Determinants of energy consumption: The case of African oil exporting countries

Ogunsola, Akindele John; Tipoy, Christian Kakese Article Determinants of energy consumption: The case of African oil exporting countries Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ogunsola, Akindele John; Tipoy, Christian Kakese (2022) : Determinants of energy consumption: The case of African oil exporting countries, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2022.2058157 This Version is available at: https://hdl.handle.net/10419/303619 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. 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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 energy consumption: The case of African oil exporting countries Akindele John Ogunsola & Christian Kakese Tipoy To cite this article: Akindele John Ogunsola & Christian Kakese Tipoy (2022) Determinants of energy consumption: The case of African oil exporting countries, Cogent Economics & Finance, 10:1, 2058157, DOI: 10.1080/23322039.2022.2058157 To link to this article: https://doi.org/10.1080/23322039.2022.2058157 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 06 Apr 2022. Submit your article to this journal Article views: 2522 View related articles View Crossmark data Citing articles: 5 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 energy consumption: The case of African oil exporting countries Akindele John Ogunsola 1 * and Christian Kakese Tipoy 1 Abstract: Persistent energy consumption issues are ascribed to the failure of energy planners to understand the various macroeconomic factors that influence energy consumption. Therefore, we investigated the factors influencing energy consumption in six net African oil-exporting countries (AOECs) between 1980 and 2018. Our contribution to the literature is the use of estimators; cross-sectional autoregressive distributed lag (CS-ARDL) and cross-sectional distributed lag (CS-DL) modelling approaches, which take into account the time dynamics, the heterogeneity of different countries and cross-sectional dependence, to explore the relationship between energy consumption and its major determinants, such as openness, economic structure and per capita income. The results revealed that per capita income did not impact significantly on energy consumption in AOECs during the period under study, while trade openness had a positive and significant effect on it. The third variable of interest, namely economic structure, had a negative and significant effect on energy consumption. These results led to various recommendation for policymakers and future research in the concluding section. ABOUT THE AUTHORS Akindele John Ogunsola is currently a PhD candidate in the School of Accounting, Economics and Finance (SAEF), College of Law and Management Studies, the University of KwaZulu- Natal, Durban, South Africa. In addition, he has served as a research assistant at the Macroeconomics Research Unit in the SAEF. He started his lecturing career in the Department of Economics, Faculty of the Social Sciences, Ekiti State University, Nigeria, as an assistant lecturer in 2016 and rose to the position of Lecturer II in 2019. He has published articles in wellrecognised journals and presented his research at both local and international conferences. Christian Kakese Tipoy is a Senior Lecturer in the School of Accounting, Economics and Finance, College of Law and Management Studies at University of KwaZulu-Natal, South Africa. He holds a PhD degree from University of Pretoria, South Africa. He is a reviewer to many notable journals and an assessor to many universities around the world. He has also published several articles in many recognized journals. PUBLIC INTEREST STATEMENT The failure of the governments of African oilexporting countries (AOECs) to improve energy consumption, which would lead to sustainable economic growth has been of major interest to scholars. Often, the inability of policymakers in these economies to obtain favourable results has been attributed to conflicting policy implementation and factors influencing energy consumption in the long run. To provide a better understanding of how energy consumption parameters drive sustainable economic growth of AOECs, the study investigated the factors determining energy consumption in these economies. We used both crosssectional autoregressive distributed lag and crosssectional distributed lag modelling approaches, which account for time dynamics, cross-sectional heterogeneity and cross-sectional dependence. The empirical findings showed that per capita income did not impact energy consumption significantly, while trade openness had a positive and significant effect. In addition, the study confirmed that economic structure tended to hinder energy consumption significantly. Thus, the study aimed to contribute to knowledge on the determinants of energy consumption in AOECs. Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 1 of 17 Received: 03 June 2021 Accepted: 21 March 2022 *Corresponding author: Akindele John Ogunsola, School of Accounting, Economics & Finance, University of KwaZulu-Natal, South Africa E-mail: akinogunsola2002@yahoo. com Reviewing editor: Goodness Aye, Agricultural Economics, University of Agriculture, Makurdi Benue State, NIGERIA 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. Subjects: Sustainable Development; Economics and Development; Economics; Environmental Economics Keywords: trade openness; economic structure; per capita income; energy consumption; cross-sectional autoregressive distributed lag; cross-sectional distributed lag JEL Classification: F10; H10; H24; Q40 1. Introduction The connection between energy consumption and economic growth has been well documented in the energy literature (Azam et al., 2015; Wang & Wang, 2020). The role of energy in propelling sustainable economic growth has been of great concern to energy economists and policymakers, especially in Africa where many countries are net African oil-exporting countries (AOECs; Bilgen, 2014; Dritsaki & Dritsaki, 2014; Fuinhas & Marques, 2012). African countries have exhibited two tendencies in energy consumption and economic growth. Firstly, the region has the lowest energy consumption per capita, and the little energy it consumes is dominated by dirty fuel, which contributes to environmental deterioration (Kebede et al., 2010; Rennings et al., 2012). Secondly, Africa has the worst development indicators in the world, with high unemployment and poverty rates, poor access to education and health, and low life expectancy, for example, (Karekezi, 2002); (International Energy Agency (IEA), 2016). A report from the World Bank shows that global poverty rate is on the decrease across various regions of the world, except in Africa where extreme poverty in sub-Saharan Africa (SSA), in particular, is on the increase (from 278 million in 1990 to 413 million in 2015). The poverty rate stood at 41% in 2015, compared to 13% in other regions. In addition, 27 out of 28 poorest countries in the World are from SSA (World Bank, 2018). This makes it difficult for the region to attain the poverty reduction target of the MDGs (Asongu et al., 2016; Asongu & Kodila-Tedika, 2015). Moreover, Shurig (2015) maintains that only 5% of countries in SSA have access to electricity (IEA, 2016). This implies that per capita electricity consumption in the sub-region is onesixth of the world’s average. In addition, the ineffective management of energy resources is another problem (Anyangwu, 2014). The governments of African countries subsidise fossil fuels, which are in high demand, at the expense of renewable energy. However, the increasing rate of fossil fuel consumption is one of the major causes of global warming due to environmental disturbance and contributes over 75% of carbon dioxide (C0 2 ) emissions (Huxster et al., 2015; Akpan and Akpan, 2012; Mundial, 2018). Nonetheless, fossil energy has improved humanity’s standard of living, which is reflected in the positive relationship between the total primary energy produced and the average gross national domestic product (GDP) per-capita. However, owing to their greenhouse effect, fossil fuels need to be replaced by alternative energy sources (Smil, 2005). According to Waheed et al. (2019), one aspect of energy consumption is concerned with economic growth, while the other relates to carbon emissions. Sustainable growth as well as averting environmental change need to be taken into cognisance while satisfying the growing energy demand. The energy produced from renewable sources can balance contradiction in the energy-mix market as well as guide the ecological environment (Ogbonnaya et al., 2019). The use of energy covers diverse human activities, which include generating power, fueling machines and providing power to homes and industries (Haider & Adil, 2019). These activities are essential for the daily survival of human beings and the improvement of their standard of living. Physical labour or animals were used as power by people in the past. However, with increasing progress in technology, modern energy 1 was substituted for traditional energy sources (Lean & Smyth, 2010; Stern, 2010). Modern energy constitutes an important input in the industrial process, mining, services, transportation and information sectors (Mallett, 2013). Hence, it is expected that, when industrial and agricultural activities increase, the demand for energy Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 2 of 17 consumption will also increase. Increasing energy consumption, precisely industrial energy consumption, is a vital indicator of a country’s economic development level (Zhang et al., 2017). Therefore, low energy consumption and a poor energy mix are indicators of an underdeveloped economy (Karekezi & Kimani, 2002). Specifically, modern energy has been a fundamental part of economic growth for many developed economies since the great industrial upheaval. As indicated above, there is a strong association between energy consumption and economic growth (Arabatzis & Malesios, 2011; Javid & Sharif, 2016; Koutroumanidis et al., 2009). Evidence from high-income countries reveals that high energy consumption correlates with high per capita income levels, whereas low energy consumption is associated with low-income countries (United States Energy Information Administration (US EIA), 2016). For instance, energy consumption in 2017 stood at 3132.2 million tonnes of oil equivalent (Mtoe) in China, 753.7 Mtoe in India and 295.9 Mtoe in South Korea, with growth rates of 6.76%, 7.17% and 3.06%, respectively. In the same year, the energy consumption of AOECs, such as Algeria, Angola and Nigeria was 51.8 Mtoe, 7.7 Mtoe and 28.2 Mtoe, respectively, with growth rates of 1.3%, −0.15% and 0.81%, respectively. This shows that affordable access to adequate and dependable energy supply is a fundamental factor in the attainment of the large productivity gains that guarantee sustainable economic growth. Despite all the efforts being made by the AOECs to improve access to energy consumption and sustainable economic growth, it should, however, be noted that these have not been impressive in recent times (see, Clark et al., 2005; Karekezi & Kimani, 2002). The inability of the AOECs to improve both their energy consumption and income levels has been blamed on inconsistent policy implementation and policymakers’ failure to obtain adequate information on the factors influencing energy consumption. The argument is that for countries that depend on crude oil exports for most of their foreign earnings (Davidson & Sokona, 2002; Wolde-Rufael, 2006), as a result of huge energy resources endowment (Kebede et al., 2010), the paradox of low energy consumption requires extensive investigation. This becomes more important when these countries also have poor developmental indicators and are affected by environmental issues resulting from the emissions of CO 2 . Given all these, it is, therefore, important to examine the determinants of energy consumption in this group of countries. This can be done by providing more precise estimates of major energy consumption parameters to understand how they drive sustainable economic growth. This approach is important for two reasons. Firstly, providing definite quantitative estimates for energy consumption parameters can help in policy making. Secondly, quantitative estimates will also help in forecasting future energy consumption. Various studies have been conducted to identify factors influencing energy consumption. For instance, Kolawole et al. (2017), point out that basic economic theory suggests that the demand of a good or service is usually dependent on factors like the price of the good, the income of the buyers, the price of a close substitute and other exogenous factors associated to the nature of the good. This was further buttressed by Xiong et al. (2020) and US EIA (2021) that prices of close substitutes, like natural gas and solar, are also important factors determining energy consumption. Similarly, Atakhanova and Howie (2007) and Bohlmann and Inglesi-Lotz (2021) conclude that the income of the consumer as well as energy price are factors influencing energy consumption. Other major variables influencing energy consumption, as pointed out by Samuel et al. (2013) and Sineviciene et al. (2017), include economic growth rate, population, foreign direct investment and industrial growth. Findings from these studies are mixed and have not reached a consensus. So, there is a need to carry out an empirical study on the topic since no study to the best of our knowledge has been conducted to investigate the major drivers of energy consumption in AOECs, using crosssectional augmented autoregressive distributed lag (CS-ARDL) and cross-sectional distributed lag (CSDL). Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 3 of 17 The focus of the study, therefore, was to identify drivers that help explain the influence of energy consumption in AOECs, using macroeconomic drivers such as per capita income, trade openness, economic structure and the reaction of the consumers to these drivers in the long run. Having enough understanding of their reaction to these drivers would serve as an important instrument for formulating an effective energy policy that will improve access to sustainable energy resources in AOECs. Our novel contribution to the literature in AOECs, unlike related studies conducted by Samuel et al. (2013) on the determinants of energy consumption and Kolawole et al. (2017) for sub-Saharan Africa, is the use of recent panel data estimators, CS-ARDL and CS-DL estimating techniques that are robust to cross-sectional dependence and small sample size bias. Banerjee and Carrion-i-silvestre (2015) maintain that the power of unit root and cointegration tests may be increased by combining the information from cross-section and time dimensions. This is especially beneficial when the time dimension is limited owing to the lack of accessibility to long series of consistent time series data, which is generally the case for African countries. Our methodologies controlled for time dynamics, cross-sectional heterogeneity and cross-sectional dependence because countries may share the same characteristics. We used the most significant determinants (per capita income, economic structure and openness) and constructed the unit root test based on the second-generation test that controlled for cross-sectional dependence. Although the degree of integration of these variables of interest was uncertain, we used the autoregressive distributed lag (ARDL) technique developed by Pesaran and Smith (1995), as extended by Pesaran et al. (1997), to evaluate the likelihood of a longrun cointegration relationship. Following the presence of cross-sectional dependence after conducting the Pesaran’s (2004) test, versions of the dynamic common correlated effects (dynamic CCE) estimator built by Chudik and Pesaran (2013), the CS-ARDL and the CS-DL estimating techniques, which are robust to small sample bias, were applied. We found that the three variables explained the variation of energy consumption in various ways. While trade openness had a positive and significant effect on energy consumption, the effect of economic structure was negative and significant. The results revealed that openness drove energy consumption in AOECs during the period under review. Conversely, economic structure did not drive energy consumption in the countries under study. Similarly, per capita income did not affect energy consumption. Most of these findings did not conform to both theoretical expectations and some previous empirical findings. These results, therefore, indicated that AOECs may behaved differently when it comes to what drives their energy consumption for a variety of reasons ranging from structural rigidity to their inability to transition to a more efficient and growth-enhancing energy regime. The following section of this article reviews the relevant literature, whilst the third section presents the theoretical framework for the study, the sources of the data and the research methodology. The empirical results are presented and discussed in the fourth section, which is followed by the conclusion and recommendations for policy and research 2. Literature review Varian’s (1992) neoclassical consumption theory postulates that consumption is based on primary economic variables, influencing the demand for a commodity. According to this theory, consumption is linked to individual preferences and other factors affecting demand, such as the price of a commodity and income. Therefore, all these variables are incorporated in conventional energy demand models. Like other goods, energy is consumed primarily because of the satisfaction derived from it and its utility, for example, in production for industrialist and in operating domestic appliances for households. In addition, therefore, an increase in income would positively impact energy consumption, although the reverse happens when a consumer’s income falls. An increase in the price of energy would cause a consumer to consume less or seek an alternative source (see, Varian, 1992). Several empirical studies have been conducted on the determinants of energy consumption (Samuel et al., 2013). For instance, Aziz et al. (2013) investigated factors driving energy demand in 16 developing countries using panel data analysis. Findings from the study suggested that price Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 4 of 17 and income were crucial factors determining energy consumption in these economies and economic structure and C02 emissions had a significant positive impact on energy use. Azam et al. (2015) employed ordinary least squares (OLS) to explore the determinants of energy consumption in Indonesia, Malaysia, and Thailand between 1980 and 2012. Their findings revealed that the inflow of foreign direct investment, economic growth, trade openness, and human capital development index had a positive and statistically significant effect on energy consumption. Urbanisation had a significant positive effect on energy consumption in Indonesia and Thailand, whilst the rate of population growth had positive and statistically significant effect on energy consumption in Malaysia. Keho (2016) investigated the drivers of energy consumption in 12 sub-Saharan African countries. Bounds test cointegration was employed to test the data set for individual countries from 1970 to 2011. Results from the study revealed evidence of cointegration with industrial output, imports, income, foreign direct investment, urbanization, population, and domestic credit to the private sector. It was further established that the share of the industrial sector in GDP, urbanization, population and per capita income had a long-run relationship with energy consumption. Keho (2016) concluded that industrial output, economic/population growth were the major variables that influenced energy consumption in virtually all the countries of interest. Using panel data analysis, Dalei (2016) investigated the drivers of energy consumption in open economies, namely China, Japan, and India. The findings of the study revealed that trade openness, GDP, and carbon emissions influenced energy consumption in these economies during the period under study. Kolawole et al. (2017) investigated factors that influenced energy demand in 16 countries in SSA from 1980 to 2014 using the panel cointegration technique. Their results confirmed income and urbanisation as major factors determining energy consumption. Sarkodie and Adom (2018) used nonlinear iterative partial least squares (NIPALS) to examine the drivers of aggregate energy consumption in Kenya. The findings revealed that price, population density, urbanisation and renewable energy from hydro sources played a vital role in promoting energy demand. However, climate change and higher income were expected to cause an increase in alternative energy consumption. Moreover, the growth in population rate was expected to increase the consumption of alternative energy, because of the fragility of the electricity infrastructure dependent on fossil fuels. Zaharia et al. (2019) used both bibliometric analysis and panel data techniques to analyse factors determining energy consumption in the EU-28 countries between 1995 and 2014. The results from the study showed that greenhouse gas emissions, GDP, population and labour growth had a positive correlation with both primary and final energy consumption. However, female population increase, healthcare expenditure and energy taxes had a negative impact. Fernandes and Reddy (2021) used the vector error correction model (VECM) to investigate the determinants of energy consumption in a sample of newly industrialised countries in Asia (China, India, Indonesia, Malaysia, Philippines and Thailand). The study found that industrialisation, the exchange rate, financial development and trade openness had a significant impact on energy consumption in China. In Thailand and India, only industrialisation had an impact, whilst GDP and trade openness had a significant impact on energy consumption in Indonesia and Malaysia, respectively. Adetola and Sunday (2021) investigated the determinants of energy consumption in Egypt using time series data from 1971 to 2017. The study adopted a wavelet coherent method for the analysis and the results revealed a positive connection between energy consumption and urbanisation at various scales and a bidirectional causal connection between the two variables. Additionally, the results revealed that imports were a significant determinant of energy consumption in diverse frequencies in Egypt. Moreover, evidence of bidirectional causality was found between economic Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 5 of 17 growth and energy consumption, whilst gross capital formation was found to drive energy consumption in Egypt during the period under review. The researcher concluded that policymakers in Egypt should formulate policies to ensure sustainable energy consumption. Canh et al. (2021) integrated the influence of a shadow economy by using the stochastic impacts by regression on population, affluence and technology (STIRPAT) model for 115 countries between 1991 and 2014 to investigate the determinants of energy consumption. The study employed the system-GMM estimation technique to examine the determinants of energy consumption according to three aspects: level, intensity and renewable. The analysis of the impact of the shadow economy and the drivers of energy consumption contributed to the literature by showing that in the global sample, a higher informal sector caused a higher level, higher intensity and higher renewable energy consumption. In studies in other fields, the CS-ARDL approach has been used to estimate long run relationships in the empirical application of macroeconomics models (Ditzen, 2019). Using the CS-ARDL approach, Mohaddes and Raissi (2017) investigated the effect of commodity terms of trade (CTOT) volatility on economic growth in commodity-dependent countries and also assessed the role of sovereign wealth funds (SWFs) and quality of institutions in their long-term growth performance. The results showed that while CTOT volatility exerted a negative effect on economic growth, the average effect was dampened if a country had an SWF and better institutional quality. Raissi and Anderson (2018) explored the long-term effect of incessant corporate debt accumulation on the productivity growth of Italian firms and examined if total factor productivity growth changed with the level of corporate indebtedness. The results revealed the significant negative impact of incessant corporate debt increase on total factor productivity growth and a weak indication of a threshold level of corporate debt, after which productivity expansion fell considerably. Ahmed (2020) examined the effect of business and consumer sentiment on stock market performance within the contexts of both advanced and emerging markets. The study revealed that for developed markets, the business sentiment had a positive impact on stock returns, covering short- and long-term time horizons, while for emerging markets, the price effect of business sentiment was short-lived. However, consumer sentiment tended to influence both market types positively, although only in the short run. Moreover, the impact of sentiment appeared stronger in emerging markets than it did in developed countries. The CS-ARDL model has been extensively used according to the literature because it is robust to omitted variable bias and the simultaneous determination of growth regressors. The validity of this method is the long-run connection amongst the variables of interest, the dynamic specification of the model, which sufficiently augmented for the regressors to become weakly exogenous and the resulting residual which is serially uncorrelated (Chudik & Pesaran, 2013). Moreover, CS-ARDL and CS-DL models account for cross heterogeneity, cross-sectional dependence arising from unobserved common factors, spillover effects dynamics and feedback effects. Therefore, they were used to investigate the factors influencing energy consumption, making the study novel, as we are not aware of any oil-related study that has employed these models to develop a CS-ARDL and CS-DL estimation for developing net oil-exporting countries in Africa. Therefore, this study contributes to knowledge by exploring CS-ARDL and CS-DL estimating techniques to investigate the relationship between energy consumption and its determinants in AOECs. 3. Methodology 3.1. Theoretical framework Based on the theory of consumer behaviour and the studies conducted by Mudakkar et al. (2013) and Tang and Tan (2014), a framework was designed to investigate the determinants of energy consumption. Given that the theory of consumer behaviour assumes that all consumers are rational and will maximise utility/satisfaction with each unit of consumption subject to given budget constraints, the energy consumption function can be written as follows: Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 6 of 17 ECt¼f Yt;PE;t;PX;t;Ht  � (1) where ECt represents energy consumption, Yt represents the level of income, PE;t represents the price of energy, PX;t represents the price of non-energy goods and Ht represents vector of factors influencing energy consumption. Assuming homogeneity, the specification of energy consumption can also be itemised as a function of relative price and real income of energy to non-energy goods as follows: ECt¼fYt PX;t ;PE;t PX;t ;Ht � � (2) In the energy consumption framework in equation (2), energy consumption is dependent on relative price, real income and other factors. According to the existing literature, other factors that affect energy consumption include economic structure, C02 emission, population size, employment and urbanisation (see, for instance, Azam et al., 2015; Aziz et al., 2013; Esen & Bayrak, 2017; Keho, 2016; Kolawole et al., 2017; Sarkodie & Adom, 2018). Therefore, the functional relationship between energy consumption and its determinants was modified as follows: ECi;t¼f PERINCi;t;OPENi;t;ESi;t  � (3) 2 In equation (3), ec denotes energy consumption, perinc denotes per capita income, open denotes trade openness and es denotes economic structure. The empirical model of energy consumption and its determinants could be written as follow: lnECi;t¼β0þβ1lnPERINCi;tþβ2OPENi;tþβ3ESi;tþεi;t(4) where ln is the natural logarithm and the error term ε is expected to be spherically distributed and white noise. The a priori expectations for the parameters in equation 4 are as follows: β1<0;β2<0;β3<0. 3.2. Data and measurement of variables The study investigated the factors influencing energy consumption in six net Africa oil-exporting countries; Angola, Algeria, Egypt, Gabon, Republic of the Congo and Nigeria (Sudan and Libya were left out because of the due non-availability of data). The choice of these countries was premised on their being exporters and producers of oil (OPEC, 2018). The study used annual data for the period between 1980 and 2018, the length of which allowed the recognition of changes in variables due to various energy-sector reforms to discover energy demand patterns (Kolawole et al., 2017; Shafiei et al., 2013). The dependent variable, energy consumption (EC), was proxied by total primary energy consumption in Mtoe. In line with Azam et al. (2015), the trade indicator was proxied by trade openness (OPEN), which was the ratio of the total value of imports and exports of goods and services to GDP expressed as a percentage. In addition, in line with Kolawole et al. (2017), economic structure (ES) was proxied by the value added of industrial output as a percentage of GDP. This comprised the value added of mining, manufacturing, construction, electricity, water and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. This is calculated without making deductions for the depreciation of fabricated assets and the degradation of natural resources. In line with the empirical work of Kolawole et al. (2017) and Keho (2016), per-capita income (PERINC) was proxied by the GDP per capita of the selected AOECs in constant 2010 US$. This was measured as the ratio of GDP to the midyear population. Data on energy consumption were obtained from the USEIA (2016) and data on the trade openness trade, economic structure and per capita income variables were sourced from the World Development Indicators (WDI; World Bank, 2016). In addition, all the variables were expressed in natural logarithms, except economic structure and trade openness, which were already in percentages. Ogunsola & Tipoy, Cogent Economics & Finance (2022), 10: 2058157 https://doi.org/10.1080/23322039.2022.2058157 Page 7 of 17 The results revealed that trade openness, per capita income and economic structure influenced energy consumption in various ways. Specifically, trade openness had a positive and significant effect on energy consumption, and this may be because these countries rely on energy exports for most of their foreign earnings. The effect of the economic structure was negative and significant, suggesting that these countries have been unable to transform their economies despite huge oil earnings. Similarly, per capita income did not affect energy consumption, indicating that these economies have reached the stage where income levels do not drive energy consumption, which may be due to structural rigidity in the energy mix. Most of these findings did not conform to both theoretical expectations and previous empirical findings. These results, therefore, indicated that AOECs may behave differently when it comes to what drives their energy consumption for a variety of reasons ranging from structural rigidity to their inability to transition to a more efficient and growth-enhancing energy regime. Based on the results of this study, some policy recommendations can be inferred. Firstly, since the structure of the economy, proxied by the share of the industrial sector, stunted energy consumption, policymakers should use oil earnings to grow manufacturing sectors in these countries. The governments of AOECs should encourage private investment from both internal and external sources, which can help to drive manufacturing value-added in these economies. Policymakers can kick start this process by providing an enabling environment to investors in industrial sectors to encourage investment and growth. Secondly, these countries should also accelerate the implementation of policies and create incentives that will stimulate the switch from fossil fuels to modern renewable energy. As stressed by Marinaș et al.’s (2018) study, countries with stronger policy implementation, which encourages renewable energy consumption or sources, are the ones that enjoy higher growth rates of consumption of such energies. Thirdly, an important finding of the study was that per capita income did not have any impact on energy consumption, and this may be due to the low level of per capita income in the sampled countries and their energy consumption. Hence, these countries need to transform their economies structurally in a way that will make economic growth more inclusive, driven by domestic value-added and not exogenous income from primary products exports. A puzzling result from the study was the negative relationship between per capita income and energy consumption in AOECs. There is a need to provide more quantitative evidence of this relationship beyond offering plausible reasons. A better way to achieve this, is to use a new dataset on the energy mix to examine whether households transition from fossil fuel to renewable energy as per capita income increases. This may be grounds for future research. Author details Akindele John Ogunsola 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-0910-571X Christian Kakese Tipoy 1 ORCID ID: http://orcid.org/0000-0002-3893-500X 1 School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban South Africa. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work was supported by the University of KwaZulu- Natal. Correction This article has been corrected with minor changes. These changes do not impact the academic content of the article. Citation information Cite this article as: Determinants of energy consumption: The case of African oil exporting countries, Akindele John Ogunsola & Christian Kakese Tipoy, Cogent Economics & Finance (2022), 10: 2058157. Notes 1. In Africa, most of the renewable energy consumed is through traditional procedures which is being replaced by modern fossil fuel-based energy. 2. Note that several variables such as energy price, urbanisation, capital and labour were considered in our model. However, we retained the most significant variables in the model based on tests conducted. The results of other tests are available upon request. 3. This will happen if the unobserved common factors are undeniably correlated with the regressors 4. This could stand as a test for the presence of cointegration where a significant speed of adjustment contributes to cointegration between the sample variables of interest in the model. 5. Pros and cons of the ARDL, the CS-ARDL and the CSDL can be retrieved from Chudik and Pesaran (2013). 6. Not with standing the contradictory results on the order of integration, we ran the Westerlund’s (2007) test. The result however showed that there were at least 2 cointegrating equations, indicating that there is long-run relationship between LNEC, LNPERINC, OPEN, and ES. Therefore, the null hypothesis of no cointegration was rejected. 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