Unlocking Sustainable Development in G-7 Economies: How Institutional Quality Shapes the Impact of Renewable Energy

Abstract

The role of renewable energy (REN) in promoting economic growth and improving environmental quality has been widely debated and extensively researched in the existing body of knowledge during the last couple of decades. However, the specific role of REN in promoting sustainable development (SUD) is yet to be researched in the presence of the moderating role of institutional quality in the context of G-7 economies. Accordingly, this research study attempts to explore the potential relationship between REN and SUD in the presence of the moderating role of institutional quality by utilizing data from G-7 economies for the period 2002–2022. Acknowledging the presence of cross-sectional dependency, serial correlation, and heteroscedasticity, the present study used the fixed effects estimator with Driscoll–Kraay standard errors. The results demonstrated a positive and statistically significant influence of REN on SUD. Furthermore, the findings demonstrated that institutional quality moderates the relationship between REN and SUD and sustainable development effectively. Moreover, we found that trade openness and unemployment rate are the main driving forces of SUD. In addition, we found that government expenditures, inflation rate, and CO₂ emissions are detrimental from the perspective of SUD. Finally, we found that industrialization and institutional quality in isolation are unable to explain variation in the level of SUD in the case of G-7 economies. The causality analysis also demonstrated a one-way causal impact of REN on SUD. The study’s findings offer valuable policy suggestions related to the transition towards REN and accelerating the pace of SUD for the consideration of the policymakers of G-7 economies.

1. Introduction

In recent years, achieving sustainable development (SUD, hereafter) has emerged as one of the main policy objectives, particularly after the launch of 17 Sustainable Development Goals (SDGs) in 2015. The term SUD means “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. The 17 SDGs of the United Nations are related to the three main dimensions of SUD, that is, social, economic and environmental [1]. Among the 17 SDGs, SDG-13 is related to climate action which stresses the need to address the problem of climate change and minimize its adverse consequences. The worsened environmental quality amid increased CO₂ emissions, climate change, and energy insecurity have deepened the need for promoting SUD. On the other hand, SDG-7 mainly focuses on ensuring clean and affordable energy for all by 2030 [2]. In light of SDG-7, renewable energy (REN, hereafter) is an important factor for addressing environmental issues and promoting SUD. It is widely accepted that the use of renewable energy generally reduces CO₂ emissions, addresses the problem of climate change, and further plays a vital role in advancing the pace of SUD [3].

The transition towards REN has become one of the main policy objectives globally due to its enormous importance from the perspective of SUD [3]. According to recent research [4], the transition towards REN from fossil fuels is an innovative approach to achieving economic, social, and environmental sustainability. The REN basket is considered environment friendly, and it includes several forms of energy, namely, wind, hydro, solar, and biomass. The use of renewable energy is associated with less emissions as compared to traditional fossil fuels and hence protects the quality of the environment, generates jobs, promotes innovations, and thus casts a positive influence on the pace of SUD. According to research [5], SUD in an economy responds positively to the supply of sustainable energy sources and the effective utilization of energy sources. In other words, the supply of sustainable energy and the effective utilization of energy sources provide a foundation for linking renewable energy to SUD. Promoting SUD requires a significant shift from fossil fuel dependency towards green and clean energy sources, including renewable energy [6].

Although the shift from fossil fuels towards renewable energy is an important step for accelerating the pace of SUD, its effectiveness is largely dependent on the quality of domestic institutions. It is a fact that improved institutional quality is a pre-requisite for shaping the transition towards renewable energy and promoting SUD. Recent research [7] endorsed that the transition towards renewable energy requires large amounts of financing and hence improved institutional quality is required to have a clear and comprehensive policy package for the successful transition. Research [8] quantitatively demonstrated that strong institutional quality and financial stability along with economic and policy variables promote the use of REN. Generally, improved institutional quality is expected to cure corruption, ensure transparency, and encourage investment in REN that ultimately affects SUD positively. To put it differently, improved institutional quality can act as a moderator factor for the relationship between REN and SUD. However, quantitative evidence on how improved institutional quality shapes the relationship between REN and SUD is indeed very scarce.

Studying the relationship between REN and SUD while considering the moderating role of institutional quality is more relevant for the Group of Seven (G-7, hereafter). The G-7 includes the advanced economies of the world, namely, Canada, France, Germany, Italy, Japan, United Kingdom, and United States. The quality of institutions is better in economies belonging to the G-7 group, and they have further set up ambitious targets regarding the transition towards REN and accelerating the pace of SUD. In 2023, the G-7 economies produced 29.5% of the total electricity from REN sources (35.5% hydropower; 34.1% wind energy; 19.9% solar energy; 9.2% bioenergy; and 1.3% geothermal energy) as mentioned in reference [9]. Moreover, it is endorsed that the G-7 group have well-established institutional quality and are enjoying effective institutional governance [10]. Therefore, examining the role REN in promoting SUD while accommodating for the moderating role of institutional quality indeed make sense in the case of G-7 member economies.

1.1. Research Gaps and Novelty

Despite the importance of REN, its role in shaping the pace of SUD is still largely unexplored in the existing body of knowledge. Previous studies have used focused on either CO₂ emissions [11,12] or ecological footprints [13] to capture sustainability. Yet only a few studies have utilized the comprehensive index of SUD which was developed by [14]. More specifically, in the context of G-7 economies, the broader measure of SUD such as SDI index is yet to be utilized for fully capturing the level of SUD The SDI index measures the ecological efficiency of human development and it reflects that the development must be achieved within the planetary boundaries [14]. The SDI index is basically the quotient of two indices such as the development index, which is based on the human development index (education, income, and life expectancy), and the ecological impact index, which is based on the consumption-based CO₂ emissions and material footprint. The SDI index ranges from 0 to 1, where higher values indicate advanced level of SUD and vice versa. The SDI index is preferred over other proxies such as ecological footprints which only capture environmental pressures but overlook human development. Similarly, the SDI index is also useful as compared to the SDG index which may overestimate SUD by not accommodating for excessive resource use.

Similarly, institutional quality is one of the main factors that can shape the transition towards REN and its ultimate influence on SUD. Recent studies [8,9] have paid some attention to exploring the role of institutional quality in the transition towards REN. However, a comprehensive study linking REN with SUD while considering the important role of institutional quality is yet to be conducted specifically in the context of G-7 economies. It is a fact that the transition towards REN requires a lot of efforts on the part of policymakers and hence a strong institutional quality in the form of effective governance is indeed necessary. Moreover, the issue of causality between SUD and REN has yet to find a convincing answer due to limited research. Therefore, to fulfill, the aforementioned research gap, the present study has attempted to study the relationship between REN and SUD in the presence of institutional quality. Using panel data from G-7 economies and applying the relevant and robust econometric estimators, the present study demonstrates comprehensive knowledge of how better and stronger institutional quality can enhance the sustainability benefits of REN, offering useful implications for the government authorities and policymakers of G-7 economies.

This research paper contributes to the ongoing body of knowledge in several ways. Firstly, this study sheds light on the potential impact of REN on SUD which is an untapped area as far as the empirical literature is concerned. The prior literature has linked REN with economic growth and environmental quality while ignoring its impact on SUD. Secondly, particularly, in the context of G-7 economies, research evidence related to REN and SUD is not only scarce but also inconsistent. Therefore, the present study makes a contextual contribution by focusing on the G-7 economies to establish a link between REN and SUD. Thirdly, our study also attempts to add to the existing body of knowledge by examining the direction of the relationship between REN and SUD using the methodology of [15].

We have split the reminder of the article into several connected sections. In Section 2, we have provided a detailed review of the existing body of knowledge on the subject. The third section reports key statistics related to REN, institutional quality, and SUD in the context of G-7 economies. In Section 4, the study presents an econometric model and estimation methods. In the proceeding section, the study reports the main findings and their interpretation. Moreover, in the penultimate section, the study demonstrates the findings of causality analysis. The final section includes summary remarks, implications, shortcomings, and directions for future research.

1.2. Literature Review

The use of REN and its potential impact on SUD have received some attention in the recent empirical literature. For instance, ref. [16] used data from 12 Asian economies to examine the link between SUD and its determinants. The study’s findings demonstrated that financial development and income have positively affected SUD, while the inflation rate and the natural resource sector have negatively impacted the pace of SUD in the sampled economies. Moreover, research [17] found that consumption per capita negatively impact SUD in the long run while unemployment and energy efficiency adversely impact SUD in the short run. Furthermore, ref. [18] conducted a research study based on the data from G-7 economies for the period 1990–2020, using several econometric estimators to examine the factors of SUD. The study’s findings showed that energy intensity has worsened environmental sustainability in USA, Germany, and Italy, while energy depletion has reduced environmental sustainability in the case of France and Canada. Therefore, the transition towards REN could be an ultimate and effective solution. Finally, recent research [19] focused on OECD economies and applied the GMM approach to examine the link between REN and SUD. The study’s findings underscore that SUD responds positively to an increase in REN. Recently, ref. [20] introduced innovative systems related to REN and demonstrated that these systems improve hydrogen production and energy efficiency, which are desirable from the perspective of increasing the level of SUD.

However, some studies have pointed out that expansion in REN in isolation may not be able to bring the desirable influence on SUD. In other words, the role of REN in accelerating the pace of SUD may be dependent on several important factors. For instance, ref. [21] pointed out that improved institutional quality promotes the use of REN, which ultimately enhances the pace of SUD. In a similar vein, recent research also documented that advanced institutional quality promotes the use of REN [22]. It is a fact that advanced institutional quality encourages investment in REN, reduces the risk, and attracts green FDI, which ultimately increases the proportion of REN in total energy basket. To put it differently, strong institutional quality enhances the use of REN, and it promotes investment in green and environment-friendly technologies which consequently cast positive impacts on SUD. In other words, strong institutional quality can effectively moderate the relationship between REN and SUD. However, research studies exploring the potential impacts of REN on SUD have rarely assessed the moderating role of institutional quality.

Several research studies have been conducted in the existing body of knowledge about the determinants of SUD by focusing on individual economies. For instance, ref. [23] conducted a research study by focusing on data from Saudi Arabia from 1998 to 2019, using the OLS estimation method. The study’s findings underscored that SUD positively responds to climate change vulnerability and oil rents. Similarly, the research based on the time series data of Pakistan for the period 1970–2018 [24] indicated that SUD is dependent on both macroeconomic factors and energy-related factors. On the other hand, ref. [25] also attempted to examine the determinants of SUD in BRICS and G-7 economies using data from 2005–2015. The research’s finding found that population and income are essential factors for advancing the journey of SUD in G-7 economies. On the other hand, in the case of BRICS economies, the research’s findings showed that both income and population are harmful from the perspective of improving SUD. However, studies focusing on the explicit role of REN in advancing the pace of SUD while considering the moderating role of institutional quality are scarce.

This brief review of the relevant literature shows that the relationship between REN and SUD is an untapped research area, particularly in the case of G-7 economies. In addition, the moderating role of institutional quality on the relationship between REN and SUD is yet to be researched. Therefore, this research attempts to fill the aforementioned research gap by conducting a comprehensive study on the relationship between REN and SUD while focusing on the important moderating role of institutional quality in the case of G-7 economies. The paper offers valuable insights about the relationship between REN and SUD in the presence of the moderating role of institutional quality. The results of this study are expected to shape policies regarding the transformation of REN and advancing the pace of SUD in G-7 economies.

The body of knowledge has attempted to figure out the main determinants of SUD in the case of G-7 economies during the last couple of decades. In this regard, ref. [26] employed the CS-ARDL methodology for analyzing the determinants of environmental sustainability using data from the period 1990–2018. The study’s findings showed that energy innovation and globalization impact ecological footprints negatively while the natural resource sector and income are positively linked with ecological footprint. Likewise, ref. [27] focused on both BRICS and G-7 member economies to figure out the true determinants of SUD. Their findings demonstrated that national innovative capacity is the main factor behind SUD. Moreover, ref. [25] also empirically proved that SUD in G-7 is dependent on both income and population. Finally, ref. [28] endorsed, based on their results, that environmental sustainability responds positively to increased REN and negatively to increased trade openness, government size, and institutional factors. Furthermore, recent research has examined the determinants of environmental sustainability in the context of G-7 economies and provided some diverse results [18]. However, a comprehensive study focusing on the broad measure of SUD and its determinants from the perspective of REN is yet to be conducted for G-7 economies. In addition, the moderating influence of institutional quality on the impact of REN on SUD is yet to be researched for economies belonging to G-7. Therefore, the present research study carries significant importance for the policymakers of G-7 economies.

1.3. Key Statistics on Selected Variables

In this section, we present key statistics and their behavior during the study period for the G-7 economies in

Table 1. Key statistics (2002–2022).

Country	Variables	2002	2022	% Change
Canada	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.302	0.162	−46.357
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	20.100	23.850	18.656
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.625	1.447	−10.953
France	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.681	0.620	−8.957
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	8.700	16.500	89.655
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.167	1.049	−10.111
Germany	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.574	0.341	−40.592
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	4.400	18.050	310.227
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.534	1.367	−10.886
Italy	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.614	0.641	4.397
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	5.600	18.100	223.214
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	0.799	0.554	−30.663
Japan	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.559	0.433	−22.540
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	3.700	8.650	133.783
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	0.964	1.368	41.908
United Kingdom	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.533	0.560	5.065
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	1.00	12.900	1190.000
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.516	1.271	−16.160
United States	${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.207	0.167	−19.323
	${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	4.800	10.950	128.125
	${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.365	1.003	−26.520

Note: ${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$: Sustainable Development Index (0–1); ${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$: Renewable Energy (renewable energy consumption (% of total final energy consumption)); and ${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$: Institutional Quality (the average value of six components of institutional quality (−2.5 to 2.5). The moment towards the positive side represents improved institutional quality.

. The descriptive statistics of SUD are disappointing. The SUD approximated by the SDI index which ranges from 0 to 1 has increased only for the United Kingdom and Italy during the study period 2002–2022. The remaining five economies have witnessed a significant decline in SUD. The statistics indicated that Canada witnessed a significant decline of 46.357 percent, followed by Germany where a reduction of more than 40 percent was recorded during the study period. Similarly, Japan and the United States have also recorded poor performances in terms of SUD. The lowest decline in SUD among the G-7 economies has been experienced by the economy of France. Overall, the SUD of G-7 economies is not satisfactory as the majority of the economies have shown a significant decline in their SDI index. Several factors could be responsible for the observed dissatisfactory performance of G-7 economies on the path towards SUD. Fiscal constraints and global economic shocks have adversely impacted the ability of all economies of the world, including the G-7 economies, to invest in REN projects and improve SUD. In addition, rapid industrialization has continuously put significant pressure on the environment, which undermines the process of SUD.

The statistics of REN “Renewable energy consumption (% of total final energy consumption)” are indeed very promising for all G-7 economies. According to the statistics, all G-7 economies have shown remarkable improvement in terms of transitioning towards REN in recent times. The highest increase in REN is witnessed by the United Kingdom, followed by Germany and Italy. Similarly, Japan and the United States have also shown improvement in terms of transitioning towards REN during the study period. Finally, France and Canada have observed the lowest increase in REN, respectively, among the G-7 members.

Finally, we observed that institutional quality has declined in all G-7 economies except the economy of Japan. The highest decline in institutional quality is witnessed in Italy, followed by the United States and the United Kingdom. Similarly, institutional quality has also fallen in Canada, France, and Germany. However, the institutional quality in G-7 economies is still reasonably strong as compared to emerging and developing economies. Following the footprints of G-7 economies, the less developing economies must also strive to enhance the quality of their institutions as strong institutions generally improve SUD, both directly as well as indirectly through the channel of REN transition. In this regard, the developing countries must cure the menace of corruption, ensure transparency, and further pay attention to the proper implementation of policies to improve the overall quality of institutions. Consequently, improved institutional quality will help the developing countries to reap the full benefits of a transformation towards REN and advanced SUD.

It is interesting to note that the use of REN has increased in all G-7 member economies during the study period. On the other hand, the pace of SUD has also fallen in all G-7 members except the United Kingdome and Italy. However, the poor performance of G-7 economies in terms of SUD despite the significant improvement in REN could be explained by the declining institutional quality. In other words, to reap the full benefits of REN in terms of SUD, the G-7 economies need to focus on improving the quality of domestic institutions.

2. Modeling and Methods

2.1. Model Specification

The study intends to specify an econometric model in this section to assess the impact of REN on SUD in the presence of the moderating role of institutional quality for G-7 economies. In this regard, REN is the prime independent variable, and SUD is the dependent variable while institutional quality is the moderating variable. However, SUD according to the existing body of knowledge also responds to increases in trade openness, industrialization, and government expenditures [29,30,31]. Therefore, to design a proper econometric model, we have specified the following functional form in the first step shown below using Expression (1).

$$\mathrm{S}\mathrm{U}\mathrm{D}=\mathrm{F}{(\mathrm{R}\mathrm{E}\mathrm{N}}^{\mathrm{a}},{\mathrm{I}\mathrm{N}\mathrm{T}}^{\mathrm{b}},{\mathrm{T}\mathrm{O}\mathrm{P}}^{\mathrm{c}},{\mathrm{I}\mathrm{N}\mathrm{D}}^{\mathrm{d}},{\mathrm{G}\mathrm{E}\mathrm{X}}^{\mathrm{e}})$$

(1)

Expression (1) demonstrates that the SUD of G-7 economies is dependent on the use of REN, institutional quality, openness to international trade, industrialization, and government expenditures. Using logarithmic transformation, we have transformed Expression (1) using Expression (2) to address the potential nonlinearity and express the coefficients in elasticities.

$${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3\left(\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}{\mathrm{N}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}\right)+{\mathsf{\beta }}_4{\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathrm{U}}_{\mathrm{i}\mathrm{t}}$$

(2)

In Expression (2), SUD is measured by the SDI index. The SDI index basically measures the ecological efficiency of human development, and it ranges from 0 to 1, where higher values represent a higher level of SUD and vice versa. The variable of interest, REN, is measured as “Renewable energy consumption (% of total final energy consumption)”. Institutional quality is measured by taking the average value of the six main components of institutional quality “government effectiveness, regulatory quality, rule of law, control of corruption, voice and accountability, political stability and absence of violence”. Trade openness is quantified by taking the “total trade as % of GDP”. For measuring industrialization, the present study used “Industry (including construction), value added (% of GDP)”. Finally, the role of government in shaping the pace of SUD is approximated by using the “total government expenditures as % of GDP”.

Data on the SDI index are taken from the website of SUD, freely available for researchers. Similarly, data on institutional quality are extracted from the “World Governance Indicators (WGI)”. On the other hand, data on REN, industrialization, trade openness, and government expenditures are taken from “World Development Indicators (WDI)”. In terms of the sample, we have utilized all seven economies of G-7. Data were collected for the period 2002–2022. In

Table 2. Variables description and data sources.

Symbol	Variable Name	Description	Source
${\mathbf{L}\mathbf{N}\mathbf{S}\mathbf{U}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	Sustainable Development	“Sustainable development index (0–1) where high values represent improved sustainable development”	https://www.sustainabledevelopmentindex.org/, accessed on 1 March 2026
${\mathbf{L}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	Renewable Energy	“Renewable energy consumption (% of total final energy consumption)”	“World Development Indicators (WDI)”
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	Institutional Quality	“The average value of six components of institutional quality (−2.5 to 2.5). The moment towards the positive side represents improved institutional quality”	“World Governance Indicators (WGI)”
${\mathbf{L}\mathbf{N}\mathbf{T}\mathbf{O}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$	Trade Openness	“Total trade as (% of GDP)”	“World Development Indicators (WDI)”
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	Industrialization	“Industry (including construction), value added (% of GDP)”	“World Development Indicators (WDI)”
${\mathbf{L}\mathbf{N}\mathbf{G}\mathbf{E}\mathbf{X}}_{\mathbf{i}\mathbf{t}}$	Government Expenditures	“Total government expenditures as % of GDP”	“World Development Indicators (WDI)”

, the study reports a detailed explanation of variables that further highlights the sources of data utilized.

2.2. Estimation Methods

This paper attempted to utilize the relevant econometric techniques for extracting reliable and meaningful results. The Fixed Effects Model (FEM, hereafter) and Random Effects Model (REM, hereafter) have been used by renowned researchers over the years to deal with models with panel data [32,33,34,35]. The FEM and REM have both advantages as well as some notable disadvantages, as illustrated by several studies in the existing body of knowledge [36]. For instance, the use of FEM is superior when the usual disturbance term and independent variables are serially correlated. However, the FEM becomes ineffective when the time-invariant or dummy variables are included in the model due to a problem which is best-known as the dummy variable trap. Likewise, the REM is an effective tool for the estimation of panel data if the serial correlation is insignificant between the usual disturbance term and independent variables. Furthermore, REM modeling is useful in exploring the influence of dummy variables or time-invariant factors on the dependent variable. The choice between the FEM and REM should be made using the Hausman test [37]. As a rule of thumb, the FEM is preferred when the probability of the Hausman test [37] is less than 5 percent. Alternatively, the use of REM is applicable when the probability value of the Hausman test [37] exceeds 5 percent level.

To begin with the empirical analysis, the present study conducted the Hausman test [37] to choose between the FEM and REM. The results supported the use of the FEM as the probability value of the Hausman test was less than 5 percent, as shown in

Table A1. Correlated random effects—Hausman test.

Test Summary	Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Cross-section random	273.143	6	0.000

reported in Appendix A. Therefore, the specified models are estimated using the FEM methodology. Similarly, the cross-sectional dependency (CD) test is applied to figure out the presence of cross-sectional dependency among the G-7 economies [38]. The findings provided in

Table A2. Cross-sectional dependency.

Test	Statistic	Prob.
Breusch–Pagan LM	124.023	0.000
Pesaran scaled LM	15.896	0.000
Pesaran CD	5.891	0.000

in Appendix A confirmed the presence of cross-sectional dependency. The cross-sectional dependency is relevant for the G-7 economies due to their developed economic and financial systems and common economic and socio-economic policies. In the presence of cross-sectional dependency, we have applied the FEM with Driscoll–Kraay standard errors, which effectively accommodates for cross-sectional dependency, serial correlation, and heteroscedasticity [39]. The Driscoll–Kraay standard errors are reliable in the presence of cross-sectional dependency as endorsed by [40]. Therefore, we have estimated the models using the FEM while utilizing the Driscoll–Kraay standard errors.

In addition, the robustness of findings is explored using the Feasible Generalized Least Squares (FGLS). Some previous studies have also followed the same methodology for the robustness of results of FEM [41,42]. The two-stage least squares (TSLS) is also used for the estimation of models as it addresses the problem of endogeneity. In the TSLS estimation, the study used the lagged values of variables as instruments to solve the endogeneity. Previous studies have also used the lagged values of variables as instruments for accommodating for the problem of endogeneity [33,43]. The GMM estimator which also address the endogeneity is skipped as the number of cross-sections is lower than the number of years (N < T).

3. Results and Interpretation

3.1. Descriptive Statistics

The study shows descriptive statistics of the selected variables in

Table 3. Descriptive analysis.

Description	${\mathbf{S}\mathbf{U}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{I}\mathbf{N}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{E}\mathbf{X}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{T}\mathbf{O}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$
Mean	0.454	11.323	1.234	22.644	19.569	53.035
Maximum	0.753	23.900	1.651	30.222	24.839	88.785
Minimum	0.162	0.900	0.478	15.769	13.927	20.447
Std. Dev.	0.183	6.131	0.315	4.147	2.523	17.072
Observations	147	147	147	147	147	147

Note: ${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$: Sustainable Development Index (0–1); ${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$: Renewable energy as % of total final energy consumption; ${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$: Institutional Quality (−2.5 to 2.5).${\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$: Industry (including construction), value added (% of GDP); ${\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$: Total government expenditures as % of GDP; ${\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$: Total trade as % of GDP.

. According to results, the mean value of SUD is 0.454 while its standard deviation is 0.183. The highest value of SUD (0.753) among the G-7 economies is observed for the economy of Italy in 2017. In contrast, the lowest value of SUD (0.162) is achieved by Canada in 2022. Current statistics of 2022 indicate that SUD is (0.162) for Canada, (0.620) for France, (0.341) for Germany, (0.641) for Italy, (0.433) for Japan, (0.560) for the United Kingdom, and (0.167) for the United States. It means that there is a significant variation among the G-7 economies in terms of their performance towards SUD.

The descriptive analysis further demonstrated that the mean value of REN is 11.323 percent for the G-7 economies during the study period. The highest use of REN (23.900) among the G-7 economies was achieved by Canada in 2020. On the other hand, the lowest statistic for REN (0.900) is recorded by the United Kingdom in 2003. However, current statistics demonstrated that all G-7 economies have performed well in encouraging the use of REN. For instance, the statistics of 2022 showed that the value of REN is (23.850) for Canada, (16.500) for France, (18.050) for Germany, (18.100) for Italy, (8.650) for Japan, (12.900) for the United Kingdom, and (10.950) for the United States.

Furthermore, the descriptive of institutional quality indicated that its average value is 1.234, having a standard deviation of (0.315). The highest value of institutional quality (1.651) within the G-7 economies is achieved by Canada in 2017. Likewise, the lowest value of institutional quality (0.478) within the G-7 members is experienced by Italy in 2018. The statistics of 2022 indicated that the value of institutional quality is (1.440) for Canada, (1.110) for France, (1.414) for Germany, (1.074) for Italy, (1.021) for Japan, (1.258) for the United Kingdom, and (0.860) for the United States. According to the current statistics, institutional quality is highest in Canada and lowest in the United States. However, it is pertinent to mention that generally institutional quality is strong in the G-7 economies as all values are on the positive side.

Moreover, the industrialization statistics show that the average value is 22.644 percent for the G-7 member economies. The standard deviation of industrialization is 4.147, which indicates significant heterogeneity among the G-7 economies in terms of their level of industrialization. The highest value of industrialization (30.222) is noted for Japan in 2002. Conversely, the lowest level of industrialization (15.769) is seen in France in 2022. The current statistics of industrialization demonstrate that its value is (23.953) in Canada, (15.769) in France, (25.560) in Germany, (23.279) in Italy, (27.717) in Japan, (16.819) in the United Kingdom, and (17.891) in the United States. The reported statistics indicates that the G-7 economies are enjoying a relatively improved level of industrialization as compared to developing and emerging economies. In other words, it could be said that the advanced economic performance of G-7 economies could be largely explained by their improved level of industrialization.

Additionally, the statistics of government expenditures confirmed the effective role played by the government sector. The average value of government expenditures is 19.569 percent of the GDP, with a marginal standard deviation of 2.523. Among the G-7 member economies, the study witnessed the maximum government expenses (24.839) in France in 2020. In contrast, the lowest government expenses (13.927) are experienced by the United States in 2018. The current statistics of 2022 revealed that the average value of government expenditures is (20.418) for Canada, (23.874) for France, (21.329) for Germany, (18.789) for Italy, (21.566) for Japan, (20.387) for the United Kingdom, and (13.942) for the United States. In other words, according to the current statistics, the government’s role in the economy is the highest in Canada and the lowest in the United States.

Finally, the statistics of trade openness demonstrated the relatively liberalized trade policies exercised by the G-7 economies. The average value of trade openness is more than 53 percent for the G-7 economies. The highest trade openness index (88.785) is noted for Germany in 2022. Likewise, the lowest trade openness index (2.447) is experienced by Japan in 2002. The statistics of 2022 displayed that the trade openness index is (67.317) for Canada, (75.963) for France, (88.785) for Germany, (72.016) for Italy, (46.812) for Japan, (69.198) for the United Kingdom, and (27.313) for the United States.

3.2. Correlation Analysis

The correlation analysis among the dependent and independent variables is shown in

Table 4. Correlation analysis.

Variables	${\mathbf{L}\mathbf{N}\mathbf{S}\mathbf{U}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{L}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{L}\mathbf{N}\mathbf{G}\mathbf{E}\mathbf{X}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{L}\mathbf{N}\mathbf{T}\mathbf{O}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1	0.248	−0.544	−0.276	0.514	0.227
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	0.248	1	0.068	0.099	0.321	0.571
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	−0.544	0.068	1	0.299	0.052	0.230
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$	−0.276	0.099	0.299	1	−0.117	−0.003
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	0.514	0.321	0.052	−0.117	1	0.566
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	0.227	0.571	0.230	−0.003	0.566	1

Note: ${\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$: Sustainable Development Index (0–1); ${\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$: Renewable energy as % of total final energy consumption; ${\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$: Institutional Quality (−2.5 to 2.5). ${\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$: Industry (including construction), value added (% of GDP); ${\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$: Total government expenditures as % of GDP; ${\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$: Total trade as % of GDP.

. The highest correlation is observed between REN and trade openness. Likewise, the lowest correlation coefficient is observed between industrialization and trade openness. Additionally, it is found that REN and SUD are positively correlated with each other. Finally, all other variables are correlated with each other in a moderate manner. The study also used the VIF test to explore the presence of multicollinearity. The results of the VIF test shown in the

Table A3. VIF testing.

Variables	Coefficients	VIF
	Variance	Centered
$\mathrm{L}\mathrm{N}{\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	0.0001	7.249
$\mathrm{L}\mathrm{N}{\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	0.0003	4.024
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.0003	1.196
$\mathrm{L}\mathrm{N}{\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	0.0008	1.738
$\mathrm{L}\mathrm{N}{\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	0.0001	2.173

(Appendix A) confirmed the absence of multicollinearity. All values of VIF test for the selected variables are below 10, which is desirable for the confirmation of the absence of multicollinearity.

3.3. Regression Results and Discussion

The regression-based results are demonstrated in

Table 5. Regression findings.

Variables	FEM	FGLS	TSLS
	Coefficients	Coefficients	Coefficients
${\mathbf{L}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	0.144 *** (0.019)	0.145 *** (0.019)	0.144 *** (0.021)
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	−0.017 (0.045)	−0.023 (0.046)	0.011 (0.024)
$\mathbf{L}\mathbf{N}({\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}\times {\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}})$	0.015 *** (0.002)	0.016 *** (0.002)	0.022 *** (0.001)
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{D}}_{\mathbf{i}\mathbf{t}}$	−0.027 (0.063)	−0.026 (0.065)	0.026 (0.036)
${\mathbf{L}\mathbf{N}\mathbf{G}\mathbf{E}\mathbf{X}}_{\mathbf{i}\mathbf{t}}$	−0.148 ** (0.068)	−0.165 ** (0.070)	0.727 *** (0.053)
${\mathbf{L}\mathbf{N}\mathbf{T}\mathbf{O}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$	0.091 *** (0.033)	0.095 *** (0.034)	0.183 *** (0.022)
C	1.256 (0.330)	1.332 (0.339)	−2.524 (0.199)
Year Fixed	Yes	No	Yes
Country Fixed	Yes	Yes	Yes
Observations	147	147	147
Diagnostics	Adj (R2): 0.959 S.E.R: 0.036 F: 289.564 ***	Adj (R2): 0.961 S.E.R: 0.036 F: 301.344 ***	Adj (R2): 0.899 S.E.R: 0.068 F: 239.527 *** Prob (Hansen J-Statistic: 0.371) Number of Instruments: 8

Note: The asterisks (***, **) stand for 1 and 5 percent significance levels. The dependent variable is the sustainable development index. The values presented in the parenthesis indicate the Driscoll–Kraay [39] standard errors.

. It is found that an increase in REN has casted a positive and statistically significant influence on SUD in G-7 economies. It means that REN is one of the main drivers of SUD. The point estimate suggests that a 1 percent rise in REN is associated with a 0.144 percent increase in SUD. The obtained findings are supported by recent research [44,45,46]. Therefore, for enhancing the speed of SUD, the G-7 economies must focus on investing heavily in REN and the related infrastructure. Furthermore, we found that the interactive term of REN and institutional quality is positive and different from zero statistically. It means that the benefits of REN are amplified when coupled with strong institutional quality. In other words, countries with strong institutional quality are in a better position to experience an advancement in SUD using the channel of REN. To put it differently, sound institutional quality moderates the relationship between REN and SUD effectively. Recent research [47] correctly pointed out that the role of institutional quality is more visible when coupled with REN policies. Therefore, bringing improvement in the quality of institutional quality should be the priority of the policymakers of the G-7 economies.

Similarly, our results underscored that increased government expenditures are detrimental for the pace of SUS in G-7 economies. The coefficient of the variable used for the approximation of government expenditures is negative and significant. The point estimate indicates that a 1 percent increase in government expenditures is responsible for about a 0.148 percent decline in the pace of SUD. It is possible that government expenditures in G-7 economies are either insufficient or misallocated due to which their influence on the acceleration of SUD is negative. In addition, increased government expenditure in the economy generally crowd out private investment related to the adoption of REN and the associated infrastructure. Research demonstrated that an increase in government expenditures hurts the SDGs related to growth [48]. Therefore, the role of the government needs to be re-assessed to figure out the true reasons behind its negative consequences from the perspective of SUD.

Moreover, we found that trade openness has accelerated the pace of SUD in G-7 economies. The coefficient of trade openness is different from zero and further carries a positive coefficient in the estimated model. It means that enhanced trade openness is one of the main factors for accelerating the pace of SUD in G-7 economies. Generally, increased trade openness facilitates the process of technology transfers and promotes innovations which are essential factors for promoting SUD. Recent research [29] demonstrated the importance of international trade for promoting SUD. Therefore, the G-7 economies must take some visible steps to integrate their economies within the global economy in terms of both international trade as well as FDI inflows to speed up the pace of SUD.

Furthermore, our results underscored that domestic industrialization is unable to shape the pace of SUD in G-7 economies. In the estimated model, the coefficient of industrialization variable is insignificant, with a negative coefficient. It means that industrial expansion alone is not sufficient to accelerate the pace of SUD. In other words, the process of industrialization must be supported by a strong institutional quality and environmental framework for shaping the pace of SUD in the case of G-7 economies.

The FGLS-based results displayed in the penultimate column of Table 5 also demonstrated the beneficial influence that REN has on SUD in the case of G-7 economies. It means that REN is the robust determinant of SUD. Furthermore, the FGLS estimation also validated the moderating role of institutional quality on the relationship between REN and SUD. The joint term of institutional quality and REN is positive and statistically significant like the earlier results. Furthermore, the positive impact of trade openness and the negative impact of the government sector on the progress of SUD did not change in the FGLS estimation. Lastly, the insignificant roles of institutional quality and industrialization also remained the same in the FGLS estimation, which are consistent with the earlier results.

The TSLS results shown in the final column of Table 5 also confirmed the positive influence of REN on SUD which is consistent with the results extracted using the FEM and FGLS. The TSLS findings underscored that REN is important for accelerating the pace of SUD. In addition, we found that the positive impact of REN on SUD is further dependent on the quality of domestic institutions. Moreover, in the TSLS estimation, the sign of government expenditures is reversed, which indicates the correction of endogeneity. Generally, government expenditures improve SUD mainly through the development of human capital, fund allocation to address environmental problems, and investment in REN projects which ultimately accelerates the progress towards SUD. The positive impact of government expenditures on the level of SUD is expected and aligned with prior research [49]. All other variables have maintained their coefficient sings and significance in the TSLS estimation.

Finally, it is important to mention that the utilized instruments are exogenous as the null hypothesis was accepted (p-value = 0.371). Hence, the acceptance of the null hypothesis has provided support for the validity of the instruments. Moreover, the study utilized a total of eight instruments in the TSLS estimation which are acceptable given the sample size.

3.4. Robustness with Additional Control Variables

In addition to the robustness analysis using the FGLS and TSLS in Section 3.3, we have also assessed the robustness of the findings by including additional control variables in the specified models. To begin with, we have included the inflation rate, unemployment rate, and CO₂ emissions step by step to figure out whether the observed positive impact of REN on SUD stays robust or alters. The findings for the robustness analysis using additional control variables are depicted in

Table 6. Robustness analysis.

Variables	FEM	FEM	FEM	FEM
	Coefficients	Coefficients	Coefficients	Coefficients
${\mathbf{L}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	0.136 *** (0.018)	0.090 *** (0.019)	0.095 *** (0.019)	0.071 *** (0.018)
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	−0.078 (0.043)	−0.039 (0.039)	0.080 * (0.045)	−0.002 (0.040)
$\mathbf{L}\mathbf{N}({\mathbf{R}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}\times {\mathbf{I}\mathbf{N}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$)	0.015 *** (0.001)	0.005 ** (0.002)	0.017 *** (0.001)	0.010 *** (0.002)
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{D}\mathbf{S}}_{\mathbf{i}\mathbf{t}}$	−0.028 (0.056)	0.098 * (0.056)	0.008 (0.056)	0.109 ** (0.051)
${\mathbf{L}\mathbf{N}\mathbf{G}\mathbf{E}\mathbf{X}}_{\mathbf{i}\mathbf{t}}$	−0.150 ** (0.063)	0.242 *** (0.061)	−0.153 *** (0.062)	−0.213 *** (0.056)
${\mathbf{L}\mathbf{N}\mathbf{T}\mathbf{O}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$	−0.002 (0.035)	−0.076 *** (0.027)	−0.098 *** (0.030)	−0.034 (0.029)
${\mathbf{L}\mathbf{N}\mathbf{I}\mathbf{N}\mathbf{F}}_{\mathbf{i}\mathbf{t}}$	−0.011 *** (0.002)			−0.006 *** (0.002)
${\mathbf{L}\mathbf{N}\mathbf{U}\mathbf{N}\mathbf{E}\mathbf{M}}_{\mathbf{i}\mathbf{t}}$		0.015 *** (0.002)		0.010 *** (0.002)
${\mathbf{L}\mathbf{N}\mathbf{C}\mathbf{O}}_{2\mathbf{i}\mathbf{t}}$			−0.309 *** (0.052)	−0.206 *** (0.050)
Constant	0.860 (0.307)	0.982 (0.279)	3.261 (0.469)	2.184 (0.446)
Year Fixed	Yes	Yes	Yes	Yes
Country Fixed	Yes	Yes	Yes	Yes
Observations	147	147	147	147
Diagnostics	Adj (R2): 0.960 S.E.R: 0.037 F: 320.980 ***	Adj (R2): 0.969 S.E.R: 0.031 F: 362.032 ***	Adj (R2): 0.966 S.E.R: 0.033 F: 325.161 ***	Adj (R2): 0.974 S.E.R: 0.029 F: 373.398 ***

Note: The asterisks (***, **, *) stand for 1, 5 and 10 percent significance levels. The dependent variable is the sustainable development index. The values presented in the parenthesis indicate the Driscoll–Kraay [39] standard errors.

. The findings of column 2 show that inflation rate has casted a detrimental influence on SUD. However, with the inclusion of the inflation rate, the main findings regarding the relationship between REN in the presence of the moderating role of institutional quality stayed robust. Similarly, the inclusion of the unemployment rate in the model did not alter the relationship between REN and SUD in the presence of the moderating role of institutional quality. The variable unemployment carries a positive and statistically significant reflecting its importance for SUD. The positive impact of unemployment on SUD needs to be interpreted with caution. The positive sign obtained for unemployment may reflect temporary structural transitions or potential measurement issues rather than a genuine positive relationship with SUD. In addition, the positive impact of unemployment on SUD could be due to technological innovation, structural adjustment, or a shift towards environment-friendly technologies that generally reduces the demand for the labor force. The recently developed Environmental Philips Curve (EPC) also believes that unemployment reduces CO₂ emissions, which ultimately improves environmental quality [50,51]. Therefore, our results regarding unemployment and SUD are aligned with the EPC hypothesis.

Moreover, the results of the penultimate column show that CO₂ emissions are harmful from the perspective of SUD. The coefficient of CO₂ emissions is negative and different from zero, statistically highlighting its detrimental influence on the pace of SUD in G-7 economies. Higher CO₂ emissions generally degrade the quality of the environment and hence the pace of SUD is adversely affected. It is pertinent to mention that the inclusion of CO₂ emissions has not affected the already established relationship between REN and SUD along with the moderating role of institutional quality. Finally, the final column of Table 6 includes results when all three additional control variables are considered in the model estimation simultaneously. We found that with the inclusion of three additional control variables together, our main results did not change. To put it differently, our results are robust and valid and hence should be considered by the worldwide policymakers in general and the policymakers of G-7 economies in particular. Lastly it is useful to highlight that trade openness has adversely impacted SUD in the robustness analysis. This change in sign of trade openness indicates that its impact is conditional on other factors. In addition, it is possible that the sole impact of trade is sensitive to the model specification. However, most importantly, the main results about the REN and SUD are robust.

4. Causality Analysis

The direction of the relationship among the selected variables based on the DH causality approach [15] is shown in

Table 7. Causality results (DH approach).

Null Hypothesis:	Zbar-Stat.	Prob.
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	2.698 ***	0.007
${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	1.102	0.963
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	2.825 ***	0.004
${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	1.285	0.821
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.208	0.226
${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	0.445	0.656
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.269	0.204
${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.049	0.960
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.157	0.247
${\mathrm{L}\mathrm{N}\mathrm{S}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	0.932	0.350
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	2.034 **	0.041
$\mathrm{L}\mathrm{N}{\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	0.680	0.495
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	2.010 **	0.044
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	0.168	0.866
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	2.631 ***	0.008
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	3.821 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}\mathrm{E}\mathrm{M}}_{\mathrm{i}\mathrm{t}}$	2.240 **	0.025
${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}\mathrm{E}\mathrm{M}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	0.405	0.684
$\mathrm{L}\mathrm{N}{\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$	5.135 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$ to $\mathrm{L}\mathrm{N}{\mathrm{R}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	1.794 *	0.072
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	2.305 **	0.021
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.285	0.198
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.840 *	0.065
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	1.023	0.306
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	2.112 **	0.034
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.553	0.120
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$	3.480 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1.366	0.171
$\mathrm{L}\mathrm{N}{\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	2.633 ***	0.008
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$	1.809 *	0.070
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	1.787 *	0.073
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.097	0.272
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	7.098 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	0.177	0.859
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to LNINFit	1.845 *	0.065
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.403	0.160
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$	1.871 *	0.061
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}$	1.666 *	0.095
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	2.036 **	0.041
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	7.297 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$	2.918 ***	0.003
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	0.617	0.536
${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}\mathrm{E}\mathrm{M}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	6.295 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{U}\mathrm{N}\mathrm{E}\mathrm{M}}_{\mathrm{i}\mathrm{t}}$	1.715 *	0.086
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$	3.066 ***	0.002
${\mathrm{L}\mathrm{N}\mathrm{G}\mathrm{E}\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$	0.684	0.493
${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	1.851 *	0.064
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$	2.017 **	0.043
${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$ to $\mathrm{L}\mathrm{N}{\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	5.479 ***	0.000
${\mathrm{L}\mathrm{N}\mathrm{T}\mathrm{O}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ to ${\mathrm{L}\mathrm{N}\mathrm{C}\mathrm{O}}_{2\mathrm{i}\mathrm{t}}$	3.254 ***	0.001

Note: The asterisks (***, **, *) stand for 1, 5, and 10 percent significance levels.

. Both directions of the relationship among the key variables are demonstrated. According to the findings of causality, REN is linked with SUD, institutional quality, industrialization, unemployment, and CO₂ emissions in a unilateral manner. In other words, REN is the main driving force behind the improvement in industrialization, SUD, and institutional quality in G-7 economies. However, the study could not find any causal impact between SUD and institutional quality, industrialization and SUD, and government expenditures and SUD. Similarly, domestic industrialization is connected in a unilateral fashion with institutional quality, government expenditures, trade openness, inflation rate, and CO₂ emissions in the case of G-7 economies. Moreover, a unilateral casual influence running from institutional quality towards trade openness and inflation rate is observed. In addition, government expenditures are related with the inflation rate and institutional quality unilaterally. Finally, unemployment and CO₂ emissions are connected unilaterally with government expenditures.

The DH approach also uncovered some two-way causal relationships among the variables. For instance, the results demonstrated a two-way causal relationship between trade openness and CO₂ emissions, government expenditures and unemployment, trade openness and inflation rate, trade openness and government expenditures, trade openness and REN, CO₂ emissions and REN, CO₂ emissions and industrializations, and CO₂ emissions and institutional quality. In addition, trade openness and REN are also bilaterally linked with each other.

5. Concluding Remarks, Implications, and Limitations

5.1. Summary Remarks

This research paper attempted to figure out the impact of REN on SUD, which is an untapped research area in the case of G-7 economies. Unlike previous studies, this study has utilized a comprehensive index of sustainable development which is based on the ecological efficiency of human development. In addition, the study paid significant attention to uncover the true moderating impact of institutional quality on the relationship between REN and SUD in the case of G-7 economies. The paper focused on panel data of G-7 economies for the period 2002–2022 and applied a battery of econometric techniques for the purpose of the estimation, including the FEM with Driscoll–Kraay standard errors, FGLS, TSLS, and the DH approach of causality.

The study’s results showed the positive and statistically significant influence of REN on the pace of SUD in G-7 economies. More specifically, the study found that a 1% increase in REN leads to a 0.144% increase in SUD which is remarkable. Therefore, focusing on increasing the share of REN in the total energy basket shall be the priority of policymakers of G-7 economies in order to enhance the speed of SUD. The results also supported the moderating impact that institutional quality has on the relationship between REN and SUD in the case of G-7 economies. The coefficient of the joint term of 0.015 demonstrates that the impact of REN increases by 0.015% for every single unit improvement in institutional quality. It means that REN contributes more to SUD in economies with an advanced level of institutional quality. Besides REN, the study’s results demonstrated that open trade policies are important for accelerating the speed of SUD in G-7 economies. Quantitatively, it is found that a 1% increase in trade openness leads to a 0.091% increase in SUD in the case of G-7 economies. Hence, the policies of trade liberalization shall be continued given their important role in accelerating the pace of SUD. In addition, our results showed that the government’s role in the economy, the inflation rate, and CO₂ emissions are detrimental factors from the perspective of SUD. Empirically, the study demonstrated that a 1% increase in government expenditures, inflation rate, and CO₂ emissions will decrease SUD in G-7 economies by −0.148%, −0.011% and −0.309% respectively. Moreover, we found that the unemployment rate has improved SUD unexpectedly. Finally, we found that the domestic industrialization and institutional quality in isolation are irrelevant factors in explaining the variation in the level of SUD in G-7 economies, as both of them are insignificant in most of the estimated specifications. In summary, this paper has enriched the ongoing literature for the context of G-7 economies by establishing a relationship between REN and SUD in the presence of the moderating role of institutional quality which has not received sufficient attention in the prior literature.

5.2. Policy Implications

The study suggests, based on its findings, that the G-7 economies must wholeheartedly embrace the transition towards REN from fossil fuels to protect the quality of the environment and accelerate the process of SUD. The successful transition towards REN would help the G-7 economies to transform their economies and achieve the goal of long-run SUD. However, it is pertinent to mention that the true benefits of the transition towards REN would only be realized in the presence of strong and effective institutional quality. It means that countries having strong institutional quality are in a much better position to experience the full benefits stemming from the REN and SUD as underscored by the results. Therefore, significant investment is required on the part of policymakers to improve the quality of existing institutional quality to reap the full benefits of REN and SUD.

Moreover, the open trade policies adopted by the G-7 economies have provided a significant boost for the acceleration of SUD as demonstrated by the results. As a policy suggestion, the G-7 economies must eradicate all sorts of restrictions to the flow of goods and services as that ultimately contributes to SUD, which is the end objective of all activities. In addition, the government’s role needs to be minimized as it misallocates the existing scarce resources and hence worsens the pace of SUD.

Finally, our results imply that the G-7 group must address the problem of the inflation rate which has risen in recent years due to multiple factors, including COVID-19, increased energy prices, and the Russo-Ukrainian war. Inflation adversely impacts all sectors of the economy, including investment in REN projects which harms the transition towards SUD. In addition, the problem of unemployment in G-7 economies also calls for the urgent attention of policymakers, although its impact on SUD is positive in the estimated specifications. Generally, there is a consensus among the researchers that unemployment is the root cause of several economic and non-economic issues. Finally, based on our findings, we suggest that CO₂ emissions must be reduced by shifting the production process to REN. Policy changes in light of the recommendations provided will encourage the use of REN and ultimately the goal of long-run SUD will be achieved.

5.3. Limitations and Future Research Avenues

In this section, the study intends to document the main limitations and highlight the future research directions. The first and foremost limitation of our study is that the results could not be generalized on a large scale as the G-7 group shares some common characteristics which are distinct from other groups such as GCC, ASEAN, and SAARC. Therefore, to address this issue of result generalization, we suggest that future researchers carry out comparative studies by focusing on different regional groups as well as on individual economies to explore the robustness of the findings reported in this paper. Secondly, the SDI index is not free from limitations. For instance, the SDI index is dependent on inconsistent and incomplete ecological and social data, and its methodology is based on subjective judgments. Therefore, alongside the SDI index, future studies should study SDG indicators as well, which also capture some aspects of SUD. Thirdly, the data on SDI index are only available up to 2022 and hence the study covers only the period 2002–2022. Hence, future research studies must explore the specified models with the latest data when available. Fourthly, due the small cross-sectional dimension, the present study adopted only the basic econometric methodologies and included only a few variables that could impact SUD. Hence, we suggest that future researchers use a comprehensive sample of countries, include all potential factors of SUD in their models, and apply the more advanced econometric techniques such as GMM or CS-ARDL to provide more robust insights about the relationship between REN and SUD. We leave these mentioned research directions for future research studies.