nigerian energy rser

Renewable and Sustainable Energy Reviews 62 (2016) 1209–1225

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Does the need for economic growth influence energy consumptionand CO2 emissions in Nigeria? Evidence from the innovationaccounting test

Abdulkadir Abdulrashid RafindadiDepartment of Economics, Usmanu Danfodiyo University, Sokoto, Nigeria

a r t i c l e i n f o

Article history:Received 1 September 2014Received in revised form9 January 2016Accepted 3 May 2016

Keywords:Trade opennessFinancial developmentCO2 emissionsEnergy demandRafindadi

x.doi.org/10.1016/j.rser.2016.05.02821/& 2016 Elsevier Ltd. All rights reserved.

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a b s t r a c t

Developing economies like Nigeria are competing strategically to ensure a rise in sustainable economicgrowth, and reduction in CO2 emission. The question is, could this be possible amidst the series of energycrises facing the country? It is against this development that this paper investigates empirically if thenexus between economic growth, energy consumption, financial development, trade openness and CO2

emissions in Nigeria could provide a clue. The study used time series data from 1971 to 2011. To ensure arobust result, the study applied the ARDL bounds testing approach to cointegration, the Zivot–Andrewstructural break test, and the Bayer–Hanck combine cointegration analysis. The causality analysis, waschecked using the VECM model and this was validated using the innovative accounting and the impulseresponse test. The findings of the study revealed that financial development stimulates energy demand,but lowers CO2 emissions. Economic growth lowers energy demand but increases CO2 emissions. Inaddition to that, the study discovered how Trade openness increases energy consumption but improvesenvironmental quality by lowering CO2 emissions. Energy consumption was on the other hand, found tohave significant increase on CO2 emissions. The Granger causality analysis revealed a bidirectional causalrelationship between financial development and energy consumption, and the same inference was foundin financial development and CO2 emissions. In this study, trade-led energy hypothesis and the existenceof a feedback effect between economic growth and CO2 emissions were discovered. The study recom-mends massive investment in Nigeria’s financial sector with the motivation for these sectors to invest inefficient, and sustainable renewable energy system. How it should be done and why it should be done arecarefully outlined in this study.

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Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12092. Empirical review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12113. The data source, model and the estimation procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1213

3.1. The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12133.2. Estimation procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214

4. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12145. Conclusion and policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1219Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1222References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224

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A.A. Rafindadi / Renewable and Sustainable Energy Reviews 62 (2016) 1209–12251210

1. Introduction

The significance of energy within the realm of economicgrowth, financial development, social and political strata of everycountry may not be sufficiently stressed; this is due to the fact thatin communication, transportation, education, health, financialoperation and industrial operations, the vital use of energy for thefull-scale functioning of these systems cannot be observed to haveany close substitute. Apart from these, the enhancement in thequality of life as noticeable in the increase on the manufactureof food, raised industrial production, accessibility to shelter,enhanced provision of healthcare as well as alternative humanamenities; for each of these, development to take place requiresthe use of energy. In addition, researchers like Rafindadi [63,64];Ucan et al. [91]; Halicioglu [30]; Rafindadi and Ozturk [65] andRafindadi and Ozturk [66]; Aliyu et al. [5]; Shaaban and Petinrin[85]; Oyedepo [53] have established that energy is an essentialfactor for the attainment of numerous economic objectives notonly in Nigeria but throughout the entire globe The reason is thatenergy assists in abolishing severe hunger and poverty, attainingglobal primary learning, advancing gender equality as well asdecreasing child deaths. Energy also helps in enhancing maternalwellbeing, fighting infection, empowering women and ascertain-ing ecological maintenance Oyedepo [53,54]; Yamusa and Ansari[93] Rafindadi [63,64]; Rafindadi and Ozturk [65]. From the fore-going, it is sufficient to argue that the high level of energy neededto achieve a given level of economic growth will definitelyincrease beyond ordinary household consumption if nationalgrowth is to be maintained abreast with the dynamics of thequality of life and other industrial requirement. Following to this,Al-mulali and Che-Sab [8] argue that insufficient provision ofenergy limits socio-economic operations hampers financialdevelopment and negatively impacts living standards.

Adding to the assertions maintained by Al-mulali and Che-Sab[8] and in attempting to progress the importance of energy utili-zation in the achievement of sustainable national economicobjective, Payne [59] and Ozturk [58] offered four opposing the-ories regarding the manner in which energy comprises the core ofeconomic development, they emphasize that: (i) in a situationwhere energy consumption Granger causes economic growth (i.e.the growth hypothesis) the authors posit that energy decreasingpolicies have to be prevented, and novel origins of sustainable andrenewable energy have to be investigated, to ensure that sufficientdemand is met with efficient supply (ii) Another potential factoridentified by the authors is that when causality was found to shiftfrom financial development to energy consumption, this meansthat energy decreasing regulations would not imply negativeconsequences for economic development as economic develop-ment of the nation does not appear to be reliant on energy, (iii) iffeedback hypothesis was found, then this infers the inter-relianceof energy consumption and economic growth. Following to this, anincrease in economic growth will result in the rise of energyrequirement, which in response encourages economic growth, andas a result of this and unlike the first case, energy conservationpolicies will inhibit the direction of economic growth (iv) in asituation where no causality connecting energy consumption andeconomic growth was found then this implies neutrality hypoth-esis, signifying that energy and development are not co-reliant. Inaddition to this and with regard to the fourth point the authorscontinued to argue that the implementation of energy conserva-tion measures as well as exploration of energy policies may nothave a constructive impact on economic growth.

Complementing the above theoretical hypotheses was theempirical findings of Sadorsky [72−75]. In his leading researcheffort, the author established that the role of financial develop-ment is equally very significant in ensuring steady, sustainable andflourishing channel of productive activities in an economic system.This is because financial development is the major conduit thatwill continue to boost economic growth that will, in turn, piqueinvestment, thus enabling a developed financial market. Followingthis, a significant development in the financial market is in turnexpected to yield an increased entrepreneurial diversification thatwill strengthen internal and external demand for energy. In asimilar pattern of thought Shahbaz [80−84] asserted that financialdevelopment permits the likelihood for the availability of a prac-tical loan for the purpose of obtaining manufacturing apparatus,namely machines, which would increase the requirement ofenergy for competent and successful exports. The author con-tinues to assert that if Granger causality moves from the utilizationof energy to exports or there is a causal association linking exportsand energy, then policies to conserve energy will decrease exports,and this could, in turn, reduce financial development. On the otherhand, If causality was established to be moving from exports toenergy or substantiation of no Granger causality in every direction,then the author asserted that the most viable and enduringalternative is to preserve energy in such a way that it will not havean impact on exports.

The contributions of this study are:

� Having regard to the foregoing, this study aims to investigate towhat extent does the need for economic growth influenceenergy consumption in Nigeria. In addition to that, what is thelikely position of CO2 emissions to the country’s economicgrowth prospects, considering it being the most populouscountry in Africa and having the leading oil reserves andexploration. Is the revived financial sector of the country pro-viding any meaningful economic contributions to the GDP? Isthe Trade openness of the country of any significant contribu-tion? From these empirical findings, the study seeks to deter-mine the continuums of policy implications that exist and thepolicy guides that could be offered in achieving a realistic andsustainable economic growth prospects in the country. Thestudy noted with keen interest the energy challenges of thecountry and the crucial role of energy as a significant factor forsustainable economic growth.

� To ensure a parsimonious analysis, and in contrast to otherempirical researches, that mainly applied the ADF, PP, DF-GLS,KPSS, and Ng-Perron tests, the present study applied the Zivotand Andrew [100] structural break test to identify possiblestructural breaks in the series. In addition to that, Pesavento[62] established that the efficiency and power of rankingcointegration approaches by other methods is sensitive withthe value of nuisance estimators. To avoid this, the Bayer andHanck [13] developed a more parsimonious cointegration tech-nique. This approach combines all non-cointegrating tests inestablishing a uniform, efficient and reliable cointegrationestimates devoid of multiple testing procedures. This test isapplied in this study in order to overcome the likely short-comings of old existing methods. After all these diagnosticanalysis, the study applied the ARDL bounds testing approachin the presence of structural break, and this was validated usingthe chow breakpoint test, the innovative accounting test, andthe impulse response test.

A.A. Rafindadi / Renewable and Sustainable Energy Reviews 62 (2016) 1209–1225 1211

Following to this, the remainder of the paper is organized asfollows: Section 2 provides an overview of the recent empiricalliterature on energy consumption, linking energy consumption tofinancial development, trade openness and economic growth andthe influence of CO2 on economic growth; Section 3 is the meth-odology section which introduces the data, the model specifica-tion, and the model estimation procedure; Section 4 contains theresults and discussion. Finally, Section 5 presents the conclusionand recommendations for policy and further research.

2. Empirical review

The pioneering work of Kraft and Kraft [43] on the nexuslinking financial development and energy consumption is stillconsidered as having the greatest influence in the area of energyeconomics. In that study, the authors were the first to establish theexistence of unidirectional causal association between GNPdevelopment and energy consumption for the case of the UnitedStates in 1947–1974. Subsequent to that development, numerousresearchers like Akarca and Long [2] re-examined the discoverymade by Kraft and Kraft [43]. The authors in their new researchendeavor used new data set and different time frame. Theempirical finding of the authors established the rejection of aunidirectional association between energy and GNP developmentas in the case of Kraft and Kraft [43]. This development resulted inthe encouragement of early writers to use different methodolo-gical approach, data sets in the form of panel and time series tocontinue with the investigation on the nexus between energyconsumptions and economic growth. For example, Erol and Yu[26] tactically carried out their research from 1952 to 1982. Theauthors in a bid to obtain robust results dichotomized the casestudy into six internationally leading industrial countries that arerenowned to be the leading energy consumers in the world as atthe time under survey. The outcomes of their research establishedthe existence of bidirectional causal relationship for the case ofJapan. In contrast to that development, a different outcome wasobtained for the case of Canada. The Canadian research findingdisplayed the existence of unidirectional causality from energyconsumption to financial development. These amazing resultscontinued up to the comparative study between Italy and Ger-many. In these two instances, the authors discovered that it isfinancial development that encourages energy consumption inItaly, and unexpectedly none in the instance of England andFrance.

Another pioneering study linking financial development andenergy consumption was that of Sadorsky [72−74]. In that study,the author argued that the sophistication and modernity of thefinancial system will elevate the extent of energy consumption,and this has a role in the inflow of FDI and the enhancement in thebanking operations. According to him, this development willstrategically help in stimulating the rise and growth of the stockmarket, and alternative economic infrastructure. The writerarguably insists that these will blossom to ensure the thriving ofentrepreneurial prospects among other things. In a relateddevelopment, Tamazian et al. [87] stated that financial develop-ment assists in encouraging local requirements of modern elec-tricity devices. In response to that increases, energy consumptionwill escalate. To analyze this logic, Karanfil [39] investigated theposition of the impacts of financial development on energy con-sumption in Guangdong, China. The finding of the author perfectlydemonstrated the existence of a unidirectional causal relation,running from financial development to energy consumption.A comparable endeavor was noted in the instance of Sadorsky[72−75] in his research of 22 developing economies between 1990and 2006. The conclusion of the author underscored the trend that

energy consumption was vital in those continents under surveyparticularly in increasing the spate of financial development. Thisresult motivated Shahbaz and Lean [81]. In that study, the authorsexplored the precision of the impact of how financial developmentcan enhance energy consumption in Pakistan. In their empiricalresearch explanations, the writers established that this can beattributed mainly to the capability of financial development toencourage the requirement of consumables in facilities as well asnon-facilities founded operations. Meaning that there is bidirec-tional Granger causality with respect to one another. Other studieslinking economic growth, financial development, energy con-sumption and CO2 emission include those of Ziaei [99]; Al-Mulaliet al. [7]; Salahuddin et al. [76]; Komal and Abbas [40] amongothers.

According to the previous research progress and the empiricalsuppositions proclaimed by earlier researchers, suffice for us tostress that an increase in efficient and effective energy system andfinancial development will pique the rise in commercial opera-tions of a country. This development will, in turn, result in thelikely growth and development of entrepreneurial productivities.The rise in these three factors (efficient and sufficient energy,financial development and entrepreneurship) will lead to anexpansion in export. All things being equal. This development willrender it necessary for the requirements of additional energyefficient machinery as well as export directed apparatus for utili-zation in delivery and shipping to the airports and harbors, wheresuch exports are subsequently packed and re-packed to interna-tional destinations. The chain of operations in this undertakingrequires energy to function. In addition to this, an increase incommercial output, exportations, and value-added economicoperations will result in the rise in energy consumption and theopposite will be true. In a related development, the export-directed energy concept maintains that a decrease in exportsaffects consumption of energy. However, the energy-directedhypothesis on its part established that any considerable reduc-tion in energy consumptionwill affect the movement of exports. Inanother perspective, it has been established by leading researcherssuch as Shahbaz and Lean [81] and Shahbaz [80−84]. In these twostudies, the authors argued that the existence of a causal rela-tionship linking exports and energy is quite considerable. Theauthors based their believe on the fact that energy is an importantaspect in establishing the direction of exports although exports aresignificant aspects in accounting for consumption of energy. Theseassociations linking energy consumption and inputs, has a com-parable dynamic inclination similar to export. In the two differentinstances, energy consumption may not be prevented. Hypothe-tically any reduction in imports will impact on the consumption ofenergy by means of a considerable hindrance in directing theimported produce to the correct location and respective networksthereby, stopping delivery, and encroaching on the structure of thesupply chain network. Overall, it is apparent to note that, when-ever, the supply chains are disrupted due to poor energy supply, itthen became obvious that the state welfare structure will beimpinged, due to considerable disruption of inputs and outputs.This development will in turn affect demand and supplystructures.

The second strand of literature on this subject offers empiricalsubstantiation regarding the association linking environmentalKuznets curve (EKC) and economic growth. The EKC hypothesisis an empirical investigation which tests the existence of aninverted-U shaped relationship between economic growth andenvironmental quality. Available researches comprising Kukla-Gryz [44]; Kohler [41]; Ozcan [56]; Cho et al. [20]; Robalino-López et al. [69]; Lau et al. [45]; Farhani et al. [27]; Al-Mulali et al.[7]; Apergis and Ozturk [12] and Ozturk and Al-Mulali [57];Tsurumi and Managi [89]; Marsiglio et al. [48]; Tutulmaz [90]; Yin


et al. [94]; Jebli and Youssef [37], and more recently Rafindadi[115] studied the various position of the Environmental KuznetsCurve (EKC) with respect to different countries, applying differenteconometric methods of investigations and amidst varying eco-nomic circumstance. In a related development. Martinez-Zarzosoand Bengochea-Morancho [47], Hettige et al. [31], Cropper andGriffiths [22], Grossman and Krueger [29], Selden and Song [77], toname a few, explored the association linking revenue and emis-sions of CO2. The authors in their findings substantiated theexistence of the EKC in the respective research areas. In contrast tothat, Dinda and Coondoo [23] employed panel data and offeredambiguous outcomes concerning financial development and CO2

emissions’ association. Shahbaz et al. [80] and Tiwari et al. [88] ontheir own part backed the empirical availability of the EKC inBrazil, India, China, Pakistan, France, Malaysia, Italy and Romania.In another research effort Soytas et al. [78] explored the dynamicassociation linking CO2 emissions, revenues and consumption ofenergy in the United States. Their outcomes illustrated that CO2

emissions is the Granger cause in revenues, while energy con-sumption has a significant role in the escalation of CO2 emissionsin the US. A similar comparable exercise was carried out inMalaysia and France by Ang [9] and Al-mulali and Che-Sab [8]respectively, the outcomes of their empirical exercise illustratedhow financial development Granger cause an increase in theconsumption of energy which result in the emission of carbon inMalaysia and France. In addition to that, a unidirectional causalityis observed stemming from financial development to energyconsumption. Narayan and Popp [104] while using a panel of 43developing countries established the existence of environmentalKuznets’s curve (EKC) hypothesis in the studied areas. The findingsof the authors established that the income of a country increasesin proportion to its ability to reduce the total content of its CO2emissions.

Studies that applied recent econometric methodologies couldbe found in the leading study of Rafindadi and Yusof [68] andDogan [24]. In that study the authors applied the second genera-tion Unit root test, cross-sectionally augmented Dickey–Fuller(CADF) and the cross-sectionally augmented Im-Pesaran-Shin(CIPS) unit root tests and the Lagrange multiplier bootstrappanel cointegration test. The aim of all these methodologies is toanalyze the long-run relationship that may subsist between car-bon dioxide emissions, real gross domestic product (GDP) in thefirst instance and the square of real GDP in the second instance.The study also added energy consumption, trade and tourismunder an Environmental Kuznets Curve (EKC) model for theorganization for economic co-operation and development (OECD)countries. The findings of the study established a linkage betweenenergy consumption and tourism. In a related development, thestudy found how a persistent rise in international trade could helpin enhancing the environmental quality of the continents undersurvey. In spite of this development a negative relationship wasdiscovered between the EKC hypothesis, GDP and also the GDP2.

The impacts and implications of CO2 emission on GDP and theenvironment absolutely depends on the type and content ofenergy emissions. For instance, CO2 emissions, sulfur dioxide, andcarbon monoxide have detrimental effects on human health andthe environment. To support this view, the study of Hettige et al.[31]; Diwan and Shafik [105] discovered how the relationshipbetween CO2 emissions and economic growth is detected to bestatistically significant. This development attested to the existenceof an inverted-U. Complementing the findings of Hettige et al.[31]; Diwan and Shafik [105]; is the study of Chen [112]. Inthat study the author established the existence of significant

relationship between economic growth and increasing CO2 emis-sions in Taiwan. To show how impacting is the existence of CO2

emission in the Taiwanese economy, the author maintain that theTaiwanese GDP would have been 34% less had it been CO2 emis-sions is stabilized as compared with that of the 1990 level. Xepa-padeas [106] in their empirical investigations discovered theexistence of a positive relationship between sustainable economicdevelopment and energy emissions. Complimenting this finding isthe noble research findings of, Managi [107] in that research effort,the author maintain the view point that economic growth and thedecrease of environmental degradation are compatible in accor-dance with the EKC hypothesis. Say and Yucel [108]; Ang [10];Fodha and Zaghdoud [109] established the existence of positiverelationship between CO2 emissions and economic growth in dif-ferent periods of their investigations. However, Lise and Montfort[111]; Gosh [110], established a negative linkage between eco-nomic growth and the per capita pollution emissions in the long-run. In another development, Chebbi [19] explored the causalassociation linking consumption of energy, revenues and emis-sions of CO2 by including investment and employment as extracontrollers of emissions of carbon but observed no causality link-ing CO2 emissions and revenues.

Chang [17] implemented the multivariate causality appraisal inan effort to assess the causal association linking financial devel-opment, consumption of energy and CO2 emissions. The authoremployed time series data from China. The outcomes from theresearch disclosed that financial development Granger causeenergy consumption which in turn results in CO2 emission.Employing Turkish data, Halicioglu [30] establish a finding thatlinks the hypotheses between financial development and CO2

emissions. Menyah and Wolde-Rufael [49] while using SouthAfrican data discovered that consumption of energy Granger causeCO2 emissions and that financial development is being Grangercaused by emissions of CO2. In contrast, Odhiambo [52] re-explored the causality linking consumption of energy, financialdevelopment and emissions of CO2. In this research a unidirec-tional causality was established moving from financial develop-ment to CO2 emissions. In another strand of energy literature,Tamazian et al. [87] explored the effect of financial developmentand economic growth on CO2 emissions in the instance of panelcountries which include China, Russia, United States, Brazil andIndia, and subsequently Tamazian and Rao [86] explored thefunction of organizations on CO2 emissions. The empirical prooffrom their research established that financial development, tradeopenness, economic growth and organizations have to function tolimit the destruction of the ecology while sustaining the avail-ability of EKC concept.

Complementing the research findings of Tamazian and Rao[86], Yuxiang and Chen [95] in their study on China, establishedthat financial sector policies should be encouraged to facilitate theemployment of modern technology by companies which possessthe prospects of not only reducing CO2 emissions but that willequally help in the efficient improvement of the local output. Inaddition to this point, the authors continue to maintain thatfinancial development encourages capitalization and financialrules that support ecological standards should equally be moti-vated with the industrial sectors. Subsequently, Jalil and Feridun[34] in their investigation studied the effect of economic growth,consumption of energy and financial development on carbonemissions for the case of China. They revealed that consumption ofenergy; economic development and trade openness are destruc-tive to ecological standards. Alternatively, financial developmentand international direct investment restrict ecological damage. Of


late Zhang [96] re-explored the finance setting nexus and deducedthat financial development raises the emissions of CO2 as a resultof incompetent distribution of monetary reserves to ventures.Panel studies carried out in the context of the Sub-Saharan Africannations also exist and notable among these studies is that of Al-Mulali and Sab [8] the authors in their empirical research effortexplored the dynamics connecting the link between consumptionof energy, revenues, financial development and CO2 emissions byincluding employment and investment as possible factors ofboosting local output. Their empirical exercise reveals that con-sumption of energy propels economic growth. This momentumwill in turn persist and consolidate up to a position where anincrease in economic development and consumption of energywill lead to an increase in the requirement for financial services.This will, in turn, allow financial development to raise the stan-dard of the ecology by managing emissions of CO2. The authorsargued that this can take place by means of managing CO2 emis-sion through the implementation of efficient and visible financialregulations. In contrast to this finding Ozturk and Acaravci [55]established that financial development does not appear to be afactor in reducing emissions of CO2 in the case of Turkey.

In an earlier development which supports the findings ofOzturk and Acaravci [55] is the noble work of Grossman andKrueger [29] in that empirical exercise, the authors asserted thatthe ecological impacts of international trade are subject to theregulations applied in an economy. The authors continue to assertthat two different schools of thought exist concerning the effect ofinternational trade on CO2 emissions. The first group insist thattrade openness offers a suggestion to each nation to have admis-sion to foreign markets which improves the market share amidnations. Following to this development Shahbaz et al. [80] arguedthat the result of this suggestion will open up a door of rivalrybetween nations and will equally raise the effectiveness ofemploying insufficient resources at the expense of supporting theimportation of cleaner technologies for the purpose of reducingemissions of CO2 only. This assertion motivated another group tosuggest that natural resources are diminishing as a result ofinternational trade. The group further argued that consequent tothe diminishing features of natural resources, this, in turn,increases CO2 emissions and results in a reduction in ecologicalstandard (Chaudhuri and Pfaff [18], Copeland and Taylor [21], andSchmalensee et al. [79]).

Dogan and Turkekul [25] in their leading study which focusedon the nexus between carbon dioxide (CO2) emissions, energyconsumption, real output (GDP), the square of real output (GDP2),trade openness, urbanization, and financial development in theUSA ranging from 1960 to 2010. The authors while applying theARDL bound testing approach to cointegration established theexistence of cointegration relationship among the variables andthis dynamically revealed that, In the long run, energy consump-tion and urbanization increase environmental degradation whilefinancial development was found to have no effect. In addition tothat, trade was found to leads to environmental improvements.Moreover, this study did not support the validity of the environ-mental Kuznets curve (EKC) hypothesis for the USA because realoutput leads to environmental improvements while GDP2 increa-ses the levels of gas emissions. The results from the Grangercausality test, on the other hand, show the existence of bidirec-tional causality between CO2 and GDP, CO2 and energy consump-tion, CO2 and urbanization, GDP and urbanization, and GDP andtrade openness while no causality is detected between CO2 andtrade openness, and gas emissions and financial development. Inaddition, the study provided sufficient evidence to support one-way causality running from GDP to energy consumption, and from

financial development to output, and from urbanization to finan-cial development.

Machado [46] the author signified a constructive associationlinking international trade and emissions of CO2 in Brazil. In arelated but more illustrative development, Mongelli et al. [50]deduced that the contamination haven theory was present in Italy.Jafari et al. [33] explored the link connecting energy consumption,economic development and CO2 emissions, and this time aroundthe author included urbanization and capital as possible factors ofconsumption of energy and contaminants of energy. The finding ofthe authors established the absences of a long run associationbetween the factors, and urbanization in addition to this theGranger causality result was found to be in support of energyconsumption. Apart from these, there are more recent studies thatapplied different economic variables that identified the significantinfluence of pollution on GDP growth. These studies include thatof Chandran and Tang [16]; Saboori and Sulaiman [70,71]; Al-mulali et al. [6]; Zhang and Da [97]; Zhang et al. [98].

In addition to the above, studies that investigated the dynamicinfluence of energy consumption on economic growth and CO2

emission include those of Boutabba [15]; Akhmat et al. [3];Rafindadi et al. [67]; Alam et al. [4]; etc. Moreover notable studiesthat include trade openness, economic growth and CO2 includethose of Kohler [41]; Onafowora and Owoye [113]; Osabuohienet al. [114] and Al-mulali et al. [6], while those that used theinfluence of financial development on energy consumptioninclude Jalil and Feridun [36]; Boutabba [15]; Alam et al. [4]; Al-mulali et al. [6]. Similar to the earlier development, differentvariables were utilized as indicators of pollution, and these includeCO2 emission such as in the leading study of Ang [9]; Halicioglu[30]; Jalil and Mahmud [35]; Apergis and Payne [11]; Acaravci andOzturk [1]; Kivyiro and Arminen[42]; etc.

In spite of the extensive literature provided above, yet, there isno study that investigated the effect of whether the need foreconomic growth influence energy consumption and CO2 emis-sions in the developing economy of Nigeria which is consideredthe leading oil producer in Africa and the leading African countryin terms of population and GDP. It is in consideration of thisdevelopment, and also following the competitive economic stra-tegies put in place by the country to ensure sustainable rise ineconomic growth, and reduction in CO2 emission, that this studyaims to make an empirical contributions to the energy, CO2 andeconomic growth literature.

3. The data source, model and the estimation procedure

The data concerning energy consumption (kt of oil equivalent),real credit to the private sector, real trade and real GDP have beenacquired from World Development Indicators (WDI) [92]. Popu-lation series have been employed to change each of the variablesinto per capita expression. This research deals with the time frame1971–2011. The aim of this paper is to explore the effect offinancial development on energy consumption and CO2 emissionsby contributing economic growth and trade openness in energyconsumption and CO2 emissions functions in the case of Nigeria.

3.1. The model

The overall state of CO2 emissions and energy demand func-tions are provided subsequently:

ECt ¼ f ðFDt � Ct � Yt � TRtÞ ð1Þ


Ct ¼ f ðFDt � ECt � Yt � TRtÞ ð2ÞEach of the variables has been transformed into logarithm. This

is in order to render the model more uniform to estimate as wellas rendering the data efficient and dependable within empiricalapproximation. The specification of our empirical model is pro-vided subsequently:

ln ECt ¼ β0þβ1 ln FDtþβ2 ln Ctþβ3 ln Ytþβ4 ln TRtþμi ð3Þ

ln Ct ¼ α0þα1 ln FDtþα2 ln ECtþα3 ln Ytþα4 ln TRtþμi ð4ÞIn Eq. (4) ln ECt comprises the natural log for energy use (kt of

oil equivalent) per capita, while ln FDt is the natural log of actualdomestic credit to the private sector proxy for financial develop-ment, ln Ct is the natural log of CO2 emission (metric ton) percapita, economic growth is signified by ln Yt which comprisesnatural log of real GDP per capita, ln TRt is for natural log of realtrade openness per capita (exportsþ imports) and μi is white noiseerror reference.

3.2. Estimation procedure

The study applied the Pesaran et al. [60] ARDL bounds testingtechnique. Several benefits are present with this technique thatmay be contrasted with the Johansen and Juselius cointegrationmethods [38].1 The ARDL bounds assessment technique differ-entiates between the dependent and explanatory variables. For thepurpose of applying the bounds assessment process, Eq. (1) isconverted to the unconditional error correction model (UECM)subsequently:

Δ ln ECt ¼ c0þXpi ¼ 1

ciΔ ln ECt� iþXpi ¼ 1

diΔ ln FDt� i

þXpi ¼ 1

diΔ ln Ct� iþXpi ¼ 1

diΔ ln Yt� iþXpi ¼ 1

diΔ ln TRt� i

þπ1 ln ECt�1 þπ2 ln FDt�1þπ3 ln Ct�1þπ4 ln Yt�1

þπ5 ln TRt�1þπDDUMtþu1t ð5Þ

Δ ln Ct ¼ c0þXpi ¼ 1

ciΔ ln Ct� iþXpi ¼ 1

diΔ ln ECt� i

þXpi ¼ 1

diΔ ln FDt� iþXpi ¼ 1

diΔ ln Yt� iþXpi ¼ 1

diΔ ln TRt� i

þπ1 ln Ct�1þπ2 ln ECt�1þπ3 ln FDt�1þπ4 ln Yt�1

þπ5 ln TRt�1þπDDUMtþu2t ð6ÞwhereΔ signifies the initial different operator, the c0 and d0 comprisethe drift aspects, DUM is dummy variable to capture the structuralbreak date,2 p is the utmost lag length3 and ut is the regular whitenoise residuals. The process of the ARDL bounds assessment techniquecomprises two phases. The initial step is F-test for the Joint sig-nificance of the lagged-level variables. The null hypothesis for theabsence of a long-run association is signified by H0: π1 ¼ π2 ¼ π3 ¼π4 ¼ π5 ¼ 0 against Ha: π1aπ2aπ3aπ4aπ5a0. Pesaran et al.[60] with this development the lower and upper critical bounds fromthe F-test were generated. The key values of the lower bounds

1 Firstly, a smaller sample size is required to compare it to the Johansencointegration technique (Ghatak and Siddiki [28]). Secondly, the ARDL boundstesting approach does not require that the variables be integrated at the sameorder. The approach can be applied whether the variables are purely I (0) or I (1), ormutually integrated. Thirdly, the approach provides a method of assessing theshort- and long-run effects of a variable on another simultaneously, and it alsoseparates the short- and long-run effects (Bentzen and Engsted [14]).

2 The inclusion of dummy variable in the ARDL F-test equation is based on CMRunit root test with single unknown structural break arising in the series.

3 Pesaran et al. [60] caution that it is important to balance choosing the laglength.

suppose that each of the variables is l(0), and the key values of theupper bound presume that each of the variables is l (1). If the F-sta-tistic surpasses the upper critical bound, thus the null hypothesis of nocointegration amid the variables may be discarded. If the F-statisticdrops under the lower bound, therefore the null hypothesis of nolong-run association is acknowledged.4 The subsequent stage com-prises approximation of the long-run as well as the short-run para-meters by employing the error correction model (ECM). To ascertainthe union of the dynamics to long-run equilibrium, the accepted signfor the coefficient of the lagged error correction term (ECMt�1) has tobe negative and statistically significant. Additionally, the diagnostictests include the assessment for the functional form, normality, serialcorrelation as well as the heteroscedasticity (Pesaran and Pesaran[61]). As soon as the variables were found to be cointegrated for thelong-run association, then the long-run and short-run causality can beexplored. The existence of long-run association between economicgrowth, energy consumption, financial development, trade opennessand CO2 emissions will necessitate us to trace which course thecausality follows amid the variables by implementing the vector errorcorrection method (VECM) Granger causality framework. The vectorerror correction method (VECM) is as subsequently:

Δ ln ECt

Δ ln Ct

Δ ln FDt

Δ ln Yt

Δ ln TR

26666664

37777775¼

b1b2b3b4b5

26666664

37777775þ

B11;1 B12;1 B13;1 B14;1 B15;1

B21;1 B22;1 B23;1 B24;1 B25;1

B31;1 B32;1 B33;1 B34;1 B35;1

B41;1 B42;1 B43;1 B44;1 B45;1

B51;1 B52;1 B53;1 B54;1 B55;1

26666664

37777775

�

Δ ln ECt�1

Δ ln Ct�1

Δ ln FDt�1

Δ ln Yt�1

Δ ln TRt�1

26666664

37777775þ :::þ

B11;m B12;m B13;m B14;m B15;m

B21;m B22;m B23;m B24;m B25;m

B31;m B32;m B33;m B34;m B35;m

B41;m B42;m B43;m B44;m B45;m

B51;m B52;m B53;m B54;m B55;m

26666664

37777775

�

Δ ln ECt�1

Δ ln Ct�1

Δ ln FDt�1

Δ ln Yt�1

Δ ln TRt�1

26666664

37777775þ

ζ1ζ3ζ3ζ4ζ5

26666664

37777775� ðECMt�1Þþ

μ1t

μ2t

μ3t

μ4t

μ5t

26666664

37777775

ð7Þ

where the difference operator is ð1�LÞ and the ECMt�1 is producedfrom the long-run association. The long-run causality is signified bythe significance of the coefficient for the ECMt�1 by employing the t-test statistics. The F statistic for the first differenced lagged indepen-dent variables is employed to assess the course of short-run causalityamid the variables.

4. Results and discussion

In this stage, the study provide descriptive statistics as well aspair-wise correlation as found in Table 1. The outcomes of thisanalysis signify that each of the series are white noised, and this isaffirmed by the Jarque–Bera test statistics. The correlation analysisdiscloses the negative association linking CO2 emission and energyuse. Financial development and trade openness are found to bepositively associated with energy consumption. Economic growthis inversely correlated with energy consumption. The positivecorrelation exists between financial development and CO2 emis-sion. Trade openness (economic development) is found to benegatively (positively) correlated with CO2 emission. In a relateddevelopment, the study also discovered how Trade openness andeconomic growth are positively associated with financial devel-opment. Similarly, a positive correlation is found between tradeopenness and economic growth.

The challenge with ADF as well as PP unit root test is that theseassessments tests do not take into account the information fromstructural breaks within the series, as a result of this, their test

Table 2Zivot–Andrews unit root test.

Variables At Level 1st Difference

T-statistic Time break T-statistic Time break

ln ECt �3.273 (2) 1980 �6.752 (1)n 1996ln Ct �4.876 (1) 2000 �7.624 (2)n 1999ln FDt �3.460 (2) 1987 �7.595 (1)n 2007ln TRt �4.607 (3) 1982 �9.338 (2)nnn 1985ln Yt �3.013 (1) 1995 �7.003 (3)nnn 1988

Note: n and nnn indicate significance at 1% and 10% levels respectively. () Indicatesthe lag length of the variables.

Table 1Descriptive statistics and correlation matrix.

Variable ln ECt ln Ct ln FDt ln TRt ln Yt

Mean 6.5764 �0.4619 4.4555 8.1033 6.5271Median 6.5899 �0.3748 4.3946 8.1505 6.4607Maximum 6.6373 0.0069 5.9005 9.1216 6.9414Minimum 6.4422 �1.1330 3.7128 7.2066 6.2030Std. Dev. 0.0504 0.3075 0.5229 0.4258 0.2183Skewness �1.2990 �0.6467 1.0517 �0.1616 0.2217Kurtosis 3.7880 2.4159 3.7878 2.9177 1.5689Jarque-Bera 2.5924 3.4410 1.6191 0.1901 3.8344Probability 0.2043 0.1789 0.3439 0.9093 0.1470ln ECt 1.0000ln Ct �0.2125 1.0000ln FDt 0.3262 0.0723 1.0000ln TRt 0.2120 �0.2402 0.4883 1.0000ln Yt �0.4429 0.3582 0.5094 0.5037 1.0000


results tend to offer vague outcomes. In this manner, the studyapplied the Zivot and Andrews [100] unit root test which takesinto account single unknown structural break within the series.The choice of the break date is based on T-statistic and the breakdate will be chosen where the substantiations are suitable for thenull hypothesis. Zivot and Andrews [100] employed the criticalvalues of ADF unit root assessment. The Zivot–Andrews [100]assessment comprising structural breaks as employed within thisresearch may be tested by employing the subsequent econometricmodels:

axt�1þbtþcDUtþXkj ¼ 1

djΔxt� jþμt ð8Þ

Δxt ¼ bþbxt�1þctþbDTtþXkj ¼ 1


Δxt ¼ cþcxt�1þctþdDUtþdDTtþXkj ¼ 1


where DUt signifies the dummy variable, and it offers the changinglikelihoods of the mean in every point while DTt is a change in thetrending variable.

DUt ¼1:::if �tZTB

0:::if �trTBDUt ¼

t�TB:::if :tZTB

0:::if :trTB

((ð11Þ

In the above equation, the null hypothesis of unit root breakdate is denoted by c¼0 which signifies that series is not motion-less with a drift and not comprising information concerningstructural break originating in the series, while co0 conjectureinfers that the variable is established to be trend-motionless withone indefinite time break. Zivot–Andrews unit root assessmentsets all points as likely for possible time break and does all esti-mation by means of regression for all likely structural breaksconsecutively. Then, this unit root assessment chooses that timebreak which reduces one-sided t-statistic to assess c(¼c�1)¼1.Zivot–Andrews further suggest that in the availability of endpoints, asymptotic allocation of the statistics will tend to deviateto an infinity point. It is essential to select an area in which endpoints of sample time frame are disqualified. Following to this,Zivot–Andrews proposed the trimming regions i.e. (0.15 T, 0.85 T)are observed. The outcomes of the ZA with single unidentifiedstructural break within the series are reported in Table 2. Fol-lowing to this, the results in that analysis established that all thevariables have special sequence of integration. As the unit root

assessment in this study illustrates that all the variables observethe I(1), unit root pattern.

The outcomes in Table 2 disclose that each of the series is non-stationary at level with the presence of structural break pointoriginating within the series. At first difference, each of the vari-ables is observed to be stationary. This signifies that each of thevariables is integrated at l(1). The outcomes of ZA with singleunknown structural break within the series are reported in Table 2from that result the study was able to established how each of thevariables have a special order of integration. As demonstrated bythe unit root assessment and following the fact that each of thevariables observe the I(1) order of integration, the combinedcointegration assessments are given in Tables 4 and 5. Beforeproceeding to the cointegration test the study first outline the VAROrder selection criterion in Table 3. In that Table 3 the studyapplied the FPE, LR, AIC, HQ and SC criterion. Based on the out-come of each, the AIC criterion is followed and 1 is found to be thestipulated lag length.

From the above analysis, the study proceeds to apply the Bayerand Hanck combined cointegration test, considering the findingthat all the variables are integrated at I (1). Table 4 highlights thecombined cointegration test which comprise the EG-JOH, as wellas EG-JOH-BO-BDM assessments. The outcome discloses thatFisher-statistics for EG-JOH and EG-JOH-BO-BDM assessments forthe case of Ct , ECt , FDt and Yt are more than 5% critical valueswhich signify that both EG-JOH and EG-JOH-BO-BDM test statis-tically reject the null hypothesis of no cointegration between thevariables. Following to this development, the study cannot rejectthe hypothesis of no cointegration once the TRt, is employed asautonomous variables in the case of Nigeria. This shows the pre-sence of 4 cointegrating vectors that affirm the availability ofcointegration between the variables. This substantiates the avail-ability of a long-run association linking energy use, CO2 emissions,trade openness and financial development during the time frame1971–2011 in the case of Nigeria.

The study noted that the combined cointegration test does notaccommodate information with respect to the issues of structuralbreaks originating within the series. To overcome this problem theARDL bounds testing approach to cointegration in the presence ofstructural breaks is employed. With this development the studyproceed with the application of the ARDL bounds testing approachto cointegration noting the relevance of the F-statistic which helpsin making decision with respect to the existence of cointegrationbetween the variables or otherwise. The outcome of this empiricalexercise is presented in Table 5. In that table, the study observedhow the estimated F-statistics surpass upper critical bounds at 5%and 1% level respectively particularly if energy consumption, CO2

emission, economic growth and financial development wereemployed as the dependent variables. In this respect our resultconfirms the presence of cointegration in the presence of struc-tural breaks within the series spanning the time frame 1971–2011.

Table 3Lag length selection.

VAR lag order selection criteria FPE AIC SC HQ

Lag LogL LR

0 63.3883 NA 3.18e�08 �3.0730 �2.8576 �2.99641 195.5965 222.6663 1.14e�10 �8.7156 �7.4227a �8.25562 215.2219 27.8886 1.63e�10 �8.4327 �6.0625 �7.58943 261.5542 53.6479a 6.44e�11a �9.5554a �6.1079 �8.3288a

LR: sequential modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion; SC: Schwarz information criterion; HQ:Hannan–Quinn information criterion.

a Lag order selected by the criterion.

Table 4The results of Bayer and Hanck cointegration analysis.

Estimated models EG-JOH EG-JOH-BO-BDM Cointegration

Ct ¼ f ðECt ; FDt ; Yt ; TRt Þ 14.850** 28.798 ✓

ECt ¼ f ðCt ; FDt ; Yt ; TRt Þ 15.897** 25.410 ✓

FDt ¼ f ðCt ; ECt ; Yt ; TRt Þ 14.832** 24.207 ✓

Yt ¼ f ðCt ; ECt ; FDt ; TRt Þ 14.448** 29.565 ✓

TRt ¼ f ðCt ; ECt ; Yt ; FDt Þ 7.373* 12.107 None

Note: * and ** indicate significance at 1% and 5% levels respectively. Critical values at1% level are 16.259 (EG-JOH) and 31.169 (EG-JOH-BO-BDM) respectively.

Table 5Results of ARDL cointegration test.

Variables ln Ct ln ECt ln FDt ln TRt ln Yt

Break Year 2000 1980 1987 1982 1995F-statistic 8.444 6.648 5.800 2.063 10.113Critical values 1% level 5% level 10% levelLower bounds 7.133 5.130 4.300a

Upper bounds 7.820 5.780 4.780R2 0.8367 0.8698 0.7934 0.7818 0.8071Adj-R2 0.6081 0.6875 0.5043 0.4763 0.5371F-statistics 3.6603n 4.7742n 2.744nn 2.559nn 2.989nnn

Diagnostic tests

χ2NORMAL 0.7169 0.8628 1.9861 2.0464 1.1347

χ2SERIAL 1.5435 9.8951 4.1248 0.1946 0.9860

χ2ARCH 0.5337 3.8554 0.2590 0.1734 1.0821

χ2WHITE 0.6209 2.6954 1.2288 1.6256 1.2266

χ2REMSAY 0.2293 0.8392 0.0873 1.3048 0.3802CUSUM Stable Stable Stable Un stable StableCUSUM sq Stable Stable Stable Stable Stable

n The significance of the variable at 1% level.nn The significance of the variable at 5% level.nnn indicates the significance of the variable at 10% level.a Critical values bounds are with restricted trend and unrestricted intercept.

Table 6Long run analysis.

Dependentvariable¼ ln ECt Dependentvariable¼ ln Ct

Variables Coefficient T-statistic Coefficient T-statistic

Constant 7.5255n 53.6346 �30.7109n �3.0285ln Ct 0.0462n 2.7522 … …ln ECt … … 3.7574n 2.7522ln FDt 0.0593n 6.1593 �0.2080nnn �1.7414ln Yt �0.2621n �9.4752 1.7197n 4.6787ln TRt 0.0653n 4.7749 �0.5874n -4.7599

R2 0.7603 0.4763

Adjusted�R2 0.7337 0.4181

F-statistic 28.5568n 8.1864n

Diagnostic tests

Test F-statistic P-value F-statistic P-value

χ2NORMAL 2.6723 0.3303 2.4333 0.2803

χ2SERIAL 1.6531 0.2910 1.9371 0.2911

χ2ARCH 0.4805 0.4972 1.2214 0.2377

χ2WHITE 0.5231 0.8971 1.2996 0.0914

χ2REMSAY 1.0471 0.3542 0.8959 0.3764

n Significance at 1% level.


This development further validates the existence of long-runassociation between energy consumption, economic growth,financial development and CO2 emission in the case of Nigeria.With this discovery, the study proceed to investigate the long-runimpact of energy consumption, financial development, and eco-nomic growth on CO2 emissions. The results are reported inTable 6.

In financial development and energy consumption, the studydiscovered that the relationship between CO2 emissions andenergy consumption to be positive and it is statistically significantat 1%level of significance. Keeping other things constant, a 1%increase in CO2 emissions leads to energy consumption by0.0462%. In addition to that, financial development was furtherdiscovered to be having a positive and significant effect on energyconsumption in Nigeria. The results highlighted that 0.1% increasein financial development increases energy consumption by0.0593%, keeping other things constant. These findings are con-sistent with existing studies of Islam et al. [32], and Shahbaz et al.[80] and Mudakkar et al. [51] for Pakistan, Malaysia and Chinarespectively. In another development, the study discovered howeconomic growth is inversely related with energy consumption,and it is statistically significant at 1% significance level. Similarly, itwas discovered that a 1% increase in economic growth declinesenergy demand by 0.2621%. This empirical evidence contradictswith Shahbaz et al. [80]. Following to this, the study further dis-covered Trade openness to have a positive impacts on energyconsumption and is also found to be statistically significant at 1%.See also Rafindadi and Yusof [116−118]. This development is found

Table 7Short-run analysis.

Dependentvariable¼ Δ ln ECt Dependent variable

Variables Coefficient T-statistic Coefficient T-statistic

Constant 0.0027 1.0897 �0.0123 �0.4382Δ ln Ct 0.0340nn 2.6210 … …Δ ln ECt … … 4.9335n 3.0809Δ ln FDt 0.0057 0.4610 0.0045 0.0351Δ ln Yt �0.0662nnn �1.7498 0.9021nn 2.2953Δ ln TRt 0.0243nn 2.0057 �0.3033nn �2.2568ECMt�1 �0.2520nn �2.3764 �0.3688n �3.0205

R2 0.3128 0.3754

Adjusted�R2 0.2118 0.2835

F-statistic 3.0963nn 4.0874n

Diagnostic tests

Test F-statistic P-value F-statistic P-value

χ2NORMAL 2.0246 0.3615 4.1350 0.1264

χ2SERIAL 0.08271 0.9208 0.7259 0.4917

χ2ARCH 1.2708 0.2678 0.3827 0.5799

χ2WHITE 2.3205 0.1170 0.4152 0.9711

χ2REMSAY 2.0905 0.0429 1.6363 0.1113

n Significance at 1% level.

Table 8Chow breakpoint test.

Chow breakpoint test: 1989

F-statistic 1.9618 Prob. F(6, 28) 0.1054Log likelihood ratio 14.0371 Prob. Chi-Square (6) 0.0292Wald Statistic 11.7708 Prob. Chi-Square (6) 0.0673


to be consistent with Sadorsky [72−75] who also reported thattrade openness increases energy consumption.

In the case of the link between financial development and CO2

emissions, the study discovered that energy consumption ispositively linked with CO2 emissions and it is statistically sig-nificant at 1% level. As a result of this, a 1% increase in energyconsumption in Nigeria leads to a corresponding increase in CO2

emissions by 3.7574% by assuming other things remain the same.The relationship between financial development and CO2 emis-sions on the other hand, is found to be negative and is statisticallysignificant at 10% level. A 0.2080% decline in CO2 emissions islinked with 1% increase in financial development. In addition tothis, the study discovered that economic growth is positively andsignificantly linked with CO2 emissions in Nigeria. Following tothis a 1% increase in economic growth will increase CO2 emissionsby 1.7197%, assuming other things remain the same. This empiricalevidence supports the view reported by Shahbaz et al. [80,82,83].In contrast to this finding on CO2 emission, the study discoveredhow Trade openness decline CO2 emissions, and it is statisticallysignificant at 1% significance level. Furthermore a 0.5874% declinein CO2 emissions in Nigeria is linked with 1% increase in tradeopenness by keeping other things constant. To validate the con-tinuum of findings obtained in this study, it was discovered thatboth our long-run models have fulfilled the assumption of classicallinear regression model (CLRM). The study also found the evidenceof normal distribution with respect to the error term. There is noissue of serial correlation and same conclusion can be drawn forthe ARCH test. The residual term is homoscedastic and the func-tional form of long run model is well constituted.

The results of the short-run dynamics are presented in Table 7.In that table the study established the dynamics of the short-runrelationship between financial development and energy con-sumption, the results show that CO2 emissions have a positive andsignificant impact on energy consumption. Financial developmenthas a positive but statistically insignificant impact on energyconsumption. Economic growth declines energy consumption.

Trade openness has a positive relationship with energy con-sumption and it is statistically significant. In the case of financialdevelopment and CO2 emissions nexus, following to that, energyconsumption and economic growth were found to be positivelyand significantly linked with CO2 emissions. Financial develop-ment, on the other hand, was found to affect CO2 emissionspositively, and it is statistically insignificant. The relationshipbetween trade openness and CO2 emissions is also found to benegative and statistically significant at 5% level. The estimate ofECMt�1 is negative and statistically significant. The lagged of ECMis equivalent to �0.2520 and �0.3688 for energy demand and CO2

emissions functions respectively. The negative value of ECM istheoretically correct, which shows the speed of convergence fromshort-run toward long-run. It implies that short-run variations arecorrected in 2.1 years and 3.07 years for energy demand and CO2

emissions function respectively. The significance of lagged errorterm further confirms our ascertained long-run relationshipbetween the variables. The short-run model also provided satis-factory results with respect to the diagnostic tests carried out onthat analysis. The analysis from the test established the absence ofserial correlation and the same is true for autoregressive condi-tional heteroskedasticity. In addition to that there is no issue ofwhite heteroskedasticity while the functional form is discoveredto be well constructed.

In investigating the stability of the model, the study proceed toapply the Cumulative Sum (CUSUM) and the Cumulative Sum ofthe Squares (CUSUM sq). The stability test explains that the esti-mated model of energy demand is stable over the study timeperiod (Table 8).

The estimated graphs of the cumulative sum (CUSUM) and thecumulative sum of the squares (CUSUMsq) are presented inFigs. 1–4. Only graph CUSUMsq in financial development-energyconsumption function shows instability as it exceeded the criticalbounds. To confirm the stability, the study applied the Chowbreakpoint test which confirmed the absence of no break pointover the selected period of time. This shows that our estimatedmodel is a good fit.

The presence of cointegration between energy consumption,financial development, economic growth, trade openness and CO2

emissions lead us to test the causality for the said variables byapplying the framework of VECM Granger causality. The VECMGranger causality divides causality results into long run as well asthe short-run. The results regarding the VECM Granger causalitytest are reported in Table 9.

In the long run, Granger causality analysis reveals the feedbackeffect between energy consumption and CO2 emissions in the caseof Nigeria. Following to this, it was discovered that financialdevelopment Granger-causes CO2 emissions (energy consumption)and as a result, CO2 emissions (energy consumption) Grangercause financial development. The relationship between economicgrowth and energy consumption is detected to be bidirectional,and the same inference is found between economic growth andCO2 emissions. There is a bidirectional causal relationship thatexists between financial development and economic growth.

-20-15-10

-505

101520

1980 1985 1990 1995 2000 2005 2010

CUSUM 5% Significance

The straight lines represent critical bounds at 5% significance level

Fig. 1. Plot of cumulative sum of recursive residuals. Financial development-energy consumption function in Nigeria. The straight lines represent critical bounds at 5%significance level.

-0.4-0.20.00.20.40.60.81.01.21.4

1980 1985 1990 1995 2000 2005 2010

CUSUM 5% Significance


Fig. 2. Plot of cumulative sum of squares of recursive residuals. The straight lines represent critical bounds at 5% significance level.

-20-15-10

-505

101520

1980 1985 1990 1995 2000 2005 2010CUSUM 5% Significance


Fig. 3. Plot of cumulative sum of recursive residuals. Financial development-CO2 emissions function in Nigeria. The straight lines represent critical bounds at 5% significance level.

1980 1985 1990 1995 2000 2005 2010CUSUM 5% Significance


-0.4-0.20.00.20.40.60.81.01.21.4

Fig. 4. Plot of cumulative sum of squares of recursive residuals. The straight lines represent critical bounds at 5% significance level.


Table 9The VECM Granger causality analysis.

Variables Direction of Granger causality

Short-run Long Run

Δ ln Ct Δ ln ECt Δ ln FDt Δ ln Yt Δ ln TRt ECTt�1

Δ ln Ct�1 …. 6.5005n [0.0047] 0.0923 [0.9120] 5.9739n [0.0067] 2.9060nnn [0.0707] -0.5150n [-3.6933]Δ ln ECt�1 3.4245nn [0.0467] …. 0.1044 [0.9011] 4.4702nnn [0.1028] 1.3221 [0.2827] -0.3475nn [-2.5087]Δ ln FDt�1 2.7025nnn [0.0845] 0.9156 [0.4119] …. 1.0853 [0.3516] 6.9829n [0.0035] -0.4166n [-3.6265]Δ ln Yt�1 4.7380nn [0.0169] 1.8141 [0.1816] 1.9080 [0.1672] …. 3.8557nn [0.0335] -0.4995n [-2.8959]Δ ln TRt�1 3.2269nn [0.0543] 1.1981 [0.3163] 2.7915nnn [0.0778] 2.0040 [0.1530] …. ….

NB: Short-run is defined to be within the range of 1–5 years and long-run is from 6 years and aboven Significance at 1%, level.nn Significance at 5% level.nnn Significance at 10% level.


Trade openness Granger causes energy consumption, CO2 emis-sions, financial development and economic growth.

The short-run result on the other hand, reveals the existence ofa feedback effect between energy consumption and CO2 emissionsin Nigeria. As a result of this, financial development in Nigeria wasfound to be the Granger cause of CO2 emissions. In another relateddevelopment, the study discovered the existence of bidirectionalcausality between trade openness and CO2 emissions but uni-directional causality was also found running from trade opennessto energy consumption. Trade openness Granger causes energyconsumption, and the same is true from financial development totrade openness. Economic growth Granger causes energy con-sumption and CO2 emissions but CO2 emissions were found toGranger cause economic growth.

Following the investigation of the causal relationshipbetween energy consumption, CO2 emissions, financial devel-opment, economic growth and trade openness using the VECMGranger causality method. It is evident that the VECM analysiscannot provide vital information with respect to the effects ofeconomic shocks and how it may affect the respective variablesunder survey. In essence, the variance decomposition method(VDM) is responsible in pointing out the exact degree withrespect to the feed effects from one variable due to innovativeshocks occurring in another variable over the various time hor-izons. For this purpose, the innovative accounting approach isapplied (see Rafindadi and Ozturk [65]). The innovativeaccounting approach is the combination of variance decom-position and the impulse response function. The results of thevariance decomposition method are reported in Table A1 (seeAppendix A). In that analysis, the study discovered how a 75.69%of CO2 emissions to be contributed by its own innovative shocksand one standard deviation shock in energy consumption,financial development, economic growth and trade opennessexplain CO2 emissions by 15.81%, 0.89%, 2.85% and 4.74%respectively. The contribution of CO2 emissions, financialdevelopment, economic growth and trade openness in energyconsumption is 25.38%, 5.56%, 6.37% and 7.46% respectively,while 55.20% of energy consumption is contributed by its owninnovative shocks. CO2 emissions and energy consumptioncontribute to financial development minimally. Economicgrowth and trade openness is found to contribute 15.13% and29.95% respectively while the rest contribute to financialdevelopment by its own innovative shocks.

Energy consumption and trade openness on the other hand,contribute to economic growth by 58.28% and 22.61% respectively

and the contribution of CO2 emissions and financial developmentthat explain economic growth is very minimal i.e. 2.52% and 0.39%respectively. A 34.54% in trade openness is explained by its ownstandard innovative shocks and the rest is explained by CO2

emissions, energy consumption, financial development and eco-nomic growth and that is 5.13%, 46.88%, 1.67% and 11.75% respec-tively. Overall, the study discovered the existence of bidirectionalcausality between CO2 emissions and energy consumption but CO2

emissions cause energy consumption dominantly. The unidirec-tional causality is found running from energy consumption andeconomic growth to financial development. Economic growth iscaused by energy consumption and trade openness. The uni-directional causality is found running from energy consumption totrade openness.

The impulse response function indicates the reaction ofdependent variables due to shock arising in the independentvariables in the model (see Fig. A1 in Appendix A). The impulseresponse function is a mirror of the variance decompositionapproach. The study found that the response in CO2 emissionsdue to forecast error stemming from energy consumption initi-ally rises and then starts to decline after 6th time horizon. CO2

emissions respond positively after 6th and 7th due to forecastingerror stemming from economic growth and trade openness. Theresponse in CO2 emissions is fluctuating due to forecasting errorin financial development. Energy consumption reacts like fluc-tuations due to shocks arising in CO2 emissions, financialdevelopment, economic growth and trade openness. Theresponse in financial development is positive due to forecasterror in economic growth and trade openness but forecastingerror in energy consumption declines financial development.The forecasting error in energy consumption was found to have apositive response in economic growth which is due to forecast-ing error in financial development and trade openness. Eco-nomic growth responds positively and negatively before 7th andafter 7th time horizon due to forecasting error in CO2 emissions.The forecasting error occurs in CO2 emissions. Energy con-sumption and economic growth affect trade openness negativelyand positively respectively.

5. Conclusion and policy implications

This paper investigated the impact of financial development onenergy consumption and CO2 emissions using annual data over theperiod of 1971–2011 for the case of Nigeria. In doing so, the study


applied the Zivot–Andrews unit root test, and the Bayer–Hanckcombined cointegration test. The dynamic relationship that existbetween the variables is tested by applying the ARDL boundstesting approach to cointegration in the presence of structuralbreaks. The VECM Granger causality test, on the other hand, isapplied to test the direction of causality between the variables,and the robustness of this causal analysis was validated using theinnovative accounting approach (IAA). The results of the studyconfirmed the existence of cointegration relationship between thevariables. Following to this, the study discovered how financialdevelopment stimulates energy demand and lowers CO2 emissionsin the case of Nigeria, and the existence of the inverted-U shapedrelationship between economic growth and environmental qualityis also confirmed in this study. In addition to that, it was dis-covered that economic growth lowers energy demand butincreases CO2 emissions. Trade openness also increases energyconsumption but improves environmental quality by lowering CO2

emissions. The study also found how energy consumptionincreases CO2 emissions, and CO2 emissions were also found tolead to the escalation of energy demand.

The causality analysis, on the other hand, disclosed the exis-tence of bidirectional causal association linking financial devel-opment and energy consumption, and similar inference is found toexist between financial development and CO2 emissions. Thestudy also discovered trade openness to Granger cause energydemand i.e. trade-led energy hypothesis and CO2 emissions i.e.trade-led CO2 emissions hypothesis. The feedback effect is alsofound between economic growth and CO2 emissions, and the sameinference is drawn for economic growth and energy consumption.The study further discovered how economic growth and carbonemissions are interrelated in the case of Nigeria. In addition, tothat financial development is discovered to be the Granger causeof all respective increases in energy consumption, energy pollu-tants, economic growth and trade openness. These findings inferthat carbon emissions may be limited at the expense of economicdevelopment, or energy efficient technologies have to be pro-moted to improve environmental quality. In another perspective,the finding that financial development Granger causes energyconsumption as discovered in this study is suggesting that theadoption of energy conservation policies in Nigeria will notadversely affect economic growth (in the Granger sense). Follow-ing to these discoveries, the study argues for the need of thefinancial sector to concentrate loanable funds to those productionentities that implement ecologically friendly technologies in theirproduction activities. The increasing inclination of carbon emis-sions in the present momentum is an arguable matter in the caseof Nigeria. To address this contentious matter, the study point tothe fact that there should be the need for reforms in the area offinancial, energy and economic growth. This is in the sense that,regulations should have to be enacted that should reflect the needfor the reduction of CO2 emission in the country.

To support the direction of the claims made above with sta-tistical indicators, the study discovered that any 1% rise in CO2

emissions was found to lead to the escalation of energy con-sumption by 0.0462%. Similar to this line of development, a rise inNigeria’s financial development, was on the other hand discoveredto produce an increase in energy consumption by 0.0593%. Sur-prisingly, a 1% decline in financial development was found toresult in the reduction of CO2 emissions by 0.2080%. However, any1% rise in the country’s economic growth will tend to increase thetrend of CO2 emission by 1.7197%. Although energy consumptionhas an inverse relationship with Nigeria’s economic growth pro-spects, in that relationship, it was detected that a 1% increase in

economic growth in Nigeria declines energy demand by 0.2621%.Parallel to this development, a 1% rise in Nigeria’s energy con-sumption, was found to result in the corresponding effect of pro-ducing 3.757% of CO2 emissions. This figure is very high, and itclearly shows the effects of oil spillage that exist in the country’senergy sector, and the high level of fossil fuel consumption bypower generating plants, and as used by other means of trans-portations in the country. Going by this high rise in CO2 emissions,Gowdy [103] cautioned that, in any given society in the world, thesocial welfare of the populace in that society should be prima facieamong all other national objectives. The author in addition to theforegoing, point out that the continuous accrual of income percapita at the expense of societal welfare particularly amidst highenvironmental degradation is an impingement on the social con-tract theory. As a result of this development, the focus on allgovernment policies should be on improving societal welfare in allmaterial respect. Complimenting this assertion, Di Tella andMacCulloch [102] emphasized that environmental degradationthrough CO2 emission or by any other means reduce life satisfac-tion, and is a means of human welfare deterioration that reduceslife expectancy. This view was also maintained by Ang [10] who, inhis empirical wisdom, argued that there is growing evidence thatenvironmental degradation does not only have a negative impacton the quality of life in a society through reduced health, but italso affects the economy significantly by impeding the quality ofhuman capital which in the long-run retards national productivity.

In another line of development, and from the findings estab-lished by this study it was discovered that Trade openness declinesCO2 emissions in Nigeria. This finding establishes that a 0.5874%decline in CO2 emissions in the country is linked with 1% increasein Trade openness. This signifies that Trade openness in Nigeria isan omen to the country’s environmental and economic growthprospects. In a related view, the finding heralds to the need for asignificant boost in the Nigerian export and import sector con-sidering it as a means that could reduce CO2 emission, and theenhancement in entrepreneurial productivity. According to Adom[101], the significant contributions of trade openness is adjudgedto have dual impacts to an economic system, and these are con-ceptualized in the form of: ‘the pull effect of trade openness’ andthe ‘the push effect of trade openness’ The concept of the pulleffect, guide and directs on the existence of those possibilities thata host country could avail its self by innovatively devising thoseskills to identify an existing gap in an international product andthereby, imitate it by learning its structural composition andimproving on that. The aim is to produce a renewed version of theproduct locally with better enhancements. The second conceptualargument (push effect) establish how a well-coordinated, econ-omy could use its beneficial position of trade openness to createcompetitive environments essentially meant to influence localentrepreneurs to opt for the use of energy efficient technologiesthat have a balance between energy consumption and CO2

reduction. This development was in addition to making theirproducts and production process to be more adept to changinginternational competitiveness.

In this study, it was discovered that the higher the degree ofeconomic growth in Nigeria the greater will be the rise in CO2

emission and the higher the energy consumption in the countrythe more worst the position of CO2 emission. The implication ofthese findings for the Nigerian economy and the environment canbe seen in two angles (i) at the current stage of economic growth(ii) when the country attained an enviable degree of economicgrowth. Following to this development, and in consideration ofNigeria’s struggle to achieve sustainable economic growth, the


need to reduce if not to eradicate the effects of CO2 emission isforemost a factor of great concern in a country with a populationof over 170 million. To mitigate these effect the present studyargue for the adoptions of those policies and strategies that willensure the use of green energy technologies at the industrial level,promoting the diffusion and use of energy efficient appliances,introduction of the Nigerian Renewable Energy (RE) Act, andlaunch a road map to increase the contribution of RE to the energymix of the country. The study believes that this will put a stoppageon energy wastage, over consumption and promote the practice ofsignificant energy efficiency use and conservation. Apart from theabove policy options, energy authorities in Nigeria could alsoembark on a penalty-based approach aimed at encouraging pru-dential energy use in the country. This can be ensured by makingthe government to promulgate a policy for clean energy use. Thisdevelopment if implemented in Nigeria will not only palliate theNigeria’s energy predicaments in the contemporary time but willequally ensure a balance between energy usage and environ-mental quality attainment through a significant reduction in CO2emission from the fossil fuel. This initiative will equally help incosts saving, and at the same time serving to sustain the welfareposition of the Nigerian citizens through a reduction in carbondioxide emission. While ensuring the accomplishment of thesepolicy options, this study is of the believe that the precedingarguments will assist with the simultaneous satisfaction of effi-cient and balanced energy production and usage by consumers.

Apart from the preceding development, the study also arguesfor the need of the country to enforce a taxation system orfinancial levy for those energy consumption outlets that exceed astandardized level of environmental pollutants. In contrast to that,subsidies may be provided for those industrial outlets that have norecord of emitting environmental pollutants in Nigeria. In additionto the aforementioned, the study is of the view that the ministry ofenvironment in conjunction with key law enforcement agencies inNigeria should formulate some means to regulate those produc-tion entities known to have a significant record of environmentalpollution by limiting their pollutants below a prescribed policytarget and specifying the maximum expected industrial emissionthrough the adoption of key accepted standards to be observednationwide. Apart from that, the fossil fuel quality should also bestandardized within a minimum quality range that is acceptablefor industrial usage in the country. Equally important is the needfor Nigeria to review its energy subsidy program to ensure thatdomestic energy prices reflect world market prices. The low priceof energy tantamount to high energy consumption and this will, inturn, lead to high CO2 emissions and other contaminants in thesociety.

To ensure the attainment of these objectives unilaterally is aherculean task considering the economic and financial turmoil thecountry is facing particularly with the drastic fall in oil prices.Notwithstanding this development, Nigeria should not afford tosee a continued decline in its economic growth prospects as thiswill continue to impede on its regional economic leadership. Fol-lowing to this, the study advocates policy actions that will re-direct massive foreign direct investment into the Nigerianrenewable energy sector. In addition to that, there is a need forpotent policies and practices that promote energy efficiencies aswell as the utilization of those equipment and products that are

congenial to the densely populated environment in Nigeria. Thiscan be attained through the creation of awareness on renewableenergy use and the establishment of key agencies that will becharged with the responsibilities of propagating the use of energyefficient practices. This can also be ensured by funding andtraining relevant departments in Universities, and Polytechnicsto aid in renewable energy project development, management,monitoring, evaluation and implementation in the country. Over-all, the implications of these findings to the Nigerian economy isthat it will continue to pose tremendous effects to the country’smacroeconomic development, thereby, constricting the possibi-lities of economic growth in the long-run particularly if oil con-sumption is reduced. This is because as industrial outputs arethreatened by insufficient and inefficient energy, the phenomenonwill undoubtedly lead to the decline in competitive industrialwherewithal. To support this claim, the study discovered theexistence of bidirectional causality between CO2 emissions andenergy consumption but CO2 emissions cause energy consumptiondominantly. The unidirectional causality is found running fromenergy consumption and economic growth to financial develop-ment. Economic growth is caused by energy consumption andtrade openness. Unidirectional causality was also detected fromenergy consumption to trade openness. This situation suggeststhat any attempt to curtail energy will greatly hamper Nigeria’seconomic growth prospects. To further mitigate these side effects,the study still insist and emphasized on a significant andresponsible investment in the exploration and exploitation of thenation’s renewable and sustainable energy reserves as theonly hope.

In conclusion, it is imperative to argue that energy is a crucialpart of production factor in the contemporary era and a cardinalmeans of achieving sustainable and veritable economic growth.Following to this, energy in Nigeria should be seen in parallel withland, labor and capital. To ensure an endless search on the wayforward of attaining energy sufficiency and efficiency in Nigeria,this study has identifies some key limitations of this study andthose that requires further investigations: these limitations anddirection for future research ranges on identifying and consideringalternative sources of energy that are connected with economicgrowth such as renewable energy, coal and natural gas con-sumption, biomass energy, etc. in addition to that, there is theneed to examine energy consumption (kg of oil equivalent percapita for the case of Nigeria using sectorial data to analyse therelationship between energy consumption, energy efficiency andenergy usage among key economic and non economic sectors ofthe country). Similar to that line of development, other studies canfocus on using different methodological approaches. Although wehave applied the most parsimonious models in this study that areup to date when it comes to time series studies, howeverintending researchers can focus on a panel or cross-sectional datato see the impact from different types of energy use on Nigeria’seconomic growth perspective. To the best of my understanding,these areas are seriously lacking for the case of Nigeria. Finally,future studies could also focus on various sources of environ-mental pollutants as against CO2 emissions that is used inthis study.


Appendix A

See Table A1 and Fig. A1.

Table A1Variance decomposition approach.

Period S.E. ln Ct ln ECt ln FDt ln Yt ln TRt

Variance decomposition of1 0.2010 100.0000 0.0000 0.0000 0.0000 0.00002 0.2678 94.7234 4.0596 0.0973 0.4400 0.67943 0.3066 92.1924 3.3564 0.1853 0.4157 3.84994 0.3133 91.4600 3.5927 0.2576 0.4131 4.27625 0.3197 91.2611 3.4699 0.2475 0.6222 4.39916 0.3206 91.0115 3.4640 0.5025 0.6461 4.37567 0.3234 89.9045 4.5560 0.5055 0.6861 4.34768 0.3269 88.1433 5.7692 0.7652 1.0167 4.30539 0.3324 85.3327 8.4044 0.7434 1.2706 4.2486

10 0.3376 83.2165 10.0529 0.8175 1.6404 4.272311 0.3421 81.1480 11.8824 0.7967 1.8411 4.331612 0.3460 79.5975 12.9614 0.8562 2.1836 4.401113 0.3495 78.0570 14.1865 0.8467 2.4181 4.491414 0.3526 76.8541 14.9537 0.9007 2.6975 4.593815 0.3554 75.6996 15.8108 0.8932 2.8517 4.7444

Variance decomposition of ln ECt

1 0.0162 34.2634 65.7366 0.0000 0.0000 0.00002 0.0217 36.3014 60.0493 1.2081 0.7289 1.71203 0.0241 30.6389 62.1344 2.6588 1.1659 3.40194 0.0252 28.9550 59.7882 2.5680 2.9159 5.77265 0.0262 27.6358 57.6499 2.9437 4.1717 7.59866 0.0273 26.9076 54.3305 4.9645 5.8745 7.92277 0.0278 26.0736 54.6022 5.4019 6.2703 7.65188 0.0281 25.5067 54.7478 5.7936 6.2279 7.72389 0.0285 25.4477 55.0567 5.6351 6.0618 7.7985

10 0.0287 25.4729 55.3078 5.5410 5.9614 7.716711 0.0289 25.2887 55.6900 5.4916 5.8961 7.633312 0.0291 25.1047 55.7548 5.4608 5.9997 7.679813 0.0293 25.1242 55.6551 5.4363 6.1415 7.642714 0.0296 25.3709 55.2657 5.5389 6.3039 7.520315 0.0297 25.3878 55.2048 5.5680 6.3705 7.4687

Variance decomposition of ln FDt

1 0.1713 1.9228 4.1373 93.9397 0.0000 0.00002 0.2170 10.4838 5.4316 68.8057 5.0840 10.19463 0.2547 22.3033 5.0391 50.4546 7.2338 14.96904 0.3063 19.7248 9.5647 36.3502 21.3478 13.01235 0.3852 14.1902 20.7953 23.2254 24.5178 17.27116 0.4540 10.9861 26.7451 17.1299 22.1673 22.97147 0.5283 9.4134 31.9400 12.6501 18.4633 27.53308 0.5991 9.1028 34.7483 9.8839 15.9310 30.33379 0.6590 8.6309 37.5058 8.2483 14.7080 30.9067

10 0.7111 7.4919 40.3122 7.0846 14.8465 30.264711 0.7638 6.5712 42.6227 6.1483 15.2636 29.394012 0.8162 6.1039 43.9527 5.4373 15.5939 28.912113 0.8683 5.7019 44.8668 4.8466 15.5908 28.993614 0.9180 5.2786 45.4791 4.3681 15.4108 29.463115 0.9638 4.8589 46.0770 3.9688 15.1374 29.9575

Variance decomposition of ln Yt

1 0.0709 7.8064 44.2742 0.5626 47.3566 0.00002 0.1142 4.8086 50.1019 0.4444 36.3051 8.33983 0.1430 3.9214 54.8675 1.3624 30.9174 8.93114 0.1752 3.0803 57.5622 1.0527 26.2544 12.05025 0.2053 4.5876 56.9521 0.7681 21.8385 15.85346 0.2295 4.9137 56.9485 0.6310 19.7382 17.76847 0.2537 4.4432 58.0250 0.5312 18.7690 18.23148 0.2766 3.7368 58.7244 0.5070 18.4401 18.59159 0.2990 3.2971 59.2239 0.4941 18.0937 18.8909

10 0.3211 3.1192 59.1295 0.5089 17.7587 19.483511 0.3421 2.9299 58.9908 0.4903 17.3014 20.287312 0.3613 2.7906 58.7516 0.4739 16.9290 21.054713 0.3790 2.6559 58.6295 0.4423 16.5928 21.679314 0.3948 2.5798 58.4522 0.4171 16.3612 22.189515 0.4088 2.5262 58.2867 0.3928 16.1766 22.6175

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14

Response of lnC to lnEC

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14

Response of lnC to lnF

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14

Response of lnC to lnY

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14

Response of lnC to lnTR

-.01

.00

.01

.02

2 4 6 8 10 12 14

Response of lnEC to lnC

-.01

.00

.01

.02

2 4 6 8 10 12 14

Response of lnEC to lnF

-.01

.00

.01

.02

2 4 6 8 10 12 14

Response of lnEC to lnY

-.01

.00

.01

.02

2 4 6 8 10 12 14

Response of lnEC to lnTR

-.4

-.2

.0

.2

.4

2 4 6 8 10 12 14

Response of lnF to lnC

-.4

-.2

.0

.2

.4

2 4 6 8 10 12 14

Response of lnF to lnEC

-.4

-.2

.0

.2

.4

2 4 6 8 10 12 14

Response of lnF to lnY

-.4

-.2

.0

.2

.4

2 4 6 8 10 12 14

Response of lnF to lnTR

-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14

Response of lnY to lnC

-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14

Response of lnY to lnEC

-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14

Response of lnY to lnF

-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14

Response of lnY to lnTR

-.3

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14

Response of lnTR to lnC

-.3

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14

Response of lnTR to lnEC

-.3

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14

Response of lnTR to lnF

-.3

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14

Response of lnTR to lnY

Response to Generalized One S.D. Innovations

Fig. A1. Impulse response function.

Table A1 (continued )

Period S.E. ln Ct ln ECt ln FDt ln Yt ln TRt

Variance decomposition of ln TRt

1 0.1788 1.1066 6.3066 5.8159 11.0881 75.68252 0.2619 2.6560 18.4255 4.6714 7.87919 66.36773 0.3547 5.9140 31.4282 4.3441 11.6866 46.62684 0.4453 5.3312 43.9144 2.8948 10.3628 37.49655 0.4947 6.5639 45.6831 2.3572 9.72491 35.67076 0.5282 6.0249 47.6837 2.3735 9.37811 34.53967 0.5512 5.9388 47.6235 2.1831 10.1814 34.07308 0.5717 5.7145 47.9944 2.0302 10.9266 33.33419 0.5933 6.1247 47.3823 2.0647 11.8560 32.5721

10 0.6136 6.0228 47.3890 1.9453 12.0292 32.613511 0.6307 5.8545 46.9993 1.8689 11.9961 33.280912 0.6460 5.5834 46.9175 1.7998 11.7486 33.950513 0.6579 5.3850 46.7949 1.7398 11.6683 34.411914 0.6683 5.2220 46.8935 1.7144 11.6334 34.536515 0.6769 5.1363 46.8840 1.6710 11.7587 34.5498



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