
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12673-4
10.1016/j.heliyon.2024.e36642
e36642
Research Article
Towards sustainable development: Examining renewable energy consumption in E−7 countries
Chen Xi xichen0313@outlook.com
a
Rahman Saif Ur saifrao12@gmail.com
b⁎
Abdullah Sehresh sehreshabdullah@gmail.com
c
Ali Shahzad ali.huzafah@gmail.com
d
Khalid Salman Salmankhalid884@gmail.com
e
a School of Management, Sias University, Zhengzhou 451150, Infrastructure University Kuala Lumpur, Malaysia Kuala Lumpur,43000, China
b Faculty of Economics and Commerce, Superior University, Lahore, Pakistan
c Faculty of Economics and Commerce, Superior University, Lahore, Pakistan
d Faculty of Business and Management Sciences, Superior University, Lahore, Pakistan
e MPhil Scholar, Faculty of Economics and Commerce, Superior University, Lahore, Pakistan
⁎ Corresponding author. saifrao12@gmail.com
28 8 2024
15 9 2024
28 8 2024
10 17 e3664229 1 2024
14 8 2024
20 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
In the contemporary landscape, sustainable development became major challenge for the economy which is tackled if environmental issues are resolved. In this regard, this study investigate renewable energy, institutional quality, foreign direct investment (FDI), economic growth on environmental pollution in E−7 countries (Brazil, Russia, China, Indonesia, India, Mexico, and Turkey). Utilizing annual data from 2002 to 2023, selected the panel Nonlinear Autoregressive Distributed Lag (NARDL) after applying stationary process. The results depict that there is short and long run relationship among the selected variables, CO2 emissions exhibit an asymmetrical response to positive and negative shocks in exogenous variables. A one-percent change in FDI, GDP, IQ and EC reduce the carbon emission. Our research concludes with policy recommendation that prioritize priorities economic growth and environmental sustainability. To achieve a healthy environment and sustainable growth in the E7 countries, it is essential to strengthen institutional frameworks, encourage green investments, and foster technical innovation.

Keywords

Renewable energy
Institutional quality
CO2 emission
E-7 countries
==== Body
pmc1 Introduction

In the contemporary landscape, there is noticeable emphasis in the media on the transition towards sustainable development [1], particularly in emerging economies. Understanding the intricate relationships among renewable energy, institutional quality, foreign direct investment (FDI), economic growth, and environmental pollution is critical. This understanding is vital as the global community confronts the challenges posed by climate change and environmental degradation. Environmental contamination affects the entire world, and countries that contribute the most to anthropogenic greenhouse gas emissions are expected to bear responsibility for reducing them [2,3]. Climate change is causing rising global temperatures, continued emissions of harmful pollutants from industries, and limited access to green energy sources or innovations in most regions [4]. Worldwide, sea level and global temperature are rising, glaciers are melting, and weather patterns are becoming more unpredictable [5]. Air pollution is a significant health risk, accounting for approximately one-third of heart attacks, strokes, and lung cancer cases [4,6]. Global economic growth projections indicate that by 2043, the world economy have nearly doubled in size, growing by 2.6 % annual rate between 2017 and 2050 [7]. Consequently, in Fig. 1 (a), emissions are not expected to decline. The forecast suggests that this growth will be largely driven by E−7 economies, including Brazil, Russia, China, Indonesia, India, Mexico, and Turkey, at a rate of nearly 2.4 % each year over the next 25 years [8]. In this regard, the element which causes of this environmental degradation is carbon emission and greenhouse gasses.Fig. 1(a) Comparison of carbon Emission of E−7 and Worldwide.

Fig. 1(a)

Energy utilization is critical for driving economic development and progress. Energy is identified as a critical factor in achieving sustainable development, particularly in climate action goals of United Nation [9]. It is worth noting that the energy sector accounts for approximately three-quarters (3/4) of greenhouse gas emissions, making it critical to mitigating the negative effects of climate change [10]. Research indicates that economic growth as a significant positive impact on carbon emission [[11], [12], [13], [14]]. Moreover, governmental effort is not ignored this situation which help to make the effective and efficient policy which help to reduce the risk related to environment [15]. Therefore, it is essential for improving the environmental quality [[16], [17], [18], [19], [20]]. The state can control emissions and carbon concentrations through democratic climate policy if the governance foundation is strong [16]. Furthermore, Foreign Direct Investment (FDI) has a variety of effects on environmental quality [13,18,21,22]. Initially, many studies focused on the relationship between FDI and environmental pollution, yielding varying results [12,[23], [24], [25], [26]]. Subsequently, extensive research has been conducted to further investigate this idea [27,28]. Compared to other regions around the world, Less Developed countries (LDCs) now receive 31 % more Foreign Direct Investment (FDI) than they did in the 1990s, a 25 % increase. LDCs have actively welcomed these inflows because they play an important role in accelerating environmental related issues [29].

This research aims to unravel these intricacies in order to address urgent contemporary challenges and inform policy decisions. Poor environmental conditions have adverse effects on individuals, resulting in social, economic, and ecological losses. These include health issues, discomfort, and premature death, reduced global tourism, soil erosion, and decreased recreational value of natural landscapes [3,4,30,31]. The study contributes in variety of way, including a novel combination of variables, particularly among E−7 countries. Furthermore, the panel nonlinear ARDL approach is used to bridge the methodology gap and addresses methodological challenges associated with panel data. This methodology is also useful for providing a nuanced understanding of the relationships between variables. The panel NARDL model allows for the examination of both short and long-term impacts, which help to improves the accuracy of policy recommendations for environmental preservation and sustainable development. Furthermore, this study also contributes in the literature review on the novel relationship, and help to make the effective and efficient policy to enhance environmental quality by utilization of foreign direct investment, economic growth and institutional quality.

2 Review of the literature

2.1 Theoretical literature

Trade openness is widely supported because it is linked to higher GDP per capita, increased productivity, and faster economic growth in open economies compared to closed ones [32,33]. However, the environment impact varies depending on technology, scale, relative advantage, and product composition [32,34,35]. Economic growth resulting from trade openness can have an impact on environmental quality, with initial deterioration followed by potential improvement, as seen in the Environmental Kuznets Curve (EKC), which shows a U-shaped relationship between GDP and pollution [32]. Initially, trade openness and overexploitation may have a negative impact on the environment; however, later stages of development may see positive effects from trade openness and compositional pathways [36,37]. Despite initial declines in environmental quality caused by factors such as transportation and industrialization, technological advancements can have a positive compositional effect as cleaner technologies, thereby improving environmental quality [30,38,39]. Moreover, trade openness may improve environmental quality if technological advancements outweigh negative factors, and stringent environmental regulations can enhance environmental quality even further [40]. Gonzalez, Tomlinson [41] and Wald, Johnson [42] exhibit the expertise of renowned academics in renewable energy by utilizing primary data from national energy agencies as well as recent climate research. Similarly, Jones and Utyuzhnikov [43] and Wang, Xia [44] use large datasets and sophisticated econometric techniques to analyze the impact of renewable energy on environmental consequences, ensuring the validity and consistency of their findings. Kawabata and Camargo Junior [45] and Wang, Gozgor [46] investigate institutional quality, drawing on empirical data from expert surveys and World Bank governance indices. Meanwhile, Le, Kim [47] and Lv, Rodríguez-García [48] used rigorous statistical techniques like panel data analysis to evaluate how institutional quality affects economic growth and environmental sustainability. This study emphasizes the need of examining the complex interactions between renewable energy, institutional quality, FDI, and economic growth on environmental pollution in E−7 nations in order to fill up research gaps. The study intends to close these gaps and offer insights for successful policy interventions and sustainable development plans.

2.2 Empirical literature

2.2.1 Renewable energy consumption and carbon emission

Investigations have indicated that utilizing renewable energy sources resulted in an increase in CO2 emissions [49,50]. Specifically, the study by Bölük and Mert [50] demonstrated that energy consumption (EC) and environmental quality (EQ) have an inverse and significant relationship in the long run, while the statistical analysis model showed positive and significant results in the short run. Furthermore, EC has been found to have a positive and significant relationship according to studies by Refs. [[51], [52], [53]]. Conversely, other research has revealed that EC has a significant but negative or inverse impact on CO2 emissions [[54], [55], [56], [57], [58]].

2.2.2 Institutional quality and carbon emission

Research into the factors influencing environmental performance has been extensive, yet there has been limited exploration of how institutional quality affects environmental sustainability [20]. Studies by Refs. [59,60] have shown that institutional quality has a significant relationship with CO2 emissions, with positive impacts on both CO2 emissions and environmental quality. Similarly, research conducted by Refs. [17,61] has demonstrated a positive correlation between institutional quality and CO2 emissions. Moreover, a separate study has indicated that institutional quality enhances environmental quality, highlighting the importance of effective governance in improving the environment within the E7 countries [62]. In regions where CO2 emissions are already high, institutional quality has a positive long-term effect on them, indicating that as institutional quality improves, so do CO2 emissions [63]. Carbon emissions are notably and negatively affected by institutional quality, and it significantly mitigates the emissions of carbon. Studies, such as those conducted by Refs. [17,[63], [64], [65]] consistently demonstrate a negative and significant relationship between institutional quality and CO2 emissions. According to Ref. [64] an additional one percent increase in institutional quality leads to a 0.114 % reduction in pollution.

2.2.3 Foreign direct investment and carbon emission

Several studies, including those by Refs. [[66], [67], [68]] have highlighted a negative and significant relationship between CO2 emissions and FDI. National industry CO2 emissions are notably impacted by FDI, with coefficients in regions such as the east, center, and west showing inverse correlations with environmental quality, including CO2 emissions, at a 1 % significance level, except for northeastern China [69]. Furthermore, research conducted by Refs. [70,71]suggests that both direct and inverse changes in foreign direct investment have a stationary or positive influence on environmental quality, with strong and direct changes having a more pronounced long-run impact compared to inverse changes. Nguyen, Tran [72] and Wu, Zong [73] used advanced modeling techniques like as geographic information systems (GIS) and spatial econometrics to examine the spatial distribution and temporal trends of environmental pollution. It is crucial to evaluate the credibility and reliability of the literature, including assessing the qualifications, affiliations, and methodological quality of the authors' work, as demonstrated by Wald, Johnson [42] and Pesaran, Shin [74]. Previous research, it is investigate that there is a positive relationship between foreign direct investment (FDI) and pollution or CO2 emissions [67,[75], [76], [77]]. FDI inflows had a statistically significant, albeit minor, long-term positive impact on India's GDP growth from 1980 to 2003. In contract, FDI inflows have had a substantial impact on increasing CO2 emissions over time [78]. Some studies have illustrated how FDI contributes to emission reduction and foster collective efforts toward emission reduction [[79], [80], [81]]. Before 2006, FDI exhibited a positive and significant impact on emissions; however, after that year, it had a negative impact [22]. Findings from the Banerjee [82] causality test suggest the existence of causal relationships. Between carbon emissions and FDI in both directions [13]. Chen, Zhang [83] and Nguyen, Tran [72] provide compelling evidence on the relationship between foreign direct investment (FDI) and economic growth, supported by large datasets from international organizations and national statistical agencies. Rao, Ali [84] and Tong, Singh [85] use sophisticated econometric techniques to methodological rigor and reliability in their analyses of the relationship between FDI inflows and environmental pollution levels.

2.2.4 Economics growth and carbon emission

The relationship between pollution and GDP has attracted increasing attention as it is believed to aid in the development of CO2 emission reduction methods. The Environmental Kuznets Curve (EKC) describes the U-shaped relationship between environmental pollution and economic development, where GDP has a negative impact on CO2 emissions or the environment [86]. Studies, such as those by Refs. [49,75], have observed that environmental damage initially increases with GDP for levels of GDP per capita less than $4686 USD, but this relationship reverses for GDP levels exceeding $4686 USD. Additionally, GDP has a positive long-term influence on CO2 emissions, with studies by Ref. [87]. Zhao, Zhang [88] and Olopade, Okodua [89] provide comprehensive insights into factors influencing economic growth, relying on empirical data and employing robust research methods such as growth accounting and time-series analysis. Environmental science studies by Li, Zu [90] and Mahalik, Patel [91] provide reliable information on environmental pollution in E7 countries, based on empirical data from satellite images and ground-level monitoring stations.

3 Data and methodology

3.1 Data

This study investigates the relationship between renewable energy, institutional quality, foreign direct investment (FDI), economic growth, and CO2 in the E7 countries, namely Brazil, Russia, China, Indonesia, India, Mexico, and Turkey. Data were collected from the world development indicator on annual bases from 2002 to 2023. Institutional quality (IQ) is estimated using Principal Component Analysis (PCA), incorporating six indicators: law and order condition, absence of violence, regulatory quality, corruption perception, political stability, voice and accountability, and government effectiveness. Table 1 further provides details about the variable measure and source of data collection.Table 1 Description of variables.

Table 1Symbols	Variables	Measurements	Resources	
CO2emissions	Carbon Emission	CO2 emissions which are measured in kt.	World Bank	
EC	Renewable Energy Consumption	Renewable energy consumption (energy consumption final %)	World Bank	
IQ	Institutional Quality	PCA of six indicators of institutional quality (Political Stability, Control of Corruption, Absence of Violence, Voice and Accountability, Government Effectiveness, Rule of Law, Regulatory Quality).	World Bank	
FDI	Foreign Direct Investment	FDI inflows at constant 2005 US$	World Bank	
GDP	Gross Domestic Product	GDP per capita growth (annual %)	World Bank	

Table 2 Cross sectional dependency test.

Table 2Overall model cross-sectional Dependence Test	
Pesaran CD	Statistics	Prob	
8.5440	0.000	

3.2 Methodology

Many studies in the existing literature have primarily investigated the linear relationships among renewable energy, institutional quality, foreign direct investment, economic growth, and CO2 emissions. In this study, descriptive statistics were used to better understand the statistical properties of the data. Furthermore, before proceeding with the data, it is necessary to apply the stationarity process in panel data, levin, Lin (LLC) unit and Im persran unit root test [92,93], as shown in Table 4. The unit root test was used to assess the random walk in the data, or, more simply, to check level of integration. The NARDL approach, on the other hand, can be used regardless of whether all variables are purely stationary at I (0), I (1), or a combination of both, with the exception of I (2). This flexibility enables a broader range of analyses and data characteristics. Initially, stationarity is assessed to prevent spurious regression and ensure the results integrity.Table 3 Descriptive statistics.

Table 3	CO2	EC	IQ	FDI	GDP	
Means	0.710307	2.311770	0.792481	1.226160	1.824172	
Medians	0.629972	2.341715	0.808574	0.879492	1.821121	
Maxi	1.855882	2.819328	1.155245	6.321598	2.378400	
Mini	0.043730	1.574767	0.419708	0.061913	1.098238	
Std.	0.497901	0.311834	0.168606	1.089292	0.158365	
Skewness	0.662932	−0.136245	−0.216770	2.046821	−1.293598	
Kurtosis	2.456065	2.550981	2.217357	8.166111	10.98813	
Jarque Bera	8.557417	1.149451	3.335358	181.0275	293.7661	
Probability	0.013861	0.562859	0.188684	0.000000	0.000000	

Table 4 Panel unit root tests.

Table 4	Level	First Difference	
With Intercept	With Trend & Intercept	With Intercept	With Trend & Intercept	
Variables	Tests	Statistics	P Value	Statistics	P Value	Statistics	P-Value	Statistics	P-Value	
CO2	LLC	2.67803	0.9963	−0.71	0.2389	−2.6899	0.0036	−2.0033	0.0227	
IPS	3.51775	0.9998	0.40494	0.6572	−2.2919	0.011	−1.3537	0.0879	
EC	LLC	−0.9	0.1841	−0.1708	0.4322	−3.6195	0.0001	−4.4025	0	
IPS	1.19025	0.883	0.08693	0.5346	−2.6239	0.0043	−2.1936	0.0141	
IQ	LLC	0.70397	0.7593	−0.17	0.4325	−5.7201	0	−3.8762	0.0001	
IPS	1.10942	0.8664	0.33087	0.6269	−5.1393	0	3.86906	0.0001	
FDI	LLC	−1.1557	0.1239	−2.3112	0.0104	−5.4835	0.009	−4.338	0	
IPS	−1.5983	0.055	−1.9068	0.0283	−5.7932	0	−4.6636	0	
GDP	LLC	−1.3769	0.0842	−1.1575	0.1235	−1.5618	0.0592	−1.0204	0.1538	
IPS	−1.6265	0.0519	−0.7104	0.2387	−5.5489	0	−4.6185	0	

There are different co-integration techniques are available to investigating the panel data Munir and Riaz [94] argue that panel data is more effective than cross sectional and timer series. Moreover, panel data analysis produces better results than time series and cross-sectional data because it can incorporate both heterogeneity and cross-sectional specific effects into the estimation process. A large sample size leads to more reliable results and robust estimation [95]. Furthermore, panel data provides more information, variability, and efficiency than pure time series data or cross-sectional data. Furthermore, panel data can mitigate estimation biases caused by aggregating groups into a single time series.

As a result, this study chose the most vulnerable technique, nonlinear ARDL, for panel data [96], based on the equation below.1 CO2it=f(EC,IQ,FDI,GDP)

According to the above equation, positive and negative shocks were separately investigated. Therefore, the following equation (2) is formulated2 CO2it=f(EC+,EC−,IQ+,IQ−,FDI+,FDI−,GDP+,GDP−)

Which were further generated the following equation;X+=∑i=1tΔXi+=∑i=1tmax(ΔXi,0)

X−=∑i=1tΔXi−=∑i=1tmin(ΔXi,0)

In the above equation which depicts that there is -ve value shows the shocks and +values show the positive shock.CD=2TN(N−1)(∑i=1N−1∑j=i+1Npijˆ)∼N(0,1)i,j

CD=2TN(N−1)(∑i=1N−1∑j=i+1Npijˆ)(T−k)pˆij2−E(T−k)pˆij2var(T−k)pˆij2

Before, applied the nonlinear ARDL approach to test the hypotheses it is recommended to check the cross-sectional dependency test in the following Table 2.

According to the results of cross-sectional dependency which idecated that there is no cross-sectional correlation exist among the cross sections. Therefore, panel nonlinear ARDL is considered more appropriate [97]. This approach is built upon the linear ARDL model developed by Pesaran, Shin [98], with enhancements provided by Pesaran, Shin [74], as detailed in Ref. [97]. Shin, Yu [97]utilized methods proposed by Granger and Yoon [99] to decompose any stationary variable into its negative and positive variations. This decomposition enables the examination of both the negative and positive impacts of a variable. For instance, for a variable X, the two components, representing the partial sum of the negative and positive variations of the variable, are considered. By integrating the methodologies proposed by Pesaran, Shin [98] and Shin, Yu [97], this study conducts estimation of the panel nonlinear ARDL model. Thus, the specification of the panel nonlinear ARDL model is as follows:ΔYit=θiECTit+∑j=1P−1γijΔYit−j+∑j=0q−1(δij*+ΔXi,t−j++δij*−ΔXi,t−j−)+μi+εit

Where, ECTit=∅iYi,t−1−(βi′+Xi,t++βi′−Xi,t−)):

4 Results and discussion

Table 3 offers descriptive information for the factors or variables from 2002 to 2023. The means, medians, maximums, and minimums of each series are presented in the table of statistics.

Verifying stationarity is crucial in ensuring the reliability of our analysis. Therefore, we conducted unit root tests to assess the stationarity of the series, findings presented in Table 4. The results indicate that all variables exhibit stationarity at the first difference. Consequently, none of.

the variables are stationary at the second difference, allowing us to proceed with the application of panel ARDL. This underscores the suitability of employing panel ARDL methodology to explore the relationships among the variables.

Table 5 presents the results of panel non-linear short-run estimations conducted for E−7 nations. The findings reveal significant relationships between various factors and CO2 emissions. Specifically, the study highlights the impact of foreign direct investment (FDI), economic growth, institutional quality, and renewable energy consumption on CO2 emissions. The analysis indicates that FDI has both positive and negative coefficients of −0.024 and −0.123, respectively, at a 1 % level of significance. This suggests that a 1 % increase in FDI results in a 0.024 % decrease in CO2 emissions, while a 1 % decrease in FDI leads to a 0.123 % increase in CO2 emissions. These findings corroborate previous research [22,100]. PHH offers an additional explanation for the positive correlation found in NARDL estimation between FDI and CO2 emissions. It states that FDI causes industrialized nations to relocate their industry to developing nations with lax environmental rules.Table 5 Panel NARDL short run estimation.

Table 5Variables	Coefficient	Prob.*	
 ECT (-1)	−0.482**	0.042	
d (FDI_POS)	−0.024***	0.002	
d (FDI_NEG)	−0.123***	0.002	
d (GDP_POS)	0.406***	0.005	
d (GDP_NEG)	−0.117***	0.003	
d (IQ_POS)	−0.789***	0.006	
d (IQ_NEG)	−0.054***	0.009	
d (EC_POS)	−0.572***	0.001	
d (EC_NEG)	−0.029***	0.002	
 C	0.069***	0.004	

Similarly, the study reveals positive and negative coefficients for economic growth, with values of 0.406 and −0.117, respectively, at a 1 % significance level. This implies that a 1 % increase in economic growth corresponds to a 0.406 % decrease in CO2 emissions, while a 1 % decrease in economic growth results in a 0.117 % reduction in CO2 emissions. The explanation for this is that the elasticity may vary over time as a result of variables influencing the drivers of CO2 emissions. Since the causes that cause emissions change over time, so too may the ways in which emissions respond to these changes. The primary discovery of this study is that, in every country under investigation, the income elasticities of CO2 emissions are found to be positive. As the very recent global trends seem to indicate, this does not clearly imply that active climate policy may cause a drop in absolute emissions levels. Furthermore, the analysis shows that institutional quality also plays a significant role, with positive and negative coefficients of −0.789 and −0.054, respectively, at a 1 % level of significance. Thus, a 1 % improvement in institutional quality leads to a substantial 0.789 % decrease in CO2 emissions, while a 1 % decline in institutional quality results in a 0.054 % increase in CO2 emissions. Moreover, the study examines the impact of renewable energy consumption, revealing positive and negative coefficients of 0.572 and −0.029 %, respectively. This suggests that a 1 % increase in renewable energy consumption reduces CO2 emissions by 0.572 %, whereas a 1 % decrease in renewable energy consumption leads to a 0.029 % increase in CO2 emissions. Lastly, the analysis highlights the speed of adjustment, indicating a rate of 48 %, which signifies a relatively fast recovery process. These findings provide valuable insights into the dynamics of CO2 emissions and the factors influencing them within the E−7 nations. It denotes advancements in factors that determine institutional quality, such as a nation's macroeconomic circumstances, the stability of Law and order, government, and corruption will lessen the CO2 emissions impact and hence enhance the environmental quality of E 7countries.

Table 6 presents the long-run panel nonlinear relationship estimates among the variables, echoing the patterns observed in the short-run analyses. Specifically, the positive and negative coefficients associated with foreign direct investment stand at −0.094 and −0.248, respectively, with a significance level of 1 %. This implies that a 1 % increase in foreign direct investment correlates with a decrease in CO2 emissions by −0.094 %, while a corresponding decrease in foreign direct investment results in an increase in CO2 emissions by −0.248 %. These findings align with prior research conducted by Refs. [5565,101,102] The reduction in CO2 emissions associated with Foreign Direct Investment (FDI) can be attributed to several factors. Firstly, FDI often brings in advanced technologies and management practices that enhance energy efficiency and promote cleaner production methods, thus lowering carbon emissions. Secondly, FDI facilitates the adoption of renewable energy sources and promotes sustainable development practices, mitigating the reliance on fossil fuels and subsequently reducing CO2 emissions. Lastly, FDI can spur economic growth in host countries, leading to a shift towards cleaner industries and investments in environmental conservation efforts, ultimately contributing to a decrease in CO2 emissions. Additionally, the coefficients for economic growth exhibit positive and negative values of 0.327 and −0.047, respectively, at a significance level of 1 %. This indicates that a 1 % increase in economic growth corresponds to a reduction in CO2 emissions by 0.327 %, while a 1 % decrease in economic growth results in an increase in CO2 emissions by 0.047 %. The findings are aligned with [Usman, Rahm an [86]. The observed increase in CO2 emissions with economic growth can be attributed to the inherent reliance of many economic activities on fossil fuels, which release CO2 upon combustion. As economies expand, industries such as manufacturing and transportation often escalate their energy consumption, predominantly sourced from fossil fuels. This intensification of energy usage contributes to higher CO2 emissions, overshadowing any potential efficiency gains or emissions reductions that may accompany economic growth initiatives. Additionally, rapid economic development can lead to increased consumption and production, exacerbating environmental stressors and further amplifying CO2 emissions.Table 6 Nonlinear ARDL long run estimation.

Table 6variables	Beta	Prob.	
FDI_POS	−0.094***	0.007	
FDI_NEG	−0.248***	0.003	
GDP_POS	0.327***	0.005	
GDP_NEG	−0.041***	0.001	
IQ_POS	−0.808***	0.006	
IQ_NEG	−0.427***	0.009	
EC_POS	−0.055***	0.003	
EC_NEG	−0.323***	0.001	
C	0.362***	0.008	
Notes: Asterisk ***, ** and * represent significance levels with 1 %, 5 %, and 10 %, respectively.

Furthermore, the positive and negative coefficients for institutional quality stand at −0.808 and −0.427, respectively, with a significance level of 1 %. This suggests that a 1 % increase in institutional quality results in a reduction in CO2 emissions by 0.808 %, while a corresponding 1 % decrease in institutional quality leads to a decrease in CO2 emissions by 0.427 %. These findings are consistent with previous studies by Ref. [100].The observed reduction in CO2 emissions associated with institutional quality underscores the vital role of governance frameworks in environmental stewardship. Nations with higher institutional quality often exhibit better enforcement of environmental regulations, fostering compliance among industries and individuals. Moreover, transparent and accountable governance structures encourage investments in sustainable technologies and infrastructure, leading to more efficient resource utilization and reduced emissions. Additionally, strong institutions facilitate coordination and collaboration among stakeholders, enabling the implementation of effective climate policies and initiatives aimed at curbing CO2 emissions.

The dynamic coefficients of carbon dioxide emissions Fig. 1(b) confirm both short- and long-term effects of positive and negative shocks in EC, FDI, GDP, and CI on CO2 levels. The bold dotted lines depict the cumulative impact of explanatory variables IQ, CE, FDI, and GDP following increases (positive shocks), while the darker dotted lines represent the effect of negative shocks on these variables and subsequently on CO2 emissions. The thicker line within the 95 % confidence intervals illustrates the disparity between positive and negative responses. CO2 is anticipated to react swiftly to both positive and negative changes in FDI, CE, IQ, and GDP, with notable responses expected in the initial year.Fig. 1 (b) dynamic multipliers Dim line----Positive change, Dark line----- Negative change, thick line-----asymmetry.

Fig. 1 (b)

Additionally, the positive and negative coefficients for renewable energy consumption are −0.055 and −0.323, respectively. This indicates that a 1 % increase in renewable energy consumption leads to a reduction in CO2 emissions by 0.055 %, while a corresponding 1 % decrease in renewable energy consumption results in an increase in CO2 emissions by 0.323 %. These findings are consistent with previous research by Ref. [16]. The decrease in CO2 emissions attributed to renewable energy stems from its inherent characteristics as a clean and sustainable alternative to fossil fuels. Renewable energy sources such as solar, wind, and hydroelectric power generate electricity without emitting greenhouse gases. By replacing fossil fuel-based energy generation with renewable sources, nations can significantly reduce their carbon footprint and dependency on non-renewable resources. Additionally, the expansion of renewable energy infrastructure often accompanies advancements in energy efficiency and conservation practices, further contributing to CO2 emissions reduction goals. This transition to renewable energy aligns with global efforts to combat climate change and promote a more sustainable energy future.

5 Discussion

This research investigated the impact of renewable energy consumption, Institutional Quality, foreign direct investment, and gross domestic product on carbon emission by adopting the NARDL approach. The results depict that the both negative and positive change of FDI on carbon emission but even it considered as opposite. Concerning FDI, both the upward and downward variations in the rate have high consequences but in opposite directions [101]. Thus, an increase in the FDI cuts down on carbon emissions; this may be because it stimulates investment in clean technology and good practices [13,18,102]. But a situation of declined FDI also reduces the emissions and this may because it leads to decline in industries which are known to emit much. In relation to GDP, the direction of the relationship that was indicated reflects the reality that economic growth is mostly connected with the increase in energy consumption, and, as a result, emissions [75,103]. On the other hand, negative change in GDP helps cut emissions levels probably because of low industrial and transport Activity rates during such periods. Another variable that also maintains a significant impact is the institutional quality. This study find that institutional quality greatly reduces carbon emission, likely because higher institutional quality would improve the compliance with environmental standards and promote sustainable environmental practices [16,17,46]. Simplified, the emissions decrease even when the quality of institutions has declined, which may be due to a decline in the overall activity during the time of weak institutions. The review with regard to the usage of renewable energy clearly depicts a reduced emission function. They give credence to the continued advancement of renewable energy sources, given that its use reduces carbon emissions thus helping combat climate change. Furthermore, it is clearly seen that reductions in energy also greatly diminish emissions, which is expected since energy expenditures are lower. The constant term is another potential source of carbon emission that were not explicitly covered in the study and thus represent a constant rate of carbon emissions. In summary, it indicates that policymakers have to be sensitive and look at the multiple impacts/neutral or asymmetric effects/of these variables on the level of carbon emissions. There are more concerns that should be considered; first, there is the need to lobby for FDI while ensuring acceptable standards of environmental control that must accompany economic development; second, economic growth and development should go hand in hand with sustainability; third, institutional upgrading is always a vital factor in the moderation of emissions. It is important that the long-term needs of the economy are also met, such as the encouragement of renewable power production. In this way, it is possible to identify the factors that relate to carbon emissions and, thus, specify preventive interventions and contributions to the sustainable development of regions and countries.

5.1 Conclusion and policy implication

The primary aim of this study is to investigate the asymmetric impacts of foreign direct investment, institutional quality, renewable energy, and economic growth on environmental pollution in E7 nations, including China, Brazil, Indonesia, Russia, India, Mexico, and Turkey, over the period from 2002 to 2023. Employing the panel NARDL method, our findings reveal significant positive relationships between positive shocks in institutional quality, economic growth, foreign direct investment, and renewable energy and environmental pollution in these nations. Conversely, negative changes in foreign direct investment and renewable energy exhibit a positive and significant correlation with environmental pollution. Moreover, our study suggests several strategies for policymakers and administrators aimed at fostering a sustainable environment.

One key finding from our analysis is the unidirectional Granger causality from positive foreign direct investment shocks to positive CO2 levels and from negative foreign direct investment shocks to negative CO2 levels. Additionally, our model indicates a rapid response to both positive and negative changes in foreign direct investment, renewable energy consumption, institutional quality, and economic growth within the first year, with responses reaching equilibrium after two years. Our recommendations for policymakers include promoting laws that diversify the origins of foreign direct investment to reduce reliance on carbon-intensive industries and encourage sustainable investments. Furthermore, strategies prioritizing sustainable economic growth can help boost GDP while simultaneously reducing carbon emissions. Additionally, initiatives focusing on energy efficiency and institutional quality improvement are essential for achieving long-term environmental sustainability. Theoretical implications of our study highlight the intricate relationships between institutional quality, environmental sustainability, and economic progress in E7 countries, shedding light on the critical role of institutional quality in mitigating the impact of foreign direct investment on CO2 emissions. Managerial and policy implications emphasize the importance of strengthening regulatory frameworks and promoting sustainable investment practices to address environmental degradation effectively.

6 Limitation

This study also has some limitation, such as using only renewable energy consumption, economic growth, institutional quality and foreign direct investment to measure the impact on carbon emission. These variables provide the fundamental and solid foundation for analyzing carbon emission. In this regard, this study opened the new horizon for the future research, which could include panel and comparative studies to deepen our understanding of these complex dynamics. Even different econometric techniques based on panel data include also incorporate which help to investigate the impact on environmental performance. to keep the importance of environmental performance, sustainable performance index, which is recommended for in-depth analysis. Furthermore, in this study the institution quality complete index is used, and institutional quality may be used as moderating variables. In conclusion, our findings highlight the importance of prioritizing foreign direct investment, renewable energy, and economic growth in order to achieve sustainable development goals. Moreover, various aspects of foreign direct investment are recommended to be uses as an index or separately to obtain more significant results, while sustainable economic growth is also considered a significant influencer, while environmental performance taken into account.

Ethics approval

This original work has not been submitted anywhere else for publication.

Consent for publication

The paper submitted with the mutual consent of authors for publication in Journal of” Heliyon”.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

CRediT authorship contribution statement

Xi Chen: Validation, Investigation, Formal analysis. Saif Ur Rahman: Writing – original draft, Methodology, Formal analysis. Sehresh Abdullah: Writing – original draft, Data curation, Conceptualization. Shahzad Ali: Software, Methodology, Data curation. Salman Khalid: Validation, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was sponsored in part by Henan Province Private university Funding Project-Financial Management major of Zhengzhou Sias University.
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