REC and DIGT are important factors that influence ENGHG emissions. Several authors have investigated this link and discovered diverse results. There are numerous studies in the existing literature on the relationship between renewable energy consumption and ENGHG emissions that demonstrate that REC has a significant influence on decreasing carbon emissions. Qing et al. (2023) studied the relationship between renewable energy and energy-related emissions using moment quantile regression and discovered that they are interrelated and that renewable energy has a favorable influence. Leng and Zhang (2023) thought that renewable energy may help to reduce carbon emissions while also assisting in the restructuring of the global energy system. Zhang and Zhang (2022) examined renewable energy and ENGHG emissions and discovered that using renewable energy significantly lowered ENGHG emissions in the region. Anser et al. (2021) investigated the impact of renewable energy consumption on BRICS countries and discovered that renewable energy has the potential to considerably cut ENGHG emissions. However, empirical studies from Lei et al. (2022) reveal that the positive shock of renewable energy consumption has a large negative influence on ENGHG emissions, whereas the negative shock of renewable energy consumption leads to an increase in ENGHG emissions in the long run. As a result, it is important to note that the effect of renewable energy consumption on ENGHG emissions may be unclear. The majority of mainstream research concludes that renewable energy consumption has a beneficial environmental impact (Ren et al., 2023a; Yuan et al., 2022; Abbas et al., 2022; Sharma et al., 2021; Mohsin et al., 2021; Hu et al., 2021; Hussain et al., 2021). Furthermore, the following body of work explores the connection between REC and ENGHG emissions (Borzuei et al., 2022; Chien et al., 2022). For causality analysis, Mohsin et al. (2021) showed causal associations in their research.

The influence of digitalization on ENGHG emissions is determined by the national level of digitalization. Huang and Zhang (2023) recently explored the relationship between digitalization, global value chain placement, and carbon emissions. The empirical findings indicated that technological advancements in digitalization can boost low-carbon growth. Digitalization has greatly lowered regional ENGHG emissions, and this effect will be long-lasting (Ma and Wu, 2023). The empirical findings of (Zhang et al., 2023b) demonstrated that digitalization may stimulate energy storage technology innovation and coordinate energy systems, hence lowering carbon emissions. Kuzior et al. (2022) examined the effect of digitalization on ENGHG emissions using EU member states as an example. The empirical findings of Dong et al. (2022) demonstrated that digitalization reduces the intensity of emissions; Ma et al. (2022) predicted that the Chinese economy’s digitalization may achieve the carbon-neutrality objective, and empirical results showed that digitalization can limit energy emissions to minimize carbon dioxide production. Chen (2022) evaluated the long-term and significant relationship between digitalization and ENGHG emissions. By lowering energy consumption and increasing the structure and efficiency of energy systems, digitalization can assist accomplish the Sustainable Development Goals (Ali et al., 2023; Ren et al., 2023b; Xu et al., 2022a; Mondejar et al., 2021).

Energy efficiency is critical in reducing CO2 emissions and managing the environment. Li et al. (2022a) researched the impact of energy efficiency and green innovation on ENGHG emissions in China between 1991 and 2019. The empirical analysis found that increasing energy efficiency and green innovation reduces emissions, whereas decreasing energy efficiency and green innovation increases China’s CO2 emissions in the long run. Qing et al. (2023) examined the significance of energy efficiency in reducing ENGHG emissions in BRICS countries. Calvillo (2023) evaluated the influence of five different energy system models on energy efficiency and gas emissions, and the empirical results demonstrate that energy system selection is significant in enhancing energy efficiency and, as a result, lowering gas emissions. Mirza et al. (2022) checked the impact of energy efficiency on energy emissions in developing nations. According to the research, energy efficiency is a substantial factor to lowering energy emissions. Tu et al. (2022) evaluated energy efficiency and CO2 emissions connected to energy in RCEP economies. The discovery reveals that energy efficiency can be used as a corrective action to dramatically cut emissions and increase environmental sustainability. Furthermore, the following publications support the favorable impact of environmental innovation on ENGHG emissions (Wang et al., 2023b; Ali et al., 2022; Sattar, 2022; Bao et al., 2022; Mahapatra and Irfan, 2021).

There are plenty of studies in the literature that suggest that economic growth exerts a significant impact on ENGHG emissions (Ren et al., 2022; Wen et al., 2022; Kartal et al., 2023; Kirikkaleli et al., 2023). Su et al. (2023) observed that the effect of economic expansion on ENGHG emissions revealed EKC features. Liu and Ma (2023) demonstrated the link between green economic growth and ENGHG emissions in Belt and Road member nations. Chen et al. (2023) investigated the influence of economic growth on emissions reduction in China’s power system, and the empirical findings imply that long-term economic measures to minimize greenhouse gas emissions should be implemented. Chen (2022) evaluated the relationship between CO2 emissions and French economic development from 1975 to 2019. Economic expansion increases CO2 emissions, according to empirical evidence. Obobisa et al. (2022) analyzed the long-term impact of institutional quality and economic growth on CO2 emissions in 25 African nations between 2000 and 2018. According to the findings, economic growth and institutional quality have a favorable effect on CO2 emissions. Other research has demonstrated a positive relationship between ENGHG emissions and GDP (Xu et al., 2022b; Sun et al., 2022). The implication is that GDP will increase, reducing environmental sustainability. The empirical conclusion of Mujtaba et al. (2022) implies that economic expansion suppresses environmental quality in OECD nations; the NARDL model estimates that each 1% rise in economic growth reduces ENGHG emissions by 0.4%. Methodology.

The impact of renewable energy consumption (REC), digitalization (DIGT), energy efficiency (ENERF), and economic growth (GDP) on energy-related greenhouse gas emissions (ENGHG) is discussed in this section. Policymakers around the world have adopted a range of strategies to reduce greenhouse gas emissions, including expanding renewable energy consumption (Xu and Ullah, 2023). Renewable energy development is a critical method for achieving carbon neutrality and mitigating climate change (Tang et al., 2023). Countries must raise the amount of renewable energy, adapt the energy structure, and discover more suitable energy sources to reduce greenhouse gas emissions and enhance environmental quality (Zhang et al., 2023a). In other words, renewable energy is critical for environmental protection, combating climate change, and attaining long-term economic and social growth. Based on the preceding explanation, this analysis assumes that the negative impact of REC on ENGHG is: δ 1 = E N G H G i t R E C i t < 0 . Another important element influencing energy-related greenhouse gas emissions is digitalization (Wu et al., 2023). Digitalization is expected to reduce greenhouse gas emissions. Through the “dematerialization effect,” or the movement of the economy from the provision of physical products to the provision of services, digitalization decreases energy-related greenhouse gas emissions (Ozcan and Apergis, 2018; Chen, 2022). Digitalization may maximize the usage of clean energy, capture these energy sources at peak supply periods, and determine the optimum way to store energy, all of which contribute to lower energy consumption and, as a result, lower energy-related greenhouse gas emissions (Wei et al., 2023). Digitalization also contributes to the growth of green innovation by providing technological assistance for firms’ green transformation (Li et al., 2022b). Furthermore, digitalization has the potential to transform the economy toward a lighter, more energy-efficient structure, which is critical for long-term sustainability. Based on the foregoing, this analysis assumes that the negative impact of DIGT on ENGHG is as follows: δ 2 = E N G H G i t D I G T i t < 0 . As technology advances, every economy strives to utilize less energy to get the most out of it. Energy efficiency can lower CO2 emissions and pollution levels immediately (Lei et al., 2022). The combustion of fossil fuels produces a considerable amount of greenhouse gases, and energy efficiency may significantly reduce greenhouse gas emissions both directly from the combustion or use of fossil fuels and indirectly from electricity production (Zhang et al., 2023a). Because these studies indicate that ENERF can lower ENGHG, the following assumptions are made in this study: δ 3 = E N G H G i t E N E R F i t < 0 . Economic growth leads to the expansion of the scale of product production and waste disposal, leading to a large amount of energy consumption, resulting in a large amount of greenhouse gas emissions (Adebayo and Rjoub, 2022; Xue et al., 2022). Rapid economic expansion, in particular, necessitates the use of huge amounts of nonrenewable energy to power conventional industrial sectors and hence raise income levels. Increased wealth encourages industrial product sector output, which increases energy consumption, resulting in high volumes of greenhouse gas emissions (Cheng et al., 2019; Liu et al., 2021). Based on the preceding explanation, this analysis assumes that GDP has the following positive influence on ENGHG: δ 4 = E N G H G i t G D P i t > 0 .

Five variables are chosen based on the theoretical framework and research aims. Energy-related greenhouse gas (ENGHG) emissions were the dependent variable. Renewable energy consumption (REC) and digitalization (DIGT) are, on the other hand, critical factors. In addition, two control variables were added: energy efficiency (ENERF) and economic development (GDP). Since the combined impact of renewable energy use and digitalization on energy-related greenhouse emissions is still unknown, this study tends to explore the true association between them in the Group of Seven (G7) economies, including United States, United Kingdom, France, Germany, Japan, Italy and Canada. This study covers the period of the last three decades, ranging from 1990 to 2020. Following the literature (Zheng et al., 2023) and (Lei et al., 2022), this study constructes the following general model: E N G H G i t = f R E C i t , D I G T i t , E N E R F i t , G D P i t (1)

For the empirical estimations, the model can be given below: E N G H G i t = γ 0 + δ 1 R E C i t + δ 3 D I G T i t + δ 2 E N E R F i t + δ 4 G D P i t + ε i t (2) where γ 0 is the intercept, δ 1 , δ 2 , δ 3 and δ 4 are the intercept of each explanatory variable, and ε i t indicates the model’s random error. Besides, i and t in the subscript reveals the cross-section and time period, respectively. The data for all these variables are extracted from various sources, which include OECD ¹ and the World Bank ² . While the importance of other developed or emerging economies in the environment cannot be overlooked, the G7 must take the necessary steps to address the pressing challenges at hand as the global leader in sustainable development and energy policy.

Initially, this research summarizes the data using descriptive statistics such as mean, median and range. Additionally, the study also evaluated the standard deviation of the data to measure the overall volatility of each data set. Further, this research investigates the regularity of each variable. Specifically, skewness and kurtosis are estimated to understand if the data have a regular distribution. In this sense, the current study calculates skewness and Kurtosis against critical values of 1 and 3, respectively. This research additionally uses the Jarque and Bera (1987) normalcy test, which assumes skewness and excess Kurtosis to be equivalent to zero. The statistics of may be calculated using the following equation: J B = N 6 S 2 + K − 3 2 4 (3)

Since this study focuses on panel data, panel data techniques are appropriate to use. This research examines panel data properties including Slope heterogeneity and Cross-section Dependence. These two panel data issues are considered crucial and if not solved, the results will be biased and inaccurate (Wei et al., 2022). Considering the G7 countries are all sophisticated economies, it is critical to determine whether they have any similarities. Using the slope coefficient homogeneity test devised by Pesaran and Yamagata (2008) is better, since it produces both the standard slope coefficient homogeneity and the adjusted slope coefficient homogeneity, as follows: Δ S C H = N 1 / 2 2 k − 1 / 2 1 N Ś − K (4) Δ A S C H = N 1 / 2 2 K T − K − 1 T + 1 − 1 / 2 1 N Ś − K (5) where Δ S C H defines the slope coefficient homogeneity and Δ A S C H specifies the adjusted slope coefficient homogeneity.

Under the influence of economic globalization, activities such as foreign trade, capital flows, and technology transfer potentially increase a country’s dependence on other economies, as well as the dependence of other economies or regions on it. Yet, ignoring P C D may lead to erroneous and misleading study findings (Campello et al., 2019). In this study, Pesaran (2004) ’s P C D test is employed to assess cross-section dependency across the G7 countries, which takes independent cross-sections as the null hypothesis, stated as: C D T e s t = 2 T N N − 1 ∑ i = 1 N − 1 ∑ k = 1 + i N T i k (6)

Due to the possible commonality of the panel data, this study uses a unit root estimator to address SCH and PCD issues. In particular, this study employs the Pesaran’s (2007) cross-sectional IPS (CIPS) test. Pesaran (2006) skillfully constructed a factor model to analysis the cross-sectional dependence of unexplained cross-sectional averages; Pesaran (2007) managed to modify the Augmented Dickey-Fuller regression by combining the average and first differed cross-section lags. This methodology produces cross-sectional dependence even though the panels are unbalanced (T > N or N > T). The basic CIPS equation is as follows: C I P S = N − 1 ∑ i = 1 N C A D F i (7)

The CIPS test assumes the existence of a unit root in the time series.

This study uses the error correction framework of Westerlund (2007) to assess the long-run equilibrium relationship between the variables under consideration. This test is designed to provide accurate estimates despite the cross-sectional dependence and slope fluctuations. Since it considers both group mean statistics, i.e., G τ = 1 N ∑ i = 1 N α i S . E α i and G a = 1 N ∑ i = 1 N T α i α i 1 , and the panel statistics, i.e., P τ = α S . E α and P a = T . α .

As the estimation results verify the cointegration between the variables, the study considers the non-normality, which leads to a new estimation method, i.e., Method of Moments Quantile Regression (MMQR) (Koenker and Bassett, 1978). Quantile regression works well when the dataset’s distribution is asymmetrical or follows the properties of non-normal distribution (Shahzad et al., 2023). Machado and Santos Silva (2019) designed the MMQR technique for assessing the dispersion of quantile estimates (Sarkodie and Strezov, 2019), which is a solution to the problem of non-normality. Equation 8 provides the conditional quantile location-scale variant Q y τ | R as follows: Y i t = α i + β R i t + γ i + ρ Z i t μ i t (8)

Here, the probability representation p γ i + ρ Z i t > 0 is equal to one, whereas α , β , γ , and ρ indicate the coefficients to estimate. The subscript “ i ” presents the fixed effect for i = 1,2 , . . . , n .In addition, R is a component of the k -vector, denoted by Z , and the symbol “ l ” indicates a distinctive variation. Z l = Z l R , l = 1,2 , . . . , k (9) where R i t is distributed symmetrically and independently for the total fixed i and t time, which is orthogonal to both i and t (Machado and Santos Silva, 2019). Therefore, the outer reserves and external components are both stabilized. Following the context, the constructed model may be modified as follows: Q y τ R i t = α i + γ i q τ + β R i t + ρ Z i t q τ (10) where R i t captures explanatory variables such as REC, ENERF, DIGT, and GDP in logarithmic form. In addition, R i t reveals the quantile dissemination of the predictor variables ( Y i t ), which is ENGHG emissions in this study, which also replies on the quantile’s position. Furthermore, − α i τ ≡ α i + γ i q τ is a scalar coefficient that demonstrates the stable influence of τ quantiles on i . In contrast, the influence of each quantile does not influence the intercept. Due to the separate temporal structure of variables, various impacts are vulnerable to modification. Lastly, q τ symbolizes the τ − t h quantiles’ sample, which are Q 0.25 , Q 0.50 , Q 0.75 , and Q 0.90 in this research. This study uses the quantile equation as follows: m i n q ∑ i ∑ t θ τ R i t − γ i + ρ Z i t q (11) where θ τ A = τ − 1 A I A ≤ 0 + T A I A > 0 , denotes the check function.

As robustness estimation, this work used Bootstrap Quantile Regression (BSQR) technique after obtaining empirical data for each variable by MMQR. The BSQR method is a gap technique for analyzing confidence intervals and statistical significance, which uses algorithmic capabilities to estimate the sample distribution of the evaluation model. The BSQR method has the merit of obtaining quantifiable information, which avoids asymptotically normal sample distribution restrictions. The BSQR approach could offer more efficient estimation and empirical results (Markus and Groenen, 1998).

Due to the lack of causality between the dependent and explanatory variables in the above method, even the presence of an unbalanced panel (T is not equal to N) will not provide an optimal and accurate estimate. This study uses the panel Granger causality test developed by Dumitrescu and Hurlin (2012), which is more powerful and deals well with the panel data including cross-section dependency and slope variability (Banday and Aneja, 2020).

To begin with, this study performed a descriptive diagnosis of the statistics including mean, median, maximum and minimum values. The means and medians of all variables in this study are positive, indicating that these variables have increased over time. The standard deviation of variables illustrates the volatility of the data and the extent to which they deviate from the mean position. Kurtosis and skewness can reflect the symmetry and peakedness of the data distribution. According to Table 1, it can be seen that DIGT, REC and ENERF show a skewed negative distribution, and ENGHG emissions and GDP show a skewed normal distribution. In this study, the non-normality of the data distribution was verified using the Jarque Bera method, and the probability statistics showed significant results, leading to the rejection of the original hypothesis and the conclusion that all variables are asymmetrically distributed.

Since this study deals with panel data from seven developed countries and spans the period 1990–2020, it is necessary to perform slope homogeneity and cross-sectional dependence tests before panel data analysis. The estimation results of these checks are presented in Tables 2, 3. According to the empirical result in Table 2, the empirical result in Table 2 indicates that SCH and ASCH statistics are significant at the 1% level of significance, thus rejecting the original hypothesis of homogeneity. According to the empirical result of PCD in Table 3, all variables are statistically significant at the 1% level of significance, thereby rejecting the null hypothesis and concluding that all variables in G7 countries are interrelated and cross-dependent.

In this study, the CIPS unit root test of Pesaran (2007) was used. Table 4 provides the stationary results of unit root analysis. The test result reveals that REC, DIGT, and ENERF are significant, but ENGHG emissions and GDP are not significant, which indicates the existence of unit roots for these two variables. In addition, by testing the first-order difference data for these two non-stationary variables, this verifies their stationary and permits this research to investigate the long-term relationship.

The results of the unit root test indicate that all variables are stationary, so the existence of a long-run cointegration relationship between them needs to be examined. The Westerlund ECM Cointegration Test is employed in this study. The empirical result from Table 5 reveals that there is no cointegration in the original hypothesis. The significant p-values demonstrate a long-term correlation between the variables, indicating that ENERF, REC, GDP, and DIGT are cointegrated with ENGHG emissions.

The above empirical findings indicate that long term relationships exist between the variables explored, therefore, this study attempts to explore their effects on ENGHG emissions. The non-normality of the information leads to the choice of a new approach, i.e., MMQR for empirical results. The primary results are provided in Table 6. First, energy efficiency is significantly and negatively correlated with ENGHG emissions in all quartiles, which suggests that in the G7, improving energy efficiency can reduce ENGHG emissions. The variable finding is consistent with (Li et al., 2022a; Lei et al., 2022; Mirza et al., 2022). Next, the coefficients of all quartiles of economic growth are negative, indicating a significant negative correlation between GDP and ENGHG emissions. The negative impact of economic growth is consistent with (Iqbal et al., 2022; Obobisa et al., 2022; Sufyanullah et al., 2022; Xue et al., 2022). This means that economic growth will largely aggravate environmental pollution. Further, renewable energy consumption is significant in the first and second quartiles and the coefficients are all negative, which indicates that increased REC could lower ENGHG emissions. The estimated results are in line with the existing studies of (Apergis et al., 2023; Mukhtarov et al., 2023). Finally, all coefficients of the digitalization are also negative, which demonstrates a negative correlation between DIGT and ENGHG emissions. The result is consistent with (Ke et al., 2022; Ma and Wu, 2023). This suggests that the advancement of digitalization allows for a reduction in ENGHG emissions while also improving environmental quality. Figure 1 depicts the trend graphs between all variables in the moment quantile regression and energy-related greenhouse gas emissions.

This study used Bootstrap Quantile regression to assess the model’s robustness, and the results indicate that the model utilized in this study is stable and dependable. Significant robustness analysis results are presented in Table 7, especially at the (Q_0.75) and (Q_0.90) quartiles. The trend of all variable coefficients in Bootstrap Quantile regression is plotted in Figure 2.

Since moment quantile regression is unable to reveal the causal relationship between variables, this study employs the Dumitrescu and Hurlin’s (2012) panel Grander causality test, and estimated results are shown in Table 8. The variable pairs ENERF≠ENGHG, ENGHG≠ENERF; REC≠ENGHG, ENGHG≠REC; GDP≠ENGHG, ENGHG≠GDP are significant. However, no significant causal relationship was found between DIGT and ENGHG emissions. For the assessment of causal relationships between variables in line with the literature (Anser et al., 2021; Eskander and Istiak, 2021; Lei et al., 2022; Tufail et al., 2022; Mukhtarov et al., 2023; Zheng et al., 2023).

The empirical econometric results above illustrate a long-term relationship between four variables in G7 advanced countries: energy efficiency (ENERF), renewable energy consumption (REC), digitalization (DIGT), and economic development (GDP). Empirical results reveal that REC has a negative influence on ENGHG of G7 economies, which is consistent with (Xiong et al., 2022; Zhang and Zhang, 2022). These two studies, conducted in BRICS and Belt and Road nations, looked at the detrimental impact of renewable energy on energy-related greenhouse gas emissions. These studies give actual evidence of renewable energy’s negative influence on greenhouse gas emissions in various economies. The essence of renewable energy’s negative impact on energy-related greenhouse gas emissions is a shift in energy mix. Renewable energy, as an alternative to traditional fossil fuels, can have a low carbon footprint or minimize greenhouse gas emissions and other chemical pollutants in the manufacturing process. According to the findings of Lei et al. (2022)’s survey on renewable energy in China, renewable energy has some benefits over traditional fossil fuels in terms of energy supply diversification and environmental sustainability. However, renewable energy confronts several challenges, including expensive construction and development costs, challenging storage, and a long payback period. Sanchez et al. (2022) illustrated this in their investigation of alternative energy choices. Therefore, finding acceptable alternative energy sources, boosting renewable energy conversion efficiency, and lowering investment prices are critical ways to minimize greenhouse gas emissions from energy sources.

DIGT, on the other hand, has a negative influence on ENGHG, as evidenced by Alina-Petronela et al. (2023)’s European nation survey. The results of our research suggest that digitisation reduces energy-related greenhouse gas emissions. Previously, the OECD (2010) estimated that digitalization would expand the manufacturing scale of information and communication technology, resulting in increased energy consumption and greenhouse gas emissions. However, current empirical studies on digitalization indicate that this viewpoint is certainly no longer prevalent. Digitalization is considered as a blessing for lowering greenhouse gas emissions (Chen, 2022). Digitalization promotes national energy system transformation, increases clean energy efficiency, decreases energy consumption in the economic system, and reduces energy-related greenhouse gas emissions. ENERF had a detrimental influence on ENGHG as well. Mirza et al. (2022) and JinRu and Qamruzzaman (2022) also corroborated this conclusion. These studies indicate that increasing energy efficiency may save the country money while also lowering energy-related greenhouse gas emissions. Empirical results reveal that GDP boosts ENGHG emission, which is consistent with (Gyamerah and Gil-Alana, 2023; Yahyaoui, 2023). Economic development is still heavily reliant on nonrenewable resources, resulting in significant greenhouse gas emissions.

In conclusion, investigating the link between energy efficiency, renewable energy consumption, digitalization, and economic growth, as well as energy-related greenhouse gas emissions in the G7, is critical for balancing economic and environmental progress. Many industries rely on energy inputs to grow, which increases greenhouse gas emissions and environmental pressures. Consumption of renewable energy and digitalization necessitate large investment expenditures that middle-income or rising nations may be unable to finance. Improvements in energy efficiency may result from technology breakthroughs, investment in R&D expenses, and other causes, but no immediate advantages should be expected. Large-scale energy efficiency gains take time, and ineffective energy efficiency switching can stymie national economic development. Storage and other issues hinder green progress when it comes to adopting and integrating renewable energy sources. In this context, the empirical outcomes of this study may give a path for academics, politicians, and regulators to take appropriate action in order to achieve low-carbon development.

The research investigated the influence of renewable energy consumption, digitalization, energy efficiency, and economic growth on energy-related greenhouse gas emissions in the G7 economies objectively. The simultaneous evaluation of these factors in G7 economies was unique. Despite substantial research, their relevance to energy-related greenhouse gas emissions has received little attention. As a result, this research investigated the genuine impact of renewable energy consumption and digitalization on energy-related greenhouse gas emissions. Advanced econometric tools were employed in this work to analyze in depth the factors influencing energy-related greenhouse gas emissions. On the one hand, empirical studies suggested that promoting renewable energy may reduce reliance on fossil fuels, achieve carbon neutrality and ameliorate climate change, and promote green and low-carbon growth. By facilitating energy system transition and improving energy sector structure, digitalization can help to reduce energy-related greenhouse gas emissions. On the other hand, economic development and energy efficiency are major elements in reducing emissions and improving the environment. The estimated results are consistent with the existing mainstream literature. Overall, our research uncovered novel connections between renewable energy use, digitalization, energy efficiency, and energy-related greenhouse gas emissions. This discovery has the potential to significantly improve environmental quality and achieve sustainable development.

Based on the findings of the empirical research, this paper suggests some policy recommendations that may assist governments and policymakers in developing and implementing effective policies to reduce energy emissions. Renewable energy consumption should be encouraged in developed countries, and government incentives and aid should be offered to sectors transitioning to clean energy. Simultaneously, investment and government spending in energy technology must be expanded, prompting policymakers to place a greater emphasis on energy-related technical innovation. A higher degree of digitalization will reduce environmental impact; consequently, the development of smart technology in the digital sphere should be supported. Furthermore, for global environmental sustainability, economic development in industrialized economies must minimize reliance on nonrenewable energy sources.

This study primarily looks at the influence of these elements in G7 nations; nevertheless, the impact may be significant in other economies throughout the world, particularly in terms of digitalization. Future studies might look into the impact of digitalization in different economies. In addition, the larger data set can improve the comprehensiveness of the study model, which future researchers can accomplish.