Abstract
Attaining cleaner production is a major challenge for BRICS economies. In this context, this study explores the effect of financial globalization on renewable energy consumption in BRICS economies from 1990 to 2018. It is probably the first research to study the linkage between financial globalization and renewable energy consumption. Therefore, this research adds to the current literature by presenting new empiric evidence on how financial globalization, in conjunction with environmental innovations, energy productivity, energy prices, and economic growth, affect renewable energy consumption in BRICS economies. In doing so, this research utilized novel econometric methods such as continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) techniques to evaluate the long-run results. The empirical findings show that financial globalization, environmental innovation, energy productivity, and energy prices promote renewable energy consumption. In contrast, economic growth impedes renewable energy consumption. This study suggests that governments and policymakers in BRICS countries should consider financial globalization and the increasing role of environmental innovations to increase the renewable energy share, which can be the appropriate solutions to the environmental challenges and achieve the Paris Climate Agreement’s goals. BRICS economies require speeding up permits for renewable energy projects, raising tax credits, including substantially more grants and loans, extending timelines for pandemic-affected projects, and investing directly in emerging clean energy sources.
1 Introduction
The most critical challenges are climate change and environmental deterioration to accomplish environmental sustainability in the 21st century (Zhenmin and Espinosa, 2019). Rapid economic expansion, industrialization, and higher energy consumption have made climate change more disastrous (Zaman and Moemen, 2017; Mesagan and Chidi, 2020). To cope with the rising energy consumption and environmental problems, countries worldwide are formulating policies considering the Paris Agreement (COP21) to curb global warming to less than 2°C (United Nations, 2020). Researchers have found different strategies and policies to combat environmental deterioration and increase energy consumption. Among others, the development and usage of renewable energy have ecological and economic benefits. Renewable energy is critical in achieving energy security and independence from fossil fuel markets (Anton and Afloarei Nucu, 2020). Therefore, higher renewable energy consumption (hereafter REC) is an essential component of the national planning agenda for smart, sustainable, and inclusive growth (Binz et al., 2017; Liu et al., 2019).
The energy sector is one of the most capital intensives compared to other industries. Renewable energy projects require a high initial investment relative to fossil fuel energy. Despite the increasing role of energy from renewable sources in sustainable economic growth, there is little about how globalization affects REC. It is argued that globalization is considered a valuable tool to mitigate environmental degradation (Wang et al., 2020) because globalization promotes REC (Padhan et al., 2020). Globalization is a part of financial liberalization and openness that increases renewable energy research and development (Gozgor et al., 2020). A well-developed and sound financial system offer more funds for renewable production at a lower cost, giving rise to improved financing, which sequentially increases renewable energy production and consumption. Thus, improvements in the financial sector through financial globalization are likely to benefit the renewable energy sector, particularly for raising external funds for renewable energy projects.
Moreover, financial globalization and liberalization of capital markets enhance the interaction between financial channels and international firms, which may have ample technological power transfer for renewable energy and research and development to the host country (Eren et al., 2019; Fan and Hao, 2020; Sabishchenko et al., 2020). Therefore, governments have options for environmentally friendly and cost-effective solutions because of increased demand for energy consumption and environmental degradation. Likewise, environmental innovations are considered efficient ways to lessen carbon emissions and promote REC (Alvarez-Herranz et al., 2017; Li et al., 2020). Geissdoerfer et al. (2017) also pointed out that the circular economy (sustainable) may not be possible without environmental innovations (Shpak, 2021).
The BRICS (Brazil, Russia, India, China, and South Africa) economies are among the top 10 largest and fastest-growing energy producers and consumers globally (BP Statistical Review of World Energy, 2020). REC in BRICS economies has also grown steadily in the last few decades. However, considerable disparities exist in the usage of renewable energy among BRICS economies (Zeng et al., 2017). Brazil has achieved a high share in renewable energy in its final gross energy use (43.79%), after that India (36.02%), South Africa (17.15%), China (12.41%), Russia (3.30%) (see Figure 1) in the year of 2018 (World Bank, 2019). Anton and Afloarei Nucu (2020) believed that REC and its development depend on countries’ financial conditions. However, other factors may affect discrepancies, such as geography and other domestic factors, despite financial and economic aspects. For Instance, Brazil has the highest forested area in BRICS, and hydroelectric power generation is being the most vital source of renewable energy with a share of 63.8%, followed by biomass, biogas (8.9%), wind (9.3%), and solar centralized (1.4%). In contrast, Russia is categorized with vast land area, low population, and rich in minerals. India has the world’s fifth-largest wind energy industry, with a volume of 22. GW. China’s renewable energy is growing faster in the hydroelectric and wind power sectors than in other countries. That seems to be evident as a circular economy is an economic model which is the reasonable use and recovery of natural resources based on reducing, reusing and recycling (Ślusarczyk, 2012; Pathak and Shah, 2019).
FIGURE 1
Why BRICS countries? This study focuses on BRICS economies for many reasons. First, BRICS countries represent an overall 21% of global GDP, covering half of the world’s population and consuming 40% of total energy. Second, for the past two decades, these BRICS countries have been at the forefront of global economic progress, and their GDP increased from $4,985 US billion (constant 2010) to $7,719 US billion from 1990 to 2018. Investment in BRICS is rapidly growing (Zeng et al., 2017). Furthermore, financial cooperation among BRICS economies is continually increasing. However, the ambitious pace of economic growth creates significant challenges to their development paths, and these countries are responsible for increasing global carbon emissions (Danishwang, 2019). Third, The BRICS economies face a severe conditions concerning environmental sustainability and energy security.
Moreover, BRICS economies face internal and external pressure to reduce environmental degradation because rapid economic growth has created far-reaching environmental issues. Four, previous research has concentrated on the relationship between globalization and REC; however, the linkage between financial globalization and REC remains un-investigated. Therefore, because of the prominence of financial globalization and environmental innovation, ongoing research aims to fill this gap and examine the linkage among financial globalization, environmental innovation, energy productivity, economic growth, and REC in BRICS economies.
This research contributed to the present literature in several respects. Firstly, the current literature is silent on how financial globalization affects REC. Therefore, this research fills this gap by using the recently developed financial globalization index (Gygli et al., 2019). Secondly, research on environmental innovation and energy productivity is very thin, so our research investigates the effect of financial globalization, environmental innovation, and energy productivity on REC in BRICS economies. Lastly, the advanced panel estimation methodology is applied for this study that counters cross-sectional dependence. Conventional panel data estimators like fully modified ordinary least squares (FM-OLS) and dynamic (D-OLS) consider the panels’ cross-sections are independent. They infer that shocks in one country do not affect other countries. In spite of this, globalization has made economies economically, politically, and socially interconnected. Therefore, this study employs continuously updated full modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) techniques presented by Bai et al. (2009) to get robust and reliable findings.
The remaining article is structured in this way. Section 2 unveils the literature review encompassing the determinants of REC. Section 3 presents the theoretical framework, model construction, and data. Section 4 explains the econometric methods. Section 5 debates the results and discussions. The conclusion and policy recommendations are summarized in Section 6.
2 Literature Review
Considering the increasing threat of climate change and global warming because of greenhouse gas emissions (GHG), A growing global consensus favours the development and usage of renewable energy to reduce fossil fuels. Nevertheless, renewable energy sources are rapidly increasing (International Energy Outlook, 2020), but it still has a relatively small share of overall energy consumption. Recently, a significant volume of literature emerged to determine renewable energy development (Sadorsky, 2009; Salim and Rafiq, 2012; Omri and Nguyen, 2014; Zhao and Luo, 2017; Ali et al., 2018; Buturache and Stancu, 2021). Bhattacharya et al. (2016) stated that energy sustainability is related to a clean environment, a productive energy structure, and fewer carbon emissions.
Besides, a substantial part of the research explores the determining factor of REC. The most widely known elements that affect REC are energy prices, regulatory system, environmental pollution, energy consumption, energy security, renewable energy potential, political environment, financial flows, and economic growth. The pivotal study of Sadorsky (2009) and Chang et al. (2009) established a sound base for forthcoming researchers to explore the determining factor of REC. In the past 10 years, most researchers deemed energy prices and economic growth are the core variables of REC (Sadorsky, 2009; Lucas et al., 2016; Romano and Scandurra, 2016; Lin and Omoju, 2017; Lu, 2017; Nicolini and Tavoni, 2017; Nyiwul, 2017; Oláh et al., 2020; Shahzad et al., 2021a; Shahzad et al., 2021b). The researchers subsequently incorporated environmental variables to investigate the link between REC and economic growth (Lucas et al., 2016; Romano and Scandurra, 2016; Lu, 2017; Nicolini and Tavoni, 2017; Nyiwul, 2017; Shahzad, 2020). Lucas et al. (2016) proposed energy consumption and energy security as the determining factor of REC. Lin and Omoju (2017) incorporated fossil fuels, renewable energy potential, and regulatory framework into the existing literature listing determinant factors of REC. Nyiwul (2017) introduced the population, and Yao et al. (2019) described human capital as determining REC. Recently, Li et al. (2020) included environmental innovation and energy productivity in explaining REC.
Following a review of the research on REC determinants, certain important conclusions can be reached. Firstly, economic growth and energy prices gained more attention. Secondly, minimal importance is devoted to financial development and other important variables that can affect REC, for example, structural and institutional variables. A few studies also examine the connection between financial development and REC; however, their findings are inconclusive. The literature’s mixed results may also be due to non-financial factors such as availability of resources, geographic location, orography, and the country’s own social and political context (Anton and Afloarei Nucu, 2020; Wang, 2022; Wang et al., 2022). The overall KOF globalization indices introduced the specific dimension as a sub-indices called the financial globalization index (Gygli et al., 2019).
Financial globalization may help countries lower energy demand by importing energy technologies (Baek et al., 2009). Also, there is a disagreement about using the dependent variable to evaluate REC determinants. For example, some researchers used all levels of REC per capita (Sadorsky, 2009). Some scholars have adopted the share of all renewable energy in whole energy consumption (Marques et al., 2010; Martínez-Zarzoso and Maruotti, 2011; Majeed et al., 2021a), whereas others employed non-hydro-renewable energy produced from electricity (Omri and Nguyen, 2014; Zeb et al., 2014; Cadoret and Padovano, 2016; Romano and Scandurra, 2016; Carley et al., 2017; Mariyakhan et al., 2020). Following previous studies of Apergis and Eleftheriou (2015); Li et al. (2020) this research utilizes total REC as the response variable. This research fills the literature gap by proposing new determinants that may impact REC. In approximating the equation for REC, we add energy productivity as a control variable. However, to our best knowledge, no single research has investigated the effect of financial globalization on REC. This research contributes to the current literature by identifying financial globalization as a new determinant of REC.
3 Theoretical Background, Data, and Model Construction
3.1 Theoretical Background
The theoretical mechanism through which financial globalization and environmental innovation, energy productivity, economic growth, and energy prices affect REC is described in this section. The existing literature is silent on the association between financial globalization and REC. So, for the intent of this research, we have explained the effect of financial development on REC because financial globalization demonstrates the global impact of financial development (Gygli et al., 2019). Existing literature has provided mixed results for the association between financial development and REC (Shahbaz et al., 2016; Topcu and Payne, 2017; Destek, 2018; Zuo et al., 2022). The overall interpretation of the positive association can be split into three ways: direct impact, business impact, and wealth impact (Sadorsky, 2011a; Sadorsky, 2011b). The direct impact is where the demand for durable products is raised by financial easing, contributing to greater energy usage. The business impact indicates the firm’s response to the growing demand occurs by utilizing financial resources (loans) to extend the production base, resulting in a rise in energy use. Finally, the wealth impact takes capital market behaviour into account and reflects customers’ and businesses’ confidence in the economy. Such confidence is fueled by the rising stock market, thereby providing customers and businesses with confidence in economic growth, rising energy demand.
The existing literature shows that financial development facilitates sustainable energy sources, resulting in renewable energy ventures (Chandrashekeran et al., 2015; Neville et al., 2019). Further, economic growth raises energy demand and places pressure on the economy to ensure the sustained availability of energy to ensure a steady growth trend (Ozcan and Ozturk, 2019). But the effect of extensive utilization of resources, especially traditional energy sources such as fossil, coal, and oil, leads to a deterioration of the environment and additional economic costs. Bhattacharya et al. (2016) assert the negative economic growth resulting from higher investment costs in renewable energies. Thus, countries have shown a keen interest in finding and using alternative renewable energy sources over the past decade and trying to replace them with the impacts of green environmental concerns. Research should focus on more significant environmental innovation (de Jesus et al., 2016; Park et al., 2017). Hojnik and Ruzzier (2016) indicate that laws serve as a mainspring for businesses to embrace environmental innovation by reducing manufacturing costs and compliance with environmental policies. This competition also pushes enterprises to be leaders in environmental innovation and gain an advantage of the first mover (Nidumolu et al., 2009; Ahmad et al., 2022).
These organisational measures encourage new green manufactured goods and focus on a new marketplace for customers and investors with environmental concerns (Porter and Van Der Linde, 2017). Therefore, environmental innovation could promote a change from non-REC to REC to reinforce their reputation in stakeholders’ eyes. Energy productivity also improves energy efficiency and reduces CO2 emission and GHG, which is linked to the greater usage of renewable energy sources (Li et al., 2020). However, energy is an essential component of residential use and commercial output. REC is obtaining traction as an alternate energy source because of increasing energy prices and the control of conventional energy supplies by a small number of vendors, which also describes the positive link between energy prices and REC (Ju et al., 2017; Troster et al., 2018). Evidence indicates that increasing energy prices often push innovation Popp (2002) which inevitably results in cheaper renewable energy sources than conventional energy sources. Leng Wong et al. (2013) also reported that increased energy prices decrease traditional energy usage and increase REC.
3.2 Model Construction
In order to look into the effect of financial globalization on REC in the existence of economic growth, environmental innovation, energy productivity, and energy prices explained in the theoretical framework. The econometric model used is as follows:
The model variables are changed into natural logarithm form for empirical estimation to reduce data sharpness and improve distributional properties. Data autocorrelation and heteroskedasticity can be curbed by natural logarithmic conversion. The results of a log-transformed model are more reliable and effective than linear transformation. The empirical model’s log-transformed version is as follows:
Eq. 2i signifies the countries, and t describes the year. expresses the error term, and β describes the coefficients. REC indicates renewable energy consumption. FG, GDP, ET, EPR, and EP show financial globalization, economic growth, environmental innovation, energy productivity, and energy prices.
The study included financial globalization as a new determinant of REC. As a subset of the KOF Globalization Index, financial globalization illustrates the global impact of financial development (Gygli et al., 2019). Countries can embrace new technology and transition the economy to a more sustainable energy source (Anton and Afloarei Nucu, 2020; Shahzad et al., 2022). Therefore, we expect that financial globalization in BRICS countries will increase REC, i.e., . Following previous studies, Anton and Afloarei Nucu (2020); Moreno et al. (2012); Uzar (2020), we employ GDP to measure economic growth when determining the economic growth effect on REC for the BRICS countries. The anticipated impact of economic growth on REC is negative, i.e., As a result of significant research and development investment, environmental innovation can transform the economy into renewable energy sources (Alvarez-Herranz et al., 2017; Li et al., 2020). Therefore, in BRICS countries, we anticipate the positive effect of environmental innovation on REC, i.e., . This study introduces energy productivity in addition to environmental innovation, which is an attempt to enhance energy efficiency and minimize carbon emission and other GHG that are directly connected to the surge REC (Li et al., 2020). We expect energy productivity to impact REC, i.e., positively . Prior studies found that energy prices are one of the most important variables to describe REC (Chang et al., 2009; Sadorsky, 2009; Carley et al., 2017; Lin and Omoju, 2017; Lu, 2017; Nicolini and Tavoni, 2017; Nyiwul, 2017; Li et al., 2020). According to Khan et al. (2020) renewable energy replaces non-renewable energy, like fossil fuels, especially caused by the substitution impact; we anticipate a positive effect on REC from the energy prices, i.e.,
3.3 Data
This research explores the determinants of REC in BRICS economies, covering the years 1990–2018. The study has used REC as a dependent variable. Financial globalization, economic growth, environmental innovation, energy productivity, and energy prices are independent variables. Data on REC, economic growth, and energy prices are taken from the World Bank database; financial globalization is from Swiss Economic Institute; environmental innovation and energy productivity are retrieved from the OECD database. Table 1 shows the description, measurement, and data resources for variables.
TABLE 1
| Variable | Symbol | Measurement | Source |
|---|---|---|---|
| Renewable energy consumption | REC | Renewable energy consumption (% total energy) | WDI |
| Financial globalization | FG | Financial globalization index | SWI |
| Economic growth | GDP | GDP (constant 2010) | WDI |
| Environmental innovation | ET | Environmental related technologies (% of total technologies) | OECD |
| Energy productivity | EPR | GDP/Primary energy consumption | OECD |
| Energy prices | EP. | Consumer price index | WDI |
Variable description.
WDI, World Development Indicators; SWI, Swiss Economic Institute; OECD, Organization for.Economic Cooperation and Development.
4 Econometric Methodology
Before testing the stationarity properties and the long-run association between the variables, we incorporate the cross-sectional dependence (CD) analysis introduced by Pesaran (2004).Because the panel data typically display CD because countries are interconnected at the global and regional levels. If the research findings do not evaluate CD, the estimation methods would be inconsistent and biased (Phillips and Sul, 2003; Paramati et al., 2017). Thus, In the panel data, the CD must be checked. In this analysis, to measure CD, two distinct methods are used. First, the CD examination was developed by Pesaran (2004). The calculation for the CD assay is given as follows:Where sample size is shown by N, time is indicated by T, and demonstrates the cross-sectional error correlation estimation of economy i and j. Breusch and Pagan (1980) proposed the Lagrange Multiplier (LM) technique to examine CD. The following equation is used to test CD for the LM test:Where i stands for cross-section measurements, t reflects the duration of the research.
4.1 Unit Root Tests
The first-generation unit root scrutiny results are unreliable in the presence of CD. Therefore, the second-generation unit root techniques have gained popularity (Zafar et al., 2019). Thus, this research analyzed the stationarity of the studied variables by applying CADF and CIPS to stand for cross-sectional augmented ADF and augmented IPS, respectively. The credibility of the analyses improves by utilizing the appropriate unit root examination within panel data in the existence of CD. Pesaran (2007) developed the following equation to evaluate the unit root:Where difference operator denotes by , evaluated variable shown by , individual intercept expressed by , time trend represented by T and error term explained by . The Schwarz Information Criterion approach defines the lag length. The null hypothesis for both measures is that neither variable is stationary, but the alternative hypothesis is that at least one individual in a panel data time series is stationary.
4.2 Panel Cointegration Test
Before calculating the long-term variables, we confirm the underlying variables are cointegrated or not. As the panel cointegration tests of the first and second generation cannot jointly cope with structural breaks and CD, i.e., (Pedroni, 2004; Westerlund, 2005; Larsson et al., 2001; McCoskey and Kao, 1998; Westerlund, 2007). Phillips and Sul (2003), Ozcan and Ozturk, (2019) described that traditional cointegration techniques produce deceptive and erroneous outcomes when the model undergoes CD and heteroscedasticity. For that reason, this research employs the Durbin Hausman Group Mean (DHGM) cointegration method proposed by Westerlund and Edgerton (2008). DHGM cointegration method is an advanced method and robust for CD and incorporates multiple structural breaks. Westerlund and Edgerton (2008) approach examines the series through the structural break and regime shift. Westerlund and Edgerton (2008) cointegration analysis presumes that there is no cointegration under null hypotheses compared to the alternate hypothesis of long-run variable relationships. Thus, this analysis should first employ Westerlund and Edgerton (2008) panel cointegration method before obtaining a long-run estimate.
The Westerlund and Edgerton (2008) cointegration analysis equations are described as:
4.3 Long-Run Estimation
Next, we evaluate the long-run connection between financial globalization and REC in the existence of environmental innovations, energy productivity, economic growth, and energy prices. For long-run analysis, Economists suggest several analytical techniques for panel data. In summary, different methods are prone to different shortcomings. Prior studies used first-generation techniques to calculate long-run elasticity, but these techniques overlook the CD (Ulucak and Bilgili, 2018). In the case of CD, the Dynamic Seemingly Unrelated Cointegrating Regressions (DSUR) method produces consistent results in recent literature. However, it ignores the problem of serial correlation and endogeneity. The Mean Group (MG) estimation method introduced by Pesaran and Smith (1995) and the Augmented Mean Group (AMG) approach suggested by Bond and Eberhardt (2013) provide accurate measurements with significantly larger sample size. However, in endogeneity and serial correlation, these techniques are not effective (Ahmed et al., 2020).
In the light of the above discussion, the present research relies on the CUP-FM presented by Bai and Kao (2006). For robustness, the CUP-BC approach was introduced by Bai et al.(2009) to consider some recent research (Ulucak and Bilgili, 2018; Zafar et al., 2019; Ahmed et al., 2020; Xiaoman et al., 2021). We have large samples with high power values by applying these two estimating methods, CUP-FM and CUP-BC. These approaches are more efficient for panel data than other estimating techniques because they can generate correct results even when CD, endogeneity, and autocorrelation are present. These techniques produce unbiased and reliable findings when used with exogenous regressors. These techniques can also deal with mixed I(1)/I(0) factors and produce reliable outcomes. These methods can estimate consistent results even if there is no endogeneity (Bai et al., 2009).
The authors adapted the CUP-BC estimation method to control the serial correlation and endogeneity resulting from the asymptotic bias. The CUP-FM estimating method maintains a consistent distribution of the limited model factors. These variables are continually updated (CUP) with time until they attain convergence via. simulations. The error term is assumed to follow the factor model. The factor model is defined as follows:where; illustrates the components demonstrates the identity matrix. The “error term” denotes the presence of common latent elements. Then, the initial estimates are assigned to F. It is performed repeatedly until the desired level of convergence is obtained.
5 Results and Discussion
The CD in the model is checked first in the empirical evaluation. The assessment of CD has become the key focus of the current literature. The failure to manage the CD could generate biased outcomes (Ahmed et al., 2020; Majeed et al., 2021b). The findings of the CD and LM examinations are summarized in Table 2. The findings are significant at the 1% significance level and confirm the null hypothesis’ rejection. The findings of Table 2 verify the existence of CD. The presence of the CD allows the use of second-generation unit root assessments to analyze the variables’ integration order. The panel unit root tests CADF and CIPS are used for this, and Table 3 summarizes the results of both tests. The CIPS test’s empirical outcomes show that REC, energy productivity, economic growth, and energy prices have a unit root at the level. These variables have no unit root in the first difference, and they are integrated at I(1). The CADF panel unit root test findings show the unit root at the level, except for environmental innovation, and in the first difference, all variables are stationary.
TABLE 2
| Variables | Breusch-pagan LM | Pesaran Scaled LM | Pesaran CD |
|---|---|---|---|
| lnRE | 79.900*** | 15.630*** | 7.411*** |
| lnFG | 204.970*** | 43.597*** | 14.303*** |
| lnGDP | 209.648*** | 44.643*** | 14.328*** |
| lnET | 13.0078 | 0.673 | 0.244*** |
| lnEPR | 134.763*** | 27.898*** | 5.728*** |
| lnEP | 216.459*** | 46.166*** | 14.670*** |
CD test findings.
Note: ***, **, * is for p-values <0.01, 0.05 & 0.10.
TABLE 3
| Variable | CIPS | CADF | ||
|---|---|---|---|---|
| Level | First-difference | Level | First-difference | |
| lnRE | −2.059 | −3.689*** | −2.119 | −3.689*** |
| lnFG | −3.027*** | −5.321*** | −2.334 | −3.859*** |
| lnGDP | −1.455 | −2.855** | −1.455 | −3.044*** |
| lnET | −4.728*** | −6.068*** | −3.083*** | −4.873*** |
| lnEPR | −2.570 | −4.027*** | −2.656 | −4.027*** |
| lnEP | −1.675 | −4.956*** | −1.675 | −4.053*** |
Unit root test findings.
Note: ***, **, * is for p-values <0.01, 0.05 & 0.10.
The Westerlund and Edgerton (2008) cointegration technique investigated the long-run cointegration connection. The statistically significant test of τ and ϕ imply a long-term relation between independent and dependent variables, in Table 4, both at the level and regime shift. For each country, the findings also found many structural breaks. The logic behind each structural break for the BRICS economies is described in-depth with their relative significance. In particular, we have observed multiple structural break periods in 2002, 2003, 2008, 2009, 2012, and the results are presented in Table 5. These breaks impact each country subject to both global and local shocks. The South American economic crisis is the economic turmoil that happened in Brazil in 2002. Russia’s debt increased to 19 billion US dollars in 2003 because of higher finance ministries and Euro-bonds disbursements. In addition to the structural crisis in 2012, Russia has to contend with cyclical and idiosyncratic economic challenges (Grant and Hansl, 2015). Furthermore, the recession in 2008-09 has had an acute impact on the Indian and South African economies. Lastly, In 2003, a $12.3–28.4 billion loss was caused by the SARS epidemic and the projected 1% fall in GDP in China (Qiu et al., 2018).
TABLE 4
| Level shift | Regime shift | |
|---|---|---|
| LMτ | −3.740*** | −4.056*** |
| LMϕ | −2.368** | −2.835**** |
Findings of Westerlund and Edgerton cointegration test.
Note: ***, **, * is for p-values <0.01, 0.05 & 0.10.
TABLE 5
| Countries | Level shift | Regime shift |
|---|---|---|
| Brazil | 2002 | 2002 |
| Russian federation | 2012 | 2003 |
| India | 2008 | 2008 |
| China | 2002 | 2003 |
| South Africa | 2009 | 2009 |
Structural breaks of Westerlund and Edgerton cointegration test.
Note: ***, **, * is for p-values <0.01, 0.05 & 0.10.
After completing the cointegration evaluation, a precondition for long-run analysis, we used the effectual CUP-FM approach. We also used the CUP-BC approach for robustness purposes. Table 6 reveals the long-run outcomes are positive and significant. The coefficients of financial globalization, environmental innovations, energy productivity, and energy prices signify that the rise in these variables improves REC. At the same time, economic growth has a negative connection with REC in BRICS countries. The CUP-FM shows coefficients values for financial globalization, environmental innovations, energy productivity, energy prices and economic growth are 0.103, 0.064, 0.421, 0.040%, and −0.542% respectively. Firstly, it is observed that economic growth reduces REC. It can be discussed from two viewpoints: First, economic growth may also entail increased energy consumption. In this sense, growing energy demands can be fulfilled from various sources. It could lead to an escalation in demand for renewables. Secondly, increasing energy consumption can raise the demand for readily available and inexpensive conventional energy rather than renewable energy. These results are consistent with earlier studies (Moreno et al., 2012; Uzar, 2020; Chen et al., 2021). Our results oppose Li et al. (2020) the OECD economies that access the GDP as a driver of the REC. The difference between Li et al. (2020) results can be justified because the BRICS countries are still emerging while OECD countries are developed.
TABLE 6
| Variable | Cup-FM | Cup-BC | ||
|---|---|---|---|---|
| Coefficient | T-Statistics | Coefficient | T-Statistics | |
| lnFG | 0.10346*** | 2.8228 | 0.08690*** | 4.29829 |
| lnGDP | −0.5428*** | 17.8395 | −0.6235*** | 17.0217 |
| lnET | 0.06450*** | 3.0158 | 0.06381*** | 3.0197 |
| lnEPR | 0.42135*** | 3.3189 | 0.4111*** | 3.115 |
| lnEP | 0.0409*** | 14.0292 | 0.05121*** | 15.1797 |
CUP-FM and CUP-BC test findings.
Note: ***, **, * is for p-values <0.01, 0.05 & 0.10.
Secondly, Financial globalization devises a significant positive effect on REC as a 1% acceleration in financial globalization is correlated with a 0.103% rise in REC, with a statistically significant impact at a 1% level. Financial globalization represents global financial development as a sub-index for the overall KOF globalization index (Gygli et al., 2019). An established financial system offers an incentive to access capital that enhances living standards and stimulates economic growth. Besides the increasing quality of life in the economic dimension, which is an essential constituent of its overall perception (Tvaronavičienė et al., 2021), it also raises energy consumption (Saud et al., 2020). The introduction of modern manufacturing methods and the procurement of progressed technology that save more energy resources result in a well-functioning financial system. Additionally, the capital market’s financial development and liberalization cause financial channel interactions and bring foreign direct investment to transfer green technology with ample financial and technical capacity (Kim and Park, 2016; Eren et al., 2019; Ji and Zhang, 2019; Anton and Afloarei Nucu, 2020; Halaskova et al., 2021; Zhuang et al., 2021).
Thirdly, environmental innovation has a positive and significant effect on REC. Environmental innovation is, on average, a rise in REC by 0.064%. The BRICS countries have developed the requisite environment to promote environmentally friendly and energy-saving technological innovations. These advances also lower the renewable energy cost, making it more convenient for the masses to switch from non-REC to REC (Murad et al., 2019; Khan et al., 2020). Therefore, environmental innovation moves economies to clean energy sources (Alvarez-Herranz et al., 2017), contributing to the low-carbon energy transition and Sustainable Development Goals achievement (Krzymowski, 2020; Zhu et al., 2020; Li et al., 2021; Štreimikienė, 2021). These findings endorse the results (Alvarez-Herranz et al., 2017; Li et al., 2020; Wang and Luo, 2022).
Fourth, similarly, energy productivity is positively connected with REC. On average, energy productivity results in a 0.790% rise in REC. Enhanced energy productivity could improve economic competitiveness, lower energy prices, and minimize carbon dioxide emissions. These outcomes confirm the research results (Bhattacharya et al., 2020; Li et al., 2020). Therefore, the endeavours to boost energy efficiency and minimize CO2 emissions are also explicitly connected to REC improvement. Lastly, energy prices (assessed by the Consumer Price Index) are significantly linked to REC. An increase of 0.040% in REC, on average, is triggered by energy prices. These results confirm that energy price strategies can play an essential role in supporting REC. These findings affirm the results of (Ike et al., 2020; Khan et al., 2020; Li et al., 2020; Yayla et al., 2021). This study utilizes the CUP-BC technique to confirm the robustness of the analysis. The CUP-BC findings are congruent with the CUP-FM findings. Figure 2 demonstrates a visual interpretation of the long-run parameters concerning their positive and negative signs. The positive effect indicates an increase in the related variable stimulates the REC, whereas the negative impact decreases the REC.
FIGURE 2
6 Conclusion and Policy Implications
There is much discussion about the positive effect of developing and removing CO2 emissions from renewable energy sources. In spite of this, it is essential to examine the aspects that influence the REC. In this study, we endeavoured the potential determinants of REC. This research empirically explores financial globalization as a new determinant of REC in BRICS countries from 1990 to 2018. This research adds to the literature on energy economics by presenting new empirical data on how REC is connected to economic activities. We used financial globalization, environmental innovation, energy productivity, and energy prices to observe the potential association between economic growth and REC to calculate the linkage between development alacrity and REC. Renewable energy in BRICS economies is developing at a high speed (Zeng et al., 2017). This study used two techniques for CD familiarized by Pesaran (2004) and the LM test by Breusch and Pagan (1980). This research applies the DHGM cointegration approach formed by Westerlund and Edgerton (2008) to evaluate the determinants of REC for the BRICS economies. The long-run coefficients are computed employing the CUP-FM methodology presented by Bai and Kao (2006). CUP-BC technique is used in this study to test the models’ robustness presented by Bai et al. (2009).
We confirmed the existence of CD in the data. Furthermore, the CIPS and CADF results of Pesaran (2007) unit root investigations reveal that the variables’ integration order is mixed, i.e., I(0) and I(1). The second-generation cointegration methods are applied due to the mixed integration of variables. The cointegration methodology developed by Westerlund and Edgerton (2008) is employed to confirm the variables’ long-run equilibrium. All variables are cointegrated with REC at both the level and the regime shift, showing significant structural breaks, e.g., financial crises of South American economic crisis in 2002, 2003 SARS outbreak, 2007–8, recession, the variables REC, financial globalization, economic growth, environmental innovation, energy productivity, and energy prices are cointegrated. The positive and significant coefficients of financial globalization, environmental innovations, energy productivity, and energy prices imply that an escalation in these factors increases REC. Simultaneously, economic growth is negatively related to REC in the BRICS economies. The coefficients values are displayed by the CUP-FM for financial globalization, environmental innovation, energy productivity, energy prices and economic growth are 0.103, 0.064, 0.421, 0.040%, and −0.542% correspondingly. The outcomes of the CUP-BC are in line with the results of the CUP-FM.
6.1 Policy Implications
In practical terms, we recommend adopting policies by BRICS economies to reshape their total energy mix for clean energy. Increasing energy prices will be another option; it will reduce commercial energy consumption and is one possible way of motivating customers to change their preferences toward renewable energy. In addition, BRICS countries should turn to using renewable energy sources through environmental innovation and increasing energy production to attain the goals outlined in the Paris Climate Agreement. More specifically, environmental innovation and financial globalization are highly complementary. It is often risky and expensive to develop renewable energy, particularly at the start. Policy support is necessary to build a favourable environment to improve this sector. The financial environment is one of the most important foundations. Financial globalization plays an important role in raising the REC in this context.
According to the International Renewable Energy Agency, converting to renewable energy sources by 2050 will increase global GDP by $98 trillion while creating 63 million jobs worldwide in the renewables and energy sectors. In India, where more than 100 million jobs were lost due to the shutdown. Increasing renewable energy capacity to 160 GW by 2022 might create 1.3 million full-time jobs. A transition to renewables will help countries like India and China save money by drastically lowering their import bills. India will save over $90 billion between 2021 and 2030 if half of its renewable energy is used to substitute imported coal.
The fact that renewables are now the more cost-effective option in many countries makes a persuasive case for redirecting expenditures on coal consumption to accelerate renewable energy sources. It is necessary to reduce fossil fuel consumption in areas that remain essential, particularly now that oil prices plummet. Other stakeholders must also take action: businesses must use sustainable and safe methods, and investors must decarbonize their investments and encourage renewable energy. Renewable energy has the potential to reinvigorate the economy by producing “green” jobs, improving energy efficiency, and increasing resilience. The Paris climate agreement targets could be met if countries commit to a renewable energy future. Policymakers have an important opportunity to promote the clean energy sector significantly. They can expedite permits for renewable projects, increase tax credits, provide more grants and loans, extend timelines for projects impacted by the pandemic, and directly invest in emerging clean energy sources such as offshore wind. This study is limited to BRICS economies. This study can be enhanced by comparing the developed and developing economies in future research.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
Project no. 132805 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the K_19 funding scheme.
Conflict of interest
The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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References
1
AhmadM.AhmedZ.BaiY.QiaoG.PoppJ.OláhJ. (2022). Financial Inclusion, Technological Innovations, and Environmental Quality: Analyzing the Role of Green Openness. Front. Environ. Sci.10, 80. 10.3389/fenvs.2022.851263
2
AhmedZ.ZafarM. W.AliS. (2020). Danish, Linking Urbanization, Human Capital, and the Ecological Footprint in G7 Countries: An Empirical Analysis. Sustain. Cities Soc.55. 10.1016/j.scs.2020.102064
3
AliQ.KhanM. T. I.KhanM. N. I. (2018). Dynamics between Financial Development, Tourism, Sanitation, Renewable Energy, Trade and Total Reserves in 19 Asia Cooperation Dialogue Members. J. Clean. Prod.179, 114–131. 10.1016/j.jclepro.2018.01.066
4
Alvarez-HerranzA.Balsalobre-LorenteD.ShahbazM.CantosJ. M. (2017). Energy Innovation and Renewable Energy Consumption in the Correction of Air Pollution Levels. Energy Policy105, 386–397. 10.1016/j.enpol.2017.03.009
5
AntonS. G.Afloarei NucuA. E. (2020). The Effect of Financial Development on Renewable Energy Consumption. A Panel Data Approach. Renew. Energ.147, 330–338. 10.1016/j.renene.2019.09.005
6
ApergisN.EleftheriouS. (2015). Renewable Energy Consumption, Political and Institutional Factors: Evidence from a Group of European, Asian and Latin American Countries. Singapore Econ. Rev.60. 10.1142/S0217590815500083
7
BaekJ.ChoY.KooW. W. (2009). The Environmental Consequences of Globalization: A Country-specific Time-Series Analysis. Ecol. Econ.68, 2255–2264. 10.1016/j.ecolecon.2009.02.021
8
BaiJ.KaoC. (2006). Chapter 1 on the Estimation and Inference of a Panel Cointegration Model with Cross-Sectional Dependence. Elsevier, 3–30. 10.1016/s0573-8555(06)74001-9
9
BaiJ.KaoC.NgS. (2009). Panel Cointegration with Global Stochastic Trends. J. Econom.149, 82–99. 10.1016/j.jeconom.2008.10.012
10
BhattacharyaM.InekweJ. N.SadorskyP. (2020). Convergence of Energy Productivity in Australian States and Territories: Determinants and Forecasts. Energy Econ85, 104538. 10.1016/j.eneco.2019.104538
11
BhattacharyaM.ParamatiS. R.OzturkI.BhattacharyaS. (2016). The Effect of Renewable Energy Consumption on Economic Growth: Evidence from Top 38 Countries. Appl. Energ.162, 733–741. 10.1016/j.apenergy.2015.10.104
12
BinzC.GosensJ.HansenT.HansenU. E. (2017). Toward Technology-Sensitive Catching-Up Policies: Insights from Renewable Energy in China. World Develop.96, 418–437. 10.1016/j.worlddev.2017.03.027
13
BondS. R.EberhardtM. (2013). Accounting for Unobserved Heterogeneity in Panel Time Series Models. Oxford: Nuff. Coll. Univ. OxfordMimeo., 1–32. Available at:https://pdfs.semanticscholar.org/2e60/ef67b62aaeb2e6db945cb8d59001b587c5c3.pdf (accessed November 3, 2020).
14
Bp Statistical Review of World Energy (2020). Available at: http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/downloads.html.
15
BreuschT. S.PaganA. R. (1980). The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics. Rev. Econ. Stud.47, 239. 10.2307/2297111
16
ButuracheA. N.StancuS. (2021). Usage Of Neural-Based Predictive Modeling And Iiot In Wind Energy Applications. Amfiteatru Econ.23, 412–428. 10.24818/EA/2021/57/412
17
CadoretI.PadovanoF. (2016). The Political Drivers of Renewable Energies Policies. Energ. Econ56, 261–269. 10.1016/j.eneco.2016.03.003
18
CarleyS.BaldwinE.MacLeanL. M.BrassJ. N. (2017). Global Expansion of Renewable Energy Generation: An Analysis of Policy Instruments. Environ. Resour. Econ.68, 397–440. 10.1007/s10640-016-0025-3
19
ChandrashekeranS.ZuckermanJ.DeasonJ. (2015). Raising the Stakes for Energy Efficiency: A Qualitative Case Study of California's Risk/reward Incentive Mechanism. Util. Pol.36, 79–90. 10.1016/j.jup.2015.09.006
20
ChangT. H.HuangC. M.LeeM. C. (2009). Threshold Effect of the Economic Growth Rate on the Renewable Energy Development from a Change in Energy price: Evidence from OECD Countries. Energy Policy37, 5796–5802. 10.1016/j.enpol.2009.08.049
21
ChenY.KumaraE. K.SivakumarV. (2021). Invesitigation of Finance Industry on Risk Awareness Model and Digital Economic Growth. Ann. Oper. Res., 1–22. 10.1007/s10479-021-04287-7
22
DanishwangZ. (2019). Investigation of the Ecological Footprint's Driving Factors: What We Learn from the Experience of Emerging Economies. Sustain. Cities Soc.49, 101626. 10.1016/j.scs.2019.101626
23
de JesusA.AntunesP.SantosR.MendonçaS. (2016). Eco-innovation in the Transition to a Circular Economy: An Analytical Literature Review. J. Clean. Prod.172, 2999–3018. 10.1016/j.jclepro.2017.11.111
24
DestekM. A. (2018). Financial Development and Energy Consumption Nexus in Emerging Economies, Energy Sources, Part B Econ. Plan. Pol.13, 76–81. 10.1080/15567249.2017.1405106
25
ErenB. M.TaspinarN.GokmenogluK. K. (2019). The Impact of Financial Development and Economic Growth on Renewable Energy Consumption: Empirical Analysis of India. Sci. Total Environ.663, 189–197. 10.1016/j.scitotenv.2019.01.323
26
FanW.HaoY. (2020). An Empirical Research on the Relationship Amongst Renewable Energy Consumption, Economic Growth and Foreign Direct Investment in China. Renew. Energ.146, 598–609. 10.1016/j.renene.2019.06.170
27
GeissdoerferM.SavagetP.BockenN. M. P.HultinkE. J. (2017). The Circular Economy – A New Sustainability Paradigm?J. Clean. Prod.143, 757–768. 10.1016/j.jclepro.2016.12.048
28
GozgorG.MahalikM. K.DemirE.PadhanH. (2020). The Impact of Economic Globalization on Renewable Energy in the OECD Countries. Energy Policy139, 111365. 10.1016/j.enpol.2020.111365
29
GrantA.HanslB. (2015). Russia Economic Report: The Dawn of a New Economic Era?http://documents.worldbank.org/curated/en/904101468295545451/Russia-economic-report-the-dawn-of-a-new-economic-era (accessed November 2, 2020).
30
GygliS.HaelgF.PotrafkeN.SturmJ. E. (2019). The KOF Globalisation Index – Revisited. Rev. Int. Organ.14, 543–574. 10.1007/s11558-019-09344-2
31
HalaskovaM.HalaskovaR.GavurovaB.KubakM. (2021). Fiscal Decentralisation of Services: The Case of the Local Public Sector in European Countries. J. Tour. Serv.12, 26–43. 10.29036/JOTS.V12I23.234
32
HojnikJ.RuzzierM. (2016). The Driving Forces of Process Eco-Innovation and its Impact on Performance: Insights from Slovenia. J. Clean. Prod.133, 812–825. 10.1016/j.jclepro.2016.06.002
33
IkeG. N.UsmanO.AlolaA. A.SarkodieS. A. (2020). Environmental Quality Effects of Income, Energy Prices and Trade: The Role of Renewable Energy Consumption in G-7 Countries. Sci. Total Environ.721, 137813. 10.1016/j.scitotenv.2020.137813
34
International Energy Outlook (2020). https://www.eia.gov/outlooks/ieo/.accessed November 28, 2020).
35
JiQ.ZhangD. (2019). How Much Does Financial Development Contribute to Renewable Energy Growth and Upgrading of Energy Structure in China?Energy Policy. 10.1016/j.enpol.2018.12.047
36
JuK.SuB.ZhouD.WuJ. (2017). Does Energy-price Regulation Benefit China's Economy and Environment? Evidence from Energy-price Distortions. Energy Policy105, 108–119. 10.1016/j.enpol.2017.02.031
37
KhanZ.MalikM. Y.LatifK.JiaoZ. (2020). Heterogeneous Effect of Eco-Innovation and Human Capital on Renewable & Non-renewable Energy Consumption: Disaggregate Analysis for G-7 Countries. Energy209, 118405. 10.1016/j.energy.2020.118405
38
KimJ.ParkK. (2016). Financial Development and Deployment of Renewable Energy Technologies. Energ. Econ. 10.1016/j.eneco.2016.08.012
39
KrzymowskiA. (2020). The european union and the united arab emirates as Civilian and Soft powers Engaged in Sustainable Development Goals. J. Int. Stud.13, 41–58. 10.14254/2071-8330.2020/13-3/3
40
LarssonR.LyhagenJ.LöthgrenM. (2001). Likelihood‐based Cointegration Tests in Heterogeneous Panels. Econom. J.4, 109–142. 10.1111/1368-423x.00059
41
Leng WongS.ChiaW. M.ChangY. (2013). Energy Consumption and Energy R&D in OECD: Perspectives from Oil Prices and Economic Growth. Energy Policy62, 1581–1590. 10.1016/j.enpol.2013.07.025
42
LiJ.ZhangX.AliS.KhanZ. (2020). Eco-innovation and Energy Productivity: New Determinants of Renewable Energy Consumption. J. Environ. Manage.271, 111028. 10.1016/j.jenvman.2020.111028
43
LiZ.WangJ.CheS. (2021). Synergistic Effect of Carbon Trading Scheme on Carbon Dioxide and Atmospheric Pollutants. Sustain13, 5403. 10.3390/su13105403
44
LinB.OmojuO. E. (2017). Does Private Investment in the Transport Sector Mitigate the Environmental Impact of Urbanisation? Evidence from Asia. J. Clean. Prod.153, 331–341. 10.1016/j.jclepro.2017.01.064
45
LiuW.ZhangX.FengS. (2019). Does Renewable Energy Policy Work? Evidence from a Panel Data Analysis. Renew. Energ.135, 635–642. 10.1016/j.renene.2018.12.037
46
LuW. C. (2017). Renewable Energy, Carbon Emissions, and Economic Growth in 24 Asian Countries: Evidence from Panel Cointegration Analysis. Environ. Sci. Pollut. Res.24, 26006–26015. 10.1007/s11356-017-0259-9
47
LucasJ. N.Escribano FrancésG.San Martín GonzálezE. (2016). Energy Security and Renewable Energy Deployment in the EU: Liaisons Dangereuses or Virtuous Circle?Renew. Sustain. Energ. Rev.62, 1032–1046. 10.1016/j.rser.2016.04.069
48
MajeedA.JiangP.AhmadM.KhanM. A.OlahJ. (2021). The Impact of Foreign Direct Investment on Financial Development: New Evidence from Panel Cointegration and Causality Analysis. J. Compet.13, 95–112. 10.7441/joc.2021.01.06
49
MajeedA.WangL.ZhangX.MunibaKirikkaleliD. (2021). Modeling the Dynamic Links Among Natural Resources, Economic Globalization, Disaggregated Energy Consumption, and Environmental Quality: Fresh Evidence from GCC Economies. Resour. Pol.73, 102204. 10.1016/j.resourpol.2021.102204
50
MariyakhanK.MohamuedE. A.KhanM. A.PoppJ.OláhJ. (2020). Does the Level of Absorptive Capacity Matter for Carbon Intensity? Evidence from the USA and China. Energies13, 407. 10.3390/en13020407
51
MarquesA. C.FuinhasJ. A.Pires MansoJ. R. (2010). Motivations Driving Renewable Energy in European Countries: A Panel Data Approach. Energy Policy38, 6877–6885. 10.1016/j.enpol.2010.07.003
52
Martínez-ZarzosoI.MaruottiA. (2011). The Impact of Urbanization on CO2 Emissions: Evidence from Developing Countries. Ecol. Econ.70, 1344–1353. 10.1016/j.ecolecon.2011.02.009
53
McCoskeyS.KaoC. (1998). A Residual-Based Test of the Null of Cointegration in Panel Data. Econom. Rev.17, 57–84. 10.1080/07474939808800403
54
MesaganE. P.ChidiO. N. (2020). Energy Consumption, Capital Investment and Environmental Degradation: The African Experience. Forum Sci. Oeconomia.8, 5–16. 10.23762/FSO_VOL8_NO1_1
55
MorenoB.LópezA. J.García-álvarezM. T. (2012). The Electricity Prices in the European Union. The Role of Renewable Energies and Regulatory Electric Market Reforms. Energy48, 307–313. 10.1016/j.energy.2012.06.059
56
MuradM. W.AlamM. M.NomanA. H. M.OzturkI. (2019). Dynamics of Technological Innovation, Energy Consumption, Energy price and Economic Growth in Denmark. Environ. Prog. Sustain. Energ.38, 22–29. 10.1002/ep.12905
57
NevilleK. J.CookJ.BakaJ.BakkerK.WeinthalE. S. (2019). Can Shareholder Advocacy Shape Energy Governance? the Case of the US Antifracking Movement. Rev. Int. Polit. Econ.26, 104–133. 10.1080/09692290.2018.1488757
58
NicoliniM.TavoniM. (2017). Are Renewable Energy Subsidies Effective? Evidence from Europe. Renew. Sustain. Energ. Rev.74, 412–423. 10.1016/j.rser.2016.12.032
59
NidumoluR.PrahaladC. K.RangaswamiM. R. (2009). Why Sustainability Is Now the Key Driver of Innovation, Harv. Bus. Rev.87, 57–64. Available at:https://hbr.org/2009/09/why-sustainability-is-now-the-key-driver-of-innovation (accessed December 20, 2020).
60
NyiwulL. (2017). Economic Performance, Environmental Concerns, and Renewable Energy Consumption: Drivers of Renewable Energy Development in Sub-sahara Africa. Clean. Technol. Environ. Pol.19, 437–450. 10.1007/s10098-016-1229-5
61
OláhJ.KrisánE.KissA.LaknerZ.PoppJ. (2020). PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector. Energies13, 2323. 10.3390/en13092323
62
OmriA.NguyenD. K. (2014). On the Determinants of Renewable Energy Consumption: International Evidence. Energy72, 554–560. 10.1016/j.energy.2014.05.081
63
OzcanB.OzturkI. (2019). Renewable Energy Consumption-Economic Growth Nexus in Emerging Countries: A Bootstrap Panel Causality Test. Renew. Sustain. Energ. Rev.104, 30–37. 10.1016/j.rser.2019.01.020
64
PadhanH.PadhangP. C.TiwariA. K.AhmedR.HammoudehS. (2020). Renewable Energy Consumption and Robust Globalization(s) in OECD Countries: Do Oil, Carbon Emissions and Economic Activity Matter?Energy Strateg. Rev.32, 100535. 10.1016/j.esr.2020.100535
65
ParamatiS. R.MoD.GuptaR. (2017). The Effects of Stock Market Growth and Renewable Energy Use on CO2 Emissions: Evidence from G20 Countries. Energ. Econ66, 360–371. 10.1016/j.eneco.2017.06.025
66
ParkC.XingR.HanaokaT.KanamoriY.MasuiT. (2017). “Impact of Energy Efficient Technologies on Residential CO2 Emissions: A Comparison of Korea and China,” in Energy Procedia (Elsevier), 689–698. 10.1016/j.egypro.2017.03.231
67
PathakL.ShahK. (2019). Renewable Energy Resources, Policies and Gaps in BRICS Countries and the Global Impact. Front. Energ.13, 506–521. 10.1007/s11708-018-0601-z
68
PedroniP. (2004). Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econom. Theor.20, 597–625. 10.1017/S0266466604203073
69
PesaranM. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence. J. Appl. Econom.22, 265–312. 10.1002/jae.951
70
PesaranM. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. SSRN Electron. J.122, 1–39. 10.2139/ssrn.572504
71
PesaranM. H.SmithR. (1995). Estimating Long-Run Relationships from Dynamic Heterogeneous Panels. J. Econom.68, 79–113. 10.1016/0304-4076(94)01644-F
72
PhillipsP. C. B.SulD. (2003). Dynamic Panel Estimation and Homogeneity Testing under Cross Section Dependence. Econom. J.6, 217–259. 10.1111/1368-423x.00108
73
PoppD. (2002). Induced Innovation and Energy Prices. Am. Econ. Rev.92, 160–180. 10.1257/000282802760015658
74
PorterM. E.Van Der LindeC. (2017). “Toward a New conception of the Environment-Competitiveness Relationship,” in Corp. Environ. Responsib. (Taylor & Francis), 61–82. 10.1257/jep.9.4.97
75
QiuW.ChuC.MaoA.WuJ. (2018). The Impacts on Health, Society, and Economy of SARS and H7N9 Outbreaks in China: A Case Comparison Study. J. Environ. Public Health2018, 2018. 10.1155/2018/2710185
76
RomanoA. A.ScandurraG. (2016). Investments in Renewable Energy Sources in Countries Grouped by Income Level, Energy Sources, Part B Econ. Plan. Pol.11, 929–935. 10.1080/15567249.2013.834006
77
SabishchenkoO.RębilasR.SczygiolN.UrbańskiM. (2020). Ukraine Energy Sector Management Using Hybrid Renewable Energy Systems. Energies13, 1776. 10.3390/en13071776
78
SadorskyP. (2011). Financial Development and Energy Consumption in Central and Eastern European Frontier Economies. Energy Policy39, 999–1006. 10.1016/j.enpol.2010.11.034
79
SadorskyP. (2009). Renewable Energy Consumption, CO2 Emissions and Oil Prices in the G7 Countries. Energ. Econ31, 456–462. 10.1016/j.eneco.2008.12.010
80
SadorskyP. (2011). Trade and Energy Consumption in the Middle East. Energ. Econ33, 739–749. 10.1016/j.eneco.2010.12.012
81
SalimR. A.RafiqS. (2012). Why Do Some Emerging Economies Proactively Accelerate the Adoption of Renewable Energy?Energ. Econ34, 1051–1057. 10.1016/j.eneco.2011.08.015
82
SaudS.ChenS.HaseebA. (2020). Sumayya, the Role of Financial Development and Globalization in the Environment: Accounting Ecological Footprint Indicators for Selected one-belt-one-road Initiative Countries. J. Clean. Prod.250, 119518. 10.1016/j.jclepro.2019.119518
83
ShahbazM.MallickH.MahalikM. K.SadorskyP. (2016). The Role of Globalization on the Recent Evolution of Energy Demand in India: Implications for Sustainable Development. Energ. Econ55, 52–68. 10.1016/j.eneco.2016.01.013
84
ShahzadU. (2020). Environmental Taxes, Energy Consumption, and Environmental Quality: Theoretical Survey with Policy Implications. Environ. Sci. Pollut. Res.27, 24848–24862. 10.1007/s11356-020-08349-4
85
ShahzadU.FerrazD.NguyenH. H.CuiL. (2022). Investigating the Spill Overs and Connectedness between Financial Globalization, High-Tech Industries and Environmental Footprints: Fresh Evidence in Context of China. Technol. Forecast. Soc. Change174, 121205. 10.1016/j.techfore.2021.121205
86
ShahzadU.LvY.DoğanB.XiaW. (2021). Unveiling the Heterogeneous Impacts of export Product Diversification on Renewable Energy Consumption: New Evidence from G-7 and E-7 Countries. Renew. Energ.164, 1457–1470. 10.1016/j.renene.2020.10.143
87
ShahzadU.RadulescuM.RahimS.IsikC.YousafZ.IonescuS. A. (2021). Do environment-related Policy Instruments and Technologies Facilitate Renewable Energy Generation? Exploring the Contextual Evidence from Developed Economies. Energies14, 690. 10.3390/en14030690
88
ShpakN. (2021). Assessing the Implementation of the Circular Economy in the EU Countries. Dąbrowa Górnicza: Forum Sci. Oeconomia. Available at: http://ojs.wsb.edu.pl/index.php/fso/article/view/343/259 (accessed January 12, 2022).
89
ŚlusarczykB. (2012). Polish Government Impact on Foreign Direct Investments. Polish J. Manag. Stud.Available at: https://econpapers.repec.org/article/pczjournl/v_3a6_3ay_3a2012_3ai_3a1_3ap_3a45-54.htm (accessed January 12, 2022).
90
ŠtreimikienėD. (2021). Externalities of Power Generation in Visegrad Countries and Their Integration through Support of Renewables. Econ. Sociol.14, 89–102. 10.14254/2071-789X.2021/14-1/6
91
TopcuM.PayneJ. E. (2017). The Financial Development–Energy Consumption Nexus Revisited. Energ. Sourc. B Econ. Plan. Pol.12, 822–830. 10.1080/15567249.2017.1300959
92
TrosterV.ShahbazM.UddinG. S. (2018). Renewable Energy, Oil Prices, and Economic Activity: A Granger-causality in Quantiles Analysis. Energ. Econ70, 440–452. 10.1016/j.eneco.2018.01.029
93
TvaronavičienėM.MazurN.MishchukH.BilanY. (2021). Quality of Life of the Youth: Assessment Methodology Development and Empirical Study in Human Capital Management. Econ. Res. Istraz.10.1080/1331677X.2021.1956361
94
UlucakR.BilgiliF. (2018). A Reinvestigation of EKC Model by Ecological Footprint Measurement for High, Middle and Low Income Countries. J. Clean. Prod.188, 144–157. 10.1016/j.jclepro.2018.03.191
95
United Nations (2020). The Paris Agreement | United Nations. Available at: https://www.un.org/en/climatechange/paris-agreement (accessed May 2, 2021).
96
UzarU. (2020). Is Income Inequality a Driver for Renewable Energy Consumption?J. Clean. Prod.255, 120287. 10.1016/j.jclepro.2020.120287
97
WangH.LuoQ. (2022). Can a Colonial Legacy Explain the Pollution haven Hypothesis? A City-Level Panel Analysis. Struct. Chang. Econ. Dyn.60, 482–495. 10.1016/j.strueco.2022.01.004
98
WangL. (2022). Role of FDI and Energy Intensity in Mitigating the Environmental Pollution in the Chinese Steel Industry: Does Technological Innovation Makes a Difference?Environ. Sci. Pollut. Res., 1, 18219. 10.1007/s11356-021-18219-2
99
WangL.VoX. V.ShahbazM. (2020). Globalization and Carbon Emissions: Is There Any Role of Agriculture Value-Added, Financial Development, and Natural Resource Rent in the Aftermath of COP21?J. Environ. Manage.268, 110712. 10.1016/j.jenvman.2020.110712
100
WangZ.GaoL.WeiZ.MajeedA.AlamI. (2022). How FDI and Technology Innovation Mitigate CO2 Emissions in High-Tech Industries: Evidence from Province-Level Data of China. Environ. Sci. Pollut. Res.29, 4641–4653. 10.1007/s11356-021-15946-4
101
WesterlundJ.EdgertonD. L. (2008). A Simple Test for Cointegration in Dependent Panels with Structural Breaks. Oxf. Bull. Econ. Stat.70, 665–704. 10.1111/j.1468-0084.2008.00513.x
102
WesterlundJ. (2005). New Simple Tests for Panel Cointegration. Econom. Rev.24, 297–316. 10.1080/07474930500243019
103
WesterlundJ. (2007). Testing for Error Correction in Panel Data. Oxf. Bull. Econ. Stat.69, 709–748. 10.1111/j.1468-0084.2007.00477.x
104
World Bank, (2019). Renewable Energy Consumption (% of Total Final Energy Consumption). Data, Available at: https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS.
105
XiaomanW.MajeedA.VasbievaD. G.EmilienneC.YameogoW.HussainN. (2021). Natural Resources Abundance , Economic Globalization, and Carbon Emissions : Advancing Sustainable Development Agenda, 1–12. 10.1002/sd.2192
106
YaoY.IvanovskiK.InekweJ.SmythR. (2019). Human Capital and Energy Consumption: Evidence from OECD Countries. Energ. Econ84, 104534. 10.1016/j.eneco.2019.104534
107
YaylaÖ.BozkurtH. Ö.ArslanE.KendirH. (2021). The Moderator Role of Environmental Interpretations in the Relationship between Planned Behavior Level and Environmental Awareness Perception of Hotel Employees. J. Tour. Serv.12, 150–168. 10.29036/JOTS.V12I23.287
108
ZafarM. W.ShahbazM.HouF.SinhaA. (2019). From Non-renewable to Renewable Energy and its Impact on Economic Growth: The Role of Research & Development Expenditures in Asia-Pacific Economic Cooperation Countries. J. Clean. Prod.212, 1166–1178. 10.1016/j.jclepro.2018.12.081
109
ZamanK.MoemenM. A.-e. (2017). Energy Consumption, Carbon Dioxide Emissions and Economic Development: Evaluating Alternative and Plausible Environmental Hypothesis for Sustainable Growth. Renew. Sust. Energ. Rev.74, 1119–1130. 10.1016/j.rser.2017.02.072
110
ZebR.SalarL.AwanU.ZamanK.ShahbazM. (2014). Causal Links between Renewable Energy, Environmental Degradation and Economic Growth in Selected SAARC Countries: Progress towards green Economy. Renew. Energ.71, 123–132. 10.1016/j.renene.2014.05.012
111
ZengS.LiuY.LiuC.NanX. (2017). A Review of Renewable Energy Investment in the BRICS Countries: History, Models, Problems and Solutions. Renew. Sustain. Energ. Rev.74, 860–872. 10.1016/j.rser.2017.03.016
112
ZhaoX.LuoD. (2017). Driving Force of Rising Renewable Energy in China: Environment, Regulation and Employment. Renew. Sustain. Energ. Rev.68, 48–56. 10.1016/j.rser.2016.09.126
113
ZhenminL.EspinosaP. (2019). Tackling Climate Change to Accelerate Sustainable Development. Nat. Clim. Chang.9, 494–496. 10.1038/s41558-019-0519-4
114
ZhuD.WangB.MaH.WangH. (2020). Evaluating the Vulnerability of Integrated Electricity-Heat-Gas Systems Based on the High-Dimensional Random Matrix Theory. CSEE J. Power Energ. Syst.6, 878–889. 10.17775/CSEEJPES.2019.00440
115
ZhuangM.ZhuW.HuangL.PanW. T. (2021). Research of Influence Mechanism of Corporate Social Responsibility for Smart Cities on Consumers' Purchasing Intention. Libr. Hi Tech.10.1108/LHT-11-2020-0290
116
ZuoS.ZhuM.XuZ.OláhJ.LaknerZ. (2022). The Dynamic Impact of Natural Resource Rents, Financial Development, and Technological Innovations on Environmental Quality: Empirical Evidence from BRI Economies. Int. J. Environ. Res. Public Health19, 130. 10.3390/ijerph19010130
Summary
Keywords
financial globalization, environmental innovation, CUP-FM, CUP-BC, renewable energy consumption (REC)
Citation
Majeed A, Ahmad M, Rasheed MF, Khan MK, Popp J and Oláh J (2022) The Dynamic Impact of Financial Globalization, Environmental Innovations and Energy Productivity on Renewable Energy Consumption: Evidence From Advanced Panel Techniques. Front. Environ. Sci. 10:894857. doi: 10.3389/fenvs.2022.894857
Received
12 March 2022
Accepted
31 March 2022
Published
26 April 2022
Volume
10 - 2022
Edited by
Umer Shahzad, Anhui University of Finance and Economics, China
Reviewed by
Lijun Wang, University of Management and Technology, United States
Mingxia Zhu, University of International Business and Economics, China
Updates
Copyright
© 2022 Majeed, Ahmad, Rasheed, Khan, Popp and Oláh.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: József Popp, popp.jozsef@uni-neumann.hu
This article was submitted to Environmental Economics and Management, a section of the journal Frontiers in Environmental Science
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