RESEARCH ARTICLE

Investigating the nexus among environmental pollution, economicgrowth, energy use, and foreign direct investment in 6 selectedsub-Saharan African countries

Max William Ssali1 & Jianguo Du1& Isaac Adjei Mensah2

& Duncan O. Hongo3

Received: 28 September 2018 /Accepted: 3 February 2019 /Published online: 22 February 2019

AbstractThis research seeks to enhance the current literature by exploring the nexus among environmental contamination, economicgrowth, energy use, and foreign direct investment in 6 selected sub-Saharan African nations for a time of 34 years (1980–2014).By applying panel unit root (CADF and CIPS, cross-sectional independence test), panel cointegration (Pedroni and Kaocointegration test, panel PP, panel ADF), Hausman poolability test, and an auto-regressive distributed lag procedure in view ofthe pooled mean group estimation (ARDL/PMG), experimental findings disclose that alluding to the related probability values,the null hypothesis of cross-sectional independence for all variables is rejected because they are not stationary at levels but ratherstationary at their first difference. The variables are altogether integrated at the same order I(1). Findings revealed that there is aconfirmation of a bidirectional causality between energy use and CO2 in the short-run and one-way causality running from energyuse to CO2 in the long run. There is additionally a significant positive outcome and unidirectional causality from CO2 to foreigndirect investment in the long run yet no causal relationship in the short run. An increase in energy use by 1% causes an increase inCO2 by 49%. An increase in economic growth by 1% causes an increment in CO2 by 16% and an increase in economic growthsquared by 1% diminishes CO2 by 46%. The positive and negative impacts of economic growth and its square approve the EKCtheory. To guarantee sustainable economic development goal, more strict laws like sequestration ought to be worked out, use ofsustainable power source ought to be stressed, and GDP ought to be multiplied to diminish CO2 by the utilization of eco-technology for instance carbon capturing, to save lives and also to maintain a green environment.

Keywords Energy use . Pool mean group estimator . Carbon emissions . Economic growth . Foreign direct investment .

Sub-SaharanAfrica . Cross-sectional augmented Dickey-Fuller . Health

Introduction

Economic growth in sub-Saharan Africa is projected to haveimproved slowly from 1.3% in 2016 to 2.4% in 2017. This

gradual change is witnessed in countries like Angola, Nigeria,and South Africa which are considered to be the largest econ-omies in the region due to the price stabilities and economicenvironment. Economic growth in sub-Saharan Africa is pre-dicted to increase to 3.2% in 2018 and to 3.5% in 2019 but theprogress will be gradually slow (World Bank 2018b). To has-ten and maintain a comprehensive growth indication,policymakers should continuously invest in human capital assupported by Ndambiri et al. (2012), minimize resourcewastage, and enhance production in the region. Zeufack(2018) reported that policymakers in sub-Saharan Africashould be empowered to manage the challenges that ariseduring the process of economic development. Not only that,they should also consider boosting the energy systems forsustainable economic development. There is no way econom-ic growth and development can advance without the use ofenergy. Energy is one of the required variables for economicgrowth in sub-Saharan Africa as well as in the entire world.

Responsible editor: Philippe Garrigues

* Max William Ssalileadershipforimpact@gmail.com

* Isaac Adjei Mensahkwameatoapoma@gmail.com

1 School of Management, Jiangsu University, Zhenjiang 212013,People’s Republic of China

2 Faculty of Science, Jiangsu University, Zhenjiang 212013, People’sRepublic of China

3 School of Finance, Department of Statistics, Jiangsu University,Zhenjiang 212013, People’s Republic of China

Environmental Science and Pollution Research (2019) 26:11245–11260https://doi.org/10.1007/s11356-019-04455-0

# The Author(s) 2019

Bazilian et al. (2012) reported that accessibility to energy ser-vices are very important because of the abilities they have tohandle the present global economic and social oppositionssuch as poverty, disparity, food security, environmental chal-lenges, welfare, and education. Africa is rich in terms of ener-gy resources which are capable of transforming Africa.Energy use is needed for economic growth, and access tocheap and reliable energy is a vital objective for economicactivities. They stated that it is unfortunate to generate energyfrom fossil fuel. For economic activities to be effective andhazard-free, there is a need for de-carbonization (decoupling)from energy globally for low carbon and zero carbon emis-sions (Fankhauser and Jotzo 2018).

According to Avila et al. (2017) and Ispy (2018), Africa isprosperous in terms of energy resources, but on the otherhand, Africa is faced with the challenge of equal distribution.That over 130 million people depend on kerosene, charcoal,lantern, candles, and fossil fuels, and on top of that, around620 million cannot access electricity. However, with the greatresource of energy, Africa has not been able to meet thedemand for energy by her citizens. It was indicated byWorld Economic ForumWEF (2016) that the need for energyin Africa is huge and that only 24% of the sub-SaharanAfrican region has access to electricity and yet the capacityof the generated energy is 28% gigawatts.

Avila et al. (2017) revealed that the ongoing scarcity ofelectricity has incapacitated the economic growth of sub-Saharan Africa and stopped it from achieving the sustainablestandards of living because of the absence of the capacity togenerate enough energy, lack of suitable and proper infra-structure for energy supply, and lack of stable financial re-sources to sustain and invest in renewable energies. Thesesetbacks have affected the progression of the sub-SaharanAfrican development and growth. They have also led tothe great use of fossil energy in sub-Saharan Africa. Lackof accessibility to constant and affordable energy supplypresents a huge hindrance to the rapid economic growthand development of Africa. For these challenges to besolved, the World Bank (2018a) reported that technical in-novation for instance investing in solar power will grantAfrica with enough supply of electricity which will boost.That when Africa embraces technology, it will not onlyboost the economy, but it will also improve the lives ofthe citizens once they get access to energy supply.

Africa as a slow-growing economy will need to boost herenergy sector by encouraging foreign investors to come andinvest in Africa. According to the World Bank (2014), foreigndirect investment (FDI) is the greatest form of capital crossingborders into emerging economies. FDI in sub-Saharan Africawent up from US$6.3 billion in 2000 to US$35 billion in2012. Rodríguez-Pose and Cols (2017) reiterated that interna-tionally, FDI is considered to be a tool for global economicamalgamation which has got the proclivity to deliver

monetary support as it promotes economic improvement forsustainable living of the citizens.

As much as sub-Saharan African region desires to developeconomically, the recent economic development, demand forenergy, and attraction of foreign investors have posed a hugeconcern about environmental pollution especially carbonemissions (Bekhet et al. 2017). On top of that, Zaman andMoemen (2017) reiterated that economic development, inthe midst of environment degradation, is something thatshould bring shame upon everyone. The effects of economicgrowth on the environment are more evident in nations whereindustrialization is growing at a high speed inducing carbonemissions, affecting human health on a daily basis as a resultof economic activities. It is also confirmed by Nazeer et al.(2016) that industrialization, urbanization, mechanization, fer-tilizer, pesticides, and poor management of wastes particularlyin developing economies have had a great deadly impact onthe environment where policies and regulations are not tightand strict. In light to what is stated above, Huisingh et al.(2015) also revealed that the main factor responsible for cli-mate change and global warming is the ever increasing globalcarbon emissions engineered by human activities for instancecutting down of trees and burning of fossil fuels. Asumadu-Sarkodie and Owusu (2016) also reiterated that economicgrowth, population, foreign direct investments, and energyuse have contributed greatly to environmental pollution.

This has made policymakers and analysts to investigate theconnection between the responsible variables. The questionsin the minds of several researchers have been, Bhow can thischallenge of environmental pollution be brought to an endwithout interfering with the economy?^ This is the questioneconomists and environmentalists are asking. The world todayis in a dilemma because of climate change as a result of envi-ronmental degradation due to human activities which hasmade climate change to become a global concern (Arbuckleet al. 2015; Bonan 2015; Frumhoff et al. 2015; Goldstone2018; Meyer 2017; Wang et al. 2017). Wang et al. (2017)posted that international governments on climate change stat-ed that global warming with an increase in temperature ush-ered in atmospheric extremes. That these extremes have af-fected nature, ecological system, and community debasement,and the practical advancement of the economy has drivenindividuals to economic poverty.

On top of that, it has been affirmed that energy utilizationand the economic activities are thought to be the most pro-ducers of greenhouse gas discharges (Zhu et al. 2016) whichhave increased in the past years due to population increase,high demand of energy, increase in economic growth, andincrease in agriculture (Asumadu-Sarkodie and Owusu2017; Kofi Adom et al. 2012; McAusland 2010; Shahbazet al. 2017). Based on the research conducted by researchers,carbon emissions is considered to be the highest pollutant andmore deadly in emerging economies (Khan et al. 2011). It has

11246 Environ Sci Pollut Res (2019) 26:11245–11260

greatly contributed to respiratory and cardiovascular illness.The evidence from World Health Organization stated that airpollution claims 7,000,000 lives annually (WHO 2018).

The prospective consequences of global warming are sosevere that researchers and policy practitioners have devotedmuch time and a lot of resources to investigate the solution toenvironmental pollution (Chai et al. 2016; Gergel et al. 2017;Matthews et al. 2017; Pittock 2017; Schuur et al. 2015; Vogeland Henstra 2015).

In spite of the distinctions of sentiment, a few activitieshave been put together to check the antagonistic impacts ofenvironmental change (Anderson et al. 2017; Melillo et al.2017). For instance, the Kyoto convention which placed anofficial instruction among developed and developing nations,with the goal to lessen GHG emanations (Sethi and Gupte2017; Weitzman 2017). To achieve the sustainable develop-ment goals, the Kyoto protocol was linked to UNFCCC(United Nations Framework Convention on ClimateChange) which was adopted in December 1997, in Kyoto,Japan. It came into action in 2005 to influence governmentPresidents to task their ministers to start working toward sus-tainability and also to remind them of the future deadly effectsof global warming if nothing is done about it (Aldy 2017;Böhringer et al. 2018; Nanda 2018; Sethi and Gupte 2017).From the result of the convention, several treaties were signedto encourage countries to vow to reduce their carbon emis-sions out of a good will to a temperature of 2 °C.Unfortunately, most developing countries have not fully paidattention to the request which has led to extreme globalwarming (de Alegría et al. 2017). During the conventions,techniques like bio-filtration for cleaning ambient gas weresuggested and more research on these techniques were en-couraged (Ben Jaber et al. 2016; Devinny et al. 2017).

As nations commence to address economic challenges, dis-cussions have taken place to whether economic growth canexist alongside stabilized climate or vice versa (Aden 2016).As discussions about the relationship between economicgrowth and environmental pollution, Hanley Wood DataStudio (2016) carried out a study and they found that 21 coun-tries globally have managed to enhance their economy as theydownsize their carbon footprint. Their findings confirmed thatnations can reduce carbon emissions without interfering withthe economic growth. They stated that at least 1 billion metrictons annually was reduced between 2000 and 2014 by nationslike the USA, UK, Australia, Bulgaria, and Uzbekistan. Forinstance, Slovakia downsized her greenhouse gas by 22% atthe same time achieving a high GDP growth by 75%. Theleading nation in the business of carbon emission reductionglobally is USA. Hanley Wood Data Studio (2016) indicatedthat based on the findings ofWorld Resource Institute, USA isconsidered to be the largest nation with a continuous GDPgrowth with a huge decline in emissions for several years.For instance, from 2010 to 2012, carbon emissions dropped

by 6% from 5.58 to 5.23 billion metric tons and the GDPincreased by 4% from $14.8 trillion to $15.4 trillion. Aden(2016) reported that in 2014 and 2015, global carbonemissions remained leveled as the GDP continued to growamong the 21 nations and they managed to reduce GHGemissions. Unlike in Canada, it has been the opposite.Hughes and Herian (2017) posited that right from 1956, boththe GDPs and emissions of Canada have frequently increasedrapidly. They stated that Canada must decouple GDP fromcarbon emissions to meet Canada’s emission reduction targetof 2030.

In regard to the above environmental distress, this studyinvestigates the nexus among environmental pollution, eco-nomic growth energy use, and foreign direct investment in 6selected sub-Saharan African countries for over the period of1980–2014. The selected countries are Ghana, Kenya,Botswana, Mauritius, Togo, and Benin. Even though severalresearchers have done studies in connection to the variablesused in this study using time series or panel data associations(Begum et al. 2015; Bekhet et al. 2017; Ghosh and Kanjilal2014; Lin and Ouyang 2014; Ozcan 2013; Salahuddin et al.2018), to the best of our knowledge, no research has yet beencarried out to determine the nexus among the 6 selected sub-Saharan African countries.

The research aims to provide more consistent estimates ofthe nexus among environmental pollution, economic growth,energy use, and foreign direct investment in the 6 selectedsub-Saharan African countries during the period of 1980–2014. The results of the study will help governments to for-mulate policies that will be useful in terms of governing theenvironment for a sustainable standard of living and even tofind out the kind of technology suitable for environmentalprotection. It will also add value to the existing literature aboutsub-Saharan African and other parts of the world.

The remaining structure of the study is structured as fol-lows: BTheoretical literature review^ discusses the theoreticalevidences from literature review, BMethodology^ is devotedto methods and data, BEmpirical results and discussions^ fo-cuses on the empirical results and discussion of the study, andBConclusion and policy implications^ details the conclusionand policy implications or recommendation.

Theoretical literature review

For the past two decades, the causal nexus among carbondischarges, economic growth, energy use, and FDI have beena subject of discussion (Achour and Belloumi 2016). As indi-cated by various researchers like Chai et al. (2016) and Gergelet al. (2017), the prospective consequences of environmentaldestruction are so severe that researchers and policy practi-tioners have devoted much time to investigate the solutionsto this challenge.

Environ Sci Pollut Res (2019) 26:11245–11260 11247

It is imperative to understand the variables involved in thisstudy. When we discuss carbon outflows (dependent vari-able), we are concentrating particularly on carbon dioxide,or CO2 discharges into the air in an enormous amount of ways(Jones 2016; Schreier 2017). In the area of research, some ofthe indicators that have been engaged as proxies for ecologicalpollution are CO2 (carbon dioxide) and SO2 (sulfur dioxide)emissions and methane. The other variables are economicgrowth, foreign direct investment inflow, and energy use asthe independent variables.

The nexus among economic growth and carbonemission

Economic growth is an extension in the production of goodsand services execution over a specific period. To be correct,the estimation must oust the effects of inflation. Economicgrowth or development makes more advantage for organiza-tions and a nation in general. Hence, stock cost rise gives firmsresources to remain in business. They contribute and contractmore laborers and as greater work opportunities are madeaccessible as well as compensation rise. Be that as it may, asmuch as nations would need to see their income enhances, thiscomes with consequences like carbon emissions. This makesit very important to understand the nexus between this eco-nomic growth and carbon emissions. According to Han et al.(2018), they reveal that it is imperative to have a decent com-prehension about the relationship between carbon emissionsand economic growth to disclose the interfaces between hu-man activities and the natural ecology to reduce greenhousegases.

The nexus between environmental defilement and econom-ic growth can be shown by EKC (Environmental KuznetsCurve) presented by Simon Kuznets in the 1960s. He revealedthat environmental contamination rises correspondently asone with economic growth. That when there is an increasein the income level and when the income ascends to a partic-ular level, at the defining moment, CO2 begins to decrease.Accordingly, an inverted U-shaped correlation between envi-ronmental defilement and economic growth is made. Ahmedand Azam (2016) exhibited that the current literature displaysthe connection between energy usage and economic progres-sion is extensively examined. However, the experimental dis-coveries are yet not made clear and irreconcilable with respectto the trend of causality, basing on their research using cross-country time-series unidirectional causality runs from grossnational product (GNP) to energy utilization, for both Chileand Argentina.

Begum et al. (2015) examined the impact of dynamic im-pacts of economic growth, energy consumption, and popula-tion growth on CO2 emissions. They used econometric meth-odologies suchARDL bound testing inMalaysia from 1970 to1980. Their results indicated that as the carbon emissions

reduced, economic growth increased. But again, from 1980to 2009, carbon emissions increased abnormally as the eco-nomic growth increased at the same time. Their result alsoindicated that both energy consumption and economic growthhave a long-term positive effect on carbon emissions. Theysuggested the use of low-carbon innovations such green ener-gy to reduce carbon emissions for sustainable economicgrowth. Kasman and Duman (2015) investigated the causalnexus among energy consumption, economic growth carbonemissions, urbanization, and trade openness for a panel of EUcountries over the period 1992–2010. They exerted panel cau-sality tests, panel unit root test, and panel cointegration meth-odologies. According to their results, the evidence ofEnvironmental Kuznets Curve hypothesis is supported. Thisproved that there is an inverted U-shaped correlation betweenenvironment pollution and economic growth for the selectedcountries. Salahuddin et al. (2017) scrutinized the empiricaleffects of economic growth, electricity consumption, foreigndirect investment (FDI), and financial development on carbondioxide (CO2) emissions in Kuwait using time-series data forover the period of 1980–2013. They exerted ARDL boundtesting method, and their report indicated that economicgrowth, electricity consumption, and FDI stimulate CO2 emis-sions in Kuwait. The results from the VECM exposed thatFDI, economic growth, and electricity consumption toughlyGranger-cause CO2 emissions. According to these results, thestudy indorsed that carbon emissions should be reduced byincreasing its present carbon capture and use of renewableenergy like solar and wind energy. Cai et al. (2018) exploredthe causal relationship among renewable energy consumption,economic growth, and carbon emissions. They exerted boot-strap ARDL bounds alongside structural break to investigatecointegration and causality for G7 nations. Their findings ofcausality test revealed that renewable energy use causes realGDP per capita for countries like Canada, Germany, and theUSA and CO2 emissions cause renewable energy use forGermany. They also revealed that their findings have got sig-nificant policy implications for G7 nations steering effectiveenergy-use approach to decrease CO2 emissions. They alsoconcluded by stating that clean energy has the capability tobridge the gap between economic growth and environmentalmanagement in G7 and the demerging economies.

Isik et al. (2018), in another exploration, observed the in-terrelationship between energy consumption, environmentalpollution, urbanization, and economic growth in China con-sidering provincial panels. They used a heterogeneous panelanalysis technique. In this technique, they included heteroge-neous panel estimation which is based on (DOLS), fully mod-ified least squares (FMOLS). They exerted heterogeneouspanel Granger test focusing on Dumitrescu and Hurlin. Theirfindings revealed that carbon emissions, GDP, energy con-sumption structure, and urbanization contain a long-run equi-librium correlations. Their findings further revealed that GDP

11248 Environ Sci Pollut Res (2019) 26:11245–11260

and energy consumption have a significant impact on carbonemission in all provincial panels. But urbanization has a sig-nificant effect on carbon emissions within national andmedium-developed regions. Based on the findings, they pro-posed policy implications to reduce carbon emissions. Koçakand Şarkgüneşi (2018) investigated the effect of foreign directinvestment on carbon emissions in Turkey over the periodfrom 1974 to 2013. Using the structural break cointegrationtest bootstrap test for causal testing and ordinary least squareestimator, their study reiterated that economic growthprocedures reduce CO2 emissions. Khobai and Le Roux(2017) examined the relations among energy consumption,carbon dioxide emission, economic growth, trade openness,and urbanization for South Africa from 1971 to 2013. Basedon the findings of Johansen test of cointegration and vectorerror correction model Granger causality, their study indicatedthat there is a long-run relationship between the variables andthere is bidirectional causality running among energy use andGDP in the long run. Their study suggested that South Africangovernment should impose energy polices for the enhance-ment economic growth.

The nexus among foreign direct investmentand carbon emission

The link between FDI inflows and CO2 emissions are broadlydiscussed in the literature to do with environmental behaviors.This connection has ignited debate among scholars andpolicymakers about the positive and the negative impact ofboth variables. The truth of the matter is that FDI inflows iscapable of providing direct capital for investment, and moti-vate economic development by the means of knowledge trans-fer, technology transfer, and spillover effects to enhance pro-duction (Lee 2013; Ning andWang 2017). On the other comeswith carbon emissions especially in regions where there aretighter policies and regulation. Jain (2016) states that there is agreat deal of confirmation recommending that through foreigndirect investment, international multi-national corporationstend to move pollution to developing countries with thought-less ecological controls as opposed to developed countrieswith incredibly strict ecological policies. In this way, contin-gent upon the idea of and the intentions behind the multina-tional companies, FDI can cause more emanations in the hostnations (Li et al. 2018). The impact of FDI on greenhouse gasdischarges specifically has likewise been a subject of level-headed discussion in the existing literature (Rafindadi et al.2018).

Salahuddin et al. (2017) analyzed the effects of economicgrowth, electricity consumption, and foreign direct investment(FDI) on carbon dioxide (CO2) emissions in Kuwait usingtime-series data for over the period of 1980–2013. ExertingARDL, their report showed that FDI motivates CO2 emis-sions. Their other result from the VECM exposed that FDI,

economic growth, and electricity consumption toughlyGranger-cause CO2 emissions. Bekhet et al. (2017) investigat-ed dynamic causal relationships among, financial develop-ment, economic growth, energy use, and carbon emissionsfor Gulf Cooperation Council nations over the period of1980 to 2011.When they employed ARDLmodel, their studyrevealed that there is an existence of long-run and causal re-lationships among the variables in all countries unlike inUnited Arab Emirates (UAE); within UAE, Oman, andKuwait, one-way causal relationship from financial develop-ment to carbon emissions was found. In another study, Tangand Tan (2015) examined the connection between carbonemissions, energy consumption, (foreign direct investment),and economic growth in Vietnam from 1976 to 2009. Theyemployed cointegration test and Granger causality test. Theirreport inveterate the presence of long-run equilibrium withinthe variables. Their other findings revealed that there is anexistence of two-way causalities among carbon emissionsand income, and between FDI and carbon emissions. Theywere able to single out energy use, FDI, and GDP as the keyfactors of carbon emissions in Vietnam. They also suggestedthat foreign direct investment is vital in the mitigation of car-bon emission through clean technologies in terms of technol-ogy transfer.

Koçak and Şarkgüneşi (2018) exerted structural breakcointegration test bootstrap test for causal testing and ordinaryleast square estimator to investigate the potential impact offoreign direct investment on carbon emissions in Turkey overthe period from 1974 to 2013. Their results indicated that thereis an existence of long-term balance relationship among FDI,GDP, energy use, and carbon emissions. The study also re-vealed that FDI has a potential impact on carbon emissions.They also found that the impact of FDI on carbon emissions ispositive with a two-way relationship. Sarkodie and Strezov(2019) examined the significant impact of foreign direct in-vestment (FDI) inflows, economic growth, and energy use oncarbon emissions from 1982 to 2016 considering China, India,Iran, Indonesia, and South Africa as the leading carbon emit-ters in the emerging economies. They discovered that the im-pact of energy use on carbon dioxide emissions is significantlypositive. Their final findings reported that foreign direct in-vestment inflows can boost eco-technological transfer,upgrading in labor and eco-friendly management in emergingeconomies.

Another study by Ojewumi and Akinlo (2017) investigatedthe dynamic relations between foreign direct investment, eco-nomic growth, and environmental quality in sub-SaharanAfrica (SSA). They used panel vector error correction(PVEC) and panel vector autoregressive (PVAR) methods.They indicated that vibrant relationships are evident amongeconomic growth, foreign direct investment, and carbon emis-sions. They advised that sub-Saharan African governmentsought to make an equilibrium between policies which are

Environ Sci Pollut Res (2019) 26:11245–11260 11249

suitable for the protection of the environment. Behera andDash (2017) explored the interrelationship between urbaniza-tion, energy consumption, foreign direct investment (FDI),and carbon emissions of 17 countries in the South andSoutheast Asian (SSEA) region over the period of 1980–2012. When they used Pedroni cointegration, they found thatboth fossil fuel energy consumption, primary energyconsumption, and FDI significantly affect carbon emissionin the SSEA province. Sung et al. (2018) investigated howforeign direct investment (FDI) affect the level of carbonemissions of a nation. They used panel data for a period of2002–2015 considering 28 industrial sectors in China. Basedon their findings, they reported that FDI is a positive prognos-ticator of environmental quality in the hosting nation. Thisproves that FDI reduces the intensities of carbon emissions.

The nexus among energy use and carbon emission

The relationship between CO2 emissions and energyconsumption is one of the present debates in the energyenvironment literature since the last two decades.Alshehry and Belloumi (2015) examined the causal con-nection between energy cost, economic exercises, and en-ergy utilization in Saudi Arabia. Their findings exhibitedthat a long-run unidirectional causality from energy con-sumption to financial development and CO2 outflows.They also found a bidirectional causality between carbondioxide discharges and financial development, and a long-run unidirectional causality keeps running from energycost to economic growth and CO2 emanations, and ashort-run, unidirectional causality running from CO2 out-flows to energy utilization and monetary yield and fromenergy cost to CO2 emanations. In Tunisia, Ben Jebli andBelloumi (2017) utilized (ARDL) methodology andGranger causality tests and their examination revealed thata bidirectional short-run causality between sea transportand CO2 transmissions and a unidirectional short-run cau-sality which runs from real GDP, burnable renewables andwaste depletion, railroad transport to CO2 radiations, andthe long-run estimates revealed that real (GDP) contributesto the abatement of CO2 emissions, while burnable renew-ables, waste depletion, sea, and railroad transportpositively encourages emissions. A study by Mirza andKanwal (2017) revealed that there is bidirectional causali-ties among energy use, economic growth, and carbon em-anations in both the long and short run.

Heidari et al. (2015) applied the panel smooth transitionregression (PSTR) model as a second-generation econometrictechnique to investigate the link between economic growth,carbon dioxide (CO2) emissions, and energy consumption infive ASEAN. Their study showed that energy consumptionincrease CO2. Mirza and Kanwal (2017) examined the exis-tence of causality presence between economic growth, energy

consumption, and environmental pollution for Pakistan.Using ARDL to examine the robustness of the long-run con-nection and the existence of Grangers’ causality results usingVECM, they indicated that there is bidirectional causalitiesamong economic growth, energy consumption, and carbonemissions. Based on their results, they suggested that thePakistan government should increase on renewable energy.Another study conducted by Isik et al. (2018) revealed that along-run equilibrium correlations exited among carbon emis-sions, GDP, energy consumption structure, and urbanization.Their study also revealed that GDP and energy consumptionhave a significant impact on carbon emission in all theprovinces of China. Ahmad et al. (2018) used ARDL modelto examine the effect of energy consumption, total population,and economic growth on carbon emissions over the period of1971 to 2013 in China. They were able to identify a linkageamong economic growth and carbon emissions which con-firmed the existence of long run connection. When theyused Granger causality test, they found the existence ofone-way causality between economic growth and carbonemissions. Their final results divulged that the energy useand economic growth have got great potential to encouragecarbon productions in the long run. The researcherssuggested that the government should pay attention on thepromotion of sustainable energy for sustainable economicdevelopment and living.

Another study by Balcilar et al. (2018) explored the con-nection between economic growth energy consumption andcarbon dioxide emissions in the G-7 countries. They usedhistorical decomposition method, and their findings providedproof that countries like Canada, Italy, Japan, and the USAneeded to give up on most economic activities to diminishCO2 emissions by banning the use of nonrenewable energyuse. But on the other hand, their results displayed contradic-tion of EKC hypothesis for Canada, Germany, Japan, the UK,and the US which indicated that economic growth does notaffect the quality of the environmental quality because theEKC hypothesis is not valid for Germany and the UK.

Topcu and Payne (2018) estimated trade and energy usenexus in OECD countries for over the period of 1990–2015.They applied panel structure with approval for hetero-geneity and cross-sectional dependence. Their study revealedthat the influence of trade on energy use displayed an invertedU-shaped pattern which indicated that the impact of carbonemission on energy use is more than economic growth.Behera and Dash (2017) examined the interrelationship be-tween urbanization, energy consumption, foreign direct in-vestment (FDI), and carbon emissions of 17 countries in theSouth and Southeast Asian (SSEA) region over the period of1980–2012. They used Pedroni cointegration and their out-come reported that both the primary energy consumptionand fossil fuel energy consumption significantly increasingthe carbon emission in the SSEA province.

11250 Environ Sci Pollut Res (2019) 26:11245–11260

Methodology

This paper explores the relationship amidst carbon emissions,economic growth, energy use, and foreign direct investment.Variables were converted into natural logarithm forms to ad-dress the issue of heteroskedasticity; every one of the factorsare changed over into common logarithm shapes. The log-linear quadratic form is utilized to analyze the connectionbetween carbon emissions, economic growth, energy con-sumption, and foreign direct investment using the followingregression model;

lCO2it ¼ βo þ β2lY it þ β3Y2it þ β4lECit þ β5lFDIit

þ εit ð1Þ

where lYit represents the natural logarithm of economic growth(GPD constant price of 2010), lECit is the natural logarithm ofenergy consumption (kg of oil equivalent per capita), lFDIitdenotes the natural logarithm of foreign direct investment netinflows (BoP, current US$), and lCO2it is the natural logarithmof carbon emissions. βo and εit, further represents the constantand error terms, respectively.

Econometric approach

To detect the stationarity of carbon-emissions, economicgrowth, energy consumption, and foreign direct investment,the panel data characteristics should further be elaborated inorder to utilize appropriate and robust panel unit root tests. Inthe case where the panel time-series data is cross-sectionallyindependent, the use of traditional panel unit root test such asIPS, LLC, and Hadri tests gives inconsistent and erroneousresults. Hence, in order to check whether the panel time-seriesdata for the study is cross-sectionally independent, the studyemployed a cross-sectional independence test developed byPesaran (2004). Under the null hypothesis of Pesaran CD test,the error terms of individual series within the panel are uncor-related (cross-sectional independence), while under the alter-native hypothesis, errors terms of individual series within thepanel are correlated (cross-sectional independence). The studylater analyzed the stationarity properties of the variables.Considering potential cross-sectional independences withinthe panel time-series data, we employed second-generationpanel unit roots test developed by Pesaran (2007) which in-clude the cross-sectional IPS (CIPS) (an extension IPS panelunit root test developed by Im et al. (2003) and cross-sectionalaugmented Dickey-Fuller (CADF) panel unit root tests. TheCIPS and CADF panel unit root tests are based on the nullhypothesis that all individual series within the panel are sta-tionary against the alternative hypothesis that at least an indi-vidual series in the panel is stationary.

Aside the Pesaran CD test, CIPS, and CADF panel unitroot tests, the study went on to investigate the long-run rela-tionship amid carbon emissions, economic growth, energyconsumption, and foreign and direct investment withPedroni panel cointegration test and Kao panel cointegrationtest developed by Pedroni (2004) and Kao (1999),respectively.

Further, the study utilized the Pooled Mean Group (PMG)estimator through a panel Autoregressive Distributed Lag(ARDL) model in order to show both the long-run and short-run estimates of carbon emissions, economic growth, energyuse, and foreign and direct investment. Recently, the ARDLhas been used more due to some useful advantages whichinclude (i) regardless of whether the series is I(1) or I(0) thistechnique can be applied (ii) both short-term and long-termestimates can be simultaneously made (Pesaran and Shin1999). The ARDL (p, q) model consists of lag p on the re-sponse variable and lag q for the explanatory variables. TheARDL (p, q) model as developed can be formulated as:

yit ¼ ∑p

j¼1μijyit− j þ ∑

q

j¼0Ω

0ijxit− j þ εit ð2Þ

wherei = 1, 2, 3,… ,N represents the number of countries usedin the study; t = 1, 2, 3, . . , T denotes the period in years, yit isthe response variable, xit − j represents am × n vector consistingof the natural logarithm of the explanatory variables, μij is ascalar vector, Ω;

ij represents the m × 1 coefficient vector,

whereas εit is the error term with zero mean and a finite vari-ance. Considering a maximum of one lag for all the variables,the autoregressive distributed lag, ARDL (1,1,1,1) modeldeveloped by Pesaran and Shin (1999) is formulated asfollows:

Δyit ¼ μ1iyit−1 þ ∑1

j¼0Ω

01 jxit− j þ εit ð3Þ

Generally, the error correction representation of Eq. (4) canbe written as:

Δyit ¼ ψi Δyit−1−Θ0ixit

� �þ ∑

p−1

j¼1μijΔyit− j þ ∑

q−1

j¼0Ω*0

ij Δxit− j

þ εit ð4Þ

where ψi ¼ − 1− ∑p

j−1μij

!, and Θ ¼

∑q

j¼0Ωij

ψi.Θ represents the

long-run relationship among the response and explanatory

variables (yit and xit) whereas Ω*ij, on the other hand, signifies

the short-run effect in the xit’s on the yit’s. Theψi again denotesthe error correction term which is used from measuring thespeed of convergence of the response variables in moving toits long-run equilibrium as the explanatory variable changes.

Environ Sci Pollut Res (2019) 26:11245–11260 11251

The error correction term is expected to be negative and sig-nificant so as to show the existence of stability in the long-runrelationship.

In this study, our modified model with carbon emission asthe response variable from (1) can, therefore, be written in theARDL format as:

ΔCO2it ¼ k þ ψi ΔCO2it−1−Θ01ilY it−Θ

02ilY

2it−Θ

03ilEUit−Θ

04ilFDIit

� �

þ ∑p−1

j¼1μijΔCO2it− j þ ∑

q−1

j¼0Ω*

0

1ijΔlY it− j þ ∑q−1

j¼0Ω*

0

2ijΔlY2it− j

þ ∑q−1

j¼0Ω*0

3ijΔlEUit− j þ ∑q−1

j¼0Ω*0

4ijΔlFDI it− j þ εit

ð5Þ

The remaining equations in similar mode can be formulat-ed as follows:

ΔY it ¼ k þ ψi ΔY it−1−Θ01ilCO2it−Θ

02ilY

2it−Θ

03ilEUit−Θ

04ilFDIit

� �

þ ∑p−1

j¼1μijΔY it− j þ ∑

q−1

j¼0Ω*

0

1ijΔlCO2it− j þ ∑q−1

j¼0Ω*

0

2ijΔlY2it− j

þ ∑q−1

j¼0Ω*0

3ijΔlEUit− j þ ∑q−1

j¼0Ω*0

4ijΔlFDIit− j þ εit

ð5:1ÞΔY 2

it ¼ k þ ψi ΔY 2it−1−Θ

01ilY it−Θ

01ilCO2it−Θ

03ilECit−Θ

04ilFDIit

� �

þ ∑p−1

j¼1μijΔY

2it− j þ ∑

q−1

j¼0Ω*

0

1ijΔlY it− j þ ∑q−1

j¼0Ω*

0

1ijΔlCO2it− j

þ ∑q−1

j¼0Ω*0

3ijΔlECit− j þ ∑q−1

j¼0Ω*0

4ijΔlFDI it− j þ εit

ð5:2ÞΔECit ¼ k þ ψi ΔECit−1−Θ

01ilY it−Θ

02ilY

2it−Θ

01ilCO2it−Θ

04ilFDIit

� �

þ ∑p−1

j¼1μijΔECit− j þ ∑

q−1

j¼0Ω*

0

1ijΔlY it− j þ ∑q−1

j¼0Ω*

0

2ijΔlY2it− j

þ ∑q−1

j¼0Ω*

0

1ijΔlCO2it− j þ ∑q−1

j¼0Ω*

0

4ijΔlFDIit− j þ εit

ð5:3ÞΔFDIit ¼ k þ ψi ΔFDIit−1−Θ

01ilY it−Θ

02ilY

2it−Θ

01ilCO2it−Θ

03ilECit

� �

þ ∑p−1

j¼1μijΔFDIit− j þ ∑

q−1

j¼0Ω*

0

1ijΔlY it− j þ ∑q−1

j¼0Ω*

0

2ijΔlY2it− j

þ ∑q−1

j¼0Ω*

0

1ijΔlCO2it− j þ ∑q−1

j¼0Ω*0

3ijΔlECit− j þ εit

ð5:4ÞEquations (5), (5.1), (5.2), (5.3), and (5.4) are estimated

respectively using the PMG estimator. As compared to otherestimators, the PMG has several merits. For instances, thisestimator restricts the long-run estimates to be constant acrossall cross sections with a panel but allows the intercepts as wellas the short-run coefficients and error variances to vary acrosscountries. Also, it can be used irrespective of whether theseries is I(1) or I(0) and inference of long- and short-run cau-salities can be drawn even if the presence of cointegration isnot officially detected.

Finally, an important diagnostic test known as theHausman poolability test was finally performed in the studyto attest whether the pooling coefficients are appropriate andefficient. This diagnostic test tests the null hypothesis ofpooling long-run coefficients that are identical for all crosssections (Ho : δi = δ ∀ i) against the alternative hypothesis thatpooling long-run coefficients are not identical for all crosssections.

Data source and description

This study utilizes an annual panel time-series data for 7sub-Saharan African countries covering the period of1980 to 2014. The data with respect to the variables(carbon emissions, economic growth, energy consump-tion, and foreign direct investment) were obtained fromthe World Bank Development Indicators (WDI) which asaccess from https://data.worldbank.org/indicator. Thedata per variable was transformed into natural logarithmso as to interpret the coefficient estimates as theelasticities of the dependent variable (carbon emissions).Sampled sub-Saharan African countries for the study in-clude Ghana, Kenya, Botswana, Mauritius, Togo, andBenin.

With respect to the dependent variable and as the maindeterminant of the time frame, we could not go past the frameas data for carbon dioxide before 1980 were missing whileobservations after 2014 for energy consumption and carbonemissions were missing as well, and for most countries likeUganda, South Soudan, and D.R. Congo, data were also miss-ing. The fact that several countries had large missing dataobservations; this left us to sample the 6 aforementionednations.

Results obtained from the data were generated withthe help of EVIEWS 9.0 and STATA 13.0 software.Table 1 presents the summary of the data set togetherwith the summary statistics which includes the meanand the standard deviation of carbon emissions, energyuse, economic growth, and foreign direct investment foreach of the 6 selected sub-Saharan African countries.Results from the summary statistics depict that the meanof carbon emissions ranges from 8062.266 in Kenya to1242.799 in Togo. As for energy consumption, Ghanaamong the 6 selected sub-Saharan African countries hasthe lowest energy usage, while Botswana has the highest.With respect to the gross domestic product (GDP),Mauritius has the highest GDP followed by Botswana,while Togo is the poorest country within the panel.Finally, for foreign direct investment (FDI) which is de-fined as net inflows (BoP, current US$), Ghana has thehighest volatility and Benin has the lowest among the 6sub-Saharan African countries.

11252 Environ Sci Pollut Res (2019) 26:11245–11260

Empirical results and discussions

Cross-sectional independence test

The CD test developed by Pesaran (2004) is utilized togetherwith the analyzed variables in order to explore whether thepanel time-series data has cross-sectional independence.Results from the various Pesaran CD tests are presented inTable 2. Referring to the related probability values, the nullhypothesis of cross-sectional independence for carbon emis-sions, economic growth, energy use, and foreign directinvestment is rejected. This results are similar to those ofOmri et al. (2015) and Shahbaz et al. (2013). This, therefore,gives the indication that the panel time’s series data whichincludes the analyzed variables has cross-sectional indepen-dence. Given the fact that the panel time’s series data

containing the variables under discussions exhibit cross-sectional independence, the study continues with panelmethods that assume cross-sectional independence. Hence,this study in the next stage employs the CIPS and CADF panelunit root test.

Panel unit root test

This study as mentioned in the methodology uses the CIPSand CADF panel unit root tests instead of conventional unitroot test such as Breitung, IPS, and LLC panel unit root tests(Gengenbach et al. 2009). This is due to the reason that theseconventional panel unit root tests have drawbacks with respectto the presence of cross-sectional independence. Most impor-tantly, the CADF and CIPS unit root tests yield reliable resultsin the presence of cross-sectional independence as supportedby the results of Dogan et al. (2017). Results of the CIPS andCADF tests are therefore stated in Table 3. Both tests illustratethat the variables under investigation are not stationary at theirrespective levels but become stationary at their first difference.Therefore, this gives the indication that the variables CO2,GDP, EU, and FDI are all integrated at the same order (I(1)).

Since the investigated variables are non-stationary at theirlevels, the study used the Pedroni and Kao panel cointegrationtest to investigate whether long-run relationship exists among

Table 1 Summary of data set anddescriptive statistics Summary of data set

Variable Definition Source

CO2 Carbon emissions (kt) WDI (2017)

Y, Y2 Gross domestic product (constant 2010 US$) WDI (2017)

EU Energy use (kg of oil equivalent per capita) WDI (2017)

FDI Foreign direct investment (net inflows (BoP, current US$)) WDI (2017)

Descriptive statistics

Country Statistic CO2 Y EU FDI

Ghana Mean 6430.451 1006.415 340.452 1,183,927,502.909

Stdev 621.939 257.345 41.313 706,385,613.429

Kenya Mean 8062.266 894.897 451.717 390,321,585.231

Stdev 3011.917 62.372 15.969 201,827,233.489

Botswana Mean 3043.715 4554.279 937.208 271,776,967.187

Stdev 1445.347 1587.127 141.165 225,425,725.223

Mauritius Mean 2210.468 5117.669 753.382 165,719,466.222

Stdev 1215.278 2081.501 231.656 116,342,018.688

Togo Mean 1242.799 521.576 383.04 123,026,885.889

Stdev 621.9395 51.311 57.387 62,312,932.025

Benin Mean 2141.109 677.844 349.185 99,422,032.236

Stdev 1843.39 69.767 31.716 59,635,370.193

Panel Mean 13,449.284 2057.509 561.447 1,398,676,804.880

Stdev 25,196.730 2054.303 244.943 565,178,353.014

Stdev, CO2, GDP, EU, and FDI represent the standard deviation, per capita carbon dioxide emissions, per capitareal GDP, per capita energy consumption, and foreign direct investment. WDI means World DevelopmentIndicators

Table 2 Results from the Pesaran (2004) cross-sectional independencetest

CO2 Y(Y2) EU FDI

CD test value 20.999*** 11.417*** 7.707*** 16.618***

p value 0.000 0.000 0.000 0.000

***represents statistical significance at 1%. The Pesaran CD test is basedon the null hypothesis of cross-sectional independence

Environ Sci Pollut Res (2019) 26:11245–11260 11253

the variables or not (Fang and Chang 2016). During the test,variables are to be in a panel data to be non-stationary at theirlevels.

Panel cointegration test

This examination searches for cointegration connections thatare conceivable between the investigated variables (CO2, Y,Y2, EU, and FDI) bymost importantly using the Pedroni panelcointegration test. The Pedroni panel cointegration test fromTable 4 provides results from seven diverse statistics.Generally, since the majority of these statistics which incorpo-rates the panel PP, panel ADF, group rho, and group PP sta-tistics are measurable, we rejected null hypothesis. This isbecause the analyzed variables have cointegrationrelationship.

Kao panel cointegration test contains coefficients that arehomogeneous coefficients at the primary stage regressors overthe cross section applying the same methods as the Pedronicointegration test. As for the results of the Kao panelcointegration test exhibited in Table 5, it is detected that theinvestigated variables are cointegrated and subsequently have

cointegration connections. This is on the grounds that there issufficient proof to reject the null hypothesis of nocointegration for the elective speculation of cointegration at1% noteworthiness level. In light of the results of both thePedroni and Kao panel cointegration tests, we reach on thepresence of cointegration between the investigated variablesutilized in the examination.

The causality test using the Pooled Mean Group estimator

We applied the Pooled Mean Group estimator because itmakes the long-run coefficients controlled to be similar forall cross section, while the short run allows it to be diversefrom nation to nation. PMG also permits idiosyncratic hetero-geneity to be accommodated by estimating separate equationsfor each country or variable and averages the parameter esti-mates. Finally, PMG can also be utilized when there is anintegration of variables at the same order or not. This makesit possible for the short- and long-run causality interpretationto be concluded even when cointegration was identified pre-viously or not.

Table 6 reports the results of the PMG cointegration esti-mation results for the 7 sub-Saharan African nations withlong-run coefficients in Eq. (5) showing the elasticity ofCO2 as for the different illustrative variables (EU, FDI, Y,and Y2). Apart from FDI, the rest of the variables proofed tobe statistically significant at 1% level. This indicates that a 1%increase in energy use leads to 49% rise in CO2. This resultconfirms the findings of Acaravci and Ozturk 2010 and Wanget al. 2016. This findings recommends that increase in energyconsumption encourages environmental pollution which issimilar to the findings of Omri et al. (2015) who stated thatan increase in energy consumption and GDP influences car-bon emissions in Vietnam. To reduce the level of CO2 emis-sion, is to increase energy efficiency, the public should besensitized, and governments should encourage energy savingfor instance switching of lights when not in use. A 1% in-crease in economic growth leads to 16% increase in CO2.These results support the findings of Ito (2017). Wang et al.(2016) stated that there is need to enhance and implementpolicies for both energy consumption and economic growthto address the issue of carbon emissions in China by encour-aging low-carbon rise in the nation of China. But 1% increasein the square of economic growth triggers CO2 to reduce by

Table 3 Results from panel unit root test

Variable CIPS CADF

Level Δ Level Δ

CO2 − 2.456 − 5.969*** − 2.159 − 4.041***Y(Y2) − 2.047 − 4.844*** − 2.214 − 3.467***EU − 2.019 − 5.326*** − 1.748 − 3.931***FDI − 2.024 − 6.195*** − 2.273 − 5.442***

*** represents statistical significance at 1%. Critical values of the CIPS arenot given due to concision but can be provided upon request. Both theCIPS and CADF are based on the null hypothesis of cross-sectionalindependence

Table 4 Results from the Pedroni board cointegration test

Common AR coefficients (within-dimension)

Statistic Weighted statistic

Panel v-statistic − 0.518 − 0.568

Panel rho-statistic 0.492 0.501

Panel PP-statistic − 0.219*** − 0.292***

Panel ADF-statistic − 0.693*** − 0.638***

Individual AR coefficient

Group rho-statistic 1.616

Group PP-statistic 0.394***

Group ADF-statistic − 0.254***

***denotes the rejection of the null hypothesis at 1% significance level.The Pedroni cointegration test assumes the null hypothesis of nocointegration

Table 5 Results from theKao panel cointegrationtest

t-statistic Probability value

ADF − 2.484*** 0.007

***represents the rejection of the null hy-pothesis at 1% significance level. The Kaopanel cointegration test is based on the nullhypothesis of no cointegration

11254 Environ Sci Pollut Res (2019) 26:11245–11260

46% which was confirmed by Ben Jebli et al. (2015). Theyconducted an investigation of the short-run and long-run affil-iations among carbon emissions, gross domestic product, re-newable energy use, and international trade considering a pan-el of 24 sub-Saharan Africa countries for a period over 1980–2010. Their findings reported that an increase in real GDP hasa great opportunity to increase carbon emissions, but an in-crease GPD squared is capable of reducing carbon emissionsin the selected sub-Saharan African countries. This resultssupport the findings of Anastacio (2017). Their findings alsoreported the existence of inverted U-shaped connection. Toback up our findings, Doğan’s (2018) findings report that inthe long run, an increase in GDP2 will decrease carbon emis-sions. As per the findings of Anastacio (2017), the positiveand negative impact of economic growth and its Y2 approvethe EKC theory. Concerning causalities in the midst of theinvestigated variables, there is a proof of a bidirectional cau-sality between energy use and CO2 in the short run as well as aone-way causality running from energy consumption to CO2

in the long run. The bidirectional causal relationship betweenenergy use and carbon emissions in the short term implies thatenergy use and carbon emissions are interconnected in theshort run in the sense that increase in energy use will lead tohigh level of carbon emissions (Ssali et al. 2018), while theincrease in carbon emissions means more consumption ofenergy. The unidirectional causality from former (energy con-sumption) to the latter (carbon emissions), on the other hand,implies that, in the long term, energy use has a significantimpact on emissions of carbon in sub-Saharan countries.This further means that changes in energy consumption ap-pear to precede changes in carbon emissions. Remarkably,there likewise exists a significant positive effect and unidirec-tional causality from CO2 to foreign direct investment in the

long-run. In the policy context, the long-run unidirectionalcausal relationship from carbon emission to foreign direct in-vestment (FDI) gives the implication that changes in carbonemissions precede changes in FDI in the long term and as wellhave significance influence on FDI. This means that the pres-ence of multinationals of counties under study could eitherincrease or even decrease the level of foreign direct invest-ment, implying that the host countries must lay down policiesto assess the environmental impact on FDI before foreigninvestors can be allowed into the country.

However, there is no causal relationship in the short run.With respect to carbon income nexus, the PMG resultsthrough the ARDL model from Table 6 interestingly reveal aproof of a unidirectional causality running from economicgrowth to carbon emanations both over the long and shortrun. The finding of one-way causality extending from eco-nomic growth to carbon emissions implies that the growth ofsub-Saharan African economies have significant effect on car-bon emissions in the sense that, with increasing carbon emis-sions, economic growth of the sample of sub-Saharan Africancountries do not decline both in the long term and short terms;thus, reduction in the increment of carbon emissions ratherthan minimizing the entire amount should be an attainablegoal for countries under discussion. Rather than economicgrowth and CO2 nexus, however, unidirectional causality runsfrom the square of economic growth to carbon emanationsover the long run and the other way around in the short run.The ECTs of carbon emanations, energy use, foreign directinvestment, and economic growth and square of economicgrowth are highly significant and correspond to 19.6%,21.2%, and 62.9% years. It indicates that every variable reactsquickly to deviations over the long-run equilibrium. TheHausman poolability test, on the other hand, implies that the

Table 6 Results of the ARDLmodel estimation using the PMGestimator

Dependent variable CO2 (5) EU (5.1) FDI (5.2) Y (5.3) Y2 (5.4)

Long run

CO2 − 0.012 1.674*** − 0.003 − 0.471ENU 1.481*** 1.861 0.017 − 0.104

FDI 0.005 − 0.002 − 0.001 0.047

Y 8.157*** 0.279 − 12.708 5.508

Y2 − 0.459*** 0.006 0.777 0.066***

Ect − 0.196** − 0.212* − 0.629*** −0.358** − 0.136**Short run

CO2 0.123** 0.142 − 0.001 0.019*

ENU 1.212** − 0.764 0.004 − 0.068FDI − 0.012 0.006 − 4.085 0.001

Y 5.379*** − 5.226 − 6.544 4.428***

Y2 −0.383 0.389 1.278 0.0697***

Hausman test value 81.880 17.988 14.454 13.377 13.593

p value 0.473 0.277 0.311 0.107 0.129

***, **, and *represent statistical significance level at 1%, 5%, and 10%, respectively

Environ Sci Pollut Res (2019) 26:11245–11260 11255

confinement of homogenous coefficients in all cases in thelong run cannot be rejected at 1% level of significance.Therefore, it indicates that the PMG estimator is effectiveand appropriate. Table 7 plots the rundown of principle dis-coveries in view of causalities.

Conclusion and policy implications

The nexus among the variables was investigated in the select-ed sub-Saharan African countries by applying cross-sectionalfreedom test created by Pesaran which incorporate the cross-sectional IPS (CIPS) (an augmentation IPS board unit root testcreated by Im et al. (2003)), ARDL model utilizing PMGapplying a board information running from 1980 to 2014.Five variables incorporated into this investigation are carbonoutflows, Y, Y2, energy use, and outside direct venture. Thedeveloping nations considered for this investigation areGhana, Kenya, Nigeria, Botswana, Mauritius, Togo, andBenin.

The present problems of climate alteration activated byecological degradation has woken up governments to dealwith this catastrophe before it destroys the whole world.Researchers around the globe have picked interest to findout the major cause and the end results of climate changeglobally. They have utilized distinctive variables to compre-hend the impact and the reasons for environmental changetoday. Understanding variables is imperative since it helps indeveloping the right strategies. Having said that, our researchis the best thing to do for the benefit of mankind. We did notutilize traditional test to avoid questionable outcomes. Weutilized second-generation test, for instance, CADF andCIPS (Pesaran 2007). The cointegration test of Kao andPedroni delineates that variables are cointegrated which im-plies they have long-run causality relationship.

Our empirical findings of the long-term peripheral effect ofthe breakdown on dependent variable for the seven nationswas focused on the ARDL approach. Empirical investigationfindings report that 1% increase in energy use and economicgrowth are destined to cause a relative increase in CO2

emission by 49% and 16% reliably. This suggests energyuse and GDP add to the growth of CO2 emissions whichmakes them perilous to the emerging economies. The findingslikewise report that an increase in Y2 by 1% diminishes CO2

by 46%. This reveals that Y2 promotes the environment of theemerging nations. This implies more accentuation ought to beput on economic growth to inspire income to invest in tech-nologies to encourage low carbon. But the results indicate thatif nothing is done, energy use and economic growth will con-tinue, causing carbon emissions in developing countries. Thisis because the majority of African nations still use poorsources of energy due to lack of accessibility to clean energy.This has resulted in intense lower respiratory sickness, cere-brovascular malady, ischemic coronary illness, obstructiveaspiratory ailment, and lung malignancy. Economic activitieshave also contributed a lot to carbon emissions in developingnations because most of them have not yet arrived at the levelof using renewable energy in huge capacity according to theEKC hypothesis.

Over the long run, the Pooled Mean Group (PMG)—esti-mator result demonstrates that Y has a positive and significantimpact on carbon emission while Y2 negatively and positivelyeffects carbon emissions. This infers the connection betweenGDP growth and carbon emanation has a U-shaped but not aninverted-U-shaped relationship in the long run, which negatesthe Kuznets’ environmental curve. It likewise keeps up thepossibility that when the economy grows, it diminishes CO2

discharges, yet as the economy grows, it prompts increasedCO2 in the long run. In the long run, the coefficients of eco-nomic growth and Y2 are positively and negatively signifi-cant. Our study revealed that the error correction coefficientto environmental degradation, energy use, and foreign directinvestments postulates long-run equilibrium. We notice thesystem runs back to equilibrium at a rate of 19.6%, 21.2%,and 62.9% for the latter three variables.

As per Table 7, it presents the long- and short-run causalityinside the variables. The outcomes depict that long-run unidi-rectional causality runs from energy use to CO2, GDP to CO2,GDP-squared to CO2, and CO2 to FDI. In any case, the short-run unidirectional causality runs from energy use to CO2, CO2

to GDP, and CO2 to GDP-squared. Basing on Tables 6 and 7,the long-run causality correlation test findings show thatunidirectionality exists between CO2 and independentvariables.

There is a need to use the existing research to helpcombat environmental pollution to acquire sustainableeconomic development. Diverse methodologies have tobe put in place to handle this challenge of carbon emis-sions in developing nations. Developing nations shouldhave research institutions to conduct more investigationson the type of carbon emitted in the space as a result ofconsuming fossil energy which has become dangerousto human health.

Table 7 Summary of results based on causalities

Sample type Short-run causality Long-run causality

SSA countries CO2↔ENU CO2←ENU

CO2∘FDI CO2→FDI

CO2←Y CO2←Y

CO2→Y2 CO2←Y2

SSA means sub-Saharan Africa, ENU, FDI, Y, and Y2 represent energyuse, foreign direct investment, economic growth, and square of economicgrowth, respectively. ↔, ←, and ∘ represent bidirectional causality, uni-directional causality, and no causality, respectively

11256 Environ Sci Pollut Res (2019) 26:11245–11260

Policy implication

Our policy implications are summarized on the fact thatthe policymakers of the sub-Saharan region must stimu-late the economic segments to come up with new invest-ment policies in renewable sources of energy by encour-aging a tax policy incentives and establishing energycodes to reduce environmental pollution. The mix ofAfrican energy is that 23% of oil is utilized as a sourceof energy, 14% coal, 14% gas, 48% bio-energy, and 1%different sources like atomic, hydro, and renewable (Ispydistributing 2018). This implies less accentuation hasbeen put on the use of renewable energy source. Moreaccentuation ought to be put on sustainable power sourcesrather than non-sustainable power source. Hydropower,wind energy, atomic energy, and sun-based power oughtto be advanced in emerging economies. Therefore, renew-able energy supplies should be enlarged and reinforced inthe selected countries and globally and as well as encour-agement of energy efficiency by the governments. Greentechnology investments should be enhanced to reduce car-bon emissions as well as venturing into carbon captureand storage. For this to happen, suitable motivational pol-icies should be considered for example boosting FDI bygovernments to buttress industrial sectors in relation torenewable energy investments and production. These pol-icies can be effective if they are made mandatory as wellas creating more public awareness concerning the newgreen technologies and concerning their positive effec-tiveness on the environment.

In conclusion, as much as emerging economies would liketo develop, governments should take it upon themselves tofight against this threat of environmental pollution before thenumber of deaths escalates globally. In this scenario, knowl-edge management strategies as a means of creating awarenessabout the dangers of climate change should be introduced inall sectors. Knowledge should be collected and then thisknowledge should be shared among citizens through differentmeans of communication.

Acknowledgements The authors would like to thank the reviewers whospared their time to review this paper. We would like to thank you for thepositive comments which helped us to revise our manuscript. We wouldalso like to thank Springer for publishing our paper in their fine and greatscientific journal.

Author contributions All authors were able to contribute equally to thestudy as presented in this paper.

Funding information This work was supported in part by the NationalScience Foundation of China under grants 71471076, 71704066,71671080, 71701090, and 71501084. This work was also supported bythe Special Funds of the National Social Science Fund of China(18VSJ038) and by the Jiangsu Provincial Natural Science Foundationof China (grant no. BK20170542).

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict ofinterest.

Open Access This article is distributed under the terms of the CreativeCommons At t r ibut ion 4 .0 In te rna t ional License (h t tp : / /creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided you give appro-priate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made.

Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.

References

Acaravci A, Ozturk I (2010) On the relationship between energy con-sumption, CO2 emissions and economic growth in Europe. Energy35(12):5412–5420. https://doi.org/10.1016/j.energy.2010.07.009

Achour H, Belloumi M (2016) Investigating the causal relationship be-tween transport infrastructure, transport energy consumption andeconomic growth in Tunisia. Renew Sust Energ Rev 56:988–998.https://doi.org/10.1016/j.rser.2015.12.023

Aden N (2016) The roads to decoupling: 21 countries are reducingcEmissions while gGDP. Retrieved from https://www.wri.org/blog/2016/04/roads-decoupling-21-countries-are-reducing-carbon-emissions-while-growing-gdp

Ahmad M, Hengyi H, Rahman ZU, Khan ZU, Khan S, Khan Z (2018)Carbon emissions, energy use, gross domestic product and totalpopulation in China. Ekonomia i Środowisko

Ahmed M, Azam M (2016) Causal nexus between energy consumptionand economic growth for high, middle and low income countriesusing frequency domain analysis. Renew Sust Energ Rev 60:653–678. https://doi.org/10.1016/j.rser.2015.12.174

Aldy JE (2017) Policy surveillance in the G-20 fossil fuel subsidiesagreement: lessons for climate policy. Clim Chang 144(1):97–110.https://doi.org/10.1007/s10584-015-1505-0

Alshehry AS, Belloumi M (2015) Energy consumption, carbon dioxideemissions and economic growth: the case of Saudi Arabia. RenewSust Energ Rev 41:237–247. https://doi.org/10.1016/j.rser.2014.08.004

Anastacio JAR (2017) Economic growth, CO2 emissions and electricconsumption: Is there an environmental Kuznets curve? An empir-ical study for North America countries. Int J Energy Econ Policy7(2):65–71

Anderson EP, Marengo J, Villalba R, Halloy S, Young B, Cordero D, . . .Herzog SK (2017) Consequences of climate change for ecosystemsand ecosystem services in the tropical Andes

Arbuckle JG Jr, Morton LW, Hobbs J (2015) Understanding farmer per-spectives on climate change adaptation and mitigation: the roles oftrust in sources of climate information, climate change beliefs, andperceived risk. Environ Behav 47(2):205–234

Asumadu-Sarkodie S, Owusu PA (2016) The relationship between car-bon dioxide and agriculture in Ghana: a comparison of VECM andARDL model. Environ Sci Pollut Res 23(11):10968–10982. https://doi.org/10.1007/s11356-016-6252-x

Asumadu-Sarkodie S, Owusu PA (2017) The causal nexus between car-bon dioxide emissions and agricultural ecosystem—an econometricapproach. Environ Sci Pollut Res 24(2):1608–1618. https://doi.org/10.1007/s11356-016-7908-2

Environ Sci Pollut Res (2019) 26:11245–11260 11257

Avila N, Carvallo JP, ShawB,KammenDM (2017) The energy challengein sub-Saharan Africa: A guide for advocates and policy makers:Part 1: Generating energy for sustainable and equitable develop-ment. Oxfam Research Backgrounder series

Balcilar M, Ozdemir ZA, Ozdemir H, Shahbaz M (2018) Carbon dioxideemissions, energy consumption and economic growth: The histori-cal decomposition evidence from G-7 countries. Retrieved fromhttps://ideas.repec.org/p/emu/wpaper/15-41.pdf.html. Accessed 22Dec 2018

Bazilian M, Nussbaumer P, Rogner H-H, Brew-Hammond A, Foster V,Pachauri S et al (2012) Energy access scenarios to 2030 for thepower sector in sub-Saharan Africa. Util Policy 20(1):1–16.https://doi.org/10.1016/j.jup.2011.11.002

Begum RA, Sohag K, Abdullah SMS, Jaafar M (2015) CO2 emissions,energy consumption, economic and population growth in Malaysia.Renew Sust Energ Rev 41:594–601. https://doi.org/10.1016/j.rser.2014.07.205

Behera SR, Dash DP (2017) The effect of urbanization, energy consump-tion, and foreign direct investment on the carbon dioxide emission inthe SSEA (South and Southeast Asian) region. Renew Sust EnergRev 70:96–106. https://doi.org/10.1016/j.rser.2016.11.201

Bekhet HA,Matar A, Yasmin T (2017) CO2 emissions, energy consump-tion, economic growth, and financial development in GCC coun-tries: Dynamic simultaneous equation models. Renew Sust EnergRev 70:117–132. https://doi.org/10.1016/j.rser.2016.11.089

Ben Jaber M, Couvert A, Amrane A, Rouxel F, Le Cloirec P, Dumont E(2016) Biofiltration of H2S in air—experimental comparisons oforiginal packing materials and modeling. Biochem Eng J 112:153–160. https://doi.org/10.1016/j.bej.2016.04.020

Ben Jebli M, Belloumi M (2017) Investigation of the causal relationshipsbetween combustible renewables and waste consumption and CO2emissions in the case of Tunisian maritime and rail transport. RenewSust Energ Rev 71:820–829. https://doi.org/10.1016/j.rser.2016.12.108

Ben Jebli M, Ben Youssef S, Ozturk I (2015) The role of renewableenergy consumption and trade: environmental Kuznets curve anal-ysis for sub-Saharan Africa countries. Afr Dev Rev 27(3):288–300.https://doi.org/10.1111/1467-8268.12147

Böhringer C, Carbone JC, Rutherford TF (2018) Embodied carbon tariffs.Scand J Econ 120(1):183–210

Bonan G (2015) Ecological climatology: concepts and applications.Cambridge University Press, Cambridge

Cai Y, Sam CY, Chang T (2018) Nexus between clean energy consump-tion, economic growth and CO2 emissions. J Clean Prod 182:1001–1011. https://doi.org/10.1016/j.jclepro.2018.02.035

Chai X, Tonjes DJ, Mahajan D (2016) Methane emissions as energyreservoir: context, scope, causes and mitigation strategies. ProgEnergy Combust Sci 56:33–70. https://doi.org/10.1016/j.pecs.2016.05.001

de Alegría IM, Molina G, del Río B (2017) Carbon markets: linking theinternational emission trading under the United Nations FrameworkConvention on Climate Change (UNFCCC) and the EuropeanUnion Emission Trading Scheme (EU ETS). In: Chen W-Y,Suzuki T, Lackner M (eds) Handbook of Climate ChangeMitigation and Adaptation. Springer International Publishing,Cham, pp 313–339

Devinny JS, Deshusses MA, Webster TS (2017) Biofiltration for air pol-lution control. CRC press, Boca Raton

Doğan N (2018) The impact of agriculture on CO2 emissions in China.Panoeconomicus. https://doi.org/10.2298/PAN160504030D

Dogan E, Seker F, Bulbul S (2017) Investigating the impacts of energyconsumption, real GDP, tourism and trade on CO2 emissions byaccounting for cross-sectional dependence: a panel study of OECDcountries. Curr Issue Tour 20(16):1701–1719. https://doi.org/10.1080/13683500.2015.1119103

Fang Z, Chang Y (2016) Energy, human capital and economic growth inAsia Pacific countries — evidence from a panel cointegration andcausality analysis. Energy Econ 56:177–184. https://doi.org/10.1016/j.eneco.2016.03.020

Fankhauser S, Jotzo F (2018) Economic growth and development withlow-carbon energy. Wiley Interdiscip Rev Clim Chang 9(1):e495.https://doi.org/10.1002/wcc.495

Frumhoff PC, Heede R, Oreskes N (2015) The climate responsibilities ofindustrial carbon producers. Clim Chang 132(2):157–171

Gengenbach C, Palm FC, Urbain J-P (2009) Panel unit root tests in thepresence of cross-sectional dependencies: comparison and implica-tions for modelling. Econ Rev 29(2):111–145

Gergel DR, Nijssen B, Abatzoglou JT, Lettenmaier DP, Stumbaugh MR(2017) Effects of climate change on snowpack and fire potential inthe western USA. Clim Chang 141(2):287–299. https://doi.org/10.1007/s10584-017-1899-y

Ghosh S, Kanjilal K (2014) Long-term equilibrium relationship betweenurbanization, energy consumption and economic activity: empiricalevidence from India. Energy 66:324–331. https://doi.org/10.1016/j.energy.2013.12.052

Goldstone JA (2018) Demography, environment, and security.Environmental conflict. Routledge, London, pp 84–108

Han J, Du TY, Zhang C, Qian XP (2018) Correlation analysis of CO2emissions, material stocks and economic growth nexus: evidencefrom Chinese provinces. J Clean Prod 180:395–406. https://doi.org/10.1016/j.jclepro.2018.01.168

Hanley Wood Data Studio (2016) Reducing greenhouse gas emissionsand growing GDP are not mutually exclusive. Retrieved fromhttps://www.architectmagazine.com/technology/reducing-greenhouse-gas-emissions-and-growing-gdp-are-not-mutually-exclusive_o. Accessed 19 Dec 2018

Heidari H, Katircioğlu ST, Saeidpour L (2015) Economic growth, CO2emissions, and energy consumption in the five ASEAN countries.Int J Electr Power Energy Syst 64:785–791. https://doi.org/10.1016/j.ijepes.2014.07.081

Hughes L, Herian A (2017) The correlation between GDP and green-house gas emissions. Retrieved from http://policyoptions.irpp.org/magazines/september-2017/the-correlation-between-gdp-and-greenhouse-gas-emissions/. Accessed 20 Dec 2018

HuisinghD, Zhang Z,Moore JC, QiaoQ, Li Q (2015) Recent advances incarbon emissions reduction: policies, technologies, monitoring, as-sessment and modeling. J Clean Prod 103:1–12. https://doi.org/10.1016/j.jclepro.2015.04.098

Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heteroge-neous panels. J Econ 115(1):53–74. https://doi.org/10.1016/S0304-4076(03)00092-7

Isik C, Dogru T, Turk ES (2018) A nexus of linear and non-linear rela-tionships between tourism demand, renewable energy consumption,and economic growth: Theory and evidence. Int J Tour Res 20(1):38–49. https://doi.org/10.3390/su10072383

Ispy P (2018) An African energy industry report: 2018 Retrieved fromhttps://www.futureenergyafrica.com/media/1751/1-mir-africa-mir-18-2-es_685804715-05-2018.pdf. Accessed 22 Dec 2018

Ito K (2017) CO2 emissions, renewable and non-renewable energy con-sumption, and economic growth: evidence from panel data for de-veloping countries. Int Econ 151:1–6. https://doi.org/10.1016/j.inteco.2017.02.001

Jain H (2016) Trade liberalisation, economic growth and environmentalexternalities: an analysis of Indian manufacturing industries.Springer, Berlin

Jones RL (2016) Process or separating and enriching carbon dioxide fromatmospheric gases in air or from atmospheric gases dissolved innatural water in equilibrium with air: Google Patents. From https://patents.google.com/patent/US9266057B1/en

11258 Environ Sci Pollut Res (2019) 26:11245–11260

Kao C (1999) Spurious regression and residual-based tests forcointegration in panel data. J Econ 90(1):1–44. https://doi.org/10.1016/S0304-4076(98)00023-2

Kasman A, Duman YS (2015) CO2 emissions, economic growth, energyconsumption, trade and urbanization in new EU member and can-didate countries: a panel data analysis. Econ Model 44:97–103.https://doi.org/10.1016/j.econmod.2014.10.022

Khan AN, Ghauri B, Jilani R, Rahman S (2011) Climate change: emis-sions and sinks of greenhouse gases in Pakistan. Paper presented atthe Proceedings of the Symposium on Changing EnvironmentalPattern and its impact with Special Focus on Pakistan

Khobai HB, Le Roux P (2017) The relationship between energy con-sumption, economic growth and carbon dioxide emission: The caseof South Africa. Int J Energy Econ Policy 7(3):102–109

Koçak E, Şarkgüneşi A (2018) The impact of foreign direct investment onCO2 emissions in Turkey: new evidence from cointegration andbootstrap causality analysis. Environ Sci Pollut Res 25(1):790–804. https://doi.org/10.1007/s11356-017-0468-2

Kofi Adom P, Bekoe W, Amuakwa-Mensah F, Mensah JT, Botchway E(2012) Carbon dioxide emissions, economic growth, industrialstructure, and technical efficiency: empirical evidence from Ghana,Senegal, and Morocco on the causal dynamics. Energy 47(1):314–325. https://doi.org/10.1016/j.energy.2012.09.025

Lee JW (2013) The contribution of foreign direct investment to cleanenergy use, carbon emissions and economic growth. EnergyPolicy 55:483–489. https://doi.org/10.1016/j.enpol.2012.12.039

Li Q, Xue Q, Truong Y, Xiong J (2018) MNCs’ industrial linkages andenvironmental spillovers in emerging economies: The case of China.Int J Prod Econ 196:346–355. https://doi.org/10.1016/j.ijpe.2017.12.008

Lin B, Ouyang X (2014) Energy demand in China: comparison of char-acteristics between the US and China in rapid urbanization stage.Energy Convers Manag 79:128–139. https://doi.org/10.1016/j.enconman.2013.12.016

Matthews TK, Wilby RL, Murphy C (2017) Communicating the deadlyconsequences of global warming for human heat stress. Proc NatlAcad Sci 114(15):3861–3866

McAusland C (2010) Globalisation's direct and indirect effects on theenvironment. Globalisation, Transport and the Environment:31–53. https://doi.org/10.1787/9789264072916-4-en

Melillo JM, Frey SD, DeAngelis KM,WernerWJ, BernardMJ, Bowles Fet al (2017) Long-term pattern and magnitude of soil carbon feed-back to the climate system in a warming world. Science 358(6359):101–105

Meyer LH (ed) (2017) Intergenerational justice. Routledge, London.https://doi.org/10.4324/9781315252100

Mirza FM, Kanwal A (2017) Energy consumption, carbon emissions andeconomic growth in Pakistan: dynamic causality analysis. RenewSust Energ Rev 72:1233–1240. https://doi.org/10.1016/j.rser.2016.10.081

Nanda SK (2018) From Rio to Doha: In Search of cooperative action forclimate change. In: Nirmal BC, Singh RK (eds) Contemporary is-sues in international law: environment, international trade, informa-tion technology and legal education. Springer Singapore, Singapore,pp 161–171

Nazeer M, Tabassum U, Alam S (2016) Environmental pollution andsustainable development in developing countries. Pak Dev Rev55(4 Part I &):589–604

Ndambiri H, Ritho C, Ng'ang'a S, Kubowon P, Mairura F, Nyangweso Pet al (2012) Determinants of economic growth in sub-SaharanAfrica: a panel data approach. International Journal of Economicsand Management Sciences 2(2):18–24

Ning L, Wang F (2017) Does FDI bring environmental knowledge spill-overs to developing countries? The tole of the local industrial struc-ture. Environ Resour Econ:1–25. https://doi.org/10.1007/s10640-017-0159-y

Ojewumi SJ, Akinlo AE (2017) Foreign direct investment, economicgrowth and environmental quality in sub-Saharan Africa: a dynamicmodel analysis. African Journal of Economic Review (AJER) 5(1):48–68

Omri A, Daly S, Rault C, Chaibi A (2015) Financial development, envi-ronmental quality, trade and economic growth: what causes what inMENA countries. Energy Econ 48:242–252. https://doi.org/10.1016/j.eneco.2015.01.008

Ozcan B (2013) The nexus between carbon emissions, energy consump-tion and economic growth in Middle East countries: a panel dataanalysis. Energy Policy 62:1138–1147. https://doi.org/10.1016/j.enpol.2013.07.016

Pedroni P (2004) Panel cointegration: asymptotic and finite sample prop-erties of pooled time series tests with an application to the PPPhypothesis. Econometric theory 20(3):597–625. https://doi.org/10.1017/S0266466604203073

PesaranMH (2004) General diagnostic tests for cross section dependencein panels

Pesaran MH (2007) A simple panel unit root test in the presence of cross-section dependence. J Appl Econ 22(2):265–312. https://doi.org/10.1002/jae.951

Pesaran H, Shin Y (1999) Econometrics and economic theory in thetwentieth century: the Ragnar Frisch Centennial Symposium:Cambridge University Press, Cambridge, UK, chapter AnAutoregressive Distributed Lag Modelling Approach toCointegration Analysisin

Pittock AB (2017) Climate change: turning up the heat. Routledge,London

Rafindadi AA, Muye IM, Kaita RA (2018) The effects of FDI and energyconsumption on environmental pollution in predominantlyresource-based economies of the GCC. Sustainable EnergyTechnologies and Assessments 25:126–137. https://doi.org/10.1016/j.seta.2017.12.008

Rodríguez-Pose A, Cols G (2017) The determinants of foreign directinvestment in sub-Saharan Africa: ehat role for governance? RSPP9(2):63–81. https://doi.org/10.1111/rsp3.12093

Salahuddin M, Alam K, Ozturk I, Sohag K (2017) The effects of elec-tricity consumption, economic growth, financial development andforeign direct investment on CO2 emissions in Kuwait. Renew SustEnerg Rev. https://doi.org/10.1016/j.rser.2017.06.009

Salahuddin M, Alam K, Ozturk I, Sohag K (2018) The effects of elec-tricity consumption, economic growth, financial development andforeign direct investment on CO2 emissions in Kuwait. Renew SustEnerg Rev 81:2002–2010. https://doi.org/10.1016/j.rser.2017.06.009

Sarkodie SA, Strezov V (2019) Effect of foreign direct investments, eco-nomic development and energy consumption on greenhouse gasemissions in developing countries. Sci Total Environ 646:862–871. https://doi.org/10.1016/j.scitotenv.2018.07.365

Schreier MR (2017) Solar-driven reduction of CO2: from homogeneousto heterogeneous catalytic systems. Ecole Polytechnique Fédéralede Lausanne (Thesis)

Schuur E, McGuire AD, Schädel C, Grosse G, Harden J, Hayes D et al(2015) Climate change and the permafrost carbon feedback. Nature520(7546):171–179

Sethi S, Gupte R (2017) Is trade openness leading to sustainable carbonemission? IJPMB 7(1):79–93

ShahbazM, Hye QMA, Tiwari AK, Leitão NC (2013) Economic growth,energy consumption, financial development, international trade andCO2 emissions in Indonesia. Renew Sust Energ Rev 25:109–121.https://doi.org/10.1016/j.rser.2013.04.009

Shahbaz M, Khan S, Ali A, Bhattacharya M (2017) The impact of glob-alization on CO2 emissions in China. Singap Econ Rev 62(04):929–957

Ssali MW, Du J, Hongo DO, Mensah IA (2018) Impact of economicgrowth, energy use and population growth on carbon emissions in

Environ Sci Pollut Res (2019) 26:11245–11260 11259

sub-Sahara Africa. JESE-B 7:178–192. https://doi.org/10.17265/2162-5263/2018.05.002

Sung B, Song W-Y, Park S-D (2018) How foreign direct investmentaffects CO2 emission levels in the Chinese manufacturing industry:evidence from panel data. Econ Syst 42(2):320–331. https://doi.org/10.1016/j.ecosys.2017.06.002

Tang CF, Tan BW (2015) The impact of energy consumption, income andforeign direct investment on carbon dioxide emissions in Vietnam.Energy 79:447–454. https://doi.org/10.1016/j.energy.2014.11.033

Topcu M, Payne JE (2018) Further evidence on the trade-energy con-sumption nexus in OECD countries. Energy Policy 117:160–165.https://doi.org/10.1016/j.enpol.2018.03.007

Vogel B, Henstra D (2015) Studying local climate adaptation: a heuristicresearch framework for comparative policy analysis. Glob EnvironChang 31:110–120

Wang S, Li Q, Fang C, Zhou C (2016) The relationship between econom-ic growth, energy consumption, and CO2 emissions: empirical evi-dence from China. Sci Total Environ 542:360–371. https://doi.org/10.1016/j.scitotenv.2015.10.027

Wang X, Jiang D, Lang X (2017) Future extreme climate changes linkedto global warming intensity. Sci Bull 62(24):1673–1680. https://doi.org/10.1016/j.scib.2017.11.004

WEF (2016) Understanding Africa’s energy needs. Retrieved fromhttps://www.weforum.org/agenda/2016/11/understanding-africas-energy-needs/. Accessed 22 Dec 2018

WeitzmanML (2017) Voting on prices vs. voting on quantities in aWorldClimate Assembly. Res Econ 71(2):199–211. https://doi.org/10.1016/j.rie.2016.10.004

WHO (2018) Air pollution: air pollution infographics. Retrieved fromhttps://www.who.int/airpollution/infographics/en/. Accessed 23Dec 2018

World Bank (2014) Foreign direct investment flows into sub-SaharanAfrica Retrieved from http://documents.worldbank.org/curated/en/505071468203651135/Foreign-direct-investment-flows-into-Sub-Saharan-Africa

World Bank (2018a) Boosting access to electricity in Africa throughinnovation, beter Regulation. Retrieved from https://www.worldbank.org/en/region/afr/publication/boosting-access-to-electricity-in-africa-through-innovation-better-regulation. Accessed22 Dec 2018

World Bank (2018b) Global economic prospects: sub-Saharan Africa.Retrieved from https://www.worldbank.org/en/region/afr/brief/global-economic-prospects-sub-saharan-africa-2018. Accessed 22Dec 2018

Zaman K, Moemen M A-e (2017) Energy consumption, carbon dioxideemissions and economic development: evaluating alternative andplausible environmental hypothesis for sustainable growth. RenewSust Energ Rev 74:1119–1130. https://doi.org/10.1016/j.rser.2017.02.072

Zeufack A (2018) Growth in sub-Saharan Africa is slower than expected.Retrieved from https://www.worldbank.org/en/news/press-release/2018/10/03/growth-in-sub-saharan-africa-is-slower-than-expected.Accessed 22 Dec 2018

Zhu H, Duan L, Guo Y, Yu K (2016) The effects of FDI, economicgrowth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression. Econ Model 58:237–248. https://doi.org/10.1016/j.econmod.2016.05.003

11260 Environ Sci Pollut Res (2019) 26:11245–11260