The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

Various scholarly inquiries have acknowledged the significance of utilizing renewable and non-renewable energy sources with economic growth and competitiveness [20, 24, 25]. Energy is an essential resource for producers, as it plays a critical role in setting production costs and subsequently impacts consumer pricing levels. The performance of an economy is ultimately dependent on various elements, as evidenced by the research conducted by Zarkovic et al. [19], Fotourehchi [1], Dai et al. [3]. Therefore, the significance of energy consumption in stimulating economic growth cannot be overstated, as it is a fundamental requirement for attaining development [6, 20]. In the study conducted by Dat et al. [26], a reciprocal relationship was identified between energy consumption in the non-renewable energy sector and economic growth. This situation has made nations increasingly emphasize allocating resources toward the energy industry, specifically focusing on renewable energy technology. Utilizing renewable and non-renewable energy sources has a crucial role in shaping economic growth and enhancing competitiveness.

Several empirical studies undertaken on a global scale have examined the impacts of renewable and non-renewable energy use on economic growth and development. However, further study must be conducted to establish certain conclusions based on the existing findings. Kumari, Kumar and Sahu [2] conducted a study employing the appropriate standard error in panel model, which revealed that the use of non-renewable energy and the quality of the environment have the potential to enhance welfare in G20 nations. The augmentation of welfare programs has a beneficial influence on the economy's expansion. The usage of both renewable and non-renewable energy in the G20 countries not only impacts economic activity but also exerts an influence on individuals' subjective well-being. Their study revealed a positive association between the consumption of renewable energy and improved environmental quality, characterized by fewer carbon emissions, and subjective well-being in G20 countries. Conversely, the consumption of non-renewable energy was found to be negatively associated with subjective well-being.

Zarkovic et al. [19] conducted a study in Europe wherein they employed Pooled Mean Group (PMG) Estimation and Autoregressive distributed lag (ARDL) models to investigate the impact of renewable energy consumption on economic growth in European countries throughout 2000-2020. Their findings revealed a favorable association between these variables. This study provides support for the notion that the utilization of renewable energy is a factor that fosters economic growth, as increased levels of renewable energy consumption are associated with more significant economic expansion. The present analysis, nonetheless, demonstrates that an increase in the utilization of non-renewable energy sources has a significant and unfavorable impact on gross domestic product (GDP) in the European countries under scrutiny, resulting in a deceleration of economic expansion over an extended period.

Likewise, Chen and Pinar [20] also found in their study of 103 countries from 1995 to 2015. Their study also found a link between renewable energy consumption and economic growth, depending on the amount of renewable energy used. The effect of renewable energy consumption on economic growth is positive and significant if developing countries or non-OECD countries exceed certain renewable energy consumption limits. However, if developing countries use renewable energy below a given threshold level, the effect of renewable energy consumption on economic growth is negative. Meanwhile, in developed countries, it was found that renewable energy consumption did not significantly affect economic growth in developed countries and had a positive and significant effect on economic growth in OECD countries. The study's findings indicate that for developing countries to achieve favorable economic growth through their investment in renewable energy, they must surpass specific thresholds of renewable energy consumption. The escalating utilization of non-renewable energy sources poses a significant threat to the environment [27]. Mohsin et al. [27] found a positive association between environmental degradation and economic development - the trend of increasing economic development activity is the leading cause of increasing environmental degradation. Their study confirmed that increasing economic growth, urbanization, and energy consumption have increased transportation-based environmental degradation urbanization.

According to a study by Mardani et al. [28], the adaptive neuro-fuzzy inference system (ANFIS) model was employed to analyze data from G20 countries from 1962 to 2016. The analysis revealed a positive correlation between economic growth and energy consumption in several countries, leading to increased CO₂ emissions. Li et al. [29] conducted research in the G20 countries, employing several econometric techniques such as cross-sectional dependence, cointegration, FMOLS, DOLS, and the pair-wise panel Granger causality test. This analysis reveals a long-term elasticity that demonstrates a notable positive impact of both renewable and non-renewable energy consumption on economic activity within various sectors and overall economic output. This study additionally presents empirical evidence supporting a bidirectional causal relationship between the consumption of renewable energy, non-renewable energy, and economic growth.

Ehigiamusoe and Lean [30] examined 22 countries to examine the cointegration relationship between energy consumption, economic growth, and financial development. They got empirical evidence of a cointegration relationship between energy consumption, economic growth, and financial development. These three variables have a detrimental effect on carbon emissions in the 22 countries studied. Then, they also conclude that financial development mitigates carbon emissions in high-income countries but has the opposite effect on low- and middle-income groups. However, Zaidi et al. [24] using FMOLS and Dynamic Ordinary Least Squares Estimator (DOLS), did not find a two-way causality relationship between energy consumption and economic growth. As with Mahmoodi [18]'s study, using panel cointegration and causality, the panel vector error correction model found a unidirectional causality of GDP and energy consumption.

Classical economic theory has provided perspectives on the relationship between population growth and economic development, and empirical results on this relationship reveal pessimistic and optimistic groups [31]. The proponent of the pessimistic Malthusian View contends that population expansion has a detrimental impact on economic development. This viewpoint was established by Malthus in 1798; for instance, population growth tends to outpace production growth [32]. Optimistic opinion (neoclassical view) asserts that an increase in population affects economic growth by increasing the labor supply [33, 34]. Consequently, population growth has a positive effect on the economy through the provision of labor; an increase in population encourages competition, which fosters innovation, competition, and technological advancement (among others, Bucci [35]). The number and proportion of the world's population living in urban areas continues to increase, and this increase impacts economic growth and energy consumption. For G20 countries, the urban population will reach around 61.2% in 2021, and it is predicted that the urban population will reach 73.6% in 2050 [36].

Based on the above discourse, it can be posited that a scientific endeavor has been undertaken to scrutinize the correlation between economic growth and the utilization of renewable and non-renewable energy sources. Despite several empirical research investigating the impact of population expansion on economic growth, a consensus has yet to be reached about the precise effects of renewable and non-renewable energy consumption, population growth, and urban population on economic growth. The study's primary contribution lies in examining the relationship between renewable and non-renewable energy consumption and the economy. This is achieved through the utilization of panel data obtained from G20 member countries, which collectively account for approximately 80% of global GDP and are responsible for approximately 80% of global greenhouse gas emissions, predominantly originating from the energy sector [37]. Prior studies predominantly utilized single-country analysis or were primarily undertaken in high-income nations, with only a limited number of studies focusing on impoverished countries.

4.3 Panel unit root test

A unit root test was conducted to evaluate the stationarity of the data, thereby ascertaining the level of integration of the variable (i.e., the integration order). The panel unit root test was conducted to assess the analyzed data's stationarity. Stationary data refers to a dataset wherein the statistical properties such as mean, variance, and autocorrelation remain constant and unaltered throughout the temporal domain. If the data under analysis lacks stationarity, it is imperative to acknowledge that regression analysis conducted on panel data may engender the phenomenon of regression spuriousness. This entails the potential for obtaining an elevated r-squared value despite the absence of any genuine correlation within the data [49].

This study employs various forms of unit root tests include Levin Lin Chu unit root test (LLC) [52], Augmented Dickey-Fuller test (ADF) [53], Fisher-Philips-Perron (Fisher-PP) [54], and Im, Pesaran, and Shin (IPS) [55] to consider the unique characteristics of individual countries [30]. Results of the unit root test are presented in Table 4. The null hypothesis provided in this test asserts the existence of a unit root. In contrast, the alternative hypothesis suggests the absence of a unit root or the presence of a stationary variable.

The findings in Table 4 indicate that none of the variables exhibit stationarity at the level, but they do exhibit stationarity at the first difference. This implies that all variables are integrated in the first order, denoted as I(1). In addition, GDP growth, renewable energy consumption, natural gas consumption, and petroleum consumption exhibit stationarity at a significant level of 1% (0.01) as determined by the LLC test, ADF Fisher PP, and IPS. In the LLC test, the variable urban population exhibits stationarity at a significance level of 5% (0.05), while in the ADF test, it demonstrates stationarity at a significance level of 10%. The Fisher PP test reveals that the urban population exhibits stationarity at a significance level of 5% (0.05). The IPS test suggests that the urban population remains constant at 1% level of significance (0.01). The stationary nature of the variable log population has been determined at 1% level of significance (0.01) by applying the LLC, Fisher PP, and IPS techniques. In the ADF unit root test, it was observed that the variable log population exhibited stationarity at a significance level of 5% (0.05). Once the variable has been established as stationary at the first difference, denoted as I(1), the subsequent procedure involves assessing the cointegration between the variables through the utilization of the Pedroni cointegration test.

Table 4. Results of panel unit root tests

4.4 Panel cointegration test

The Pedroni cointegration technique is employed to examine the presence of cointegration in panel data to ascertain the existence of a durable association between variables over the long run. The null hypothesis posits an absence of a cointegration relationship between the variables. According to the findings presented in Table 5, it is observed that four out of the seven statistics, specifically the PP-Statistics Panel, ADF-Statistics Panel, PP-Statistics Group, and ADF-Statistics Group, exhibit statistical significance at both the 1% and 5% significance levels. Hence, a cointegration and long-term association exists among variables such as GDP growth, renewable energy consumption, natural gas consumption, petroleum consumption, urban population, and total population size.

Table 5. Results of Pedroni's residual-based cointegration test

*** & ** indicate statistically significant at 1%, and 5%, correspondingly.

4.5 Long-run estimation using FMOLS

Following the identification of cointegration and long-term association among variables, FMOLS method proposed by Pedroni [56] was employed. The FMOLS is employed to address the issues of small sample bias and endogeneity bias by incorporating the inclusion of leads and lags of the first-differentiated regressors, as discussed by Pedroni [56] and Dauda et al. [57]. The FMOLS estimator employs initial estimates of both symmetric and one-sided long-term covariance matrices of the residuals, as discussed by Verma, Dandgawhal and Giri [58]. The FMOLS methodology is employed to estimate long-term parameters because it considers cross-sectional heterogeneity, serial correlation, and endogeneity concerns [30]. Furthermore, it is worth noting that the FMOLS estimator has the desirable property of consistency in estimating parameters, especially when dealing with limited sample sizes [56]. The estimator Phillips and Moon [48] presented incorporates a semi-parametric correction to address the challenges arising from long-run correlations between cointegration equations and stochastic regression.

Furthermore, when panel data is employed, FMOLS method yields an estimator that follows an asymptotic distribution approximated by a normal distribution. Consequently, the FMOLS estimator is not subject to asymptotic bias, enabling the application of the standard Wald test utilizing asymptotic inference chi-square statistics [47]. According to the study conducted by Chen et al. [59], the equation for the FMOLS panel estimator is as follows:

$\hat{\beta}=\left[\sum_{i=1}^N \sum_{t=1}^T\left(X_{i t}-\bar{X}_i\right)\left(X_{i t}-\bar{X}_i\right)\right]^{-1}$             (3)

$\left[\sum_{i=1}^N\left(\sum_{t=1}^T\left(X_{i t}-\bar{X}_i\right) \hat{Y}_{i t} T \Delta_{\varepsilon \mu}\right)\right]$            (4)

where, Y_it  is the endogeneity correlation term; Δ_εμ  is the serial correlation correction term. Correction is achieved by assuming that there is a relationship between the residuals of the static regression and the first difference of the lead, lag and contemporaneous values of the regressor in the first difference [51, 56].

The results of the FMOLS estimation are presented in Table 6. Based on the findings obtained from the FMOLS estimation, it can be concluded that there exists a strong and positive association between renewable energy consumption and economic growth. This conclusion is supported by the statistical analysis, which yielded a t-statistic of 3.33 and a coefficient (β) value of 17.79. This discovery suggests that a rise in the utilization of renewable energy sources will lead to a corresponding increase in economic development. The use of renewable energy has been found to benefit economic growth, indicating that a 1% increase in renewable energy consumption leads to a significant 17,893% rise in economic growth, assuming all other factors remain constant. The results obtained from the FMOLS calculation indicate no statistically significant impact of natural gas usage on economic growth.

In addition, it is observed that petroleum consumption exhibits a noteworthy and favorable impact on economic growth, as evidenced by the coefficient β = 7,032 and the corresponding t-statistic of 6,801. The factors related to petroleum consumption suggest that a 1% rise leads to a substantial increase of 7,032% in economic growth, provided all other independent variables remain constant. The observed probability of 0.000, less than the predetermined significance level of 0.05, indicates that the effect under consideration is statistically significant. The present discovery demonstrates a statistically significant and adverse effect on economic growth, with a magnitude of -3.128 and a significance level of 1%. This suggests that a more significant proportion of urban population is associated with declining economic growth. The findings indicate that a one percent increase in urban population is linked to a substantial fall of 3,128 percent in GDP growth. The regression coefficient associated with the log population variable is determined to be 11,281. However, the analysis conducted on the G20 countries reveals that this coefficient does not yield a statistically significant impact on economic growth.

Table 6. Results of FMOLS estimation GDP growth is the dependent variable

Notes: ***, and ** indicate that the coefficients are significant at the 5% and 10% level of significance, respectively.

Source: Author's own calculation using EVIEWS 12.