Globalization, renewable energy consumption and sustainable development

Abstract

Since the launch of the Brundtland Report in 1987, developing countries continue to face problematic trade-offs between balancing the current use of natural resources and how to minimize the level of harm to their future use. This paper examines how renewable energy consumption and globalization affect sustainable development for a sample of 24 sub-Saharan African countries (SSA) between 1990–2015. Nevertheless, the evidence from the panel ARDL/PMG estimation shows that renewable energy consumption stimulates sustainable development. However, the heterogeneous analysis between our variables reveals that renewable energy is likely to exert a greater impact on countries that have made modest progress in sustainable development agenda, these countries still need to significantly reduce the consumption of non-renewable energy sources in their total energy mix. We further observe that the integration of global economies and society is relevant for sustainable development. Therefore, since SSA countries have a huge potential for renewable energy including solar power, wind, hydropower, biomass, and geothermal energy, global partnerships in the areas of green policy innovation and research are critical. More importantly, the green partnerships should aim at facilitating unfettered access to clean energy by ensuring that the use of renewables is less expensive than fossil-based energy sources. This is likely to smoothen the path to achieving sustainable development goals in SSA.

Keywords:

• Renewable
• non-renewable
• globalization
• sustainable development
• institutional quality
• ARDL/PMG

1. Introduction

Sustainable improvement of economic, social, and ecological life for future generations requires making the right decisions from today. In 2015, the United Nations General Assembly adopted 17 Sustainable Development Goals (SDGs) as a purpose vehicle to achieve sustainable development by 2030 (United Nations, 2019). Yet, policymakers continue to face problematic trade-offs that must balance the pursuit of economic development, reduce poverty, and ensure environmental sustainability (Botchway , 2021; Uzar, 2020b). A catalyst in this conundrum is the phenomenon of globalization, which is understood as the growing integration of markets and nation-states mediated by a variety of flows including people, information, ideas, capital, and goods (Adebayo et al., 2022; Murshed et al., 2022), leading to the transformation of economic, environmental, and social foundations of societies (Miao et al., 2022). Indeed, the growth of economic activities due to globalization stimulates the use of natural resources—renewable and non-renewable (International Energy Agency, 2019), which is inextricably linked to the (un)sustainable welfare of the current and future generations (Adebayo, Kartal, & Ullah, 2023). The renewable energy has become critical in this debate due to the alarming rate of carbon dioxide emission—a stimulator of greenhouse gases (GHG)—and its impact on air quality, natural resources, global warming, and climate change (Langnel & Amegavi, 2020).

Conversely, the SDG 7 specifically seeks to “ensure access to affordable, reliable, sustainable, and modern energy for all”. Its menu includes but not limited to ensuring global access to affordable and reliable state-of-the-art energy infrastructure; increase the share of renewable energy mix, ability to double energy efficiency, promote international cooperation to facilitate unfettered access to clean energy and expand the needed innovative technology to achieve sustainable energy services especially in the developing world (United Nations, 2020). Within this framework, scholars contend that for the world to leapfrog into a new era of sustainable development trajectory, it will require a transition into a new carbon efficient modern energy for all (Adebayo, Ullah, et al., 2023). In fact, the intensification of environmental crisis has made the renewable energy sources such as biomass, solar, geothermal, and wind energy very crucial for achieving the SDGs (Adebayo, Kartal, Ağa, et al., 2023). Consistent with this view, the global estimate report suggests that energy is the principal cause of climate change and responsible for over 60% of the total greenhouse gas emissions (International Energy Agency, 2020). In that regard, the recently held United Nations Conference of parties (COP) on climate change including COP 26 and COP 27 offered a global platform for countries to commit to significantly incorporate renewable energy sources into their energy mix. However, while an active body of the literature has examined the effect of (non)renewable energy, particularly within economic growth-CO₂ emissions nexus (Adebayo et al., 2023c), there is a dearth of empirical evidence on how renewable energy mix influences sustainable development, particularly in low- and middle-income countries (Ahmed et al., 2022; Güney, 2019).

Moreover, the proponents of globalization have touted its significant benefits on a global scale. International trade in goods, services, energy efficient technology, capital, labour, and cross-fertilization of ideas all move freely within and across national borders. It has positively impacted health, education, human rights, security, gender equality, and the entire political system (Dreher, 2006; Jordá & Sarabia, 2015). Arguably, this has resulted in allocative efficiency, increased economic growth, and ultimately improved the quality of life (Sirgy et al., 2004; Tsai, 2007). Globalization has been criticized on some fronts, particularly as being the key driver of high poverty rates and inequality in developing countries, and the erosion of social and environmental standards (Stiglitz, 2002; You & Lv, 2018). However, most studies on globalization have examined its impact on economic growth (Colen & Swinnen, 2016; Villaverde & Maza, 2011). Yet, the evidence suggests that the computation of a country’s level of economic growth (gross domestic product/GDP) does not take into consideration the environmental damages such as global warming, air pollution, forest depletion, and carbon footprint caused by economic activities (Dincer, 2000; Dincer & Rosen, 2005). Therefore, since economic development is about sustainable improvements in human welfare, GDP may correlate with a fall in inter-temporal social welfare when the consequences of future generations are considered (Aidt, 2010).

The SSA is important in the sustainable development debate because the transition from the Millennium Development Goals (MDGs) to the SDGs shows that the region has the worst human development outcomes (World Bank, 2018). The 2019 human capital report by the World Economic Forum showed that while the global average human capital gap is 38%, that of SSA is 47%, which suggests that the region is leveraging less than half of its human capital. The region hosts about 30% of the world’s mineral resources (International Resource Panel, 2019), and the abundance of natural resources including gold, timber, oil and gas has not only made SSA a key partner in the global trade system but also, with implications on the sustainable development agenda.

The dependence on these primary products as the main engine of export does not only risk the problem of domestic trade volatilities (as a result of global instabilities) including unstable exchange rates, poverty, and unemployment but with consequences on the environment due to the overexploitation of natural resources (Nwaka et al., 2020). Moreover, SSA is an attractive place for renewable energy investors due to its huge potential for the generation and use of wind, biomass, solar power, hydro, and other forms of green energy (da Silva et al., 2018). Yet, the recent report suggests that land degradation, extreme poverty and inequality, deforestation, lack of access to safe water, energy deficit, and loss of biodiversity compounded by climatic variability have been underscored as key hindrances to the realization of UN SDGs in SSA (Botchway & Meissner, 2021; United Nations, 2020). Moreover, SSA countries have rectified several international trade agreements, which resulted in remarkable foreign direct inflows of 29.2 billion in 2019 (World Bank, 2019). Relatedly, most countries in the region are signatories to the Paris Agreement, Kyoto Protocol, and the current United Nations Sustainable Development Goals (SDGs) and are regarded as part of countries that can effectively contribute more sustainably to reducing carbon footprints of greenhouse gases. Although the economies in this region emit less pollutants (Langnel et al., 2021), SSA countries are more susceptible to the vagaries of climate change including hunger due to food insecurity and flooding.

The objective of this paper is to examine the nexus between (non) renewable energy consumption, globalization, and sustainable development in SSA. The main question is as follows: how do (non) renewable energy consumption and globalization influence sustainable development of sub-Saharan African economies? The current paper contributes to the extant literature in several respects. First, previous studies on the effects of renewable and non-renewable energy on the economic growth have employed GDP as a measure of economic activities. However, Güney (2020) argued that the computation of GDP does not account for the damage caused to the environment as a result of economic activities. Therefore, the use of adjusted-net savings as a measure of sustainable development is a novelty.

Second, studies on the nexus between globalization and the environment have traditionally employed trade openness index—reflecting the inflows and outflows of goods and services (Wang & Zhang, 2021). It is noteworthy that globalization is a multidimensional concept that includes not only trade and capital flows but also involves citizens of different countries communicating and exchanging ideas and information as well as governments working together to tackle political and environmental problems with global spillovers (Langnel & Amegavi, 2020). Therefore, the use of globalization which measures social, political and economic dimensions of global interconnectedness and relationships and adjusted-net savings as a measure of sustainable development to account for the damages caused to the environment fills important gaps in the literature of economic growth-environment nexus.

Third, the use of multidimensional measures such as globalization and adjusted-net savings also allows for the verification of these complex relationships without risking the omitted variable bias often observed in the empirical literature. Fourth, the use of quantile regression to examine the conditional distribution of sustainable development is also a novelty.

The paper further offers some practical policy implications. It provides ample evidence to policymakers on how to pursue development opportunities without harming the environment, particularly in low- and middle-income countries. Specifically, knowledge on the interplay between globalization, renewable energy consumption, and their concomitant effect on sustainable development will enable countries in the sub-region to strengthen their environmental norms, reexamine trade negotiations and the international protocols to which they are signatories as well as the need to incorporate renewable energy sources into their energy mix.

The rest of the paper proceeds as follows: the related literature is discussed, the methodology is presented, the empirical findings are discussed, and the policy implications are offered.

2. Theoretical underpinning

There is no doubt that globalization is a controversial topic and its economic, social, and environmental consequences remain unclear. It should be noted that the idea of sustainable development seeks to explain how human activity can successfully sustain itself without depleting the resources on which it feeds (Güney, 2019). Although many scholars agree that the notion of sustainability encompasses constant consumption over time, views about how to meet the needs of the current generation without compromising that of the future are different (Botchway, 2021).

Concerning the economic aspect of globalization, the neoliberal school of thought contends that globalization encourages technological transfer, innovation, research, and scientific advancements, which engender productive efficiency and sustainable growth (Greenes, 2003; Tsai, 2007). Thus, cleaner technology may be employed by foreign investors (Farazmand & Moradi, 2015), which may be diffused to other sectors of the economy engendering more environmentally efficient production and consumption patterns. It is argued that although the process of technological advancements may result in unintended consequences such as temporal loss of jobs, especially, for the unskilled labour, the gains can be widespread if people will be responsive to changes in the labour market. Thus, globalization manages this potential trade-off by ensuring that employment opportunities are created through rapid industrialization and economic growth, leading to pay-offs such as reduced poverty and inequality, environmental quality, institutional renewal, and overall economic development of countries in the long-run (Greenes, 2003; Rodrik et al., 2004).

This view resonates with the popular notion of the Environmental Kuznets Curve (EKC) hypothesis which posits a U-shape relationship between income per capita growth and environmental pollution (Stern, 2004). The underpinning idea of the EKC hypothesis is that economic growth at the initial stages of development tends to correlate positively with environmental deterioration resulting in higher emissions of carbon dioxide (Alam et al., 2016). Thus, at the initial stages of the economic development, the path of rapid economic growth is characterized by natural resource depletion (renewable and non-renewable) and emission of pollutants – scale effect. However, as incomes grow due to structural changes in the economy – composition effect – people begin to demand cleaner activities that arguably produce less pollution (Dinda, 2004). At higher economic development, dirty and obsolete technology is replaced with upgraded new and cleaner technology particularly from renewable energy sources – technique effect.

Kumar and Agarwala (2016) contended that the transfer/diffusion of renewable energy technology by foreign firms has the potential to contribute to reliable and sustainable energy in the host country. However, globalization may stimulate the growth of more pollution-intensive industries (Dinda, 2004) and amplify social inequality (Dreher & Gaston, 2008) particularly in developing societies where institutions are weak. Analogous to this view, Dasgupta et al. (2001) argued that developing countries risk being “pollution haven” in situations where environmental standards may be relaxed to attract foreign direct investors.

Moreover, the hegemonic school of thought viewed globalization as the creation of a new world order, which perpetuates injustice in developing countries through multinational corporations (MNCs) and global financial institutions such as the World Bank (WB) and International Monetary Fund (Petras, 2016). These institutions (WB and IMF) only seek to facilitate capitalist accumulation in an environment of unconstrained market forces and under a very limited state capacity (Veltmeyer et al., 2016). Therefore, an unequal relationship does not only disproportionately allocate trade benefits to the already advantaged but also allows environmental costs and pollution to be shifted to developing countries—leading to domestic environmental damages, and the destruction of human and ecosystem (Hornborg, 2009).

The social dimension of globalization, however, forges linkages through information dissemination, personal contact, cultural exchanges, and the promotion of tourism activities (Dreher, 2006). The media pluralism and the proliferation of online social media handles may ignite the environmental consciousness of many people by exposing bad environmental practices (Rudolph & Figge, 2017). It may also expose bad governance practices, particularly in developing countries, which exacerbate economic, social, and environmental conditions. Langnel and Amegavi (2020) argued that greater awareness enables people to observe sustainable practices and standards. With respect to the political globalization, countries are integrated into the global governance system to show commitment for the implementation of international agreements and protocols including the Paris Agreement, Kyoto Protocol, and the current United Nations Sustainable Development Goals (SDGs). This global governance system urges member countries to comply with international standards that could reduce poverty and inequality, ensure shared growth and prosperity, and ultimately improve environmental quality (Ahmed et al., 2019).

3. Empirical literature

3.1. Globalization and sustainable development

Dreher (2006) examined the relationship between globalization and economic growth. The per capita GDP was used. The paper found that globalization was positively correlated with economic growth around the world. Villaverde and Maza (2011) also observed that globalization as measured by the overall, economic, and social globalization has a positive relationship with economic growth. In a time-series analysis of the effect of globalization on economic growth, Rao et al. (2011) demonstrated that the effect of globalization on economic growth differs across countries.

Employing a dynamic panel estimation to investigate the impact of globalization on economic growth in the Organization of Islamic Countries (OIC), Samimi and Jenatabadi (2014) observed that economic globalization exerts a positive influence on economic growth. The result showed that the magnitude of the effect was strong in countries with better-educated workers as well as well-developed financial systems. The authors further showed that economic globalization tends to benefit middle-income countries compared to low-income countries. Whilst the social and economic dimensions of globalization positively impacted economic growth in new member countries of the European Union, Gurgul and Lach (2014) observed that political globalization was found to be insignificant. Employing trade openness as a measure of globalization, Zahonogo (2018) found that the relationship between globalization and economic growth is not linear for sub-Saharan African countries.

Analyzing the effect of globalization on life expectancy, Bergh and Nilsson (2010b) found that whereas economic globalization had a robust positive effect on life expectancy, the political and social dimensions of globalization had no such effects. A similar conclusion was drawn when the same authors investigated the effect of globalization on inequality. Thus, social and political globalization showed no relationship with inequality (Bergh & Nilsson, 2010a). This means that social and political globalizations appear to show little or no relationship on the social dimension of sustainable development. Employing data on industrial wage inequality and household income inequality, Dreher and Gaston (2008) reported that overall globalization worsens inequality in OECD countries. In contrast, the paper found no robust effect of globalization on inequality in developing countries. These findings appeared to refute the skeptical view that the current wave of globalization is the key driver of inequality in developing countries (Stiglitz, 2002). According to Dreher and Gaston (2008), developed countries opposed greater integration because of increased inequality and political costs on their societies.

With regards to gender equality, Cho (2013) concluded that whereas trade openness, information flows, and personal contacts were positively correlated with women’s economic and social rights, no relationship was established for the political rights of women. Goryakin et al. (2015) demonstrated that social and political globalizations had the most profound positive association with women’s propensity to be overweight and dominate the influence of economic and the overall index of globalization.

Figge et al. (2017) investigated the effect of various measures of globalization on ecological footprints. The authors observed that the overall index of globalization significantly impacted on ecological footprints. However, the decomposition of globalization into different domains (political, economic, and social) revealed that apart from the political dimension, all dimensions drive human pressures on the environment. Twerefou et al. (2017) examined how economic growth and globalization affect environmental quality in sub-Saharan Africa. Using trade openness as a measure of globalization, the paper finds that globalization had a worsening impact on environmental quality. However, Farazmand and Moradi (2015) found in Middle and North African (MENA) countries that trade openness (a measure of globalization) reduces the level of pollution and improves environmental quality. Dreher et al. (2008) found that globalization abates sulfur emissions and water pollution. From the preceding discussions, the effects of globalization on economic, social, and environmental outcomes remain decidedly mixed.

3.2. Renewable energy and sustainable development

An active body of the literature has examined the impact of renewable energy on economic growth and carbon dioxide emissions. Tugcu et al. (2012) established that renewable and non-renewable energy sources matter for economic growth in G7 countries. Notably, the relationship was bidirectional for all countries, and inconclusive results were reported for each country when the production function was augmented. Sbia et al. (2014) demonstrated that renewable energy consumption promotes economic growth in the United Arab Emirates. Moreover, recent works by Leitão (2021), Balsalobre-Lorente et al. (2021) and Leitão et al. (2022) found that renewable energy reduces environmental pollution. Nevertheless, Ocal and Aslan (2013) showed that renewable energy consumption has a negative impact on economic growth in Turkey.

With regard to non-renewable energy, Payne (2011) observed no relationship between coal consumption and economic growth in the United States. Lin and Moubarak (2014) interrogate the relationship between renewable energy and economic growth in China for the period 1977–2011. The study found that renewable energy consumption positively affects economic growth. However, no relationship was reported between carbon dioxide emission and renewable energy, suggesting that China has not explored renewable energy enough to mitigate carbon dioxide emission. Ito (2017) evaluated the impact of energy consumption in developing economies within 2002 and 2011. Their results suggested that whereas non-renewable energy sources had a negative impact on economic growth, a positive relationship was reported for renewable energy sources. This means that non-renewable energy sources tend to hinder the economic growth potentials of developing countries.

Inglesi-Lotz and Dogan (2018) observed that whilst an increased use of non-renewable sources intensifies pollution, the dependence of renewable sources has the potential to mitigate greenhouse gas emission. Tiwari et al. (2015) concluded that whilst the economic growth of some sub-Saharan African countries could be impaired by energy conservation policies, the same could enhance the growth process in some other countries in the sub-region. However, the empirical studies that employed adjusted-net savings (a more reliable measure of sustainable development) are limited but emerging. The following studies employed adjusted-net savings as a measure of sustainable development (Güney & Kantar, 2020; Güney, 2017, 2019, 2020; Spaiser et al., 2019). These papers observed a positive relationship between renewable energy and an investment in the current and future generations.

4. Data and methodology

4.1. Data and the model specification

The paper examines a panel of 24 sub-Saharan Africa countries (Benin, Botswana, Burkina Faso, Cameroon, Cote D’Ivoire, Ethiopia, Gabon, Ghana, Kenya, Malawi, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Seychelles, Tanzania, Togo, Uganda, Zambia, and Zimbabwe) within the period of 1990–2015. The study considered only 24 countries due to unavailability of recent and complete dataset for other SSA countries.

Given the multidimensional nature of sustainable development, it is difficult to get an index that captures the three dimensions inter alia economic, social, and the environment. However, following the existing literature (Aidt, 2010; Güney, 2017, 2019, 2020; Spaiser et al., 2019), we employed the adjusted-net savings (ANS) as a proxy for sustainable development. Nevertheless, our work is different from the existing literature which failed to consider the impact of external forces (globalization) and the national level institutions as key drivers of industrialization and economic activities, and their consequent impact on both the current and future generations.

Data were sourced from the World Development Indicators (WDI) database. The sustainable development index (ANS), which aims to capture the capital stock of the economy, is computed with respect to the manufacturing industry, human capital, social capital, and natural capital at current prices (Güney & Kantar, 2020, p. 763). Arrow et al. (2004) argued that adjusted-net savings can be derived from the standard national accounting measure of gross national savings by making four adjustments reflecting genuine investment in the productive base of an economy. The first correction was to deduct an estimate of fixed consumption capital to obtain net national savings. This helps to replenish the value of capital depleted during the production process. The second was the addition of current expenditures on education, signaling an investment in human capital development. Third, estimates of the depletion of a variety of natural resources such as forests, minerals, and energy were further deducted from net national savings. Finally, carbon dioxide emission as well as the financial deduction of an estimate of the health damages due to environmental pollution was done.

After the adjustments from the gross national savings, the result gave a figure of genuine investment (adjusted-net savings) as a percentage of gross national income (GNI). In short, ANS reflects the rate of gross national savings as a percentage of gross national income (GNI) after taking into account the depletion of fixed capital, education expenditure, depletion of natural resources (energy, minerals, and forest resources) and pollution damages (Hamilton, 2003). When the ANS is positive, it shows an investment in the future well-being as a nation accumulates the assets needed to drive economic growth and at the same time continues to sustain current levels of consumption (Hamilton, 2003). Based on the existing literature, other variables were added to avoid omitted variable bias.

The composite index of globalization (GLO) was employed. It captures the economic, social, and political dimensions of globalization. The economic dimension captured actual flows (trade, foreign direct investments, and portfolio investments) and restrictions on trade and capital (such as import barriers, taxes on international trade, and capital accounts restrictions). Social globalization measures integration with regards to information flows and dissemination of ideas. Whereas political aspect denoted formal international linkages such as membership of international organizations, and the rectification of international and regional agreements and protocols. Data were obtained from the KOF database of the Swiss Economic Institute (SEI) developed by Dreher (2006) and Dreher et al. (2008). It is measured on a scale of 1(minimum globalization) to 100 (maximum globalization). The KOF index is arguably the most reliable measure of globalization (Gygli et al., 2019; Villaverde & Maza, 2011).

Renewable energy is the share of energy consumed from renewable sources including geothermal, solar, wind, biomass, and biofuels to the total electricity consumption. Non-renewable energy refers to the fossil-based energy consumption sources such as coal, crude oil, and natural gas as a percentage of total energy consumption. The data were taken from the WDI. The GDP per capita captures the income growth. The institutional quality was sourced from the International Country Risk Guide (ICRG). The variables were transformed into their logarithmic forms to enhance their elasticity for interpretation except the institutional quality variable. The initial model is operationalized as:

(1) $\mathrm{A}\mathrm{N}{\mathrm{S}}_{\mathrm{i}\mathrm{t}}={\alpha }_0+{\theta }_1\mathrm{R}{\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\theta }_2\mathrm{N}\mathrm{R}{\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\theta }_3\mathrm{G}\mathrm{L}{\mathrm{O}}_{\mathrm{i}\mathrm{t}}+{\theta }_4\mathrm{G}\mathrm{D}\mathrm{P}{\mathrm{C}}_{\mathrm{i}\mathrm{t}}+{\theta }_5\mathrm{I}{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}+{\mu }_{\mathrm{i}\mathrm{t}}$(1)

Where ANS is the adjusted-net savings (measure of sustainable development), RE and NRE denote renewable and non-renewable energy sources, GLO is globalization, GDPC is income growth, IQ is the institutional quality, and µ_it is the residual term.

4.2. Panel estimation techniques

The paper adopted a three-pronged approach. First, we performed cross-sectional dependence (CD) and the unit root test to ascertain the degree of dependence of the cross-sections and the stationarity of the variables. Pesaran (2004) argued that an analysis of CD is crucial to avert spurious estimates. In that regard, Im et al. (2003) and Levin et al. (2002) were engaged. Though Liven-Lin-Chu (LLC) and Im-Pesaran -Shin (IPS) minimizes the impact of CD, they do not completely eradicate it (Uzar, 2020a). Therefore, we further employed the cross-sectional augmented IPS (CADF and CIPS) as proposed by Pesaran (2007). The CIPS estimates heterogenous panels and effectively accounts for the presence of CD by allowing for the heterogeneity in panel data (Im et al., 2003). CADF regression, however, uses cross-sectional averages and their initial differences to remove the influence of dependencies across countries. Drawing insights from Pesaran (2007), CADF statistics can be denoted as follows:

(2) $\mathrm{\Delta }{y}_{\mathit{\text{it}}}={\phi }_i+{\beta }_i{y}_{\mathit{\text{it}}}+{\tau }_i{\bar{y}}_{\mathit{\text{it}}-1}+d_i\bar{y}{\mathrm{\Delta }}_t+{\epsilon }_{\mathit{\text{it}}}$(2)

where φ_i is the deterministic term and y_t is the mean of CD for time t. An average of CADF individual ADF statistics are used for the calculation of CIPS statistics. The use of one period lag will yield the following equation:

(3) $\mathrm{\Delta }{y}_{\mathit{\text{it}}}={\phi }_i+{\beta }_i{y}_{\mathit{\text{it}}-1}+{\tau }_i{\bar{y}}_{\mathit{\text{it}}-1}+\sum _{j=0}^1{\tau }_{\mathit{\text{it}}}\mathrm{\Delta }{\bar{y}}_{\mathit{\text{it}}-1}+\sum _{j=0}^1{\tau }_{\mathit{\text{it}}}\mathrm{\Delta }{y}_{\mathit{\text{it}}-1}+{\epsilon }_{\mathit{\text{it}}}$(3)

Based on the CADF statistic, the CIPS can be estimated as:

(4) $\mathit{\text{CIPS}}=\frac{1}{N}\sum _{j=i}^N{t}_i\left(N,T\right)$(4)

Where t_i (N, T) denotes the t-statistics in the CADF regression.

Second, having confirmed the unit root properties of our series, cointegration techniques were performed to examine the spurious long-run relationship in our data series. Therefore, Pedroni (2004) panel cointegration technique was engaged. Based on the Eagle and Granger 2-step methodology, the cointegration test by Padroni eliminates short-run parameters and individual-specific deterministic trends in the first step and thus effectively control for heterogeneity. Pedroni provided seven different test statistics based on the estimated residuals, which are grouped under common process (within-dimension) and individual processes (between-dimension). The null hypothesis assumes the condition of no cointegration. Also, the panel cointegration proposed by Westerlund (2007) was used. Contrary to Pedroni (2004), Westerlund (2007) relaxed the imposition of common factor restrictions on tests based on residual dynamics, which are deemed to be structural in nature instead of residual. The bootstrap approach is able to handle the negative impact of cross-sectional dependence and yields more robust critical values (Westerlund, 2007).

Third, we then proceeded to estimate the model with panel autoregressive distributive lag/pooled mean group (ARDL/PMG) proposed by Pesaran et al. (1999) and the quantile regression. One inherent advantage for using ARDL/PMG is that it provides efficient results whether the variables are integrated of 1(0) or 1(1), but not 1(2). This allows for homogeneity with respect to the cointegration coefficients; however, heterogeneity is imposed on the short-run coefficients and error variances (Pesaran et al., 1999). Within this framework, the predictor variables are treated as exogenous, suggesting that the error terms are not serially correlated but individually dispersed among the regressors. Uzar (2020a) argued that the panel ARDL/PMG provides reliable results in the face of CD. Pesaran and Smith (1995) first developed the mean group (MG), which is computed by taking the averages of the estimated coefficients. Contrary to the MG, the PMG assumes the long-run homogeneity of the parameters. The PMG relies on a combination of pooling and averaging coefficients and ensures that the parameters vary freely across groups and units (Pesaran et al., 1999). By using the Hausman test to decide between MG and PMG, the statistical significance of the estimated coefficients suggests that PMG is the most preferable. The ARDL (p, q, q…, q) generalized model is operationalized as:

(5) $\mathit{\text{lnAN}}{S}_{\mathit{\text{it}}}=\sum _{j=1}^p{\lambda }_{\mathit{\text{ij}}}\mathit{\text{lnAN}}{S}_{i,t-j}+\sum _{j=0}^q{\delta }_{\mathit{\text{ij}}{\mathrm{X}}_{i,}t-j}+{\mu }_i+{\epsilon }_{\mathit{\text{it}}}$(5)

Where y_it captures the dependent variable for group i and x_i denotes the vector of independent variables. The equation (5)(5) $\mathit{\text{lnAN}}{S}_{\mathit{\text{it}}}=\sum _{j=1}^p{\lambda }_{\mathit{\text{ij}}}\mathit{\text{lnAN}}{S}_{i,t-j}+\sum _{j=0}^q{\delta }_{\mathit{\text{ij}}{\mathrm{X}}_{i,}t-j}+{\mu }_i+{\epsilon }_{\mathit{\text{it}}}$(5) can be rewritten as follows:

(6) $\mathrm{\Delta }\mathrm{I}\mathit{\text{nAN}}{S}_{\mathit{\text{it}}}={\psi }_i\left(\mathrm{I}\mathit{\text{nAN}}{S}_{i,t-1}-{\beta }_i{\mathrm{X}}_{\mathit{\text{it}}}\right)+\sum _{j=1}^{p-1}{\varphi }_{\mathit{\text{ij}}}\mathrm{\Delta }\mathrm{I}\mathit{\text{nAN}}{S}_{i,t-j}+\sum _{j=0}^{q-1}{\tau }_{\mathit{\text{it}}}\mathrm{\Delta }{\mathrm{X}}_{i,t-j}+{\gamma }_i+{\epsilon }_{\mathit{\text{it}}}$(6)

Where X denotes the regressors (lnGLO, lnRE, lnNRE, and IQ). β captures the long-run parameters of the effect of regressors on the sustainable development. ψ is the mechanism of error correction, Ɛ_it is the stochastic noise, and Y_i is the group effect. The bracket $\left(\mathrm{I}\mathit{\text{nAN}}{S}_{i,t-1}-{\beta }_i{\mathrm{X}}_{\mathit{\text{it}}}\right)$ indicates the error correction term, which captures the long-run coefficients. The other parameters showed the short-run coefficients. However, because SSA countries are at different stages of development, we supplemented our analysis with the new method of moment quantile regression as proposed by Machado and Silva (2019) and Machado et al. (2020). The quantile regression provides detailed information on the heterogenous relationship between sustainable development, globalization and renewable energy. The model for quantile regression is operationalized as:

(7) ${\mathrm{Q}}_{\mathrm{y}}\left(\tau |{\mathrm{X}}_{\mathrm{i}\mathrm{t}}\right)=({\alpha }_{\mathrm{i}}+{\delta }_{\mathrm{i}}\mathrm{q}\left(\tau \right))+{\mathrm{X}}_{\mathrm{i}\mathrm{t}}\prime \beta +{\mathrm{Z}}_{\mathrm{i}\mathrm{t}}\prime \gamma \mathrm{q}\left(\tau \right)$(7)

From Eq. 7, X_it ” denotes independent variables: lnGLO, lnRE, lnNRE, lnGDPC, lnIQ. Q_y (τ|X_it) designates the quantile distribution of the dependent variable—lnANS, which depends on the location of independent variables. α_i (τ) = α_i + δ_i q (τ) is the scaler coefficient which shows the fixed effect of quantile-t on individual i. The heterogenous effects are made to vary through the quantiles based on the conditional distribution of the endogenous factors. Specifically, the parameters are estimated as:

(8) $\mathrm{m}\mathrm{i}{\mathrm{n}}_{\mathrm{q}}{\mathrm{\Sigma }}_{\mathrm{i}}{\mathrm{\Sigma }}_{\mathrm{t}}{\rho }_{\tau }\hspace{0.17em}\left({\hat{\mathrm{R}}}_{\mathrm{i}\mathrm{t}}-\left({\delta }_{\mathrm{i}}+{{\mathrm{Z}}_{\mathrm{i}\mathrm{t}}}^{\prime }\gamma \right)\mathrm{q}\right)$(8)

where ρ_τ (A) = (τ − 1) Al {A <0} + τ Al{A >0} represents the check-function.

5. Empirical results

5.1. Results of cross-sectional dependence and unit root

In order to examine the unit root properties of our data series, we first checked the CD properties in the data. Consistent with the standard practice in the literature, Breusch-Pagan LM, Pesaran Scaled LM, and Pesaran CD tests developed by Breusch and Pagan (1980) were conducted. As can be seen in the Table 2, the results from the three tests suggested that our variables exhibit significant cross-sectional dependence. Table 1 depicts the descriptive statistics and the correlation matrix.

Table 2. Cross-sectional dependence

Test	Statistic	Probability
Breusch-Pagan LM	2229.89	0.0000
Pesaran Scaled LM	64.0006	0.0000
Pesaran CD	10.2419	0.0000

Source: Authors’ computation. Note: Significant at 1%.

Table 1. Descriptive statistics and correlation matrix

	ANS	RE	NRE	GLO	GDPC	IQ
PANEL A
Mean	3.999	65.726	39.233	45.383	1.848	.382
Std. Dev.	12.689	27.038	36.507	10.498	5.114	.171
Min	−56.445	44.4	6.4	12.4	−47.503	0
Mx	45.693	96.842	100	72.576	37.535	.806
PANEL B
ANS	1.000
RE	−0.223	1.000
NRE	0.325	−0.368	1.000
GLO	0.217	−0.410	0.310	1.000
GDPC	0.085	−0.009	0.010	0.130	1.000
IQ	0.177	0.031	0.219	0.047	0.003	1.000

Source: Authors’ computation.

Proceeding further, the IPS and LLC unit root tests were implemented having ascertained the CD properties of our series. The results revealed that with the exception of non-renewable energy (NRE), all our variables are integrated of 1(1).

However, literature suggests that though IPS and LLC tests are corrected versions of the first-generation panel unit root tests through some econometric transformations robust to CD; nonetheless, the potential challenges likely to be posed by the presence of the CD are only minimized and not completely eliminated (as depicted in Table 3). Analogous to this theory, second-generation panel unit root tests, which are able to avert the problem related to the presence of CD were conducted. The null of hypothesis of CADF and CIPS assumes the condition of non-stationarity of the variables. The results (Table 4) revealed that with the exception of IQ, all our variables were stationary at first difference within CADF unit root test. With respect to CIPS, the null hypothesis can be annulled when the test statistics are greater than the critical values: and can conclude the presence of stationarity. The CIPs results also showed that the null hypothesis of non-stationarity can be rejected at 1% level of significance, not at level, but at first difference, suggesting that the variables were integrated of 1(1).

Table 3. Im et al. (2003) and Levin et al. (2002) unit root tests

	IPS		LLC
Variables	Level	Δ	Level	Δ
ANS	−3.121(0.314)	−13.996(0.000)	−1.897 (0.028)	−13.287(0.000)
GLO	−0.538(0.295)	−20.991 (0.000)	−0.371(0.355)	−8.309 (0.000)
RE	1.689 (0.954)	−18.206(0.000)	−1.7668(0.0386)	−7.840 (0.000)
NRE	−0.743(0.228)	−14.788(0.1302)	-	-
1Q	−11.105(0.000)	−17.424 (0.000)	−9.3417 (0.000)	−17.340 (0.000)

Source: Authors’ computation.

Table 4. Pesaran (2003, 2007) unit root properties

	CADF		CIPS
Variables	LEVEL	Δ	LEVEL	Δ
ANS	−2.004 (0.962)	−3.159 (0.000)	−3.247	−5.758
GLO	−2.479 (0.164)	−3.637 (0.000)	−2.960	−4.707
RE	−1.879 (0.994)	−2.607(0.043)	−2.072	−4.640
NRE	−1.056(1.000)	−2.806 (0.002)	−1.203	−2.806
GDPC	−2.308(0.005)	−3.479(0.000)	−4.423	−6.141
IQ	−2.181(0.073)	−3.821(0.000)	−2.366	−4.033
Critical Values			−2.58 (10%) −2.66 (5%) −2.81 (1%)	−2.58 (10%) −2.66 (5%) −2.81 (1%)

Source: Authors’ computation.

5.2. Panel cointegration results

Having confirmed the stationarity of our variables, the long-run association between the variables was verified. Though cointegration among the series is not strictly necessary, da Silva et al. (2018) argued that where the long-run association between the variables do exist, the panel ARDL/PMG estimator tends to offer stronger results. Pedroni (2004) cointegration, which is popular in the existing literature assumes no cointegration for all units in the panel as null hypothesis. It drives four statistics as panel and other three as group statistics. The findings captured both the constant and trend. The results (Table 5) revealed that six of the test statistics are statistically significant for group and panel. Therefore, the null hypothesis of no cointegration has been rejected, suggesting that there exist a long-run association among the variables. Also, Westerlund (2007) panel residual-based cointegration technique showed that the four test statistics: G_t, G_a, P_t, and P_a are statistically significant (see Table 6 below). Overall, our cointegration tests signaled a long-run relationship between renewable energy, non-renewable energy, globalization, and the sustainable development.

Table 5. Pedroni (2004) panel residual-based cointegration test

Statistics
	Within-dimension(panel)		Between-dimension (group)
	Statistics	Weighted statistics	Statistics
V-Statistics	−2.1596(0.9846)	−3.4697 (0.9997)
Rho-Statistics	−1.5674 (0.0585)	−0.2032(0.5805)	1.8866 (0.9704)
PP-Statistics	− 13.1264(0.0000)	−8.5431 (0.0000)	−10.83325 (0.0000)
ADF-Statistics	−13.8946 (0.0000)	−9.970186(0.0000)	−10.3868 (0.0000)

Source: Authors’ computation.

Table 6. Westerlund (2007) Bootstrap error-correction-based cointegration

Statistic	Value	Z-Value	p-value	Robust-P-value
Gt	−3.677	−4.827	0.000	0.030
Gq	−12.671	2.924	0.998	0.030
Pt	−19.141	−4.995	0.000	0.010
Pq	−13.367	0.327	0.628	0.020

Authors’ computation.

Note: Gt and Ga denote the group mean tests. Pt and Pa are panel mean tests.

5.3. Panel ARDL/PMG estimation results

After the cointegration analysis, the long and short run parameters of the association among the variables can be estimated. In that regard, panel ARDL/PMG estimator was used to analyze the effect of globalization and renewable energy consumption on sustainable development across the 24 SSA countries. Hausman (1978) tests was used, and the probability value of 0.653 (Table 7) signals that the PMG estimator is more efficient compared to the MG. Through the error correction coefficient, the PMG estimator allowed the short-run parameters to vary among the units, whereas the long-run parameters are the same across the units. An analysis of the error correction term (ECT)—speed of adjustment—showed that the value of −0.399 is between the expected range of −1 and 0, and statistically significant at 1% level. This did not only imply a long-run relationship between the variables but also indicated that a deviation from the equilibrium would be corrected by 39% in the long-run. Therefore, the ARDL/PMG results in Table 7 showed that renewable energy consumption, non-renewable energy consumption, globalization, and institutional quality influence sustainable development. Specifically, renewable energy was positive and statistically significant at 5% level, which implies that a 5% increase in renewable energy consumption is likely to improve sustainable development by 0.1242% in the long run among SSA countries.

Table 7. Panel ARDL/PMG estimation results (1,1,1,1,1,1)

Variables	coefficients	Std. error	z	p-value
Long-run results
lnRE	0.1242**	0.0666	0.36	0.026
lnNRE	−0.184***	0.0383	−4.81	0.000
lnGLO	0.200***	0.0336	5.94	0.000
lnGDPC	0.0878*	0.0726	−1.21	0.096
IQ	21.04***	3.289	6.40	0.000
Short-run results
ECT (−1)	−0.399***	0.0722	−5.52	0.000
lnRE	−0.226*	0.136	−1.67	0.096
lnNRE	2.900	2.988	0.97	0.332
lnGLO	0.377*	0.202	1.87	0.061
lnGDPC	0.112**	0.0438	2.56	0.010
IQ	−11.94*	7.074	−1.69	0.092
Constant	−4.080**	1.9019	−2.15	0.032
Hausman (MG vs PMG		0.653
N		525

*** p<0.01, ** p<0.05, * p<0.1 show significance of 1%, 5% and 10% respectively.

We further observed that globalization has strong positive effects on sustainable development in both the long run and short run at 1% and 10%, respectively. Thus, in the long run, a 1% increase in the value of globalization is likely to stimulate sustainable development by 0.200% in among SSA countries. It suggests that the aggregation of the economic, social, and political dimensions of globalization appears to increase sustainable development in SSA. This is consistent with the basic argument of neoliberal school though (Greenes, 2003; Tsai, 2007). There are several ways to explain the positive effect of globalization on sustainable development. The economic aspect of globalization integrates international markets and promotes industrialization and consequently spur economic activities. This means that countries’ ability to attract foreign investment through economic integration may facilitate easy access to goods and services and create employment opportunities.

The SDG 8 recognizes the need to create decent works through sustainable economic growth. As part of its target, SDG 8 aims to achieve full and productive employment for all women and men by 2030. Therefore, since 80% to 90% of labour in SSA is not engaged in decent work due to structural challenges including limited economic transformation (International Labour Organization, 2017), the ability to attract foreign investors may help contribute to an improved social welfare. More so, foreign firms may adopt innovative and cleaner technology (renewable energy) for production processes, which may not only minimize the trade-offs between development activities and the environment but will stimulate more environmentally efficient production and consumption patterns. This is particularly important because the limited exploitation of the huge renewable energy potential in SSA is partly due to inadequate capital investment and technological innovation (Suberu et al., 2013).

With respect to political dimension of globalization, which reflects formal integration into the global governance system, the data suggests that membership in international organizations such as the United Nations and its allied bodies may yield dividends such as the global fight against epidemics, prevention of human rights abuses, and the fight against environmental threats like climate change and global warming, which are critical ingredients for sustainable human development. Through the United Nations some countries in SSA have agreed and rectified global agendas such as Paris Agreement, Kyoto Protocol, and the current SDGs. Through social globalization, information flows through media pluralism may expose some bad governance practices that have adverse consequences on economic, social, and environmental conditions (Botchway & Meissner, 2021; Langnel & Amegavi, 2020).

With respect to other variables, institutional quality exerts a positive impact on sustainable development. This means that the ability to build resilient and strong institutions as articulated under the SDG 16 is relevant for improving sustainable development in SSA. Conversely, the coefficients of GDP per capita is positive and significantly affects sustainable development. This result suggests that income growth is likely to improve sustainable development. This is consistent with the composition effect (Dinda, 2004). This is partly because the measure of sustainable development is composed of gross national savings, education expenditure, depletion of fixed capital and others, which tends to respond to changes in the national income. Therefore, SSA countries need to grow their economies. However, redistributive mechanisms should be put in place to ensure that the growth trickles down to the majority of the population, since a rise in per capita income is likely to be associated with an improvement in sustainable development.

However, we complemented our findings with evidence from the method of moment quantile regression. Since it is possible that SSA countries may be at different levels of sustainable development, quantile regression is able to structure the conditional distribution of sustainable development. The findings showed that the relationship between renewable energy, globalization, and sustainable development were heterogeneous across quantiles.

From Table 8, Q1- Q4 correspond to countries at a lower level of sustainable development. While Q5 denotes the mid-point, Q6-Q9 capture countries that have advanced in sustainable development path. The findings revealed that the coefficient on renewable energy is positive and statistically significant in lower quantiles (Q2-Q4) and upper quantiles (Q6-Q9). However, in the upper quantiles, it can be observed that the impact of renewable energy is greater compared to the lower quantiles, meaning that renewable energy is likely to increase sustainable development in countries that have made significant progress in sustainable development agenda. Interestingly, the finding regarding non-renewable energy also suggests that fossil-based energy sources may limit the sustainable development effort of countries at the higher quantile levels.

Table 8. Results from quantile regression

VARIABLES	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9
lnRE	0.02	0.05**	0.06***	0.07***	0.08***	0.09***	0.1***	0.11***	0.14***
lnNRE	0.02	−0.01	−0.03	−0.04	−0.05**	−0.06***	−0.08***	−0.09***	−0.12***
lnGLO	0.19**	0.15**	0.14**	0.12*	0.11*	0.090	0.07	0.05	0.02
lnGDPC	0.23	0.20*	0.19*	0.18*	0.17*	0.16*	0.148	0.13	0.11
IQ	9.80*	9.59**	9.49***	9.39***	9.32***	9.23***	9.13***	9.03**	8.85**
Constant	−24.25***	−15.63***	−11.59***	−7.53**	−4.64	−0.87	3.26	7.67*	14.99***

Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1.

Globalization is positive and statistically significant from Q1-Q4, meaning that globalization may facilitate the resources use for the current generation and minimize the harm to their use by future generation particularly for countries at lower quantiles. However, globalization shows no relevance on sustainable development for countries at the highest quantile. Moreover, income growth (GDP per capita) is likely to improve sustainable development for countries the lower quantiles. The result with respect to the institutional quality shows that improving intuitional environment is relevant for promoting sustainable development across the quantiles.

The direction of causality was examined by employing Dumitrescu and Hurlin (2012) panel causality test. It is based on the individual Wald statistic of Granger non-causality averaged across cross-sections. The findings from Table 9 show that lnRE, lnNRE, lnGLO, and lnIQ drive sustainable development.

Table 9. Dumitrescu and Hurlin (2012) panel causality test

Null hypothesis	W-stat	Z-bar stat	Prob.
lnRE ≠ lnANS	2.0326	0.0748	0.0004
lnNRE ≠ lnANS	4.4203	5.5457	0.0000
lnGLO ≠ lnANS	8.9916	16.0198	0.0000
lnGDPC ≠ lnANS	2.2663	0.6102	0.5418
lnIQ ≠ lnANS	7.4646	2.3535	0.0363

Source: Authors’ construction. ≠ stands for no causality.

6. Discussion

This paper examines how (non) renewable energy consumption and globalization affect sustainable development of SSA countries. The result regarding the positive contribution of renewable energy consumption to social, economic and environmental outcomes is consistent with similar findings observed by Sbia et al. (2014), Leitão (2021), Balsalobre-Lorente et al. (2021), Leitão et al. (2022), and Wu et al. (2023) but contradict the study conducted by Ocal and Aslan (2013) in Turkey, who found a negative impact of renewable energy on economic growth. The positive impact of renewable energy further validates the EKC hypothesis (Alam et al., 2016; Stern, 2004), which posits that at a high level of economic development, the population begins to transition to renewable energy sources and cleaner technologies that cause less pollution and less harm to the environment (Dinda, 2004). It must be emphasized that despite SSA’s great potential for renewables, particularly for solar power, the International Energy Agency (IEA) reported that about 600 million people in SSA do not have access to electricity and over 900 million lack access to clean cooking (International Energy Agency, 2020). Indeed, solar technology has a limited role in the economies of SSA. The current use is limited to lightening and the operation of simple appliances (Suberu et al., 2013). Moreover, the use of equipments such as solar-powered cookers, building integrated photovoltaics, solar-powered water heaters, and other complex solarized appliances are limited to few countries including South Africa and Mauritius. Nevertheless, the finding implies that an investment in renewable energy sources including wind, biomass, hydropower, solar power, wind, and geothermal will boost the prospect towards the achievement of a sustainable development agenda in SSA. Moreover, the result with respect to the negative impact of non-renewable energy consumption on the sustainable development efforts of SSA countries echoes the work by Güney (2017, 2019, 2020), Spaiser et al. (2019), Güney and Kantar (2020), Adebayo et al. (2023d), and Adebayo (2022). This implies that SSA countries need to transition from dirty sources of energy including crude oil, fossil fuels, coal consumption, and to replace obsolete technology with modern and cleaner energy in their energy mix (Ahmed et al., 2019; Langnel & Amegavi, 2020). Furthermore, finding shows that globalization has a significant and positive impact on sustainable development, suggesting that globalization is good for meeting the needs of the current generation without compromising that of the future. This finding generally confirms similar results by Villaverde and Maza (2011), Samimi and Jenatabadi (2014), and Goryakin et al. (2015) who observed that globalization promotes economic growth. However, the finding tends to contradict the results reported in the studies conducted by Tiwari et al. (2015), Figge et al. (2017), and Langnel and Amegavi (2020). Globalization may be beneficial to sustainable development of SSA countries because of the ongoing economic, social, and political partnerships at the global level. Thus, SSA countries have signed and rectified several international agreements and protocols including Kyoto Protocol and Paris Agreement, which are aimed at reducing the global carbon footprint level.

7. Conclusion and policy implications

In light of the problematic trade-offs between achieving high economic growth and reducing the level of harm to the environment, this paper examined how renewable energy consumption and globalization affect the sustainable development in SSA—a region dotted with huge renewable energy potential including biomass, geothermal, wind energy, solar power, and hydropower.

A panel of 24 countries was analyzed spanning between 1990–2015. A three-pronged approach was used cross-sectional dependence and unit root properties were verified, cointegration was conducted, the appropriate panel estimation technique was used, and the causality analysis was done. Applying the panel ARDL/PMG estimator, the following results were established.

First, renewable energy consumption stimulates sustainable development in SSA. Second, non-renewable energy consumption suffocates the sustainable development of SSA countries. Third, globalization is relevant for sustainable development. Fourth, an improvement in the incomes (GDP per capita) of the people is good for sustainable development. Lastly, creating quality institutions has the potential of meeting the needs of the current generation without necessarily compromising that of the future generations. Unlike the previous studies, the application of quantile regression revealed that the impacts of renewable energy consumption, globalization, and sustainable development were heterogeneous across quantiles.

Based on the above findings, the following policy implications are offered. Indeed, globalization creates both threats and opportunities. The challenge for SSA is to become flexible in order to take advantage of the numerous opportunities offered by globalization and overcome the threats. It requires institutional reforms and strict enforcement of environmental standards and practices. Given that SSA has a huge potential for the generation and use of renewable energy sources, it is crucial for these energy efficient resources to be marketed to foreign investors through economic integration.

More so, countries in SSA should insist that foreign firms transfer energy efficient technologies, and their subsequent diffusion to other sectors of the economy such as for industrial use, household consumption, and transportation services. Globalization through economic integration should create a readily available market for green products since this move is likely to persuade and motivate countries in the sub-region to invest in renewable energy research and development for export. Moreover, as limited capital may be hindering renewable energy consumption in SSA, global partnership should result in sustainable energy-specific aid and programs that will allow cheaper access to renewable energy sources and technical expertise, which can help rapid deployment and use of energy-efficient technologies. This is important in achieving the global target of promoting international cooperation to facilitate unfettered access to clean energy under the SGD 7. Within this framework, it is important for countries to strictly enforce environmental laws and standards to induce compliance from the economic agents since economic integration may stimulate the growth of more pollution-intensive industries in societies where laws are weakly enforced.

Furthermore, countries in the sub-region should strengthen ties with the regional and international organizations such as the African Union and United Nations in a manner that will ensure a global fight against global warming and climate change. However, the political alliances with international bodies should be built in such a way that the policy space of individual countries will be respected. This is critical for the effective enforcement of domestic laws, practices, and standards (Botchway & Hlovor, 2019). More so, countries in the sub-region need to reexamine the kind of trade negotiations and alliances to which they are signatories to. Future studies can improve on the extant scholarship by investigating how globalization affects sustainable development based on case studies reflecting country-level realities (Samimi & Jenatabadi, 2014).

Acronyms

ARDL	=	Autoregressive Distributive Lag
ANS	=	Adjusted-net Savings
CD	=	Cross-sectional Dependence
CIPS	=	Cross-Sectional Augmented Im-Pesaran-Shim
CO2	=	Carbon Dioxide
EKC	=	Environmental Kuznets Curve
ECT	=	Error Correction Term
GDPC	=	Gross Domestic Product per Capita
GDP	=	Gross Domestic Product
GHG	=	Greenhouse Gases
GNI	=	Gross National Income
GLO	=	Globalization
ICRG	=	Integrational Country Risk Guide
IPS	=	Im-Pesaran-Shin
IQ	=	Institutional Quality
IMF	=	International Monetary Fund
IEA	=	International Energy Agency
LLC	=	Liven-Lin-Chu
MG	=	Mean Group
MGD	=	Millennium Development Goals
MENA	=	Middle and North African Countries
MNCs	=	Multinational Corporations
NRE	=	Non-Renewable Energy
OECD	=	Organization for Economic Cooperation and Development
OIC	=	Organization of Islamic Countries
PMG	=	Pooled Mean Group
RE	=	Renewable Energy
SGDs	=	Sustainable Development Goals
SSA	=	Sub-Saharan Africa
SEI	=	Swiss Economic Institute
UN	=	United Nations
WB	=	World Bank
WDI	=	World Development Index

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Desmond Tweneboah-Koduah

Desmond Tweneboah-Koduah is a seasoned lecturer who has taught at all levels of the Ghanaian Educational system. He holds a doctorate degree in Political Science from the University of Ghana. He is currently a lecturer in the Department of Political Science Education, University of Education, Winneba. His teaching and research interests are in the areas of Inclusive Development, Environmental Sustainability, Public Policy Analysis, Local Government Administration and Democratization.

Matthew Lobnibe Arah

Matthew Arah is a Political Scientist with deep interest in Comparative Politics and Political Thought. He does research in elections, governance, environment and sustainable development.

Thomas Prehi Botchway

Thomas Prehi Botchway holds Bachelor of Arts and Master of Philosophy degrees in Political Science and a Doctor of Law (PhD). His research covers the interdisciplinary fields of Political Science, International Relations and Law. Of particular interest to him are issues of environmental governance, sustainable development and state-society relations.