Determinants of electricity production from renewable source excluding hydroelectricity in selected East African countries: Panel ARDL approach

Asratie, Teshager Mazengia Article Determinants of electricity production from renewable source excluding hydroelectricity in selected East African countries: Panel ARDL approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Asratie, Teshager Mazengia (2022) : Determinants of electricity production from renewable source excluding hydroelectricity in selected East African countries: Panel ARDL approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-12, https://doi.org/10.1080/23322039.2022.2080897 This Version is available at: https://hdl.handle.net/10419/303664 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Determinants of electricity production from renewable source excluding hydroelectricity in selected East African countries: Panel ARDL approach Teshager Mazengia Asratie To cite this article: Teshager Mazengia Asratie (2022) Determinants of electricity production from renewable source excluding hydroelectricity in selected East African countries: Panel ARDL approach, Cogent Economics & Finance, 10:1, 2080897, DOI: 10.1080/23322039.2022.2080897 To link to this article: https://doi.org/10.1080/23322039.2022.2080897 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 31 May 2022. Submit your article to this journal Article views: 1686 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Determinants of electricity production from renewable source excluding hydroelectricity in selected East African countries: Panel ARDL approach Teshager Mazengia Asratie 1 * Abstract: Though East Africa has ample resource endowments for electricity production, the region has the lowest performance in generating electricity, and millions of people are living without access to electricity. To fill the electricity gap countries used fossil fuels as the major source of energy, but electricity production from a renewable resource is lower. Therefore, this study aimed to identify determinant factors of electricity production from renewable resources excluding hydropower sources. Panel data for five East African countries for the period 1998 to 2019 were used, and it was examined using pooled mean group panel ARDL estimation technique. The estimation result revealed that in the long- and short-run GDP per capita growth, population growth, energy consumption per capita, and energy import have a positive significant effect on electricity production from renewable resources other than hydropower, while political instability, electricity production from hydropower, and electricity production from oil, gas, and coal have a negative significant effect. However, in the short run, energy use and resource rent percentage of GDP have positive and negative significant effects, respectively, but in the long run, the two variables have no significant effect. Error correction coefficient is negative 0.64, which indicates that the deviation from long-run disequilibrium adjusts toward equilibrium at a rate of 64% per year. Based on the results, this study recommends that the government improve the performance of GDP growth by quality education, lower lending interest rate, improving political ABOUT THE AUTHOR Teshager Mazengia Asratie, the author of this article, is MSc in economics holder from Addis Ababa University with specialization in resource and environmental economics. Currently, I am working for Debre Berhan University as a lecturer. I am interested in conducting a study on financial-sector performance, renewable and non-renewable resource use efficiency, and valuation, climate change adaptation practices and its role in agricultural productivity. PUBLIC INTEREST STATEMENT The limited availability of fossil fuels, on which countries have been highly dependent on for last few decades, makes it costlier, scarce, and greenhouse gas emission increases. Because of these reasons many countries shift their energy source from non-renewable to renewable. However, unlike the ample renewable resources in the region, its performance is the lowest in the world. To improve this performance great efforts, mostly on hydropower, were devoted by both the government of a country and international organizations, but it is not as expected. Therefore, identifying the bottlenecks of renewable energy production has paramount importance for the region. Thus, the researcher tried to do so. Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 1 of 12 Received: 13 August 2021 Accepted: 17 May 2022 *Corresponding author: Teshager Mazengia Asratie, Department of Economics, Debre Birhan University, 445, Ethiopia E-mail: [email protected] Reviewing editor: Aviral Tiwari, Finance and Economics, Rajagiri Business School, India Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. stability through controlling internal conflicts caused by differences in religion and ethnicity, and improving energy security. Subjects: Environmental Sciences; Environmental Management; Renewable Energy Keywords: electricity production; panel ARDL; renewable; East Africa 1. Introduction Though electricity is essential for life, millions of people in the world have no access to electricity. According to the International Energy Agency (IEA), an individual is in energy poverty if and only if he/she cannot access a minimum kWh of 120 per annum (UN-WB, 2011). IEA’s projection shows that a household has enough access to electricity if they can get 1250 kWh per year per household with standard appliance and 420 kWh of efficient appliance (Pérez-Fargallo et al., 2020). However, most African countries do not fulfill this criterion. Besides, in terms of electricity access, Africa is the lowest-performing continent. For instance, the share of the world population with access to electricity increases from 71.4% in 1990 to 87.35% in 2016 (World Bank, 2018). Despite this, the average annual increase in access to electricity is low. From 1990 to 2010 average annual increase in access to electricity is 0.61% which increases to 0.75% from 2010 to 2012, but declines back to 0.33% from 2012 to 2014. From 2014 to 2016, it is 0.83%, and the target rate from 2016 to 2030 is 0.9% (world bank, 2018). Despite this rate, sub-Saharan Africa’s electricity access deficit has more than doubled between 1990 and 2016. For instance, in 1990, only 28% of sub-Saharan Africa faces electricity deficit, but in 2016, it reaches 60%. Moreover, in the continent from 2002, each year around 9 million people are added to have no access to electricity per year. In Africa from 2000 to 2020, populations without electricity ranged from 522 to 609 million, and the maximum population without access to electricity was reached in 2013. 1 To fill the electricity gap, great effort was devoted by both countries and international organizations. For the last few decades, fossil fuels were the major source of energy used by households, firms, and the government. However, the limited availability of fossil fuels makes the price of energy higher and the resource becomes scarcer, and the greenhouse gas emission increases. In addition, nowadays “climate change” has been receivin great interest among politicians, economists, and the government (Adra, 2014). Due to this, many countries are shifting their source of energy from non-renewable to renewable sources. Africa, in general, and East Africa, in particular, has great endowments and potential for renewable energy from water, wind, solar, and other renewable sources. For instance, Africa has 1.1 gigawatts of hydropower, 9,000 megawatts of geothermal, and other abundant potentials of biomass and solar energy (Karekezi & Kimani, 2002). Despite the potentials, the region has the lowest performance in generating electricity from renewable sources. For instance, at the end of 2014 only 2462 megawatts of wind energy was generated, and a total of 21 gigawatts of new capacity is expected to be operational in 2020. According to the African energy industry report (AEIR, 2018) Africa faces many energy challenges, which require the accelerated use of modern renewable energy sources and the development of energy infrastructures. In Sub-Saharan Africa, the number of people without access to reliable electricity reaches more than 620 million. Sustainable Development Goal 7 plans to close the electrification gap and provide reliable and sustainable energy for all by 2030. However, recent rates of growth in electricity access indicate that Africa will not meet this target. 2 Lack of access to electricity is endemic in Africa regardless of income. Africa is the leading continent that has most countries whose electrification performance is below their income predictions. Increasing the rate of electrification from 20% to 80% will take 25 years (Blimpo & Cosgrove-Davies, 2019). The consumption and production of energy in East Africa are similar. Most of the countries in the region are heavily dependent on fossil fuels as a primary source of energy use. In addition to fossil fuel energy, they use traditional biomass fuels ranging from 70% to 90% of total energy Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 2 of 12 production. This high dependence on traditional biomass fuels causes large area deforestation and environmental degradation. However, energy production from renewable resources is small accounting for 2% for hydropower, solar, and geothermal sources. From the total renewable resource of energy production, hydropower sources constitute 70% of production. Despite this, the region has geothermal energy potentials of 15,000 MW. However, only Kenya generates electricity from geothermal energy. From the total of worldwide geothermal energy generation of 10,000 MW, Kenya and Ethiopia generated 209 and 5 MW, respectively (Omenda & Teklemariam, 2010). There was also only 5.5 GW of wind power across the whole of Africa in 2018, an increase from 1 GW in 2010. Meanwhile, there was 4.5 GW of solar PV across the continent as of 2019 (IEA, 2019). Looking at other renewable technologies, Kenya is leading other East African countries in the development of geothermal power in the region (Sawyer, 2020). International Energy Agency (International Energy Agency (IEA), 2011) estimates indicate that 17% of electricity in SSA was lost in 2018, and from this, Kenya loses over 20%. Climate-driven issues with rainfall can also lead to outages or price fluctuations when low water levels prevent hydropower plants from operating and force utilities to turn to backup generators (IEA, 2019a). The large share of hydropower in the region’s electricity mix has contributed to electricity shortages or being forced to turn to backup generators as climate-change-induced rainfall changes have resulted in depleted water resources across the region. Except for Kenya, where wind and geothermal are also being invested in, and in Rwanda, where solar PV is also seeing investments, in East African countries, investment in power is still predominantly in hydropower (REN21, 2016). As a result, other electrical power sources account for only small amounts of the region’s power, with solar PV having only 9.15 MW in the EAC in 2015, 8.75 MW of which was in Rwanda. At the same time, geothermal power had nearly 600 MW of capacity, and the wind had 25.5 MW (REN21, 2016). Thus, this study was intended to examine determinant Electricity Production from Renewable Sources other than Hydropower in selected East African countries. The rest of the paper is organized as follows. The next section discusses the methodological approach of the panel ARDL approach and source of data. Section 3 is devoted to the presentation and discussion of empirical findings. Concluding remarks and recommendations are presented at the end. 2. Literature review In this section, literature reviews related to renewable energy production and its determinants are presented. According to Da Silva et al. (2018) economic growth is one of the drivers of renewable energy consumption. A recent study concluded that renewable energy sources, based on wind, water, and sunlight (abbreviated as WWS; not including biomass), could provide all new energy globally by 2030, and replace all current non-renewable energy sources by 2050 (Jacobson & Delucchi, 2011). The decline in energy import dependency diminishes the impact of fossil fuel energy prices, in other words, countries’ vulnerability to external shocks and restrictions imposed on the economy because of fossil fuel reserve limits (Da Silva et al., 2018). Marques and Fuinhas (2011) found negative effects of fossil fuel and nuclear contribution to electricity generation and positive effects of energy imports. Investment in the renewable energy sector is very sensitive to the country’s institutions’ quality (e.g., Becker and Fischer, 2013). Theoretically, weak institutions have various harmful impacts on energy-sector policies, in particular, the electricity sector. Accordingly, Gutermuth (2000) considers that the legal and institutional framework is of great importance in the transition to clean energies. The findings reveal that political stability is a key determining factor of renewable energy production. By using panel quantile with non-additive regression model, Belaid et al. (2019) concluded that the effect of political stability has a heterogeneous effect on renewable energy production. For instance, at the lower (10 th ) and higher (90 th ) quantile political stability has a negative significant effect. However, its elasticity has a positive significant effect. They also found that economic growth and natural resource dependency negatively affects renewable energy production in the lower (10 th ) quantile, while on the remaining quantile, it has a positive significant effect. Moreover, Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 3 of 12 they found a positive relationship between renewable energy production and economic growth. This can be interpreted as a resource curse in countries where renewable energy production is lower. Finally, the quantile regression model results show that the total energy consumption elasticities are positive and statistically significant throughout the renewable energy production distribution. Mengova (2019) examined determinants of production of energy from renewable sources in Europe, the Former Soviet Union, the Middle East, and North Africa. The author found that energy use per capita has a negative significant effect on renewable energy production, while energy use per capita has a positive effect. The result confirms that the energy needs of one’s country should be fulfilled by utilizing both traditional and renewable energy sources efficiently. GDP per capita coefficient estimate was small, but positive and significant. The coefficient of energy imports, i.e., the variable accounting for the energy security of a country, has a positive and significant effect on renewable energy production. Finally, all the alternative traditional sources of electricity production in each country were statistically significant and had a negative effect. 3. Methods The data for this study were collected from East African countries over the years 1998 to 2019. Electricity production from renewable energy sources excluding hydropower, gross domestic product per capita growth, population growth, electricity production from hydropower energy as a percentage of total electricity production, energy imports, energy use, political instability, electricity production from oil, gas, and coal as a percentage of total electricity production, resource rent as a percentage of GDP variables for five East African countries, namely, Ethiopia, Kenya, Tanzania, Zimbabwe, and Mauritius,was were collected from the World Bank database. Only these countries were selected because other East African countries have no data on electricity production from renewable sources excluding hydropower for the years 1998 to 2019. In this study, panel ARDL estimation technique is used. It is used because it has many advantages over the traditional Johansson co-integration approach in that the panel ARDL approach could be used with the studied factors regardless of whether they were I(0), I(1), or both I(0) and I (1). Based on the study conducted by Paseran and Shin (1995) the following basic ARDL specification is used as a benchmark for this study: Yi;t¼α0þ∑p l¼1θilYi;tlþ∑q l¼0β0 ilXi;tlþεit (1) εit ¼γiftþUit (2) Where i = 1, 2 . . . N stands for cross sections and t = 1, 2, 3 . . . . represents for time periods; Xit�is k*1 vector of regressors and β0is k*1 coefficient vectors; ft is an m × 1 vector of unobserved common factors; γi is the corresponding factor loading. From equation 2, the error correction model is parameterized as follows: ΔYi;t¼λiYi;t1δ0iXi;t  �þ∑p1 �¼1θi;�ΔYi;t1þ∑q1 �¼0β0i;�ΔXi;t�þεi;t(3) Where λi represents the speed of adjustment from a long-run relationship; δ0i is a vector of longrun coefficients is which can be computed regardless of whether the variables are I(0) or I(1), and whether the explanatory variables are endogenous or exogenous (Paseran et. al, 2001; as cited in Chen & Vujic, 2016). After specifying the model under the panel ARDL model, there are three estimation techniques. These include the mean group estimator, pooled mean group estimator, and dynamic fixed effect estimator. Mean group considers heterogeneity in both slope and constant, while PMG only assumes fixed effect heterogeneity in the long-run specification, which means Pooled Mean Group uses Maximum-likelihood to get the long-run equation, but MG estimates different coefficients (constant and slope) for each panel separately. Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 4 of 12 By contrast, the DFE estimator restricts the speed of adjustment, slope coefficient, and short-run coefficient to exhibit non-heterogeneity across countries. Accepting this estimator as the main analysis tool requires the strong assumption that countries’ responses are the same in the shortrun and long-run, which is less compelling. Another drawback is that this approach may suffer from simultaneity bias in a small sample case due to the endogeneity between the error terms and lagged explanatory variables (Baltagi et al., 2000). The PMG restricts long-run equilibrium to be homogeneous across countries while allowing heterogeneity for the short-run relationship. The short-run relationship focuses on the country-specific heterogeneity, which might be caused by different responses of stabilization policies, external shocks, or financial crises for each country. The MG estimator allows for heterogeneity in the short-run and long-run relationships. To be consistent, this estimator is appropriate for a large number of countries. For a small number of N, this method is sensitive to the permutation of non-large models and outliers (Favara, 2003). To choose among the three models, the Hanuman specification test was used. Dependent and independent variables used in this study are discussed as follows: the dependent variable is electricity produced from renewable resources excluding hydropower (EPRREHT). It is electricity production from solar, wind, biomass, and tides. Gross domestic product per capita growth (GDPPCG) is used as an independent variable, which is used to measure the prosperity of a nation computed by dividing GDP by the population of a country. It is expected that an increase in GDP per capita will cause an increase in electricity production from renewable energy sources excluding hydropower. However, in developing countries, the fast growth rate of population offsets the growth of the gross domestic product, which in turn causes GDPPCG to decline or be constant. The second variable used as an independent variable is Population growth (POPGROWTH) in that as the size of the population increases the energy demand also increases. Hence, it was expected that there is a positive relationship between population growth and electricity production from renewable energy sources excluding hydropower. Energy consumed per capita (ECPC) residing in the country is the third variable used in this study. Energy imports (ENERIMPOR), a proxy for the energy dependency of a country represents that as energy import increases the country’s energy source becomes insecure. As a result, the country is required to invest more in domestic renewable resources, and the production of electricity from renewable resources increases. Electricity production from oil, gas, and coal as a percentage of total electricity production (EPOGCT), which is a non-renewable resource, and relying on these sources will have a consequence of scarcity and declining welfare of the society. It was expected that an increase in electricity production from non-renewable resources will cause a decline in electricity production from renewable resources of wind, solar, and biomass. Energy use (ENERUSE) is energy use measured as kg of oil equivalent per capita. It refers to the use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. An increase in energy use could increase energy production and create incentives for more production, particularly from renewable sources. Total natural resources rents % of gross domestic product (RESRENTGDP) is the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. Electricity production from hydropower energy as a percentage (EPHPT) is also used as an independent variable. Finally, political instability (POLINST) is used as an independent variable, which involves whether the institutional development of one’s country depends on democracy and rule of law, i.e., the involvement of the population in the decision-making process. The data is yearly reported by the World Freedom House survey since 1972 for 195 sovereign countries. The measure of political freedom incorporates two broad subgroups: political rights (PR) and civil liberties ratings (CL). The political right includes subgroups such as the process of election, level of participation and political pluralism, and government functioning. While civil liberties include the level of freedom of expression and belief, rights associated with freedom to join and leave existing groups of the organization, rule of law, protection of individual rights, and personal freedom. Both were evaluated in the range of 1 to 7. If the index ranges between 1 and 2.5 a country is considered as free, 3 to 5 partially free, and 5.5 to 7 is considered as not free (Bzhalava, 2014). In estimating the level of freedom, Freedom House survey control for Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 5 of 12 cultural differences and provides generalized estimation built on worldwide standards of political and civil rights such as the Universal Declaration of Human Rights. 4. Result and discussion Figure 1 presents each country’s mean electricity production from only hydropower sources and electricity production from renewable resources excluding hydropower energy as a percent of total electricity production. In Ethiopia, more than 90% of electricity production is from hydropower and the mean electricity production from renewable resources excluding hydropower energy as a percentage of total electricity production is less than 10%. In Kenya, the mean electricity production as a percentage of total electricity production from hydropower energy is less than 60% and the mean electricity produced from renewable resources excluding hydropower is 20%, which is the highest mean electricity production as compared to other countries production considered in this study. In Mauritius, the mean electricity production excluding hydropower is greater than electricity production from hydropower. The mean electricity production from renewable resources excluding hydropower in Tanzania and Zimbabwe is closer to zero, while the mean electricity production from hydropower energy is greater than 60% and 50%, respectively. Figure 2 presents the time trend of electricity production from renewable resources excluding hydropower energy by country. It shows that in Ethiopia, Kenya, and Tanzania, electricity production from renewable resources excluding hydropower sources as a percentage of total electricity production is increasing, while in Mauritius and Zimbabwe, it shows great fluctuations. For instance, in Zimbabwe, electricity production is declining after 2010. 4.1. Stationary test Before reporting the long-run estimation of the panel ARDL model, the researcher tested the stationary of the variables used in the estimation. This study adopted the test of stationary developed by Im et al. (2003), which relaxes the assumption of a common autoregressive parameter. The starting point for the IPS test is a set of Dickey–Fuller regressions of the form: Δyit ¼ �iyi;t1þZ0itγiþεit where �i is panel-specific indexed by I and I’m Pesaran assume that εit is independently distributed normal for all i and t, and allow εit have heterogeneous variances across panels. The IPS test allowing for heterogeneous panels with serially uncorrelated errors assumes that the number of periods, T is fixed. While N is assumed to be either fixed or ! 1. Hence, the ETH KEN MUS TZA ZWE mean of eprreht mean of ephpt Figure 1. Electricity production from renewable resources (hydropower and other sources), a comparison. Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 6 of 12 test is tested based on the null hypothesis that all panels have unit roots against the alternate hypothesis that some panels are stationary. As can be seen in Table 1, five variables are stationary at the first difference and the remaining four variables are stationary at level. Stationary test political instability is not conducted because of an insufficient number of periods to compute W-t-bar. 4.2. Model selection Under the panel ARDL model estimations there are three estimation techniques.. These include the mean group estimator, pooled mean group estimator, and dynamic fixed effect estimator. Mean 0 2 4 6 8 10 12 EPRREHP%T 1995 2000 2005 2010 2015 2020 year Ethiopia 10 15 20 25 30 35 40 45 50 EPRREHP%T 1995 2000 2005 2010 2015 2020 year Kenya 17 18 19 20 21 22 23 24 25 EPRREHP%T 1995 2000 2005 2010 2015 2020 year Mauritius 0 .2 .4 .6 .8 1 1.2 EPRREHP%T 1995 2000 2005 2010 2015 2020 year Tanzania 0.2.4.6.81 1.2 1.4 1.6 1.8 EPRREHP%T 1995 2000 2005 2010 2015 2020 year Zimbabwe Figure 2. Time trends of electricity production from renewable resources excluding hydropower by country. Table 1. Stationary test statistics of variables used in this study Variables Im-Pesaran-Shin unit-root test statistics p-value Stationary Level First difference Level First difference EPRREHT 2.6731 −4.3086 0.9962 0.0000 I(1)*** GDPPCG −3.5250 −9.8410 0.0002 0.0000 I(0)*** popgrowth −4.3005 −5.0409 0.0000 0.0000 I(0)*** EPOGCT −0.9869 −6.5481 0.1619 0.0000 I(1)*** ECPC 0.3208 −6.1941 0.6258 0.0000 I(1)*** ENERIMPOR 1.5160 −3.1702 0.9352 0.0008 I(1)*** ENERUSE 2.1358 −4.4707 0.9837 0.0000 I(1)*** RESRENTGDP −2.7174 −4.7154 0.0033 0.0000 I(0)*** EPHPT −1.7716 −5.4992 0.0382 0.0000 I(0)** ** and *** represents stationary at 5% and 1% level of significance level. Asratie, Cogent Economics & Finance (2022), 10: 2080897 https://doi.org/10.1080/23322039.2022.2080897 Page 7 of 12