2. methods Summary to of im-recent empirical literature regarding energy/emissions–growth nexus (2016–2019). prove on earlier works. Table 2 details some studies and theirAuthors conclusions thatCountries studied the Years Variables Methods Results EKC Hypothesis EKC hypothesis in Africa. The research that confirmed the EKC hypothesis is illustrated CO2, Y, Y2, POP, by ✓, and the other considerable works that failed to validate the EKC5 model African are econo- repre- Lin [47] 1980–2011 EI/S STIRPAT model EI/S→CO2 ˟ ˟ mies sented by . Indeed, the foregoing review (see Table 2) revealed that no study examined UBR EKC in Africa considering the four different income levels in the region, except for Ogun- 10 Middle East CO2, Y, EC CO2→Y dipe [14], who employed different variables and empiricalMagazzino methods. [48] Therefore, it is better 1971–2006 Panel VAR ˟ economies EC→CO2 to consider the four different income groups in the region in the EKC’s theory estimation. 33 Sub-Saharan Additionally, while related variables such as CO2, economic growth, agriculture, non-re- CO2, Y, Y2, CIN, Panel cointegra- Y→CSF Ojewumi [49] African coun- 1980–2012 ˟ newable, and renewable energy consumptionSustainability are used2021 in ,the13, 5634literature, they are rarely CLQ, CSF tion Y→CFE 6 of 22 tries adopted jointly in the EKC framework. Furthermore, the research on African countries 3 North African Y→ CO2 is very inadequate, and several of the available studiesKais analyzed [50] only sub-regional 1980–2012 CO2, Y, Y2, EC VECM Not investigated → 2 datasets. economies UR CO 16 WestIn Africa the context of Africa, theCO available2, Y, Y2, WA, empirical results are also inconclusive. As a Ogundipe [12], result, new studies1990 employed–2012 new datasets and improvedWA econometric Y→ methods CO2 to improve˟ Table 2. Summary of recent empirical literature regarding energy/emissions–growth nexus (2016–2019)Countries. SA on earlier works. Table2 details some studies and their conclusions that studied the EKC North Africa VECM Granger RE→CO2 Mehdi [10] 1980−2011 CO2, RE, AGR, Y ✓ Authors Countries Years Variables Methods Results EKCeconomieshypothesis Hypothesis in Africa. The research that conﬁrmed thecausality EKC hypothesis (LR) isillustrated by , CO2, Y, Y2, POP, and the other considerable works that failed to validate the EKC model are represented 5 African econo- CO2, RE, NG, Y, Panel VECM Lin [47] 1980–2011 EI/S STIRPAT model EI/SDong→CO [51]2 byBRIC˟ . Indeed, the1985−2016 foregoing review (see Table2) revealed that no studyRE→ examinedCO2 EKC in˟ mies Y2 causality UBR Africa considering the four different income levels in the region, except for Ogundipe [14], CO2, Y, Y2, 10 Middle East CO2, Y, EC CO2→Y 16 whoWest employed Africa different variables and empirical methods.fixed effects Therefore, it is better to consider → 2 ˟ Magazzino [48] 1971–2006 Panel VAR Adu [1] the˟ four different1970−2013 income groups COWASTE, in the region TO, in the EKC’s theory estimation.Y CO Additionally, economies EC→CO2 Countries model (FEM), while related variables such as COPOP,, economic OER growth, agriculture, non-renewable, and 33 Sub-Saharan 2 CO2, Y, Y2, CIN, Panel cointegra- Y→CSF 25 Africanrenewable coun- energy consumption are used in the literature, they are rarelyRE→CO adopted2 jointly in Ojewumi [49] African coun- 1980–2012 Zoundi [52] ˟ 1980–2012 Y, Y2, CO2, RE FMOLS, DOLS ✓ CLQ, CSF tion Y→CFE thetries EKC framework. Furthermore, the research on African countries(LR) is very inadequate, tries 19 Africanand several coun- of the available studiesCO2 analyzed, Y, Y2, EI, only sub-regional datasets.EI→CO2 3 North African ShahbazY→ CO [53]2 1971–2012 ARDL ✓ Kais [50] 1980–2012 CO2, Y, Y2, EC VECM Not investigatedtries GL GL→CO2 economies URTable→CO 2. Summary2 of recent empirical literature regarding energy/emissions–growth nexus (2016–2019). 16 West Africa CO2, Y, Y2, WA, Ogundipe [12], 1990–2012 WA Y→ CO2 ˟ Countries SA Authors Countries Years Variables Methods Results EKC Hypothesis CO , Y, Y2, POP, North Africa VECM Granger RE→CO2 2 Mehdi [10] 1980−2011 CO2, RE, AGR, Y Lin [47] 5 African economies✓ 1980–2011 EI/S STIRPAT model EI/S → CO2 economies causality (LR) UBR 10 Middle East CO → Y CO2, RE, NG, Y, Panel VECMMagazzino [48] 1971–2006 CO , Y, EC Panel VAR 2 Dong [51] BRIC 1985−2016 RE→CO2 economies ˟ 2 EC → CO 2 2 Y causality 33 Sub-Saharan CO , Y, Y2, CIN, Panel Y → CSF Ojewumi [49] 1980–2012 2 CO2, Y, Y2, African countries CLQ, CSF cointegration Y → CFE 16 West Africa fixed effects 3 North African 2 Y → CO2 Adu [1] 1970−2013 COWASTE, TO, Kais [50] Y→CO2 ˟ 1980–2012 CO2, Y, Y , EC VECM Not investigated Countries model (FEM), economies UR → CO2 2 POP, OER 16 West Africa CO2, Y, Y , WA, Ogundipe [12], 1990–2012 WA Y → CO2 25 African coun- RE→CO2 Countries SA Zoundi [52] 1980–2012 Y, Y2, CO2, RE FMOLS, DOLS North Africa ✓ VECM Granger Mehdi [10] 1980−2011 CO , RE, AGR, Y RE → CO (LR) tries (LR) economies 2 causality 2 2 CO , RE, NG, Y, Panel VECM 19 African coun- CO2, Y, Y , EI, Dong [51]EI→CO2 BRIC 1985−2016 2 RE → CO Shahbaz [53] 1971–2012 ARDL ✓ Y2 causality 2 tries GL GL→CO2 CO , Y, Y2, 16 West Africa 2 ﬁxed effects Adu [1] 1970−2013 COWASTE, TO, Y → CO Countries model (FEM), 2 POP, OER 2 2 Zoundi [52] 25 African countries 1980–2012 Y, Y , CO , RE FMOLS, DOLS RE → CO2 (LR)

2 EI → CO2 Shahbaz [53] 19 African countries 1971–2012 CO2, Y, Y , EI, GL ARDL GL → CO2 CO , Y, Y2, 2 Panel Y → CO Sarkodie [54] 17 African countries 1971–2013 AGLND, CBRT, 2 cointegration AGLND → CO ECF, ENC, 2 Panel Granger P → CO2 Dong [40] 128 Economies 1990–2014 Y, CO2, POP, RE Not investigated Causality Y → CO2 14 Panel Granger Dong [31] 1970–2016 Y, Y2, CO , NG NG → CO Asia-Pacificcountries 2 Causality 2 RE → CO 19 nations of G20 Y, Y2, CO , AGR, FMOLS 2 Qiao [18] 1990−2014 2 AGR → CO countries RE VECM 2 Y → CO2 RE → CO2 Y, Y , CO , AGR, FMOLS AGR → CO Our Contribution 54 African countries 1990−2015 2 2 2 -income RE, NRE VECM Y → CO2 group-specific NRE → CO2 2 Y: per capita GDP; Y : GDP (squared); CO2: carbon emissions; RE: renewable energy; NRE: non-renewable energy; TO: trade openness; POV: poverty; EI/S: energy intensity/system; POP: population; IQ: institutional quality; UBR: Urbanization; AGR: agricultural value-added per-capita; GL: grasslands, AGLND: Agricultural lands; CBRT: Birth rate; ECF: Ecological footprint; EU Energy use; FECs: various fossil fuels; NG: natural gas; OER: Official exchange rate; WA: water access; SA: sanitation access, CSF: composite solid emission; CFE: composite factor of emission; CIN: industrial emission; CLQ: liquid emissions; is EKC validated, is EKC hypothesis not validated; VECM: Vector Error Correction Model; OLS: Ordinary Least Squares; FMOLS: Fully Modified OLS; VAR: Vector Autoregressive Model; ARDL: Autoregressive Distributed Lag Model; DOLS: Dynamic OLS; ECM: Error Correction Method. Sustainability 2021, 13, 5634 7 of 22

3. Materials and Methods 3.1. Data

To tackle the dynamic link between CO2 emissions, agriculture, renewable and non- renewable energy consumption, and economic growth across Africa, we utilize a panel dataset from 1990 to 2015 for the 54 African economies. The selection of data is subjected to data availability. Tables S1 and S2 in the Supplementary Materials give the descriptive statistics and correlation analysis of the variables, respectively. The economies are classified into different income categories per their capita gross national income (GNI) according to the classification by the World Bank Atlas method [20]. This categorization allows us to grasp the effects of the improvements in CO2 emissions and how the variables influence CO2 emissions in each income group. The different income groups can be seen in Table S3 in the Supplementary Materials. Table3 describes the variables used and their sources.

Table 3. Description of variables.

Deﬁnition of Variables Symbol Unit Data Source Measuring method Primarily from the Carbon dioxide consumption of fossil World Development CO Metric Tons emissions 2 fuels and other Indicators [55] emissions The net outputs minus Agricultural intermediate primary World Development AGR US$ value-added agricultural sector Indicators [55] inputs Energy consumption Renewable Sustainable Energy RE TJ from all renewable energy for All [56] resources Total ﬁnal energy Non-renewable consumption— Sustainable Energy NRE TJ energy renewable energy for All [56] consumption Gross domestic World Development GDP US$ GDP product Indicators [55] Note: All variables are in per capita given by the ratio of each variable to the total population.

3.2. Methodology To fulfil this study’s purpose, we consider the apparent differences in the different income levels, which presupposes that the motivation forces for CO2 emissions may be dissimilar due to the variations in income levels. Consequently, we conduct our empirical analysis on the separate sub-income categories and the full sample. To this end, our study extends the standard EKC model of Qiao [18] by introducing non-renewable energy use as a new independent variable. In our analysis, the quadratic form of the empirical equation is defined as: 2 CO2it = f (AGRit, REit, NREit, GDPit, GDPit) (1) By definition, i signifies country samples (i = 1, 2. 3, ... , N), t designates the period (1990–2015), CO2it indicates the CO2 emissions per capita of the country i in the year t, GDPit is country’s i GDP per capita in year t,AGRit denotes agricultural value-added per capita of country i in year t,REit is country’s i renewable energy consumption per capita in year t, and NREit defines non-renewable energy consumption per capita of country i in year t. The model is converted to its natural logarithmic form to eliminate issues correlated with the data’s distributional characteristics, allowing the interpretation of each estimated Sustainability 2021, 13, 5634 8 of 22

coefﬁcient as elasticity in the regression model [57]. The new equation is then written as follows:

2 ln CO2it = β0 + β1 AGRit + β2REit + β3NREit + β4GDPit + β5GDPit + Uit (2)

By definition, β0 defines the intercept and Uit defines the error term. The parameters β1 − β5 signify the long-run elasticity estimated coefficients of CO2 emissions on per capita GDP (squared), agricultural value-added (AGR) per capita, renewable energy consumption (RE) per capita, and non-renewable energy consumption (NRE) per capita. The parameter β4 is envisaged to be positive, and β5 is envisaged to be negative. Figure S1 in the Supplementary Materials is the procedure adopted in the analysis of the results.

3.2.1. Panel Unit Root Tests The stationarity test of the variables is ﬁrst assessed to avoid spurious regression. The following panel unit root tests are used: Fisher-augmented Dickey-Fuller (ADF) [58], Im-Pesaran-Shin (IPS) [59], and Fisher-Phillips-Perron (PP) [60]. The null and the alternate hypotheses suggest the presence of panel non-stationary unit root and panel stationary unit root, respectively.

3.2.2. Panel Cointegration Tests Next, the cointegration relationship between the variables is determined using the Fisher Johansen cointegration technique by Maddala [58]. A determination of the cointegrating relationship between the parameters will allow for the estimation of their effects on CO2 emissions. Paramati [57] and Paramati [61] intimate that the Fisher-type Johansen cointegration test gives more robust test results than the conventional Engle-Granger two- step procedure panel cointegration test. “The Fisher Johansen cointegration test technique employs both trace and maximum eigenvalue (Max-Eigen) tests to corroborate the number of cointegrating vectors, with the null hypothesis stating that there is no cointegration between the variables” [18].

3.2.3. Panel Long-Run Parameter Estimates After conﬁrming the cointegration relationship, the EKC effect and the long-run estimates for Equation (2) are examined. The FMOLS model is used to estimate the long- run elasticity in Equation (2). “It can correct the biased and incoherent results associated with the ordinary least square (OLS) and control possible endogeneity of the regressors and serial correlation of the long run” [44], as well as generate consistent estimates in ﬁnite samples [62]. The FMOLS model is as follows:

N ˆ −1 ˆ βGDPFMOLS = N ∑ βFMOLS,n (3) n−1

By deﬁnition, βˆGDPFMOLS,n signiﬁes the FMOLS estimator applied to country n. The t-statistic is observed as follows:

N ˆ −1/2 βGDPFMOLS = N ∑ tβGDPFMOLS,n (4) n−1

3.2.4. The Turning Point of GDP per Capita After estimating the model of the EKC hypothesis in Equation (2), a turning point can be estimated by taking the derivative of the known quadratic functions of the EKC hypothesis as follows:

d β1t 2β2 ln GDPit ln (CO2)it = + = 0 (5) d(Yit) GDPit GDPit Sustainability 2021, 13, 5634 9 of 22

Thus, the threshold of the GDP per capita is given by: β1 GDPit = exp − 2β2

3.2.5. Vector Error Correction Model (VECM) Panel Granger Causality Test In the fourth step, the short- and long-run directional causalities are investigated by the vector error correction model (VECM) Granger causality approach. Causality testing is the most critical phase in examining the relationship between macroeconomic indicators, especially in the formulation of comprehensive and reliable policies to tackle CO2 emissions. The approach analyzes the long-run relationships, utilizing the Equation (2) residuals. Furthermore, we investigate the short-run causalities using the VECM Wald test. Following the example of Qiao [18], the VECM empirical equations can be constructed as follows:             ∆ ln CO2it α1 β11jβ12jβ13jβ14jβ15jβ16j ∆ ln CO2it γ1 µ1it ∆ ln AGR  β β β β β β  ∆ ln AGR  it   α2   21j 22j 23j 24j 25j 26j   it   γ2   µ2it      k          ∆ ln REit   α3   β31jβ32jβ33jβ34jβ35jβ36j   ∆ ln REit   γ3   µ3it    =   + ∑   ×   +   × (ECTit−1) +   (6)  ∆ ln NREit   α4  j=1 β41jβ42jβ43jβ44jβ45jβ46j   ∆ ln NREit   γ4   µ4it               ∆ ln GDPit   α5   β51jβ52jβ53jβ54jβ55jβ56j   ∆ ln GDPit   γ5   µ5it  2 2 ∆(ln GDPit) α6 β61jβ62jβ63jβ64jβ65jβ66j ∆(ln GDPit) γ6 µ6it

where ∆ stands for the first difference operator, µ denotes a random error term, ECTt−1 defines the lagged error correction term, and j is the lag length. k is based on the VAR lag order selection criteria. α is the fixed country effect, β signifies the short-run coefficient, which measures the explained variable’s dynamic impact, and γ represents the long-run adjustment coefficient. When the value of ECTit−1 is statistically significant and negative, there is a long-run causal relationship from the regressors to regressands in Equation (3).

4. Results 4.1. Panel Unit Root Test Results Table4 illustrates the unit root results. The null hypothesis suggests the presence of a unit root and stationarity for the alternative hypothesis. The panel root unit root tests used include IPS, Fisher-ADF, and Fisher-PP. The test uses two regressions to allow for comparison [18,63]. Regression 1 includes only the constant term, and regression 2 considers both the constant and time trends. The results from regression 1 and 2 in Table4 illustrate that almost all the variables have unit roots at levels. However, in the ﬁrst difference, all the variables are stationary at the 1% signiﬁcance level in both regressions. Thus, the long-run equilibrium relationship between the variables can be evaluated using cointegration test techniques. In the high-income economies, the African high-income economy is only one; hence, the stationarity is investigated using time series unit root tests, namely ADF, Dickey-Fuller Generalized Least Squares (DF-GLS), and PP. The results are similar to the rest of the samples that use panel unit root test.

4.2. Panel Cointegration Test Results The Johansen Fisher panel cointegration estimates for both trace and maximum eigenvalue statistics are presented in Table5. The null hypothesis of no cointegrating relation (R = 0) at the 1% signiﬁcance level is rejected. Therefore, the cointegration test supports the long-run equilibrium between the variables for all the income groups and the full African sample. Sustainability 2021, 13, 5634 10 of 22

Table 4. Panel unit root tests.

Different Income Levels of African Countries Variables Level First Difference Level First Difference Intercept Intercept Intercept Intercept Intercept Intercept Intercept Intercept and Trend and Trend and Trend and Trend IPS High Income Lower-Middle-Income a b a a ln CO2 −1.815 −1.027 −3.929 −3.739 −0.202 −0.807 −17.508 −16.592 ln AGR −0.334 −3.143 −3.587 a −3.515 b 0.628 −3.140 a −14.628 a −9.571 a ln RE −1.440 −0.145 −4.583 a −5.241 a 0.503 2.518 −12.467 a −12.299 a ln NRE −1.884 −0.972 −4.269 a −4.690 a 1.461 0.189 −18.250 a −16.808 a ln GDP −0.334 −3.143 −3.587 a −3.515 c 5.097 0.033 −11.952 a −8.721 a (ln GDP)2 −0.379 −0.575 −3.287 b −5.122 a 1.788 0.190 −8.680 a −8.444 a Fisher-ADF a a b a a ln CO2 −1.189 −1.091 −3.885 −4.345 51.840 54.175 317.20 279.02 ln AGR −1.633c −2.591 −4.485 a −4.643 a 45.931 88.938 a 279.13 a 233.35 a ln RE −1.191 −0.746 −4.685 a −5.415 a 40.271 30.494 245.77 a 213.04 a ln NRE −1.259 −1.040 −4.222 a −4.909 a 34.760 41.953 331.10 a 278.91 a ln GDP −0.447 −3.288 b −3.660 a −3.679 b 23.401 52.050 c 199.41 a 152.57 a (ln GDP)2 −0.554 −0.876 −3.331 a −4.952 a 42.544 42.448 154.21 a 142.94 a Fisher-PP a a a a ln CO2 −1.810 −1.142 −17.944 −4.132 30.002 48.134 465.46 604.80 ln AGR −2.489 −2.740 −4.502 a −4.410 a 49.824 83.670 a 361.81 a 628.53 a ln RE −1.440 −0.145 −4.583 a −5.241 a 26.151 22.263 264.28 a 312.83 a ln NRE −1.884 −0.972 −4.269 a −4.690 a 25.347 40.438 370.61 a 419.52 a ln GDP −0.334 −3.143 −3.587 b −3.515 c 14.056 42.817 198.16 a 244.98 a (ln GDP)2 −0.379 −0.575 −3.287 b −5.122 a 24.823 31.192 184.30 a 219.83 a IPS Upper−Middle-Income Low Income a a a a a ln CO2 −1.516 −11.132 327.94 −16.349 2.961 0.567 −14.605 −12.545 ln AGR −1.011 −4.256 a −13.966 a −8.270 a 0.788 −1.198 −16.67 1a −9.602 a ln RE 2.187 −2.074 a −9.466 a −7.711 a 1.627 −4.941 a −14.321 a −16.086 a ln NRE 0.147 1.089 −7.814 a −7.246 a 3.543 −4.307 a −14.573 a −16.337 a ln GDP 2.373 −0.571 −9.125 a −6.750 a 1.584 −1.513 c −14.557 a −10.729 a (ln GDP)2 −2.491c 1.840 −6.348 a −7.063 a −0.783 −0.628 −13.166 a −17.139 a Fisher-ADF b a a a a a ln CO2 28.403 286.66 551.84 356.91 27.658 56.466 278.01 223.76 ln AGR 22.444 b 48.335 a 154.81 a 154.09 a 46.081 70.950a 327.82 a 296.01 a ln RE 8.739 a 28.435 b 106.35 a 81.899 a 39.506 117.651a 277.84 a 314.81 a ln NRE 20.033 10.912 86.864 a 73.180 a 32.922 108.340a 275.21a 307.36 a ln GDP 9.955 24.337 c 98.095 a 70.376 a 45.647 65.353b 287.84 a 242.49 a (ln GDP)2 55.917 a 8.667 71.543 a 69.609 a 302.28 a 75.563 a 272.09 a 535.08 a Fisher-PP b a a a a a ln CO2 66.450 296.13 1126.2 617.51 26.740 48.400 317.55 300.76 ln AGR 28.172 a 34.276 a 139.91 a 249.74 a 56.736 85.252 a 388.94 a 830.53 a ln RE 10.749 a 12.734 108.42 a 98.509 a 54.556 54.116 314.87 a 359.35 a ln NRE 17.160 12.007 97.525 a 141.17 a 39.566 40.138 317.11 a 344.40 a ln GDP 9.266 24.597 c 125.92 a 306.51 a 45.122 66.437 b 319.86 a 388.92 a (ln GDP)2 12.215 a 15.160 70.643 a 72.056 a 46.405 53.180 315.32 a 568.82 a IPS Full Africa Sample Full World Sample a a a a a a a ln CO2 1.192 −4.229 −27.982 −25.336 −3.519 −2.538 −41.275 −35.262 ln AGR 0.417 −4.254 a −26.015 a −15.743 a 0.976 −3.556 a −37.000 a −26.698 a ln RE 1.444 −4.304 a −22.234 a −19.997 a 1.347 −4.494 −43.966 a −35.549 a ln NRE 2.126 −0.841 −26.012 a −23.604 a −1.230 −2.561 a −42.063 a −36.094 a ln GDP 5.290 −1.422 c −21.002 a −15.385 a 7.891 −3.492 a −30.857 a −25.345 a (ln GDP)2 1.092 −0.370 −18.326 a −16.259 a 1.905 −6.014 a −29.637 a −26.316 a Sustainability 2021, 13, 5634 11 of 22

Table 4. Cont.

Different Income Levels of African Countries Variables Level First Difference Level First Difference Intercept Intercept Intercept Intercept Intercept Intercept Intercept Intercept and Trend and Trend and Trend and Trend Fisher-ADF a a a a a a a ln CO2 109.923 397.46 933.16 867.74 503.81 462.346 2034.77 1736.3 ln AGR 118.54 c 211.16 a 774.60 a 692.84 a 283.189 409.701 a 1737.51 a 1360.6 a ln RE 88.599 217.17 a 653.54 a 546.49 a 348.96 a 497.023 a 2118.80 a 1850.3 a ln NRE 198.41 a 128.58 c 743.81 a 619.98 a 476.54 a 487.352 a 2082.78 a 1758.2 a ln GDP 79.201 145.99 a 593.89 a 471.06 a 215.398 711.262 a 1568.76 a 1218.7 a (ln GDP)2 145.78 a 131.83 b 541.03 a 494.95 a 432.63 a 760.972 a 1538.18 a 1367.0 a Fisher-PP a a a a a a a ln CO2 125.200 392.88 1345.1 1531.1 435.75 718.816 3060.07 5034.0 ln AGR 138.81 a 206.13 a 903.42 a 1718.0 a 254.500 343.177 b 1994.65 a 2375.2 a ln RE 99.079 96.702 667.47 a 584.05 a 405.41 a 404.183 a 2255.14 a 3766.8 a ln NRE 65.796 110.220 803.34 a 818.56 a 517.17 a 566.913 a 2334.32 a 3695.1 a ln GDP 68.700 134.86 b 651.82 a 945.38 a 241.896 389.078 a 1485.72 a 1618.1 a (ln GDP)2 108.217 112.817 631.50 a 946.59 a 595.43 a 759.302 a 1489.38 a 1876.3 a Notes: a, b, and c represent 1%, 5%, and 10% signiﬁcance levels, respectively. The speciﬁcation of the optimal lag length is automatically based on Akaike Information Criterion.

Table 5. Johansen Fisher panel cointegration test.

AHIC AUMICs ALMICs ALICs Full Africa Sample Max-Eigen Max-Eigen Max-Eigen Max-Eigen Max-Eigen Hypothesized Trace Test Trace Test Trace Test Trace Test Trace Test Test Test Test Test Test None 145.7 a 0.923 a 134.5 a 88.28 a 660.6 a 364.9 a 741.8 a 387.5 a 1385.0 a 780.7 a At most 1 86.73 a 0.794 a 60.70 a 43.96 a 370.3 a 201.9 a 396.9 a 220.2 a 752.2 a 422.3 a At most 2 50.3 b 0.69b 25.83 a 24.97 a 209.4 a 116.3 a 219.5 a 126.3 a 414.2 a 226.3 a At most 3 23.01 0.375 9.180 6.499 119.2 a 64.51 a 120.9 a 77.70 a 246.3 a 152.7 a At most 4 12.18 0.275 7.796 7.844 85.50 a 67.87 a 72.54 a 59.21 a 155.3 a 138.2 a At most 5 4.767 0.187 9.328 9.328 66.70 a 66.70 a 60.36 a 60.36 a 111.6 a 111.6 a Note: a, b, and c indicate 1%, 5%, and 10% levels of signiﬁcance, respectively.

4.3. Panel Long-Run Parameter Results The panel long-run estimates of the FMOLS estimators for each income subpanel and the full Africa panel are presented in Table6. In most cases, the high values for adjusted R2 and R2 suggest that the model best fits the data. For the EKC model, the coefficients for ln GDP and (ln GDP)2 have different signs across the panels. The analysis confirms an inverted U-shaped relationship for the AUMICs, ALMICs, and ALICs subpanels since the ln GDP coefficients are significant and positive, and its square term (ln GDP)2 coefficients are significant and negative in these subpanels. The implication is that CO2 emissions are decreasing at a higher GDP rate. However, in the high-income economy and the full African sample, the findings do not validate the EKC hypothesis. In the high-income subpanel, EKC theory is not validated because the ln GDP and (ln GDP)2 coefficients are both significantly positive. Regarding the full sample, although the GDP coefficient is positive and significant, its square term is negative and insignificant. Following previous literature, we compute for the threshold, which shows the point where increasing CO2 turns to decreasing tendency with GDP. For the AHICS, AUMICs, ALMICs, and ALICs, the turning points are $1.16325563, $1.53066581, $2.332220479, and $1.245069024, respectively. When the countries are pooled together as a full sample, the turning point is $8545.768177. The turning points are low, showing that Africa could be turning on EKC at lower income levels. The finding is in keeping with the results of Ogundipe [12], who argued that the income groups in Africa could not attain reasonable turning points. Sustainability 2021, 13, 5634 12 of 22

Table 6. FMOLS estimation (dependent variable: CO2 emissions) results.

Variables AHICs AUMICs ALMICs ALICs FULL AFRICA 0.1512 0.426 0.847 0.2191 9.053 Threshold λˆ = β1 2β2 [1.163] [1.531] [2.332] [1.245] [8545.768] 0.235 a 0.636 a 0.586 a 0.466 a 0.851 a ln GDP (0.001) (0.000) (0.000) (0.000) (0.000) 0.777 a −0.747 a −0.346 a −1.063 a −0.047 (ln GDP)2 (0.019) (0.001) (0.001) (0.000) (0.232) 0.207 a −0.201 −0.154 a −0.447 a −1.068 a ln AGR (0.003) (0.228) (0.000) (0.004) (0.000) −0.072 a −0.003 −0.033 a 10.894 a −0.120 a ln RE (0.013) (0.924) (0.182) (0.000) (0.000) 0.780 a 0.837 a 0.790 a −10.289 a 0.346 a ln NRE (0.000) (0.000) (0.000) (0.00) (0.000) R2 0.991690 0.594493 0.871796 0.244991 0.638930 Adjusted R-squared 0.989940 0.582653 0.870569 0.237918 0.63746 Note: a represents 1% statistical signiﬁcance. The p-values are in the parenthesis. The values in [] points reported in US$.

The findings also confirm that a 1% change in agriculture per capita negatively affects CO2 emissions in the ALMICs, ALICs, and the full sample subpanels by −0.154%, −0.447%, and −1.068%, respectively. CO2 emissions in the high-income economy also have a positive impact of 0.207%. However, the result in the upper-middle-income economies is insignificant. The results affirm that the influence of AGR on CO2 emissions is inconsistent throughout the different income groups. The findings establish the impact of RE as significant and negative on CO2 emissions in the AHIC and the full sample. More precisely, a 1% rise in renewable energy decreases CO2 emissions by −0.072% in the AUMICs, and −0.120% in the full African sample. While the estimated values are very marginal, the signs are expected. The significant and negative findings are consistent with the work of Bento [64], who concluded that in Italy, RE use mitigates CO2 emissions between 1960 to 2011, and the work of Dong [51], who found RE to have a reducing impact on CO2 emissions in Brazil, Russia, India, China, and South Africa (BRICS) economies between 1985 and 2016. Generally, this attests that RE is a cleaner substitute to fossil fuel for the AUMICs subpanel and Africa as a whole. However, there are no statistically significant mitigating impacts of RE use on CO2 emissions for the AUMICs and ALMICs. The results suggest that the RE mitigating influence on CO2 emissions is limited in the total energy mix, thereby making no impact on these economies. Only when the total amount of RE is above a certain limit in the entire energy structure can the mitigation effect be achieved [19]. In the ALICs, RE has a statistically significant and positive influence on CO2, which corroborated the conclusion of Apergis [65], who established that RE positively impacts CO2 emissions for 19 developing and developed countries. Also, the results indicate that NRE exerts a significant and positive influence on CO2 emissions in the whole of Africa, AHIC, AUMICs, and ALMICs. However, its influence on CO2 emissions for ALICs is significantly negative. Notably, in the AHIC, AUMICs, ALMICs, and full sample, a 1% rise in non-renewable energy consumption raises CO2 emissions by 0.346%, 0.780%, 0.837%, and 0.790%, respectively. The results of this study authenticate the finding that in the long run, increasing NRE usage will significantly increase pollutant emissions in these economies and Africa as a whole.

4.4. VECM Panel Granger Causality Test Results Table7 illustrates the results of the long and short directional causality estimates. Considering the distinctly speciﬁc relationships between the selected variables around the various income levels described in Section 4.3, we evaluate the directional causalities for each different income level distinctly. This separate evaluation of each income group and the whole Africa sample can provide sensible policy suggestions for each speciﬁc Sustainability 2021, 13, 5634 13 of 22

income group. Given that CO2 emission is our interest variable, the results are exclusively interpreted for the nexus between the other selected variables and CO2 emissions given in Table7.

Table 7. Panel Granger causality results.

Dependent Variable Short-Run Long-Run 2 ∆ ln CO2it−1 ∆ ln AGRit ∆ ln REit ∆ ln NREit ∆ ln GDPit(∆(ln GDPit) ) ECTt−1 F-Stat (p-Value) t-Stat (p-Value) High-Income Countries 2.875 0.295 0.056 4.039 c −1.350 b ∆ ln CO - 2it (0.109) (0.594) (0.815) (0.061) (0.052) 0.032 0.157 0.253 0.341 0.145 ∆ ln AGR - it (0.858) (0.696) (0.620) (0.566) (0.636) 18.854 a 0.078 23.649 a 0.329 1.695 a ∆ ln RE - it (0.000) (0.782) (0.0001) (0.573) (0.000) 0.892 1.563 0.068 2.951 −0.953 ln ∆NRE - it (0.358) (0.228) (0.796) (0.103) (0.198) 0.052 1.178 0.645 0.307 0.044 ∆ ln GDP (∆(ln GDP )2) - it it (0.822) (0.292) (0.432) (0.586) (0.791) Causality Direction GDP → CO2, CO2 → RE, NRE → RE CO2 Upper-Income Countries 0.789 0.820 0.002 0.054 −0.081 a ∆ ln CO - 2it (0.375) (0.366) (0.960) (0.815) (0.003) 0.029 0.460 0.677 6.393 b −0.021 b ∆ ln AGR - it (0.864) (0.498) (0.411) (0.012) (0.014) 0.687 0.084 1.250 1.958 0.002 ∆ ln RE - it (0.408) (0.771) (0.265) (0.164) (0.480) 0.490 0.014 0.542 0.897 −0.021a ∆ ln NRE - it 0.484 (0.905) (0.462) (0.345) (0.001) −0.009 a 0.129 0.336 0.839 0.943 ∆ ln GDP (∆(ln GDP )2) - it it (0.001) (0.719) (0.562) (0.361) (0.333) Causality Direction GDP → AGR, CO2 → GDP CO2 → AGRCO2 → NRE Lower-Middle-Income Countries 0.103 3.091 b 2.652 c 3.392 b −0.090 b ∆ ln CO - 2it (0.901) (0.046) (0.071) (0.034) (0.003) 1.384 0.212 0.118 5.203 a −0.001 ∆ ln AGR - it (0.251) (0.808) (0.888) (0.005) (0.943) 1.533 0.151 0.449 0.110 −0.029 b ∆ ln RE - it (0.217) (0.859) (0.638) (0.895) (0.056) 3.848 b 1.241 1.844 5.466 a 0.039 ∆ ln NRE - it (0.022) (0.290) (0.159) (0.004) (0.162) 0.853 10.644 a 0.130 0.225 −0.003 ∆ ln GDP (∆(ln GDP )2) - it it (0.426) (0.000) (0.877) (0.798) (0.565) Causality Direction RE → CO2, NRE → CO2, GDP → CO2, GDP → AGR, GDP → NRE CO2 → RE Low-Income Countries 0.458 0.435 0.392 2.794 c 0.0004 ∆ ln CO - 2it (0.498) (0.509) (0.531) (0.095) (0.511) 2.342 6.391 b 6.503 b 4.714 b 0.001 b ∆ ln AGR - it (0.126) (0.011) (0.011) (0.030) (0.021) 0.466 0.050 1.109 8.693 a 4.080 ∆ ln RE - it (0.494) (0.821) (0.292) (0.003) (0.932) 0.480 0.069 1.509 8.954 a −6.460 ∆ ln NRE - it (0.488) (0.792) (0.219) (0.002) (0.894) 3.189 b 4.170 b 3.072 c 10.454 a 0.421 a ∆ ln GDP (∆(ln GDP )2) - it it (0.074) (0.041) (0.080) (0.001) (0.008) Causality Direction GDP → CO2, RE → AGR, NRE → AGR, GDP → AGR, GDP → RE, GDP → NRE Sustainability 2021, 13, 5634 14 of 22

Table 7. Cont.

Dependent Variable Short-Run Long-Run Full Africa Sample 0.104 0.587 1.906 5.245 b −0.034 b ∆ ln CO - 2it (0.747) (0.443) (0.167) (0.022) (0.000) 1.850 0.137 0.628 4.183 b 0.009 c ∆ ln AGR - it (0.999) (0.710) (0.428) (0.041) (0.074) 1.525 0.122 0.016 0.0008 0.007 ∆ ln RE - it (0.217) (0.726) (0.898) (0.976) (0.114) 1.484 0.243 0.680 0.618 0.0017 ∆ ln NRE - it (0.223) (0.621) (0.409) (0.431) (0.817) 1.720 9.532 a 0.060 3.506 c 0.004 ∆ ln GDP (∆(ln GDP )2) - it it (0.189) (0.002) (0.805) (0.061) (0.124) Causality Direction GDP → CO2, GDP → AGR, NRE → GDP ln CO2 Note: a, b, and c represent 1%, 5%, and 10% level of signiﬁcance, respectively. ∆ First-difference operator.

With the regression for the high-income economy, the ECTt−1 column shows that the error correction term (ECT) coefficient of CO2 emission is significantly negative at a 1% level of significance, signifying that when the system deviates from long-term equilibrium, CO2 emission responds to the adjustment process at 1.350% yearly. The short-run results reveal that GDP’s influence is significantly positive on CO2 emissions, signifying that GDP does have Granger causality with CO2 in the short term. The coefficients of AGR, NRE, and RE are all positive; however, they are not statistically significant. These results indicate that AGR, NRE, and RE do not have Granger causality with emissions in the short term. The findings affirm CO2 emissions’ positive influence on RE, suggesting that increases in CO2 sources stimulate renewable energy usage. For the upper-middle-income subpanel, the three equations having ln CO2, ln AGR, and ln NRE as the dependent variables, have their ECT coefficients negative and statistically significant, suggesting bidirectional relationships between CO2 and AGR, and CO2 and NRE in the long term. Furthermore, the error terms’ coefficients indicate that the deviations from CO2 or AGR or NRE from the short term to the long term are corrected by 0.08%, 0.021%, or 0.021% after a shock, annually. The results affirm that AGR, RE, NRE, and GDP do not have Granger causality with emissions in the short-run. Furthermore, CO2 increases with GDP and vice versa, signifying that in the short term, increases in CO2 stimulate GDP and vice versa. In the lower-middle-income panel, the ECTt−1 coefficients for CO2 and RE equations are negatively significant at a 1% significance level, suggesting a hypothetical feedback connection between emissions and RE in the long term. The implication is that in the long term, CO2 influences RE and vice versa in the ALMICs. The short-run findings reveal that Granger causalities run from RE, NRE, and GDP to CO2. The agriculture sign is positive and insignificant, which means that in the short-run, agriculture does not have a Granger trigger with CO2. CO2 emissions exert an impact on NRE. Also, the evidence adduced affirm that emissions negatively impact AGR, RE, and GDP in the short term; however, their associated coefficients are statistically insignificant. A short-term bidirectional relationship between output and emission is reported in the low-income subgroup, connoting that GDP is a Granger trigger for CO2 emissions and vice versa. The coefficients of the other variables that demonstrate the influence of AGR, RE, and NRE on CO2 have positive signs but are insignificant. This evidence indicates that AGR, RE, and NRE are not Granger causative of CO2 emissions in the short-term. The effects of CO2 as an explanatory variable on the AGR, RE, and NRE prove positive but insignificant. Meanwhile, in the long term, the lagged ECTs are significant but not with negative signs when ln GDP and ln NRE are the dependent variables, suggesting no hypothetical nexus between emissions and the other variables in the long-run. In the full sample panel, the ECTt−1 column reveals that at a 1% significance level, the ECT term’s coefficient in the CO2 equation is significant and negative, connoting that when the system deviates from long-term equilibrium, CO2 responds to the adjustment process. Sustainability 2021, 13, 5634 15 of 22

SustainabilitySustainability 2021 2021, 13, 13, x, xFOR FOR PEER PEER REVIEW REVIEWThe CO error term’s coefficient suggests that a deviation from the short- to long-term1616 of of 23 23 Sustainability 2021, 13, x FOR PEER REVIEW 2 16 of 23 is fixed by 0.034% after a shock each year. With the short-run results, GDP impact on CO2 is significant and positive, implying that GDP does have Granger causality with CO2 inCO the2 in short the short term. term. The The estimated estimated coefficients coefficients of of AGR, AGR, RE, RE, and and NRE NRE are are all all positive positive butbut CO2 in the short term. The estimated coefficients of AGR, RE, and NRE are all positive but statisticallyCOstatistically2 in the short insignificant,insignificant, term. The implyingimplying estimated thatthat coefficients AGR,AGR, RE,RE, of andand AGR, NRENRE RE, dodo and notnot NRE havehave are GrangerGranger all positive causalitycausality but statisticallystatistically insignificant, insignificant, implying implying that that AGR, AGR, RE, RE, and and NRE NRE do do not not have have Granger Granger caus causalityality withwith COCO22 emissions inin thethe shortshort term.term. TheThe VECMVECM GrangerGranger causalitycausality analysisanalysis isis presentedpresented with CO2 emissions in the short term. The VECM Granger causality analysis is presented inwithin FiguresFigures CO2 3emissions 3––77.. in the short term. The VECM Granger causality analysis is presented inin Figures Figures 3 3––7.7.

GDP& GDP&2 GDP&GDP2 GDPGDP2

CO2 AGR CO2 AGR CO2 AGR

RERE NRENRE RE NRE

FigureFigure 3.3. High-incomeHigh-income economies.economies. FigureFigure 3. 3. High High-income-income economies. economies.

GDP& GDP&2 GDP&GDP2 GDPGDP2

CO2 AGR CO2 AGR CO2 AGR

RE NRE RERE NRENRE

Figure 4. Upper-middle-income economies. FigureFigureFigure 4 4.4. .U Upper-middle-incomeUpperpper-middle-middle-income-income economies economies.economies. .

GDP& GDP&2 GDP&GDP2 GDPGDP2

CO2 AGR CO2 AGR CO2 AGR

RE NRE RERE NRENRE

Figure 5. Lower-middle-income economies. FigureFigureFigure 5. 5.5. Lower Lower-middle-incomeLower-middle-middle-income-income economies economies.economies. . Sustainability 2021, 13, x FOR PEER REVIEW 17 of 23 Sustainability 2021, 13, 5634 16 of 22 Sustainability 2021, 13, x FOR PEER REVIEW 17 of 23

GDP& 2 GDPGDP& GDP2

CO2 AGR

RE NRE RE NRE Figure 6. Low-income economies. FigureFigure 6.6. Low-incomeLow-income economies.economies.

Note Note

Bidirectional long run Granger causality Bidirectional long run Granger causality

Bidirectional short-run Granger causality Bidirectional short-run Granger causality

Figure 7. Full Africa Sample. Figure 7. Full Africa Sample. 5.Figure Further 7. Full Discussion Africa Sample. 5. 5.1.Further CO2 DiscussionEmissions and Economic Growth 5. Further Discussion 5.1. COExcept2 Emissions for theand fullEconomic African Growth sample and the high-income economy, the inverted U- shaped5.1.Except CO link2 Emissions for between the full and CO African Economic2 and GDPsample Growth is confirmed and the high in all-income the other economy, different incomethe inverted subpanels, U- signifying that emissions decrease with increasing per capita output in ALICs, ALMICs, shaped Exceptlink between for the CO full2 and African GDP sampleis confirmed and the in allhigh the-income other different economy, income the inverted subpan- U- and AUMICs. It also means that when nations’ environment and economy are correctly els,shaped signifying link between that emissions CO2 and decrease GDP is confirmed with increasing in all the per other capita different output income in ALICs, subpan- managed through constructive and sustainable ways, CO emissions can be curbed, which ALMICs,els, signifying and AUMICs. that emissions It also means decrease that when with nations increasing’ environment2 per capita and output economy in ALICs, are is in keeping with the ecological sustainability theory. The results here demonstrate that correctlyALMICs, managed and AUMICs. through It constructivealso means that and when sustainable nations ’ ways, environment CO2 emissions and economy can be are the EKC hypothesis can be validated in any income category irrespective of the income curbed,correctly which managed is in keeping through with constructive the ecological and sustainablesustainability ways, theory. CO The2 emissions results here can be status, which is compatible with the evidence by Bilgili, Dong, and Dong [19,39,42]. They demonstratecurbed, which that the is inEKC keeping hypothesis with can the be ecological validated sustainability in any income theory. category The irrespective results here found that the EKC hypothesis’ validation has nothing to do with any particular country of thedemonstrate income status, that the which EKC ishypothesis compatible can with be validated the evidence in any by income Bilgili, category Dong, and irrespective Dong or region’s income level. As for the full African sample, according to the panel analysis, [19,39,42]of the income. They foundstatus, that which the isEKC compatible hypothesis with’ validation the evidence has bynothing Bilgili, to Dong, do with and any Dong the GDP per capita at the turning point was $8545.768177, and that in year 2014, the particular[19,39,42] country. They orfound region that’s incomethe EKC level. hypothesis As for ’the validation full African has nothingsample, accordingto do with to any GDP per capita was $137,938.698, which indicates that the carbon emission of the total theparticular panel analysis, country the or GDP region per’ scapita income at thelevel. turning As for point the full was African $8545.768177 sample,, and according that in to African sample has already reached the turning point, yet the hypothesis is not validated. the panel analysis, the GDP per capita at the turning point was $8545.768177, and that in yearShuai 2014, [66 the] concluded GDP per capita that there was are $137 carbon,938.698, emission whichintensity, indicates carbonthat the emission carbon emission per capita, year 2014, the GDP per capita was $137,938.698, which indicates that the carbon emission of andthe total total African carbon sample emission has thresholds. already reached Instead the of turning Africa striving point, yet to the reach hypothesis carbon emission is not of the total African sample has already reached the turning point, yet the hypothesis is not validated.per capita, Shuai it should [66] concluded strive to achieve that there its totalare carbon carbon emission emission intensity, threshold. carbon emission pervalidated. capita,In the and high-incomeShuai total [66] carbon concluded category, emission that our thresholds. resultsthere are confirm Insteadcarbon the emissionof findings Africa strivingintensity, of Anam to [carbon 67reach], who carbon emission found emissionper capita, per capita,and total it should carbon striveemission to achieve thresholds. its total Instead carbon of Africa emission striving threshold. to reach carbon no U-shaped railways Kuznets curve for CO2 but only for nitrous oxide for high-income countries.emissionIn the highper In thecapita,-income lower- it should category, and upper-income strive our to results achievesubpanels, confirm its total the carbon our findings findings emission of affirm Anam threshold. the [67] results, who of foundMaladoh noIn U the [-17shaped high], who- incomerailways validated category, Kuznets the EKC ourcurve in theresults for lower- CO confirm2 andbut upper-middle-incomeonly the for findings nitrous of oxide Anam countriesfor [67] high, who- in incomethefound Sub-Saharan countries. no U-shaped In Africa the railways lower region.- andKuznets In upper the low-incomecurve-income for COsubpanels, subpanels,2 but only our Azamfor findings nitrous [15] affirm foundoxide the for re- EKChigh- sultstheoryincome of Maladoh to countries. be supported [17] In, whothe in lower low-validated and- and lower-middle-income theupper EKC-income in the subpanels, lower subpanels- and our upper findings but-middle not inaffirm the-income upper-the re- countriesmiddlesults of inand Maladoh the high-income Sub- Sahara[17], whon subpanels. Africa validated region. Again, the In theEKC our low in findings- incomethe lower are subpanels,- aand clear upper departure Azam-middle [15] from found-income the theworkcountries EKC of theory Qiao in the[ 18to Sub],be who supported-Sahara assertedn Africa in that low region. the- and EKC Inlower theory the -lowmiddle is- foundincome-income only subpanels, insubpanels developed Azam but economies [15] not found in butthe notEKC in theory low- and to be lower-middle supported in incomes low- and (developing lower-middle economies)-income of subpanels G20 countries. but not Our in Sustainability 2021, 13, 5634 17 of 22

findings are in line with Ogundipe’s [14] conclusion that the EKC theory does not hold for 53 African countries in the whole African sample. Also, Ogundipe [12] determined that the EKC theory is unsupported in western African countries, and Sunday [49] established no evidence of EKC for Sub-Saharan African countries. However, our finding contradicts Sarkodie [54], who found EKC validity for 17 African countries, Osabuohien [11], who also established the applicability of EKC for 50 African countries, and Adu [1], who affirmed the EKC hypothesis for West African counties. In the AUMICs subpanel, there is confirmed evidence that CO2 increases with GDP, indicating that increases in CO2 stimulate GDP in AUMICs.

5.2. CO2 Emissions and Agriculture Regarding the FMOLS findings, agriculture has statistical significance and a positive influence on CO2 in high-income economies. The discovery follows Liu [44], who found agriculture to be positively correlated with CO2 emissions in the BRICS but contradicts Ben [10], who evidenced that a percentage increase in agriculture decreases emissions in North African countries. This is particularly justified given that only the high-income countries in Africa have a total share of 128% mean global share. Liu [44] stated that emissions from modern agriculture, which is dependent on the high use of fossil fuel, the production of fertilizers, and livestock and crops, are the primary source of greenhouse gases. In the low- and upper-middle-income economies, agriculture is negatively correlated with CO2, which supports the evidence by Jebil [10] that a percentage increase in agriculture triggers a decline in emissions in North African economies. This can be attributed to the hoe-cutlass farming methods mainly used in agriculture with little employment of a mechanized farming system and the sinking effects of agriculture in these regions. In our case, the negligible impact of agriculture on emissions for ALMICs can be attributable to the decreasing mean share of agriculture in 2014 compare to 1990 (see Table1). Besides, in the ALICs, ALMICs, and AUMICs subpanels, only short-run bidirectional relationships are observed from agriculture to output, signifying that in the long term, economic growth in these income categories would not be impacted by agriculture. The unidirectional connection between RE and NRE sources and agriculture shows that both energy sources have an impact on agriculture in these income categories. Indeed, in addition to ensuring continuous economic growth, African governments should invest finances in ensuring non-renewable energy efficiency improvements and expand the use of RE as an alternative to NRE to ensure sustainable environment.

5.3. CO2 Emissions and Renewable Energy Consumption In the ALMICs subpanel, both short- and long-run correlations are established from RE to emissions, implying that incremental change in RE signiﬁcantly lowers CO2 for the ALMICs in both periods. The long-run estimates validate the assertion that environmental sustainability can be boosted by renewables [44]. Indeed, the ALMICs have seen a relative increase in investment in renewable energy. Again, CO2 is afﬁrmed to be positively correlated with RE in the high-income economy, suggesting that upward change in CO2 sources positively affects the use of renewables. Also, a bidirectional relationship between renewable energy use and GDP exists in the short term, with a positive effect on agriculture in the ALICs, suggesting that cutting down emissions will not hinder GDP in the short term. It also implies that renewable energy utility can harmonize economic growth and environmental sustainability.

5.4. CO2 Emissions and Non-Renewable Energy Consumption

In the AUMICs subpanel, a long-run bidirectional connection between CO2 and NRE was found, suggesting that NRE inﬂuences CO2 and vice versa in the long term. Also, short-term bidirectional relationships between NRE and CO2 in the ALMICs were found, implying that in the short term, a percentage rise in CO2 emissions increases the use of Sustainability 2021, 13, 5634 18 of 22

NRE resources and vice versa. African governments can appropriate funds to be used in raising the efﬁciency of NRE.

6. Conclusions and Policy Recommendations This research attempted to test the EKC hypothesis for a panel of 54 African economies with different income levels using per capita CO2 emissions, agriculture, renewable and non-renewable energy consumption, and economic growth from 1990 to 2015. The panel cointegration test established a long-run relationship between the variables selected. Next, the long-run coefficients of the explanatory indicators were examined using the panel FMOLS estimator. To conclude, the analysis used the panel VECM Granger causality approach to examine the variables’ directional causalities. The focal findings and recommendations are as follow: (1) From the estimations, the results substantiated the EKC hypothesis in the low-income, lower-middle-income, and upper-middle-income economies in Africa. In these income groups, ln GDP and its square term (ln GDP)2 coefficients were significantly positive and negative respectively, signifying that as GDP growth deepens, emissions at the different income levels will increase before peaking and then decrease with rising GDP growth. Also, it connotes that the EKC phenomenon’s validity is not income group-specific, meaning that the EKC phenomenon can occur in any region/economy, irrespective of the income status. However, the long-run estimates for ln GDP and (ln GDP)2 failed to meet the EKC assumption in the full African sample and the high-income economy even though their GDP per capita reached their turning points. As a matter of policy, African governments should focus on achieving the threshold of their total carbon emission rather than carbon emission per capita in these groups. (2) The findings of the panel FMOLS evaluations revealed agriculture to have a significant positive influence on emissions in the high-income economy, while it reduced CO2 emissions in the lower-middle-income, low-income, and full sample sub-groups. In the full-sample and high-income economy, renewable energy use mitigated CO2 emissions, while it had no statistically significant effects in reducing emissions for the upper- and lower-middle-income economies. Lastly, in all the sub-groups, except for the low-income subpanel, NRE exerted a positive effect on emissions. The following policy options are advised on renewable energy, agriculture, and non-renewable energy, respectively: (a) Agriculture policy: African governments, particularly in the high-income economy, should invest in agricultural research and extension services to promote environmentally sustainable farming practices and adopt agricultural policies that target the use of solar-powered biogas plants and power stations as an alternative to NRE sources in generating heat and electricity to power agricultural activities. The other subpanels where agriculture ameliorates emissions’ effect should be a model for the high-income economy. (b) Renewable energy policy: policy framers in Africa should initiate and adopt effective policies to optimize the RE consumption potential in those sub- categories where RE has no emission mitigation effect. Budgetary allocations and renewable expansion plans must be adopted to maximize the share of renewable energies in the total energy mix, especially in the low- and lower- middle-income economies, where there is a tremendous and unexploited potential for renewable energy sources. The following pragmatic actions can be taken to promote renewable energy: (i) African governments can directly undertake wide-ranging reassess- ment, identification, and mapping out of the renewable energy resources and their sources. It will enable private energy investors, the public, and entrepreneurs to access and reliably exploit these potentials. Sustainability 2021, 13, 5634 19 of 22

(ii) Adopting tax holidays policy to promote investors’ interest in the “clean” energy markets can largely boost investment in the sector and low prices of clean energy sources. (c) Non-renewable energy policy: On NRE, considering the significant influence of NRE on increasing CO2, there is an urgent need to implement a range of policies that would significantly increase the RE stake in the total energy mix time and limit the over-reliance on NRE. (3) The VECM Granger causality evaluations provided mixed outcomes. The results found a hypothetical unidirectional causality from output to CO2 in the high-income, lower-middle-income, and the full samples. In contrast, a bidirectional relationship from output to CO2 in the low and upper-middle-income economies existed in the short-run. There was also a unidirectional relationship from RE to CO2 and bidirectional causality from NRE to CO2 in the lower-middle-income economies. Moreover, this study observed a short-run Granger causality from CO2 to RE in the high-middle-income category.

This research offers only an initial empirical analysis for the CO2 emission, agriculture, economic growth, and non-renewable and renewable energy consumption nexus in the context of the EKC hypothesis, and there still exist some drawbacks. Firstly, methane is the second largest GHG that signiﬁcantly contributes to global warming. Therefore, exploring environmental quality with methane’s inclusion in the EKC model in future work will be informative. Secondly, other economic sectors were not considered in this study, aside from the agricultural sector. Therefore, studying the emissions–economic growth–energy consumption–sectoral output nexus would signiﬁcantly help policymakers to create individual sector-tailored policies to mitigate global warming impacts.

Supplementary Materials: The following are available online at https://www.mdpi.com/2071-1 050/13/10/5634/s1, Figure S1: Estimation procedure for analyzing CO2 emissions, agriculture, renewable energy, non-renewable energy, and economic growth, Table S1: Descriptive statistics for the variables (1990–2015), Table S2: correlations analysis for the variables (1990–2015), Table S3: new thresholds for classification by income-Africa Category. Author Contributions: Conceptualization, M.A.T.; methodology, D.A. and W.A.; software, X.Y. and D.A.; validation, M.A.T., X.Y., W.A.; formal analysis, M.A.T., X.Y., and Y.L.; investigation, M.A.T. and Y.L.; resources, X.Y.; data curation, M.A.T. and J.G.; writing—original draft preparation, M.A.T. and W.A.; writing—review and editing, M.A.T., X.Y., Y.L., D.A., M.G. and J.G.; visualization, M.A.T., X.Y. and M.G.; supervision, X.Y. and Y.L.; project administration, X.Y. and Y.L.; funding acquisition, X.Y. and Y.L. All authors have read and agreed to the published version of the manuscript. Funding: This study is funded by Program for the Innovative Talents of Higher Education Institutions of Shanxi (“PTIT”), Program for the National Natural Science Foundation of China (Project No. 41401655), Program for Soft Science Research Project of Shanxi Province (No.201803D31051), Program for Soft Science Research Project of Jinzhong City (No.201905D01111111), and Program for General Project of Philosophy and Social Science Research in Colleges and Universities of Shanxi Province (No.201803058). Data Availability Statement: Data is from World Bank Indicators. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Sustainability 2021, 13, 5634 20 of 22

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