Renewable Energy Consumption Determinants: Do They Differ between Oil-Exporting Countries and Oil-Importing Ones?

Abstract

This paper delves into the critical determinants of renewable energy consumption, focusing on the contrasting roles of oil imports and exports. It aims to bridge the knowledge gap by comparing these determinants across both oil-importing and oil-exporting nations, offering a comprehensive and nuanced perspective to inform policy recommendations. Using annual data from 1990 to 2018 sourced from the World Bank database, the study employs panel multiple regression analysis and adopts a fixed effects model to explore two main questions: What drives the use of renewable energy sources? How does a country’s oil importer or exporter status affect these factors? The findings reveal a significant but inverse relationship between oil rents and renewable energy consumption (REC) for both types of countries. Additionally, there is a notable negative correlation between GDP growth and REC for both oil-exporting and oil-importing countries. Interestingly, the crude oil average closing price and inflation show an insignificant impact on REC in both contexts. The study also highlights that net energy imports significantly affect REC, with a much stronger inverse relationship in oil-importing countries compared with oil-exporting ones. For oil-importing countries, diversifying energy sources is a crucial investment. Governments should prioritize research and development in renewable energy to spur technological advancements, enhancing efficiency and affordability. Economic growth-promoting policies, such as tax incentives and subsidies for renewable energy businesses, are vital for encouraging sustainable practices. Consistent, long-term policies are essential for providing investor confidence and supporting the transition to renewable energy. For oil-exporting countries, similar strategies are recommended. Additionally, allocating a portion of oil revenues to renewable energy infrastructure and funding research and development in renewable technologies through local universities and startups are crucial steps. This dual approach will not only enhance energy diversification but also foster innovation and sustainability in the energy sector.

1. Introduction

Growing alarms regarding climate change and energy supply have revitalized the prominence of renewable energy as the most viable solution to meet global energy demands in the foreseeable future. A significant opportunity arises for developing nations to amplify their adoption of renewable sources as their economies expand and energy requirements escalate. However, various factors may impede this transition toward renewables unless accompanied by requisite policies and regulations.

This paper explores the role of importing and exporting oil in the context of the topic of renewable energy consumption. Understanding a country’s reliance on oil imports is vital for ensuring its energy security and stability, as we have learned from the experiences of Europe over the previous 2 years and the Middle East over the past 30 years [1,2].

Previous literature has shown that the crude oil average closing price is significantly related to renewable energy consumption but with contradicting directions [3,4]. Energy use and oil rents [5,6,7,8,9], economic growth [9,10], and energy imports [11,12,13,14,15] are all determining factors of renewable energy consumption.

Previous research has frequently overlooked a crucial distinction in understanding the factors that drive renewable energy consumption: the differing dynamics between oil-importing and oil-exporting nations. This distinction is important because the economic and policy environments in these two groups of countries can lead to vastly different outcomes in their energy strategies and adoption of renewable sources. While some studies have explored the determinants of renewable energy consumption within oil-exporting countries, they often do so in isolation, failing to contextualize their findings by comparing them with the patterns observed in oil-importing nations. As a result, a comprehensive understanding of how these factors operate across different economic contexts remains underdeveloped.

This paper aims to bridge this significant gap by conducting a detailed comparative analysis of the determinants of renewable energy consumption in both oil-importing and oil-exporting countries. By examining these two groups side by side, the research provides a more nuanced perspective on the interplay between a country’s oil dependency and its renewable energy adoption. This approach not only highlights the unique challenges and opportunities each group faces but also offers valuable insights into how global energy policies can be tailored to accommodate the specific needs of oil-importing and oil-exporting nations.

While Ogunsola and Tipoy [16] explore the determinants of renewable energy consumption among oil-producing and exporting nations, they omit the crucial aspect of comparing these findings with oil-importing countries. This paper addresses this shortfall by conducting a comprehensive comparative analysis that encompasses both types of countries. Energy security as defined by the International Energy Agency refers to continuous physical availability at an affordable price while considering environmental concerns, and this concept forms the backdrop against which this research unfolds [17]. In an era where sustainable energy practices are paramount, discerning the determinants of renewable energy consumption is essential for shaping effective energy policies. By exploring how the oil-importing and exporting status of countries influences these determinants, the paper addresses a critical aspect of energy security, and contributes valuable insights to both academic discourse and practical policymaking.

The paper addresses two primary questions: What factors influence the use of renewable energy sources? How does a country’s status as either an oil importer or exporter impact these factors? Data extracted from the World Bank database and organized in two subsets (one for oil-exporting countries and one for oil-importing countries) were used to answer these questions. The unit roots test was conducted prior to the estimations; first, differencing was performed where necessary; correlation matrices were constructed; cross-section dependence tests were performed; and regressions were applied after correcting for cross-section dependency.

The findings reveal a significant correlation between energy consumption and oil rents and renewable energy consumption in both oil-exporting and importing countries. Economic growth demonstrates a significant association with renewable energy consumption across both types of countries. Furthermore, net energy imports significantly influence renewable energy consumption, showing an inverse relationship in oil-exporting countries and being ten times more pronounced in oil-importing nations.

This paper emphasizes critical policy recommendations for oil-exporting and importing countries to encourage the use of renewable energy. Governments in oil importers should encourage economic growth through subsidies and stable policies, and oil importers should diversify their energy sources. Furthermore, oil exporters should invest in renewable infrastructure and fund international research programs with the proceeds from oil sales to help advance global sustainability efforts.

Following the introduction and literature review, this paper presents the methodology and discussion of the results and then provides the recommendations. Figure 1, below, summarizes the process followed to develop this paper.

2. Literature Review

Based on the reviewed research, the key drivers of renewable energy consumption are country-specific macroeconomic factors, as well as crude oil prices and oil rents. Country-specific macroeconomic factors include gross domestic product, economic growth, real interest rates, taxes on non-renewable energy-based goods and services, effective exchange rate efficiency, and increased exports. In addition, countries’ net energy imports also play a role in determining renewable energy consumption. The following parts of this section explore the determinants of renewable energy consumption.

2.1. Macroeconomic Determinants of Renewable Energy Consumption

Economic growth and environmental degradation have been explored in several studies based on the Kuznets curve [18,19], but the theory also suggests that economic growth can provide a solution for environmental problems by increasing development and renewable energy use [19]. Researchers are particularly interested in the use of renewable energy sources and economic development. Acheampong, Boateng, Amponsah, and Dzator [20] studied the developed G7 countries (United States, Canada, France, Italy, Germany, Japan, and the United Kingdom) along with the N-11 countries—Bangladesh, Egypt, Indonesia, Iran, Mexico, Nigeria, Pakistan, Philippines, South Korea, Turkey, and Vietnam—and recognized that developed countries have benefited from renewable energy (RE) boosting production and raising gross domestic product over the past ten years (2010–2021). While RE stimulates gross domestic product growth, developing countries do not fully realize its benefits. The research concludes that while clean energy may not be advantageous in the initial production stages due to the high price of maintenance, production, and skilled labor, it is beneficial in the latter stages. The high cost of RE in developing nations and the need for immediate and quick benefits are reasons why non-RE is used in industry [21]. To reduce RE’s price and create regulations promoting these technologies, policymakers in developing countries should take advantage of globalization and international competition [22]. Doytch and Narayan [10] estimated the impact of non-renewable energy consumption on the growth of manufacturing and services. They found that the manufacturing industry sectors in middle-income economies in both developing and developed countries and the services sector in high-income economies contribute to the growth of high industries. By 2050, the transition to renewable energy will increase global employment by 0.2% and GDP by 2.5 percent [10].

The relationship between renewable energy consumption and economic growth is affected by new risk-based perspectives, including political risk, financial risk, economic risk, and total risk. The results of a study conducted by Wang, Dong, Li, and Wang [23] showed that when aggregate risk and political risk were used as threshold variables, there was a unique threshold between renewable energy consumption and economic growth. When this threshold was exceeded, the positive impact of renewable energy on economic development increased. When economic risk and financial risk were used as threshold variables, there was a double threshold between renewable energy consumption and economic growth. When exceeding the first threshold value but not exceeding the second threshold value, renewable energy had a positive impact on economic development. However, when economic risk and financial risk did not lie between the two threshold values, there was found to be a negligible negative correlation between renewable energy consumption and economic growth.

In addition, the decreased cost of financing renewable energy is an important factor in relation to renewable energy consumption. The decreased cost of financing is one frequently overlooked factor affecting the competitiveness of RE. Low interest rates reduce the cost of the implementation of renewable energy [24,25]. Renewable energy is currently quite affordable, but this will not always be the case with increased interest rates. While renewable energy costs have significantly decreased in the past, Polzin, Egli, Steffen, and Schmidt [26] demonstrate how rising interest rates (IRs) can reverse that trend, especially in Europe where IRs are historically low. The research shows that in Germany, levelized electricity costs for solar and onshore wind could increase by 11% and 25%, respectively, in five years, as IRs return to pre-financial crisis levels, at a financial cost of approximately one million dollars. The viability of renewable energy investments would significantly decline because rising IRs would have a negative impact on the costs of fossil-fuel-based electricity, which would be reduced, and this may be detrimental. Their study concludes that increasing IRs could endanger the transition to a sustainable energy source and suggests a self-adjusting thermostatic policy strategy to prevent this. As a result, policymakers have introduced several support measures for renewable energy, particularly in the EU, and these have been successful. In many European countries today, the generation costs of alternative energy are comparable to the (marginal) prices of existing gas- or coal-fired power stations. Low interest rates have contributed to alternative energy sources’ increased economic viability due to the low cost of capital [27,28]. In particular, because of significant improvements in financial conditions, renewable energy has become more affordable. On the other hand, increasing financial expenditure will cause disproportional increases in the cost of renewable energy [27,28].

Walmsley, Walmsley, Varbanov, and Klemeš [29] evaluated five energy ratios. The ratios considered were energy return on investment (EROI)—standard and external, energy payback time (EPT), primary energy factor (PEF), and resource utilization factor (RUF). Based on return on (external) energy investment, production methods fall into three levels: (1) combined cycle nuclear, natural gas and geothermal (in New Zealand) with a ratio greater than 30, (2) hydropower, wind and geothermal (in Iceland) with a ratio of 5 to 30, and (3) solar photovoltaics with a ratio of less than 5. A high return on energy investment corresponds to a short return on investment period and vice versa. The energy rate efficiency level for renewable energy generation sources—hydroelectric, wind, geothermal and solar—depends greatly on the quality of the available primary natural resources.

Furthermore, polluting energy sources are subject to taxes to reduce emissions that are harmful for the environment and human health [30]. Thus, several measures have been taken to limit the negative implications of pollution, and the money raised in some countries to mitigate the implications can also help vulnerable families transition to low-carbon energy sources [30,31]. However, a recent OECD analysis found that 70% of energy-related CO₂ emissions from developed and developing economies were untaxed, providing no incentive to switch to greener energy [32]. According to a quick glance at Taxing Energy Use 2019, tariffs on energy from polluting sources are nowhere near being established at the levels required to lessen the risks and effects of climate change and air pollution in 44 nations that account for more than 80% of energy emissions [32]. Ninety-seven percent of energy-related CO₂ emissions outside of road transportation were taxed in all 44 nations evaluated, at amounts substantially below those that would represent environmental harm. Only four nations—Denmark, the Netherlands, Norway, and Switzerland—tax non-road energy at rates higher than EUR 30/t CO₂, which is regarded as the low end of the costs associated with carbon emissions on the environment. Energy taxes have even decreased in a few nations recently. On the other hand, increasing the tax burden on fossil fuels, and occasionally nuclear energy as well, and then implementing tax incentives for renewable energy sources are two popular ways to use fiscal instruments to help the energy transition [33,34]. This is carried out to provide sustainable energy sources with a financial edge. When handled wisely, taxes on non-renewable energy sources can fund the increase in renewable energy production [35]. The purpose of taxes is to dissuade individuals from consuming certain commodities that may adversely affect their well-being, the ecosystem, or other concerns [36]. Due to taxes on non-renewable energy-dependent goods and services, consumers and investors shift their supply and demand toward alternatives that do not contribute to pollution [37]. Taxes imposed on non-renewable energy sources promote investment in renewable energy and help investors reduce their reliance on non-renewable energy in their investments [38,39].

On the other side, currency devaluation heightens the earnings of individuals, particularly those employed in export industries [40,41]. It can also lead to a surge in domestic investment, which in turn can profoundly impact energy consumption within the economy [42,43,44]. As per Borozan [45], the rise in people’s earnings increases the demand for more energy, resulting in a surge in energy consumption. So, the variation of exchange rates can potentially impact the economy’s energy consumption. The combination of growing urbanization and economic development drives a surge in energy usage, leading to environmental degradation, hence increasing the importance of renewable energy [46]. In addition, the recent literature has evaluated renewable energy consumption’s impact on climate transition risk. The correlation between renewable energy and sovereign yields/spreads is positive in developing economies facing challenges in transitioning from fossil fuels to renewables due to resource constraints, potentially leading to lower prioritization of climate change targets [47]. Moreover, renewable energy consumption impacts geopolitical risk; Ren, Yang, and Jin [48] convey that the convergence of renewable energy consumption is inversely related to geopolitical risk for developed countries due to the fluctuation of oil prices. An essential component of efforts to reduce CO₂ emissions is based on renewable energy. The Paris Agreement’s goal of limiting global warming to below 2 °C relative to the pre-industrial period would not be met without it [49]. Compared with fossil fuels, renewable energy is the key solution to climate change and the core part of reducing air pollution [50]. Recent scientific studies conducted in OECD countries have demonstrated that the use of renewable energy is expanding quickly and that CO₂ levels are decreasing [51,52,53,54]. According to data from the International Energy Agency (IEA), emissions from the power sector fell by more than 170 million tonnes (Mt) (or 1.3%) worldwide in 2019 [49]. Due to their quick adoption, renewable energy sources contributed the most in 2019 to reducing global energy-related CO₂ emissions [55]. Enhancing the use of renewable energy resulted in a 330 Mt reduction in the potential growth in emissions [49]. Despite the revolutionary attempt to adopt renewable energy technologies, some industrialized nations are regrettably still steadfast in using fossil fuels to achieve faster and more impressive economic growth [56].

The recent literature also highlights socio-economic factors and especially poverty in relation to energy consumption. In fact, poverty impacts people’s choices and opportunities and can perpetuate disadvantage. Furszyfer Del Rio, Sovacool, Griffiths, Foley, and Furszyfer Del Rio [57] indicate that poverty remains a widespread problem globally due to the uneven distribution of wealth, often leading to inequities in energy consumption and emissions, reporting that low-income and minority households are at higher risk of experiencing concurrent energy and transportation poverty, regardless of the national context in which they live. They show that even in relatively wealthy countries, many people still face financial poverty that prevents them from switching to renewable energy consumption, and they conclude that renewable energy consumption on a global scale requires significant changes in political action, resource allocation, and investment in social services.

Furthermore, the factors that promote reduction of renewable energy costs across a technology’s innovation include learning by research, learning by deployment, and economies of scale [58].

2.2. The Impacts of Oil Rents and Oil Prices Renewable Energy Consumption

Various investigations have used distinct methods to explore the relationship between oil revenues and sustainable energy use. According to data for the period 1990 to 2015, there was a negative and significant correlation between oil revenues and renewable energy consumption in the most developed and populous African countries, such as Nigeria, Democratic Republic of Congo, and South Africa [7], implying that reduced oil revenues led to increased use of sustainable energy sources [6,7]. Conversely, the utilization of renewable energy in the countries of the Balkans remains uninfluenced by oil revenue [8]. Another perspective posits that in Canada, oil price variations directly impact the financing and the use of long-lasting goods [5].

Increased oil prices generate much-needed revenue and make renewable energy sources more economically feasible than oil. This has resulted in a growing inclination toward using renewable energy, as customers become more conscious of oil substitutes due to the surge in crude oil costs. Countries like Egypt, Jordan, Mauritania, Morocco, Tunisia, Turkey, and Yemen, which import oil, have developed their renewable energy sectors to reduce their dependence on oil demand and minimize their governments’ financial outlays [59]. Other nations that trade goods, such as Saudi Arabia, are weary of the competitive disadvantage caused by exorbitant oil prices; abandoning oil will be a complex task as the revenue from oil sales is essential for this transition [3]. As a result, for nations that import oil, the cost of oil and the use of sustainable energy are positively linked; high oil prices lead to increases in sustainable energy consumption [4]. In contrast, oil-exporting countries have little motivation to convert to sustainable energy. It can be argued that these nations will not be incentivized to develop sustainable energy sources unless their reserves of oil and the costs of transitioning to sustainable energy are reduced or policy changes are imposed at national or international level [4]. Studies conducted in 2008 and 2009 demonstrated a strong correlation between the stock prices of solar energy companies and the price of crude oil [60,61]. However, in recent years, this correlation has weakened [62].

In general, crude oil and renewable energy are used to meet different energy demand segments globally. Crude oil remains an important primary energy source; it is mainly used to produce transportation fuel, while sustainable energy is used to produce electricity [62,63]. Thus, crude oil and renewable energy are not direct substitutes and an increase in the price of one does not necessarily lead to an increase in demand for the other [64]. Inadequate (or perhaps too broad) linkages between crude oil and renewables can create disincentives for policymakers, project sponsors, and private investors, leading to unjustifiable political and financial consequences [65]. Observers and stakeholders in the renewable energy sector must recognize the influence of factors such as legislation or regional access, which have a larger impact on the energy outlook for renewables compared with oil prices [66]. Nevertheless, geopolitical challenges significantly impact both natural gas and renewable energy markets [65].

2.3. The Impact of Energy Imports, Exports, and Energy Use on Renewable Energy Consumption

The impact of oil imports, oil exports, and energy use on renewable energy consumption is limited. Makki, Mawad, and Khalili [9] investigated the linkage between energy prices, oil rents, and other macroeconomic factors on renewable energy consumption, and concluded that the existing relationships may differ between oil-importing countries and oil-exporting ones, while recognizing that the energy policy of the countries plays a crucial role. On the other hand, Ogunsola and Tipoy [16] explored the same question, for only countries producing and exporting oil, and their study found that trade openness positively affected energy consumption due to reliance on energy exports.

In fact, the degree to which a nation or region depends on oil imports is crucial to the security of its overall energy supply [1,2] and impacts policies encouraging domestic renewable energy consumption over energy imports. The International Energy Agency (IEA) describes energy safety as affordable, sustained physical accessibility that takes into account environmental concerns [17]. Other definitions of this concept focus on issues of accessibility and affordability of energy resources [17]. Recent advancements in renewable energy technology offer the possibility of domestic electricity and biofuel production, which theoretically could reduce the use of fossil fuels and, consequently, imports [67].

Wei, Ni, and Shen [11] argue that nations with substantial energy requirements should import oil and use local resources. The amount of energy a nation imports should roughly equal its domestic energy production minus its total energy demand. A significant body of additional relevant work in the literature suggests that locally produced fossil fuels or renewable energy could at least partially replace imported energy [12,15]. On the other hand, York [13] points out that non-fossil gasoline use is not a substitute for fossil gasoline utilization on a single scale. Alternatively, it might be necessary to substitute between four and ten units of non-fossil fuel energy for every unit of energy from fossil fuels.

Kahia, Aïssa, and Lanouar [68] show that with respect to domestic fossil fuel production, claims that domestic products are largely substitutable for imports, or the converse, may be wrong. Additionally, of the 15 industrial holdings the authors studied in the MENA region, energy or fuel products (oil, natural gas, coal) had the lowest inelasticity and a lower ability to absorb imports through domestic product substitution. It was suggested that this may have been due to domestic resource constraints, trade regulations and restrictions, or both. Product similarity or dissimilarity can affect substitutability between industry groups. For the period from 1960 to 2011 in the United States, York [14] analyzed the correlation between domestic production and fossil fuel imports, focusing on the elasticity of substitution of domestic imports and the domestic exports. Like Kahia, he found that national production substituted indirectly for imports; about three-quarters of the units of imported energy were replaced by domestically produced energy. Qiang, Lin, Zhao, Liu, Liu, and Wang [43] examined the factors affecting oil imports into China. They found that substitutability varied depending on the type of energy; national energy production had the effect of displacing oil and coal, but not natural gas or hydroelectricity.

3. Data and Methodology

3.1. Data

The main goal of this study was to identify the determinants of renewable energy consumption while comparing between oil exporters and oil importers. Data were retrieved from the World Bank database, including renewable energy consumption (% of total final energy consumption), energy imports, net (% of energy use), energy use (kg of oil equivalent per capita), oil rents (% of GDP), GDP growth (annual %), energy use per capita, urban population as a percent of population, inflation, per capita growth rate, and crude oil average closing price, for the years between 1990 and 2018. Some observation data for the years 2016 to 2018 were forecasted for some variables in order to complete the dataset.

Data for several other variables were not available for most of the sample countries, like industry (including construction), value added (% of GDP), financial development factor, and fossil fuel energy consumption (% of total). This imposed limitations on the possible number of exogenous variables included in the model. Added to that, the sample was limited to the time period that extended between 1990 and 2018, since most of the data were not available for all countries before 1990 or after 2018.

The sample included 24 countries, divided into two sub-samples. The first one represented exporters of oil, including the United Arab Emirates, United States, United Kingdom, Saudi Arabia, Russian Federation, Nigeria, Kazakhstan, Norway, Angola, Brazil, Oman, Mexico. The second one represented importers of oil, including China, India, Korean Rep., Japan, Germany, Netherlands, Spain, Thailand, Italy, Singapore, France, Belgium. Each panel data sample constituted 12 countries while considering the same independent variables and time period.

This paper has a few limitations. Primarily, the available data span from 1990 to 2018. Although our results are significant and encompass a substantial period, they do not account for two recent and crucial market shocks: the COVID-19 pandemic and the Ukraine war.

3.2. The Renewable Energy Consumption (REC) Equation

The renewable energy consumption equation is defined by the independent variables net energy imports (NEIs), energy use per capita (EUPC), oil rents (OR), GDP growth (G), crude oil average closing price (COP), inflation (I), per capita growth rate (PCGR), urban population (UP) and by the dependent variable renewable energy consumption (REC). Equation (1) includes the tested determinants:

$$REC={\alpha }_0+{\alpha }_{1}NEI+{\alpha }_{2}EUPC+{\alpha }_{3}OR+{\alpha }_{4}G+{\alpha }_{5}COP+{\alpha }_{6}I+{\alpha }_{57}PCGR+{\alpha }_{8}UP\epsilon$$

(1)

where ${\alpha }_0$ is the model intercept, ${\alpha }_1$ to ${\alpha }_8$ are partial slope coefficients.

Indicator data were exported from the World Bank open access database.

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NEIs were estimated as the annual energy use less production, both measured in oil equivalents;

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EUPC refers to 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;

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OR is the difference between the value of crude oil production at regional prices and total costs of production;

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annual percentage growth rate of GDP (G) is estimated at market prices based on constant local currency;

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COP is exported from open access financial data;

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I represents consumer prices (annual %);

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PCGR (annual %) is the GDP per capita, calculated by dividing annual GDP by the same year’s population;

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UP is estimated based on the United Nations Population Division’s World Urbanization Prospects. Urban percentages are the numbers of persons residing in an area defined as “urban” per 100 total population.

3.3. Methodology

To identify the impact of the importer or exporter status of countries on REC, regressions were performed separately on both sub samples, and the results were compared. First, a pre-estimation unit roots test was performed [69], then first differencing was applied on the variables REC, COP, EUPC, NEIs, and UP for both exporter and non-exporter countries sub-samples, and on the variable OR for the non-exporter countries sub-sample. Correlation matrices were generated to detect correlations between the variables. The panel cointegration test was applied to detect any long-term relationship between the variables. Cross-section dependence testing and panel least squares multiple regressions were applied considering fixed effects estimation that eliminated the effects of time-invariant unobserved heterogeneity by including individual-specific fixed effects in the model. Figure 2 summarizes the major steps used in the data analysis methodology.

4. Empirical Results and Discussion

4.1. Univariate Analysis

Based on

Table 1. Descriptive statistics for the oil-exporting (exporters) countries.

	REC	COP	EUPC	G	I	NEIs	OR01	PCGR	UP
Mean	23.25	47.53	4015.97	3.03	87.61	−216.67	14.22	1.05	71.16
Median	4.72	41.51	3784.92	2.86	6.67	−117.89	9.20	1.44	76.58
Maximum	88.74	99.67	12,172.41	18.33	4800.53	42.32	55.38	13.69	86.57
Minimum	0.00	14.42	433.57	−23.98	−25.13	−885.24	0.00	−26.34	29.68
Std. Dev.	29.80	28.64	2821.52	4.57	406.05	249.79	14.41	4.61	13.83
Skewness	0.94	0.57	0.61	−0.73	7.19	−0.93	0.96	−1.03	−1.38
Kurtosis	2.31	1.88	2.82	8.00	66.21	2.55	2.84	7.63	3.91
Jarque–Bera	58.89	36.76	22.31	392.21	60,764.48	53.36	53.84	371.82	123.40
Probability	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Sum	8069.21	16,493.95	1,393,542	1052.478	30,401.98	−75,187.64	4934.518	365.31	24,691.89
Sum Sq. Dev.	307,329.5	283,777.8	2.75 × 109	7231.373	57,047,499	21,590,272	71,830.23	7359.69	66,223.77
Observations	347	347	347	347	347	347	347	347	347

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), per capita growth rate (PCGR), inflation (I), renewable energy consumption (REC), and URBAN_POP_OF_POP (UP). Source: authors’ work.

and

Table 2. Descriptive statistics for the oil-importing (importer) countries.

	REC	COP	EUPC	G	I	NEIs	OR01	PCGR	UP
Mean	12.00	47.46	3346.30	3.56	2.65	57.20	0.28	2.79	70.76
Median	7.72	41.51	3781.09	2.92	1.98	61.84	0.01	2.23	76.60
Maximum	58.65	99.67	7370.65	14.52	20.61	99.39	5.21	13.63	100.00
Minimum	0.19	14.42	350.07	−7.63	−3.57	−4.56	0.00	−8.76	25.54
Std. Dev.	13.38	28.62	1562.12	3.56	2.88	29.20	0.61	3.30	22.33
Skewness	1.71	0.57	−0.40	0.45	1.76	−0.47	3.12	0.33	−0.71
Kurtosis	5.46	1.89	2.18	3.54	8.71	2.02	17.06	4.08	2.41
Jarque–Bera	257.21	37.01	19.14	16.18	653.13	26.91	3435.80	23.47	34.48
Probability	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Sum	4176.28	16,518.48	1,164,513	1242.00	923.09	19,906.51	100.04	972.56	24,627.64
Sum Sq. Dev.	62,123.12	284,305.4	8.47 × 108	4419.78	2892.82	295,896.4	130.52	3792.10	173,129.9
Observations	348	348	348	348	348	348	348	348	348

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), Oil Rents (OR01), per capita growth rate (PCGR), inflation (I), renewable energy consumption (REC), and URBAN_POP_OF_POP (UP). Source: authors’ work.

, below, renewable energy consumption is on average higher for oil-exporting countries (23.25) than for oil-importing countries (12.00). This could be true because oil-exporting countries have higher budgets and may be more able to invest in and consume new costly technologies, compared with oil-importing countries that usually have energy import cost burdens affecting their budgets. The minimum renewable energy consumption for exporting countries is zero, which indicates that not all countries have equal renewable energy consumption. This is also evident with the high standard deviation (29.803) of REC for oil exporter countries.

Moreover, the oil rents for oil-exporting countries (14.22) are higher than the oil rents for oil-importing countries (0.287), which reflects the fact that most exporters of oil benefit from oil rents. Furthermore, the net energy import value for oil exporter countries is negative 216.679, which indicates an export value of energy of 216.679. For non-exporters of oil, the net energy import average value is a positive 57.202, indicating an import of energy of 57.202. The standard deviation of the net energy import variable for exporter countries is 249.799, indicating a large discrepancy in the export power of the sampled countries. The average economic growth rates for exporters and non-exporters countries are close, which indicates that the two samples are comparable. In addition, the average energy use per capita for exporters and non-exporters countries is also very close, which also indicates that the countries on average have similar behaviors in terms of energy use and consumption, making the sample comparable.

For the years between 1990 and 2018, the average price of crude oil was USD 47.533, reaching a high of USD 99.670 in the year 2008 and a minimum of USD 14.42 in the year 1998.

The correlation matrices for both samples are provided in

Table 3. Oil-Exporting Countries—Correlation Matrix.

	COP	EUPC	G	I	NEIs	OR01	PCGR	UP
COP	1
EUPC	0.03	1
G	0.14	0.00	1
I	−0.18	−0.16	−0.11	1
NEIs	0.10	−0.08	−0.17	0.01	1
OR01	0.12	0.04	0.30	0.05	−0.57	1
PCGR	0.08	−0.09	0.88	−0.10	−0.03	0.06	1
UP	0.16	0.52	−0.09	−0.18	0.11	−0.19	−0.06	1

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), per capita growth rate (PCGR), inflation (I), and URBAN_POP_OF_POP (UP). Source: authors’ work.

and

Table 4. Oil-Importing Countries—Correlation Matrix.

	COP	EUPC	G	I	NEIs	OR01	PCGR	UP
COP	1
EUPC	0.06	1
G	−0.14	−0.35	1
I	−0.19	−0.52	0.44	1
NEIs	0.05	0.54	−0.30	−0.36	1
OR01	0.05	−0.64	0.47	0.48	−0.62	1
PCGR	−0.13	−0.34	0.97	0.39	−0.32	0.46	1
UP	0.12	0.92	−0.38	−0.52	0.69	−0.70	−0.38	1

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), per capita growth rate (PCGR), inflation (I), and URBAN_POP_OF_POP (UP). Source: authors’ work.

, below.

Table 3 shows the correlation matrix for the oil-exporting countries. It was evident that per capita growth rate was highly correlated with GDP growth rate; thus, it was dropped from the model.

For the oil-importing countries, the correlation matrix shows that per capita growth rate highly correlates with GDP growth rate; thus, it was dropped from the model. On the other side, urban population also highly correlated with several variables including OR, NEIs, and EUPC; thus, it was also dropped from the model.

Panel cointegration testing was performed for both samples and based on the augmented Dickey–Fuller results, it was concluded that there was no long-term cointegration between the variables for the sample of oil exporters, with a p-value of 0.1365; the null hypothesis that there is no cointegration is retained. Similarly for the oil importers sample, the augmented Dickey–Fuller test resulted in a p-value of 0.7961, also indicating no cointegration between the variables.

4.2. Unit Root Tests

Unit root testing was performed for all the variables in the model, for each of the samples.

The results for oil-exporting countries are shown in

Table 5. Oil-Exporting Countries, Variables Unit Root Tests.

	No Differencing		Differencing		Result
	Levin, Lin, and Chu t	p-Value	Levin, Lin, and Chu t	p-Value
REC	2.63	0.99	−9.33	0.00	Stationary After 1st differencing
COP	−0.28	0.38	−10.21	0.00	Stationary After 1st differencing
EUPC	3.41	0.99	−6.68	0.00	Stationary After 1st differencing
G	−4.27	0.00			No unit root
I	−4.52	0.00			No unit root
NEIs	−0.31	0.38	−5.97	0.00	Stationary After 1st differencing
OR01	−2.69	0.00			No unit root
PCGR	−3.94	0.00			No unit root
UP	−0.54	0.29	−27.48	0.00	Stationary After 1st differencing

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), per capita growth rate (PCGR), inflation (I), and URBAN_POP_OF_POP (UP). Source: authors’ work.

, below.

The results for oil-importing countries are shown in

Table 6. Oil-Importing Countries, Variables Unit Root Tests.

	No Differencing		First Differencing		Result
	Levin, Lin, and Chu t	p-Value	Levin, Lin, andChu t	p-Value
REC	3.81	0.99	−3.64	0.00	Stationary after first differencing
COP	−0.28	0.38	−10.21	0.00	Stationary after first differencing
EUPC	1.30	0.90	−6.76	0.00	Stationary after first differencing
G	−7.23	0.00			No unit root
I	−4.31	0.00			No unit root
NEIs	−1.20	0.11	−6.37	0.00	Stationary after first differencing
OR01	−0.97	0.16	−9.73	0.00	Stationary after first differencing
PCGR	−7.63	0.00			No unit root
UP	−0.94	0.17	−7.06	0.00	Stationary After 2nd differencing

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), per capita growth rate (PCGR), inflation (I), and URBAN_POP_OF_POP (UP). Source: authors’ work.4.3. Regression Results and Discussion

, below.

Table 7. Oil-Exporting Countries Panel Least Squares Regression Results.

Variable	Coefficient	Prob.
D(COP)	−0.00	0.32
D(EUPC)	0.00	0.20
G	−0.02 *	0.08
I	−2.58 × 10−5	0.87
D(NEIs)	−0.00 **	0.01
OR01	−0.00	0.27
D(UP)	−0.23	0.21
C	0.22 **	0.02
R-squared	0.049
Adjusted R-squared	0.028
Prob(F-statistic)	0.020
Breusch–Pagan LM		0.01
Pesaran CD		0.00

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I), and URBAN_POP_OF_POP (UP). **: significant at a 5% level of significance, *: significant at a 10% level of significance. Source: authors’ work.

, below, shows the result of the regression tests applied using Equation (1) for the two samples, oil-exporting and oil-importing countries.

Oil-Exporting Countries:

The least squares estimation was performed; the R-squared value was very small (0.049) and the coefficients of all the variables were insignificant at a 5% level of significance.

A cross-section dependence test was performed; the Breusch–Pagan LM (0.01) and Pesaran CD (0.00) p-values were lower than the 5% level of significance; thus, the null hypothesis that there is no cross-sectional dependence is rejected. Cross-sectional SURs (seemingly unrelated regressions) were selected to correct for that.

Using cross-sectional SUR, most of the variables are now significant with an improved R-squared value as seen in

Table 8. Oil-Exporting Countries Panel Least Squares Regression Results—Corrected.

Variable	Coefficient	Prob.
D(COP)	−0.00 *	0.09
D(EUPC)	0.00 ***	0.00
G	−0.01 ***	0.00
I	−2.42 × 10−5	0.79
D(NEIs)	−0.00 ***	0.00
OR01	−0.00 ***	0.00
D(UP)	−0.14 ***	0.00
C	0.14 ***	0.00
R-squared	0.19
Adjusted R-squared	0.17
Prob(F-statistic)	0.00
Breusch–Pagan LM		0.99
Pesaran CD		0.31

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I), and URBAN_POP_OF_POP (UP). ***: significant at a 1% level of significance, *: significant at a 10% level of significance. Source: authors’ work.

.

The cross-sectional dependence test was performed again, and the Breusch–Pagan LM (0.99) and Pesaran CD (0.31) p-values were higher than the 5% level of significance; thus, the null hypothesis that there is no cross-sectional dependence is accepted.

Due to insufficient instruments, GMM with random effects could not be performed. Since the cross-sectional dependence affects the estimation with random effects, as the unobserved individual-specific effects are assumed to be uncorrelated with the independent variables, which in this case is not valid, then estimating the model with fixed effects allows us to correct for cross-sectional dependence.

Table 9. Oil-Exporting Countries Panel Least Squares Regression Results—Fixed Effects.

Variable	Coefficient	Prob.
D(COP)	−0.00	0.64
D(EUPC)	0.00 *	0.08
G	−0.02	0.14
I	7.74 × 10−5	0.65
D(NEIs)	−0.00 ***	0.00
OR01	−0.02 *	0.07
D(UP)	0.03	0.91
C	0.34 *	0.09
R-squared	0.08
Adjusted R-squared	0.03
Prob(F-statistic)	0.03
Breusch–Pagan LM		0.00
Pesaran CD		0.00

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I), and URBAN_POP_OF_POP (UP). ***: significant at a 1% level of significance, *: significant at a 10% level of significance. Source: authors’ work.

, below, provides the panel regression results with fixed effects.

All the coefficients were found to be insignificant with very small R-squared values. The cross-sectional dependence test was performed, and the Breusch–Pagan LM (0.00) and Pesaran CD (0.00) p-values were lower than the 5% level of significance; thus, the null hypothesis that there is no cross-sectional dependence is rejected. Cross-sectional SURs (seemingly unrelated regressions) were selected to correct for that.

To ensure that there was no cross-sectional dependence, the test was performed and the Breusch–Pagan LM (1.00) and Pesaran CD (0.36) p-values were higher than the 5% level of significance as seen in

Table 10. Oil-Exporting Countries Panel Least Squares Regression Results—Fixed-Effect-Corrected.

Variable	Coefficient	Prob.
D(COP)	−0.00	0.69
D(EUPC)	0.00 ***	0.00
G	−0.02 ***	0.00
I	−1.13 × 10−5	0.91
D(NEIs)	−0.00 ***	0.00
OR01	−0.01 ***	0.00
D(UP)	0.02	0.70
C	0.15 **	0.01
R-squared	0.37
Adjusted R-squared	0.34
Prob(F-statistic)	0.00
Breusch–Pagan LM		1.00
Pesaran CD		0.36

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I), and URBAN_POP_OF_POP (UP). ***: significant at a 1% level of significance, **: significant at a 5% level of significance. Source: authors’ work.

; thus, the null hypothesis that there is no cross-section dependence is accepted.

For oil-exporting countries, the fixed-effect model results are provided in the below equation:

$$REC=0.149+0.00022EUPC-0.0163G-0.00378NEI-0.0095OR$$

(2)

The equation shows that, for oil-exporting countries, there was a positive significant relationship between energy use per capita (EUPC) and renewable energy consumption (REC). Renewable energy consumption (REC) increases by 0.00022 for each energy use per capita (EUPC) unit increase. In addition, the results of the regression showed that there was a negative significant relationship between GDP growth (G) and renewable energy consumption (REC). Renewable energy consumption (REC) decreased by 0.0163 for each 1% economic growth. Net energy imports (NEIs) had a negative significant relationship with renewable energy consumption (REC). Renewable energy consumption (REC) decreased by 0.00378 for each unit increase in net energy imports (NEI). Moreover, the regression results showed a negative significant relationship between oil rents (OR01) and renewable energy consumption (REC). Renewable energy consumption (REC) decreased by 0.0095 for each dollar increase in oil rents (OR01).

Crude oil average closing price (COP) and inflation (I) had an insignificant impact on the level of renewable energy consumption (REC) for oil-importing countries.

Oil-Importing Countries:

The least squares estimation was performed; the R-squared value showed a low percentage (0.29), and the model was significant at a 5% level of significance.

A cross-sectional dependence test was performed, in which the Breusch–Pagan LM (0.00) and Pesaran CD (0.09) p-values were mostly lower than the 5% level of significance as seen in

Table 11. Oil-Importing Countries Panel Least Squares Regression Results.

Variable	Coefficient	Prob.
D(COP)	0.00 *	0.05
D(EUPC)	−0.00	0.45
G	−0.09 ***	0.00
I	−0.06 ***	0.00
D(NEIs)	−0.05 ***	0.00
OR01	−0.02	0.90
C	0.49 ***	0.00
R-squared	0.29
Adjusted R-squared	0.28
Prob(F-statistic)	0.00
Breusch–Pagan LM		0.00
Pesaran CD		0.09

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I). ***: significant at a 1% level of significance, *: significant at a 10% level of significance. Source: authors’ work.

; thus, the null hypothesis that there is no cross-sectional dependence is rejected. Cross-sectional SURs (seemingly unrelated regressions) were selected to correct for that.

For oil-importing countries,

Table 12. Oil-Importing Countries Panel Least Squares Regression Result—Corrected.

Variable	Coefficient	Prob.
D(COP)	0.00 **	0.05
D(EUPC)	−7.82 × 10−5	0.22
G	−0.07 ***	0.00
I	−0.04 ***	0.00
D(NEIs)	−0.02 ***	0.00
OR01	0.18	0.27
C	0.37 ***	0.00
R-squared	0.41
Adjusted R-squared	0.39
Prob(F-statistic)	0.00
Breusch–Pagan LM		1.00
Pesaran CD		0.66

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I). ***: significant at a 1% level of significance, **: significant at a 5% level of significance. Source: authors’ work.

provides the least squares regression results after correcting for cross-sectional dependence followed by the cross-sectional dependence test.

Growth, inflation, and NEIs were significant while the other variables were not. The R-squared value was calculated to be 0.41, and the cross-sectional dependence test was performed, in which the Breusch–Pagan M (1.00) and Pesaran CD (0.66) p-values were mostly lower than 5% level of significance; thus the null hypothesis that there is no cross-sectional dependence is rejected.

Regression with fixed effects was also performed, and the results are shown in

Table 13. Oil-Importing Countries Panel Least Squares Regression Results—Fixed Effects.

Variable	Coefficient	Prob.
D(COP)	0.01 *	0.08
D(EUPC)	−0.00	0.24
G	−0.07 ***	0.00
I	−0.05 **	0.01
D(NEIs)	−0.05 ***	0.01
OR01	−0.05	0.80
C	0.39 ***	0.00
R-squared	0.36
Adjusted R-squared	0.33
Prob(F-statistic)	0.00
Breusch–Pagan LM		0.00
Pesaran CD		0.08

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I), ***: significant at a 1% level of significance, **: significant at a 5% level of significance, *: significant at a 10% level of significance. Source: authors’ work.

, below. The R-squared value was 0.31, and only growth rate, inflation, and NEIs were significant.

The cross-sectional dependence test was performed, and the Breusch–Pagan LM (0.0002), Pesaran scaled LM (0.0000), and Pesaran CD (0.0804) p-values were mostly lower than the 5% level of significance; thus, the null hypothesis that there is no cross-sectional dependence is rejected.

Table 14. Oil-Importing Countries Panel Least Squares Regression Results—Fixed-Effect-Corrected.

Variable	Coefficient	Prob.
D(COP)	0.00	0.69
D(EUPC)	−7.75 × 10−5 ***	0.00
G	−0.05 ***	0.00
I	−0.03	0.91
D(NEIs)	−0.03 ***	0.00
OR01	0.04 ***	0.00
C	0.29 **	0.01
R-squared	0.52
Adjusted R-squared	0.49
Prob(F-statistic)	0.00
Breusch–Pagan LM		1.00
Pesaran CD		0.76

Notes: crude oil average closing price (COP), energy use per capita (EUPC), GDP growth (G), net energy imports (NEIs), oil rents (OR01), inflation (I). ***: significant at a 1% level of significance, **: significant at a 5% level of significance. Source: authors’ work.

, below, provides the regression results after correcting for cross-sectional dependence.

The cross-sectional dependence test was performed, and the Breusch–Pagan LM (1.000), Pesaran scaled LM (0.0000), and Pesaran CD (0.7673) p-values were mostly higher than the 5% level of significance; thus, the null hypothesis that there is no cross-section dependence is accepted.

The results of the corrected fixed effects model indicated that EUPC, G, NEIs, and ORs were significant with R-squared of 0.52. The results are provided in the below equation:

$$REC=0.29-0.00007EUPC-0.05G-0.03NEI+0.04OR$$

The equation shows that, for oil-importing countries, there is a negative significant relationship between energy use per capita (EUPC) and renewable energy consumption (REC). Renewable energy consumption (REC) decreases by 7.75 × 10⁻⁵ for each energy use per capita (EUPC) unit increase. In addition, the results of the regression show that there is a negative significant relationship between GDP growth (G) and renewable energy consumption (REC). Renewable energy consumption (REC) decreases by 0.05 for each 1% economic growth. Net energy imports (NEIs) have a negative significant relationship with renewable energy consumption (REC); renewable energy consumption (REC) decreases by 0.03 for each net energy imports (NEIs) unit increase. Moreover, the regression results show a positive significant relationship between oil rents (OR01) and renewable energy consumption (REC). Renewable energy consumption (REC) increases by 0.04 for each dollar increase in oil rents (OR01).

Crude oil average closing price (COP) and inflation (I) have insignificant impact on the level of renewable energy consumption (REC) for oil-importing countries.

Comparative discussion of Oil-Exporting Countries vs. Oil-Importing Countries:

For both oil-exporting and oil-importing countries, crude oil average closing price (COP) and inflation (I) have insignificant impact on the level of renewable energy consumption (REC). Our findings are contradictory to the findings of Slav [3] and Deniz [4], who found a positive significant relationship between the crude oil average closing price and renewable energy consumption. The result is also inconsistent with Boyer and Filion [5], Le [70], and Deane [71]’s findings on decrease in fuel dependency in Europe because of crude oil price increases.

Our findings show that for oil-importing countries, there is a negative significant relationship between energy use per capita (EUPC) and renewable energy consumption (REC), but there is a positive significant relationship between energy se per capita (EUPC) and renewable energy consumption (REC) for oil-exporting countries. This result is divergent from studies conducted in Africa [7]; there is a negative and significant correlation between oil revenues and renewable energy consumption in the most developed and populous African countries, such as Nigeria, Democratic Republic of Congo, and South Africa, which, while not defined as such by the authors, are considered oil-exporting countries. Similarly, in the works of Eder, Provornaya, Filimonova, Kozhevin, and Komarova [6] and Boyer and Filion [5] in Canada, and Makki, Mawad, and Khalili [9], where no distinction was made between the two types of countries, the results were also contradictory to studies in the Balkan countries [8], importers of energy where no significant relationship was found between oil rents and renewable energy consumption.

In addition, our findings show that there is a negative significant relationship between GDP growth (G) and renewable energy consumption (REC) for oil-exporting countries and oil-importing countries. Economic growth is significantly related to renewable energy consumption at the 5% significance level; a 1% increase in economic growth leads to a decrease in renewable energy consumption by 0.01 for oil-exporting countries, and 0.05 for oil-importing countries. The direction of the relationship is similar for both samples, which indicates similar conduct and behavior for countries in terms of growth whatever their net import of energy status. This result contradicts the findings of Makki, Mawad, and Khalili [9] and Doytch and Narayan [10].

Moreover, the regression results show a significant relationship between oil rents (OR01) and renewable energy consumption (REC) for both oil-importing countries and oil-exporting countries, but in different directions. Renewable energy consumption (REC) increases by 0.04 for each dollar increase in oil rents (OR01) for oil-importing countries but decreases by 0.0095 for each dollar increase in oil rents (OR01) for oil-exporting countries. Our findings match those of the work of Olanrewaju, Olubusoye, Adenikinju, and Akintande [7] and Eder, Provornaya, Filimonova, Kozhevin, and Komarova [6] in African oil-exporting countries.

Finally, and most importantly, net energy imports are significantly related to renewable energy consumption at a 1% significance level, similar to the findings of Wei, Ni, and Shen [11], Solangi, Islam, Saidur, Rahim, and Fayaz [12], and Schmidt, Gruber, Klingler, Klöckl, Camargo, Regner, Turkovska, Wehrle and Wetterlund [15] and contrary to the findings of York [13] and York [14]. An increase in net energy imports of 1% decreases renewable energy consumption by 0.00378 for oil-exporting countries and by 0.03 for oil-importing countries, which is ten times more.

5. Conclusions

This study delves into the factors influencing renewable energy consumption, with a specific emphasis on the roles of oil importation and exportation. Notably, past research has often overlooked the distinction between oil-importing and exporting countries in this context. Addressing this gap, the study focuses on two pivotal questions: what drives renewable energy consumption, and how does a country’s status as an oil-importing or exporting country impact these factors? To answer these queries, data spanning from 1990 to 2018 were meticulously collected from the World Bank database. The dataset was divided into two subsets, distinguishing between nations exporting oil and those importing it. Various statistical tests were conducted to ensure robust analysis.

The findings reveal significant patterns: significant factors influencing renewable energy consumption are the same for oil-importing countries and oil-exporting countries, but with different directions and power. First, there is a negative significant relationship between energy use per capita and renewable energy consumption for oil-importing countries but a positive significant relationship between those two variables for oil-exporting countries. The country’s economic growth has a negative impact on renewable energy consumption, for both oil-importing and oil-exporting countries. A contradicting result was observed for the impact of oil rents on renewable energy consumption. Our findings show a negative impact of oil-rents on renewable energy consumption for oil-exporting countries and a positive impact for oil-importing countries. Intriguingly, net energy imports exhibit a notable negative correlation with renewable energy consumption for oil-exporting countries, with a far more pronounced impact observed for oil-importing nations, underscoring the intricate dynamics at play in these diverse energy contexts.

Based on this study’s results, several important policy recommendations can be presented to promote renewable energy consumption and enhance energy security in both oil-exporting and importing countries. For oil-importing countries, diversification of energy sources is an important investment that should be considered and employed. Governments should allocate resources to research and development in the renewable energy sector. This investment should lead to technological advancements, making renewable energy sources more efficient and affordable, thereby encouraging their adoption. Since economic growth positively influences renewable energy consumption, governments can implement policies that foster economic development. These can include tax incentives, subsidies, and grants for businesses and individuals engaged in renewable energy production and consumption. These incentives can stimulate economic growth while simultaneously promoting the adoption of sustainable energy practices. Governments should formulate clear and consistent policies supporting renewable energy adoption. Long-term planning and policy stability provide investors with confidence, encouraging them to invest in renewable energy projects. These policies should address regulatory frameworks, grid integration, and financial mechanisms to facilitate a smooth transition to renewable energy sources.

As for oil-exporting countries, the above recommendations are also applicable, but alongside others. Oil-exporting countries are invited to allocate a portion of oil revenues to invest in renewable energy infrastructure, including solar, wind, and hydroelectric power plants, and invest in research and development in renewable energy technologies by funding local universities, research institutions, and startups, which will help all countries.

The successful implementation of the above-listed policy recommendations should be based on identifying the most effective strategies for integrating renewable energy into existing energy systems in both oil-exporting and importing countries, which could be the subject of future research. For example, the electrification of the economy via the widespread adoption of electric vehicles, involving collaborative management schemes, can have a high impact on sustainable and decentralized energy systems. Borroy Vicente, Fernández, Galan, Llombart Estopiñán, Salani, Derboni, Giuffrida, and Hernández-Callejo [72] examined the impact of increasing electric vehicle (EV) integration on local community-based low-voltage energy distribution networks, particularly within the context of broader energy transition. Their report highlights the importance of active systems management, such as smart grid monitoring, to manage the stress on the grid caused by higher EV penetration. The research shows that strategic EV charging can mitigate load violations, emphasizing the need for effective load management. It advocates for a more predictive and proactive approach to systems management, encouraging collaboration among distribution system operators, demand aggregators, and local energy community managers to enhance grid flexibility and prevent severe grid issues as EV adoption rises. An exploration of the socio-economic and environmental impacts of renewable energy adoption should be conducted; additionally, studies should examine the potential barriers to renewable energy adoption, such as technological limitations [73], financial constraints [74], and public perception [74].

In conclusion, the successful implementation of these policies ultimately hinges on the genuine commitment of countries toward transitioning to green energy and fostering global energy sustainability efforts. Political decisions, driven by national priorities and international collaborations, are instrumental in driving the world toward a more sustainable energy future.