i Gusau Journal of Accounting and Finance (GUJAF) Vol. 4 Issue 2, October, 2023 ISSN: 2756-665X A Publication of Department of Accounting and Finance, Faculty of Management and Social Sciences, Federal University Gusau, Zamfara State -Nigeria ii © Department of Accounting and Finance, 2023 Vol. 4 Issue 2 October, 2023 ISSN: 2756-665X A Publication of Department of Accounting and Finance, Faculty of Management and Social Sciences, Federal University Gusau, Zamfara State -Nigeria All Rights reserved Except for academic purposes no part or whole of this publication is allowed to be reproduced, stored in a retrieval system or transmitted in any form or by any means be it mechanical, electrical, photocopying, recording or otherwise, without prior permission of the Copyright owner. Published and printed by: Ahmadu Bello University Press Limited, Zaria Kaduna State, Nigeria. Tel: 08065949711, 069-879121 e-mail: abupress2013@gmail.com abupress2020@yahoo.com Website: www.abupress.com.ng mailto:abupress2013@gmail.com iii EDITORIAL BOARD Editor-in-Chief: Prof. Shehu Usman Hassan Department of Accounting, Federal University of Kashere, Gombe State. Associate Editor: Dr. Muhammad Mustapha Bagudo Department of Accounting, Ahmadu Bello University Zaria, Kaduna State. Managing Editor: Umar Farouk Abdulkarim Department of Accounting and Finance, Federal University Gusau, Zamfara State. Editorial Board Prof.Ahmad Modu Kumshe Department of Accounting, University of Maiduguri, Borno State. Prof Ugochukwu C. Nzewi Department of Accounting, Paul University Awka, Anambra State. Prof Kabir Tahir Hamid Department of Accounting, Bayero University, Kano, Kano State. Prof. Ekoja B. Ekoja Department of Accounting, University of Jos. Prof. Clifford Ofurum Department of Accounting, University of PortHarcourt, Rivers State. Prof. Ahmad Bello Dogarawa Department of Accounting, Ahmadu Bello University Zaria. Prof. Yusuf. B. Rahman Department of Accounting, Lagos State University, Lagos State. Prof. Suleiman A. S. Aruwa Department of Accounting, Nasarawa State University, Keffi, Nasarawa State. Prof. Muhammad Junaidu Kurawa iv Department of Accounting, Bayero University Kano, Kano State. Prof. Muhammad Habibu Sabari Department of Accounting, Ahmadu Bello University, Zaria. Prof. Okpanachi Joshua Department of Accounting and Management, Nigerian Defence Academy, Kaduna. Prof. Hassan Ibrahim Department of Accounting, IBB University, Lapai, Niger State. Prof. Ifeoma Mary Okwo Department of Accounting, Enugu State University of Science and Technology, Enugu State. Prof. Aminu Isah Department of Accounting, Bayero University, Kano, Kano State. Prof. Ahmadu Bello Department of Accounting, Ahmadu Bello University, Zaria. Prof. Musa Yelwa Abubakar Department of Accounting, Usmanu Danfodiyo University, Sokoto State. Prof. Salisu Abubakar Department of Accounting, Ahmadu Bello University Zaria, Kaduna State. Prof. Isaq Alhaji Samaila Department of Accounting, Bayero University, Kano State. Dr. Fatima Alfa Department of Accounting, University of Maiduguri, Borno State. Dr. Sunusi Sa'ad Ahmad Department of Accounting, Federal University Dutse, Jigawa State. Dr. Nasiru A. Ka’oje Department of Accounting, Usmanu Danfodiyo University Sokoto State. Dr. Aminu Abdullahi v Department of Accounting, Usmanu Danfodiyo University Sokoto, State. Dr. OnipeAdebenege Yahaya Department of Accounting, Nigerian Defence Academy, Kaduna State. Dr. Saidu Adamu Department of Accounting, Federal University of Kashere, Gombe State. Dr. Nasiru Yunusa Department of Accounting, Ahmadu Bello University Zaria. Dr. Aisha Nuhu Muhammad Department of Accounting, Ahmadu Bello University Zaria. Dr. Lawal Muhammad Department of Accounting, Ahmadu Bello University Zaria. Dr. Farouk Adeza School of Business and Entrepreneurship, American University of Nigeria, Yola. Dr. Bashir Umar Farouk Department of Economics, Federal University Gusau, Zamfara State. Dr Emmanuel Omokhuale Department of Mathematics, Federal University Gusau, Zamfara. State vi ADVISORY BOARD MEMBERS Prof. Kabiru Isah Dandago, Bayero University Kano, Kano State. Prof A M Bashir, Usmanu Danfodiyo University Sokoto, Sokoto State. Prof. Muhammad Tanko, Kaduna State University, Kaduna. Prof. Bayero A M Sabir, Usmanu Danfodiyo University Sokoto, Sokoto State. Prof. Aliyu Sulaiman Kantudu, Bayero University Kano, Kano State. Editorial Secretary Yazid Ibrahim Kabir Department of Accounting and Finance, Federal University Gusau, Zamfara State. vii CALL FOR PAPERS The editorial board of Gusau Journal of Accounting and Finance (GUJAF) is hereby inviting authors to submit their unpublished manuscript for publication. The journal is published in two issues of April and October annually. GUJAF is a double-blind peer reviewed journal published by the Department of Accounting and Finance, Faculty of Management and Social Sciences, Federal University Gusau, Zamfara State Nigeria The Journal accepts papers in all areas of Accounting and Finance for publication which include: Accounting Standards, Accounting Information System, Financial Reporting, Earnings Management, , Auditing and Investigation, Auditing and Standards, Public Sector Accounting and Auditing, Taxation and Revenue Administration, Corporate Governance Issues, Corporate Social Responsibility, Sustainability and Environmental Reporting Issue, Information and Communication Technology Issues, Bankruptcy Prediction, Corporate Finance, Personal Finance, Merger and Acquisitions, Capital Structure, Working Capital Management, Enterprises Risk Management, Entrepreneurship, International Business Accounting and Finance, Banking Crises, Bank’s Profitability, Risk and Insurance Issue, Islamic Finance, Conventional and Islamic Banks and so forth. 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PAYMENT DETAILS Bank: FCMB Account Number: 7278465011 Account Name: Gusau Journal of Accounting and Finance FOR INQUIRY The Head, Department of Accounting and Finance, Federal University Gusau, Zamfara State. elfarouk105@gmail.com +2348069393824 FOR MORE INFORMATION, CONTACT The Editor-in-Chief on +2348067766435 The Associate Editor on +2348036057525 OR visit our website on www.gujaf.com.ng or journals.gujaf.com.ng http://www.gujaf.com.ng/ http://www.gujaf.com.ng/ ix CONTENTS Board Characteristics and Financial Performance: Evidence from Listed Deposit Money Banks in Nigeria 1 Abdullahi Bala Ado, Norfadzilah Nik Mohd Rashid, Sa’adatu B. Adam, Binta Abubakar Nuhu, Hassanat Salawu Salihu and Tariro Masunda Welfare, Inflation, and Pension Income Inequality Among the Bottom and Top Income Quintiles and Decile: An Implication of Kaduna State Pension Reform 18 Prof. Salamatu I. Isah, Ibrahim Kekere Sule (PhD) Political Connection, Audit Fees, Audit Quality, and Tax Avoidance 31 Novita Dwi Damayanti, M KhoiruRusydi, WuryanAndayani Firm Attributes and Shareholder’s Wealth of Listed Deposit Money Banks in Nigeria 47 A.A. Mustapha, Prof. M.S. Tijjani, S. Salami PhD Financial Determinants of Entrepreneurship in Nigeria 67 Precious Adukwu, Hyeladi Stanley Dibal Work Environment, Remuneration and Accounting Lecturers’ Performance in Polytechnics in North West, Nigeria 88 Dr. Aliyu Abdullahi Ahmed, Rabiatu Ahmed Relative Efficiency of the Capital Market Over the Money Market in a Growth-Financing Economy 110 Adedeji Daniel Gbadebo Board Education, Director's Age and Earnings Management of Listed Deposit Money Banks in Nigeria 131 Idris IbrahimPhD, Prof. Luka Mailafia, Salami Suleiman PhD Ownership Concentration’s Moderating Effect on Dividend Payout And Tobin’s Q in the Nigerian Consumer Goods Sector. 149 Ovbe Simon Akpadaka x Foreign Direct Investment, Renewable Energy and Economic Growth: An Empirical Analysis from South Africa. 167 Ahmed Oluwatobi Adekunle Impact of Digital Financial Services on Savings Development in Nigeria 182 Iro, Onyinyechi Adanna, Eke, Patrick Omoruyi, Yunisa, Simon Amodu, Shekoni, Nurudeen Adebayo Account Receivable Management and Financial Performance of Listed Consumer Goods Firms in Nigeria 209 Umar Suleiman Abubakar Dabai, Biyai Shepnaan, Hajara Abubakar Jimoh, Haruna Halimah Sani Sambo PhD Stable Dividend Policy and Value of Listed Healthcare Firms in Nigeria 227 Maimuna Adamu Salihu, Aminu Danladi Ahmad, Zaharaddeen Salisu Maigoshi, Naja'atu Bala Rabiu The Impact of Monetary Policy on Small and Medium Scale Enterprises (SMES) in the Period of Economic Crises. 240 Ahmed Oluwatobi Adekunle. CEO Age and Gender on Financial Distress Likelihood of Listed Deposit Money Banks in Nigeria: Moderated by Risk Committee Gender 254 Idris Mohammed, Joshua Okpanachi, OnipeAdabenege Yahaya, Suleiman Tauhid 167 FOREIGN DIRECT INVESTMENT, RENEWABLE ENERGY AND ECONOMIC GROWTH: AN EMPIRICAL ANALYSIS FROM SOUTH AFRICA Ahmed Oluwatobi Adekunle Department of Accounting Science Kwara State University, Malete1 Walter Sisulu University, South Africa2 Abstract This paper explores the connection amid renewable energy, FDI and economic growth in South Africa using the vector error correction model (VECM) and exogeneity granger causality test. The data employed in this study covered 1990-2020, all obtainable from World development indicator (WDI) and International Energy Agency (IEA). The results revealed that a significant causal connection from economic growth to renewable energy is observed over the long-run while renewable energy does not granger cause economic growth, a uni-directional causality exist amid the economic growth and renewable energy. The study recommends that the government should offer some sort of help to make sure organizations or companies are adequately influenced to depend largely on renewable energy which will facilitate ecological friendly system in the country. Keywords: FDI, Renewable energy, Growth, South Africa, VECM 1. Introduction Climate change has become one of the most urgent issues facing the planet, particularly in recent decades. Human-caused carbon-emissions from energy creation and usage are the primary factor causing climate change, mostly CO2 emissions are attributable to the usage of non-renewable elements such as coal and fossil fuels facedrise in usage in recent time. Since 1755, 399.8 billion tonnes of CO2 released to the environment as a result of usage of cement production and usage of fossil fuels in production activities (IEA, 2016). According to Can et al. (2019), significant level of this CO2has emerged in the meantime, 1980s.However, one of the most certain means of attaining the global climate targets is through the use of renewable energy. Renewable energy (REn), as opposed to non-renewable fuels, improves the environment, the economy, and energy security (IEA, 2016).Energy resources must be consumed in order for nations to experience economic growth (Y). Research on climate change should therefore take into account the connection amid CO2, energy consumption (including total energy, non-renewable energy (NREn) and REn, and economic growth (EcGr). DOI: https://doi.org/10.57233/gujaf.v4i2.10 168 Many study subjects in the empirical work on energy finance make use of econometric techniques such as cointegration, causation, and unit root. According to the Environmental Kuznets Curve (EKC) theory, ecologicaldilapidation rises along with economic advancement until it crosses a certain edge, at which point it falls (Adekunle et al., 2023; Bekun et al., 2023).The second is the Pollution Halo Hypotheses (PHH), which looks at how CO2 and foreign direct investment are related. According to the PHH, wealthy nations with strong environmental policies gravitate towards those with lax environmental laws (Zeraibiet al., 2019; AlNemer et al., 2023). On the other hand, the PHH contends that FDI results in a cleaner environment since advanced technology and superior management are transferred to the host country (Ahmed et al., 2019; Djellouli et al., 2023; Pao et al., 2013). 2. Empirical Review Several advanced econometric methods employed in several studies in the energy finance to examine the connection among NREnREn, urbanization, EcGr and FDI. Yet, there are stock-still no conclusive findings in this area from researchers. Researchers that are looking to solve these difficulties opt to multi-data analysis rather than single country analysis because issues related to level of data frequencies may occur in the analysis especially in time series. Additionally, according to Shahbaz et al. (2020), the ARDL methodology is the most often utilised technique for assessing the EKC hypothesis. The ARDL technique put forth by Pesaran et al. (1999) has several drawbacks, though. For instance, the limit test presupposes that there is no feedback amid the exogenous and endogenous variables. As a result, the ARDL test encounters the endogeneity problem (Tuna et al., 2019). Basically, an increase in environmental consciousness brought on by international agreements like the Kyoto Protocol or Paris Agreement has led to an increase in the number of scholars conducting studies on climate change issues. In a world where climate regulations are becoming more common, researchers continue to look into the causes of environmental degradation. Three categories are used by Grossman and Krueger to categories the variations in contamination levels. Environmental pollution increases during the initial stage of development (i.e., as GDP grows) as a result of increased production and resource use. Scale effects become apparent as a result (Appiah-Otoo et al., 2023; Akadiriet al., 2019). Second, the composition effect is particularly noticeable in the shift from industrial sectors that require more intensive energy resources to environmentally friendly sectors, particularly in those nations that have made the knowledge economy transition (Shahbaz et al., 2019). Finally, thanks to technical advancements, manufacturers progress towards a clean 169 production method. As a result, technology prevents the environment from degrading (Sunet al, 2022). The literature supports the EKC theory (Bilgili et al., 2016; Pata, 2018; Destek et al., 2019), although other empirical works (Pata, 2018; Belloumi, 2009; Zhang et al., 2023; Sharif et al., 2019) have reached the opposite conclusion, contending that the indication does not backing this supposition. The choice of the nations, the time frame, the differences between the model variables, the selection of the quadratic or cubic EKC model, the demographics of the studied country, and the use of econometric techniques are some of the factors that contribute to this ambiguity in the literature. Ocal et al. (2013) and Sarkodie (2018) have noticed that when the EKC is evaluated for the same country, various results are accessed even in study. In this case, Ozturk et al. (2015) suggest that researchers should use novel econometric methodologies and alternate viewpoints in order to obtain more accurate and consistent results. Because countries with loose environmental laws are appealing to those with sterneco friendly guidelines, climate dereliction in the host country may intensify and demonstrate the scale effect (Sarkodie et al., 2018). Moreso, ecological advances and innovative technologies transfer can stimulate ecology friendly system in the host nation. As a result, the scale effect proves the Pollution Halo Hypothesis (PHH), while the composition and technology effects also support the PHH (Xu et al., 2020; Maji et al., 2019). On the question of whether FDI inflows can hasten ruin of the environment in the host country, there is no consensus. The pollution halo hypothesis, which holds that FDI influx will lower ecological pollution through high technical transfer to the host country, is correct, according to various research (Fan et al., 2020; Maji et al., 2019; Rafindadi et al., 2017; Baz et al., 2021). Essentially, many studies (Yildirim et al., 2012; Yilcan et al., 2019; Usama et al., 2020; Dogan et al., 2020; Pata, 2021; Shahbaz et al., 2019) corroborate the soundness of the PHH. Essentially, this current study revisits the claims of PHH for South Africa economy using VECM, Cointegration analysis and causality method. The study aims to find whether the PHH holds for South Africa given the current energy situation of the country viz-a-viz foreign direct investment. 3. Methodology Theoretically, magnitude, composition, and method effects are how FDIs affect REC and EG. According to the scale effect, foreign direct investments (FDIs) 170 impede the development of renewable energy by encouraging the transmission of dirty energy technologies for industrial uses, typically during the early stages of economic expansion, which are frequently typified by a rampant use of dirty energy (Suki et al., 2020). However, because of the severe environmental burdens associated with the use of dirty energy, manufacturing processes are starting to shift in the direction of more ecologically maintainable practices through reallocation of resources. According to the method effect, FDI speeds up REC through accelerating economic growth, typically through the transference of sustainable advanced machineries, expertise and free market accessibility, researchers like (Qamruzzaman et al., 2022; Tiwari et al., 2022) providing empirical support for the method effect, whereas (Khan et al., 2022) for 69 Belt and Road nations have provided empirical support for the scale effect. Essentially, following the theoretical relationship established above, this study formulates the empirical model as GDP = f(FDI, REN, IMP, EXP) (1) Where GDP = gross domestic product REN = renewable energy consumption FDI = Foreign Direct Investment IMP = Import EXP = Export The econometric specification of the model is specified below: GrDP = REWE + FDIv + EXPT + IMPT (3) GrDP = β0 + β1REWE + β2FDIv + β3EXPT + β4IMPT (4) GrDP = β0 + β1REWE + β2FDIv + β3EXPT + β4IMPT + ϰ (5) GrDP = β0 + β1REWE + β2FDIv + β3EXPT + β4IMPT+ ȇ (6) GrDP is the endogenous variable while REWE, FDIv, EXPT and IMPT are the exogenous variables. Equation (6) is modelled to show the connection amid GDP and other specified variables in South Africa (SA). β0 – β4 are the parameters to be estimated in the model.Essentially, this study will employ VECM and granger causality test to validate the current connection amid FDIv, REWE and GrDP for South Africa economy. The data employed are secondary data source from WDI 171 4. Findings and Discussions Unit Root Test Table 1 presents the unit root test in order to ascertain the order of integration of the variables. As shown by both ADF and DF-GLS test. The Table confirmed that the variables are I(0) and I(1) order. Table 1: Unit Root Testing ADF Null (𝐻0): Non-stationary DF-GLS Null (𝐻0): Non-stationary 𝐴𝐷𝐹𝛼 𝐸𝑅𝑆𝛼 𝑧.𝑡 𝜏.𝜇 1% 5% Prob. 𝜏𝜏 1% 5% Prob. In te rc ep t w it h o u t T im e T re n d REN -1.62 -3.67 -2.97 0.45 -1.35 -2.65 -1.95 0.18 GDP -1.59 -3.57 -2.97 0.47 -1.37 -2.65 -1.95 0.18 IMP -1.51 -3.67 -2.96 0.51 1.09 -2.65 -1.95 0.28 EXP -3.06 -3.67 -2.96 0.04 2.83 -2.64 -1.95 0.00 FDI -2.73 -3.68 -2.97 0.08 -1.83 -2.65 -1.95 0.08 ∆REN -2.67 -3.67 -2.96 0.04 -2.21 -2.65 -1.95 0.03 ∆𝐺𝐷𝑃 -3.44 -3.67 -2.97 0.01 -3.52 -2.65 -1.95 0.00 ∆𝐼𝑀𝑃 -3.36 -3.67 -2.97 0.02 -4.57 -2.60 -1.95 0.00 ∆𝐸𝑋𝑃 -6.31 -3.69 -2.97 0.00 -8.97 -2.65 -1.95 0.00 ∆𝐹𝐷𝐼 -5.09 -3.69 -2.98 0.00 -7.03 -2.65 -1.95 0.00 In te rc ep t w it h T im e T re n d REN -0.89 -4.30 -3.57 0.94 -1.53 -3.77 -3.19 0.13 GDP -3.44 -3.68 -2.97 0.01 -3.52 -2.65 -1.95 0.00 IMP -0.34 -4.29 -3.57 0.98 -1.45 -3.77 -3.19 0.16 EXP -5.24 -4.29 -3.57 0.00 -5.43 -3.77 -3.19 0.00 FDI -3.43 -4.30 -3.56 0.06 -3.54 -3.77 -3.19 0.00 ∆REN -2.75 -4.30 -3.57 0.04 -2.64 -3.77 -3.19 0.01 ∆𝐺𝐷𝑃 -3.43 -4.31 -3.57 0.05 -3.59 -3.77 -3.19 0.00 ∆𝐼𝑀𝑃 -3.61 -3.77 -3.57 0.05 -3.77 -3.77 -3.19 0.00 ∆𝐸𝑋𝑃 -6.19 -4.32 -3.58 0.00 -6.42 -3.77 -3.19 0.00 ∆𝐹𝐷𝐼 -6.05 -4.34 -3.59 0.00 -7.31 -3.77 -3.19 0.00 Source: Author’s Compilation, 2023 The Table 1 above represents the unit root test which shows that all the variables were not stationary at level. Essentially, the study ensure stationarity of the variables used at I(0) and I(1) which necessitate the use of Vector Error Correction Model (VECM). 172 Selection of Lags The optimum lag selection is established in Table 2 below, in order to avoid spurious regression analysis as this can lead to misguided validations. Since, the OpLS is established at 1 following the Schwarz information criterion. The estimation of Johansson cointegration test and thereafter the VECM procedure is established. Lag LogL LR FPE AIC SC HQ 1 - 713.9068 NA 5.86e+16* 52.77906* 53.96852* 53.14269* 2 - 697.0882 21.62385 1.22e+17 53.36345 55.74238 54.09071 3 - 678.4363 17.31962 3.03e+17 53.81688 57.38529 54.90778 Source: Author’s Compilation, 2023 Johansen Cointegration Test (JCT) JCT was used in the study to determine the long-term relationships between the variables. It will be employed in this study since Johansen's (1999) method provides the greatest likelihood for finite-order vector auto-regressions (VARS) and is simple to compute for such systems. The outcome is displayed in Table 3 below. Table 3: Test of Unrestricted Cointegration (Trace). Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.763558 89.50430 69.81889 0.0006 At most 1 0.521311 47.68484 47.85613 0.0519 At most 2 0.362173 26.32041 29.79707 0.1194 At most 3 0.274975 13.27947 15.49471 0.1049 At most 4 * 0.127474 3.954533 3.841466 0.0467 Trace test indicates 2 cointegrating eqn(s) at the 0.05 level Source: Author’s Compilation, 2023 173 Table 4: Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.763558 41.81947 33.87687 0.0046 At most 1 0.521311 21.36442 27.58434 0.2548 At most 2 0.362173 13.04094 21.13162 0.4485 At most 3 0.274975 9.324938 14.26460 0.2601 At most 4 * 0.127474 3.954533 3.841466 0.0467 Eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level Source: Author’s Compilation, 2023 There exista long run stable cointegration relations among the variables, as shown in Table 4 above, in both the trace and maximum eigenvalue tests. VEC modelling can be carried out in further detail based on the assumption that cointegration linkages exist. Vector Error Correction Model (VECM) It should be emphasized that the error correction mechanism (ECM) aims to link the cointegrating equations' short-run dynamics to their long-run static dispositions. The Vector Error Correction Method (VECM) was used to capture the short run variation, and the outcome is shown in Table 5 below. This model is estimated in the study so that causality and diagnostic tests can be performed. Table 5: VECM Output Cointegrating Eq: CointEq1 GDPC(-1) 1.000000 REN(-1) -1.77E+10 (8.9E+09) [-1.99567] FDI01(-1) 6.14E+10 (1.2E+10) [ 5.08572] LOGEXP(-1) -2.87E+11 (3.8E+10) [-7.59992] 174 LOGIMP(-1) -1.20E+11 (6.9E+10) [-1.74535] C 8.73E+12 Error Correction: D(GDPC) D(REN) D(FDI01) D(LOGEXP) D(LOGIMP) CointEq1 -0.015292 4.17E-13 -5.94E-12 6.64E-12 -8.89E-13 (0.02521) (2.6E-12) (4.3E-12) (1.7E-12) (4.9E-13) [-0.60661] [ 0.15820] [-1.37592] [ 3.81632] [-1.81009] D(GDPC(-1)) 0.353444 -1.99E-11 1.22E-10 7.47E-12 -9.69E-12 (0.51566) (5.4E-11) (8.8E-11) (3.6E-11) (1.0E-11) [ 0.68542] [-0.36854] [ 1.37993] [ 0.20980] [-0.96400] D(GDPC(-2)) -0.112474 5.39E-11 -9.95E-11 -6.19E-12 -3.49E-12 (0.47356) (5.0E-11) (8.1E-11) (3.3E-11) (9.2E-12) [-0.23751] [ 1.08682] [-1.22738] [-0.18929] [-0.37826] D(REN(-1)) -6.08E+09 0.483612 -0.372945 0.328021 -0.169683 (2.6E+09) (0.27625) (0.45156) (0.18225) (0.05143) [-2.30283] [ 1.75063] [-0.82589] [ 1.79983] [-3.29954] D(REN(-2)) -1.25E+08 0.269139 0.394295 0.489052 -0.077642 (3.4E+09) (0.35838) (0.58582) (0.23644) (0.06672) [-0.03643] [ 0.75098] [ 0.67306] [ 2.06843] [-1.16376] D(FDI01(-1)) -1.96E+08 -0.043264 -0.21145 -0.205907 0.026499 (1.2E+09) (0.12839) (0.20987) (0.08470) (0.02390) [-0.15940] [-0.33697] [-1.00755] [-2.43096] [ 1.10873] D(FDI01(-2)) -7.19E+08 -0.068376 -0.043313 -0.115323 -0.013134 (1.0E+09) (0.10553) (0.17251) (0.06962) (0.01965) [-0.71271] [-0.64791] [-0.25108] [-1.65637] [-0.66851] D(LOGEXP(- 1)) -2.28E+09 0.065392 -1.210733 0.523011 -0.165316 (5.1E+09) (0.53808) (0.87956) (0.35499) (0.10017) [-0.44304] [ 0.12153] [-1.37652] [ 1.47331] [-1.65038] D(LOGEXP(- 2)) -1.81E+09 0.067441 -0.421609 0.253318 -0.071923 (3.3E+09) (0.34929) (0.57096) (0.23044) (0.06502) [-0.54152] [ 0.19308] [-0.73843] [ 1.09929] [-1.10611] 175 D(LOGIMP(- 1)) 1.51E+09 0.089798 -0.735266 2.797161 0.118234 (1.7E+10) (1.79912) (2.94087) (1.18693) (0.33492) [ 0.08780] [ 0.04991] [-0.25002] [ 2.35664] [ 0.35302] D(LOGIMP(- 2)) -2.23E+09 0.155535 1.333078 0.572628 -0.292815 (1.5E+10) (1.56052) (2.55086) (1.02952) (0.29050) [-0.14983] [ 0.09967] [ 0.52260] [ 0.55621] [-1.00796] C 2.55E+09 -0.289186 0.171907 0.092179 0.072272 (2.8E+09) (0.28861) (0.47176) (0.19040) (0.05373) [ 0.92376] [-1.00200] [ 0.36439] [ 0.48412] [ 1.34518] R-squared 0.445210 0.395595 0.551586 0.678931 0.542715 Adj. R- squared 0.063792 -0.019933 0.243301 0.458195 0.228332 Sum sq. resids 7.42E+20 8.134425 21.73497 3.540449 0.281896 S.E. equation 6.81E+09 0.713023 1.165519 0.470402 0.132735 F-statistic 1.167250 0.952030 1.789207 3.075768 1.726285 Log likelihood -665.8673 -22.42489 -36.18433 -10.77897 24.64763 Akaike AIC 48.41909 2.458920 3.441738 1.627069 -0.903402 Schwarz SC 48.99004 3.029865 4.012683 2.198014 -0.332457 Mean dependent 5.62E+09 -0.307143 0.244462 0.043126 0.044421 S.D. dependent 7.04E+09 0.706021 1.339855 0.639069 0.151102 Determinant resid covariance (dof adj.) 3.30E+16 Determinant resid covariance 2.01E+15 Log likelihood -691.9618 Akaike information criterion 54.06870 Source: Author’s Compilation, 2023 176 Table 6: Stability test for VECM Lags LM-Stat Prob 1 13.92668 0.963 2 30.51903 0.2054 Probs from chi-square with 25 df. Source: Author’s Compilation, 2023 Granger Causality Test The results of the cointegration test shows a stable link over time between the two variables such as FDI, renewable energy and economic growth, but more research is required to determine whether there is a causal connection. When the regression of EcGr is based on previous values of EcGr and past values of FDI are added, the independence power of the regression can be considerably increased if variable EcGr is useful in predicting EcGr. If so, FDI is the Granger cause of FDI; otherwise, it is the non-Granger cause. P value is below the 5% level of significance, indicating that the null hypothesisthat Granger causality existsmust be accepted. Table 7: Granger Causality Output Null Hypotheses (H0) Chi-Square Probability Remarks GDPC does not Granger Cause REN 5.31 0.07 Uni-directional Causality (UDC) REN does not Granger Cause GDPC 1.19 0.55 GDPC does not Granger Cause FDI 0.55 0.70 no-directional Causality (UDC) FDI does not Granger Cause GDPC 2.40 0.30 GDPC does not Granger Cause EXP 0.30 0.80 No Causality EXP does not Granger Cause GDPC 0.05 0.97 GDPC does not Granger Cause IMP 0.02 0.89 No Causality EXP does not Granger Cause GDPC 1.69 0.42 Source: Author’s Compilation, 2023 In summary, as the results shown in Table 7 above, there is no bidirectional granger causality among any of the variables. However, there is Unidirectional Causality 177 between GDPC and REN which is supported by previous studies such as Adekunle et al., 2023a, Adekunle et al., 2023b and Kiliçarslan, 2019. 5. Conclusion and Recommendations The connection amid FDI, Ren and EcGr for the South African economy from 1990 to 2020 was examined in this paper. The study determined that GDP granger causes REN in the South African economy, but REN does not granger cause GDP. The sightings of this study establish the GDP growth potential of the REC-EG nexus. Therefore, policies that encourage South Africa's GDP growth ought to be given top priority. Thorough general and local policy had better develop for the implementation and expansion of REn. The regional governments' lawmaking bodies should implement the required action to create a strong framework for environmental sustainability that overcomes the current obstacles to sustainability adoption. Incentives and a supportive environment for thriving domestic manufacturing of RE technologies and the parts they require could be included in policies, but this is not the only option. To facilitate the best adoption and use of RE, policymakers must put various strategic measures into place. For instance, education can increase knowledge about RE's existence, application, and importance. Education laws that mandate the inclusion of energy courses, particularly RE, in academic curricula should be established by policymakers. This would enable both the current generation and succeeding ones to implement and accommodate the usage of REn. Additionally, the cost of RE deters firms, especially big enterprises, from implementing and utilising it. Large upfront expenses are associated with the majority of corporate and private RE programmes, which discourages people from taking part. The government must offer some sort of help to make sure organisations or companies are adequately influenced to depend largely on Ren which will facilitate ecological friendly system in the country. The study is constrained by the following factors. The results of this study cannot be used to reflect the economies of other nations in the sub-region because it exclusively focused on South Africa. As a result, later studies may include more sub-regions to increase the study's reach. Additionally, this study ignored sub- measures of RE like solar and wind energy and utilised REC as the only measure of RE. Therefore, the sub-components of RE should be used in additional investigations in the future. Additionally, this analysis focused on total FDI. In light of this, future investigations may employ the portion of FDI to RE for additional research. Lastly, although this study focused on the aggregate dataset, there are regional variances in economic growth, export, foreign direct investment, and 178 import. So that effective multi-country policies may be created, imminentwork can conduct studies that are panel in nature. References Adekunle, A. O., Bekun, F. V., Gbadebo, A. D., Akande, J. O. (2023). 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